From 3cd4134d9b63ebafacca7080f3602ad4f4b91bb5 Mon Sep 17 00:00:00 2001 From: Codex Date: Wed, 8 Jul 2026 10:18:49 +0800 Subject: [PATCH] feat: bundle anima xpred runtime --- .gitignore | 5 + README.md | 13 +- __init__.py | 29 +- src/rum_xpred/__init__.py | 39 + src/rum_xpred/adapters/__init__.py | 1 + src/rum_xpred/adapters/anima_sd_scripts.py | 438 + src/rum_xpred/anima.py | 311 + src/rum_xpred/cache_batches.py | 443 + src/rum_xpred/cache_import.py | 135 + src/rum_xpred/chunking.py | 303 + src/rum_xpred/config.py | 285 + src/rum_xpred/train_schedule.py | 77 + src/rum_xpred/vendor_paths.py | 18 + vendor/sd-scripts/.gitignore | 21 + vendor/sd-scripts/LICENSE.md | 201 + vendor/sd-scripts/XTI_hijack.py | 204 + vendor/sd-scripts/anima_minimal_inference.py | 1082 + .../sd-scripts/configs/qwen3_06b/config.json | 30 + .../sd-scripts/configs/qwen3_06b/merges.txt | 151388 +++++++ .../configs/qwen3_06b/tokenizer.json | 303282 +++++++++++++++ .../configs/qwen3_06b/tokenizer_config.json | 239 + .../sd-scripts/configs/qwen3_06b/vocab.json | 1 + vendor/sd-scripts/configs/t5_old/config.json | 51 + vendor/sd-scripts/configs/t5_old/spiece.model | Bin 0 -> 791656 bytes .../sd-scripts/configs/t5_old/tokenizer.json | 1 + vendor/sd-scripts/library/__init__.py | 0 vendor/sd-scripts/library/adafactor_fused.py | 138 + vendor/sd-scripts/library/anima_models.py | 1671 + .../sd-scripts/library/anima_train_utils.py | 628 + vendor/sd-scripts/library/anima_utils.py | 317 + vendor/sd-scripts/library/attention.py | 268 + .../library/attention_processors.py | 227 + vendor/sd-scripts/library/chroma_models.py | 744 + vendor/sd-scripts/library/config_util.py | 749 + .../library/custom_offloading_utils.py | 345 + .../library/custom_train_functions.py | 561 + vendor/sd-scripts/library/deepspeed_utils.py | 179 + vendor/sd-scripts/library/device_utils.py | 108 + vendor/sd-scripts/library/flux_models.py | 1329 + vendor/sd-scripts/library/flux_train_utils.py | 690 + vendor/sd-scripts/library/flux_utils.py | 563 + .../library/fp8_optimization_utils.py | 482 + vendor/sd-scripts/library/huggingface_util.py | 84 + .../library/hunyuan_image_models.py | 489 + .../library/hunyuan_image_modules.py | 863 + .../library/hunyuan_image_text_encoder.py | 661 + .../sd-scripts/library/hunyuan_image_utils.py | 525 + .../sd-scripts/library/hunyuan_image_vae.py | 755 + vendor/sd-scripts/library/hypernetwork.py | 223 + vendor/sd-scripts/library/ipex/__init__.py | 207 + vendor/sd-scripts/library/ipex/attention.py | 119 + vendor/sd-scripts/library/ipex/diffusers.py | 126 + vendor/sd-scripts/library/ipex/hijacks.py | 466 + vendor/sd-scripts/library/jpeg_xl_util.py | 186 + vendor/sd-scripts/library/leco_train_util.py | 522 + vendor/sd-scripts/library/lora_utils.py | 287 + .../library/lpw_stable_diffusion.py | 1271 + vendor/sd-scripts/library/lumina_models.py | 1387 + .../sd-scripts/library/lumina_train_util.py | 1093 + vendor/sd-scripts/library/lumina_util.py | 259 + vendor/sd-scripts/library/mask_generator.py | 354 + vendor/sd-scripts/library/model_util.py | 1430 + .../sd-scripts/library/optimizers/__init__.py | 0 .../library/optimizers/automagic.py | 291 + .../sd-scripts/library/optimizers/emosens.py | 243 + .../library/optimizers/optimizer_utils.py | 151 + vendor/sd-scripts/library/original_unet.py | 1928 + .../library/qwen_image_autoencoder_kl.py | 1735 + .../sd-scripts/library/safetensors_utils.py | 478 + vendor/sd-scripts/library/sai_model_spec.py | 693 + vendor/sd-scripts/library/sd3_models.py | 1428 + vendor/sd-scripts/library/sd3_train_utils.py | 945 + vendor/sd-scripts/library/sd3_utils.py | 302 + .../library/sdxl_lpw_stable_diffusion.py | 1311 + vendor/sd-scripts/library/sdxl_model_util.py | 586 + .../library/sdxl_original_control_net.py | 272 + .../sd-scripts/library/sdxl_original_unet.py | 1293 + vendor/sd-scripts/library/sdxl_train_util.py | 393 + vendor/sd-scripts/library/slicing_vae.py | 682 + vendor/sd-scripts/library/strategy_anima.py | 302 + vendor/sd-scripts/library/strategy_base.py | 651 + vendor/sd-scripts/library/strategy_flux.py | 271 + .../library/strategy_hunyuan_image.py | 218 + vendor/sd-scripts/library/strategy_lumina.py | 375 + vendor/sd-scripts/library/strategy_sd.py | 179 + vendor/sd-scripts/library/strategy_sd3.py | 420 + vendor/sd-scripts/library/strategy_sdxl.py | 306 + vendor/sd-scripts/library/train_util.py | 6933 + vendor/sd-scripts/library/utils.py | 522 + vendor/sd-scripts/networks/__init__.py | 0 vendor/sd-scripts/networks/loha.py | 643 + vendor/sd-scripts/networks/lokr.py | 683 + vendor/sd-scripts/networks/network_base.py | 545 + vendor/sd-scripts/requirements.txt | 49 + vendor/sd-scripts/setup.py | 3 + 95 files changed, 503698 insertions(+), 36 deletions(-) create mode 100644 .gitignore create mode 100644 src/rum_xpred/__init__.py create mode 100644 src/rum_xpred/adapters/__init__.py create mode 100644 src/rum_xpred/adapters/anima_sd_scripts.py create mode 100644 src/rum_xpred/anima.py create mode 100644 src/rum_xpred/cache_batches.py create mode 100644 src/rum_xpred/cache_import.py create mode 100644 src/rum_xpred/chunking.py create mode 100644 src/rum_xpred/config.py create mode 100644 src/rum_xpred/train_schedule.py create mode 100644 src/rum_xpred/vendor_paths.py create mode 100644 vendor/sd-scripts/.gitignore create mode 100644 vendor/sd-scripts/LICENSE.md create mode 100644 vendor/sd-scripts/XTI_hijack.py create mode 100644 vendor/sd-scripts/anima_minimal_inference.py create mode 100644 vendor/sd-scripts/configs/qwen3_06b/config.json create mode 100644 vendor/sd-scripts/configs/qwen3_06b/merges.txt create mode 100644 vendor/sd-scripts/configs/qwen3_06b/tokenizer.json create mode 100644 vendor/sd-scripts/configs/qwen3_06b/tokenizer_config.json create mode 100644 vendor/sd-scripts/configs/qwen3_06b/vocab.json create mode 100644 vendor/sd-scripts/configs/t5_old/config.json create mode 100644 vendor/sd-scripts/configs/t5_old/spiece.model create mode 100644 vendor/sd-scripts/configs/t5_old/tokenizer.json create mode 100644 vendor/sd-scripts/library/__init__.py create mode 100644 vendor/sd-scripts/library/adafactor_fused.py create mode 100644 vendor/sd-scripts/library/anima_models.py create mode 100644 vendor/sd-scripts/library/anima_train_utils.py create mode 100644 vendor/sd-scripts/library/anima_utils.py create mode 100644 vendor/sd-scripts/library/attention.py create mode 100644 vendor/sd-scripts/library/attention_processors.py create mode 100644 vendor/sd-scripts/library/chroma_models.py create mode 100644 vendor/sd-scripts/library/config_util.py create mode 100644 vendor/sd-scripts/library/custom_offloading_utils.py create mode 100644 vendor/sd-scripts/library/custom_train_functions.py create mode 100644 vendor/sd-scripts/library/deepspeed_utils.py create mode 100644 vendor/sd-scripts/library/device_utils.py create mode 100644 vendor/sd-scripts/library/flux_models.py create mode 100644 vendor/sd-scripts/library/flux_train_utils.py create mode 100644 vendor/sd-scripts/library/flux_utils.py create mode 100644 vendor/sd-scripts/library/fp8_optimization_utils.py create mode 100644 vendor/sd-scripts/library/huggingface_util.py create mode 100644 vendor/sd-scripts/library/hunyuan_image_models.py create mode 100644 vendor/sd-scripts/library/hunyuan_image_modules.py create mode 100644 vendor/sd-scripts/library/hunyuan_image_text_encoder.py create mode 100644 vendor/sd-scripts/library/hunyuan_image_utils.py create mode 100644 vendor/sd-scripts/library/hunyuan_image_vae.py create mode 100644 vendor/sd-scripts/library/hypernetwork.py create mode 100644 vendor/sd-scripts/library/ipex/__init__.py create mode 100644 vendor/sd-scripts/library/ipex/attention.py create mode 100644 vendor/sd-scripts/library/ipex/diffusers.py create mode 100644 vendor/sd-scripts/library/ipex/hijacks.py create mode 100644 vendor/sd-scripts/library/jpeg_xl_util.py create mode 100644 vendor/sd-scripts/library/leco_train_util.py create mode 100644 vendor/sd-scripts/library/lora_utils.py create mode 100644 vendor/sd-scripts/library/lpw_stable_diffusion.py create mode 100644 vendor/sd-scripts/library/lumina_models.py create mode 100644 vendor/sd-scripts/library/lumina_train_util.py create mode 100644 vendor/sd-scripts/library/lumina_util.py create mode 100644 vendor/sd-scripts/library/mask_generator.py create mode 100644 vendor/sd-scripts/library/model_util.py create mode 100644 vendor/sd-scripts/library/optimizers/__init__.py create mode 100644 vendor/sd-scripts/library/optimizers/automagic.py create mode 100644 vendor/sd-scripts/library/optimizers/emosens.py create mode 100644 vendor/sd-scripts/library/optimizers/optimizer_utils.py create mode 100644 vendor/sd-scripts/library/original_unet.py create mode 100644 vendor/sd-scripts/library/qwen_image_autoencoder_kl.py create mode 100644 vendor/sd-scripts/library/safetensors_utils.py create mode 100644 vendor/sd-scripts/library/sai_model_spec.py create mode 100644 vendor/sd-scripts/library/sd3_models.py create mode 100644 vendor/sd-scripts/library/sd3_train_utils.py create mode 100644 vendor/sd-scripts/library/sd3_utils.py create mode 100644 vendor/sd-scripts/library/sdxl_lpw_stable_diffusion.py create mode 100644 vendor/sd-scripts/library/sdxl_model_util.py create mode 100644 vendor/sd-scripts/library/sdxl_original_control_net.py create mode 100644 vendor/sd-scripts/library/sdxl_original_unet.py create mode 100644 vendor/sd-scripts/library/sdxl_train_util.py create mode 100644 vendor/sd-scripts/library/slicing_vae.py create mode 100644 vendor/sd-scripts/library/strategy_anima.py create mode 100644 vendor/sd-scripts/library/strategy_base.py create mode 100644 vendor/sd-scripts/library/strategy_flux.py create mode 100644 vendor/sd-scripts/library/strategy_hunyuan_image.py create mode 100644 vendor/sd-scripts/library/strategy_lumina.py create mode 100644 vendor/sd-scripts/library/strategy_sd.py create mode 100644 vendor/sd-scripts/library/strategy_sd3.py create mode 100644 vendor/sd-scripts/library/strategy_sdxl.py create mode 100644 vendor/sd-scripts/library/train_util.py create mode 100644 vendor/sd-scripts/library/utils.py create mode 100644 vendor/sd-scripts/networks/__init__.py create mode 100644 vendor/sd-scripts/networks/loha.py create mode 100644 vendor/sd-scripts/networks/lokr.py create mode 100644 vendor/sd-scripts/networks/network_base.py create mode 100644 vendor/sd-scripts/requirements.txt create mode 100644 vendor/sd-scripts/setup.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5fcf5b8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +__pycache__/ +*.py[cod] +.pytest_cache/ +.venv/ +venv/ diff --git a/README.md b/README.md index f681bb9..2ddeddf 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,6 @@ Custom nodes for sampling RUM Anima x-pred checkpoints in ComfyUI. -Repository: https://github.com/leafmoone/ComfyUI-RUM-Anima-XPred - ## Why This Node Exists Anima x-pred checkpoints do not output the original Flow Matching velocity directly. They output clean latent `x`, and the sampler must read out the Euler update direction as: @@ -16,16 +14,11 @@ Regular Anima/FM ComfyUI samplers expect a velocity-prediction checkpoint, so wi ## Install -Clone this repository into `ComfyUI/custom_nodes/`: +Clone or copy this repository into `ComfyUI/custom_nodes/`: ```bash -git clone https://github.com/leafmoone/ComfyUI-RUM-Anima-XPred.git -``` - -The nodes need the RUM Anima X-Pred backend repository because model loading and sampling reuse `src/rum_xpred`. If the backend is not at `/root/shared-nvme/RUM-anima-xpred` or a sibling directory, set: - -```bash -export RUM_ANIMA_XPRED_ROOT=/path/to/RUM-anima-xpred +cd ComfyUI/custom_nodes +# place the ComfyUI-RUM-Anima-XPred folder here ``` Put model files under ComfyUI's normal `models/` folders: diff --git a/__init__.py b/__init__.py index 7669a81..30dfd97 100644 --- a/__init__.py +++ b/__init__.py @@ -1,7 +1,6 @@ from __future__ import annotations import gc -import os import sys from dataclasses import dataclass from pathlib import Path @@ -12,31 +11,9 @@ import torch NODE_ROOT = Path(__file__).resolve().parent - - -def _backend_root() -> Path: - candidates = [] - env_root = os.environ.get("RUM_ANIMA_XPRED_ROOT") - if env_root: - candidates.append(Path(env_root).expanduser()) - candidates.extend( - [ - NODE_ROOT.parent / "RUM-anima-xpred", - NODE_ROOT.parent / "RUM-anima-x", - Path("/root/shared-nvme/RUM-anima-xpred"), - ] - ) - for candidate in candidates: - if (candidate / "src" / "rum_xpred").is_dir(): - return candidate - raise ImportError( - "Could not locate the RUM Anima X-Pred backend. " - "Set RUM_ANIMA_XPRED_ROOT to the repository containing src/rum_xpred." - ) - - -BACKEND_ROOT = _backend_root() -SRC_ROOT = BACKEND_ROOT / "src" +SRC_ROOT = NODE_ROOT / "src" +if not (SRC_ROOT / "rum_xpred").is_dir(): + raise ImportError(f"Bundled RUM Anima X-Pred package not found: {SRC_ROOT / 'rum_xpred'}") if str(SRC_ROOT) not in sys.path: sys.path.insert(0, str(SRC_ROOT)) diff --git a/src/rum_xpred/__init__.py b/src/rum_xpred/__init__.py new file mode 100644 index 0000000..4fa0658 --- /dev/null +++ b/src/rum_xpred/__init__.py @@ -0,0 +1,39 @@ +"""RUM reflow latent x-pred utilities.""" + +from .anima import ( + CacheMetadata, + ToyXPredNet, + anima_euler_step, + apply_flow_shift, + load_object, + load_xpred_cache_sample, + make_shifted_sigma_schedule, + reflow_latent_z, + reflow_training_target, + sample_train_sigmas, + sample_with_vpred_student, + sample_with_xpred_student, + save_xpred_cache_sample, + train_one_xpred_step, + validate_latent_pair, + xpred_to_anima_v, +) + +__all__ = [ + "CacheMetadata", + "ToyXPredNet", + "anima_euler_step", + "apply_flow_shift", + "load_object", + "load_xpred_cache_sample", + "make_shifted_sigma_schedule", + "reflow_latent_z", + "reflow_training_target", + "sample_train_sigmas", + "sample_with_vpred_student", + "sample_with_xpred_student", + "save_xpred_cache_sample", + "train_one_xpred_step", + "validate_latent_pair", + "xpred_to_anima_v", +] diff --git a/src/rum_xpred/adapters/__init__.py b/src/rum_xpred/adapters/__init__.py new file mode 100644 index 0000000..15344e1 --- /dev/null +++ b/src/rum_xpred/adapters/__init__.py @@ -0,0 +1 @@ +"""Adapters that connect RUM x-pred code to local model runtimes.""" diff --git a/src/rum_xpred/adapters/anima_sd_scripts.py b/src/rum_xpred/adapters/anima_sd_scripts.py new file mode 100644 index 0000000..af75b2b --- /dev/null +++ b/src/rum_xpred/adapters/anima_sd_scripts.py @@ -0,0 +1,438 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +import torch +from PIL import Image +from safetensors.torch import load_file, save_file + +from rum_xpred.vendor_paths import ensure_local_sd_scripts + + +@dataclass +class AnimaSdScriptsAdapter: + """RUM x-pred adapter backed by the vendored Anima sd-scripts code. + + This adapter deliberately exposes only the x-pred experiment surface. It is + not a LoRA or ordinary full-finetune training entrypoint. + """ + + args: Any + device: torch.device + dtype: torch.dtype + + def __post_init__(self) -> None: + ensure_local_sd_scripts() + import anima_minimal_inference as anima_inference # type: ignore + from library import anima_models # type: ignore + from library import strategy_anima, strategy_base # type: ignore + + self.anima_inference = anima_inference + self.anima_models = anima_models + self.strategy_anima = strategy_anima + self.strategy_base = strategy_base + self.teacher = None + self.student = None + self.vae = None + self.shared_text_models: dict[str, Any] = {} + self.conds_cache: dict[str, Any] = {} + + if self.strategy_base.TokenizeStrategy.get_strategy() is None: + tokenize_strategy = self.strategy_anima.AnimaTokenizeStrategy( + qwen3_path=self.args.text_encoder, + t5_tokenizer_path=None, + qwen3_max_length=512, + t5_max_length=512, + ) + self.strategy_base.TokenizeStrategy.set_strategy(tokenize_strategy) + if self.strategy_base.TextEncodingStrategy.get_strategy() is None: + self.strategy_base.TextEncodingStrategy.set_strategy(self.strategy_anima.AnimaTextEncodingStrategy()) + + def _convert_lora_for_sd_scripts(self, lora_path: str | Path) -> str: + source = Path(lora_path) + if not source.exists(): + raise FileNotFoundError(f"teacher_lora not found: {source}") + sd = load_file(str(source), device="cpu") + keys = list(sd) + if any(key.startswith("lora_unet_") for key in keys): + return str(source) + if not any(key.startswith("diffusion_model.") and key.endswith((".lora_A.weight", ".lora_B.weight", ".alpha")) for key in keys): + raise ValueError( + "teacher_lora must use sd-scripts lora_unet_* keys or diffusion_model.*.lora_A/B keys; " + f"got first key {keys[0] if keys else ''}" + ) + + repo_root = Path(__file__).resolve().parents[3] + converted_dir = repo_root / "tmp" + converted_dir.mkdir(parents=True, exist_ok=True) + converted_path = converted_dir / f"{source.stem}.sd-scripts.safetensors" + if converted_path.exists() and converted_path.stat().st_mtime >= source.stat().st_mtime: + return str(converted_path) + + converted: dict[str, torch.Tensor] = {} + for key, value in sd.items(): + if not key.startswith("diffusion_model."): + continue + body = key[len("diffusion_model.") :] + if body.endswith(".lora_A.weight"): + module = body[: -len(".lora_A.weight")].replace(".", "_") + converted[f"lora_unet_{module}.lora_down.weight"] = value + elif body.endswith(".lora_B.weight"): + module = body[: -len(".lora_B.weight")].replace(".", "_") + converted[f"lora_unet_{module}.lora_up.weight"] = value + elif body.endswith(".alpha"): + module = body[: -len(".alpha")].replace(".", "_") + converted[f"lora_unet_{module}.alpha"] = value + if not converted: + raise ValueError(f"teacher_lora conversion produced no weights: {source}") + save_file( + converted, + str(converted_path), + metadata={"source": str(source), "format": "converted_for_sd_scripts_lora_utils"}, + ) + return str(converted_path) + + @classmethod + def from_cli(cls, args: Any, device: torch.device | str, dtype: torch.dtype) -> "AnimaSdScriptsAdapter": + return cls(args=args, device=torch.device(device), dtype=dtype) + + def _inference_args( + self, + *, + prompt: str | list[str], + seed: int | None = None, + steps: int | None = None, + cfg: float | None = None, + require_dit: bool = True, + ): + required = ["text_encoder"] + if require_dit: + required.append("dit") + missing = [name for name in required if not getattr(self.args, name, None)] + if missing: + raise ValueError(f"real Anima adapter requires CLI argument(s): {', '.join('--' + name for name in missing)}") + return SimpleNamespace( + dit=self.args.dit, + vae=getattr(self.args, "vae", None), + text_encoder=self.args.text_encoder, + lora_weight=[self._convert_lora_for_sd_scripts(self.args.teacher_lora)] + if getattr(self.args, "teacher_lora", None) + else None, + lora_multiplier=[float(getattr(self.args, "teacher_lora_weight", 1.0))], + include_patterns=None, + exclude_patterns=None, + guidance_scale=1.0 if cfg is None else cfg, + prompt=prompt if isinstance(prompt, str) else prompt[0], + negative_prompt=getattr(self.args, "negative_prompt", ""), + image_size=[getattr(self.args, "height", 1024), getattr(self.args, "width", 1024)], + infer_steps=steps or getattr(self.args, "teacher_steps", 40), + save_path=str(getattr(self.args, "output_dir", "/tmp")), + seed=seed, + flow_shift=getattr(self.args, "flow_shift", 3.0), + fp8=bool(getattr(self.args, "fp8", False)), + fp8_scaled=bool(getattr(self.args, "fp8_scaled", False)), + text_encoder_cpu=bool(getattr(self.args, "text_encoder_cpu", False)), + device=str(self.device), + attn_mode=getattr(self.args, "attn_mode", "torch"), + output_type="latent", + no_metadata=True, + latent_path=None, + lycoris=False, + ) + + def _load_teacher(self): + if self.teacher is None: + infer_args = self._inference_args(prompt="") + self.teacher = self.anima_inference.load_dit_model(infer_args, self.device, self.dtype) + return self.teacher + + def _encode_prompt(self, prompt: str, *, cfg: float, anima_model: torch.nn.Module | None = None): + if anima_model is None: + anima_model = self._load_teacher() + require_dit = True + else: + require_dit = False + infer_args = self._inference_args(prompt=prompt, cfg=cfg, require_dit=require_dit) + if "conds_cache" not in self.shared_text_models: + self.shared_text_models["conds_cache"] = self.conds_cache + if "text_encoder" not in self.shared_text_models: + text_encoder_device = torch.device("cpu") if infer_args.text_encoder_cpu else self.device + self.shared_text_models["text_encoder"] = self.anima_inference.load_text_encoder( + infer_args, + dtype=torch.bfloat16, + device=text_encoder_device, + ) + return self.anima_inference.prepare_text_inputs(infer_args, self.device, anima_model, self.shared_text_models) + + def _ensure_text_encoder(self, *, text_encoder_cpu: bool): + if "text_encoder" not in self.shared_text_models: + infer_args = self._inference_args(prompt="", require_dit=False) + infer_args.text_encoder_cpu = text_encoder_cpu + text_encoder_device = torch.device("cpu") if text_encoder_cpu else self.device + self.shared_text_models["text_encoder"] = self.anima_inference.load_text_encoder( + infer_args, + dtype=torch.bfloat16, + device=text_encoder_device, + ) + return self.shared_text_models["text_encoder"] + + @torch.no_grad() + def _encode_prompt_batch(self, prompts: list[str], *, cfg: float, anima_model: torch.nn.Module): + negative_prompt = getattr(self.args, "negative_prompt", "") + cond_embeddings: list[torch.Tensor | None] = [] + missing_prompts: list[str] = [] + missing_positions: list[int] = [] + for position, prompt in enumerate(prompts): + cached = self.conds_cache.get(prompt) + if not isinstance(cached, torch.Tensor): + cond_embeddings.append(None) + missing_prompts.append(prompt) + missing_positions.append(position) + else: + cond_embeddings.append(cached) + + text_encoder_cpu = bool(getattr(self.args, "text_encoder_cpu", False)) + text_encoder = self._ensure_text_encoder(text_encoder_cpu=text_encoder_cpu) + text_encoder_device = torch.device("cpu") if text_encoder_cpu else self.device + if getattr(text_encoder, "device", None) != text_encoder_device: + text_encoder.to(text_encoder_device) + + tokenize_strategy = self.strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy = self.strategy_base.TextEncodingStrategy.get_strategy() + if tokenize_strategy is None or encoding_strategy is None: + raise RuntimeError("Anima text strategies are not initialized") + + if missing_prompts: + tokens = tokenize_strategy.tokenize(missing_prompts) + embed = encoding_strategy.encode_tokens(tokenize_strategy, [text_encoder], tokens) + crossattn = anima_model._preprocess_text_embeds( + source_hidden_states=embed[0].to(anima_model.device), + target_input_ids=embed[2].to(anima_model.device), + target_attention_mask=embed[3].to(anima_model.device), + source_attention_mask=embed[1].to(anima_model.device), + ) + crossattn[~embed[3].to(anima_model.device).bool()] = 0 + crossattn = crossattn.cpu() + for row, prompt, position in zip(crossattn, missing_prompts, missing_positions): + cached = row.unsqueeze(0).contiguous() + self.conds_cache[prompt] = cached + cond_embeddings[position] = cached + + cached_uncond = self.conds_cache.get(negative_prompt) + if cached_uncond is None: + tokens = tokenize_strategy.tokenize(negative_prompt) + negative_embed = encoding_strategy.encode_tokens(tokenize_strategy, [text_encoder], tokens) + negative_crossattn = anima_model._preprocess_text_embeds( + source_hidden_states=negative_embed[0].to(anima_model.device), + target_input_ids=negative_embed[2].to(anima_model.device), + target_attention_mask=negative_embed[3].to(anima_model.device), + source_attention_mask=negative_embed[1].to(anima_model.device), + ) + negative_crossattn[~negative_embed[3].to(anima_model.device).bool()] = 0 + cached_uncond = negative_crossattn.cpu().contiguous() + self.conds_cache[negative_prompt] = cached_uncond + + cond = torch.cat([embedding for embedding in cond_embeddings if embedding is not None], dim=0) + uncond = cached_uncond.expand(len(prompts), *cached_uncond.shape[1:]).contiguous() + return cond.to(self.device, dtype=torch.bfloat16), uncond.to(self.device, dtype=torch.bfloat16) + + def _encode_prompts(self, prompts: list[str], *, cfg: float, anima_model: torch.nn.Module | None = None): + if anima_model is not None and len(prompts) > 1: + return self._encode_prompt_batch(prompts, cfg=cfg, anima_model=anima_model) + cond_embeds = [] + uncond_embeds = [] + for prompt in prompts: + context, context_null = self._encode_prompt(prompt, cfg=cfg, anima_model=anima_model) + cond_embeds.append(context["embed"][0].to(self.device, dtype=torch.bfloat16)) + uncond_embeds.append(context_null["embed"][0].to(self.device, dtype=torch.bfloat16)) + return torch.cat(cond_embeds, dim=0), torch.cat(uncond_embeds, dim=0) + + @torch.no_grad() + def teacher_sample_latent( + self, + prompt: str | list[str], + eps_latent: torch.Tensor, + sigmas: torch.Tensor, + guidance_scale: float, + ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]: + """Run the frozen Anima teacher from the provided latent noise to sigma=0.""" + teacher = self._load_teacher() + prompts = [prompt] if isinstance(prompt, str) else prompt + cond_embed, uncond_embed = self._encode_prompts(prompts, cfg=guidance_scale, anima_model=teacher) + + if eps_latent.ndim == 4: + latents = eps_latent.unsqueeze(2) + elif eps_latent.ndim == 5: + latents = eps_latent + else: + raise ValueError(f"expected BCHW or BCFHW eps_latent, got {tuple(eps_latent.shape)}") + latents = latents.to(self.device, dtype=torch.bfloat16) + if cond_embed.shape[0] != latents.shape[0]: + raise ValueError(f"prompt batch size {cond_embed.shape[0]} != latent batch size {latents.shape[0]}") + + padding_mask = torch.zeros( + latents.shape[0], + 1, + latents.shape[-2], + latents.shape[-1], + dtype=torch.bfloat16, + device=self.device, + ) + sigmas = sigmas.to(self.device, dtype=torch.float32) + do_cfg = guidance_scale != 1.0 + for index, sigma in enumerate(sigmas[:-1]): + sigma_next = sigmas[index + 1] + timestep = sigma.reshape(1).expand(latents.shape[0]).to(self.device, dtype=torch.bfloat16) + noise_pred = teacher(latents, timestep, cond_embed, padding_mask=padding_mask) + if do_cfg: + uncond = teacher(latents, timestep, uncond_embed, padding_mask=padding_mask) + noise_pred = uncond + guidance_scale * (noise_pred - uncond) + latents = latents + noise_pred * (sigma_next - sigma).to(latents.dtype) + + x_teacher = latents.squeeze(2).to(dtype=self.dtype) + return x_teacher, {"prompt_embeds": cond_embed.detach().to(dtype=self.dtype)} + + def load_student_xpred(self, init_checkpoint: str | None): + # The student architecture is the same Anima DiT; the experiment treats + # its output as clean latent x rather than velocity. + if init_checkpoint is None: + init_checkpoint = getattr(self.args, "student_init", None) or getattr(self.args, "dit", None) + if init_checkpoint is None: + raise ValueError("load_student_xpred requires --student_init or --dit") + original_dit = getattr(self.args, "dit", None) + self.args.dit = init_checkpoint + try: + self.student = self._load_teacher() if self.teacher is None and init_checkpoint == original_dit else None + if self.student is None: + infer_args = self._inference_args(prompt="") + self.student = self.anima_inference.load_dit_model(infer_args, self.device, self.dtype) + finally: + self.args.dit = original_dit + self.student.train().requires_grad_(True) + return self.student + + def _forward_with_conditioning( + self, + model: torch.nn.Module, + z: torch.Tensor, + sigma: torch.Tensor, + text_conditioning: dict[str, torch.Tensor], + *, + guidance_scale: float = 1.0, + ) -> torch.Tensor: + if "prompt_embeds" not in text_conditioning: + prompt = getattr(self.args, "prompt", "") + context, context_null = self._encode_prompt(prompt, cfg=guidance_scale, anima_model=model) + text_conditioning = { + "prompt_embeds": context["embed"][0].detach().to(dtype=self.dtype), + "negative_prompt_embeds": context_null["embed"][0].detach().to(dtype=self.dtype), + } + if z.ndim == 4: + model_z = z.unsqueeze(2) + else: + model_z = z + embed = text_conditioning["prompt_embeds"].to(self.device, dtype=torch.bfloat16) + if embed.shape[0] == 1 and model_z.shape[0] > 1: + embed = embed.expand(model_z.shape[0], *embed.shape[1:]).contiguous() + if embed.shape[0] != model_z.shape[0]: + raise ValueError(f"prompt embed batch size {embed.shape[0]} != latent batch size {model_z.shape[0]}") + padding_mask = torch.zeros( + model_z.shape[0], + 1, + model_z.shape[-2], + model_z.shape[-1], + dtype=torch.bfloat16, + device=self.device, + ) + timestep = sigma.reshape(sigma.shape[0], -1)[:, 0].to(self.device, dtype=torch.bfloat16) + model_z = model_z.to(self.device, dtype=torch.bfloat16) + out = model(model_z, timestep, embed, padding_mask=padding_mask) + if guidance_scale != 1.0: + uncond_embed = text_conditioning.get("negative_prompt_embeds") + if uncond_embed is None: + _context, context_null = self._encode_prompt(getattr(self.args, "prompt", ""), cfg=guidance_scale, anima_model=model) + uncond_embed = context_null["embed"][0].detach().to(dtype=self.dtype) + uncond_embed = uncond_embed.to(self.device, dtype=torch.bfloat16) + if uncond_embed.shape[0] == 1 and model_z.shape[0] > 1: + uncond_embed = uncond_embed.expand(model_z.shape[0], *uncond_embed.shape[1:]).contiguous() + if uncond_embed.shape[0] != model_z.shape[0]: + raise ValueError(f"negative prompt embed batch size {uncond_embed.shape[0]} != latent batch size {model_z.shape[0]}") + uncond = model(model_z, timestep, uncond_embed, padding_mask=padding_mask) + out = uncond + guidance_scale * (out - uncond) + return out.squeeze(2).to(dtype=z.dtype) + + def student_forward_xpred( + self, + student: torch.nn.Module, + z: torch.Tensor, + sigma: torch.Tensor, + text_conditioning: dict[str, torch.Tensor], + *, + guidance_scale: float = 1.0, + ) -> torch.Tensor: + return self._forward_with_conditioning(student, z, sigma, text_conditioning, guidance_scale=guidance_scale) + + def teacher_forward_vpred( + self, + teacher: torch.nn.Module, + z: torch.Tensor, + sigma: torch.Tensor, + text_conditioning: dict[str, torch.Tensor], + *, + guidance_scale: float = 1.0, + ) -> torch.Tensor: + return self._forward_with_conditioning(teacher, z, sigma, text_conditioning, guidance_scale=guidance_scale) + + def save_student_xpred(self, student: torch.nn.Module, checkpoint_path: str | Path) -> None: + checkpoint_path = Path(checkpoint_path) + checkpoint_path.parent.mkdir(parents=True, exist_ok=True) + state = {key: value.detach().cpu().contiguous() for key, value in student.state_dict().items()} + save_file(state, str(checkpoint_path)) + + def _load_vae(self): + if self.vae is None: + vae_path = getattr(self.args, "vae", None) + if not vae_path: + raise ValueError("decode requires --vae / [common].vae") + from library import qwen_image_autoencoder_kl # type: ignore + + self.vae = qwen_image_autoencoder_kl.load_vae( + vae_path, + device="cpu", + disable_mmap=True, + spatial_chunk_size=getattr(self.args, "vae_spatial_chunk_size", None), + disable_cache=bool(getattr(self.args, "vae_disable_cache", False)), + ) + self.vae.to(torch.bfloat16) + self.vae.eval() + return self.vae + + @torch.no_grad() + def decode_latents_to_images(self, latents: torch.Tensor, output_dir: str | Path, prefix: str = "sample") -> list[Path]: + vae = self._load_vae() + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + latents = latents.to(self.device, dtype=vae.dtype) + vae.to(self.device) + pixels = vae.decode_to_pixels(latents) + if pixels.ndim == 5: + pixels = pixels.squeeze(2) + pixels = pixels.to("cpu", dtype=torch.float32) + vae.to("cpu") + + paths: list[Path] = [] + for index, image_tensor in enumerate(pixels): + image_tensor = torch.clamp(image_tensor, -1.0, 1.0) + image_tensor = ((image_tensor + 1.0) * 127.5).to(torch.uint8) + array = image_tensor.permute(1, 2, 0).contiguous().numpy() + path = output_dir / f"{prefix}-{index:04d}.png" + Image.fromarray(array).save(path) + paths.append(path) + return paths + + +def create_adapter(args: Any, device: torch.device | str, dtype: torch.dtype) -> AnimaSdScriptsAdapter: + return AnimaSdScriptsAdapter.from_cli(args, device=device, dtype=dtype) diff --git a/src/rum_xpred/anima.py b/src/rum_xpred/anima.py new file mode 100644 index 0000000..973b0a5 --- /dev/null +++ b/src/rum_xpred/anima.py @@ -0,0 +1,311 @@ +from __future__ import annotations + +import importlib +import math +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable + +import torch +import torch.nn.functional as F +from safetensors.torch import load_file, save_file + + +DEFAULT_TEACHER_STEPS = 40 +DEFAULT_FLOW_SHIFT = 3.0 +DEFAULT_TEACHER_CFG = 1.0 +DEFAULT_SIGMA_MIN_TRAIN = 0.02 +DEFAULT_EPS_FLOOR = 1e-4 +DEFAULT_LEARNING_RATE = 1e-6 + + +@dataclass(frozen=True) +class CacheMetadata: + prompt: str + width: int + height: int + seed: int + sample_index: int + teacher_steps: int + flow_shift: float + teacher_cfg: float + teacher_lora: str | None = None + teacher_lora_weight: float = 1.0 + + +def apply_flow_shift(sigma: torch.Tensor, flow_shift: float) -> torch.Tensor: + if flow_shift <= 0: + raise ValueError("flow_shift must be > 0") + return flow_shift * sigma / (1 + (flow_shift - 1) * sigma) + + +def make_shifted_sigma_schedule(steps: int, flow_shift: float, device: torch.device | str, dtype: torch.dtype) -> torch.Tensor: + if steps < 1: + raise ValueError("steps must be >= 1") + base = torch.linspace(1.0, 0.0, steps + 1, device=device, dtype=dtype) + shifted = apply_flow_shift(base, flow_shift) + shifted[0] = 1.0 + shifted[-1] = 0.0 + return shifted + + +def reflow_latent_z(x0_latent: torch.Tensor, eps_latent: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor: + return (1 - sigma) * x0_latent + sigma * eps_latent + + +def reflow_training_target(prediction_type: str, x_teacher_latent: torch.Tensor, eps_latent: torch.Tensor) -> torch.Tensor: + if prediction_type == "x": + return x_teacher_latent + if prediction_type == "v": + return eps_latent - x_teacher_latent + raise ValueError(f"prediction_type must be 'x' or 'v', got {prediction_type!r}") + + +def xpred_to_anima_v(z: torch.Tensor, x_pred_latent: torch.Tensor, sigma: torch.Tensor, eps_floor: float = DEFAULT_EPS_FLOOR) -> torch.Tensor: + if eps_floor <= 0: + raise ValueError("eps_floor must be > 0") + return (z - x_pred_latent) / sigma.clamp_min(eps_floor) + + +def anima_euler_step(z: torch.Tensor, v_anima: torch.Tensor, sigma: torch.Tensor, sigma_next: torch.Tensor) -> torch.Tensor: + return z + v_anima * (sigma_next - sigma) + + +def sample_train_sigmas( + batch_size: int, + sigma_min_train: float, + flow_shift: float, + device: torch.device | str, + dtype: torch.dtype, + generator: torch.Generator | None = None, + time_sampling: str = "uniform_shifted", + logit_mean: float = -0.8, + logit_std: float = 0.8, +) -> torch.Tensor: + if not 0 < sigma_min_train < 1: + raise ValueError("sigma_min_train must be in (0, 1)") + if time_sampling == "uniform_shifted": + shifted_sigma = sigma_min_train + (1 - sigma_min_train) * torch.rand( + batch_size, + device=device, + dtype=dtype, + generator=generator, + ) + elif time_sampling == "jlt_logit_normal": + t = torch.sigmoid(torch.randn(batch_size, device=device, dtype=dtype, generator=generator) * logit_std + logit_mean) + shifted_sigma = (1 - t).clamp(min=sigma_min_train, max=1.0) + else: + raise ValueError(f"unsupported time_sampling: {time_sampling!r}") + return shifted_sigma.view(batch_size, 1, 1, 1) + + +def validate_latent_pair(x_teacher_latent: torch.Tensor, eps_latent: torch.Tensor) -> None: + if x_teacher_latent.shape != eps_latent.shape: + raise ValueError(f"x_teacher_latent and eps_latent shapes differ: {x_teacher_latent.shape} != {eps_latent.shape}") + if x_teacher_latent.ndim != 4: + raise ValueError(f"expected BCHW latent tensors, got shape {tuple(x_teacher_latent.shape)}") + if x_teacher_latent.shape[1] != 16: + raise ValueError(f"expected Anima latent channel count 16, got {x_teacher_latent.shape[1]}") + if not torch.isfinite(x_teacher_latent).all(): + raise ValueError("x_teacher_latent contains non-finite values") + if not torch.isfinite(eps_latent).all(): + raise ValueError("eps_latent contains non-finite values") + + +def save_xpred_cache_sample( + path: str | Path, + x_teacher_latent: torch.Tensor, + eps_latent: torch.Tensor, + text_conditioning: dict[str, torch.Tensor], + metadata: CacheMetadata, +) -> None: + validate_latent_pair(x_teacher_latent, eps_latent) + tensors: dict[str, torch.Tensor] = { + "x_teacher_latent": x_teacher_latent.detach().cpu().contiguous(), + "eps_latent": eps_latent.detach().cpu().contiguous(), + "width": torch.tensor([metadata.width], dtype=torch.int32), + "height": torch.tensor([metadata.height], dtype=torch.int32), + "seed": torch.tensor([metadata.seed], dtype=torch.int64), + "sample_index": torch.tensor([metadata.sample_index], dtype=torch.int64), + "teacher_steps": torch.tensor([metadata.teacher_steps], dtype=torch.int32), + "flow_shift": torch.tensor([metadata.flow_shift], dtype=torch.float32), + "teacher_cfg": torch.tensor([metadata.teacher_cfg], dtype=torch.float32), + } + for key, value in text_conditioning.items(): + if not torch.is_tensor(value): + raise TypeError(f"text_conditioning[{key!r}] must be a tensor") + tensors[f"text_{key}"] = value.detach().cpu().contiguous() + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + file_metadata = { + "prompt": metadata.prompt, + "format": "anima_rum_xpred_cache_v1", + "teacher_lora": metadata.teacher_lora or "", + "teacher_lora_weight": str(metadata.teacher_lora_weight), + } + save_file(tensors, str(path), metadata=file_metadata) + + +def load_xpred_cache_sample(path: str | Path, device: torch.device | str, dtype: torch.dtype) -> dict[str, Any]: + tensors = load_file(str(path), device="cpu") + from safetensors.torch import safe_open + + with safe_open(str(path), framework="pt", device="cpu") as handle: + file_metadata = handle.metadata() or {} + x_teacher_latent = tensors["x_teacher_latent"].to(device=device, dtype=dtype) + eps_latent = tensors["eps_latent"].to(device=device, dtype=dtype) + validate_latent_pair(x_teacher_latent, eps_latent) + text_conditioning = { + key.removeprefix("text_"): value.to(device=device, dtype=dtype) + for key, value in tensors.items() + if key.startswith("text_") + } + return { + "x_teacher_latent": x_teacher_latent, + "eps_latent": eps_latent, + "text_conditioning": text_conditioning, + "width": int(tensors["width"][0]), + "height": int(tensors["height"][0]), + "seed": int(tensors["seed"][0]), + "sample_index": int(tensors["sample_index"][0]), + "teacher_steps": int(tensors["teacher_steps"][0]), + "flow_shift": float(tensors["flow_shift"][0]), + "teacher_cfg": float(tensors["teacher_cfg"][0]), + "teacher_lora": file_metadata.get("teacher_lora", "") or None, + "teacher_lora_weight": float(file_metadata.get("teacher_lora_weight", "1.0")), + } + + +def load_object(spec: str) -> Callable[..., Any]: + if ":" not in spec: + raise ValueError("adapter spec must be 'module.submodule:factory_name'") + module_name, object_name = spec.split(":", 1) + module = importlib.import_module(module_name) + obj = module + for part in object_name.split("."): + obj = getattr(obj, part) + if not callable(obj): + raise TypeError(f"{spec} resolved to a non-callable object") + return obj + + +class ToyXPredNet(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.net = torch.nn.Sequential( + torch.nn.Conv2d(17, 32, kernel_size=1), + torch.nn.SiLU(), + torch.nn.Conv2d(32, 16, kernel_size=1), + ) + + def forward(self, z: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor: + sigma_map = sigma.expand(z.shape[0], 1, z.shape[2], z.shape[3]).to(dtype=z.dtype) + return self.net(torch.cat([z, sigma_map], dim=1)) + + +def make_toy_teacher_endpoint(eps_latent: torch.Tensor, prompt_index: int) -> torch.Tensor: + phase = 0.05 * (prompt_index + 1) + return torch.tanh(0.72 * eps_latent + phase) + + +def train_one_xpred_step( + student: torch.nn.Module, + optimizer: torch.optim.Optimizer, + x_teacher_latent: torch.Tensor, + eps_latent: torch.Tensor, + sigma: torch.Tensor, +) -> torch.Tensor: + z = reflow_latent_z(x_teacher_latent, eps_latent, sigma) + x_pred = student(z, sigma) + loss = F.mse_loss(x_pred.float(), x_teacher_latent.float()) + if not torch.isfinite(loss): + raise FloatingPointError("non-finite x-pred loss") + optimizer.zero_grad(set_to_none=True) + loss.backward() + for parameter in student.parameters(): + if parameter.grad is not None and not torch.isfinite(parameter.grad).all(): + raise FloatingPointError("non-finite x-pred gradient") + optimizer.step() + return loss.detach() + + +@torch.no_grad() +def sample_with_xpred_student( + student_forward: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + eps_latent: torch.Tensor, + sigmas: torch.Tensor, + eps_floor: float = DEFAULT_EPS_FLOOR, +) -> torch.Tensor: + if sigmas.ndim != 1: + raise ValueError("sigmas must be a 1D schedule") + if sigmas[-1].item() != 0.0: + raise ValueError("sigmas must end at 0") + z = eps_latent + for index, sigma_value in enumerate(sigmas[:-1]): + sigma = sigma_value.reshape(1, 1, 1, 1).to(device=z.device, dtype=z.dtype).expand(z.shape[0], 1, 1, 1) + sigma_next = sigmas[index + 1].reshape(1, 1, 1, 1).to(device=z.device, dtype=z.dtype) + x_pred = student_forward(z, sigma) + v = xpred_to_anima_v(z, x_pred, sigma, eps_floor) + z = anima_euler_step(z, v, sigma, sigma_next) + if not torch.isfinite(z).all(): + raise FloatingPointError("non-finite latent during x-pred sampling") + return z + + +@torch.no_grad() +def sample_with_vpred_student( + student_forward: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + eps_latent: torch.Tensor, + sigmas: torch.Tensor, +) -> torch.Tensor: + if sigmas.ndim != 1: + raise ValueError("sigmas must be a 1D schedule") + if sigmas[-1].item() != 0.0: + raise ValueError("sigmas must end at 0") + z = eps_latent + for index, sigma_value in enumerate(sigmas[:-1]): + sigma = sigma_value.reshape(1, 1, 1, 1).to(device=z.device, dtype=z.dtype).expand(z.shape[0], 1, 1, 1) + sigma_next = sigmas[index + 1].reshape(1, 1, 1, 1).to(device=z.device, dtype=z.dtype) + v_pred = student_forward(z, sigma) + z = anima_euler_step(z, v_pred, sigma, sigma_next) + if not torch.isfinite(z).all(): + raise FloatingPointError("non-finite latent during v-pred sampling") + return z + + +@torch.no_grad() +def sample_with_mixed_velocity( + teacher_forward_v: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + student_forward_x: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + eps_latent: torch.Tensor, + sigmas: torch.Tensor, + *, + alpha: float, + eps_floor: float = DEFAULT_EPS_FLOOR, +) -> torch.Tensor: + if not 0.0 <= alpha <= 1.0: + raise ValueError("alpha must be in [0, 1]") + if sigmas.ndim != 1: + raise ValueError("sigmas must be a 1D schedule") + if sigmas[-1].item() != 0.0: + raise ValueError("sigmas must end at 0") + z = eps_latent + for index, sigma_value in enumerate(sigmas[:-1]): + sigma = sigma_value.reshape(1, 1, 1, 1).to(device=z.device, dtype=z.dtype).expand(z.shape[0], 1, 1, 1) + sigma_next = sigmas[index + 1].reshape(1, 1, 1, 1).to(device=z.device, dtype=z.dtype) + v_teacher = teacher_forward_v(z, sigma) + if alpha == 0.0: + v = v_teacher + else: + x_student = student_forward_x(z, sigma) + v_student = xpred_to_anima_v(z, x_student, sigma, eps_floor) + v = (1.0 - alpha) * v_teacher + alpha * v_student + z = anima_euler_step(z, v, sigma, sigma_next) + if not torch.isfinite(z).all(): + raise FloatingPointError("non-finite latent during mixed-velocity sampling") + return z + + +def cache_file_sort_key(path: Path) -> tuple[int, str]: + stem_digits = "".join(ch for ch in path.stem if ch.isdigit()) + return (int(stem_digits) if stem_digits else math.inf, path.name) diff --git a/src/rum_xpred/cache_batches.py b/src/rum_xpred/cache_batches.py new file mode 100644 index 0000000..3dcac3c --- /dev/null +++ b/src/rum_xpred/cache_batches.py @@ -0,0 +1,443 @@ +from __future__ import annotations + +import dataclasses +import math +import os +import random +from pathlib import Path + +import torch + +from rum_xpred.anima import cache_file_sort_key, load_xpred_cache_sample, xpred_to_anima_v + + +DEFAULT_CACHE_BUCKETS: tuple[tuple[int, int], ...] = ( + (1024, 1024), + (832, 1216), + (1216, 832), + (896, 1152), + (1152, 896), + (768, 1344), + (1344, 768), +) + + +@dataclasses.dataclass(frozen=True) +class CacheBucket: + name: str + files: list[Path] + + +def read_prompts(path: str | Path, limit: int | None) -> list[str]: + prompts = [line.strip() for line in Path(path).read_text(encoding="utf-8").splitlines() if line.strip()] + if limit is not None: + prompts = prompts[:limit] + if not prompts: + raise ValueError(f"no prompts found in {path}") + return prompts + + +def chunked(items: list, batch_size: int): + if batch_size < 1: + raise ValueError("batch_size must be >= 1") + for start in range(0, len(items), batch_size): + yield items[start : start + batch_size] + + +def make_seeded_eps_batch( + sample_indices: list[int], + *, + seed: int, + height: int, + width: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + latents = [] + for sample_index in sample_indices: + generator = torch.Generator(device=device).manual_seed(seed + sample_index) + latents.append( + torch.randn( + 1, + 16, + height // 8, + width // 8, + device=device, + dtype=dtype, + generator=generator, + ) + ) + return torch.cat(latents, dim=0) + + +def choose_cache_bucket(sample_index: int, seed: int) -> tuple[int, int]: + rng = random.Random((int(seed) << 32) + int(sample_index)) + return DEFAULT_CACHE_BUCKETS[rng.randrange(len(DEFAULT_CACHE_BUCKETS))] + + +def bucket_name(width: int, height: int) -> str: + return f"{int(width)}x{int(height)}" + + +def bucket_cache_path(cache_dir: Path, sample_index: int, *, width: int, height: int, bucket_enabled: bool) -> Path: + filename = f"sample-{sample_index:06d}.safetensors" + if not bucket_enabled: + return cache_dir / filename + return cache_dir / bucket_name(width, height) / filename + + +def collect_cache_buckets(cache_dir: str | Path) -> list[CacheBucket]: + root = Path(cache_dir) + if not root.exists(): + return [] + + bucket_files: dict[str, list[Path]] = {} + seen_files: set[Path] = set() + + def add_files(bucket_name: str, files: list[Path]) -> None: + for file in files: + resolved = file.resolve() + if resolved in seen_files: + continue + seen_files.add(resolved) + bucket_files.setdefault(bucket_name, []).append(file) + + add_files("root", sorted(root.glob("*.safetensors"), key=cache_file_sort_key)) + for dirpath, dirnames, filenames in os.walk(root, followlinks=False): + current = Path(dirpath) + dirnames[:] = sorted(name for name in dirnames if not (current / name).is_symlink()) + files = sorted( + [current / filename for filename in filenames if filename.endswith(".safetensors")], + key=cache_file_sort_key, + ) + if files and current != root: + add_files(current.name, files) + + return [ + CacheBucket(name=name, files=sorted(files, key=cache_file_sort_key)) + for name, files in bucket_files.items() + if files + ] + + +def merge_cache_samples(paths: list[Path], device: torch.device, dtype: torch.dtype) -> dict: + samples = [load_xpred_cache_sample(path, device=device, dtype=dtype) for path in paths] + shapes = {tuple(sample["x_teacher_latent"].shape[1:]) for sample in samples} + if len(shapes) != 1: + raise ValueError(f"mixed latent shapes in one train batch are not supported: {sorted(shapes)}") + text_keys = set(samples[0]["text_conditioning"]) + if any(set(sample["text_conditioning"]) != text_keys for sample in samples): + raise ValueError("all samples in a train batch must have the same text conditioning keys") + return { + "x_teacher_latent": torch.cat([sample["x_teacher_latent"] for sample in samples], dim=0), + "eps_latent": torch.cat([sample["eps_latent"] for sample in samples], dim=0), + "text_conditioning": { + key: torch.cat([sample["text_conditioning"][key] for sample in samples], dim=0) + for key in text_keys + }, + } + + +def cache_source_name(cache_dir: str | Path) -> str: + path = Path(cache_dir) + name = path.parent.name if path.name.startswith("chunk-") and path.parent.name else path.name + safe = "".join(char if char.isalnum() or char in {"-", "_"} else "_" for char in name) + return safe or "cache" + + +def unique_cache_source_names(cache_dirs: list[str]) -> list[str]: + counts: dict[str, int] = {} + names: list[str] = [] + for cache_dir in cache_dirs: + base = cache_source_name(cache_dir) + count = counts.get(base, 0) + counts[base] = count + 1 + names.append(base if count == 0 else f"{base}_{count + 1}") + return names + + +def cache_source_indices_for_paths(paths: list[Path], cache_dirs: list[str]) -> list[int]: + roots = [Path(cache_dir).resolve() for cache_dir in cache_dirs] + indices: list[int] = [] + for path in paths: + resolved = path.resolve() + for index, root in enumerate(roots): + try: + resolved.relative_to(root) + except ValueError: + continue + indices.append(index) + break + else: + raise ValueError(f"cache sample path does not belong to any cache_dir: {path}") + return indices + + +def prediction_to_x( + prediction_type: str, + prediction: torch.Tensor, + z: torch.Tensor, + sigma: torch.Tensor, +) -> torch.Tensor: + if prediction_type == "x": + return prediction + if prediction_type == "v": + return z - sigma * prediction + raise ValueError(f"unsupported prediction_type: {prediction_type!r}") + + +def reflow_loss( + prediction_type: str, + loss_weighting: str, + prediction: torch.Tensor, + target: torch.Tensor, + z: torch.Tensor, + sigma: torch.Tensor, + eps_floor: float, +) -> torch.Tensor: + if loss_weighting == "none": + return torch.nn.functional.mse_loss(prediction.float(), target.float()) + if loss_weighting == "jlt_velocity_readout": + if prediction_type != "x": + raise ValueError("loss_weighting='jlt_velocity_readout' is only valid with prediction_type='x'") + v_pred = xpred_to_anima_v(z.float(), prediction.float(), sigma.float(), eps_floor) + v_target = xpred_to_anima_v(z.float(), target.float(), sigma.float(), eps_floor) + return torch.nn.functional.mse_loss(v_pred, v_target) + raise ValueError(f"unsupported loss_weighting: {loss_weighting!r}") + + +@torch.no_grad() +def loss_by_cache_source( + *, + prediction_type: str, + loss_weighting: str, + prediction: torch.Tensor, + target: torch.Tensor, + z: torch.Tensor, + sigma: torch.Tensor, + eps_floor: float, + source_indices: list[int], + source_count: int, +) -> dict[int, float]: + losses: dict[int, float] = {} + if not source_indices: + return losses + source_tensor = torch.tensor(source_indices, device=prediction.device) + for source_index in range(source_count): + mask = source_tensor == source_index + if not bool(mask.any()): + continue + losses[source_index] = float( + reflow_loss( + prediction_type, + loss_weighting, + prediction.detach()[mask], + target.detach()[mask], + z.detach()[mask], + sigma.detach()[mask], + eps_floor, + ).detach() + ) + return losses + + +@torch.no_grad() +def x_mse_by_cache_source( + *, + x_pred: torch.Tensor, + x_target: torch.Tensor, + source_indices: list[int], + source_count: int, +) -> dict[int, float]: + losses: dict[int, float] = {} + if not source_indices: + return losses + source_tensor = torch.tensor(source_indices, device=x_pred.device) + for source_index in range(source_count): + mask = source_tensor == source_index + if not bool(mask.any()): + continue + losses[source_index] = float(torch.nn.functional.mse_loss(x_pred.detach()[mask].float(), x_target.detach()[mask].float()).detach()) + return losses + + +class CacheBatchCursor: + def __init__(self, cache_files: list[Path], batch_size: int, *, shuffle: bool, seed: int, drop_last: bool) -> None: + if batch_size < 1: + raise ValueError("train_batch_size must be >= 1") + self.cache_files = list(cache_files) + self.batch_size = batch_size + self.shuffle = shuffle + self.drop_last = drop_last + self.rng = random.Random(seed) + self.position = 0 + if self.shuffle: + self.rng.shuffle(self.cache_files) + + def next_count(self, count: int) -> list[Path]: + if count < 1: + return [] + selected = [] + while len(selected) < count: + remaining = len(self.cache_files) - self.position + need = count - len(selected) + take = min(remaining, need) + if take: + selected.extend(self.cache_files[self.position : self.position + take]) + self.position += take + if len(selected) == count: + return selected + self.position = 0 + if self.shuffle: + self.rng.shuffle(self.cache_files) + if self.drop_last and selected: + selected = [] + return selected + + def next(self) -> list[Path]: + return self.next_count(self.batch_size) + + +class CacheBucketBatchCursor: + def __init__(self, buckets: list[CacheBucket], batch_size: int, *, shuffle: bool, seed: int, drop_last: bool) -> None: + if not buckets: + raise ValueError("at least one cache bucket is required") + self.cursors = [ + CacheBatchCursor(bucket.files, batch_size, shuffle=shuffle, seed=seed + index, drop_last=drop_last) + for index, bucket in enumerate(buckets) + ] + self.rng = random.Random(seed) + self.shuffle = shuffle + self.position = 0 + self.order = list(range(len(self.cursors))) + if self.shuffle: + self.rng.shuffle(self.order) + + def next(self) -> list[Path]: + if self.shuffle: + index = self.rng.randrange(len(self.cursors)) + return self.cursors[index].next() + index = self.order[self.position] + self.position = (self.position + 1) % len(self.order) + return self.cursors[index].next() + + +def allocate_weighted_counts(batch_size: int, weights: list[float]) -> list[int]: + if batch_size < 1: + raise ValueError("batch_size must be >= 1") + if not weights: + raise ValueError("at least one weight is required") + if any(weight < 0 for weight in weights) or sum(weights) <= 0: + raise ValueError("cache mix weights must be non-negative and sum to > 0") + total = float(sum(weights)) + raw = [batch_size * float(weight) / total for weight in weights] + counts = [int(math.floor(value)) for value in raw] + remaining = batch_size - sum(counts) + order = sorted(range(len(weights)), key=lambda index: raw[index] - counts[index], reverse=True) + for index in order[:remaining]: + counts[index] += 1 + return counts + + +def allocate_random_weighted_counts(batch_size: int, weights: list[float], rng: random.Random) -> list[int]: + if batch_size < 1: + raise ValueError("batch_size must be >= 1") + if not weights: + raise ValueError("at least one weight is required") + if any(weight < 0 for weight in weights) or sum(weights) <= 0: + raise ValueError("cache mix weights must be non-negative and sum to > 0") + total = float(sum(weights)) + counts = [0] * len(weights) + for _ in range(batch_size): + pick = rng.random() * total + cumulative = 0.0 + for index, weight in enumerate(weights): + cumulative += float(weight) + if pick < cumulative: + counts[index] += 1 + break + return counts + + +class MultiCacheBucketBatchCursor: + def __init__( + self, + cache_bucket_sets: list[list[CacheBucket]], + batch_size: int, + *, + weights: list[float] | None, + shuffle: bool, + seed: int, + drop_last: bool, + ) -> None: + if len(cache_bucket_sets) < 2: + raise ValueError("multi cache cursor requires at least two cache dirs") + if batch_size < 1: + raise ValueError("train_batch_size must be >= 1") + self.batch_size = batch_size + self.shuffle = shuffle + self.rng = random.Random(seed) + if weights is None: + weights = [1.0] * len(cache_bucket_sets) + if len(weights) != len(cache_bucket_sets): + raise ValueError("cache_mix_weights length must match cache_dirs length") + self.weights = [float(weight) for weight in weights] + if any(weight < 0 for weight in self.weights) or sum(self.weights) <= 0: + raise ValueError("cache_mix_weights must be non-negative and sum to > 0") + + bucket_names = sorted({bucket.name for buckets in cache_bucket_sets for bucket in buckets}) + self.bucket_groups: list[tuple[str, list[tuple[int, CacheBatchCursor]]]] = [] + for bucket_name in bucket_names: + source_cursors: list[tuple[int, CacheBatchCursor]] = [] + for source_index, buckets in enumerate(cache_bucket_sets): + bucket = next((candidate for candidate in buckets if candidate.name == bucket_name), None) + if bucket is None: + continue + source_cursors.append( + ( + source_index, + CacheBatchCursor( + bucket.files, + batch_size, + shuffle=shuffle, + seed=seed + source_index * 1009 + len(self.bucket_groups), + drop_last=drop_last, + ), + ) + ) + if source_cursors: + self.bucket_groups.append((bucket_name, source_cursors)) + if not self.bucket_groups: + raise ValueError("no shared cache buckets found across cache dirs") + self.position = 0 + self.order = list(range(len(self.bucket_groups))) + if self.shuffle: + self.rng.shuffle(self.order) + + def next(self) -> list[Path]: + if self.shuffle: + group_index = self.rng.randrange(len(self.bucket_groups)) + else: + group_index = self.order[self.position] + self.position = (self.position + 1) % len(self.order) + _, source_cursors = self.bucket_groups[group_index] + source_indices = [source_index for source_index, _ in source_cursors] + weights = [self.weights[source_index] for source_index in source_indices] + if self.shuffle: + counts = allocate_random_weighted_counts(self.batch_size, weights, self.rng) + else: + counts = allocate_weighted_counts(self.batch_size, weights) + selected: list[Path] = [] + for count, (_, cursor) in zip(counts, source_cursors): + selected.extend(cursor.next_count(count)) + if self.shuffle: + self.rng.shuffle(selected) + return selected + + +def prune_checkpoints(output_dir: Path, keep: int | None, *, prediction_type: str = "x") -> None: + if keep is None or keep < 1: + return + checkpoints = sorted(output_dir.glob(f"{prediction_type}pred-checkpoint-step-*.safetensors"), key=cache_file_sort_key) + for path in checkpoints[:-keep]: + path.unlink() diff --git a/src/rum_xpred/cache_import.py b/src/rum_xpred/cache_import.py new file mode 100644 index 0000000..e285a9f --- /dev/null +++ b/src/rum_xpred/cache_import.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +import re +import tarfile +from dataclasses import dataclass +from pathlib import Path, PurePosixPath + + +SAMPLE_RE = re.compile(r"sample-(\d+)\.safetensors$") + + +@dataclass(frozen=True) +class CacheImportStats: + archives: int = 0 + extracted: int = 0 + skipped_existing: int = 0 + skipped_out_of_range: int = 0 + skipped_non_sample: int = 0 + skipped_unsafe: int = 0 + + def add(self, **values: int) -> "CacheImportStats": + data = self.__dict__.copy() + for key, value in values.items(): + data[key] += value + return CacheImportStats(**data) + + +def sample_index_from_name(name: str) -> int | None: + match = SAMPLE_RE.search(PurePosixPath(name).name) + return int(match.group(1)) if match else None + + +def chunk_id_for_sample(sample_index: int, *, start_index: int, chunk_size: int) -> int: + if chunk_size <= 0: + raise ValueError("chunk_size must be > 0") + return (sample_index - start_index) // chunk_size + + +def target_path_for_member( + member_name: str, + *, + dst_cache_root: Path, + start_index: int, + chunk_size: int, + chunk_offset: int = 0, + min_index: int | None = None, + max_index: int | None = None, +) -> Path | None: + path = PurePosixPath(member_name) + sample_index = sample_index_from_name(member_name) + if sample_index is None: + return None + if sample_index < start_index: + return None + if min_index is not None and sample_index < min_index: + return None + if max_index is not None and sample_index > max_index: + return None + + chunk_id = chunk_id_for_sample(sample_index, start_index=start_index, chunk_size=chunk_size) + chunk_offset + if chunk_id < 0: + return None + + resolution = path.parent.name + if not resolution or resolution == ".": + return dst_cache_root / f"chunk-{chunk_id:04d}" / path.name + return dst_cache_root / f"chunk-{chunk_id:04d}" / resolution / path.name + + +def is_safe_tar_member(name: str) -> bool: + path = PurePosixPath(name) + return not path.is_absolute() and ".." not in path.parts + + +def import_cache_archives( + src_dir: Path, + dst_cache_root: Path, + *, + start_index: int, + chunk_size: int, + chunk_offset: int = 0, + min_index: int | None = None, + max_index: int | None = None, + overwrite: bool = False, +) -> CacheImportStats: + archives = sorted(src_dir.glob("*.tar.gz")) + sorted(src_dir.glob("*.tgz")) + sorted(src_dir.glob("*.tar")) + if not archives: + raise FileNotFoundError(f"no tar archives found in {src_dir}") + + stats = CacheImportStats(archives=len(archives)) + dst_cache_root.mkdir(parents=True, exist_ok=True) + + for archive in archives: + with tarfile.open(archive, "r:*") as tf: + for member in tf: + if not member.isfile(): + continue + if not is_safe_tar_member(member.name): + stats = stats.add(skipped_unsafe=1) + continue + + sample_index = sample_index_from_name(member.name) + target = target_path_for_member( + member.name, + dst_cache_root=dst_cache_root, + start_index=start_index, + chunk_size=chunk_size, + chunk_offset=chunk_offset, + min_index=min_index, + max_index=max_index, + ) + if target is None: + if sample_index is None: + stats = stats.add(skipped_non_sample=1) + else: + stats = stats.add(skipped_out_of_range=1) + continue + if target.exists() and not overwrite: + stats = stats.add(skipped_existing=1) + continue + + extracted = tf.extractfile(member) + if extracted is None: + stats = stats.add(skipped_non_sample=1) + continue + target.parent.mkdir(parents=True, exist_ok=True) + with extracted, target.open("wb") as out: + while True: + block = extracted.read(1024 * 1024) + if not block: + break + out.write(block) + stats = stats.add(extracted=1) + + return stats diff --git a/src/rum_xpred/chunking.py b/src/rum_xpred/chunking.py new file mode 100644 index 0000000..58cff46 --- /dev/null +++ b/src/rum_xpred/chunking.py @@ -0,0 +1,303 @@ +from __future__ import annotations + +import argparse +import dataclasses +import json +import os +import shutil +from pathlib import Path + +from rum_xpred.cache_batches import bucket_cache_path, choose_cache_bucket + + +@dataclasses.dataclass(frozen=True) +class ChunkPlan: + chunk_id: int + start_index: int + num_samples: int + + +def make_chunk_plan( + *, + start_index: int, + total_samples: int, + chunk_size: int, + max_chunks: int | None, +) -> list[ChunkPlan]: + if total_samples < 1: + raise ValueError("total_samples must be >= 1") + if chunk_size < 1: + raise ValueError("chunk_size must be >= 1") + remaining = total_samples + plans: list[ChunkPlan] = [] + chunk_id = 0 + current_index = start_index + while remaining > 0 and (max_chunks is None or chunk_id < max_chunks): + num_samples = min(chunk_size, remaining) + plans.append(ChunkPlan(chunk_id=chunk_id, start_index=current_index, num_samples=num_samples)) + current_index += num_samples + remaining -= num_samples + chunk_id += 1 + return plans + + +def make_prompt_set_chunk_plan( + *, + prompt_sets: list[dict], + chunk_size: int, + max_chunks: int | None, +) -> list[ChunkPlan]: + if chunk_size < 1: + raise ValueError("chunk_size must be >= 1") + chunk_scope_totals = [] + all_totals = [] + for index, prompt_set in enumerate(prompt_sets): + num_samples = prompt_set.get("num_samples") + if num_samples is None: + raise ValueError(f"prompt_sets[{index}] requires num_samples when chunked_rum.total_samples is omitted") + effective_total = int(num_samples) * int(prompt_set.get("repeat", 1)) + all_totals.append(effective_total) + if prompt_set_cache_scope(prompt_set) == "chunk": + chunk_scope_totals.append(effective_total) + totals = chunk_scope_totals or all_totals + total_samples = max(totals) if totals else 0 + return make_chunk_plan(start_index=0, total_samples=total_samples, chunk_size=chunk_size, max_chunks=max_chunks) + + +def prompt_set_effective_total(prompt_set: dict) -> int: + set_total = prompt_set.get("num_samples") + if set_total is None: + raise ValueError(f"prompt set {prompt_set.get('name', '')} requires num_samples") + return int(set_total) * int(prompt_set.get("repeat", 1)) + + +def prompt_set_weight_total(prompt_set: dict) -> int: + set_total = prompt_set.get("num_samples") + if set_total is None: + raise ValueError(f"prompt set {prompt_set.get('name', '')} requires num_samples") + return int(set_total) + + +def prompt_set_cache_scope(prompt_set: dict) -> str: + scope = str(prompt_set.get("cache_scope", "chunk")) + if scope not in {"chunk", "all"}: + raise ValueError(f"prompt set {prompt_set.get('name', '')} has unsupported cache_scope: {scope!r}") + return scope + + +def prompt_set_slices_for_plan(prompt_set: dict, plan: ChunkPlan) -> list[dict]: + set_start = int(prompt_set.get("start_index", 0)) + set_total = prompt_set.get("num_samples") + if set_total is None: + raise ValueError(f"prompt set {prompt_set.get('name', '')} requires num_samples") + set_total = int(set_total) + effective_total = prompt_set_effective_total(prompt_set) + effective_offset = plan.start_index + remaining = effective_total - effective_offset + if remaining <= 0: + return [] + to_take = min(plan.num_samples, remaining) + slices: list[dict] = [] + while to_take > 0: + offset_in_set = effective_offset % set_total + repeat_cycle = effective_offset // set_total + take = min(to_take, set_total - offset_in_set) + adjusted = dict(prompt_set) + adjusted["start_index"] = set_start + offset_in_set + adjusted["num_samples"] = take + adjusted["cache_chunk_offset"] = 0 + adjusted["_repeat_cycle"] = repeat_cycle + slices.append(adjusted) + effective_offset += take + to_take -= take + return slices + + +def prompt_set_slice_for_plan(prompt_set: dict, plan: ChunkPlan) -> dict | None: + slices = prompt_set_slices_for_plan(prompt_set, plan) + return slices[0] if slices else None + + +def load_chunk_manifest(path: Path) -> dict: + if not path.exists(): + return {"chunks": []} + data = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(data, dict) or not isinstance(data.get("chunks"), list): + raise ValueError(f"invalid chunk manifest: {path}") + return data + + +def write_chunk_manifest(path: Path, manifest: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False), encoding="utf-8") + + +def update_chunk_manifest(path: Path, plan: ChunkPlan, **values) -> None: + manifest = load_chunk_manifest(path) + chunks = manifest.setdefault("chunks", []) + existing = next((chunk for chunk in chunks if chunk.get("chunk_id") == plan.chunk_id), None) + if existing is None: + existing = { + "chunk_id": plan.chunk_id, + "start_index": plan.start_index, + "num_samples": plan.num_samples, + } + chunks.append(existing) + else: + existing["start_index"] = plan.start_index + existing["num_samples"] = plan.num_samples + existing.update(values) + write_chunk_manifest(path, manifest) + + +def completed_chunk_ids(manifest: dict) -> set[int]: + return { + int(chunk["chunk_id"]) + for chunk in manifest.get("chunks", []) + if isinstance(chunk, dict) and chunk.get("status") == "complete" and "chunk_id" in chunk + } + + +def chunk_train_steps(chunk: dict, fallback_output_dir: Path | None, previous_chunk: dict | None = None) -> int: + value = chunk.get("train_steps") + if isinstance(value, int) and value >= 0: + return value + total_completed_steps = chunk.get("total_completed_steps") + if isinstance(total_completed_steps, int) and total_completed_steps >= 0: + previous_total = previous_chunk.get("total_completed_steps") if previous_chunk else None + if isinstance(previous_total, int) and 0 <= previous_total <= total_completed_steps: + return total_completed_steps - previous_total + output_dir = Path(chunk.get("output_dir") or fallback_output_dir) + summary_path = output_dir / "train-summary.json" + if not summary_path.exists(): + return 0 + try: + data = json.loads(summary_path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + return 0 + losses = data.get("losses", []) + return len(losses) if isinstance(losses, list) else 0 + + +def resolve_chunk_student_init(args: argparse.Namespace, previous_checkpoint: str | None) -> str | None: + return previous_checkpoint or args.student_init + + +def resolve_chunk_optimizer_state(args: argparse.Namespace, previous_optimizer_state: str | None) -> str | None: + return previous_optimizer_state or getattr(args, "optimizer_state", None) + + +def normalize_prompt_sets(args: argparse.Namespace) -> list[dict] | None: + prompt_sets = getattr(args, "prompt_sets", None) + if not prompt_sets: + return None + if not isinstance(prompt_sets, list): + raise ValueError("prompt_sets must be a list of tables") + normalized: list[dict] = [] + for index, prompt_set in enumerate(prompt_sets): + if not isinstance(prompt_set, dict): + raise ValueError("each prompt_sets entry must be a table") + prompts = prompt_set.get("prompts") + cache_dir = prompt_set.get("cache_dir") + if not prompts or not cache_dir: + raise ValueError(f"prompt_sets[{index}] requires prompts and cache_dir") + normalized.append( + { + "name": prompt_set.get("name") or f"set-{index}", + "prompts": prompts, + "cache_dir": cache_dir, + "start_index": int(prompt_set.get("start_index", getattr(args, "start_index", 0))), + "num_samples": prompt_set.get("num_samples", getattr(args, "num_samples", None)), + "cache_chunk_offset": int(prompt_set.get("cache_chunk_offset", 0)), + "repeat": int(prompt_set.get("repeat", 1)), + "cache_scope": prompt_set_cache_scope(prompt_set), + } + ) + if normalized[-1]["repeat"] < 1: + raise ValueError(f"prompt_sets[{index}].repeat must be >= 1") + return normalized + + +def prompt_set_mix_weights(prompt_sets: list[dict]) -> list[float]: + return [float(prompt_set_weight_total(prompt_set)) for prompt_set in prompt_sets] + + +def prompt_set_chunk_name(prompt_set: dict, training_chunk_id: int) -> str: + return f"chunk-{training_chunk_id + int(prompt_set.get('cache_chunk_offset', 0)):04d}" + + +def prompt_set_cache_dir_for_plan(prompt_set: dict, training_chunk_id: int) -> str: + base = Path(prompt_set["cache_dir"]) + if prompt_set_cache_scope(prompt_set) == "all": + return str(base) + return str(base / prompt_set_chunk_name(prompt_set, training_chunk_id)) + + +def link_or_symlink_cache_file(source: Path, target: Path) -> None: + target.parent.mkdir(parents=True, exist_ok=True) + try: + os.link(source, target) + except OSError: + try: + target.symlink_to(source) + except OSError: + shutil.copy2(source, target) + + +def link_repeated_prompt_set_cache( + *, + prompt_set: dict, + adjusted: dict, + chunk_size: int, + seed: int, + bucket_enabled: bool, + width: int, + height: int, + skip_existing: bool, +) -> tuple[int, int]: + base_start = int(prompt_set.get("start_index", 0)) + base_cache_dir = Path(prompt_set["cache_dir"]) + dst_cache_dir = Path(adjusted["cache_dir"]) + linked = 0 + skipped = 0 + for sample_index in range(int(adjusted["start_index"]), int(adjusted["start_index"]) + int(adjusted["num_samples"])): + offset = sample_index - base_start + if offset < 0: + raise ValueError(f"repeat sample index {sample_index} is before prompt set start_index {base_start}") + source_chunk_id = offset // chunk_size + source_cache_dir = base_cache_dir / prompt_set_chunk_name(prompt_set, source_chunk_id) + if bucket_enabled: + sample_width, sample_height = choose_cache_bucket(sample_index, seed) + else: + sample_width, sample_height = width, height + source = bucket_cache_path( + source_cache_dir, + sample_index, + width=sample_width, + height=sample_height, + bucket_enabled=bucket_enabled, + ) + target = bucket_cache_path( + dst_cache_dir, + sample_index, + width=sample_width, + height=sample_height, + bucket_enabled=bucket_enabled, + ) + if skip_existing and target.exists(): + skipped += 1 + continue + if not source.exists(): + raise FileNotFoundError( + f"repeat cache source is missing: {source}. " + "Run earlier chunks first or disable repeat for this prompt set." + ) + if target.exists(): + target.unlink() + link_or_symlink_cache_file(source, target) + linked += 1 + return linked, skipped + + +def chunk_cache_dirs_for_prompt_sets(prompt_sets: list[dict], training_chunk_id: int) -> list[str]: + return [prompt_set_cache_dir_for_plan(prompt_set, training_chunk_id) for prompt_set in prompt_sets] diff --git a/src/rum_xpred/config.py b/src/rum_xpred/config.py new file mode 100644 index 0000000..94f8bd7 --- /dev/null +++ b/src/rum_xpred/config.py @@ -0,0 +1,285 @@ +from __future__ import annotations + +import argparse +from pathlib import Path +from typing import Any + +try: + import tomllib +except ModuleNotFoundError: # pragma: no cover - Python < 3.11 fallback + import toml as _toml # type: ignore + + class tomllib: # type: ignore + @staticmethod + def loads(text: str) -> dict[str, Any]: + return _toml.loads(text) + + +COMMANDS = {"build_cache", "train_xpred", "sample_xpred", "sample_compare", "chunked_rum"} +PROMPT_SET_KEYS = {"name", "prompts", "cache_dir", "start_index", "num_samples", "cache_chunk_offset", "repeat"} + +COMMON_DEFAULTS: dict[str, Any] = { + "device": None, + "mixed_precision": "bf16", + "flow_shift": 3.0, + "seed": 20260701, + "toy_smoke": False, + "adapter": "rum_xpred.adapters.anima_sd_scripts:create_adapter", + "dit": None, + "student_init": None, + "text_encoder": None, + "vae": None, + "vae_spatial_chunk_size": None, + "vae_disable_cache": False, + "negative_prompt": "", + "attn_mode": "torch", + "fp8": False, + "fp8_scaled": False, + "text_encoder_cpu": False, +} + +COMMAND_DEFAULTS: dict[str, dict[str, Any]] = { + "build_cache": { + "prompts": None, + "cache_dir": None, + "prompt_sets": None, + "num_samples": None, + "start_index": 0, + "cache_batch_size": 1, + "skip_existing": True, + "bucket_enabled": False, + "width": 1024, + "height": 1024, + "teacher_steps": 40, + "teacher_cfg": 1.0, + "teacher_lora": None, + "teacher_lora_weight": 1.0, + }, + "train_xpred": { + "cache_dir": None, + "cache_dirs": None, + "cache_mix_mode": "single", + "cache_mix_weights": None, + "output_dir": None, + "prediction_type": "x", + "global_step_offset": 0, + "optimizer_state": None, + "max_train_steps": None, + "num_train_epochs": 1.0, + "train_batch_size": 1, + "gradient_accumulation_steps": 1, + "learning_rate": 1e-6, + "lr_scheduler": "constant", + "lr_warmup_steps": 0, + "lr_cosine_min": 0.1, + "lr_scheduler_total_steps": None, + "weight_decay": 0.0, + "adam_beta1": 0.9, + "adam_beta2": 0.999, + "adam_epsilon": 1e-8, + "max_grad_norm": 1.0, + "sigma_min_train": 0.02, + "time_sampling": "uniform_shifted", + "time_sampling_logit_mean": -0.8, + "time_sampling_logit_std": 0.8, + "loss_weighting": "none", + "loss_eps_floor": 5e-2, + "shuffle_cache": True, + "drop_last": False, + "log_every": 1, + "save_every_steps": None, + "checkpoints_total_limit": None, + "gradient_checkpointing": False, + "gradient_checkpointing_cpu_offload": False, + "gradient_checkpointing_unsloth_offload": False, + "sample_every_steps": 0, + "sample_prompt": "", + "sample_steps": 40, + "sample_num_samples": 1, + "sample_cfg": 1.0, + "sample_eps_floor": 1e-4, + "sample_width": None, + "sample_height": None, + "sample_seed": None, + "sample_output_dir": None, + "sample_decode_images": False, + "sample_image_prefix": "train-sample", + "sample_wandb_log_images": True, + "sample_lora": "__inherit__", + "sample_lora_weight": None, + "sample_lora_steps": None, + "sample_lora_cfg": None, + "sample_lora_eps_floor": None, + "sample_compare_every_steps": 0, + "sample_compare_prompt": "", + "sample_compare_steps": 40, + "sample_compare_num_samples": 1, + "sample_compare_cfg": 1.0, + "sample_compare_eps_floor": 1e-4, + "sample_compare_width": None, + "sample_compare_height": None, + "sample_compare_seed": None, + "sample_compare_output_dir": None, + "sample_compare_baseline_source_dir": None, + "sample_compare_baseline_output_dir": None, + "sample_compare_baseline_wandb_log_images": True, + "sample_compare_lora": "__inherit__", + "sample_compare_lora_weight": None, + "sample_compare_lora_cfg": None, + "sample_compare_teacher_sanity": False, + "sample_compare_teacher_sanity_lora": "__inherit__", + "sample_compare_teacher_sanity_lora_weight": None, + "sample_compare_decode_images": False, + "sample_compare_image_prefix": "compare", + "sample_compare_wandb_log_images": True, + "dry_run": False, + }, + "sample_xpred": { + "checkpoint": None, + "output": None, + "prediction_type": "x", + "prompt": "", + "num_samples": 1, + "steps": 40, + "width": 1024, + "height": 1024, + "eps_floor": 1e-4, + "decode_sample_images": False, + "sample_image_dir": None, + "sample_image_prefix": "sample", + "wandb_log_sample_images": True, + }, + "sample_compare": { + "student_checkpoint": None, + "teacher_checkpoint": None, + "teacher_lora": "__inherit__", + "teacher_lora_weight": None, + "output_dir": None, + "prediction_type": "x", + "prompt": "", + "num_samples": 1, + "steps": 40, + "width": 1024, + "height": 1024, + "eps_floor": 1e-4, + "alphas": [0.0, 0.25, 0.5, 0.75, 1.0], + "teacher_cfg": 1.0, + "decode_sample_images": False, + "sample_image_prefix": "compare", + "wandb_log_sample_images": True, + }, + "chunked_rum": { + "chunk_root": None, + "total_samples": None, + "chunk_size": 1024, + "start_index": 0, + "max_chunks": None, + "train_steps_per_chunk": None, + "delete_cache_after_train": False, + "resume": True, + }, +} + +WANDB_DEFAULTS: dict[str, Any] = { + "wandb_enabled": False, + "wandb_project": "rum-anima-xpred", + "wandb_entity": None, + "wandb_run_name": None, + "wandb_run_id": None, + "wandb_resume": None, + "wandb_mode": None, + "wandb_tags": [], + "wandb_notes": None, + "wandb_log_config": True, + "wandb_metrics_file": None, + "wandb_metrics_log_every": 1, +} + + +def load_toml_config(path: str | Path) -> dict[str, Any]: + config_path = Path(path) + if not config_path.exists(): + raise FileNotFoundError(f"config file not found: {config_path}") + data = tomllib.loads(config_path.read_text(encoding="utf-8")) + if not isinstance(data, dict): + raise ValueError(f"config root must be a TOML table: {config_path}") + return data + + +def _section(data: dict[str, Any], name: str) -> dict[str, Any]: + value = data.get(name, {}) + if value is None: + return {} + if not isinstance(value, dict): + raise ValueError(f"[{name}] must be a TOML table") + return dict(value) + + +def config_to_namespace(config: dict[str, Any], *, command_override: str | None = None) -> argparse.Namespace: + command = command_override or config.get("command") + if command not in COMMANDS: + raise ValueError(f"config command must be one of {sorted(COMMANDS)}, got {command!r}") + + common = dict(COMMON_DEFAULTS) + common.update(_section(config, "common")) + command_values = dict(COMMAND_DEFAULTS[command]) + command_values.update(_section(config, command)) + if command == "build_cache" and isinstance(command_values.get("prompt_sets"), list): + build_cache_global_keys = set(COMMAND_DEFAULTS["build_cache"]) - {"prompt_sets", *PROMPT_SET_KEYS} + for index, prompt_set in enumerate(command_values["prompt_sets"]): + if not isinstance(prompt_set, dict): + continue + misplaced = sorted(set(prompt_set) & build_cache_global_keys) + if misplaced: + raise ValueError( + "build_cache.prompt_sets entries contain global build_cache key(s): " + f"prompt_sets[{index}] has {', '.join(misplaced)}. " + "Move these keys before the first [[build_cache.prompt_sets]] table." + ) + build_cache_values = _section(config, "build_cache") + if command == "train_xpred": + if command_values.get("sample_compare_lora") == "__inherit__": + command_values["sample_compare_lora"] = build_cache_values.get("teacher_lora") + if command_values.get("sample_compare_lora_weight") is None: + command_values["sample_compare_lora_weight"] = build_cache_values.get("teacher_lora_weight", 1.0) + if command_values.get("sample_lora") == "__inherit__": + command_values["sample_lora"] = build_cache_values.get("teacher_lora") + if command_values.get("sample_lora_weight") is None: + command_values["sample_lora_weight"] = build_cache_values.get("teacher_lora_weight", 1.0) + if command_values.get("sample_compare_teacher_sanity_lora") == "__inherit__": + command_values["sample_compare_teacher_sanity_lora"] = build_cache_values.get("teacher_lora") + if command_values.get("sample_compare_teacher_sanity_lora_weight") is None: + command_values["sample_compare_teacher_sanity_lora_weight"] = build_cache_values.get("teacher_lora_weight", 1.0) + elif command == "sample_compare": + if command_values.get("teacher_lora") == "__inherit__": + command_values["teacher_lora"] = build_cache_values.get("teacher_lora") + if command_values.get("teacher_lora_weight") is None: + command_values["teacher_lora_weight"] = build_cache_values.get("teacher_lora_weight", 1.0) + wandb_values = dict(WANDB_DEFAULTS) + wandb_values.update(_section(config, "wandb")) + + unknown_sections = sorted( + key + for key, value in config.items() + if isinstance(value, dict) and key not in {"common", "wandb", *COMMANDS} + ) + if unknown_sections: + raise ValueError(f"unknown config section(s): {', '.join(unknown_sections)}") + + unknown_keys = sorted( + key + for key, value in config.items() + if not isinstance(value, dict) and key != "command" + ) + if unknown_keys: + raise ValueError(f"unknown config key(s): {', '.join(unknown_keys)}") + + args = argparse.Namespace(command=command, **common, **command_values, **wandb_values) + return args + + +def apply_overrides(args: argparse.Namespace, overrides: dict[str, Any]) -> argparse.Namespace: + for key, value in overrides.items(): + if value is not None: + setattr(args, key, value) + return args diff --git a/src/rum_xpred/train_schedule.py b/src/rum_xpred/train_schedule.py new file mode 100644 index 0000000..dbbf03f --- /dev/null +++ b/src/rum_xpred/train_schedule.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +import argparse +import math + +import torch + +from rum_xpred.chunking import ChunkPlan, prompt_set_cache_scope, prompt_set_effective_total, prompt_set_weight_total + + +def lr_scale_for_step(args: argparse.Namespace, step: int) -> float: + if step < 1: + raise ValueError("step is 1-indexed and must be >= 1") + if args.lr_warmup_steps > 0 and step <= args.lr_warmup_steps: + return step / args.lr_warmup_steps + if args.lr_scheduler == "constant": + return 1.0 + if args.lr_scheduler == "cosine": + total_steps = args.lr_scheduler_total_steps or getattr(args, "resolved_max_train_steps", None) or args.max_train_steps + decay_steps = max(total_steps - args.lr_warmup_steps, 1) + decay_step = min(max(step - args.lr_warmup_steps, 0), decay_steps) + cosine = 0.5 * (1.0 + math.cos(math.pi * decay_step / decay_steps)) + return args.lr_cosine_min + (1.0 - args.lr_cosine_min) * cosine + raise ValueError(f"unsupported lr_scheduler: {args.lr_scheduler}") + + +def set_optimizer_lr(optimizer: torch.optim.Optimizer, lr: float) -> None: + for group in optimizer.param_groups: + group["lr"] = lr + + +def resolve_max_train_steps(args: argparse.Namespace, cache_sample_count: int) -> int: + if args.max_train_steps is not None: + return args.max_train_steps + effective_batch_size = args.train_batch_size * args.gradient_accumulation_steps + steps_per_epoch = math.ceil(cache_sample_count / effective_batch_size) + return max(1, math.ceil(steps_per_epoch * args.num_train_epochs)) + + +def planned_chunk_train_steps( + train_args: argparse.Namespace, + chunk_args: argparse.Namespace, + plans: list[ChunkPlan], + *, + cache_multiplier: int = 1, +) -> int: + if chunk_args.train_steps_per_chunk is not None: + return chunk_args.train_steps_per_chunk * len(plans) + if train_args.max_train_steps is not None: + return train_args.max_train_steps * len(plans) + return sum(resolve_max_train_steps(train_args, plan.num_samples * cache_multiplier) for plan in plans) + + +def planned_prompt_set_train_steps(train_args: argparse.Namespace, chunk_args: argparse.Namespace, plans: list[ChunkPlan], prompt_sets: list[dict]) -> int: + if chunk_args.train_steps_per_chunk is not None: + return chunk_args.train_steps_per_chunk * len(plans) + if train_args.max_train_steps is not None: + return train_args.max_train_steps * len(plans) + total = 0 + for plan in plans: + cache_sample_count = planned_prompt_set_cache_sample_count(plan, plans, prompt_sets) + if cache_sample_count > 0: + total += resolve_max_train_steps(train_args, cache_sample_count) + return total + + +def planned_prompt_set_cache_sample_count(plan: ChunkPlan, plans: list[ChunkPlan], prompt_sets: list[dict]) -> int: + chunk_scope_total = sum(plan.num_samples for plan in plans) + cache_sample_count = 0 + for prompt_set in prompt_sets: + if prompt_set_cache_scope(prompt_set) == "all": + cache_sample_count += math.ceil(prompt_set_weight_total(prompt_set) * plan.num_samples / chunk_scope_total) + continue + remaining = prompt_set_effective_total(prompt_set) - plan.start_index + if remaining > 0: + cache_sample_count += min(plan.num_samples, remaining) + return cache_sample_count diff --git a/src/rum_xpred/vendor_paths.py b/src/rum_xpred/vendor_paths.py new file mode 100644 index 0000000..7941ded --- /dev/null +++ b/src/rum_xpred/vendor_paths.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +import sys +from pathlib import Path + + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +SD_SCRIPTS_ROOT = PROJECT_ROOT / "vendor" / "sd-scripts" + + +def ensure_local_sd_scripts() -> Path: + if not (SD_SCRIPTS_ROOT / "anima_minimal_inference.py").exists(): + raise FileNotFoundError(f"vendored sd-scripts Anima code not found: {SD_SCRIPTS_ROOT}") + value = str(SD_SCRIPTS_ROOT) + if value in sys.path: + sys.path.remove(value) + sys.path.insert(0, value) + return SD_SCRIPTS_ROOT diff --git a/vendor/sd-scripts/.gitignore b/vendor/sd-scripts/.gitignore new file mode 100644 index 0000000..4fb6655 --- /dev/null +++ b/vendor/sd-scripts/.gitignore @@ -0,0 +1,21 @@ +logs +__pycache__ +wd14_tagger_model +venv +*.egg-info +build +.vscode +wandb +CLAUDE.md +GEMINI.md +.claude +.gemini +MagicMock +.codex-tmp +references + +# inpainting test artefacts (synthetic data, training outputs, downloads, mask viz) +tests/test_data/ +tests/test_output_*/ +tests/downloaded_data/ +tests/mask_viz/ diff --git a/vendor/sd-scripts/LICENSE.md b/vendor/sd-scripts/LICENSE.md new file mode 100644 index 0000000..56765e7 --- /dev/null +++ b/vendor/sd-scripts/LICENSE.md @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2022] [kohya-ss] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/vendor/sd-scripts/XTI_hijack.py b/vendor/sd-scripts/XTI_hijack.py new file mode 100644 index 0000000..93bc1c0 --- /dev/null +++ b/vendor/sd-scripts/XTI_hijack.py @@ -0,0 +1,204 @@ +import torch +from library.device_utils import init_ipex +init_ipex() + +from typing import Union, List, Optional, Dict, Any, Tuple +from diffusers.models.unet_2d_condition import UNet2DConditionOutput + +from library.original_unet import SampleOutput + + +def unet_forward_XTI( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + return_dict: bool = True, +) -> Union[Dict, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a dict instead of a plain tuple. + + Returns: + `SampleOutput` or `tuple`: + `SampleOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + # デフォルトではサンプルは「2^アップサンプルの数」、つまり64の倍数である必要がある + # ただそれ以外のサイズにも対応できるように、必要ならアップサンプルのサイズを変更する + # 多分画質が悪くなるので、64で割り切れるようにしておくのが良い + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + # 64で割り切れないときはupsamplerにサイズを伝える + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + # logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # 1. time + timesteps = timestep + timesteps = self.handle_unusual_timesteps(sample, timesteps) # 変な時だけ処理 + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + # timestepsは重みを含まないので常にfloat32のテンソルを返す + # しかしtime_embeddingはfp16で動いているかもしれないので、ここでキャストする必要がある + # time_projでキャストしておけばいいんじゃね？ + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + # 2. pre-process + sample = self.conv_in(sample) + + # 3. down + down_block_res_samples = (sample,) + down_i = 0 + for downsample_block in self.down_blocks: + # downblockはforwardで必ずencoder_hidden_statesを受け取るようにしても良さそうだけど、 + # まあこちらのほうがわかりやすいかもしれない + if downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states[down_i : down_i + 2], + ) + down_i += 2 + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + down_block_res_samples += res_samples + + # 4. mid + sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states[6]) + + # 5. up + up_i = 7 + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # skip connection + + # if we have not reached the final block and need to forward the upsample size, we do it here + # 前述のように最後のブロック以外ではupsample_sizeを伝える + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states[up_i : up_i + 3], + upsample_size=upsample_size, + ) + up_i += 3 + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + + # 6. post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return SampleOutput(sample=sample) + + +def downblock_forward_XTI( + self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None +): + output_states = () + i = 0 + + for resnet, attn in zip(self.resnets, self.attentions): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i] + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample + + output_states += (hidden_states,) + i += 1 + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +def upblock_forward_XTI( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, +): + i = 0 + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i] + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample + + i += 1 + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/vendor/sd-scripts/anima_minimal_inference.py b/vendor/sd-scripts/anima_minimal_inference.py new file mode 100644 index 0000000..6190dff --- /dev/null +++ b/vendor/sd-scripts/anima_minimal_inference.py @@ -0,0 +1,1082 @@ +import argparse +import datetime +import gc +from importlib.util import find_spec +import random +import os +import time +import copy +from types import SimpleNamespace +from typing import Tuple, Optional, List, Any, Dict, Union + +import torch +from safetensors.torch import load_file, save_file +from safetensors import safe_open +from tqdm import tqdm +from diffusers.utils.torch_utils import randn_tensor +from PIL import Image + +from library import anima_models, anima_utils, hunyuan_image_utils, qwen_image_autoencoder_kl, strategy_anima, strategy_base +from library.device_utils import clean_memory_on_device, synchronize_device + +lycoris_available = find_spec("lycoris") is not None +if lycoris_available: + from lycoris.kohya import create_network_from_weights + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +class GenerationSettings: + def __init__(self, device: torch.device, dit_weight_dtype: Optional[torch.dtype] = None): + self.device = device + self.dit_weight_dtype = dit_weight_dtype # not used currently because model may be optimized + + +def parse_args() -> argparse.Namespace: + """parse command line arguments""" + parser = argparse.ArgumentParser(description="HunyuanImage inference script") + + parser.add_argument("--dit", type=str, default=None, help="DiT directory or path") + parser.add_argument("--vae", type=str, default=None, help="VAE directory or path") + parser.add_argument( + "--vae_chunk_size", + type=int, + default=None, + help="Spatial chunk size for VAE encoding/decoding to reduce memory usage. Must be even number. If not specified, chunking is disabled (official behavior)." + + " / メモリ使用量を減らすためのVAEエンコード/デコードの空間チャンクサイズ。偶数である必要があります。未指定の場合、チャンク処理は無効になります（公式の動作）。", + ) + parser.add_argument( + "--vae_disable_cache", + action="store_true", + help="Disable internal VAE caching mechanism to reduce memory usage. Encoding / decoding will also be faster, but this differs from official behavior." + + " / VAEのメモリ使用量を減らすために内部のキャッシュ機構を無効にします。エンコード/デコードも速くなりますが、公式の動作とは異なります。", + ) + parser.add_argument("--text_encoder", type=str, required=True, help="Text Encoder 1 (Qwen2.5-VL) directory or path") + + # LoRA + parser.add_argument("--lora_weight", type=str, nargs="*", required=False, default=None, help="LoRA weight path") + parser.add_argument("--lora_multiplier", type=float, nargs="*", default=1.0, help="LoRA multiplier") + parser.add_argument("--include_patterns", type=str, nargs="*", default=None, help="LoRA module include patterns") + parser.add_argument("--exclude_patterns", type=str, nargs="*", default=None, help="LoRA module exclude patterns") + + # inference + parser.add_argument( + "--guidance_scale", type=float, default=3.5, help="Guidance scale for classifier free guidance. Default is 3.5." + ) + parser.add_argument("--prompt", type=str, default=None, help="prompt for generation") + parser.add_argument("--negative_prompt", type=str, default="", help="negative prompt for generation, default is empty string") + parser.add_argument("--image_size", type=int, nargs=2, default=[1024, 1024], help="image size, height and width") + parser.add_argument("--infer_steps", type=int, default=50, help="number of inference steps, default is 50") + parser.add_argument("--save_path", type=str, required=True, help="path to save generated video") + parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") + + # Flow Matching + parser.add_argument( + "--flow_shift", + type=float, + default=5.0, + help="Shift factor for flow matching schedulers. Default is 5.0.", + ) + + parser.add_argument("--fp8", action="store_true", help="use fp8 for DiT model") + parser.add_argument("--fp8_scaled", action="store_true", help="use scaled fp8 for DiT, only for fp8") + + parser.add_argument("--text_encoder_cpu", action="store_true", help="Inference on CPU for Text Encoders") + parser.add_argument( + "--device", type=str, default=None, help="device to use for inference. If None, use CUDA if available, otherwise use CPU" + ) + parser.add_argument( + "--attn_mode", + type=str, + default="torch", + choices=["flash", "torch", "sageattn", "xformers", "sdpa"], # "sdpa" for backward compatibility + help="attention mode", + ) + parser.add_argument( + "--output_type", + type=str, + default="images", + choices=["images", "latent", "latent_images"], + help="output type", + ) + parser.add_argument("--no_metadata", action="store_true", help="do not save metadata") + parser.add_argument("--latent_path", type=str, nargs="*", default=None, help="path to latent for decode. no inference") + parser.add_argument( + "--lycoris", action="store_true", help=f"use lycoris for inference{'' if lycoris_available else ' (not available)'}" + ) + + # arguments for batch and interactive modes + parser.add_argument("--from_file", type=str, default=None, help="Read prompts from a file") + parser.add_argument("--interactive", action="store_true", help="Interactive mode: read prompts from console") + + args = parser.parse_args() + + # Validate arguments + if args.from_file and args.interactive: + raise ValueError("Cannot use both --from_file and --interactive at the same time") + + if args.latent_path is None or len(args.latent_path) == 0: + if args.prompt is None and not args.from_file and not args.interactive: + raise ValueError("Either --prompt, --from_file or --interactive must be specified") + + if args.lycoris and not lycoris_available: + raise ValueError("LyCORIS support requires the lycoris package to be installed") + + if args.attn_mode == "sdpa": + args.attn_mode = "torch" # backward compatibility + + return args + + +def parse_prompt_line(line: str) -> Dict[str, Any]: + """Parse a prompt line into a dictionary of argument overrides + + Args: + line: Prompt line with options + + Returns: + Dict[str, Any]: Dictionary of argument overrides + """ + parts = line.split(" --") + prompt = parts[0].strip() + + # Create dictionary of overrides + overrides = {"prompt": prompt} + + for part in parts[1:]: + if not part.strip(): + continue + option_parts = part.split(" ", 1) + option = option_parts[0].strip() + value = option_parts[1].strip() if len(option_parts) > 1 else "" + + # Map options to argument names + if option == "w": + overrides["image_size_width"] = int(value) + elif option == "h": + overrides["image_size_height"] = int(value) + elif option == "d": + overrides["seed"] = int(value) + elif option == "s": + overrides["infer_steps"] = int(value) + elif option == "g" or option == "l": + overrides["guidance_scale"] = float(value) + elif option == "fs": + overrides["flow_shift"] = float(value) + elif option == "n": + overrides["negative_prompt"] = value + + return overrides + + +def apply_overrides(args: argparse.Namespace, overrides: Dict[str, Any]) -> argparse.Namespace: + """Apply overrides to args + + Args: + args: Original arguments + overrides: Dictionary of overrides + + Returns: + argparse.Namespace: New arguments with overrides applied + """ + args_copy = copy.deepcopy(args) + + for key, value in overrides.items(): + if key == "image_size_width": + args_copy.image_size[1] = value + elif key == "image_size_height": + args_copy.image_size[0] = value + else: + setattr(args_copy, key, value) + + return args_copy + + +def check_inputs(args: argparse.Namespace) -> Tuple[int, int]: + """Validate video size and length + + Args: + args: command line arguments + + Returns: + Tuple[int, int]: (height, width) + """ + height = args.image_size[0] + width = args.image_size[1] + + if height % 32 != 0 or width % 32 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.") + + return height, width + + +# region Model + + +def load_dit_model( + args: argparse.Namespace, device: torch.device, dit_weight_dtype: Optional[torch.dtype] = None +) -> anima_models.Anima: + """load DiT model + + Args: + args: command line arguments + device: device to use + dit_weight_dtype: data type for the model weights. None for as-is + + Returns: + anima_models.Anima: DiT model instance + """ + # If LyCORIS is enabled, we will load the model to CPU and then merge LoRA weights (static method) + + loading_device = "cpu" + if not args.lycoris: + loading_device = device + + # load LoRA weights + if not args.lycoris and args.lora_weight is not None and len(args.lora_weight) > 0: + lora_weights_list = [] + for lora_weight in args.lora_weight: + logger.info(f"Loading LoRA weight from: {lora_weight}") + lora_sd = load_file(lora_weight) # load on CPU, dtype is as is + # lora_sd = filter_lora_state_dict(lora_sd, args.include_patterns, args.exclude_patterns) + lora_sd = {k: v for k, v in lora_sd.items() if k.startswith("lora_unet_")} # only keep unet lora weights + lora_weights_list.append(lora_sd) + else: + lora_weights_list = None + + loading_weight_dtype = dit_weight_dtype + if args.fp8_scaled and not args.lycoris: + loading_weight_dtype = None # we will load weights as-is and then optimize to fp8 + + model = anima_utils.load_anima_model( + device, + args.dit, + args.attn_mode, + True, # enable split_attn to trim masked tokens + loading_device, + loading_weight_dtype, + args.fp8_scaled and not args.lycoris, + lora_weights_list=lora_weights_list, + lora_multipliers=args.lora_multiplier, + ) + if not args.fp8_scaled: + # simple cast to dit_weight_dtype + target_dtype = None # load as-is (dit_weight_dtype == dtype of the weights in state_dict) + if dit_weight_dtype is not None: # in case of args.fp8 and not args.fp8_scaled + logger.info(f"Convert model to {dit_weight_dtype}") + target_dtype = dit_weight_dtype + + logger.info(f"Move model to device: {device}") + target_device = device + + model.to(target_device, target_dtype) # move and cast at the same time. this reduces redundant copy operations + + # model.to(device) + model.to(device, dtype=torch.bfloat16) # ensure model is in bfloat16 for inference + + model.eval().requires_grad_(False) + clean_memory_on_device(device) + + return model + + +def load_text_encoder( + args: argparse.Namespace, dtype: torch.dtype = torch.bfloat16, device: torch.device = torch.device("cpu") +) -> torch.nn.Module: + lora_weights_list = None + if args.lora_weight is not None and len(args.lora_weight) > 0: + lora_weights_list = [] + for lora_weight in args.lora_weight: + logger.info(f"Loading LoRA weight from: {lora_weight}") + lora_sd = load_file(lora_weight) # load on CPU, dtype is as is + # lora_sd = filter_lora_state_dict(lora_sd, args.include_patterns, args.exclude_patterns) + lora_sd = { + "model_" + k[len("lora_te_") :]: v for k, v in lora_sd.items() if k.startswith("lora_te_") + } # only keep Text Encoder lora weights, remove prefix "lora_te_" and add "model_" prefix + lora_weights_list.append(lora_sd) + + text_encoder, _ = anima_utils.load_qwen3_text_encoder( + args.text_encoder, dtype=dtype, device=device, lora_weights=lora_weights_list, lora_multipliers=args.lora_multiplier + ) + text_encoder.eval() + return text_encoder + + +# endregion + + +def decode_latent( + vae: qwen_image_autoencoder_kl.AutoencoderKLQwenImage, latent: torch.Tensor, device: torch.device +) -> torch.Tensor: + logger.info(f"Decoding image. Latent shape {latent.shape}, device {device}") + + vae.to(device) + with torch.no_grad(): + pixels = vae.decode_to_pixels(latent.to(device, dtype=vae.dtype)) + # pixels = vae.decode(latent.to(device, dtype=torch.bfloat16), scale=vae_scale) + if pixels.ndim == 5: # remove frame dimension if exists, [B, C, F, H, W] -> [B, C, H, W] + pixels = pixels.squeeze(2) + + pixels = pixels.to("cpu", dtype=torch.float32) # move to CPU and convert to float32 (bfloat16 is not supported by numpy) + vae.to("cpu") + + logger.info(f"Decoded. Pixel shape {pixels.shape}") + return pixels[0] # remove batch dimension + + +def process_escape(text: str) -> str: + """Process escape sequences in text + + Args: + text: Input text with escape sequences + + Returns: + str: Processed text + """ + return text.encode("utf-8").decode("unicode_escape") + + +def prepare_text_inputs( + args: argparse.Namespace, device: torch.device, anima: anima_models.Anima, shared_models: Optional[Dict] = None +) -> Tuple[Dict[str, Any], Dict[str, Any]]: + """Prepare text-related inputs for T2I: LLM encoding. Anima model is also needed for preprocessing""" + + # load text encoder: conds_cache holds cached encodings for prompts without padding + conds_cache = {} + text_encoder_device = torch.device("cpu") if args.text_encoder_cpu else device + if shared_models is not None: + text_encoder = shared_models.get("text_encoder") + + if "conds_cache" in shared_models: # Use shared cache if available + conds_cache = shared_models["conds_cache"] + + # text_encoder is on device (batched inference) or CPU (interactive inference) + else: # Load if not in shared_models + text_encoder_dtype = torch.bfloat16 # Default dtype for Text Encoder + text_encoder = load_text_encoder(args, dtype=text_encoder_dtype, device=text_encoder_device) + text_encoder.eval() + tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() + # Store references so load_target_model can reuse them + + # Store original devices to move back later if they were shared. This does nothing if shared_models is None + text_encoder_original_device = text_encoder.device if text_encoder else None + + # Ensure text_encoder is not None before proceeding + if not text_encoder: + raise ValueError("Text encoder is not loaded properly.") + + # Define a function to move models to device if needed + # This is to avoid moving models if not needed, especially in interactive mode + model_is_moved = False + + def move_models_to_device_if_needed(): + nonlocal model_is_moved + nonlocal shared_models + + if model_is_moved: + return + model_is_moved = True + + logger.info(f"Moving Text Encoder to appropriate device: {text_encoder_device}") + text_encoder.to(text_encoder_device) # If text_encoder_cpu is True, this will be CPU + + logger.info("Encoding prompt with Text Encoder") + + prompt = process_escape(args.prompt) + cache_key = prompt + if cache_key in conds_cache: + embed = conds_cache[cache_key] + else: + move_models_to_device_if_needed() + + tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() + + with torch.no_grad(): + # embed = anima_text_encoder.get_text_embeds(anima, tokenizer, text_encoder, t5xxl_tokenizer, prompt) + tokens = tokenize_strategy.tokenize(prompt) + embed = encoding_strategy.encode_tokens(tokenize_strategy, [text_encoder], tokens) + crossattn_emb = anima._preprocess_text_embeds( + source_hidden_states=embed[0].to(anima.device), + target_input_ids=embed[2].to(anima.device), + target_attention_mask=embed[3].to(anima.device), + source_attention_mask=embed[1].to(anima.device), + ) + crossattn_emb[~embed[3].bool()] = 0 + embed[0] = crossattn_emb + embed[0] = embed[0].cpu() + + conds_cache[cache_key] = embed + + negative_prompt = process_escape(args.negative_prompt) + cache_key = negative_prompt + if cache_key in conds_cache: + negative_embed = conds_cache[cache_key] + else: + move_models_to_device_if_needed() + + tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() + + with torch.no_grad(): + # negative_embed = anima_text_encoder.get_text_embeds(anima, tokenizer, text_encoder, t5xxl_tokenizer, negative_prompt) + tokens = tokenize_strategy.tokenize(negative_prompt) + negative_embed = encoding_strategy.encode_tokens(tokenize_strategy, [text_encoder], tokens) + crossattn_emb = anima._preprocess_text_embeds( + source_hidden_states=negative_embed[0].to(anima.device), + target_input_ids=negative_embed[2].to(anima.device), + target_attention_mask=negative_embed[3].to(anima.device), + source_attention_mask=negative_embed[1].to(anima.device), + ) + crossattn_emb[~negative_embed[3].bool()] = 0 + negative_embed[0] = crossattn_emb + negative_embed[0] = negative_embed[0].cpu() + + conds_cache[cache_key] = negative_embed + + if not (shared_models and "text_encoder" in shared_models): # if loaded locally + # There is a bug text_encoder is not freed from GPU memory when text encoder is fp8 + del text_encoder + gc.collect() # This may force Text Encoder to be freed from GPU memory + else: # if shared, move back to original device (likely CPU) + if text_encoder: + text_encoder.to(text_encoder_original_device) + + clean_memory_on_device(device) + + arg_c = {"embed": embed, "prompt": prompt} + arg_null = {"embed": negative_embed, "prompt": negative_prompt} + + return arg_c, arg_null + + +def generate( + args: argparse.Namespace, + gen_settings: GenerationSettings, + shared_models: Optional[Dict] = None, + precomputed_text_data: Optional[Dict] = None, +) -> torch.Tensor: + """main function for generation + + Args: + args: command line arguments + shared_models: dictionary containing pre-loaded models (mainly for DiT) + precomputed_image_data: Optional dictionary with precomputed image data + precomputed_text_data: Optional dictionary with precomputed text data + + Returns: + tuple: (HunyuanVAE2D model (vae) or None, torch.Tensor generated latent) + """ + device, dit_weight_dtype = (gen_settings.device, gen_settings.dit_weight_dtype) + + # prepare seed + seed = args.seed if args.seed is not None else random.randint(0, 2**32 - 1) + args.seed = seed # set seed to args for saving + + if shared_models is None or "model" not in shared_models: + # load DiT model + anima = load_dit_model(args, device, dit_weight_dtype) + + if shared_models is not None: + shared_models["model"] = anima + else: + # use shared model + logger.info("Using shared DiT model.") + anima: anima_models.Anima = shared_models["model"] + + if precomputed_text_data is not None: + logger.info("Using precomputed text data.") + context = precomputed_text_data["context"] + context_null = precomputed_text_data["context_null"] + + else: + logger.info("No precomputed data. Preparing image and text inputs.") + context, context_null = prepare_text_inputs(args, device, anima, shared_models) + + return generate_body(args, anima, context, context_null, device, seed) + + +def generate_body( + args: Union[argparse.Namespace, SimpleNamespace], + anima: anima_models.Anima, + context: Dict[str, Any], + context_null: Optional[Dict[str, Any]], + device: torch.device, + seed: int, +) -> torch.Tensor: + + # set random generator + seed_g = torch.Generator(device="cpu") + seed_g.manual_seed(seed) + + height, width = check_inputs(args) + logger.info(f"Image size: {height}x{width} (HxW), infer_steps: {args.infer_steps}") + + # image generation ###### + + logger.info(f"Prompt: {context['prompt']}") + + embed = context["embed"][0].to(device, dtype=torch.bfloat16) + if context_null is None: + context_null = context # dummy for unconditional + negative_embed = context_null["embed"][0].to(device, dtype=torch.bfloat16) + + # Prepare latent variables + num_channels_latents = anima_models.Anima.LATENT_CHANNELS + shape = ( + 1, + num_channels_latents, + 1, # Frame dimension + height // 8, # qwen_image_autoencoder_kl.SCALE_FACTOR, + width // 8, # qwen_image_autoencoder_kl.SCALE_FACTOR, + ) + latents = randn_tensor(shape, generator=seed_g, device=device, dtype=torch.bfloat16) + + # Create padding mask + bs = latents.shape[0] + h_latent = latents.shape[-2] + w_latent = latents.shape[-1] + padding_mask = torch.zeros(bs, 1, h_latent, w_latent, dtype=torch.bfloat16, device=device) + + logger.info(f"Embed: {embed.shape}, negative_embed: {negative_embed.shape}, latents: {latents.shape}") + embed = embed.to(torch.bfloat16) + negative_embed = negative_embed.to(torch.bfloat16) + + # Prepare timesteps + timesteps, sigmas = hunyuan_image_utils.get_timesteps_sigmas(args.infer_steps, args.flow_shift, device) + timesteps /= 1000 # scale to [0,1] range + timesteps = timesteps.to(device, dtype=torch.bfloat16) + + # Denoising loop + do_cfg = args.guidance_scale != 1.0 + autocast_enabled = args.fp8 + + with tqdm(total=len(timesteps), desc="Denoising steps") as pbar: + for i, t in enumerate(timesteps): + t_expand = t.expand(latents.shape[0]) + + with torch.no_grad(), torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=autocast_enabled): + noise_pred = anima(latents, t_expand, embed, padding_mask=padding_mask) + + if do_cfg: + with torch.no_grad(), torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=autocast_enabled): + uncond_noise_pred = anima(latents, t_expand, negative_embed, padding_mask=padding_mask) + noise_pred = uncond_noise_pred + args.guidance_scale * (noise_pred - uncond_noise_pred) + + # ensure latents dtype is consistent + latents = hunyuan_image_utils.step(latents, noise_pred, sigmas, i).to(latents.dtype) + + pbar.update() + + return latents + + +def get_time_flag(): + return datetime.datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S-%f")[:-3] + + +def save_latent(latent: torch.Tensor, args: argparse.Namespace, height: int, width: int) -> str: + """Save latent to file + + Args: + latent: Latent tensor + args: command line arguments + height: height of frame + width: width of frame + + Returns: + str: Path to saved latent file + """ + save_path = args.save_path + os.makedirs(save_path, exist_ok=True) + time_flag = get_time_flag() + + seed = args.seed + + latent_path = f"{save_path}/{time_flag}_{seed}_latent.safetensors" + + if args.no_metadata: + metadata = None + else: + metadata = { + "seeds": f"{seed}", + "prompt": f"{args.prompt}", + "height": f"{height}", + "width": f"{width}", + "infer_steps": f"{args.infer_steps}", + # "embedded_cfg_scale": f"{args.embedded_cfg_scale}", + "guidance_scale": f"{args.guidance_scale}", + } + if args.negative_prompt is not None: + metadata["negative_prompt"] = f"{args.negative_prompt}" + + sd = {"latent": latent.contiguous()} + save_file(sd, latent_path, metadata=metadata) + logger.info(f"Latent saved to: {latent_path}") + + return latent_path + + +def save_images(sample: torch.Tensor, args: argparse.Namespace, original_base_name: Optional[str] = None) -> str: + """Save images to directory + + Args: + sample: Video tensor + args: command line arguments + original_base_name: Original base name (if latents are loaded from files) + + Returns: + str: Path to saved images directory + """ + save_path = args.save_path + os.makedirs(save_path, exist_ok=True) + time_flag = get_time_flag() + + seed = args.seed + original_name = "" if original_base_name is None else f"_{original_base_name}" + image_name = f"{time_flag}_{seed}{original_name}" + + x = torch.clamp(sample, -1.0, 1.0) + x = ((x + 1.0) * 127.5).to(torch.uint8).cpu().numpy() + x = x.transpose(1, 2, 0) # C, H, W -> H, W, C + + image = Image.fromarray(x) + image.save(os.path.join(save_path, f"{image_name}.png")) + + logger.info(f"Sample images saved to: {save_path}/{image_name}") + + return f"{save_path}/{image_name}" + + +def save_output( + args: argparse.Namespace, + vae: qwen_image_autoencoder_kl.AutoencoderKLQwenImage, + latent: torch.Tensor, + device: torch.device, + original_base_name: Optional[str] = None, +) -> None: + """save output + + Args: + args: command line arguments + vae: VAE model + latent: latent tensor + device: device to use + original_base_name: original base name (if latents are loaded from files) + """ + height, width = latent.shape[-2], latent.shape[-1] # BCTHW + height *= 8 # qwen_image_autoencoder_kl.SCALE_FACTOR + width *= 8 # qwen_image_autoencoder_kl.SCALE_FACTOR + # print(f"Saving output. Latent shape {latent.shape}; pixel shape {height}x{width}") + if args.output_type == "latent" or args.output_type == "latent_images": + # save latent + save_latent(latent, args, height, width) + if args.output_type == "latent": + return + + if vae is None: + logger.error("VAE is None, cannot decode latents for saving video/images.") + return + + if latent.ndim == 2: # S,C. For packed latents from other inference scripts + latent = latent.unsqueeze(0) + height, width = check_inputs(args) # Get height/width from args + latent = latent.view( + 1, + vae.latent_channels, + 1, # Frame dimension + height // 8, # qwen_image_autoencoder_kl.SCALE_FACTOR, + width // 8, # qwen_image_autoencoder_kl.SCALE_FACTOR, + ) + + image = decode_latent(vae, latent, device) + + if args.output_type == "images" or args.output_type == "latent_images": + # save images + if original_base_name is None: + original_name = "" + else: + original_name = f"_{original_base_name}" + save_images(image, args, original_name) + + +def preprocess_prompts_for_batch(prompt_lines: List[str], base_args: argparse.Namespace) -> List[Dict]: + """Process multiple prompts for batch mode + + Args: + prompt_lines: List of prompt lines + base_args: Base command line arguments + + Returns: + List[Dict]: List of prompt data dictionaries + """ + prompts_data = [] + + for line in prompt_lines: + line = line.strip() + if not line or line.startswith("#"): # Skip empty lines and comments + continue + + # Parse prompt line and create override dictionary + prompt_data = parse_prompt_line(line) + logger.info(f"Parsed prompt data: {prompt_data}") + prompts_data.append(prompt_data) + + return prompts_data + + +def load_shared_models(args: argparse.Namespace) -> Dict: + """Load shared models for batch processing or interactive mode. + Models are loaded to CPU to save memory. VAE is NOT loaded here. + DiT model is also NOT loaded here, handled by process_batch_prompts or generate. + + Args: + args: Base command line arguments + + Returns: + Dict: Dictionary of shared models (text/image encoders) + """ + shared_models = {} + # Load text encoders to CPU + text_encoder_dtype = torch.bfloat16 # Default dtype for Text Encoder + text_encoder = load_text_encoder(args, dtype=text_encoder_dtype, device=torch.device("cpu")) + shared_models["text_encoder"] = text_encoder + return shared_models + + +def process_batch_prompts(prompts_data: List[Dict], args: argparse.Namespace) -> None: + """Process multiple prompts with model reuse and batched precomputation + + Args: + prompts_data: List of prompt data dictionaries + args: Base command line arguments + """ + if not prompts_data: + logger.warning("No valid prompts found") + return + + gen_settings = get_generation_settings(args) + dit_weight_dtype = gen_settings.dit_weight_dtype + device = gen_settings.device + + # 1. Prepare VAE + logger.info("Loading VAE for batch generation...") + vae_for_batch = qwen_image_autoencoder_kl.load_vae( + args.vae, device="cpu", disable_mmap=True, spatial_chunk_size=args.vae_chunk_size, disable_cache=args.vae_disable_cache + ) + vae_for_batch.to(torch.bfloat16) + vae_for_batch.eval() + + all_prompt_args_list = [apply_overrides(args, pd) for pd in prompts_data] # Create all arg instances first + for prompt_args in all_prompt_args_list: + check_inputs(prompt_args) # Validate each prompt's height/width + + # 2. Load DiT Model once + logger.info("Loading DiT model for batch generation...") + # Use args from the first prompt for DiT loading (LoRA etc. should be consistent for a batch) + first_prompt_args = all_prompt_args_list[0] + anima = load_dit_model(first_prompt_args, device, dit_weight_dtype) # Load directly to target device if possible + + shared_models_for_generate = {"model": anima} # Pass DiT via shared_models + + # 3. Precompute Text Data (Text Encoder) + logger.info("Loading Text Encoder for batch text preprocessing...") + + # Text Encoder loaded to CPU by load_text_encoder + text_encoder_dtype = torch.bfloat16 # Default dtype for Text Encoder + text_encoder_batch = load_text_encoder(args, dtype=text_encoder_dtype, device=torch.device("cpu")) + + # Text Encoder to device for this phase + text_encoder_device = torch.device("cpu") if args.text_encoder_cpu else device + text_encoder_batch.to(text_encoder_device) # Moved into prepare_text_inputs logic + + all_precomputed_text_data = [] + conds_cache_batch = {} + + logger.info("Preprocessing text and LLM/TextEncoder encoding for all prompts...") + temp_shared_models_txt = { + "text_encoder": text_encoder_batch, # on GPU if not text_encoder_cpu + "conds_cache": conds_cache_batch, + } + + for i, prompt_args_item in enumerate(all_prompt_args_list): + logger.info(f"Text preprocessing for prompt {i+1}/{len(all_prompt_args_list)}: {prompt_args_item.prompt}") + + # prepare_text_inputs will move text_encoders to device temporarily + context, context_null = prepare_text_inputs(prompt_args_item, device, anima, temp_shared_models_txt) + text_data = {"context": context, "context_null": context_null} + all_precomputed_text_data.append(text_data) + + # Models should be removed from device after prepare_text_inputs + del text_encoder_batch, temp_shared_models_txt, conds_cache_batch + gc.collect() # Force cleanup of Text Encoder from GPU memory + clean_memory_on_device(device) + + all_latents = [] + + logger.info("Generating latents for all prompts...") + with torch.no_grad(): + for i, prompt_args_item in enumerate(all_prompt_args_list): + current_text_data = all_precomputed_text_data[i] + height, width = check_inputs(prompt_args_item) # Get height/width for each prompt + + logger.info(f"Generating latent for prompt {i+1}/{len(all_prompt_args_list)}: {prompt_args_item.prompt}") + try: + # generate is called with precomputed data, so it won't load Text Encoders. + # It will use the DiT model from shared_models_for_generate. + latent = generate(prompt_args_item, gen_settings, shared_models_for_generate, current_text_data) + + if latent is None: # and prompt_args_item.save_merged_model: # Should be caught earlier + continue + + # Save latent if needed (using data from precomputed_image_data for H/W) + if prompt_args_item.output_type in ["latent", "latent_images"]: + save_latent(latent, prompt_args_item, height, width) + + all_latents.append(latent) + except Exception as e: + logger.error(f"Error generating latent for prompt: {prompt_args_item.prompt}. Error: {e}", exc_info=True) + all_latents.append(None) # Add placeholder for failed generations + continue + + # Free DiT model + logger.info("Releasing DiT model from memory...") + + del shared_models_for_generate["model"] + del anima + clean_memory_on_device(device) + synchronize_device(device) # Ensure memory is freed before loading VAE for decoding + + # 4. Decode latents and save outputs (using vae_for_batch) + if args.output_type != "latent": + logger.info("Decoding latents to videos/images using batched VAE...") + vae_for_batch.to(device) # Move VAE to device for decoding + + for i, latent in enumerate(all_latents): + if latent is None: # Skip failed generations + logger.warning(f"Skipping decoding for prompt {i+1} due to previous error.") + continue + + current_args = all_prompt_args_list[i] + logger.info(f"Decoding output {i+1}/{len(all_latents)} for prompt: {current_args.prompt}") + + # if args.output_type is "latent_images", we already saved latent above. + # so we skip saving latent here. + if current_args.output_type == "latent_images": + current_args.output_type = "images" + + # save_output expects latent to be [BCTHW] or [CTHW]. generate returns [BCTHW] (batch size 1). + save_output(current_args, vae_for_batch, latent, device) # Pass vae_for_batch + + vae_for_batch.to("cpu") # Move VAE back to CPU + + del vae_for_batch + clean_memory_on_device(device) + + +def process_interactive(args: argparse.Namespace) -> None: + """Process prompts in interactive mode + + Args: + args: Base command line arguments + """ + gen_settings = get_generation_settings(args) + device = gen_settings.device + shared_models = load_shared_models(args) + shared_models["conds_cache"] = {} # Initialize empty cache for interactive mode + + vae = qwen_image_autoencoder_kl.load_vae( + args.vae, device="cpu", disable_mmap=True, spatial_chunk_size=args.vae_chunk_size, disable_cache=args.vae_disable_cache + ) + vae.to(torch.bfloat16) + vae.eval() + + print("Interactive mode. Enter prompts (Ctrl+D or Ctrl+Z (Windows) to exit):") + + try: + import prompt_toolkit + except ImportError: + logger.warning("prompt_toolkit not found. Using basic input instead.") + prompt_toolkit = None + + if prompt_toolkit: + session = prompt_toolkit.PromptSession() + + def input_line(prompt: str) -> str: + return session.prompt(prompt) + + else: + + def input_line(prompt: str) -> str: + return input(prompt) + + try: + while True: + try: + line = input_line("> ") + if not line.strip(): + continue + if len(line.strip()) == 1 and line.strip() in ["\x04", "\x1a"]: # Ctrl+D or Ctrl+Z with prompt_toolkit + raise EOFError # Exit on Ctrl+D or Ctrl+Z + + # Parse prompt + prompt_data = parse_prompt_line(line) + prompt_args = apply_overrides(args, prompt_data) + + # Generate latent + # For interactive, precomputed data is None. shared_models contains text encoders. + latent = generate(prompt_args, gen_settings, shared_models) + + # # If not one_frame_inference, move DiT model to CPU after generation + # model = shared_models.get("model") + # model.to("cpu") # Move DiT model to CPU after generation + + # Save latent and video + # returned_vae from generate will be used for decoding here. + save_output(prompt_args, vae, latent, device) + + except KeyboardInterrupt: + print("\nInterrupted. Continue (Ctrl+D or Ctrl+Z (Windows) to exit)") + continue + + except EOFError: + print("\nExiting interactive mode") + + +def get_generation_settings(args: argparse.Namespace) -> GenerationSettings: + device = torch.device(args.device) + + dit_weight_dtype = torch.bfloat16 # default + if args.fp8_scaled: + dit_weight_dtype = None # various precision weights, so don't cast to specific dtype + elif args.fp8: + dit_weight_dtype = torch.float8_e4m3fn + + logger.info(f"Using device: {device}, DiT weight weight precision: {dit_weight_dtype}") + + gen_settings = GenerationSettings(device=device, dit_weight_dtype=dit_weight_dtype) + return gen_settings + + +def main(): + # Parse arguments + args = parse_args() + + # Check if latents are provided + latents_mode = args.latent_path is not None and len(args.latent_path) > 0 + + # Set device + device = args.device if args.device is not None else "cuda" if torch.cuda.is_available() else "cpu" + device = torch.device(device) + logger.info(f"Using device: {device}") + args.device = device + + if latents_mode: + # Original latent decode mode + original_base_names = [] + latents_list = [] + seeds = [] + + # assert len(args.latent_path) == 1, "Only one latent path is supported for now" + + for latent_path in args.latent_path: + original_base_names.append(os.path.splitext(os.path.basename(latent_path))[0]) + seed = 0 + + if os.path.splitext(latent_path)[1] != ".safetensors": + latents = torch.load(latent_path, map_location="cpu") + else: + latents = load_file(latent_path)["latent"] + with safe_open(latent_path, framework="pt") as f: + metadata = f.metadata() + if metadata is None: + metadata = {} + logger.info(f"Loaded metadata: {metadata}") + + if "seeds" in metadata: + seed = int(metadata["seeds"]) + if "height" in metadata and "width" in metadata: + height = int(metadata["height"]) + width = int(metadata["width"]) + args.image_size = [height, width] + + seeds.append(seed) + logger.info(f"Loaded latent from {latent_path}. Shape: {latents.shape}") + + if latents.ndim == 5: # [BCTHW] + latents = latents.squeeze(0) # [CTHW] + + latents_list.append(latents) + + vae = qwen_image_autoencoder_kl.load_vae( + args.vae, + device=device, + disable_mmap=True, + spatial_chunk_size=args.vae_chunk_size, + disable_cache=args.vae_disable_cache, + ) + vae.to(torch.bfloat16) + vae.eval() + + for i, latent in enumerate(latents_list): + args.seed = seeds[i] + save_output(args, vae, latent, device, original_base_names[i]) + + else: + tokenize_strategy = strategy_anima.AnimaTokenizeStrategy( + qwen3_path=args.text_encoder, t5_tokenizer_path=None, qwen3_max_length=512, t5_max_length=512 + ) + strategy_base.TokenizeStrategy.set_strategy(tokenize_strategy) + + encoding_strategy = strategy_anima.AnimaTextEncodingStrategy() + strategy_base.TextEncodingStrategy.set_strategy(encoding_strategy) + + if args.from_file: + # Batch mode from file + + # Read prompts from file + with open(args.from_file, "r", encoding="utf-8") as f: + prompt_lines = f.readlines() + + # Process prompts + prompts_data = preprocess_prompts_for_batch(prompt_lines, args) + process_batch_prompts(prompts_data, args) + + elif args.interactive: + # Interactive mode + process_interactive(args) + + else: + # Single prompt mode (original behavior) + + # Generate latent + gen_settings = get_generation_settings(args) + + # For single mode, precomputed data is None, shared_models is None. + # generate will load all necessary models (Text Encoders, DiT). + latent = generate(args, gen_settings) + + clean_memory_on_device(device) + + # Save latent and video + vae = qwen_image_autoencoder_kl.load_vae( + args.vae, + device="cpu", + disable_mmap=True, + spatial_chunk_size=args.vae_chunk_size, + disable_cache=args.vae_disable_cache, + ) + vae.to(torch.bfloat16) + vae.eval() + save_output(args, vae, latent, device) + + logger.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/vendor/sd-scripts/configs/qwen3_06b/config.json b/vendor/sd-scripts/configs/qwen3_06b/config.json new file mode 100644 index 0000000..43c79dc --- /dev/null +++ b/vendor/sd-scripts/configs/qwen3_06b/config.json @@ -0,0 +1,30 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151643, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "max_position_embeddings": 32768, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 1000000, + "sliding_window": null, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 151936 +} diff --git a/vendor/sd-scripts/configs/qwen3_06b/merges.txt b/vendor/sd-scripts/configs/qwen3_06b/merges.txt new file mode 100644 index 0000000..3134955 --- /dev/null +++ b/vendor/sd-scripts/configs/qwen3_06b/merges.txt @@ -0,0 +1,151388 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. 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+ÙĬ ج +íĴ Ī +à¸ŀ à¸ļ +ห ัว +з на +ר ק +à¹Ĥ ร +Ġ×ij ס +ĠBaÅŁ kan +ĠëĶ ° +à¸Ń ัà¸Ļ +ีà¹Īย ว +н еÑģ +à¹Ģà¸Ķ ิà¸Ļ +ÙĬ اÙĨ +×ķ׾ ×Ļ +ا خت +צ ×ķת +ãģĵ ãģĵ +ĠاÙĦ اÙĨ +ĠпÑĢо ÑĨ +ãģ¾ ãģł +׼ ס +ĠاÙĦ Ø¢ +ÙĬ ز +ĠاÙĦد ÙĪÙĦ +Ġíķĺ ëĤĺ +ض ع +ê» ĺ +ÅĽ wi +ย ิ +ãģ¡ãĤĥ ãĤĵ +ĠÙħ Ø´ +à¸ĺ ี +ãģ¨ ãģį +׳×Ļ ×ķת +Ġë ¯ +Ġë¯ ¸ +Ġs ı +ëĭĪ ê¹Į +Ġп л +غ ÙĦ +à¹ģ รà¸ĩ +ب ÙĬر +ãģĤãĤĬ ãģ¾ãģĽãĤĵ +ê· ¼ +Ġy üz +ĠdeÄŁ er +åł´ åIJĪ +á» ¡ +м аÑĤ +รา à¸Ĭ +ÙĪر ÙĬ +ж ен +ãģ¾ ãĤĬ +ãģ® ä¸Ń +×Ļ×ĵ ×¢ +à¸Ń ุ +à¸ļ à¸Ńล +à¸Ľà¸±à¸į หา +ز Ùħ +ÄŁ a +à¸Ń ืà¹Ī +à¸Ńืà¹Ī à¸Ļ +п л +Ġне обÑħодим +׼ ×ij +à¹Ģ ศ +קר ×Ķ +ì² ĺ +ëł ¨ +×ŀק ×ķ×Ŀ +jÄħ c +Ùĩ ÙĦ +Ġ×¢ ×ij×ķ×ĵ +à¹Ħม à¹ī +à¸ģล ัà¸ļ +×ķ׼ ׾ +ק ×ĵ +اÙĦ ÙĬØ© +ر Ùĩ +ãģij ãĤĮãģ° +ĠÙĨ Ùģس +ãĤ¢ ãĥ« +ìĹ Īëĭ¤ +ק ×ķר +н еÑĢ +ب اب +ãĤ ¶ +سب ب +ÙĦ ÙĬÙĦ +ص ÙĨ +ص در +ế m +à¸Ĭà¹Īว à¸ĩ +ØŃ ÙĨ +Ġ×ij ×Ĵ +×ŀ ×ķ×¢ +׾ ×Ĺ +大 ãģį +ت ب +н еÑĤ +×Ļ×ij ×Ķ +б л +ãĥĹ ãĥª +اص Ø© +ãģ¤ ãģij +×Ļ×ŀ ×ķש +ãģĮ ãģĤ +ëĭ ´ +ãģĭãĤĤ ãģĹ +ãģĭãĤĤãģĹ ãĤĮ +ãģ¡ ãĤī +×ij ×ĺ +Ġba ÄŁ +×Ļ×Ĺ ×¡ +×ij ×ķ×¢ +ล ี +פע ×Ļ׾ +им и +g ÅĤ +Ġим е +خد اÙħ +×IJ ×Ļר +Ġy apt +ãģ¨ ãģĦ +à¸ĩ à¹Īาย +׾×Ļ ×ķ +ØŃد Ø« +را ÙĤ +ĠÄIJ i +اد ر +ãģĵãģ¨ ãĤĤ +×ij ×Ļר +Ġв з +ض اÙģ +ת ×ķ׼ +ÑĢ ом +ر ات +à¹Ģà¸Ĺ à¹Īา +ãģĺ ãĤĥ +ãģĿ ãģĵ +اج تÙħاع +à¹īà¸Ń à¸Ļ +ÙĤ Ùħ +ë³ ¸ +Ä ŀ +ש ×Ļ×ķ +×ij ׳×Ļ +ìľĦ ìĽIJ +à¹ģ à¸Ī +×Ĺ ×ķר +دÙĬ ÙĨØ© +ت Ø· +ằ m +ò a +ย à¸Ńà¸Ķ +Ġëĭ ¹ +สุ à¸Ĥ +×ĵר ×ļ +د ÙĨ +س ÙĬÙĨ +ÙĪÙĤ Ùģ +ÑĨ Ñĭ +г оÑĤов +еж дÑĥ +à¸ŀ วà¸ģ +اÙĤ تص +اÙĤتص اد +cz ÄĻ +ni ÄĻ +ÑĢ еб +ØŃ ÙĪ +à¸Ĺ à¹Į +ãĤĪ ãģŃ +д ж +à¸ģล à¹Īาว +دÙĬ Ø« +ãĤ³ ãĥŁ +ÙĤ ÙĪÙħ +Ġت ØŃ +à¹Ģ à¸ķิ +اÙģ ظ +à¸Ī ุ +رÙĬ اض +×ŀש ×ļ +à¹Ĥ ย +еÑĢ е +ãģ¿ ãģŁãģĦ +ìĿ´ ëĿ¼ +ĠاÙĦÙħ ÙĪ +ĠÑģÑĤ о +à¹Ģรà¹ĩ ว +Ġд еÑĤ +ĠÑģ дел +à¹Ģà¸Ĭ ืà¹Īà¸Ń +פ ׳×Ļ +ÙĪض ÙĪع +×ij ס +à¹ģ à¸Ķ +ó c +ริ ม +ÑĢаР´ +ìĪ ł +ãĥ¼ãĤ º +ãģ« ãģĬ +и но +פ ×Ļ׾ +à¸Ĭั à¹Īà¸Ļ +×Ĺ×ĵ ש +à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ +׳ ×Ļס +غ رب +ãĤ¸ ãĥ£ +ส ัà¸ĩ +à¹Ģ à¸Ĺีà¹Ī +à¹Ģà¸Ĺีà¹Ī ยว +ëŁ ¼ +à¹ģ à¸Ł +ãĥ¼ãĤ · +ãĥ¼ãĤ· ãĥ§ãĥ³ +Ġвоз мож +جÙħ ÙĪع +×ijר ×Ļ×Ŀ +ãĥĪ ãĥ© +ĠкаÑĩ еÑģÑĤв +Ø· ÙĬ +ÑĤ Ñı +צ ×ķ×¢ +ÄŁ ını +ع ÙĦÙī +ا Ø° +ÙĪاÙĤ ع +Ùħ ÙĪا +ائ ÙĬÙĦ +к ол +á»ģ m +à¸ľà¸¥ ิà¸ķ +×Ļ׳ ×ĺר +س Ùĥ +ש ×Ļר +ศึà¸ģ ษา +à¸ļ ั +Ñĩ аÑģ +×ķפ ×Ķ +×Ļפ ×ķ׾ +ĠاÙĦس اب +رÙĬ ب +ĠاÙĦ بÙĬ +ãĤ¹ ãĥĨ +Ñĩ ен +à¹ģ à¸ľ +Ġ׳ ש +ز ÙĬد +ØŃ Ø§Ø¯ +ëį Ķ +رÙĪ ع +à¸Ĺุ à¸Ļ +ส มา +c zeÅĦ +×Ļ×ĵ ×Ķ +ãģ§ ãģĤ +Ġçoc uk +Ø® ب +à¸ļ าย +à¸Ľà¸£à¸° à¸Ĭา +×ŀש ׾ +ãģª ãģĭ +à¸ģ าย +ãĥģ ãĥ£ +аÑĢ и +ĠÑĩ а +à¸Ķ ำ +à¸Ĺั à¹Īว +Ñĥ Ñħ +Ġö z +Ġì¢ ĭ +ج رÙĬ +ائ ÙĤ +à¸ł ัย +Ø· ار +د ارة +Ä© nh +Ø« ÙĨ +zell ik +اÙĦ ت +Ġg eli +ãĥķãĤ © +ол од +رب ع +שת ×ŀש +à¸ļร ร +íĿ ¬ +Ġü rün +Ġê·¸ ëłĩ +ศาส à¸ķรà¹Į +ãģ ľ +×Ļ×ij ׾ +ĠпÑĢед ÑģÑĤав +سط ÙĬÙĨ +ãĤĴ 使 +Ġпом оÑī +×ķק ר +ãĥ¯ ãĥ¼ +Ġyö net +×Ļק ר +à¸Ĥ า +еÑĢи ал +ØŃ Ùģ +Ġ×Ļ צ +à¸Ĺ ิ +å£ ² +à¸Ļ à¸Ńà¸ģ +×ķ׼ ר +íĻ ľ +ủ y +ĠاÙĦÙĤ ر +×Ļ×ij ×ķת +ÅĽ ni +Ùħ شار +ượ t +ĠÙĦ دÙĬ +ÑĤ ел +ĠØ¥ ÙĦÙĬ +عÙĦ ÙĪÙħ +ìķ ĺ +в иÑĤ +à¸Ħ ะ +yr ı +ãģ¨ ãģ£ãģ¦ +à¹Ģ à¸ī +à¸ĸ าม +ÙĤ ار +عÙĦ اÙħ +ặ ng +Ùħ ÙĴ +×Ļ×ŀ ת +سب Ø© +ãĤ¯ ãĥ© +×ķס ×£ +ĠпÑĢ ин +ãģĦ ãĤį +س اس +عت بر +วิ à¸Ĺย +วิà¸Ĺย า +س Ùĥر +ãĤ· ãĥ§ +ãģ ģ +ัà¸ģ ษ +×ij ×ķ×Ķ +ห ย +ãģ¾ ãĤĮ +ĠоÑĢг аниз +каз ал +ĠÑģв Ñıз +uy ết +ĠпÑĢо из +Ġק ×ĺ +à¹ģà¸ģ à¹ī +п ÑĥÑģ +Ġê·¸ ê²ĥ +ëĬ IJ +л екÑģ +ãĥ¼ãĥ Ĺ +à¸ķ ำ +ת×Ĺ ×Ļ׾ +à¸Ńà¸ĩ à¸Ħà¹Į +Ạµ +׳ צ +Ø£ Ø´ +Ø´ Ùĩ +ย ะ +à¸ģ à¸İ +ĠاÙĦØ¥ سÙĦاÙħ +ед ÑĮ +ãģ² ãģ¨ +ëıĦ ë¡Ŀ +ãģ© ãģ® +Ñĥ в +еÑĩ ение +ĠاÙĦت ج +ãģ« è¡Į +Ġп озв +ãĤı ãĤĬ +ÙĦ اث +íķĺ ìĺĢ +Ġм аÑĢ +Ġkon uÅŁ +ãĥ¬ ãĤ¹ +ãĤĴ æĮģ +ĠоÑģ нов +×Ĺ ×ij +ÙĪج ÙĪد +פ ×ķף +в оÑĢ +Ġн ик +ãģĭ ãĤĭ +ÅŁtır ma +×Ļס ×ĺ +Ø£ ÙĦ +ห à¹Į +и она +лÑĮ н +Ġг оÑģ +ĠÐľÐ¾Ñģ к +ÑĢ об +×ķ×IJ ×Ļ +ãģĬãĤĬ ãģ¾ãģĻ +ãģ£ãģ ± +к л +à¸Ļ à¸Ķà¹Į +رÙĬ Ùģ +اس ب +ĠÑĢ еÑĪ +Ġд ол +ãģ¹ ãģį +×Ļ×ij ×ķר +м еÑī +Ġна ÑĪ +à¹ģ à¸Ľà¸¥ +ÑĢ иÑĤ +кÑĥ Ñģ +и ÑĢа +аÑĤ ÑĥÑĢ +ÙĪا صÙĦ +à¹Ģà¸ľ ย +à¸Ń ำ +à¹Ģà¸ģ ิà¸Ļ +غ Ùħ +ãģĻ ãģİ +lı kl +ÅĦ sk +ê² ¬ +×Ļ׼ ×Ķ +׊ש×ij +ÙĪر ÙĬØ© +Ġд ейÑģÑĤв +×Ĺ׾ ×ĺ +Ġ׾ ×ŀ×¢ +צ׾ ×Ļ×Ĺ +еÑĩ а +Ùģ اع +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ еб +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ е +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ е +ק ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ãģ¨æĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ ا +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ اÙĨ +Ġ׾×Ķ ת +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ од +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +ק ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ פ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +Ø° Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ Ø¨ +ан Ñģ +س ÙĪ +ĠпÑĢ е +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +ק ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ùģض ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ Øª +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ Ø§ +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ãģ¨æĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +ק ×ķ׾ +ÑĢ ез +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ ан +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ سط +Ġв оп +Ġвоп ÑĢоÑģ +Ùħ ÙĬÙĨ +à¸Ħ à¸ĩ +×Ļר ×Ļ×Ŀ +c ów +ê² © +Ġê·¸ 룰 +Ġì§ Ħ +Ġש ׾×Ķ +à¹Ģร ิà¹Īม +à¸Ĭ à¸Ńà¸ļ +д еÑĤ +ÑİÑī иÑħ +à¸ļ à¸Ńà¸ģ +æĢĿ ãģĦ +ع ÙĬد +ס ×ŀ +×Ĵ ×Ļ×¢ +צ ×ĵ +ب ات +ĠëĶ° ëĿ¼ +à¸Ī ัà¸ĩ +ãģłãģij ãģ§ +×¢ ×Ļר +ĠÑĩ ел +ĠÑĩел ов +ĠÑĩелов ек +ãĥĥ ãĥģ +à¹Ģà¸ģ ีà¹Īยว +à¸Ķ ิ +Ġפ ×¢ +×Ļ×ŀ ×Ļ +ë° ĺ +Ø® ار +×ij ×Ļת +×¢ ×Ļ×Ŀ +ü yor +ãĤģ ãģ¦ +к лад +Ġ à¸Īาà¸ģ +à¹Ģà¸Ħ ย +ส à¸Ńà¸ĩ +à¹ģ à¸Ħà¹Ī +ẫ u +หà¸Ļ ัà¸ĩ +ש׾ ×ķ×Ŀ +اÙĨ ÙĬØ© +åĩº ä¼ļ +åĩºä¼ļ ãģĦ +à¸ł าย +à¸ļา à¸Ĺ +à¸Ĭา ว +mu ÅŁ +Ġ׾ק ×ij׾ +ãĤ· ãĥ£ +ĠÄ° ÅŁ +×Ĵ×ĵ ×ķ׾ +ج عÙĦ +ë³ Ģ +ยิ à¹Īà¸ĩ +à¸Ļ าย +à¸Ļ ีà¹Ī +วิ à¸ĺี +ãĤī ãģªãģĦ +ëł Ī +Ġ문 ìłľ +Ġ à¸ģ +à¸Ĺำ à¸ĩาà¸Ļ +à¹Ģว à¹ĩà¸ļ +ÑĦ е +楽 ãģĹ +สำ à¸Ħ +สำà¸Ħ ัà¸į +ر Ùħ +ãģķãĤĮ ãģ¦ +Ġоб ла +ר×IJ ×Ļ +หม à¸Ķ +ÙĨ ÙĬØ© +ли н +Ġe ÄŁ +it im +ëł ¹ +ص اÙĦ +ÅĽ l +à¸ľ ิà¸Ķ +ãĥŀ ãĥ³ +åħ¥ ãĤĮ +à¹Ģà¸ķ à¸Ńรà¹Į +ار ÙĬ +ĠÐ ¦ +d ür +ส วย +ë¦ ½ +رÙĥ Ø© +Ġh ã +×Ļת ×Ķ +à¸Ĥ à¸Ļา +à¸Ĥà¸Ļา à¸Ķ +à¸Īำ à¸Ļ +à¸Īำà¸Ļ วà¸Ļ +ש ×ķק +Ġд ом +ì± ħ +ãģĭ ãģij +פ ×ķ׾ +à¸Ĭ าย +Ñģ моÑĤÑĢ +Ñģл Ñĥж +ש ×IJ׾ +кÑĢÑĭ ÑĤ +Ġìŀ ĺ +é«ĺ ãģĦ +ĠÑĢ Ñĥк +ÙĨ ص +д ав +Æ°á» ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪا ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪد +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +ĠÄ° l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ ز +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ׾ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ од +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ اء +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +ק ×ķת +ÙĪج د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° à¸Īำ +ов ание +ãģĦ ãģ¤ +ãĥĥãĤ¯ ãĤ¹ +åIJĪ ãĤı +åIJĪãĤı ãģĽ +×Ļ׳ ×ķ×Ļ +ạ y +Ø« ÙĤ +ĠпÑĢ об +ĠпÑĢоб лем +ÅŁ eh +ÅŁeh ir +ع ادة +اÙĨ ÙĪÙĨ +à¸ķัว à¹Ģà¸Ńà¸ĩ +ì¶ ķ +ı lan +б ан +ãĥ³ ãĥī +à¸Ī ี +Ġ×Ķש ׳×Ļ +п оÑĤ +×ķ׾ ×Ļ×Ŀ +ล ัà¸ļ +ĠÑį ÑĤи +×ijק ש +ë¹Ħ ìĬ¤ +à¸Ńยà¹Īาà¸ĩ à¹Ħร +×Ļ׾ ×Ļ +à¹ĥà¸Ĭ à¹Ī +ĠاÙĦ ÙĥÙĦ +ãĥļ ãĥ¼ãĤ¸ +ص Ø© +ÑĤи ÑĢ +ãĤĵ ãģ© +зÑĭ к +wy ż +Ùĩ ÙĬ +ĠÙħ ÙĦÙĬ +Ġвид е +ظ اÙħ +دا ÙĪÙĦ +×ŀ ת×Ļ +Ġs ık +à¹Ģà¸ķิ ม +ãĤ¢ ãĤ¤ +ка Ñħ +צ ×Ļ׾ +à¹Ģà¸Ĭ à¹Īà¸Ļ +м аг +маг аз +магаз ин +à¸Ľ ั +à¸Ľà¸± à¸Ī +Ġש ×Ļר×ķת +ีย ม +ãĥĸ ãĥ« +Ġد ÙĪÙĦ +קר ×Ļ×Ŀ +Ùĩ Ùı +ов о +Ġü ret +د ÙĪÙĨ +à¹ģà¸Ļ ว +à¹Ģà¸Ļ ืà¹īà¸Ń +ĠÑĦ оÑĤ +ãĥ ĺ +ãģ¤ ãģĭ +Ñı Ñģ +ĠíķĺëĤĺ ëĭĺ +ائ ع +Ġп лаÑĤ +ìĺ Ī +Ġdost ÄĻp +ÙĪج Ùĩ +Ġ×Ķ ×Ĺ×Ļ +׳ ×Ļק +д ей +í ĽĦ +ı y +بØŃ Ø± +à¹Ģส ริม +Ġ׾ ×Ĵ +Ø°Ùĩ ب +ج ÙĬÙĦ +رÙĥ ز +Ġë ħ +Ġëħ ¸ +פ×Ļ׾ ×ķ +ãģ¾ ãģļ +iri ÅŁ +ĠÙĥ ÙĬÙģ +Ġ×ij צ +Ġêµ IJ +ÑĢоÑģ Ñģ +ĠØ´ ÙĬ +Ġiç er +×Ĵ ×ķ×ij×Ķ +мен но +×¢ ×ij×Ļר +×ķ×ŀ ×Ķ +ãĤī ãģĹãģĦ +ãģ ¼ +Ñī ин +è²· ãģĦ +جÙħÙĪع Ø© +Ġdön em +Ġ×ij ×IJר +в еÑģÑĤ +×ķר ×ķת +س Ùģ +à¹ģà¸Ĺ à¸Ļ +Ġд окÑĥменÑĤ +Ġا ÙĬ +ج اÙĨ +צ×ķ×¢ ×Ļ +ĠоÑģ об +ĠاÙĦÙħ س +ÑĢаР± +à¸ł ู +à¸Ķ าว +л екÑĤ +ع ÙĤ +×ķ×ĵ ×ķת +Ġol u +Ġolu ÅŁtur +ãģ¾ ãģ¾ +ед ин +à¹Ģ à¸Ńà¸ģ +ãĤµ ãĤ¤ +ëĦ Ī +Ø· ÙĨÙĬ +Ø· ÙĤØ© +ĠÐł аз +ÙĦ Ùij +Ñĩ ем +Ġ׾ ×ĺ +สั à¹Īà¸ĩ +سر ائÙĬÙĦ +Ġפר ×ĺ×Ļ +д еÑģÑĮ +Ġ׳ ׼ +اÙĨ ب +ÙĬا Ø© +Ùħ بر +Ġk ı +à¸Ľ à¸ı +à¸Ľà¸ı ิ +à¸ļั à¸ķิ +׳ ת×Ļ +ìĨ ¡ +ر اب +à¹ĥ à¸ķ +à¹ĥà¸ķ à¹ī +×Ļ׳ ת +ÙĪ ÙĬر +Ġ×Ķ×ŀ 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+èIJ½ ãģ¡ +åĩº ãģ¦ +ล ัà¸ģษ +Ġ×Ĵ ×ij×ķ×Ķ +ãĥĻ ãĥ« +ê±° ëĤĺ +ë§ IJ +×Ļ׾ ×ĵ×Ļ×Ŀ +ĠëĦ Ī +×ŀר ×Ļ +ร ส +ãĥŃ ãĥ³ +и ло +ноÑģÑĤÑĮ Ñİ +×ĸר ×Ĺ +п он +Ġ×Ķש ׾ +ê²ł ìĬµëĭĪëĭ¤ +Ġki ÅŁ +ĠÐļ и +ว ร +د اع +ÅŁ im +ÙĨ Ùij +в аÑĤ +را Ùĥ +ب اÙĦ +ид е +Ġ×Ķ×ŀ ×Ĺ +ìĸ µ +تÙģ اع +Ø£ ت +ëĬ ĺ +ש ×Ļת +ست Ùħر +ĠÑĦ ак +ĠاÙĦØ£Ùħ رÙĬ +ëŀ ¨ +اس Ùħ +Ġa ÄŁ +Ġç ev +Ùĥ ÙĪر +ãģķ ãģ¾ +Ġç öz +Ġر س +Äħ da +สà¸Ļ ุ +ãģĹãģ¦ ãģıãĤĮ +н Ñİ +leÅŁ me +ãĤª ãĥ³ +ãģ¨ ãģªãĤĬ +ava ÅŁ +×ĺ ×Ļ×ij +ØŃ Ø¶ +×ķצ ×IJ×ķת +ÙĨ ÙħÙĪ +ı t +ĠÑħ а +ĠÑħа ÑĢак +ĠÑħаÑĢак ÑĤеÑĢ +Ġd ÅĤ +ãĥĹ ãĥ© +à¸Ĭ ุม +à¹Ī à¸Ńà¸Ļ +×ķ×ij ׾ +Ñģ ол +×ĵ ×Ĵ +аÑĢ аÑĤ +n ivers +Ġgerçek leÅŁtir +ĠاÙĦ ÙĦÙĬ +ระ ยะ +ĠÙħ ختÙĦÙģ +Ġgö nder +Ùģ ار +do ÄŁ +doÄŁ an +ص ÙĦاØŃ +Ġyay ın +ãĥĨ ãĥ³ +รว à¸Ī +×Ļ×Ĺ ×Ļ×ĵ +ünk ü +ÑĨи алÑĮн +à¸ļ ู +ม ุ +h ä +Ø® Ùģ +å¢ Ĺ +å¢Ĺ ãģĪ +еÑĩ но +ĠاÙĦس ÙĨ +à¸Ĥ าว +im di +Ð « +à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ +à¸ļา ล +ת ש +Ġdüzen le +мÑĭ Ñģл +ãģı ãģª +ż u +Ġwsp óÅĤ +Ġн аз +ınd aki +تر Ø© +ÅŁ ek +Ġö d +ĠÙĪ Ùĥ +Ġпозв олÑı +Ġת ×ķ׼ +ÙħÙĨ تج +ë§ ī +ĠاÙĦØ« ÙĦاث +аÑĨи Ñİ +ÙĪر ÙĪ +Ñĭв 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ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh Æ°ng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +ĠØ° ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt Æ° +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ק +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪاÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch ủ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij Æ°á»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth Æ°á»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ Øª +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ ت +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth Æ°Æ¡ng +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ ت +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪر +Ġtr ÃŃ +ĠÐŁÑĢ о +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch Æ°a +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph Æ°Æ¡ng +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ о +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth ủ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ а +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд 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+ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +ק ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ Ø¯Ø© +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij ủ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġק ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ׼×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth Æ° +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ פר +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦØ° ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ Ø° +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk ý +Ġch ân +Ġ à¹Ħมà¹Ī +اØŃ Ø© +r án +׳×Ļ ×Ļ×Ŀ +Ġ×ij ף +ĠÐ ĸ +à¸ķร à¸ĩ +д Ñĭ +Ġs ắc +ÙĦ ت +ãĥŃ ãĥ¼ +ĠÙĦ ÙĨ +Ġר ×ķ +Ġd Æ°á»Ľi +à¹Ģ à¸ĺ +à¹Ģà¸ĺ à¸Ń +e ÄŁi +Ġ×ķ ש +ĠÙĦ Ø£ +Ġg ặp +Ġc á»ij +ãģ¨ ãģ¦ãĤĤ +رÙĪ س +Ġ׾×Ķ ×Ļ +Ġë³ ¸ +ä¸Ĭ ãģĴ +Ġm ức +Ñħ а +Ġìŀ ¬ +à¸ī ัà¸Ļ +ÑĢÑĥ ж +Ġaç ık +ÙĪ اÙĦ +Ġ×ĸ ×ŀף +人 ãģ¯ +ع ÙĬÙĨ +Ñı Ñħ +Ġ×Ĵ×ĵ ×ķ׾ +ר ×ķ×ij +g ó +ëĿ¼ ê³ł +Ġark adaÅŁ +ÙĨ شر +Ġгод Ñĥ +ĠболÑĮ ÑĪе +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġcâ u 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Ñĭе +ëĤ ł +Ġв ам +بÙĦ غ +Ġê°Ļ ìĿĢ +íĮ IJ +ÙĦ ب +Ġnas ıl +Ġод ин +м ан +ĠعÙĦÙĬ Ùĩا +б и +Ġפ ש×ķ×ĺ +×ijר ×Ļ +Ġש ׳×Ķ +Ġëı Ħ +ĠÄIJ ại +Ġ×IJ×ķת ×Ŀ +ĠاÙĦØŃ Ø± +Ġб о +à¸Ī ุà¸Ķ +Ġr õ +ĠdeÄŁi ÅŁ +Ġëĭ ¨ +ĠÑģлÑĥÑĩ а +ĠÑģлÑĥÑĩа е +Ġ×IJ׳ ש×Ļ×Ŀ +×ĵ ×£ +ש×ij ת +Ġש׾ ׼×Ŀ +Ġch ú +nik ów +Ġtan ı +Ġcá o +ĠÄij á +Ġ×IJ ×ĵ×Ŀ +Ġê° ķ +Ġnhi á»ĩm +Ġ׾ ס +Ġ×Ľ×ª ×ij +Ġ×Ķס פר +ĠÄij Äĥng +Ġë ijIJ +à¸ľ ิ +à¸ľà¸´ ว +ج ا +Ġê° IJ +ر Ø£ +ست خدÙħ +ãģ«ãģªãĤĬ ãģ¾ãģĻ +Ġtá» · +×ĺ ×ķר +г овоÑĢ +Ġв оÑģ +ĠÙħÙĨ Ùĩا +иÑĢов аÑĤÑĮ +ĠÄij ầy +׳ ×Ĵ +ĠÙħ ÙĪ +ĠÙħ ÙĪÙĤع +ר׼ ×Ļ +ت Ùı +ëª ¨ +Ġת ×ķ +ÙĬا Ùĭ +à¹ĥ à¸Ķ +ãĤĬ ãģ¾ãģĻ +à¸Ńยูà¹Ī à¹ĥà¸Ļ +ĠØ£ ÙĪÙĦ +ĠØ£ خرÙī +Ġc Æ° +ص ار +×ŀ׊ש×ij +б ÑĢа +ÅĦ ski +б ÑĢ +ĠÙĬ Ùı +à¸ģ ิà¸Ļ +Ġch á»ijng +Ùħ Ùı +Ġ à¸Ħืà¸Ń +Ġت ÙĨ +t ÃŃ +y Äĩ +Ġm ạng +Ùģ ÙĪ +Ġdü nya +ק ר×IJ +Ġק ׾ +ĠØŃ Ø§ÙĦ +c ÃŃa +Ġà¹Ģ รา +Ġר ×ķצ×Ķ +Ġá p +ë° ķ +ا ÙĤØ© +ни Ñİ +Ġ×IJ ׾×ķ +Ġ×ŀס ×ķ +ãģ§ãģ¯ ãģªãģı +Ġtr ả +Ġק שר +mi ÅŁtir +Ġl Æ°u +Ġh á»Ĺ +ĠбÑĭ ли +Ġl ấy +عÙĦ Ùħ +Ġö zel +æ°Ĺ ãģĮ +Ġ×ĵ ר×ļ +Ùħ د +s ını +׳ ×ķש×IJ +r ów +Ñĩ еÑĢ +êµIJ ìľ¡ +ĠÐľ о +л ег +ĠV Ỽi +วัà¸Ļ à¸Ļีà¹ī +ÑİÑī ие +ãģĬ ãģĻ +ãģĬãģĻ ãģĻ +ãģĬãģĻãģĻ ãĤģ +ëı ħ +Ġ×Ļ×Ķ ×Ļ×Ķ +×ŀ ×ĺר +Ñı ми +Ġl á»±a +ĠÄij ấu +à¹Ģส ียà¸ĩ +Ġt Æ°Æ¡ng +ëĵ ± +ĠÑģÑĤ аÑĢ +à¹ĥ à¸ļ +ว ัà¸Ķ +ĠÄ° stanbul +Ġ à¸Īะ +à¸ķ ลาà¸Ķ +Ġب ÙĬ +à¹ģà¸Ļ ะ +à¹ģà¸Ļะ à¸Ļำ +س اعد +Ġب Ø£ +Ġki á»ĥm +ØŃ Ø³Ø¨ +à¸Ĭั à¹īà¸Ļ +Ġ×ķ ×¢×ķ×ĵ +ов ÑĭÑħ +оÑģ нов +Ġtr Æ°á»Łng +צ ×ij×¢ +ĠÃŃ t +Ġk ỹ +cr é +Ñı м +êµ ° +ãģĮ ãģªãģĦ +ÙĬÙĦ Ø© +ãĥķ ãĤ£ +ر Ùī +ĠÙĬ جب +Ġ×IJ ×£ +Ġc á»±c +ãĤīãĤĮ ãģŁ +Ġ à¸ľà¸¹à¹ī +Ġ à¸Ń +lar ımız +Ġkad ın +Ġê·¸ ëŀĺ +Ġê·¸ëŀĺ ìĦľ +ĠëĺIJ ëĬĶ +ĠÄij ả +ĠÄijả m +Ġ×IJ ×ķ×ŀר +Ġy ếu +ci Äħ +ciÄħ g +Ġt á»ij +Ġש×IJ ׳×Ļ +Ġdz iaÅĤa +Ñī а +ĠÄij Ãłn +s ına +ãģĵãĤĮ ãģ¯ +Ġ×ij ׾×Ļ +Ġ×ij ×Ļשר×IJ׾ +л оÑģÑĮ +Ġgi ữ +ê° IJ +ÑĢ он +تج ار +г лав +в ин +Ġh ạn +Ġyapı lan +ب س +Ġ à¸ŀรà¹īà¸Ńม +ê´Ģ 리 +mÄ±ÅŁ tır +b ü +r ück +ĠBaÅŁkan ı +ĠÙĦ ÙĬس +Ġs Æ¡ +à¸Īัà¸ĩ หว +à¸Īัà¸ĩหว ัà¸Ķ +د اء +Ġ×Ķ ׼ +v ÃŃ +ש ×IJר +Ġh Æ°á»Łng +Ġb óng +ĠCh ÃŃnh +Äħ c +à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ +Ġtá» © +Ġtứ c +ĠÑĨ веÑĤ +Ġt á»iji +ĠnghÄ© a +ÙĦا عب +د ÙĦ +Ġפע ×Ŀ +h ör +à¸Ĭ 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×ķ×Ļ×ķת +ei ÃŁ +ãĥ ¨ +ìĥ Ī +ĠÃĩ a +Æ ¯ +ש ×Ĵ +ÙĬÙĨ Ø© +ร à¹īà¸Ńà¸ĩ +ãĤµ ãĥ³ +ÑĢоÑģÑģ ий +ÑĢоÑģÑģий Ñģк +a ÄŁa +ĠнаÑĩ ина +Ġص ÙĦÙī +à¸Ĺุà¸ģ à¸Ħà¸Ļ +íļĮ ìĤ¬ +Ġли ÑĨ +Ø´ ÙĬر +ĠØ´ÙĬ Ø¡ +ÙĬÙĨ ا +Ġפ ×Ĺ×ķת +Ġiçer is +Ġiçeris inde +ĠØ£ ØŃÙħد +Ġże by +ì´ Ŀ +Ġп оказ +Ġи менно +หà¸Ļัà¸ĩ ส +หà¸Ļัà¸ĩส ืà¸Ń +ĠÑĤÑĢ е +สัà¸ĩ à¸Ħม +Ø¥ ÙIJ +ãģĮ å¿ħè¦ģ +ÙĬÙij Ø© +פ צ +íĭ ° +ĠÙħ جاÙĦ +׳ פש +к ан +×Ĺ ×ķפ +×Ĺ×ķפ ש +ì²ĺ ëŁ¼ +ов аÑı +з ов +Ġh ạ +Ġdzi ÄĻki +×Ļר ×ķ +Ġ׾ ×ŀצ +Ġ׾×ŀצ ×ķ×IJ +×Ļ×ĵ ×ķ +Ġs ợ +Ġ׾×Ķ ×Ĵ×Ļ×¢ +ק ×ij×¢ +Ġchi á»ģu +ãĥŀ ãĤ¤ +Ġd Ãłng +à¹ģà¸Ł à¸Ļ +Ġü ye +×Ļ׳ ×Ĵ +à¹Ģรีย à¸ģ +ç§ģ ãģĮ +th é +ĠÑĦ илÑĮ +ĠÑĦилÑĮ м +ĠNg Ãły +Ġж ен +Ġжен Ñīин +ج ÙĬد +n ç +à¸Ľ รา +×Ļ×ŀ ×ķ +Ġn á»ģn +×IJ ×ķ׾×Ŀ +Ġвозмож ноÑģÑĤÑĮ +Ġëĭ¤ ìĭľ +è¦ĭ ãģŁ +à¸ĸ à¸Ļ +à¸ĸà¸Ļ à¸Ļ +mız ı +ĠÙħ جÙħÙĪعة +c jÄħ +ĠÐł Ф +à¸ģำ หà¸Ļ +à¸ģำหà¸Ļ à¸Ķ +ĠìŬ 기 +land ı +ни ÑĨ +ÑģÑĤв е +Ġ×ĵ ×ijר×Ļ×Ŀ +Ġsk ÅĤad +ãĤĬ ãģ¾ãģĹãģŁ +ĠоÑĤ кÑĢÑĭÑĤ +нÑı ÑĤ +ĠÑģво ей +à¸Ī ิà¸ķ +ĠкаÑĩеÑģÑĤв е +Ġet tiÄŁi +ìĤ¬ íķŃ +ĠاÙĦÙĬ ÙħÙĨ +иÑĩеÑģки й +ë¸ Į +Ġ×ij×IJר ×¥ +Ġا سÙħ 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+تط ÙĪÙĬر +Ġsay ı +ĠÑģ ейÑĩаÑģ +Ġ×IJ×Ĺר ת +ק ×ķפ×Ķ +ק×ķר ס +Ġس Ùħ +Ġ×ĺ ×Ļפ×ķ׾ +ìĿ´ëĿ¼ ëĬĶ +دراس Ø© +èµ· ãģĵ +×Ĺ ×Ļ׳ +×Ĺ×Ļ׳ ×ķ×ļ +×ĵ ק +Ġë§ ŀ +Ġком анд +ĠÐij о +Ġиг ÑĢÑĭ +à¸ļ ี +ĠØ£ Ùİ +в ен +ĠاÙĦج دÙĬد +ĠÙĦ Ø¥ +Ġ×ķ×IJ ׳×Ļ +Ġ×Ķס ×Ļ +иÑĩеÑģк ого +رÙĪ ØŃ +à¸ģาร ศึà¸ģษา +ĠTr Æ°á»Ŀng +иг ÑĢа +ıl ması +Ġм аÑģÑģ +ãģ¨ãģį ãģ« +à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ +à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา +ĠاÙĦساب ÙĤ +Ġ×ŀ×¢ ×ĺ +в аÑĤÑĮ +m Ã¼ÅŁ +Ġ׾ ׼×ļ +Ġt á»ĭch +Ùģ ÙĩÙħ +تد رÙĬب +Ø´ Ùĥ +Ġ×ij ×ŀ×Ļ +Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ +ÙĤØ· اع +ãģª ãģĹ +×ķצ ×Ļ×IJ +ĠÙĪ سÙĬ +з Ñĥ +Ġy at +Ġyat ırım +ë§ İ +Ġth ắng +ãģĬ 客 +ãģĬ客 æ§ĺ +ĠThi ên +ãģ«å¯¾ ãģĹãģ¦ +ÑĢ иÑģ +ÙĨت ائ +ÙĨتائ ج +Ġ×ŀ שר +Ġ×ŀשר ×ĵ +Ġتع اÙĦ +ĠتعاÙĦ Ùī +ש ׳×Ļ +Ùĩ اÙħ +×IJ׳ ש×Ļ×Ŀ +Ġżyc ia +ĠÑĢÑĥб лей +ÙĬ ض +Ġkat ıl +ĠÙħ ÙĪضÙĪع +Ġvard ır +ĠÙħÙĨ Ø·ÙĤØ© +ĠTr ần +Ġв еÑģ +ü p +Ùħ ÙĪÙĨ +ÑĪ ли +Ġn óng +Ø® ÙĦÙģ +ĠС ÑĤа +Ġд оÑĢ +ĠдоÑĢ ог +ĠwÅĤa ÅĽnie +eÄŁ in +Ġhi á»ĥm +ĠС ам +ê»ĺ ìĦľ +ĠÑĦ а +ãģ» ãģĨ +ãģ»ãģĨ ãģĮ +×ķפ ×Ļ×¢ +ê° Ī +د ÙĪÙĦ +Ġthu ê +Ġch á»Ĺ +Ġëĭ¹ ìĭł +ãģij ãĤĮ +ãģijãĤĮ ãģ© +ë³´ íĺ¸ +ãģķãĤĮ 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½ +ðĿĴ ° +ðĿĴ · +ðĿĴ ¿ +ðĿĵ ģ +ðĿĵ ĭ +ðĿĵ İ +ðĿĵ Ĵ +ðĿ ĵĺ +ðĿĵ ¢ +ðĿĵ ¦ +ðĿĵ « +ðĿĵ ¿ +ðĿĶ İ +ðĿĶ ± +ðĿĶ ´ +ðĿĶ · +ðĿĶ ¸ +ðĿĶ ½ +ðĿķ Ĥ +ðĿķ ĥ +ðĿķ ĭ +ðĿķ ı +ðĿķ IJ +ðĿķ ¥ +ðĿķ ´ +ðĿķ º +ðĿĸ IJ +ðĿĸ Ľ +ðĿĸ Ŀ +ðĿĸ ŀ +ðĿĹ © +ðĿĹ ³ +ðĿĹ ½ +ðĿĺ Ĭ +ðĿĺ ĭ +ðĿĺ Ķ +ðĿĺ ± +ðĿĺ ´ +ðĿĺ ¿ +ðĿĻ Ĵ +ðĿĻ Ŀ +ðĿĻ Ł +ðĿĻ ¬ +ðĿĻ Ń +ðĿĻ » +ðĿĻ ¾ +ðĿļ Ī +ðĿļ ĭ +ðĿļ ij +ðĿļ Ł +ðĿļ ł +ðĿļ £ +ðĿĽ ½ +ðĿľ Ĥ +ðĿľ Ķ +ðĿľ Ļ +ðŁ Ģ +ðŁĢ Ħ +ðŁĦ ² +ðŁĦ ¶ +ðŁħ IJ +ðŁħ ĸ +ðŁħ ļ +ðŁħ Ľ +ðŁħ ¦ +ðŁħ ¶ +ðŁħ » +ðŁħ ¼ +ðŁĨ ĥ +ðŁĨ Ĩ +ðŁĨ İ +ðŁĪ ¯ +ðŁĪ ² +ðŁĪ ¹ +ðŁĮ ĩ +ðŁĮ ĵ +ðŁį ĺ +ðŁİ ij +ðŁİ ¿ +ðŁı ı +ðŁı Ĵ +ðŁı © +ðŁı ¯ +ðŁIJ Ģ +ðŁij Ŀ +ðŁĴ ¹ +ðŁĴ º +ðŁĵ Ł +ðŁĵ ª +ðŁĵ ¼ +ðŁĶ Ģ +ðŁĶ Ĥ +ðŁĶ ĥ +ðŁĶ ĩ +ðŁĶ ĵ +ðŁĶ ¢ +ðŁĶ ¤ +ðŁĶ © +ðŁķ ĸ +ðŁķ ļ +ðŁķ ľ +ðŁķ Ŀ +ðŁķ ŀ +ðŁķ ł +ðŁķ ¢ +ðŁķ ³ +ðŁĸ ĩ +ðŁĸ ij +ðŁĸ ¶ +ðŁĹ ģ +Ñ ¨ +Ú İ +á¡ Į +Ḡ° +ẠĢ +á¼ ® +á½ Ŀ +âĦ ¬ +âļ § +⼠¤ +ã³ ¬ +êĻ ĭ +ê¸ ij +ëĶ ī +ëĹ į +ë¡ ij +ë¯ ij +ë» ħ +ë¼ Ŀ +ìĦ IJ +ìī ¡ +ìĭ ² +ìı ± +ìĹ ¤ +ìĿ © +ìĿ ¿ +ìŁ Ļ +ìł ° +ì¥ ī +íĬ Ń +íķ ® +ï® ı +ðŁħ ± +ðŁĨ Ĵ +ðŁķ ĭ +É ĺ +Ê ĵ +Õ 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"vely": 16239, + "});čĊ": 16240, + "_ENT": 16241, + "#+#+": 16242, + "articles": 16243, + "ĠSouthern": 16244, + "Ġstronger": 16245, + "ĠGiven": 16246, + "ĠEric": 16247, + "ĠIR": 16248, + "abstract": 16249, + "Under": 16250, + "nable": 16251, + "Ġincrement": 16252, + "oven": 16253, + "Ġcoin": 16254, + "_timer": 16255, + "Ġsuffered": 16256, + "ĠFREE": 16257, + "'].\"": 16258, + "ĠQueen": 16259, + "stats": 16260, + "Ġmeetings": 16261, + "Ġentering": 16262, + "Ġalongside": 16263, + "(session": 16264, + "itals": 16265, + "Ġfoundation": 16266, + "ĠCredit": 16267, + ".div": 16268, + "_ALL": 16269, + "pcion": 16270, + "_stat": 16271, + "icking": 16272, + "Defaults": 16273, + "_src": 16274, + "Ġoutputs": 16275, + "/B": 16276, + "Ġenthus": 16277, + "-bl": 16278, + ".ForeColor": 16279, + "ĉtemp": 16280, + "Face": 16281, + "Ġinteract": 16282, + "Ġweird": 16283, + "Mount": 16284, + "rell": 16285, + "udents": 16286, + "Ġrequirement": 16287, + "ĠSus": 16288, + "IER": 16289, + "Ġelected": 16290, + "reference": 16291, + "ĠME": 16292, + "Ġservers": 16293, + ".wait": 16294, + "Ġsnapshot": 16295, + "ilton": 16296, + "Ġtries": 16297, + "Ġtipo": 16298, + ".Time": 16299, + ">w": 16300, + "Ġmountain": 16301, + "Ġpounds": 16302, + "Ġ[...": 16303, + "exists": 16304, + "ĠngOn": 16305, + "_MAP": 16306, + "Ġflying": 16307, + "xiety": 16308, + "ĉvalue": 16309, + "_DB": 16310, + "uno": 16311, + "Ġseats": 16312, + "TURN": 16313, + ".author": 16314, + "!)": 16315, + "orce": 16316, + "Ġindicated": 16317, + ".sin": 16318, + "Ġassignment": 16319, + "imiento": 16320, + "ĠFrame": 16321, + "_gen": 16322, + "inery": 16323, + "_)": 16324, + "messages": 16325, + ".settings": 16326, + "ĠMean": 16327, + "ĠMuseum": 16328, + "irq": 16329, + "attach": 16330, + "ĠPalestin": 16331, + "_QU": 16332, + "_tags": 16333, + "Ġcasual": 16334, + "emen": 16335, + "ASSWORD": 16336, + "$s": 16337, + "ĠCirc": 16338, + "ой": 16339, + "etric": 16340, + "/P": 16341, + "Ġepoch": 16342, + "The": 16357, + "ĠAk": 16358, + "Ġgrass": 16359, + "/*čĊ": 16360, + "(dis": 16361, + "Ġguns": 16362, + "Ġtb": 16363, + "ĠKevin": 16364, + ".args": 16365, + "ĠAh": 16366, + "oped": 16367, + "(J": 16368, + "columns": 16369, + "arguments": 16370, + "ĠWithEvents": 16371, + "_full": 16372, + "ĠDefense": 16373, + "Simple": 16374, + "Ġdeaths": 16375, + "Ġextensive": 16376, + "ĠStill": 16377, + "ĠExpression": 16378, + "ĠAgency": 16379, + "Ġperforming": 16380, + "FX": 16381, + "Ġusuario": 16382, + "UAL": 16383, + "Side": 16384, + "odos": 16385, + "aptop": 16386, + "Ġcredentials": 16387, + "_cap": 16388, + "atient": 16389, + "ĠDisney": 16390, + "Ġai": 16391, + "Ġchip": 16392, + "Ġvolt": 16393, + ".makeText": 16394, + "%%%%%%%%%%%%%%%%": 16395, + "Ġbelief": 16396, + "_LOC": 16397, + "ĠCivil": 16398, + "Navigation": 16399, + "Ġreveal": 16400, + "Ġviolent": 16401, + "ĠFil": 16402, + "Ġcatalog": 16403, + "emed": 16404, + "scan": 16405, + ".control": 16406, + "Ġconstitution": 16407, + "Country": 16408, + "Separator": 16409, + "_APP": 16410, + "topic": 16411, + "uetooth": 16412, + "MIN": 16413, + "Ġdescriptor": 16414, + "yt": 16415, + "ETHER": 16416, + "Ġdistribute": 16417, + "'}Ċ": 16418, + ".trim": 16419, + ".Line": 16420, + "Ġlbl": 16421, + "assertEquals": 16422, + "ĠDet": 16423, + "ombok": 16424, + "(width": 16425, + "Ġtort": 16426, + "ĠEXPRESS": 16427, + "aco": 16428, + "Using": 16429, + "ĠBrand": 16430, + "wall": 16431, + "EMENT": 16432, + "ĠCommunic": 16433, + "(Ċ": 17055, + "?>\"": 17056, + "Ġ///Ċ": 17057, + "Ġeiner": 17058, + "Ġweekly": 17059, + "ĉlogger": 17060, + "_pop": 17061, + "_man": 17062, + "Ġmigrations": 17063, + "Ġasks": 17064, + "Ġbs": 17065, + "Ġfalls": 17066, + ".Where": 17067, + "-height": 17068, + "_feature": 17069, + ".Min": 17070, + "Ġhyper": 17071, + "Ġvolatile": 17072, + "Ġtwenty": 17073, + "Typography": 17074, + "Unable": 17075, + "Det": 17076, + ",f": 17077, + "-mod": 17078, + "Ġsettlement": 17079, + "Ġcontracts": 17080, + "nome": 17081, + "Bad": 17082, + "ĠBrian": 17083, + "(username": 17084, + "!!!!": 17085, + "Ġhack": 17086, + ".Field": 17087, + "HR": 17088, + "ĠJordan": 17089, + "iza": 17090, + "ĠÂł": 17091, + "ĠSher": 17092, + ".header": 17093, + "(other": 17094, + "ĠDub": 17095, + "(op": 17096, + "ĠRound": 17097, + "Ġvie": 17098, + "Ġappl": 17099, + "ĉJ": 17100, + "ĠInsert": 17101, + "ĠLP": 17102, + "regon": 17103, + "ĠMPI": 17104, + "Ġanchor": 17105, + "aca": 17106, + "ør": 17107, + "Ġade": 17108, + "anchor": 17109, + "quee": 17110, + "ĠTreeNode": 17111, + "Ġtargeted": 17112, + "Ġlaid": 17113, + "ABEL": 17114, + "vet": 17115, + "ĠOrigin": 17116, + "Ant": 17117, + ".');Ċ": 17118, + "expect": 17119, + "edReader": 17120, + "ĠMajor": 17121, + "Ġinch": 17122, + "Compar": 17123, + "Ġpreview": 17124, + "Ġillness": 17125, + "ĠCONTRACT": 17126, + "ĠIndepend": 17127, + "uuid": 17128, + "Ġnome": 17129, + "Ġtc": 17130, + "ĠAvenue": 17131, + "isan": 17132, + "Ġphrase": 17133, + "_move": 17134, + "\")[": 17135, + "Ġprovision": 17136, + "Ġconcentr": 17137, + "_IR": 17138, + "ĠUt": 17139, + "()+": 17140, + "Ġnas": 17141, + "!,": 17142, + "ĠRobin": 17143, + "iations": 17144, + "atitude": 17145, + "Ġpx": 17146, + "ĠWithout": 17147, + "/bash": 17148, + "ekt": 17149, + "reement": 17150, + "Observer": 17151, + "ĠRegion": 17152, + "UBLIC": 17153, + "Ġ{//": 17154, + "KN": 17155, + "å·": 17156, + "GameObject": 17157, + "å¾": 17158, + "encoding": 17159, + "Ġ***": 17160, + "projects": 17161, + "Ġtk": 17162, + "Ġcheese": 17163, + "EMPL": 17164, + "aro": 17165, + "ĠاÙĦ": 17166, + "Ġconsists": 17167, + "refresh": 17168, + "ureau": 17169, + "ĠScanner": 17170, + "Ġsoil": 17171, + "Ġflavor": 17172, + "DataSource": 17173, + "Execute": 17174, + "ение": 17175, + "Ġshit": 17176, + "åĪĨ": 17177, + "Ċ": 17419, + "Ġsubsequent": 17420, + "posable": 17421, + "-fluid": 17422, + "Ġthorough": 17423, + "Ġpublicly": 17424, + "apters": 17425, + "ĠWilson": 17426, + "_PRE": 17427, + "yard": 17428, + "ä¼": 17429, + "ĉin": 17430, + "Ġrevers": 17431, + "Ġbullet": 17432, + "cribed": 17433, + "nesota": 17434, + "Ġ($_": 17435, + "annon": 17436, + "cursor": 17437, + "Ġclothing": 17438, + "ĠMulti": 17439, + ":',": 17440, + "Ġvess": 17441, + "ordinator": 17442, + "Ġeinem": 17443, + "Cannot": 17444, + "Ġarmed": 17445, + "ĉV": 17446, + "ä¸Ĭ": 17447, + ".Flat": 17448, + "ĠSep": 17449, + "ĠSubject": 17450, + "_font": 17451, + "Ġcharacteristics": 17452, + "Done": 17453, + "eln": 17454, + "############": 17455, + "POS": 17456, + "Ġdensity": 17457, + "ĠPlatform": 17458, + "-items": 17459, + "Ġovers": 17460, + "Ġpushing": 17461, + "ç¤": 17462, + ".Connection": 17463, + "_term": 17464, + "Ġinitialization": 17465, + "________________________________": 17466, + "ç¬": 17467, + ".document": 17468, + "lesh": 17469, + "ĉdocument": 17470, + "ĠPin": 17471, + "ça": 17472, + "Ġdefinitions": 17473, + ".Path": 17474, + "_WRITE": 17475, + "ĠĉĊ": 17476, + "?>ĊĊ": 17477, + "Ġterrible": 17478, + "bean": 17479, + "ickets": 17480, + "ĠSV": 17481, + "Buy": 17482, + "(task": 17483, + "Ġregime": 17484, + "google": 17485, + "Ġcrack": 17486, + ".visit": 17487, + "NUM": 17488, + "energy": 17489, + "Ġstruck": 17490, + "_sample": 17491, + ".payload": 17492, + "Ġrevis": 17493, + "ĠScene": 17494, + "Ġpg": 17495, + "Ġbreakfast": 17496, + "URRENT": 17497, + ".charAt": 17498, + "_exception": 17499, + "ĠAnton": 17500, + "Ġguidelines": 17501, + "Ġexhaust": 17502, + "ĠFinancial": 17503, + "Ġindent": 17504, + "Ġdesktop": 17505, + "Hidden": 17506, + "Failure": 17507, + "Ġprinciple": 17508, + "Ġiv": 17509, + "Ġseks": 17510, + "network": 17511, + "ĠnumberOf": 17512, + "ĠAlbert": 17513, + "ĉlong": 17514, + ",.": 17515, + "Ġzeros": 17516, + "fade": 17517, + "ĠTyp": 17518, + "ĠTerm": 17519, + "ĠArts": 17520, + ".Application": 17521, + "Ġbehalf": 17522, + "æĪ·": 17523, + "Ġmere": 17524, + "(`${": 17525, + "Ġawareness": 17526, + "elpers": 17527, + "flix": 17528, + "Ġweigh": 17529, + "Ġestimates": 17530, + ".child": 17531, + "/O": 17532, + "ĠBitmap": 17533, + ".bottom": 17534, + "Ġ**************************************************************************": 17535, + "Expect": 17536, + "ento": 17537, + "ĠForum": 17538, + "veral": 17539, + "Ġjail": 17540, + "Ġabilities": 17541, + "ĠHOLD": 17542, + "ĠCit": 17543, + "Ġdynam": 17544, + "Ġgray": 17545, + "ĉĉĉĉĉĉĉĉĉĉĉĉĉ": 17546, + ".nextInt": 17547, + "antly": 17548, + "ĠARISING": 17549, + "(private": 17550, + "Ġrejected": 17551, + "ĠNic": 17552, + "Ġleather": 17553, + "={Ċ": 17554, + "alytics": 17555, + "thetic": 17556, + ".Top": 17557, + ".Page": 17558, + "={`": 17559, + "Ġ;čĊ": 17560, + "depth": 17561, + "mann": 17562, + "WD": 17563, + "ĠSom": 17564, + ".Right": 17565, + "Ġ)}Ċ": 17566, + "Ġtrait": 17567, + "ÃĹ": 17568, + "iac": 17569, + "Ġrv": 17570, + "Sample": 17571, + ".Xml": 17572, + "opped": 17573, + "ĠÑĦ": 17574, + "lists": 17575, + "Ġtear": 17576, + "iversary": 17577, + ".collection": 17578, + "ĠConstitution": 17579, + "ĠHttpResponse": 17580, + "Ġbrill": 17581, + "ĠProm": 17582, + "hover": 17583, + "ĠMiami": 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17638, + "Ġcolleagues": 17639, + "maps": 17640, + "âĺ": 17641, + "ĊĉĊ": 17642, + "(al": 17643, + "_req": 17644, + "Ġfut": 17645, + "Ġarchitecture": 17646, + "ĠWHETHER": 17647, + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 17648, + "_screen": 17649, + "ĠstyleUrls": 17650, + "Ġmonster": 17651, + ".up": 17652, + "phia": 17653, + "Ġprocessor": 17654, + "ĠTerr": 17655, + "=',": 17656, + "ĠManufact": 17657, + "ĠNT": 17658, + "kel": 17659, + "ibern": 17660, + "ĉfile": 17661, + "Ali": 17662, + "rientation": 17663, + "Ġ//!": 17664, + "apore": 17665, + "aneous": 17666, + "ĠCreat": 17667, + "folder": 17668, + "Ġhay": 17669, + "Suppress": 17670, + "(left": 17671, + "Ġeuro": 17672, + "Ġdisclaimer": 17673, + "ustry": 17674, + "ships": 17675, + "_fd": 17676, + "ĠFa": 17677, + "_insert": 17678, + "Ġrol": 17679, + "ifting": 17680, + "ĠComments": 17681, + "_br": 17682, + "Ġlosses": 17683, + "ĠAdded": 17684, + "charg": 17685, + "Ġпо": 17686, + "_system": 17687, + "ĠSometimes": 17688, + "ĠSpain": 17689, + "(group": 17690, + "ialis": 17691, + "Ġdollar": 17692, + "ĠArgs": 17693, + "quires": 17694, + "ĠTen": 17695, + ".scss": 17696, + "Ġsurvive": 17697, + "usage": 17698, + "Ġjun": 17699, + "imiter": 17700, + "ï¼ģĊĊ": 17701, + "Ġfifth": 17702, + "toggle": 17703, + "Ġdecline": 17704, + "($\"": 17705, + "(Long": 17706, + "inge": 17707, + "Ġpilot": 17708, + "-light": 17709, + "-radius": 17710, + "Ġpodcast": 17711, + "Ġnaturally": 17712, + "Pages": 17713, + "为": 17714, + "ĠDespite": 17715, + "Ġlighting": 17716, + "Ġcrate": 17717, + "ĠBinary": 17718, + "Ġreducing": 17719, + "Ġeleg": 17720, + "ĠMouse": 17721, + "ĠTestBed": 17722, + "ĠbeforeEach": 17723, + "_ARRAY": 17724, + "Redirect": 17725, + "Ġflood": 17726, + "Ġships": 17727, + "Ġelectricity": 17728, + ")*(": 17729, + "ê¸": 17730, + "ĠViet": 17731, + "hero": 17732, + "Ġdia": 17733, + "ĠKent": 17734, + "heart": 17735, + "Ġthreats": 17736, + "_acc": 17737, + "Ġsymbols": 17738, + "ischen": 17739, + "_inst": 17740, + "Criterion": 17741, 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"_editor": 33752, + "Ġcrashed": 33753, + "dead": 33754, + "kat": 33755, + "agh": 33756, + "ĠEXT": 33757, + "asser": 33758, + "-small": 33759, + "Ġrealiz": 33760, + "(Entity": 33761, + "ús": 33762, + "ĠActually": 33763, + "ĠElite": 33764, + "Ġhelm": 33765, + "(nonatomic": 33766, + "asher": 33767, + "Community": 33768, + "alleng": 33769, + "iry": 33770, + "ĠGrowth": 33771, + "Ġsue": 33772, + "Ġfrequencies": 33773, + "_descriptor": 33774, + ".Attribute": 33775, + "Ġrecipients": 33776, + "_NS": 33777, + "/\"+": 33778, + "iban": 33779, + "Ġathlete": 33780, + "ĠIgn": 33781, + "_DMA": 33782, + "(ds": 33783, + "ĠRequirements": 33784, + "ADI": 33785, + "erez": 33786, + "\\Admin": 33787, + "braska": 33788, + "ĠRust": 33789, + "Relation": 33790, + "COD": 33791, + "ĠVERSION": 33792, + "emma": 33793, + ")){": 33794, + ".Duration": 33795, + "ĠCamb": 33796, + "-logo": 33797, + "Ġreadable": 33798, + "Ġcreators": 33799, + "()];Ċ": 33800, + "UpDown": 33801, + "-half": 33802, + ".getMonth": 33803, + 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"BYTE": 33908, + "ĠDistribution": 33909, + "Ġuso": 33910, + "ĠRecovery": 33911, + "approved": 33912, + "Ġdenial": 33913, + "/share": 33914, + "LinkedList": 33915, + ")čĊčĊčĊ": 33916, + "uddy": 33917, + "Ġfines": 33918, + "Ġry": 33919, + "Unicode": 33920, + "ĉrender": 33921, + "Ġpremises": 33922, + "Ġpon": 33923, + "aliases": 33924, + "/Foundation": 33925, + "cuda": 33926, + "ĠCock": 33927, + ",:)": 33928, + "(folder": 33929, + "Ġméd": 33930, + "drag": 33931, + "Ġtalents": 33932, + "ĠĠĠĊĊ": 33933, + "еÑģÑĤв": 33934, + "mob": 33935, + ".yml": 33936, + "Ġaster": 33937, + "Ġdiscre": 33938, + "goal": 33939, + "ĠGTX": 33940, + "ĠSUCCESS": 33941, + "ĠLONG": 33942, + "(find": 33943, + "Ġsingular": 33944, + "_sz": 33945, + "ĠEthereum": 33946, + "..Ċ": 33947, + "Ġirres": 33948, + "')){Ċ": 33949, + "Ġministers": 33950, + "Steps": 33951, + "iversal": 33952, + "ĠNevertheless": 33953, + "-led": 33954, + "Ġ(%)": 33955, + "ç¡®": 33956, + "Ġtimezone": 33957, + "Ġstranger": 33958, + "(render": 33959, + "Ġshutil": 33960, + "Ġmph": 33961, + "Ġtrio": 33962, + "ppy": 33963, + "Ġpredomin": 33964, + "Ġendors": 33965, + "ĠRussians": 33966, + "ĉrow": 33967, + "Ġwizard": 33968, + ".serialize": 33969, + "Ġcomplained": 33970, + "Ġsido": 33971, + "Ġdelighted": 33972, + "-me": 33973, + "ĠRav": 33974, + "Human": 33975, + "adays": 33976, + "recv": 33977, + "Working": 33978, + "Jump": 33979, + "ĠÃ¥r": 33980, + "ĠAutomatic": 33981, + "_Base": 33982, + "æł¼": 33983, + "aurants": 33984, + "¯": 33985, + "æ¸": 33986, + "(CType": 33987, + "IFI": 33988, + "(amount": 33989, + "Ġbelieving": 33990, + "=mysql": 33991, + "Ġfir": 33992, + "Ġrestoration": 33993, + "ereco": 33994, + "Т": 33995, + "_'+": 33996, + "Ġebook": 33997, + "Ġdebris": 33998, + "(inputs": 33999, + "AYOUT": 34000, + "Ġscreaming": 34001, + "avia": 34002, + "lander": 34003, + "Ġdistress": 34004, + "Ġassembled": 34005, + "ĠAvoid": 34006, + "(thread": 34007, + "ĠRPC": 34008, + "_EXIT": 34009, + "(queue": 34010, + "иÑģÑĤ": 34011, + "Dll": 34012, + "Ġskull": 34013, + "_pub": 34014, + "chez": 34015, + "minate": 34016, + "ensen": 34017, + "Ġinsane": 34018, + "bounds": 34019, + "ĠRosen": 34020, + "Ġconditioning": 34021, + "processed": 34022, + "videos": 34023, + "four": 34024, + ".Conv": 34025, + "|;Ċ": 34026, + "Personal": 34027, + "cerpt": 34028, + ":UIControlStateNormal": 34029, + "Ġdoses": 34030, + "ĠKarl": 34031, + "ĠFrequ": 34032, + ".BASE": 34033, + "ĠVote": 34034, + "Ġconcurrent": 34035, + "ĠMessageBoxIcon": 34036, + "ĠÃĸ": 34037, + "ĠDubai": 34038, + "ĠRetail": 34039, + ":number": 34040, + "ĠObserver": 34041, + "ĠBigInteger": 34042, + "_origin": 34043, + "_WORK": 34044, + "Frames": 34045, + "Ġnotably": 34046, + ".âĢľ": 34047, + "Ġtropical": 34048, + "Ġniche": 34049, + "amina": 34050, + ".sys": 34051, + "(tokens": 34052, + "modify": 34053, + "osit": 34054, + "strom": 34055, + "ĠComics": 34056, + "OPTION": 34057, + "Ticket": 34058, + "Ġfactories": 34059, + "Ġdisput": 34060, + "_File": 34061, + "ĠFinn": 34062, + "eee": 34063, + "ĠDiscord": 34064, + "_money": 34065, + ".tpl": 34066, + "_safe": 34067, + "LB": 34068, + "Ġglut": 34069, + "JK": 34070, + ".flow": 34071, + "-cont": 34072, + "gos": 34073, + "Ġhorizon": 34074, + "ĠRush": 34075, + "::*": 34076, + "Pipe": 34077, + "ulla": 34078, + "borough": 34079, + "heimer": 34080, + "(move": 34081, + "(Text": 34082, + "});čĊčĊ": 34083, + "welcome": 34084, + "ĠComponents": 34085, + "Ġgovernance": 34086, + "closed": 34087, + "ĉmargin": 34088, + "Ġlaundry": 34089, + "ĠTerminal": 34090, + "izards": 34091, + ".âĢĶ": 34092, + ".remote": 34093, + ".radius": 34094, + "ĠQuebec": 34095, + "Ġdh": 34096, + "Tech": 34097, + "ĠMist": 34098, + "seller": 34099, + "_literal": 34100, + "Ġgenius": 34101, + "Ġbrains": 34102, + "gem": 34103, + "ĠMeasure": 34104, + "Ġcatast": 34105, + "rance": 34106, + ".TextField": 34107, + "Ġconsuming": 34108, + "Ġ'\\''": 34109, + "oubtedly": 34110, + "ĠCertain": 34111, + "Ev": 34112, + "erti": 34113, + "being": 34114, + "Experience": 34115, + "Ġ//[": 34116, + "ĠArabic": 34117, + "ĠCrist": 34118, + "ĠAzure": 34119, + "Ġhora": 34120, + "ladesh": 34121, + "\\Blueprint": 34122, + "dar": 34123, + ".rel": 34124, + "Ġsuprem": 34125, + "ĠReagan": 34126, + "ĠAttributes": 34127, + "-sidebar": 34128, + "ĠuseStyles": 34129, + "ĠAirlines": 34130, + "Ġhills": 34131, + "/xhtml": 34132, + "vinc": 34133, + "_mock": 34134, + "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ": 34135, + "ĠPill": 34136, + ".LayoutStyle": 34137, + "ĠCommander": 34138, + "]<": 34139, + "signature": 34140, + "Ġ{}čĊ": 34141, + "Ġhatred": 34142, + "Ġëĭ": 34143, + "olesterol": 34144, + "Ġ********": 34145, + "ancellor": 34146, + "crop": 34147, + "TIM": 34148, + "ĉĉĊĊ": 34149, + "ysqli": 34150, + "uitive": 34151, + "ĉunset": 34152, + "_sel": 34153, + "Ġmenus": 34154, + "tick": 34155, + "Ġconstitute": 34156, + "ĠElements": 34157, + "ĠRedis": 34158, + "aggio": 34159, + "_fp": 34160, + "_depend": 34161, + "emas": 34162, + "CAST": 34163, + "orange": 34164, + "jon": 34165, + "ĠEmily": 34166, + "Ġpotatoes": 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138717, + "Ġساعات": 138718, + "ĠиÑģполÑĮзованиÑı": 138719, + "ר×ķצ×Ļ×Ŀ": 138720, + "ĠзнаÑĩиÑĤ": 138721, + "ĠÑĢам": 138722, + "ĠÑĢамкаÑħ": 138723, + "거리": 138724, + "ĠпÑĭÑĤа": 138725, + "ãĥģãĥ³": 138726, + "ĠпоÑģк": 138727, + "ĠпоÑģколÑĮ": 138728, + "ĠпоÑģколÑĮкÑĥ": 138729, + "إبر": 138730, + "إبراÙĩ": 138731, + "إبراÙĩÙĬÙħ": 138732, + "ĠÑĤÑĢеÑħ": 138733, + "ĠGenç": 138734, + "سÙĪÙģ": 138735, + "ĠveÃŃculo": 138736, + "ĠNgân": 138737, + "ĠоÑĩеÑĢедÑĮ": 138738, + "à¸Ħรึà¹Īà¸ĩ": 138739, + "×IJ×ij×Ļ": 138740, + "à¸ķà¹īม": 138741, + "ãĤĴè¡ĮãģĦ": 138742, + "ĠاÙĦسابÙĤØ©": 138743, + "наÑĨи": 138744, + "наÑĨиона": 138745, + "наÑĨионалÑĮн": 138746, + "Ġgestión": 138747, + "تÙĤد": 138748, + "ĠاÙĦبÙĬاÙĨ": 138749, + "ĠاÙĦبÙĬاÙĨات": 138750, + "ĠاÙĦاÙĨتخاب": 138751, + "ĠاÙĦاÙĨتخابات": 138752, + "à¹Ģà¸Ĭà¹Īา": 138753, + "×ĵ×IJ×Ĵ": 138754, + "Ġ׾×Ĵ×ŀר×Ļ": 138755, + "ĠتØŃتاج": 138756, + "Ġthôn": 138757, + "à¸ķà¹īà¸Ńà¸Ļ": 138758, + "à¸ķà¹īà¸Ńà¸Ļรัà¸ļ": 138759, + "女ãģ®": 138760, + "女ãģ®åŃIJ": 138761, + 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"Ġ×ŀת×Ĺת": 138810, + "еннÑĥÑİ": 138811, + "Ġзим": 138812, + "à¸ģาà¹ģà¸Ł": 138813, + "Ġ×ijת×ķר": 138814, + "Ġnghè": 138815, + "Ġnghèo": 138816, + "ĠÐĽÑİ": 138817, + "ĠÐĽÑİб": 138818, + "תקצ×Ļ×ij": 138819, + "×ŀעש×Ķ": 138820, + "ĠاÙĦبÙĬت": 138821, + "צ×Ļפ": 138822, + "ĠобÑıзан": 138823, + "ĠMá»Ĺi": 138824, + "ĠТÑĥÑĢ": 138825, + "ĠÙĪباÙĦت": 138826, + "ĠÙĪباÙĦتاÙĦÙĬ": 138827, + "Ġdécision": 138828, + "Ġبد": 138829, + "Ġبدأت": 138830, + "Ġcục": 138831, + "Ġbask": 138832, + "Ġbaskı": 138833, + "Ġhatırl": 138834, + "Ġhatırla": 138835, + "å°ıãģķãģĦ": 138836, + "Ġgerçekten": 138837, + "à¸ľà¸±à¸ģ": 138838, + "åı¯èĥ½ãģª": 138839, + "×ŀ×IJס": 138840, + "ĠcrÃŃtica": 138841, + "ĠìĿĺìĽIJ": 138842, + "عÙĤÙĪد": 138843, + "×ĺ׼׳": 138844, + "×ĺ׼׳×ķ׾×ķ×Ĵ×Ļ×Ķ": 138845, + "è¨ĢãģĪãģ°": 138846, + "ĠÙĤÙĨا": 138847, + "ĠÙĤÙĨاة": 138848, + "ĠìĿ´ê²ĥìĿĢ": 138849, + "تصر": 138850, + "à¸Łà¸±à¸Ļ": 138851, + "ĠÑĢеÑĨеп": 138852, + "ĠÑĢеÑĨепÑĤ": 138853, + "ĠبÙĨÙģس": 138854, + "ÑĢоÑĪ": 138855, + "ĠмаÑĢÑĤа": 138856, + "Ġsonras": 138857, + "Ġsonrası": 138858, + "×ķ×ijש": 138859, + "ãĥªãĤ¹ãĤ¯": 138860, + "ĠFrançais": 138861, + "á»ļ": 138862, + "ê°Ķ": 138863, + "Ġ×Ķ×ijר×Ļת": 138864, + "פ×Ļצ": 138865, + "פ×Ļצ×ķ×Ļ": 138866, + "ĠÙĦÙħاذا": 138867, + "ĠÐļиев": 138868, + "ĠÑģмÑĭÑģл": 138869, + "ê¸Īìľµ": 138870, + "ãĤ·ãĥ£ãĥ«": 138871, + "ãĥ©ãĤ¤ãĥĪ": 138872, + "ìĽĥ": 138873, + "×ŀ×Ĺר": 138874, + "ãĨį": 138875, + "Ġkullanım": 138876, + "Ġ×IJצ׾׳×ķ": 138877, + "ĠtÃłn": 138878, + "ãĥıãĥ¼": 138879, + "ãģ¨ãģ¨ãĤĤ": 138880, + "ãģ¨ãģ¨ãĤĤãģ«": 138881, + "ÑĢег": 138882, + "ÑĢеги": 138883, + "ÑĢегион": 138884, + "ãģªãģıãģªãĤĭ": 138885, + "Ġchảy": 138886, + "ĠجÙĩØ©": 138887, + "ÅĦskiej": 138888, + "à¸Ńีà¹Ģม": 138889, + "à¸Ńีà¹Ģมล": 138890, + "ãģįãģ£ãģ¨": 138891, + "ĠìĺĪìĤ°": 138892, + "Ġkitabı": 138893, + "Ġeducação": 138894, + "ĠbuluÅŁ": 138895, + "ологиÑı": 138896, + "ĠконкÑĢ": 138897, + "ĠконкÑĢеÑĤ": 138898, + "×Ĵ×Ļר": 138899, + "ĠпÑĢедлаг": 138900, + "ĠпÑĢедлагаеÑĤ": 138901, + "ĠYên": 138902, + "Ġíķľë²Ī": 138903, + "Ġ×ŀר׼×ĸ×Ļ": 138904, + "à¹Ģà¸Ľà¸´à¸Ķà¹Ģà¸ľà¸¢": 138905, + "ÑĤвеÑĢд": 138906, + "ĠHá»ĩ": 138907, + "ĠÐĵÑĢ": 138908, + "à¸Ŀà¹īา": 138909, + "×Ķשק": 138910, + "×Ķשקע×Ķ": 138911, + "ĠнаÑĥк": 138912, + "ìłIJìĿĦ": 138913, + "ĠнелÑĮ": 138914, + "ĠнелÑĮз": 138915, + "ĠнелÑĮзÑı": 138916, + "гин": 138917, + "ĠBöl": 138918, + "ĠBölge": 138919, + "Ġвла": 138920, + "ĠвлаÑģÑĤи": 138921, + "à¹Ģà¸Ļà¹ĩ": 138922, + "à¹Ģà¸Ļà¹ĩà¸ķ": 138923, + "골": 138924, + "Ġöld": 138925, + "Ġöldür": 138926, + "×Ľ×ł×¢": 138927, + "ĠاÙĦÙĩÙĬئة": 138928, + "تارÙĬØ®": 138929, + "ĠÐijÑĢ": 138930, + "ĠÑģмож": 138931, + "ĠÑģможеÑĤе": 138932, + "ĠLúc": 138933, + "à¹Ħà¸Ľà¸ĸึà¸ĩ": 138934, + "ĠBakanı": 138935, + "Ġerklärt": 138936, + "ĠÐIJна": 138937, + "Ġscène": 138938, + "åķıãģĦ": 138939, + "åķıãģĦåIJĪãĤıãģĽ": 138940, + "ÙħÙĩÙĨد": 138941, + "ÙħÙĩÙĨدس": 138942, + "Ġназвание": 138943, + "иваниÑı": 138944, + "ãĤĴå¤īãģĪ": 138945, + "ä»ĺãģįåIJĪ": 138946, + "ãĥijãĤ½": 138947, + "ãĥijãĤ½ãĤ³ãĥ³": 138948, + "æĺİãĤī": 138949, + "æĺİãĤīãģĭ": 138950, + "à¹Ģà¸Ńà¸ģสาร": 138951, + "à¹Ģà¸ģิà¸Ļà¹Ħà¸Ľ": 138952, + "леп": 138953, + "ãģĹãģŁãĤĤãģ®": 138954, + "ĠCâm": 138955, + "ĠCâmara": 138956, + "ק×ķ׾׳×ķ×¢": 138957, + "Ġ×ij×Ĵ×Ļף": 138958, + "Ġoczy": 138959, + "ĠoczywiÅĽcie": 138960, + "attivitÃł": 138961, + "ãĥĵãĥ¥ãĥ¼": 138962, + "Ġeducación": 138963, + "Ä°YE": 138964, + "ê¹ĮìļĶ": 138965, + "ãĤ¨ãĥªãĤ¢": 138966, + "неÑģÑĤи": 138967, + "Ġmóg": 138968, + "ĠmógÅĤ": 138969, + "Ġק×ĺ׳×Ļ×Ŀ": 138970, + "ĠPrä": 138971, + "Ġ×ľ×¢×ij×ķר": 138972, + "بÙĨÙī": 138973, + "зол": 138974, + "золоÑĤ": 138975, + "ĠwnÄĻtr": 138976, + "ĠwnÄĻtrz": 138977, + "Ġconstrução": 138978, + "รัà¸ļรà¸Ńà¸ĩ": 138979, + "سجÙĨ": 138980, + "Ġק×ķ׳": 138981, + "ס×Ļפ×ķר": 138982, + "ĠÙħدÙī": 138983, + "رضÙī": 138984, + "плав": 138985, + "ï¼¥": 138986, + "Ġila": 138987, + "Ġilaç": 138988, + "ãĤĭãģ¹ãģį": 138989, + "ĠÙħÙĪÙĤÙģ": 138990, + "à¸ģรุ": 138991, + "à¸ģรุà¸ĵา": 138992, + "chodzÄħc": 138993, + "ĠÑĤÑĭÑģ": 138994, + "ÐķвÑĢо": 138995, + "ĠÙĬØŃدث": 138996, + "ãĥ¡ãĤ¤ãĥ³": 138997, + "ĠاÙĦصØŃÙĬ": 138998, + "ĠÐĶан": 138999, + "دعاء": 139000, + "ãĤ´ãĥ¼ãĥ«": 139001, + "×©×ł×ª×Ļ": 139002, + "×©×ł×ª×Ļ×Ļ×Ŀ": 139003, + "à¸Ķà¹īวยà¸ģัà¸Ļ": 139004, + "Ġolacaģı": 139005, + "Ġ×ij×ŀ×Ĺ×Ļר": 139006, + "×Ķק": 139007, + "×Ķק×ŀת": 139008, + "ãĥ¢ãĥİ": 139009, + "ĠçalÄ±ÅŁtı": 139010, + "Ġjóvenes": 139011, + "ãģĦãģıãĤī": 139012, + "ĠÙħعدÙĦ": 139013, + "ĠCÅ©ng": 139014, + "ĠSegún": 139015, + "Ġdönemde": 139016, + "Ġ׾×Ļ×ĵ×Ļ": 139017, + "ãģįãģ¡": 139018, + "ãģįãģ¡ãĤĵ": 139019, + "ãģįãģ¡ãĤĵãģ¨": 139020, + "ÙģرÙĨس": 139021, + "ÙģرÙĨسا": 139022, + "åIJijãģį": 139023, + "Ġcampaña": 139024, + "ĠÑģамоÑģÑĤоÑı": 139025, + "ĠÑģамоÑģÑĤоÑıÑĤелÑĮно": 139026, + "á»Ģ": 139027, + "ÙĤÙĪا": 139028, + "سÙĦاØŃ": 139029, + "à¸ģระà¹ģ": 139030, + "à¸ģระà¹ģส": 139031, + "ĠполÑĮзÑĥ": 139032, + "nqu": 139033, + "nquête": 139034, + "รà¹Īวมà¸ģัà¸ļ": 139035, + "ëĬIJëĥIJ": 139036, + "à¸Ĺีมà¸Ĭาà¸ķิ": 139037, + "Ġyıllık": 139038, + "ìĬ¬": 139039, + "ĠأصØŃاب": 139040, + "illé": 139041, + "Ġdóla": 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"lös": 141850, + "lösung": 141851, + "ĠТаким": 141852, + "ÑĪед": 141853, + "ÑĪедÑĪ": 141854, + "عزÙĦ": 141855, + "Ġרש×Ļ×ŀת": 141856, + "Ġ׾×Ķ×Ļ׼": 141857, + "Ġ׾×Ķ×Ļ×Ľ×ł×¡": 141858, + "ĠпÑĥÑĤ": 141859, + "ĠпÑĥÑĤеÑĪ": 141860, + "ĠпÑĥÑĤеÑĪеÑģÑĤв": 141861, + "ĠnotÃŃcia": 141862, + "ĠalÄ±ÅŁ": 141863, + "ĠalÄ±ÅŁver": 141864, + "ĠalÄ±ÅŁveriÅŁ": 141865, + "ĠwÅĤos": 141866, + "ĠwÅĤosów": 141867, + "Ġبغ": 141868, + "Ġبغداد": 141869, + "Ġveröffent": 141870, + "Ġveröffentlicht": 141871, + "ĠKhá": 141872, + "Ġtán": 141873, + "ëIJĺ기": 141874, + "Ġ방문": 141875, + "ÙģÙĬÙĦ": 141876, + "à¹Ģà¸ģิà¸Ķà¸Īาà¸ģ": 141877, + "åı¯æĦĽ": 141878, + "åı¯æĦĽãģĦ": 141879, + "à¸ĸุà¸ĩ": 141880, + "ĠzewnÄĻtrzn": 141881, + "à¸łà¸²à¸©à¸²à¸Ńัà¸ĩà¸ģฤษ": 141882, + "Ġmáxima": 141883, + "Ġulus": 141884, + "Ġuluslararası": 141885, + "Ġ׳×Ķ׳": 141886, + "à¸Ĥà¹Īาวสาร": 141887, + "ĠìĿĺìĤ¬": 141888, + "à¹Ģหลืà¸Ńà¸ĩ": 141889, + "ĠدÙĤ": 141890, + "ĠدÙĤائÙĤ": 141891, + "สืà¹Īà¸Ńสาร": 141892, + "먼": 141893, + "ĠÑģоÑģÑĤоÑıнии": 141894, + "สมาà¸Ħม": 141895, + "á»Ĥ": 141896, + "ĠÐľÐ¾Ñģков": 141897, + "ĠÐľÐ¾ÑģковÑģк": 141898, + "×ŀס×ķ×Ĵ׾": 141899, + "ãģĭãģĭãĤĬ": 141900, + "ĠTruyá»ģn": 141901, + "à¹ģà¸Ĥà¹ĩà¸ĩà¹ģรà¸ĩ": 141902, + "×ŀ×Ĺ×ĸ×Ļק": 141903, + "à¹Ĥà¸ģà¹ī": 141904, + "ÙĬسر": 141905, + "ìĶ©": 141906, + "×IJ×ķק": 141907, + "×IJ×ķק×ĺ": 141908, + "×IJ×ķק×ĺ×ķ×ijר": 141909, + "Ġproximité": 141910, + "ÙħÙĨÙĩج": 141911, + "ĠاÙĦجز": 141912, + "ĠاÙĦجزائ": 141913, + "ĠاÙĦجزائرÙĬ": 141914, + "ĠÄIJiá»ĥm": 141915, + "Ġденеж": 141916, + "Ġденежн": 141917, + "ÙģØŃص": 141918, + "Ùģئ": 141919, + "ĠÐijÑĥд": 141920, + "×Ĵ×Ļ×ĵ×ķ׾": 141921, + "ĠÐĴедÑĮ": 141922, + "عÙĦاÙħØ©": 141923, + "Ġ×IJ×Ĺר×ķ׳×ķת": 141924, + "ãģĦãģŁãģłãģĦãģ¦": 141925, + "سÙĦØŃ": 141926, + "ØŃÙĦÙħ": 141927, + "زÙĪار": 141928, + "Ùĥسر": 141929, + "×ĺקס": 141930, + "Ġбан": 141931, + "Ġбанков": 141932, + "ĠпÑĢож": 141933, + "ĠпÑĢожива": 141934, + "liwo": 141935, + "liwoÅĽci": 141936, + "ĠTiếp": 141937, + "ĠاÙĦÙħÙĨاسب": 141938, + "ĠاÙĦØ®ÙĬار": 141939, + "ãģĬãģĭ": 141940, + "ãģĬãģĭãģĴ": 141941, + "à¸Ķà¸Ńà¸ģà¹Ħมà¹ī": 141942, + "ämp": 141943, + "ämpfe": 141944, + "à¸ķัà¹īà¸ĩà¹ĥà¸Ī": 141945, + "ĠзаÑīиÑĤ": 141946, + "ĠзаÑīиÑĤÑĭ": 141947, + "ĠThÆ°á»Ŀng": 141948, + "ĠصÙģ": 141949, + "ĠصÙģØŃØ©": 141950, + "×Ĺ×ķרף": 141951, + "ãĥIJãĥĥãĤ°": 141952, + "Ġ×ĵ×Ļ×Ĵ": 141953, + "Ġ×ĵ×Ļ×Ĵ×Ļ×ĺ": 141954, + "Ġ×ĵ×Ļ×Ĵ×Ļ×ĺ׾×Ļ": 141955, + "Ġ×Ķ×Ĺ×ķ׾×Ļ×Ŀ": 141956, + "веÑī": 141957, + "веÑīа": 141958, + "ĠкÑĥлÑĮÑĤ": 141959, + "ĠкÑĥлÑĮÑĤÑĥ": 141960, + "ĠкÑĥлÑĮÑĤÑĥÑĢÑĭ": 141961, + "ĠاÙĦاÙĨترÙĨت": 141962, + "Ġhöch": 141963, + "Ġhöchst": 141964, + "Ġíĺķ": 141965, + "Ġíĺķíĥľ": 141966, + "Ġвой": 141967, + "ĠвойнÑĭ": 141968, + "ÐĽÐŀ": 141969, + "ìĭłìļ©": 141970, + "Ġ×ŀ×ij×ķס": 141971, + "Ġ×ŀ×ij×ķסס": 141972, + "×ŀ׳×Ļ×¢": 141973, + "Ġfiyatı": 141974, + "ĠÑģлÑĥж": 141975, + "ĠÑģлÑĥжбÑĭ": 141976, + "à¸Ĺัศ": 141977, + "à¸Ĺัศà¸Ļ": 141978, + "ãģĵãģ¨ãģĮå¤ļãģĦ": 141979, + "Ġ×Ķ×ŀשת": 141980, + "Ġ×Ķ×ŀשת×ŀש": 141981, + "å¯ĦãģĽ": 141982, + "×ŀש׾×ķ×Ĺ": 141983, + "æĻĤçĤ¹": 141984, + "æĻĤçĤ¹ãģ§": 141985, + "à¸ŀรี": 141986, + "à¸ŀรีà¹Ģมีย": 141987, + "à¸ŀรีà¹Ģมียรà¹Į": 141988, + "à¸ŀรีà¹Ģมียรà¹Įลีà¸ģ": 141989, + "Ġdifficolt": 141990, + "ĠdifficoltÃł": 141991, + "ãĥ¬ãĤ¹ãĥĪ": 141992, + "ãĥ¬ãĤ¹ãĥĪãĥ©ãĥ³": 141993, + "สมà¹Ģà¸Ķà¹ĩ": 141994, + "สมà¹Ģà¸Ķà¹ĩà¸Ī": 141995, + "Ġжид": 141996, + "Ġжидк": 141997, + "ĠzupeÅĤ": 141998, + "ĠzupeÅĤnie": 141999, + "ĠÙħجر": 142000, + "ĠÙħجرد": 142001, + "ãģĮå§ĭ": 142002, + "ãģĮå§ĭãģ¾": 142003, + "ãĤŃãĥ£ãĥ©": 142004, + "Ġ×IJ×ķ×ķ×Ļר": 142005, + "ãģĬäºĴ": 142006, + "ãģĬäºĴãģĦ": 142007, + "ĠpotrÃł": 142008, + "ĠPaÅĦst": 142009, + "ĠPaÅĦstwo": 142010, + "ĠبÙĬاÙĨ": 142011, + "ĠبÙĬاÙĨات": 142012, + "Ġиногда": 142013, + "ĠÑĢа": 142014, + "ĠÑĢаÑģÑĤв": 142015, + "ĠÑĢаÑģÑĤвоÑĢ": 142016, + "Ġ×ĸ×ŀ׳": 142017, + "ยิà¹īม": 142018, + "ÄĨ": 142019, + "ãģ¾ãģķ": 142020, + "ãģ¾ãģķãģ«": 142021, + "ãĥķãĤ¡ãĤ¤ãĥ«": 142022, + "ĠgördÃ¼ÄŁÃ¼": 142023, + "สà¸ĩà¸Ħร": 142024, + "สà¸ĩà¸Ħราม": 142025, + "ĠArkadaÅŁ": 142026, + "ĠrozwiÄħzania": 142027, + "×ŀ×ķ×ĺ": 142028, + "piÄĻ": 142029, + "piÄĻt": 142030, + "صغر": 142031, + "สย": 142032, + "สยาม": 142033, + "ãĤĨãģ£ãģıãĤĬ": 142034, + "Ġtrần": 142035, + "ĠeconomÃŃa": 142036, + "Ġgehören": 142037, + "ãĤ·ãĥ§ãĥ¼": 142038, + "ĠsÅĤucha": 142039, + "à¸ŀà¸Ńà¹ĥà¸Ī": 142040, + "ĠоÑĤмеÑĤил": 142041, + "ÙĨتÙĤÙĦ": 142042, + "Ġpropósito": 142043, + "ĠваÑĪего": 142044, + "Ġnhắn": 142045, + "à¹ģà¸ĸว": 142046, + "ĠкомиÑģ": 142047, + "ĠкомиÑģÑģи": 142048, + "ważnie": 142049, + "ĠyavaÅŁ": 142050, + "×ŀ×Ļק": 142051, + "×ŀ×Ļק×ķ×Ŀ": 142052, + "ש×IJ×ľ×ª": 142053, + "Ġyıllarda": 142054, + "ĠЮ": 142055, + "ĠЮÑĢ": 142056, + "×ł×¡×Ļ×ij×ķת": 142057, + "תצ": 142058, + "תצ×ķ×Ĵ": 142059, + "ĠоднÑĥ": 142060, + "Ġà¸Ńยà¹Īาà¸ĩà¹Ħร": 142061, + "Ġà¸Ńยà¹Īาà¸ĩà¹Ħรà¸ģà¹ĩà¸ķาม": 142062, + "ëģ¼": 142063, + "à¹Ħลà¹Ī": 142064, + "تسÙĦÙĬÙħ": 142065, + "بÙĦاغ": 142066, + "Ġìī": 142067, + "Ġìī½": 142068, + "Ġìī½ê²Į": 142069, + "ãĥļãĥ³": 142070, + "звÑĥÑĩ": 142071, + "ĠWäh": 142072, + "ĠWährend": 142073, + "Ġ×Ļ×Ļת": 142074, + "Ġ×Ļ×Ļ×ª×Ľ×Ł": 142075, + "Ġkhuyên": 142076, + "Ġvẽ": 142077, + "ĠамеÑĢ": 142078, + "ĠамеÑĢик": 142079, + "ĠамеÑĢикан": 142080, + "ĠамеÑĢиканÑģк": 142081, + "عجب": 142082, + "ãĥĽãĥ¼ãĥłãĥļãĥ¼ãĤ¸": 142083, + "ĠникÑĤо": 142084, + "ĠÙĤÙİ": 142085, + "ĠÙĤÙİاÙĦ": 142086, + "ĠÙĤÙİاÙĦÙİ": 142087, + "ÐIJÐĹ": 142088, + "ÙħجÙħÙĪع": 142089, + "ÙħجÙħÙĪعات": 142090, + "ĠnecessitÃł": 142091, + "Ġpobli": 142092, + "Ġpobliżu": 142093, + "Ġphấn": 142094, + "ĠСообÑī": 142095, + "ÙħÙĤاط": 142096, + "ÙħÙĤاطع": 142097, + "Ġ×Ķצ×ķר×ļ": 142098, + "laÅŁtırma": 142099, + "วิà¸Ķ": 142100, + "วิà¸Ķี": 142101, + "วิà¸Ķีà¹Ĥà¸Ń": 142102, + "Ġ그리ìĬ¤": 142103, + "Ġ그리ìĬ¤ëıĦ": 142104, + "ãĤ¿ãĤ¤ãĥŁ": 142105, + "ãĤ¿ãĤ¤ãĥŁãĥ³ãĤ°": 142106, + "ק×ĺ×Ĵ×ķר": 142107, + "ק×ĺ×Ĵ×ķר×Ļ×Ķ": 142108, + "Ġ×Ĺ×ķפ": 142109, + "Ġ×Ĺ×ķפש×Ļ": 142110, + "أجر": 142111, + "Ġимени": 142112, + "ĠÑĢанее": 142113, + "à¹Ģà¸ŀืà¹Īà¸Ńà¸Ļà¹Ĩ": 142114, + "ĠJesús": 142115, + "Ñģоедин": 142116, + "Ñģоединен": 142117, + "Ġר×Ĺ×ķק": 142118, + "à¹Ĥà¸ļรา": 142119, + "à¹Ĥà¸ļราà¸ĵ": 142120, + "ĠHÆ¡n": 142121, + "ĠtháºŃp": 142122, + "تعÙĬÙĬÙĨ": 142123, + "ĠtartÄ±ÅŁ": 142124, + "ĠtartÄ±ÅŁma": 142125, + "ĠGespr": 142126, + "ĠGespräch": 142127, + "תר×ķפ": 142128, + "תר×ķפ×ķת": 142129, + "Ġcatégorie": 142130, + "ĠоказÑĭва": 142131, + "ĠналиÑĩие": 142132, + "Ġprésenté": 142133, + "Ġkull": 142134, + "Ġkulland": 142135, + "Ġkullandı": 142136, + "Ġünl": 142137, + "Ġünlü": 142138, + "ĠÙģÙĥرة": 142139, + "изаÑĤоÑĢ": 142140, + "×IJ×ķ׳": 142141, + "×IJ×ķ׳×Ļ×ij": 142142, + "×IJ×ķ׳×Ļ×ijרס": 142143, + "×IJ×ķ׳×Ļ×ijרס×Ļ×ĺת": 142144, + "ĠÑĢаÑģÑģмаÑĤ": 142145, + "ĠÑĢаÑģÑģмаÑĤÑĢ": 142146, + "ĠÑĢаÑģÑģмаÑĤÑĢива": 142147, + "تÙĥÙĦÙħ": 142148, + "ÙĥترÙĪ": 142149, + "ÙĥترÙĪÙĨÙĬ": 142150, + "ĠÑģоÑĩеÑĤ": 142151, + "ĠÑģоÑĩеÑĤа": 142152, + "ãĤĴè¦ĭãģĽ": 142153, + "Ġngừa": 142154, + "ĠÐłÐµÑģп": 142155, + "ĠÐłÐµÑģпÑĥб": 142156, + "ĠÐłÐµÑģпÑĥблик": 142157, + "ãĤ¦ãĤ©": 142158, + "ãĤ¦ãĤ©ãĥ¼": 142159, + "ĠÐľÐµÐ¶Ð´Ñĥ": 142160, + "ĠìŀĪê²Į": 142161, + "Ġmâ": 142162, + "ĠìļĶì²Ń": 142163, + "ضار": 142164, + "ลุà¹īà¸Ļ": 142165, + "ëĮĢíķĻêµIJ": 142166, + "×ĸ×Ļ׼": 142167, + "×ĸ×Ļ׼ר×ķף": 142168, + "ãĤ¹ãĥļ": 142169, + "ãĤ¹ãĥļãĥ¼ãĤ¹": 142170, + "ĠкÑĢаÑģоÑĤ": 142171, + "Ｈ": 142172, + "ê¼Ń": 142173, + "ãĤĴéĽĨ": 142174, + "ãĤĴéĽĨãĤģ": 142175, + "ë°Ŀ": 142176, + "Ġ×Ķ׳×IJ": 142177, + "Ġ×Ķ׳×IJש×Ŀ": 142178, + "Ġê°Ģìļ´": 142179, + "Ġê°Ģìļ´ëį°": 142180, + "تÙĥÙĦÙģØ©": 142181, + "ĠØŃÙĤÙĬÙĤÙĬ": 142182, + "Ġhalk": 142183, + "Ġhalkın": 142184, + "ÑİÑīÑĥÑİ": 142185, + "ĠÑģпин": 142186, + "סר×ĺף": 142187, + "ĠпеÑĢвого": 142188, + "Ġполож": 142189, + "ĠположиÑĤелÑĮн": 142190, + "Ġдл": 142191, + "ĠдлиÑĤелÑĮн": 142192, + "ĠVÄ©nh": 142193, + "ê´´": 142194, + "ĠÑģÑĭÑĢ": 142195, + "ĠíĨµíķĺìŬ": 142196, + "ë³ijìĽIJ": 142197, + "à¹Ĥรà¸ĩà¸ĩาà¸Ļ": 142198, + "รัà¸ļà¸ľà¸´à¸Ķ": 142199, + "รัà¸ļà¸ľà¸´à¸Ķà¸Ĭà¸Ńà¸ļ": 142200, + "تجÙĨب": 142201, + "sÅĤ": 142202, + "sÅĤuch": 142203, + "ãĤ¢ãĥ«ãĥIJ": 142204, + "ãĤ¢ãĥ«ãĥIJãĥł": 142205, + "ëī´ìĬ¤": 142206, + "Ġpatië": 142207, + "Ġpatiënt": 142208, + "Ġìĺ¤í": 142209, + "Ġìĺ¤íŀ": 142210, + "Ġìĺ¤íŀĪ": 142211, + "Ġìĺ¤íŀĪ볤": 142212, + "ĠDerne": 142213, + "ĠDerneÄŁi": 142214, + "wróci": 142215, + "wróciÄĩ": 142216, + "ĠобÑī": 142217, + "ĠобÑīеÑģÑĤв": 142218, + "ĠобÑīеÑģÑĤвенно": 142219, + "ĠêµIJìĪĺ": 142220, + "tıģımız": 142221, + "Ġ×Ķ×ŀש×Ļ×ij": 142222, + "körper": 142223, + "Ġпозвол": 142224, + "ĠпозволиÑĤ": 142225, + "ĠChiến": 142226, + "أخÙĪ": 142227, + "ĠAydın": 142228, + "à¸Ķà¹īาà¸Ļล": 142229, + "à¸Ķà¹īาà¸Ļลà¹Īาà¸ĩ": 142230, + "Ġdru": 142231, + "Ġdruż": 142232, + "Ġdrużyn": 142233, + "Ġë°ľíijľ": 142234, + "ĠThảo": 142235, + "جÙĩاد": 142236, + "à¸ģระà¸Ĺูà¹ī": 142237, + "ĠкÑĢов": 142238, + "ĠкÑĢови": 142239, + "Ġiçerik": 142240, + "Ġnadzie": 142241, + "ĠnadziejÄĻ": 142242, + "ĠСмоÑĤÑĢ": 142243, + "Ġphức": 142244, + "جتÙħاع": 142245, + "جتÙħاعÙĬØ©": 142246, + "компон": 142247, + "компоненÑĤ": 142248, + "Ġбил": 142249, + "ĠбилеÑĤ": 142250, + "ãĥIJãĥ³ãĥī": 142251, + "ĠPolÃŃcia": 142252, + "اÙĦتÙĩ": 142253, + "اÙĦتÙĩاب": 142254, + "ØŃرÙģ": 142255, + "تخط": 142256, + 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P", + "ut e", + "Ġas s", + "I nt", + "as k", + "lo c", + "Ġli ke", + "od y", + "Ġle t", + "lo ad", + "Ġa m", + "ro l", + "Ġg r", + "y p", + "Ġal so", + "ĠI t", + "ur l", + "if ic", + "or s", + "_ P", + "_ n", + "ig h", + "Ġth an", + "C om", + "A N", + "U L", + "at ing", + "ĠTh is", + "re f", + "_ S", + "Ġst atic", + "ro ll", + "Ġj ust", + "Ġres ult", + "i an", + "id th", + "Ġthe m", + ") );Ċ", + "d er", + "re ak", + "C on", + ": //", + "u le", + ".. .", + "ar ch", + "em ent", + "Ġ< <", + "us h", + "en se", + "ar r", + "Ġint o", + "c ess", + "am p", + "i ed", + "um ent", + "Ġ \\", + "] ,", + "w o", + "al s", + "Ġwh at", + "an c", + "V alue", + "= '", + "ol um", + "Ġp os", + "ag es", + "ay er", + "Ġs c", + "u es", + "\" )Ċ", + "_ T", + "Ġl ist", + "( s", + "Ġc ase", + "C h", + "ĉĉĉĉ ĉ", + "//// ////", + "pon ent", + "Ġ z", + "Ġk n", + "le t", + "D E", + "re d", + "Ġf e", + "Ġ} ,Ċ", + "Ġ ,", + "( t", + "Ġf irst", + "' );Ċ", + "w ord", + "Ġ import", + "Ġa ct", + "Ġch ar", + "C T", + "ĠT r", + "op le", + "= {", + "ĉ f", + "i ent", + "c ent", + ". j", + "le ction", + ") )Ċ", + "Ġon ly", + "Ġpr int", + "m er", + ". W", + "o ck", + "Ġ --", + "T ext", + "Ġo p", + "an k", + "Ġit s", + "Ġb ack", + "[ \"", + "Ġne ed", + "Ġc l", + "Ġs ub", + "Ġl a", + "( (", + ". \"", + "O bject", + "Ġst art", + "f ile", + "( self", + "n er", + "e y", + "Ġus er", + "Ġ ent", + "ĠC om", + "it s", + "ĠC on", + "ou ble", + "ow er", + "it em", + "ver y", + "ĠW e", + "lic k", + "Ġ Q", + "ph p", + "t tp", + "' :", + "ic s", + "Ġu nder", + "Ġ* Ċ", + ". L", + ") ;", + "ic es", + "Ġre g", + ") čĊ", + "ĉ public", + "S S", + "Ġth en", + "re at", + "i ous", + ". G", + "e k", + "ire ct", + "he ck", + "cri pt", + "n ing", + "ĠU n", + "Ġm ay", + "ĠW h", + "B o", + "I tem", + "str uct", + ". st", + "re am", + "ib le", + "lo at", + "Ġor g", + "u nd", + "s um", + "_ in", + ".. /", + "_ M", + "Ġh ow", + "r ite", + "' Ċ", + "T o", + "w w", + "Ġpe ople", + "ind ex", + ". n", + "ht tp", + "( m", + "ect or", + "Ġin d", + "Ġj av", + "] ,Ċ", + "ĠH e", + "_ st", + "f ul", + "o le", + ") {Ċ", + "Ġsh ould", + "op y", + "el p", + "i er", + "_ name", + "ers on", + "I ON", + "ot e", + "Ġt est", + "Ġb et", + "rr or", + "ul ar", + "ã Ģ", + "Ġ Ð", + "b s", + "t ing", + "Ġm ake", + "T r", + "Ġa fter", + "ar get", + "R O", + "olum n", + "r c", + "_ re", + "def ine", + "Ġr ight", + "r ight", + "d ay", + "Ġl ong", + "[ ]", + "( p", + "t d", + "con d", + "ĠP ro", + "Ġre m", + "ption s", + "v id", + ". g", + "Ġ ext", + "Ġ __", + "' )Ċ", + "p ace", + "m p", + "Ġm in", + "st ance", + "a ir", + "a ction", + "w h", + "t ype", + "ut il", + "a it", + "< ?", + "I C", + "t ext", + "Ġp h", + "Ġf l", + ". M", + "cc ess", + "b r", + "f ore", + "ers ion", + ") ,Ċ", + ". re", + "ate g", + "Ġl oc", + "in s", + "- s", + "tr ib", + "ĠI nt", + "Ġa rray", + ", \"", + "P ro", + "( c", + "ess ion", + "> ĊĊ", + "Ġs he", + "\" ]", + "ap h", + "Ġex p", + "ert y", + "ĠS e", + "Ġp ar", + "un c", + "E T", + "Ġre ad", + "pr int", + "Ġre l", + "Ġfor m", + "Ġd r", + "Ex ception", + "in put", + "Ġtr ans", + "#### ####", + "ord er", + "B y", + "Ġa w", + "it ies", + "u ff", + "pl ay", + ". add", + "ĠâĢ ĵ", + "Ġw ant", + "Ġcom p", + "ment s", + "Ġ| |", + "a z", + "b e", + "Ġn umber", + "Ġre quire", + "ĠE x", + "Ġc ol", + "Ġ key", + "em ber", + "Ġt wo", + "Ġs ize", + "Ġwh ere", + "U T", + "res ult", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ou gh", + "or ld", + "o od", + "u ch", + "at ive", + "g er", + "are nt", + "Ġ/ *", + "Ġar g", + "Ġwh ile", + "( this", + "Ġre c", + "Ġd if", + "St ate", + "Ġs pec", + "r ide", + "_ F", + "Ġlo ok", + "A M", + "il ity", + "et er", + "âĢĻ t", + "ĊĊ Ċ", + "ay out", + "---------------- ----------------", + "ag er", + "Ġc ould", + "Ġb r", + "end s", + "u res", + "Ġkn ow", + "et s", + "ĠI f", + "ĠS h", + ". w", + "b ack", + "Ġs er", + "Ġ+ =", + "Ġf r", + "() );Ċ", + "Ġh and", + "I nd", + "UL L", + "I m", + "() ;ĊĊ", + "Ġm ost", + "Ġtr y", + "Ġn ow", + "rou gh", + "> čĊ", + "ack age", + "Ġh im", + ". _", + "if y", + "Ġb reak", + "Ġ );Ċ", + "re n", + "# define", + "it t", + "Ġa p", + "ĉ c", + "( n", + "ĠY ou", + ": ĊĊ", + "- m", + "Ġe very", + "ust om", + "li ent", + "oc ument", + "cri ption", + "E rror", + "- b", + "Ð ¾", + "] [", + "tr ans", + "Ġp oint", + "Ġst d", + "Ġf il", + "T ime", + "Ġm od", + "Ġ ->", + "Ġ error", + "a h", + "Ġt ext", + "roll er", + "lo se", + "q l", + "Ġp ol", + "> <", + ". B", + "- c", + "Ġop en", + "Ġe st", + "ĠĠĠĠĠĠĠĠ Ċ", + "Ġn ext", + "I M", + "Ñ Ĥ", + "O T", + "à ³", + "Ġf ollow", + "cont ent", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "Ġin clud", + "H E", + "ĠR es", + "Ġh ref", + "Ð ¸", + "Ġc ar", + "yp es", + "im age", + "U n", + "Ġbo ol", + "A D", + "Ġg ame", + ".F orm", + "row s", + "* /", + "vel op", + ".D rawing", + "Ġp ath", + "is ion", + "Ġe ach", + "ĠP l", + "_t ype", + "P ath", + "ne ction", + "Ġa v", + "' ).", + "Ġsup port", + "EN T", + "re m", + "\" ).", + "Ġo wn", + "Ġc or", + "c ount", + "m iss", + "u ally", + "Ġm em", + "st d", + "i ence", + "se arch", + "\" ĊĊ", + "F orm", + "Ġs ex", + "en ame", + "Ġs ign", + "Ġ et", + "ĠĠĠĠĠĠĠĠ ĠĠ", + "', '", + "ĠA pp", + "Ġth ose", + "o ff", + "Ġ err", + "Ġs ystem", + "Ġbe st", + "c ode", + "Ġs ame", + "Ġd i", + "us s", + "Ġc reate", + "ath er", + "A rray", + ". in", + "f e", + "S ervice", + "U N", + "at s", + "Ġ Z", + "al th", + "Ġm ade", + "tr ue", + "A B", + "Ġm ark", + "r id", + "if ied", + ", čĊ", + "y n", + "p ress", + "Ġg roup", + "Ġf in", + "ĠL icense", + "F ield", + "eg er", + "Ġw orld", + "in ess", + "t y", + "Ġpro cess", + "( b", + "Ġc re", + "ar n", + "iv es", + "Ġm ain", + "ide o", + "_ g", + "A G", + "val id", + "im g", + "P I", + "Ġc olor", + "Ġre port", + "Ġt ake", + "ri b", + "O M", + "Ġd ay", + "Re quest", + "Ġs k", + "b ers", + "ĉ s", + ".A dd", + "o ot", + "Im age", + "Ġcom ple", + "ol lection", + "Ġto p", + "Ġf ree", + "A S", + "D e", + "ĠO n", + "I G", + "et a", + "D ate", + "Ġa ction", + "O ver", + "it or", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "n ot", + "Ġind ex", + "h er", + "ic on", + "O n", + ";čĊ čĊ", + "iv ity", + "m and", + ".W indows", + "O L", + "Ġre al", + "Ġm ax", + "l and", + ".. ..", + "r aph", + "Ġbu ild", + "le g", + "ass word", + "? ĊĊ", + "âĢ ¦", + "o ok", + "u ck", + "Ġm essage", + "t est", + "iv ers", + "Ġin put", + "Ġar t", + "Ġbet ween", + "G et", + "ent er", + "g round", + "en e", + "à ¡", + ".l ength", + "N ode", + "( i", + "C lass", + "f or", + "ĠâĢ Ķ", + "t en", + "o in", + "Ġ ke", + "u i", + "ĠI N", + "Ġt able", + "s ub", + "ĠL e", + "Ġhe ad", + "Ġm ust", + "//////// ////////", + ". util", + "Cont ext", + "Ġor der", + "Ġm ov", + "o ver", + "Ġcont in", + "Ġs ay", + "st atic", + ".T ext", + "Ġclass Name", + "pan y", + "Ġt er", + "he ad", + "r g", + "Ġpro duct", + "Th is", + ". âĢĿ", + "ĠB ut", + "lo y", + "Ġd ouble", + "s g", + "Ġpl ace", + ". x", + "m essage", + "Ġin formation", + "pr ivate", + "Ġo per", + "c ed", + "d b", + "\"> ", + "ater ial", + "ile d", + "Ġp ut", + "Q u", + "Ñ Ģ", + "un g", + "m ap", + "ĉĉĉĉ ĉĉĉĉ", + "Ġle vel", + "Com ponent", + "bo ok", + "cre en", + "_ RE", + "Ġcon fig", + "ã ģ", + "O r", + ". data", + "Ġd ocument", + "\", \"", + "trib ute", + "u x", + "L og", + "fer ence", + "p ost", + "_ e", + "Ġloc al", + "and om", + "ass ert", + "V al", + "lect ed", + "in a", + "atab ase", + "A dd", + "Ġcont ent", + ".p rint", + "s igned", + "r ic", + ".\" ĊĊ", + "Ġf a", + "! ĊĊ", + "- f", + "iv ed", + "Ġ quest", + ". ex", + "Ġf loat", + "Ġde velop", + "о Ð", + "M ap", + "ad ing", + "Ġpos s", + "U E", + "n amespace", + "_ O", + "ĉ b", + ".G et", + "> (", + "j son", + "etail s", + "Ġto o", + "Ġext ends", + "ĠN one", + "Ġf ore", + "( String", + "form at", + "Ġg reat", + "int er", + "ca le", + "Ñ ģ", + "r on", + "iv ing", + "E nt", + "enc y", + "x t", + "o y", + "Ġmon th", + "Ġh app", + "Ġsup er", + "b ar", + "def ault", + "_ de", + "ord s", + "l n", + "( {Ċ", + "ĠI nd", + "as es", + "Ġt itle", + "Ġcont ext", + "o h", + "- p", + "E m", + "Ġm et", + "T est", + "Ġl ife", + "_ v", + "ĠU S", + "U I", + "oc ation", + "m d", + "Ġ[ Ċ", + "Ġ ]", + "s w", + "Ġin cre", + "s cript", + "ent ial", + "w ays", + ". de", + "Ġs rc", + "Ġc atch", + "ĠA meric", + "// Ċ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "Ġp ay", + "pl it", + "âĢ Ķ", + "Ġc oun", + "ob j", + ".ph p", + "Ġch ange", + "eth ing", + "' re", + "ast er", + "lo s", + "l ation", + "ĠĠ Ċ", + "L e", + "à ¤", + "( {", + "read y", + "ĠN o", + "Ġpos ition", + "Ġo ld", + "Ġbo ok", + "able d", + "b ug", + "H and", + "} ;ĊĊ", + "is play", + "av ing", + "Ġgo ver", + "Ġv ersion", + "S ystem", + "n ect", + "res ponse", + "St yle", + "U p", + "ang u", + "Ġth ree", + "in it", + "er o", + "Ġl aw", + "end if", + "Ġb ase", + "em ail", + "( l", + "_ V", + "Ġcon f", + "AT E", + "Ġd uring", + "t es", + "Ġcon sole", + "ĠP r", + "Ġs pe", + "v es", + "p ath", + "ial og", + "d ition", + "_t o", + "ard s", + "Ġagain st", + "et work", + "ĠP h", + "_ L", + "c ur", + "im it", + "W ith", + "Ġp ower", + "i um", + "' ;ĊĊ", + "Ġw om", + "le ft", + "our ces", + "at ri", + "ĠI m", + "ĠM an", + "or th", + "$ {", + "qu als", + "es e", + "_s ize", + "Ġis s", + "ot al", + "- g", + "i que", + "r ame", + "Ġw idth", + "er g", + ") (", + "itt le", + "T R", + "ĠThe y", + "enc es", + "r l", + "on s", + "Ġl abel", + ". y", + "- t", + "up date", + "an el", + "s c", + ".t o", + "Ġpro ject", + "à ¼", + "Ġe lement", + "Ġsu ccess", + "ĉĉ Ċ", + ".s h", + "r am", + "ch ed", + "() )Ċ", + "Ġ( Ċ", + "Ġd ate", + "Ġto t", + "_ ST", + "A ll", + "ific ation", + "ĉ var", + "Ġt ri", + "ch em", + "m y", + "Ġb ig", + "ĠA d", + "ĠA t", + "ot s", + "n um", + "A ct", + "Ġm ap", + "er a", + "co pe", + ". $", + ", âĢĿ", + "Ġp op", + "Ġf ew", + "Ġl en", + "u id", + "et ers", + "u les", + "à Ń", + "s ource", + "http s", + "Ġd em", + "Ġe ar", + "######## ########", + "Ġm atch", + "or ies", + "ac es", + "ĠC l", + "Ġn ode", + "ir c", + "loc al", + "un ity", + "} ;Ċ", + "Ġan other", + "< <", + "og le", + "Ġs it", + "ew ork", + "T E", + ". I", + "N S", + "olog y", + "ou ght", + ".C ont", + "> >", + "Ġc are", + "st ate", + "ĉ private", + "Ġe ffect", + "++ )", + "_f ile", + "end ing", + "L ine", + "F or", + "i or", + "ĠS c", + "Ġf un", + ".S ize", + "ĉ else", + "] )", + "st art", + "v ious", + "Ġ} ,", + "our s", + "Ġle g", + "Ġs ervice", + "Ġs ince", + "ir on", + "L abel", + "Ġn on", + "Ġl os", + "ict ion", + "Ġf ull", + "act er", + "bo ard", + "g ress", + "Ġt urn", + "ith er", + ".s ize", + "Ġb ody", + "res h", + "et urn", + "( _", + "y les", + "orm al", + "p i", + "Ġsom ething", + "! --", + "u int", + "Ġpro du", + "Ġst and", + "Ġpro ble", + "Ġav ailable", + "m t", + "ĠB l", + "Ġ ...", + "Ġb lock", + "In put", + "Ġke ep", + "C ount", + "op en", + "Ġ[ '", + "Ġth row", + "uild er", + "A ction", + "Ġth ings", + "Tr ue", + "Ġ url", + "ĠB o", + "print f", + "Ġre d", + "j s", + ".c reate", + "ĠO r", + "St atus", + "In stance", + "Ġcont rol", + "Ġcom e", + "Ġc ustom", + "loc ation", + "m odel", + "Ġ čĊ", + "Ġs ource", + "Ġe as", + ". out", + "] ĊĊ", + "one y", + "Ġaw ait", + "Ġpart ic", + "A P", + "ub lish", + "od es", + "_p ro", + "p ly", + "rit er", + "Ġpro v", + "Ġm ill", + "H T", + "] )Ċ", + "Ġch ang", + "Ġas k", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "Ġout put", + "Ġem ail", + ".p ush", + "Ġ} čĊčĊ", + "in ation", + "atri x", + "T able", + "u ccess", + "] );Ċ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġdis c", + "( [", + "Ġb usiness", + "he ight", + ". html", + "t a", + "f ield", + "Ġrequire d", + "_ R", + "Ġgover n", + "} čĊčĊ", + "le x", + ". ,", + "ĠS et", + "ur ch", + "// /", + "t s", + "a f", + "Ġm ight", + "ist ory", + "S tr", + "Ġne ver", + "Res ponse", + "ar se", + "ad a", + "ĠH ow", + "Ġ* )", + "Ġ ;", + "Ġh ard", + "A d", + "Ġinter n", + "us ed", + "( data", + "m od", + "ann el", + "Ġn p", + "ug g", + "Ġ/ >Ċ", + "Ġcal led", + "b ody", + "Ġch o", + "( r", + "_s et", + "ir d", + "Ġ> =", + "Ġ} ;Ċ", + "Ġo ptions", + "ĠG ener", + "Ġhe ight", + "P oint", + "Y ou", + "et y", + "C lick", + "Ġsm all", + "Ġ ide", + "Ġacc ess", + "angu age", + "Ġprot ected", + "Ġj ob", + "ĠTh ere", + "D ef", + "Ġadd ress", + "Ġu int", + "N ot", + "o o", + "ap s", + "< div", + "ain ed", + "at ur", + "Ġs um", + "- w", + "ĠD ate", + "Ġl ittle", + "Ġf ri", + "Y PE", + "Ġp ort", + "e h", + "pr ing", + "_p ath", + "Ġst atus", + "a im", + "bo ol", + "Ġap pe", + "Ġo s", + ". name", + "ens ion", + "_ G", + "Ġup date", + "Con fig", + "a ff", + "ER R", + "Ġ< =", + "at ely", + "# if", + "u ction", + "ĠT e", + "Ġl ink", + "ĠU ser", + ".f ind", + ". org", + "m e", + "Ġg iven", + "O ut", + "# endif", + "Ġbet ter", + "P age", + "Ġfe el", + "en n", + "M L", + "Ġal ready", + "Ġinclud ing", + "o ogle", + "r u", + "ic ally", + "pro p", + "le an", + "out er", + "Ġal ways", + "ord ing", + "I f", + "or age", + "Ġp arent", + "v is", + "ĉĉĉĉ ĉĉĉ", + "Ġg ot", + "st and", + "Ġle ss", + "/ s", + "ĠA ss", + "ap t", + "ire d", + "ĠA dd", + "Ġacc ount", + "p loy", + "Ġd er", + "res ent", + "Ġl ot", + "Ġval id", + "ĉ d", + "Ġb it", + "pon ents", + "Ġfollow ing", + "_ ex", + "S ON", + "Ġs ure", + "oc ial", + "Ġp rom", + "ert ies", + "he ader", + ".p ro", + "Ġbo olean", + "Ġse arch", + "k en", + "Ġor ig", + "Ġ er", + "E d", + "E M", + "a ut", + "l ing", + "al ity", + "By Id", + "b ed", + "ĉc ase", + "eth er", + "pos it", + "Ġinv est", + "ĠO R", + "Ġs ays", + "miss ion", + "AM E", + "Ġtem p", + "o ad", + "Ġre st", + "in fo", + "Ġinter est", + "A rg", + "Ġper form", + "pon s", + "ĠV iew", + "Ġv er", + "l ib", + "( const", + "U til", + "List ener", + "ar ge", + "Ġm ult", + "Ġd ie", + "Ġs ite", + "../ ../", + "E L", + "Ġval ues", + "Ġ} )Ċ", + "p en", + "N o", + "ic ro", + "Ġbe h", + "Ġ' ./", + "ac y", + "re c", + "() ->", + "ĉ ĠĠĠ", + "\" ))", + "Cont ent", + "_ W", + "ple ment", + "Ġw on", + "Ġv ideo", + "ad i", + "p oint", + "% %", + "Ġg l", + "erv ed", + "v iron", + "I F", + "ut ed", + "ã ĥ", + "' m", + "Ġc ert", + "Ġpro f", + "Ġc ell", + "ar i", + "Ġpl ayer", + "a is", + "Ġc ost", + "Ġh um", + "( R", + "Ġoff ic", + "k s", + ".t ext", + "at ures", + "Ġtot al", + "Ġ*/ ĊĊ", + "o pe", + "Ġst at", + "U M", + "Ġlo ad", + "ight s", + "Ġc lear", + "u ro", + "Ġte chn", + "up port", + "I R", + "Ġ row", + "Ġse em", + "Ġ q", + "Ġsh ort", + "ĠN ot", + "ip p", + "G roup", + "se ction", + "m ax", + "ir l", + "Ġover ride", + "Ġcom pany", + "Ġd one", + "\" );čĊ", + "Ġg re", + ". Re", + "Ġbel ie", + "r ist", + "Ġhe alth", + "AN T", + "() ĊĊ", + "ĠB e", + ". value", + "ĠG r", + "ott om", + "Ġarg s", + "P T", + "st atus", + "f unc", + "um ents", + "- h", + "N umber", + ": čĊ", + "ĠL og", + "er ver", + "Ġ) ,Ċ", + "am ent", + "Ġob j", + "in c", + "Ġchild ren", + "ic y", + "I Z", + "and s", + "ab ly", + "Ġdist rib", + "Ġc ur", + "er ial", + "Ġd ays", + "re ated", + "re ct", + "- l", + "ir m", + "idd en", + "om b", + "Ġin itial", + ".j s", + "Ġ â", + "Qu ery", + "Ġon line", + "im al", + ". con", + "a u", + "U rl", + "cont rol", + "ire ction", + "Ġin stance", + "OR T", + "ĠF r", + "wh ere", + "Ġjav ax", + "Ġorg an", + "ap ter", + "Ġre ason", + "o ptions", + "ĠM ar", + "( a", + "Ġwith in", + ".âĢĿ ĊĊ", + "O DE", + "_ DE", + "ad min", + "end ed", + "Ġdes ign", + "ĠD ata", + "un e", + "ĠF ile", + "ro ot", + "Ġc ent", + "Ġa rr", + "_ add", + "l en", + "p age", + ", '", + "_ str", + "Ġb ro", + "ab ility", + "ou th", + "/ c", + "p ose", + "irt ual", + "ear ch", + "_ url", + "arg in", + "H ttp", + "Ġs chool", + "av a", + "Ġcons ider", + ".l abel", + "ĠA rray", + "we b", + "o pt", + ".print ln", + "ul ation", + "Ġf unc", + "P L", + "Ġ\" \\", + "ĠT ext", + "act ory", + "(f unction", + "n ull", + "Ġen g", + "d own", + "Ġin clude", + "ĠE n", + "ĠD r", + "Ġd b", + "! !", + "s ide", + "Ġin it", + "quire d", + "ĠS he", + "C olumn", + "re act", + "Ġan n", + "Ġst op", + "Ġl ater", + "ĠTh at", + "ent ion", + "d f", + "U G", + "I LE", + "Ġc lient", + "ra ft", + "ff er", + "PO ST", + "el per", + "Ġlo ve", + "qu ote", + "ou d", + "Ġj son", + "Ġab le", + "Ġm en", + "A X", + "ĠC opyright", + "à ¶", + "av ig", + "re q", + "C lient", + "} );Ċ", + ".C om", + "er c", + "il t", + "pec ial", + "_c om", + "ro om", + ". Name", + "Ġg ive", + "am b", + "i ke", + "Ġcon dition", + "cl ient", + "ator s", + ": \"", + "Ġc opy", + "ut ure", + "ivers ity", + "ern al", + "{ {", + "ĠC an", + "ou nc", + "d o", + "Ġo cc", + "Ġapp ro", + "th ers", + "z e", + "Ġe ither", + "ĠF l", + "Ġimport ant", + "Ġle ad", + "at tr", + "AR T", + "E qual", + "Ġd a", + "et ch", + "ent ity", + "Ġfam ily", + "add ing", + "Ġo ption", + "Ġex ist", + "ic a", + "ĠO bject", + "' ve", + "v ers", + "ition al", + "out put", + "ĠTr ue", + "ĠO F", + "_t ime", + "Ġof fer", + "Ġ} );ĊĊ", + "H ER", + "eg in", + "\" \"", + "Ġw ater", + "Ġc he", + "ĠM y", + "ore d", + "Ġst ep", + "anc es", + "C K", + "A Y", + "à ¸", + "str uction", + "( C", + "ou ch", + "St ream", + "act ive", + "am a", + "Ent ity", + "pro duct", + "() {Ċ", + "Ġgovern ment", + "ĠI D", + "aj or", + "A nd", + "Ġdis play", + "Ð »", + "Ġt imes", + "Ġf our", + "Ġf ar", + "Ġpres ent", + "ĠN S", + "Ġ\\ Ċ", + "ue st", + "Ġb as", + "e cho", + "ch ild", + "if ier", + "Hand ler", + "Ġl ib", + "Prop erty", + "trans lation", + "Ġro om", + "Ġon ce", + "Ġ[ ]", + "cent er", + "================ ================", + "Ġresult s", + "Ġcontin ue", + "Ġt alk", + "_ get", + "Ġg row", + ".s w", + "e b", + "ĠP ublic", + "O P", + "ec ute", + "ol s", + "Ġ **", + "\" );ĊĊ", + "Ġm ass", + "ure d", + ".c lass", + "om ic", + "Ġme an", + "ip s", + "Ġa ut", + ");čĊ čĊ", + "Ġun til", + "Ġmark et", + "Ġare a", + "u it", + "Ġl ength", + "ĠW ith", + "struct or", + "e vent", + "\"> <", + "ĠS p", + "I V", + "Ġm us", + "if f", + "Ġk ind", + "a uthor", + "ound s", + "m b", + "_ key", + "w idth", + "posit ory", + "Ġl ight", + "u k", + "R ow", + "oh n", + "al f", + "viron ment", + "app er", + "ollection s", + "Ġs ide", + "_in fo", + "Ġex ample", + "im ary", + "Ġw r", + "Ġc amp", + "cri be", + "\" /", + "Ġm iss", + "w ay", + "Ġb ased", + "Ġpl an", + "V is", + "om ain", + "un k", + "Ġaw ay", + "U P", + "< T", + "O S", + "i od", + "ĠM on", + "âĢĻ re", + "Ġli k", + "à §", + "iv ely", + ". v", + "im er", + "iz er", + "S ub", + "Ġbut ton", + "ĠU p", + "Ġexper ience", + "C L", + "Ġre nder", + "_ value", + "Ġn ear", + "UR L", + "al t", + "Ġcoun try", + "ib ility", + "() ,Ċ", + "e ad", + "Ġa uthor", + "Ġspec ific", + "b ase", + "( name", + "on es", + "ĠD o", + "Ġal ong", + "y ear", + "Ġexp ress", + ". '", + "en v", + "Ġbeg in", + "Ġso ftware", + "Ġim p", + "Ġw in", + "ó n", + "Ġth ing", + "Tr ans", + "ĠT HE", + "Ġ< ?", + "Ġwh y", + "Ġdoes n", + "i j", + "g ing", + "ĉ g", + "Ġs ingle", + "off set", + "ar ning", + "og raph", + "le y", + "_c ount", + "Ġan al", + "cre ate", + "/ m", + "ĠR eg", + "un ch", + "= $", + "is k", + "Ġright s", + "( M", + "Ġ\"\" \"Ċ", + "ap er", + ".m odel", + "Ġp o", + "em pty", + "art ment", + "Ġa nt", + "ĠWh en", + "Ġwom en", + "ĠE d", + "Ġse ason", + "Ġde st", + "à £", + "( h", + "Ġposs ible", + "Ġse ver", + "Ġb tn", + "Ġdid n", + "Ġs ent", + "Ġen c", + "Ġcomm and", + "Ġ ],Ċ", + "_ x", + "Ġre cent", + "ol ution", + "v ector", + "ĠB y", + "ĠM ay", + "ĠA ct", + "» ¿", + "Ġm oney", + "IN T", + "bs ite", + "ĉ p", + ". čĊ", + "ï »¿", + "s l", + "atter n", + "ĠC lass", + "Ġto ld", + "ud io", + "c urrent", + "Ġe qu", + "Ġa uto", + "ĠSt ate", + "d a", + "ms g", + ")) ;ĊĊ", + "Ġwork ing", + "Ġqu ery", + "ĠB r", + "Ġw indow", + "a uth", + "on ly", + "ĉ t", + "Ġle ast", + "ag n", + "Ġex pl", + "it ter", + "ar ing", + "Ġc olumn", + "ĠGener al", + "\": \"", + "er al", + "ri or", + "Ġrec ord", + "I B", + "E X", + "Ġd at", + "Ġm aking", + "u ed", + "ĠC ar", + "em p", + "\" .", + "ĠM ed", + "Ġc lose", + "Ġper cent", + "Ġp ast", + "( g", + ": (", + "Ġw rite", + "Ġm ove", + "Ġp at", + "Cont rol", + ".T o", + "Ġv i", + "*/ Ċ", + "in ate", + "' ll", + "ag ed", + "N ull", + "Ġspec ial", + "IZ E", + "Ġc ity", + "/* Ċ", + "ĠE ng", + "ix ed", + "in ary", + "p y", + "Ġe ff", + "ar io", + "Ġt ell", + "av or", + "Ġse lect", + "le vel", + "im um", + "op er", + "B uilder", + "I P", + "') ,Ċ", + "es c", + "Ġf ont", + "\" ;ĊĊ", + "ĠA m", + "ish ed", + "ill s", + "Int er", + "O W", + "Ġcour se", + "Ġl ate", + "idd le", + "Ġam ount", + "Ġas ync", + "in o", + "c ul", + "Ġ ì", + "and le", + "_ user", + "Ġb en", + "ĠC al", + "Ġ$ _", + "ĠR ep", + "Ġen ough", + "T oken", + ". user", + "( j", + "S c", + "W idth", + "n ow", + "at form", + "Ġlook ing", + "Ġh old", + "M odule", + "IT Y", + "v o", + "is on", + ".D ata", + "y c", + "Ġp ot", + "ĠTr ump", + "id ual", + "id es", + "r t", + "Ġprop erty", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "am ework", + "g o", + "Ġl ow", + "Ġpar a", + "Ġpr ice", + "ur y", + "Ġto day", + "ro y", + "Ġ' /", + "Ġpol it", + "Ġ' '", + "ym b", + "P h", + "Ġad v", + "Ġatt ack", + "ĠS te", + "RO M", + "an a", + "Ġme ans", + "Ġst ory", + "id s", + "ak en", + "Ġme et", + "Ġm om", + "ĠâĢ ĺ", + "Ġ? >", + "Ġd en", + "ob ile", + "ch ange", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ic i", + "n a", + "ĠF orm", + "Ġs ort", + "Se lect", + "p are", + "Ġth ought", + "_ con", + "Ġt ask", + "oc us", + "ĠD E", + "ĠM in", + "Ġo pt", + "ĉb reak", + "um er", + "K E", + "th en", + "Ġd et", + "ĠT est", + "port s", + "Ġre view", + "(' /", + "m ove", + "Ġsw itch", + "ER T", + "p atch", + "ann ot", + "ã Ĥ", + "Ġab ove", + "it ive", + "Ġquest ion", + "ĠQ u", + "ãĢĤ ĊĊ", + "g le", + "Ġw ord", + "Ġprov ide", + "ĠR eturn", + "Ġre search", + "ã o", + "u str", + "Ġp ublish", + "chem a", + "} }", + "ĠC ON", + "- in", + "all back", + "Ġco ver", + "\\ \\", + "c olor", + "ĠI S", + "Ġwh ether", + "im ate", + "is c", + "B ar", + "Ġd iv", + "B e", + "our n", + "Ġh aving", + "le m", + "pl ayer", + "ab s", + "am era", + "ne y", + "Ġex c", + "get her", + "pl ied", + "a o", + "[ $", + "Ġ+ +", + "i pe", + "sh ow", + "/ d", + "[ :", + "ag ement", + "le v", + "_ ID", + "r ary", + "ad es", + "_ se", + "a use", + "Ġem ploy", + "Ġ*/ čĊ", + "Ġf re", + "Ġ' @", + "Ġcomple t", + "Ġl arge", + "r al", + "\\ x", + "Ġf ac", + "< String", + "Ġcre ated", + "up er", + ".st ate", + "Ġh ost", + "ener ic", + "/ b", + "( !", + "wh ile", + "i as", + "B UG", + "Ġ );ĊĊ", + "Ġro le", + "Re g", + "ĠC olor", + "St art", + "Ġp orn", + "t op", + "Ġwe b", + "Ġde v", + "Ġde al", + "++ )Ċ", + "Int eger", + "pos ition", + ". on", + "Ġ( \"", + "ä ¸", + "Ġproble m", + "s v", + "Ġp ress", + "AB LE", + "AT ION", + "ĠSe e", + "an ch", + "Ġth ough", + "le ep", + "Ġ< !--", + "Ġpoint s", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + ". J", + "Ġ ::", + "p tr", + "D B", + "++ ;Ċ", + ".p ng", + "n ode", + "so ft", + "pon d", + "Ġe ver", + "-------------------------------- --------------------------------", + "M enu", + "(' #", + "Ġs ervices", + "p g", + "} )Ċ", + "param s", + "Ġact ually", + "Ġ\" /", + "Em pty", + "M ethod", + "Ġid ent", + "un ic", + "Ġmill ion", + "Ġa ff", + "st yle", + "Ġcon c", + "i os", + "ign ment", + "UL T", + "P r", + "\" ;čĊ", + "Ġunder stand", + "u ary", + "Ġhapp en", + "Ġser ver", + "ĠC o", + "S C", + "Ġle s", + "Ġfile s", + "G rid", + "s ql", + "Ġof ten", + "Ġin fo", + "_ tr", + "s rc", + "on y", + "Ġsp ace", + "um b", + "Ġpass word", + "Ġst ore", + ", ĊĊ", + "ĠWh at", + "g ed", + "ĠF alse", + "U s", + "sw er", + "_ index", + "Ġform at", + "m ost", + "s m", + "N ew", + "Ġd etails", + "Ġpro b", + "ĠAN D", + "() čĊ", + "il ar", + "Ġ$ {", + "ry pt", + ".C ollections", + "$ this", + "ĠF ree", + "_ of", + "(f alse", + "d ated", + "Ġ> >", + "Ġf ace", + "CT ION", + "Ġs ave", + "Ġt yp", + "de v", + "(\" #", + "AG E", + "cont ainer", + "ed it", + "Q L", + "Ġitem s", + "Ġs ocial", + "i en", + "ĠRe act", + ") .ĊĊ", + "Ġm ar", + "Ġre du", + "ĠR E", + ".p ut", + "Ġm ajor", + "C ell", + "n ext", + "Ġexpect ed", + "Ġy et", + "Ġin div", + "trib utes", + "at is", + "am ed", + "Ġf ood", + "S ource", + "( string", + "Ġ+ Ċ", + "it es", + "d r", + "Ġmem bers", + "Ġcom b", + "item s", + "ĠP er", + "T H", + "= True", + "Ġb ar", + "_ SE", + "com m", + "( w", + ")ĊĊ Ċ", + "Ġs end", + "Ġin c", + "un signed", + "F A", + "Ġparam s", + "app ing", + "ro s", + "ug in", + "f a", + "Ġcon nection", + "Ġ} ;ĊĊ", + "Ġbe come", + "M ode", + "Ġe v", + "Ġdif f", + "ĠUn ited", + "He ight", + "ful ly", + "im ages", + "Ġm akes", + "Ġg lobal", + "Ġcont act", + "' :Ċ", + "Ġab s", + "а Ð", + "f loat", + "Ġex cept", + "ĠP ol", + "Ch ild", + "t yp", + "Ġcert ain", + "i ón", + "O UT", + "Ġim pro", + "ile s", + "Ġ-- >Ċ", + "ĠP art", + "val ues", + "os s", + "/ **", + "il it", + "ĠE vent", + "cur ity", + "st er", + "Ġchar acter", + "Ġnew s", + "Ġ\" ,", + "Ġde vice", + "c el", + "log in", + "he et", + "Def ault", + "@ \"", + "ĉ Ġ", + "c lick", + "( value", + "ĠA b", + "Ġpre vious", + "ERR OR", + "oc al", + "Ġm aterial", + "Ġbel ow", + "ĠCh rist", + "Ġmed ia", + "co ver", + "ĠU I", + "Ġf ail", + "Ġbl ack", + "Ġcom ponent", + "ĠAmeric an", + "Ġadd ed", + "Ġbu y", + "st it", + "Ġc ame", + "Ġde lete", + "prop erty", + "od ing", + "Ġc ard", + "rop s", + "Ġhttp s", + "Ġro ot", + "Ġhand le", + "C C", + "B ack", + "em plate", + "Ġget ting", + "_b y", + "m ail", + "_s h", + ". assert", + "ĠD ec", + "( true", + "Ġcom put", + "Ġcl aim", + "' =>", + "ĠS ub", + "Ġa ir", + "op s", + "n av", + "em ents", + "( id", + "Ġent er", + "ang ed", + "E nd", + "Ġloc ation", + "Ġn ight", + "Ġdo ing", + "ĠR ed", + "l in", + "}ĊĊ Ċ", + "vid er", + "Ġp ick", + "Ġw atch", + "ess ages", + "Ġhum an", + "Ġd am", + "p end", + "d ir", + "Ġt ax", + "Ġg irl", + "re et", + "Ġbo x", + "Ġstr ong", + "( v", + "re l", + "Ġinter face", + "Ġm sg", + "f ect", + "_ at", + "Ġh ouse", + "Ġtr ack", + "' );ĊĊ", + "j e", + "ĠJ ohn", + "ist r", + "( S", + "ub e", + "Ġc e", + "itt ed", + "V ER", + "* )", + "p arent", + "Ġapp lication", + "an y", + ".sw ing", + "Ġp ack", + "\\ u", + "Ġpr act", + "Ġse ction", + "ct x", + "Ġun signed", + ".P oint", + "ĠO ne", + "Ä ±", + "ip le", + "a id", + "Ñ ĥ", + "V ector", + "by te", + "Ġw ait", + "Ġà ł", + "à ¥", + "Ġto gether", + "Ġth rows", + "F O", + "' ))", + "h ost", + "is ing", + ". view", + "Ġter ms", + "fr amework", + "- r", + "Ġapp ly", + "Ġs ession", + "O ptions", + "ugg est", + "Ġo thers", + "w itter", + "Ġf und", + "In it", + "__ (", + "ens or", + "G ET", + "Ġsever al", + "i i", + "[ j", + "I O", + "Ġtem plate", + "P osition", + "Ġe con", + "ach ine", + "Ġ il", + ".s pring", + "m ain", + "el t", + "im ent", + "Re c", + "m m", + "ĠUn iversity", + "urs or", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "G L", + "ict ure", + "ith ub", + "c er", + "c ast", + "F rom", + "a les", + "Ġsub ject", + "p assword", + "n y", + "Ġes c", + ".w rite", + "ï¼ Į", + "Wh at", + ". H", + "Ġh istory", + "ĠF e", + "Ġindiv idual", + "un it", + "Ġ-- >", + "Ġd u", + "I ST", + "Ġus ers", + "f s", + "f alse", + "un t", + "T itle", + "Ġm ot", + "Ġf uture", + "ach ed", + "Ġstart ed", + "Ġm ode", + "Ġ' <", + "_ array", + "Ġa x", + "'] ;Ċ", + "i res", + "Th ere", + "ug ht", + "t ml", + "pos ed", + "ic ult", + "Ġto ok", + "Ġg ames", + "Ġ} }", + "Ġ? >Ċ", + "Ġproduct s", + "I s", + "Ġb ad", + "ĠD es", + ".p ath", + "' ĊĊ", + "ĠP ost", + "av el", + "( :", + "Ġneed s", + "Ġkn own", + "F l", + "Ġex ec", + "Ġse en", + "um e", + "Ġb order", + "Ġl ive", + "tem p", + "P er", + "Ġvar iable", + "i et", + "ĠD ef", + "Ġg e", + "em e", + "_b ack", + "f irst", + "Ġprovid ed", + "//////////////// ////////////////", + "Ġfil ename", + "Ġh ope", + "ul y", + "a uto", + "f ind", + "_ string", + "b tn", + "it ude", + "At tribute", + "Ġyou ng", + ".t xt", + "Ġwe bsite", + "ĠP rop", + "Ġe y", + "> ();Ċ", + "ion al", + "AR R", + "iction ary", + "ur ther", + ". ", + "t x", + "Ġp ur", + "u el", + "ymb ol", + "u ation", + "ang er", + "Ġback ground", + "ec ess", + "ef ined", + ".... ....", + "Ġdes cription", + "Ġrep resent", + "\") );Ċ", + "press ion", + "row ser", + "Ġser ies", + "ward s", + "($ _", + "a ise", + "Ġh ot", + "ac ity", + "ri es", + "action s", + "C reate", + "ad io", + "amp les", + "Ġorig inal", + "ens ive", + "f ont", + "st ream", + " using", + ".spring framework", + "ser ver", + "Ġb ill", + "AC K", + "il ename", + "Ġfr ame", + "Ġ= Ċ", + "Ed it", + "adi us", + "Ġd raw", + "ank s", + "Ġd eter", + "Ġcom es", + "_ int", + "Ġfore ach", + "ang le", + "Ġe lect", + "pect ed", + "He ader", + "ist ration", + "F alse", + "ĠG ame", + "Ġfil ter", + "Act ivity", + "Ġl arg", + "in ition", + "Ġ\" <", + "is ed", + "Ġrem ove", + "ĠTr ans", + "m et", + "se e", + "Form at", + "Com mand", + "ĠE X", + "N one", + "Ġfr ont", + "A SE", + "ĠR ec", + "ound ation", + "Ġv o", + "= \\\"", + "( *", + "Ch ange", + ".W rite", + "g roup", + "i ents", + "u y", + "******************************** ********************************", + "Ġd ig", + "h r", + "( -", + "Ġg en", + "n umber", + "ve c", + "uro pe", + "ent ry", + "L L", + "Ġst e", + "Val id", + "'] ,", + "_p aram", + "Ġse lected", + "Ġacc ording", + "ĠD is", + "Ġ util", + "B uffer", + "_ error", + "Ġass oci", + "_S IZE", + "Ġw or", + "Ġprint f", + "r ag", + " ł", + "D D", + "ĠV al", + "Ġact iv", + "E ng", + "et ime", + "Ġv irtual", + "a ign", + "a ur", + "ĠP res", + "ĠEx ception", + "Ġany thing", + "ĠO ff", + "Ġh ours", + "Ġw ar", + "Arg s", + "ag ing", + "Ġmodel s", + "ĠT ime", + "O b", + "am s", + "j oy", + "Ġear ly", + ". read", + "Ġc enter", + "ĠIn itial", + "Ġl anguage", + "l ength", + "x y", + "Ġs n", + "Ġin f", + "P ost", + "Ġag o", + "Ġeas y", + "_c ode", + "ĠAN Y", + "_ ch", + "Ġdown load", + "( T", + "av ed", + "âĢ ĵ", + "Ġstud ents", + "Ġf ig", + "l ight", + "x x", + "Ġbu ffer", + "ĠD ep", + "ĠM ath", + "IT H", + "Ġvar i", + "Ġd ue", + "F actory", + "Ġp or", + "Ġe p", + "ot ype", + "Ġcan not", + "Ġwh ite", + "< int", + "ter n", + "Ġreg ister", + "Ġpre d", + "cl us", + "_d ate", + "Ġ/ **", + "Ġa uth", + "Ġ[ ]Ċ", + "Ġper iod", + "n own", + "Ġv ot", + "Ġs creen", + "' d", + "T ypes", + "Ġt mp", + "е Ð", + "ur al", + "Ġben ef", + "_ y", + "Ġn et", + "ĠSt ates", + "'] ['", + "ĠN e", + "ĠN OT", + "Ġn eg", + "Ġcomm on", + "s cope", + "Ġc red", + "g es", + "_T YPE", + "Ġs uggest", + "o om", + ".ĊĊ Ċ", + "Ġac cept", + "Ġr andom", + "er m", + "ĠV ector", + "w ith", + "T ER", + "( str", + "Ġres pons", + "Ġh it", + ".S et", + "gr id", + "ri a", + "Ġc lick", + "und le", + "C ase", + "ins ert", + "Util s", + "Ġ\"\" \"", + "Ġim plement", + "at al", + "tem pt", + "tem plate", + "oc r", + "return s", + "Ġplay ers", + "us ers", + "ed ef", + "ĠTh ese", + "Ġam ong", + "Ġde b", + "h a", + ".get Element", + "Ġc irc", + "Ġan swer", + "Ġw alk", + "Ġt reat", + "ĠG e", + "ĠC reate", + "Ġa ge", + "Ġre q", + "O ST", + "ang ular", + "Ñ ı", + "Ġf ive", + "Ġdistrib uted", + "Ġfri end", + "T P", + "Ġc lean", + "ow s", + ".Control s", + "d is", + "Ġw ords", + ". io", + "z y", + "Ġhe ader", + "ĠC heck", + "âĢĻ m", + "j ust", + "h older", + "=\" čĊ", + ". annot", + "Ġcol lection", + "' .", + "Ġsim ilar", + "Ġt aken", + "(\" %", + "Or der", + "'] Ċ", + "-m d", + "ĠT H", + "ac ed", + "Ġis n", + "/ j", + "Ġs on", + "gr aph", + "ĠInt eger", + "Ġn ecess", + "re en", + "Ġ um", + "Ġ\\ <", + "Ġmom ent", + "Ġbr ing", + "Ġind ic", + "ys is", + "Le vel", + "ver se", + "urre nc", + "_t est", + "Ġent ire", + "D own", + "Ġ}ĊĊ Ċ", + "( result", + "ĠRe ad", + "à ¨", + "M od", + "Ġtry ing", + "\") ,Ċ", + "Ġm ember", + "ĠC or", + "OD O", + "- control", + "un time", + "ĠS im", + "D ialog", + "pl ot", + "_ on", + "Ġph ys", + "} /", + "Ġn amespace", + "ĉ čĊ", + "ac c", + "Pl ayer", + "A RE", + "Ġf oot", + "Ġbo ard", + "p art", + "Ġs us", + "w ise", + "ĠM c", + "Ġp ush", + "AT A", + "Ġp lease", + "ri ed", + "we et", + "b it", + "id ed", + "V E", + "ĠS w", + "U B", + "Ġt ypes", + "ed ia", + "Ġc los", + "ace book", + "Wh en", + "Ġed it", + "ig ger", + "Ġen erg", + "Cont ainer", + "Ġph ot", + "ĠC ount", + "ĠE urope", + ".I s", + "ĠR uss", + "pe ed", + "ĠS tr", + "Ġp y", + "Ġc ult", + "Ġdef ined", + "cc ount", + "Ġob t", + ".L ocation", + "Ġth read", + "il le", + "Ġinst ead", + "str ong", + "ĠS ec", + "U RE", + "Ġide a", + ". se", + "em y", + "select ed", + "Con nection", + "ac ing", + "th read", + ".n ext", + "Ġc oll", + "Ġfil m", + "ist ic", + "Ġcomp et", + "Ġcon n", + "th ough", + "Ġcom pan", + "ock et", + "Ġte ach", + "= (", + "Ġph one", + "Ġact ive", + "de lete", + "tr ies", + "Ġm o", + "Ġde ath", + "} );ĊĊ", + "oc ol", + "W idget", + "Ġart icle", + "ro du", + "and id", + "Ñ ĭ", + "ĠC r", + "k a", + "() :", + "lo od", + "ĉĉĉ Ċ", + "Ġal most", + "Ġs ell", + "erv let", + "ri p", + "Un it", + "Ġapp lic", + "Ġcon nect", + "Ġfe ature", + "Ġv ia", + "' ),", + "Ġl im", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠG u", + "Eng ine", + "Ġen s", + "Ġen vironment", + "b lock", + "HER E", + "N ULL", + "g y", + "t ag", + ") ).", + "ex p", + "Ġcom pl", + "Ġinst all", + "Ġcomple te", + "que ue", + "atur al", + "Ġgener al", + "th on", + "Ġask ed", + "o res", + "( res", + "Ġres erved", + "S P", + "ĠâĢ ¦", + "Å Ĥ", + "Ġsign ific", + "O ff", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠA g", + "ĠJ ust", + "ĠE rror", + "Ġin fl", + "ad ata", + "Ġ icon", + "ask s", + "' '", + "_ LO", + "? .", + "ac count", + "Ġ( *", + "' )ĊĊ", + "r ap", + "_ var", + "ĠF OR", + "Ġpart y", + "ĠY our", + "c at", + "str y", + ". new", + "bo ot", + "ĠN ov", + "Ġv ector", + "Ġn ormal", + "Ġf urther", + "Re pository", + "Ġd atabase", + "att le", + "Ġmus ic", + "Ġspe ed", + "Ġd oc", + "pro cess", + "IG HT", + ".p arse", + "Ġt aking", + "Ġvi ol", + "ce ed", + "ĠA fter", + "Ġfor ward", + "Ġc rit", + "\"/ >Ċ", + "ro t", + "Ġfa iled", + "ef ore", + "Ġconc ern", + "o e", + "b a", + "Ġs ender", + "Ġter m", + "h as", + "=\" #", + "Ġpot ential", + "N um", + "Ġpublish ed", + ".c lose", + "ĠIm age", + "str aint", + "U D", + "ĠO b", + "Ġprob ably", + "l im", + "\" :Ċ", + "olum e", + "Ġcon sum", + "ag ue", + "ens ions", + "Ġinvest ig", + "- year", + "') ;", + "-s m", + "Ġen joy", + "or ig", + "er ing", + "c p", + "le ased", + "ple ments", + "Ġreturn s", + "p at", + "B O", + "ĠH ouse", + ".L abel", + "Ġwe ight", + "igh b", + "Ġcondition s", + "Ġex ception", + "d escription", + "Ġtr ad", + "- to", + "Ġ{ }", + "Ġmod ule", + "EN D", + ". ap", + ".p rops", + "Ġcon structor", + "av es", + "Ġf avor", + "ĠN ow", + "; i", + "ĠM ain", + "_ k", + "er ies", + "âĢĻ ll", + "trans form", + "imest amp", + "P re", + "Ġm er", + ". res", + "st ant", + "L ocation", + "_N AME", + "Ġlos s", + "Ġ ĊĊ", + "n et", + "Ġeng ine", + "B lock", + "Ġiss ues", + "Ġpar se", + "ĠB ar", + "Ġst ay", + "ĠJ SON", + "Ġd om", + "air s", + "w ner", + "Ġl ower", + "\", čĊ", + "ĠD em", + "uf act", + "Ġp s", + "Ġper fect", + "R L", + "Ġed uc", + "l s", + "em ory", + "ARR ANT", + "u ge", + "Ġex act", + ". key", + "al led", + "e ch", + "ie f", + "\\ /", + "o ke", + "Ġfor mer", + "al loc", + "Ġs ix", + "id a", + "Ġm argin", + "Ġhe art", + "al d", + "p ack", + ".getElement ById", + "ĠW ARRANT", + "Ġr ather", + "Ġbuild ing", + "er man", + "lic e", + "Ġquest ions", + "iz es", + "le ge", + "irect ory", + "Ġj e", + "Ġc as", + "pro ps", + "ut f", + "Ġse curity", + "Ġhow ever", + "we ight", + "Ġins ide", + "Ġpres ident", + "Ch ar", + "ĠW ITH", + ".m ap", + "Ġgr aph", + "Ġt ag", + "_st atus", + "Ġat tempt", + "op p", + "us es", + "ĉ const", + "Ġr ound", + ", $", + "Ġfri ends", + "Em ail", + "? >", + "Res ource", + "KE Y", + "os p", + ". query", + "ĠN orth", + "able s", + "ist rib", + "_c lass", + "el lo", + "Th at", + "Ð º", + "pecial ly", + "ĠPres ident", + "Ġcamp aign", + "Ġal t", + "are a", + "Ġch all", + "Ġop port", + ".C on", + "Ġenerg y", + "li ke", + ". string", + "ing ton", + ") *", + "y y", + "Ġprof ession", + "ir th", + "Ġse g", + "æ ľ", + "Ġh or", + "i ers", + "c an", + "Ġbeh ind", + "Pro duct", + "f g", + "ĠS k", + ".j pg", + "? :", + "] ;ĊĊ", + "Ġcall back", + "ĠH ttp", + "Ñ Į", + "l ong", + "M S", + "AT H", + "Ġr aise", + "Ġwant ed", + "row n", + "ut or", + "l t", + "] =", + "el ine", + "M A", + "Ġse par", + "c s", + "se mb", + "D is", + "bs erv", + "ĠW ill", + "Ġpol icy", + "Ġth ird", + "ph one", + "Ġb ed", + "/ g", + ". __", + "ĠIn c", + "iz ing", + ".re move", + "in stance", + ".t ype", + "Ġs erv", + "E ach", + "Ġh ar", + "ĠM essage", + "( key", + "SE LECT", + "P os", + ")) ;čĊ", + "Ġre comm", + "Ġtr aining", + "ĠE nt", + "ĠCh ar", + "ic ht", + "(f ile", + "Ġp rior", + "G ame", + "Ġex it", + "Param s", + ".c ore", + "P C", + "n es", + "anc ed", + "( request", + "P assword", + "} >Ċ", + "Ġm ag", + "Ġre lease", + "Ġsh all", + "ud ent", + "ĠS outh", + "and o", + ": '", + ".Tab Index", + "s k", + "ann er", + "is set", + "Ġout side", + "led ge", + "Ġ å", + "ĠR ob", + "Ġim m", + "! Ċ", + "ĠWe b", + "D es", + "B C", + "anc ial", + "R oute", + "D ec", + "fer ences", + "Ġp urch", + "ĠM odel", + "ct or", + "g n", + "_st art", + "_ un", + ". *", + "is es", + "Ġg round", + "Ġun ique", + "Ġbe aut", + "{ \"", + "Ġp our", + "ĠO ct", + "Ġt ree", + "set s", + "_ res", + "') ->", + "_re g", + "(\" \\", + "Ġby te", + "B l", + "Ġd ating", + "Ġm atter", + "ĠR em", + "Ġ' ../", + "ĠA ug", + "ĠL a", + "Ġ$ (", + "ourn al", + "i am", + "Ġshow s", + "w rite", + "Ġb all", + "Ġsim ply", + "Ġf ast", + "Ġmem ory", + "A SS", + "ĠO f", + "ov ed", + "ant e", + "a ul", + "ist ry", + ")) );Ċ", + "Ġf it", + "< string", + "Ġpolit ical", + "anc el", + "_ .", + "c ard", + ".c urrent", + "o ch", + "_ image", + "\\ t", + "# Ċ", + "( L", + "Ġindu stry", + "com ing", + "Ġex tra", + "Ġreport ed", + ".st art", + "Ġres ources", + "Ġim g", + "fl ow", + "_E X", + "(n ull", + "ĠP re", + "Ġwr ong", + "inter face", + "Param eter", + "n ers", + "á »", + "t ure", + "ers ist", + "oun try", + "Ġseem s", + "al ance", + "de st", + "ĉ String", + "Ġm aint", + "Ġun it", + "act ers", + "ĠT R", + "if ul", + "export s", + "pro ject", + "App lication", + "leg ate", + "Ġt akes", + "ter m", + "Ġet c", + "ust er", + "Ġappe ar", + "add ress", + "Ġf em", + "h s", + "Ġh om", + ", -", + "Ġdiff icult", + "Ġcom ing", + "O pen", + "Ġset tings", + "ĠW ar", + "ĠTh en", + "Ġaut om", + "ĠF oundation", + "Ġqu ite", + "D escription", + "Ġb log", + "i qu", + "P S", + "_f ield", + "J son", + "SS ION", + "ĠS ch", + "ĠL O", + "Ġdes cri", + "Ġevery one", + "Ġpret ty", + "Ġlong er", + "Ġm enu", + "Ġcurrent ly", + "se c", + "Ġrelations hip", + "################ ################", + "ĠM ap", + "as et", + "Ġparam eters", + "Ġcr ush", + "\" čĊ", + "IL ITY", + "ig ration", + "Ġc out", + "t otal", + "Ġn ames", + "nd ef", + "\") ;", + "ri end", + "yn amic", + "Ġeff ort", + "Ġact ual", + "Ġfield s", + "O UN", + "t ers", + "Ġf ix", + "_m odel", + "Ġc ases", + "C A", + "M y", + "Inter face", + "ĠS E", + "] ]", + "al le", + "ĠN ational", + "ĠArray List", + "in line", + ". V", + "ar a", + "ref ix", + "as c", + "Re ader", + "ĠÐ ¿", + "ast ic", + "( ()", + "C l", + ".annot ation", + "Ġperform ance", + "ail y", + ".to String", + ".n et", + "view s", + ". end", + "ay ers", + "l ate", + "ĠA pr", + "ed eral", + "'] )", + ".b ody", + "Ġhigh er", + "_f l", + "c r", + "al ert", + "_n ode", + "ĠG oogle", + "Ġit self", + "A uth", + "urrenc y", + "Ġsignific ant", + "app end", + "Ġres pect", + "str ap", + "Ġun a", + "riter ia", + "P ORT", + ".ap ache", + "Out put", + "Ġpro gress", + "Ġm id", + "ĠM icrosoft", + "Ġres ource", + "ab lish", + "Ġd im", + ". load", + ".A pp", + "Ġd irection", + "Ġadd itional", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "Ġnum bers", + "Ġcompan ies", + ".T h", + "Ġs ound", + "user name", + "Ġstat ement", + "Ġal ert", + "Ġcon tract", + "h ome", + "_l ength", + ".Com ponent", + "e v", + ". Ex", + "ï¼ ļ", + "\" ;", + "ĠH igh", + "Ġ )ĊĊ", + "ĠP oint", + "op h", + "Ġl ines", + "-> _", + "\" )ĊĊ", + "o x", + "app lication", + "Ġ ]Ċ", + "ĊĊĊĊ ĊĊ", + "Ġso on", + "ction s", + "ing er", + "Ġj oin", + "ĠP e", + "Ġ ë", + "Ġl as", + ". E", + "c ss", + "/ or", + "ĠSt art", + "ĠT O", + "Ġsub s", + "con n", + "com ponents", + "DE BUG", + "qu are", + "F unction", + "end ar", + ". index", + "Ġf ill", + "Ä Ļ", + "Ġcho ose", + "h ow", + "ĠAmeric a", + "ass ets", + "-------- ----", + "ĠV alue", + "Ġoff ice", + "Ġv eh", + "Ġtrans form", + "ĠAr t", + "Ġin de", + "Ġf n", + "Ġim plements", + "ang o", + "ple te", + "+ \"", + "t mp", + "am ily", + "Ġhas h", + "miss ions", + "E ST", + "g t", + "Pro vider", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "Ġfl ag", + "Ġpartic ip", + "d en", + "ĠReturn s", + "Ġnot e", + "ü r", + "p m", + "ide os", + "Ġspec ified", + "ĠE N", + "est er", + "ol id", + "Ġup on", + "( std", + "ĉ v", + "Ġ' \\", + "u z", + "Ġv ert", + "Ġv ict", + "ĉ self", + "Ġ\" $", + ". k", + "Ġgroup s", + "g ithub", + "l ang", + "Ġm ut", + "T O", + "Ġv e", + "ĠP lease", + ";ĊĊ Ċ", + "ac cess", + "Ġ{ \"", + "re a", + "Ġr isk", + "ick er", + "og gle", + "ĉ while", + "AN G", + ".s end", + "Ġwom an", + "Ġget s", + "Ġ ign", + "ĠI d", + "_ log", + "ON E", + "Ġe vid", + "ĠH ar", + "_s ub", + "Ġend l", + "Ġinclud ed", + "() );ĊĊ", + "ĠA p", + "ig r", + "Ġs em", + "ĠBl ack", + "d oc", + "_t able", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "- up", + "Ġca use", + "Ġ ..", + "Ġv an", + "_d ict", + "Ġf ocus", + "IN D", + "CE SS", + ".L og", + "Ġmult iple", + "id o", + "Ġreg ard", + "- M", + "and ler", + "our se", + "Ġde g", + ". U", + "Ġadd ition", + "Ġvar ious", + "Ġrece ive", + "е н", + "ĠH T", + "Ob j", + "D F", + "Ġincre ase", + "ĠO pen", + "] ;", + "Ġcomm it", + "? Ċ", + "ateg ories", + "at ory", + "sh ip", + "ĠM ich", + "Ġh tml", + "rom ise", + "Ġle ave", + "Ġstr ateg", + "av en", + "ĠCon sole", + "k nown", + "- n", + "_ LE", + ".com ponent", + "Ġb re", + "S ession", + "i ance", + "Ġal ign", + "typ edef", + "_ result", + "ĠW HERE", + ".s plit", + "Ġread ing", + "FA ULT", + "Ġc lo", + "Ġnot ice", + "_p r", + "ar ter", + "Ġlo ck", + "Ġstand ard", + "et ic", + "ell ow", + "Ġp adding", + "ĠH is", + "Ġst ates", + "_c ast", + "( P", + "a a", + "Ġintern al", + "e an", + "ĠP RO", + "ĠK ey", + "Ġes pecially", + "m ing", + "Ġc ross", + "Ġn ational", + "_ object", + "f ilter", + "Ġs cript", + ". update", + "_ i", + "ĠAss ert", + "/ core", + "%% %%", + "Ġproble ms", + "ist or", + "Ġ. =", + "Ġar ch", + "Ġwrit ten", + "Ġm ilit", + "M ENT", + ". ch", + "ca pe", + "ĠM us", + "_ config", + "ĠA PI", + "fo ot", + "Ġim ages", + "end l", + ". In", + "F irst", + "Ġpl atform", + ".pro t", + "O ption", + "st e", + "ĠT ODO", + "Ġfor ce", + ". cont", + "ĉ echo", + "ĠD av", + "P tr", + "( B", + "R T", + "ĠB ase", + "] ['", + "Ġann ounc", + "con sole", + "ĠP y", + "d s", + ". as", + "Ġpre vent", + "ap an", + "Ġ{ '", + "} '", + "Ġde ad", + "V AL", + "Q UE", + "**************************************************************** ********", + "Ġch arg", + "R eturn", + "Ġf ul", + "d om", + "Ġr ules", + "Ġmod ify", + "Ġe val", + "h am", + "at ement", + "\\ <", + "ul a", + "= False", + "R A", + "Ġcont ains", + "Ġst ack", + "m ar", + "Ġ{ }Ċ", + "Ġund efined", + "A ss", + "ĠCh ina", + "ve y", + "* Ċ", + "Ġplay ing", + ") /", + "act or", + "Ġb ottom", + "li er", + "ĠN umber", + "Ġcou ple", + "D C", + "ĠS O", + "g or", + ".set Text", + "s uccess", + "com mand", + "F ilter", + "ĠO ur", + "_ item", + "Ġc tx", + "Ġro ad", + "V ersion", + "c ase", + "ur t", + "av ior", + "y ch", + "semb ly", + "ĠPro duct", + "Ġh eld", + "a fe", + "Ġinclud es", + "< quote", + "Ġa void", + "ĠF in", + "ĠM od", + "Ġt ab", + "an o", + "à ±", + "ipp ing", + "- e", + "Ġins ert", + "t arget", + "ch an", + ".M odel", + "IM E", + "\\ Ċ", + "Ġm achine", + "av y", + "ĠN O", + "ĠInt er", + "Ġoper ation", + "mod al", + "T ag", + "] :", + "Ġprodu ction", + "Ġare as", + "Ġre n", + "_f rom", + "n bsp", + "Ġoper ator", + "m en", + "app ed", + "_p er", + "z en", + "(\" .", + ".s ave", + "=\" {{", + "Ġt or", + "( response", + "Ġc andid", + "Ġcon v", + "a iled", + "ĠL ib", + "com p", + "ur a", + "ï¿ ½", + "ĠH ere", + "Ġarg ument", + "h ood", + "Ġest ablish", + "ograph y", + "Ġon Click", + "amb da", + "Ġs ch", + "Ġmov ie", + "Ġse c", + "Ġact ivity", + "Ø §", + "Ġs ql", + "_ all", + "inc ip", + "Ġprovid es", + "Ġs ys", + "ack et", + "Ġwas n", + "Ġus es", + "ĠF unction", + ".g oogle", + "ĠRes ult", + "Vis ible", + "ag ma", + "el come", + "ĠS y", + "ĠC ent", + "AL SE", + "ac ión", + "EX T", + "Ġl icense", + "ĠL ong", + "Ġacc om", + "Ġab ility", + ". height", + "Act ive", + "olog ical", + "ol y", + ")) ,", + ".S e", + "Ġparam eter", + "pr ite", + "AB ILITY", + ".s ervice", + "ĠG roup", + "_ query", + "ĠI tem", + "in ing", + "Ġj ud", + "im s", + "f ix", + "ind er", + "ag ram", + "Ġfunction s", + "Ġexper i", + "ĠE m", + "Ġro t", + "Ġp en", + ".b tn", + "ĠA S", + "#if def", + "Ġcho ice", + "ĠP age", + "_P RO", + "Q U", + "å ı", + "ant ity", + " Ń", + "word s", + "Ġread only", + "Ġf lex", + "prot ected", + "ĠAn y", + "Ġchar acters", + "enc ed", + "ĠJ uly", + "il er", + "C ard", + "ur ance", + "Ġre v", + ".e vent", + "al y", + "Ġwon der", + "ĠP ort", + "Ġleg al", + "ro le", + "Ġt en", + "Ġgo es", + "M P", + "wh ite", + "): čĊ", + ")) čĊ", + "Ġre ference", + "Ġm is", + "ĠPro ject", + "ick s", + "> &", + "C ON", + "Ġre pl", + "Ġreg ular", + "St orage", + "ram ework", + "Ġgo al", + "Ġt ouch", + ".w idget", + "Ġbu ilt", + "d es", + "P art", + "( re", + "Ġw orth", + "h ib", + "g ame", + "ĠÐ ²", + "ac ion", + "ĠWh ite", + "(t ype", + "( `", + "Ġn atural", + "Ġin j", + "Ġcal cul", + "ĠApr il", + ". List", + "Ġassoci ated", + "ĉ System", + "~ ~", + "= [", + "Ġst orage", + "Ġby tes", + "Ġtr avel", + "Ġs ou", + "Ġpass ed", + "! =", + "as cript", + ". open", + "Ġgr id", + "Ġb us", + "Ġrec ogn", + "A b", + "Ġh on", + "ĠC enter", + "Ġpre c", + "b uild", + "HT ML", + "ĠS an", + "Ġcoun tries", + "a led", + "t oken", + "k t", + "Ġqu al", + "L ast", + "ad ow", + "Ġman ufact", + "id ad", + "j ango", + "N ext", + "x f", + ". a", + "Ġporn o", + "ĠP M", + "er ve", + "it ing", + "_ th", + "c i", + "= None", + "g s", + "Ġlog in", + "at ives", + "'] );Ċ", + "Ä ħ", + "Ġ ill", + "I A", + "child ren", + "D O", + "Ġlevel s", + "Ġ{ {", + "Ġlook s", + "Ġ\" #", + "To String", + "Ġnecess ary", + "ĠĠĠ Ċ", + "c ell", + "En try", + "Ġ' #", + "Ġext rem", + "Select or", + "Ġplace holder", + "L oad", + "Ġre leased", + "O RE", + "En umer", + "ĠT V", + "SE T", + "in q", + "P ress", + "ĠDep artment", + "Ġprop erties", + "Ġres pond", + "S earch", + "a el", + "Ġre qu", + "ĠB ook", + "/ Ċ", + "( st", + "Ġfin ancial", + "ick et", + "_in put", + "Ġth reat", + "( in", + "Str ip", + "ì Ŀ", + "ç ão", + "Ġevid ence", + ")) ;", + "ĠB ro", + "Ġ[ ];Ċ", + "Ġ ou", + "b uf", + "S cript", + "d at", + "Ġr ule", + "# import", + "=\" /", + "S erial", + "Ġstart ing", + "[ index", + "a e", + "Ġcon trib", + "s ession", + "_ new", + "ut able", + "o ber", + "Ġ\" ./", + "Ġlog ger", + "Ġrecent ly", + "Ġreturn ed", + "č čĊ", + ")) )Ċ", + "ition s", + "Ġse ek", + "Ġcomm unic", + "Ġ\" .", + "Ġuser name", + "E CT", + "D S", + "Ġother wise", + "ĠG erman", + ". aw", + "Ad apter", + "ix el", + "Ġsystem s", + "Ġd rop", + "Ġstruct ure", + "Ġ$ (\"#", + "enc ies", + "ann ing", + "ĠL ink", + "ĠRes ponse", + "Ġst ri", + "Å ¼", + "ĠD B", + "æ Ĺ", + "and roid", + "sub mit", + "ot ion", + "( @", + ".t est", + "ĊĊĊĊ ĊĊĊĊ", + "] ;čĊ", + "Ġdirect ly", + "Ġ\" %", + "r is", + "el ta", + "A IL", + ") {čĊ", + "m ine", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "( k", + "b on", + "as ic", + "p ite", + "__ _", + "M ax", + "Ġerror s", + "ĠWh ile", + "Ġarg uments", + "Ġens ure", + "R ight", + "-b ased", + "We b", + "Ġ- =", + "Ġint rodu", + "ĠIn st", + "ĠW ash", + "ord in", + "j oin", + "D atabase", + "Ġgr ad", + "Ġus ually", + "IT E", + "Prop s", + "? >Ċ", + "ĠG o", + "@ Override", + "RE F", + "Ġ ip", + "ĠA ustral", + "Ġ ist", + "View ById", + "Ġser ious", + "Ġcustom er", + ".prot otype", + "od o", + "c or", + "Ġdo or", + "ĠWITH OUT", + "Ġpl ant", + "Ġbeg an", + "Ġdist ance", + "() ).", + "Ġch ance", + "Ġor d", + "c ame", + "pr agma", + "Ġprot ect", + "rag ment", + "ĠN ode", + "en ing", + "Ñ ĩ", + "Ġr oute", + "ĠS chool", + "h i", + "Ġne ighb", + "A fter", + "lic it", + "Ġcon tr", + "Ġpr imary", + "A A", + ".Write Line", + "util s", + "Ġb i", + "R ed", + ".L inq", + ". object", + "Ġlead ers", + "un ities", + "Ġg un", + "on th", + "ĠDe v", + "F ILE", + "Ġcom ments", + "_l en", + "ar row", + "am ount", + "R ange", + "s ert", + "Grid View", + "Ġup dated", + "ĠM o", + "Ġin form", + "oci ety", + "al a", + "A ccess", + "Ġh ab", + "Ġc reat", + "_ arg", + "ĠJan uary", + "ĠD ay", + "\") čĊ", + "up le", + "d ocument", + "gor ith", + "m enu", + "ĠO ver", + "b b", + ".t itle", + "_ out", + "Ġle d", + "ur i", + "Ġ? >Ċ", + "r un", + "Ġsc ene", + "( array", + "de vice", + "_t itle", + "ag on", + "] čĊ", + "ab y", + "Ġbe came", + "bo olean", + "Ġp ark", + "ĠC ode", + "up load", + "rid ay", + "ĠSept ember", + "F e", + "Ġs en", + "c ing", + "F L", + "C ol", + "ut s", + "_p age", + "in n", + "Ġim plied", + "al ing", + "Ġyour self", + ".C ount", + "con f", + "Ġa ud", + "_in it", + ". )", + "Ġw rote", + "N G", + ". Error", + "ä »", + ".f or", + "Ġe qual", + "ĠRe quest", + "Ġser ial", + "Ġallow s", + "X X", + "Ġm iddle", + "ch or", + "à ¸", + "erv al", + ".C olumn", + "read ing", + "Ġesc ort", + "ĠAug ust", + "Ġquick ly", + "Ġwe ap", + "ĠC G", + "rop ri", + "h o", + "Ġc op", + "( struct", + "ĠB ig", + "Ġv s", + "Ġfre qu", + ". Value", + "Ġaction s", + "Ġpro per", + "Ġin n", + "Ġobject s", + "Ġm atrix", + "av ascript", + "Ġon es", + ".g roup", + "Ġgre en", + "Ġp aint", + "ool s", + "y cl", + "enc ode", + "ol t", + "com ment", + ". api", + "D ir", + "Ġun e", + "iz ont", + ".p osition", + "Ġdes igned", + "_ val", + "av i", + "ir ing", + "t ab", + "Ġl ayer", + "Ġview s", + "Ġre ve", + "ra el", + "ĠO N", + "r ics", + "n p", + "Ġc ore", + "() );čĊ", + "M ain", + "Ġexp ert", + "ĉĉ čĊ", + "_ en", + "Ġ/ >", + "ut ter", + "I AL", + "ail s", + "ĠK ing", + "*/ ĊĊ", + "ĠM et", + "_ end", + "add r", + "or a", + "Ġ ir", + "M in", + "Ġsur pr", + "Ġre pe", + "Ġdirect ory", + "P UT", + "- S", + "Ġe lection", + "h aps", + ".p re", + "c m", + "Val ues", + "Ġ\" Ċ", + "c olumn", + "iv il", + "Log in", + "in ue", + "Ġbeaut iful", + "Ġse cret", + "(e vent", + "Ġch at", + "um s", + "Ġorig in", + "Ġeffect s", + "Ġman agement", + "ill a", + "t k", + "Ġset ting", + "ĠC our", + "Ġmass age", + "ĉ end", + "Ġhapp y", + "Ġfin ish", + "Ġc amera", + "ĠV er", + "ĠDem ocr", + "ĠH er", + "( Q", + "con s", + "it a", + "Ġ' .", + "{ }", + "ĉ C", + "Ġst uff", + "Ġ :Ċ", + "ĠA R", + "T ask", + "h idden", + "er os", + "IG N", + "at io", + "ĠHe alth", + "ol ute", + "Ent er", + "' >", + "ĠT witter", + "ĠCount y", + "s cribe", + "Ġ= >Ċ", + "Ġh y", + "f it", + "Ġmilit ary", + "Ġsa le", + "re quired", + "n on", + "boot strap", + "h old", + "r im", + "- old", + "ĠD own", + "Ġm ention", + "cont act", + "_g roup", + "od ay", + "Ġto wn", + "Ġsol ution", + "u ate", + "ell ing", + "] ->", + "ot es", + "ent al", + "om en", + "osp ital", + "ĠS up", + "_ EN", + "Ġsl ow", + "SE SSION", + "Ġbl ue", + "ag o", + "Ġl ives", + "Ġ ^", + ". un", + "in st", + "en ge", + "Ġcustom ers", + "Ġc ast", + "ud get", + "ï¼ ģ", + "ic ens", + "Ġdeter min", + "Se lected", + "_ pl", + "ue ue", + "Ġd ark", + "// ĊĊ", + "s i", + "ther n", + "ĠJ apan", + "/ w", + "P U", + "ĠE ast", + "ov ie", + "Ġp ackage", + "Ġn or", + "Ġap i", + "b ot", + "\" ];Ċ", + "_p ost", + "ul ate", + "Ġcl ub", + "') );Ċ", + "Ġlo op", + "PI O", + "ion e", + "sh ot", + "In itial", + "Ġplay ed", + "reg ister", + "rou ght", + "_m ax", + "ac ement", + "m atch", + "raph ics", + "A ST", + "Ġexist ing", + "Ġcomple x", + "D A", + ".C h", + ".com mon", + "m o", + "Ġ' ../../", + "it o", + "Ġanal ysis", + "Ġdel iver", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "id x", + "à ł", + "ong o", + "ĠEng lish", + "< !--", + "Ġcomput er", + "EN SE", + "Ġp as", + "Ġr ais", + "H ash", + "Ġm obile", + "Ġo wner", + "F IG", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "th es", + "Ġat tr", + "w d", + ".t ime", + "aw n", + "Ġtreat ment", + "ĠA c", + ". View", + "im pl", + "m ore", + "p ass", + "Ġh a", + ".f rom", + "Ġle ading", + "FF FF", + "( error", + ". ui", + "at ar", + "ad ers", + "d ates", + "Ġz u", + "Ġfl ow", + "T arget", + "Ġinvol ved", + "Ġi o", + "par se", + "$ _", + "he st", + ". int", + "- item", + "as y", + "S p", + "Ġsh ift", + "N T", + "Ġt f", + "_T R", + ". web", + "C S", + "Ġ} )", + "Ġey es", + "_ z", + "' );čĊ", + "if orn", + "Ġ{ @", + "Ġn ice", + ".l ist", + "ĠĠĠĠ čĊ", + "Ġf loor", + "Ġred irect", + "ĠU K", + "( ['", + "Ġw ish", + "Ġcap t", + "leg al", + "ĠI O", + "Ġst age", + ". String", + "ĠA fr", + "ig en", + "ĠS H", + "De lete", + "ell s", + "Ġsol id", + "Ġmeet ing", + "Ġwork ed", + "Ġed itor", + "in y", + "Ð ¼", + "_ read", + ". Id", + "e ff", + "Off set", + "ch a", + "US ER", + "ĉĉ ĠĠĠ", + "ipp ed", + "Ġd ict", + "ĠR un", + ".h pp", + "Ġan g", + "x ml", + "im ple", + "Ġmed ical", + "_t oken", + "con nect", + "Ġh our", + "Ġcont roller", + "_m essage", + "U ID", + "G r", + "and ed", + "_C H", + "Ġbook s", + "Ġspe ak", + "am ing", + "Ġm ount", + "Rec ord", + "ĉ struct", + ".W eb", + "ond on", + "Ġ// Ċ", + "Ġf elt", + ".A uto", + "id ge", + "_p os", + "P R", + "Ġmod ern", + "C ollection", + "_m sg", + "C D", + "ĠL o", + "Ġsecond s", + "ib ly", + ".e quals", + "Ġintern ational", + "# pragma", + "oo th", + "W riter", + "i ate", + "Ġce le", + "ĠB it", + "iv o", + "iv ery", + "r d", + "HE CK", + "Ġc ache", + ".c ount", + "Ġro ll", + ".Re ad", + "RE D", + "Ġset up", + "izont al", + "model s", + "arg v", + "Ġconsider ed", + "=\" ../", + "set tings", + "ĠR el", + "Ġgrow th", + "Ġm ix", + "ĠWash ington", + "Ġpl t", + "ĠI M", + "á º", + "Ġturn ed", + "ĠDate Time", + "ĠW ed", + "( url", + "Ġ\" -", + "Ġlet ter", + "As ync", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠOct ober", + "_l ine", + "Ġatt ention", + "Ġcol lect", + "ĠH ash", + "Ġim ag", + "T ree", + "Ġsit uation", + "et te", + "_n o", + "IV E", + "Ġv on", + ".t arget", + "Ġknow ledge", + "Ġdr ive", + ".p ost", + "Ġb lood", + "Ġc it", + "pr imary", + "Ġconfig uration", + "te e", + "Ġph oto", + "is ode", + "Tr ace", + "Ġg ave", + "Ġsh ot", + "ĠA ir", + "Ġm other", + "pr ice", + "Ġmor ning", + ")) {Ċ", + "- x", + "Ġtr ade", + "Ġdes c", + "Ġ&& Ċ", + "Ġparent s", + "A pi", + "å Ī", + "t ed", + "w er", + "Ġ æ", + "Ġs y", + "ĠK e", + "Par ser", + "å ħ", + "anc y", + "Ġpie ce", + "iforn ia", + "to String", + "r an", + "id ing", + "PT ION", + "com es", + "/ lic", + ".c lient", + "E l", + "L ong", + "Ġprofession al", + "ru pt", + "v a", + "Ġcomplet ely", + "Ġpract ice", + "Ġse lection", + "R em", + "in i", + "Ġc am", + "RE E", + "Ġsit es", + "p a", + "AT US", + "Ñģ ÑĤ", + "arr ant", + "* (", + "_ KEY", + "ĠB utton", + "ĠF riday", + "se qu", + "Ġre ader", + "Ġm essages", + "è ¯", + "Ġbu f", + "K e", + "Ġn ov", + "H P", + "M sg", + "al ign", + "ar ily", + "Ġ' ,", + "_w ith", + "Ġd as", + "Ġhe ard", + "at omic", + "ri al", + ") [", + "Ġdis e", + "@ end", + "Ġg old", + "Ġf air", + "Ġsa les", + ". Button", + "str ict", + "s ave", + "Ġme asure", + "Ġ\" +", + "ec ause", + "View Controller", + "ĠT able", + ".p aram", + "Ġdec ided", + "(( (", + "IN FO", + "Ġopport unity", + "T e", + "IC ENSE", + "cc ording", + "k i", + "ĠU N", + "Ġcont ain", + "Ġman ager", + "Ġp ain", + "ĠF ire", + "rom e", + "Ġpl ans", + "F ound", + "l ay", + "ĠDec ember", + "Ġinfl u", + "à º", + "ren ch", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "az ing", + "b rief", + "c all", + "wo od", + "Ġload ed", + "Ġgr and", + "/ f", + "im p", + "_ U", + "ST R", + "âĢ ¢", + "Ġcred it", + ".C olor", + "or ge", + "QUE ST", + "Ġdiffer ence", + "ĠP C", + "w args", + "Ġp ub", + "und ay", + "Ġf ra", + ".m ax", + "Ġtri ed", + "ann els", + "s end", + "Ġreport s", + "Ġad ult", + "ä º", + "Ġcons ist", + "ĠSt reet", + "ĠPro gram", + "S QL", + "M atrix", + "ounc il", + "- A", + "ĉ w", + "Ġwho se", + "Ġrel ig", + "ĠS ex", + "Ġg ives", + "n one", + ".m essage", + "( G", + ".aw t", + "- right", + "ĠNov ember", + "ell ig", + "ut ive", + "Ä ĥ", + "over n", + "Ġeas ily", + "Ġide as", + "ĠÐ ½", + "/c ss", + "ly ing", + "el le", + "C an", + "_c olor", + "оР²", + "Ġp air", + "ng th", + "Ġs plit", + "d rop", + "art y", + "on a", + "Ġcap ital", + "Ġhe ar", + "Ġex ists", + "ĉ log", + "em o", + "R un", + "o i", + "Ġpar ser", + "ĠM ethod", + "Ġeduc ation", + "[ k", + "Ġlib rary", + "> \";Ċ", + "_ UN", + "ĉ std", + "od ed", + "Ġcall s", + "h ere", + "R el", + "Ġbr and", + "back ground", + "g a", + "_add ress", + "_param s", + "C ategory", + "ĠInd ia", + "_e vent", + "Ġ ing", + "R ender", + ".c l", + "ump y", + "Ġp et", + "F C", + "ĠA nt", + "Ex t", + "Ġchar ge", + "en ed", + "gr ad", + "E O", + "Ġdep end", + "Ġ .ĊĊ", + "fr ame", + "Ġd f", + "Ġh uge", + "ĠP ART", + "ed s", + "; ;", + "ĠA M", + "Ġbas ic", + "ĠL et", + "lic h", + "Ġar m", + "Ġst ar", + "Ġf ederal", + "W ork", + "Ġcar ry", + "ĠIs rael", + "( obj", + "={ {", + "Ġs aved", + "Ġs yn", + "Ġconst ant", + "V ENT", + "Ġpos itive", + "Ġcon duct", + "Ġsk in", + "Ġear lier", + "Ġl ayout", + "ĠI P", + "O UR", + "Ġt im", + "styles heet", + "_ cl", + "ĠC ard", + "++ ){Ċ", + "Ġtem per", + "ĠDav id", + "ĉ try", + ".d art", + "Ġwant s", + "Ġp icture", + "Ġv ideos", + "ĠCom m", + "is ions", + "_M AX", + "M apping", + "- content", + "ĠE ar", + "- de", + "Ġpre m", + "br uary", + "Ġcom ponents", + "Ġthrough out", + "Ġp ull", + "Ġp ages", + "ent e", + "res pond", + "Ġg as", + "cript or", + "Ġed ge", + "Ġb ound", + "A CT", + "**** **", + "Ġcre ating", + "ĠC H", + "Ġnull ptr", + "B r", + "+ '", + ".c o", + "> ::", + "Ġle arning", + ".L ength", + "_S H", + "Ġpat ients", + "A IN", + "Ġk ids", + "Ġcom fort", + "Ġsh own", + "ug ins", + "ĠB ack", + "ell a", + "_C L", + "Ġl at", + "Ġdis patch", + "Ġclass es", + ". at", + ".b egin", + "Ġsuccess ful", + "b an", + "Ġobt ain", + "ĠS l", + "Ġl ack", + "iter ator", + "Th read", + "(s ize", + "Ġn one", + ".h as", + "_ X", + "s ort", + "n ap", + "p et", + "b in", + "ĠCan ada", + "The y", + "Ġd ans", + "ĠM at", + "< td", + "Ġh air", + "Ġ' ',Ċ", + "Ġc u", + "Ġlaw s", + "let ed", + "p ed", + "Ġp ow", + "Ġk new", + "_C OM", + "_ ,", + "ĠM ag", + "id ents", + "( req", + "Ġ ),", + "- center", + "Ġw ide", + "ĠA uthor", + "st ants", + "Ġjob s", + "Ġm ath", + "et imes", + "Bo olean", + "Ġs cope", + "_ is", + "Ġme as", + "Ġkey s", + "el ay", + "Ġexact ly", + "'=> '", + "ĠP aul", + "m as", + "ĉ print", + "(l en", + "f d", + "Ġ) ;", + ". Event", + "q li", + "ir it", + "ield s", + "om an", + "ĠT op", + "Ġv ote", + "Ġm ask", + "Ġthem e", + "- Ċ", + "Ġpro ps", + "Ġf ine", + "Ġwrit er", + "_ offset", + "c ar", + "Ġal tern", + "Ġc opyright", + "Ġdest roy", + "pp er", + "Ġgener ate", + "pp ed", + "âĢĻ d", + "ĠĠĠĠĠĠ Ċ", + "m ake", + "ĠSh ow", + "Ġb rowser", + "Ġfavor ite", + "Ġcare er", + "Ġhappen ed", + "( char", + "Ġrecomm end", + "Ġl iter", + ".f ilter", + "gr ade", + "Ġ £", + "Ph one", + "om s", + "Ġn amed", + "- label", + "ip o", + "ĠO ther", + "Ġp anel", + "Ġro ck", + "S cale", + "ĉ assert", + "Ð ´", + "Ġtr ust", + "fr ont", + "Ġdem on", + "A r", + "N et", + "Ġecon omic", + "foot er", + "Ġr ace", + "(n ode", + "ĠO ption", + "s plit", + "Ġphys ical", + "if est", + "Ġrem oved", + ". http", + ")) ,Ċ", + "Ġlook ed", + "' ;", + "d ing", + "g est", + "atur day", + "/lic enses", + "Pr ice", + "Ġd ro", + "Ġto wards", + "Ġun s", + "ĠC L", + "ĉ static", + "Ġ rows", + "Ġdef ine", + ".re place", + "Ġf ather", + "ĠDes ign", + "ass ign", + "m ut", + "De vice", + "D id", + "') )Ċ", + "omet ry", + "ay load", + "Ġh istor", + "ĠP aram", + "ĠBo olean", + "Ġn ature", + "Ġj s", + "Ġn ation", + "i h", + "Ġdis cover", + "se m", + "Hand le", + "ĉ r", + "ĠTe chn", + "Ġw all", + "{ $", + "@ property", + "Ġ\" ../", + "Ġex am", + ".d raw", + "opp ing", + "Ġnear ly", + "Ġco ol", + "Ġinde pend", + "RE S", + "Ġhand ler", + "ĠMon day", + "Ġs un", + "St yles", + "ous ly", + "Ġ ĉ", + "v est", + "D isplay", + "( y", + "atic ally", + "Ġpred ict", + "y ing", + "Ġsom etimes", + "\" ]Ċ", + "Ġdr ink", + "Ġb ul", + "ific ations", + ". insert", + ".re g", + "Ġtest s", + "Al ignment", + "Ġal leg", + "Ġat tribute", + "ĠN ote", + "Ġmy self", + "art s", + "N ow", + "Ġinterest ing", + "li ents", + "Ġpop ulation", + "ĠCal ifornia", + "\" I", + "å ¹", + "Ġgre ater", + "ues day", + "Ġth ous", + "Ġcost s", + "Ġla unch", + "\\ Http", + "k er", + "b and", + "ĠPl ay", + "Ġb and", + ".sh ape", + "es ome", + "art icle", + ".r f", + "Ġw er", + "á s", + "em bers", + "us r", + "B A", + "ic an", + "et t", + "valid ate", + "ult i", + "Ġimmedi ately", + "z er", + "Ġfig ure", + "o es", + "ell er", + "irc le", + "ĠS ign", + ".d b", + "Ġr ank", + "By tes", + "Ġproject s", + "_re c", + "UL AR", + "A PI", + "ĠL ine", + "P ort", + "Ġp oll", + "Ġg iving", + "id ence", + "-- Ċ", + "Ġpl ot", + "ic ial", + "Ġw arrant", + "IT ION", + "ĠD ouble", + "Ġbill ion", + "gorith m", + "Ġequ ipment", + "D ATE", + "Ġ@ \"", + "E E", + "Ġp le", + "i ation", + "Ġhead ers", + "Ġpro ced", + ".Component Model", + "ĠOb ama", + "Ġp a", + "ĠB est", + "im ately", + ".get String", + ". \\", + "mp loy", + "Ġr aw", + "_b lock", + "und red", + "\" },Ċ", + ".Group Layout", + "Ġb rought", + "NS String", + "th row", + "cre ated", + ".N ew", + "_ view", + "C P", + "ep s", + "O p", + "Ġgr atis", + "Ġ' \"", + "Ġinter view", + "\"\" \"Ċ", + "Ġpart ial", + "Ġa ria", + "b ing", + "A uthor", + "Bo ok", + "ĠP at", + "um an", + "Us ers", + "pl us", + "ĠD irect", + "ven ue", + "al pha", + "UC CESS", + "ĠC all", + "Ġ );čĊ", + "im ated", + "Ġrem ain", + "Ġant i", + "ĠL ondon", + "Ġsaf ety", + "PO SE", + "o les", + "cont roller", + "By te", + "ĠCour t", + "ĠPh il", + "ĠAss oci", + "en a", + "å IJ", + "_ST R", + "co in", + "resh old", + "Ġb atch", + "_C lick", + "entic ation", + "> ';Ċ", + "ent y", + "Ġbegin ning", + "Ġz ero", + "ĠCon vert", + "Ġt err", + "Ġp aid", + "Ġincre ased", + "c atch", + "-s ize", + "act ivity", + "e quals", + "Ġque ue", + "Ġ\" '", + "ĠIntern ational", + "Ġf ür", + "urs day", + "Ġsc ient", + "all ow", + "ax is", + "Ġapp ropri", + "ed ge", + "Ġid x", + "S uccess", + "ent ifier", + ": \\", + "x is", + "Ġmax imum", + "ark s", + "Ġb irth", + "( index", + "Ġmay be", + ".p y", + "file s", + "Ġlim ited", + "_ check", + "lo ok", + "pl ies", + "Ġmov ement", + "'] .", + "Ġbro ad", + "ĠB E", + "ĠUn ityEngine", + ".c pp", + "ĠE very", + "Ad min", + "Ġf ans", + "p ared", + "Ċ ĠĠĠĠĊ", + "Ġfore ign", + "Ġp an", + "Ġt our", + "ĠOr der", + "Ġmov ing", + "Ġa uf", + "C all", + "c b", + "Å Ł", + "vent ory", + "ĠS ql", + "Ġful ly", + "Click Listener", + "W ORD", + "Ġannounc ed", + ") čĊčĊ", + "Ġagre ed", + "ri e", + "Ġe arn", + "_l ink", + ". array", + "(t ext", + "Ġmaterial s", + ", p", + "ff ff", + "v g", + "Ġ ©", + "Ġun less", + "aj ax", + "LO G", + "Ġsex ual", + "Ġ\\ \"", + "- time", + "Ġco ach", + "Ġsupport ed", + "Ġphot os", + "if orm", + ".C reate", + ") ]", + "ri er", + "Ġd ialog", + "av er", + "ig e", + ") +", + "_id x", + ": [", + "_m in", + "ĠC ong", + "Ġpress ure", + "Ġteam s", + "S ign", + "b egin", + "ri an", + "NE SS", + "L S", + "Ġimpro ve", + "ĠS unday", + "Ġdef inition", + "ig er", + "roll ers", + "Ġthink ing", + "T emplate", + "- F", + "Ġem erg", + "pl ates", + "ĠUS A", + ".set State", + "ĠAl so", + "re v", + "Ġen able", + "ĠC O", + "PE CT", + "Ġcon cept", + ") -", + "ĠâĢ ¢", + "Ġset s", + "Ġmean ing", + "em on", + "ĠCon s", + "c mp", + "ed er", + "ann ed", + "icens ed", + "ĠS uper", + "Ġd aily", + "Ġmult i", + "_ u", + "Ġchall eng", + "_m ode", + "ĠP romise", + "Ġstr ict", + "j o", + "int on", + "( list", + "On ly", + "> {", + "Ġveh icle", + "í ķ", + "ĠPl ayer", + "ĠD el", + "Ġp ool", + ". url", + "nes day", + "();čĊ čĊ", + "Ġ\" );Ċ", + "L ocal", + ". \");Ċ", + "Ġorgan ization", + "re nder", + "ĠApp lication", + "Ġsum mer", + "ex pected", + "N A", + "Ġr ap", + "_ obj", + "Ġsur face", + "ĠP UR", + "Ġ}, ĊĊ", + "Ġvariable s", + "(m essage", + "Ġop in", + ".b ack", + "а н", + "Ġwork ers", + "v m", + "C o", + "ught er", + "Ġm aster", + "Ġ\" \",", + "Ġst ories", + ". User", + "Ġcele br", + "ines e", + "B S", + "ĠCom mand", + "ash board", + "Ġo g", + "k g", + ". image", + ".st yle", + "Ġstep s", + "ĠB en", + "( args", + "ĠP erson", + ", y", + "Ġofficial s", + "| Ċ", + "Ġsk ills", + "v c", + "Ġbuild er", + "Ġg ar", + "A ccount", + "ĠA uth", + "ç Ķ", + "'] )Ċ", + "ĠA T", + "n n", + ". Int", + "SS ERT", + "Ġeffect ive", + "LE TE", + "Ġto ols", + "AR D", + "Ġdig ital", + "D ouble", + "ĠF ind", + "R C", + "Ġin line", + "/ r", + "AR AM", + "AS K", + "Ġint ent", + "a ight", + "_add r", + "Ġrequest s", + ".f irst", + "Ġde bug", + "Ġsp ent", + "() ));Ċ", + "Å Ľ", + "Ġpr incip", + "Log ger", + "clud es", + ". use", + "Ġsur v", + "med ia", + "ĠFe bruary", + "ĠM ac", + "Ġmiss ing", + "Ġw ife", + "Ġtalk ing", + "ĠM ake", + "Ġc art", + "Ġloc ated", + "E nc", + "- a", + "ch ron", + "Ġc ards", + "Ġgu y", + "Ġp ers", + "ĠY es", + "ate ver", + "ĠA ng", + "ol ar", + "ĠE ven", + "Ġacc ur", + "ĠP ower", + "ĠG old", + "c lear", + "Pro cess", + "Ġrec ords", + "Ġk illed", + ".c lear", + "ĠWARRANT IES", + "Ġpur pose", + "pan el", + "J ECT", + "ÃŃ a", + "Ġex erc", + "W S", + "/ L", + ". exports", + "Ġ__ _", + "Ġs in", + "S ervlet", + "Ġd é", + ".de lete", + "ro ke", + "S l", + "ug h", + "ear s", + "Ġpoint er", + "Ġh op", + "all ery", + "Ġo bs", + "co very", + "ĉ char", + "ĉĉĉĉ ĉĉĉĉĉĉ", + "ĉ def", + "oc ity", + "itch en", + "ul ations", + "ĠF IT", + "Ġ ).", + "straint s", + "vent ion", + "Ġrequ ires", + "ĠO per", + "M E", + "OUN T", + "al let", + "Ġn orm", + "I RE", + "ex as", + "Ġprogram s", + "Ġwe ak", + "' .$", + "u ing", + "ĉ ĠĠĠĠĠĠĠ", + "Ġm il", + "Ġf irm", + "init ely", + "_VAL UE", + "ap se", + "atis f", + "Ġdem and", + "_m od", + "Ġdescri bed", + "Ġpl aces", + "V ID", + "Ġal one", + "Ġex port", + "Ġv ec", + "ĠM ax", + "Ġactiv ities", + "ict ures", + "g ener", + "Ġm a", + "Ĥ ¬", + "Ġexpress ion", + "C allback", + "_ content", + "ĠM ost", + "Ġtest ing", + "E C", + "CH ANT", + "Ġad just", + ".Th reading", + "( ctx", + "Ġag ree", + "ig hest", + "Ġu i", + "ĠL aw", + ". Y", + "> ĊĊ", + ".ex ample", + "ber g", + "Ġmov ed", + "ĉ e", + "ĠS aturday", + "Ġpay load", + "Ä ĩ", + ") :ĊĊ", + "Ġbe y", + "ur er", + "< script", + "Ġs ymbol", + "Ġass um", + "Ġp ul", + "E ffect", + "Ġh undred", + "To ol", + "ak ed", + "con nection", + "Ġvo ice", + "Ġp d", + "Ġtrans action", + "Ġlink s", + "E rr", + "ĠInd ian", + "T C", + "atal og", + "n i", + "s ign", + "<< \"", + "j i", + "y a", + "Ġdemon str", + "ul ated", + ". St", + "Ġinst it", + "Ġbo ost", + "Ġcell s", + "ol ic", + ".P ro", + ": ,", + "\"> \\", + "Ġth us", + "ĠReg ister", + "h ol", + "ĠCh inese", + "Ġpost ed", + "Ġm agn", + "ab ilities", + "Ġdise ase", + "Ġrem ains", + "ĠPro f", + "- form", + "Ġc in", + "org an", + "ic ate", + "Ġst ress", + "] *", + "Ġ ----------------------------------------------------------------", + "_ context", + "or ry", + "Ġd ied", + "m at", + "Ġstart s", + ".M essage", + "Ġrun s", + "Ġgu ide", + "Ġwarrant y", + "ential s", + "d ict", + "ĠS ize", + "ul er", + "Ġrespons ible", + "_SE T", + "Ġcont aining", + "ĠPr ice", + "| |", + "F S", + "Ġem p", + "_b utton", + "( uint", + "Ġsu ff", + "p th", + "Ġdef initely", + "put e", + "Ġmarket ing", + "ĠW H", + "ĠS ie", + "+ =", + "OL OR", + "Ġcons ult", + "Ġs igned", + "Ġse quence", + "le e", + "Ġrequire ments", + "h y", + "Ex press", + "M T", + "se y", + "Ġ ult", + "å ®", + "ellig ence", + "Ġanal y", + "Ġd ress", + "eng ine", + "ĠG reat", + "ĠAnd roid", + "ĠA lex", + "m ode", + "D ictionary", + ".D ate", + "ä ½", + "V ICE", + "Ġfam ilies", + "ĠRuss ian", + "ĠT imes", + ".c all", + "$ (", + "Pro file", + "Ġf older", + "ch es", + "Ġleg is", + "_ row", + "un es", + "Ù Ħ", + "Ġ} ).", + "Ass ert", + "ag en", + "ĠH and", + "I ter", + "Ġbig gest", + "ore ach", + "Ġpol ic", + "Ġper missions", + "Ġshow ed", + "ĠE lement", + "Ġtop ic", + "âĢĶ âĢĶ", + "ro ad", + "ĠB ank", + "rec ord", + "Ġpart ners", + "ĠR ef", + "ess ions", + "Ġass ess", + "U ST", + "ĠPart y", + "pro du", + "L C", + "Ġ ul", + ". form", + "h ide", + "c opy", + "UT F", + "ĠSO FTWARE", + "čĊčĊ čĊ", + "ĠL in", + "un a", + "ug ar", + "Ġadmin istration", + "Ġopen ing", + "Ġsc an", + "Ġcontin ued", + "com ponent", + ".s p", + "Ġhapp ens", + "um my", + "ĠP R", + ".F ile", + "ĠDown load", + "Lo ading", + "d i", + "Ġwait ing", + "_A DD", + "T ab", + ".query Selector", + "Ġecon omy", + "ĠF rench", + "t xt", + "Ġf ant", + "_ ;Ċ", + "H older", + "S H", + "Ġn umpy", + "Ġst reet", + "Ġm ale", + "\\ Model", + "ang ing", + "ĠB ill", + "Ġprevious ly", + "B I", + "ĠSec ret", + "Ġm ist", + "ĠF ield", + "up s", + "ĠPro cess", + "Ġke pt", + "ĠO T", + "Ġtrad itional", + ". i", + "am in", + "Ġhelp s", + "An y", + "orig in", + "ilt ers", + "j u", + "d esc", + "ĠA ccount", + "Ġ) čĊ", + "k top", + "ol ly", + "Ġf s", + "Ġ ê", + "Ġ ut", + "Ġcent ral", + "(t est", + ".A n", + "Ġs atisf", + "G R", + "ĠF ull", + "Ġhe at", + "ib er", + "Ġon to", + "m os", + "S chema", + "Ġfact ory", + "\" .$", + "aw s", + "St atement", + "(t arget", + "ĉ new", + ".b e", + "Ġg uest", + "Ġm al", + "AR Y", + "Ġre ached", + "Ġm ouse", + "Ġchall enge", + "ĉd ouble", + "ĠT em", + "Ġt error", + "Ġex tract", + "_T O", + "Ġsepar ate", + "Ġm ir", + "h elp", + "Ġcap acity", + "ĠProp erty", + "k an", + "_c reate", + "ĠL ight", + ".p arent", + "Ġunderstand ing", + "Ġeas ier", + "Ġ| =", + "Ġen h", + "Ġf at", + "Ġprot est", + "am m", + "_ AT", + "- of", + "il s", + "ĠO h", + "Ġps ych", + "Ġ$ .", + "ind s", + "Ġrel ative", + "sh op", + "sh ort", + "ĠS and", + "uest ion", + "Ġf ear", + "/ ĊĊ", + ". context", + "Ġschool s", + "Ġser ve", + "z one", + "_d b", + "Ġmajor ity", + "ex ample", + "Ġl ang", + "ĉ ĠĠ", + "Reg ister", + "end o", + "Ġprocess ing", + "_t emplate", + "- user", + "Ġe g", + "C OM", + "ĠBl ue", + "i ro", + "Ġrem ote", + "ĠI T", + "#! /", + "Ġred istrib", + "ra z", + "ĠS ince", + "ĠT ur", + "Back ground", + "== =", + "Ġref lect", + "Ġpro s", + "c md", + "Ġwh om", + "Com pat", + "ĠA re", + "Id entifier", + "ĠTh om", + "_ port", + "g u", + "Ġmon itor", + "r m", + "Ġpat ient", + "ver ter", + "Ġg ain", + "- ui", + "In st", + "Ġd ies", + "A rea", + "_f ilter", + "Ġgr at", + "Ġreal ity", + "ord inate", + "ol ved", + "Cont act", + "Ġcompl iance", + "_ or", + "ĠV ar", + "d l", + "Ġapp end", + "G ER", + "(m ax", + ".re nder", + "Ġd ynamic", + "ordin ates", + "_ options", + "_c olumn", + "Ġb atter", + "s pace", + "L a", + "ĠS ource", + "/b in", + "Ġd os", + "ĠBo ard", + "ĠTh read", + "ĠA L", + "( config", + "ĠM er", + "Ġm iles", + "_ header", + "ETH OD", + "iz z", + "Ġbenef it", + "Ġinteg r", + "(c urrent", + "ul o", + ". default", + "ĠD iv", + "Ġt on", + "o th", + "erv ation", + "ed om", + "Ġb aby", + "ce ived", + ".t op", + "rior ity", + "ĠL ocal", + "ri age", + "Ġattack s", + "Ġh ospital", + "Ġfem ale", + "ĠLog in", + "ĠFl or", + "Ġch ain", + "ash ion", + "Text ure", + "S ave", + "Ġf arm", + ".cont ains", + ".T est", + "Ġknow s", + "Ġgener ally", + "ip eline", + "Ġme ant", + "enc ia", + "Ġn icht", + "Ġcont ents", + "P M", + "ched ule", + "( line", + "C G", + "j ob", + "ĠRe al", + "u er", + "f irm", + "Ġ Ø", + "et ro", + "\" `Ċ", + "Ġspe ech", + "Ġth r", + "fore ach", + "Ġw arn", + "ĉ l", + "Ġhe avy", + "< li", + "N e", + "Ġinvestig ation", + "M ath", + "- title", + "Ġch urch", + "Ġdes pite", + "ch ain", + "Ġwh atever", + "ar ian", + "f n", + "Ġm eta", + "} )ĊĊ", + "U FF", + "Ġregard ing", + "_S UCCESS", + "m es", + "ĠInt ent", + "Ġres olve", + "pos s", + "ir a", + "for ce", + "o ice", + "à ¢", + "Ġp m", + "Ġup dates", + "A rr", + "Ġ Ñ", + "test ing", + "Ġto ward", + "nt ax", + "ë ĭ", + "Ġlist en", + "Ġgo als", + "Instance State", + "D r", + "Ġr are", + "Ġtr ail", + "Ke ys", + "C al", + "C ar", + "ĠPe ople", + "ĉ local", + "class es", + "Re ference", + ".for Each", + "em b", + "act iv", + "Ġpr im", + "red ict", + "Ġr ad", + "æķ °", + ".B ack", + "Ġsp read", + "Ġc lock", + "Ġv ir", + "ed itor", + "Ġeffort s", + "Ġbr anch", + "Ġind ust", + "Ġmot or", + "Ġam b", + "Ġdat etime", + "Ġren cont", + "ĠChrist ian", + "ĠAmeric ans", + "f ull", + "Ġf mt", + ".m ain", + "Ġca used", + "_ update", + "ĠCont ent", + "AT CH", + "Ġb ath", + "ĠE ach", + "Ġr adio", + "ach ment", + "uz z", + "Sub mit", + "Ġre strict", + "ab in", + "ĠL oad", + "Ġext ension", + "Ġess ay", + "Ġh at", + "avi our", + "to Be", + "\": [", + "Ġoffer ed", + "Ġv ill", + "(d ouble", + "æĹ ¥", + "b c", + "_f ree", + "ĠM iss", + "ĠB er", + "Ġ è", + "ĠL ike", + "Ġhelp ed", + ".get Name", + "_ AL", + "Ġsp irit", + "ĠAp ache", + "w s", + "Ġthere fore", + "( params", + "_ img", + "Ġpe ace", + "Ġinc or", + "ĠEX PECT", + "Ġmin or", + "ip es", + "ĉ data", + "select or", + "c ity", + "tr ie", + ".b ase", + "_f rame", + "Ġopen ed", + "/ json", + "L Y", + "n u", + ".D e", + "t f", + "m argin", + ".P arse", + "Ġp i", + "Ġe q", + "b d", + "Field s", + "ĠT ree", + "Ġb an", + "ist an", + "Ċ ĠĠĠĠĠĠĠĠĊ", + "ĉg l", + "Ġprodu ced", + "s ystem", + "M ark", + "_h ash", + "Ġb g", + "Ġconst it", + "ĠLe ague", + "Ġmiss ion", + "_ format", + "([ Ċ", + "clus ion", + "! \"", + "Ð ·", + "b reak", + "ĉs witch", + "Ġth er", + "Trans form", + "Ġfoot ball", + "- link", + "r oute", + ". auth", + "Ġb ag", + "ov ers", + "Ġen abled", + "Ġr ac", + "( I", + "C R", + "anc ing", + "Ġman aged", + "_ q", + "NG TH", + "Ġm ac", + "ĠA uto", + "ament e", + "Ġ' ',", + ".App end", + "Ġp in", + ". item", + "ack ing", + "Ġocc as", + "p erson", + "Ġt i", + ".Re g", + "Ġh aven", + "Ġg lass", + "Ġ\" )", + "_ char", + "res ource", + "Ġep isode", + "Ġ' _", + "ĠE s", + "ĠEar th", + "Âł Âł", + "UP DATE", + "ĠS ou", + "u is", + "t ypes", + "Ġm as", + "Ġf av", + "Ġcon struct", + "_r ate", + "er as", + "Ġ| Ċ", + "rop erties", + "Ġext ernal", + "Ġap plied", + "Ġpre fix", + "ot ed", + "l ers", + "Ġc old", + "ĠS P", + "ĠCh urch", + "ĠOut put", + "los ed", + "ç ļ", + "ific ate", + "oper ation", + "her it", + "x FF", + ". env", + "_ err", + "os h", + "D irection", + "C ancel", + "ĠFr ank", + "Ġfind ing", + ". )ĊĊ", + "Ġr outer", + "ãĥ »", + "s es", + "Ġc row", + "== '", + "Ġs and", + "Ġr id", + "it ure", + "Ġent re", + "Ġo bserv", + "Ġv ac", + "ð Ł", + "- T", + "A rt", + "n ight", + ". search", + "Ġex change", + "Ġdistr ict", + ". os", + "Ġdep artment", + "Ġdoc uments", + "Ġcent ury", + "ĠN ext", + "H ost", + "ĠK IND", + "Ġsus p", + "- P", + "re nd", + ". em", + "u ite", + "ist ers", + "( json", + "ĠAn n", + "w t", + "at i", + "ĠHT ML", + "wh en", + "D irectory", + "Ġsh ut", + "< a", + "ed y", + "Ġhealth y", + "Ġtemper ature", + "ĠG en", + "Ġmet al", + "Ġsub mit", + "ĠD O", + "Ġat tract", + "Ġ{ };Ċ", + "ĠW ord", + "Ġl l", + "Ġseem ed", + "k o", + "I ED", + "Ġl abor", + ".Cont ext", + "Ġas set", + "y ou", + "Ġc ars", + "ĠC olumn", + "Ġr é", + "Ġs quare", + "ĠNS String", + "âĢĿ ,", + "ap es", + ".. .Ċ", + "Ġthan ks", + "( props", + "Ġt ick", + "Ġexper iment", + "Ġpr ison", + "t ree", + "- text", + "ĠIO Exception", + "-w idth", + "_ST ATUS", + "f ast", + "-b ody", + "- header", + "Ġgu ar", + "cre te", + "ĠT im", + "Ġclear ly", + "ĠRepublic an", + "Ġjust ify", + "и ÑĤ", + "ĉ ĠĠĠĠ", + "c ache", + "; //", + "Ġpres ence", + "Ġfact ors", + "Ġemploy ee", + "] ))", + "M ember", + "Ġselect or", + "b or", + "ĠM ex", + "çļ Ħ", + "ut ex", + "_t ag", + "ail ure", + "ĠN et", + "Ġre li", + "E G", + "Ġf printf", + "Ġte en", + "lo ss", + "Ġle aving", + "De legate", + "Ġbe at", + "Ġmin ute", + "sub scribe", + "Ġredistrib ute", + "Con stants", + "Ġcan cer", + "/ {", + "B L", + "Ġs pan", + "ĠCh ild", + "C enter", + "Ġear th", + "Y S", + "ĠLe vel", + "Ġse a", + ".s upport", + ".in ner", + ". Item", + "ill ing", + "ĠĠĠĠĊ ĠĠĠĠĊ", + "ĠL abel", + "ĠE st", + "( arg", + "bo Box", + "ĉf oreach", + "c os", + "F ailed", + "sw ers", + "Ed itor", + "r ont", + "ĠM P", + "ex pr", + "ĠL ife", + "Ġ? ?", + "ö r", + "Ġatt end", + "ĠQ ue", + "Ġspec ies", + "- D", + "Ġa us", + "Str uct", + "Ġadvant age", + "ost on", + "-b lock", + "in itial", + "C RE", + "Ġtr uly", + "Ġcomp are", + "or ney", + "Ġs pect", + "F ull", + "b es", + "Ġvis ible", + "Ġm ess", + "st ances", + "Ġcl oud", + "_v ersion", + "Ġf urn", + "ic ago", + "LO W", + "Ġtraff ic", + "Ġf ol", + "rypt o", + "Ġdecl ar", + "Ġsl ot", + "ĠEx t", + "ĠEng land", + "ĠU nder", + "Ġt a", + "let ter", + "Ġoffic er", + "ĠDon ald", + "Y es", + "_ json", + "IT ableView", + "ĠU SE", + "mploy ee", + "Ġopin ion", + "ĠA ut", + "b order", + "Ġad vice", + "Ġautom atically", + "is co", + "Ġm m", + ". vis", + "am l", + "Ġinitial ize", + "Ġ( {", + "Ġ ;ĊĊ", + "Ġgener ation", + "Ġb its", + "clip se", + "Ġun f", + "ut ors", + "pl t", + "Ġdel ta", + "est roy", + "is is", + "< br", + "Ġlimit ations", + "Ġend ed", + "ĠM ad", + "il m", + "Th ese", + "ĠMin ister", + "Ġch art", + "F ragment", + "Ġindepend ent", + "Y ear", + "Ġin str", + "Ġt ags", + "A VE", + "ĠAr ch", + "st op", + "Pro gress", + "Ġm i", + "Ġlearn ed", + "G e", + "Ġhot el", + "S M", + "T YPE", + "Ġc y", + "ERS ION", + "un ately", + "l imit", + "s el", + "Ġmov ies", + "Ġste el", + "o z", + "g b", + "ĠC amp", + "s ite", + "ĠLog ger", + "P LE", + "оР´", + ". right", + "ĠC ore", + "Ġm ixed", + "st ep", + "Ġput s", + "s uper", + "R outer", + ". Http", + "ly ph", + "ĠColor s", + "Ġandroid x", + ". str", + "Ġinn ov", + "Ġde ck", + "' >Ċ", + "ap ers", + "] (", + "cont inue", + "s pec", + "ĠR oad", + "AS H", + "ili ar", + "Ġcontin ues", + "Ġapp oint", + "Ġ# Ċ", + "ĠV ir", + "Ġ?> \"", + "Ġb in", + "} \",", + "go ing", + "e ach", + "B D", + "ĠA ccess", + "D oc", + "ĠMan agement", + "B ER", + "ask et", + ".get Instance", + "Ġestablish ed", + "so cket", + "IN S", + "ĉv irtual", + "ĉ result", + "RE AD", + "_ height", + "ĠF ont", + "Ġ( );Ċ", + "_ html", + "Ġneighb or", + "l or", + "Ġg ather", + "Ġ} )ĊĊ", + "Ġid entity", + "Ġf ab", + "p adding", + "ĠR oute", + "Enumer able", + "à ´", + "Ġfor ced", + "/j query", + ".ĊĊ ĊĊĊĊ", + "res ents", + "_ left", + ".P aram", + "ĉ throw", + "ĠH am", + "Ġevent ually", + "ac er", + "p ub", + "Ġtr a", + "un ique", + "d el", + "ĠFlor ida", + "ĠC lean", + "x a", + "Ġ ·", + "Ġvalid ate", + "Vis ual", + "Ex pression", + "_f unc", + "m ember", + "ĉ h", + "tr l", + "ĉ G", + "nap shot", + "ĠProp Types", + "v in", + "] )ĊĊ", + "ow l", + "if ies", + "Ġ$ ('.", + "ĠCont ext", + "ĠTo ast", + ". Key", + "Ġoffic ers", + "/ n", + "s n", + "und efined", + ". items", + "ut ow", + "am age", + "Ġaccount s", + "ook ie", + "Se ction", + "ici ans", + "Ġad vis", + "( is", + "[: ,", + "ĠFr ance", + "F unc", + "ic ious", + "Ġto k", + "Ch annel", + "ĠA D", + "_N UM", + "Ġtime out", + "lem ma", + "rem e", + "u j", + ".A l", + "uc lear", + "( os", + "(\" <", + "[ Ċ", + "f etch", + "Ġb al", + "Ġgu id", + "- align", + "ĠW rite", + "ĠOn ce", + "utow ired", + "OD ULE", + "Ġp itch", + "C F", + "by tes", + "ĠCom mission", + "Ġincre d", + "P ER", + "_ response", + "ĠL os", + "par ser", + "Ġass ume", + ". Request", + "ĠT oken", + "_p osition", + "Ġn om", + "- term", + "Ġrem aining", + "i ostream", + "Ġpie ces", + "ap y", + "ĠL ess", + "r ange", + "umb n", + "pr ise", + "_ option", + "Im pl", + "k wargs", + "Ġbusiness es", + "Al ert", + "Ġpart ies", + "ĠCont ainer", + "ĠPr ivate", + "ĠPl an", + "Ġregister ed", + "Ġj our", + "ack er", + "ен и", + "/ >", + "ch at", + "se ct", + "Ġcre ation", + "olut ely", + "Ġinst ant", + "Ġdel ivery", + "ick en", + "y es", + "ĠFr anc", + "bl ing", + "end a", + "[ (", + "_r ange", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "Ġsched ule", + "Con n", + "Ġthan k", + "x d", + "Ġh ook", + "Ġdocument ation", + "Param eters", + "H ello", + "v t", + "Ġart icles", + "Ġw est", + "def ined", + ". select", + "ok ens", + "ĠV AL", + ".f ile", + "res et", + "Ġmy s", + "ĠM A", + "] ),", + "Ġc ities", + "rel ated", + "å Ľ", + "Ġappe ared", + "Ġw id", + ".p anel", + "ĠIn s", + ". entity", + "Ġde cre", + "ĠL ou", + "(t ime", + "ĠTh ank", + ".create Element", + "Ġmention ed", + "oun ce", + "ĠT ry", + "ĠW all", + "/ images", + "ĠM enu", + "' čĊ", + "ĠE r", + "Ġcrit ic", + "ĠY ear", + "( param", + "Ġf lo", + "N N", + "oot er", + "Ġ ];Ċ", + "ĠA ff", + "\" github", + "room s", + "Ġh yp", + "g lobal", + "Ġa vec", + "æľ Ī", + "Ġcomplet ion", + "Ġcon d", + "onym ous", + "( temp", + "Ġst ars", + "Ġre levant", + "Ġcover ed", + "Ġel im", + "_t ypes", + "( bool", + "Ġt u", + "_ex ists", + "Ġsec ure", + "Ġst ored", + "] /", + "x F", + "ĠCont roller", + "Ġm igr", + "M I", + "ĠD en", + "Ġann ual", + "U IL", + "- and", + "Ġcr ime", + "b el", + "Ġk itchen", + "@ g", + "_p h", + "ourn ament", + "ĠS ocial", + "ĠS pecial", + "log ger", + "Ġt ail", + "Ġun known", + "d ed", + "Ġapp rec", + "(d b", + "c f", + "Ġass ign", + "- out", + "ĠM ont", + "d p", + "w idget", + "Ġst one", + "- primary", + ". grid", + "Result s", + "az z", + "Ġda ughter", + "Ġcur r", + "Ġl in", + "Ġs outh", + "form s", + "ĠO UT", + "let te", + "ak s", + "ig ure", + "ĠE U", + "var iable", + "Ġb rief", + "ĠSc ott", + "Ġcon ference", + "and a", + "_ lock", + "or al", + "Ġe ine", + "OR S", + "//////////////////////////////// ////////////////////////////////", + "ess o", + "Ġr is", + "Ġg ender", + "est ic", + "L icense", + "( out", + "Ġm s", + "Se e", + "Ġwill ing", + "az e", + "Ġs ports", + "Ġy es", + "l u", + "Ġp urs", + "/j avascript", + "- pro", + "nav bar", + "_pro duct", + "/ bootstrap", + "Ġdr iving", + "Ġ Ä", + "Ġpro pos", + "ult ip", + "up lic", + ". email", + "Ġappro x", + "( cl", + "Ġwe ar", + "Ġrep ly", + "ass et", + "Ġ ice", + "Ġt x", + "k r", + "ĠGerman y", + "ĠGe orge", + "Ġc b", + "ĉ err", + "M ove", + "Ġpol y", + "vo ice", + "} \"", + "Ġan imal", + "A v", + "ĠL ocation", + "Ġn ative", + "] [\"", + "< double", + "Ġm ais", + ", int", + "Ġpre par", + "Ġinter val", + "plement ation", + "_ ERR", + "Ġb ug", + "> \"", + "st at", + "Ġ} ,čĊ", + "< span", + "Ġfa ith", + "Ġ rom", + "pre v", + "ĠE lect", + "F ind", + "Ġg od", + "ot or", + "// ----------------------------------------------------------------", + "orig inal", + "C pp", + "ĠSen ate", + "Ġposition s", + "Ġweap ons", + "Ġco ff", + "Ġpur poses", + "p ol", + "Ġim press", + "Ġanim als", + ". Entity", + "(n p", + "Ġmur der", + "Ġ` `", + "fl ag", + "Ġsol utions", + "ĠAct ive", + "Ġb right", + ".d ate", + "Ġsit u", + "ï¼ Ī", + ". ID", + "Ġs ie", + "), čĊ", + "ak t", + "S pace", + ".d at", + ".index Of", + "h an", + "az ine", + "ĠZ e", + "Ġcr ash", + "( /", + "> =", + "Ð ±", + "iv a", + ".Auto Size", + "ĠL at", + "_ ext", + "Initial ize", + ".reg ister", + "OP Y", + "Ġre verse", + "_d is", + "'] [", + "Ġprom pt", + "ont o", + "ĠJ ournal", + "r outer", + "Ġmys qli", + "# else", + ") \"", + "-x s", + "let s", + "ph an", + ". LE", + "W ill", + "Ġaff ord", + "Ġsk ill", + "-t oggle", + "N C", + "B ind", + "T S", + "J ust", + "iter al", + "Y P", + "ĉ unsigned", + "Ġw ind", + ")) :Ċ", + "Ġw arning", + "ĠW ater", + "Ġd raft", + "Ġc m", + "Ġs am", + "Ġhold ing", + "z ip", + "ĠSc ience", + "Ġsup posed", + "G en", + "Ġdi et", + "< h", + "ĠP ass", + "v i", + "Ġhus band", + "� �", + "n ote", + "ĠAb out", + "ĠIn stitute", + "Ġcl imate", + ".Form at", + "Ġn ut", + "est ed", + "Ġapp arent", + "Ġhold s", + "f i", + "new s", + "C M", + "v ideo", + "': '", + "D ITION", + "p ing", + "Ġsen ior", + "w a", + "-- >Ċ", + "_ default", + "ĠD atabase", + "re p", + "E SS", + "ner gy", + ".F ind", + "_m ask", + "Ġr ise", + "Ġk ernel", + ":: $", + ". Q", + "Ġoffer ing", + "de cl", + "ĠC S", + "Ġlist ed", + "Ġmost ly", + "eng er", + "Ġblock s", + "ol o", + "Ġgover ning", + "\\ F", + "Ġcon cent", + ".get Text", + "Ġm b", + "Ġocc urred", + "Ġchang ing", + "Sc ene", + "_C ODE", + "B eh", + "\" The", + "Ġt ile", + "ĠAssoci ation", + "ĉ P", + "al ty", + "_ ad", + "od ies", + "i ated", + "Ġpre pared", + "poss ible", + "Ġm ort", + "TE ST", + "Ġign ore", + "Ġcal c", + "Ġr s", + "Ġassert Equals", + "Ġs z", + "ĠTH IS", + ". \"Ċ", + "Ġcan vas", + "j ava", + "Ġd ut", + "VAL ID", + ".s ql", + ". input", + "Ġa ux", + "S up", + "Ġart ist", + "V ec", + "_T IME", + ".string ify", + "et ween", + "ĠC ategory", + "Ġ[ -", + "ĠDev Express", + "ĠJ ul", + "Ġr ing", + ". ed", + "Y Y", + "L et", + "Text Field", + "Ġfl at", + "_p rint", + "ĠOT HER", + "ad ian", + "Ġcheck ed", + "e le", + "Al ign", + "stand ing", + "Ġ[ ],", + "Ġl ab", + "uck y", + "ĠChrist mas", + "( image", + ".m odule", + "Ġl ots", + "Ġslight ly", + "(f inal", + "er ge", + "è ¿", + "ĠPol ice", + "ĠR ight", + "Ġaw ard", + "ĠO S", + "Ġ{ }ĊĊ", + "Ġp tr", + "ov es", + "ic ated", + "еР¼", + "Ġman age", + "olid ay", + "Am ount", + "ool Strip", + "t body", + "N av", + "w rap", + "B B", + "Ġwatch ing", + "ari os", + "Ġoption al", + "_ K", + "ĠL icensed", + ".M ap", + "T imer", + "ĠA P", + "ĠRe v", + "( o", + ", c", + "um in", + "eta iled", + "ĠH y", + "Ġbl ank", + "ag ger", + "ĠS elf", + "() [", + ".m ake", + "ear n", + "ch annel", + "< pre", + "ble m", + "_p assword", + "_s p", + "ic ing", + "e z", + "Ġthe ory", + "ĠT er", + ", n", + "log o", + "ĠHT TP", + "() ))", + ".h andle", + "> ;Ċ", + "W orld", + "Ġpy thon", + "Ġl if", + "Ġtr av", + "Ġcon ven", + "com pany", + "ĠCl ub", + "V er", + "B tn", + "Ġz one", + "product s", + "ĠE duc", + "Ġver ify", + "ĠM il", + "on o", + "] );ĊĊ", + "EN CE", + "Ġpack et", + "Ġc er", + "Ġen umer", + "Ġpar s", + "form ed", + "Ġocc up", + "t re", + "Ġexerc ise", + "D ay", + "_s um", + "Ġask ing", + "apt ion", + "Ġord ers", + "Ġsp ending", + "ĠE RR", + ".D is", + "ĠU til", + "âĢľ I", + "\\ '", + "? )", + "/ >Ċ", + "Ġem ot", + "Ġinflu ence", + "ĠAfr ica", + "att ers", + "Ù ħ", + ".s ession", + "Ġch ief", + "ĉĉĉĉĉĉĉĉ ĉĉĉ", + "Ġto m", + "clud ed", + "ser ial", + "_h andler", + ".T ype", + "ap ed", + "Ġpolic ies", + "- ex", + "- tr", + "bl ank", + "mer ce", + "Ġcover age", + "Ġr c", + "_m atrix", + "_ box", + "Ġcharg es", + "ĠB oston", + "P e", + "Ġcirc um", + "Ġfil led", + "Ġn orth", + "icture Box", + "ĉ res", + "è ®", + "Ġter min", + "Ġ[ âĢ¦", + "IRE CT", + "Ġb er", + "Ġ\" ../../", + "ret ch", + ".c ode", + "_c ol", + "ĠGovern ment", + "Ġarg v", + "ĠL ord", + "as i", + "Ex ec", + "ĉ let", + "vert is", + "Ġdiscuss ion", + "en ance", + "out ube", + "type of", + "Ġs erved", + "ĠP ut", + "ĉ x", + "Ġs weet", + "B efore", + "ateg y", + ". of", + "ĠM aterial", + "S ort", + "ON T", + "ig ital", + "Wh y", + "Ġs ust", + "Ġ ç", + "ab et", + "Ġseg ment", + "Ġ[ ],Ċ", + "ĠMus lim", + "Ġfind ViewById", + "c ut", + "_T EXT", + "ĠM ary", + "Ġlo ved", + "Ġl ie", + "ĠJ O", + "Ġis set", + "mon th", + "Ġpr ime", + "t i", + "ĠCar ol", + "U se", + "ĠP op", + "ĠS ave", + "Int erval", + "ex ecute", + "d y", + "ĠI ran", + "_ cont", + "ĉ T", + "Ġph ase", + "check box", + "we ek", + "Ġh ide", + "Ġt il", + "Ġj u", + "C ustom", + "b urg", + "/ M", + "T ON", + "Ġqu ant", + "Ġr ub", + "ix els", + "Ġinst alled", + "Ġd ump", + "Ġproper ly", + "( List", + "Ġdec ide", + "app ly", + "H as", + "Ġkeep ing", + "Ġcitiz ens", + "Ġj oint", + "p ool", + "S ocket", + "_ op", + "Ġweap on", + "gn ore", + "ĠEx ec", + "ott en", + "ĠM S", + "Ġ( -", + "ĠRe view", + "Ġex amples", + "Ġt ight", + "! (", + "D P", + "ĠMessage Box", + "Ġphot ograph", + "UR I", + "é t", + "l ow", + "ĠGr and", + ".p ersistence", + "Ġmaint ain", + "Ġnum s", + "Ġz ip", + "ial s", + "ĠG ets", + "pe g", + "ĠB uffer", + "~~ ~~", + "ra structure", + "ĠP L", + "u en", + "ob by", + "size of", + "Ġp ic", + "Ġse ed", + "Ġexperi enced", + "Ġo dd", + "Ġk ick", + "Ġproced ure", + "avig ator", + "- on", + ", j", + "ĠAl though", + "Ġuser Id", + "ac cept", + "Bl ue", + "IC olor", + "l ayer", + "av ailable", + "Ġend s", + ".t able", + "Ġdat aset", + "b us", + "Ġexpl ain", + "( pro", + "ĠCommit tee", + "Ġnot ed", + "] :Ċ", + "D im", + "std io", + ". \",Ċ", + "_s ource", + "ĠWe ek", + "ĠEd ge", + "Ġoper ating", + "Ġest e", + "i pl", + "ag ination", + "Ġpro ceed", + "Ġanim ation", + ".Model s", + "ĠW atch", + "i at", + "Ġopp on", + "/ A", + "Re port", + "Ġs ounds", + "_b uf", + "IEL D", + "Ġbu nd", + "ĉ get", + ".p r", + "(t mp", + "Ġk id", + ">ĊĊ Ċ", + "Ġy ang", + "Not Found", + "Ñ Ĩ", + "m ath", + "@g mail", + "ĠL IMIT", + "red ients", + "Ġv ent", + "avig ate", + "L ook", + "Ġrelig ious", + "Ġr and", + "ri o", + "( GL", + "_ ip", + "u an", + "ici ency", + "ĠCh ange", + "> čĊčĊ", + "ĠEnt ity", + "Ġrencont re", + "ĠR et", + "pl an", + "é n", + "BO OL", + "ur ies", + "tr ain", + "Def inition", + "======== ====", + "z z", + "An imation", + "ĠO K", + "_m enu", + ".b l", + "_s core", + "Ġac ad", + "( System", + "Ġref resh", + "'=> $", + ".G raphics", + "ament o", + "p id", + "t c", + "Ġt ips", + "Ġhom es", + "Ġf uel", + "â ĸ", + "_h elper", + "ĠĠ čĊ", + "ĠR oom", + ".C lose", + "_ attr", + "ĠM ount", + "ĠE v", + "ar ser", + "_t op", + "e ah", + "ĠDe lete", + "ãĢ į", + "u ke", + "Ġus age", + "ar ia", + "_de v", + "Ġtext ure", + "Ġconvers ation", + "e per", + "Be an", + "d one", + "non atomic", + "ĠSe cond", + "Ġshoot ing", + "_p re", + "Com ponents", + "Ġ] ĊĊ", + "__ ,", + "stit ution", + ".Ch ar", + "> ();ĊĊ", + "Ġpresent ed", + "Ġw a", + "ok er", + "- ĊĊ", + "in er", + "Ġbe coming", + "Ġinc ident", + "At t", + "Ġreve aled", + "for c", + "Ġbo ot", + ".p age", + "Enumer ator", + "_ ->", + "Ph oto", + "Ġs pring", + ". \",", + "ĠD ictionary", + "B JECT", + "Ġloc ations", + "Ġs amples", + "Input Stream", + "ĠB rown", + "Ġst ats", + "qual ity", + "Ñ ħ", + "-d is", + "Ġhelp ing", + "Ġp ed", + "( se", + "ĠWh o", + "al ian", + "int ernal", + "Ġf t", + "> ().", + "-> {", + "Ġm ine", + "Ġs ector", + "Ġg ro", + "Ġopport unities", + "Ġà ¼", + "Ġm p", + "Ġalleg ed", + "Ġdoub t", + "M ouse", + "Ab out", + "_p art", + "Ġch air", + "Ġstop ped", + "lo op", + "ent ities", + "Ġapp s", + "ans ion", + "Ġm ental", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "F R", + "Ġdef end", + "c are", + "Ġide al", + "/ api", + "ur face", + "Ġe le", + "ul ator", + "ĠR ights", + "angu ages", + "Ġfund s", + "Ġad apt", + "At tributes", + "Ġdep loy", + "opt s", + "Ġvalid ation", + "Ġconcern s", + "u ce", + ".n um", + "ult ure", + "il a", + "Ġc up", + "Ġp ure", + ".F ore", + "ĠHash Map", + ".value Of", + "as m", + "M O", + "Ġc s", + "Ġst ores", + "Ġ ************************************************************************", + "Ġcommunic ation", + "m em", + ".Event Handler", + ". Status", + "_ right", + ".set On", + "S heet", + "Ġident ify", + "ener ated", + "order ed", + "Ġ\" [", + "Ġs we", + "Con dition", + "ĠA ccording", + "Ġpre pare", + "Ġro b", + "P ool", + "Ġs port", + "r v", + "ĠR outer", + "Ġaltern ative", + "( []", + "ĠCh icago", + "ip her", + "is che", + "ĠDirect or", + "k l", + "ĠW il", + "key s", + "Ġmy sql", + "Ġw elcome", + "k ing", + "ĠMan ager", + "Ġca ught", + ") }Ċ", + "S core", + "_P R", + "Ġsur vey", + "h ab", + "He aders", + "AD ER", + "Ġdec or", + "Ġturn s", + "Ġr adius", + "err upt", + "C or", + "Ġm el", + "Ġin tr", + "( q", + "ĠA C", + "am os", + "M AX", + "ĠG rid", + "ĠJes us", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + ".D E", + "Ġt s", + "Ġlink ed", + "f ree", + "ĠQ t", + "Ġ/** čĊ", + "Ġf aster", + "ct r", + "_ J", + "D T", + ".C heck", + "Ġcomb ination", + "Ġint ended", + "- the", + "- type", + "ect ors", + "am i", + "ut ing", + "Ġum a", + "X ML", + "U CT", + "A p", + "ĠR andom", + "Ġr an", + ".s ort", + "Ġsort ed", + ". Un", + "_P ER", + "it ory", + "Ġprior ity", + "ĠG al", + "ĠO ld", + "h ot", + "ĠD isplay", + "(s ub", + "_T H", + "_ Y", + "ĠC are", + "load ing", + "K ind", + "_h andle", + ", ,", + "r ase", + "_re place", + ".add EventListener", + "ĠR T", + "Ġenter ed", + "g ers", + "Ġ ich", + "( start", + "/ app", + "Ġbro ther", + "M emory", + "Out let", + "Ġ utf", + "pre c", + "Ġn avigation", + "OR K", + "Ġd st", + "D etail", + "Ġaud ience", + "Ġd ur", + "Ġcl uster", + "un ched", + "Ġ ],", + "Ġcomfort able", + ". values", + "ĠT otal", + "Ġsn ap", + "Ġstand ards", + "Ġperform ed", + "h and", + "(\" @", + "å Ń", + "Ġph il", + "ib r", + "tr im", + "Ġfor get", + "Ġdo ctor", + ".Text Box", + "icon s", + ", s", + "ĠO p", + "S m", + "St op", + "ĉ List", + "ĉ u", + "Com ment", + "_V ERSION", + ".X tra", + "P erson", + "r b", + "LO B", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠCent ral", + "IC K", + "ra q", + "Ġput ting", + "Ġm d", + "ĠL ove", + "Pro gram", + "B order", + "o or", + "Ġallow ing", + "a fter", + "Ġent ries", + "ĠMay be", + "] ).", + "ĠSh ort", + ") \\", + ".n ow", + "f riend", + "Ġpre fer", + "ĠG PIO", + "os is", + "ĠGame Object", + "Ġsk ip", + "Ġcompet ition", + "_m atch", + "lic ations", + "_CON T", + ".group Box", + "Ġal s", + "\" We", + "_e q", + "l an", + "_ search", + "ĠMus ic", + "as is", + "Ġb ind", + "ĠIs land", + "r um", + "( E", + "Ġse at", + "V ideo", + "Ġa ck", + "ree k", + "={ ()", + "Ġr ating", + "Ġrestaur ant", + "DE X", + "(b uf", + "pp ing", + "ual ity", + "Ġle ague", + "Ġfoc used", + "ap on", + "$ data", + "CL UD", + "CLUD ING", + "Ġabs olute", + "( query", + "Ġtell s", + "A ng", + "Ġcomm unities", + "Ġhon est", + "ok ing", + "Ġap art", + "ar ity", + "/ $", + "_m odule", + "ĠE nc", + ". an", + ".Con fig", + "C re", + "Ġsh ock", + "ĠAr ab", + "I ENT", + "/ re", + "Ġre trie", + "ycl er", + "is a", + "ĠO rgan", + ". graph", + "Ġ í", + "ĠB AS", + "En um", + "Ġposs ibly", + "ÑĢ аÐ", + "ĠJapan ese", + "Ġc raft", + "ĠPl ace", + "Ġtal ent", + "Ġfund ing", + "Ġconf irmed", + "Ġc ycle", + "/ x", + "G E", + "Ġhe aring", + "Ġpl ants", + "Ġm outh", + "p ages", + "or ia", + "ĠRem ove", + "_t otal", + "Ġo d", + "oll apse", + "do or", + "Ġb ought", + "Ġadd r", + "AR CH", + "_d im", + "dd en", + "Ġdec ades", + "RE QUEST", + "Ġvers ions", + "f ire", + "Ġmov es", + "f b", + "Ġcoff ee", + ".con nect", + "ĠR ow", + "Ġs chema", + "S cope", + "- Type", + "Ġfight ing", + "Ġret ail", + "Ġmod ified", + "T F", + "File s", + "n ie", + "_com mand", + "st one", + "Ġ ÑĤ", + "_ thread", + "Ġb ond", + "ĠDevelop ment", + "Ġp t", + "F ORM", + "ple t", + "Ġident ified", + "c pp", + "Ġc oding", + "ok ed", + "ĠM aster", + "ID TH", + "Ġres idents", + "red it", + "ĠPh oto", + "= -", + "un te", + "ate ur", + "_ST ATE", + "ĠS ing", + "Ġshe et", + ". val", + "or se", + "Ġh ers", + "Ġdetermin ed", + "Com mon", + "Ġw ed", + "_ queue", + "P H", + "ĠAt l", + "cre d", + "/L ICENSE", + "Ġm es", + "Ġadv anced", + ".j ava", + ".S h", + "G o", + "k ill", + "f p", + "_set tings", + "Ġp al", + "Ġtr uck", + "Ġcomb ined", + "Ġ\" ${", + "ĠCor por", + "Ġjo ined", + "ĠJ ose", + "ĠC up", + "un s", + "est ival", + "lev ision", + "Ġbro ken", + "Ġmar riage", + "ĠWest ern", + "Ġrep resents", + "ĠT itle", + "Ġs s", + ".A ss", + "ongo ose", + "ient o", + "< >();Ċ", + "Ġabs olutely", + "Ġsm ooth", + "TER N", + "ĠUn less", + "W ord", + "Ġmer ge", + "ig an", + "ĠV ol", + "Ġn n", + ".get Id", + "ĠÐ ·", + "Ġsex y", + "Ġseek ing", + "S ingle", + ". this", + "Ġk om", + "b ound", + "; \"", + "Ġfont Size", + "_d f", + "Ġinj ury", + "( H", + "Ġiss ued", + "_ END", + ": self", + "Ġp atch", + "Ġle aves", + "Ġad opt", + "File Name", + "ãĢ IJ", + "Ġexec utive", + "ĠBy te", + "] ))Ċ", + "Ġn u", + "out ing", + "clud ing", + "- R", + ". options", + "Ġsub stant", + "av ax", + "ĠB UT", + "Ġtechn ical", + "Ġtw ice", + "Ġm ás", + "Ġun ivers", + "y r", + "Ġdr ag", + "ĠD C", + "Ġs ed", + "Ġb ot", + "ĠP al", + "ĠH all", + "forc ement", + "Ġa uch", + ".m od", + "not ation", + "_file s", + ".l ine", + "_fl ag", + "[ name", + "Ġres olution", + "Ġb ott", + "(\" [", + "end e", + "( arr", + "F ree", + "( @\"", + "ĠD istrict", + "PE C", + ": -", + "P icker", + "ĠJ o", + "ĠĠĠĠĠ Ċ", + "ĠR iver", + "_ rows", + "Ġhelp ful", + "Ġmass ive", + "--- Ċ", + "Ġmeas ures", + "ĠR untime", + "Ġwor ry", + "ĠS pec", + "ĉ D", + "ãĢ ij", + "Ġ) {Ċ", + "Ġwor se", + "(f ilename", + "Ġl ay", + "Ġmag ic", + "ĠThe ir", + "ou l", + "st roy", + "ĠWh ere", + "Ġsu dden", + "Ġdef e", + "Ġb inding", + "Ġfl ight", + "ĠOn Init", + "ĠW omen", + "ĠPol icy", + "Ġdrug s", + "ish ing", + "(' ../", + "ĠM el", + "pe at", + "t or", + "Ġpro posed", + "Ġst ated", + "_RE S", + "Ġe ast", + "ĠCON DITION", + "_d esc", + "Ġwin ning", + "fol io", + "M apper", + "ĠP an", + "ĠAn ge", + ".s ervlet", + "Ġcop ies", + "L M", + "Ġv m", + "å į", + "Ġd ictionary", + "S eg", + "el ines", + "ĠS end", + "Ġ iron", + "ĠF ort", + ".d omain", + "Ġdeb ate", + "Not Null", + "e q", + "ach er", + "l f", + "ĉf mt", + "Ġlaw y", + "Ä Ł", + "ĠM en", + "Ġtr im", + "( NULL", + "Ġ! !", + "Ġp ad", + "Ġfollow s", + "\"] [\"", + "re qu", + "ĠE p", + ".g ithub", + "( img", + "et o", + "(' \\", + "S ervices", + "umbn ail", + "_m ain", + "ple ted", + "fort unately", + "Ġw indows", + "Ġpl ane", + "ĠCon nection", + ". local", + "u ard", + "} \\", + "== \"", + "and on", + "ĠR oy", + "w est", + "ig inal", + "em ies", + "it z", + "') :Ċ", + "ĠP eter", + "Ġt ough", + "Ġredu ced", + "Ġcalcul ate", + "Ġrap id", + "c ustomer", + "Ġeff icient", + "Ġmed ium", + "Ġf ell", + ". ref", + "ĠC as", + "Ġfeed back", + "S peed", + "( output", + "aj e", + "Ġc ategories", + "Ġfe e", + "} ;", + "Ġde leted", + "re h", + "Ġpro of", + "D esc", + "B uild", + "Ġs ides", + ".Array List", + "- %", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "Ø ±", + ".m atch", + "л и", + "Ġfe els", + "Ġachie ve", + "Ġcl im", + "_ ON", + "ĠC D", + "Ġteach er", + "_c urrent", + "b n", + "_P L", + "ist ing", + "En able", + "G EN", + "Ġt v", + "Ġso ck", + "Ġpl ays", + "Ġdis count", + "ĠK E", + "ĠDe bug", + "F ore", + "ĠI raq", + "Ġappear ance", + "M on", + "Ġst yled", + "ĠH uman", + "i ot", + "ĠH istory", + "Ġs ac", + "ĠC ollection", + "Ġrecomm ended", + ".Se lected", + "Ġorgan izations", + "Ġdiscover ed", + "co hol", + "ad as", + "ĠThom as", + "M ay", + "Ġcons erv", + "Ġdom in", + "ĠF ollow", + "ĠSe ction", + "ĠTh anks", + "User name", + "Ġrec ipe", + "Ġwonder ful", + ".s leep", + "_ if", + "ĉĊ ĉĊ", + "orn o", + "Ġr u", + "_t arget", + ".\" \"", + "à ¦", + "Event Args", + "Ġinput s", + "Ġf if", + "Ġv ision", + "c y", + "ĠS eries", + ") (((", + "Ġtr ading", + "Ġmark er", + "B egin", + "Ġtyp ically", + "Ġca uses", + "drop down", + "_DE BUG", + "Ġdet ect", + "c ountry", + "! \");Ċ", + "ĉ R", + "app y", + "Ġc ref", + "(' <", + "\" =>", + "ĠL E", + "read er", + "Ġadmin istr", + "à µ", + "uck et", + "Ġf ashion", + ". char", + "iz ar", + "Ġdis able", + "Ġsu c", + "ĠL ive", + "iss ue", + "Ġmet adata", + "fl ags", + "Ġ ðŁ", + "Ġcomm itted", + "Ġv a", + "Ġr ough", + "Ġ'' 'Ċ", + "Ġhigh light", + "_var s", + "V O", + "Ġenc oding", + "- Z", + "_s ign", + "$ (\"#", + "Ġr ain", + "reate st", + "ĠEN D", + "Se lection", + "Ġcandid ates", + "Ġs av", + ". Empty", + "Ġdec isions", + "Ġcoll abor", + "rid ge", + "fe ed", + "ress ion", + "Ġperson s", + "V M", + "eg a", + "_B IT", + "A ccording", + "ack ed", + "Ġdoll ars", + "_lo ss", + "ĠC ost", + "} \"Ċ", + "Not ification", + "Ġpro stit", + "Ġauthor ity", + ".re c", + "Ġsp okes", + "ĠT oday", + "ist ant", + "ĠHe ad", + "âĢĿ .", + "ertain ment", + "ce an", + "cul ate", + "Ġv en", + "How ever", + "_ arr", + "Ġtok ens", + "G raph", + "ĠJ ud", + "ĠVir gin", + "ĠS erial", + "un ning", + "M utable", + "ag ers", + ".c sv", + "Ġdevelop ing", + "Ġinstruction s", + "Ġprom ise", + "Ġrequest ed", + "_ encode", + "/ \"", + "ĠI con", + "u ilt", + "- day", + "Ġint elligence", + ". IS", + "ĠO bservable", + "ĠH ard", + "Bo ol", + "ident ial", + ".An chor", + "Ġsell ing", + "C I", + "AG ES", + "t le", + "b ur", + "UFF ER", + "R Y", + "Ġbig ger", + "Ġr at", + "Ġfam ous", + "Ġtyp ename", + "Ġexpl ained", + "} }Ċ", + "Ġn uclear", + "- N", + "Ġcr isis", + "ĠEnt er", + "Ġan swers", + "/ ${", + "/ pl", + "Ġse qu", + "_n ext", + "m ask", + "Ġstand ing", + "Ġpl enty", + "ĠC ross", + "ĉ ret", + "d ro", + "ĠC ast", + "= true", + "ĠCh ris", + "ic io", + "ĠM ike", + "Dec imal", + "add Component", + "L en", + "Ġco ck", + "Ġ# {", + "UR N", + "< tr", + "Ġauthor ities", + "Res ources", + "- H", + "B ottom", + "_ qu", + "put er", + "ester day", + "Dis patch", + "s ince", + "Ġfam iliar", + ", i", + "V C", + "Ġm ent", + ", C", + "Ġfre edom", + "Ġr outes", + "ĠB uy", + "Ġcomm ands", + "Ġm esh", + "/ C", + "ĠSet tings", + "- style", + "Ġw itness", + "Ġc le", + "Ġun ion", + "ef ault", + "are t", + "Ġthought s", + "Ġ ----", + "_pro cess", + "_ us", + "ing ly", + "U ES", + "T ouch", + "ĠÐ ¼", + "_ open", + "ĠV ec", + "Ġre ward", + ".C lick", + "/ :", + "Ġn ie", + "Ch anges", + "M onth", + "ï¼ Ł", + "Ġexec ution", + "Ġbe ach", + "( Integer", + "ĉ a", + "/ '", + ".Font Style", + "Ġab ort", + "ĠS ingle", + "( isset", + "Ġd p", + "Ġ}} ", + "Ġ* =", + "ĠP S", + "Ġdanger ous", + "[ p", + "OM E", + "O ther", + "ĠString Builder", + "Point s", + "head ing", + "Ġc urrency", + "Ġpercent age", + "_A PI", + "Ġclass ic", + "the ad", + "ĠM O", + "F E", + "Id x", + "aw ait", + "Ġà ¨", + "Ġacc ident", + "Ġvari ant", + "Ġm yst", + "ĠL and", + "ĠB re", + "Ġh arm", + "ĠA cc", + "Ġcharg ed", + "ion es", + "Vis ibility", + "ar ry", + "ĠL anguage", + "Ġwalk ing", + "\" .ĊĊ", + "if er", + "Ġleaders hip", + ".F rom", + "yn am", + "Ġt imestamp", + "i pt", + "ĠH as", + "REF ER", + "ĠIt s", + "Ġlist ener", + "UT E", + "_d escription", + "Ġexperi ences", + "Ġcre ates", + "R S", + "c art", + "bl ack", + "Ġcho ices", + "w ar", + "Ġ'' '", + "Ġorder ed", + "Ġeven ing", + "Ġp il", + "Ġt un", + "ĠB ad", + "( app", + "r andom", + "Ġexp licit", + "Ġarr ived", + "Ġf ly", + "Ġecon om", + "-m ail", + "Ġlist s", + "Ġarch itect", + "ĠP ay", + "Ġd s", + "ĠS ol", + "Ġveh icles", + "H z", + "- com", + "Ġk ing", + "_e qual", + "ĠH elp", + "Ġab use", + "-- ;Ċ", + "Ġex tr", + "Ġchem ical", + "ä ¿", + "Ġor ient", + "Ġbre ath", + "ĠS pace", + "(e lement", + "w ait", + "DE D", + "ig ma", + "Ġent r", + "Ġs ob", + "- name", + "Ġaff ected", + "ik a", + "Ġco al", + "_w ork", + "Ġhundred s", + "Ġpolit ics", + "sub ject", + "Ġconsum er", + "ANG E", + "Ġrepe ated", + "S end", + "Ġ# [", + "Ġprot ocol", + "Ġlead s", + "use um", + "E very", + "Im port", + "(c ount", + "Ġchalleng es", + "Ġnov el", + "Ġdep art", + "b its", + ".C urrent", + "Ġ` ${", + "ot ing", + "( \\", + "Ġcreat ive", + "Ġbu ff", + "Ġintrodu ced", + "us ic", + "mod ules", + "A re", + "-d oc", + "l anguage", + "_c ache", + "Ġto d", + "? > {{", + "ĠRes ource", + "ĠSt andard", + "ĠP rem", + "up dated", + "ival ent", + "Ġas sets", + "_t emp", + "Ġinterest s", + "Ġhard ware", + "ĠR om", + "ĠSh are", + "Ġ' 'Ċ", + "Ġ* ,", + "ĠT ake", + "ĠIm ages", + "_C HECK", + "(type of", + "ĠJ un", + "\\< ^", + "Ġli qu", + "Ġwor st", + "ymb ols", + "ĉĉĉ ĠĠĠ", + "Ġdr ivers", + "ĠD ocument", + "en o", + "ĠTechn ology", + "Ġappro ved", + "ump s", + "Ġs now", + "form ance", + "_A SSERT", + "u its", + "Ù Ĩ", + "Ġdiffer ences", + ". Visible", + "ĉĉĉ čĊ", + "ĠP s", + "_f etch", + "Ġto do", + ". ',Ċ", + "Ġs el", + "ur ers", + "in valid", + "Ġt weet", + "V EL", + "Ġresearch ers", + "Ġs printf", + "ĠR O", + "Ġp el", + ".Tr ans", + "Ġil legal", + "d ialog", + "sm arty", + "l g", + "_M IN", + "Ġher o", + "f inal", + "Ġp p", + ".L e", + "Ġc i", + "ĉ RT", + "Ġsuggest ed", + "p df", + "ach ing", + "ĠR o", + "ĠProp erties", + "ĠS i", + "Ġbuy ing", + "Ġm u", + "Ġl ands", + "if iers", + "ĠF ILE", + "RO UP", + "Ġh older", + "ĠS on", + "Ġsym pt", + ".r oute", + ") ?", + "Ġarg c", + "Ġfor t", + "Ġcas ino", + "_c ategory", + "Ġfor um", + "p refix", + "apt ure", + "T ube", + "em s", + "im ize", + "Ġn ue", + "a us", + "c ourse", + "AT OR", + "() ),", + "Ad vertis", + "ING S", + "Ġack now", + "ĠKore a", + "pl ing", + "Ġwork er", + "PL IED", + "h al", + "ĠRich ard", + "Element s", + "ĉĉĉ Ġ", + "st ar", + "Ġrelationship s", + "Ġche ap", + "AC H", + "ĠX ML", + ", &", + "ĠLou is", + "Ġr ide", + "_F AIL", + "Ġch unk", + "[ s", + "_O UT", + "Ġch osen", + "_ [", + "/ (", + "ĠJ eff", + "_s l", + "pr iv", + "ĠCan adian", + "Ġun able", + "_F LAG", + "Ġn os", + "h igh", + "Ġl ift", + "f un", + "() {", + "el ly", + "ycler View", + "_ as", + "_L IST", + "Ġr adi", + ".get Value", + "ĠAnge les", + "ĠS pan", + "_in stance", + "it ors", + "Ġm igration", + "A K", + "O h", + " ®", + ". selected", + "ĠG T", + "Ġadv ance", + "ĠSt yle", + ".Data GridView", + "e ction", + "Ñ İ", + "p io", + "ro g", + "Ġsh opping", + "ĠR ect", + "I lluminate", + "O U", + "ĉ array", + "Ġsubstant ial", + "Ġpre gn", + "Ġprom ote", + "IE W", + ".L ayout", + "Ġsign s", + "/ .", + "Ġlet ters", + "Bo ard", + "ct rl", + "\" \\", + "ĠJ ones", + "Ġvert ex", + "Ġj a", + "Ġaff ili", + "Ġwe alth", + "ĉ default", + "Ġsignificant ly", + "Ġe c", + "Ġx s", + "act ual", + ".p er", + "_st ep", + "an vas", + "m ac", + "Ġtrans l", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Iter ator", + "Ġo ch", + "agnost ic", + "ĠD uring", + "ĠDE FAULT", + "Ġt ill", + "Ġsign ature", + "Ġb ird", + "ĠO l", + "ĠI r", + "H S", + "av atar", + "ESS AGE", + "Ġe lev", + "Ġm t", + "ĠN av", + "Ġrel ax", + "Ġpl ate", + "IT EM", + "( date", + ".n ot", + "Ġgr ade", + "Ġ} ),Ċ", + "? \"ĊĊ", + "i ences", + "H igh", + "ĠD IS", + "dis abled", + "Q UI", + "Ġno ise", + "a ux", + "ĠU P", + "os a", + "Ġv oc", + "Ġ ))", + "oc om", + "_O FF", + "ĠD b", + "L ock", + ".e clipse", + ", d", + "ĠD raw", + "Ġ\" (", + "Ġvis ited", + "Ġâ Ī", + "Ġsuc ceed", + "Ġim possible", + "a ire", + "ĠT urn", + "Ġd ish", + "F G", + "Ġs ensor", + "AN N", + "ab a", + "Ġsur g", + "] );čĊ", + "Ġf p", + "_ an", + "- J", + "- G", + "ĠJ ob", + "Con vert", + "ĠKE Y", + "Ġauth ors", + "_s erver", + "\\ r", + "Ġ-* -", + "f lex", + "Ġs oc", + "R et", + "Ġs alt", + "ĠâĢ¦ ĊĊ", + "ĠC lear", + "(p age", + "-d anger", + "Ġroom s", + "con v", + "# {", + ". op", + "ĠA rea", + "_S C", + "h en", + "Ġbeg ins", + "- y", + "Ġexc ited", + "Ġign ored", + "Ġbon us", + "st udent", + "ĠM ember", + "Ġrel atively", + "ĠL ow", + "ĠPro du", + "ate way", + "pos ure", + "Ġth ick", + "ani el", + "( view", + "ĠCr ush", + "Ext ension", + "I l", + "e ed", + "LO C", + ". im", + ". Items", + "Ġconflic t", + ".pre vent", + "Ġon Create", + "u v", + "is er", + "Ġw ave", + "M ar", + "ĠComm unity", + "ic he", + "ĠNo thing", + "[ m", + "ĠLe e", + "ri ends", + "è re", + "!! !", + "an z", + ". result", + "ĠS K", + "_P ARAM", + "Ġdem ocr", + "Back Color", + ".ex ists", + "\" It", + "( options", + "ra zy", + "as er", + "\\ Database", + "al endar", + "_ ass", + "; }Ċ", + "vert ex", + "ine craft", + "W arning", + "arg o", + "Ġact or", + "ĠInst ead", + "ĠUs ing", + "S elf", + "@ interface", + "Ġspe aking", + "ĠPar is", + "ĠL ICENSE", + ".n ode", + "ĠF ood", + "E IF", + "ĠB i", + ". Start", + "ĠI B", + "Ġun iversity", + "ĠHe ader", + ".pro duct", + "C opy", + "et c", + "r ical", + "Ġ> >>", + "book s", + "Ġal gorithm", + "Ġ' __", + "(j avax", + "Ġnumer ous", + "Sh are", + "H ave", + "Ġrec ru", + "Ġpro ve", + ".sub string", + "he alth", + "е л", + "Ġdec imal", + "Ġcomm ission", + "s cription", + "x C", + "Ġsum mary", + "att ed", + "Ġclo ser", + "fin ished", + "() ){Ċ", + "ĠW ood", + "_field s", + "k u", + "_ items", + "Fl ag", + "Ġconf idence", + "ĠF ederal", + "du x", + "Ġcomp at", + "Ġvert ical", + "Ð ¹", + "è s", + "; \">Ċ", + "_m anager", + "() ))Ċ", + "ID E", + ": \",", + "__ Ċ", + "ĠW ay", + "Ñ Ī", + "T emp", + "ĠS TR", + "rit ten", + "S ync", + "ĠA V", + "ĠC EO", + "ĠG uid", + "Ġenvironment al", + "Ġcorrespond ing", + "ĉ console", + "Ġjust ice", + "ĠJ S", + "Ġl ived", + "g ar", + "ĠG raph", + "ĠSt at", + "Ġi Phone", + ". al", + "ĠH D", + "Ġocc ur", + "Ġth reshold", + "Ġon click", + "RE G", + ".Graphics Unit", + "M eta", + "Å ¾", + "Ġc um", + ".g nu", + "à «", + "Ġobt ained", + "Ġcompl aint", + "Ġe ating", + "Ġt ar", + "_t ask", + "Ġopt s", + "( to", + "P ass", + "Ġpl astic", + "t ility", + "ĠW in", + ".prevent Default", + "p ile", + "ĠG ar", + "Ġqu antity", + "_l ast", + "Ġg reatest", + "D ao", + "_D IS", + "ĠUs ed", + "ĠH P", + "rit ing", + "S ION", + "bl ue", + "d omain", + "Ġs cores", + "N ormal", + "_ admin", + "ĠA SSERT", + "Th en", + "** *", + "d ist", + "l on", + "Ġh ate", + "sh al", + "Image View", + "d atabase", + "Ġp and", + "Ġlog ic", + "= false", + "b g", + "ĠConfig uration", + "Ġn ur", + "O G", + "Ġmar ried", + ": +", + "Ġdro pped", + "Ġreg istration", + "оР¼", + "ult iple", + "iz ers", + "sh ape", + ".c opy", + "Ġwe aring", + "ĠC ath", + "Ġded icated", + "Ġ.. .Ċ", + "Ġadv oc", + "ĠF amily", + "Ġstat ements", + "em atic", + "ampions hip", + "Ġmot iv", + "ĠH ave", + "Ġbl ow", + "J ob", + "c ert", + "_v ector", + "inst all", + "ĠC OPY", + "em bed", + "D IR", + "ĠS pring", + "Ġex hib", + "cd n", + "ĠCom ment", + "ĠOption al", + ". player", + "ĠD ark", + "( pos", + "ĠSh ould", + "Ġcent re", + "ĠGu ard", + "ó w", + "Ġtr ouble", + "EN ER", + "( unsigned", + "_s ervice", + "Ġn s", + "ul ing", + "ĠMex ico", + "ĠN Y", + "mys ql", + "Ġl ic", + "å ľ", + "M r", + "- fl", + "ĠC ustomer", + "id i", + "Ġ? >ĊĊ", + "ri ble", + "Ġп ÑĢ", + "Ġs izes", + "_STR ING", + "valid ation", + "ĠJ on", + "( Http", + "add Class", + "N odes", + "Ġfrag ment", + "Ġsp oke", + "Ġw aste", + "J oin", + "Ġill ustr", + "el i", + "c ient", + "Ġa id", + "Ġpro sec", + "') {Ċ", + "Ġpass ing", + "Ġf aces", + "Sh ape", + "_ Z", + "it i", + "Ġal le", + "Ġro bot", + "ĠĠĠĠĠĠĠ Ċ", + "ĠS pe", + "Ġrece iving", + "ĠD etails", + "Ġ\" )", + "m g", + "_RE F", + "Ġcompar ison", + "* ,", + "ĠF ound", + "_s ession", + "( U", + "/ F", + "Ġx xx", + "N etwork", + "d ers", + "Ġcap ture", + "Ġcor re", + "ĠL td", + "ĠAd v", + "[ @", + "Ġcl ip", + "M ill", + "ĠPro file", + "Ġend if", + "Ġob lig", + "des cribe", + ".e lement", + "riter ion", + "L D", + "er ed", + "Ġfav our", + "s core", + "ĠF ilter", + "at tributes", + "Ġcheck s", + "In flater", + "ĠPl us", + "Ġscient ific", + "Ġpriv acy", + "He ad", + "Ġfe at", + "Ġdeg rees", + "ĠP ale", + "; \">", + "Ġfil ms", + "ĠA udio", + "ĠT ag", + "ĠE nergy", + "it ar", + "par ator", + "Ġf ellow", + "Ġev t", + "ĠT ri", + "ĠD AM", + "cl oud", + "ĠP assword", + "ĠDemocr ats", + "ĠAc ad", + "$ lang", + "Ġre b", + "() )ĊĊ", + "н Ñĭ", + "ĠB ur", + "read cr", + "Ġh ex", + "Con sole", + "ct l", + "ous el", + "ĠWill iam", + "Ġa z", + "_P ORT", + "Ġpract ices", + "Ġany where", + "ĠP osition", + "Ġ- >Ċ", + "i ams", + ".user name", + "place holder", + "Ġo der", + "ĠSecret ary", + "Ġi T", + "mon d", + "event s", + "? âĢĿ", + ".S ub", + "Ġatt ached", + "Ġn ão", + "Ġest ate", + ". action", + "Ġfig ures", + "Ġ} );čĊ", + "Ġsubs cri", + ".t ag", + "n am", + ". plot", + "no on", + "li ament", + "Char acter", + ".t ab", + "Ġw inter", + "ĠVar iable", + "Ġtre es", + "Ġpr oud", + "( V", + "_ load", + "Ġh ier", + "ĠE con", + "Ġf d", + "Ġvict ims", + "R est", + "ian a", + "Ġf ake", + ".Print ln", + "Ġstr len", + "Ġs ad", + "Ġb le", + "Pro t", + "Ġbutton s", + "Ġte levision", + "Ġlog o", + "ext ension", + "ĉ j", + "ste in", + "acion es", + "Ġ\"\" \"ĊĊ", + "Ġsim p", + "Ġrecord ed", + "Ġbr ings", + "Ġprincip al", + "Ġfe es", + "(s ource", + "k dir", + "Ġutil s", + "Ġcorrect ly", + "f il", + "Ġw el", + "P air", + "-b utton", + "s cale", + "ver ify", + "[ c", + "Ġ-- -", + "Ġes cape", + "ik es", + "Lower Case", + "ic ian", + "Ġch apter", + "ĠT YPE", + "Ġsh adow", + "Ġaw esome", + "W E", + "el if", + "Ġl ambda", + "Ġdist inct", + "Ġb are", + "- off", + "Ġcol our", + ".append Child", + "ole c", + "ag a", + ".f ill", + "ĉs uper", + "Ġad j", + "( position", + ".get Item", + "Sh ort", + "Ġtot ally", + "V D", + "ĠT re", + "_ ep", + "v ements", + "ĠS olution", + "Ġfund ament", + "F ollow", + "Ġfac ility", + "Ġhappen ing", + "O F", + ".text Box", + "S pan", + "Ġ «", + "id en", + "Ġex ceed", + "(p arent", + "Ġc p", + "ç »", + "Ġhas n", + "Ġp ri", + "Ġcon sequ", + "n en", + "ĠIN TO", + "I gnore", + "ĠF uture", + "Ġcar bon", + "ĠSte el", + "f mt", + "ok ie", + "Ġs pl", + "(t itle", + "- info", + "Ġde als", + "Ġfix ture", + "e a", + "D iv", + "Ġtest ed", + "_ return", + ")ĊĊ ĊĊ", + "upport ed", + "ĠC ook", + "Ġpay ing", + "ĠI ll", + "Ġarrest ed", + "ĠPr ime", + "_c allback", + "> ,Ċ", + "dr iver", + "On ce", + "ab b", + "_by tes", + "ĠS ets", + "( Object", + "Ġc c", + "Ġsh ell", + "al o", + "); //", + "( log", + "ct ors", + ") ", + "Ġ$ (\".", + ".p os", + "Ġbo ys", + "Ġwed ding", + "Ġag ents", + "=\" _", + "ĠAr my", + "Ġh int", + "v ision", + "Ġte ch", + "ĠCon nect", + "Ġleg end", + "ĠB et", + ".B ase", + "Sub ject", + "Ġl it", + "Rem ove", + "Ġ\" :", + "ĠF inal", + "pear ance", + "ĠiT unes", + "Ġparticip ants", + "ĠPy thon", + "Ġbus y", + "i el", + "vert ices", + "Ġtemplate Url", + "ĠC lose", + "Im g", + "ĠCorpor ation", + "t imestamp", + "Ġext end", + "Ġwe bsites", + "Ġposs ibility", + "о ÑĤ", + "Ġk ö", + "Ġme at", + "Ġrepresent ation", + "Ġ ĉĉ", + "_ST ART", + ".app ly", + "ĠVal ley", + "ĠS uccess", + "H i", + "Ġn ob", + "ĠI Enumerable", + "_ select", + "ge o", + ". \")Ċ", + "Ġturn ing", + "Ġfab ric", + "(\" \");Ċ", + "Ġpers pective", + "é Ĺ", + "ĠS n", + "Th ank", + "; j", + ".Param eters", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠ", + "Ġfact s", + "Ġun t", + ".in stance", + "################################ ################################", + "- end", + "ĠJO IN", + "ĠH en", + "Ġur i", + "åIJ į", + "Ġн а", + "ĠIn fo", + "Ġconduct ed", + "Ġà ¥", + "OUR CE", + "Ġw ine", + "J ohn", + ".Error f", + "ĠA ge", + "ound ed", + "Ġreal ize", + "Ġ] ;", + "Ġsub sequ", + ", m", + "( User", + "ian o", + "Ġaccom pl", + "is p", + ".st d", + "é ĩ", + "ĠB ed", + ".set Attribute", + "B R", + "ke ep", + "ĠA LL", + "Ġis ol", + "am ma", + "P ackage", + "Ġoccas ion", + "-s uccess", + "еР´", + "ĠLIMIT ED", + "st rip", + "() ĊĊĊ", + "istrib ution", + "Color s", + "Ġ+ :+", + "Did Load", + "al er", + "Ġt id", + "ĠL ED", + "ĠLink ed", + "ĠC art", + "() )čĊ", + "_RE AD", + "Ġkill ing", + "ĠP HP", + "fe ction", + "Ġinst ances", + "c v", + "\"/ >", + "Ġs f", + "Ġtax es", + "_ location", + "ĠBit coin", + "u able", + "r ank", + "ign ore", + "tr ack", + "к а", + "Ġshould n", + "ĠO P", + "=> {Ċ", + "Ġk m", + "Ġh elper", + "_ head", + "ĠWh ether", + "oc o", + "_b l", + "Ġstat istics", + "Ġbeaut y", + "Ġto g", + "t ip", + "ëĭ ¤", + "Ġc sv", + "(s ql", + "std lib", + "we ak", + "Ġlik es", + "Ä į", + "Ġrepe at", + "Ġap artment", + "Ġem ph", + "_ edit", + "Ġv it", + "ĉ type", + "E ven", + "ut en", + "Ġcircum stances", + "b ian", + "Ġs ugar", + "W indows", + "ì ŀ", + "Ġobs erved", + "/ data", + "Ġcal endar", + "Ġstri ke", + "ĠR ES", + "_s c", + "f ony", + "ore m", + "( z", + "p ower", + "et ect", + "ĠS at", + ".d escription", + "Ġg ang", + "ĠS ports", + "ong s", + "ĠB undle", + ".s um", + "on ce", + "Ġacc used", + "Ġexplo re", + "Ġapprox imately", + "Ġlos ing", + "thes is", + "ĠF und", + "Ġdi agn", + "A utowired", + "prop erties", + "Ġ_ .", + "Ġc nt", + "ced ure", + "Ġy y", + "Ġgr ant", + "so ck", + ".inner HTML", + "Ġ] );Ċ", + "ĠCON FIG", + "=' $", + "] ];Ċ", + "UN D", + "Ġg lob", + "Ġd ire", + "uff le", + "_M EM", + "Ġauth entic", + "> (\"", + "Ġdec ade", + "ĠIm port", + "Ġorigin ally", + "Ġj Query", + "Ġindic ate", + "Ġours elves", + "S w", + ".l bl", + "ener ate", + "Ġbas ically", + "ĠH om", + "Ġ+ #+", + "ĠBrit ain", + "ĠK ar", + "to Equal", + ".st op", + "Ġmod al", + "is i", + "Ġsuggest s", + "Ġd type", + "Ġt ur", + "b f", + "Ġconnection s", + "ĠB efore", + "ist ed", + "m ouse", + "Ġpul led", + ".b uild", + "Ġlegis lation", + "Ġfor th", + "p ad", + "eg o", + ".N ow", + "Ġexc iting", + "}ĊĊ ĊĊ", + "Ġcom pr", + "Ġsh ares", + "Ġr ig", + "g reen", + "_ vec", + "Ġenumer ate", + "A uto", + "ic ator", + "ĠR ay", + "as se", + "Ġh oliday", + "Ġnull able", + "g un", + "_d etails", + "Ġwr apper", + "se q", + "ĠYou ng", + "ju ana", + "Ġ\" __", + "lic ense", + "ser ve", + "^ (", + "id ers", + ".Rem ove", + "rop down", + "' S", + "p in", + "(t oken", + ".D efault", + "Ġreason able", + "amp ion", + "ĠS ociety", + "Ġbe i", + "erv es", + "r ad", + "ĠF ox", + "_ images", + "Ġw heel", + "') [", + "Ġc fg", + "( By", + "Con structor", + "Ġv ary", + ".sw ift", + "Ġpro xy", + "ĉ H", + "ĠAn other", + "ĠP en", + "Ġcheck ing", + "Ġj est", + "man ager", + "Or igin", + "ug s", + "o ir", + ">< !--", + "Ġexpress ed", + "Ġmod er", + "Ġag encies", + "Ġi h", + "-h idden", + "ious ly", + "ĠR od", + "Ġso le", + "M ed", + ".A ny", + "Ġp c", + "b al", + "Ex ample", + "ĠS ale", + "Ġst rip", + "ĠCom p", + "Ġpresident ial", + "M ost", + "put ation", + "( ref", + "ĠF our", + "_f ilename", + "Ġen forcement", + "Ø ¯", + "ĠGe org", + "we ights", + "/ l", + "Ġag gress", + "Ġd rawing", + "and y", + "< I", + "- j", + "ak a", + "h ref", + "Ġteach ers", + "_ Q", + "( it", + "ĠM B", + "Ġtemp orary", + "ire base", + "str a", + "æĹ ¶", + "è ´", + "( label", + "ou p", + "Ġtop ics", + "Ġport ion", + "id os", + "ĠJew ish", + "Ġre covery", + "Ġstand s", + "# [", + "Ġafter noon", + "ĠArt icle", + "_ att", + "Ġexpl an", + "ĠP ak", + ".setOn ClickListener", + ". children", + "Ġi k", + "+ (", + "l ag", + "Ġdis k", + "Ġcont rovers", + "\"> &", + "as p", + "Ġw ie", + "ĠAustral ian", + "ĠYou Tube", + "At tr", + "cont ains", + "du ce", + "ĠM att", + "at ern", + "Ġvol unte", + "Ġnew sp", + "V P", + "olt ip", + "Ġde legate", + "_m eta", + "Ġaccur ate", + "ĠEx ample", + "% ,", + "ĠD aily", + "Ġc abin", + "ĠS W", + "Ġlim its", + "k ip", + "Ġar my", + "Ġend ing", + "Ġb oss", + "ĠD ialog", + "Al so", + "=\"# \"", + "ord an", + "row se", + "- min", + "Ġ\" &", + "_ loc", + "U X", + "Ġdevelop ers", + "Ġaccur acy", + "Ġmaint enance", + "Ġhe av", + "Ġfil ters", + ".T oolStrip", + "Ġn arr", + "ĠE mp", + "ORD ER", + "ĠM obile", + ".S erial", + ".out put", + ".c ol", + "M aterial", + "um a", + "Ġconsum ers", + "sh ift", + "Ġp ued", + "Ġmin i", + "c ollection", + "Ġk an", + ".c enter", + "H istory", + "Ġben ch", + "() );", + "itor ies", + "Ġcrow d", + "_c all", + "Ġpow ers", + "- E", + "Ġdis miss", + "Ġtalk s", + "ĠCh annel", + "for ward", + "_ control", + "/s rc", + "i est", + "**************** ********", + "Ġbet a", + "(c olor", + "_O BJECT", + "ĠA pi", + "Ġeffect ively", + "C amera", + "s d", + "uss y", + "D ict", + "ĠE ffect", + "ib ilities", + "Ġreturn ing", + "ĠF ar", + "Ġ' ')", + "Ġmod ules", + "il ation", + "Ġ( %", + "TR GL", + "Ġst orm", + "on na", + "ĠEX P", + "Ġs pons", + "Ġdis pl", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "f all", + "å Į", + "ign Key", + "_ US", + "et rics", + "Ġhand les", + "T L", + "_ amount", + "ow a", + "br and", + "ĠT ool", + "Ġus ual", + ". Z", + "cre ment", + "ad ium", + "st ock", + "Ġserv ing", + "ĠB on", + "Ġline ar", + "ĠT arget", + "ĠR adio", + "H L", + "Sh ader", + "om atic", + "ag ues", + "in ity", + "d iff", + "_ iterator", + "qu ot", + "Ġ ,Ċ", + "c allback", + "Ġsympt oms", + "[ _", + "ĠB ul", + "ĠF eb", + "und o", + "_ account", + "Ġtyp edef", + "и Ñģ", + "tr as", + "User Id", + "ĠP enn", + "ĠSup reme", + "} >", + "user Id", + "ĠK im", + "Ġg a", + "Ġart ists", + "å ¸", + "ĠAb stract", + "ok emon", + "Ġh am", + "o val", + "Ġch a", + "at en", + "å Ĩ", + "F ixed", + "Ġvul ner", + "ĠParam eters", + "qu antity", + ".C lear", + "Servlet Request", + "Ġy a", + "Ġsou l", + "trans action", + "Ġsol o", + "Ġp airs", + "æ Ķ", + "ĠG re", + "_ word", + "ĠC C", + "Ġg i", + "z ie", + "Ġsched uled", + "rot ation", + "gy pt", + "ul ous", + ":: _", + "ĠE ll", + "< !", + "ĉĉ ĠĠ", + "l p", + "ah a", + "C opyright", + "Ġdr am", + "Ġdi agram", + "ĠM em", + "Ġg arden", + "Com p", + "Ġattempt s", + "uff ix", + "> ()", + "Ġphil osoph", + "_re l", + "å ¼", + "Ġs v", + ".se cond", + "ant o", + ".J son", + "ĠTe le", + "_ local", + "_s end", + "Ġas pects", + "ì Ĺ", + "IB LE", + "Ġr ail", + "Ġwid ely", + "ash ed", + "i ar", + "in f", + "up per", + "d jango", + "_result s", + "iss ing", + "Ġequ ivalent", + "OUN D", + "Ġt y", + "Ġpotential ly", + "Advertis ement", + "ĠRec ord", + "resent ation", + "_w idget", + "ound ing", + "Ġrelig ion", + "Ġcons c", + "ĠL im", + ". am", + "H tml", + "Ġ' :", + "P ATH", + "_s pec", + "ort ed", + "id ades", + "_sh ape", + "Ġkeep s", + ".S ave", + "ĠL oc", + "or i", + "ĠT EST", + "unic ip", + "Ġreg ions", + "Ġbelie ves", + "/ en", + "pos ite", + "{ '", + "pre pare", + "_ const", + "s ample", + "ĠWill iams", + "Ġstr t", + "_ Get", + "ĠAnd rew", + ". active", + "Ġl ayers", + "Visual Style", + "az y", + "ĠK n", + "Ġac id", + "ĠAs ia", + "Ġex cess", + "ĉm y", + "Ġkey board", + "ens us", + "Ġcre w", + "Ġmiss ed", + "m aster", + "ĠW ild", + "Ġnew ly", + "Ġwin ner", + "Ġst ub", + "ic ode", + ".m ove", + "D omain", + "ĠS ar", + "Ġfore st", + "LE D", + "claim er", + ".ex it", + "ĠW indow", + "Ġres istance", + "ĠC HECK", + "(\" -", + "ĠR yan", + "Ġp ipe", + "Ġco ast", + "DE F", + "// !", + "_ off", + "ex it", + "Ġult imately", + "imit ive", + "ĠKe ep", + "Ġhistor ical", + "Ġany way", + "ĠJack son", + "ock er", + "ER N", + "ĠU INT", + "y ntax", + "ER Y", + "is ms", + "Ġc n", + "Ġocc urs", + "Ġ; ;", + "Text View", + "A E", + "/ img", + "Ġy esterday", + "- default", + "Ġt iny", + "Ġpro c", + "Ġal ive", + "ĠRE G", + ". th", + "ear ing", + ".get Logger", + "< link", + "_ login", + "F older", + "ab c", + "lyph icon", + "н о", + "Ġnot iced", + "od igo", + "Ġed ition", + "im ator", + ". Enabled", + ".parse Int", + "Ġy ards", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉ", + "Ġver bose", + "л Ñı", + "_B Y", + ".log in", + ".* ;Ċ", + "ĠM id", + "é es", + "Ġg lo", + "Ġbuild ings", + "Ġz e", + "ĠI ter", + "Ġt ube", + "ĠP ot", + "\\ M", + "< th", + "br idge", + "ĠS cript", + "ĠM odule", + "Ġv acc", + "Ġinstall ation", + "v y", + "VisualStyle BackColor", + "ĠS M", + ".t otal", + "b at", + "Ġfind s", + "Ġat mos", + "Sub view", + "iz ard", + "Ġrepl acement", + "lic ated", + "ap is", + "Ġlog ged", + "ĠLe ft", + "G ui", + "_ Type", + "t m", + "P ad", + "Ġhouse hold", + "Ġre le", + "Ġpropos al", + "_CL ASS", + ":: ::", + "Ġinf rastructure", + "In ject", + "/ html", + "Ġad s", + "iz za", + "Ġm g", + "ctr ine", + "% Ċ", + "< html", + "- image", + "Ġatt orney", + "< m", + "(' ,", + "Ġcan n", + "Ġprint ln", + "o ose", + "Ġy ellow", + ".ex p", + "p ayment", + "Ġtable View", + "aw ay", + "Ġopp osition", + "ĠAg ain", + "ĠH andle", + "Ġex clusive", + "in ar", + "é r", + "оР±", + "ĠC ODE", + "emp orary", + "Ġre act", + "pi pe", + "c z", + ". activity", + "Ġlarg ely", + "Ġdis s", + "ax y", + "es is", + "ĠR en", + "Ġc orn", + ".Use VisualStyleBackColor", + "d ays", + "Ġfr uit", + "In sert", + "_ enc", + "E st", + "_de c", + "ĠL uc", + "Ġü ber", + "param eters", + "P ERT", + "ex press", + "_pro file", + "Un known", + "Ġrev olution", + ".add ress", + "_re quire", + "Ġun iform", + "ĠP ack", + "l ar", + "ĠU ITableView", + "Ġdep ends", + "Valid ation", + "conf irm", + "O wner", + "Ġt rib", + "h et", + "ĠI de", + "ans as", + "L anguage", + "u et", + "ĠP o", + "ĠSte ve", + "Ġcont est", + "_DE FAULT", + "Ġapparent ly", + "RE EN", + "Ġfrequ ently", + "Ġtrad ition", + "ocol ate", + "S I", + "ĠArg ument", + "F ocus", + "ert e", + "ĠL ayout", + "Ġd x", + "Ġgener ator", + "ĠW ait", + "P olicy", + "l ights", + ".Ex ecute", + "P y", + "Ġbed room", + "ed a", + "ra id", + "ĉs ize", + "Ġan cient", + "Ġp ump", + "Ġd w", + "Ġ(! (", + "Ġspec ify", + "( status", + "ĠF BI", + ".ex ception", + "Ġrem ark", + "ly mp", + "ant ee", + "Up load", + "ern et", + "é ¡", + "in ent", + "ĠR ender", + "d m", + "ĠM emory", + "r ich", + "ĠT ools", + "Ġk ne", + "Ġper m", + "b ad", + "Ġd inner", + ".res et", + "Ġj Label", + "Fe ature", + ".S ervice", + "Ġ( {Ċ", + "Ġre ferred", + ".class List", + "Ġinit With", + "ĠText View", + "Ġne ither", + "Ġcount y", + "Ġ\" {", + "ç §", + "Ġt ack", + "class Name", + "ĠUS ER", + "Ġre new", + "` `", + "get Name", + "Ġb rown", + "Err ors", + "ert o", + "Ġsust ain", + "S O", + "let es", + "ĠIn valid", + "Ġen emies", + "un ge", + "Ġexist ence", + "err a", + "Ċ ĠĠĊ", + "utor ial", + "# a", + "p ay", + "char ge", + "ĠI re", + "ate st", + "Ġexp los", + "Ġf ired", + "N ER", + "ĠT y", + "ic ion", + "U ri", + "Ġobvious ly", + "ĠC olum", + "Ġ' +", + "ĠDe vice", + "- related", + "_ ARG", + "Ġv or", + "ĠLess er", + "_O P", + "Serial izer", + "Ġup grade", + "L ight", + "Ġc odes", + "++ ;čĊ", + "Ġwrit es", + "fo od", + "Ġé t", + "@ section", + "Ġtrack s", + "Ġserious ly", + "ch t", + "(size of", + "Ġimmedi ate", + "Ġscient ists", + "Ġ{ $", + "_ ne", + ".Anchor Styles", + "Ġaccom mod", + "ĠHar ry", + "Ġs ight", + "ĠPale st", + "ersist ent", + "Ġ Ñĥ", + "- input", + "Ġco ordinates", + " ·", + "W elcome", + ".con f", + "Ġgre w", + "Ġb old", + "ĠC PU", + "(m y", + "Ġperfect ly", + "Ġmom ents", + "ĠM ovie", + "- data", + "yst al", + "_W IDTH", + "ĠS creen", + "æ Ŀ", + "Ġdis ap", + "Ġredu ction", + ".Get Component", + "_M ODULE", + "Ġgener ic", + "Ġd y", + "all er", + "Ġc url", + "ĠB ody", + "Ġb anks", + ", t", + "av g", + "Ġev il", + "Ġmanufact urer", + "Ġrece iver", + "Column s", + "Ġing redients", + "ĉ out", + "qu es", + ".L oad", + "Ġslow ly", + "ĠT own", + "ĠC ell", + "_n ormal", + "_p refix", + "ĠAl ert", + "(\" {", + "ä r", + "âĢľ The", + "ĠM D", + "Ġcour ses", + "ath an", + "é Ļ", + "oc c", + "ĠS ER", + "es ign", + "Add r", + "= ['", + "(\" ./", + "] }", + ".f ont", + "ĠInst agram", + "ĠB order", + "od a", + "Ġh all", + "Ġr um", + "_b it", + "Ġs aving", + "_d own", + "R andom", + "_reg ister", + "( Context", + "Ġoppos ite", + "R oom", + "Y ES", + "ан и", + "Ġenjoy ed", + "_r un", + "C lear", + "âĢ ĺ", + "ĠF ord", + "on ic", + "ost en", + "\"] )", + "_ auth", + "// čĊ", + "Ġsuff icient", + "LE S", + "Ġph en", + "Ġo h", + "_c sv", + "Ġrout ine", + ".Are Equal", + "ay lor", + "Ġb asket", + "_COM M", + "rypt ed", + "S im", + "ĠSh op", + "Ġstud io", + "at os", + "( W", + "[ string", + "ä t", + "og a", + "Ġsh r", + "Ġs ick", + "An other", + "Ġdo ors", + "_N E", + "ĠTH REE", + ". order", + "raz il", + "Ġmap s", + "_TR UE", + "trans late", + "Ġnear by", + "Ġn ach", + "LO AT", + "b atch", + "Ġl ux", + "ash es", + "ang ers", + "âĢ¦ âĢ¦", + "_E VENT", + "_ UP", + "Ġact s", + "in v", + "_M ETHOD", + "cc ion", + "Ġret ain", + "ut ch", + "ĠÐ ±", + "Ġknow ing", + "Ġrepresent ing", + "N OT", + "p ng", + "Con tract", + "Ġtr ick", + "ĠE dition", + "uplic ate", + "Ġcontrol led", + "c fg", + "j avascript", + "Ġmil k", + "Wh ite", + "Se quence", + "aw a", + "Ġdiscuss ed", + "ĠB ush", + "ĠY ES", + ".f actory", + "t ags", + "Ġt act", + "Ġs id", + "$ $", + "ĠE num", + "Ġfr ames", + "} );", + "Ġreg ul", + "'] ;čĊ", + "Reg ion", + "ff f", + "Ġc ro", + "( com", + "=\" +", + "St udent", + "Ġdis appoint", + "RES ULT", + "Count er", + "Ġbut ter", + "ĠH a", + "ĠD igital", + "Ġb id", + "\"> {{", + "ing ers", + "ĠC ountry", + "_t pl", + "\"] )Ċ", + "/ k", + "d ating", + ": #", + "ĠD ATA", + "yn chron", + "_b ody", + "olly wood", + "Ġval or", + "ip ient", + "o ft", + "UB L", + "doc s", + "Ġsyn chron", + "Ġform ed", + "ru ption", + "Ġlist a", + "Request Mapping", + "Ġvill age", + "Ġkn ock", + "oc s", + "\" {", + "_fl ags", + "Ġtrans actions", + "Ġhab it", + "ĠJ e", + "ed en", + "Ġa ircraft", + "ir k", + "ĠA B", + "Ġfair ly", + ". inter", + ".A ct", + "Ġinstr ument", + "remove Class", + ".com mand", + "Ñ ī", + "ĉm em", + "( min", + "Ġo t", + "Ġcol le", + "= s", + "time out", + "Ġid s", + "ĠM atch", + "ij n", + "z ero", + "Ġnetwork s", + ".g ov", + "Ġint el", + "Ġsection s", + "out ine", + "(c md", + "(d ir", + "ĠLI ABILITY", + "ĠB log", + "Ġbr idge", + "ĠC V", + "con vert", + "Ġ\" )Ċ", + "ĠB ern", + "_P O", + "e val", + "( set", + "to ol", + "Ġpay ments", + "Beh aviour", + "Ġcon crete", + "Ġel ig", + "Ġacc eler", + "Ġh ole", + "_ o", + "TE GER", + "Ġgraph ics", + "O wn", + "Form atter", + "on der", + "Ġpack ages", + "/ a", + "ĠK now", + "Or Default", + "Ġdut y", + "W ait", + "н а", + "_rec ord", + "[ t", + "M esh", + "Ġon going", + ".be ans", + "Ġt an", + "Ġinter pret", + "ast ers", + "QU AL", + "Ġleg s", + "\\ Request", + "- file", + "_m utex", + "ĠS aint", + "// #", + "Ġpro hib", + "( info", + ": =", + "lin ux", + "Ġb lo", + "ot ic", + "ĉf inal", + "_ex p", + "ĠSt op", + "ap ing", + "(s aved", + "_p ush", + "Ġe ase", + "_F R", + "pons ive", + "str cmp", + ": ĊĊĊĊ", + "ä» ¶", + "ol i", + "Ġextrem e", + "Ġprof essor", + "Im ages", + ".IO Exception", + "Ġaddress es", + "plement ed", + "Ġincor por", + "Ġuse Effect", + "_O F", + "ĠD a", + "n ombre", + "IR ST", + "Ġdisc rim", + "Ġcomp ens", + "greg ate", + "anc ell", + "ach es", + "ĠC riteria", + "$ result", + "D estroy", + "Ġsecond ary", + "W atch", + "ĠS em", + "ĠMc C", + "Ġacad emic", + "U pper", + ":: ~", + "ut ral", + "ĠD og", + "ad ed", + "Valid ator", + "Ġder ived", + "Ġset Timeout", + "ĠK en", + "Ġtyp ical", + "ĠB ob", + "Ġb ounds", + "ĠSe ason", + "Ġc razy", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "-r outer", + "itt est", + "ĠM ir", + "Ġemot ional", + ", v", + "c n", + "/ st", + "å ½", + "on om", + "Ġdecl ared", + "> .", + "ail ing", + "Ġ/* <<<", + "Ġnorm ally", + "(M e", + "ev in", + "lik ely", + "Ġpoint ed", + "ĠSt ack", + "Ġw alls", + ". Vector", + "me an", + "] ]Ċ", + "Ġlist ening", + "ad v", + "Ġsw ap", + "IF T", + "Ø ª", + ". argv", + "ul s", + "< option", + "not ations", + "Ġemail s", + "ĠU kr", + "ast a", + "ĠTh us", + "ĠSt one", + "Ġappe al", + ". âĢĻ", + "Ġreg ulations", + "Pre ferences", + "ĠPh one", + "ul f", + "ĠD R", + "Ġtechn ologies", + "Ġpar agraph", + "Ġnecess arily", + ".e ach", + "< float", + "res a", + "Ġunder st", + "Ġf inger", + "press ed", + "-b y", + "if fer", + "w atch", + "ĠB a", + "A IM", + "Ġwe ights", + "ĠR on", + "') }}", + "[ self", + "-------- --Ċ", + "per iment", + "Ġto String", + "x ic", + "ĠC amera", + "! ĊĊĊĊ", + "aur ant", + "P refix", + "Ġinstit utions", + ": int", + "Ġex posure", + "p attern", + "ĠLin ux", + ".n umber", + "red ient", + "Argument Exception", + "ĠCh ief", + "\" },", + "Ġelect ronic", + "r ong", + "er d", + "sp Net", + "ra it", + "/ ',", + "ĠOh io", + "Cont rollers", + "Ġcontin uing", + "ĠT emplate", + "ĠE th", + "s z", + "/ env", + "En v", + "% .", + "art ers", + ") ((", + "ĠT ABLE", + "Ġà ®", + "per ature", + "pro gress", + "P res", + "ê °", + "im plementation", + "Ġb ien", + "Ġstre ets", + "_M SG", + "New s", + "## #", + ": /", + "Ġcut ting", + "x B", + "ress ed", + "_EN ABLE", + "l ab", + "Ġca using", + "] ));Ċ", + "b ra", + "x FFFF", + "il ly", + "plet ion", + "w ill", + "_b ar", + "Ġstruct ures", + "ĠI mp", + "Û Į", + "Ġ< >", + "Ġ ----------------", + "_B UFFER", + ".d ir", + "Ġpl ain", + "Ġpe er", + "g g", + "oint s", + "Ġsomew hat", + "Ġw et", + "Ġemploy ment", + "Ġtick ets", + "ir ms", + "Ġt uple", + "s is", + "$ sql", + "r ig", + "Ġcon version", + "Ġg es", + "Ġconfig ure", + "eg r", + "ĠC a", + "Ġ__ ('", + "ou ston", + ".t oken", + "Bl ack", + "Ġmag azine", + "A W", + ". IN", + "os ing", + "Ġbro ke", + "ĠC ru", + "DE LETE", + "Ġdestroy ed", + "(M ath", + "Ġappro val", + "-d om", + "ĠI II", + "table View", + "Ġdesign s", + "Ġcrush ing", + "Ġcons ent", + "dir name", + "om p", + "Ġc rypt", + "? (", + "or ough", + ". o", + "ĉ list", + "ams ung", + ".\"\" \"Ċ", + "err ing", + "G oogle", + "_p air", + "_IN IT", + "rem arks", + "Ġg ear", + "F ill", + "l ife", + "} \")Ċ", + "Ġsuit able", + "Ġsurpr ised", + "_RE QUEST", + "Ġman ifest", + "att en", + "Ġfr ustr", + "ov ement", + ".c lick", + "Ġi i", + "Ġexp ansion", + "ig s", + "P arse", + ".Reg ular", + "R ob", + "_l ayout", + "ì ł", + "Ġtrans lation", + "ĠBe aut", + "B est", + "_C OLOR", + "< label", + "Ġliqu id", + "IT S", + "Ġpro d", + "Ġoper ate", + "UI Kit", + "Ġn atur", + "arg ument", + "_d etail", + "ĠCent re", + "Ġ\" --", + "Ġ}} \"", + "lo cale", + ".t v", + "_se q", + "Ġup coming", + "Ch art", + "ĠDiv ision", + "Ġclin ical", + "Com pany", + "S epar", + "l as", + "ĠH un", + ": s", + "Ġhead ing", + "оР³", + "Ġ\" \");Ċ", + "[ id", + "b ia", + "Ġst retch", + "ic ide", + "Ġre produ", + ".pro ject", + "leg end", + "end ers", + "Ġrespons es", + "Ġon t", + "rit ical", + "Ġref uge", + "ĠL i", + "Ġ: ĊĊ", + "ĠTh ree", + ".cont roller", + "_IN DEX", + "_F OR", + "\\Model s", + "j ax", + "ĉex it", + "Ġâ ĸ", + "Ġc overs", + "ĉ y", + "- .", + "IND OW", + "Ġfail s", + "in cludes", + "Ġf ault", + "Ġl y", + "ñ o", + ".s lice", + "ILE D", + "ĠP ur", + "ĠAs ian", + "_b atch", + ".M ax", + "v l", + "ĠCOPY RIGHT", + "Ġg iant", + "ĠMan ual", + "ĠC opy", + "Class Name", + "He alth", + "C ursor", + "IB Outlet", + "Ġt we", + "æ ³", + "_label s", + "Ġcol lected", + "Ġfurn iture", + "Ġdeal ing", + "Control s", + "ĠHot el", + "ck s", + "Ġch ose", + "âĶ Ģ", + "od d", + "S R", + "Ù Ĭ", + "ì Ħ", + "Ġacc ord", + "ĠM ove", + "ĠM ode", + "ĠM ock", + "Ġthread s", + "++ ++", + "ĠO ptions", + "Ref resh", + "ĠD id", + "'] ->", + "u cc", + "_ch annel", + ". abs", + "Ġ{ },Ċ", + "ĠW al", + "er ior", + "Ġmain ly", + "ĠDr iver", + "NotFound Exception", + "Ġcount s", + "e am", + "Ġ& =", + "Q uestion", + "ĠA li", + "Ġany more", + "d etail", + "t ail", + "Ġm ile", + "ĠF air", + "Ġs orry", + "Ġsurround ing", + "Ġad m", + "De v", + "Ġmari juana", + "ĠS ound", + "ĠA sh", + "F D", + "Te am", + ". port", + "Ġ[ ]ĊĊ", + "ub ble", + "Ġas c", + "Ġint ention", + "A cc", + "ch i", + "ust ers", + "Ġins pired", + "se g", + "CL U", + "Ġman ip", + "M etadata", + "Con nect", + "ĠB eh", + "Ġfind ings", + "Ġas sembly", + "w orld", + "Ġrem ained", + "Ġu id", + "( .", + "Ġm x", + "Lo op", + "ĊĊĊĊ Ċ", + "Ġfant astic", + "wh o", + "ak i", + "ĠB asic", + "ĠY et", + "ĠUs ers", + "ik ip", + "Ġhead s", + "ĠMich igan", + "_ it", + "ĠTor onto", + "Ġrec ording", + "Ġsub mitted", + "_var iable", + "medi ate", + ".graph ics", + "Ġst ood", + "Ġre ar", + "vel ocity", + "_M ESSAGE", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ro les", + "ĠT our", + "_ year", + "end ment", + "amp s", + "ĠIre land", + "m al", + "Ġyoung er", + "Ġstrugg le", + "Ġc able", + "ĠSD L", + "(' -", + "an es", + "ĠNe ed", + ".R ow", + "P ol", + "ĠP H", + "_s cript", + "ag em", + "ĠB as", + "_s pace", + ". loc", + ": i", + "ad r", + "Ġengine ering", + "it en", + ") &", + "Ġu k", + "ĠL ittle", + "_C OUNT", + "x A", + "Array List", + "æ į", + "Ġ\" \")Ċ", + "An chor", + "Ġh ang", + "t witter", + "Ġcompet itive", + ".s rc", + "ãģ Ĺ", + "Ġtrans late", + "ĠCre ates", + "ook s", + "ĠR oll", + "'' 'Ċ", + "/ sh", + "s ome", + "Enc oding", + ".res olve", + "Ġdesign er", + "ĠSt orage", + "Ġz a", + "ĠN ever", + "Ġsomew here", + "Ġbox es", + ".s ource", + "Ġpy game", + "Ġgrow n", + ".t w", + "() ),Ċ", + "', ['", + "Ġoppon ent", + "(s rc", + ".l ayer", + "AP P", + "ĠAct iv", + "Ġguest s", + "ĠVAL UES", + "};ĊĊ Ċ", + ".n ative", + "Ġamount s", + ". RE", + "Ġcl one", + "Ġwer en", + "Ġ\" <<", + "_ ac", + "Ġbreak ing", + "Ġreli able", + ".P OST", + "ĠSk y", + "Ġ' &", + "Ġsaved InstanceState", + "ast ing", + "ill ion", + "com ments", + "ult y", + ".m enu", + "/ config", + "Ġ ĊĊĊ", + "T ODO", + "Ġpurch ased", + "_c or", + "ĉ auto", + "Compat Activity", + "com plete", + "_ graph", + "is odes", + "Ġsitu ations", + "ĠH or", + "Re ceive", + "âĢľ We", + "Ġent ities", + ".assert Equals", + "оРº", + "ĠS ans", + "v ince", + "rom pt", + "= Ċ", + "Ġ/ .", + ".Se lect", + "yl v", + "Ġb att", + "A udio", + "Ġincreasing ly", + ".B undle", + "Ġexpl ains", + "the ast", + ". offset", + "Ġh al", + "Ġtechn ique", + "_l imit", + "Ġdraw n", + "AY ER", + "Ġfeature d", + "yy yy", + "at in", + "ph en", + "ach el", + "! \\", + "l ower", + "ĠG R", + "Ġp ag", + "ĠP arse", + "Ġt ou", + "ä¸ Ģ", + "D istance", + "Index Path", + "Ġh ell", + "s im", + "UT TON", + "Us age", + "elen ium", + "ĠF all", + "Ġ\" .$", + "ĠM u", + "Ġcr uc", + "Ġs ont", + "REF IX", + "Ġinter ior", + "ĠO lymp", + ".Auto Scale", + "par a", + "Axis Alignment", + "Ġr iver", + "D to", + "Ġwith draw", + "Re act", + "- class", + "b efore", + "_ alloc", + "Cont ents", + "ĠW as", + "I CT", + "Ġform ula", + "Ġindic ates", + "ĠĠĠĠ ĊĊ", + "_st ore", + "it ting", + "ĠIt alian", + "_S et", + "_re port", + "Ġp id", + "_V ER", + "Ġw ins", + "ĠCl oud", + "\") {Ċ", + "ch ester", + "Ġden ied", + "Ġw ird", + "ĠSte p", + "Ġinvest ors", + "b old", + "_d isplay", + "ou ver", + "or er", + "Res et", + "Ġsurg ery", + "Ġstrateg ies", + "/m aterial", + "_ unit", + "Ġc ouncil", + ".P er", + "ĠâĢ ŀ", + "Ġre form", + "F ramework", + "Ġlist ing", + "_b tn", + "Ġb is", + "% d", + "eg as", + "Ġsudden ly", + "_S ER", + "Ġa o", + "_d irectory", + "f as", + "Ġprem ium", + "Ġtrack ing", + "ĠB L", + "Ġm ature", + "Ġbath room", + "Ġ'/ '", + "ĠÄ ij", + "Per formed", + "Ġsold iers", + "arn ings", + "Ġwalk ed", + "- con", + "b ottom", + "Ġsurpr ising", + "Ġg ene", + "Us uario", + ".DE FAULT", + "ĠM IT", + "C ODE", + "ĠE gypt", + "p icker", + "ys ql", + "AT URE", + "d etails", + "ĠCon ference", + "In formation", + "ĠM ail", + "-d own", + "r aries", + "b ro", + "Ġsubject s", + "Ġ' *", + "è¯ ·", + "or ient", + ": @", + "ver bose", + "E F", + "Ġto ler", + "eng ers", + "Ġend point", + "Ġstr ange", + "Ġcol on", + "Ġpre ferred", + "de p", + "ĠE V", + "ARR AY", + "Ġw he", + "Ġp up", + "_n odes", + "Ġtalk ed", + "Ġinstit ution", + "db c", + "Ġex posed", + "te en", + "ĠFr ont", + "T T", + "_N ONE", + "\\/ \\/", + "pro gram", + "Ġencour age", + ". `", + "sh ire", + "ĠIsl am", + "e en", + "N I", + "' \"", + ".W idth", + "Ġlik ed", + "Ġ{ ...", + "ĠSystem s", + "Ġvot re", + "Ġmanufact uring", + "Con verter", + "ĠIn f", + "ì ļ", + "D TO", + "Ġin ches", + "Ġ à¤", + "à ¹", + "ĠChar les", + "B U", + "\")) ;ĊĊ", + "ĠL abor", + "un n", + "Ġest im", + "m obile", + "ĠL earn", + "_C ALL", + "â Ħ", + "Ġind ices", + "Ġt ub", + "ikip edia", + "C ost", + "row able", + "ë ¡", + "g age", + "Ġfunction ality", + "uzz le", + "em os", + ".l ib", + "Ġd ass", + "еРº", + "enn a", + "Ġsh ots", + "Ġrest ore", + "/ D", + "For Key", + "], [", + "al ias", + "l int", + ".st ream", + "æ ł", + "_FORM AT", + "Ġsil ver", + ".re pository", + "Ġlegis l", + ".B order", + "_fe atures", + "Per mission", + "Ġhous es", + "ĠW ars", + "_COM P", + "Ġinj uries", + "Ġconstant ly", + "fl utter", + "EN U", + "ĠCon f", + "Ġrecogn ized", + "Ġpract ical", + "Ġde cent", + "B J", + "] );", + "ast y", + "ĠAct ivity", + "-m ode", + "Ġsl ide", + ".IsNullOr Empty", + "ĠY OU", + "P ower", + "ind ices", + "Ġqual ified", + "Ġthrow n", + "h ello", + "ĠN ick", + "l ah", + "as sembly", + "ĠSm all", + "old ing", + "Sh ould", + "ĠSil ver", + "(saved InstanceState", + "Ġtog gle", + ".N ot", + "C trl", + ": nil", + "ĠCont inue", + "ĠB oot", + "æ ī", + "ĠM ur", + "d on", + "ĠF A", + "S napshot", + "Ġassoci ation", + "fo x", + ", a", + "az ione", + "] )čĊ", + "CT YPE", + "Ġf ade", + "ĠD ar", + ".n avigation", + "Ġl uck", + "SC RI", + "ĠDe ad", + "Ġterm inal", + "_LE NGTH", + "Ġeff iciency", + "Ġun w", + "Ġn arrow", + "iment o", + "( Color", + "ĠSe a", + "_ area", + ", A", + "_ opt", + "ĠHill ary", + ".t ask", + "ĠJ ac", + "ast ed", + "ĠAd am", + "ĠIl legal", + "Ġsearch ing", + "Instance Of", + "J ava", + "ĠForm at", + "Ġreal ized", + "ĠChild ren", + "Ġk il", + "(f rame", + "âĢĿ .ĊĊ", + "Ġscen ario", + "\"] );Ċ", + "Ġincred ible", + "li x", + "IO Exception", + "ĠQ uest", + "il ty", + "Ġun lock", + "â Ĥ¬", + "Ġre ferences", + "ĠV ert", + "B inding", + "eg ative", + "Ġwr ap", + ".d atabase", + "( content", + "B uf", + "ĠTr ad", + "ĠA ud", + "tr ace", + ".m ock", + "Ġther apy", + "ĉ L", + ".To Int", + "ĠKing dom", + "B us", + "ha ust", + "\"\" \"ĊĊ", + "( end", + ".draw able", + "[ ];Ċ", + "ĠH ospital", + "Ġph arm", + "---- -", + "ĠA G", + "é d", + "> \");Ċ", + "Ġw allet", + "at able", + ") $", + "Ġmonth ly", + "Ġdi agnostic", + "S ymbol", + "Ġiter ator", + "un finished", + "Ġimm igration", + "s r", + "RO W", + "(g ame", + "Ġclo thes", + "ĠU nt", + "Ġactiv ation", + "_C on", + ".h ash", + "Ġinitial ly", + ".H ash", + "Ġcut s", + "f ound", + "ĠSt ory", + "ÑĨ и", + "ac ao", + "_T YP", + "pro to", + "est r", + "-p age", + "ah r", + "Ġincor rect", + "ĠJose ph", + "TextBox Column", + "_st yle", + "ĠD aniel", + "s heet", + "Ġl iv", + "l ined", + "Ġr a", + "R untime", + "_ empty", + "sl ug", + "_ struct", + "ë Ĭ", + "m u", + "Ġper mitted", + "Ġreg ional", + "Ġsob re", + "ĠS uch", + "Ġ[ _", + "Ġro of", + ".Al ignment", + "t imes", + ".m sg", + "Ġche st", + "ĠT ab", + "Ġest a", + "ä n", + "Ġsubs cription", + "( command", + "s pecial", + "Ġme al", + "\") :Ċ", + "_ ctx", + "Ġclos ely", + "et ry", + "- be", + "ad el", + "ĠR am", + "ig est", + "ĠSpan ish", + "Ġcommit ment", + "Ġw ake", + "* >(", + "P HP", + "_ {", + "ck er", + "< List", + "_n ull", + "ĠRes erved", + "Ġin her", + ".Column s", + ".A spNet", + "_IN VALID", + "ĠParam eter", + "Ġex pr", + "} {", + "Cell Style", + "Ġval uable", + "Ġfun ny", + "In v", + "Ġst able", + "* t", + "Ġp ill", + "pl iers", + "ĠC SS", + "ĠCon dition", + "ĠS peed", + "ublish er", + "Ġoff ensive", + "ce st", + "ic as", + "Ġsp ark", + "ĠPro te", + "set up", + "IF Y", + "ĠT ax", + "Wh o", + "F amily", + "- for", + ". uk", + "Ġf asc", + "sv g", + "\") ).", + "Ġbirth day", + "âĸ Ī", + "ve h", + "el led", + "Ġimport s", + "ĠIsl amic", + "T A", + "ĠSt an", + "we ather", + "Ġsus pect", + "e ature", + "enn es", + "W M", + ".m inecraft", + "av id", + "è ½", + ".se curity", + "in os", + "G ood", + "Ġm arch", + "Ġposs ess", + "us uario", + "Con s", + "am ber", + "ched uler", + "Ġhor se", + "ç ½", + "(b ody", + "ĠTrans form", + "_de code", + ".s vg", + "Ġf oo", + "Ġd ella", + "ext ends", + "am er", + "Ġprocess ed", + "ĠH arr", + "ĠA I", + "Ġk o", + "CH AR", + "( %", + "Ġt ap", + "({ '", + "c roll", + "D OM", + "Ġte a", + "Ġre in", + "Ġworld wide", + "_f n", + "sh a", + "Ġb ir", + "ç ões", + "=\"# \">", + "Ġrepresent ed", + "ill er", + "(ex pected", + "Ġd ance", + "Ġvisit ors", + ".con cat", + "-b it", + "UR RE", + "ĠR og", + "v p", + "ip h", + "ĠL LC", + "it led", + "iam i", + "C oll", + "_re al", + "_sh ow", + "_f older", + "Ġd ar", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġl atter", + "arch y", + "Ġb ow", + "Ġout come", + "ĠPost ed", + "Ġris ks", + "ĠThere fore", + "Ġowners hip", + "Ġpar allel", + "Ġp ending", + "ge ometry", + "Ġrecogn ize", + "ST EM", + "ĠC P", + "Ġimm igr", + "IT LE", + "ĠĠĠĠ ĉĉ", + "conn ected", + "Ġsm ile", + "(d ocument", + "\\ Component", + "vert ical", + "Ġconsum ption", + "Ġsh oes", + ". impl", + "un ks", + ". \";Ċ", + "Ġfood s", + "_ );Ċ", + ".assert True", + "Ġp ipeline", + "Ġcollection s", + "Ġearn ed", + "ĠC ert", + "Ġpartners hip", + "( action", + "Ġc d", + "ĠV ery", + "Option al", + "Ġscre ens", + "Ġtit les", + "ener ator", + "Ġab andon", + "k ind", + "IL TER", + "Ġclos ing", + "lic a", + "_ inter", + "Ġcamp us", + "set ting", + "S prite", + "ãģ ¯", + "_re ply", + "To List", + ": \\/\\/", + "ed e", + "Ġfol ks", + "Ġbo at", + "( argv", + "Ġperman ent", + "Ġcarry ing", + "Ġconserv ative", + "import ant", + ". img", + "ĠIm m", + "Ġdim ensions", + "al and", + "s ingle", + "Ex it", + "-------- --", + "ari ant", + "tern al", + "Se conds", + "ĠIt aly", + "ot lin", + ".Res ume", + "=' \"", + ") ==", + "cept or", + "Ġs ca", + "/m ain", + "Sec urity", + "_d at", + "Ġlet s", + "Ġa qu", + "Ġwhen ever", + "b erry", + "Ġact ing", + "ant i", + "p d", + "& gt", + "æ Ń", + "Z one", + "T oday", + "! .", + "To Props", + "ab is", + "it able", + "Ġg al", + "] {", + "iz ona", + "Ġin contri", + "N ET", + "/// Ċ", + "[ in", + "_s ave", + "Ġex em", + "ĠK enn", + "Ġev olution", + "var s", + "_st ats", + "- only", + "ĠColor ado", + "Ġwatch ed", + "b our", + "Ġsever e", + "Ġprofession als", + "port ion", + "Ġguar ante", + "Ð ³", + "Ġpush ed", + "ĠG i", + "ï ½", + "Ġt um", + "ĠA z", + "ĠEdge Insets", + "\")) ;čĊ", + "is se", + ". ac", + "Set ting", + "Ġapprec iate", + "ĠValue Error", + "Ġsur ve", + "ĠR ole", + ". Inter", + "plot lib", + "j et", + "d am", + "Ġplatform s", + "te le", + "UT O", + "ĠInt ernal", + "+ :", + "} ;čĊ", + "Gener al", + "\\ Entity", + "Ġlawy er", + "qu iv", + "ĠPost s", + "is o", + "Ġacc um", + "ob e", + "Ġmark s", + "Ġ] ;ĊĊ", + "ĉ text", + ".s uccess", + "cur r", + "as a", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġth in", + "_ over", + "are st", + "ĠO s", + "( address", + "Ġvel ocity", + "Ġ[] ;ĊĊ", + "=\" ../../", + "ĠPr iv", + "b ow", + "Ġguar antee", + "% ĊĊ", + "Ġeval uate", + ".LE NGTH", + "Ġin ventory", + "q a", + "_de bug", + ".On ClickListener", + "Ġl ies", + "Ġassess ment", + "dat etime", + ".background Color", + "Ġ*/ čĊčĊ", + "ra f", + "un wrap", + "ĠF oot", + "Ġnot ify", + "Ġlow est", + "DO CTYPE", + "Ġl anguages", + "ex tra", + "- back", + "Ġein en", + "tem plates", + "_p ass", + "ĠM ust", + "Ġest á", + "_c ore", + "ĠSc ot", + "A I", + "Ġb ias", + "ations hip", + "Con stant", + "Ġprogram ming", + "In s", + "uspend Layout", + "ĠPRO VID", + "ant es", + "Ġsh irt", + "in ated", + ". OK", + "[ a", + "Ġthink s", + "? ĊĊĊĊ", + "Ġregard less", + "ĠMag ic", + "ul ating", + "ĉ class", + "add Group", + "RE ATE", + "ĠS U", + "Ġsim pl", + "c opyright", + "Ġb unch", + "Ġun iverse", + "ĠE rr", + "Ġpresent ation", + "c ategories", + "Ġatt ach", + ".s ign", + "_A C", + "Ġdisc ipl", + "Ġregular ly", + "Ġprim arily", + "ink s", + "[ [", + ".r and", + ".sh ould", + "ownt own", + "=\" '", + "Ġs ans", + "Ġsupport ers", + "se quence", + "G O", + ". .ĊĊ", + "ĠS pr", + "Ġcare fully", + "U IColor", + "dest roy", + "Ġtod os", + "ĠOR DER", + "ott ed", + "Ġd ont", + "aud i", + "_ player", + "g re", + "ĠO il", + "< body", + "_st ack", + ".P adding", + "ĠProduct s", + "Ġpriv ile", + "Ġinj ured", + "ĠF urther", + "Ġal ias", + ".Resume Layout", + "_LE N", + "Ġs es", + "'] ;ĊĊ", + "cre ens", + "Ġdirect ed", + ".S uspendLayout", + "od ge", + ".A t", + "mark s", + "ĠUn ivers", + "ert s", + "ĠE sc", + "Ġnav bar", + "Ġutil ity", + "agnost ics", + "Ġin ject", + "ĠD NA", + "Ġ\" ,\"", + "am ar", + "Ġe u", + "Ġrestaur ants", + "_p ut", + "ut ers", + "Tool Strip", + "t w", + "ist ro", + "Ġz oom", + "Ġleg it", + "pec ific", + "ĠC ome", + "Ġlocal Storage", + "Ġabs or", + ".P anel", + "ĠDesign er", + "Ġo w", + "IC AL", + "_ uri", + "(f ield", + "Ġsup erv", + "Ex ists", + "Ġrespect ively", + "ĠSt and", + "Con f", + "uss ian", + "Ġar c", + "Ġ nd", + "uck s", + "Ġre str", + "Ġseason s", + "ĠCh apter", + "ĠSw itch", + "p ic", + "Ġh i", + "load ed", + "Ġfl uid", + "-b tn", + "Ġrun time", + ". it", + "B N", + "Op acity", + "as ant", + "ry ption", + "-n ative", + "Ġta ught", + "å ¯", + "ag ment", + "Ġm ul", + "Reg istry", + "_ grid", + "ĠBro ok", + ": Set", + "Ġm ongoose", + "AM ES", + "inner HTML", + "Ġs oci", + "ĠInt el", + "get Id", + "C md", + "Ġaccess ible", + "r ames", + "le ton", + "Ġ__ (", + "ĉ delete", + "ĠS quare", + "\" ĊĊĊ", + "Ġbu cket", + "avor ite", + "ĠB reak", + "++ ]", + "Ġbr ush", + "Ġt ensor", + "/ http", + "T ile", + "Ġfunction al", + "Ġ\" *", + "wh el", + "Ġt ent", + "ĠChar acter", + "Ġse es", + ". ST", + "B ig", + "Ġext ern", + "Url s", + ")) )),", + "ĠJ r", + ".B uilder", + ". ;", + "n l", + "_ Init", + "ĠH ER", + "ż e", + "mys qli", + "_ icon", + "v an", + "Ġfeel ings", + "Ġle an", + "Ġhop ing", + "T V", + "=\"čĊ", + "b est", + "all as", + "ent ed", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "_con nection", + "Ġrep o", + "en abled", + "аРº", + "Ġsh a", + "Ġmembers hip", + "Status Code", + "in ating", + "_s m", + "_c ustom", + "_ weight", + "Ġc ss", + "St at", + "_ env", + "link s", + "TR L", + "ĠH it", + ", r", + "up id", + "Ġop ens", + "Ġg ent", + "_v is", + "Ġj oy", + "< w", + "_c ost", + "ĠPy Object", + "ren ce", + "ĠGeorg ia", + "ĠBro ad", + "m ma", + "â Ĥ", + "p f", + "Ġ\" \\\"", + "Ġ( &", + "om o", + "Ġliter ally", + "Ī ĺ", + "met ric", + "Ġb ars", + "z ed", + "(w indow", + "ĠIsrael i", + "Ġform al", + "ident ifier", + ".d ao", + "ĠDe ath", + "% ;Ċ", + "Ġdecl are", + "ar ms", + "RE AM", + "PERT Y", + "Ġconsequ ences", + "to ols", + "Pe ople", + "ĠWh ich", + "> ();čĊ", + ".de code", + "_A CT", + "Button s", + ".f loat", + ".F irst", + "ë ¥", + "ĠPol it", + "ĠX CT", + "T ags", + "ĠCG Float", + "= str", + "Ġle af", + "- check", + "ĠI ss", + ".s ystem", + "log out", + "ach t", + "Ang le", + "s in", + "ch art", + "INT ER", + "ĠN UM", + "B asic", + ".P roperties", + "ä¸ Ń", + "_ change", + "ĠB razil", + "Ab stract", + "Ġ: +:", + "_ use", + "а л", + "ĠL y", + "IB UT", + "Ġout er", + "Ġ-- >čĊ", + "Ġrel ief", + "l ap", + "qu er", + "_p arent", + "he ap", + "LO SE", + "Ġcomb ine", + "ĠR ose", + "ow ers", + "Ġproced ures", + "ĠS ort", + "an im", + "var iant", + "eh icle", + "Ġsign ing", + "Pr imary", + "c urrency", + "Ġsex e", + "o en", + "th eta", + "em an", + "Ġimpress ive", + "(' _", + "ĉ U", + "ĠText Style", + "_c nt", + "Ġs lice", + "(' :", + "Ġunderst ood", + "H is", + "Ġinform ed", + "Ġn ick", + "(T AG", + "h d", + "Ġelection s", + "est ure", + "ĠS anta", + "ĠCo ast", + ".p df", + "inc iple", + ".cl one", + "b orn", + "ut a", + "Ġl icensed", + "C r", + "Ġb read", + "ĠH ouston", + "Ġn od", + "Ġhop es", + "ĠCG Rect", + "Ġgu ilty", + ".g if", + "Ġro se", + ".Com mon", + "T ip", + "AN K", + "ĠF C", + "D uring", + "ĠSym fony", + "Ġdef ensive", + "k m", + ") >", + "arch ive", + "ĠU RI", + "ycl ing", + "- o", + "ĠWe bsite", + "AM P", + "ish ment", + "Ġdo ctors", + "D irect", + "AR I", + "ĠRed irect", + "ier en", + "_d ist", + "y o", + "ĠPro gress", + "Ġz um", + "Ġmem or", + "ĠE D", + "Ġj ur", + "æį ®", + "_T ABLE", + "Ġu uid", + "Ex pr", + ". head", + "(' %", + "point er", + "Ġest imate", + "ĠG reg", + "Ġlo ader", + "Ġi OS", + "Ġm ens", + "[ y", + "Ġref used", + "Ġprec ision", + "is ch", + "ĠA CTION", + "Cl oud", + "s With", + "( ret", + "_ADD R", + "_con f", + "(d f", + "Ġlock ed", + "Ġr ising", + "ãĥ» ãĥ»", + "ĠM s", + "Ġscen es", + "_EX T", + "_ raw", + "_ the", + "pe ople", + "Ġre con", + "ĠF un", + "Ġb less", + "ĠUp dated", + "ü n", + "ĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", + "pe ction", + "Re lease", + ".log ger", + "ĠS Y", + "Ġcoun sel", + "ur d", + "_ true", + "Ġevery body", + "iv ot", + "Ġh ence", + "ĠN AS", + "Ġoppos ed", + "unk nown", + "ĠDES C", + "ĠCh air", + "fa iled", + "ĠIN CLUDING", + "Ġwrit ers", + "{ }Ċ", + "ÃŃ t", + "_c opy", + "} :", + "ĠB at", + "Ġconvert ed", + "ed ing", + "pl acement", + "ĠH ost", + "S ound", + "и м", + "Ġs ought", + "m id", + "Ġsal ary", + "og g", + "âĦ ¢", + "b ul", + "Ġw ir", + "valid ator", + "_ST AT", + ".st ore", + "ĠB attle", + "ı n", + "Ġ-- >ĊĊ", + "Tr ump", + "d ot", + "ĠCON T", + ".f etch", + "Ġcontin u", + "w as", + "Ġfra ud", + "_t mp", + "mit ter", + ".p ictureBox", + "G A", + "Ġt ournament", + ". Input", + "[ r", + "ex ion", + "cent age", + "ĠKore an", + "und ef", + "ĠAv ailable", + "resh ape", + "Ġk it", + "ĠStr uct", + "ĠS UB", + "An swer", + "_l ib", + ".t witter", + "Ġo re", + "ĠDr agon", + ".Ex t", + ", k", + "Ġexplan ation", + "ref s", + "ĠDr ive", + "ĠTr aining", + ".H as", + "int age", + "b ig", + "olog ist", + "enn is", + "Ù ĩ", + "Ġch icken", + "ĠĠĠĠĠĠĠĠĠĠ Ċ", + "ç Ľ", + "ãģ §", + "Ġpe ak", + "Ġdrink ing", + "Ġen code", + "ĠNE W", + "m alloc", + "ĉf printf", + "Ġ= ================================================================", + "in cluding", + "Ġprincip les", + "ĠM ah", + "st orage", + "- key", + "Ġkey word", + "% ;", + "Ġtr ained", + ".con trib", + "Ġk v", + "__ ':Ċ", + "ĠB oy", + "param eter", + "Ġsu ite", + "Ġthous and", + "Ġco ordinate", + "-g enerated", + "íķ ĺ", + "gener ated", + "Ġad mitted", + "Ġp ussy", + "# w", + "Ġsw im", + "un ion", + "N a", + "ĠRoy al", + ".ch annel", + "Up dated", + "_RO OT", + "Ġv ital", + "ra ction", + "ĠCrush er", + "Ġpre ced", + "Ġhor izontal", + "Blue print", + "Ġattr s", + "Ġsm oke", + "Ð Ĵ", + ". Equals", + "F B", + "ĠRes ources", + "roll ing", + "Ġpass es", + "ĠN um", + "rot ate", + "et ype", + "\\ \",", + "Ġsens itive", + "Ġt all", + "? âĢĿĊĊ", + "Pro xy", + "i y", + "_ section", + "âĢĶâĢĶ âĢĶâĢĶ", + "br id", + "Ġcirc uit", + "at an", + "EN C", + "Ġdr iven", + "Ġvot ed", + "Ġeduc ational", + "Ġinter action", + "abet es", + "Ġt one", + "ĠInitialize Component", + "Ġmer ely", + "Ġì ŀ", + "co okie", + "_ div", + "ĠUIL abel", + "vel y", + "} );čĊ", + "_ ENT", + "#+ #+", + "art icles", + "ĠSou thern", + "Ġstrong er", + "ĠG iven", + "ĠE ric", + "ĠI R", + "ab stract", + "U nder", + "n able", + "Ġincre ment", + "ov en", + "Ġco in", + "_t imer", + "Ġsuffer ed", + "ĠF REE", + "'] .\"", + "ĠQue en", + "st ats", + "Ġmeet ings", + "Ġenter ing", + "Ġalong side", + "(s ession", + "it als", + "Ġfound ation", + "ĠC redit", + ". div", + "_ ALL", + "pc ion", + "_st at", + "ick ing", + "Default s", + "_s rc", + "Ġoutput s", + "/ B", + "Ġent hus", + "-b l", + ".Fore Color", + "ĉ temp", + "F ace", + "Ġinter act", + "Ġwe ird", + "M ount", + "re ll", + "ud ents", + "Ġrequire ment", + "ĠS us", + "I ER", + "Ġe lected", + "re ference", + "ĠM E", + "Ġserv ers", + ".w ait", + "Ġsnap shot", + "il ton", + "Ġtri es", + "Ġt ipo", + ".T ime", + "> w", + "Ġmount ain", + "Ġp ounds", + "Ġ[ ...", + "ex ists", + "Ġng On", + "_M AP", + "Ġf lying", + "xi ety", + "ĉ value", + "_D B", + "un o", + "Ġse ats", + "T URN", + ". author", + "! )", + "or ce", + "Ġindic ated", + ".s in", + "Ġass ignment", + "im iento", + "ĠF rame", + "_g en", + "in ery", + "_ )", + "m essages", + ".set tings", + "ĠMe an", + "ĠM useum", + "ir q", + "att ach", + "ĠPalest in", + "_ QU", + "_t ags", + "Ġcas ual", + "em en", + "ASS WORD", + "$ s", + "ĠC irc", + "оР¹", + "et ric", + "/ P", + "Ġep och", + "< head", + "_C MD", + "Ġg it", + "Ġpen alty", + "or ph", + "_ users", + "ours es", + ".Date Time", + "atern ion", + "_pro ject", + "Ġsuper ior", + "ĠD am", + "ĠSe attle", + "X Y", + "> The", + "ĠA k", + "Ġgr ass", + "/* čĊ", + "(d is", + "Ġgun s", + "Ġt b", + "ĠK evin", + ". args", + "ĠA h", + "op ed", + "( J", + "column s", + "arg uments", + "ĠWith Events", + "_f ull", + "ĠDef ense", + "S imple", + "Ġdeath s", + "Ġext ensive", + "ĠSt ill", + "ĠEx pression", + "ĠAg ency", + "Ġperform ing", + "F X", + "Ġus uario", + "U AL", + "S ide", + "od os", + "apt op", + "Ġcred entials", + "_c ap", + "at ient", + "ĠDis ney", + "Ġa i", + "Ġch ip", + "Ġvol t", + ".make Text", + "%%%%%%%% %%%%%%%%", + "Ġbelie f", + "_LO C", + "ĠC ivil", + "N avigation", + "Ġreve al", + "Ġviol ent", + "ĠF il", + "Ġc atalog", + "em ed", + "sc an", + ". control", + "Ġconstit ution", + "C ountry", + "Separ ator", + "_A PP", + "top ic", + "uet ooth", + "M IN", + "Ġdes criptor", + "y t", + "ET HER", + "Ġdistrib ute", + "' }Ċ", + ".tr im", + ".L ine", + "Ġl bl", + "assert Equals", + "ĠD et", + "omb ok", + "( width", + "Ġt ort", + "ĠEXP RESS", + "ac o", + "Us ing", + "ĠBr and", + "w all", + "EM ENT", + "ĠComm unic", + "< uint", + "ĠG UI", + "EG IN", + "ĠR ange", + "/ i", + "ĠT aylor", + "c ost", + "Ġrespond ed", + "ĠTh eme", + "n ce", + "IS H", + "Ġfeat uring", + "Return s", + "ĠK r", + "Ġ .Ċ", + "Ġn am", + "_c b", + "Test ing", + "Ġ{ },", + "y al", + ".f ield", + "Ġ/ =", + "_SH ORT", + "m ates", + "Test Case", + "ain less", + "Ġeval uation", + "_ ITEM", + "ĠPac ific", + "ĉ k", + "Ġc ant", + "ĠR os", + ") s", + "Ġf et", + "STR ING", + "ĠDis pose", + "g al", + "ĠJ oin", + "ĠP orn", + "ĠCath olic", + "AR GET", + "cp u", + "ç łģ", + ".sc roll", + "IS ING", + "ifest yle", + "anc ement", + "Ġm erc", + "ĠB rowser", + "eter min", + "Ġover flow", + "Av ailable", + "Ġbott le", + ": UI", + "ific ial", + "Ġco ord", + "clar ation", + "Ġcon j", + "G LOBAL", + "ok u", + "Ġk wargs", + "cond itions", + "ul um", + "Ġg enu", + "ĠH ero", + "å İ", + "Ġun expected", + "ĠDAM AGES", + "Ġk a", + "ĠC ould", + "UP PORT", + "ĠPh otos", + "Ġconf ident", + "Ġdet ected", + "de g", + "rg b", + "Ġstrong ly", + "Ġ} ;čĊ", + "Ġ) :", + "Ġle ct", + "urs ive", + "RO L", + "ĠWe ight", + "Ġent ertainment", + "Ġ) );Ċ", + "Ġg onna", + "Ġb b", + ".d o", + "G S", + "Ġmist ake", + "D L", + "ĠPROVID ED", + "ear ning", + "L imit", + "iss ions", + "[ v", + "ä¸ į", + "ir ty", + "D el", + "Ġunder lying", + "pre ne", + "Ġj aw", + "ĠD I", + "pe er", + "Ġobject ive", + "Ġde posit", + "Ġk on", + "Ġes p", + ".set Visibility", + "/ login", + "< typename", + "Ġfr anch", + "/ e", + "Par allel", + "Ġsc ored", + "ĠH on", + "ĠV ill", + "ig a", + "Ġant icip", + "_ assert", + "ĠO pt", + "Ġdescri bes", + "w an", + "m ount", + "Ġmonitor ing", + "Ġt out", + "ëĬ Ķ", + "}, {", + "................ ................", + "= int", + "Ġc ust", + "---- --", + "Ġatmos phere", + "P AR", + "ort e", + "IS IBLE", + "ĠI ron", + "ĠNot ification", + ".log ging", + "ĠBO OL", + "-p oint", + "Ġaf raid", + "ent a", + "Ġtom orrow", + "@ implementation", + "Ġeng age", + "ĠAn th", + "ĠF loor", + "ĠU l", + "To ols", + "Ġb ab", + "Ġcare ful", + "ãģ Ħ", + "Ġcruc ial", + "Ġcalcul ated", + "ĠS A", + "Ġw y", + "D X", + "_T AG", + "ind ed", + "Ġj et", + "ĠEngine ering", + ".M AX", + "en z", + "v d", + "Ġpublic ation", + "Ġ## #", + "Ġfac ed", + "ra ham", + "ĠC apt", + "As set", + "ĠCon stants", + "Ġlo ans", + "_ IP", + "ĠF ish", + "Red uc", + "_m at", + "Date Format", + "_m e", + "[] []", + "Ġintegr ity", + "ĠC ourse", + "lob als", + "Ġfac ilit", + "Ġem br", + "ĠN g", + ".S ystem", + "Ġmanufact urers", + "Ġpro ven", + ".on Create", + "Ġal arm", + "Ġ §", + "Ġcomm only", + "ic os", + "æĸ °", + "ĠSt ation", + "} ).", + "ĠF ilm", + "w i", + "ç ī", + "Ġeng aged", + "St ats", + "Ġgovern ments", + "Ġafford able", + "_p roperty", + "Ġag es", + "(' --", + "Ġf ör", + "ĠProf essor", + "Ġhy dro", + "P ush", + "Ġorgan ized", + "Ac cept", + "é m", + "_c ell", + "Ġn b", + "p b", + "Art icle", + "Ġrem oval", + "Ġauth entication", + "ĠF R", + "l ide", + "Ġple asure", + "ap ol", + "Ġpart ition", + "ĠS ide", + "Ġcr imes", + "Ġdem o", + "hold ers", + "ĠPak istan", + "In struction", + "Ġexpect ations", + ".sc ene", + "Ġ' )", + "h es", + "ino is", + "_P ro", + "Ġm olec", + "and al", + "_sh ort", + "Ġdefault s", + "Ġn ations", + "in en", + "Ġr t", + "O CK", + "P acket", + "S B", + "ĠSH ALL", + "_cont ents", + "ise conds", + "vert y", + "á t", + "G uid", + "n om", + "Ġcon clusion", + ". Update", + "Ġlo vely", + "Ġem it", + "b ec", + "ĉĉĉĉ Ġ", + "Ġintel lect", + "Ġb rew", + "ec ycle", + "F ire", + "Ġad mit", + "Ġar bit", + "Ġarr ang", + "ĠM IN", + "M ail", + "ĠN ative", + "C ur", + "Ġcon vent", + ".R untime", + "\" }Ċ", + ".R un", + "Ġprint ed", + "Ġconven ient", + ". ar", + "m ock", + "ĠAdmin istration", + "ãģ ¾", + "Ġelect ron", + "fl ate", + "Ġl ombok", + "Ġjava fx", + "n h", + "Ġsup plies", + "Ġvisit ing", + "ah l", + "Ġpow der", + "Ġult imate", + "Ġorient ation", + "ut as", + "_s cale", + "Con firm", + "ph ones", + "ĠOper ation", + "/ T", + "_IN TER", + "Ġair port", + "Ġmet rics", + "Ġphen omen", + "a udio", + "Ġm ai", + "( K", + "h u", + "all ing", + "rodu ction", + "ĠTrans port", + "ĠNOT E", + "æĸ ĩ", + "Ġfew er", + "_T IM", + "ì §", + "к и", + "A ge", + "F IN", + "Ġì Ŀ", + "ĠAt tribute", + "group s", + "er k", + "at to", + ". define", + ".AspNet Core", + "ategor ia", + "ĠS ir", + "( form", + "< User", + ". round", + "_d ay", + ".A ll", + "Servlet Response", + ".N o", + "l arge", + "IG H", + "qu ent", + "Ġvir us", + "Ġret ro", + "Ġim per", + "Bit map", + "Ġv ice", + "Ġoff ense", + "ist e", + "ĠA UTH", + "Ġê °", + "ToolStrip MenuItem", + "G u", + "Ġr ape", + "ĠDav is", + "Ġover whel", + ": flutter", + "- table", + "ĠCon structor", + "Pr ivate", + "e ven", + "ch r", + "Ġap plies", + "_at tribute", + "Ġcon tribute", + "E VER", + "L ines", + "ĠAf ghan", + "Vis itor", + "ĠS L", + "se ason", + "C U", + "Ġintrodu ction", + "Ġmat plotlib", + "Å ij", + "Ġnewsp aper", + "âĢĶ and", + "< tag", + "Ġin i", + "Ġd iverse", + "Ignore Case", + "ĠU r", + "Ag ent", + "Ġb ull", + ".em it", + "( Exception", + "ar Layout", + "Ġincred ibly", + "ĠTr ust", + "={ (", + "- nav", + "Ġe quals", + "Ġl ady", + "ĠP od", + "d isc", + "al am", + "ĠI V", + "â Ļ", + "iv idual", + "ph i", + "add ed", + "Ġdifficult y", + "Ġcomp act", + "ĠAction Result", + "c ers", + "_class es", + "Non Null", + "Ġqu it", + "Ġp ou", + "S witch", + "ir s", + "- test", + "ĠK ind", + "ĠCal endar", + "Ġstream ing", + "} ',", + "S W", + "Ġst ead", + "oc a", + "Ġprov ince", + "Ġcol span", + "Ġperson nel", + "ĠE mployee", + "Ġprodu cer", + "Ġevery where", + "od b", + "Ð Ł", + "bs olute", + "act ivate", + "Ġgr inding", + "ĠBuild ing", + "ĠSand ers", + "(s c", + "ĠOff set", + "//////// ////", + "} ;čĊčĊ", + "({ \"", + "Ġscan f", + "ĠY Y", + "ĉdef er", + "Ġj ew", + "Ġrestrict ions", + ".m p", + "[ l", + "ä¸ ĭ", + "label s", + "red icate", + "aw esome", + "Ġw aves", + "Ġcon front", + "Ġmeas ured", + "Ġdat as", + "_ex it", + "ot ton", + "Ġshould er", + "ask a", + "+ #", + "ĠĠĠĠĠĠĠĠĊ ĠĠĠĠĠĠĠĠĊ", + "Ġtro ops", + "ĠU nd", + "_c ard", + "w ich", + "Ġn ous", + "Ġ\"/ \"", + "s b", + "Ġcommunic ations", + "Ex port", + "Ġdec ode", + "th s", + "inter pret", + "By Name", + "ĠSp irit", + "ed ges", + "O LE", + "ĠE M", + "t it", + "ĠTh rough", + "Ġb io", + "ĠP ackage", + "or ne", + "Ġ} .", + "` ;Ċ", + "Ġok ay", + "ĠZe aland", + "ident ity", + "(n ext", + "ĠB ang", + "Lib rary", + "Ġheav ily", + "il on", + "Ġdi pl", + "Ġrot ate", + "put s", + ") ',Ċ", + "ĠData Table", + "Ġmay or", + ".to LowerCase", + "Ġsome how", + "ĠNor thern", + "al c", + "Ġcap abilities", + "Ġv ibr", + "+ Ċ", + "ĠS u", + "ĠRes et", + "_m ean", + "Ġc ig", + ".cl oud", + "ĠB and", + "ĠF actory", + "ĠAr izona", + "_ io", + "op her", + "Ġconsc ious", + "Ġà ¶", + "\\ Controllers", + "_s peed", + "ĠF ac", + "_C om", + "ĠB ible", + "w en", + "ED IT", + "Ġun n", + "ĠSt aff", + "ĠIn n", + "Ġmechan ism", + "ĠM embers", + "Ġmigration Builder", + "'] .'", + ".get Int", + "< void", + "ĉf ree", + "oid s", + "\\ Support", + "Ġautom atic", + "Ġch ances", + "Ð ¶", + "Ġcomp licated", + "[ row", + "ah oo", + "Ġ}ĊĊ ĊĊ", + "Model s", + "W in", + "Ġt ape", + "ir us", + "iz on", + "on omy", + "(\" _", + ": .", + ".st ereotype", + "( env", + "_re ct", + "(w ith", + "Ġassert That", + "Ġcon straints", + "put y", + "E mployee", + "T D", + "Ġgu itar", + "ĠJew s", + ".pro cess", + "Ġf iction", + "ĠSh ared", + "âĶĢ âĶĢ", + "Ġprop ag", + ".N et", + "Ġachie ved", + "ĉ Q", + "Ġn urs", + "Sh ared", + "_FAIL URE", + "Ġbeh aviour", + "Ġcol s", + "ism o", + "Ġfem in", + "Ġchalleng ing", + "Ġpost ing", + "enc il", + "Ġcapt ured", + "ĠD ou", + "( word", + "ĠTur key", + "pan ies", + "Ġre putation", + "ORM AL", + "Ġelig ible", + "prot ocol", + "id as", + "(f rom", + "Ġfin ance", + "- per", + "Ġg otten", + "H A", + "d uration", + "ĠP arent", + "Ġin vent", + "Ġre start", + "ол ÑĮ", + "r ition", + "(r s", + "< bool", + "i ert", + "Ġmod ification", + "ĠT X", + "readcr umb", + "b ank", + "$ /", + "ĠMill er", + "] ),Ċ", + ".Check ed", + "Ġsac r", + "se curity", + "Ġp ose", + "ĠBr ad", + "Ġfit ness", + "Ġannounc ement", + "ation Token", + "Ġserv es", + "ne ed", + "Ġge ometry", + "AR S", + "æ Ģ", + "andid ate", + "Ġs prite", + "_s plit", + "We ek", + "ad ies", + "> (Ċ", + "?> \"", + "Ġ/// Ċ", + "Ġein er", + "Ġweek ly", + "ĉlog ger", + "_p op", + "_m an", + "Ġmigr ations", + "Ġask s", + "Ġb s", + "Ġfall s", + ".W here", + "- height", + "_fe ature", + ".M in", + "Ġhy per", + "Ġvol atile", + "Ġtw enty", + "Typ ography", + "Un able", + "D et", + ", f", + "-m od", + "Ġsett lement", + "Ġcontract s", + "n ome", + "B ad", + "ĠB rian", + "(user name", + "!! !!", + "Ġh ack", + ".F ield", + "H R", + "ĠJ ordan", + "iz a", + "Ġ ł", + "ĠSh er", + ". header", + "( other", + "ĠD ub", + "( op", + "ĠR ound", + "Ġv ie", + "Ġap pl", + "ĉ J", + "ĠIn sert", + "ĠL P", + "reg on", + "ĠM PI", + "Ġan chor", + "ac a", + "ø r", + "Ġa de", + "anch or", + "que e", + "ĠTree Node", + "Ġtarget ed", + "Ġla id", + "AB EL", + "v et", + "ĠOr igin", + "A nt", + ". ');Ċ", + "ex pect", + "ed Reader", + "ĠM ajor", + "Ġin ch", + "Com par", + "Ġpre view", + "Ġill ness", + "ĠCONTR ACT", + "ĠInd epend", + "u uid", + "Ġn ome", + "Ġt c", + "ĠA venue", + "is an", + "Ġph rase", + "_m ove", + "\") [", + "Ġprov ision", + "Ġconcent r", + "_ IR", + "ĠU t", + "() +", + "Ġn as", + "! ,", + "ĠRob in", + "i ations", + "at itude", + "Ġp x", + "ĠWith out", + "/b ash", + "ek t", + "re ement", + "Ob server", + "ĠReg ion", + "UBL IC", + "Ġ{ //", + "K N", + "å ·", + "Game Object", + "å ¾", + "enc oding", + "Ġ** *", + "project s", + "Ġt k", + "Ġche ese", + "EM PL", + "ar o", + "Ġا ÙĦ", + "Ġcons ists", + "ref resh", + "ure au", + "ĠSc anner", + "Ġso il", + "Ġfl avor", + "Data Source", + "Ex ecute", + "ени е", + "Ġsh it", + "åĪ Ĩ", + "< any", + "Ġretrie ve", + "Ġbelong s", + ".st rip", + "abs olute", + "Ġexp anded", + "bo y", + "): -", + "Ġresc ue", + ".J Label", + "Ġre ly", + "Ġal ignment", + "-f amily", + "Ġre nd", + "OLUM N", + "Ġb orrow", + "Ġqu otes", + "ĠL ew", + "Ġsh ower", + "ĠDE LETE", + "_lo op", + "! \"ĊĊ", + "ĉ re", + "Ġattempt ed", + "aver age", + "ĠP aint", + "quis ition", + "ol en", + "Ġliter ature", + "ĠRe ference", + "_TEXT URE", + "ĠS eg", + "ĠInd ust", + "ct ype", + "D UCT", + "_H OST", + "ĠTr ade", + "Ġpl ugins", + "Ġbre ast", + "ul se", + "Ġcreat ure", + "ãģ Ļ", + "ĠW i", + "Ġsup plied", + "c oll", + "! (\"", + "Ġfuck ing", + "ĠCh rome", + "ĠU ri", + "ĠN ation", + "Ġvert ices", + "T HE", + "ĠOr iginal", + "on de", + "Ġsh arp", + "Ġcook ing", + "Ġ{ /*", + "ĠPs ych", + "ĠH ollywood", + "=$ _", + ".D ock", + "Ġg er", + "Ġb one", + "_con n", + "_se c", + "ys ics", + "Ġ= \"", + "S al", + "s f", + "Ġdeep ly", + "ang les", + "T erm", + "b ell", + "ĠQu ick", + "ener ation", + "adio Button", + "åħ ¥", + "}čĊčĊ čĊ", + "Ġcapt ion", + "l c", + "ĠE L", + ", [", + "ĠĠĠĠĠĠ čĊ", + "ret t", + "(m ethod", + "ĠFl ash", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "W ISE", + ".s cale", + "Ġrough ly", + "_ child", + "m emory", + "ay ing", + "Ġinitial ized", + "in ator", + "а ÑĢ", + "Ġsc alar", + "ĠH o", + "ai res", + "(c olumn", + ".de stroy", + "P ACK", + "Ġh em", + "ang el", + "_S UB", + ". qu", + "Ġ ×", + "DE FAULT", + "pos itories", + "ĠL ength", + "ĠF ast", + "Ġsign als", + "Ġ// $", + "ri ers", + "Ġd ummy", + "AN Y", + "Ġperson ality", + "Ġa gricult", + "Pl atform", + "ER O", + "ĠT ra", + "Ġen orm", + "ĉ W", + "Action Result", + "Ġa ver", + "[ str", + "Ġ' --", + ".S printf", + "Ġdeb ut", + "Ġ Ñĩ", + "h ex", + "_ utils", + "Ġp b", + "U ITableView", + "Ġz ur", + ". encode", + "Ġv ag", + ".error s", + "о н", + "Ġm r", + "ĠA ward", + "Ġc pu", + "Ġpress ed", + "' est", + "ĠF estival", + "' T", + "Ġa k", + "res olve", + ".m e", + "Ġn ic", + "Ġgen re", + "Ġat trib", + "ĠMo on", + "Ġarr ive", + "ĠD ating", + "Ġt m", + ".Config uration", + ". red", + "Ġgl m", + "Ġst ations", + "sw itch", + "Ġt ied", + "äº º", + "Ġ/ >Ċ", + "Ġsubsequ ent", + "pos able", + "-fl uid", + "Ġth orough", + "Ġpublic ly", + "apt ers", + "ĠWil son", + "_P RE", + "y ard", + "ä ¼", + "ĉ in", + "Ġre vers", + "Ġbul let", + "cri bed", + "nes ota", + "Ġ($ _", + "ann on", + "c ursor", + "Ġclo thing", + "ĠM ulti", + ": ',", + "Ġv ess", + "ordin ator", + "Ġein em", + "C annot", + "Ġar med", + "ĉ V", + "ä¸ Ĭ", + ".F lat", + "ĠS ep", + "ĠSub ject", + "_f ont", + "Ġcharacter istics", + "D one", + "el n", + "######## ####", + "PO S", + "Ġd ensity", + "ĠPl atform", + "- items", + "Ġo vers", + "Ġpush ing", + "ç ¤", + ".Con nection", + "_ term", + "Ġinitial ization", + "________________ ________________", + "ç ¬", + ".d ocument", + "les h", + "ĉd ocument", + "ĠP in", + "ç a", + "Ġdefinition s", + ".P ath", + "_W RITE", + "Ġ ĉĊ", + "? >ĊĊ", + "Ġter rible", + "be an", + "ick ets", + "ĠS V", + "B uy", + "(t ask", + "Ġreg ime", + "g oogle", + "Ġcr ack", + ".vis it", + "N UM", + "ener gy", + "Ġstr uck", + "_s ample", + ".p ayload", + "Ġre vis", + "ĠSc ene", + "Ġp g", + "Ġbreak fast", + "URRE NT", + ".char At", + "_ex ception", + "ĠAnt on", + "Ġguid elines", + "Ġex haust", + "ĠFin ancial", + "Ġind ent", + "Ġdes ktop", + "H idden", + "F ailure", + "Ġpr inciple", + "Ġ iv", + "Ġse ks", + "n etwork", + "Ġnumber Of", + "ĠAl bert", + "ĉ long", + ", .", + "Ġz eros", + "f ade", + "ĠT yp", + "ĠT erm", + "ĠAr ts", + ".App lication", + "Ġbeh alf", + "æĪ ·", + "Ġm ere", + "(` ${", + "Ġaware ness", + "elp ers", + "f lix", + "Ġwe igh", + "Ġestim ates", + ". child", + "/ O", + "ĠBit map", + ".b ottom", + "Ġ************************************************************************ **", + "Ex pect", + "ent o", + "ĠFor um", + "ver al", + "Ġj ail", + "Ġab ilities", + "ĠH OLD", + "ĠC it", + "Ġd ynam", + "Ġgr ay", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", + ".next Int", + "ant ly", + "ĠAR ISING", + "( private", + "Ġreject ed", + "ĠN ic", + "Ġle ather", + "= {Ċ", + "aly tics", + "th etic", + ".T op", + ".P age", + "={ `", + "Ġ ;čĊ", + "de pth", + "m ann", + "W D", + "ĠS om", + ".R ight", + "Ġ) }Ċ", + "Ġtr ait", + "à Ĺ", + "i ac", + "Ġr v", + "S ample", + ".X ml", + "opp ed", + "ĠÑ Ħ", + "list s", + "Ġt ear", + "ivers ary", + ".c ollection", + "ĠCon stitution", + "ĠHttp Response", + "Ġbr ill", + "ĠP rom", + "h over", + "ĠM iami", + "Ġarg ue", + "_f loat", + "Ġ ãĤ", + "Ġn at", + "ĠT al", + "Ġinteg ration", + "(c ur", + "Ġrem oving", + "Ġco eff", + "ĠTh ough", + "Ġfore cast", + "ĠV egas", + "S ite", + "Ġtr ab", + "ĠHen ry", + "- i", + "Ġinvol ves", + "B T", + "Ġs lo", + "In voke", + "Ġl ucky", + "r at", + "Ġ? Ċ", + "Ġhand led", + "(f d", + "cont ents", + "ĠO FF", + "R F", + "Ġst y", + "ĠM otor", + "ter y", + "t ax", + "M AP", + "ĠMr s", + "Ġph ones", + "ĠUI View", + "\")) );Ċ", + "( dev", + "ĠIr ish", + "Ġw s", + "D I", + "_OFF SET", + "ĠEvent s", + "Ġst ages", + "Ġ} //", + "Ġhab en", + "ST ANCE", + "ĠS in", + "ĠM oney", + "(t op", + "Ġappoint ment", + "VER SION", + "met adata", + "_com ment", + "Ġcolle agues", + "map s", + "â ĺ", + "Ċ ĉĊ", + "( al", + "_re q", + "Ġf ut", + "Ġarchitect ure", + "ĠWH ETHER", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "_s creen", + "Ġstyle Urls", + "Ġmon ster", + ". up", + "ph ia", + "Ġprocess or", + "ĠT err", + "= ',", + "ĠMan ufact", + "ĠN T", + "k el", + "ib ern", + "ĉf ile", + "A li", + "rient ation", + "Ġ// !", + "ap ore", + "ane ous", + "ĠC reat", + "f older", + "Ġh ay", + "Sup press", + "( left", + "Ġe uro", + "Ġdis claimer", + "ustr y", + "sh ips", + "_f d", + "ĠF a", + "_in sert", + "Ġro l", + "if ting", + "ĠCom ments", + "_b r", + "Ġloss es", + "ĠAdd ed", + "ch arg", + "Ġп о", + "_s ystem", + "ĠS ometimes", + "ĠSp ain", + "(g roup", + "ial is", + "Ġdoll ar", + "ĠAr gs", + "qu ires", + "ĠT en", + ".s css", + "Ġsurv ive", + "us age", + "Ġj un", + "im iter", + "ï¼ģ ĊĊ", + "Ġfif th", + "t oggle", + "Ġdecl ine", + "($ \"", + "(L ong", + "ing e", + "Ġpil ot", + "-l ight", + "-r adius", + "Ġpod cast", + "Ġnatur ally", + "P ages", + "ä¸ º", + "ĠDes pite", + "Ġlight ing", + "Ġcr ate", + "ĠB inary", + "Ġredu cing", + "Ġe leg", + "ĠM ouse", + "ĠTest Bed", + "Ġbefore Each", + "_ ARRAY", + "Red irect", + "Ġf lood", + "Ġsh ips", + "Ġelectric ity", + ")* (", + "ê ¸", + "ĠV iet", + "her o", + "Ġd ia", + "ĠK ent", + "he art", + "Ġthreat s", + "_ acc", + "Ġs ymbols", + "is chen", + "_in st", + "C riterion", + "ĠT IM", + ". Height", + "Ġ âĢĻ", + "();ĊĊ Ċ", + "Product s", + "_S P", + "ĠC y", + "Ġdepend ent", + "est e", + "Ġdat os", + "d it", + "аР²", + "IGN AL", + "Ġless on", + "\"> '", + "ĠC over", + "ĠH ope", + "ĠT imer", + "Ġd ad", + "vid ers", + "ĠPh ot", + "/ ?", + "rop y", + "om ing", + "as ion", + "Ġ\\ (", + "ĠE T", + "ĠRe ading", + "Ġep isodes", + "l m", + "ech a", + "Ġne uro", + "Ġhar mon", + "Ġlib eral", + "- ind", + "D ATA", + "Ġevery day", + "Ġdiv ided", + "ĠActive Record", + "fig ure", + "U A", + "ä ¹", + "riend ly", + "te ch", + ".game Object", + "иÑĤ ÑĮ", + "Ġmo on", + "ft ime", + "Ġno ch", + "ĠT ORT", + "ĠV M", + ".in itial", + "( child", + "Ġmus ical", + "Ġo c", + "b as", + "ĠH ay", + "_l ong", + "Ġmem set", + "ile y", + "adel phia", + "S V", + "ro at", + "_t x", + "Ġl on", + "ĠngOn Init", + "b p", + "ĠGold en", + "AC HE", + "Ġwor ried", + "az i", + "E ar", + "T ake", + "(f p", + "bur gh", + "_ Data", + "g res", + "ĠO nt", + "p us", + "Ġtrans parent", + "Ġp ocket", + "Ġr am", + "igr ations", + ". čĊčĊ", + "Ġ[ (", + "Ġadopt ed", + "Ġreported ly", + "ĠD ream", + "Ġ} ));Ċ", + "los ing", + "Ġte eth", + "ĠBook s", + "\", &", + "enn y", + "LE MENT", + "Ġg el", + "ĠPl ant", + "! âĢĿ", + ".h ost", + "ĠRep ly", + "re ngth", + "Ġrecogn ition", + "Ġ}} >Ċ", + "L A", + "Ġmir ror", + "Ġassist ant", + "( device", + "Ġspirit ual", + "b uilder", + " §", + "Ġou tr", + "Ġt t", + "ĠP ER", + "Ġrad ical", + "Method s", + "Ġp ace", + "ud y", + "Ġg ut", + "ĠG reek", + "Ġnon atomic", + "ĠP aper", + "_G PIO", + "Ġob st", + ".A d", + "viron ments", + "ĠS ov", + "( con", + "ĠTrans action", + ". assign", + "ĉc atch", + "el ter", + "Ġbit coin", + "_G R", + "ĠčĊ", + "met ic", + "Ġtrans formation", + "åı ·", + "Ġr gb", + "istrib utions", + "Ġimp licit", + "/ in", + "dest ination", + "аÑĤ ÑĮ", + "Z ero", + "Ġun set", + ". where", + ".g o", + "Ġform ation", + "Ġdeclar ation", + "() čĊčĊ", + "ĠEx pl", + "ĉĉĉ ĠĠ", + "/ pro", + ".J SON", + "Ġdes k", + ".sub str", + "//---------------------------------------------------------------- ------------", + "ly n", + "p son", + "dis able", + "ĠF unc", + "ĉ Assert", + "ĠM ARK", + "Ġdefe at", + "Ġbl ind", + "Ġconst ants", + ". headers", + "UIL D", + "Ġexp enses", + "P ixel", + "Ġh r", + "Ġf el", + "ĠEast ern", + "_d el", + "ĠC ub", + "Ġs q", + "ĉc ount", + "ĠD irectory", + "Ġex clus", + "Ġhistor ic", + "Ġ ------------------------------------------------", + "Ġcom position", + "Ġdata GridView", + "ĠB urn", + "ĠB C", + "M aster", + "Ġsp awn", + "Ġbe aring", + ".Set Active", + "il o", + "Ġg allery", + "Ġfound ed", + "Ġav ailability", + ".s qrt", + "Ġp es", + "ĠD OM", + "m ate", + "O ct", + "Ġmatch ed", + "it ivity", + "Ġan xiety", + ".pr ice", + "ĠIn stant", + "ì Ĭ", + "Ġt ut", + "IC ollection", + ".sh ared", + "_s ql", + "t bl", + "lib rary", + "_de stroy", + "erm al", + "ĠNot es", + "ĠE in", + "Ġsou thern", + "ĠOTHER WISE", + "Ġmac ro", + ".l ower", + "cl s", + "Content View", + ".l ink", + "const ant", + "ĠB es", + "Ġsome body", + "n b", + "\"> {", + "( local", + ".. ...", + "ĠN ull", + "m x", + "Ġà §", + "Ġp ause", + "-------- ---", + "_M O", + "ĠC M", + "Ġfor Key", + "ĠD VD", + "Ġclose st", + "_DE VICE", + "ĠSte phen", + "ĠB BC", + "ĠTr avel", + "P aint", + "ĠResult s", + "ĠR ule", + "Ġt p", + "Ġrat ings", + "c in", + "c sv", + "> /", + "ĠG OP", + "l ad", + "Ġ ÑĢ", + "Ġindex Path", + "m atrix", + "= f", + "ars ed", + "Ġ} );", + "ĠC os", + "ĠS core", + "Ġt ak", + "ĠE SP", + "ĠIN C", + "_N ULL", + "-f lex", + "\"] [", + "int o", + "el and", + "Author ization", + "_F ALSE", + "Ġg ate", + "Ġv id", + "ist ent", + "T IME", + "Ġre write", + "Ġt ie", + "Ġarch ive", + ".event s", + ".get Parameter", + "ĠPer mission", + "Ġprogram me", + "Ġ é", + "j ud", + "Ġcam eras", + "(s ys", + "ĠSy rian", + "Ġimpro vements", + "Ġh ip", + "Ġsu icide", + "Ġsch olar", + "Ġcompat ible", + "rem ote", + ".d own", + "F UNCTION", + "Ġman aging", + "ĠUI Kit", + ". raw", + ">> >>", + "Ġdem ands", + "ell ite", + "Ġd ent", + "ĠM icro", + "åı ĸ", + "'] [$", + "ĠI E", + "im ension", + "Ġt rem", + "Ġg ained", + ".w ith", + ". ok", + "h ou", + "Ġb om", + "amp aign", + "Ġjoin ing", + "f ish", + "Ġadd Subview", + "Ġnor thern", + ".c or", + "ore t", + "D ie", + "in ish", + "_com p", + "Ġatt ended", + "Ġcoll apse", + "ĠS S", + "ac ent", + "_E QUAL", + "ĠDe ep", + "R GB", + "ĉ test", + "ol ves", + "us et", + "Un ityEngine", + "w riter", + "Res olver", + ", %", + "if ference", + "_re move", + "ond a", + "Ġfem me", + "de code", + "Br anch", + "Ġfl ush", + "Ġinnov ative", + "Test s", + "Ġ[' ./", + "Ġcover ing", + ". admin", + "ultip art", + "(l ambda", + " namespace", + "ĠS port", + "Ġ! (", + "ac les", + "Ġde pression", + "ĠK ong", + "Ġp ert", + "ĠCon n", + "ĠOther wise", + "/ home", + "s upported", + "Ġp ink", + "Ġinv ited", + "ñ os", + "_en abled", + "Ġ- Ċ", + "F W", + "en ers", + "ĠM Y", + "Ġsuggest ions", + "Can vas", + "Ġf er", + "ĠMarket ing", + "@ Test", + "unt u", + "ĠV en", + "ĠC ou", + "iv als", + "Don ald", + "lim ited", + "ĉĉĉĉĉĉ Ċ", + "Ġanal yst", + "( entry", + "Ġrepresent ative", + "_at tributes", + "Ġf ur", + ".h ide", + "res p", + "ado res", + "rid es", + "ĠJ osh", + "ro bot", + "ĠN AT", + "Ġs esso", + "Ġintegr ated", + ": true", + "part s", + "Ġst upid", + ": event", + "@end section", + "Ġp u", + ".T able", + "ĠY ii", + "` ;ĊĊ", + "Ġcl ang", + "=\" \">", + "eng an", + "_param eters", + ".int ernal", + "ĠMod ern", + "Ġmet ric", + "Ġsem i", + "={ {Ċ", + ".am azon", + "ĠB B", + "aint y", + "view port", + "Ġstart Activity", + "dis patch", + "**** *", + "Ġfl av", + "iffer ent", + "[ this", + "Ġst ake", + "Ġarg ued", + "vious ly", + ".w ork", + "ĠO ak", + "O ld", + "( async", + "not es", + "Ġfl ip", + "Ġdis ag", + "ĠT E", + "ĉ error", + "< '", + "Ġ» ĊĊ", + "Ġfilter ed", + "ĠM ach", + "Ġh ung", + "_d ump", + "_s amples", + "-dis miss", + "Ġr ay", + "Im plemented", + "D K", + "Ġj ed", + "Ġbreak s", + "Ġf its", + ". gr", + "ĠZ ero", + "or o", + "Ġequ ally", + "Ġ' [", + "Ġconcern ing", + "< meta", + "play ers", + "_P OS", + "_s im", + "J an", + "Ġyour s", + "ĉ N", + "Ġsp ir", + "Ġch ampion", + "ĠAn alysis", + "ap a", + "ĠNS Log", + "_l ines", + "ñ a", + "ĉĉ ĠĠĠĠĠĠĠ", + ".S c", + "Re p", + "etro it", + "ur able", + "M IT", + "com pat", + "own ed", + "_ind ices", + "], čĊ", + "Ġdis covery", + "ĠDie go", + "ob i", + ". Index", + "Ġtrend s", + "PL AY", + ".n o", + "Ġl ens", + "_c fg", + "Ġan no", + "ag an", + "Ġperiod s", + "ter ms", + "y z", + "Ġattack ed", + "ib ration", + "PEC IAL", + "_ grad", + "Ġaccord ance", + ".Read Line", + ".de vice", + "ri x", + ". container", + "m ay", + "erc ise", + "ĠL u", + "Ġr g", + "ĠÑģ ÑĤ", + "ĉĉĊ ĉĉĊ", + "( un", + "TERN AL", + "Ġless ons", + "Ġalleg ations", + "Ġtrans mission", + ".Re f", + "M obile", + "ĠT ournament", + "ĠN ut", + "ĠG a", + "ĠCap ital", + "def inition", + "- exp", + "c lean", + "Ġfant asy", + "Ġenh ance", + "ent ence", + "'] :Ċ", + "ack ets", + "Ġcelebr ate", + "@ \",", + "Serialize Field", + "Ġarray s", + "t b", + "ĉ st", + "[ assembly", + "( reg", + ".c ategory", + "Ġimpro ving", + "Ġsal ope", + "Byte Array", + "Or iginal", + "Ġ[ {Ċ", + "åĽ ŀ", + "ĠCl in", + "oen ix", + "ĠS amsung", + "Ġmaint ained", + "Ġag enda", + "f ail", + "Ġpres ents", + "Ġtim ing", + ".m ark", + "' ><", + "Ġprom ot", + "Ġin cl", + "_ only", + "ë¥ ¼", + "ĠAtt orney", + "- date", + "Ġlands cape", + "Ġf u", + "S Y", + ".p rop", + "ĠA rr", + "p ag", + "Parallel Group", + "': čĊ", + "Ġlog s", + "a unch", + "unc i", + "n ama", + "Table Cell", + "iss ues", + ". {", + "ec urity", + "_ex ec", + "old s", + "Ġhost s", + "Ġpro to", + "_ import", + "_s ort", + "ĠB ow", + "ĠN ormal", + "ĠF arm", + ".create ParallelGroup", + "R otation", + ". err", + "Ġp leased", + "it age", + ".W h", + "ĉĉ ĠĠĠĠ", + "M R", + "ĠM ORE", + "ĠN atural", + "_ transform", + "B ASE", + "ener al", + "ut down", + ".common s", + "W T", + "Ġa an", + ". Result", + "d og", + "Ġclick ing", + "), ĊĊ", + "# line", + "Oper ator", + "Ġc iv", + "Ġm erg", + "ob uf", + "ng then", + "Ġ[ {", + "Ġcan cell", + "tr igger", + ". :", + "W ORK", + "decl are", + "Ġdecre ase", + "ÅĽ ci", + "lo om", + ".N one", + "ĠM I", + "ĠJ ason", + "Ġhealth care", + "iam ond", + "s ylvania", + "* x", + "ĠR a", + "[ b", + "Ġprint ing", + "ph abet", + "ĠLab our", + "op per", + "Ġz ijn", + "-t arget", + "_F UNCTION", + "Ġo ct", + "ени Ñı", + "åľ ¨", + "Ġwest ern", + "Ġcomput ers", + "ĠR ET", + "Hash Map", + "[ String", + "get Value", + "_D ATE", + ".N ext", + "ĠF if", + "é l", + "ick ed", + "æ İ", + "-M M", + "Ġ{ ĊĊĊ", + "Ġcontact s", + "Ġdig its", + "Pro du", + "Ġunus ual", + "Ġrapid ly", + "t ures", + "Ġang ry", + "c ancel", + "xx xx", + "_p arser", + "id ity", + "_P REFIX", + "Ġme hr", + "Ġrare ly", + "et he", + "op es", + "Ġ% .", + "work s", + "Ġthe ta", + "Ġcontrib ution", + "ĠT ony", + "Ġsqu ad", + "аР¹", + "Ġî n", + "th ere", + "out ed", + "ĉ q", + "Ļ Ĥ", + "g ood", + "L I", + "é¡ µ", + "ĠL iving", + "iz abeth", + "Ġk t", + "ĠD allas", + "] ],Ċ", + "Ġ/ >ĊĊ", + "Ġrais ing", + "/r outer", + "_g ame", + "ĠC UR", + "z ens", + ". es", + "Ġfont Weight", + "(f unc", + "not ification", + "Ġ'../../ ../", + "Ġbl ame", + "ãĢĤ ĊĊĊĊ", + "an co", + "Id entity", + "f ollow", + "Ġart s", + "x s", + "Ġofficial ly", + "ĠSt udio", + "Ġrecommend ations", + "Ġloc ale", + "Ġam ateur", + "ĠEn able", + "Ġcap s", + ". End", + "- add", + "_g shared", + "ĠC T", + "For ce", + "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "Ġor ange", + "Ġl p", + "Ġanswer ed", + ".G rid", + "Ġd ual", + "Ġstrateg ic", + "Ġnob ody", + "Ġf atal", + "_ est", + "( el", + "Ġì ł", + "ĠB udd", + "A IT", + "_f actor", + "- one", + "ĠH AVE", + "\" čĊčĊ", + "Pro f", + "Ġä r", + "str ings", + "Ġdir ty", + "ĠF ace", + "ĠB egin", + "ĠB us", + "Ġw is", + "åŃ Ĺ", + "Ġspe aker", + "Ġcar rier", + "ĠO m", + "Ġhad n", + "All ow", + ":: __", + "Ġver b", + "ĠCom plete", + "ĠE asy", + "Ġb ills", + "ĠĠ ĊĊ", + "Vert ical", + "Ġpr on", + "ĠDef ine", + "Ġlook up", + "variable s", + "Ġpand as", + "um es", + "Ġinn oc", + "Ġset Up", + "ĠCh ampionship", + "art ist", + "ĠC Type", + "F oundation", + "๠Ī", + "ĠSet up", + "Ġrec ipes", + "ĠU IColor", + "ĠF ight", + "Ġauthor ized", + "_c lick", + "_s uccess", + "ang an", + "ĠMount ain", + "ĠDo ctor", + "Ġeg g", + "ĠMedic ine", + "c les", + "` .Ċ", + "[ int", + "d ashboard", + "ĠApp ro", + "-d r", + "Ġprodu ces", + "Ġrent al", + "Ġre load", + "Ġarr ival", + "sp ot", + "Ġund ert", + "Ġequ ipped", + "Ġpro ved", + "Ġcent ers", + "Ġdef ines", + "al so", + "Ġop acity", + "ĠUn fortunately", + "ĠIll inois", + "Ġн е", + "ĠTem ple", + "ĠTr ail", + "ĠK elly", + "Ġmeasure ment", + "Ġsepar ated", + "-c ircle", + "H ey", + "ĠRE AD", + "ig its", + "Ġ ib", + "ĠM OD", + "atter y", + "аР·", + "Ġv end", + "ен ÑĤ", + "ĠHttp Client", + "s afe", + "_A SS", + "ic it", + "ĠCon struct", + "ĠC lo", + "ĠS ix", + "_T OKEN", + "(b lock", + "Ġwarn ed", + "/* !", + "! Ċ", + "Ġinnov ation", + "_ \"", + "Ġ );čĊčĊ", + "Ġsp ots", + "Ġcho osing", + ".c s", + "Ġflex ible", + "U Int", + "Ġscr atch", + "- al", + "Ġf estival", + "Ġout standing", + "================================ ================", + "M ean", + "ĠO regon", + "s ymbol", + ". account", + "d ney", + "'' '", + "! \",", + "Ġpart icle", + "à ĥ", + "[ MAX", + "IV ER", + "ER ENCE", + "NS Mutable", + "ĠColum bia", + "_ ĊĊ", + ".f r", + "Ġc ogn", + "V R", + "ĠMethod s", + "ĠM ade", + "ĠB R", + "ĠEl se", + "Ġeg gs", + "Ġsw ing", + "ĠIn v", + "Ġdise ases", + "Ġf irms", + "Ġle mma", + "}` );Ċ", + "l ings", + "Ġg ym", + "umin um", + ".T rim", + "M em", + "Ġcritic ism", + "ibern ate", + "_T X", + "ion i", + "Ġguid ance", + "Ġrepeated ly", + "Ġsup plier", + "Ġpaint ing", + ".F ragment", + "ed Exception", + "Ġw iring", + "Ġcour ts", + "W EB", + "æľ ī", + "\\ .", + "ill ance", + "Ġb rows", + "ĠP attern", + "PL ICATION", + "ĠSum mer", + "Ch ain", + "Ġc ute", + "mer cial", + "Ġd il", + "ĠFrank lin", + "ĉg lobal", + "IN CLUDING", + "h istory", + "Ġl st", + "Q t", + "SD L", + "al ia", + "i ere", + "( ...", + "ĉc in", + "iff s", + "vel ope", + "ĠR oot", + "cl uster", + "User Name", + "ign e", + "< S", + "Ġf est", + "Ġindic ating", + "ke eper", + "Ġc ada", + "é g", + "cons in", + "ĠG B", + "Ġl b", + "em ony", + "-icon s", + "_d oc", + "Act or", + "e lem", + ".De lete", + "Ġin fection", + "ĠPriv acy", + "Ġgreat ly", + "ĠP os", + "ĠT reat", + "Fl ow", + "Ġattract ive", + "ĠMar c", + "s udo", + "tes y", + "- an", + "ab ama", + "ĠW ould", + "Ġsu ck", + "index Path", + "ĠE t", + "T imes", + "Ġclub s", + "_ass oc", + "Ġac quired", + "(\" :", + "Ġint ense", + ".m aps", + "Ex pected", + "T oggle", + "Ġa y", + "Ġl ifestyle", + "-c alled", + "ĠS now", + "V olume", + "Ġcann abis", + "ĠD irection", + "ĠLim ited", + "-s pecific", + "Ġd owntown", + "/ icons", + "Ġre ven", + "L eg", + "= null", + "Key board", + "') ).", + "Ġ\"\" ;čĊ", + "Ġatt itude", + ".n avigate", + "- error", + "AM PLE", + "ĠJ ay", + "v r", + "c ow", + ".com pile", + "Ġmem ories", + "_m ark", + "ĠMin nesota", + "Ġk osten", + "Ġprob ability", + "w arning", + "Ġgen etic", + "F ixture", + "ĠHash Set", + "N ombre", + "_m onth", + "Æ °", + "- start", + "xy gen", + "ĉ ft", + "i agnostics", + "ĠMat thew", + "Ġconcept s", + "Ġcon str", + ". State", + "и н", + "N ov", + "Î ±", + "ĠP anel", + "ä¸ ª", + "com pare", + "> ()Ċ", + "Ġapply ing", + "Ġprom ised", + "Ġo x", + "nc ia", + "ĠValid ation", + "ort s", + "_c ur", + "e lect", + "ey e", + "( Data", + "Ġreport er", + "ĠB uff", + "Ġs r", + "Ġ\" ;", + "ick y", + "Ġtemp or", + "S N", + "Ġres ident", + "pi res", + "ys ical", + "Ġend orse", + "ĠS ong", + "is Empty", + "le et", + "_ util", + "Ġdist ingu", + "ĠT alk", + "ĠM ot", + "( default", + ".A rg", + "gorith ms", + "_ words", + "im mer", + "_res et", + "f amily", + "W W", + "Ġsav ings", + "ĠâĢ Ŀ", + "_en able", + "side bar", + "Run ning", + "Ġal i", + "Ġtest im", + "Ġwarn ings", + "ĠCh em", + "ĠEx it", + "Ġfound er", + "pect or", + "Ġr m", + "_d ataset", + "ĠD as", + "Ġh an", + "Get ty", + "á l", + "Ġn y", + "Ġpo verty", + "Ġresult ed", + ".b y", + "ĠVis it", + "Ġobt aining", + "/ '.$", + "ĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "sh all", + "_LE FT", + "UI Image", + "_ Name", + "h ave", + "ĠN ob", + "l r", + "- footer", + "Ġn aked", + "ĠG arden", + "\\F acades", + "Ġgrad uate", + "Ġfranch ise", + "pl ane", + "Ġcontrib utions", + "Ġstring With", + "Ġc rypto", + "Ġmov ements", + "ath ers", + "Ġlif etime", + "Ġcommunic ate", + "j ar", + "ĠFr agment", + "_ IF", + "ĠN avy", + "ĠF igure", + "Ġsim ulation", + "_st op", + "Ġreport ers", + "Ġvers us", + "aj a", + "ĠÎ ±", + "Ġgovern or", + "List Item", + "Ġse aled", + ".Back ground", + "ed i", + "ash ing", + "Ġl ip", + "ĠI h", + "mer ge", + "Ġn ec", + "el ocity", + "ATE G", + "Ġse eds", + "Ġflo ating", + "_F A", + "w alk", + "ĉ user", + "_de pth", + "Ġw age", + "@ app", + "N il", + "( [\"", + "( vector", + "Ġsecret ary", + "Ġj Panel", + "ve z", + "³³ ³³", + "d irection", + "ĠE P", + "Ġh unt", + "Json Property", + "ĠP ORT", + "] \",", + "аР¿", + "ĠFore ign", + "pan ic", + "Ġtri als", + "ĠA le", + "Ġr ural", + "- value", + "author ized", + "ĠScot land", + ".d rop", + "ĠM T", + "ç ±", + "row th", + "File Path", + "Ġrec all", + "if le", + "Ġc el", + "ĠSE LECT", + "k n", + "_c ase", + "Ġc rop", + "s ure", + "p ot", + "IC S", + "Ġst em", + "Ġindust ries", + "P ut", + "Ġa ber", + "road cast", + "Icon s", + ") \")Ċ", + "æĪIJ åĬŁ", + "g ui", + "Ġassum ed", + "Ġr x", + "E A", + "è §", + "EL L", + "Ġdo se", + "Ġin e", + "Ġde eper", + "l ider", + "Ġord inary", + "Ġg olf", + "_IM AGE", + "ĠN AME", + "(m odule", + "Ġat om", + "Ġbel t", + "Ġoff ices", + "b eta", + "Ġphilosoph y", + "( JSON", + "-f ield", + "Ġintrodu ce", + "Ġconven ience", + "opt im", + "> \"Ċ", + "ath y", + "Ġemploy er", + "qu ate", + "Ġed ited", + "Arg uments", + "ĠN ations", + "__ )", + "Ġno se", + "ĠS ample", + "' )ĊĊĊ", + "Ġc ake", + ".get Attribute", + "H D", + "Mod ified", + "Ġpredict ed", + "Å Ħ", + "an ie", + "S orry", + "(d oc", + "w ind", + "ie ve", + "Ġprov isions", + "AT ER", + "OT E", + "M Y", + ".A utowired", + "ĠB ath", + ". Boolean", + "Ġback end", + ".M ouse", + "ater al", + "p aper", + "Con st", + "ĠV R", + "_ entity", + "_C TRL", + "ĠProte ction", + "ĠG M", + "ĠStud y", + "Ġsou p", + "ot ime", + "' use", + "] \"", + "/ users", + "a ug", + "ĠH ong", + "_n orm", + "ãģ ¨", + "Ġse cre", + "(B uild", + "ĠCon tract", + "ol as", + "Ġsa uce", + "Ġaggress ive", + "Ġrac ial", + "char acter", + "@ @", + "Ġcomp ile", + "ĠV oid", + "_re m", + "_m emory", + "k k", + "Ġm ic", + "S ame", + "U tility", + "ĠH tml", + "ĠX ml", + "Read y", + "Ġg all", + "Ġalleged ly", + "ĉĉĉĉ ĠĠĠ", + "ĠMet al", + "ĠPerson al", + "Ġborder Radius", + "rx js", + "object s", + "Ġwant ing", + "Ġb owl", + "v endor", + "offset of", + "ĠR s", + "ĠR ating", + "Ġr ally", + "_N ODE", + "ĠM ix", + "Ġadvert is", + "Ġnarr ative", + "s al", + "Ġm c", + "SE rror", + "Ġf ingers", + "Ġaccom pany", + "Ġt ired", + "Ġstr ide", + "Ġgu i", + "el ist", + "Loc ale", + "Ġrele ases", + "ik ing", + "Ġan ger", + ")) )ĊĊ", + "alle st", + "Sum mary", + "( O", + "(f or", + "Ġbasket ball", + "Ġroad s", + "ĠInst all", + "ĠF ab", + "it map", + "Ġ) )Ċ", + "Ġinter section", + "ighb or", + "ĠB ry", + "ĠHER E", + "So ftware", + "elf are", + "ac s", + "Ġtrail er", + ".get Class", + "ch ars", + "Ġreg ulation", + "Ġref ers", + "Ġde struction", + "Ġcontin uous", + "ĠAust in", + "é ¢", + "ak an", + ".w indow", + "ĠTem plates", + "Ġabs ence", + ": n", + "Ġdis order", + "fl ash", + "Ġde let", + "bo ards", + "ĠĠ ĉ", + "RO P", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġac qu", + "Ġlaws uit", + "ĠRe views", + "Ġgar age", + "t imer", + "Ġe j", + "ĠRect angle", + "Ġflow ers", + "il st", + "ĠIn stance", + "S uper", + "d et", + "dis posing", + "ĠE S", + "ĠI C", + "ver e", + "S k", + "_ch annels", + "put ed", + "/ null", + "nn en", + "ĠG allery", + "_g lobal", + "Auth entication", + "ĠR ank", + "Ġblock ed", + "Ġcal m", + "mark et", + "ĉ val", + "Ġa ug", + "per iod", + "ĠCon stant", + "Ġ?> \">Ċ", + "Ġl obby", + "p al", + "Ġs ink", + "ia h", + "Ð ¡", + "urn ame", + "Ġcon ver", + "Ġinvestig ate", + "Ch rist", + "H ub", + "ĠIN D", + "ĠP ed", + "ur as", + "ĉ url", + "ĠT ro", + "Ġpre ferences", + "Ġguarante ed", + "` ĊĊ", + "Ġport ions", + "Ġeval u", + "' > ;ĊĊ", + ".AutoScale Mode", + "Ġc ats", + "Ġreg istry", + "ul us", + "F I", + "p ayload", + "- search", + "Ġstay ing", + "ac ious", + "Dec oration", + "Re view", + "In f", + "Ke ep", + "it is", + ", String", + "Co ord", + "Ġper o", + "S ex", + "ĠAtl anta", + "uest a", + "Arg b", + "> *", + "} _", + "F ooter", + "Ġemploy ed", + "_b ound", + "v ide", + ".f unc", + "$ scope", + "Ġsp o", + "ĠAn al", + "ounc ed", + "ar ound", + "Ġrestr iction", + "Ġsh ops", + "å Ģ", + "ĠLat in", + "-c ol", + "Ġbare ly", + "ĠE uro", + "E r", + "Ġfa ire", + "_d istance", + "_un lock", + "Qu ote", + "IV ATE", + "Ġå Ī", + "Ġaim ed", + "ĠRet rie", + ". iter", + "Ġwr apped", + "Ġagre ements", + "str ument", + "( product", + "Ġstud ied", + ".set Value", + "Ġy e", + "ĠC ache", + "MB OL", + "Ġquarter back", + "Ġsy ntax", + ".getElements By", + ".v ersion", + "we bsite", + "Run ner", + "_s ingle", + "at iv", + "ĠAl tern", + "ĠBeaut iful", + "right arrow", + "Ġd iversity", + "pl ash", + "( co", + ".F ill", + "Ġtyp ing", + "Ġcl ar", + "H it", + "O O", + "ac co", + "w orth", + "Ġscript s", + "ĠMuslim s", + "ĠL L", + "erv ing", + "( boolean", + "Ġbase ball", + "ĠC AN", + "MA IL", + "de pend", + "Ġrespect ive", + "Ġconst expr", + ".* ;ĊĊ", + "'] ))Ċ", + "Ġy ard", + "Ġident ical", + "if ecycle", + "US H", + "up iter", + ". validate", + "cl i", + "IST ER", + "Ind icator", + "F ail", + "Ġdemocr acy", + ". var", + "Ġsatisf ied", + "------------ -", + "enc er", + "h or", + "Ġr ounds", + "DA O", + "o a", + "Ġfl ask", + "= c", + "[ ]Ċ", + "/d ist", + "Ġpart e", + "Ġconfirm ation", + "er on", + "aw are", + "", + "Ġdepend encies", + "ĠV ideos", + "- row", + "Ġ** /Ċ", + "Ġn ou", + "Ġh over", + "æ ŀ", + "Ġn in", + "ĠUS D", + "M ac", + "_L oad", + "Ġout comes", + "_s ocket", + "Ġqu eries", + "w m", + "Ġhit ting", + "in ux", + "M ich", + "ud ge", + "AT AB", + "Ġvulner able", + "ä ¾", + "Ġport folio", + ": YES", + "ĉm ap", + "B ound", + "Ġiter ation", + "in cess", + "Ġact ors", + "ĠQ ual", + "_c lean", + "ãĢij ãĢIJ", + "MS G", + "G reen", + "ĠOff icer", + "Ġsm oking", + "> ',", + "ĠF lo", + "++ ;", + "oly gon", + "Ġbul k", + "Ġdr ama", + "Ġexception s", + "os ed", + "Ġ+ čĊ", + "Ġleg acy", + "C V", + "Ġcontrib uted", + "ĠTer ms", + "Ġb t", + "Ġunt uk", + "Ġal ien", + "=== Ċ", + "ĉ Vector", + "Ġl s", + "On line", + ".f acebook", + "num eric", + "ock ets", + "A ut", + "b ury", + "-re dux", + "ĠRed istributions", + "GLOBAL S", + "urrenc ies", + "Ġt ons", + "âĢĻ ,", + "Ġà ª", + "(c ol", + "ĠS ymbol", + "Ġstay ed", + "ĠM L", + "Ġm unicip", + "Ġsex o", + "S en", + "n r", + "Ġg ains", + "Ġshort ly", + ".M enu", + "à ½", + "KN OWN", + "Ġoper ators", + "- V", + "ĠPat rick", + "/ add", + "_C O", + "ir ation", + "(p ost", + "Post s", + "/ _", + "Ġpl ug", + "Ġintellect ual", + "Ġmet ab", + "Ġpregn ancy", + "ĠPrem ier", + "n m", + "Ġpred iction", + "ĠMin istry", + "Th ree", + "val uate", + "ĠMin i", + "b u", + "оР·", + "< ul", + "Ġd d", + "ol ving", + "ĠC ut", + "Ġs chem", + ".tr ain", + "it ate", + "Ġr ice", + "Ġbird s", + "ãģ «", + "m iddle", + "struction s", + "Ġn erv", + "a que", + "Ġfl u", + "Ġsurv ival", + "ĠGal axy", + "ĠF ant", + ". Order", + "At trib", + "irt s", + "é c", + "M ovie", + "Ġcon ce", + "qu arters", + "Ġm ood", + ".Add Range", + "Ġres olved", + "ãĥ Ī", + "Ġburn ing", + "ĉĉĉĉ čĊ", + "ĠW E", + "Ġhost ing", + "L AB", + "Ġman agers", + "Ġstre ngthen", + "< const", + "ĠFire base", + "on ed", + "ĠJ ean", + "' \";čĊ", + "ĠS av", + ".B old", + "Ġen ables", + "ĉt mp", + "Ġman ually", + "ĠS qu", + "user id", + ".f unction", + ".c ache", + "LO PT", + ".S ervices", + "dd it", + "t im", + "< img", + "ĠTh ings", + "ĠEvery thing", + "Ġa pt", + "em and", + "Ġroll ing", + "ë ¦", + ". level", + "Ġst om", + "ĠW inter", + "Ġview ing", + "( values", + "ocom plete", + "v ia", + "up o", + "Ġabort ion", + "i ère", + "ï¼ ij", + "_B UTTON", + "_d omain", + "Ġb ra", + "ĠA st", + "in as", + "Ġstat ist", + "c od", + "L R", + "Ġdr ives", + "Ġfollow ers", + "Ġall ies", + "ĉc urrent", + "ecess ary", + "Ġdam aged", + "_ pt", + "and les", + "oun tries", + "Ġsim ult", + "e u", + "Ġcontrovers ial", + "_G ROUP", + "Ġr ib", + ". Info", + ": mm", + ".n ormal", + "_ADD RESS", + "Ġ íķ", + "add le", + "ĠD ur", + ". Element", + "W arnings", + "Ġcred its", + "Ġin hib", + "Ġem issions", + "Ġh az", + ".y outube", + "ugg ed", + "Ġbo ther", + "ĠK ansas", + "ĠF ixed", + "ĠTest s", + "ĠF IX", + "Un iform", + "Ġk ont", + ">> >", + "st ation", + "lo re", + "at ype", + "ish op", + "/ ****************************************************************", + "Com boBox", + "Ġvac ation", + "Ġiniti ative", + "Ġdefault Value", + "con cat", + "ĠK h", + "ĠW elcome", + "ized Name", + "M igration", + "Ġgrad ient", + "H ot", + "Ġhard ly", + "el o", + "ĠStud ents", + "Ġlo ose", + "at z", + ".S end", + "' /", + "Ġunivers al", + "Ġenter prise", + "Ġreg ex", + "Ġvis itor", + "ĠF ly", + "Se q", + "ภĻ", + "ĠVis ual", + "Ġlib raries", + "ato es", + "P ayment", + "Ġp ent", + "Ġgather ed", + "VRT X", + "ĠD M", + "S plit", + "Ġlet ting", + "Ð Ŀ", + "_error s", + "ep och", + "P ARAM", + "c u", + "ÑģÑĤ в", + "ol utions", + "Edit ing", + "font s", + "Ġalloc ated", + "ĠB ased", + "( Y", + "ĠJud ge", + "Ġbro thers", + "FILE S", + "ç o", + "w b", + "_P I", + "' ^", + "Ġs word", + ".s ervices", + "Ġn l", + "T im", + "ig g", + "ĠMo ore", + "Ġcrypt oc", + "åĩ º", + "_post s", + "ot ate", + "? '", + "... .ĊĊ", + "Ġk l", + "=\" $", + "Ġdec oration", + "Ạ¡", + "ĠD IRECT", + "G UI", + ") =>{Ċ", + "Ġnews letter", + "Ġprec is", + "(p oint", + "ĠEqu ipment", + "ut y", + "ĠD ave", + "Ġparticip ation", + "u arios", + "x it", + ".A s", + "ET ER", + "or ous", + "Ġsh ield", + "[] >", + "ilit ary", + ". origin", + "Ġprom otion", + "U nt", + "Ġc t", + "TR A", + "View Holder", + "Ġsig ma", + "d elta", + "are house", + "con tract", + "( Vector", + "Ġcompet e", + "/ form", + "/ components", + "Ġn r", + "ĠInd ones", + "Ġо ÑĤ", + "ĠV olume", + ".f iles", + "(res p", + "/ models", + "Ġsur f", + "stand ard", + "/ o", + "ĠXCT Assert", + "V ICES", + ".C ode", + "SE D", + "Ġact ivate", + "D elta", + "Ġlimit ation", + "ri j", + "Ġpregn ant", + ": ^(", + "Ġs our", + "p ie", + "Ġexp ense", + "ic ation", + "ĠL arge", + "Ġ ±", + "ĠB owl", + "(model s", + "/ N", + "P a", + ".re load", + "Ġwonder ing", + "Exec ution", + "ĉ ĠĠĠĠĠĠ", + "ĠG raphics", + "ĠCont in", + "_j ob", + "Ġget Name", + "ĠM agn", + "ĠD WORD", + "m ad", + "Ġn h", + "fe atures", + "} \");Ċ", + "he ets", + "(tr ain", + "z n", + "Ġrecru it", + ".con nection", + "Ġbar rel", + "Ġste am", + "_set ting", + "Ġang ular", + "ane ously", + "Ġb il", + "ĠN orm", + "(! $", + "ib t", + "% (", + "Ġpos it", + "ĠF ather", + "int endo", + "L ive", + "Ġport s", + "Ġme j", + "Ġland ing", + "pon der", + "Ġc od", + "_HE ADER", + ".M argin", + "Ġball s", + "Ġdiscuss ions", + "Ġbl end", + "H ex", + "Ġfarm ers", + "Ġmaint aining", + "ĠĠĠ čĊ", + "s yn", + "[ T", + "r us", + "uff ers", + "Ġcontrib utors", + "_s ys", + ".De bug", + "Ġconstruct ed", + "om es", + "? id", + "sl ider", + "Ġsup pliers", + "scri ber", + "p es", + "Ð ŀ", + "\": čĊ", + "\\ Controller", + ")) ĊĊĊ", + "Ġl ua", + "M ulti", + "EN S", + "S rc", + "Ġpet ition", + "Ġsl ave", + "look ing", + "V ERT", + "ĉ vector", + "S pecial", + "h h", + "an ne", + "ĠN iger", + "/ views", + "z ing", + "end ant", + "< C", + "s peed", + "Ġ{ };ĊĊ", + "Begin Init", + "Ġf open", + "@ RequestMapping", + "End Init", + "Ġp unch", + "S ender", + "é Ķ", + "get Message", + "/t ypes", + ".P I", + "(' ');Ċ", + "oc used", + "( all", + "Ġdrop down", + "). __", + "ĠV in", + ".Fore ignKey", + "can f", + "ou red", + "ĠOrgan ization", + "ĠÐ °", + "ĠC ulture", + "(cl s", + ", _", + "rg ba", + "ìĿ ĺ", + ".data GridView", + "Ġdo zen", + "ĠG es", + "_sh ared", + "n ick", + "Ġh osp", + "om eter", + "Ġclaim ing", + "ib les", + "ri k", + "æĺ ¯", + "en ario", + "Ġd engan", + "ob b", + "m ont", + "_r ank", + "('/ ',", + "Ġap olog", + "P s", + "_p ower", + "ĠG ree", + "Ġful fill", + "Ġfire base", + "Ġf are", + "ĠH im", + "Ġbe an", + "âĢ¦ .", + "ĠS PI", + "_R X", + "Ġper ception", + "rel ative", + "comp ile", + "u um", + "ut os", + "a uc", + "ĠAs k", + "Ġindic ator", + "/ th", + ".set String", + "ĠWis consin", + ".D omain", + "Ġart ificial", + "De velop", + "ĠSar ah", + "Ġl ying", + "( search", + "ĠEmp ire", + "urr ing", + "æŶ éĹ´", + "=\" ${", + "Ġget Id", + "ĠP ayment", + "trans ition", + "Ġ ].", + "ix in", + "V T", + "- select", + "Ġdemonstr ated", + "Ġlast Name", + "employ ment", + ".get Property", + "Ġf ought", + "file Name", + "ĠP ers", + "-c ard", + "a str", + "attr s", + "Ġprom inent", + "Des ign", + "anc ouver", + "ãģĹ ãģ", + "ard o", + "se cret", + "Ġr ag", + "Ġpo ison", + "-m an", + ", omitempty", + "ĉ un", + "it zer", + "ĠCas ino", + "ĠR oss", + "- foot", + "(result s", + "Pl an", + "Ġlas er", + "ê¸ °", + "_D R", + "F acebook", + "Ġbo ards", + "st a", + "] ],", + "Ġt iles", + "S IZE", + "Ġ= ~", + "Ġprem ier", + "oc ab", + "Ġenc oded", + "Ġres erve", + "ĠAfghan istan", + "ĠList Node", + "url s", + "Ġsub mission", + "Ġne u", + "Ġ# +#", + "_P OST", + "Ġmo ist", + "ell i", + "ellig ent", + ". alert", + "ó d", + "b re", + "ĠCol lect", + "Ġgraph ic", + "Ġlong itude", + "ĠPro vid", + "ĠCal culate", + "x ffff", + "c riteria", + "Ġw aters", + "ro ck", + "lo quent", + "ĠT rib", + "Ġbur st", + "Ġsuff ix", + ".Ext ensions", + "ish es", + "iv el", + "ĠLI KE", + "ĠGet ty", + ".Action Event", + ".s lf", + "ĠH AL", + "up al", + "E AR", + "ud i", + "_time out", + "U F", + "ĠSing apore", + "ĠAd vent", + "_int erval", + "cha ft", + "ĠE mer", + "Ġtele phone", + "ĠTur k", + "_ interface", + "ĠO wn", + "Ġencour aged", + "< Object", + "_T ext", + "ĠOnt ario", + "ĠApp ly", + ".f irebase", + "Ġant ib", + "P riority", + "ene z", + "D ays", + "c id", + "urre nce", + "; /", + "inn ed", + "Ñģ Ñı", + "Ġve z", + "f w", + "// $", + "att ack", + "Ġstart up", + "ain ers", + ".f ragment", + "op acity", + "( conn", + "he im", + ".n etwork", + "( stream", + "ĠN ON", + "t ol", + "ĠX box", + "ĠD S", + "Ġc ached", + "Ġprostit utas", + "ĠB alt", + "(' [", + "Ġno except", + "\" '", + "Ġs d", + ". valid", + "_ ag", + "Ġr aces", + "Ġro d", + "itud es", + "< >(", + ".Pro duct", + "Form s", + "NE W", + "P ay", + "ĉ boolean", + "_ contact", + "ĠElect ric", + "sk ip", + "Ġw ur", + "Ġch ronic", + "_d river", + "ĠS ab", + "ĠU lt", + "ĠR ad", + "ST ATUS", + "ĠLew is", + "O B", + "Ġgift s", + ".Re c", + "TR UE", + "Ġint ensity", + "Mark er", + ".com pare", + "ff ic", + "C ookie", + "ĠB aby", + "ĠBig Decimal", + "ile t", + "ĠHOLD ERS", + "ĠL ady", + "Ġl ung", + "ĠAl abama", + "Ġd ess", + "` );Ċ", + "ĠB uilder", + "_reg ion", + "Ġne utral", + "Bo th", + "Ġh p", + "Ġh orn", + "Ġseg ments", + "ĠE C", + "\"=> \"", + "( rec", + "ĠP i", + "G M", + "Ġl aptop", + "Sc alar", + "is d", + "-d ialog", + "ĠAnd erson", + "Ġmist akes", + "ĠH an", + "j es", + "est ination", + "Ġprom ises", + "b id", + "ĠSc ient", + "G IN", + "ĠPer formance", + "b age", + ". users", + "le ading", + "Ġor al", + "G raphics", + "_P TR", + "h ang", + "Ġin ev", + "process ing", + "F actor", + "ĠN A", + "$ string", + "Ġground s", + ".Save Changes", + "c lock", + "cri pcion", + "ĠNew ton", + "g c", + ".in cludes", + "Ġbl ast", + "Ġ'- '", + "Ġpued e", + ".S ession", + "Ġgre p", + "_f inal", + "ĠG ay", + "ĠG ive", + "ir i", + "-st ar", + "ĠUI Image", + "_ep och", + "ub b", + "ent h", + "Ġel ite", + "Ġcampaign s", + "ĠP orno", + "_ assign", + "Prot ocol", + "ĠBe ing", + "ĠAir port", + "Ġconvent ional", + "ĠW at", + "ĠC I", + "ET A", + "ĠAnth ony", + "Ġtable t", + "( format", + "Ġconsist ently", + "ĠI owa", + "Ġav atar", + ".c ursor", + "! [", + "Ġh anging", + "H er", + "S uch", + "';ĊĊ Ċ", + "orge ous", + "() ==", + "Ġview Model", + "Ġ ãĥ", + "Ġel s", + "ĠAg ent", + "F etch", + "ap or", + "Ġc x", + "p read", + "ĠP ier", + "oe ff", + "S n", + "ĠV irtual", + "A pr", + ".Wh ite", + "_M OD", + "ĠPoint s", + "å¤ ±", + "Ġgen es", + "Ġv endor", + "Ġmain stream", + "< src", + "ĠEl izabeth", + "Dec oder", + "- state", + "ĠG lass", + "nc y", + "adi ans", + "_m on", + "ĠRem ote", + "Ġwire less", + "ĠM i", + "å ī", + "è¡ ¨", + "st age", + "ĠT ile", + "ll ib", + "V ariant", + "== Ċ", + "Ġgold en", + "(Q String", + ".put Extra", + "ĠD om", + "ĠAn imation", + "Ġinter active", + "if act", + "éĻ ¤", + "LE T", + "Ġfrequ ent", + "Ġ< >Ċ", + "F ilename", + "Ġs ne", + "ĠFoot ball", + "Ġr ival", + "Ġdis aster", + "ion ic", + "ĠD amage", + ". Resource", + "- en", + "ĠT ypes", + "get String", + "( board", + "Ġb ol", + "pl ain", + "z ym", + "ภ²", + "Ġsc anner", + "ild er", + "_msg s", + "æ ı", + "(int ent", + "Ġde struct", + "Ġb ust", + "ĠE mploy", + "on i", + "ĠUI ViewController", + "Ġodd s", + "ear er", + "Ge ometry", + "Ġy ii", + "_EX PORT", + "ĠAtt ack", + "Ġn iet", + "Ġim pression", + "ĠG il", + "_pro b", + "ĠC F", + "ĠEx perience", + "/pl ugins", + ".M ethod", + "Ġbelie fs", + "N ative", + "_b uild", + "Ġv ig", + "Ġr anks", + "cover ed", + "s uch", + "G uard", + ".p ack", + "add er", + "iv ia", + "l ng", + "Ġв Ñĭ", + "T imestamp", + "_n ow", + "Ġp oker", + "Ġun c", + "Ġsh apes", + "-t ypes", + "_per iod", + "p k", + "Ġveter an", + "Ġson o", + "Ġappoint ed", + "over flow", + ".d river", + "_c at", + "ut t", + "pl ant", + "im b", + "ĠAc cept", + "Ġconc ert", + "ĉ node", + "ĉ z", + "? >čĊ", + "Ġb anned", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġto xic", + "Ġdisap pe", + "È Ľ", + "Ġgr ace", + "ate ful", + "Re ply", + "ĠCru z", + "Ġsc rap", + "Ġkey words", + "s imp", + "Ġmort gage", + "Ġcy ber", + "ĠEx ecute", + "Ġlat itude", + "if u", + ".C OM", + "d bo", + "Ġsort s", + "ĠG as", + "om ial", + ".L ocal", + "Cell s", + ".Re place", + "String s", + ".f it", + "ĠTh ird", + "% \",Ċ", + "Ġ{} \".", + "ĠS ony", + "Ġ[ :", + "Ġfall en", + ". ')Ċ", + "in h", + "ĠM C", + "Ġred is", + "C odes", + "Ġprofile s", + "h ook", + "Reduc er", + "_F UNC", + "Ġn avigate", + "str len", + "Ġh orm", + "á ŀ", + "ĠS R", + ". boot", + "Ġdig est", + "ĉ header", + ".find One", + "æ ģ", + "Db Type", + "n ia", + "_m erge", + "Ġdon ne", + "/ Getty", + "_CH AR", + "Ġb ands", + ". URL", + "art ial", + "Ġf req", + "Ġs ist", + "N g", + "Ġrender ing", + "\\ Core", + "Widget s", + "ĠV A", + "Ġactiv ists", + "St e", + "= _", + "all a", + "St amp", + "Ġload s", + "Ġx x", + "ĠL earning", + ".M vc", + "u ir", + "(\" $", + "Ġconnect ing", + "Read Only", + "ur u", + "ĠE ag", + "B IT", + "_DE L", + "å §", + "arr ass", + "ext ernal", + "ĠY OUR", + "ĠB rew", + "ĠF ive", + "Ġres ize", + "ig id", + "er ation", + "ĠÑ į", + "åĬ ł", + "ĠC atch", + "Ù ģ", + "ĠLe on", + "am il", + ".B ody", + "Cl ip", + "/ list", + ".b r", + "Edit Text", + "ĉ db", + ".G ame", + "(Build Context", + "back end", + ".R ed", + "face book", + ".url s", + "m r", + "rol led", + "---- ---", + "Ġinter vention", + "Ġretire ment", + "ĠK it", + "ĠP RE", + "Upper Case", + "ĠS ocket", + "Ġ: -", + "Ġstudy ing", + "ĠMet ro", + "ard ed", + "Ġconvers ations", + "C alled", + "Ġexam ine", + "ert ificate", + ".g z", + "-res ponsive", + "Ġref und", + "_n etwork", + "allow ed", + "em pt", + "Ġme als", + "C ategories", + "Ġtravel ing", + "Ġk g", + "Ġsh ame", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġexplicit ly", + "Ġmath ematic", + "ĠS uite", + "ĠR GB", + "****** /", + "Ġmix ture", + "lear ning", + ".t emplate", + "att s", + "w x", + "ĉ ctx", + ".p roperties", + "Ġdrink s", + "ĠE ither", + "set Text", + ".get Data", + ".z ip", + "Ġreve als", + "< table", + ".Hash Map", + "ĠH ur", + ") \");Ċ", + ".f ramework", + "ĠST ART", + "feed back", + "Ġsaf ely", + ". icon", + "config ure", + ". lock", + ".l ayers", + "/> .Ċ", + "Ġrank ed", + "_ impl", + "ĠHand les", + "Ġhost ed", + "Ġup dating", + "al bum", + "é Ŀ", + "Ġsh ader", + "Edit ors", + "- round", + "[] {", + "Ġse p", + "ĠH i", + "TE M", + "look up", + ".m an", + "_IN PUT", + "Ġthreat ened", + "_IM PORT", + "Ġd rops", + "ru it", + "s id", + "bo th", + "ĠEx cel", + "Ġj er", + "ord inary", + "еР¹", + "V IEW", + "re ply", + "Ġ) :Ċ", + "color s", + "ver ified", + "_T r", + "_p arse", + "Ġcon gress", + "P romise", + "int s", + "ĠM other", + ".A pi", + "ĠD uration", + "Ġfirst Name", + "inherit doc", + "ĠM ars", + "Ġa pr", + "OD Y", + "Ġvis its", + "Ġhe aling", + "let ters", + ")) );čĊ", + "f uture", + ".F ramework", + "Ġk iss", + "Ġinv olve", + "Ġsil ent", + "ad ows", + "Ġany body", + "s ch", + "Ġsole ly", + "- img", + "Ġprop ri", + "Ġin struct", + "Ġlic enses", + "Ġm eth", + "Ġcond em", + "ĠD omain", + "ĠHarr is", + "Ġs Ã¥", + "CE PT", + "B atch", + "@ extends", + "ĠCONTR IBUT", + ".Data Frame", + "_p acket", + "rec ision", + "Ġfoc using", + ". ht", + "__ \":Ċ", + ": Get", + "ĠK C", + "Ġpass age", + "Seg ment", + "_c enter", + "-z A", + "_B L", + "Ġconv in", + "Ġclass ified", + "ĠNS Mutable", + "_ ap", + "t ile", + "Rect angle", + "(n ums", + "v ens", + "ĠUI Button", + "ĠF eder", + "am o", + "Ġout line", + "ĠPar ser", + "Ġâ ī", + "ĠWork s", + ".S chema", + "Ġeng ines", + "_com mon", + "_ old", + "Ġset ContentView", + "Ġ/// <", + "ĠB T", + "f m", + "Ġd ivers", + "_ weights", + "em ark", + "ĠA CT", + "Ġpro portion", + "over lay", + ".dir name", + "ĠG it", + "_REF ERENCE", + "< >", + "l b", + "_r ule", + "è´ ¥", + "ĠPut in", + "Ġsleep ing", + "() :čĊ", + "Ġpres erve", + "Ġpar liament", + "ĠLook ing", + "Ġpick ing", + "ĠDis patch", + "Ġsl ip", + "ë ĵ", + "ĠL yn", + "_sign al", + "config uration", + "ĠP itt", + "ad en", + "pro cedure", + "Ġenthus i", + "f ight", + "ĠCons ider", + "Ġt orn", + "Conn ected", + ".c os", + "_group s", + "ĠTh ink", + "Ġdel iber", + "Ġres id", + "work ing", + ".column s", + "ĠCal led", + "Ġes lint", + "> \",", + "_D OWN", + "h ist", + "ĠAdv anced", + "Ġre wards", + "act ors", + "Ġsil ence", + "Ġmy th", + "Ġne ur", + "Ġa uction", + ".Get String", + "ek s", + "( project", + "ĉ msg", + "ĉ output", + "Ġcomplaint s", + ", S", + "Ġt bl", + "Ġ, ĊĊ", + "ri ors", + "ah ren", + "Ġlawy ers", + "re dux", + "_s ymbol", + "off ee", + "_RES ULT", + "( Name", + "UT C", + ".current Time", + "Ġorgan is", + ". arg", + "Ġmin im", + "w ick", + "Ġrece ives", + "B alance", + "Ġspeak s", + "ĠD ays", + "ĠBel ow", + "t ipo", + "P resent", + "Ġres erv", + "h p", + "Ġr it", + "_R IGHT", + "-- )", + "Ġchair man", + "D IS", + "ĠBO OST", + "Ġexper iments", + "__ );Ċ", + "Ġst amp", + "Ġf ert", + "Ġf ond", + "T er", + "el ve", + "ure n", + "+ i", + "end ency", + "Ġvirt ually", + "... \"", + "ï½ ŀ", + "- cent", + "_un ique", + "Ġpr icing", + "m ic", + "RES H", + "Ġ:: :", + "Ġan notation", + "ĠC ircle", + "ong odb", + "it as", + "Ġ% (", + "( component", + "Ġо б", + "( port", + "-h our", + ". obj", + "L BL", + "Ġj ury", + "GB T", + "Ġsp y", + "ĠProf essional", + "Ġ\"\" ;ĊĊ", + "Ġstri king", + "Ġdiscrim ination", + "Ġp ays", + "lic t", + "ent es", + "Ġthrow ing", + "ĠPl ugin", + "( def", + "ĠRuntime Exception", + "ĠM igration", + "Ġd ic", + "b ag", + "on ia", + "Ġcor ruption", + "( Map", + "Ġpr z", + ".d to", + "Ġac quire", + "State ToProps", + "Ġlo ving", + "оР¶", + "_p attern", + "Ġemot ions", + "Ġpublish er", + "_b e", + "Ġcoup les", + "o j", + "ĠCh art", + "Ġt rop", + ".t ool", + "Ġestablish ment", + "Ġd ol", + "Ġto wer", + "Ġl ane", + "ĠSy dney", + "Ġfill ing", + "claim ed", + "Ġdialog ue", + "Ġcon vention", + "book ing", + "pare ncy", + "æ ±", + "ĠGener ic", + "\\ Schema", + "Ġr anges", + "/ ch", + "Ġpan els", + "Ġr uled", + "çĶ Ł", + ".t s", + "_s ets", + "Ġclean up", + "Pre vious", + "ĠAn imal", + "($ (", + "ĠA ve", + "oll ar", + "_e val", + "ĉ Name", + "(t ree", + "Ġ\" ]", + "Ġdut ies", + "=' /", + "Click ed", + "Ġdifferent ly", + "ĠCl ark", + "Ġd it", + "olog ists", + "Ġsy nd", + "Ġs ends", + "- known", + "k b", + "ĠMod al", + "it ative", + "Ġr acing", + "Ġhigh lights", + "ĠSim on", + "ĠCapt ain", + "ä¿ ¡", + "ĠC B", + "cont in", + "ar an", + "Ġphys ics", + "ret ty", + "et al", + ".m d", + "ax ios", + "Ġspeak ers", + "Ġpre p", + "Ġaward ed", + "ì§ Ģ", + "ĠC orn", + "ĠN ature", + "UD IO", + "Ġpro j", + "- pre", + "[ u", + "Fe atures", + "Ġis Equal", + "B inary", + "s ig", + "Ġconf usion", + "ĠH at", + "Ġkt ó", + ".config ure", + "M ON", + "/ edit", + "_A dd", + ", true", + "Ġc li", + "Error Message", + "- loader", + "Dim ensions", + "ultip ly", + "Ġ{ !!", + "ĠSql Command", + "Ġsp oken", + "Ġp ics", + "Ġto y", + "( Key", + "ĠLo op", + "Ø ¨", + "E ATURE", + "in ction", + "_set up", + "w rapper", + "Ġt ong", + "c ular", + "O pt", + ".P l", + "=\" ,", + "(l ength", + "um n", + "Ġch rom", + "Ġse vent", + "ĠIllegal ArgumentException", + "ĉ start", + "Ġbeg un", + "CE PTION", + "dat aset", + "ĠF ailed", + "col s", + "Ġkne e", + "im ore", + ".sp lice", + "sh ell", + "ig gers", + "Ġthem es", + "ĠD J", + "ĠAss istant", + "- $", + "May be", + "Ġorder ing", + "ĠInt elligence", + "ĠMass achusetts", + "Ġfail ing", + "el son", + "G reat", + "= i", + ".re st", + "Ġinv ite", + "-dis able", + ".Group Box", + "âĢĻ est", + "Ġtack le", + "g v", + "et ter", + "Ġ), čĊ", + "_r ules", + ".w arn", + "function s", + "ĠChrist ians", + "Ġback ed", + "Ġsl ider", + "Ġenjoy ing", + "n est", + "Ġh ij", + "_m s", + "// *", + "An notations", + "ĠVariable s", + "< V", + "( server", + "ĠOr acle", + "element s", + "Ġorgan isation", + "_point er", + "ĠHe aders", + "[ d", + "Ġdead line", + "iss a", + "Ġkn ife", + "ĠNAS A", + "ĠHe ight", + "ĠAs ync", + "Ġven ue", + ".d om", + "bour ne", + "ĠHaw ai", + "Ġmem o", + "ict ions", + "Ġsurve illance", + "om i", + "/ assets", + "Ġed u", + "Ä Ľ", + "Ġro ster", + "Ġh ired", + "ĠT ok", + "Ġpl acement", + "ur ations", + "Ġset State", + "ĠMag azine", + "Ġhor ror", + "T ry", + "Ġl ag", + "ĠEvery one", + "th ur", + ")) ;čĊčĊ", + ". return", + "Ġsy mp", + "âĸĪ âĸĪ", + "Ġn ights", + "work er", + "Ġa le", + "ennes see", + ".st ep", + "Ġsynchron ized", + "our i", + "Do es", + ". change", + "f on", + ".set Background", + "irc ular", + "+ -", + "ĠC IA", + "ĠJ ane", + "ĠSim ilar", + "- I", + "level and", + "Ġpros pect", + "_f ound", + "ĉc olor", + ".D iagnostics", + "Ġann ounce", + "Ġassum es", + "/ tr", + "Ġb d", + "ĠCar bon", + "Ġanal ys", + ".de st", + "n ik", + "ĠL ie", + "- index", + "Draw able", + "ĠT AG", + "Ġtri angle", + "_F LOAT", + "ĉĉ ĠĠĠĠĠ", + ".bl ack", + "v ue", + "cur acy", + "Ġaffect s", + "Ġsure ly", + "Sl ider", + "uk i", + "c ery", + "Ġun ter", + ".pro file", + "ord on", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "le ave", + "Ġsmart phone", + "g ie", + "Ġcons pir", + "Ġt utorial", + "ç± »", + "Ġc ab", + "ĠSum mary", + "* ĊĊ", + "ä h", + "\" This", + "Ġsl ides", + "\" ", + "c ycle", + "ĠB ull", + "path s", + "Ġun p", + "Ġview DidLoad", + "_M odel", + "Ġassert True", + "Ġr ated", + "De cl", + "vert ed", + "ĠD at", + "b rew", + "Ġpoint ing", + "M s", + "ĠPoint er", + ") '", + "_n on", + "ĠSE C", + "Ġy eah", + "g ency", + "initial ize", + "f ly", + "[ pos", + ", g", + "Te le", + "Ġj oke", + "Ġcl ause", + ".find ById", + "en es", + "( instance", + " £", + "Ġs lic", + "_h ome", + "Ġ*/ }Ċ", + "_p ages", + "(s ervice", + "R P", + "ĠAm ong", + ".get Current", + "ãĤ ¹", + "Ġs lee", + "= [Ċ", + "ol er", + "Ġlib ert", + "Ġ` Ċ", + "Ġw enn", + "l ated", + "Ġimm une", + "( Node", + "ĠPro blem", + "ĠA bs", + "log s", + "Ġ ../", + "ĠA DC", + "Ġ}} \">Ċ", + "> ');Ċ", + "= b", + "ĠW ind", + "lah oma", + "Ġalloc ate", + "or ian", + "Ġpres cription", + "- quality", + "ĠMay or", + "in ely", + "end foreach", + "ĠCom plex", + "k om", + "T Y", + "] ].", + ". Style", + "_m any", + "',' $", + "Ġbar rier", + "ĠF etch", + "ĠMar vel", + "Ġres ist", + "ог о", + "b idden", + "ĠRun nable", + ": false", + "Ġbuild s", + "ĠSt age", + "Ġd ub", + "emp o", + ".s ite", + ";ĊĊ ĊĊ", + "ĠDen ver", + "Ġre vel", + "Ġtrigger ed", + "Ġd ice", + "_f ail", + "Ġg c", + "ĉ X", + "ĠTh rowable", + ".r outer", + "ĠRev olution", + "ÑĢ а", + "_N ON", + "Ł ¥", + "Ġel der", + "Ġab road", + "ĠÐ µ", + "ĠAd ult", + "bl r", + "g lyphicon", + "Ġprom oting", + "Ġ iz", + "ĠS olid", + "_lo ader", + "ear ly", + ".en abled", + "- edit", + "ĠU L", + "_ play", + "ĠInt errupt", + "Ġadvant ages", + "uc le", + "Ġmechan ical", + ".table LayoutPanel", + "ĠWork ing", + "Ġan onymous", + "R ating", + "ig ious", + "_ph one", + ".addAction Listener", + "Ġfr an", + "und en", + "Ġ*) &", + "_ bool", + "ul ative", + "Ġcon e", + "ĠM ult", + "Ġm ö", + "ĠFor ward", + "] ):Ċ", + "Ġconvin ced", + "act ed", + "ãģ ĵ", + "ĠConfig ure", + "Ġce iling", + "D er", + "Ġpass engers", + "Group s", + "Ġsoc cer", + "/ W", + "avi ors", + "sw ith", + "ĠZ one", + ". Options", + "ĠM om", + "ied er", + "Array s", + "Ġtreat ments", + "Ġprotect ing", + "f ac", + "Ġpick le", + "Button Item", + "Ġblock ing", + "str ar", + "à ²", + "ĠEx port", + "Ġth rew", + "ott a", + "ĠB ASE", + ".w s", + ".LE ADING", + "order By", + "_d elay", + "ĠP u", + ".d ll", + "ĠCh oose", + "Pol ice", + "ĠBE GIN", + "box es", + "Ġdiam ond", + ", l", + "Ġ ĉĉĉ", + "Ġcur ious", + "t v", + "Ġerot ische", + "ack ages", + "ĉ Set", + "T ick", + ".b order", + "static method", + "Ġch er", + "in voice", + "Ġcr u", + "Ġdef ect", + "_m etadata", + "re lation", + "ik an", + "[ N", + "(Q t", + "( Base", + "æģ ¯", + "be at", + "ĠEm pty", + "ĉ o", + "_sh ift", + "Ġreg ret", + "Th ose", + "C ent", + "ĠPort ug", + "ĠIs lands", + "ĠT IME", + "Man agement", + "-s p", + "ê me", + "Ġnot ion", + "un ifu", + "P K", + "è¡ Į", + "ĠCUR LOPT", + "\\\" \\", + "U V", + "ç º", + "d ra", + "c ou", + "= `", + "ĠD estroy", + "r p", + ".c ancel", + "G G", + "r untime", + "ĠV ue", + "Ġprogress ive", + "/s ervices", + "Ġrun ner", + "_FR AME", + ".ToolStrip MenuItem", + "Ġ' ,'", + "d elay", + "= utf", + "Ġscreen ing", + "Ġpull ing", + "om as", + "Ġan th", + "- new", + "/ local", + "Ġi Pad", + "Ġt witter", + "Ġd ying", + "Ġhe aven", + "ĠU Int", + "ĠSen ator", + "Ġpres um", + "ĠWalk er", + "Ġover come", + "ete ction", + "Ġemb arrass", + "Ch ina", + "In clude", + "RO LL", + "Ġdata Type", + "D avid", + "ภ£", + "lo p", + "-m onth", + "Ġsc ar", + "ĠS afe", + "Ġ ****************************************************************", + "Ġaccess ories", + "Ġr amp", + "_U SE", + "Ġcontr ad", + ")) ]Ċ", + "Ġpre st", + "ĠH R", + "ĠR ap", + "Ġus ize", + "Ġcap ability", + "Ġc ort", + "- next", + "Ġbur den", + "_read er", + "Ġ@ @", + "reg ular", + "ĠK a", + "M AN", + "Ġa str", + "Ġ' ')Ċ", + "Ġf ed", + "Ġpars ing", + "ĠY ears", + "Ġbro ker", + "\": {\"", + "Ġa kt", + "In ventory", + "abe led", + "Ġarg parse", + "****** *Ċ", + "vers ation", + "Ġc ord", + "ĠT i", + "Ġhope fully", + "Ġa h", + "ver b", + "Ġst olen", + ". Entry", + "Ġexpect ing", + "O rientation", + "Ġpower ed", + "Ġp ersist", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "'] );", + "')) ,Ċ", + "ĠC ash", + "ĉ item", + "gr ades", + "rop ol", + "b asic", + "Ġ\" );čĊ", + "Ġaw ards", + "(r ange", + "- all", + "ĠIB Outlet", + "ĠInd eed", + "---------------------------------------------------------------- ------------", + "Ġstom ach", + "Ġfl ower", + "Ġs ew", + "_t imes", + "av is", + "Q String", + "ĠR outes", + "_pro t", + "Ġcom edy", + "Ġlog out", + "Ġwood en", + "Ġpost er", + "p iece", + ".J oin", + "ĠP ok", + "cel ona", + "mut ex", + ";čĊ čĊčĊ", + "Ġstri kes", + "Load ed", + ") arg", + "es a", + "Un ited", + "E p", + "PE LL", + "ĠAtl antic", + "ul let", + "app le", + "Ġsett led", + "a con", + "Ġprint er", + "ĠG C", + "å® ļ", + "Ġrender ed", + ", âĢĻ", + "he it", + "s ocial", + ". ge", + "ĠR ick", + "ĠUt ah", + "g ot", + "on ical", + "ĠSc roll", + "ĠSc iences", + "Ġj ug", + "Ġam pl", + "ent i", + "LE FT", + "Ġt abs", + "Ġenorm ous", + ".get Key", + "loc ate", + ". EX", + ".st orage", + ".W e", + "Ġto ast", + "ĠAdd itionally", + "ĠN OW", + "_ UPDATE", + "Ġtrans ferred", + "th a", + ".D isplay", + "_ ui", + "ID EO", + "Ġmeaning ful", + "ĠMos cow", + ", this", + "ĠVict oria", + "æĶ ¹", + "ĠÐ Ł", + ".st ack", + "ĠB arn", + "pared Statement", + ": string", + "Ġb ij", + "ĠST ATE", + "Ġemploy ers", + "ĉ input", + "( |", + "Ġle x", + "in voke", + "ĉ num", + "++ ,", + "at ial", + "ors es", + "Ġfor k", + "_t xt", + "ĠAnton io", + "Ġ( <", + "aver se", + "Ġdev ast", + "ãĢ Ģ", + ".D ec", + "ĠG ard", + "/ ui", + ". %", + "tr i", + "Ġrol led", + "Value Pair", + "itt en", + "ĠTh er", + "Ġv rou", + "ĠFl ow", + "ĠFin ance", + "ĠCom b", + "H C", + ".set Visible", + "is l", + "Ġp k", + "Ġup set", + "( raw", + "ĠV ice", + "e atures", + "ĠL ang", + "Look ing", + "ĠA ST", + "Ġtri ps", + "ĠJust in", + "b rowser", + "=\" '.$", + ". vertices", + "- co", + "}/ {", + "Ġ? ,", + "ĠD omin", + "ĠBel g", + "\" <", + "Ġsup pose", + "add y", + "Ġwalk s", + "ERR U", + "_f ilters", + "Pre ferred", + "sc ene", + "е Ñģ", + "ĠAff airs", + "Ġ\"# {", + "Ġon Submit", + "Ġstock s", + "/ view", + "g ree", + "- get", + "h it", + "J o", + ".get C", + "Initial ized", + "ÑĤ и", + "c uts", + "( Type", + "ĠAg reement", + "ĠViet nam", + "Ġ/* !", + "Ġp izza", + "- view", + "_ em", + "Ġl hs", + "Ġm uy", + "ĠId ent", + "ĠF riends", + "Ġab und", + "_A D", + ".t imestamp", + "- '", + "Ġd uplicate", + "Ġhun ting", + "Ġregul atory", + "ia o", + "am ous", + "ĠEnt ertainment", + "[ A", + "iat ric", + "_CL IENT", + "ĠK ids", + "/p kg", + "B reak", + ")) );ĊĊ", + "ĠSh ape", + "Ġrel ating", + "Int errupt", + "able Opacity", + "emb re", + "Ġmyst ery", + "Ġjournal ists", + "rit able", + ".L ink", + "Ġstop ping", + "CRE T", + ".D B", + "Ġpopular ity", + "Ġg ew", + "Ġim pr", + "set Value", + "FL AG", + "ĉm ax", + "Ġb ake", + "w y", + "ĠEcon omic", + "Ġen contr", + "Ġf name", + "/ de", + "R ank", + "Ġbug s", + ".s m", + "Ġmed ian", + "D OWN", + "ĠS ure", + "At Index", + "ĠD ick", + "Ġ( __", + ".d elta", + "F r", + "Ġsuggest ing", + "ĠRec yclerView", + ", e", + "ST ART", + "/************************************************************************ ****", + "xf ord", + "Ġrece ipt", + "CL AIM", + "read only", + "Ġeng aging", + "C a", + "as ma", + "Ġens uring", + "Eng lish", + "ĠV ancouver", + "hy th", + "Ġpurch asing", + "ĠP I", + ". word", + "(s p", + ".h ome", + ": def", + "Ġg ig", + "ĠV e", + "for um", + "ĠM itch", + "B ay", + "_F L", + "Ġs oll", + "_column s", + "Ġminor ity", + "b ird", + "Ġhand ed", + "SS L", + "ST AT", + "Ġnerv ous", + "ĥ ½", + "Ġfile Path", + "CRE ATE", + "A w", + "Ġp ens", + "se ed", + "ĠCom pute", + "ol k", + "ĠAs set", + "re ach", + "'), čĊ", + "n avigation", + "L F", + "/ util", + "ĠP ub", + "Ġâ Ķ", + "c ion", + "## Ċ", + "II I", + "Tag Name", + "Ġam id", + "per mission", + "if iable", + "xFFFF FFFF", + "н и", + ".B uffer", + "_ irq", + "d ark", + "Ġret val", + ".f ire", + "produ ction", + ".list en", + "ĠWe ather", + "Ġbuy ers", + ". ne", + "er p", + "ĠP ent", + "Ġw elfare", + "Ġpage Size", + "ĠSt adium", + "ert a", + "Ġle v", + "amp a", + "P ager", + "Ġcharg ing", + "ĠNet flix", + "| null", + "_r andom", + ".x path", + "Ġst ere", + "ĠIS IS", + "pons es", + "( loc", + "ey ond", + "ĠOff icial", + "ĠMary land", + "Data Type", + "_p ar", + "{ },", + "ĠEn joy", + "_SH IFT", + "ĠA wards", + "_ENT RY", + "Ġseem ingly", + "entic ate", + "Ġheart s", + "_ ;ĊĊ", + "ĠH IV", + "Ġindiv id", + "ĠFl ag", + "_ ctrl", + "ĠC allback", + ", z", + "ĠG PU", + "ĉ obj", + "ĠPh oenix", + "ĠB US", + "Ġrub ber", + "_A UTH", + "ĠSol utions", + "( location", + "Variable s", + ".set Enabled", + "_h igh", + "W O", + "G esture", + "Ġre try", + "Ġobject ForKey", + "allow een", + "Ġm os", + "ĠC ele", + "Ġik ke", + "(c ell", + "ĠM ODE", + "ren a", + "Ġdescri bing", + "Ġph i", + "Ġr d", + "Ġdes erve", + "Ġwhe els", + "å¸ Ĥ", + "Ġcrit ics", + "N amespace", + "ĠF ra", + "Ġ ĊĊĊĊ", + "Ġall a", + "Ġrequ iring", + "æľ Ł", + "ut ation", + "Ġdelay ed", + "Ġadministr ative", + "Ġb ay", + ".h idden", + "T ex", + "Ġbound aries", + "Ġ] );ĊĊ", + "ĠFollow ing", + "~ /", + "F i", + "_con v", + "_T ITLE", + "Ġdes de", + "ICollection View", + "Ali as", + "Ġb ite", + "pat ient", + "_COMM AND", + "Com pleted", + "ĉ elif", + "( <", + "B usiness", + "ĠP ool", + "Ġpurs ue", + "ĠB an", + "_st eps", + "_DE CL", + "um ble", + "Ġcom bo", + "ĠL ayer", + ".x r", + "Ġd up", + "-------- -", + "Ġmod ifier", + "ro b", + "re z", + "Ġath letes", + "Us ed", + "w ear", + "Ġlegit imate", + "Ġ\" ĊĊ", + "Ġh v", + "St d", + "ĠH old", + "Ġsurv iv", + "ĠAll iance", + "ĠEar ly", + "Beh avior", + "(f ont", + "/lib s", + "Ġrect angle", + "Ġs inger", + "Ġam p", + "Equal To", + "Ġ\" .\"", + "Ġgirl friend", + "å ±", + "line ar", + "obs erv", + "Ġpi ù", + "Ġcomple ment", + "With Value", + "(p assword", + "t ake", + "Bl ank", + "ĠCom par", + "' \",", + "_p olicy", + "m ongoose", + "_FA ILED", + ".re port", + "R atio", + ".Perform Layout", + "us able", + "m ers", + "_re nder", + "PE ED", + "Ġles b", + "ĉ E", + "_t ool", + "Ġl adies", + "о Ñģ", + ")) ))Ċ", + ";; ;;", + ".d ot", + "Ġn est", + "pe ak", + "uk kit", + "ec a", + "_S W", + "Ġ& (", + "ĠOk lahoma", + "Ġbank ing", + "ĠN intendo", + "Ġreprodu ce", + "_element s", + "_m ac", + "pro xy", + "Ġremark able", + "}/ ${", + "Ġout s", + ".has Next", + "M ODE", + "Ġan ime", + ".con n", + "Un ique", + "D om", + "Ġimportant ly", + "itt y", + "Ġju ice", + "T w", + "ĠPart ners", + "Ġattack ing", + "Ġport able", + "am iento", + ".P ictureBox", + ".g en", + "Ġopt imal", + "Ġre cre", + "Ġjournal ist", + "ĠEx tract", + "ĠMore over", + "Ġmargin Top", + ".A p", + "Ġf iring", + "Na N", + "ĉ template", + "аР´", + ". En", + "Ġdef ence", + "ĠT el", + "il en", + "j an", + "= data", + "ĠU rl", + "ĠRe uters", + "(t otal", + "ĠFif th", + "Ġess ays", + "Ġinterpret ation", + "Ġchar ity", + "ĠR ules", + "Ġsub section", + "st yled", + "az er", + "l ags", + "L IST", + "Ġupload ed", + "Ġtr ash", + "Ġreg istr", + "Ġsell er", + ">' ;čĊ", + "Ġstart Time", + "ç Ļ", + "s y", + "(Http ServletRequest", + "Ġtr ap", + "G C", + "Ġembed ded", + "Ġsurround ed", + "im its", + "T X", + "yl inder", + "ĠF al", + "Ġsent ences", + "ĠJ a", + "IF ICATION", + "we apon", + "ov ation", + "Ġco at", + "Ġinter pol", + "Ġl ips", + "ĠK y", + "Ġv ectors", + "_ am", + "Ġint ake", + ".w orld", + "Ġin box", + "ĠM AC", + "_ ab", + "(name of", + "Ġent ert", + "Ġgather ing", + "ĠS IM", + "++ .", + "ny a", + "' }}", + "ĠUP DATE", + "Ġp ac", + "( html", + "ĠS ant", + "i ating", + "ĠIde as", + "Ġspr ay", + "ĠH art", + "Ġver ification", + "ades h", + "/ modules", + "ĠM ind", + "ĠSized Box", + "Ġsh elter", + "Ġher oes", + "att y", + "Ġcert ified", + "s j", + "Ġê tre", + "ÅĤ o", + "Ġpublish ing", + "ĠMal ays", + ".get User", + "ĠPro vider", + "ĠLinked List", + "ĠB or", + "RO UND", + "d id", + "t ain", + "p ire", + "ĠJ enn", + "t el", + "and e", + "_f ront", + "ĠMc G", + "Test Method", + "ภŃ", + "Ġoccasion ally", + "ĠW ales", + "Ġexerc ises", + "ĠÐ Ĵ", + "- plus", + "Ġvalid ator", + "Ġpr ayer", + "L ATED", + "_ author", + "Ġlab our", + "++ Ċ", + "-e quiv", + "ĠG PL", + "Ġface book", + "s imple", + "g ly", + "Process or", + "ip y", + "Ġ* >", + "Ġcle ared", + "ĠP ush", + "Ġpen is", + "Struct ure", + "li j", + "ĠM organ", + "Ġhand ful", + "\" .Ċ", + "| \\", + "Ġ ********************************", + "ĠA qu", + "_ IC", + ".load s", + "Ġm eter", + "ĠMar ine", + ":: {", + "ĠT S", + "ĠArray s", + ".T itle", + "GR AM", + "ter min", + "Ġco inc", + "El se", + "_st ates", + "-r un", + "m embers", + "ast ro", + "Ġon Press", + "Ġbe ings", + "Ġabandon ed", + "Ġtax p", + "own ers", + ".m ode", + "Ġdiagn osis", + "Ġ_ Ċ", + "ĠK night", + "ĉ A", + "Ġob serve", + "), '", + "! \")Ċ", + "ĠPar a", + "Ġvari ation", + "( False", + "ĠAnt i", + "Ġg ri", + "Ġhome less", + "? v", + "Ġbe z", + ".S erver", + "re lease", + "ĠP atri", + "Ġchar s", + "Ġrank ing", + "activ ation", + "Ġw ides", + "q r", + ".S ql", + "ac ular", + "ĠB ot", + "_s ync", + "Ġhapp iness", + "Ġvolunte ers", + "Ġs its", + "/ <", + "[ e", + "(file Name", + "Ġcap ac", + "ĠMar ia", + "f ather", + "Ġgr am", + "* i", + "Ġcas o", + "_d raw", + "ĠR aw", + "ĠIter ator", + "ĠP adding", + "P D", + "BO X", + "ĠS PECIAL", + "Ġfe cha", + "Ġv ide", + "ĠLe ader", + "ä» ¥", + "$ (\".", + "Ġdiam eter", + "Ġm ild", + "Ġrock s", + "app ings", + "d irectory", + ".fl ush", + "ĠJ ess", + "UN IT", + "ĠP ear", + "Ġmand atory", + "S ur", + "q t", + "Ġstream s", + "Ġco operation", + "ĠS ac", + "Ġche aper", + "ĉ ch", + "an imation", + "f are", + "( height", + "( True", + "N Y", + "Ġw rest", + "Ġpoll s", + "Ġencounter ed", + "ĠMarket able", + "_P ASSWORD", + "_SE LECT", + "ĠArab ia", + "_c lock", + "Ġv oy", + "Ġи з", + "Ġst ir", + "is ible", + "-e ffect", + ".c reated", + "Ġto ys", + "ĠTrad able", + "Ġr ust", + "Ġstr cpy", + "_t imestamp", + "Ġtalent ed", + ", null", + "ĠJ obs", + "ĠPort land", + "Ġweak ness", + "Th row", + "ĠAng el", + "ä¿ ®", + "Ġun cert", + "ï¼ī Ċ", + "ĠìĿ ´", + "Wh ich", + "Ġ[- ]:", + "S omething", + "Ġconv icted", + "k le", + "ed ium", + "Ġbranch es", + "Ġb ases", + "ç ®", + "Ġcomplex ity", + "ĠF ig", + ". reshape", + "$ db", + "_CON ST", + "ĠT es", + ".r untime", + "Ġden y", + "ĠB SD", + "Ġk r", + "h att", + "ĠSt atic", + "Ġunivers ities", + "Re place", + "Ġdro ve", + "Ġad oles", + "_pl ugin", + "ĠL GBT", + "Ġt ex", + "du ction", + "ED I", + "ĠT ed", + "_ URI", + "Ġre ception", + "art en", + ".S ingle", + "r ice", + "sc ious", + "_b g", + "Ġw ages", + "ĠS ervlet", + "UIL ayout", + "Ġform atted", + ".M od", + "< class", + "is en", + "Ġrepresent atives", + "\"] =", + "Ġport al", + "ĠHun ter", + "Ġh iring", + "__ )Ċ", + "ric ulum", + "u o", + "li est", + "Ġt ears", + "L at", + "Ġliter al", + ".In sert", + "Ġc urs", + "ĠCom put", + "Ġterror ism", + "Ġswe ep", + "Ġ[] čĊ", + "Ġpass enger", + "Ġeast ern", + "Ġtwe ets", + "Ġoper ated", + "w nd", + "ĠS yn", + ".t ools", + "ĠW M", + "ul ates", + "Ġbacter ia", + "( bytes", + ".set Data", + "Ġvis ibility", + "// ================================================================", + "el m", + "Ġgener ating", + "Ġm v", + "Ġk h", + "j en", + "/ search", + "Ġaccount ing", + "se gment", + "act ic", + ". ip", + "Ġdeploy ment", + "Ġfoot er", + "> ',Ċ", + "Ġexpand ing", + "ĠHam ilton", + "ĠCon trib", + ".T ables", + "Act iv", + "H H", + "ocom merce", + "_ ;", + "Ġamong st", + "ow ing", + "ĠC old", + "AP H", + "Ġpsych ological", + "_t ensor", + "Ġpack aging", + "ĠSw eden", + "Ġp are", + "Ġag gregate", + "Ġmoder ate", + "_h and", + "Ġdesign ated", + "Ġdr um", + "Ġget User", + "ĠC reek", + "_s cope", + "ĠTrans fer", + "ĠM arg", + "Ġfight ers", + "W nd", + "ĠS el", + "ĠLa unch", + "Ġemerg ing", + "if rame", + "ĠAdd itional", + "Ġf ears", + "Ġsat ellite", + "_ :", + "Ġdis posing", + "Get Value", + "Http Post", + "AT IVE", + "ul ary", + "View s", + "Ġatt ending", + "ĠT ennessee", + "ĠM ission", + "Ġmedic ation", + "ĠW y", + "ĠAn na", + "Ø ¹", + "ĠVert ex", + ".t ypes", + "O rgan", + ".DataGridView TextBoxColumn", + "ĠR S", + "Ġtemp o", + "( App", + "Version UID", + ".p oint", + "ĠD utch", + "H ours", + "L U", + "Ġqu oted", + ".b uilder", + "ĠPer fect", + "ĠAl ways", + "_t wo", + "Ġexclus ively", + "ĠC ra", + "ific ar", + "ĠA WS", + "ing ham", + "com plex", + "k ernel", + "Ġgr avity", + "Ġw i", + "Ġover view", + "ĠW ant", + "ĠW P", + "( sh", + ". rotation", + "St ates", + "ĠTe en", + "_com ponents", + "ì Īĺ", + "Re ceived", + "Ġly rics", + "rit es", + "ĉĉĉĉĉ Ġ", + "-A merican", + "[ num", + "/ python", + "ĠU ART", + "Ġapp le", + "ĠJon athan", + "Ġmoment um", + "ภ±", + "Ĥ ¹", + "Ġm ich", + "and ra", + "Ġbi ological", + "ĠM ens", + "Ġ% %", + "else a", + "ĠMex ican", + ".rand int", + "Ġt ale", + "ĠValid ate", + "Ġdefe ated", + ".ht m", + "Ġcop per", + "= /", + "cos ystem", + "Ġr ip", + "dec imal", + ".V ISIBLE", + "ĠT a", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", + "Ġdownload ed", + "en vironment", + "Ġnom ine", + "build ing", + "ĠSp ot", + "ipher al", + "Ġal to", + "qu et", + "ĠF T", + "/ get", + "/m aster", + "W IN", + "åħ ĥ", + "W est", + "arg c", + "Ġprodu cers", + "ĠM uch", + "_st orage", + "cred it", + "CON T", + "Ġv et", + "Ġvo ices", + "(' ',", + "Ġinstr uments", + "ĠM SG", + "es se", + "re pository", + "om ics", + "Ġdeal er", + "St ill", + "Ġb anner", + "asc ii", + "Ġrem arks", + "[ js", + "Ġshort er", + "g ulp", + "Ġmyst er", + "Ġk un", + "ĠB ird", + "Ġti ene", + "n ut", + "ĠU m", + "Ġw ise", + "Y eah", + "INE SS", + "_b egin", + "- heading", + "C ourse", + "Ġ čĊčĊ", + "omb ie", + "grad ed", + "ĠG PS", + "Ġ że", + "F it", + "c aption", + "ö n", + "/ image", + "l ia", + "(m od", + "Ġle ak", + "en za", + "/ H", + "ĠH appy", + "D ist", + "n x", + "ĠGovern or", + "(l ast", + "te acher", + "ĠS ent", + "s upport", + "ject ory", + "Ġ Ùħ", + "Reg istration", + "ĠGr ay", + ", false", + "Ġadjust ed", + "( settings", + "< R", + "ĠM age", + "Ġpl aint", + "_ )Ċ", + "ĉ it", + "omet ric", + ". bootstrap", + "Ġcar ries", + "I p", + "Ġ! $", + "Ġswim ming", + "ĠMar io", + "ĠQuest ions", + "P ACE", + "æĸ ¹", + "e or", + "}} \"", + "Ġo ven", + "ĠK on", + "Ġwis dom", + "Ġac quisition", + "ess ment", + "ag ine", + "Ġexpress ions", + "Sequential Group", + "F ront", + "ul pt", + "aw k", + "'] )ĊĊ", + "_ AR", + "Ġanal og", + "ul in", + "_PR INT", + "ĠL G", + "Ġb lob", + "ĠFurther more", + "_com ponent", + "ĠC ole", + "L AN", + "SCRI PTION", + "Ġl ap", + "icens ing", + "_TIME OUT", + "ĠF ro", + "Ġli ability", + "Ġcom posed", + ".create SequentialGroup", + "_p erson", + "Ġbe am", + "ĉ ĠĠĠĠĠĠĠĠ", + "ĠNot Found", + ". 'Ċ", + "ÃŃ s", + ".Text View", + "P DF", + "Ġk ar", + "__ ('", + "Ġ\" :\"", + "_m essages", + "Ġhar vest", + ".h istory", + "> 'Ċ", + "-f old", + "æ Ĭ", + "ĠBet ter", + "Ġ\"\\ <", + "sp acing", + "Ġfurn ished", + "os er", + "] }Ċ", + "Ġ$ \"", + "p ull", + ".P ost", + "( ip", + "Ĺ ı", + ".f ront", + "nt e", + "ĠF M", + "g uid", + "Ġnegot iations", + "agon al", + "Ġtrem end", + "unge on", + "Ad v", + "car ousel", + "ÃŁ e", + "_DE SC", + "Ġham mer", + "ẠŃ", + "ĠĠĠĠĠĠĠĠ ĊĊ", + "-c ore", + "-s ervice", + "Ġcorn ers", + "ĠS F", + "p red", + "> A", + "ĠJ Label", + "Ġrom antic", + "Ġtestim ony", + "os c", + "ĠGener ation", + "as ures", + "_int ernal", + "Ġprint s", + "Ġ] )Ċ", + "ĠC leveland", + "re po", + "D isc", + "Ġ\" >Ċ", + "�� ��", + "Ġne arest", + "_t b", + "( require", + "EO F", + "- child", + "Ġbu dd", + ".Xtra Editors", + "alt ies", + "\\\": \\\"", + "W ords", + "Ġloc ally", + "Ġpurch ases", + "Draw er", + "ex tract", + "Ġexec ut", + "} '.", + "user data", + "Ġfocus es", + "-min ute", + "ĠP ublish", + "og o", + "Ġmount ains", + "B ot", + "} >{", + "Ġt ension", + "ro d", + "m esh", + "Ġtransform ed", + ", R", + "() }Ċ", + ".l ong", + "Ġg orgeous", + "ĠS chedule", + "Ġol dest", + "Ġsub process", + "( IN", + "y ect", + "ĠCo oper", + "arn ess", + "ĠMon itor", + ".p art", + "ĠN BC", + "Ġc otton", + "Ġh ol", + "Ġrg ba", + "ĠB io", + "Cont inue", + "P od", + "Ġparticip ating", + "clus ions", + "(By Val", + "à ¬", + "ĠH OW", + "_set opt", + "Ġaccompany ing", + "at on", + "Ġ/ \\", + "ĠAuth entication", + "i én", + "ĠBar ack", + "/* .", + "Ġe ager", + "ĠC ancel", + "< lemma", + "ep h", + "ĉ window", + "Ġinc idents", + "), (", + ".D es", + "ib e", + "ĠFunction s", + "Ġhosp itals", + "Ġo xygen", + "root Scope", + "Ġd rew", + "ĉ request", + "not ice", + "ak u", + "am ents", + "f ar", + "Ġprec ise", + "_w rapper", + "Ġlisten ers", + "A Z", + ".b ounds", + "ĠA verage", + "field set", + "_ axis", + "Ġexam ination", + "' .Ċ", + "mon s", + "++) {čĊ", + "ĠForm s", + "íķ ľ", + "Cpp Method", + "_tr ace", + "Ġengine er", + "ĠFl at", + "Ġrev ision", + "Ġhe ating", + "/ profile", + ".r u", + "p riority", + "Ġin fer", + "_ST REAM", + "Ġ* )(", + "> $", + "OLE AN", + "OK IE", + "IB ILITY", + "U AGE", + "ĠSur vey", + "Ġres ign", + "w ing", + "Ġsecre ts", + "Ġch ips", + "JSON Object", + "Des ktop", + "_SY MBOL", + "(res ource", + "ĠĊ", + "Ġnew est", + "ul i", + "Ġdes ert", + "Ġd ip", + "ĠP ow", + "Ġequ ation", + "Ġposs ibilities", + "ĠF ed", + "os ph", + "Ġ[ %", + "Ġb ubble", + "ether lands", + "Ġc ement", + ". auto", + "_ AN", + "âĢĻ .", + "se lection", + "ĠB ond", + "D en", + "- O", + ".get Type", + ".W indow", + "p res", + "Ġsw inger", + "\" })Ċ", + "Ġp ip", + "Ġm ice", + "Ġcomp ound", + "- plugin", + "ik o", + "Ġcent uries", + "ic ular", + "-in line", + "ĉ key", + "> \\<", + "EN SION", + "Ġ[ čĊ", + "Ġprecis ely", + "Ġét é", + "ĠP ast", + "ĠCam bridge", + "-f ull", + "Ġanaly ze", + "ĠSte ven", + "Ġn em", + "d ue", + "ore n", + "Ġmus cles", + "ij ing", + "/ -", + "ĠKenn edy", + "R M", + "oss ible", + "Ġact ress", + "Ġd olor", + "å½ ķ", + "Ne ed", + ".t oggle", + "ĠR ace", + "w ers", + ".m aterial", + "ĠD ue", + "ĠP el", + "# print", + "Ġindepend ence", + "ex us", + "Sh adow", + "Ġenc oder", + "( level", + "ĠSw ift", + ".d oc", + "_se lection", + "Ġserial VersionUID", + "Label s", + "Ġperform ances", + ".T ag", + "ĠN HL", + "iz en", + "/ UIKit", + "_CONT ROL", + "Ġearn ings", + "ĠAl t", + "_H ANDLE", + "C tx", + "Ġpers u", + "Ġtr an", + "ç ¨", + "_CH ANNEL", + "Ġsatisf action", + "ĠG P", + "io x", + "m itt", + "land o", + "Ġp ig", + "inal s", + "ê ncia", + "S urface", + "ĠU UID", + "Ġbenef icial", + "Ġsequ ences", + "ĉmem set", + "Ġmag ical", + " «", + "Ġw orn", + "AS C", + "pop up", + "COM P", + "_b efore", + "en ess", + "U i", + "L es", + ".re quire", + ".Serial izable", + "add Gap", + "Ġauthor ization", + ".py plot", + "urr ay", + "lat itude", + "fr ames", + "aj s", + "Ġcomp ass", + "Ġobserv ations", + "_s up", + ".en viron", + "Ġtri ple", + "ĠRub y", + "Ġdr ain", + "_F ILTER", + "S an", + "UM P", + "Null Exception", + "ĠG ab", + "ow e", + "ĠTurk ish", + "_se quence", + "ĠGr ant", + "uel a", + "Ġw o", + "Ġc ube", + "i q", + "Ġdis orders", + "Ġextra ordinary", + "Ġc trl", + "ĠSe q", + "ent r", + "Ġsan ctions", + "uts ch", + "Re ports", + "Ġin herit", + "Per iod", + "Ġphot ography", + "ĠF ramework", + "Ġspecial ist", + "Ġ? ĊĊ", + "_ selected", + ".P layer", + "Ġal location", + "( account", + "Ġstruct ural", + "v able", + "- offset", + ".App CompatActivity", + "аР¼", + ".Add WithValue", + "Ġicon s", + "Ġshut down", + "_l ow", + "ĠCom pare", + "ĠC e", + "= head", + "l am", + ".p redict", + "_DE C", + "ĠS leep", + "ĠGr atis", + "Ġsuggest ion", + "ĠD EL", + "ca ff", + "av irus", + "No thing", + "ŀ ĭ", + "Ġwides pread", + "Ġmechan isms", + "Ġtext Align", + "occ up", + "ĠR ail", + ": NS", + "Ġf iber", + "Ġm k", + "Ġv intage", + "-l ong", + ".re duce", + ". Entities", + "( record", + "Ġple asant", + "FR ING", + ".C ells", + "OT T", + "ĉelse if", + "_con firm", + "ĠView Group", + "s ym", + "Ġpr ay", + "Ġsus pected", + "Cont ains", + "Ġb orders", + "Ġcomponent Did", + "ASS ERT", + "Ġinf inite", + "- order", + "Ġh ello", + "ĠGr ade", + ".currentTime Millis", + "apol is", + "z h", + "ĉ Object", + ": \\\\", + "H O", + "val uation", + "Ġvoc ab", + "Ġcou pon", + "atab ases", + ".Get Type", + "L earn", + "] =\"", + "ĠG ary", + "ot ive", + "Ġas h", + "Ġb ib", + "XX XX", + "Ġbal anced", + "VAL UE", + "ĠN at", + "_A d", + "< E", + "åĮ º", + "ĠMethod Info", + "L IB", + "Ġconsider able", + "ĠInd ustry", + "test s", + ".set Title", + "ĠBl uetooth", + "Ġm apped", + "ĠBru ce", + "ĠMain Window", + "ĉ status", + "Ġr az", + "ĠM and", + "Ġclass ification", + "Per missions", + "Ġ---------------------------------------------------------------- ------------", + "Ġcontain ers", + ": set", + "_x ml", + "Ġwh ilst", + "Th rough", + "Ġval ign", + "Ġworld s", + "C ORD", + "ED IA", + "ÑĢ ов", + "Ġsp are", + "ĠH ad", + "ĠDE F", + "(p tr", + "Ġwarm ing", + "ठ¾", + "Ġcons ensus", + "ag ne", + "CT L", + "Ġì ķ", + ".M ain", + "web Element", + "Ġp ist", + "Fl ash", + "App end", + ".tw img", + "T ap", + "Ġveget ables", + "al g", + ".s ample", + "Ġcoach ing", + "( ind", + "Cell Value", + "Check Box", + "ĠH ell", + "RO OT", + "Ġst adium", + "Ġinvestig ating", + ") %", + "st ed", + "ĠW riting", + "Ġê ²", + "Ġun o", + "Ġ{{ --", + "Ġco ords", + "Ġun ser", + "organ ization", + "ĠCr ime", + "ĠDemocr at", + "Ġv in", + "/ file", + "- api", + "ĠA y", + "Ġfund ed", + "ĠBre xit", + "ĠG h", + "ent ina", + "c ases", + "Ġd ash", + "Ġ!! }Ċ", + "H I", + "Off ice", + "Ġcapt ain", + "Ġwor ship", + "\\ C", + "Ġglo be", + "_ board", + "Ġbab ies", + "Ġconsec utive", + "Ġenh anced", + "ere um", + "ĠAd vis", + "Ġgr ain", + "Ġc raw", + "ancell ationToken", + ". alpha", + "_W ITH", + "ĠO tt", + "ĠC ool", + ".b atch", + "Ġver ified", + "(c allback", + "Ġreg ards", + "ĠInt Ptr", + "ouch er", + "Ġk in", + "Ġtou ched", + "it Ãł", + "ath on", + "Ġadj acent", + "Ġaccom panied", + "LE AR", + "Ġim plies", + "Ġh ill", + "ĠBalt imore", + "=\" -", + "Fin ally", + "S am", + "ic opt", + "Ġs od", + "Ġm aj", + "ĠSh ipping", + "Ġget All", + "Ġcoach es", + "Ġdon ations", + "il ot", + "ĠT ar", + "c err", + "Ġbad ge", + "Ġmark ers", + "ĠR and", + "ais ed", + "iss ance", + "Ġexpl oring", + "uc ed", + "ĠIndones ia", + "Ġbene ath", + "Ġmagn etic", + "Ġm useum", + "match Condition", + "Ġdis rupt", + "Ġrem ind", + "ĠT M", + "Ġ/ ><", + "Ġf ool", + "Ġes k", + ".N ull", + "ĠD ies", + "_OUT PUT", + "_TYP ED", + "Ġpaint ed", + "Ġsoph istic", + "ĠB ear", + "* n", + "_P ACK", + "Ġdeliver ing", + "ĠC OUNT", + "åį ķ", + "Ġj eg", + "-c ar", + "f name", + "Ġr anging", + "ĠN eg", + "/ ******/", + "ĠCH AR", + "Ġul tra", + "Gr ad", + "= t", + "Ġjud ges", + "ĠD ise", + "ann ers", + "Ġsc al", + "_c al", + "ĠCON NECTION", + "_ embed", + "(f n", + "ĠC raft", + "ĠP as", + "\") ->", + ".con vert", + ".res ource", + "ĠST ATUS", + "ô ng", + "ĠT it", + "Ġclass room", + "ĠArch itect", + "ĠK ings", + "Ġstead y", + "/* !Ċ", + "ĠG ene", + ") \";Ċ", + "ic ia", + "st an", + "ĠCon struction", + "um per", + "w c", + "ĠC BS", + "ing ing", + "-p arty", + "(d river", + "M ARK", + "Ġn ested", + "ew ard", + "Ġdepend ency", + "Ġm ales", + "ĠO NE", + "ĠProdu ction", + "][ $", + "ãĥ¼ ãĥ", + "_LO AD", + "ĠB ol", + "el ry", + "ł éĻ¤", + "ĠRe quire", + "Ġpl acing", + "xx x", + "CA LE", + "Ġth umb", + "Ch oose", + "Ġprot otype", + "VO ID", + "Ġles bian", + "Ġtra its", + "Sh arp", + "Ġconsum e", + "Tr uth", + "Ġaction Performed", + "ĠEnvironment al", + "ĠDe an", + "Ġest ado", + "s ame", + "Ġnumer ic", + "Ġtrans it", + ". Email", + "-s ide", + "_R UN", + "ĠVill age", + "_OP EN", + "è ¦", + ".re m", + "-w arning", + "any a", + "Property Changed", + "Ġ(! _", + "( check", + "il ia", + "ĠSo ft", + "st eps", + "ĠMad rid", + "Memory Warning", + "Ġhand lers", + "Ġexperi encing", + "Ġins pect", + "button s", + "Receive MemoryWarning", + "chem y", + "Link s", + "Ġur llib", + ".System Colors", + "ĠE igen", + "Ġpun ishment", + ":UI Control", + "bar a", + "- set", + "Ġ}čĊčĊ čĊ", + "Ġtoler ance", + "Ġinter faces", + ". redirect", + "ighb ors", + "cs rf", + "_back ground", + ". Utils", + "_H T", + "ĠInter est", + "im os", + "Ġgr ants", + "Ġexam ined", + "Ð Ķ", + "Ġc f", + "for ge", + "back s", + "ĠObject s", + "_s ent", + ". entry", + "ĠTH EN", + "ell ido", + "c ia", + ", res", + "/std c", + ". nd", + "( Int", + "ĠAuth ors", + "ĠApp CompatActivity", + "' {", + "Ġmed i", + "M usic", + "ig m", + "ce ipt", + "Ġa uss", + "Ġtarget ing", + "ĠKe ys", + "h n", + ": ]Ċ", + "Ġmin eral", + "à ®", + ".c a", + "om ed", + "Ġshe ets", + "Ġc amb", + "Ġdead ly", + ".in ject", + "( unit", + "ĠSe lection", + ".g ms", + "( connection", + "Ġ$ (\"", + "é mon", + "ĠCurrent ly", + "pt e", + "_path s", + "le af", + "Ġimp lications", + "pos al", + "ä½ į", + "[ /", + "anc ia", + "é Ľ", + "m ul", + "c ie", + "Ġge ile", + "im als", + "UI View", + "Ġs urre", + "serial ize", + "IS O", + "Ġarbit rary", + "Ġsock addr", + ".f n", + "ĠM erc", + "Ġcast ing", + "Key Down", + "Ġnew Value", + "op ens", + "T odo", + "Ġflex ibility", + "ĉĉĉĉ ĠĠ", + "V elocity", + "ú n", + "row ing", + "Ġcomput ed", + "` )Ċ", + "st atement", + "Ġr i", + "_c art", + "L ow", + "trans fer", + ".n av", + "Ġgr ave", + "ĠDo or", + "ĉ alert", + ".sub scribe", + "- profile", + "ĉb ase", + "ĠâĪ Ĵ", + "__ ĊĊ", + "Ġengine ers", + "Ġexplos ion", + "Ġd ari", + "ĉ Log", + "on al", + "Ġisol ated", + "{ i", + "ĠM sg", + "F uture", + "Ġrac ist", + "-w rap", + "ĠV ers", + "b org", + "IS ION", + "Ġ ÑĢаÐ", + "ĠY an", + "init With", + "Ġn omin", + "( empty", + "ÃŃ n", + "ãĤ ¤", + "ĉ width", + "Ġch amber", + "/ ajax", + "EM P", + "Ġnec es", + "iv os", + "log ic", + "*) &", + "cript s", + "Row At", + "ib lings", + "Ġe ars", + "Ġcomput ing", + "Ġm aker", + "ĠNe ither", + "b readcrumb", + "Ġserial ize", + "ĠWith in", + "Ġd ell", + "_TR ACE", + "= a", + "Ġwish es", + "-in ch", + "ĠD or", + "Ġinnoc ent", + "ĠD ol", + "Ġint ens", + "for ced", + "ĠB IT", + "Ġphotograph s", + "Ġcas a", + "ĠL en", + "\\F ramework", + ".S imple", + "Ġde ar", + ")/ (", + "ip pi", + "Ġown s", + "Pl ayers", + "Ġpropos als", + ".p i", + "us alem", + "D amage", + "Ġcal ories", + "ĠCreat ive", + "Ġ[ $", + "Ġ// čĊ", + "And View", + "è me", + ".c ustom", + "_f actory", + "command s", + "_lo ok", + "Ġstr cmp", + "Y N", + "a ired", + "Ġaud it", + "о ÑģÑĤ", + "ĠRe verse", + "ropri ate", + "et ics", + "< vector", + ".s elenium", + ". or", + "Ġpred icate", + "Ġfinish ing", + "Ġk le", + "ĠRep os", + "ĠK han", + "ĠM aking", + "ĠF S", + "Ġp ute", + "ĉ state", + "_S UPPORT", + "' -", + "orient ation", + "Ġexist ed", + "atur a", + "Ġexpect s", + "ĠSh adow", + "Ġorgan iz", + "å ŀĭ", + "Ġsusp ension", + "Ġu it", + "Ġsimult aneously", + "ĠAff ero", + ": \");Ċ", + "Ġro cket", + "c as", + "eter mine", + "ace ut", + "x l", + "ĠA MD", + "( graph", + "ass oci", + "_C R", + ".ar ange", + "(j Label", + "Ġbe ef", + "Qu ick", + ".c ard", + "] ):", + "- gr", + ".G ONE", + "_C LOSE", + "ĠNe v", + "ÃŃ as", + "Ġste pped", + "ĠFre edom", + "ĠW R", + "NS Array", + "_r x", + "_d ialog", + "Ġhot els", + "Ġ( \\<", + "ĠD iamond", + "Ġassum ption", + "um i", + "( items", + "č ččĊ", + "æ³ ķ", + "Ġn el", + "Book s", + "åİ ¿", + "us b", + "ĠF IN", + "æ ¬", + "Ġcorpor ations", + "US A", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + ".p roperty", + "ew ise", + "_ plot", + "\"> ';Ċ", + "Ġpe pper", + "Ġsh ed", + "ĠMed ium", + "ĠC ookie", + "Ġoverse as", + "ed or", + "asure ment", + "åŃ ĺ", + "Ġ' .'", + "Ġph p", + "ĠPRO C", + "Ġexception al", + "( th", + "ĠJ et", + "Ġoccup ied", + ".set Image", + "ĠRel ated", + "uck er", + "M embers", + "PR INT", + "ĠG lo", + "_V IEW", + "} \",Ċ", + "Ġad option", + "[] )Ċ", + "ĠMiss ouri", + "ĠLin coln", + "eral d", + "Pop up", + "Ġf ate", + "- bootstrap", + "fe ctions", + "ĠP oll", + "_ARG S", + "in ance", + "-h ome", + ". ),", + "_d one", + ": ĊĊĊ", + "Ġdiscuss ing", + "ĠSQL Exception", + "Ġelect ro", + "ĉ req", + "Ġz w", + "Ġl ui", + "Ġover night", + "$ user", + "ĠW AY", + "Ġall erg", + "Ġdisappoint ed", + "Ġradi ation", + "Ġimpress ed", + "ific ates", + "Ġto b", + "CL ASS", + "Ġc uda", + "_d et", + "- post", + "ul u", + "Trans lation", + "-h and", + ".y ear", + "ĠM ongo", + "Ġun clear", + ". engine", + "WEB PACK", + "r ices", + "_AC CESS", + "Ġh olidays", + "per cent", + ".Id entity", + "ĠG ov", + "Ġpassion ate", + "!! .", + "ĠGree ce", + "plus plus", + "')) ;", + "G P", + "Ġexc it", + ".tab Page", + "_ cond", + "Ġspons or", + "M ODULE", + "_pro c", + "Ġ$ Ċ", + "Ġr ational", + ".T ool", + "Ġi hr", + "cc a", + "åĵ ģ", + "ĠE state", + "IB UTE", + "Action Performed", + "ĠS olar", + "¦ Ĥ", + "Ġequ ity", + "t id", + "Ġrec ip", + ".s imple", + "m k", + "ĠL uke", + "ĠGuard ian", + "Ġenc rypted", + "Ġdomin ant", + ". place", + "ĠN V", + "Ġtong ue", + "( Get", + "Ġst ainless", + ".P lay", + "Ġe b", + "ac i", + ".b uffer", + "readcr umbs", + "Ġvacc ine", + "p rom", + "Ġuser Info", + "Ġsl ug", + "Serial izedName", + "-w ide", + "Ġre actions", + "ĠY ang", + "ĠAdd s", + "(user Id", + "Ġpl ates", + "ĠM EM", + "Ġb ail", + "In side", + "et ed", + "Ġels if", + "Ġs ake", + "Ġc ycles", + "Ġì Ĺ", + "ĉ I", + "-c ollapse", + "ĠG MT", + "De claration", + "Ġg ros", + "Ġreach es", + "Ġcust ody", + "Unt il", + "t u", + "ĠCh en", + "Ġn x", + "( addr", + "ĠO ffer", + "Ġcol leg", + "ass ador", + "Ġm apper", + "ĠS IGNAL", + "ĠB loom", + "ĠH oll", + "ĠIm per", + "-d es", + "_s ite", + "Pro c", + "E qu", + "Ġat omic", + "ĠW oman", + "s ent", + "sc ar", + "Ġint elligent", + "ĠGet ting", + "ĠReg istration", + "ĠPh ill", + "Ġkill er", + "unic ode", + "Ċ ĉĉĊ", + "ĠJac ob", + "ĠCon st", + "Ġloc ate", + "Ġca us", + "ĠSch olar", + "Ġconstitution al", + "Ġinfl ation", + "ĠG ot", + "= array", + "end um", + "Ġtransl ated", + "Ġdiv orce", + "En tries", + "Ġs or", + "ĠQu ote", + "irl ines", + "U K", + "Ġexc el", + "( opt", + "ĠAD V", + ",: ,", + "Ġcontact ed", + "ĠD A", + "Ġr ings", + "ĠIndust rial", + ".get Context", + "Ġforg otten", + "ĠT an", + "Ġp ants", + "Ġo v", + "Ġdec oder", + "ĠPart ial", + "Ġv c", + "Ġbatt les", + "A rial", + "FRING EMENT", + "ir ates", + ", w", + "aint enance", + "ĠO d", + "ĠTechn ologies", + "åī į", + "ĠCar ter", + ".find All", + "N ome", + "B en", + "ĠUs age", + "ĠP icture", + "Ġbad ly", + "_p anel", + "Ġpat ent", + "ĠProt ocol", + "lot te", + "ĉ player", + "je ctions", + "Ġd ou", + "_re lease", + "urn iture", + "_t ax", + "ĠF ields", + ".d ataset", + "_m aster", + "CLU DE", + "ĠPh arm", + "b st", + "Ġoper ational", + ".c ell", + "Ġident ifying", + "Ġj wt", + "t uple", + "ĠT C", + "ĠC ro", + "ix map", + "- components", + "gener al", + "Ġo z", + "_D e", + "_d ouble", + "ĠTo o", + ".View Group", + "g ate", + "d ings", + "ph otos", + "Ġgrand e", + "ol lect", + "_l in", + "Ġaw ful", + "f ilters", + "Ġaltern ate", + "es p", + "Ġcomp ress", + "e o", + "ĠS cale", + "Ġind irect", + "Ġinv oice", + "ĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊ", + "Start ing", + "ĠPl ayers", + "ie le", + ". then", + "Or d", + "ĠT uple", + "Ġb out", + "ĠStat istics", + "Pre view", + "Ġp uzzle", + "ĠW idth", + "ST ATE", + "Ġover lay", + "ĉ on", + "Ġin fr", + "Ġsm allest", + "lock ed", + "ÑĤ о", + "ss l", + "Ġde emed", + "Ġs co", + "re ck", + "Ġj Button", + "Ġmiss ions", + "ç§ °", + ".Selected Index", + "T ABLE", + "Se pt", + "Ġacknow ledge", + "Ġstrt otime", + "ĠT ell", + "ĠD ak", + "Ġal uminum", + "Ġf ence", + "ĠSt ars", + "CON FIG", + "Ġretro fit", + "Ġemph asis", + "/ header", + "ĠS omething", + "in ished", + "=' \".$", + "ĠValid ators", + "Ġpol ar", + "section s", + ".as px", + "Ġas pir", + ".M ock", + "Code Gen", + "Ġpe ut", + "Ġaccept ing", + "Ġback ing", + "P icture", + "/ ap", + "еР³", + "_SE C", + "- use", + "annot ation", + "Ġcogn itive", + "Ġg rip", + "h our", + "ĠLeg al", + "Ġep ic", + ".t oolStrip", + ".not ify", + ".L ast", + "OR IZ", + "M iddleware", + "cri ptions", + "l ash", + "_F OUND", + "ĠLiver pool", + "Ġ{} \",", + "Inst all", + "Ġn it", + "Ġfig ured", + "[ len", + ".W in", + ".pl atform", + "Ġgam bling", + "(d t", + "av ery", + "ĉ include", + "Wh ether", + "R outing", + "Ġther ap", + "Rem ote", + "ĠL oss", + "y ll", + "Ġappro ached", + "ĠV ehicle", + "ĠAl pha", + "Ġvoc ê", + "ans wers", + "NS Dictionary", + "cons ider", + "un used", + "ĠF an", + "or able", + "f re", + "ĠDIS CLAIM", + "ĠAct or", + ". ]", + "to Have", + ".user Id", + "Ġspeed s", + "ew ay", + "Ġrec urs", + "ĠÐ ³", + "_pr iv", + "! âĢĿĊĊ", + "Ch oice", + "Ġsett le", + "Ġplan es", + "' },", + "T om", + "IT ER", + "! \"Ċ", + "å »", + "achel or", + "Ġsepar ation", + "Ġd al", + "ad j", + "Ġreg isters", + "r iz", + "ĠNot ice", + "Ġl u", + "Ġcour age", + "Ġax es", + "cell ent", + ".as ync", + "Ġcompat ibility", + "ç «", + "Ġ! ĊĊ", + "ĉ title", + "Y LE", + "ĉ message", + "U UID", + "OLD ER", + "ĠH H", + "ĠStyle Sheet", + "Ġaccess ed", + ". validation", + "t asks", + "Ġpoll ution", + ".c anvas", + "Ġing redient", + "ĠC abin", + "A h", + "old own", + "ĠNO I", + "Ġà Ĺ", + "[ f", + "ed uc", + "y alty", + "(n ot", + "_ State", + "am en", + "Ġda o", + "ud ad", + "ell ers", + "} &", + "lic ity", + "_W INDOW", + "Ġt atto", + "val or", + ".R ange", + "Ġrefer enced", + "ĠRes erve", + "M oney", + "SCRI PT", + "/ product", + "cho ices", + "Ġt in", + "ãĤ ĵ", + "Ġsepar ator", + "Ġp kg", + "am med", + "ĠM AT", + "! !ĊĊ", + "Ġr aid", + "Ġmotiv ation", + "ĠX P", + "ĠBack ground", + "ĠQu aternion", + ".define Property", + "ik er", + "ĉp arent", + "ĠOrigin ally", + "ant age", + "ĠH ans", + "Ġtim eline", + ".c ur", + "op ic", + "ĠSe qu", + "m ust", + "ĠCo al", + "Ġform atter", + "_R GB", + "Ġ_ (\"", + "'} ),Ċ", + "Ġ= ================", + "ĠF UNCTION", + "Ġl ng", + "ic ates", + "l ive", + "_ engine", + "Ġtown s", + "')) ĊĊ", + "ĠP K", + "( api", + "ĉs canf", + "pack et", + ".ph one", + "á Ģ", + "ĠAnd y", + "_N AMES", + "PL Y", + "Ġmin s", + "im i", + "Ġbr ick", + "Ġbl ade", + ".std out", + "}` ;Ċ", + "Sh ift", + "ĉs b", + "ĠCheck s", + "Ġphenomen on", + "Av atar", + "Ġmin istry", + "ro se", + "ĉ File", + "Ġtit led", + "( LOG", + "Ġg an", + "des ign", + "(), čĊ", + "Ġb ones", + "st m", + "ÅĽ Äĩ", + "ĠInput Stream", + "Ġvol unt", + "ĠSerial izable", + "Ġfight er", + "ĠDr ag", + "T witter", + "Ġsubs id", + "ç ¼", + "Ġfor ums", + ".load ing", + "log ged", + "_ this", + "Ġterr ain", + "Ġir re", + "ĠIn g", + "ĠC N", + "_object s", + ". uid", + "Ġconscious ness", + "T INGS", + "ĠG all", + "Ġport ray", + "ĠDevelop er", + "Ġparticip ant", + "Ġ\" ;čĊ", + "/ model", + "ĠOper ations", + "^ \\", + "ĠL ater", + "Ġrais es", + "-n one", + ".m eta", + "=' .$", + "Fin ished", + "Ġrepl acing", + "Ġsam pling", + "ĠJ en", + "\" There", + "RE AL", + "A LE", + "ìĬ ¤", + "Or ders", + "_param eter", + "ĠOlymp ic", + "Ġtr ès", + "Ġare na", + "i ol", + "; ?>", + "Ġimpact s", + "ĠW S", + ": get", + "Ġfl ights", + "ĠRuss ell", + "c amera", + "F n", + "s igma", + "Ġfor cing", + "Ġloc als", + "Ġdepart ure", + "Ġcelebr ation", + "ĠS ay", + "ï¼ Ĵ", + "ĠH ills", + ".has OwnProperty", + "Ġtyp ings", + ".A PI", + "Ġdon ation", + "Operation Exception", + ".Act ivity", + "c plusplus", + "ĠChar lie", + "Ġimport ed", + "Ġd ann", + "Ġoccas ions", + "Ġimplement ing", + "Ġpur ple", + ".d ialog", + "SQL Exception", + "ern o", + "Ġw ars", + "Ġpast e", + "Ġdecre ased", + "Ġhar sh", + "Ġel abor", + "input s", + "ĠView s", + "Ġerror Message", + "_m ul", + "ĉ write", + "ĠC op", + "ĠAnn ual", + "(b utton", + "Ġv ida", + "b ars", + "ĠHar vard", + "ĉex pect", + "Ġindex es", + "Ġdocument ary", + "Ġf lesh", + "OR LD", + "ĠD elta", + "M AND", + "Br ush", + "-c olumn", + "Ġdevelop ments", + "method Visitor", + "s lice", + "ĠP DO", + "Ġinvest ing", + "ir able", + "Ġxml ns", + "ï¼ Ľ", + "art a", + "Ġthe ories", + "_c ity", + "Ġ$ __", + "Cre ating", + "( pr", + "D ropdown", + "ism atch", + "ĠN ET", + "'] )){Ċ", + "ĠVal ues", + "ĠSE O", + "ĠST AT", + "Ġe cosystem", + "Ġtem pt", + "Ġ\\ \\", + "Ġ// {Ċ", + "ĠChrist opher", + "ĠKent ucky", + "ĠHttp ServletResponse", + "Ġhy brid", + "y on", + "Ġfeed ing", + "ĠEx tra", + "N orm", + "IT CH", + "ĠSe an", + "ĠUp load", + "m un", + "p ur", + "Ġp ersistent", + "ĠID C", + "ĠPer form", + ".m erge", + "_ room", + "Mean while", + "! ='", + "ĠW el", + "Args Constructor", + ".D atabase", + "Ġcount ing", + "() *", + "Ķ åĽŀ", + "ĠT OP", + "m ill", + "ĠD T", + "IGN ED", + "ĠK B", + "Ġcomp ly", + "S outh", + "_c ollection", + "Ch apter", + "Ġexpl aining", + "_ AM", + "_t s", + "c ards", + "Ġqu el", + "Ġp ole", + "Ġtouch down", + "ĠO thers", + "Ġpe ers", + "ĠType Error", + "Ġsix th", + "Ġche er", + "Ġdis pute", + "us c", + ") ],", + "th umb", + "Ġh iding", + "ĠS IG", + "lik es", + "ĠP AGE", + ".Ref lection", + "Ġhead quarters", + "T ING", + "ĠG host", + "M LE", + "$ Ċ", + "Ġcontr ary", + "ext end", + "'] ).", + "FF ECT", + "ĠP interest", + "úmer o", + "ric ane", + "ĉs ession", + "Ġcr ystal", + "- Control", + "overn ment", + "og raf", + "- action", + "v olume", + "ft en", + "Ġun con", + "Ġan imate", + "Ġle ase", + "sc r", + "Ġref use", + "ãĢ ĭ", + "ft p", + "in formation", + "Ġeval uated", + "Ġin jection", + "Ġj ack", + "Ġwork shop", + "æ³ ¨", + "PT H", + "ĠT s", + "off er", + "ĉ os", + "Ġking dom", + "M issing", + "Ġlaw makers", + "ext Field", + "Ġsing ing", + "ab i", + "/ client", + ".m edia", + "ATEG ORY", + "Sign ature", + "% ',Ċ", + "ĠF uck", + "][ :", + "Ġsens ors", + "/ com", + "ĠPr imary", + ".S QL", + "_pro gram", + "Ġp ills", + "Ġinteg ral", + "Ġfle et", + "Ġdro pping", + ".s l", + "Be en", + "Ġp ets", + "Ġadvis ed", + "Ġdr agon", + "_ EDIT", + "( im", + "F ER", + "ĠDr ug", + "(r andom", + "Ġcomp ression", + "ou st", + "[ %", + "Ġbuy er", + "h op", + "R oles", + "man age", + "Ġpain ful", + "ĠBr anch", + "-mod al", + "en ant", + "ĠM esh", + "/ font", + "ĠG raham", + "Ġâ ĺ", + "Ġn c", + "ĠFranc is", + "Ġspec ification", + "Ġdam ages", + "- config", + "Ġthe oret", + "sec ure", + "_m ulti", + "aceut ical", + "Ġdemand ing", + "en ne", + "IST S", + "() ));ĊĊ", + "Re ason", + "Re cent", + "ph ase", + "Ġps y", + "_M AN", + "Ġvolunte er", + "å ¿", + "istrib uted", + "li o", + "Ġproduct ivity", + "_com m", + "S pring", + "n is", + ". weight", + "ĠC ancer", + "Al loc", + "ĠT weet", + "Ġsepar ately", + "ĉ check", + "_p roperties", + ". Unit", + "_CL K", + "Ġg t", + "Ġ( );ĊĊ", + "Ġhand y", + "ĠThom pson", + "Ġunn ecessary", + "ĠRe ader", + "G N", + "= request", + "ĠU tility", + ".Re pository", + "ĠA x", + "hy dr", + "ie u", + "Ġth y", + "Ġl t", + "_m ail", + "ä¿® æĶ¹", + "ail and", + "ĠPhil ip", + "Ġbit ter", + "Ġbet ting", + "Ġtim ed", + "ock s", + "' a", + "Ġal gorithms", + "Ġre interpret", + "Ġto ss", + "ro gen", + "Ġhop ed", + "( selected", + "Ġvent ure", + "TE X", + "ĠLe ave", + ".Sub string", + "Ġgr ateful", + "uk a", + "ĠCon sumer", + "Ġag greg", + "C ircle", + "ภģ", + "_block s", + "Ġleg ally", + "Ġ\" |", + "ãĥ ĥ", + ". board", + ".A b", + "Function s", + "rec ipe", + "è ĩ", + "ĠO xford", + "Ġwho les", + ".B uild", + "_ch anged", + "h ai", + "Ġdepart ments", + "I mp", + "Ġcoal ition", + "IN FRINGEMENT", + "Ġemp ower", + "itch es", + "N orth", + "Ġinfl amm", + "ON SE", + "Ġmiss ile", + "ĠR aj", + "ĠIss ue", + "Ġat oi", + "ca led", + ".Cont rollers", + "ĠW olf", + "Ġcrush ers", + "á» ĩ", + ".A uth", + ".add Attribute", + "h is", + "Ġbo ots", + ".c lean", + "c amp", + "Ġten ant", + "Ġt une", + "Ġ{} '.", + "Ġwork out", + "Re po", + "Ġpartial ly", + "MI SSION", + "j amin", + "ĠS B", + "Ġdetermin ation", + "Ġ' ');Ċ", + "ĠB eng", + "Ġv os", + "Ġin hab", + "/ lang", + "s burgh", + "Exec utor", + "h one", + "ĠCh allenge", + "_link s", + ".Le vel", + "Ġunder ground", + "-c ode", + "Ġoptim ization", + "log ging", + "_de st", + "Ġsn ake", + "Ġchemical s", + "_IMPORT ED", + "ado op", + "ĠTH AT", + "man aged", + "Ġredu ces", + "ĠRE AL", + "ĠG uy", + "_GENER IC", + "/ ********************************", + ". amount", + "Ġd ere", + "get Time", + "Ġp ant", + "an onymous", + "Ġharmon y", + "ĠAl an", + "Ġscen arios", + "Ġd irt", + "ht ags", + "M c", + "Sh ell", + "r in", + "{ čĊčĊ", + ".p ow", + "ĉ client", + "Ġconspir acy", + "Ġad mission", + "ĠReg ional", + "ĠView Controller", + "ĠPhilipp ines", + "Ġde pos", + "Ġp ap", + "ĠP ad", + "P aul", + ".Com boBox", + "Ġt utor", + "ĠRec ipe", + "w riting", + "Ġcontrib utor", + "OT H", + "Sm all", + "V I", + "Ġh acer", + "e qu", + "ĠEx amples", + "h uman", + ".m essages", + "ĉt yp", + "Ġ( čĊ", + "ĠS SL", + "LE N", + "ĠRom ney", + "( grid", + "ĉ min", + "Ġ> ĊĊ", + "Ġfr uits", + "Ġvot er", + "In line", + "pan e", + "ĠC ollections", + "char set", + "Ġsp am", + "z b", + "item ap", + "Ġsucceed ed", + "_C OL", + "Ġel apsed", + "im eter", + "Ġrecover ed", + "T ensor", + "hatt an", + ".set up", + "ist o", + "( head", + "ĠS IZE", + "Ġtact ics", + "Ġdist ur", + "Ġpre val", + "ici os", + "( Value", + "_c ols", + "ĠF at", + "Ġse al", + "Ġs ons", + "Ġens ures", + "Ġpress ing", + "= &", + "igen ous", + "Ġharass ment", + "_ JSON", + "Ġign or", + "yn omial", + "om er", + "_st atic", + "Ġsignific ance", + "Ġcirc les", + "_S ystem", + "Ġdiscipl ine", + "Ġdress ed", + "Ġs phere", + "Ġclim b", + "_ actions", + "ĠB ab", + "Ġ' =',", + "_s chema", + "\" use", + "Ġund ers", + "Ġc ups", + ".s creen", + "/ new", + "Ġappe aring", + "T OP", + "vis ed", + "cl ang", + "Ġinvestig ators", + "Ġmyster ious", + "Ġprom ising", + "Ġqual ify", + "Ġc ave", + "Ġequ ip", + "= x", + "G T", + "( link", + ". velocity", + ". erase", + "ot er", + "++++ ++++", + "pro fit", + "Ġz ones", + "_ uid", + "- ser", + "Ġobject ives", + "Ġmil f", + "web kit", + "(m atch", + "ne h", + "ĠAssoci ated", + "ĠT odo", + "= d", + "C am", + "Ġv ocal", + "Ġs udo", + "( EX", + "Ġtr ou", + "AB C", + ".b ean", + "ĠG round", + "ĠRE ST", + "we ets", + "In g", + "im on", + "_b us", + "ĠC OLOR", + "un to", + "Ġf oss", + "ĠLink s", + "ä ng", + "/ forms", + "pr ises", + "Ġachie vement", + "C ALL", + "ел ÑĮ", + "ĠVer ify", + "_S OURCE", + "apt cha", + "ID D", + "_re ference", + "G old", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "Re ceiver", + "Ġa j", + "_d irection", + "} ]", + "ĠCom pet", + "Ġb ang", + "ĠC ass", + "- url", + "te chn", + "ĠJer usalem", + "long itude", + "' );čĊčĊ", + "Ġwin ners", + "T asks", + "ĠD MA", + "Ġtool tip", + "İ ·", + "ĠB ra", + "_d uration", + "cur y", + "parent s", + "---- >(", + "ĠK ir", + "Ġint ros", + "Ġsk etch", + "Ġsk illed", + "Ġim mer", + "Ġade quate", + "_re p", + "( header", + "_ like", + "Ġper ceived", + "ss h", + "Ġassum ing", + "Ġf f", + "_u uid", + "ul as", + "Ġdemocr atic", + ". entities", + "S eries", + "aph ore", + "Ġnew er", + "} (", + "SE C", + "ai ro", + "Ġcomm od", + "Ġprivile ge", + "Ġde ux", + "ĠH op", + ".' /", + "ct ic", + ". ';Ċ", + " C", + "ĠWar ren", + "Ġoptim izer", + "ĠSER VICES", + "_ oper", + "get Attribute", + "ĠMc K", + "_s elf", + ".r s", + "\" )ĊĊĊ", + "Get Component", + "er ce", + "Ġt ous", + "un its", + "'] );čĊ", + "Z oom", + "/ E", + "Ġobs c", + "Ġfast est", + "on line", + "Ġpeace ful", + "ff en", + "Ġc argo", + "ĉ pr", + "Ġseek s", + "z u", + "Tr im", + "Ġw ard", + "Ġver d", + "Ġblog s", + ".exception s", + "ĠPrem ium", + "ĠN etherlands", + "S afe", + "Fin ish", + "ĠAl bum", + "_A CC", + "= this", + "v irtual", + "] >", + "_L ABEL", + "ĠN ich", + "_w in", + "ĠA aron", + "W P", + "; $", + "aim s", + "ĠImage View", + "Ġend less", + "ER A", + "_DIS ABLE", + "Ġcancel led", + "- us", + "Ġins pection", + "em in", + "ĠG rey", + "- open", + "Ġiter ations", + ". owner", + "Ġk eras", + ".P assword", + "ĠR y", + "ĠIN S", + "A ir", + "ĠSe veral", + ".Tab Stop", + "ING LE", + "ĠH air", + "ĠCan vas", + "AA AA", + "Ġfl aw", + "ced es", + ".Re port", + "í Ĭ", + "ĠT ips", + "cript ors", + ".trans action", + ".S pring", + "Ġview er", + "Ġins ights", + "è¾ ĵ", + "ord ion", + "U INT", + "se ek", + "ĠA uf", + "ìŀ IJ", + "Ġstr ain", + "To oltip", + "Ġd z", + "ign al", + "ad t", + "Ġu c", + "fin ite", + "Ġn m", + ".c md", + "ĠMy Sql", + "[ data", + ".j ackson", + ".t ree", + "Request Param", + "_ agent", + "\") ]čĊ", + "Ġass ass", + "( Constants", + ": ss", + "ĠM AN", + "+- +-", + "ĠB ottom", + "print s", + "ĠS ame", + "@ Autowired", + "sw ap", + "ici ón", + "Ġprotest ers", + "Ġh oney", + "ĠV eter", + "(C alendar", + "- ad", + "ĠBrook lyn", + "L ife", + "_V AR", + "ze ch", + "ĠC ALL", + "_C AST", + "ĠE lection", + "Ġthick ness", + "V ery", + "_IN TEGER", + "- dev", + ")) ))", + "ap at", + "oo oo", + "d emo", + "Ġparse Float", + "ĠR ather", + "ST IT", + "m aker", + "[ current", + "chron o", + "Ġch rist", + "ãģ ª", + "ĠD etail", + "Æ° á»", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġs ul", + "id ency", + "Q ue", + "Ġeleg ant", + "ap ons", + "Ġdish es", + "Ġinteg ers", + "( read", + "find ViewById", + "ĠAm ount", + "ĠSk ip", + "Ġhab its", + "* )(", + "Ġmon sters", + "M AC", + ": end", + "Ġfr ank", + "As sembly", + "Ġd fs", + "Ġne ut", + "_TYP ES", + "e qual", + "loy d", + "( uri", + "Ġch i", + "Ġdefend ant", + "Ġconflic ts", + "Ġv il", + "- js", + "ĠPe ace", + "Ġmut able", + ") sender", + "ĠF ocus", + "å» º", + "Ġapprec iated", + "s leep", + "ĠR ED", + "C ulture", + "Ġdesign ers", + "_g enerator", + "c odes", + "/ ex", + ".Get Value", + "umb led", + ".scal ajs", + "per or", + "Ġveter ans", + "Ġ} )čĊ", + "Ġun fortunately", + "_C REATE", + "M ass", + "ĠCL AIM", + "ĠMe et", + "_s upport", + "B ank", + "() .Ċ", + "D ark", + "_LO W", + "ĠMin ing", + "ĠO wner", + "ier a", + "Client e", + "Ġencour aging", + "> S", + "Ġboy friend", + "ĠH alf", + "ĠA CC", + "A ff", + "_ ar", + "-l ife", + "c x", + ".J Button", + "iz ado", + ".z ero", + ".open qa", + "ot on", + ".text Content", + "Ġto ll", + "at ie", + "Ġball ot", + "- number", + ". Exception", + "ĉ params", + "c ircle", + "-m ap", + "Ġn ap", + "ĠRob ot", + "ĠI ch", + "reg istration", + "Am azon", + "roll ment", + "( exp", + "Ġt anks", + "ĠG ordon", + "Ġmach inery", + "Ġbas eline", + "æ ĭ", + "Ø ©", + "ĠCon vention", + "ĉ config", + "ook ies", + "m ult", + "Rec ords", + "ĠE ST", + "Ġgar bage", + "Ġcon form", + "id al", + "Ġb arg", + "Ġsurv ived", + "Ġinvestig ations", + ".contains Key", + "---------------------------------------------------------------- ----------Ċ", + "ort ion", + "Ġhor r", + "_ http", + "Ġm ant", + "] ;čĊčĊ", + "b inary", + "em pl", + "Ġin quiry", + "ĠMean while", + "Ġcollect ing", + ".Entity Framework", + "\", ĊĊ", + "ĠP ic", + "@ Inject", + "ick ness", + "ĠB inding", + "Ġcont rolling", + "re verse", + "Ġch airs", + "semb led", + "( add", + "Dis abled", + "an as", + ".trans late", + "-------- ---Ċ", + "Ġref lected", + "\"] ĊĊ", + "Ex ternal", + "Ar row", + "Single ton", + "% x", + "Ġ Å", + "Ġan cest", + "ĠOr leans", + "ĉc md", + "Ġprohib ited", + "ith metic", + "(ch annel", + "_c ss", + "For ward", + ".s ocket", + "Ġl uc", + "â Ĩ", + "ĠFire fox", + "ĠM ovies", + ") _", + ". ends", + "( shape", + "Ġde alt", + "Ġs aves", + "Ġgl ory", + "Ġmej or", + "Ġbreath ing", + "Ġ eller", + "get Data", + "Ġang les", + "Ġtool bar", + "Ġsp acing", + "IP S", + "Ġflo ors", + "_ACT IVE", + "Ġsh uffle", + "/ shared", + "ĠE le", + "ed ish", + "Ġweb cam", + ".ex pect", + "il oc", + "ĠIn cludes", + "Ġtweet ed", + "Ġ: )", + "ĠEss ay", + "F ix", + "-b etween", + "_ web", + ".con v", + "Ġrac ism", + "Ġreflect s", + "um m", + "иÑĤ е", + "_f ooter", + "/d ocs", + "ĠP our", + "Ng Module", + ".initial ize", + "pattern s", + "_ In", + "ĠAb b", + "* čĊ", + "Ġsent iment", + "b uff", + "_count s", + "Ġre use", + "ch unk", + "Ġim posed", + "Primary Key", + "Fore ground", + "Ġconsum ed", + "? !", + "Ġd ick", + "Ġch ron", + "ĠF ern", + "Ġrespons ive", + "Ġin sect", + "icult y", + "Ġr w", + "Ġal ike", + "Ġsub set", + "ĠCook ies", + "ĠP air", + "Ġt ier", + "IF O", + "av our", + "ĠQ U", + ", sizeof", + "Ġmerg ed", + "m v", + "it ol", + "yl on", + "Ġjump ed", + ". role", + "ens aje", + "R ules", + "Ġb rowse", + "An imator", + "Ġy oga", + "Ġvari ants", + "Ġcour tesy", + "ur an", + "p bs", + "else if", + "Al t", + "ĠL ane", + "CL K", + "IM ARY", + "_PRO PERTY", + "ï¼ IJ", + "Ġch an", + "Ġgrad ually", + "Ġsh ake", + "Ġbl onde", + "... \");Ċ", + "-se x", + "Ġgame play", + "ac ies", + ".ref resh", + "US B", + "ĠPl ot", + "W as", + "iss ippi", + "ĠT ensor", + "Ġcryptoc urrency", + "Ġdifficult ies", + "De leted", + "With out", + "_ append", + "_ ver", + "\")) čĊ", + "Ġhonest ly", + "Ġp ivot", + "Ġtem ps", + "_p s", + "ĠUn like", + "[: -", + "V S", + "_in f", + "Ġjun ior", + "Ġanim ations", + "Ġfile path", + "? {{ $", + "Ġun icode", + "pl aces", + "ĠC offee", + ".S E", + "ĠP AR", + "(t xt", + "ge bra", + "Ġf ires", + "Main Window", + "med ium", + "Ġ( âĢľ", + "Ġl g", + "Ġc mp", + "/ base", + "_l ayers", + "_ entries", + "Ġadmin ister", + "ĠSU CH", + "B P", + "ĠScott ish", + "ĉčĊ ĉčĊ", + "gu ard", + "ĠStr ong", + "In sn", + "ĠC AP", + "as ury", + "ĠSE E", + "C lock", + "er ie", + "\\ models", + "Ġ$ $", + "ĠC ab", + "Ġwur de", + "Ġsold ier", + "Ġcl ips", + "Ġarrang ement", + "ĠW onder", + "ĠH orn", + "Ġsc ared", + "Ġc ure", + "m kdir", + "Ġal igned", + "ĠP ink", + "Ġland ed", + "Dim ension", + "Scroll Pane", + ".ch at", + ".W ith", + "ĠTr ain", + "] .Ċ", + "Ġth irty", + "Ġdur able", + "Ġl d", + "Ġlate init", + "Ġch arts", + "Ġins ult", + ".F atal", + "_ ct", + "Ġm asks", + "CLU DED", + "Pres ident", + "Ġcol ours", + "g ments", + ".at tributes", + "ĠF lex", + "ĠC lock", + "ÃŃ cul", + "im en", + "J O", + "ĠReg ex", + "_L INK", + "Ġc ouch", + "ĠIN PUT", + "Ġbe ating", + "b usiness", + "pre ced", + ". unit", + "ĠF el", + "N ever", + "osp el", + ".start swith", + "ĠE PA", + ". only", + "Ġprevent ing", + "y er", + "Column Name", + "Ġelev ation", + "fl u", + "icy cle", + "Ġoff line", + "Tool bar", + "Ġcompet ing", + ") ].", + "Ġm og", + "Ġis Valid", + "As k", + "_ av", + "_l at", + "AN C", + "ĠJ oh", + "k ers", + "Ġgu ards", + "Ġch ains", + "ĠSimple DateFormat", + ".st atic", + "Ġvess el", + "Ġm ud", + "Ġst abil", + "Ġst ret", + "g m", + "am ation", + "ç ľ", + "-w ith", + "Ġro s", + "_P A", + "Ġresult ado", + "Ġconf idential", + "ĠTok yo", + "ĉ using", + "ĠMath f", + "omb ine", + "ĠESP N", + "Ġdeal ers", + "Ġdismiss ed", + "TR Y", + "Ġte ens", + "rec ords", + "Ġw ings", + "g allery", + "account s", + "_L IB", + "Ġj acket", + "ĠNS Object", + "Ġst ones", + "ĠDel ivery", + "ĠD iet", + "/w atch", + "Ġto ilet", + "ĠG uest", + ".d ay", + "Ġint val", + "Vis it", + "Ġinvestig ated", + "Ġpent ru", + "ĠThe atre", + "andid ates", + "L ang", + "ĠS erv", + "Ġcont rollers", + "Ġset Title", + "N P", + "am y", + "fl at", + "( ui", + "_d ocument", + "è ĥ½", + "ĠC oin", + "ĠAd ams", + "pt ic", + "Ġproduct ive", + "Ġaccompl ished", + "čĊčĊ čĊčĊ", + "Ġdefer red", + "ient es", + "Ġs inc", + "ol ars", + "Right arrow", + "Ġvari ations", + "( offset", + ".Layout Inflater", + "Ġsus pend", + "Ġprevent ion", + "_pr ivate", + "_ js", + "âĺ ħ", + "Ġw ieder", + "at um", + "Ĵ Į", + "Ġappear ances", + ".D ocument", + "Ġvalid ates", + "cal endar", + "} \";Ċ", + ".d emo", + "con ut", + "Ġcorre ction", + "ĠDe al", + "Ġbatter ies", + ".d uration", + ", \\", + "_m arker", + "m ulti", + "Ġh alt", + "Ġc ms", + "Ġsh aped", + "B ro", + "re duce", + "Ġ ####", + "CT OR", + "ĠBen ef", + "Ġicon ic", + "Ġp iano", + "Ġeffect iveness", + "| .Ċ", + "Ġa jax", + "Ġv olumes", + "ภ¡", + "Ġcl js", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ath s", + "ra its", + "å¤ §", + "Ñ ĸ", + "_m ult", + "Ġfasc inating", + "A verage", + "Ġpr é", + "ĠChair man", + ".find Element", + "_p in", + "Ġcomp aring", + "Ġdark ness", + "-F i", + "- server", + "Ġselect ing", + "ster dam", + "ĠPart s", + "FORM ATION", + "Ġnot ing", + "Ġp ile", + "og s", + "Ġpa lette", + "_d o", + "it ize", + "() (", + "Ġdef ining", + "Ġremain der", + "Un its", + "_T ASK", + "Http Client", + "S ocial", + "Ġfund ra", + "N R", + "ch est", + "C urrency", + ".ad apter", + "Ġd op", + "un ting", + "ANG UAGE", + "\" He", + "ĉ index", + "_p ackage", + ".I con", + "Ġrep et", + "m ass", + "=\" .$", + "ĠS ud", + "Ġl id", + "pro vince", + "ì ľ", + "G PIO", + "Ð ļ", + "ĠMy SQL", + "Ġdoc s", + "ĠG A", + "Ġip sum", + "K ernel", + "Ġaccept s", + "Ġfit ting", + "Ġcu ando", + "Ġd uplic", + "ĠBro ther", + "ĠK le", + "num s", + "Ġmor ph", + "Ġ ########", + "ĠCG Point", + "< unsigned", + "ä¾ ĭ", + "ĠD uke", + ".set Bounds", + "q s", + "or ic", + "j er", + "Ġregard ed", + "Http Request", + "Ġbond s", + "Ġthorough ly", + "enc ent", + "Ġhighlight ed", + "Ġac res", + "Ġwork place", + "ĠL ux", + "Ġqu ot", + ".in flate", + "Ġdocument ed", + "Ġadd iction", + "Ġmut ation", + ".c ity", + "Ġbott les", + "ĠRepos itory", + "on n", + "err no", + "ARI ABLE", + "åº ¦", + "_B EGIN", + "gl as", + "' })Ċ", + "ĠMass age", + "ĠWh it", + "reg ex", + "W A", + "Ġout let", + "- head", + "Ġexp ired", + "ĠTh ai", + "/ include", + "grad ient", + "scan f", + "Ġse am", + "w al", + "ĉb uf", + "B earer", + "Ġprec ious", + "if acts", + "co ord", + "Ġexpl oration", + ".get Y", + "(h andle", + "Top ic", + "ĠV ent", + "r hs", + "---- --Ċ", + "ĠB right", + "Ġg uild", + "m other", + "st orm", + "Ġmunicip al", + "Ġin k", + ".T YPE", + "w l", + "... manual", + "ĠTechn ical", + "Ġcorpor ation", + "ĠH W", + "ank a", + "T AIL", + "ist as", + "Ġperform s", + "ĠBeh avior", + ".F or", + "_ ORDER", + "ĠK ick", + "Ġcallback s", + "_d r", + "ue go", + "h ub", + "uff icient", + "sk y", + "Ġb p", + "ht able", + "ĠON LY", + "ĠAUTH ORS", + ".Arg ument", + "\" };Ċ", + "ĠTh under", + "ĠK om", + ".Sh ould", + "A UTH", + "ah u", + "_p ayment", + "Ġst arter", + "ìĦ ľ", + "ìļ ©", + "B log", + ".p atch", + "Ġgovern ed", + "ass y", + "-f ound", + "Ġthe ater", + "ĠFont Weight", + "ĠBat man", + "\" If", + ".R andom", + "_d elta", + "ĠC E", + "Auth enticated", + "Ġdr one", + "Ġc ous", + "r adius", + "M er", + "( None", + "ĠN J", + "_ headers", + "Ġam er", + "py test", + "ĠA ctions", + "ĉĉĉ ĠĠĠĠ", + "Ġet t", + "Ġh oly", + "Ġun comfort", + "ĠN in", + "ĠDec imal", + "ĠM essages", + ".s ender", + "] ])Ċ", + "Ġembr ace", + "Th ough", + "/ sp", + "Ġcult ures", + "Ġhigh way", + "t ar", + ".f ail", + "_h idden", + "ĠcomponentDid Mount", + "ĠW right", + "Ġj ag", + "_ il", + "../../ ../", + "ig u", + "F ood", + "Ġa ce", + "Ġa ños", + "US D", + "Ġmut ual", + "Log ic", + "Ġtem ple", + "Ġbrief ly", + "ĠT rip", + "class method", + "default s", + "Ġch unks", + ",, ,,", + "ĠRe ason", + "$ id", + "-up s", + "Ġdam n", + "Ġtruck s", + "Ġun limited", + "Ġsc ulpt", + "ĠC ards", + "Ġaut or", + "ĠTest ing", + "Ġdies e", + "sh ops", + "ç ´", + "(p ayload", + "ĠP ATH", + "ĠMem orial", + "Ġridic ulous", + "eg ree", + "-w inning", + "Ġre hab", + "Ġsophistic ated", + "wp db", + "ĉ path", + "! \";Ċ", + "_S YS", + ".s peed", + "Ġso ap", + "s uffix", + "W rap", + "Ġenh ancement", + "à ī", + "ú b", + "Ġplay list", + "Ġmix ing", + "ant idad", + "=\" \";Ċ", + "ĠRev ision", + "ĠBe at", + ".in c", + "-w ay", + "enc ias", + "ul ers", + "C at", + "id el", + "ĠSh ip", + ".set Color", + "Ġthreat ening", + ".mod ules", + "Ġafter wards", + "ĠD ashboard", + "Ċ ĠĊ", + "Sign al", + "Ġpr imer", + "orne ys", + "ici ary", + "Ġl igne", + "_p redict", + "Ġa est", + "_ https", + "> :", + "ĠL ex", + "Ġrencont res", + "eg ral", + "sc ala", + "_f amily", + "ÃŁ en", + "_s ym", + "Ġuncert ainty", + "ĠVAL UE", + "Ġ} ;čĊčĊ", + "Ġbro ader", + "Ġh orses", + "ãģ Ŀ", + "ĠK al", + "ob a", + "_IN ET", + "ĠK ill", + "j query", + "am ination", + "[ @\"", + "Ġm uj", + "## #Ċ", + "First OrDefault", + "then Return", + "C he", + "/ footer", + "Ġpark s", + "as je", + "ĠG ulf", + "Ġmod est", + ". Init", + "ï¼Ł ĊĊ", + "Ġpros pects", + "Ġs vg", + "Ġå ı", + ".D ialog", + "_N ET", + "Ġ( ($", + "Ġe k", + "ĠW arning", + "ĠM K", + "< LM", + "Ġ' čĊ", + "i em", + "h etic", + "Ġi x", + "th ink", + "-sh adow", + "ĠE ld", + "ĠNev ada", + "ĠLe af", + "ĠG ROUP", + "Ġprom o", + "ent ine", + "ĉ Map", + "ĠModel s", + "ĠK rist", + "_k ernel", + "-m ade", + "Ġc err", + "As sets", + "ell ar", + "Ġinv oked", + ".v ue", + "Ġcult iv", + "C losed", + "Ġgener ates", + "ffff ff", + "thes ize", + "s qrt", + "ĠCast le", + ".c ar", + "Ġke en", + "und a", + "ĠC row", + "ĠSing h", + "y thon", + "Ġbe ans", + "l arg", + "æĸĩ 件", + "Aw esome", + "unc ate", + "Path s", + "o ji", + "(c urr", + "CON DS", + "Ġm im", + "Ġshould ers", + "H ard", + "ast es", + "а еÑĤ", + "Ġconv ince", + "de cess", + "m ade", + "ĠC MD", + ". Im", + "Ġcha os", + "ens ively", + "Ġcool ing", + "Ġbur ied", + "(' @", + "_S e", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", + ".com pany", + ".sub mit", + "ph ant", + "Ġboot strap", + "_h elp", + "à §", + ".d ump", + "Ġdif er", + "_m apping", + "Ġcirc ular", + "Ġescort s", + "Ġb ere", + "Ġgrad u", + "ĠLeg end", + "im edia", + "ĠBar celona", + "Ġbed s", + "åĪ °", + "ãĢ Ĭ", + "_v olume", + "Ġtremend ous", + "Ġsc aling", + "Ġp ins", + "en as", + "type param", + "D ashboard", + "render er", + "Ġsp i", + "Ġ& $", + "ĠSk in", + "alm art", + "Ġh ockey", + "Ġ'\" .$", + "Ġerr no", + "Ġb ew", + "Follow ing", + ".M odule", + "er able", + "ĠM ilitary", + "ĠR io", + "_ available", + "ĠSur face", + "Ġst ab", + "IF IER", + "ĠL IST", + "Ġd ashboard", + "Ġcl usters", + ".pl ugin", + "Ġj ou", + "ĠDec or", + "F our", + "Ġdel le", + "****** /Ċ", + "ia z", + "in de", + "ch ing", + "Ġget Item", + ".Add ress", + "ment ed", + "A meric", + "Pl ain", + "Ġus b", + "ĠPract ice", + "_ ment", + ".bl ue", + "H int", + "ÑĢаР²", + "Ġconn ector", + "Ġinher ited", + "и в", + "Ġinterval s", + "Ġc ere", + "Ġu d", + "Ġin con", + ".Ex ists", + "ĠM ic", + "F K", + "(c ard", + ".Set tings", + "Ġexhib ition", + "Ġon Pressed", + "Ġrest ored", + "eng u", + ". def", + "Ġrec v", + ".\" );čĊ", + "enc oder", + "ather ine", + "( dest", + "az ed", + "# endregion", + "sem bl", + ", M", + "ob y", + "Ġп еÑĢ", + ".C all", + "Ġattend ance", + "-b order", + "Ġaddress ing", + "ê n", + "ĠLe v", + "Ġb ash", + "ben ch", + "C redentials", + "Sp acing", + "( of", + "_RE SET", + "ig uous", + "Ġcr uel", + "Ġcross ed", + "Ġle ur", + "ĠG olf", + "or rect", + "Ġpack ets", + "ĠData Set", + "Ġpart ly", + "SEQU ENTIAL", + "Ġindic ation", + "ĠS alt", + "ac ia", + "Ġ* );Ċ", + "ĉ info", + "ĠView Bag", + "on z", + "Ġeditor ial", + "ĠA rena", + "Ġs ir", + "_ Static", + "( socket", + "s u", + "cho ose", + ".m onth", + ".M y", + "é ri", + "; font", + "do es", + "Ġcon verter", + "Ġsal v", + "Ġl r", + "Ġinflu enced", + "(f eature", + "ĠQue ens", + "let t", + "_M ON", + "& amp", + "Touch ableOpacity", + "O FF", + "Ġmetab ol", + "( iter", + "Ġvit amin", + "ĠIND IRECT", + "aut om", + "_p ublic", + "Ġadjust ment", + "Ġspecial ized", + "w indows", + ".add All", + "Ġaccording ly", + "ĠJ OptionPane", + "Ġcell spacing", + "Ġqu ad", + "Ġcre ep", + "Ġout lets", + "}` )Ċ", + "Ġpri est", + "_TH READ", + "ĠMar x", + "ĠBy Val", + "Ġc ual", + "éĿ ¢", + "Ġtempor arily", + "An n", + "ke leton", + "å ¥", + "ĠLO C", + "au er", + "der ive", + "Ġbeh aviors", + "as ename", + "ĠCent ury", + "Ġhor rible", + "ME SS", + "_ List", + "we i", + "P at", + "ĠCh oice", + "_F ROM", + "ĉ line", + ".in voke", + ".B ottom", + "Ġnow here", + ".\" ĊĊĊĊ", + "_ export", + "Ġstrugg led", + ".Ap pearance", + "ĠJ Button", + "ĠJer emy", + "([ [", + "Ġkick ed", + "mar shal", + "st aff", + "es ity", + "Ġqu iz", + "_e ffect", + "Ġ} ));ĊĊ", + "m el", + "b anner", + "ĠP IN", + "Ġin vention", + "Ġcons olid", + "Ġop s", + "ĠB etween", + "j ack", + "ern ational", + "Ġsacr ifice", + "ag ation", + "ĠJ oy", + "Ġam endment", + "ĠS old", + "Ġprison ers", + "ан нÑĭ", + "Doc uments", + ") ])Ċ", + "ust ed", + "ĠLine arLayout", + "os o", + "_E M", + ".s elf", + ".M iddle", + ") //", + "Ġ\\ '", + "Ġfuck ed", + "ĠM urray", + "Ġprof ound", + "_E LEMENT", + "ult a", + "il ers", + "port folio", + "J une", + "t cp", + "mod ified", + "ĠTr ace", + "ĠK el", + "aly zer", + ") =>", + "ĠRep air", + "_B E", + "Br and", + "u art", + "pre view", + "Ġiniti atives", + "run ning", + "b ang", + "ĉ update", + "ĠCo ach", + "R ich", + "Ġy outube", + "Ġrit ual", + "app a", + "ĠRobin son", + "prec ision", + "//////////////////////////////////////////////////////////////// ////////////", + "=[ ]Ċ", + "Ġcelebr ated", + "OT O", + "Ġin clusion", + "J P", + "' ;čĊčĊ", + "Ġnot able", + "(_ .", + "Man aged", + "Ġgu ides", + "& nbsp", + "ated Route", + "ĠAd just", + "Ġcol ored", + "_s cores", + "ĠTes la", + "_pro gress", + ".in st", + "[' _", + ".fl ags", + "Ġf close", + "_O PER", + "ż y", + "_n ote", + "Ġtrans gender", + "å ķ", + "RI PT", + "Ġabs ent", + "Ġam et", + "Ġoper and", + "ë ©", + "Ġh ood", + "to LowerCase", + "av o", + "ĠCirc uit", + "ĠL ind", + "-- }}Ċ", + "= m", + "Ġsup press", + "ĠM AP", + "i ang", + "- admin", + "Ġside bar", + "ĠB u", + "ĠH ex", + ", F", + "ĠSign al", + "Ġtrans parency", + "ĠFeder ation", + "/ V", + "Re q", + "Ġpul se", + "Ġt ends", + "Num bers", + "% '", + "Ġde port", + "dat as", + "_U INT", + "_ tra", + "ok o", + "Ġ\" ?", + "comp et", + "sole te", + "und ry", + "Ġover lap", + "}` ,Ċ", + ". ly", + "_sum mary", + "ĠL ost", + ".C enter", + "Ġdis ability", + ".Serial ization", + "Ġge om", + "Ġ? :", + "ĠW o", + "Ġsh ipped", + "Ĥ æķ°", + "Ġu gly", + "Ġexcit ement", + "Ġext erior", + "Ġcheck out", + "Ġk ur", + ", D", + "ĠAl aska", + "Ġsyn thetic", + "ĠB udget", + "ĠSub scribe", + "Ġ& Ċ", + "ÈĻ i", + "ĠY u", + "ĉ query", + "} .Ċ", + "Ġtr aged", + "ass en", + "Ġaccommod ation", + "Ġphys ician", + "Ġren amed", + "Ġtid ak", + "z Äħ", + "Ġmin us", + "ny ch", + "_EX CEPTION", + "thread s", + "Ġt ire", + "_c reated", + "ens ure", + "Ġworth y", + "Ġexc use", + "Ġclo th", + ".parent Node", + "/pl atform", + "ĠU FC", + "ĠG tk", + "un ny", + "Ġg ibt", + "ke ley", + "h um", + "(t x", + "ĉ dev", + "Ġout fit", + "do ors", + "Ġf on", + "ic ut", + "vol atile", + "Ġhom osex", + "Max imum", + "Ġexp end", + "Ġ});ĊĊ Ċ", + "E q", + "ond ers", + "dep artment", + "ĠPhys ics", + "\" });Ċ", + "Ġpar ad", + ".S tr", + "Ġse le", + "IF IED", + "Ġdel ivers", + "iv an", + "Ġrespons ibilities", + "Ġadvoc ates", + "è µ", + "ĠR ID", + ".param eters", + "M etrics", + "ron ics", + "ĠUITableView Cell", + "A bsolute", + "ip se", + "yl um", + "MLE lement", + "_VAL ID", + "< title", + "D lg", + "p aces", + "Ġsynd rome", + "be ans", + "_d atabase", + "oz illa", + "ĠM eg", + "DB G", + "Ġl ub", + "Bag Constraints", + "ab ad", + "Ġproject ed", + "_BY TE", + ".Size F", + "st reet", + "ĊĊĊĊ ĊĊĊĊĊĊ", + "ĠLO SS", + "Ġdirect ors", + "/ news", + "Ġnurs ing", + "ĠD one", + ". HTTP", + "dis count", + "ĠR ot", + "To Many", + "Ġen abling", + "Ġauss i", + "ost a", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", + "è½ ½", + "Ġhel icopt", + "ĠIn side", + "ä¿¡ æģ¯", + "is per", + "ĠAll ah", + "ARCH AR", + "Ġroll s", + "Com pare", + "X P", + "Index Of", + "S UM", + "Ġass ured", + "ĠPhys ical", + "End point", + ".G lobal", + ".d etail", + "Ġthe ft", + ".j upiter", + "Ġhum or", + ".R ender", + "A lex", + ".c ap", + "Ġbuff ers", + "Ġdis pose", + "t ion", + ".p resent", + "z el", + ", P", + "Ġdesper ate", + ".get Column", + "Ġtw in", + "ì ĸ", + ".c an", + "Ġf lee", + "ĠIran ian", + "Ġstick y", + "ĠU TC", + "L T", + "//////////////////////////////// ////////////////", + "Ġl icensing", + "_PO INT", + "ĠM aps", + "Ġl ol", + "= models", + "-t ab", + "ĠN ash", + "_log ger", + "tor ch", + "ĠCON SEQUENTIAL", + "Not Empty", + "/ react", + "Ġp f", + "Ġassert ion", + "Ġsubsequ ently", + "_c an", + "Ġpand emic", + "og ue", + "\"+ Ċ", + "_ ent", + "_P aram", + ".ĊĊ ĊĊĊĊĊĊ", + "Res earch", + "C apture", + "Ġbel oved", + "d em", + "Ġextract ed", + "Ġf ights", + "ER C", + "(a uth", + "position s", + "Ġrevers ed", + "(st ack", + "Ġ_ )", + "uto ff", + "_fl ow", + "ç Ĥ¹", + "( Game", + "Ġex cluded", + "ĠCS V", + "c g", + "ĠT itan", + "p ause", + "Ġcer ca", + "Ġdump ster", + "L ess", + "Ġkotlin x", + "aster xml", + "Ġpoint ers", + "Ġfl ows", + "ĠT un", + "ĠMain Activity", + "Ġdis cret", + "Ġcomb inations", + "vis it", + "_b ind", + "oot ing", + "d ater", + "_look up", + ".n io", + "Ġswe at", + "ĠR d", + "Ġscient ist", + "ĠP ixel", + "@ NgModule", + "Play ing", + "Ġunf old", + "Trans late", + "ĠLaw rence", + "ĠFIX ME", + "B ill", + "ĠR IGHT", + "Ġwhere ver", + "Ġo ok", + "vid ence", + "Ġ] ];", + "ĠSk ill", + "unist d", + "ĠðŁ ĻĤ", + "Ġfem ales", + "-- )Ċ", + "İ· åıĸ", + "ĠF red", + "Over all", + "Ù Ĥ", + "Ġess ence", + "Ġthere by", + "Ġw ounded", + "ĠD OWN", + "les son", + "text ure", + "R ound", + "Ġautom ated", + "ĠÐ ¡", + "ĠUp dates", + "Ġsh ade", + "p ublish", + "ĠG ear", + "= lambda", + "Ġle ver", + ") +\"", + "h ill", + "Ġrad ar", + "ry ing", + "Ġ\" ).", + "f illed", + "Ġline up", + "Ġd l", + "Ġworks pace", + "V o", + "_d t", + "ë ²", + "_ Item", + "NS URL", + ". verify", + "ĠHawai i", + "G od", + "M arch", + "Ġ[âĢ¦ ]", + "Ġpel o", + "ur ious", + "ĠPitt sburgh", + ". It", + "C lean", + "> \\<^", + "Ġi os", + "s ound", + "\"] ;", + "Ġfre ed", + "rot tle", + "ĠL ower", + "[ count", + "å Ŀ", + "Ġp ale", + "ĠWay ne", + "ear th", + "_c ategories", + "U CK", + ".m etadata", + "Ġsum mon", + "H OME", + "олÑĮ з", + "Ġmanufact ured", + "Ġdo ck", + "Ġcompet itors", + "_MODE L", + "ok ia", + "ĠH ey", + "Î ¿", + "Ġback ward", + "ĠPO SS", + "rop a", + "Ġc ri", + "_O BJ", + "Trans port", + "-h igh", + "Ġerot ik", + "_s lot", + "Ġart ic", + "_f ramework", + "-ser if", + "ĠSql DbType", + "') (", + "+ \"/", + "Ġw ore", + "S il", + "Ġst oring", + "ĠPh ase", + "u ant", + "Ġb ump", + "in ho", + "Ġd ign", + "Ġback s", + "q q", + "(h ash", + "Ġge o", + "Ġt ender", + "Log o", + "! )Ċ", + "ĠM X", + "ĠAr thur", + "esso a", + "_C h", + "Ġbed rooms", + "=\"# \"><", + "Ġth roat", + "ins ic", + ".int eger", + "Ġpr imitive", + "Truth y", + "Ġfacilit ate", + "Ġcreat ivity", + "ĠD NS", + "Ġg ra", + "ue z", + "Ġcount less", + "ĠPol and", + "' M", + "ĠD ist", + "Ġv est", + "Ġcert ification", + "á» ij", + "h eld", + "ext ensions", + "( static", + "Ġgr ades", + "ĠU ber", + "ãģ Ł", + "Ġ[ ])Ċ", + "dat os", + "Ġget Data", + "ĠCh arg", + "ĠB S", + ".m icrosoft", + ".v ideo", + ".d irection", + "->{ '", + "l ua", + "ape st", + "Ġbo iler", + "ere k", + "Ġdec ides", + ".j ar", + "IS C", + "ĠW ords", + "(C ON", + "EMPL ATE", + "ree ze", + "sh ots", + "app s", + "unt ed", + ".set Name", + ":: <", + "-b old", + "ê ²", + "å¯ Ĩ", + "Long rightarrow", + "Ġunf air", + "Ġear ning", + "Ġsh elf", + "URE MENT", + "Ġid le", + "_M ENU", + ".C ustom", + "AG ER", + "- \"", + "_s witch", + "b ecause", + ") view", + "m are", + "_ condition", + "ĠStart ing", + "M vc", + "(p re", + "d ump", + "_LO CK", + "at etime", + ".c allback", + "ĠC er", + "op ol", + "ib rary", + "Ġres ervation", + "ĉĉĉĉĉĉĉ Ċ", + "lect or", + "grad uate", + "Ġgener ous", + "Ġ ion", + "ric ao", + "m q", + "_com plete", + "(c ursor", + "ĠForm Control", + ": center", + "Ġsub stitute", + "ĠPl anning", + "Ġp ension", + "Ġrecommend ation", + "ĠT ags", + "Ġg ef", + "Ġalbum s", + "Ġwash ing", + "ro c", + "Ġtr ains", + "at ings", + "Ġex ponent", + "ack bar", + "- ln", + "á g", + ".Data Annotations", + "ĠE IF", + "ĠMalays ia", + "ĉ PORT", + "on us", + "Ġcle ver", + "Ġpe u", + "> ĊĊĊĊ", + "ĠArg uments", + "Ġdebug ging", + "( right", + "' D", + "com pute", + "Ġfin est", + "OR AGE", + "Ġspect acular", + "ph rase", + "Ġind ia", + "Ġlegend ary", + "b irth", + "Ġcom posite", + "Ġg rows", + "ĠT D", + "Ġep id", + "Ġlaunch ing", + "] ][", + "Min utes", + "ĠCh a", + "Ġclean ed", + "Ġwitness es", + "uk an", + "ĉ Type", + "Ġhab e", + "par agraph", + "ĠJ Panel", + "ĠH ann", + "Ġvar ied", + "ĠP okemon", + "ĠM UST", + "åĬ ¨", + ".vis ibility", + "op up", + "^ [", + ".exp and", + "Ġ\" ',", + ".f asterxml", + "_ auto", + "ĠShe et", + "mark er", + "Par cel", + "ew s", + "ĠStr ategy", + "-m aking", + "Ġun ve", + "Ġtrail ing", + "Ġclick s", + "ĠGet Component", + "ĉ content", + "IG ENCE", + "ERN EL", + "NSMutable Array", + "Ġb reat", + "Ġharm ful", + "¶ Ī", + "Ġbes ides", + "Ġb oring", + "Ġbrut al", + "v ang", + "(p arse", + "qu ick", + "Ġpy test", + "Ġswitch ing", + "() ]Ċ", + "Ġì Ħ", + "L ER", + "ĉf ont", + "Ġnet t", + ") ]ĊĊ", + "(/ \\", + "æŀ ľ", + "to Array", + "Ġbre ed", + "ĠC AR", + "ĠWe apon", + "A bs", + "t ot", + "Ġset Name", + "apt ive", + "Ġ: ,", + "Ġesc aped", + "ord en", + "ĠP ri", + "th umbnail", + "Ġdescri ptions", + "/ styles", + "ĠPC I", + "Ġal phabet", + "astic search", + "NOT E", + "Ġc ialis", + "ĠGr iff", + "Ġpor que", + "Ġprote ins", + "pl ays", + "Ġst ating", + "Ġimag ination", + "Ġfac ial", + "ĠMe chan", + "Ġarr anged", + "_ used", + "Ġarrang ements", + "ĠP ipe", + "host name", + "Ġprov inc", + "T it", + ".Flat Style", + "ĠS plit", + "ĠLo ader", + ".c c", + "Ġclin ic", + "---------------- ------------", + "Ġb aking", + "ĠEN T", + "ne ath", + "ãĢģ ĊĊ", + "AN E", + ".EntityFramework Core", + "app ers", + ". ic", + "ĠNg Module", + "ĠF ORM", + "Ġ' ;", + "-pro fit", + "h w", + "en emy", + "ĠE ye", + "Ġca ution", + "t own", + "Ġur ged", + "ĠJim my", + "ynchron ous", + "-s ized", + "m aking", + ", {", + "] ',", + "_ Object", + "ah oma", + "Ġactiv ist", + "IN VAL", + "ĠCom mercial", + "ĠOr lando", + "(t ab", + "ĠØ ¨", + "Al gorithm", + "Ġher itage", + "Get Mapping", + "Ġfail ures", + "ri os", + "at iva", + "Ġt et", + "Ġcar pet", + "( Z", + "th ree", + "Ġdisc losure", + ". ERROR", + "_c alled", + "Ġd ial", + "Ġoccas ional", + ".E rr", + "Ġfunc ion", + "caff old", + "Ġrele asing", + "ï¼ī ĊĊ", + "_ Value", + "ĠV ari", + "y ellow", + "Ġstrugg les", + ".c al", + "ĠDak ota", + "ĉc lose", + "Ġsand wich", + "Ġanaly tics", + "Ġ** )", + "& #", + "ĠJ os", + "Ġpass ive", + "AT TR", + "Th rowable", + "ĠM un", + "ĠU int", + "(dis posing", + "ar ak", + "ĠLe aders", + "Ġaffect ing", + "Ġitem View", + "Ġeconom ics", + "f v", + "๠Ģ", + ".r b", + "ĠOver all", + "Ġwealth y", + "Ġev olved", + "nd a", + "ĠH us", + "re strict", + "um en", + "ĠA gricult", + "! ĊĊĊ", + "Ġexp ires", + "Ġspokes person", + "int erval", + "Ġà ¢", + "Ġque en", + "(n il", + "ing o", + "He ap", + "Ù İ", + "Ġcompl ain", + "S ym", + "ĠCl one", + "ĠR u", + "ĠW ILL", + "ĠCr ystal", + "/ content", + "ing en", + "oint ment", + "Last Name", + "av icon", + "ĠIB M", + "ĠDim ension", + "an h", + "icip ants", + "ĠAn ne", + ".pro gress", + "Ġal go", + "ob il", + "ĠV oice", + "ĠF E", + "Ġg li", + "Ġv ed", + "Ġprevent s", + "\\ Column", + "Ġfol k", + "ett i", + "Ġm n", + "ĠCL ASS", + "Ġdisplay ing", + "ĠK l", + "ĠF err", + "d uto", + ". ib", + "Ġd ados", + "' name", + "-s pace", + "Ġit alian", + "Ġin verse", + "Ġd ense", + "ut er", + "ĠI Enumerator", + "-s ign", + "Ġnation wide", + "Ġperson a", + "Ġsol ved", + "Ġdram atically", + "Log out", + "Ġgr av", + "Ġanalys es", + "ol lo", + "Ġl amp", + ". team", + "ĠE rot", + "= [\"", + "Ġd ancing", + "Ġ?> /", + "Ġc ater", + "ff e", + "ĠSh a", + "ĠB os", + "ĠRE QUIRE", + "ĠMon ster", + "ĠR B", + "ĠI DE", + "Ġsu its", + "Ġform Data", + "( theta", + "Ġsp atial", + "= NULL", + "ĠSql Connection", + "Ġ à", + "ĠV enez", + "ĠMor ning", + "Ġpublic ations", + "ĠNON INFRINGEMENT", + "first Name", + "ud s", + "W ould", + "_HE AD", + "Ġinvest ed", + "st able", + "f red", + "Ġcommand er", + "SE S", + "âĢĶ a", + "an che", + "ĠM ovement", + "ë ³", + "S uite", + "Ġjur isdiction", + "ë¦ ¬", + "ĠB eth", + "j Query", + "ĠIs a", + "Ġd ental", + ", *", + "ĠL imit", + "ili ation", + "=\" {", + "b ast", + "Ġt urb", + "is y", + "O OK", + "Ġadvoc ate", + "im ag", + "LE CTION", + "л ÑĮ", + "(c ategory", + ".de c", + "Ġun iqu", + "_s n", + "Ġattract ed", + "Ġà ī", + "ĠRun ning", + "_ edges", + "ĠDis able", + "_A S", + "åĽ ¾", + "Ġnetwork ing", + "_br anch", + "H aving", + "toBe Truthy", + "G I", + "Ġcamp s", + "se p", + "-p art", + "Ġ)ĊĊ ĊĊĊĊĊĊ", + "ustral ia", + "ĠRe ports", + "rit o", + "Ġwa ist", + "_pl us", + "ĠW W", + "-p erson", + "Apr il", + "Ġs ar", + ".t ar", + "Ġagricult ural", + "t ic", + "Ġt cp", + "Ġset Value", + "agent o", + "ĠAp pe", + "p iler", + "CA DE", + "Ġan che", + "atch er", + "Ġcom ics", + "Ġl bs", + "_se gment", + "'] =$", + "itt ers", + "ich er", + "G INE", + "Ġutil ize", + "ĠC ursor", + "_ex pression", + "Ġd ag", + "< long", + "Ġr hyth", + "æı IJ", + "Ġconsult ation", + "Y et", + "\")) ĊĊ", + "_M AC", + "c ould", + "Ġ' \\\\", + "ĠV o", + "ĉ http", + "Ġg s", + "ph er", + "- grid", + "J ames", + "J ul", + "Ġsch on", + "Ġtensor flow", + "ĠLOG GER", + "am as", + "Ġsc ipy", + "Ġconv iction", + ". ag", + "Ġadministr ator", + ")) {čĊ", + "Ġn un", + "\" group", + "P or", + "Ġnur se", + "ex pression", + "ak y", + "ĠHe avy", + ". opt", + ".get All", + "Ġover l", + "/ \",", + "_c ountry", + "ç İ", + "ĠG ENER", + "_r oute", + "ĠD al", + " ´", + "ol oad", + "Ġuncomfort able", + "(m enu", + "Ġhost name", + "' \");Ċ", + "Ġcalcul ations", + "-c lick", + "Ġprotect ive", + "ãĤ ¯", + "_F orm", + "ung s", + "Act ual", + "m f", + "ĠProcess ing", + "ĠIn ventory", + "(m atrix", + "app ropriate", + "w eg", + "ij a", + "Ġch r", + "Ġr ifle", + "-w sj", + "k ar", + "Ġindepend ently", + "I OS", + "Ġconsist ency", + "v n", + "/s ystem", + "ĠCh anges", + "Ġexp ose", + "ici ents", + "Ġrel ate", + "ĉ next", + "è ¨", + "ud es", + "Ġglass es", + "F XML", + ".... ..", + "ĠP df", + "Ġappro ve", + "Ġ{ \\", + "Ġexist e", + ")) (", + "ARE NT", + "оР¿", + "ĠL atest", + "ĠNiger ia", + ".Inter faces", + "Ġrem oves", + "En emy", + "Ġen force", + "vert s", + "ĉ pos", + "_text ure", + "W ARD", + "ĠINC IDENT", + "( container", + "Ġdef ending", + "ĠR X", + "ĠH ook", + "br is", + "ĠFl ask", + "Gr ay", + ". )Ċ", + "vis ibility", + "ĠRedirectTo Action", + "err al", + "_e lem", + "Ġres on", + "front end", + "_variable s", + "ater ia", + "Ġ+ \"", + "ave led", + "RI X", + "Ġdef icit", + "_C heck", + "YY YY", + "To One", + "sp y", + "Ġun ited", + "end ent", + "Ġp ode", + "ãģ Į", + "C AT", + "(f mt", + "ĠBon us", + "Ġre ck", + " º", + "Mod ules", + "Ġvac uum", + "R adio", + "ĠDAM AGE", + "P en", + "ĠPark er", + "; ;Ċ", + "ĠRe ally", + "_n eg", + "p ending", + "Ġnomine e", + "ĠC ategories", + "ĠUl tra", + "We apon", + "Ġdef ender", + "I ss", + "ĠG ender", + "ĠD ress", + "Ġimpr ison", + "Ġbank rupt", + "imension al", + "PH A", + "ĠStr ateg", + "ĠPROF ITS", + "Ġp atri", + "//////////////////////////////////////////////////////////////// ////////////////", + "de legate", + "Ġfor State", + "Ġdev oted", + "_m ake", + "Ġterror ists", + "ĠS nap", + "_n av", + "ĠA A", + "ĠI an", + "ĉ app", + "Pl acement", + "_h dr", + "< K", + "Ġs ang", + "st roke", + "- Q", + "> x", + ".T ask", + "m oney", + "ib aba", + "' });Ċ", + "ĠSpec ific", + "ĠLine ar", + "_O PT", + "Hash Code", + "( Player", + ".Contains Key", + "Ġcoll apsed", + "trans parent", + "_R ANGE", + "View er", + "(c fg", + "Ġsort ing", + "Ġinf ected", + "ĠN ach", + "Ġaccommod ate", + ".element s", + "_P ART", + "ĠSex y", + "= get", + "( year", + "Ġx hr", + ": ]", + "ows ki", + "Ġsum mar", + "Ġ ¿", + "Ġint e", + "Ġwork flow", + "ĠTai wan", + "vers ions", + "åı ij", + "Ġsurprising ly", + "Ġopt ical", + "Ġpro ces", + "Ġdisag ree", + "Ġnue vo", + "ĠC AM", + "sort ed", + "le ases", + "ist le", + "Id ent", + "ĉ event", + "ject ed", + "Ch unk", + "V ars", + ".pro vider", + "Ġproceed ings", + "Ġin clusive", + "Ġart work", + "end ants", + "ï¼ļ Ċ", + "se en", + "Ġl ig", + "Ġm akers", + "_f un", + "Ġlength s", + "Path Variable", + "[ item", + "ภµ", + "De ad", + "FFFF FF", + "ĠUr ban", + "up les", + "ich en", + "(null ptr", + ".s pec", + ", System", + "UR ATION", + "(j ob", + "å¼ ı", + "Ġtrack er", + "Å Ļ", + "ĠM R", + "ĠSQL ite", + "Ġd to", + "Ġ; ;Ċ", + "Ġm int", + "ĠInt roduction", + "ca o", + "Ġquestion ed", + "Ġf itted", + "rev ision", + "s q", + "Ġm ig", + "_un its", + "_ async", + "Ġf lick", + "});ĊĊ Ċ", + "Ġnot re", + "}` ,", + "F ilters", + "Ġm undo", + "_d ays", + "Ġfr m", + "ut c", + "Ġval s", + "ew idth", + "ĠGener ator", + "ĠArt ist", + "ĠID s", + "ĠArt icles", + "re ater", + "ĠComponent Fixture", + ". =", + "Ġr ou", + "- no", + ".b ukkit", + "eg g", + "ĠD iff", + "atic s", + "Ñĥ Ñĩ", + "âĢĶ ĊĊ", + "ĠChar lotte", + "by e", + "Ġ} );čĊčĊ", + "ĠV ik", + "ĠB row", + "Ġl v", + "ĠG ib", + "-w ing", + "GL IGENCE", + "(I l", + "ĠEngine er", + ".W ait", + "ĠP ictures", + "Ġr het", + "Ġth ermal", + "Ġpr aise", + "< >();ĊĊ", + "ĠSp ider", + "P ause", + "ĠB aker", + "Ġsl ower", + "Ġ} ]Ċ", + "_en queue", + "Ġdisappe ared", + "ĠT icket", + "IN UX", + "_LOC AL", + "аÑģ Ñģ", + "@Inject able", + "comm unity", + "Gesture Recognizer", + "åĽ ½", + "Ġsca les", + "Ġ- (", + "/ '+", + "ĠS it", + "Ġexecut ives", + "ard ing", + "Ġad vers", + "Ġback wards", + "ĉ context", + "ĠH amp", + "ĠP F", + "ĠDe ck", + "ĠCra ig", + "A merican", + "Ġb ell", + "Ġpro l", + "uf en", + "Ġr ng", + "ar shal", + "ĠSim ply", + "first name", + "sh ore", + "J uly", + "Ġmort ality", + "ĠâĨĴ ĊĊ", + "Help ers", + "Ġbench mark", + "em ade", + "Ġorganis ations", + ".g son", + "ĠText Field", + "Ġciv ilians", + ".Array s", + "ĠMiss issippi", + "Ġinter mediate", + "get User", + "_cl uster", + "Rel ative", + "fore ign", + ".querySelector All", + "Fore ignKey", + "Ġreason ably", + "-------- -Ċ", + "C ards", + "ĠK am", + "ĠTh or", + "Ġroll er", + "-e lement", + "ĠC urrency", + "dd ie", + "ALL Y", + "ĠR A", + "Ġper met", + "aa aa", + "Ġhom ework", + "ĠV it", + "Ġm old", + "ĠF er", + "[ start", + "Ġstatist ical", + "Ġsc ary", + "_H OME", + ".B egin", + "Con struct", + "ogen ic", + "ĠDEAL INGS", + "Ġtamb ién", + "ix on", + ". ind", + "ac re", + "Ġtransform s", + "ĠN ap", + ".B lock", + "uss ia", + "pir ation", + "ul ent", + "Ġce il", + "Cl ause", + "na ire", + "T ES", + "Ġne at", + "ST D", + "ĠReg Exp", + "per form", + ": )", + "Ġun ions", + "Ġs ublic", + "Ġw inds", + "lo ating", + "g lich", + "Ġp agination", + "S kill", + "App ly", + "ĠOper ator", + "ist ogram", + "Ġqual ities", + "C ross", + "Ġde com", + "], \"", + "ĠJ uan", + ".mod al", + ".Ch ild", + "ĠRog er", + "STIT UTE", + ":CGRect Make", + "a lette", + "Ġst a", + "as ide", + "Ġbl ur", + "ĠW a", + "if etime", + "re ed", + "control s", + "Ġb ins", + "Ġп ол", + "*/ ,Ċ", + "U IS", + "ĠR ou", + "ĠDem o", + "- awesome", + "ĠCh ain", + "Ġh asta", + "ĠB art", + ". KEY", + "Ġvend ors", + "nof ollow", + "ĠD est", + "_b uilder", + "Ġarg ues", + "_ answer", + "g oto", + "ĠRES ULT", + "ĠM ON", + "Ġp oder", + "o ons", + "_C ASE", + "Ġrep lic", + "Ġfin ancing", + "ĠD ATE", + "c ern", + "_tr ack", + "t ies", + "/ logo", + "ĠNE GLIGENCE", + "get Type", + "> T", + "b et", + "g irl", + "ĠINCIDENT AL", + "-s ite", + ".tr igger", + "ĠL isa", + "_input s", + "Ġrel atives", + "Logged In", + "Config ure", + "I K", + ". accept", + "Res ume", + "ĠD raft", + "Ġ* >(", + "ĠW A", + "ed ian", + "ern ess", + "ĠLayout Inflater", + "*/ čĊčĊ", + "oth y", + "Ġoblig ation", + "Sub scribe", + "Ġth umbnail", + "ex ist", + "Ġins isted", + "ĠU ICollectionView", + "ĠAng ular", + "Ġtable ts", + "ĠImp act", + "ãĢį ĊĊ", + "ah o", + "Ġcharacter istic", + "g d", + "Ġ= ================================================", + "our t", + "` .", + "App ro", + "Co ordinate", + "Rem ember", + "Ġmar ine", + "] =='", + "ĠAdmin istrator", + ".get Default", + "Ġforg ot", + "ĠStruct ure", + "V ue", + "ars ing", + "m oment", + "k w", + "_c ursor", + "Att ack", + "Ġath letic", + "Ġdiagn osed", + "Ġend e", + "åĪ łéĻ¤", + "H ouse", + "ĠP ARAM", + "Ġw iki", + "ĠO pp", + "Ġcons ervation", + "Ġs nd", + "_t em", + "sub str", + "ĠC ape", + ".s im", + "UT ION", + "an an", + "âĢĻ un", + "Ġg y", + "- work", + "Ġcomp elling", + "=' #", + "ĉs ub", + "Ġdirect ories", + "íĬ ¸", + "Ġtouch es", + "out ines", + ".C ollection", + "s chedule", + ".l at", + "ĠDo ctrine", + "CA A", + "ĠRe fer", + "Ġshift s", + "Ġlik elihood", + "pre ter", + "ĠF emale", + "Ġinter cept", + "Ġl ou", + "çĻ »", + "Ġr ug", + "ĠC rown", + "Ġ************************************************************************ ****", + "- product", + "Ġprompt ed", + "ung le", + "d ocker", + "ĠT u", + "ĠUn ique", + "_ Error", + "ul os", + "Ġâ Ħ", + "Ġ( `", + "Get ting", + "_s cal", + "ĠEn h", + "ü t", + "Ġsust ained", + "Ġp atches", + "Ġpros per", + "ĠG aza", + "_l ight", + "Ġin cons", + "-------- Ċ", + "ĉĉ ĠĠĠĠĠĠ", + "S F", + "C N", + ": \";Ċ", + "ĠColl ins", + "( *)", + "Ġcomp ilation", + "'] čĊ", + "Ġcon sequence", + ", ...", + "Ġd m", + "ĠB LOCK", + "Cl uster", + "Ġsk i", + "(arg c", + "T uple", + "Ġjo ins", + "ĠSher iff", + "W ar", + "ind i", + "Ġcomment ed", + "H OST", + "Ġinv itation", + "apan ese", + "Ġperm its", + "preced ented", + "_z one", + "ĠA my", + "_R D", + "Min imum", + "Ġinv ocation", + ".en able", + "icht en", + "- owned", + "\" id", + "_PO INTER", + "F ac", + "Ġspecific ations", + "Ġnom ination", + "Ġg p", + "< (", + "Ġrob ots", + "ĠJ erry", + "Ġhold ers", + "Ġw and", + "c ms", + "Ġ} ))Ċ", + ".To ast", + "ĠI List", + "B ased", + "z oom", + "/ style", + "ĠBe ck", + "M en", + "Ġcontrib uting", + "Ġund o", + "ĠO H", + "Ġadd Object", + "Ġe igen", + "sign up", + "éĶ Ļ", + "Ġdist ant", + "PAR ATOR", + "ĠM ari", + "Ġm á", + "E mp", + "ó s", + "Ġì Īĺ", + "ev t", + "+ j", + "p ark", + "ĠSt ay", + "ĠD un", + "Ġso y", + "> %", + "az ines", + "Ġti empo", + "(m e", + "p resent", + ".Th is", + "Ġedit ors", + "F IELD", + ".W ork", + "ĠUn iverse", + "Ġdr unk", + ".t imer", + "Ġalter ed", + "ĠN ar", + "ëł ¥", + ".Act ive", + "id or", + "ç Ń", + ".delta Time", + "Ġawk ward", + "& quot", + "ĠSaf ari", + "Ġtr icks", + "MENT S", + "div ision", + "Ġvary ing", + "ĠHigh way", + "Ġphotograph er", + "ĠSt ewart", + "Ġlast ing", + ".P re", + ".amazon aws", + "ĠL uck", + ".D escription", + "ĠN az", + "n eg", + "Ġc ó", + "<<\" \\", + "ĠSur v", + "ĠU nc", + "Rec ipe", + ".Border Style", + "Ġmod ifications", + "- at", + "AT FORM", + "h dr", + "ak o", + "Ġsublic ense", + "ĠJ ump", + "Ġbe im", + "ĠMan hattan", + ". bool", + "_h w", + "ÑĤ ÑĮ", + "B in", + "Ġg ateway", + "\" \":", + "ĠU IS", + ":\" +", + "- def", + "ĠReg ular", + "/ testing", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "string stream", + "Ġdis par", + "Ġmob il", + "- read", + "ĠAd apter", + "ĠCh ampions", + "Ġsched uler", + "Ġk ills", + "ĠM ultiple", + "ir ror", + "Ġgod s", + "AD O", + "ak te", + "ĠUs uario", + ".c ircular", + "Ġre cept", + "ĠEx pr", + "Ġelder ly", + "Ġnic ely", + "Ġbest e", + "W ant", + "Ġclass ical", + ".s prite", + "obj c", + "ĠM ason", + "Ġsist ema", + ".Bl ack", + "es o", + "ĠZe it", + "Ġdiv id", + "Ġent ers", + "_sub ject", + "ĠPlan et", + ".w arning", + "ĠG ram", + "_t okens", + "Ġhousehold s", + "_c ustomer", + "user Name", + "c ross", + "Ġp ione", + "Ġass ists", + "_S M", + "ib o", + "Ġlo yal", + "Ġuse less", + "# elif", + "ĠUlt imate", + "C ome", + "g el", + "Ġd ich", + "xy z", + "ik el", + "ob ra", + "_s can", + "ĠInter ior", + "ĠN ice", + "Ġpl ac", + "ĉt arget", + "Ġvir al", + "ass o", + "() /", + "und e", + "ĠAd obe", + "O s", + "vis ited", + "ĠO W", + "ĠFe ed", + "ĠSe quence", + "Ġman ages", + "in son", + "ĠLouis iana", + "{ })", + "ĠH ab", + "ĠL D", + "Ġb ip", + "pr ites", + "(e lem", + ".h ibernate", + "él é", + "Ġoh ne", + "_trans action", + "Ġann unci", + "P ublished", + "ĠH onda", + "ĠT am", + "ĠP acket", + "_ selector", + "Ġchalleng ed", + "Process ing", + "-h over", + "Ġtr ainer", + "_c ancel", + "ĠNS Dictionary", + "ab ric", + "ĠM LS", + "_s ensor", + "Ġshr ink", + "ĠF X", + "th reshold", + "ĉH X", + "-m ark", + "` .`", + "S cheme", + "(f ull", + "_w riter", + "ĠS ys", + "Ġf led", + "ĠC in", + "-w idget", + "ĠPre vious", + "G ender", + "_ question", + "Fe ed", + "Ġscr ut", + "(p refix", + "ãĢĤ ãĢĤ", + "Ġin fections", + "Part s", + "Ġhier archy", + "_DE LETE", + "ĠPat ient", + "_p ay", + "Ġprom oted", + "Ġì ĭ", + "Ġcivil ian", + "Ġagricult ure", + "ĠP iece", + "Ġst ance", + "uts che", + "Ass ign", + ".A CTION", + "F ig", + "_r adius", + "ĠS ync", + "du cer", + "f ailure", + "ens ed", + "pt ime", + "B M", + "_dat etime", + "qu ivo", + "QUE UE", + "èĢ ħ", + "Ap pear", + "Ġsum mit", + ": void", + "Ġv ine", + "è® ¤", + "on ne", + "_TR ANS", + ".g reen", + "_ cc", + "Ġhung ry", + "Ġ\" >", + "() );čĊčĊ", + "Ex tract", + "iz ens", + "Ġsol ver", + "Not ify", + "Ġeng lish", + "ĠSh opping", + "inter faces", + "RE Q", + "Ġil leg", + "ĠUI ImageView", + "Ġdis connect", + "ĠUnt il", + "ĠConserv ative", + "@ Column", + "Ġshift ed", + "Ġ: čĊ", + "Ġf ich", + "Ġd la", + "Ġsh oe", + "\"), čĊ", + "ular ity", + "_RE SP", + "We ather", + "UI Application", + ". iterator", + "Ġag ing", + ".P arent", + "ow ie", + "(e qual", + "ĠCon v", + "/ default", + "Ġmeas uring", + ".pre v", + ".Is Valid", + ".F at", + "Ġs Äĥ", + "key words", + "with out", + "Ġso vere", + "Ġex changes", + "Ġm elt", + "Ġis lands", + "ĠInt egr", + "Ġjump ing", + "Ġg le", + "Ġjournal ism", + "Ġd ated", + "Local ized", + "ĠRef resh", + "Part icle", + "Ġa a", + "ĠSTR ICT", + "Ġb od", + ".Pro cess", + "_A UTO", + "ĠP ublished", + "e very", + "Ġtechn ological", + "ls x", + "Ġir rit", + "Add itional", + "Ġdel imiter", + "_l anguage", + "- area", + "bo ys", + "ĠT ube", + "Ġw at", + "Ġmechan ics", + "_ owner", + "Sp ell", + "ĠSt ories", + ".Append Line", + "Table View", + "h em", + "st ick", + "oll ower", + "I FF", + "ĠU V", + "oll ision", + "S UB", + "Ġcompar able", + "Ġdon de", + "s ales", + "ll vm", + "Ġ} ],Ċ", + "OTT OM", + "ĠPur pose", + "L ab", + "Ġinterview ed", + "o is", + "as il", + ".set Id", + "ĠIn struction", + "-- >", + "ĠMod ified", + "ation ally", + "ĠMe eting", + "è¯ ¯", + "# region", + "Ġrout ing", + ".f ocus", + "ĠYou th", + "< D", + "ĠN ag", + "contact s", + "Ġform ing", + "Ġm ie", + "',[' ../", + "ĠB P", + "Ġapp et", + "ĠTe acher", + "ĠT P", + "Ġann ually", + "outed EventArgs", + "ĠSpe aker", + "Ġre name", + "CF G", + "(\" //", + "æİ ¥", + "/p ages", + "Ġpr és", + "ĠSp ell", + ".All ow", + "ĠINT ERRU", + "Ġ( #", + "âĢĻ ĊĊ", + "_G eneric", + ".im show", + "_t im", + "- face", + "(& (", + "atin um", + "Ġrevolution ary", + "ĠH ours", + "r ain", + "Ġany time", + "Ġab b", + ".j sp", + "Scroll View", + "ĠTr uth", + "Ġanticip ated", + "Ġacc ent", + ". checked", + "Ġspec ifies", + "Ġca f", + "Ġcell padding", + "Ġcook ed", + "ĠH ugh", + "pe ek", + "_R ATE", + "Ġd orm", + "/ čĊ", + "IV ITY", + ".Cont roller", + "(p art", + ".con straint", + "Ġinv asion", + "MO VE", + "Ġgl uc", + "l ename", + "Ġam en", + "eng lish", + "ĠSw itzerland", + "\";ĊĊ Ċ", + "pe st", + ".col lect", + "N ib", + "ĠD ict", + "ĠE mb", + "(sub ject", + "Ġoutr age", + "Ġdec iding", + "Ġsent enced", + "F echa", + "\" A", + "Ġqu er", + "Ġfont Family", + "Ġqu adr", + "- Y", + "_C ACHE", + "Ġanaly zed", + "Ġg aining", + "ĠAgain st", + "ĠSou l", + "ta u", + "Ġlight weight", + "ĠT F", + "ĠEffect s", + ".T ypes", + ".add Class", + "Ġv egan", + "é ģ", + ".' \"", + "ĠExpl orer", + ".d etect", + ".sh ift", + "Ġoblig ations", + "last Name", + "Ġassoci ations", + "ĠTime Span", + "un ter", + "ĠF resh", + "Compat ible", + "P ub", + "id ges", + ". option", + "var i", + ".hash Code", + "Ġg eb", + ". section", + "- not", + "ĠSub mit", + "T N", + "reg istry", + "_m edia", + "Ġn aj", + "ff t", + "Ġm ate", + "-th ird", + "Ġp ockets", + "est a", + "Ġb ent", + "ĠN ord", + "Ġretail ers", + "ĠMor ris", + ".\"\" \"ĊĊ", + "W rong", + "Ġ ÅĽ", + "R ay", + ". ec", + "ĠB ind", + "_H AND", + "(n on", + "is Valid", + "Ġsimilar ly", + "_L IMIT", + "Ġdynam ics", + "Ġdist inction", + "ãģ Ĩ", + "< N", + "Ġor th", + "ĠToy ota", + "ĠK ate", + "ĠL S", + "or ie", + "ĠSpr ings", + "Ġf reak", + "last name", + "_M ULT", + "-st ep", + "\" (", + "AD DR", + "Ġentert aining", + "_CON F", + "Ġdec oded", + "Ġst reak", + "Ġwait ed", + "Ġnot ified", + "rodu ced", + "vis ual", + ".Layout Params", + "æ °", + "es ian", + "f its", + "s pring", + "ĠBern ie", + "User Defaults", + "Ġped est", + "Ap pearance", + "ĠW iki", + "ĠNOT ICE", + "Ġs sh", + "Ġdur ante", + "ĠZ ip", + "ı r", + "ĠNAT O", + "Ġtw elve", + "Ġro yal", + "ï ¸", + "Ġmer chant", + "ĠF urniture", + "'] ),Ċ", + ", X", + "Ġfold ers", + "ĠG ate", + "ĉf unc", + "p ick", + "_us uario", + "ĠV erm", + "ment ion", + "ur pose", + "Ġalert s", + "x ious", + "_s ig", + "ĠF u", + "Ġ( :", + "Ġd umb", + "åħ ³", + "Ġaccur ately", + "éĩ į", + "R B", + "-s creen", + "ĠV ER", + "j our", + "Ġrom ance", + "uc ceed", + ". choice", + "Ġad ip", + "_d ims", + "Serial izable", + "ãĤ ĭ", + ".j ob", + "Ġpro g", + "uch ar", + "Ġg ently", + "ĠR SS", + "ict ured", + "_ENABLE D", + "ĉ label", + "aw ks", + "ĠEn sure", + "rem ember", + "ìł ķ", + "Ġtrans mit", + "{{ $", + ".Trans action", + "ur se", + "_rel ative", + "Ġs ized", + "ĠX X", + "ĠPr incess", + "ĠL arry", + "Ġpr ó", + "ĠÑģÑĤ ÑĢ", + "Ġs isters", + "estr uct", + "Ġcheck point", + ": length", + "ĠCar los", + "/ icon", + "_T ARGET", + "T okens", + "Ġpat ience", + "ĠSe lected", + "q ty", + ".show Message", + "Ġwild life", + "ĠP rops", + "b m", + "- arrow", + "Ġpar cel", + "fire base", + "ĠBen jamin", + "cess o", + ".t im", + "ĠG arc", + ". any", + "ĠHOW EVER", + "ĠK o", + "Ġgrab bed", + "_f rames", + "Ġobject AtIndex", + "ĠADV ISED", + "Ġsub ur", + "ĉ GL", + "Ġ}) }Ċ", + "-l ength", + "ìĭ ľ", + "ĠPot ter", + "_b uff", + ".g ui", + "ĠEnc oding", + "E lect", + "-m essage", + "Ġ �", + "Ġ ÈĻi", + "ĠArgument NullException", + "а ÑĨи", + "Ġmin imize", + "Ġrespond ing", + "$_ ['", + "ĠInd ividual", + "á c", + "ĠIN TER", + "Ġmast urb", + "ĠB in", + "(' $", + "ëĵ ľ", + "Ġopen ly", + "Ġ> <", + "Ġun to", + "olog ically", + "ĠM ul", + "VID IA", + "Ġsl im", + "ĠCommission er", + "( on", + "Ġunder neath", + "/ db", + "v ote", + "( Message", + "ĠP ope", + "Def ined", + "Ġsw ift", + "ur f", + "Ġadapt ed", + "SE L", + "Ġreven ues", + "Ġdiv ine", + "= y", + "Grad ient", + "_ act", + "Ġ/*! <", + "Ġpoly gon", + "ĠF DA", + "ĠC arr", + "at ables", + "(std out", + "Ġrefr iger", + "Ġco ordin", + "avor ites", + "ÑĪ и", + "Ġcompass ion", + "ĠPOSS IBILITY", + "- secondary", + "ur acy", + "Ġcomp romise", + "_A V", + "_ os", + "Ġbes ide", + "ĥ Ŀ", + "Ġl n", + ".pl ugins", + "Cap acity", + "al ah", + ".b in", + "ĠC RC", + "_b alance", + "Ġflex Direction", + "Ġam bit", + "Ġnick name", + "ĠFor ces", + "C LE", + "ĠSh ell", + "Ġs ail", + "ĠW riter", + "ĠA lice", + "d w", + "ĠInd ians", + "ĠMar shall", + "_S RC", + "Ġnormal ized", + "ĠJ ag", + "ãĤ Ĵ", + "ze it", + "r pc", + "ÃŃ c", + ".in line", + "Ġtrav ers", + "_n umeric", + "Ġutil ities", + "Ġev ac", + "IN PUT", + "ĉ register", + "M X", + "ĠCamp bell", + "Ġdatas ets", + "Ġdem anded", + "Ġinitial State", + "g an", + "Ġe i", + "Un expected", + "- web", + "tr ait", + ", Y", + "ĠT odd", + "Ġske leton", + "Ġoptim ize", + "ç¬ ¬", + "ĠU pon", + "ĠSt Object", + "Ġap lic", + ".' P", + "v ron", + ". UN", + "Ġpaint er", + "izar re", + "Ġl av", + "Ġp om", + "p reg", + "= function", + "( serial", + "ific a", + "um ing", + "åľ °", + "ãģ Ĥ", + "- op", + "U CH", + "ĠH end", + ".prop Types", + "Ġy o", + "Ġrout ines", + "Ġcar ing", + "S em", + "Ġres erves", + "Ġprior ities", + "red its", + "IST R", + "Content Type", + "ĠSch w", + "/ media", + "Ġe str", + "Ġclim bing", + "- week", + "cher che", + "s ensor", + "To Array", + "ĠMont real", + "Ġcloud s", + "ĠInject able", + "ĠR ice", + "Ġpropag anda", + "_pro vider", + "Ġind oor", + "Ġin aug", + "Ġdipl om", + "Ġmess aging", + "_m ut", + "å ¦Ĥ", + "Ġk w", + "ON S", + "ari ans", + "R PC", + ") ]čĊ", + "-r ay", + "ĠS or", + "m all", + "Ġmarket place", + "Ġv tk", + "M a", + "og an", + "ig i", + "Ġspons ored", + "ĠD ani", + ".S EVER", + ">' .$", + "m ultipart", + "ĠW ol", + "Ġtable Name", + "ĠUser name", + "Background Color", + "Ġf right", + "_E MAIL", + "Sept ember", + "_val s", + "op ia", + "Ġsp otted", + "- Ch", + "Ġdata Source", + "/ \"Ċ", + "ек ÑĤ", + "ĠRequest Method", + "ĠRe place", + "-d o", + "ah n", + "ĠPh D", + "] .ĊĊ", + "N ON", + "g ement", + "ĠTh r", + "Ġquiet ly", + "Ġtort ure", + "Ġte as", + "ĠC Y", + "Ġa tr", + "develop ment", + "-d etail", + "Ġlight er", + "Ġarg uing", + "Ġdes erves", + "Ġcur riculum", + "_CON TEXT", + "ÅĤ y", + "H ITE", + "ĉ ID", + "/ uploads", + "Ġt its", + "re o", + "_d rop", + ". UTF", + "Ġpick up", + "Ġgro cery", + "ĠP ure", + "Ġeas iest", + "Ph il", + ".f eature", + "(\" *", + "Ġinvest or", + "t ok", + "Ġj ar", + "L os", + "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", + ". queue", + "-s peed", + "M al", + "um blr", + "ĠCON ST", + "ĠH RESULT", + "ĠD ance", + "(file Path", + "Ġattrib uted", + "ॠį", + "ĠB und", + "co ins", + "Ġs ão", + "Ġp ir", + "person al", + "Ġpre lim", + "Ġprop ose", + "ĠT L", + "] ])", + "ĠSub scription", + "ĠK re", + ", len", + ".First OrDefault", + ") --", + "_product s", + ".Get Bytes", + "Sh ip", + "Ġenc rypt", + "ĠS G", + "ĠM yst", + "h ir", + "Ġiter ate", + "Ġint end", + ".mock ito", + "Ġch apters", + "( angle", + "ĠV lad", + "è® ¾", + "' .ĊĊ", + "Response Body", + "ĠAb d", + "de al", + "Ġbar riers", + "-out line", + "b ill", + "ĠF alls", + "_se cond", + ". include", + ". ceil", + "Ġoccup ation", + "ph ony", + ".move To", + "ĠJenn ifer", + "AST ER", + "; \"><", + "ĠEn abled", + "Ġtermin ate", + "ĠI o", + "l ations", + "ĠTHE ORY", + "Ġear liest", + "Ġr ack", + "ĠSc ar", + "sh ake", + "ch ip", + "Ġu v", + "Ġall iance", + "п иÑģ", + "ĠGOOD S", + "z ione", + "ĠV I", + "Ġ{ -", + "Ġfilter ing", + "Ġmis con", + ".Dock Style", + "Ġb ush", + "Ġj unk", + "æ Į", + "ĠQ UE", + "Ġhook s", + "Ġfirm ware", + "Ġmiddle ware", + "d ic", + "ĠOak land", + "Ġarr ives", + "P ayload", + "p ixel", + "] |", + "Ġstart Date", + ".P RO", + "_a udio", + "Ġmid field", + "igid body", + "ĠSw iss", + "ĠCl ip", + "ĠD ump", + "ĠText Box", + "Ġg eh", + "y ield", + "od s", + "Ġrefer endum", + "Back end", + "ĠC ream", + "Ġdomin ated", + "ĠArch ive", + "Ġrid ers", + ".prepare Statement", + "Ġqu ando", + "Ġche f", + "w iki", + "in el", + "am pling", + "(\" \\\\", + "Ġs ag", + "_pro xy", + "ãģ ķ", + "p do", + ".getElementsBy TagName", + "Ġdemonstr ation", + "ĠN PC", + "Ġarch ivo", + "end ance", + "Ġefficient ly", + "( actual", + ".t ableView", + "Ġm ush", + "Ġbe ars", + "_thread s", + "j as", + "ah un", + "Ġne ural", + "Ġdesign ing", + "ĠG DP", + "Ġlift ed", + "çĽ ®", + "ĠJ oint", + "ĠIn clude", + "ĠGi ants", + "Ġwithdraw al", + "ĠR ent", + "n ative", + "ĠSe ek", + "gress ion", + "_C PU", + "\\ S", + "ĠSh ield", + "Ġsol ic", + "Ġbo om", + "yect o", + "Ġmanufact ure", + "ĠâĢ ĭ", + "Ġb box", + "Ġearth qu", + "ollect ors", + ":@\" %", + "Ġlo ops", + "J e", + "alk ing", + "ĠWh ats", + "ĠBo ys", + ". book", + "ARG E", + "_p ixel", + "Ġsus pects", + "Î ¹", + "us p", + "ĠBM W", + "ie ces", + "(p erson", + "å¼ Ģ", + "é »", + "ĠPod cast", + "Ġb ou", + "( Item", + "à »", + "( Input", + "Http Get", + "Ġb urg", + ") ^", + "BO ARD", + "*/ ,", + "Ġg ulp", + "ĠB enn", + "Ġdeck s", + ".status Code", + "Ġac ute", + "Ġh ug", + "ug u", + "Ġp led", + ",\" %", + "h ape", + "Ġз ап", + "ĠMain e", + ".re al", + "Ġd alam", + "ĠMin or", + ".F loat", + "dis p", + "Ġt l", + "Ġen count", + "=> $", + "Ġf g", + "te es", + "ĠRec omm", + "ä l", + "Ġchem istry", + "Block s", + "O ID", + "Ġfore x", + "ĠApp end", + "Ġ{ *", + "ĠSup ply", + "CG Float", + "(b l", + "Ġat e", + "ador a", + "Ġg ust", + "Ass oci", + "> .Ċ", + "F ETCH", + ".s erial", + "widget s", + "ard less", + "ie fs", + "_F ULL", + "ernet es", + "ĠP red", + "Ø Ń", + "äº ĭ", + "ub ernetes", + "ĠL aura", + "Ġl abeled", + "High light", + "Ġanno ying", + "/ update", + "(d escription", + "Ġintim id", + "$ c", + "\")) )Ċ", + ".A P", + "Ġ[] *", + "ĠEX IT", + ".H ost", + "ĠOP EN", + ".send Message", + "_c amera", + "_t ile", + "Ġth erm", + "onom ous", + "Ġdis adv", + "Ġna ar", + "index Of", + "ĠP P", + ".prot ocol", + "AF E", + "Ġtext ures", + "################################ ################", + "umb ai", + ".st ats", + "ĠG E", + "Ġi e", + "ĠST D", + "ĠM ann", + ".ref lect", + "K B", + "Ġd ive", + ".w av", + "/* ----------------------------------------------------------------", + "/ settings", + ".l ifecycle", + "Ġda ughters", + "or us", + "ub er", + "N ING", + "st ri", + "ĠT ip", + "Ġz n", + "Ġswitch ed", + "in et", + "uff y", + "ĠTransport ation", + "( conf", + "fr ica", + "ĠX L", + "ĠLe ad", + "_per cent", + "< Map", + "Ġthr ust", + "or b", + "ik k", + "Ġtra uma", + "Access or", + "ĠF it", + "ĠString Buffer", + "ex pl", + "(s creen", + "Ġaud iences", + "ĠO PTION", + "_ round", + "[ node", + "be h", + "-> __", + "per missions", + "ĠD etermine", + ".M an", + "Ġadv ances", + ". InputStream", + "Ġstrong est", + "Ġe Bay", + "Ġ# -", + "Ġdir name", + "ĠS MS", + "Ġmedic ations", + "Ġam ended", + "Ġchurch es", + "ĠImper ial", + "$ row", + "ĠMad ison", + "ĠIn sp", + "Ġaff air", + "Ġpsych ology", + "v h", + "Ġsever ity", + "âĢ IJ", + "Ġstri ps", + "A H", + "vert ising", + "Ġcon se", + "IM AGE", + "ĠSt ats", + "ĉs c", + ".C ursor", + "Ġfree ze", + "ss on", + "(x ml", + "ĠSus an", + ".t ile", + "ed ed", + "ĠĠĠĠ ĉĉĉ", + "uel le", + "ĠMitch ell", + "b ased", + "Oper and", + "½ æķ°", + "ĠF F", + "ĉstr cpy", + "ounc es", + "ild o", + ".execute Query", + "Ġapproach ing", + "ĠSe ven", + "Ġn uts", + "Ġr ic", + "ass ignment", + "Ġcalcul ator", + "ĠMur phy", + "ĠB ou", + "í Ħ", + "Ġbut t", + "Ġt icks", + "Project s", + "il ib", + ".text Color", + "m ov", + "_log o", + "( template", + "ĠIN IT", + "Ġimage View", + "scri ptions", + "OR ITY", + "Con sumer", + "Ġun precedented", + "Ġtour ist", + "Ġbr on", + "Ġcontract or", + "Ġlic ence", + "ĠN am", + "æ ¯", + "( transform", + "_AT T", + "P ref", + "ĠG am", + "Ġvess els", + "Ġh av", + "L ater", + ".To Lower", + "Ġurl s", + "Ġbreak down", + "Ġpen alties", + "Ġf oster", + "ĠU E", + "Ġcl ue", + "com ed", + "åIJį 称", + "-m ain", + "Ġp ts", + "Ġcount ed", + "ict s", + "/ post", + "Ġget attr", + "Ġp ing", + "ANCE L", + "Ġp ec", + "Ñħ од", + "ant om", + "ĠBlue print", + "ĠEvent Emitter", + "Ġl ä", + "æ ²", + "Ġstr aw", + "( comp", + "' une", + "> N", + "- client", + "es Module", + "-b ase", + "Ġret reat", + "_s imple", + "ĉĉĉĉĉĉ Ġ", + "fe e", + "') čĊčĊ", + "Control Item", + "Ġsubscri bers", + "ple ase", + "ĠE ff", + "Ġp ound", + "ĠBy tes", + "ĠTe a", + "_ activity", + "Ġmax im", + "Ġop code", + "B SD", + ". constant", + "; }", + "omb res", + "Ġcare ers", + ") .ĊĊĊĊ", + "Ġsp reading", + "-exp anded", + "ĠOr d", + "amar in", + "Ġmob ility", + "Un fortunately", + "ak k", + "N L", + "_ redirect", + "ĠP G", + "ĠS ensor", + "b ol", + "t ap", + "_MEM ORY", + "ĠUI Alert", + "plit ude", + "We bsite", + "ĠLog o", + "lo ve", + "[ ind", + "Ġalto gether", + "Ġwonder ed", + "Ġes per", + "ĠLib eral", + "Ġo ss", + "Ġel it", + "Ġst iff", + "od ox", + "_ment ions", + "ĠDou glas", + "_p id", + "ĠC K", + "ĠinitWith Frame", + ".b log", + "p kg", + "ang hai", + "QUI RED", + "u u", + "Ġm kdir", + "AT AL", + "Ġun h", + "in ces", + "st h", + "Ġhypo thesis", + "Ġc ata", + "ĠT B", + "ĠCl ar", + "Ġpre decess", + "Ġsitu ated", + "-w orld", + ")) /", + "Ġhead lines", + ".st at", + "Ġout break", + "sp ath", + "_FLAG S", + "ĠServlet Exception", + "S un", + "F ROM", + "ĠD ir", + "ãĥ»ãĥ» ãĥ»", + "_co ord", + "ĠOpt im", + "Mon itor", + ".b it", + "XX X", + "Ġtod as", + "f eld", + "ÑĢ и", + "im ir", + "Ġpolit ically", + "Ġmolec ular", + "Ġtrad ed", + "Ġ{{ $", + "ĠSw edish", + "Ġ'@ /", + "_RE AL", + "Ġw arehouse", + "t oday", + ", L", + "or p", + "< section", + "- br", + "ym e", + "ĠUser Service", + "Ġlib erty", + "Ġmoment o", + "( Image", + "< size", + "S ch", + "Ġj og", + "i ology", + "arent ly", + "Ġquant um", + "ĠAb u", + "Ġr im", + "Ġman a", + "Font Size", + "Build ing", + "st airs", + "AIL ABLE", + "Ġ& '", + "Ġs ect", + "Ġs igh", + "(b atch", + ".I Container", + "p oll", + "ĠCor ps", + "Î µ", + "ar u", + "ĠK ay", + ".r ange", + "_click ed", + "ĠRobert s", + ".N etwork", + "fin ish", + "- Man", + "Ġcolleg es", + "ĠF ine", + "\")) ,Ċ", + "f ilm", + "Ġrem inded", + "Ġgest ure", + "out il", + "Ġthread ing", + "Ġobj et", + "Ġt ours", + "activ ated", + ".m kdir", + "= user", + "Ġre de", + "f ü", + "_SY STEM", + "p v", + "Ġcon gr", + "Ġmass asje", + "Ġpract ition", + "Un iversity", + "Ġtab index", + "Ð ĺ", + "S ets", + "Ġcount ies", + "g uest", + "f an", + "Ġword en", + ".d i", + "на Ñĩ", + " ¿", + "ig Decimal", + "Ġsh ore", + "Ġg ö", + "Ġrep airs", + "Ġhelp ers", + "Ġcenter ed", + "OL LOW", + "Ġmap StateToProps", + "Ġc ents", + "< A", + "Ġexpect ation", + "Oct ober", + "Ġbg color", + "ca les", + ".C ON", + "ĠV el", + "Ġcry ing", + "-se ason", + "Ġfunction ing", + "_LOC ATION", + "ü ss", + "ber y", + "Par a", + "omin ator", + "- le", + "Ġeth ical", + "has htags", + "emp lo", + "Ġn úmero", + "( activity", + ".St op", + ".str ftime", + "IL D", + "Ġto e", + "ĉ Node", + "\") čĊčĊ", + "ĠPu erto", + "Ġexec uting", + "ĠG UID", + "Ġoppos ing", + "al ph", + "Ġexhib it", + "_fl ash", + "Ġme ille", + "Ġjson Object", + "H ero", + "aint ed", + "_D OM", + "Ġw il", + "Ġslo pe", + "Ġm Ã¥", + "ĠIraq i", + "Ġorgan ize", + "ĉj Query", + "H UD", + "sh ine", + ". we", + "ĠSk ills", + "pons or", + "Ġcon clusions", + "Ġre forms", + "Ġrel uct", + "n amed", + "ĠOl iver", + "Ġ// }Ċ", + "- looking", + "Ġf og", + "ĠH O", + "ĠF ried", + "Ġinev itable", + "ĠData GridView", + "H our", + "il les", + "log ical", + "Ġconnect ivity", + ".tw ig", + "ĠK yle", + "(d st", + "- Sh", + "ĠStud ios", + "( Level", + ".j et", + "_PRO TO", + "-de coration", + "OT HER", + "Ġread ily", + ".Param eter", + "Ġmultip ly", + "ĠL IB", + "ar med", + "Ġsoon er", + "æ Ħ", + "_ ES", + "Ġfoss il", + "ĠA nc", + "âĢľ This", + "l odash", + "Py thon", + "Ġhist ogram", + "west ern", + "Ġinf ant", + "Ġco ordinator", + "Ġn ib", + ": m", + "Ġres pected", + "Ġdef init", + "& T", + "_p ad", + "ĠTr igger", + "th al", + "Ġimage Named", + "Ġbeat en", + "ĉ rc", + "ĠPal ace", + "Ġhaz ard", + "Ġisol ation", + "_ rc", + "cont re", + "OUT PUT", + "Ġre ign", + "ĠPl ate", + "AT ES", + "Ġfl ux", + "Ġpack s", + ".get Selected", + "Ġparticip ated", + "Ġneed le", + "-de pth", + ":::: ::", + "-l aw", + "ins pace", + "on itor", + "= no", + "ĠAt omic", + "ĠBr ain", + "Edit able", + "-s c", + "red ential", + "ĠP erry", + "k ie", + "Ġ ----------Ċ", + ".st roke", + "( Intent", + "Ġun ity", + "um lah", + "F urther", + "Ġpr ze", + "Ġs ø", + "ãĤ Ĭ", + "ĠPROC UREMENT", + "ĠH ousing", + "Ġatt orneys", + "Ġcomp ose", + "atter ing", + "\" What", + "dra ul", + "Ġstraight forward", + "In stant", + ".J TextField", + "Ġtr ades", + "л а", + "Ġ{ !", + "Ġl ately", + "IM G", + "ĠA ld", + "ĠIN NER", + "Ġcart oon", + ".S ource", + "F ALSE", + "Ġd ough", + "f en", + "( rect", + "Data Table", + "N ick", + "ĠBut ter", + "read s", + "_com ments", + "EN V", + "ĠConnect icut", + "-F IRST", + "ĉĉĉ ĠĠĠĠĠ", + "ach i", + ".M sg", + "re ction", + "Ġrelax ed", + "Ġsha ft", + "Ġe f", + "ĠAdd ing", + "Ġbre ach", + "Ġ ï¼ļ", + "ram a", + "Ġconduct ing", + "Ġ( ;", + "(g l", + "ĠCA USED", + "ash i", + "ĠF LAG", + "ĠCom merce", + "ĠIN TEGER", + "h ours", + "ĠSchool s", + "Ġn ucle", + "Ag ain", + "pro j", + "Ġsevent h", + "EMPL ARY", + "(m ock", + "'] ,čĊ", + "_S PEED", + "> false", + "Ġsp a", + "ĠN ear", + "ì ķ", + "Ġintr ig", + "_m embers", + "w ave", + "Ġanalyst s", + "_O S", + "ed in", + "ĠF ri", + "Ġretrie ved", + "Reg ular", + "_ obs", + "EX PORT", + "')}} \"", + "\" class", + "__ ((", + "b ucket", + "Ġst ro", + "ĠP atch", + "yst ick", + "ful ness", + "ap os", + "D a", + "ĉĉĉĉĉ ĠĠĠ", + "Ġen rich", + "un ordered", + "h ole", + "C ong", + "< Product", + "ĠC urt", + "( the", + "_l ower", + "Ġavoid ing", + "Ġbu zz", + "Ġv iable", + "ub a", + "- is", + "are l", + "Ġact ed", + "-d etails", + "ภĩ", + "ĠThe ory", + "ĠP un", + "ĠAn onymous", + "... \"Ċ", + "è res", + "åı ¯", + "ĠV ision", + "_se m", + "ash a", + "Ġcelebr ity", + "Ġend Date", + "Ġpop ulate", + "Ġcu is", + "qu ant", + "f loor", + "Ġglob ally", + "Ġcru ise", + "ĠStan ley", + "Ġb ikes", + ".get Connection", + "Ġpoor ly", + "_ other", + "amp ing", + ".\" );ĊĊ", + "od i", + "_A DMIN", + ".color s", + "ĠG aming", + "> ';ĊĊ", + "STR UCT", + "Q R", + "ID s", + "(arg uments", + "_a ux", + "( Event", + "_PR IVATE", + "ĠTre k", + "Ġdownload s", + "m utable", + "_STR UCT", + "(w x", + "Ġdom ains", + "js px", + "ĠVi agra", + "Command s", + "J s", + ".c fg", + "Content Pane", + "ĠEdit Text", + "à¥į à¤", + "Att ach", + "ĠAR M", + "posit ive", + "ĠGener ated", + "Ġse ized", + "= :", + "Ġelectron ics", + "ĠApp Component", + "/ ',Ċ", + ".equals IgnoreCase", + "Do ctrine", + "d isk", + "ĠPolit ical", + "CH O", + "< F", + "ĉ height", + "ĠB ug", + ". le", + "ik h", + "Ġmill iseconds", + "Ġconstit u", + "m ag", + ".n l", + "-r ange", + "ang gal", + "', [", + "ropol itan", + "Ġà ľ", + "ĠU C", + ".d esc", + "-L AST", + "f stream", + "ib il", + "Ġf ier", + "VER Y", + "Ġë ³", + "IR T", + "_ UI", + "( abs", + "Ġkne es", + "Ġro okie", + "ĠV ac", + "are na", + "comm end", + "- \\", + "ĠSUB STITUTE", + "So ft", + "Ġpart ir", + "we alth", + "è¦ ģ", + "(d ataset", + "ĠCl imate", + "- show", + "Ġreli ability", + "_ch unk", + "ä» £", + "_st ock", + "ĠEX EMPLARY", + "ï¸ ı", + "Ġv ÃŃ", + "Ġsm iled", + "Ġdr ill", + ".F unction", + "ĠS I", + "Ġreg ression", + "- X", + "ĠJ ar", + "p ref", + "ĉs uccess", + "ĠHit ler", + "Ġinst inct", + "Ġfem mes", + "Ġlo ver", + "< Ċ", + "Ġmulti plier", + "r il", + "Res ize", + "ĠAuthor ization", + "ĠK an", + "Dispatch ToProps", + "Ġc rops", + "t okens", + "ec n", + "ential ly", + "ĠINTERRU PTION", + "f ake", + "Und efined", + "ĠA K", + "ĠTest Case", + "Ġr ab", + "Ġtor rent", + "ĠO t", + "B ars", + "Ġlect ure", + "Ġen jo", + "Ġrespond s", + "Ġindex ed", + "Of Work", + "_ch ain", + ")) ->", + "ĠBeaut y", + "Ġ` <", + "Ġtouch ing", + "Ġ| --", + "ĉf lag", + "normal ize", + "Ġtr apped", + "Ġestablish ing", + "/b uild", + "A J", + "f y", + "- react", + "av n", + "RI PTION", + "Ġk ut", + "ĠF ashion", + "ĠIn form", + "cur ities", + "< byte", + "ĠUkr ain", + "Ġs ug", + "Ġconsist ing", + "ood le", + ". ctx", + ".To List", + "Ġcomment ary", + "Ġtransf ers", + "Ġn ost", + "ih ad", + "ĠU pper", + "Ġconf using", + "miss ing", + "- cl", + "Ġbound ing", + "Ġcongress ional", + "Ġreve aling", + "d h", + "r up", + "Ġt res", + "re peat", + ", ĊĊĊĊ", + "_t ac", + "Ġexp ed", + "G irl", + "h orizontal", + "Ġ\"../../ ../", + "( option", + "Ġwe iter", + "ĉs ql", + "Ġ=> {Ċ", + "Ġgar lic", + "Ġre pr", + "Ġrepl ies", + "( prop", + "Ġspir its", + "Ġins pire", + "Ġbas ement", + ".re ject", + "Ġhint s", + "Ġpoll ing", + "ĉ ĠĊ", + "_r ating", + "Ġc ath", + "av ier", + "Ġcomp ressed", + "ĠV S", + "] '", + "Ġjud icial", + "ĠT rend", + "tr aining", + "EST AMP", + "ogn ition", + "Ä ģ", + "SE NT", + "vent ions", + "Ġconsult ant", + "um ph", + "Ġuser Service", + ", NULL", + "k h", + "D ear", + "_B AD", + "it ations", + "Ġmet aph", + "' é", + "and ise", + "-f ont", + ".ch art", + "Ġs g", + "_ Controller", + ".j peg", + "ĠUL ONG", + "ĉg ame", + "( ss", + "ĠM aj", + "ĉg o", + "ĠS ad", + "ĠB erg", + "ĠM ine", + "P ack", + "Ġres istant", + "ĠR OM", + "Ġp eg", + "ĠStan ford", + "ĠY ahoo", + "Ġsca led", + "Ġl an", + "= []", + "\"/ > ččĊ", + "Ġs ud", + "ĉ background", + "Ġsch olars", + "-m uted", + "ar á", + "Ġ= ====", + "Ġ__ __", + "C reat", + "ene ver", + "/w p", + "ĠV PN", + "Error Code", + ") ],Ċ", + "(b uilder", + "ĠEn emy", + "S ensor", + "us a", + "Ġtr iggers", + "Ġplayoff s", + "_RE Q", + "Ġ( ~", + "ĠBar ry", + "Ġperman ently", + "ĠR UN", + "Ġb ure", + ".Fat alf", + "Ġch ick", + "ĉ panic", + "ps i", + "ok a", + "éĢ ī", + "> [", + "Ġunderstand s", + "ĠJun ior", + "ĠIN FO", + "= mysqli", + "ust ain", + "-s ource", + "s erv", + "ĠC REATE", + ". au", + "Ġsell s", + "ĠĠĊ ĠĠĊ", + "E urope", + "z w", + "pre h", + "ĠNS A", + "Ġx y", + "ภ´", + "ĠB eyond", + "Inst ead", + "Non Query", + "Ġar ise", + "Ġavoid ed", + ".em place", + "_model s", + "} ),Ċ", + "Ġh id", + "Ġ& _", + ".p oints", + ".get Width", + ".Ex ec", + "Ġ// //", + "ĠS essions", + "... \\", + "ĠCol omb", + "Ġacceler ation", + "rest ore", + "Ġ ile", + "ob ic", + "< Node", + "ĠD X", + "ĠBes ides", + ". age", + "ĠCont ains", + "N ational", + "ĠIm plementation", + "Ġeff ic", + "ĠR M", + "H y", + "ĠWed ding", + "ok ies", + "Ġrec ursive", + "Ġprosec utors", + ".Se lection", + "ĠForm ula", + "Been Called", + "[i i", + "ĠFr an", + "Ġtraged y", + "_F EATURE", + "Ļ ¨", + "comp ass", + "ĠB h", + "? ĊĊĊ", + ".w riter", + "ĠH our", + "Db Context", + "io v", + "am on", + "re pr", + "é ĥ", + "ĉf i", + "'] ]", + "ĠD ry", + ". ro", + "ĠO bserv", + "æł ĩ", + "Form er", + "ĠB alance", + "ĉ json", + "Ġpr zy", + "I SS", + "( sock", + "ĠL INE", + "Ġde ce", + "Ġal ly", + "Ġtend ency", + "F un", + "Ġschem es", + "Ġinter ven", + "æĺ İ", + "Ġad verse", + "quote lev", + "Ġsacr ific", + "_s ide", + "Ġmut ex", + "AG IC", + "Ġocc urring", + "ĠCommunic ation", + "um ar", + "ç¼ ĸ", + "ĠTreat ment", + ".p erson", + "ĠL C", + "Ġe ch", + "( (\"", + "ĠDise ase", + "ä d", + "ĠA Z", + ".A ccount", + "Ġcontinu ously", + "END ING", + "ĠRET URN", + "- string", + ".f ilename", + "syn thesize", + "Res ponder", + "( opts", + "reg s", + "Ġn uest", + "Pe er", + "// ------------------------------------------------", + "Ġg auge", + "ĠK in", + ".s chema", + "Ġarr ange", + "ĠBl ake", + "_Type Info", + "C over", + "ĠHamp shire", + "P aper", + "-in ner", + "util ity", + "Ġcross origin", + "F OR", + "Ġign oring", + "ĠD D", + "av an", + "Ġtrad itions", + "Ġget String", + "Ġeth ics", + "ĠMaterial s", + "DE SC", + "Ġen zym", + "io let", + "ĠCh ip", + "ĠMc Donald", + "Ġn erve", + "ç Ħ", + "\") ]", + "æ± Ĥ", + "ĠS ugar", + "_S IM", + "j peg", + "Ġdiscret ion", + "ĠT N", + "bo ve", + "ĠMin imum", + "ĠForm Group", + "Ġwork force", + "ĠExec ution", + "err er", + "ĉ ĠĠĠĠĉ", + "Ġpres cribed", + ".Text Align", + "OP EN", + "ĠP B", + "im ity", + "ĠEx ternal", + "° C", + "ĠApplication Controller", + "Ġb arr", + "imp licit", + "_d ot", + "ĠCol on", + "C OLOR", + ".Pro ject", + "* }Ċ", + "pl aint", + "get Text", + "Ġindivid ually", + "Ġcheck box", + "U Y", + "ĠL amb", + "Ġdys function", + "ĠL ar", + "à °", + "ĠCre ating", + "');ĊĊ Ċ", + "\" They", + "loc ations", + "_C ORE", + "Inter action", + "umbn ails", + "ĠPart ner", + "b rit", + "Ġless er", + "ĠSl ot", + "set Attribute", + "ĠW ave", + ".p o", + "/ store", + "Ġbrows ing", + "_p d", + "sum e", + "s ed", + "Cur ve", + "Ġpl asma", + "Ġsusp icious", + "ìĿ ¸", + "ĠB ah", + "ĠExp licit", + "_C C", + ".Client Size", + "\\ View", + "Ġsub stit", + "lo on", + "ĠG AME", + "ĠB rid", + "Ľ 建", + "_ User", + "Ġsqu ares", + "f one", + "Ġsac red", + "ug hs", + "] interface", + "ĠTh row", + "ĠK irk", + "Ġemp ire", + "Ġassess ed", + "T ax", + "ĠHe aven", + "-b uffer", + "_STAT IC", + "én é", + "-b ordered", + "Ġpun ct", + "(m ode", + "Ġke ine", + "S ent", + "ĠCal cul", + "ĠE ve", + "Ġsty lish", + "Ġoil s", + ".Test Case", + "Ġtrad emark", + "Ġliter ary", + "Ġconcentr ations", + "ĠRel ations", + "( Class", + "Ġstd in", + "Ġv æ", + "back up", + ". VERSION", + ".AutoScale Dimensions", + "st arter", + "Transaction al", + "- panel", + "St udio", + "k c", + "ĠCh amber", + "ĠSpi el", + "Ġr ho", + "ا ÙĦ", + "! '", + ".At tributes", + "Ġmurder ed", + "apeut ic", + "Ġint imate", + "Ġtext Field", + "ĠBuff alo", + "d ummy", + "\" %", + "ĠLib erty", + "ob ar", + "ĠT ank", + "ĠPop ular", + "erv isor", + "ĠIn iti", + "ĠM all", + "ĠP rior", + "C AP", + "ĠCl ay", + "ĠCert ificate", + ".L ock", + "-st rip", + "-dr iven", + "/ all", + "ĠMessageBox Buttons", + "_SE CRET", + "_p b", + "Ġr ats", + "ा à¤", + "Ġn t", + ".R outer", + "_top ic", + "Ġt ennis", + "ĠP UBLIC", + "ĠActiv atedRoute", + "Ġ' ,Ċ", + "Ġcost ume", + "Ġj okes", + ". Handle", + "ĉ byte", + "Ġflav ors", + "( cc", + "Ġperson as", + "ĉ image", + "ĠN azi", + "Ġgram mar", + "Ġú lt", + "Ġval ve", + "Ġv ic", + "ĠR achel", + "_in valid", + "P refs", + "std int", + "(r oute", + "Ġhtml specialchars", + "Ġpe oples", + "pl ine", + "Ġn v", + "ĠQu ant", + "opp ers", + "Ġcurrent User", + "ĠC atal", + "Ġrecon c", + "Ġconj unction", + "l x", + "amb urg", + "Ġinflu ential", + "d anger", + "ind ers", + "Ġ% @\",", + ".config uration", + "os ome", + ". identity", + "Ġpick er", + "n ost", + "ĠDI Y", + "Aug ust", + "ab lo", + "Le af", + "ĠRec o", + "ck o", + "DO C", + "ĠH erm", + ": any", + "ĠInt erview", + "ĠT ex", + "x fe", + "( work", + "Ġle ap", + "He ading", + "Ġqu arters", + "\\ Bundle", + "re b", + "Per haps", + "ĠG mbH", + "B irth", + "ĉ sum", + "ĠWat son", + ".n il", + "ç ¡", + "{ }ĊĊ", + "ica id", + "Get ter", + "\" name", + "Ġ\" čĊ", + "_n one", + "z m", + "ac ute", + "uest o", + "Ġs ous", + "Ġre build", + "Ġnewsp apers", + "ĠH az", + "Ġk its", + "if o", + "Bl ur", + "Ġsu ited", + "- In", + "à ¯", + "ĠKe ith", + "ĠNor way", + "IN IT", + "ire ccion", + "iet ies", + "_us age", + "ĠDou g", + "r ise", + "Ġtr illion", + "im ited", + "ĠR EL", + "al ic", + "Ġcritic ized", + "the orem", + "Ġce ase", + "Ġsid ew", + "ĠT erry", + "Ġsubs idi", + "Ġfirm ly", + "Ġaw s", + "Ġh ott", + "Ġdress ing", + "bad ge", + "ĠApp lications", + "è¿ ĶåĽŀ", + "Ġlaugh ed", + "Ġh obby", + "Ġmus icians", + "Ġ* .", + ". placeholder", + "Ġcount ers", + "ĠCap itol", + "SD K", + "Ġhel met", + "and box", + "qu it", + "Ġcriminal s", + "Ġteen ager", + "( update", + "G l", + ".se lection", + "Ġdis charge", + "Ġpresent ing", + "ufact urer", + "_UN KNOWN", + "Ġstress ed", + "å Ļ¨", + "Pro to", + "_cor rect", + "ha us", + "Ġren ov", + "Ġfire arms", + "Ġtechn ically", + "-b rowser", + "Ġc andy", + "St roke", + "Ġexec utor", + "Ġocc urrence", + "ĠIP v", + "_INTER FACE", + "ĠRetrie ve", + ".b ad", + "Ex change", + "Nav bar", + "ĠK id", + "(get ApplicationContext", + "_ST OP", + "ĠB oss", + "List eners", + "Ġshoot er", + "ĠAl b", + "ä ch", + "Ġp ix", + ".key Code", + "al one", + "Ġabs urd", + "ĠC um", + "ĠNewton soft", + "ik t", + "Ġlaugh ing", + "Ġcapital ism", + "ree Node", + "T x", + "_QU ERY", + ".S leep", + "( login", + "Web Element", + "Ġcelebr ating", + "Ġde precated", + "Ġma ar", + "Ġart istic", + "_ASS OC", + "ĠBorder Radius", + "ĉw p", + "Ġsurviv ors", + "In ner", + "- red", + "Ġprosec ution", + "_ pp", + "(\" $", + "Ġcomm a", + "un checked", + "graph ics", + "r ors", + "G ROUND", + "( public", + "Ġcustom ized", + "ĠArk ansas", + "ĠR ew", + "Ġexp iration", + "× ķ", + "ĠC ul", + "Ġn ons", + ".F ilter", + "Ġsen ator", + "_def inition", + "ash ington", + "ym ph", + "/ J", + "Ġf use", + "ram id", + "ĠSup plier", + "Ġaut ocomplete", + "Ġ} ),", + ".\" ĊĊĊ", + "_function s", + "ĉ to", + ".e val", + "ĠT Object", + "Re ferences", + "Ġhe ated", + "H AL", + "Ġ)) }Ċ", + "} $", + "ĠB arr", + "_UN IT", + "+ $", + "Ġget Value", + "ip ed", + "ch ied", + "(v m", + "c ue", + "_int eger", + "_c ourse", + "th ird", + "Ġrevis ed", + "** /Ċ", + "_D IRECT", + "Out Of", + "(\" (", + "ĠFe el", + "Ġre ass", + "Ġsub title", + "per i", + "n f", + "Ġenjo ys", + "Ġtreat s", + ") this", + "-t abs", + "anc ers", + "Ġcontin ent", + "Ġcard io", + "S er", + ". question", + "Ġph rases", + "Valid ators", + "Ġpop ul", + "Ġl ÃŃ", + "s ong", + "_IN TERNAL", + "Ġadvis er", + "Ġp uzz", + "Ġambit ious", + "ĠT ob", + "ĠD P", + "Ġpres idency", + "Ġsurre nder", + "Ġwatch es", + "_b inary", + "ĠSo on", + "Ġcan ada", + "(\" \")Ċ", + "] ='", + "ĠBr andon", + "eps ilon", + "r w", + ".add Child", + ".C opy", + "Pr incipal", + "Ph otos", + "Ġmarg inal", + "Ġbas ics", + "e ing", + "M ust", + "_ String", + "Ġo le", + "M agento", + ".c ustomer", + "(p rev", + "ภ¥", + "Ġlo yalty", + "C og", + "Ġprot ocols", + "ĠCom panies", + "Ġtheoret ical", + "Ġaccess ing", + "ĠZ en", + ". ones", + "att ice", + "_w orld", + "z es", + "Ġtatto o", + "Ġmen os", + "Ġinter sect", + "\"] ;ĊĊ", + "bel ie", + "Ġin active", + ".read line", + "-label led", + ".d one", + "lick r", + "ĠW ORK", + "Ġderiv ative", + "Ġd atabases", + "âĤ Ĥ", + "Ġs x", + ".is Array", + "Ġy s", + "Ġp ada", + "ĠBul let", + "(` /", + "is Active", + "ĠCG Size", + "(equal To", + "ĠColum bus", + "Ġmar ry", + "DE V", + "_l imits", + "ron es", + "I AS", + "Ġt au", + "min o", + "_W rite", + "ĠW ine", + "Ġ[ ['", + "ĠP ull", + "rit ers", + "ri ents", + "Ġsh ifting", + "up p", + "_TIM ER", + "ĠCondition s", + "Ạ¥", + "ĠOr ders", + "ĠSt rength", + "æī Ģ", + "Ġvalid ity", + "Ġf ot", + "et ur", + "Ġb olt", + "åĨ ħ", + "ĠAl ong", + "os hi", + "Ġassum ptions", + "Ġmag azines", + "_S PI", + "Ġp unt", + "_PRO DUCT", + "Ġrel ay", + "ĠJ avascript", + ". te", + "- es", + "Ġwidget s", + "(f s", + "< Item", + "_ex tra", + "Ġrecru iting", + "E t", + "Ġnecess ity", + "p w", + "Ġnov els", + "uss els", + "Cre ator", + "ĠM VP", + "ĠO C", + "th ood", + "cl ients", + ")) *", + "Ġcharacter ized", + "_SE ND", + "ut i", + "T y", + ".from Json", + "@ Service", + "ãĤ Ĥ", + "Ch ris", + "_ Is", + "ĠJohn ny", + "Ġclean er", + "ĠInitial izes", + "UN K", + "( axis", + "еР·", + "ie val", + "ĠWar riors", + "} )(", + "DM I", + "âĻ Ģ", + "ĠTre asury", + "Ġfe as", + "Ġsl a", + "_EN UM", + "l hs", + "ĠIn stit", + "ipp ers", + "Line ar", + "Re ading", + "quir ies", + "-c ell", + "ch rome", + ".S earch", + "IN A", + "ç±» åŀĭ", + "ĠĊ ĠĊ", + "ĠSam uel", + "Ġmill s", + "Ġdon ate", + "ĠGe o", + "( rows", + "Ġshe ep", + "Ġé l", + "ä½ ĵ", + "Ġb em", + "_UN USED", + "ĠR CC", + "Ġintrodu cing", + "att a", + "ĠP riority", + "ĠF B", + "ĠSer ge", + "> \";", + "atch ing", + "ĠKnow ledge", + "ĉ The", + "; margin", + "less ness", + "op ard", + "um atic", + "() ));čĊ", + "Ġf als", + "(c ache", + "Type Id", + "éĢ ļ", + "_ choice", + "ĠGo th", + "ĠS ites", + "M G", + "_b order", + "Ind ices", + "Compar er", + "ĠRed istribution", + "Ġclo set", + "Ġvers atile", + "Input s", + "**************** ****", + "Ġob esity", + "qu iz", + "gr a", + "(g lobal", + "åĬ ¡", + "Ġcollect or", + "Ġk or", + "ov able", + "AD C", + "ĠEvent Handler", + ". nc", + "Ġplay back", + "ient os", + "_p erm", + "_W ARNING", + "ĠOlymp ics", + ".n orm", + "ĠBroad cast", + "_sm all", + "dr ive", + ". iloc", + "Ġtyp ed", + "M EM", + "_con s", + "DM ETHOD", + "Ġl un", + ".d istance", + "(p ar", + "po on", + "Ġb ast", + "activ ities", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + ": čĊčĊ", + "S ER", + ") &&", + "_l st", + "ĠPol ish", + "Ġknock ed", + "Ġfrustr ation", + "au kee", + "Ġph osph", + "iqu id", + "_c oeff", + "æŃ ¤", + "L atest", + "ĠD ust", + "T ipo", + "Ġmaint ains", + "Ġmar sh", + "inc inn", + "l bl", + "C are", + "Ġneighborhood s", + "_g pio", + "ĠAr senal", + "D em", + "ĠW he", + "_h ook", + "Ġl dc", + "ĠHar per", + "ĠBer keley", + "Ġgrad uated", + "Per cent", + "Ġarr iving", + "ĠAdvent ure", + "(s cope", + "(' *", + "qu arter", + "ĠMar ie", + "Spe aking", + "_code gen", + "Ġimm un", + "c aster", + "ãĤ Į", + "åķ Ĩ", + "ĠDim ensions", + ".rec ord", + "Ġtext o", + "ĠMich elle", + "P ending", + "( by", + "_P AR", + "uch t", + "be e", + ".Th read", + "amp ire", + "k now", + "ĠClin ical", + "Ġmargin Bottom", + "Ġdistingu ish", + ".F ull", + ". undefined", + "ĠSequ elize", + "################################################################ ############", + "Ġeduc ated", + "_O VER", + "åº ı", + "ĠÂł ĠÂł", + "_e ach", + "Ġur ge", + "de part", + "Ġdon ors", + "ĠA u", + "Ġbill ions", + "Ġbelong ing", + "_ age", + "_ Int", + "Ġsub stances", + "m achine", + "!! !ĊĊ", + "Ġjson ify", + "ib bean", + "ĠC ad", + "Ġend Time", + "Ġc ycling", + "ĠUIT extField", + "Ġle verage", + "Ġvan illa", + "e at", + "La unch", + "( pt", + "st ates", + "ĠControl s", + "ĠRes pons", + "ĠJ ake", + "Ġas leep", + "fort unate", + ".next Line", + "Size Mode", + "ìĿ ¼", + "Testing Module", + "G erman", + "ĠInvest ig", + ".re verse", + "ĠB ACK", + "( DateTime", + "Ġnon profit", + "ĠEx pect", + "Ġt anto", + "'] ),", + "ĉ the", + "M ultiple", + "(get Activity", + "_W AIT", + "Ġj á", + "de cor", + "lev ance", + "ĠGit Hub", + "min ation", + "_qu antity", + ".Sc anner", + "ĠL ion", + "éĶĻ 误", + "Ġd re", + "Ġtan tra", + "Ġcontent Type", + "Ġf id", + "_ alt", + "NS IndexPath", + "- pl", + "åĮ ĸ", + "Ġantib iot", + "table s", + "ac ial", + "ĠReg istry", + "Ġol ive", + "ig ers", + "Ġsubscri ber", + "_p res", + "ĠSy ntax", + "Ġlo vers", + ". Byte", + "old ers", + "_for ward", + "al ways", + "C aption", + "Pr iv", + "ĠT ampa", + "is ateur", + "-labelled by", + "ĠTo String", + "Ġì Ĥ¬", + "Ġinit iated", + "W F", + "Ġinstitution al", + "in ject", + "ĠSc r", + "Ġdo ctrine", + "Ġsp acious", + "is ure", + "ĠAn a", + "\" time", + "ess aging", + "Ġc id", + "ĠN an", + "Ġin complete", + "T AG", + "-b uild", + "Dec ember", + "Ġres idual", + "(P DO", + "ĠList en", + "Ġg lyph", + "Ġg aps", + "ne a", + ".R ect", + "Ġsa u", + "ĠPhot ograph", + "Ġexec utable", + "ĠExp ert", + "Cor outine", + "_s izes", + "ĠN L", + ".is Valid", + "); }Ċ", + "- reg", + "Ġc iting", + "c wd", + "ĠOtt awa", + "ĠB att", + "Ġrenew able", + "Ġprelim inary", + "Ġas ylum", + "Ġw rist", + "Ġutil iz", + "Ġdet ention", + "F ast", + "Ġan ge", + "incinn ati", + "Ġste ering", + "ĠNa N", + "ios ity", + "/ page", + "Ġè ¿", + "ster ol", + "Ġdis g", + "( DB", + "ĠDESC RIPTION", + "Ġ_ $", + "Ġobst acle", + "Ġb izarre", + "Ġextr action", + "_ex pected", + "Ġlos es", + "ĠCele br", + "Ġhtml For", + "Ġexplo it", + "олÑĮз ов", + "XY Z", + "Ġmagn et", + "amp ed", + "Ġat oms", + "S ources", + "pect ives", + "Ñģ ли", + "Ġ= čĊ", + "Ġd are", + "ĠWal ter", + "Ġbright ness", + "Ġan notations", + "ë ı", + "is ke", + "S chedule", + ". images", + "ros so", + "Ġ\" ..", + "g amma", + "Ġin structor", + "Ġover write", + "- am", + "Ġdevast ating", + "ĠSaint s", + "Ġh s", + "Ġbon uses", + "$ output", + "ij d", + "(Action Event", + "mon itor", + "Ġmatt ress", + "Jan uary", + ".j p", + "Ġcar acter", + "Ġim pose", + "_re st", + "ĠSign ature", + "Ġcoron avirus", + "ãģ Ĭ", + "_com pare", + "Me asure", + "it ated", + "el ijk", + "ig os", + "es ar", + "Ġrush ed", + "met ry", + "_SE PARATOR", + "_W E", + "_ATTR IBUTE", + "Ġy aml", + "Ġspec s", + "ĠR ah", + "ph eric", + "ĠInvest ment", + "ä ll", + "Ġappe aling", + "Ġview port", + "ç ©", + "Ġmargin Left", + "Ġsub tract", + "ĠED IT", + "ĉ ArrayList", + "gr ading", + "ĠF ailure", + "as per", + "EE K", + "(n ow", + "< object", + "ĠAl ignment", + "ple ado", + "q tt", + "( ERROR", + "ĠIN VALID", + "Ġuser id", + "ra ises", + "ID I", + "Ġvari ance", + "ĠN il", + "/ delete", + "_M AIN", + ".T oken", + ".C ategory", + "> )Ċ", + "Coll ision", + "ĠGre ater", + "ĠR acing", + "al an", + "Ġmon etary", + ", new", + "ĠS orry", + ". Enable", + "ĠInstant iate", + "oll en", + "ë© ´", + "ĠCall ing", + "_h our", + "AD A", + "Ġsh y", + ") **", + "Ġ== >", + "Ġes pecial", + "Ġinterpre ted", + "! =\"", + "Ġpharm acy", + ".s ingle", + "ĠC ialis", + "Ġpar as", + ".to UpperCase", + "ĠDem on", + "Pr ime", + "Ġrank ings", + "Add ing", + "_H ASH", + "ĠEx am", + "Ú ©", + "ĠVict or", + "Ok ay", + "\"] ;čĊ", + "Ġfort une", + "ĠF ETCH", + "exp and", + ".Inter op", + "Ġb arn", + "æ ¶Ī", + "ue vo", + "Ġspec ulation", + "âĶĢâĶĢ âĶĢâĶĢ", + "ĠN u", + "ĠBl ues", + "(f name", + "Ġinhab it", + "Ġ\\\" %", + "C ES", + "ular io", + "_c r", + "Ġvalid ated", + "Ġmid night", + "ank ing", + "Ġincorpor ate", + "Ġpurs uit", + "EX P", + "pr ime", + "P id", + "- US", + "ĠN urs", + "ĠW heel", + "é ĺ", + "Ġin p", + "Ġsupport ive", + ".m ember", + "ĠSh ot", + ".Check Box", + "Ġaff irm", + "T or", + "Full Year", + "Ġconsider ably", + "cred entials", + "_ opts", + "R oll", + "( round", + "Ġcom ent", + "_U ART", + "Ġext ending", + "R G", + "result ado", + "it u", + ".get Session", + "Ġattr action", + "& D", + "$ html", + "ĠJess ica", + "ĠAssoci ate", + "a ñ", + "_ ed", + "ĠL ag", + "Ġorig ins", + "()) ->", + "add EventListener", + "IAL OG", + "åIJ ¦", + ".Com pare", + "Al bum", + "ĠK u", + "< Q", + "arg est", + "Ġpro long", + "Ġconfig urations", + "Ġaccident ally", + "_ph oto", + "Ġ'' ;čĊ", + "Ġver se", + "B ob", + "Ġfarm ing", + "del ivery", + "ĠM ack", + "Ġuse Selector", + ".bootstrap cdn", + "keep ing", + "en y", + ". upload", + "ĠM ETHOD", + "cre ator", + "< _", + "ĠE aster", + ". --", + "UI Button", + "ãĤ ī", + "om eters", + "Ġsh ine", + "Ġh ogy", + "\\ s", + "Ġh arness", + ".C ell", + "Ġlif ting", + "Ġcomb ines", + "ĠOcc up", + "ex clude", + "pat ial", + "Ġres pir", + "_f it", + "Ġfif ty", + "ĠM ol", + "Ġtun ed", + "-d imensional", + "Ġq s", + "Ġto ps", + "> \";ĊĊ", + "quis ite", + "ch annels", + "/ res", + "ĠAn alytics", + ".app compat", + "/ to", + "Ġon Error", + "( attr", + "IR M", + "Ġrag az", + "- as", + ".Se cond", + "orient ed", + "Ġdon n", + "Ġlight ning", + "f id", + "ĠP le", + "ãģ¾ ãģĻ", + "t ro", + ".Tr ue", + "O bservable", + "× Ļ", + "umb ing", + "Ġpros pective", + "-f ilter", + "Ġpurs uant", + "(p oints", + ".B ind", + "Ġp alm", + "clear fix", + "ö s", + "ĠG onz", + "Ġwe aken", + "Dr ive", + "en ido", + "l ld", + "ob ox", + "ane an", + "G ot", + "ä¿ Ŀ", + "Reg ex", + "æ ĥ", + "Ġsal ad", + "ass is", + "\" net", + "inherit Doc", + "ĠR V", + "qu ier", + "Ġcl azz", + "ı ÅŁ", + "oster one", + "Ġair line", + ".list dir", + "Ġdownload ing", + "ĠP alm", + "w aukee", + "& lt", + ".B L", + "_IN LINE", + "off s", + "<< (", + "_new s", + "Ġch ase", + "/ ><", + "Ġeuro s", + "ĠEgypt ian", + "ĠSt ainless", + "_BO OL", + "ĠG uild", + "ĠD ynam", + "[index Path", + "Ġ ï", + "Ġmemor able", + "ĠCh ampion", + "Resource Manager", + ".Log in", + "ĠForm er", + "yp ed", + "Ġl leg", + "; \",", + "D WORD", + "Ġtax i", + "Ġbom bs", + "ra h", + ".t ags", + "_test s", + "st ones", + "âĢĿ )", + "[ g", + "r type", + "Ġv u", + "Ġhost ile", + "Ch ars", + "ĠPatri ots", + "/ status", + "< B", + "ĠIn come", + "ĠD ad", + "Ġpat rol", + "_CH ANGE", + "Ġup graded", + "Ġch ina", + "set q", + "Start ed", + ".U ndef", + "Ġcheck sum", + "Ġfrustr ated", + "{ o", + "Ġen f", + "Ġwood s", + "ĠAny one", + "Enc ode", + "ĠQt Widgets", + "are as", + "Ġshe er", + "sk i", + "end point", + "_T est", + "S oup", + "~~~~~~~~ ~~~~~~~~", + "(f iles", + "ĉĉĉĉĉ čĊ", + ".sp ark", + "Ġval ued", + "Ġ% Ċ", + ".control s", + "ĠXCTAssert Equal", + "Ġf ame", + "ĠR ic", + "D OT", + "ĠAlbert a", + "ä½ ¿", + "os al", + ".Web Controls", + "Ġ ------------", + "ĠM is", + "ĠS YS", + "Non null", + "= item", + "Ġexp ire", + "Dec ode", + "_ operation", + "ĠValid ator", + ".C ENTER", + "uff s", + "* m", + "Ġav ant", + "æ¬ ¡", + "âĢľ You", + ".per mission", + "... )", + "ĠL ic", + "_co ords", + ".n ombre", + "c lo", + ".Int ernal", + "ĠCh o", + "_s w", + "ĉ Il", + "cl k", + "Ġcast le", + "(l ayer", + "p it", + "Ġgu ided", + "Ġâĸ Ī", + "Ġsuper b", + "Ġsup plements", + "_c ent", + "Ġpe ek", + "IN ARY", + ".Content Alignment", + "f alls", + "\")) ;", + "W all", + "). čĊ", + "ĠD anny", + "irm ingham", + "IAL IZ", + "( create", + "\" In", + "Service Provider", + "Ġpr iced", + "mac ro", + "am ac", + ". box", + "---- Ċ", + "ãĥ «", + "ĠS uit", + "ur st", + "br u", + "ourn als", + "num ero", + "__ ()Ċ", + "D as", + "ĠM itt", + "ud er", + "? \\", + "f u", + "[ B", + "Ġ: )ĊĊ", + "(int er", + "br ains", + "Ġatt itudes", + "Ver ify", + "Ġsign atures", + "ack Bar", + "Ġg d", + "J ack", + ".c at", + "Ġz z", + "war f", + "FT ER", + "\");ĊĊ Ċ", + "Al ive", + "IC LE", + "ĠWh atever", + "Ġout lined", + "s prite", + "еР²", + "_A B", + "_DE PTH", + "Ġcrush ed", + "aa a", + "(e v", + "æľ º", + "Ant i", + "IC O", + "is EqualTo", + ".s un", + "ic ulo", + "s ale", + "_h ex", + "ĠV k", + "apt or", + "Un ion", + "ĠDis count", + "list a", + ".Undef Or", + "Ġautom ation", + "N or", + "å¯ ¹", + "åı Ĥæķ°", + "Ġref lex", + "ĠLa ure", + ".showMessage Dialog", + ".t emp", + "Ġa kan", + "Ġ__ ____", + ".Is True", + "ARE D", + "ag le", + "E nergy", + "Ġquant ities", + "âĢĻ é", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġcitizens hip", + "m outh", + "Ġin appropriate", + "ĠOut door", + "White Space", + "An onymous", + "load s", + "webElement Properties", + "T en", + "Ġacc idents", + "Ġadvertis ement", + "ĠY emen", + "(c all", + "Ġsl avery", + "Ñģ п", + "ĠL am", + "_BIT S", + "ome ga", + "ĠO le", + "Ġkid n", + "_A n", + "ĠR aid", + "Cre ation", + "s aved", + "Ġpro port", + "W ARNING", + "\\ P", + "Ġp wd", + "Data Reader", + "is cher", + "ade on", + "ĠP redict", + "Ġreason ing", + "Ġdestroy ing", + "H el", + "* d", + "ĠLeg isl", + "_P r", + "ĉĉĉ ĠĠĠĠĠĠĠ", + "Ġsymp ath", + "Ġch ess", + "Ġm am", + ": hover", + "Ġconvert s", + "Ġp ela", + "Ġprogress ion", + "Ġ\"_ \"", + "ĠG ill", + "ĉ show", + "Ġsupposed ly", + "ac curacy", + "el in", + "Ġunf olding", + "ĠHy per", + "Ġw anna", + "Ġup s", + "( #", + "ĠCr iminal", + "( Point", + "at Lng", + "act ly", + "Ġcontract ors", + "'] }", + "draul ic", + "ód igo", + "ĠT T", + "ĠW ide", + "ĠAR G", + "_ ic", + "FLAG S", + "S chool", + "Ġclear ing", + "-be ing", + "={ [", + ", const", + "man ent", + "Over lay", + "(' \"", + "éĩ ı", + "ĠT imestamp", + "Ġmail ing", + "ĠC ake", + ".Th at", + "Ġmed itation", + "q p", + "Ġemp resa", + "ĠL ions", + "Ġw eld", + "ĠLinked In", + "Ġc ush", + "Ġgen ome", + ".Index Of", + "ag ain", + "Ġf allback", + "Ġcamp ing", + "re dd", + "-strip ed", + "Ġd v", + "Fe bruary", + "ĠPro xy", + "us k", + "Ġdies el", + "W RITE", + "RE AK", + "L orem", + ".In voke", + "- div", + "Inter ceptor", + "ĠD H", + "ia les", + "Ġvill ages", + "Ø ´", + "ĠEN V", + "S ys", + ".X R", + "Ġpo em", + "à Ĥ", + "c ade", + "pl ots", + "Ġ{ (", + ".g it", + "/s vg", + "nc mp", + "ĠÄ į", + "ain es", + "åĩ ½æķ°", + "Ġ( )ĊĊ", + "ops is", + "ĠRel ationship", + "_ aut", + "ĠB omb", + "ĉ com", + "* sizeof", + "off icial", + "_p ayload", + "ĉĉĉĉĉ ĠĠ", + ".m anager", + "ĠA round", + "ĉs end", + "ĠEx ercise", + "ĠB illy", + "iv i", + "Ġneed ing", + "_url s", + "_t asks", + "ĠH em", + "Ġtear Down", + "enc rypt", + ".t ie", + "Ġas m", + "IC H", + "ĠCGRect Make", + "ìĦ ±", + "ul ong", + "Ġit r", + "ĠG ST", + "Ġoffer ings", + "ro be", + "EE E", + "oper ators", + "_PRO P", + "ind ent", + "A DE", + "or f", + "ë IJ", + "Ġbless ed", + "vas cular", + "Ġcon oc", + "H appy", + "B ridge", + "ilit ation", + "j oint", + "ĠAdmin istr", + "- transform", + "Ġmeant ime", + "/ K", + "ĠBed room", + "Ġrig id", + "Ġbrows ers", + "EM PTY", + ".S erialize", + "_ ED", + "Ġst itch", + "Ġj an", + "ell t", + "Ġbr ace", + "Ġtr ails", + "p ublished", + "å¯Ĩ çłģ", + "} ')Ċ", + "Ġac ids", + "Ġ! !!", + "_d irect", + "> ());Ċ", + "aj Äħ", + "_O CC", + "Ġplan ets", + "æ Ł¥", + "ĠDub lin", + "Ġser ie", + ".print f", + "de ep", + "` )", + "Ġ\\ $", + "ĠÎ ¼", + "_V IDEO", + "end ors", + "ĠC rypto", + "F ar", + ".Trans parent", + ".T R", + "ias m", + "_tr aining", + "Ġteach es", + "ĠB elt", + "Ġlimit ing", + "ĠK ath", + "ĠIndex Path", + "Ġachie vements", + "Ġser á", + "interop Require", + "Ġdis se", + ".I f", + "arm ing", + "uls ion", + "P o", + "_DE TAIL", + "Prot otype", + "ĠC AL", + "Ġagre es", + ".v o", + ".Execute NonQuery", + "ĠTop ic", + "Ġ' {}", + "Ar m", + "Ġe cc", + "M ag", + "Ġserial ized", + "ĉ conn", + "c ached", + "= tf", + "ĠByte Array", + "prot obuf", + "var char", + "ĉ ASSERT", + "Ġlist e", + "_tr igger", + "· ¸", + "Fe el", + "T ahoma", + "ĠL ik", + "Ġstruct ured", + "erg us", + ".In itial", + "_ ge", + "cl js", + ".cont act", + "Ġand ere", + "$ stmt", + "_C URRENT", + "ĠDis cover", + "$ res", + "form atter", + "H a", + "vang st", + "Ġem erge", + "ãĢĤ âĢĿ", + "ĠCabin et", + "-s quare", + "éĥ ¨", + "Ġr age", + "ĠA J", + "ĠV T", + "sh adow", + "ĠFa ith", + "en ames", + "pret ty", + "has il", + "part y", + "Ġvar char", + "Ġf otos", + "Ġal um", + "ĠBelg ium", + ".y label", + "Ġde j", + "_num bers", + "Ġh u", + ".set Adapter", + "ĠUs ually", + "(s ample", + ".Sh ared", + "Ġbook ed", + "Ġ>> =", + "Ġmin erals", + "\">", + "pro g", + "bo o", + "_m d", + "_p ack", + "(ex press", + "ut z", + "\\ Auth", + ", id", + "ĠCh ile", + "act ice", + "Ġrecruit ment", + "Ġpos es", + "Ġvulner ability", + "inst anc", + "or um", + "d ess", + "Ġx l", + "%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%", + "( fig", + "Ġdelet ing", + ".d el", + ") ')Ċ", + "ĠWeek ly", + "?? ?", + "(str cmp", + "sm ith", + "Ġpurs uing", + "- so", + "ĠApp s", + "/ 'Ċ", + "Ġdec is", + "FO RE", + "Every one", + "Ġl anes", + "V irtual", + ". attach", + "( Log", + "ĠMed icaid", + "( Path", + "ĠTurn er", + "/ application", + "Ġport rait", + "Ġopp ose", + "check out", + "Ġfinish es", + "_M E", + "Bar rier", + "S ong", + "V AR", + "Ear lier", + "rell a", + "Ġh ast", + "az ar", + "Ġpull s", + "ng x", + "Ġinspir ing", + "Ñĥ Ñİ", + "-d irection", + "Ġexplos ive", + "Ġcreated At", + "st o", + "Ġwhe at", + "ĠB uilt", + "' ai", + "Ġtrack ed", + "ham mad", + "RowAt IndexPath", + "_ heap", + "D ue", + "Ġconnect s", + ".p ublish", + "em u", + "Ġbul lets", + "B AR", + "ol ate", + "Ġintern ally", + "Ġcatch ing", + "-p assword", + "ou ched", + "æĢ §", + "e ous", + "Ġx range", + "Q uality", + "v v", + "Man age", + "( ($", + "ac ements", + "ĠBro thers", + "ĠHE AD", + "ĠUn supported", + "s an", + "es i", + "** *Ċ", + "Ġadapt ation", + "ĠWork er", + "'] /", + ".save fig", + "( trans", + "Ø ¬", + "ne e", + "Cor rect", + "... \")Ċ", + "Ġsubmit ting", + "-p ath", + "ĉ last", + "iss an", + ".x label", + "ĠS epar", + "/ no", + "_b est", + "ĠM ills", + "_s ock", + "(f lag", + "Ġdest inations", + "em ption", + "ĠF AIL", + "å ĴĮ", + "Ġr p", + "f act", + "ĉ len", + "D AY", + "Ġse iz", + "_d st", + "l ip", + ".Line ar", + "ĠB asket", + "$ t", + "$ i", + "- brand", + "ĠNe il", + "ĠE q", + "Ġth ou", + "og ene", + "Ġscholar ship", + "æĽ ´", + "Ġs wo", + "ag inator", + "en i", + "( book", + "Ġbl ink", + "th us", + "Ġcancell ationToken", + "ĠPalestin ians", + "Ġprofit able", + "Ġback pack", + "ens on", + "< Long", + "Ġp ools", + "Ġst icks", + "Ġspokes woman", + "Be ing", + "ĠHer itage", + "ĠN ike", + "SH A", + "ĠNotImplemented Exception", + "$ core", + "ĠR ico", + "/ latest", + "ĠC zech", + "ner Radius", + "(l ines", + "Ġsem ester", + "Ġw ounds", + "Pro cedure", + ".m ail", + "() ):Ċ", + "Ġcor rid", + "ter ed", + "ĠN CAA", + "Ġgal axy", + "_k ind", + "il k", + "Ġtr as", + "_P OL", + "ĠH et", + "Ġrefuge e", + "Ġteen age", + ".b inding", + "post al", + "Ġiç in", + "ĠData Type", + "é ĸ", + "ycl erview", + ", value", + "_id entifier", + "< b", + "Ġout file", + "čĊ ĠĠĠĠčĊ", + "Ġcr é", + "Ġrespond ents", + "ĠBe ast", + "ce led", + "Ġinter f", + "-th eme", + "g if", + "ĠR angers", + "IT AL", + "Ġauthentic ate", + "Com pletion", + "urs ors", + "Ġcin ema", + "Ġdisc our", + "ĠJ aw", + "OCK ET", + "Ġpr ayers", + "ĠL uis", + "fr ag", + "=[ Ċ", + "Ġbr ave", + "_p ose", + "C ertificate", + "- fe", + "ifer ay", + "ĠFl ags", + "Container Gap", + "ĠC rit", + "Result Set", + "ĉc ur", + "Ġcorrespond s", + "St aff", + ".Http ServletRequest", + "Ġneur ons", + "ĠMain AxisAlignment", + "ed ar", + "Ġg ad", + "_p arts", + "ĠÎ ²", + "Ġf x", + "/ files", + "ĠB ros", + "hip s", + "Ġgluc ose", + "Ġfar ms", + "Ġment ally", + "rest aurant", + "Table Name", + "ĠMer cedes", + ". Visual", + "Ġan ch", + "inal g", + "_r untime", + "Ġpropri etary", + "Ġintent ions", + "iz i", + "S lice", + "; \"> true", + "ĠNY C", + "Ġb ored", + "ĠD etect", + "Ġapp ar", + "Ġje ans", + "ĠT ak", + "I OD", + "ĠH orse", + "( FILE", + "( ?", + "ri que", + "optim izer", + "n at", + "lo ys", + "ĉ Token", + "oub ted", + "u ess", + "oco a", + "Data Member", + "_P OWER", + "class List", + "Push Button", + "ĠWi Fi", + ". Stream", + ".g uild", + "Ġn og", + "ĠPortug al", + "ĠUnt er", + "Pr imitive", + "b oss", + "ĠDe utsch", + "Ġerot ic", + "Ġstr conv", + ".Try Parse", + "Ġgr ams", + ".S uccess", + "_p k", + "ĠHar vey", + "-m inded", + ".c ountry", + "[] \"", + "Ġang el", + "Ġbe ats", + "ĠV or", + "il io", + ".m aster", + "s omething", + "ĠP ACK", + "( if", + "Request Body", + "Ġant es", + "/w idget", + "Ġmod o", + "ĠA W", + "find er", + "Ġoptim ized", + "Ġmiss iles", + "N B", + "ĉint ernal", + "t ex", + "ĠS ri", + "Ġdam aging", + "ĠM ais", + "- Allow", + "ĠZ h", + "- alt", + "Ġ ));ĊĊ", + "è ī", + "Ġinflu ences", + "Ġc atal", + "_REG ISTER", + "ĠAPI s", + "-cent ury", + "Ġbi ology", + "ĠAct ual", + "Ġhe els", + "TR ACE", + "_D IG", + "D ataset", + "ĠM atter", + "Ġclass ifier", + ".w ikipedia", + "ĠRog ers", + "Ġdon ated", + "raw ler", + "en en", + "Ġcas inos", + "ort al", + "Ġpr ive", + "s pe", + "duc ers", + ". ep", + "Ġgr asp", + "ac ji", + "Ġd airy", + "Ġb uses", + ".com m", + ". ins", + "ĠI RS", + "ĠBe er", + "ad c", + "o ard", + "_M ET", + "Ġ' +'", + "r ans", + "Ġkind a", + "ĠâĶ Ĥ", + "ĠM aur", + "аР³", + "Ġband width", + "ib us", + "ĠD ifferent", + "(m at", + "ĠRes ume", + "_UN S", + "est ablish", + "Ġfon ction", + "Sub scription", + "_com pany", + "Ġlight ly", + ".con firm", + ".y aml", + "ĠBo ost", + "Com merce", + "- template", + "_DEL AY", + "ĠH I", + "Ġn avig", + "(S ender", + "ĠH S", + "_ \"+", + "ĠRE QUEST", + "Ġw ifi", + "=\" \"Ċ", + "]) ->", + "Ġro pe", + "Ġviol ated", + "Ġgl ance", + "ĠK urd", + "Ġè ®", + "de ck", + "ĠIS BN", + "Ġin fect", + "ĠF oo", + "Ġget ter", + "Ġt ener", + "ap pe", + ".h h", + "_h ot", + "< AM", + "p oly", + "! \",Ċ", + "Ġconver ting", + "ĠW WE", + "RO S", + "(' {", + "Com mit", + ") L", + "ĠO re", + "Ġsp arse", + "Ġdis posal", + "Ġcan celed", + "åIJ İ", + "Ġa er", + "Ġvin yl", + "á» ĥ", + "rec ogn", + "ark ing", + "Ġtrick y", + "* s", + "Ġproceed s", + "Ġis o", + "Ġco conut", + "Ġcraft ed", + "IEL DS", + "Ġquest o", + "Ġcomm un", + "_CON NECT", + "Ġtraff icking", + "De ep", + "a ções", + "c odigo", + "ve au", + "Ġbet ray", + "int a", + "T ED", + "æ r", + "m art", + "_B US", + "/ sc", + "ial ly", + "Ġcigaret tes", + "è¯ ģ", + "(n n", + "Ġmodel ing", + "/ products", + "w arn", + "Ġmet ro", + "ĠI v", + "& )", + "ĠC able", + "Î »", + "Compar ison", + "g ary", + "ĠB A", + "P ART", + "Ġp v", + "_up dated", + "C redit", + "orth y", + "observ able", + "Ġthe atre", + "B LE", + "; }ĊĊ", + "la unch", + "_str ings", + "ug o", + "ĠR PG", + "- auth", + "Ð ł", + "hol m", + "ĠP and", + "U id", + "Ġim ply", + "ìľ ¼", + "'] ='", + "/ User", + "Ġstr cat", + "нÑĭ й", + "Data Adapter", + "Ġland sc", + "Ġdipl omatic", + "ï¼ ĵ", + "************************************************************************ ****", + "ĠCh icken", + "Ġbc rypt", + ".In f", + "[ col", + "ĠQu antity", + "- position", + "Ġdiet ary", + "Ġfil mm", + "Is rael", + "Pre v", + "ĠMill ion", + "Ġrem ed", + "Ġbill ing", + "Ġout doors", + ".t m", + "Ġn ad", + "F org", + "Z Z", + "Ġs sl", + "], '", + "K T", + "f req", + "= document", + "bl ur", + "¬ ¸", + "ĠJeff erson", + "C s", + "(s ave", + "Ġstr ap", + "Ind ia", + "Ġide ology", + "BO SE", + "ĠF P", + "( ans", + "Ġfe ver", + "ĠY am", + "K ing", + "à ²", + "AT ING", + "bo hydr", + "roll back", + "Ġnew Node", + "ĠN VIDIA", + "Ġhon our", + "ĠCon firm", + "xb d", + "Ġsuccess or", + "/ u", + "l iv", + "ourn aments", + "Att achment", + "Ġgr up", + "Ġtri be", + "Ġca res", + "e ft", + "_s ame", + "' label", + "Ġ ãĢIJ", + "M otor", + "Ġin exp", + "Ġ\" (\"", + "_POS ITION", + "Ġval ley", + "ĠResult Set", + "Ġpres erved", + "Ġmut ations", + "Ġquestion ing", + "mun ition", + "parse Int", + "ĠS r", + "ĠMet adata", + "âĢĿ ï¼Į", + "timestamp s", + "Ġtrans itions", + "í Ļ", + "Ñ Ĭ", + "i om", + ".D o", + "Ġp ine", + "Ġf ung", + "Ġtrans mitted", + "ct ime", + "ĠF am", + "Re vision", + "B as", + "UP ER", + "D estination", + "toHave BeenCalled", + "Ġun fortunate", + "IN ES", + "_pro f", + "Am ong", + "ĠCy ber", + "ĠB attery", + "gen re", + "ĠView Model", + "- =", + "Ġutil ized", + "p aint", + ".Integer Field", + "ern ity", + "comp iler", + "âĢĭ ĊĊ", + "ĠM asters", + ".To Array", + "Ġstrt ol", + "ĠUkrain ian", + "} ));Ċ", + "Ġsh emale", + "\" That", + "for all", + "/ download", + "Ġrhet oric", + ".l atitude", + "ĠWH EN", + "Ġshock ing", + "IF IC", + ".N ormal", + "_F OLDER", + "Ġdr ift", + "Ġmount ing", + "- book", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠWire less", + "> \".$", + "Ġrel ies", + "( Console", + "Int ernational", + "-> {$", + "M id", + "Ġdis sert", + "dd s", + "Ġdepos its", + "ĉd river", + "# ga", + "pr ising", + "print ln", + "Ġpres enter", + "Ġmin es", + "C SS", + "ĠD ual", + "(! (", + "Ġk am", + "Ġis Loading", + "ĠProt ect", + ". upper", + "ar ium", + "]: ĊĊĊ", + "Y ii", + "-sh irt", + "ĠIM AGE", + "_color s", + "Ġur gent", + ".Cont ainer", + "! (Ċ", + "S aturday", + "Ġsoci eties", + "ĠTh an", + "ĠC od", + "= @", + "Ġattach ments", + ".m obile", + "Ġsp ite", + "Ġb ounce", + "raw l", + "instanc etype", + "ĠTr uck", + "Ġmanip ulation", + "( Config", + "-in st", + "Ġst or", + "it ution", + "Preferred Gap", + "Ġmain AxisAlignment", + "Ġlist ened", + "'' 'ĊĊ", + "ott age", + "- project", + ".AP PLICATION", + "ĉ root", + "Ġwh it", + "Ġb ilder", + "Ġk er", + "Ġappl iances", + "row ave", + "ìĿ Ģ", + "ematic s", + "ĠO rg", + "op ing", + "_SE ARCH", + "Ġch am", + "add ContainerGap", + "Ġ( ).", + "ĠAr row", + "Il legal", + "Current ly", + "Ġus a", + "Ġpassword s", + "Ġre nown", + "av ern", + "ĠEv il", + "Ġconc at", + "Ġdu o", + "Ġv ale", + "ĠBe an", + "Ġindic ators", + "cm ath", + "ĠP ump", + "Nov ember", + "ific ant", + "_DOM AIN", + "reg ar", + "ĠPort al", + "\" $", + "Ġformer ly", + "\"] :Ċ", + "ĠVis ibility", + ".getElementsBy ClassName", + "_RE D", + "Ġch ampions", + "à ´", + "Val or", + "_ es", + "* a", + "-re peat", + "B and", + ".st age", + "Ġbure auc", + "C nt", + "et en", + "- function", + "Ġm uito", + "P ID", + "_ editor", + "Ġcrash ed", + "de ad", + "k at", + "ag h", + "ĠEX T", + "ass er", + "-sm all", + "Ġreal iz", + "( Entity", + "ú s", + "ĠAct ually", + "ĠEl ite", + "Ġhel m", + "(non atomic", + "ash er", + "Comm unity", + "all eng", + "ir y", + "ĠG rowth", + "Ġs ue", + "Ġfrequ encies", + "_des criptor", + ".At tribute", + "Ġrecip ients", + "_N S", + "/ \"+", + "ib an", + "Ġath lete", + "ĠI gn", + "_D MA", + "(d s", + "ĠRequire ments", + "AD I", + "ere z", + "\\ Admin", + "br aska", + "ĠR ust", + "Rel ation", + "C OD", + "ĠV ERSION", + "em ma", + ")) {", + ".D uration", + "ĠC amb", + "- logo", + "Ġread able", + "Ġcre ators", + "() ];Ċ", + "Up Down", + "-h alf", + ".get Month", + "(s f", + "P ic", + "Ġhun ger", + ".t x", + "Ġexceed ed", + "_se ed", + "( ^", + "_s k", + ".per form", + "Ġ> ::", + "Ġm ongo", + "= float", + "bind Param", + "Sm art", + "if a", + "Ġse curities", + "Ġpre jud", + "Ġ, \"", + "Ġcor ps", + "Ġv ra", + "amac are", + "it err", + "(M edia", + "uch e", + "Ġc ob", + "Ġlib er", + ". geometry", + "Loc ator", + "Ġsl iding", + "Ġsurg ical", + "_C UR", + "Ġcon sect", + "[ *", + "ĠRes ort", + "St ub", + "_DO UBLE", + "ĠS oph", + "Ġelect oral", + "_dis able", + "ĠÑģ о", + "ĠLight ning", + "Ġment ions", + "oc y", + "Ġle aked", + "Ġrelax ing", + "Pres enter", + "v sp", + "Ġgu ilt", + "=- =-", + ".re ply", + "ĠMir ror", + "C amp", + "Ġ+#+ #+#+", + "Ġ+#+#+#+ #+#+", + ".A uthor", + "Ġdirect ive", + "-h ook", + "íĦ °", + "}ĊĊ ĊĊĊ", + "@ pytest", + "_r and", + "m is", + "Ġcolor ful", + "u je", + "lass es", + "ĠClass es", + ".h ave", + "% ),", + "é¢ ĺ", + "Ġdistur bing", + "sub string", + "ĠK oh", + "In vest", + "p urchase", + "Ġrec ycling", + "ĠA RT", + "ier archy", + "Ġf ps", + ".check Box", + "íķ ´", + "_m aterial", + "duc ation", + "Ġf w", + "ud it", + "Ġreview ing", + "ĠS id", + "S yntax", + "ĠW ritten", + "arg ar", + "UM E", + "/ q", + "Class ifier", + "Off icial", + "Ġj azz", + "Ġom ega", + "Ph ysics", + "Ġl ugar", + "_access or", + ".command s", + "Ab ility", + "ĠB atch", + "R AM", + "Ġencount ers", + ". Qu", + "BY TE", + "ĠD istribution", + "Ġus o", + "ĠReco very", + "appro ved", + "Ġden ial", + "/sh are", + "Linked List", + ")čĊčĊ čĊ", + "udd y", + "Ġf ines", + "Ġr y", + "Un icode", + "ĉ render", + "Ġprem ises", + "Ġp on", + "ali ases", + "/F oundation", + "c uda", + "ĠC ock", + ",: )", + "(f older", + "Ġm éd", + "dr ag", + "Ġtal ents", + "ĠĠĠ ĊĊ", + "е ÑģÑĤв", + "m ob", + ".y ml", + "Ġa ster", + "Ġdis cre", + "go al", + "ĠGT X", + "ĠS UCCESS", + "ĠL ONG", + "(f ind", + "Ġsing ular", + "_s z", + "ĠEth ereum", + ".. Ċ", + "Ġir res", + "')) {Ċ", + "Ġmin isters", + "St eps", + "ivers al", + "ĠNever theless", + "- led", + "Ġ( %)", + "ç¡ ®", + "Ġtime zone", + "Ġstr anger", + "(re nder", + "Ġsh util", + "Ġm ph", + "Ġtri o", + "pp y", + "Ġpred omin", + "Ġend ors", + "ĠRuss ians", + "ĉ row", + "Ġw izard", + ".s erialize", + "Ġcompl ained", + "Ġs ido", + "Ġdelight ed", + "-m e", + "ĠR av", + "H uman", + "ad ays", + "rec v", + "Work ing", + "J ump", + "ĠÃ¥ r", + "ĠAut omatic", + "_B ase", + "æł ¼", + "aur ants", + " ¯", + "æ ¸", + "(C Type", + "IF I", + "( amount", + "Ġbelie ving", + "= mysql", + "Ġf ir", + "Ġrest oration", + "ere co", + "Ð ¢", + "_ '+", + "Ġe book", + "Ġde bris", + "(input s", + "AY OUT", + "Ġscre aming", + "av ia", + "land er", + "Ġdist ress", + "Ġas sembled", + "ĠA void", + "( thread", + "ĠR PC", + "_EX IT", + "( queue", + "и ÑģÑĤ", + "D ll", + "Ġsk ull", + "_p ub", + "che z", + "min ate", + "ens en", + "Ġins ane", + "b ounds", + "ĠR osen", + "Ġcondition ing", + "process ed", + "v ideos", + "f our", + ".Con v", + "| ;Ċ", + "Person al", + "cer pt", + ":UIControlState Normal", + "Ġdos es", + "ĠKar l", + "ĠFre qu", + ".B ASE", + "ĠV ote", + "Ġcon current", + "ĠMessageBox Icon", + "Ġà ĸ", + "ĠDub ai", + "ĠR etail", + ": number", + "ĠOb server", + "ĠBig Integer", + "_ origin", + "_W ORK", + "F rames", + "Ġnot ably", + ". âĢľ", + "Ġtrop ical", + "Ġn iche", + "am ina", + ".s ys", + "(t okens", + "mod ify", + "os it", + "st rom", + "ĠCom ics", + "O PTION", + "T icket", + "Ġfact ories", + "Ġdis put", + "_F ile", + "ĠFin n", + "ee e", + "ĠDisc ord", + "_m oney", + ".t pl", + "_s afe", + "L B", + "Ġgl ut", + "J K", + ".fl ow", + "- cont", + "g os", + "Ġhor izon", + "ĠR ush", + ":: *", + "P ipe", + "ull a", + "bor ough", + "he imer", + "(m ove", + "( Text", + "} );čĊčĊ", + "w elcome", + "ĠCom ponents", + "Ġgovern ance", + "c losed", + "ĉm argin", + "Ġla undry", + "ĠTerm inal", + "iz ards", + ". âĢĶ", + ".rem ote", + ".r adius", + "ĠQue bec", + "Ġd h", + "T ech", + "ĠM ist", + "s eller", + "_l iteral", + "Ġgen ius", + "Ġbr ains", + "g em", + "ĠMe asure", + "Ġcata st", + "r ance", + ".Text Field", + "Ġconsum ing", + "Ġ'\\ ''", + "oubted ly", + "ĠC ertain", + "E v", + "ert i", + "be ing", + "Ex perience", + "Ġ// [", + "ĠArab ic", + "ĠC rist", + "ĠAz ure", + "Ġhor a", + "l adesh", + "\\ Blueprint", + "d ar", + ".re l", + "Ġsup rem", + "ĠRe agan", + "ĠAt tributes", + "-s idebar", + "Ġuse Styles", + "ĠA irlines", + "Ġh ills", + "/x html", + "v inc", + "_m ock", + "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠP ill", + ".Layout Style", + "ĠCommand er", + "] <", + "sign ature", + "Ġ{ }čĊ", + "Ġhat red", + "Ġë ĭ", + "ole sterol", + "Ġ ********", + "ancell or", + "c rop", + "T IM", + "ĉĉ ĊĊ", + "ys qli", + "uit ive", + "ĉun set", + "_s el", + "Ġmen us", + "t ick", + "Ġconstit ute", + "ĠElement s", + "ĠRed is", + "agg io", + "_f p", + "_de pend", + "em as", + "CA ST", + "or ange", + "j on", + "ĠEm ily", + "Ġpot atoes", + "Ġre ceptor", + "ĠElect ronic", + "ĠL ights", + "Ġcomb ining", + "ĠSome one", + "Ġ######## .", + "ĠT OD", + "/ show", + "X d", + ".\" '", + "af x", + "Ġtr agic", + "St yled", + "ĠMar co", + "G allery", + "d ale", + ".âĢĿ ĊĊĊĊ", + "é rie", + "/s ervice", + "äº Ĩ", + "Ġamb ient", + "_SET TINGS", + ".Ad apter", + "l ene", + "Ġtrav els", + "Not ice", + "Ġcle ans", + "ĠF em", + "ch air", + "Ñĥ н", + "/ my", + "_b ad", + "ĠEcon omics", + "IS A", + "_C NT", + "(M enu", + "äº İ", + "ĠR idge", + "Ġlength y", + "D ot", + "Ġjump s", + "Ġhe y", + "$ pdf", + "Ġw orm", + "Ġs ut", + "Ġsh er", + "iam o", + "ĠCal c", + "trie ve", + "Ġc ops", + "ĠCh rom", + "Ġreg ulated", + "reat ment", + "ĠHigh er", + "ok s", + "Ġde ze", + "LOC ATION", + "ongs To", + "Ġfin ite", + "Ġvar ies", + "Ġposition ed", + "' il", + "éĩ ij", + "Ġh ike", + "(d one", + "play list", + "Ġad a", + "Ġcoast al", + "ĠN ancy", + ".DateTime Field", + "Cpp CodeGen", + "ĠSimilar ly", + "re ur", + "ĠCon tr", + "ĠH idden", + "ĠB eta", + "atch ed", + "_inst all", + ". Output", + "Look up", + "ĠRich mond", + "qu ared", + "Ġm anga", + "-control s", + "ĠBern ard", + "L arge", + "Ġslic es", + "Ġoff ence", + "ĠM ega", + "Ġest ar", + "Ġjoint s", + "Ġsum m", + "_pl atform", + "B uff", + ".add Subview", + "Ġret ained", + "Let ter", + ".d im", + "Ġess ere", + "ĠS caffold", + "EX PECT", + "ĉ RE", + ".long itude", + "ü nd", + "Ġstat ue", + ".add Widget", + "ĠCar ibbean", + "add PreferredGap", + "il de", + "UIL abel", + "ĠOp port", + "Ġimper ial", + "urs ion", + "Ġmand ate", + "Ġpromot ional", + "Ġv k", + "ia ÅĤ", + "Ġp yl", + "ĠCre ation", + "оз д", + "Ġsim pler", + ". what", + "ĠRec ent", + "St orm", + ". quantity", + "ĠL ov", + "\" -", + "ubb les", + "_not ification", + "(w orld", + "ur ger", + "* (-", + ": \"Ċ", + "h m", + "ans hip", + "ĠAl most", + "Ġmotor cycle", + "_f ee", + "Ġabsor b", + "ĠVin cent", + "Ġsound ed", + "ÃŃ st", + "Ġpharm aceutical", + "ht ag", + "ĠKind le", + "ital ize", + "ĠEm peror", + "oust ic", + "Ġspecial ists", + "åħ ¬", + "Border Style", + "/ \\", + "RE LATED", + "(', ',", + "(ex pr", + "Ġh t", + "åį Ī", + "_C reate", + "Ġspecial ly", + "Ġ[] ;čĊ", + "Ġhe el", + "Ġse pt", + "_ arch", + "(in itial", + "% .ĊĊ", + "\\\", \\\"", + "Ġdiscuss es", + "Ġu pt", + "Ġ[ &", + "Ġman us", + ".h and", + "ĠM AIN", + "ĠDen mark", + "Ġ], čĊ", + "Ġcr yst", + "Ġn ack", + "Co ords", + "_in ner", + "Ġmid st", + "Ġaw ake", + "ĠÐ ŀ", + "-b reak", + "ÃŃ vel", + "_P ASS", + "ĠParam s", + "Ġdet r", + "Ġsp ider", + "ĠCon cept", + "Ġpre nd", + "CH ED", + ".Ex it", + "Ġpop ulated", + "Ġvirt ue", + "_SE SSION", + "Ġnou vel", + "o auth", + "Ġд аннÑĭ", + "r ink", + ".Header Text", + "atur ated", + "Ġer st", + "Ġå ħ", + "ॠĩ", + "_vis ible", + "ey er", + "Ġli able", + "Ġde be", + "Ġb w", + "{- #", + "_W IN", + "df s", + "H over", + "ĠP UT", + "- angle", + "Ġnob le", + "Ġtr aces", + "enc v", + "Ġuser Data", + "_in s", + "ĠS uz", + "Ġnews letters", + "ĠMod i", + "Ġentreprene urs", + "Ġtrib ute", + "Ġrum ors", + "Ġr r", + "ĠQu arter", + "ê³ ł", + "Ġfeed s", + "ó g", + "Ġen velope", + "Ġle ar", + "Ġk ø", + "develop er", + "Sim ilar", + ": \")Ċ", + "sub scription", + "Mod ifier", + "ital ic", + "Ġn asty", + "Ġtermin ation", + "Ġchar ming", + "Ġâ Ł", + "ton s", + ".tr ace", + "h ots", + "ĠU R", + "M ont", + "Ġjust ified", + "ĠG ang", + "ine a", + "Ġb og", + "( ap", + "_ $", + "Ġcont amin", + ".D ot", + "ĉ Debug", + "( exports", + "Ġpa ired", + "ĠAss ignment", + "Ġautom obile", + "ĵ į", + "Ġph ases", + "v w", + "@ SuppressWarnings", + "= \\", + "r ant", + "- ed", + "ĉ await", + "Ġcert ificates", + "'> \"", + "Ġint act", + "CT RL", + "M ike", + "greg ation", + "AT TERN", + "Ġre public", + "_up per", + "ili ary", + "Ġcomput ation", + "h ire", + "ĠSh in", + "_ ANY", + "ĠManufact urer", + "ĠC arm", + "Ġbear ings", + "_c omb", + "c ad", + "ur istic", + "Ġwholes ale", + "Ġdon or", + ".inter faces", + "press o", + "ĠBr un", + "-c lose", + "pro ve", + "_S K", + "ĉf rame", + "et ros", + "ĠP ain", + "_EX P", + "ĠL T", + "_f s", + ".dat as", + "ĉ ss", + "vo ir", + "ĠA xis", + "M ajor", + "=\" <", + "[ h", + "Ġprof ess", + "igr ate", + "(s core", + "Key word", + "\" os", + "ĠĠĠĠ ĉĊ", + "an alysis", + "Ġre play", + ".p ass", + "\\ d", + "t ls", + "Ġsan ct", + ".l ight", + "_m obile", + "ÑģÑĤ ÑĮ", + "ĉt otal", + "u ity", + "Ġpa used", + "N AS", + "Ġen core", + "lo e", + "Ġ-* -ĊĊ", + ".h igh", + "am pler", + "ĠSec ure", + "Ġfrag ments", + "_ vel", + "ill ary", + "ĠSte in", + "ĠD awn", + "Ġmax imize", + "ภ¢", + "Ġ/ ^", + "Ġcontin ually", + "Ġsh adows", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠI ActionResult", + "Ġinform ación", + "C HECK", + ".Selected Item", + "b undle", + "ol ley", + "< Int", + "AIN ER", + "ĠW ing", + "tit les", + "ount ain", + "C Y", + "ĠLoc ale", + "form er", + "< context", + "R adioButton", + "_s chedule", + "Ġfab ulous", + "Rob ert", + "_PRO FILE", + "Ġg ates", + "IM P", + "ĠPent agon", + "g old", + "b ach", + "employ ees", + "R otate", + "Ġch amp", + "Ġsel bst", + "Al tern", + "Ġconvert View", + "/ ,", + "Ġ~ (", + "St reet", + "_ place", + "Ġpersonal ized", + "P ublisher", + "ĠSO CK", + "_NAMES PACE", + "ĠStand ards", + "so ever", + "_C ENTER", + "Inter est", + "ô t", + "tem perature", + "View port", + "get Resource", + "Ġeat en", + "Ġsem pre", + "Ġab normal", + "Ġc ylinder", + "Ġtroub les", + "n od", + "Ñĭ в", + "g ames", + "_g l", + "Pl ane", + "g rey", + "_t bl", + ".Component Placement", + "ĠCh ase", + "Log ging", + "man y", + "ì Ĩ", + "Ġfl ame", + "=\"<", + "Ġtra jectory", + "_r ing", + "Ġhydro gen", + "tr on", + "Ġstat ute", + "Ġcondition al", + "Ġtr ay", + "-s chool", + "(w idget", + "$ config", + "Ġrequest ing", + ". uint", + "et on", + "brit ies", + "Of Type", + "AD MIN", + "p redict", + "Ġg egen", + "ĠH app", + "OC UMENT", + "ĠA part", + "Ġ---- -", + "ro e", + "u ide", + "just ify", + "ĠSqu ad", + "Ġprof es", + ".b ot", + "_c urrency", + "inn en", + "ĠM umbai", + "ĠNum bers", + "avana ugh", + "agn itude", + "âĢľ There", + "= http", + "çī ĩ", + "Ġv b", + "+' {{ $", + "Ġin ode", + "s il", + "Ġh ace", + "Ġsever ely", + "ĠOver view", + "Ġspr aw", + "Ġbeach es", + ": left", + "· »", + "($ {", + "ĠF IRST", + "ĠSp a", + "- ass", + "Ġb aise", + "ĠN ODE", + "ĠP izza", + "P et", + "(se q", + "\\ \">Ċ", + "CppMethod Pointer", + "Ġv p", + "Ġi a", + "_se conds", + "em et", + "/b lob", + "_TH RESH", + "... čĊ", + "D est", + "ĠN H", + ".data Source", + "it és", + "ĠJ ak", + "s ell", + "Ġwork shops", + "< u", + "Ġr ivals", + "ĠEX ISTS", + "h om", + "-t oken", + "compat ible", + ".J Panel", + "Ġphys icians", + "art in", + "Ġdes irable", + "Ġdistinct ive", + ".D ep", + "g id", + "ili ate", + ", max", + "Ġprem iere", + "Ġq Debug", + "Ġadvoc acy", + "Ġwh isper", + "P t", + "Ġun changed", + "_q ty", + "请 æ±Ĥ", + "Se ason", + "avel ength", + "ĠP ul", + "Ġd ÃŃa", + "'] ]],Ċ", + "al is", + "(\" &", + "bor o", + "Ġb m", + "ĠR adi", + "w rong", + "ĠGo ing", + "ime Type", + "ij i", + "- feedback", + "ĠN ames", + "ĠB apt", + "Ġprob able", + "ĠE ther", + "ĠPolit ics", + "_prot ocol", + "lin ing", + "S at", + "Ġcor rel", + ".Pr imary", + "(null able", + "RI ORITY", + "Ġcolor ing", + "Ġutil izing", + "d as", + "Ġexport ed", + "Ġcar riers", + "Con v", + ". editor", + "i ó", + "(h andles", + "Ġapprec iation", + ". import", + "ĠAust ria", + "ĠStr ip", + "il ight", + "Ġappropri ately", + "ĠP rest", + "ĠW ir", + "ĠUI Application", + "al chemy", + "ĠM ob", + "ĠD etermin", + "ergus on", + "register ed", + "_con vert", + "ĠVlad imir", + ".Show Dialog", + "ref lect", + "Ġsh ook", + "Ġass ure", + "ĠO ften", + "Ġcivil ization", + "Ġvocab ulary", + "fore ground", + "ĠS cope", + "Ġunw anted", + "act ing", + "Ġ( []", + "Ġmark ing", + ". original", + "ĠMO VE", + "Ġsport ing", + "ception s", + "NS Number", + "S izes", + "Ġprovinc ial", + "_Tr ans", + "Ġproblem atic", + "d igit", + "ĠEm ma", + "lock s", + "ĠC rew", + "ib a", + "') :", + "ish a", + "Ġm amm", + "Ġocc ured", + "w cs", + "(r ule", + "Ġmerch andise", + "es pecially", + "ĠT win", + "Ġn aming", + "Ġs log", + "Ġimpro ves", + "Ġad her", + ": text", + ".h adoop", + "_HT TP", + ".to List", + ".dis abled", + "Ġl enses", + ".in i", + "ĠR are", + "ĠUb untu", + "Ġsc ram", + "ol ation", + "tit ulo", + "Every thing", + "Ġnod ded", + "icht ig", + "_const ant", + "z c", + "l ift", + "ĠNot ify", + "ond o", + "ĠIN F", + "(\" +", + "ĠK az", + "Ġd read", + ".m apper", + "le ur", + "ĠCome y", + "ĠN B", + "ic ers", + ".P ush", + "ĠH ack", + "ĠBrazil ian", + "_pro d", + "Ġ// ĊĊ", + "Ġb icycle", + "Ġun available", + "Ġadoles cent", + "bl k", + "Ġmit ig", + "_bl ue", + "ì ĺ", + "fade In", + "ĠUtil ities", + "ĠM N", + "; k", + "< style", + "- status", + "ind o", + "Ġinn ings", + "Ġg j", + "Ġ|| =", + ".e u", + ": Number", + "Ġcuis ine", + "ĠURL s", + "ie k", + "Ġw ires", + "ĉ ps", + "ie g", + ".m k", + "so ap", + "Ġsom etime", + "Ġst ap", + "_s eries", + ".T arget", + "æ º", + ".dest ination", + "OUN TER", + "R aises", + "& A", + "Ġsmart phones", + "NI Env", + ".s dk", + "Ġhelicopt er", + "Ġim pe", + "ĠB irth", + "A U", + "b readcrumbs", + "co ords", + "Ġexplo red", + "Ġl od", + "ĠI p", + "g able", + "ian e", + "Ġart ifacts", + "Box Layout", + "ا ر", + "list ener", + ".c art", + "ĠH uff", + "ĠHind u", + "ĠData Types", + "ĠDr upal", + "IGN ORE", + "Ġoffset s", + "ĠR TC", + "- login", + "æ ®", + "ĠQ Object", + "Ġprosec utor", + "R ock", + "_ch at", + "W ay", + "ì ²", + "Ġneg lig", + "Ġd ude", + "; <", + "Ġdeleg ates", + "_f ailed", + "/ dev", + "/ work", + "( New", + "et able", + "() \"", + "( Icons", + "Ġp ork", + "ĠModel AndView", + "ĠV IP", + "ĠK or", + "m ix", + "Ġox id", + "ĠSC REEN", + "ĠFour th", + "/ \",Ċ", + "Ġte e", + "ĠSte vens", + "t icks", + "Ġp ledge", + "ib bon", + "ĠLo an", + "Ġne o", + "n umpy", + "ĠShared Preferences", + "- oriented", + "ĠLogger Factory", + "ĠGraph QL", + "zen ia", + "\" _", + "W omen", + ".c ast", + "Ġdeliber ately", + "+ b", + "ĠAr n", + "font Size", + "Ġm aze", + "Ġbl amed", + ".m as", + "} )čĊ", + "eler ik", + "Ġsc anning", + "ĠWork shop", + "Ġfind en", + "Ġca ut", + "UI Font", + "( return", + "al in", + "cast le", + "//////////////////////////////////////////////////////////////// ////////", + "Ġincent ive", + "op ath", + "b lob", + "Ġcigaret te", + "Ġfert il", + "*/ ĊĊĊ", + "ĠSh ar", + "Ċ ĠĠĠĠĠĠĊ", + "Ġunc ertain", + "ĠS ton", + "Oper ations", + "ĠSp encer", + "Ġdef in", + "ĠS olo", + "on est", + "·» åĬł", + "Ġu omo", + "G ive", + "Ġdent ro", + "; padding", + "ent ai", + "ĠC ars", + "Ġenthus iasm", + "ĠOper ating", + "S kip", + "par ation", + "Ġprotect s", + "Ġre ver", + "d g", + "ĠC incinnati", + "Ġconsect etur", + "Ġm uss", + "employ ed", + "a uses", + "ink le", + ". Values", + "£ ¼", + "lo v", + "_W ARN", + "Ġbook mark", + "ĠAp ollo", + ". axis", + "Ġm ét", + "Ġop ener", + "Ġtum or", + "d an", + "Ġelement ary", + "Ġsk ipped", + "ĠK er", + "as ia", + "_res p", + "Ġdem ol", + "ĠCan adians", + "Ġt astes", + "U Integer", + "Ġ' ${", + ".aw s", + "RO ID", + "ri ans", + "M Q", + "ord able", + "Ġcous in", + "Prop agation", + "(S ession", + "ph alt", + "UL D", + "ĠSc alar", + "Ġblo ody", + "Ġ à¦", + ".m ask", + ", q", + "ĠUn its", + "Ġcent res", + "ĠPr im", + ". ]ĊĊ", + "ĠSh aw", + "P rom", + "ĠTh ought", + "Check er", + "_output s", + "( chan", + "E INVAL", + "Ġb ob", + "_c mp", + "P ed", + "Ġmat rices", + "Ġvrou wen", + "Ġgenu inely", + "high light", + "(d isplay", + ") !=", + "Ġdel icate", + "ĠL uther", + "ĠM iles", + "Ġuser ID", + "% =", + "ate urs", + "_B UF", + "---- ---Ċ", + "imit ives", + "Ġsh elves", + "sl ow", + "_in formation", + "LE G", + "W r", + ".form s", + "cel and", + "/ un", + ": &", + ".âĢĻ ĊĊ", + "=\" %", + "Ġpro st", + "Ġfont size", + "uc ión", + "get ic", + "am t", + "=\" .", + "Dec or", + "B rit", + "Ġ\"\" ).", + "Ġfound ing", + ".File Name", + "ĠT ier", + "Ġdisc lose", + "á m", + ".s yn", + ".View Holder", + "lic ant", + "_st age", + "Mon day", + "Ġdes erialize", + "t alk", + "Ġtradition ally", + "æĢ ģ", + "Ø ®", + "LE X", + "Ġe h", + "ĉ ROM", + "Ġ{ })Ċ", + "Quest ions", + "nc py", + "Ġfix ing", + "к Ñĥ", + "_ Key", + ": x", + "ĠSTR ING", + "ĠÑĦ ай", + "ĉ left", + "ĠBen ch", + "ell ij", + "UR RED", + "ĠDi agram", + "} catch", + "/ time", + "ĠMiss ing", + "db name", + "Ġs ore", + "ĠW alt", + "ugg ing", + "rep resent", + "ĠG S", + "ne ys", + "ĉ page", + "Ġvol can", + "(b tn", + "Ġexceed s", + "Ġ erg", + "Ġpil ots", + "ĠS ed", + "ers ions", + "Ġpat ron", + "R V", + "/ top", + ". asset", + "_c ross", + ". Editor", + ".t b", + "Ġwel coming", + "SC REEN", + ") findViewById", + "C oder", + " \",Ċ", + "_P in", + "ues e", + "Ġover rides", + "_ ready", + "Adv anced", + "Ġop i", + "-c art", + "(\"/ \",", + "ĠDe b", + "CR Y", + "ĠVert ical", + "ĠO VER", + "ĠCorpor ate", + "Ġ\"\" ;", + "Ġste pping", + "e j", + "Ġaccus ations", + "Ġor az", + "_t ail", + "Ġindu ced", + "Ġel astic", + "Ġbl own", + ", //", + "Ġbackground s", + "âĢĻ une", + "-s dk", + "Ġset Interval", + "Ġincent ives", + "Ġveget able", + "_ On", + "exp anded", + "p ix", + "_sh ader", + "ĠSP DX", + "@ example", + "ĠW rapper", + ".Z ero", + "Pos itive", + "Ġsp inner", + "Ġinvent ed", + "ĠG ates", + "оÑĤ оÑĢ", + "Ġcompar isons", + "è ·", + ".pr imary", + "data Provider", + "add itional", + "ĉ options", + "s napshot", + ".set Horizontal", + "Ġ\" {}", + "ĠFish er", + "hal ten", + "< Type", + "Ġmax Length", + "ĠM t", + "Ġê° Ģ", + ".jet brains", + "Ġident ifies", + "Ġflow ing", + "ĠDisc ussion", + "ats by", + "Ġsch w", + "ught y", + "Ġr ivers", + ".un ique", + "_PH Y", + "ed ral", + "( ll", + "Ġcs rf", + "pp ers", + "ü l", + "ĠEs pecially", + "port ed", + "ĠHarr ison", + "****** */Ċ", + "Text Color", + "ìĬ µ", + "w ire", + "Ġstatus Code", + "ĠFin ish", + "c ence", + "ĠMcC ain", + "ĠW or", + "( await", + "Ġ) ->", + "ĠRegister ed", + "IN ED", + "k al", + "par ison", + "Ġobj eto", + "V i", + "mand a", + "Ġrenew ed", + "ĠS of", + "ess el", + ".nd array", + "Ġcr ap", + "ç® ¡", + ".ab spath", + "( up", + "Ġclear ance", + "ĠT W", + "_C OPY", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", + "Ġforest s", + "Ġarg uably", + "ĠA SS", + "he y", + "am el", + "_f ore", + "ĠSou theast", + "Ġab used", + "Ġpract icing", + "aked irs", + "ä¸ »", + "_res ources", + "Ġp ond", + ".F ixed", + "Last Error", + "ĠPsych ology", + "Ġ\" //", + "! :", + "Re usable", + "Ġmens aje", + "Ġro spy", + "Ġb our", + "Ġvar ieties", + "Ġem path", + "(( {", + "_ org", + "ĠM es", + "ĠMag ento", + "IST ORY", + "Un less", + "Ġh j", + "ĠD uty", + "J un", + ", size", + "Ġpaint ings", + "Ġdisp ens", + "d art", + "Ġbehavior al", + "Ġr pc", + "cal culate", + "fr uit", + "_m m", + "ĉp thread", + "Max Length", + "Ġc urrencies", + "_cap acity", + "ĠO z", + "Ġfire arm", + "Ġcoeff icient", + "Ġbankrupt cy", + "w art", + "Ġfat igue", + "AV A", + "Ġes pa", + "_p c", + "ĠQu otes", + "_L IGHT", + "ĠT ickets", + "Ġrel ates", + "Ġpublish ers", + "Ġunlock ed", + "Ġ// ----------------------------------------------------------------", + "ĠInterrupt edException", + "Ġout look", + "r n", + "Ġreb els", + "W ritten", + "Ġas ian", + "ot to", + "Ġ ĉĉĉĉ", + "_g pu", + "T xt", + ".Image View", + "Ġsu is", + "_t ables", + ".Rec yclerView", + "Ġwhat soever", + "è ģ", + "] ++;Ċ", + "assert True", + "_ verify", + "ĠR ivers", + "Ġ ][", + "J et", + "id ian", + "S ibling", + "Ġgen res", + ".A ccess", + "OP S", + "Ġtr ivial", + "ภª", + "al en", + "в ед", + "ĠS word", + "Ġscrut iny", + "(c b", + "Ġcomm erce", + "Ġguarante es", + "_ad v", + "ĠL ET", + "rec io", + "Ġh ilar", + "Ġback yard", + "ãĢ ı", + "Ġillustr ated", + "/v endor", + ". Util", + "Ġw ow", + "LO Y", + "ĠMar shal", + "\"> '.$", + "ĠB ak", + "Ġmod ifiers", + "d ictionary", + "ĠSt re", + "m ultiple", + "\")) ,", + "ĠC ort", + "'] \").", + "( admin", + "ĠCre ator", + "Int ernet", + "( ms", + "log y", + "DECL ARE", + "ĠMarc us", + "<< <<", + "ãģ ł", + "_m y", + "(in st", + "Ġsc iences", + "ND ER", + ". enter", + "Ġit u", + "Ġbeh ave", + "P an", + "omb ies", + "=' <", + "')) ;čĊ", + "ĠM ENU", + "ĠWork ers", + ".No Error", + "Ġbind ings", + "Ġdis abilities", + "{ \\", + "ĠM unicip", + "Ġco res", + "ur ple", + "ĠN okia", + "us ions", + "ĠF itness", + ".handle Change", + "Ġjav ascript", + "ìļ Ķ", + "( dec", + "Ġpack ing", + "-de pend", + "Ġtrans cript", + "z eros", + "_ alert", + "? \",Ċ", + "lib s", + "± оÑĤ", + "Ġ| ĊĊ", + "tr ained", + "ĠG ent", + "ĠR ab", + "x p", + "_config uration", + "å¤ ©", + "_ accept", + ".rec yclerview", + ": url", + "ĠMu hammad", + "Ġprivile ges", + "_b ank", + "uk u", + "w allet", + "ĠRO OT", + "Ġenc uent", + "? family", + "ĉ position", + "Ġc g", + "Ġprec ip", + "method s", + "_f ast", + "in crement", + "ĠT iger", + "_OCC URRED", + "qu ip", + "ĠH AS", + "_d om", + "Ġw reck", + "b j", + "Ġd ern", + "Ġorg ans", + ". entries", + "Ġ_ ('", + "ram ento", + "ĠJam ie", + "Ġp unk", + "IP P", + "Ġprogram a", + "Ġatt ain", + "Ġpro ves", + "/s ign", + "Ġanswer ing", + "Ġl adder", + "************************ ****", + "ĠW almart", + "ĠCONT ENT", + "duct or", + "Ġver bal", + "ĠP ID", + "c rypto", + "_CALL BACK", + "Ġ= ================================", + "Ġpot ent", + "Ġshort s", + ".U ri", + ".un iform", + "; border", + "ĠW er", + "Ġhere in", + "ll a", + "ĠI hr", + "P ixmap", + "l iteral", + "! )ĊĊ", + "g eneric", + "r ust", + "_script s", + "ost o", + "it us", + "ĠCoal ition", + "Ġrem ot", + "de ploy", + "ĠEag le", + "ãĢģ ãĢĮ", + "Ġimportant e", + "ĉ object", + "Ġseason al", + "ne j", + "aid u", + "Bind View", + "ĠSi erra", + "-b g", + "Ġmake Styles", + "[ offset", + "G ames", + "Ġhorm one", + "AR IO", + "head s", + "( select", + "ĠStart ed", + "@ param", + "_de cl", + "_b log", + "Ġa ño", + "\\ Api", + "ĠMil waukee", + "Pro vid", + "An imated", + "Ġcool er", + "ĠSe ed", + ". Edit", + "Ï Ħ", + "ĠT aking", + "Ġborder Color", + "-found er", + ".Logger Factory", + "Ġ\"\" ĊĊ", + "AL T", + "ĠL ate", + "EDI ATE", + "Ġ);ĊĊ Ċ", + "af a", + "Ġcancell ation", + "At om", + "ĠB irmingham", + "emp resa", + "HE MA", + "asc al", + "Ġup side", + ".V ersion", + "ĠF older", + "ĠE ight", + "ĠV intage", + "ĠApp Delegate", + "ĠPre vention", + ".se parator", + "ST M", + "( room", + "gener ator", + "Ġc attle", + "ĉ Z", + "ĠPart icle", + "' };Ċ", + "Ġneighb ours", + "ĠState less", + "Ġalt itude", + "Ġsa int", + "об ав", + "Ġconv inc", + "ĠCont ents", + "Ġje une", + "(t s", + "Serial ization", + "(c ollection", + "ĠJ azz", + "ĠD od", + "ĠR och", + "ac io", + "comm ended", + "DEF INE", + ".on load", + "Ġspecial ty", + "PL ACE", + "_MO VE", + "Ġaccount able", + "Re uters", + "Ġf icken", + "Ġde pr", + "W ow", + "V oid", + ".s pace", + "ภĹ", + "Ġt q", + "ĠP ets", + "< $", + "(C urrent", + "ber ries", + "plan ation", + "Ġlist Of", + "ĠTh u", + "ĠPR INT", + "Ġm ismo", + "Ġdo i", + "ch k", + "ĠUn icode", + "( role", + "Ġvir gin", + "< Point", + "_RESP ONSE", + "-h ouse", + "ĠVenez uela", + "EM AIL", + "Ġp úb", + "_ex ist", + "B all", + ".C L", + "re ferences", + "ĠBeautiful Soup", + "ĉ Expect", + "TH IS", + "Ñĥ д", + "b ane", + "Ġtemp oral", + "ER IC", + "et as", + "Ġrefresh ing", + "Ġsec ular", + "@ synthesize", + "ac cur", + "Ġn ella", + "ĠS OL", + ".p ipe", + "Ch annels", + "èĩ ª", + "Ġinsert ion", + "á» ĭ", + "el ia", + "Ġadjust able", + "Can ada", + "ĠI TEM", + "Ġcur ves", + "ĠChe ap", + "let ing", + "Ġoptim istic", + "al lo", + "Ġpolit ician", + "_down load", + "= edge", + "ORT H", + "Ġmodel o", + "art o", + ". rotate", + "Ġs elenium", + "æĪ ij", + "_al ias", + "Ġrenown ed", + ".' .", + "Ġc zy", + "Ġal les", + ".Com piler", + "ĠB ass", + "Conn ector", + ".R ole", + "L INK", + "Ġc riterion", + "lem etry", + "Success fully", + "/p ng", + "Ġey eb", + "asp berry", + "( gr", + "Ġd angers", + "Ġcorrect ed", + "Ġgl ow", + "Ġelabor ate", + "ĠB ears", + "aw ai", + "=\" '+", + "Ġpromot ions", + "Ġmathematic al", + "Ġ\" `", + "_Generic Class", + "ĠChe f", + ".S ort", + "table Name", + "R IC", + "Ġvolunt ary", + "ĠBl ade", + "-e lect", + "ĠCom bat", + "ĠAb ility", + "Ġab dom", + "Ġd uck", + "T mp", + "åħ ¨", + "Ġer ase", + ".P h", + "ĠDefault s", + "p artment", + "_US B", + "ê te", + "; '", + "Ġp ads", + "ĠOb amacare", + ".T otal", + "Ġdiv ert", + "Ġcr icket", + "Ġrecre ational", + "( red", + "ĠC le", + "R U", + "Ġmist aken", + "ĠMont ana", + "Ġstr ive", + "_sl ider", + "ĠPl astic", + "Ġdecor ated", + "ĠV P", + "lic o", + "ĉf alse", + "Ġpre fs", + "( \\\"", + "_f alse", + "i endo", + "Ġ@ $", + "B ucket", + "act ical", + "ĠZ hang", + ".c ols", + ".B inding", + "Ġw ax", + "_ST ORAGE", + "Ġlaw n", + "Ġr f", + ".Sc ene", + "ĠCal culator", + ".d esign", + "Ġres il", + "л ем", + "E mploy", + "ĠPr ices", + "ĠP WM", + "ag i", + ".e valuate", + "ĉ param", + "Ġbr ass", + "bb en", + "Ġinflamm ation", + "ull ivan", + "Ġan not", + "Ġp H", + "iam eter", + "ĠB TC", + "( box", + "Story board", + "Ġcl ay", + ".assert Raises", + "| string", + ".App ly", + "Ġmatch er", + "und ed", + "Ġsatisf ying", + "Ġìł ķ", + "Render ing", + "_app ro", + "ind rome", + "AN EL", + "_f ix", + "br ush", + ".M atch", + "Ġsm iling", + "on aut", + "S unday", + "Ġdelet ion", + "Ġencour ages", + "P ull", + "Ġreven ge", + "Ġqu arry", + "tr ade", + "Ġc ables", + "(d elta", + "ites pace", + "Ġf h", + ".b unifu", + "Ġvi el", + "_IN CLUDED", + "ĠT ail", + "ad ar", + "of s", + "Ġmet als", + "g om", + "_method s", + "Ġn j", + ".St d", + "(w in", + "$ ('", + "Ġt urtle", + "ur on", + "Ġen rolled", + "ĠH z", + "ĠBox Decoration", + "Ġp ont", + "rel ationship", + "B i", + "³ »", + "Ġmas cul", + "Ġsh ades", + "Ġv r", + "ĠLog ic", + "Ġa in", + "ĠD IST", + "Ġcoll ar", + "\" profile", + "Generated Value", + "ĠP ossible", + "Ġe ines", + "ĥ ģ", + ".time out", + "ĠE c", + "Ġjer sey", + ".D ouble", + "Ġqual ifying", + "v or", + "CRE EN", + "_A pp", + "_rec v", + "Ġali ens", + "It s", + "E sc", + "i ator", + "ĠE clipse", + "Ġg h", + "V ict", + "ĉ html", + "to o", + ". const", + "Ġant erior", + "ĠW u", + "(key s", + "Ġul tr", + "_p oly", + "ĠT ap", + "ĠB ud", + "A WS", + "Ġcrash es", + "_t ot", + "Cont in", + "-h anded", + "alth ough", + "ภļ", + "ific ent", + "Ġde ve", + "ut ory", + "ĠW orth", + "_M S", + "Ġfloor ing", + "Ġsell ers", + "ĠThank sgiving", + "Ġp ng", + "Ġval ores", + "Ġslee ve", + "Ġfil le", + "Ð IJ", + "Ġappoint ments", + "Ġv im", + "User Info", + "BO OST", + "Ġpos ed", + "initial ized", + ".product s", + "ĠLeaders hip", + "man uel", + "' %", + "em arks", + "Per centage", + "(d ist", + ". avatar", + "(h Object", + "ä» Ĭ", + "_ iff", + "ic one", + "; )", + "_n il", + "Ġab ol", + "е ÑģÑĤ", + "Ġven ues", + ".Con vert", + "! ')Ċ", + ".B itmap", + "sk in", + "_C OLUMN", + "Re v", + "G RESS", + "g ow", + "Ġw ished", + "tract s", + ".assert False", + "Ġscreens hot", + "Ġfo is", + "Com b", + "Line Width", + "ĠGr ab", + "Ġint ensive", + "ĉ sh", + "+ )", + ".first Name", + "_PRO CESS", + "Ġt ilt", + "it ored", + ".L OG", + "Ġb ak", + "Ġintention ally", + ".play ers", + "(c anvas", + ")) )čĊ", + ".Pro vider", + "_P UBLIC", + "T alk", + "ĠL iv", + "ched ulers", + "Ġl c", + "ad ic", + "feature d", + ".res ources", + "Full Name", + "Ġmean while", + "B uffers", + "Ġres olver", + "ĠS AP", + "_T E", + "G NU", + "ĠForms Module", + "_ wh", + "ĠS we", + ".widget s", + "Ġcabin ets", + "Ġsus cept", + "ĠB ott", + "activ ex", + "av ar", + "ant ics", + "Ġ\" =\"", + "_k wargs", + "Ġgame Object", + "ĠAng le", + ".I ter", + "mar sh", + "ĠB irthday", + "ĠC MS", + "request s", + "ĠPear l", + "_E OL", + "Ġlin ux", + "( org", + "_M ouse", + ".con structor", + "Ġz d", + "Ġk icks", + "art isan", + "Ġe ax", + "K n", + "pon ge", + "ĠFin land", + "Ġmet res", + "ĠAss essment", + "part ner", + "/ pre", + "! ',Ċ", + "[ Int", + "Ġos lo", + "date picker", + "/ String", + "op lay", + "ĠHe brew", + ", double", + "Ġtrab al", + "+\" \\", + "ĉ EIF", + "/ text", + "_F IRST", + "ĠP ete", + "Ġe go", + "Ġextr as", + "P DO", + "Ġreg ulate", + "ĠQ Widget", + "st s", + "ĠSh ows", + "ĠN HS", + ".c ourse", + "p thread", + "ĠF uel", + ".t imes", + "Ġ °", + "Ġstr ides", + "($ ('#", + "( words", + "Ġrhyth m", + "Ġsp ont", + "Ġsens ation", + "Ġsp ike", + "C losing", + "页 éĿ¢", + "N umeric", + "Ġbreat he", + "Ġfin ale", + "_F ACT", + "in ion", + "Ġch ill", + "Ġform ally", + "ANG ED", + "Ġ' :'", + "ĠпÑĢ и", + "a q", + "ĠFab ric", + "(l at", + "ĠPr incipal", + "Ġer ro", + "oc ale", + "N om", + "Ġf ost", + "_C USTOM", + ".int ellij", + "ert ools", + "Ġcl asse", + "adi ents", + "Ġfundra ising", + "EN E", + "_OPTION S", + "_ ob", + "// }Ċ", + "Ġprote ctions", + ".se ed", + "N V", + "term inal", + ";; ;", + "P redicate", + "Ġì ¶", + "Ġbomb ing", + "G F", + "Ġch ew", + ")) ).", + "qual ified", + "] ={", + "list en", + "C ENT", + "d igest", + "E ast", + "Ġd iver", + "Ġend points", + "Ġe e", + "Ġcolle ague", + "Ġdissert ation", + "_com mit", + "_D AT", + ". rc", + "Ġbre asts", + "ĠR ug", + "ĠP il", + "Contract s", + "ĠBry an", + "Web View", + "Ġconcent rate", + "ĠIn ner", + "Ġ' |", + "std out", + "_S ub", + "> -->Ċ", + "V ol", + "ĠS SD", + ")) ),", + ". Optional", + "Ġnurs es", + "Ġor b", + "_ pe", + ");čĊ čĊčĊ", + "pl aced", + "ess er", + "Ġther apeutic", + "Ġwhites pace", + "Ġa ston", + "Success ful", + "Ġpr aised", + "ĠW es", + "Ġe ighth", + "ir al", + "Ġvrou w", + "Ġf action", + "_b ias", + "Ġw itch", + "Ġnp c", + "(s b", + "ĠRod rig", + "_b ig", + "Dep endency", + "ĠAb raham", + "ard i", + "C AR", + "n os", + "Ġabund ance", + "Ġnut rients", + "in stein", + ".V ert", + "ĠI SS", + "< U", + "Ġsum s", + "_h ist", + "Ġfar mer", + "ĠA br", + "Sh ot", + "ĠBad Request", + "Ġh ass", + "ĠR ails", + "Ġaffili ated", + "æĿ ¥", + "Ġer f", + "IN F", + "ĠView Holder", + "min i", + "ĠR oth", + "Ġfaith ful", + "ĠPhill ips", + "AND OM", + "]. [", + "_P AY", + "ĠAr ctic", + "f aker", + "D igit", + "M ale", + "std err", + "se ys", + "Ġ Å¡", + "_rem ote", + "li que", + "Ġin def", + "ĠIndust ries", + "it ra", + "_p airs", + "< iostream", + "Ġsal aries", + "ik en", + ".F rame", + "PL IC", + "_S PEC", + "ĠMed iterr", + "Ġsystem atic", + "Ġinter rog", + "Icon Button", + "se a", + "int ro", + "ĠIss ues", + "enc rypted", + "Ġintern ationally", + "Ġsn printf", + "Ġpast a", + "ĠBrad ley", + "_ Status", + "AL K", + "_P AD", + ".l aunch", + "< select", + "Ġhar dest", + "Ġph y", + "Ġ(( *", + "-s lide", + "ĠNob ody", + "S u", + "Ġas ÃŃ", + "close st", + "_initial izer", + "Ġsupport er", + "-g en", + "Ġt ales", + "Ġcor p", + "_f u", + "s at", + "ne ighbor", + ".M igrations", + "Ġal gun", + "Ġsin on", + ".S pec", + "? ,Ċ", + ".G L", + "m ale", + "Ġmon itors", + "yl an", + "-L icense", + ".m atches", + "ĠA BS", + "ĠM ast", + "ĠW allet", + "($ (\"#", + "Dir ty", + "Ġco pe", + "Ġinterpol ation", + "ous ed", + "ĠJ ets", + ".F LAG", + ".C ancel", + ".Event s", + "ne ver", + "ĠM Hz", + "> D", + "Ġs ervlet", + "bast ian", + "Ġ> &", + "S ID", + "_cl k", + "Ġdiv isions", + "} ',Ċ", + "Ġd ildo", + "Ġpar ade", + "m ajor", + "Ġab oard", + "; ++", + "Ġf usion", + "\"}, {\"", + "ĠDialog Result", + "ĉ arr", + "- em", + "_n r", + "(h andler", + ".N ET", + ".Xtra Reports", + "ĠSh ah", + "ĠB rief", + "- ,", + "Ġprec io", + "ĉĉĉ ĠĠĠĠĠĠ", + "Ġt ant", + "ĠGrand e", + "/ xml", + "_IC ON", + "ĠR etro", + "un que", + "Ġn ag", + "to Fixed", + "X L", + "Ġdecl aring", + "ĠCon crete", + "ĠAm azing", + "ĉprint k", + "Ġdeb ates", + "D ATED", + "Ġaest hetic", + "emet ery", + "Routing Module", + "ĠNash ville", + "W AYS", + "Ġw olf", + "Ġobserv ers", + "OT A", + "ans on", + "Ġe a", + "Ġgreen house", + "ĵį ä½ľ", + "Ġst air", + "Ġimmigr ant", + "_app ly", + "pe are", + "ĠBloom berg", + "_PL AYER", + "Res p", + "æŃ £", + "Cho oser", + "ĠI Collection", + "P eter", + "Er ro", + ".detect Changes", + "Map s", + "Ġs queeze", + "ĠHom es", + "weg ian", + "Ġformat ting", + "Ġnegot iate", + "ul d", + "ĠN ep", + "ĠQ B", + "Ġeconom ies", + "Ġ*/ ,", + "Ġredu nd", + "ĠA ber", + ".IsNullOr WhiteSpace", + "yc led", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "_S h", + "Ġske pt", + "Ġre created", + "Ġget Type", + "Ġmarg ins", + "Ġcolon ial", + "ch arts", + "// @", + "Ġprocess ors", + "è¯ ´", + "b atis", + "æĦ ı", + "ator io", + "mention ed", + "P atient", + "Ġpre y", + "Check box", + "_x path", + ".s kip", + "ĠMorm on", + "ĠMemory Stream", + "CRE MENT", + "Ġk u", + "m eld", + "\\ Data", + "ĠK ernel", + "il tr", + "éĢ ģ", + "( profile", + "Car bon", + "RO LE", + "( pl", + "] *(", + ".m emory", + "Ġmed al", + "Ġadvis or", + "it ät", + "Ġh dr", + "ier ung", + "ĠProvid es", + "( alpha", + "Ġteen agers", + "- parser", + ".L atLng", + "] ()Ċ", + "Ġfel ony", + "ĉĉĉĊ ĉĉĉĊ", + "BO OK", + "Ġsl ash", + "Ġclear fix", + "ĠPro phet", + "å® ¹", + "right ness", + "-f i", + ".k ind", + "ert on", + "J im", + "Ġmanip ulate", + "Ġworks heet", + "ol in", + "st ars", + "Ġart ifact", + "_EM PTY", + "ĉm ain", + "------------- ' ;", + "Ġexpress ing", + "ĠI Q", + "ĠF act", + "/************************************************************************ *******Ċ", + "_m ass", + ")) :", + "Ġcon dom", + "Ġcreate State", + "omet own", + "Ġir r", + "Ġ> (", + "> B", + "iter ation", + "ãĥ ª", + "Ġshirt s", + "ount y", + "-> $", + "_S IGN", + "ĠD ale", + "Ġj j", + "E asy", + "F re", + "ĠN y", + "Ġch lor", + "match ed", + "ĠG erm", + "- UA", + "ĠN athan", + "educ ation", + "-y ard", + "- che", + "h ouses", + "r itional", + "Ġprox imity", + "Ġdies em", + "áºŃ p", + "Ġd rought", + ".a udio", + "ĠLe o", + "Ġfavor able", + "in ch", + "ĠD aw", + "rib ly", + "_st udent", + "id able", + "O VE", + "Ġlack s", + "ounc ing", + ".b usiness", + "Ġre open", + "may be", + "_G LOBAL", + "Ġdress es", + "ĠEd wards", + "ens ible", + "ĠHard ware", + "ĠEx cellent", + "ĠTime Unit", + "CTION S", + "Ġsched ules", + "Ġseg ue", + "Op ens", + "am men", + "- Identifier", + "Ġst aring", + "Ġhapp ily", + "ĠH ob", + "' _", + "Ġ\" );", + "ament os", + "et ched", + "Ġ/> }Ċ", + ". Users", + "Ġinterrupt ed", + "Contact s", + "Ġreg istro", + "in burgh", + "CH A", + "_ imp", + "ph is", + "s ay", + "Ġretail er", + ".N ODE", + "/ maps", + "_L AST", + "ĠCh arge", + "_g uard", + "Coll ider", + "ĠStateless Widget", + "\": [\"", + "(\" ../../", + "iox ide", + "ĠS und", + "Ġ'' ;", + "un set", + "add Widget", + "л Ñİ", + "el les", + "alk er", + "A rc", + "Ġded uct", + "G UILayout", + "ĠV illa", + "Ġfor bidden", + "_ where", + "Ġ\\ /", + "ĠT ib", + "_A X", + "] čĊčĊ", + "ĠB ir", + "Ġb end", + "ĠMA KE", + "ĠM ET", + "Ġfut ures", + "Ġweight ed", + "\"\" \"čĊ", + "Ġauthor ize", + "(pro gram", + "}, {\"", + "Ġcoeff icients", + "ê s", + "Per Page", + "ĠBath room", + "ĠPublish ing", + "G PL", + "Ġsub missions", + "ĠNUM BER", + "j Äħ", + "Ġaddition ally", + "em pre", + "ĠSh el", + "ot yp", + "S olution", + "Ġth under", + "_ ec", + "ĠĊ ĠĠĠĠĊ", + "ĠF ellow", + "Ġk ay", + "Ġnew State", + "ONT AL", + "Im plementation", + ".L ook", + "Ġ ents", + "Ġl ors", + "ĠB IG", + "f ab", + "Ġaver aged", + "ĠFe edback", + "ĠW ells", + "Ġm artial", + "Ġind ul", + "ĠComm unist", + "ĠFore x", + "ĠAgricult ure", + "\" [", + "Ġqu ar", + "ĠK ont", + "ĉ view", + ". Bytes", + "des ktop", + "ĠM akes", + "akes peare", + ".Null able", + "Ġspot light", + "V B", + "ow y", + "(t orch", + "tr idge", + "_b ounds", + "Ġapolog ize", + ".add Item", + "ant d", + "* );Ċ", + ", u", + "(g en", + "ç» ĵ", + "re ator", + "ĠC ord", + "ou pper", + ".m etro", + "Ġ ew", + "ĠW ORD", + ".A fter", + "Ġdet ained", + "ĠHam mer", + "ex isting", + "Ġo st", + "Ġmon ument", + "-c ustom", + "User ID", + "ĠN om", + "Ġre jection", + "(d im", + "Ġsingle ton", + "ĉd ie", + "ari ance", + "re ports", + "] !=", + "eld a", + "Ġpreval ence", + "_reg s", + ".\" .", + "Ġfemin ist", + "Code c", + "Ġ **Ċ", + "(label s", + "_M ARK", + "FA ILED", + "Ġadminister ed", + "W N", + "ĠĠĠĠĠĠĠĠ ĉĉ", + "Ġn oun", + "w ig", + "Ġg otta", + "Ġr if", + "- im", + "ĠPaul o", + "ĠCommand Type", + "] ))ĊĊ", + "-z ero", + "Tr aining", + "Ġl ord", + "_ art", + "re ddit", + "C ert", + "Ġpes o", + "R ot", + "Ġend anger", + ".d r", + "user Info", + "un ts", + "n v", + "ĠTrail er", + "-f irst", + "(m ake", + "Ġbenef ici", + "-bl ack", + "i ÃŁ", + "Ġund oubtedly", + "Ġm ex", + "ĠAnc ient", + "( as", + "Ġdes cent", + "P ick", + "Ġrep lica", + "$ obj", + "ä hr", + "Ġar rows", + "ft y", + "ĠLib ya", + "ug a", + "charg ed", + "T ur", + "Ġh omic", + "iss en", + "ĠF ake", + "Ġbe ers", + "Ġsc attered", + "( Time", + "UT IL", + "Ġbureauc r", + "/pl ain", + "Ġstick ing", + "FA IL", + "ĠC ovid", + "Th ird", + "_p resent", + "ĠPier re", + "Ġë ª", + "Ġ[... ]ĊĊ", + "Pro b", + "ĠTra ffic", + "ica o", + "do ctor", + "Ġ), ĊĊ", + "T abs", + "al u", + "ï¼ļ âĢľ", + "Ġinher ent", + "_N o", + "rit is", + "ĠPro of", + ".b asename", + "ä¼ ļ", + "Ġch im", + "ĠProt ected", + "c rit", + "Ġpr one", + "Ġк он", + "ĠHero es", + "Ġan xious", + "Ġan os", + "Ġweek ends", + "Ġs ext", + "Ġredu cer", + "= UTF", + "h alf", + "ĠS aw", + ".m m", + "Ġnue va", + ".current Target", + ".l ua", + "_EXT ENSION", + "ĉ reg", + "ĠC trl", + "_ align", + "accept able", + "Ġrush ing", + "fr ac", + "Ġbo asts", + "F ive", + " ±", + "ĠTem perature", + "> ):", + "Ġchar ter", + "RE ATED", + "Ġsubject ed", + "Ġop c", + "health y", + "使 çĶ¨", + "ĠScient ific", + "Ġfra u", + "ri ages", + "ภĶ", + ".in ventory", + "ation ale", + "M ad", + "min utes", + ">> ();Ċ", + "ĠEn v", + "Ġrecord ings", + "Ġsusp icion", + "sql ite", + "ĉ read", + "ãģ ¦", + "Ġwor ries", + ".put String", + "ĠSh anghai", + "( uid", + "r er", + "ĠvÃŃ de", + "\") :", + "Ġmethod ology", + "Ġк оÑĤоÑĢ", + "cc c", + "av ad", + "Ġindu ction", + "ĉ Thread", + ", string", + "ạ i", + "neh men", + "u ition", + "Ġ* __", + ".em f", + "Ġì ľ", + "/th emes", + "ĠN ine", + ". One", + "ĠEm bed", + "Ġf az", + "u ations", + "Ġpriv ately", + "Ġl ing", + "[ F", + "ush i", + "Ġlaunch es", + "( KEY", + "G MT", + "Ġaim ing", + "pat ible", + "ĠB iden", + "i w", + "ĠD egree", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġ$ ('<", + "á rios", + "to UpperCase", + "ìł ľ", + "ĠE UR", + "Ġovers ight", + "Ġtable sp", + "Up dates", + ".m akedirs", + "Ġhum idity", + "/ template", + "Al ways", + "( IS", + "_c ert", + "D ig", + "Ġunder way", + "ort on", + "ĠHur ricane", + "Ġsp ends", + "ĠSeg ment", + "Ġfl ies", + "ĠT oggle", + "ĠLyn ch", + "Ġs enses", + "ĠK os", + "set Enabled", + "ist ically", + "Ġtest er", + "Ġadministr ators", + "Ġtag ged", + "Ð ĵ", + "Ġshort cut", + "ĠRes olution", + "Ġsuperv ision", + "ĠAsh ley", + "Tr acking", + "ul atory", + "and el", + "ist en", + "Ġun re", + "(d iff", + "ANT S", + "Ġr ider", + "Ġs Äħ", + ".S eries", + "_ orders", + "ORIZ ONTAL", + "Ġret ention", + "ãĢĤ čĊčĊ", + "Ġdi agonal", + "ĠC ancellationToken", + "_ Internal", + "Ġru in", + ".Q t", + "ocr atic", + "T el", + "ĠAn swers", + "m atic", + "Ġx p", + "at em", + "_j obs", + "_ any", + "Ġsen iors", + "Ġland mark", + "ĠQ List", + "Ġman eu", + "ot ify", + "/ \";Ċ", + "/ server", + "ĠPhil osoph", + "uten ant", + "( io", + "h z", + "Ġauthentic ated", + "d v", + "- Compatible", + "Origin ally", + ", function", + "ãĢĤ čĊ", + "ĠRepresent ative", + "as ily", + "irc uit", + ".d t", + "(m ath", + ".M arshal", + "[ ,", + "ĠC ities", + "_ turn", + "| )Ċ", + "Ġcant idad", + "al ter", + "ĉ ui", + "ĠNe braska", + "Ġsk irt", + ".b g", + "Shared Preferences", + "( style", + "Ġg rief", + "g ew", + "Ġsaf eg", + "ol ang", + "_l ists", + "ì Ľ", + "Ġgran ite", + "Ġhott est", + ".j dbc", + ".C ustomer", + "Ġâī ¤", + "Ġwa ar", + "_sc ene", + "+' /", + "ĠJ TextField", + "Ġse ating", + "Ġwe ars", + "Ġ` /", + "C ases", + "ĠY outube", + "ı m", + "Ġbal con", + ", G", + "Meta Data", + "- price", + "SC R", + "Un ity", + "Ġtr unk", + "={` ${", + "Ġearthqu ake", + "Part ial", + "Ġsub st", + "Ġelim in", + "=\" '.", + "//* [@", + "Ġsuperv isor", + "vro let", + "_ article", + "Ġp ane", + "b io", + "Ġmot ors", + "N M", + "F rank", + "Ġon ion", + "- word", + "Item ClickListener", + "Ġb rit", + "end encies", + "Com puter", + "_r unning", + "( day", + "- he", + "(n amed", + "ĠS ach", + "о Ñĩ", + "c ampaign", + ".Ab stract", + "(w rapper", + ".p ay", + "Ġu w", + "Ge o", + "r ails", + "/ select", + "icht e", + "son s", + "E VENT", + "Ġal iment", + "Pro viders", + "A wait", + "_INTER VAL", + ". off", + "Ġgl uten", + "_cl oud", + "Ġw en", + ".ex tract", + "ĉ button", + "/ MM", + "Part y", + "Ġdem ographic", + "_err no", + "Ġh iking", + "(' ')Ċ", + "\", @\"", + "Ġw it", + "r á", + "olog ie", + "ĠSt yles", + "ĠBrowser Module", + ".Request Mapping", + "ic ans", + "P AGE", + "cre ation", + "ĠF erguson", + "ud ed", + "num bers", + "ĠGT K", + "Ġpresent ations", + "ĠB obby", + "_s pan", + "est yle", + "Ġilleg ally", + "abel a", + "Ġbattle field", + "cap acity", + "ter ror", + "] \");Ċ", + "Ġwar rior", + "le ader", + "ĠDB G", + "ĠRe venue", + "Ġvig il", + "Ġcounter parts", + "( Error", + "ACT ER", + "Ġhe eft", + "Ġselection s", + "ze ug", + "t om", + "-t wo", + ". ;Ċ", + "_st atement", + "ĠA id", + "ĠV ul", + "_r gb", + "Ġpr izes", + "Ġedit able", + "ĉ form", + "ın ı", + ".de cor", + "D emo", + "lic es", + "Ġen ctype", + "rat ulations", + "ĠR OS", + "_ch ars", + "ĠJ ahr", + "part ial", + "Ñĥ ÑĤ", + "ĠRe ceive", + "ĠL ands", + "AP TER", + "Ġch opped", + ".. \"", + "ĠAn aly", + "ĠU ID", + "ĠR adeon", + "ĠB ee", + "Ġun m", + "> M", + ".find all", + "Token izer", + "ĠWH AT", + "Ġs j", + "D rawing", + "E ss", + "ON D", + "Ĭ ¶", + "(p acket", + "âĢĶ but", + "Inv ocation", + "ĠN uclear", + "? ;Ċ", + "Ġgrand es", + "ĠC rypt", + "rem ark", + "Ġ'../../ ../../", + "Ġin ability", + "m agic", + "c ats", + "Ġsim ulate", + ": ${", + "in flate", + "Ġen er", + ": NO", + "ip les", + "Ġmer it", + "ĠR ated", + "Ġgl ue", + "/b log", + "Ġg ren", + "Ġthr illed", + ".C H", + "unc an", + "ĠPR IMARY", + "Ġper sec", + "Ġfe ared", + ".M IN", + "ĠThe ater", + "é Ĵ", + "ategor ie", + "æ® µ", + "Ġappet ite", + "s quare", + "ĠAlex and", + ".User Id", + "_g t", + "_ enter", + "Ġgradu ates", + "Fragment Manager", + "Author ize", + "-N LS", + "(M y", + "Ġtri umph", + "ust ing", + "_PARAM S", + "Char acters", + "(: ,:,", + "_B UILD", + "M Hz", + "Ġwash ed", + "Ġun cle", + "Ste ve", + "ard own", + " ${", + "_confirm ation", + "Ġtro phy", + "Work s", + "ĠElect ronics", + "ĠMediterr anean", + "_m etrics", + "Ġannounc ing", + "ĠD AY", + "_pro to", + "Ġp ear", + "base Url", + "ĉĉĉĉĉĉĉĉ Ċ", + "Ġcoord ination", + ": N", + ".an imate", + "ĠC otton", + "_h it", + "â ľ", + "Ġjet zt", + "if ter", + "(f ields", + "own load", + "ific acion", + ".c uda", + "ĠLi u", + "> equals", + "ĠA ce", + "ÑĢаР¼", + "ĠSuper man", + "ĠGarc ia", + "Ġarrest s", + "ag ar", + "Ġ{} )", + "Ġmac ros", + "rou pe", + "ê tre", + "Ġtw isted", + "str uments", + "_ (\"", + "_ vertices", + "ĠTrans ition", + "и к", + "[ max", + "m ind", + "Ġaccess Token", + "Ġun le", + "m us", + "c op", + "ĠF actor", + "Ġcon ced", + "Ġre tr", + ".l inalg", + "-s lider", + "ob l", + "_Static Fields", + "Ġz ombie", + "s elling", + "Ġch ap", + "Ġsh aking", + "ĠTrans late", + "ĠAm sterdam", + "ĠE TH", + "_EX TERN", + "k d", + "_d isc", + "Ġpreced ing", + "Ġpri x", + "Object Name", + "_mod ified", + "ard ware", + "Ġ?> \">", + "ĠD W", + "` ${", + "Ġ?> \">ĊĊ", + "Ġspin ning", + "_p ending", + "Match ers", + ". Keys", + "ĠP V", + "en us", + "ant is", + "Ġdisc ard", + "Ġh aul", + "Ġem pir", + "Ġpath way", + "Ġo ak", + "м ен", + "-ind uced", + "Ġimp air", + "ĠCal gary", + ".is Hidden", + "d z", + "_ include", + "Ġg m", + "Ġ' ('", + "P Y", + "uggest ions", + "Ġcommod ity", + "c ro", + "/ sub", + "Ġget Instance", + "ĠLeg acy", + "ĠK il", + "B al", + "( short", + "In form", + "+ x", + "* r", + "ĠHope fully", + "or ate", + "Ġmach en", + "Ġtreat y", + "ĠO ri", + ".p ublic", + "-h orizontal", + "Ġtact ic", + "Ġb ord", + "w ares", + "Ġam mo", + "ĠL ists", + "Ġequ ations", + "/ her", + "ĠNS W", + "B ounding", + "_C ollections", + "Ġav ail", + ".Drop Down", + "è °", + "Ġh h", + "Ġl Ãł", + ".p b", + "Ġmemor ial", + "ĠAT TR", + "Ġexhaust ed", + "Ġt sp", + "ĉ redirect", + "Ġlik ewise", + "ST ER", + "L java", + "Ġcondem ned", + "oca ust", + "(str ict", + "Ġexem pt", + "Ġs ms", + "Ġex agger", + "S YS", + "Ġl ounge", + ": ^", + "Ġto dd", + "de b", + "ator ial", + "ĠPort er", + "Ġtu ition", + "Ġexem pl", + "Ġp aren", + ".line To", + "Ġkid ney", + "Ġç a", + "Ġc ui", + "ï¼Į 请", + "X C", + "Ġmo ż", + "Ġnomin ated", + "l ung", + "Im Gui", + "ĠB uzz", + "Ġstere o", + "port al", + "res as", + "Ġk lass", + "Ġdraft ed", + "Ġproject ile", + "/g pl", + "(param eters", + "* )Ċ", + "Ġassist ed", + "ĠNS Integer", + "s itemap", + ":n th", + ".View s", + ".Argument Parser", + "Ġme er", + "z ier", + "ĠD ig", + "Ċ", + "Ġpl ag", + "p ine", + "Ġblank et", + "Ġ: -", + "Ġl cd", + "------------ ---", + "(\" \"", + "Ġtact ical", + "ĠRon ald", + "ex tr", + "ĠF est", + "Ġf uer", + "-n avigation", + "Ġk b", + "gh ost", + "Ġhandle Change", + "_cl s", + "() !=", + "Com parator", + ".v m", + "ĠCo x", + "_re view", + "/ @", + "_c ookie", + "Ġrecogn ised", + "ld ap", + "Thread s", + "ĠSex ual", + "ĠB earing", + "(S QL", + "Ġx r", + "Ġth igh", + "URL Connection", + "ĠSU V", + "Ġm Context", + "Ġinc idence", + "ĠE ste", + ".s up", + "_t e", + "(EX IT", + "C MD", + "/ \">", + "Al most", + "ĠU ne", + "Ġand eren", + "ĠSingle ton", + "Ġb ore", + "Th ink", + "Ġn arc", + "] initWith", + "_sh op", + "(str ategy", + "! ',", + "her its", + "ĠDes k", + "_m achine", + ".net ty", + "ı nda", + "= <", + "ĠQ R", + "ĠS idebar", + ".split Container", + "Ġon Success", + "Ġmon key", + "En joy", + "(n odes", + "pect rum", + "Ġ(* (", + "ĉU INT", + ", height", + "ĠNetwork s", + ".t ail", + ".l inspace", + "Ġ\" ...", + "List en", + "Æ ¡", + ".Ch annel", + "- defined", + "Re peat", + "ad just", + "ER M", + "_ application", + ".assert NotNull", + "- stream", + "Ġr abbit", + "Ġposition ing", + "Ġw oke", + "Ġf ing", + "Ġmulti player", + "Ġregister ing", + "un til", + "Ã¥ n", + "( ::", + "uss ions", + "Ġpot ato", + "ĠE quals", + ".S up", + "/ap ache", + "Ġ( =", + ". \")", + ".p tr", + "ĠSpe ech", + ".cl ip", + "ĠGab riel", + "Ġmusic ian", + "/ issues", + ".sh op", + "ĠH ier", + "_RE T", + "_b ucket", + "ãĥ ¡", + "av s", + "Ġro z", + "fl ower", + "Write Barrier", + "ĠMil an", + "Ġlegisl ature", + "ĠD oll", + "Ġprov ing", + ".concat enate", + "âķ IJ", + "Ġg char", + "cdn js", + "b les", + "ĠList ing", + "л о", + ".xr Label", + "ĠS ak", + "just ice", + "ĠVal entine", + "un less", + "Ġp iger", + "(r un", + "Ġtest ified", + "AN A", + "ĠRem oves", + ")) ));Ċ", + "rec ated", + "ĠRuntime Method", + "Ġcon qu", + "ãĤ ¢", + "Ġt issues", + "ail er", + "ét é", + "- Star", + "Ġfl ames", + ".set Icon", + "Ġsup ern", + "Ġvag ina", + "- variable", + "Ġwell ness", + "C UR", + "Ġbel le", + ".get Request", + "Ġp oco", + "ben h", + "ag ens", + "Ġsp ill", + "ĠJ ur", + "Ġdispatch er", + "н ого", + "emon ic", + "(dir name", + "ĠÐ Ķ", + "Ġpas se", + "Ġg anz", + "ric ing", + "E U", + "Ġmuj eres", + "ess en", + ".at tribute", + "j j", + "ĉĉ ĠĊ", + "[ ^", + "Ġstrtol ower", + "lex er", + "ect ar", + "hot el", + ".s quare", + "Ġr all", + "Ġlower ed", + "hand led", + "Mark et", + "ĠUs es", + "iv as", + ".B usiness", + "ãģĹãģ ¦", + "D IV", + "Ġw asted", + "Ġav oir", + "ê m", + "_ACC OUNT", + ". et", + "ĉ SDL", + "k ap", + "Ġf ox", + "up pet", + "{ },Ċ", + "\", '", + "F avorite", + "P END", + "ĠA ES", + "} ),", + "Ġded uction", + "Ġpol ÃŃt", + "Ġcomponent Will", + "ĠT elerik", + "_SE LF", + "Ġm use", + "C raft", + "Ġd ens", + "ठ¿", + "( tp", + "Ġt asty", + "Ġbal ances", + "Ġded ication", + "ĠWall ace", + "Ġun law", + "\\\"> \\", + "Ġm um", + "- update", + "ement e", + "Ġs oda", + "Re public", + "as mine", + "é ric", + "( Status", + "ĠJson Convert", + "ĠD isk", + ".Red irect", + "Ġfilm ing", + "/m ol", + "R o", + "Ġv ille", + "Ġtrab aj", + "Ġsyn thesis", + "reg a", + "Ġr l", + "S cheduler", + "ISH ED", + "current User", + "(error s", + "' h", + "_b ot", + "x imo", + "ĠUS ART", + "_s uper", + "_DEC REF", + "н ой", + "_RO W", + "Ġprom otes", + "ĠT A", + "Ġhor as", + "ĠRep resents", + "Ġname of", + "ĠEx c", + "ĠGar age", + "Ġse ine", + ", #", + "Ġher b", + "/ resources", + "Ġple aded", + ".r adioButton", + "Ġæ ĺ", + "O ps", + "ĠN est", + "c string", + "ĠDef ence", + "Ġref ere", + "_le af", + "Ġrevel ation", + "ë §", + ".execute Update", + "_W ORLD", + "Ġexp ans", + "(\" \\\"", + "j ab", + "Ġdoub ts", + "ĠGe ometry", + "Ġintrodu ces", + "Ġsen ators", + "Ġcan al", + ".h elper", + "ĠBi ology", + "_SE NS", + ".pre vious", + "-t ouch", + "ab it", + "Ġimpact ed", + "Ġbr ackets", + ".d irect", + "acc um", + "Ġtest osterone", + "ĉ action", + "ĠCh ance", + "Ġpe aks", + "CppCodeGen WriteBarrier", + "Ġun belie", + "_p ress", + ".R el", + "ang led", + "/ templates", + "-- >čĊ", + "l ime", + "Ġsufficient ly", + "_ nt", + "Exp and", + ".is file", + "Ġis Empty", + "Ġq t", + "Ġmul her", + "ac ob", + "Ge orge", + "å¸ ¸", + "Ġass im", + "as o", + "Ġcompr ised", + "O V", + "(CON FIG", + "ĉw riter", + "Ġdes p", + "Ġten ure", + "(c r", + ".p ool", + "ĠB rend", + "Ġc ensor", + "(time out", + "Ġple a", + ".W rap", + "Ġtight ly", + "ĠW ere", + "ĠI gnore", + "abe i", + "Ġbr idges", + "Ġcondem n", + "Ġsimp licity", + "Ġrout inely", + "Ġblack s", + "j b", + "ĠP it", + "U tf", + "Ġ/ Ċ", + "re load", + "Ġset Object", + "/g lobal", + "Ġf atty", + "Ġsock s", + "Could n", + "Ġerot isk", + "æĿ ¡", + "ĠPress ure", + "ĠM az", + "n pos", + "tol ower", + "ĠE Q", + "ute ur", + "ĠM oment", + "Ġet a", + "{{ --", + "Ġgraph s", + "ĠGu ar", + "r ine", + "( --", + "ĠHttp Status", + "(st udent", + "* np", + "Ġrail way", + "Ġas ynchronous", + "_v m", + "'] ,'", + ", text", + "mer chant", + "(G uid", + "ĠG ra", + "ix er", + "fetch All", + ".add Listener", + "fl ip", + "* $", + "> (),", + "Ġsun light", + "ass igned", + "Ġab c", + "ĠC OLUMN", + "ĠðŁĻĤ ĊĊ", + ") ...", + "Ġen semble", + "Ġnew line", + "_S INGLE", + "ied ad", + "Ġdark er", + "orm ap", + "Ġl ion", + "pl its", + "Ġillustr ation", + "ĠI EEE", + "Ġv ista", + "ous ands", + "****** *", + "ĠTom my", + "Ġh ue", + "S el", + "Ġa ura", + "ĠTher apy", + "Ġanim ator", + ".con straints", + "Ġv ague", + "(\" \")", + "Ġvill ain", + "Ġbless ing", + "Ġstring Builder", + "ĠM isc", + "ĠD IR", + "f ax", + "- node", + "ĠWalk ing", + "ĠA U", + "s ess", + "Ġgr ill", + "VERT ISE", + "ĠF oods", + "Ġt ournaments", + "à ĵ", + "ĠMar sh", + "Ġw onders", + "Long itude", + ".Command Text", + "= input", + "_enc oder", + "page Size", + "Ġget State", + "> >Ċ", + ".g rey", + "p od", + "Ġread ings", + "Ġre consider", + "Start up", + "Ġexc er", + ".b alance", + "_c ycle", + "_T ime", + "LOC AL", + "ĠE FI", + "ĠRe yn", + ".set Foreground", + "by n", + "Ġdis connected", + "ACT IVE", + "Ġembed ding", + "ick ers", + "Ġsurround ings", + "* c", + "Ġgar ant", + "Ġb f", + "Ġw ipe", + "Ġ ä¸ĭ", + "_T RA", + "ado x", + "ç ķ", + "Ġsu cks", + "ĠS ongs", + "ĠAssoci ates", + "ĠB ald", + "ĠB rett", + "ven ile", + "Ġv t", + "Ġin ade", + "Ġres igned", + "ĠGl enn", + ".p attern", + ".Data Bind", + "Ñĥ м", + "Layout Inflater", + "ch et", + "ĠTest ament", + ".m s", + "Ġp av", + "ĠReact DOM", + "ur dy", + "AD ATA", + "M u", + "/ actions", + "ĠJ s", + "_ex tract", + "ĠBr ing", + ": id", + "str t", + "iv ation", + "Ġoutr ight", + "az u", + "loy ment", + "и Ñı", + "al do", + "ĠP ublisher", + "E ducation", + "Pa lette", + "_d rv", + "Ġ($ (", + "ĠAnd a", + "Ġrem edy", + "Ġincons istent", + "te ction", + "Ġregul ators", + "Ġshort est", + "(p air", + "ĠInstall ation", + "Ġdefend ants", + "Ġ( );", + "-l arge", + "M el", + "Ġthreat en", + "н Ñı", + "Ġfet ish", + "ot ine", + "_d ic", + "Ġ< $", + "Ġst agger", + "sp i", + "$ response", + "S erv", + "-b orn", + "j os", + "ĉ img", + "ĉW HERE", + "_l t", + "å½ ĵ", + ".c ost", + "ĠT ue", + ".label s", + "ĠL V", + "wcs store", + "ĠJes se", + "ภ«", + "Tr ade", + "Ġpredecess or", + "ë Ĥ", + "fin ally", + "_g eneral", + "ogg ler", + "_REG ION", + "n ement", + "Ġblog ger", + "ĠHar bor", + "ĠD ataset", + "[ w", + "Ġattend ees", + ". ico", + "max imum", + ".Un lock", + "_SY NC", + "ág ina", + "Ġdown s", + "ĠW ii", + "]) /", + "Ġkick ing", + "unic ation", + "ĠD AC", + "ĠID S", + "ĠR ental", + "Ġcurrent Time", + "Ġvacc ines", + "ĠDev il", + "Ġn ors", + "_m ouse", + "urre ction", + "(n o", + "Ġ> čĊ", + "Ġaggress ion", + "Ġbre eding", + ".s ymbol", + "im an", + "Absolute Path", + "ĠWH O", + "_fl ush", + "- root", + "arn a", + "& M", + "Ġf athers", + "ĠR ocket", + "ive au", + "Ġw ander", + "Ġcom pos", + "ĠWar rior", + "ĠSe at", + "ĠClin ic", + "_in voice", + "(dis patch", + "Product o", + "at uring", + "oss ier", + "ĠM AY", + "Ġd agger", + "Ġsanit ized", + "ĠR FC", + "Ġpro ph", + "Ġur ine", + "Ġgr ind", + "ĠExp anded", + "des cripcion", + "-f w", + "ĠK erry", + "= name", + "Ġch k", + "Ġnation ally", + "Ġthe e", + "In c", + "Ġ? >>", + ".R adioButton", + ".Http ServletResponse", + "/ Y", + "ĉf ield", + "Ġhom me", + "y per", + "Ph ysical", + "= v", + "Ġdr iv", + "ĠErr ors", + "Ġc Äĥ", + "De ath", + "ĠW INDOW", + "Ġpo et", + "ĠSh arp", + "ĠImm utable", + "ĉ create", + "Ġge ht", + "ĠRe form", + "ais er", + "ĠInitial ization", + "Ġimm unity", + ".com pose", + "Ġlat ency", + "ĠLeban on", + "ĠPar ad", + "Ġfu els", + "ĠEx hib", + "co h", + "% \">Ċ", + "ĠCL I", + ") initWith", + "-Z a", + "_C LEAR", + "reg n", + "Ġfin ances", + ".st andard", + "_C ATEGORY", + ".lib rary", + "Ġtravel ers", + "_w p", + "ĠE valuation", + "start ing", + "Ġ )),Ċ", + "ep isode", + "ĠV ariant", + "Ġda emon", + "ĠJul ia", + "ĠN R", + "Ġdoub les", + "< v", + "/r untime", + "Ġinterpre ter", + "ĠIN DEX", + "ĠHol mes", + "_D IM", + "Ġp addle", + "_ex ample", + "Ġfore ground", + ".r outes", + "Ġs owie", + "S UCCESS", + "ĠC DC", + "ĠB D", + "_ -", + "as ured", + "W riting", + "Ġcurrent Page", + "( answer", + "ĠASC II", + "à ¨", + "Ġsocial ly", + "yy y", + "ĠSpecial ist", + "(c ustomer", + "ist ani", + "ke st", + "ĠM ak", + "Ġth o", + ". pt", + "( comment", + "ĠCon verter", + "g am", + "b ins", + ". tele", + "ĠVeter ans", + "_AL LOC", + "олÑĮзов аÑĤ", + "inn amon", + "; width", + "oh l", + "Ġfant as", + "Ġs ung", + "ĉ K", + "( Json", + "Ġneighbour hood", + "Ġv ow", + "Ġs ins", + "on acci", + "Ġepoch s", + "im agen", + ".Ch ange", + ".my batis", + "Se ek", + "W ER", + "管 çIJĨ", + "Ġinter ess", + "_ Event", + "eder land", + "Ġterr itor", + "Ġci udad", + "uck ed", + "Ġsn ack", + "Ġtransport ed", + "ĠMan ifest", + "ĠD AT", + "_th eta", + "Ġw ont", + ".ĊĊ ĊĊĊĊĊĊĊĊ", + "Ĭ¶ æĢģ", + "ĠEp ic", + "De ck", + "l tra", + "_Z ERO", + "Ġ[] ;", + "/ scripts", + "Ġ---------------------------------------------------------------- ----------------", + "æĥ ħ", + "Ġwe ed", + "N BC", + "Ġrap ed", + "ĠG ateway", + "[ M", + "ĠTime out", + "ench mark", + ".View Model", + "Ġporn os", + "ĠY a", + "th ritis", + "ĠFly nn", + "Ġme ga", + "ac in", + "Ġtrib al", + ".app le", + "ĠB lo", + "â n", + "ib i", + "ro v", + "ĠL ives", + "^ .", + "get Request", + "ĠEst ablish", + "cont ainers", + "Ġst arring", + "Ġcele brities", + "ĠRel ative", + "ĠHe ights", + "Ġtq dm", + "ĠNorth west", + "iv ic", + "ĉ cl", + "Ġautom otive", + "ent ric", + "Ġfort unate", + "Ġfire place", + "se ud", + "nick name", + "; s", + "_C AL", + "h alt", + "(n s", + "_de leted", + "Develop ment", + "m ovies", + "Ġident ities", + "Ġprompt ly", + "ا ÙĨ", + "Ġant e", + "Ġ\" ','", + "åı £", + "imp se", + "Ġy ap", + "Type Name", + "Ġb itch", + "Ġassoci ates", + "HE ME", + "- empty", + "ĠØ ª", + "ol vers", + "Ġpist ol", + "Sc oped", + "ag ner", + "'] =='", + "ĠI MP", + "ex c", + "Ġo mitted", + "Ġmind set", + "Ġ[] (", + "Ġor n", + "_C AM", + "A vg", + "Localized String", + "ĠN atur", + "Ġcom poser", + "ĠPlay ing", + "Ġover d", + "_ utf", + ".s k", + "ĠF ol", + "$ page", + ", Object", + "Ġbe es", + "al ary", + "bul let", + "_lib rary", + "O ffer", + "loc ated", + "Ġ(_ ,", + "âĢľ He", + "ĠOwn ers", + ") ).Ċ", + "Ġb ri", + ".Ad min", + "kt ion", + "лÑİ Ñĩ", + "Ġerot ici", + "Cancel led", + "Ġa gr", + "re views", + "_d ma", + "RI CT", + "Ġg fx", + "mp i", + "pp o", + "Ġ// @", + "Ġupper case", + "Ġcommit ting", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "User Data", + "Ġv ai", + "ĉs ort", + "Ġcongr at", + "Ġd ioxide", + "д а", + ". area", + "ĠJosh ua", + "ĠK och", + "_b reak", + "az ure", + "ist ical", + "_AL PHA", + "_ views", + "Ġelim inating", + "OM B", + "en umer", + "ĠHy dro", + "(* (", + "ERT ICAL", + "Ġinev itably", + "Ġst ole", + "-e ast", + "ier on", + "Ġl inger", + "/d oc", + "Å º", + "ĠAl ready", + "as io", + "Ġ-- Ċ", + "Ġabb rev", + "ĠAt om", + "h im", + "ĠINS ERT", + "s un", + "âĻ ª", + "CON NECT", + "er ator", + "ĠM anning", + "Ġ: (", + "g as", + "=> '", + "Ġquery set", + "; }čĊ", + "ĠPop ulation", + "uted String", + "res ident", + "_F ONT", + "ĠRes pond", + "Ġobsc ure", + "Ġo bservable", + "ĠContrib utors", + "k on", + "ĠMus k", + "ex ao", + "ĠT ub", + "Boot Application", + "S OR", + ".H orizontal", + ".find By", + ".p ower", + "Ġposit ively", + "ven ience", + "ĠJ ong", + "Ġwh istle", + "Ġз наÑĩ", + "Ġl ending", + "Ġdestruct ive", + "Ġon Delete", + "author ization", + "(); ?>", + "_ original", + "sc ience", + "at ra", + "?, ?,", + "ĠAs c", + "Ġconvinc ing", + "$ a", + "org en", + "_D ate", + "ĠPro vide", + "Ġlon ely", + ") 'Ċ", + "ex change", + "; ?>Ċ", + ".f ast", + "S amples", + "L ondon", + "'] )čĊ", + "ĠI onic", + "Ġp esso", + "ĠKn ights", + "ĠR af", + "_attr s", + "Ġrepe al", + "> Main", + "ĠOrder ed", + "_N ew", + "=\" \"> \";Ċ", + "ĠS ERVER", + "ĠHE ADER", + "_ velocity", + "ĠIn voke", + ".timestamp s", + "Ġs ulf", + "I QUE", + "Ġinhabit ants", + "ph ins", + "azz o", + "Ġmon o", + "Leg end", + "Ġnon ce", + "IF E", + "; \";Ċ", + "- create", + "\" \",Ċ", + "per mit", + "ĠImm igration", + "Ġpath name", + "ffect ive", + "âĻĢ âĻĢ", + "Ġex ams", + "- event", + "ĠT ill", + "[m id", + "F IX", + "; color", + "( Order", + "_tra its", + "Ġorder By", + "Ġs unt", + "ĠNich olas", + "Ø ²", + "Ġsun ny", + "in ers", + "Ġaccess ibility", + "ĠH B", + ".com p", + "ĉ op", + "Ġminor ities", + "ethe us", + "Ġcollabor ative", + "pr it", + "H IR", + "Ġwr aps", + "ĉd raw", + "g od", + "ĠI X", + ".app s", + "ĠN M", + "Ġirre levant", + "ĠT igers", + "Ġdi ag", + "G V", + "ĠAccess ories", + "k ont", + "Ġsimpl ify", + "ĠF avorite", + "_t ools", + "([] );Ċ", + "Ġtow ers", + "B es", + "Ġhun ter", + "Ġsal on", + "(b uff", + "ĉ debug", + "Ġmal ware", + "M oving", + "- options", + ") +'", + "ĠLO VE", + "_S OCKET", + "_f in", + "ĠDel aware", + "Ġsher iff", + "-in valid", + "ĠF ULL", + "Ġп од", + "el as", + "\" strings", + "ĠRepresent atives", + "s urface", + "res olved", + "ht docs", + ")) :čĊ", + "Ġpress ures", + "Ġnorm s", + "Ġpl a", + "Ġs urname", + "Ġpost al", + "ĠDep art", + "Ġsla ughter", + "or ida", + "Ġhe bben", + "Ġdes ar", + "comp act", + "_L ANG", + "åIJ Ī", + "op oly", + "_r ad", + "ĠST DMETHOD", + "L azy", + "ĠĠĠ ĉ", + "... ,", + "( web", + "ĠP ont", + "Ġet was", + "Ġup ward", + "_h at", + "Ġ], ĊĊ", + "Ġbase Url", + "Ġworry ing", + "-add on", + "(get Class", + "S PI", + "Ġcapt uring", + ") },Ċ", + "Effect s", + "Ġcompet ent", + "Ġf oul", + "Ġsubscri bing", + "ĠO BJECT", + "IX EL", + "b ucks", + "( edge", + "(p ass", + "ĠPet erson", + "Ġbo obs", + "ĠD elay", + "_s quare", + "el im", + "ot ers", + "_P C", + "% E", + "on click", + "ĠSV G", + "Ġto pped", + "Ġf ist", + "sm art", + "ĠR alph", + "( owner", + "j ours", + "Ġbron ze", + "ĠArgument Exception", + "( original", + "_S CALE", + "_c p", + "Ġrecomm ends", + ".set Style", + "S ure", + "L AND", + "Ġrepe ating", + "M att", + ". Visibility", + "Ġenter prises", + ".Set up", + "(sc ene", + "ĠRe active", + "ur ge", + "b w", + ".P ut", + "p ersist", + ".c ookie", + "ĠAud i", + "` s", + "sup plier", + "( Form", + " ¡", + "_s o", + "Į Ģ", + "ĠLeg ion", + "t te", + "N d", + "L oss", + "( attrs", + ".sc atter", + "Ġg room", + "Ġgl impse", + "Ġn ails", + "Ġcum ulative", + "Ġf azer", + "_s ervices", + ".N um", + "ib ilit", + "_res olution", + "ĠT x", + "umin ium", + "op a", + ".s chedule", + "sm tp", + "ภķ", + "ur ry", + "ü k", + "go og", + "_sign ature", + ".int o", + "ĠSte ps", + "Ġhome owners", + "ĠNS URL", + "ĠP AC", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĊĊ", + "> ')Ċ", + "en h", + "Ġinc ap", + "$ MESS", + "Ġmo ins", + "ĠF i", + "Ġoff season", + "press ions", + "> .Ċ", + "ĠGr ass", + "ĠGo al", + "_p df", + "Hand lers", + "Ġstack s", + ".get FullYear", + "=[ ];Ċ", + "è½ ¦", + ", V", + "(s plit", + "Ñĥн к", + "Ġbake ca", + "Ġ~ /.", + "pe z", + "t ails", + "ĠG len", + "Ġset Image", + "ĠCom ic", + "B LOCK", + "ĉ This", + "o ader", + "Ġcapital ist", + "_ST EP", + "( Boolean", + "ĠCor rect", + "r ina", + "Ġconc aten", + "å® ŀ", + "() :ĊĊ", + "Ġun anim", + "ll i", + "al ars", + "- ne", + "Ġdiv or", + "ĠKick starter", + "]. _", + "< number", + "/m enu", + "GR APH", + "vis itor", + "Ġimpro per", + "_N EXT", + "Ġb isa", + "background Color", + "/ input", + "Ġmo i", + "Go al", + "li qu", + "Ġmiscon duct", + "Ġcompr ises", + "aw ns", + "ĠP ie", + "ra is", + "role um", + "Ġcur se", + "y u", + "_p oll", + ".current User", + "ES H", + "]) [", + "Ġstory t", + ")? ;Ċ", + "* =", + "ĠB urg", + "/ layout", + "_back end", + "; ?> * '+", + "åĿ Ģ", + "ac ency", + "( URL", + "_h alf", + "= l", + "Ġlist View", + "( section", + ".to Array", + "+ /", + "ĠRodrig uez", + "ist ream", + "Ġelig ibility", + ":: -", + ".new Instance", + "P B", + "ĠAs sets", + "ĠCom posite", + "ĠL abs", + "ĠHam as", + "++ );Ċ", + "Ġbl k", + "ĠNe o", + "L uc", + "@ login", + "Ġun aware", + ".m et", + "_RE LEASE", + "( ST", + "AM IL", + "ri ke", + "Ġ( ){Ċ", + "(s printf", + "ĠAccount s", + "ĠV IEW", + "ĠA j", + "ãĤ °", + "Ġwh isk", + "Ġid i", + "Ġro de", + "Ġih n", + "ĠElement ary", + "Q ty", + "Ġintrig uing", + "Ġå ¤", + "J obs", + "ĉ offset", + "ĠAh med", + "ĠTal iban", + "Ġè İ·åıĸ", + "Ġinject ed", + ".Auth entication", + "_line ar", + ".Dec imal", + "Ġapp les", + "Ġshare holders", + "Ġb aked", + ".d iff", + "ĠE ddie", + "ok ers", + "Ġconfront ed", + "vo ices", + "Ġt us", + "ĠSp in", + "N ODE", + "_ Un", + "CT X", + "/g oogle", + "Tem perature", + "Ġ' ').", + "Ġmagn ificent", + "Ġstart Index", + "semb les", + "Any one", + "z k", + "eh en", + "ĠD ame", + ". strict", + "Ġrepl aces", + "Ġline back", + "Ġpush es", + "Ġche ek", + "ĠSh i", + "_BY TES", + "RE A", + "ả n", + "_CON NECTION", + "G ateway", + "ĠTr avis", + "ĠA X", + "ĠBas ically", + "ĠUp grade", + "à ª", + "th emes", + "erm o", + "k or", + "F emale", + "_att ach", + "ĠìĤ¬ ìļ©", + "Ġpo z", + "============ ==Ċ", + "(s ymbol", + "ĠS ector", + "__ )ĊĊ", + "_p adding", + "ï¼ļ \"", + "Ġf abs", + "Ġr anged", + "set Name", + "Ġp error", + "â Ĺ", + "ĠFile Reader", + "Ġful filled", + "_C urrent", + "Ġdom inate", + "Ġsm ugg", + "Post Mapping", + "_for ce", + "Ġb loc", + "ĠG iant", + "(v ideo", + "ĠC U", + "System Service", + "Ġ elf", + "Ġkont akt", + "ë ª", + "ke es", + "gt k", + "Ġparam Int", + "Ġmark up", + "u ales", + "Ġaccount ed", + "Ġgang bang", + "RY PT", + "ĠW rong", + "Ġcred ited", + "ĠM ESSAGE", + "Ġfl aws", + "Ġbb w", + "Ġmetab olic", + "ĠO EM", + "/ event", + "(C ollectors", + "mont on", + "ap pear", + "Ġopt ed", + "Ġche at", + "Ġd av", + "ĠPro ceed", + "Ġê ¸", + "ank ed", + "и з", + "ans k", + "ĠH ang", + "ĠC ler", + "Ġdis gu", + "Ġc map", + ".cl js", + "Ġa ument", + "le z", + "ĠJo ined", + "_re ceived", + "Ġa erial", + "ot el", + "Ġgre et", + "\" s", + "ĠGen esis", + "ĠCal if", + "pan ion", + "Ġtail ored", + "m apping", + "and Expect", + ".tr ack", + "at omy", + "ĠO w", + "ull ah", + ".Y es", + "ĠSimple Name", + "db h", + "' en", + "Ġnons ense", + "Ġphilosoph ical", + "(get Context", + "Ġis so", + "ĠA CE", + "start Date", + "Ġb ÄĻd", + "ĠAUTH OR", + "ĠGlo be", + "Ġinsect s", + "_A l", + "ush ing", + "è® °", + "/ Home", + "ĠLocal Date", + "need ed", + "hes ive", + "Ġill usion", + "äº Į", + "Ġtr at", + "x o", + "/d etail", + "_M ATCH", + "Ġbroad band", + "Ġw al", + "ĠIllegal StateException", + "IRE CTION", + "Ġnor theast", + "es ium", + "ĠClient e", + "ul ance", + "nt y", + "Ġt ecn", + "Dev ices", + "Ġgr ains", + "ĠO g", + "ĠS EL", + "ud iant", + "Ġ++ ;Ċ", + "Ġexplan ations", + "oc co", + "Ġdi ets", + "Ġco hort", + "( controller", + ".Iter ator", + "-r ich", + "ro cess", + "G D", + "Ġcar bohydr", + "Ġfri ed", + "ĠEmploy ment", + "ìŀ ¥", + "ĠLeon ard", + "_ ${", + "qu ares", + "Ġcompan ions", + "Ġpar is", + "Ġstim ulation", + "ĠZ oo", + "Ġre levance", + "ĠCol our", + "Ġspe ar", + "ot ional", + "ĠL ite", + "ĠK osten", + "Ġà ³", + "_att achment", + "orph ic", + "Ġdam it", + "Ġd lg", + "Ġthr ive", + "CH ANGE", + "ĠApp arently", + "Ġat ual", + "Ġroot ed", + "( images", + "aw i", + "ari at", + "Ġch erry", + "STAT IC", + "m nt", + "ĠUser Id", + "il let", + "ĠHis panic", + "Ġn ak", + "Ġcent ro", + "Ġdim s", + "_initial ize", + "ı k", + "ĠCent ers", + "RE N", + "Ġevolution ary", + "ĠTop ics", + "_d amage", + "em er", + "Ġr und", + "Ġpun ished", + "Ġcub ic", + "f air", + "[] ;ĊĊ", + "Ġinstant iate", + "Ġover see", + "- delete", + "unte er", + "start Time", + "ĠP ipeline", + "_G AME", + "ĠC ir", + "ĉ Null", + ".Format ting", + "uc umber", + "ĠR ide", + "Ġz oo", + "Ġcheck er", + "åIJ Į", + "= C", + "Ġg rit", + "\"); //", + "_x y", + "ĠDe claration", + "Ġcall able", + "F oo", + "ĠList Item", + "Ġin accur", + "ml in", + "ĉ Data", + "Ġev olving", + "aw an", + "Ġca fe", + "fol k", + "_ID X", + "ĠAny thing", + "ĠPalest ine", + "ĠGrid View", + "Ġcol ony", + "ĠGerm ans", + "( +", + ".p id", + ".js x", + "ĠSuper ior", + "Christ ian", + "ĠL ect", + "ĉ Game", + "Ġinstrument al", + "Anim ations", + "д ал", + "ĠMos es", + "ĉĉčĊ ĉĉčĊ", + "z s", + "k te", + "ä¸ ļ", + "_D IST", + "bit map", + "d B", + "Ġp ersistence", + "ÑĢ оÑģ", + "$ l", + "B ron", + "Ġ{ |", + "_ch art", + "ĠCon sum", + "Ġh emp", + "Ġ\" ))Ċ", + "Ġattack ers", + "Ġknowledge able", + "Ġc et", + "Ġvir uses", + "' I", + "Ġpitch er", + "Ġsweep ing", + "= list", + "apt ops", + ".de pth", + "Ġinstruct ed", + "ĠR us", + "benh avn", + "Ġи н", + "S ports", + "Ġon set", + "æĿ ĥ", + ". RED", + "_s i", + "ĠP ST", + ".on Change", + "> tag", + "ĠR oh", + "_char acter", + "ĠLaw s", + "ĠB achelor", + "_s wap", + ".re activex", + "Ġreward ing", + "Med ium", + "- [", + "ĠRec ently", + "J oint", + "part ition", + "ĠMin utes", + "Ġind o", + "Ġabsor bed", + "ĠG N", + "_IN D", + "Ġsab er", + "Sp awn", + "output s", + "ĠJeff rey", + "Ġmed ieval", + "h ed", + "Gu ide", + "Ġpsy cho", + "Ġgl am", + "E lim", + "äd chen", + "_pl ain", + "ĠS au", + "-f our", + "Ġanaly zing", + "QU ERY", + "Ġtom ato", + "_button s", + "V EN", + ".set Status", + ". Url", + "+ ĊĊ", + "Ġcompl aining", + "deg ree", + "conf irmed", + "Ġsub t", + "p arsed", + "Ġtor que", + "Ġtroub led", + "ĠT ARGET", + "Ġtrad emarks", + "ĠCo ordinate", + "ĠV iv", + "Ġ// }ĊĊ", + "Ġapr ès", + ".get Position", + "(Key Code", + "ĠSil va", + "Ġmet eor", + "Ġendorse ment", + "Over view", + "ĠP oss", + ".In ject", + "Ġeven ly", + "Ġvisual ization", + "Ġw char", + "ĠH DMI", + "Ġfun ct", + "ick name", + "',' ','", + "Ġfor wards", + "Managed Object", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĉ server", + "ĠOut look", + "ĠChron icle", + "Ġdub bed", + "Ġd ok", + "ĠW ear", + ".A L", + "pare n", + ". Interface", + "Inter faces", + ".c od", + "Ġd ib", + ".Global ization", + "ĠAcad emic", + "Ġass ms", + "Aut om", + "Ġl w", + "ĠN W", + "Ġ&& čĊ", + "Ġproble ma", + "ĠManufact uring", + "lim its", + "-m obile", + "Ġfil me", + "/ map", + "Ġdo it", + "ĠIn k", + "Ġsu ed", + ". arr", + "Ġunder min", + "ĠPro c", + "croll View", + "__ $", + "Ġsidew alk", + "( that", + "ภ·", + "[ q", + "gram mar", + "Ġt ë", + "qu ito", + "Ġspir al", + "ext ended", + "Ġf ocal", + "Ġdig ging", + "p as", + "ĠT all", + ".pro xy", + "it ures", + "TR ACT", + "ĠRe alm", + "Ġf eder", + "Ġorient ed", + "ĠAltern ative", + "Ġo we", + "Ġsour ced", + "ink er", + ".d et", + "S ep", + "ĠQ ui", + "ĠPal mer", + "(_ ,", + "s amples", + "oy er", + "ull an", + "que z", + "Ed ges", + "Ġsh out", + "ĠA chie", + "Ġha ar", + "_Con struct", + "Ġprem ature", + "Ġre vert", + "'). Ċ", + "Ġs chn", + "filter ed", + "null ptr", + "S aved", + "itect ure", + "CL A", + "Ġv l", + "st ell", + "ĉ Me", + "ĠL ip", + "n ational", + "Ġwh olly", + "Ġspr ings", + ".T imer", + "ĉs rc", + "els en", + "åħ ¶", + "Ġcommunic ating", + "ĠQu iz", + "Ġt eng", + "Ġge z", + "ĠOut side", + ".S ign", + "(c s", + "Ġdisput es", + "ĠWe iss", + "ann es", + "> No", + "ĠB ach", + ".remove All", + "re fer", + "/d ashboard", + "ĠA jax", + "Index Changed", + "ĠWe ak", + "' \"Ċ", + "Ġs ights", + "access Token", + "ĠJ oi", + "(d omain", + "ĉc v", + "Ġcontin uation", + "Ġpl um", + "ad ir", + ".set Message", + "Ġ ï¼Į", + "Ġsw allow", + "ĠL amp", + "Ġq w", + "Ġu u", + "C oin", + "ub ic", + "ĠDe als", + "r ace", + "Ġdict ator", + "Ġmem e", + "turn ed", + "ĠJul ie", + ".grid Column", + "Ġpup py", + "Ġp am", + "Ġ) {čĊ", + "Ġinv iting", + "Ġf rench", + "v im", + "Ġwr apping", + "Ġ#- }Ċ", + "([ -", + "Ear ly", + "Ġsh iny", + ".f aces", + "Ġreb ell", + "abc def", + "ä lt", + "Ġest imation", + "ph ys", + "los ures", + "_RE L", + "Ġex clusion", + "ĠSk ype", + "we ise", + "-st op", + "no thing", + "ĠE gg", + "is ors", + "Rich ard", + "Ġcounsel ing", + "Ġcomm em", + "ĠQ MessageBox", + "ĠSy nd", + "ĠFro st", + "ĠCompet ition", + "ĠAw ake", + "Ġt ed", + "ic iones", + "ĠDev Components", + "VERTISE MENT", + "ott i", + ".run ner", + "Ġuniqu ely", + ".fl ag", + "ĉ rs", + "_g eneric", + "Ġ`` `Ċ", + "ACH INE", + "Ġme in", + "( Application", + "( br", + "Ġrat ios", + ": ,", + "ĠXCT est", + "ustain able", + "- www", + "it les", + "_T EMP", + "Ġs yst", + "umeric UpDown", + "ĉassert True", + "Ġw f", + ". peek", + "ĠBul g", + "Ġterr ifying", + ".M ODE", + "ĠG W", + "á r", + "Ġf ic", + "Ġcommit ments", + "- tech", + "ĠL iquid", + "ope z", + "z heimer", + "a ña", + "-m edia", + "( animated", + "_go al", + "Ġg um", + "yst one", + ".S ET", + "ĠW end", + "set CellValue", + "Ġmsg s", + "c ash", + "AL LOC", + "/ aws", + "Ġmic rowave", + ".Point er", + "ĉ Console", + "_s orted", + "ĠFil ip", + "Pro d", + "Ġ//! <", + "ing roup", + "Ġk s", + "_T RI", + "Ġteas poon", + "ĠAT T", + "Ġrecover ing", + "ĠG LOBAL", + ".P ar", + "Ġ/> ;Ċ", + "Ġmar ble", + "ul ators", + "ĠC ycle", + "Ġher bs", + "_m etric", + ") !", + "_C LOCK", + "_ Button", + "H arry", + "è¿ Ľ", + "Ġstr ains", + "ĠApp Bar", + "ĠCh an", + "/v ideo", + "Ġb am", + ".Pro gress", + "$ f", + "lem en", + "Ġir regular", + "ĠD uncan", + "ĠM int", + "-v ideo", + "ঠ¾", + "ó wn", + "ĠEM PTY", + "Ġstack ed", + "ĠH A", + "_c ut", + "Ġwhere in", + "ĠW ays", + "(count er", + "è¯ ķ", + "Form Group", + "Ġble w", + "c ourses", + "Ġproduct os", + "ry s", + "ĠRest r", + "Ġsty ling", + "> s", + "Ġp iv", + "Ġit ertools", + "get Repository", + "ĠI k", + "_dev ices", + "lay ui", + "Ġhalf way", + "Ġfran ç", + "Ġtun ing", + "O A", + "_N ode", + "ar de", + "Ġfier ce", + "lic ted", + "# čĊ", + "Ġbreak through", + "ĠE rik", + "Ġb ride", + "Ġ. \"", + "cul us", + "ins ide", + "ĠIndian apolis", + "ĠE E", + "Ġy og", + "urre t", + ".f s", + ". grad", + "_c ards", + "_ac curacy", + "_ep i", + "qu eda", + "/ org", + "é ªĮ", + "Ġcom pte", + ")) [", + "Out side", + "G reater", + "ĠRender er", + ". actor", + "Account s", + "Id le", + "_h ours", + "ern er", + "Jo ined", + "Ġmen j", + "requ ires", + "ĠO PER", + ".remove Child", + "ĉs p", + "Ġes se", + "r ift", + "xF E", + "ĠSh akespeare", + "________ ____", + "Ġbudget s", + "Model State", + "fill able", + "- component", + "oc os", + "ĠBUT TON", + "/ io", + ", out", + "s ms", + "Th omas", + "ĠAr med", + "res ume", + "Ġrot ating", + "ĠV ault", + "Ġse us", + ". (*", + "Ġa mino", + "Ġ[] );ĊĊ", + "Ġprov oc", + "no x", + ".Get Enumerator", + "==== ===Ċ", + "æĸ Ļ", + "_sc roll", + "Ġfil med", + "ĠS oci", + "g ap", + "g ro", + "V ote", + "\" But", + "_R C", + "An imal", + " Ģ", + "ib ile", + "Ġaw aken", + "ore st", + "in ja", + "ĠI van", + "( Command", + "Ġ *****", + "Î ·", + "Ġkv inder", + "/h elpers", + "_c ases", + "t g", + "ìĦ ¸", + "Register ed", + "ĉp ass", + "_d igits", + "Ġcont our", + "Ġinf ants", + "Ġjust ification", + "ĠFort unately", + "Con tr", + "ĠonCreate View", + "_S AMPLE", + "Ġallow Null", + "Ġn ud", + "Ġfet ched", + "_e qu", + "ĠUn able", + "=\\\" \"", + "> {Ċ", + "Ġcommit tees", + "ist ema", + "+ \".", + "ÃŃ an", + "m ant", + "Ġsou theast", + "ï¼Į Ċ", + "dialog s", + "PRO JECT", + "charg er", + "- port", + "(u uid", + ". export", + "S ix", + "ĠR P", + "P rem", + "Ġconsc ience", + "Ġmargin Right", + "_d istribution", + "y aml", + "res izing", + "D ock", + "ĠLoc ations", + "G Y", + "Se ed", + "B UFFER", + "oss ip", + "ull en", + "Th ings", + "- self", + ".p oll", + "PL AYER", + "Ġå ®", + "G ROUP", + "ĠA way", + "Ġg ospel", + "xf d", + "M ary", + "ĠPort able", + "T URE", + "Ġutil is", + "Ġse it", + "Ġstr and", + "Ġtrans c", + "Ġ( ^", + "ĠAl fred", + ".m em", + ".c ircle", + "Ġ~ /", + "for cing", + "Ġr iot", + "pro x", + "TH ON", + "iz ación", + "ĠN I", + "ro st", + "Ġdis pro", + "_in stances", + "ï¼Į âĢľ", + "ograph er", + "end as", + "ĠIsa ac", + "ĠP ine", + "/d is", + "Ġcolor With", + "iter ate", + "_str ide", + "Ġpun to", + ".Event Args", + "( center", + "Ġneighb oring", + "ĠPr ison", + "ĠMess enger", + "Ġepid emic", + "da o", + "_com plex", + "Ġgr avel", + "_D IP", + "é ment", + "ĠA ri", + "_bit map", + ".qu it", + "( valid", + "Ġp end", + "Ġrespir atory", + "Ġre bound", + "Default Value", + "ãĥ Ń", + "Ġcomm its", + ".test s", + "_f r", + "it et", + ".s f", + "Ġspace craft", + "c ritical", + "Ġde pressed", + "ĠAny Object", + "Ġun b", + "Ġdisc ern", + "(m ysql", + "L atin", + "ĠB og", + "ĠWild life", + "To File", + "iox id", + "@ RestController", + "Ġ\"$ (", + "Ġ<< \"", + "Ġdefect s", + "Ġdat um", + "h in", + "Ġreal izar", + "any ahu", + "ĠS ig", + "@ Data", + "ad aptive", + "ĠC atherine", + ".c r", + "ĠCO OKIE", + "Ġp ictured", + "ĠFight er", + "Query able", + "ĠAny way", + "ĠGL FW", + "_n amespace", + "_ ft", + "Ġ] )", + "Organ ization", + "Ġconstit utes", + "Ġqu and", + "(ch unk", + "\"/ >čĊ", + "ĠL akes", + "main window", + "Car thy", + "sp in", + "(c sv", + ": red", + "-com merce", + "ภ¹", + "Ġdiscover ing", + "Ġe co", + "_f ac", + "inc eton", + "ĠGre ens", + "j wt", + "Ø µ", + "ĠBron cos", + "ĠGood s", + "(G TK", + "Ġreturn Value", + "Ġsi empre", + "Ġneut r", + "w ent", + "ĠN atal", + "Ġenthusi astic", + "á» į", + "F N", + "/d atabase", + "C atalog", + "Ġbr un", + "ĠK ash", + "_P l", + "isc rim", + ", width", + "Ġin mates", + "Ass ignment", + "ĠH aven", + "Ġplay ground", + "ex am", + "@ Controller", + "ul iar", + ".get Parent", + "Ġ\" ;ĊĊ", + ": size", + "iss ors", + "Ġf is", + "Ġal c", + "ens ation", + "ĠN ixon", + "Ġmight y", + "- str", + "_s pecial", + "_A DC", + "ĠTw ig", + "um bling", + "- address", + "Ġher oin", + "Y TE", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "F riend", + "Ġa ve", + "ĠP NG", + "ĠKurd ish", + "DataSet Changed", + "Ġbl ades", + "br al", + "St eam", + "Ġsig u", + "IRT UAL", + "ac os", + "UD P", + "(d atabase", + "he c", + "ĠString s", + "_scal ar", + "ĉd esc", + "ĠT LS", + "; \"Ċ", + "ĠCor byn", + "Simple Name", + "u ell", + "ĠEnt re", + "ell ites", + "- place", + "Ġfrank ly", + "ĠE rf", + "CE L", + "Ġpa ÃŃs", + "Ġh edge", + "Ġlat ent", + "ĠIR Q", + "ĠH erald", + "ĠP rec", + "ë³ ´", + ".T EXT", + "Sal ary", + "Ġaut umn", + "Ġtrav ail", + ".S um", + "Ġc ared", + "M or", + "Ġint uitive", + "Ġj ournals", + "_ IT", + "ĠT rou", + "ä¼ ł", + "Has ColumnName", + "Com posite", + "Ġsp ice", + "_d isk", + "_CODE S", + "ĠInt roduced", + "ion a", + "Ġnue stra", + "o ct", + "ĠĠĠĠĊĠĠĠĠĊ ĠĠĠĠĊ", + "(param eter", + "Ġstud ios", + "Ġproject Id", + "Ġbd sm", + ".Sql Client", + "im izer", + "ĠC ARD", + "+ t", + "a an", + ".s ol", + "_Ad just", + "Ġright eous", + "ĠLog ging", + ".f ilters", + "_T AB", + "ĉs ys", + "roph ic", + "other apy", + "ĠB rowse", + "key board", + "R ON", + "+ \\", + "ro pped", + "Ġext ensively", + "f k", + "Ġl ime", + "year s", + "Ex c", + "Ġs ph", + "Ġche ating", + "and ro", + "ÃŃ o", + "Ġpr ince", + "o ire", + "ĠD estination", + "ĠConvert s", + "Ġup stream", + "o led", + "Ġserv ants", + "Ġsem antic", + "Ġcr unch", + "Ġevent ual", + "run ner", + "/ error", + "Sp in", + "Ġsecret ly", + "Ġas semble", + ".P erson", + "end error", + "_ <", + "Ġp endant", + "S leep", + "ĠChem istry", + "Ġboss es", + "l k", + ")) ),Ċ", + "Block ly", + "DE VICE", + "Ġreflect ing", + "Ġam ple", + "Mill iseconds", + "ĠPresident ial", + "Ġus uarios", + "ĠN Z", + "ĠSal ary", + "ĠA manda", + "_n p", + "j ury", + "Ġkö n", + "Ġtherap ist", + "Ġhomosex ual", + "ĠDr ake", + "-w indow", + "ĠLoc ated", + ".D river", + "ĠV IDEO", + "Ġmerch ants", + "ĠC hest", + "- lock", + "/ php", + "Ġmil ano", + "_ST YLE", + "arg er", + "ide a", + "G UID", + "adv anced", + "me al", + "Options ItemSelected", + "=' %", + "ĠCh am", + ": data", + "(st at", + "Will Appear", + "Ġinform al", + "aj i", + "Ġre productive", + "ĠC AS", + "ãģ £", + "F UNC", + "ĠR uth", + ")+ (", + "CON ST", + "ĠF ans", + "Ġgroup Id", + "xffff ffff", + "Ġsam pler", + "Ġ}} \">", + ". the", + "Ġh ollow", + "W AY", + "ĠFac ulty", + "Attrib utedString", + "ĠLook s", + "ĠR ex", + "j k", + "ĠM IL", + "Ġb ard", + ".L ong", + "Ġliv est", + "Ġsk al", + "ic ism", + "MA IN", + "Ġmu cho", + "B ODY", + "Ġes e", + "ĉ use", + "F oot", + ".SQL Exception", + "Ġinherit ance", + "re ceived", + "Ġput as", + "ed is", + "als a", + "ĠError Message", + "Book ing", + "Ġtr act", + "ac z", + "ĠC ant", + "_reg ex", + "Ġide ological", + "Ġj ihad", + "h os", + "/s ys", + "col m", + "(p ool", + "Ġest án", + "ĠP ending", + "em ás", + "Ġktó ry", + "));ĊĊ Ċ", + "trans actions", + "Ġw ield", + "it ere", + "ert ure", + "_s s", + "Ġstretch ing", + "Ġprison er", + ".Read All", + "Ġbes ch", + "-- ;čĊ", + "Ġcr isp", + "_SC AN", + "Ġa e", + "Str ict", + "ĠMin neapolis", + "ĠBo eing", + "ar is", + "re k", + "_p ipe", + "Ġpri ests", + "(E IF", + "eh icles", + "ĠInter active", + "b etween", + "ĉNull Check", + "ĠBl air", + "ĠL t", + "_in line", + "eth yl", + " ¼", + "_p ackages", + "Ġbarrel s", + "_ he", + "Ġreg exp", + "_ pts", + "_H andler", + "ing ular", + "ĠN issan", + "ĠR anch", + "Ġper ch", + "Un supported", + "Sm ith", + "ĠLeg ends", + "M i", + "Ġg f", + "st eder", + "Ġacqu iring", + "Ġsim ulator", + "() ,\"", + "re ceive", + "Ġin place", + "A CTION", + "ĠWeb Driver", + "files ystem", + "< Order", + "lo pen", + "ĠHE IGHT", + ".set Border", + "į °", + "__ [\"", + "Ġcl amp", + "Seg oe", + "b ands", + "to List", + "amb a", + ">' +Ċ", + "Ġcred ible", + "am at", + "play ing", + ".setImage Resource", + "qu el", + "Ġpod r", + "ge om", + "E k", + "ĠQ atar", + "Ġg eld", + "? ',Ċ", + "Ġc yl", + "( ax", + "ĠW I", + "ur ally", + "ĠBr asil", + "Ġsen za", + "ale y", + "on en", + "Ġb ah", + "Ġmolec ule", + "R ad", + "è¿ °", + "AN CH", + "- background", + "- agent", + "Ġprol ifer", + ": boolean", + "Ġt ide", + "erial izer", + "_ ;čĊ", + "F ee", + "** )", + "erg y", + "ĠHon or", + ".Log ging", + "ir is", + "Ġunder mine", + "ĠD y", + "Ġt yr", + "Ġde que", + "Ġdam er", + "([] )Ċ", + ".layout ControlItem", + "pe ated", + "C AN", + "rag ments", + "L and", + ") ]);Ċ", + "ĠS ah", + "ĠDE CL", + "With in", + "ĠN amespace", + "an other", + "sem bling", + ".des cribe", + "Con sum", + "ĠF ear", + "g iven", + "Or ange", + "< boolean", + "Ġstead ily", + "pa Repository", + "Ġresult Set", + "_ ENTER", + "_re peat", + "Ġt ones", + "ĠPRO P", + "n al", + "part icle", + "Ġsign aling", + "Ġaccess ory", + "ĉĉĉĉĉĉ ĠĠ", + "Ġvie le", + "ĠNo ah", + "- ag", + "Ġmur ders", + "Ġa ired", + "ĠPL AY", + "ĠS ullivan", + "_C ore", + "Ġul ong", + "Ġblog ging", + "> This", + "Ġdata Index", + "Ġprint able", + "ĠE yes", + "_target s", + "(P y", + ". over", + "Ġbr u", + "am pton", + "Ġplaint iff", + "< Key", + "b ull", + "Ġ⣠¨", + "Iss ue", + ".cor nerRadius", + "C ritical", + "_p hi", + ". angle", + "Ġdynam ically", + "! \");čĊ", + "> );Ċ", + "in vest", + ".* ĊĊ", + "Ġt élé", + "Ġsuper f", + "Ġcas cade", + "DT D", + "Ġviv id", + "Ġsubsid ies", + "ĠH ass", + "Ġcoll aps", + "Ġcer amic", + "{} \".", + "ĠLeak age", + "-tr ash", + "coll apsed", + "-s ocial", + "ĠCh ad", + "Ġincl ined", + "Ġst o", + "Ġstory board", + ".p ayment", + "stack overflow", + "ĠRaid ers", + "Ġ# '", + "olic ies", + "ìľ¼ ë¡ľ", + "em ap", + "Ġk j", + "Ġqu ota", + "ĠGard ens", + "ë² Ī", + "ĠAng els", + "Ġof t", + "Ġlower case", + "Ġi Param", + "Ġche apest", + "un ta", + "_p kt", + "ic ators", + "Ġle urs", + "Ġdecre ases", + "ĉ define", + "PRE C", + "amm ers", + "ĠPre paredStatement", + "(d irection", + "Ġcre ws", + "ark ed", + "ĠMem phis", + "ĠS ell", + "G TK", + "Ġm aid", + ": disable", + "éĽ Ĩ", + "ĠP f", + "Ġal beit", + "open h", + "?> \">Ċ", + ".get Source", + "(s cale", + "D u", + "ĠP IL", + "_ref resh", + "Ġbet s", + "(c ar", + "ĠV on", + "| --------------------------------------------------------------------------Ċ", + "ĠGr at", + "M uch", + "( Dialog", + ".stop Propagation", + "Ġte k", + "Ġex its", + "'], $", + "Ġphone Number", + "uc s", + "ec imal", + "------------ --", + "in p", + ".po jo", + "Ġcor pus", + "Ġpractition ers", + ".p ic", + "\" testing", + "Ġstring By", + ".Not Null", + "Ġr ang", + ".D ynamic", + "_R ender", + "аÑĤ а", + "Wait ing", + "ĠW ik", + "Ġoverwhel med", + "% \">", + "ĠA E", + "}} >Ċ", + "u w", + "_t yp", + "Ġbuck ets", + "Ġgre eting", + "Ġla ughter", + "Ġant agon", + "uggest ion", + "- email", + "ĉt op", + "Ġer os", + "_tr i", + "Ġiss uing", + "Ġh á", + "Ġisol ate", + "Over flow", + ", E", + "Ġnut ritional", + "ĠAbb ott", + "Ġn f", + ".t ouch", + ".fetch all", + "_z ip", + "\") }Ċ", + "Ġam at", + "ĠC isco", + "Ġn Ã¥", + "PLE X", + "Ġse i", + "f oto", + ".to Json", + "å¤ ļ", + "ĠKle in", + "Ġlib c", + "Ġmin ers", + "å ¢", + "- print", + "ĠP ride", + "T odos", + "Ġmask ed", + "Ġset Data", + "Ġtele fon", + "Ġunh appy", + "ĠT ables", + "ge b", + "( debug", + "_all owed", + "- access", + "Ġlog istics", + "Ġg ems", + "ĠM ature", + "Ġr sp", + "ĠAl le", + ".get Bytes", + "\\ web", + "ynchron ized", + "Par agraph", + "Ġth rottle", + ".sql ite", + "cons ulta", + "ĠSe ah", + "C e", + "Ġsub mar", + "ER E", + "V ous", + "Ġre ddit", + "Ġsql alchemy", + "-m ile", + "oc ide", + "P our", + "}} \">Ċ", + "st ead", + "Ġ@ (", + "Ġ[ ])", + "ĠAd s", + "Ġover load", + "r idden", + "ĠDes ert", + "ĠW rap", + "ĠPortug uese", + "et z", + "ĉf irst", + "Ġmile stone", + "æĹ ł", + "Ñĥ Ñī", + "(s uccess", + "< Vector", + "co ol", + "Ġ[ ]);Ċ", + "erv als", + "Ġin vert", + "\" io", + "cur so", + "fr agment", + "Ġfeas ible", + ".set Position", + "Ġel m", + "Ġimag in", + "@ Spring", + "Ġb ats", + "pu és", + "ga lement", + "ns ic", + "gi ene", + "ell ation", + "ĠBa iley", + "Sh ar", + "ĠT ul", + "ĠH K", + "Ġfree zing", + "gl m", + "ce ans", + "-c ut", + "_c ircle", + "åij ĺ", + "n egative", + "Ġind ian", + "s alt", + "Ġt ing", + "ĉm od", + "Ġs int", + "ak in", + "um l", + "ĠText Input", + "Ġpop ped", + "T MP", + "Ġpark ed", + "×Ļ ×", + "ĠF usion", + "Ġhe ater", + "ET F", + "ro zen", + "h all", + "ĠM ik", + "lev ard", + "- heart", + "ĉ order", + "M aking", + "Ġpled ged", + "Ġdir s", + "$ post", + "ĠH err", + "stant iate", + ", \"Ċ", + ".get Color", + "ĠS AT", + "Ġtimed elta", + "ĠM ai", + "ĉm ethod", + "Ġid iot", + "ĠTr av", + "ident ified", + "ĠDiv ine", + ".get Path", + "D ash", + "Ġinf iltr", + "Ġhandle Submit", + "bro ok", + ".g eneric", + ".short cuts", + "................................ ................................", + "Ġdat ings", + "ĠM V", + " #", + "} \"ĊĊ", + "Ġimprison ment", + "ason ic", + "rou d", + "uc ion", + "æĬ ¥", + "Ġdia lect", + "Ġon Mouse", + "const expr", + ".label Control", + "Ġwe aker", + "Ġman kind", + "ĠRE CE", + "Ġd iz", + "Ġapp Bar", + "Ġqu é", + "f ra", + "_default s", + "Ġal iqu", + "_at om", + ": indexPath", + "Ġmiss es", + "Ġvis ually", + "ĠH ands", + "STR U", + "i ates", + "_ asset", + "F inder", + "mid t", + "Ġsn acks", + "(__ ('", + ". uri", + "ĠIn strument", + "ven ir", + "($ __", + ".Dot NetBar", + "Ġconfig s", + "Ġguess ed", + "ि à¤", + "Ġinitial izer", + "Ġ? \",", + "ĠVer izon", + "man ifest", + "ge ben", + ".d etails", + "G ate", + "pons ible", + "ĠEl im", + ", str", + "Ġwrit ings", + "ĠD erek", + "ĠCo ordinator", + "Ġpill ow", + "Ġnotice able", + "R s", + "Ġduplic ates", + "ern els", + "k J", + ".z z", + "oll and", + "ĠSE CTION", + "_f name", + "uff led", + "'].' \")Ċ", + "ĠD ollar", + "Ġem oji", + "Car ousel", + "- player", + "Ġadjust ing", + "Ġjug a", + "alleng es", + "g ene", + "(body Parser", + "lop edia", + "ĠBeh ind", + "Ġslee ves", + "Ġdrag ging", + "ĠChe vrolet", + "Ġb iz", + "iv ities", + "ĠFrequ ency", + ", char", + ".W HITE", + "_pre view", + ") ';Ċ", + "_ ax", + "ION S", + ".c pu", + ".input s", + "UB E", + "_fe ed", + "ĠSup plement", + "! ).", + "es us", + "ĠU DP", + "Ġmicro phone", + "Ġconf irms", + ".is NotEmpty", + "\":\" \",Ċ", + "_S CREEN", + "ĉ expected", + "+-+- +-+-", + "ĠH ait", + "fast call", + "Ġdep ict", + "v b", + "_p icture", + "ĉd escription", + "ĠW ife", + "uc i", + "Ġv icious", + "ä» ĸ", + "ue ba", + "Ġset User", + "ãģ ¡", + "Ġd iving", + "Ġoper a", + "user content", + "ar ah", + ") },", + "y un", + "vel t", + "Ġun covered", + "Ġh ips", + "Ġosc ill", + "Ġassert ing", + "ĠX i", + ".re store", + "ke a", + "Ġsp elling", + "Ġder ive", + "ab we", + "ĠD ow", + ".set Type", + "_v s", + "Ġco zy", + ".c ategories", + "O rg", + "_m gr", + "Ġd ungeon", + "collection View", + "ĠBl ank", + "ac ias", + "ä ä", + "_clean up", + "_ACT IVITY", + "Ġtri angles", + ".Menu Item", + "Ġip hone", + "ĠW on", + "] ]ĊĊ", + "ĠCompar ison", + ".D oc", + "Ġcan onical", + "ĠSud an", + "') {", + "Up Inside", + "b uiltin", + "ENC Y", + "x be", + "Ġch uck", + "Ġcontrad ict", + "Ġnuest ro", + "Ġarchitect ural", + "ĠF ib", + "Ġcomp ares", + "* k", + "C fg", + "çĦ ¡", + "nt en", + "Match es", + "ĠDOWN LOAD", + "_HAND LER", + "man agement", + "[ S", + "EN G", + "ÂĢ Â", + "f ang", + "Ġsl ipped", + "ĠL anka", + "esc aping", + "Ġtack les", + "ĠPed ro", + ".P rop", + ".' '", + ".G enerated", + ".New Guid", + "at rigesimal", + "ill on", + "Ġstat istic", + "spec ies", + "hold ing", + "Dr upal", + "Ġfundament ally", + "Ġbond age", + "Ġres olutions", + "Inline Data", + "\\ Type", + "est ion", + ".w rap", + "Ġwar riors", + "ĠLOC AL", + "Arch ive", + "Ġembr aced", + "á» §", + ".V er", + "ĠAff ordable", + "oles ale", + "ĠAp plied", + "ĠCon version", + "m ega", + "_c am", + "Ġcer emon", + "aur us", + "ĠVol k", + ".op ens", + "/ about", + "ĠSt d", + "j ournal", + "()) {čĊ", + ",\" \\", + "( Arrays", + "ĠD ense", + "ase ña", + "än ner", + "/ stat", + "user Data", + "Ġg erman", + "Ġt z", + "worth y", + "Format Exception", + "ph erd", + "Ġsm iles", + "ĠWh enever", + "( adapter", + ".bad logic", + "Ġbrief ing", + ".Grid Column", + "- char", + "dim ension", + "ĠC opper", + "Ġnin th", + "Ġ' {{", + "Ġr av", + "_T able", + "Ġderiv atives", + "ĠR aise", + "ĠF ut", + "arm or", + "-p adding", + "Ġre min", + "ĉ style", + "ĠMembers hip", + "Ġspread s", + "Ġgall eries", + "ĠClar ke", + "Ġcon ception", + "min ute", + "Ġab usive", + "_ad j", + "Ġterr ific", + "Ġover t", + "our cing", + "Ġentr ada", + "level s", + "Ġcrit ique", + "Ġrespect s", + "ĠM MA", + "i ene", + "Ġenc aps", + "ĠRay mond", + "Div ider", + "iv able", + "b az", + "Ġ@ _;Ċ", + "ĠCl aire", + "Ġur ging", + "CE E", + "Ġtransform er", + "disc ord", + "ĠJ ourney", + "t os", + "Ġcompet itions", + "ĠO BJ", + "ĠB is", + "Ġrelax ation", + "id y", + "_IN STANCE", + "ĠP ref", + "d ados", + "ici encies", + "ĠMedia Query", + "ĠC ube", + "ĠStr ange", + "g pu", + "(d ays", + "_Init Struct", + "Ġfinger print", + "em at", + "ĠGe cko", + "Ġr ails", + "ĠL um", + "str action", + "ig ung", + "(m ovie", + "_d ictionary", + "_int errupt", + "ĠQ C", + "ik ed", + "append Child", + "rec ipient", + "r é", + "V e", + "Ġtow el", + ".last IndexOf", + "Ġplace bo", + "ĠW ie", + ".es p", + "( Debug", + "oper ative", + "Ġdece ased", + "& id", + "ĉm utex", + "el ic", + "Ġb apt", + "ĉ čĊčĊ", + "Ġfar ther", + "H alf", + ".dis able", + ".menu Strip", + "le ccion", + "Ġresult Code", + "Ġc ans", + "-e lection", + "f emale", + "_F IX", + "aus ible", + "ĠP OWER", + "Ġrecon struction", + "Ġsc ans", + ".Xtra Bars", + "âĢĺ s", + "Rem oved", + "Ġparagraph s", + "_m argin", + "Ġl ymph", + "Ġb os", + "ling ton", + "ĠBapt ist", + "Ġadvertis ements", + "ĠMan age", + "/ yyyy", + "IO US", + "ENC ES", + "ĠF iction", + "ĉm enu", + "ĠFile OutputStream", + "ov an", + "ĠF eng", + "Ġsk ipping", + "get Class", + "ann i", + "Ġreb ounds", + "Ġpublic ity", + "Ġing res", + "use ment", + "Ġthought ful", + ".Ch art", + "Ġhat te", + "pass port", + "Ġhook ed", + "ĠL ens", + "Ġflag ship", + "Ġst ip", + "ĠG EN", + "Ġcl ues", + "ip v", + "ĠR ise", + "ĠG ew", + "tab lename", + "Ġfore most", + "_ validate", + "_an alysis", + "oll a", + "Ġqual ifications", + "Ġdistrib utions", + "ĠFl ower", + "Ġt ense", + "Ġthank ful", + "Ġcl utch", + "Ġun ified", + "ro ads", + "Ġsit i", + "Ġst all", + "_P RIORITY", + "c stdlib", + "_USER NAME", + ".by tes", + "? page", + "ermal ink", + "ĠVe get", + "/v nd", + "- author", + ".N ONE", + "ĠCon current", + "ĠC ry", + "Ġstart ers", + "ĠInter action", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠLE VEL", + "E ll", + "Ġcom boBox", + "ĠTh eresa", + "te k", + "_H andle", + "Ġab y", + ".g dx", + ", end", + "(L ocal", + "O l", + "kn ife", + "ar ial", + "ĠH off", + "Ġprostituer ade", + "Do ctor", + "Inst ances", + ".Set Value", + "ĉf rom", + "Ġlux urious", + "Ind ent", + "Alloc ator", + "_D RAW", + "(\", \",", + "ĠFr ances", + "Ġgroup Box", + "(s chema", + "Print f", + "OR IES", + "- gradient", + "Ġre put", + "ar in", + "_D ONE", + "in cre", + "ig nty", + "Ġex ert", + "Ġ- .", + "/ App", + "-th rough", + "Ġdecl ining", + "Ġdess ert", + "Ġinc umb", + "Ġdesign ation", + ".P ORT", + ", strong", + "Ġsand box", + "Ġw ines", + "ĠP av", + "$ str", + "ask ell", + "Ġh ö", + "ĠP Y", + "Get Instance", + "Text Input", + "game Object", + "/ events", + "created At", + "Ġlocal Var", + "ĠWH ITE", + "per ed", + "ile ge", + "eff icient", + ", color", + "c ate", + "ĠC afe", + "Ġsimilar ities", + "Ġp umps", + "ĠHung ary", + ".User name", + "Ġsk ate", + "Ġtouchdown s", + "Ġacceler ate", + "ĠH elen", + "OM EM", + "ĠK un", + "_v ol", + "Ġfind All", + "ĠMens chen", + "a head", + "); \"", + "kom men", + "Ġpossess ed", + ".arg max", + ".trans ition", + "AR P", + "OLUM E", + "(s cript", + "ĠÐ ĺ", + "ĠF inding", + "on ces", + "I o", + "B old", + "Ġrenew al", + "_D IALOG", + "Ġdis reg", + "INT ERN", + "Ġt oute", + "Ġelect r", + "ĠG ross", + "ĉ true", + ".F ields", + "ĠW IDTH", + "ĠD ent", + "Ġà ģ", + "NS Notification", + "Ġa os", + "Ġme lee", + ". Validation", + "ĠDE C", + "-depend ent", + "Ġsu ic", + "T raits", + "$ message", + "ĠD ear", + "ĉ FILE", + "l anguages", + ".P rot", + ".add r", + "-g eneration", + "IC ON", + "Ġtrans plant", + "-d escription", + "Ġch asing", + "Ġche es", + "Ġ} */Ċ", + "Tr ad", + "qu eries", + "/widget s", + "sub package", + "Ġes pec", + "Ġcr acked", + "Ġcompet itor", + "P urchase", + "- team", + "olec ular", + "or Thunk", + "& P", + "Ġrel ent", + "/ #{", + "Ġproduct Id", + "Ġè ¾", + "ĠL av", + "ĠAl ter", + ".M ode", + "AD IO", + "gr p", + "æ ·»åĬł", + "Qu it", + "Ġdepth s", + "-c ategory", + "ĠD ATABASE", + "S PELL", + "ĠFal con", + "ĠQString List", + "Ġ'' .", + "ĠIn stitution", + "d amage", + "az or", + "bel ongsTo", + "ver ages", + "ĠN ONE", + "ipp ets", + ", \\Ċ", + "Ġfoot print", + "_ archive", + "n ak", + ".get Field", + "ĠRef lection", + "Ġ' ]", + "ĠH BO", + "_dis count", + "Ġin cest", + "ĠD odge", + "ĠW ade", + ".N O", + "\" encoding", + "ĠBlock chain", + "Ġlaws uits", + "ĠM aint", + "ch ten", + "Ġét ait", + "Ġktó re", + "_ ctl", + "(t imer", + "B attle", + "iz o", + "ay ed", + "I OR", + "ĠGlas gow", + "Ġsyn th", + "_log s", + ".p ose", + "_Adjust orThunk", + "(( &", + "Ġuns ure", + "yst ate", + "íķĺ ëĬĶ", + "O ULD", + ". ng", + "Ġdefault dict", + "work space", + "Ġselect ive", + "Picker Controller", + "YNAM IC", + ".method s", + "Ġpath ways", + "ĠF ew", + "K G", + "CRY PT", + "follow ing", + "ĠD LC", + "ĠS ara", + "Ġpres et", + "estruct or", + "ĠK urt", + "Ġair plane", + "Ġo mp", + "ĠParent s", + "ĠMart inez", + ".com plete", + "Ġbroad ly", + "Ġsc are", + "ĠM é", + "Ġelim ination", + "Ġpou red", + "/ sw", + "Ġcom un", + "Ġm asc", + "ĠOrgan ic", + "ĠString Utils", + "il ateral", + "Ġreluct ant", + "- age", + "Ġn z", + ".\" \\", + "Ġpast or", + "ale z", + "Ġe fect", + "pro v", + "/ init", + "Ġp enn", + "und s", + "Ġs size", + "ĠPro j", + "bas ename", + "Ġsh ells", + "ĠNe ck", + "ĠEn forcement", + "vid ed", + "st own", + "S phere", + "$ r", + "uss en", + "af il", + "ĠTele gram", + "Ġanaly tical", + "нÑĭ е", + "us ually", + "x n", + "Ġhistor ian", + "ĠGreg ory", + "ol ph", + "ĠUn a", + "Ġcon tributes", + "% -", + "anti ago", + "ÑĢ ед", + ".reg ion", + "Ġab rupt", + "ĠUnsupported OperationException", + "ĠT ASK", + "_f inish", + "Ġnot orious", + "ĠV s", + "ĠM Q", + "Ġsun set", + "Ġun acceptable", + "ar cer", + "Ġill umin", + "ĠOr b", + "Ġb h", + "E ste", + "_dis patch", + "Ġr ipped", + "Ġtou jours", + "ĠPar cel", + "_ ll", + ".user Name", + ".class es", + "S OURCE", + "( Number", + "ел Ñı", + "Ġhead phones", + "(s ide", + "const itution", + "ann ah", + "čĊ ĠĠĠĠĠĠĠĠčĊ", + "Ġcl iff", + "- ref", + "Ġmo strar", + "ĠPow ell", + "+ y", + "ĠB G", + "_f ragment", + ".P ort", + "Ġreal izing", + "param ref", + "Ġh ometown", + "@ Table", + "+\" --}}Ċ", + "F rench", + "Entity Manager", + "ĠPl ain", + "//////////////////////////////////////////////////////////////// ////", + " ³", + "( RE", + "c apt", + "Ġorgan isms", + "Ġj ets", + "ol ocation", + "ĠApp RoutingModule", + "Ġgl orious", + "æľ į", + "Ġdisc arded", + "ĉĉĉĉ ĠĠĠĠĠ", + "ĠArn old", + "l ug", + "Ġpar l", + "Ġhorm ones", + "Ġm ah", + "ĠSon ic", + "Ġorgan izers", + "_PL ATFORM", + ".in v", + "Ġch ord", + "vent ional", + "ĉ of", + "Ep isode", + ". Enum", + "unk t", + "ĠD h", + "ĠJ ared", + "ĠN ak", + "Ġint ends", + "End ian", + "Ġa ustralia", + "_c v", + "(res olve", + "Ġclin ics", + "lik ed", + "ASH INGTON", + "in ha", + "' *", + "ĠN P", + "_b eh", + "Ġh f", + "Ġw ür", + "c ategoria", + "$ form", + "Ġsub way", + "Ġis Active", + "pop ular", + "C our", + "Ġco oldown", + "Ġa insi", + "ĠGL uint", + "ere al", + "Ġarray Of", + "Ġh atch", + "======== ==", + "ress es", + "_P P", + ". ^", + "_dec ay", + "ĠB less", + "met rics", + "ĠCOPY ING", + "ĠDump ster", + "ĠJos é", + "ĠDesign s", + "<", + "Ġ\" }Ċ", + "time zone", + "Ġe er", + "max cdn", + "ĠE SC", + "ig aret", + "_conn ected", + "_re verse", + "Ġquestion able", + "ĠUS C", + "Ġtut ti", + "Ġdrop out", + "ĠActiv ities", + "ĠW inds", + "')) );Ċ", + "Ġcon gest", + "ÄŁ ı", + "Ġprolong ed", + "è¿ Ļ", + "ĠCross AxisAlignment", + "LE EP", + "ĠVAL ID", + "ĠG az", + "Ġdepend ence", + "ĠP rix", + ".Compiler Services", + "j ump", + "Ġstr at", + "c irc", + "ĠC USTOM", + "x aa", + "Ġb mp", + "Ġb ureau", + "Ġw aren", + "N X", + "( Window", + "ĠChrist ie", + "_F E", + "Ġt n", + "ĠOm ega", + "communic ations", + "Home Page", + "com pletion", + "Ġsupply ing", + "YP ES", + "á vel", + "åĪ ¶", + "(c lick", + "\\ Contracts", + "/ questions", + "Ġe z", + "AM S", + ".m esh", + "Ġ' \\Ċ", + "Rob ot", + "Json Object", + "ĠD F", + "ĠProcess or", + "_sh ould", + ".prot obuf", + "- users", + "Ġemb ry", + "F ONT", + "Ġstart ups", + "ĠData Source", + ") #", + "uro s", + "_C olor", + "Ġstand alone", + "} [", + "j d", + "Ġforg ive", + "Ġng x", + "ĠGener ally", + "Ġconfig urable", + "/ order", + "Ġv as", + "') \";Ċ", + "ĠR R", + "ĠT roy", + "Ġcomprom ised", + "ĠSw an", + "int endent", + "Cent ral", + "_ keeper", + "Ġar quivo", + "ĠRead Only", + "_cur ve", + "k v", + "ent in", + "è ±", + "ĠE y", + ".im read", + "ĠP am", + "if fe", + "at ivity", + "xb c", + "Ġgr im", + "-f illed", + "names e", + "'] :", + "Ġa ur", + "ĠGib son", + ".Mouse Event", + "Ġl ado", + "avad oc", + "Ġfam il", + "ĠM oder", + "f ps", + "ãĢĢ ãĢĢ", + "- example", + "ĠAl zheimer", + "ĠU tf", + "_arg uments", + "Con clusion", + "text Content", + "rem aining", + "Ġinterrupt s", + "ĠBack up", + "ĠM ong", + "Ġrecept ors", + "h istor", + ".cor outines", + "Ġsh outed", + "Al arm", + "Ġcomb ust", + "Ġg rote", + "ult ural", + "( ids", + "---------------------------------------------------------------- ----------------", + "ipl inary", + "O pts", + "ĠY ale", + "local Storage", + "Ġequ ival", + "ĠF leet", + "\\ b", + "* pi", + "ĠQ Label", + "æ ¡", + "Ġv x", + "ĠA CL", + "Ġsu cesso", + "Ġper c", + "ĠNot re", + "Ġan arch", + "R ing", + "sp b", + "Ġstr pos", + "st ores", + "ĠMap le", + "(Main Activity", + "(\" \"))", + "Ġview Holder", + "Qu ad", + "Ġig ual", + "ors che", + ".m argin", + "Ġind ie", + "Ġfr anc", + "ĠForm Builder", + "ĠPart icip", + ".fl ash", + "Ġstorm s", + "U lt", + "Ġf en", + "[ new", + "E ver", + "=\" Ċ", + "Ġlocal ized", + "_f ollow", + "Ġn ave", + "Ġdomin ance", + "(t ile", + "J ournal", + "ĠV C", + "Ġpenet ration", + "ï¼ ķ", + "Ġcomp artment", + "Ġb ids", + "Form atted", + "****** /ĊĊ", + "(c ity", + "âĢĶ it", + "[ C", + "Ġuse Callback", + "a ub", + ") ?.", + "ĠV AR", + "ĠSe bastian", + "ĠM oss", + "Ġabund ant", + "G reg", + "ÑĤ а", + "_c i", + "Ġbib li", + "CR M", + "ĠAt tempt", + "ism e", + "d ash", + "ãĢ İ", + "_m u", + ".Formatting Enabled", + "Ind eed", + "-d irect", + "Ġsuck ing", + "Ġp ne", + "ocab ulary", + "ĠPack ers", + ".N avigation", + "Ġp ied", + "cri bing", + "ĠSt uart", + ".To Double", + "ĠSecond ary", + "S aving", + "ĠD ut", + "ĠM add", + "M agic", + ", H", + ".document Element", + "ĠB ST", + "Ġdiff ers", + "Ġmore over", + "_ nd", + "SE ARCH", + "п ÑĢав", + "æ ´", + "to Match", + "Ġdecre asing", + "-m ember", + "amp us", + "( boost", + "D aily", + "Data GridView", + "ĠHttp Context", + "Ġh ipp", + "_work ers", + "-l anguage", + "é ĵ", + "Ġconsist ed", + "ath ing", + "ĠMer cury", + "$ content", + "Ġpract iced", + "ĠMod ules", + "_D AY", + "Ġweakness es", + "ĠL odge", + "Ġn ar", + "ĠM ate", + "Ġj p", + "ĠHttp Headers", + "Ġsm o", + "ĠT OKEN", + "] )(", + "Ġaqu i", + "sw agen", + "Ġs rv", + "ĉ ans", + "A round", + "ĠMan uel", + "Ġfiction al", + "ĠIM G", + "Ġ. '", + "ĠB erry", + "Ġwall paper", + "sex ual", + "ier o", + "Ġ çļĦ", + "ìĨ Į", + "Backing Field", + "ĠAd rian", + "BASE PATH", + "Ġrepe ats", + "Ġbl ues", + "Ġunp redict", + "_c oll", + "st acle", + "ĠT umblr", + "ĠEl f", + "Ġass urance", + "Ġc ensus", + "ĠIM PORT", + "END ER", + "an os", + "Ġ= (", + "ĠEll is", + "\" ĊĊĊĊ", + ".w in", + "ĠA bove", + "al on", + "_t ick", + "Ġrepresent ations", + "Ġæ ķ", + "w id", + "ĠAr ms", + "List a", + "_f ailure", + "_c m", + ".Flat Appearance", + "Ġthr one", + "P atch", + "ĠV oy", + "eng l", + "Ġnegot iating", + "> `", + "Ġshoot s", + "ĠF PS", + ".Y ear", + "ĠK iss", + "enc ión", + "reet ing", + "From File", + "Ġresign ation", + "Ø ·", + "Ġtw ins", + "Æ°á» £", + "Ġge bru", + ".get Content", + ".T ree", + "ĠEmploy ees", + "ĠF IFA", + "Ġcert ainty", + "(C l", + "Ġtot als", + "edit able", + "ॠĢ", + ".Report ing", + "M as", + "qu iet", + ".r ules", + "ĠV O", + "con exion", + ", K", + "Ġalloc ator", + "ĠPow der", + "\\ Repository", + "Be at", + "_t ipo", + "Ġ[' ',", + "_IN TR", + "Ġ<< <", + "< hr", + "\") ==", + "ugg age", + "ĠC raw", + "Ġé galement", + "Ġg inger", + "Ġprim era", + "Ġprod uto", + "lt k", + ".User Name", + "Ġstr error", + "m ith", + "_n b", + "Ġdis comfort", + "']; ?> \");čĊ", + "drop IfExists", + "ĠB eg", + "_H AL", + "Ġcross AxisAlignment", + "ĠE vidence", + "Ġpec uliar", + "Ġinstit ute", + "ve is", + "Ġf ft", + "à ģ", + "Ġzo ekt", + "an aly", + "ĠHom eland", + "Ġpen etr", + "udden ly", + "ĉ element", + "ĠB ren", + "ĠTr udeau", + "ĠCub an", + "j am", + "us lim", + "_e v", + "Ġst ems", + "} %", + "Ŀ å§ĭ", + "Ġbrand ing", + "Ġcorrespond ence", + ".j query", + "¢ åįķ", + "ĠRead s", + "(Http StatusCode", + "ass in", + "(s lot", + "ĠGrad uate", + "/// <", + "Ġinform ations", + "EN ABLE", + "Ġp uis", + "Ġfind er", + "ĠBr is", + "Ġnett steder", + "_m id", + "Ġo gs", + "ĠSter ling", + "Ġar rog", + "str ftime", + "| ĊĊ", + "Ġvo x", + "ĠReg ardless", + "Ġes o", + "ĠCom fort", + ".Boolean Field", + "Ġu h", + "AC Y", + "Ġsque ez", + "ĠV ic", + "cont ro", + ". lo", + "Ġ ire", + "ĠCom edy", + "ë ¶", + "Ġorigin ated", + "Ġsh ipment", + "| max", + "_g uid", + "lev ation", + "на Ñı", + "( undefined", + "ĠD DR", + "Ġshoot ings", + "ĠLat ino", + "END OR", + "Ġaver aging", + "Ġgre eted", + "Ġthe aters", + "о е", + "Ġd B", + "Ġg st", + "Ġdef inite", + ". Storage", + ".h er", + "Ġa fore", + "ĠRe ality", + "ĠGod s", + "vers ed", + "Ġhands ome", + "Ġex cluding", + "( ad", + "Qu otes", + "ĠS cheme", + "? q", + "ĠT amil", + "T icks", + "Ġp est", + "' n", + "Ġporn ography", + "_mod al", + "Ġ ----------", + "Ġdis posable", + "F REE", + "Ġsh ark", + "C HE", + "Ġdep icted", + "Ġdemonstr ations", + "ĠK illed", + "ĠR ULE", + "Ġobs essed", + "Ġsimpl ified", + "Post al", + "Ġconcept ual", + "Ġp st", + "L as", + "_PRO JECT", + "ucceed ed", + "ol u", + "ÄŁ i", + "Ġpersonal ities", + "Ġres hape", + "Ġenc losed", + "ĉp tr", + "Ġtutor ials", + "Ġexpl oded", + "_DIRECT ORY", + "åĨħ 容", + "Ġcan on", + "Ġrecogn ise", + "P AD", + "ĠAppro x", + "ĠRest ore", + "ĠImport ant", + "Ġheav ier", + ".Se quential", + "Ear th", + "ĠMil k", + ".set Request", + ".t em", + "Ġre construct", + "Ġskept ical", + "_Pr ivate", + "BU F", + "qu a", + ": a", + "Ġse k", + "Ġd well", + "oss a", + "Ġreward ed", + "и й", + "(top ic", + "_part ition", + "Ġ__ ________________", + "Key words", + "ĠFr anco", + "L ite", + "Ġn aken", + "Ġз а", + "O BJECT", + "Ġcraft s", + "ĠSw ap", + ".X na", + ".Con nect", + "Ġbalcon y", + "(re al", + "ĠBarn es", + "b ir", + "ĠTw enty", + "ay an", + "at ars", + "ĠProp el", + "ĠIh nen", + "Up grade", + "Ġcur b", + "- second", + "Ġn eph", + ".p res", + "ìŀ ħ", + ".se q", + "Ġp added", + "\" ?", + "j l", + "ãĥ ¬", + "') a", + "Co ordinates", + "Ġen acted", + "ENT S", + "Ġl ac", + ".f inal", + "ĠPhp Storm", + "c alled", + "Ġin quiries", + ".m iddleware", + "ĠD owntown", + "/ ';Ċ", + "Ġkil omet", + "ac cel", + "Ġqu ien", + "w string", + "set Data", + "Ġman era", + "Ġmod ular", + "rim p", + "Ġtar iffs", + "âĢĻ il", + "_TH ROW", + "/c olor", + "ĠHT MLElement", + "Ġcar ro", + "Ġpr ere", + "Ġplot ting", + "ĠPos itive", + "ĠMach ines", + "OT ES", + "á» Ľ", + "ple asant", + "Ġal te", + "Ġa inda", + "th ese", + "Ġc ors", + "ip ay", + "ĠAdvis ory", + "ĠRub io", + "j q", + "Ġl imestone", + "Ġdet ached", + "设 ç½®", + "ten ant", + "ĠDep th", + "al ore", + "ĠÑģÑĤÑĢ ок", + "ĠF ORE", + "ĠL ay", + "p resentation", + ") ');Ċ", + ".sub plots", + "Ï ĥ", + "N OW", + "G ar", + "hand les", + "ab ra", + "put ies", + "ĠElect rical", + "M iddle", + "rop ic", + "ĠJ D", + "ĠD yn", + "ĠB ristol", + "ĠMc Carthy", + "Ġstri ker", + "Ġenumer able", + "ĠEv an", + ".default s", + "qu ences", + ") ||", + "ĉt oken", + "â Ĺı", + "-d ropdown", + "ST ORE", + "ĠGraph ic", + "( pp", + "Ex pl", + "Ġup wards", + "ĠD istributed", + "ĠW EB", + "J er", + "is NaN", + "çĶŁ æĪIJ", + "> R", + "üss en", + "ef s", + "Ġun cover", + "Ġl ud", + ".cal culate", + "Ġint ptr", + "Ġmidfield er", + ". Headers", + "Ġm f", + "ere f", + ".M etro", + "ĠSpe aking", + ": b", + "Ġcryptoc urrencies", + "Ġdem ons", + "ĉ EXPECT", + "Ġw icked", + "y outube", + ": Int", + "ĠHind i", + "ĠC AT", + "ĠØ ¹", + "r ar", + "om ore", + "/ per", + "/lic ense", + "Ġre im", + "Ġawait ing", + "Ġle thal", + "ĠE F", + "round ed", + "ĠPl atinum", + "ĠвÑģ е", + ".co ords", + ".De vice", + "/ item", + "ĠW enn", + "compile Components", + "ĠK inder", + ".remove Item", + "Ġand a", + "bn b", + "Ġpr a", + "( transaction", + "Ġembarrass ing", + "ĉ BOOL", + ".content View", + "Ġevent data", + "at ore", + "Ġprovided In", + "ir ma", + "Ġz ona", + "_H W", + "æ Ļ", + "Ġst ove", + "Ġcounter part", + "_Pro duct", + "_MAN AGER", + "Ġinfr ing", + "ĠE RA", + "_p arty", + "Ñ ij", + "Ġin ici", + "_ Request", + "Ġmir acle", + "Ġcancel Button", + "S py", + "at ó", + "Ġpol ish", + "ĠNic ole", + ".display Name", + "\\Request s", + "Ġuse History", + "Router Module", + "Ġst ared", + "ID ER", + "Ñĥнк ÑĨи", + "Ġnot a", + "$ arr", + "pec ified", + "Ġto pp", + "_DR IVER", + "/ ng", + "å ł", + "_t m", + "% timeout", + "< s", + "Ġ( *)", + "ĠHttp Request", + "_TR ACK", + "(n ote", + "ĠExp lore", + "_s erv", + "Ġç »", + "B inder", + "+ \",", + ". att", + "ĠEth i", + "Ġc ódigo", + "=' \\", + ".l ines", + "( Of", + "å° Ĩ", + "miss ible", + "Ġv é", + "Ġac oustic", + "Ġcraft ing", + "n it", + ".b a", + "ĠLuc y", + "Ġi Pod", + "Ġpup ils", + "-m ax", + "_w r", + "(c p", + "ĠRE PORT", + "Ġd ns", + "ĠRe ferences", + "Ġundert aken", + "Ġkø benhavn", + "Ġch ai", + "ĠC roat", + "_ Log", + "rown ed", + "_m ed", + "ĉ date", + "# __", + "Ġcost umes", + "ĠRe quires", + "aff le", + "ç Ĭ¶æĢģ", + "-S emit", + "ela ide", + "еÑĤ од", + "Ġp estic", + "Ġd ra", + "DOC UMENT", + "Ġ... čĊ", + "}` }Ċ", + "ĠA uction", + "ĠD ock", + "xxxx xxxx", + "(get String", + "ħ į", + "Ġborder Width", + "ĠMach inery", + "Ġpredict able", + ".S H", + "Ġam plitude", + ".for Root", + "IN avigation", + "Table Model", + "at trib", + "Ġmaneu ver", + "Ġexc av", + "B ERS", + "Ġd apat", + "Ġinstall ations", + ".A sync", + "Ġr ays", + "= âĢĿ", + "; ččĊ", + ".c rypto", + "_db g", + "ĠEnum erable", + "Of Size", + "_epoch s", + "m w", + "M ENU", + "out line", + "ĠP apers", + "============ Ċ", + "Ġuniform s", + "ĠG ig", + "- package", + "ĠJen kins", + "ĠHome Page", + ".is Selected", + "Ġmechan ic", + "M K", + "ĠS ounds", + "//---------------------------------------------------------------------------- -Ċ", + "Ġresearch ing", + "Ġinf os", + "ograph ics", + "ers et", + "([' /", + "ĠTim ber", + ". agent", + ".to JSON", + "_command s", + "par ing", + "_ad just", + ".n ome", + "(g lm", + "Status Bar", + "file path", + "? âĢĻ", + "Ġdetect ive", + "Ġunser er", + "ĠTib et", + "EN DED", + "(se ed", + "Ġsne ak", + "Ġam or", + "=\" //", + "ĠPan thers", + "all ax", + "ĠL IVE", + "ĉD WORD", + "]= -", + "Ġtorn ado", + "/ min", + "Ġlung s", + "-c urrent", + "ĠBook ing", + "åĪĹ è¡¨", + "Ġenjoy ment", + "ठ°", + "J A", + "typ ed", + ".B tn", + "f at", + "ug al", + "ĠSh ares", + "Ġdis gr", + "ĠB AR", + "ĠFO X", + "Op code", + "ĠS z", + "key down", + "iction aries", + "Ġdetail ing", + "} ))Ċ", + "Ġp ok", + "Ġdemonstr ating", + "Ġnot ation", + "l ayers", + "@ if", + "ĠN PR", + ".strict Equal", + "ĠRec ipes", + ".T ensor", + "Ġliqu or", + "Ġdeb ts", + ".ends With", + "W heel", + ".P os", + "CS V", + "$ arity", + "Ġun stable", + "( loss", + "ENS OR", + "Ġele ven", + "ĠL opez", + "ĠHop kins", + "con om", + "ĠS eth", + "Ġpo ems", + "Qu ant", + "Ġg sl", + "Ġsy rup", + "Ġs ibling", + "Ġc ass", + "-v ous", + "ö t", + "_P ATTERN", + "_SE CTION", + "est imated", + "up grade", + ".m ongodb", + "ĠBo at", + "_C TX", + "Ġfetch ing", + "ust in", + "pi el", + "M arg", + "Ref lection", + "Ġd uct", + "ĠMunicip al", + "Ġb x", + ".Get Current", + "ml ink", + "ĠAccount ing", + "ĠGene va", + "_P os", + "Ġpass er", + "Ġhear ings", + "com pan", + "Ġfrag ile", + "Initial izer", + "walk er", + ".M aterial", + "ĠHun ting", + "trys ide", + "Ġk at", + "Ġcl erk", + "á Ł", + "do ing", + "ĉg roup", + "Ġsan ction", + ".l b", + "ĠL azy", + "ĠCon straint", + "P agination", + "Ġpou vez", + "ĠInd icates", + "M ER", + "Ġcour s", + "Ġyear ly", + "Ġgros se", + "abb rev", + "ĠD ON", + "Ġproceed ed", + "ent lich", + "Ġproperty Name", + "ĠTe aching", + "st adt", + "Ġc utoff", + "orn ers", + "Ġa frica", + "Ġrend ers", + "ĠYan kees", + "ĠTool bar", + "sp aces", + ".fill Style", + "Ġseg undo", + "_str len", + ".F irebase", + "å¤ Ħ", + "Ġmention ing", + "\\ (", + "ĠVal ve", + "Set ter", + "Ġsp ans", + "ĠAl cohol", + "ĠLet ters", + "\\x e", + "ĠT K", + "_B LE", + ".get Result", + "< Player", + "ĠP att", + "Ġeas ing", + "Ġtur key", + "ĠF en", + "') \"", + "Ġconf ined", + "Ġin clus", + "Sup erview", + "(with Identifier", + "enc ial", + "Ġstuff ed", + "Th eta", + "Ġeconom ists", + "} ));ĊĊ", + "co okies", + "ĠRo ose", + "ĠChe ese", + "Ġfich ier", + "Ġen forced", + "AB B", + "no ÅĽci", + "_AL LOW", + "Ġrecru ited", + "Ġexpend iture", + "-n ight", + "Ġassert NotNull", + "_ex ecute", + "ĠØ ¯", + "IN DEX", + "_F MT", + "Ġresc ued", + "ĠMonth ly", + "ĠCons ervation", + "ĠG eb", + "Ob ama", + "Ep och", + "ic ies", + "ĠOr t", + "Ġso it", + "( icon", + "F riends", + "m ol", + "Ġground ed", + "ĠC ause", + "ad ena", + "WE EN", + "ĠL un", + "IT IVE", + ". loop", + "_un til", + "Ġcor r", + ".ed ges", + "Ġhyp oth", + "ched uling", + "trans lator", + "ĠÐ ľ", + "R om", + "ãĢij ĊĊ", + "ĠX amarin", + "Ġviol ating", + ". anchor", + "--- ĊĊ", + "Ġtr ader", + "AD VERTISEMENT", + "Ġuns ere", + "ĠD AO", + "Ġbl ond", + "ĠP AT", + ".g lob", + "Ġè¾ ĵ", + "Ġsplit ting", + "Ġun subscribe", + "Ġatmos pheric", + "ĠTr im", + "Ġcit ation", + "Ġin ference", + "ĠF t", + "ĠDar win", + "find One", + "ĠG el", + "( Convert", + "Ġaccess or", + "; text", + "(s orted", + "Ġjud ged", + "); \\", + ": p", + "Ġme ine", + "ĠS lim", + ".Command s", + "Ġper ceive", + "coh olic", + "< Data", + ".entry Set", + "Ġassert False", + "ĠPat rol", + "ense m", + "ÅĤ Äħ", + "¨ ¡", + "W IDTH", + "ĠRes cue", + "ĠU IF", + "_THRESH OLD", + "ĠMich el", + "ATER IAL", + "opens ource", + "ĠD iana", + "Ġinv ites", + "_B ODY", + "Ġreserv oir", + "Ġro i", + "c ust", + "(t c", + "ï¼ģ \");Ċ", + "Ġfest ivals", + "Ġperform ers", + "Ġclim bed", + "Ġj ungle", + "String Length", + "Ġunlaw ful", + "ier re", + "vertis ement", + "Ġst akes", + "Ġh ats", + "Mod ify", + "ĠLET TER", + ".H ide", + "Ġstat utory", + "_ white", + "ĠPer l", + "uten berg", + "em ple", + ".W orld", + "Ġoverlook ed", + "Ġcon cludes", + "/* ================================================================", + "-w ise", + "ĉ stream", + "pop ulation", + "Ġevent o", + "Ġillustr ations", + "ft s", + "Ġaut of", + "ĠPro cedure", + "Ġdes erved", + "-t imes", + "Ġg ol", + "N SError", + "cre st", + "ĠPak istani", + "any ch", + "get Current", + "Ġl ar", + "nt l", + "ĠRe becca", + "Ġm ateria", + "Ġfind By", + "/ ad", + "Callback s", + "ĠAl s", + "ĠKat ie", + "ĠObservable Collection", + "ĠDocument ation", + "Typ ed", + "ĠCulture Info", + "ĠTim othy", + "Ġlater al", + "\" type", + "Ġun authorized", + "Ġteach ings", + "Ġdebug ger", + "[ value", + "Ġal ors", + "Ġu z", + "Ġsc atter", + "Ġdown ward", + "Ġmig li", + "status Code", + "Ġ( ))", + "ĠM W", + "Ġм ож", + "RO SS", + ".b uf", + "Ġfair y", + "ĠInf rastructure", + "=> \"", + "t lement", + "$ (\"", + "From String", + "ĠB ild", + "Ġconvent ions", + "_n ative", + "ĠIns pector", + "ĠP ist", + "ub ar", + "Ġreg s", + "ĠP ilot", + "Th us", + ">' +", + "Ġc ela", + ".new s", + "( Product", + "L iving", + "R ussia", + "Ġfac et", + "et ical", + "Ġ[' $", + "/ [", + "ĠD ire", + "Ġg ases", + "ĠIN FORMATION", + "ĠE at", + "ĠFor ums", + "ĠChar acters", + "_m et", + "Ġìĭ ľ", + "Ġk ings", + "ach ie", + "ĠL ambda", + "Ġtim ers", + "ĠLight ing", + "ĠCase y", + "add ir", + "and ex", + ". answer", + "ĠH ip", + "ĠPr incip", + "Start Date", + "Ġ ãĢĮ", + "t res", + "Ġ& #", + ".Max Value", + "ĠPro blems", + "Ġlat ex", + "Of Class", + "ĠLyn n", + "// '", + "Ġvoy age", + "Ġshut tle", + "ĠRoll er", + "ĠRuntime Error", + "uy a", + "D ic", + "ĉb uilder", + "Ġbul lying", + "Ġsimple st", + ".c alled", + "ĠL R", + "Ġmor ality", + "Ġst urdy", + "tr acking", + ".sw agger", + "_B IND", + "IT OR", + "-url encoded", + "ĠÑ ħ", + "ĠTr inity", + "Ġtr aps", + "Ġ| -", + "Ġset Text", + "Ġbarg ain", + "Ġbr akes", + ".get Code", + "Ġmigr ate", + "Ġrib bon", + ") return", + "Ġcharg er", + "ac om", + "ADI US", + "ĠAmb assador", + "-a fter", + "Ġann i", + "ĉs pin", + "Con cept", + "ĠHend erson", + "ĠH OST", + ".r ank", + "ĠNor theast", + "Ġber lin", + "Ġrequ is", + ".f eed", + "Ġsource Mapping", + "ĠRen contre", + ". ajax", + "nest js", + "Ġtre k", + "ĠN acional", + "Ġ& [", + "Ġpay able", + "ort ex", + "Ġde pt", + "field Name", + "Ġcomple tes", + "ĠR VA", + "Ġon ions", + "al ignment", + "Form ats", + "Ġ' {$", + "Hash Set", + "ĠB od", + ".Invariant Culture", + "Ġsettlement s", + "Ġhy dr", + ". updated", + "vent h", + "( seconds", + "=\"/ \"", + "Ġweb page", + "( ĊĊ", + "Ġt ir", + "Ġto es", + "ĠBr ick", + "Ġamb ition", + "P ot", + "= max", + "ET IME", + "Ġdep ot", + "c alls", + "ĠNor wegian", + "` :", + "Ġbur ger", + "Ġprofess ors", + "ĠAl locate", + "-third s", + "-ch art", + "Ġfor d", + "* N", + ".k otlin", + "Ġpaper work", + "ĠDE VICE", + "% @\",", + "res pect", + "(m p", + "é «ĺ", + "- if", + "Ġcush ion", + "ob ot", + "Ġpar c", + "SP ACE", + "ĠNet anyahu", + "Ġself ish", + "fe at", + "Ġclient es", + "-to ols", + "Ġpor ch", + "Ġj q", + ". verbose", + "Ġlib erals", + "] )ĊĊĊ", + "p ies", + "Not Blank", + "( term", + "ÈĽ i", + "_Param s", + ".normal ize", + "B ullet", + "AS IC", + "(h ex", + "_client e", + "+ ,", + "_D I", + "Ġforth coming", + "} \")]Ċ", + "se o", + "U m", + "> Name", + "Ġcomfort ably", + "irection al", + "W ITH", + "/ pr", + "ĠP oor", + "ĠVit amin", + "v ic", + "G H", + "Ġprior it", + "ĠN N", + "ĠC losed", + "¤ í", + "Ġis Open", + "\\ Console", + "And Feel", + ".S UCCESS", + "_OPER ATION", + "pol ation", + "ĠT as", + "ps z", + "> '.", + "C URRENT", + "V endor", + "host s", + "ĠE rd", + ">tag ger", + "ĠsourceMapping URL", + "Ġmar athon", + "_c losed", + "Ġexem ption", + "Ġrecogn izes", + "ides how", + "' $", + "('/ ');Ċ", + "m its", + "war z", + "ĠCh erry", + "µ ¬", + "n or", + "port e", + "Ġw l", + "_back up", + ".get Boolean", + ".get Resource", + "Ġdefinit ive", + ". EditText", + "Ġs ÃŃ", + ".C ONT", + "ĠPL AYER", + ".c ards", + "ĠSh ore", + "('/ ')Ċ", + "cl uir", + "Web Driver", + "(m onth", + "-re lease", + "Ġins pector", + "å £", + "ĠN F", + "_cl ip", + "åŃ IJ", + "Ġinteract ing", + ".t mp", + "Ġ'' 'ĊĊ", + "Ġde e", + "Ġfro st", + "\"] ))Ċ", + "ĠPl aces", + "Th rows", + "f ork", + "/ day", + "i Phone", + "ĠM IC", + "Ġfold ing", + "Ġcro re", + "ĠCh iefs", + "pher ical", + "( price", + ".Write String", + "Ġexit ing", + "] ',Ċ", + "ight ing", + "Ing redient", + "( vertex", + "Ġscroll View", + "h f", + ": new", + "SE N", + "se ctor", + "Ġsp ins", + "ĠS cheduler", + "ote chn", + "sem icolon", + "Font OfSize", + "ĠSpecific ally", + "fl amm", + ".Object Id", + "Ġcont a", + "_per missions", + "ĉF ROM", + "IC ODE", + "/ kg", + "ĠHot els", + "-m ed", + "ĠD in", + "Ġn avy", + "get Param", + "Ġm end", + "Ġportray ed", + "ĠMet ropolitan", + "Paint er", + "Ġref erral", + "_g ood", + "Ġmar vel", + "osa ic", + "> (&", + ". ur", + "Ġest os", + "Will iam", + "Ġtim ber", + "Ġquel ques", + "ĠDoc uments", + ".X aml", + "Ġbatch es", + "éģ ĵ", + "ĠRe leased", + "T ail", + "CO OKIE", + "he id", + "_st ation", + "ĠV ia", + "S ale", + "ĠRe peat", + "Ġprom in", + "ĠZ o", + "- forward", + "ĠI on", + "it ary", + "Ġj us", + "- request", + "Ġproud ly", + "ĠStream ing", + "(Mouse Event", + "ĠS print", + "_ rotation", + "Re positories", + "Ġt art", + "ĠÑģ в", + "Ġm appings", + "è ª", + "C u", + "C ycle", + "Ġb un", + "ĉl ua", + "ãĥ ī", + "Ġ(( !", + "Ġcollect ively", + "ĠCon d", + "Ġwsz yst", + "(l ib", + "openh agen", + "_s kip", + ".Column Header", + "é Ĥ", + "peri enced", + "ı è¿°", + "_p rops", + "Ġcontr ace", + "Ġmatch up", + "ab etic", + ".m embers", + "RE CT", + "(d at", + "Ġs og", + "ren om", + "_M ethod", + "Custom ers", + "full name", + "Z N", + "re try", + "Ġk ap", + "ĠNe u", + "è Ĭ", + "add Child", + "will Return", + "_p ermalink", + "Ġener getic", + "ĠW et", + "ĠMor r", + "Ġg cd", + "count s", + ", type", + "d ig", + "( Login", + "Ġcr acks", + "Ġbacter ial", + "ĠMe at", + "ĠArm strong", + "ĠBron ze", + "Ġapprox imate", + "_dir s", + "lig a", + "ÅĤ ad", + "Ġkind ness", + "Ġcont re", + "ĠE VERY", + "M ET", + "Ġannounc ements", + "g pio", + "ĠWaitFor Seconds", + "ĠPhotos hop", + "Ġdis contin", + "/ dd", + "Ġtop ology", + "an ical", + ". interface", + "auc oup", + ".Hash Set", + "ARI ANT", + "(r outes", + "ĠT eh", + "Ġh ype", + "] \").", + "Ġsl am", + "Ġbro th", + "- inter", + "ĠR id", + "-m anager", + "Cancel ar", + "ĠP agination", + "Ġsound track", + "Ġpost erior", + "Ġscr ub", + "cre ating", + "- *", + "ir teen", + ".d y", + ".s ymmetric", + "Ġ\"\" .", + "============ ===", + "Ġch assis", + "ĠnumberOf Rows", + "Develop er", + "_b ins", + "ĠO UR", + "ri eb", + "Pro s", + "Ġwi ÄĻ", + "\" d", + "Ġasync io", + "ze igen", + "_s pi", + ".A LL", + "Ġscre ws", + "Ch inese", + "Ġapi Key", + "Ġun successful", + "ĠSeah awks", + "OR G", + "ç« ł", + "Ġprofession ally", + "ĠCou pon", + "åŃĹ æ®µ", + "Con vention", + "Ġpol ym", + "æī ĭ", + "Ġsalv ation", + "Ġengine ered", + "ĠW rest", + "ĠG CC", + "Ġwar mer", + "Layout Constraint", + "Ġag grav", + "Script s", + "vent ure", + "Ġrefriger ator", + "Ġinnov ations", + "ĠRun ner", + "N IC", + "ĠRoll ing", + "Control Events", + "Ġlo os", + "p ac", + "ĉ panel", + "ef e", + "ĠBudd ha", + "------------ --Ċ", + "åº ĵ", + "(for Key", + "Ġl umin", + "Ġ( ?", + "ĠA IDS", + ", user", + "im ientos", + "content Type", + "ant lr", + "é ¦", + "ĠW elt", + "Produ ction", + "m ight", + "ĠV II", + "\", (", + "Ġobserv ing", + "Ġdeliber ate", + "( control", + "Ġwith d", + "Ġsem ana", + "ST ACK", + "uch en", + "N ice", + "ĠDeutsch land", + "ĠSpec ifies", + "d ma", + "iz io", + "ĠF acts", + "_pop up", + "ĠDirect ors", + "{ :", + "[ R", + "ĠÑį леменÑĤ", + "Ġpl at", + "Ġdirect ing", + "ä¸ ī", + "ĠGil bert", + "âĢ¦ .ĊĊ", + ".q ml", + "Ġthere after", + "Ġdis position", + "d raft", + "Ġsurge on", + "ĠIns ider", + "Bl end", + "ĠT rev", + "tr insic", + "Top ics", + "rie ve", + "_FILE NAME", + "Ġaut res", + "J ose", + "Produ cer", + "er us", + "Ġpet it", + "ĠN EXT", + "ĠF ilters", + "Ġreplic ate", + "\"] ).", + "Ġl enders", + "] \",Ċ", + "; charset", + "Cpp Object", + "Ġfl oral", + "ĠT ipo", + "Ġcirc uits", + "e asy", + "(& $", + "itt a", + "ery l", + "_COMM ON", + "'}} >Ċ", + "-back ed", + "(var iable", + "( Index", + "Ġvo ir", + "_loc ations", + "++) {", + "ĠLouis ville", + "Ġgrat itude", + ".Mock ito", + "ĠP owers", + "ie urs", + "Ġge ographic", + "ra le", + "Ġc ra", + "ĠSp urs", + "iph ertext", + "AC ION", + "- common", + "Ġvict ories", + "ĠFinal s", + ".sh uffle", + "-m illion", + "_PRO C", + "ass ume", + "Ġil s", + "DB C", + "Boot Test", + "Ġl avor", + ".test ing", + ". ast", + "\"] /", + "m oid", + "Ġqual ification", + "ges ch", + "ĉ put", + "Ġair ports", + "J I", + "Te acher", + "_un iform", + "Ġn ama", + "ĠB ast", + "ert ype", + "c apture", + "get All", + "ĠReyn olds", + "oo led", + ".com ments", + "Ġch in", + "). *", + "Ġи ли", + "t gl", + "ud os", + "Ġd ÃŃas", + "ch ai", + ".pro gram", + "Ġps z", + "ĉ icon", + "ph il", + "ent ral", + "_WR AP", + "ov i", + "Ġnost alg", + "In finity", + "ĉy ield", + "Ġvit amins", + "Qu aternion", + "S ink", + "_g oods", + "Ġ ........", + "ĠW ings", + "ur idad", + "-st ory", + "\"] )ĊĊ", + "idel ity", + "Type Def", + "G tk", + "Ġí Į", + "_M ain", + "Ġche z", + "ĠR aven", + "Ġpay roll", + "Ġfreel ance", + "LL U", + "ĠM end", + "ed ay", + "Api ModelProperty", + ".Form BorderStyle", + "Ġeconom ist", + "stan bul", + "Ġfre ight", + "-A gent", + "(m eta", + "Ġsym metry", + "Ġ' ..", + ".C alendar", + "- aut", + "g f", + "p ent", + "yc lopedia", + "Ġwish ing", + "ĊĊĊĊĊĊĊĊ ĊĊĊĊ", + "Ġgentle man", + "Ġê ³", + "= #", + "Ġlect ures", + "âĢľ In", + "Ġ! _", + "Ġh b", + "ĠV endor", + "Recent ly", + "_n otes", + "æıIJ 示", + "\" My", + "Headers Height", + "_S O", + "Ġunw illing", + "Ġsuper hero", + "g io", + "ps y", + "ĠPe er", + "j avax", + "& apos", + "ĠCr isis", + "ord inal", + "Mem cpy", + "++++++++ ++++++++", + "- val", + "Ġwork book", + "- ap", + "= k", + "Ġmetal lic", + "_ peer", + "By PrimaryKey", + "_S D", + "u ator", + "_SH ADER", + ") Math", + ".Trans form", + "Ġc ows", + "Ph i", + "ĠC lem", + "(_ (\"", + "ĠL ud", + "-d elay", + "ĠSec urities", + "ĠOrth odox", + "Sym fony", + "(re port", + "Ġent ertain", + "E PS", + "iz oph", + "ex ual", + "IR D", + "ä» İ", + "Ġl ith", + "Ġsanit ize", + "Ġfemin ine", + "IS BN", + ".auth entication", + "_p ipeline", + "/ constants", + "ĠCON F", + "Ġluc r", + "ric ia", + ".t tf", + ".set Content", + "Ġst an", + "ore an", + "ĠL loyd", + ".raw Value", + "Ġg or", + "ĠBrow ns", + "Re gression", + "Ġlower ing", + "na issance", + "Ġbl ows", + "Ġam azed", + "Ġun related", + "Re views", + "Ġrub y", + "ĠMod ifier", + "Ġgi ants", + ". thread", + "Ġcontain ment", + "ĠStart Coroutine", + "um at", + "ore lease", + "ĠR andy", + "@ endif", + "D igest", + "Ġsubur ban", + "=\" );Ċ", + "Ġann once", + ". variable", + "\\F oundation", + "Ġa cre", + "V an", + "Ġt uples", + "d ns", + "ĠStand ing", + "_l arge", + "Ġbox ing", + "Support ActionBar", + "ĠFort une", + "ĠR um", + "_m ultiple", + "arch ical", + "Ġf write", + "_ quote", + "Ġfool ish", + "Ġcompr ising", + "Ġо п", + "- selected", + "v f", + "ma id", + "N ama", + "(d atetime", + "Ġindirect ly", + "g art", + "fix tures", + "ch os", + "ĠH alo", + "Ġrec urring", + "- news", + "v il", + "ĠNurs ing", + "- produ", + "ĠH Q", + "\\Http Foundation", + "enc i", + "au en", + "Ġv y", + "ocr acy", + "Ġdeleg ation", + "Ġas phalt", + "Ġset Selected", + "k ok", + "/ rest", + "met ics", + "ĠNS Date", + "Ġtravel led", + "Ġrec ib", + "Ġm ime", + "CL IENT", + "ĠG U", + "ĠH ANDLE", + "/ Q", + "[ z", + "Ġbother ed", + "ĠBB Q", + "ç as", + "_ex amples", + "_F IN", + "Ġwhite Color", + "Ġastr onom", + "-d ir", + "Ġsovere ign", + "Ġb reeze", + "Ġin ning", + "ĠEd monton", + "g li", + ".blog spot", + "js x", + "Ġvers a", + "ĠMoh ammed", + ".J ob", + "-t oggler", + "Ġп олÑĮзоваÑĤ", + "ard on", + "Ġnew born", + "Ġnav al", + "note q", + "Ġtum blr", + "Ġh entai", + "ĠTyp ically", + "Ġlo ot", + ".S prite", + "Fl ight", + "Ġw avelength", + "-s k", + "ĠEl le", + "_ exports", + "Ġ Ñı", + "ĠI H", + "izoph ren", + "Ġí ģ", + "_pr imary", + "Ġmo is", + "ĠB N", + "Ġsystem ic", + "Ġdifer entes", + "IN CT", + "Ġ'' ĊĊ", + "$ q", + "Widget Item", + "cl ide", + "$ file", + "L emma", + "/ table", + "ag rid", + "ĠMongo DB", + "int e", + "Ġapp rent", + "ÂŃ ing", + ".D b", + "Ġà Ĥ", + "ham mer", + "=' ';Ċ", + "Ġbro kers", + "it lement", + "sembl ies", + "E le", + "{ x", + "Ġlast name", + "< -", + "Ġfl atten", + "_b and", + ".R oot", + ".read FileSync", + "==== ==", + ".r x", + "? čĊ", + "Ġmetaph or", + "T i", + "con te", + "Ġdeb it", + "Ġcont empt", + "Cpp Type", + "æĶ ¯", + "Form Field", + "r atio", + "os opher", + "Ġimpl ant", + "P URE", + "Ġal ta", + "_man agement", + "Ġref ine", + "ĠCheck Box", + "ĠChar l", + "- version", + "cond itional", + "ven ues", + "Ġrif les", + "Ġoff spring", + "Ġmill ing", + "Ġshar ply", + "Ġunder water", + "( origin", + "_ Control", + "Ġ. $", + "Pl ugins", + "Ġdry ing", + "Ġillustr ates", + "- u", + "Ġveget arian", + "n pc", + "He art", + "; ',Ċ", + "com ma", + "te enth", + "as an", + "/s pec", + "_m oves", + "-m argin", + "Ġing en", + "³³ Âł", + "Ġpro jet", + "Ġo tra", + "Ġbr as", + ". utc", + "Ġsle pt", + "= sub", + "ab ilit", + "post er", + "Ġs dk", + "ounc ill", + "Ġw d", + "Pre paredStatement", + "ĠDr um", + "( attribute", + "ĠEther net", + "ĉ DB", + "Cal ifornia", + "c ube", + "[ I", + ".C reated", + "ĠH M", + "Ġtr acing", + "Forms Module", + "- you", + ".c urrency", + "feed ing", + "Ġt body", + "L i", + "acc ion", + "n as", + "Ġtr ouver", + "N ONE", + "\"} ,čĊ", + "Ġf tp", + "With Identifier", + "pol ate", + "File Info", + "Ġpurs ued", + "ĠĠĠĠčĊ ĠĠĠĠčĊ", + "DE SCRIPTION", + "} */Ċ", + "From Nib", + "Ġdecor ative", + "_S SL", + "(ch at", + "T LS", + "Ġsurpr ises", + "al culate", + "ĠS plash", + "( Configuration", + "ĠS EM", + "im son", + "/lib rary", + "< Double", + ". robot", + "³³³³ ³³³³", + "ĠCP F", + "ĠUnder standing", + "Ġcos metic", + "ĠX t", + "t ips", + "+ k", + "(\" '", + "ĠP DT", + "W AR", + ".get Object", + "ĠTrad itional", + ".sl ug", + "ĠDi pl", + "=\" \",", + "ĠFil ms", + "ĠAn im", + ".h elp", + "Ġemb assy", + "ĠBoot s", + "Ġb unk", + "-r isk", + "Ġp ci", + "Ġ/ \\.", + "ĠI PT", + "Ġcrash ing", + "Ġip v", + "_ ke", + "ĠRES P", + ".Log Error", + "Ġinade quate", + "I on", + "ĠF ür", + "ric ula", + "Ġshould Be", + "al ready", + "'].\" ", + "G ED", + "fa q", + "Ġoption ally", + "_D is", + "ĠSuccess ful", + "ĠC ensus", + "Ġinc arcer", + "_C ARD", + "Ġav iation", + "ĠG ym", + "Author ity", + ".B ean", + "sh ader", + "Not Exist", + "_Text Changed", + "ĠST OP", + "( team", + "\" H", + "w g", + "Ġgr inder", + "Ġstri pe", + "Ġpres ervation", + "Cl aim", + "avers al", + "ware house", + "target s", + "Tr ust", + "Ġal lev", + ", www", + "ous se", + "_ch an", + "_S ize", + "system s", + "Ġobj ection", + "ĠK ane", + "Ġcor ros", + "ĠD SL", + "Ġu a", + "ĠM H", + "ĠStrateg ic", + "_t cp", + "Ġê° Ĵ", + "Ġborrow ed", + "ĠA ch", + "ĉ command", + "Ġg ps", + "le ston", + "iche ver", + "ĠU A", + "Ġassault ed", + "Ġspecial izes", + "ĉ search", + "Hot el", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", + "ĠP itch", + "Ġ Ùģ", + "READ Y", + "Ġparent al", + "Ġg éné", + "Ġdonn ées", + "Ġdet ain", + "T ARGET", + "Ġprotagon ist", + "Ġclear Interval", + "ĠIcon Button", + "ĠGet All", + "Type Info", + "E H", + "âĢľ They", + "Ġ{ [", + "Ġg ag", + "Ġ Ú©", + "ĠD ropdown", + ".f ree", + "g one", + "im ens", + "Ġinst al", + "ĉc url", + "_C AN", + "ĠB one", + "ï¼ Ķ", + "ony ms", + "-g overnment", + ".binding Navigator", + "ĠD ans", + "ĠMc L", + "( en", + ">( _", + "ÐĴ Ñĭ", + ".* ;čĊ", + "= j", + "-c or", + "S on", + ".ToolStrip Item", + "- around", + "_X ML", + "end Date", + "Ġsl ack", + "Ġrot ated", + "Ġno qa", + "Ġc ottage", + "Ġencontr ar", + "_s kill", + "hou ette", + "! čĊ", + ". weather", + "Ġemphas ized", + "å® ¶", + "ĠÑģ пиÑģ", + "ĠComp iler", + "( android", + "ĠâĢ º", + ". turn", + "Ġsup pression", + "_c alls", + "Ġ* @", + "(str len", + ".h ex", + "ĠB ills", + "ĠR SA", + "Ï Ĥ", + "ĠEs cape", + "ement ia", + "Ġfront end", + "Ġp int", + "_ex c", + "zz o", + "[ ],Ċ", + "Ġ\"',' \"", + ". Environment", + "Ġafore mentioned", + "Ġend ure", + "prot otype", + "ther apy", + "ss i", + "D eg", + "_pl ugins", + ".user Info", + "Print er", + "ĠPRO GRAM", + "Ġru ins", + "Ġempir ical", + "Ġcraw l", + "ĠBo iler", + "- comment", + ".sub plot", + "_ et", + "Ġ'. ',", + "min or", + "ĠCustom s", + "Ġy aw", + "under line", + "ĠCom o", + "( ('", + "(m ean", + "Ġcha que", + "ĠBlock s", + ".r ad", + "ilib rium", + "Ġweb driver", + "Ġmel hor", + "d ana", + "ĠAb use", + "ĠSouth west", + "ĠP aren", + "PERT IES", + "ĉ IL", + "Ġscre am", + "v u", + "Ġin comes", + "Ġn im", + "Ġl ace", + "Ġcompens ate", + "Re verse", + "D at", + "_att ack", + "Ġn our", + "ach en", + "ce k", + "< Func", + "w ie", + "com pressed", + "-m atch", + "(\" \")]Ċ", + "im ized", + ". orientation", + ".compare To", + "Ġmass aggi", + "Ġìľ Ħ", + "Ġel bow", + "Ġant ioxid", + "undred s", + "/ tools", + "ĠR OW", + "an mar", + "ĠW ow", + "_t icket", + "Program ming", + "Ġthe or", + "-re view", + "() )));Ċ", + "ĠRichard son", + "ĠP ocket", + "] []", + "am pp", + "_ health", + "ĠP OP", + "ĠNav al", + "Gu ess", + "Ġancest or", + ".Get All", + ".local Scale", + "ĠM apper", + "Ġaccum ulation", + "Ġsim ulated", + "ĠDr ivers", + "Ġd és", + "cur ring", + "Ġele phant", + "Ġadvert ised", + "Ġmail box", + "SH IFT", + "ĠMon ica", + "Ġan c", + "Ġward robe", + "Ing redients", + "Ġ|| čĊ", + "ipp y", + "Ġantibiot ics", + "av ings", + "(c x", + "ĠFerr ari", + "ĠAn imator", + ".d type", + "rem oved", + "order by", + "Ġc res", + "oc ê", + "Ġp ym", + "ĠCirc ular", + "@ index", + "ĠW arm", + "S ay", + "ĠAss istance", + "Ġcur tain", + "ĠMont e", + "IL ER", + "ĠC VE", + "ĠD uck", + "ĠAll ows", + "_f ire", + "ĠDer by", + "Ġre pos", + "Ġhttp Client", + "Ġpsych iat", + "Ġnow adays", + "Ġcaut ious", + "ĠComput ing", + "Ġcompletion Handler", + "ĠWel sh", + "ĠB EST", + "Ġstress ful", + "_P E", + "æĹ¥ æľŁ", + "ĠData Frame", + "ĉ Integer", + "_P rint", + "M oves", + "Ġtransform ing", + ".B atch", + "y ahoo", + "Position s", + "ze j", + "Ġno od", + "io res", + "_ *", + "Ġcl k", + "ĠF loyd", + "Ġh ap", + "font size", + "Ġn az", + ".not ification", + "ĠDep ression", + "Ġac ne", + "*** ĊĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + ".cont ents", + "yn th", + "ĠStra ight", + "')}} \"> \"+", + "Ġtoken izer", + "Ġsovere ignty", + "ĠP ence", + "() \");Ċ", + "Ġpesso as", + ".G e", + "ĠIn cluded", + "Ġpag ina", + "Ġex posing", + "е ÑĪ", + "_SC RIPT", + "/$ ',", + "Th umbnail", + "× Ķ", + "webElement X", + "webElementX paths", + "press ure", + "ĠCur ry", + "_C P", + "OL UTION", + "ILE S", + "prot ect", + "ool a", + "Work space", + "{ };Ċ", + "ĠU NS", + "Ġsymp athy", + "ro ker", + "Ġrem odel", + "ĉc ell", + "Ġat op", + ".Full Name", + "Ġfa ut", + "ĠE asily", + "_d ynamic", + "Ġfr amed", + "Ġmot ive", + "è· ¯", + "s am", + "Ġmar ca", + "ĠText EditingController", + "Ġde structor", + "cre am", + "Ġr ude", + "ĠB old", + "ĠInd igenous", + "Ġg ens", + "Ġrel acion", + "(s ystem", + "ĠUIF ont", + "_char ge", + "UST ER", + "E V", + ".N amespace", + "Ġmer ger", + "Ġcal loc", + "g ang", + "Bad Request", + "Ġs per", + "-d esign", + "Ġâ ĩ", + "Ch an", + "Ġorgan ism", + ", )", + "= id", + "_pl ane", + "ĠC ases", + "elf ast", + "ĠLegisl ature", + "ĠF aker", + "Ġinv oking", + "- utils", + "(). '", + ".f ace", + "Ġguard ian", + "my Modal", + "Ġclip board", + "ĠAT M", + "Ġpe as", + "ĠS ylv", + ".c alc", + "ĠContact s", + "int Value", + "Ġmodify ing", + "ĠBar b", + ". loss", + "_per centage", + "Ask ed", + "(l st", + "ategor ical", + "- files", + "ĠRoman ia", + ".A c", + "Ġh ai", + "ĠF lying", + "Ġ ż", + "j p", + "ĠTr ainer", + ". arc", + "_de g", + "Ġtrace back", + "Or Fail", + "F LOW", + ". old", + "oy a", + "g mt", + "is empty", + "Ġvacc ination", + "Ġob solete", + "recogn ized", + "Ġru ined", + "ĠRe in", + "ĠTr acking", + "xf b", + "ا ÛĮ", + "Ġvæ re", + "Ġbr yster", + "ĠIT S", + "Ġdest iny", + "Ġsw ear", + "Ġred es", + "Ġcl f", + "Ġfl ipped", + "ĉ head", + "Bl uetooth", + "ĠOver rides", + ": Boolean", + "_ =", + "_l r", + "sp awn", + ": index", + "VAL UES", + "is key", + "? \");Ċ", + ".syn thetic", + "ĠCheck ing", + "struct ures", + "ip ing", + "Ġvoc als", + "- Up", + "ĠManufact urers", + "ĠMar riage", + "代 çłģ", + "Ġgar ner", + "_C lient", + "par allel", + "RI END", + "Ġvine gar", + "seg ue", + "J B", + "Ġcontact ing", + "ĠCar roll", + "Ġout reach", + "t ensor", + "_var iant", + "Ġthe at", + "lic able", + "{ |", + "t iny", + "_ letter", + "Ġp encil", + "HeadersHeight SizeMode", + "ilt ro", + ".auto configure", + ".d rag", + ".use State", + "ĠB MI", + "h int", + "Com pile", + "* \\", + "en ary", + "Ġl vl", + ".C ache", + "+ =\"", + "_t v", + "ruit ment", + "Ġf read", + "Art icles", + "f ila", + "Ġpack aged", + "âĺ Ĩ", + "AT HER", + "ĠPl anned", + "s cheme", + "Ġdi ary", + "Ġoff enses", + "/ F", + "ĠSt ick", + "Ġc erc", + "ĠS lee", + "ĉĉ ĠĠĠĠĠĠĠĠ", + "< Image", + "Ġè® ¾", + "- editor", + "pie ces", + "ĠD rama", + "Ġ// ////////////////", + "ĠT asks", + "AR C", + "g ateway", + ".get cwd", + ".M etadata", + "Ġguess ing", + "åľ° åĿĢ", + "Ġsm arter", + "ĠGet Enumerator", + "Ġe fter", + "/ operators", + "ĠGL float", + "Ġf ør", + "Ġop aque", + "ä¿Ŀ åŃĺ", + "Sp read", + "SY STEM", + "Ġinv ersion", + "ĠBasket ball", + "Ġsim ulations", + "Ġden ies", + "Ġa vez", + "_list ener", + "Ġenh ancing", + "ĠMy th", + "ĠL akers", + "_M D", + "Nd Ex", + "D ATABASE", + "Ġt á»", + "ar th", + "[ left", + "Ġcontest s", + "st ile", + "(K ERN", + "_f c", + "_p m", + "Ġpres idents", + "Ġhospital ity", + "Ġfade In", + "RO PERTY", + "_m aps", + "ĠDefinition s", + "Ġassess ing", + "Ġus ar", + "Ġquant itative", + "mo z", + "Be autiful", + "[ ((", + "b ons", + "f requency", + "Cont ain", + "Ġpuzz les", + "ĠCast ro", + "Ġv illa", + "Ġkind ly", + "Font Awesome", + "ern a", + "epoch s", + "_dat as", + "ĉ ip", + ".p adding", + "ĠCont est", + "Ġed itions", + "Ġdispro portion", + "ĠI CO", + "Ġcome back", + "= value", + "ri ad", + "-s ort", + "Sub mitted", + "(n etwork", + "ĠC el", + "Ġinstall ment", + "l ashes", + ".List View", + "ĠV atican", + "(Media Type", + "IV ED", + "reach able", + ": Is", + "ĠC ITY", + "äº ¬", + "ĠHelp ful", + "Ġba ÅŁ", + "% čĊ", + "Ġpsych iatric", + "Ġrec ycled", + "FORM AT", + "ĠG row", + "b ine", + "G it", + ".s s", + "ĠWe apons", + "ĠSt y", + "_ arrow", + "* self", + "ire ment", + "Ġdeg li", + "App Delegate", + "_b anner", + "Ġcoordin ated", + "ĠWeb cam", + "Ġcelebr ations", + ". act", + "******************************** ****************", + "( show", + "Ġweek day", + "Ġconc erts", + "ол н", + "cl in", + "Ġcr on", + "ĠN im", + ".set Vertical", + "ĠEll en", + "س ت", + "ĠS AM", + "E ff", + "g z", + "ste am", + "Ġant ique", + "ph ysical", + "ĠForm Data", + ".set ter", + "ĠPO INT", + "B on", + "Ġflav our", + "erv ention", + "_ENT ITY", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġintr insic", + "Ġæ İ", + "append To", + "aram el", + ") ])", + "ĠRecomm end", + ") m", + "OutOf Range", + "Ġkn ight", + "Ġsat ellites", + "ĠTit ans", + "Ġweigh ed", + "ĠD ana", + "e ase", + "Ġs ip", + "S IM", + "ĠDevelop ers", + "mal ink", + "/ check", + "_P LL", + "n ung", + "Ġdry er", + "= A", + ".d w", + "_S QL", + "Ġsub plot", + "D ROP", + "Ġprot otypes", + "Ġhour ly", + "display Name", + "Ġas i", + "ĠViol ence", + "Ġastr onaut", + "Ġdat atype", + "Ġinformation al", + "Ġinvestig ative", + "etermin ed", + "ren al", + "; '>", + "ĉc ol", + "V G", + "_ boolean", + "re cent", + "Ġ* )ĊĊ", + "ĠRain bow", + "om men", + "Ġl ur", + "Ġopp ression", + "(\", \");Ċ", + "ĠFac ility", + "DEF INED", + "Ġne on", + "Ġoff ender", + "AF P", + "ĠClean ing", + "[] ):", + "Ġund ocumented", + ".Re positories", + "ĠG uitar", + "аÑģÑģ ив", + "Sk ills", + "Ġtestim on", + "rypt ography", + "ĠAm ber", + "ĠSt alin", + "Ġl one", + "Ġap enas", + "Ġdies es", + "ĠAr duino", + "è½ ¬", + "== -", + "_A ct", + "Ġc oded", + "âĸ ł", + "amb urger", + "-link s", + "Ġarm our", + ".H igh", + "get Content", + "st ag", + "Ġhe ck", + "ĠìĹ Ĩ", + "ĠMc Connell", + "ĠCon cert", + "ĠAl loc", + "ä re", + ".replace All", + "Ġpart itions", + "rot t", + "ĠF le", + "_T REE", + "reason able", + "ĠReport ing", + "Ġbillion aire", + "s cores", + "min s", + "- eye", + "M ORE", + "ab ort", + "ĠSW T", + "Ġin verted", + "ĠTe achers", + "; n", + "Ġast ro", + "н ов", + "ани ÑĨ", + "product o", + "c ountries", + "ĠO wen", + "Ġcont amination", + "Ġv ibe", + "ĠEll i", + ".s cript", + "ĠOl ive", + "D MA", + "v ier", + ": semicolon", + "-m odule", + "gress ive", + "ag u", + "_ players", + "Ġresult ados", + "start ed", + "scroll Top", + "==== =", + "Ġweigh ing", + "Ġ[[ [", + "z ahl", + "( NS", + "ĠAssert ion", + "le ague", + ".setText Color", + "ĉ Message", + "Ġmom s", + "_A F", + ". wh", + "AL S", + "Ġaut re", + "] ĊĊĊĊ", + ".op acity", + "ĠBudd hist", + "Ġde af", + "ĠOrgan isation", + "(G lobal", + "ens ch", + "Ġhead ache", + "ĠAli en", + "_in ode", + "ĠSt ark", + "Ġæ ī", + "-l nd", + "ore f", + "_fe at", + "Ġpedest rian", + "Ġnom inal", + "Ġbal loon", + "Ġspr ites", + "Prototype Of", + "ĠA post", + "ĠF EATURE", + "O H", + "Ġre cess", + "ĠDon na", + "con sumer", + "$ GLOBALS", + "ĠG IF", + "- frame", + "In icio", + "Ġpass ages", + "Date String", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + ".by te", + "B ug", + "initial izer", + "p kt", + "od ium", + "ĠD ER", + ". ops", + "ler i", + "Ġgift ed", + "Ġdet ach", + "ter rain", + "elt ers", + "ãģ ı", + ". loader", + "ĠN GO", + "str ncmp", + "K h", + "(font Size", + "ro cket", + "Ġpreced ent", + "ĠAur ora", + "ĠEx periment", + "is phere", + "Enc oded", + "ĠâĢĵ ĊĊ", + "Ġpy ramid", + "ĠAnn iversary", + "of il", + "ë Ł", + "( plugin", + "C oeff", + "Ġcooper ate", + "Ġpredomin antly", + "IS M", + "Ph rase", + "_DEF INE", + "Fl ip", + "AMIL Y", + "ĠMark ets", + "ĠStream Reader", + "ĠComb ine", + "Ġmanus cript", + "z za", + ", tp", + "Wh atever", + "IT ICAL", + "ighb our", + "Data Provider", + ".Text ure", + "priv acy", + ".S DK", + "Ġre charge", + "Ġc pp", + "ĠC FG", + "(h older", + "(p y", + "m ot", + "Ġsav oir", + "ĠR osa", + "ĠPC s", + "Ġí Ļ", + ".her oku", + "Ġf ren", + "ĠR iley", + "ag ate", + "Ġs ond", + ".x lsx", + "Ġh acked", + "st ad", + "G i", + "Ġsan ity", + "ĠSql DataAdapter", + "... \",", + "ĠP ussy", + "Ġ ****************", + "Ġhass le", + "_P ARENT", + "ĠU AE", + "Ġbegin ners", + "( Client", + "Ġstatist ically", + ".h our", + "ed elta", + "Ġtr action", + "uel ve", + "ar at", + "Ġsa una", + "IN VALID", + "Ġindict ment", + "AL LE", + "Ġdiss ent", + "ĠTyp ography", + "Ġintention al", + "s it", + "ĠAn imals", + "Ġcoun tryside", + "Ġu art", + "} \\\"", + "Ġseam less", + "¾ 示", + "Ġaut os", + "Ġ\"' \";Ċ", + "Fl ush", + "ANN OT", + "Ġal gebra", + "ass oc", + "ĠW aters", + "Ġprepar ations", + "ron ym", + "[, ]", + "S ans", + "Ġarm ies", + "ipe g", + "Ġcream y", + ". art", + "et re", + "ĠAn imated", + "Ġun pleasant", + "eme an", + "g reat", + "i Äħ", + "ĠEar lier", + "Ġch ic", + "Ġpres erving", + "(ex ec", + "ĠInvest igation", + "ĉG PIO", + "Ġrig orous", + "ij o", + "= num", + "Ġtool Strip", + ") set", + "+\" &", + "ĠAcc eler", + "Ġdevelopment al", + "is posable", + "Ġflaw ed", + "re ne", + "Up dating", + "Ġwatch dog", + "Ġden ominator", + "Ġsubur bs", + "Ġ... )", + "Ġconv ictions", + "c losure", + ".I P", + "Ġtransl ates", + ".sw t", + ".Tr ace", + "Ġmet tre", + ".is Enabled", + "ĠEffect ive", + ".to Int", + "Ġen chant", + "Ġst unned", + "Ġpo i", + "/ code", + "ad m", + ".datab inding", + "ĠL orem", + "________________________________ ________________________________", + "Ġled ger", + "Ġcar a", + "ĠG ir", + "Ġwa its", + "Un o", + "Ġc wd", + "è¾ ij", + "ĠT Result", + "Ġre jo", + "Ġem itted", + "ĠWest minster", + "ä¸Ģ 个", + "ne k", + "_T is", + "Ġen act", + "ĉ with", + "org ia", + "Ġj ue", + "Per form", + "SP ATH", + ".top ic", + "ĠD aten", + "Ạ§", + "Ġsit io", + "_M M", + "\" So", + "b ial", + "Ġsc oped", + "Re quires", + "ĠT OTAL", + "ĠCh ancellor", + "( contents", + "Ġste alth", + "dev ices", + "-p ass", + "ili h", + "ĠMal colm", + "ĠDep ot", + "Ġconfig ur", + "a ussian", + "_con straint", + "в еÑĤ", + "G RA", + "ĠR ates", + ".dataGridView TextBoxColumn", + "ĠNob el", + "it ics", + "Ġignor ant", + "ĠReport er", + "ĠEb ola", + "ĠSh ock", + "_re lation", + "ĠNin ja", + ") c", + "Ġt icker", + ".is Checked", + "ĠSup pliers", + "ĠRap id", + "Level s", + "âĤ¬ âĦ¢", + "ĉ queue", + "Ġch op", + "ĠUn ix", + "re ject", + "-c alendar", + "(s ort", + "è ne", + "erc icio", + "Ġh ect", + "CALL TYPE", + "rou pon", + "Ġrent als", + "auth ors", + "{ name", + "ĠF IFO", + "Ġl assen", + "ĠN ous", + "Ġsn apped", + "Ġfert ility", + "\" log", + "click ed", + "Ġplant ing", + "Ġg b", + "/ output", + "PE AT", + "Ġc ategoria", + "Ġb ach", + "Prof essor", + "in th", + "\"] čĊ", + "Rec order", + "ser de", + "ĠTrans mission", + "tr ad", + "Ġtur bo", + "_VER TEX", + "\\ Event", + "il ver", + "Ġbod ily", + "ĠS ources", + "Ġkill ings", + ".xr TableCell", + "Ġfold ed", + "/ legal", + "un er", + "ĠR ifle", + "ĠM IDI", + "_Selected IndexChanged", + ".Size Type", + "ĠWeb Socket", + "Ġsele ccion", + "S and", + "ot ros", + "Ġenv ision", + "/ etc", + "ĠMel issa", + "Sp ot", + "но е", + "_ ARM", + "At tempt", + "ĠB I", + "ãģ Ķ", + "ĠD U", + "Ġback lash", + "str ide", + "/ classes", + "Ġtext Color", + "_st aff", + "ob lin", + "agent a", + ".c ollections", + "ill age", + "' čĊčĊ", + "fl atten", + "_s ales", + "_M ASTER", + "T W", + "_d a", + "P itch", + "ph ies", + "Ġz ombies", + "ĠV ERY", + "ĠPharm acy", + "Ġprogress Bar", + "Ġhas htag", + "S idebar", + "@ stop", + "(p c", + "ол ж", + "MA KE", + "ĠCor on", + "Ġkv inner", + "ĠM aid", + "b ob", + ".title Label", + "Ġsuccess es", + "ĠDemocr acy", + "ĠSurg ery", + "Ġcou gar", + "Ġcur so", + "Ġl oro", + "ist ency", + "Sen ior", + "æ k", + "ĠA AA", + "ĠBO OK", + "к о", + "W STR", + "Ġ*/ ,Ċ", + "oy al", + ".v ector", + "ĠS PEC", + "SS F", + "Ġcomp uls", + "ĠAppe als", + "ĠW inston", + "ĠMock ito", + "con trib", + ". available", + "entity Manager", + "ari as", + "_s ale", + "_r s", + "Ġdec oding", + "Ġloc ator", + "ol ith", + "Ġk ol", + "Ġasc ii", + "ĠR ut", + "/ interface", + "ĉĉĉĉĉĉ ĠĠĠ", + "ĠN umer", + ".fl ip", + "-d el", + "Ġbol ster", + "on omic", + "Ġz m", + "L G", + "Find By", + "Ġadapt ive", + "lo o", + "Ġv ue", + "(re verse", + "_c anvas", + ". roles", + "ific ado", + "ven ient", + "\" As", + "ĠEn tr", + "al igned", + "Ġbere its", + "/// ĊĊ", + ".g wt", + ". employee", + "_cl i", + "Ġanticip ate", + "éĻ IJ", + "Ġp ik", + "Ġmush rooms", + "(t t", + "Ġo ma", + "ĠSan chez", + "_g oogle", + ". Valid", + "ĠFile Name", + "iv ative", + "k ed", + "-w ar", + "Ġm aturity", + "и д", + "Ġmin er", + "Reduc ers", + "ĠLat Lng", + "_ST D", + "D igits", + "Cal c", + "-up load", + "Ġhand ic", + "ี à¹Ī", + "egr ated", + "ĠST M", + "C lients", + "ĠTur bo", + "SY NC", + "Ġphotograph ers", + ". Out", + ".char acter", + "B UILD", + ".un lock", + "Ġar ises", + "ĠCommand s", + "(\" \");čĊ", + "_F ORE", + "; ',", + "+\" '", + ". Images", + "\") {", + "ĠM eyer", + "Ġneg atively", + "ĠD LL", + "Ġex e", + "Ġdef iciency", + "Ġwild ly", + "-s witch", + "con struction", + "Ġexception ally", + "ĠL iz", + "/j ava", + "Ġtheir s", + "ĠCont emporary", + "l is", + ".fill Rect", + "ĠN FC", + "Ġre he", + "(num bers", + "Ġr aster", + "Ġfig uring", + "Ġshow c", + "ĠJ ill", + "Ġarc ade", + "ĠConstruct s", + "md l", + "(' |", + "Ġident ifiers", + "Ġst ellar", + "( Connection", + "Ġ\" {{", + "y or", + "(m ysqli", + "Ġdo ve", + "Of Birth", + ".dis connect", + "_h i", + "Ġzw ischen", + "ĠGr und", + "i ros", + "_A rray", + ".on click", + "ans om", + "An swers", + "ĉ remove", + "F a", + "Ġhur ry", + "-in f", + "Ġget Class", + "ĠReg ulation", + "ĠFLAG S", + "m isc", + "K en", + "_ heading", + "G Hz", + "- entry", + "Ġbi ography", + "S ig", + "-m f", + "Watch er", + "âĢľ A", + "} px", + "Ġsp icy", + "_s q", + "L ost", + "(tr ack", + "а ли", + "Desc ending", + "< bits", + "qu ine", + "ĠAdv oc", + "_S N", + "ĠHann ah", + "PO P", + "Ġem itter", + "Ġc yn", + "ĠC AD", + "? ).", + "/ set", + "ĠS ister", + "ĠEnd point", + "Ġmen or", + "Ġinter p", + "r k", + "id le", + "Ġout fits", + ". vertex", + "Ġc lic", + "ARE N", + "Ġpost ure", + "ĠOpport unity", + "v x", + "ĠFor bes", + ".D irection", + "Ġres ide", + "Ġremember ing", + "nest y", + "Auto resizing", + "pro viders", + "ĠA H", + "Ġhur ting", + "ĠL ily", + "eval uate", + "lij k", + "p apers", + "ĠSm ash", + "ĠL AST", + "Ġwell s", + "w asher", + "_RO LE", + "ĠD anger", + "* ((", + "_re pository", + "ĠRes olve", + "ĠRoom s", + "_R G", + "ĠQ T", + "o op", + "ĠHe ap", + "Ġslow ing", + "Ġgrat uite", + "_c atalog", + "Ġpol ynomial", + "L y", + "pc s", + "F ox", + "ĠC yr", + "Ġdim in", + "/ month", + "S alt", + "Ġh ind", + ".P ER", + "For um", + "c en", + "_p ol", + "íĺ ¸", + "Ġin ser", + "( ~", + "@ test", + "ĠGold man", + "Ġupload ing", + "F c", + "Ġkom mer", + "Ġm itt", + "_log ged", + "Ġbu cks", + "-l ayer", + ") };Ċ", + "ĠO M", + "Ġv eg", + "col our", + "Ġоб ÑĬ", + "Std String", + "_ que", + "ĠT ian", + "Ġspecial ize", + "и п", + "Ġк л", + "tr ial", + "- edge", + "Ġm ars", + "OG LE", + "Ġempath y", + "ĠB om", + "Ġcoll isions", + "Ġcart e", + "ĠTe il", + "ĠM PL", + "Ġporn ô", + "Ġa irlines", + "A ws", + "N s", + "ĠSp awn", + "( use", + "é» ĺ认", + "Ġy acc", + "st or", + "Ġconf ess", + "Ġpe que", + "r age", + "? \"Ċ", + "/dat atables", + "ĠSh ower", + "__ /", + "Ġcryst als", + "Ġbus car", + "ĠH aus", + "iz ação", + "_ entities", + "ķ Į", + "ļ Į", + "x cc", + "v irt", + "-che vron", + "( Result", + "c ake", + "COM E", + "Ġprohib it", + "ĠCh ess", + "Ġbe aucoup", + "ĠÑĩ ÑĤо", + "R UN", + "ĠI K", + "ó ÅĤ", + "_ Update", + "Ġsle ek", + "ĠSpec ify", + "_c redentials", + "ÅŁ t", + "ĠUser Name", + "ĉ Value", + "Ġarray List", + "Ġex changed", + "ips is", + ".re lated", + "ĠSe ite", + "_B AR", + "ĠL em", + "ĠW ATCH", + "ĠC lients", + "Ġ. *", + "ĠEar l", + "-re port", + "Ġforeign ers", + "Ġstrengthen ing", + "ĉ Description", + "(g o", + ".tool bar", + "Ġcalcul ates", + "ĉs ource", + "Ġcz as", + "Ġre cl", + "ab o", + "Ġlocal host", + "Ġ^ {Ċ", + ".P op", + "ĠDes igned", + "\\ Abstract", + "H old", + "ĠGuid elines", + "ipl ine", + "Ġc aching", + ".Re ader", + "_ext ernal", + ".str ptime", + "ĠWeek end", + "-M ar", + "ĠBe i", + "Ġ{* }", + "ĠR ud", + "Ġexpl or", + "ĠBou levard", + "C ash", + "Ġprep ares", + "Ġserial ization", + "ew ater", + "Ġad c", + ": ĊĊĊĊĊĊ", + "Re fer", + "Ġsc anned", + "} }ĊĊ", + "ĠF ul", + "Ġtour ing", + "ãĥĥ ãĤ¯", + "> ((", + "sur vey", + "Ġí ĺ", + "... ')Ċ", + "ĠDiv ider", + "os l", + "_C ANCEL", + "_pre pare", + "st in", + "ĠHe ath", + ".Primary Key", + "ĠâĨ IJ", + "ĠLocal DateTime", + "Ġcooper ative", + "L earning", + ".en queue", + "Ġgo og", + "ĠReg ression", + "im ates", + "Ġvoy eur", + "ĠDr ink", + "pl ug", + "Ġl ender", + "man a", + "Ġperson nes", + "yp se", + "Ġun link", + "ĠRav ens", + "Ġhur d", + "Ġperiod ically", + "ARG S", + "ĠG H", + "char acters", + "... \"ĊĊ", + "- establish", + "Ġd n", + "( condition", + "ĠGr avity", + "Ġest as", + "_f ocus", + "Creat ure", + "(s ite", + "Ġc arr", + "ĠR L", + "ĠR I", + "ĠM oto", + "AS F", + "ĠLuck ily", + "ĉ Route", + "Ġent ropy", + "(\" ,\"", + "Col lect", + "( contact", + "ĠFlo rence", + "Ġpremium s", + "Ġlif ecycle", + "Ġb ans", + "x ef", + "Web Kit", + "ĠFlo ating", + "Ġcos a", + "Spec ific", + "ĠLo ans", + "b read", + "Ġdes criptors", + "Ġ{ :.", + "TH READ", + "ĠT rent", + "Ġsc op", + "Q A", + "ĠAnt ar", + "p el", + "_d ifference", + "_ch anges", + "(... )", + "ĠR otation", + "ĠLG PL", + "ĠJ UST", + "(T ask", + "_sub set", + "ĠTR ANS", + "åĬ Ľ", + "ĠSc out", + "-p opup", + "Ġsm oked", + "_C lass", + "Ġturn over", + "br akk", + "ĠRock y", + "t as", + ".Regular Expressions", + "ĠElli ott", + "ĠSp inner", + "DU CTION", + "Ġlib re", + "Ġmol to", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠF TP", + "m peg", + "(f eatures", + "Ġb ald", + "ĠV id", + "Ġsh outing", + "L int", + "Ġsock ets", + "Ġpro w", + "Ġnouvel le", + "isc ard", + "ĠS ponsor", + "Ġconsult a", + ")) );", + "Ind ian", + "ĠR aspberry", + "Ġteam mate", + "ĠJ WT", + "ĠGh ana", + "Ġc akes", + "pr imer", + "form a", + "erg arten", + "_M anager", + "Ġpre season", + "G AME", + "| \"", + "ĠBro ck", + "Ġoccup y", + "Ġdecor ations", + "á nd", + "Ġc ot", + "Ġpar an", + "D isk", + "rem ain", + "> ?", + "Str ong", + "Ġfr ance", + "ĠE ra", + "-c r", + ".Buffer edReader", + "ĠParad ise", + "ĠV AT", + "ĠAnd ers", + "Ġlim b", + "amp oo", + "Ġimper ative", + "UT ILITY", + "ĠRec ognition", + "Ġragaz ze", + "Ġpop s", + "yp ress", + "Ġemb argo", + "// {Ċ", + "Ġsy ll", + "P TR", + "åŃĺ åľ¨", + "Ġdid nt", + "Mail er", + "Ġacad emics", + "ĠFra uen", + "ne ider", + "- rel", + "Ġrain bow", + "( In", + "Ġslic ed", + "============ =Ċ", + "(s end", + "NSMutable Dictionary", + "v os", + "(p ackage", + "Ġord inance", + "view er", + "ĠSant os", + "-s elling", + "Ġgo v", + "ett le", + "Ġfound ers", + "Ġw aking", + "sl ashes", + "-p ound", + "re cht", + "ا ت", + ".on Click", + "Ġn ord", + "st änd", + "_ when", + "UT ERS", + "ic c", + "Ġcaps ule", + "ĠW id", + "M arc", + "ภ¸", + "ro red", + "UG E", + "LO UD", + "ĠAud it", + "ip ients", + "op ian", + "ĠS ue", + "Ġwur den", + ".H elpers", + "Ġf actions", + "[ np", + "-th an", + "Ġre co", + "Ġk as", + "Ġcmd s", + "/n etwork", + "xb f", + "get Color", + "Ġbi ased", + "ĠL ak", + "D atas", + "vent s", + "Ġë ²", + "_P S", + ". Validate", + "Inv oker", + "Ġne uen", + "Ġju venile", + "V ISION", + "Ġdev ote", + "Ġlin ha", + "Ġdiscount ed", + "\\ Config", + "Ġworth while", + "Ġskin ny", + "ĠC ourses", + "le ys", + "ĠMort gage", + "K evin", + "Ġannounc es", + "]) *", + "res ervation", + "Ġæķ °", + "Ġprejud ice", + "ĠString Comparison", + "Ġbe ard", + "-w in", + "ĠS ão", + "ĉ ms", + "j al", + "ĠE arn", + "_ ports", + "ĠN ombre", + "_C OR", + "ĠB UILD", + ".s ound", + "Y ellow", + "Ġlineback er", + "Ġchar itable", + "j ug", + "_NON NULL", + "ĠD ental", + "\"> ${", + "ĉm atch", + "R ussian", + "Ġvers ch", + "Ġp inned", + "Ġadopt ing", + "Options Menu", + "P ag", + "Ġpair ing", + "Ġt read", + "erc ises", + "ĠSp read", + ") i", + "ĠB AD", + "_t f", + "UI ImageView", + "pop ulate", + "b ab", + "ĠÏ ĥ", + "[ ++", + "Ġopi oid", + "Ġ## Ċ", + "d type", + "ĠStart s", + "('/ ')", + "Ġperson als", + "-mark et", + "Ġredund ant", + "ĠEss ential", + "Ġscrap y", + "Ġи м", + "a cl", + "Ġcre ar", + "ĠB end", + "Ġrel ieve", + "- room", + "w ife", + "Ġv Ãł", + "ĠQ Point", + "Ġqu asi", + "Ġmethod Name", + "\\x c", + "ĠPer u", + "/ The", + ". orm", + "Ġv iz", + "/p df", + "Loc ated", + "Ġconfront ation", + "ĠChampionship s", + "Ġhyp ert", + "Ġd j", + "ĠUser Info", + "ĠåĪ Ľå»º", + "\\x b", + "(s im", + "Ġ== Ċ", + "Ġst aging", + "Ġdr astically", + "åŃ ¦", + "l ords", + ". less", + "вед иÑĤе", + "ĠB ucket", + "ĠM am", + ". term", + "_p i", + "c zy", + ".p ub", + "prec io", + "ĠV irt", + "Ġrom an", + "it at", + "L ex", + "_inf os", + "Ä °", + ". other", + "VE LO", + "Ġp onder", + "Ġh anno", + "( Page", + "do i", + "Ġpol ite", + "Ġprogram mer", + "D ies", + "$ d", + "Ġrep lication", + "add Column", + "fr ican", + "Ġl eng", + "be er", + "o it", + "Ġw asting", + "yl im", + "me asure", + "N eg", + "Ġpart ie", + ".con sole", + "ĠGu inea", + "TE L", + "_f act", + ".ch unk", + "Ġl ent", + "Ġall er", + "Ġठķ", + "_id le", + "Ġad missions", + "JSON Array", + "Ġv ibration", + ".h elpers", + "å¤ ĸ", + "Ġh en", + "j ohn", + "Ġì ĥĿ", + "Ġjud gement", + "Ġge en", + "ter ra", + "^ {", + "ĠI z", + "Ġc â", + "inst ances", + "Ġthreat ens", + "Ġm üssen", + "Kind OfClass", + "Ġstoryt elling", + "_d emo", + "ri as", + "Priv acy", + "h ift", + "ĠY i", + "es or", + "íķ ł", + "ens itivity", + ".W riter", + "ภĤ", + "D istrict", + ".get JSONObject", + "Im pro", + "(get Resources", + "ĠS PELL", + "rodu ce", + "Ġslow ed", + "Ġlin ewidth", + "Ġhonest y", + "ĠCo ord", + "ĠF ork", + "ĠDispatch Queue", + "ĠCl iff", + "ĠW iring", + "_TIM ESTAMP", + "oll ah", + "av oid", + "++ ];Ċ", + "sem antic", + "-c ss", + "Ġv eto", + "ĠM err", + "Ġlegisl ators", + "CEE DED", + "Ġquestion naire", + "ĠP ills", + "Cal culate", + "(c ore", + "' e", + "Ġdis like", + "ĠPre ferences", + "_EX TERNAL", + "è° ĥ", + "Ġd odge", + "æľį åĬ¡", + ".n ames", + ".draw Image", + "_p rom", + "uck land", + "Ġ<$ >", + "ı z", + "/s ite", + "é¡ ¹", + "rop he", + "Ġcomp elled", + "Ġl aptops", + "Ġun i", + "C LOSE", + "Ġcasual ties", + "ĠUn iform", + "Term inal", + ". \",\"", + "D AT", + "(T reeNode", + "ĠGand hi", + "(st mt", + "AX B", + "* M", + "Ġumb rella", + "an imal", + "Ġgr pc", + "Ġwhere by", + "Ġfloat s", + "ĉ arg", + "Ġdb g", + "Ġexceed ing", + "Event Type", + ".SaveChanges Async", + "Ġ{ {{", + "Ġow ed", + "ahren heit", + "Ġì §", + "Ġequ ipo", + "ur ai", + "Ġid ol", + "] \")Ċ", + "_m ajor", + "Ġentire ty", + "inger print", + "ç os", + "/ account", + "ĉ right", + "urs os", + "ĠE DT", + "_INS ERT", + "Ġsh ining", + "Ġ< :", + "Edge Insets", + "Ġcolon ies", + ". IM", + "ĉĠ ĉ", + "RO AD", + "CC CC", + "pl acing", + "Ġget Activity", + "em acs", + "' %(", + ".click ed", + "ĠTh em", + "is ia", + "Bus car", + ".re name", + "Ġo ath", + "Ġafter ward", + "ĠU FO", + "AP S", + "ĠJackson ville", + ".s ome", + "Conf irmed", + ".s can", + "ig Integer", + "Decor ator", + "sh ield", + "ress ive", + ".d id", + "请 è¾ĵåħ¥", + "Ġsh utter", + "D am", + "Ġparent ing", + "ey ed", + "$ item", + "-de velop", + "Ġextract s", + "Ġdecentral ized", + "ĠEl sa", + "_sp in", + "]) +", + "-in itial", + "Ġmult itude", + "Ġsens ory", + "ĠMODE L", + "Ġsafeg uard", + "ì ¹", + "Ġhunt ers", + "ĠT iny", + "IN O", + "decor ate", + "ĠNo Such", + "H o", + "( Response", + "Ġr uler", + "ĉ short", + "Ġc aster", + "Ġclient Id", + "Ġp db", + "ëı Ħ", + "it ic", + "ĠGame State", + "Ġnew Item", + ")ĊĊ ĊĊĊĊ", + "ou is", + "n oc", + ".BL ACK", + "_V ECTOR", + "---------- ();", + ".get P", + "any e", + "Ġneur on", + "if old", + "ĠK nown", + "Bit coin", + "Any way", + "ay ette", + "Ġ' ['", + "Ãł nh", + "m gr", + "Ġcor related", + "Ġn ause", + "Ġment ality", + "has Many", + "ĠF G", + "amp ie", + "IT U", + "F s", + ".S p", + "_b etween", + "Dep endencies", + "ou g", + "Place holder", + "= text", + "ĠMan aging", + "ocal ypse", + "åĮ Ĺ", + "_m ag", + "f ld", + "â ij", + "C AM", + "ĠHelp ers", + "Ġd ost", + "/ out", + "Ġassass ination", + ".get Image", + "ĠKenn y", + ".' )ĊĊ", + "){ //", + "ĠR anger", + "Ġg ek", + "Ġsinc ere", + "< Value", + "ĠD OT", + "ĠVict ory", + "Ġleg ends", + "Ġpr isons", + "(ex pression", + "ĠR abbit", + "_s entence", + "Ġbit es", + "Ġon Failure", + "ĠâĪ Ī", + "K im", + ".g ender", + "ĠÎ »", + "Ġ[ .", + "\"] );", + "land ing", + "-d igit", + "TE MP", + "ĉ entry", + "Ġstrt ok", + "Ġdesc endants", + "um no", + "Ġlean ing", + "Ġspecific s", + "q n", + "ĠSp art", + "Ġpor r", + "EDIATE K", + "Ġse per", + "' aut", + "ĠSTE P", + "ĠBorder Layout", + "Ġret ros", + "ĠSalv ador", + "ĠEN GINE", + "x dc", + "T weet", + "v k", + "Ġì ²", + "] <<", + "het ics", + "c oding", + "Re ach", + ".re q", + "gu ide", + ".s cope", + "sh irt", + "rog ate", + "SET TING", + "ĠProte in", + "Ġe ing", + ". EMPTY", + ".d f", + "Ġclear er", + "Ġc rossover", + "ĠTo ys", + "Ġco ated", + ".M onth", + "ĠAtt ach", + "/ run", + ".t abs", + "Ġogs Ã¥", + "B rown", + ".D ATE", + "Ġf os", + "åŃŠ符", + "W ood", + "-th ree", + "her ited", + "Ġ rop", + "( ac", + "Ġembod iment", + "ĠKenn eth", + "Ġcan non", + "Ġb idding", + "čĊ", + ".get Resources", + "Ġl ump", + "_const s", + "( ext", + "ĉd ir", + "â Ŀ", + "Ġpadding Top", + "Ġobs ession", + "Ġb anning", + "ĠApp Module", + "Ġpart isan", + "Ġcatalog ue", + "Ġmin ors", + "Ġpitch es", + "we ep", + "Ġundert ake", + "Ġthem ed", + "aud it", + ".scroll Top", + "Ġr er", + "Ġsympt om", + "Ġopen ings", + ".block s", + "open id", + "Ġas sh", + "-s ave", + "ĠP ig", + "Ġreg ain", + "Ġin icial", + "/f avicon", + "ĉ exp", + "Ġsp ices", + "isk a", + "claim s", + "m ak", + "definition s", + "Ġcorrespond ent", + "ĠCann abis", + "__ ,Ċ", + "ĠL ucky", + "ĠGa ussian", + "ĠN early", + "C AD", + "'] ]Ċ", + "Ġadequ ately", + "ĠT ITLE", + "constitution al", + "-m m", + "_ override", + "Ġbl as", + ".ready State", + "Ġremin is", + "Ġrein forced", + "ĠColl abor", + "Ġdecor ating", + "Ġb achelor", + "ERRU PT", + "Ġup right", + "ip ation", + "ĠNob le", + "Ġvalue ForKey", + "Ġset Loading", + ".I gnore", + "å ģ", + "G lobals", + "ĠM ent", + "AS SES", + "Ġlim bs", + "ĠH UD", + "inc i", + ". iv", + "ĠQ ModelIndex", + "F use", + "Ġped al", + "_F REQ", + "( verbose", + "Ġlong itud", + "ĠChar ter", + "ê ·¸", + "Ġbund les", + ". ignore", + "um bo", + "EM A", + ".... ...", + "s x", + ".C ard", + "Ġhe ute", + "Ġste er", + "j umlah", + "Ġ{ _", + "_Check ed", + "Ġf ax", + "ĠG ust", + "itch ens", + "Ġ ))ĊĊ", + "Ġremark ably", + "/ XML", + "- remove", + "_b t", + "Ġinc ub", + ".p ackage", + ".current Thread", + "ĠHigh lander", + ".s ide", + "s plash", + "Ġ ici", + "= D", + "Ġp uck", + "Ġball ots", + "Ġhug ely", + "co eff", + "Ġp Data", + ".C OLUMN", + "ĠHe aling", + "Ġord in", + "! ),", + "Ġ' ',čĊ", + "(m d", + "ĠS ask", + "< strong", + "Ġsurviv or", + ".s eries", + "Ġcaffe ine", + "Ġ` (", + ".TRA ILING", + "_ Input", + "(\" ^", + "z d", + "& );Ċ", + "ĠP ing", + "Ġv oucher", + ".r ating", + "-sh irts", + "ĠRetrie ves", + ".al ibaba", + "Or acle", + "_MO V", + "Old Data", + "Ġ/* čĊ", + "Ġg boolean", + "Ġ=> čĊ", + "Ġr á", + "Ġbl unt", + "ĠImage Icon", + "if ik", + "RT C", + "Ġfib ers", + "Ġto ile", + ".s ent", + "ĠPy Qt", + "$ app", + "Ġmed io", + "Ġgrant ing", + "Ġtsl int", + "ĠM ö", + "(fig size", + "Ġhur ricane", + "Ġlif es", + "Ġà Ħ", + "rocess ing", + "_st andard", + "- option", + "')) )", + "Ġvac ant", + "å· ¥", + "ĠH ollow", + "handle Change", + "Ġdiv ider", + "ĠEngine ers", + "Ġsv ens", + "Ġcompl iant", + "t anggal", + "ĠC redits", + "ĠEm irates", + "Rule Context", + "Ġreal ization", + "Ġdistr acted", + "]+ =", + "Ġaug ment", + "ĠD w", + "ot p", + "or rent", + "Edit ar", + ".st ock", + "St udy", + "pe ctions", + "ĠGame Manager", + "= cut", + "Ġf lock", + "ĠRom ans", + "th em", + "-h op", + "Ġscreens hots", + "Ġ/* !Ċ", + "Ġconvers ions", + "Ġnormal ization", + "(config uration", + "Ġa eros", + "_se curity", + "! 'Ċ", + "B onus", + "ĠDR IVER", + "ĉ Date", + "t ie", + "ĠWy oming", + "St and", + "it re", + "Ġsh oppers", + "Ġdisadv antage", + "Ġlik ing", + "ç¬ ij", + "Ġunderstand able", + "SE E", + "Ġh oy", + "Ġnin ete", + "Ġcon fer", + "Ġnow rap", + "ĠV ern", + ", čĊčĊ", + "imest ep", + "Layout Manager", + "à ·", + "ĉw ait", + "PLE TED", + "J apan", + "Ġindu ce", + "Ġå ¯", + "оз в", + "_END POINT", + ".h orizontal", + "Ġacceler ated", + "rim on", + "IV ES", + "Trans actions", + "Le an", + "ĠSO UR", + "wh ether", + "y g", + "Ġo id", + "ĠEntity Manager", + "OUN TRY", + "Ġfil a", + "OLUM NS", + "IN UE", + "ĠAn chor", + "TR AN", + "wo o", + "block quote", + "ĠN urse", + "ĠCar p", + "Ġrede em", + ". try", + "ĠJ P", + "Ġtimestamp s", + "Ġ?> \"><", + "ĠREM OVE", + "ĠStar bucks", + "Re ally", + "Ġflood ed", + ".C allback", + "Drop Down", + "ip ro", + "Ġt ended", + "l te", + "Ġproport ions", + "- te", + "ĠR ena", + "lic ate", + "for ces", + ".ex tra", + ".auth enticate", + "в од", + "¡ °", + "Ġfor ControlEvents", + "Ġsen ha", + "Ġke in", + "Ġmin ist", + "ĠPre ference", + "ĠTele graph", + "Ñĥ п", + "str pos", + "Ġillness es", + "Ġp igs", + "Ġget Intent", + "S ol", + "Ġ ¡", + "(c pu", + "[ prop", + "s creens", + "'); ?>", + "ĠAct s", + "Ġstr dup", + "Ġaver ages", + "an al", + "ĠCas ual", + "Group Box", + "ĠHand book", + "/ comments", + "Ġnumber ed", + "Ġbroadcast ing", + "çĽ ij", + ".native Element", + ".m u", + "Ġupdated At", + "ĠDoes n", + ".A C", + ".c oll", + "Ġrec order", + "_sh a", + "B g", + "b il", + "Ġbol ts", + "Ġç ¬", + "Ġim posing", + "ĠInformation en", + "_flash data", + "e conomic", + "Rem ark", + "uc as", + "ĠOff icers", + "ĠT ER", + "W alk", + "Ġmerc ado", + "_g enerate", + "H Y", + "Call ing", + "s nap", + "script Id", + ". operation", + "ĠFl ame", + "l iness", + "Ġrent ed", + "_t oggle", + "-ch anging", + "ĠT Y", + "' util", + "EE P", + "Ġgraph ql", + "ĠUn i", + "Ġimp ulse", + ".B asic", + "Ġenerg ies", + "M ARY", + "ĠMar cel", + "Ġmort al", + "Ġf res", + "m ens", + "m otion", + "Ġsample d", + "âĢľ That", + "id ay", + "qu ipment", + "get Int", + "ĠA bsolute", + ",' \"", + "un ed", + ".sh are", + "Ġ} )(", + "mm m", + "ĠR ising", + "ä» »", + "Ġun employed", + "x fa", + ".f ollow", + "ĉĉĉĉ ĠĠĠĠĠĠ", + "sl t", + ".P hone", + "Ġkn ives", + "Ġe ve", + "on Click", + "] ))čĊ", + "ĠW itness", + "ĉ NS", + "ĠE OS", + "ĠSte fan", + "ĠPri est", + "âĢĶ which", + "Get String", + ". By", + "Ġup stairs", + "Ġdetr iment", + "bro ken", + "emb ro", + "Ġnic otine", + "il ion", + "Ġaston ishing", + "_ aff", + "ĠLess on", + "Ġaccident al", + "od or", + "Ġdec ir", + "Ġnew Name", + "+ .", + "çĽ ¸", + "igs list", + "ĠG ithub", + "Ġsuccess ive", + "rac ial", + "Ġen viron", + "éªĮ è¯ģ", + "Ġredirect ed", + "T OTAL", + "Ġgrab bing", + "ĠL ance", + "Ġfor fe", + "_C B", + "å¾ ®", + "El apsed", + "_w ay", + "(Dialog Interface", + "_me asure", + "x bb", + "D og", + "Dep art", + "-s rc", + "res olver", + "with standing", + "_sh ell", + "ĠLast Name", + "ĠAv iation", + "Ġbegin ner", + "(\"% .", + "(to ol", + "Ġн ов", + ": init", + "(A PI", + "ĠMorr ison", + "vt Color", + "Ġstap le", + "/ INFO", + "Ġsupern atural", + "Ġste ak", + "tim eline", + "zz le", + "\" `ĊĊ", + "Second ary", + "ĠNep al", + ".String Utils", + "Ġad am", + "Ġ( ...", + "Ġsub stitution", + "Ġboard ing", + "ĠKey word", + "ĠAss ault", + "dbc Template", + "Ġorder Id", + "( engine", + ".assert That", + "ĠVen us", + "Ġhomic ide", + "ĠA val", + "Ġg utter", + "ĠSupport ed", + "/p art", + "Ġac claimed", + "H istor", + "Ġmes es", + "ü ber", + "ĠRen ew", + "Ġgr as", + "ĠE k", + "Ġin file", + "ind y", + ".m usic", + ".S croll", + "ĠA ges", + "ĠNar uto", + "ĠG ather", + "Ġconfirm ing", + "= (\"", + "Ġpitch ed", + "ole y", + "Fr ance", + "+' \"", + "$ total", + "Ġon de", + "Ġd itch", + "_s igma", + "Ġcontinu ity", + "re ward", + "- load", + "Ġproces o", + "Lock ed", + "st aw", + "Ġsp inal", + "l azy", + "! ==", + "j est", + "Ġd un", + "ĠRod gers", + "ĉ grid", + "Ġlog os", + "ĠBeng al", + ".s uper", + "Provid es", + "Ġnut rient", + ".T imestamp", + "IZ ATION", + "åĨ Į", + "Ġf ats", + "ĠX xx", + "ct ica", + "Target s", + "Ġcont ours", + "Ġre ordered", + ": Array", + "Ġtoler ate", + "V ir", + "Ġter ribly", + "Ġbr icks", + "(& _", + "h b", + "Port al", + "ĠB read", + ". which", + "ÂŃ t", + "as InstanceOf", + "Ġj object", + "ĉ length", + "_M T", + "; \">čĊ", + "_EX IST", + "Ġmat ernal", + "RE L", + "Ġê²½ ìļ°", + "he e", + "Ġlayout s", + "ĠL ap", + "ais y", + "Ġst umbled", + "ĠU IG", + "ĠS co", + "Ġimp aired", + "RES SED", + "Ġab uses", + "V F", + "AR B", + ".N AME", + "r ch", + "prim ir", + "_com pleted", + "Ġp enny", + "Ch rome", + "(b egin", + "ern en", + "- checkbox", + "Plain OldData", + "ĠL PC", + "r ade", + "sp ir", + "Ġcon ceived", + "T ips", + "ĠIo T", + "ĠG an", + "èģ Ķ", + "Ġbi ases", + "Ġconsult ants", + "ple d", + "_ ht", + "associ ated", + "], ĊĊ", + "Ġdelight ful", + "ĠÑĤ ек", + "Hel vetica", + "( load", + "-exp and", + "_W IDGET", + "to a", + "ĠA kt", + "Ġom n", + "Ġcl auses", + "Int el", + "*/ }Ċ", + "_reg istration", + "Ġold Value", + "Ġrest oring", + "Ġun real", + "O VER", + "ĉĊĉĊ ĉĊ", + "AT S", + "_pro be", + "Ġdiv isor", + ".update Dynamic", + "å¹ ³", + "Produ ces", + "st amp", + ".j boss", + "ĉt ask", + "! (:", + "Ġpsych ic", + "@ class", + "M artin", + "ĠPass ed", + "clar ations", + "h el", + "а Ñĩ", + "ĉc opy", + "-b in", + "z an", + "ig ram", + "া à¦", + "(s ig", + "ĠC aval", + "_ ##", + "Ġ% =", + "out lined", + "ĠAc id", + "Ġunpredict able", + "-d ashboard", + "Hex String", + "+ c", + ".P ublic", + "Ạ©", + "Ġconvey or", + "ĠE B", + "Ġselect s", + "Ġknock ing", + "ĠC ec", + "IBUT ES", + "owa Äĩ", + "g atsby", + "* v", + "ent ropy", + "Ġdispatch ed", + "Ġcam el", + "ĠSat urn", + "Ġover weight", + "( phone", + "par able", + "% B", + "_v ectors", + "Ġbrew ing", + "ĠT k", + "ĠDownload s", + "ĠS aved", + ".Pr ice", + "Ġcur ved", + "ĠParen thood", + "è ¶", + ".p nl", + "plet ely", + ".D ay", + "Ġadvertis ers", + "Ġej ec", + "Ġpr zed", + "ë ¯", + "! ';Ċ", + "ĠK ush", + "ĠT AB", + "Ġquest s", + "Ġcoinc idence", + "umm ies", + "ĠKash mir", + "ĠEth ics", + "_g rowth", + "Ġakt iv", + "Ġgroup ing", + "å¢ ŀ", + "_tr uth", + "åIJ ¬", + "t odos", + "is et", + "Tex Coord", + "ä tt", + "ĠZ ur", + "ro ys", + "_M AGIC", + "Ġbrew ery", + "( State", + "ĠSM ALL", + "ĠPl ants", + "it bart", + "each er", + "ĠAd elaide", + "L u", + "Ġf ick", + "und les", + "_load ed", + "и е", + "P oll", + "rit ic", + "EL Y", + "Ġ+ '", + "ĠProf ession", + "Ġst amps", + "ĠS ew", + "scroll View", + "Ġcomm unist", + "/pro blems", + "}čĊčĊ čĊčĊ", + ", o", + "Ġu dp", + "Ġob ese", + "appro ve", + "ancell ation", + "_G ame", + "ĠHas htable", + "adaptive Styles", + "Ġpossess es", + ".match er", + "function al", + "M rs", + "ĉs ave", + "ĠDb Type", + "Ġk en", + "get Context", + "Ġm ans", + "( rel", + "ĠBrother hood", + ") `Ċ", + "è§ £", + ".In formation", + "OutOfRange Exception", + "ĠS ek", + "C as", + "Ġblog gers", + "E ither", + "(\" \"\"", + "Ġpin ch", + "Ġco arse", + ") p", + "ĠP ulse", + "Ġlear nt", + "Ġdent ist", + "Ġon change", + "Ġdirect ives", + "( actions", + "ny der", + "ĠSh ir", + "T rait", + "_de p", + "ĠP ET", + "ĠRE P", + ".App Settings", + "cu ador", + "iden av", + "Ġenv i", + "Ġsl ammed", + "ĠSh oot", + "Ġdate Format", + ".j oda", + "ve ys", + "Ġ) .ĊĊ", + "Ġcare g", + "ĠPar allel", + "_ translation", + ".function s", + ". obs", + "Runtime Exception", + "[] =", + "over view", + "ĠSch l", + "Ġno isy", + "ĠOn PropertyChanged", + "S ending", + "Ġunf amiliar", + "U pon", + "ĠPrint s", + ".t yp", + "Ġflee ing", + "ĉm ove", + "( Un", + "Ġq r", + "× ľ", + "_b eta", + "Ġsk ies", + "ĉm e", + "W ND", + "Ġstick ers", + "bl as", + "Ġinsert s", + "Ġvers es", + "ĠD ew", + "Ġtang ible", + "Ġhe cho", + "P OL", + "Ġte ardown", + "om nia", + "IB E", + ".c over", + "_str ategy", + "^ -", + "set Position", + "u ale", + "S igned", + "Ġif ace", + "as eline", + ".set Time", + "ĠMin eral", + "ĠFight ing", + "sk ins", + "Ġdiscrim in", + "Ġdans k", + "ĠPr inceton", + "ac ist", + "Ġ( ));Ċ", + "tr acks", + "imon ial", + "ad ecimal", + "EP ROM", + "ugg le", + ".Not ification", + "$ mail", + "c antidad", + "ĠJ ung", + "Ġseek ers", + "Ġpl ausible", + "t ier", + "еР¶", + "Ġr apper", + "ĠMan a", + "ĠHttp StatusCode", + "Ġburn t", + "los es", + "ĠF oto", + "ĠJson Object", + "Inst agram", + "Ġsys call", + "Ġreal ities", + "ĠMAT LAB", + ":^ {Ċ", + "TER M", + "ĠC bd", + "ĠPar agraph", + "Ġtrav és", + "Ġconstruct ing", + "Ġsw al", + "Ġp ige", + "LL LL", + "-ex isting", + "G ets", + "Ġmelt ed", + "Ġmitig ate", + "H en", + "Ġh m", + "im as", + "ĠA o", + "ĠP erez", + "ĠD AL", + "Ġëĭ ¤", + "Ġdiv is", + "Storyboard Segue", + "ĠMod ify", + "ĠÃľ ber", + "_O VERRIDE", + ".p em", + "unt os", + "Ġespa ñ", + "Ġ{ ?", + "ĠP AY", + "_ip v", + "ĠF ury", + "__ .__", + "el ow", + "-center ed", + "check s", + "_ Reg", + "-J avadoc", + "ĉ load", + "ĠLik ewise", + "ا Ùħ", + "UN E", + ".se m", + "x cb", + "ĠC ave", + "_s leep", + "Ġsil ently", + "ĠExt reme", + ".To Upper", + "ĉC HECK", + "Ġc ue", + "ĠQ ByteArray", + "Ġcorrupt ed", + "ĠD é", + "Ġimp ed", + "Get Name", + "Ġinaccur ate", + "Ġso ber", + "е е", + "Ġbar code", + "-- ){Ċ", + "ink i", + "Ġé p", + "Ġd ri", + "ĠAL T", + ">>>> >>>>", + "ont a", + "[ L", + "Ġinter es", + "ver ting", + "Ġdi agnostics", + "p dev", + "è ©", + "ĠIntegr ated", + "). '", + "_g c", + "$ text", + ".g ames", + "ĠT erra", + "' Re", + ".trans fer", + "_F IFO", + "get Model", + "Ġbl and", + "ĠCole man", + "Ġpr imes", + "Ġæ Ī", + "Ġcross es", + "n k", + "G ING", + "Ġ' ^", + "ĠB lob", + "Ġinter course", + "ĠBl vd", + "Ġweigh s", + "_reg ular", + "ĠPer th", + "Ġsepar ating", + "Ġb illed", + ".tab Control", + "Ġpup pet", + "Ġutil ization", + "Ġâĸ ł", + "Ġsucc es", + "Ġl amps", + "_pro j", + "E ric", + "Ġren ovation", + "ĠFam ilies", + "ĠB its", + "part ials", + "-M en", + "s olution", + "Ġd warf", + ".IN TEGER", + "ĠLO CK", + ". ct", + "Ġexcer pt", + "ĠP ix", + "ĠFirst Name", + "ANT ED", + "ĠAd mir", + "-h elp", + "P rior", + "ĠAl ign", + ".IN STANCE", + "Line Edit", + "('/ :", + "Ġin et", + "od us", + ".p kl", + "ĠK Y", + "up ert", + "Ġn erves", + "_grad ient", + "} ','", + "_un ref", + "Ġs aturated", + "ĠConn ected", + "ĠF N", + "EX IT", + "Ġtele port", + "Ġav ait", + "Page Route", + "Ġdivor ced", + "(l ang", + "f st", + "ĠT yr", + "Ġmess enger", + "if stream", + "X S", + "ĠBank ing", + "Ġinfect ious", + "ĠM ons", + "_LO OP", + "Ġzur ück", + "Ġobt ener", + "/re pos", + "V el", + "ac ro", + "Ġuser Repository", + "style Type", + "ĠS RC", + "VML INUX", + "rec ursive", + "/ bar", + "_ch ip", + "omin ated", + "ĠN it", + "âĢĶ to", + "ĠBudd h", + "ом еÑĢ", + "ĠM AG", + "ĠC HE", + "_d en", + ". raises", + "_de gree", + "Ġpump kin", + "_tem plates", + "_M EDIA", + "ĠTim eline", + "Ġb ots", + "Object Type", + "Ġbu ys", + ".post s", + "C AL", + "wait ing", + "ĠDani els", + "Ġd abei", + "ĠS igma", + "il or", + "ig el", + ", W", + "AD S", + "( panel", + "ì² ´", + "it ating", + ".p alette", + "Ġmos quito", + "Ġt ego", + "(parse Int", + "Ġdes pués", + "p romise", + "Ġw ij", + "types cript", + "ĠT v", + "_IDENT IFIER", + ").ĊĊ Ċ", + "_fl at", + "its u", + "US R", + "ex perience", + "-f it", + "ph inx", + "_th resh", + "Ġide ally", + "ĠFre eman", + ", DB", + "_r w", + "çŃ ī", + "U b", + "_stat istics", + "=\" \"><", + "Ġch ore", + "Ġy ork", + "inst alled", + "Add itionally", + "Ġp stmt", + "yl ko", + ":: Ċ", + "Fore st", + "Ġhead set", + "Ġgall on", + "ÑĢ ем", + "Ġwithdraw n", + "ĠC andidate", + "Ġmel ting", + "Ġfree zer", + "Ġh l", + "_HE LP", + "m ime", + "( /*", + "Ġth irst", + "$ return", + "member of", + "еР±", + "ĠHttp ServletRequest", + "( ob", + "_ Result", + "Ġassert ed", + "Ġfulfill ing", + "Ġstret ches", + "par ated", + "-f unded", + "Ġå Ľ", + "ing les", + "_c a", + ". condition", + "ĠDis plays", + "Ġor ang", + "ĠC RE", + "Ġgl Bind", + "ĠSelect or", + "/ type", + "ĠAlex a", + "ched ules", + "ĠPen insula", + "Ġpar ity", + "ĉ dest", + "ĠDo ors", + "čĊ ĉčĊ", + "_dim ension", + "Ġa load", + ".St oredProcedure", + "(p aren", + "ĠBur ke", + "') ]Ċ", + "- engine", + "Ġqu ir", + "ĠHy brid", + "ĠDo e", + "Ġout lines", + "ĠTrend s", + "_N V", + "per iments", + "ĠH in", + "? ',", + "ĉ Text", + "F UL", + "Ġsm ells", + "Ġs lick", + "Ġmis erable", + "ĠArray Adapter", + "Ġparam String", + "H om", + "_l iterals", + "us uarios", + "Ġprompt ing", + "_l azy", + "ĠActiv ation", + "_ oc", + "We ak", + "Ġan ecd", + "ĠU CLA", + "= re", + "isse ment", + "ĠEsc orts", + "Ex cellent", + "ĠP ause", + "Ġre positories", + "T OR", + "ari ate", + "_is o", + "up dates", + "hal b", + "udi ante", + "ë¡ Ŀ", + "Ġna ive", + "ĠP eg", + "ĠL ounge", + "ARG IN", + "(b in", + "On ClickListener", + "ĠFA ILED", + "Ġl ite", + "Ġd zie", + "ĠL iteral", + "iv or", + "fc ntl", + "Ġe ats", + "Ġq ed", + "Un lock", + "rid ing", + "und ai", + "= M", + "AT TER", + "Configure Await", + "ici as", + "ustom ed", + "Ġsuccess ion", + "end Time", + "ĠJ upiter", + "Ġjud ging", + "d ration", + "_d ocs", + ".m o", + "Ġeduc ators", + "ĠV ine", + "Con d", + "[ out", + "q b", + "\\ Validator", + "Ġmean ings", + "Ġpresent ly", + "Ġdiv iding", + "otten ham", + "asc ular", + "Ġtrail ers", + "ĠC LOSE", + "ам и", + "âĢĻ ai", + "ĠG ain", + "w or", + "Ġpl anner", + "Ġdistrib uting", + "v at", + "month s", + "x label", + "H F", + "V iol", + ".BASE LINE", + "еÑĤ ÑģÑı", + "ĠR otate", + "Ġtx n", + ": bold", + "Ġb loss", + "Forg ery", + "( embed", + "Ġjak o", + "s printf", + "the ir", + "Ġexhib its", + "- static", + "he cy", + "get ActiveSheet", + ".c lients", + "ãģ į", + "_h ide", + "[ word", + "C b", + "add Item", + "ax e", + "_r adio", + "al ion", + "mod ifier", + "Ġsat uration", + "Ġden om", + "_p ixels", + "m ess", + "(f l", + "at if", + "Ġse cs", + "Ġpro stitution", + "Ġgrand children", + "Ġparad ise", + "ĠF eld", + "_B INARY", + "it ous", + "๠Ħ", + "Ġflash ing", + "-s ided", + "Ġcontrad iction", + "/* ĊĊ", + "y label", + "ĠT et", + "Ġadm ire", + "res o", + "Ġlet z", + "ĠSE ARCH", + "sl ots", + "ĠRew ards", + "ĠH og", + "ĠNS Data", + "st ash", + "F all", + "ĠA mer", + "Line arLayout", + "/ photos", + "Ġfe ather", + "Ġ| čĊ", + "Download s", + ".Start sWith", + "Ġ// #", + "ine Transform", + "Ġaff id", + "V tbl", + "ĠRog ue", + "scri bed", + "Ġfa uc", + "ĠMon roe", + "Ġdecl ares", + "mod ern", + "re on", + "ay be", + "P ASS", + "f ers", + "_MULT I", + "ĠMath ematics", + "Ġsud ah", + "_ATT ACH", + "Ġnumber With", + "ĠSol omon", + "j in", + "ograf ia", + "ö l", + "_d esign", + "cul ated", + "ĠL una", + "ies z", + "Ġ=> '", + "Ġrevel ations", + "Al ong", + "( ed", + "ĠF ilename", + "Ġy label", + "Sec ure", + "Ġbus ca", + "agn osis", + "_RE CE", + "Ġoverl apping", + "Ext ent", + "Ġanticip ation", + "Check s", + "ĠALS O", + "or c", + "iling ual", + "it ational", + "Ġadv ancement", + "ou ro", + "ĠP redicate", + "å¾ Ĺ", + "er ia", + "ĠPier ce", + "or io", + "Ġmer its", + "Ġpe anut", + ".P ackage", + "ĠCon duct", + "_SENS OR", + "Ġbo iling", + "Ġin tra", + "ĠI GN", + "ĠF ur", + ".Ref resh", + "ĠRe ach", + "_dec oder", + ".Ex p", + "ĠÑĤ ак", + "p ill", + ", Q", + "ĠGr ill", + "Ġpop ping", + ".A g", + "Ġpro yecto", + "Ġmile age", + "Ġec ological", + "] ]);Ċ", + "Ġ Ń", + "sub plot", + "ac ad", + "ĠTry ing", + "rec ipes", + "$ criteria", + "ĠPers ian", + "-b ound", + "M ASK", + "ĠG esture", + "Ġk k", + "ĠP VC", + "Ġprohib ition", + "Ġcom ando", + "ĠLO OK", + "Sh opping", + "Ġdist ortion", + "< Boolean", + ".Get Length", + "um pt", + "\\ Product", + "ell ery", + "Ġfire wall", + "form atted", + ".red is", + "Ġes a", + "ĠRh ode", + "S om", + ".n on", + "Ġ' ).", + "Ġget View", + "ạ n", + "pr us", + "Mat thew", + "Ġs ia", + "ĠF ors", + "G PU", + "ient ras", + "_IN ST", + "Ġol arak", + "Ġimport ing", + "T CP", + "/ \");Ċ", + "e ither", + "Ġfresh ly", + "c ascade", + "(char acter", + "ĠJe ep", + "ot ics", + "_ UTIL", + ".Xtra Printing", + ".first Child", + "ĠEx cell", + "Ġd vd", + "Ġt aller", + "Ġr as", + "yp ass", + "Ġassign s", + "Ġgri ev", + "-m ore", + "J D", + "ĠBurn s", + "' >čĊ", + ".D ependency", + ".Query String", + ".O wner", + "Ġexp iry", + "Th u", + "( Vec", + "Ġhazard ous", + "Ġr pm", + "AP ON", + "Ġadd Target", + "sv ille", + "p Net", + "ĠIm g", + "ĠTIM ER", + ".An imation", + "Ġbe k", + "Ġass ort", + "Ġle bih", + "Ġbody Parser", + "Ġvibr ating", + "ID L", + "Ġbutter knife", + "int ers", + "Ġpersu ade", + "ĠLGBT Q", + "è ĭ", + ".s oft", + "Ġbe ams", + "_s ur", + ".D ef", + "Ġl abs", + "ĉ plt", + "Ġsk ins", + "Ġtransf erring", + "Ġimag inary", + "_E nd", + "; background", + "Ġl aps", + "_COM MENT", + "(S DL", + "ond s", + ".Rec ord", + "ĠIm plements", + "_t icks", + "() ))ĊĊ", + "Ġa rose", + "] ?", + "ĠM p", + "ĠI Command", + "Ġsculpt ure", + "Ġcontract ed", + "< HTML", + "Ġcal end", + "at y", + "/ Sub", + "Ġkv inn", + "_ IGNORE", + "ĠSh ane", + "ML S", + "Ġstim ulate", + "Part ition", + "Ġm un", + "ó m", + "eral a", + "- account", + ".B inary", + "c é", + "Ġse ize", + "connection s", + "ĠĊ ĠĠĠĠĠĠĠĠĊ", + "ĠDi agnostic", + "V ISIBLE", + "ĠRun s", + "Ġimpress ions", + "s uite", + "ob le", + "~ -", + "ak ukan", + "< Person", + "ĠN os", + "ĠG ui", + ".wait For", + "RE SET", + "Ġpost pon", + "Dis cover", + "arr ison", + "sh aw", + "b lood", + "AJ OR", + "æĽ´ æĸ°", + "ĠM use", + "æĶ ¶", + "Ġret aining", + "ot te", + "Ġmos que", + "ĠS ne", + "Ġstandard ized", + "Ġmain land", + "_th ree", + "unge ons", + "get Doctrine", + "Ġwh ale", + "Ġag g", + "ĠP orsche", + "now led", + "lat ent", + "ĠRel ation", + "Ġ// '", + "Ġshut ting", + "ĠRem ix", + "_c ov", + "Ġs ailing", + "Ġv owed", + "Ġp ots", + "out u", + "Ġhair y", + "cast s", + "Rel oad", + "Ġre connect", + "ter a", + ".child Nodes", + "ĠR ack", + "Ġcurrent Index", + "Ġall en", + "Ġ çĶ¨æĪ·", + "ĠC ubs", + "[ X", + "_SE Q", + "_RE MOVE", + ".get Action", + "(/ ^", + "err ar", + "Ġ ether", + "cur ve", + "Ġsl ap", + "Ġu om", + "O thers", + "Ġen gr", + "Dis position", + "Ġst aged", + "E ye", + "ĠA ux", + "auth enticate", + "Ġ$ ?", + "ĠAndre as", + "Ġset w", + ".A rt", + "Ġforecast s", + "Ġa unt", + "-m iddle", + "Ġmis d", + "des k", + "Ġescort e", + "ĠCas a", + "rop ical", + "Ġexem ple", + "plan et", + "(U INT", + "Ġwh ip", + "ĠPC B", + "clide an", + "=\" \\", + "Ġox ide", + "Ġsucceed s", + "der ived", + "ĠEcon om", + "_co ordinates", + "ir as", + "D raft", + "Ġvisual ize", + "B rian", + "_ASS UME", + "ĠObject Id", + "Ġtrain ers", + "_FOR CE", + "Ġcon soles", + "- process", + "lic her", + "ĠSim mons", + "T aking", + "ĠCl aims", + "Ġdiffé rent", + "Activity Result", + "Ġsn s", + "éĢī æĭ", + "ĠCr us", + "Ġll am", + "r ab", + "ĠJo an", + "AA A", + "ĉf ilter", + "ish ops", + "get ting", + "à µ", + "Ġquant o", + "P ast", + "ov ich", + "Ġin justice", + "ĠF LOAT", + "Ġal right", + "\\ DB", + "( GameObject", + "u ish", + "(b ot", + "Ġgall ons", + "ĠR é", + "ĠS aid", + "ĠSTDMETHOD CALLTYPE", + "ais ing", + "_process or", + "ell idos", + "ter dam", + "ĠBe am", + "Text Area", + "Ġret orno", + ".M ake", + "Ġ$ (\"<", + "Ġlock down", + "Ġremed ies", + "Ġve el", + "x ee", + "do ctype", + "F il", + "ĠExp and", + "Ġemp loys", + "Ġsession Storage", + "Ph p", + "P ublish", + "Ġret al", + "f abs", + "ynam ics", + "Ġtoss ed", + "ĠnumberOfRows InSection", + "x path", + "\\ modules", + "Ġdis astr", + "ĠM ULT", + ".M esh", + "-st age", + "Ġs df", + "it ung", + "ug es", + "Ġ?> \">'", + "kin son", + "Ġк ол", + "ogn itive", + "_ li", + "Ġim minent", + "Ġaff inity", + ".sign al", + "Ġnot ch", + "ĠSteel ers", + "max length", + "K K", + "ĠEug ene", + "_P WM", + "ro i", + "Ġâ Ĺı", + "ĠH amburg", + ".M ust", + "Ġax e", + "en ef", + "Ġamb itions", + "ĠSpec ies", + "ĠSt ress", + "Ġa while", + "Ġб Ñĥд", + "Ġwith stand", + "ĠDec oder", + "_in ventory", + "Ġ{ ččĊ", + "Ġt gt", + "Ġrail road", + "W ASHINGTON", + "Ġnegot iated", + "N ST", + "- phone", + ", U", + "Ġexerc ising", + "á» ¥", + "_P IXEL", + "av ors", + "iter ated", + "Ġv ampire", + "ad al", + "In grese", + "Ġun g", + "ject ive", + ".c ells", + "Ġn ano", + "Ġmark down", + "_R ULE", + "(event s", + "Ġl uggage", + "MESS AGE", + "ig keit", + "$ count", + "Attribute Name", + "IG INAL", + "_E nt", + "ĠB F", + "ĠCOM MENT", + "_in i", + "ĠEurope ans", + "ĠB elle", + "åij ½", + ") ['", + "åº Ķ", + "ĠUse ful", + ".re ference", + "() \",", + "_ grade", + "ĠK aw", + "Ġsent encing", + "Ġsocial ism", + "mon ster", + "_L AYER", + "Ġdee pest", + "w k", + "ĠNo ise", + "### ĊĊ", + "Ġpr éc", + "ot le", + "ÑĤ е", + "a uf", + "ib al", + "Ġcon quer", + "> Email", + "Ġamb ulance", + "O AD", + "Ġ(\" %", + "ĠF I", + ".f ixture", + "Ġter se", + "ĠĠĠĠ ĉĉĉĉ", + "Ġsanct uary", + "ug i", + "ĠCom parator", + "Definition s", + "Ġast hma", + "Ġl act", + "Ġhard wood", + ".c lock", + "Ġattract ing", + "ĠM our", + "(d istance", + "ic its", + "Ġbon ne", + "ĠAC CESS", + ".Deserialize Object", + "ĠTyp ed", + "Ġje u", + "Ġapp Id", + "ĠCl ara", + "ĠH F", + "ĠRe ich", + "ipp les", + "//---------------------------------------------------------------- ----------------", + "_del ivery", + "erial ization", + "Ġplaint iffs", + "Sc ient", + "sh opping", + "ĠD ummy", + "ĠW ald", + "Group Name", + "Ġins cription", + "el og", + ":::: ::::", + "_ ld", + "Back Pressed", + ".R aw", + "ĠOn Trigger", + "Ġmuse ums", + "ĠBe en", + "ĠAdvent ures", + "Ġsl ate", + "Ġlet t", + "Ġsu nd", + "ĠG in", + "ĠMechan ical", + ".s hip", + "App Component", + "Ġdest ined", + "Ġdw elling", + "Prof iler", + "Pre pare", + "ze ich", + "Ġsil icon", + "(h as", + "Ġ# %", + "VID EO", + "Ġcollabor ate", + "L in", + "Ġsc opes", + "( className", + "(s d", + "and in", + ".h am", + "Service Impl", + "-des cribed", + "Ġiron y", + "st ial", + "ĠHu awei", + "(re po", + "Ġunexpected ly", + "ĠK ai", + ".inst all", + "\\x f", + "Ġexhib ited", + "_T CP", + "ĠO x", + "_CH O", + "Ġprostitu erte", + "Ġv ä", + "Ġsit o", + "Ġconstitu ents", + "ĠContin ued", + "ĠS AVE", + "r ss", + "/ message", + "ub es", + "Ġmisd emean", + "Ġtax ation", + "Ġstory line", + "h air", + "ĠFind s", + "S IG", + "ver ification", + "~ =", + ".h p", + "Iter able", + "Ñĭ е", + "ator i", + "Ġc tr", + "R x", + "_ );ĊĊ", + "d ag", + ".p in", + "Ġp seud", + "Ġinv o", + "ÑģÑĤ ÑĢ", + "_p ix", + "为 空", + "Ġsw orn", + "âĢĶ or", + "_reg istry", + "Ġdis asters", + "ĠRO I", + "ĠâĢ ķ", + "akt u", + "fore st", + "be iten", + "âĢĶ I", + "ue va", + "eg t", + "Ġsp ikes", + "URE S", + "ĠRecomm ended", + "Ġexplo ited", + "ĠFreder ick", + "_COMP LETE", + "ĠDr ugs", + "!!!! !!!!", + "ĠR iv", + "ST OP", + "RO OM", + "ĠP ASSWORD", + "C ookies", + ".E l", + "á» Ń", + "ĠB ert", + "Ġhash ed", + "ic ester", + "Ġdecor ator", + "Ġquery String", + ": ;Ċ", + "Ġ\" [\"", + "oto pe", + "-A meric", + "ĠMatthew s", + "UR AL", + "âĢľ ,", + "Sum mer", + "f os", + "_CONT AINER", + "_A CK", + "Ġfil tr", + "_dis p", + "_ Re", + "Ġfac ile", + "а ÑĪ", + "Ġìķ Ĭ", + "Ġe ben", + "Ġspr ink", + "ĠQ uint", + "> V", + "Ġhistor ians", + "our met", + "ĠMonitor ing", + "led ger", + "c ott", + "Ġw are", + "GG LE", + "c ars", + "ĠM EDIATEK", + "Ġvol upt", + "_ View", + "HE L", + "(c opy", + "(st ats", + "Ġchrom osome", + "ĠCurt is", + "- conf", + "( asset", + "Ġhv or", + "File System", + "< >();čĊ", + "oc oder", + "ĠC annon", + ") x", + "ĠSm ooth", + "ĠS AS", + "_ ce", + "ĉ prev", + "_m ovie", + "E c", + "_w all", + "< Button", + "ĠF AST", + "Ġon View", + "ul an", + "ĠS UPPORT", + "Ġgesch ichten", + "ĠS ons", + "Im m", + "$ IFn", + "Ġfair ness", + "Ġd pi", + "ats u", + "J osh", + "Equal ity", + "Ġ} ()Ċ", + "_ less", + "ĠR atio", + "ĠC ats", + "ĠS tern", + "Mon ster", + "Ġmer cury", + "ü hr", + "Ġplus ieurs", + ".des erialize", + "sc opy", + ".F alse", + ") animated", + "ĠExp erts", + "Ġ\"\") {Ċ", + ".W hen", + "see also", + ".un pack", + "LE M", + ".select All", + "Ġperception s", + "ud ing", + "ir ling", + "ĠPrint ing", + "gram s", + "ĠFile Stream", + "erv ille", + "il og", + "ic mp", + "_C ount", + "Ġlivest ock", + "- ca", + "doc uments", + "Ġpo les", + "ĉw ant", + "Ġflu ores", + "Ġstand point", + "ĠH uge", + "Ġradi ans", + "ĠUIB ar", + "EDI UM", + "ĠHistor ic", + "_h older", + "ĠMar ines", + "Ġt ä", + ".L ight", + "quir er", + "ason ry", + "div ider", + "ĠFl utter", + "_f b", + "restrict ed", + "ĠEvery body", + "N ão", + "Ġkn ot", + "ĠT witch", + "Ġhall way", + "(C ollider", + "Input Element", + "? )Ċ", + "/ off", + "/ )", + "play ed", + "[ OF", + "Ġbat ting", + "_d l", + "Ġcom edian", + "Ġé v", + "ĠD EM", + "ĠEd en", + ": white", + "' ',", + "Con struction", + "acer b", + "Ġtask ed", + ".man age", + "Rel ationship", + "Ġph on", + "n z", + "_B GR", + "Validate AntiForgeryToken", + "_ air", + "âĢľ When", + "Ġgl fw", + "ĠCon versation", + "_T OTAL", + ", Z", + "Ġg raz", + "Ġiter able", + "ĠP ASS", + "Ġadvert ise", + "Ġmö glich", + "/ train", + "ĠVolk swagen", + "Ġcreep y", + "Ġ\" )čĊ", + "QU ENCE", + "Ġalt ar", + "Ġed its", + "comp iled", + "aw ning", + "ĠD ungeon", + "Ġo sg", + "Navigation Bar", + "Ġtrend ing", + "ĠE co", + "ogg les", + "cd ot", + "| -", + "S ie", + "ec ret", + "ĠN egative", + "ĠL ing", + "ĠD IM", + "ĠC WE", + "ĠCar rier", + "Ġcar tridge", + "_us b", + "= os", + "ĠJack ie", + "Ġo tras", + "Ġcommod ities", + "ĠP resentation", + ")&& (", + "ĠMar tha", + "ĠCath olics", + "ĠM ond", + "об Ñĭ", + "_ absolute", + "Ġash amed", + "pons ors", + "t al", + "Ġsad ness", + "Ġpu ò", + "F ade", + "-pre view", + "ĠRequest s", + "ĠCal vin", + "h orn", + "Reuse Identifier", + "(pro vider", + "/app s", + "ime o", + "ĉ Class", + "S amsung", + "ĠW ORLD", + "Ġc innamon", + "dot env", + "ĠI User", + "ĠDE V", + "_C har", + ".ib atis", + "et i", + "/ me", + "s st", + ".s ym", + "ĠRug by", + "-m aster", + "aj ar", + "ĠY EAR", + "Ġo dp", + "ĠR oles", + "Ġbip artisan", + "ail le", + "Ġblock er", + "Ġgre ens", + ".SE CONDS", + "Ġbelie vers", + "ĠL ikes", + "F LOAT", + "Ġm ak", + "Ġg cc", + "âķIJ âķIJ", + "(\" ~/", + "SCRIPT OR", + "Ġton nes", + "ĠS ang", + "Ġtrans pose", + "enn ai", + "P red", + "Ġsoll te", + ".github usercontent", + "( print", + "ĠH ole", + "çľ ĭ", + "ad get", + "Ġprompt s", + "Ġgen etically", + "ĠH od", + "Ġvert ically", + "_control s", + "ÑģÑĤ ан", + "\") {čĊ", + "$ title", + "Ġ} ),ĊĊ", + "Ġstate wide", + "ĠCor respond", + "ĠAt tr", + "it ant", + "Element Type", + "Ġout ward", + "Ġfam ilia", + "( article", + "Ġbl at", + "Âł Ċ", + "Ġgl Get", + "ĠRe ceiver", + "Ġ% -", + "ad am", + "W inner", + "Ġtail or", + "_p wd", + "ert en", + "St an", + "ĉ all", + "al ive", + "strt otime", + "� s", + "s essions", + "$ conn", + "ass ist", + "Ġchat ting", + "ĠM ant", + "Ġ% @", + "Ġ\"\" );ĊĊ", + "Ġd gv", + "Ġíķ ¨", + ".re peat", + "_M essage", + "Ġadvis ers", + "/ path", + "Ġk es", + ") } .ĊĊ", + "ogen esis", + "ĠOPTION S", + "upt ools", + "Ġmilit ant", + "Ġex ited", + "ig ar", + "ĠCOM M", + "ĠDis posable", + "ay cast", + "Ġrow span", + "Ġsyn thes", + "Ġsond ern", + "ĠĊ", + "ĠJ acket", + "R ATION", + ".getSelected Item", + "- init", + "ĠReg isters", + "_se p", + "ĠTool kit", + ".d ict", + "Ġx label", + "\\ Table", + "t oc", + "_com bo", + "ĠComp act", + "Ġr ugged", + "à¥ĩ à¤", + "-man agement", + "')}} \">Ċ", + "ĠSt amp", + "ı l", + "ro x", + "Ġlandsc apes", + "_NOT E", + "mon ary", + "c ab", + "Ġmo et", + "x af", + "rc ode", + "- cli", + "_g ate", + "[ event", + "SP ORT", + "g ia", + "ĠS UPER", + "/ Login", + "_sh utdown", + "int errupt", + "Ġpret ending", + "Ġfr inge", + "ĠRed s", + "ĠC UDA", + "ĠUN IX", + "v it", + "Ġbr ig", + "dr v", + "ĠConn ector", + "There fore", + "Ġl ia", + "D etection", + "_ actor", + "Ġtemp file", + "Ġecc entric", + "- role", + "Ġpad x", + "d ent", + "West ern", + "Ġê ·¸", + "ĠApplication Record", + "Ġcampaign ing", + "_run ner", + "ĠC ivic", + "ale igh", + "Ġdire kt", + ".s ul", + "ĠĠ ĉĉĉ", + "ant en", + "Ġiss uer", + "Ġassert ions", + "( orig", + "AT IO", + "Ġlean ed", + "ä s", + ".D TO", + "expl ode", + ".O bservable", + "Ġstagger ing", + "Ġkidn apped", + "Ġprogram mers", + "ĠInn ov", + ".param eter", + "Ġdom ination", + "Ġske ptic", + "Ġæĺ ¯", + "Ġavoid s", + ".Ver ify", + "ub by", + "ĠAS N", + "Ġformat o", + "ĠBeat les", + "_b rand", + "Ġin set", + "y outu", + "Ġto c", + "-f inal", + "Show ing", + "ĠD oub", + "ĠM esa", + "Ad j", + "_m edium", + "Cre ates", + "(end point", + "ĉ UP", + "bb ie", + "Ġst alk", + ".datab ind", + ".S can", + "ag ents", + "$ ,", + "ind ividual", + "+ )/", + "ĉv m", + "(not ification", + "Ġin ex", + "ĠClass ification", + "ren o", + "Ġo lig", + "-r ated", + "Ġform ulation", + "', {", + "Ġa cept", + "_un pack", + "_C A", + ".P ow", + "ĉ im", + "Ġal uminium", + "AN O", + "Ġx n", + "Ġcó mo", + "ĠIng redient", + "Ġseiz ures", + "åħ ±", + "ific ador", + "Ġsigu iente", + "ĠIn fragistics", + "Ġduplic ated", + "ĠDe e", + "Ġn ø", + "ĠAC CEPT", + "(c rate", + "иÑĤ елÑĮ", + "- less", + "Ġinf inity", + "An alyzer", + "-D ay", + "rit t", + "(c in", + "ĠG y", + "Ġmulti plied", + "uch i", + "ĠBald win", + "/ ip", + "Ġshort cuts", + ".A DD", + "Ġvig or", + "_in struction", + "( ;", + "_ eta", + "è¿ ŀ", + "utor ials", + "Ġboost ing", + "b v", + "Ġacknowled ges", + "List ening", + "FA Q", + "; b", + "(( -", + "Ġarchitect s", + "Ġz we", + "Ġpul s", + "Ġget Count", + "ver bs", + "ãĢ ľ", + "(C ollection", + "k re", + "Ġjuris dictions", + "_b ridge", + "ĠCr ack", + "ĠDiff iculty", + "K O", + "Res ervation", + "_re quires", + "T our", + "ãģĹãģ Ł", + ".set Current", + "Ġk y", + "ĠAlb any", + "Ġè §", + "ll er", + "agn a", + "work ers", + ".bl ank", + "ĠPr ayer", + "M IC", + "Ġresil ience", + "Te X", + "ĠL anguages", + "st udy", + "ĉc urr", + "Ġenzym es", + "Sl ug", + "ĠíĮ Į", + "str al", + "Ġtum ors", + "Ġseg unda", + "=' {", + "in struction", + "ĠL isp", + "/ info", + "Ġ\" {$", + ",: ),", + "Ġg v", + "( ErrorMessage", + "Ġ' =", + "}- ${", + ".Doc uments", + "\" Well", + "Ġreminis cent", + "Ġg az", + "iro pr", + "eh r", + "Ġsup pressed", + "ers h", + ".scroll To", + "Ġcad ena", + "Ġgame State", + "ÃŃ m", + "( conv", + "ĠTom orrow", + "ĠC CT", + "M ongo", + "ul g", + ".C amera", + ".hand lers", + "m ph", + "Ġst k", + "Ġgen etics", + "AC ING", + "Tr ivia", + "ĠB am", + "(m arker", + ".St retch", + "ĠSun ni", + "ĠBet ty", + ".t olist", + "un likely", + ".Rect angle", + "ob solete", + "IL ON", + "inner Text", + "emb ourg", + "a N", + "ĠV ehicles", + "un lock", + ": utf", + "n ob", + "ĠSee ing", + "ĠNE VER", + "Ġt ls", + "Ġfil les", + "Ġbenef ited", + "ĠCl int", + "*/ ),", + ".f old", + "Ġpos ible", + "A DED", + "th ouse", + ".D AL", + "ĠO dd", + "ro kes", + "ĠSun ny", + "ĠPartial Eq", + "_B uffer", + "ĠLe vi", + "long rightarrow", + "eld on", + "g ages", + "_w arn", + ".Create Table", + "ĠD ip", + "_ questions", + ".log ic", + "Ġ# \"", + "={() =>", + "Ġt ep", + "Ġju icy", + "ì Ĥ¬", + "en ko", + "ia lect", + "Ù ī", + "Ġon board", + "Ġæ ı", + "ĉ rt", + "_ UTF", + "ĠQ Action", + "âĢ ŀ", + "( Component", + "(a udio", + ".h it", + "g te", + "Ġprogram med", + "state Params", + "Ġpoly ester", + "f ires", + "by ss", + "] =(", + "_ quality", + "Of Day", + "ĠFair y", + "Ġy elled", + "op l", + "(user Name", + "ĠD ifference", + "Ġevalu ations", + "iff any", + "Ġcycl ists", + "Ġc idade", + "Ġtext book", + "Ġprof iling", + "__ ),", + "de a", + ". activate", + "Ġindic ations", + "Ð ķ", + "Touch UpInside", + "Ġinval uable", + "ĠM ASK", + "Ġcont end", + "F req", + "Ġrecru its", + "(int erval", + "ĠUser Profile", + "Ġ'./ ../", + "ed u", + "_C allback", + "Ġanal ogy", + "ĠTro phy", + "app hire", + "V ideos", + "ĠCh er", + "ĠH av", + "âĢ¦ \"", + ". validator", + "g fx", + "ĠU Object", + "class names", + "tri angle", + "ĠEnc oder", + ".s py", + "Ġpred ators", + "= status", + "-s afe", + ": \",Ċ", + "ĠIn cluding", + "Ġ{} ;čĊ", + "* cos", + "Ġend ured", + ".sul ake", + "Ġnurs ery", + "Ġfrag rance", + "Ġre building", + "Ġn th", + "ĠFr aser", + ".set Date", + "ĠV ince", + "_RE ST", + "Ġvent ilation", + "æµ ·", + "cri bes", + ".as m", + "lp Vtbl", + "ĠA be", + "uis ine", + ", array", + "ĉ className", + "err als", + "Ġ' ĊĊ", + "Check out", + "Ġsol icit", + "A ux", + "_c apture", + "Ġrib s", + "rag on", + "vi ol", + "top ics", + "Function Flags", + "ĠM arty", + "b ike", + "ĠT ucker", + "(k ernel", + "ĠO ps", + "Close Operation", + "/d emo", + "ild a", + "ĠlÃŃ nea", + "APP ING", + "Ġsu ites", + ".visit VarInsn", + "ur us", + "ĠMin ute", + "(m anager", + "Ġbutter fly", + "Ġap are", + "Ġw olves", + "J WT", + "ĠSal on", + "ĉd elay", + "-es lint", + "is ations", + ".r pc", + ")| (", + "ĠSnap chat", + "/m m", + "M N", + "cer ies", + ".text Alignment", + "ĠFrank furt", + "Ġad o", + "(new Value", + "( access", + "( Expression", + "ĠSign In", + "ĠHait i", + "_t p", + ".set Parameter", + "Min ute", + "Ġmanual s", + "ric anes", + "ĠP TR", + "ĠOut er", + "Ġget line", + "oc ations", + "_C D", + "ĠLy on", + "/g ui", + "_l ive", + "id an", + ".ge om", + "Ġborder Bottom", + "im uth", + "_check point", + "Ġme u", + "ĠIr ving", + "Ġpeu vent", + "(M AX", + "ĠAR CH", + "Ġp ov", + ".source forge", + "Ġjam ais", + "Ġar k", + "ĠBaghd ad", + "ĠC LEAR", + "Menu Bar", + "Ġtro is", + "CHED ULE", + "Ġ# čĊ", + "(C all", + "$ order", + "(M aterial", + "Ġencontr ado", + "$ list", + "ĠMETHOD S", + ".begin Transaction", + "_M AG", + "Style Sheet", + "Ġmaj ors", + "Ġindef initely", + "clean up", + "Ġhom eland", + "(d to", + "D ates", + "P resentation", + "ĠD K", + "={` /", + "ĉ Key", + "( Block", + "_check box", + "ne eds", + "Ġon Complete", + "ric o", + "Ġgle ich", + "Ġx m", + "O OD", + "B etter", + "ĠSQL ITE", + ". Book", + "x ad", + "ĠG one", + "ĉd p", + "Ġdev otion", + "Ġst m", + "Ġobs ess", + "ĠBack end", + "Qu eries", + "I k", + "// ****************************************************************", + "Ġdivid ends", + ".parent Element", + "} \")ĊĊ", + "ĠMaterial PageRoute", + ": num", + "Ġexp lic", + "ĠO L", + "le ast", + "O ops", + "iment os", + "Ġins urers", + "Ġhero ic", + "ĉf ields", + ".img ur", + ".btn Cancel", + "ĠDetect ive", + "(s m", + "ĠMutable LiveData", + ".l ab", + "(( [", + "Ġha irst", + "ĠTrans actions", + "å¼Ģ å§ĭ", + "Ġstd Class", + "uent o", + "G IS", + "_c od", + "Instruction s", + "C alls", + "Pointer Type", + "ĠR w", + "Ġassort ment", + "ĠD IG", + "+ r", + "_C ERT", + "Ġinst ability", + "Ġv ib", + "on as", + "Ġro ku", + "ap ellido", + "Ġan gl", + "prene ur", + "Ġfluid s", + "ise ase", + "Ġde ed", + "qu ist", + "_CONST ANT", + "Ġequ ilibrium", + "_de legate", + "ĠQuant um", + "re i", + "Cap abilities", + "rect angle", + "? ><", + "al ien", + "ĠJ ug", + "D NA", + "T ickets", + "Occ urs", + "ĠHaw k", + ".setHorizontal Group", + "\\ Collection", + "ff iti", + "Ġre arr", + ".setVertical Group", + "Ġc avity", + "Ġadult e", + "Fac ade", + "- wh", + "ĠL OL", + "Ø °", + "Ġgrand parents", + "Sw ift", + "ĉw x", + "æīĢ æľī", + "if en", + "ff set", + "B eyond", + "// }ĊĊ", + "Ġw ager", + "Ġb ury", + "Ġcomm ence", + "reg istro", + "sc ient", + "ĠPer cent", + "Ġд олж", + "( identifier", + ".set Model", + "Ġs eldom", + "nt on", + "Ġappl iance", + "am us", + "rys ler", + "Ġpant ies", + "engu ins", + "Ġmim ic", + "Ġon Changed", + "Ġal coholic", + ".reload Data", + "Ch arge", + "ĠF ax", + "Ġj ScrollPane", + "Emp resa", + "Ġsh attered", + "x ba", + "Font s", + "? s", + "Ġpost season", + "ret ain", + "_r ates", + "Ġrequest Code", + ".t odo", + "´ s", + "CH K", + "ĠKeep ing", + "enge ance", + "Ġvs code", + "IPP ING", + "Default CloseOperation", + "_ raise", + "ĠO culus", + "ogram s", + "ra j", + "pc i", + "Ġcorros ion", + ".handle Submit", + "Access ible", + "ĠP iano", + "l ittle", + "AC L", + "Äĩ e", + ".un wrap", + "ĠCon vers", + "ĠLe ben", + "ione er", + "ĠMer chant", + "ĠJ orge", + "Ġembr acing", + "Ġvent a", + "á st", + "Ġvi ene", + "< QString", + "Ġexplos ions", + "Ġdistur bed", + ".\" <", + "m emo", + "ĠAb original", + "Ġcomple to", + "Tex Parameter", + "Ġuom ini", + "( agent", + "Ñĥ ÑĢ", + "ĠWh olesale", + "/ am", + "ĠBook mark", + "dr agon", + "Ġglo ve", + "Ġ\" \"));Ċ", + "iv ariate", + "now rap", + "In Children", + ".B r", + "Ġcon exion", + "Ġback bone", + "Ġe clipse", + "Ġpersec ution", + "': ĊĊ", + "/ link", + "ĠP ero", + "and as", + "ĠT ek", + ". \");", + "-an alysis", + "Ġer ad", + "Mar shal", + "Ġanch ors", + "og er", + "Ġconver gence", + "st icky", + "Ġnave g", + "int ern", + "_DE SCRIPTOR", + "ĠConsult ant", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠA uch", + "Ġer re", + "ÅĽ li", + "ĠHor izon", + "col a", + "Install ation", + "hot mail", + "C NN", + ".C ollectors", + "ch s", + "(tr ace", + "ĠEnc rypt", + "Ġ---- --", + "ĠBase Controller", + "Ġag ua", + "Ġre active", + "id l", + "Ġclass Names", + "ĉ Session", + "ĠDod gers", + "H ad", + "_l v", + "Is Valid", + "ĠHEL P", + "ut to", + "ĠVer ification", + "Ġget env", + "_p a", + ".b mp", + ": f", + "ĠLou ise", + "(' ;", + "/ socket", + "Gr anted", + ".c alendar", + "( IP", + "ĠP X", + ".R oom", + "Ġprogram m", + "ens i", + "Ġtablesp oons", + "Ġle ve", + "Ġmo str", + ".t ipo", + "/ an", + "(d i", + "Ġb iod", + "Ġdb Context", + "ĠJS X", + "ĉ results", + ". END", + "ht e", + "l ify", + "P recision", + "èĬ Ĥ", + "ARS ER", + ")did ReceiveMemoryWarning", + "at tempt", + "IS P", + "& a", + "_P OP", + "ĠT ac", + "Ġprepared Statement", + "Ġзап иÑģ", + "Ġow ing", + ", start", + "Ġreview er", + "Ġr st", + "Ġprop Types", + "Ġrock y", + "_lo cale", + "ĠStrateg ies", + "ĠWe ber", + ".C ascade", + "_equal To", + "Ġcos as", + "ĠDe letes", + "ĠMax im", + "Ġsh rimp", + "re trieve", + ".In clude", + "IG IN", + "ĠO E", + "] );čĊčĊ", + ".en umer", + "Ġco ef", + "_N ull", + "R a", + "ty ard", + "ĠSh awn", + "keep ers", + "Ġq q", + "_s b", + "om ens", + "ĠExec utes", + "# \"", + "TT Y", + "ĠValue Type", + "); */Ċ", + "ĠAbs olutely", + "ĠT ottenham", + "/ art", + "Ġbless ings", + "Ġswift ly", + "b uster", + "Ġa vid", + "COM M", + ", temp", + "Ġ} ?>Ċ", + "-g rowing", + "Ġdeep copy", + "A ck", + "egg ies", + "Ġ__ (\"", + "Ġno ir", + "terror ism", + "Ġanth em", + "ag ency", + "_PACK AGE", + "ĠC losure", + ".reg istry", + "Ġmamm als", + "< L", + "U ICollectionView", + "ĠLED s", + "Ġvol ley", + "( Buffer", + "_N ATIVE", + "lib c", + "impl ode", + "Scroll Bar", + "ĠMar ion", + ".Con tracts", + "_A t", + "ĠWe instein", + "compare To", + "ĠH ose", + "en ity", + ".create Query", + "_r outer", + "Ġstim uli", + "Ġ++ )", + "ĠCh amp", + "ĠBay ern", + "ass a", + ".v a", + "Ġdistrib utors", + "Ġfile private", + "Ġdepart ed", + "cc cc", + "@ click", + "ĠL unch", + "> L", + "Ġbl uetooth", + ".De ep", + "- standing", + "ác il", + "Ġro oft", + "ĠPath s", + "_iter ations", + "Invalid ArgumentException", + ".s pi", + "ĠUIAlert Action", + "uy e", + "sign in", + ".p riority", + "ĠEss ays", + "=' {$", + "Ġè¿ ĶåĽŀ", + "_s igned", + ".p ersist", + "Ġred esign", + "To Lower", + "ĠNew man", + "= start", + "ĠIsrael is", + "asis wa", + "Spe ech", + "Ġnum eros", + "hand lers", + "ĠW ong", + "Ġм еÑĤод", + "We ights", + "ĠGu jar", + "te il", + "ĠNon etheless", + "_E FFECT", + "Ġv ect", + "ĠO sc", + "Ġco ats", + "ĠW heat", + "Ġge ek", + "ĠPRO PERTY", + "w orm", + "_const ants", + "ĠB oulder", + "ĠP arm", + "co le", + "Ġdefault Center", + "ĠRou ge", + ": A", + "xc f", + "ĠVen ice", + "med ian", + "Ġred emption", + "F resh", + "Ġcos m", + "Ġfig ur", + "Ġref urb", + "CO PE", + ".c d", + "Ġch ords", + "ĠS gt", + "Å į", + "VP N", + "ĠS END", + "ain en", + "_account s", + "Ġtent h", + "Ġdiss olved", + "< App", + "ĠCover age", + "use State", + "é ro", + ".. <", + "Ġì £¼", + "Ġdream ing", + "ĠFore cast", + ".C ursors", + "Ġvis as", + "/ script", + "_start ed", + "Ġga str", + "(P RO", + "]; //", + ".T ile", + "* sin", + "( Adapter", + "ĠSand ra", + "_S IG", + "ard ash", + "ĠO val", + "Ġdescri pcion", + "(s l", + "ĠDes criptor", + "Ġ` $", + "/f ree", + "ĠKey words", + "Ġt udo", + "ion ale", + "(f ound", + ".x yz", + "ĠGeneration Type", + "_DISABLE D", + "( area", + "Ġel ites", + "Ġh ombre", + "(m essages", + "ĠR ac", + "Ġext ingu", + "ĠEst a", + "op o", + ". vel", + "mouse out", + "Ġconv olution", + "ĠHand ling", + "Ġceil ings", + "T ek", + "ĠAre as", + ".writer ow", + "< View", + "ĠCorn ell", + "_B IN", + ".in valid", + "'' 'čĊ", + "ie ż", + "_P osition", + "Ġk idding", + "PC ODE", + "Ġwatch er", + "lo x", + "Ġâ Ĺ", + "D ave", + "_all ow", + "Ġbis exual", + "Ġun ordered", + "ĠSch we", + "_se gments", + "Ġt earing", + "IN LINE", + "Ġund es", + ".g oods", + ".c am", + "ĠL W", + "ĉ where", + "Cal culator", + "-th reat", + "- alert", + "ĠSuz uki", + "ĠIP A", + "ĠAtt achment", + "AC CESS", + "(d type", + "O pp", + "_s ymbols", + "Ġdans ke", + "l age", + "or get", + "res olution", + "е Ñĩ", + "ĠQ Color", + "ĠBar rett", + "аÑĨи Ñı", + "= \\'", + "ĠNav Controller", + "/ ref", + "(c ountry", + "_H DR", + "Ġterse but", + "pet ition", + "Ġsu f", + "cred its", + "๠Į", + "x m", + "ĠDav ies", + ".re ddit", + "Ġw oven", + "ĠO bl", + "ĠK M", + "ĠConsider ing", + "ens ored", + ".per iod", + "Ġd dl", + "$ wp", + "Ġextrem ist", + "; \\Ċ", + "Ġk im", + "al ers", + "Ġspan ning", + "Ġco herent", + "Ġconse gu", + ".text Label", + ".g eneral", + "_d ashboard", + "л ение", + "k ick", + "_P ID", + "ĠExt ensions", + "reg exp", + "ĠCl ause", + "_m ov", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠR eward", + "ĠLEG O", + "A k", + "=-=- =-=-", + "ĉ parser", + "Ġon ze", + "éĢ Ģ", + "âĢĿ ãĢĤ", + "_b all", + "(r hs", + "Ġch orus", + "< count", + "as urable", + "Ġwirk lich", + "ĠEr in", + "ĠMS NBC", + "Ġet ter", + "ĠC ron", + "_F LOW", + "Ġ, čĊ", + "Ġcal idad", + "ĠFile Writer", + "ĉ stmt", + "( Byte", + "_p at", + "Ġte lescope", + "Ġgre ed", + "ĠT ort", + "(w rite", + "\\ application", + "ĉRT LR", + "ĠConfiguration Manager", + "Un ix", + "End Time", + "In cludes", + "ĠHar vest", + "en berg", + "ĠAustral ians", + "Ġë ĵ", + "Ġr n", + "Ġreput able", + "Ġbl ending", + "UL ATION", + "ĠBrend an", + "d ad", + "Ġm ø", + "ĠW oo", + "_d c", + "U ne", + "Ġr ue", + "with in", + "ang ep", + "Ġp ouch", + "\\\" \",", + "ĠS ic", + "âĢĿ ),", + "aly ze", + "ĠG ef", + "c overs", + "Ġd bo", + "replace All", + "ĉ Logger", + "Try ing", + "[ state", + "-p iece", + "éĸ ĵ", + "beh avior", + "all ows", + "l rt", + "_p ython", + "ert ura", + "-c ountry", + "ĠT G", + ".UI Manager", + "b ens", + "ale x", + "ĠBre itbart", + "b ac", + "Ġpredict s", + "Ġg ab", + "Ġcard inal", + ".Time Unit", + "ĠVis itor", + "ĠM ing", + "Ġliv re", + "Ġparent Id", + "port un", + "Ġdimension al", + "ĠV est", + "en ic", + "à ³", + "Ġ Ùĩ", + "ĠBL UE", + "Ġitem Count", + "Ġfe athers", + "ĉp stmt", + "ĠPol ar", + "{ //", + "und i", + "Ñĥ ж", + "z ar", + "Error Response", + "ì ĥģ", + "Rep resentation", + "* _", + "+ ]", + "pre pend", + "Ġ' >", + "Ġlegitim acy", + "Ġo o", + "S linky", + "Ġnation als", + ". words", + "; p", + "tr ap", + "oman ip", + "Ġc ues", + "Ġgradu ating", + "Ġsem aphore", + "\"] );ĊĊ", + "ace y", + "RE ET", + "Gr ab", + "ĠFel ix", + "( Id", + "_ne ighbors", + "Ġmeaning less", + "(d el", + "Ġj eder", + "ĠContent Values", + ".abs olute", + "/ cl", + "Ġx b", + "dat um", + "Ġtort ured", + "Ġrub bing", + "S cores", + "ĠðŁĺ ī", + "Ġav ons", + "Ġam sterdam", + "E OS", + "H al", + "Ġtrust worthy", + "# =", + ".EX TRA", + "Ġman o", + "is icing", + "-s upport", + "ĉc ursor", + "ĠSp o", + "aim assage", + "M ission", + "[] {\"", + "Ġprint ers", + "G REEN", + "Ġt eg", + "Ġabdom inal", + "! ĊĊĊĊĊĊ", + ".Sh ort", + "аз в", + "ĠGift s", + "} \")", + "(b inding", + "x ce", + "âĢ ij", + "inf os", + "Form Data", + "Ġd art", + "Ġele ms", + "(in v", + "Y L", + "t in", + "GEN ER", + "á» ¯", + "ĠT aken", + "uck le", + ": e", + "Ġspect ral", + ".b aidu", + "/ ');Ċ", + "Ġgre edy", + "es ion", + ",,,, ,,,,", + "Ġ/> ,Ċ", + "Internal ServerError", + "NSNotification Center", + "ĠA i", + "Ġsp it", + "Ġaug mented", + "Ġstandard UserDefaults", + "FIN ITY", + "R ace", + ": C", + "ĠRE CORD", + "ĠHigh light", + "Ġ' `", + "Ġdef icits", + "Ġne i", + "Ġresearch ed", + "T a", + "Ġc opp", + ".Get HashCode", + "): čĊčĊ", + "On Click", + "ĠWell ington", + "Ġrev ival", + "æ¯ Ķ", + "éĹ ®", + "ĠN SS", + "Ġfor n", + "Ġint é", + "ĠKu wait", + "_fl ip", + "_ bo", + "_ \\", + "Ġocc urrences", + "ĠScient ists", + "S RC", + "og ens", + "igr ant", + "RE MOTE", + "ĠS ID", + ". opts", + "u ve", + "() ])Ċ", + "Ġlibert arian", + "ĠGl ide", + "les en", + "Ġform e", + "ow ania", + "Ġannoy ed", + "Def s", + "ĠExec utor", + "Ġcast s", + ".set Checked", + "ĠSh aring", + ".Serialize Object", + "Ġselect ors", + "_ OTHER", + "ë¯ ¸", + "(s uper", + "( OS", + "_VER IFY", + "id unt", + "< header", + "Ġ/> ';Ċ", + "Ġvidé o", + "ĠNeg ro", + "ĠL ords", + "ĠT ours", + "Ġsoft ly", + ".re ceive", + "ĠE RC", + "Ġdata Set", + "Bad ge", + "ĉ Event", + "Ġper l", + "Ġ{} \\", + "(s entence", + "Or Update", + "Ġdim inish", + "P IN", + "(d raw", + ".To DateTime", + ".Equal To", + "(p in", + "-p encil", + "lu ent", + "ĠCall er", + "Ġplay ful", + "- '+", + "x ca", + "sw ick", + "){ }Ċ", + "}: ${", + "ĠM eth", + ".get Cell", + ".b reak", + "Ġy max", + "=' Ċ", + "ĠH iro", + "( TRUE", + "as urer", + "Ġcu er", + "U ber", + ". Operation", + "Ġol an", + "Ġthr illing", + "< Response", + "ĠF emin", + "Ġtravers al", + "Ġp oc", + "Ġset Status", + "decl ar", + "std afx", + "Ġaddict ive", + "ĠB tn", + "Ġexplos ives", + "ĠCook ing", + "ĠPl aint", + "Ġaccum ulator", + "ĠApp ointment", + ", password", + "ĠF AR", + "lu et", + "Further more", + "decl spec", + "_Static s", + ".D ictionary", + "\"> '.", + "ĉ valid", + "\" \",", + "In strument", + "> J", + "Ġno str", + "ĠR ift", + "_P ort", + "Ġvec es", + "[ ['", + "Ġrall ies", + "- series", + "Ġv v", + ". uc", + "Ġr tn", + "State Changed", + "( ins", + "ĠCl a", + "------------ Ċ", + "c us", + "ĠRel oad", + "//---------------------------------------------------------------- --------------------------------", + ".se conds", + "_dest ination", + "Ġscrew ed", + "> c", + "Th ickness", + "Design er", + "Ġgr ids", + "n Äħ", + "( cookie", + "T rip", + "-M obile", + "Ġv oll", + "Ġgen ital", + "Ġconf isc", + "ĠConfeder ate", + "Ġweb View", + "Ġm ise", + "Ġcl er", + "(se lection", + "$ date", + "Ġshar pen", + "rag en", + "And Update", + "Ġrem ix", + "Ġh tons", + "R W", + "M PI", + "Ġretrie val", + "Ġric hest", + ".Dec ode", + ":init Components", + "ĠT Value", + "S aint", + "@ include", + "ĠPER SON", + ".se p", + "ĠLD AP", + "g ba", + "Ġgro ÃŁe", + "Ġreli ably", + "ĠD FS", + ".getItem Id", + "Ġprés ent", + ".get Token", + "Ġch inese", + "ĠMe al", + "Y OU", + "\"> >ĊĊ", + "b ower", + "Ġsw apped", + "/ install", + "Ġs inks", + "etr ize", + "Ġdecl ines", + "ĉm ysql", + "ĠC String", + "ĠMotion Event", + ".L anguage", + "R oad", + "ÑĤ еÑĢ", + "asc imento", + "')) ->", + ". about", + "( editor", + "ĠR atings", + "in come", + "Å¡ e", + ".de queueReusableCell", + "ĠAust rian", + "Ġs ulla", + "ĠTrib unal", + "ĠDid n", + "ов аÑĢ", + "Ġins pections", + "B oss", + "Ġcock tails", + "Ġapolog ized", + "_sub plot", + "op al", + "+ =(", + "Ġreson ance", + "ib u", + "Ġë ¦¬", + "rom a", + "res erve", + "pl s", + "ĠT ah", + "ax ies", + "OP LE", + "ĠDar ren", + "ĠZ ombie", + "_M ap", + "Ġ] )ĊĊ", + "ĠQ i", + "ĠS ail", + "Ġrestrict ive", + "Ġeros ion", + "- par", + "WH ITE", + "Ġold u", + "Ġap erture", + "Ġbit coins", + "text o", + "ĠCom cast", + "Ġtime less", + "en kins", + "Ġfeed er", + "/ tmp", + "res den", + "+' _", + ".D estroy", + "Ġç ok", + "ĠD OCUMENT", + ".l ng", + ".tag Name", + "Ġk ullan", + "eg rate", + "Ġ(* .", + "ç¼ĸ è¾ij", + "Ġhand shake", + "s oc", + "_ geometry", + "ĠDam ascus", + "Min or", + "ĠK afka", + "ìĹ ¬", + "Fl orida", + "_com pute", + ".ex pr", + "Ġpar alle", + "ĠD iaz", + "c ir", + "[ target", + "Ġj oking", + "Ġgl or", + "(set q", + "_hand lers", + "H ang", + "Ġf err", + "rim inal", + "ĉĠĠĠĠ ĉĉ", + "ent ies", + "def ines", + "-t ax", + "json p", + "ĠU PS", + "met ro", + "__ ;Ċ", + "ĠUg anda", + "])) :Ċ", + "_t d", + "x ae", + "l w", + ". OS", + "ĠLog ged", + "ac id", + "ĠMay o", + "as pect", + "Ġvag inal", + "Ġinitial izing", + "Ġster oids", + "f iction", + "G RE", + "g end", + "Ġli abilities", + "ĠL ets", + "M ech", + "( nc", + "( change", + "Ġconnect ors", + ": k", + "Ġt ast", + "! \");ĊĊ", + "th ings", + "ro phy", + "luet ooth", + "ĠSign Up", + ". ctrl", + "Ġthere in", + "ord a", + ". escape", + "ig ator", + "Ġpet rol", + "Ġspec imen", + "Ġdeb uted", + "- Pro", + "Ġcr ises", + ".add View", + "ëı Ļ", + "-d oor", + "Ġmon et", + "Ġmill is", + "Ġv ier", + "Internal Enumerator", + "Ġadmin s", + "ĠL air", + "z in", + "get Query", + "umb les", + "L IMIT", + "ĠV ig", + "_s ong", + "< Character", + ":: .", + "_h om", + "_b p", + "ĠSup ervisor", + "sub mission", + "ab ile", + "Ġno i", + "Or Create", + "Ġpe el", + "Ġon Start", + "Ġsent iments", + "veh icles", + "Ġclass rooms", + "Ġs zer", + "Ġb ending", + "Ġlong evity", + "Ġa cl", + "ĠAle ppo", + "ĠU M", + "ĠR icht", + "Ġmultip rocessing", + "DOM AIN", + "\",\" +", + "_Y EAR", + "Ġsc rape", + "Ġsol itary", + "Ġ\"] \";Ċ", + "/ errors", + "ìŀ ¬", + "ľ ëł¥", + "b etter", + "ĉ number", + "ĠL F", + "ĠAc ross", + "Pub Med", + "\\\" \"", + "ĠExcell ence", + "Ġus ando", + "ĠU IP", + "Activity Indicator", + "_V OID", + "Ġbre eds", + "ï½ ¥", + "uest as", + "ĠTre asure", + "ustral ian", + "(f ace", + "ĠT ennis", + "ĉ Int", + "ĠHans en", + "ç µ", + ": I", + "Ġâľ Ķ", + "GR AY", + "O USE", + "Ġhe pat", + "ł í", + "A IR", + "ó ż", + "Ġque ued", + "vinc ia", + "ĠChrom ium", + "Ġcompet ence", + "ung al", + "ill i", + "Ġget By", + "ĠF inder", + "Ġincap able", + "Ġs add", + "Ġc ites", + "ĠChurch ill", + "S dk", + "More over", + "As pNet", + "( Float", + "$ password", + "ĠConn or", + "-s ession", + "_d m", + "* ))", + "Ġde utsch", + "ĠN X", + "Ġper ks", + "_S ORT", + "_TO OL", + "_V ISIBLE", + ".as p", + "æĪ ĸ", + "ĠBre ath", + "D etect", + "ĠD uel", + ".c mb", + "[ it", + ".Set Bool", + "Ġnarc iss", + "Ġab ide", + "Ġej emplo", + "ĠâĦ ķ", + "Ġm ornings", + "Ġcomput es", + ".s sl", + "j t", + "Ġmuch os", + "_S S", + "[ end", + "Ġbas in", + "Ġalgun os", + "ĠCroat ia", + "lin ewidth", + "(t ags", + "(h idden", + "ÃŃc io", + "Ġap ar", + "ĠÐ ¶", + "ä¸ İ", + ". food", + "ĠR ural", + "Ġbread th", + "å½ ±", + "(s ess", + "+ \")", + "ĠP aste", + "Ġserv idor", + "ĠBit Set", + "ĠTr an", + "la us", + "v ette", + "ey es", + "ĠCL ICK", + "ĠV III", + "ĠTurn s", + "ĠLe Bron", + "ĠM uj", + "ĠD eg", + "ĠAdult s", + "_s uite", + "process able", + "ĠPH Y", + "g hest", + ".F ail", + "ĠSl ack", + "ce j", + "\\ Carbon", + "Ġsuper star", + "Ġhold ings", + "( forms", + "Ġ'# '", + "M ultip", + "(\"[ %", + "-s olid", + "/ url", + "-t ier", + "[ length", + "ĠStream Writer", + "ĠMarket place", + "get text", + "_T ICK", + "ĠFor ge", + "Ġblack jack", + "ĠDO ES", + "ĠM atters", + "w aves", + "Ġwhisper ed", + "Ġl ush", + "ìĺ ¤", + "d igital", + "Ġwr ink", + "ĠH ogan", + "Ġrust ic", + ".Apply Resources", + "ĠHard y", + "os omes", + "A UT", + ".ST ATE", + "Ġnarr atives", + "ĉ store", + "b ib", + "ĉ Scanner", + "ĠC ody", + "\\ Repositories", + "Ġre union", + "and um", + "âĢĻ h", + "Ġsn iff", + "NS Bundle", + "Ġcompreh end", + "_US AGE", + "_ occ", + "URRE NCY", + "J NI", + "Ġspecial izing", + "Ġvis ions", + "Ġdol ore", + "Ġv á", + "ĠChe vy", + "ĠSt yled", + "imp act", + "all en", + "Ġk art", + "ĠTable t", + "st uff", + "re esome", + "аÑĤ оÑĢ", + "//---------------------------------------------------------------- -----------Ċ", + "_Ad min", + "Ġcell phone", + "Ġaut oplay", + "Ġcamb io", + "Ġmar itime", + "_BO OT", + "- quarter", + "Ġlat ina", + "ĠAJ AX", + "e quiv", + "ĠFront ier", + "ĠX Y", + "} ]Ċ", + "ĠR ough", + ".pro to", + "Ġcorrect ness", + "Ġfac il", + "ĠRe ached", + "ãģĿ ãģ®", + "V IS", + ".p s", + "Ġstr ncpy", + "Ġdiff usion", + ".start Activity", + "�� �", + "Ġaccom p", + "AMES PACE", + "imon ials", + "ĠBl ast", + "aby rin", + "Ġd ome", + "Ġextr av", + "Ġy en", + "Ġcul inary", + "P RI", + "ĠComm unities", + "n id", + "_oper ations", + ".h s", + "ĠMil ton", + "Ġno ises", + "Autoresizing Mask", + "(c id", + "}ĊĊ ĊĊĊĊ", + "] },Ċ", + "ĠD etection", + "tab la", + "Ġlib erties", + "_D YNAMIC", + "w get", + "ĠT ür", + "ĠP ascal", + "Trans parent", + "Delay ed", + "] ()", + "ĠHer bert", + "< ActionResult", + "ch allenge", + "Ġmush room", + ".insert Before", + "ĠR in", + "Ġhum our", + "Ġf ø", + "api Key", + "alloc ated", + "Ġconf ession", + ". \",čĊ", + "ĉassert That", + "ĠS ORT", + "ĠL ORD", + "Ġexport er", + ".set Level", + "p okemon", + "ash tra", + "Ġf é", + "ur ator", + "(M SG", + "Ġt up", + "ĠH ull", + "Ġyield ed", + ".Sub ject", + "\\ Route", + "! ?", + "ĠÑĥ дал", + "\\ Security", + "- ar", + "Ġalleg ation", + "( Settings", + "ä nder", + "Ġell ipse", + "ĠRetro fit", + "Ġregul ating", + "ĠM olly", + "ĠL ok", + "_C ustom", + "ĠProm o", + "is in", + "Ġres umed", + "Ġmet ropolitan", + ".error Message", + ": -------------", + "Ġpas ado", + "th ank", + "_De lete", + "ĠBright on", + ", unsigned", + "ä½ľ èĢħ", + "Ġaspir ations", + "-h ow", + "R ose", + "= ((", + "_ne eded", + "_pl ural", + "< Application", + "ĠW EEK", + "ĠUn lock", + "ĠT EMP", + "S ou", + "Ġschizophren ia", + "Ġt roll", + "Ġcomplement ary", + "ĠNET WORK", + "Ġbl ir", + "Ġprogress Dialog", + "\" %(", + "ĠAttribute Set", + "ĉ ts", + ".iter items", + "è¯ Ŀ", + "Ġesc rit", + "v ous", + "_pl aces", + "H K", + "Ġseg uir", + "_f w", + "ĠR ounded", + "Ġdis posit", + "è§ Ĩ", + "par m", + "w ow", + "STRU CTION", + ". allow", + "ĠChar Sequence", + "ĉ extern", + "Ġprosec uted", + "Ġmort ar", + "ĠJ uda", + "- msg", + "Ġest ud", + ".get Description", + "Ġs ow", + "amb re", + "Ġrom a", + "En h", + "bon us", + "Ġsqu at", + "Ġdist ra", + "ed Image", + "Ġpe ppers", + "-per formance", + ", ĊĊĊ", + ", file", + "ĠM IME", + "_con cat", + "AB S", + "-f ashion", + "Ġunder cover", + "One ToMany", + "Ġre claim", + "C OPY", + "Ġb inds", + "ĠT ape", + "Ġg ossip", + "ĠEqu ity", + "/ Card", + ". activ", + "' am", + "Ġdrain age", + "< Scalars", + "ĠonBind ViewHolder", + "() ?.", + "Ġs orrow", + "ĠI b", + "up y", + "_U UID", + "ĠCh arm", + "ĠElection s", + ".on Destroy", + "ĠInterest ingly", + "ounding Box", + "_d etection", + "-h eld", + "_ unknown", + "Ġrefr ain", + "Ġmét odo", + "Ġe Book", + "EN OMEM", + "Ġd ang", + "Prof essional", + "Ġd ictionaries", + "/m ysql", + "ĠST UD", + "Ġmas se", + "s cape", + "Ġdre i", + ": name", + ".log o", + "Sign Up", + "Ġt ahun", + "( theme", + "ĠFem me", + "Ġbom ber", + "ĠJ ade", + "ĠT ay", + "Ġsubmar ine", + "_cl ause", + "zy ch", + "Ġsimult aneous", + "Ġcas os", + ". boolean", + "(l hs", + "Ġcontin ental", + "-s ale", + "ĉ env", + "ĠC ute", + "ĠFactory Girl", + "ab us", + "/ value", + "Ġj adx", + "Ġst ern", + "> >ĊĊ", + "Ġsurf aced", + "Ġìł Ģìŀ¥", + "pl atz", + "ĉ email", + "cept ors", + "\"> (", + "Ġep ile", + "è¯ »", + "ĠDe bt", + "åij Ĭ", + "N OP", + "\" https", + ": j", + "Form Item", + "_L ICENSE", + ".get Double", + "ĠAg enda", + "ĉf inally", + "(f ilters", + "( av", + "ç¾ İ", + "AP ER", + "Ġl ava", + "еÑĢ ж", + ")) ))ĊĊ", + "Ġfault y", + "_n m", + "Ġtr ava", + "(B itmap", + "Ġspeed ing", + "> ').", + "Ġscreen ed", + "_ roll", + "ĠMac Book", + "ĠA UD", + "Ġdiagn ose", + ".G enerate", + "Ġ^ ^", + "Ġstr s", + "[ Test", + "Ġr ansom", + "ĠDH CP", + "eld en", + "Ġinterpret ations", + "() ].", + "flat Map", + "Ġline Height", + "_m ount", + "ĠW izards", + "Ġsl uts", + "eh ler", + "od al", + "Ġmilit ia", + "å ²", + "earn ed", + "Ġmis ery", + "int val", + "f und", + "Ġh ides", + "Ġdi arr", + "ĠWes ley", + "Ġx mm", + "Ġqu em", + "ĠAr abs", + "if th", + "ategor ized", + "Dis posable", + "P ure", + "_NOT IFY", + "sn ippet", + "ĠGar rett", + ".run ning", + ". weights", + "Ġ( --", + "Ġin variant", + "äºĭ 件", + "ĠAll owed", + "dir s", + "Ġpass ions", + "Ġl ad", + "ĠFl ush", + "men us", + ": block", + "Ġcompr a", + ".ch omp", + "alloc ator", + "Ġcur ated", + "ĠKnow ing", + "ĠPatt erson", + "Ġtel ah", + "' ex", + "Ġdo omed", + "Ġphil anth", + "ott y", + ".st yles", + "Own ed", + "Ġallerg ies", + "= params", + "oc ese", + "it elist", + "ĠS ending", + "b ef", + "orr ar", + "ĠN ão", + "ĠF argo", + "ĠL ub", + "ĠComb ined", + "_g iven", + "ĉĉĉĉĉ ĠĠĠĠ", + "Ġreconc iliation", + "Pattern s", + "az ard", + "Ġbiom ass", + "ĠH ouses", + "resp uesta", + "cc o", + "/top ics", + "ĠY uk", + "Ġweaken ed", + "_c alendar", + "Ġmulher es", + "ĠMar l", + "Ġs ine", + "ĠT il", + "ĠSou ls", + "ĠDe utsche", + "ĠF OLLOW", + "Ġpip elines", + "ĠBever ly", + "_DIP SETTING", + "\" #", + "ĠPro to", + ".b ig", + "ĠSav ings", + "ĠT anz", + "j un", + "ĠG amma", + "ĠS add", + "Ġadvis ors", + "Ġro ast", + "Ġun ters", + "ud ies", + "_l on", + "-point er", + "ĠElement Ref", + "\\ Builder", + "example Input", + ".web driver", + "data Type", + "ĠQu ite", + "ĠCelt ics", + "u il", + "-def ense", + "b ish", + "ĠUI Window", + "ĠS uddenly", + ".h ot", + ".re ason", + "Ġg ör", + "AM D", + ".M ulti", + "auth enticated", + "reg ions", + "; (", + "а ÑĢам", + "ĠKir by", + "$ route", + "PREC ATED", + "ĠDur ham", + "ow o", + "ĠPer forms", + "Ġdisreg ard", + "n st", + "ĠP ols", + "Ġget P", + "\"] :", + "-col ored", + "( Keys", + "ĠAl leg", + "_mod ify", + "_ loading", + "str ained", + "Ġat roc", + "_p hr", + "< Sprite", + "Ġsatisf actory", + "m anship", + ".p ipeline", + "T ony", + "Ġth ief", + "pol ator", + "( lock", + "bur st", + "ĠOptim ization", + "Ġsurf ing", + "\" Yes", + "Ġdesc ended", + "æ Ĵ", + "_C lear", + "Ġc ries", + "ĠFro zen", + "D IRECT", + "- Con", + "ĠLe icester", + "å¥ ³", + "O OM", + "= db", + "Ġget Message", + "< Student", + "_b atches", + ".M ask", + "_ eth", + "\\ )", + "Ġsom a", + "C atch", + "[ ch", + "Own ers", + "ind le", + ": auto", + ". vert", + "iv r", + ".set Location", + "Ġfl uent", + "_END IAN", + "ĠCar lo", + "cept s", + "add Action", + ".o auth", + "< UnityEngine", + "re ements", + ".S kip", + "? )ĊĊ", + ".default Props", + "Ġc abe", + "ĠSh en", + "eros is", + "ĠPro fit", + "Ġpo is", + "_C REATED", + "Ġremove From", + "(w s", + "? action", + "( Field", + "Ġerr one", + ".min imum", + "ĠRetrie ved", + "Ġd ado", + "ĠPR IVATE", + "-s pec", + "Ġg zip", + "p data", + "Ġpos Y", + "(l ow", + "Ġqual quer", + "/ cloud", + "ê² Į", + "( common", + "ĠAr beit", + "organ isation", + "Ġtid y", + "ĠRol and", + "( ph", + ".z one", + "Ġgent lemen", + "ượ c", + "å± ±", + "Ġenc losure", + "ĠMan afort", + "ĉ Color", + "St encil", + "N ic", + "Ġthe orem", + "ĠV G", + "Ġcol oured", + "V BoxLayout", + "uls ive", + "Drag on", + "c ff", + "et est", + "ens a", + "of day", + ".A zure", + ":UIControlEvent TouchUpInside", + "_up dates", + "Ġtrend y", + "ug as", + "weak Self", + "Ġr idge", + "ib ri", + "Ġì¶ Ķ", + "(C G", + "ĠMon key", + ".write Int", + ".tim edelta", + "ViewController Animated", + "ĠProvid ence", + "ãģ Ī", + "Ġbl ends", + "/Sub threshold", + "ĠAp pl", + "Ġat an", + "Ġreload Data", + "umb otron", + "st üt", + "O Auth", + "ĠG iving", + "ĠìĦ ¤", + "ĠFinn ish", + "check ing", + ". Embed", + "sequ elize", + "Ġinitial izes", + "ĠOs lo", + "Ø ¶", + "get Extension", + "_AL T", + "(bl ank", + "Ġfatal Error", + "Ġdem ise", + "**** *Ċ", + "ĠX S", + "(A F", + "ĠEn s", + "an tha", + "ĠP OR", + "Ġn ich", + ".N amed", + "Ġgig antic", + "ĠObserv atory", + ".Res olve", + "ĠPay ments", + "g uild", + "Ġcurrent State", + "============ ===Ċ", + "ĠS ey", + "p Data", + "Ġdead lines", + "Ġcentral ized", + "ĠScholar ship", + "_s upported", + ".ch rome", + "() ]);Ċ", + "Ġc yan", + "ĠC age", + "Auth ors", + "_ čĊ", + "/ os", + "k im", + "de e", + ".t ex", + "Ġyours elves", + "Ġm gr", + "Ġal k", + "-inst all", + "Ġdraft ing", + "Ġrum or", + "Ġstat ues", + "Pool ing", + "ol ina", + "AAAA AAAA", + "/* ----------------------------------------------------------------------------", + "Ġextrem ists", + "Cal cul", + "ighth ouse", + "In set", + "(IN PUT", + "Ġsynchron ization", + "iv irus", + ". axes", + "ĠG ap", + "- An", + "_T emplate", + "Ġgam er", + "ĠCr icket", + "Ġl int", + "Ġauthor itarian", + "NS UInteger", + "Ġred o", + "Ġadip iscing", + "_F ETCH", + "che id", + "ĠF ang", + ". indices", + "t one", + "д ел", + "Ġ{{-- <", + "bra him", + "Ġsal a", + "get Code", + "Ġcommunic ated", + "start sWith", + "ert z", + "Read able", + "Item Id", + "oref errer", + "cred ible", + "á ria", + "Ġcombine Reducers", + "** /ĊĊ", + "Ġbl iss", + "Ġad orn", + "dep ends", + "ĠRO OM", + "Ġfr aming", + "Ġ? ',", + "aut y", + "_p ot", + "_t abs", + "Ex act", + ", \",", + "Ġ'} ';Ċ", + "Ġarbit r", + "ahr ain", + ".getString Extra", + "Ġ$ \\", + "Ġoutput Stream", + "Ġcomm enc", + "an us", + "ch y", + "< Employee", + "Ġhex atrigesimal", + "Ġn acional", + "(serial izers", + "_put char", + "_S AFE", + "ential Action", + "ItemSelected Listener", + ".Dis patch", + "Conf lict", + "_ about", + "os aur", + "Bound ary", + "Ġclear Color", + "( Location", + "ĠMON TH", + "ĠT aste", + "- General", + "ĠW AR", + "Ġer halten", + "-s aving", + "Ġcou pling", + "-tr igger", + "m otor", + "Ġy yyy", + "ĠPat ent", + "pt o", + "Ġmisdemean or", + "vas ion", + "ĠAdmir al", + "à¹ī า", + "_P WR", + "Ġdevast ated", + "fol ios", + "ITU DE", + "urre ct", + "Ġrobot ic", + "ĠSan ct", + "ĠHawai ian", + ".R oute", + "- condition", + "Ġr k", + "/**************************************************************************** Ċ", + "create Element", + "ĠK op", + "ign ant", + ". rollback", + "Ġsal ud", + "_ ',", + "ĠAN SI", + "Ex cept", + "ĠDraw able", + ".Utc Now", + "\":[ {Ċ", + "Ġk ole", + "L ua", + "ĠBel ieve", + "Com put", + "Ġhall uc", + "ĠSign s", + "r st", + ".h u", + "ĠKN OW", + "W i", + "ĠBr ass", + "ĠR as", + "@ hotmail", + "Ġsed iment", + "Ġap k", + "Ġì ĥģ", + "_reg ions", + "Ġpod ium", + "< Book", + "ж е", + "Ġsix teen", + "ĠAli as", + "Ġinfr ared", + "ĠV ander", + "ĠLe ading", + "uc ing", + ",: ,:", + "_h or", + "w at", + "Ġdé cou", + "_W idget", + "S ounds", + "_n avigation", + "Ġschn ell", + "(g enerator", + "uc ene", + "Ġrem ake", + "IP v", + "Ġré al", + "_IN CREMENT", + "Ġhypoth etical", + "_ ang", + "Ġof s", + "Ġ! Ċ", + ".com pleted", + "Get Type", + "Ġkom men", + "ál ido", + "add On", + "Ġz ÅĤ", + "UL A", + "_ind icator", + "'] ĊĊĊ", + "ap ache", + "_S elect", + "ĠGre ene", + "Wh ats", + "_an im", + "Ġrepet itive", + "m uch", + "ĠTh reshold", + "Ġl f", + "(C ategory", + "con e", + "M ix", + "_MET ADATA", + "ays ia", + "Ne ighbors", + "ĉĊ ĉĉĊ", + "IP HER", + "ĠFr ag", + "ĠC ells", + "Ġnames paces", + "( back", + "ĠRest aurants", + "sv c", + "Ġл и", + "ote ch", + "-s l", + "¥ ¿", + "ĠW T", + "ĠRed uction", + "Ġd otted", + "ĉf ound", + "ĠTE AM", + "B orn", + "ĠM ush", + "ĠCompar able", + "Ġh itch", + "AT O", + "Ġmax Height", + "begin Transaction", + "ÃŃ v", + "_b n", + "Ġher d", + "Ġrevers al", + "ĠH ond", + "del imiter", + "Ġconf use", + "Ġh ops", + "Ġcent roid", + "Ġcourt room", + ".decor ators", + "Ġm pi", + "ĠImpro ved", + "IN NER", + "ĠBang alore", + "ĠT amb", + "Ġbo ast", + "() ))čĊ", + "Ġil licit", + "ĠMor occo", + "greg ator", + "_res ume", + "Ġcrack down", + "Ġport raits", + "/h igh", + "( \\'", + "Ġay ud", + "_fe edback", + "Ġc ate", + "/ avatar", + "Ġhe b", + "Point Cloud", + "Ġå ĴĮ", + "Ġ< ![", + "Ġget Resources", + "} :{", + "Oper ating", + "ĠF og", + "ĉt ab", + "ĠResearch ers", + "Ġfabric ation", + ".datas ets", + "ĠCamp o", + "ĠKa uf", + "Ġd ll", + "lig t", + "] ));ĊĊ", + "st ellen", + "ACK ET", + "l vl", + "ĠGl ory", + ".date Time", + "Ġcomm ute", + "ĠonCreate ViewHolder", + "ĠX Element", + "ĠT okens", + "< thead", + "_p ick", + "ì ¤", + "v on", + "depart ure", + "(render er", + "phone Number", + "(P erson", + "gen es", + "ĠL ars", + "Ġ) {ĊĊ", + "ĠJson Result", + "Ġmet odo", + "VO KE", + ".get UserId", + "Acc eler", + "ĉ required", + "Ġchampionship s", + "Build Context", + "/t ask", + "/re leases", + "C ategoria", + "_over lay", + "Ġscar ce", + "_l im", + "n gr", + "ah len", + "ĠArt ificial", + "sp read", + "Ġbow ling", + ".an alysis", + "SM TP", + "ĉp assword", + "Ġbath s", + "] )){Ċ", + "current ly", + "ac iente", + "_se parator", + "Ġde ber", + "ĠDis abled", + "i ères", + "Ġâ ķ", + "_process ing", + "Ġprotest ing", + "ĠR OT", + "gr ab", + "Ġз ак", + "Ġpro active", + "word press", + "ĠSe ver", + "ind en", + "Ġw ikipedia", + "){ čĊčĊ", + "_w indows", + "is lation", + "Ġun rest", + "Ġdismiss al", + ".N UM", + "_F AST", + "iss ued", + "ĠF ACE", + "_u nder", + "Ġpl ugged", + "Ġå °", + "ĠbÄĻd zie", + "ĠI CC", + "Ġcombust ion", + "Ġkiss ed", + "Ġstar red", + "ĠW atts", + "Ġspi elen", + "-p urpose", + "ĠE val", + "arg es", + ", result", + "techn ology", + "Ġnational ity", + "ic us", + "ĠN ug", + "ĠÑĤ о", + "ĉĉĉĉĉĉĉ ĠĠ", + "col o", + "Ġg astro", + "ante ed", + "OL ID", + ".b ias", + "_t ele", + ".ins pect", + "Ġve il", + ". footer", + "Ġneglig ence", + "Ġjud gments", + "Room s", + "yn n", + "ĉcount er", + "occup ation", + "Ġ çĶŁ", + "un as", + "Ġ(^ )(", + "L ambda", + "f el", + ".Param s", + "Ġд обав", + "set Layout", + "Ġdeport ation", + "Ġlocal Object", + "ĠPharm aceutical", + "cept ive", + "ĠN ome", + "Equ ipment", + "F an", + "Un iversal", + "ĉ socket", + "Ġgr in", + "Ġex poses", + "Ġhab er", + "Ġsincer ely", + "Ġc ams", + "Ġm ü", + "en ia", + "E mer", + "C rypto", + "Sl ow", + "(x hr", + "! =(", + "-s ervices", + "ĠP W", + "Ġprend re", + "Ġm ädchen", + "em ons", + "озв ÑĢаÑī", + ".M anager", + "ì Ļ", + "Ġg raf", + "- ra", + "met rical", + "/ fl", + "Ġc emetery", + "g ens", + "Ġp ÅĻ", + "ĠMySql Command", + "- To", + "Ġv Ã¥", + "Ġa irst", + "oment um", + "Ġserv o", + "m illion", + "ĠMir anda", + "\" She", + "Ġadvoc ating", + "-c aption", + "ĠAt tribution", + "Ġwel che", + "_v endor", + "ĉ Status", + "arr is", + "Ġprint k", + "\",\" #", + "Ġrel ativ", + "if ferences", + "izz es", + "Ġdec imals", + "ĠPro v", + ".max imum", + "Ar n", + "Ġhelicopt ers", + "_B OTTOM", + "ch ure", + "od ings", + "' (", + "\")) );čĊ", + "( bean", + ".f d", + "F und", + "Ġhang s", + "app id", + "/k ernel", + ".p oi", + ".Min Value", + "- validation", + "L uke", + "c df", + "ĠFun eral", + "ĠS amples", + "ĉ de", + "Ġto astr", + "Ġtax able", + "Ġcl ustering", + "Ġ'\\ '", + "Ġre straint", + "ec ed", + "ch ains", + "ãĢĤ ï¼Ī", + "_GR APH", + "Ġfue led", + "éľ Ģ", + "H p", + "å¤ į", + "T iles", + "Ġa unque", + "J C", + "Ġhost age", + "ĠE sk", + "Ġm av", + "Ġgest ion", + "Ġb anners", + "} {$", + ".int Value", + ".' \"ĊĊ", + "_M ATRIX", + "Ġce ased", + "ĠG OD", + "_CAM ERA", + ".Allow User", + "tr acked", + "C ook", + "b airro", + "( company", + "Ġview point", + ".get Writer", + "ĠN ets", + "w ives", + "Ġ( ))Ċ", + "example Modal", + "ĉ child", + "Ġmyth ology", + "Ġ// \"", + "_ axes", + "ib old", + ".D ark", + "ĠMax well", + "Ġg pointer", + "olic itud", + "B at", + "ul ner", + "bal anced", + "mail er", + "Ġcont empor", + "æīĭ æľº", + "(\" __", + "Ġ\" )\"", + "re ar", + "ĠHu ang", + "] ')Ċ", + "× ©", + "FT A", + "ĠCalling Convention", + "ĠOutput s", + "P k", + ".Re ference", + "lect ual", + "Ġ) :ĊĊ", + "Ġbrace let", + "ug er", + "ĉ Error", + "S weet", + "(\"/ \");Ċ", + "h x", + "Ġun reasonable", + "Inter preter", + "Ġlo ft", + "_product o", + "Ġsoci etal", + ".P arser", + "ĠAd apt", + ". foo", + "( where", + ".F eature", + "ĠYam aha", + "g lass", + "For ge", + "Ġprohib its", + "Ġcapac ities", + "Ġíķ¨ ìĪĺ", + "Ġper mutation", + "Ġih m", + "F ld", + "el ial", + "======== ===Ċ", + "@ Configuration", + "Ġge ared", + "ios o", + "iest a", + "trans lations", + "Input Change", + "Pop ular", + "ĠPL US", + "Ġv f", + "_F ree", + "b box", + "Ġcaus al", + "PI LE", + "Ġsch ö", + "Ġiron ic", + "M ir", + ". @", + "åį Ĺ", + "Ġè ĩ", + "R ew", + "ul ence", + "fl en", + "Ġcan Activate", + "- response", + "Ġacc ents", + "ign ored", + "° F", + ".Dependency Injection", + "ĉ point", + "Ġconting ent", + "Ġsqu ash", + "Ġpar ms", + "ĠC emetery", + "Ġdelta Time", + "ĠD OS", + "Ġvan ished", + "аÑĢам еÑĤ", + "ĠD PS", + "t foot", + "ĠZ us", + "_IN STALL", + "G AN", + "Ġar b", + "Ġmunicipal ities", + "Into Constraints", + "AutoresizingMask IntoConstraints", + ", image", + "_ ignore", + "Ġdanger ously", + "quis a", + "pl uck", + "Ġhar us", + "up pe", + "Http Exception", + "Br acket", + ".' 'ĊĊ", + "ĠT ol", + "ĠView er", + "zb ollah", + ".Code Analysis", + "ì nh", + "Ġcorrect amente", + ".d a", + "ĠAl ger", + "× IJ", + "ba um", + "ĠPan ther", + "part icipant", + "å¿ ħ", + "-s up", + "Ġem ulator", + "Ġf ading", + "ĠW olver", + "cre ates", + "Ġbook ings", + ".Q uestion", + "§ è¡Į", + "Ġstress es", + "Ġre written", + ".PI PE", + "ed es", + "Ġc bd", + "\": \"/", + "Ġenh ancements", + "_s y", + "B IN", + "ĠSl ip", + "Ins pect", + "ĠW eg", + "Ġcon gregation", + "Ġ_ :", + "_r m", + "Frame buffer", + "Ġ'& #", + "ĠFall out", + "Is Required", + "ĠPear son", + "ĠF ACT", + "Ġrel ie", + "ĉ box", + "ĠShe pherd", + "ĠWiki Leaks", + "ĠCollect or", + "Ġres ized", + "method Name", + "Ġevent Type", + "ĠA then", + "Des criptors", + "Ġb ers", + "- oper", + "ĠInitial ly", + "å ¡", + "_B TN", + "ĠĠĠĠĠĠĠĠĠ čĊ", + "á b", + "_c ampaign", + "_w atch", + "F ord", + "-date picker", + "Ġvis c", + "Ġsat u", + "_s ms", + "Ġcont ador", + "-s vg", + "ĠDO I", + "$ args", + "Ġkn ob", + ".B OLD", + "Ġdeb ated", + "img s", + "sock opt", + "tr uth", + "ĠFe es", + "Ġh Wnd", + "_f ood", + "Ġab ras", + "Ġnot ions", + "ĠT od", + ": create", + "ĠConf lict", + "Us uarios", + "OT OS", + "Ġm sm", + "K HTML", + "([ (", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "Ġ} ]", + "w izard", + "Ġm ientras", + "Ġdata List", + "Ġemerg es", + "Äĥ ng", + ".Read Int", + "PG A", + "ILL ISE", + "I Enumerator", + "(t uple", + "Christ mas", + "Look AndFeel", + "og enerated", + "Ġ# ĊĊ", + "control led", + "Ġex quisite", + "Ġa cest", + "Read Write", + "G ain", + "ãĢį ãĢĮ", + "Ġcopyright ed", + "Ġdo om", + ".Table LayoutPanel", + "ĠD ort", + "Ġch ili", + "Ġwer k", + "ĠEVENT S", + "ĠBe acon", + "Ġship ments", + "Ġse bagai", + "up on", + "ut om", + ".con verter", + ".Drop Table", + "={ }Ċ", + "f ic", + "~ ĊĊ", + "Ġlesb ians", + "_n a", + "Fore ign", + "ĉ then", + "/ ms", + "Ġor i", + "get Property", + "ĉsn printf", + "hes ion", + "ãģ ¤", + "\"} ,\"", + "Ġac rylic", + "P ers", + "@ Enable", + "I sl", + "(C ard", + ". Stack", + "L icensed", + "_G UID", + ": title", + "Ġh ust", + "Ġprincipal Table", + "an itize", + "/ embed", + "Ġens ured", + "ĠE GL", + "ÙĪ ر", + "ĠåĪ Ĩ", + "/ ,Ċ", + "Ġfundra iser", + "Key Name", + "Ġmarch ed", + "_VAL UES", + "ĠSc enario", + "Ġmet ic", + "_ass oci", + "ĠPast or", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", + "er ate", + "Ġinv itations", + "quo ise", + "Ġbl aming", + "Ġd aring", + "UM MY", + "Ġrich er", + "em aker", + "ĠIdent ification", + "ĠìĿ ¸", + "ĠBinding Flags", + "ch as", + "Ġresil ient", + "_p g", + "Ġre leg", + "ĠI RA", + "ST E", + "Ġtr actor", + "- loading", + "ĠPre viously", + "ĠV acc", + "/ be", + "Ġn Ã¥r", + "Ġurl encode", + "ĠNor folk", + ".Re lease", + "ĠNe utral", + "ä¸Ń åĽ½", + "ĠAr lington", + "Ġalleg es", + "ĠW riters", + "Test er", + "ĠR ally", + "Ġc á", + "ĉ Print", + "Ġâĩ Ĵ", + "ĠUser Controller", + "ĠSeek ing", + ".V AL", + "List Node", + "_ ff", + "ĠPhill ip", + "FA CT", + "Ġc aramel", + "ĠM ultip", + "ĠCom pared", + "ĠSer bia", + "Ł ³", + "Ġrev ive", + "ĠK anye", + "Ġver ge", + "ĠBulg aria", + "get Body", + "Ġ| >", + "ce ph", + ".DateTime Picker", + ".\" ;ĊĊ", + "ĠT ie", + ", item", + "Ġm enn", + "G as", + "och a", + "_v irtual", + "Ġmaster piece", + "_se quences", + "L TE", + "ĠSub mission", + "Call er", + "$ \\", + "S port", + "ag us", + "Constraint Maker", + "Ġcol oc", + "Ġw ig", + "ĠÐ £", + "ĉ Array", + "Look s", + "ĠGT A", + ".st eps", + "atch ewan", + "_r anges", + "ext Alignment", + "ĠBren nan", + "Ġab straction", + "uler Angles", + ".m isc", + "Ġantib odies", + "Ġexponent ial", + "ĠCH ANNEL", + "exp ense", + "' y", + "Ġdetect ives", + "Ġpur ported", + "Y STEM", + "Ġradio active", + "ĠLat ina", + ".Enc oding", + ".T AG", + "x in", + "D egree", + "ur acion", + "pr ices", + "ĠRefer entialAction", + "Ġr arity", + "Ġp iles", + "g ende", + "_project s", + "_g lobals", + ".start Time", + "Ġê µ¬", + "SE CTION", + "_p ublish", + "F ault", + "DD L", + "_p rior", + "M om", + "Ġth icker", + "Ġsequ elize", + "Ġessential s", + "str as", + "in tr", + ">( ()", + ".man agement", + "e il", + "éĹ Ń", + "A ware", + ".C ity", + "ĠAr bit", + "_D M", + "_key board", + "L Object", + "- webpack", + "ĠNew port", + "Ġprincipal Column", + "leg ant", + "Ġp allet", + "Ġfract ure", + "Ġg mail", + ".M eta", + "A bove", + ".Key Event", + "j it", + "_mac ro", + "_P USH", + "á» ©", + "/ controller", + "åĬł è½½", + "Ġsuperf icial", + "exter ity", + "Ġmens agem", + "W ind", + "ist on", + ".open api", + "и ÑĢов", + "ĠSerial izer", + "uct ive", + "Ġz ar", + "Pl aces", + ".St atic", + "B a", + "Ġin advert", + "ĠIndones ian", + "_IP V", + "(h orizontal", + "Ġget Title", + "ide press", + "ĠConsole Color", + "ip ers", + "$ out", + "Ġfest ive", + "Ġeven ings", + ".Get Data", + "uit ka", + "ĠManual s", + "uss ed", + "_M ax", + ".Ch at", + "ĠA ircraft", + "= com", + "FO UND", + "ap ro", + "Ġtre asures", + "_al ive", + "Ġgad get", + "ek ing", + "Button Down", + "B rowsable", + ".PER MISSION", + "P ASSWORD", + "ĠH ASH", + "f é", + "\\ TestCase", + "LO SS", + "o thers", + ", J", + "Ġassh ole", + "wer k", + "Ġm ã", + ". ie", + "ev il", + "kont akte", + "//////////////////////////////////////////////////////////////////////////////// Ċ", + "= sys", + "ĉ lock", + "-- ;ĊĊ", + "_F UN", + "Fill Color", + "ó a", + "pre nd", + "Ġcompress or", + "M other", + "ĠAr cher", + ".g oto", + "Ġwür de", + "Ġbam boo", + "ï¼ İ", + "ĠT rees", + "Ġb umper", + "Ġsa usage", + "ĠEl asticsearch", + "Ġhor izontally", + "ĠG ul", + "Im mutable", + "Ġlos er", + "Ġabort ed", + "-d emo", + "ĠH atch", + "Ġund e", + "Ġprocess o", + "-c all", + "In come", + "å ĥ", + "_ returns", + "'].\" '", + "(s w", + "C BS", + "am ilies", + "ĠYour self", + "ĠH olt", + ".M ON", + "ৠĩ", + "ÑĪ е", + "an on", + "ĠFont Awesome", + "produ cer", + "j r", + "Ġm au", + "ĉint er", + "Ġdish onest", + "Ġmagn a", + "ĠCollect ive", + "Ġvra iment", + "Ġcho ix", + "st ay", + "Ġweld ing", + "r ising", + ", min", + "ĠF ate", + "g lob", + "RGB A", + "Ġdet te", + "V en", + "Ġembarrass ment", + ".DE LETE", + "greg ar", + "-re nder", + "(b ucket", + "\"> ĊĊĊ", + ".wait Key", + "Bus y", + "Ġdifferent iation", + "ĠC ST", + ".Con stant", + "Ġline Number", + "(m atches", + "Ġweb socket", + "Ġbar red", + "Ġpued es", + "M ono", + "C ORE", + "I ID", + "ĠĠĠĠ čĊčĊ", + "Ġpúb lico", + "lean ing", + "Ġcleans ing", + "Ġcr is", + "ĠDev ils", + "_SET TING", + "unt ary", + ". );Ċ", + "Ċ ĠĠĠĊ", + "[ curr", + "ts y", + "ĠAlex is", + "rit el", + "Ġpet roleum", + ".pre processing", + "m atter", + "For Result", + "- license", + "Ġtrav ellers", + "ĠDispatch er", + "enn ifer", + "Ġdigest ive", + "P ED", + "hib ition", + "MAS ConstraintMaker", + "ĠW att", + "Ben ef", + ".set View", + "d to", + "TE E", + "ĠPel osi", + "_EX TRA", + "Ġmed als", + "x hr", + "fore cast", + "Ġn argin", + "oun s", + "-f ill", + "_CUR SOR", + "Ġsuperv ised", + "Ġtur f", + "ĠEd gar", + "POS ITION", + "Ġcategory Id", + "â ī", + "_ ER", + "ủ a", + "Sh own", + ". ll", + "_POL ICY", + "(), '", + "ĠPre v", + "ĠString Field", + "ĉG lobal", + "ass ed", + "Through out", + "o stringstream", + ".awt extra", + "Ġslo pes", + "ĠSe quential", + "Ġgi orn", + "Ġz elf", + "Ġvers atility", + "lene ck", + ".c gi", + "Ġdou bling", + "ĠBang kok", + "Ġbu urt", + "Ġusu ário", + "st udio", + "Ġje unes", + "Ġm uted", + "Ġ ips", + "_f raction", + "&& (", + "Ġst unt", + "'); ?>čĊ", + "Ġev apor", + "b able", + "ĠPR ICE", + "Ġæ ³", + "lu cent", + "Ġv amp", + "ĠTechn ician", + "Ġuniqu eness", + "M es", + "ur ban", + ".param etrize", + "ĠRe play", + "S essions", + "em br", + "-Americ ans", + "_PRO XY", + "Ġp ian", + "Ġtri e", + "ĠD estructor", + "Game State", + "ĠIM F", + "ch in", + "Ġport e", + "ĠSw al", + "åŁ İ", + "Sub string", + "im ing", + "/L ibrary", + "Ġfright ened", + "w rites", + "Ġrecurs os", + "ar Result", + "_INIT IALIZ", + "ĠBad ge", + "_c rc", + "E ight", + "ĠDIST INCT", + "Ġth ro", + "@ Xml", + "ĠLegend ary", + "-t witter", + "_e asy", + "Ġ+ ++", + "(D ATA", + ".L ocale", + "Ġk ä", + "Ġn urt", + "Ġcr uis", + "_ ios", + "Ġsens ing", + "_L ine", + "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "pon g", + "ole on", + "Ġwild card", + "çĶ¨æĪ· åIJį", + "Ġbeg ging", + "R od", + "Ġà İ", + "_C ELL", + "Research ers", + ". selector", + "_ ing", + "Ġaspir ing", + "Ġimm ortal", + "Ġy min", + "_ robot", + "Ġpl ur", + "B TC", + "ĠD ID", + "Ġpier cing", + "* u", + "_DEFIN ED", + "ĠTh i", + "ita ire", + "(m edia", + "- ons", + "Ġche fs", + "Ġ\"* .", + "/ AP", + "Ġraz or", + "Ġsearch Data", + "Ġ= &", + "Ġ ãĢĤ", + "Ġm ourn", + "ting ham", + "Ġo li", + "ĠVern on", + "_R S", + "ŀ æĢ§", + "Ġf ácil", + "ang en", + "cel ain", + "Ġa il", + "le st", + "ĠQ COMPARE", + "g ain", + "ĠÎ µ", + "ĠK ob", + "ĠF ault", + "_config s", + "ç»ĵ æŀľ", + ". +", + "cal ar", + "(color s", + "M ul", + "_ ART", + "Ġexperiment ing", + "erm en", + "ĠAng lo", + ".Fixed Single", + "Se a", + "Ġc txt", + ".s lider", + "C ollapse", + "G rey", + "Ġf ld", + "-pro of", + ".cap acity", + "get Parent", + "ĠCom pliance", + "Ġburg l", + "- rec", + "Ġover written", + "M U", + "Ġrout ers", + "ĉ Model", + "Ġfantas ies", + "av ian", + "_p rec", + "ĠSc andin", + "Ġ// <", + "/o ct", + "Ġceremon ies", + "Month s", + "und y", + "Ġqu ed", + "ĠN ou", + "ĠV ibr", + ".r gb", + "Ġcit rus", + "Ġbr aces", + "-upper case", + "get Table", + "Ġdop o", + "ĠK err", + "_CH ILD", + "- cloud", + "ĉ Matrix", + "Ġgard ening", + "S ing", + "al most", + "Require ments", + "ugu ay", + "( Property", + "sub scriber", + "FA ST", + "re action", + "(l p", + ") })Ċ", + "` ).", + ".w allet", + "_ex change", + ".Max imum", + "ĠVer b", + "âĶ ģ", + "() <", + "ï¼Ľ Ċ", + "RO T", + "C ARD", + "ub it", + "{ @", + "_k el", + "ĠTool tip", + "My SQL", + "Main Activity", + "ar f", + "Ġm align", + "Ġse inen", + "ap ist", + "Ġ< %", + "Method Impl", + "M il", + "ĠM ick", + ".de pend", + "< ID", + "Ġpredict ive", + "ĠAP PLICATION", + "le f", + "dim ensions", + "Ġconoc er", + "/ conf", + "ĠTr acy", + "F oto", + "_rem aining", + "= file", + "Ġpage Index", + "ĠPar ish", + "Ġt exas", + "ĠM AGIC", + "ĠH ew", + "d ifference", + "Ġalt ura", + "c um", + "ĉdata Type", + "Ġcaracter es", + "avi ours", + "ĠV OID", + "è¿ ij", + "P UBLIC", + "B io", + "ĠstringBy Appending", + "Parse Exception", + "ĠS uff", + "ĠN orton", + "/d etails", + ".n ull", + ">> &", + "ĉ ok", + "-l ow", + ". usuario", + "n ested", + "X B", + "OUR S", + ".Border Color", + "Ġb row", + "ĠÐ ķ", + "cor r", + "ĠRed skins", + ".get Tag", + ".get Transaction", + "Ġst igma", + "hard t", + "ĠPlayer Prefs", + "als y", + "uc son", + "L anguages", + "ĠOl ivia", + "Ġt ac", + "Ġb li", + "Ġc aval", + "Ġconsolid ated", + "Ġper il", + "Ġde le", + "Ġform ulated", + "Ġhigh ways", + ".sp awn", + "== $", + "ĠN iet", + "Ġv eggies", + "yp o", + "-r ule", + "ĠV ie", + "/e pl", + "Ġenf ants", + "string Literal", + "Ġtou ghest", + "buy er", + "Ġcov ariance", + "Ġil i", + "ĠSoph ie", + "ĠB AB", + "Ġ\" ),", + "ĠU k", + "current Index", + "_user data", + ".code c", + "ĠPun jab", + "ĠSN P", + "l ol", + "adv ance", + "Ġcom fy", + "Json Ignore", + "Ġfashion able", + "ĠI CON", + "Ġor a", + "ĠP ricing", + "< num", + "ĠI RC", + "ER V", + "ĠMe in", + "ĠID ictionary", + "AD OW", + "is New", + "ĠDev on", + "at l", + "(request Code", + "ĉ PreparedStatement", + "IM PORT", + "Ġmar ital", + "_SELECT ED", + "get Response", + "ar Down", + "B V", + "ib Name", + "ĠP ATCH", + "ä än", + "Ġda ar", + "ĠFile Mode", + "Ġm arty", + ".Spring Application", + "c ene", + "amp oline", + "get Size", + "Rest art", + "æķ Ī", + ".project s", + "ĠEthi opia", + "Ġstatus es", + "T ION", + "(b g", + "ĠX unit", + "Temp orary", + "ĠEng agement", + "Ġx f", + "Ġprox ies", + "Ġgen esis", + "Pager Adapter", + "ĠSl ave", + "Ġsung lasses", + "ĠCh loe", + "Ġko ji", + "ad em", + "ĉ JSONObject", + "Î ³", + "Ġh ors", + "* w", + "ó r", + "es ch", + "Ġcritic ised", + "z ial", + "ĠSale m", + ".Vert ical", + "ĠR ash", + "> E", + "ter ing", + "/s creens", + "Ġheight ened", + "аÑĢ ÑĤ", + "Author ities", + "_b box", + "ün st", + ".font Size", + "ĠBO OLEAN", + "div ide", + "ĠSlo ven", + "uc er", + "Ù Ĵ", + "st ub", + "Ġnavig ating", + ": animated", + "_N OW", + "_v ect", + "} {Ċ", + "@ (", + "Ġtele com", + "Ġcontract ing", + "ĠAss ange", + "Ġextract ing", + "Ġgr ö", + "c obra", + ".D IS", + "Ġcr ab", + "Ġtw itch", + "Ġvert s", + "Ġreject s", + "ĉ format", + "Ġreg eneration", + ".S ys", + "s olve", + "ĉd ialog", + "sh i", + "m eter", + "(b est", + "valid ators", + "Ġon wards", + "Ġg uru", + "Ġmoder ator", + "ow ied", + "ex periment", + "r ub", + "Ġm qtt", + "ĠCa ucas", + "Ġnational ism", + "Ġm ange", + "ĉ ImGui", + "/ Edit", + "Ġin h", + "Ġint ellig", + "ero kee", + "ĉ export", + "Ġdiscrim inate", + "sub tract", + "ĠM oodle", + "ens er", + "ĠGuid es", + "R AP", + "-h ot", + "_gr p", + ".p icture", + "X A", + "Ġinit View", + "_Com m", + "Ġoverd ose", + "Ġ+ ĊĊ", + "ĠSil ent", + "show s", + "Ġinterpol ate", + "Form ation", + "Ġb isc", + "mark ets", + "( SC", + "Z e", + "ĠNetwork ing", + "Ġad renal", + "ĠG uns", + "ete or", + "Decl ared", + "orget own", + "Ġk arena", + "/ password", + "_address es", + "ITER AL", + "B uzz", + "ĠCon way", + "(c ase", + "P WD", + "he iro", + "( act", + "** čĊ", + "());ĊĊ Ċ", + "Ġan v", + "Ġ. .ĊĊ", + "(Menu Item", + "(m ail", + "_section s", + "ĉ net", + "Ġpl ut", + "Ġw rench", + "/ object", + "ĠI st", + "ĠV IS", + "/p ub", + "al ten", + "Ġguit ars", + "Ġantibiot ic", + "ï¼ ĸ", + " ¹", + "Ġ\" +\"", + "form ula", + "Ġbab es", + "ĠP rompt", + "Ġen im", + "/ player", + "ĉ ref", + "Ġby Äĩ", + "Ġconsum es", + "ĠH ast", + "ĠT ao", + "Ġ' ))Ċ", + "Ġcl am", + "Ġthigh s", + "Ġmot if", + "Api Operation", + "ĠW L", + "get C", + "ĉf lags", + "oint ments", + "Ġeconom ical", + "need le", + "x ls", + "pr actice", + "ut zer", + "time ofday", + "- output", + "Ġfind ById", + "ĠBudd y", + "Ðŀ ÑĤ", + "Se ven", + "ĠB ark", + "Ġenv oy", + "_al gorithm", + "åĪ ©", + "Ġball istic", + "ç§ »", + "r ades", + "ĉd oc", + "rodu cing", + "ĠE ating", + "Un mount", + "/data Tables", + "_b onus", + "Ġl itt", + "pp s", + ") localObject", + "per f", + "ĠHel vetica", + "sh utdown", + "/ ml", + ".t okens", + "ĠHard core", + ", row", + "/b g", + "Sc aler", + "âĢĶ as", + "_log its", + "âĢĻ int", + "ĉ App", + "Imp licit", + ".F printf", + "ET O", + "Ġterr a", + "Ġpossess ing", + ".r strip", + ", ),", + "= yes", + "ĠStr ipe", + "? =", + "ne utral", + ".g ood", + "Ġk ennen", + "ĠS ung", + "f ault", + "ystate change", + "Can adian", + "',' \".$", + "ĠM its", + "æ nd", + "ĠSTR UCT", + "ĠURL WithString", + "ĠCom pass", + "Ġ-- ĊĊ", + "ĠNS LayoutConstraint", + "| min", + "-ad just", + "Ġreb uilt", + "L IGHT", + "/ se", + "-m ount", + "vp n", + "valid ated", + "(Q Object", + "Ġign ition", + "ĠCharg ers", + "RYPT O", + "]initWith Frame", + "ĠFl uid", + "Ġcad re", + "Ġnomin ations", + "Ne ill", + "ĠH ou", + "Ġcurrent s", + "_g ene", + "(in p", + "Par is", + "z ÄĻ", + "ag gregate", + "Ġass oc", + "weet ed", + "err at", + "âĢĵ ĊĊ", + "Ġ'/ ',Ċ", + "fix ture", + "ĠH ighest", + "amb ient", + "Ġch mod", + "Ġcon te", + "Ġsens ual", + "Ġgar ment", + "z ers", + "ĠPower ed", + "dom ains", + "R eward", + "i omanip", + "Ġcock pit", + "out file", + "Ġbuilt in", + "Ġins isting", + ". vars", + "zip code", + "Ġ ����", + "f ails", + "Ġconsolid ation", + "_ oid", + "Plan et", + "Ġ= \",", + "ĉ el", + "UIL T", + "ät z", + "af ari", + "ĠMc Cl", + "Tim eline", + "Est a", + "Ġfr am", + "Y E", + "Ġcere bral", + "Of Month", + "ĠP regn", + "Ġкл аÑģÑģ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠF res", + "Appro ved", + ".S pecial", + "ĠProtest ant", + "Ġallerg y", + "_p cm", + "ĉC opyright", + "Ġsuper Class", + "\" strconv", + "ĠMoh amed", + "Ġ' //", + "Fore Color", + "Ar thur", + "ĠJ ungle", + "Ġve ins", + "S ad", + "Ġback ups", + "ĠOp inion", + "û t", + "Ġinter mitt", + "ody n", + "ĠChrist ina", + "Ġand re", + "Ġevac uation", + "pa lette", + "h orse", + "ĠRes ident", + "ĠHass an", + ".N il", + "Ġa isle", + "ĠG rowing", + "Ġblog info", + "/s ql", + "_io ctl", + "Sc aling", + "ĠMon ad", + "_c pp", + "ĠH utch", + "ĠApple WebKit", + "Exp ense", + "_J OB", + "Ġpoint less", + "From Body", + "ant al", + "Ġdepict ing", + "ĠC ELL", + "Ġref in", + "ĠC NC", + "ì¹ ĺ", + "_dim ensions", + "ĠS AN", + "Ġa ft", + "Ġfoot steps", + "cc oli", + "_PH ONE", + "/m ath", + "-k ind", + "ĠMe ans", + "ich ael", + ".g una", + "Ġinaug uration", + "-dr iving", + "( delete", + "Ġtotal Count", + "_M C", + ".Ext ension", + "Com mercial", + "Ġz Index", + "< Customer", + "\" g", + "-sh are", + "Ġp act", + "ag ara", + "ĠS IL", + "_m odes", + "ĠM olecular", + "Ġsystem atically", + "< G", + "_s cr", + "ĠO ro", + "as ers", + "Ġb ic", + "Ġdest roys", + "PI PE", + ".Start Position", + "Ġc ủa", + "ire z", + ".B unifu", + "_F unction", + "Ġs ü", + "_f uture", + "ĠWe alth", + "ĠNatur ally", + "æĢ »", + "_y es", + "Ġabrupt ly", + "String Encoding", + "ĠCGPoint Make", + "Ġz h", + "Ġimp erson", + "Ġpiv otal", + "ĠSom alia", + "Ġsegment ation", + "_AN AL", + "ĠLogin Component", + "Cons ult", + "Ġtr uncated", + "] \";Ċ", + ".get Config", + "Ġintern ship", + "B aby", + "ê° ľ", + "Ġstrengthen ed", + "_M I", + "b asket", + "Ġnicht s", + "ĠTV s", + "ĠSh an", + "ãĤ µ", + "rac use", + ".Re LU", + "/ interfaces", + "ĠgetItem Count", + "Ġret iring", + "Ġspecial s", + "Ġentity Manager", + "bel ief", + "Ġs older", + "da ughter", + "ij kl", + "Ġutil izes", + ".f ixed", + "S U", + "Ġdr astic", + "Ġh acks", + "gr und", + "ĠM U", + "ĠSt arter", + ".Com ponents", + "_m otor", + "Gold en", + "Ġl odge", + "Ġ ));", + "ĠCor inth", + "иÑĩ еÑģÑĤво", + "ón ico", + "gre SQL", + "ĠFl uent", + "Ġmar c", + ".Load Scene", + ".Group s", + "Ġer h", + "ĠAut umn", + "St opped", + "Ġitalian o", + "Ġmin ions", + "ĠAssert ions", + "Ġm ux", + "B u", + "Ġ---------------------------------------------------------------- --------------------------------", + "ĉ up", + "read ystatechange", + "_M eta", + "Ġcurrent Date", + "ĠChap man", + "Und o", + "Se an", + "ap r", + "Ġpar m", + "_ icons", + "ĠSt a", + "á z", + "Ġsub division", + "Ġalter ing", + "P NG", + "ponent ial", + "Ġpost gres", + "ĠB DS", + "-ex istent", + "ĠBrad ford", + "ĠO MX", + "_W HITE", + "_PRO GRAM", + "q c", + "Ġtypings Slinky", + "ĠP ics", + "_M ETA", + "IT TER", + "_sub scription", + "IRON MENT", + "ĠHy undai", + "();ĊĊ ĊĊ", + "ĠØ ³", + "Ġj ac", + "Ġelimin ates", + ") });Ċ", + "Ġcomp rend", + "ĉ insert", + "_f aces", + "\"> $", + "Ġeb ay", + "Ġcapt ive", + "pl iant", + "ĠCalcul ates", + "ol ta", + "est ing", + "_re vision", + "Ġm ús", + "+ m", + "\",\" \",\"", + "WH AT", + "Ġcompassion ate", + "h arga", + "[ random", + "Ġmod ulo", + "(s n", + "Ġoccup ations", + "//// Ċ", + "ĉ board", + "ĠB alk", + "wi Äħ", + "ĠW ifi", + ".Pro file", + ":m aj", + "ĉm at", + "LOCK S", + "(j Button", + "Ġ(' $", + "M ur", + "æĮ ī", + "b ble", + "Ġf rog", + "-h ide", + "Ġbroad caster", + "ภŀ", + "ha led", + "Ġam using", + "_predict ions", + "_in tr", + "Ġe agle", + "аÑĤ елÑĮ", + "Ġget List", + "ps ilon", + "Ġcharacter ization", + "AR DS", + "Ġre location", + "Ġr ulers", + "P AY", + "ĠDef initely", + "_A ction", + "Ġclos ures", + "Ġfact ual", + "odyn amic", + "Ġpreca utions", + "nie j", + "ĠPart ies", + "ĠSub aru", + "Ġcous ins", + "ar beit", + ".m oney", + "gun ta", + "( and", + "get item", + ".Style Priority", + "Ġsl id", + "single ton", + "Ġg arn", + "ĠP AS", + "Ġd azz", + "a ż", + "Ġbog us", + "ĠM og", + "Ġrival ry", + "is ol", + "Ġland marks", + "ñ as", + "B ern", + "ĠSach s", + "Ġ\" )ĊĊ", + "Ġhost ility", + "_m ex", + "m ere", + "M ot", + "p ictureBox", + "Def ense", + "Ġaffid avit", + "other wise", + ".d irectory", + "_ UnityEngine", + "-b log", + ".s kin", + "ph em", + "Ap ellido", + "er chant", + "[ class", + "Ġw art", + ".\" [", + "ale ur", + "/ back", + "ĠĠĠĠ ĉĠĠĠ", + "Ġprecip itation", + "Ġob struction", + "Ġp Obj", + "Ġr upt", + "UCK ET", + "ay e", + "æİ Ĵ", + "g x", + "Ġe cl", + "Ġsecre cy", + "/ Header", + "ĠLes b", + "Ġle i", + "ĠBullet in", + "Ġgive away", + ".H ome", + "_RO OM", + "\" W", + "Ġcow ork", + "_ ra", + "ĠC ycling", + "ĠP aw", + "Ġpup il", + "/ arch", + "ĠFile Utils", + "é¦ ĸ", + "r sp", + "Ġfreed oms", + "ĠL ear", + "}` ).", + "Ġbow ls", + "/b lock", + "_log ging", + "Ġmeth ane", + "Ġhorn s", + "Ġwonder fully", + "Ġalter ations", + "Ġex ile", + "ls en", + "_p ause", + "_L ANGUAGE", + "ĠUS DA", + "_m ysql", + "_AM OUNT", + "ĠL IFE", + "Ġyoung sters", + "Ġri ots", + "[ E", + "Ġun forgettable", + ", },Ċ", + "Dis posed", + "ĠAss assin", + "UN G", + "ĠNew sp", + "User Service", + ": aload", + "+ ',", + "Ġsett lers", + "Ġscre ams", + "Ġincon venience", + ".R otate", + "Ġj ars", + "ĠP uzzle", + "Ġm est", + "ars i", + "ĠSh arma", + "| (", + ".d s", + "ĠSac red", + "_e vt", + "Ġexpress es", + "Ġh och", + "ĠD uch", + ".c alls", + "th r", + "ĠShe ffield", + ".Alert Dialog", + "Ġrad ically", + "Ġtr ous", + "Ġprev ailing", + "ĠWW II", + "âĢĻ n", + "ens ely", + "ĠY esterday", + "ĠSir ius", + "Ġkill ers", + "ĠF FT", + "Ġo val", + "') :čĊ", + "Ġìłķ ë³´", + "our age", + "ĠCheck box", + "Work book", + ".def er", + "_f loor", + "Ġc ouncill", + "Ġnors ke", + "mo il", + "ore a", + "Ġmarket ed", + "_S UR", + "x AA", + "Ġst ained", + "e ut", + "ĠM eng", + "Ġi eee", + ". extern", + "eg ie", + "Ġr app", + "ĠPy ongyang", + "' class", + "M ob", + "Ġinitial Value", + "_w ave", + "Ġj ab", + "Ġmascul ine", + "Ġampl ifier", + "Ġt ty", + "Path Component", + "_ xt", + "ĠG FP", + "/ sec", + "ĉdis patch", + "mark down", + "ĠS chn", + "bo le", + "· ·", + "mouse move", + "Ġerr Msg", + "Ġas ign", + "_m ono", + "To Selector", + "ĠZ u", + "(R ect", + "ĠError Code", + "lat in", + "ang ible", + "v tk", + "CG Size", + "P okemon", + "Ġclass mates", + "Ġattract s", + "ĠT atto", + "ult an", + "ol óg", + "Ġhalt ed", + "ठ¨", + "ĠK art", + "Ġ ue", + "_Init Structure", + "Test Class", + "ĠAir bnb", + "_ \",", + "Ġchar coal", + "Ġip c", + "ĠSt retch", + ".g lide", + "lates AutoresizingMaskIntoConstraints", + "Ġpot ion", + "ITT LE", + "Ġcount ert", + "_h d", + "pre pared", + "Ad s", + "ĠV ampire", + "rob ots", + ".Create Index", + "Status Label", + "Ġt ucked", + "af ür", + "U t", + "Ġswe ater", + "_F N", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", + "ata ka", + "Ġeyeb rows", + "ac oes", + "ud en", + ".LinearLayout Manager", + "Ġsw ay", + "Ġmult in", + "() )))Ċ", + "ĠNS UInteger", + "ĠMy Base", + "Part ner", + "uts chen", + "ĠC ater", + ".setBackground Color", + "Ġaccompl ishment", + "_pro blem", + ".d td", + "Ġpage Number", + "Ġj ackets", + "Ġcro pped", + "u els", + "ĠH ep", + "Ġc apped", + "* Math", + "_callback s", + "Ġpub b", + "ĠBrun swick", + ".res pond", + "[\" _", + "Ġbed ding", + "hyth m", + "O X", + "(s peed", + "Ġpestic ides", + "Ġ---- ---", + ".Bl ue", + "Ġnood les", + "ĠGo es", + "Ġs aver", + "o xy", + "_com pletion", + "ĠSw inger", + "Ġget Date", + "Ġmind ed", + "int egration", + "ĠLot us", + "(st op", + "(', ');Ċ", + "Ġflood s", + "ĠWork flow", + "Ġerupt ed", + "Mac ro", + "ĠSau ce", + "Ġevent Name", + "\\ Input", + "Break ing", + "ĉ when", + "_p w", + "IND ER", + "ĠWell ness", + "Ġvox el", + "ĠM ell", + "ĠM EDIA", + "SE NS", + "ĠFund s", + "ĠM ild", + "< Array", + "- this", + "ump ed", + "/f w", + "ĠDb Context", + "W I", + "girl s", + "H OW", + "'); ?>Ċ", + "Ġtempt ing", + "Ġtest ament", + "Ġb ible", + "Ġconsult ed", + "ĠIndex Error", + "è¨ ĺ", + "Ġkey pad", + "izz o", + "( ok", + "Ġwhats app", + "ĠRemote Exception", + "Ġteam ed", + "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", + "» ,", + "Ġget Time", + "di ag", + "iss y", + "Ġh ed", + "Ġkn ots", + "j om", + "Ġfun nel", + "-m ails", + "Ġexport ing", + "ĠV L", + "ĠK arn", + "ĠBuddh ism", + "ĠAll an", + "_R ADIUS", + "Ġw ording", + "ĠFor get", + "ĠCor ona", + "ip hy", + "Ġlim burg", + "ugg y", + "ĠUser Repository", + "im in", + "(e le", + "Ġlabel led", + "ç¤ ¾", + "ĠH erman", + ".q q", + "Ġ\" ));Ċ", + "ie ber", + ".Trans late", + "ry n", + "Ġdes env", + "um d", + "Sim ply", + "ĉm ode", + "R pc", + "ĠVal encia", + "Ġstaff ers", + "Ġsel v", + "ĠSpi ke", + "Ġdel ic", + "Ġer u", + "_D T", + "J udge", + "á» ķ", + "ĠBas in", + ".m utable", + "\" url", + "Ġtar iff", + "ĠSlee ve", + "Ġfl are", + ".drop out", + "Ġbr ides", + ")) ,čĊ", + "_con straints", + "de struct", + "Out line", + "Ġdisappe ars", + "_lock ed", + "ĠNS LocalizedString", + "ck e", + "ĉ null", + "ad resse", + "Ġto pping", + "ĠJ oker", + "b ishop", + "но ÑģÑĤÑĮ", + "and ering", + "_ amp", + "= time", + "_S pace", + "_P ULL", + "' =", + "Ġant iqu", + "Ġc ach", + "___ ĊĊ", + "ON ES", + "о Ñı", + "Ġun read", + ".p olicy", + "oooo oooo", + "ëŁ ¬", + "Ġu sted", + "ĠRe ce", + "Ġal lem", + "ãĥ¼ ãĤ¹", + "ĠThought s", + "ve illance", + "istr ate", + "_l ane", + "Ġfam ed", + ".Get Name", + "Ġsmo other", + "ĠQual ified", + "az ers", + "_ geo", + "F ax", + "ĠM inds", + "ĠR aises", + "Ġtrans cripts", + "Con versation", + "Ġremark ed", + "ëĤ ĺ", + "d ling", + "Ġdeploy ing", + "Ġshared Application", + "Ġk p", + "FontAwesome Icon", + "_d ummy", + "reib en", + "ĠJane iro", + "Direction s", + ".get Bean", + "s ass", + "Ġcommand ers", + "v ation", + "error Code", + "ĠAl loy", + ".local ized", + "Ð ij", + "Ġdish washer", + "ĠSou p", + "N u", + "_D efault", + "Ġune ven", + "Ġ/> \";Ċ", + "-B ased", + "Ġseam lessly", + "- null", + "ĠX C", + "Ġst ew", + "(d elay", + "AT ORS", + "ĠWhe eler", + "\" H", + "e ast", + ". air", + "âĢľ But", + "Object Context", + "success fully", + "_l and", + "Ġfold s", + "_CO ORD", + "Ġsub po", + ".get Address", + "in str", + "Material s", + "Ñĥ ÑģÑĤ", + "de posit", + "-l ast", + "_GR AY", + "= find", + "Ġmut ant", + "Ġlesb ienne", + "let cher", + "RO UGH", + "ure ka", + ".c apture", + "Ġen n", + "Ġ([ [", + "ĠFl u", + "Ġtask Id", + "ĠHus sein", + ".f older", + "Ġa usterity", + "ISTR ATION", + "_ Impl", + "注 æĦı", + "Ġdec ree", + "- chat", + "Ġimp lication", + "Ġguess es", + "ul kan", + "An alytics", + ". plus", + "COM MAND", + "е ли", + "» ĊĊ", + "_S ITE", + "Ġequal To", + "Support FragmentManager", + "ĠRec ording", + "å®Į æĪIJ", + "Ġbag gage", + "Ġpitch ers", + "ĠE h", + "o que", + "ĉc nt", + "Ġ=> $", + "/ foo", + "IR A", + "ĠSat ellite", + "bor ah", + "Ġ}} \"Ċ", + "ĠEnd s", + "ĠSpr ay", + ", param", + ".Ch rome", + "* q", + "th ought", + "ibr ated", + "Ġth ieves", + "Ġbenefici aries", + "Enter ed", + "ottes ville", + "Ġveter in", + "By ID", + "qu ipe", + "um ption", + "- unit", + "Execution Context", + "@ s", + "ĠG iov", + ".Tool Tip", + "_f riend", + "( attributes", + "Ġdump ing", + "ĠJ C", + "_D OCUMENT", + "ĠArm our", + "( insert", + ".Horizontal Alignment", + "ĠQ ed", + "ãģĦ ãģ¾ãģĻ", + "/g it", + "ĠY YYY", + "ĠCard iff", + "Ġap a", + "organ ic", + "ĠWhere as", + "Ġæ Ŀ", + "ĠM ia", + "Ġdemol ition", + "Ġsc ars", + "Ġp ai", + "Ġre tries", + "Ġr q", + "ĠDen is", + "( Utils", + "Ġallev iate", + "ĠP IC", + "id ue", + "Ġacknowled ging", + "Ġ// ////////////////////////////////", + "ç¡® å®ļ", + "Ä «", + "\\ Json", + ".b inary", + "Ġx type", + "sign als", + "ĠAp pearance", + "& r", + "} s", + "C i", + "ĠI llum", + "por ate", + "h og", + "Ġindex Of", + "\\ Command", + "_par allel", + "ĠSher lock", + "í ĥ", + "Ġ\" \")čĊ", + "//////////////////////////////////////////////////////////////// ////////////////////////////////", + "Ġcritic ize", + "ĠSo ap", + "ĠMatch er", + "Ġgr illed", + "* T", + "Ġad ore", + "ull ing", + "Ġjed och", + "_ref s", + "lean up", + "ĠJ AXB", + "Ġro ses", + "ĠL iam", + "size i", + "Ġget char", + "Ġtar de", + "-to oltip", + "Ġqual ifier", + "ĠInter mediate", + "_W indow", + "ĠMal ta", + "Dis connect", + "ew here", + "Camp o", + "Ġirr ational", + "led o", + "ĠD N", + "ARG V", + "Ġout ro", + "Ġth irteen", + "Jose ph", + "M AR", + "/g l", + "J ess", + "ĠPsych iat", + "Ġpadding Bottom", + "- loop", + "/ fonts", + "_se en", + "Te ams", + "React DOM", + "(m an", + "(x path", + ".get SimpleName", + ">( *", + "ĠP vt", + "Ġel ders", + "Ġp ies", + ".user Agent", + "- region", + "ĠGree ks", + "(f ragment", + "st u", + "Ġcouncil s", + "Ġst amina", + "ĠGod dess", + "è ¥¿", + "Ġphilosoph ers", + "Ġpers one", + "ĠL ose", + "ĠCL R", + "ĠD ocs", + "Ġso ak", + "ĠHOLD ER", + "Ġb ells", + "hash Code", + "R ATE", + "_WE IGHT", + "in ous", + "end ra", + "oph obic", + "Ġpro se", + "Ġfin ely", + "/o auth", + "(s pace", + "ad ge", + "ĠM ama", + "Ġstring Buffer", + "Ġst int", + "Ġmis ma", + "Ġvill ains", + "ĠCrime a", + "Ġdipl oma", + "Ġпо Ñģл", + "ĠBe a", + "(j oin", + "Ġíķ ´", + "CH AT", + "per ing", + "ĠC ros", + "Ġmon keys", + "Ġpred s", + "yl a", + ",, ,", + "Ġvibr ator", + "ĠN U", + "åħ Ī", + "f ant", + "z et", + "Ġb ietet", + "un ft", + "sw orth", + ".F low", + "Ġpsy ched", + "ĠContin ental", + "> t", + "Ġqu ilt", + ". UP", + "Ġexpans ive", + "Dis pose", + "(l anguage", + "C aps", + "_Z ONE", + "Ġrec ycle", + "ĠMan aged", + "current Color", + ".b roadcast", + "sign In", + ".p rom", + "ll u", + "ue blo", + "Ġpunch es", + "Ġautom at", + "Ġassign ing", + "Ġcreate User", + "ĠAll ied", + "Ġconduct or", + "Ĥ ¨", + "Ġs addle", + "Ġd ni", + "omed ical", + "-W est", + "Positive Button", + "Ġit alic", + "? [", + "(tr igger", + "Ġele phants", + "\":\" \",\"", + "Ġcal iber", + "raft ed", + "d igits", + "Ġmar shal", + "mill iseconds", + "mark ers", + "m om", + "/ place", + "Ġhol istic", + ": t", + "# ,", + "Ġb oto", + "Ġnause a", + "ĠSh ooting", + "ite ch", + "Ġtext Status", + "< Class", + "ĠDes cribe", + "Ġbuff et", + "g il", + "Ġlog its", + "std call", + "mod s", + "ĠSk ull", + "ĠB are", + "h ope", + "ĠIn tr", + "F air", + "ĉ pt", + "Ġacompan h", + "Ġf kk", + "_r pc", + "Inst alled", + "_ ans", + ".get Minutes", + "âĢ¦ \"ĊĊ", + "- thread", + "Ġpres chool", + "AIL S", + "Ġdiff ic", + "( convert", + "ĠN ath", + "ĠDO J", + "Ġreg imes", + "Ġenthusi ast", + "Ġwarrant ies", + "Ġfasc inated", + "_b inding", + "_N ot", + "oft en", + "_R W", + "/m ail", + "Ġtitle Label", + "Ġvill agers", + "ĠJ iang", + "Ġsw agger", + ".Row Index", + "_img s", + "rap y", + "VER AGE", + ". Up", + "Ġno op", + "c io", + "ĉ ST", + "Ġdecre ment", + "Ġmagn esium", + "_ rotate", + "S it", + "Ġnieu we", + "Ġter med", + "íķ ©ëĭĪëĭ¤", + "Ġur g", + "_t ouch", + "Ġsw arm", + "Ġcl ave", + "th est", + "ĠL af", + "H X", + "ĠH ulk", + "Ġplaint ext", + "ĠSof a", + "get Session", + "L ed", + "Ġecosystem s", + "he i", + "ĠK ills", + "Ġhus bands", + "Ñħ ÑĢан", + "(d om", + "_t iles", + "Nib Name", + "Ġdon ating", + ". acc", + "Ġlifes pan", + ".b n", + "_RG CTX", + "æ ¥", + "ans en", + "Ġmod elling", + "Layout Params", + "ĠonChange Text", + "rs a", + "- location", + ".P e", + "(b us", + "(s ong", + "Ġprodu k", + "ĠSH OULD", + "ĠC J", + "Ġs os", + "ĠHome Controller", + ".load ed", + "(D ocument", + ".s ocial", + "t iles", + "Ġl ame", + "= df", + ".parse Long", + "Ġpr ac", + "Ġdet ox", + "ĠV E", + "Ġpunt os", + "Ġdo ctr", + "Ġan cor", + "CA PE", + "Ġc mb", + "çĦ ¶", + "*) \"", + ":// /", + "Value Type", + "Ġmort gages", + "; q", + "ĠRock ets", + "s port", + "UG C", + "ct s", + "ãĤ ģ", + "ie ur", + "ĠAppe al", + "(n b", + "//////////////////////////////////////////////// ////////", + "IM ATION", + "ĠC res", + "ĠMan ip", + "C ause", + "at ypes", + "man ufacturer", + "# ----------------------------------------------------------------------------", + "Ġsp or", + "es on", + "Ġpun ched", + "Ġbook marks", + "ĠBul k", + "Complete Listener", + "ĠTalk ing", + "ĠEr nest", + "Ġrub bish", + "k ills", + "ĠDE FIN", + "Ġneighbour ing", + "ar lo", + "ĠP CA", + "ĉm atrix", + "lo k", + "Ġat las", + "ĠG ur", + "Ġw yn", + "-n egative", + "Ġt ul", + "Ġre lic", + "ĠV oltage", + "ĠPre is", + "ĠJ NICALL", + "ĠPM ID", + "ak et", + "ĉ attr", + "Ġet iqu", + "ĠM J", + "ĠG mail", + "cl r", + "_exec ution", + "éĶ ®", + "pos itor", + ". af", + "N r", + "Ge orgia", + "Top ology", + "Ġperch é", + "Ġmus lim", + "Ġepid emi", + "Ġsab ot", + "act us", + "Ġë ĮĢ", + "ĠIO Error", + ". est", + "p refs", + "ĠKr ish", + ".Read Key", + "NAS A", + "u ção", + "_D b", + "umer ator", + "W ide", + "(st atement", + ".end point", + ".... .....", + "Ġ[ *", + "stream s", + "m time", + "P x", + "at r", + "Ġt pl", + "R oman", + "Ġscen ic", + ".n z", + "ĠSe conds", + "sub menu", + "Ġìĭ ¤í", + "_b undle", + "Ġde ÄŁ", + "ĠS isters", + "pre ferences", + "Ġport a", + "Ad visor", + "max Length", + "ĠG REAT", + "__ (Ċ", + "ole st", + "ĠLabel s", + "Ġen fer", + "ĠĠĠĠĠĠ ĊĊ", + "ĠThe ft", + "_F ILL", + "ĠW ise", + ") application", + "un ami", + "> ())Ċ", + "ADD RESS", + "B ST", + "et zt", + "ĠQ gs", + "S ense", + "Exception Handler", + "ĠCh u", + ".get OwnProperty", + "Ġexerc ised", + "iot ic", + "ĠRe leases", + "Ġp interest", + "ol ie", + "is oft", + "Ġsequ encing", + "Ġpad re", + "] ));čĊ", + "(r adius", + ".m ed", + "aint ies", + ".Object Model", + "Ġem ple", + "Ġseg uro", + "St ars", + "Ġqual itative", + "lem n", + "á» ±", + "> \").", + "Ġg x", + "-c ert", + "ĠAST M", + "Ġfull name", + "Ġte lemetry", + "ĠCamb odia", + "_ ul", + "ĠCl are", + "C USTOM", + "Q C", + "ĠUn s", + "ĠHTTP S", + "ĠPark inson", + "ancy box", + "',' .", + "T ue", + ".get Last", + "Ġab i", + "Äħ d", + "A st", + "ĠEd iting", + ".Un ity", + "j mp", + "Ġm ats", + "Ġshared Preferences", + "Capt ain", + ".page Size", + "Ġr tl", + "Ġan meld", + "Runtime Object", + "Ġdemand e", + "(\" ;", + "se ite", + "-head ed", + "ĠK ra", + "ĠF ONT", + "` \\", + "Class NotFoundException", + ". avg", + "atic al", + "A j", + "Ġpermit ting", + "Pro j", + "ERR Q", + "Ġcre ampie", + "ĠBuy er", + "-mod ules", + "ĠSund ays", + "| `Ċ", + "Ġday time", + "Ġ+ (", + "Ġgl itch", + "ĠOper and", + "Ġtox ins", + "iny a", + "D NS", + "ĠS as", + "C ake", + "ĠNation als", + ".add To", + "Ġs inking", + "Ġcompreh ension", + "Ġsc or", + "ag ements", + "Ġt ard", + "Ġmarch ing", + "ĠM TV", + "Ġs ane", + "Create Info", + "Ạ¯", + "Ġend Index", + "ĉ layout", + "ĠåIJ į", + "S ITE", + "ĠT HERE", + "Ġ[ {'", + "opath ic", + "Ġtrans mitter", + "/ body", + "Ġp und", + "ĠC losing", + "Ġset attr", + "Ġbound ed", + "At las", + "sum ing", + "(t imes", + "par er", + "yn om", + "fe it", + "Ġf rem", + "- leg", + "ĠBr as", + "> #", + "Ġì¶ ľëł¥", + "ĠIN STANCE", + "ĠC ouch", + "_host s", + "lik elihood", + ".M arker", + "ĠM asks", + "Ġcere al", + "util ities", + "Ġelement al", + "Ġdist orted", + "in active", + "c ry", + "W L", + "UPPORT ED", + ".Th rows", + "/s chema", + "ser ie", + ".\" ',", + "ĠBened ict", + "-p icker", + "ig gs", + "ĠPir ate", + "åij¨ æľŁ", + "ĠTh ema", + "ĠSouth ampton", + "Ġarray With", + "ĠPaul a", + "Ġpredict or", + "- Ass", + ".user id", + "Ġper i", + "Ġexagger ated", + "ur ate", + "arse ille", + "ĠCon cent", + "ĠP ik", + "Ġ@ _;ĊĊ", + "Ġform ations", + "Ġden omin", + "\"/> .Ċ", + "ended or", + "Ġpan cre", + "Ġam t", + "Ġon Resume", + "on Delete", + "ĠB CH", + ") (\"", + "m ovement", + "Ġpot assium", + " čĊčĊ", + "ĠMah m", + "} \";ĊĊ", + "Ġd q", + "ĠPublish ers", + "ĠAm pl", + "ĠDani elle", + "Ġt ern", + "èµ ·", + "no ÅĽÄĩ", + "e in", + "ĠAsync Storage", + "un ger", + "rou w", + "Ġsc issors", + "/ assert", + ".b ucket", + "/ archive", + "_M an", + "Ġint oler", + "Ġ() =>", + "ĠÐĴ Ñĭ", + "Ġsa i", + ".x y", + ".\" čĊ", + "Ġur inary", + "es ub", + "IST ICS", + "ĠÎ º", + "Ġcompl iments", + "Ġtypings Japgolly", + "ih ar", + "Exp ansion", + "ĠS erving", + "_st udents", + "ĠX BOOLE", + "( il", + "Ġì² ĺ", + "Ġj ó", + "(t ol", + "( JS", + "ĉC G", + "ĠD RAW", + "tw ig", + "Ġo at", + "_sm ooth", + "ĠC SL", + "Ġos ob", + "Ġens uing", + "Ġbank er", + "ĠBack pack", + "_p ing", + "Ġwish list", + "= ax", + "ĉĠĠĠ Ċ", + "Dis ney", + "stead y", + "\"> %", + "Ġproph ets", + "ĠZ X", + "Ġminimal ist", + ".PL AIN", + "Se attle", + ". ordinal", + "ĠPI PE", + "Ġret orna", + "Ġjug ador", + "ĠB ret", + "ĠâĶ ľ", + "Ġpl ush", + "UL ATOR", + "Sort ing", + ".grid y", + "ect omy", + "_ activ", + "r ack", + "Inter active", + "ĠAntar ctica", + "Ġv engeance", + "en so", + "_k nown", + "up plier", + ".Mod ules", + "ĠConnection State", + "éļ IJèĹı", + "@ FindBy", + "Ġpl acer", + "\\ model", + "< ()>", + ".is Successful", + "-g ood", + "b z", + "ĠDr aco", + "Ass istant", + "-ex tra", + "аб лиÑĨ", + "Ġhyp ocrisy", + "Ġt st", + "ĠA gr", + "$ txt", + "Ġlog istic", + "lic ensed", + "ĠH of", + "Ġt at", + "( iv", + "Ġinto xic", + "post Id", + "_st rike", + "Ġhum iliation", + "pc odes", + "\" sync", + "(rec ipe", + "+ N", + "rent e", + "ĉ Client", + "ycop g", + "ĠZur ich", + "ĠPro files", + "C ountries", + "Ġp ict", + "Ġroll out", + "requ encies", + "Ġpatch ed", + "Ġcar tridges", + "Ġsh ading", + "J ar", + "Ġsalv age", + "ĠTax es", + "Ġstand by", + "apor an", + "E igen", + ". angular", + "ĠN ested", + "äº «", + "Ġis Visible", + "ĠDw ight", + "_BR ANCH", + ".D elay", + "Ġk end", + "Ġfacilit ated", + ".flat Map", + "Ġs anta", + "ĉS end", + "/m essages", + "Ġof Type", + "ĉs wap", + "# plt", + "ĠTur ks", + "N ES", + "Ġprogress ively", + "ĠRes idence", + "ĠT REE", + "Ġno en", + "d io", + "Ġn elle", + "Ġsog ar", + "itt i", + "week ly", + "Ġambigu ity", + "_Set tings", + "W are", + ".ne o", + "_D ST", + "Ġæĸ ¹", + "pre p", + "lob by", + "@ email", + "/m ovie", + "Ġfun kc", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ÂŃ s", + "Ġguard ians", + "- pos", + "Ġconfig uring", + "ĠC PS", + "ĠDe us", + "Ġvidé os", + "_ empresa", + "Ġsl apped", + "< Model", + "Ġunders cores", + "U h", + ".access Token", + "SET S", + "ĠS parse", + "ĠCal d", + ": path", + "ĠS ervers", + "= batch", + "Ġkn itting", + "Ġx a", + "Ġsearch Bar", + "Ġsn ag", + "Ġinf used", + ".b am", + "le ver", + "Ġtax onomy", + "à İ", + "Ġatt aching", + "Ġh ern", + "_N OP", + "Click able", + "(P arse", + "ĠDynam o", + "-b uilder", + "Ġdere g", + "Ġsc attering", + "è¿Ľ è¡Į", + "an zi", + "ĠShe pard", + "\"> ',Ċ", + "_X DECREF", + "ĠBuzz Feed", + "_M ARGIN", + "P LOY", + ".sm all", + "Ġm imeType", + "Ġh olog", + "ĉc amera", + "li as", + "Ġsusp ense", + "ody nam", + "b au", + "Ġgrave yard", + "_n amed", + "\":\" '", + "Ġ******************************** ****************", + "Ġgame Over", + "ĠLENG TH", + "ĉs creen", + "Ġdo InBackground", + "_depend encies", + "Ġr tc", + "/ up", + "_ ROM", + "H all", + "Ġdef iciencies", + "( te", + "' #", + "_e quiv", + "Ġpre order", + "ĠA xe", + "ом Ñĥ", + ".send File", + "Ġfil t", + "ĠLim its", + "ĠCaval iers", + ".dis count", + "âĨ IJ", + "ĠW it", + "QRST UV", + "Ġi j", + "Ġt egen", + "Ġ: \",", + "diff iculty", + "p unkt", + "ĠEmail s", + "ch lor", + "(f un", + ".U int", + "ĠSt all", + "_ verified", + "u D", + "File Type", + "Ġple asures", + "Ġjud iciary", + "Ġsh am", + "ip ur", + "_PL US", + "off ers", + "( foo", + "_G T", + "ĉc ore", + "ENT ION", + "ĠLib eration", + "Command Line", + "_de partment", + ".A r", + "_ne ighbor", + "ĠSub mitted", + "ĠĊ", + "Ġdro its", + "Ġhomosexual s", + "Ġab duction", + "ĉw idget", + "$ headers", + "ĠD AR", + "Ġfl a", + "th reat", + "Ġlou is", + ".Get Property", + "\" Just", + "(f rames", + "ry o", + "prof ession", + "| i", + "íķ´ ìĦľ", + "(s v", + "Ġun recognized", + "I onic", + "F ashion", + "Screen State", + "ĠIn coming", + "Not Nil", + "Ġsync ing", + "em ie", + "Ġtherm o", + "_pro cs", + "Ġincons istency", + "rel igious", + ".m j", + "Ġperson n", + "Ġmoment os", + "or arily", + "Ġæ Ĭ", + "_ne urons", + "Ill ustr", + "im oto", + "il ik", + "ĠW oj", + "Tr ading", + "Ġapp are", + "Ġentre prises", + "ach at", + "Ġ ¬", + "Ġne igh", + "BUTTON DOWN", + "ĠMah er", + "ag han", + "-h ash", + "\" f", + "Ġclient ele", + ".add Button", + "ĉ SP", + "Q i", + "Ġgr ated", + "POS ITE", + ": >", + "ĠHow ell", + "ĠCompar ative", + "ĠIS C", + "ÂŃ i", + "O cean", + "D avis", + "ĠFil me", + "W ins", + "ĠJ IT", + "oc cer", + "ĠC orm", + "ENCH MARK", + "rch ive", + "ica ção", + "Ġm ata", + "Ġchild birth", + "ĠOption ally", + "En s", + "Ġx http", + "Ġel ucid", + "_Osc InitStruct", + ")) ):Ċ", + "Ġint uit", + "ĠDon ate", + "Ġcorrel ates", + "> Delete", + "Ġequ ipe", + "Ġb oca", + "Ġinfl atable", + "er ah", + "ĠDateTime Kind", + "Ġcal ves", + "\\ Lib", + "Ġem lrt", + "ĠTr ilogy", + "ĠP anc", + "ĠD uis", + "ĠpelÃŃcul a", + "WAR DS", + "_DE TECT", + "-section al", + "dh cp", + "For Row", + "-de struct", + "ĠPres enter", + "/s lick", + ", on", + "ĠCit adel", + "logged in", + "_sub type", + "Ġsig ue", + "Ġc uring", + "ĠFire wall", + "Ġfluores cence", + "ĠItal ians", + "иÑĤ ÑģÑı", + ".get Style", + "In Seconds", + "j ie", + "-S mith", + "Ġx link", + "Ġsub missive", + "он ÑĤ", + "arbon ate", + "ĠF aul", + "_go als", + "ĠCommission ers", + "chart Instance", + "_POST FIELDS", + "Ġmed ial", + "Ġman os", + "Ġdel t", + "sv m", + ".Ap is", + "ep hy", + "Ġasym pt", + "Ġapp Delegate", + "Ġimpro bable", + "ck a", + "sim d", + "/ Error", + ". âĢĵ", + "ĠP TS", + "de er", + "Ġs ina", + "m agnitude", + "ID ADE", + "'] }'", + "Ġmay ores", + "ĉ comment", + "/ console", + "\" @", + "v olt", + ".s ell", + "ĠM acy", + "Ġmel od", + "Ġim ágenes", + "_ch g", + "Ġin out", + "ident e", + ") '),Ċ", + "d ni", + ".b lob", + "Ġtyp ography", + "Ġe erie", + "_O ID", + "pes an", + "aj an", + "Ġch opping", + "Ġbl uff", + "ad f", + "_b ases", + ".Form atter", + "Ġ\\ %", + "ĠPage Info", + "Car rier", + "ĠCal ibration", + "com o", + "-b odied", + "Ġfinanc ier", + "ĠIN A", + ". ERR", + "Ġhood ie", + "ĠSan ity", + "gu arded", + ".opend aylight", + "ISM ATCH", + "High lights", + "ün k", + "ani em", + "anger ed", + "assign ments", + "Ġregistr ado", + "ĠU PPER", + "ampil kan", + "ash ire", + "ĠNik ola", + "ĠC FL", + "ĠH DC", + "Ġp oids", + "ĠIP s", + "Ġprevent ative", + "ips oid", + "if ix", + ".c amel", + ".g a", + "V olumes", + "- ste", + "Y ahoo", + "_s ibling", + "H ighest", + "opt group", + "Ġkvin na", + "âĢĿ ãĢĤĊĊ", + "ĠAppl iances", + "Ġ\" ><", + "') \")Ċ", + "ht t", + "ĠIdent ified", + "Ġpenc ils", + "Ġmember Id", + "Ġappend String", + ".load Data", + "Ġmock Mvc", + "Ġj ub", + "ĠSl ut", + "ĠTai pei", + "st att", + "Pol it", + "Ġpart ager", + "Did Change", + "Incre ases", + ") }.", + "ĠB aba", + "_CL IP", + "[ unit", + "Ġк лÑİÑĩ", + "Ġalc uni", + "ĠL ola", + "Ġcl inging", + "@ PostMapping", + "(con cat", + "Ġss id", + "ĠFa uc", + "ok it", + "ĠRecord ed", + "á lez", + "($ ('<", + ".assertIs Not", + "Ġk ali", + "V olt", + "Ġwarm ly", + "Ġsca res", + "get ti", + "füh rt", + "_d oes", + ". EMAIL", + "im ations", + "Ġspring fox", + "ĠDec om", + "arc y", + "Ġgl itches", + "ĠM off", + "ĠV oll", + ".b etween", + "Ġcoord en", + "ĠPart icularly", + "GB P", + "Ġsem ble", + "East ern", + "_M SB", + "]) {čĊ", + "m organ", + "ĠE VAL", + "d ere", + "HO USE", + "mo ire", + "ist ique", + "_l stm", + "-com mit", + "yster ious", + "Ġtw ink", + "-th umbnails", + "en ÃŃ", + ":' ',", + "Ġblack out", + "ĠFlo ors", + "Ġso fas", + "Ġou i", + "lesh oot", + "ĠRa q", + "- abs", + "Ġk ra", + "M ining", + "sha ft", + ".set Columns", + "Cl azz", + "PRE TTY", + ".play list", + "éĸ ¢", + "-Sah aran", + "M ING", + "ĉ bl", + "è® ®", + "j f", + "DO CKER", + "hope fully", + "( ignore", + "ĠUsers Controller", + "ĠMitar beiter", + "ĠL ES", + "Ham ilton", + "-m etadata", + "ĠK K", + "ikt ig", + "Ġwoll te", + "egr ator", + "] bool", + ", current", + "Ġvalue Type", + "Ġexcav ation", + "ol and", + "Ġv erv", + "/file path", + "Auth Provider", + "Ġpro crast", + "ĉ ULONG", + "_MEM BERS", + "Ġup lift", + "ĠAut onomous", + "Ġart works", + "ĠOut reach", + "Ġp ore", + "Home page", + "Dialog Title", + "ĠGener ating", + "PAR SE", + "Ġsem anas", + "Ġhuman o", + "JSGlobal Scope", + "Ġvol te", + "Ġb ella", + "(is instance", + "Ġpl c", + "\\C atalog", + "Ġeste emed", + "éĽ ·", + "(s uffix", + "Ġswe eps", + "ĉ ORDER", + "Ġdo ivent", + "ĠSw arm", + "ĠComp iled", + "get Page", + "AD R", + ".R ichTextBox", + "ĠN aming", + "ag ged", + "ĠG ANG", + "r asing", + "ode led", + "Ġg ala", + "ĠJS Name", + "dd f", + "Ġill ust", + "ĠLans ing", + "[ port", + "-de ath", + "Ġdin heiro", + "ĠE ighth", + "Ġb ian", + "st Ã¥", + "Ġvers ión", + "ĠLinear Gradient", + "ĠHard ing", + ". *)", + "ec zy", + "$ header", + "Ġv Ã¥r", + "Un checked", + "Ġko je", + "ĠPal adin", + "() )),", + "G iving", + "() })Ċ", + "Ġd ips", + "F riendly", + "Ġport rays", + "Ġhel ium", + "Ġinsurg ency", + "_ex piry", + "ĠstringByAppending String", + "Ġa antal", + "s lope", + "m ast", + ".get Integer", + "Ġ################ ########", + "_PIPE LINE", + "Ġdens ely", + "Ġmut ating", + "m idi", + "ĠSe it", + "ay ne", + "NOW LED", + "ĠDes mond", + "ĠF Name", + "ĠN airobi", + "\\ Context", + "Ġcalc ular", + "-d en", + "Ġc ott", + "] ):čĊ", + "ĠRecommend ation", + "ĠRole x", + "Ġvalidation Result", + ".p at", + "Ġn Ãły", + "ĠRest Client", + "ĠG PI", + "ĠAshe ville", + "ĠO SP", + "ĠPER MISSION", + "ÐĶ аÑĤа", + "/ notification", + "K night", + "_W ord", + "ĠB ender", + "rank ing", + "Ġpart ida", + "_res ervation", + "Ì Ģ", + "Ġm Name", + "Ġget ch", + "Ġb orr", + "Ġdilig ent", + "Disc uss", + "æŃ£ åľ¨", + "ape ake", + "ion ed", + "-N azi", + ".c um", + "ĠK ron", + "=$ ('#", + "/s ingle", + "Ġerot isch", + "ĠV ib", + "Ġrat ified", + "Ġconcert ed", + "ĠREG ARD", + "Ġdo br", + ".Driver Manager", + "' r", + "Port able", + "ĉs uite", + "Ġrel aciones", + "ĠD op", + "emplo i", + "DO B", + "Ġcr umbs", + "Ġx ls", + "_App lication", + "(': ',", + "Ġ---------------------------------------------------------------- --------Ċ", + "m se", + "Ġber k", + "ĠReturn Value", + "ĠBel ly", + "Ġcam ar", + "ĠPe ek", + "els ing", + "Ġnot ifies", + "ĠTr istan", + "ĠG AR", + "em me", + "ĠElev ated", + "_C SV", + "(ch alk", + "Ġtw enties", + "ĠSearch Result", + "= search", + "ĠMix ing", + "ý t", + "Ġrecru iter", + "ĠIDE OGRAPH", + "ĠA go", + "( Operation", + "$ values", + "Ġworld ly", + "ĠRosen berg", + "ĠConfigure Services", + ">* Ċ", + "Ġsn ork", + "_op acity", + "ĠinitWith NibName", + "i ado", + "A AC", + "Ġ] ).", + "; z", + "_par agraph", + "Ġnos es", + "stand s", + "if r", + "_m E", + "I raq", + ".P redicate", + "ena ire", + "]] ];Ċ", + "Ġun idad", + "Ġretire es", + "_h ello", + "Ġmode le", + "ĠUIT ableViewController", + "f write", + "_num ero", + "_vis ited", + "Ġrece be", + "( Notification", + "Fant astic", + "_sub menu", + "ĠP EM", + "ĠCup ertino", + "approx imately", + "class ed", + ".Read String", + "Ġdomic ile", + "_P W", + "Ġball park", + "ĠK ale", + "con tra", + "_f avorite", + "/ of", + "Qu ite", + "ĠOT A", + "Ġacceler ometer", + "did n", + "| ^", + "ĠRohing ya", + "ivic rm", + "ann abin", + "обÑĭ ÑĤи", + "or ado", + "') +", + "Ha unted", + ", ID", + "( UIAlertAction", + "ur v", + "_b el", + "ĠMex icans", + "/ terms", + "ĠPaint er", + "Input Label", + "ĠV inci", + "ĠRos ie", + "\\ uc", + "< Menu", + "Ġcool ant", + "(current User", + "_d ual", + ") \"},Ċ", + "& p", + "Ġconver ged", + "Ġrestr ain", + "ĠYugosl avia", + "= target", + "Ġimp uls", + "ds a", + "Search Tree", + "Ġh box", + "ĠImp ress", + "§ Ãĥ", + "get FullYear", + "(d a", + "ĠY YS", + ".al ignment", + ".Get Text", + ".token ize", + "ĠOlymp us", + "Ġmur ky", + "ore station", + "Ġdiss atisfaction", + "ĉT Array", + "_ kses", + ".Add Singleton", + "ĠStart Time", + "Ġfan atic", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", + "Ġentity Type", + ". override", + "Ġ -------------", + "ĠDat agram", + "f out", + "(with Id", + "Ġ# __", + "Ł èĥ½", + "ek yll", + ".f riends", + "ame leon", + "Ġz ach", + ".simple Button", + "ret orno", + "Ġkon k", + "/s mall", + "ĠQuick ly", + "un read", + "Don ate", + "Detail View", + "Ġdu a", + "Ġpenetr ated", + "OM UX", + "Ġn ir", + "_p data", + "\"], [\"", + "Ġlow es", + "Ġdop ing", + "Ġas ymmetric", + "Ġneed less", + "our cem", + "Ġup ro", + "ĠGu zzle", + "af b", + "Ġsext reffen", + "-c ollar", + "Ġcol ossal", + "Mon key", + "n ish", + "Ġhandle Message", + "Incre ased", + "* dx", + "ĠChatt anooga", + "f org", + "ĠOr den", + "Ġsh ri", + "ĠV and", + "Ġ\" @\"", + "Image Sharp", + "ĠWild cats", + "pon ible", + ".sc enes", + "Ġpaint ers", + "ĠPf izer", + "ĠZ ah", + "To Local", + "ĠFl am", + "Ġé taient", + ")) ^", + "ĠSand box", + "ĠTR ADE", + "Ġchrom ium", + "Ġac claim", + "Ġpac man", + "´ t", + ") reader", + "M ari", + ".Dispatch er", + ".A DMIN", + "ĠRem ed", + "Sw eden", + "Ġoverl ays", + ". er", + "Ġp ang", + "Ġclean ly", + "aven port", + "Toy ota", + "patch es", + "Ġv tx", + "ĠE is", + "cl ado", + "ĠR itch", + "RO LS", + "Ġh ade", + "Ġconspic uous", + "Ġdo cks", + "(j q", + "ĠPrem iership", + "ĠBe z", + "ĠâĦ ĸ", + "ĠÑĥ Ñģл", + "_tot als", + "Ġprov a", + "ĠC ue", + "Ġsa úde", + "ĠGame Controller", + "IM IZE", + ", port", + "ãĢĤ (", + ".C decl", + "Instant iationException", + "Ġcoll age", + "ĠIO C", + "Ġb ais", + "Ġon Finish", + "-st ars", + "set Size", + "Ġmog ul", + "Ġdis illusion", + "Ġche vy", + "(S chedulers", + "( IR", + "_loc s", + "Ġcann ons", + "Ġcancell ing", + "/b us", + "Ġbuf io", + "ĠY ours", + "ĠPik achu", + "Ġter me", + "r Ã¥", + "f ahren", + "Ġowner Id", + "Ġoblig atory", + "Ġcul p", + "Ġacid ity", + "-m ult", + "ĠBam boo", + "Ġ' \">", + "_g s", + "Ġcomp il", + "n ard", + "-ex c", + "Ġrh yme", + "Ġbut to", + "s ays", + "ant asy", + "ë ¸", + "Ġcitt Ãł", + "Ġche g", + "Time String", + "Ġpos itivity", + "ĠD abei", + "Ġw ang", + "Ġes cre", + "\" c", + "ĉv ideo", + "ĠRank ed", + ".str ings", + ">> >(", + "Ġин ÑĤеÑĢ", + "Ġrest a", + "[: ,:", + "Ġrend re", + "Ġdes er", + "J os", + "Ġdis ruptions", + "Ġоп еÑĢ", + "s ampling", + "sup press", + "Ġcontainer View", + "ĠSeam less", + "Ġair y", + "Ġon load", + ".Window Manager", + "ĠPL A", + "br aco", + ".set PositiveButton", + "Ġp du", + "Ġg si", + "ĠC li", + "_gr adients", + "Ñı д", + "ĠWh isper", + "c stdint", + "Ġl äng", + "Ġform ulations", + "én om", + "ourn emouth", + "[$ _", + "Ġordin arily", + ".set Username", + "Ġfacult ies", + "MIT TED", + "/ values", + "Ġwe ir", + "ĠA pt", + "M Z", + "ĉc f", + "uck en", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", + "def ense", + "[i Var", + "ĠBusiness Exception", + "Select ors", + "(co ordinates", + "ĠRes ets", + "ĠDr inks", + "ole ans", + "(st ypy", + "_IO C", + ".x xx", + "ĠSl ater", + "ĠBel ize", + "Ġ/ ************************************************************************", + "add in", + "_ep isodes", + "Ġis chem", + "legal ArgumentException", + "D anny", + "Ġp ared", + ".code haus", + "ĠAss y", + "ĉ Rect", + "â ŀ", + ".list a", + "Ġв аÑĪ", + "Ġv ets", + "HW ND", + "ison er", + "Ġx o", + "Ġor ally", + "ĠSt mt", + ".r nn", + "ĠD PI", + "ĠStr ikes", + ".setViewport View", + "Ġèĩª åĬ¨çĶŁæĪIJ", + "Y ELLOW", + "GL enum", + "part ners", + "ĠImp licit", + "Ġtak o", + "âĢĻ elle", + "Ġerm ög", + "total Count", + "G il", + "ĉ work", + "Ġpr atic", + "in ati", + "ab ies", + "ĠSk inner", + "Ġspir ited", + "Ġpancre atic", + "Ġh df", + "' em", + "Ġpsych osis", + "olic it", + "Ġ\" {\"", + "_at ual", + "Ġé lect", + "TE AM", + "Ġd ak", + "ĠSW AT", + ".Fragment Manager", + "Ġprovision ing", + "l ifetime", + "_EXTENSION S", + "ĠC ASCADE", + "Ġ! [", + "(K P", + "Ġv em", + "ĠInterr acial", + "'] },Ċ", + "sp acer", + "_k v", + "W arehouse", + "R DD", + "_f sm", + ".Stretch Image", + ", Yes", + "ĠRefuge e", + "ĠBr inging", + "Ġv álido", + ".inter section", + "Ġsp ooky", + "_port al", + "Ġmo th", + "ĠZ odiac", + "ĠSOC IAL", + "M imeType", + "'] }}", + "_Bl ue", + "Ġbot anical", + "Ġfr ags", + "Ġfamil ial", + "- du", + "Ġse izing", + "(block s", + ".r d", + ".check NotNull", + "Ġmis er", + "Ġmax x", + "ĠK nee", + "View Item", + "Inner HTML", + "D anger", + "(( __", + "Ġprz ypad", + "create Url", + "** ,", + "ĠDecor ating", + "ATEG Y", + "?> /", + ".Design er", + "hex digest", + "ĠEvery where", + "all eries", + ".TEXT URE", + ".Block s", + "z ell", + "Ġpre ço", + "S uddenly", + "input Email", + "(s ync", + ".b d", + "gold en", + "> ');", + "ĠDick inson", + ">> (Ċ", + "ĠQUE UE", + "Ġget Column", + "ĠS AND", + ".p iece", + "lic er", + "Fl utter", + "Ġget Version", + "Ġresource Id", + "og l", + "ÅĤ aw", + ".Br anch", + "ĉ web", + "Ġfr amerate", + "PP P", + "Ġfr ay", + "C NT", + "Ġinformat ie", + "'] čĊčĊ", + "ne as", + "Header Code", + "Ġæ ¸", + "Ġtr g", + "raw types", + "H onda", + "Ġmark eter", + "Ġrequest Data", + "ĠP g", + "ĉ not", + "Ġpage Info", + "Ġakt uellen", + "ãģķ ãĤĵ", + "ĠA MS", + "push ViewController", + "ĉ AL", + "Ġv ests", + "produ ce", + "-m ême", + "ĠRah man", + "F unny", + "E Z", + "_ Valid", + "Ġsquad ron", + "Ġl ash", + "Ġ irm", + "ias co", + "ĠPar an", + "Ġpet ites", + "ĠDec ay", + "Ġun initialized", + "priv ileged", + "Ġm bedtls", + "å¤ĩ 注", + "Ġ^ .", + "Ġec static", + "D etroit", + "Ġpart en", + "Ġsou venir", + ".get Login", + "моÑĤ ÑĢ", + "en ção", + "ĠmÃŃn imo", + "ĠAccess ed", + "ri ó", + "M ic", + "ĠV ocal", + ".Set String", + "Ġmens ajes", + "åĢ į", + "Ġattr avers", + "ĠA ph", + "Ġ' );čĊ", + "ünd e", + "Ġench anted", + "ĠRoot State", + "ĠCLOSE D", + "ĉĉĉĉĉĉĉĉ čĊ", + "Ġcal iente", + "or ris", + "Ġphysic ists", + "h wnd", + "_v i", + "Ġráp ido", + "Ġcapital ized", + "ed By", + "Ġmach ining", + "Ġhub by", + "ĠSt acy", + ".B us", + "dr ink", + "H ur", + "Ġprop ia", + "Unit Test", + "Ġmiscon ception", + "__ ));Ċ", + "/d c", + "ĠMay weather", + "_m C", + ".create From", + "ĠQ Painter", + "rops ych", + "inn itus", + "ay as", + "Ġg eg", + "(d w", + "Ġus ado", + "Ġtrick le", + "Ġann ihil", + "ĠP asta", + "Ġ++ Ċ", + "(Expected Conditions", + ".post Value", + "ic ap", + "ĠDon etsk", + "_s oup", + "-p ublish", + "ĠP b", + "ment ions", + "AC CEPT", + ".P ull", + ",âĢĻ âĢĻ", + "Ġret arded", + "_AT OM", + "ĠTermin ator", + "-c ourt", + "ĠCLLocation Coordinate", + "Ġrever ence", + "ĠS SC", + "ut ely", + "ĠW ON", + "ĠG SL", + "fre i", + ".get Longitude", + "Ġopen FileDialog", + ".B utter", + "- important", + "_M ANY", + "ĠG ong", + "âĢľ How", + "Ġg orge", + "= msg", + "ĠEz ek", + "create Command", + ": checked", + "Ġinf ographic", + ".W EST", + "Dir s", + "Ġguard a", + "Ġbeet le", + "< small", + "- android", + "Ġcred itor", + "ĠM éd", + "Ġfinal ist", + "Ġab l", + "ne v", + "_inter action", + "ĠMonter ey", + "j ah", + "Ġcand ies", + "ĠQu incy", + "èª Ń", + "Ġbatch Size", + "ak it", + "Ġo be", + "(p ara", + "Ġexperiment ed", + "Ġcouncill ors", + "Ġcl ashed", + "s qu", + "-st rokes", + "ĠG K", + "ĠEx pires", + "Ġprosec utions", + "ĠCreat ures", + "Ġy ö", + "x lim", + "_IM P", + "Entry Point", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + ".Default CellStyle", + "Ġbre ve", + "ĠBrit ann", + "Ġsweat y", + "Ġle th", + "Ġflash back", + "per manent", + "ĠJ DK", + "_D etails", + "E uro", + "p pt", + "Ġrich TextBox", + "/ board", + "Ġtr ance", + ".c ycle", + "'); \");Ċ", + "Ġtox in", + "_de init", + "Ġover arching", + "Ġconfig parser", + "ĠKaw asaki", + ".th umb", + "Ġplay a", + "ĠJose f", + "+ _", + "Ġzero es", + "Ġa up", + "ĠH ari", + "comm itted", + "N it", + ".file Path", + "ĠDis abilities", + "man ufact", + "-al igned", + ".RE SET", + "Ġrust y", + "E y", + "Ġou sted", + "cos a", + "Struct ured", + ".get D", + "Ġs ábado", + "> Loading", + "_m A", + ".get Random", + "bl ings", + "Ġchees es", + "tt i", + ". âĢ¢", + "ĠBurg ess", + "ender it", + ". ',čĊ", + "(\" \"+", + "ac b", + "% p", + "index ed", + "_pred icate", + "nes ia", + "Ġb ied", + "ĠC IT", + "( Pos", + "_r adi", + "ä»· æł¼", + "B iz", + "ĠAdoles cent", + "Ġvi ên", + "c ycl", + "_C ancel", + "Ġcon clusive", + "Ġappell ate", + "inform atics", + "S J", + "Ġelect ive", + "role Id", + "Fetch er", + "ĉ Command", + "(\" (%", + "Ġf art", + "IL A", + "get Block", + "A USE", + "Ġд ан", + "ĠAr te", + "Ġnot ifying", + "Ġge le", + ".s ame", + "ĠReg el", + "ĠBa ÅŁ", + ".c reation", + "ĠV N", + "_comm unity", + "Ġuns ustainable", + "SE X", + "Ġgrid Size", + "res cia", + "avers able", + "(', ')[", + "ĠPh elps", + "á»ķ i", + "ANCE LED", + "- IS", + ".run ners", + "ĠSt okes", + ".P rodu", + "Ġwh ipping", + "_ac quire", + "Ġinvestig ación", + "f ried", + ".copy With", + "ĠHard cover", + "- Se", + "áŀ¶ áŀ", + "inv itation", + "les ai", + "ĠD orm", + "ĠÑģпиÑģ ка", + "Ġconcaten ated", + "oph il", + "Ġthink er", + "/font awesome", + "ĠLe opard", + "Ġ\"/ \");Ċ", + "Ġresidual s", + "ĠMic rowave", + "Ġconform e", + "th rop", + "Ġdis emb", + "ĠO MG", + "ĠDisc ipline", + "ĠAc robat", + "/re pository", + "df a", + "_M ED", + "buf io", + "Ġméth ode", + "_H OLD", + "ias i", + "_ legacy", + ") ččĊ", + "æ£ Ģ", + "Get ProcAddress", + "Ġy ay", + "ot ence", + "order id", + "-t w", + "Ġdear ly", + "In coming", + "/ il", + "Ġneu rop", + "uc z", + "); čččĊ", + "ĠInnov ative", + "Ġprof und", + "ig mat", + "Selection Mode", + "re levant", + ".G O", + "Ġbru ises", + "Ġs ach", + "ode f", + "Ġre imb", + "/d esktop", + "-s pot", + "und ance", + "Ent ropy", + "\\ core", + "Ġsug er", + "ĠM vc", + "ĠGN OME", + "_ind x", + "ĠYY STYPE", + "ĠMat lab", + "ĠC IF", + "Ġ* ))", + "Ġproduct List", + "ĠAl right", + "ac emark", + "ÑĤи в", + "mod ification", + "int ernational", + "Ġhom ers", + "Ġdict s", + "ĠQ Font", + ".SQL ite", + "Ġtransplant ation", + "ĠMessageBox Button", + "ĠEl ves", + "'] ])Ċ", + "(Q Icon", + "Ġcin emas", + "CO ORD", + "- China", + "Ġkh ẩu", + "æĪij çļĦ", + "Ġskull s", + "Ġpain staking", + "f ce", + ".XR Label", + "Ġspec ifier", + "Ġpref erring", + "/ activity", + "( Photo", + "á lt", + ".l ot", + "' '.", + "ann once", + ".google code", + "-p df", + "ĠP oke", + "_A CL", + "Ġend owed", + "dis cover", + ".om g", + "Ġwood land", + ".M agic", + "Ġvol ont", + "Not Allowed", + "Ġch ave", + "BM W", + "',' =',", + "ĠS IX", + "æĪij 们", + "Ġkos her", + "Ġaspir ation", + "int l", + "_ref ptr", + "'+ Ċ", + "ment or", + ".cl ub", + "Window State", + ".A RR", + "Ġz za", + "Ġmessage Type", + ".e qu", + "Th or", + "Ġin just", + "Ġg ums", + "Ġborder Side", + "//// /", + "ĠTrans mit", + "Ġbuf size", + "Ġh ak", + "Ġell as", + "R ANDOM", + "ĉm c", + "Ġpe a", + "ek o", + "document o", + "Ġhyster ia", + "Ġaren as", + "Ġgun men", + "Ġm ike", + "Ġimp unity", + "atis ation", + "_Z ero", + "_COMP ANY", + "ĠG ors", + "Ġuse Class", + "( redis", + "ĠRUN NING", + "ĠB air", + "vel te", + "Ġ',' .", + "аÑĤÑĮ ÑģÑı", + "ö st", + "encode URIComponent", + "_re strict", + "Ġdec als", + "ĠPed ido", + "Ġalter cation", + "Dis plays", + "ĠApp licants", + "C US", + "Text area", + "ĠAng ola", + ".f uture", + "ĠUS HORT", + "Ġsuppress ing", + "Ġset zen", + "AP olynomial", + "Ġto ch", + "Ġhall mark", + "Ġ$ $$", + "ĠCHAR SET", + ".r pm", + "ĠD ich", + "---------------- ----", + "_p arm", + "è¿ ĺ", + "acc iones", + "h ait", + "WAR DED", + "_r outing", + "ĠN OM", + "Ġen clave", + "ĠLot to", + "ĉf r", + "complex Content", + "ĠBall ard", + "k ube", + "/w in", + ".getColumn Model", + "_RE PLACE", + "Header Value", + "Ġest udiantes", + "Ġap is", + "Ġb pm", + "ĠType Name", + "And Get", + "rit a", + "Pl ans", + "> Note", + "Ġfet isch", + "Ġton ed", + "_g oto", + "ons ense", + "Ġm olds", + "Ġinfiltr ation", + "ĠGuerr ero", + "ub bo", + "ck i", + "($ (\".", + "_ activities", + "(ch anges", + "Ġof App", + "ĠKe pler", + "ĠD emp", + "ĠCont inent", + ".T icks", + "ĠUn signed", + "ĠJah res", + "Ġfresh men", + "ĠArch ived", + "ĠкоÑĤоÑĢ Ñĭй", + "Ġ' ::", + "T utorial", + "C c", + "Ġtable LayoutPanel", + "from Json", + ".level s", + "_trans ient", + "Ġendors ing", + "ĠD IC", + "la uf", + "Ġsh red", + "_E MIT", + "ific antly", + "AL A", + "/ proto", + "Ġnarrow ing", + "U tc", + "Fact ors", + "Ġsent ient", + "æŀ IJ", + "lix ir", + "ĠC ROSS", + "met eor", + "Ġgro in", + "Ġm db", + "ĠRot terdam", + "Ġcom ida", + "ĠOp Code", + "ĠDefault Value", + "Permissions Result", + "Ġheter ogeneous", + "Ġm oot", + "Ġde ceived", + "-in dependent", + "ĠObject OutputStream", + "Ġover power", + ".d up", + "Ġl db", + "Ġdomest ically", + "Ġbest ellen", + "Ġlo v", + "ĠContract ors", + "Tri angles", + "Ġfod der", + "Ġfilm es", + "ä¼ ģ", + "Ġrev olver", + "Startup Script", + "/ validation", + "ĠResource Type", + "i ÅŁ", + "ĠL az", + "f ef", + "Ġlst m", + "{ *", + ". attachment", + ".h its", + "ew ith", + "DO G", + "Al abama", + "Ġmedium s", + ".m Context", + "-c ols", + "åı ĭ", + ".not ice", + "Ġat tn", + "ĠP acking", + "ĠL n", + "_COM PLEX", + "/ Users", + ".sav etxt", + "ĠR ounds", + "?,?, ?,?,", + "Ġing l", + "ĠR OC", + "_f emale", + "ĠSt ard", + "]] ;", + "Ġwrest lers", + "Ġtorrent s", + "Ġsin h", + " ĊĊ", + "ë³ µ", + "s ense", + "how ever", + ".Ph ysics", + "Inf rastructure", + "ĠSac r", + "F el", + "ĠD ISTRIBUT", + "é ments", + "ĠValid ates", + "################################################ ############", + "Ġ| /", + "Ġes l", + "Ġré seau", + "ĠB ip", + "BY TES", + "_W ATER", + "Turn ing", + "EL S", + "Ġj uxtap", + "Ġlesb ische", + "ý ch", + "( Unknown", + "Ne o", + "@ JsonProperty", + "Ġal umnos", + "ĠRaq qa", + "ime i", + ".get Bounds", + ".Mouse EventHandler", + "#### ###", + "Generic Type", + "/c ms", + "Ġturn o", + "Ġм ин", + "Ġfolk lore", + "ĠE vo", + "Ġconduct ivity", + "Ġle ben", + "Ġgear box", + "-v s", + "ĠÏ Ĩ", + "Ġdrink ers", + "Ġcon exao", + "ĠTe eth", + "Ġget Arguments", + "ĠR AT", + "ent ious", + "E duc", + "+ W", + "ĠInstitution al", + "ĠB ord", + "is Equal", + "(p wd", + "Ġign ited", + "ĠR ousse", + "Ġimpact ful", + "ĠM alk", + "Ġg eral", + "ĠP ivot", + "Ġa zt", + "Ġcsv file", + "ĠR ope", + "ĠSOL UTION", + "ĠArbit rary", + "Ġlet to", + ".Mouse Adapter", + "Ġ} }}", + "ĠSail or", + "der a", + "Put ting", + "Ġconcentr ates", + "Ġauth Domain", + "âĢĿ çļĦ", + "-f inals", + ", strlen", + "Mu on", + "ĠOrd inary", + "fire fox", + "ĠLa TeX", + "ĠH und", + "engine ering", + "/ blue", + "ed TextBox", + "(\" \");", + "ĠC DDL", + "ke pt", + "ĠGet String", + "K ir", + "() ='", + "ĠO CD", + "ant ium", + "$ menu", + "ĠAppalach ian", + "Secret ary", + "ë¥ ĺ", + "ี ย", + "Sem antic", + "Ġ* [", + "est one", + "ung kin", + "Max Y", + "-t one", + "\"} ;čĊ", + "_P art", + "< Member", + "tr am", + "Ġtrans istor", + "Ġ---------------------------------------------------------------- ----------Ċ", + "ĠDes de", + "Ġright ful", + "ĠCorn el", + "æ ij", + ".H OUR", + "Ġsidel ined", + "ref errer", + "m aze", + "Ġhol ster", + "Ġcripp led", + "ĠDate Formatter", + "oph age", + "_m D", + "Ġdes elect", + "ra ud", + "ĠPK K", + "row Data", + "Ġlock smith", + ".res ponses", + "(product Id", + "_ST MT", + "Key Type", + ".Th en", + "z ee", + "Ġcr t", + "ĠGrand ma", + "@ Resource", + "Ġbit wise", + "-c mpr", + "ãĢĤ www", + "zeit ig", + "& display", + "Cart Item", + "- No", + "Ġnum éro", + "Ġm aur", + "Ġinst ancia", + "ĉd t", + "_n pc", + "Ġskate board", + "âĢľ All", + "ĠCrow d", + "Ġä n", + "Ġb raz", + "ca e", + "yn et", + "/p m", + "/s creen", + "OPT ARG", + "ĠV Box", + "Ġle opard", + "_g reater", + "c pt", + "< dd", + "Ġmechan ically", + "osp els", + ") f", + ".l wjgl", + ".get Port", + "ĠP REF", + ".Add Transient", + "pp ard", + "Ġí ļĮ", + "Ether net", + "Ġsal ine", + "(level s", + "Ġservice Provider", + ".A ngle", + "alt itude", + "illa ume", + "Ġs cape", + "_CAL C", + "_ quest", + "ĠDiss ertation", + "ĠE DM", + "-C ds", + "Ġhon orary", + "st ops", + "Ġsub dir", + "ĠV H", + "ĠChe at", + "Ġright fully", + "Q E", + ".Write Byte", + "fig ures", + "enn ie", + "( DBG", + "Ġvoks ne", + "Ġexp ended", + "UN ICATION", + "il inx", + "ĠRec ap", + "_ verts", + "Ġtra umat", + "Ġget Player", + "Ġverb ess", + "Ġcultiv ating", + "Ġiniti ator", + "Th ông", + "find First", + "_per ms", + "Ġbu c", + "Ġ\"\"\" čĊčĊ", + "T YPES", + "object Manager", + "(Configuration Manager", + "Ġtim id", + "Ġsnap chat", + "Ġcon seg", + "ĉd istance", + "_right s", + "_D es", + "ĠF lesh", + "- ver", + "Ġa fl", + "fra uen", + "Ġblas ph", + "ĠQual ität", + "ma f", + "Monitor ing", + ".D iff", + "Ġshore line", + "Ġresponse Body", + "mem set", + "< decimal", + "Smarty HeaderCode", + "Ġin sets", + "ĠBinary Tree", + "amed a", + "Ġn ihil", + "ĠN ay", + "ym ology", + "ĠW G", + "Ġt api", + "ĠInst alled", + "m aintenance", + ")} \"Ċ", + "ĠX O", + "-per iod", + "s ar", + "Ġning una", + "ORM AT", + ".set PrototypeOf", + "ĠK b", + "ĠHen rik", + "ét ique", + "ĠLah ore", + "ĉ Address", + "Ġmel ts", + "N y", + "_adv ance", + "Ġveloc idad", + "Ġalum no", + "Ġsanit izer", + "Ġph ishing", + "ĠCom et", + "Ġch iar", + "ĉs pec", + "trim med", + "(state arr", + "on nen", + "Re venue", + "L ens", + "Ġcha ired", + "ĠAss umes", + "Tr ash", + "_un set", + "\\ Bridge", + "Point Size", + "ĠPol ic", + "Ġsex uales", + "ĉd fs", + "ĠWide String", + "Ġaccru ed", + "Y W", + "_S CHEDULE", + "Ġk ite", + "Ġparach ute", + "[ table", + "Ġactive ClassName", + ".Qu ad", + "Israel i", + "ĠÅ ĵ", + "Ġho og", + "Ġch á»ī", + "ew ear", + "Ġtire lessly", + "set Error", + ".get Amount", + ".set Items", + "ĠM anson", + "ĠBay esian", + "_F lag", + "AC HER", + "/ original", + "Ġimm ac", + "ĠLos ing", + "' >ĊĊ", + "L ic", + "ĠMir age", + "ĠAssembly FileVersion", + "Te V", + "ĠValue EventListener", + "-s olving", + "Th o", + "rou lette", + "_W P", + "Ġunint errupted", + "Ġfield Type", + ".T yped", + "Ġam our", + "Ġmock ery", + "(v ol", + "ĠSub committee", + "ĠR uf", + "ero x", + ":UIButtonType Custom", + "ĠBl ur", + "Ġwy kon", + "nc es", + "ASH BOARD", + "!! \");Ċ", + "Ġmurder ers", + ".d aily", + "ĠDI AG", + "j ing", + "Ġdol phin", + "Ġl òng", + "Ġb ö", + "ĠV ocabulary", + ".St Object", + "') \">", + "Ġz un", + "Ġscrim mage", + "tr éal", + "ĠL ig", + "[ vi", + "C ole", + "Ġfrost ing", + ".Pl ayers", + "- translate", + "Fe els", + "=\\\" /", + ".Butter Knife", + "Ġ?> ;Ċ", + "Ġav i", + "inn ie", + ".F ailure", + "Ġsp indle", + "Configuration Exception", + "_h op", + "Ġpos ição", + "ĠA wait", + "UIImage PickerController", + "ĉ day", + "Ġgen om", + "C ab", + "ĠÑĢ езÑĥлÑĮÑĤаÑĤ", + "OR IGINAL", + "Ġejac ulation", + "(t cp", + "SE COND", + "Ġton ic", + "ĠList Box", + "Ġ ĉĉĊ", + "() >Ċ", + "Ġqu atre", + "ượ ng", + "with Errors", + ".M aybe", + ", âĢ¦", + "token Id", + "_UN DEF", + "Ġfresh ness", + "ĠAmend ments", + ".map box", + ".C V", + "(b log", + "_get time", + ". quest", + "s parse", + "Ġres ale", + "Ġenthusi astically", + "ĠProstit utas", + "W a", + "C argo", + ".Parcel able", + "SENS OR", + "ĠRy u", + "La ughs", + "_N ative", + "/ pg", + "yst s", + "Ġphot oc", + "ç® Ģ", + "ado pt", + ".spec ies", + "conc iliation", + "Adjust ed", + ".Firebase Auth", + "ut tle", + "ord ination", + "Ġm unch", + "ĠSt ake", + ".p ing", + "ank er", + "(QString Literal", + "Ġsub script", + "ĠĠ ĉĊ", + "ĠM CC", + "_C md", + "se xy", + "i ou", + "ĠM ANY", + "Ġn anny", + "TR AIN", + "Ġflour ishing", + "ĠW atches", + "ĠQ Map", + "ĠF erm", + "Ġwas m", + "ĠA bed", + "_ UD", + "ĠGlass es", + "+ v", + "Att end", + ".Ch ain", + "Ġdec ency", + "ĠSupplement ary", + "h unter", + "-t xt", + "Ġ\" }\";Ċ", + ".set WindowTitle", + "(\" ", + "Ġmasc ara", + "( Profile", + "åĬŁ èĥ½", + "imit é", + "Ġwild fires", + "- ROM", + ".is On", + "(group Id", + "Re pair", + "accum ulate", + "Ġ< \",", + "Ġhand written", + "Ġach eter", + "ĠM GM", + "ĠIr ma", + "->{ _", + "ge e", + "cr iminal", + "Ġèĭ¥ è¦ģ", + "Ġmoment arily", + "\") !=", + "_l it", + "Ġexpires In", + ".\" ).", + "éķ¿ 度", + "Ġfr ække", + "vl c", + "Ġor bs", + "), $", + "Ġvent ured", + "/ >\\", + "char m", + "N uitka", + "eld ig", + "aton in", + "W itness", + "-l at", + "Ġset Hidden", + "Ġrelic s", + "Ġcons ulate", + ". IGNORE", + "\" After", + "Ġset Address", + "Ġbeste ht", + "Ġ'' )ĊĊ", + ".x axis", + "Ġser ão", + "Ġmis led", + "_UN IFORM", + "ĠV IA", + "inc r", + "Ġzen ith", + "Ġvis cosity", + "Ġthin ly", + ".get SharedPreferences", + ".Error Code", + "\"), \"", + "ĠMillion en", + "Ġ/> )Ċ", + "Scroll Indicator", + "-se eking", + "ĠPOLIT ICO", + "as ca", + "_r l", + "N avig", + "(full file", + "Ġsol itude", + "Ġju ven", + "Ġhaul ing", + "ĠMac ros", + "ĠG ry", + "Ġexerc itation", + "ĠATT ACK", + "Tick Count", + "Ġr ites", + "Ġdo e", + "Particle System", + "Ġsl u", + "Window Text", + "ĠClass Name", + "Ġsl ander", + "ĉ Port", + "j ong", + "? a", + ".D ial", + "âĢĶ at", + "$obj PHPExcel", + "Ġso ar", + "EN N", + "appe ared", + "Ġquot id", + "em achine", + "Ġn ip", + "Ġmicro time", + "ĠAl ma", + "; !", + "---------------------------------------------------------------- --------------------------------", + "ĠPass age", + "Ġdump sters", + "ĠEx clude", + "Ġsuggest ive", + "ĠCircularProgress Indicator", + "_cl r", + "Array Type", + "ILL A", + "Elapsed Time", + "Dr iven", + "Ġresource Name", + "ĠG arrison", + "ser ir", + "-a head", + "Ġp innacle", + "ĠEs presso", + "S parse", + "Ġass ays", + "ĠGirl friend", + "im id", + "]=' \\", + "ONGL ONG", + "Ġportray ing", + "L ane", + "Ġb úsqueda", + "Ġrein forcements", + "ĠSpread sheet", + "ĠArray Collection", + ", arr", + "light box", + "ic ana", + "< \"", + "build ers", + "K id", + "ĠMat SnackBar", + "EX PR", + "od cast", + "ĠFound ations", + "Ġind s", + "=' ${", + "F izz", + "-function al", + "(work space", + "Ġstem med", + "_p atches", + "ĠJar vis", + "READ ING", + "Ġdisrespect ful", + "ĠQ Dom", + "Ġ$ {Ċ", + "est atus", + "Re ached", + "! .ĊĊ", + "IL T", + "ĠN DEBUG", + "ĠCour age", + "birth date", + "ĠT ing", + "Ġutil izado", + "án chez", + "Out door", + "Ġhand guns", + "Ref Count", + "É Ļ", + "rom o", + "Ġt ts", + ".S he", + "ĠP ane", + "ãĢij, ãĢIJ", + "ĠIO CTL", + "/ black", + "ins cription", + "Ġbi opsy", + "ĠTime Interval", + ".Test Check", + "ĠGUI Style", + "ĠCap ability", + "ĠBeit rag", + "don nees", + "T reatment", + ".back up", + "Ġsign ings", + "ĠB oca", + "dr m", + ".M AIN", + "Ġgo ede", + "ĠMark up", + "G REE", + "ĠBase Service", + ".C reator", + "Ġj ails", + "ĠK ahn", + "Ip Address", + "ACH I", + "Ġinhib ited", + "Ġ@ $_", + "ĠAss ass", + "Ġenvi ado", + "Hero es", + "ÐŁ еÑĢ", + "ĠM aven", + ".l s", + "Ġ ive", + "| RF", + "Ġresize Mode", + "Ġrum pe", + "_attach ments", + "T U", + "Ġtact ile", + "Attempt ing", + "Ġro bin", + "y aw", + "Ġmerc enaries", + "ĠHab itat", + "end date", + "Ġo xy", + "ĉR andom", + "oh on", + "Is Null", + "ĠValidation Result", + "ãĥ ļ", + "um bed", + "pp v", + "Ġar p", + "ich ick", + "_r nn", + "ĠT FT", + "Tex Image", + "\" On", + "ĠSam pler", + "top l", + "Ġj ane", + "y ling", + "ĠUN ICODE", + "Tab Index", + "< {Ċ", + "s uspend", + "uv ian", + ", application", + "ол иÑĩеÑģÑĤво", + "y at", + "ez ier", + "ĠCH UNK", + "ĠAd ler", + "/ Add", + "ĠKey Value", + "Ġspos ób", + "Sam pling", + "ch ers", + "_AM D", + "R u", + ".Must Compile", + "N ation", + "Ass oc", + "Man aging", + "ĠEng l", + "_G B", + "Ġsucc inct", + "Ġdis liked", + "ĠI ke", + "Bullet in", + "_ARCH IVE", + "Prop osal", + "Ġjog ging", + ".C REATED", + "Ġch ol", + "è£ ħ", + "Į ¨", + "-p ush", + "Ġreserv a", + "core v", + "è tre", + "TH R", + "Ġincompet ence", + "Ġchar isma", + "æĦ Ł", + "Ġ\" ==", + "BT N", + "ĠLoc ator", + "iv et", + "('. ')Ċ", + "Ġfor IndexPath", + "ô me", + "Ġcapac it", + "w aters", + "ĠWR ONG", + "ho a", + "ĠM IPS", + "Ġem iss", + "ĠJacqu eline", + "(c mp", + "Ġe ens", + "Le o", + ".tim ing", + "CLUS ION", + "Ġ(\" -", + "åĵ Ī", + ".k ode", + "ĠUnd ert", + "Ġbew ild", + "ĠEss en", + ".h d", + "Ġren egot", + "Ġm ower", + "Ġl sp", + "Ġpen chant", + "Ġman oe", + "Ġag li", + "Ġrec al", + "ĠOPER ATION", + "(^ )(", + "ĠÎ ½", + "ĠSc oped", + "Ġ@ \"Ċ", + "= label", + "[ loc", + "Int l", + "ĠN z", + "table t", + ".Column Name", + "Ġscreen Size", + "DB us", + "co oked", + "- registration", + "âĢľ One", + "-n on", + "ĠwiÄĻ c", + "Ġcost a", + ".add Tab", + ". conditions", + "ĠH ess", + "MEM ORY", + "ĠAval anche", + "() }}Ċ", + "Ġtri plet", + "Ġl abyrinth", + "ĠNode List", + "ĠNY T", + "Ġy eni", + "d ff", + ".Html Controls", + "AV IS", + "/ Math", + "Ġmem cmp", + "Ø§Ø ¡", + "оÑģ ÑĮ", + "c rap", + "(p ages", + "Ġl xml", + "ĠQ DateTime", + "_t cb", + "Ġopen id", + "Ġsyn aptic", + "ĠMD MA", + "(s lug", + "igm atic", + "en or", + "Ġcr amped", + "G OP", + "Ń IJ", + ".is File", + "ĠD ifferential", + "Ġ=\" \";Ċ", + "ĉĉĉ ĠĠĠĠĉ", + "ĠC ooke", + "ĉU FUNCTION", + "Ġpersever ance", + "Relative Layout", + "IMPORT ANT", + "Ġex on", + "Ġо н", + "ib ase", + "(C ONT", + "n ovation", + "ä½ ķ", + "[ sub", + "Admin Controller", + "HTTP Header", + "cre ar", + "ĠN IR", + "ĠDrop DownList", + "Ġval ide", + "Ġde hydration", + ". ']", + "(W IN", + "Ġ... \\", + "Ġphotos hop", + "ĉ Init", + "_c ou", + "Ġtime Zone", + "dar win", + "rom atic", + "Navigation ItemSelectedListener", + "br ates", + "] --;Ċ", + "Ġtraged ies", + "ĠPed iatrics", + "SM ART", + "-A PI", + "ĠMessage Lookup", + "ĉ vo", + "Ġprejud ices", + "Ġm A", + "U ps", + "ĠMISS ING", + "ĉ ad", + "C ream", + "ĠT b", + "ĠMon a", + "_ ghost", + "ĉt ypes", + "Em b", + "ĠDocument ary", + "');ĊĊ ĊĊ", + "Ġl up", + "_ Reference", + "ĠB ATCH", + "Ġintertw ined", + "< Cell", + "ĠCab r", + "n ation", + "Ġis Connected", + ".remove Listener", + "Ġcon g", + "_t i", + "ĠSil icone", + "Ġê²° ê³¼", + "ĠW AN", + "ĠG ibraltar", + "/ response", + "ĉp erson", + "ch ants", + "V IP", + "em ergency", + "Pixel Format", + "- Am", + "Ġsouth western", + "_pl l", + "if ers", + "_ON CE", + "ĠF ayette", + ".nc bi", + "_P anel", + ".Q ual", + "Ġpol ys", + "Ġcreate StackNavigator", + "� t", + "Ġlay offs", + "ĠBl anco", + "Fe at", + "ĠV imeo", + "_ch i", + "_l ifetime", + "POINT S", + ", private", + "Ġunb earable", + "print ing", + "Ġc gi", + ".B ACK", + "Ġintern s", + "ĠNew ly", + "inf eld", + "( IB", + "ĠK ata", + "ĠDef endants", + "Th r", + "é¢ Ħ", + "_V F", + "FFFF FFFF", + "Ġdavid jl", + "Ġbitter ly", + "S uggestions", + ".set Cancelable", + "FIN AL", + "ason s", + "_rw lock", + "_WRAP PER", + "Ġhapp iest", + "(row Index", + "ós ito", + "TOT YPE", + "Autom ation", + "Log File", + "Ġcons olation", + "ãĥ Ģ", + "Ġt êm", + "Ġpr er", + "rg yz", + "ĠG eg", + "ĉd to", + ".default Value", + "ĠK ami", + "ĠA SE", + "optim ized", + "Ġíı ¬", + "Ġorigin ates", + "err Msg", + "Ġespa ço", + "(S YS", + "ĠMc B", + "d ance", + "_det ected", + "Ġfr ü", + "ĉĉ ĠĠĠĠĉĉ", + "< Date", + "(com b", + "ĠDec ide", + "\\ Field", + "ĠProp osed", + "R ib", + "Ġdis likes", + "ĠW ien", + "ĉ Document", + "Ġtr af", + "Ġst oria", + "ĠT ells", + "') ==", + "C ri", + "( VALUE", + "ĠBurn ett", + ", void", + "Ġdan h", + "Ġc cp", + "Block chain", + ":\"- \"`Ċ", + "IC lient", + "IS ODE", + "Iss uer", + ") }čĊ", + ", but", + "ĠU ph", + "( Sub", + "Ġtélé phone", + "ĠonData Change", + "Ġmarsh aller", + "-an alytics", + ", content", + "Ġdeb acle", + "_Value Changed", + "Ġfa una", + "Ġ# =>", + "Ġf oyer", + "'util isation", + "ĠMü ller", + "ĠFet ish", + "Ġdefault Manager", + "Ġback track", + "B ah", + "Exp licit", + "_A SCII", + "Ġm Activity", + "(M sg", + "Ġê² Į", + "ĠTER MS", + "ĠAng ie", + "HS V", + "ĠMos que", + ".N ames", + "íĬ ¼", + "rest e", + "_p arms", + "Ġgap ing", + "Ġcro pping", + "Data Frame", + "Ġrespons iveness", + "_ undo", + "_tr an", + ". terminate", + "Ġitalian e", + "Ġwalk through", + "Ġattract iveness", + "д е", + "_ST S", + "_ learn", + "Ġchocol ates", + "ier archical", + "-th inking", + "Ġ )))", + "ish ments", + ".Log f", + "ĠTM Z", + "ĠCan ary", + "fo il", + "ĠVacc ine", + ".v x", + "ĠSur round", + "Inter mediate", + "Ġi ov", + "v ais", + "'; \";Ċ", + "ï½ŀ ĊĊ", + "éĢģ æĸĻ", + "âĢ¦ it", + "Se ats", + "Cl ar", + "W ars", + "ĠHutch inson", + "ĠHas an", + "! ')ĊĊ", + "ĠRich ie", + "che iden", + "($ ('", + "Y ork", + "Ġl ids", + "Ġal phanumeric", + "ĠG lock", + ".sh apes", + "Ġspark ing", + "_ epsilon", + "uplic ated", + ".dir ty", + "]) ==", + "ĠìľĦ ì¹ĺ", + "Ġsc n", + "Ġ/ ****************************************************************", + "_PRE VIEW", + "_H C", + "ield ing", + "f gets", + "ĠAdd ison", + "Ġproduct Service", + "- figure", + "(ret val", + "z ano", + "Ġaut ob", + "ĉs d", + "_n umer", + "ĠSet LastError", + "ĠF ior", + "ific ance", + "Unt itled", + "Ġin field", + "Ġ{} ));Ċ", + "Ġsp ac", + "Ġro okies", + "(des cribing", + "ng en", + "ி à®", + ".r df", + ".M utex", + "Ġkne eling", + "ĠQ E", + "set Max", + "Read Stream", + "Ġvent as", + "s ut", + "cm peq", + ".WriteAll Text", + "ĠEx perienced", + "$ __", + "Ġka um", + "ĠL IS", + "Ġdocument os", + "_HE ALTH", + "icont ains", + "Ġart isans", + "OWN ER", + "Ġblink ed", + "get Display", + "Ġto en", + "Ġrow Num", + "Ġav ril", + "Ġinv is", + "ĠK ear", + "toBe InTheDocument", + "ap ur", + "Ġr acked", + "ĠMc Master", + "_ATTR IB", + "H az", + "Ġfact ura", + "/ ts", + "ĠÑĢаз меÑĢ", + "Ġz f", + "Ġshort fall", + ".f asta", + "ĠCONST ANT", + ".man aged", + "g ems", + "Shared Pointer", + "Ġblur ry", + "b rightness", + "( components", + "Ġ... \"ĊĊ", + "SE LL", + "ĠIllustr ator", + ".get Channel", + "Ġtrou vé", + "yst ers", + "Ġvo is", + "ĠLind en", + "Ġem ojis", + "Ġb rawl", + "ĠMS R", + "ĠE lo", + "ĠCroat ian", + "Popup Menu", + "L ewis", + ".J WT", + "Ġaston ished", + "B ush", + "(item Id", + "Ġdet achment", + "ĠEnc ore", + "å° Ķ", + "Ġre kl", + "Ġcr am", + ")$ /", + ".get Host", + "_re commend", + "- HT", + "_cal ibration", + "Auth enticate", + ".firebase app", + "UN IX", + "ĉC amera", + "ĠHE AP", + "I deal", + ". office", + "Ġgoof y", + "(S ymbol", + "Ġjou er", + "_part itions", + "Ġrapid ement", + "ĠGN UNET", + "id User", + "Ġsuperv ise", + "( Contact", + "AW N", + "ãģ ĺ", + "Ġna am", + "Ġa ust", + "åľ¨ 线", + "_soft max", + "Allow Anonymous", + "amm able", + "RO UTE", + "* D", + "Ġad en", + "ĠCrist ina", + "ĠCrist iano", + "Ġblood stream", + "sub class", + "_person a", + "CH ILD", + "-k now", + "Ġnavigation Options", + "ĠZuk unft", + "ĠPix ar", + "Ty ler", + "Ġunder world", + "Ġsincer ity", + "Ġdispens er", + "Ġk ter", + "idd ers", + ".add Node", + "- checked", + "Ġke yst", + "ĠW TO", + ".sign als", + "Ġadvent urer", + "ĠP ang", + "\\ R", + "= pos", + "Ġdispens aries", + "ĠClo set", + "(\"{ \\\"", + "ide on", + "Ġnécess aire", + "() \"Ċ", + "_RECE IVED", + "Ġrésult ats", + "Ġmod en", + "ĠIceland ic", + "; d", + ". allowed", + "(new User", + "Ġmerc iless", + ".Wait For", + "Ġday care", + "ĠCon veyor", + "ç ĸ", + "ð ¬", + "ç ĥ", + "ç Ĺ", + "ç ł", + "è Ħ", + "é ²", + "å ¦", + "çĿ Ģ", + "å¾ Ī", + "é ħ", + "ç ĭ", + "é ª", + "æ Ĥ", + "é ¥", + "è ħ", + "æĥ ³", + "å ¨", + "é ¹", + "ç Ĥ", + "å Ĵ", + "ç Į", + "è´ ¨", + "æ ¢", + "æ° Ķ", + "ð «", + "æķ Ļ", + "ç Ł", + "å Ħ", + "åıij å±ķ", + "åĪ Ľ", + "è ij", + "æ ħ", + "å ŀ", + "åģ ļ", + "æĪ ĺ", + "æ IJ", + "å¼ º", + "æ· ±", + "åĩ ł", + "ç ¿", + "å ©", + "è ŀ", + "å§ Ķ", + "åIJ Ħ", + "è İ", + "é ¸", + "é º", + "åı Ĺ", + "èģ Į", + "å ĺ", + "æ ½", + "é£ İ", + "èIJ ¥", + "åħ ļ", + "è ľ", + "éĤ £", + "é¢ Ĩ", + "ç ij", + "é ³", + "æľ ¯", + "ä» Ģ", + "æĪ ¿", + "ç² ¾", + "å ª", + "é Ĩ", + "å¤ ª", + "èĤ ¡", + "è Ľ", + "åħ ī", + "æŀ ģ", + "åĬ ŀ", + "è ĵ", + "ç ĺ", + "å ´", + "å Ĺ", + "èĬ ±", + "çł Ķ", + "å¿ «", + "å¸ Ī", + "è¶ Ĭ", + "è§ Ĥ", + "æ ¤", + "æ ¦", + "ç ŀ", + "èĤ ²", + "çĪ ±", + "çĻ ½", + "ä¸ ĸ", + "ä»Ģ ä¹Ī", + "çľ ¼", + "å ³", + "è Ĵ", + "æ ĵ", + "è¢ «", + "å¹ ²", + "çĹ ħ", + "å£ «", + "ç Ĵ", + "è ¸", + "æ ¾", + "å·¥ ä½ľ", + "è® ©", + "çĥ Ń", + "è¾ ĥ", + "åĦ ¿", + "åĬ ©", + "ç§ ¯", + "ç ³", + "ç ĵ", + "ç £", + "å Ĥ", + "è ¹", + "è ļ", + "å· ±", + "çĻ ¾", + "åĬ ¿", + "èµ Ľ", + "æ ¨", + "æ ¿", + "è ĸ", + "æĿ ij", + "å¸ ¦", + "å¢ ĥ", + "æĬ ¤", + "é Ń", + "å «", + "èĩª å·±", + "æµ İ", + "ä½ İ", + "åĮ »", + "éĺ ²", + "åĨ ľ", + "è Ĩ", + "ç Ĩ", + "é «", + "åĨ Ľ", + "æĪ ı", + "åį ĩ", + "æĸ ¯", + "ä½ ı", + "èIJ ½", + "åħ »", + "èĩ ´", + "ç Ĭ", + "ç ĩ", + "ç ħ", + "è Ķ", + "ä¼ģ ä¸ļ", + "åĽ ¢", + "æī į", + "æł ¡", + "åĩ Ĩ", + "å¥ ĩ", + "åī ¯", + "é ¼", + "æ¼ Ķ", + "é© ¬", + "èµ °", + "ç¥ ŀ", + "åħ ĭ", + "æľ Ľ", + "æ² ¹", + "è¾ ¹", + "åį ĥ", + "å¾ Ģ", + "åĪ ĩ", + "æ ©", + "ç ¶", + "å Ļ", + "éĻ ħ", + "çī Į", + "社 ä¼ļ", + "游 æĪı", + "æĸ ½", + "ç ħ§", + "æİ §", + "æ» ¡", + "è¯ Ĩ", + "éĩį è¦ģ", + "è¶ ³", + "çķ Ļ", + "ç» Ĩ", + "åį ı", + "éĢ Ĥ", + "æ ĩ", + "æ §", + "é Ħ", + "è Ŀ", + "å¸Ĥ åľº", + "ç»ı æµİ", + "ä¹ ł", + "æĸĩ åĮĸ", + "éļ ¾", + "ä¹ IJ", + "åĨ ³", + "æ¬ ¢", + "è§ ī", + "åĽ Ń", + "åħ ´", + "åħ ħ", + "ä¸ ¾", + "æī ¹", + "è ķ", + "æĬ Ĭ", + "æĬĢ æľ¯", + "ç© ¶", + "第 ä¸Ģ", + "ä¾ ¿", + "åĵ į", + "çİ ©", + "åĿ ļ", + "èŀ į", + "åį Ĭ", + "åĸ ľ", + "å± Ĥ", + "ç¦ »", + "ä» ħ", + "é Ł", + "åij ³", + "å¿ µ", + "åŃ £", + "ç´ §", + "ä¹ ħ", + "é ¤", + "é ŀ", + "è ¤", + "åĢ Ļ", + "åĨ µ", + "ç Ł³", + "åģ ¥", + "æĢ İ", + "å® Ŀ", + "è¡ Ģ", + "åŁ Ł", + "æĹ ©", + "çŁ¥ éģĵ", + "è´ Ł", + "åį ļ", + "å· ´", + "äº ²", + "å± ŀ", + "ä¸ ¥", + "äº ī", + "å¯ Ł", + "è º", + "ç °", + "建 设", + "产 ä¸ļ", + "åIJ ĥ", + "åŃ ©", + "æĹ ħ", + "æł ¹", + "æĿ IJ", + "ä¼ Ĺ", + "éļ ı", + "å® ĺ", + "åº ķ", + "å½ ©", + "å¯ Į", + "æ¸ ©", + "åį «", + "åī §", + "çĽ Ĭ", + "æĬ Ĺ", + "è´ ¢", + "çº ª", + "æ Ĩ", + "çĶŁ æ´»", + "çº ¢", + "çĶŁ 产", + "è¿ ľ", + "éĴ ±", + "åĶ ®", + "ç¾ ¤", + "çı Ń", + "æ¥ ¼", + "éĩ ĩ", + "èī º", + "å± ħ", + "åģ ĩ", + "è° Ī", + "æĻ ļ", + "é ¬", + "èĪ ª", + "å® ³", + "è Ĺ", + "ç į", + "å µ", + "çİ ĭ", + "åº ·", + "è İ·", + "ç» Ń", + "äº ļ", + "é£ Ł", + "åİ ĭ", + "æĭ Ľ", + "èĮ ĥ", + "è® ¸", + "åĽ ´", + "é ½", + "éĻ į", + "çº ³", + "åĵ ª", + "æķĻ èĤ²", + "å·² ç»ı", + "å¾ ·", + "æŀ Ĺ", + "å®ī åħ¨", + "é¾ Ļ", + "大 家", + "éĿ Ĵ", + "åº ľ", + "æ² ³", + "åı ¤", + "èį ¯", + "åĿ ĩ", + "æĻ º", + "ä¹ ¡", + "çķ ¥", + "åĨ ·", + "ç¦ ı", + "å® ¤", + "ç» ´", + "æī ¿", + "å± Ĭ", + "è¯ ī", + "åĪ »", + "è Ł", + "æ ª", + "å°± æĺ¯", + "è¿Ļ 个", + "ä¸Ń å¿ĥ", + "ä¸ĸ çķĮ", + "åŁİ å¸Ĥ", + "éĿŀ 常", + "åĪ Ĵ", + "åı Į", + "æĢİ ä¹Ī", + "åĪ° äºĨ", + "æľ ĥ", + "åı ²", + "ä¾ Ĩ", + "å¾ ĭ", + "å¥ ĸ", + "ç» Ī", + "åª Ĵ", + "å® ģ", + "è¯ ¾", + "èģĮ ä¸ļ", + "åħ į", + "æµ ĭ", + "æĢ ¥", + "æķ ij", + "çĭ ¬", + "èŃ ¦", + "é¤ IJ", + "æĦ ¿", + "è´ «", + "çĸ ij", + "å ļ", + "å¥ ¹", + "åı Ī", + "åĽł 为", + "ä¸į æĺ¯", + "å¤ Ł", + "æĸ¹ éĿ¢", + "éķ ĩ", + "äº Ĵ", + "éħ Ĵ", + "è® ²", + "çĸ Ĺ", + "æĺ ¥", + "æ¹ ĸ", + "å¤ ľ", + "è´£ ä»»", + "人 æ°ij", + "åħ °", + "çŁ Ń", + "æķ ħ", + "åĩ ı", + "æĻ ®", + "äº ®", + "ä¾ Ŀ", + "åį °", + "éĿ Ļ", + "åĢ ĭ", + "å¾ ģ", + "åIJ ¸", + "ç¼ º", + "æĶ »", + "åĩ Ģ", + "åħ ¸", + "åĽ º", + "è® ¿", + "ç ¹", + "ç Ģ", + "æıIJ ä¾Ľ", + "ç» ĩ", + "å¾Ī å¤ļ", + "çłĶ 究", + "è· Ł", + "主 è¦ģ", + "æĥħ åĨµ", + "çŃ ĸ", + "æŃ »", + "大 åѦ", + "æĶ¿ åºľ", + "å½± åĵį", + "ä¹ °", + "åħ Ń", + "éĻ ©", + "åħ «", + "æŁ IJ", + "è´¨ éĩı", + "åį ł", + "å· ®", + "æĽ´ å¤ļ", + "æľ ĭ", + "éĿ ©", + "å® £", + "çł ´", + "è½ »", + "åº §", + "æĺ ¾", + "ç¨ ³", + "è´ µ", + "èĥ Į", + "èī ¯", + "çĸ «", + "æ¯ Ĵ", + "ä¹ İ", + "åĢ Ł", + "è¿ ·", + "çŃ Ķ", + "æ¿ Ģ", + "åij ¼", + "äºĨ ä¸Ģ", + "è¶ £", + "ä¼ ´", + "ä¼ Ļ", + "è ¼", + "ð¬ Ń", + "åĽ½ 家", + "æ´» åĬ¨", + "çİ° åľ¨", + "ç§ij æĬĢ", + "åį ¡", + "ä¸į åIJĮ", + "个 人", + "è®° èĢħ", + "ä¸į æĸŃ", + "éĹ »", + "ä¹ Ŀ", + "èij Ĺ", + "ç» ¼", + "ä¸ ĥ", + "æł ij", + "æľĭ åıĭ", + "åį ĸ", + "ä¼ ¤", + "æ² Ļ", + "åĸ Ħ", + "å¥ Ĺ", + "è½ ®", + "ç© ¿", + "è¡ ¥", + "ä¸Ģ å®ļ", + "çª ģ", + "çĿ £", + "è¿ ½", + "å¨ ģ", + "åı ¦", + "åĽ °", + "æŀ ¶", + "ç» Ŀ", + "æķ £", + "æİ ¢", + "æ´ Ĺ", + "ä¸ ´", + "ä¼ ¼", + "è´ ¸", + "ä¸ °", + "æĺ¯ ä¸Ģ", + "ç« ŀ", + "è¿ İ", + "èģ ļ", + "è «", + "æį Ł", + "æī §", + "é© ¾", + "è¿ Ŀ", + "è ¥", + "è ł", + "ä»ĸ 们", + "æŶ åĢĻ", + "å® ĥ", + "人 åijĺ", + "è¿Ļ æł·", + "å·¥ ç¨ĭ", + "åĪĽ æĸ°", + "åŃ© åŃIJ", + "å¸ Į", + "éĥ¨ åĪĨ", + "éĵ ¶", + "代 表", + "é¦ Ļ", + "å¸ ®", + "æݨ è¿Ľ", + "çĽ ĺ", + "积 æŀģ", + "éĥ¨ éŨ", + "åŁ ¹", + "æŃ ¦", + "ä¸į ä¼ļ", + "çŃ ij", + "éĢ Ļ", + "çİ© 家", + "æĭ ¿", + "åİ Ĥ", + "æ¯ Ľ", + "çģ µ", + "æŃ Į", + "ç »¿", + "å¦ Ī", + "çĽ Ľ", + "é¦ Ĩ", + "é¡ º", + "èĦ ¸", + "å° ¼", + "ä¸ ½", + "å¥ ¥", + "éģ ĩ", + "è¯ į", + "å° ģ", + "ä¸ Ŀ", + "好 çļĦ", + "æĭ ħ", + "èĦ ±", + "æģ ¶", + "åİ ļ", + "åĬ ³", + "çĽ Ł", + "æĬ ĺ", + "åı ¥", + "æĢ Ģ", + "æŁ ĵ", + "书 è®°", + "åĨ ł", + "é² ľ", + "æ ¦Ĥ", + "éļ IJ", + "å¹ ħ", + "èµ ŀ", + "å¹ ķ", + "æ¥ Ń", + "éģ Ĺ", + "åĪ ¤", + "è ĺ", + "å ¶", + "æĬķ èµĦ", + "è¡Į ä¸ļ", + "äº ij", + "çݯ å¢ĥ", + "åѦ çĶŁ", + "åIJĪ ä½ľ", + "åģ¥ 康", + "é£ ŀ", + "ä¸Ģ æŃ¥", + "ä¸Ģ 缴", + "åıij çĶŁ", + "éĺ ¿", + "é¢Ĩ 导", + "åĸľ 欢", + "åºĶ 该", + "çĤ º", + "è® Ń", + "æĿ Ģ", + "æ¸ ¯", + "交 éĢļ", + "éĺ ¶", + "éĴ ¢", + "ä» ¤", + "å° ½", + "æ¯ į", + "è¡ £", + "ç² ī", + "é¡ ¶", + "ä¹Ł ä¸į", + "æĬ ĵ", + "èĭ ¦", + "å¹ ¸", + "ç¤ ¼", + "第 ä¸ī", + "大 çļĦ", + "éģ İ", + "çĥ Ł", + "éģ ¿", + "ä» į", + "åº Ĩ", + "æĢ ķ", + "è° ¢", + "çĽ ĸ", + "å° Ħ", + "éľ ²", + "æĸ Ĺ", + "ç Ĭ¶", + "åŃ ¸", + "æ¯ ķ", + "å· ¨", + "çŁ ¿", + "çļ ĩ", + "å¸ Ń", + "çĹ ĩ", + "æī ¬", + "å» ¶", + "ä¾ §", + "æ· ¡", + "çļĦ ä¸Ģ", + "ç¶ ²", + "æ´ ģ", + "ç ¸", + "è§ Ī", + "çŃ ¹", + "ç§ ĺ", + "è¯ Ĭ", + "çı ¾", + "èª ī", + "æ¯ «", + "ð ¨", + "åį ´", + "æĪIJ 为", + "èĥ½ åĬĽ", + "é» Ħ", + "æĹħ 游", + "èĪ ¬", + "æ¯Ķ è¾ĥ", + "èµ· æĿ¥", + "äºĨ 解", + "èĩª çĦ¶", + "ä¸Ģ 次", + "åŁº æľ¬", + "æĽ ¾", + "综 åIJĪ", + "èı ľ", + "è§ī å¾Ĺ", + "第 äºĮ", + "è· ij", + "æ³ ¢", + "åĢ Ĵ", + "ç¡ Ģ", + "åħ µ", + "èį ī", + "çĶ ³", + "çĶ °", + "æĤ £", + "è§Ħ å®ļ", + "èĥ ľ", + "èµĦ 产", + "æ¢ ¦", + "æľ Ŀ", + "è¿Ļ éĩĮ", + "å¤ «", + "æĮ ¥", + "ä½ Ľ", + "å® Ī", + "éĽ ¶", + "æĸ ¼", + "ç¯ ĩ", + "å² Ľ", + "åĵ ¥", + "éŃ Ķ", + "ä¸į åĪ°", + "æī ĺ", + "åº Ĭ", + "æ¬ §", + "èį £", + "æ± ĩ", + "æī ©", + "åģ ı", + "å¢ Ļ", + "è® ¯", + "å© ļ", + "æĥ ł", + "æ´ ĭ", + "å® ľ", + "æ¶ ¦", + "æħ ¢", + "éĢ ı", + "å® ½", + "é¡ ¾", + "ç´ ¯", + "æ± ¡", + "çĪ Ĩ", + "ç§ Ł", + "æĥ Ĭ", + "æ¶ ¨", + "é¥ °", + "éĺ µ", + "é¥ ®", + "æļ ĸ", + "åº Ł", + "æĹ Ĺ", + "éļ Ķ", + "ç¶ ĵ", + "åĭ Ļ", + "å¯ ¦", + "éĢ Ķ", + "æī «", + "çĥ Ī", + "éĽ »", + "åĪ ij", + "éĹ ľ", + "éĹ ª", + "å¥ ĭ", + "å Ĥ¨", + "ç¼ ©", + "ä¾ µ", + "å ¬", + "𬠶", + "åĽ½ éĻħ", + "ç»Ħ ç»ĩ", + "ä¸ĵ ä¸ļ", + "åıij çİ°", + "å¸Į æľĽ", + "ç»ı èIJ¥", + "åı «", + "æĿ¥ 说", + "éļ ľ", + "ä»» ä½ķ", + "交 æĺĵ", + "éĩį çĤ¹", + "çļ ®", + "ç» į", + "æ´ ¾", + "ç§ij åѦ", + "åºĶ çĶ¨", + "建 çŃij", + "èĤ ī", + "æĶ¹ éĿ©", + "åŁº ç¡Ģ", + "æ± ī", + "åĩº æĿ¥", + "è¿Ļ ä¹Ī", + "åĪ ļ", + "åĿ IJ", + "ä¸į ä»ħ", + "ä¼ļ è®®", + "éĿ ł", + "åªĴ ä½ĵ", + "æ° ¸", + "åĨ ²", + "èĭ ı", + "å¤ ®", + "çĪ ¶", + "åł Ĥ", + "å®ŀ éĻħ", + "è¡ Ĺ", + "ç« ¥", + "éĺ ħ", + "äºĭ æĥħ", + "åİŁ åĽł", + "éħ ¸", + "以 æĿ¥", + "å¨ ±", + "å® «", + "åĿ Ĺ", + "ç» ©", + "éĩ İ", + "ä¸į å¾Ĺ", + "ä¼ł å¥ĩ", + "ç¡ ¬", + "åİ ħ", + "æĹ ¢", + "ç» ĥ", + "èĦ ij", + "å¼ ±", + "æİ Į", + "è´ ´", + "æĮ Ĥ", + "åħ³ éĶ®", + "å° ļ", + "é¥ Ń", + "åº Ħ", + "çĻ ¼", + "åľ ĭ", + "æİ Ī", + "个 æľĪ", + "äº Ī", + "å¸ ģ", + "è· Ŀ", + "æ² ī", + "ç« Ł", + "åĨ ¬", + "æĬ ½", + "éĨ Ĵ", + "å¼ Ł", + "è§ ¦", + "èģ ĺ", + "è± Ĩ", + "æļ ´", + "åijĬ è¯ī", + "è± ª", + "èµ ¢", + "è· ¨", + "è³ ĩ", + "çĪ ¸", + "æĬ ±", + "æµ ª", + "éº »", + "ä» ª", + "è¡ ¡", + "å¥ ¶", + "çģ ¾", + "èµ ¶", + "èĤ ¥", + "å§ IJ", + "åĢ º", + "éľ ĩ", + "è® ¢", + "æ¬ Ĭ", + "ç ·", + "å» ī", + "ä¿ Ĺ", + "å¿ ĺ", + "å¦ ĩ", + "ç¼ ĵ", + "åŃ ķ", + "æ¼ «", + "è£ ģ", + "çĩ ĥ", + "é» ĺ", + "çī ¢", + "çĪ ·", + "æĬ µ", + "å® ¾", + "æľī ä¸Ģ", + "è¿ ¹", + "è¿ «", + "è² Į", + "æľī çļĦ", + "ð¬ ĺ", + "è¿ĺ æĺ¯", + "æīĢ 以", + "ä¹Ł æĺ¯", + "è¿Ļ äºĽ", + "对 äºİ", + "åIJ §", + "缮 åīį", + "èĩªå·± çļĦ", + "èĥ½ å¤Ł", + "å¦Ĥ ä½ķ", + "æľº æŀĦ", + "åıª æĺ¯", + "ç½ij ç«Ļ", + "åħ¨ éĿ¢", + "为 äºĨ", + "å¼Ģ åıij", + "æĸ° éĹ»", + "éĩij èŀį", + "ç» §", + "客 æĪ·", + "ä¸Ģ èµ·", + "èĮ ¶", + "åħ³ 注", + "æ°´ å¹³", + "åİĨ åı²", + "å¢ŀ éķ¿", + "é ±", + "åŁº éĩij", + "åº Ń", + "åı ¶", + "ä¿ ĥ", + "éĽ ¨", + "æ¶Ī è´¹", + "èĪ ¹", + "çŁ¥ è¯Ĩ", + "æĪĺ çķ¥", + "ç»ı éªĮ", + "å³ °", + "æĽ ²", + "èĦ ļ", + "åĨ °", + "å¤ ı", + "å½ Ĵ", + "ç¬ Ķ", + "èĻ ij", + "çĶ ²", + "åľ Ī", + "è¯ Ĺ", + "é½ IJ", + "容 æĺĵ", + "çłĶ åıij", + "éª ¨", + "çº ¸", + "è· µ", + "æĹ §", + "çķ ¶", + "åĪ ¸", + "è´ ·", + "åı ¬", + "ç§ ĭ", + "æ¶ ²", + "è¡Į æĶ¿", + "çĮ ®", + "èĤ ¤", + "éĢ IJ", + "è¶Ĭ æĿ¥", + "è¶ĬæĿ¥ è¶Ĭ", + "æĦı è§ģ", + "èĪ ŀ", + "åī Ĥ", + "æ¶ ī", + "ç¨ĭ 度", + "åħ¬ åħ±", + "æ¢ °", + "æľ «", + "çº ¯", + "åĶ ±", + "æ´ ²", + "æĬ ¢", + "æ¤ į", + "å¿ Ļ", + "ä¼ °", + "å¼ ¹", + "æ³ ī", + "æľĢ 大", + "è¶ ĭ", + "å· §", + "ç¦ ģ", + "æī ¶", + "åį ±", + "çı ł", + "çĨ Ł", + "æĭ ľ", + "主 ä¹ī", + "æĿ Ĥ", + "éĻ Ħ", + "éģ į", + "æIJ Ń", + "æĮ ¯", + "å¤ļ å¹´", + "æķ ¬", + "æij Ħ", + "çº ·", + "å¼ ĥ", + "æ¹ ¿", + "å¨ ĺ", + "æ¡ £", + "é© ¶", + "æľ Ĺ", + "æ® ĸ", + "æ¦ ľ", + "åĵ ¡", + "ä¸Ģ ä½ĵ", + "æŁ¥ çľĭ", + "ç¹ ģ", + "æµ ĵ", + "åħ¬ å®ī", + "æ½ ľ", + "è´ ¯", + "éª Ĺ", + "æ IJľ", + "å· ¡", + "è ¬", + "é Ĭ", + "å§Ķ ä¼ļ", + "æĤ ł", + "åī ©", + "æı Ń", + "åŃ£ 度", + "ð «ĺ", + "𬠬", + "ä ´", + "ð ª", + "ä½Ĩ æĺ¯", + "éĥ½ æĺ¯", + "å¹³ åı°", + "åѦ ä¹ł", + "åĵģ çīĮ", + "ä¸ Ķ", + "è¿Ļ ç§į", + "æĶ¿ çŃĸ", + "æĭ ¬", + "认 为", + "ä¸Ģ èĪ¬", + "æłĩ åĩĨ", + "æĶ¯ æĮģ", + "模 å¼ı", + "åħ³ ç³»", + "çļĦ æĺ¯", + "è¿Ļ ä¸Ģ", + "ä¸į è¦ģ", + "çĶ ļ", + "ç²¾ ç¥ŀ", + "æĭ ¥", + "åĪ© çĶ¨", + "ä¿Ŀ æĬ¤", + "ä½ľ çĶ¨", + "èĭ ¥", + "åĽ½ åĨħ", + "ä»ĭ ç»į", + "ä¸Ģ ä¸ĭ", + "å·¥ ä¸ļ", + "缮 æłĩ", + "æľĢ åIJİ", + "ä»· åĢ¼", + "å° į", + "éĵ ģ", + "è° ģ", + "ç»ĵ æŀĦ", + "éĽ ª", + "æĻº èĥ½", + "ä¼ł 绣", + "ä½ĵ èĤ²", + "çĶŁ æĢģ", + "æĭ į", + "æİ ª", + "åĨľ ä¸ļ", + "çī¹ èī²", + "è§Ħ 模", + "æŶ 代", + "è¿ĩ ç¨ĭ", + "éĴ Ī", + "æĿ ¾", + "åĶ IJ", + "åĮ» çĸĹ", + "çģ ¯", + "åĪ¶ éĢł", + "æł¸ å¿ĥ", + "ä¸į åı¯", + "ç³» åĪĹ", + "åIJ ī", + "åľ £", + "åĢ ij", + "ä½ ³", + "æĿ¥ çľĭ", + "æ¯Ķ èµĽ", + "ä¸ĭ æĿ¥", + "åĩº äºĨ", + "å¹² éĥ¨", + "å¾® ä¿¡", + "å½ĵ åľ°", + "åį ·", + "åį« çĶŁ", + "ä¼ Ł", + "çĸ« æĥħ", + "è° ·", + "åĩł 个", + "éĺ ´", + "çĶŁ çī©", + "å° ¤", + "ä¼ Ĭ", + "èĤ ¯", + "éĿ¢ 积", + "åĪĽ éĢł", + "æı ¡", + "åľ Ĩ", + "æĻ ĵ", + "æĪIJ äºĨ", + "åĩ ¡", + "çĸ ¾", + "ç«ŀ äºī", + "è® ¨", + "主 é¢ĺ", + "é² ģ", + "è¿ ª", + "ä¿ Ħ", + "æĢ ª", + "ä¸ ¦", + "èĻ ļ", + "æ½ ®", + "çĥ §", + "èĢ ³", + "æ± ł", + "éĢĤ åIJĪ", + "æł¹ æľ¬", + "åĬł 缣", + "çĶµ è§Ĩ", + "æ· ·", + "ç¼ ĺ", + "çª Ĺ", + "çĬ ¯", + "æĥ ¯", + "æĦı ä¹ī", + "åĬŀ æ³ķ", + "ä¼ ij", + "æ» ij", + "åĭ ĩ", + "æķ ¢", + "å¯ »", + "è¦ Ĩ", + "éĢ ĥ", + "ç»ı çIJĨ", + "åĿ ı", + "æ³ ½", + "ä¹ ĺ", + "åĪ º", + "å± ı", + "é¡ ¿", + "äº ¡", + "éĤ Ģ", + "åħ ¼", + "åĭ ¤", + "æ® ĭ", + "æĺ ł", + "æ¯ķ ä¸ļ", + "æĪ ª", + "è· Į", + "å£ ģ", + "åı¦ ä¸Ģ", + "羣 å®ŀ", + "ç£ ¨", + "è¯ ļ", + "å¿ħ è¦ģ", + "æģ ĭ", + "æĩ Ĥ", + "å¾ Ĵ", + "è° ĵ", + "æķ ı", + "æ Ļ¨", + "èĥ ¸", + "æĭ ¼", + "å¦ Ļ", + "è¯ ¸", + "èģ Ĭ", + "æĤ ī", + "éº ¼", + "åĩ Ń", + "èĪ Ĵ", + "æ¶ Ĥ", + "è¿ ģ", + "æ² ¿", + "å¡ ij", + "æĽ ¿", + "æ¾ ³", + "å¿ į", + "èĢ Ĺ", + "éľ ¸", + "åĩł å¹´", + "åĪ Ĭ", + "èĦ ī", + "èħ IJ", + "æ¡ Į", + "çº ł", + "æ» ļ", + "æĤ ²", + "åĨ Ĵ", + "å¦ ¹", + "çķ ħ", + "çº µ", + "æij ĩ", + "å¤ º", + "è·¯ ä¸Ĭ", + "å¿ ½", + "èĸ ª", + "æģ IJ", + "æĦı æĢĿ", + "å« Į", + "æı ´", + "æ° §", + "èĢ Ģ", + "éĺ »", + "è½ ¨", + "å¹ »", + "æį ķ", + "åĿ ¦", + "åĵĪ åĵĪ", + "çĭ IJ", + "æ» ¨", + "è² »", + "è¿ Ł", + "人 éĥ½", + "ç» ĺ", + "åı ¹", + "çµ IJ", + "æī °", + "æ» ĭ", + "å¥ ij", + "åĭ Ł", + "ç¢ º", + "ð ¦", + "éĽĨ åĽ¢", + "æĿ İ", + "å¼Ģ å±ķ", + "æıIJ åįĩ", + "åħ¨ åĽ½", + "æ±½ 车", + "åѦ æł¡", + "æł¹ æį®", + "è¿Ļ æĺ¯", + "åĩº çİ°", + "éĻ Ī", + "ç½ Ĺ", + "èİ· å¾Ĺ", + "åĪ ĺ", + "éĶĢ åĶ®", + "æľª æĿ¥", + "éľĢ æ±Ĥ", + "å®ŀ æĸ½", + "åĿļ æĮģ", + "åħ¨ çIJĥ", + "éĵ¶ è¡Į", + "æݧ åĪ¶", + "é¡ »", + "åľ° åĮº", + "æīĵ éĢł", + "çļĦ è¯Ŀ", + "帮 åĬ©", + "ä½ĵ ç³»", + "è¾¾ åĪ°", + "è§Ħ åĪĴ", + "åŁ¹ è®Ń", + "两 个", + "æĬ¥ åijĬ", + "åľ° æĸ¹", + "å®Į åħ¨", + "æİ ī", + "ç»ĵ åIJĪ", + "宣 ä¼ł", + "æ³ķ å¾ĭ", + "èīº æľ¯", + "çĶµ å½±", + "èª ª", + "ä¸Ģ çĤ¹", + "è¶ħ è¿ĩ", + "çĶµ åŃIJ", + "æĢĿ æĥ³", + "æķĻ åѦ", + "éĺ¶ æ®µ", + "åķĨ ä¸ļ", + "çī© æµģ", + "åĪĽ ä¸ļ", + "æĸ¹ æ¡Ī", + "çİ° 代", + "æ¡ ¥", + "èIJ½ å®ŀ", + "带 æĿ¥", + "产 çĶŁ", + "ç§ Ģ", + "æ³ °", + "ä¹ ±", + "åħ· ä½ĵ", + "åĸ Ŀ", + "èĵ Ŀ", + "å® Ĺ", + "åįĩ 级", + "æ·± åħ¥", + "ä¿Ŀ éĻ©", + "ç®Ģ åįķ", + "çĹ Ľ", + "稳 å®ļ", + "è¾ Ĩ", + "å±ŀ äºİ", + "å· Ŀ", + "ä¸į å°ij", + "åĴ ¨", + "举 西", + "å½¢ å¼ı", + "娱 ä¹IJ", + "æŃ£ 常", + "é¸ ¡", + "åħħ åĪĨ", + "å®ŀ è·µ", + "éĩĮ éĿ¢", + "è· ³", + "èĻ İ", + "æĪIJ éķ¿", + "æļ Ĺ", + "çĿ ¡", + "ç½ ª", + "çIJĨ 念", + "æĮ ij", + "èµĦ æľ¬", + "å¤ļ å°ij", + "ä¸ĭ éĿ¢", + "å¸ Ŀ", + "åħ¬ å¼Ģ", + "æ¸ IJ", + "éķ ·", + "å± ĭ", + "欢 è¿İ", + "å¿ĥ çIJĨ", + "çĤ İ", + "æ¹ ¾", + "è® ĵ", + "éĤ Ħ", + "ç³ ĸ", + "ä¹ Į", + "åĬ ±", + "çī Ļ", + "èħ ¿", + "å² Ĺ", + "ä¼ į", + "æĪIJ åijĺ", + "åŃ Ķ", + "å°ı ç¼ĸ", + "èij £", + "æ³ ¡", + "åħĪ è¿Ľ", + "åħ §", + "åĺ ´", + "è´ Ŀ", + "è »", + "æIJ ŀ", + "æ³ Ľ", + "é¸ Ł", + "ç½ ²", + "èĽ ĭ", + "主 ä»»", + "缮 çļĦ", + "ä¹ ı", + "æ´ ¥", + "æĪ ´", + "严 æł¼", + "çħ ¤", + "çĮ «", + "åĶ ¯", + "å° Ĭ", + "çĶ ľ", + "åŀ ĥ", + "åľ ¾", + "æĭ Ł", + "çĦ ¦", + "é« Ķ", + "å® ı", + "æ© Ł", + "é© »", + "æĹ ģ", + "å½ »", + "éĥ½ ä¸į", + "æij ©", + "ä» ĵ", + "ä¹ ³", + "å² ¸", + "è° ĭ", + "大 å¤ļ", + "çģ Ń", + "èħ ¾", + "æŁ ľ", + "èĪ į", + "åħļ çļĦ", + "å° ĺ", + "åįģ å¹´", + "æĭ Ĵ", + "è£ ¡", + "æŁ Ķ", + "å¹ ¼", + "éĶ ģ", + "ä¸ĵ 项", + "æī İ", + "驾 驶", + "ç¢ İ", + "è¢ ĭ", + "éĶ ĭ", + "å£ ®", + "å° ĸ", + "çĶµ æ±ł", + "è¿ Ķ", + "æ¼ ı", + "å¾ ª", + "èı Į", + "èĥ ĥ", + "è¾ ħ", + "éĢ Ĵ", + "èĥ İ", + "éĻ ª", + "å¯ ¿", + "å¥ Ķ", + "çĮ Ľ", + "çº ¹", + "çŁ¥ åIJį", + "å¿ Ĩ", + "æ¡ ĥ", + "æ£ ĭ", + "éĢ Ĩ", + "çĤ ¼", + "ç± į", + "çī §", + "æł· çļĦ", + "è¾ Ľ", + "åł Ĩ", + "å®ŀ åľ¨", + "ä¼ ı", + "å® ¿", + "èµ ı", + "è£ Ĥ", + "åįĬ å¹´", + "åĢ ¾", + "满 æĦı", + "æ¢ ¯", + "æĦı åij³", + "åŃ ¤", + "ç¥ Ŀ", + "æĻ ¶", + "èµ Ķ", + "åģ ¿", + "èĦ Ĥ", + "ç½ ļ", + "ç¢ į", + "æ² ĥ", + "æ ĵį", + "å´ ĩ", + "æļ Ĥ", + "è· ĥ", + "æIJ ¬", + "å© Ĩ", + "é ī", + "éī ´", + "åħ´ 趣", + "èIJ¥ ä¸ļ", + "è® Ĭ", + "èĦ ı", + "è¾ Ī", + "å·ŀ å¸Ĥ", + "è´« åĽ°", + "ç© ·", + "ä¸Ń å°ı", + "æ¼ Ĥ", + "çĻ Į", + "èľ ľ", + "ä¼Ļ ä¼´", + "çī µ", + "æĤ Ł", + "éĻ ·", + "èµĽ åŃ£", + "æ¨ £", + "åģ ¶", + "æĺ Ĩ", + "è¢ Ń", + "æį IJ", + "èī °", + "æ Ĥ¬", + "çĶ ¢", + "èij ¡", + "çĽ Ĺ", + "å© ´", + "å° İ", + "çº ½", + "åĢ ¡", + "æī ®", + "è¨ Ń", + "æĬ ij", + "ç¡ ķ", + "è¾ ĸ", + "éĥ ģ", + "è¾ ©", + "éĤ »", + "çİ° åĩº", + "è¦ ı", + "å½ ¹", + "éĺ Ķ", + "åī µ", + "è¯ ±", + "æĥ ij", + "æ· Ģ", + "é¢ Ī", + "ä¾ ¦", + "æģ °", + "æ£Ģ å¯Ł", + "éĨ «", + "çĦ¶ æĺ¯", + "åĭ ĥ", + "èĮ «", + "ä ĵ", + "𠬸", + "ä½ľ 为", + "çļĦ 人", + "éĤ£ ä¹Ī", + "ç¾İ åĽ½", + "è¿ĺ æľī", + "æıIJ é«ĺ", + "èĻ ½", + "åħ· æľī", + "åĮħ æĭ¬", + "æĪĸ èĢħ", + "ä¸į è¿ĩ", + "ä¸Ĭ æµ·", + "åĮ» éĻ¢", + "èµĦ éĩij", + "çĶļ èĩ³", + "åĪ¶ 度", + "解 åĨ³", + "èģĶ ç½ij", + "继 ç»Ń", + "建 ç«ĭ", + "è¿Ľ ä¸ĢæŃ¥", + "æĿIJ æĸĻ", + "ä»Ĭ 天", + "å¿ħ é¡»", + "åIJĦ ç§į", + "çİ° åľº", + "ä»ĸ çļĦ", + "å¢ŀ åĬł", + "é¢Ĩ åŁŁ", + "åıĤ ä¸İ", + "æĮģ ç»Ń", + "ä¹ĭ ä¸Ģ", + "çī¹ åĪ«", + "é± ¼", + "åħ± åIJĮ", + "åĬ ª", + "çİ ī", + "人 们", + "åħĪ çĶŁ", + "ä¼ĺ åĬ¿", + "ä¿Ŀ æĮģ", + "ä½ľ åĵģ", + "çī Ľ", + "æĪIJ æľ¬", + "æĶ¶ åħ¥", + "åıĬ æŶ", + "è´Ł è´£", + "æİ¥ åıĹ", + "èį IJ", + "åıª è¦ģ", + "羣 çļĦ", + "导 èĩ´", + "æľº åĪ¶", + "è¡Į åĬ¨", + "æĸ° çļĦ", + "å®Į åĸĦ", + "为 ä»Ģä¹Ī", + "ä¸Ń 央", + "æĪIJ ç«ĭ", + "æĦŁ è§ī", + "åıĺ åĮĸ", + "åıĹ åĪ°", + "并 ä¸į", + "åŃ Ļ", + "æĸ½ å·¥", + "æĺİ æĺ¾", + "è¿ĩ åİ»", + "åıij æĮ¥", + "羣 æŃ£", + "åŁº åľ°", + "æĺİ ç¡®", + "èĥ ¡", + "许 å¤ļ", + "ä¸Ģ å¹´", + "æĸ¹ åIJij", + "æģ ©", + "缸 ä¿¡", + "åľ ³", + "详 ç»Ĩ", + "äºĭ ä¸ļ", + "çĶŁ åij½", + "åĴ¨ 询", + "æĸĩ æĺİ", + "çij ŀ", + "绿 èī²", + "èİ «", + "æĦı è¯Ĩ", + "æĬķ åħ¥", + "åĬł å¿«", + "æ¢ ħ", + "ç¿ »", + "å¼Ģ æĶ¾", + "æĻ® éĢļ", + "åįı ä¼ļ", + "æĪIJ 绩", + "ä» Ļ", + "å¯ Ĵ", + "è¯ģ åĪ¸", + "认 è¯Ĩ", + "ä¸ ¹", + "大 éĩı", + "è¿ ħ", + "åģļ åĪ°", + "设 æĸ½", + "è´¸ æĺĵ", + "èĥ½ æºIJ", + "æŶ æľŁ", + "ä¸Ģ 天", + "æ²» çIJĨ", + "åĺ ī", + "å® ĩ", + "丰 å¯Į", + "举 è¡Į", + "æĪIJ æŀľ", + "èĤ¯ å®ļ", + "çĭ Ĺ", + "åĬ¨ åĬĽ", + "æ£ ®", + "åĩł ä¹İ", + "åĽł ç´ł", + "æ°ij æĹı", + "æ´ ŀ", + "ç½ij åıĭ", + "åIJĪ çIJĨ", + "广 大", + "æ® Ĭ", + "æ´ Ľ", + "æĿ ¯", + "èĴ Ļ", + "çĶ¨ äºİ", + "èŀį èµĦ", + "ç¥ ĸ", + "æľº 械", + "举 åĬŀ", + "èĩª åĬ¨", + "åĬŀ åħ¬", + "é» ŀ", + "éĽ Ħ", + "åĢ¼ å¾Ĺ", + "çĮ ª", + "以 为", + "æĺ Į", + "è·Ŀ 离", + "åIJ¸ å¼ķ", + "ç» ķ", + "éļ Ĩ", + "计 ç®Ĺ", + "éĺŁ ä¼į", + "大 ä¼ļ", + "å¼ķ èµ·", + "çī¹ çĤ¹", + "èĥ ¶", + "å¹´ è½»", + "æľ¬ 身", + "æľº åħ³", + "å®ĺ æĸ¹", + "éĥ ij", + "æµ Ļ", + "è§Ĵ èī²", + "èij£ äºĭ", + "为 主", + "æĹł 论", + "ä¹ł æĥ¯", + "æ¥ ļ", + "æĭ ĵ", + "绣 计", + "åħ Ħ", + "广 æ³Ľ", + "åį Ģ", + "污 æŁĵ", + "è« ĭ", + "èĬĤ 缮", + "ä¼ ¦", + "è¦Ĩ çĽĸ", + "èĢ IJ", + "æī¶ è´«", + "ç»ı åİĨ", + "éĩįè¦ģ çļĦ", + "èĤ¡ 举", + "æĭĽ èģĺ", + "åĽĽ 个", + "æĩ ī", + "èĥ ŀ", + "æij Ĩ", + "é«ĺ éĢŁ", + "éº ¦", + "åİŁ åĪĻ", + "èİ ±", + "æĽ´ 好", + "éķ ľ", + "åĩ Į", + "åŀĥ åľ¾", + "éĢ ²", + "çģ °", + "éĵ º", + "äºĭ æķħ", + "çĶ ĺ", + "空 æ°Ķ", + "é¾ Ħ", + "èı ²", + "çĵ ¶", + "æĺ ¨", + "æĹ¥ æĬ¥", + "æµ ®", + "åľ° åĽ¾", + "åij Ī", + "大 åĬĽ", + "ç» ª", + "å¸ ħ", + "æľį åĭĻ", + "ä¸į éĶĻ", + "乡 æĿij", + "å± ¥", + "å¹³ æĸ¹", + "éĹ ²", + "æī £", + "ç´ł è´¨", + "èµ ´", + "éģ Ń", + "èIJ ¨", + "èĩª 主", + "éĩij å±ŀ", + "èī¯ 好", + "两 å¹´", + "æ³ ¥", + "é¢ ľ", + "ç²¾ 彩", + "ä¸Ń åįİ", + "æĻ ĭ", + "ä¹ł è¿ij", + "ä¹łè¿ij å¹³", + "æĪĺ 士", + "åģļ çļĦ", + "éª ij", + "æ» ´", + "çĵ ľ", + "çīĪ æĿĥ", + "èĤ ł", + "æľĥ åĵ¡", + "çı į", + "ç¨ ®", + "ä »¿", + "çī© ä¸ļ", + "åĢĭ 人", + "å¦ »", + "ä¼ ¸", + "æ± Ĺ", + "æĹ º", + "çIJĨ æĥ³", + "æij ¸", + "è¿Ŀ æ³ķ", + "å®Į æķ´", + "åİ ¦", + "è¸ ı", + "æĸ ij", + "æ¡ Ĥ", + "ä½ĵ åĪ¶", + "å¸ «", + "æĿ Ĩ", + "æ® ¿", + "æ¯ ģ", + "é¦ Ī", + "è§Ĵ 度", + "æ¬ £", + "çĥ ¦", + "èĤ º", + "éĩĩ 访", + "æij ĺ", + "æĮ ¡", + "æ· ĺ", + "åħ» èĢģ", + "çĤ ¸", + "è¿ Ī", + "åİ ī", + "åĿ Ĭ", + "è¾ £", + "åĩ Ŀ", + "æ³ ª", + "çĸ ı", + "æİ ĺ", + "åĥı æĺ¯", + "éĽ ķ", + "ç¼ Ŀ", + "èį ·", + "æį ·", + "åł ¡", + "åı¥ è¯Ŀ", + "çĸ ¼", + "æł ı", + "éģ µ", + "ç¢ ³", + "å·¥ åķĨ", + "æIJ º", + "åĪ ¥", + "ä¹ Ļ", + "æĹ ĭ", + "æĥ ľ", + "ä¸Ģ 大", + "å±Ĥ 次", + "èµ ĸ", + "æĬ ¬", + "æ¨ Ĥ", + "è¯ ŀ", + "åħ Ĵ", + "ç¯ ®", + "èĤ ĥ", + "å§ ¿", + "æĬ ļ", + "çĵ ·", + "çĶµ åĬ¨", + "æĸ° åĨł", + "æ¶ µ", + "ç¢ ij", + "æ· ®", + "æĹ ¨", + "è¸ ª", + "æ¸ Ķ", + "æĦ Ī", + "åı Ķ", + "åįĹ çľģ", + "ç¾ ©", + "å§Ķ 书记", + "è² ¸", + "æ¶ Į", + "è« ĸ", + "èIJ Ħ", + "æı ı", + "å¿ §", + "è¾ ¦", + "å¦ Ĩ", + "æī Ń", + "åij µ", + "éģ ¥", + "è¨ ±", + "ä» ĩ", + "åįģ ä¸ī", + "åī ²", + "èª į", + "èĪ °", + "é¢ ĩ", + "é¥ ±", + "çĭ ł", + "é«ĺ çļĦ", + "çµ ±", + "æħ İ", + "é¢ ģ", + "åIJĪ éĢĤ", + "æµ ´", + "èµ ĭ", + "æĬ ¼", + "å¦ ¥", + "éĻ¢ éķ¿", + "èĢ ķ", + "è¾ ¨", + "æħ °", + "åįģ åĽĽ", + "æľ µ", + "èĵ Ħ", + "æŀ ¢", + "å» ·", + "æĤ Ħ", + "æ¶ ¯", + "çŁ ©", + "åŃIJ éĩĮ", + "çĬ ¹", + "å±Ģ éķ¿", + "é IJ", + "å¥ ł", + "ä¼ļ éķ¿", + "æĵ ļ", + "ä¸į åıĬ", + "åįģ ä¹Ŀ", + "æ¬ º", + "èº º", + "éĺ IJ", + "çº Į", + "è¨ »", + "åĨ Ĭ", + "èŃ ĺ", + "é«ĺ çŃī", + "èħ º", + "å¤ ķ", + "ç» ij", + "åĶ ¤", + "èķ ´", + "çķ ľ", + "æħ ĭ", + "åı Ļ", + "åı ĥ", + "å³ ¡", + "人 大", + "éħ ¿", + "éģ ©", + "å¥ ¢", + "åı£ æ°Ķ", + "éĮ Ħ", + "é ı", + "åĭ ĺ", + "è´ ¿", + "éļ ª", + "é ĭ", + "éļ ¶", + "ð ¥", + "𬠣", + "ð £", + "ð« į", + "𬠳", + "ð« ĵ", + "ð« Ħ", + "ð« Ł", + "𨠱", + "ä Ĺ", + "以 åıĬ", + "æľī éĻIJ", + "åij ¢", + "åIJ Ĺ", + "çľĭ åĪ°", + "计 åĪĴ", + "è¿Ľ åħ¥", + "缴 æİ¥", + "åĪĨ æŀIJ", + "åıª æľī", + "设 å¤ĩ", + "åħ¶ å®ŀ", + "åĬł 强", + "ä¸Ń çļĦ", + "ä¿Ŀ éļľ", + "èĢģ å¸Ī", + "人 æīį", + "å¾Ĺ åĪ°", + "é£İ éĻ©", + "ä¸Ģ ç§į", + "空 éĹ´", + "æĪij åĽ½", + "ä¹ĭ åīį", + "ä¸ĵ 家", + "æĿ ¨", + "æĹ¥ æľ¬", + "群 ä¼Ĺ", + "åıĤ åĬł", + "æķĪ æŀľ", + "æľī åħ³", + "家 åºŃ", + "åĮº åŁŁ", + "åĬª åĬĽ", + "éļı çĿĢ", + "æĹł æ³ķ", + "交 æµģ", + "è¡Į 为", + "æ£Ģ æŁ¥", + "æľŁ éĹ´", + "å¦Ĥ æѤ", + "èĤ¡ 份", + "å½ĵ æŶ", + "è£ħ å¤ĩ", + "åĩĨ å¤ĩ", + "éħĴ åºĹ", + "è¿IJ åĬ¨", + "æıIJ åĩº", + "å·¦ åı³", + "æݪ æĸ½", + "é£Ł åĵģ", + "æ¶Īè´¹ èĢħ", + "åѦ éĻ¢", + "æĮĩ 导", + "è¿IJ èIJ¥", + "éĩį 大", + "åĨľ æĿij", + "éĢł æĪIJ", + "æĶ¿ æ²»", + "éĴĪ 对", + "æŃ£ å¼ı", + "åıĸ å¾Ĺ", + "éĤ£ 个", + "éĽĨ ä¸Ń", + "åıª èĥ½", + "å¿« éĢŁ", + "身 ä½ĵ", + "åħļ åijĺ", + "èģĶ åIJĪ", + "åĬĽ éĩı", + "éĥ½ æľī", + "æ ħ§", + "å¡ Ķ", + "åĪ« 人", + "表 çİ°", + "æķħ äºĭ", + "ä¸Ģ åĪĩ", + "å° ĩ", + "èµĦ æĸĻ", + "åŁ¹ åħ»", + "éĺħ 读", + "æľī 人", + "èIJ¥ éĶĢ", + "çĽij çĿ£", + "çݯ ä¿Ŀ", + "èĢĥ èĻij", + "æ·± åľ³", + "严 éĩį", + "èĮĥ åĽ´", + "å§Ķ åijĺ", + "çĽij 管", + "ä¸ī 个", + "è£ħ ä¿®", + "åħ¬ éĩĮ", + "åĪĨ åĪ«", + "çIJĨ 解", + "éŁ ©", + "åĬł å·¥", + "认 羣", + "ä¸į 好", + "åİ» å¹´", + "éĻį ä½İ", + "æľº ä¼ļ", + "åįı è®®", + "符 åIJĪ", + "å¢ŀ 强", + "æĬĢ èĥ½", + "é¦ĸ åħĪ", + "ç§ ¦", + "ä¸ ģ", + "å° ¾", + "æľī äºĨ", + "åľ° 产", + "æ¸ ł", + "æĸ¹ 便", + "移 åĬ¨", + "éĢŁ 度", + "å°¤ åħ¶", + "éĢļ çŁ¥", + "åĿ Ľ", + "éģ¿ åħį", + "æģ ¢", + "è´ ¡", + "èģĮ å·¥", + "å®ŀ åĬĽ", + "æĺ¯ä¸Ģ ç§į", + "åIJ¯ åĬ¨", + "çĸ¾ çĹħ", + "æĿ¥ äºĨ", + "缸 对", + "çİ° å®ŀ", + "èŀį åIJĪ", + "åIJĮ æł·", + "åħ¬ åijĬ", + "çī¹ æ®Ĭ", + "ç´ «", + "ä¸ĭ åİ»", + "ä¼ł æĴŃ", + "æľĢ 好", + "ä¼ĺ è´¨", + "æ² Ĵ", + "æĮ º", + "æĹ ¦", + "è¯ º", + "ä¸Ģ åIJį", + "éģĵ è·¯", + "示 èĮĥ", + "è¿ĩ æĿ¥", + "åIJĮ åѦ", + "é¼ ĵ", + "æĿ Ń", + "æľ¬ 次", + "åIJĮ æĦı", + "ä¸ĸ 纪", + "ç¾ Ĭ", + "æ¬ ²", + "å·¥ èīº", + "çĵ ¦", + "人 士", + "æľī æīĢ", + "ä»İ äºĭ", + "æľī å¾Īå¤ļ", + "ä¸į äºĨ", + "å²Ĺ ä½į", + "åıĺ å¾Ĺ", + "åĬ³ åĬ¨", + "å¤Ħ äºİ", + "å¹³ åĿĩ", + "å½¢ 象", + "å¡ ŀ", + "åħ± 享", + "çĿ Ľ", + "åĪ© 润", + "æŃ£ æĺ¯", + "å¾Ģ å¾Ģ", + "缸 æ¯Ķ", + "æ¨ ª", + "åĪ ·", + "æµĻ æ±Ł", + "大 éĥ¨åĪĨ", + "å¤ļ 个", + "æĤ¨ çļĦ", + "çĶµ åķĨ", + "å¾® åįļ", + "å§ĭ ç»Ī", + "çĬ¯ 罪", + "æĺ¯ åľ¨", + "ç»Ħ åIJĪ", + "åİŁ æĿ¥", + "æ¸ħ æ¥ļ", + "åIJĦ åľ°", + "æĦŁ åıĹ", + "å½ĵ ä¸Ń", + "è¶ĭ åĬ¿", + "æĻ¯ åĮº", + "羣 æĺ¯", + "ä¾Ľ åºĶ", + "转 åŀĭ", + "çĭ Ĥ", + "èĨ ľ", + "èĭ Ĺ", + "å¿ ł", + "å¾Ī 大", + "èĤ¡ æĿĥ", + "ç¾İ åħĥ", + "æİĴ åIJį", + "åĬ¨ çī©", + "éĶ ħ", + "å¢ ¨", + "主 å¸Ń", + "å¾Ī 好", + "ç»Ŀ 对", + "æĿ ľ", + "转 è½½", + "çĴ ĥ", + "æĿij æ°ij", + "åIJ ¨", + "åĽŃ åĮº", + "é«ĺ 度", + "çī© è´¨", + "è¾ ī", + "æĹ¥ 常", + "æı Ĵ", + "ä¸ī å¹´", + "ä½ĵ çİ°", + "æīį æĺ¯", + "代 çIJĨ", + "ä¸į 管", + "æģ Ĵ", + "åľ° ä½į", + "ç² ®", + "èĸ Ħ", + "æĺİ çĻ½", + "ä¸Ģ èĩ´", + "æĽ ¼", + "åĵ Ń", + "åĩ ¤", + "åĬ ²", + "æķ Į", + "æĪĺ æĸĹ", + "主 ä½ĵ", + "åħ¬ å¸ĥ", + "åıĤ èĢĥ", + "èĪª 空", + "å¯ º", + "åѦ ä¼ļ", + "åıį æĺł", + "ç¾İ 丽", + "太 éĺ³", + "建 æĪIJ", + "æħ¢ æħ¢", + "åIJĦ 个", + "éĤ ¦", + "ç»Ħ æĪIJ", + "ä¸ī 大", + "éĶ ¦", + "大å¤ļ æķ°", + "æ¦Ĥ 念", + "éŃ Ĥ", + "åħ¬ çĽĬ", + "èį Ĵ", + "身 份", + "æ·± åĪ»", + "åħ ©", + "ç»ı åħ¸", + "åIJĦ 项", + "èĻ ķ", + "è¿Ľ æŃ¥", + "åįģ äºĮ", + "æī§ æ³ķ", + "æĥ³ åĪ°", + "æĦŁ æŁĵ", + "åķĨ åĬ¡", + "å°ı ç»Ħ", + "èĶ ¬", + "çıŃ åŃIJ", + "åIJĮ å¿Ĺ", + "éĿ¢ 临", + "çĤ Ĵ", + "å¤ļ ç§į", + "è§Ĥ çĤ¹", + "åĵª éĩĮ", + "å° Ŀ", + "å§ Ĩ", + "èħ ¹", + "åŁİ åĮº", + "太 å¤ļ", + "çĹħ æ¯Ĵ", + "åľ¨ äºİ", + "æīĢ è°ĵ", + "æĻ °", + "æŀ Ŀ", + "æĭ ĸ", + "å® ħ", + "æķ´ æ²»", + "ä½ı æĪ¿", + "åģ ·", + "çĨ Ĭ", + "èµ ģ", + "æ° Ľ", + "æł¼ å±Ģ", + "åŁºç¡Ģ ä¸Ĭ", + "èĥ Ĩ", + "åħ ½", + "鼶 åĶ®", + "åĿ ¡", + "女 åŃ©", + "æĴ ŀ", + "åħ¨ åĬĽ", + "åĴ ĸ", + "èĤ ©", + "çľ ī", + "èĩ³ äºİ", + "åħļ ç»Ħ", + "ä¸Ģ 件", + "æĭ Ĩ", + "äºĭ å®ŀ", + "åĤ ³", + "æ¹ ĺ", + "網 ç«Ļ", + "循 çݯ", + "åIJĮ æ¯Ķ", + "æĭ Ķ", + "åĮ» èį¯", + "åħ» æ®ĸ", + "åĽº å®ļ", + "å®ŀéĻħ ä¸Ĭ", + "è®° å¾Ĺ", + "åĪ© äºİ", + "æĤ ¦", + "æĭ ³", + "èĤ Ŀ", + "æķĪ çĽĬ", + "è© ²", + "æ°ij 主", + "çĹĩ çĬ¶", + "é¢ ¨", + "å¹¼ åĦ¿", + "å§ ij", + "æĪ Ĵ", + "ä¸ĭ çļĦ", + "æ¸ ¡", + "å¹´ åºķ", + "è®° å¿Ĩ", + "åIJ IJ", + "大 å¹ħ", + "å¾ ½", + "åħ¬ ä¼Ĺ", + "ä¿¡ å¿ĥ", + "çİ Ľ", + "ä¼ļ ä¸Ĭ", + "ä¹ Ķ", + "æijĦ å½±", + "æ£ĭ çīĮ", + "éĻ ķ", + "åºĶ æĢ¥", + "æĶ¶ è´¹", + "æݧ èĤ¡", + "仪 å¼ı", + "çŀ ¬", + "æīĢ åľ¨", + "ç¢ °", + "å§ ĵ", + "é¡ Į", + "æĶ¯ éĥ¨", + "使 åij½", + "çĤ ī", + "å¯ Ħ", + "ç¿ ¼", + "åľ° ä¸ĭ", + "è¾ ŀ", + "ä¿ ±", + "主 æĮģ", + "è´§ å¸ģ", + "æģ ¨", + "èĤ Į", + "çĽ Ī", + "éĶ »", + "å¿Ĺ æĦ¿", + "ç±» ä¼¼", + "æĮ ĸ", + "éĢ »", + "ç¸ ½", + "纪 念", + "åķ ¥", + "å¼ ¯", + "åIJį åŃĹ", + "åģ¥ 身", + "çļĦ å¿ĥ", + "é© ±", + "èĥĮ åIJİ", + "æ³ķ å¸Ī", + "ç² Ĵ", + "èĥ½ éĩı", + "è¾ °", + "èī ³", + "å½ ¼", + "段 æŶéĹ´", + "åIJĪ æ³ķ", + "æĵ ¦", + "ç¾ ½", + "åİ ¨", + "æĪij 说", + "äºĭ åĬ¡", + "åĩł 天", + "åħ ģ", + "ç¼ ´", + "åį ĵ", + "两 ç§į", + "çĭ¬ çī¹", + "å¸ ¶", + "éĴ »", + "æĥ ©", + "é¢Ĩ åħĪ", + "足 å¤Ł", + "å£ ³", + "æĦıåij³ çĿĢ", + "åĪĨ å¸ĥ", + "ä¹ ĥ", + "éģ ĭ", + "ä½ ©", + "è° ±", + "çģ £", + "èį ¡", + "è´¯ å½»", + "å¹ ¾", + "ç£ ģ", + "åħ¸ åŀĭ", + "åī ĩ", + "åĨ »", + "æ¬ ł", + "ä¸į ä¹ħ", + "æµ ¦", + "éŃ ħ", + "å¼Ģ äºĨ", + "使çĶ¨ èĢħ", + "è¿Ļ 款", + "å° Ī", + "èĦ± è´«", + "æĶ» åĿļ", + "ç®Ĺ æĺ¯", + "ç¨ Ģ", + "æĹł 人", + "åł µ", + "å¥ ı", + "éĥ½ å¸Ĥ", + "åı¯ è§ģ", + "ä¸į åĩº", + "æ ·»", + "äº ı", + "ç¾İ 好", + "èĥ ĸ", + "éŁ µ", + "æłĩ å¿Ĺ", + "èĬĤ èĥ½", + "æĬ «", + "å° º", + "å¯ ¸", + "ä¸Ģ 代", + "é¢ Ĺ", + "èĢ ¶", + "èĴ ¸", + "åĸ ®", + "æ »¿", + "çĮ ľ", + "æµ Ĩ", + "åŁ ĥ", + "åįĥ ä¸ĩ", + "èµ Į", + "èģ ²", + "ä½ľ é£İ", + "è³ ª", + "å¯ ¨", + "å¹´ 人", + "åį° 象", + "æ¡ ¶", + "æĴ ¤", + "åįģ äºĶ", + "æ¯ ħ", + "æ² ª", + "åĽ½ æľī", + "大éĩı çļĦ", + "å¾ ¡", + "å¯ ĵ", + "è¦ ĸ", + "æ¼Ĥ 亮", + "çľ ł", + "ç ĤŃ", + "é» İ", + "èĻ ¹", + "åĪ© äºļ", + "èŃ ī", + "æµ ı", + "åįģ åħ«", + "ä¸ ¢", + "è¾ ½", + "æľīä¸Ģ äºĽ", + "æħ Ī", + "åģľ 车", + "å® ł", + "解 æĶ¾", + "æľī å¤ļ", + "éĤ Ĭ", + "常 è§ģ", + "æĬ ¹", + "çº ¤", + "è¦ ª", + "æ¡ Ĩ", + "èİ ŀ", + "æ°§ åĮĸ", + "è¿Ļ 件", + "åĩ °", + "æŁ ´", + "åıij çĶµ", + "é¼ ł", + "转 åĮĸ", + "å¨ ĥ", + "æĮ ¤", + "ç½ ©", + "å¯Ĩ åĪĩ", + "æĪij ä¸į", + "é«ĺ æĸ°", + "ä¸Ģ ç¯ĩ", + "è¿Ľ ç¨ĭ", + "è¡ °", + "è¿ĺ ä¸į", + "ç ħĮ", + "æĸ° åįİ", + "èĤ ¿", + "æ» ©", + "ä¸Ģ æµģ", + "è¯ Ī", + "å®ŀ ä½ĵ", + "å¤ĸ åĽ½", + "èº ²", + "èµ ł", + "è¦ º", + "æ¢ Ŀ", + "ä¸į è§ģ", + "è¨ Ĭ", + "åĮ ¹", + "åį µ", + "çĩ ¥", + "æħ ķ", + "é½ ¿", + "å® ´", + "é¥ ¼", + "èij¡ èIJĦ", + "å°ı å¿ĥ", + "æģ ¼", + "éĻ Į", + "æĺ Ĥ", + "åĥ ¹", + "èĬ Ŀ", + "æ¯ı 个人", + "åīį æıIJ", + "ä½ĵ ä¼ļ", + "æ¨ Ļ", + "æIJľ çĭIJ", + "对 åħ¶", + "ä¸ §", + "èľ Ĥ", + "æµ ¸", + "èª ¿", + "åĿ ª", + "é¢ ĸ", + "åIJį 为", + "ç¬ ¼", + "èĪ Į", + "æľ¬ 书", + "èģ ¯", + "çº º", + "ç®Ģ 缴", + "éĽ ¢", + "ç¾İ çļĦ", + "éļ ¨", + "é«ĺ å³°", + "è¿Ļ 家", + "å Ĥ¬", + "å° ¸", + "ç¡ķ 士", + "èŃ ·", + "è° ¨", + "æĺ ı", + "æĶ¿ åįı", + "è¡ Ķ", + "ç¿ Ĵ", + "åľ Ĵ", + "åĽ½ æ°ij", + "主 è§Ĵ", + "è£ ķ", + "ä¼ ª", + "åº ŀ", + "æ°ij èIJ¥", + "æĥ §", + "ç§ĺ 书", + "çĹ ķ", + "çĻ¾ åĪĨ", + "æº ¶", + "æĹł çĸij", + "çļĦ çľ¼", + "æĵ İ", + "ä¼Ł 大", + "å½ °", + "åħ¬å®ī å±Ģ", + "ç³ ķ", + "å¼ ¥", + "åĤ Ļ", + "ä¹ ¾", + "毫 ä¸į", + "注 æĺİ", + "åī¯ æĢ»", + "æĦ ī", + "æķ ¦", + "é¦ ¨", + "æĶ Ģ", + "éĢ Ŀ", + "åı¯ éĿł", + "å¤ ¸", + "åľ ĺ", + "éĿ¢ ä¸Ĭ", + "æĬ ĸ", + "èĦ Ĩ", + "é© °", + "ä¼ IJ", + "å¦ ¨", + "å®ļ äºĨ", + "ç³ Ĭ", + "æŃ ¡", + "éĥ¨ éķ¿", + "ç§ ī", + "èĪ Ĩ", + "åĪij äºĭ", + "åIJ µ", + "æ¤ Ĵ", + "è¡ ĵ", + "è± «", + "èı ©", + "åŃ µ", + "é¥ ²", + "å°± 好", + "åł ª", + "ä¸ī è§Ĵ", + "åľº æ¯ĶèµĽ", + "ä¸į åģľ", + "æĵ ħ", + "åħ¨ æĸĩ", + "æ³ ģ", + "åѦ ä½į", + "æ± °", + "éł ĺ", + "åı ł", + "éļ Ľ", + "å¸ IJ", + "çľĭ åĩº", + "åĮ ł", + "å±Ģ éĿ¢", + "æ³ Į", + "è° Ĭ", + "åIJĮ æľŁ", + "æĬķ æłĩ", + "å¥ ´", + "æĿ¥çľĭ çľĭ", + "èĦ ¾", + "èŀ º", + "æŃ ī", + "çĽ ¯", + "ç¨İ åĬ¡", + "å» Ĭ", + "æİ ©", + "æħ ¨", + "çĽ ¼", + "èĬ Ĵ", + "è® Ģ", + "æĮ £", + "èĮ ħ", + "æĸ ¥", + "æ¤ ħ", + "åĪ° æĿ¥", + "èijĹ ä½ľ", + "çĭ ±", + "äºĮ æīĭ", + "ä»İ æĿ¥", + "çĸ ²", + "åºĬ ä¸Ĭ", + "æĸ° 浪", + "æ³ Ħ", + "å¢ŀ åĢ¼", + "ä¸ Ľ", + "æļ ij", + "ä»İ ä¸ļ", + "æ· ĭ", + "å¤ļ æł·", + "æľ ´", + "份 é¢Ŀ", + "æŀ £", + "西 çľģ", + "æľ¬ è´¨", + "æ·± æ·±", + "èī ĩ", + "ç» µ", + "产 åĢ¼", + "æ¼ ł", + "èħ »", + "çŃ Ľ", + "åİ Į", + "æģ Ń", + "å«Į çĸij", + "æĪ ¶", + "æ» ŀ", + "èĨ Ģ", + "åĬ £", + "座 è°Ī", + "常 æĢģ", + "çļĦ æĥħ", + "è¦ ½", + "å¯ Ĥ", + "åĮ Ĩ", + "èĩ º", + "é¡ ¯", + "çķ ı", + "éģ £", + "åį ľ", + "çŃī å¥ĸ", + "è² ¬", + "æº ¯", + "é İ", + "çĤ¹ 头", + "èĵ ¬", + "æ± º", + "éħ ¬", + "éģ Ĭ", + "è³ ¼", + "註 åĨĬ", + "æľ¬ æĬ¥", + "çµ ķ", + "æ´» æĢ§", + "åħ ij", + "éĮ ¯", + "åĨ ¶", + "åĸ »", + "æº ĸ", + "èĤ ¢", + "æº ĥ", + "æĹ ¬", + "åī Ĭ", + "çIJĨ äºĭ", + "å± ł", + "æ² §", + "èļ Ģ", + "鼻 åŃIJ", + "为 æŃ¢", + "常 å§Ķ", + "çµ Ĥ", + "éĬ ·", + "çĭ Ģ", + "ä¾ £", + "èĥ Ģ", + "èŃ °", + "çĶ¨ 车", + "åĻ ª", + "æŃ ·", + "åį Ķ", + "åĪ ¹", + "ç«Ł æĺ¯", + "é© Ĺ", + "èIJ Ŀ", + "çĻ «", + "çĹ «", + "æŃ §", + "å¼ Ĭ", + "åª ½", + "çı Ĭ", + "è¡ ·", + "éľ ī", + "åŁº çĿ£", + "éļ ±", + "æ° ¨", + "ç» ¸", + "å°¼ æĸ¯", + "çĥ ĺ", + "æľŁ åĨħ", + "è° ħ", + "éĽ ĩ", + "éļ Ļ", + "å ĸī", + "åī ¥", + "çĹ ĺ", + "æĮ ½", + "çĵ £", + "æ¹ Ľ", + "æ¨ ±", + "æ¾ İ", + "æ¹ ĥ", + "åĨ¬ 奥", + "æ£ µ", + "å® °", + "åŀ Ĵ", + "æ§ ĭ", + "ä¾ Ī", + "èĮ Ħ", + "åĺ ¿", + "èı ĩ", + "ç ĻĤ", + "åĬ ĥ", + "é į", + "èĶ ½", + "çŀ Ń", + "æķ ŀ", + "ä¹ ĸ", + "éŁ §", + "è¾ ľ", + "æĩ Ī", + "ä½ £", + "çŀ »", + "åŁ Ķ", + "èĪ ħ", + "å®ŀ äºĭ", + "é ¨", + "å§ ¥", + "çµ ¡", + "åĺ »", + "çķ ¢", + "æ²ĥ å°Ķ", + "è¿ Ħ", + "èĤ ĩ", + "æħ ij", + "ã §", + "ä ı", + "ð ł", + "ð¬ ĩ", + "ð« Ń", + "ð« IJ", + "ã ³", + "© ½", + "ð« ł", + "ã Ľ", + "ð¬ į", + "é ¿", + "ð¬ Ĵ", + "ã Ļ", + "𬠤", + "ð ¬´", + "ð« ĸ", + "ð ¤", + "ã ¬", + "ä ²", + "ð« Ķ", + "ð« ļ", + "è¦ģ æ±Ĥ", + "ä¸Ģ äºĽ", + "å®ŀ çİ°", + "èĢĮ ä¸Ķ", + "åĽł æѤ", + "çĶ± äºİ", + "åħ³ äºİ", + "çĦ¶ åIJİ", + "æݨ åĬ¨", + "ä¸Ģ æł·", + "æĮī çħ§", + "è¿Ļæł· çļĦ", + "å½¢ æĪIJ", + "æľī äºĽ", + "æĽ´ åĬł", + "ç»ı è¿ĩ", + "建 è®®", + "æ²» çĸĹ", + "ä½ł 们", + "æīį èĥ½", + "ä¿ĥ è¿Ľ", + "åijĺ å·¥", + "ä½ĵ éªĮ", + "èĪ ĩ", + "åģļ 好", + "ä¿Ŀ è¯ģ", + "æķ´ 个", + "æĺ¯ ä¸Ģ个", + "éĩĩ çĶ¨", + "çIJĨ 论", + "æ¯Ķ å¦Ĥ", + "ä¸Ĭ çļĦ", + "æݨ èįIJ", + "çĶ³ 请", + "天 空", + "éĥ¨ èIJ½", + "åįģ åĪĨ", + "æĿ¥ èĩª", + "ä¹ĭ éĹ´", + "è°ĥ æķ´", + "æ¯ı 天", + "è°ĥ æŁ¥", + "æĤ£ èĢħ", + "è¿ĩç¨ĭ ä¸Ń", + "é¦Ļ 港", + "广 åijĬ", + "éĿ¢ 对", + "满 足", + "éķ¿ æľŁ", + "è§Ħ èĮĥ", + "æķ´ ä½ĵ", + "æĶ¹ åıĺ", + "æĻº æħ§", + "å¦Ī å¦Ī", + "å¦Ĥ ä»Ĭ", + "åIJĪ åIJĮ", + "éĥ½ ä¼ļ", + "åĦ¿ ç«¥", + "åĩı å°ij", + "éŁ³ ä¹IJ", + "ç»ı 常", + "ä¸Ĭ å¸Ĥ", + "ä¼ĺ ç§Ģ", + "çļĦ éĩįè¦ģ", + "ä¸Ģ æĿ¡", + "æµ· å¤ĸ", + "åı¦ å¤ĸ", + "ä¸Ģ 家", + "åİĭ åĬĽ", + "大 åŀĭ", + "çľĭ çĿĢ", + "åĪ Ģ", + "幸 ç¦ı", + "æݨ 广", + "åIJ Ľ", + "å¾ IJ", + "æī¾ åĪ°", + "äºİ æĺ¯", + "èĩª 身", + "ä¸Ģ ä½į", + "åľŁ åľ°", + "åĬł åħ¥", + "æİ¢ ç´¢", + "æ¢ ģ", + "主 åĬ¨", + "å°± ä¸ļ", + "女 æĢ§", + "çªģ çł´", + "ä¸įåIJĮ çļĦ", + "è¿IJ è¾ĵ", + "èĩª çĶ±", + "å±ħ æ°ij", + "æѤ 次", + "çļĦ æŶéĹ´", + "家 éķ¿", + "ä¸Ģ个 人", + "æ£Ģ æµĭ", + "åĨħ éĥ¨", + "广 å·ŀ", + "缴 æĴŃ", + "ä»İ èĢĮ", + "è´· 款", + "åı¬ å¼Ģ", + "æĶ¹ éĢł", + "人 çĶŁ", + "å±ķ 示", + "æ¯ı å¹´", + "女 人", + "çļĦ æĸ¹å¼ı", + "æķĪ çİĩ", + "å±± 举", + "æ¸ł éģĵ", + "ä¼¼ ä¹İ", + "æ¡Ī 件", + "åĪ© çĽĬ", + "çľĭ çľĭ", + "å¿ĥ éĩĮ", + "ç»´ æĬ¤", + "å®Ŀ å®Ŀ", + "ç½ij ä¸Ĭ", + "论 åĿĽ", + "å°± åı¯ä»¥", + "ä¸į 足", + "æģ¢ å¤į", + "å¸ĥ å±Ģ", + "è´¡ çĮ®", + "ä¸ĭ éĻį", + "æİĮ æı¡", + "çļ® èĤ¤", + "å·¥ åħ·", + "éĩį åºĨ", + "åĵģ è´¨", + "æݨ åĩº", + "çĶ· 人", + "æī¿ æĭħ", + "çªģ åĩº", + "èĢĮ è¨Ģ", + "æ² Ł", + "åįı è°ĥ", + "æĺ¯ ä»Ģä¹Ī", + "æ± ¤", + "æĴ ij", + "çĭ¬ ç«ĭ", + "çݯ èĬĤ", + "æī© 大", + "æ´ ª", + "æĿ °", + "çĽ IJ", + "ä» ģ", + "æ¶ī åıĬ", + "èĢģ 人", + "åį³ 使", + "åįĹ äº¬", + "éħį åIJĪ", + "é¬ ¼", + "çĪ¶ 亲", + "ç½Ĺ æĸ¯", + "å°ı åĮº", + "æķĻ æİĪ", + "åĨ³ çŃĸ", + "é¢Ħ 计", + "æľ¬ 人", + "ä¼ ¯", + "ç« ¹", + "åĪ° åºķ", + "å¸Ĥ æ°ij", + "åĩº åı£", + "éĩĩ è´Ń", + "æĢ» ç»ĵ", + "æѦ æ±ī", + "åĬł 大", + "广 举", + "æµģ ç¨ĭ", + "人 åı£", + "å¦Ĥæŀľ ä½ł", + "åĩº åİ»", + "åĩ ī", + "åĨľ æ°ij", + "çİ° 象", + "åĬĽ 度", + "ç»Ļ äºĪ", + "åħļ å§Ķ", + "è¯Ń è¨Ģ", + "线 ä¸Ĭ", + "æĢİ æł·", + "åĦ¿ åŃIJ", + "ç¡® å®ŀ", + "ä¹ĭ å¤ĸ", + "éĥ½ åľ¨", + "èī ¾", + "çļĦ æĥħåĨµ", + "éĩĮ çļĦ", + "åĽ´ ç»ķ", + "æĽ´å¤ļ çļĦ", + "ä¾Ŀ æ³ķ", + "åħ¬ åĽŃ", + "家 éĩĮ", + "æ¯į 亲", + "ä¸į åĨį", + "èĭ ¹", + "æ³ķ éĻ¢", + "飩 åĽ½", + "缸 å½ĵ", + "ä¸į çŁ¥", + "è¯Ħ ä¼°", + "ä¸į çĶ¨", + "顺 åĪ©", + "éĩį è§Ĩ", + "è´¢ åĬ¡", + "ä»ĸ åĢij", + "åıij è¡Į", + "ä¸ĵ éŨ", + "åħ· å¤ĩ", + "并 ä¸įæĺ¯", + "足 çIJĥ", + "é ŀĭ", + "åıij 表", + "æ°¸ è¿ľ", + "èIJ¥ åħ»", + "éħį å¥Ĺ", + "æķ´ åIJĪ", + "è´ º", + "åĽŀ çŃĶ", + "æĶ¶ çĽĬ", + "ä¹Ł 许", + "è» Ĭ", + "æİ¥ 触", + "æĶ» åĩ»", + "åĽĽ å·Ŀ", + "æĢ§ èĥ½", + "åĽŀ åĪ°", + "èħ °", + "ä¹Ł 没æľī", + "å¼ Ħ", + "设 ç«ĭ", + "éĺ² æݧ", + "æĬĢ å·§", + "éĢļ 常", + "è´¢ æĶ¿", + "éĥ¨ ç½²", + "åľº æĻ¯", + "æ±Ł èĭı", + "表 è¾¾", + "åĸ ·", + "女 åĦ¿", + "èĪ ¶", + "çµ ¦", + "ä¼ļ åijĺ", + "æĪĸ 许", + "äº ©", + "举 æĸ¹", + "天 æ´¥", + "è¿ij å¹´", + "çľĭ æĿ¥", + "æ¯Ķ ä¾ĭ", + "å² ©", + "éĵ ľ", + "çİ »", + "å®ŀ éªĮ", + "æĢĿ ç»´", + "æĭħ å¿ĥ", + "æ² Ī", + "身 è¾¹", + "æ·± åĮĸ", + "ç²¾ åĩĨ", + "ç§ģ æľį", + "æ¶Ī éĺ²", + "åİ» äºĨ", + "ç»Ĩ èĥŀ", + "çIJĥ éĺŁ", + "æĺİ æĺŁ", + "é£Ł çī©", + "å¾Ī å¿«", + "让 ä½ł", + "ä¿¡ çĶ¨", + "åĶ¯ ä¸Ģ", + "åħ¶ å®ĥ", + "çŃī æĸ¹éĿ¢", + "å¾ĭ å¸Ī", + "æŃ» 亡", + "æ Ł³", + "ä¸Ģ æī¹", + "ä¸Ĭ 涨", + "æľº åľº", + "å½¢ åĬ¿", + "æĦ¿ æĦı", + "éĽĨ ä½ĵ", + "æĸ° åŀĭ", + "æįŁ 失", + "æĽ ¸", + "ä¸ĭ åįĪ", + "æ¯ı 次", + "æĪIJ å°±", + "åħ¬ è·¯", + "èĻ «", + "åĴ ±", + "西 å®ī", + "æľĢ ä½³", + "ç§ij çłĶ", + "å¤į æĿĤ", + "æľº åĻ¨", + "çĪ± æĥħ", + "çħ§ çīĩ", + "å¹´ é¾Ħ", + "è³ĩ æĸĻ", + "ç² Ĺ", + "åĩĨ ç¡®", + "åĬł ä¸Ĭ", + "åĩº çīĪ", + "è° IJ", + "家 å±ħ", + "èĥĮ æĻ¯", + "ä¸Ģ 线", + "äºĭ 项", + "åĬ¨ ä½ľ", + "ç¥ ¥", + "æĢ» ä½ĵ", + "æĪ¿ åŃIJ", + "ä¹Ł å°±æĺ¯", + "大 æ¦Ĥ", + "é«ĺ æķĪ", + "åIJ ¹", + "æİ ĪæĿĥ", + "éĻĦ è¿ij", + "æ¡Ī ä¾ĭ", + "éĹ ¹", + "çĪ¸ çĪ¸", + "彩 票", + "æĢ Ĵ", + "举 æĬ¥", + "æĻ® éģį", + "çķĻ ä¸ĭ", + "è¡£ æľį", + "æĹłè®º æĺ¯", + "åħħ 满", + "æ·± 度", + "æ¡ ij", + "æĪª èĩ³", + "带æĿ¥ çļĦ", + "éĻ µ", + "æĦŁ æĥħ", + "èµ ļ", + "åĵª äºĽ", + "æķ´ æĶ¹", + "æĪIJ çĨŁ", + "å¨ ľ", + "é¼ »", + "çŁ Ľ", + "çĽ ¾", + "好 好", + "第 åĽĽ", + "åĨł åĨĽ", + "è´¢ å¯Į", + "æľĢ 好çļĦ", + "车 åŀĭ", + "éĸ Ģ", + "åį³ å°Ĩ", + "åĪĨ 为", + "éĿĴ å²Ľ", + "纷 纷", + "ä»Ĭ æĹ¥", + "å¹³ è¡¡", + "å¹³æĸ¹ ç±³", + "éĤ£ ç§į", + "åĩº çĶŁ", + "éĿĴ æĺ¥", + "人 群", + "人 å·¥", + "ä¹ĭ ä¸ĭ", + "æ¹ĸ åĮĹ", + "åľ¨ æѤ", + "åįļ 士", + "æŶ åĪ»", + "æ²³ åĮĹ", + "æĶ¾ å¼ĥ", + "éĢļ éģĵ", + "森 æŀĹ", + "çĸ Ĩ", + "æķ ¸", + "èĬ ³", + "æīĵ åĩ»", + "æĽ ¹", + "åĮĸ åѦ", + "æĥ³ 象", + "ä¸ĩ 人", + "è´¢ ç»ı", + "åħĥ ç´ł", + "ä¼ļ 计", + "åħ¨ ä½ĵ", + "æĦ Ľ", + "é«ĺ ä¸Ń", + "æľº éģĩ", + "声 éŁ³", + "æĹħ è¡Į", + "æµ ©", + "æŁ ±", + "å°ij å¹´", + "åĽ½ å¤ĸ", + "èijĹ åIJį", + "çĶŁ åŃĺ", + "å§ ľ", + "带 é¢Ĩ", + "é¢ľ èī²", + "ä¸Ĭ ä¸ĭ", + "产ä¸ļ éĵ¾", + "æĽ´ 好çļĦ", + "å² Ń", + "ä¼ĺ æĥł", + "便 æĺ¯", + "åħ§ 容", + "ä¸Ģ åıª", + "çIJ ´", + "梦 æĥ³", + "秣 èµģ", + "å¼Ģ åIJ¯", + "è´Ń çī©", + "åĮħ åIJ«", + "åĪ© çİĩ", + "èµ· äºĨ", + "æľī åĬĽ", + "éĤ£ éĩĮ", + "审 æī¹", + "对 æīĭ", + "çİ° éĩij", + "天 çĦ¶", + "çĽ Ĵ", + "çĪ ½", + "å¿ħ çĦ¶", + "åĮĸ å·¥", + "ä¸ĵ åĪ©", + "åķ ¡", + "å¼Ģ å¿ĥ", + "人 ä½ĵ", + "éģĵ 士", + "æĢģ 度", + "空 è°ĥ", + "æĭĽ åķĨ", + "å§ »", + "第 äºĶ", + "æ£ Ĵ", + "ä¸Ģ ç³»åĪĹ", + "åį± æľº", + "转 åıĺ", + "åľº æīĢ", + "é¸ £", + "æĪ¿ éĹ´", + "éĢ ¼", + "è¯ķ çĤ¹", + "对 å¤ĸ", + "åĩº åı°", + "åľ¨ è¿Ļ", + "åİĤ 家", + "å·¨ 大", + "ç®Ģ ä»ĭ", + "çľĭ äºĨ", + "åħļ 建", + "æĮĩ æĮ¥", + "çŁ³ æ²¹", + "ä¸į åı¯èĥ½", + "èİ ²", + "ä¸į 太", + "åĪĽ æĦı", + "第 ä¸Ģ个", + "è´µ å·ŀ", + "è¿ĩ äºĨ", + "æľ¬ æĿ¥", + "éģĵ å¾·", + "çŃĶ æ¡Ī", + "éĻ ¶", + "ä¸Ģ è·¯", + "èĤ ĸ", + "æ¸ħ æ´ģ", + "æľī æľº", + "åIJį åįķ", + "æĿ ±", + "åij¼ åIJ¸", + "ä¸ Ī", + "ç¦ı 建", + "è¯ķ éªĮ", + "å¼ķ åıij", + "ä¹Ł 没", + "ä¸į ä½ı", + "çĨŁ æĤī", + "èIJ ¬", + "ä¸į èī¯", + "çł ĸ", + "èĩ´ åĬĽ", + "çŃ¾ 订", + "åIJ Ĭ", + "ä¾ ¯", + "çĺ ¦", + "å§ij å¨ĺ", + "æĸ ¤", + "妻 åŃIJ", + "æĺ¥ èĬĤ", + "çĪ ¬", + "æĽ Ŀ", + "çĥŃ æĥħ", + "éķ¿ æ²Ļ", + "èIJ¥ éĢł", + "éħ ·", + "éĵ Ŀ", + "åŁºæľ¬ ä¸Ĭ", + "åij¨ åĽ´", + "ä»Ģ 麼", + "认 åı¯", + "åĪĨ åŃIJ", + "ä¸Ģ æĸ¹éĿ¢", + "è½ ´", + "å¼ ·", + "马 ä¸Ĭ", + "éĽ ¾", + "èĩ £", + "å° ¿", + "çĶŁ æĦı", + "å®ī å¾½", + "ç¥ŀ ç»ı", + "åĩº å¸Ń", + "èį¯ åĵģ", + "çIJĨ çĶ±", + "åįı åIJĮ", + "æµģ åĬ¨", + "åıij åĬ¨", + "åĿļ å®ļ", + "表 æĺİ", + "åIJİ éĿ¢", + "ä¹ī åĬ¡", + "å¦ ĸ", + "æľī åı¯èĥ½", + "å¹´è½» 人", + "大 éĻĨ", + "å² ³", + "ä¸į èµ·", + "çŀ¬ éĹ´", + "ä¸įå¾Ĺ ä¸į", + "çŃ¾ 约", + "åIJĪ æł¼", + "åħļ æĶ¯éĥ¨", + "æµİ åįĹ", + "便 åĪ©", + "éļı æŶ", + "å¥ ī", + "称 为", + "产 æĿĥ", + "åIJ ķ", + "çĽ Ĩ", + "课 åłĤ", + "ç· ļ", + "æ£ ī", + "线 ä¸ĭ", + "èĩª è¡Į", + "举 æݪ", + "åݦ éŨ", + "èĩª ä¿¡", + "å½± è§Ĩ", + "ä» Ķ", + "çĶŁæ´» ä¸Ń", + "æĿĥ çĽĬ", + "çĻ½ èī²", + "å°± ä¸į", + "è¿Ľ å±ķ", + "æ¯ı æĹ¥", + "ä¾Ľ ç»Ļ", + "æĿĥ åĪ©", + "æĹł æķ°", + "çIJĨ è´¢", + "ä¾Ŀ æŧ", + "ä¸Ĭ åįĪ", + "è¯Ĩ åĪ«", + "çĽĪ åĪ©", + "çł Ĥ", + "许 åı¯", + "åIJĮ äºĭ", + "åĺ Ľ", + "éģ ¸", + "çĿĢ åĬĽ", + "éŨ åı£", + "ä¸į å¤ļ", + "åħ¶ 次", + "ç¢ §", + "çī© çIJĨ", + "åĨħ å¿ĥ", + "çĻ¾ å§ĵ", + "æĢ» 绣", + "å¹² åĩĢ", + "积 ç´¯", + "åıį é¦Ī", + "æłij ç«ĭ", + "社 交", + "ç§ ©", + "åįģ ä¸Ģ", + "éĤ ĵ", + "驱 åĬ¨", + "å±ķ è§Ī", + "èĪĴ éĢĤ", + "åŁº åĽł", + "å·® å¼Ĥ", + "转 让", + "å°ı å§IJ", + "æł· åŃIJ", + "ç¿ Ķ", + "é«ĺ åħ´", + "å½±åĵį åĬĽ", + "æīĭ ç»Ń", + "缸 åIJĮ", + "缸 åºĶ", + "æĻ Ĵ", + "è§ Ģ", + "å¸Ĥ å§Ķ", + "èĬ ¯", + "å±ķ çİ°", + "åľ° çIJĥ", + "éĤ ª", + "ä¸Ģå®ļ çļĦ", + "åħģ 许", + "ä¿¡ ä»»", + "æī ij", + "éĻ¢ æł¡", + "ç®Ģ 称", + "åģļ æ³ķ", + "ä¹ĭ è·¯", + "æĹĹ ä¸ĭ", + "èħ Ķ", + "æ¶Ī 失", + "ä¸ĸçķĮ ä¸Ĭ", + "åŁİ 乡", + "èĪŀ åı°", + "å¾Ī 大çļĦ", + "绣 çŃ¹", + "åħ¬ å¹³", + "èĤ ¾", + "çļĦ 好", + "æ± ģ", + "çľ¼ åīį", + "éĽ £", + "å¹ ½", + "åħ± 产", + "主 åĬŀ", + "å¤Ħ ç½ļ", + "åº Ļ", + "éģĵ çIJĨ", + "å¼ µ", + "æİ¥ çĿĢ", + "çĮ İ", + "çģ Į", + "çĶ± æѤ", + "人 åĬĽ", + "æµģ è¡Į", + "ä¾ ł", + "åı¯ä»¥ 说", + "èĴ ĭ", + "å½¢ æĢģ", + "æĹ¥ åŃIJ", + "æ¼ Ĩ", + "çķĻ åѦ", + "缸 éĹľ", + "æľĢ å¤ļ", + "åĩŃ åĢŁ", + "åħ¬ 交", + "æĮĸ æİĺ", + "æĿĤ å¿Ĺ", + "主 人", + "éļľ ç¢į", + "æł¡ éķ¿", + "æĸ¹ ä½į", + "ä¸Ĭ çıŃ", + "å¤ļ åħĥ", + "è ĥģ", + "éŃħ åĬĽ", + "èĮ Ĥ", + "åħħ çĶµ", + "强 大", + "çĥ ¤", + "å¥ĭ æĸĹ", + "å®ŀ çĶ¨", + "éĺ ģ", + "ç»Ļ äºĨ", + "æľ¬ ç§ij", + "æł ĭ", + "æĭ ¨", + "æķĻ ç»ĥ", + "éĥ½ çŁ¥éģĵ", + "æ¯ķä¸ļ çĶŁ", + "ç¢ Ĺ", + "åŀ Ĥ", + "è® ¼", + "å®ģ æ³¢", + "åѦ èĢħ", + "è°¢ è°¢", + "åŁİ éķĩ", + "æĢİä¹Ī åĬŀ", + "éģ Ķ", + "æĪIJ 交", + "æ½ľ åĬĽ", + "åį §", + "æĸ° å¼Ģ", + "éħį å¤ĩ", + "主 åĬĽ", + "åij³ éģĵ", + "çĥ Ĥ", + "é£ŀ è¡Į", + "å« ģ", + "大 大", + "ç»Ļ 大家", + "å¤ĸ éĿ¢", + "éĨ ī", + "åıij è¨Ģ", + "æĹ© é¤IJ", + "åIJĦ èĩª", + "å® Ļ", + "èį£ èªī", + "æĬ« éľ²", + "é¡ ŀ", + "åĨħ çļĦ", + "èĤ ª", + "è¾ IJ", + "æ³ µ", + "æĬ Ľ", + "æĺŁ æľŁ", + "ä¸Ģ 带", + "çĶŁ ç´ł", + "ç»ı éĶĢ", + "åĩ ¶", + "åľ° ä¸Ĭ", + "åij½ è¿IJ", + "åĵ ²", + "ä¸Ĭ åİ»", + "æĸĩ çī©", + "è¯ ij", + "æĮ¯ åħ´", + "éķ¿ æŶéĹ´", + "ç¥ Ń", + "åIJĪ èĤ¥", + "è¿Ŀ è§Ħ", + "èģ ª", + "ä½İ äºİ", + "éĢĤ å½ĵ", + "æľī åºı", + "æľ¬ ç½ij", + "çķĻ è¨Ģ", + "æĥ³ æ³ķ", + "çŃ¾ ç½²", + "å§ ļ", + "æĢ§ æł¼", + "èĴĻ åı¤", + "æŁ ı", + "åŀ «", + "åѦ åİĨ", + "ä»ħ ä»ħ", + "讲 è¯Ŀ", + "éĶ IJ", + "æĢ ĸ", + "åī ª", + "èĭ į", + "åIJ ĵ", + "强 çĥĪ", + "åģ¥ åħ¨", + "çĸ ¯", + "åı¤ 代", + "å¥ Ī", + "ä¸į çĦ¶", + "乡 éķĩ", + "æľĭåıĭ 们", + "åĤ ħ", + "èģ ½", + "个 æĢ§", + "æ³ķ è§Ħ", + "å°ı éķĩ", + "çĶ» éĿ¢", + "第 åħŃ", + "網 è·¯", + "åīį æĻ¯", + "åIJ¬ 说", + "ä¼ł åªĴ", + "æĿ¡ ä¾ĭ", + "åĪ« çļĦ", + "ä¸į æĩĤ", + "顾 éĹ®", + "强 度", + "éĺ¿ éĩĮ", + "èµ° åĬ¿", + "å¸ ½", + "çļĦ ç¡®", + "åĮº åĪ«", + "éĮ ¢", + "主 管", + "ä¸Ģ çľĭ", + "æĸ ľ", + "åŃĺåľ¨ çļĦ", + "ä» ²", + "åį± 害", + "éĵ Ń", + "游æĪı ä¸Ń", + "éħ ±", + "é¾Ļ 头", + "人 å¿ĥ", + "éĢĢ ä¼ij", + "æµı è§Ī", + "åĬ «", + "éĺ² æ²»", + "ç® Ń", + "å± Ī", + "è¾½ å®ģ", + "å£ ¤", + "è¿İ æĿ¥", + "éŀ į", + "çĶ¨ æĿ¥", + "大 åľ°", + "ä» °", + "éĢļ 讯", + "å¼Ģ å·¥", + "è£ ¤", + "å¦Ĥ åIJĮ", + "éª ¤", + "éĺŁ åijĺ", + "è½ ©", + "ç¾İ æľ¯", + "èĻ Ł", + "åIJĮ ä¸Ģ", + "åľ ĸ", + "书 æ³ķ", + "æīĵ åį°", + "åIJ« æľī", + "éĽĨ æĪIJ", + "éĹ ·", + "å¸Ĥåľº ä¸Ĭ", + "æĹģ è¾¹", + "åľ° æĿ¿", + "产çĶŁ çļĦ", + "ç² ¤", + "éĩį ç»Ħ", + "è¡Ģ 液", + "çŃ ĭ", + "åĬŀ äºĭ", + "常è§ģ çļĦ", + "ä¸Ĭ åįĬå¹´", + "å±ı å¹ķ", + "åIJī æŀĹ", + "å· ©", + "åĸľ çĪ±", + "ç¿ ł", + "ä¸ī ç§į", + "æ¡Ĩ æŀ¶", + "举 èİŀ", + "çĶĺ èĤĥ", + "èĬ ¬", + "åĽ¾ 书", + "åĩ¤ åĩ°", + "æ°Ķ åĢĻ", + "å° ´", + "å° ¬", + "两 天", + "è¾ħ 导", + "åĢŁ 款", + "æĹ¥ èµ·", + "æ´ Ĵ", + "ä¸Ģ 度", + "è¹ Ī", + "æ½ Ń", + "æī ĩ", + "çĻ ľ", + "æĸ° åħ´", + "åĤ ²", + "诸 å¤ļ", + "è´ ª", + "éĻ· åħ¥", + "èĪ Ł", + "èĤº çĤİ", + "ä¸Ģ æł·çļĦ", + "åİ ĺ", + "åľ° çIJĨ", + "æĬķ 注", + "éļ Ĭ", + "åħī ä¼ı", + "ä¿Ŀ åģ¥", + "åħ Ķ", + "åħ¬ åĬ¡", + "æīĵ çł´", + "çĶ· åŃ©", + "åĬ³ åĬ¡", + "ä½ł ä¼ļ", + "çĶ¨ åľ°", + "æº ¢", + "åıij è¾¾", + "èĤ ļ", + "è¿ĩ äºİ", + "èĩ Ĥ", + "éĢĻ 樣", + "è½» è½»", + "ä¸Ń åħ±", + "åIJĦ åĽ½", + "åĶ ĩ", + "å®ŀ ä¹ł", + "èĻ ¾", + "æ§ ½", + "ä¸į ä¸Ĭ", + "åħį çĸ«", + "åįł æį®", + "å·¥ ä¼ļ", + "åĽ Ĭ", + "èĪª 天", + "åı¯ çĪ±", + "æĸĹ äºī", + "çĺ ¤", + "å¦Ĥ æľī", + "éĽ ĸ", + "对 æĪij", + "åĩº 秣", + "好 çľĭ", + "太 大", + "æ°´ åĪ©", + "åĬ¿ åĬĽ", + "åħ¨ æ°ij", + "ç½ ¢", + "èµ¢ å¾Ĺ", + "çĶµ ä¿¡", + "车 éĹ´", + "æĻĤ åĢĻ", + "å°ij æķ°", + "éĵ ¸", + "åħ³ èģĶ", + "ä¸įä»ħ ä»ħ", + "为 æĤ¨", + "åĴ ¸", + "æľº åĬ¨", + "è£ Ļ", + "åĵį åºĶ", + "éģ ł", + "è² ·", + "ç© ´", + "å¢ ħ", + "éĶ ¡", + "çµ Ħ", + "çģ« 车", + "è³ĩ è¨Ĭ", + "åĨ³ èµĽ", + "污 æ°´", + "èª ŀ", + "å´ Ľ", + "ç´§ å¯Ĩ", + "缺 å°ij", + "å¤ļ 人", + "æĢ» 书记", + "éĶ Ī", + "èij Ľ", + "å¿ĺ è®°", + "éĻĮ çĶŁ", + "éķ¿ 大", + "åħĪè¿Ľ çļĦ", + "ç¡ ħ", + "åıij æĺİ", + "å©´ åĦ¿", + "æīİ å®ŀ", + "èĽĭ çĻ½", + "ä¸Ģ çĻ¾", + "缮 åħī", + "æ ħĮ", + "åĬł æ²¹", + "åIJ ŀ", + "ä¸Ģ 群", + "ä¸Ń ä»ĭ", + "å¸ ĸ", + "å¿ Į", + "èģĮ èĥ½", + "广 æĴŃ", + "çĽij å¯Ł", + "ç§ĺ å¯Ĩ", + "çĭ ®", + "è¿Ļ æĿ¡", + "éĢ ¢", + "æĢ ¨", + "åįģ åħŃ", + "è© ¦", + "说 åĪ°", + "åĩĿ èģļ", + "æĮĩ 示", + "æ° ¢", + "å¼ ĺ", + "éĺ Ģ", + "æĸ ©", + "éł ħ", + "ä¸Ģ å¼Ģå§ĭ", + "æİĴ è¡Į", + "åľ¨ æĪij", + "纪 å½ķ", + "æĬ Ħ", + "æł ª", + "说 æ³ķ", + "ä¸Ń èį¯", + "好 å¤ļ", + "åıª ä¸įè¿ĩ", + "çķĻ åľ¨", + "个 å°ıæŶ", + "认 çŁ¥", + "çķ «", + "è§ģ è¿ĩ", + "å°ı å¾®", + "ä½Ľ å±±", + "çľ ¾", + "讲 è¿°", + "æ¢ ³", + "称 åı·", + "æĹ¥ æĻļ", + "è¢ ĸ", + "åķ ¤", + "æľª ç»ı", + "æľĢ æĹ©", + "æī® æ¼Ķ", + "è¡Ģ 管", + "çº ±", + "æĥħ èĬĤ", + "第 ä¸ĥ", + "æį §", + "ä» Ĺ", + "æ¿Ģ çĥĪ", + "æĹł 线", + "ä¸į 容æĺĵ", + "å¼Ģ å¹ķ", + "æĸ° çĶŁ", + "ä¸ĵ 注", + "èij ±", + "åįĹ æµ·", + "çĩ Ł", + "èµ· ä¾Ĩ", + "æ´¾ åĩº", + "åĦ Ĵ", + "ä¾ ¨", + "è¼ ĥ", + "åįļ è§Ī", + "éĢ ¾", + "åĮ Ģ", + "ç»ıæµİ åѦ", + "æ¸ Ĺ", + "ä¿Ŀ èŃ·", + "çī º", + "çī ²", + "çİ «", + "çij °", + "æľĢåIJİ ä¸Ģ", + "æĶ¿ åĬ¡", + "æ§ Ľ", + "èĻķ çIJĨ", + "éļIJ æĤ£", + "æī¿ åĮħ", + "æ¥ µ", + "æ¡ ©", + "çĽ ²", + "导 åIJij", + "èĩ´ å¯Į", + "ç¼ Ĩ", + "æģĭ çĪ±", + "ä¸į åĬ¨", + "ç»Ļ 人", + "å· ¢", + "表 æĥħ", + "举 åįĹ", + "åĨħ å¤ĸ", + "è¾Ī åŃIJ", + "åı ī", + "åįļ ä¼ļ", + "åĬŁ æķĪ", + "æ¸ ´", + "å± ¬", + "æİĴ éĻ¤", + "éĢ Ľ", + "ä¸Ģ ä¼ļ", + "ä¸į å¼Ģ", + "å¼Ģ å¥ĸ", + "é»ij é¾Ļ", + "é»ijé¾Ļ æ±Ł", + "å¿« ä¸ī", + "度 åģĩ", + "åĿ ¤", + "éĤ® 件", + "æĩ Ĵ", + "ä¾Ľ çĶµ", + "å» £", + "好 è¯Ħ", + "ç§ĺ书 éķ¿", + "æĪĺ åľº", + "好 å¥ĩ", + "ä¾µ æĿĥ", + "æĨ ¾", + "æľĢ åĪĿ", + "æī¹ åıij", + "åİ ķ", + "è¼ ķ", + "æŀ ¯", + "ä¸ļ åĨħ", + "è´Ń æĪ¿", + "ä¸į åľ¨", + "纪 å§Ķ", + "æīĢ éľĢ", + "å¸Ĥ éķ¿", + "è³ ½", + "å¼ķ æĵİ", + "çģµ éŃĤ", + "éĬ Ģ", + "æ» ¤", + "çĿ IJ", + "å¤ļ 项", + "åĽŀ 头", + "èī ĺ", + "å¤į å·¥", + "éĥ¨ 件", + "ç´§ ç´§", + "æŁIJ ç§į", + "使 åħ¶", + "æĸ° 人", + "æŀ ļ", + "æ³ķ å®ļ", + "å·´ å·´", + "涵 çĽĸ", + "ç¨ »", + "æĭ ¾", + "æĻ ķ", + "è½ ¿", + "éĢļ è¡Į", + "åĵ Ģ", + "æ³ Ĭ", + "温 馨", + "éĽĨ èģļ", + "çĨ Ļ", + "åĩ ij", + "åįģ ä¸ĥ", + "æ°Ķ æģ¯", + "æıIJä¾Ľ çļĦ", + "æ³ ³", + "奥 è¿IJ", + "çģ¾ 害", + "åĩĢ åĮĸ", + "è·¨ è¶Ĭ", + "åĵª æĢķ", + "éŁ ¿", + "å¢ŀ æ·»", + "çĦ Ĭ", + "æ®ĭ çĸ¾", + "ç¢ Į", + "æĤ Ķ", + "è§ģ è¯ģ", + "è¾ĸ åĮº", + "å¿ĥ èĦı", + "éļ §", + "åį ¸", + "åı¯èĥ½ æĢ§", + "æľī 趣", + "åī¯ 书记", + "åĮĸ å¦Ĩ", + "ä¿ Ĥ", + "æ£ ļ", + "éĨ ĩ", + "带 头", + "éł Ī", + "追 究", + "æij Ķ", + "è¿Ļ éĥ¨", + "ä¸į 论", + "ç¥ ¸", + "å ³»", + "éģ ķ", + "çĶŁ èĤ²", + "å¤ ł", + "å¤ĸ 交", + "è¯Ħ 为", + "ä»İ å°ı", + "å°ı å°ı", + "é ¥¿", + "æĴ ¼", + "è·¨ å¢ĥ", + "被 åijĬ", + "åįĹ å®ģ", + "身 å¿ĥ", + "åĨį çĶŁ", + "æīĢ 说", + "æŶéĹ´ åĨħ", + "åĪĹ åħ¥", + "éĿĴ æµ·", + "çĪ± 好", + "çª Ħ", + "èĪ Ī", + "è¿ĩ 渡", + "æ¿ Ł", + "éĽ Ģ", + "审 è®®", + "åĽ½ èµĦ", + "æŃ¥ ä¼IJ", + "轨 éģĵ", + "ä¿¡ 念", + "ä¸ī åĪĨ", + "çĨ ¬", + "åѵ åĮĸ", + "ç¼ ł", + "éĥ Ĭ", + "èĪĴ æľį", + "纪 æ£Ģ", + "ä¸Ģä¸ĭ åŃIJ", + "鼻 話", + "è² ł", + "éĴ ¥", + "åĮ Ļ", + "çĹ ´", + "è¶ ģ", + "ç» £", + "çĪ µ", + "è½ °", + "éª Ħ", + "å§ ¨", + "æĭ ĺ", + "çĮ ´", + "è® ¶", + "è¿Ļ 座", + "çį ¨", + "æ·ĺ æ±°", + "çĹħ ä¾ĭ", + "æ²Ļ åıij", + "è§Ĩ 为", + "头 æĿ¡", + "å¿ħè¦ģ çļĦ", + "åı¯ è°ĵ", + "è¯Ŀ 说", + "ç¯ Ħ", + "æĹ© çĤ¹", + "æŀ¢ 纽", + "ç¾ ¡", + "çĪ± åĽ½", + "çªģ åıij", + "éĢ Ĭ", + "æ½ į", + "èį£ èĢĢ", + "èŁ ¹", + "æ¦Ĥ çİĩ", + "å¾Ī ä¹ħ", + "æĥ ķ", + "è¨ ´", + "åľĨ 满", + "çļ ±", + "åĪĨ æ³Į", + "åħħ 足", + "çľĭ æ³ķ", + "è¾ Ł", + "æĭ ¦", + "æĭ ©", + "对 åºĶ", + "为 æł¸å¿ĥ", + "èħ Ĭ", + "å¤ļ ä¹Ī", + "æµ ij", + "å®ı è§Ĥ", + "èĦ ĸ", + "åIJĪ èµĦ", + "çĶŁ 涯", + "å®ŀ è´¨", + "ä¼ĺ çĤ¹", + "çĶ¨ æ°´", + "寿 åij½", + "æ² «", + "åIJ ģ", + "è© ¹", + "åĽ½ éĺ²", + "å´ ©", + "åĿ İ", + "èĨ ı", + "ä¸Ģ è½®", + "éģĹ äº§", + "æ¹¾ åĮº", + "ç» İ", + "åįķ 纯", + "æ¾ Ħ", + "åīį åĪĹ", + "身 å½±", + "é»ĺ é»ĺ", + "æį ī", + "çĴ °", + "èı Ĭ", + "æĢ ľ", + "åħĭ æĢĿ", + "æĢ» å±Ģ", + "çĩĥ æĸĻ", + "ä¸ļ æĢģ", + "åIJĦ æł·", + "åĴ ½", + "åĩº èī²", + "åĪĿ å¿ĥ", + "åı Ľ", + "çłĶ 讨", + "è¡ «", + "åİĨ ç¨ĭ", + "ç¦ ½", + "è¶³å¤Ł çļĦ", + "èį Ĩ", + "çľĭ å¾ħ", + "è´ ©", + "åĨ³ å¿ĥ", + "è£ ¹", + "å¸Ī èĮĥ", + "åŀ Ħ", + "æĿ ł", + "åĩ ¸", + "çĬ¹ 豫", + "çĥŃ è¡Ģ", + "åIJĪ ä¼Ļ", + "éħ µ", + "èIJ½ åľ¨", + "åįł åľ°", + "è¡ ¬", + "èĵ ī", + "æĦ ¤", + "æ¸ Ĭ", + "åĪĨ æķ°", + "ç¬ij çĿĢ", + "太 å¹³", + "çĤ «", + "æݨ ä»ĭ", + "æĸ¯ åĿ¦", + "å½¢ 容", + "æĵ Ĭ", + "æĦŁ åħ´è¶£", + "åĨĽ 人", + "åĩĮ æĻ¨", + "对 çħ§", + "åıij çĹħ", + "å· ¾", + "èĪ ī", + "æª ¢", + "ç¬ij äºĨ", + "ç¡® è¯Ĭ", + "è´Ł åĢº", + "壮 大", + "æĪ ļ", + "äºĴ èģĶ", + "èª ²", + "èħ ¦", + "æĹ ±", + "åıĹ æ¬¢è¿İ", + "åį ī", + "éĻ¢ 士", + "æ© ¡", + "ä¸Ģ 对", + "è¾ ±", + "æ² Ĥ", + "åı² ä¸Ĭ", + "æIJ ı", + "å´ ĸ", + "代 è°¢", + "ç£ ·", + "é¡ ĺ", + "æµ ĩ", + "常 çĶ¨", + "åį ij", + "åĩº åĽ½", + "è¯ ł", + "稳 æŃ¥", + "ç»ı 纪", + "å¤ļ å¤ļ", + "æīĢ å¾Ĺ", + "为 主é¢ĺ", + "ä¸Ģ åĪĨ", + "æł ½", + "é¡ §", + "çº ²", + "åĥ ħ", + "å£ ĵ", + "åĦ ª", + "ç¿ °", + "æİ Ģ", + "人 为", + "åª ³", + "æ´ ½", + "èĿ ¶", + "å¤į åħ´", + "ä¼ļ å½±åĵį", + "åIJĦ çķĮ", + "éĤ£ ä¸Ģ", + "é¢ ¤", + "çĢ ı", + "çĢı 覽", + "å¯ ŀ", + "åı¯ æĢķ", + "åį³ æŶ", + "çķ ´", + "ä¸ĭ åįĬå¹´", + "ç¬Ķ è®°", + "éĻĦ åĬł", + "çĥŃ æ°´", + "å¥ ¸", + "ç£ ħ", + "æĿ ī", + "æ¸ħ åįİ", + "éĸ ±", + "ç° ¡", + "å¤Ħ å¤Ħ", + "åIJĪ éĩij", + "æ²³ æµģ", + "ç´ °", + "è´Ł éĿ¢", + "çļĦ 羣å®ŀ", + "åĻ¨ 械", + "èĴ IJ", + "西 äºļ", + "å· ħ", + "ç² ¹", + "åİŁ æĸĩ", + "æŀ ķ", + "è¡Ģ åİĭ", + "åļ ´", + "å¸ ĺ", + "åĨ Ģ", + "æĮ «", + "çĶµ è·¯", + "å°ı ä¼Ļä¼´", + "èĿ ´", + "æľĢ å¿«", + "æĭ Į", + "å® ª", + "æĸ ·", + "ç¿ ħ", + "åĴ ³", + "åĹ ½", + "ç¾ ŀ", + "躺 åľ¨", + "èµĽ 车", + "æ² IJ", + "éĻIJ 度", + "为 ä¸Ģä½ĵ", + "èĴ ľ", + "å¹ «", + "æIJ ħ", + "åĭ ĭ", + "åī ĸ", + "纳 ç¨İ", + "éķ¿ æķĪ", + "ç½ ķ", + "åī¯ æľ¬", + "ç© į", + "éĴ ©", + "ç¹ ¼", + "åĽ½ åľŁ", + "è¼ ī", + "ä¸į å¿ĺ", + "èѦ 示", + "çģ ¿", + "å¿ĥ å¾Ĺ", + "æĦ ļ", + "忽 çķ¥", + "åĽŀ äºĭ", + "åįł æľī", + "æ· Ħ", + "çī ¡", + "çĽij äºĭ", + "ç¿ ¡", + "éĴĪ对 æĢ§", + "çª ĥ", + "è£ ½", + "èĨ Ŀ", + "ç³ Ł", + "港 æ¾³", + "太 太", + "æ¾ ¡", + "ç»Ĩ åĮĸ", + "åĶ® åIJİ", + "å®ŀåľ¨ æĺ¯", + "ç« £", + "çį ²", + "åĢ¾ åIJij", + "å¼ķ çĶ¨", + "é¹ ħ", + "ç¬ij 容", + "ä¹IJ 趣", + "æ°ij æĶ¿", + "éŨ æĪ·", + "å± ģ", + "è¿· 失", + "éĶ Į", + "å°ı 康", + "åĭ ī", + "æ³ ¼", + "ä¾ĭ åŃIJ", + "ä¸ī ä½į", + "å» ł", + "èĶ ĵ", + "广 éĺĶ", + "èĢ į", + "èĢģ èĻİ", + "åĭŁ éĽĨ", + "èĦļ æŃ¥", + "æĭ ¯", + "åŃĹ åı·", + "çĦ °", + "é¢ ł", + "èļ Ĥ", + "èļ ģ", + "é£ ¯", + "人 æĢ§", + "æĴ °", + "åİ ¢", + "å±Ģ éĻIJ", + "æľª æĪIJ", + "åĵª åĦ¿", + "大 åıij", + "ä¸į å®ļ", + "å¾ģ æ±Ĥ", + "éĥ µ", + "åĢº æĿĥ", + "çĪ± ä½ł", + "èº ģ", + "ä»ħ ä¾Ľ", + "è¿ľ å¤Ħ", + "éĨ Ľ", + "åĥ µ", + "积æŀģ æĢ§", + "æİ ¡", + "åīį ä¸ī", + "äºİ ä¸Ģä½ĵ", + "çŀ Ħ", + "çĿ ģ", + "æ² ¸", + "åħ± èµ¢", + "éĢĢ å½¹", + "è´Ŀ å°Ķ", + "æİ ı", + "æĪ ²", + "è¡ į", + "éĶ Ĥ", + "ä¸ĩ ä½Ļ", + "ç§ij åĪĽ", + "æ¼Ķ åĶ±", + "欧 åħĥ", + "æ·¡ æ·¡", + "éĿĴ å±±", + "èĹ Ŀ", + "ç» ½", + "令 çīĮ", + "éĽĨ 群", + "ä½ľ çī©", + "çĢ ij", + "å¤ ¯", + "ç½ij 游", + "åħ« 大", + "éª ļ", + "èª ĵ", + "ä¼ļ å±ķ", + "åħļ åı²", + "æ£Ģå¯Ł éĻ¢", + "åĸ ĺ", + "éĺ ±", + "èĢĮ åĩº", + "éĢļ 车", + "éĴ ĵ", + "æĥħ 人", + "æ¸ Ľ", + "ä¸Ń ç§ĭ", + "çĪ Ń", + "åıª åī©", + "æĺ Ķ", + "éĩİ çĶŁ", + "ç¡ «", + "èIJĿ åįľ", + "æĬµ æĬĹ", + "çĻ« çĹ«", + "éĻ Ģ", + "èĶ ļ", + "å¸ ľ", + "满 满", + "èı ±", + "éļĨ éĩį", + "æĺŁ çº§", + "æ½ ĩ", + "åħ¬ åħĥ", + "è° £", + "æ¯Ķ äºļ", + "æ¡Į åŃIJ", + "èµ £", + "è² ¼", + "æĦ¿ æľĽ", + "é¡ ½", + "æ´¾ éģ£", + "ç¥ Ľ", + "åª ļ", + "éĺ ľ", + "èij «", + "èĬ ¦", + "æ³ »", + "å¡ Į", + "çĭ Ń", + "å»ī æĶ¿", + "å¥ij æľº", + "æĹĹ èĪ°", + "æĥ «", + "严 åİī", + "åıĭ æĥħ", + "å¦ Ĭ", + "å¨ ł", + "åĵª 家", + "èĨ ¨", + "è¶ Ł", + "æĮ ª", + "èĻ IJ", + "é łģ", + "çŀ ©", + "éº Ł", + "ç¨ £", + "èģĶ éĢļ", + "åı ®", + "çİĭ èĢħ", + "ä¸į ç¡®å®ļ", + "ç ijľ", + "è° İ", + "çī¢ è®°", + "ç¢ ¼", + "æĬ¤ èĤ¤", + "é¡ ·", + "çĦ ķ", + "åģļ 强", + "éļ± ç§ģ", + "éļ±ç§ģ æ¬Ĭ", + "åıĹ å®³", + "ä¸į çĶ±", + "çĥ ¹", + "é¥ ª", + "é© ³", + "ä¼ ½", + "ä¸Ŀ 绸", + "è¥ Ħ", + "åįģ ä½Ļ", + "éº Ĺ", + "æ¬Ĭ åĪ©", + "èģ ŀ", + "åı¤ èĢģ", + "éģ ı", + "åIJĦ å¼ı", + "å°± è¡Į", + "åħ¥ å¢ĥ", + "ç ĥģ", + "èľ ĺ", + "èĽ Ľ", + "çº ¬", + "çŁ «", + "è» Ł", + "æ´Ĺ è¡£", + "æĦ §", + "é¢Ħ æ¡Ī", + "éľ Ĩ", + "æ·± åİļ", + "éĺ¿ æĭī", + "åĨĻ åŃĹ", + "åį ¦", + "éķ Ģ", + "模 æł·", + "åĤ į", + "æIJ į", + "èĸ ¯", + "åł ħ", + "åħ¬ 积", + "è¨ İ", + "ä¼ł æŁĵ", + "æ¯ ¯", + "çIJĨ å·¥", + "åĨ· éĵ¾", + "ç«ĭ æĸ¹", + "æ¢ Ń", + "åľ£ è¯ŀ", + "综 èīº", + "çİ© ç¬ij", + "æĥ³ ä¸įåĪ°", + "æijĩ 头", + "æ· ¹", + "åģĩ æĹ¥", + "åĢ ĺ", + "èĢ ½", + "èİ ĵ", + "åŁ ·", + "èĩª è´¸", + "åįĬ 天", + "æª Ķ", + "æ¾İ æ¹ĥ", + "éķ ij", + "ä¸ «", + "éĩĮ ç¨ĭ", + "å¼Ģ èįĴ", + "èı ı", + "å®Ŀ è´µ", + "èŃ ¬", + "åķ Ł", + "æŁ ł", + "æª ¬", + "é© Ń", + "æ± Ľ", + "çĨĬ çĮ«", + "èķ ī", + "éļı ä¹ĭ", + "å± ij", + "è¾ĥ 强", + "èĥ ³", + "èĨ Ĭ", + "éĿĻ éĿĻ", + "åĴ ª", + "æĭĽ åij¼", + "代 è¨Ģ", + "ä¿¡ ç®±", + "è£ħ éħį", + "æĤ į", + "åįķ 车", + "èIJ İ", + "å¤ļ 彩", + "éĻ ¸", + "ä»İ 严", + "æ© Ħ", + "æ¦ Ħ", + "éĢ ®", + "éĩĮ æĸ¯", + "姿 æĢģ", + "太 æŀģ", + "éĩ Ŀ", + "æº ī", + "è¿ Ń", + "ç§ ¸", + "ç§ Ĩ", + "å·¥ å§Ķ", + "æ± ķ", + "èģ Ĩ", + "ä½ ¬", + "ç¼ ħ", + "çĶ ¸", + "åī¯ å±Ģéķ¿", + "éĹ º", + "èª ¤", + "è¤ IJ", + "ä¸į éĻIJ", + "èħ ķ", + "åij ķ", + "çŁ ¶", + "åĨľ 家", + "管 å§Ķä¼ļ", + "é¥ º", + "èĬ ľ", + "æ¾ Ī", + "è© ¢", + "å¨ģ å°¼æĸ¯", + "ä½ķ åĨµ", + "å°ı ä¼Ļ", + "奢 ä¾Ī", + "è¿Ļ ç¯ĩ", + "è¯ µ", + "ç«ł ç¨ĭ", + "ç´ Ģ", + "éIJ ĺ", + "éĤ ¢", + "ç³ Ļ", + "ç¼ Ģ", + "ä¹ Ĵ", + "ä¹ ĵ", + "çī¢ åĽº", + "åĿ ŀ", + "å¼ Ī", + "ä¾ĭ å¤ĸ", + "å» ³", + "è§Ħ ç«ł", + "èĬ Ļ", + "ç¯ ·", + "èº ¯", + "æł Ī", + "åĿļ å®ŀ", + "åŁº 建", + "çĿĢ çľ¼", + "ç· ´", + "èij ©", + "ç¼ ļ", + "æ¦ Ĩ", + "主 åĭķ", + "ç¥ Ģ", + "äºĴ éĢļ", + "å°¤ 为", + "å® Ľ", + "éª ¼", + "æ± ²", + "ä¾ ĥ", + "æĤł ä¹ħ", + "æij §", + "æĭ ĩ", + "é« ĵ", + "éº Ĵ", + "éĻ Ľ", + "æŀ ¸", + "æĿ ŀ", + "è´ ¬", + "å°ı é¾Ļ", + "åĵ ®", + "èĵ¬ åĭĥ", + "åĮ Ī", + "çķľ çī§", + "å¨ ©", + "个 å¤ļ", + "æ² ¥", + "æĺ §", + "çĦ ļ", + "æĬij éĥģ", + "çĸ ¡", + "èĺ ij", + "éģİ ç¨ĭ", + "æ© ±", + "éĿ ĵ", + "大 çIJĨ", + "é« ¦", + "åĪĨ 辨", + "æ¸ ¤", + "çĸ ¤", + "åĬ¨ èĥ½", + "å¼ł 家", + "ä¸ĩ åįĥ", + "æ» ¥", + "é¥ ¥", + "åºŁ å¼ĥ", + "å¸ ³", + "æ¼ ³", + "è± IJ", + "ä» ij", + "å« ī", + "å¦ Ĵ", + "çŀ Ĵ", + "è¡ ħ", + "çĭ ¸", + "å¾ģ ç¨ĭ", + "éĤ ¯", + "éĥ ¸", + "ç¥ Ī", + "ç¥ ·", + "è¶ ´", + "ç»ĵæŀĦ æĢ§", + "è§Ĩ åIJ¬", + "è¬ Ŀ", + "çĴ Ģ", + "çĴ ¨", + "åĩº å¤Ħ", + "è¯ Ģ", + "å¾ ĺ", + "å¾ Ĭ", + "çľ ¨", + "åĸ ĩ", + "åı Ń", + "åĺ ²", + "çķ ¸", + "å¹² äºĭ", + "æļ §", + "æ² Ľ", + "åĦ Ħ", + "å» ĵ", + "åİ¿ éķ¿", + "èĥ ļ", + "çIJ ¢", + "çŃ ·", + "éĩ ĭ", + "ä¾ ®", + "åIJ ©", + "åĴ IJ", + "åĮ ¿", + "æĬ¬ èµ·", + "æ³ £", + "æ¶ ¤", + "éº ½", + "æĽ Ļ", + "åī¯ éĻ¢éķ¿", + "åħļ åĴĮ", + "æķ£ åıij", + "润 æ»ij", + "åĵ º", + "æĥ ¬", + "漫 éķ¿", + "ä¸į æĩĪ", + "åŁ ł", + "åĹ ĵ", + "èĢģ çĪ·", + "è® ½", + "æĪĺ ç»ĦåIJĪ", + "æ£ ł", + "åħ¨ åŁŁ", + "èł ¢", + "è¯ ¡", + "åīį çŀ»", + "æķ Ľ", + "ä¸Ģ å°ģ", + "å¹ Ĥ", + "èİ Ĩ", + "è¯Ŀ è¯Ń", + "ç»Ĩ åĪĻ", + "å± ¿", + "åµ Į", + "éĢ į", + "åĺ ±", + "æ¸ ²", + "çĥ ¯", + "çĿ ¹", + "é¦ Ĵ", + "èħ ¥", + "æĬĹ åĩ»", + "çĿ «", + "èį Ķ", + "éļ İ", + "æ³ī æ°´", + "è¬ Ĥ", + "ç Ĥ¬", + "åĩı æİĴ", + "è¸ Ĭ", + "è ·»", + "æ· Į", + "éľ ¾", + "å¥ĩ 纳", + "å¯ Ŀ", + "æ¤ İ", + "æŁ ¬", + "æĸ¯ åŁº", + "åħ¬ ç«ĭ", + "è¨ ĵ", + "é£ Ļ", + "é© ¿", + "åĤ µ", + "èĽ Ļ", + "ç¯ĩ ç«ł", + "åĪĨ æĶ¯", + "ä¸Ĭ å¹´", + "çŃ Ŀ", + "ç¼ ¤", + "èĢģ æŧ", + "åĻ ¬", + "æľ ¦", + "èĥ §", + "æ¶Ī è²»", + "æĵ Ķ", + "æ¦ ´", + "æ¿ Ĵ", + "ç³ ¯", + "æ³ ¸", + "æį Ĩ", + "ç» ļ", + "èµ İ", + "çIJ IJ", + "èµ Ĥ", + "æħ ®", + "æ² Į", + "çĦ Ļ", + "æĴŃ æĬ¥", + "æ· ĩ", + "åĪĩ åħ¥", + "çij ķ", + "çĸ µ", + "éģ ´", + "ç¨ ļ", + "ç© ©", + "èŀ ĥ", + "æ£ ķ", + "æĨ §", + "æĨ ¬", + "ä¼ º", + "æ¯ Ĺ", + "æį į", + "æĬ ī", + "ç´ Ĭ", + "å¼ Ľ", + "æĭ Ń", + "æĹı èĩªæ²»", + "åĿ ·", + "ç« ¶", + "è© ³", + "è¿Ħ ä»Ĭ", + "è° ´", + "çŀŃ è§£", + "æŁ ¿", + "é¢ Ĭ", + "ç° §", + "çĥŁ èĬ±", + "ä¾ ¥", + "çĿ ¦", + "éħ Ŀ", + "æ° ĵ", + "çIJ ī", + "å§ Ĭ", + "æ² ®", + "æħ ·", + "èľ ķ", + "çij ļ", + "éĩĩ çŁ¿", + "åł °", + "åºķ èķ´", + "èĨ ³", + "è¾ ķ", + "éŁ Ń", + "åĴ Ļ", + "ç² ½", + "åī Ķ", + "æ² ¦", + "èĤ ´", + "éķ ¶", + "æĺ ¼", + "è¾ Ĺ", + "å© ª", + "åĮ ®", + "æĸ ĵ", + "æ± ¶", + "éĥ ´", + "éł »", + "çª Ĵ", + "è¢ ±", + "åĽ ±", + "èĢ ĺ", + "è ļĮ", + "çĭ Ļ", + "çĹ ¹", + "ç¥ ī", + "æı ®", + "æ· Ĩ", + "ç£ ĭ", + "éĺ ª", + "æ «", + "ã ¸", + "Ļ ¶", + "ã ij", + "𣠲", + "ä ¢", + "ã Ń", + "𬠨", + "ð¬ Ģ", + "𬠮", + "𬠯", + "ð¬ ľ", + "𪠨", + "ð« Ĺ", + "ð¬ Ĭ", + "𬠱", + "ð¬ Ł", + "ä İ", + "ð ¡", + "ä ĥ", + "ã ł", + "ð ©", + "ð© ¾", + "𬠺", + "ð¬ Ļ", + "ãĢ Ķ", + "ãĢ ķ", + "çļĦ æŶåĢĻ", + "æľīéĻIJ åħ¬åı¸", + "ä¹ĭ åIJİ", + "ä¸ļ åĬ¡", + "åķ Ĭ", + "èĻ½ çĦ¶", + "æĭ¥ æľī", + "äºĴ èģĶç½ij", + "éĤ£ äºĽ", + "ä½ł çļĦ", + "åĨ³ å®ļ", + "éĻ¤ äºĨ", + "åĽ¢ éĺŁ", + "åı¯ æĺ¯", + "以 åIJİ", + "社 åĮº", + "çļĦ éĹ®é¢ĺ", + "并 ä¸Ķ", + "æķĻ å¸Ī", + "å°± ä¼ļ", + "天空 éĥ¨èIJ½", + "æľĢ ç»Ī", + "å½ĵ çĦ¶", + "ä¹Ł æľī", + "ç¡® ä¿Ŀ", + "æĥ³ è¦ģ", + "è´Ń ä¹°", + "人 çļĦ", + "åIJ ´", + "çļĦ åıijå±ķ", + "ä¸į çŁ¥éģĵ", + "软 件", + "æĪij们 çļĦ", + "çĪ¶ æ¯į", + "åī ij", + "èĢĮ æĺ¯", + "å®ī æİĴ", + "åIJİ æĿ¥", + "çļĦ åľ°æĸ¹", + "èµ µ", + "èĢĥ è¯ķ", + "çªģ çĦ¶", + "ä¸Ģå®ļ è¦ģ", + "åĪ¶ ä½ľ", + "è¯Ħ ä»·", + "åħį è´¹", + "è´¹ çĶ¨", + "绣 ä¸Ģ", + "çĦ¶ èĢĮ", + "è¿Ļ 次", + "éĿĴ å¹´", + "人 ç±»", + "äº ¦", + "让 人", + "è´Łè´£ 人", + "éĩĩ åıĸ", + "çļĦ äºĭæĥħ", + "ä¹Ł ä¼ļ", + "车 è¾Ĩ", + "æĽ´ æĺ¯", + "强 åĮĸ", + "æĪij åĢij", + "以 åīį", + "ä¼ĺ åĮĸ", + "å§Ķåijĺ ä¼ļ", + "åĽ° éļ¾", + "å¹´ 度", + "ä½į äºİ", + "æĮĩ åĩº", + "åĨį 次", + "åĬŀ çIJĨ", + "æ¯ı 个", + "对 æĸ¹", + "è¿Ľè¡Į äºĨ", + "æľĢ é«ĺ", + "课 ç¨ĭ", + "身 ä¸Ĭ", + "æĽ¾ ç»ı", + "åĮ» çĶŁ", + "å®ī è£ħ", + "æľ ±", + "è¿IJ è¡Į", + "åıĮ æĸ¹", + "æľĢ 大çļĦ", + "æŀĦ 建", + "è¿ŀ ç»Ń", + "çļĦ å°ı", + "她 çļĦ", + "çŃī çŃī", + "æĶ¹ åĸĦ", + "åIJĦ ç±»", + "éģĩ åĪ°", + "æľī çĿĢ", + "人 çī©", + "æĢ» æĺ¯", + "è¿ħ éĢŁ", + "åĪ¶ å®ļ", + "å®ĥ 们", + "å®ĺ ç½ij", + "è¿ĺ è¦ģ", + "ç»Ī äºİ", + "æĪ¿ åľ°äº§", + "è¯ģ æĺİ", + "èĤ¡ 票", + "åºĶ å½ĵ", + "èĭ± åĽ½", + "è¿IJ çĶ¨", + "æľĢ æĸ°", + "享 åıĹ", + "让 æĪij", + "æĻļ ä¸Ĭ", + "å¾ ŀ", + "å°ı 说", + "å°¤åħ¶ æĺ¯", + "è®Ń ç»ĥ", + "åħ¨ å¸Ĥ", + "æĮij æĪĺ", + "æľī çĤ¹", + "带 çĿĢ", + "çļĦ ä¸ľè¥¿", + "é£İ æł¼", + "é»Ħ éĩij", + "å¼ķ 导", + "æѤ å¤ĸ", + "æľĢ è¿ij", + "追 æ±Ĥ", + "强 è°ĥ", + "ä¹Ł åı¯ä»¥", + "æĦŁ åĪ°", + "èĩª æĪij", + "çī¹åĪ« æĺ¯", + "æĪIJ éĥ½", + "éĢIJ æ¸IJ", + "å¿« ä¹IJ", + "ä¹ĭ ä¸Ń", + "æĬķèµĦ èĢħ", + "ä»ĸ们 çļĦ", + "æ° ı", + "å·¥ä½ľ 人åijĺ", + "äºĨ ä¸Ģ个", + "åķ ¦", + "ä¸Ģ åĢĭ", + "åŁº å±Ĥ", + "æ²Ł éĢļ", + "第ä¸Ģ 次", + "并 没æľī", + "çļĦ å·¥ä½ľ", + "åľ¨ è¿ĻéĩĮ", + "æŀ ª", + "æĶ¯ æĴij", + "æŶ å°ļ", + "æĿ¥ åĪ°", + "æĶ¶ è´Ń", + "éĿ© åij½", + "æĺ¯ ä¸įæĺ¯", + "讨 论", + "ä¸ļ 绩", + "å°± èĥ½", + "ç«ĭ åį³", + "è¡Ĺ éģĵ", + "åľ¨ ä¸Ģèµ·", + "æľĪ 份", + "é«ĺ 端", + "å¾Ī éļ¾", + "ä¿Ħ ç½Ĺæĸ¯", + "æīĭ 段", + "åģļ åĩº", + "ä¼Ĺ å¤ļ", + "å®ŀ è¡Į", + "æīĵ å¼Ģ", + "游 客", + "ä¾Ŀ çĦ¶", + "å°± åĥı", + "离 å¼Ģ", + "说 éģĵ", + "æĸ° èĥ½æºIJ", + "æº ª", + "äº ķ", + "令 人", + "ä¸Ģ åľº", + "æĪij æĥ³", + "两 人", + "èĩ³ å°ij", + "çļĦ çĶŁæ´»", + "æĺ¯ ä¸ª", + "èĭ± è¯Ń", + "æ²Ĵ æľī", + "æĢĿ èĢĥ", + "éĻIJ åĪ¶", + "åı° æ¹¾", + "ä¸Ģ æŦ", + "çļĦ ä¸Ģ个", + "é«ĺ 级", + "åĬŀåħ¬ 室", + "å¾· åĽ½", + "æĪij å°±", + "å®ļ ä½į", + "éĢĤ åºĶ", + "æĮĩ æłĩ", + "åħ¨ çľģ", + "ä¸Ĭ è¿°", + "å®ĥ çļĦ", + "åĽŀ 家", + "欧 æ´²", + "éĵģ è·¯", + "é¼ĵ åĬ±", + "çļĦ å½±åĵį", + "é«ĺ æł¡", + "天 ä¸ĭ", + "é«ĺ è´¨éĩı", + "æĿŃ å·ŀ", + "èµĦ 讯", + "æĶ¾ åľ¨", + "æľī ä¸Ģ个", + "å°± è¦ģ", + "ä¸Ĭ éĿ¢", + "解 éĩĬ", + "éĢIJ æŃ¥", + "å°½ 管", + "æľī ä»Ģä¹Ī", + "çļĦ äºĭ", + "çĻ» è®°", + "人æ°ij å¸ģ", + "è§Ĥ ä¼Ĺ", + "è§Ĥ å¯Ł", + "çĶµ èĦij", + "çļĦ åIJĮæŶ", + "ä½ľ ä¸ļ", + "宣 å¸ĥ", + "çļĦ ä½ľçĶ¨", + "åĽŀ æĿ¥", + "éļ¾ 以", + "æīĢæľī çļĦ", + "å°ı åѦ", + "æıIJ åīį", + "æ¤į çī©", + "åĩ ¯", + "ä¸Ĭ äºĨ", + "å°± åľ¨", + "åħĪ åIJİ", + "æīĭ æľ¯", + "éĥ Ń", + "éĿ¢ åīį", + "æ¯ķ ç«Ł", + "äºĮ æĺ¯", + "红 èī²", + "éĺ³ åħī", + "èĭ¹ æŀľ", + "å¾Īå¤ļ 人", + "ç»Ļ æĪij", + "åĵ ¦", + "çľ¼ çĿĽ", + "éł Ń", + "ä¸Ģ æĺ¯", + "åıijå±ķ çļĦ", + "åıį åºĶ", + "æĪ¿ å±ĭ", + "æľŁ å¾ħ", + "ç§į æ¤į", + "æĸĩ åѦ", + "åį³ åı¯", + "é¦ĸ 次", + "èĭ± éĽĦ", + "å¤ļ 次", + "åĮħ è£ħ", + "æ²³ åįĹ", + "ä¹ĭéĹ´ çļĦ", + "ä»į çĦ¶", + "åIJ¬ åĪ°", + "èij£äºĭ éķ¿", + "è§Ħ åĪĻ", + "ä¸Ģ 份", + "大 ä¼Ĺ", + "使 å¾Ĺ", + "è¿Ľ åı£", + "ä¸Ģ çīĩ", + "æĢ§ çļĦ", + "çļĦ 大", + "æĪij æĺ¯", + "äºĴ åĬ¨", + "æ° £", + "çļ Ĩ", + "åħ¬åı¸ çļĦ", + "ä¸Ģ è¾¹", + "åıĬ åħ¶", + "èī¯ 好çļĦ", + "æĭĵ å±ķ", + "å½ĵ å¹´", + "广 åľº", + "åģļ äºĨ", + "åŁº äºİ", + "æıIJ éĨĴ", + "åħĦ å¼Ł", + "èĢģ æĿ¿", + "è¿ij æĹ¥", + "çĬ¶ åĨµ", + "注 éĩį", + "åĪļ åĪļ", + "è°ĥ çłĶ", + "å¿ĥ ä¸Ń", + "æĬĬ æı¡", + "éļı åIJİ", + "ä¸į å¤Ł", + "åĪĽ ä½ľ", + "ç«Ļ åľ¨", + "缸 äºĴ", + "çĸ«æĥħ éĺ²æݧ", + "å¹´ 代", + "带 åĬ¨", + "伤 害", + "ç«Ł çĦ¶", + "å¼ķ è¿Ľ", + "ç´¯ 计", + "让 æĪij们", + "åĽŀ æĶ¶", + "æĬ¥ åIJį", + "åĬ© åĬĽ", + "èģĶ 缣", + "çŃĸ çķ¥", + "åij¨ è¾¹", + "åĭ Ĵ", + "è¿ĺ åľ¨", + "æµģ éĩı", + "寻 æī¾", + "çĶµ åĬĽ", + "èĪ¹ èĪ¶", + "è¿ĺ èĥ½", + "æĭħ ä»»", + "çļĦæĥħåĨµ ä¸ĭ", + "çļĦ åİŁåĽł", + "缺 ä¹ı", + "çIJĥ åijĺ", + "å²ģ çļĦ", + "çĶ· åŃIJ", + "å·¥ èµĦ", + "è¿ijå¹´ æĿ¥", + "åij Ģ", + "æıIJä¾Ľ äºĨ", + "她 们", + "家 åħ·", + "çĩ ķ", + "è½» æĿ¾", + "æł¡ åĽŃ", + "èĢĥ æł¸", + "åį± éĻ©", + "åħļ ç»Ħç»ĩ", + "æĢ» ç»ıçIJĨ", + "çļĦ æĸ°", + "çİ» çĴĥ", + "è¿Ļ ä½į", + "对 æѤ", + "家 人", + "çļĦ è¦ģæ±Ĥ", + "温 度", + "æĮĩ æķ°", + "缴 åĪ°", + "æѤ æŶ", + "æ¹ĸ åįĹ", + "éĥ½ è¦ģ", + "ä½ľ åĩº", + "åIJĦ ä½į", + "èĢĥ çĶŁ", + "ä¾Ŀ æį®", + "说 è¯Ŀ", + "æĪij ä¹Ł", + "å·¥ åİĤ", + "åıĺ æĪIJ", + "ä»ĸ 人", + "æĪij è§īå¾Ĺ", + "åIJĦ 级", + "ä¼łå¥ĩ ç§ģæľį", + "ä¸Ĭ åįĩ", + "好 åĥı", + "åĬł éĢŁ", + "äºĮ åįģ", + "è¢ ģ", + "è£ħ 饰", + "éĥ½ èĥ½", + "ä¸Ģ å¼ł", + "åĬ¨ æĢģ", + "å¹´ çļĦ", + "è¿Ļ å°±æĺ¯", + "ä¹Ł è¦ģ", + "èµĦ æł¼", + "æĪĺ äºī", + "æĦŁ è°¢", + "åŁ¹ èĤ²", + "天 æ°Ķ", + "女 士", + "åı¯èĥ½ ä¼ļ", + "çļĦ 产åĵģ", + "ä¹Ł å°±", + "主è¦ģ æĺ¯", + "åĪº æ¿Ģ", + "ç»Ļ ä½ł", + "大 æķ°æį®", + "åĮ» åѦ", + "åĪ ¤æĸŃ", + "ä»ĸ 说", + "表 æ¼Ķ", + "äºļ æ´²", + "ä¸ĵ é¢ĺ", + "ç«ŀäºī åĬĽ", + "éĤ£ æł·", + "å±ķ å¼Ģ", + "å¹³ æŶ", + "æİ¥ ä¸ĭæĿ¥", + "æī¿ 诺", + "æ³ķ åĽ½", + "åħ³ å¿ĥ", + "ä¼ļ æľī", + "éĤĢ 请", + "é¢Ħ éĺ²", + "对 æİ¥", + "好 äºĨ", + "åĴ± 们", + "çļĦ æĦŁè§ī", + "æĢĿ è·¯", + "éĥ½ 没æľī", + "çļĦ æĸ¹æ³ķ", + "女 åŃIJ", + "åı¸ æ³ķ", + "è¿ĺ ä¼ļ", + "è¶ĬæĿ¥è¶Ĭ å¤ļ", + "åĽł çĤº", + "æµ· åįĹ", + "人 æķ°", + "å°Ĩ ä¼ļ", + "ä¸ļ 主", + "é¤IJ 饮", + "å±ħ ä½ı", + "åıij åĩº", + "è¿ij æľŁ", + "å¼ķ é¢Ĩ", + "æľºåĻ¨ 人", + "åĩºæĿ¥ çļĦ", + "çľĭ è§ģ", + "ä¿ Ĭ", + "让 ä»ĸ", + "ä¸į æĥ³", + "å·¥ä½ľ çļĦ", + "è¡¥ åħħ", + "æµ ħ", + "çī¹ å¾ģ", + "ä¸Ĭå¸Ĥ åħ¬åı¸", + "ç¾İ é£Ł", + "广 西", + "æ¯ı ä¸Ģ个", + "èIJ½ åľ°", + "åĵģ ç§į", + "åĴĮ è°IJ", + "å½» åºķ", + "é«ĺ èĢĥ", + "æĺ¨ å¤©", + "åīį å¾Ģ", + "çĽij æµĭ", + "çĻ¾ 度", + "åľ¨ ä¸ŃåĽ½", + "çļĦ éľĢæ±Ĥ", + "亿 ç¾İåħĥ", + "åѦ æľ¯", + "æĶ¶ åĪ°", + "æĿ¿ åĿĹ", + "ä¸Ģ 段", + "æŀĦ æĪIJ", + "ä¼ģä¸ļ çļĦ", + "表 éĿ¢", + "æķ´ çIJĨ", + "ç»ĵ å©ļ", + "人 家", + "åģľ æŃ¢", + "åѦ ç§ij", + "æĺ¾ å¾Ĺ", + "ä¼ij æģ¯", + "é¢Ħ æľŁ", + "æĪĸ æĺ¯", + "çļĦ 主è¦ģ", + "åºĶ 对", + "èµ° äºĨ", + "ä¸Ń éĹ´", + "èµ° è¿Ľ", + "åijĪ çİ°", + "æIJŃ éħį", + "é¹ ı", + "æĺ¯ åĽłä¸º", + "æĥħ 绪", + "å®ļ æľŁ", + "社ä¼ļ 主ä¹ī", + "çŃī 级", + "磼 çĽ¾", + "é£ŀ æľº", + "èĩ³ ä»Ĭ", + "æĶ¶ éĽĨ", + "çļĦ æķħäºĭ", + "åĪĩ å®ŀ", + "å®ŀçİ° äºĨ", + "å½¢ æĪIJäºĨ", + "åįĹ æĸ¹", + "ä¸Ń åѦ", + "æµ· æ´ĭ", + "åIJ¦ åĪĻ", + "æĭį æijĦ", + "大åѦ çĶŁ", + "åĩºçİ° äºĨ", + "æĦı å¤ĸ", + "ä¹Ł èĥ½", + "çļĦ èĥ½åĬĽ", + "åĿIJ åľ¨", + "åĪĻ æĺ¯", + "èĢĥ å¯Ł", + "å°Ĭ éĩį", + "éĺ² æŃ¢", + "ç´§ å¼ł", + "读 书", + "åĩº è¡Į", + "å°± æľī", + "å±¥ è¡Į", + "çݰ代 åĮĸ", + "åĽ½ åĬ¡", + "åĽ½åĬ¡ éĻ¢", + "ç»´ ä¿®", + "åİŁ åĪĽ", + "æĺ¯ æĮĩ", + "ä¼ij éĹ²", + "çĤ ®", + "æĸ° æĹ¶ä»£", + "éĢĻ åĢĭ", + "ä¸į æķ¢", + "å®Į ç¾İ", + "ç»Ĩ èĬĤ", + "éŃ ı", + "èĶ¬ èıľ", + "é¢Ĩ导 çıŃåŃIJ", + "è¶ħ 级", + "è¡Į æĥħ", + "人工 æĻºèĥ½", + "åį° 度", + "åŁºç¡Ģ 设æĸ½", + "åıĪ æĺ¯", + "èį¯ çī©", + "åIJ¸ æĶ¶", + "åį´ æĺ¯", + "éĥ İ", + "å¥ĸ åĬ±", + "çļĦ æľĭåıĭ", + "ä¿Ŀ çķĻ", + "è§Ħ å¾ĭ", + "æĸ° çĸĨ", + "è¿ĺ åı¯ä»¥", + "æİ¥ è¿ij", + "æѤ åīį", + "æī¹ åĩĨ", + "æĢİä¹Ī æł·", + "çļĦ ä½įç½®", + "ä¸Ģ åĿĹ", + "æĭĴ ç»Ŀ", + "顾 客", + "ä¹Ł åľ¨", + "ä¸Ģ çĶŁ", + "éĥ¨ éĺŁ", + "å¹´ åīį", + "æĸ¹éĿ¢ çļĦ", + "å°Ŀ è¯ķ", + "羣æŃ£ çļĦ", + "ç¦ģ æŃ¢", + "è¿ĺ 没æľī", + "æ°ij çĶŁ", + "èµ° åIJij", + "èĦ¸ ä¸Ĭ", + "å½ĵ 天", + "éĽĨåĽ¢ åħ¬åı¸", + "çļĦä¸Ģ ç§į", + "西 æĸ¹", + "åĽŀ åºĶ", + "ä¸Ģ 声", + "常 常", + "æıIJ åĪ°", + "èħ¾ 讯", + "æľį è£ħ", + "为 ä½ķ", + "äºij åįĹ", + "å°± ç®Ĺ", + "ä¼ł æī¿", + "åıį èĢĮ", + "ä¸ĩ åIJ¨", + "è´¢ 产", + "å¦Ĥ ä¸ĭ", + "æĹ¥ åīį", + "åİŁ æľ¬", + "æľĢ éĩįè¦ģçļĦ", + "认 è¯ģ", + "ä¸Ģ éģĵ", + "ä¿¡æģ¯ åĮĸ", + "å¾Ĺ åĪ°äºĨ", + "éĢ² è¡Į", + "æĪij è¦ģ", + "éĢļ ä¿¡", + "室 åĨħ", + "èµļ éĴ±", + "æĶ¶ èĹı", + "解åĨ³ æĸ¹æ¡Ī", + "æĪ¿ 产", + "çĭ ¼", + "æ´» åĬĽ", + "ç»ıæµİ åıijå±ķ", + "çŃī å¾ħ", + "ä¹Ł å¾Ī", + "åĿ ij", + "å¾Ī 好çļĦ", + "éļ¾ 度", + "ä¸į å¦Ĥ", + "人æ°ij æĶ¿åºľ", + "åĩº åıij", + "åīį æľŁ", + "æ¼Ķ åijĺ", + "女 çĶŁ", + "èģļ çĦ¦", + "审 计", + "é¢Ħ æµĭ", + "ä¾Ŀ æīĺ", + "äºĶ å¹´", + "è¡¥ è´´", + "æ¸ħ æĻ°", + "éª Ĥ", + "çľĭ èµ·æĿ¥", + "çļĦ åŃ©åŃIJ", + "é¢ij éģĵ", + "ä½ı å®ħ", + "éĿ¢ åIJij", + "æľĢ ä½İ", + "æĹ¢ çĦ¶", + "ä¸Ģ å¥Ĺ", + "æķ° åѦ", + "群 ä½ĵ", + "åĮĹ京 å¸Ĥ", + "å±ħ çĦ¶", + "æ°Ľ åĽ´", + "éĢĶ å¾Ħ", + "çļĦ åŁºç¡Ģä¸Ĭ", + "èģĮ è´£", + "åı¯èĥ½ æĺ¯", + "åĨĽ äºĭ", + "æĪIJ æķĪ", + "åŃ©åŃIJ 们", + "计ç®Ĺ æľº", + "èµ ¤", + "产ä¸ļ åıijå±ķ", + "å·¨ 大çļĦ", + "å·¥ 人", + "çĶŁ éķ¿", + "éĥ½ åı¯ä»¥", + "çļĦ æľºä¼ļ", + "èµĦ è´¨", + "çĹĽ èĭ¦", + "ç²ī ä¸Ŀ", + "å¢ ĵ", + "å¹³ å®ī", + "管 éģĵ", + "è·Ł çĿĢ", + "饮 é£Ł", + "åķĨ 家", + "å¤ļ 家", + "åı¸ æľº", + "åºĶ该 æĺ¯", + "éĢı éľ²", + "认 å®ļ", + "è¡Įä¸ļ çļĦ", + "çļĦ ä¼ģä¸ļ", + "æ¯ı ä¸Ģ", + "èĮĥåĽ´ åĨħ", + "è¾ĥ 大", + "è´ ¤", + "大 èµĽ", + "å¤ļ äºĨ", + "é¸ ¿", + "临 åºĬ", + "åľ¨ è¿Ļ个", + "çļĦ åĨħ容", + "éĶĢ éĩı", + "å¾Ī å°ij", + "åŃ Ł", + "ç»´ æĮģ", + "åĴĸ åķ¡", + "æľ¬ åľ°", + "èī² 彩", + "并 éĿŀ", + "èĢĮ å·²", + "温 æļĸ", + "èIJ §", + "æĬĵ ä½ı", + "èĢĮ ä¸įæĺ¯", + "åĸ Ĭ", + "çļĦ åħ³ç³»", + "çī© åĵģ", + "éĤ£ æĺ¯", + "åĨľ 产åĵģ", + "è¿Ļ æŶ", + "å©ļ 姻", + "æ°´ æŀľ", + "æĶ¶ èİ·", + "ä»ĺ åĩº", + "客æĪ· 端", + "æ¼Ķ åĩº", + "åħ¨ æĸ°", + "è¿Ļ ä¹Łæĺ¯", + "æĺ¯ çĶ±", + "è§Ĥ 念", + "æľī 个", + "éĢł åŀĭ", + "èĥľ åĪ©", + "ä¸ī æĺ¯", + "è¶ħ å¸Ĥ", + "åħļ建 å·¥ä½ľ", + "æĶ¾ å¿ĥ", + "线 è·¯", + "æĭĽ çĶŁ", + "åIJĥ é¥Ń", + "è½ ī", + "å°½ éĩı", + "è§ģ åĪ°", + "åIJĮæ¯Ķ å¢ŀéķ¿", + "åįİ ä¸º", + "æĪij å¸Ĥ", + "æıIJ åĩºäºĨ", + "æ°ij èѦ", + "åįļ çī©", + "åįļçī© é¦Ĩ", + "è¯ļ ä¿¡", + "åīį éĿ¢", + "å±± 西", + "è¾ħ åĬ©", + "转 移", + "æĽ´ 为", + "丰å¯Į çļĦ", + "åį ¢", + "å¿« éĢĴ", + "æĺ¾ èijĹ", + "çī© èµĦ", + "åĪ° è¾¾", + "æľī åĪ©äºİ", + "åij Ĩ", + "åŃ©åŃIJ çļĦ", + "ä¸į ä½Ĩ", + "çłĶ究 éĻ¢", + "çĶ³ æĬ¥", + "æļ ¨", + "æ°ij éĹ´", + "åį »", + "çļĦ å£°éŁ³", + "å¸Ĥåľº çļĦ", + "ä¸Ģ åı¥", + "çľģ 级", + "æĿ¥ çļĦ", + "åĵª 个", + "æīį ä¼ļ", + "åĪĨ éħį", + "èĶ ¡", + "ä»ĸ åľ¨", + "åħ± æľī", + "å¡ ĺ", + "èĴ Ĥ", + "éľ į", + "åıĤ è§Ĥ", + "ä¸Ī 夫", + "ä¾Ŀ éĿł", + "æľī æŶ", + "äºĨ å¾Īå¤ļ", + "ä¸ĸçķĮ æĿ¯", + "家 æĹı", + "ä¸į éľĢè¦ģ", + "大 å¸Ī", + "èŀį åħ¥", + "éĿŀ æ³ķ", + "çĹħ 人", + "åIJİ æľŁ", + "大家 éĥ½", + "ç½ij åĿĢ", + "åİŁ æĸĻ", + "便 å®ľ", + "æ¶ Ľ", + "仿 ä½Ľ", + "å·® è·Ŀ", + "åı¦ä¸Ģ æĸ¹éĿ¢", + "产åĵģ çļĦ", + "èµ «", + "æĥħåĨµ ä¸ĭ", + "éĴ¢ éĵģ", + "æľ¬ ç«Ļ", + "纳 åħ¥", + "å·² æľī", + "æľī 没æľī", + "ä¼° 计", + "é£ ĺ", + "æľŁ è´§", + "åĢĭ人 è³ĩæĸĻ", + "ä¸ĵä¸ļ çļĦ", + "çĪĨ åıij", + "èĩ´åĬĽ äºİ", + "çİ°åľ¨ çļĦ", + "æľī åĵªäºĽ", + "çł´ åĿı", + "æķ°åŃĹ åĮĸ", + "åľ° éĿ¢", + "é»ij èī²", + "å¹¼åĦ¿ åĽŃ", + "çļĦ ç²¾ç¥ŀ", + "äº Ń", + "导 æ¼Ķ", + "çİ° æľī", + "æѦ åĻ¨", + "èĭı å·ŀ", + "çİ Ħ", + "æ±Ł 西", + "延 伸", + "论 æĸĩ", + "è¾ĥ 为", + "çİ© æ³ķ", + "é¼ İ", + "åIJĮ æŃ¥", + "éĩĬ æĶ¾", + "æĽĿ åħī", + "åĿļ åĨ³", + "å§Ķ æīĺ", + "å°Ĩ åľ¨", + "äºĪ 以", + "ä½ľ æĸĩ", + "èĢĮ åľ¨", + "ä¼ĺ åħĪ", + "åĽŀ åİ»", + "ä¿® å¤į", + "åĽ½åĨħ å¤ĸ", + "çŃĸ åĪĴ", + "åıij æĶ¾", + "å¿ĥ æĥħ", + "çļĦ åİĨåı²", + "éĿ¢ è¯ķ", + "举 åĮĹ", + "ä¿¡ åı·", + "ç²® é£Ł", + "è¯ģ 书", + "æŁIJ äºĽ", + "è¿IJ ä½ľ", + "åĨ² åĩ»", + "çĥŃ çĤ¹", + "æŶ æŶ", + "æŶæŶ 彩", + "åľ° çĤ¹", + "ä¸Ģä½ĵ åĮĸ", + "éļ¾ é¢ĺ", + "æĽ °", + "ç«ĭ åĪ»", + "æĺ¯ éĿŀ常", + "åħ± åĴĮ", + "åħ±åĴĮ åĽ½", + "æ¿Ģ åĬ±", + "æľīæķĪ çļĦ", + "å¤Ħ ç½®", + "该 åħ¬åı¸", + "æ£Ģ éªĮ", + "èѦ æĸ¹", + "è´ ¾", + "äºĨä¸Ģ ä¸ĭ", + "ä»Ĭ åIJİ", + "çħ ®", + "çĶ¨ åĵģ", + "读 èĢħ", + "æĪij åľ¨", + "åĽŀ å¤į", + "ä¸Ģ 座", + "è¿ĺ 没", + "å®ļ åĪ¶", + "没 æĥ³åĪ°", + "å¤ ¹", + "ä¼ł éĢĴ", + "ä¸Ģ 款", + "强 大çļĦ", + "çļĦ è¡Į为", + "å¤ı 天", + "åıijåĬ¨ æľº", + "é¢ĨåŁŁ çļĦ", + "å®ŀéªĮ 室", + "ä¸Ģ æĬĬ", + "æĺ¯ ä¸ºäºĨ", + "éĻķ 西", + "æĭħ ä¿Ŀ", + "è¾¾ æĪIJ", + "è¦ģ æĺ¯", + "æĺİ å¤©", + "ç»Ļ ä»ĸ", + "建ç«ĭ äºĨ", + "ä¸į è¡Į", + "ä¸Ń æĸĩ", + "åľ° 说", + "åIJİ çļĦ", + "çĽij æݧ", + "éĢ ¸", + "æĢ» éĥ¨", + "æľ¬ æĸĩ", + "é¹ ¿", + "æĻ¯ è§Ĥ", + "çļĦ 缮æłĩ", + "èĽ ĩ", + "åĨ ¯", + "ä¸Ń åĮ»", + "æķĪ åºĶ", + "产 éĩı", + "åŃ Ŀ", + "è´¦ æĪ·", + "è¿Ŀ åıį", + "èij£äºĭ ä¼ļ", + "京 举", + "责任 ç¼ĸè¾ij", + "åķı é¡Į", + "çĪ± å¿ĥ", + "èѦ å¯Ł", + "é¤IJ åİħ", + "å¸Ĥ æĶ¿åºľ", + "天 天", + "æĸ° é²ľ", + "éĥij å·ŀ", + "è¶ħ è¶Ĭ", + "å½ Ń", + "çŁ¥è¯Ĩ 产æĿĥ", + "åĽŀ å¿Ĩ", + "è·¯ 线", + "å»ī æ´ģ", + "éĿĴ å°ijå¹´", + "åıĸå¾Ĺ äºĨ", + "çľĭ åĪ°äºĨ", + "é¦ ¬", + "ç²¾ åĵģ", + "åľ° éĵģ", + "æĮģ æľī", + "ä¸ĭ äºĨ", + "æľī æŶåĢĻ", + "ä¸Ģ 人", + "æĴ Ĵ", + "ä»Ķ ç»Ĩ", + "èĢģ åħ¬", + "äºĭå®ŀ ä¸Ĭ", + "èģĶ èµĽ", + "ä¾ĽåºĶ éĵ¾", + "é¢Ħ ç®Ĺ", + "åĪ¶éĢł ä¸ļ", + "å®īåħ¨ çĶŁäº§", + "俱 ä¹IJ", + "俱ä¹IJ éĥ¨", + "çļĦ æł¸å¿ĥ", + "æīĵ ç®Ĺ", + "å½± çīĩ", + "æIJŃ å»º", + "ä¹Ł ä¸įä¼ļ", + "æĭħ å½ĵ", + "å±Ĥ éĿ¢", + "åѦ åijĺ", + "临 æŶ", + "缸 ç»ĵåIJĪ", + "对 æ¯Ķ", + "ä»ĸ æĺ¯", + "æĸ° åĮº", + "è¿Ľ åİ»", + "çĻ¾ å¹´", + "ä¿ ©", + "å°½ å¿«", + "çĶµåŃIJ åķĨåĬ¡", + "æĽ´ æľī", + "æ¸ħ çIJĨ", + "åı¦ ä¸Ģ个", + "åĤ »", + "ä»Ģä¹Ī æł·çļĦ", + "æĺ¯ æľĢ", + "åij¨ å¹´", + "å¾Ī 容æĺĵ", + "åĽ¢ ç»ĵ", + "ç´ Ħ", + "æĹ© å·²", + "çļĦ åıĺåĮĸ", + "éľ ŀ", + "æĹ¥ ä¸ĬåįĪ", + "失 åİ»", + "ä¸Ń åľĭ", + "çļĦä¸Ģ äºĽ", + "å°ı åŃ©", + "ä¸ĭ è·Į", + "éĶ» çĤ¼", + "é ij", + "éij «", + "å¿ĹæĦ¿ èĢħ", + "èĤ¡ å¸Ĥ", + "èµĽ äºĭ", + "许åı¯ è¯ģ", + "åı¯ æĮģç»Ń", + "åijĬè¯ī è®°èĢħ", + "éĢ» è¾ij", + "å¼ķ åħ¥", + "çļĦ è¿ĩç¨ĭä¸Ń", + "è§Ĩ è§ī", + "èĩªæ²» åĮº", + "è¯ģ æį®", + "è£ħ ç½®", + "第ä¸ī æĸ¹", + "å¹´ æĿ¥", + "å¹¿ä¸ľ çľģ", + "带æĿ¥ äºĨ", + "éķ¿ æ±Ł", + "访 éĹ®", + "å·® ä¸įå¤ļ", + "æĺ¯ æĪij", + "éģŃ éģĩ", + "æĬĵ 好", + "é«ĺ è¾¾", + "并 åľ¨", + "èĩª è§ī", + "ä¾ĽåºĶ åķĨ", + "æĥħ æĦŁ", + "ä½ı äºĨ", + "çļĦ èģĮä¸ļ", + "çļĩ å¸Ŀ", + "西 éĥ¨", + "åĴĮ å¹³", + "çļĦ åĬĽéĩı", + "æ± ª", + "åħħåĪĨ åıijæĮ¥", + "æĬķ è¯ī", + "èµ· åĪ°", + "äºĴ 缸", + "æ¾³ éŨ", + "æİ¥ åĪ°", + "æ°´ æ³¥", + "模 åŀĭ", + "ä¸Ģ åįĬ", + "秩 åºı", + "æĪij们 åľ¨", + "æī¿ 认", + "ä¸Ģ éĥ¨åĪĨ", + "åįł æ¯Ķ", + "å¦ĩ 女", + "ç² ĺ", + "äºĨ解 åĪ°", + "ä¸Ģå®ļ ä¼ļ", + "åIJĦ 大", + "èµ° åĩº", + "为 大家", + "é«ĺ éĵģ", + "åı¯ä»¥ åľ¨", + "ä½Ĩ åľ¨", + "çĶŁæĢģ çݯå¢ĥ", + "èı ¯", + "çļĦ ä»·æł¼", + "麻 çĥ¦", + "æ¿Ģ åıij", + "éĤ£ å°±", + "çļĦ æł·åŃIJ", + "为 æѤ", + "天 åľ°", + "çļĦ 缮çļĦ", + "åĢº åĪ¸", + "å·² ç¶ĵ", + "åĽĽ 大", + "åIJĮæŶ ä¹Ł", + "å½¼ æѤ", + "æĭ¿ åĪ°", + "åIJ« éĩı", + "åįģ 大", + "éļ¾ éģĵ", + "å¼ Ĺ", + "ä¸Ģ 段æŶéĹ´", + "çħ§ 顾", + "æķ°æį® æĺ¾ç¤º", + "æĪIJ为 äºĨ", + "èµ° åĪ°", + "æľ¬ åħ¬åı¸", + "ç»Ī 端", + "ä¹Ł ä¸įæĺ¯", + "头 åıij", + "大 约", + "é£İ æĻ¯", + "æ¶Ī èĢĹ", + "审 æŁ¥", + "äºī åıĸ", + "æ³ķ æ²»", + "äºĭ çī©", + "ç¼ĵ 解", + "æĥ ¨", + "缸åºĶ çļĦ", + "çļĦ æķĪæŀľ", + "åıį å¤į", + "åıijçĶŁ äºĨ", + "éĢĻ äºĽ", + "ç»ĥ ä¹ł", + "åݨ æĪ¿", + "å¼Ģ æĭĵ", + "欣 èµı", + "夫 妻", + "ä¸į ä¸Ģæł·", + "产 èĥ½", + "èĬ¯ çīĩ", + "è¦ģ ç´ł", + "åıį 对", + "çİĩ åħĪ", + "è´§ çī©", + "æĹ¥ çĶµ", + "ä½ľ 家", + "æĶ¹ è¿Ľ", + "æĪIJ åĪĨ", + "åĽł èĢĮ", + "åĩı èĤ¥", + "æ½ ĺ", + "å±±ä¸ľ çľģ", + "åĬ Ŀ", + "åŁ ĭ", + "æѦ è£ħ", + "æ±ĩ æĬ¥", + "ä¸Ģ个 æľĪ", + "çĥŃ éŨ", + "大 éģĵ", + "æ´» åĭķ", + "éĥ½ å¾Ī", + "çĶµ 梯", + "ç´§ æĢ¥", + "åĢº åĬ¡", + "客 æľį", + "ä¸Ģ éĥ¨", + "ä½ł æĺ¯", + "çİ° çĬ¶", + "æŃ£ç¡® çļĦ", + "ä¹ĭ å¤Ħ", + "ç¼ĸ åĪ¶", + "ä½ł åı¯ä»¥", + "çŃī åľ°", + "èİ ī", + "对 è¯Ŀ", + "æ·ĺ å®Ŀ", + "è°ĥ èĬĤ", + "æİĴ æĶ¾", + "åºĵ åŃĺ", + "ç´ ļ", + "çļĦ ä¼ĺåĬ¿", + "æĿĥ å¨ģ", + "以ä¸ĭ ç®Ģ称", + "ä¸Ģ 项", + "èģļ éĽĨ", + "ä¼łç»Ł çļĦ", + "æ·· åIJĪ", + "è¿Ļä¸Ģ çĤ¹", + "ä¸Ģ çľ¼", + "æĹł éĻIJ", + "èİ·å¾Ĺ äºĨ", + "éĢī æīĭ", + "åĪ¶ åĵģ", + "åįı ä½ľ", + "çĭ¬çī¹ çļĦ", + "ä¸Ģ 级", + "è¿Ļ个 éĹ®é¢ĺ", + "æĸ Į", + "æĺ¯ æĪij们", + "æķĮ 人", + "æ¸ħ æ´Ĺ", + "ä¸Ģ缴 åľ¨", + "å°ı ç±³", + "çļĦ è¿ĩç¨ĭ", + "åľ¨ åĮĹ京", + "ä¸Ģ æĶ¯", + "æĹ© ä¸Ĭ", + "æĸĩ èīº", + "ç¦ı åĪ©", + "é£Ł çĶ¨", + "æĦŁ åĬ¨", + "åħ¨ ç¨ĭ", + "æĶ¯ åĩº", + "æĸ° 建", + "å¸ ķ", + "æĺ¾ çĦ¶", + "羣 çļĦæĺ¯", + "æĸ°éĹ» ç½ij", + "èĥ½ åIJ¦", + "åįı åĬ©", + "亲 èĩª", + "å¾Ī æľī", + "çĻ¼ å±ķ", + "æĦı 大", + "æĦı大 åĪ©", + "çĶµ ç½ij", + "æĹ¥ çĽĬ", + "çĨ ±", + "èĤĮ èĤ¤", + "çĶ· æĢ§", + "ç»Ħ 建", + "çŃī éĹ®é¢ĺ", + "æ¶Ī éĻ¤", + "æĬ¤ çIJĨ", + "å¡ij æĸĻ", + "ä¹Į åħĭ", + "ä¹Įåħĭ åħ°", + "åķĨ æłĩ", + "çIJ ³", + "æĸ° æīĭ", + "çļĦ çī¹çĤ¹", + "åĴ ¬", + "å½ĵ ä¸ĭ", + "设计 å¸Ī", + "èµĶ åģ¿", + "第 åįģ", + "æĻºèĥ½ åĮĸ", + "å¼Ģåıij åĮº", + "åı¯ä»¥ éĢļè¿ĩ", + "åħ±äº§ åħļ", + "åİī 害", + "çģµ æ´»", + "æŶ åħī", + "éĥ¨ ä½į", + "人 æĸĩ", + "è¿Ľ æĿ¥", + "ä¹ĭ æīĢ以", + "ä¸ī åįģ", + "çļĦ åѦçĶŁ", + "éĺ² æĬ¤", + "åĽ½ 产", + "æ·±åľ³ å¸Ĥ", + "éĤ£ å°±æĺ¯", + "åĪ° ä½į", + "çī¹ æľĹ", + "çī¹æľĹ æĻ®", + "å®ŀ æŶ", + "åı° çģ£", + "èĢĮ ä¸į", + "æĮĩ å®ļ", + "åĿ Ŀ", + "èħIJ è´¥", + "çī¹ å®ļ", + "å¢ŀ éĢŁ", + "æłĩ çŃ¾", + "æĪ¿ ä»·", + "æĦ ģ", + "贯彻 èIJ½å®ŀ", + "æĢ§ è´¨", + "çłĶ究 çĶŁ", + "ç¾İ 容", + "æī¹ è¯Ħ", + "究 ç«Ł", + "人åĬĽ èµĦæºIJ", + "éĸĭ å§ĭ", + "åĽŀ å½Ĵ", + "èIJ¥ åķĨ", + "èIJ¥åķĨ çݯå¢ĥ", + "ä¸ŃåĽ½ 人", + "çļĦ åŁºæľ¬", + "è¯Ŀ é¢ĺ", + "æłĩåĩĨ åĮĸ", + "西 èĹı", + "åĭ ¾", + "çļĦ 设计", + "ç®Ģåįķ çļĦ", + "å¤į åĪ¶", + "æ¸IJ æ¸IJ", + "以 å¤ĸ", + "èģĶ åĬ¨", + "两 次", + "æĢ§ åĴĮ", + "æĽ´ 大", + "çļĦ åIJįåŃĹ", + "éŁ ¦", + "ä½ł è¦ģ", + "å¢ĥ å¤ĸ", + "æĹ© æľŁ", + "åĪĿ æŃ¥", + "è´¦ åı·", + "害 æĢķ", + "æĺ¨ æĹ¥", + "åĪļ æīį", + "ç¥ŀ ç§ĺ", + "ç²¾ å¿ĥ", + "æµģ éĢļ", + "åħ¨ æĸ¹ä½į", + "以 å¾Ģ", + "ä¹Ł å°Ĩ", + "æĺ¯ ä¸ŃåĽ½", + "åĽ½å®¶ 级", + "å°Ĩ åĨĽ", + "æij Ĭ", + "æľĢ 为", + "第ä¸Ģ æŶéĹ´", + "æ¶Ī æ¯Ĵ", + "å°Ĩ äºİ", + "å¨ģ èĥģ", + "èĭ± æĸĩ", + "æīĭ ä¸Ń", + "çIJĥ è¿·", + "è§Ĥ çľĭ", + "离 å©ļ", + "æľ¬ åľŁ", + "åĪĨ æķ£", + "æĻ ´", + "è¦ģ 注æĦı", + "浪 è´¹", + "管 æݧ", + "åĩº åĶ®", + "æĢ» è£ģ", + "ä¸Ģ éĺµ", + "å¨ ĩ", + "äºĶ 个", + "å½ĵ åĪĿ", + "çºł 纷", + "ä¸ĵ çĶ¨", + "å¤ĩ æ¡Ī", + "åĪĿ æľŁ", + "å®ĥ æĺ¯", + "åĮº åĿĹ", + "åĮºåĿĹ éĵ¾", + "大 è¿ŀ", + "è¿Ļ ç±»", + "åıĺ æĪIJäºĨ", + "éĤĦ æĺ¯", + "åįļ 客", + "çı¾ åľ¨", + "ä¸Ģ æĸ¹", + "å®ĮæĪIJ äºĨ", + "è¿Ļ个 æŶåĢĻ", + "åħ¨ å¹´", + "ä¸Ĭ 线", + "ç½ IJ", + "ç«ŀ èµĽ", + "åĩºçīĪ 社", + "åĵ¥ åĵ¥", + "å¯ «", + "å¾Ĺ 以", + "èĬ± åĽŃ", + "äºĨ èµ·æĿ¥", + "èĦ±è´« æĶ»åĿļ", + "çļĦ åİŁåĪĻ", + "讲 解", + "æ¶Ī åĮĸ", + "æįŁ 害", + "æļĤ æŶ", + "å¾Ĺ çŁ¥", + "éĢĤ çĶ¨", + "éŨ åºĹ", + "解 读", + "æĻ® åıĬ", + "人æ°ij æ³ķéĻ¢", + "åī¯ 主任", + "å¿ĥ çģµ", + "è¯Ĭ æĸŃ", + "ç¾İ 女", + "æŁ ¯", + "å¹´ 以æĿ¥", + "æ´» è·ĥ", + "åĢŁ åĬ©", + "åħ± 建", + "è¯ī 讼", + "æĶ¾ æĿ¾", + "çªĹ åı£", + "ä¼ģ æ¥Ń", + "åĬł æĭ¿", + "åĬłæĭ¿ 大", + "ä¹° äºĨ", + "主 æµģ", + "æĩĤ å¾Ĺ", + "å°Ĩ åħ¶", + "éĢı æĺİ", + "å·¥ä½ľ ä¸Ń", + "èĤ¡ ä»·", + "æ¡£ æ¡Ī", + "没æľī ä»»ä½ķ", + "åijĬ çŁ¥", + "å¹´ åĪĿ", + "æĹ¥ ä¸ĭåįĪ", + "åİĤ åķĨ", + "èĬĤ å¥ı", + "主 导", + "è£ Ŀ", + "åħ³éĶ® è¯į", + "èģĬ 天", + "åĨĻ ä½ľ", + "æĶ¹éĿ© å¼ĢæĶ¾", + "æľī æľĽ", + "éĢļ æĬ¥", + "èIJ Į", + "æĢ» é¢Ŀ", + "çŁŃ æľŁ", + "ä¸Ģ çķª", + "çĶŁæ´» çļĦ", + "åĮĸ çļĦ", + "æĺ¥ å¤©", + "è¿Ļ åľº", + "æĸ°å¼Ģ ä¼łå¥ĩ", + "æĺ¯ è¦ģ", + "å°ļ æľª", + "åıĺ æĽ´", + "ä¸Ģ åij¨", + "客 è§Ĥ", + "æĹ¥ èĩ³", + "é¹ °", + "çİ ²", + "å°Ĩ æĿ¥", + "客 人", + "åıĺ éĿ©", + "说 äºĨ", + "åİŁ çIJĨ", + "èģĮ åĬ¡", + "åıĪ æľī", + "ä¸Ģ åı¥è¯Ŀ", + "æĦŁ åıĹåĪ°", + "ç¬Ķ èĢħ", + "移 æ°ij", + "西 åįĹ", + "ä¹ĥ èĩ³", + "æŃ£ è§Ħ", + "åĪĿ ä¸Ń", + "çĬ ¬", + "å½ĵ äºĭ", + "å½ĵäºĭ 人", + "æĪij们 è¦ģ", + "åħ¥ åı£", + "éĤ£ æŶ", + "æľīéĻIJ 责任", + "å°ij 女", + "è¿Ļä¹Ī å¤ļ", + "åĪĨ åħ¬åı¸", + "å®ĩ å®Ļ", + "çļĦ éĢīæĭ©", + "å§IJ å§IJ", + "åıij èµ·", + "è» į", + "æĽ´å¥½ åľ°", + "éĻĨ ç»Ń", + "æľ¬ æľįåĭĻ", + "å« ©", + "赶 ç´§", + "èĦĤ èĤª", + "第äºĮ 天", + "æĪij ä¼ļ", + "两 ä½į", + "æķ ²", + "åħ¬å®ī æľºåħ³", + "ç§ijæĬĢ åĪĽæĸ°", + "å°º 寸", + "è¾IJ å°Ħ", + "å®Ĺ æķĻ", + "转 æį¢", + "åĩº çİ°åľ¨", + "ä¸Ģ é¢Ĺ", + "æľŁ éĻIJ", + "åIJĮåѦ 们", + "åĮĹ æĸ¹", + "ä½ł å°±", + "ä¸Ģ带 ä¸Ģè·¯", + "èĢģ å©Ĩ", + "游æĪı çݩ家", + "çļĦ ç»ĵæŀľ", + "è¡¥ åģ¿", + "å¤ĸ è´¸", + "对 å¾ħ", + "ç»´ çĶŁç´ł", + "ç»ıéĶĢ åķĨ", + "è¿ĺ å°Ĩ", + "åŃIJ 女", + "æĽ´ é«ĺ", + "ä¸į 大", + "éī´ å®ļ", + "让 ä»ĸ们", + "æīĢè°ĵ çļĦ", + "æŃ» äºĨ", + "帮 æī¶", + "åĵ² åѦ", + "以ä¸Ĭ çļĦ", + "çļĦ åħ³éĶ®", + "æĹ© å°±", + "æĬ¥ ä»·", + "éģµ å®Ī", + "æī© å¼ł", + "æĺ¯ å¾Ī", + "å¼Ģ éĢļ", + "æĸ° åĬł", + "æĸ°åĬł åĿ¡", + "ç¿» è¯ij", + "询 éĹ®", + "é¸ Ń", + "ä½ĵ åĨħ", + "两 个人", + "çĪ ¹", + "éľ ľ", + "乡æĿij æĮ¯åħ´", + "çĿ¡ è§ī", + "å®ĺ åijĺ", + "åĪĽ å§ĭ", + "åĪĽå§ĭ 人", + "ä¼Ĺ 人", + "åį³ 便", + "çĸ« èĭĹ", + "ä¼ģä¸ļ 家", + "æ¸ £", + "ç²¾ åĬĽ", + "å¤ĸ éĥ¨", + "èģª æĺİ", + "è¿Ļ ä¹Ł", + "å½ķ åıĸ", + "åĨ² çªģ", + "åħ¨ 身", + "åŃ£ èĬĤ", + "忽 çĦ¶", + "çļĦ æĢģ度", + "åĤ¨ å¤ĩ", + "ä¿Ŀ åħ»", + "çļĦ æĥ³æ³ķ", + "ä¸Ĭæµ· å¸Ĥ", + "æIJº æīĭ", + "çļĦ ä¿¡æģ¯", + "åķĨ åľº", + "çļĦ æĢĿæĥ³", + "æĿĥ åĬĽ", + "毫 æĹł", + "æĢĢ åŃķ", + "硬 件", + "åĨħ èĴĻåı¤", + "æİ¢ 讨", + "åħ» çĶŁ", + "çļĦ 表çİ°", + "空 ä¸Ń", + "æģIJ æĢĸ", + "å¾Ī é«ĺ", + "ç»ıæµİ 社ä¼ļ", + "ä¸Ĭ æĿ¥", + "延 ç»Ń", + "éĩį å¤į", + "éĺ² èĮĥ", + "çļĦ å½¢å¼ı", + "æľĪ åºķ", + "èĢģ 年人", + "绿 åĮĸ", + "å±± åĮº", + "æĭ¿ åĩº", + "æĹħ 客", + "æĽ´ æį¢", + "åħ¬ 主", + "èĬĤ 约", + "åħ¨ åİ¿", + "åĽŀ æĬ¥", + "çIJĨ æĢ§", + "çĸ¯ çĭĤ", + "æ¶ī å«Į", + "åī§ æĥħ", + "åĨ¬ åŃ£", + "åIJİ ç»Ń", + "è¿Ļæĺ¯ ä¸Ģ个", + "æ¼Ķ 讲", + "ä¸Ģ å±Ĥ", + "æľīåħ³ éĥ¨éŨ", + "æĹł å¥Ī", + "ç§į ç±»", + "缸åħ³ çļĦ", + "æĪĸèĢħ æĺ¯", + "æī¶ æĮģ", + "å¤ļ æķ°", + "çļĦ ä½ľåĵģ", + "ä¸ĭ ä¸ĢæŃ¥", + "å¸Ī åĤħ", + "é«ĺéĢŁ åħ¬è·¯", + "好 åıĭ", + "ä¼ĺç§Ģ çļĦ", + "è¿Ľ äºĨ", + "æģIJ æĢķ", + "äºĨ åIJ§", + "大 è§Ħ模", + "çļĦ ä¸ĸçķĮ", + "æĢĢ çĸij", + "å· ·", + "åħ´ å¥ĭ", + "æĪ °", + "æĿij éĩĮ", + "æľĭåıĭ åľĪ", + "åĨ¬ 天", + "ä¸Ńåįİ äººæ°ij", + "åįı åķĨ", + "è¯Ħ éĢī", + "æĹ Ń", + "å¢ŀåĬł äºĨ", + "åıĹ ä¼¤", + "ä¸Ģ èĤ¡", + "便 æį·", + "ä¸ ij", + "é¹ ¤", + "å¤ĸ è§Ĥ", + "å·¥ç¨ĭ å¸Ī", + "åĴĮ åħ¶ä»ĸ", + "è¿Ļ å°±", + "ä¸Ńå°ı ä¼ģä¸ļ", + "西 åĮĹ", + "åĽ½æľī ä¼ģä¸ļ", + "èĭ¥ æĺ¯", + "åı¯ æĥľ", + "çĶŁ æĹ¥", + "åĩ ½", + "ä¹° åįĸ", + "ç¥Ŀ ç¦ı", + "人æ°ij 群ä¼Ĺ", + "åħī æĺİ", + "åħ¬ å¯ĵ", + "æĺ¯ è°ģ", + "æĪij çŁ¥éģĵ", + "è¯Ń æĸĩ", + "æķı æĦŁ", + "ä¸įéĶĻ çļĦ", + "æĿ¥ 讲", + "æ³¢ åĬ¨", + "çļĦ 第ä¸Ģ", + "åľ° éľĩ", + "åľ¨ åħ¨åĽ½", + "骨 å¹²", + "å®ī ç½®", + "家 çĶµ", + "ä¸İ æѤ", + "ä¸İæѤ åIJĮæŶ", + "åıĹ çģ¾", + "çĥŃ çº¿", + "çļĦ æĬĢæľ¯", + "æµĭ éĩı", + "ä¾Ŀ èµĸ", + "ä¸ŃåĽ½ çļĦ", + "çī¹ æĢ§", + "è¾ĥ é«ĺ", + "è¸ ©", + "ä¼ļ åľ¨", + "建 éĢł", + "导 èĪª", + "æĥ³ èµ·", + "åħ¨ ä¸ĸçķĮ", + "建 æĿIJ", + "ç¯ Ģ", + "çļĦ åŁºç¡Ģ", + "èĩªåĬ¨ åĮĸ", + "åīį åIJİ", + "çĿ¡ çľł", + "æݨ è¡Į", + "æį® äºĨ解", + "ä»Ģä¹Ī æŶåĢĻ", + "ä¸į åĸľæ¬¢", + "çħ¤ çĤŃ", + "éĤ£ä¹Ī å¤ļ", + "å¸Ĥåľº åĮĸ", + "ä¸į管 æĺ¯", + "ç«ĭ åľº", + "éĥ½ 没", + "课 é¢ĺ", + "æĪij们 å°Ĩ", + "è¿ĩ çļĦ", + "åĨį åĬłä¸Ĭ", + "çĪ ¾", + "身 æĿIJ", + "çĶ· 女", + "è¿ľ è¿ľ", + "çĶ· çĶŁ", + "èĩªèº« çļĦ", + "è´Ł æĭħ", + "çĻ¾ ä¸ĩ", + "西 çıŃ", + "西çıŃ çīĻ", + "åĩĢ åĪ©æ¶¦", + "æ¾³ 大", + "澳大 åĪ©äºļ", + "ä¸į åİ»", + "æī¿ åıĹ", + "楼 çĽĺ", + "å¢ĥ åĨħ", + "æ·· åĩĿ", + "æ··åĩĿ åľŁ", + "æĢĿæĥ³ æĶ¿æ²»", + "å¸Ĥ åĮº", + "æĭĽ æłĩ", + "åĽ¢ ä½ĵ", + "è¿Ľ 度", + "åĨĽ éĺŁ", + "åıį å¼¹", + "äºĨä¸Ģ äºĽ", + "æİ¥ å¾ħ", + "çļĦ åŃ¦ä¹ł", + "éħį éĢģ", + "é£Łåĵģ å®īåħ¨", + "æĽ¿ 代", + "æĺ¯ ä»¥", + "éĢļ çĶ¨", + "çłĶ究 æīĢ", + "ç¦ ħ", + "æī Ķ", + "éļĶ 离", + "ä¸ĩ å¹³æĸ¹ç±³", + "çļĦ è§Ħå®ļ", + "ç»Ļ æĪij们", + "æ¿Ģ åħī", + "ä¼ļ åĩºçİ°", + "çŁŃ ä¿¡", + "ç©¿ çĿĢ", + "æ²Ī éĺ³", + "æķĻ æĿIJ", + "éĺ² çĸ«", + "ä¼ĺ èī¯", + "约 å®ļ", + "æĪij çľģ", + "åħ¬ æ°ij", + "éģ¸ æĵ", + "éģ¸æĵ ĩ", + "å·² æĪIJ为", + "ä¸į å¿ħ", + "ç¥ĸ åĽ½", + "并 æľª", + "åľŁ 壤", + "å¾® ç¬ij", + "äºĭä¸ļ åįķä½į", + "çļĦ 游æĪı", + "åħ¬ 示", + "åIJĪçIJĨ çļĦ", + "çª Ŀ", + "æ°Ķ 象", + "家 ä¸Ń", + "亮 缸", + "åį« æĺŁ", + "è®° è½½", + "è§Ĩ éĩİ", + "åľ°åĮº çļĦ", + "ä½Ĩ ä»ĸ", + "èĤĮ èĤī", + "äºı æįŁ", + "åĬŀ åѦ", + "ä¸Ģ è¡Į", + "è¯ŀ çĶŁ", + "åıijå¸ĥ çļĦ", + "çļĦ æľįåĬ¡", + "çļĦ çłĶ究", + "åij¨ æľ«", + "产ä¸ļ åĽŃ", + "é«ĺ 温", + "æĪIJåĬŁ çļĦ", + "æŃ¥ 骤", + "åŃĺ åĤ¨", + "åŃIJ åħ¬åı¸", + "让 她", + "ä¸Ń æľī", + "åĺī 宾", + "å¦ ®", + "æĺİ å¹´", + "äºĨ åIJĹ", + "äºī è®®", + "æĪ Ī", + "ä¸Ģ æľ¬", + "ç¾İ丽 çļĦ", + "ä½ł 说", + "大 人", + "æĶ» çķ¥", + "ä¸į æľĥ", + "å¾ħ éģĩ", + "ä¸Ģ è¾Ĩ", + "çīĪæĿĥ æīĢæľī", + "æ°ij ä¼Ĺ", + "åĬŁ 夫", + "å±ķ ä¼ļ", + "大 èĦij", + "æ¯ı æľĪ", + "å°ı 麦", + "æµĻæ±Ł çľģ", + "çļĦ æīĢæľī", + "ä¸ĭ æ»ij", + "èĵĿ èī²", + "è¦ģ æĥ³", + "åѦçĶŁ çļĦ", + "å½ĵ ä½ł", + "ä½ľ æĪĺ", + "家 乡", + "å¤ļ åIJį", + "é«ĺ äºİ", + "åĿļ 强", + "è¿ŀ éĶģ", + "åIJİ æŀľ", + "人 äºĭ", + "ç´ ħ", + "æ¿Ģ åĬ¨", + "è¿Ľ æĶ»", + "ç© Ĩ", + "ä¸ ĺ", + "让 èĩªå·±", + "以 æѤ", + "夫 人", + "å¼Ģ 设", + "æ°Ķ è´¨", + "鸡 èĽĭ", + "çĦ¡ æ³ķ", + "åIJĥ äºĨ", + "åĪĨåĪ« 为", + "èģĶåIJĪ åĽ½", + "å½ĵ 代", + "å¦Ĥæŀľ æĺ¯", + "è¿ľ ç¨ĭ", + "åĸ Ĥ", + "è®° ä½ı", + "æ¸ħ åįķ", + "åIJĪä½ľ ä¼Ļä¼´", + "åİ» åģļ", + "æķħ éļľ", + "模 æĭŁ", + "å¸Ī çĶŁ", + "åīį æĿ¥", + "çĶµè§Ĩ åī§", + "çĥŃ çĪ±", + "éľ² åĩº", + "é«ĺ å±Ĥ", + "çĶµ åĻ¨", + "纪 å¾ĭ", + "å¼Ģåıij åķĨ", + "éķ¿ å®ī", + "è½½ ä½ĵ", + "çļĦ å°±æĺ¯", + "被 人", + "åıĹ çIJĨ", + "篮 çIJĥ", + "èİ İ", + "交 ç»Ļ", + "æľªæĿ¥ çļĦ", + "两 大", + "åIJķ å¸ĥ", + "çŃī 人", + "çļĦ æĹ¥åŃIJ", + "åIJĪä½ľ 社", + "æĮij éĢī", + "åŃĺ 款", + "ç³»ç»Ł çļĦ", + "æĬĬ å®ĥ", + "没æľī ä»Ģä¹Ī", + "ä»İ æѤ", + "ä¸Ń åįĪ", + "çĸ¼ çĹĽ", + "å·© åĽº", + "浪 漫", + "缸åħ³ éĥ¨éŨ", + "éķ¿ åŁİ", + "纤 ç»´", + "ä¸Ĭ éŨ", + "çĪĨ çĤ¸", + "èµ· çĤ¹", + "çļĦ éĢļçŁ¥", + "èĢĮ æĿ¥", + "çļĦ èĢģ", + "æīĭ éĩĮ", + "è¯Ń éŁ³", + "è¾Ľ èĭ¦", + "æ±Łèĭı çľģ", + "çĶ¨ äºĨ", + "身份 è¯ģ", + "æľī åĬ©", + "æľīåĬ© äºİ", + "çī© èģĶç½ij", + "åĩº éŨ", + "å¼Ł åŃIJ", + "æĥ ¹", + "è¿Ļ件 äºĭ", + "æĪij们 åı¯ä»¥", + "çļĦ çĶŁåij½", + "æľīä¸Ģ ç§į", + "åºĹ éĵº", + "åıĮ æīĭ", + "çļĦ æ¶Īæģ¯", + "èĢIJ å¿ĥ", + "å°´ å°¬", + "éĤ£ 天", + "é¦ĸ æī¹", + "æĺ¯ä¸Ģ 家", + "人 æ°Ķ", + "åıį æŃ£", + "æĪij åĴĮ", + "å®ł çī©", + "ä¸į 对", + "寻 æ±Ĥ", + "缸 ä¼¼", + "åľ¨ ç¾İåĽ½", + "åı« åģļ", + "åĹ İ", + "ç«ĭ 足", + "çĶ¨ éĢĶ", + "åħ Ĩ", + "大 æ°Ķ", + "åIJij ä¸Ĭ", + "ä»ĸ å°±", + "é¡¹çĽ® 建设", + "èĭ¥ å¹²", + "æĺ¯ æľī", + "æ¿Ģ æĥħ", + "çļĦ æĦıä¹ī", + "æĺ Ń", + "严éĩį çļĦ", + "å¯Ĩ éĽĨ", + "èĪŀ è¹Ī", + "èį£ èİ·", + "èİ· æĤī", + "æ±Ł åįĹ", + "åģĩ å¦Ĥ", + "æĪ· å¤ĸ", + "线 ç´¢", + "ç§ģ 人", + "转åŀĭ åįĩ级", + "çļĦ ä»·åĢ¼", + "åįķ çĭ¬", + "èĢģ çĻ¾å§ĵ", + "å°į æĸ¼", + "åĽ½éĻħ åĮĸ", + "ä¼° åĢ¼", + "æľįåĬ¡ ä¸ļ", + "èĩ Ń", + "æİī äºĨ", + "解åĨ³ äºĨ", + "ä¹Ł ä¸įèĥ½", + "åħ ¹", + "æĸ¯ çī¹", + "æķħ æĦı", + "è¿ĩ 度", + "èĬĤ æĹ¥", + "çĻ½ çĻľ", + "çĻ½çĻľ é£İ", + "继 æī¿", + "äºĨ ä¸įå°ij", + "äºĮ 人", + "è§ģ éĿ¢", + "æĥ³ æĥ³", + "å¤į åIJĪ", + "康 å¤į", + "åİ¿ åŁİ", + "åľ¨ åĽ½åĨħ", + "åľº åľ°", + "éĻ¶ çĵ·", + "è¿Ļ 项", + "çľ¼ ä¸Ń", + "çł ¸", + "æĦŁè§ī åĪ°", + "æŀľ çĦ¶", + "æĶ¾ åħ¥", + "约 æĿŁ", + "æİĴ æŁ¥", + "车 主", + "çļĦ æĦıæĢĿ", + "æĸ° åŁİ", + "æĥ³ çĿĢ", + "éģ Ĥ", + "èĮ¶ åı¶", + "ä¹° æĪ¿", + "åĨľ æĪ·", + "é«ĺ æīĭ", + "çİī ç±³", + "æĸ°åĨł èĤºçĤİ", + "çħ§ æĺİ", + "æĮĩ åįĹ", + "è¸ ¢", + "æķij æı´", + "æĻ¯ çĤ¹", + "ç¨İ æĶ¶", + "çļĦ æīĭ", + "æŃ£ 好", + "è¦ģ æĬĬ", + "éļı æĦı", + "åħ¶å®ŀ æĺ¯", + "ç»Ļ èĩªå·±", + "è°Ī åĪ¤", + "æ¯ı天 éĥ½", + "æĢģ åĬ¿", + "é¢Ħ 约", + "åİĨåı² ä¸Ĭ", + "å®Ŀ è´Ŀ", + "åīį è¿Ľ", + "ä¹Łå°±æĺ¯ è¯´", + "çļĦ æĦıè§ģ", + "åı£ 罩", + "åİĺ ç±³", + "èĬ± è´¹", + "ä½ĵèĤ² æĬķ注", + "åħ¬ä¼Ĺ åı·", + "èijĹåIJį çļĦ", + "å¼Ģ æĪ·", + "æĭį åįĸ", + "å²ģ æľĪ", + "åĨħ 涵", + "å®Įæķ´ çļĦ", + "é«ĺ åİĭ", + "åħ¬åĬ¡ åijĺ", + "使çĶ¨ çļĦ", + "çĶŁäº§ 线", + "妹 妹", + "èµ° 访", + "æĺ¯ åı¯ä»¥", + "åľ¨ 家", + "æļ´ åĬĽ", + "æ³° åĽ½", + "è´¨ çĸij", + "ä¸į éģİ", + "天çĦ¶ æ°Ķ", + "缺 çĤ¹", + "å°ı åŀĭ", + "ä¸įä»ħ æĺ¯", + "é»ij æļĹ", + "æ¢ ¨", + "æĸĩ æĹħ", + "è¦ģ æľī", + "ä¸Ń å±±", + "çļĦ æķ°æį®", + "å¾Ĺ å¾Ī", + "以 便", + "对 ä»ĸ", + "åĬł 以", + "çĻ¼ çı¾", + "设 å®ļ", + "èĤļ åŃIJ", + "éĿ ĸ", + "å¥ī çĮ®", + "ä¸į åıĺ", + "åı£ ç¢ij", + "åľ¨ åĵªéĩĮ", + "ä½ IJ", + "è¿Ļ 两个", + "çļĦ æĸ¹åIJij", + "æŀ «", + "äºĮ 次", + "çīĩ åĮº", + "éł IJ", + "ç£ Ĭ", + "æĭ¿ çĿĢ", + "å·²ç»ı æĪIJ为", + "ä¹ĭ ä¸Ĭ", + "å®Ĺ æŨ", + "奶 奶", + "é«ĺæĸ° åĮº", + "社 æľĥ", + "è·Ł 踪", + "æľįåĬ¡ ä¸Ńå¿ĥ", + "æī ¯", + "æīĭ æĮĩ", + "礼 çī©", + "宿 èĪį", + "çĶ¨ å¿ĥ", + "æıIJé«ĺ äºĨ", + "亮 çĤ¹", + "ä¸į æĦ¿æĦı", + "æĴŃ æĶ¾", + "å¤ļå°ij éĴ±", + "没 ä»Ģä¹Ī", + "æķ° åįģ", + "æĢ» çĽij", + "çļĦ åŁİå¸Ĥ", + "æī¾ åĪ°äºĨ", + "åĨħ åľ°", + "åĪ° çİ°åľ¨", + "æĪĺæĸĹ åĬĽ", + "åİŁ å§ĭ", + "åĥ §", + "åĢĴ æĺ¯", + "æľĢ åħ·", + "è´«åĽ° æĪ·", + "éĢģ åĪ°", + "级 åĪ«", + "åĩº èµĦ", + "æĪª æŃ¢", + "ç§į åŃIJ", + "èĥ½ ä¸įèĥ½", + "幸 è¿IJ", + "èĸ ĩ", + "项 éĵ¾", + "æĮĤ çīĮ", + "ä¸Ģ 樣", + "ä¹ĺ 客", + "èIJ½ åIJİ", + "ä½Ĩ æĪij", + "æĹ© åľ¨", + "åĬ¨ 漫", + "å¹³ çŃī", + "对 ä½ł", + "ä¸į æĢķ", + "å¤ĸ çķĮ", + "å¤ļå¹´ æĿ¥", + "é¦ĸ 个", + "æ²³ åįĹçľģ", + "æĪĸ åħ¶ä»ĸ", + "éķľ 头", + "åįĹ æĺĮ", + "ä¸Ģ éĿ¢", + "éĢłæĪIJ çļĦ", + "å´ Ķ", + "çŃ Ĵ", + "æķĻèĤ² éĥ¨", + "åľ° åŁŁ", + "æĺĨ æĺİ", + "å·´ é»İ", + "æīĭ 游", + "ä¸Ģ æŶ", + "çł į", + "顶 级", + "åħ± 计", + "åİŁ æ²¹", + "è¾ī çħĮ", + "说 æĺ¯", + "æĸ°åįİ ç¤¾", + "ç»ıåİĨ äºĨ", + "ä¸į æŃ¢", + "è¦ģ ä¹Ī", + "èĢħ çļĦ", + "æĢ» æĬķèµĦ", + "è¡Į 驶", + "ä¸Ĭ å¸Ŀ", + "å¹´ 纪", + "çIJ ¼", + "ä¼ł 说", + "ç²¾ èĭ±", + "æĸ¹ éĴĪ", + "æ±Ł æ¹ĸ", + "æĪIJ çĤº", + "æĢ» éĩı", + "æĬķ æĶ¾", + "åĬ¨ çĶ»", + "èĹ ¤", + "çĶµ æºIJ", + "éĴ Ļ", + "åIJĮ è¡Į", + "æĻ®éĢļ çļĦ", + "åĽ¾ä¹¦ é¦Ĩ", + "è¯Ī éªĹ", + "æħĪ åĸĦ", + "è¿Ļ 份", + "主æĮģ 人", + "å°± è¿Ļæł·", + "èĢĮ æĪIJ", + "èĩªè¡Į 车", + "ä¸ŃåĽ½ çī¹èī²", + "èĤ¿ çĺ¤", + "åIJ ¾", + "å¼Ł å¼Ł", + "åıĹ çĽĬ", + "éĢīæĭ© äºĨ", + "æĺİæĺ¾ çļĦ", + "æĬ¥ èĢĥ", + "ç¬ij éģĵ", + "éĽĸ çĦ¶", + "温 å·ŀ", + "éĿŀ æ´²", + "ç§į ç§į", + "åıĤåĬł äºĨ", + "è´§ è¿IJ", + "éļı 便", + "å°± 没æľī", + "ç¸ £", + "央 è§Ĩ", + "ç©¿ è¶Ĭ", + "çļĦ çݰ象", + "åĩł 次", + "çļĦ é£İéĻ©", + "æŃĮ æĽ²", + "æľ¬ å±Ĭ", + "å¹´ åĨħ", + "ä¸į è¶ħè¿ĩ", + "è¿ĩ å¤ļ", + "å¿ħé¡» è¦ģ", + "ç»ĵ 论", + "åĢŁ éī´", + "ç¥ŀ å¥ĩ", + "æľŁ æľĽ", + "ä¸ĵ 享", + "éĿŀ常 éĩįè¦ģ", + "æĦıè¯Ĩ åĪ°", + "åIJĪ 并", + "æĬĬ èĩªå·±", + "å¥Ĺ è£ħ", + "éŃĶ æ³ķ", + "å¤ı åŃ£", + "ä¸į åĥı", + "å¢ĥ çķĮ", + "æĥĬ åĸľ", + "æľīä¸Ģ 天", + "çĦ¦ çĤ¹", + "æĪij 认为", + "åħ° å·ŀ", + "çĶµ æ°Ķ", + "èģĶç³» æĪij们", + "ç§ij æĻ®", + "她 说", + "çļĦ æĸĩç«ł", + "å¥ĩ æĢª", + "åıĭ 好", + "饮 æĸĻ", + "çļĦ æĶ¯æĮģ", + "çŃĶ åºĶ", + "éĩį éĩı", + "çij ¶", + "åĩı è½»", + "ç§ijåѦ 家", + "å·´ 西", + "éĩijèŀį æľºæŀĦ", + "åħļ å§Ķ书记", + "貸 款", + "ç²¾ èĩ´", + "ä»İ æľª", + "åį° åĪ·", + "åĽŀ 顾", + "é¦ĸ éĥ½", + "åıij èĤ²", + "éĹ® éģĵ", + "è¾¾ åĪ°äºĨ", + "å¿į ä¸įä½ı", + "æīį æľī", + "æįIJ èµł", + "ä½Ľ æķĻ", + "ä¸į æ¸ħ", + "éĺŁ éķ¿", + "缸 åıį", + "æĬ¥ èѦ", + "大 åħ¨", + "欧 缣", + "帮 å¿Ļ", + "çļĦ æĻĤåĢĻ", + "缮 å½ķ", + "足 以", + "èī° éļ¾", + "ä»ĸ ä¹Ł", + "å·¥ ä½ľèĢħ", + "头 èĦij", + "缺 éĻ·", + "æĪIJç«ĭ äºĨ", + "å°± å¼Ģå§ĭ", + "认 åIJĮ", + "é»Ħ èī²", + "çĹħ æĥħ", + "覺 å¾Ĺ", + "è¿Ļ 两", + "ä¿¡ ä»°", + "åľĭ 家", + "ä¸įä»ħä»ħ æĺ¯", + "çĭ¬ 家", + "èĪ¬ çļĦ", + "æĿIJ è´¨", + "æµ· ä¸Ĭ", + "çĤº äºĨ", + "æľºåĬ¨ 车", + "缸å½ĵ äºİ", + "å¤ļåħĥ åĮĸ", + "æĽ´ 大çļĦ", + "èĽ ®", + "åģĩ æľŁ", + "å¼ı çļĦ", + "交éĢļ è¿IJè¾ĵ", + "çľģ å§Ķ", + "ä¸į ç®Ĺ", + "æĶ¾ ä¸ĭ", + "éĹ ¯", + "人 åľ¨", + "港 åı£", + "æŨ åľ¨", + "åij½ 令", + "æŁIJ 个", + "å¹³ 稳", + "åıª 好", + "人 人", + "äº ŀ", + "äºĮ ç»´", + "äºĮç»´ çłģ", + "æŀģ 为", + "åĪ« å¢ħ", + "åħ¶ ä½Ļ", + "大 äºĭ", + "主管 éĥ¨éŨ", + "æĹł éĶ¡", + "éĹ µ", + "éģŃ åĪ°", + "说 è¿ĩ", + "为 ä½ł", + "解 çŃĶ", + "éªĮ æĶ¶", + "çļĦ ç»ıéªĮ", + "åĮ¹ éħį", + "çģ« ç®Ń", + "豪 åįİ", + "æŁIJ æŁIJ", + "çļĦ æĹ¶ä»£", + "书 éĿ¢", + "æģĴ 大", + "延 éķ¿", + "ä¸Ģ åIJĮ", + "æľª èĥ½", + "交 æį¢", + "çĶ¢ åĵģ", + "çŃī åĪ°", + "åĪĨ 离", + "æīĵ çĶµè¯Ŀ", + "å¹² çĩ¥", + "è¾ĥ å¤ļ", + "å¤ļå¹´ çļĦ", + "èĥĮæĻ¯ ä¸ĭ", + "为 ä¾ĭ", + "æijĺ è¦ģ", + "å´Ľ èµ·", + "æѤ åĪ»", + "æľī æľºä¼ļ", + "æĿ¡ 款", + "é¢Ĩ导 å°ıç»Ħ", + "çļĦ 身ä½ĵ", + "åįķ ä¸Ģ", + "央 è¡Į", + "ä¸įæĸŃ æıIJé«ĺ", + "ä»·åĢ¼ è§Ĥ", + "èĬ ½", + "èIJ į", + "æ³ķå¾ĭ æ³ķè§Ħ", + "ä¸į éĶĪ", + "ä¸įéĶĪ éĴ¢", + "åĩº äºİ", + "èĻļ æĭŁ", + "æį® æĤī", + "çĥ¦ æģ¼", + "åħ¨ æĸ°çļĦ", + "æī« æıı", + "çĻ» éĻĨ", + "èīºæľ¯ 家", + "çļĦ é£Łçī©", + "çļĦ åŃĺåľ¨", + "客 åİħ", + "æĪij们 å°±", + "æŁ¥çľĭ æĽ´å¤ļ", + "è¯Ħ 审", + "å¸Ĥ åł´", + "è¬ Ľ", + "å·¨ 头", + "ä¸ŃåĽ½ ç»ıæµİ", + "äºĨ èĩªå·±çļĦ", + "åĨ³ è®®", + "çĽijçĿ£ 管çIJĨ", + "æĬķ 票", + "åĨį 度", + "è¡Į çĤº", + "注 åħ¥", + "ä½ľä¸º ä¸Ģ个", + "æ¯ı个人 éĥ½", + "åįķ åħĥ", + "è¦ģ çŁ¥éģĵ", + "被 称为", + "ä¹ĭ éĻħ", + "解 éĻ¤", + "ä¸ ¸", + "æº «", + "ä¸ī æĺŁ", + "é²ľ æĺİ", + "ä¹Ł éĥ½", + "æŶ æľº", + "åĩº æīĭ", + "æĥħ å½¢", + "åķĨ è´¸", + "éĢī 举", + "对 èĩªå·±", + "çĶŁ åĬ¨", + "åħĭ æľį", + "个 ä½ĵ", + "èĭ ij", + "ç¨ ±", + "大 åݦ", + "æĺ¯ å¯¹", + "åĪ© æģ¯", + "è¿IJåĬ¨ åijĺ", + "åĮĸ 解", + "åīį 沿", + "æĦŁ æģ©", + "æĢ» ä¹ĭ", + "é«ĺæĸ° æĬĢæľ¯", + "åĿĩ 为", + "åħ¨ åĮº", + "æ°Ķ æ°Ľ", + "åı¯ä»¥è¯´ æĺ¯", + "ä½ı 宿", + "åħļåijĺ å¹²éĥ¨", + "åĹ ¯", + "è·µ è¡Į", + "çļĦ ä¸ĵä¸ļ", + "èĢĥ éªĮ", + "èķ ¾", + "åħ¬ åŃIJ", + "çļĦ çĬ¶æĢģ", + "æ½® æµģ", + "ä¿¡ æīĺ", + "è´ ¼", + "åIJĦ æĸ¹", + "æķij åĬ©", + "éĿŀ常 çļĦ", + "æ¡¥ æ¢ģ", + "åħ¬ æĸ¤", + "ä¼¼ çļĦ", + "çľĭ 好", + "å±Ģ éĥ¨", + "å®ī éĿĻ", + "éħį 件", + "常 è§Ħ", + "å¼Ģ 车", + "第äºĮ 次", + "ä¸Ĭ 级", + "åıĤ èµĽ", + "家 å±ŀ", + "强 åĬ¿", + "åľ¨ ä»ĸ", + "åIJij åīį", + "ä¹ĭ åľ°", + "éĥ ¡", + "è¡Į ç¨ĭ", + "èѦ åijĬ", + "è§Ħå®ļ çļĦ", + "åķĨ åŁİ", + "äºĶ 大", + "æķĻ 室", + "åįģ 足", + "æīĢ以 åľ¨", + "å°Ĩ 继ç»Ń", + "çŃī æĸ¹å¼ı", + "家 ä¼ģä¸ļ", + "交 ä»ĺ", + "çĤ¹ è¯Ħ", + "ç»ĵ ç®Ĺ", + "ä¹Ł åı¯", + "å¤ĸ æ±ĩ", + "è¿Ļç§į æĥħåĨµ", + "æİĪ äºĪ", + "å¸ĥ ç½®", + "æĪIJç«ĭ äºİ", + "é¢Ħ èѦ", + "管çIJĨ 人åijĺ", + "å©ļ 礼", + "ç»ĵæĿŁ åIJİ", + "åħ¥ éĢī", + "æĹł æ¯Ķ", + "åĴĮ åıijå±ķ", + "çĻ½ éħĴ", + "çİ© åħ·", + "ä¸ĩ ç¾İåħĥ", + "çļĦ æĪIJ绩", + "æĭį çħ§", + "èĢĥèĻij åĪ°", + "ä¼ģä¸ļ åıijå±ķ", + "äºĨ 个", + "çĶŁ æ°Ķ", + "çļĦ 女人", + "äºĶ åįģ", + "çĪ· çĪ·", + "纽 约", + "éĥ½ 被", + "ä¸Ĭ 课", + "çĽ ¡", + "ä¼łç»Ł æĸĩåĮĸ", + "æ½ľ åľ¨", + "åıij å°Ħ", + "ä¸Ģ 身", + "éĺ² å®Ī", + "åĪ ®", + "é¢ĺ 缮", + "åľ¨ åĨħçļĦ", + "ç¾İ 好çļĦ", + "è¿ĻéĩĮ çļĦ", + "ä¸Ģ ä¸Ŀ", + "人 åĿĩ", + "åĢ¡ 导", + "身 åIJİ", + "æī© å±ķ", + "大 éŨ", + "å°± 被", + "该 é¡¹çĽ®", + "æŀ¶ æŀĦ", + "ä¸Ģ åı£", + "ä¿¡æģ¯ æĬĢæľ¯", + "å¼Ģ ä¸ļ", + "æĶ¶ åıĸ", + "ç½ij 页", + "æĶ¯ æı´", + "å°ģ éĹŃ", + "å¡ij éĢł", + "大 èĥĨ", + "å¿«éĢŁ åıijå±ķ", + "çľĭ ä¼¼", + "æ¸ Ŀ", + "è¿Ļæł· ä¸Ģ个", + "模 åĿĹ", + "注æĦı åĪ°", + "çł´ 解", + "èĩª ä»İ", + "åijµ åijµ", + "ä¹ĭ å¾Į", + "ä¹ĭ æĹħ", + "è·Ł æĪij", + "æ³ķ 人", + "æİĴè¡Į æ¦ľ", + "åĿļ å®Ī", + "好 å¤Ħ", + "çŁ³ 头", + "并 å°Ĩ", + "èĪ ±", + "æŃ ĩ", + "两 岸", + "å¤ļ ä¹ħ", + "象 å¾ģ", + "个æĢ§ åĮĸ", + "çļĦ è§Ĵ度", + "å¸ Ĩ", + "ç¦ı å·ŀ", + "æŁ¥ å¤Ħ", + "两 åĽ½", + "åIJ¸å¼ķ äºĨ", + "é¦ĸ å¸Ń", + "大 åĵ¥", + "é¤ Ĭ", + "涨 å¹ħ", + "éĢī çĶ¨", + "許 å¤ļ", + "èIJ½ æĪ·", + "åĵĪ å°Ķ", + "åĵĪå°Ķ 滨", + "åģļ ä»Ģä¹Ī", + "以 åħį", + "é¾ į", + "æĹł éľĢ", + "åĪ°åºķ æĺ¯", + "æĢ ¡", + "åijĬè¯ī ä½ł", + "éĺ² æ°´", + "è¿Ļ æŶåĢĻ", + "欢 ä¹IJ", + "转 åIJij", + "è¿Ļ个 åľ°åĽ¾", + "åħ¥ é©»", + "èįī åİŁ", + "æĹ¶ä»£ çļĦ", + "åıĺ åĬ¨", + "åĬłå¼º 对", + "åģ¶ å°Ķ", + "å®Ī æĬ¤", + "æ°Ķ 温", + "人 éĹ´", + "æľĿ é²ľ", + "ç»ı è´¹", + "åĽŃ æŀĹ", + "å·¥ åľ°", + "è§Ħ æł¼", + "åĩł åįģ", + "è¯ķ åĽ¾", + "å¦ ĥ", + "éĤ£ æŶåĢĻ", + "å¼ĺ æī¬", + "ä¸ļ çķĮ", + "çļĦ éĢŁåº¦", + "ä¼ļ ä¸įä¼ļ", + "èIJ¥ æĶ¶", + "å°ıå¾® ä¼ģä¸ļ", + "çľĭ è¿ĩ", + "æĬĬ ä»ĸ", + "éģµ 循", + "è¿Ļ è¾¹", + "没æľī 人", + "å£ ¶", + "æ¹ĸ åįĹçľģ", + "æŀģ åħ¶", + "çļĦ人 çĶŁ", + "ä»ĸ è¿ĺ", + "转åĮĸ 为", + "èµ° è¿ĩ", + "æĬ± çĿĢ", + "çīĽ 奶", + "ä¸ĩ 亩", + "å¿ĥ æĢģ", + "æĹ¥å¸¸ çĶŁæ´»", + "ä½ĵ æ£Ģ", + "æĻ ĥ", + "çŃī é¢ĨåŁŁ", + "æĩī 該", + "åı¯ä»¥ çľĭåĪ°", + "æī¾ ä¸įåĪ°", + "èĢģ å¹´", + "æĬĬ æĪij", + "积 åĪĨ", + "梳 çIJĨ", + "ç» ³", + "çļĦ æĶ¿æ²»", + "å¸Ŀ åĽ½", + "éĻª ä¼´", + "æ´Ľ éĺ³", + "åħ¬ æŃ£", + "å¼Ģ åı£", + "çī¹èī² çļĦ", + "åĽ° å¢ĥ", + "ä¸Ĭ æľī", + "ç«ĭ ä½ĵ", + "æīĵ å·¥", + "åķ¤ éħĴ", + "åľ¨ éĤ£éĩĮ", + "éĤ£ è¾¹", + "个 åĪ«", + "ä¸Ģå®ļ æĺ¯", + "çļĦéĩįè¦ģ æĢ§", + "主 å¼ł", + "åĴĮ æľįåĬ¡", + "ä¸Ĭ ç½ij", + "è¡¥ åĬ©", + "åıª éľĢ", + "å¼ ¦", + "éģ ®", + "åĬĽ äºī", + "度 è¿ĩ", + "èij ¬", + "é¡¿ æŶ", + "éĦ ī", + "纺 ç»ĩ", + "åľ° åĿĹ", + "ä¿¡çĶ¨ åį¡", + "ç½ļ 款", + "åijĬè¯ī æĪij", + "éĽ Ļ", + "书 çĶ»", + "è¨Ń è¨Ī", + "æĢ» ä¼ļ", + "åĪ¤ åĨ³", + "ä¿¡ èªī", + "个 èĤ¡", + "å¹³ 常", + "æĢİ éº¼", + "ä½ĵ çİ°åľ¨", + "é»Ħ æ²³", + "åĽĽå·Ŀ çľģ", + "羣 缸", + "åIJĦ项 å·¥ä½ľ", + "åĬ¨ åijĺ", + "å³° ä¼ļ", + "ä¸Ģ æľŁ", + "æľī ä¸Ģå®ļçļĦ", + "é«ĺ度 éĩįè§Ĩ", + "ç¹ģ èį£", + "åıijçİ° äºĨ", + "ç½ij 红", + "æīĭ æ³ķ", + "家 åĽŃ", + "仪 åĻ¨", + "è¾ĥ ä½İ", + "çļĦ å®īåħ¨", + "æ¡ IJ", + "ä»ĺ 款", + "æĬij åĪ¶", + "åįĵ è¶Ĭ", + "æŃ£ éĿ¢", + "åĵ ij", + "强 åĪ¶", + "ä»Ĭ天 çļĦ", + "æĪĺ èĥľ", + "楼 å¸Ĥ", + "æĭ¿ ä¸ĭ", + "é¢ľ åĢ¼", + "举 éĥ¨", + "çłĶ åĪ¶", + "çļĦ æĪĺçķ¥", + "åľ¨ ä¸Ģ个", + "ä¸ī 人", + "å®Į äºĨ", + "æĸ° æĬĢæľ¯", + "ç»ıæµİ æķĪçĽĬ", + "å¯Į æľī", + "æ¾³ æ´²", + "åĬ© çIJĨ", + "é¢Ĩ åıĸ", + "è° Ń", + "çĩĥ çĥ§", + "ç´ł åħ»", + "éĤĦ æľī", + "è¿Ľ èĢĮ", + "ä»Ģä¹Ī æĺ¯", + "çłĶ究 ä¸Ńå¿ĥ", + "éĢĤ çĶ¨äºİ", + "æİ¥ æĶ¶", + "失 æľĽ", + "äºĮ 级", + "éĹ´ çļĦ", + "åİŁ æłĩé¢ĺ", + "èªį çĤº", + "æį ¡", + "对 çĿĢ", + "对 éĿ¢", + "ä¸Ń åİŁ", + "éĵ ĥ", + "çĶŁäº§ çļĦ", + "åıijå¸ĥ ä¼ļ", + "士 åħµ", + "è¿Ļ åı¥è¯Ŀ", + "ç¼´ 纳", + "ä¸Ģ个 个", + "åѸ çĶŁ", + "çĸij éĹ®", + "交 èѦ", + "示èĮĥ åĮº", + "天 使", + "åľ¨ ä¸Ĭæµ·", + "åIJĮ æĻĤ", + "è½» æĺĵ", + "åĶ¯ä¸Ģ çļĦ", + "çĥŃ éĹ¹", + "ä¹IJ è§Ĥ", + "çļĦ 身份", + "åĸĦ äºİ", + "大 åİħ", + "èĤ¯å®ļ æĺ¯", + "éĺ² çģ«", + "å¤ĸ åĩº", + "æį® 说", + "é¡¹çĽ® çļĦ", + "ä¸Ģ åı°", + "èĻļ åģĩ", + "ä¸Ģ ç¬Ķ", + "ç«ĭ æ³ķ", + "严 èĤĥ", + "æī¿ åĬŀ", + "åįģ åĩł", + "çļĦ 空éĹ´", + "æľ¬ ç½ijç«Ļ", + "åģļ å¾Ĺ", + "ä¿Ŀ 温", + "æľĪ åĪĿ", + "åľ¨ ç½ijä¸Ĭ", + "åIJĦ æĸ¹éĿ¢", + "ä¸ī 天", + "交æĺĵ æīĢ", + "解 æŀIJ", + "åħļ ä¸Ń央", + "è¿Ľ åĩºåı£", + "åĴĮ 社ä¼ļ", + "次 æķ°", + "ä¹ĭ 家", + "ç»´ 度", + "æ´¾åĩº æīĢ", + "产çĶŁ äºĨ", + "带 æľī", + "å¾Ī 强", + "æľīäºĽ 人", + "å¹´ åIJİ", + "äºĨ 许å¤ļ", + "å¯Ĩ 度", + "åѦ æľŁ", + "çıł æµ·", + "æľĢå¤ļ çļĦ", + "è¾¹ ç¼ĺ", + "容 éĩı", + "第äºĮ 个", + "ä¸Ģ缴 æĺ¯", + "ä¸į ç¦ģ", + "æŃ ²", + "ä»ĭç»į äºĨ", + "ä¼ĺ éĽħ", + "æ¯Ķ è¼ĥ", + "èģĮ ä½į", + "温 æŁĶ", + "æľī éĴ±", + "æľĢ é«ĺçļĦ", + "åįļè§Ī ä¼ļ", + "ä¸į æĪIJ", + "éĶĻ äºĨ", + "è¯ģ çĽij", + "è¯ģçĽij ä¼ļ", + "æĪIJ 人", + "åĿĩ åĮĢ", + "æľī åĪ©", + "è¶Ĭ åįĹ", + "æīĵ äºĨ", + "好 åIJĥ", + "ç³» çµ±", + "è·Ł éļı", + "çļĦ åľ°ä½į", + "æŃ£ å¦Ĥ", + "ç¨į å¾®", + "åį° åıij", + "åĪĽ ç«ĭ", + "é£İ åħī", + "å°Ĩ æĪIJ为", + "ä¸į é«ĺ", + "é¢ij ç¹ģ", + "设 æľī", + "ä¼ ŀ", + "æĭĨ éĻ¤", + "å½± åĥı", + "æ¸Ĺ éĢı", + "å¹´ å¼Ģå§ĭ", + "ç½ij æĺĵ", + "è¦ģ åģļ", + "çĶµåĬ¨ 车", + "羣 å¿ĥ", + "æµ· åĨĽ", + "ä¼ł æĿ¥", + "å·® åĪ«", + "è°¨ æħİ", + "çĥŁ åı°", + "åįĥ å¹´", + "è¯ģ å®ŀ", + "çIJ ª", + "çļĦ åħ·ä½ĵ", + "åĪ° å¤Ħ", + "ä¸į å®ľ", + "èľ Ģ", + "èĥ½åĬĽ åĴĮ", + "çīº çī²", + "çļĦ éĴ±", + "大 éĺŁ", + "é¦ĸ è¦ģ", + "ä¸į æĦ¿", + "çİ« çij°", + "人æ°ij ç½ij", + "è¿ĺæĺ¯ è¦ģ", + "åĽĽ å¹´", + "æįŁ 伤", + "çļĦ åģļæ³ķ", + "éĿ Ī", + "è¡Ķ æİ¥", + "åIJĪ æĪIJ", + "没 人", + "éŨ æ§Ľ", + "ä¿¡ è´·", + "çļĦ 缸åħ³", + "举 é£İ", + "社 ä¿Ŀ", + "ä¸ĭ 游", + "åĿĹ éĴ±", + "è¿ĩ åIJİ", + "çļĦ åºĶçĶ¨", + "é¥ ¶", + "é¢ģ åıij", + "ä¸Ģ å¤Ħ", + "åįİ å¤ı", + "为 ä¼ģä¸ļ", + "åıª ä¼ļ", + "ä¾µ 害", + "çļĦ åĬŁèĥ½", + "åѸ ç¿Ĵ", + "ä¸Ńåįİ æ°ijæĹı", + "åıijå¸ĥ äºĨ", + "è¿İ æİ¥", + "æĪij èĩªå·±", + "è¿ĺ éľĢè¦ģ", + "太éĺ³ èĥ½", + "åİ» ä¸ĸ", + "æĺ¯ ä½ł", + "åIJĪ åĬĽ", + "ç»ĺ çĶ»", + "åı° åĮĹ", + "çĿ£ ä¿ĥ", + "åĮĹ éĥ¨", + "æľī å¤ļå°ij", + "å¾Ī éĩįè¦ģ", + "åĪĴ åĪĨ", + "åı· 线", + "æĶ¾ 大", + "ä¼ļ 被", + "èİ· å¥ĸ", + "ä¹ĭ åĨħ", + "失 åİ»äºĨ", + "çݩ家 们", + "éĩĩ éĽĨ", + "å£ ¹", + "家 ä¼Ļ", + "çĻ½ 天", + "åĽłä¸º ä»ĸ", + "社ä¼ļ æ²»çIJĨ", + "å¼Ģ åĪĽ", + "çĶµ ç¼Ĩ", + "æĸ° ä¸Ģ代", + "并 è´Ń", + "å°± å·²ç»ı", + "çļĦ 社ä¼ļ", + "éĻ¤ éĿŀ", + "åı¯ä»¥ çĶ¨", + "å© ī", + "æ¯Ķè¾ĥ 好", + "å®ŀ ä¸ļ", + "åĪĽ åĬŀ", + "æıIJ èµ·", + "é» ĥ", + "ä½ı åľ¨", + "å¸Ĥ æĶ¿", + "éĿ¢ä¸´ çļĦ", + "èĥ½ åľ¨", + "çŁŃ çŁŃ", + "羣 人", + "æĺİ æĺİ", + "èµĦ åĬ©", + "çļĦ ä¸įåIJĮ", + "å°ı æľĭåıĭ", + "é¢ĺ æĿIJ", + "ç¾İ åij³", + "æĺŁ åº§", + "ä¸į ä¸Ģæł·çļĦ", + "çľĭ ä¸Ĭåİ»", + "ä¸Ģ æł¹", + "广 å·ŀå¸Ĥ", + "åıijçĶŁ çļĦ", + "é«ĺ ç§ijæĬĢ", + "ä¸Ģ è¾ĪåŃIJ", + "交 åıī", + "ä½ĵç³» 建设", + "åĽłä¸º æĪij", + "çıį æĥľ", + "ä¸Ĭ åѦ", + "æĪĺ æľ¯", + "æѤ ç±»", + "交 å¾Ģ", + "æĮī æij©", + "人们 çļĦ", + "åħ¶ 實", + "åİŁ æĿIJæĸĻ", + "渴 æľĽ", + "缸 å¤Ħ", + "å¾® å¾®", + "æ® ·", + "ä¹ĺ åĿIJ", + "å¼Ģå±ķ äºĨ", + "é«ĺ åĵģè´¨", + "æĹłäºº æľº", + "ä¸įæĺ¯ å¾Ī", + "çļĦ æĬķèµĦ", + "èĬĤ çľģ", + "èĩ ī", + "ç²¾ éĢī", + "çļĦ æłĩåĩĨ", + "åįĹ éĥ¨", + "认è¯Ĩ åĪ°", + "å¹³ éĿĻ", + "èĹ ¥", + "æī« é»ij", + "æī«é»ij éĻ¤", + "æī«é»ijéĻ¤ æģ¶", + "éĢĻ 種", + "建çŃij éĿ¢ç§¯", + "ç¡® ç«ĭ", + "管çIJĨ åĬŀæ³ķ", + "æĦı å¿Ĺ", + "ä¸ ¨", + "让 åŃ©åŃIJ", + "æķij çģ¾", + "å½ĵ ä»Ĭ", + "çģ« çģ¾", + "åIJĦ éĥ¨éŨ", + "ä¾µ çĬ¯", + "æ¯ı åij¨", + "æı ½", + "ä¸Ģ次 æĢ§", + "åħ¶ä»ĸ 人", + "éĶĻ è¿ĩ", + "ä¸İ åħ¶", + "åĭĩ æ°Ķ", + "çĩĥ æ°Ķ", + "é¦ĸ å±Ĭ", + "æľį 饰", + "ç² ¥", + "å®Į æ¯ķ", + "å°± æĬĬ", + "åĬŀäºĭ å¤Ħ", + "ä¸Ģä¼ļ åĦ¿", + "离 ä¸įå¼Ģ", + "å¦Ĥæŀľ æĤ¨", + "ä»ĵ åºĵ", + "导 å¸Ī", + "åIJĪéĢĤ çļĦ", + "毫 ç±³", + "å®īåħ¨ æĢ§", + "ä¾Ŀ çħ§", + "产ä¸ļ åĮĸ", + "ä½ł çľĭ", + "羣çļĦ å¾Ī", + "åѤ çĭ¬", + "éĺ² å¾¡", + "å¾Ī ç®Ģåįķ", + "é£İ æ°´", + "ä½Ĩ ä¹Ł", + "æݨ åĩºäºĨ", + "æ°ijèIJ¥ ä¼ģä¸ļ", + "çłģ 头", + "å¤įæĿĤ çļĦ", + "ç»ĦæĪIJ éĥ¨åĪĨ", + "åħħ满 äºĨ", + "è¿ij åĩłå¹´", + "çľģ æĶ¿åºľ", + "æľī å¿ħè¦ģ", + "éĻ ³", + "ä¹ĭ ç±»", + "ä¹ĭç±» çļĦ", + "æĢ§ ä»·", + "æĢ§ä»· æ¯Ķ", + "åķĨ åºĹ", + "å¸Ĥ åĢ¼", + "人æīį åŁ¹åħ»", + "æ·± åıĹ", + "管çIJĨ å±Ģ", + "æģIJ æĥ§", + "ä»ħ æľī", + "æĬµ è¾¾", + "æµ· åħ³", + "èµĭ äºĪ", + "äºĭ åĦ¿", + "ä»· éĴ±", + "æīĭ ä¸Ĭ", + "èĩª å¾ĭ", + "åħ³ çĪ±", + "享 æľī", + "éģĹ æĨ¾", + "å¾Īå¿« å°±", + "æĽ´ å¿«", + "æłĩ è¯Ĩ", + "åºĨ ç¥Ŀ", + "ä¹Ł 好", + "ä¸į æĺĵ", + "æĪij å¾Ī", + "æĶ¹éĿ© åıijå±ķ", + "å¤ĸ åľ°", + "æĬµ æĬ¼", + "è¯Ĺ 人", + "åİķ æīĢ", + "æĸ° åªĴä½ĵ", + "èĸ Ľ", + "è°Ī è¯Ŀ", + "ä¸Ģå®ļ ç¨ĭ度", + "èµ° åľ¨", + "æľĢ 强", + "åĬŁ çİĩ", + "åħ± è¯Ĩ", + "大 æ¡¥", + "ä¸ĭ æĸ¹", + "å¤ĸ èµĦ", + "ç¢ ±", + "å·¡ è§Ĩ", + "æ¹ĸåĮĹ çľģ", + "个 çĻ¾åĪĨ", + "个çĻ¾åĪĨ çĤ¹", + "çļĦ 责任", + "çļĦ åĵģçīĮ", + "åĬ© æݨ", + "åĪĽéĢł äºĨ", + "ä»» èģĮ", + "å¿« æį·", + "æĿij åºĦ", + "åİ» çľĭ", + "æīį èĥ½å¤Ł", + "å± ¤", + "æĪij 家", + "æĺ¯ä¸Ģ 款", + "ç¾ ħ", + "åĨ° éĽª", + "æŀģ 大", + "çģ¯ åħī", + "éĨ ĭ", + "ä¸İ åħ¶ä»ĸ", + "æıIJåĩº çļĦ", + "éĿł è¿ij", + "è°ĥ åĬ¨", + "å°½ åı¯èĥ½", + "åıij åĬĽ", + "ç»Ļ 她", + "éĢĤ éĩı", + "è·¨ åĽ½", + "åħĪ è¡Į", + "æĸ° æĿIJæĸĻ", + "ä½ľ äºĨ", + "满 äºĨ", + "ä¸į 满", + "çļĦçľ¼ çĿĽ", + "çľĭ å¾Ĺ", + "è¿Ļ ä¸Ģ次", + "é½IJ åħ¨", + "çļĦä¸Ģ éĥ¨åĪĨ", + "ä¸ Ļ", + "æ¸ħ æĸ°", + "說 æĺİ", + "身边 çļĦ", + "æīĢæľī 人", + "å½° æĺ¾", + "è± ¹", + "åį ¿", + "è¿IJ 转", + "æĮĩ å¼ķ", + "å¸Ĥ åħ¬å®īå±Ģ", + "åıĤ å±ķ", + "ä¹ĭ æŶ", + "éĩijèŀį æľįåĬ¡", + "èµĦæľ¬ å¸Ĥåľº", + "èĥ½ 让", + "å¿ĺ äºĨ", + "天 åłĤ", + "æ¯Ķå¦Ĥ 说", + "éĬĢ è¡Į", + "èĽĭ ç³ķ", + "çĶ ©", + "æł¸ å®ŀ", + "æĻ® 京", + "ä¼ĺ ç¾İ", + "åı£ èħĶ", + "漫 çĶ»", + "çľ¼ éĩĮ", + "äºĨ ä¸ĭæĿ¥", + "æĪij们 ä¹Ł", + "ä¾ į", + "为 ä¸Ńå¿ĥ", + "å¥ĩ 迹", + "éĿĴ çĿIJ", + "æĪªèĩ³ 缮åīį", + "åĩº ä¾Ĩ", + "æĢ» åħ¬åı¸", + "å¼¥ è¡¥", + "ç®Ĺ æ³ķ", + "å·¥ä½ľ 室", + "æīĢ以 æĪij", + "æ°´ åĪĨ", + "æīĢ å±ŀ", + "ä¸į 说", + "ä½Ĩæĺ¯ åľ¨", + "è¦ģ åİ»", + "åĪĽä¸ļ èĢħ", + "ä¸į æ¸ħæ¥ļ", + "åĽĽ åij¨", + "æĺ¯ ä»İ", + "çļĦ æł¹æľ¬", + "çģ ¶", + "æ¯Ľ æ³½", + "æ¯Ľæ³½ 举", + "æµ· åı£", + "åĽĽ åįģ", + "ä¹Ł 被", + "èģ ·", + "ä¸Ģ æīĭ", + "绩 æķĪ", + "çļĦ çĶ·äºº", + "书 ç±į", + "ä¸Ģ èĦ¸", + "大 äºİ", + "鼶 éĥ¨ä»¶", + "åħ³ æĢĢ", + "å¹³ ç±³", + "æļ´ éľ²", + "å¾Ĺ å¤ļ", + "ä¸ī 级", + "æľ¬ åij¨", + "两 èĢħ", + "对 ä¸ŃåĽ½", + "åıª è§ģ", + "欧 ç¾İ", + "å¦Ĥæŀľ æľī", + "å·²ç»ı æĺ¯", + "çľĭ å®Į", + "çģ« éĶħ", + "èµ IJ", + "ä¸Ģ éģį", + "æĦŁ åĨĴ", + "ç»ĵ å±Ģ", + "ä»ĵ åĤ¨", + "å®ŀ åľ°", + "åī¯æĢ» ç»ıçIJĨ", + "ä¹Łä¸į çŁ¥éģĵ", + "碰 åĪ°", + "åIJĪ 计", + "客æĪ· çļĦ", + "ç½Ĺ 马", + "æĦī å¿«", + "é£ Ľ", + "çĥŃ çĥĪ", + "伦 æķ¦", + "åĮ» ä¿Ŀ", + "éĺ¿éĩĮ å·´å·´", + "åĨį 说", + "为 åŁºç¡Ģ", + "çĶŁäº§ ç»ıèIJ¥", + "è¿ĻäºĽ 人", + "åĪĹ è½¦", + "æ²³åĮĹ çľģ", + "è¿Ļ 段", + "æ´»åĬ¨ ä¸Ń", + "å© ·", + "çĶŁ çIJĨ", + "ä¸ŃåĽ½ 人æ°ij", + "éĦ Ĥ", + "åIJ¬ åıĸ", + "å¤į ä¹ł", + "æľī çĽĬ", + "æĶ¶ æĭ¾", + "å¾Ī åı¯èĥ½", + "ç½ij绾 游æĪı", + "们 çļĦ", + "èµĭ èĥ½", + "éļ¾ å¾Ĺ", + "åĪĨ æīĭ", + "羣 è¯ļ", + "åħ¬åı¸ åľ¨", + "åĿĩ è¡¡", + "åı£ åij³", + "çīµ 头", + "ä¸ĢèĪ¬ çļĦ", + "轿 车", + "çŃī äºİ", + "æ²ī é»ĺ", + "æĪij éĥ½", + "å°ı ç¨ĭåºı", + "ä¸Ģ åī¯", + "æī¿ è½½", + "åľ° è´¨", + "çķĮ éĿ¢", + "çĶµ æľº", + "çĦ¦ èĻij", + "éĶĢåĶ® é¢Ŀ", + "æĸ° 车", + "ä¸Ĭ 游", + "主 æ¼Ķ", + "éļIJ ç§ģ", + "åıijå±ķ æĪĺçķ¥", + "çļĦ åĬªåĬĽ", + "å¼Ģ åħ³", + "解åĨ³ éĹ®é¢ĺ", + "çĿ£ 导", + "对 æĬĹ", + "å¾Īå¤ļ 人éĥ½", + "æĹł æķĪ", + "产åĵģ è´¨éĩı", + "å®ī å¿ĥ", + "åįİ äºº", + "ä¸į 符åIJĪ", + "èĩª 家", + "éĺµ å®¹", + "çļĦ åIJĦç§į", + "çļĦ çIJĨ念", + "çļĦ æĸĩåĮĸ", + "为 èĩªå·±", + "å±± æ°´", + "游 æ³³", + "éľĩ èį¡", + "çĶŁæ´» æĸ¹å¼ı", + "è¿ľ 离", + "çŁ³ åĮĸ", + "æѤ äºĭ", + "æĺ¯ çľŁçļĦ", + "çļĦ æ¯Ķä¾ĭ", + "çĶ¨ çĶµ", + "奥è¿IJ ä¼ļ", + "ä¿Ŀ å®ī", + "èĽĭçĻ½ è´¨", + "çļĦ å¿ĥçIJĨ", + "å· «", + "åı· çłģ", + "æ°Ķ ä½ĵ", + "åıij æĶ¹", + "åıijæĶ¹ å§Ķ", + "åĮ» å¸Ī", + "æ¶Ĥ æĸĻ", + "æĺ Ĭ", + "å¸Ĥ 级", + "ä¸ĸçķĮ çļĦ", + "åĪĨåĪ« æĺ¯", + "çł´ 产", + "ä¸Ģ æĿ¯", + "æĭī å¼Ģ", + "å¹³ åĩ¡", + "çļĦ åıijçĶŁ", + "åĬ¨ æīĭ", + "ä¸Ģ缴 以æĿ¥", + "æīĭ å·¥", + "éĩĮéĿ¢ çļĦ", + "æĹł åħ³", + "ä»ĭ åħ¥", + "èµ° ä¸Ĭ", + "å°±æĺ¯ è¦ģ", + "å¹´ éĹ´", + "åĩº çı¾", + "å½± éŁ¿", + "å¹ħ 度", + "éĽ ģ", + "éģĵ åħ·", + "缮çļĦ åľ°", + "åIJİ èĢħ", + "ä¸Ĭ æ¼Ķ", + "äºĨ åĩł", + "æ®ĭçĸ¾ 人", + "å¿Ļ ç¢Į", + "æĺ¯åIJ¦ æľī", + "并 对", + "ä¼ļ 导èĩ´", + "æ°´ åºĵ", + "ç»Ĩ èĩ´", + "åIJİ æĤĶ", + "å¿ĥ æĢĿ", + "åģļ äºĭ", + "åİĤ æĪ¿", + "çĿ ¿", + "è¿IJèIJ¥ åķĨ", + "头 éĥ¨", + "çļĦ è§Ĵèī²", + "æĺ¯ ä»ĸ", + "æĹ¢ æľī", + "å°ıæŶ åĢĻ", + "强 åĬ²", + "主 æĴŃ", + "åħ¨åĽ½ åIJĦåľ°", + "æį ı", + "æįŁ åĿı", + "åķĨ ä¼ļ", + "ä¿Ŀ ç½Ĺ", + "çľģ å¸Ĥ", + "éļ§ éģĵ", + "æľī ä¸įå°ij", + "è¦ģ åľ¨", + "建设 é¡¹çĽ®", + "ç³ĸ å°¿", + "ç³ĸå°¿ çĹħ", + "æĿ¡ä»¶ ä¸ĭ", + "ä¼ĺè´¨ çļĦ", + "é¦ĸ åıij", + "å½ĵæŶ çļĦ", + "丰 çĶ°", + "大 çĽĺ", + "缸 继", + "å®ģ å¤ı", + "åħ¥ ä½ı", + "æĪij è¿ĺ", + "åħĭ æĸ¯", + "å®ļ ä»·", + "å¹³æĸ¹ åħ¬éĩĮ", + "çļĦ çŁ¥è¯Ĩ", + "æĪij们 ä¼ļ", + "åħĥ å®Ŀ", + "ä½ĵ éĩį", + "è³ £", + "对 æĪij们", + "çŁ³ 家", + "çŁ³å®¶ åºĦ", + "ç²¾ åįİ", + "å½¢ çĬ¶", + "åıĹ åĪ°äºĨ", + "ä¿® 订", + "ç¾İ åľĭ", + "é«ĺ æ¸ħ", + "çľ¼ éķľ", + "è§īå¾Ĺ èĩªå·±", + "带 ç»Ļ", + "åĶ® ä»·", + "éŨ 票", + "åŃķ å¦ĩ", + "çĶµè§Ĩ åı°", + "åıij ä½ľ", + "çļĦ åij³éģĵ", + "éķ¿ è¿ľ", + "åħ¬åħ± æľįåĬ¡", + "æŃ£å¸¸ çļĦ", + "æľī è¿ĩ", + "é£İ æĥħ", + "æ¯Ķ éĩį", + "åIJ »", + "管çIJĨ å·¥ä½ľ", + "综åIJĪ æĢ§", + "å·² 被", + "说 èµ·", + "æİĴ æ°´", + "ä¸įæĸŃ åľ°", + "æĥħ æĢĢ", + "è¾ĵ éĢģ", + "è¿ĩ æķı", + "çļĦ åı¯èĥ½æĢ§", + "æľį çĶ¨", + "æľī 许å¤ļ", + "å§Ķ åī¯ä¹¦è®°", + "åĮĸå¦Ĩ åĵģ", + "æļĤ åģľ", + "æĬķèµĦ 人", + "çıŃ çº§", + "说 çĿĢ", + "åįĹ åĮĹ", + "åĪĨ è¡Į", + "çıł å®Ŀ", + "å¯ ¶", + "å¢ŀ å¤ļ", + "被 åĬ¨", + "çī¹æ®Ĭ çļĦ", + "éĹľ ä¿Ĥ", + "çļĦ èĦ¸", + "æĥ Ł", + "ä¸į ä¸Ģå®ļ", + "ç¶ Ń", + "çģ« çĪĨ", + "秣 éĩij", + "çŀ §", + "éĩį 建", + "è· ª", + "ä¸Ģ 種", + "çļĦ åIJĪä½ľ", + "å®ī æħ°", + "ä»į æĺ¯", + "ä¸ĵä¸ļ åĮĸ", + "è°ĥ 解", + "ä¸į 妨", + "éĢĻ æĺ¯", + "å¿ħ éłĪ", + "ä¼Ĭ æľĹ", + "å¾Ĺ äºĨ", + "æľįåĬ¡ å¹³åı°", + "å§ ¬", + "åħĪ éĶĭ", + "çİĭ åŃIJ", + "çļĦä¸Ģ åĪĩ", + "æĢ» çIJĨ", + "åĵ ¼", + "çª ij", + "çļĦå¿ĥ æĥħ", + "çļĦ éĩį大", + "çij Ł", + "ä¸Ģ ç¬ij", + "åıijå±ķ ä¸Ń", + "åģ¥åº· åıijå±ķ", + "åĵģçīĮ çļĦ", + "ç¦ ®", + "ä½Ļ 人", + "ä»Ĭå¹´ 以æĿ¥", + "æķ° çłģ", + "çŃ¾ è¯ģ", + "åİ» æī¾", + "åŁºéĩij ä¼ļ", + "æĬ± æĢ¨", + "æŃ£ å½ĵ", + "çıŃåŃIJ æĪIJåijĺ", + "ä¸į åIJĪæł¼", + "åĪ¶ å®ļäºĨ", + "ç¼ĵ æħ¢", + "åĪ¶ 约", + "æłı 缮", + "å¸Ĥåľº ç»ıæµİ", + "ç»ĦæĪIJ çļĦ", + "严 å³»", + "æĹ¥ 讯", + "ä¸ĢçĤ¹ çĤ¹", + "æĺ¯ æĢİä¹Ī", + "çļĦ çħ§çīĩ", + "éĺ» æŃ¢", + "模 ç³Ĭ", + "ç¼ ¸", + "éģķ åıį", + "æIJ¬ è¿ģ", + "éĩij éĴ±", + "å½ ¬", + "ä¸į å®ī", + "æĪĺçķ¥ åIJĪä½ľ", + "å¡« åĨĻ", + "讲 究", + "åħħåĪĨ åĪ©çĶ¨", + "èĥ½ å¤ł", + "èij¡èIJĦ éħĴ", + "éĩĩçĶ¨ äºĨ", + "åľ¨ ä»Ĭå¹´", + "ä¸Ńå°ı åѦ", + "åľ¨ æĦı", + "çļĦ åİĭåĬĽ", + "ä¸į 幸", + "åĪ¶ èį¯", + "åı¯ä»¥ 让", + "被 è¯Ħ为", + "ç»Ĩ èıĮ", + "æĪı åī§", + "åįĬ 导", + "åįĬ导 ä½ĵ", + "è§Ĩ è§Ĵ", + "åĸľ æŃ¡", + "å¾ģ æĶ¶", + "è°ĭ åĪĴ", + "æŀģ 大çļĦ", + "çĤ¹ èµŀ", + "è®°èĢħ ä»İ", + "两 åIJį", + "èĩª åĬ©", + "èµ· æŃ¥", + "æĬ¤ 士", + "å®Ŀ 马", + "太 åŃIJ", + "å°ıå°ı çļĦ", + "温 æ³ī", + "åĩºç§Ł 车", + "秣 æĪ¿", + "两 家", + "éľĩ æĴ¼", + "ç§ī æī¿", + "ä¸Ģ件 äºĭ", + "çĥĪ 士", + "å®ĺ åħµ", + "转 身", + "ä¹IJ åĽŃ", + "çĻĮ çĹĩ", + "模 èĮĥ", + "æĦ £", + "è¿ĩåİ» çļĦ", + "代 ä»·", + "çļĦ æ¦Ĥ念", + "åĩł çĻ¾", + "è´µ éĺ³", + "æĭħ 忧", + "éĢĤ å®ľ", + "çݯå¢ĥ ä¿ĿæĬ¤", + "çĥ «", + "ä½ł æĥ³", + "æѤ åIJİ", + "ä½ł ä¹Ł", + "çį İ", + "éĻ¤ æѤ", + "éĻ¤æѤ ä¹ĭå¤ĸ", + "è°ĥ 度", + "ç§ij 缮", + "æīĢ说 çļĦ", + "åĬ ĩ", + "忽 è§Ĩ", + "ä¸ī 次", + "ä¸Ģ æĹ¥", + "åŀĤ 缴", + "ç«ŀ æĬĢ", + "éĿ¢ åĮħ", + "大 æĪĺ", + "æIJº 带", + "å¦Ĥæŀľ 没æľī", + "åħ» æĪIJ", + "åĩº è¡Ģ", + "çĪ±å¥½ èĢħ", + "æīĵ éĢļ", + "èµ· è¯ī", + "åijĪ çİ°åĩº", + "æŃĮ æīĭ", + "åľ¨ å¤ĸ", + "é¢Ĩ导 å¹²éĥ¨", + "åĨ ¥", + "èĪĨ 论", + "æıIJ åıĸ", + "éĺ¿ å°Ķ", + "æľĽ çĿĢ", + "ä¸ī äºļ", + "è² ¡", + "åĪ ·æĸ°", + "æĻļ æĬ¥", + "è¿ĺæľī ä¸Ģ个", + "åĨ° ç®±", + "ç½ij çĤ¹", + "åĩº åħ·", + "强çĥĪ çļĦ", + "æĪij çĽ¸ä¿¡", + "å¸ĮæľĽ èĥ½", + "çīĻ 齿", + "äºĭ å®ľ", + "ä¸ļåĨħ 人士", + "代 æĽ¿", + "åıĺ å½¢", + "éĽ ²", + "è°ĥ æݧ", + "åĪĽæĸ° åĪĽä¸ļ", + "æĭĨ è¿ģ", + "æł¸ æŁ¥", + "éĢ Ĺ", + "åħ¥ åѦ", + "æĦı åIJij", + "æı Ľ", + "ä¸ĭ 次", + "ä¼ł è¾ĵ", + "ä»ĸ们 åľ¨", + "èĢĮä¸Ķ è¿ĺ", + "æĹ¥ åľ¨", + "æķĻ è®Ń", + "æ´» çĿĢ", + "çļĦ æľīæķĪ", + "å¤įå·¥ å¤į", + "å¤įå·¥å¤į 产", + "æĺ¯ä¸Ģ 件", + "çŃī çĿĢ", + "å¾ ©", + "åĭĩ æķ¢", + "éģŃ åıĹ", + "å¥Ķ é©°", + "讲 座", + "说 å®Į", + "ç»Ļ åĩº", + "è° ¦", + "è¯Ĭ çĸĹ", + "çĽ² 缮", + "客 è¿IJ", + "å°± è¿ŀ", + "å¼Ģ åħĥ", + "å¼Ģåħĥ æ£ĭçīĮ", + "ä¸įæĸŃ æıIJåįĩ", + "çĶ¨æĪ· çļĦ", + "æĴ ķ", + "ä¾Ľ æ°´", + "ç¶ĵ æ¿Ł", + "ä¸Ń åĮ»èį¯", + "èģĶ æĥ³", + "åħ¬äº¤ 车", + "èĪª çıŃ", + "æĬĢ è¡ĵ", + "å¼ķèµ· çļĦ", + "å° ¹", + "èµĦ æ·±", + "åĽ½èµĦ å§Ķ", + "èĺ Ń", + "é¼» åŃIJ", + "éĹ ½", + "æİĴ éĺŁ", + "è§Ĥ åħī", + "éģĹ åĿĢ", + "举 京", + "é¥Ń åºĹ", + "ä¸įæĸŃ çļĦ", + "å°±æĺ¯ ä¸Ģ个", + "éķ¿ ä¹ħ", + "çļĦ è§ĤçĤ¹", + "å¨ ¶", + "æĪij çİ°åľ¨", + "çķ °", + "å¾Ĺ åĩº", + "å¿ħ å®ļ", + "ä¸į åıĹ", + "åıª éľĢè¦ģ", + "åĽ° æī°", + "ç§ijåѦ æĬĢæľ¯", + "çīĽ èĤī", + "è¾ĥ é«ĺçļĦ", + "è·ij æŃ¥", + "æ² ¾", + "èı© èIJ¨", + "æľĢ å¾Į", + "ä¿Ŀ å¯Ĩ", + "æ²» å®ī", + "éĤ ±", + "常 è¯Ĩ", + "èĦ¸ èī²", + "åĮĹ å¤§", + "æ±ĩ èģļ", + "æijĨ èĦ±", + "é¾Ļ头 ä¼ģä¸ļ", + "女 åıĭ", + "çŃī å·¥ä½ľ", + "ä¸Ń ç¾İ", + "èģĮ åľº", + "èĦij è¢ĭ", + "åĨĻ çļĦ", + "饲 æĸĻ", + "åĬ³ åĬ¨åĬĽ", + "å± ¯", + "æĮģ èĤ¡", + "åĽ¾ åĥı", + "è¿ĩåİ» äºĨ", + "è² ¨", + "è¾ ²", + "éĹ® æĪij", + "è·Ł ä½ł", + "çĶŁ æŃ»", + "审 ç¾İ", + "é¢Ĺ ç²Ĵ", + "ä¸Ń æĸ¹", + "åĬł çĥŃ", + "æĹħè¡Į 社", + "çĻ¼ çĶŁ", + "ä¸į åłª", + "åĤ ·", + "æ¥ ł", + "åĬŀ æ¡Ī", + "æŁ Ħ", + "æĹ¢ æĺ¯", + "å¤Ħ åĪĨ", + "羣å®ŀ çļĦ", + "æĬ¥ 纸", + "å¸Ī çĪ¶", + "å®īå¾½ çľģ", + "åī¯ 主å¸Ń", + "ä¹ĭ éģĵ", + "导 å¼¹", + "åŃ¦æł¡ çļĦ", + "åŁİå¸Ĥ çļĦ", + "è°Ī åĪ°", + "æ¢ Ĺ", + "å¹³ éĿ¢", + "说 ä»Ģä¹Ī", + "é¢ij çİĩ", + "éķ¿ ä¸īè§Ĵ", + "çļĦ åĪ©çĽĬ", + "é» ¨", + "è±Ĩ èħIJ", + "å®ŀéĻħ æĥħåĨµ", + "æŀĹ ä¸ļ", + "纪æ£Ģ çĽijå¯Ł", + "ä½ı éĻ¢", + "çļĦ æķ´ä½ĵ", + "åīį è¡Į", + "æĮ ¨", + "çħ¤ çŁ¿", + "åī¯æĢ» è£ģ", + "å°ı åIJĥ", + "æŀģ 端", + "å©Ĩ å©Ĩ", + "çİ° è´§", + "è¯Ĺ æŃĮ", + "éĴ¥ åĮĻ", + "缩 çŁŃ", + "ä½Ĩ è¿Ļ", + "æĸ° åĵģ", + "è¿Ļ 对", + "çŁ¥åIJį 度", + "å¿ĹæĦ¿ æľįåĬ¡", + "大 å±Ģ", + "è¡¡ éĩı", + "ä½ĵçİ° äºĨ", + "æ¡ĥ èĬ±", + "åIJ¸å¼ķ åĬĽ", + "åł ¤", + "æĵħ éķ¿", + "åĴ Ĵ", + "缸 æľº", + "ä¸Ģ ç«Ļ", + "ä¸Ģç«Ļ å¼ı", + "æľĢ ç¾İ", + "æ°¸ ä¹ħ", + "çļĦ éĥ¨åĪĨ", + "åĪĨ å·¥", + "å·¥ç¨ĭ 建设", + "æIJŃ è½½", + "æ°´ ä¸Ń", + "èĮ ¨", + "çļĦ æĵįä½ľ", + "绣 æ²»", + "çķħ éĢļ", + "åħļçļĦ åįģ", + "è¼ ¸", + "æ¸ ¬", + "ç¾İ è§Ĥ", + "ä¸į åĪ©", + "åıį æĢĿ", + "éªĦ åĤ²", + "æłĩ çļĦ", + "æĿĢ 人", + "éĺ¿ å§¨", + "é£Ł æĿIJ", + "åIJĥ çļĦ", + "åIJİ åĨį", + "çŁ £", + "两 侧", + "æ¸ħ æ°´", + "è¿Ľ çIJĥ", + "å¼Ģå§ĭ äºĨ", + "åIJ¬ äºĨ", + "çĦĬ æİ¥", + "çŁ ®", + "å¨ Ł", + "为 人", + "éĢģ ç»Ļ", + "åĨĴ éĻ©", + "æķ ·", + "ç»Ī æŃ¢", + "æīį çŁ¥éģĵ", + "è¿IJ æ°Ķ", + "éĢļ é£İ", + "æĥĬ 讶", + "ç§ijåѦ éĻ¢", + "æıIJ éĹ®", + "太 åİŁ", + "缸åIJĮ çļĦ", + "ä» ķ", + "èģ ĸ", + "æĥħ æ³ģ", + "é¢Ĩ导 人", + "åĩºæĿ¥ äºĨ", + "沿 线", + "éĻ ½", + "æĦŁ 覺", + "ä»į åľ¨", + "æ© Ļ", + "约 为", + "åĸĿ éħĴ", + "çĶ¨ èį¯", + "ä¸ĭ ä¸Ģ", + "æ³ķ å®ĺ", + "顺 åºı", + "åģļ ä¸Ģ个", + "åĭ ¢", + "æŃ ª", + "çĶµ ç«ŀ", + "ä¼´ éļıçĿĢ", + "ä¹ĭ åĬĽ", + "ä¹ĭ 人", + "äºij 计ç®Ĺ", + "åĪ«äºº çļĦ", + "ç§ijåѦ åıijå±ķ", + "第 åħ«", + "å¹² æī°", + "女 ç¥ŀ", + "è¿Ļæł· åģļ", + "å¤Ħ åľ¨", + "æ°´ è´¨", + "éķ¿ æĺ¥", + "å¸Ĥåľº éľĢæ±Ĥ", + "ç»´ æĿĥ", + "èĢ³ æľµ", + "æĸĩåĮĸ çļĦ", + "奶 ç²ī", + "ä¼ł è¾¾", + "æīĭæľº çīĪ", + "æĽ¾ åľ¨", + "äºĮ æľŁ", + "åİŁåĽł æĺ¯", + "æºIJ 头", + "åıĪ èĥ½", + "è£ ¸", + "æĬĢæľ¯ åĪĽæĸ°", + "æĸĩåĮĸ æĹħ游", + "åıij 票", + "å¹´ 级", + "ä½ł ä¸į", + "ä¹ĭ å¿ĥ", + "æķ° çĻ¾", + "åIJij å¾Ģ", + "èĢģ 家", + "åľĭ éļĽ", + "çļĦ é«ĺ度", + "æľĿ éĺ³", + "æ¸ħ éĻ¤", + "èĩª æľī", + "书 ä¸Ń", + "游æĪı è£ħå¤ĩ", + "ä¸ĩ å¤ļ", + "驾驶 åijĺ", + "ä½ł çŁ¥éģĵ", + "åĽ½ åºĨ", + "é£Ł åłĤ", + "æİ¥ åı£", + "æĢ» æķ°", + "åħ¶ä»ĸ çļĦ", + "çĶŁåij½ çļĦ", + "ä½ł åľ¨", + "çļĦ 缮åħī", + "è¿Ļ æĸ¹éĿ¢", + "éĥ½ 说", + "çĸĹ æ³ķ", + "åĭĩ 士", + "åľ¨ åħ¨çIJĥ", + "ä¿ĿéĻ© åħ¬åı¸", + "çĿ£ æŁ¥", + "åĸĦ èī¯", + "表 å½°", + "è¹ ²", + "è·¯ 段", + "æľĥåĵ¡ è¦ı", + "æľĥåĵ¡è¦ı ç¯Ħ", + "æĪ· åŀĭ", + "ä¿ĥ 使", + "ä¿® 建", + "é«ĺ æ°´å¹³", + "åģļ åĩºäºĨ", + "主 åľº", + "è¡Į èµ°", + "空 çĻ½", + "æľī人 说", + "è¿Ļ个 ä¸ĸçķĮ", + "åIJį ä¹ī", + "å®Į ç¾İçļĦ", + "羡 æħķ", + "åıĬ åħ¶ä»ĸ", + "åı¯ çĶ¨", + "æĭ IJ", + "è¾ĥ 大çļĦ", + "æĬĢæľ¯ åĴĮ", + "å°¼ äºļ", + "çĻ¾ è´§", + "æı ī", + "éĢī è´Ń", + "éĺŁ åıĭ", + "ä¼ł æĦŁ", + "ä¼łæĦŁ åĻ¨", + "åıªè¦ģ ä½ł", + "为ä»Ģä¹Ī è¦ģ", + "ä¸ĵ注 äºİ", + "ä½Ļ é¢Ŀ", + "åħ¸åŀĭ çļĦ", + "缮åīį å·²", + "欲 æľĽ", + "èģĶ 绾", + "æµģ ä¼ł", + "çļĦ 家åºŃ", + "åı· åı¬", + "çıį è´µ", + "ä¼Ł 大çļĦ", + "éī´ äºİ", + "è·Ł ä»ĸ", + "产 çī©", + "ä¸į å·²", + "è¿Ŀæ³ķ è¡Į为", + "头 ä¸Ĭ", + "åĪĨ 解", + "åı¯ä»¥ çľĭåĩº", + "æł¡ åĮº", + "åŃĹ ä½ĵ", + "ä¿® çĤ¼", + "çĶļèĩ³ æĺ¯", + "微信 åħ¬ä¼Ĺ", + "åıĸ 代", + "èIJ¥ä¸ļ æĶ¶åħ¥", + "æ½į åĿĬ", + "ä½ł èĥ½", + "社ä¼ļ ä¿Ŀéļľ", + "æ¯ĶèµĽ ä¸Ń", + "污水 å¤ĦçIJĨ", + "夫 å¦ĩ", + "ä¸Ģ å¹ħ", + "沿 æµ·", + "åı£ æĦŁ", + "ä½Ĩ åį´", + "å½ĵ æĹ¥", + "çļĦ æľĢ大", + "æ¯ı ä¸Ģä½į", + "没 äºĭ", + "çī¹ åĪ¥", + "å¼Ģ åѦ", + "è·¯ éĿ¢", + "å¿ĥçIJĨ åѦ", + "æĶ¾ ç½®", + "éĩįåºĨ å¸Ĥ", + "ä½ł èĩªå·±", + "æ¶Īè´¹èĢħ çļĦ", + "ä¸Ģ æ³¢", + "èѦ æĥķ", + "åį§ 室", + "注 å°Ħ", + "é£İ 鼨", + "沿 çĿĢ", + "åijĬ 訴", + "表 çİ°åĩº", + "åĽĽ æĺ¯", + "åı¤ åħ¸", + "æĽ´ éĩįè¦ģçļĦ", + "好 äºĭ", + "çľ¼ 泪", + "æ¨ ĵ", + "审 åĪ¤", + "碰 æĴŀ", + "车 ç«Ļ", + "è¿Ľåħ¥ äºĨ", + "éĽĨ åIJĪ", + "æł¼ å¤ĸ", + "宾 é¦Ĩ", + "æĶ¯ä»ĺ å®Ŀ", + "她 æĺ¯", + "æĺ¯ å¦Ĥä½ķ", + "人 次", + "çļĦ æĪIJåĬŁ", + "æĹł åĬĽ", + "æµ· æĭĶ", + "æĺ¥ åŃ£", + "éĥ½ ä¸įä¼ļ", + "çŃī å¤ļç§į", + "ä¸Ģ个 å°ı", + "åģľè½¦ åľº", + "让 æĽ´å¤ļ", + "è¿Ļ çĤ¹", + "æĪIJ åĵģ", + "éĴ ī", + "éģĩ è§ģ", + "çıŃ ä¸»ä»»", + "æĦı æĦ¿", + "çļĦ åIJĮåѦ", + "游 è§Ī", + "åİĭ 缩", + "åľ¨ ä¼łå¥ĩ", + "å¼¹ æĢ§", + "æĹ¥ åĨħ", + "ç¦ı建 çľģ", + "è§Ĵ èIJ½", + "åĪĨ å¼Ģ", + "ä¼ļ 让", + "å¤ĸ åĽ´", + "çĨŁæĤī çļĦ", + "çĨ Ķ", + "ä¸ĩ è¾Ĩ", + "å¤ľ éĹ´", + "车 身", + "ä¸Ń æľŁ", + "å®ĮåĸĦ çļĦ", + "åĵģ ç±»", + "åıĭ è°Ĭ", + "éĢīæĭ Ķ", + "éªij 士", + "å½ ¦", + "çļĦ çľĭæ³ķ", + "åĽ½ çİĭ", + "è¾£ æ¤Ĵ", + "åıijå¸ĥ æŶéĹ´", + "åı¤ åŁİ", + "éļı æľº", + "ç« ĸ", + "å¼Ģ è¾Ł", + "ä¼Ĺ çĶŁ", + "没 åĬŀæ³ķ", + "åįĥ éĩĮ", + "æĿ¥æºIJ äºİ", + "çļĦ æĿĥåĪ©", + "æ¯Ķ åĪĨ", + "满æĦı çļĦ", + "ä¿® è¡Į", + "åĿ ł", + "大 æµ·", + "èİ ¹", + "åĩº 身", + "è« ĩ", + "åħ³ èĬĤ", + "åIJį 人", + "éľĢè¦ģ 注æĦı", + "æĹ© æĻ¨", + "å¤ĸ åįĸ", + "åıĪ è¦ģ", + "æ¶ī æ¡Ī", + "çĶ³è¯· 人", + "éĻĦè¿ij çļĦ", + "åĬłå¿« æİ¨è¿Ľ", + "æĸ° å¹´", + "大 è¡Ĺ", + "ä¸Ģ é»ŀ", + "èĭı å®ģ", + "æĤĦ æĤĦ", + "èĦ¾ æ°Ķ", + "å¸Į èħĬ", + "éļı åį³", + "æķ¢ äºİ", + "å®ŀè·µ ä¸Ń", + "æĺ¯ æ²¡æľī", + "æľī趣 çļĦ", + "æĿ¥èĩª äºİ", + "è£ģ åĪ¤", + "女 åŃ©åŃIJ", + "èĩ³ åħ³", + "èĩ³åħ³ éĩįè¦ģ", + "æĻº åĬĽ", + "èµ° åĩºåİ»", + "çŁŃ æĿ¿", + "大 åĽ½", + "çļĦ 认è¯Ĩ", + "å¹´ å¤ľ", + "åĨį åĪ°", + "åIJĮ æł·çļĦ", + "å¯Ĩ å°ģ", + "å¤ĸ交 éĥ¨", + "çĶŁ æķĪ", + "æĤ¨ åı¯ä»¥", + "ä½ł åĢij", + "è¿ĩ å¹´", + "å¼ ĵ", + "è¡Į æĿİ", + "æ¯Ķ èµ·", + "身 é«ĺ", + "è¿Ļ个 人", + "ä¸Ń å¤ĸ", + "éģĵ æŃī", + "çĽ¯ çĿĢ", + "亲 åŃIJ", + "éĹ ¸", + "çĻ½ äºij", + "èĦĸ åŃIJ", + "ä¸ĢåĪĩ éĥ½", + "æ· ij", + "è° ľ", + "åģ¶ çĦ¶", + "éĿł è°±", + "é«ĺ 管", + "ä¸ĭ åıij", + "æĶ¾ åĪ°", + "ç±» åĪ«", + "ä¸ĭ åĪĹ", + "æ·· ä¹±", + "åIJĪæ³ķ æĿĥçĽĬ", + "çݯ çIJĥ", + "æľīæķĪ åľ°", + "åķĨ æĪ·", + "æ¹ĸ 人", + "æµ· 岸", + "æĬķ 产", + "两 个æľĪ", + "éĥ½ éĿŀ常", + "å¢ŀ强 äºĨ", + "æĿ¥ åĪ°äºĨ", + "åī© ä½Ļ", + "æĤ¨çļĦ åŃ©åŃIJ", + "æµģ æ°´", + "æŃ£ ä¹ī", + "天 çĮ«", + "åģļ è¿ĩ", + "ä½ķ æŶ", + "æĪij åİ»", + "çľģ 份", + "å¥ĸ éĩij", + "该 å¦Ĥä½ķ", + "ä¸ĭ çıŃ", + "åģ¶ åĥı", + "æijĨ æĶ¾", + "æĸ° 模å¼ı", + "æĬķ è³ĩ", + "è·¯ åı£", + "åĨľæ°ij å·¥", + "大 åѸ", + "件 äºĭ", + "æł¹æľ¬ ä¸į", + "æµĵ 度", + "æµĵ åİļ", + "è½® èĥİ", + "æĪ¿ ä¼ģ", + "éĿŀ常 好", + "ä»İ ä¸Ń", + "人 æł¼", + "ç¿ ģ", + "æŶéĹ´ åĴĮ", + "è¿Ļ ä¸įæĺ¯", + "åĪ¸ åķĨ", + "æĥĬ 人", + "åĻ¨ å®ĺ", + "åĩĨ åĪĻ", + "æĥħ æĻ¯", + "æĽ´ é«ĺçļĦ", + "åѦ 家", + "泡 沫", + "åľ°æĸ¹ æĶ¿åºľ", + "å°± çŁ¥éģĵ", + "åij¼ åIJģ", + "ç»ı è´¸", + "èĬ± éĴ±", + "æľī ä¸Ģ次", + "æĦŁ æħ¨", + "ä¸Ģ åįĥ", + "å¤ľ æĻļ", + "詹 å§Ĩ", + "詹å§Ĩ æĸ¯", + "è¦ģ éĹ»", + "ç» Ĵ", + "æºIJ äºİ", + "çļĦ è´¨éĩı", + "注æĦı äºĭ项", + "æħ¢ æĢ§", + "稳å®ļ çļĦ", + "建设 åĴĮ", + "æĻ¯ 象", + "éĩı åĮĸ", + "çļĦ 話", + "è¯Ħ 级", + "æº ľ", + "红 åĮħ", + "éĢļ éģİ", + "社ä¼ļ 责任", + "æĸ° 产åĵģ", + "åĨ· éĿĻ", + "çľĭ ä¸įåĪ°", + "èģĶ éĤ¦", + "éŃ Ħ", + "çļĦ åīįæıIJ", + "çļĦåīįæıIJ ä¸ĭ", + "è¾ĥ 好", + "çļĦ æĦŁæĥħ", + "客æĪ· æıIJä¾Ľ", + "çĭ¬ èĩª", + "å¢ŀ æĶ¶", + "æĸĩ çĮ®", + "æĭ¼ åij½", + "管çIJĨ åĴĮ", + "æµģåĬ¨ æĢ§", + "åħ¨ 家", + "ä¸Ĭ æĸ¹", + "æݨåĩº çļĦ", + "ä¸ī åĽ½", + "ä¸Ģ个 æĺ¯", + "æĸ° ä¸Ģè½®", + "æĸĩåĮĸ éģĹ产", + "æ® º", + "大 æ¹¾åĮº", + "éĥ½ éľĢè¦ģ", + "çļĦ å®ŀéĻħ", + "ç· Ĭ", + "大 å¥ĸ", + "åħī èĬĴ", + "便 äºİ", + "çļĦ 表æĥħ", + "æ¼Ķ ç»İ", + "红 åĨĽ", + "å½ĵ æĪij", + "æ²» æĦĪ", + "é¢Ŀ 度", + "éĿ ľ", + "ä»»ä½ķ 人", + "è¡Ĺ 头", + "çī¹ æĸ¯", + "çī¹æĸ¯ æĭī", + "åĮ»çĸĹ æľºæŀĦ", + "ç»Ļ åŃ©åŃIJ", + "è§Ħ 磩", + "è£ ľ", + "çļĦ 身影", + "ä¸ĵ æłı", + "æĿ¥ 临", + "ç«¥ å¹´", + "å¤į èĭı", + "è¨ Ĥ", + "åŀĭ åı·", + "åĽ¾ æ¡Ī", + "ç®Ģ åİĨ", + "æĭ ±", + "èį· åħ°", + "ä»» æĦı", + "æī¿ æİ¥", + "è¿Ļ æīį", + "客 车", + "æľĿ çĿĢ", + "éłħ 缮", + "åı° é£İ", + "çļĦ æĪ¿åŃIJ", + "éª ı", + "æĿ± 西", + "éģĹ ä¼ł", + "è¶Ĭ å¤ļ", + "äºĨ ä»ĸçļĦ", + "ä¸Ĭ åij¨", + "管çIJĨ åĪ¶åº¦", + "失 ä¸ļ", + "çĶ· åıĭ", + "æİ¥ ç§į", + "å¨ģ åIJį", + "çĴ° å¢ĥ", + "åıijçĶŁ åľ¨", + "个 åĽ½å®¶", + "åĪĽæĸ° åıijå±ķ", + "æĶ¹åıĺ äºĨ", + "åģ¥åº· çļĦ", + "åĢ¼å¾Ĺ ä¸Ģ", + "åĢ¼å¾Ĺä¸Ģ æıIJ", + "åĽ¢ ä¼Ļ", + "åģĩ 设", + "åı° ä¸Ĭ", + "è§ĦèĮĥ åĮĸ", + "éĻª åIJĮ", + "座 æ¤ħ", + "åı¯ æĢľ", + "åħĭæĢĿ 主ä¹ī", + "æ³ķå¾ĭ 责任", + "ä¸Ģ é¡¿", + "æĬ¬ 头", + "为 éĩįçĤ¹", + "è¿ľ æ´ĭ", + "éĢı è¿ĩ", + "åħ¨çIJĥ åĮĸ", + "趣 åij³", + "票 æĪ¿", + "æ¯ı 人", + "åIJĦç§į åIJĦæł·", + "äºĨ åĩºæĿ¥", + "ç»Ŀ对 æĺ¯", + "ä¸ĭ å±ŀ", + "ä¸Ģ åıĮ", + "è¿Ļ åĿĹ", + "æĬĹ çĸ«", + "è¦ģ çĤ¹", + "å½¢æĪIJ çļĦ", + "æĪij çľĭ", + "ä¸ĩ éĩĮ", + "èĢĥ çłĶ", + "为 åħ¶", + "æ°ij 宿", + "å¤ļ ä½į", + "大 èĩ´", + "ä»ĺ è´¹", + "åħ¥ æīĭ", + "å±ħ 家", + "æīĢåľ¨ åľ°", + "人 身", + "è¿ĩ å¾Ĺ", + "è¯ķ è¯ķ", + "访 è°Ī", + "åĬł éĩį", + "å°± ä¸įä¼ļ", + "çĶŁäº§ ä¼ģä¸ļ", + "åĽŀ åĽ½", + "åºķ 线", + "赶 åĪ°", + "æĶ¯ éĺŁ", + "æĪij们 éĥ½", + "éĤ® æĶ¿", + "缴 èĩ³", + "éĴ¢ çIJ´", + "åħ ľ", + "çłĶ讨 ä¼ļ", + "æľĪ 亮", + "åĿļæĮģ 以", + "åħ¬å®ī éĥ¨", + "éĴ¢ 管", + "å°ı çĻ½", + "ç½® ä¸ļ", + "èģ ĭ", + "书 åĨĻ", + "æĿ ı", + "éħį æĸ¹", + "èĢĮ åıĪ", + "çijŀ 士", + "çķĮ çļĦ", + "èĢģ 大", + "æĪIJçĨŁ çļĦ", + "å¹² ä»Ģä¹Ī", + "ä¸ĵ项 æĸĹäºī", + "çŃī å¤ļ个", + "èĦ± 离", + "ä¸ī 个æľĪ", + "çłĶ究 åijĺ", + "æĹĭ 转", + "æŀģ èĩ´", + "åħį è´£", + "åħįè´£ 声æĺİ", + "å¾Īå¤ļ çݩ家", + "车 ä¸Ĭ", + "交 äºĴ", + "å·² æĺ¯", + "ä¸Ģ å°ı", + "çļĦ éĩįçĤ¹", + "èĬ± äºĨ", + "ä¸į æĺİ", + "æľīåħ³ è§Ħå®ļ", + "çĬ¹ å¦Ĥ", + "çľ ¸", + "å¯ ¡", + "çļĦ è¡£æľį", + "åĮħ 裹", + "身 åŃIJ", + "å¸ĪèĮĥ 大åѦ", + "äºĭ åħĪ", + "线 æĿ¡", + "æ³ķ åĪ¶", + "åħ» æĬ¤", + "稳å®ļ æĢ§", + "éĤ µ", + "åŀĦ æĸŃ", + "é¡ į", + "èĢĥ åı¤", + "æĿł æĿĨ", + "èĭı èģĶ", + "æ°´ çĶµ", + "åħ·ä½ĵ çļĦ", + "æ¿Ģ æ´»", + "æĪij æł¡", + "åĪļ å¼Ģå§ĭ", + "åĩ¸ æĺ¾", + "ç¦ ¾", + "åħ¼ èģĮ", + "éĢı éģİ", + "åľ¨ 游æĪıä¸Ń", + "社ä¼ļ åıijå±ķ", + "好 çİ©", + "å¹» æĥ³", + "ä¸į 代表", + "注æĦı åĬĽ", + "æ£ į", + "çĶ¨ æīĭ", + "ç¾İ 人", + "许å¤ļ 人", + "å¾Ī æĺ¯", + "çļĦ çłĶåıij", + "æīĵ åĩº", + "åIJĪä¼Ļ 人", + "ä¸Ģ å¤ľ", + "ç¼ĵ ç¼ĵ", + "ä¿® æŃ£", + "æĦŁ çŁ¥", + "ç»Ī 身", + "æ¿Ģ ç´ł", + "çݯå¢ĥ ä¸ĭ", + "次 ä¼ļè®®", + "ç»ıæµİ å¢ŀéķ¿", + "æī Ľ", + "åıij éħµ", + "åĪĨæŀIJ å¸Ī", + "åľ¨ æľªæĿ¥", + "主è¦ģ æľī", + "ä¸Ģ åŃ£åº¦", + "çļĦ 说æ³ķ", + "ä»İæĿ¥ 没æľī", + "è´§ 车", + "缩 å°ı", + "太 è¿ĩ", + "æķĪ åĬĽ", + "ä¸į ä¸ĭ", + "æĬķ 稿", + "èį¯ ä¸ļ", + "ç»Ħ éķ¿", + "ç«Ļ çĤ¹", + "å¾Ī åĸľæ¬¢", + "éIJ µ", + "åĬ¿ 头", + "æ¼ı æ´ŀ", + "æĦ¤ æĢĴ", + "åħħ å®ŀ", + "åĪĽä¸ļ æĿ¿", + "çĪ ª", + "æľª å¿ħ", + "åºķ éĥ¨", + "å¾Ĺ åĪĨ", + "人æ°ij åĮ»éĻ¢", + "äºĮæīĭ æĪ¿", + "å·²ç»ı 被", + "大 楼", + "æĸ° æĪ¿", + "辦 æ³ķ", + "çĶ¨ åĬĽ", + "æĭĵ 宽", + "åĨħ åľ¨", + "æĴŃ åĩº", + "饰 æ¼Ķ", + "ä¹Ł 让", + "ä½ľ çĤº", + "çī©ä¸ļ 管çIJĨ", + "åį´ ä¸į", + "为 ä¸ŃåĽ½", + "å±Ģ åĬ¿", + "ä¸į èĤ¯", + "æľĢ æĸ°çļĦ", + "åı¯ä»¥ éĢīæĭ©", + "æĺ¾ çİ°", + "å°± ç®Ĺæĺ¯", + "åľ¨ æł¡", + "é¾ Ł", + "两 æĿ¡", + "çļĦ å®ŀåĬĽ", + "è¶Ĭ 好", + "她 åľ¨", + "å¿ł è¯ļ", + "ä¹Ł éľĢè¦ģ", + "游æĪı æĵįä½ľ", + "è¶ħ åĩº", + "å¦Ĥæŀľ ä¸į", + "æīĢåľ¨ çļĦ", + "ä½ł è¿ĺ", + "以 åĨħ", + "æľī ä¸Ģå®ļ", + "åı¯ è¾¾", + "è·ij åĪ°", + "åī Ľ", + "建ç«ĭ åģ¥åħ¨", + "æķ´ 车", + "åīį æĸ¹", + "éĹ´ æİ¥", + "çŃ¹ å¤ĩ", + "çĸ² åĬ³", + "离 å¼ĢäºĨ", + "æ± Ŀ", + "éĿ¢ éĥ¨", + "ä¹ĭåīį çļĦ", + "åıĺ 为", + "å¦Ĥæŀľ 说", + "对 ä»ĺ", + "åĿĩ åı¯", + "被åijĬ 人", + "ç²¾ ç¾İ", + "èģļ ä¼ļ", + "çĿĢ æĢ¥", + "è°· æŃĮ", + "ä¸Ģ åı·", + "红 åĪ©", + "ä¼łå¥ĩ 游æĪı", + "å» ĸ", + "è´ ŀ", + "ä¹° åĪ°", + "éŃ ļ", + "ä½ĵ è´¨", + "å°ij äºĨ", + "æ³ī å·ŀ", + "åIJ Ł", + "ç»Ŀ ä¸į", + "é»ij æģ¶", + "é»ijæģ¶ åĬ¿åĬĽ", + "ä¸Ĭ æĺł", + "çļĦè¯Ŀ é¢ĺ", + "ä¸ĩ人 次", + "ä¸ĸ éĹ´", + "çĶ¨ å·¥", + "è´¯ ç©¿", + "å®Ŀ çŁ³", + "ä½ł 好", + "åĪĩ åī²", + "强 åĽ½", + "åĽŀ èIJ½", + "æ°´ æĻ¶", + "模 仿", + "æ´ª æ°´", + "éĢĻ 麼", + "åįģä¸ī äºĶ", + "ä½ ij", + "éĻ Ħ件", + "çļĦ å¢ŀéķ¿", + "éĻĦ å±ŀ", + "çİ° å·²", + "帮 ä½ł", + "éĩij çīĮ", + "é«ĺ åİŁ", + "åľ¨ 家éĩĮ", + "éĺ² èħIJ", + "ç¡®å®ŀ æĺ¯", + "宣 讲", + "天 æīį", + "ç»ıèIJ¥ 管çIJĨ", + "éĶħ çĤī", + "åIJĪ ä¸Ģ", + "è§Ĥ èµı", + "éķ¿ è¾¾", + "主ä¹ī æĢĿæĥ³", + "éĤ£ 麼", + "é£İ äºij", + "为主 çļĦ", + "æļij åģĩ", + "æĮģ ä¹ħ", + "å¼Ĥ åľ°", + "å¼Ģ éŨ", + "模 æĿ¿", + "æī¹ 次", + "ä¸į 便", + "天 çĶŁ", + "åĩł 个æľĪ", + "ä¸ĵ ç§ij", + "åı¦ æľī", + "åħ¬å¸ĥ çļĦ", + "æĩ ·", + "åľº åIJĪ", + "çļĦå¿ĥ æĢģ", + "è¿ĺ 好", + "å®ŀ æĪĺ", + "èĢģå¸Ī çļĦ", + "åħ© åĢĭ", + "åı¯ åľ¨", + "éĤ£ ä½į", + "å¥ł å®ļäºĨ", + "ä¿ĥ éĶĢ", + "æı´ åĬ©", + "ä¸ĩ çī©", + "æĥħ æĬ¥", + "é¦ĸåħĪ è¦ģ", + "æĸĩåĮĸ åĴĮ", + "éĥ½ å·²ç»ı", + "ä¸Ĭ ä¸ĸ纪", + "åĨľ åľº", + "大 æī¹", + "æĺİçĻ½ äºĨ", + "çļĦ æĪIJéķ¿", + "çļĦ æ¯ĶèµĽ", + "失 误", + "åģļ æĪIJ", + "ä»Ĭ天 å°ıç¼ĸ", + "é¢Ĩ è¢ĸ", + "æıIJåįĩ äºĨ", + "å¾IJ å·ŀ", + "ä»į æľī", + "è¿ĩ 滤", + "å¹½ é»ĺ", + "çĥŃ éĩı", + "ä¸Ģ é¦ĸ", + "æ¼Ĥ亮 çļĦ", + "åĩł ç§į", + "åĢ¡ è®®", + "å°±åı¯ä»¥ äºĨ", + "æİĴ åĪĹ", + "éĩį éĩį", + "ä¼ģä¸ļ åĴĮ", + "ä¸ĵ å±ŀ", + "çħ İ", + "亲 æĪļ", + "çĻ¾åĪĨ ä¹ĭ", + "稿 件", + "è¿ĺ å¾Ĺ", + "人 åĵ¡", + "äºī 夺", + "æĽ´ 容æĺĵ", + "大 èĩªçĦ¶", + "鼻 èħ¦", + "太 空", + "åľ° å¤Ħ", + "å¤ ¢", + "ä»ĸ 对", + "å¿ħ å°Ĩ", + "ä¸į å½ĵ", + "严 è°¨", + "åĩº åľº", + "å·²ç»ı æľī", + "é¢Ĩ åĨĽ", + "é«ĺ æ¡£", + "ä¸Ģ æīĢ", + "æł Ĺ", + "让 åѦçĶŁ", + "æĽ¹ æĵį", + "æŁIJ ä¸Ģ", + "伸 åĩº", + "èĬ± åįī", + "æ¸ħ éĨĴ", + "èģĶç³» æĸ¹å¼ı", + "åĪĨ å±Ģ", + "èħ ³", + "æ©¡ èĥ¶", + "éķ¿ å¾Ĺ", + "绿 åľ°", + "è¢ į", + "çļĦ èīºæľ¯", + "女 æľĭåıĭ", + "ä¸Ń è¶ħ", + "离 åŃIJ", + "å¤ļæł· åĮĸ", + "éĺ³ åı°", + "ä½İ 碳", + "ä¸Ģ ç±»", + "çŃīæĸ¹éĿ¢ çļĦ", + "å¾Ĺ 好", + "模 åħ·", + "ä¸ĩ 亿", + "çķĻ æĦı", + "临 æ²Ĥ", + "å°ij éĩı", + "çľĭ åIJij", + "ç»ıèIJ¥ èĢħ", + "çķĻä¸ĭ äºĨ", + "åĿı äºĨ", + "åijĬ åĪ«", + "羣 çIJĨ", + "ç¼´ è´¹", + "æĬĬ ä½ł", + "çļĦ ä»»åĬ¡", + "æĪij 对", + "ä¹° åħ¥", + "çĻ» ä¸Ĭ", + "æľī 两个", + "ä¸Ģ 头", + "æĵį æݧ", + "åħ¨ è¦ĨçĽĸ", + "çĿĢ æīĭ", + "å¢Ļ éĿ¢", + "å¤ļ æĸ¹", + "åı¯çĪ± çļĦ", + "ä¹Ł åı¯èĥ½", + "æľĢ æľī", + "è¿ĻäºĽ éĥ½æĺ¯", + "æĥ ¡", + "å® ®", + "å¾Ī å°ı", + "éĹ®é¢ĺ æĺ¯", + "åĿĩ æľī", + "å¾ģ éĽĨ", + "说 åĩº", + "æľī æĦı", + "é¢ Ĥ", + "æī¬ å·ŀ", + "åķĨä¸ļ 模å¼ı", + "çĶŁ èĤĸ", + "æįIJ 款", + "å² Ĥ", + "ç¾İ æĻ¯", + "è¿ĺ 羣", + "æĭ¥ æĬ±", + "身ä½ĵ åģ¥åº·", + "æ·± å¤Ħ", + "çľ¼ ç¥ŀ", + "çļĦ 形象", + "ä¼ĺ è¶Ĭ", + "å½ĵ æĪIJ", + "åĮº åĪĨ", + "åİ» éĻ¤", + "注 å®ļ", + "å§IJ 妹", + "åĮº åĨħ", + "é© ļ", + "æļĹ ç¤º", + "æĺİ äº®", + "æħ° éĹ®", + "å¸Ĥåľº 份é¢Ŀ", + "çĮª èĤī", + "çļĦ èµĦéĩij", + "åİĨ ç»ı", + "å§ĭç»Ī åĿļæĮģ", + "çĶŁ æľº", + "ä¸į 顾", + "éĩij åĪļ", + "大 声", + "éĻķ 西çľģ", + "é² į", + "åĨľä¸ļ åĨľæĿij", + "æľī 害", + "éŨ è¯Ĭ", + "æ¯ı ä¸Ģ次", + "çļĦ åĽłç´ł", + "é¢Ŀ å¤ĸ", + "åİ¿ 级", + "çļĩ åIJİ", + "åĽ½ ä¼ģ", + "é¦ĸ éĢī", + "ç¼ĸ åĨĻ", + "æĭ¿ èµ·", + "åģ· åģ·", + "ä¸İ ä¸ŃåĽ½", + "åįĸ 家", + "ç»Ļ ä»ĸ们", + "ç¥ŀ è¯Ŀ", + "åѸ æł¡", + "æĪij ä¸Ģ缴", + "çŁ¥éģĵ äºĨ", + "åį Ĵ", + "åĴĮ åľ°åĮº", + "ä»Ģä¹Ī éĥ½", + "çĶ» 家", + "æľ¬ çĿĢ", + "ä½Ļ åIJį", + "审 çIJĨ", + "ä¸Ģ åIJij", + "åıijå±ķ è¶ĭåĬ¿", + "åĮº éĹ´", + "注åĨĮ èµĦæľ¬", + "çIJ ¦", + "ä¸į åı¯ä»¥", + "çļĦ åĦ¿åŃIJ", + "åĢ¼ çıŃ", + "ä¸¥æł¼ çļĦ", + "å®ŀä½ĵ ç»ıæµİ", + "æľī æĿĥ", + "æĪij åıĪ", + "éĵ¶ æ²³", + "ç«ĭ 马", + "æĿĢ äºĨ", + "åĮħ 容", + "管 家", + "身 é«Ķ", + "éĵ ħ", + "å°ı åŃIJ", + "管çIJĨ ç³»ç»Ł", + "æľīçļĦ 人", + "é£İ çĶµ", + "æĻºèĥ½ åĪ¶éĢł", + "ç²¾ ç¡®", + "æĭĽåķĨ å¼ķ", + "æĭĽåķĨå¼ķ èµĦ", + "äºĮæīĭ 车", + "åİ¿ å§Ķ", + "èīº 人", + "å¥ ķ", + "è¿İ æĿ¥äºĨ", + "ç»ĵæĿŁ äºĨ", + "çļĦ ä¼łç»Ł", + "æĭ¼ æIJı", + "奥 迪", + "çĸij æĥij", + "ä¹ĭ æĹ¥èµ·", + "æłĩå¿Ĺ çĿĢ", + "åľ° åįĢ", + "è¯ł éĩĬ", + "åĪ° æľŁ", + "åħ¨ éĥ½", + "çŁŃ æļĤ", + "æĺ¯ æĪijåĽ½", + "æĪij å·²ç»ı", + "æ»´ æ»´", + "天 èµĭ", + "对 她", + "åį«çĶŁ éĹ´", + "çĶŁäº§ åŁºåľ°", + "æĹ¥ è®°", + "çļĦ æķĻåѦ", + "åĵ ĩ", + "æ°ij äºĭ", + "è¿ĺ åİŁ", + "æīĭ ä¸ŃçļĦ", + "çļĦ èī¯å¥½", + "æ· «", + "ä¸Ńåħ± ä¸Ń央", + "åĪ ĥ", + "åĵ Ħ", + "åľ¨ ä»ĸçļĦ", + "å°Ī æ¥Ń", + "åľº éĿ¢", + "éĤ» å±ħ", + "çĹ Ĵ", + "å¦ Ħ", + "å¤ĸ ç§ij", + "ä¸į éĢĤ", + "举åĬŀ çļĦ", + "é Ĥ¹", + "åħļçļĦ 建设", + "çĻ¼ 表", + "è·¨ çķĮ", + "æ²ī æ·Ģ", + "大 çīĩ", + "è¶Ĭ é«ĺ", + "å°Ĩ æĺ¯", + "è§ī éĨĴ", + "åĤ¨ åŃĺ", + "å¢ŀ 大", + "ä¸į 让", + "æķ´ å½¢", + "å¹³åı° ä¸Ĭ", + "åĩł ä½į", + "è¯ī æ±Ĥ", + "好 ä¸į好", + "åľ į", + "æĸĩ æľ¬", + "éĢ² åħ¥", + "ç´ į", + "æł¹ æĵļ", + "èįī æ¡Ī", + "åħŃ ä¸ª", + "åĭ ¿", + "åĪ¶ æĪIJ", + "饮 æ°´", + "æ°¸ æģĴ", + "èĩª æĿĢ", + "åı¸ 马", + "éļ¾ çĤ¹", + "为 æĪij们", + "å¼ §", + "åī© ä¸ĭçļĦ", + "åĩĨå¤ĩ 好", + "çļĦ æľĢä½³", + "èģĶåIJĪ ä¼ļ", + "æĤ£èĢħ çļĦ", + "æĪijä¸į çŁ¥éģĵ", + "ä¸ĭ ä¸Ģ个", + "åıijå±ķ æĸ¹åIJij", + "ç¬ ¨", + "æīĢ以 æĪij们", + "åĨĻ äºĨ", + "éĢł æĪIJäºĨ", + "æ²Ļ æ¼ł", + "çŃĽ éĢī", + "çģ¾ åĮº", + "ä¸Ĭ çľĭ", + "éħ ¶", + "æ»ļ åĬ¨", + "éļ¾ åħį", + "åIJī åĪ©", + "ä¸Ģ ä¸Ģ", + "ç²¾ å¯Ĩ", + "伸 æīĭ", + "礼 仪", + "åħ¨ æĺ¯", + "è¶Ĭ 大", + "ä¸Ń æłĩ", + "åıĸ åĨ³", + "åıĸåĨ³ äºİ", + "éĢĶ ä¸Ń", + "讨 åİĮ", + "æīĭ åĨĮ", + "第 ä¹Ŀ", + "åŃĶ åŃIJ", + "çĦ¶ å¾Į", + "ä¸Ģ åħ±", + "æµ· æĬ¥", + "款 å¼ı", + "æķ´ 天", + "è¾¹ çķĮ", + "è·¯ è¾¹", + "æĻĭ 级", + "åIJIJ 槽", + "çļĦ åħ³æ³¨", + "æĪij 没æľī", + "å°±æĺ¯ åľ¨", + "缮 çļĦæĺ¯", + "åį³ä½¿ æĺ¯", + "顶 å°ĸ", + "å·²ç»ı åľ¨", + "å®īåħ¨ éļIJæĤ£", + "æłĩ æĿĨ", + "åįĹ éĢļ", + "ä¼ļ 对", + "座 ä½į", + "èµ¢å¾Ĺ äºĨ", + "åİŁæĿ¥ çļĦ", + "身 为", + "书 åºĹ", + "è¢Ń åĩ»", + "ä»Ĭ æĻļ", + "以 èī²", + "以èī² åĪĹ", + "æĬĸ éŁ³", + "åį´ 没æľī", + "丧 失", + "çļĦ å±ĢéĿ¢", + "åįģåĽĽ äºĶ", + "çŃī 缸åħ³", + "æ±ĩ æĢ»", + "å¤ĸ 表", + "为 æ°ij", + "éľĩ æĥĬ", + "å¥Ĺ è·¯", + "çĬ¯ç½ª å«Įçĸij", + "å°Ĩ 以", + "çİĩ é¢Ĩ", + "éħĴ åIJ§", + "è¡Įä¸ļ åıijå±ķ", + "å¹´ èĩ³", + "åĻ¨ æĿIJ", + "åĴĮ æĬĢæľ¯", + "æľĢ å°ı", + "è¿Ļä¸Ģ åĪĩ", + "èģĮ 称", + "å½ĵ ä½ľ", + "æİĢ èµ·", + "åĴ ĭ", + "ä¸Ń éĥ¨", + "æīĭ èĩĤ", + "ç½¢ äºĨ", + "媳 å¦ĩ", + "æ´½ è°Ī", + "æĹ¶ä»£ ä¸ŃåĽ½", + "人çĶŁ çļĦ", + "æŀģ éĻIJ", + "ç¦ Ħ", + "åĮº æĶ¿åºľ", + "æľ¬ éĴ±", + "礼 åĵģ", + "çļĦ éĤ£ä¸ª", + "侦 æŁ¥", + "太å¤ļ çļĦ", + "å®ŀæĸ½ æĸ¹æ¡Ī", + "é«ĺ æłĩåĩĨ", + "æĮĩæĮ¥ éĥ¨", + "åĢ¾ æĸľ", + "çī¹èī² 社ä¼ļ", + "çµIJ æŀľ", + "éĴ» çŁ³", + "移 æ¤į", + "çī¹ ç§į", + "èĩª æĦ¿", + "æĭľ çĻ»", + "åįķ 身", + "åį´ åıĪ", + "åĪ¥ 人", + "åIJĪ è§Ħ", + "æľº çĶµ", + "çī¹ æĦı", + "å½ĵåīį ä½įç½®", + "ä¹° 家", + "åIJĪ 约", + "èĤ© èĨĢ", + "为 åĩĨ", + "家 è£ħ", + "çļĦ çĥŃæĥħ", + "éĿŀ éģĹ", + "çļĦ éŃħåĬĽ", + "åİŁ åijĬ", + "社ä¼ļ åIJĦçķĮ", + "ä¹° çļĦ", + "å¤ļ åIJĥ", + "éĽķ å¡ij", + "èµ· ä¹ī", + "åĬł åī§", + "éĤ£ä¸Ģ åĪ»", + "å°Ĩ è¿Ľä¸ĢæŃ¥", + "æ¡Ĥ æŀĹ", + "æĽ´ 强", + "对 ä¼ģä¸ļ", + "æĹł æĦı", + "ä¹łè¿ijå¹³ æĸ°", + "æµģ 失", + "å¾® 软", + "缸 对äºİ", + "座è°Ī ä¼ļ", + "主 èIJ¥ä¸ļ", + "主èIJ¥ä¸ļ åĬ¡", + "ç§ģ åĭŁ", + "å±ķ示 äºĨ", + "常æĢģ åĮĸ", + "è² ´", + "符 åı·", + "å¹´è½» çļĦ", + "å°± éľĢè¦ģ", + "ä¹Ł æĽ¾", + "çļĦæĥħ 绪", + "è¾¾ æłĩ", + "èĩ ¨", + "ä½į å±ħ", + "ä»ħ 为", + "é¦ĸ 家", + "éĺ´ éĺ³", + "ä¸įåĨį æĺ¯", + "åĽłä¸º å®ĥ", + "ä¼ģä¸ļ åľ¨", + "çĺ ¾", + "åIJ¬ è§ģ", + "åİŁ æľī", + "åĪ¶ è£ģ", + "å¯Ĥ å¯ŀ", + "éĢļè¿ĩ 对", + "æ»ij éĽª", + "è¿Ļ å¼ł", + "çļĦ çIJĨ解", + "æĸ° ä¸ŃåĽ½", + "è¿Ļ åĦ¿", + "ä½İ ä»·", + "æĥ³ è¿ĩ", + "çļĦ ä¿¡å¿ĥ", + "建çŃij çī©", + "çļĦ é¢ľèī²", + "ä¸į åºĶ该", + "æĹłçĸij æĺ¯", + "å¼ķèµ· äºĨ", + "åħ¨ åijĺ", + "æĿ° åĩº", + "è¿Ļæĺ¯ æĪij", + "èª °", + "èĺ ĩ", + "éĺµ åľ°", + "åħħ åĢ¼", + "çŁ¿ ä¸ļ", + "çĿĢ ä»ĸ", + "ä¿¡ 访", + "ä¸ĩ è¾¾", + "æij© æĵ¦", + "å¼Ģ 端", + "èı² å¾ĭ", + "èı²å¾ĭ 宾", + "车 åŃIJ", + "æľ¬èº« çļĦ", + "çģ«è½¦ ç«Ļ", + "常 å·ŀ", + "为 代表", + "为代表 çļĦ", + "广 çĶµ", + "亲 人", + "åı³ æīĭ", + "éĽĨ è£ħ", + "éĽĨè£ħ ç®±", + "çļĦ åį°è±¡", + "æ©Ł æľĥ", + "åĮĨ åĮĨ", + "åħī çĶµ", + "大 æĸ¹", + "è¿ĺ æľª", + "åĪ© 好", + "ç»Ŀ 大å¤ļæķ°", + "åľ¨ è¿Ļç§į", + "ä¸Ģ ç»Ħ", + "æĸ° èĤ¡", + "转 åıij", + "æ³ķ åºŃ", + "æĹł æīĢ", + "éģĵ è·¯ä¸Ĭ", + "çŁ¿ å±±", + "èij ī", + "æĶ¶ åĽŀ", + "称 ä¹ĭ", + "称ä¹ĭ 为", + "æıŃ éľ²", + "åı£ 岸", + "åIJ ¼", + "å¿ĥ æĥ³", + "çļĦ 梦æĥ³", + "éĽ ¯", + "ä¹ĭ åĪĿ", + "å¥ĸ 项", + "订 éĺħ", + "èĵĿ 天", + "åĿ¦ åħĭ", + "ç«ĭ æ¡Ī", + "èģĶ æīĭ", + "ä½Ĩæĺ¯ æĪij", + "帮 æĪij", + "ä»ħ 代表", + "说 æĪij", + "çļĦ è¶ĭåĬ¿", + "æ¯Ķè¾ĥ 大", + "èµ° å»Ĭ", + "éĩįçĤ¹ é¡¹çĽ®", + "èµĮ åľº", + "åIJį çīĩ", + "æĦŁ åı¹", + "åľ¨ åľ°ä¸Ĭ", + "åıij çĥŃ", + "èĮĥ çķ´", + "çļĦ éģĵè·¯", + "éĩij èī²", + "ä»ĸ åıĪ", + "ä¼ļ 产çĶŁ", + "æ°ij åĽ½", + "å®ĺæĸ¹ ç½ijç«Ļ", + "æĶ¶çĽĬ çİĩ", + "çļĦ åĪ°æĿ¥", + "çļĦ åĬŀæ³ķ", + "æĶ¹ åĪ¶", + "ä¸ĩ ç§ij", + "ä¸į äºĪ", + "è¿ĻäºĽ éĹ®é¢ĺ", + "çĪ± ä¸Ĭ", + "çIJĥ åľº", + "è´£ 令", + "æİĪ 课", + "åľ¨ é¦Ļ港", + "ç»Ĩ èħ»", + "å¤ļ ä¸ĩ", + "åIJĮ å¹´", + "大 使", + "æĸ ĭ", + "ä¹Ł 为", + "æĥł å·ŀ", + "åIJī 祥", + "çĶ° åĽŃ", + "åĽ½å®¶ éĺŁ", + "éĩį çĶŁ", + "åľ¨ åħ¶", + "é¦Ļ åij³", + "è´Ł èį·", + "亲 åĪĩ", + "èĩª 豪", + "没 éĶĻ", + "åĽłä¸º åľ¨", + "æĺŁ æĺŁ", + "éĤ ij", + "è¿ĺæľī å¾Īå¤ļ", + "æij© æīĺ", + "æij©æīĺ 车", + "æŃ¥ è¡Į", + "管çIJĨ ä½ĵç³»", + "èĦļ ä¸ĭ", + "éģİ åİ»", + "æ±ī è¯Ń", + "对 ä¸įèµ·", + "çļĦ ç»ıåİĨ", + "åıĬ 缸åħ³", + "ä¸įå°ij 人", + "éĩį ç£ħ", + "åĬ³åĬ¨ èĢħ", + "大åĬĽ åıijå±ķ", + "æĢİä¹Ī åģļ", + "çĭĹ çĭĹ", + "举åįĹ äºļ", + "åĭĩ äºİ", + "åħ¬ éĸĭ", + "çĵ· çłĸ", + "åıĤ çħ§", + "广æĴŃ çĶµè§Ĩ", + "举 åĬ¨", + "æ±Ł 西çľģ", + "æķĪ èĥ½", + "åĶ¯ æľī", + "éĿ¢ è²Į", + "èĩªåĬ¨ 驾驶", + "æ¦ľ åįķ", + "å½ĵ æĪij们", + "仲 è£ģ", + "æľ¨ æĿIJ", + "ç±³ åħ°", + "çĻ½ éĵ¶", + "çļĦ 人éĥ½", + "å°± åĥıæĺ¯", + "æŃ¥ åħ¥", + "åįł çĶ¨", + "åĩ» è´¥", + "让 大家", + "ä¼ļ è®©ä½ł", + "åİ¿ æĶ¿åºľ", + "è¦ģ çĶ¨", + "çŃī å½¢å¼ı", + "åįĩ é«ĺ", + "责任 æĦŁ", + "å¤ĩ çĶ¨", + "ä»ĸ 认为", + "æ¸ħåįİ å¤§åѦ", + "ä»ĸ èĩªå·±", + "éĸ± è®Ģ", + "太平 æ´ĭ", + "éĶģ å®ļ", + "çŃ Ĩ", + "è¿Ļ çīĩ", + "æī§ æĶ¿", + "è¿ĶåĽŀ æIJľçĭIJ", + "å°± æѤ", + "éģĩ åĪ°äºĨ", + "å¼Ģå¹ķ å¼ı", + "管çIJĨ éĥ¨éŨ", + "姿 åĬ¿", + "设 æĥ³", + "åĽĽ åŃ£", + "æĬĢæľ¯ 人åijĺ", + "å·® çĤ¹", + "è¾ŀ èģĮ", + "èĢģ 師", + "çļĦ æĦŁåıĹ", + "ä¹Ł éĿŀ常", + "å¹´ ä¸ĬåįĬå¹´", + "æĢª çī©", + "èĮĥ æĸĩ", + "æĪĺ å½¹", + "åIJ« ä¹ī", + "åħ¨ è¿ĩç¨ĭ", + "èĢĮ éĿŀ", + "éĢļ讯 åijĺ", + "è¿Ļæł· æīįèĥ½", + "æľº ç»Ħ", + "è£ ı", + "çķ¶ çĦ¶", + "èµĮ åįļ", + "åIJĦ æľī", + "å·¥ä½ľ æľºåĪ¶", + "äºĭ åIJİ", + "åī§ éĻ¢", + "å±Ĭ æŶ", + "åĺ´ éĩĮ", + "主 线", + "ä¸Ģ åľĪ", + "主è¦ģ åİŁåĽł", + "å°¸ ä½ĵ", + "åĮ»çĸĹ åĻ¨æ¢°", + "ä½ł æĢİä¹Ī", + "ä½Ĩ çĶ±äºİ", + "æŶ 空", + "çĶ· æľĭåıĭ", + "çĶľ èľľ", + "é«ĺ åľ°", + "æĻ ĸ", + "èĴIJ éĽĨ", + "åĩĿèģļ åĬĽ", + "å¤ĩ åıĹ", + "æĸĩ åĪĽ", + "马 æĿ¥", + "马æĿ¥ 西äºļ", + "æŁ´ æ²¹", + "使 人", + "æķĻ ä¼ļ", + "ç§ĭ 天", + "æĺİ çıł", + "åħŃ åįģ", + "çݯå¢ĥ ä¸Ń", + "æ¸ħ æĻ¨", + "积æŀģ åıĤä¸İ", + "å·ħ å³°", + "为 æľŁ", + "çŃ¾ åŃĹ", + "æĦŁ æ¿Ģ", + "ç§ĭ åŃ£", + "æĿij åŃIJ", + "æ¢ħ 西", + "æļ´ 鼨", + "çĶŁæ´» åľ¨", + "çªĹ æĪ·", + "æģ¶ åĬ£", + "纯 ç²¹", + "åľ¨ æİ¥åıĹ", + "没 èĥ½", + "è¡Į 人", + "åĭ º", + "æĭ¨ æīĵ", + "ä½ľ åĩºäºĨ", + "çļĦ 主é¢ĺ", + "æľª ä¾Ĩ", + "ä¸Ń æľĢ", + "æ¾ ľ", + "é«ĺ è¡Ģåİĭ", + "åħ´ èµ·", + "æŃ£ èĥ½éĩı", + "åŁ¹è®Ń çıŃ", + "æİ¥ åħ¥", + "çĦ¶åIJİ åĨį", + "åѦçĶŁ 们", + "é¢ĨåħĪ çļĦ", + "çģ« çĥŃ", + "ä¸ĵ èģĮ", + "æĪĸèĢħ 说", + "建 è¨Ń", + "é» ı", + "对 åħ¬åı¸", + "çī¹ æľīçļĦ", + "åħī èį£", + "å½ĵ åľº", + "éĿ¢ åŃIJ", + "èµĦ产 管çIJĨ", + "æĹ¶æľŁ çļĦ", + "çŀ İ", + "åįİ ä¸ľ", + "åıĪ ä¸Ģ次", + "èĥİ åĦ¿", + "å®ļ çĤ¹", + "头 çĹĽ", + "液 ä½ĵ", + "æĺ¯ä¸Ģ ä½į", + "帽 åŃIJ", + "å¹´ èµ·", + "ä¸į ä½İäºİ", + "è¾ĥ å°ij", + "éĿ¢ä¸´ çĿĢ", + "å±Ĥ å±Ĥ", + "èĿ´ èĿ¶", + "èī° èĭ¦", + "éĺ¿ æł¹", + "éĺ¿æł¹ å»·", + "æ¦Ĥ æĭ¬", + "请 éĹ®", + "èµ· åºĬ", + "å±Ģ å±Ģéķ¿", + "稳 åģ¥", + "å¦Ĥæŀľ æĪij们", + "éħĴ ç²¾", + "æĪ· åı£", + "æĦŁ æĤŁ", + "æĪij们 éľĢè¦ģ", + "æĬĢ èīº", + "èĩª åªĴä½ĵ", + "è¿Ľ åĮĸ", + "æ¿ĢçĥĪ çļĦ", + "ä½ĵ 温", + "èļ ķ", + "èĩ´ è¾ŀ", + "宪 æ³ķ", + "ä¸Ģ çŃīå¥ĸ", + "çĵ¶ é¢Ī", + "æĥł æ°ij", + "èµ° è·¯", + "çİ° ä»»", + "åķĨ éĩı", + "ä¸ĭ 车", + "åĪ ł", + "責 ä»»", + "èŀįåIJĪ åıijå±ķ", + "ç´ł æĿIJ", + "æ²¹ ä»·", + "åģļ 人", + "çŀ ª", + "æĶ¹éĿ© åĪĽæĸ°", + "çļĦ åĮºåĪ«", + "è·¨å¢ĥ çĶµåķĨ", + "æ¶īåıĬ åĪ°", + "æīĺ 管", + "æĪij è¿ĺæĺ¯", + "åĿIJ æłĩ", + "ç½ij 讯", + "å½ĵåľ° çļĦ", + "追 溯", + "åľŁ èĢ³", + "åľŁèĢ³ åħ¶", + "åºķ ä¸ĭ", + "åĩł åįģå¹´", + "ç©¿ è¿ĩ", + "çĶŁæĢģ æĸĩæĺİ", + "æݨ èĸ", + "æݨèĸ ¦", + "éł Ĩ", + "åĴ³ åĹ½", + "åĪĨ æĪIJ", + "çĹķ 迹", + "æĪ· ç±į", + "éĥ½ ä¸įèĥ½", + "æĻļ ä¼ļ", + "åĢ ©", + "ä½ĵ åĬĽ", + "è¿Ļ个 èģĮä¸ļ", + "æĹł å½¢", + "åıª æĥ³", + "è¿Ľ åıĸ", + "æĿĢ æŃ»", + "èĦ Ĭ", + "äºij åįĹçľģ", + "æľª çŁ¥", + "ç¾İ èģĶ", + "ç¾İèģĶ åĤ¨", + "å¤ĸ å½¢", + "诱 æĥij", + "çĽ £", + "è¡Į 使", + "åłĨ 积", + "çĨŁ ç»ĥ", + "éĺIJ è¿°", + "æľĢ大 éĻIJ度", + "å·¡ æŁ¥", + "夺 åĨł", + "ä¼ģä¸ļ æĸĩåĮĸ", + "çĭ® åŃIJ", + "ä¿Ŀ å®Ī", + "ä¸ºæł¸å¿ĥ çļĦ", + "æī© æķ£", + "åĪ¶éĢł åķĨ", + "æŁĶ 软", + "为ä¸Ģä½ĵ çļĦ", + "游 çİ©", + "çĶŁ çĹħ", + "幫 åĬ©", + "åĶ± æŃĮ", + "æīį åı¯ä»¥", + "宽 æĿ¾", + "è¦ģ æ¯Ķ", + "æĺ¯ æĢİæł·", + "çģ° èī²", + "çİĭ åĽ½", + "æIJħ æĭĮ", + "计 éĩı", + "åij¨åĽ´ çļĦ", + "æĻºèĥ½ æīĭæľº", + "常 åĬ¡", + "常åĬ¡ åī¯", + "é© ´", + "å°Ĩ è¿ij", + "寻 常", + "ä¸ŃåĽ½ å¸Ĥåľº", + "容 åĻ¨", + "å±± ä¸Ĭ", + "èĥĮåIJİ çļĦ", + "亲 å¯Ĩ", + "æīĢ以 说", + "éİ ®", + "çļĦ çIJĨçĶ±", + "大 åŁİå¸Ĥ", + "常 å¹´", + "æĹħ游 ä¸ļ", + "å°±æĺ¯ è¿Ļæł·", + "åĨį æĿ¥", + "é«ĺ ä½į", + "åĨħ 饰", + "æŀĦ éĢł", + "ä¸Ģ èµ·æĿ¥", + "çĶ³ è«ĭ", + "å·²ç»ı å¼Ģå§ĭ", + "çļĦ åĬ¨ä½ľ", + "被 è¿«", + "éģį å¸ĥ", + "åīĸ æŀIJ", + "å°ı äºĭ", + "å¿ĥ ä¸ŃçļĦ", + "ä½ĵåĪ¶ æĶ¹éĿ©", + "çļĩ 家", + "æķĻ åłĤ", + "åIJĥ å®Į", + "åĽ½æ°ij åħļ", + "æĺİç¡® äºĨ", + "åıijå±ķ è§ĦåĪĴ", + "第ä¸Ģ æŃ¥", + "å¾Ĺ èµ·", + "åľ¨ åĵª", + "çļĦ è·¯ä¸Ĭ", + "é» Ķ", + "çķ¶ æĻĤ", + "大åĬĽ æĶ¯æĮģ", + "åıĮ éĩį", + "çŁ¥éģĵ èĩªå·±", + "åIJĪä½ľ åįıè®®", + "æ°Ķ åĬ¿", + "éķ¿æķĪ æľºåĪ¶", + "ç½ķ è§ģ", + "åĽŀ æĿ¥äºĨ", + "ä»ĸ ä¼ļ", + "ä¸Ń æĸ°", + "ä¸Ńæĸ° ç½ij", + "çļĦ åķĨåĵģ", + "èµł éĢģ", + "決 å®ļ", + "å¸Ĥåľº çĽij管", + "çķĻ åѦçĶŁ", + "çĶµ åİĭ", + "äºļ 马", + "äºļ马 éĢĬ", + "è¿ĺæĺ¯ æ¯Ķè¾ĥ", + "ä¿ĥè¿Ľ äºĨ", + "æµģ åħ¥", + "æijĦ åĥı", + "æijĦåĥı 头", + "æıIJ åıĬ", + "åıij æİĺ", + "æī¾ åĩº", + "æ¢Ŀ 件", + "ç¹¼ çºĮ", + "æĪij åĸľæ¬¢", + "å¥ İ", + "æ¦ľ æł·", + "å¼Ģ èĬ±", + "æ²ī éĩį", + "åŁº åĩĨ", + "ä»ħä»ħ æĺ¯", + "轨éģĵ 交éĢļ", + "åĶIJ å±±", + "çŃī ä¸Ģç³»åĪĹ", + "ä¸įè¿ĩ æĺ¯", + "åŃĺåľ¨ çĿĢ", + "èĬ± çĶŁ", + "å¤ ·", + "ç»Ī 究", + "ä¹Łæĺ¯ ä¸Ģ个", + "åįģ åŃĹ", + "èĸª éħ¬", + "伤 å¿ĥ", + "æĺ¥ ç§ĭ", + "åĨ· åį´", + "ç²¾ çģµ", + "çļĦ åľ°åĽ¾", + "æ¯Ķ çī¹", + "æ¯Ķçī¹ å¸ģ", + "æĢ§ åĪ«", + "ä½Ļ ä¸ĩåħĥ", + "ä¸įå¿ĺ åĪĿå¿ĥ", + "å¿ĥ çĸ¼", + "æĽ² 线", + "é«ĺ ä½İ", + "è¦ı å®ļ", + "æĻ¯ èī²", + "è¦ģ 说", + "åħ¬åı¸ å°Ĩ", + "液 åİĭ", + "è¿Ŀ 约", + "åİļ 度", + "åºŀ 大çļĦ", + "è¿ĺæĺ¯ å¾Ī", + "é¦ĸåħĪ æĺ¯", + "çµ ²", + "åĬ¡ å®ŀ", + "並 ä¸Ķ", + "å¢ŀ è¿Ľ", + "ç»Ħç»ĩ å¼Ģå±ķ", + "èµ·æĿ¥ äºĨ", + "è¾ĥ å°ı", + "导 游", + "两 åľ°", + "ç¿ ĺ", + "çģ¿ çĥĤ", + "é£İ éĩĩ", + "æĶ¯ 线", + "æĶ¯çº¿ ä»»åĬ¡", + "娱ä¹IJ åľĪ", + "天津 å¸Ĥ", + "åĮħ åĽ´", + "æľ¬ èµĽåŃ£", + "éĩįè¦ģ 讲è¯Ŀ", + "åıĮ åIJij", + "åįİ ä¸½", + "éĶ ¤", + "åĦ¿ 女", + "åįĸ åĩº", + "ä¾Ĩ 說", + "ä»ĭç»į ä¸Ģä¸ĭ", + "åIJ¦ 认", + "åĭ Ŀ", + "æĻ®éĢļ 人", + "çļĦ åĬ¨åĬĽ", + "涨 åģľ", + "åŁºéĩij 管çIJĨ", + "ä¸Ģ个 éĩįè¦ģ", + "è¿IJ æ²³", + "çħ ŀ", + "è´¢æĶ¿ éĥ¨", + "è¡Įä¸ļ åįıä¼ļ", + "éĥ½ å°Ĩ", + "è¨Ģ 论", + "ä¸ĭ ä¾Ĩ", + "墨 西", + "墨西 åĵ¥", + "åĽłä¸º ä»ĸ们", + "æĢİä¹Ī åĽŀäºĭ", + "åĬłå¤§ 对", + "èĬ Ń", + "çīĮ åŃIJ", + "ä¼ļ 使", + "妹 åŃIJ", + "ç«Ļ éķ¿", + "å¿ħ å¤ĩ", + "æłij æľ¨", + "æģ¶ æĦı", + "æ²³ éģĵ", + "å¯Į è£ķ", + "ç¹ģ åįİ", + "代表 åĽ¢", + "æµij 身", + "é¦ĸ ä½į", + "èĪªç©º åħ¬åı¸", + "鼻 å½±", + "ä¸ĵ è¾ij", + "æ°´ æºIJ", + "ä¸Ń æ¯Ĵ", + "並 ä¸į", + "èĢĮ åİ»", + "é ĥĿ", + "äºİ æѤ", + "æĸĩåĮĸ 建设", + "èĤ¯å®ļ ä¼ļ", + "å¸ĮæľĽ 大家", + "æıı åĨĻ", + "ä½İ è°ĥ", + "æĸ°åħ´ 产ä¸ļ", + "æ·Ħ åįļ", + "æĶ¾ å¼Ģ", + "çļĦ æĢ§æł¼", + "çĸ¾çĹħ çļĦ", + "æķ´ é¡¿", + "线ä¸Ĭ 线ä¸ĭ", + "éĢī 项", + "çļĦ 认åı¯", + "æķ´ é½IJ", + "çĶļ ä¹Ī", + "çľģ åĨħ", + "åı¤ 人", + "æ°ij ä¿Ĺ", + "çī¡ 丹", + "éŨ çªĹ", + "éĤ£ æł·çļĦ", + "çĽijäºĭ ä¼ļ", + "ç¿¡ ç¿ł", + "ç¦ ¹", + "åįĥä¸ĩ ä¸įè¦ģ", + "æĶ¶ 缩", + "çļĦ æĸĩåŃĹ", + "åĴĮ å°ļ", + "æĮĩ 令", + "åħ±äº§ åħļåijĺ", + "çļĦ çĪ¶äº²", + "å®Į å·¥", + "åĬ¡ å·¥", + "马 æĭī", + "马æĭī æĿ¾", + "æµĭ è¯Ħ", + "å² ļ", + "ä¸į åģļ", + "ä¸ĥ å¹´", + "åĿĩ ä»·", + "主 è§Ĥ", + "å¾Ī ä¸įéĶĻ", + "èĤ¡ä¸ľ 大ä¼ļ", + "äºĶ ä¸Ģ", + "é£İ åIJ¹", + "å¼Ģ éĩĩ", + "è¿Ļä¹Ī 大", + "èĥ½ çľĭåĪ°", + "èĢĥ è¯Ħ", + "åį³ 便æĺ¯", + "çݰ代 åĨľä¸ļ", + "æ¯Ķè¾ĥ é«ĺ", + "è¦ģ çľĭ", + "没 äºĨ", + "解 決", + "çݯ æ¯Ķ", + "åĨ² åĬ¨", + "æ·± å¤ľ", + "åĩł åįĥ", + "ä¿ ı", + "ç½ij æ°ij", + "å°± 没", + "ä»ĸ 表示", + "éĩı åŃIJ", + "æĹ©é¤IJ åĬłçĽŁ", + "åįĬ å²Ľ", + "æIJŀ ç¬ij", + "ä¸Ĭ æĬ¥", + "å¯ ©", + "é¢Ħ 订", + "èľĤ èľľ", + "æŁ¥ æī¾", + "ä¼Ĺ æīĢ", + "ä¼ĹæīĢ åij¨", + "ä¼ĹæīĢåij¨ çŁ¥", + "æĹ© æĹ¥", + "åıij æī¬", + "åĴĮ 个人", + "åĬłåħ¥ äºĨ", + "åĸ® ä½į", + "åĪĨ æĺİ", + "第ä¸Ģ æī¹", + "ç¾İ åĨĽ", + "æĿĢ æīĭ", + "éŨ å¤ĸ", + "åķĨ åľĪ", + "ä¸Ģ åĪ»", + "çļĦçľ¼ ç¥ŀ", + "éľ Ħ", + "äºĽ ä»Ģä¹Ī", + "åĬł æ·±", + "æ¯ı ä½į", + "å¸Ĥ éĿ¢ä¸Ĭ", + "åıĶ åıĶ", + "çļĦ éĤ£ç§į", + "粤 港澳", + "è´´ å¿ĥ", + "æĸĩåĮĸ 产ä¸ļ", + "红 æĹĹ", + "åĺī åħ´", + "æĶ¶ çĽĺ", + "å®ĮæĪIJ åIJİ", + "ä¼ģä¸ļ 管çIJĨ", + "纵 横", + "ä¸į ä¿¡", + "æĪIJ éĥ½å¸Ĥ", + "æ´Ĺ 澡", + "举è¡Į çļĦ", + "çĶ¢ çĶŁ", + "ç©¿ ä¸Ĭ", + "åĪļ 好", + "åħī 线", + "æīĵ æŀ¶", + "è¿Ļ æľ¬ä¹¦", + "åĶ®åIJİ æľįåĬ¡", + "åĩł åĪĨ", + "ä¸Ĭ 次", + "ä¸į åĪĨ", + "产 åIJİ", + "éģ¿ å¼Ģ", + "ç»Ī æŀģ", + "代表 大ä¼ļ", + "æ¼Ķ æĬĢ", + "åĽŀ è´Ń", + "åѦ è´¹", + "éĺ» ç¢į", + "ä¸Ģ大 æī¹", + "ç«£ å·¥", + "åĨ³ å®ļäºĨ", + "ä½Ĩ å¦Ĥæŀľ", + "çĶµ æµģ", + "ä¸Ŀ 毫", + "èĥ½å¤Ł åľ¨", + "éĶĢåĶ® æĶ¶åħ¥", + "åľ¨ åŃ¦æł¡", + "æ°´ åĩĨ", + "è§Ĩ 线", + "èĩª åľ¨", + "åķĨä¸ļ éĵ¶è¡Į", + "为äºĨ 让", + "çį² å¾Ĺ", + "çݩ家 æľĭåıĭ", + "éĿ¢ èĨľ", + "åĪĨ åī²", + "åī§ æľ¬", + "ç« Ń", + "说 å¾Ĺ", + "æĥ³ çŁ¥éģĵ", + "çļĦ人 çī©", + "èĮħ åı°", + "åIJĮ ä¸Ģ个", + "æķ°æį® ä¸Ńå¿ĥ", + "çĶ Ħ", + "åĸľ æĤ¦", + "ä¸ĭæĿ¥ çļĦ", + "å®ļ åIJij", + "æŀģ åħ·", + "çļĦ åľŁåľ°", + "éĤ£ åĢĭ", + "æijĦ åħ¥", + "äºĨ æĪijçļĦ", + "马 è·¯", + "åħ¨ 社ä¼ļ", + "è®® æ¡Ī", + "å±ĭ åŃIJ", + "åIJį åı«", + "åĮ ª", + "åľ¨ å¤ĸéĿ¢", + "åįİ åįĹ", + "åıij è´§", + "å¯Ĵ åĨ·", + "é«ĺçŃī æķĻèĤ²", + "详ç»Ĩ çļĦ", + "个 é¡¹çĽ®", + "çĶŁäº§ åĬĽ", + "æŶ 常", + "å°± æľĥ", + "ä¸ĩ èĤ¡", + "éĻĮçĶŁ 人", + "æıı ç»ĺ", + "å½ĵ çĦ¶æĺ¯", + "æĭī åĬ¨", + "éĵ¾ æĿ¡", + "æī£ éĻ¤", + "ä¸Ģ缴 éĥ½", + "å°ı åŃ©åŃIJ", + "伤 åı£", + "第äºĮ å±Ĭ", + "è´Ń ç½®", + "çļĩ 马", + "æĹł èģĬ", + "表 åĨ³", + "诸 å¦Ĥ", + "åĵį èµ·", + "é£İ æļ´", + "ä¸Ģæµģ çļĦ", + "ç ·¨", + "解æĶ¾ åĨĽ", + "室 å¤ĸ", + "å°± è¿Ļä¹Ī", + "å³ ¶", + "æīĢæľī 人éĥ½", + "æIJľç´¢ å¼ķæĵİ", + "çļĦ æĪIJæľ¬", + "åħļ æĶ¿", + "åıijè¡Į 人", + "çļĦ äºĭå®ŀ", + "对 该", + "åıĹ æįŁ", + "ä¿Ħ ä¹Į", + "é²ľ èĬ±", + "åĨľ èį¯", + "æŀģ éĢŁ", + "æĢ¥ æĢ§", + "两 ä¼ļ", + "ä¸ĢèĪ¬ æĿ¥è¯´", + "æµ· é²ľ", + "åĨ Ī", + "çĶ¨ 人", + "çĶ¨äºº åįķä½į", + "åĢ ª", + "åĦª æĥł", + "æł¹ æºIJ", + "åĽ¢ è´Ń", + "ç¾İ æ´²", + "ä¸ĭ è¡Į", + "å¹´ æľ«", + "èľ ¡", + "è¯ģ 件", + "åľ¨ æĪijåĽ½", + "ä¸į åºĶ", + "æĮī æŶ", + "åłª 称", + "åľº ä¸Ĭ", + "å¹²éĥ¨ èģĮå·¥", + "æľī å¾Ī大çļĦ", + "æķ°åŃĹ ç»ıæµİ", + "æ¼Ķ ç»ĥ", + "æį® ç»Łè®¡", + "å¾Ģ æĿ¥", + "广åijĬ æľįåĬ¡", + "çļĦ è·Ŀ离", + "æŃ ¸", + "è¨Ģ è¯Ń", + "被 èªī", + "被èªī 为", + "åĭī 强", + "å°Ĭ æķ¬", + "ä¸ĩ 亿åħĥ", + "ä¸ŃåĽ½ åĽ½éĻħ", + "å¹² é¢Ħ", + "å¹´ 产", + "èĢķ åľ°", + "èĮ İ", + "åį³ æĺ¯", + "æĺ¨ æĻļ", + "æĪIJ为 ä¸Ģ个", + "çºł æŃ£", + "åij½ åIJį", + "é¢ģ å¸ĥ", + "çĮľ æµĭ", + "ä¿ĿèŃ· æĶ¿çŃĸ", + "æĭ ¢", + "æ´» æ³¼", + "çŃī éĥ¨éŨ", + "åѦ åĪ°", + "å¢ŀåĢ¼ ç¨İ", + "èĪª 线", + "åĨ ¤", + "åįģ åĩłå¹´", + "æݧèĤ¡ èĤ¡ä¸ľ", + "ä¸Ģ éŨ", + "个 å·¥ä½ľ", + "ä¸ªå·¥ä½ľ æĹ¥", + "æĸ° 西", + "æĸ°è¥¿ åħ°", + "论 è¯ģ", + "ä» Ĩ", + "åı¦å¤ĸ ä¸Ģ个", + "æĶ¹ ç¼ĸ", + "严 ç¦ģ", + "åĸľ 好", + "个人 ä¿¡æģ¯", + "满æĦı 度", + "åĵ ¨", + "å¸Ī èµĦ", + "æĶ¹ 为", + "ç«ŀäºī 对æīĭ", + "åĩº çĤī", + "åķĨ 人", + "大 æ£ļ", + "æĮĩ导 ä¸ĭ", + "å¦ĩ ç§ij", + "è¼ ª", + "æī ģ", + "åIJĮæŶ è¿ĺ", + "并 éĢļè¿ĩ", + "æĪĺ éĺŁ", + "èĶĵ 延", + "ä¿ ŀ", + "éĢĤå½ĵ çļĦ", + "åīį è¾Ī", + "åĵģ åij³", + "湿 åľ°", + "æĪIJ åŀĭ", + "ä¸į åıªæĺ¯", + "æĥ© ç½ļ", + "åĩºåı° äºĨ", + "çİ© 游æĪı", + "æīį åıijçİ°", + "åºĶ èģĺ", + "å¤ĸ æĿ¥", + "åįł é¢Ĩ", + "å±ķ æľĽ", + "å« Ĥ", + "港 èĤ¡", + "æ¡Į ä¸Ĭ", + "æĶ¯ æŁ±", + "çļĦæĥħ å½¢", + "广éĺĶ çļĦ", + "æĶ¯ è¡Į", + "å´© æºĥ", + "æľĪ ä¸Ń", + "æľĪä¸Ń æŬ", + "ç»į åħ´", + "临 è¿ij", + "æĬ¤ æłı", + "æļ ®", + "åįķ èģĮä¸ļ", + "è¾¹ å¢ĥ", + "æĹ¥ çħ§", + "ä¸Ģ åłĨ", + "缴 å¾Ħ", + "åħ±åIJĮ ä½ĵ", + "æĸ°åįİ ç½ij", + "æīĵ 好", + "çĶµåĬ¨ 汽车", + "ä¸į æĺİçĻ½", + "éĢĻ 裡", + "缼 大", + "çİĭ æľĿ", + "åĨį ä¸Ģ次", + "åĬŀåħ¬ åİħ", + "è´¨ æĬ¼", + "åIJĪ åĩ»", + "人们 对", + "鼶 é£Ł", + "éĥ½ä¸į çŁ¥éģĵ", + "çļĦ è¯Ńè¨Ģ", + "åĭŁéĽĨ èµĦéĩij", + "åĬ¨ èĦī", + "å½ ¤", + "è¿Ļ åĩłå¹´", + "çŁŃ è§Ĩé¢ij", + "太 é«ĺ", + "常 å§Ķä¼ļ", + "åĬł çıŃ", + "éĩį å¿ĥ", + "åªĴä½ĵ æĬ¥éģĵ", + "没 æ³ķ", + "éĹ» åIJį", + "çĥŃ åº¦", + "å¹¿æ³Ľ çļĦ", + "åħŃ å¤§", + "çī© ä½ĵ", + "ä¸į 该", + "é¢ĺ 主", + "精彩 çļĦ", + "为 è¿Ľä¸ĢæŃ¥", + "èĻ ŀ", + "åĽº çĦ¶", + "è´µå·ŀ çľģ", + "çºł ç»ĵ", + "代çIJĨ 人", + "æ³ķå®ļ 代表", + "åı¦ä¸Ģ ç§į", + "ä¸į åIJ«", + "æĭ¯ æķij", + "ä¼ļ ç»Ļ", + "è¯Ĺ è¯į", + "åIJĮ ç±»", + "å¾Ĺ ä¸įåĪ°", + "æĬĵ ç´§", + "以 åħ¶", + "åħ¥ åħļ", + "è¿ĺ åı¯", + "æľŁ åĪĬ", + "å¾Īå¤ļ æŶåĢĻ", + "æĹ¥ åIJİ", + "åħ¬ 约", + "ä¸Ģ 举", + "æ¯Ķè¾ĥ å¤ļ", + "éĩij æ²Ļ", + "æį ŀ", + "æİĴ åĩº", + "æѦ æľ¯", + "ä¸į æĸ·", + "ä¸Ń èĢĥ", + "ä¿¡ èµĸ", + "ä»İä¸ļ 人åijĺ", + "çģ« çĦ°", + "éĨĴ æĿ¥", + "ä½İ 温", + "éĢ¾ æľŁ", + "åĬ± å¿Ĺ", + "éħ ¥", + "åı¯è°ĵ æĺ¯", + "è¿Ļ æĦıåij³çĿĢ", + "é¢ł è¦Ĩ", + "åĮĹ京 大åѦ", + "ä¸ĵ 线", + "åıĬ 以ä¸Ĭ", + "è¨ ª", + "èĢĮ åIJİ", + "çŁ¥ ä¹İ", + "ä¸Ģ对 ä¸Ģ", + "å¨ĥ å¨ĥ", + "çģ¾ éļ¾", + "åħ¨ å±Ģ", + "æīĢå¾Ĺ ç¨İ", + "å®ŀ æĥł", + "èļĤ èļģ", + "ä¹Ł çŁ¥éģĵ", + "温 åĴĮ", + "èIJ½ ä¸ĭ", + "åŀĭ ä¼ģä¸ļ", + "åĨį ä¹Ł", + "ä¾Ľ çĥŃ", + "é«ĺ æ½®", + "çĢı覽 åĻ¨", + "çļĦ 巨大", + "åħĪ 天", + "å¹´ ä¸ŃåĽ½", + "类似 çļĦ", + "çIJĨäºĭ ä¼ļ", + "空 éĸĵ", + "çģµ æĦŁ", + "åĬĽ æ°Ķ", + "带 ä¸Ĭ", + "ä¸į好 æĦıæĢĿ", + "æľī ä½ķ", + "å·² åľ¨", + "åıĸ åĩº", + "è¿Ŀæ³ķ çĬ¯ç½ª", + "åŃ¦ä¹ł 贯彻", + "åľ° 带", + "楼 梯", + "çŃī æĥħåĨµ", + "ä»İ åīį", + "çļĦ ä¹łæĥ¯", + "ç³Ł ç³ķ", + "å°± èĥ½å¤Ł", + "è© ķ", + "ä¸Ģ å¾ĭ", + "æĮ« æĬĺ", + "åİŁæĸĩ åľ°åĿĢ", + "å½ĵ å±Ģ", + "ä¸į éĢļ", + "æķ° åįĥ", + "éĺŁä¼į 建设", + "æŶ èĬĤ", + "åģļ èµ·", + "çļĦ è®°å¿Ĩ", + "ç½ij绾 å®īåħ¨", + "åĩ¡ æĺ¯", + "æ° ¯", + "éĽķ åĪ»", + "åŁĥ åıĬ", + "æĪij åı¯ä»¥", + "çĽij çIJĨ", + "æĽ´ åħ·", + "åŁİ 管", + "èĭ ¯", + "åı¥ åŃIJ", + "èĭ¥ æľī", + "ä»İæĿ¥ ä¸į", + "缸åħ³ è´Łè´£", + "å®īåħ¨ æĦŁ", + "æĽ´ è¦ģ", + "çļĦæĥħ æĦŁ", + "çī¢ çī¢", + "è¾ĥ 好çļĦ", + "æ° ®", + "ç¬ij è¯Ŀ", + "车 å±ķ", + "ä¹ĭ ç¾İ", + "ç®Ģ 约", + "ç±»åŀĭ çļĦ", + "èĢģ åĮĸ", + "çľĭ ä½ł", + "è¿ĩ åĪĨ", + "éŨ åīį", + "ä¸Ģ éĹ´", + "æĥ³ åİ»", + "åª Ľ", + "åľŁ è±Ĩ", + "åıĪ 称", + "ä¸Ń ä¿¡", + "åŃĺ éĩı", + "马 äºij", + "èĩ´ 使", + "åħĪ åīį", + "èĢģ åŃIJ", + "æīĵ æī®", + "æ¯ķä¸ļ äºİ", + "æ¯ķä¸ļ åIJİ", + "ç¾İ好 çĶŁæ´»", + "å·¥ä¸ļ ä¼ģä¸ļ", + "就好 äºĨ", + "èħIJ èļĢ", + "çıį çıł", + "åĪ° è¿ĻéĩĮ", + "æīĢéľĢ çļĦ", + "è¿Ļæĺ¯ åĽłä¸º", + "çIJĨæĥ³ çļĦ", + "å·®å¼Ĥ åĮĸ", + "é ®", + "é® ®", + "äºļ 太", + "æĹł ç©·", + "æıIJ çİ°", + "ä¸ĵä¸ļ æĬĢæľ¯", + "çĶ¢ æ¥Ń", + "åѦ åŃIJ", + "ç§ij å¹»", + "åįłåľ° éĿ¢ç§¯", + "ä¸į åĩĨ", + "æľªæĪIJ 年人", + "æĶ¶ å½ķ", + "è¿ĺ 款", + "éĴ¢ çŃĭ", + "æ¼ ¢", + "å¾Ĺ æĦı", + "综åIJĪ ä½ĵ", + "æŀģ é«ĺ", + "åįķ è¯į", + "é«ĺæķĪ çļĦ", + "骨 头", + "æī§ çĿĢ", + "缼 ä¸ĸ", + "模 çī¹", + "æĽ´ èĥ½", + "ç»Ŀ æľĽ", + "对åºĶ çļĦ", + "æ¨ Ĭ", + "æĸ° ä¸ī", + "æĸ°ä¸ī æĿ¿", + "æģ° æģ°", + "åIJį 家", + "æł¸å¿ĥ æĬĢæľ¯", + "个 å°ı", + "æĢİä¹Ī ä¼ļ", + "说 ä¸įå®ļ", + "西 çĵľ", + "åĵ İ", + "ç¢ Ł", + "å¿ħ ä¸įåı¯", + "å¿ħä¸įåı¯ å°ij", + "ä¹ĭ éĸĵ", + "åĪĨ 管", + "交éĢļ äºĭæķħ", + "å¼Ģ åĬŀ", + "å¾ģæ±Ĥ æĦıè§ģ", + "äº ¨", + "鼻åŃIJ éĥµ", + "鼻åŃIJéĥµ 件", + "ä¿¡æģ¯ æľįåĬ¡", + "ä½ł è§īå¾Ĺ", + "缴 è§Ĥ", + "å·² å®ĮæĪIJ", + "åĪĨ ä¼ļ", + "åĽŀ åįĩ", + "éļ »", + "好 人", + "äºĨ解 ä¸Ģä¸ĭ", + "åį« æµ´", + "æľĢ çĪ±", + "åºŀ 大", + "客 æĪ¿", + "çijŀ åħ¸", + "éĥ½ ä¸įæĺ¯", + "é¤ ¨", + "èĹ ī", + "çļĦ åIJĦ项", + "为 缮æłĩ", + "çļĦ è®¤çŁ¥", + "å½±åĵįåĬĽ çļĦ", + "夸 å¼ł", + "佩 æĪ´", + "æ±ĩ çİĩ", + "çļĦ çĪ±æĥħ", + "æĺ¥ é£İ", + "æĺ¯ æĪijçļĦ", + "æ¨ ¹", + "åįĬ å°ıæŶ", + "å±± åİ¿", + "å±± 西çľģ", + "èĢĮ è¿Ļ", + "æĽ´å¤ļ ä¿¡æģ¯", + "è¿ĺ æľīä¸ĢäºĽ", + "ç²¾ ç»ĨåĮĸ", + "ç¾İ åѦ", + "çĶ± æĸ¼", + "ä»ħä¾Ľ åıĤèĢĥ", + "å¾Ī é«ĺçļĦ", + "åıł åĬł", + "è¿Ļä¹Ī 说", + "å±ķ åĩº", + "åĽĽ å¤Ħ", + "ä¸ĩ 家", + "æĭĽ åĭŁ", + "çļĦ 强大", + "æĤ£ æľī", + "å°ı äºİ", + "ä¹Łè®¸ æĺ¯", + "对 èĩªå·±çļĦ", + "èģĮä¸ļ æķĻèĤ²", + "æĿ¥ è¿Ľè¡Į", + "æ¡£ 次", + "æīĵ èµ¢", + "éĥ½æľī çĿĢ", + "åº ¸", + "è¯Ń æ°Ķ", + "çĶ² éĨĽ", + "空 åĨĽ", + "车 åĨħ", + "åĽłä¸º ä½ł", + "å®ŀ æķĪ", + "æĥħ ä¾£", + "åıijè¾¾ åĽ½å®¶", + "éķľ åŃIJ", + "æ¯į å©´", + "ä½Ĩæĺ¯ ä»ĸ", + "积æŀģ æİ¨è¿Ľ", + "大å¹ħ 度", + "çļĦ 女åĦ¿", + "é¤IJ æ¡Į", + "åIJ¬ å¾Ĺ", + "çļĦ 积æŀģæĢ§", + "好 åIJ§", + "æĹ¥ æ¶Īæģ¯", + "æľī ä»»ä½ķ", + "æ¯Ĵ åĵģ", + "æĹ©çĤ¹ åĬłçĽŁ", + "第ä¸Ģ 天", + "å°½ åĬĽ", + "æł ĸ", + "主 æīĵ", + "æĺ¯ä¸Ģ åIJį", + "çĪĨ æĸĻ", + "äºĭä¸ļ åıijå±ķ", + "å¾® åķĨ", + "äºİä¸Ģä½ĵ çļĦ", + "çĶŁ çĮª", + "èĩªçĦ¶ èµĦæºIJ", + "çŀĦ åĩĨ", + "è§Ħ模 åĮĸ", + "并 ä¸İ", + "èĤ¥ èĥĸ", + "家 çĶ¨", + "大 çĪ·", + "é¢Ħ åijĬ", + "æĿ¥ åģļ", + "éĺ³ åİ¿", + "æŀĦ çŃij", + "é¢ģ å¥ĸ", + "åİĨåı² æĸĩåĮĸ", + "æľįåĭĻ æĪĸ", + "æĢ» åĨ³èµĽ", + "åıij åŀĭ", + "æĪij 羣çļĦ", + "æĽ ¦", + "åıĤ ä¼ļ", + "èĦĨ å¼±", + "åĩĨ åħ¥", + "èħ¹ éĥ¨", + "åı¸ 令", + "æĤ² åī§", + "天 ä¸Ĭ", + "åı£ ä¸Ń", + "ä¸ĩ 个", + "åѦ ä¸ļ", + "æıIJ åĢ¡", + "两 è¾¹", + "大 èĤ¡ä¸ľ", + "åı¤ éķĩ", + "è¡Ģ ç³ĸ", + "çļĦ ç¨ĭ度", + "æ£ī èĬ±", + "åIJİ åı°", + "å°± åĮ»", + "æķ´ æķ´", + "èĴ ²", + "çĽĪåĪ© èĥ½åĬĽ", + "ç± ½", + "èĦ «", + "çľĭ éĩį", + "家 éķ·", + "èģĺ çĶ¨", + "èµĽ éģĵ", + "åīį èĢħ", + "建 èŃ°", + "å¾ĭå¸Ī äºĭåĬ¡", + "èīºæľ¯ åĵģ", + "æľī èĩªå·±çļĦ", + "åIJ¦ å®ļ", + "社 åĽ¢", + "åij¨ äºĶ", + "带 åĪ°", + "å·¥ä½ľ ä¼ļè®®", + "èĤ¡ æľ¬", + "å¤ĸ åĮħ", + "家 åħ¬åı¸", + "çĽij çĭ±", + "èĪ Ĭ", + "åIJį æł¡", + "西 æ¹ĸ", + "è¶ħè¿ĩ äºĨ", + "åįĹ å±±", + "ç»Ħ 件", + "åĢ¼å¾Ĺ 注æĦı", + "æĮ£ æīİ", + "äºĭ 迹", + "ç¶ĵ çĩŁ", + "ç§ij 室", + "好 åIJĹ", + "æ¤ħ åŃIJ", + "åľĪ åŃIJ", + "ä½Ĩ 她", + "æµģ çķħ", + "åIJĦèĩª çļĦ", + "èģĮ åijĺ", + "è¡į çĶŁ", + "åħ¨ åľº", + "æĴ¤ éĶĢ", + "åį´ 被", + "å®ģ éĿĻ", + "åīį æīĢ", + "åīįæīĢ æľª", + "åīįæīĢæľª æľī", + "主 ä¸ļ", + "åĮĹ ç¾İ", + "è¯Ħ å®ļ", + "åĵģ å°Ŀ", + "大家 éĥ½åľ¨", + "主 å¸ħ", + "ç»Ĩ å¿ĥ", + "ä¿¡æģ¯ æĬ«éľ²", + "çļĦ ç«ŀäºī", + "éĢĻ樣 çļĦ", + "ç§ijåĪĽ æĿ¿", + "éĩĩ æijĺ", + "票 æį®", + "éĢIJ å¹´", + "èĭ± è¶ħ", + "è¡Įä¸ļ åĨħ", + "人 寿", + "åIJİ åĭ¤", + "å¦Ĥ æĦı", + "ç¬Ķ è¯ķ", + "æ·¡æ·¡ çļĦ", + "ä¸į èĪĴæľį", + "ä½ĵ 积", + "ä¹Łä¸į è¦ģ", + "éĿ¢ æĸĻ", + "æł· æľ¬", + "ç¥ ģ", + "æĮī è§Ħå®ļ", + "大æ¦Ĥ æĺ¯", + "æĥħåĨµ è¿Ľè¡Į", + "åIJĦ åįķä½į", + "çļĦ ç¬ij容", + "åĩºèī² çļĦ", + "代表 æĢ§", + "çļĦ ç¾İ好", + "éĴ ¦", + "å¾® çĶŁçī©", + "è¶Ĭ æĺ¯", + "æĸ¹ åı¯", + "å¹² èĦĨ", + "éģĬ æĪ²", + "çļĦ åħ´è¶£", + "éĹ® è´£", + "åĽłä¸º æĪij们", + "èĢĥ éĩı", + "çĶŁ çĶŁ", + "éĺ» åĬĽ", + "ä¸į åħģ许", + "æıIJ è®®", + "åĩı æĮģ", + "åıªæĺ¯ ä¸Ģ个", + "æĪij æĬĬ", + "åıijçİ° èĩªå·±", + "å¢ŀ å¹ħ", + "å¦ į", + "èĹĿ è¡ĵ", + "ä¸Ģ家 人", + "åĪĨ 级", + "çļĦ æķ°éĩı", + "è½® èŀįèµĦ", + "çŃī åĽłç´ł", + "大 夫", + "èģĺ 请", + "é£İ æľº", + "绽 æĶ¾", + "ä»»ä½ķ ä¸Ģ个", + "éł Ĥ", + "éĺ¶ çº§", + "æĬĬ 她", + "è¿Ľ åĨĽ", + "èĥ½ åģļåĪ°", + "åŁ¹è®Ń æľºæŀĦ", + "çī© æĸĻ", + "ç«¥ è¯Ŀ", + "æĮĩ导 æĦıè§ģ", + "éĺ ®", + "æ·±åħ¥ æİ¨è¿Ľ", + "主 æľº", + "æ¸Ķ ä¸ļ", + "ä¸į æľį", + "æµĵ éĥģ", + "è¡Ĺ ä¸Ĭ", + "ä¾Ŀ 次", + "æŶ 段", + "æ¢ µ", + "çļĦ åĸľçĪ±", + "å¾Ī éķ¿", + "åĪĿ 级", + "æŀľ æĸŃ", + "æĬ¢ æķij", + "é¼ĵ èĪŀ", + "ä¾Ľ éľĢ", + "æ·±åħ¥ å¼Ģå±ķ", + "产ä¸ļ éĽĨ群", + "åĻª éŁ³", + "åIJ¬ çĿĢ", + "æ·±åĪ» çļĦ", + "å¿į åıĹ", + "çĶµ ç£ģ", + "强 èĢħ", + "æ»ĭ åij³", + "æĽ¼ èģĶ", + "åı¯ä»¥ 缴æİ¥", + "大 ç±³", + "æŃ· åı²", + "æĶ¿åĬ¡ æľįåĬ¡", + "åħ¬ å¼ı", + "社 群", + "éģĵ士 èģĮä¸ļ", + "ä¹ĭ æĥħ", + "æµ· æ°´", + "æ¼Ķ å¥ı", + "åºĹ éĩĮ", + "迹 象", + "åıijå±ķ çIJĨ念", + "é«ĺ 空", + "åij¨ åĪĬ", + "åĽŀ åĪ°äºĨ", + "ä¸į éĢĤåIJĪ", + "åłµ å¡ŀ", + "åĬ Ī", + "æ°´ ä¸Ĭ", + "çĢij å¸ĥ", + "纳ç¨İ 人", + "çĩĥ æ²¹", + "å·¥ç¨ĭ é¡¹çĽ®", + "峡 è°·", + "æľī éĴĪ对æĢ§", + "åľĨ å½¢", + "æľ¬ å¸Ĥ", + "è¿Ļ è¯Ŀ", + "管çIJĨ èĢħ", + "ç¡®è¯Ĭ çĹħä¾ĭ", + "æĬĬ æīĭ", + "彩 èī²", + "ä¸Ĭ åīį", + "夯 å®ŀ", + "ç¾Ĭ èĤī", + "å¾Ģ å¹´", + "æĵħ èĩª", + "è¿· 人", + "èĪª æ¯į", + "ç²¾ ç»Ĩ", + "åľ¨ æĪijçļĦ", + "åĪĽ æĬķ", + "麦 åħĭ", + "æľĪ ç»ı", + "åĮĹ æµ·", + "ä¹ĭ æĺŁ", + "åı¶ åŃIJ", + "å¸Ĥåľº ç«ŀäºī", + "è¿Ļ äºĭ", + "åıĥ èĪĩ", + "产 åľ°", + "åĶ ī", + "åķĨåĵģ æĪ¿", + "èĪª è¿IJ", + "ä¼ĺ å¼Ĥ", + "ä»ĸ们 æĺ¯", + "鼨 æ°´", + "è¯į æ±ĩ", + "åĨľ çĶ°", + "欧 éĺ³", + "çŁŃ 线", + "管 ç½ij", + "æł¹ åŁº", + "åıªæľī ä¸Ģ个", + "éŀĭ åŃIJ", + "å¸Ĥ å§Ķ书记", + "åĪ» æĦı", + "è¡Į 车", + "åıĪ 被", + "åı¯éĿł æĢ§", + "è´ ±", + "ä»» åij½", + "åºĶ åľ¨", + "å°± å¾Ĺ", + "æľįåĬ¡ ä½ĵç³»", + "æĶ¿ æĿĥ", + "åıijè¨Ģ 人", + "è¿ĩ å¾Ģ", + "两 åıª", + "èĻ½ 说", + "éĢģ ä¸Ĭ", + "ä»Ģä¹Ī äºĭ", + "æķ£ æĸĩ", + "æİĮ æݧ", + "èĸĦ å¼±", + "ä¸ĭéĿ¢ å°±", + "主è¦ģ åĨħ容", + "å¾Ī éĩįè¦ģçļĦ", + "å°± 说", + "çĻ½èī² çļĦ", + "éĤ£ä¸ª æŶåĢĻ", + "ç»ı纪 人", + "çļĦ æ¯į亲", + "ç¬Ķè®° æľ¬", + "åºķ å±Ĥ", + "è¿ij 代", + "解 说", + "è²ł 責", + "æľĢ大 åĮĸ", + "åķĨ éĵº", + "æł¡ åıĭ", + "æ² ģ", + "ä¸į åĩºæĿ¥", + "éĻ· éĺ±", + "ç¨ ħ", + "åħ¬å¸ĥ äºĨ", + "åĩĢ åĢ¼", + "çĽ¸å¯¹ è¾ĥ", + "ç¬ Ľ", + "æł¸ ç®Ĺ", + "åįİ ä¾¨", + "æĢ¥ æķij", + "æĮº 好", + "åħĴ ç«¥", + "äºĮ èĥİ", + "åĩº èĩª", + "åĿ Ł", + "æīĭ ä¸ĭ", + "å± ¡", + "åĪĽéĢł æĢ§", + "ä¸¥æł¼ æĮīçħ§", + "åĨį åİ»", + "举 缣", + "人 æµģ", + "äºĨä¸Ģ 声", + "å°ıæŶ åīį", + "è´µ æĹı", + "éľ ĸ", + "ä¹Łæĺ¯ éĿŀ常", + "éĢ ±", + "çľĭäºĨ çľĭ", + "ç¹ģ æ®ĸ", + "èĩ³ æѤ", + "é¢Ħ å¤ĩ", + "å¾Ī æĺİæĺ¾", + "æ¼Ķ èīº", + "åĿIJ çĿĢ", + "ä¿Ħ åĨĽ", + "åľ¨ è¿ĩåİ»", + "ä¹ĭ äºĭ", + "æĬĵ èİ·", + "åĿIJ ä¸ĭ", + "çĶ± ä¸ŃåĽ½", + "ä¹Ł å¼Ģå§ĭ", + "çŃĶ å¤į", + "åŀĥåľ¾ åĪĨç±»", + "éĴĵ é±¼", + "åIJĦ 種", + "缸 éģĩ", + "ä¸įåģľ çļĦ", + "æī¹ éĩı", + "éĩįè¦ģ ä½ľçĶ¨", + "å§Ķ å±Ī", + "åħŃ å¹´", + "ä¸ĥ åįģ", + "ä¹ĭ æĪĺ", + "é£İéĻ© 管çIJĨ", + "éŁ³ æ¨Ĥ", + "è¡ĮæĶ¿ å¤Ħç½ļ", + "æľ¬ äºĭ", + "æĴ° åĨĻ", + "èģļ åIJĪ", + "éĢĤ æŶ", + "æIJ¬ 家", + "ç¢İ çīĩ", + "缼 å®´", + "ç®Ģ æ´ģ", + "åı¬ éĽĨ", + "ç®Ģ åĮĸ", + "åĮĹ京 æŶéĹ´", + "第ä¸ī å±Ĭ", + "æĿ¥ åĽŀ", + "常çĶ¨ çļĦ", + "京 æ´¥", + "京津 åĨĢ", + "梦 å¹»", + "è¯ķ è¡Į", + "æľº åºĬ", + "åĪ° æľĢåIJİ", + "åĬ© æīĭ", + "åĪĨ 彩", + "åĩº åĵģ", + "åĪ¹ 车", + "åIJ¯ åıij", + "侧 éĿ¢", + "æ¯ı å½ĵ", + "缸åħ³ è§Ħå®ļ", + "ä¸ĸ 人", + "è´Ń 车", + "å¿ĥ 缮", + "å¿ĥ缮 ä¸Ń", + "äºĶ éĩij", + "è¿ĺ è®°å¾Ĺ", + "ä¾Ŀ çĦ¶æĺ¯", + "æıIJ æ¡Ī", + "çĶµåķĨ å¹³åı°", + "åģļ åĪ°äºĨ", + "æĿľ ç»Ŀ", + "å®ī åįĵ", + "ä¸ĸçķĮ åIJĦåľ°", + "åīį éĢĶ", + "æ´Ĺ åĩĢ", + "å¥ĭ åĬĽ", + "åŁİå¸Ĥ 建设", + "å¤ļ åĬŁèĥ½", + "ä¼ļ éĢłæĪIJ", + "åıijå¸ĥ ä¼ļä¸Ĭ", + "究 ç«Łæĺ¯", + "åĪĨ 红", + "çŁ¥ èŃĺ", + "éĿ¢ æĿ¿", + "æĹł 声", + "æĢ¥ éľĢ", + "失 çľł", + "çĪ¸ å¦Ī", + "äº Ĥ", + "åħ¨ æĻ¯", + "ç»ıåħ¸ çļĦ", + "åī§ ä¸Ń", + "é¢Ĩ导 ä¸ĭ", + "åħļ åĨħ", + "åħ¥ ä¾µ", + "æĭī æĸ¯", + "ä¸Ģ å¹ķ", + "åĬł ä¹ĭ", + "èĤ Ĩ", + "èĭ± æł¼", + "èĭ±æł¼ åħ°", + "å·§ åħĭ", + "å·§åħĭ åĬĽ", + "ä¸Ģ å¿ĥ", + "èģ Ĥ", + "å¾Ģå¾Ģ æĺ¯", + "管çIJĨ å±Ĥ", + "çĻ» åħ¥", + "建ç«ĭ èµ·", + "建 åĽ½", + "åŃIJ 宫", + "åºĶ ä»ĺ", + "æİ¢ 究", + "第ä¸Ģ ä½į", + "ä½Ļ 家", + "çŃī æ´»åĬ¨", + "æīĢ èĩ´", + "è¾ĥ å¿«", + "æĺ¯ éĿŀ", + "æıIJ åIJį", + "äºĮ èĢħ", + "åıªåī© ä¸ĭ", + "åħ¶ä¸Ń åĮħæĭ¬", + "ç¼ĸ ç¨ĭ", + "çł´ ç¢İ", + "ä¸Ń 举", + "å·¥ä½ľ æĬ¥åijĬ", + "çŃ¾ åIJį", + "éħĴ ä¸ļ", + "çŁ¥ æĻĵ", + "çĥŃ å¿ĥ", + "éĿŀ åĩ¡", + "èIJ¥ä¸ļ æī§", + "èIJ¥ä¸ļæī§ çħ§", + "人大 代表", + "ä¸Ģ个 æĸ°çļĦ", + "å¨ģ æµ·", + "éĤ£ 人", + "涨 ä»·", + "æ¶Ī çģŃ", + "éļ¾ å¿ĺ", + "ç¶ĵ é©Ĺ", + "åı£ è¢ĭ", + "ç³» æķ°", + "æĸĩ ä¸Ń", + "好 转", + "æĸ° 鼶åĶ®", + "讲述 äºĨ", + "å¼Ģ çĽĺ", + "çķĻ ç»Ļ", + "æħ¢æħ¢ çļĦ", + "æĤ² 伤", + "æľ¬ æľŁ", + "äºĨ å¤ļå°ij", + "è¿Ļ 让", + "åIJĮ çŃī", + "æ¸ħ æĺİ", + "个 åŁİå¸Ĥ", + "æºĸ åĤĻ", + "åĩłä¹İ æĺ¯", + "强 åĬĽ", + "ä¿ ¯", + "æ°´ 稻", + "åĽºå®ļ çļĦ", + "æł¸ åĩĨ", + "说 æľį", + "顯 示", + "è¿Ļ å¥Ĺ", + "æĻºæħ§ åŁİå¸Ĥ", + "å±ĭ 顶", + "ä¸į æĿ¥", + "çĶŁ é²ľ", + "çŁ¥ æĥħ", + "æĬķ 身", + "åijĬè¯ī æĪij们", + "ä¸ī åĽĽ", + "ä¸ĩ ä¸Ģ", + "è¾Ĩ 车", + "为 ä¹ĭ", + "åĪ° æŶåĢĻ", + "è¿Ļ æīįæĺ¯", + "åIJį çīĮ", + "åºŁ æ°´", + "åݻ年 åIJĮæľŁ", + "å¹´ éĻIJ", + "éģĭ åĭķ", + "åıĮ çľ¼", + "è¦ģ ç´§", + "对 çŃĸ", + "åľº é¦Ĩ", + "çĻ¾ ç§ij", + "è¶Ĭ éĩİ", + "å¯Į åIJ«", + "大å¤ļæķ° 人", + "æľĢ å°ij", + "åı¬ åĶ¤", + "åħ¸ èĮĥ", + "åĨľ æľº", + "æŃ£ æĸĩ", + "åºĶçĶ¨ äºİ", + "æ·± èĢķ", + "ä¿ Ń", + "ä»Ģä¹Ī ä¸ľè¥¿", + "å¥Ĺ é¤IJ", + "å½ĵ éĢī", + "å·¦ æīĭ", + "è°ĥ çIJĨ", + "æĻļ é¤IJ", + "éļ¾ åħ³", + "åĩŃ è¯ģ", + "çĪ± 人", + "æĮĩ è´£", + "è´£ ç¼ĸ", + "çļĦä¸Ģ 款", + "éĵ ²", + "åįģ 个", + "èĢ »", + "æľįåĬ¡ åķĨ", + "åľ° çĭ±", + "è¿ŀ å¿Ļ", + "åĽ° æĥij", + "çļ ĵ", + "ä¸į åIJĥ", + "çİ°åľ¨ å·²ç»ı", + "çĽĺ çĤ¹", + "ä¸įåģľ åľ°", + "管çIJĨ 模å¼ı", + "è¿Ļ 段æŶéĹ´", + "æ¤ °", + "礼 åĮħ", + "æµģ 转", + "æī« çłģ", + "éĽĨä¸Ń åľ¨", + "æ±Ĥ åĬ©", + "åįĬ 个", + "å¿«éĢŁ å¢ŀéķ¿", + "å¾Ģ ä¸ĭ", + "è¯Ħ åĪĨ", + "å°± æĥ³", + "åķĨåĬ¡ éĥ¨", + "æľī éĹ®é¢ĺ", + "èİ· åĪ©", + "æ¯Ľ çĹħ", + "æĦŁ åºĶ", + "èī¯ æĢ§", + "åĪĨ æѧ", + "åĨ ī", + "æĪij们 çİ°åľ¨", + "è¦ģ åĬłå¼º", + "å·§ å¦Ļ", + "èŀº æĹĭ", + "åĪĩ æį¢", + "çĭ Ħ", + "顺 çķħ", + "å°¤åħ¶ æĺ¯åľ¨", + "èĬĿ 麻", + "éļ¾ è¿ĩ", + "æĹĹ å¸ľ", + "å¤į åį°", + "å¤įåį° 件", + "å¿ħ éľĢ", + "对å¤ĸ å¼ĢæĶ¾", + "éļ¾ åıĹ", + "åİŁæĿ¥ æĺ¯", + "ç®Ĺ äºĨ", + "é«ĺ å±±", + "离 èģĮ", + "çµĦ ç¹", + "çµĦç¹ Ķ", + "å±ģ èĤ¡", + "çĻ¾ 家", + "éģĩ ä¸Ĭ", + "æĺĶ æĹ¥", + "ä¸į 容", + "çĽij管 éĥ¨éŨ", + "主 æĦı", + "æµģ åŁŁ", + "è·Į å¹ħ", + "èĩ³ ä¸Ĭ", + "åĪ« 说", + "æĺ¯ æ¯Ķè¾ĥ", + "å®ıè§Ĥ ç»ıæµİ", + "å¸Ĥåľº 主ä½ĵ", + "污æŁĵ çī©", + "æķij æ²»", + "丰 æĶ¶", + "åŃĺ æĶ¾", + "åĩ Ħ", + "éĩij å±±", + "æį¢ äºĨ", + "ä¸ĵ 人", + "éĹľ æĸ¼", + "æĹ¢ è¦ģ", + "åĽ½ 足", + "éļ ĭ", + "åıį åĩ»", + "èµ· 身", + "åħĪ æĺ¯", + "å¸ĮæľĽ èĥ½å¤Ł", + "åĪ¶ 订", + "åºĹ éĿ¢", + "åĸ Ģ", + "æķĻ ä½ł", + "éĻį 温", + "åĬĽ æ±Ĥ", + "ä¸ī çĻ¾", + "çī© ä»·", + "丢 失", + "å¢Ļ ä¸Ĭ", + "éĥ¨ 份", + "æł· æĿ¿", + "ä¹ĭ æĦı", + "ç½ij å°ıç¼ĸ", + "ä¸ĸ ä¸Ĭ", + "è°ĥ è¯ķ", + "污æŁĵ éĺ²æ²»", + "å½± éĻ¢", + "å®Įåħ¨ åı¯ä»¥", + "éĢļ åħ³", + "ä¹īåĬ¡ æķĻèĤ²", + "没æľī åĬŀæ³ķ", + "èĢ ¿", + "å¦ ³", + "æĹł æĥħ", + "å¾Ĺ çĽĬ", + "å¾ĹçĽĬ äºİ", + "æľŁ çĽ¼", + "娱ä¹IJ åľº", + "çĶ² æĸ¹", + "ä¸Ģ æ±½", + "çĹ °", + "çĸij ä¼¼", + "æĸ°æµª å¾®åįļ", + "强 è¡Į", + "å½ĵ ä»ĸ", + "èĥ º", + "çĶ¨æĪ· æıIJä¾Ľ", + "åĮº å§Ķ", + "æĦ¿ æĻ¯", + "æĬĺ æī£", + "失 踪", + "è¿« åĪĩ", + "åŃĹ æ¯į", + "åĴ ¯", + "èªį èŃĺ", + "ä»Ģä¹Ī æĦıæĢĿ", + "çĽĴ åŃIJ", + "å½ķ éŁ³", + "建设 å·¥ç¨ĭ", + "ä¸ļ ä½Ļ", + "å®ŀè·µ æ´»åĬ¨", + "羣 空", + "çĤ ĸ", + "åľ¨ è·¯ä¸Ĭ", + "主è¦ģ åĮħæĭ¬", + "该 æĢİä¹Ī", + "æĢ» æľī", + "æĢ§ æĦŁ", + "æ°ij èĪª", + "å¼Ģ åºĹ", + "欺 éªĹ", + "çªģ åĩ»", + "缺 失", + "æī§ ä¸ļ", + "åľ° éģĵ", + "并 æĹł", + "æ°ij åĬŀ", + "ç»Ħç»ĩ çĶŁæ´»", + "æĪij å¦Ī", + "è¨ĺ èĢħ", + "管 åĪ¶", + "æī¾ 个", + "èĹ »", + "çĤİ çĹĩ", + "äºĴ åĬ©", + "æµıè§Ī åĻ¨", + "çݩ家 æĿ¥è¯´", + "éĻįä½İ äºĨ", + "è£ Ķ", + "æĮ£ éĴ±", + "åķĨ æľº", + "æĶ¹ è£ħ", + "æµģ 浪", + "æĶ¿ æ³ķ", + "èĢģ 头", + "çĶŁäº§ åĴĮ", + "ç© Ĺ", + "亲 çĪ±", + "亲çĪ± çļĦ", + "å±¥ èģĮ", + "åŁİ éĩĮ", + "ç»Ĩ åĪĨ", + "åĬ³åĬ¨ åIJĪåIJĮ", + "åľ¨ æĹ¥æľ¬", + "å¨ģ å°Ķ", + "åį« è§Ĩ", + "éĢ£ çµIJ", + "çĿĢ éĩį", + "æĬĺ 磨", + "åĽ¾ 为", + "çľ ·", + "å·¥ åºı", + "æĵ ģ", + "æĵģ æľī", + "ç½ijç«Ļ åľ°åĽ¾", + "çļĦä¸Ģ 大", + "ç»Ħç»ĩ å®ŀæĸ½", + "æĬĽ å¼ĥ", + "åĴĮ æĶ¯æĮģ", + "æ³ķ åĪĻ", + "浪 æ½®", + "çİ° æľīçļĦ", + "åĩł çİĩ", + "为 客æĪ·", + "åįģ ä¸ĩ", + "è ¹Ħ", + "çªģåĩº éĹ®é¢ĺ", + "åıĥ åĬł", + "éĥ½ä¼ļ æľī", + "çĽ ¤", + "è°ģ éĥ½", + "æīĭ åĬ¨", + "缴 è¾¾", + "çĤ¹ å¤ļ", + "éĺ¶ å±Ĥ", + "ä¸į ä½³", + "éĤ£ 段", + "滨 æµ·", + "æĺ¯ åĽ½åĨħ", + "æĪij å¸ĮæľĽ", + "åIJĽ åŃIJ", + "è§Ĥ éŁ³", + "åģļ é¥Ń", + "æ±½ è»Ĭ", + "åħ³ ç¨İ", + "çľ¼åīį çļĦ", + "æ°´ éĿ¢", + "èĢ³ æľº", + "追 踪", + "æݨ éĢģ", + "éĴ± åĮħ", + "æģ¶ å¿ĥ", + "æµ· åŁŁ", + "å· į", + "å¼Ģ æĿ¥", + "表 æĢģ", + "仪 表", + "å¹³ åİŁ", + "åįģ å¤ļå¹´", + "ä¹Ł æĹłæ³ķ", + "åħ¼ 顾", + "è¡£ æŁľ", + "æł½ åŁ¹", + "æĪ¿ æºIJ", + "设ç«ĭ äºĨ", + "ä¸ĩ åIJį", + "æķ° é¢Ŀ", + "è¦ģ åĿļæĮģ", + "åIJīæŀĹ çľģ", + "请 èģĶç³»", + "ç»ıåİĨ è¿ĩ", + "çļĦ æľ¬è´¨", + "åħ¥ éŨ", + "æľ¬ æ¡Ī", + "çİĩ è¾¾åĪ°", + "åı° éĺ¶", + "éĴ ŀ", + "æĪij èĥ½", + "èݲ èĬ±", + "éĴ ł", + "ä¸Ģ äºĭ", + "åİŁ æľīçļĦ", + "æ¯ı åĢĭ", + "æ¯Ķäºļ 迪", + "æ£ĭçīĮ 游æĪı", + "ä¸įä¼ļ æľī", + "å½Ĵ æĿ¥", + "äºĶ çĻ¾", + "è¿ĩ é«ĺ", + "鼷 è¾¾", + "ä¸Ģèµ· åİ»", + "æķĻ 导", + "å°± è¯Ĭ", + "å°± å¾Ī", + "ä¸įåIJĮ äºİ", + "ä¿ º", + "å¸ĸ åŃIJ", + "æĶ¿åįı å§Ķåijĺ", + "çĸ«æĥħ å½±åĵį", + "åĪĨ è£Ĥ", + "为ä»Ģä¹Ī ä¼ļ", + "äºĶ æĺŁ", + "å°ij åĦ¿", + "æĬ¢ éĻ©", + "梦 è§ģ", + "è®°èĢħ éĩĩ访", + "å±± è·¯", + "æĪij 个人", + "æ²Ļ 滩", + "è¹ Ń", + "æĶ¹ è®Ĭ", + "æĸ°åŀĭ åĨł", + "æĸ°åŀĭåĨł çĬ¶", + "åĮ» æĬ¤", + "åĮ»æĬ¤ 人åijĺ", + "æµ· å°Ķ", + "åħ³äºİ æĪij们", + "éĻ¤ å¤ĸ", + "åº ļ", + "宣 åijĬ", + "ä¸ī åįĥ", + "æ¦ ¨", + "ç§ijæĬĢ 大åѦ", + "ä¸ĥ åħ«", + "顺 åºĶ", + "çĪ¸çĪ¸ å¦Īå¦Ī", + "éĢī åıĸ", + "åī§ çĥĪ", + "乡æĿij æĹħ游", + "积æŀģ æݢ索", + "表çİ° 为", + "å¾Ī æ¸ħæ¥ļ", + "大 åĨĽ", + "æĿ¥ çĶµ", + "å¥Ĺ æĪ¿", + "çİ° è¡Į", + "享 åıĹåĪ°", + "çľĭ çĤ¹", + "åĽºå®ļ èµĦ产", + "以 人为", + "以人为 æľ¬", + "ä¸į å®Į", + "éĻį 鼨", + "åģļçļĦ äºĭæĥħ", + "并 äºİ", + "顽 强", + "èĢ ¸", + "åĺ´ å·´", + "缸åħ³ ä¿¡æģ¯", + "æĪij 没", + "æĪĺçķ¥ æĢ§", + "æĢĿ 念", + "åĪĺ å¤ĩ", + "åĬ© æĶ»", + "é£İ è²Į", + "éĿ¢å¯¹ éĿ¢", + "积æŀģ å¼Ģå±ķ", + "çĸĹ æķĪ", + "çľĭ 书", + "缺 åı£", + "åĽ½æ°ij ç»ıæµİ", + "使çĶ¨ æĿĥ", + "éģ¥ è¿ľ", + "å¡« è¡¥", + "第ä¸ī 人", + "åįĬ å¤ľ", + "æѦæ±ī å¸Ĥ", + "æĪij åıijçİ°", + "ä¼ĺæĥł æĶ¿çŃĸ", + "é£İ åı£", + "å°± ä¸įèĥ½", + "为 主è¦ģ", + "æµģ åĩº", + "å´ĩ æĭľ", + "并 ä¸įèĥ½", + "é«ĺ ä¸ī", + "ä¸ĸçķĮä¸Ĭ æľĢ", + "æĥ³ å¿ħ", + "åħ¶ æīĢ", + "åĢĻ éĢī", + "åĢĻéĢī 人", + "ä¸į çĪ±", + "åī¯ ä½ľçĶ¨", + "人æ°ij æĹ¥æĬ¥", + "æĪij ä¸įæĺ¯", + "å®ŀ çī©", + "çĶµ åİĤ", + "ä¹Ł ç®Ĺæĺ¯", + "æľī éĹľ", + "æľī èĥ½åĬĽ", + "æĮĤ åľ¨", + "çľ¼ ä¸ĭ", + "约 ç¿°", + "å°ı åѦçĶŁ", + "èµ· åĪ°äºĨ", + "å·¥ 夫", + "åIJĮ å¿ĥ", + "åĿ¦ è¨Ģ", + "çł Į", + "åıijæĮ¥ äºĨ", + "èģĮä¸ļ éģĵå¾·", + "è¿ĻäºĽ å¹´", + "念 头", + "èĢģ é¼ł", + "åħ¨ èµĦ", + "åħ¨èµĦ åŃIJ", + "ä¸Ģ åij³", + "å¤ļ ä¸ĩåħĥ", + "æł¼ æľĥ", + "éķ¿ éĢĶ", + "带 èµ°", + "èĭ± 寸", + "æĸĩ ä½ĵ", + "对 ä»ĸ们", + "åĵŃ äºĨ", + "å¡« æĬ¥", + "çīĪæĿĥ 声æĺİ", + "çĶµ 线", + "è´Ńçī© ä¸Ńå¿ĥ", + "饱 满", + "ä½İ 头", + "强 è¿«", + "ä¿Ŀ æ´ģ", + "欧 åĨł", + "缸 è¿ŀ", + "认 è´Ń", + "çģ« æĺŁ", + "é«ĺ å°Ķ", + "é«ĺå°Ķ 夫", + "èij« èĬ¦", + "æłĩ 注", + "çļĦ çIJĨæĥ³", + "æł¸ éħ¸", + "æł¸éħ¸ æ£Ģæµĭ", + "åĬ ī", + "ä¸ĢèĪ¬ æĺ¯", + "æĢĿ ç´¢", + "轨 迹", + "çĥŃ å¸¦", + "éĻ £", + "åĩĨç¡® æĢ§", + "æĪ´ çĿĢ", + "åľ¨ çĶŁæ´»ä¸Ń", + "æīĢ èĥ½", + "æľ¯ åIJİ", + "带 ä½ł", + "ç¥ ł", + "æ®ĭ éħ·", + "ä¹Ł åıªæĺ¯", + "çĶ³ è´Ń", + "举åĬŀ äºĨ", + "æľī æĦıä¹ī", + "æĹº 缼", + "åľ¨ 網", + "åľ¨ç¶² è·¯ä¸Ĭ", + "å¾Ī大 ç¨ĭ度", + "管 è¾ĸ", + "çĸ«æĥħ æľŁéĹ´", + "触 æij¸", + "éĺ¶æ®µ æĢ§", + "ä¼ļ è§īå¾Ĺ", + "çļĦ çĶ»éĿ¢", + "æİ¥åıĹ äºĨ", + "表达 äºĨ", + "éĤĵ å°ı", + "éĤĵå°ı å¹³", + "åħļ é£İ", + "åħļé£İ å»īæĶ¿", + "åķĨ åѦéĻ¢", + "åħij æį¢", + "é£Łåĵģ èį¯åĵģ", + "éĿŀ常 好çļĦ", + "çľ ¯", + "纳 ç±³", + "åĬ¨ æijĩ", + "åĽŀ éģ¿", + "çľĭ èijĹ", + "款 项", + "åħ« å¹´", + "åģļ 个", + "æĸĩ æ¡£", + "éĩijèŀį ç§ijæĬĢ", + "åħ¶ä¸Ń æľī", + "äºĨä¸Ģ ç³»åĪĹ", + "æĹĹèĪ° åºĹ", + "称 èµŀ", + "éĽ¢ éĸĭ", + "åĪ¶ åĨ·", + "家 éŨåı£", + "åįģ å¤ļ", + "ä¼´ ä¾£", + "çľĭ çĹħ", + "æĭī çĿĢ", + "æī Ĵ", + "çĸ² æĥ«", + "å°ijæķ° æ°ijæĹı", + "åĽ¾ å½¢", + "è½ §", + "å¢ŀ éĩı", + "饲 åħ»", + "çģ« å±±", + "æ¯ı 个æľĪ", + "ä½ľä¸º ä¸ĢåIJį", + "è½´ æī¿", + "æĸĩ 书", + "ç¼ ķ", + "åħ·ä½ĵ æĥħåĨµ", + "çĹĽ çĤ¹", + "缴 éĶĢ", + "å¡ Ĭ", + "ä¹Ł æľĥ", + "çĥŃ æ½®", + "å¹³ æ°ij", + "æ¼ĶåĶ± ä¼ļ", + "æķĻ çłĶ", + "éĢĥ éģ¿", + "ä¸Ģ è´¯", + "å°± è¶Ĭ", + "å®ŀ å®ŀåľ¨", + "å®ŀå®ŀåľ¨ åľ¨", + "ä¹łè¿ijå¹³ æĢ»", + "æº º", + "å¿ĥ åºķ", + "éķ¿ å¾ģ", + "媽 媽", + "第ä¸ī 次", + "åĩº æ¼Ķ", + "çĭĢ æ³ģ", + "å°Ķ æĸ¯", + "代çIJĨ åķĨ", + "çĨ ı", + "çļĦ 对象", + "çĶµ éĩı", + "è¡Į åĪĹ", + "åĽ½ 人", + "è·ij äºĨ", + "åįĶ åĬ©", + "èIJ¥ è¿IJ", + "å¸Ī åħĦ", + "æ¦ ®", + "æĥ³ åĥı", + "æĢ§ 强", + "ç§ijåѦ çłĶ究", + "延 å®ī", + "ä¸¥æł¼ èIJ½å®ŀ", + "é¢Ĩ ä¼ļ", + "缸 å·®", + "è·¯ 人", + "çĶ «", + "æľī ä»·åĢ¼", + "æľīä»·åĢ¼ çļĦ", + "ç¾İ åĽ¢", + "æ°ij主 çĶŁæ´»", + "æĪij æīį", + "ç¾İåĽ½ 人", + "æ°Ķ åij³", + "åıį å°Ħ", + "çļĦ åĨ³å¿ĥ", + "大 è±Ĩ", + "交 代", + "è¿Ľ åĩº", + "åıį æĬĹ", + "æĮĩ çļĦæĺ¯", + "ä»· ä½į", + "è¿Ľ é©»", + "ä¸Ĭ çĻ¾", + "ä½į åĪĹ", + "ä¸ŃåĽ½ ä¼ģä¸ļ", + "çļĦ好 å¤Ħ", + "主 ç¼ĸ", + "æ±½ æ²¹", + "ä½Ĩ æĪij们", + "æĢİä¹Ī çľĭ", + "é»Ħ å±±", + "å¤ļ åªĴä½ĵ", + "åIJİ åį«", + "èİ·å¾Ĺ æĽ´å¤ļ", + "åĬ¡ å¿ħ", + "为 å¥ijæľº", + "é¦ĸ 饰", + "ä¸ĩ åįļ", + "è¶ĬæĿ¥è¶Ĭ 大", + "ä¸ĵ项 è¡ĮåĬ¨", + "å¥ĭ è¿Ľ", + "ä»į çĦ¶æĺ¯", + "è´¨ æĦŁ", + "å¦Ĥæŀľ ä¸įæĺ¯", + "ç«Ļ èµ·æĿ¥", + "ä¹¾ éļĨ", + "åı¯æĢķ çļĦ", + "å¯Į è´µ", + "æ¸ħ ç®Ĺ", + "åIJij ä¸ĭ", + "åĢ ļ", + "çļĦ çŃĶæ¡Ī", + "èĪ¹ ä¸Ĭ", + "çļĦ羣å®ŀ æĢ§", + "çŃī åĬŁèĥ½", + "åĸľ åī§", + "å¨ģ åĬĽ", + "æĸ° é¢ĸ", + "æł¸ çĶµ", + "æĬ¥ éĶĢ", + "æķħ 乡", + "ä¼´ éļı", + "éŀ Ń", + "å¦Ĭ å¨ł", + "åĪĨ åĮĸ", + "æľī å¾Ī大", + "æĢİä¹Ī 说", + "æĻĤ 代", + "产 åĩº", + "ä»ĭç»į 说", + "å¤ĦçIJĨ åĻ¨", + "èĨ¨ èĥĢ", + "åī¯ å¸Ĥéķ¿", + "çļĦ 妻åŃIJ", + "æł· åĵģ", + "åIJĮæ¯Ķ ä¸ĭéĻį", + "åħĥ å·¦åı³", + "çĶ¨ èĩªå·±çļĦ", + "é«ĺ éĽĦ", + "æĺ¥ æĻļ", + "ä¹Ł æľīå¾Īå¤ļ", + "çľ¼ çIJĥ", + "æķ£ æŃ¥", + "ä»ĸ们 éĥ½", + "第ä¸Ģ 家", + "åĬŀ 好", + "å®ī éĺ²", + "ä¸Ģ ä¸ĩ", + "åľ¨ éĩĮéĿ¢", + "éŁ³ é¢ij", + "åı£ åı·", + "ä¸Ģ è¶Ł", + "ç¦ı çī¹", + "é³ ŀ", + "æĥĬ èī³", + "æĸ° å¨ĺ", + "绿èī² åıijå±ķ", + "ä¸Ń å¼ı", + "ä¹Ł åıªæľī", + "çİ° 身", + "åı¯ ä¾Ľ", + "æ¯ı ä¸Ģ个人", + "第ä¸ī èĢħ", + "åľ° å½¢", + "éĴ¢ ç»ĵæŀĦ", + "çĽijçĿ£ æ£ĢæŁ¥", + "åı« æĪij", + "èĩ´ æķ¬", + "æ´Ĺ æīĭ", + "ä¸ĭ è°ĥ", + "康 çĨĻ", + "æĪIJ交 éĩı", + "ä¹Ł æĪIJ为", + "åħī æ»ij", + "å®Įæķ´ æĢ§", + "çģ ¼", + "網 éłģ", + "éķ¿ 寿", + "éģ© çĶ¨", + "çļĦä¸Ģ 项", + "çŀ© 缮", + "æĬĬ èĩªå·±çļĦ", + "éĵ¶è¡Į åį¡", + "å°± å¿ħé¡»", + "ç¾İ çĻ½", + "éŀį å±±", + "æľ¬ é¢Ĩ", + "ä¸Ģ ç¢Ĺ", + "æīĵ æ³ķ", + "æĤ¨ 好", + "对 åŃ©åŃIJ", + "æĬ¥éģĵ 称", + "ä¼ł åĩº", + "大 èĩ£", + "ç¬ ĭ", + "çĽ ı", + "é¾ ļ", + "缴 线", + "æĻº åºĵ", + "秣 车", + "é£İ åij³", + "çľĭ ä¸Ģä¸ĭ", + "æݨ éĶĢ", + "éĥ¨ éĥ¨éķ¿", + "è´¨éĩı åĴĮ", + "åĪĬ çĻ»", + "å·¥ä¸ļ åĮĸ", + "çİĩ 为", + "鼶 件", + "硬 åĮĸ", + "ä¸Ĭ åįĥ", + "ç»ıéªĮ åĢ¼", + "å¹³ è¡Į", + "声 éģĵ", + "æľįåĬ¡ è´¨éĩı", + "çĶŁ çĶ¢", + "æľĢ 容æĺĵ", + "ä¸Ģ æŀļ", + "å¹´ æĬ¥", + "åħ¬ ç½ij", + "åħ¬ç½ij å®ī", + "åħ¬ç½ijå®ī å¤ĩ", + "çļĦ èĥ½éĩı", + "å®ŀéĻħ è¡ĮåĬ¨", + "è¦ģ ä¸įè¦ģ", + "æĹ¥æľ¬ 人", + "èĢ¶ 稣", + "ç¼ĸ åī§", + "æ¶ ©", + "åį° å°¼", + "ä¸Ĭä¸ĭ 游", + "åĩł åı¥", + "ä¸Ń éĵģ", + "ç°¡ åĸ®", + "èĩª 带", + "çĶŁ äºİ", + "ä¸Ģ åı£æ°Ķ", + "åĭ¤ å¥ĭ", + "éĻį ä»·", + "å±ķçİ° äºĨ", + "å¸ĥ æĭī", + "ä¼ļ éĢīæĭ©", + "çļĦ ç»ıåħ¸", + "好 æľĭåıĭ", + "车 éģĵ", + "æķ´ åĢĭ", + "åľ ĵ", + "éķ¿æľŁ 以æĿ¥", + "æĬķ å½±", + "çļĩ åĨł", + "è¿ĩ 大", + "åijĬè¯ī ä»ĸ", + "ä¼ģä¸ļ æıIJä¾Ľ", + "æĬ½ 象", + "éĢĤ 度", + "çļĦ 女åŃ©", + "èµ· ä¼ı", + "çļĦ åĬŁæķĪ", + "ä¸ĵ项 æķ´æ²»", + "åı¯ éĢļè¿ĩ", + "ä¸įåIJĮ ç¨ĭ度", + "å¼Ĥ è®®", + "åĩĢ èµĦ产", + "åij Ĺ", + "ä»Ģä¹Ī åij¢", + "å·¡ éĢ»", + "è¸ı ä¸Ĭ", + "ä½Ĩ å®ĥ", + "ç²¾ 度", + "管 å±Ģ", + "第ä¸Ģ åIJį", + "åĨħ åŃĺ", + "æijĨ åľ¨", + "åī© ä¸ĭ", + "主ä½ĵ 责任", + "çĤ¹ åįĬ", + "以 èĩ³äºİ", + "åħ»èĢģ ä¿ĿéĻ©", + "æĦŁåıĹ åĪ°äºĨ", + "çŁ¥åIJį çļĦ", + "å¯Į 豪", + "妥 åĸĦ", + "åŃĻ åŃIJ", + "éĵ Ĥ", + "说 èĩªå·±", + "让 æĤ¨", + "æķ° æݧ", + "çļĦçľ¼ åħī", + "注 éĶĢ", + "çļĦ çģµéŃĤ", + "è¿ĺ ä¸įéĶĻ", + "éĹ® ä»ĸ", + "èĩªä¸» çłĶåıij", + "èĵ ĭ", + "ç´« èī²", + "åĽ½å®¶ å®īåħ¨", + "è¾½å®ģ çľģ", + "ä¹Ł æ¯Ķè¾ĥ", + "ç¾İ èĤ¡", + "ä¸įç¡®å®ļ æĢ§", + "å¿ĥ 头", + "æĪ ³", + "级 åĪ«çļĦ", + "论 è¿°", + "çļĦ åĽŀçŃĶ", + "ä¿Ŀè¯ģ éĩij", + "çŃī è¡Įä¸ļ", + "幸ç¦ı æĦŁ", + "æѧ è§Ĩ", + "æľº 票", + "æ´¾ 人", + "èĩ´ åij½", + "åĺ´ è§Ĵ", + "æĸ°éĹ» ä¸Ńå¿ĥ", + "æĶ¾å¼ĥ äºĨ", + "å®ľ å±ħ", + "åĨĻ ä¸ĭ", + "éĹ® çŃĶ", + "è¿ĻéĩĮ æĺ¯", + "å¤ļ åľ°", + "åĮºåŁŁ åĨħ", + "åīµ æĸ°", + "çľĭ ä»ĸ", + "æī§æ³ķ 人åijĺ", + "åĬ¨ æľº", + "éŁ³ åĵį", + "çļĦ åij½è¿IJ", + "顶 éĥ¨", + "åĵ Ł", + "éĥ½ æľĥ", + "æīĵéĢł æĪIJ", + "æĦı åĽ¾", + "çļ ĸ", + "åĢĴ åħ¥", + "å·´ èIJ¨", + "åĬ© åѦ", + "å¤į åı¤", + "åIJ¯ çĶ¨", + "åĽ½éĻħ å¸Ĥåľº", + "åĤ¨ èĥ½", + "é»ijé¾Ļæ±Ł çľģ", + "ä¹ĺ 车", + "è¿IJåĬ¨ ä¼ļ", + "ä¿Ŀ åĪ©", + "çŁ³ æĿIJ", + "çµ ®", + "çĤĴ ä½ľ", + "çļĦ ä¿¡ä»»", + "å°± æĪIJäºĨ", + "åı¯ è§Ĥ", + "çļĩ ä¸Ĭ", + "è¿Ļ åĩłå¤©", + "ä¸Ģ éĶ®", + "åĨ· åĨ»", + "ä¿Ŀ åį«", + "æł¸ æ¡ĥ", + "åIJĪä½ľ åħ³ç³»", + "éĢģ åĩº", + "æĹĹ ä¸ĭçļĦ", + "åľ¨ ä¹İ", + "为 广大", + "åįĪ é¤IJ", + "ä¸ĵ 访", + "æĪĸ å°Ĩ", + "éĿĴå²Ľ å¸Ĥ", + "å¥Ķ è·ij", + "æĹ¥ æĬ¥éģĵ", + "å¥ij åIJĪ", + "æĸ° æĺ¥", + "ä¸į å°ıå¿ĥ", + "两 ä¸ī", + "æĦıæĢĿ æĺ¯", + "åĨ· èĹı", + "çļĦ çĹĩçĬ¶", + "æĢ§ åij½", + "è¶ħ æłĩ", + "å¯Ĩ 碼", + "ç§ijæĬĢ èĤ¡ä»½", + "äºĨä¸Ģ æī¹", + "çĿ£ å¯Ł", + "åªĴ ä»ĭ", + "å°Ħ æīĭ", + "ä¿® åħ»", + "çīĩ åĪ»", + "éĢĤåIJĪ èĩªå·±", + "åıªè¦ģ æĺ¯", + "åIJĥ è¿ĩ", + "éĩij éĵ¶", + "缴 å±ŀ", + "åѦ éĹ®", + "åİĭ åĪ¶", + "çªĹ å¤ĸ", + "æĶ¶ åĪ°äºĨ", + "åħ¨åĽ½ 人大", + "ä½Ĩæĺ¯ å¯¹äºİ", + "åľ¨ æķ´ä¸ª", + "çļĦ èĥĮåIJİ", + "åĩıå°ij äºĨ", + "åıį èħIJ", + "åıįèħIJ åĢ¡", + "åıįèħIJåĢ¡ å»ī", + "æĹ ·", + "åĪĨ æľŁ", + "åľ¨ æ·±åľ³", + "æīĵ çĿĢ", + "æī« ä¸Ģ", + "æī«ä¸Ģ æī«", + "æĶ¿åºľ éĥ¨éŨ", + "æİ¥ è¿ŀ", + "å±ŀäºİ èĩªå·±", + "åŃIJ å¼¹", + "åIJĮæł· æĺ¯", + "æĢ» åħ±", + "车 ä¼ģ", + "æ¢ ĵ", + "åħ¬ é¡·", + "åıij 声", + "éĴ Ľ", + "èµ°åĬ¿ åĽ¾", + "主 èIJ¥", + "åĸ Ķ", + "æķ°æį® åĪĨæŀIJ", + "ä¸į è¿ľ", + "æľī åIJį", + "æľīåIJį çļĦ", + "åģ¿ è¿ĺ", + "å¾Ī ä½İ", + "è®ĵ 人", + "èĿ ī", + "é«ĺ è´µ", + "å°ij 许", + "æ° Ł", + "å¹ ¢", + "亲 æĥħ", + "è¿Ļ件 äºĭæĥħ", + "çĶ¨ é¤IJ", + "缸åħ³ æĸ°éĹ»", + "å°± åºĶ该", + "ç»Ī çĤ¹", + "æĺ¯ å¤ļå°ij", + "çĻ» åľº", + "è¯ķ 管", + "è¯ķ管 å©´åĦ¿", + "åģļ 大", + "åģļ大 åģļ强", + "çļĦ ä¾ĭåŃIJ", + "åħ« 个", + "æĺİ æĹ¥", + "çĤ ³", + "èµ° åİ»", + "éģ º", + "å¢ ©", + "ä½ĵä¼ļ åĪ°", + "åĴ ı", + "ä¸ĭ è¾¾", + "å¤į åıij", + "追 éĢIJ", + "æīĵ åĵį", + "çļĦ éļ±ç§ģæ¬Ĭ", + "åħ·æľī ä¸Ģå®ļ", + "è¿Ļä¹Ī å¤ļå¹´", + "æłij æŀĹ", + "æľĢ éķ¿", + "åIJĮ èĥŀ", + "åħī æ³½", + "åŁŁ åIJį", + "æĮĩ åIJij", + "åıĹ害 èĢħ", + "æłij èĦĤ", + "æľīå¤ļ 大", + "大 éĿ¢ç§¯", + "æĹł ç¼Ŀ", + "æĶ¹ æŃ£", + "æĽ´å¤ļ çļĦæĺ¯", + "æľŁ æľ«", + "æŃ ¼", + "ä¹ī ä¹Į", + "éĤ£ ä½ł", + "çļĦ 第ä¸Ģ个", + "èĮ µ", + "å° §", + "èį «", + "ä¸įä»ħ åı¯ä»¥", + "æ¶Į çİ°", + "æĢ» éĿ¢ç§¯", + "æĸ°éĹ» åıijå¸ĥ", + "æ°ij çĶ¨", + "å°± 读", + "æīĵ è´¥", + "å¤ĸ è¯Ń", + "æĪij们 ä¸Ģèµ·", + "é¢Ħ å®ļ", + "çĥ¹ 饪", + "æľĢ 主è¦ģ", + "æľĢ主è¦ģ çļĦ", + "çīĮ çħ§", + "åĽł åħ¶", + "ä½İ ä¸ĭ", + "ä¼ļ åIJĮ", + "è§ģ 解", + "éĹ´ éļĶ", + "æķĻ ç¨ĭ", + "å° ī", + "å¸Ĥ ä¸Ńå¿ĥ", + "åħ³éĶ® æĺ¯", + "æµ· åįĹçľģ", + "çī¹åĪ« æĺ¯åľ¨", + "ä¸ŃåĽ½ 大éĻĨ", + "åħħ足 çļĦ", + "æĹ¢ èĥ½", + "åĤ³ çµ±", + "çijľ ä¼½", + "åħ¥ åĽ´", + "æħ¢æħ¢ åľ°", + "æĬ¥ éħ¬", + "æī¹ å¤į", + "å·¥ä¸ļ åĽŃåĮº", + "ä¸İ åıijå±ķ", + "èĥ¸ éĥ¨", + "åľ¨ ç½ij绾", + "åľ¨ç½ij绾 ä¸Ĭ", + "交 è°Ī", + "æĽ´ æĶ¹", + "åįłæľī çİĩ", + "ä¸Ŀ绸 ä¹ĭè·¯", + "è¡ Ľ", + "çłĶ åĪ¤", + "åĪ ª", + "åĪª éĻ¤", + "è¿Ļ åıª", + "çļĦ æ°Ķæģ¯", + "åĬł å·ŀ", + "éĴ §", + "çIJĨäºĭ éķ¿", + "ä¸ĸ 家", + "æµģè¡Į çļĦ", + "å¾Ī æľīåı¯èĥ½", + "们 éĥ½", + "ç»ıèIJ¥ 模å¼ı", + "è¡Įä¸ļ ä¸Ń", + "éĢļçŁ¥ 书", + "åij½ é¢ĺ", + "æľ¬ 網ç«Ļ", + "æ²Ļ çī¹", + "åıij åħī", + "é«ĺ ä»·", + "å·² çĦ¶", + "åıĮ åįģä¸Ģ", + "ä¸Ĭ è¯ī", + "ç¿ħ èĨĢ", + "è¿Ļä¸Ģ å¹´", + "大ä¼ļ ä¸Ĭ", + "éĩ ī", + "å®Įåħ¨ æĺ¯", + "å¾Ĺ 太", + "ä¸ĢèĪ¬ 人", + "è¿ĺ ç®Ĺ", + "æĬĺ åıł", + "æĬķ æľº", + "çĤ¹ çĩĥ", + "çİ°éĩij æµģ", + "åħĶ åŃIJ", + "ç½ij æł¼", + "æİ¥ è¿ĩ", + "ä¾Ľ è´§", + "éĺ´ å½±", + "åİŁ åħĪ", + "æį £", + "å·¦ 侧", + "åħĭ æĭī", + "æīĵ åį¡", + "ç§ij æ¯Ķ", + "æ±ĩ éĽĨ", + "åľ°çIJĨ ä½įç½®", + "è¯Ħ å§Ķ", + "ç»ĵåIJĪ èµ·æĿ¥", + "è¿Ľåħ¥ åĪ°", + "åı¯ è¡Į", + "åı¯è¡Į æĢ§", + "让 å®ĥ", + "åĪ¶åº¦ æĶ¹éĿ©", + "çĶĺèĤĥ çľģ", + "åĵ Ĺ", + "åģı åģı", + "è¡£ çī©", + "ç¥Ŀ è´º", + "æºIJ èĩª", + "并ä¸į 代表", + "åĽ½ 度", + "好 åĿı", + "æĿ ĸ", + "æĿŃ å·ŀå¸Ĥ", + "湿 度", + "é² ¸", + "åįļ 彩", + "æ³° å±±", + "æĿij èIJ½", + "æĸ° èģŀ", + "èĤ ĭ", + "åı¤èĢģ çļĦ", + "çļĦ ç§ĺå¯Ĩ", + "ä¸Ģ个 éĹ®é¢ĺ", + "éģı åĪ¶", + "åįĥ 亿", + "è¿ĩ 硬", + "å°Ħ åĩ»", + "èĩªçĦ¶ æĺ¯", + "产 åĮº", + "çĤ¹ çĤ¹å¤´", + "åı¯ä»¥ 帮åĬ©", + "说 å®ŀ", + "说å®ŀ è¯Ŀ", + "æĪij åıªæĺ¯", + "ä¹ĭ ä½Ļ", + "åIJĮæŶ ä¹Łæĺ¯", + "ä¸ŃåĽ½ éĺŁ", + "建æĪIJ åIJİ", + "ä¹IJ è§Ĩ", + "åij¨ å²ģ", + "èį¯ åºĹ", + "éĩij åįİ", + "严éĩį å½±åĵį", + "è´¨ åľ°", + "æĹħ éģĬ", + "åħµ åĻ¨", + "æķĻèĤ² æķĻåѦ", + "离 åİ»", + "åIJĦå¼ı åIJĦæł·", + "ä»ĭ ç´", + "ä»ĭç´ ¹", + "å¼Ģ 头", + "å°Ĩ èĩªå·±çļĦ", + "åIJ¬ åĬĽ", + "ä¿¡æģ¯ ç³»ç»Ł", + "ä»İ æł¹æľ¬", + "ä»İæł¹æľ¬ ä¸Ĭ", + "æİĮ 声", + "欢 åĸľ", + "å±ķ åĮº", + "åķ ¸", + "太å¤ļ äºĨ", + "éĹ² ç½®", + "èĥ¡ èIJĿåįľ", + "å§Ķ å®£ä¼ł", + "å§Ķå®£ä¼ł éĥ¨", + "åįĹ éĺ³", + "å·ŀ åĮº", + "ä¸İ æŶ", + "ä¸İæŶ 俱", + "ä¸İæĹ¶ä¿± è¿Ľ", + "å«Įçĸij 人", + "èī¯ å¿ĥ", + "头 顶", + "è´¢ æĬ¥", + "ä½Ľ æ³ķ", + "å¾ µ", + "åİŁ 件", + "åĭ ŀ", + "çĶ· 篮", + "å¤ĸåĽ½ 人", + "è¿Ŀ 纪", + "æī¾ äºĨ", + "æįķ æįī", + "缸 è¯Ĩ", + "æIJľ éĽĨ", + "çļĦ ä¼Łå¤§", + "ä¸ī ç»´", + "å°±è¡Į äºĨ", + "çĭIJ æľĪ", + "çĭIJæľĪ å±±", + "å¸ĮæľĽ éĢļè¿ĩ", + "èĢĮ 对äºİ", + "éĿ¢ å°į", + "åĨĽ åĽ¢", + "è¡Ĺ åĮº", + "æĤ¬ æĮĤ", + "便 ç§ĺ", + "æľīä¸Ģ çĤ¹", + "ä¼ļè®® ä¸Ĭ", + "ä¸ĭ æīĭ", + "廣 åijĬ", + "äºĶ è¡Į", + "çŃī åĢĻ", + "紧紧 åĽ´ç»ķ", + "æĭ¿ äºĨ", + "æ¡Į éĿ¢", + "ç¥ŀ æĥħ", + "éĽĦ åİļ", + "çŀ ³", + "楼 ä¸ĭ", + "å½ ª", + "äºĭ åıij", + "åĨį è§ģ", + "é¤ ĺ", + "é¢Ħ åĶ®", + "åİ» çľĭçľĭ", + "æĪij们 åºĶ该", + "ä¸ī 家", + "æµ Ĭ", + "ä¹IJ éĺŁ", + "çľĭ ä¸įè§ģ", + "èĦij åŃIJ", + "æĮģ æľīçļĦ", + "çĻ½ èıľ", + "éĹª çĥģ", + "åĸĿ æ°´", + "æݧåĪ¶ ç³»ç»Ł", + "ä¸ĵ åĮº", + "æľĿ å»·", + "æĪij å¿ĥéĩĮ", + "å±ķ åİħ", + "èľĺ èĽĽ", + "åĨ» ç»ĵ", + "ç² ª", + "åº IJ", + "åIJij 社ä¼ļ", + "åĨ³çŃĸ éĥ¨ç½²", + "çŁŃ æľŁåĨħ", + "æĸ° ä¸ļæĢģ", + "æľ Ķ", + "æŶ æĬ¥", + "使 ä¹ĭ", + "åĽł åŃIJ", + "åıĤä¸İ èĢħ", + "çļĦ 年轻人", + "æīĭ 表", + "å°ģ éĶģ", + "为ä»Ģä¹Ī ä¸į", + "åIJ¸ çĥŁ", + "æ¯Ĵ ç´ł", + "åĪij æ³ķ", + "磫 æŃ£", + "身 æĹģ", + "åİŁ è°ħ", + "çĽij æĬ¤", + "æѤ å¤Ħ", + "éļ¨ æĻĤ", + "æŀľ å®ŀ", + "åĮ»çĸĹ æľįåĬ¡", + "ä¸į åIJĪçIJĨ", + "æIJŀ 好", + "çļĦ èĦļæŃ¥", + "å¤ĸ å¥Ĺ", + "ç¶ĵ éģİ", + "æĶ¾ ç¼ĵ", + "åģľ çķĻ", + "æĺŁ çIJĥ", + "çļĦä¸Ģ éĿ¢", + "åĩł ä½ķ", + "è½® åĽŀ", + "æ¯Ľ å·¾", + "ä¿® çIJĨ", + "ä¸įçŁ¥ ä¸į", + "ä¸įçŁ¥ä¸į è§ī", + "æķ´ 个人", + "æ¯ģ çģŃ", + "åı° å·ŀ", + "使çĶ¨ 寿åij½", + "é»ij çĻ½", + "æij¸ ç´¢", + "é¼ł æłĩ", + "éĿ© æĸ°", + "éº µ", + "ä¸ĵéŨ 为", + "å¾Īå¤ļ æľĭåıĭ", + "å·¥ä½ľ ç»Ħ", + "åIJĪ å½±", + "çĤº ä»Ģ麼", + "æŀģ 度", + "çļĦ è¿ĽæŃ¥", + "å½ĵ ä¹ĭ", + "å½ĵä¹ĭ æĹł", + "å½ĵä¹ĭæĹł æĦ§", + "è´´ è¿ij", + "å°º 度", + "åľ¨ çİ°åľº", + "éĻį 临", + "åħ»èĢģ éĩij", + "ç£ ķ", + "åı¯ä»¥ 使", + "管çIJĨ æ°´å¹³", + "æľ¬æĬ¥ è®°èĢħ", + "æ³ķ 令", + "åį¡ 车", + "举 æµ·", + "å¤ļ éĩį", + "åħ¶ éĹ´", + "ç´ Ļ", + "éĩį大 é¡¹çĽ®", + "æ±Ĺ æ°´", + "ç»Ħ å§Ķä¼ļ", + "ä¿¡æģ¯ åħ¬å¼Ģ", + "ä¸į论 æĺ¯", + "ä¸Ģ åIJ¬", + "èĴ¸ æ±½", + "æıŃ ç§ĺ", + "è¶ħ éģİ", + "触 åıij", + "å© ¦", + "åħ³èģĶ 交æĺĵ", + "å°± ç»Ļ大家", + "好 ä¹ħ", + "åĢŁ è´·", + "游æĪı è§Ĵèī²", + "å¼ĢåIJ¯ äºĨ", + "æİ ł", + "åħļçļĦ åįģä¹Ŀ", + "ä¸ĭ 鼨", + "çŁŃ æŶéĹ´åĨħ", + "å¯ ħ", + "导 åħ¥", + "å·¥ä½ľ ç»ıéªĮ", + "ä¹Ł åıªèĥ½", + "鼷 éľĨ", + "è·Ł è¿Ľ", + "åį¡ éĢļ", + "é¢ĩ æľī", + "æľº ä½ĵ", + "æĪĺ士 èģĮä¸ļ", + "女 主", + "ä½ĵåĪ¶ æľºåĪ¶", + "足 åįı", + "èĪĴéĢĤ çļĦ", + "åĢŁ åı£", + "æī¹ åĪ¤", + "æķ° åĢ¼", + "è« ¾", + "éĺ¿æĭī 伯", + "åĺ İ", + "æħ ¶", + "è¾¾ 人", + "å¼Ģ æ°´", + "大 鼨", + "温 室", + "ä½İ è¿·", + "ä»į æŧ", + "éªĹ åŃIJ", + "亲 å±ŀ", + "çIJĨ æĻº", + "æľ¬ åŁºéĩij", + "å¨ ħ", + "åĨĻåŃĹ æ¥¼", + "å¢Ļ å£ģ", + "å® µ", + "èĻ½ çĦ¶æĺ¯", + "顺 çĿĢ", + "åħ« åį¦", + "åķĨ çĶ¨", + "ä¸į 失", + "è¿· èĮ«", + "顺 便", + "æļij æľŁ", + "欺 è´Ł", + "é¢ij é¢ij", + "该 æł¡", + "æĸĻ çIJĨ", + "æ·± æĥħ", + "åīį éĶĭ", + "ä¿Ŀ èŃī", + "èģĮä¸ļ çĶŁæ¶¯", + "åħ¬ å¼Ģåıij", + "åħ¬å¼Ģåıij è¡Į", + "åħ¥ æĪ·", + "éł ĵ", + "åĢ¾ åIJ¬", + "éŃ ģ", + "æĦī æĤ¦", + "åĽŀ åIJĪ", + "åħ¨åĬĽ 以", + "åħ¨åĬĽä»¥ èµ´", + "åĥ¹ åĢ¼", + "èĥ½åĬĽ 强", + "ç»ı å¼Ģ", + "ç»ıå¼Ģ åĮº", + "è¿ľ æĸ¹", + "çļĦ éģĵçIJĨ", + "缴 åįĩ", + "缴åįĩ æľº", + "为主é¢ĺ çļĦ", + "ç»Ļ æĤ¨", + "è¿ĺ æĥ³", + "æ¯Ķ æĪij", + "åĨľ çī§", + "æµ· åºķ", + "çŃ¾è®¢ äºĨ", + "对äºİ æĪij们", + "æŶ 许", + "éĶ® çĽĺ", + "å®ŀéĻħ æݧåĪ¶", + "çļĦ æ¨¡æł·", + "åıįæĺł äºĨ", + "代 åĬŀ", + "åĮ» çĶ¨", + "éĽĨ ç»ĵ", + "åıijå±ķ åīįæĻ¯", + "æĮĩ çĿĢ", + "åįİ åĮĹ", + "è¿Ļ åĩłä¸ª", + "åIJį æ°Ķ", + "åĤį æĻļ", + "èĩª åıij", + "æ³¢ åħ°", + "大åĬĽ æİ¨è¿Ľ", + "èĩª 称", + "èįĨ å·ŀ", + "æIJį 害", + "äºĨä¸Ģ åı¥", + "æľĢåĪĿ çļĦ", + "éĩijèŀį åį±æľº", + "æĢĢ 念", + "è¡Į åĭķ", + "女 æİĴ", + "ä¸į 解", + "ä¼ł éĶĢ", + "转载 请", + "饰 åĵģ", + "åıª 为", + "ä¸İ ä¼Ĺ", + "ä¸İä¼Ĺ ä¸įåIJĮ", + "èĥ½ èĢĹ", + "èı© æıIJ", + "è¿ij 两年", + "è¿Ķ 乡", + "马ä¸Ĭ å°±", + "äºĮ çŃīå¥ĸ", + "æ°´ 管", + "æ³ķ åѦ", + "çģŃ çģ«", + "大 å§IJ", + "åij¨ 转", + "æľī æľŁ", + "æľīæľŁ å¾Ĵ", + "æľīæľŁå¾Ĵ åĪij", + "å°į æĸ¹", + "ç¥ŀ èī²", + "æ²¹ èĦĤ", + "ä¸ī çĤ¹", + "ä¸į åĪ©äºİ", + "äºĭä¸ļ éĥ¨", + "å°± è·Ł", + "å¼Ģ æĶ¯", + "å°ı 女åŃ©", + "åħ±åIJĮ åĬªåĬĽ", + "çĶļèĩ³ è¿ĺ", + "è¿Ļ åIJį", + "è¿Ļ ç¬Ķ", + "çݯ åį«", + "æľī ç§į", + "è§Ĩ åĬĽ", + "çĨŁ çŁ¥", + "åħ¬ç§¯ éĩij", + "æ¶Īéĺ² å®īåħ¨", + "é¢ĩ 为", + "大 èħ¿", + "éĿ ¶", + "çī¹ æķĪ", + "æľįåĬ¡ åĮº", + "å¼Ģ åĩº", + "深度 èŀįåIJĪ", + "æĹł 忧", + "æŁ¥ éĺħ", + "ç»Ī ç»ĵ", + "ä¿Ŀ ç¨İ", + "è¨İ è«ĸ", + "å½ĵ åģļ", + "è·³ èĪŀ", + "å¯ §", + "女 çİĭ", + "è®°èĢħ åľ¨", + "åħ¨ 产ä¸ļéĵ¾", + "è´¯ éĢļ", + "åħ´ ä¸ļ", + "éĻį åĪ°", + "å°ģ éĿ¢", + "åħ¨éĿ¢ æİ¨è¿Ľ", + "奶 èĮ¶", + "éĢī åĿĢ", + "äºĨä¸Ģ åľº", + "åIJĮ ä¼´", + "è®® 论", + "æIJ ĵ", + "诸 èijĽ", + "诸èijĽ 亮", + "å¹² åĺĽ", + "æµģ æĦŁ", + "ä¸ĵä¸ļ çŁ¥è¯Ĩ", + "çĶµ ç«Ļ", + "åĩı å¼±", + "åĩº åħ¥", + "åIJĦ çľģ", + "éĿŀ常 é«ĺ", + "åľ° 毯", + "åıij æĸĩ", + "çĦ ī", + "çĥ§ çĥ¤", + "å£ģ 纸", + "æģ¶ åĮĸ", + "èĬ ¸", + "èĥĸ åŃIJ", + "çĩ Ĵ", + "çľģ éĴ±", + "çĻ¾ 强", + "çIJĨå·¥ 大åѦ", + "éĴ¢ æĿIJ", + "åĽ½æľī èµĦ产", + "æĪĺ æľº", + "æ³Ħ éľ²", + "åIJİéĿ¢ çļĦ", + "æ°´ èµĦæºIJ", + "æ¢ħ èĬ±", + "åĨĻ çĿĢ", + "ä¹ĭ 声", + "æĹł åı¯", + "æĺİ æľĿ", + "ç«ĭæĸ¹ ç±³", + "ç· £", + "æĶ¾ è¿ĩ", + "ç¦ı çĶ°", + "å¾Ĺ ä½ı", + "åıĹ ä¼Ĺ", + "ä¸Ń 级", + "çĹħ åıĺ", + "ä¸Ģ çŀ¬éĹ´", + "æĿĥ éĩį", + "人æĢ§ åĮĸ", + "åĮ»çĸĹ åį«çĶŁ", + "ä¸įåĪ° ä½į", + "æĻºèĥ½ 家å±ħ", + "饮 çĶ¨", + "æ¼Ķ åıĺ", + "é«ĺ ç´łè´¨", + "ä¹Ļ æĸ¹", + "åģľ çķĻåľ¨", + "èİ· æī¹", + "ç©¿ æ¢Ń", + "客 åľº", + "æĮ½ åĽŀ", + "京 åŁİ", + "çĶŁåij½ åĬĽ", + "實 éļĽ", + "çĩ Ī", + "åĨį çİ°", + "çİ°å®ŀ ä¸Ń", + "æľī ä¿¡å¿ĥ", + "çĸı éĢļ", + "åĺ´ åĶĩ", + "鼷 éĶĭ", + "èıľ åįķ", + "éħ ¯", + "è¶ħ é«ĺ", + "å¾Ī é«ĺåħ´", + "çĶŁ æ®ĸ", + "éĢł ä»·", + "误 åĮº", + "æĨ ĭ", + "好 æ¶Īæģ¯", + "å´ Ń", + "以 èĩ´", + "å¼Ģ çİ©ç¬ij", + "çĽij è§Ĩ", + "å·¡ å¯Ł", + "å¾· å·ŀ", + "æĹ© æĹ©", + "éĹª çĶµ", + "æĪª åĽ¾", + "åı¯ä»¥ æł¹æį®", + "æīĭ èīº", + "æİ¥ 轨", + "ç§į æĹı", + "æĢĢ éĩĮ", + "åİ» åĮ»éĻ¢", + "ä¸Ģ äºĮ", + "å¼Ģ éĺĶ", + "åĩı éĢŁ", + "ä½Ĩ ä»İ", + "éĢĻ ä¸Ģ", + "åĩı åħį", + "主é¢ĺ æķĻèĤ²", + "å¼Ģå·¥ 建设", + "è¹ ¦", + "æľĪ 饼", + "ä¸ĭ æ²ī", + "å°Ĭ 严", + "éĻ ĩ", + "å®ŀ æľ¨", + "å»ł åķĨ", + "声 称", + "èĢĥ åľº", + "å¸ĥ é²ģ", + "èĩª æĿ¥", + "èĩªæĿ¥ æ°´", + "éĴ ¾", + "å¹´ 以ä¸Ĭ", + "大 åıĶ", + "ä»ĸ å·²ç»ı", + "åħ¨ æĿij", + "èģĶç³» çĶµè¯Ŀ", + "为 导åIJij", + "åĪ¤ å¤Ħ", + "对 éĺµ", + "缮 æ¨Ļ", + "åIJį é¢Ŀ", + "客 æ°Ķ", + "横 åIJij", + "çŃī åĨħ容", + "åĩł çĤ¹", + "è°Ī 论", + "ä¸į ä¹ı", + "å±ķ çİ°åĩº", + "è¾ĥ éķ¿", + "éĢĨ 转", + "å°ı æĻĤ", + "æĺ¯ å¤ļä¹Ī", + "æľ¬ æľĪ", + "è¿ij è§Ĩ", + "æĪIJç«ĭ 以æĿ¥", + "代表 çĿĢ", + "æĬ¥ å¤į", + "æĪı æĽ²", + "è¨Ń åĤĻ", + "åħ¥ èĤ¡", + "å¾ģ æľį", + "é«ĺ åĩº", + "èĪŀåı° ä¸Ĭ", + "å¿ĥ åĬ¨", + "两 çĤ¹", + "缸 çķ¶", + "èĻ Ľ", + "主 页", + "åĩł 家", + "æĹł ä¸į", + "åįı å®ļ", + "æĸ IJ", + "å¯ĵ æĦı", + "åħ¨ 线", + "æįķ é±¼", + "åı¯ä»¥ ä»İ", + "æľī è¿Ļæł·çļĦ", + "æģ¶ éŃĶ", + "åĮħ åŃIJ", + "æģ ¤", + "å¼Ģå¥ĸ ç»ĵæŀľ", + "ä¸į æŃ»", + "èĹ į", + "弯 æĽ²", + "æµ· 峡", + "éĶĢ æ¯ģ", + "çļĦ çĭ¬çī¹", + "示 æĦı", + "ä¸įèĥ½ åĨį", + "èĥ½ æĬĬ", + "éĺ² çº¿", + "ä¸įå°ij äºİ", + "æ± Ģ", + "çļĦ éĤ£ä¸Ģ", + "羣 æĥħ", + "åŀ ®", + "被 æīĵ", + "åĽ½ å®ī", + "ç¾İ å¦Ļ", + "è¿Ļ åĩł", + "åĩº éģĵ", + "æľįåĬ¡ äºİ", + "æĪIJæŀľ 转åĮĸ", + "æīį åįİ", + "天 é¹ħ", + "åĩł 个人", + "åĢĺ èĭ¥", + "èĢ½ 误", + "æĬĹ æĪĺ", + "è¡Į éĬ·", + "æĿ¥ è¢Ń", + "åĢŁ éĮ¢", + "èįī èİĵ", + "ä¸¥æł¼ æī§è¡Į", + "举è¡Į äºĨ", + "å¤ĸ ç±į", + "å·² è¾¾", + "æĿij åħļæĶ¯éĥ¨", + "è¡ Ŀ", + "éĻį èĩ³", + "æµ· éĩı", + "é¤IJ é¦Ĩ", + "æĢ¥ å¿Ļ", + "æ·± è¿ľ", + "å¾Ģ è¿Ķ", + "ç¨İåĬ¡ å±Ģ", + "å¹¿æ³Ľ åºĶçĶ¨", + "è®® åijĺ", + "æĹł æķĮ", + "çľ¼ åħī", + "çĥŃè¡Ģ ä¼łå¥ĩ", + "æŃ IJ", + "äºĨ äºĽ", + "è¿Ŀ èĥĮ", + "è¿Ļ æĺ¯ä¸Ģç§į", + "ä¸į 稳å®ļ", + "大家 åĪĨ享", + "表 çı¾", + "åīį åįģ", + "è·¯ è¿ĩ", + "æĴ ©", + "åIJĮ æĥħ", + "ä¹ł ä¿Ĺ", + "åıij è´¢", + "åºĶ æľīçļĦ", + "æĿİ æŁIJ", + "èĤ Ľ", + "马 åħĭ", + "éĢļ åijĬ", + "å·¨ 人", + "ä¸Ģ åĽ¢", + "éĢĻ 次", + "ä¸į äºĨ解", + "æĸ½ è¡Į", + "èij¡èIJĦ çīĻ", + "åıĺå¾Ĺ æĽ´åĬł", + "æı £", + "åĪĽæĸ° èĥ½åĬĽ", + "çķħ éĶĢ", + "表 æī¬", + "æ¯Ķ åĪ©", + "æ¯ĶåĪ© æŶ", + "åĮ»çĸĹ ä¿ĿéĻ©", + "æĵį 纵", + "伤 亡", + "æµİ å®ģ", + "åıĺ äºĨ", + "æľ¬æ¬¡ æ´»åĬ¨", + "åľŁ 豪", + "æĥ³ åĬŀæ³ķ", + "æĺ ķ", + "å½ĵ æĻļ", + "åĩº å±Ģ", + "çĥŃ è®®", + "è°Ī è°Ī", + "æĻĭ åįĩ", + "åĬ¿ å¿ħ", + "çĻ» å±±", + "éĤ£ åĦ¿", + "åIJĥ åĪ°", + "ä¹ĭ åŁİ", + "å¿« æĿ¥", + "æ¹Ľ æ±Ł", + "第ä¸ī 个", + "åħ¨éĿ¢ æıIJåįĩ", + "å¥ĸ åѦ", + "å¥ĸåѦ éĩij", + "æĬķåħ¥ 使çĶ¨", + "é½IJ é²ģ", + "åı¯ä»¥ æĬĬ", + "åĴĮ ä»ĸçļĦ", + "è´ŃæĪ¿ èĢħ", + "æŃ£å¼ı åIJ¯åĬ¨", + "åįİ æ¶¦", + "ä¸įæĸŃ å®ĮåĸĦ", + "éĴ¢ æĿ¿", + "ç´¯ 积", + "满 èĦ¸", + "åĽĽ æĸ¹", + "è´¢ çī©", + "ä»ĸ们 ä¼ļ", + "å¤ı æĹ¥", + "éĤ£ 个人", + "éĿł çĿĢ", + "çĤ¹ äºĨ", + "çĤ¹äºĨ çĤ¹å¤´", + "æ© ĭ", + "åıĪ 好", + "åıĪ好 åıĪ", + "åıĪ好åıĪ å¿«", + "éĺµ éĺµ", + "å°ģ 建", + "æľ¬ çĶ°", + "çī©ä¸ļ æľįåĬ¡", + "èĩªè´¸ åĮº", + "åIJ ı", + "便åĪ© åºĹ", + "åĽ½å®¶ æłĩåĩĨ", + "éĿ¢ ç²ī", + "èī° è¾Ľ", + "æĶ» åħ³", + "æīĵ åĮħ", + "车 éĺŁ", + "人 éĢī", + "åı¯ ä¸įæĺ¯", + "äºĮ åįģå¹´", + "åIJį å¸Ī", + "浦 举", + "åħ¬ è¯ģ", + "è¿IJ éĢģ", + "æĺ¯ æľĢ好çļĦ", + "æŁĶ åĴĮ", + "çİĭ æŁIJ", + "çĹħ æĪ¿", + "åĨ¶ éĩij", + "ä¸Ģ件 äºĭæĥħ", + "åį ¤", + "åı¯ æݧ", + "çī Ł", + "æĭ Ĥ", + "å·² äºİ", + "人 éĢł", + "çĶŁçī© åĮ»èį¯", + "ä½ĵ çİ°åĩº", + "èĤ² åĦ¿", + "èĢģ å®ŀ", + "åľĸ çīĩ", + "è« ¸", + "ç´¯ äºĨ", + "æĦŁåħ´è¶£ çļĦ", + "åĽ¾çīĩ æĿ¥æºIJ", + "ä¹Ł æĺ¯ä¸Ģç§į", + "æ¾İæ¹ĥ æĸ°éĹ»", + "æŶ 表示", + "åħī è¾ī", + "æĬ¥ åºŁ", + "å²ģ æŶ", + "éħ ®", + "æ£Ģ ä¿®", + "åıĺ éĢŁ", + "åıĺéĢŁ ç®±", + "åľ¨ èģĮ", + "éı ¡", + "æį Ĥ", + "çĿ£ åĬŀ", + "æ°¸ ä¸į", + "åģļ ä¸ĢäºĽ", + "åİĨ æŶ", + "å·¥ç¨ĭ æľºæ¢°", + "æģ° å½ĵ", + "å°± åľ¨äºİ", + "称 åij¼", + "éĢļ常 æĺ¯", + "æł· å¼ı", + "åij¨ ä¸Ģ", + "èĭ± éķij", + "åĿĩ 线", + "ä¼ł éĹ»", + "çĶ¨æĪ· ä½ĵéªĮ", + "èµŀ åIJĮ", + "骨 æĬĺ", + "为主 ä½ĵ", + "æ±Ł å±±", + "æ¸ħ æľĿ", + "æĶĢ åįĩ", + "ä¸į çĽ¸ä¿¡", + "éĿ ´", + "æѦ åĬŁ", + "åĭ¤ åĬ³", + "æĿ¥ æī¾", + "å°Ĩ æĮģç»Ń", + "丫 头", + "æ¨Ļ æºĸ", + "è£ ´", + "深深 çļĦ", + "åŃķ èĤ²", + "è§ĦåĪĴ 建设", + "æ¸ħ çĪ½", + "ç²¾åĩĨ æī¶è´«", + "æīĵçł´ äºĨ", + "è¿Ļä¸Ģ 天", + "å·¥ä½ľ æĢ»ç»ĵ", + "æĹħ ç¨ĭ", + "举 èIJ¥", + "æĶ¾ å°Ħ", + "æľī åĩłä¸ª", + "éĿŀ çī©è´¨", + "åIJĥ å¾Ĺ", + "åĹ ¨", + "ä¼ļ åıijçĶŁ", + "篮 æĿ¿", + "å¼Ģ å°ģ", + "麻 å°Ĩ", + "èıı æ³½", + "ä¸į åIJĪ", + "ç³»åĪĹ äº§åĵģ", + "èѬ å¦Ĥ", + "ç¾İ èªī", + "èĩªå·± åĸľæ¬¢", + "交æĺĵ ä¸Ńå¿ĥ", + "åIJĪ åĶ±", + "使 æĪij", + "åĥı ç´ł", + "带 éĺŁ", + "ä½Ĩ 对äºİ", + "æĬĬ è¿Ļ个", + "èĤĿ èĦı", + "åįķ纯 çļĦ", + "æĶ»åĿļ æĪĺ", + "缼 ä¼ļ", + "åijµ æĬ¤", + "æª Ģ", + "赶 ä¸Ĭ", + "æ¥ Ĭ", + "ä¹ħ äºĨ", + "ç¡ Ŀ", + "çŃĶ é¢ĺ", + "ä¿ĿæĮģ çĿĢ", + "è§ģ è¯Ĩ", + "çĤ¹ åĦ¿", + "åįĬ 个æľĪ", + "æ» ĩ", + "浸 泡", + "ä¼ł éĢģ", + "åľ¨ å¸Ĥåľºä¸Ĭ", + "ä¹ĭ 乡", + "çī¹ éķ¿", + "éĽ ŀ", + "èª ł", + "身 å¤Ħ", + "æŁł 檬", + "身 ç©¿", + "çľģ åħ¬å®ī", + "çľģåħ¬å®ī åİħ", + "åıĻ åĪ©äºļ", + "åĩł åĪĨéĴŁ", + "人 åĢij", + "åľ° 段", + "èĩª åѦ", + "ä¹Ł è¶ĬæĿ¥è¶Ĭ", + "èģĮ æĿĥ", + "æĸ §", + "èĩ »", + "å½Ĵ 纳", + "驾 é©Ń", + "éĥ¨åĪĨ åľ°åĮº", + "没æľī æĥ³åĪ°", + "æĴ ĩ", + "ä¹Į é²ģ", + "ä¹Įé²ģ æľ¨", + "ä¹Įé²ģæľ¨ é½IJ", + "èĤ² 人", + "çļĦ æŃ¥ä¼IJ", + "延 æľŁ", + "æ²¹ æ°Ķ", + "åģļ å®Į", + "åľ£ åľ°", + "丰 åİļ", + "宽 带", + "åı¯éĿł çļĦ", + "åºŃ éĻ¢", + "åŃ ľ", + "å°ı康 社ä¼ļ", + "å®īåħ¨ 管çIJĨ", + "å¹´ 第", + "æİĴ 污", + "èĥĮ åĮħ", + "家 ä½ı", + "åħ¶å®ŀ å°±æĺ¯", + "ä¼ļ è§ģ", + "帮åĬ© ä¼ģä¸ļ", + "ç½ij è´Ń", + "æĺ¯ ä¸įä¼ļ", + "飯 åºĹ", + "æŃ» åİ»", + "åħįçĸ« åĬĽ", + "æľ ķ", + "åĸĿ äºĨ", + "è½» å¾®", + "个æľĪ åĨħ", + "ç»Ħ åĽ¢", + "åĴĮ å®ĮåĸĦ", + "é¸ ½", + "æıIJ éĢŁ", + "西å®ī å¸Ĥ", + "ä¸Ńå¿ĥ 主任", + "æŶéĹ´ 为", + "æľŁ æĿĥ", + "è¶ ķ", + "ä¸įä»ħ è¦ģ", + "æľį ä»İ", + "é¡ĺ æĦı", + "ä¸į å°ı", + "ä¸įå°ı çļĦ", + "ç° ĩ", + "çª ¦", + "åĪĩ æĪIJ", + "åĵĪ åĪ©", + "天 羣", + "ä¸Ģ次 次", + "éĩij å¸ģ", + "æĢİä¹Ī èĥ½", + "ç½ij è´·", + "ä¼ļ计 å¸Ī", + "çŁŃ 缺", + "对 æłĩ", + "åıĺå¾Ĺ æĽ´", + "åīį åĩłå¤©", + "éĺ² æ±Ľ", + "彩 èĻ¹", + "åĵģ ä½į", + "表 æł¼", + "严 å¯Ĩ", + "æ¯Ľ åĪ©çİĩ", + "çļĦ åį±å®³", + "å½ķ åĪ¶", + "æ°´ åĬ¡", + "èĥ½å¤Ł 让", + "å¹³ æĿ¿", + "ä¹³ æĪ¿", + "è¸ı å®ŀ", + "é¦ĸ åĪĽ", + "é¦Ļ èķī", + "æĬ¥ 表", + "ä¸Ģ æĬ¹", + "åĩºçĶŁ äºİ", + "è²» çĶ¨", + "åĩº 让", + "åIJĪæ³ķ æĢ§", + "å°¼ åħĭ", + "åĨ° åĨ·", + "é¦Ļ æ°Ķ", + "åı· 称", + "èµ· çłģ", + "åŁİ åİ¿", + "çİ© èĢį", + "ä¸Ĭ éĻIJ", + "ä¼ļè®® ç²¾ç¥ŀ", + "æĹģè¾¹ çļĦ", + "便 ä¼ļ", + "æıŃ æĻĵ", + "çİ© æĦı", + "éĽª å±±", + "åIJij çĿĢ", + "ä½ĵèĤ² åľ¨çº¿", + "说æĺİ ä¹¦", + "åĮĸ èĤ¥", + "åħļç»Ħ 书记", + "åĬ¨ 人", + "ä¹ĭ æīĢ", + "æľĪ èĩ³", + "æľĢå¿« çļĦ", + "èĬĤ åģĩæĹ¥", + "ä¸ĵ åľº", + "èĢĥ ä¸Ĭ", + "çª Ł", + "é²ľ è¡Ģ", + "è¾ĥ强 çļĦ", + "æĤĦ çĦ¶", + "å¤ļ个 åĽ½å®¶", + "çªĹ å¸ĺ", + "æŀģ å¤§åľ°", + "ä¸įçĶ¨ æĭħå¿ĥ", + "è¿Ļä¹Ī åģļ", + "åĥ¹ æł¼", + "ç¾İ丽 乡æĿij", + "å°ıæŶ åĨħ", + "ç´§ è¿«", + "大 çģ«", + "èĥ³ èĨĬ", + "æĵįä½ľ ç³»ç»Ł", + "æ®ĭ çķĻ", + "åĨĻ åĩº", + "ç¦ģ å¿Į", + "åĬłçĽŁ åºĹ", + "è¿ij çĻ¾", + "便 åı¯", + "æķ´æĶ¹ æݪæĸ½", + "éĩĩ访 æŶ", + "åĶIJ 代", + "æ·±åĮĸ æĶ¹éĿ©", + "çŁ ¢", + "éĥ½ åĸľæ¬¢", + "è¶ĬæĿ¥è¶Ĭ é«ĺ", + "èĬ± æľµ", + "头 çĸ¼", + "å®ī 康", + "å¢ŀéķ¿ çİĩ", + "çľ¼ çľĭ", + "å°±æĺ¯ ä¸ºäºĨ", + "èĢĮ 导èĩ´", + "åĬłå¿« 建设", + "èĬ± æł·", + "åĨħå¿ĥ çļĦ", + "æĺĨ å±±", + "è³ĩ æºIJ", + "åĽŀåĪ° 家", + "èıĬ èĬ±", + "æ°´ éĩı", + "å¾ģ ä¿¡", + "è¡ĮæĶ¿ åĮº", + "ä¹ĥ æĺ¯", + "æĬķèµĦ é¡¹çĽ®", + "å«ģ ç»Ļ", + "ç¥ŀ åľ£", + "ç¨ ł", + "æľ¬æĿ¥ å°±", + "éĢIJ ä¸Ģ", + "èģĮä¸ļ æĬĢæľ¯", + "ä¸įèī¯ ä¿¡æģ¯", + "æīĺ è¿IJ", + "åIJ¯ 示", + "ä¹ĭ åħ§å®¹", + "éŁ ¶", + "奢 åįİ", + "æıŃ ç¤º", + "æĪIJ为 ä¸ŃåĽ½", + "æ¶Īè´¹ åĵģ", + "åħ¬ çĶ¨", + "æIJŀ å®ļ", + "请 ä½ł", + "æŁ ļ", + "åĨħ è¡£", + "ä½Ĩ ä»ĸ们", + "ä¿Ŀ 湿", + "该 åİ¿", + "饱 åĴĮ", + "æݨ åIJij", + "èµĦæĸĻ æĺ¾ç¤º", + "ä¸į å½±åĵį", + "人 人éĥ½", + "åıijå±ķ 壮大", + "åħ»èĢģ æľįåĬ¡", + "çĶŁæ´» æ°´å¹³", + "åIJĦ åİ¿", + "ä½ł éľĢè¦ģ", + "说 çļĦæĺ¯", + "å¤ĸ åªĴ", + "æѤ 人", + "次 è¦ģ", + "追 赶", + "åºĶ该 å¦Ĥä½ķ", + "æĹ¥ åĩĮæĻ¨", + "çķ¥ æľī", + "éĥ½ æĥ³", + "游 ä¹IJ", + "è¿Ļ款 游æĪı", + "å¹³ æ·¡", + "æĺ¯ä¸Ģ åĢĭ", + "å¤ĩ èĢĥ", + "åĪ¶ æŃ¢", + "ä¸Ģå®ļ èĥ½", + "å¾Ĵ å¼Ł", + "以 çĤº", + "åįĥ åħĥ", + "äºĶ åħŃ", + "迪 士", + "迪士 å°¼", + "éĺ³ æĢ§", + "åĨ¬å¥¥ ä¼ļ", + "å°±æĺ¯ åĽłä¸º", + "æĮĤ éĴ©", + "æ¦Ĥ åĨµ", + "åıªè¦ģ æľī", + "æ²¹ çĶ»", + "åľ° æłĩ", + "ä¸Ĭ è°ĥ", + "产ä¸ļ åĽŃåĮº", + "åħ« åįģ", + "æ£ ±", + "液 æĻ¶", + "æĿij å§Ķä¼ļ", + "çŃ¾çº¦ 仪å¼ı", + "è¿Ļ åħ¶ä¸Ń", + "åĨĻ éģĵ", + "示èĮĥ åŁºåľ°", + "éĩİçĶŁ åĬ¨çī©", + "鼻åŃIJ ä¿¡ç®±", + "åĽ½éĻħ è´¸æĺĵ", + "人 æĿĥ", + "ä¿Ŀ 管", + "èĭ¥ æĤ¨", + "åİĭ æĬij", + "é» Ľ", + "åľ° çľĭçĿĢ", + "éĻ °", + "ä¸Ģå¹´ å¤ļ", + "ä»İ 容", + "ä¸Ń æĸŃ", + "å¯Ł è§ī", + "移 交", + "éĶ ¯", + "æĪĸ许 æĺ¯", + "ç¶ ł", + "两 项", + "æľĢ åĸľæ¬¢", + "æľĢåĸľæ¬¢ çļĦ", + "å¤ľ éĩĮ", + "åIJĮ ä»ģ", + "åĪĽæĸ° 驱åĬ¨", + "è°ģ èĥ½", + "é£ ¾", + "åħī åѦ", + "åİ Ħ", + "èĦ± é¢ĸ", + "èĦ±é¢ĸ èĢĮåĩº", + "è¿ ¦", + "æĺ¯ ä¸įåı¯èĥ½", + "çª ¥", + "èĥ½ 满足", + "宽 度", + "伦 çIJĨ", + "åı¯ä»¥ èİ·å¾Ĺ", + "转 ä¼ļ", + "å±± æĿij", + "éĵº 设", + "åĩº åĩ»", + "æĸĩåĮĸ èīºæľ¯", + "ä¼ļè®® 室", + "æŃĮ 声", + "æ» Ķ", + "èIJİ ç¼©", + "æľįåĬ¡ åijĺ", + "åıij表 äºĨ", + "æĸ¼ æĺ¯", + "æĺİç¡® è§Ħå®ļ", + "ç»´ å¥ĩ", + "æ°´ 产", + "æĬķ ä¿Ŀ", + "éĺ´ éģĵ", + "赶 å¿«", + "夺 å¾Ĺ", + "ä¸ĭ åįķ", + "çī©æµģ åħ¬åı¸", + "çݯ ç»ķ", + "å½ Ī", + "ä½ľé£İ 建设", + "æĹħ游 æĻ¯åĮº", + "æľī æĽ´å¤ļçļĦ", + "丰å¯Į å¤ļ彩", + "çIJĨè´¢ 产åĵģ", + "åĩº å·®", + "ä»İ严 æ²»", + "ä»İ严治 åħļ", + "缸 å¹²", + "æ»ĭ 润", + "主åĬŀ æĸ¹", + "åī§ åľº", + "æ»ļ çIJĥ", + "æ©Ħ æ¦Ħ", + "èĩªä¸» åĪĽæĸ°", + "éĢļ å¾Ģ", + "æł¼ å°Ķ", + "çļĦ ä¼ĺçĤ¹", + "èĥĮ ä¸Ĭ", + "çª ľ", + "çĪĨ åĩº", + "å¹³ æķ´", + "ä¸Ģ èĦļ", + "åħ¨ä½ĵ åijĺå·¥", + "éĻIJ å®ļ", + "åŁİéķĩ åĮĸ", + "æ· ³", + "éĢ® æįķ", + "è¡ĮåĬ¨ 计åĪĴ", + "æīĵ å¾Ĺ", + "åİļ éĩį", + "纪å½ķ çīĩ", + "åĿļ ä¿¡", + "央 ä¼ģ", + "åĨį ä¹Łä¸į", + "天 涯", + "åıĤèĢĥ èµĦæĸĻ", + "æľī æ¯Ĵ", + "åIJ¸ 纳", + "è¶Ĭ åıij", + "éĩįè¦ģ æĦıä¹ī", + "åĽ½éĺ² éĥ¨", + "è¿Ļ个 è¡Įä¸ļ", + "æĻ® æŁ¥", + "å¼Ĥ æĢ§", + "延 è¿Ł", + "å°ı å¹ħ", + "èī² æĥħ", + "综åIJĪ æ²»çIJĨ", + "æŃ£æĺ¯ åĽłä¸º", + "产ä¸ļ ç»ĵæŀĦ", + "çłĶ究 æĬ¥åijĬ", + "åģľ ä¸ĭ", + "éķ¿ èĢģ", + "éĩĿ å°į", + "åįĹ京 å¸Ĥ", + "çģĮ æºī", + "转 è¿IJ", + "欺 è¯Ī", + "éĢł åģĩ", + "åĪĨå¸ĥ å¼ı", + "æĦŁ 触", + "æĪij å½ĵæŶ", + "åıij è§ī", + "åĽ¾ 纸", + "æĶ¹ èī¯", + "çĭł çĭł", + "åĨ² åĪº", + "æĸ° 京", + "æĸ°äº¬ æĬ¥", + "ç¥ŀ åĻ¨", + "秸 ç§Ĩ", + "çĪ º", + "å°Ĩ è¿İæĿ¥", + "å·¥ ä¿¡", + "工信 éĥ¨", + "éĻIJ éĩı", + "æŃ¢ æįŁ", + "åѦä¼ļ äºĨ", + "åįİ çĽĽ", + "åįİ缼 é¡¿", + "å¾Į ä¾Ĩ", + "ä¸ĭéĿ¢ æĺ¯", + "ä¸ĭéĿ¢æĺ¯ å°ı", + "æIJ¬ è¿IJ", + "ç¾İæľ¯ é¦Ĩ", + "æ¸ħ åĩī", + "å¤ļå¹´ åīį", + "è© ŀ", + "åįĥ ç±³", + "表 è¿°", + "æ±Ł éŨ", + "åĬłæ²¹ ç«Ļ", + "æľ¬ èĥ½", + "导 读", + "åĽ´ è§Ĥ", + "并 åIJij", + "åŁºæľ¬ æĥħåĨµ", + "æīĵ å¼ĢäºĨ", + "è¿Ļ ä¸ī个", + "æ±ķ 头", + "强 æľīåĬĽ", + "强æľīåĬĽ çļĦ", + "è¿Ľ åľº", + "ä¹Ŀ æ±Ł", + "çIJĥ æĺŁ", + "好çľĭ çļĦ", + "大 æĪ·", + "æ¹ ¯", + "å¥ĩ å¦Ļ", + "ä¹IJ åĻ¨", + "æĪijçļĦ å¿ĥ", + "çľī 头", + "åĨľä¸ļ çĶŁäº§", + "ç¼ĸ çłģ", + "åŁº ç¤", + "åŁºç¤ İ", + "天 æĸĩ", + "åĢĭ人 è³ĩè¨Ĭ", + "åİ» è¿ĩ", + "èģĨ åIJ¬", + "æĶ¾ åģĩ", + "ä¸į åħ·å¤ĩ", + "æ·Ģ ç²ī", + "大 佬", + "åħ¨ 天", + "åħ¨éĿ¢ 建æĪIJ", + "éļIJ å½¢", + "ç¼ħ çĶ¸", + "åIJ ³", + "è¡ĮæĶ¿ æī§æ³ķ", + "åŁİ åł¡", + "èİ« æĸ¯", + "èİ«æĸ¯ ç§ij", + "æīĢæľī æĿĥ", + "éĽĨ åľĺ", + "å±Ģ åī¯å±Ģéķ¿", + "åĩłä¹İ 没æľī", + "æ´ģ åĩĢ", + "çĶµå½± èĬĤ", + "åŃ© ç«¥", + "æīĢ åģļçļĦ", + "æ¸ħ 代", + "æĸ° çīĪ", + "éĵĿ åIJĪéĩij", + "为 æĬĵ", + "为æĬĵ æīĭ", + "åĪ¤ å®ļ", + "çī¹ 产", + "æīĭ æ©Ł", + "ä¸įåı¯ æĪĸ", + "ä¸įåı¯æĪĸ 缺", + "å¸Ĥåľº è§Ħ模", + "åĿ ¯", + "åĮ» åѦéĻ¢", + "å¿« è¦ģ", + "èĮ ľ", + "æĬĺ èħ¾", + "äºĨ è¿ĩæĿ¥", + "æĬ¥åijĬ æľŁåĨħ", + "çī© ç§į", + "ç»Łè®¡ å±Ģ", + "æī© 建", + "æ¶ ħ", + "责任 人", + "éĺ İ", + "è¯Ħ è®®", + "å¾Ģ äºĭ", + "æīĢ 示", + "æķ´ æ´ģ", + "éĹº èľľ", + "æĹħ éĢĶ", + "å®ŀ è®Ń", + "ä¹ĭ 称", + "å·´ 士", + "éĢŁåº¦ å¿«", + "ä¸įä»ħ å¦ĤæѤ", + "å®Ŀè´µ çļĦ", + "åºŁ çī©", + "æ²³ æ°´", + "æİ¥ 纳", + "ç²¾ æ¹Ľ", + "åħ¶æ¬¡ æĺ¯", + "顺 å¾·", + "åħ¬åħ± åį«çĶŁ", + "è¤IJ èī²", + "ä¸į æĥľ", + "æĬĢæľ¯ æľįåĬ¡", + "æİ ·", + "æ±Ĥ èģĮ", + "ä¸ī 峡", + "æĬķåħ¥ åĪ°", + "太 åIJİ", + "åIJ¯åĬ¨ 仪å¼ı", + "缴æİ¥ å½±åĵį", + "æĸ° 款", + "个 乡éķĩ", + "çĻ¾ 亿", + "åº «", + "ä¹Ł æŃ£æĺ¯", + "åı¶ çīĩ", + "æľĢæĹ© çļĦ", + "æĪĺ 绩", + "å·¥ æľŁ", + "æĻļ æľŁ", + "è¿Ļæł· 说", + "è¯į è¯Ń", + "ä¾ Ħ", + "æķ£ çĥŃ", + "éĽĨæĪIJ çĶµè·¯", + "åIJį è¯į", + "æĻº åķĨ", + "æĭ¥ åłµ", + "çĭĤ 欢", + "è¿Ļ èĪ¬", + "æµ´ 室", + "åijķ åIJIJ", + "æľªæĿ¥ åıijå±ķ", + "ä¸īä½į ä¸Ģä½ĵ", + "åªĴ é«Ķ", + "ä¸įå¾Ĺ 转载", + "åĽłä¸º 她", + "æĺ¾ç¤º å±ı", + "ä¾Ľ æļĸ", + "éĨ« éĻ¢", + "æľī æĦıæĢĿ", + "æľīæĦıæĢĿ çļĦ", + "娱ä¹IJ åŁİ", + "åįµ å·¢", + "åĪĽéĢł åĬĽ", + "ç«ł èĬĤ", + "人大 常å§Ķ", + "èĢĮ çİ°åľ¨", + "å¤ĸ å©Ĩ", + "å¢ŀ æĮģ", + "äºĶ åįĥ", + "èĢģå¸Ī 们", + "æ´Ľ æĿī", + "æ´ĽæĿī 磶", + "æİĮæı¡ äºĨ", + "ä¸ŃåĽ½ æĸĩåĮĸ", + "æĸ° æĶ¿", + "主è¦ģ çĶ¨äºİ", + "åıij çĥ§", + "类似 äºİ", + "åĮĹ æŀģ", + "æĪij们 认为", + "å¼¥ 漫", + "åħ¨çIJĥ ç»ıæµİ", + "é¢ IJ", + "ä¸Ģèµ· è£ħä¿®", + "æĶ Ĵ", + "æĭī èIJ¨", + "帶 ä¾Ĩ", + "åĨ· æ°´", + "ä¸ī åĨľ", + "æĿ¿ æĿIJ", + "è¿ŀ è¿ŀ", + "éĵ ®", + "ç»ıèIJ¥ çIJĨ念", + "å±± 顶", + "å¾Ī æĥ³", + "çĺ «", + "å§ĭç»Ī ä¿ĿæĮģ", + "åľ¨ 广å·ŀ", + "ä¸įåIJĮ æĦı", + "åıĺ åİĭ", + "åıĺåİĭ åĻ¨", + "产 éĶĢ", + "表 éĿ¢ä¸Ĭ", + "æīĢ以 ä»ĸ", + "ç»ıéªĮ 丰å¯Į", + "éĥ¨ å§Ķ", + "åħµ åĽ¢", + "æīĢ è¿°", + "æķ¦ çħĮ", + "ç»ıèIJ¥ èĮĥåĽ´", + "åı£ è¯Ń", + "失 ä¿¡", + "æ¯ı个人 çļĦ", + "æīĭ æĮģ", + "æģIJ æħĮ", + "åł¡ åŀĴ", + "é¦ ħ", + "éĵ¸ éĢł", + "æĭ¿ åĩºæĿ¥", + "æİ¢ æµĭ", + "大家 ä¸Ģèµ·", + "å¥ §", + "å®ŀè´¨ æĢ§", + "å°ı åĦ¿", + "èĩº åįĹ", + "èĩºåįĹ å¸Ĥ", + "å¼Ģåıij èĢħ", + "åı¯ æł¹æį®", + "ç®± åŃIJ", + "饺 åŃIJ", + "å¿Ļ çĿĢ", + "æĿ¥ ä¸įåıĬ", + "缸 ä¼ł", + "åĽ½ ç½ij", + "èħ¹ æ³»", + "è¿ĻéĩĮ æľī", + "é£İ æĻ¯åĮº", + "åıĤ ä¿Ŀ", + "æŃ» èĢħ", + "æĪ´ ä¸Ĭ", + "æ©Ł æ§ĭ", + "è¯ķéªĮ åĮº", + "ä¼ł æİĪ", + "æµ· è¾¹", + "泪 æ°´", + "缸åħ³ åĨħ容", + "éĥij å·ŀå¸Ĥ", + "åħij çİ°", + "两 åij¨", + "èĬľ æ¹ĸ", + "çĶµåŃIJ ä¿¡æģ¯", + "红 å¤ĸ", + "æĹħ游 å±Ģ", + "å¾Ģå¾Ģ ä¼ļ", + "è¿ħ çĮĽ", + "ä¼ł 羣", + "æ¸ħ æ¾Ī", + "å°± è¿ij", + "微信 群", + "ç³»åĪĹ æ´»åĬ¨", + "ç»ı常 ä¼ļ", + "è§Ĥ æµĭ", + "å¿ĥå¾Ĺ ä½ĵä¼ļ", + "éĻĪ åĪĹ", + "åĮĹ æĸĹ", + "è« ®", + "è«® è©¢", + "è¿ĺæĺ¯ ä¼ļ", + "æµĭ ç®Ĺ", + "æĺŁ ç©º", + "宽 容", + "çī©ä¸ļ åħ¬åı¸", + "æĪĴ æĮĩ", + "å¸ħ æ°Ķ", + "ä¸ĢæŃ¥ æŃ¥", + "åħ± 鸣", + "åĨ³ ä¸į", + "æİ¥ 管", + "å¦ĩ èģĶ", + "æ¯Ķ åĸ»", + "é²ģ è¿ħ", + "æĮģ çºĮ", + "缸 亲", + "å¨ģå°¼æĸ¯ 人", + "ç«ĭ 项", + "åĪ Ŀå§ĭ", + "èĩª åĪ¶", + "è¿Ī è¿Ľ", + "ä¸Ĭ æ±½", + "å®ı ä¼Ł", + "æł¹æľ¬ 没æľī", + "æĸ°åĨł çĹħæ¯Ĵ", + "åĵª ç§į", + "康 åħ»", + "è¡° èĢģ", + "å½ķ åĥı", + "é«Ķ é©Ĺ", + "ç»ij å®ļ", + "é¢Ŀ 头", + "äºĶ æľĪ", + "èĬ± å¼Ģ", + "ä¸Ģ线 åŁİå¸Ĥ", + "åĪ° åľº", + "æĬķ éĻį", + "çĹĺ çĹĺ", + "åıĹ ä¸įäºĨ", + "æīİ æł¹", + "æĽ´ ä½ķåĨµ", + "æĬ½ æŁ¥", + "åĩº è·¯", + "审议 éĢļè¿ĩ", + "ä¸į åĥħ", + "èī² è°ĥ", + "çĻ¾ ä½Ļ", + "èĤł éģĵ", + "æ·±åİļ çļĦ", + "马 åĬĽ", + "æĹ© æĻļ", + "æŃĮ èĪŀ", + "éĺ² æĻĴ", + "æľĢåIJİ ä¸Ģ个", + "樱 èĬ±", + "å°ıä¼Ļ åŃIJ", + "åľ¨ å½ĵåľ°", + "å°ıä¼Ļä¼´ 们", + "èµ· æºIJ", + "åħ¨ åªĴä½ĵ", + "ç° ½", + "éħ± æ²¹", + "æĹłè®º å¦Ĥä½ķ", + "裤 åŃIJ", + "åģľ 产", + "ä¸įçĶ± å¾Ĺ", + "çīµ å¼ķ", + "ä¼ł åĬ¨", + "ä¹Ŀ é¾Ļ", + "åĬł åĽº", + "ä¹Łä¸į æķ¢", + "æĬĢæľ¯ æĶ¯æĮģ", + "ä¸Ĭ å²Ĺ", + "ç»ıéªĮ åĴĮ", + "æł¼ æŀĹ", + "åIJ¸ éĻĦ", + "æľªæĪIJ å¹´", + "奢ä¾Ī åĵģ", + "追 æį§", + "好 ä¸į容æĺĵ", + "èķ´ åIJ«", + "ä¿Ŀ å®ļ", + "æĬ¥ ä¸ļ", + "æµ· åĨħå¤ĸ", + "ä½ł çİ°åľ¨", + "æ²¹ èĢĹ", + "è´¨éĩı 管çIJĨ", + "æ½ľ æ°´", + "丽 æ±Ł", + "转 åħ¥", + "è¿Ļä¹Ī ä¹ħ", + "æĺİ ä»£", + "责任 åĪ¶", + "éĩį å·¥", + "大 å·´", + "触 åıĬ", + "èµ· åĪĿ", + "大 å¦Ī", + "æĸ¯ å¡Ķ", + "åĨĽ å·¥", + "书 éĻ¢", + "å³ ¨", + "æݨ çIJĨ", + "è¿Ļç¯ĩ æĸĩç«ł", + "è¿ģ 移", + "åľ¨ åIJĮä¸Ģ", + "ç»Ĩ ç»Ĩ", + "åīĬ å¼±", + "书 æĪ¿", + "ç¶ĵ 常", + "è¯ķ é¢ĺ", + "æĤ£ ä¸Ĭ", + "çĻ«çĹ« çĹħ", + "åĨ² æ´Ĺ", + "å¤ĸ æı´", + "åħĭ åĪ¶", + "åįģ æľĪ", + "åģļ ä¸įåĪ°", + "ç¾İ åĮĸ", + "å¦Ĥ æľŁ", + "è¿ĺ éľĢ", + "天 åºľ", + "å°± æĦıåij³çĿĢ", + "çļĦç¡® æĺ¯", + "éªĹ å±Ģ", + "å°ıç»Ħ èµĽ", + "è© ©", + "ä¹Ŀ å¹´", + "æĻĵ å¾Ĺ", + "çłĶ究 人åijĺ", + "大 éħĴåºĹ", + "ç§ij åѸ", + "åħŃ åIJĪ", + "çķĮ å®ļ", + "车 è½½", + "å¼Ģ çĿĢ", + "毫 æĹłçĸij", + "毫æĹłçĸij éĹ®", + "è¿IJ ç»´", + "ç¦ģ åĮº", + "èĦ± èIJ½", + "讲 å¸Ī", + "产ä¸ļ åŁºåľ°", + "é«ĺ æĢ§èĥ½", + "åħī 彩", + "çİ° éĺ¶æ®µ", + "åĩ ¿", + "è¾ĥ å·®", + "饮 çĶ¨æ°´", + "éĸĭ çĻ¼", + "ç½ij åIJ§", + "çĮ´ åŃIJ", + "æѦ æŀĹ", + "å®ī åİ¿", + "ä¸įåı¯ æĢĿ", + "ä¸įåı¯æĢĿ è®®", + "éĬ· åĶ®", + "è´« ç©·", + "为 åķ¥", + "éº ĵ", + "å¹¾ åĢĭ", + "è§Ħ模 以ä¸Ĭ", + "æı ļ", + "被 åĽ°", + "缺 å¸Ń", + "å¿« é¤IJ", + "æĬ¢ åįł", + "æĻ Ł", + "å¤į æ´»", + "æľ¬æĬ¥ 讯", + "åĪĽ ä¸ĭ", + "æµ· 滩", + "éĩı 产", + "å¦Ĥä½ķ åİ»", + "车 ä½į", + "å¯ ĩ", + "äºĮ åįģåĽĽ", + "ç»ıæµİ æįŁå¤±", + "éħįå¥Ĺ 设æĸ½", + "åŁºæľ¬ éĿ¢", + "äºī 论", + "就好 åĥı", + "çłĶ究 æĪIJæŀľ", + "éĻĪ è¿°", + "æīĵ åĬ¨", + "ä¸ĭ å·´", + "ç§Ĵ éĴŁ", + "对 人ä½ĵ", + "æĬĢæľ¯ çłĶåıij", + "åİŁ åŃIJ", + "æĺ¯ä¸Ģ 项", + "äºĨä¸Ģ 份", + "æĮĩ çĶ²", + "çĶ¨ éĩı", + "è¿ĺä¸į å¤Ł", + "æĶ¿åºľ éĩĩè´Ń", + "çŁ¥è¯Ĩ çĤ¹", + "ä¸ŃåĽ½ 梦", + "å¾Ī å¼Ģå¿ĥ", + "礼 è²Į", + "éĿŀ常 å¤ļ", + "éĿŀ常å¤ļ çļĦ", + "åĽ ļ", + "æĹħ é¦Ĩ", + "å°½ æĥħ", + "æŃĮ åĶ±", + "æ²Ļ é¾Ļ", + "车 åİ¢", + "客 æµģ", + "åģı å·®", + "积累 äºĨ", + "æ¡ Ķ", + "çĶ» çĶ»", + "ä¹Ł åºĶ该", + "åºĶçĶ¨ ç¨ĭåºı", + "èĥĥ èĤł", + "以 å¾Į", + "豪 å®ħ", + "æ·± åĬłå·¥", + "缴 è¨Ģ", + "åĮĸ çŁ³", + "åĽ½ éģĵ", + "ä¸ĥ 个", + "ä»İèĢĮ 使", + "èĤł èĥĥ", + "æĹ¥ è¶ĭ", + "çĪ¶ åŃIJ", + "ç· ©", + "æĭĽ çīĮ", + "产 å¦ĩ", + "çķª èĮĦ", + "æĪij éĻ¢", + "建çŃij å·¥ç¨ĭ", + "å±ķè§Ī ä¼ļ", + "家éķ¿ 们", + "åĨľ ä½ľçī©", + "æĹ¥ å¤ľ", + "æĶ» æĵĬ", + "è§Ħ éģ¿", + "èĪŁ å±±", + "便 æ°ij", + "åħ« åŃĹ", + "ä¸į æĽ¾", + "æĶ¯ éħį", + "çĨ¬ å¤ľ", + "人 é¡ŀ", + "ç´Ģ éĮĦ", + "ç»ıèIJ¥ æ´»åĬ¨", + "大 涨", + "å¸Ĥå§Ķ 常å§Ķ", + "åĪĨ éIJĺ", + "ä¸Ģ个 èģĮä¸ļ", + "çĹħ åĽł", + "è¿Ļ 对äºİ", + "ä¸įå¾Ĺä¸į 说", + "åıijçĶµ æľº", + "æľīæīĢ 帮åĬ©", + "缮æłĩ ä»»åĬ¡", + "åĽł åľ°", + "åĽłåľ° åĪ¶", + "åĽłåľ°åĪ¶ å®ľ", + "å°Ĩ è¾¾åĪ°", + "ç²Ĺ ç³Ļ", + "稳 åĽº", + "å« £", + "çİ°åľ¨ å¾Īå¤ļ", + "ä¸ĸçķĮ 级", + "å¼ł æŁIJ", + "çĤ¹ ç¼Ģ", + "èij µ", + "社ä¼ļ ç»Ħç»ĩ", + "å¾Ģ åIJİ", + "åĬł æģ¯", + "åĻª 声", + "æľī åħ´è¶£", + "为æĤ¨ æıIJä¾Ľ", + "æ²¹ æ¼Ĩ", + "ç¬¬åĽĽ å±Ĭ", + "çļĩ 宫", + "ä¹Ĵ ä¹ĵ", + "ä¹Ĵä¹ĵ çIJĥ", + "éļ¨ èijĹ", + "éģ© åIJĪ", + "åįĹ éĿŀ", + "æĵ ´", + "西 æ´ĭ", + "åĬł å¯Ĩ", + "æĪIJåĬŁ 举åĬŀ", + "åı£ æ°´", + "æĪIJ 年人", + "æīĢ æıIJä¾ĽçļĦ", + "éļĶ å£ģ", + "åľ¨ 京", + "å½ĵåľ° æŶéĹ´", + "çŃī åIJĦç§į", + "é£İ æ°Ķ", + "å±ĭ éĩĮ", + "ä¸Ģ åŃĹ", + "çļĦæŶéĹ´ éĩĮ", + "åĺ¿ åĺ¿", + "å¿« 讯", + "ä¸Ń åľº", + "ä¸Ģ çĵ¶", + "æ» ķ", + "é¢Ĩ è·ij", + "好 èݱ", + "好èݱ åĿŀ", + "没 åħ³ç³»", + "åĩº å¢ĥ", + "ä¸įæĺ¯ ä¸Ģ个", + "éĥ½æĺ¯ éĿŀ常", + "éľĩ åĬ¨", + "èİ· èĥľ", + "åįļ å¼Ī", + "æĬļ åħ»", + "对 ç«ĭ", + "æľįåĬ¡ æľºæŀĦ", + "è°£ è¨Ģ", + "社ä¼ļ ç§ijåѦ", + "åIJ¬è¯´ è¿ĩ", + "æī ³", + "æīĵ 磨", + "åı£ æľį", + "好 åĥıæĺ¯", + "以åıĬ åħ¶ä»ĸ", + "çī¹ è´¨", + "亲 è¿ij", + "ä¸Ģ ç»ı", + "æ¶ Ŀ", + "éŃĶ æľ¯", + "éģĵè·¯ 交éĢļ", + "è§Ħ模 æľĢ大", + "å®ŀæĸ½ æĦıè§ģ", + "ä¹ ŀ", + "ä¸Ģ ä¸ĸ", + "åŁ· è¡Į", + "è±Ĩ çĵ£", + "åĪĹ ä¸º", + "æķħ 宫", + "çĶŁ åij½åij¨æľŁ", + "ä¸īç§į èģĮä¸ļ", + "详ç»Ĩ ä»ĭç»į", + "å®Į å¤ĩ", + "岩 çŁ³", + "éļı æīĭ", + "é£ ²", + "æķĪæŀľ åĽ¾", + "ç§ĭ åĨ¬", + "åĬŁ å¾·", + "è§Ħç«ł åĪ¶åº¦", + "æĹ¥ æ¸IJ", + "æīĢ éľĢè¦ģ", + "æīĢéľĢè¦ģ çļĦ", + "å²Ľ ä¸Ĭ", + "åĩº åľŁ", + "åĽ¾ æĸĩ", + "ç§ijæĬĢ è¿ĽæŃ¥", + "éĢļ èĥĢ", + "èĢģ 太太", + "èĭĹ æľ¨", + "éĵ¶ å·Ŀ", + "å¸IJ 篷", + "éĿŀ è¦ģ", + "éħį çĶµ", + "å¤Ħ å¢ĥ", + "èĤ¡æĿĥ æĬķèµĦ", + "ä¸Ģ缴 åĪ°", + "åĿĩ çĶ±", + "æĬĹ æĹ¥", + "æį® ä»ĭç»į", + "ä½ł åĸľæ¬¢", + "åĪĽæĸ° åŀĭ", + "åıĺ è¿ģ", + "è§Ĩ å¯Ł", + "å®Įåħ¨ 没æľī", + "åħĥ æŦ", + "åı¯ ä¿¡", + "åı¦ è¡Į", + "æĿij 级", + "åħ¥ åľº", + "æIJŃ æ¡£", + "ä¹Ł åĽłæѤ", + "æį¢ æĪIJ", + "ä¸į è´Ł", + "äºĨ 大éĩıçļĦ", + "éģĶ åĪ°", + "å¸Ĥ åİ¿", + "å¹´ è¼ķ", + "å¿« æīĭ", + "å¸Į å°Ķ", + "èĩª èIJ¥", + "éĽª èĬ±", + "æIJ ģ", + "çľ¼ ç§ij", + "æŃ£ 確", + "çļĦ 姿æĢģ", + "åĿļå®ŀ çļĦ", + "æĮĩ 纹", + "æªĶ æ¡Ī", + "ç½® äºİ", + "佩 æľį", + "豪 éŨ", + "åĵ Ĵ", + "æģ° 好", + "檢 æŁ¥", + "åĪĿ è¡·", + "大 åĶIJ", + "约 ä¼ļ", + "èĴ¸ åıij", + "çŃ¹ åĪĴ", + "å¹´ ç»Ī", + "è¡Į æ¥Ń", + "åħ± éĿĴ", + "åħ±éĿĴ åĽ¢", + "ä¼ļ å¼ķèµ·", + "ä¸Ń ç§ij", + "ä¸Ńç§ij éĻ¢", + "æĮ¯ åĬ¨", + "åį´ åıijçİ°", + "ä¸įåĬ¨ 产", + "èĮ ¹", + "æĪ¿éĹ´ éĩĮ", + "è´§å¸ģ æĶ¿çŃĸ", + "æ²» çĻĤ", + "æħİ éĩį", + "å¡ŀ å°Ķ", + "åĽ½ ç±į", + "åĽł æŀľ", + "çŃī çī¹çĤ¹", + "å±± è°·", + "ä¸ĭ è¼ī", + "è®ĵ æĪij", + "饮 éħĴ", + "è¿Ļ个 游æĪı", + "ç»Ŀ 大éĥ¨åĪĨ", + "åĴ¨è¯¢ æľįåĬ¡", + "å¹² æ´»", + "è®® ä¼ļ", + "æ¦Ĥ è¿°", + "åĪĨ åĮº", + "æŃ» åIJİ", + "ç«Ļ çĿĢ", + "主è¦ģ é¢Ĩ导", + "åIJĮ åŁİ", + "大 æłij", + "对 åѦçĶŁ", + "社ä¼ļ ä¿ĿéĻ©", + "å¢ŀ èµĦ", + "主人 åħ¬", + "å®£ä¼ł æķĻèĤ²", + "æĸĩåĮĸ 交æµģ", + "客 æĪ¶", + "çŁ¥åIJį åĵģçīĮ", + "æ»ŀ åIJİ", + "äºĴ è¡¥", + "æĦŁ 人", + "åī ¿", + "åIJİ ä»£", + "äºī 龸", + "æķĻèĤ² åŁ¹è®Ń", + "éĿĻ èĦī", + "ä¹ı åĬĽ", + "说 åĩºæĿ¥", + "çİĭèĢħ èį£èĢĢ", + "åĢ «", + "åįĩ èµ·", + "éķ ģ", + "åĩº 游", + "éĢļè¡Į è¯ģ", + "å·¥ä½ľ å²Ĺä½į", + "åĮł å¿ĥ", + "æĭ¿ æĿ¥", + "æ´Ĺè¡£ æľº", + "æĪijä¸į æĥ³", + "é¢Ħ è§ģ", + "æ¼Ķ 示", + "ä¸Ģ缴 没æľī", + "è·Ł 她", + "对çħ§ æ£ĢæŁ¥", + "ç° ¿", + "ä¸ĵ å¿ĥ", + "è®® äºĭ", + "åīį 端", + "åį¡ å°Ķ", + "è¨Ń å®ļ", + "设置 äºĨ", + "å©ļ 纱", + "åľ¨ åĽ½å¤ĸ", + "åı³ 侧", + "è³¼ çī©", + "å¥ĩ èij©", + "å¢ŀåĬł åĢ¼", + "好 è¿IJ", + "åĽ½éĻħ æľºåľº", + "ä¸ĭ 称", + "缮åīį 为æŃ¢", + "ç¥ŀ ä»Ļ", + "å®ĥ åı¯ä»¥", + "æ¾Ħ æ¸ħ", + "èĥ½ 使", + "游 åĩ»", + "游åĩ» éĺŁ", + "åĩ ¹", + "ä¸įè¦ģ åĨį", + "åĨ³ èĥľ", + "åĨ³ æĪĺ", + "æĭ ½", + "缼 åħ¸", + "å¾Ī好 åľ°", + "æľĢ ç¾İçļĦ", + "åĥ ļ", + "å·´ åŁº", + "å·´åŁº æĸ¯åĿ¦", + "æľĢ éĢĤåIJĪ", + "é«ĺ èģĮ", + "ä¿Ŀ å§Ĩ", + "æİĪ æ¬Ĭ", + "说åĪ° è¿ĻéĩĮ", + "æݨ å¼Ģ", + "çİĩ è¾¾", + "ä¸īåĪĨ ä¹ĭä¸Ģ", + "管çIJĨ ä¸Ńå¿ĥ", + "交 æ±ĩ", + "森æŀĹ åħ¬åĽŃ", + "å¾Ģ ä¸Ĭ", + "éªij è¡Į", + "æį® æѤ", + "纽 带", + "ç» ŀ", + "ä¸ī æĸ¹", + "æĦıä¹ī ä¸ĬçļĦ", + "æݨ è¿Ł", + "å¤ļæł· æĢ§", + "æĥ³ èµ·äºĨ", + "æİĴåIJį 第", + "å·¨ é¢Ŀ", + "æĿŁ ç¼ļ", + "å®ī å®ļ", + "äºĭ 實", + "çļĦ æĦ¿æľĽ", + "è£ħå¤ĩ åĪ¶éĢł", + "人 å±ħ", + "人å±ħ çݯå¢ĥ", + "å¿ĺè®° äºĨ", + "该 游æĪı", + "楼 ä¸Ĭ", + "å¼Ģ ä¼ļ", + "æģ ³", + "åıĭæĥħ éĵ¾æİ¥", + "ç¡ Ĵ", + "ç»ĻäºĪ äºĨ", + "åģı 好", + "åĵ ī", + "交éĢļ å®īåħ¨", + "éĽ Į", + "æ²» çĹħ", + "è§īå¾Ĺ å¾Ī", + "衬 è¡«", + "å¿ĥ æĦ¿", + "æ´ŀ å¯Ł", + "æ°ij æ£Ģå¯ŁéĻ¢", + "æıIJ çĤ¼", + "è¦ģ è¿Ľä¸ĢæŃ¥", + "驾 车", + "æĻ® æĥł", + "æķ ĸ", + "ç¦ı éŁ³", + "éĢģ è¾¾", + "è§ĦåĪĴ 设计", + "æīĭ å¥Ĺ", + "å®ī ä¿Ŀ", + "è¿ĺä¸į å¦Ĥ", + "åīį è¿°", + "æłĩ è®°", + "ç´§ æİ¥çĿĢ", + "æ§ IJ", + "深深 åľ°", + "满满 çļĦ", + "æĺ¥ è¿IJ", + "æĹ¥ 产", + "çĪ± æĬ¤", + "åħ¨ æĹ¥", + "åħ¨æĹ¥ åĪ¶", + "转 åĬ¨", + "ç¥Ń ç¥Ģ", + "ä¹° ä¸ľè¥¿", + "对 æľªæĿ¥", + "æ¶Ī失 äºĨ", + "åļ´ éĩį", + "ä¸ī æĿ¡", + "éħ¸ 奶", + "éĽĨåĽ¢ èĤ¡ä»½", + "西 è·¯", + "åıª å¾Ĺ", + "éĢģ åİ»", + "çĭł æĬĵ", + "åĪ©çĶ¨ çİĩ", + "ä¸ĭ åij¨", + "å¥ĭ æĪĺ", + "æĺ¥èĬĤ æľŁéĹ´", + "è´Ł 责任", + "æĺĤ è´µ", + "å°¾ å·´", + "ç¯ĩ æĸĩç«ł", + "åħ ®", + "è®Ĭ æĪIJ", + "å¹ ¹", + "çĻ» éĮĦ", + "ä½ Ī", + "å·¥ åĮł", + "åĵªæĢķ æĺ¯", + "åıį åĵį", + "ç§ ĥ", + "åĩº 轨", + "æĹ¥ åĨĽ", + "åIJį èªī", + "æķı éĶIJ", + "æľįåĬ¡ æ°´å¹³", + "çħ§ å°Ħ", + "ä¼Ĭ æĭī", + "ä¼Ĭæĭī åħĭ", + "åĨħ éĺģ", + "èĬĴ æŀľ", + "ä¸ĩ åĪĨ", + "éĢĢ 款", + "缴æĴŃ éĹ´", + "æĭ¿ åĪ°äºĨ", + "å°İ èĩ´", + "空æ°Ķ ä¸Ń", + "客æĪ· æľįåĬ¡", + "è¿IJ åĬ¿", + "ç»ĵ çŁ³", + "ä¸į å¿ħè¦ģçļĦ", + "èĥ¶ åĽĬ", + "çIJĨ ä¼ļ", + "æĬ½ åĩº", + "空æ°Ķ è´¨éĩı", + "æ¯ķ ç«Łæĺ¯", + "åĨ· æ¼ł", + "ä¸Ģ å¦Ĥ", + "ä¸Ģå¦Ĥ æĹ¢", + "ä¸Ģå¦ĤæĹ¢ å¾Ģ", + "æĤ£ çĹħ", + "åĬł æĮģ", + "èµŀ åĬ©", + "é« ®", + "åij½ ä¸Ń", + "æĦıä¹ī ä¸Ĭ", + "ä¸į èĪį", + "åģļ 梦", + "æīĵ æī«", + "æĺŁ åħī", + "æĸŃ è£Ĥ", + "åħ¨ å¥Ĺ", + "è£ģ å®ļ", + "马 åħĭæĢĿ", + "骨 骼", + "ä¸Ģ è·¯ä¸Ĭ", + "å®ļ æŶ", + "å·¥ç¨ĭ æĬĢæľ¯", + "å½¼ å¾Ĺ", + "æ±² åıĸ", + "ä¸Ģ è§Ī", + "åIJµ æŀ¶", + "ä¿Ĺ 称", + "æłª æ´²", + "åºŁ æŧ", + "è¡Į æĺŁ", + "åıijçĶŁ åıĺåĮĸ", + "é¦ĸ ä»ĺ", + "åįģåĪĨ éĩįè¦ģ", + "æĬĬ è¿ĻäºĽ", + "ç¥ŀ å·ŀ", + "æıIJä¾Ľ åķĨ", + "æ¥ ·", + "å± İ", + "çĬ¶ åħĥ", + "åŁİ å¢Ļ", + "çľĭ ä¸Ģçľĭ", + "çĶŁäº§ èĥ½åĬĽ", + "åŁºæľ¬ä¸Ĭ éĥ½", + "æīĵ æī°", + "åĪĿ 次", + "åĩº 示", + "åħ¶ä¸Ń ä¸Ģ个", + "çĶŁæĢģ ç³»ç»Ł", + "æīĭ æİĮ", + "æµİåįĹ å¸Ĥ", + "åľĭ åħ§", + "æŃ£ åĢ¼", + "å¹¾ ä¹İ", + "æݨèįIJ éĺħ读", + "è¿Ń 代", + "è°ĥ ä¾ĥ", + "饮 åĵģ", + "å¢Ļ ä½ĵ", + "åıĺ çİ°", + "äºĨ 好", + "äºĨ好 åĩł", + "ä¸į çķĻ", + "çĪ ²", + "å°½ æĹ©", + "æŃ£åľ¨ è¿Ľè¡Į", + "åĩº éĻ¢", + "æĿĢ 害", + "æıIJ 款", + "åıijå±ķ 空éĹ´", + "åīį 身", + "ä¸įæĸŃ å¢ŀ强", + "æ·± å±Ĥ次", + "容 纳", + "éĤ£ 份", + "å·¥ä½ľ æķĪçİĩ", + "æľ¬ åĽ½", + "失 èIJ½", + "æŃ£ åĽłä¸º", + "èĬĤ æ°´", + "ä¸ĭ ä¸Ģ代", + "çłĶåıij ä¸Ńå¿ĥ", + "ä¸į çIJĨ", + "å®Į 好", + "ä¿ĿæĬ¤ åĮº", + "ç»ĵæŀĦ è°ĥæķ´", + "å¥ł å®ļ", + "宣 称", + "éĺ» æĮ¡", + "æĴ¤ 离", + "ä¸į æĸ¹ä¾¿", + "åĴ ķ", + "ç¬ijäºĨ ç¬ij", + "çݯå¢ĥ 污æŁĵ", + "ä½ı æĪ·", + "ç»Ŀ ç¼ĺ", + "éĻ¤ å°ĺ", + "é«ĺ å°ļ", + "æĢİä¹Ī åı¯èĥ½", + "éĿ¢ èī²", + "åķĨ æ¥Ń", + "çĸ ¹", + "èµĦæºIJ ä¼ĺåĬ¿", + "è¾ĸåĮº åĨħ", + "èĢĢ çľ¼", + "æij§ æ¯ģ", + "ä¸ĸçķĮ ç»ıæµİ", + "å¼ķ æĿ¥", + "ä¸Ģ åĪĻ", + "æĭĩ æĮĩ", + "æĬµ 御", + "éĽ į", + "åĩĨå¤ĩ å·¥ä½ľ", + "çıł ä¸īè§Ĵ", + "ç¨Ģ åľŁ", + "èİ·å¾Ĺ æĦŁ", + "æĪIJåĬŁ çİĩ", + "ç½ij 约", + "ç½ij约 车", + "èĦ IJ", + "æķ¬ ä¸ļ", + "éĩij ä»·", + "ç²¾ é«ĵ", + "ä¹° 车", + "åħ³ åı£", + "åĨį å¤ļ", + "æŀģ åĵģ", + "åIJĦ 家", + "举æĬ¥ çĶµè¯Ŀ", + "èļ Ĭ", + "æĸ¹ å½¢", + "ç§ijæĬĢ æĪIJæŀľ", + "æľĢ好 æĺ¯", + "éĹ® åĢĻ", + "红 éħĴ", + "åĽĽ ç§į", + "ç¿Ĵ æħ", + "ç¿Ĵæħ £", + "åŀ ¦", + "éĤ£ åıª", + "é¢Ĩ æĤŁ", + "çľ¼ éĥ¨", + "æ³° å®ī", + "ä»» æľŁ", + "磨 æįŁ", + "æĽ¿ æį¢", + "åħ¸ 礼", + "符åIJĪ æĿ¡ä»¶", + "è¿ĺæľī ä»Ģä¹Ī", + "åħ±äº« åįķ车", + "åı¯ åĪĨ为", + "åŃ£ åIJİ", + "åŃ£åIJİ èµĽ", + "举èİŀ å¸Ĥ", + "å¿ĥ æĦı", + "æīŃ æĽ²", + "ä½ľä¸º ä¸Ģç§į", + "è¿Ļ éĥ¨åĪĨ", + "åıĤä¸İ åĪ°", + "ç½ij çIJĥ", + "實 çı¾", + "ç»Ħ è£ħ", + "åIJij å¤ĸ", + "å·¥ä½ľ æĸ¹æ¡Ī", + "åįģ æĿ¡", + "課 ç¨ĭ", + "颤 æĬĸ", + "åĵ ©", + "éĤ® å¯Ħ", + "äº ¢", + "åħį è²»", + "ç§ ¤", + "åºĶæĢ¥ 管çIJĨ", + "åĽĽ äºĶ", + "éºĴ éºŁ", + "å¾Ĵ æŃ¥", + "è¨ĺ å¾Ĺ", + "çĴ IJ", + "æĺ¯åIJ¦ ä¼ļ", + "æĦıè§ģ åıįé¦Ī", + "éļ¾ æĢª", + "çª į", + "交 æİ¥", + "两 åįĥ", + "æĩī çĶ¨", + "æľŁ éĸĵ", + "æIJ¬ åĪ°", + "è®® é¢ĺ", + "碧 æ¡Ĥ", + "碧æ¡Ĥ åĽŃ", + "åģļ çĶŁæĦı", + "éĻĽ ä¸ĭ", + "è· ĭ", + "èĢģ人 家", + "带 åĽŀ", + "æŀ¸ æĿŀ", + "è¡Į éķ¿", + "åĨħ容 ç®Ģä»ĭ", + "æ¢ ¢", + "æĮĩ æݧ", + "éĩį çĹĩ", + "ç½ijåıĭ 们", + "çı¾ 代", + "ç±» 产åĵģ", + "å¥Ķ æ³¢", + "æ¸ º", + "ç²ī ç¢İ", + "è¿Ļ åıªæĺ¯", + "æ£Ģå¯Ł æľºåħ³", + "é½ Ĭ", + "æĪ¿ 秣", + "å¾· æĭī", + "å²ģ 以ä¸Ĭ", + "纯 åĩĢ", + "åĪĨå¸ĥ åľ¨", + "èĥ½ å¾ĹåĪ°", + "ä¸į å°½", + "ç«ŀ ä»·", + "çļĦ 带é¢Ĩ", + "çļĦ带é¢Ĩ ä¸ĭ", + "ä¸Ńèį¯ æĿIJ", + "æĿij éķĩ", + "ä¸įåı¯ éģ¿åħį", + "éľ² 天", + "å°ı å§ijå¨ĺ", + "çī© 件", + "èijĹä½ľ æĿĥ", + "æĭĺ çķĻ", + "éĥ½ è§īå¾Ĺ", + "æĽ² æĬĺ", + "æ·»åĬł åīĤ", + "åı¬ åĽŀ", + "æīİå®ŀ æİ¨è¿Ľ", + "æĬĦ è¢Ń", + "åĮĸ 身", + "缴 èIJ¥", + "ä¹Ł å¸ĮæľĽ", + "èį£èªī 称åı·", + "åįĸ ç»Ļ", + "æľī ä¸įåIJĮçļĦ", + "å¥ĩ çī¹", + "éĥ½ 认为", + "å¦ ŀ", + "æĪIJéķ¿ 为", + "辩 æĬ¤", + "主 æķĻç»ĥ", + "æ³ķå¸Ī èģĮä¸ļ", + "æ¤į åħ¥", + "ç´¢ å°¼", + "åIJ¬ è¿ĩ", + "ä¹łæĥ¯ äºĨ", + "夺 åıĸ", + "éŁ ĵ", + "æľ¬è´¨ ä¸Ĭ", + "æİ¥ åĬĽ", + "äºij 端", + "è¦ģ åģļ好", + "è·¯ çģ¯", + "åįıåIJĮ åıijå±ķ", + "æľī å¾ħ", + "æ°´ åŁŁ", + "æIJľçĭIJ é¦ĸ页", + "è´¨éĩı å®īåħ¨", + "åįģäºĮ äºĶ", + "åĵ® åĸĺ", + "èĵ¬åĭĥ åıijå±ķ", + "åIJį 声", + "身 亡", + "çİĭ åºľ", + "åİŁåĪĻ ä¸Ĭ", + "çĥĺ å¹²", + "éģĹ æ¼ı", + "éĿ¢ 缮", + "åĽ½ ä¼ļ", + "ä¸Ģ缴 éĥ½æĺ¯", + "æľīä¸Ģ ä½į", + "éħį æľī", + "éĻª çĿĢ", + "ä¼ģ åĽ¾", + "æĮī ä¸ĭ", + "èĵĿ åĽ¾", + "æ© ĺ", + "大å¤ļ æĺ¯", + "辩 论", + "æĹĭ å¾ĭ", + "æĬ¥ éĢģ", + "æĿ¡ è§Ħå®ļ", + "åĬ¨ éĿĻ", + "åĮĪ 奴", + "æĭľ 访", + "ä¸Ģ åĪĢ", + "ä»ĸ çŁ¥éģĵ", + "主 æĿĥ", + "ä»ĸ æĽ¾", + "æĴŃ ç§į", + "å£ģ åŀĴ", + "çī¢è®° 使åij½", + "åľ¨è¿Ļ æĸ¹éĿ¢", + "æīĭ èħķ", + "æĶ¯ æŀ¶", + "ä¾Ĩ èĩª", + "éĩį å¡ij", + "å¤ļ å±Ĥ次", + "ä»ĭ è´¨", + "éĿ¢ åŃĶ", + "æ½® 湿", + "åİ¿ åŁŁ", + "游æĪı å½ĵä¸Ń", + "å£ ŀ", + "åĪĹ åĩº", + "èµĽ åĮº", + "å¤ļ åįĬ", + "éĩįçĤ¹ å·¥ä½ľ", + "æĪij们 å¿ħé¡»", + "æŁı æŀĹ", + "é²ģ èĥ½", + "æĸ½ å±ķ", + "åIJĦ åĮº", + "åħį ç¨İ", + "èµĽ åIJİ", + "æľĢ éĩįè¦ģ", + "ä¸Ģ个 好çļĦ", + "è¿Ŀæ³ķ è¿Ŀè§Ħ", + "äºĨ解 æĽ´å¤ļ", + "æķ¬ 请", + "ç¬ijçĿĢ 说", + "ä¸įæĸŃ åıijå±ķ", + "æijĦå½± å¸Ī", + "以 éĺ²", + "çĤ¸ å¼¹", + "声 åĵį", + "ç¤ ģ", + "æĩ ¿", + "èĪĨ æĥħ", + "èĩªçĶ± è´¸æĺĵ", + "æķı æį·", + "ä¸ī大 éĺ¶æ®µ", + "èĭ Ķ", + "æĹº åŃ£", + "ä¸į 满æĦı", + "微信 åı·", + "ä¿® 为", + "çł´ è£Ĥ", + "éĢĥ 离", + "æ¯ı èĤ¡", + "è¾¾ ä¸įåĪ°", + "æ¯ıå¹´ éĥ½", + "çģ¯ 笼", + "æѤ åŁºç¡Ģä¸Ĭ", + "åĥı 个", + "åĪĨ 娩", + "æĻ ¾", + "ä¸į èĩ³äºİ", + "红 线", + "误 解", + "举 è·¯", + "æ·® å®ī", + "产 åѦ", + "产åѦ çłĶ", + "èī¾ æ»ĭ", + "èī¾æ»ĭ çĹħ", + "åīįæıIJ æĺ¯", + "æ¯ı ä¸Ģ天", + "ä¸ĥ 大", + "æłij åı¶", + "èµ° å¾Ĺ", + "è¿Ļ 两ç§į", + "æİı åĩº", + "æİ IJ", + "é¢Ĩ导 èĢħ", + "ä¸Ģ æľµ", + "个å¤ļ æľĪ", + "ä¸Ń åħ³", + "ä¸Ńåħ³ æĿij", + "课åłĤ æķĻåѦ", + "大 åĴĸ", + "éģĭ çĶ¨", + "è¯ļ æĦı", + "ç»Ħ åĽ¾", + "è¯ķ çĿĢ", + "ä¹Ķ æ²»", + "è¿ĺ ä¸įæĺ¯", + "æľī æĽ´å¥½çļĦ", + "åIJİ å¤ĩ", + "æĸ°çĶŁ åĦ¿", + "æ°Ķ è¡Ģ", + "æ²¥ éĿĴ", + "å±ı éļľ", + "æ¥Ń åĭĻ", + "æĪij 以为", + "éķ¿ 缸", + "èĢģ çĪ¸", + "éķĩ æ±Ł", + "æľºæ¢° 设å¤ĩ", + "ä½Ĩæĺ¯ å¦Ĥæŀľ", + "åĿļå®ļ ä¸į", + "åĿļå®ļä¸į 移", + "åĨ² éĶĭ", + "ç®Ģ缴 æĺ¯", + "åĤ¨ èĵĦ", + "纯 çĶµåĬ¨", + "漫 æŃ¥", + "举 èµ·", + "æģ¶ æĢ§", + "è¨ĺ éĮĦ", + "èģĮèĥ½ éĥ¨éŨ", + "åħ¨ éķ¿", + "鼻 è¦ĸ", + "ä¹³ èħº", + "ä½ķ å¤Ħ", + "æ¶Ī æŀģ", + "æŃ£ å¤Ħäºİ", + "å®ī å®ģ", + "æĪIJ éķ·", + "åıĻ è¿°", + "æºĥ çĸ¡", + "ä½Ĩ çİ°åľ¨", + "女 æĺŁ", + "å©´ å¹¼åĦ¿", + "æĬķ èŀįèµĦ", + "éĹ® éĹ®", + "æıŃ å¼Ģ", + "è¯ ı", + "åIJį å½ķ", + "èĺij èıĩ", + "åIJĬ 顶", + "æ¹ĸ åĮº", + "åįĸ åľº", + "建 ç¯", + "å»ºç¯ ī", + "èİ ½", + "åIJ¬ åIJ¬", + "ç«ŀäºī ä¼ĺåĬ¿", + "åĩº ä»»", + "æľī 两ç§į", + "橱 æŁľ", + "è¤ ª", + "è¯ķ åį·", + "ç»ıæµİ æĬĢæľ¯", + "æ·± å±Ĥ", + "éĩįè¦ģ åĨħ容", + "é£İ æݧ", + "çĬ¶æĢģ ä¸ĭ", + "éĥ¨ éĸĢ", + "广 æ±½", + "è§Ĥ æij©", + "éģĹ çķĻ", + "转 è´¦", + "æĮģ ä»ĵ", + "æĢ» 计", + "åľĺ éļĬ", + "æĪ¿ 举", + "éĺĢ éŨ", + "åħ¬ åħ³", + "åħ³ åĪĩ", + "èĤ ĺ", + "æķ¸ æĵļ", + "ä¸ī åįģå¹´", + "è§ģè¯ģ äºĨ", + "å± Ĩ", + "çģ° å°ĺ", + "æ¦ľ é¦ĸ", + "è¦ĨçĽĸ çİĩ", + "ä»Ļ 女", + "çĶŁäº§ æĢ»", + "çĶŁäº§æĢ» åĢ¼", + "æĪ¿ è´·", + "æ±Ł åĮº", + "åħħçĶµ æ¡©", + "çĻ¾ åIJĪ", + "確 èªį", + "转 移åĪ°", + "éĥ½ æĹłæ³ķ", + "纪念 é¦Ĩ", + "çŃ¾ç½² äºĨ", + "并ä¸į å¤ļ", + "æĮ ł", + "ä¸į太 好", + "ä¸ĸ 代", + "误 导", + "é«ĺå³° 论åĿĽ", + "åħ¼ 容", + "龸 æ°Ķ", + "æĿ¥ 访", + "æīĢ 带æĿ¥çļĦ", + "æĺ¯ä¸Ģ éĥ¨", + "æĻļ é¥Ń", + "åİĨ 代", + "åIJ¦ åīĩ", + "ä¹ħ ä¹ħ", + "æľīæķĪ æľŁ", + "诱 åıij", + "æĢ» èµĦ产", + "æľ¬èº« å°±æĺ¯", + "çĶŁäº§ åİĤ家", + "æŶ 髦", + "èĢIJ çĶ¨", + "ä»İå°ı å°±", + "æĿ¡ 约", + "èĭ± åĭĩ", + "ä¿Ĺ è¯Ŀ说", + "寺 åºĻ", + "å¿ĥçIJĨ åģ¥åº·", + "ä»Ģä¹Ī äºĭæĥħ", + "æ±ī åŃĹ", + "çķĻ ä½ı", + "åįĹ è·¯", + "ä¸ī 项", + "丢 äºĨ", + "æĥ³ åĪ°äºĨ", + "çŃ¹ éĽĨ", + "éĻĦåĬł åĢ¼", + "西 è£ħ", + "ä¹ĭ ä½ľ", + "åģļçļĦ äºĭ", + "çķ¶ æĤ¨", + "çķ¶æĤ¨ åľ¨", + "é¦ĸ 款", + "ä¸įåľ¨ ä¹İ", + "å·¥ç¨ĭ æĸ½å·¥", + "éļIJ éļIJ", + "åıĺ 身", + "沿 éĢĶ", + "æĤł æĤł", + "ä¿Ŀ æļĸ", + "çĶŁæ´» åŀĥåľ¾", + "渤 æµ·", + "æѦ ä¾ł", + "女 主è§Ĵ", + "举 ä¾ĭ", + "æ ·¨", + "çĻ½ é¢Ĩ", + "è£Ļ åŃIJ", + "è¿Ķ è¿ĺ", + "è¿Ī åĩº", + "é¾Ļ éŨ", + "ç»ıæµİ ä½ĵ", + "æĶ¶ å®ĺ", + "çķĮ éĻIJ", + "è·³ åĩº", + "åįĩ åĢ¼", + "绵 éĺ³", + "çĸ¤ çĹķ", + "çľĭ æ¸ħ", + "æĭĴ çµķ", + "è¥Ħ éĺ³", + "课 å¤ĸ", + "åŃIJ åŃĻ", + "æŃĮ è¯į", + "æĪIJ åIJį", + "溶 液", + "åĦĴ 家", + "åķĨä¸ļ åĮĸ", + "辨 åĪ«", + "å¤ļ è¾¾", + "ç½ij åºĹ", + "ä¹Ŀ 大", + "ä¹Ŀ大 ç²¾ç¥ŀ", + "æѤ 举", + "è¿ŀ è½½", + "ä¸Ģ åĢĭ人", + "èī² æ³½", + "涵çĽĸ äºĨ", + "è¦ı åĬĥ", + "åĽ½ æĥħ", + "åį«çĶŁ åģ¥åº·", + "积æŀģ åĵįåºĶ", + "æĭ Ļ", + "åĪ¶ åĬ¨", + "æĥ³è±¡ åĬĽ", + "çļĦ ä¹IJ趣", + "å¼łå®¶ çķĮ", + "å´ İ", + "éĩį åŀĭ", + "å¤ĸ å¢Ļ", + "æĶ¾ åѦ", + "è®¤çľŁ åŃ¦ä¹ł", + "è´¬ åĢ¼", + "æ³ķ æ¡Ī", + "æĬ¤èĤ¤ åĵģ", + "éĻ·åħ¥ äºĨ", + "请 æĤ¨", + "åŀ ¢", + "æķĻèĤ² èµĦæºIJ", + "交æĺĵ å¹³åı°", + "æŶ è£ħ", + "ä¼łæŁĵ çĹħ", + "æ¹ĸ æ³Ĭ", + "èµĦ 管", + "åݨ å¸Ī", + "éĹľ éį", + "éĹľéį µ", + "åĵĪåĵĪ åĵĪ", + "çĽĹ çªĥ", + "çĶľ ç¾İ", + "åºĦ åĽŃ", + "缮åīį å·²ç»ı", + "è¾¹ ä¸Ĭ", + "çģ« èĬ±", + "æĬ¥ è®°èĢħ", + "æģĭ æĥħ", + "ç´§ åĩij", + "æ°´ æµģ", + "è¿Ļæĺ¯ æĪij们", + "æ³¥ åľŁ", + "æĽ¾ ä»»", + "æĸ¹ è¨Ģ", + "åij¨ åħŃ", + "åı· 楼", + "ä¼ij åģĩ", + "误 ä¼ļ", + "åĽ½ åĢº", + "åīį å¤ķ", + "两 å¼ł", + "éĹ «", + "éŃĶ 鬼", + "æĬĬ æĮģ", + "èĬĤèĥ½ çݯä¿Ŀ", + "æ¸ħæ´ģ èĥ½æºIJ", + "èĤ¥ æĸĻ", + "é«ĺ é¢ij", + "å°± æľīäºĨ", + "交 ä¼ļ", + "没 éĴ±", + "éĽħ æĢĿ", + "è¦ģ åıĬæŶ", + "åŁ¹åħ» åѦçĶŁ", + "欣 åĸľ", + "çĥŃæ°´ åĻ¨", + "é¾Ļ æ¹ĸ", + "äºĮ 楼", + "æĸ°æµª è´¢ç»ı", + "æĸ° åĬ¨èĥ½", + "èµ£ å·ŀ", + "æĭ³ 头", + "æµģ åIJij", + "ä¹Łæĺ¯ å¾Ī", + "åıij åĶ®", + "ä¸Ń åIJ«æľī", + "åIJĵ å¾Ĺ", + "å·¨ æĺŁ", + "æĹł æīĢè°ĵ", + "æ¯Ľ åŃĶ", + "åħ¬åħ± 交éĢļ", + "çĤİ çĥŃ", + "èµ· èįī", + "åĬłçĽŁ åķĨ", + "说 ä¸įåĩº", + "大åѦ æ¯ķä¸ļ", + "å·¥ä¸ļ åĽŃ", + "éłĺ åŁŁ", + "åºĨ åħ¸", + "æµģ 产", + "èģ² éŁ³", + "ä¼¼ä¹İ æĺ¯", + "è´§ æºIJ", + "æ·± åĪĩ", + "æ²»çĸĹ æĸ¹æ³ķ", + "èµĦæºIJ éħįç½®", + "網 åıĭ", + "çĶ £", + "äº ¥", + "躲 åľ¨", + "社 ç§ij", + "è»Ł é«Ķ", + "女 è£ħ", + "æŃ¡ è¿İ", + "综åIJĪ å®ŀåĬĽ", + "æł¼ å°ĩ", + "åħļåı² åŃ¦ä¹ł", + "æľĢ åŁºæľ¬", + "æľĢåŁºæľ¬ çļĦ", + "çľĭ æľĽ", + "åıĹ è´¿", + "ä¸įä»ħ èĥ½", + "ä½ķ å¿ħ", + "ä¸Ģ个 å°ıæŶ", + "ç¾ Į", + "æĭĽ æĶ¶", + "çĤĴ èĤ¡", + "æĿij å¹²éĥ¨", + "缸 çĪ±", + "æ½ľ èĥ½", + "ä¹ į", + "æŶ è¾°", + "欣 æħ°", + "éĵ¶ è¡Įä¸ļ", + "çĭŃ çªĦ", + "éĩįçĤ¹ é¢ĨåŁŁ", + "çİ°å®ŀ çĶŁæ´»", + "éĮ¯ 誤", + "æĸ° è§Ħ", + "滥 çĶ¨", + "æŶ ä¸į", + "æŶä¸į æŶ", + "帳 èĻŁ", + "ç¨Ģ 缺", + "åIJij 举", + "ä¿Ŀåģ¥ åĵģ", + "çıŃ éķ¿", + "äºĴ åĭķ", + "笼 罩", + "æ½ Ľ", + "æļĸ å¿ĥ", + "è½° çĤ¸", + "åºĨ 幸", + "è²Į ä¼¼", + "æĵ º", + "èĢIJ 磨", + "ä¸ĵä¸ļ 人士", + "ä¸ĢèĪ¬ éĥ½æĺ¯", + "æ¼³ å·ŀ", + "åħ¨ èĩªåĬ¨", + "å½ķ çĶ¨", + "大 è·Į", + "æľīæķĪ æĢ§", + "èĩª åĭķ", + "ä¸ī个 æĸ¹éĿ¢", + "港 åĮº", + "ä¿¡ 貸", + "éĢļ è¯Ŀ", + "é«ĺ 涨", + "æ³Ħ æ¼ı", + "éħį ä¸Ĭ", + "åħļ å·¥å§Ķ", + "被 认为", + "被认为 æĺ¯", + "ä¸įä¼ļ åĨį", + "è°ĥ åīĤ", + "åıĤ èĤ¡", + "èĦ± åıij", + "å¿ł å®ŀ", + "åĨħ åĪĨæ³Į", + "ç¹ģ å¿Ļ", + "åıĮ åĪĽ", + "é©» æĿij", + "åĪĴ ç®Ĺ", + "éģİ ä¾Ĩ", + "åľ£ ç»ı", + "èıľ 鸣", + "æĭ¼ å¤ļå¤ļ", + "ä¸ŃåĽ½ 汽车", + "çĥŁ èįī", + "缴 æµģ", + "äºĨä¸Ģ åı£æ°Ķ", + "ä½İ æĪIJæľ¬", + "æī¾ åĽŀ", + "èĩª åįij", + "總 æĺ¯", + "æĸĩåĮĸ åĪĽæĦı", + "天 æ²³", + "樱 æ¡ĥ", + "éªij åħµ", + "éĩĮéĿ¢ æľī", + "çİ ®", + "èĥ½ æī¾åĪ°", + "éĢĥ è·ij", + "åĪĩ å°Ķ", + "åĪĩå°Ķ 西", + "以ä¸ĭ æĺ¯", + "å²³ éĺ³", + "çļĦ æ¦Ĥçİĩ", + "æĬµ åĪ¶", + "å¸Ī äºĭåĬ¡", + "å¸ĪäºĭåĬ¡ æīĢ", + "åĩĨ æŶ", + "屬 æĸ¼", + "订 è´Ń", + "åįłæį® äºĨ", + "ä¸Ń éĢĶ", + "å° ĭ", + "é»ij 马", + "åİ¿ åħ¬å®īå±Ģ", + "ä¸ĥ æľĪ", + "èī² ç´ł", + "å¿ĥèĦı çĹħ", + "æŶ éĻIJ", + "æ¯į åħ¬åı¸", + "å¹ķ åIJİ", + "ä¸Ĭ æ¦ľ", + "åĢ¾åIJij äºİ", + "纸 ä¸Ĭ", + "æ¡ ĵ", + "éĽĨä½ĵ ç»ıæµİ", + "æĥħ å¢ĥ", + "è¦ģ åģļåĪ°", + "ç©į 極", + "åıª æĢķ", + "æ¹ĺ 西", + "çļ± 纹", + "åħ¨ åľĭ", + "çĦ¡ è«ĸ", + "好 æĦŁ", + "åįķ ä»·", + "è¿Ľç¨ĭ ä¸Ń", + "æĺĨ ä»ij", + "åĪĽ 客", + "åħħ æĸ¥", + "åħĪ æĬĬ", + "该 æĢİä¹ĪåĬŀ", + "åĵģ å¾·", + "åħ¨éĿ¢ åıijå±ķ", + "è¨Ī åĬĥ", + "æĢ» å·¥ä¼ļ", + "ä½Ľå±± å¸Ĥ", + "æĬĹ è¡¡", + "å¼Ģ åľº", + "éĴ± å¸ģ", + "åıĭ 们", + "å«ī å¦Ĵ", + "ç´¢ èµĶ", + "è®Ĭ åĮĸ", + "æĮ¤ åİĭ", + "æĮij è¡ħ", + "çŃī ä¸Ģæī¹", + "æĿ¨ 欢", + "ä¸ĵ家 åѦèĢħ", + "èĥ½ è¾¾åĪ°", + "èµ° è¿ij", + "è´«åĽ° åľ°åĮº", + "éĻIJ æľŁ", + "ä¸į 平衡", + "åĽ½åĨħ å¸Ĥåľº", + "èµĽ åľº", + "éħį èµĦ", + "è¦ģ èĢĥèĻij", + "ä¸ĩ åı°", + "æľĪ æľ«", + "éĶ ¥", + "åŃ «", + "æݥ触 åĪ°", + "åĩº 产", + "æķĻ åѸ", + "ä½ľ å¼Ĭ", + "çļĦ æľĢåIJİä¸Ģ", + "ä¿ĥ æĪIJ", + "åIJ¸ åıĸ", + "æ½ľ èīĩ", + "被 éªĹ", + "è¾ĵ äºĨ", + "çĭIJ çĭ¸", + "åįĩ éĻį", + "è¿ĻäºĽ ä¸ľè¥¿", + "æĬķèµĦ åŁºéĩij", + "çĶŁçī© åѦ", + "ç½ij绾 èIJ¥éĶĢ", + "åIJij è®°èĢħ", + "èįī åľ°", + "æĢ ¯", + "æľįåĬ¡ èĥ½åĬĽ", + "éĥģ éĹ·", + "åįķ åĵģ", + "å¾Ĺ 罪", + "æĺĵ äºİ", + "个å¤ļ å°ıæŶ", + "éĩį ä»»", + "ä¸Ĭ å®ĺ", + "æľ¬ éĩij", + "çı¾ åł´", + "溢 ä»·", + "æĺŁ è¾°", + "æ´»åĬ¨ çİ°åľº", + "丹 麦", + "å¸Ŀ çİĭ", + "æŁ¥ æĺİ", + "åŃĺåľ¨ äºİ", + "é¦Ļ æ°´", + "æĬ½ æ£Ģ", + "å®ŀéĻħä¸Ĭ æĺ¯", + "æĸ° å¾ģç¨ĭ", + "è´¢åĬ¡ 管çIJĨ", + "æİ Ľ", + "åĨľ åİĨ", + "éĥ½ èĥ½å¤Ł", + "éĤ¯ éĥ¸", + "羣 實", + "ç» Ĭ", + "åĨµ ä¸Ķ", + "ç½® 身", + "ç¥Ī 祷", + "çĿģ å¼Ģ", + "æĮĩ çĤ¹", + "å¼Ģ æľº", + "西 å®ģ", + "åĮĹ çº¦", + "积 æ°´", + "åĩº åĬ¨", + "åıijå±ķ 模å¼ı", + "转 æĬĺ", + "èĢĥ çĤ¹", + "æľī ç½ijåıĭ", + "è´«åĽ° æĿij", + "æĪij们 çŁ¥éģĵ", + "åĪĨ éĶĢ", + "å±± èĦī", + "æ¯Ķ æĭŁ", + "ä¼° ç®Ĺ", + "æĶ¹ 建", + "壮 è§Ĥ", + "ç§ī æĮģ", + "æı ª", + "ç¦ Ģ", + "åĮĸåѦ åĵģ", + "ä¸ŃåĽ½ åĪ¶éĢł", + "ä¸Ģ æŀ¶", + "æīį è¡Į", + "æĭĽ å¾ħ", + "åıĺ æį¢", + "åīį 线", + "幸 好", + "è¿Ļæł· çļĦè¯Ŀ", + "å¿ĥ è¡Ģ管", + "æĢ§ çĸ¾çĹħ", + "åħ¨ èĥ½", + "åĪij 侦", + "ä¿¡æģ¯ åıijå¸ĥ", + "æĺ¾ çĦ¶æĺ¯", + "éĿĴ éĵľ", + "åIJĥ ä»Ģä¹Ī", + "çĶµ ä»·", + "æ³ķå¾ĭ è§Ħå®ļ", + "çħ ²", + "çĵ· åĻ¨", + "èĤī ç±»", + "æıĴ åħ¥", + "åĹ ľ", + "è¿Ł è¿Ł", + "ä¸ĢçĤ¹ éĥ½ä¸į", + "è¿ĺ åĮħæĭ¬", + "èĪį ä¸įå¾Ĺ", + "æłĩå¿Ĺ æĢ§", + "æľĪ 以æĿ¥", + "ç³ĸ æŀľ", + "éĥ½ åºĶ该", + "çݯå¢ĥ åį«çĶŁ", + "èĪª è¡Į", + "éĥij éĩį", + "ç½ij æĬķ", + "åįģ ä½³", + "ç§ģ ä¸ĭ", + "æļ´ è·Į", + "åĬłå¿« åıijå±ķ", + "产åĵģ çłĶåıij", + "åĪĽéĢł åĩº", + "æĢ» è§īå¾Ĺ", + "åºķ çĽĺ", + "èķ Ĭ", + "åĩºå¸Ń ä¼ļè®®", + "主 æĿ¿", + "æĹ¥æĻļ éĹ´", + "å®ĺæĸ¹ å¾®åįļ", + "å¼ķçĶ¨ æĹ¥æľŁ", + "åī¯ æķĻæİĪ", + "çĶµåŃIJ 产åĵģ", + "è¡° éĢĢ", + "çķĻ åŃĺ", + "çģ« åĬĽ", + "çĴ §", + "çļ Ĥ", + "åħ¼ åħ·", + "éĩį è¿Ķ", + "é¢Ĩ çķ¥", + "åĪĩ éĻ¤", + "åĨįçĶŁ èĥ½æºIJ", + "å®ŀåľ¨ 太", + "çIJĨ论 ä¸Ĭ", + "ä¸ī å±Ĥ", + "ä¸ĸçķĮ åIJĦåĽ½", + "å®ľ æĺĮ", + "èĢ³ è¾¹", + "宽 æķŀ", + "æ±ī æĹı", + "çĻ½ çĻ½", + "è¿ĻéĩĮ éĿ¢", + "çĶŁæ´» ä¹łæĥ¯", + "èµŀ èµı", + "çĶ· 士", + "ä¸Ń ä¿Ħ", + "车 祸", + "åīĤ éĩı", + "éĻ¤ åİ»", + "å·¦ è¾¹", + "çŃij çī¢", + "çīĽ å¸Ĥ", + "家 åĬ¡", + "åķ ĥ", + "ç½® æį¢", + "ç´« å¤ĸ", + "ç´«å¤ĸ 线", + "å¾Ģ åīį", + "åĬĽ åѦ", + "ç´§ è·Ł", + "缮çļĦ åľ¨äºİ", + "ç» ®", + "ç¥ Ĥ", + "宣 è¨Ģ", + "äºĮ æ°§åĮĸ", + "äºĮæ°§åĮĸ 碳", + "æĹł ç¼ĺ", + "ç²¾ éĢļ", + "è¨ º", + "å¼ķåıij äºĨ", + "æľĢ åħĪ", + "æ´¾ é©»", + "ä¸į å¿į", + "æĪij çĪ¸", + "å¹´ ä¸ĭåįĬå¹´", + "æ·ĭ å·´", + "没 éĹ®é¢ĺ", + "åºĹ åĨħ", + "è·Ł æĪij说", + "çĶŁäº§ çĶŁæ´»", + "è§Ĥ æľĽ", + "æ¸ į", + "被 æī§è¡Į", + "被æī§è¡Į 人", + "èĪ ľ", + "æİ º", + "ä¸Ģ ç§Ĵ", + "èįī åĿª", + "åij¼ åĴĮ", + "åij¼åĴĮ 浩", + "åij¼åĴĮ浩 çī¹", + "人æ°ij éĵ¶è¡Į", + "çĦķ åıij", + "è¯ģåĪ¸ 交æĺĵ", + "çķ Ķ", + "æľº èĥ½", + "å¦ ¾", + "æĻļ å¹´", + "å·¥åķĨ èģĶ", + "åİŁ åŀĭ", + "è§Ĵ度 çľĭ", + "æĬ¥ 社", + "è¯į æĿ¡", + "躲 éģ¿", + "éĩį åIJ¯", + "å¤ķ éĺ³", + "èĤ¡æĿĥ 转让", + "åľ¨ ä¸Ģ", + "åľ¨ä¸Ģ æĹģ", + "社ä¼ļ åĮĸ", + "åıijå±ķ åİĨç¨ĭ", + "æĭĸ æ¬ł", + "使 èĢħ", + "ä¸İ åIJ¦", + "æĸ° å±ĢéĿ¢", + "ä»Ĭ天 æĪij们", + "é½IJ èģļ", + "对 æĪij说", + "éĢĴ 交", + "æľª æĽ¾", + "èİ Ĭ", + "éĸ ī", + "亲 æīĭ", + "è§Ĵ éĢIJ", + "æľī é»ŀ", + "ç¨İ çİĩ", + "ä½İ 声", + "é»ĺ å¥ij", + "æĻ® æ³ķ", + "大 ä¸ĵ", + "第äºĮ 大", + "ä½ı åĿĢ", + "æĶ¾ è¿Ľ", + "äºĮ æĪĺ", + "亲 身", + "åĽº åĮĸ", + "ä¸ĭ 乡", + "åħ³éĶ® æĬĢæľ¯", + "åĽŀ æĥ³", + "æĬ¥ åĪĬ", + "æ¶Ĥ æĬ¹", + "èĹı çĿĢ", + "ç¥Ŀ æĦ¿", + "åįĩ 温", + "çĶļèĩ³ è¿ŀ", + "åħ¬åħĥ åīį", + "ç¾İ æĸ¹", + "è¯ļ å®ŀ", + "æĹł åģ¿", + "åīµ æ¥Ń", + "å°ıå¿ĥ 翼", + "å°ıå¿ĥ翼 翼", + "两 æīĭ", + "温馨 æıIJ示", + "仿 羣", + "æĥ ¶", + "èĥ¡ åŃIJ", + "å·¥ä½ľ ç«Ļ", + "硬 çĽĺ", + "ç« ¿", + "åĤ³ éĢģ", + "åħ¨ æł¡", + "é²ľ æ´»", + "çĴĢ çĴ¨", + "ç»ĵ å°¾", + "æį¢ æĿ¥", + "æĪ Ģ", + "ä½İ ä½į", + "ä¸ĩåħĥ 以ä¸Ĭ", + "åĬł åĪĨ", + "æݨä»ĭ ä¼ļ", + "çIJĨ èµĶ", + "å¾· å°Ķ", + "æĬĹ è®®", + "æ´ ¼", + "åĸ §", + "åŁİ éĻħ", + "å¾Ī æ£Ĵ", + "人 æŃ»äº¡", + "ä¼ļå±ķ ä¸Ńå¿ĥ", + "äºĴèģĶ äºĴéĢļ", + "èĸĦ èĨľ", + "éĩį é»ŀ", + "ç¦ģ æ¯Ĵ", + "åĨ· ç¬ij", + "大家 åı¯ä»¥", + "é¦ĸ 缸", + "è¿ij è·Ŀ离", + "æµ® çİ°", + "ç§ĺ è¯Ģ", + "èµ· é£ŀ", + "æIJ ¶", + "羣 åģĩ", + "æģ ķ", + "å°ı åºĹ", + "æ°ij çľ¾", + "åıijå¸ĥ åħ¬åijĬ", + "侧 éĩį", + "å¾ĺ å¾Ĭ", + "æĢ Ķ", + "æª IJ", + "æķ° 缮", + "åī¯ ç§ĺ书éķ¿", + "两 åı¥", + "éļIJ çŀĴ", + "åıĮ åıĮ", + "æīĭ æĦŁ", + "èij¡ 京", + "éģĹ å¿ĺ", + "é¬ ¥", + "è¿Ļ个 åľ°æĸ¹", + "说 çļĦè¯Ŀ", + "å·¡ åĽŀ", + "è¿Ŀ ç«ł", + "æī¾ å·¥ä½ľ", + "æĶ¯ çIJĥéĺŁ", + "裡 éĿ¢", + "æĺ¾ç¤º åĩº", + "èĩ³ å°Ĭ", + "两 级", + "åīį 段æŶéĹ´", + "çĺ¦ èº«", + "èĤ¢ ä½ĵ", + "æ¯į 親", + "æīĭç»Ń è´¹", + "汽车 è¡Įä¸ļ", + "æİ© çĽĸ", + "æݧèĤ¡ éĽĨåĽ¢", + "åı£ å¾Ħ", + "æĶ¿çŃĸ æݪæĸ½", + "æµ· 绵", + "åħ¨ éķĩ", + "äºĭ åħ³", + "å¸Ń æī§è¡Į", + "å¸Ńæī§è¡Į å®ĺ", + "éĤ£ 次", + "åı¯èĥ½ åĩºçİ°", + "ä¸Ńå¿ĥ åŁİå¸Ĥ", + "ç¿» 身", + "ä¹Ł ç®Ĺ", + "ä¾µ çķ¥", + "åĸĩ åıŃ", + "æ¯ı次 éĥ½", + "è§ ħ", + "éĻ¢ éĻ¢éķ¿", + "å§ĭ äºİ", + "èѦ åĬ¡", + "èį¯ æĿIJ", + "å±ł æĿĢ", + "æľ¬èº« å°±", + "éļıæŶ éļı", + "éļıæŶéļı åľ°", + "åĶ® åįĸ", + "æĹłäºº 驾驶", + "é¢ ħ", + "åĵģ 質", + "åĺ² ç¬ij", + "è·ij åİ»", + "åħĭ éĩĮæĸ¯", + "çķ¸ å½¢", + "ä¿® 饰", + "磩 éĺµ", + "éŁ³ä¹IJ ä¼ļ", + "æŁ³ å·ŀ", + "é½ ¡", + "ä¼ļ è°Ī", + "æŃ£ çīĪ", + "ä¹Ł åIJĮæł·", + "æļ§ æĺ§", + "è¡ĮæĶ¿ éĥ¨éŨ", + "ä¹ĸ ä¹ĸ", + "èĤ¤ èī²", + "æŶ ä»»", + "羣 åĪĩ", + "æľĪ ä¸ĭ", + "æľĪä¸ĭ æŬ", + "举æĸ¹ è´¢å¯Į", + "è£ħä¿® åħ¬åı¸", + "éĢĢ è¿ĺ", + "åĭĺ å¯Ł", + "åĵ¥ 伦", + "åĵ¥ä¼¦ æ¯Ķäºļ", + "çĭ¬ ä¸Ģ", + "çĭ¬ä¸Ģ æĹł", + "çĭ¬ä¸ĢæĹł äºĮ", + "è°ĥ åij³", + "åİĭ è¿«", + "åħ¨çIJĥ æľĢ大", + "åī¯ æł¡éķ¿", + "æĽ´ ä½İ", + "åĪĨéĴŁ åIJİ", + "åĽŀ ä¾Ĩ", + "åĪ¶ åīĤ", + "åijĬè¯ī 大家", + "çĤ¹ éĴŁ", + "åįģä¸ī å±Ĭ", + "åij¨ åĽĽ", + "è¿Ļæł· ä¸Ģ", + "è¿Ļæł·ä¸Ģ æĿ¥", + "èĭ Ł", + "æľĽ åİ»", + "æĪIJ è¯Ń", + "å½ĵ åį³", + "ç¬ij 声", + "ä¹ĭ åĬ¿", + "åĪijäºĭ æ¡Ī件", + "æĮĤ çĿĢ", + "ä½ķ ç§į", + "å°ı 游æĪı", + "åĽ½å®¶ æĪĺçķ¥", + "åĨ· åĨ·", + "å®ľ 宾", + "æIJº ç¨ĭ", + "è¶ĭ äºİ", + "åıį çľģ", + "常 说", + "ä¸ĩ æĪ·", + "åĥµ å°¸", + "åįĥä¸ĩ åĪ«", + "åıijçİ° éĹ®é¢ĺ", + "åı¯ çŁ¥", + "éŨæĪ· ç½ijç«Ļ", + "åģ¥åº· 产ä¸ļ", + "åı³ è¾¹", + "æµ· è¿IJ", + "è¿ij ä¹İ", + "åĮ» æ²»", + "æĢ» ç®Ĺ", + "ä¸Ģ åĪĨéĴŁ", + "æĭ §", + "ä¹Ł æľīä¸ĢäºĽ", + "ä¾ĽçĶµ åħ¬åı¸", + "å»ī ä»·", + "帮 ä»ĸ", + "æŃ¤æ¬¡ æ´»åĬ¨", + "åıªèĥ½ 说", + "èĬ ĭ", + "çīĩ 段", + "åŃĺåľ¨ éĹ®é¢ĺ", + "ä½łä¼ļ åıijçİ°", + "è½® å»ĵ", + "ç½ij éĢļ", + "滨 æ±Ł", + "æİĪ ä¿¡", + "é»İ æĺİ", + "ä¸į å±ŀäºİ", + "约 åįł", + "éķ¿æ²Ļ å¸Ĥ", + "èĥļ èĥİ", + "åħĥ 件", + "éĻĨ åĨĽ", + "è³¼ è²·", + "æĮĩ æľĽ", + "å®ŀä¹ł çĶŁ", + "çī¹çĤ¹ æĺ¯", + "çıł æ±Ł", + "çľĭ ä¸įåĩº", + "ä¸įè§ģ äºĨ", + "ç¼ ī", + "éĺµ èIJ¥", + "åĶIJ æľĿ", + "没 å¿ħè¦ģ", + "åĽ½åľŁ èµĦæºIJ", + "ç»ıæµİåѦ 家", + "åIJĪèĤ¥ å¸Ĥ", + "çIJ¢ 磨", + "ç¡® åĪĩ", + "åŁİå¸Ĥ åıijå±ķ", + "çŃ· åŃIJ", + "人æ°ij æľįåĬ¡", + "满 åĪĨ", + "è¿· ä¿¡", + "ä½ľèĢħ æľ¬äºº", + "æĸĩç«ł æĿ¥æºIJ", + "ç«Ļ ç«ĭ", + "æŀĦ æĪIJäºĨ", + "è¾Ľ åĭ¤", + "è¶ħ 强", + "éĶ ļ", + "åīįä¸ī åŃ£åº¦", + "å°± è§īå¾Ĺ", + "å´ĩ é«ĺ", + "è¶Ĭ ä¾Ĩ", + "è¶Ĭä¾Ĩ è¶Ĭ", + "å¸Ĥåľº èIJ¥éĶĢ", + "综åIJĪ ç´łè´¨", + "åŃ ļ", + "ä¾® è¾±", + "äºĮ åŃĹ", + "å·¥ä½ľ ä»»åĬ¡", + "åı²ä¸Ĭ æľĢ", + "æľĢ ä¼ĺ", + "åIJ© åĴIJ", + "表 çĻ½", + "èİ« åIJį", + "èİ«åIJį åħ¶", + "èİ«åIJįåħ¶ å¦Ļ", + "å¹ £", + "åIJĮå¿Ĺ 们", + "建设 çĶ¨åľ°", + "åĦ Ģ", + "éħį åģ¶", + "å¼ ©", + "åĶ± çīĩ", + "æīĭ èĦļ", + "åħ¼ ä»»", + "åģľ æĶ¾", + "æŃ£ å®Ĺ", + "æĸ° åĨľæĿij", + "åĤ¬ çĶŁ", + "æīĢ åŃ¦æł¡", + "念 ä½Ľ", + "åĶ¤ éĨĴ", + "åħ± åĪĽ", + "æĭī ä¸ģ", + "èĥĮ çĿĢ", + "çĶŁæĢģ ä¿ĿæĬ¤", + "åı£ 头", + "æĸ¹åIJij çĽĺ", + "調 æķ´", + "æĭĽèģĺ ä¿¡æģ¯", + "åħ¶ä»ĸ åĽ½å®¶", + "ç®Ģ æĺĵ", + "åĮ¿ åIJį", + "è¯Ħ æµĭ", + "æĺ¯ä¸Ģ 座", + "çīµ æīĭ", + "足 迹", + "çIJĨ解 åĴĮ", + "æľĢ åıĹ", + "å¿ĥ è·³", + "çĪ¶ 親", + "éĿŀ常 åĸľæ¬¢", + "èĭ¦ éļ¾", + "æĬĢ å¸Ī", + "æ°ij æĦı", + "æĪĺ åĽ½", + "æĽ¿ è¡¥", + "æ´¥ è´´", + "ä¸ŃåĽ½ ä¼łç»Ł", + "åIJĦ è¡Į", + "åIJĦè¡Į åIJĦ", + "åIJĦè¡ĮåIJĦ ä¸ļ", + "第äºĶ å±Ĭ", + "èį· èĬ±", + "æĦı èŃĺ", + "票 ä»·", + "åĪĨ æµģ", + "æĿİ çĻ½", + "æ±Ł åĮĹ", + "æİĴ æĸ¥", + "ä½ĵ éĩı", + "åĮħåIJ« äºĨ", + "åĪĺ æŁIJ", + "çİ° å¦Ĥä»Ĭ", + "å·¥èīº åĵģ", + "è¿Ļç§į æĸ¹æ³ķ", + "åĬŀåħ¬ 楼", + "çĶµ å·¥", + "çħ Ļ", + "åį¡ çīĩ", + "å¹´ å¹´åºķ", + "ä¸ĵ项 èµĦéĩij", + "åĮ» ç§ij", + "åĮ»ç§ij 大åѦ", + "åĽŀ头 çľĭ", + "ä¸į å±ij", + "èĩª 驾", + "没 æĶ¶", + "æīĵ çĮİ", + "èĦ¸ éĥ¨", + "åıĥ èĢĥ", + "å°Ĩ 士", + "è´«åĽ° 人åı£", + "çIJĨæĥ³ 信念", + "é£İ å°ļ", + "人æīį éĺŁä¼į", + "çij ¾", + "æĿ¥ è¿ĻéĩĮ", + "æ´Ĺ 涤", + "å¹´ èĸª", + "èĭį çĻ½", + "ä¸ĩ äºĭ", + "课 æľ¬", + "åºĵ éĩĮ", + "çī¹ æ´¾", + "çī¹æ´¾ åijĺ", + "èµŀ ç¾İ", + "ç©¿ æĪ´", + "製 ä½ľ", + "èµŀ æĪIJ", + "ä¸Ģ 侧", + "å½ĵåľ° 人", + "æĭ İ", + "纸 è´¨", + "ä½Ļ 个", + "éĶĤ çĶµæ±ł", + "æľº åŀĭ", + "éĻ¢ éĻ¢å£«", + "åģļ å·¥", + "å¼ł è´´", + "ç¥Ľ æĸij", + "æ®ĸ æ°ij", + "å¥ij 约", + "æ¹ĺ æ½Ń", + "æIJ ĸ", + "åŃĺ è´§", + "交éĢļ 大åѦ", + "è¶ģ çĿĢ", + "æĸĩçī© ä¿ĿæĬ¤", + "å¤ĩ æĪĺ", + "éĩĩ 纳", + "åįĬ æľĪ", + "æľĢ åħ³éĶ®", + "æľĢåħ³éĶ® çļĦ", + "æİ¥ éĢģ", + "æĶ¶ åī²", + "åıį åĢĴ", + "çĥ Ľ", + "æ ½Ķ", + "ä¼Łå¤§ å¤įåħ´", + "çļĦè¯Ŀ è¯Ń", + "容 å¿į", + "å®ļ éĩı", + "æķ Ĺ", + "åĵģçīĮ 形象", + "æīŃ è½¬", + "åĽ½å®¶ éĩįçĤ¹", + "èĨĿ çĽĸ", + "ä¸Ģ 楼", + "大 éĻ¸", + "éĤª æģ¶", + "åĽŀ åij³", + "çĮ ¿", + "çĿ¡ åīį", + "æĹł è¾ľ", + "çĹħæ¯Ĵ æĦŁæŁĵ", + "æľºæ¢° åĮĸ", + "çĤ¹ 亮", + "溶 解", + "åĩłä¹İ æīĢæľī", + "è·ij éģĵ", + "çĶµè§Ĩ æľº", + "åı ¨", + "æijĩ äºĨ", + "æijĩäºĨ æijĩ头", + "èĩª è´Ł", + "综åIJĪ åĪ©çĶ¨", + "èĩª å¦Ĥ", + "åİŁ ä¾Ĩ", + "ä¹Łä¸į æĥ³", + "èĬĤ 课", + "è¿ĩ åī©", + "çĶ² çĬ¶", + "çĶ²çĬ¶ èħº", + "æĸ° ä¸ĸ纪", + "èĩªä¸» åĵģçīĮ", + "é«ĺ å±Ĥ次", + "ä¸Ģ è§Ĵ", + "è¡Į äºĭ", + "ç¥ĸ åħĪ", + "å©ļ åIJİ", + "éĹ´ éļĻ", + "ç¼Ŀ éļĻ", + "è¿Ļ æĶ¯", + "ä¸įæĸŃ åĪĽæĸ°", + "å¾® åŀĭ", + "æĽĻ åħī", + "享 çĶ¨", + "ä¸ŃåĽ½ 移åĬ¨", + "éĹŃ çݯ", + "æī§ æĦı", + "åıijå±ķ æł¼å±Ģ", + "æł¸å¿ĥ åĮº", + "éªļ æī°", + "åħļåĴĮ åĽ½å®¶", + "ä¸ŃåĽ½ æĶ¿åºľ", + "帶 èijĹ", + "ä¸ĩåįĥ çĵ¦", + "åħ© 人", + "äºİæĺ¯ æĪij", + "åĽº ä½ĵ", + "çªģ å¦Ĥ", + "çªģå¦Ĥ åħ¶", + "çªģå¦Ĥåħ¶ æĿ¥", + "éĩĮç¨ĭ ç¢ij", + "çĪ± ç¾İ", + "æŁ¥ éªĮ", + "åıĮ èµ¢", + "éĹª åħī", + "楼 å®ĩ", + "æĻ ı", + "æľī è¶³å¤ŁçļĦ", + "æŁĶ æĢ§", + "ä¿¡æģ¯ å®īåħ¨", + "管 线", + "并 ä¸įä¼ļ", + "åĻ¨ 件", + "ä½ł åºĶ该", + "çĿĢ å®ŀ", + "æĺİ æ¸ħ", + "æĬĹ çĶŁç´ł", + "æīĵ æŃ»", + "å®Įåħ¨ ä¸įåIJĮ", + "èĬ± æ¤Ĵ", + "æĶ¾ 宽", + "ä½İ 端", + "åĽĽ èĤ¢", + "åĮĹ京 èµĽè½¦", + "éĽĨ å¸Ĥ", + "æľª å©ļ", + "大å¹ħ æıIJåįĩ", + "建çŃij 设计", + "çĭ¬ æľīçļĦ", + "æİ¢ éĻ©", + "æ²³æµģ åŁŁ", + "æħķ 容", + "被 çĽĹ", + "åĵº ä¹³", + "èı ģ", + "æĥ¬ æĦı", + "è¶ĬæĿ¥è¶Ĭ 好", + "广大 群ä¼Ĺ", + "å¾· èĤ²", + "å¸Ĥåľº ä»·æł¼", + "奥 å·´", + "奥巴 马", + "èĬĤ缮 ä¸Ń", + "两 款", + "ä¸ĩä½Ļ åħĥ", + "ç»´ å°Ķ", + "çĶŁçī© ç§ijæĬĢ", + "åIJ¬ èµ·æĿ¥", + "çł ļ", + "æĭŁ å®ļ", + "æ²¹ çĶ°", + "声 èªī", + "建çŃij ä¸ļ", + "éĻIJ è´Ń", + "çīĩ åŃIJ", + "çķľ 禽", + "ç½ij é¦ĸ页", + "ä¼Ĺ çŃ¹", + "æĴŀ åĩ»", + "åīį ä¸įä¹ħ", + "åīį ä¸ĸ", + "åĽĽä¸ª æĦıè¯Ĩ", + "æµĭ ç»ĺ", + "éĺ² ç©º", + "漫éķ¿ çļĦ", + "æ²IJ æµ´", + "æ¯Ķè¾ĥ ç®Ģåįķ", + "æµĭ å®ļ", + "åĽŀ è°ĥ", + "让 人们", + "èĴĭ ä»ĭ", + "èĴĭä»ĭ çŁ³", + "ç»ĵ æĻ¶", + "å¢ŀæ·» äºĨ", + "æĿ¡ è¯Ħ论", + "åī¯ ä¼ļéķ¿", + "ä½ı æīĢ", + "ç»Ļ åĩºäºĨ", + "è°ĥ éħį", + "æ² ĸ", + "æľī çĶ¨", + "æľīçĶ¨ çļĦ", + "ä¸ĢæĿ¡ é¾Ļ", + "éĩİ å¤ĸ", + "ç¼ĺ åĪĨ", + "æ°¸è¿ľ ä¸įä¼ļ", + "æŀľ æłij", + "大åıij å¿«ä¸ī", + "麻 éĨī", + "äºij éĽĨ", + "åİ» åĵªéĩĮ", + "åħ¥ å¸Ĥ", + "ä»» æĢ§", + "建 æ¡£", + "建档 ç«ĭ", + "建档ç«ĭ åį¡", + "ä¸Ģ 棵", + "社 åįĢ", + "缸 ä¼´", + "åļ ·", + "å¡« åħħ", + "ä¸Ģ æĹı", + "ç¾ ģ", + "åıĸ è¯ģ", + "èĪ° éĺŁ", + "åİĤ åĮº", + "è¡· å¿ĥ", + "åıijå±ķ éĺ¶æ®µ", + "é«ĺ 强度", + "åĹĵ åŃIJ", + "é¢Ĩ è¡Ķ", + "楼 主", + "大 èĴľ", + "æŀķ 头", + "ç²® æ²¹", + "é»Ħ çĵľ", + "æĵ Ĵ", + "å°ı çĭĹ", + "æĶ¹éĿ© å§Ķ", + "åįģ åĪĨéĴŁ", + "é²ľ èī³", + "åħ³ ç¾½", + "çĭĢ æħĭ", + "å®ŀçĶ¨ æĢ§", + "å°ij è§ģ", + "é£ŀ æī¬", + "çĶ° éĩİ", + "æIJ Ĥ", + "è¿Ļ个 è¯į", + "åºĶæĢ¥ é¢Ħæ¡Ī", + "è§Ĵ度 æĿ¥çľĭ", + "æķ¬ çķı", + "æ³ķ å®Ŀ", + "åĸĦ æĦı", + "æīĵ æĸŃ", + "对 åĨ³", + "çµķ å°į", + "åĢŁ æѤ", + "å¼Ģ æºIJ", + "å°ı 說", + "ç¥ º", + "å²ģ 以ä¸ĭ", + "éĢĢå½¹ åĨĽäºº", + "ä¸įä¹ħ åīį", + "åĩº åİĤ", + "讽 åĪº", + "æĿ¥çľĭçľĭ åIJ§", + "éŃĶ åħ½", + "çķĻ ä¸ĭæĿ¥", + "å±ħ 室", + "åłħ æĮģ", + "çľĭ äºĨä¸Ģ", + "çľĭäºĨä¸Ģ çľ¼", + "éĽĨåĽ¢ æĹĹä¸ĭ", + "æĪĺ æĪĺç»ĦåIJĪ", + "è®¤çľŁ èIJ½å®ŀ", + "汽车 产ä¸ļ", + "çī©çIJĨ åѦ", + "æķ µ", + "éĴ Ŀ", + "åĽ¢ éķ¿", + "ä¸įæĸŃ æī©å¤§", + "èĤ© è´Ł", + "åıijå±ķ 缮æłĩ", + "è³ĩ éĩij", + "åīį ç½®", + "ä¸ŃåĽ½ åı¤ä»£", + "æŃ» åĪij", + "åħħåĪĨ ä½ĵçİ°", + "åħ³ éŨ", + "ç¾İ æĦŁ", + "æīĵ åħ¥", + "æĬijéĥģ çĹĩ", + "å°ij çĪ·", + "æłij æŀĿ", + "æ¶Īæģ¯ 称", + "æ´Ľ åħĭ", + "åį ¯", + "è¿Ī åIJij", + "æݨ åĭķ", + "ä»İä¸ļ èĢħ", + "åİ» ä¹°", + "欢 å¿«", + "æĭ¥ æĮ¤", + "马 桶", + "æĬĬ æݧ", + "æĶ¿ åħļ", + "å¼ł æī¬", + "客 æłĪ", + "红 æĺŁ", + "éĢģ æĿ¥", + "åħ¨åŁŁ æĹħ游", + "èĩª ç§ģ", + "åįģäºĮ æĿ¡", + "åı¹ æģ¯", + "ä¸Ģ èīĺ", + "ä¿Ŀ è´¹", + "æĸ½å·¥ çİ°åľº", + "æľī 幸", + "ç»Ń èĪª", + "åı¯èĥ½ æľĥ", + "èĥĮ åıĽ", + "ä½£ éĩij", + "ä¸ī çŃīå¥ĸ", + "å¾Ī 满æĦı", + "游æĪı åī¯æľ¬", + "群 éĩĮ", + "æŀĦ 件", + "åºı å¹ķ", + "太 æ¹ĸ", + "æľ¨ è´¨", + "æĻĭ æ±Ł", + "çµĤ æĸ¼", + "è·³ è·ĥ", + "åĢºæĿĥ 人", + "çŃī 诸å¤ļ", + "æĶ¾ åĩº", + "åħ³éĶ® æŶåĪ»", + "æĦŁæŁĵ èĢħ", + "é£ŀè¡Į åijĺ", + "èĥĨ åĽº", + "èĥĨåĽº éĨĩ", + "æĬ± æŃī", + "åij¨ äºĮ", + "æĸ° æĹ¶æľŁ", + "åĨ·éĵ¾ çī©æµģ", + "è¿Ļç§į æĸ¹å¼ı", + "该 æĿij", + "åĽŀ é¦Ī", + "åŁºçĿ£ æķĻ", + "人 åıĤ", + "æŀ¯ çĩ¥", + "æī¹åıij å¸Ĥåľº", + "åħħåĪĨ èĤ¯å®ļ", + "å¸Ĥ æĶ¿åįı", + "äºĭ æ¥Ń", + "龸 çİĭ", + "çĥŃ æIJľ", + "åįģä¹Ŀ 大", + "ä¼´ æľī", + "ç¾İåĽ½ æĢ»ç»Ł", + "åŁİå¸Ĥ 管çIJĨ", + "ä¸ĭ 令", + "èĥ¸ åı£", + "åıª çŁ¥éģĵ", + "åij¨ ä¸ī", + "çĶ¨ æĪ¶", + "éŃ ¯", + "å¿ĥ è¡Ģ", + "带头 人", + "åĮ» åĬ¡", + "åĮ»åĬ¡ 人åijĺ", + "æݧåĪ¶ åĻ¨", + "ä½ľåĵģ åĨħ容", + "æĪĺ åıĭ", + "åİĨ å¹´", + "ä¸į åħĭ", + "ä¸įåħĭ ä¸įåıĬ", + "æĹ¥ æŃ£å¼ı", + "è±IJ å¯Į", + "ç¨İ è´¹", + "æŶ æķĪ", + "å±ķ ä½į", + "è¡¡ éĺ³", + "æĪ¿ 貸", + "çĪĨ 款", + "ä¹IJ æĦı", + "çĶ· 主", + "å¯ ¬", + "æľĥ èŃ°", + "ä¹ĭ å¤ľ", + "åIJĮ 樣", + "ä¸įè¦ģ 太", + "ä¼Ĭ æĸ¯", + "ä¼Ĭæĸ¯ åħ°", + "åŁºæľ¬ åİŁåĪĻ", + "åİ» æİī", + "ä½İ ä¿Ŀ", + "个 交æĺĵ", + "个交æĺĵ æĹ¥", + "èģĬ èģĬ", + "åĽĽ ä½į", + "åħļç»Ħ æĪIJåijĺ", + "主è¦ģ ä»İäºĭ", + "å½± éŁ³", + "åĨĴ åĩº", + "åij¼åIJ¸ éģĵ", + "è¾¾ å°Ķ", + "æľ¨ åľ°æĿ¿", + "诡 å¼Ĥ", + "çģ¯ åħ·", + "çģ« çĥ§", + "解 èĦ±", + "æĦĪ åıij", + "æ¹ĸ å·ŀ", + "é£İ ä¿Ĺ", + "æĸ° å½¢åĬ¿", + "æĸ°å½¢åĬ¿ ä¸ĭ", + "è² Ŀ", + "èĦ ĵ", + "åĬ¨åĬĽ çĶµæ±ł", + "é£ŀ èĪ¹", + "飧 æĢ§", + "åĪ© çī©", + "åĪ©çī© 浦", + "ä¸į 认è¯Ĩ", + "ç¼ĸ ç»ĩ", + "ä½ľ åĿĬ", + "èģĮä¸ļ æĬĢèĥ½", + "çľĭ è¦ĭ", + "åĽ´ æ£ĭ", + "æĺı è¿·", + "å½Ĵ å±ŀäºİ", + "æĤ¬ å´ĸ", + "éĨ« çĻĤ", + "å®ĭ 代", + "åºĦ æĿij", + "èĹ ķ", + "çĮĽ çĦ¶", + "çĩĥæĸĻ çĶµæ±ł", + "å®ŀä½ĵ åºĹ", + "ä¸į足 以", + "æĥħ ç·", + "æĥħç· Ĵ", + "å»Ĭ åĿĬ", + "çĶµ åı°", + "åºĶ åĬĽ", + "ä¸Ńå°ı åѦçĶŁ", + "èĥ¡ åIJĮ", + "éī´ åĪ«", + "åĨħ ç½®", + "ä¹± 象", + "æ¬Ĭ çĽĬ", + "å¼ĢæĶ¾ å¼ı", + "åįļ æĸĩ", + "讲 课", + "çŃī åİŁåĽł", + "ç©· 人", + "交 æĽ¿", + "æĬ¤ çħ§", + "åıijå±ķ æľºéģĩ", + "客 åķĨ", + "åıį ä¹ĭ", + "ç±³ é¥Ń", + "并 åıij", + "并åıij çĹĩ", + "æ±ī åŃIJ", + "æŀľ åĽŃ", + "对æĪij æĿ¥è¯´", + "åģı åIJij", + "æī¹ 示", + "读 åIJİ", + "读åIJİ æĦŁ", + "æĺİ æĻº", + "åĽ´ çĿĢ", + "åıį 转", + "æĿ¨ å¹Ĥ", + "ä¸ĵ åįĸ", + "ä¸ĵåįĸ åºĹ", + "åıĹ éĻIJ", + "åºŁ è¯Ŀ", + "æŀģ å°ij", + "åįĪ åIJİ", + "è¿Ľ ä¿®", + "åīĬ åĩı", + "æľ¬ç§ij çĶŁ", + "ä¼ĺ éĢī", + "åħī çħ§", + "åıĻ äºĭ", + "åıĸ æļĸ", + "åĮĹ è·¯", + "æ¦ ķ", + "èİĨ çĶ°", + "楼 å±Ĥ", + "天 èĬ±", + "天èĬ± æĿ¿", + "çĤ ľ", + "å·²ç»ı æľīäºĨ", + "è¶ ¾", + "çĶ³ åįļ", + "çĶµ éĺ»", + "åĬŁ 课", + "æŃ¥ æŃ¥", + "éĤ£ä¹Ī 容æĺĵ", + "æѤ æĸĩ", + "ä½ °", + "计 è¾ĥ", + "çīĩ éĿ¢", + "çĶµå½± éĻ¢", + "ä¸į åħ¬å¹³", + "ä¸ī æľŁ", + "æĹħ游 èµĦæºIJ", + "å¤ļç§į å½¢å¼ı", + "è£Ĥ ç¼Ŀ", + "åIJİ æİĴ", + "硬 度", + "åĽŀ æļĸ", + "éģĵ æķĻ", + "è´« è¡Ģ", + "æ¸ħ é¦Ļ", + "伤 çĹħ", + "æĦı 義", + "çļĦ ç¼ĺ", + "çļĦç¼ĺ æķħ", + "åºĦ 严", + "åıªæĺ¯ ä¸ºäºĨ", + "æīĵ æĬĺ", + "以 ä¾Ĩ", + "滿 足", + "çİĽ 丽", + "風 éļª", + "æĸĩ ç§ij", + "éħįå¤ĩ äºĨ", + "è¿Ľ é£Ł", + "æ¶ ¡", + "è·¯ ç¨ĭ", + "åı« 声", + "ä¸Ńå¿ĥ åŁİåĮº", + "æľīæīĢ ä¸įåIJĮ", + "å¼µ è²¼", + "é¢Ħ æĬ¥", + "æľīå¤ļ ä¹Ī", + "è¿Ľè¡Į åħ¨éĿ¢", + "æĽ¾ ç¶ĵ", + "ä¸ī 代", + "å®ı 大", + "æ¸ħ æī«", + "éĢī åĩº", + "åĵª ä¸Ģ个", + "主 義", + "ä¾Ŀ æĵļ", + "çļ® éĿ©", + "赶 æĿ¥", + "çŃĽ æŁ¥", + "æ¨ Ł", + "ä¿Ŀ èįIJ", + "åIJĥ æĥĬ", + "æľĭåıĭ们 对", + "ä»ĸ æĺ¯ä¸Ģ个", + "åºŁ æ°Ķ", + "æ» ħ", + "è´¢ ç¨İ", + "æĿij æĿijæ°ij", + "èµĦ产 è´ŁåĢº", + "å®ī å¨ľ", + "缮åīį åĽ½åĨħ", + "æĦŁè§ī èĩªå·±", + "çµIJ åIJĪ", + "éĶ¦ æłĩ", + "éĶ¦æłĩ èµĽ", + "æĽ´ æ·±", + "åŁº æķ°", + "éħ¿ éħĴ", + "çī¹èī² 产ä¸ļ", + "åİĭ å®ŀ", + "ä¾Ŀæ³ķ 追究", + "æ·¡ å®ļ", + "ç®Ģ缴 å°±æĺ¯", + "å£ĵ åĬĽ", + "æ°ij å¿ĥ", + "ä¸į åIJĪéĢĤ", + "çĶ±æѤ åı¯è§ģ", + "èµŀ èªī", + "æ¾ ¤", + "åĩłå¹´ åīį", + "åIJī ä»ĸ", + "çł´ æįŁ", + "轻轻 åľ°", + "å²Ľ 屿", + "æĦı å¢ĥ", + "ä»Ģä¹Ī åı«", + "åģĩ è£ħ", + "éĢģ è´§", + "å¹ķ å¢Ļ", + "妥 åįı", + "åĽ½ æĹĹ", + "äºĨ å¾Īä¹ħ", + "åĪĨ辨 çİĩ", + "ç´ Ķ", + "éĺ³ åĮº", + "åĩŃ çĿĢ", + "åģľè½¦ ä½į", + "京 éĥ½", + "éĶ £", + "æĵ ¾", + "è¿Ľ éŨ", + "åĪĺ æµ·", + "åĽĽ 级", + "女 足", + "è¡ĮæĶ¿ 审æī¹", + "éģ¥ æݧ", + "ä¸į éĮ¯", + "å¾Ĺ å¾Ī好", + "为 缮çļĦ", + "ä»į æľª", + "ç²¾ è£ħ", + "éĢį éģ¥", + "å°½ 头", + "çºł ç¼ł", + "éłĺ å°İ", + "æĭħ è´Ł", + "æĪĸèĢħ åħ¶ä»ĸ", + "åıªä¸įè¿ĩ æĺ¯", + "åı® åĺ±", + "åģĩ åĨĴ", + "æļĸ æ°Ķ", + "çĽIJ åŁİ", + "被 è§Ĩ为", + "诺 è´Ŀå°Ķ", + "ç»ĻäºĨ æĪij", + "è¿ij åįĥ", + "éĩį åĽŀ", + "éĨĴ äºĨ", + "çĶµ 解", + "忽çķ¥ äºĨ", + "èĥĮ éĥ¨", + "æĸĩæĺİ åŁİå¸Ĥ", + "æº ħ", + "è² ĵ", + "æĬµ æĮ¡", + "åĸľæ¬¢ åIJĥ", + "éĿĻéĿĻ åľ°", + "å¾Ī æ·±", + "åŁºç¡Ģ çŁ¥è¯Ĩ", + "è¿ĩ éĶĻ", + "çIJĨ ç§ij", + "交æµģ åIJĪä½ľ", + "èĪ Ķ", + "調 æŁ¥", + "æħĪ æĤ²", + "éĴ °", + "èĩ´ çĶµ", + "å®£ä¼ł æ´»åĬ¨", + "åıĺ éĩı", + "çļĦ人 æĿ¥è¯´", + "æŶ éļĶ", + "ä¸į管 ä½ł", + "缸 è¿ij", + "è´µ éĩijå±ŀ", + "ä¹Łä¸į åı¯èĥ½", + "ç²ī æľ«", + "åįĹ çĵľ", + "çĻ½ 马", + "åħī æºIJ", + "éĩij å¥ĸ", + "çĭ¬ è§Ĵ", + "çĭ¬è§Ĵ åħ½", + "妨 ç¢į", + "ç»Ļ åĬĽ", + "ä½Ĩ ä»į", + "å¼łå®¶ åı£", + "èIJ¬ åħĥ", + "渲 æŁĵ", + "éķ¿å¤§ äºĨ", + "è®°èĢħ äºĨ解", + "æĢĢ çĿĢ", + "è¦ģ åѦä¼ļ", + "游æĪı 代", + "游æĪı代 ç»ĥ", + "äºĮ çĻ¾", + "æĦıè¯Ĩ å½¢æĢģ", + "çİ º", + "计åĪĴ çĶŁèĤ²", + "æī¾ åĩĨ", + "åħ° èĬ±", + "è¿Ļ座 åŁİå¸Ĥ", + "污 æ³¥", + "å®ĺæĸ¹ 微信", + "å½Ĵ å±ŀ", + "æ°§ æ°Ķ", + "éģİç¨ĭ ä¸Ń", + "åį°è±¡ æ·±åĪ»", + "稳 妥", + "çµIJ æĿŁ", + "åŃķ æľŁ", + "çī¹ æĿĥ", + "åĿļ åĽº", + "顺 åĬ¿", + "æŀľ èĶ¬", + "éĨ« 師", + "åİ ®", + "ä¹Łæĺ¯ å¦ĤæѤ", + "é¦Ĵ 头", + "缸 åĬ©", + "å¹² 线", + "ä¸Ģ æľ¬ä¹¦", + "ç» ¥", + "æĮ¯ å¥ĭ", + "èĤ¾ èĦı", + "åĭķ çī©", + "é£ŀ è·ĥ", + "èıľ åĵģ", + "å¤ļ ä½Ļ", + "å¤ļä½Ļ çļĦ", + "éĢĿ ä¸ĸ", + "æģĭ 人", + "å¼Ģåıij åĪ©çĶ¨", + "顺 丰", + "éĩİ å¿ĥ", + "æł¡ å¤ĸ", + "æģIJ é¾Ļ", + "éĿ¢ åħ·", + "éķ¿ è¾Ī", + "éļı å¤Ħ", + "éļıå¤Ħ åı¯è§ģ", + "ç´§ 缺", + "éĩį ä¸Ń", + "éĩįä¸Ń ä¹ĭ", + "éĩįä¸Ńä¹ĭ éĩį", + "奥 æĸ¯", + "奥æĸ¯ åį¡", + "ä¸Ģ个 å¤ļ", + "ä¸Ģ个å¤ļ æľĪ", + "ä¸įåı¯ 缺å°ij", + "æĸ° æł¼å±Ģ", + "æıIJ æĮ¯", + "è¡Į è´¿", + "æ¼Ĥ æµģ", + "èģĬ åŁİ", + "åħ´ 建", + "è´¨ æ£Ģ", + "ç§ģæľį 游æĪı", + "æĽ´ éĩįè¦ģ", + "è´ ®", + "çħ ľ", + "转åıĺ 为", + "è¿Ļ 两年", + "ä¿Ŀ é²ľ", + "æī§ æķĻ", + "çĥ ¨", + "å¼Ģåıij 建设", + "è¿IJèIJ¥ 管çIJĨ", + "误 å·®", + "京 åī§", + "å¸IJ åı·", + "å·¥ä½ľ ä½ľé£İ", + "ä¸ĸ ä¿Ĺ", + "çĻ½ 宫", + "天 åĽ½", + "å¤©åĽ½ 继ç»Ń", + "å·´ æĸ¯", + "èIJ¥ åĪ©", + "åĵģ æł¼", + "æĿijæ°ij 们", + "æĪ¿ 车", + "çŃī çĹĩçĬ¶", + "å¦Ĥ å®ŀ", + "å® ¸", + "å±Ĥ 级", + "éĶĻ è¿ĩäºĨ", + "ç»ĵ å®ŀ", + "ç¬ij èĦ¸", + "羣å®ŀ æĢ§", + "éĥ½å¸Ĥ æĬ¥", + "é¥Ń èıľ", + "åºĶ 注æĦı", + "æĬ½ çĥŁ", + "伪 éĢł", + "åīį ä¸Ģ天", + "éŃĶ é¾Ļ", + "éŃĶé¾Ļ 令çīĮ", + "约 è°Ī", + "绣çŃ¹ æİ¨è¿Ľ", + "让 çĶ¨æĪ·", + "åħ¨éĿ¢ èIJ½å®ŀ", + "å¼Ħ å¾Ĺ", + "è°Ī æģĭçĪ±", + "鸣 æĪIJéķ¿", + "鸣æĪIJéķ¿ è®°", + "æ´ĭ æ´ĭ", + "çĸı æķ£", + "éĿ¢ç§¯ 约", + "æµĵ 缩", + "æĸ¯ é¡¿", + "çĶŁæĢģ åľĪ", + "æī§ 导", + "移 éĢģ", + "齿 è½®", + "æł¹æľ¬ å°±ä¸į", + "缩 åĩı", + "èµ° ä¸ĭåİ»", + "çĿ« æ¯Ľ", + "ä¹Łä¸į éĶĻ", + "åıįæĺł åĩº", + "èĭ¦ æģ¼", + "缸åħ³ æĶ¿çŃĸ", + "é«ĺ 楼", + "ç²ī èī²", + "æĬķèµĦ é¢Ŀ", + "ä¸į ç»ı", + "ä¸įç»ı æĦı", + "å®ģ æĦ¿", + "èĪĮ 头", + "æ»ĭ çĶŁ", + "å®ģ åİ¿", + "åīįåĪĹ èħº", + "åĩ ³", + "é£Ł 欲", + "åıĸ èĥľ", + "éĻ¢ åŃIJ", + "ç´łè´¨ æķĻèĤ²", + "滨 å·ŀ", + "æĬ¢ æĬĵ", + "å¼Ĥ åij³", + "åĴ ļ", + "åĬ į", + "宽 éĺĶ", + "æļ´ 涨", + "æĥł åıĬ", + "è§Ħ ç¨ĭ", + "ä¾Ľ åħ»", + "éĢģ å¾Ģ", + "å±± åºĦ", + "举 äºļ", + "å±ķ é¦Ĩ", + "解 éĶģ", + "æĹł è§Ĩ", + "éĻį èIJ½", + "è¿ŀ äºij", + "è¿ŀäºij 港", + "åıĤ è°ĭ", + "çİ ĸ", + "ç¬ ĥ", + "èĢĹ è´¹", + "æī¿ å¾·", + "社ä¼ļ æķĪçĽĬ", + "åįĹæµ· ç½ij", + "åĪĽ 伤", + "èIJ ±", + "åħħ æ²Ľ", + "ç½ijç«Ļ 建设", + "大 åºĨ", + "åĨį éĢł", + "åŃĹ æł·", + "åħ¨æ°ij åģ¥èº«", + "èĮ« èĮ«", + "æµ® åĬ¨", + "åīį åı°", + "å¢ŀ 设", + "éĢĽ è¡Ĺ", + "åĢĴ éĹŃ", + "æ³ķå¾ĭ 顾éĹ®", + "çĸ ®", + "çĹħ çĹĩ", + "空 åīį", + "请 æķĻ", + "èĥľ ä»»", + "æĿĢ èıĮ", + "æĪĺæĸĹ æľº", + "ç»ĺ åĪ¶", + "å¤Ħ æĸ¹", + "çªģ åĽ´", + "çĮ« åĴª", + "æĬ¥åijĬ æĺ¾ç¤º", + "ç¿ Ł", + "çķ¶ åľ°", + "æľĢ éļ¾", + "纪 å§Ķ书记", + "ä½İ åİĭ", + "èĻļ 空", + "è¿Ļéĥ¨ çĶµå½±", + "产ä¸ļ åįĩ级", + "è°· çĪ±", + "è°·çĪ± åĩĮ", + "æĬ¼ éĩij", + "女 æĸ¹", + "éĴ» çłĶ", + "æļĹ æļĹ", + "è¿· ä½ł", + "æīĢ è¬Ĥ", + "å¨ģ å»ī", + "å¼Ģ æľĹ", + "å² Ķ", + "çģ« çĤ¬", + "åIJĪçIJĨ æĢ§", + "åħ¬ åĬŀ", + "ä¼ļ ä¼ļéķ¿", + "éĺ´ è°ĭ", + "å¼Ģ å±Ģ", + "æĻ®éĢļ è¯Ŀ", + "åį¡ æĭī", + "å°ij åIJĥ", + "éĹª èĢĢ", + "æŀľ æ±ģ", + "æī§è¡Į åĬĽ", + "è° Ľ", + "æĬ¢ åĬ«", + "é«ĺéĢŁ åıijå±ķ", + "éŁ ¬", + "åįĹ æ²Ļ", + "é«ĺçŃī åŃ¦æł¡", + "æį¢ 个", + "åı¯èĥ½ åŃĺåľ¨", + "æĬ Ĵ", + "è°± åĨĻ", + "被 æĬĵ", + "æĿ¯ åŃIJ", + "èĬĤèĥ½ åĩıæİĴ", + "æ°ĶåĢĻ åıĺåĮĸ", + "åĪĨ åĪ¥", + "ä¸Ń æŀ¢", + "欢 åij¼", + "åħī 纤", + "è¿Ļ 群", + "çľ¼ çķĮ", + "åħ±åIJĮ åıijå±ķ", + "çİ° ä»Ĭ", + "éĹ» è¨Ģ", + "çī¹èī² å°ıéķĩ", + "æķij 人", + "éĻį æ°´", + "ä¸ĸçķĮ ä¸Ģæµģ", + "å°± é¤IJ", + "çŀ ¥", + "å¤į ä»ĩ", + "ç¾½ æ¯Ľ", + "ç¾½æ¯Ľ çIJĥ", + "è´© åįĸ", + "æºIJ æ³ī", + "æĢ»ä½ĵ è§ĦåĪĴ", + "åĬ¨ æĦŁ", + "ä¸Ģ 审", + "åĢŁ éĴ±", + "è§ģ æķĪ", + "èĬ± èįī", + "åIJĮ ä¸ļ", + "æŁ¥ è©¢", + "åĽ½éĻħ åIJĪä½ľ", + "ä¾Ľ åĽ¾", + "åģ ´", + "æł ĵ", + "缸 éĢļ", + "è°Ī åıĬ", + "è¿ĩç¨ĭ å½ĵä¸Ń", + "é¦Ļ èıĩ", + "åįģåĽĽ æĿ¡", + "ä¸Ģå¼Ģå§ĭ å°±", + "ä¸ĵ åijĺ", + "æĺİ é¡¯", + "æīĵéĢł åĩº", + "ä¸ĭéĿ¢ æĪij们", + "æľº æ²¹", + "åı° è¯į", + "åŃIJ å¼Ł", + "æľĢ 常è§ģçļĦ", + "æĪij è®°å¾Ĺ", + "ç» °", + "æĤ¬ æµ®", + "è¿ĺ 羣æĺ¯", + "æĮĤ åı·", + "åıĭ åĸĦ", + "éĩį 伤", + "çħ§ 亮", + "æѦ èѦ", + "åĩºçİ° éĹ®é¢ĺ", + "è¸Ĭ è·ĥ", + "åľ°çIJĥ ä¸Ĭ", + "å¸Ĥ 人大", + "åıĹ害 人", + "å² IJ", + "åIJĮ åѸ", + "éĩijèŀį å¸Ĥåľº", + "æľīçļĦ çݩ家", + "å¸Ĥ æķĻèĤ²", + "å¸ĤæķĻèĤ² å±Ģ", + "åIJĦ å¼Ĥ", + "ç·ļ ä¸Ĭ", + "æģ º", + "æľī 大éĩıçļĦ", + "åķĨ æĬ¥", + "åįķ åįķ", + "åħ¨ é¢Ŀ", + "ä¾Ŀæŧ æĺ¯", + "好 åĩłä¸ª", + "åĸ µ", + "éĩį æķ´", + "çĶŁæ´» è´¨éĩı", + "æİ¢ 访", + "åį° èĬ±", + "缼 è¡Į", + "å¾® è§Ĥ", + "èĪį å¾Ĺ", + "åºŁå¼ĥ çī©", + "积 èĵĦ", + "å®ļ å±ħ", + "æĤ ¼", + "èĮ ¸", + "çļĦ 帮åĬ©", + "çļĦ帮åĬ© ä¸ĭ", + "亿 åIJ¨", + "åŃĶ éĽĢ", + "è¿ĻæĿ¡ è·¯", + "é¥ µ", + "æĦĪ åĬł", + "éķ į", + "ä½ľ æ¡Ī", + "èįĶ æŀĿ", + "太 å°ij", + "è·» 身", + "åħ¬çĽĬ æ´»åĬ¨", + "çĻ½ æĸij", + "æĬĢæľ¯ æ°´å¹³", + "å¸ §", + "æĹł çŁ¥", + "åºĶ该 æĢİä¹Ī", + "éĢĢ å¸Ĥ", + "æ¸ Ń", + "åħ» çĮª", + "é© ¼", + "群 å²Ľ", + "大 åį«", + "ä¹ĺ çĶ¨è½¦", + "èı² å°Ķ", + "è´´ åIJ§", + "åģľ ä¸ĭæĿ¥", + "æľīæľº ç»ĵåIJĪ", + "åĪ» èĭ¦", + "çļĦ åľ°", + "çļĦåľ° æŃ¥", + "è¯Ĭ æīĢ", + "å¼Ģ æĪĺ", + "èĢģ çīĮ", + "çŃ¹ çłģ", + "åħ«å¤§ 以æĿ¥", + "楼 æĪ¿", + "åŃĻ æĤŁ", + "åŃĻæĤŁ 空", + "åħĴ åŃIJ", + "第ä¸Ģ æĿ¡", + "社交 åªĴä½ĵ", + "æĥ³ èµ·æĿ¥", + "大 æ´ĭ", + "æĭ¼ éŁ³", + "è¿Ľ åįļä¼ļ", + "è¿ĩ åħ³", + "æ² ¼", + "ç©¿ æIJŃ", + "éĤ£ ä¸Ģ天", + "çł´ éŨ", + "æĬķæłĩ 人", + "èµ¢ 家", + "èĻļ å¼±", + "æ¿ ĥ", + "å®ī æ£Ģ", + "客 家", + "çĭ¬ç«ĭ èij£äºĭ", + "æīĭ åĬ¿", + "åīµ éĢł", + "åľĨ满 å®ĮæĪIJ", + "为主 线", + "好å¥ĩ å¿ĥ", + "é¢Ĩ åľŁ", + "çª ĸ", + "åħ¸åŀĭ æ¡Īä¾ĭ", + "çªģåıij äºĭ件", + "åºķ æ°Ķ", + "头 æĻķ", + "å®Ľ å¦Ĥ", + "è§ ¸", + "æ¸ħ æ·¡", + "åļ ¼", + "åģľ çĶµ", + "ç²ī å°ĺ", + "éĻįä½İ æĪIJæľ¬", + "æĶ¾ æīĭ", + "è®°èĢħ 表示", + "æĭĸ 延", + "éª ĩ", + "æ®ĭ å¿į", + "çľģ æķĻèĤ²", + "çľģæķĻèĤ² åİħ", + "é«ĺ é¢Ŀ", + "éĦ Ļ", + "æ¥ ŀ", + "åĨħ ç§ij", + "èIJ¥ä¸ļ é¢Ŀ", + "åŁº çŁ³", + "æµģ æ·Į", + "主 æŨ", + "éĺIJ éĩĬ", + "建 åįİ", + "æĥĬ åı¹", + "çī¢åĽº æłijç«ĭ", + "æĺ¯åIJ¦ åŃĺåľ¨", + "建 åĨĽ", + "éĽ¾ éľ¾", + "åħ¬ 认", + "åħ¬è®¤ çļĦ", + "æ°¨ åŁº", + "æ°¨åŁº éħ¸", + "åīį åĩłå¹´", + "åĪ¹ éĤ£", + "æ±Ł 举", + "å·¥ æ¥Ń", + "ä¸ĢçĤ¹ ä¹Łä¸į", + "ä¿® 士", + "äºĨä¸Ģ éģį", + "åĪ ģ", + "æ»ļ æ»ļ", + "åĪĨ æł¡", + "羣 çĪ±", + "è¡Ģ èĦī", + "æĢ¥ åī§", + "ä¸Ģ群 人", + "ç¾ ¯", + "æĪIJ é¾Ļ", + "ç²¾ç¥ŀ çĹħ", + "缸åħ³ 人åijĺ", + "éĿĵ 丽", + "ä¸ī åŃ£åº¦", + "åĪĴ å®ļ", + "ä¸ĸçķĮ 第ä¸Ģ", + "éĢļ ä¿Ĺ", + "åķĨä¸ļ åľ°äº§", + "åĬŁèĥ½ æĢ§", + "èµĦæľ¬ 主ä¹ī", + "详 è§ģ", + "æĬĵ æįķ", + "æĸĩ æĺĮ", + "å®Ŀ å®ī", + "è£ħéħį å¼ı", + "æºIJ æºIJ", + "æºIJæºIJ ä¸įæĸŃ", + "çĶŁ æĢķ", + "纵 åIJij", + "å£ ½", + "çľ¼ è¢ĭ", + "èĤī ä½ĵ", + "åı¤ ä»Ĭ", + "èŀį åªĴä½ĵ", + "åģ ī", + "æł¼ æľĥåĵ¡", + "çĥ ·", + "åĬŁ çĶ¨", + "æīŃ çŁ©", + "绿èī² éĢļéģĵ", + "åī§ ç»Ħ", + "å¼± åĬ¿", + "è´¨éĩı éĹ®é¢ĺ", + "éĻIJ é¢Ŀ", + "éª Ĩ", + "éģµ ä¹ī", + "å¯Ŀ 室", + "æĥ³ 念", + "åł± åijĬ", + "ä»ħ 次", + "ä»ħ次 äºİ", + "èŀį åĪĽ", + "æĭĽèģĺ ä¼ļ", + "åºĬ åŀ«", + "转åŀĭ åıijå±ķ", + "ä¸ŃåĽ½ çĶµä¿¡", + "åIJ¬ è¯Ŀ", + "è«ĭ æ±Ĥ", + "大éĥ¨åĪĨ 人", + "æ´» å¾Ĺ", + "åĵŃ æ³£", + "è¶ Ļ", + "åıijçĹħ çİĩ", + "ä¸į 符", + "åĨĽ å®ĺ", + "é¢Ī æ¤İ", + "æĸ°åĨł çĸ«æĥħ", + "æŁ¬ åŁĶ", + "æŁ¬åŁĶ 寨", + "ä»»ä½ķ å½¢å¼ı", + "人 éĻħ", + "人éĻħ åħ³ç³»", + "æĢ» æī¿åĮħ", + "å¹³åĿĩ æ¯ı", + "æģŃ åĸľ", + "åĦ ĺ", + "åħµ 马", + "è¿Ł åĪ°", + "å·¥ 伤", + "çīĪæĿĥ å½Ĵ", + "çīĪæĿĥå½Ĵ åİŁ", + "æĭ¥ æĬ¤", + "ç³Ĭ æ¶Ĥ", + "å¹² æ¶ī", + "å°ij ä¸įäºĨ", + "æĥ³ æī¾", + "è´¹ çİĩ", + "该 éĻ¢", + "èŀį åĮĸ", + "è¿İ åIJĪ", + "è§ĨåIJ¬ èĬĤ缮", + "æł¼ 網ç«Ļ", + "çľī æ¯Ľ", + "欢è¿İ 大家", + "家åºŃ æķĻèĤ²", + "ä¾µ èļĢ", + "ç»Ļ ä½łä»¬", + "è¡Ģ液 循çݯ", + "å¯Ħ æīĺ", + "å°ĸ åı«", + "以ä¸ĭ åĩłä¸ª", + "è¿ĺ 以为", + "åħ¶ä»ĸ çݩ家", + "ç¬ij ç¬ij", + "æīĵ åIJ¬", + "èĩªçĦ¶ ç§ijåѦ", + "åŁº ç«Ļ", + "ä¹Ŀ å·ŀ", + "ä¿Ŀ 驾", + "ä¿Ŀ驾 æĬ¤", + "ä¿Ŀ驾æĬ¤ èĪª", + "æĶ¾ çľ¼", + "çŁ¥åIJį ä¼ģä¸ļ", + "ç¸ ®", + "ç¨ ½", + "æļ ĩ", + "使çĶ¨ 網路", + "é¢Ħ çķĻ", + "大 象", + "åıijæĺİ ä¸ĵåĪ©", + "æĸĩ 娱", + "éĢł ç¦ı", + "湿 润", + "éĿ¢ æĿ¡", + "æ¶Īè´¹ åįĩ级", + "è®Ĭ å¾Ĺ", + "åĩł åIJį", + "ä» Ħ", + "认 æ¸ħ", + "è¿ľ æĻ¯", + "æıĴ 座", + "诸 侯", + "åıĺ æĢģ", + "ç¦ı 彩", + "è´§ æŀ¶", + "失 æݧ", + "移åĬ¨ 端", + "ä¸Ĭ åı¸", + "éĢł 纸", + "å¸ĥ æľĹ", + "çĴ ĩ", + "åı° åįĹ", + "åĮĹ京 åĨ¬å¥¥", + "èĵĿ çīĻ", + "éķ¿ çŁŃ", + "æĬĺ å°Ħ", + "ç»ij æŀ¶", + "å¯Ĵ åģĩ", + "转 åŁºåĽł", + "æĢ¥ äºİ", + "æŃ£ åĵģ", + "åħħ 滿", + "大 纲", + "æĬĹ ä½ĵ", + "è¨ĵ ç·´", + "æĶ¶ ç´§", + "æ¯Ķ è³½", + "åħµ åĬĽ", + "æľ¬ æĽ¸", + "äºĮ 代", + "æĢ¥ è¯Ĭ", + "æĸĩ æ¡Ī", + "ç»ı åķĨ", + "æĻ¨ æĬ¥", + "æ£ ĺ", + "æĢ»ä¹¦è®° åľ¨", + "åıĹ éĤĢ", + "äºĶ åĽĽ", + "å²Ń åįĹ", + "çĪ± åIJĥ", + "åŁĥ å°Ķ", + "å¿ĥ å¢ĥ", + "è¦ĨçĽĸ éĿ¢", + "å®ŀåľ¨æĺ¯ å¤ª", + "æł¹ åºķ", + "纷纷 表示", + "åĹ ħ", + "éļıçĿĢ æŶéĹ´", + "åİĨåı² æĤłä¹ħ", + "éħ ī", + "æĢ» éĺŁ", + "主é¢ĺ æ´»åĬ¨", + "éĹ® åį·", + "é©¿ ç«Ļ", + "æı¡ ä½ı", + "åı¯èĥ½ 导èĩ´", + "æ°ij éĸĵ", + "éĸĭ åķŁ", + "ä½Ĩ ä¸įéĻIJ", + "ä½Ĩä¸įéĻIJ äºİ", + "åįģ éĩĮ", + "å¨ ¥", + "æįŁ èĢĹ", + "çĸı 导", + "çݯ æ°§", + "ç¥ŀ éĢļ", + "çĪ± å°Ķ", + "çĪ±å°Ķ åħ°", + "æľ´ å®ŀ", + "å¿« æĬ¥", + "æĶ¶ åıĹ", + "æĪĸ 許", + "èĥĮ éĿ¢", + "æĸĩåĮĸ ä¼łåªĴ", + "ä¸ī åĢĭ", + "æĶ» åĬ¿", + "å®ī 举", + "å®ī举 å°¼", + "åĿĩ å·²", + "顾 èĻij", + "éĦ Ń", + "è¿Ļ家 åħ¬åı¸", + "åħ¬åijĬ 称", + "æıIJä¾Ľ ä¼ĺè´¨", + "稳æŃ¥ æİ¨è¿Ľ", + "å¤į è¯ķ", + "å°Ĩ é¢Ĩ", + "è°Ī èµ·", + "å¨ Ħ", + "è¿ŀ 线", + "æ©Ł éĹľ", + "åºĶçĶ¨ åľºæĻ¯", + "çĶ» åĥı", + "è´¢ è¿IJ", + "ä¿Ŀ éļª", + "çĹħ çIJĨ", + "æ¯Ľ 主å¸Ń", + "ä¸Ŀ 毫ä¸į", + "çĪ± å¥ĩ", + "çĪ±å¥ĩ èīº", + "ä¸ĵ家 ç»Ħ", + "åij¼ åĶ¤", + "éĭ ¼", + "çģ ¸", + "é¢ĨåħĪ åľ°ä½į", + "æıIJ æĭĶ", + "龸 éģĵ", + "å±± åĿ¡", + "èĿ İ", + "沸 èħ¾", + "该 项", + "ä»Ĭ çĶŁ", + "ä¸Ģç¯ĩ æĸĩç«ł", + "æĸ¹å¼ı è¿Ľè¡Į", + "é»ij 客", + "æĶ¹ åĬ¨", + "主 é¡Į", + "æķ£ å¸ĥ", + "ä»Ģä¹Ī åľ°æĸ¹", + "åĮĸ åIJĪ", + "åĮĸåIJĪ çī©", + "éĿĻ çĶµ", + "æĢ» æĶ¶åħ¥", + "å§Ķ ç»Ħç»ĩ", + "å§Ķç»Ħç»ĩ éĥ¨", + "éĿĻ æĢģ", + "èĢģ åŃĹåı·", + "室 åıĭ", + "éĥ½ä¸į æķ¢", + "æŀ¶ åŃIJ", + "çģµ æķı", + "审 è§Ĩ", + "æĤ£ åĦ¿", + "å±± 寨", + "èĸª èµĦ", + "é©° æı´", + "éĥ¨åĪĨ åĨħ容", + "好 ä¼¼", + "æĪIJåijĺ åĽ½", + "åľ¨æĪij çľĭæĿ¥", + "åħ³æ³¨ 度", + "éĻĪ æŁIJ", + "è¿Ļç§į äºĭæĥħ", + "éĢī å®ļ", + "ç²¾ åŃIJ", + "å£ģ çĶ»", + "æ±Ł æ·®", + "é«ĺ æĺĤ", + "æł¼ åĬĽ", + "è¼ ©", + "åѦ åłĤ", + "æĤ¨ åIJĮæĦı", + "ä¸ĢåĪĩ éĥ½æĺ¯", + "æ½ ¤", + "éĸ ĥ", + "å¸ĮæľĽ èĩªå·±", + "ä¿ ĺ", + "æ±Ł åİ¿", + "æ³ ¾", + "ç§ij æķĻ", + "æīĵ è¿Ľ", + "ä¸į æħİ", + "å¯Ĵ åĨ¬", + "æ¸Ķ æ°ij", + "鼷 æĸ¯", + "主 å®°", + "æĹħ游 度åģĩ", + "çĶµåŃIJ éĤ®ä»¶", + "æ±Ĥ å©ļ", + "éļİ æ®µ", + "åģ¥èº« æĪ¿", + "注æĺİ åĩºå¤Ħ", + "äºĭæķħ åıijçĶŁ", + "级 以ä¸Ĭ", + "åŃĺ æ´»", + "æĸ½ èĤ¥", + "èľľ èľĤ", + "åµ ©", + "æĮĸæİĺ æľº", + "æĬĹ æĭĴ", + "ä¼ł 导", + "æĺ¯ä»Ģä¹Ī åij¢", + "ä¸Ĭå¹´ åIJĮæľŁ", + "建 åħļ", + "çĶŁ æħĭ", + "ä¿Ŀ ä½ı", + "款 车åŀĭ", + "人 èĦī", + "éļIJ èĶ½", + "失 æķĪ", + "éģ¿ åŃķ", + "ç®Ģ 便", + "谢谢 ä½ł", + "å®Ī ä½ı", + "æĶ¾ æĺł", + "è¨Ī çķ«", + "çݰ代 çī©æµģ", + "é¤IJ 廳", + "æķħ å±ħ", + "大 大å°ı", + "大大å°ı å°ı", + "çī¹åĪ« 声æĺİ", + "éģį åıĬ", + "å¿ĥçIJĨ åĴ¨è¯¢", + "è³ ´", + "çĮ® è¡Ģ", + "å·²ç»ı è¾¾åĪ°", + "æīĵ æĭĽåij¼", + "åıĮ è¾¹", + "ä¸Ģæĸ¹éĿ¢ æĺ¯", + "å´ĩ å°ļ", + "éĺ¿ å¯Į", + "éĺ¿å¯Į æ±Ĺ", + "æĮģ æľī人", + "è± ģ", + "é£İ çŃĿ", + "åĬ¨ èį¡", + "äºĨä¸Ģ ä¼ļ", + "äºĨä¸Ģä¼ļ åĦ¿", + "ä¸ĩ 象", + "çľĭ çĶµè§Ĩ", + "åįģä¸ī æĿ¡", + "çĮĽ çĥĪ", + "è¦ģ ä¸įçĦ¶", + "太æŀģ æĭ³", + "å¼ķ çĪĨ", + "ç»ıè¿ĩ å¤ļå¹´", + "游æĪı éĩĮçļĦ", + "é¾Ļ æ³ī", + "æłĩ éħį", + "è®ĵ ä»ĸåĢij", + "éĢł æŀĹ", + "åĮºåŁŁ æĢ§", + "亿 ä¸ĩ", + "æĪĺçķ¥ å¸ĥå±Ģ", + "éķĩ æĶ¿åºľ", + "åĶ® 票", + "çĶŁäº§ å·¥èīº", + "éķĩ åħļå§Ķ", + "ä¸Ńå°ı åŀĭ", + "æľ¨ èĢ³", + "æ²³ è¾¹", + "èĦ¾ èĥĥ", + "欢è¿İ æĤ¨", + "åıĺ å¼Ĥ", + "缤 纷", + "åŀĥåľ¾ 桶", + "辩 è¯ģ", + "车 åºĵ", + "æ¯Ķ çİĩ", + "åħ´ æĹº", + "详ç»Ĩ äºĨ解", + "å®ī å±ħ", + "çħ§ æĸĻ", + "æĸ¹ æīį", + "èµ ¦", + "åĨ ķ", + "å¥Ķ èµ´", + "å®Ŀ 鸡", + "åľº åĿĩ", + "缮åīį æŃ£åľ¨", + "åIJŀ åĻ¬", + "è¿° èģĮ", + "æĩ µ", + "å¥ĩ çijŀ", + "ä»į å°Ĩ", + "èĪī 辦", + "å·¥åķĨ å±Ģ", + "å¡ij èĥ¶", + "åĬŀ å®ŀäºĭ", + "æĸ¹ æĸ¹éĿ¢", + "æĸ¹æĸ¹éĿ¢ éĿ¢", + "æĸĩåĮĸ èĬĤ", + "åħ¥ èģĮ", + "é¸ ¥", + "ç©¿ éĢı", + "以 ä¹łè¿ijå¹³", + "åį± éļª", + "æľ¦ èĥ§", + "åİĨåı² æĢ§", + "æķŀ å¼Ģ", + "ä¼Ļä¼´ åħ³ç³»", + "çŁ¿ åĮº", + "åĽ½éĻħ åľ¨çº¿", + "ä¼łå¥ĩ éĩĮéĿ¢", + "è¿ij äºĽ", + "è¿ijäºĽ å¹´", + "åĬ£ åĬ¿", + "æĶ»åĩ» åĬĽ", + "æĻº éĢł", + "ç¦ §", + "çİĭ åħĪçĶŁ", + "éĨ« çĶŁ", + "åĽĽ 项", + "å®ŀ æĻ¯", + "åĪĿ åĪĽ", + "å¿ĥ 裡", + "æĻ¶ ä½ĵ", + "交 éĻħ", + "让 æ¶Īè´¹èĢħ", + "课 æĸĩ", + "æİĴ æ°Ķ", + "并ä¸į æĦıåij³", + "缸 声", + "第ä¸Ģ å±Ĭ", + "åİŁ èijĹ", + "éĽ ľ", + "没æľī 太大", + "è¡¥ æ°´", + "çī©æµģ ä¼ģä¸ļ", + "第äºĮ æī¹", + "åħ¶å®ĥ éĹ®é¢ĺ", + "æİĮ éŨ", + "责任 å¿ĥ", + "é¤IJ åħ·", + "ç¾Ĭ æ¯Ľ", + "没æľī å¿ħè¦ģ", + "ä¹IJ åĽ¢", + "è¿Ľ åŁİ", + "ä¸ĢçĤ¹ åĦ¿", + "身 å½¢", + "çļ®èĤ¤ çĹħ", + "æĺ ±", + "å¢ŀ èĩ³", + "èģ² æĺİ", + "æıIJ è´¨", + "ä½ĵèĤ² åľº", + "çŃ¹ 建", + "é¬ Ĩ", + "车 çīĮ", + "éļĶ éŁ³", + "è´Łè´£ åIJĮå¿Ĺ", + "丰 ç¡ķ", + "ä½Ľ éĻĢ", + "äºī åIJµ", + "åº ¶", + "æ·¡ æ°´", + "å°ı çĶ·åŃ©", + "ç§ģ èĩª", + "åĮĸ è¿Ľç¨ĭ", + "æĪĺ士 æĿ¥è¯´", + "æ²¹ èħ»", + "èĦ±è´« èĩ´å¯Į", + "æĹ¥å¸¸ å·¥ä½ľ", + "交 èŀį", + "åĨľ è´¸", + "åĨľè´¸ å¸Ĥåľº", + "åĵĪ çĻ»", + "çĶµ è´¹", + "èµ ĺ", + "åıĮ èħ¿", + "æĵĶ å¿ĥ", + "æĿ¥ 形容", + "使åij½ æĦŁ", + "éĤ£ä¹Ī ç®Ģåįķ", + "èĬĻ èĵī", + "åĢŁæ¬¾ 人", + "ç§Ģ 丽", + "è®ĵ ä»ĸ", + "严åİī æīĵåĩ»", + "è³ ŀ", + "æļ «", + "çħ¤ æ°Ķ", + "çĪ¬ ä¸Ĭ", + "æ½ĩ æ´Ĵ", + "太 ä¹ħ", + "åij½ åIJį为", + "è·¯ çĶ±", + "è·¯çĶ± åĻ¨", + "é© ¯", + "æıIJ æĹ©", + "æĬĹåĩ» çĸ«æĥħ", + "åĩ Ľ", + "交 åıĭ", + "éĶĢåĶ® æ¸łéģĵ", + "毫ä¸į çĬ¹è±«", + "èIJ¥ åľ°", + "çłĶ究 表æĺİ", + "é±¼ ç±»", + "æį¢ å±Ĭ", + "æİ¡ åıĸ", + "çī Ĩ", + "缼 å¼Ģ", + "沧 æ¡ij", + "åºŃ 审", + "ç»ı æŁ¥", + "åĬł å¼·", + "缸æ¯Ķ äºİ", + "ä¸ĵ çıŃ", + "ä½ĵ åŀĭ", + "被 害", + "被害 人", + "æĶ¶ 款", + "åħ·æľī èī¯å¥½", + "é«ĺå³° æľŁ", + "åģı ä½İ", + "åĦ Ł", + "åĨľä¸ļ ç§ijæĬĢ", + "çī¹æ®Ĭ æĥħåĨµ", + "å¦Ĥæŀľ çݩ家", + "éķ¿ 约", + "第åħŃ å±Ĭ", + "åħ¬å¼Ģ æĭĽèģĺ", + "åĪĩ æĸŃ", + "è¿« 使", + "çĸĹ ç¨ĭ", + "第äºĮ ç§į", + "ä¸į åħį", + "å¹² èѦ", + "çŁ³ 榴", + "åĹ £", + "两 ç±»", + "çĪµ 士", + "åŁİ乡 å±ħæ°ij", + "æѤ 项", + "缴 è¾ĸ", + "缴è¾ĸ å¸Ĥ", + "åij¼ åºĶ", + "éĴ ¯", + "ç¦ı å¾·", + "æľº 身", + "æĵį åľº", + "æ¿Ĵ 临", + "人群 ä¸Ń", + "èĤ¡ æ°ij", + "åŃ ½", + "æ³ķ åħ°", + "é¨ İ", + "糯 ç±³", + "æĢ» çļĦ", + "æĢ»çļĦ æĿ¥è¯´", + "åħ¸ éĽħ", + "æĸ° éĻĪ", + "æĸ°éĻĪ 代谢", + "缮 çĿ¹", + "é¢Ħ è¨Ģ", + "è·Į çł´", + "æĸ° ç¯ĩç«ł", + "æ¯Ĵ æĢ§", + "åĸĿ èĮ¶", + "æŁ¥ èİ·", + "亮 丽", + "çĶŁäº§ åķĨ", + "æĶ¹ æĪIJ", + "为äºĨ æĽ´å¥½", + "æ·± 交", + "深交 æīĢ", + "æİ ĥ", + "ä¹Ļ èĤĿ", + "泸 å·ŀ", + "åħĪè¿Ľ æĬĢæľ¯", + "è¾ĵ ç»Ļ", + "æķ£ æĪ·", + "æĢĿç»´ æĸ¹å¼ı", + "åºĹ 主", + "è°ĭ æ±Ĥ", + "游æĪı æĬĢå·§", + "ä¸Ģå¹´ 级", + "çľ¼ è§Ĵ", + "ä¸Ńä»ĭ æľºæŀĦ", + "å·§ åIJĪ", + "éĺ² çĽĹ", + "导 è´Ń", + "æĪ Ĭ", + "æĽ´ éĢĤåIJĪ", + "åŁºæľ¬ ä¿¡æģ¯", + "马 ä¸ģ", + "åħ»æ®ĸ åľº", + "åıį è¿ĩæĿ¥", + "æݨ å´ĩ", + "å¯ĨåĪĩ åħ³æ³¨", + "åŁºéĩij ç»ıçIJĨ", + "æĮī éĶ®", + "åĨħéĥ¨ æݧåĪ¶", + "æĪIJåijĺ åįķä½į", + "æľ¯ è¯Ń", + "åĪ¶ æľį", + "åĪļ éľĢ", + "æ£Ģ ç´¢", + "大大 æıIJé«ĺ", + "åģ¥åº· 管çIJĨ", + "èĩª æѤ", + "客æĪ· éľĢæ±Ĥ", + "丰 èĥ¸", + "èµ· éĩį", + "èµ·éĩį æľº", + "æ¬ł 缺", + "æ¡Ī åŃIJ", + "æĥħ人 èĬĤ", + "åħļ æł¡", + "è¢ ľ", + "该 åī§", + "迷失 ä¼łå¥ĩ", + "ç»ļ 丽", + "åķ ª", + "æĹł ç§ģ", + "éĢ² ä¸ĢæŃ¥", + "第ä¸Ģ ç«ł", + "åĻ¨ åħ·", + "åĨľ èµĦ", + "確 實", + "åºı åĪĹ", + "娱ä¹IJ å¹³åı°", + "èŀįèµĦ 秣èµģ", + "èµĦæºIJ åħ±äº«", + "èģ½ åĪ°", + "æIJŀ å¾Ĺ", + "继ç»Ń ä¿ĿæĮģ", + "åIJ¯ èĴĻ", + "çľ º", + "ä¸Ŀ è·¯", + "设æĸ½ 建设", + "æİ¥ åľ°", + "æİ¥åľ° æ°Ķ", + "第ä¸ī åŃ£åº¦", + "åŁº è°ĥ", + "åıij éŁ³", + "社ä¼ļ èµĦæľ¬", + "éĽĩ 主", + "è¿ŀ èĥľ", + "没 åķ¥", + "å» ¢", + "赶 èµ´", + "æ¼Ķ åĮĸ", + "åı¤ æĢª", + "çİĭ çĪ·", + "é¢Ħ åħĪ", + "å¼Ģ åħ·", + "åĽŀ é¦ĸ", + "åľ°ä¸ĭ æ°´", + "å°ıç¼ĸ ä¸Ģèµ·", + "èµİ åĽŀ", + "åľ° è²Į", + "åĪĿ ä¸ī", + "åı¯ çĶ¨äºİ", + "éģĹ è¿¹", + "è¿Ļ æī¹", + "èĸª æ°´", + "å¿ħçĦ¶ ä¼ļ", + "æ² ½", + "éį ĭ", + "第ä¸Ģ éĥ¨", + "åĪĬ çī©", + "å®ŀ ä¾ĭ", + "æ¸ħ åĩĢ", + "ä¸Ĭ èµĽåŃ£", + "åĽ¾ 表", + "éĤ® è½®", + "åĵª 裡", + "缸 è§ģ", + "æī° ä¹±", + "æ¯ı æ¯ı", + "è¿Ļ è¾ĪåŃIJ", + "ç¡« éħ¸", + "äºī 缸", + "溯 æºIJ", + "åĩº ä¼Ĺ", + "çİī çŁ³", + "åħ± çĶŁ", + "æŶéĹ´ 段", + "éĩįè¦ģ æĮĩ示", + "æ¶Īè´¹ éľĢæ±Ĥ", + "éķ¿ éķ¿", + "éķ¿éķ¿ çļĦ", + "å®ī æĬļ", + "å¢ŀ é«ĺ", + "æľ¬ è½®", + "亲 çľ¼", + "é£İ æ³¢", + "èĢģ å¦Ī", + "æĶ¶è´¹ æłĩåĩĨ", + "åĨħ éĻĨ", + "æĮ¥ åıij", + "åįĩ åѦ", + "èĥ¸ åīį", + "åģı è¿ľ", + "纯 æ´ģ", + "æĸ½å·¥ åįķä½į", + "身 ä»·", + "è´¢ åĬĽ", + "çº ¶", + "è£ħ çĶ²", + "æĺ¾ç¤º åĻ¨", + "毫 åįĩ", + "æ·± çŁ¥", + "èĢ¶ ç©", + "èĢ¶ç© Į", + "è¾ĥ éĩı", + "åľ¨ è¿ĩ渡", + "åľ¨è¿ĩ渡 æľŁ", + "èĮ Ĺ", + "ä¸Ģ个 æĺŁæľŁ", + "èĬ ·", + "è´¿ èµĤ", + "æ¿ ķ", + "æĩĤ äºĭ", + "ç§ §", + "åħħ å½ĵ", + "åĽ½ ç«ĭ", + "èĬ± çĵ£", + "éĤĦ è¦ģ", + "åħ¬ åľĴ", + "触 åĬ¨", + "æ³° å·ŀ", + "ä»Ģä¹Ī æł·", + "æ»ĭ åħ»", + "è¯Ħ åĪ¤", + "æĮ¥ æīĭ", + "èĦ Ī", + "姥 姥", + "è¿IJ è´¹", + "æ¯ħ åĬĽ", + "å¿ĥ æĻº", + "ä¸į æİĴéĻ¤", + "第ä¸ī 代", + "éĢĢ è´§", + "æĺŁ éĻħ", + "æ°¸ åĪ©", + "æĬ¤ åį«", + "çıŃ è½¦", + "è¨Ģ è¡Į", + "ç¹ ª", + "主åĬ¨ æĢ§", + "å·¥ç¨ĭ è´¨éĩı", + "éĥĬ åĮº", + "ä¸Ģ æłĭ", + "ä½Ĩ å®ŀéĻħä¸Ĭ", + "ä¸ī大 èģĮä¸ļ", + "åij¼ åı«", + "女 åħĴ", + "è¯ģåĪ¸ æĬķèµĦ", + "èĢĥ æħ®", + "çĤ« èĢĢ", + "æ²» 好", + "åĺ ¶", + "èĥ ¤", + "åħīä¼ı åıijçĶµ", + "åĩł æŃ¥", + "æīĢ æīĢ", + "æīĢæīĢ éķ¿", + "çħ§ æł·", + "åĵ¥ 们", + "è¯ Ľ", + "è¿Ļä¸Ģ åĪ»", + "çŁ¿ çī©è´¨", + "ä¸įå¾Ĺ å·²", + "åIJĮ 缣", + "ç»Ĩ å¾®", + "è·¯ èĻİ", + "çĻ¾ èĬ±", + "æ·· æ²Į", + "ä¸Ĭæµ· è¯ģåĪ¸", + "éĢĢ ç¨İ", + "èµŀ åı¹", + "æī®æ¼Ķ 游æĪı", + "åIJį åĪĹ", + "åIJįåĪĹ åīį", + "åIJįåĪĹåīį èĮħ", + "ç±³ å°Ķ", + "ä»Ģä¹Ī åİŁåĽł", + "å®īåħ¨ ä¿Ŀéļľ", + "ä¸Ģåıª æīĭ", + "ä¹³ ä¸ļ", + "ä¸į çĶĺ", + "æĥħ åķĨ", + "æĮ¡ ä½ı", + "åİŁåĽł ä¹ĭä¸Ģ", + "è¿Ļ 两天", + "çĥĺ çĦĻ", + "è± ¬", + "ä½ł 以为", + "没 è§ģè¿ĩ", + "åĵªå®¶ 好", + "åīį ä»»", + "è¿Ľ è´§", + "éĢĢ åĽŀ", + "串 èģĶ", + "èĩ³ æĸ¼", + "åĨ° æ·ĩ", + "åĨ°æ·ĩ æ·ĭ", + "æŁ¥çľĭ 详æĥħ", + "çı¾ 實", + "æݨ æµĭ", + "æİ¥ æīĭ", + "éļ¶ å±ŀäºİ", + "åŁİå¸Ĥ 群", + "æĿİ åħĪçĶŁ", + "çŁ¿ æ³īæ°´", + "çī¹ ä»·", + "æĽ´å¤ļ 精彩", + "ç¨ĭ å¼ı", + "读 æĩĤ", + "å±ı èĶ½", + "奥 æŀĹ", + "奥æŀĹ åĮ¹", + "奥æŀĹåĮ¹ åħĭ", + "红 èĸ¯", + "å¥ ®", + "å®Ŀ çİī", + "網 絡", + "è² §", + "欧 å¼ı", + "çĻ½ ç³ĸ", + "èĩªçĦ¶ çģ¾å®³", + "åijĬè¯ī 她", + "å» ļ", + "çĤ¹åĩ» æŁ¥çľĭ", + "é£İ 湿", + "èµĦ产 éĩįç»Ħ", + "ä¹Łä¸į ä¾ĭå¤ĸ", + "åįĬ 个å°ıæŶ", + "åIJ¸å¼ķ æĽ´å¤ļ", + "æŶéĹ´ èĬĤçĤ¹", + "æĶ¶ 纳", + "åIJ¸ æ¯Ĵ", + "èĢģ 乡", + "çIJ ħ", + "æľĢ çµĤ", + "åıį æĦŁ", + "çĶ¨ 微信", + "çĶ¨å¾®ä¿¡ æī«", + "éĢŁ çİĩ", + "大 çĨĬçĮ«", + "åı¯ æĥ³", + "åı¯æĥ³ èĢĮ", + "åı¯æĥ³èĢĮ çŁ¥", + "åĴ §", + "èµ° åħ¥", + "碳 éħ¸", + "èĮĥ åĨ°", + "èĮĥåĨ° åĨ°", + "被 åĪ¤", + "积æŀģ æݨåĬ¨", + "足 足", + "ç²Ĵ åŃIJ", + "大 å®Ĺ", + "大å®Ĺ åķĨåĵģ", + "ç½ij绾 ç§ijæĬĢ", + "æĽ¼ åŁİ", + "å·² ä¹ħ", + "å·²ä¹ħ çļĦ", + "秦 çļĩ", + "秦çļĩ å²Ľ", + "ä»» æķĻ", + "åĶ¯ ç¾İ", + "æ·¡ åĮĸ", + "æ¡Ĥ èĬ±", + "çŁ¥è¯Ĩ åĪĨåŃIJ", + "æĩĴ å¾Ĺ", + "主 åħ¬", + "设计 çIJĨ念", + "è³ º", + "æīĢ æıIJä¾Ľ", + "æīĢæıIJä¾Ľ ä¹ĭ", + "æĶ» åħĭ", + "åĤ ¾", + "è¯Ń æ³ķ", + "åįĥ åı¤", + "éĸĭ æĶ¾", + "第ä¸Ģ èĬĤ", + "éĤĦ æ²Ĵ", + "éĢĥ çĶŁ", + "æ³ Ĺ", + "åİ¿ å§Ķ书记", + "ä½ľèĢħ æīĢæľī", + "çħ ½", + "ç» ħ", + "æł ħ", + "æľ´ ç´ł", + "çijķ çĸµ", + "åĮħ åĮħ", + "æ°ij主 åħļ", + "ä¸į è¿ľå¤Ħ", + "å¥ĩ å¼Ĥ", + "åĺ» åĺ»", + "æī ¼", + "ç¿» å¼Ģ", + "æĢİ èĥ½", + "éģ´ éĢī", + "解 éĩĭ", + "å¹¼ ç¨ļ", + "è¦ģ 好好", + "趴 åľ¨", + "ç´¢ åıĸ", + "ç»Ī çĶŁ", + "åħ¨ æµģç¨ĭ", + "éģ© çķ¶", + "åįıè°ĥ åıijå±ķ", + "æĬ¥ ä»ĩ", + "ç§ijæĬĢ åĽŃ", + "ä»Ģä¹Ī éĥ½ä¸į", + "æľĢåIJİ ä¸Ģ次", + "ç»Ļ人 ä¸Ģç§į", + "æł¸ å®ļ", + "被 åĪĹåħ¥", + "æĦı æĥ³ä¸įåĪ°", + "èĢĥ æŁ¥", + "åľ¨æѤ ä¹ĭåīį", + "æīĵ çIJĥ", + "è¶ĬæĿ¥è¶Ĭ å°ij", + "å®ļ å¾ĭ", + "è¡ĮæĶ¿ æľºåħ³", + "ä½ıæĪ¿ åħ¬ç§¯", + "å°ıå§IJ å§IJ", + "ä¸ī èı±", + "ä¿® è¡¥", + "èŀĥ èŁ¹", + "西 çĶ²", + "æĢ ł", + "çŃī å¤ļ项", + "产ä¸ļ éĽĨèģļ", + "ä»·æł¼ ä¸Ĭ涨", + "åħ¬åħ± åľºæīĢ", + "è¢ĭ åŃIJ", + "æĨ§ æĨ¬", + "çļĦæĸ¹å¼ı æĿ¥", + "åĪ° è´¦", + "çģ ½", + "å·´ èı²", + "å·´èı² çī¹", + "æ¼Ķ ä¹ł", + "èŃ¦ç¤º æķĻèĤ²", + "çķı æĥ§", + "å¼ķ æµģ", + "æĶ¶ æĶ¯", + "å±Ĥ åĩº", + "å±Ĥåĩº ä¸į", + "å±Ĥåĩºä¸į ç©·", + "æijĩ æ»ļ", + "辦 çIJĨ", + "纵 è§Ĥ", + "æķij æµİ", + "家 éĥ½çŁ¥éģĵ", + "åĮ ¯", + "å°ı 鸣", + "ä»» åĭĻ", + "计 åħ¥", + "ç«ŀ éĢī", + "å¼ĢèįĴ æĹ¶æľŁ", + "åij¨ æģ©", + "åij¨æģ© æĿ¥", + "交 ç»ĩ", + "çķ¢ æ¥Ń", + "æł¹æį® èĩªå·±", + "æĸ°äºº çݩ家", + "åѵåĮĸ åĻ¨", + "éĩĩ æļĸ", + "å¹³åĿĩ æ°´å¹³", + "åħ¬å¼Ģ 课", + "失 åĪ©", + "伺 æľį", + "çĬ ģ", + "忽 æĤł", + "主è¦ģ éĽĨä¸Ń", + "æ¤į æłij", + "æ¯Ĺ éĤ»", + "èĩº çģ£", + "åĩºåĽ½ çķĻåѦ", + "æĬĹ éľĩ", + "æĥ© æĪĴ", + "å¹´åºķ åīį", + "åĴ¸ éĺ³", + "æ°ij å±ħ", + "大çIJĨ çŁ³", + "éĿ ³", + "éķ ĸ", + "æ¸ħ è¿ľ", + "è£ħ è½½", + "èĩ Ģ", + "å½± ä¸ļ", + "å¼Ł åħĦ", + "æĤ² è§Ĥ", + "çĿĢçľ¼ äºİ", + "æįį åį«", + "åī¥ 夺", + "ç¯ Ĩ", + "å¾Ī éķ¿æŶéĹ´", + "è¥ Ł", + "第ä¸Ģ çĻ¾", + "ä¸ĢåĪĨ éĴ±", + "æĸ°éĹ» è®°èĢħ", + "éķ· æľŁ", + "æ³ķ æĪĺç»ĦåIJĪ", + "è°ģ çŁ¥éģĵ", + "èħ° éĥ¨", + "æ±ī åł¡", + "åħ¥ çĿ¡", + "åįĸ æİī", + "æ¶Īè²» èĢħ", + "æĥ¯ ä¾ĭ", + "æĥ³ äºĨ", + "æĥ³äºĨ æĥ³", + "èĢģæŧ å°ıåĮº", + "ä¼ł è¨Ģ", + "åĪĨæķ° 线", + "æµģ 泪", + "ç»Ħç»ĩ é¢Ĩ导", + "äºļ åĨĽ", + "å¢ŀåĢ¼ æľįåĬ¡", + "å¾ ¹", + "ä¼ ¶", + "äºĽ 许", + "å¸ĥ èݱ", + "强 æĤį", + "宫 å»·", + "绿 èĮ¶", + "åĮ ¡", + "å¾Ī æŃ£å¸¸", + "æĺ¥ å¤ı", + "æ¯ Ļ", + "è¯Ħ æ¯Ķ", + "åĩ¡ äºĭ", + "æĬī æĭ©", + "åĢĴ éľī", + "éĩį 度", + "åįıä¼ļ ä¼ļéķ¿", + "忧 èĻij", + "ä¸ĭ ä¸Ģç¯ĩ", + "沪 æ·±", + "æĪ İ", + "æīĵ ä»Ĺ", + "åįĪ é¥Ń", + "å¹´é¾Ħ 段", + "ä¸ŃåĽ½ 足çIJĥ", + "设计 æĸ¹æ¡Ī", + "åºĶçĶ¨ æŁ¥çľĭ", + "é¢Ħ æĸĻ", + "åĹ ¡", + "ç¥ĸ çĪ¶", + "çļĦä¸Ģ åijĺ", + "æ´Ĺ å¹²åĩĢ", + "åİĨåı² æĸ°", + "åİĨåı²æĸ° é«ĺ", + "çĭ¬ åħ·", + "æħĭ 度", + "æīĵ 交", + "æīĵ交 éģĵ", + "é»Ħ çŁ³", + "çĽ¼ æľĽ", + "çī§ åľº", + "转 弯", + "åįĩ åįİ", + "åĨį ä¹Łæ²¡æľī", + "èĭ± æīį", + "æĽ´ åIJį为", + "åĢŁ çĶ¨", + "çºł éĶĻ", + "ç»Ŀ对 ä¸įä¼ļ", + "çİĭ çīĮ", + "çĽĨ åľ°", + "失 è°ĥ", + "好 象", + "é³ ¥", + "ä¿Ŀ ä¿®", + "åĽĽä¸ª èĩªä¿¡", + "头 çļ®", + "åİŁ åīĩ", + "æĬ¥ æ¡Ī", + "奴 éļ¶", + "å³ Ļ", + "è°ĥ æĸĻ", + "ä¹Ł 許", + "èIJ½ åĪ°", + "èIJ½åĪ° å®ŀ", + "èIJ½åĪ°å®ŀ å¤Ħ", + "çĦļ çĥ§", + "çĶŁæ´» çݯå¢ĥ", + "åºĶ åıĬæŶ", + "è¶Ĭ è¿ĩ", + "æĦŁ è¬Ŀ", + "æĻ¯ å¾·", + "æĻ¯å¾· éķĩ", + "çĬ Ģ", + "身 éĤĬ", + "ç¨İåĬ¡ æĢ»å±Ģ", + "åĩĢ åľŁ", + "ä¾µ åįł", + "åĬ¨ å·¥", + "å¹´ ä¹ĭ", + "å¹´ä¹ĭ ä¹ħ", + "第äºĮ èĬĤ", + "åĬ¨çī© åĽŃ", + "第ä¸Ģ 书记", + "éħ ļ", + "çĶŁäº§ 设å¤ĩ", + "æŁIJç§į ç¨ĭ度", + "åľ Ń", + "åĩŃåĢŁ çĿĢ", + "éĺħ è§Ī", + "çĻ½ æ²Ļ", + "æ²¹ çĥŁ", + "çªģçł´ åı£", + "åıĹ å½±åĵį", + "åı¯ä»¥ æĽ´å¥½", + "å³° åĢ¼", + "æĿĤ è´¨", + "宿 è¿ģ", + "çĽĺ æ´»", + "æ¿Ģ èµ·", + "åĦ¿ ç§ij", + "åĿIJ èIJ½åľ¨", + "æĮª å¨ģ", + "æµ· å²Ľ", + "绣 绣", + "éĻ ¨", + "ä¼ĺ äºİ", + "å°Ī 家", + "ä¸Ģ éĤĬ", + "èIJ Ĭ", + "äºĨä¸Ģ åı£", + "æ²ĥå°Ķ æ²ĥ", + "æŃ£å¸¸ 使çĶ¨", + "æĻ®éģį åŃĺåľ¨", + "丰 满", + "çĶ» åį·", + "åºĶ æĶ¶", + "åºĶæĶ¶ è´¦", + "åºĶæĶ¶è´¦ 款", + "å®Įæķ´ çĥŃ", + "å®Įæķ´çĥŃ æ¦ľ", + "注 è§Ĩ", + "çĨ Ħ", + "èº ¬", + "éĶĢåĶ® 人åijĺ", + "è¶ĭ åIJij", + "çĦ¦ æĢ¥", + "åįģå¹´ åīį", + "ä¼łç»Ł 产ä¸ļ", + "質 éĩı", + "åĩ¤åĩ° ç½ij", + "èµĦæºIJ æķ´åIJĪ", + "æ¶Į åħ¥", + "æĸĩåĮĸ ä¼łæĴŃ", + "çķĮ 第ä¸Ģ", + "æ°´ æ³µ", + "宫 殿", + "æİ¢ 寻", + "ä¿® åīª", + "æĦı è¦ĭ", + "ç´Ĭ ä¹±", + "æĽ ī", + "çĻ½ è¡£", + "èĻİ åį«", + "ç´§ æī£", + "å¤Ħå¤Ħ éķ¿", + "åĪĽå»º å·¥ä½ľ", + "红 æŀ£", + "饼 å¹²", + "äºĨ åįĬ天", + "ä¼ļå½±åĵį åĪ°", + "çĽ¸ä¿¡ 大家", + "èħ¾ é£ŀ", + "å°± å¦ĤåIJĮ", + "ä¸ĭéĿ¢ å°ıç¼ĸ", + "æ°ijèIJ¥ ç»ıæµİ", + "æĻ ¦", + "è£ħ æī®", + "é»ij å¤ľ", + "常 å¾·", + "å·¥ä¸ļ 大åѦ", + "æĺİ çŁ¥", + "éĺŁåijĺ 们", + "åIJ¬ 课", + "æ¯ı éļĶ", + "羣æĺ¯ å¤ª", + "åIJĪä½ľ åħ±èµ¢", + "çIJĨ åıij", + "æīį å¹²", + "çľĭ èµ·ä¾Ĩ", + "殿 ä¸ĭ", + "å®ī éĺ³", + "æīĢ 产çĶŁçļĦ", + "éĽĩ ä½£", + "æĬ¬èµ· 头", + "æį® æĬ¥éģĵ", + "éļĨéĩį 举è¡Į", + "交 éĶĻ", + "è¶ħ é¢Ŀ", + "åĮĸ çĸĹ", + "é¡ Ĩ", + "纵 æ·±", + "çĪ±åĽ½ 主ä¹ī", + "éĻ¢ åī¯éĻ¢éķ¿", + "è® ³", + "羣æŃ£ åģļåĪ°", + "åѤ åįķ", + "èĩªçĦ¶ èĢĮ", + "èĩªçĦ¶èĢĮ çĦ¶", + "ä¿® 身", + "èĬ ¹", + "æģ¯ æģ¯", + "æģ¯æģ¯ 缸åħ³", + "驾 æł¡", + "æİ© 饰", + "æ³½ è¿ŀ", + "æ³½è¿ŀ æĸ¯åŁº", + "举 æŃ¢", + "管çIJĨ ä½ĵåĪ¶", + "åħ¶ä¸Ń ä¹ĭä¸Ģ", + "æĿ¾ å¼Ľ", + "æĭ¦ æĪª", + "åį« åģ¥", + "åį«åģ¥ å§Ķ", + "ä»İ åݻ年", + "åĤ ¢", + "è´Ń 票", + "åĽ¾ æłĩ", + "æ²³ 西", + "æ°ijæĶ¿ å±Ģ", + "ç§ģ èIJ¥", + "å¤ĸåĽ½ è¯Ń", + "å¹² è´§", + "æĵ¦ æĭŃ", + "åľ° ä¸Ń", + "åľ°ä¸Ń æµ·", + "æµĵ æµĵ", + "æµĵæµĵ çļĦ", + "å§ĭ 建", + "å§ĭ建 äºİ", + "ç¶ĵ æŃ·", + "è·¯ æ¼Ķ", + "æļ´ é£İ", + "åŁº è¾ħ", + "æī¶è´« å·¥ä½ľ", + "ä¸Ģ缴 å¤Ħäºİ", + "æĥħ 趣", + "äºĮ åŃ£åº¦", + "åİĮ æģ¶", + "顺åĪ© å®ĮæĪIJ", + "æŁ¥ å°ģ", + "顶 端", + "ä¸į åŃķ", + "ä¸Ģ大 åłĨ", + "被 æ·ĺæ±°", + "æĺ¯ çĶ¨æĿ¥", + "æľĢ åIJĪéĢĤ", + "亮 çľ¼", + "并ä¸įæĺ¯ å¾Ī", + "ç§ijçłĶ éĻ¢", + "ç§ijçłĶéĻ¢ æīĢ", + "ç² Ł", + "é¢Ī éĥ¨", + "é»ĺé»ĺ åľ°", + "é«ĺä¸Ń çĶŁ", + "æĹıèĩªæ²» åİ¿", + "æķĻåѦ è´¨éĩı", + "æĪĺ çģ«", + "åĿİ åĿ·", + "æIJŃ ä¹ĺ", + "è¯Ĺ æĦı", + "åĪij èѦ", + "åĩº æ±Ĺ", + "åįģåħŃ æĿ¡", + "请 åıĬæŶ", + "åĨľä¸ļ 大åѦ", + "èIJ½ åı¶", + "æĢ» èĢĮè¨Ģ", + "æĢ»èĢĮè¨Ģ ä¹ĭ", + "æĿľ åħ°", + "æĿľåħ° çī¹", + "éĻª ä½ł", + "åħ¬ æĬ¥", + "çķĻè¨Ģ æĿ¿", + "éĺħ åİĨ", + "競 çĪŃ", + "ç»Ļ åĪ«äºº", + "æĹ¥æĬ¥ 社", + "åĿIJ èIJ½", + "åĿIJèIJ½ äºİ", + "éĩij åŃĹ", + "éĩijåŃĹ å¡Ķ", + "åĽ ¤", + "è¯Ŀ åī§", + "æĮģç»Ń æİ¨è¿Ľ", + "æ¼ı æ°´", + "詳 ç´°", + "æĢĢ æĬ±", + "åıĺ å¹»", + "饥 饿", + "éļIJ 身", + "个 èµĽåŃ£", + "åĵ¡ å·¥", + "æģ¢å¤į æŃ£å¸¸", + "äºĨ 好å¤ļ", + "æĺŁ å·´", + "æĺŁå·´ åħĭ", + "åħī çݯ", + "å¸ħ åĵ¥", + "çĻ½ éĽª", + "ç¨į ç¨į", + "计 æıIJ", + "æĦĽ æĥħ", + "éİ ĸ", + "ä¿¡ éĺ³", + "è§Ģ å¯Ł", + "å¦Ĥæŀľä½ł æĥ³", + "缸æ¯Ķ ä¹ĭä¸ĭ", + "解 å¼Ģ", + "æīĵåį° æľº", + "身 躯", + "ç²¾ç¥ŀ æĸĩæĺİ", + "èĤ¡ æĮĩ", + "å¾® åĪĽ", + "红 èĮ¶", + "èĩ´ çĻĮ", + "æģ© æĸ½", + "èħ¿ éĥ¨", + "大åŀĭ å¤ļ人", + "å®ī åĢį", + "è¾ħ导 åijĺ", + "èĪª éģĵ", + "å¸ĥ å°Ķ", + "åįĹå®ģ å¸Ĥ", + "ä¸ĬçıŃ æĹı", + "侧 ç»ĵæŀĦæĢ§", + "追 éļı", + "å½ĵåľ° æĶ¿åºľ", + "èµ° åĩºæĿ¥", + "éĩijèŀį ä¸ļ", + "丼 书", + "é¡¹çĽ® ç»ıçIJĨ", + "è¿ĩ æĪ·", + "骨 æŀ¶", + "è¡ Ļ", + "ä»Ģ 麽", + "èħ ĭ", + "è¦ģ 害", + "åľ¨ åºĬä¸Ĭ", + "代è¨Ģ 人", + "並 å°ĩ", + "åIJĦ个 æĸ¹éĿ¢", + "è°´ è´£", + "åħ± æĮ¯", + "åį³å°Ĩ åĪ°æĿ¥", + "èĤº çĻĮ", + "ä¾Ľ éĶĢ", + "丼 æŀĹ", + "èµ ĥ", + "åįģä½Ļ å¹´", + "åĭĺ æİ¢", + "飵 åij³", + "èĭ¦ ç¬ij", + "æľĢ大 ç¨ĭ度", + "éĩįçĤ¹ åħ³æ³¨", + "ä¹ĭ 举", + "满 æĢĢ", + "åıĹåĪ° å½±åĵį", + "æĭĽ æĬķæłĩ", + "è¡¥ é½IJ", + "西 红", + "西红 æŁ¿", + "é¬ §", + "è£ħ åį¸", + "éĤ» éĩĮ", + "èĤĩ äºĭ", + "æİĴ æ¯Ĵ", + "åѤ åĦ¿", + "鼶 è·Ŀ离", + "å®ŀ å¹²", + "çľĭ æŁ¥çľĭ", + "æĶ¶è´¹ ç«Ļ", + "ç» ·", + "åħ¬çĽĬ æĢ§", + "éĢĴ ç»Ļ", + "æĶ» æīĵ", + "æĺŁçº§ éħĴåºĹ", + "æĺİ åªļ", + "çį¨ ç«ĭ", + "è¯Ŀè¯Ń æĿĥ", + "ä¸ĢæŃ¥ ä¸ĢæŃ¥", + "书æ³ķ 家", + "æľªç»ı æİĪæĿĥ", + "çŁ³ èĨı", + "åĩŃ ä»Ģä¹Ī", + "çļĦ æĹ¥", + "çļĦæĹ¥ åŃIJéĩĮ", + "诱 人", + "çĻ¾åĪĨ çĻ¾", + "èĪĪ 趣", + "å¼ł åħĪçĶŁ", + "èĢģçĪ· åŃIJ", + "æ³¢ çī¹", + "åŁºéĩij 份é¢Ŀ", + "æ²Ļåıij ä¸Ĭ", + "å¥ĭæĸŠ缮æłĩ", + "æ°¢ èĥ½", + "æ²ĥå°Ķ çİĽ", + "義 åĭĻ", + "éŁ³ ç®±", + "æ²ī 浸", + "æ²ī浸 åľ¨", + "èĭ± åľĭ", + "çģ¯ çģ«", + "è¿Ľ 项", + "两 端", + "ä¹Ķ 丹", + "èĦ¸ é¢Ĭ", + "åıijå±ķ æ½ľåĬĽ", + "åĭķ ä½ľ", + "åĵĪ ä½Ľ", + "å®´ ä¼ļ", + "æ§ į", + "ç«ĭ å¿Ĺ", + "ç¡ķ士 åѦä½į", + "åĭĭ ç«ł", + "è¿Ļ åľºæ¯ĶèµĽ", + "æĮģ å¹³", + "éķĢ éĶĮ", + "èĭ± çī¹", + "èĭ±çī¹ å°Ķ", + "æķĻ èģĮå·¥", + "åĬŁ åĬĽ", + "该 æ¡Ī", + "ä¸Ģ æ¢Ŀ", + "åĺī å¹´", + "åĺīå¹´ åįİ", + "è¿« ä¸įåıĬ", + "è¿«ä¸įåıĬ å¾ħ", + "è¿Ļ个 æĹ¶ä»£", + "精彩 æĴŃæĬ¥", + "人 èĦ¸", + "人èĦ¸ è¯ĨåĪ«", + "æ£Ģå¯Ł å®ĺ", + "å°ı èħ¿", + "éĨĴ 缮", + "åħļ æĢ»", + "åħļæĢ» æĶ¯", + "æĪ Ł", + "èĮ« çĦ¶", + "è±Ĩ æµĨ", + "主 æ²»", + "éĿĴæµ· çľģ", + "åĪijäºĭ 责任", + "çł °", + "ä¹ĭ æ¬ĬåĪ©", + "äºĶ å®ĺ", + "è¿· æĥij", + "åħ¥ åºĵ", + "家 纺", + "å¼¹ ç°§", + "åįģäºĶ æĿ¡", + "ç»Ļ å®Ŀå®Ŀ", + "èĪªç©º èĪªå¤©", + "å¾Ģ å¤ĸ", + "å¼ķ åĬĽ", + "çľ¼ çļ®", + "æ¶ī 足", + "æĿ¥ 宾", + "åľ¨çº¿ è§Ĵèī²", + "çĥŃ éĶĢ", + "æµģ éĢĿ", + "泡 泡", + "éĻį å¹ħ", + "è´ŁéĿ¢ å½±åĵį", + "红 楼", + "红楼 梦", + "éļĶ çĿĢ", + "ä¾¥ 幸", + "许 ä¹ħ", + "åĴĮ çĿ¦", + "èŃ ½", + "使çĶ¨èĢħ æĪĸ", + "ä¹° åįķ", + "è¿ ´", + "é£İ æīĩ", + "æķĻ 師", + "æ¡ĮåŃIJ ä¸Ĭ", + "å¾Ī æ¼Ĥ亮", + "åł± å°İ", + "第ä¸Ģ åŃ£åº¦", + "ç©© å®ļ", + "æĤ² åĵĢ", + "çĿĢåĬĽ æīĵéĢł", + "æĮ Ł", + "è·¯ æ¡¥", + "åij IJ", + "åľ£è¯ŀ èĬĤ", + "çļĩ åŃIJ", + "ä»ĩ æģ¨", + "éħĿ éħ¿", + "ä¸į éĹ´", + "ä¸įéĹ´ æĸŃ", + "æĮĩ å°ĸ", + "ä¸ŃåĽ½ ç½ij游", + "åŀ £", + "æĦıè§ģ 建议", + "æ¯ħ çĦ¶", + "亮 度", + "èģĶ è°Ĭ", + "å½ķ åħ¥", + "åĦ ²", + "å¨ĺ 家", + "ç§ij å°Ķ", + "ä¹Łæ²¡ ä»Ģä¹Ī", + "æł¹æį® ä¸įåIJĮ", + "åı¶ ä¿®", + "åĢ¼ å®Ī", + "æľ« 端", + "åĪ ¨", + "åĤµ åĭĻ", + "èģ¯ åIJĪ", + "å¥ĩ å¹»", + "èĻļ æŀĦ", + "é»Ħ æĺı", + "å¹³ åĿ¦", + "æµģ æ°ĵ", + "æĸ° åŁºå»º", + "æĮ½ æķij", + "åįİ å°Ķ", + "åįİå°Ķ è¡Ĺ", + "æľĢ åıĹ欢è¿İ", + "ç»Ń 约", + "å¼Ĭ 端", + "éŃĶ æ³ķå¸Ī", + "éŃĶæ³ķå¸Ī åĴĮ", + "åħ·ä½ĵ åĨħ容", + "çIJī çĴĥ", + "æī© 容", + "èĮ¶ åĽŃ", + "主ä¹ī èĢħ", + "ç«ĭ éĿ¢", + "æİ¥åıĹ éĩĩ访", + "åĩº åħ¥å¢ĥ", + "ç§ij åįı", + "éĴ ³", + "çµIJ æ§ĭ", + "ç»ĵæŀľ æĺ¾ç¤º", + "åı° è´¦", + "å°± æĿ¥çľĭçľĭ", + "èĩª æķij", + "åıį æĩī", + "åİ» åĵªåĦ¿", + "è¿Ļ é¦ĸ", + "è¿Ļé¦ĸ æŃĮ", + "åIJ¬ ä¼Ĺ", + "å¤ĸ 壳", + "ä½ĵèĤ² é¦Ĩ", + "實 æĸ½", + "èŀº ä¸Ŀ", + "æĭī åįĩ", + "çĮĽ åľ°", + "åħ¨åĽ½ 人æ°ij", + "æĤī å°¼", + "æĹı 群", + "åĽ¢ åijĺ", + "两个 å°ıæŶ", + "åľ¨ çݩ家", + "åľ¨çݩ家 ä¸Ń", + "çĶľ çĶľ", + "æĬķ è¡Į", + "åįĶ æľĥ", + "éĻ ¡", + "åĬłå·¥ åİĤ", + "æ¦Ĩ æŀĹ", + "æŃ» è§Ĵ", + "åĨħ å¹ķ", + "æīĢæľī æĥħèĬĤ", + "åĪ· åį¡", + "æ°´ èĤ¿", + "èĥĥ åı£", + "å«Į å¼ĥ", + "æ²® 丧", + "ä¸īå¹´ 级", + "æ¶Ĥ å±Ĥ", + "å¿ĥ 仪", + "å¿ĥ仪 çļĦ", + "å¤ Ń", + "é¦ĸ è½®", + "æĹłè®ºæĺ¯ åħ¶", + "éĢı æ°Ķ", + "äºĮ åįģäºĶ", + "ç® «", + "åĬŁ åĬ³", + "çŃ¾ ä¸ĭ", + "æ²ī è¿·", + "æķij åij½", + "éĹª éĹª", + "åIJĥ äºı", + "å±ķ åĵģ", + "åį³æŶ åıijçĶŁ", + "ç¶ ľ", + "綾 åIJĪ", + "æłĩ æĺİ", + "çľĭ çĶµå½±", + "åħ¬ ç«ł", + "éĺ¿ æ£®", + "éĺ¿æ£® 纳", + "身 åĪĽéĢł", + "身åĪĽéĢł çļĦ", + "æ¸Ľ å°ij", + "åĢ¼å¾Ĺ åħ³æ³¨", + "鼶åĶ® åķĨ", + "æįĨ ç»ij", + "è¸ı åħ¥", + "èĽ Ł", + "æŁ´ 纳", + "èĢģ åħµ", + "绿èī² çݯä¿Ŀ", + "é¹ Ń", + "麻 æľ¨", + "æıŃ çīĮ", + "è¿Ļ款 车", + "ç¾İ å¾·", + "ç¾İå¾· åħ¬åı¸", + "æ¶ §", + "è°ģ çŁ¥", + "æ´ĭ èij±", + "æ¯į æł¡", + "ä¸Ģ éĹª", + "çĶ· 主è§Ĵ", + "æĹłçº¿ çĶµ", + "å±ł å®°", + "æĺ¯ éŁ©åĽ½", + "æĺ¯éŁ©åĽ½ 娱", + "容 è²Į", + "åĿĩ 使åħ¶", + "太 å¿«", + "å¹´ çĶ±", + "å¹´çĶ± 缼", + "èĭ¦ èĭ¦", + "åĬĽ è¿ĺæĺ¯", + "åĬĽè¿ĺæĺ¯ èĩª", + "æĨ ©", + "èģ¯ 絡", + "åĶ ¾", + "åħ·æľī æĪĺ士", + "追 éĹ®", + "åłĨ æĶ¾", + "åıį 驳", + "å®ŀäºĭ æ±Ĥ", + "å®ŀäºĭæ±Ĥ æĺ¯", + "åѸ éĻ¢", + "åįģ åĩłä¸ª", + "æķij æĬ¤", + "æķijæĬ¤ 车", + "ç½ij绾 ä¼łæĴŃ", + "åįģåħ« å±Ĭ", + "éĥ¨ åī¯", + "éĥ¨åī¯ éĥ¨éķ¿", + "çĹ´ è¿·", + "管çIJĨ æĿ¡ä¾ĭ", + "èŀį 为ä¸Ģä½ĵ", + "æĢ» 产åĢ¼", + "è³ ĵ", + "ä¸ĥ æĺŁ", + "çıŃ ç»Ħ", + "绣 é¢Ĩ", + "请 大家", + "éĩij éĻµ", + "èĪħ èĪħ", + "æµ· æ¹¾", + "æĸ½ çŃĸ", + "享 èªī", + "éº ¥", + "端 åįĪ", + "绿 åŁİ", + "確 ä¿Ŀ", + "å·´ æĭī", + "åĨĴ çĿĢ", + "æħ· æħ¨", + "个人 è§ĤçĤ¹", + "ä¹Ļ çĥ¯", + "ç¡ħ è°·", + "éĸĭ å±ķ", + "å°ļ 书", + "åĿļ 飧", + "åº µ", + "èĢģ é¾Ħ", + "èĢģé¾Ħ åĮĸ", + "羨 çľ¼", + "绿 æ°´", + "绿水 éĿĴå±±", + "书 é¦Ļ", + "主åĬĽ åĨĽ", + "æīįæĺ¯ çľŁæŃ£", + "æĬ¢ åħĪ", + "æĪIJå°± æĦŁ", + "éĩį æŀĦ", + "éĴ¢ åİĤ", + "æĪIJ 份", + "èĬ± 纹", + "ä¹ĭ äºī", + "å¹² ç»Ĩèĥŀ", + "æĹ¢ åı¯ä»¥", + "ç¹ģ çIJIJ", + "æĦļ èł¢", + "éĿŀ常 æĺİæĺ¾", + "ä½ĵ 彩", + "æĬĢ æ³ķ", + "æĿĨ èıĮ", + "å¹¿æ³Ľ åħ³æ³¨", + "åĮĹ å®ĭ", + "å§Ĭ 妹", + "åįı åĬŀ", + "æ·® åįĹ", + "çĥ ı", + "æ´Ĺ èĦ¸", + "åıĹ è®¿", + "åıĹ访 èĢħ", + "éĩįè¦ģ åĽłç´ł", + "å½±è§Ĩ åī§", + "综èīº èĬĤ缮", + "èľķ åıĺ", + "äºĮ 线", + "äºĮ线 åŁİå¸Ĥ", + "ä¼Ĭ å§ĭ", + "çıĬ çijļ", + "èĩª æŁ¥", + "åħ¥ åĽŃ", + "åĩ¶ æīĭ", + "åħ¬ è¯ī", + "éģĩ éļ¾", + "éĩĩçŁ¿ çŃī", + "èĩª çIJĨ", + "åĸ· æ¶Ĥ", + "æī© åħħ", + "éĢı è§Ĩ", + "é«ĺéĢŁ å¢ŀéķ¿", + "åĽ¾ çĶ»", + "ç¾ ¹", + "èĤĩ åºĨ", + "è¾ľ è´Ł", + "èµĶ ä»ĺ", + "è· ¡", + "åģ¥åº· æĪIJéķ¿", + "以ä¸Ĭ åѦåİĨ", + "åıĸå¾Ĺ 以åıĬ", + "æ²ī 积", + "åįģä¹Ŀ å±Ĭ", + "缸éĹľ æľįåĭĻ", + "æī§ åĭ¤", + "åī¯ åİ¿éķ¿", + "å¯ °", + "åģľ æ»ŀ", + "æ·¹ 没", + "çŁ³ çģ°", + "çį ¸", + "åĢ ¦", + "ç¾İ åªĴ", + "æķĻ æ¡Ī", + "åĬł çĽĸ", + "åħ¬å¼Ģ èµĽ", + "å¥ł åŁº", + "æĺĨ èĻ«", + "çŀ ħ", + "磷 éħ¸", + "äºī åĪĽ", + "çİĭ æĻĵ", + "ç¼ĵ åĨ²", + "åİļ åİļ", + "åİļåİļ çļĦ", + "æŀ£ åºĦ", + "ç²¾ çĽĬ", + "ç²¾çĽĬ æ±Ĥ", + "ç²¾çĽĬæ±Ĥ ç²¾", + "åĪĨæĶ¯ æľºæŀĦ", + "å®ŀæĸ½ ç»ĨåĪĻ", + "æĸ° èµĽåŃ£", + "總 çµ±", + "éĢł è¡Ģ", + "é¢ĩ åħ·", + "é»Ħ åŁĶ", + "è¡Ģ èĦĤ", + "交éĢļ å·¥åħ·", + "å³ ¥", + "æĹıèĩªæ²» å·ŀ", + "寺 éĻ¢", + "確 å®ļ", + "æ¦Ĥ念 èĤ¡", + "æĦŁ å®ĺ", + "æŁľ åı°", + "åĶ Ķ", + "çŀŃ解 並", + "æĢ» ä»·", + "åIJ¸ åħ¥", + "æĢ ¼", + "æĻļ éĹ´", + "å±Ĭ æ¯ķä¸ļçĶŁ", + "çĶŁ å§ľ", + "éĺħ读 åħ¨æĸĩ", + "å¾ĹåĪ° æľīæķĪ", + "æIJľ æķij", + "åİĨ æĿ¥", + "èŃī æĺİ", + "åĥ »", + "èĨ³ é£Ł", + "åĦĦ åħĥ", + "æīĵ åİĭ", + "宾 客", + "åķ ¼", + "ä¸ĢçĻ¾ å¤ļ", + "æ·±åħ¥ 人å¿ĥ", + "æ¢ħ å·ŀ", + "çłĶ åѦ", + "åħ³ ä¹İ", + "è¼ Ľ", + "亲 åıĭ", + "éħį æĸĻ", + "æĪij çĪ±ä½ł", + "è´¸æĺĵ æĪĺ", + "æľī èī²", + "æľīèī² éĩijå±ŀ", + "æįIJ åĬ©", + "为 é¦ĸ", + "为é¦ĸ çļĦ", + "å¯Į åĬĽ", + "çĶ· ç¥ŀ", + "é³ ³", + "æµĩ æ°´", + "åIJ ±", + "æĺİç¡® æıIJåĩº", + "åı¹ äºĨ", + "åı¹äºĨ åı£æ°Ķ", + "礼 æĭľ", + "è¿Ļ个 åIJįåŃĹ", + "ä¿¡ å¾Ĵ", + "å¿Ĺ 强", + "éĻIJ æŶ", + "æĶ¶ è²»", + "åĨľå®¶ ä¹IJ", + "å°ıé¾Ļ èĻ¾", + "èIJ½ å¹ķ", + "æ§ Ł", + "åѦ 龸", + "æĪĸ å¤ļ", + "æĪĸå¤ļ æĪĸ", + "æĪĸå¤ļæĪĸ å°ij", + "座è°Ī ä¼ļä¸Ĭ", + "æ¶ ¼", + "éŃĶ çİĭ", + "å² ±", + "顶 å±Ĥ", + "顶å±Ĥ 设计", + "èĦij åŃIJéĩĮ", + "éĻ¢ åŃIJéĩĮ", + "轩 è¾ķ", + "身å¿ĥ åģ¥åº·", + "èħ ij", + "éĹľ 注", + "åıĤåĬł ä¼ļè®®", + "ä¸Ńåįİ æĸĩåĮĸ", + "追 寻", + "å®ī çĦ¶", + "é£Ļ åįĩ", + "éŁŃ èıľ", + "é¸ ¦", + "åĤ¨ éĩı", + "çĶ· æĸ¹", + "å¤ĩ 份", + "æijĶ åĢĴ", + "润æ»ij æ²¹", + "éĢ¼ è¿ij", + "çĶ³ è¯ī", + "鸣 ç±»", + "çŁ³æ²¹ åĮĸå·¥", + "åĿļ æŀľ", + "è¿Ļ家 ä¼Ļ", + "æĭĴ ä¸į", + "羣 çļ®", + "è·Ŀ éĽ¢", + "è¿ĺ æĮº", + "éĽķ åĥı", + "åĪĿ æģĭ", + "æıIJä¾Ľ æĽ´å¤ļ", + "æŁ¥çľĭ åħ¨æĸĩ", + "æķ°åŃĹ è´§å¸ģ", + "åĸī åĴĻ", + "åı¦ä¸Ģ ä½į", + "åĤ¬ åĮĸ", + "åĤ¬åĮĸ åīĤ", + "ä»İæĿ¥ 没", + "å¯ĨåĪĩ 缸åħ³", + "éĥ¨ 主任", + "产åĵģ ç»ıçIJĨ", + "並 åIJĮæĦı", + "èIJ½ åħ¥", + "å±ıå¹ķ ä¸Ĭ", + "åħ¬åı¸ ç«łç¨ĭ", + "æį¢ åı¥è¯Ŀ", + "æį¢åı¥è¯Ŀ 说", + "ä½į æĸ¼", + "ä½ Ķ", + "åĩ» æĿĢ", + "缸 è¾ĥ", + "缸è¾ĥ äºİ", + "ç²½ åŃIJ", + "åįĹ æŀģ", + "宫 é¢Ī", + "è£ģ åijĺ", + "æĺİ ç»Ĩ", + "ä»·åĢ¼ éĵ¾", + "åĽĽä¸ª æĸ¹éĿ¢", + "æĥħåĨµ æĿ¥çľĭ", + "æĮij åīĶ", + "æ® ĺ", + "æŀģ åĬĽ", + "çĸij éļ¾", + "æĬµæĬĹ åĬĽ", + "æĢ¥ éĢŁ", + "æĪ Į", + "ä½İ ä¼°", + "éĹª è¿ĩ", + "æģ ¬", + "èµŀ æī¬", + "ä»ĸ å¦Ī", + "æĪIJ为 ä¸ĢåIJį", + "æ´Ĺ 礼", + "é¢Ħ计 å°Ĩ", + "åħĪè¿Ľ åįķä½į", + "è¼ Ķ", + "éĢĥ èĦ±", + "çİ° åŃĺ", + "èĢģèĻİ æľº", + "åįģä¸ĥ æĿ¡", + "åı¦ä¸Ģ åįĬ", + "温 æĥħ", + "åī¥ 离", + "ä¸ĸ è´¸", + "å®ĺ åı¸", + "å¾Ī å·®", + "éĹ´ è·Ŀ", + "请 注æĦı", + "åı² è¯Ĺ", + "åĪ© åĻ¨", + "è¿IJ ç®Ĺ", + "沦 为", + "該 使çĶ¨èĢħ", + "èĮ ¬", + "éĶ¦ 绣", + "åı² æĸĻ", + "çģµ æ´»æĢ§", + "èģĶ 社", + "æĹł åĬ©", + "æĬĹ æ°§åĮĸ", + "èıľ èĤ´", + "éĢł èĪ¹", + "æİī èIJ½", + "å¤į æŁ¥", + "åĭĥ åĭĥ", + "åij¼ 声", + "給 äºĪ", + "åIJĮäºĭ 们", + "ç½ °", + "è¯ķ æİ¢", + "åħ³éĶ® åŃĹ", + "æįIJ çĮ®", + "ç»Łè®¡ æķ°æį®", + "åĪĽ ä½ľèĢħ", + "ä¸ĭ åįĬ", + "ä¸ĭåįĬ åľº", + "æī¿æĭħ 责任", + "端 æŃ£", + "ç©¿ è¡£", + "ä¼ł çIJĥ", + "åĬ© éķ¿", + "åĩ ±", + "éķ¶ åµĮ", + "é£ŀ ç¿Ķ", + "è¾ĵ åįµ", + "è¾ĵåįµ 管", + "ä¸ĩ åħ¬éĩĮ", + "æݨ广 åºĶçĶ¨", + "å¿« æ¨Ĥ", + "ç§ ½", + "èī° å·¨", + "åIJ¬ å®Į", + "åĿļ 硬", + "奥 åľ°", + "å¥¥åľ° åĪ©", + "é¢ ĵ", + "èĻIJ å¾ħ", + "ä¾Ľ æ±Ĥ", + "éľī ç´ł", + "伪 è£ħ", + "乡 åľŁ", + "åĩ¡ æľ¬ç½ij", + "åĩ¡æľ¬ç½ij 注", + "ä¼Ĭ åĪ©", + "è¡¡ æ°´", + "æĽ´ åĥıæĺ¯", + "åĪĨéĴŁ å·¦åı³", + "è¦ı 模", + "äºĶ åĪĨéĴŁ", + "åºĹ åĬłçĽŁ", + "åĽ° éĽ£", + "åħ³ åģľ", + "æĢĿ 绪", + "åĴ½ åĸī", + "缸 符", + "çĥ¦ èºģ", + "æĻĤ æľŁ", + "åijĪ çı¾", + "解 æķ£", + "诱 导", + "éļĶ çĥŃ", + "çĮ ¶", + "åįĹ å®ĭ", + "æ·±åħ¥ äºĨ解", + "çŃĶ çĸij", + "æĺ¼ å¤ľ", + "åįĥ ä¼ı", + "åĬ³åĬ¡ æ´¾éģ£", + "红 è±Ĩ", + "åĿı äºĭ", + "çĤ¹ æ»´", + "å°±ä¸ļ å²Ĺä½į", + "约 åIJĪ", + "åħį éĻ¤", + "éĢĨ åĬ¿", + "éĩį éĩijå±ŀ", + "å®ĺ 宣", + "ä½İ å»ī", + "æģ¨ ä¸įå¾Ĺ", + "å¾Ĺ 天", + "å¾Ĺ天 çĭ¬", + "å¾Ĺ天çĭ¬ åİļ", + "ä¸Ģå°ģ ä¿¡", + "æĬ½ å¥ĸ", + "è¾Ĺ 转", + "çķĻ å®Ī", + "çķĻå®Ī åĦ¿ç«¥", + "çŃĶ åį·", + "å·¨ åŀĭ", + "æľĢ好 ä¸įè¦ģ", + "æµĻæ±Ł 大åѦ", + "æĨ ¨", + "æı¡ æīĭ", + "éĴĪ ç»ĩ", + "æİĴ 骨", + "çĤ ½", + "å°ģ è£ħ", + "åįĢ åŁŁ", + "空æ°Ķ åĩĢåĮĸ", + "åħī å½±", + "åĢĴ å¡Į", + "å§ļ æĺİ", + "æ¤į 被", + "åѦ åīį", + "åѦåīį æķĻèĤ²", + "èĬĿ åĬł", + "èĬĿåĬł åĵ¥", + "缩 æ°´", + "ä½ Ł", + "åľ¨çº¿ åĴ¨è¯¢", + "èµı æŀIJ", + "éĿĴ èĽĻ", + "æĬ± ä½ı", + "èĮĤ åIJį", + "åħ¨åĬĽ æīĵéĢł", + "åįļ士 åѦä½į", + "沧 å·ŀ", + "åĻ ¢", + "æĿĤ çī©", + "åĪ» çĶ»", + "æį ħ", + "å¾® éĩı", + "å¾®éĩı åħĥç´ł", + "ä¸Ģ åĽŀäºĭ", + "鸡 èĤī", + "åĪ©æ¶¦ çİĩ", + "æīį ç®Ĺ", + "å¾® å¦Ļ", + "棵 æłij", + "è´ª 婪", + "åĩı åĢ¼", + "梦 å¢ĥ", + "åı¯ è§Ĩ", + "åı¯è§Ĩ åĮĸ", + "广大 å¸Ĥæ°ij", + "ä¸ĵä¸ļ ä»İäºĭ", + "ç»ı 纬", + "ç´§ çĽ¯", + "çŁ¥ å·±", + "è¤ ļ", + "æĸĩåĮĸ åºķèķ´", + "åݦéŨ å¸Ĥ", + "临 港", + "对åħ¶ 羣å®ŀ", + "岸 è¾¹", + "è¦ĸ çĤº", + "æĬĹ çĻĮ", + "åĶIJ å®ĩ", + "ä¸įå¾Ĺ è¶ħè¿ĩ", + "å¨ģ æħij", + "æ¡Ĩæŀ¶ åįıè®®", + "èµ° ç§ģ", + "åĽ¢ å§Ķ", + "夸 大", + "æ¬ Ħ", + "ç¥ŀç»ı ç³»ç»Ł", + "æijĦå½± ä½ľåĵģ", + "èĬ ¥", + "å®ī åºĨ", + "æµ· 滨", + "æŀĦ æĢĿ", + "çīµ æĮĤ", + "åı ©", + "éĺIJ æĺİ", + "éģ ģ", + "ç²¾ æ²¹", + "ç©´ ä½į", + "æĬ¤ 身", + "æĬ¤èº« 符", + "æĮĩ å°İ", + "åŃĺåľ¨ ä¸Ģå®ļ", + "å¯Ĥ éĿĻ", + "æµ·å¤ĸ å¸Ĥåľº", + "éĿ ¡", + "综åIJĪ å¾ģ", + "ä¿ IJ", + "è¨Ī ç®Ĺ", + "æĺİ æľĹ", + "äºļ è¿IJ", + "äºļè¿IJ ä¼ļ", + "åīįçŀ» æĢ§", + "åĮ® ä¹ı", + "产ä¸ļ æī¶è´«", + "èĦij æµ·", + "èĦijæµ· ä¸Ń", + "åħļçļĦ é¢Ĩ导", + "åĪĺ éĤ¦", + "æµģ æĺŁ", + "æĵ Ĥ", + "æĶĢ çĻ»", + "åĴ Ķ", + "ä¸Ģä¸ĭåŃIJ å°±", + "è¯Ĭ æ²»", + "使 åĬ²", + "åīµ ä½ľ", + "éĵŃ è®°", + "éĴ± è´¢", + "æĹ¥æĬ¥ è®°èĢħ", + "çĥŁ çģ«", + "èĥľ è´Ł", + "åįļ 主", + "ä¸ŃåĽ½ èģĶéĢļ", + "ç½ijç«Ļ é¦ĸ页", + "å°± å¤Ł", + "å°±å¤Ł äºĨ", + "æīij åħĭ", + "å±ħ å§Ķä¼ļ", + "è° ¬", + "å®īåħ¨ äºĭæķħ", + "åķĨ çĶ¨è½¦", + "循çݯ ç»ıæµİ", + "æ· ¤", + "èĢĥ è¯ģ", + "å®Ŀ èĹı", + "å®Į ç»ĵ", + "çłĶåıij æĬķåħ¥", + "å² ij", + "æģŃ æķ¬", + "离 éĢĢä¼ij", + "æ°´ 墨", + "å© ¶", + "è¯Ĺ åı¥", + "å®ģæ³¢ å¸Ĥ", + "å¼± çĤ¹", + "åģľ çīĮ", + "奶 æ²¹", + "å¥ĩ纳 æ²³", + "æĨ Ĥ", + "社ä¼ļ å®ŀè·µ", + "è´Ŀ 壳", + "çłĤ æµĨ", + "èĪ¹ åıª", + "宣 æī¬", + "综åIJĪ æķ´æ²»", + "åĤ ij", + "æ°ijæĹı æĸĩåĮĸ", + "éĩį çİ°", + "积 æ·Ģ", + "åħ¬ çĦ¶", + "çħ ī", + "缸 èģļ", + "æ± ¾", + "纹 çIJĨ", + "çĩĥ çħ¤", + "æѤ ç§į", + "ç¾İ å¦Ĩ", + "åįĥ çĵ¦", + "çIJ Ľ", + "驾驶 è¯ģ", + "éĺ¶ æ¢¯", + "ä¸Ŀ ä¸Ŀ", + "å¾Īå¤ļ äºĭæĥħ", + "åħī éĺ´", + "èijĹä½ľ æ¬Ĭ", + "åħ§ éĥ¨", + "çĽ¸å¯¹ æĿ¥è¯´", + "éĸ Ĵ", + "éľĩ æħij", + "說 話", + "æĨ ij", + "ç«¥ è£ħ", + "ä½ıæĪ¿ åĴĮ", + "ä½ıæĪ¿åĴĮ åŁİ", + "å·²ç»ı è¶ħè¿ĩ", + "侦 å¯Ł", + "çŁ¿ çī©", + "ä¾Ľ 大家", + "çī¹ éĤĢ", + "ç¨ĭåºı åijĺ", + "çķľçī§ ä¸ļ", + "æ° ª", + "çij ª", + "åĢĴ åľ¨", + "åĢĴåľ¨ åľ°", + "æ¯ Ģ", + "梯 éĺŁ", + "æİ¥ èijĹ", + "æĬĹ èıĮ", + "è¤ ĩ", + "ç¬ Ļ", + "æ¯Ķ ä¸Ĭå¹´", + "鸡 汤", + "åŃ¦ä¹ł æĪIJ绩", + "æĸij æĸĵ", + "åħĪ 导", + "åĪĹ ä¸¾", + "è°ĥæŁ¥ æĺ¾ç¤º", + "æ© «", + "ä¹Ŀ åįģ", + "è°¢ 飵", + "è·¨è¶Ĭ å¼ı", + "女æĢ§ æľĭåıĭ", + "èIJ¥åħ» ä»·åĢ¼", + "å®ŀè·µ ç»ıéªĮ", + "èĭı å·ŀå¸Ĥ", + "çĵ¶ åŃIJ", + "æĸ° çļĦä¸Ģ", + "æĸ°çļĦä¸Ģ å¹´", + "æĺİ æĻ°", + "å®ł çĪ±", + "åŃŠ第", + "æľĹ 诵", + "纳 æĸ¯", + "éĢĨ è¡Į", + "è«ĭ æĤ¨", + "è«ĭæĤ¨ æıIJä¾Ľ", + "èĥ¸ æĢĢ", + "第ä¸ĥ å±Ĭ", + "强 壮", + "代 åŃķ", + "汶 å·Ŀ", + "家 åĸ»", + "家åĸ» æĪ·", + "家åĸ»æĪ· æĻĵ", + "èħ ®", + "åIJ¯ 迪", + "æĹł éļľç¢į", + "èĻķçIJĨ åıĬ", + "æĿ¥ åİĨ", + "å®ŀ åĬ¡", + "ä¹Ł éļıä¹ĭ", + "æĬĢèĥ½ åŁ¹è®Ń", + "åѤ ç«ĭ", + "åī ģ", + "éĥ´ å·ŀ", + "æĶ¶ æķĽ", + "éł» éģĵ", + "èį£ 幸", + "èİ« è¿ĩäºİ", + "æѤ æĻĤ", + "纪å§Ķ çĽij", + "纪å§ĶçĽij å§Ķ", + "缸 éĤ»", + "åı¦ä¸Ģ è¾¹", + "çªĴ æģ¯", + "æľīå¾Īå¤ļ ç§į", + "æ¯ı éĢ¢", + "éĹ® ä¸ĸ", + "ç´¯ ç´¯", + "éĿĴæĺ¥ æľŁ", + "è·¯ åĨµ", + "åħĭ èݱ", + "è¿Ħä»Ĭ 为æŃ¢", + "æĥĬ å¥ĩ", + "è·¨ 度", + "éħ¿ éĢł", + "åĩ ĭ", + "è¿ij ä¸īå¹´", + "åĨħ 马", + "åĨħ马 å°Ķ", + "æı į", + "è¿Ľå±ķ æĥħåĨµ", + "èĮ §", + "æľīåºı æİ¨è¿Ľ", + "æĢ» åĨłåĨĽ", + "æĪIJ绩 åįķ", + "éĽ»è©± åıĬ", + "ç´§å¯Ĩ ç»ĵåIJĪ", + "åºĬ ä½į", + "é¹ Ĭ", + "æķ£åıij çĿĢ", + "åĭŁ èµĦ", + "æ°¨ éħ¸", + "彩 ç¥ŀ", + "è®Ģ åıĸ", + "éĩį 温", + "ä¸Ń åŃĺåľ¨çļĦ", + "ç¾İ éºĹ", + "ä¸įæĸŃ å¢ŀåĬł", + "è½® æµģ", + "æİ¥ åIJ¬", + "å¹´ 产åĢ¼", + "åįĥ åħĭ", + "æĪĺåľº ä¸Ĭ", + "çħ§ 顧", + "å¹²éĥ¨ éĺŁä¼į", + "åį° ç«ł", + "ä¸Ģèĩ´ æĢ§", + "è¿ŀ å¤ľ", + "åħħ è£ķ", + "é»ij åIJįåįķ", + "åĩĢ æ°´", + "ä¸Ģ大 æĹ©", + "åĮħ 袱", + "çĬ¯ è§Ħ", + "çIJĨ è«ĸ", + "æŀģ æĺĵ", + "éª ¸", + "å¨ĺ å¨ĺ", + "åĽ¢ åľĨ", + "亿åħĥ 以ä¸Ĭ", + "åĪ©çĶ¨ æĤ¨çļĦ", + "带æĿ¥ æĽ´å¤ļ", + "ä¸Ń央 空è°ĥ", + "æľĪ èĸª", + "çĮľ æĥ³", + "åĪº 客", + "ä½ľ æģ¯", + "åįķ è°ĥ", + "äºĴ åĪ©", + "å¦Ĥæľī ä¾µæĿĥ", + "å°ı å·§", + "åįģ åł°", + "åĵĪåĵĪ åĵĪåĵĪ", + "è¾¹ éĻħ", + "æłĩ è¯Ń", + "åĪĩåħ¥ çĤ¹", + "éĢĨ è¢Ń", + "è¯ķ åīĤ", + "绿 è±Ĩ", + "è® ļ", + "åŁºçĿ£ å¾Ĵ", + "å£ ¬", + "åħ¨ æĺİæĺŁ", + "éĢī ç§Ģ", + "èĪĮ å°ĸ", + "ä¸įåIJĮ ç±»åŀĭ", + "çĥŁ åĽ±", + "çģµ æ°Ķ", + "åĮº 管å§Ķä¼ļ", + "åĨľ åī¯", + "åĨľåī¯ 产åĵģ", + "èĶļ æĿ¥", + "沪 æĮĩ", + "åħ»æ®ĸ æĪ·", + "æĸĹ å¿Ĺ", + "é¦ĸ é¢Ĩ", + "è¡Ģ èħ¥", + "åĬł ç´§", + "ä¸Ģèĩ´ 好è¯Ħ", + "第ä¸ī èĬĤ", + "æī¬ å°ĺ", + "交éĢļ æŀ¢çº½", + "鼶 ç¢İ", + "é»ij æ´ŀ", + "çľĭ ä¸įæĩĤ", + "å±ŀ å®ŀ", + "主 åŁİåĮº", + "å¨ Ľ", + "å¨Ľ æ¨Ĥ", + "ç¬ij æĦı", + "èĻ¹ æ¡¥", + "åIJĦ个 çݯèĬĤ", + "çķ¥ å¾®", + "èĢķ èĢĺ", + "æľ¬ åľºæ¯ĶèµĽ", + "æĪIJ è´¥", + "éĢī èĤ¡", + "èªŀ è¨Ģ", + "çŃĶ 辩", + "èĩª ä¹ł", + "æ£ º", + "ä¸ĩ 欧åħĥ", + "åģľ å·¥", + "对åħ¶ è¿Ľè¡Į", + "积æŀģ éħįåIJĪ", + "ä¹¾ åĿ¤", + "å¦ĸ æĢª", + "èļĮ åŁł", + "èµĦ产 è¯Ħä¼°", + "è°ĥ çļ®", + "éĻ¤ å¤ķ", + "åĽ´ å¢Ļ", + "æľį å½¹", + "æ·± æ¸Ĭ", + "é¢Ħ åĪ¶", + "ç ĥ½", + "å®ī 稳", + "建 æŀĦ", + "çĭĻ åĩ»", + "主åĭķ 註åĨĬ", + "éĥ½æľī èĩªå·±", + "æİĴåIJį 第ä¸Ģ", + "麻 è¾£", + "çĢ ļ", + "çĥŁèĬ± çĪĨ", + "çĥŁèĬ±çĪĨ 竹", + "èĩªçĦ¶ ä¿ĿæĬ¤", + "ä»Ļ å¢ĥ", + "为äºĨ éģ¿åħį", + "åĨ· åºĵ", + "解æĶ¾ æĢĿæĥ³", + "åĪĿ äºĮ", + "ä½ĵ è´´", + "é¦ĸ å¯Į", + "迪 æĭľ", + "æļĤ ç¼ĵ", + "æĶ¯æĮģ åĬĽåº¦", + "侦 æİ¢", + "马 åĪº", + "åĮĹ æ±½", + "ç¹ ŀ", + "è°İ è¨Ģ", + "éĢ£ çºĮ", + "å· ³", + "ä»»ä½ķ æŶåĢĻ", + "车 èģĶç½ij", + "åįķ 项", + "å¸Ń åį·", + "建çŃij æĿIJæĸĻ", + "ä¸Ńç§ĭ èĬĤ", + "ç¡ķ士 çłĶ究", + "ç§ģ ç«ĭ", + "åħļåĴĮ æĶ¿åºľ", + "æľ¬æ¬¡ 交æĺĵ", + "èººåľ¨ åºĬä¸Ĭ", + "ç½ijåıĭ è¯Ħ论", + "å¦ Ŀ", + "害 ç¾ŀ", + "åħ¬ç«ĭ åĮ»éĻ¢", + "ä¸ ŀ", + "çĶŁçī© è´¨", + "åºĶ éĤĢ", + "æĬ½ åıĸ", + "åĩł å¼ł", + "æijĺ ç¼ĸ", + "ç»ĺ æľ¬", + "详 解", + "强 硬", + "æľĢ åħĪè¿ĽçļĦ", + "æĭĽ èĤ¡", + "æĭĽèĤ¡ 书", + "åįĥ æĸ¹", + "åįĥæĸ¹ çĻ¾", + "åįĥæĸ¹çĻ¾ 计", + "éħį éŁ³", + "驾 çħ§", + "å¾ģ æĪĺ", + "èªĵ è¨Ģ", + "æĭľ å¸Ī", + "æĭľå¸Ī åѦ", + "æĭľå¸ĪåѦ èīº", + "æĬ± åĽ¢", + "ç±³ ç²ī", + "éĿŀ常 éĢĤåIJĪ", + "èĪª æµ·", + "å±¥ 约", + "åįģåħ« æĿ¡", + "éĶ» éĢł", + "éĩįè¦ģ 举æݪ", + "åıijæĮ¥ ä½ľçĶ¨", + "æ· ļ", + "人 社", + "人社 å±Ģ", + "è¯ķçĤ¹ å·¥ä½ľ", + "éĺľ éĺ³", + "æ¡ĥ åľĴ", + "æ°ij ä¼ģ", + "æ´ģ çĻ½", + "è´µ 宾", + "åħ¬ 社", + "è§ī æĤŁ", + "è®°å¿Ĩ åĬĽ", + "æľĥåĵ¡ 註åĨĬ", + "æѤ æ¡Ī", + "麻 çĹ¹", + "çı Ģ", + "æĸ© èİ·", + "çĶ· åŃ©åŃIJ", + "å±ĢéĻIJ äºİ", + "åĭĺ æŁ¥", + "åIJĥ 饱", + "èĬ¬ åħ°", + "æ£ķ èī²", + "ç¦ı ç¥ī", + "çĶ³ èĬ±", + "æµ· çĽĹ", + "èĶ ij", + "æĸĩ åѸ", + "æ´»æĢ§ çĤŃ", + "缴 éĢļ车", + "è°¢ éĤĢ", + "躺 çĿĢ", + "åľ ĥ", + "æ¯ıæĹ¥ ç»ıæµİ", + "åħ¬åħ± æĸĩåĮĸ", + "讲 æķħäºĭ", + "å¯Ł çľĭ", + "æĤł éĹ²", + "åľ° åĿª", + "æ¶Į çİ°åĩº", + "é«ĺçŃī éĻ¢æł¡", + "èĮĦ åŃIJ", + "éĺ² åį«", + "ä¾ĭ è¡Į", + "æĺ¾ éľ²", + "æĸ° 常æĢģ", + "ç»Ŀ ä½³", + "å¯Į æ°ij", + "以 人æ°ij", + "以人æ°ij 为", + "éĤ¢ åı°", + "å±ķ æ¼Ķ", + "çĻ¼ å¸ĥ", + "è´Ł è½½", + "åģı 离", + "æ°¸ éģł", + "éĩįè¦ģ åİŁåĽł", + "åįıä¼ļ ä¼ļåijĺ", + "éļ¾ æ°ij", + "çĶŁäº§ 车éĹ´", + "çģµ åĬ¨", + "两年 åīį", + "æĸ¹ åľĨ", + "æ´» ä¸ĭåİ»", + "ä¸ĸçķĮ è§Ĥ", + "éªĹ åıĸ", + "ç¾İ è²Į", + "èĥ½ çľĭåĩº", + "çĻ¼ æı®", + "è§Ĥ å½±", + "åī ĥ", + "åIJĪèµĦ åħ¬åı¸", + "å© §", + "å¹² æű", + "åħŃ ä¸ªæľĪ", + "尤为 éĩįè¦ģ", + "èĤ ½", + "秦 åĽ½", + "æīĺ ç¦ı", + "建çŃij å¸Ī", + "åįĩ级 æĶ¹éĢł", + "å°ı é¢Ŀ", + "å°ıé¢Ŀ 贷款", + "两个 ç»´æĬ¤", + "æĭį æĭį", + "åı¯ çĸij", + "æį¢ åıĸ", + "æѦ 士", + "èµĸ 以", + "èµĸ以 çĶŁåŃĺ", + "æĮ ļ", + "殿 åłĤ", + "èĩªçĦ¶ çķĮ", + "ç£ģ åľº", + "å¦Ĥä½ķ çľĭå¾ħ", + "ä»ĬæĹ¥ 头æĿ¡", + "西 åŁŁ", + "èİ· è¯Ħ", + "風 æł¼", + "ä¿Ħ åĽ½", + "æīĵ æĭ¼", + "å®£ä¼ł çīĩ", + "å¾Ī æĸ¹ä¾¿", + "ä¾Ľç»Ļ 侧", + "纪念 ç¢ij", + "毫 åħĭ", + "èĬ³ é¦Ļ", + "å·¥åķĨ éĵ¶è¡Į", + "请 çĤ¹åĩ»", + "ç¼ ª", + "æĹłæķ° 次", + "èį¯ å¸Ī", + "èħ ¸", + "游 èīĩ", + "åĮ ¾", + "å·¡ èĪª", + "æ²»çIJĨ ä½ĵç³»", + "èIJ¥éĢł èī¯å¥½", + "æ·· æ·Ĩ", + "éĢļ çķħ", + "åĬ³ ç´¯", + "ä»ĵ ä½į", + "å¢ŀ éķ·", + "éļIJ 约", + "æĿĤå¿Ĺ 社", + "åħ» èĤ²", + "åı¯èĥ½ åıijçĶŁ", + "èĢĥ 試", + "西 侧", + "åĬł åĢį", + "主æĮģ åı¬å¼Ģ", + "çķ¢ ç«Ł", + "éĹ® 询", + "æµ· æ£ł", + "èĹ ©", + "注æĺİ æĿ¥æºIJ", + "æ£Ģ çĸ«", + "请 åģĩ", + "æĬļ æij¸", + "èĵĦ çĶµæ±ł", + "è·Ł ä¸įä¸Ĭ", + "çݰ代 社ä¼ļ", + "çŃ¹ èµĦ", + "ä½ĵèĤ² 彩票", + "延 误", + "è¾Ľ è¾£", + "éĿ¢ 容", + "åį° è®°", + "çģŃ äº¡", + "ç´ł é£Ł", + "åħ´ èĩ´", + "éľĢè¦ģ çĶ¨", + "éľĢè¦ģçĶ¨ åĪ°", + "å®Ŀ å¦Ī", + "ç£ĭ åķĨ", + "éļ¶ å±ŀ", + "è´¡çĮ® åĬĽéĩı", + "åħ¬åħ± èµĦæºIJ", + "大 éĺª", + "åĨĽ è®Ń", + "æĤ¬ 念", + "社ä¼ļ 稳å®ļ", + "å¹²äºĭ åĪĽä¸ļ", + "æľī æĿ¡ä»¶", + "æľīæĿ¡ä»¶ çļĦ", + "ä¸Ģå¹´ ä¸Ģ度", + "åİ ¥", + "强 奸", + "豪 车", + "æİĮ æŁľ", + "æ°´åĪ© å·¥ç¨ĭ", + "å³ ª", + "积æŀģ ä½ľçĶ¨", + "æµ· æ·Ģ", + "æµ·æ·Ģ åĮº", + "çĥŃ æĴŃ", + "åĿļæĮģ ä¸įæĩĪ", + "åıĮ èĦļ", + "绣 æĪĺ", + "ä»»ä½ķ 人éĥ½", + "åľ°ä¸ĭ 室", + "åĨ¶ çĤ¼", + "è°ħ 解", + "æ¸Ķ èĪ¹", + "太éĺ³ åŁİ", + "被 æįķ", + "计ç®Ĺ åĻ¨", + "西 åĮ»", + "èĪĴ å¿ĥ", + "æ¡ ¦", + "éģ ²", + "åĬ ij", + "è¨ Ĺ", + "èİ º", + "åĸ ¬", + "çĵ ¯", + "åĺ ĺ", + "åł ķ", + "æķ Ŀ", + "åij ¦", + "èĭ ŀ", + "æŃ ¹", + "æĵ ¬", + "æ£ Ħ", + "èĪ µ", + "å¥ ª", + "çļ ĭ", + "æĶ ¸", + "åľ ©", + "ç¤ Ļ", + "ç¢ ĺ", + "éı Ī", + "æĦ ķ", + "ç¹ ³", + "èĺ ¸", + "è² Ĥ", + "æ¼ ²", + "æij ¹", + "æĶ Ŀ", + "åŃ ¢", + "èķ Ń", + "é¨ °", + "æ½ ¼", + "éħ °", + "æĴ ¥", + "è¹ ¬", + "é¨ Ļ", + "è¸ ¹", + "éģ IJ", + "çĺ Ģ", + "èĽ ¤", + "æĤ ĸ", + "çĴ ŀ", + "ç£ IJ", + "æİ °", + "è¾ Ĭ", + "å¾ ij", + "æİ ĸ", + "éģ ŀ", + "éĤ ¸", + "éĽ ı", + "æĨ İ", + "æľ ½", + "çį »", + "ç® Ķ", + "è¤ ¶", + "æļ ¢", + "æĺ µ", + "çı Ĥ", + "æĤ ¸", + "åģ µ", + "åĻ ľ", + "å£ ¯", + "æĴ ®", + "æģ į", + "å© ķ", + "ç¯ ±", + "éĺ Ļ", + "çī ł", + "è£ ĺ", + "è³ ¢", + "éĩ ľ", + "éĵ ł", + "èİ ĺ", + "æ® Ĩ", + "çĻ ¸", + "è´ ı", + "ç² ±", + "å« ¡", + "åĨ ¢", + "è¤ Ĵ", + "æĩ Ĭ", + "éľ ĵ", + "å¡ µ", + "æĭ £", + "å» Ł", + "é£ ½", + "é¢ Į", + "åļ İ", + "æ· º", + "èĨ ł", + "åİ Ń", + "åļ ĩ", + "åij ĥ", + "çĴ ĭ", + "çŃ ±", + "æĭ ·", + "èį §", + "éĶ °", + "åŃ °", + "èĵ ĵ", + "èĨ ½", + "æŀ ī", + "åĸ ½", + "çĽ Ķ", + "çŃ IJ", + "ç¾ ļ", + "è ħĮ", + "è¾ «", + "æ³ ĵ", + "çĶ ¬", + "èŁ ²", + "åĸ ª", + "å¦ ĵ", + "è¬ Ģ", + "çĤ Ĭ", + "æĽ ľ", + "æ± IJ", + "è´ Ī", + "èį Ģ", + "æĬ ł", + "ç¢ ¾", + "æ« ĥ", + "éŀ ł", + "èij Ĩ", + "ç¥ ¯", + "å½ Ŀ", + "é¦ į", + "åĮ £", + "æľ Ń", + "åĿ Ĥ", + "ä¿ ij", + "èĵ ®", + "çij Ľ", + "æī ī", + "èĩ Ł", + "è² «", + "çİ ¥", + "æ· ¼", + "åİ ²", + "é³ Į", + "å³ Ń", + "åij Ľ", + "é §", + "é§ IJ", + "éģ ·", + "ä¿ ª", + "æĢ Ĥ", + "è¾ į", + "å± į", + "åĭ ģ", + "å¥ ļ", + "éļ ħ", + "éĴ ´", + "è¼ Ŀ", + "å® ¦", + "èIJ ĥ", + "çĺ ĭ", + "æĨ ¶", + "æĤ ħ", + "è¾ Ļ", + "åij ľ", + "çł º", + "éĢ ŀ", + "æµ ļ", + "éĸ £", + "èĸ ©", + "éĻ ĭ", + "çĤ Ļ", + "èª ķ", + "ä¸ Ł", + "é¹ ½", + "ç± Į", + "è´ °", + "éĭ ª", + "çľ ©", + "æĴ IJ", + "èĨ º", + "éŀ ĺ", + "ç¾ ²", + "çª ®", + "ç´ IJ", + "æ® ´", + "çº ¾", + "èº į", + "ç´ ĭ", + "çĦ ĸ", + "çĶ º", + "çī ½", + "çĤ ¯", + "ç¼ Ķ", + "æ¯ ĵ", + "å¬ °", + "æ¢ §", + "äº Ł", + "è¢ ħ", + "çį Ħ", + "è¿ ¥", + "æ¼ ¾", + "çĿ ij", + "ç¸ ¾", + "é¦ ĭ", + "é¤ ħ", + "æ ¹Ħ", + "æĺ ĩ", + "æŀ Ń", + "èĸ °", + "æŁ ij", + "æ¦ »", + "åĻ Ĺ", + "åĻ ´", + "æ£ £", + "åĶ §", + "çĨ ¹", + "è¼ ¯", + "å¢ Ł", + "é² ²", + "æĪ Ľ", + "èī ¦", + "èĬ ®", + "åĺ Ł", + "å¸ ¥", + "å¿ »", + "çĮ Ŀ", + "å¯ µ", + "è³ ¦", + "èĽ ¾", + "æ» ¾", + "çĤ ķ", + "éĵ ¬", + "èĴ ¿", + "éĴ ¨", + "çĥ Ļ", + "ç² ķ", + "æĥ ¦", + "æº §", + "é¢ į", + "éħ £", + "å³ ¦", + "ç± ģ", + "çĥ ĥ", + "åĨ Ĺ", + "åı ģ", + "çĽ §", + "ç½ µ", + "éĴ Ĺ", + "å¬ ī", + "è° ı", + "ç³ §", + "è¾ Ń", + "æ· ¬", + "èŁ Ĵ", + "è¯ ©", + "è¦ ĥ", + "çĻ ĸ", + "é½ Ĵ", + "çĪ IJ", + "ç® į", + "ç¼ İ", + "ç£ º", + "è¯ «", + "è¤ ²", + "æĵ ł", + "èIJ ¦", + "çĿ ¬", + "è° į", + "éĦ °", + "æł ¾", + "é¡ ı", + "ç¸ ±", + "æ¡ ¨", + "éĨ ¬", + "è¥ ²", + "è® ª", + "å© º", + "èį Ł", + "åĮ Ŀ", + "çĨ ł", + "èĽ Ĭ", + "æ¸ ļ", + "å´ ½", + "é² ¤", + "åķ °", + "åĮ ķ", + "ä¸ IJ", + "è® ¥", + "åı ½", + "åı ¼", + "çļ ¿", + "è¿ Ĥ", + "åIJ Ĩ", + "å± ¹", + "èĩ ¼", + "è® ¹", + "é© ®", + "çº «", + "æ± ŀ", + "æĬ ¡", + "èĭ ĩ", + "åIJ ł", + "åIJ Ń", + "åIJ ®", + "å² ĸ", + "ä½ ĥ", + "çĭ Ī", + "åº ĩ", + "åIJ Ŀ", + "éĹ °", + "æ± ¹", + "å¿ ±", + "æĭ Ħ", + "æĭ Ĺ", + "èĮ ī", + "èĭ Ľ", + "èĮ ģ", + "çŁ ¾", + "èĻ ı", + "åij »", + "åĴ Ħ", + "å¿ ¿", + "èĤ ®", + "çĭ ŀ", + "çĸ Ł", + "çĸ Ļ", + "çĸ ļ", + "æ³ ŀ", + "å¸ ļ", + "å± ī", + "è¿ ¢", + "é© ¹", + "ç İ·", + "çıĬ ó", + "çıĬó ł", + "çıĬół Ħ", + "çıĬółĦ ģ", + "æĮ İ", + "æĭ ´", + "åŀ Ľ", + "èį ¤", + "æ® ĥ", + "çĽ ¹", + "åĵ Ĩ", + "è´ »", + "æ¯ ¡", + "çĭ °", + "çĭ ¡", + "æŁ Ĵ", + "æģ ĥ", + "è¯ ¬", + "è¢ Ħ", + "è¯ ²", + "èļ ¤", + "èĢ Ļ", + "åŁ Ĥ", + "æį İ", + "æį Į", + "æ¢ Ĩ", + "é ħĮ", + "çł ¾", + "æ® ī", + "åĶ ł", + "æĻ Į", + "èļ £", + "èļ ª", + "èļ ĵ", + "é¸ ¯", + "åĶ ģ", + "åĶ Ĩ", + "åĢ Ķ", + "èĪ Ģ", + "è± º", + "èĥ °", + "é¸ µ", + "é¸ ³", + "é¦ ģ", + "ç¾ Ķ", + "æ¶ £", + "æ¶ ķ", + "æĤ ¯", + "è¯ ½", + "è° Ĩ", + "ç¥ Ł", + "ç» ¢", + "æį º", + "æį ¶", + "æį »", + "æİ Ĥ", + "èı ł", + "èIJ ¤", + "éħ Ĺ", + "çľ ¶", + "åķ Ħ", + "èļ ¯", + "èĽ Ģ", + "åĶ ¬", + "å¸ ·", + "éĵ IJ", + "éĵ Ľ", + "åģ İ", + "å¾ Ļ", + "èĦ ¯", + "è± ļ", + "çĮ ĸ", + "çĹ Ĭ", + "æ¶ ®", + "æĥ Ń", + "æĤ ´", + "æĥ ĭ", + "è° ļ", + "æı ©", + "æIJ Ģ", + "æIJ Ķ", + "æ¦ Ķ", + "æ¤ Ń", + "éĽ ³", + "åĸ ³", + "è· Ľ", + "èľ ĵ", + "èľ Ĵ", + "é¹ ĥ", + "éĶ Ħ", + "çĶ ¥", + "çŃ ı", + "çĮ ©", + "çĮ ¬", + "çĮ ¾", + "çĹ ¢", + "çĹ ª", + "æĥ °", + "çª ĺ", + "è° ¤", + "éļ ĺ", + "å© ¿", + "é¹ ī", + "çij Ļ", + "æĸ Ł", + "æ¤ ¿", + "éħ ª", + "éĽ ¹", + "åĹ ¦", + "è· ·", + "è· º", + "è· ¤", + "èľ Ī", + "èľ Ĺ", + "å¹ Į", + "é¦ ı", + "èª Ĭ", + "æ¼ ĵ", + "è¤ Ĥ", + "èĶ Ĺ", + "èĶ ¼", + "åħ ¢", + "è£ ³", + "èľ »", + "èĿ ĩ", + "åĺ Ģ", + "éĶ ¹", + "ç® ķ", + "ç® ©", + "çĺ ©", + "çĺ Ł", + "æ¼ ±", + "å¯ ¥", + "éª ¡", + "æĴ µ", + "æĴ ¬", + "è± Į", + "åĺ ¹", + "èĿ ł", + "èĿ Į", + "èĿ Ĺ", + "èĿ Ļ", + "éķ IJ", + "ç¨ ¼", + "ç¯ ĵ", + "èĨ Ľ", + "é² «", + "çĺ ª", + "é² ¨", + "æĨ Ķ", + "ç¿ ©", + "è¤ ¥", + "ç¼ Ń", + "åĻ ©", + "çĵ ¢", + "éľ İ", + "è¸ ±", + "è¹ Ĥ", + "èŁ Ĩ", + "é¹ ¦", + "ç¯ ¡", + "çĺ ¸", + "çª ¿", + "ç¼ °", + "èĹ IJ", + "è¹ ĭ", + "èŁ ĭ", + "èŁ Ģ", + "èµ ¡", + "èĩ Ĭ", + "é³ Ħ", + "ç³ ł", + "æĩ ¦", + "åļ £", + "éķ °", + "é³ į", + "ç° ¸", + "çĻ £", + "é³ ĸ", + "é¬ ĵ", + "èł ķ", + "éľ ¹", + "èº ı", + "é» ¯", + "çĵ ¤", + "çŁ Ĺ", + "ä¹ Ĥ", + "ä¹ ľ", + "åħ Ģ", + "å¼ ĭ", + "åŃ ij", + "åŃ ĵ", + "å¹ º", + "äº ĵ", + "å »¿", + "ä¸ ı", + "åį ħ", + "ä» ĥ", + "ä» ī", + "ä» Ĥ", + "åĪ Ī", + "çĪ »", + "åį ŀ", + "éĹ ©", + "è® £", + "å¤ ¬", + "çĪ ¿", + "æ¯ ĭ", + "éĤ Ĺ", + "éĤ Ľ", + "èī ½", + "èī ¿", + "åı µ", + "ä¸ ķ", + "åĮ ľ", + "åĬ ¢", + "åį Ł", + "åı ±", + "åı »", + "ä» ¨", + "ä» Ł", + "ä» ¡", + "ä» «", + "ä» ŀ", + "åį ®", + "æ° IJ", + "çĬ °", + "åĪ į", + "éĤ Ŀ", + "éĤ Ļ", + "è® ¦", + "è® §", + "è® «", + "å° »", + "éĺ ¡", + "å° ķ", + "å¼ ģ", + "èĢ Ĵ", + "çİ İ", + "çİ ij", + "åľ ¬", + "æī ¦", + "åľ ª", + "åľ ¹", + "æī ª", + "åľ ®", + "åľ ¯", + "èĬ Ĭ", + "èĬ į", + "èĬ Ħ", + "èĬ ¨", + "èĬ ij", + "èĬ İ", + "èĬ Ĺ", + "äº ĺ", + "åİ į", + "å¤ ¼", + "æĪ į", + "å° ¥", + "ä¹ ©", + "æĹ ¯", + "æĽ ³", + "å² Į", + "å± º", + "åĩ ¼", + "åĽ ¡", + "éĴ ĩ", + "ç¼ ¶", + "æ° ĺ", + "æ° ĸ", + "çī Ŀ", + "ä¼ İ", + "ä¼ Ľ", + "ä¼ ¢", + "ä½ ¤", + "ä» µ", + "ä¼ ¥", + "ä¼ §", + "ä¼ ī", + "ä¼ «", + "åĽ Ł", + "æ± Ĩ", + "åĪ ĸ", + "å¤ Ļ", + "æĹ ®", + "åĪ İ", + "çĬ ·", + "çĬ ¸", + "èĪ Ľ", + "åĩ «", + "é Ĥ¬", + "é¥ §", + "æ± Ķ", + "æ± ľ", + "æ± Ĭ", + "å¿ ĸ", + "å¿ ı", + "è® ´", + "è® µ", + "è® ·", + "èģ ¿", + "èī ®", + "åİ ¾", + "å¦ ģ", + "çº ¡", + "çº £", + "çº ¥", + "çº ¨", + "çİ ķ", + "çİ Ļ", + "æĬ Ł", + "æĬ Ķ", + "åľ »", + "åĿ į", + "æĬ ĥ", + "㧠IJ", + "èĬ «", + "èĬ ¾", + "èĭ Ī", + "èĭ £", + "èĭ ĭ", + "èĬ ¼", + "èĭ Į", + "èĭ ģ", + "èĬ ©", + "èĬ ª", + "èĬ ¡", + "èĬ Ł", + "èĭ Ħ", + "èĭ İ", + "èĭ ¡", + "æĿ Į", + "æĿ ĵ", + "æĿ Ī", + "å¿ ij", + "åŃ Ľ", + "éĤ ´", + "éĤ ³", + "å¥ ģ", + "è± ķ", + "å¿ Ĵ", + "æ¬ ¤", + "è½ «", + "è¿ ĵ", + "éĤ ¶", + "å¿ IJ", + "åį £", + "éĤ º", + "æĹ °", + "åij ĭ", + "åij Ĵ", + "åij ĵ", + "åij Ķ", + "åij ĸ", + "æĹ ¸", + "åIJ ¡", + "èĻ ¬", + "åIJ ½", + "åIJ £", + "åIJ ²", + "å¸ ı", + "å² Ī", + "å² ĺ", + "åħ ķ", + "åĽ µ", + "åĽ «", + "éĴ Ĭ", + "éĴ ĭ", + "é ĴĮ", + "è¿ ķ", + "æ° Ļ", + "æ° ļ", + "çī ¤", + "ä½ ŀ", + "ä½ ļ", + "ä½ Ŀ", + "ä½ Ĺ", + "å½ ·", + "ä½ ĺ", + "ä½ ¥", + "è± ¸", + "åĿ Į", + "èĤ Ł", + "å¥ Ĥ", + "åĬ ¬", + "çĭ ģ", + "é¸ ł", + "é¥ ¨", + "é¥ ©", + "é¥ «", + "é¥ ¬", + "åº ij", + "åº ĭ", + "çĸ Ķ", + "çĸ ĸ", + "èĤ ĵ", + "éĹ ±", + "éĹ ³", + "çĤ Ģ", + "æ² £", + "æ² ħ", + "æ² Ķ", + "æ² ¤", + "æ² ı", + "æ² ļ", + "æ± ©", + "æ± ¨", + "æ² ¨", + "æ± ´", + "æ² Ĩ", + "æ² ©", + "æ³ IJ", + "æĢ ĥ", + "æĢ Ħ", + "å¿ ¡", + "å¿ ¤", + "å¿ ¾", + "æĢ ħ", + "å¿ ª", + "æĢ Ĩ", + "å¿ Ń", + "å¿ ¸", + "è¯ Ĥ", + "è¯ ĥ", + "è¯ ħ", + "è¯ ĭ", + "è¯ Į", + "è¯ Ĵ", + "éĻ Ĥ", + "éĻ ī", + "å¦ ©", + "å¦ ª", + "å¦ £", + "å¦ Ĺ", + "å¦ «", + "å§ Ĵ", + "å¦ ¤", + "åĬ Ń", + "åĪ Ń", + "éĤ °", + "çº Ń", + "çº °", + "çº ´", + "çİ ¡", + "çİ Ń", + "çİ ł", + "çİ ¢", + "çİ ¦", + "çĽ Ĥ", + "å¿ Ŀ", + "åĮ ¦", + "åĿ ©", + "æĬ ¨", + "æĭ ¤", + "åĿ «", + "æĭ Ī", + "åŀ Ĩ", + "æĬ »", + "åĬ ¼", + "æĭ ĥ", + "æĭ Ĭ", + "åĿ ¼", + "åĿ »", + "㧠Ł", + "åĿ ¨", + "åĿ Ń", + "æĬ ¿", + "åĿ ³", + "èĭ ·", + "èĭ ¤", + "èĮ ı", + "èĭ «", + "èĭ ľ", + "èĭ ´", + "èĭ Ĵ", + "èĭ ĺ", + "èĮ Į", + "èĭ »", + "èĭ ĵ", + "èĮ ļ", + "èĮ Ĩ", + "èĮ ij", + "èĮ ĵ", + "èĮ Ķ", + "èĮ ķ", + "è ĮĢ", + "èĭ ķ", + "æŀ ¥", + "æŀ ĩ", + "æĿ ª", + "æĿ ³", + "æŀ §", + "æĿ µ", + "æŀ ¨", + "æŀ ŀ", + "æŀ ĭ", + "æĿ »", + "æĿ ·", + "æĿ ¼", + "çŁ ¸", + "ç łĢ", + "åĪ ³", + "å¥ Ħ", + "æ® ģ", + "éĥ ı", + "è½ Ń", + "éĥ ħ", + "é¸ ¢", + "çĽ ±", + "æĺ Ļ", + "æĿ ²", + "æĺ ĥ", + "åĴ Ĥ", + "åij ¸", + "æĺ Ģ", + "æĹ »", + "æĺ ī", + "çĤ ħ", + "çķ Ģ", + "èĻ ®", + "åĴ Ģ", + "åij ·", + "é» ¾", + "åij ±", + "åij ¤", + "åĴ Ĩ", + "åĴ Ľ", + "åij ¶", + "åij £", + "åĴ Ŀ", + "å² ¢", + "å² ¿", + "å² ¬", + "å² «", + "å¸ Ļ", + "å² £", + "å³ ģ", + "åĪ ¿", + "å² ·", + "åī Ģ", + "å¸ Ķ", + "å³ Ħ", + "æ² ĵ", + "åĽ ¹", + "ç½ Ķ", + "éĴ į", + "éĴ İ", + "éĴ ı", + "éĴ Ĵ", + "éĴ ķ", + "éĤ ¾", + "è¿ ®", + "çī ¦", + "ç« º", + "è¿ ¤", + "ä½ ¶", + "ä¾ ij", + "ä¾ ī", + "èĩ ¾", + "ä¾ Ĺ", + "ä¾ ı", + "ä¾ ©", + "ä½ »", + "ä½ ¾", + "ä¾ ª", + "ä½ ¼", + "ä½ ¯", + "ä¾ ¬", + "å¸ Ľ", + "ä¾ Ķ", + "å¾ Ĥ", + "åĪ ½", + "éĥ Ħ", + "ç± ´", + "çĵ ®", + "æĪ Ĺ", + "èĤ ¼", + "äı Ŀ", + "èĤ ±", + "èĤ «", + "è¿ ©", + "éĥ ĩ", + "çĭ İ", + "çĭ į", + "çĭ Ĵ", + "åĴ İ", + "é¥ ¯", + "é¥ ´", + "åĨ ½", + "åĨ ¼", + "åº ĸ", + "çĸ ł", + "çĸ Ŀ", + "åħ ĸ", + "åĬ ¾", + "ð¬ ī", + "ð¬ī ¼", + "çĤ ĺ", + "çĤ Ŀ", + "çĤ Ķ", + "æ³ Ķ", + "æ² Ń", + "æ³ ·", + "æ³ ±", + "æ³ ħ", + "æ³ ł", + "æ³ º", + "æ³ ĸ", + "æ³ «", + "æ³ ®", + "æ² ±", + "æ³ ¯", + "æĢ Ļ", + "æĢ µ", + "æĢ ¦", + "æĢ Ľ", + "æĢ ı", + "æĢ į", + "ã ¤", + "㤠ĺ", + "æĢ ©", + "æĢ «", + "æĢ ¿", + "å® ķ", + "ç© ¹", + "å® ĵ", + "è¯ ĵ", + "è¯ Ķ", + "è¯ ĸ", + "è¯ ĺ", + "æĪ ¾", + "è¯ Ļ", + "æĪ ½", + "éĥ ĵ", + "è¡ ©", + "ç¥ Ĩ", + "ç¥ İ", + "ç¥ ĩ", + "è¯ ľ", + "è¯ Ł", + "è¯ £", + "è¯ ¤", + "è¯ §", + "è¯ ¨", + "æĪ ķ", + "éĻ Ķ", + "å¦ ²", + "å¦ ¯", + "å§ Ĺ", + "å¸ ij", + "åŃ ¥", + "é© ½", + "èĻ ±", + "è¿ ¨", + "ç» Ģ", + "ç» ģ", + "ç» Ĥ", + "é© ·", + "é© ¸", + "ç» ī", + "ç» Į", + "éª Ģ", + "çĶ ¾", + "çı ı", + "çı IJ", + "çı ij", + "çİ ³", + "é¡ ¸", + "çı ī", + "çı Ī", + "æĭ ®", + "åŀ Ń", + "æĮ Ŀ", + "æĮ ŀ", + "åŀ ¤", + "èµ ³", + "è´ ²", + "åŀ ±", + "åŀ Į", + "åŀ §", + "åŀ ĵ", + "æĮ ¦", + "åŀ ł", + "èį ļ", + "èį ij", + "è´ ³", + "èį ľ", + "èİ Ĵ", + "èĮ ¼", + "èĮ ´", + "èĮ ±", + "èİ Ľ", + "èį ŀ", + "èĮ ¯", + "èį ı", + "èį ĩ", + "èį ĥ", + "èį ł", + "èĮ Ń", + "åŀ ©", + "èį ¥", + "èį ¦", + "èį ¨", + "èį ©", + "åī ĭ", + "èį ª", + "èį ¬", + "èį ®", + "æŁ °", + "æł ī", + "æŁ ĺ", + "æł Ĭ", + "æŁ ©", + "æŀ °", + "æł Į", + "æŁ Ļ", + "æŀ µ", + "æŀ ³", + "æŁ ŀ", + "æŁ Ŀ", + "æł Ģ", + "æŁ ¢", + "æł İ", + "æŁ Ī", + "æŁ ģ", + "æŀ ·", + "æŁ ½", + "åī Į", + "éħ Ĭ", + "éĥ ¦", + "çĶ Ń", + "çł Ĺ", + "çł ĺ", + "çł Ĵ", + "æĸ «", + "çł Ń", + "çł ľ", + "èĢ ·", + "èĻ º", + "æ® Ĥ", + "æ® ĩ", + "æ® Ħ", + "è½ ±", + "è½ ²", + "è½ ³", + "è½ ¶", + "è½ ¸", + "èĻ ¿", + "æ¯ ĸ", + "è§ ĩ", + "å° ľ", + "åĵ IJ", + "çľ Ħ", + "çľ į", + "ðł ³", + "ðł³ IJ", + "éĥ ¢", + "çľ ĩ", + "çľ Ĭ", + "çľ Ī", + "ç¦ º", + "åĵ Ĥ", + "åĴ ´", + "æĽ ·", + "æĺ ´", + "åĴ ¦", + "åĵ ĵ", + "åĵ Ķ", + "çķ İ", + "åij ²", + "èĥ Ħ", + "çķ ĭ", + "çķ Ī", + "èĻ ¼", + "èĻ »", + "çĽ ħ", + "åĴ £", + "åĵ ķ", + "åī IJ", + "éĥ §", + "åĴ »", + "åĽ ¿", + "åĴ ¿", + "åĵ Į", + "åĵ Ļ", + "åĵ ļ", + "åĴ ©", + "åĴ ¤", + "åĵ Ŀ", + "åĵ ı", + "åĵ ŀ", + "å³ £", + "ç½ ĺ", + "å³ Ĵ", + "å³ ¤", + "å³ ĭ", + "è´ ¶", + "éĴ ļ", + "éĴ ¡", + "éĴ £", + "éĴ ¤", + "éĴ «", + "æ° ¡", + "çī ¯", + "éĥ ľ", + "ç§ ķ", + "ç§ Ń", + "ç« ½", + "ç¬ Ī", + "ä¿ ¦", + "ä¿ ¨", + "ä¿ ħ", + "åı Ł", + "åŀ ¡", + "çī ®", + "ä¿ £", + "ä¿ ļ", + "çļ Ī", + "ä¿ Ł", + "éĢ ħ", + "å¾ ĩ", + "å¾ ī", + "èĪ ¢", + "éĥ Ĺ", + "ä¿ İ", + "éĥ ¤", + "çĪ °", + "éĥ Ľ", + "çĵ ´", + "èĥ ¨", + "èĥ ª", + "èĥ Ľ", + "èĥ Ĥ", + "èĥ Ļ", + "èĥ į", + "èĥ Ĺ", + "è ĥĿ", + "æľ IJ", + "èĥ «", + "é¸ ¨", + "åĮ į", + "çĭ ¨", + "çĭ ¯", + "é£ ij", + "çĭ ©", + "çĭ ²", + "è¨ ĩ", + "éĢ Ħ", + "æĺ Ŀ", + "é¥ ·", + "é¥ ¸", + "é¥ ¹", + "åŃ ª", + "å¨ Ī", + "åº ¥", + "çĸ ¬", + "çĸ £", + "çĸ ¥", + "çĸ Ń", + "åº ł", + "ç« ij", + "é£ Ĵ", + "éĹ ¼", + "éĹ ¾", + "éĹ ¿", + "éĺ Ĥ", + "ç¾ ij", + "è¿ ¸", + "ç± ¼", + "éħ ĭ", + "çĤ »", + "çĥ Ģ", + "çĤ ·", + "æ´ ±", + "æ´ ¹", + "æ´ §", + "æ´ Į", + "æµ ĥ", + "æ´ ĩ", + "æ´ Ħ", + "æ´ Ļ", + "æ¶ İ", + "æ´ İ", + "æ´ «", + "æµ į", + "æ´ ®", + "æ´ µ", + "æµ Ĵ", + "æµ Ķ", + "æµ ķ", + "æ´ ³", + "æģ ¸", + "æģ ĵ", + "æģ ¹", + "æģ «", + "æģ »", + "æģ Ĥ", + "æģ ª", + "æģ ½", + "å® ¥", + "æī ĥ", + "è¡ ²", + "è¡ ½", + "è¡ ¿", + "è¢ Ĥ", + "ç¥ ľ", + "ç¥ ĵ", + "ç¥ ļ", + "è¯ ®", + "ç¥ Ĺ", + "ç¥ ¢", + "è¯ °", + "è¯ ³", + "é¸ ©", + "æĺ ¶", + "åĴ «", + "å¼ Ń", + "çī ģ", + "èĥ ¥", + "éĻ Ł", + "å§ ®", + "å¨ Ĩ", + "å§ Ŀ", + "å§ £", + "å§ ĺ", + "å§ ¹", + "ç¾ ¿", + "çĤ ±", + "çŁ ľ", + "ç» Ķ", + "éª ģ", + "éª ħ", + "ç» Ĺ", + "ç» Ľ", + "éª Ī", + "èĢ ĸ", + "æĮ Ī", + "çı ¥", + "çı Ļ", + "é¡ ¼", + "çı °", + "çı ©", + "çı §", + "çı £", + "çı ŀ", + "çIJ ¤", + "çı ²", + "æģ ļ", + "åŁ ķ", + "åŁ ĺ", + "åŁ Ļ", + "åŁ ļ", + "æĮ ¹", + "èĢ Ĩ", + "èĢ Ħ", + "åŁ Ĵ", + "æį ĭ", + "è´ ½", + "åŀ ¸", + "æį ĥ", + "çĽ į", + "èį ¸", + "èİ ³", + "èİ ´", + "èİ ª", + "èİ ł", + "èİ ľ", + "èİ ħ", + "èį ¼", + "èİ ©", + "èį ½", + "èİ ¸", + "èį »", + "èİ ¨", + "é¸ ª", + "èİ ¼", + "æł ²", + "æł ³", + "æ¡ ¡", + "æ¡ İ", + "æ¡ ¢", + "æ¡ ¤", + "æ¢ ĥ", + "æł Ŀ", + "æ¡ ķ", + "æ¡ ģ", + "æ¡ §", + "æ¡ ħ", + "æł Ł", + "æ¡ ī", + "æł ©", + "éĢ ij", + "éĢ ĭ", + "å½ §", + "é¬ ²", + "è± ĩ", + "éħ IJ", + "éĢ ¦", + "åİ Ŀ", + "åŃ ¬", + "çł Ŀ", + "çł ¹", + "çł §", + "çł ·", + "çł Ł", + "çł ¼", + "çł ¥", + "çł £", + "åī ŀ", + "çł »", + "è½ ¼", + "è½ ¾", + "è¾ Ĥ", + "é¸ «", + "è¶ ¸", + "é¾ Ģ", + "é¸ ¬", + "èĻ Ķ", + "çľ ¬", + "åĶ Ľ", + "çľ Ļ", + "åĵ §", + "åĵ ½", + "æĻ ģ", + "é¸ ®", + "è¶ µ", + "è¶ ¿", + "çķ Ľ", + "èļ ¨", + "èļ ľ", + "èļ į", + "èļ ĭ", + "èļ ¬", + "èļ Ŀ", + "èļ §", + "åĶ ¢", + "åľ Ħ", + "åĶ £", + "åĶ ı", + "çĽ İ", + "åĶ ij", + "å´ Ĥ", + "å´ ĥ", + "ç½ ¡", + "ç½ Ł", + "è§ Ĭ", + "èµ ħ", + "éĴ ²", + "éĴ µ", + "éĴ ¹", + "éĴ º", + "éĴ ½", + "éĴ ¼", + "éĴ ¿", + "éĵ Ģ", + "éĵ Ħ", + "éĵ Ĩ", + "éĵ Ī", + "éĵ ī", + "éĵ Ĭ", + "éĵ ĭ", + "éĵ Į", + "é ĵį", + "ä ¥", + "ä¥ ½", + "éĵ İ", + "æ° ©", + "æ° ¤", + "æ° ¦", + "æ¯ ª", + "èĪ IJ", + "ç§ £", + "ç§ «", + "çĽ ī", + "ç¬ Ħ", + "ç¬ ķ", + "ç¬ Ĭ", + "ç¬ ı", + "ç¬ Ĩ", + "ä¿ ¸", + "ä¿ µ", + "åģ Į", + "ä¿ ³", + "ä¿ ¶", + "åĢ ¬", + "åĢ ı", + "æģ ģ", + "åĢ Ń", + "ä¿ ¾", + "åĢ ľ", + "éļ ¼", + "éļ ½", + "åĢ Į", + "åĢ ¥", + "èĩ ¬", + "éĥ «", + "åĢ ¨", + "è¡ Ħ", + "é¢ Ģ", + "å¾ ķ", + "èĪ «", + "è¡ ¾", + "èĥ ¯", + "èĥ ±", + "èĥ ´", + "èĥ Ń", + "èĦ į", + "èĥ ¼", + "èĦ Ĵ", + "é¸ ±", + "é¸ ²", + "çĭ ·", + "çĮ ģ", + "çĭ ³", + "çĮ ĥ", + "çĭ º", + "éĢ ĸ", + "æ¡ Ģ", + "é¥ ½", + "åĩ ĩ", + "æĮ Ľ", + "äº ³", + "çĸ ³", + "çĸ ´", + "çĸ ¸", + "çĸ ½", + "çĹ Ī", + "çĸ ±", + "çĹ Ĥ", + "çĹ ī", + "è¡ ®", + "é¢ ĥ", + "æģ £", + "æĹ Ĩ", + "æĹ Ħ", + "æĹ ĥ", + "éĺ ĥ", + "éĺ Ħ", + "è¨ ļ", + "éĺ Ĩ", + "æģ Ļ", + "ç² ij", + "çĥ ľ", + "çĥ ©", + "çĥ Ĭ", + "åī ¡", + "éĥ ¯", + "çĥ ¬", + "æ¶ ij", + "æµ ¯", + "æ¶ ŀ", + "æ¶ Ł", + "å¨ ij", + "æ¶ ł", + "æµ ŀ", + "æ¶ ĵ", + "æµ ¥", + "æ¶ Ķ", + "æµ ľ", + "æµ ł", + "æµ £", + "æĤ ļ", + "æ ĤŃ", + "æĤ Ŀ", + "æĤ Ĵ", + "æĤ Į", + "æĤ Ľ", + "çª Ī", + "åī ľ", + "è¯ ¹", + "è¯ ¼", + "è¢ Ĵ", + "è¢ ¢", + "è¯ ¿", + "è° Ģ", + "è° Ĥ", + "è° Ħ", + "è° ĩ", + "å± IJ", + "å± Ļ", + "éĻ ¬", + "åĭ IJ", + "å¥ ĺ", + "çī Ĥ", + "èļ ©", + "éĻ ²", + "å¨ Į", + "å¨ ī", + "å¨ ²", + "å¨ ´", + "å¨ £", + "å¨ ĵ", + "å© Ģ", + "çķ ļ", + "éĢ ¡", + "ç» ł", + "éª Ĭ", + "ç» ¡", + "éª ĭ", + "ç» ¦", + "ç» ¨", + "éª İ", + "éĤ ķ", + "é¸ ¶", + "å½ Ĺ", + "èĢ ľ", + "çĦ ĺ", + "èĪ Ĥ", + "çIJ ı", + "çIJ ĩ", + "éº ¸", + "æı ¶", + "åŁ ´", + "åŁ ¯", + "æį ¯", + "æİ ³", + "æİ ´", + "åŁ ¸", + "åŁ µ", + "èµ §", + "åŁ ¤", + "æį Ń", + "éĢ µ", + "åŁ Ŀ", + "åł ĭ", + "åł į", + "æİ ¬", + "é¸ ·", + "æį ½", + "æİ Ĭ", + "åł ī", + "æİ ¸", + "æį ©", + "æİ ®", + "æĤ «", + "åŁ Ń", + "åŁ ½", + "æİ ĩ", + "æİ ¼", + "èģ ĥ", + "èIJ ģ", + "èı ĺ", + "åł ĩ", + "èIJ ĺ", + "èIJ ĭ", + "èı ½", + "èı ĸ", + "è IJľ", + "èIJ ¸", + "èIJ ij", + "æ£ »", + "èı Ķ", + "èı Ł", + "èIJ ı", + "èı ¹", + "èı ª", + "èı ħ", + "èı Ģ", + "èı °", + "èı ¡", + "æ¢ ¿", + "æ¢ ı", + "è§ ĭ", + "æ¡ ´", + "æ¡ ·", + "æ£ ģ", + "æ¡ «", + "æ£ Ĥ", + "åķ ¬", + "éĥ ¾", + "æķ ķ", + "è± ī", + "éĦ Ħ", + "éħ ŀ", + "ç¡ İ", + "ç¡ Ń", + "ç¡ ĸ", + "ç¡ Ĺ", + "ç¡ IJ", + "ç¡ ĩ", + "ç¡ Į", + "é¸ ¸", + "çĵ ł", + "åĮ ı", + "åİ ©", + "æ® Ĵ", + "æ® ĵ", + "æ® į", + "èµ ī", + "éĽ ©", + "è¾ Ħ", + "åł ij", + "çľ Ń", + "çľ ¦", + "åķ §", + "æĻ ¡", + "æĻ ¤", + "çľ µ", + "åľ Ĭ", + "åĸ ı", + "åķ ī", + "åĭ ĸ", + "æĻ ŀ", + "åĶ µ", + "æĻ Ĺ", + "åķ Ń", + "çķ ¦", + "è¶ º", + "åķ ®", + "è· Ħ", + "èļ ¶", + "è ĽĦ", + "èĽ İ", + "èĽ Ĩ", + "èļ °", + "åľ ī", + "èļ ±", + "èĽ ī", + "èĽ ı", + "èļ ´", + "åķ ģ", + "åķ ķ", + "åĶ ¿", + "åķ IJ", + "åĶ ¼", + "åĶ ·", + "åķ ĸ", + "åķ µ", + "åķ ¶", + "åķ ·", + "åĶ ³", + "åĶ °", + "åķ ľ", + "å¸ »", + "å´ ļ", + "å´ ¦", + "å¸ ¼", + "å´ ®", + "å´ ¤", + "å´ Ĩ", + "èµ ĩ", + "èµ Ī", + "èµ Ĭ", + "éĵ ij", + "éĵ Ĵ", + "éĵ Ĺ", + "éĵ Ļ", + "éĵ Ł", + "éĵ ¡", + "éĵ ¢", + "éĵ £", + "éĵ ¤", + "éĵ §", + "éĵ ¨", + "éĵ ©", + "éĵ ª", + "éĵ «", + "éĵ ¯", + "éĵ °", + "éĵ ±", + "éĵ ³", + "éĵ µ", + "éĵ ·", + "çī ¾", + "é¸ ¹", + "ç§ ¾", + "éĢ ¶", + "ç¬ º", + "çŃ ĩ", + "ç¬ ¸", + "ç¬ ª", + "ç¬ ®", + "ç¬ ł", + "ç¬ ¥", + "ç¬ ¤", + "ç¬ ³", + "ç¬ ¾", + "ç¬ ŀ", + "åģ ¾", + "åģ ĥ", + "åģ ķ", + "åģ Ī", + "åĤ Ģ", + "åģ ¬", + "åģ »", + "çļ ij", + "çļ İ", + "é¸ »", + "å¾ ľ", + "èĪ ¸", + "èĪ »", + "èĪ ´", + "èĪ ·", + "é¾ Ľ", + "ç¿ İ", + "èĦ ¬", + "èĦ ĺ", + "èĦ ²", + "åĮ IJ", + "çĮ Ĺ", + "çĮ ¡", + "çĮ ŀ", + "æĸ Ľ", + "çĮ ķ", + "é¦ Ĺ", + "é¦ ĥ", + "é¦ Ħ", + "é¸ ¾", + "åº ¹", + "åº ¾", + "çĹ Ķ", + "çĹ į", + "ç¿ Ĭ", + "æĹ Į", + "æĹ İ", + "è¢ ¤", + "éĺ ĩ", + "éĺ Ī", + "éĺ ī", + "éĺ Ĭ", + "éĺ ĭ", + "éĺ į", + "éĺ ı", + "ç¾ Ł", + "ç² Ŀ", + "çĦ IJ", + "çĦ ĵ", + "çĦ Ĺ", + "æ· ħ", + "æ· ŀ", + "æ¸ İ", + "æ¶ ¿", + "æ· ĸ", + "æĮ ²", + "æ· ł", + "æ¶ ¸", + "æ¸ ij", + "æ· ¦", + "æ· Ŀ", + "æ¶ ª", + "æ· Ļ", + "æ¶ «", + "æ¸ Į", + "æĤ »", + "æĤ ±", + "æ ĥĿ", + "æĥ ĺ", + "æĥ Ĩ", + "æĥ ļ", + "æĥ ĩ", + "æĥ ®", + "çª ķ", + "è° Į", + "æī Ī", + "çļ ²", + "è° ij", + "è£ Ĩ", + "è¢ ·", + "è£ ī", + "è° Ĵ", + "è° Ķ", + "è° ķ", + "è° ĸ", + "è° Ĺ", + "è° Ļ", + "è° Ŀ", + "éĢ ¯", + "éĥ ¿", + "éļ Ī", + "ç² ľ", + "éļ į", + "éļ Ĺ", + "å© Ĭ", + "å¨ ¼", + "å© ¢", + "å© µ", + "èĥ ¬", + "è¢ Ī", + "ç¿ Į", + "æģ ¿", + "æ¬ ¸", + "ç» «", + "éª IJ", + "ç» ¯", + "ç» ±", + "éª Ĵ", + "ç» ²", + "éª ĵ", + "ç» ¶", + "ç» º", + "ç» »", + "ç» ¾", + "éª ĸ", + "ç¼ ģ", + "èĢ ł", + "çIJ «", + "çIJ µ", + "çIJ ¶", + "çIJ ¥", + "çIJ ¨", + "çIJ °", + "çIJ ®", + "çIJ ¯", + "çIJ ¬", + "çIJ ļ", + "è¾ ĩ", + "é¼ ĭ", + "æı ³", + "åł ŀ", + "æIJ ½", + "æı ¸", + "æı ł", + "åł Ļ", + "è¶ Ħ", + "æı ĸ", + "é¢ ī", + "å¡ Ħ", + "æı ¿", + "èĢ ĭ", + "æı Ħ", + "èĽ ©", + "èĽ °", + "å¡ Ĩ", + "æij Ĵ", + "æı Ĩ", + "æİ ¾", + "èģ Ĵ", + "èij ij", + "èij ļ", + "éĿ °", + "éĿ ¸", + "èij ³", + "èij º", + "èij ¸", + "èIJ ¼", + "èij ¶", + "è ĴĮ", + "èij Ń", + "æ¥ ®", + "æ £¼", + "æ¤ Ł", + "æ£ ¹", + "æ¤ ¤", + "æ£ °", + "èµ į", + "æ¤ ĭ", + "æ¤ ģ", + "æ¤ ª", + "æ¤ IJ", + "é¹ ģ", + "éħ ¤", + "éħ ¢", + "éħ ¡", + "é¹ Ĥ", + "æ® ļ", + "æ® Ľ", + "éĽ ±", + "è¾ ĭ", + "æ¤ ł", + "è¾ İ", + "çĿ Ħ", + "çĿ ĩ", + "çĿ ĥ", + "æĪ ¢", + "åĸ ĭ", + "åĹ Ĵ", + "åĸ ĥ", + "åĸ ±", + "åĸ ¹", + "æĻ ·", + "åĸ Ī", + "è· ĸ", + "è· Ĺ", + "è· ŀ", + "è· ļ", + "è· İ", + "è· ı", + "è· Ĩ", + "èĽ ±", + "èĽ ²", + "èĽ Ń", + "èĽ ³", + "èĽ IJ", + "èĽ Ķ", + "èĽ ŀ", + "èĽ ´", + "èĽ ĺ", + "åĸ ģ", + "åĸ Ł", + "åķ ¾", + "åĹ ĸ", + "åĸ ij", + "åĹ Ł", + "åĹ ŀ", + "åĸ Ļ", + "åµ ĺ", + "åµ ĸ", + "å´ ´", + "éģ Ħ", + "è© Ī", + "åµ İ", + "å µ¬", + "åµ Ľ", + "åµ ¯", + "åµ Ŀ", + "åµ «", + "å¹ Ħ", + "åµ ĭ", + "èµ ķ", + "éĵ »", + "éĵ ¼", + "éĵ ¿", + "éĶ ĥ", + "éĶ Ĩ", + "éĶ ĩ", + "éĶ ī", + "éĶ ı", + "éĶ ij", + "éĶ Ĵ", + "éĶ Ķ", + "éĶ ķ", + "æİ £", + "çŁ ¬", + "æ° °", + "æ¯ ³", + "æ¯ ½", + "çĬ Ĭ", + "çĬ Ħ", + "çĬ ĭ", + "é ¹Ħ", + "çĬ į", + "åµ ĩ", + "é» į", + "ç¨ ĥ", + "ç¨ Ĥ", + "çŃ ļ", + "çŃ µ", + "çŃ Į", + "åĤ £", + "åĤ Ī", + "èĪ Ħ", + "çī į", + "åĤ ¥", + "åĤ §", + "éģ ij", + "åĤ ©", + "å¾ ¨", + "åª Ń", + "çķ ²", + "å¼ ij", + "ç¿ ķ", + "é¹ Ĩ", + "èħ Ī", + "èħ ĵ", + "èħ Ĩ", + "èħ ´", + "èħ ļ", + "èħ ±", + "é± ¿", + "é² Ģ", + "é² Ĥ", + "çĮ ¢", + "çĮ ¹", + "çĮ ¥", + "é£ ĵ", + "è§ ŀ", + "è§ ļ", + "çĮ ±", + "é¢ İ", + "é£ §", + "é¦ ĩ", + "é¦ Ĭ", + "äº µ", + "èĦ Ķ", + "è£ Ĵ", + "çĹ £", + "çĹ ¨", + "çĹ ¦", + "çĹ ŀ", + "çĹ ¤", + "çĹ §", + "èµ ĵ", + "ç« ¦", + "çĵ ¿", + "åķ »", + "é¢ ı", + "é¹ ĩ", + "éĺ ij", + "éĺ Ĵ", + "éĺ ķ", + "ç² ŀ", + "éģ Ĵ", + "åŃ ³", + "çĦ ¯", + "çĦ ľ", + "çĦ ±", + "é¹ Ī", + "æ¸ «", + "æ¹ ®", + "æ¹ İ", + "æ¹ ľ", + "æ¹ į", + "æ¹ «", + "æº ²", + "æ¹ Ł", + "æº Ĩ", + "æ¹ ²", + "æ¹ Ķ", + "æ¹ ī", + "æ¸ ¥", + "æ» ģ", + "æĦ ł", + "æĥ º", + "æĦ ¦", + "æĥ ´", + "æĦ Ģ", + "æĦ İ", + "æĦ Ķ", + "åĸ ¾", + "å¯ IJ", + "è° Ł", + "è£ ¢", + "è£ İ", + "è£ ¥", + "ç¥ ¾", + "è° ł", + "è° ¡", + "è° ¥", + "è° §", + "åŃ ±", + "å¼ ¼", + "å· ½", + "éª ĺ", + "åª ª", + "å· ¯", + "ç¿ ļ", + "çļ ´", + "éª Ľ", + "ç¼ Ĥ", + "ç¼ ĥ", + "ç¼ Ħ", + "å½ ĺ", + "ç¼ ĩ", + "ç¼ Ī", + "ç¼ Į", + "ç¼ ij", + "ç¼ Ĵ", + "ç¼ Ĺ", + "é£ ¨", + "èĢ ¢", + "çij ģ", + "çij Ĺ", + "çij Ħ", + "éģ ¨", + "éª ľ", + "éŁ «", + "é« ¡", + "å¡ ¬", + "éĦ ¢", + "è¶ Ķ", + "è¶ ij", + "æij ħ", + "æij ģ", + "èľ ĩ", + "æIJ ĭ", + "æIJ ª", + "æIJ IJ", + "æIJ Ľ", + "æIJ ł", + "æij Ī", + "å½ Ģ", + "æ¯ Ĥ", + "æIJ ¦", + "æIJ ¡", + "èĵ ģ", + "æĪ ¡", + "è ĵį", + "éĦ ŀ", + "èĵ IJ", + "èĵ ¦", + "é¹ ĭ", + "èĴ ½", + "èĵ ĸ", + "èĵ Ĭ", + "èĴ ¯", + "èĵ Ł", + "èĵ ij", + "èĴ º", + "èĵ ł", + "èĴ Ł", + "èĴ ¡", + "èĴ ¹", + "èĴ ´", + "èĴ Ĺ", + "èĵ ¥", + "æ¥ Ķ", + "æ¥ Ĥ", + "æ¥ Ŀ", + "æ¥ «", + "æ¥ ¸", + "æ¤ ´", + "æ§ Į", + "æ¥ ¯", + "çļ Ļ", + "æ¦ Ī", + "æ§ İ", + "æ¦ ī", + "æ¥ ¦", + "æ¥ £", + "æ¥ ¹", + "æ¤ ½", + "åī ½", + "éħ ©", + "èľ ĥ", + "ç¢ Ľ", + "ç¢ ĵ", + "ç¡ ¼", + "ç¢ ī", + "ç¢ ļ", + "ç¢ ĩ", + "ç¢ ľ", + "é¹ Į", + "è¾ ı", + "é¾ ĥ", + "é¾ ħ", + "è¨ ¾", + "ç² ²", + "çĿ ļ", + "åĹ ª", + "éŁ ª", + "åĹ ·", + "åĹ ī", + "çĿ ¨", + "çĿ ¢", + "éĽ İ", + "çĿ ¥", + "åĹ ij", + "åĹ «", + "åĹ ¬", + "åĹ Ķ", + "åĹ Ŀ", + "æĪ ¥", + "åĹ Ħ", + "çħ ¦", + "æļ Ħ", + "éģ ¢", + "æ ļĮ", + "è· ¬", + "è· ¶", + "è ·¸", + "è· IJ", + "è· £", + "è· ¹", + "èĽ ¸", + "èľ Ĭ", + "èľ į", + "èľ ī", + "èľ £", + "çķ ¹", + "èĽ ¹", + "åĹ ¥", + "åĹ ²", + "åĹ ³", + "åĹ Į", + "åĹ į", + "åĹ IJ", + "åĹ ¤", + "åĹ µ", + "ç½ ¨", + "åµ Ĭ", + "åµ ´", + "éª °", + "éĶ Ĺ", + "éĶ Ľ", + "éĶ ľ", + "éĶ Ŀ", + "éĶ ŀ", + "éĶ Ł", + "éĶ ¢", + "éĶ ¨", + "éĶ ©", + "éĶ Ń", + "éĶ ±", + "éĽ ī", + "æ° ²", + "çĬ ı", + "æŃ ĥ", + "ç¨ ŀ", + "ç¨ Ĺ", + "ç¨ Ķ", + "çŃ ł", + "çŃ ¢", + "çŃ ®", + "çŃ ²", + "çī Ĵ", + "æķ «", + "å¾ Ń", + "æĦ Ĩ", + "èī Ħ", + "è§ İ", + "æ¯ ¹", + "è² Ĭ", + "è² ħ", + "è² ī", + "é¢ Ķ", + "èħ ł", + "èħ ©", + "èħ ¼", + "èħ Ń", + "è ħ§", + "å¡ į", + "åª µ", + "é² ħ", + "é² Ĩ", + "é² ĩ", + "é² Ī", + "é² ĭ", + "é² IJ", + "èĤ Ħ", + "é¹ IJ", + "é£ ķ", + "è§ ¥", + "éģ Ľ", + "é¦ IJ", + "é¹ ij", + "äº ¶", + "çĺ ĥ", + "çĹ ±", + "çĹ ¼", + "çĹ ¿", + "çĺ IJ", + "çĺ ģ", + "çĺ Ĩ", + "éº Ĥ", + "æŃ Ĩ", + "æĹ Ĵ", + "éĺ ĸ", + "éĺ Ĺ", + "ç¾ §", + "è± ¢", + "ç² ³", + "çĮ ·", + "çħ ³", + "çħ ¨", + "çħ ħ", + "çħ Ĭ", + "çħ ¸", + "çħ º", + "æ» Ł", + "æº ±", + "æº ĺ", + "æ¼ Ń", + "æ» ¢", + "æº ¥", + "æº ½", + "è£ Ł", + "æº »", + "æº ·", + "æ» Ĺ", + "æ» «", + "æº ´", + "æ» ı", + "æ» ĥ", + "æ» ¦", + "æº ı", + "æ» Ĥ", + "æ» ĵ", + "æº Ł", + "æ» ª", + "æĦ «", + "æħ Ĭ", + "é² İ", + "éª ŀ", + "çª ł", + "çª £", + "è£ ±", + "è£ ¨", + "è£ ¾", + "è£ °", + "ç¦ Ĭ", + "è° ©", + "è° ª", + "åª ¾", + "å« «", + "åª ²", + "å« Ĵ", + "å« Ķ", + "åª ¸", + "ç¼ Ļ", + "ç¼ ľ", + "ç¼ Ľ", + "è¾ Ķ", + "éª Ŀ", + "ç¼ Ł", + "ç¼ ¡", + "ç¼ ¢", + "ç¼ £", + "éª Ł", + "èĢ ¥", + "çĴ Ī", + "çij Ń", + "çį Ĵ", + "è§ ı", + "æħ Ŀ", + "å« ł", + "åı Ĩ", + "æij ½", + "å¢ ģ", + "æĴ Ĥ", + "æij ŀ", + "æĴ Ħ", + "ç¿ ¥", + "è¸ ħ", + "æij Ń", + "å¢ ī", + "å¢ Ĵ", + "æ¦ ĸ", + "ç¶ ¦", + "èĶ «", + "èĶ ·", + "éĿ º", + "éĿ ¼", + "éŀ ħ", + "éĿ ¿", + "çĶ į", + "èĶ ¸", + "èĶ Ł", + "èĶ º", + "æĪ ¬", + "èķ ĸ", + "èĶ »", + "èĵ ¿", + "æĸ ¡", + "é¹ ķ", + "èĵ ¼", + "æ¦ Ľ", + "æ¦ §", + "æ¦ «", + "æ¦ Ń", + "æ§ Ķ", + "æ¦ ±", + "æ§ ģ", + "æ§ ł", + "æ¦ ·", + "åĥ °", + "éħ ½", + "éħ ¹", + "ç¢ ¡", + "ç¢ ´", + "ç¢ £", + "ç¢ ²", + "èĩ §", + "è± ¨", + "æ® ¡", + "éľ ģ", + "èľ ļ", + "é¾ ĩ", + "é¾ Ī", + "ä ģ", + "äģ ĸ", + "çĿ ½", + "åĺ ŀ", + "åĺ Ī", + "åĺ Į", + "åĺ ģ", + "æļ Ŀ", + "è¸ Į", + "è¸ ī", + "èľ ŀ", + "èľ ¥", + "èľ ®", + "èĿ Ī", + "èľ ´", + "èľ ±", + "èľ ©", + "èľ ·", + "èľ ¿", + "èŀ Ĥ", + "èľ ¢", + "åĺ ¡", + "é¹ Ĺ", + "åĺ £", + "åĺ ¤", + "åĺ ļ", + "åĹ ¾", + "åĺ §", + "ç½ ´", + "ç½ ±", + "å¹ Ķ", + "å¶ Ĥ", + "å¹ Ľ", + "èµ Ļ", + "ç½ Ĥ", + "éª ·", + "éª ¶", + "é¹ ĺ", + "éĶ ²", + "éĶ ´", + "éĶ ¶", + "éĶ ·", + "éĶ ¸", + "éĶ µ", + "éķ Ĥ", + "çĬ Ĵ", + "ç® IJ", + "ç® ¦", + "ç® §", + "ç® ¸", + "ç® ¬", + "ç® ħ", + "ç® ª", + "ç® ľ", + "ç® ¢", + "ç® ĵ", + "åĥ ĸ", + "åĦ Ĩ", + "åĥ ³", + "åĥ Ń", + "åĬ ģ", + "åĥ ®", + "éŃ ĥ", + "éŃ Ĩ", + "çĿ ¾", + "èī ĭ", + "éĦ ±", + "èĨ Ī", + "èĨ ij", + "é² ij", + "é² Ķ", + "é² ļ", + "é² Ľ", + "é² Ł", + "çį IJ", + "è§ «", + "éĽ Ĵ", + "å¤ ¤", + "é¦ ij", + "éĬ ®", + "å¡ ¾", + "çĺ Į", + "çĺ Ĭ", + "çĺ ĺ", + "çĺ Ļ", + "æĹ ĸ", + "èĨ Ĥ", + "éĺ ļ", + "éĦ ¯", + "é² ŀ", + "ç² ¿", + "ç² ¼", + "ç³ ģ", + "æ§ Ĭ", + "é¹ ļ", + "çĨ ĺ", + "çĨ ¥", + "æ½ ¢", + "æ¼ ķ", + "æ» ¹", + "æ¼ ¯", + "æ¼ ¶", + "æ½ ĭ", + "æ½ ´", + "æ¼ ª", + "æ¼ ī", + "æ¼ ©", + "æ¾ ī", + "æħ µ", + "æIJ ´", + "çª ¨", + "å¯ ¤", + "ç¶ ®", + "è° ®", + "è¤ ¡", + "è¤ Ļ", + "è¤ ĵ", + "è¤ Ľ", + "è¤ Ĭ", + "è° ¯", + "è° °", + "è° ²", + "å± £", + "é¹ Ľ", + "å« ±", + "å« ĸ", + "å« ¦", + "å« ļ", + "å «ĺ", + "é¼ IJ", + "çŀ Ģ", + "é¹ ľ", + "éª ł", + "ç¼ ¥", + "ç¼ ¦", + "ç¼ §", + "ç¼ ¨", + "éª ¢", + "ç¼ «", + "èĢ ¦", + "èĢ §", + "çĴ ľ", + "çĴ İ", + "çĴ ģ", + "å¥ Ń", + "é« ¯", + "é« «", + "æĴ ·", + "æĴ ħ", + "èµ Ń", + "æĴ ¸", + "éĭ Ĩ", + "æĴ Ļ", + "æĴ º", + "å¢ Ģ", + "èģ ©", + "è§ IJ", + "éŀ ij", + "èķ Ļ", + "éŀ Ĵ", + "èķ Ī", + "èķ ¨", + "èķ ¤", + "èķ ŀ", + "èķ º", + "çŀ ¢", + "èķ ĥ", + "èķ ²", + "èµ ľ", + "æ§ ¿", + "æ¨ ¯", + "æ§ Ń", + "æ¨ Ĺ", + "æ¨ ĺ", + "æ§ ²", + "éĨ Į", + "éĨ ħ", + "éĿ ¥", + "éŃ ĩ", + "é¤ į", + "ç£ Ķ", + "ç£ Ļ", + "éľ Ī", + "è¾ ĺ", + "é¾ ī", + "é¾ Ĭ", + "è§ ij", + "çŀ Į", + "ç ŀĭ", + "çŀ ij", + "åĺ Ń", + "åĻ İ", + "åĻ ¶", + "é¢ Ļ", + "æļ ¹", + "åĻ ĺ", + "è¸ Ķ", + "è¸ Ŀ", + "è¸ Ł", + "è¸ Ĵ", + "è¸ ¬", + "è¸ ®", + "è¸ ¯", + "è¸ º", + "è¸ ŀ", + "èĿ ½", + "èĿ ¾", + "èĿ »", + "èĿ °", + "èĿ ®", + "è ŀĭ", + "èĿ ĵ", + "èĿ £", + "è Ŀ¼", + "åĺ ¬", + "é¢ ļ", + "åĻ į", + "åĻ Ļ", + "åĻ Į", + "åĻ Ķ", + "é¢ Ľ", + "å¹ ŀ", + "å¹ ¡", + "å¶ Ļ", + "å¶ Ŀ", + "éª º", + "éķ Ĭ", + "éķ ī", + "éķ Į", + "éķ ı", + "éķ Ĵ", + "éķ ĵ", + "éķ Ķ", + "ç¨ ·", + "ç® ´", + "ç¯ ij", + "ç¯ ģ", + "ç¯ Į", + "çī ĸ", + "åĦ ĭ", + "èĻ ¢", + "é¹ ŀ", + "èĨ ĺ", + "é² ł", + "é² ¡", + "é² ¢", + "é² £", + "é² ¥", + "é² §", + "é² ©", + "çį Ĺ", + "çį ł", + "è§ ¯", + "é¦ ĵ", + "é¦ Ķ", + "éº ¾", + "å» Ľ", + "çĺ Ľ", + "çĺ ¼", + "çĺ ¢", + "çĺ ł", + "é½ ij", + "ç¾ °", + "𥠻", + "𥻠Ĺ", + "ç³ Į", + "ç³ į", + "ç³ ħ", + "çĨ ľ", + "ç Ĩµ", + "æ¾ į", + "æ¾ Į", + "æ½ ¸", + "æ½ ¦", + "æ½ ²", + "éĭ Ī", + "æ½ Ł", + "æ½ º", + "å¯ ®", + "çª ³", + "è° ³", + "è¤ ´", + "è¤ Ł", + "è¤ «", + "è° µ", + "çĨ ¨", + "å± ¦", + "åĭ °", + "æĪ ®", + "èĿ ¥", + "ç¼ ¬", + "ç¼ ®", + "ç¼ ¯", + "éª £", + "çķ ¿", + "èĢ ©", + "èĢ ¨", + "èĢ ª", + "çĴ Ł", + "éĿ Ľ", + "çĴ ł", + "çĴ ĺ", + "èģ ±", + "èŀ ¯", + "é« »", + "é« Ń", + "é« ¹", + "æĵ Ģ", + "çĶ ı", + "æĵ ŀ", + "ç¸ ł", + "ç£ ¬", + "é¢ ŀ", + "èķ »", + "é¢ Ł", + "èĸ ¤", + "èĸ ¨", + "æª ł", + "èĸ ı", + "èĸ ®", + "èĸ ľ", + "èĸ ħ", + "æ¨ ¾", + "æ© Ľ", + "æ© ĩ", + "æ¨ µ", + "æª İ", + "æ© ¹", + "æ¨ ½", + "æ¨ ¨", + "æ© ¼", + "å¢ ¼", + "æ© IJ", + "ç¿ ®", + "éĨ IJ", + "éĨ į", + "éĨ ļ", + "ç£ ²", + "èµ Ŀ", + "æ® ª", + "éľ ı", + "éĮ ¾", + "è¾ ļ", + "éģ ½", + "æ° ħ", + "çŀ Ł", + "çŀ ł", + "çŀ °", + "åļ Ħ", + "åļ Ĩ", + "åĻ ¤", + "æļ ¾", + "è¹ Ģ", + "è¸ µ", + "è¸ ½", + "è¹ ī", + "è¹ ģ", + "èŀ ¨", + "èŀ Ī", + "èŀ ħ", + "èŀ Ń", + "èŀ ł", + "èŀ Ł", + "åĻ ±", + "åĻ «", + "åĻ »", + "åĻ ¼", + "ç½ ¹", + "åľ ľ", + "ä ¦", + "ä¦ ĥ", + "éķ Ĺ", + "éķ ĺ", + "éķ ļ", + "éķ Ľ", + "éķ Ŀ", + "éķ ŀ", + "éķ ł", + "æ° ĩ", + "æ° Ĩ", + "ç© ij", + "ç¯ Ŀ", + "ç¯ ¥", + "ç¯ ¦", + "ç¯ ª", + "ç¯ Ļ", + "çĽ ¥", + "åĬ ĵ", + "ç¿ ±", + "éŃ ī", + "éŃ Ī", + "å¾ ¼", + "æŃ Ļ", + "èĨ ¦", + "èĨ Ļ", + "é² ®", + "é² ±", + "é² ³", + "é² ´", + "é² µ", + "é² ·", + "é² »", + "çį ´", + "çį Ń", + "çį ¬", + "éĤ Ĥ", + "é¹ §", + "å» ¨", + "èµ Ł", + "çĺ °", + "å» ª", + "çĺ ¿", + "çĺ µ", + "çĺ ´", + "çĻ ĥ", + "çĺ ³", + "éº ĩ", + "éº Ī", + "å ¬´", + "å£ ħ", + "ç³ Ĺ", + "çĶ ij", + "çĩ İ", + "çĩ ł", + "çĩ Ķ", + "çĩ §", + "æ¿ ij", + "æ¿ ī", + "æ½ ŀ", + "æ¾ §", + "æ¾ ¹", + "æ¾ ¥", + "æ¾ ¶", + "æ¿ Ĥ", + "è¤ °", + "çª ¸", + "å¬ ĸ", + "çĬ Ł", + "éļ °", + "å¬ Ĺ", + "é¢ ¡", + "ç¼ ±", + "ç¼ ²", + "ç¼ ³", + "çĴ ©", + "çĴ ª", + "èŀ «", + "æĵ ¤", + "å£ ķ", + "è§ ³", + "ç½ Ħ", + "æĵ ¢", + "èĸ ¹", + "éŀ ¡", + "éŀ ¬", + "èĸ ·", + "èĹ ĵ", + "èĹ ģ", + "æª Ħ", + "æª ©", + "æĩ ĭ", + "éĨ ¢", + "ç¿ ³", + "ç¤ ħ", + "ç£ ´", + "é¹ ©", + "é¾ ĭ", + "é¾ Į", + "è± ³", + "å£ ij", + "é» »", + "åļ ı", + "åļ ħ", + "è¹ ij", + "è¹ Ĵ", + "è¹ Ĭ", + "è Ł¥", + "èŀ ¬", + "èŀ µ", + "çĸ ĥ", + "èŀ ³", + "èŁ ij", + "åļ ĵ", + "ç½ ½", + "ç½ ¾", + "å¶ ·", + "é» ľ", + "é» Ŀ", + "é« ģ", + "é« Ģ", + "éķ ¡", + "éķ ¢", + "éķ £", + "éķ ¦", + "éķ §", + "éķ ©", + "éķ ª", + "éķ «", + "ç½ ħ", + "ç° Į", + "ç¯ ¾", + "ç¯ ¼", + "ç° ĸ", + "ç° ĭ", + "é¼ ¢", + "åĦ ¡", + "é¹ ª", + "é¼ ¾", + "çļ ¤", + "éŃ į", + "é¾ ł", + "ç¹ ĩ", + "è² ĺ", + "éĤ Ī", + "è² Ķ", + "èĩ Į", + "èĨ »", + "èĩ Ĩ", + "èĩ ĥ", + "é² ¼", + "é² ½", + "é³ Ģ", + "é³ ĥ", + "é³ ħ", + "é³ ĩ", + "é³ Ĭ", + "èŀ ½", + "çĩ ®", + "é¹ «", + "ç³ ľ", + "ç¸ »", + "çĻ į", + "éº ĭ", + "æĩ ij", + "æ¿ ¡", + "æ¿ ®", + "æ¿ ŀ", + "æ¿ ł", + "æ¿ ¯", + "è¹ ĩ", + "è¬ ĩ", + "éĤ ĥ", + "è¥ ģ", + "æª Ĺ", + "æ ĵĺ", + "åŃ º", + "éļ ³", + "å¬ ·", + "èŁ Ĭ", + "é¹ ¬", + "éį ª", + "éı Ĭ", + "é¬ Ī", + "é¬ ĥ", + "çŀ ½", + "éŀ ¯", + "éŀ ¨", + "éŀ «", + "éŀ §", + "éŀ £", + "èĹ ľ", + "èĹ ł", + "éĨ ª", + "è¹ Ļ", + "ç¤ ĵ", + "çĩ ¹", + "é¤ ®", + "çŀ ¿", + "æĽ Ľ", + "é¢ ¢", + "èº ĩ", + "è¹ ļ", + "èŁ Ľ", + "èŁ ª", + "èŁ ł", + "èŁ ®", + "é¹ ®", + "é» ł", + "é» Ł", + "é« ħ", + "é« Ĥ", + "éķ ¬", + "éķ Ń", + "éķ ¯", + "é¦ ¥", + "ç° Ł", + "ç° ª", + "é¼ ¬", + "éĽ ł", + "èī Ł", + "é³ İ", + "é³ ı", + "é³ IJ", + "çĻ ŀ", + "çĻ Ķ", + "ç³ ¨", + "è¹ ©", + "éİ ı", + "éĤ ĭ", + "é¬ ı", + "æĶ ī", + "éŀ ²", + "éŀ ´", + "èĹ ¿", + "èĺ §", + "èĺ ħ", + "éĨ ®", + "éĨ ¯", + "éħ ĥ", + "éľ ª", + "éľ Ń", + "éľ ¨", + "é» ¼", + "åļ ¯", + "è¹ °", + "è¹ ¶", + "è¹ ½", + "è¹ ¼", + "è¹ ´", + "è¹ ¾", + "è¹ ¿", + "èł ĸ", + "èł ĵ", + "èŁ ¾", + "èł Ĭ", + "é» ¢", + "é« ĭ", + "é« Į", + "éķ ²", + "ç± Ģ", + "é½ ģ", + "éŃ ij", + "èī ¨", + "é³ ĵ", + "é³ Ķ", + "é³ ķ", + "é³ Ĺ", + "é³ Ļ", + "éı ĸ", + "ç¾ ¸", + "㸠Ĩ", + "çĢ £", + "çĢ Ľ", + "è¥ ¦", + "è° ¶", + "è¥ ŀ", + "éª ¥", + "ç¼ µ", + "çĵ Ĵ", + "æĶ ĺ", + "èĺ ©", + "èĺ ĸ", + "éĨ ´", + "éľ °", + "éħ Ĩ", + "çŁ į", + "èº ħ", + "é¼ į", + "å· ī", + "é» ©", + "é» ¥", + "é» ª", + "éķ ³", + "éķ ´", + "é» §", + "çº Ĥ", + "çĴ º", + "é¼ ¯", + "èĩ ľ", + "é³ ľ", + "é³ Ŀ", + "é³ Ł", + "çį ¾", + "åŃ Ģ", + "éª §", + "ç ĵĺ", + "é¼ Ļ", + "éĨ º", + "ç¤ ´", + "é¢ ¦", + "æĽ ©", + "é³ ¢", + "éº Ŀ", + "å¤ Ķ", + "çĪ Ŀ", + "çģ ı", + "ç¦ ³", + "éIJ ¾", + "ç¾ ¼", + "èł ¡", + "èĢ ±", + "é¹ ³", + "æ° į", + "é¥ ķ", + "èº IJ", + "é« ij", + "éķ µ", + "ç© °", + "é¥ Ķ", + "é¬ »", + "é¬ Ł", + "è¶ ±", + "æĶ «", + "æĶ ¥", + "é¢ §", + "èº ľ", + "é¼ ¹", + "çĻ ¯", + "èł ²", + "èł ¹", + "èº ŀ", + "è¡ ¢", + "çģ ŀ", + "è¥ »", + "çº Ľ", + "é¬ £", + "æĶ ®", + "åĽ Ķ", + "é¦ ķ", + "æĪ Ĩ", + "çĪ ¨", + "é½ ī", + "äº į", + "å° ¢", + "å½ ³", + "åį ¬", + "æ® ³", + "ðł Ļ¶", + "æ¯ Į", + "éĤ ĺ", + "æĪ ĭ", + "åľ ¢", + "æ° ķ", + "ä¼ ĭ", + "ä» Ŀ", + "åĨ ®", + "æ° ¿", + "æ± Ī", + "æ° ¾", + "å¿ ī", + "å® Ħ", + "𬣠Ļ", + "è® ±", + "æī ŀ", + "åľ ²", + "åľ «", + "èĬ ı", + "èĬ ĥ", + "æľ ³", + "æľ ¸", + "ð¨ Ļ", + "ð¨Ļ ¸", + "éĤ ¨", + "åIJ Ĵ", + "åIJ ĸ", + "å± ¼", + "å± ¾", + "è¾ ¿", + "éĴ Ĩ", + "ä» ³", + "ä¼ £", + "ä¼ Ī", + "çĻ ¿", + "çĶ ª", + "éĤ ł", + "çĬ ´", + "åĨ ±", + "éĤ ¡", + "ð¬ĩ ķ", + "æ± ĭ", + "ä ľ", + "äľ £", + "è® »", + "𬣠ŀ", + "åŃ ĸ", + "ð¬ĺ ĵ", + "çº ©", + "çİ Ĵ", + "çİ ĵ", + "çİ ĺ", + "çİ ļ", + "åĪ ¬", + "ð«Ń Ł", + "åĿ ľ", + "åĿ ī", + "æī ½", + "ð«Ń ¢", + "åĿ ĭ", + "æī º", + "㧠ij", + "æ¯ IJ", + "èĬ °", + "èĬ £", + "èĭ Ĭ", + "èĭ ī", + "èĬ ĺ", + "èĬ ´", + "èĬ ł", + "ð« ĩ", + "ð«ĩ Ń", + "èĬ ¤", + "æĿ ķ", + "æĿ Ļ", + "æĿ Ħ", + "æĿ §", + "æĿ ©", + "å° ª", + "å° ¨", + "è½ ª", + "ð«IJ Ħ", + "åĿ Ĵ", + "èĬ Ī", + "æĹ ´", + "æĹ µ", + "åij Ļ", + "ã ķ", + "ãķ ®", + "å² į", + "ð« µ", + "𫵠·", + "å² ł", + "å² ľ", + "åij ĩ", + "åĨ ı", + "è§ ĥ", + "å² Ļ", + "ä¼ ¾", + "ãij ĩ", + "ä¼ Ń", + "ä½ ĸ", + "ä¼ ²", + "ä½ ģ", + "é£ ı", + "çĭ ĥ", + "éĹ ¶", + "æ± §", + "æ± «", + "𣲠ĺ", + "𣲠Ĺ", + "æ² Ħ", + "æ² ĺ", + "ð¬ĩ Ļ", + "æ± Ń", + "ã³ ĩ", + "æ² ĩ", + "å¿ ®", + "å¿ ³", + "å¿ º", + "𬣠¡", + "ç¥ ĥ", + "è¯ ĩ", + "éĤ ²", + "è¯ İ", + "è¯ IJ", + "å± ĥ", + "ð« ¸", + "𫸠©", + "å² Ĭ", + "éĺ ½", + "ä¢ º", + "éĺ ¼", + "å¦ §", + "å¦ ĺ", + "ð¨ ļ", + "ð¨ļ ķ", + "çº ®", + "é© ²", + "ð«ĺ ľ", + "çº »", + "ð¬ĺ ĺ", + "ð«ĺ Ŀ", + "çº ¼", + "çİ ¤", + "çİ ŀ", + "çİ ±", + "çİ Ł", + "éĤ ½", + "éĤ ¿", + "åĿ ¥", + "åĿ °", + "åĿ ¬", + "åĿ ½", + "å¼ Ĩ", + "èĢ µ", + "ä¢ ¼", + "ð¦ Ń", + "ð¦Ń ľ", + "èĮ ĭ", + "èĭ §", + "èĭ ¾", + "èĭ ł", + "æŀ ħ", + "ãŃ İ", + "æŀ ĺ", + "æŀ į", + "çŁ ¼", + "çŁ »", + "åĮ ¼", + "𬨠Ĥ", + "ð¬Ģ ©", + "ð¬Ģ ª", + "æĹ ¿", + "æĺ Ħ", + "æĺ Ĵ", + "æĺ Ī", + "åĴ ī", + "åĴ ĩ", + "åĴ į", + "å² µ", + "å² ½", + "å² ¨", + "å² ŀ", + "å³ Ĥ", + "ã Ł", + "ãŁ ĥ", + "åĽ ·", + "𬬠©", + "éĴ IJ", + "éĴ Ķ", + "éĴ ĸ", + "çī ¥", + "ä½ ´", + "åŀ Ī", + "ä¾ ģ", + "ä¾ ¹", + "ä½ ¸", + "ä½ º", + "éļ ¹", + "ãij Ĭ", + "ä¾ Ĥ", + "ä½ ½", + "ä¾ ĺ", + "éĥ Ī", + "èĪ ł", + "éĥ IJ", + "éĥ ĥ", + "æĶ ½", + "èĤ Ń", + "èĤ ¸", + "èĤ ·", + "çĭ ī", + "çĭ Ŀ", + "é¥ ³", + "å¿ ŀ", + "çĤ Į", + "çĤ Ĩ", + "æ³ Ļ", + "æ² º", + "æ³ Ĥ", + "æ³ ľ", + "æ³ ĥ", + "æ³ ĩ", + "æĢ Ĭ", + "å³ ĥ", + "ç© ¸", + "ç¥ ĭ", + "ç¥ Ĭ", + "ð«į £", + "𬣠³", + "𬠩½", + "é¸ ¤", + "å¼ ¢", + "å¼ ¨", + "éĻ ij", + "𬮠¿", + "éĻ İ", + "𬯠Ģ", + "åį º", + "ä¹ ¸", + "å¦ Ń", + "å§ Ī", + "ð« °", + "ð«° Ľ", + "è¿ ³", + "åı ķ", + "𬳠µ", + "é© µ", + "𬳠¶", + "ä Į", + "äĮ ¹", + "é© º", + "ð«ł Ĭ", + "ç» ĭ", + "ç» IJ", + "çł ī", + "èĢ Ķ", + "ãĽ ĥ", + "çİ ¶", + "çı ĩ", + "çı ħ", + "ð¬į Ľ", + "çı ĭ", + "çİ ¹", + "çı Į", + "çİ ¿", + "éŁ ¨", + "åŀ ļ", + "åŀ ¯", + "åŀ Ļ", + "åŀ ²", + "åŁ ı", + "åŀ į", + "èĢ ĩ", + "é¿ į", + "åŀ İ", + "åŀ ´", + "åŀ Ł", + "åŀ ŀ", + "æĮ ĵ", + "åŀ µ", + "åŀ ı", + "æĭ ¶", + "èį ĸ", + "èį ģ", + "èį Ļ", + "èį Ľ", + "èĮ Ī", + "èĮ ½", + "èį Ħ", + "èĮ º", + "ð¬ľ ¬", + "èį ĵ", + "èĮ ³", + "𦠰", + "𦰠¡", + "èĮ Ľ", + "èį Ń", + "ãŃ ķ", + "æŁ ·", + "æŁ ĥ", + "æŁ Ĭ", + "æŀ ¹", + "æł IJ", + "æŁ ĸ", + "éĥ ļ", + "åī ħ", + "ä´ ĵ", + "è¿ º", + "åİ ĸ", + "çł Ĩ", + "çł ij", + "çł Ħ", + "èĢ ı", + "å¥ ĵ", + "ä ¶", + "ä¶ ®", + "è½ µ", + "è½ ·", + "è½ ¹", + "è½ º", + "æĺ º", + "𪠾", + "𪾠¢", + "æĺ ½", + "çĽ ·", + "åĴ ¡", + "åĴ º", + "æĺ ³", + "æĺ £", + "æĺ ¤", + "æĺ «", + "æĺ ¡", + "åĴ ¥", + "æĺ ª", + "èĻ ·", + "èĻ ¸", + "åĵ ĥ", + "å³ ĺ", + "èĢ ij", + "å³ Ľ", + "𪨠°", + "å³ Ĺ", + "å³ §", + "å¸ ¡", + "éĴ ĺ", + "ð«ĵ §", + "éĴ ľ", + "𬬠®", + "𬬠±", + "𬬠Ń", + "éĴ ª", + "éĴ ¬", + "éĴ Ń", + "çŁ §", + "ç§ ¬", + "ä¿ «", + "èĪ ģ", + "ä¿ ľ", + "ä¿ Ļ", + "ä¿ į", + "åŀ ķ", + "è¡ İ", + "èĪ £", + "å¼ ĩ", + "ä¾ ´", + "é¸ §", + "äı ¡", + "èĥ ł", + "ð¦ Ļ¶", + "èĥ Ī", + "èĥ ©", + "èĥ £", + "æľ ı", + "é£ IJ", + "è¨ Ħ", + "é¥ »", + "åº ¤", + "çĸ ¢", + "çĤ £", + "çĤ Ł", + "ã ¶", + "㶠²", + "æ´ Ń", + "æ´ ĺ", + "æ´ ĵ", + "æ´ ¿", + "ã³ ļ", + "æ³ ļ", + "æµ Ī", + "æµ ī", + "æ´ ¸", + "æ´ ij", + "æ´ ¢", + "æ´ Ī", + "æ´ ļ", + "æ´ º", + "æ´ ¨", + "æµ IJ", + "ã³ ĺ", + "æ´ ´", + "æ´ £", + "æģ Ķ", + "å® ¬", + "çª Ģ", + "æī Ĥ", + "è¢ Ĩ", + "ç¥ ı", + "ç¥ IJ", + "ç¥ ķ", + "åı ļ", + "éĻ §", + "éĻ ŀ", + "å¨ Ģ", + "å§ ŀ", + "å§ ±", + "å§ ¤", + "å§ ¶", + "å§ ½", + "æŀ ²", + "ç» ĸ", + "éª ĥ", + "ð¬ĺ ¡", + "𬳠½", + "ð¬ĺ ©", + "ð«Ħ §", + "å½ ĸ", + "éª ī", + "æģ Ŀ", + "çı ª", + "çı Ľ", + "çı ¹", + "çIJ Ĭ", + "çİ ¼", + "çı ĸ", + "ðª Ł", + "ðªŁ Ŀ", + "çı ½", + "çı ¦", + "çı «", + "çı Ĵ", + "ð¬į ¤", + "çı ¢", + "çı ķ", + "çı Ŀ", + "ð«Ń ¼", + "åŁ Ĺ", + "åŀ ¾", + "åŀ º", + "åŁ Ĩ", + "åŀ ¿", + "åŁ Į", + "åŁ ĩ", + "èİ °", + "èĮ Ŀ", + "ð¬ľ ¯", + "éĦ Ģ", + "èİ ¶", + "èİ Ŀ", + "äĵ ĸ", + "èİ Ļ", + "æł »", + "æ¡ ł", + "ð¬ Ĥ", + "ð¬Ĥ ©", + "æ¡ Ħ", + "æ¢ ł", + "æł ´", + "æ¢ ´", + "æł Ĵ", + "éħ İ", + "éħ ı", + "ð«ł Ĩ", + "çł µ", + "çł ł", + "çł «", + "çł ¬", + "ç¡ ģ", + "æģ §", + "ç¿ ĥ", + "éĥ ª", + "ð¨ IJ", + "ð¨IJ Ī", + "è¾ Ģ", + "è¾ ģ", + "ð¬ Į", + "ð¬Į Ĺ", + "åī ķ", + "èµ Ģ", + "åĵ ¢", + "æĻ ħ", + "æĻ Ĭ", + "åĶ Ŀ", + "åĵ ³", + "åĵ ±", + "åĨ Ķ", + "æĻ Ķ", + "æĻ IJ", + "çķ ĸ", + "èļ Ħ", + "èļ Ĩ", + "ð« ij", + "ð«ij ¡", + "å¸ ±", + "å´ ģ", + "å³ ¿", + "𪨠¶", + "å´ Ħ", + "å¸ ¨", + "å ´Ģ", + "èµ Ĩ", + "𬠬¸", + "éĴ ·", + "𬬠»", + "𬬠¹", + "𬬠¿", + "ð¬Ń ģ", + "çľ ļ", + "çĶ ¡", + "ç¬ «", + "åĢ »", + "åĢ ´", + "èĦ ©", + "åĢ ®", + "åĢ ķ", + "åĢ ŀ", + "ð« ¢", + "ð«¢ ¸", + "åĢ ĵ", + "åĢ §", + "è¡ ĥ", + "èĻ Ĵ", + "èĪ Ń", + "èĪ ¯", + "èĪ ¥", + "çĵ ŀ", + "é¬ ¯", + "é¸ °", + "èĦ İ", + "æľ ĵ", + "èĥ ²", + "èĻ ĵ", + "é± ½", + "çĭ ´", + "å³ ±", + "çĭ »", + "çľ ¢", + "ð«Ĺ §", + "åĭ į", + "çĹ Ħ", + "çĸ °", + "çĹ ĥ", + "ç« ĺ", + "ç¾ ĸ", + "ç¾ ĵ", + "æ¡ Ĭ", + "æķ ī", + "çĥ ł", + "çĥ Ķ", + "çĥ ¶", + "çĥ »", + "ð¬Ĭ Ī", + "æ¶ į", + "æµ ¡", + "æµ Ń", + "æµ ¬", + "æ¶ Ħ", + "æ¶ ¢", + "æ¶ IJ", + "æµ °", + "æµ Ł", + "æµ Ľ", + "æµ ¼", + "æµ ²", + "æ¶ ĺ", + "æĤ Ī", + "æĤ ĥ", + "æĤ ¢", + "ð¬Ĵ Ī", + "å® §", + "çª ħ", + "çª Ĭ", + "çª İ", + "æī ħ", + "æī Ĩ", + "è¢ ª", + "è¢ Ĺ", + "è¢ ¯", + "ç¥ §", + "éļ º", + "åł ²", + "çĸ į", + "𨠺", + "𨺠Ļ", + "éĻ ´", + "ç ĥĿ", + "çł ®", + "ãĽ ļ", + "åĵ ¿", + "ç¿ Ģ", + "ç¿ Ĥ", + "åī Ł", + "𬳠¿", + "ð«Ħ ¨", + "ç» ¤", + "éª į", + "ð¬ĺ «", + "ä Ĥ", + "äĤ ®", + "çIJ İ", + "çı ¸", + "çı µ", + "çIJ Ħ", + "çIJ Ī", + "çIJ Ģ", + "çı º", + "æİ Ń", + "åł İ", + "åł IJ", + "åŁ ¼", + "æİ İ", + "åŁ «", + "åł Į", + "æĻ ¢", + "ð« ®", + "ð«® ĥ", + "æİ ŀ", + "åŁ ª", + "å£ ¸", + "ãĻ į", + "èģ į", + "èı Ŀ", + "èIJ ļ", + "èı ¥", + "èİ ¿", + "äĵ «", + "åĭ ļ", + "äĵ ¬", + "èIJ Ĩ", + "èı Ĥ", + "èı į", + "èı ¼", + "èIJ £", + "äĵ ¨", + "èı ī", + "äĵ Ľ", + "æ¢ ¼", + "æ¢ ½", + "æ¡ ²", + "æ¢ ¾", + "æ¡ ¯", + "æ¢ £", + "æ¢ Į", + "æ¡ ¹", + "æķ Ķ", + "åİ £", + "ç¡ Ķ", + "é¿ İ", + "ç¡ Ļ", + "ç¡ ļ", + "ç¡ Ĭ", + "ç¡ į", + "åĭ Ķ", + "ä´ ķ", + "é¾ ģ", + "éĢ ´", + "åĶ ª", + "åķ «", + "ç¿ Ī", + "ã «", + "ã« °", + "æĻ Ļ", + "çķ ¤", + "𬱠ĸ", + "è¶ ¼", + "è· Ĥ", + "èĽ ĥ", + "èļ ²", + "ð¬Ł ½", + "èļ º", + "åķ ´", + "äİ ĥ", + "å´ §", + "å´ Ł", + "å´ ŀ", + "å´ Ĵ", + "å´ Į", + "å´ ¡", + "éĵ ı", + "ð«ĵ ¯", + "ð«Ł ¹", + "éĵ ķ", + "ð«Ł ¼", + "éĵ ĸ", + "éĵ ĺ", + "éĵ ļ", + "éĵ ŀ", + "éĵ ¥", + "éĵ ´", + "çī »", + "çī ¿", + "ç¨ Ĩ", + "ç¬ ±", + "ç¬ ¯", + "åģ °", + "åģ ¡", + "é¸ º", + "åģ Ń", + "åģ ²", + "åģ ģ", + "ã ¿", + "ã¿ ł", + "éĦ ħ", + "åģ ĵ", + "å¾ Ľ", + "è¡ Ĵ", + "èĪ ³", + "èĪ ²", + "é¸ ¼", + "æĤ Ĩ", + "éĦ ĥ", + "çĵ »", + "ä Ŀ", + "äĿ Ļ", + "èĦ ¶", + "èĦ ŀ", + "èĦ Ł", + "äı ²", + "é± ¾", + "çĮ ĩ", + "çĮ Ĭ", + "çĮ Ħ", + "è§ ĸ", + "ðł ħ", + "ðłħ ¤", + "åº ±", + "åº ¼", + "åº ³", + "çĹ ĵ", + "ä´ Ķ", + "ç« «", + "åł ĥ", + "éĺ Į", + "ç¾ Ŀ", + "ç¾ ķ", + "çĦ Ĩ", + "çĥ º", + "çĦ Į", + "æ· ı", + "ð¬ĩ ¹", + "æ· Ł", + "æ· ľ", + "æ· ´", + "æ· ¯", + "æ¹ ´", + "æ¶ ´", + "ð¬į ¡", + "ã ¥", + "㥠Ħ", + "æĥ Ľ", + "æĥ Ķ", + "æĤ °", + "æĥ Ļ", + "å¯ ģ", + "éĢ Ń", + "𬤠ĩ", + "ð«į ¯", + "è¢ ¼", + "è£ Ī", + "ç¥ ²", + "𬤠Ĭ", + "ð«į ²", + "è° ŀ", + "èī ´", + "å¼ ¸", + "å¼ ¶", + "𬯠İ", + "éļ ĥ", + "å© ŀ", + "å¨ µ", + "å© ¼", + "åª ĸ", + "å© ³", + "å© į", + "å© Į", + "å© «", + "å© ¤", + "å© ĺ", + "å© ł", + "ð¬ĺ ¬", + "ð¬ĺ Ń", + "𬴠Ĥ", + "ð«ĺ ¦", + "ç» ¹", + "ð«Ł ħ", + "ð¬ĺ ¯", + "éª ķ", + "ð«ĺ §", + "çµ ľ", + "çı ·", + "çIJ ²", + "çIJ ¡", + "çIJ Ł", + "çIJ Ķ", + "çIJ Ń", + "åł ¾", + "åł ¼", + "æı ķ", + "ãĻ ĺ", + "åł §", + "åĸ Ĩ", + "åł ¨", + "å¡ ħ", + "åł ł", + "çµ ·", + "𪠣", + "𪣠»", + "ð¡ İ", + "ð¡İ ļ", + "è ijľ", + "æĥ İ", + "èIJ ³", + "èij Ļ", + "éĿ ¬", + "èij ´", + "èĴ ĩ", + "èĴ Ī", + "éĦ ļ", + "èĴ ī", + "èĵ ĩ", + "èIJ ©", + "èij °", + "èij İ", + "éĦ ij", + "èĴ İ", + "èij ĸ", + "èĴ Ħ", + "èIJ ¹", + "æ£ ¤", + "æ£ ½", + "æ£ «", + "æ¤ ĵ", + "æ¤ ij", + "ð¬ ĥ", + "ð¬ĥ Ĭ", + "é¹ Ģ", + "æ¤ Ĩ", + "æ£ ĵ", + "æ£ ¬", + "æ£ ª", + "æ¤ Ģ", + "æ¥ Ĺ", + "𬠷", + "𬷠ķ", + "çĶ ¦", + "éħ ¦", + "è§ Į", + "å¥ ¡", + "çļ ķ", + "ç¡ ª", + "æ¬ ¹", + "è© Ł", + "ð«IJ IJ", + "è¾ Į", + "æ£ IJ", + "é¾ Ĥ", + "𬠹", + "𬹠¼", + "é» ¹", + "çī ļ", + "çĿ İ", + "æĻ «", + "æĻ ª", + "æĻ ±", + "ð §", + "𧠿", + "𧿠¹", + "èĽ ij", + "çķ ¯", + "æĸ Ŀ", + "åĸ ¤", + "å´ ¶", + "åµ ģ", + "ð« ¶", + "𫶠ĩ", + "å´ ¾", + "åµ ħ", + "å´ ¿", + "åµ ļ", + "ç¿ Ļ", + "ð«ĸ ®", + "åľ Į", + "åľ IJ", + "èµ ij", + "èµ Ĵ", + "é¿ ı", + "éĵ ¹", + "ð¬Ń Ĭ", + "éĵ ½", + "𨱠ĩ", + "ð«ĵ ¶", + "éĶ Ĭ", + "éĶ į", + "éĶ İ", + "ð¬Ń İ", + "éĶ ĵ", + "çĬ ĩ", + "é¢ ĭ", + "ç¨ Į", + "çŃ Ģ", + "çŃ ĺ", + "çŃ ľ", + "çŃ ¥", + "çŃ ħ", + "åĤ ĥ", + "åĤ ī", + "ç¿ Ľ", + "åĤ Ĵ", + "åĤ ķ", + "èĪ ¾", + "çķ ¬", + "ð«ĸ ¯", + "èĦ ¿", + "èħ ĺ", + "ä IJ", + "äIJ ĥ", + "èħ Ļ", + "èħ Ĵ", + "𬱠Ł", + "é² ĥ", + "çĮ °", + "ð« Ľ", + "ð«Ľ Ń", + "çĮ ¯", + "ã º", + "㺠Ħ", + "é¦ ī", + "åĩ ĵ", + "éĦ Ĺ", + "ð« ·", + "ð«· ·", + "å» ĭ", + "å» Ĩ", + "éĦ Į", + "ç² ¢", + "éģ Ĩ", + "æĹ IJ", + "𬮠±", + "çĦ ŀ", + "ð¬Ĭ ¤", + "æ¬ »", + "𣠸", + "𣸠£", + "æº ļ", + "æº ģ", + "æ¹ Ŀ", + "æ¸ °", + "æ¹ ĵ", + "ã ´", + "ã´ Ķ", + "æ¸ Ł", + "æº ł", + "æ¸ ¼", + "æº ĩ", + "æ¹ £", + "æ¹ ij", + "æº ŀ", + "æĦ IJ", + "æĦ ĥ", + "æķ ©", + "çĶ ¯", + "æ£ ¨", + "æī Ĭ", + "è£ £", + "ç¥ ¼", + "å© »", + "åª Ĩ", + "åª ŀ", + "ãĽ ¹", + "åª ĵ", + "åª Ĥ", + "åª Ħ", + "æ¯ µ", + "çŁ ŀ", + "𬴠ĥ", + "ð«ĺ ¨", + "ç¼ Ĭ", + "ç¼ IJ", + "éª Ļ", + "çij ĥ", + "çij ĵ", + "çij ħ", + "çij Ĩ", + "ä´ ĸ", + "çij ĸ", + "çij Ŀ", + "çij Ķ", + "çij Ģ", + "𤠧", + "𤧠Ľ", + "çij ³", + "çij Ĥ", + "å¶ ħ", + "çij ij", + "éģ ĺ", + "é« ¢", + "å¡ ¥", + "åł ½", + "èµ ª", + "æij Ľ", + "å¡ Ŀ", + "æIJ Ĵ", + "æIJ Į", + "èĴ ±", + "èĴ ¨", + "èĵ ı", + "èĶ Ģ", + "èĵ ¢", + "èĵ Ĥ", + "èĴ »", + "èĵ £", + "æ¤ ¹", + "æ¥ ª", + "æ¦ ĥ", + "æ¦ ħ", + "æ¥ Ĵ", + "æ¥ ©", + "æ¦ ĩ", + "æ¤ ¸", + "æ¥ Ļ", + "æŃ ħ", + "𬠪", + "𬪠©", + "ç¢ ĥ", + "ç¢ ı", + "ð¬Ĵ Ķ", + "ç¢ Ī", + "äĥ ħ", + "ç¡ ¿", + "éĦ ł", + "è¾ Ĵ", + "𬨠İ", + "ð«IJ ĵ", + "é¾ Ĩ", + "è§ ľ", + "ä £", + "ä£ ĺ", + "æļ ķ", + "é¹ į", + "ð« «", + "ð«« ĩ", + "㬠Ĭ", + "æļ ħ", + "è· ±", + "èľ IJ", + "èľ İ", + "åµ ²", + "èµ Ĺ", + "éª ±", + "éĶ ĸ", + "ð«ĵ ¹", + "éĶ ĺ", + "éĶ ³", + "éĶ §", + "éĶ ª", + "ð¬Ń ļ", + "éĶ «", + "éĶ ¬", + "ð¬Ń Ľ", + "ç¨ ij", + "ç¨ Ļ", + "ä ħ", + "äħ Ł", + "ð¬ ķ", + "ð¬ķ Ĥ", + "çŃ »", + "çŃ ¼", + "çŃ ¶", + "çŃ ¦", + "çŃ ¤", + "åĤ º", + "é¹ İ", + "åĥ ĩ", + "èī ħ", + "èī ī", + "è° ¼", + "è² Ĩ", + "èħ ½", + "èħ ¨", + "èħ ¯", + "é² ī", + "é² Ĭ", + "é² Į", + "ä² Ł", + "𬶠ĭ", + "𬶠į", + "é² ı", + "éĽ Ĭ", + "çĮ º", + "é£ Ķ", + "è§ Ł", + "ð¦ Ŀ¼", + "é¦ Į", + "è£ Ľ", + "å» Ĵ", + "çĺ ħ", + "éĦ ĺ", + "é¹ Ĵ", + "éĦ ľ", + "éº Ģ", + "éĦ £", + "éĺ ĺ", + "ð«Ķ ¶", + "çħ ģ", + "çħ ĥ", + "çħ ´", + "çħ ĭ", + "çħ Ł", + "çħ ĵ", + "æ» ł", + "æº į", + "æº ¹", + "æ» Ĩ", + "æ» ī", + "æº ¦", + "æº µ", + "æ¼ ·", + "æ» §", + "æ» ĺ", + "æ» į", + "æĦ Ń", + "æħ ¥", + "æħ Ĩ", + "å¡ ±", + "ð« ĮĢ", + "è £¼", + "ç¦ ĭ", + "ç¦ Ķ", + "ç¦ ĺ", + "ç¦ Ĵ", + "è° «", + "é¹ Ķ", + "ð«ĸ ³", + "æĦ į", + "å« Ħ", + "åª ±", + "æĪ ¤", + "åĭ ł", + "æĪ £", + "ð«ĺ ª", + "ð«ĺ ¬", + "ç¼ ŀ", + "èĢ ¤", + "çij §", + "ð« ŀ", + "ð«ŀ ©", + "çij ¨", + "çij ±", + "çij ·", + "çij ¢", + "æĸ ł", + "æij ı", + "å¢ ķ", + "å¢ Ī", + "å¢ IJ", + "å¢ ĺ", + "æij ´", + "éĬ İ", + "ð¡ IJ", + "ð¡IJ ĵ", + "å¢ ļ", + "æĴ ĸ", + "𪠤", + "𪤠Ĺ", + "éĿ ½", + "éŀ ģ", + "èĶ Į", + "èĶ Ī", + "èĵ °", + "èĶ ¹", + "èĶ Ĭ", + "åĺ ı", + "æ¦ °", + "æ¦ ij", + "æ§ ļ", + "ð£ Ĺ", + "ð£Ĺ ĭ", + "æ§ ľ", + "æ¦ į", + "çĸ IJ", + "𬸠ĺ", + "éħ º", + "éħ ¾", + "éħ ²", + "éħ ´", + "ç¢ ¶", + "äĥ İ", + "ð¬Ĵ Ĺ", + "ç¢ ¨", + "ð¥ Ķ", + "ð¥Ķ ²", + "ç¢ ¹", + "ç¢ ¥", + "åĬ Ĥ", + "ð«ļ ĸ", + "ä´ Ĺ", + "å¤ ¥", + "çŀ į", + "é¹ ĸ", + "㬠İ", + "è· ½", + "èľ ¾", + "å¹ ĸ", + "å¶ į", + "åľ Ļ", + "𨱠ı", + "éĶ º", + "éĶ ¼", + "éĶ ½", + "ð¬Ń ¤", + "éĶ ¾", + "éĶ ¿", + "éķ ĥ", + "éķ Ħ", + "éķ ħ", + "é¦ Ŀ", + "é¹ Ļ", + "ç® ¨", + "ç® ĸ", + "åĬ Ħ", + "åĥ ¬", + "åĥ ¦", + "åĥ Ķ", + "åĥ İ", + "æ§ ĥ", + "ãĻ ¦", + "é² Ĵ", + "é² ķ", + "ð«ļ ķ", + "é² ĸ", + "é² Ĺ", + "é² ĺ", + "é² Ļ", + "𬶠IJ", + "𬶠ı", + "ð ©½", + "𩽠¾", + "å¤ IJ", + "çį į", + "é£ Ĺ", + "𬸠ļ", + "åĩ ĺ", + "å» ij", + "å» Ļ", + "çĺ Ĺ", + "çĺ ¥", + "çĺ ķ", + "é² Ŀ", + "éĦ «", + "çĨ ĩ", + "æ¼ ¹", + "æ¼ ĸ", + "æ½ Ĩ", + "æ¼ ¤", + "æ½ ©", + "æ¼ ¼", + "æ¼ ´", + "ã ½", + "ã½ ı", + "æ¼ Ī", + "æ¼ ĭ", + "æ¼ »", + "æħ ¬", + "çª ¬", + "çª Ń", + "ã ®", + "ã® ¾", + "𬤠Ŀ", + "è¤ ķ", + "ç¦ Ľ", + "ç¦ ļ", + "éļ ©", + "å« ķ", + "å« Ń", + "å« ľ", + "å« ª", + "ð¬ ĻĤ", + "ã »", + "ã» ¬", + "éº ¹", + "çĴ Ĩ", + "æ¼ ¦", + "åı ĩ", + "å¢ £", + "å¢ ¦", + "å¢ ¡", + "åĬ IJ", + "èĸ ģ", + "èķ °", + "èĶ ĥ", + "é¼ Ĵ", + "æ§ ±", + "é¹ Ŀ", + "ç£ ı", + "ç£ ī", + "æ® £", + "æħ Ń", + "éľ ħ", + "æļ µ", + "æļ ²", + "æļ ¶", + "è¸ ¦", + "è¸ £", + "äĹ ĸ", + "èĿ ĺ", + "èĿ ²", + "èĿ ¤", + "åĻ ĩ", + "å ĻĤ", + "åĻ Ģ", + "ç½ ¶", + "å¶ ²", + "å¶ ĵ", + "ãł ĩ", + "å¶ Ł", + "å¶ Ĵ", + "éķ Ĩ", + "éķ Ī", + "éķ ĭ", + "éķ İ", + "ð¬Ń ©", + "éķ ķ", + "ç¨ ¹", + "åĦ ĩ", + "çļ ŀ", + "çļ Ľ", + "ä´ ĺ", + "èī İ", + "èī ı", + "é¹ Ł", + "𩾠ĥ", + "é² ¦", + "é² ª", + "é² ¬", + "æ© ¥", + "è§ Ń", + "é¹ ł", + "é¹ ¡", + "ç³ ĩ", + "ç³ Ī", + "ç¿ ¦", + "é¹ ¢", + "é¹ £", + "çĨ Ľ", + "æ½ ĸ", + "æ½ µ", + "ã µ", + "ãµ IJ", + "æ¾ Ĥ", + "æ¾ Ľ", + "çij ¬", + "æ½ ½", + "æ½ ¾", + "æ½ ı", + "æĨ Ń", + "æĨ ķ", + "𬸠£", + "æĪ Ń", + "è¤ ¯", + "ç¦ ¤", + "ð«į ½", + "å« ½", + "éģ ¹", + "𬴠Ĭ", + "çĴ ¥", + "çĴ ²", + "çĴ Ĵ", + "æĨ Ļ", + "æĵ IJ", + "éĦ ¹", + "èĸ ³", + "éŀ Ķ", + "é» ĩ", + "ð¬ ŀ", + "ð¬ŀ Ł", + "èķ Ĺ", + "èĸ ¢", + "èķ ¹", + "æ© ŀ", + "æ© ij", + "æ© ¦", + "éĨ ij", + "è§ ±", + "ç£ ¡", + "ð¥ ķ", + "ð¥ķ ¢", + "ç£ ľ", + "è± ®", + "ð«Ł ¦", + "𬺠Ī", + "ð«ł ľ", + "é¹ ¾", + "èĻ ¤", + "æļ ¿", + "æĽ Į", + "æĽ Ī", + "㬠ļ", + "è¹ ħ", + "è¸ ¶", + "äĹ Ľ", + "èŀ Ĺ", + "çĸ ģ", + "ãł ĵ", + "å¹ ª", + "𪠩", + "𪩠ĺ", + "å¶ ¦", + "ð¬Ń ¬", + "𨱠ij", + "ð¬Ń ¯", + "é¦ ŀ", + "ç© Ħ", + "ç¯ ļ", + "ç¯ ¯", + "ç° ī", + "é¼ ½", + "è¡ ł", + "çĽ ¦", + "èŀ £", + "ç¸ ¢", + "é² Ń", + "é² ¯", + "é² °", + "é² º", + "é² ¹", + "ð«Ĺ ´", + "äº ¸", + "çĻ Ģ", + "çĺ Ń", + "𬸠¦", + "ç¾ ±", + "ç³ Ĵ", + "çĩ ĭ", + "çĨ »", + "çĩ Ĭ", + "çĩ ļ", + "çĩ ı", + "æ¿ ©", + "æ¿ ĭ", + "æ¾ ª", + "æ¾ ½", + "æ¾ ´", + "æ¾ Ń", + "æ¾ ¼", + "æĨ ·", + "æĨ º", + "æĩ Ķ", + "é» ī", + "å¬ Ľ", + "é¹ ¨", + "ç¿ ¯", + "ð«Ħ ·", + "çĴ ±", + "𤠩½", + "çĴ ¬", + "çĴ ®", + "é« ½", + "æĵ ¿", + "èĸ ¿", + "èĸ ¸", + "æª ij", + "æ« Ĩ", + "æª ŀ", + "éĨ ¨", + "ç ¹Ħ", + "ç£ ¹", + "ç£ »", + "çŀ «", + "çŀ µ", + "è¹ IJ", + "èŁ ı", + "ã ĺ", + "ãĺ İ", + "ð¬Ń ³", + "éķ ¤", + "ð¬Ń ¶", + "ð«Ķ į", + "éķ ¥", + "éķ ¨", + "ð¬Ń ¸", + "𨱠Ķ", + "ð¬Ń ¼", + "ð«Ķ İ", + "çŁ °", + "ç© Ļ", + "ç© ľ", + "ç© Ł", + "ç° ķ", + "ç° ĥ", + "ç° ı", + "åĦ ¦", + "éŃ ĭ", + "æĸ ¶", + "èī ļ", + "𬸠ª", + "è° ¿", + "ä² ł", + "𬶠Ł", + "é² ¾", + "𬶠ł", + "é² ¿", + "é³ ģ", + "é³ Ĥ", + "é³ Ī", + "é³ ī", + "çį ¯", + "äĹ ª", + "é¦ ĺ", + "è¥ ķ", + "è¥ ļ", + "𬶠¨", + "èŀ ±", + "çĶ ĵ", + "å¬ ¬", + "å¬ ¥", + "ð¦ Ī", + "ð¦Ī ¡", + "ð«Ħ ¸", + "çĵ Ģ", + "éĩ IJ", + "é¬ ¶", + "çĪ ĩ", + "éŀ ³", + "éŀ ®", + "ð¬Ł ģ", + "èĹ Ł", + "èĹ ¦", + "èĹ ¨", + "é¹ ²", + "æª «", + "é» ¡", + "ç¤ ŀ", + "ç¤ Į", + "ð¥ ĸ", + "ð¥ĸ ¨", + "è¹ ¢", + "è¹ ľ", + "èŁ «", + "äĹ ´", + "åļ ļ", + "é« ĥ", + "éķ ®", + "éķ ±", + "éħ Ĥ", + "é¦ §", + "ç° ł", + "ç° Ŀ", + "ç° °", + "é¼ «", + "é¼ ©", + "çļ ¦", + "èĩ ij", + "ä² ¢", + "é³ ij", + "é³ Ĵ", + "é¹ ±", + "é¹ ¯", + "çĻ Ĺ", + "ð¦ Ĵ", + "ð¦Ĵ į", + "æĹ ŀ", + "ç¿ ·", + "åĨ ģ", + "äİ ĸ", + "çĢ Ķ", + "çĢ į", + "çĢ Į", + "è¥ ľ", + "ä´ Ļ", + "ð¬Ļ Ĭ", + "åļ Ń", + "ã °", + "ã° Ģ", + "é¬ ·", + "éĨ Ń", + "è¹ ¯", + "èł ĭ", + "ç¿ ¾", + "é³ ĺ", + "åĦ ³", + "åĦ ´", + "é¼ Ĺ", + "𬶠Ń", + "𩾠Į", + "é³ ļ", + "é³ Ľ", + "éº ij", + "éº ĸ", + "èł ĥ", + "å½ Ł", + "å¬ ¿", + "é¬ Ĵ", + "èĺ ĺ", + "æ¬ Ĥ", + "é Ĩµ", + "é¢ ¥", + "çĶ Ĺ", + "ð¨ Ł", + "ð¨Ł ł", + "å· ĩ", + "éħ ħ", + "é« İ", + "çĬ ¨", + "𬶠®", + "ð¨ Ń", + "ð¨Ń ī", + "㸠Į", + "çĪ Ķ", + "çĢ ±", + "çĢ ¹", + "çĢ ¼", + "çĢ µ", + "è¥ «", + "åŃ ħ", + "éª ¦", + "ð¬Ļ ĭ", + "èĢ °", + "𤠫", + "𤫠ī", + "çĵ ĸ", + "é¬ ĺ", + "è¶ ¯", + "𬺠ĵ", + "ç½ į", + "é¼ ±", + "é³ ł", + "é³ ¡", + "é³ £", + "çĪ Ł", + "çĪ ļ", + "çģ Ī", + "éŁ Ĥ", + "ç³ µ", + "èĺ ¼", + "ç¤ µ", + "é¹ ´", + "èº Ķ", + "çļ Ń", + "é¾ ¢", + "é³ ¤", + "äº ¹", + "ç± ¥", + "é¼ ·", + "ð«ļ Ń", + "çİ ĥ", + "éĨ ¾", + "é½ ĩ", + "è§ ¿", + "èł ¼", + "× §", + "× ¤", + "× Ľ", + "×ķ× ª", + "× ¡", + "×Ļ× Ŀ", + "× ¦", + "× Ĵ", + "× ĺ", + "×ķ× ¨", + "× Ŀ", + "×ķ× ľ", + "× ĸ", + "๠Ĥ", + "ï º", + "ðŁ į", + "ðŁ IJ", + "×Ļ× ¨", + "ï »", + "ðŁ ij", + "ðĿ IJ", + "ðŁ ı", + "ðŁ Ķ", + "ðŁ Į", + "ðŁ İ", + "ðŁ ĵ", + "× Ł", + "ðĿ ij", + "×ķ× ĵ", + "ï ¦", + "Ġ× ķ", + "×ķ× ij", + "à¸Ń à¸ĩ", + "ðĿ ĺ", + "×Ļ× ª", + "ðĿ ķ", + "à¸Ĺ ีà¹Ī", + "Ø§Ø ¦", + "ðŁ ¤", + "×ķ× Ł", + "ر ÙĬ", + "×Ļ× ľ", + "ร ะ", + "า ย", + "ï ¯", + "ï ®", + "า ม", + "â ĩ", + "ðŁ ¥", + "ï Ń", + "ðĿ Ļ", + "×ķ× ł", + "á ½", + "Ġ× Ľ", + "ðŁ ļ", + "â ļ", + "ï §", + "×ij ר", + "×Ļ× ł", + "á ´", + "Ġ× Ĺ", + "á ¼", + "ðĿ Ĺ", + "Ġ× ¢", + "×Ļ× Ķ", + "ãģ£ ãģŁ", + "ãģĵ ãģ¨", + "á ¸", + "ÙĬ ÙĨ", + "ãģª ãģĦ", + "ا ع", + "ภ¨", + "à¹Ī à¸ĩ", + "×Ļ× ĵ", + "×ŀ ש", + "á Ī", + "׳ ×Ļ", + "×Ļ× ij", + "ï ¥", + "ðĿ ĵ", + "Ġ× Ļ", + "× ļ", + "ั à¸ĩ", + "â ĵ", + "ï ¤", + "ĠاÙĦ Ø£", + "า à¸ģ", + "à¹ī à¸Ļ", + "à¹Ģ ร", + "×ķ× Ŀ", + "á ¹", + "ภ¶", + "×Ļ× §", + "ภĭ", + "à¸Ħ ร", + "ภĺ", + "ั à¸ģ", + "ðŁ ķ", + "ÙĪ ÙĨ", + "à¸Ń ย", + "â Ĭ", + "ðĿ Ĵ", + "ĠاÙĦ ع", + "า à¸Ļ", + "×Ļ× Ł", + "ÙĦ ÙĬ", + "×Ļ× ©", + "à¸Ľ ระ", + "à¹Ģ à¸Ľ", + "Ġ× ł", + "×ķ× ¡", + "ภł", + "Ùħ ÙĨ", + "×ķ× ¢", + "×ķ× ŀ", + "â Į", + "ðŁ §", + "à¹ĩ à¸Ļ", + "ภį", + "ã İ", + "á µ", + "ĠاÙĦ س", + "×ķ× §", + "ห ล", + "ðŁ ĩ", + "â ı", + "ðŁ ¦", + "Ġ×Ķ ×ŀ", + "ÙĪ ا", + "Ġ× ª", + "ר ×IJ", + "à¸Ń à¸Ļ", + "ภ©", + "à¹Ī ว", + "×ķ× ¦", + "í Ĺ", + "ã Ħ", + "ï ¨", + "ï ¹", + "â İ", + "ï ²", + "ðĿ ļ", + "ð IJ", + "à¸Ħ ว", + "ห à¸Ļ", + "Ġ× ¨", + "ب ÙĬ", + "ร à¹Į", + "ر ا", + "Ø´ ر", + "×ķ× Ĺ", + "×ķ× ¤", + "×ķ× ©", + "×ķ× Ĵ", + "í Ŀ", + "â Ľ", + "à¸ķ ิ", + "à¹Ģ à¸ģ", + "ï ³", + "ï ±", + "à¸Ķ à¹ī", + "ë ¹", + "ï ¬", + "á ¿", + "ðŁ Ľ", + "ðĿ ĸ", + "à¹Īา à¸ĩ", + "ู à¹ī", + "Ġ×Ķ ×IJ", + "ĠاÙĦ ØŃ", + "פ ר", + "ÙĪ Ùħ", + "à¹Ģ ล", + "í ĸ", + "×Ļ× ¢", + "ì Ī", + "í ĵ", + "ðŁ ħ", + "á ł", + "à¸Ħว าม", + "à¸Ī ะ", + "׳ ×Ķ", + "Ġ× §", + "ภŁ", + "à¹ī à¸ĩ", + "ห ม", + "ت Ùħ", + "׾ ×Ļ", + "ÙĬ د", + "à¹Ī à¸Ļ", + "׊ר", + "ש ר", + "à¹Ģ à¸Ĺ", + "×ŀ ר", + "ë ĸ", + "ع ÙĦ", + "×ŀ ×¢", + "â ²", + "׾ ×Ķ", + "Ġ× ¤", + "à¸Ń à¸ģ", + "س ÙĦ", + "×Ļ× ŀ", + "ÙĤ ÙĬ", + "í İ", + "ت ØŃ", + "×Ļ× ¡", + "×Ļ× Ĺ", + "í Ľ", + "ï °", + "â ½", + "á ī", + "á Ĭ", + "á ¨", + "Ùĩ ا", + "Ġ׾ ×Ķ", + "×ķ× IJ", + "Ùħ ا", + "à¹īà¸Ń à¸ĩ", + "ر ب", + "ĠاÙĦ ج", + "×ŀ ×ĵ", + "Ùħ ÙĦ", + "ت ر", + "à¹Ģ à¸Ķ", + "ק ר", + "í ħ", + "ì ¼", + "ê ¿", + "ã Ī", + "á IJ", + "ðŁ Ĺ", + "ê ¦", + "á ĭ", + "ðĿ Ķ", + "à¹Ģà¸Ľ à¹ĩà¸Ļ", + "à¹ĥ ห", + "ม า", + "ว à¹Īา", + "ม ี", + "ี à¹ī", + "à¹Ħม à¹Ī", + "ÙĨ ÙĬ", + "Ø ¤", + "ร า", + "×ķ ×Ļ", + "ãĤĪ ãģĨ", + "ิ à¸Ķ", + "×Ļ× ¤", + "׊׾", + "ÙĤ د", + "à¹Ģ ส", + "×Ļ× ĺ", + "à¸ģ ล", + "ר ׼", + "×ķ× Ľ", + "×Ļ× Ľ", + "ë Ī", + "ë ĥ", + "ðŁ ĸ", + "á ħ", + "â ¼", + "ã ī", + "à¹Ħ à¸Ķà¹ī", + "ת ×Ļ", + "×Ļ× IJ", + "ĠاÙĦ Ø¥", + "à¸ł า", + "ร ิ", + "ÙĤ Ø©", + "ØŃ Ø¯", + "ê »", + "ì ±", + "ת ×Ĺ", + "ì º", + "â ĭ", + "á Ħ", + "á ¾", + "â µ", + "â ¾", + "ĠÙĪ اÙĦ", + "׳ ×ķ", + "Ù Ģ", + "ÙĬ ا", + "à¸ģ à¹ĩ", + "×ŀ ×Ķ", + "ãģĦ ãĤĭ", + "ع د", + "ĠاÙĦ ÙĨ", + "Ġ×Ķ ש", + "Ø ¦", + "ั à¹īà¸ĩ", + "ร ัà¸ļ", + "ÙĪ ÙĤ", + "ãģ§ ãģį", + "à¹Ģ à¸ŀ", + "׼ ׾", + "×ĺ ר", + "ั à¸Ķ", + "à¸Ń า", + "ì ¢", + "à¸Ń à¸ļ", + "à¸ķ ร", + "à¹Ģ à¸Ĭ", + "ì Ķ", + "ãģĹ ãģ¾", + "ë ģ", + "ë ķ", + "ðŁ Ļ", + "â Ĵ", + "á ¶", + "à¹ģ ล", + "ÙĨ ا", + "à¹ĥห à¹ī", + "à¹Ħ à¸Ľ", + "× £", + "ั ว", + "า à¸ĩ", + "×ĵ ר", + "×ij ׾", + "פ ×Ļ", + "Ġ× ĵ", + "ĠاÙĦ Ùģ", + "à¹Ģ à¸Ĥ", + "ש ×Ķ", + "×IJ ר", + "ë ¬", + "ãģ« ãģª", + "ÑĢ о", + "ว ิ", + "Ùħ ر", + "×IJ ת", + "Ùĥ ر", + "س ب", + "ÙĨ ت", + "ãģĹ ãģĦ", + "ا ج", + "à¸Ń รà¹Į", + "Ùĥ ÙĦ", + "س Ùħ", + "ส ิ", + "×Ļ× ¦", + "ë Ŀ", + "í ľ", + "ì ī", + "á Ĩ", + "Ùĩ Ùħ", + "à¸Ļ ีà¹ī", + "ãģĤ ãĤĭ", + "ãģĦ ãģ¦", + "س ÙĬ", + "׾ ×IJ", + "د ر", + "ãģ ļ", + "ÙĪ ج", + "ĠاÙĦ Ø®", + "ص ر", + "í ı", + "à¹īา à¸ĩ", + "ุ à¸Ķ", + "×ķ× ĺ", + "×ij ×¢", + "í Ĩ", + "à¸Ĭ า", + "ร ม", + "ש ×ŀ", + "×ŀ ס", + "ê ´", + "ì ´", + "ë ľ", + "ì ¿", + "ì ©", + "ë »", + "â ¤", + "ðŁ Ĩ", + "á Į", + "á ķ", + "Ø° ا", + "à¸Ĺ ำ", + "à¸ķ à¹Ī", + "ĠاÙĦ ÙĤ", + "ÙĦ Ùĥ", + "ู à¹Ī", + "à¸Ħ ุ", + "ÙĬ Ùħ", + "׳ ×Ļ×Ŀ", + "ืà¹Ī à¸Ń", + "ÙĪ ع", + "ãĤ ĩ", + "ا ÙĤ", + "Ġ×ij ×¢", + "à¹Ģ ม", + "ج Ùħ", + "á» «", + "ãģĵãģ¨ ãģĮ", + "ب د", + "×ķ× Ķ", + "ש ׾", + "Ùĩ ر", + "à¹Ģ à¸Ļ", + "ãģ ¹", + "í ĭ", + "ì »", + "ì ½", + "ë Ń", + "ì Į", + "í Ģ", + "ë Į", + "ë º", + "ã Ĭ", + "à¹ĥ à¸Ļ", + "Ġ× Ĵ", + "๠Ĩ", + "à¸Ī าà¸ģ", + "ว ย", + "à¹ĥ à¸Ĭ", + "à¸ĩ าà¸Ļ", + "ĠاÙĦ Ø´", + "ا ØŃ", + "à¹īา à¸Ļ", + "ืà¹Ī à¸Ńà¸ĩ", + "×IJ ×Ļ", + "ب ÙĦ", + "ãģ¨ æĢĿ", + "׳ ס", + "ãģ¾ ãģĽ", + "Ùĥ ÙĨ", + "×¢ ר", + "ĠاÙĦ د", + "ש ת", + "í ŀ", + "Ùħ س", + "ص ÙĦ", + "×ķ׳ ×Ķ", + "ار Ø©", + "ÙĦ Ùħ", + "ส ม", + "Ø£ ÙĨ", + "ת ר", + "×IJ ×ŀ", + "ع ب", + "Ø® ت", + "ãĤ ĥ", + "ì ¡", + "ì £", + "ив а", + "ส ั", + "ึ à¸ģ", + "ì ¸", + "ë Ĩ", + "алÑĮ н", + "ì ³", + "ì į", + "ê ¼", + "ê ½", + "ì ı", + "ã Į", + "ã ı", + "ï ©", + "ê ª", + "á İ", + "Ġ× ĸ", + "à¸ģ ัà¸Ļ", + "×Ļ ×ķ", + "à¸Ħ à¸Ļ", + "׳ ×ķת", + "à¸ľ ูà¹ī", + "à¹ĥ à¸Ī", + "ãģĦ ãģŁ", + "Ùģ ر", + "×ĺ ×Ļ", + "צ ×Ļ", + "ãĤĤ ãģ®", + "ĠاÙĦ ص", + "ãģ¾ãģĽ ãĤĵ", + "د Ø©", + "×ij ×Ļ", + "ĠاÙĦ ر", + "Ġ×ŀ ×IJ", + "ส ำ", + "à¹Ģ ห", + "ع ر", + "ãģª ãģı", + "à¸ģร ะ", + "×ij ×ĵ", + "à¹Ģ à¸Ī", + "×Ļ× ļ", + "×Ĺ ×Ļ", + "ÙĬ ع", + "ש ×ij", + "ÙĨ Ø©", + "ÙĪ ض", + "ÙĦ Ùģ", + "ÙĢ ÙĢ", + "פ ×¢", + "í Ī", + "×ŀ ק", + "ภIJ", + "ØŃ Ø©", + "ا ص", + "Ñĭв а", + "à¸Ħ ม", + "ว ั", + "à¸Ľ ล", + "ì Ł", + "í ļ", + "ë ´", + "ë ij", + "ë ī", + "ë ĩ", + "ì ¨", + "ë ±", + "ë İ", + "â ¬", + "á ¥", + "á Ĺ", + "á Ľ", + "á į", + "Å ©", + "à¸Ķ ี", + "ô i", + "Ġ× ¡", + "׾ ×ķ", + "á»Ŀ i", + "à¸Ħุ à¸ĵ", + "â y", + "à¸Ļ า", + "×Ĺ ×ĵ", + "×ĵ ×Ļ", + "ห า", + "ج ÙĦ", + "à¹Ģ ว", + "ãĤĩ ãģĨ", + "Ùħ Ø©", + "ĠاÙĦ Ùĥ", + "Ġ×Ķ ×¢", + "ج ر", + "×ĸ ר", + "ا Ø·", + "׼ ת", + "×ķ׳ ×Ļ×Ŀ", + "ØŃ Ùħ", + "ê ¶", + "ر Ùĥ", + "Ġ׾ ×¢", + "×ķ× ĸ", + "ส ร", + "צ ׾", + "Ø ¢", + "ا ست", + "à¹Ī ม", + "Ø® ر", + "צ ×¢", + "×Ļר ×ķת", + "اد Ø©", + "Ø´ ار", + "×ŀ ×Ĺ", + "í Ĵ", + "à¹Ģร ีย", + "׊ק", + "Ø§Ø «", + "ร à¸ĩ", + "à¹Ģ à¸ķ", + "à¸Ī ำ", + "ภĿ", + "à¹Īา ย", + "à¸Ħ ล", + "ÙĤ ÙĪ", + "иÑĩеÑģ к", + "à¸ĵ à¹Į", + "ั ย", + "Ùħ ع", + "ë ¨", + "ë ¿", + "ë ®", + "ï ´", + "ì ¥", + "ì «", + "ë µ", + "á ¡", + "â į", + "ð ĵ", + "â °", + "à¸Ĥ à¸Ńà¸ĩ", + "Ù ĭ", + "à¸ģ ัà¸ļ", + "ãģ® ãģ§", + "à¹ī ว", + "à¸Ńย à¹Īาà¸ĩ", + "ãģ Ń", + "á»ĩ t", + "à¸ķ à¹īà¸Ńà¸ĩ", + "×ŀ ×Ļ", + "à¹ģ à¸ļ", + "×Ĵ ר", + "ÙĪ Ùģ", + "ÙĤ ÙĦ", + "à¸łà¸² à¸ŀ", + "ר ×Ļ", + "ล า", + "ÙĬ س", + "Ġ× ¦", + "ÙĬ Ùģ", + "Ġ× ĺ", + "à¸ľ ล", + "á ng", + "ร ว", + "Ġ×ŀ ש", + "×IJ ×ķת", + "×ĸ ×Ķ", + "ู à¸ģ", + "à¸Ļ ัà¸ģ", + "اÙĨ ÙĬ", + "د ا", + "ãģ ³", + "׼ ף", + "ãĤī ãĤĮ", + "ãĤĮ ãģ°", + "ת ק", + "ú c", + "ÙĪ ز", + "×Ļר ×Ķ", + "Ġn gh", + "án h", + "Ġ×ķ ×IJ", + "á» ħ", + "ส ุà¸Ķ", + "ë į°", + "ا ض", + "اÙĦ ÙĬ", + "ب ار", + "ع Ùħ", + "à¸ļ า", + "ت ج", + "à¸ŀ ร", + "×ķר ×Ķ", + "ả ng", + "Ø® ÙĦ", + "ภī", + "ắ c", + "ש ×Ļ×Ŀ", + "í Ķ", + "Ùģ س", + "×Ļ× Ĵ", + "п ÑĢ", + "ĠاÙĦ Ø«", + "س Ø·", + "ร ูà¹ī", + "ีà¹Ī ย", + "à¸Ń à¸Ķ", + "ãģª ãĤĬ", + "×Ĵ ×ĵ", + "ãģĦ ãģ¾ãģĹãģŁ", + "ס ק", + "Ø® ص", + "la ÅŁ", + "ен но", + "ب ØŃ", + "ส à¸Ļ", + "ภ®", + "ר×IJ ש", + "Ùħ ÙĪ", + "دÙĬ د", + "ษ า", + "×ķ× ļ", + "ãĥ§ ãĥ³", + "à¸ķ ุ", + "Ġê µ", + "ĠÑģв о", + "צ ×ij", + "à¸Ń ม", + "à¸Ľ ร", + "ت ع", + "×Ķ ת", + "اÙħ ÙĦ", + "×ŀ ׳", + "ç ¶ļ", + "ภ¤", + "í į", + "ë ĺ", + "ë ¤", + "ì ij", + "â ´", + "ã ĭ", + "Ġب اÙĦ", + "á»ģ u", + "ĠاÙĦ ÙĦ", + "à¸ķ ัว", + "Ø° Ùĩ", + "ึ à¸ĩ", + "à¹ĥà¸Ĭ à¹ī", + "á»ĵ ng", + "à¸Ļ ั", + "ม าà¸ģ", + "ãĥ Ł", + "×ŀ ×ķ", + "à¸Ĺ ย", + "á»Ļ i", + "Ạ±", + "ả o", + "à¹Ĥ à¸Ķ", + "×IJ ׾", + "ส าม", + "ÙĪ ب", + "à¸Ĺ ุ", + "ย ัà¸ĩ", + "×¢ ת", + "×ķ׳ ×ķת", + "à¸Ĥ ึ", + "à¸Ĥึ à¹īà¸Ļ", + "à¸ģ à¹Ī", + "Ạ«", + "á»ij c", + "ãģĹ ãĤĩãģĨ", + "á»ĭ ch", + "Ġ×IJ ×ķת", + "Ġש ×IJ", + "׼ ×ķ׾", + "á»Ļ c", + "ع Ø©", + "à¸Ĺ ี", + "à¹Ģ à¸Ń", + "Ùĥ ت", + "ãģ »", + "Ạ»", + "ìĹ ħ", + "à¸Ń à¸Ńà¸ģ", + "اÙĨ ت", + "à¹Ħ ร", + "Ġ×IJ ×Ĺר", + "Ø· ر", + "ÙĨ د", + "ื à¹īà¸Ń", + "Ø· ÙĦ", + "×IJ ×Ķ", + "uy ên", + "í ĸī", + "×ij ×Ķ", + "à¸Ħ à¹Ī", + "à¸Ĭ à¹Īว", + "ãģĤãĤĬ ãģ¾ãģĻ", + "ÙĬ ب", + "ק ׾", + "ãĥ Ļ", + "Ä ©", + "س ر", + "า ว", + "ãĤ ±", + "à¸ļ ริ", + "ר ×Ĵ", + "á»ĥ u", + "ØŃ Øª", + "×ķ×ŀ ×Ļ", + "ب ÙĨ", + "êµ IJ", + "ÄŁ u", + "ãģª ãĤĵ", + "×ij ק", + "Ġפ ר", + "ắ n", + "ØŃ ÙĦ", + "×ij ×Ĺ", + "ấ u", + "×ij ×ķ×ĵ", + "ãĥ ¯", + "Ġ׾ ק", + "ั à¸į", + "à¸ŀ ิ", + "×Ĺ ×Ķ", + "×ĸ ׼", + "ãĥ¼ãĥ ł", + "ÑĤ елÑĮ", + "×ŀ ×Ļ×ĵ", + "ÙĬ Ø®", + "Ạ³", + "ت ص", + "à¸ĺ ิ", + "è¾ ¼", + "ì ĵ", + "Ùĥ Ø©", + "ÙĤ ب", + "à¸Ħ à¹Į", + "à¹īา ย", + "à¸ĵ ะ", + "า ะ", + "ë Ĵ", + "ê ¾", + "ë ·", + "ì ĩ", + "ê º", + "ì ģ", + "ë Ģ", + "ì ¾", + "ë ½", + "ë ļ", + "ì Ń", + "ì İ", + "á ij", + "ë Ĺ", + "ê Ĵ", + "à ¡", + "à ¬", + "ðIJ Į", + "ã ĩ", + "ðĿ Ħ", + "Ġ׾ ×IJ", + "ãģ¨ ãģĦãģĨ", + "Ġn hi", + "×Ļ ×ķת", + "Ġש ×Ķ", + "à¹ģล à¹īว", + "Æ°á»Ľ c", + "à¸Ķà¹ī วย", + "à¸Ĺ าà¸ĩ", + "׳ ת", + "פ ת", + "à¹ģ à¸ķà¹Ī", + "Æ° ng", + "à¸Ńย ูà¹Ī", + "à¹ī ำ", + "Ġ×IJ ׾", + "Ùĥ Ùħ", + "ấ p", + "ล à¸ĩ", + "ãģŁ ãĤģ", + "×Ĵ ׾", + "ห ร", + "ĠÑĢ е", + "à¹Ģà¸Ĥ à¹īา", + "ÙĤ ر", + "Ġ×Ķ ס", + "ÙĪ ÙĬ", + "สาม าร", + "สามาร à¸ĸ", + "Äĥ n", + "à¸Ń ี", + "פ ×ķ", + "×Ļ׳ ×ķ", + "ว ัà¸Ļ", + "ặ c", + "íķ Ļ", + "×ŀ ת", + "ê u", + "Ạ¹", + "Ùģ ÙĬ", + "×ŀ צ", + "à¸Ħ า", + "ãģĿ ãģĨ", + "ãĢ ħ", + "ا ز", + "ا Ùĩ", + "ר ×Ļ×Ŀ", + "ấ n", + "ห าร", + "ạ t", + "ÙĨ Ùĩ", + "à¹Ģ à¸Ħร", + "ج Ùĩ", + "׼ ×Ļ", + "ắ t", + "à¸Ħ à¹īา", + "ر Ø©", + "ãĥ ı", + "Ùĥ ÙĪÙĨ", + "ứ ng", + "Ġìļ °", + "ย à¹Į", + "à¹Īว à¸Ļ", + "à¸ģ ำ", + "Ø« ر", + "Ñģ и", + "ĠاÙĦ Ø·", + "Ġ×Ķ צ", + "ĠØ ·", + "ĠاÙĦ ÙĪ", + "ê¹ Į", + "ØŃ ÙĬ", + "ار ات", + "à¹Ģ à¸ĭ", + "ب ا", + "г ÑĢ", + "ร ี", + "ืà¸Ń à¸Ļ", + "ع ت", + "ÙĤ اÙĦ", + "د Ùħ", + "Ø ¡", + "Ġ×ŀ ק", + "×ĵ ×Ļ×Ŀ", + "×¢ ׾", + "ãģ Ĵ", + "ëĭ ĺ", + "×¢ ×Ķ", + "Ġìĸ ´", + "Ñģ ÑĮ", + "ÙĤ Ø·", + "ãĥ Ľ", + "èĢĥ ãģĪ", + "à¹ģ à¸Ļ", + "ÙĪ ات", + "â u", + "ĠìĤ¬ ëŀ", + "ห ว", + "ĠاÙĦØ£ Ùħ", + "Ġ×Ķ ×ŀש", + "ب ÙĪ", + "à¸Ĭ à¸Ļ", + "ãĤĵ ãģ§ãģĻ", + "ว à¸Ļ", + "à¸ģร รม", + "×ŀ ×ķ×ĵ", + "Ùĥ اÙĨ", + "×ķ× £", + "ол ог", + "ت ÙĨ", + "à¸ķ à¹Į", + "ê² ĥ", + "ר ×ĺ", + "ừ ng", + "×ķ×ij ×Ķ", + "Ùħ ØŃ", + "ĠÐ §", + "פ ×Ĵ", + "ส à¸ĸ", + "ãģĭ ãĤĬ", + "ını z", + "à¹Ģ ย", + "ãĥ¼ ãĥ³", + "ãģĬ ãĤĬ", + "פ ש", + "ิ à¸ķ", + "Ø· ÙĨ", + "×Ļת ×Ļ", + "×IJ ׳", + "ç ek", + "ì ª", + "×ŀ ×ij", + "ศ า", + "ãĤ¹ ãĤ¿", + "à¸ļ ุ", + "×ĵ ×ijר", + "ãģĦ ãģı", + "ส ะ", + "à¹Ģ หล", + "ิ à¸ĩ", + "à¸ŀ ัà¸Ļ", + "ãģĦ ãģŁãģł", + "ãĤĤ ãĤī", + "à¹ī ม", + "ãģĵãģ¨ãģĮ ãģ§ãģį", + "าร à¹Į", + "ุ à¸ĩ", + "í ij", + "ì ¯", + "ë ¼", + "í Ĥ", + "ì ·", + "ê ¡", + "á ı", + "á Ĵ", + "ðĿ ľ", + "á ©", + "ðŁ Ħ", + "ðIJ ¤", + "Ġש ׾", + "Ġ×ŀ ×Ķ", + "à¹ģล ะ", + "Ġ׼ ׾", + "Ạ½", + "á»Ļ ng", + "Ø° ÙĬ", + "л е", + "× ¥", + "ãģª ãģ©", + "ĠÙĪ Ø£", + "หà¸Ļ à¹īา", + "ãģ¾ ãģ§", + "à¸ķà¹Ī à¸Ń", + "à¸Ĺ ัà¹īà¸ĩ", + "ãģł ãģij", + "à¹ģà¸ļ à¸ļ", + "à¹Ģร า", + "פ ׾", + "ãģŁ ãģĦ", + "à¹Ģล ย", + "ãģ£ãģ¦ ãģĦãĤĭ", + "ế p", + "ึ à¹Īà¸ĩ", + "ê ´Ģ", + "ê³ Ħ", + "׼ ×ķ", + "à¹Ģร ืà¹Īà¸Ńà¸ĩ", + "ק ×Ļ", + "êµ Ń", + "פ ס", + "ت ÙĬ", + "ãĥ Ħ", + "Ġ×Ķ ×Ĺ", + "г и", + "ר×IJ ׾", + "×ŀ ׾", + "ĠØ£ ÙĬ", + "Ġع ÙĦÙĬ", + "ãģĭ ãģ£ãģŁ", + "ש ×Ļ", + "д Ñĥ", + "×ŀ ף", + "׳ ×ĺ", + "׳ ×Ļת", + "mi ÅŁ", + "׼ ×Ŀ", + "Ġ×ij ר", + "Ġ׾ ×ij", + "ĠÐ Ľ", + "ç e", + "×ķ׳ ×Ļ", + "ãĤĪãģĨ ãģ«", + "פ ×ķר", + "ãĥ į", + "Ùĥ ÙĬ", + "×Ĺ ×ª", + "Ùģ ÙĦ", + "Ġ×Ķ ק", + "Ġ×Ķ ×ij", + "Ġ×ŀ ס", + "à¹Īา à¸Ļ", + "п еÑĢ", + "à¹Īา ว", + "Ġ×ij ×IJ", + "ĠÙĪ Ùĩ", + "à¸Ļ ำ", + "Ġ×ij ש", + "׳ ק", + "ãģ© ãģĨ", + "ש ×ķת", + "×ĵ ×Ķ", + "à¹Ģ à¸ļ", + "ÙĨ س", + "Ġìļ° 리", + "ส à¹Īวà¸Ļ", + "ล ัà¸ĩ", + "ج ز", + "Ġ×Ĺ ×Ļ", + "Ùĥ ثر", + "ล ะ", + "Ùĩ د", + "ĠÙĪ ب", + "اÙĦ Ùħ", + "à¹ģ ม", + "Æ¡ i", + "Ġ×ij ×Ĺ", + "ữ a", + "à¹Ģà¸Ĺ ศ", + "à¸ķ ัà¹īà¸ĩ", + "ог да", + "׾ ק", + "د د", + "สร à¹īาà¸ĩ", + "à¸Ĭ ี", + "Ùģ ض", + "à¹ģ ห", + "uy á»ĩn", + "ร ัà¸ģ", + "á»ĩ m", + "ส า", + "פ ק", + "ีย à¸ĩ", + "à¸ķ à¹Īาà¸ĩ", + "à¸Ħร ัà¹īà¸ĩ", + "ØŃ ÙĤ", + "à¹Ģ à¸Ńà¸ĩ", + "ائ ÙĬ", + "×ĺ ×¢", + "اÙĦ Ø©", + "ิ à¹Īม", + "ãĤ ½", + "د Ùī", + "Ġר ×IJ", + "ãģ£ ãģ¨", + "ãĥĥ ãĥĹ", + "ÙĬر Ø©", + "ê± ´", + "×ŀ ×IJ", + "×ķ ×ķ", + "ب ع", + "ãģ ²", + "ร าย", + "×ĵ ×Ŀ", + "ت Ùģ", + "à¸ķ à¸ģ", + "ạ ng", + "ãĤĴ è¦ĭ", + "à¸Ĭ ั", + "Æ°á» Ł", + "Æ°á»Ł ng", + "ج ب", + "×ķ×ŀ ר", + "ĠìĤ¬ëŀ Į", + "ó ng", + "ร ั", + "Ġ×Ķ ×ĸ", + "ר צ", + "Ġ×Ĺ ×ĵ", + "Ø° ÙĦÙĥ", + "×ķר ×Ļ", + "ãģ¡ ãĤĥ", + "Ùģ ع", + "Ġ׾ צ", + "á i", + "à¹ĩ à¸ļ", + "ãģ İ", + "à¸ģ ิ", + "ạ c", + "ë© °", + "ãģª ãĤĭ", + "×ķ׾ ×Ŀ", + "à¹ģ à¸Ĺ", + "×ķ× ¥", + "м еÑĤ", + "ü ÅŁ", + "ÑĢ Ñı", + "ภĴ", + "ÑģÑĤ оÑı", + "ع ÙĪد", + "Ùħ ار", + "Ø· Ø©", + "à¸ŀ ื", + "к ÑĢ", + "à¹ģ à¸ģ", + "à¹Ĥ รà¸ĩ", + "×ij ×Ļ×ĺ", + "ê² ł", + "×ķ׾ ×Ķ", + "ØŃ Ø±", + "ืà¹Ī à¸Ńà¸Ļ", + "×ķ×ij ר", + "׊ש", + "ãĥķãĤ ¡", + "×ŀ ×ĺ", + "ú t", + "Ġd ön", + "ắ ng", + "ëł ĩ", + "ẳ ng", + "ว à¸ģ", + "ص د", + "Ø® Ø·", + "à¸Ń ั", + "ãĤı ãĤĮ", + "سÙĦ اÙħ", + "à¹Ģร à¹ĩ", + "×Ļש ×Ļ", + "ج اÙĦ", + "ãģij ãĤĭ", + "à¸Ĭา à¸ķิ", + "ÙĪا ÙĤ", + "à¹Ĥ à¸Ļ", + "ãģ¦ ãģĹãģ¾", + "اع Ø©", + "ãĤŃ ãĥ£", + "à¸į า", + "ÙĦا ÙĤ", + "ิ à¸ģ", + "ĠÑģ ов", + "ÑĢаРº", + "×Ļ׳ ×Ļ", + "ü ÄŁ", + "Ã¼ÄŁ ü", + "ק ×ij", + "à¹Ī à¸Ńà¸ĩ", + "Ġger çek", + "à¸Ĺ ั", + "ов аниÑı", + "×ŀ ׼", + "س Ø©", + "×Ļ× £", + "le ÅŁ", + "Ùħ ؤ", + "ĠìĿ ĺ", + "à¸IJ าà¸Ļ", + "ĠÑģ об", + "Ġêµ Ń", + "×¢ צ", + "з в", + "ส à¸ĩ", + "ز ÙĦ", + "ãģı ãĤĮ", + "и ÑĢÑĥ", + "ت Ø£", + "п олн", + "ìĺ Ģ", + "ÙĨ Ø´", + "׼ ×IJ", + "Ùħ Ø´", + "à¸Ķ à¹Į", + "ÙĪ ÙĬÙĦ", + "à¹ģ à¸Ĥ", + "ãģ£ãģ¦ ãģĹãģ¾", + "но ÑģÑĤ", + "в л", + "Ùħ ÙĤ", + "را ج", + "å¤ ī", + "ë Ľ", + "â ¸", + "ì IJ", + "à »", + "á ļ", + "â »", + "ê Ļ", + "â §", + "ð Ĵ", + "ðĿ ĩ", + "Ġ×IJ ת", + "ĠÙĦ ÙĦ", + "ĠØ£ ÙĨ", + "Ġ×ķ ×Ķ", + "ãģ« ãģ¯", + "Ġ×Ļ ש", + "ت Ùĩ", + "ÃŃ nh", + "ÙĬ ات", + "Ġ×ij ×ŀ", + "à¸Ļั à¹īà¸Ļ", + "à¸Ļ à¹īำ", + "Ãł o", + "à¸ķ าม", + "ãģ® ãģ¯", + "d ır", + "Ġn ghi", + "ặ t", + "×ŀ ×Ļ×Ŀ", + "ãģ¦ ãģĦãĤĭ", + "Ġ×ij ת", + "หร ืà¸Ń", + "Ġس ÙĬ", + "ãģª ãĤī", + "à¹Ĥà¸Ķ ย", + "ı yor", + "à¸Ńี à¸ģ", + "á»ĩ nh", + "Ñĭ м", + "à¸Ĺุ à¸ģ", + "Ġ׾ ×Ĺ", + "Ġ×Ķ ר", + "Ġ×Ķ ×Ļ", + "à¸ŀ ระ", + "à¹Ģว ลา", + "ĠØ º", + "ẫ n", + "m Ä±ÅŁ", + "׼ ×Ķ", + "á»ij n", + "ãģ§ ãģĹãĤĩãģĨ", + "ãĥ ¢", + "à¸Ľ ี", + "ס ×Ļ", + "ãģĵ ãĤį", + "Ġ׾ פ", + "ร à¸ĸ", + "ê¸ Ī", + "à¸ģ วà¹Īา", + "ë ¬´", + "á»į ng", + "ãĤĵ ãģ§", + "ãĤĪãģĨ ãģª", + "á»ĵ i", + "ãĤ ¬", + "ส à¹Īà¸ĩ", + "×Ļ׳ ×Ķ", + "à¸ĸ ูà¸ģ", + "à¸Ī ัà¸Ķ", + "Ġ×Ķ ×Ĵ", + "ãĥ ľ", + "×ŀ ×ķת", + "ÙĪ Ùĥ", + "ëĭ ¨", + "ĠØ «", + "ãģ® ãģĮ", + "à¹Ģห à¹ĩà¸Ļ", + "ع ا", + "à¸Ļ ิ", + "Å ŀ", + "à¸Ń ะ", + "ãģĪ ãĤĭ", + "Ø« ÙĦ", + "ØŃÙħ د", + "à¹Ģà¸ģ ิà¸Ķ", + "פ שר", + "פ ×Ķ", + "ม ิ", + "ئ ÙĬس", + "à¸Ĺำ à¹ĥหà¹ī", + "×¢ ×ĵ", + "ìĭ ¤", + "à¸Ĭà¹Īว ย", + "ĠاÙĦÙħ ÙĨ", + "ز ÙĬ", + "ع ÙĬ", + "Ġ׼ ×IJ", + "ạ nh", + "á» ¹", + "ãĤĵ ãģª", + "ส ู", + "צ ר", + "Æ°á»Ľ ng", + "×ķ ×ķ×Ķ", + "à¹Ĥ ล", + "ĠاÙĦ Ùĩ", + "ว า", + "หล าย", + "Ñī е", + "à¸Ĥ à¹īà¸Ń", + "à¹īà¸Ń ย", + "ب Ø·", + "ка Ñı", + "ĠØ ¢", + "Ġи Ñģ", + "ĠاÙĦ غ", + "à¸ģ า", + "à¸Ļ à¹Īา", + "ÙĬ ÙĪ", + "×ij ×ķר", + "á»ħ n", + "ว à¸ĩ", + "×Ļ× ĸ", + "ì² Ń", + "н им", + "ëŁ °", + "×Ĵ ×ķר", + "ص ØŃ", + "ÙĦ ÙĪ", + "×Ĺ ×ķת", + "ส ุ", + "رÙĬ ÙĤ", + "ס ×ĺ", + "Ġ×ŀ ×¢", + "ãĥĨ ãĤ£", + "à¸Ħ ิà¸Ķ", + "ãĤį ãģĨ", + "à¹Ħ ล", + "à¸Ļ à¹Į", + "á»ı i", + "ÑģÑĤÑĢ о", + "ส à¸Ķ", + "ส าร", + "ÙĪÙĦ Ø©", + "ầ m", + "ร à¹Īว", + "รà¹Īว ม", + "ร ุ", + "ĠاÙĦس ÙĬ", + "ìĺ ģ", + "Ġ×ŀ ×ij", + "פ ×ĺ", + "à¸ķิ à¸Ķ", + "×ĺ ×Ļ×Ŀ", + "Ġë ¬´", + "ÙĤد Ùħ", + "Ġdü ÅŁ", + "ائ ÙĦ", + "м Ñĭ", + "ØŃ Ø³", + "ÙĪ ص", + "×Ļק ×Ķ", + "ãģ§ãģ¯ ãģªãģĦ", + "à¹Ģ หม", + "оÑĢ ÑĤ", + "í Ĩµ", + "ãģ IJ", + "к ÑĢа", + "ีย ว", + "ع ار", + "ئ Ø©", + "íĥ Ģ", + "ãģ«ãģª ãĤĬ", + "ج Ø©", + "ÙĪÙĤ ع", + "ÑĮ Ñı", + "×ķצ ×Ķ", + "ש ×Ŀ", + "ب ÙĤ", + "Ġ×Ļ ×Ķ", + "ÙĬ Ø·", + "ım ız", + "д еÑĢж", + "×Ļש ר×IJ׾", + "غ ÙĬر", + "ร à¸Ńà¸ĩ", + "à¹Ģรีย à¸Ļ", + "Ġ×Ķ ×ĺ", + "หม าย", + "Ùħ Ùĩ", + "اÙģ Ø©", + "Ġо ÑĢг", + "ÙĪ Ùī", + "ãĥ© ãĤ¤", + "×ŀ ׳×Ķ", + "ĠÄij o", + "Ġг оÑĢ", + "اÙħ Ø©", + "æ¥ ½", + "Ø« ÙĬر", + "à¸ģิ à¸Ī", + "á»ĵ n", + "ÙĨ ب", + "ÑĢÑĥ д", + "ìĹ Ī", + "Ġ×Ĺ ×ijר", + "ÑĢаР¶", + "ạ ch", + "ت ÙĪ", + "à¹Ĥ ม", + "×ij ×Ļ×ij", + "Ġí Ĩµ", + "aca ģı", + "جÙĦ س", + "à¹Ģà¸Ľ ล", + "ว à¸Ķ", + "à¸Ń ล", + "ãģŁ ãĤĬ", + "à¸Ľ ัà¸į", + "Ġìķ Į", + "عر Ùģ", + "à¹Ħ à¸Ł", + "Ø£ Ø®", + "å¤ļ ãģĦ", + "à¸Ķ ัà¸ĩ", + "Ø´ Ùģ", + "ãģ£ãģ¦ ãģĦãģ¾ãģĻ", + "׼ ×ł×¡", + "ÑĨ е", + "еÑģ п", + "Ùħ اÙħ", + "à¸ŀื à¹īà¸Ļ", + "иÑĩеÑģ ки", + "Ø® د", + "Ùĥ ÙĪÙħ", + "Ġ×Ķ ר×IJש", + "ت اب", + "é£Ł ãģ¹", + "ื à¸Ļ", + "оÑĢ о", + "Ġb öl", + "×ķ×Ĺ ×ĵ", + "دÙĬ ر", + "ắ m", + "د ع", + "ãģķ ãģĽ", + "à¸ĺ ร", + "à¸ĺร รม", + "ãģĭ ãĤĤ", + "å¤ļ ãģı", + "r ä", + "س ع", + "×Ļ׾ ×Ķ", + "ض ر", + "ĠاÙĦ شر", + "×ĸ ×ķר", + "×¢ ×ijר", + "ạ m", + "алÑĮ но", + "ر ÙĨ", + "اÙħ ج", + "׼ ×ļ", + "d ıģ", + "д ен", + "ض ا", + "ÙĦÙĬ Ùħ", + "Ġê·¸ 룬", + "تÙħ اع", + "ار ÙĬØ®", + "à¹Ĥ à¸ķ", + "ĠÑģ ÑĢед", + "Ġ׳ ×ķס", + "ÙĤ بÙĦ", + "оÑĤ ов", + "le ÅŁtir", + "Ġм еÑģÑĤ", + "سÙĦ Ùħ", + "Ġ×¢ צ", + "ĠاÙĦس ÙĦ", + "еÑĤ ÑĮ", + "اب Ø©", + "н ак", + "สà¸ĸ าà¸Ļ", + "Ġ×ij ׳", + "à¸ļ ัà¸Ļ", + "׼ ׳", + "Ġö ÄŁ", + "ãģ¨ è¨Ģ", + "uy ến", + "di ÄŁ", + "áºŃ u", + "ÑĢ аÑģ", + "ãĤ· ãĥ§ãĥ³", + "n ız", + "×ķ×ĵ ×Ķ", + "ت س", + "Ùħ اÙĦ", + "à¹Ģห à¸ķุ", + "ย ว", + "à¸ŀ ัà¸ģ", + "ãģĦ ãģªãģĦ", + "Ġк аÑĩ", + "ล à¹Į", + "ר׼ ת", + "ÅŁt ur", + "×ŀ ×ķס", + "ãģ ¥", + "б ол", + "عÙħ اÙĦ", + "×ķר ת", + "ÑĨи он", + "ศ ึà¸ģ", + "ภı", + "ÑĢ ен", + "اس ÙĬ", + "ائ ر", + "à¹Ĥ à¸Ľà¸£", + "Ġse ç", + "غ ÙĬ", + "Ñį ÑĤ", + "ен н", + "ãģª ãģ®", + "×Ļש ×Ķ", + "×Ļפ ×ķר", + "ãģŁãĤģ ãģ«", + "ز Ø©", + "Ġç oc", + "ãĤ¯ ãĥª", + "ÑĪ ен", + "ãĤı ãģij", + "رÙĬ د", + "ĠÑĢ аÑģÑģ", + "Ùĥ ات", + "ส à¸Ńà¸ļ", + "ce ÄŁi", + "ãĤ¿ ãĤ¤", + "à¸ļ ร", + "ĠاÙĦ بر", + "׳ ×ķ×¢", + "r ün", + "را ض", + "ศา ส", + "à¸ķ รà¹Į", + "ãģį ãģŁ", + "×ķ׾ ×ĵ", + "еÑĢ и", + "íĹ ĺ", + "ắ p", + "ت عÙĦ", + "Ùĥ د", + "иÑĤелÑĮ но", + "Ø· Ùģ", + "Ġав ÑĤом", + "Ġ×ŀ צ", + "ÑĪи Ñħ", + "ات Ùģ", + "ĠÑħ оÑĤ", + "Ùİ Ø§", + "ãģı ãĤĭ", + "×Ķ פ", + "à¹Ĥ à¸Ĺ", + "à¹ģ à¸ŀ", + "à¹Ī à¸Ńย", + "ĠاÙĦÙħ Ø´", + "à¸ģาร à¸ĵà¹Į", + "ани з", + "×Ķ ׾", + "ظ Ùħ", + "ย ุ", + "li ÄŁ", + "à¹Ħ à¸Ĥ", + "à¸ĸ ืà¸Ń", + "ö z", + "ãģij ãģ¦", + "à¹Ģ à¸ľ", + "ุ ม", + "ãĥĹ ãĥ¬", + "Ġ×Ķ×IJ ×Ĺר", + "خت ÙĦÙģ", + "ภİ", + "ÙĦا ØŃ", + "Ġdü zen", + "צ ×Ķ", + "س اء", + "×ķר ×ļ", + "×ķ×ĵ ×Ļ", + "ÑĢа ÑĦ", + "ÅŁt ır", + "ãģ« åħ¥", + "ãģĪ ãģ°", + "ص ÙĪÙĦ", + "ĠÐľ оÑģ", + "ا Ùĩر", + "ãģ£ ãģ", + "ĠлÑİ Ð±", + "×Ļ×¢ ×Ķ", + "Ġ×Ķ×ŀ ק", + "สิ à¸Ĺ", + "สิà¸Ĺ à¸ĺิ", + "×Ļ׳ ×Ŀ", + "ÙĦا Ùģ", + "à¸ŀัà¸Ļ à¸ĺ", + "×ķ×IJ ×Ķ", + "ม ั", + "à¸Ĥ à¸ĵะ", + "д оÑĢ", + "ãģ¨ ãģª", + "à¸ģระ à¸Ĺ", + "ac ı", + "×ķ׾ ×ķ×Ĵ", + "Ñĥ ÑĪ", + "ãĥ¥ ãĥ¼", + "ãĥ ¦", + "Ùħ ست", + "Ġa ÅŁ", + "ש ק", + "פ ת×Ĺ", + "าย à¸Ļ", + "í ĩ", + "ë ¢", + "ï ·", + "í ī", + "ì µ", + "ì ¬", + "ðĿ Ľ", + "ì Ĵ", + "ë Ļ", + "ê §", + "á ĸ", + "â ¨", + "â ±", + "á ĺ", + "ð ĸ", + "à ł", + "á Ķ", + "ðIJ Ń", + "ữ ng", + "Å© ng", + "Ġ×Ķ ת", + "ĠاÙĦ ا", + "Ġ×ŀ ת", + "à¸ĸ ึà¸ĩ", + "ò n", + "á»ĭ nh", + "нÑĭ м", + "Ġc ả", + "à¸Ķ ู", + "Ġ à¹ģà¸ķà¹Ī", + "Ġ×ij ×Ķ", + "ó i", + "ãģ¨ ãģĹãģ¦", + "ú ng", + "ĠØ °", + "Ġ×Ķ ׳", + "Ġب ÙĨ", + "ÙĦ اÙĦ", + "à¹Ħ à¸Ĺย", + "á»ĩ p", + "t ı", + "ม ัà¸Ļ", + "ằ ng", + "á»ij t", + "к ом", + "à¸ĭ ึà¹Īà¸ĩ", + "à¸Ħร ัà¸ļ", + "à¸ļ à¹īาà¸Ļ", + "ĠاÙĦ ÙĬ", + "l ü", + "ÙĪ س", + "ãģł ãģ£ãģŁ", + "à¹Ģ à¸ĩ", + "Ġê³ µ", + "н Ñĥ", + "ãĤĪ ãĤĬ", + "м Ñĥ", + "à¹Ģà¸Ĥ า", + "ãĤ Ģ", + "ни е", + "ãģ«ãģª ãĤĭ", + "áºŃ y", + "ĠÙĪ ا", + "ëł ¤", + "ש ×ķ", + "á p", + "×ĵ ×ķ", + "ãģ§ ãģĹãģŁ", + "ع ض", + "Ñģк ой", + "æĦŁ ãģĺ", + "ÑİÑĤ ÑģÑı", + "Ġ×Ļ ׼×ķ׾", + "ãĤĵ ãģł", + "в и", + "à¹Ģล à¹Īà¸Ļ", + "ìĿ´ ëĭ¤", + "ĠÙĦ Ùĩ", + "à¸Ħ ืà¸Ń", + "ت Ùĥ", + "Ùħ ÙĥÙĨ", + "a ģı", + "׳ ×ĵ", + "ë¯ ¼", + "à¹Ħ ว", + "สำ ห", + "สำห รัà¸ļ", + "Ñģл ед", + "t ır", + "ĠÙĦ ÙĬ", + "ĠاÙĦع ÙħÙĦ", + "×ij ×ķת", + "×ij ×Ļ×Ŀ", + "à¸Ħ ำ", + "à¹Ģà¸Ħร ืà¹Īà¸Ńà¸ĩ", + "lı ģı", + "ืà¸Ń à¸ĩ", + "ج د", + "íŀ Ī", + "ìĭ ¬", + "×¢ ×ķת", + "ส ิà¸Ļ", + "Ñĩ и", + "ر ض", + "à¹Ģà¸Ľ ิà¸Ķ", + "à¸Ħ à¹Īา", + "ìĦ ł", + "ÙĪر Ø©", + "ק ×ĺ", + "ìľ ł", + "ع ÙħÙĦ", + "×IJ ×Ļ×Ŀ", + "׾ ×Ļ×Ŀ", + "à¹ĥห à¸į", + "à¹ĥหà¸į à¹Ī", + "ừ a", + "á»į i", + "ãģ ¶", + "ÃŃ ch", + "ãĥĩ ãĤ£", + "×ķר ×Ļ×Ŀ", + "Ñģ о", + "ìķ ½", + "ов а", + "Ñĩ аÑģÑĤ", + "à¹Ģà¸Ī à¹īา", + "п ÑĢо", + "Ġ×ŀ ×Ĺ", + "ãĥ İ", + "×ķ×Ļ ×ķת", + "Ġд е", + "ë§ Ī", + "ì§ ģ", + "×Ļפ ×Ķ", + "ĠاÙĦع اÙĦÙħ", + "ë¥ ´", + "ר×IJ ×Ķ", + "uy á»ĥn", + "×¢ ×Ļ", + "ม ืà¸Ń", + "Ø¥ ÙĨ", + "ร ู", + "ĠØ ²", + "×Ļ ×ķ×Ŀ", + "à¸ķ à¹īà¸Ļ", + "ãģ¦ ãģĦãģ¾ãģĻ", + "Ùħ اÙĨ", + "ĠÐ ¥", + "à¸Ľà¸£à¸° à¹Ģà¸Ĺศ", + "á» ³", + "׾ ×ij", + "à¹Ģà¸Ķ à¹ĩ", + "ãģŁ ãģ¡", + "à¸Ĺี ม", + "à¸Ļ ะ", + "ìĹ °", + "Ġìł Ģ", + "ÙĦ Ùĩ", + "ợ i", + "ĠاÙĦ ز", + "د ار", + "ãĤ³ ãĥ³", + "м ин", + "à¹ģห à¹Īà¸ĩ", + "à¸Ķ ัà¸ļ", + "׼ ר", + "ж а", + "íĸ Ī", + "×ŀ ×ĸ", + "ợ i", + "à¸Ķ า", + "Ġع بد", + "à¹ģ ร", + "×IJת ר", + "×¢ ׳×Ļ", + "à¹Ģ à¸Ħ", + "×ķצ ר", + "ì§Ģ ë§Į", + "ائ Ùħ", + "Ø£ س", + "uy á»ģn", + "Ġ×IJ ׳", + "׊׳×ķ", + "×ĸ ×Ļ", + "ร à¹īาà¸Ļ", + "ĠÐł оÑģ", + "ĠÐłÐ¾Ñģ Ñģ", + "رب ÙĬØ©", + "t ür", + "ãĤĭ ãģĵãģ¨", + "ظ ر", + "б Ñĭ", + "à¸Ĺีà¹Ī สุà¸Ķ", + "Ġצ ר", + "èĩª åĪĨ", + "л аÑģ", + "ĠÑı в", + "ĠÑıв лÑı", + "à¸ŀร à¹īà¸Ńม", + "à¸Ńา à¸Ī", + "à¸ļริ à¸ģาร", + "Ġç ı", + "ëį ĺ", + "ĠاÙĦÙħ ست", + "ت Ø´", + "ש ×ķ×ij", + "ãĤ ´", + "Ġyap ıl", + "ĠاÙĦ Ø°", + "ุ à¹Īม", + "à¸ĸ à¹īา", + "ìĦ ¤", + "ì° ¨", + "в аÑĢ", + "à¹Ģà¸ŀ ิà¹Īม", + "Æ°á»Ľ i", + "Ùĥ س", + "à¸Ńย าà¸ģ", + "ãģ¦ ãĤĤ", + "Ġг од", + "ÙĬ ار", + "à¸ķ à¸Ńà¸Ļ", + "Ġиг ÑĢ", + "à¹Ħà¸Ķà¹ī รัà¸ļ", + "ĠاÙĦÙħ ر", + "ÙĤ ت", + "Ġë ĺ", + "Ġëĺ IJ", + "ẩ n", + "ãģĻãĤĭ ãģĵãģ¨", + "×Ĵ ×Ŀ", + "Ġ×ij ×ij", + "ت د", + "ÙĪ ار", + "ãĤ ®", + "п ол", + "Ġм ог", + "تر Ùĥ", + "ÙĪ Ø«", + "Ġç ık", + "ا Ø©", + "à¹Ģà¸Ķ ียว", + "มี à¸Ħวาม", + "Ġ×ŀ ×Ĵ", + "ص Ùģ", + "ĠТ ак", + "Ġ׼ ת", + "×Ļ×ĵ ×Ļ", + "ов оÑĢ", + "ầ y", + "สิ à¹Īà¸ĩ", + "ب ت", + "ür ü", + "ÙĨ ج", + "หล ัà¸ģ", + "×Ļ×Ķ ×Ŀ", + "ÙĤ ص", + "з Ñĭ", + "×Ľ×ª ×ij", + "Æ° u", + "m ız", + "ĠìĦ ¸", + "л ог", + "Ùħ ÙĬÙĦ", + "ÙĬ ج", + "íĴ Ī", + "à¸ŀ à¸ļ", + "ห ัว", + "з на", + "ר ק", + "à¹Ĥ ร", + "Ġ×ij ס", + "ĠBaÅŁ kan", + "ĠëĶ °", + "à¸Ń ัà¸Ļ", + "ีà¹Īย ว", + "н еÑģ", + "à¹Ģà¸Ķ ิà¸Ļ", + "ÙĬ اÙĨ", + "×ķ׾ ×Ļ", + "ا خت", + "צ ×ķת", + "ãģĵ ãģĵ", + "ĠاÙĦ اÙĨ", + "ĠпÑĢо ÑĨ", + "ãģ¾ ãģł", + "׼ ס", + "ĠاÙĦ Ø¢", + "ÙĬ ز", + "ĠاÙĦد ÙĪÙĦ", + "Ġíķĺ ëĤĺ", + "ض ع", + "ê» ĺ", + "ÅĽ wi", + "ย ิ", + "ãģ¡ãĤĥ ãĤĵ", + "ĠÙħ Ø´", + "à¸ĺ ี", + "ãģ¨ ãģį", + "׳×Ļ ×ķת", + "Ġë ¯", + "Ġë¯ ¸", + "Ġs ı", + "ëĭĪ ê¹Į", + "Ġп л", + "غ ÙĦ", + "à¹ģ รà¸ĩ", + "ب ÙĬر", + "ãģĤãĤĬ ãģ¾ãģĽãĤĵ", + "ê· ¼", + "Ġy üz", + "ĠdeÄŁ er", + "åł´ åIJĪ", + "á» ¡", + "м аÑĤ", + "รา à¸Ĭ", + "ÙĪر ÙĬ", + "ж ен", + "ãģ¾ ãĤĬ", + "ãģ® ä¸Ń", + "×Ļ×ĵ ×¢", + "à¸Ń ุ", + "à¸ļ à¸Ńล", + "à¸Ľà¸±à¸į หา", + "ز Ùħ", + "ÄŁ a", + "à¸Ń ืà¹Ī", + "à¸Ńืà¹Ī à¸Ļ", + "п л", + "Ġне обÑħодим", + "׼ ×ij", + "à¹Ģ ศ", + "קר ×Ķ", + "ì² ĺ", + "ëł ¨", + "×ŀק ×ķ×Ŀ", + "jÄħ c", + "Ùĩ ÙĦ", + "Ġ×¢ ×ij×ķ×ĵ", + "à¹Ħม à¹ī", + "à¸ģล ัà¸ļ", + "×ķ׼ ׾", + "ק ×ĵ", + "اÙĦ ÙĬØ©", + "ر Ùĩ", + "ãģij ãĤĮãģ°", + "ĠÙĨ Ùģس", + "ãĤ¢ ãĥ«", + "ìĹ Īëĭ¤", + "ק ×ķר", + "н еÑĢ", + "ب اب", + "ãĤ ¶", + "سب ب", + "ÙĦ ÙĬÙĦ", + "ص ÙĨ", + "ص در", + "ế m", + "à¸Ĭà¹Īว à¸ĩ", + "ØŃ ÙĨ", + "Ġ×ij ×Ĵ", + "×ŀ ×ķ×¢", + "׾ ×Ĺ", + "大 ãģį", + "ت ب", + "н еÑĤ", + "×Ļ×ij ×Ķ", + "б л", + "ãĥĹ ãĥª", + "اص Ø©", + "ãģ¤ ãģij", + "×Ļ×ŀ ×ķש", + "ãģĮ ãģĤ", + "ëĭ ´", + "ãģĭãĤĤ ãģĹ", + "ãģĭãĤĤãģĹ ãĤĮ", + "ãģ¡ ãĤī", + "×ij ×ĺ", + "Ġba ÄŁ", + "×Ļ×Ĺ ×¡", + "×ij ×ķ×¢", + "ล ี", + "פע ×Ļ׾", + "им и", + "g ÅĤ", + "Ġим е", + "خد اÙħ", + "×IJ ×Ļר", + "Ġy apt", + "ãģ¨ ãģĦ", + "à¸ĩ à¹Īาย", + "׾×Ļ ×ķ", + "ØŃد Ø«", + "را ÙĤ", + "ĠÄIJ i", + "اد ر", + "ãģĵãģ¨ ãĤĤ", + "×ij ×Ļר", + "Ġв з", + "ض اÙģ", + "ת ×ķ׼", + "ÑĢ ом", + "ر ات", + "à¹Ģà¸Ĺ à¹Īา", + "ãģĺ ãĤĥ", + "ãģĿ ãģĵ", + "اج تÙħاع", + "à¹īà¸Ń à¸Ļ", + "ÙĤ Ùħ", + "ë³ ¸", + "Ä ŀ", + "ש ×Ļ×ķ", + "×ij ׳×Ļ", + "ìľĦ ìĽIJ", + "à¹ģ à¸Ī", + "×Ĺ ×ķר", + "دÙĬ ÙĨØ©", + "ت Ø·", + "ằ m", + "ò a", + "ย à¸Ńà¸Ķ", + "Ġëĭ ¹", + "สุ à¸Ĥ", + "×ĵר ×ļ", + "د ÙĨ", + "س ÙĬÙĨ", + "ÙĪÙĤ Ùģ", + "ÑĨ Ñĭ", + "г оÑĤов", + "еж дÑĥ", + "à¸ŀ วà¸ģ", + "اÙĤ تص", + "اÙĤتص اد", + "cz ÄĻ", + "ni ÄĻ", + "ÑĢ еб", + "ØŃ ÙĪ", + "à¸Ĺ à¹Į", + "ãĤĪ ãģŃ", + "д ж", + "à¸ģล à¹Īาว", + "دÙĬ Ø«", + "ãĤ³ ãĥŁ", + "ÙĤ ÙĪÙħ", + "Ġت ØŃ", + "à¹Ģ à¸ķิ", + "اÙģ ظ", + "à¸Ī ุ", + "رÙĬ اض", + "×ŀש ×ļ", + "à¹Ĥ ย", + "еÑĢ е", + "ãģ¿ ãģŁãģĦ", + "ìĿ´ ëĿ¼", + "ĠاÙĦÙħ ÙĪ", + "ĠÑģÑĤ о", + "à¹Ģรà¹ĩ ว", + "Ġд еÑĤ", + "ĠÑģ дел", + "à¹Ģà¸Ĭ ืà¹Īà¸Ń", + "פ ׳×Ļ", + "ÙĪض ÙĪع", + "×ij ס", + "à¹ģ à¸Ķ", + "ó c", + "ริ ม", + "ÑĢаР´", + "ìĪ ł", + "ãĥ¼ãĤ º", + "ãģ« ãģĬ", + "и но", + "פ ×Ļ׾", + "à¸Ĭั à¹Īà¸Ļ", + "×Ĺ×ĵ ש", + "à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ", + "׳ ×Ļס", + "غ رب", + "ãĤ¸ ãĥ£", + "ส ัà¸ĩ", + "à¹Ģ à¸Ĺีà¹Ī", + "à¹Ģà¸Ĺีà¹Ī ยว", + "ëŁ ¼", + "à¹ģ à¸Ł", + "ãĥ¼ãĤ ·", + "ãĥ¼ãĤ· ãĥ§ãĥ³", + "Ġвоз мож", + "جÙħ ÙĪع", + "×ijר ×Ļ×Ŀ", + "ãĥĪ ãĥ©", + "ĠкаÑĩ еÑģÑĤв", + "Ø· ÙĬ", + "ÑĤ Ñı", + "צ ×ķ×¢", + "ÄŁ ını", + "ع ÙĦÙī", + "ا Ø°", + "ÙĪاÙĤ ع", + "Ùħ ÙĪا", + "ائ ÙĬÙĦ", + "к ол", + "á»ģ m", + "à¸ľà¸¥ ิà¸ķ", + "×Ļ׳ ×ĺר", + "س Ùĥ", + "ש ×Ļר", + "ศึà¸ģ ษา", + "à¸ļ ั", + "Ñĩ аÑģ", + "×ķפ ×Ķ", + "×Ļפ ×ķ׾", + "ĠاÙĦس اب", + "رÙĬ ب", + "ĠاÙĦ بÙĬ", + "ãĤ¹ ãĥĨ", + "Ñĩ ен", + "à¹ģ à¸ľ", + "Ġ׳ ש", + "ز ÙĬد", + "ØŃ Ø§Ø¯", + "ëį Ķ", + "رÙĪ ع", + "à¸Ĺุ à¸Ļ", + "ส มา", + "c zeÅĦ", + "×Ļ×ĵ ×Ķ", + "ãģ§ ãģĤ", + "Ġçoc uk", + "Ø® ب", + "à¸ļ าย", + "à¸Ľà¸£à¸° à¸Ĭา", + "×ŀש ׾", + "ãģª ãģĭ", + "à¸ģ าย", + "ãĥģ ãĥ£", + "аÑĢ и", + "ĠÑĩ а", + "à¸Ķ ำ", + "à¸Ĺั à¹Īว", + "Ñĥ Ñħ", + "Ġö z", + "Ġì¢ ĭ", + "ج رÙĬ", + "ائ ÙĤ", + "à¸ł ัย", + "Ø· ار", + "د ارة", + "Ä© nh", + "Ø« ÙĨ", + "zell ik", + "اÙĦ ت", + "Ġg eli", + "ãĥķãĤ ©", + "ол од", + "رب ع", + "שת ×ŀש", + "à¸ļร ร", + "íĿ ¬", + "Ġü rün", + "Ġê·¸ ëłĩ", + "ศาส à¸ķรà¹Į", + "ãģ ľ", + "×Ļ×ij ׾", + "ĠпÑĢед ÑģÑĤав", + "سط ÙĬÙĨ", + "ãĤĴ 使", + "Ġпом оÑī", + "×ķק ר", + "ãĥ¯ ãĥ¼", + "Ġyö net", + "×Ļק ר", + "à¸Ĥ า", + "еÑĢи ал", + "ØŃ Ùģ", + "Ġ×Ļ צ", + "à¸Ĺ ิ", + "å£ ²", + "à¸Ļ à¸Ńà¸ģ", + "×ķ׼ ר", + "íĻ ľ", + "ủ y", + "ĠاÙĦÙĤ ر", + "×Ļ×ij ×ķת", + "ÅĽ ni", + "Ùħ شار", + "ượ t", + "ĠÙĦ دÙĬ", + "ÑĤ ел", + "ĠØ¥ ÙĦÙĬ", + "عÙĦ ÙĪÙħ", + "ìķ ĺ", + "в иÑĤ", + "à¸Ħ ะ", + "yr ı", + "ãģ¨ ãģ£ãģ¦", + "à¹Ģ à¸ī", + "à¸ĸ าม", + "ÙĤ ار", + "عÙĦ اÙħ", + "ặ ng", + "Ùħ ÙĴ", + "×Ļ×ŀ ת", + "سب Ø©", + "ãĤ¯ ãĥ©", + "×ķס ×£", + "ĠпÑĢ ин", + "ãģĦ ãĤį", + "س اس", + "عت بر", + "วิ à¸Ĺย", + "วิà¸Ĺย า", + "س Ùĥر", + "ãĤ· ãĥ§", + "ãģ ģ", + "ัà¸ģ ษ", + "×ij ×ķ×Ķ", + "ห ย", + "ãģ¾ ãĤĮ", + "ĠоÑĢг аниз", + "каз ал", + "ĠÑģв Ñıз", + "uy ết", + "ĠпÑĢо из", + "Ġק ×ĺ", + "à¹ģà¸ģ à¹ī", + "п ÑĥÑģ", + "Ġê·¸ ê²ĥ", + "ëĬ IJ", + "л екÑģ", + "ãĥ¼ãĥ Ĺ", + "à¸ķ ำ", + "ת×Ĺ ×Ļ׾", + "à¸Ńà¸ĩ à¸Ħà¹Į", + "Ạµ", + "׳ צ", + "Ø£ Ø´", + "Ø´ Ùĩ", + "ย ะ", + "à¸ģ à¸İ", + "ĠاÙĦØ¥ سÙĦاÙħ", + "ед ÑĮ", + "ãģ² ãģ¨", + "ëıĦ ë¡Ŀ", + "ãģ© ãģ®", + "Ñĥ в", + "еÑĩ ение", + "ĠاÙĦت ج", + "ãģ« è¡Į", + "Ġп озв", + "ãĤı ãĤĬ", + "ÙĦ اث", + "íķĺ ìĺĢ", + "Ġм аÑĢ", + "Ġkon uÅŁ", + "ãĥ¬ ãĤ¹", + "ãĤĴ æĮģ", + "ĠоÑģ нов", + "×Ĺ ×ij", + "ÙĪج ÙĪد", + "פ ×ķף", + "в оÑĢ", + "Ġн ик", + "ãģĭ ãĤĭ", + "ÅŁtır ma", + "×Ļס ×ĺ", + "Ø£ ÙĦ", + "ห à¹Į", + "и она", + "лÑĮ н", + "Ġг оÑģ", + "ĠÐľÐ¾Ñģ к", + "ÑĢ об", + "×ķ×IJ ×Ļ", + "ãģĬãĤĬ ãģ¾ãģĻ", + "ãģ£ãģ ±", + "к л", + "à¸Ļ à¸Ķà¹Į", + "رÙĬ Ùģ", + "اس ب", + "ĠÑĢ еÑĪ", + "Ġд ол", + "ãģ¹ ãģį", + "×Ļ×ij ×ķר", + "м еÑī", + "Ġна ÑĪ", + "à¹ģ à¸Ľà¸¥", + "ÑĢ иÑĤ", + "кÑĥ Ñģ", + "и ÑĢа", + "аÑĤ ÑĥÑĢ", + "ÙĪا صÙĦ", + "à¹Ģà¸ľ ย", + "à¸Ń ำ", + "à¹Ģà¸ģ ิà¸Ļ", + "غ Ùħ", + "ãģĻ ãģİ", + "lı kl", + "ÅĦ sk", + "ê² ¬", + "×Ļ׼ ×Ķ", + "׊ש×ij", + "ÙĪر ÙĬØ©", + "Ġд ейÑģÑĤв", + "×Ĺ׾ ×ĺ", + "Ġ׾ ×ŀ×¢", + "צ׾ ×Ļ×Ĺ", + "еÑĩ а", + "Ùģ اع", + "×Ĵ ×Ļ×ĵ", + "áºŃ m", + "ÄĻ b", + "Ø´ ع", + "ãģı ãĤĬ", + "à¸ŀ ุ", + "ед еÑĢ", + "à¸Ĥ à¸Ļ", + "à¸Ħ าร", + "ĠболÑĮ ÑĪ", + "ãģı ãģªãĤĬ", + "à¸ĵ า", + "×ĵ ×ķ×Ĵ", + "Ġм н", + "ä¸Ĭ ãģĮ", + "ç¶ļ ãģį", + "ฤ ษ", + "ภĨ", + "Ø® ÙĬ", + "à¹Ģà¸Ĺ à¸ŀ", + "สั ม", + "à¹Ģส à¸Ļ", + "à¹Ģสà¸Ļ à¸Ń", + "ãĥ ´", + "Ġи ÑģÑĤ", + "با شر", + "ĠÑĥ ÑĢов", + "×ŀ ×ķ×ĸ", + "ab ı", + "wa ż", + "×ķצ ×IJ×Ķ", + "ÑĤ веÑĢ", + "à¸ŀัà¸Ļà¸ĺ à¹Į", + "׳ ×Ĵ×ĵ", + "ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį", + "ĠÑĤÑĢ еб", + "à¸ģร ุà¸ĩ", + "ØŃت اج", + "à¹Ģ à¸Ħล", + "ã Ĩ", + "ÄĻ tr", + "Ġszcz eg", + "Ġר ש", + "à¸Ĺ à¸ĺ", + "Ġн ек", + "Ġнек оÑĤоÑĢ", + "в ÑĪ", + "Ð ¬", + "à¹Īว ย", + "ล ุ", + "б ÑĢÑı", + "หม ูà¹Ī", + "à¹ģ à¸ķà¸ģ", + "ר׼ ×Ļ×Ŀ", + "Ġí ĸī", + "ã i", + "Ùĥر Ø©", + "â Ń", + "í IJ", + "ã į", + "á ģ", + "â ®", + "â ¥", + "ì ®", + "à ¿", + "â ¿", + "á Ĥ", + "á ¤", + "â ł", + "í Ł", + "ðIJ į", + "ðIJ °", + "ðĿ Ĩ", + "ðŁ Ī", + "Ġ×¢ ׾", + "Ġع ÙĨ", + "ĠÙħ ع", + "Ġ×ĸ ×Ķ", + "ĠÙħ ا", + "Ġm Ãł", + "Ġd ụ", + "á»ĩ c", + "а Ñħ", + "s ı", + "íķĺ ê³ł", + "Ġ×ķ ×ij", + "ĠÐŁ о", + "×ķת ר", + "ĠÙĦ Ùħ", + "Ġ×ķ ׾", + "ãģĹãģ¦ ãģĦãĤĭ", + "Ġ×ŀ ×Ļ", + "Ġب ÙĬÙĨ", + "з а", + "ĠÙĥ اÙĨ", + "Ġ×Ķ ×Ļ×Ķ", + "ëħ Ħ", + "×IJ ×ķ", + "д и", + "ĠпеÑĢ е", + "d ı", + "Ġ׾ ש", + "Ġש ×ŀ", + "ãģĮ ãģĤãĤĭ", + "ãģĦ ãģĦ", + "ÑĢ е", + "ק ×ķ", + "и ли", + "м е", + "ÙĬ ت", + "ãģ§ ãģĤãĤĭ", + "Ġв о", + "à¹ĥ หม", + "à¹ĥหม à¹Ī", + "Ġש ×ij", + "Ġ à¹Ĥà¸Ķย", + "ÙĬ Ùĩ", + "ãģ§ãģĻ ãģĮ", + "ãģ¨ ãģ¯", + "ר ×ķ", + "Ġ à¸ĭึà¹Īà¸ĩ", + "ãģ§ãģį ãĤĭ", + "м о", + "à¹Ģà¸ŀ ืà¹Īà¸Ń", + "צ ×ķ", + "×ĺ ×ķ", + "ìķ Ī", + "Ġh á»į", + "à¹Ģà¸ĩ ิà¸Ļ", + "ĠاÙĦ ب", + "Ġ มี", + "ë¬ ¼", + "Ñģ е", + "ëĵ¤ ìĿ´", + "Ġë§ IJ", + "Ġl Ỽ", + "a ÅĤ", + "×Ĺ ×ijר", + "Ġd á»±", + "ÙĬ Ø«", + "Ġth á»ĭ", + "à¸ģà¹Ī à¸Ńà¸Ļ", + "Ġ×ij ׼׾", + "ãģ ¸", + "ãģ¨æĢĿ ãģĦãģ¾ãģĻ", + "ả nh", + "ย า", + "Ùģ ا", + "ส ี", + "à¸ķ า", + "ë² ķ", + "ãĥª ãĥ¼", + "รา à¸Ħา", + "Ġ×ķ ׾×IJ", + "ãģ¨ ãģĵãĤį", + "à¹Ģล ืà¸Ń", + "di ÄŁi", + "ÙĪ اÙĨ", + "Ġ׾×Ķ ת", + "รว ม", + "פ ×Ļ×Ŀ", + "à¸ľ ม", + "ж и", + "c ı", + "ÑĢ од", + "Ġkar ÅŁÄ±", + "×Ĵ ×ķ", + "ãģ« ãģ¤", + "ãģ«ãģ¤ ãģĦãģ¦", + "r Ãł", + "×Ļ×ķת ר", + "ĠìĨ Į", + "ק ×Ķ", + "ÑģÑĤв о", + "ãģij ãģ©", + "g é", + "à¸Ķ à¹īาà¸Ļ", + "çļĦ ãģ«", + "ĠÙĬ ÙħÙĥÙĨ", + "ìĨ į", + "ÙĬ Ùĥ", + "à¹Ħว à¹ī", + "Ñģки й", + "ì m", + "Ġ׾×IJ ×Ĺר", + "à¸Ńา หาร", + "Ġà¹Ģ à¸ŀ", + "รา ะ", + "ล ูà¸ģ", + "ÑģÑĤ а", + "Ġìľ ł", + "ÙĤ ÙĪÙĦ", + "б оÑĢ", + "Ñģк ого", + "หล ัà¸ĩ", + "à¸Ĥ à¹Īาว", + "à¹Ģม ืà¸Ńà¸ĩ", + "ê° ģ", + "t Ãł", + "ÙĬ ÙĬÙĨ", + "عر ض", + "ë° ©", + "Ġëı Ļ", + "Ġà¹Ģ à¸Ľ", + "Ġà¹Ģà¸Ľ à¹ĩà¸Ļ", + "ç i", + "li ÄŁi", + "ìĹIJ ê²Į", + "ãĤ¿ ãĥ¼", + "Ġ׾ ת", + "פ ×ķת", + "à¸Ĥ à¸Ń", + "ر س", + "ìł IJ", + "à¸ľ à¹Īาà¸Ļ", + "ÑĦ и", + "ج ÙĨ", + "ì¢ ħ", + "Ġ×Ķ פ", + "Ġn go", + "á»ĭ a", + "Ġtá» ķ", + "Ġê·¸ 리", + "à¹Ģม ืà¹Īà¸Ń", + "Ø° Ùĥر", + "ìĸ ij", + "ìĹ Ń", + "×ĺ ׾", + "k ı", + "Ġع ÙħÙĦ", + "Ġع ÙĨد", + "à¸ĭ ืà¹īà¸Ń", + "Ġê± °", + "в е", + "r ü", + "à¹Ģ à¸Ńา", + "ส à¹Į", + "à¸Ī à¸Ļ", + "ס ת", + "Ġgi ả", + "ãĤĭ ãģ¨", + "à¸ģำ ลัà¸ĩ", + "н ей", + "à¸Ī ริ", + "à¸Īริ à¸ĩ", + "Ġë į", + "Ġëį Ķ", + "à¸Ħà¹Ī ะ", + "ì n", + "Ġsü re", + "Ġqu y", + "à¸ļ าà¸ĩ", + "åıĸ ãĤĬ", + "ר ×Ĺ", + "×ij ת", + "ãģĮ ãģĤãĤĬãģ¾ãģĻ", + "ר ש", + "ìĹIJ ëĬĶ", + "Ġ×IJ פשר", + "ay ı", + "ãģĮ ãĤī", + "ØŃ Ø¨", + "ан Ñģ", + "س ÙĪ", + "ĠпÑĢ е", + "د ÙĪ", + "ãģ« ãĤĪ", + "à¹Ģà¸ģ ม", + "สู à¸ĩ", + "m akt", + "makt ad", + "maktad ır", + "Ġön em", + "×Ļ×ŀ ×Ļ×Ŀ", + "б о", + "ÙĪ ÙĬØ©", + "รู à¸Ľ", + "à¹Ĥล à¸ģ", + "Ùħ ÙĬع", + "ÑģÑĤ Ñĥп", + "à¹Ĥ à¸Ń", + "دÙĬ ÙĨ", + "ì¤ ij", + "ãģĹãģ ı", + "à¹Ģส ีย", + "в Ñĭ", + "Ùħ ت", + "íĺ Ħ", + "ãĥIJ ãĥ¼", + "ا Ø´", + "ק ס", + "Ġtá» ¥", + "ล à¸Ķ", + "Ùģ Ø©", + "í ijľ", + "ر ج", + "k ÅĤad", + "ĠÅŁ ey", + "ĠØ£ Ùħ", + "Ġà¹Ģ ม", + "Ġب ÙĦ", + "Ñģ каÑı", + "ãģ¨ ãģ®", + "Ġìĭ ¤", + "ấ m", + "ห à¹īà¸Ńà¸ĩ", + "à¸Ĭ ม", + "d ü", + "Ġç ek", + "Ġê³ ł", + "×Ĵ ×ij", + "à¸Ĭี วิ", + "à¸Ĭีวิ à¸ķ", + "Ùģض ÙĦ", + "ภ¯", + "ç ı", + "Ġب Ø´", + "ĠÙĩ ÙĨا", + "ãģį ãģ¾ãģĹãģŁ", + "t ü", + "Ġìĺ ģ", + "ĠTür k", + "к ÑĤ", + "פר ס", + "ãģ¨ãģĦãģĨ ãģĵãģ¨", + "í ĶĦ", + "à¹ģร à¸ģ", + "ר ×ķף", + "Ġar as", + "×ŀצ ×IJ", + "Ġtá» ī", + "س ا", + "à¸ŀ à¸Ń", + "ĠاÙĦÙħ ØŃ", + "ãĥ ¤", + "ĠاÙĦ است", + "Ùģ ÙĨ", + "×Ļ×ŀ ×Ķ", + "ر ت", + "ãģ¨ ãĤĤ", + "Ġна Ñģ", + "п ÑĢи", + "Ġ×Ĺ ×ķ", + "и ла", + "ÙĬ Ø´", + "Ġgö z", + "Ġ×ij ׳×Ļ", + "ım ı", + "ĠÑĤ еÑħ", + "Ġh á»Ļ", + "غ ر", + "к он", + "اØŃ Øª", + "Ġ à¸ŀ", + "à¸Ń à¸Ńà¸Ļ", + "à¸Ńà¸Ńà¸Ļ à¹Ħล", + "à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į", + "Ñħ о", + "Ñı в", + "à¹ģ สà¸Ķ", + "à¹ģสà¸Ķ à¸ĩ", + "à¹Ģà¸ŀ ียà¸ĩ", + "ÑĤ ов", + "ا ÙĬ", + "Ġ×Ķ ×ĵ", + "Ġ×ķ ׼", + "ãĤī ãģĦ", + "×ķפ ף", + "Ġë ¶Ī", + "ล à¸Ńà¸ĩ", + "Ø· اÙĦ", + "Ġн и", + "ĠÙħ ست", + "ế c", + "Ġש ׼", + "ĠëķĮ 문", + "วัà¸Ļ à¸Ĺีà¹Ī", + "×Ļ׾ ×ĵ", + "ØŃ Ø§", + "е ÑĨ", + "Ġc ứ", + "×ĵ ×ķר", + "ĠÙħ ØŃ", + "ר׼ ×ij", + "بÙĬ ع", + "ни и", + "ĠاÙĦØ£ ÙĪÙĦ", + "à¸Ħว ร", + "ãģ¨æĢĿ ãģĨ", + "ĠС о", + "ائ ÙĬØ©", + "ر اء", + "оÑģ об", + "Ġب Ø£ÙĨ", + "×¢ ×ķ×ĵ", + "ĠÑĤ е", + "ãģĵ ãģĨ", + "ÑģÑĤ ÑĢа", + "ай н", + "Ġsö z", + "ت ÙĨا", + "à¸Ń ิ", + "ặ p", + "ĠìķĦ ëĭĪ", + "íķ Ń", + "Ġר×IJ ש", + "Ġ à¹Ħà¸Ķà¹ī", + "Ġ×Ĵ ×ĵ", + "Ġס פר", + "обÑī е", + "ĠÙĪ Ø¥", + "ada ÅŁ", + "ãģ¡ ãĤĩ", + "ק ×ķ׾", + "ÑĢ ез", + "ĠdÃ¼ÅŁ ün", + "Ġ×ij ×IJ×ŀ", + "Ġìĸ´ ëĸ", + "ער ×ij", + "н ее", + "ĠÑģÑĤÑĢ ан", + "س اÙĨ", + "yn ı", + "ĠاÙĦر ئÙĬس", + "ãģĹãģ ª", + "Ġ׳ ת", + "ãģ«ãģª ãģ£ãģŁ", + "g ü", + "åıĹ ãģij", + "׾ ת", + "ìł Ī", + "ëĬĶ ëį°", + "Ø® ÙĬر", + "à¸ķà¹īà¸Ńà¸ĩ à¸ģาร", + "ĠÙĦ Ø£ÙĨ", + "Ġch á»ĭ", + "ÙĪ Ø©", + "à¹ĥ ส", + "ë¶Ģ íĦ°", + "íķĺ ë©´", + "ữ u", + "à¹Ģหม ืà¸Ńà¸Ļ", + "б еÑĢ", + "ĠìĿ´ ìļ©", + "ĠÑģ еб", + "wiÄĻ ks", + "Ġ׳ ×¢", + "ÑĤ ÑĥÑĢ", + "Ġngh Ä©", + "ש ×ķ×ĺ", + "ti ÄŁi", + "Ġde ÄŁi", + "×IJ ×ij", + "Ġ×ŀ ×ŀ", + "ãĥĹ ãĥŃ", + "wa ÅĤ", + "à¸Ī ึà¸ĩ", + "Ø® دÙħ", + "×IJ ×Ŀ", + "Ä±ÅŁ ı", + "cz Äħ", + "ר ×ĵ", + "ĠÑĢ Ñĥб", + "خر Ùī", + "ãģ® æĸ¹", + "Ġд енÑĮ", + "×Ĺ ×Ļ×Ŀ", + "еÑĤ е", + "ëĤ ľ", + "×IJ ×Ĵ", + "×¢ ×ķר", + "ë³ Ħ", + "åIJĮ ãģĺ", + "ãĤ ²", + "ר ×ļ", + "×ķש ×IJ", + "ìľ ¡", + "ا Ø®", + "צ ×Ļ×Ķ", + "á»± a", + "ãģĪ ãģ¦", + "ש×Ķ ×ķ", + "ан ÑĤ", + "ลา à¸Ķ", + "ин г", + "ë¡ ł", + "اع د", + "ÙĪ سط", + "Ġв оп", + "Ġвоп ÑĢоÑģ", + "Ùħ ÙĬÙĨ", + "à¸Ħ à¸ĩ", + "×Ļר ×Ļ×Ŀ", + "c ów", + "ê² ©", + "Ġê·¸ 룰", + "Ġì§ Ħ", + "Ġש ׾×Ķ", + "à¹Ģร ิà¹Īม", + "à¸Ĭ à¸Ńà¸ļ", + "д еÑĤ", + "ÑİÑī иÑħ", + "à¸ļ à¸Ńà¸ģ", + "æĢĿ ãģĦ", + "ع ÙĬد", + "ס ×ŀ", + "×Ĵ ×Ļ×¢", + "צ ×ĵ", + "ب ات", + "ĠëĶ° ëĿ¼", + "à¸Ī ัà¸ĩ", + "ãģłãģij ãģ§", + "×¢ ×Ļר", + "ĠÑĩ ел", + "ĠÑĩел ов", + "ĠÑĩелов ек", + "ãĥĥ ãĥģ", + "à¹Ģà¸ģ ีà¹Īยว", + "à¸Ķ ิ", + "Ġפ ×¢", + "×Ļ×ŀ ×Ļ", + "ë° ĺ", + "Ø® ار", + "×ij ×Ļת", + "×¢ ×Ļ×Ŀ", + "ü yor", + "ãĤģ ãģ¦", + "к лад", + "Ġ à¸Īาà¸ģ", + "à¹Ģà¸Ħ ย", + "ส à¸Ńà¸ĩ", + "à¹ģ à¸Ħà¹Ī", + "ẫ u", + "หà¸Ļ ัà¸ĩ", + "ש׾ ×ķ×Ŀ", + "اÙĨ ÙĬØ©", + "åĩº ä¼ļ", + "åĩºä¼ļ ãģĦ", + "à¸ł าย", + "à¸ļา à¸Ĺ", + "à¸Ĭา ว", + "mu ÅŁ", + "Ġ׾ק ×ij׾", + "ãĤ· ãĥ£", + "ĠÄ° ÅŁ", + "×Ĵ×ĵ ×ķ׾", + "ج عÙĦ", + "ë³ Ģ", + "ยิ à¹Īà¸ĩ", + "à¸Ļ าย", + "à¸Ļ ีà¹Ī", + "วิ à¸ĺี", + "ãĤī ãģªãģĦ", + "ëł Ī", + "Ġ문 ìłľ", + "Ġ à¸ģ", + "à¸Ĺำ à¸ĩาà¸Ļ", + "à¹Ģว à¹ĩà¸ļ", + "ÑĦ е", + "楽 ãģĹ", + "สำ à¸Ħ", + "สำà¸Ħ ัà¸į", + "ر Ùħ", + "ãģķãĤĮ ãģ¦", + "Ġоб ла", + "ר×IJ ×Ļ", + "หม à¸Ķ", + "ÙĨ ÙĬØ©", + "ли н", + "Ġe ÄŁ", + "it im", + "ëł ¹", + "ص اÙĦ", + "ÅĽ l", + "à¸ľ ิà¸Ķ", + "ãĥŀ ãĥ³", + "åħ¥ ãĤĮ", + "à¹Ģà¸ķ à¸Ńรà¹Į", + "ار ÙĬ", + "ĠÐ ¦", + "d ür", + "ส วย", + "ë¦ ½", + "رÙĥ Ø©", + "Ġh ã", + "×Ļת ×Ķ", + "à¸Ĥ à¸Ļา", + "à¸Ĥà¸Ļา à¸Ķ", + "à¸Īำ à¸Ļ", + "à¸Īำà¸Ļ วà¸Ļ", + "ש ×ķק", + "Ġд ом", + "ì± ħ", + "ãģĭ ãģij", + "פ ×ķ׾", + "à¸Ĭ าย", + "Ñģ моÑĤÑĢ", + "Ñģл Ñĥж", + "ש ×IJ׾", + "кÑĢÑĭ ÑĤ", + "Ġìŀ ĺ", + "é«ĺ ãģĦ", + "ĠÑĢ Ñĥк", + "ÙĨ ص", + "д ав", + "Æ°á» ¡", + "ưỡ ng", + "ر اÙħ", + "×Ļ׳ ×Ļ×Ŀ", + "ãĥ© ãĥ¼", + "ëĦ ¤", + "Ġت ع", + "l ke", + "好 ãģį", + "æĮģ ãģ¡", + "Ġë§ İ", + "Ġy ük", + "ĠÑģоÑģÑĤ ав", + "енÑĤ ÑĢ", + "pe ÅĤ", + "à¹Ģà¸Ľà¸¥ ีà¹Īย", + "à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ", + "íı ī", + "ãĤĦ ãģĻ", + "×Ĺ ×ĸ", + "×ijר ×Ķ", + "ë£ ¨", + "ìĶ Ģ", + "بØŃ Ø«", + "à¹Ģà¸ķ à¹ĩ", + "ów i", + "ب Ùĩ", + "ãģį ãģ¾ãģĻ", + "Ġ×¢ ×ŀ", + "×Ĵ ×ķ׾", + "ез д", + "ÙĬÙģ Ø©", + "สà¸Ļ à¹ĥà¸Ī", + "Ġת ׾", + "Ñı Ñī", + "Ġس ÙĨ", + "ĠÙĪا ØŃد", + "ĠÑģ м", + "lad ı", + "ı ld", + "×Ļר ת", + "ีย à¸Ļ", + "ת×Ĺ ×ª", + "Ġж из", + "à¸ŀ ั", + "à¸ŀั à¸Ĵ", + "à¸ŀัà¸Ĵ à¸Ļา", + "à¸Ĭ ิ", + "ا Ø®ÙĦ", + "ãģ£ãģ¦ ãģĦãģŁ", + "รั à¸IJ", + "ãĤģ ãĤĭ", + "à¹Ĥ à¸ģ", + "ĠT á»ķ", + "Ġh akk", + "ر Ùģ", + "ìł Ģ", + "Ñģ об", + "ãģª ãģijãĤĮãģ°", + "Ùĩ ÙĪ", + "Ġë² ķ", + "ãĤ Ĩ", + "ĠاÙĦس عÙĪد", + "Ġ×IJ תר", + "Ø§Ø º", + "Ġ׾ ×ĵ", + "à¹ģ à¸ķ", + "à¹ģà¸ķ à¹Īà¸ĩ", + "íĮ Į", + "Ñĥп иÑĤÑĮ", + "à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī", + "×ij ת×Ļ", + "à¹ĩ à¸ģ", + "ÅĤ at", + "Ġê°ľ ìĿ¸", + "ìłķ ë³´", + "ÑĤ ал", + "Ġgü ven", + "ĠÄ° l", + "Ġê° ģ", + "Ġب ت", + "×ŀ ×ķ׳×Ķ", + "ĠاÙĦØŃ ÙĥÙĪÙħ", + "ÙĤ ات", + "à¹ģ à¸ģà¹Ī", + "ห าà¸ģ", + "н ÑĮ", + "à¸Ľ รัà¸ļ", + "มา à¸ĵ", + "Ġне Ñģк", + "ĠØ ¶", + "สม ั", + "สมั à¸Ħร", + "ãģĮ ãģĤãĤĬ", + "м еÑģÑĤ", + "Ġ×IJ צ׾", + "Ġкомп ани", + "ס ר", + "ÙĬÙħ Ø©", + "ĠÑħ оÑĢо", + "ĠÑħоÑĢо ÑĪ", + "Ġ×Ļ ×ķ×ĵ", + "ü s", + "×Ĵ ×Ļש", + "à¸ļ à¸Ĺ", + "تÙĨ ظ", + "ว าà¸ĩ", + "ม หา", + "Ġ׼ ×ķ׾", + "à¸Ĥ à¹īาà¸ĩ", + "ë° ľ", + "г од", + "д ан", + "ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ", + "ãģĵ ãģ¡ãĤī", + "ãĥIJ ãĤ¤", + "ece ÄŁi", + "دÙĬ دة", + "ÙĨ Ùī", + "Ġëĭ¤ ìĿĮ", + "ว ี", + "غ ا", + "ли з", + "à¹Ģà¸Ķ ิ", + "à¹Ģà¸Ķิ ม", + "ĠÙĬ ست", + "Ġy ılı", + "ko ÅĦ", + "ãģ§ãģĹãĤĩãģĨ ãģĭ", + "ãģĤ ãģª", + "ãģĤãģª ãģŁ", + "ÑĨ ен", + "ĠÙĪ ز", + "×IJ ×Ļש", + "à¹Ī à¸Ń", + "ر ØŃ", + "ê´ ij", + "ÑĢа ÑģÑĤ", + "Ġ×Ķ ׾", + "ãģĹãģ¦ ãĤĤ", + "×ŀר ׼", + "×ŀר׼ ×ĸ", + "éģķ ãģĦ", + "ãģŁ ãģı", + "ĠÑģ Ñĥд", + "в еÑģÑĤи", + "ĠíķĦ ìļĶ", + "ãĥķ ãĤ§", + "ÑĤелÑĮ но", + "à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ", + "ÅĤu ż", + "à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ", + "ש ×ķר", + "Ġ×ŀ ×ĵ", + "×ķ×¢ ׾", + "ÙĦ اÙħ", + "à¹Ħ à¸ĭ", + "л ей", + "кÑĥ ÑĢ", + "Ạ¢", + "à¸Ĺ าà¸Ļ", + "ì§ ij", + "ĠгоÑĢ од", + "ר ס", + "׾ ×ķ×Ĵ", + "mas ını", + "Ġл ÑĥÑĩ", + "ล à¹Īา", + "ìļ ¸", + "ש ×ĺ", + "ĠÐĺ н", + "í Ĥ¤", + "ÙĪÙĦ ا", + "ìķ ł", + "ĠØ£ÙĬ ضا", + "Ùĥ ار", + "ĠاÙĦت ع", + "ส ูà¹Ī", + "ãĤ ¼", + "×ij ×Ļ×IJ", + "ย à¸ģ", + "ĠØŃ ÙĤ", + "ر بÙĬ", + "ãģĺãĤĥ ãģªãģĦ", + "รัà¸ģ ษา", + "Ñħод иÑĤ", + "à¸ķ à¸Ńà¸ļ", + "׳ ×ĺ×Ļ", + "ĠاÙĦÙħ ج", + "تÙħ ع", + "ов аÑĤÑĮ", + "ÙĦ ÙĬÙĨ", + "×Ļ×ŀ ×ķת", + "Ġm ù", + "n ÄĻ", + "Ġد ÙĬ", + "׼ ש×Ļ×ķ", + "Ġhi ç", + "ë ijIJ", + "ÙĪ اء", + "ÙĪ Ø·", + "ĠاÙĦ بÙĦ", + "à¹ģม à¹ī", + "ק ×ķת", + "ÙĪج د", + "å§ĭ ãĤģ", + "ÙĬ ئة", + "Ġë§ ¤", + "ص بØŃ", + "פ ×IJ", + "г оÑĢ", + "ס ×Ķ", + "بÙĬ ÙĤ", + "ย าà¸ģ", + "Ġн ад", + "ÙĬ Ùij", + "Ġب ÙĪ", + "ס ×ķר", + "Ùħ ÙĥاÙĨ", + "ר ×ij", + "×Ĵ ×ĸ", + "צ ת", + "b ilit", + "л аг", + "ĠN go", + "×IJ ×ķר", + "à¸ķ à¸Ļ", + "íĬ ¹", + "à¸Ĺีà¹Ī à¸Ķี", + "à¸Ľà¸£à¸° à¸Īำ", + "ов ание", + "ãģĦ ãģ¤", + "ãĥĥãĤ¯ ãĤ¹", + "åIJĪ ãĤı", + "åIJĪãĤı ãģĽ", + "×Ļ׳ ×ķ×Ļ", + "ạ y", + "Ø« ÙĤ", + "ĠпÑĢ об", + "ĠпÑĢоб лем", + "ÅŁ eh", + "ÅŁeh ir", + "ع ادة", + "اÙĨ ÙĪÙĨ", + "à¸ķัว à¹Ģà¸Ńà¸ĩ", + "ì¶ ķ", + "ı lan", + "б ан", + "ãĥ³ ãĥī", + "à¸Ī ี", + "Ġ×Ķש ׳×Ļ", + "п оÑĤ", + "×ķ׾ ×Ļ×Ŀ", + "ล ัà¸ļ", + "ĠÑį ÑĤи", + "×ijק ש", + "ë¹Ħ ìĬ¤", + "à¸Ńยà¹Īาà¸ĩ à¹Ħร", + "×Ļ׾ ×Ļ", + "à¹ĥà¸Ĭ à¹Ī", + "ĠاÙĦ ÙĥÙĦ", + "ãĥļ ãĥ¼ãĤ¸", + "ص Ø©", + "ÑĤи ÑĢ", + "ãĤĵ ãģ©", + "зÑĭ к", + "wy ż", + "Ùĩ ÙĬ", + "ĠÙħ ÙĦÙĬ", + "Ġвид е", + "ظ اÙħ", + "دا ÙĪÙĦ", + "×ŀ ת×Ļ", + "Ġs ık", + "à¹Ģà¸ķิ ม", + "ãĤ¢ ãĤ¤", + "ка Ñħ", + "צ ×Ļ׾", + "à¹Ģà¸Ĭ à¹Īà¸Ļ", + "м аг", + "маг аз", + "магаз ин", + "à¸Ľ ั", + "à¸Ľà¸± à¸Ī", + "Ġש ×Ļר×ķת", + "ีย ม", + "ãĥĸ ãĥ«", + "Ġد ÙĪÙĦ", + "קר ×Ļ×Ŀ", + "Ùĩ Ùı", + "ов о", + "Ġü ret", + "د ÙĪÙĨ", + "à¹ģà¸Ļ ว", + "à¹Ģà¸Ļ ืà¹īà¸Ń", + "ĠÑĦ оÑĤ", + "ãĥ ĺ", + "ãģ¤ ãģĭ", + "Ñı Ñģ", + "ĠíķĺëĤĺ ëĭĺ", + "ائ ع", + "Ġп лаÑĤ", + "ìĺ Ī", + "Ġdost ÄĻp", + "ÙĪج Ùĩ", + "Ġ×Ķ ×Ĺ×Ļ", + "׳ ×Ļק", + "д ей", + "í ĽĦ", + "ı y", + "بØŃ Ø±", + "à¹Ģส ริม", + "Ġ׾ ×Ĵ", + "Ø°Ùĩ ب", + "ج ÙĬÙĦ", + "رÙĥ ز", + "Ġë ħ", + "Ġëħ ¸", + "פ×Ļ׾ ×ķ", + "ãģ¾ ãģļ", + "iri ÅŁ", + "ĠÙĥ ÙĬÙģ", + "Ġ×ij צ", + "Ġêµ IJ", + "ÑĢоÑģ Ñģ", + "ĠØ´ ÙĬ", + "Ġiç er", + "×Ĵ ×ķ×ij×Ķ", + "мен но", + "×¢ ×ij×Ļר", + "×ķ×ŀ ×Ķ", + "ãĤī ãģĹãģĦ", + "ãģ ¼", + "Ñī ин", + "è²· ãģĦ", + "جÙħÙĪع Ø©", + "Ġdön em", + "Ġ×ij ×IJר", + "в еÑģÑĤ", + "×ķר ×ķת", + "س Ùģ", + "à¹ģà¸Ĺ à¸Ļ", + "Ġд окÑĥменÑĤ", + "Ġا ÙĬ", + "ج اÙĨ", + "צ×ķ×¢ ×Ļ", + "ĠоÑģ об", + "ĠاÙĦÙħ س", + "ÑĢаР±", + "à¸ł ู", + "à¸Ķ าว", + "л екÑĤ", + "ع ÙĤ", + "×ķ×ĵ ×ķת", + "Ġol u", + "Ġolu ÅŁtur", + "ãģ¾ ãģ¾", + "ед ин", + "à¹Ģ à¸Ńà¸ģ", + "ãĤµ ãĤ¤", + "ëĦ Ī", + "Ø· ÙĨÙĬ", + "Ø· ÙĤØ©", + "ĠÐł аз", + "ÙĦ Ùij", + "Ñĩ ем", + "Ġ׾ ×ĺ", + "สั à¹Īà¸ĩ", + "سر ائÙĬÙĦ", + "Ġפר ×ĺ×Ļ", + "д еÑģÑĮ", + "Ġ׳ ׼", + "اÙĨ ب", + "ÙĬا Ø©", + "Ùħ بر", + "Ġk ı", + "à¸Ľ à¸ı", + "à¸Ľà¸ı ิ", + "à¸ļั à¸ķิ", + "׳ ת×Ļ", + "ìĨ ¡", + "ر اب", + "à¹ĥ à¸ķ", + "à¹ĥà¸ķ à¹ī", + "×Ļ׳ ת", + "ÙĪ ÙĬر", + "Ġ×Ķ×ŀ ×Ļ", + "ей ÑĩаÑģ", + "ק ×ķ×ij", + "در اس", + "ĠÙħ ÙĤ", + "رÙĬ ÙĨ", + "Ø® اص", + "ãģĬ éĩij", + "Ġج دا", + "ãģĨ ãģ¡", + "ëħ ¸", + "ır ım", + "æ§ ĺ", + "ãģ« å¯", + "ãģ«å¯ ¾", + "ÑĨ ев", + "Ġv ard", + "ĠÐIJ н", + "e ÄŁ", + "ÑģÑĤв енно", + "Ð ¨", + "س د", + "à¸ģ ุ", + "à¹ģà¸ľ à¸Ļ", + "รูà¹ī ส", + "รูà¹īส ึà¸ģ", + "ات ØŃاد", + "Ñij ÑĤ", + "×Ĺ ×ķק", + "ãģĻ ãģIJ", + "Ø· ÙĦاÙĤ", + "Ġק ×ķ×ĵ", + "à¹ĥà¸Ĭ à¹īà¸ĩ", + "à¹ĥà¸Ĭà¹īà¸ĩ าà¸Ļ", + "ãĥ¼ãĤ ¿", + "Ġs ür", + "ÑĢ ок", + "ë³ ij", + "สมา à¸Ĭ", + "สมาà¸Ĭ ิà¸ģ", + "ãĥķ ãĥ¬", + "è¾¼ ãģ¿", + "ãĤ» ãĥ³", + "Ġê°Ģ ì§Ģ", + "à¸ľ à¹īา", + "ÑįÑĤ омÑĥ", + "иÑĤ ел", + "à¸ł ั", + "ภij", + "ãĥĸ ãĥ©", + "×Ľ×ª ×ķ×ij", + "׳ ×Ŀ", + "ен нÑĭе", + "×¢ ×¨×Ľ×ª", + "Ġì Ĥ", + "ĠìĤ ´", + "à¸Ĥ à¹īา", + "׳ ×ķס", + "ãĥ¬ ãĥĵ", + "ÑĢ еÑģ", + "à¹Ģล à¸Ĥ", + "Ø« اÙĦ", + "ìĹ Ĩ", + "ĠÑĩ аÑģÑĤ", + "า ศ", + "ãĥª ãĤ¢", + "u ç", + "×Ļ׼ ×ķת", + "ล à¹īาà¸Ļ", + "i ë", + "ãĤ¸ ãĤ§", + "à¸Ī à¸Ń", + "ÙĪ ØŃد", + "×Ļצ ×ķ×ij", + "Ġ×ij ש׾", + "ок о", + "ض Ø©", + "Ø° ر", + "ĠÑĥ д", + "Ä° L", + "×ķצ ×Ļ×Ŀ", + "×ĸ ×ŀף", + "à¸Ľ à¸ģ", + "íķĻ êµIJ", + "س اÙħ", + "à¹Ħ à¸Ķ", + "ละ à¹Ģà¸Ń", + "ละà¹Ģà¸Ń ีย", + "ละà¹Ģà¸Ńีย à¸Ķ", + "ả y", + "аÑĨи он", + "ãĤ¹ ãĤ¯", + "פ ×ķס", + "ร à¹Īาà¸ĩ", + "ен нÑĭй", + "ع ÙĨ", + "عÙĦ ÙĨ", + "ائ Ùģ", + "d ÄĻ", + "ؤ ÙĪÙĦ", + "׾×ķ ×ķ", + "Ġ×ij ש×ij", + "ä»Ĭ åĽŀ", + "ĠاÙĦج ÙĨ", + "د اد", + "wa Äĩ", + "ãĥª ãĥ³", + "ĠìŀIJ ìĭł", + "اÙĨ ÙĬا", + "ãĥ¡ ãĥª", + "ÙĦ ÙĪÙĨ", + "à¸Ĺ à¹Īà¸Ńà¸ĩ", + "à¸Ĺà¹Īà¸Ńà¸ĩ à¹Ģà¸Ĺีà¹Īยว", + "اÙģ ÙĬ", + "Ġли ÑĪ", + "Ùħ ÙĬØ©", + "оÑĤ веÑĤ", + "Ñĩ ин", + "à Ĭ", + "ãĥ¡ ãĥ³", + "å® Ł", + "éļĽ ãģ«", + "ĠÑĢаР¹", + "ãĤ¦ ãĥ³", + "×Ļר ×ķש", + "×Ļר×ķש ׾×Ļ×Ŀ", + "ม ะ", + "Ġar a", + "каз аÑĤÑĮ", + "à¸ķ ัà¸Ķ", + "ÑĥÑİ ÑĤ", + "Ġü st", + "×Ĵ ×ķ×ij", + "×Ĵ×ķ×ij ×ķת", + "mal ı", + "ег од", + "егод нÑı", + "اÙģ ÙĤ", + "à¸Ĭ à¹Īà¸Ńà¸ĩ", + "Ġö zellik", + "×Ļצ ×ķר", + "Ġmi ÄĻd", + "Ġili ÅŁ", + "Ġна Ñħод", + "×¢ ×ĸר", + "׾ ×Ľ×ª", + "ÙĨت اج", + "ĠÑģ ем", + "à¸Ī à¹Īาย", + "à¸ķร ว", + "à¸ķรว à¸Ī", + "פר ×ķ", + "à¸Ĥ ัà¸ļ", + "ãģ ŀ", + "Ġп ло", + "к олÑĮ", + "×ŀ×¢ ×ĺ", + "íķĺ ìĭľ", + "jÄħ ce", + "ÙĨ اÙĨ", + "ลี à¸ģ", + "н ÑĥÑĤ", + "Ġоб ÑĢаз", + "Ùĥ بر", + "ĠاÙĦÙĪ Ø·ÙĨ", + "ãģķãģĽ ãģ¦", + "ÙĤ اء", + "×ŀ×ĵ ×Ļ׳", + "y ü", + "פ ×Ļת", + "׳ ×ķף", + "ÙħÙĨ ظ", + "หà¸Ļ ัà¸ģ", + "ìŀ Ī", + "ãĤ« ãĥ¼ãĥī", + "ع ÙĨÙĬ", + "п од", + "ض اء", + "à¸Ļ à¸ķà¹Į", + "×ŀש פ", + "ว à¹Į", + "ר ×ķק", + "ส ืà¹Īà¸Ń", + "פק ×Ļ×ĵ", + "ãģªãĤī ãģªãģĦ", + "ĠìŬ 룬", + "ÙĦ ج", + "Ñī иÑĤ", + "ãĥĥ ãĤ·", + "ÙĦÙĬ س", + "ĠÙĦ Ùħا", + "ìł ij", + "×ij ×Ļף", + "ãĥģ ãĤ§", + "Ġgü ç", + "Ġch ứ", + "×ķצ ×IJ", + "קר ×ij", + "à¹Ĥ à¸ŀ", + "оÑĩ но", + "סק ×Ļ", + "ש׾ ×Ŀ", + "صر Ùģ", + "ĠL Ãł", + "×¢ ×Ļת", + "á» ·", + "à¹Ĥ à¸Ńà¸ģ", + "à¹Ĥà¸Ńà¸ģ า", + "à¹Ĥà¸Ńà¸ģา ส", + "Ġ×Ķ ×ĵ×ijר", + "à¸Ļั à¹Īà¸Ļ", + "ز ر", + "нак о", + "íļ į", + "ãĤĤ ãģ¡", + "ãĤĤãģ¡ ãĤį", + "ãĤĤãģ¡ãĤį ãĤĵ", + "اÙħ ت", + "عد اد", + "и нÑĭ", + "ÅĤy w", + "à¸Ħ à¸ĵะ", + "à¸Ĺ ะ", + "kt ör", + "×Ļ×Ĺ ×Ķ", + "Ġм е", + "Ġме ÑģÑı", + "׳×Ķ ×Ĵ", + "ĠÑģ ÑĥÑīеÑģÑĤв", + "à¸Ļ ัà¸Ļ", + "ÑĦ ÑĦ", + "ек ÑĤив", + "عÙĦÙĪÙħ ات", + "б Ñĥд", + "à¸Ļัà¸ģ à¸ĩาà¸Ļ", + "หà¸Ļà¹īา à¸Ĺีà¹Ī", + "ÙĤÙĬ ÙĤ", + "ãĤ· ãĥ³", + "ãģ« éĸ¢", + "×IJר ×Ĵ", + "ĠпÑĢ оÑĤ", + "ĠпÑĢоÑĤ ив", + "ĠìŀĪ ìĸ´", + "ÙĤÙĬ ÙĤØ©", + "ìĹ ĩ", + "k ür", + "ãģ«ãģªãĤĬ ãģ¾ãģĹãģŁ", + "Ġде ÑıÑĤ", + "ĠдеÑıÑĤ елÑĮ", + "פ×ķר ×ĺ", + "à¸Ł à¹īา", + "à¹Ģ à¸ł", + "ĠавÑĤом аÑĤ", + "×ĸ ×Ļק", + "Ġold uk", + "ع اÙħ", + "ĠÑĤ оÑĢ", + "yrı ca", + "ê Ì", + "ãĤŃ ãĥ³ãĤ°", + "ãģ« ãģ¨ãģ£ãģ¦", + "à¹Ģà¸ī à¸ŀ", + "à¹Ģà¸īà¸ŀ าะ", + "ãģ¯ ãģļ", + "×ŀ ×IJ×Ļ", + "สะ à¸Ķ", + "สะà¸Ķ วà¸ģ", + "ìľ¼ ë©°", + "à¸ģ ี", + "ภ¬", + "Ġ×¢ ×ķש", + "à¸łà¸² ษา", + "à¸Ĺ ัà¸Ļ", + "ac akt", + "acakt ır", + "اع دة", + "ĠÑĥÑģл Ñĥг", + "ס ר×ĺ", + "×ķ×ŀ ×ķת", + "×Ķ ×ķר", + "×ŀ ×ķ×ij", + "×ŀ×ķ×ij ף", + "سÙĬ اس", + "اتÙģ اÙĤ", + "×Ķ צ׾", + "Ùħؤ س", + "Ġp ó", + "Ġк ни", + "×Ļ׼ ×ķ׾", + "à¹Ģหล ืà¸Ń", + "׼׾ ׼", + "׳ ×ĸ", + "ÑĪи е", + "r ès", + "ĠاÙĦØŃ ÙĤ", + "лÑı ÑĢ", + "ห à¸į", + "หà¸į ิà¸ĩ", + "ר×Ĵ ×Ļש", + "à¹Ģส à¹īà¸Ļ", + "ש×ij ×ķף", + "ô tel", + "ап ÑĢ", + "апÑĢ имеÑĢ", + "اب ÙĦ", + "ĠÑĢаз виÑĤ", + "Ġп олÑĮз", + "ĠС еÑĢ", + "×ķ×ij ×Ļ", + "r óż", + "ìĭ Ń", + "ãĤ¯ ãĥĪ", + "ãģĹ ãĤĪãģĨ", + "à¸ģร ม", + "ØŃ ÙĥÙĪÙħ", + "à¹Ĥ à¸ļ", + "à¸Ĺ à¹īาย", + "ĠM á", + "ĠÑĤ Ñĭ", + "à¸Ħร ัว", + "ÑĢÑĥ б", + "ạ p", + "Ġm ÅĤ", + "ĠmÅĤ od", + "Ġgör Ã¼ÅŁ", + "Ġgeli ÅŁ", + "Æ°Æ¡ i", + "×ŀש ק", + "ÙĢÙĢ ÙĢÙĢ", + "รา ว", + "ãģĹãģ £", + "ãģĹãģ£ ãģĭãĤĬ", + "ĠÐļ он", + "Ġk ê", + "à¹Ĥà¸Ĺ ร", + "èIJ½ ãģ¡", + "åĩº ãģ¦", + "ล ัà¸ģษ", + "Ġ×Ĵ ×ij×ķ×Ķ", + "ãĥĻ ãĥ«", + "ê±° ëĤĺ", + "ë§ IJ", + "×Ļ׾ ×ĵ×Ļ×Ŀ", + "ĠëĦ Ī", + "×ŀר ×Ļ", + "ร ส", + "ãĥŃ ãĥ³", + "и ло", + "ноÑģÑĤÑĮ Ñİ", + "×ĸר ×Ĺ", + "п он", + "Ġ×Ķש ׾", + "ê²ł ìĬµëĭĪëĭ¤", + "Ġki ÅŁ", + "ĠÐļ и", + "ว ร", + "د اع", + "ÅŁ im", + "ÙĨ Ùij", + "в аÑĤ", + "را Ùĥ", + "ب اÙĦ", + "ид е", + "Ġ×Ķ×ŀ ×Ĺ", + "ìĸ µ", + "تÙģ اع", + "Ø£ ت", + "ëĬ ĺ", + "ש ×Ļת", + "ست Ùħر", + "ĠÑĦ ак", + "ĠاÙĦØ£Ùħ رÙĬ", + "ëŀ ¨", + "اس Ùħ", + "Ġa ÄŁ", + "Ġç ev", + "Ùĥ ÙĪر", + "ãģķ ãģ¾", + "Ġç öz", + "Ġر س", + "Äħ da", + "สà¸Ļ ุ", + "ãģĹãģ¦ ãģıãĤĮ", + "н Ñİ", + "leÅŁ me", + "ãĤª ãĥ³", + "ãģ¨ ãģªãĤĬ", + "ava ÅŁ", + "×ĺ ×Ļ×ij", + "ØŃ Ø¶", + "×ķצ ×IJ×ķת", + "ÙĨ ÙħÙĪ", + "ı t", + "ĠÑħ а", + "ĠÑħа ÑĢак", + "ĠÑħаÑĢак ÑĤеÑĢ", + "Ġd ÅĤ", + "ãĥĹ ãĥ©", + "à¸Ĭ ุม", + "à¹Ī à¸Ńà¸Ļ", + "×ķ×ij ׾", + "Ñģ ол", + "×ĵ ×Ĵ", + "аÑĢ аÑĤ", + "n ivers", + "Ġgerçek leÅŁtir", + "ĠاÙĦ ÙĦÙĬ", + "ระ ยะ", + "ĠÙħ ختÙĦÙģ", + "Ġgö nder", + "Ùģ ار", + "do ÄŁ", + "doÄŁ an", + "ص ÙĦاØŃ", + "Ġyay ın", + "ãĥĨ ãĥ³", + "รว à¸Ī", + "×Ļ×Ĺ ×Ļ×ĵ", + "ünk ü", + "ÑĨи алÑĮн", + "à¸ļ ู", + "ม ุ", + "h ä", + "Ø® Ùģ", + "å¢ Ĺ", + "å¢Ĺ ãģĪ", + "еÑĩ но", + "ĠاÙĦس ÙĨ", + "à¸Ĥ าว", + "im di", + "Ð «", + "à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ", + "à¸ļา ล", + "ת ש", + "Ġdüzen le", + "мÑĭ Ñģл", + "ãģı ãģª", + "ż u", + "Ġwsp óÅĤ", + "Ġн аз", + "ınd aki", + "تر Ø©", + "ÅŁ ek", + "Ġö d", + "ĠÙĪ Ùĥ", + "Ġпозв олÑı", + "Ġת ×ķ׼", + "ÙħÙĨ تج", + "ë§ ī", + "ĠاÙĦØ« ÙĦاث", + "аÑĨи Ñİ", + "ÙĪر ÙĪ", + "Ñĭв аеÑĤ", + "خص ص", + "ĠاÙĦÙģ ÙĦ", + "ĠاÙĦÙģÙĦ سطÙĬÙĨ", + "Ø¥ جر", + "إجر اء", + "اÙĨت Ø®", + "اÙĨتخ اب", + "ار ÙĬØ©", + "×ķ Ö", + "Ø¢ ÙĨ", + "×ŀ×¢ ×ķת", + "Ġм ал", + "Ġ×IJ ×Ĺ", + "à¸Ĺ à¹īà¸Ńà¸ĩ", + "ze ÅĽ", + "Ġë§Į ëĵ¤", + "رÙĬ ع", + "äºĭ ãĤĴ", + "à¸ļริ หาร", + "׾ ×ŀ×Ļ×ĵ", + "Ġм Ñĥж", + "ت رÙĪ", + "ĠباÙĦ Ø¥", + "פ ×Ļק", + "ز ÙħØ©", + "ĠÃ¶ÄŁ renc", + "ãĥ ¶", + "اÙħ عة", + "ק×ij ×ķצ", + "×ŀ ׳×ķת", + "رÙĬ Ùħ", + "Ġо каз", + "ãģłãģij ãģ©", + "Ġh ız", + "Ġש ×IJת", + "ãĤ¢ ãĥ¼", + "Ġmożli wo", + "ìĦ ¼", + "ÙĪ اب", + "ог ÑĢаÑĦ", + "Ġعبد اÙĦ", + "ãĤĴ è¡Į", + "ب ÙĬÙĦ", + "ĠÄ° ç", + "ย าย", + "ĠÑĥ ÑĩаÑģÑĤ", + "ÑĦ еÑģÑģ", + "ÑĦеÑģÑģ иона", + "Ạ¤", + "ÙĨ ÙĬÙĨ", + "عد ÙĦ", + "สร ร", + "دÙĬ ÙĦ", + "×ij ×Ļק", + "czy ÅĤ", + "ÑĢом е", + "Ġм ед", + "ìĻ Ķ", + "ãĥ© ãĤ¤ãĥ³", + "ĠÑĤ еп", + "еÑĢ ÑĮ", + "i ÄŁi", + "в ели", + "ÑĢи ÑģÑĤ", + "ס ×ķפ", + "×ŀ׾ ×Ĺ", + "ĠاÙĦØ¥ ÙĨ", + "Ġ׾×Ķ ש", + "è¶Ĭ ãģĹ", + "ĠÑĢ Ñĭ", + "×ķ×IJ ר", + "رÙĩ اب", + "פ ×ķ×IJ×Ļ", + "ĠгоÑģ Ñĥд", + "ĠгоÑģÑĥд аÑĢ", + "ĠгоÑģÑĥдаÑĢ ÑģÑĤв", + "ĠاÙĦØ£Ùħ ÙĬر", + "Ùħ ج", + "à¹Ģหม าะ", + "ÑĢ ев", + "à¸Ĭี à¸ŀ", + "ãĥķ ãĥĪ", + "иÑĩ но", + "ĠاÙĦÙħ ؤ", + "Ġi ht", + "íħ ľ", + "د ÙĨÙĬ", + "ر ص", + "ла ÑģÑĤ", + "à¹Ģหล à¹Īา", + "ılı r", + "ร à¸ĵà¹Į", + "×ŀש ×Ļ×ļ", + "Ġd á»ĭ", + "Ø·Ùģ اÙĦ", + "×ĺ ×ķף", + "Ġ×ij ×Ļ׳", + "ãģ¾ ãģ£ãģŁ", + "лож ениÑı", + "تØŃ Ø±", + "ب اØŃ", + "à¹Ģส ืà¹īà¸Ń", + "ãģĻ ãģĶ", + "lt ür", + "à¸ĩ าม", + "Ġt ü", + "ĠпÑĢ им", + "ĠпÑĢим ен", + "Ġhay at", + "ëĥ IJ", + "ëĭ Į", + "׳×Ļ ×ķ", + "вед ен", + "ìħ ¨", + "à¸Ī ัย", + "à¸ģà¹Ī à¸Ń", + "Ġв од", + "оÑģÑĤ оÑı", + "н аÑĤ", + "à¹ģ หล", + "سÙħ ÙĬ", + "à¸Ķำ à¹Ģà¸Ļ", + "à¸Ķำà¹Ģà¸Ļ ิà¸Ļ", + "w ód", + "ö yle", + "ãĥĢ ãĤ¤", + "ÑĪи й", + "меÑī ен", + "ãģĹãģ¾ ãģĨ", + "ãĥī ãĥ©", + "ÙĪض ØŃ", + "à¸Ńà¸Ļ ุ", + "ĠاÙĦ اجتÙħاع", + "laÅŁ ma", + "à¸Ħ à¸Ńà¸Ļ", + "×ŀר ×Ļ×Ŀ", + "ÙĨ اÙħج", + "שר ×ķת", + "اÙĦ Ø£", + "Ġksi Äħż", + "Ġа н", + "ÑĢаР¹", + "اÙĩر Ø©", + "×ŀ×ĵ ×Ķ", + "ä¸Ģ ç·", + "ä¸Ģç· Ĵ", + "ä¸Ģç·Ĵ ãģ«", + "ÑĢиÑĤ оÑĢ", + "d ıkl", + "à¹ģ à¸ĸ", + "à¹ģà¸Ĥ à¹Īà¸ĩ", + "екÑĤ оÑĢ", + "×ŀס ×¢", + "ÑĢак ÑĤи", + "u ÄŁu", + "×ķ×ij ת", + "สู à¸ķร", + "ĠçalÄ±ÅŁ m", + "ĠçalÄ±ÅŁm alar", + "Ġа на", + "ãĥĽ ãĥ¼ãĥł", + "Ġböl üm", + "Ġب ص", + "ол оÑģ", + "ĠìķĬ ëĬĶ", + "à¹Ī ะ", + "ÙĪ تر", + "ä¹ Ĺ", + "ست خداÙħ", + "פ×Ļ ×Ļס", + "פ×Ļ×Ļס ×ij", + "פ×Ļ×Ļס×ij ×ķק", + "Ġк ÑĢаÑģ", + "ли к", + "رÙĬ ØŃ", + "×ŀש ׾×Ķ", + "à¹Ģย ีà¹Īย", + "à¹Ģยีà¹Īย ม", + "в иÑģ", + "ом н", + "ÄŁ un", + "ãĥŃ ãĥ¼ãĥ³", + "Ø£ تÙĬ", + "à¸ķร ี", + "çĶ³ ãģĹ", + "تÙħ ر", + "ìĹ ĪìĬµëĭĪëĭ¤", + "ĠÙĪ غÙĬر", + "red ni", + "ĠاÙĦص Ùģ", + "Ġна ÑģÑĤоÑı", + "ĠнаÑģÑĤоÑı Ñī", + "à¸ķ รา", + "ĠÑĥÑģл ов", + "ĠÑĥÑģлов иÑı", + "ÑĨ еп", + "×Ķ ×Ĺ׾×ĺ", + "Ø· ÙĬع", + "ĠB akan", + "ĠاÙĦ رÙĪ", + "илÑĮ но", + "Ġм еÑĤ", + "à¸Ķ à¸Ńà¸ģ", + "ãģĭãĤī ãģªãģĦ", + "Ġпо ÑģÑĤоÑı", + "ĠпоÑģÑĤоÑı н", + "ĠÑĩ аÑģ", + "ü c", + "wr ó", + "б ÑĥÑĢ", + "ãĥIJ ãĥĥãĤ¯", + "ãĥ©ãĥ³ ãĥī", + "Ġо гÑĢ", + "สั à¸į", + "สัà¸į à¸įา", + "มั à¹Īà¸Ļ", + "à¸Ħ à¸Ńม", + "al ık", + "Ġн ед", + "üm üz", + "ĠÅĽ wie", + "é rio", + "×Ļ×IJ ×Ķ", + "دÙħ ات", + "ı rl", + "ĠоÑĤ з", + "ĠоÑĤз Ñĭв", + "ä»ĺ ãģį", + "Ġkaż de", + "мин иÑģÑĤ", + "ãĤ° ãĥ«", + "ë° ĸ", + "ез н", + "اÙĦ Ùģ", + "Ġש ק׾", + "Ùħ ض", + "ãĥĿ ãĥ¼ãĥĪ", + "ÙħÙĨ ت", + "ÙĤÙĬ اÙħ", + "Ø´ ÙĨ", + "×Ļר ×ķ×¢", + "ãĤŃãĥ£ ãĥ³", + "доÑĢ ов", + "×ŀ ×Ļת×Ļ", + "ÙĪÙĦ ÙĪج", + "Ùĥ اÙģ", + "ĠÑĢаз лиÑĩ", + "иÑĤ еÑĤ", + "н олог", + "ลà¸ĩ à¸Ĺุà¸Ļ", + "Ġyak laÅŁ", + "ãĥ¬ ãĤ¤", + "ê²ł ëĭ¤", + "æ±Ĥ ãĤģ", + "رÙĪ Ùģ", + "Ġí Ĭ", + "ĠíĬ ¹", + "ãģ£ ãģıãĤĬ", + "à¸Ħวาม à¸Ħิà¸Ķ", + "×Ķ ×Ļס×ĺ", + "Ø¥ ÙĤ", + "ãģ¦ ãģĦ", + "à¹Ĥ à¸Ĭ", + "ĠBü yük", + "ĠФ едеÑĢ", + "ÑĨи н", + "ÑĢов а", + "ĠاÙĦ اÙĤتصاد", + "Ġch á", + "à¸ĺ าà¸Ļ", + "ë¥ ł", + "à¹Ħ à¸ķ", + "ÃŃ pio", + "Ùĭ ا", + "Ġоб Ñıз", + "Ùĩ ج", + "Ġì¤ij ìļĶ", + "ãģ® ãģ§ãģ¯ãģªãģĦ", + "بار اة", + "ãĤ¤ ãĥ«", + "Ġн оÑĢм", + "á»ī nh", + "m ö", + "mö glich", + "ÑĨи п", + "ãĤ¢ ãĤ¯", + "×Ķ ×Ļ", + "ÑĨи алÑĮно", + "ĠÅĽ wi", + "ت ÙĤ", + "ĠÑģÑĤо им", + "بÙĬ عÙĬ", + "Ġ׾ ש×ŀ", + "г лÑı", + "глÑı д", + "ãģ¦ ãģıãĤĮ", + "ÄĻd zi", + "à¸Ĥ ั", + "à¸Ĥั à¹īà¸Ļ", + "Ø· ÙĤ", + "ĠìĹ Ń", + "ãģ£ãģ¦ãģĹãģ¾ ãģĨ", + "ĠdeÄŁer l", + "ĠdeÄŁerl endir", + "Ġü lk", + "Ġмн ог", + "๠ĭ", + "ë¿ IJ", + "ĠУ кÑĢа", + "ÄŁ ini", + "Ġбез оп", + "Ġбезоп аÑģ", + "à¸Ńà¸Ńà¸ģ à¹ģà¸ļà¸ļ", + "Ø§Ø ¸", + "ØŃد اث", + "л еÑĢ", + "×Ļ× ¥", + "×Ļ׳×ĺר ׳×ĺ", + "lar ınız", + "ØŃÙĬ ØŃ", + "ż eli", + "à¸Ń ัà¸ĩ", + "à¸Ńัà¸ĩ à¸ģ", + "à¸Ńัà¸ĩà¸ģ ฤษ", + "ĠоÑĤ лиÑĩ", + "ั ส", + "ëŀ į", + "ож но", + "ãĤ¹ ãĥĿ", + "ĠÑħ оÑĩ", + "Ġк ап", + "еÑĩ ен", + "ØŃÙĦ Ø©", + "ÙĬا Ùĩ", + "на л", + "×ķצ ר×Ļ×Ŀ", + "Ġk ald", + "åĥ į", + "ĠاÙĦØ´ خص", + "Ġз на", + "Ġwz gl", + "ż ycz", + "ê° Ŀ", + "à¸ŀ ลัà¸ĩ", + "íģ ¼", + "Ġö l", + "Ġb ụ", + "Ø´ Ùĩر", + "Ġз ам", + "Ġд ев", + "×Ļ×ĺ ת", + "تعÙĦ ÙĤ", + "ÙĪÙħ Ø©", + "ãĤĴ ä½ľ", + "ãģį ãģ¦", + "í ĥĿ", + "ras ında", + "ãĤĴ æİ¢", + "ĠÙħ باشر", + "راج ع", + "Ġв озд", + "ÙħØŃ Ø§", + "×ķש ר", + "ĠиÑģÑĤ оÑĢ", + "ม ัà¸ģ", + "t ıģ", + "Ø« ار", + "تر ÙĨت", + "à¹ģà¸Ĥ à¹ĩ", + "à¹ģà¸Ĥà¹ĩ à¸ĩ", + "п оÑĩ", + "Ġ×ij ×IJ×ķת", + "ë¯ Ģ", + "ëĿ¼ ëıĦ", + "à¸Ĭ ัà¸Ķ", + "ส à¸ķà¹Į", + "ãĥĭ ãĥĥãĤ¯", + "ид енÑĤ", + "Ġг ÑĢÑĥпп", + "ت Ø®", + "Ạł", + "ย ืà¸Ļ", + "ย ัà¸Ļ", + "ó ry", + "T Ãľ", + "ãģĹ ãĤĥ", + "ĠпÑĢов ед", + "лÑı еÑĤ", + "Ùħ Ø®", + "ย à¸Ńม", + "×Ľ×ł×¡ ת", + "ĠاÙĦÙħ ÙĨت", + "Ġol mad", + "ר׼ ×ĸ×Ļ", + "Ġв ÑģÑĤÑĢ", + "ĠиÑģ Ñģлед", + "ÑĤвеÑĢ ж", + "بد ÙĪ", + "еÑĢ ÑĤ", + "ï» ·", + "± ħ", + "สัม à¸ŀัà¸Ļà¸ĺà¹Į", + "ิ à¹Īà¸Ļ", + "צ ×Ļ×ij", + "wiÄĻ t", + "Ġì° ¸", + "Ġz wiÄħz", + "سب ÙĪع", + "ãĥĥ ãĤ°", + "à¸Ľà¸¥ à¸Ńà¸Ķ", + "à¸Ľà¸¥à¸Ńà¸Ķ à¸łà¸±à¸¢", + "ãĤĤ ãĤĬ", + "ÙĤد س", + "Ġspr z", + "Ġsprz eda", + "Ġist edi", + "Ġk hu", + "Ġд ен", + "Ġko ÅĦ", + "Ġ×ij ×Ĺ×Ļ", + "à¹Ģà¸Ĺ à¹īา", + "×ķס ×Ļ×£", + "ãĥĭ ãĥ¥ãĥ¼", + "ĠпÑĢед оÑģÑĤ", + "ĠпÑĢедоÑģÑĤ ав", + "à¹Ĥ à¸Ł", + "é v", + "ĠاÙĦص ØŃ", + "صØŃ Ø§Ø¨", + "à¹Ģà¸Ī à¹ĩà¸ļ", + "вл ек", + "วั à¸ķ", + "à¸ĸ ุ", + "ãģĵãģ¨ãģĮãģ§ãģį ãģ¾ãģĻ", + "ÙĤÙĬ ÙĤÙĬ", + "×ķ׊ר", + "Ñĭ ÑĪ", + "ĠоÑĤ но", + "ĠоÑĤно ÑĪ", + "об илÑĮ", + "Ùģ ØŃ", + "ı nt", + "ınt ı", + "Ġ׾ ×ij×ĵ", + "í İĺìĿ´ì§Ģ", + "ãĥĬ ãĥ«", + "ĠÙħ ساء", + "×Ļ×ĺ ×ij", + "ÑĮ еÑĢ", + "ëĦ ·", + "Ñĭ ÑĤа", + "ĠоÑĩ еÑĢ", + "à¸Ķ ืà¹Ī", + "à¸Ķืà¹Ī ม", + "ĠN gh", + "ت عب", + "ÙĦاÙĤ ات", + "×ķ׾×ķ×Ĵ ×Ļ×Ķ", + "ĠìĿ´ ê²ĥ", + "Ġ×Ķ ×ijר", + "ìľ µ", + "à¹Ģà¸Ħล ืà¹Īà¸Ńà¸Ļ", + "Ùĩ Ø©", + "à¸Īำ à¹Ģà¸Ľà¹ĩà¸Ļ", + "å¤ī ãģĪ", + "wi ÅĽcie", + "ch od", + "chod zÄħ", + "в ÑĢо", + "×ŀ×Ĺ ×Ļר", + "Ġy ı", + "Ġyı ll", + "ì¡ Į", + "à¹Ħ หว", + "ãģªãģı ãģª", + "Ġзав иÑģ", + "ĠìĺĪ ìĪĺ", + "Ùģ Ø°", + "ủ ng", + "à¸ŀุ à¸Ĺà¸ĺ", + "з н", + "lay an", + "ãĤ ¡", + "à¸ģà¹ĩ à¸ķาม", + "ĠsaÄŁ lam", + "ร à¸ĵ", + "ĠÑģ иÑĤ", + "ĠÑģиÑĤ Ñĥ", + "ĠاÙĦت ÙĨ", + "×Ķ ×ĸ", + "ĠØ· ÙĪÙĬÙĦ", + "ta ÅĤ", + "Ġgö rd", + "å¤ī ãĤı", + "ëĥ ¥", + "à¸Ħà¹Ī à¸Ńย", + "×IJ ×ķ×ĺ", + "ëħ IJ", + "ãĥ©ãĥ³ ãĤ¹", + "วั à¸Ĵ", + "วัà¸Ĵ à¸Ļ", + "Ġol uÅŁ", + "פע ×ķ׾", + "Ġszczeg óÅĤ", + "à¸Ħา สิ", + "à¸Ħาสิ à¹Ĥà¸Ļ", + "pow ied", + "ĠÑĤ еб", + "หà¸Ļ à¹Īวย", + "Ġм ил", + "ØŃ Ùĥ", + "à¸Ĺ à¸Ķ", + "ĠмаÑĤ еÑĢиал", + "ÅĤ ow", + "à¹Ģà¸ģ ีย", + "ĠÑģов еÑĢ", + "ãĤ ©", + "à¸Ľ ริ", + "Ġи Ñİ", + "наÑĩ ен", + "ÑĢен д", + "mu ÅŁtur", + "ĠпÑĢод Ñĥк", + "з д", + "Ñı ÑĤи", + "ÑıÑĤи Ñı", + "à¹Ģม ีย", + "رات ÙĬج", + "Ġam acı", + "ש ×ķ׾", + "ש×ķ׾ ×Ĺ", + "สะ à¸Ńา", + "สะà¸Ńา à¸Ķ", + "פ×Ĵ ×¢", + "عب Ø©", + "d ın", + "íħ Ķ", + "Ġ×ŀש ×Ĺק", + "Ġfi yat", + "Ġз аÑı", + "ĠзаÑı в", + "à¹Ĥ หล", + "à¹Ĥหล à¸Ķ", + "à¸ģรุà¸ĩ à¹Ģà¸Ĺà¸ŀ", + "צ×Ļ ×Ļף", + "ìļ ±", + "Ùħ ب", + "Ùħب اد", + "land ır", + "Ġв еÑģÑĮ", + "Ġh ük", + "ĠÐĴ оз", + "ÑĩиÑĤ Ñĭва", + "ว ล", + "×ķצ ×¢", + "à¸Ĥà¸ĵะ à¸Ĺีà¹Ī", + "ĠaÅŁ aģı", + "׾×IJ ×ķ×ŀ×Ļ", + "tr zym", + "Ã¤ÃŁ ig", + "owo ÅĽci", + "ãģĿ ãĤĤ", + "Ġroz wiÄħz", + "ĠgÅĤ ówn", + "м онÑĤ", + "×ŀ ×ķ×ŀ", + "ĠÑģÑĤ ан", + "ÙĦا ÙĤØ©", + "p rowad", + "prowad zi", + "ĠÑģоÑģÑĤ оÑı", + "×Ļ×IJ ×ķת", + "r ı", + "g ı", + "ãĥij ãĥij", + "Ġна лиÑĩ", + "×Ķ צע", + "Ġ׳ ×Ķ", + "à¸Ħ ัà¸ļ", + "ع راض", + "и ж", + "Ùĩ ائÙĬ", + "ãĤī ãģı", + "ож еÑĤ", + "Ġоб оÑĢ", + "ĠобоÑĢ Ñĥд", + "Ø£ سÙĦ", + "à¹ĩ à¸Ķ", + "ÑĢÑĥ ÑĤ", + "دÙĬ ÙħÙĤ", + "دÙĬÙħÙĤ را", + "Ġjest e", + "×ķ×ķ ×Ļר", + "×ij×ĵ ×Ļק", + "деÑĢж ива", + "ãģĬ ãģı", + "ewn ÄĻtr", + "ewnÄĻtr zn", + "à¸ŀ ฤ", + "Ġ×IJ ×ķ×Ķ", + "ת×Ĺ ×ķש", + "Ġz ob", + "д Ñĥм", + "ĠÑģ Ñĭ", + "ÙĬر ا", + "ĠwiÄĻ ks", + "à¹ģà¸ķà¸ģ à¸ķà¹Īาà¸ĩ", + "lar aras", + "lararas ı", + "íĺ Ģ", + "ëī ´", + "×ķ×Ĵ ׾", + "ĠоÑĤ меÑĤ", + "ĠÑĢ ан", + "ت ÙĥÙĦ", + "иÑĤелÑĮ н", + "à¸Ľà¸£à¸° วั", + "à¸Ľà¸£à¸°à¸§à¸± à¸ķิ", + "ìŀ ĸ", + "мож но", + "pie czeÅĦ", + "pieczeÅĦ st", + "ëª »", + "ìĬ ¨", + "×ŀס ×ŀ", + "á» ¦", + "ศ ิ", + "ศิ ล", + "ศิล à¸Ľ", + "ĠÅļ w", + "ãĥĥ ãĤ·ãĥ§ãĥ³", + "unit Ãł", + "Ġmiesz ka", + "Ġmieszka ÅĦ", + "pr zed", + "przed si", + "przedsi ÄĻb", + "przedsiÄĻb ior", + "à¸Ľà¸£à¸° สิà¸Ĺà¸ĺิ", + "à¸Ľà¸£à¸°à¸ªà¸´à¸Ĺà¸ĺิ à¸łà¸²à¸ŀ", + "ย à¹Ī", + "ìķ Ļ", + "รว à¸Ķ", + "รวà¸Ķ à¹Ģรà¹ĩว", + "å½ĵ ãģŁãĤĬ", + "äl le", + "Ñĥ еÑĤÑģÑı", + "ã n", + "ëł µ", + "th è", + "ãĤĴ åĪ©çĶ¨", + "ì µľ", + "íĵ ¨", + "à¸Ĺ ัà¸ļ", + "า à¸Ħม", + "ãģ ĩ", + "ëĤ Į", + "à¹Ģà¸Ľà¸¥ à¹Īา", + "â ¦", + "ë ¾", + "ê Ģ", + "ê ĩ", + "â ¡", + "ðŁ Ł", + "ã IJ", + "â º", + "á Ń", + "á Ļ", + "á ĵ", + "á ²", + "ðĵ ı", + "á ¬", + "â ¯", + "ä ¨", + "ê Ŀ", + "ê «", + "ð ij", + "ðĵ ĥ", + "ðĿ ħ", + "< unk", + "", + "", + "", + "Ġع ÙĦÙī", + "Ġm á»Ļt", + "Ġv Ỽi", + "Ġng Æ°á»Ŀi", + "ĠØ¥ ÙĦÙī", + "Ġnh ững", + "Ġth á»ĥ", + "Ġ×IJ ×ķ", + "Ġ×¢ ×Ŀ", + "ا Ùĭ", + "Ġ à¹ģละ", + "ĠÙĦ ا", + "Ġnh Æ°", + "ĠاÙĦت ÙĬ", + "Ġ×Ķ ×ķ×IJ", + "ĠÄij ến", + "ĠØ£ ÙĪ", + "Ġv á»ģ", + "ĠlÃł m", + "Ġs ẽ", + "Ġc Å©ng", + "Ġ ợ", + "ĠÄij ó", + "Ġnhi á»ģu", + "Ġt ại", + "Ġtr ên", + "Ġ×Ĵ ×Ŀ", + "Ġnh Ãł", + "Ġ׼ ×Ļ", + "Ġs á»±", + "ĠÄij ầu", + "Ġb á»ĭ", + "ĠÙĩ ذا", + "Ġnh ất", + "Ġph ải", + "Ġhi á»ĩn", + "Ġdụ ng", + "ĠÄij á»Ļng", + "ĠاÙĦÙĦ Ùĩ", + "ĠØ Į", + "ĠÙĥ ÙĦ", + "Ġvi á»ĩc", + "Ġn Äĥm", + "Ġth ì", + "Ġh á»įc", + "ĠÙĪ ت", + "t é", + "Ġا ÙĨ", + "Ġt ôi", + "Ġ×IJ ׳×Ļ", + "Ġ׾ ×Ļ", + "Ġ×ŀ ×ķ", + "Ġng Ãły", + "Ġn Æ°á»Ľc", + "Ġ×Ķ ×Ļ×IJ", + "Ġ×IJ ×Ļ", + "Ġh Æ¡n", + "ĠÙĩ Ø°Ùĩ", + "ĠÙĪ ÙĬ", + "ĠاÙĦ Ø°ÙĬ", + "Ġ×ķ ×ŀ", + "Ġgi á", + "Ġnh ân", + "Ġch ÃŃnh", + "Ġm ình", + "ĠÐĿ а", + "Ġth ế", + "Ġ×Ļ ×ķתר", + "Ġ×IJ ×Ŀ", + "Ġn ên", + "Ġh ợ", + "Ġhợ p", + "Ġc òn", + "ĠÙĩ ÙĪ", + "Ġc Æ¡", + "Ġr ất", + "ĠVi á»ĩt", + "Ġب عد", + "Ġש ×Ļ", + "Ġth á»Ŀi", + "Ġc ách", + "ĠÄij á»ĵng", + "Ġн о", + "Ġtr Æ°á»Ŀng", + "Ø Ł", + "ĠÄij á»ĭnh", + "ĠÄiji á»ģu", + "×Ļ ×Ļ×Ŀ", + "Ġth á»±c", + "n ın", + "Ġh ình", + "Ġn ói", + "Ġc ùng", + "Ġ×Ķ ×Ķ", + "ĠØ¥ ÙĨ", + "Ġ×IJ ×ij׾", + "Ġnh Æ°ng", + "Ġbi ết", + "Ġж е", + "Ġch úng", + "ĠÄij ang", + "ĠØ° ÙĦÙĥ", + "Ġl ên", + "Ġkh ách", + "Ġn Ãło", + "Ġs á»Ń", + "Ġkh ác", + "Ġë° ı", + "Ġl ý", + "×Ļ ×Ļ", + "ĠÄij ây", + "Ġ׾ ×ŀ", + "Ġc ần", + "Ġtr ình", + "Ġph át", + "ãģ« ãĤĤ", + "п о", + "Ġn Äĥng", + "Ġb á»Ļ", + "Ġv ụ", + "ĠÄij á»Ļ", + "Ñĩ е", + "Ġnh áºŃn", + "Ġtr Æ°á»Ľc", + "Ġ×¢ ×ĵ", + "Ġh Ãłnh", + "ĠØ® ÙĦاÙĦ", + "Ġl ượng", + "Ġc ấp", + "Ġtá» ±", + "Ġv ì", + "Ġt Æ°", + "Ġch ất", + "Ġ׼ ×ŀ×ķ", + "Ġg ì", + "Ġש ׳", + "Ġt ế", + "ת ×ķ", + "Ġnghi á»ĩp", + "Ġm ặt", + "ĠÙĥ Ùħا", + "Ġ×ij ×Ļף", + "Ġר ק", + "Ġth ấy", + "Ġmá y", + "ĠÙģ Ùī", + "Ġd ân", + "Ġ×IJ ×Ĺ×ĵ", + "Ġt âm", + "Ġ׼ ×ļ", + "Ġ׾ ×ķ", + "в о", + "Ġt ác", + "Ġto Ãłn", + "ĠÙĪ Ùħ", + "Ġk ết", + "Ġ หรืà¸Ń", + "ĠÙĪاÙĦ Ùħ", + "ĠÄiji á»ĥm", + "Ġ×ĸ ×ķ", + "Ġ×ij ×ķ", + "׼ ×ķת", + "Ġh á»Ļi", + "Ġb ằng", + "ت Ùĩا", + "Ġ׼ ×ĵ×Ļ", + "Ġ×Ķ ×Ŀ", + "Ġxu ất", + "ĠÙĤ د", + "Ġb ảo", + "Ġt á»ijt", + "Ġt ình", + "ĠÙĩ ÙĬ", + "ĠÄij á»iji", + "Ġthi ết", + "Ġhi á»ĩu", + "Ġti ếp", + "Ġt ạo", + "ת ×Ķ", + "Ġch ủ", + "o ÅĽÄĩ", + "Ġgi ú", + "Ġgiú p", + "Ġà ½", + "Ġqu ả", + "Ġlo ại", + "Ġc ô", + "Ġà ´", + "Ġô ng", + "Ġ×Ķ ×ķ", + "ĠاÙĦÙĬ ÙĪÙħ", + "ĠtÃŃ nh", + "г а", + "Ġph òng", + "Ġ Äĥn", + "Ġع اÙħ", + "Ġv á»ĭ", + "lar ını", + "r ÃŃa", + "Ġt Ỽi", + "ĠÄij Æ°á»Ŀng", + "Ġgi Ỽi", + "Ġb ản", + "Ġc ầu", + "Ġnhi ên", + "Ġb á»ĩnh", + "Ġth Æ°á»Ŀng", + "Ġ×IJ ×Ļף", + "ĠÄij á»ģ", + "Ġh á»ĩ", + "Ġ×Ļש ר×IJ׾", + "Ġqu á", + "ĠÐĹ Ð°", + "ãģ® ãģ§ãģĻãģĮ", + "ĠÐŁ ÑĢи", + "Ġph ần", + "ĠÙĪ ÙĦا", + "ĠlỼ n", + "Ġtr á»ĭ", + "Ġcả m", + "Ġм о", + "Ġd ùng", + "ĠاÙĦ Ùī", + "ĠعÙĦÙĬ Ùĩ", + "ĠìŀĪ ìĬµëĭĪëĭ¤", + "ÙĬ ÙĤ", + "ĠÙĤ بÙĦ", + "Ġho ặc", + "ĠØŃ ÙĬØ«", + "Ġ à¸Ĺีà¹Ī", + "Ġغ ÙĬر", + "ĠÄij ại", + "Ġsá»ij ng", + "нÑĭ ми", + "Ġth ức", + "Ġפ ×Ļ", + "ĠÄiji á»ĩn", + "ãģª ãģĭãģ£ãģŁ", + "Ġgi ải", + "Ġv ẫn", + "Ġи Ñħ", + "Ġö nce", + "Ġv áºŃy", + "Ġmu á»ijn", + "Ġ ảnh", + "à¹ĥà¸Ļ à¸ģาร", + "ĠQu á»ijc", + "Ġk ế", + "׳ ×IJ", + "Ġס ×Ļ", + "Ġy êu", + "ãģ® ãģĭ", + "ĠÄij ẹ", + "ĠÄijẹ p", + "Ġch ức", + "Ġy ıl", + "ĠTür kiye", + "d é", + "ĠÙĤ اÙĦ", + "Ġd á»ĭch", + "ĠolduÄŁ u", + "Ġch á»įn", + "Ġت Ùħ", + "หà¸Ļ ึà¹Īà¸ĩ", + "ãģķãĤĮ ãģŁ", + "Ġph áp", + "ìĽ Ķ", + "Ġti á»ģn", + "ãģĹ ãģ¾ãģĹãģŁ", + "Ġש ׾×IJ", + "ÙĦ Ø©", + "Ġ׾פ ׳×Ļ", + "Ġ×ij ×Ļת", + "ĠH Ãł", + "ĠØŃ Øª", + "ĠØŃت Ùī", + "Ġ×¢ ×ķ×ĵ", + "Ġn ó", + "Ġth áng", + "à¹Ģลืà¸Ń à¸ģ", + "ר ×Ķ", + "Ġt Äĥng", + "Ġcá i", + "Ġtri á»ĥn", + "Ġ×IJ×ķת ×ķ", + "ìłģ ìĿ¸", + "ĠC ông", + "Ġ׾×Ķ ×Ļ×ķת", + "Ġг ода", + "и Ñİ", + "Ġب عض", + "Ġ à¸ģาร", + "èī¯ ãģĦ", + "ÙĪ ت", + "Ġli ên", + "ĠÐĿ о", + "ĠÐĿ е", + "çļĦ ãģª", + "ĠÙħ ت", + "ĠÑĤак же", + "ĠкоÑĤоÑĢ Ñĭе", + "Ġ×Ļ ×ĵ×Ļ", + "Ġtr á»įng", + "ãĤµ ãĤ¤ãĥĪ", + "ìłģ ìľ¼ë¡ľ", + "Ġt áºŃp", + "Ġש ׾×Ļ", + "íķĺ ê²Į", + "Ġt Ãłi", + "ĠÐ ¯", + "Ġr á»ĵi", + "ا Ùĥ", + "Ġth Æ°Æ¡ng", + "Ġ×Ķ ×ĸ×Ķ", + "ĠÙĪ ÙħÙĨ", + "à¸Ĺีà¹Ī มี", + "Ġcu á»Ļc", + "Ġbü yük", + "ãģ¨ ãģĭ", + "Ġ×ij ×Ļ×ķתר", + "Ġl ần", + "Ġgö re", + "Ġtr ợ", + "Ġ×ĺ ×ķ×ij", + "ÑĤÑĮ ÑģÑı", + "Ġth á»ijng", + "Ġ׼ ש", + "Ġti êu", + "Ġ×ŀ×IJ ×ķ×ĵ", + "Ø Ľ", + "k Äħ", + "Ġ à¹ĥà¸Ļ", + "Ġv ấn", + "Ġש ׾×ķ", + "ĠÄij á»ģu", + "Ùģ ت", + "Ġê²ĥ ìĿ´", + "Ġh óa", + "ĠاÙĦع اÙħ", + "ĠÙĬ ÙĪÙħ", + "к ой", + "Ġbi á»ĩt", + "ÑģÑĤ о", + "Ġ×Ķ ×Ļ×ķ", + "à¸Ĺีà¹Ī à¸Īะ", + "Ġ×ĵ ×Ļ", + "Ġ×IJ ×ļ", + "Ġá n", + "ص ÙĪر", + "Ġtr ÃŃ", + "ĠÐŁÑĢ о", + "Ġl á»±c", + "ãģĹãģ¦ ãģĦãģ¾ãģĻ", + "Ġb Ãłi", + "Ġ×ĸ ×IJת", + "Ġb áo", + "à¸ļ à¸Ļ", + "ĠëĮĢ íķľ", + "Ġti ế", + "Ġtiế ng", + "Ġb ên", + "ãģķãĤĮ ãĤĭ", + "s ión", + "Ġt ìm", + "×¢ ×ķ", + "m é", + "ни Ñı", + "ãģ» ãģ©", + "Ġà¹Ģà¸ŀ ราะ", + "ب Ø©", + "Ġë¶ Ħ", + "Ġ×IJ ×ĸ", + "à¸Ĺ à¹Īาà¸Ļ", + "ת ×Ŀ", + "Ġth êm", + "Ġho ạt", + "y ı", + "×ĸ ×ķ", + "Ġgi á»Ŀ", + "Ġb án", + "à¸Ĥ าย", + "Ñĩ а", + "Ġ à¹Ĩ", + "ĠاÙĦÙħ ت", + "ĠоÑĩ енÑĮ", + "Ġb ất", + "Ġtr ẻ", + "ÑĤ ÑĢ", + "ĠØ£ ÙĨÙĩ", + "ĠØ« Ùħ", + "Ġ׼ ×ŀ×Ķ", + "Ġkh ó", + "Ġr ằng", + "ĠÙĪ ÙģÙĬ", + "ни й", + "Ġho Ãłn", + "t ó", + "Ġ×IJ שר", + "ĠìĥĿ ê°ģ", + "Ñģ а", + "Ġ׼ ×ijר", + "ĠÑįÑĤ ом", + "lar ının", + "Ġch Æ°a", + "з и", + "Ġd ẫn", + "ĠÐļ ак", + "ج ÙĪ", + "ĠбÑĭ ло", + "ĠÙĬ ت", + "n ı", + "ÅĤ am", + "ĠÙĪÙĩ ÙĪ", + "×ij ×ķ", + "п и", + "ר ת", + "Ġqu á»ijc", + "ж д", + "ĠÄij Æ¡n", + "Ùĥت ب", + "Ġm ắt", + "ระ à¸ļ", + "ระà¸ļ à¸ļ", + "ĠÙĥ اÙĨت", + "Ġth ân", + "สิà¸Ļ à¸Ħà¹īา", + "×Ĵ ×Ļ", + "Ġph Æ°Æ¡ng", + "à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī", + "ĠìĦ ±", + "ĠC ác", + "Ġ×Ķ×ŀ ×ķ", + "ĠÑĤ ем", + "Ġ×ĵ ×ķ", + "à¸Ńะ à¹Ħร", + "Ġv Äĥn", + "ãģª ãģ®ãģ§", + "ĠN á»Ļi", + "Ġ×¢ ×ķ", + "ãĤīãĤĮ ãĤĭ", + "Ġs áng", + "Ġgö ster", + "ãģĵãģ¨ ãĤĴ", + "Ġtaraf ından", + "Ġм а", + "ĠпоÑģл е", + "Ġ׳ ×Ļת", + "Ġ׳×Ļת ף", + "Ġл еÑĤ", + "Ġ׾ ׳×ķ", + "Ñģ Ñģ", + "Ġ×Ļ ×ķ", + "п е", + "ĠÙĪ ÙĦÙĥ", + "ĠÙĪÙĦÙĥ ÙĨ", + "Ġngo Ãłi", + "ĠÄij á»ĭa", + "r zÄħd", + "dz iaÅĤ", + "ĠÙħ ر", + "иÑĤÑĮ ÑģÑı", + "Ġ×IJ×Ĺר ×Ļ", + "Ġ׾ ׼׾", + "à¸Ĥ à¹īà¸Ńม", + "à¸Ĥà¹īà¸Ńม ูล", + "Ġб ол", + "Ġбол ее", + "جÙħ ع", + "л еÑĤ", + "Ġl á»ĭch", + "ĠÙħ Ø«ÙĦ", + "Ġ그리 ê³ł", + "Ġth ứ", + "ĠdeÄŁ il", + "ÙĪ ØŃ", + "Ġש׾ ×ļ", + "ĠÙħ ØŃÙħد", + "Ġn ếu", + "ĠÄij á»ķi", + "Ġv ừa", + "Ġm á»įi", + "Ġо ни", + "Ġl úc", + "ĠÙĬ ÙĥÙĪÙĨ", + "ì§ Ī", + "Ġש׾ ׳×ķ", + "ĠÐĶ о", + "Ġש ׳×Ļ", + "ล ิ", + "×IJ פשר", + "Ġs ức", + "ê¶ Į", + "Ġ ứng", + "à¹Ħมà¹Ī มี", + "Ø·ÙĦ ب", + "ĠÑĩ ем", + "Ġch uyên", + "Ġth ÃŃch", + "Ġ×ķ ×Ļ", + "íķ ©", + "ĠÙħ صر", + "д о", + "ĠÄij ất", + "Ġch ế", + "à¸Ĭ ืà¹Īà¸Ń", + "Ġìĭ ł", + "ĠØ¥ ذا", + "Ġر ئÙĬس", + "Ġש ×Ļש", + "Ġgiả m", + "Ñģ ка", + "lar ında", + "Ġs ợ", + "ĠtÃŃ ch", + "ĠÙĦ ÙĥÙĨ", + "Ġب Ùħ", + "×¢ ×ķ×ij", + "×¢×ķ×ij ×ĵ", + "ÅĤÄħ cz", + "ları na", + "Ġש ×Ŀ", + "ĠÙĦ ت", + "Ġש×Ķ ×ķ×IJ", + "t ów", + "Ġëĭ¤ 른", + "ĠØ£ Ùĥثر", + "ãģ® ãģ§ãģĻ", + "׼ ×Ļ×Ŀ", + "ĠolduÄŁ unu", + "ãģĭ ãģª", + "ãĤĤ ãģĨ", + "ÙĬ ØŃ", + "Ġnh ìn", + "Ġngh á»ĩ", + "ãģ«ãģª ãģ£ãģ¦", + "п а", + "Ġquy ết", + "ÙĦ ÙĤ", + "t á", + "Ġlu ôn", + "ĠÄij ặc", + "Ġ×IJ ר", + "Ġtu á»ķi", + "s ão", + "ìĻ ¸", + "ر د", + "ĠبÙĩ ا", + "Ġ×Ķ×Ļ ×ķ×Ŀ", + "×ķ ×ķ×Ļ", + "ãģ§ãģĻ ãģŃ", + "ĠÑĤ ого", + "Ġth ủ", + "ãģĹãģŁ ãģĦ", + "ر ÙĤ", + "Ġb ắt", + "г Ñĥ", + "Ġtá» Ń", + "ÑĪ а", + "Ġ à¸Ľà¸µ", + "Ġ×Ķ×IJ ×Ŀ", + "íı ¬", + "ż a", + "Ġ×IJת ×Ķ", + "Ġn á»Ļi", + "Ġph ÃŃ", + "ĠÅŁek ilde", + "Ġl á»Ŀi", + "d ıģı", + "Ġ׼×IJ ף", + "Ġt üm", + "Ġm ạnh", + "ĠM ỹ", + "ãģĿ ãĤĵãģª", + "Ġnh á»ı", + "ãģª ãģĮãĤī", + "Ġb ình", + "ı p", + "à¸ŀ า", + "ĠÄij ánh", + "ĠÙĪ ÙĦ", + "ר ×ķת", + "Ġ×IJ ×Ļ×ļ", + "Ġch uyá»ĥn", + "Ùĥ ا", + "ãĤĮ ãĤĭ", + "à¹ģม à¹Ī", + "ãĤĪ ãģı", + "ĠÙĪ ÙĤد", + "íĸ Īëĭ¤", + "Ġn Æ¡i", + "ãģ«ãĤĪ ãģ£ãģ¦", + "Ġvi ết", + "Ġà¹Ģà¸ŀ ืà¹Īà¸Ń", + "ëIJĺ ëĬĶ", + "اد ÙĬ", + "ĠÙģ Ø¥ÙĨ", + "ì¦ Ŀ", + "ĠÄij ặt", + "Ġh Æ°á»Ľng", + "Ġx ã", + "Ġönem li", + "ãģł ãģ¨", + "Ġm ẹ", + "Ġ×ij ×Ļ", + "Ġ×ĵ ×ijר", + "Ġv áºŃt", + "ĠÄij ạo", + "Ġdá»± ng", + "ĠÑĤ ом", + "ĠÙģÙĬ Ùĩا", + "Ġج ÙħÙĬع", + "Ġthu áºŃt", + "st ÄĻp", + "Ġti ết", + "Ø´ ÙĬ", + "Ġе Ñīе", + "ãģĻãĤĭ ãģ¨", + "ĠmÃł u", + "ĠÑįÑĤ ого", + "Ġv ô", + "ĠÐŃ ÑĤо", + "Ġth áºŃt", + "Ġn ữa", + "Ġbi ến", + "Ġn ữ", + "Ġ׾ ׼×Ŀ", + "×Ļ ×Ļף", + "Ġس ت", + "ĠÐŀ ÑĤ", + "Ġph ụ", + "ê¹Į ì§Ģ", + "Ġ׾ ×ļ", + "Ġk ỳ", + "à¹ĥ à¸Ħร", + "Ġg ây", + "ĠÙĦ ÙĦÙħ", + "Ġtụ c", + "ت ÙĬÙĨ", + "Ġtr ợ", + "Ġ׾ פ×Ļ", + "Ġb á»ij", + "ĠÐļ а", + "ĠÄij ình", + "ow Äħ", + "s ında", + "Ġkhi ến", + "s ız", + "Ġк огда", + "ס ׾", + "ĠбÑĭ л", + "à¸Ļ à¹īà¸Ńย", + "обÑĢаР·", + "Ġê²ĥ ìĿ´ëĭ¤", + "ëĵ¤ ìĿĢ", + "ãģ¸ ãģ®", + "Ġà¹Ģม ืà¹Īà¸Ń", + "Ġph ục", + "Ġ׊׾ק", + "Ġh ết", + "ĠÄij a", + "à¹Ģà¸Ķà¹ĩ à¸ģ", + "íĺ ķ", + "l ÃŃ", + "ê¸ ī", + "Ġع دد", + "ĠÄij á»ĵ", + "Ġg ần", + "Ġ×Ļ ×ķ×Ŀ", + "Ġs Ä©", + "ÑĢ Ñıд", + "Ġquy á»ģn", + "Ġ×IJ ׾×IJ", + "Ùĩ Ùħا", + "׳ ×Ļ×Ķ", + "׾ ×ķת", + "Ġ×Ķר ×ij×Ķ", + "Ġti ên", + "Ġal ın", + "Ġd á»ħ", + "人 ãģĮ", + "но Ñģ", + "л ÑģÑı", + "ĠÄij Æ°a", + "ส าว", + "иÑĢов ан", + "Ġ×ŀס פר", + "×Ĵ ף", + "Ġki ến", + "ĠÐ ¨", + "p é", + "б Ñĥ", + "ов ой", + "б а", + "ĠØ¥ ÙĦا", + "×IJ ׾×Ļ", + "Ġx ây", + "Ġb ợi", + "Ġש ×ķ", + "人 ãģ®", + "ק ×Ļ×Ŀ", + "à¹Ģà¸Ķ ืà¸Ńà¸Ļ", + "Ġkh á", + "Ġ×ķ ׾×Ķ", + "×ĵ ×ķת", + "Ġ×¢ ×ij×ķר", + "Ġبش ÙĥÙĦ", + "ĠÙĩÙĨا Ùĥ", + "ÑĤ ÑĢа", + "Ġ íķĺëĬĶ", + "ร à¸Ńà¸ļ", + "owa ÅĤ", + "h é", + "Ġdi á»ħn", + "Ġ×Ķ ׼׾", + "ĠØ£ س", + "Ġch uyá»ĩn", + "ระ à¸Ķัà¸ļ", + "ĠNh ững", + "Ġ×IJ ×Ĺת", + "ĠØŃ ÙĪÙĦ", + "л ов", + "׳ ר", + "Ġ×ķ ׳", + "Ġch Æ¡i", + "Ġiç inde", + "ÑģÑĤв Ñĥ", + "Ġph á»ij", + "ĠÑģ Ñĥ", + "ç§ģ ãģ¯", + "Ġch ứng", + "Ġv á»±c", + "à¹ģ à¸Ń", + "Ġl áºŃp", + "Ġtừ ng", + "å°ij ãģĹ", + "ĠNg uy", + "ĠNguy á»ħn", + "ĠÙģÙĬ Ùĩ", + "Ġб а", + "×Ļ ×Ļת", + "Ġ×ľ×¢ ש×ķת", + "Ġ×ŀ ׼", + "Ġnghi á»ĩm", + "Ġм ного", + "Ġе е", + "ëIJĺ ìĸ´", + "Ġl ợi", + "Ġ׾ ׾×IJ", + "Ġ׼ ף", + "Ġch ÃŃ", + "ãģ§ ãģ®", + "×Ĺ ×ķ", + "ש ×ķ×Ŀ", + "Ġ×ŀ ר", + "ĠÐĶ лÑı", + "Å ģ", + "Ġ׼×IJ שר", + "ĠM á»Ļt", + "ĠÙĪاÙĦ ت", + "ĠìĿ´ 룰", + "ÅŁ a", + "Ġchi ến", + "Ġaras ında", + "Ġ×ij ×IJתר", + "ãģķãĤĮ ãģ¦ãģĦãĤĭ", + "Ø´ ÙĥÙĦ", + "Ġt ượng", + "Ġت ت", + "ĠC ó", + "Ġb á»ı", + "Ġtá»ī nh", + "Ġkh ÃŃ", + "ĠпÑĢ оÑģÑĤ", + "ĠпÑĢоÑģÑĤ о", + "ĠÙĪ ÙĤاÙĦ", + "Ġgi áo", + "ĠN ếu", + "×IJ ×ŀר", + "×¢×ł×Ļ ×Ļף", + "íİ ¸", + "Ùĩد Ùģ", + "ĠB á»Ļ", + "Ġb Ãłn", + "Ġng uyên", + "Ġgü zel", + "ส าย", + "ì² ľ", + "×ŀ ×ķר", + "Ġph ân", + "ס פק", + "ק ×ij׾", + "ĠاÙĦÙħ تØŃ", + "ĠاÙĦÙħتØŃ Ø¯Ø©", + "ائ د", + "Ġ×IJ ×ŀר", + "Ġki ÅŁi", + "ì¤ Ģ", + "Ġtr uyá»ģn", + "ĠÙĦ Ùĩا", + "ĠÐľ а", + "à¸ļริ ษ", + "à¸ļริษ ั", + "à¸ļริษั à¸Ĺ", + "Ġש ׳×Ļ×Ŀ", + "Ġмен Ñı", + "ÅŁ e", + "Ġdi á»ĩn", + "Ġ×IJ׳ ×Ĺ׳×ķ", + "k ü", + "Ġc á»ķ", + "Ġm á»Ĺi", + "w ä", + "Ùħ ÙĬ", + "Ġhi á»ĥu", + "ëĭ ¬", + "Ġ×Ķ ×Ĺ׾", + "Ġt ên", + "Ġki á»ĩn", + "ÙĨ ÙĤÙĦ", + "Ġv á»ĩ", + "×ĵ ת", + "ĠÐłÐ¾ÑģÑģ ии", + "л Ñĥ", + "ĠاÙĦع ربÙĬØ©", + "ĠØ· رÙĬÙĤ", + "Ġ×Ķ×ij ×Ļת", + "Ñģ еÑĢ", + "Ġм не", + "ä u", + "Ġtri á»ĩu", + "ĠÄij ủ", + "Ġר ×ij", + "ت ÙĩÙħ", + "à¸ĭ ี", + "Ġì§Ģ ê¸Ī", + "li ÅĽmy", + "د عÙħ", + "ãģł ãĤįãģĨ", + "Ñģки е", + "Ġh á»ıi", + "Ġק ×ķ", + "ÑĢÑĥ Ñģ", + "ÙĨ ظر", + "ãģ® ãĤĤ", + "Ġ×Ķ ׼×Ļ", + "ĠìĽ IJ", + "ÙĪ Ùĩ", + "ĠÙĪ Ùİ", + "ĠB ạn", + "п лаÑĤ", + "Ġ×ŀ ×ŀש", + "лÑİ Ð±", + "ĠнÑĥж но", + "Ġth Æ°", + "ãģ µ", + "ãģı ãĤīãģĦ", + "ر Ø´", + "ר ×ķ×Ĺ", + "ĠÙĬ تÙħ", + "Ġצר ×Ļ×ļ", + "Ġph á", + "ม à¸Ńà¸ĩ", + "Ġ×ij×IJ ×ķפף", + "Ġcả nh", + "Ġíķľ ëĭ¤", + "Ġ×Ķ×ŀ ת", + "à¸ķà¹Īาà¸ĩ à¹Ĩ", + "มี à¸ģาร", + "Ñģки Ñħ", + "ĠÐĴ Ñģе", + "Ġا ÙĪ", + "ج ÙĬ", + "ãģĵãģ¨ ãģ¯", + "Ġd Ãłi", + "Ġh á»ĵ", + "èĩªåĪĨ ãģ®", + "à¹Ħ หà¸Ļ", + "ëĵ¤ ìĿĦ", + "ĠV Äĥn", + "Ġд аж", + "Ġдаж е", + "Ñĭ ми", + "лаÑģ ÑĮ", + "ÙĬ ÙĪÙĨ", + "ÙĨ ÙĪ", + "c ó", + "ãģĹãģ¦ ãģĦãģŁ", + "ãģł ãģĭãĤī", + "طاÙĦ ب", + "Ġc á»Ńa", + "п ÑĢоÑģ", + "ãģªãģ© ãģ®", + "รุ à¹Īà¸Ļ", + "Ġchi ếc", + "л Ñĭ", + "ĠÑıвлÑı еÑĤÑģÑı", + "Ġn á»ķi", + "ãģ® ãģĬ", + "Ġ×IJת ×Ŀ", + "ĠëķĮ문 ìĹIJ", + "à¸ģล าà¸ĩ", + "ĠbaÅŁ ka", + "ìĦ Ŀ", + "ĠÑĨ ел", + "Ùģ ÙĤ", + "ãģ«ãĤĪ ãĤĭ", + "ÙĤ ا", + "Ġçı kar", + "Ġcứ u", + "Ø· ا", + "Ġש ת", + "à¹Ĥ à¸Ħ", + "Ġ×ŀ ׾", + "Ġ×Ķ פר", + "Ġг де", + "ĠØ® Ø·", + "åīį ãģ«", + "c jÄĻ", + "Ġ׊ש×ķ×ij", + "ר×Ĵ ×¢", + "Ġkho ảng", + "ĠÄij á»Ŀi", + "ĠÐł е", + "Ġо на", + "Ġ×IJ ׳×ķ", + "ãģ® ãģ«", + "ĠاÙĦØ° ÙĬÙĨ", + "кÑĥ п", + "ãĤµ ãĥ¼ãĥ", + "ãĤµãĥ¼ãĥ ĵ", + "ãĤµãĥ¼ãĥĵ ãĤ¹", + "в ал", + "г е", + "Ġgi ữa", + "ĠKh ông", + "ĠâĹ ĭ", + "à¸ģล ุà¹Īม", + "ĠÙħÙĨ Ø°", + "à¸Ń à¹Īาà¸Ļ", + "ĠÑģп оÑģоб", + "ĠÄij á»Ļi", + "Ġdi ÄŁer", + "Ġ à¸ĸà¹īา", + "Ùħ Ø«ÙĦ", + "Ġ×Ķ×IJ ×Ļ", + "Ġد ÙĪÙĨ", + "ÙĬر اÙĨ", + "Ñī и", + "بÙĨ اء", + "ĠØ¢ خر", + "ظ Ùĩر", + "Ġ×ij ׼", + "ĠاÙĦÙħ ع", + "ãĥ Ĵ", + "Ġt ất", + "Ġm ục", + "ĠdoÄŁ ru", + "ãģŁ ãĤī", + "Ġס ×ķ", + "Ġx ác", + "ร à¸Ń", + "ĠcÄĥ n", + "Ġон л", + "Ġонл айн", + "Ġk ý", + "Ġch ân", + "Ġ à¹Ħมà¹Ī", + "اØŃ Ø©", + "r án", + "׳×Ļ ×Ļ×Ŀ", + "Ġ×ij ף", + "ĠÐ ĸ", + "à¸ķร à¸ĩ", + "д Ñĭ", + "Ġs ắc", + "ÙĦ ت", + "ãĥŃ ãĥ¼", + "ĠÙĦ ÙĨ", + "Ġר ×ķ", + "Ġd Æ°á»Ľi", + "à¹Ģ à¸ĺ", + "à¹Ģà¸ĺ à¸Ń", + "e ÄŁi", + "Ġ×ķ ש", + "ĠÙĦ Ø£", + "Ġg ặp", + "Ġc á»ij", + "ãģ¨ ãģ¦ãĤĤ", + "رÙĪ س", + "Ġ׾×Ķ ×Ļ", + "Ġë³ ¸", + "ä¸Ĭ ãģĴ", + "Ġm ức", + "Ñħ а", + "Ġìŀ ¬", + "à¸ī ัà¸Ļ", + "ÑĢÑĥ ж", + "Ġaç ık", + "ÙĪ اÙĦ", + "Ġ×ĸ ×ŀף", + "人 ãģ¯", + "ع ÙĬÙĨ", + "Ñı Ñħ", + "Ġ×Ĵ×ĵ ×ķ׾", + "ר ×ķ×ij", + "g ó", + "ëĿ¼ ê³ł", + "Ġark adaÅŁ", + "ÙĨ شر", + "Ġгод Ñĥ", + "ĠболÑĮ ÑĪе", + "ãģ¡ãĤĩ ãģ£ãģ¨", + "Ġcâ u", + "Ġs át", + "íĶ ¼", + "Ġti ến", + "íķ´ ìķ¼", + "ĠÙĪ Ø£ÙĨ", + "à¸Ļ าà¸Ļ", + "Ġ×ij×IJ×ŀ צע", + "Ġ×ij×IJ×ŀצע ×ķת", + "Ġ׾ ר", + "Ġqu ản", + "ĠÙĪاÙĦ Ø£", + "Ġ×IJ×ķת ×Ķ", + "Ġìĸ´ëĸ ¤", + "Ġê²ĥ ìĿĢ", + "ØŃس ÙĨ", + "Ġm ất", + "à¸Ħ ูà¹Ī", + "ãĥ¬ ãĥ¼", + "ĠÐĶ а", + "Ġol ması", + "Ġthu á»Ļc", + "׳ ×Ĺ", + "íĨ ł", + "Ġsö yle", + "ãģĿãģĨ ãģ§ãģĻ", + "Ġت ÙĥÙĪÙĨ", + "л ÑĥÑĩ", + "׾ ×Ļ×ļ", + "ĠØ£ ØŃد", + "ли ÑģÑĮ", + "ĠвÑģ его", + "Ġ×Ķר ×ij", + "Ġëª »", + "o ÄŁ", + "oÄŁ lu", + "ĠìĦ ł", + "Ġк аÑĢ", + "à¸łà¸² à¸Ħ", + "e ÅĦ", + "Ġ à¸ģà¹ĩ", + "Ġa ynı", + "Ġb Ãł", + "ãģªãĤĵ ãģ¦", + "Ġ모 ëĵł", + "ÙĤر ار", + "ãģĹãģª ãģĦ", + "ĠÐĴ о", + "ĠÙĪÙĩ ÙĬ", + "ни ки", + "ãĤĮ ãģŁ", + "Ġchu ẩn", + "ר ×¢", + "Ùģ رÙĬÙĤ", + "ãĤĴ åıĹãģij", + "ĠÄij úng", + "б е", + "׼ ×ķ×Ĺ", + "п Ñĥ", + "Ġ×ķ ×Ĵ×Ŀ", + "×ŀ ׳×Ļ", + "íĸ ¥", + "צ ×Ļ×Ŀ", + "à¸ĭ ิ", + "Ùĩ ÙĨ", + "н ем", + "Ġ×ij×ij ×Ļת", + "ر ع", + "Ġ ส", + "ĠÄIJ Ãł", + "íķĺ ëĭ¤", + "Ġ ấy", + "×Ĺ ×ķ×ĵ", + "×Ĺ×ķ×ĵ ש", + "ĠÑĩеÑĢ ез", + "Ñĥ л", + "ĠB ình", + "Ġê²ĥ ìĿĦ", + "Ġ×Ĵ ר", + "ä»ĺ ãģij", + "×Ĺ׾ ק", + "Ġت ÙĦÙĥ", + "à¹ĥส à¹Ī", + "sz Äħ", + "ÙĤ اÙħ", + "د ÙĪر", + "ĠÙģ ÙĤØ·", + "Ġh ữu", + "Ġмог ÑĥÑĤ", + "Ġg á»įi", + "Ġק ר", + "à¸Īะ มี", + "ت ÙĤدÙħ", + "Ġع بر", + "Ġ׾×Ķ ×Ŀ", + "ĠÑģам о", + "ס ×ĵר", + "Ġc Ãłng", + "r ÃŃ", + "Ġìŀ ¥", + "ëĵ¤ ìĿĺ", + "ĠÙĦ Ùĥ", + "п оÑĢÑĤ", + "Ġkh ả", + "ĠÑģеб Ñı", + "׳ ף", + "Ġد ÙĪر", + "Ġm ợ", + "Ġcâ y", + "Ġf ark", + "Ġfark lı", + "а ÑİÑĤ", + "Ġtr á»±c", + "wiÄĻks z", + "Ġthu á»ijc", + "Ġت ØŃت", + "ت ÙĦ", + "ов Ñĭе", + "ëĤ ł", + "Ġв ам", + "بÙĦ غ", + "Ġê°Ļ ìĿĢ", + "íĮ IJ", + "ÙĦ ب", + "Ġnas ıl", + "Ġод ин", + "м ан", + "ĠعÙĦÙĬ Ùĩا", + "б и", + "Ġפ ש×ķ×ĺ", + "×ijר ×Ļ", + "Ġש ׳×Ķ", + "Ġëı Ħ", + "ĠÄIJ ại", + "Ġ×IJ×ķת ×Ŀ", + "ĠاÙĦØŃ Ø±", + "Ġб о", + "à¸Ī ุà¸Ķ", + "Ġr õ", + "ĠdeÄŁi ÅŁ", + "Ġëĭ ¨", + "ĠÑģлÑĥÑĩ а", + "ĠÑģлÑĥÑĩа е", + "Ġ×IJ׳ ש×Ļ×Ŀ", + "×ĵ ×£", + "ש×ij ת", + "Ġש׾ ׼×Ŀ", + "Ġch ú", + "nik ów", + "Ġtan ı", + "Ġcá o", + "ĠÄij á", + "Ġ×IJ ×ĵ×Ŀ", + "Ġê° ķ", + "Ġnhi á»ĩm", + "Ġ׾ ס", + "Ġ×Ľ×ª ×ij", + "Ġ×Ķס פר", + "ĠÄij Äĥng", + "Ġë ijIJ", + "à¸ľ ิ", + "à¸ľà¸´ ว", + "ج ا", + "Ġê° IJ", + "ر Ø£", + "ست خدÙħ", + "ãģ«ãģªãĤĬ ãģ¾ãģĻ", + "Ġtá» ·", + "×ĺ ×ķר", + "г овоÑĢ", + "Ġв оÑģ", + "ĠÙħÙĨ Ùĩا", + "иÑĢов аÑĤÑĮ", + "ĠÄij ầy", + "׳ ×Ĵ", + "ĠÙħ ÙĪ", + "ĠÙħ ÙĪÙĤع", + "ר׼ ×Ļ", + "ت Ùı", + "ëª ¨", + "Ġת ×ķ", + "ÙĬا Ùĭ", + "à¹ĥ à¸Ķ", + "ãĤĬ ãģ¾ãģĻ", + "à¸Ńยูà¹Ī à¹ĥà¸Ļ", + "ĠØ£ ÙĪÙĦ", + "ĠØ£ خرÙī", + "Ġc Æ°", + "ص ار", + "×ŀ׊ש×ij", + "б ÑĢа", + "ÅĦ ski", + "б ÑĢ", + "ĠÙĬ Ùı", + "à¸ģ ิà¸Ļ", + "Ġch á»ijng", + "Ùħ Ùı", + "Ġ à¸Ħืà¸Ń", + "Ġت ÙĨ", + "t ÃŃ", + "y Äĩ", + "Ġm ạng", + "Ùģ ÙĪ", + "Ġdü nya", + "ק ר×IJ", + "Ġק ׾", + "ĠØŃ Ø§ÙĦ", + "c ÃŃa", + "Ġà¹Ģ รา", + "Ġר ×ķצ×Ķ", + "Ġá p", + "ë° ķ", + "ا ÙĤØ©", + "ни Ñİ", + "Ġ×IJ ׾×ķ", + "Ġ×ŀס ×ķ", + "ãģ§ãģ¯ ãģªãģı", + "Ġtr ả", + "Ġק שר", + "mi ÅŁtir", + "Ġl Æ°u", + "Ġh á»Ĺ", + "ĠбÑĭ ли", + "Ġl ấy", + "عÙĦ Ùħ", + "Ġö zel", + "æ°Ĺ ãģĮ", + "Ġ×ĵ ר×ļ", + "Ùħ د", + "s ını", + "׳ ×ķש×IJ", + "r ów", + "Ñĩ еÑĢ", + "êµIJ ìľ¡", + "ĠÐľ о", + "л ег", + "ĠV Ỽi", + "วัà¸Ļ à¸Ļีà¹ī", + "ÑİÑī ие", + "ãģĬ ãģĻ", + "ãģĬãģĻ ãģĻ", + "ãģĬãģĻãģĻ ãĤģ", + "ëı ħ", + "Ġ×Ļ×Ķ ×Ļ×Ķ", + "×ŀ ×ĺר", + "Ñı ми", + "Ġl á»±a", + "ĠÄij ấu", + "à¹Ģส ียà¸ĩ", + "Ġt Æ°Æ¡ng", + "ëĵ ±", + "ĠÑģÑĤ аÑĢ", + "à¹ĥ à¸ļ", + "ว ัà¸Ķ", + "ĠÄ° stanbul", + "Ġ à¸Īะ", + "à¸ķ ลาà¸Ķ", + "Ġب ÙĬ", + "à¹ģà¸Ļ ะ", + "à¹ģà¸Ļะ à¸Ļำ", + "س اعد", + "Ġب Ø£", + "Ġki á»ĥm", + "ØŃ Ø³Ø¨", + "à¸Ĭั à¹īà¸Ļ", + "Ġ×ķ ×¢×ķ×ĵ", + "ов ÑĭÑħ", + "оÑģ нов", + "Ġtr Æ°á»Łng", + "צ ×ij×¢", + "ĠÃŃ t", + "Ġk ỹ", + "cr é", + "Ñı м", + "êµ °", + "ãģĮ ãģªãģĦ", + "ÙĬÙĦ Ø©", + "ãĥķ ãĤ£", + "ر Ùī", + "ĠÙĬ جب", + "Ġ×IJ ×£", + "Ġc á»±c", + "ãĤīãĤĮ ãģŁ", + "Ġ à¸ľà¸¹à¹ī", + "Ġ à¸Ń", + "lar ımız", + "Ġkad ın", + "Ġê·¸ ëŀĺ", + "Ġê·¸ëŀĺ ìĦľ", + "ĠëĺIJ ëĬĶ", + "ĠÄij ả", + "ĠÄijả m", + "Ġ×IJ ×ķ×ŀר", + "Ġy ếu", + "ci Äħ", + "ciÄħ g", + "Ġt á»ij", + "Ġש×IJ ׳×Ļ", + "Ġdz iaÅĤa", + "Ñī а", + "ĠÄij Ãłn", + "s ına", + "ãģĵãĤĮ ãģ¯", + "Ġ×ij ׾×Ļ", + "Ġ×ij ×Ļשר×IJ׾", + "л оÑģÑĮ", + "Ġgi ữ", + "ê° IJ", + "ÑĢ он", + "تج ار", + "г лав", + "в ин", + "Ġh ạn", + "Ġyapı lan", + "ب س", + "Ġ à¸ŀรà¹īà¸Ńม", + "ê´Ģ 리", + "mÄ±ÅŁ tır", + "b ü", + "r ück", + "ĠBaÅŁkan ı", + "ĠÙĦ ÙĬس", + "Ġs Æ¡", + "à¸Īัà¸ĩ หว", + "à¸Īัà¸ĩหว ัà¸Ķ", + "د اء", + "Ġ×Ķ ׼", + "v ÃŃ", + "ש ×IJר", + "Ġh Æ°á»Łng", + "Ġb óng", + "ĠCh ÃŃnh", + "Äħ c", + "à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ", + "Ġtá» ©", + "Ġtứ c", + "ĠÑĨ веÑĤ", + "Ġt á»iji", + "ĠnghÄ© a", + "ÙĦا عب", + "د ÙĦ", + "Ġפע ×Ŀ", + "h ör", + "à¸Ĭ ุà¸Ķ", + "à¸ŀ ู", + "à¸ŀู à¸Ķ", + "п аÑģ", + "ĠÅŁ u", + "Ġt Æ°á»Łng", + "خار ج", + "Ġâ m", + "ĠинÑĤеÑĢ еÑģ", + "ен нÑĭÑħ", + "×IJ ׳×Ļ", + "بد Ø£", + "ëĿ¼ ëĬĶ", + "ì¹ ´", + "æĸ¹ ãģĮ", + "ли в", + "Ġ à¸Ħà¸Ļ", + "ער ×ļ", + "à¸Ĥà¸Ńà¸ĩ à¸Ħุà¸ĵ", + "п ад", + "Ġc ạnh", + "ĠëĤ ¨", + "ĠÄij âu", + "Ġbi á»ĥu", + "ãĤĤ ãģĤãĤĭ", + "׾ ×Ĵ", + "Ġ สำหรัà¸ļ", + "Ġxu á»ijng", + "ס ×ķ", + "ĠØ° ات", + "ĠÐľ е", + "ع اÙĦÙħ", + "×IJ ס", + "ب ÙĬØ©", + "Ø´ ا", + "и ем", + "ĠNg Æ°á»Ŀi", + "íĺ ij", + "Ñģл ов", + "Ġп а", + "Ġm ẫu", + "ĠпÑĢоÑĨ еÑģÑģ", + "ĠNh Ãł", + "пÑĢо из", + "пÑĢоиз вод", + "à¸łà¸²à¸¢ à¹ĥà¸Ļ", + "Ġ à¸ļาà¸Ĺ", + "×ŀ ׳×ķ", + "ĠоÑĢг ан", + "רצ ×ķ", + "×ķ×ŀ ×Ļ×Ŀ", + "Ġyaz ı", + "Ġd ù", + "ãĥ¬ ãĥ³", + "ÙĪÙĦ ÙĬ", + "ย ู", + "Ġtr ò", + "à¹Ģà¸ŀ ลà¸ĩ", + "Ġ×ŀ ׾×IJ", + "à¸ķ ล", + "à¸ķล à¸Ńà¸Ķ", + "ĠÄij ạt", + "Ġ×Ĺ×ĵ ש", + "p óÅĤ", + "Ġ×ŀ ×ĵ×Ļ", + "ujÄħ c", + "×ŀ׳×Ķ ׾", + "Ġש×ij ×ķ", + "Ġ×Ķ×ŀש פ×ĺ", + "Ġ×IJ ׾×Ķ", + "ĠÙĪ Ø°ÙĦÙĥ", + "à¹Ģà¸ŀ ราะ", + "ĠÄijo Ãłn", + "Ġíķ¨ ê»ĺ", + "Ġd ục", + "Ø´ ت", + "Ġ ula", + "Ġula ÅŁ", + "Ġqu ý", + "Ġ×Ķ ×Ĵ×ĵ×ķ׾", + "à¸ķัà¹īà¸ĩ à¹ģà¸ķà¹Ī", + "Ġש ר", + "Ø´ Ùĩد", + "׳ ש×Ļ×Ŀ", + "à¸ŀ ล", + "رÙĪ ا", + "ãĤĮ ãģ¦", + "Ġн иÑħ", + "Ġдел а", + "ãģ§ãģį ãģªãģĦ", + "ÅĤo ż", + "×IJ ×Ĺר", + "ì ½Ķ", + "ãĤ¢ ãĥĥãĥĹ", + "د Ùģع", + "Ġti á»ĩn", + "Ġkh á»ı", + "Ġkhá»ı e", + "ĠاÙĦع اÙħØ©", + "ãģ« ãģĤãĤĭ", + "ĠÄij á»Ļc", + "ì¡ ±", + "Ġc ụ", + "й ÑĤе", + "Ġзак он", + "ĠпÑĢо екÑĤ", + "ìĸ ¸", + "ÙĦ ØŃ", + "ĠçalÄ±ÅŁ ma", + "ãĤĴ ãģĻãĤĭ", + "Ñħ и", + "ع اد", + "Ġ׳ ×ŀצ×IJ", + "Ġר ×Ļ", + "à¸Ńà¸Ńà¸ģ มา", + "ĠT ôi", + "Ġth ần", + "ĠÙĬ ا", + "ล าย", + "Ġав ÑĤо", + "Ġsı ra", + "ĠÙĥ Ø«ÙĬر", + "Ùħ ÙĬز", + "ĠاÙĦع ÙĦÙħ", + "æĸ¹ ãģ¯", + "×ķ×¢ ×ĵ", + "Ġобла ÑģÑĤи", + "×Ļ׾ ×Ļ×Ŀ", + "ãģĮ åĩº", + "à¸ĺ ุ", + "à¸ĺุ ร", + "à¸ĺุร à¸ģิà¸Ī", + "ÙĤت ÙĦ", + "ר×IJ ×ķ", + "Ġng u", + "Ġngu á»ĵn", + "Ġ มา", + "Ġпл ан", + "t ório", + "Ġcu á»iji", + "Ñģк ом", + "ĠاÙĦÙħ اض", + "ĠاÙĦÙħاض ÙĬ", + "Ġ×ij×¢ ׾", + "Ġר ×ij×Ļ×Ŀ", + "Ġlu áºŃn", + "Ùĥ ÙĪ", + "à¸Ĺัà¹īà¸ĩ หมà¸Ķ", + "в ан", + "Ġtho ại", + "à¹Ħ à¸Ń", + "б иÑĢ", + "ĠاÙĦ ض", + "ت ا", + "ĠÑĢ од", + "ĠV Ãł", + "×ŀ ×Ļף", + "ĠбÑĭ ла", + "к ами", + "ĠÐĶ е", + "t ık", + "קר ×Ļ", + "ĠeÄŁ itim", + "ĠÙĥ بÙĬر", + "ب Ùĥ", + "ĠÙĦ ÙĪ", + "в ой", + "Ġ ãģĵãģ®", + "ĠÑĤ ÑĢÑĥд", + "my ÅĽl", + "Ġs Æ°", + "à¸ŀ ีà¹Ī", + "Ġ à¹ģลà¹īว", + "×¢ ק", + "Ġ×Ĺ×ijר ת", + "ระ หว", + "ระหว à¹Īาà¸ĩ", + "×Ļ ×Ļ×Ķ", + "ĠاÙĦÙĨ اس", + "ün ü", + "Ġ׾ ×ŀ×Ķ", + "Ġch Æ°Æ¡ng", + "ĠH á»ĵ", + "ار ت", + "ãĤĪãģĨ ãģ§ãģĻ", + "l á", + "ק×Ļ ×Ļ×Ŀ", + "æľ¬ å½ĵ", + "æľ¬å½ĵ ãģ«", + "ãģĵãĤĵ ãģª", + "Ñģ ов", + "Ġ×ķ ×Ĺ", + "à¹Ģà¸ģ à¹ĩà¸ļ", + "Ġк ÑĤо", + "à¹Ĥร à¸Ħ", + "ĠØ´ رÙĥØ©", + "ع زÙĬ", + "عزÙĬ ز", + "Ø·ÙĦ ÙĤ", + "п ÑĥÑģÑĤ", + "Ùģ تØŃ", + "ëŀ Ģ", + "Ġhã y", + "ض Ùħ", + "ë¦ °", + "åł´åIJĪ ãģ¯", + "ãĤª ãĥ¼", + "Ġh ắn", + "Ġ×IJ ×ij×Ļ×ij", + "Ġש׾×Ķ ×Ŀ", + "Ġ×Ķ×Ļ ×Ļת×Ķ", + "ĠاÙĦد ÙĪÙĦØ©", + "ĠاÙĦ ÙĪÙĤ", + "ĠاÙĦÙĪÙĤ ت", + "ãģĤ ãģ¾ãĤĬ", + "Ġta ÅŁÄ±", + "Ä° N", + "×¢ סק", + "ãģ¦ ãģĦãģŁ", + "Ġtá»ķ ng", + "ĠاÙĦØ¥ ÙĨس", + "ĠاÙĦØ¥ÙĨس اÙĨ", + "ÑĢ еÑĪ", + "Ġg ái", + "ĠÑĨ ен", + "ĠÙģ ÙĤد", + "Ùħ ات", + "ãģķãĤĵ ãģ®", + "Ġph ù", + "×ĺ ×Ķ", + "ĠÙĪاÙĦ تÙĬ", + "Ġب Ùĥ", + "ìĿ´ ëĤĺ", + "к Ñģ", + "Ùħ ÙĬر", + "Ġv ùng", + "ĠاÙĦØ´ عب", + "ĠNh Æ°ng", + "ãĥĢ ãĥ¼", + "Ġ×Ĺ×Ļ ×Ļ×Ŀ", + "ĠØ´ خص", + "ק ×ķ×ĵ", + "ê² Ģ", + "×¢ ש", + "×¢ ×ķ׾×Ŀ", + "צ ×ķר", + "ع ÙĤد", + "ĠiÅŁ lem", + "Ġ×Ķ×ij ×IJ", + "Ġd ưỡng", + "à¸Ł รี", + "Ġph ÃŃa", + "ãģ®ä¸Ń ãģ§", + "Ġп и", + "Ġng Ãłnh", + "ним а", + "ĠÙĩ ÙĦ", + "Ġ×ķ ×IJת", + "ĠÄij áng", + "é quipe", + "ĠÑįÑĤ оÑĤ", + "Ġgö rev", + "ë§ ¤", + "Ġqu ân", + "å¼ķ ãģį", + "æĻĤ ãģ«", + "Ġب Ùħا", + "×ŀ ×Ļת", + "Ġü lke", + "Ġ×ŀק ×ķ×Ŀ", + "×ij ף", + "æ°Ĺ æĮģãģ¡", + "Ġë§İ ìĿĢ", + "Ġyük sek", + "ÑĨ енÑĤÑĢ", + "ĠÙħ جÙĦس", + "ç§ģ ãģ®", + "ÙĤد ر", + "Ġë¶Ģ ë¶Ħ", + "Ġì° ¨", + "خر ج", + "ãģĭ ãģªãĤĬ", + "ë³´ ëĭ¤", + "Ġ×ŀ ×Ļ×ĵ×¢", + "peÅĤ ni", + "Ġx á»Ń", + "ìĹIJìĦľ ëĬĶ", + "ĠباÙĦ Ùħ", + "ĠÙĪ Ùħا", + "ĠÑįÑĤ ой", + "ب ÙĬÙĨ", + "n ü", + "ØŃ Ø²", + "ØŃز ب", + "ĠÑĢабоÑĤ а", + "ĠNh áºŃt", + "ÙĦ اء", + "Ġëĵ ¤", + "Ġëĵ¤ ìĸ´", + "ãĤĦãģĻ ãģĦ", + "×Ĺ×ĸ ק", + "Ġ×Ķ×Ĺ ×ijר×Ķ", + "п иÑĤ", + "ãģĭãĤī ãģ®", + "Ġë§IJ ìĶĢ", + "Ġפ ×ķ", + "ÙĦ Ùİ", + "à¹Ģà¸ķà¹ĩ ม", + "ĠÐļ о", + "Ġm ówi", + "Ġt ÃŃn", + "ר×Ĵ ש", + "פר ק", + "Ġtr ạng", + "ĠÐŀ н", + "×Ĺ ×ķ×¥", + "ĠعÙĨد Ùħا", + "Ġب ر", + "使 ãģĦ", + "Ġr á»Ļng", + "ëĮĢ ë¡ľ", + "íĪ ¬", + "Ġktóry ch", + "в ид", + "ลูà¸ģ à¸Ħà¹īา", + "Ġmog Äħ", + "Ġש ×Ĺ", + "×ij ×Ĺר", + "ãĥĸ ãĥŃãĤ°", + "ĠTh Ãłnh", + "Ġ×Ķ ר×Ļ", + "ĠÑģÑĤ аÑĤÑĮ", + "ĠH á»Ļi", + "à¸ļ à¹īาà¸ĩ", + "çī¹ ãģ«", + "ĠÄIJ ức", + "èĢħ ãģ®", + "×¢ ×ŀ×ķ×ĵ", + "×ĺר ×Ķ", + "Ð ¥", + "ĠÙħ Ùħا", + "Ġe ÅŁ", + "ĠнеобÑħодим о", + "ник ов", + "Ġüzer inde", + "a ÅĤa", + "Ġchá»ĭ u", + "ĠاÙĦ دÙĬÙĨ", + "أخ بار", + "ĠÄij au", + "ãģĮ å¤ļãģĦ", + "jÄħ cych", + "د Ø®ÙĦ", + "ları nd", + "larınd an", + "Ġs ẻ", + "à¸ŀิ à¹Ģศ", + "à¸ŀิà¹Ģศ ษ", + "ת ף", + "t ıģı", + "Ġlu áºŃt", + "ĠÅŀ e", + "ãĤ« ãĥ¼", + "ãģ® ãģĤãĤĭ", + "Ġ×Ķ×IJ תר", + "ĠاÙĦØ¢ ÙĨ", + "ıld ı", + "Ġá o", + "ĠнаÑĩ ал", + "Ġvi á»ĩn", + "Ġ×ij×¢ ×ķ׾×Ŀ", + "з наÑĩ", + "×Ļ×ĺ ×Ķ", + "к ам", + "ĠÐĺ з", + "à¹Ģà¸Ĥ ียà¸Ļ", + "à¸Ļ à¹īà¸Ńà¸ĩ", + "ÑĤ ÑĢо", + "à¹Ģ à¸Ł", + "Ġжиз ни", + "Ġ สà¹Īวà¸Ļ", + "Ġv áºŃn", + "Ġê´Ģ 볨", + "Ġl âu", + "ס ×ĺר", + "ק ש", + "س ÙĬر", + "Ġ×IJ×ķת ×Ļ", + "Ġm ôi", + "ائ ب", + "Ġо ÑģÑĤа", + "Ġm ón", + "Ġ×ij ×ŀק×ķ×Ŀ", + "Ġد اخÙĦ", + "Ġ×IJ ×ķר", + "Ġв аÑģ", + "Ùĥ Ø´Ùģ", + "ìĺ ¨", + "à¸ĸ à¹Īาย", + "Ġkullan ıl", + "Ġt ô", + "ãģ« ãĤĪãĤĬ", + "ĠëĺIJ íķľ", + "Ġ×¢×ij×ķ×ĵ ×Ķ", + "Ġri ê", + "Ġriê ng", + "Ġyak ın", + "ز ا", + "Å »", + "×IJ ×ķ׼׾", + "شار Ùĥ", + "Ġб еÑģ", + "× ´", + "Ġا بÙĨ", + "ĠTá»ķ ng", + "ÙĨ ظ", + "ÅĽwi ad", + "ãĤµ ãĥ¼", + "ห าย", + "ĠG ün", + "Ġhakk ında", + "à¹Ģà¸Ĥà¹īา มา", + "ز ÙĨ", + "ĠÐł о", + "Ġbi á»ĥn", + "ãģ© ãģĵ", + "Ùģ عÙĦ", + "ز ع", + "פר ×ĺ", + "Ġ×Ķ ף", + "Ø£ ÙĩÙĦ", + "Ġth ất", + "ØŃ ÙħÙĦ", + "Ñĩ Ñĥ", + "ĠìĤ¬ ìĭ¤", + "ì° ¸", + "ĠìľĦ íķ´", + "ÙĪ ظ", + "ĠÐŁ од", + "Ġkho ản", + "ÑĤ ен", + "ĠÙģ اÙĦ", + "Ñģ ад", + "à¸Ļ à¸Ńà¸Ļ", + "ĠاÙĦسعÙĪد ÙĬØ©", + "\" ØĮ", + "ĠاÙĦ ÙĴ", + "ãĤī ãģļ", + "Ġto án", + "Ġch ắc", + "׼ ×Ļר", + "m éd", + "méd ia", + "ز ÙĪ", + "Ġyan ı", + "פ ׳×Ļ×Ŀ", + "ØŃ Ø¸", + "Ġб еÑģп", + "ĠбеÑģп лаÑĤ", + "ĠбеÑģплаÑĤ но", + "ĠØ£ ÙħاÙħ", + "à¸Ń าย", + "à¸Ńาย ุ", + "ר שת", + "Ġg á»ĵ", + "Ġgá»ĵ m", + "Ġu á»ijng", + "ص ب", + "k ır", + "ãĥij ãĥ¼", + "Ġ׾×ĵ עת", + "Ġк ÑĥпиÑĤÑĮ", + "׾ ×ķ×Ĺ", + "ÙĪض ع", + "ÙĤÙĬ Ùħ", + "à¸Ľ า", + "ж ив", + "à¸Ķ ิà¸Ļ", + "×IJ ×ķפ", + "à¹Ģล à¹ĩà¸ģ", + "ãĥĥ ãĥī", + "иÑĩеÑģки Ñħ", + "ĠCh ủ", + "кÑĢ аÑģ", + "ÙĪ صÙĦ", + "p ÅĤat", + "м оÑĢ", + "Ġ×Ķ×IJ ×ķ", + "à¸Ń ิà¸Ļ", + "Ġíķľ êµŃ", + "гÑĢ е", + "Ġìłľ ê³µ", + "ì° ½", + "Ġê°ľìĿ¸ ìłķë³´", + "Ġngh á»ĭ", + "à¸ĭ า", + "ØŃس اب", + "Ġby ÅĤa", + "ÙħÙĦ Ùĥ", + "иÑĩеÑģки е", + "Ġb ác", + "ض ØŃ", + "ê¸ ¸", + "ש ×ŀ×¢", + "Ġìĸ´ëĸ »", + "Ġìĸ´ëĸ» ê²Į", + "ìĽ Į", + "ات Ùĩ", + "à¹Ĥรà¸ĩ à¹ģ", + "à¹Ĥรà¸ĩà¹ģ รม", + "خد ÙħØ©", + "ĠÐł а", + "׼×ķ׾ ×Ŀ", + "×ŀש ×Ĺק", + "ĠÙĪ ÙĥاÙĨ", + "ס ×ķ×£", + "ĠاÙĦØŃÙĥÙĪÙħ Ø©", + "Ġ×ij ×ĺ", + "Ġtr áºŃn", + "Ġ×Ķ×¢ ×ķ׾×Ŀ", + "ĠÃŃ ch", + "t Äħ", + "ש×ŀ ×ķ", + "Ġ×Ķר×IJש ×ķף", + "Ġíķĺ ê³ł", + "ãģķ ãĤī", + "ãģķãĤī ãģ«", + "ãģ« ãģĹãģ¦", + "Ġ à¸ľà¸¡", + "ãģ® ãĤĪãģĨãģª", + "ĠÙĪ ÙĤت", + "ãĥį ãĥĥãĥĪ", + "ÙĦ عب", + "ÙĪ Ø´", + "ìĺ ¬", + "Ġ หาà¸ģ", + "Ġm iaÅĤ", + "à¸Ĺ à¸Ńà¸ĩ", + "иÑĤ а", + "ا صر", + "ил ÑģÑı", + "з е", + "à¸Ľà¸£à¸° มาà¸ĵ", + "ãģĿãĤĮ ãģ¯", + "Ġb ır", + "Ġbır ak", + "صÙĨ اع", + "Ð ®", + "Ø´ عر", + "Ġ׳ ×Ĵ×ĵ", + "Ġب سبب", + "ãĥĿ ãĤ¤", + "ãĥĿãĤ¤ ãĥ³ãĥĪ", + "ĠاÙĦج ÙĪ", + "ĠнеÑģк олÑĮко", + "Ġki ếm", + "Ùģ Ùİ", + "Ġض د", + "×ij×Ļ×ĺ ×ķ×Ĺ", + "تاب ع", + "ÙĨ ز", + "ĠB ản", + "Ġaç ıkl", + "Ġaçıkl ama", + "Ġ à¸Ħุà¸ĵ", + "à¸Ĺ า", + "ÅĤ ów", + "Ø· ب", + "ÙĨ ØŃÙĨ", + "Ġ×ŀק ×ķר", + "ĠÄ° s", + "Ġдом а", + "Ġ วัà¸Ļ", + "Ġd Ãłnh", + "Ñı н", + "ми ÑĢ", + "Ġm ô", + "ĠvÃł ng", + "ص اب", + "s ının", + "à¸Ħ ืà¸Ļ", + "Ø® بر", + "×ĸ׼ ×ķ", + "Ġ×ŀ ש×Ķ×ķ", + "m ü", + "Ġкомпани и", + "Ġ×Ķ×¢ ×Ļר", + "ĠÙĥ ÙĪ", + "ÙĤÙĦ ب", + "ĠlỼ p", + "и ки", + "׳ ×ij", + "à¹Ĥ à¸Ħร", + "à¹Ĥà¸Ħร à¸ĩ", + "à¹Ĥà¸Ħรà¸ĩ à¸ģาร", + "×ŀ×ķ×¢ ×ĵ", + "ÑıÑĤ ÑģÑı", + "หลัà¸ĩ à¸Īาà¸ģ", + "ени Ñİ", + "Ġש ×¢", + "Ġb Æ°á»Ľc", + "ãĥ¡ ãĥ¼ãĥ«", + "ãĤĦ ãĤĬ", + "Ġ×Ļ×ķ×ĵ ×¢", + "Ġê´Ģ íķľ", + "ĠاÙĦØ£ Ùħر", + "Ġböl ge", + "ĠÑģв ой", + "ÙĦ س", + "Ġ×ŀ×Ļ ×ķ×Ĺ×ĵ", + "ĠëĤ´ ìļ©", + "ĠØ£ جÙĦ", + "ĠÄIJ ông", + "Ġ×ŀ ×ł×ª", + "Ġìĭľ ê°Ħ", + "Ùĥ Ùİ", + "ãģ¨ãģĦãģĨ ãģ®ãģ¯", + "Ġnale ży", + "تÙĨظ ÙĬÙħ", + "ĠÑģозд а", + "Ġph é", + "Ġphé p", + "ãģ§ãģį ãģ¾ãģĻ", + "Ġع ÙĦÙħ", + "大ãģį ãģª", + "ãĤ² ãĥ¼ãĥł", + "í ħĮ", + "Ġ׼×ķ׾ ׾", + "ĠинÑĤеÑĢ неÑĤ", + "ĠT ừ", + "ãģ¨ ãģªãĤĭ", + "ز اÙĦ", + "Ġktóry m", + "Ġnh é", + "ìĪ ľ", + "н ев", + "д еÑĢ", + "ãĤ¢ ãĥĹãĥª", + "i á»ĩu", + "×ij ×Ļ׾", + "Ġت س", + "ĠÄIJ ây", + "ĠاÙĦØ® اصة", + "Ġà¹Ģ à¸Ĭ", + "Ġà¹Ģà¸Ĭ à¹Īà¸Ļ", + "ص اد", + "Ġd ạng", + "س عر", + "Ġש ×Ļ×ŀ×ķש", + "×Ĵ ×Ļ×Ŀ", + "ãģĮãģĤ ãģ£ãģŁ", + "п ÑĢов", + "пÑĢов од", + "Ġ×IJ ×Ļ׳×ķ", + "Ġ׾ ר×IJ", + "Ġ׾ר×IJ ×ķת", + "ĠØ£ ÙģضÙĦ", + "ĠØŃ ÙĦ", + "ĠØ£ بÙĪ", + "ê° ķ", + "Ġì§ ij", + "ãģ® ãĤĪãģĨãģ«", + "Ġפ ׳×Ļ", + "ס ×Ļ×Ŀ", + "ĠÙĪÙĩ ذا", + "Ġka ç", + "Ġé én", + "Ġê± ´", + "ë° Ķ", + "Ñĥ з", + "à¸Ĥà¸Ńà¸ĩ à¹Ģรา", + "i ÅĤ", + "ĠÐľ Ñĭ", + "Ġch ết", + "ĠاÙĦØ« اÙĨÙĬ", + "×IJ ק", + "Ġ×ķ ×¢×ľ", + "ĠاÙĦØ· ب", + "×ij×ĺ ×Ĺ", + "Ġج دÙĬدة", + "Ġع دÙħ", + "ع ز", + "สิà¹Īà¸ĩ à¸Ĺีà¹Ī", + "ãģĻ ãĤĮãģ°", + "ĠÄij ô", + "ì£ ł", + "د ÙĤ", + "н омÑĥ", + "Ġk á»ĥ", + "ãĤ¢ ãĥ³", + "å¤ļãģı ãģ®", + "à¸Ľà¸£à¸° à¸ģ", + "à¸Ľà¸£à¸°à¸ģ à¸Ńà¸ļ", + "פע×Ļ׾ ×ķת", + "ĠÑģÑĤ ол", + "may ı", + "ãģ¤ ãģĦ", + "Ġyılı nda", + "Ġ à¸Īึà¸ĩ", + "koÅĦ cz", + "ĠTh ông", + "Ġак ÑĤив", + "н ÑģÑĤ", + "нÑģÑĤ ÑĢÑĥ", + "ĠÃĸ z", + "Ġת ×ŀ×Ļ×ĵ", + "ĠÙĥ ÙĨت", + "Ñģ иÑģÑĤем", + "pr és", + "prés ent", + "Ġn â", + "Ġnâ ng", + "gÅĤ os", + "ĠÙĪز ÙĬر", + "ØŃ ØµÙĦ", + "Ġиме еÑĤ", + "ØŃ Ø±ÙĥØ©", + "à¸ŀ à¹Īà¸Ń", + "ãĤĴ ãģĬ", + "Ġاست خداÙħ", + "×IJ×Ļר ×ķ×¢", + "ä»ĸ ãģ®", + "Ġש×Ķ ×Ŀ", + "ãģĹãģŁ ãĤī", + "ש×ŀ ×Ļ", + "Ñģ ла", + "m ı", + "Ġbaz ı", + "Ġíķĺ ì§Ģë§Į", + "×ĵ ׾", + "Ġyapt ıģı", + "ãĥĬ ãĥ¼", + "׾ ×Ļ׾×Ķ", + "ãģ¨ãģĦ ãģ£ãģŁ", + "änd ig", + "ĠÅŁ a", + "ĠÙģÙĬ Ùħا", + "иÑĤ елÑı", + "×ŀ ×ķש", + "à¸Ĥ à¸Ńà¸ļ", + "l ük", + "Ġh á»ĵi", + "Ġëª ħ", + "ĠاÙĦÙĥ Ø«ÙĬر", + "צ ×IJ", + "Ġhaz ır", + "طر Ùģ", + "ا ÙĬا", + "ĠÄij ôi", + "ен д", + "ÙĦ غ", + "×Ĺ ×ĸ×ķר", + "ĠвÑģ ег", + "ĠвÑģег да", + "ëIJĺ ê³ł", + "×ĵ ×ķ×ĵ", + "ан а", + "د ÙĪÙĦØ©", + "Ġho ạch", + "ع ÙĦا", + "عÙĦا ج", + "Ġ×ķ ×¢×ĵ", + "×Ķ ×Ŀ", + "ки й", + "ÙĦ ÙIJ", + "Ġ×¢ ׾×Ļ×ķ", + "ÑİÑī ий", + "Ġng ủ", + "صÙĨ ع", + "ĠاÙĦع راÙĤ", + "à¸ķà¹Īà¸Ń à¹Ħà¸Ľ", + "ãģŁãģı ãģķãĤĵ", + "Ġph ạm", + "ÙĦ اÙĨ", + "ات Ùĩا", + "Ġbö yle", + "تÙĨ ÙģÙĬ", + "تÙĨÙģÙĬ Ø°", + "Ġש×Ķ ×Ļ×IJ", + "Ñģ Ñĥ", + "ย าว", + "Ġש ×ķ׳×Ļ×Ŀ", + "Ġ×ŀ ×ķ׾", + "ĠÑģ ил", + "Ġ×IJ×Ĺר ×Ļ×Ŀ", + "Ġph ủ", + "ÙĤØ· ع", + "ĠTh ủ", + "à¸Ľà¸£à¸°à¹Ģà¸Ĺศ à¹Ħà¸Ĺย", + "ÙĨ ÙĤ", + "ĠÄijo ạn", + "Ġب Ø¥", + "п ÑĢедел", + "×ķת ×ķ", + "Ġy arı", + "пÑĢ е", + "ĠczÄĻ ÅĽci", + "ØŃ ÙĥÙħ", + "×ķ׳ ×Ļת", + "פע ׾", + "ãĤĴ ãģĹãģ¦", + "Ġktó rzy", + "׾ ×Ŀ", + "ĠÄIJi á»ģu", + "ĠкоÑĤоÑĢ аÑı", + "ĠìĿ´ ìĥģ", + "ãģĤ ãģ£ãģŁ", + "Ġ×ŀ×ĵ ×ķ×ijר", + "פ ×ķ×¢×ľ", + "d ım", + "éĢļ ãĤĬ", + "ĠбÑĥд ÑĥÑĤ", + "à¹Ģวà¹ĩà¸ļ à¹Ħà¸ĭ", + "à¹Ģวà¹ĩà¸ļà¹Ħà¸ĭ à¸ķà¹Į", + "ا خر", + "×Ĺ ×Ļ׾", + "Ġ×Ļ ׾", + "Ġ×Ļ׾ ×ĵ×Ļ×Ŀ", + "×Ĺ ×Ļפ", + "×Ĺ×Ļפ ×ķש", + "Ġd òng", + "Ġש ×ĸ×Ķ", + "ÑĮ е", + "ãģĤ ãģ¨", + "ìŀIJ ê°Ģ", + "×IJ ×ĵ", + "Ġü z", + "Ġüz ere", + "ظ ÙĦ", + "Ġ×IJ ×ķ׾×Ļ", + "Ġ×ij ×Ļ×ķ×Ŀ", + "ÙĦ ات", + "Ġm ê", + "ì¹ ¨", + "تØŃ Ø¯", + "تØŃد Ø«", + "ĠØ® اصة", + "Ġب رÙĨ", + "ĠبرÙĨ اÙħج", + "ĠH Ãłn", + "×Ĺ ×¡", + "ĠÙĪ ÙĦÙħ", + "×¢ ×Ŀ", + "Ġm ı", + "à¸Ł ัà¸ĩ", + "ש ×¢×Ķ", + "ÙĪÙģ ÙĤ", + "ס ×ij×Ļר", + "алÑĮ нÑĭй", + "×Ĺש ×ķ×ij", + "Ġn Ãłng", + "ë³ ¼", + "ĠкоÑĤоÑĢ ÑĭÑħ", + "Ġ×Ĺ ×ķק", + "t ör", + "ĠлÑĥÑĩ ÑĪе", + "ãĥij ãĥ³", + "ลà¹Īา สุà¸Ķ", + "Ġج دÙĬد", + "ÙĬد Ø©", + "à¸Ĺ รà¸ĩ", + "ãĤĪãĤĬ ãĤĤ", + "ÙĦ ÙĦ", + "ãĤĤ ãģ£ãģ¨", + "ש×ĺ ×Ĺ", + "Ġ×ķ ×IJ×Ļ", + "Ġgi á»ijng", + "Ø¥ ضاÙģ", + "ק ת", + "ë§ Ŀ", + "Ġzosta ÅĤ", + "ÑĢ оз", + "×Ļפ ×Ļ×Ŀ", + "Ġ׼׾ ׾", + "ת×ķ׼ ף", + "dıģ ını", + "ÙĤ سÙħ", + "ĠÑģ ÑĩиÑĤ", + "ĠÑģÑĩиÑĤ а", + "×ĺ ×ķת", + "Ġ Æ°u", + "ĠØ¢ ÙĦ", + "Ġм ом", + "Ġмом енÑĤ", + "ĠاÙĦتع ÙĦÙĬÙħ", + "×¢×ľ ×ķת", + "Ġch ữa", + "Ġy ön", + "Ġtr Ãł", + "ĠØŃ ÙĬÙĨ", + "à¸ĭ ั", + "ĠC á", + "×¢ ×ĸ", + "ĠاÙĦØ£ ÙħÙĨ", + "c ÃŃ", + "Ġv á»ijn", + "Ġ à¸Ļาย", + "об ÑĢа", + "ק ×IJ", + "Ġthi ếu", + "ãĥŀ ãĥ¼", + "ส วà¸Ļ", + "Ġg á»Ń", + "Ġgá»Ń i", + "Ġê ¹", + "Ġê¹ Ģ", + "Ġthi á»ĩn", + "ÙĤ ع", + "w ÄĻ", + "Ġн ам", + "ÑĤ ол", + "Ġs ân", + "ס ×ķ×Ĵ", + "Ġgeç ir", + "ÑĤ он", + "ев а", + "ĠÙĪ ضع", + "Ġع شر", + "Ñģ ло", + "à¸Ī ัà¸ļ", + "ãĤ· ãĥ¼", + "ãĤĤ ãģĤãĤĬãģ¾ãģĻ", + "Ġv ẻ", + "ĠÄIJ á»ĥ", + "ر Ùģع", + "ĠاÙĦØ£ÙĪÙĦ Ùī", + "ÑĤ аÑĢ", + "ãģªãģı ãģ¦", + "Ùħ Ùİ", + "qu ÃŃ", + "×¢×ł×Ļ ×Ļ׳", + "г ен", + "Ġh ôm", + "à¸Ī า", + "Ġnh Ỽ", + "ĠاÙĦع ربÙĬ", + "×IJ ף", + "Ġl á»Ļ", + "Ġje ÅĽli", + "à¹Ģà¸Ĺà¹Īา à¸Ļัà¹īà¸Ļ", + "ĠØ£ÙĨ Ùĩا", + "Ġt uy", + "Ġtuy á»ĩt", + "Ġت ص", + "Ġتص ÙĨÙĬ", + "ĠتصÙĨÙĬ Ùģ", + "Ġê·¸ëŁ¬ ëĤĺ", + "о ÑĨен", + "à¸ģิà¸Ī à¸ģรรม", + "ãĤĦ ãģ£ãģ¦", + "Ġkh á»ıi", + "Ġl á»ĩ", + "ĠاÙĦÙħج تÙħع", + "à¸Ńาà¸Ī à¸Īะ", + "à¸Īะ à¹Ģà¸Ľà¹ĩà¸Ļ", + "ов Ñĭй", + "ר ×Ŀ", + "ร à¹īà¸Ńà¸Ļ", + "ש ×ŀש", + "人 ãģ«", + "Ġüzer ine", + "פר ×Ļ", + "du ÄŁu", + "Ñĩ ик", + "Ġmù a", + "Ġ×ŀת ×ķ×ļ", + "Ġc áºŃp", + "Ġت ارÙĬØ®", + "×ij׾ ת×Ļ", + "Ġì¢ Ģ", + "ÙĦ ع", + "ب اÙĨ", + "Ġch út", + "Ġ×Ķ×ĸ ×ŀף", + "n ée", + "ĠLi ên", + "ĠÙĦÙĦ Ø£", + "ØŃد ÙĪد", + "Ġ×¢ ׼ש×Ļ×ķ", + "в оз", + "Ġyapt ı", + "Ġоб о", + "à¹ĥหà¹ī à¸ģัà¸ļ", + "Ġ×ij×Ķ ×Ŀ", + "ãģı ãģ¦", + "ر أس", + "ĠÑģÑĢед ÑģÑĤв", + "ĠB Ãłi", + "ãģĵãģ¨ ãģ«", + "ĠìĤ¬ íļĮ", + "Ġ모 ëijIJ", + "×ij ×IJ", + "Ġtr ắng", + "ĠاÙĦبÙĦ د", + "ĠHo Ãłng", + "ли бо", + "ĠдÑĢÑĥг иÑħ", + "Ä° R", + "Ñĥм а", + "ĠJe ÅĽli", + "ãĤĤ ãģĹ", + "Ġv òng", + "Ġ×IJתר ×Ļ×Ŀ", + "ĠÄij á»įc", + "Ġв оÑĤ", + "ãģł ãģĮ", + "ë° °", + "à¸Ķู à¹ģล", + "Ġ×ŀ ׼׾", + "ìĹIJ ëıĦ", + "г аз", + "Ġ׳×ķס פ×Ļ×Ŀ", + "ãģĵãģ¨ ãģ§", + "Ġت ÙĪ", + "ãģ§ ãģĤãĤĬ", + "à¸Ļั à¹Īà¸ĩ", + "ĠможеÑĤ е", + "sz ÄĻ", + "ãģ® ãģł", + "ĠÙħÙĨ Ùĩ", + "Ġb á»ķ", + "Ġb üt", + "Ġbüt ün", + "ë³´ ê³ł", + "Ġch á»ĵng", + "à¹ģà¸Ī à¹īà¸ĩ", + "ĠV ì", + "ĠØŃ Ø±", + "Ġgi ản", + "ĠÙħ دÙĬÙĨØ©", + "تط بÙĬÙĤ", + "à¸Ī ิ", + "æĹ¥ ãģ®", + "б ил", + "à¸ģ à¸Ńà¸ĩ", + "ê³ ³", + "ĠØ£ Ùħا", + "ìĨ IJ", + "Ġtr ái", + "ĠвÑģ ем", + "Ġس ÙĨØ©", + "ĠÑģай ÑĤ", + "Ġг оÑĤов", + "п Ñĭ", + "ĠëIJ ł", + "ĠاÙĦØ® Ø·", + "ĠاÙĦرئÙĬس ÙĬØ©", + "Ġíķ ©ëĭĪëĭ¤", + "ĠìķĦëĭĪ ëĿ¼", + "ĠìĿ´ ëłĩ", + "ĠìĿ´ëłĩ ê²Į", + ") ØĮ", + "h ält", + "ĠØ£ Ùħر", + "Ġع Ùħر", + "à¸ģà¹ĩ à¸Īะ", + "Ġ à¸Ĺำà¹ĥหà¹ī", + "Ġc ân", + "Ġ×ij ׾", + "Ġ×ij׾ ×ij×ĵ", + "פ סק", + "ĠÙĬ ÙĤÙĪÙĦ", + "н ÑĥÑĤÑĮ", + "à¹ģ à¸Ħ", + "Ġק צת", + "Ġn ằm", + "Ġh òa", + "bilit Ãł", + "ĠìĹĨ ëĭ¤", + "Ġ׼ פ×Ļ", + "ÑĢ ож", + "лаг а", + "Ġ×Ķש ×Ļ", + "ĠNgo Ãłi", + "ĠÙĪ ج", + "ĠÙĪج ÙĪد", + "ĠìľĦ íķľ", + "Ġus ÅĤug", + "Ġtu ần", + "d ź", + "×ŀ ×ķף", + "ĠاÙĦع دÙĬد", + "Ġch ẳng", + "สุà¸Ĥ à¸łà¸²à¸ŀ", + "Ġ×ij ×ĵר×ļ", + "ĠÑģеб е", + "ĠìŀĪ ìĿĦ", + "ĠاÙĦØŃ Ø§ÙĦ", + "Ġd á", + "Ġc Æ°á»Ŀi", + "Ġnghi ên", + "ie ÅĦ", + "ĠD Æ°Æ¡ng", + "ï¼ ħ", + "Ø´ د", + "ãģĦãģ¤ ãĤĤ", + "ĠвÑĭб оÑĢ", + "Ġc á»Ļng", + "ש ×Ļ׳×ķ×Ļ", + "Ġch ạy", + "Ġ×ij×¢ ׾×Ļ", + "اخ بار", + "íķĺ ë©°", + "ż Äħ", + "ج از", + "Ġ׳ ר×IJ×Ķ", + "ศ ู", + "ศู à¸Ļ", + "ศูà¸Ļ ยà¹Į", + "×Ĵ ×¢", + "Ġ×¢ ×ĵ×Ļ", + "Ġ×¢×ĵ×Ļ ×Ļף", + "بر ا", + "ÑĨи й", + "ĠÄIJ á»ĵng", + "ÙĤ اÙĨÙĪÙĨ", + "ĠÄij ứng", + "ãģĹãģŁ ãĤĬ", + "Ġ×Ĺ×Ļ ×Ļ", + "Ġë IJľ", + "ĠëIJľ ëĭ¤", + "Ġм еждÑĥ", + "à¸ŀวà¸ģ à¹Ģà¸Ĥา", + "ĠB ắc", + "ล ำ", + "ë° ±", + "ĠíĻ ķ", + "มาà¸ģ ม", + "มาà¸ģม าย", + "бан к", + "à¸Ńา à¸ģาร", + "Ġh Ãł", + "Ġ׾ ׳", + "à¸Ń à¸Ń", + "Ġë°Ķ ë¡ľ", + "л ом", + "m ática", + "ĠØŃ Ø¯", + "اب ت", + "à¸Ĺีà¹Ī à¸Ļีà¹Ī", + "Ġco ÅĽ", + "ÙģÙĬ دÙĬ", + "ÙģÙĬدÙĬ ÙĪ", + "ĠмеÑģÑĤ о", + "Ġph út", + "มาà¸ģ à¸ģวà¹Īา", + "×IJ פ", + "ب ÙIJ", + "ĠPh ú", + "ì± Ħ", + "ĠÙĪ سÙĦÙħ", + "à¸Īี à¸Ļ", + "поÑĤ ÑĢеб", + "Ġ×Ĺ×ĵ ש×ķת", + "Ø´ ÙĪ", + "Ġעצ ×ŀ×ķ", + "ĠعÙħÙĦ ÙĬØ©", + "à¸Ħุà¸ĵ à¸łà¸²à¸ŀ", + "ãģ¾ãģĻ ãģĮ", + "دع ÙĪ", + "طر ÙĤ", + "à¹Ħมà¹Ī à¸ķà¹īà¸Ńà¸ĩ", + "ë² Ķ", + "ìĬ ¹", + "Ġk ÃŃch", + "ĠìĹĨ ëĬĶ", + "ĠÑĤ ам", + "ĠÙĨ ØŃÙĪ", + "ĠاÙĦÙĤ اÙĨÙĪÙĨ", + "×Ĺ ×ķ×Ŀ", + "Ġk ız", + "Ġ×ĵ ×Ļף", + "ĠвÑĢем ени", + "ãģ£ãģŁ ãĤĬ", + "ĠØ´ Ùĩر", + "ĠìĦľ ë¹ĦìĬ¤", + "×¢ ש×Ķ", + "Ġgi ác", + "ĠاÙĦسÙĦ اÙħ", + "Ġ×IJ ש", + "ĠполÑĥÑĩ а", + "à¸Īัà¸Ķ à¸ģาร", + "к оÑĢ", + "Ġ×Ķ×ĺ ×ķ×ij", + "ราย à¸ģาร", + "주 ìĿĺ", + "à¹ģà¸ķà¹Ī ละ", + "Ġê·¸ëŁ° ëį°", + "à¸Ĺีà¹Ī à¹Ģà¸Ľà¹ĩà¸Ļ", + "Ġת ×ķ×ļ", + "بÙĬ اÙĨ", + "Ð Ļ", + "oÅĽci Äħ", + "ÑĤ ок", + "Ġà Ķ", + "ĠÃĶ ng", + "à¹Ħมà¹Ī à¹ĥà¸Ĭà¹Ī", + "ãģ¿ ãģ¦", + "ÐŁ о", + "ĠЧ ÑĤо", + "íĻ ©", + "×ĺ ×ij×¢", + "меÑĤ ÑĢ", + "Ġ×ij ×ŀ×Ķ", + "Ġ×ij×ŀ×Ķ ׾", + "Ġ×ij×ŀ×Ķ׾ ×ļ", + "Ñĩ ÑĮ", + "ק ש×Ķ", + "з нак", + "знак ом", + "uj ÄĻ", + "×Ļצ ר", + "ĠاÙĦÙħ ÙĦÙĥ", + "ı yla", + "×IJ×ŀ ת", + "à¸Ľ ิà¸Ķ", + "×IJ ×Ĺ×ĵ", + "ر اد", + "Ġm áºŃt", + "ëĭ¤ ëĬĶ", + "Ġl ạnh", + "ש׾ ×ķש", + "ØŃ Ø¯ÙĬØ«", + "ت ز", + "å¹´ ãģ®", + "Ġк ваÑĢ", + "ĠкваÑĢ ÑĤиÑĢ", + "ä½ľ ãĤĬ", + "رÙĪ ب", + "ов ан", + "ĠТ е", + "à¸Īำ à¸ģ", + "à¸Īำà¸ģ ัà¸Ķ", + "ب اط", + "×Ĵ ת", + "Ġм аÑĪ", + "ĠмаÑĪ ин", + "×Ļצ ×Ķ", + "ãģ» ãģ¨", + "ãģ»ãģ¨ ãĤĵãģ©", + "ÃŃ do", + "ĠÑı зÑĭк", + "à¸ļ ิà¸Ļ", + "สà¸ĸาà¸Ļ à¸Ĺีà¹Ī", + "ĠìĹ ´", + "ãĤ¦ ãĤ§", + "Ġc Ãł", + "п ан", + "åı£ ãĤ³ãĥŁ", + "Ġر د", + "اÙĤ ت", + "ĠÙĥ ب", + "ĠÙĥب ÙĬرة", + "ÑģÑĤ ал", + "ש×ŀ ×Ĺ", + "pos ición", + "ĠÙħÙĦÙĬ ÙĪÙĨ", + "ĠìĿ´ ìķ¼", + "ĠìĿ´ìķ¼ 기", + "Ġh út", + "ĠÅĽw iat", + "Ġë°© ë²ķ", + "ĠÑģв еÑĤ", + "Ġвиде о", + "ĠاÙĦÙĨ ظاÙħ", + "Ġtr á»Ŀi", + "ĠëĮĢ íķ´ìĦľ", + "ר ×ŀת", + "ت داÙĪÙĦ", + "×ķר ×ĵ", + "ת ×ŀ", + "ת×ŀ ×ķ׳×ķת", + "Ġ×ŀ ף", + "Ġдв а", + "Ġ×Ķק ×ķ", + "æĹ¥ ãģ«", + "Ġ×Ķ×Ĵ ×Ļ×¢", + "à¹Ģà¸ŀิà¹Īม à¹Ģà¸ķิม", + "Ùħار س", + "Ġê²ĥ ìŀħëĭĪëĭ¤", + "ãģªãģĦ ãģ¨", + "Ġnhi á»ĩt", + "ëIJ ©ëĭĪëĭ¤", + "Ġ×ij׳ ×ķש×IJ", + "Ġê°Ģ ìŀ¥", + "Ġv ợ", + "ĠÄij óng", + "צ×Ļ׾ ×ķ×Ŀ", + "ê´Ģ ê³Ħ", + "в аÑı", + "×IJ ×Ļ×ĸ", + "×IJ×Ļ×ĸ ×Ķ", + "ĠÙĨ ظاÙħ", + "ÙħØŃ Ø§Ùģظ", + "Ġt ải", + "기 ëıĦ", + "à¸Ľà¸±à¸Ī à¸Īุ", + "à¸Ľà¸±à¸Īà¸Īุ à¸ļัà¸Ļ", + "׼ ×ĵ×ķר", + "ĠìķĦ ìĿ´", + "׼׳ ×Ļס", + "à¹Ģ à¸ķร", + "à¹Ģà¸ķร ียม", + "Ġngo ại", + "ĠدÙĪÙĦ ار", + "Ġr ẻ", + "Ġkh Äĥn", + "عد د", + "Ø´ عب", + "czy Äĩ", + "ĠاÙĦ Ùĥر", + "ĠÑĩеловек а", + "ĠÙĪ Ø¥ÙĨ", + "×IJ ×ĺ", + "Ġth Æ¡", + "ĠاÙĦ رÙĬاض", + "оп ÑĢедел", + "опÑĢедел ен", + "×Ķ ×ŀש×ļ", + "ĠÐĿ ово", + "з Ñĭва", + "ĠاÙĦدÙĪÙĦ ÙĬ", + "ĠÄij áp", + "Ġк ÑĢед", + "ĠкÑĢед иÑĤ", + "ов ого", + "Ġm ôn", + "à¸Ľà¸£à¸° à¹Ĥย", + "à¸Ľà¸£à¸°à¹Ĥย à¸Ĭà¸Ļ", + "à¸Ľà¸£à¸°à¹Ĥยà¸Ĭà¸Ļ à¹Į", + "ÑģÑĤ е", + "ĠTh á»ĭ", + "د ÙĬØ©", + "×ŀצ ×ķ", + "Ùģ ات", + "ק ×ĵ×Ŀ", + "ìĿ´ëĿ¼ ê³ł", + "ÙĪ Ø®", + "Ġ×Ĺ ×ĸ", + "ĠÑĦоÑĤ о", + "׾ ×Ļת", + "ت Ùİ", + "ÙĪ بر", + "й ÑĤи", + "ĠÃ¶ÄŁ ren", + "Ġ×Ķ×ĸ ×ķ", + "Ġv á»įng", + "ÙĤÙĪ Ø©", + "ĠT ây", + "ĠÐĿ и", + "Ġש ×ķ×ij", + "ãģ¨è¨Ģ ãĤıãĤĮ", + "ãģ© ãĤĵãģª", + "׊צ×Ļ", + "ï½ ľ", + "Ġ×ķ×Ķ ×ķ×IJ", + "ä¸Ģ ãģ¤", + "ĠÑģÑĤо иÑĤ", + "ni Äħ", + "×ĺר ×Ļ", + "ĠдеÑĤ ей", + "нÑı ÑĤÑĮ", + "ĠÑģдел аÑĤÑĮ", + "Ġë§İ ìĿ´", + "ä½ķ ãģĭ", + "ãģĽ ãĤĭ", + "à¹Ħ หม", + "à¸ķิà¸Ķ à¸ķà¹Īà¸Ń", + "Ġ×ij ת×Ĺ", + "Ġ×ijת×Ĺ ×ķ×Ŀ", + "ìĻ Ħ", + "ì§Ģ ëĬĶ", + "ÑģÑĤ аÑĤ", + "ÑıÑģ н", + "ü b", + "Ġth ả", + "Ġ×ij×IJ×ŀ ת", + "Ġt uyến", + "×ĵ ×Ļר×Ķ", + "Ġ×IJ ×Ļש×Ļ", + "×ĸ׼ ר", + "ãģ° ãģĭãĤĬ", + "Ġx ét", + "׼ ×Ļ×ķ", + "׼×Ļ×ķ ×ķף", + "diÄŁ ini", + "ĠاÙĦÙħ ÙĪضÙĪع", + "Ġh áºŃu", + "à¸Īาà¸ģ à¸ģาร", + "×ijס ×Ļס", + "Ġ×ŀ×Ĵ ×Ļ×¢", + "×ij ×Ļ×¢", + "ĠÙĪ جÙĩ", + "à¹ģà¸Ķ à¸ĩ", + "à¸Ļ าà¸ĩ", + "ĠÅŀ a", + "ì ¡´", + "ë¡ Ģ", + "à¸ķ ะ", + "Ġ×Ķ×Ĺ×Ļ ×Ļ×Ŀ", + "Ùģ ÙĬد", + "ãģ§ãģĻ ãģĭãĤī", + "ê· ľ", + "ź ni", + "ĠлÑİ Ð´ÐµÐ¹", + "Ġyüz de", + "ıy orum", + "ĠاÙĦ بØŃر", + "e ño", + "п аÑĢ", + "ÙĬ ÙĤØ©", + "об ÑĢ", + "ר ×ķ×ļ", + "ت ÙĪÙĤع", + "ĠاÙĦØ´ ÙĬØ®", + "åĪĿ ãĤģãģ¦", + "ĠÑĤ елеÑĦ", + "ĠÑĤелеÑĦ он", + "Ġth ôi", + "Ġ×Ļ׼×ķ׾ ×Ļ×Ŀ", + "ĠÅŁ irk", + "ĠÅŁirk et", + "Ġìļ°ë¦¬ ê°Ģ", + "ĠÄij ông", + "Ġת ×ķ×ĵ×Ķ", + "ÑģмоÑĤÑĢ еÑĤÑĮ", + "ĠÙĦ ÙĩÙħ", + "Ġ׾ ׼", + "ĠN ó", + "ĠØŃ Ø§ÙĦØ©", + "ãģĦ ãģij", + "קר ×ķ", + "az ı", + "ãĤ³ ãĥ¼", + "ĠÙĦÙĦ ت", + "s ınız", + "ĠH ải", + "기 ìĪł", + "ยัà¸ĩ à¹Ħมà¹Ī", + "ëĭ¤ ê³ł", + "פ ×Ĺ", + "Ġ׾×Ĵ ×ij×Ļ", + "Ġع ÙĨÙĩ", + "Ġк аз", + "Ġказ ино", + "ب ÙĪر", + "ÑĦ еÑĢ", + "Ġê°Ļ ìĿ´", + "تس جÙĬÙĦ", + "ĠاÙĦÙħ رÙĥز", + "ĠTh ái", + "д аÑĤÑĮ", + "×ŀ×Ļ ×Ļ׾", + "Ġpay laÅŁ", + "ãģ¤ ãģ®", + "à¹Ģร ืà¸Ń", + "n ça", + "׳ ×ķ×Ĺ", + "Ġ×IJ פ×Ļ׾×ķ", + "ãģ¨ èĢĥãģĪ", + "ãģ¨ãģĹãģ¦ ãģ¯", + "à¹Ģà¸Ī à¸Ń", + "×ŀ פ", + "Ġg iriÅŁ", + "л иÑĤ", + "ÑĤ елÑı", + "Ñij н", + "æ°Ĺ ãģ«", + "Ġg ó", + "Ġgó p", + "åĪĩ ãĤĬ", + "Ġ×Ķ ×Ĺ×ĵש", + "ж ал", + "Ġ×ĵ עת", + "éģķ ãģĨ", + "à¹Ģà¸Ĥà¹īา à¹Ħà¸Ľ", + "Ġס ר×ĺ", + "e ña", + "æĸ° ãģĹãģĦ", + "ر Ùİ", + "ĠÐIJ ÑĢ", + "Ġph ản", + "à¸Īะ à¹Ħà¸Ķà¹ī", + "Ġ×ijצ ×ķר×Ķ", + "Ø´ اÙĩ", + "شاÙĩ د", + "ÙĪر د", + "à¹Ģà¸Ļืà¹Īà¸Ńà¸ĩ à¸Īาà¸ģ", + "или ÑģÑĮ", + "à¹ģละ à¸ģาร", + "Ġ×Ķ ×ĸ׼", + "Ġ×Ķ×ĸ׼ ×ķ×Ļ×ķת", + "ei ÃŁ", + "ãĥ ¨", + "ìĥ Ī", + "ĠÃĩ a", + "Æ ¯", + "ש ×Ĵ", + "ÙĬÙĨ Ø©", + "ร à¹īà¸Ńà¸ĩ", + "ãĤµ ãĥ³", + "ÑĢоÑģÑģ ий", + "ÑĢоÑģÑģий Ñģк", + "a ÄŁa", + "ĠнаÑĩ ина", + "Ġص ÙĦÙī", + "à¸Ĺุà¸ģ à¸Ħà¸Ļ", + "íļĮ ìĤ¬", + "Ġли ÑĨ", + "Ø´ ÙĬر", + "ĠØ´ÙĬ Ø¡", + "ÙĬÙĨ ا", + "Ġפ ×Ĺ×ķת", + "Ġiçer is", + "Ġiçeris inde", + "ĠØ£ ØŃÙħد", + "Ġże by", + "ì´ Ŀ", + "Ġп оказ", + "Ġи менно", + "หà¸Ļัà¸ĩ ส", + "หà¸Ļัà¸ĩส ืà¸Ń", + "ĠÑĤÑĢ е", + "สัà¸ĩ à¸Ħม", + "Ø¥ ÙIJ", + "ãģĮ å¿ħè¦ģ", + "ÙĬÙij Ø©", + "פ צ", + "íĭ °", + "ĠÙħ جاÙĦ", + "׳ פש", + "к ан", + "×Ĺ ×ķפ", + "×Ĺ×ķפ ש", + "ì²ĺ ëŁ¼", + "ов аÑı", + "з ов", + "Ġh ạ", + "Ġdzi ÄĻki", + "×Ļר ×ķ", + "Ġ׾ ×ŀצ", + "Ġ׾×ŀצ ×ķ×IJ", + "×Ļ×ĵ ×ķ", + "Ġs ợ", + "Ġ׾×Ķ ×Ĵ×Ļ×¢", + "ק ×ij×¢", + "Ġchi á»ģu", + "ãĥŀ ãĤ¤", + "Ġd Ãłng", + "à¹ģà¸Ł à¸Ļ", + "Ġü ye", + "×Ļ׳ ×Ĵ", + "à¹Ģรีย à¸ģ", + "ç§ģ ãģĮ", + "th é", + "ĠÑĦ илÑĮ", + "ĠÑĦилÑĮ м", + "ĠNg Ãły", + "Ġж ен", + "Ġжен Ñīин", + "ج ÙĬد", + "n ç", + "à¸Ľ รา", + "×Ļ×ŀ ×ķ", + "Ġn á»ģn", + "×IJ ×ķ׾×Ŀ", + "Ġвозмож ноÑģÑĤÑĮ", + "Ġëĭ¤ ìĭľ", + "è¦ĭ ãģŁ", + "à¸ĸ à¸Ļ", + "à¸ĸà¸Ļ à¸Ļ", + "mız ı", + "ĠÙħ جÙħÙĪعة", + "c jÄħ", + "ĠÐł Ф", + "à¸ģำ หà¸Ļ", + "à¸ģำหà¸Ļ à¸Ķ", + "ĠìŬ 기", + "land ı", + "ни ÑĨ", + "ÑģÑĤв е", + "Ġ×ĵ ×ijר×Ļ×Ŀ", + "Ġsk ÅĤad", + "ãĤĬ ãģ¾ãģĹãģŁ", + "ĠоÑĤ кÑĢÑĭÑĤ", + "нÑı ÑĤ", + "ĠÑģво ей", + "à¸Ī ิà¸ķ", + "ĠкаÑĩеÑģÑĤв е", + "Ġet tiÄŁi", + "ìĤ¬ íķŃ", + "ĠاÙĦÙĬ ÙħÙĨ", + "иÑĩеÑģки й", + "ë¸ Į", + "Ġ×ij×IJר ×¥", + "Ġا سÙħ", + "Ġиз веÑģÑĤ", + "r ão", + "Ġatt ivitÃł", + "à¹Ģà¸Ľà¹ĩà¸Ļ à¸ģาร", + "ĠاÙĦد Ùĥت", + "ĠاÙĦدÙĥت ÙĪر", + "ĠÙĪاØŃد Ø©", + "ĠÑģ ÑĩеÑĤ", + "ĠпÑĢ иÑĩ", + "ĠпÑĢиÑĩ ин", + "ĠÙĪز ارة", + "Ġh uyá»ĩn", + "ĠÙĥ تاب", + "à¹ģà¸Ļ à¹Īà¸Ļ", + "à¹ģà¸Ļà¹Īà¸Ļ à¸Ńà¸Ļ", + "Ġgün ü", + "г ÑĢÑĥз", + "ĠاÙĦØ® اص", + "Ġgör ül", + "׾ ×ŀ×ĵ", + "Ġìłķ ëıĦ", + "×ķ×ij ×Ļ׾", + "Ġ×ŀק צ×ķ×¢×Ļ", + "ĠоÑģоб енно", + "à¸Ľà¸£à¸° à¸ģา", + "à¸Ľà¸£à¸°à¸ģา ศ", + "aca ģını", + "ë¶ ģ", + "à¸łà¸¹ มิ", + "ĠÑį лекÑĤ", + "ĠÑįлекÑĤ ÑĢо", + "Ġק ש×Ķ", + "سÙĦ Ø·", + "à¸Ĭà¸Ļ ะ", + "×¢ ×Ļ׾", + "ĠЧ е", + "à¹ģà¸Ļ à¹Ī", + "lı ÄŁ", + "lıģ ın", + "Ġ×ŀ×¢ ×¨×Ľ×ª", + "好ãģį ãģª", + "มาà¸ģ à¸Ĥึà¹īà¸Ļ", + "×ŀ×¢ ×ijר", + "ĠاÙĦÙħ غرب", + "ĠпеÑĢ и", + "ĠпеÑĢи од", + "Ġnh ạc", + "ا ÙĪÙĬ", + "ĠÙĪ عÙĦÙī", + "أخ Ø°", + "ĠC ô", + "תר ×ij×ķת", + "×Ĵ ×Ķ", + "Ġktóre j", + "×IJ ×Ļת", + "×ij ×ķ×IJ", + "д елÑĮ", + "รี วิ", + "รีวิ ว", + "ж Ñĥ", + "Ġ×ij×Ĺ ×ķ", + "еÑĪ ÑĮ", + "ĠØ£ ÙĦÙģ", + "ĠاÙĦÙĪ Ø·ÙĨÙĬ", + "ĠاÙĦÙħÙĨ Ø·ÙĤØ©", + "nÄħ Äĩ", + "Ġthi ên", + "иÑĩеÑģк ой", + "ĠاÙĦÙħ ÙĦ", + "Ġع Ùħ", + "ס פר", + "Ġnh óm", + "ÙĪص Ùģ", + "ĠCh úng", + "Ġر ÙĤÙħ", + "ãģ¾ãģĹãģŁ ãģĮ", + "al ité", + "ล ม", + "ĠëĤ´ ê°Ģ", + "׾ק ×ķ×Ĺ", + "ĠS Æ¡n", + "pos ição", + "mi ÄĻ", + "Ġtr ánh", + "ĠÄIJ á»Ļ", + "׼ ×Ĺ", + "ãģĤ ãģ£ãģ¦", + "à¸Ńย à¹Īา", + "Ġ×ŀ×Ĺ ×Ļר", + "Ġ×Ķ ×Ļת×Ķ", + "à¸Ľ à¹Īา", + "à¸Ńืà¹Īà¸Ļ à¹Ĩ", + "Ø´ ÙĤ", + "×ł×¡ ×Ļ", + "ë¦ ¼", + "ãģ¦ãģĹãģ¾ ãģĨ", + "Ġ×ŀ צ×ij", + "ãģ« åĩº", + "ÙħÙĪا Ø·ÙĨ", + "ยัà¸ĩ มี", + "алÑĮ нÑĭе", + "san ız", + "Ø¥ سرائÙĬÙĦ", + "ĠvÃł i", + "ì¤ Ħ", + "ãģ¨æĢĿ ãģ£ãģ¦", + "×Ļ ×ķ׳×Ļ", + "çĶŁ ãģį", + "Ġs âu", + "Ñĩ иÑģÑĤ", + "Ġl á»ħ", + "ĠGi á", + "à¸Ńุ à¸Ľ", + "à¸Ńà¸¸à¸Ľ à¸ģร", + "à¸Ńà¸¸à¸Ľà¸ģร à¸ĵà¹Į", + "Ġnh ẹ", + "r ö", + "ס ×ĺ×Ļ", + "ãģķãĤĵ ãģĮ", + "Ġd ầu", + "ع Ùİ", + "ت را", + "×Ĵ×ĵ ׾", + "Ġtécn ica", + "׼ ׳×Ļ×Ŀ", + "תק ש", + "תקש ×ķרת", + "Ġн его", + "ét ait", + "Ġm á»ģm", + "Ñģ еÑĤ", + "Ġnh áºŃt", + "Ġ×ŀ ×¢×ľ", + "Ġ×Ķ×¢ ×ij×ķ×ĵ", + "Ġ×Ķ×¢×ij×ķ×ĵ ×Ķ", + "Ġ×Ĵ ×Ļ׾", + "ãģ¯ ãģªãģĦ", + "ائ ØŃ", + "Ġз деÑģÑĮ", + "×IJ ×Ļ׳×ĺר", + "Ùħ ÙIJ", + "Ġ×Ļ ×Ĺ×ĵ", + "ر اÙģ", + "ì²ĺ 리", + "×ĵ ×¢×ķת", + "ì¹ ľ", + "ĠТ о", + "ĠTh ế", + "ì¶ ©", + "Ġ׳׼ ×ķף", + "عÙĬ Ø´", + "ни з", + "Ġج اÙĨب", + "×ŀק צ×ķ×¢", + "à¹Ĥ à¸ĭ", + "Ñģ ÑĥÑĤ", + "ìĸ´ ìļĶ", + "ãĤĴè¦ĭ ãģ¦", + "ار د", + "Ġaç ıl", + "ĠاÙĦØŃ ÙĬاة", + "à¸ģà¹ĩ à¹Ħà¸Ķà¹ī", + "ãģĿãĤĮ ãĤĴ", + "عض ÙĪ", + "Ġг ÑĢаж", + "ĠгÑĢаж дан", + "à¸Īะ à¸ķà¹īà¸Ńà¸ĩ", + "ĠìĿ´ 룬", + "ĠìĿ´ëŁ¬ íķľ", + "Ġtr ách", + "ÙĨ Ùİ", + "Ġkı sa", + "à Ķ", + "ÑĪ ка", + "ãģ® 人", + "ĠÐŁ оÑģ", + "ĠÐŁÐ¾Ñģ ле", + "Ñĥ лÑĮ", + "ÙĪا جÙĩ", + "ÙĤ رب", + "à¸Ľà¸ıิ à¸ļัà¸ķิ", + "ê° Ļ", + "Ġ×ŀ ׳", + "ĠÑģво и", + "بر اÙħج", + "Ġر ÙĪ", + "пÑĢ од", + "пÑĢод аж", + "Ġby ÅĤy", + "วั ย", + "Ġgör ün", + "Ġà Ī", + "ÑİÑī им", + "ĠÑĤак ой", + "Ùģ ÙĪر", + "ĠÙģ عÙĦ", + "Ġб ел", + "ëIJ ł", + "er ÃŃa", + "ĠÑģво Ñİ", + "Ġl ã", + "Ġlã nh", + "à¹Ģà¸ŀืà¹Īà¸Ń à¹ĥหà¹ī", + "ÙĤ ÙĨ", + "تط ÙĪÙĬر", + "Ġsay ı", + "ĠÑģ ейÑĩаÑģ", + "Ġ×IJ×Ĺר ת", + "ק ×ķפ×Ķ", + "ק×ķר ס", + "Ġس Ùħ", + "Ġ×ĺ ×Ļפ×ķ׾", + "ìĿ´ëĿ¼ ëĬĶ", + "دراس Ø©", + "èµ· ãģĵ", + "×Ĺ ×Ļ׳", + "×Ĺ×Ļ׳ ×ķ×ļ", + "×ĵ ק", + "Ġë§ ŀ", + "Ġком анд", + "ĠÐij о", + "Ġиг ÑĢÑĭ", + "à¸ļ ี", + "ĠØ£ Ùİ", + "в ен", + "ĠاÙĦج دÙĬد", + "ĠÙĦ Ø¥", + "Ġ×ķ×IJ ׳×Ļ", + "Ġ×Ķס ×Ļ", + "иÑĩеÑģк ого", + "رÙĪ ØŃ", + "à¸ģาร ศึà¸ģษา", + "ĠTr Æ°á»Ŀng", + "иг ÑĢа", + "ıl ması", + "Ġм аÑģÑģ", + "ãģ¨ãģį ãģ«", + "à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ", + "à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา", + "ĠاÙĦساب ÙĤ", + "Ġ×ŀ×¢ ×ĺ", + "в аÑĤÑĮ", + "m Ã¼ÅŁ", + "Ġ׾ ׼×ļ", + "Ġt á»ĭch", + "Ùģ ÙĩÙħ", + "تد رÙĬب", + "Ø´ Ùĥ", + "Ġ×ij ×ŀ×Ļ", + "Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ", + "ÙĤØ· اع", + "ãģª ãģĹ", + "×ķצ ×Ļ×IJ", + "ĠÙĪ سÙĬ", + "з Ñĥ", + "Ġy at", + "Ġyat ırım", + "ë§ İ", + "Ġth ắng", + "ãģĬ 客", + "ãģĬ客 æ§ĺ", + "ĠThi ên", + "ãģ«å¯¾ ãģĹãģ¦", + "ÑĢ иÑģ", + "ÙĨت ائ", + "ÙĨتائ ج", + "Ġ×ŀ שר", + "Ġ×ŀשר ×ĵ", + "Ġتع اÙĦ", + "ĠتعاÙĦ Ùī", + "ש ׳×Ļ", + "Ùĩ اÙħ", + "×IJ׳ ש×Ļ×Ŀ", + "Ġżyc ia", + "ĠÑĢÑĥб лей", + "ÙĬ ض", + "Ġkat ıl", + "ĠÙħ ÙĪضÙĪع", + "Ġvard ır", + "ĠÙħÙĨ Ø·ÙĤØ©", + "ĠTr ần", + "Ġв еÑģ", + "ü p", + "Ùħ ÙĪÙĨ", + "ÑĪ ли", + "Ġn óng", + "Ø® ÙĦÙģ", + "ĠС ÑĤа", + "Ġд оÑĢ", + "ĠдоÑĢ ог", + "ĠwÅĤa ÅĽnie", + "eÄŁ in", + "Ġhi á»ĥm", + "ĠС ам", + "ê»ĺ ìĦľ", + "ĠÑĦ а", + "ãģ» ãģĨ", + "ãģ»ãģĨ ãģĮ", + "×ķפ ×Ļ×¢", + "ê° Ī", + "د ÙĪÙĦ", + "Ġthu ê", + "Ġch á»Ĺ", + "Ġëĭ¹ ìĭł", + "ãģij ãĤĮ", + "ãģijãĤĮ ãģ©", + "ë³´ íĺ¸", + "ãģķãĤĮ ãģ¦ãģĦãģ¾ãģĻ", + "Ġнад о", + "ĠìĤ¬ëŀĮ ëĵ¤", + "à¹Ģà¸Ĥ à¸ķ", + "สม ัย", + "z ÅĤ", + "ت ÙĪر", + "Ġש ת×Ļ", + "v ê", + "Ġ×ijת ×ķ×ļ", + "à¸Ĭ ัย", + "ãģĦ ãģ£ãģŁ", + "ìĿ ij", + "Ġt ầ", + "Ġtầ ng", + "ש ׼ר", + "Ġê¸ Ģ", + "Ġ×Ķש ׳×Ķ", + "Ġا ÙĨÙĩ", + "ç«ĭ ãģ¡", + "r és", + "füh ren", + "ر ØŃÙħ", + "ê· ¹", + "ĠâĢ «", + "Ġsu ất", + "à¸Ł ิ", + "ÙĬ Ùĩا", + "ĠاÙĦ اتØŃاد", + "Ġt uyá»ĥn", + "ãģ¾ ãĤĭ", + "Ġm ại", + "Ġng ân", + "ãĤ° ãĥ©", + "欲 ãģĹãģĦ", + "س ار", + "ãĤĤãģ® ãģ§ãģĻ", + "ки е", + "Ġseç im", + "åħ¥ ãĤĬ", + "ãģªãģ© ãĤĴ", + "ÑĤ ÑĢи", + "ĠÑģп еÑĨ", + "ĠØ£ د", + "Ġод но", + "ÑĪ ел", + "ãĥĩ ãĥ¼ãĤ¿", + "ãĤ· ãĤ¹ãĥĨ", + "ãĤ·ãĤ¹ãĥĨ ãĥł", + "è¡Į ãģį", + "ãģ¨æĢĿ ãģ£ãģŁ", + "à¹Ģà¸ģิà¸Ķ à¸Ĥึà¹īà¸Ļ", + "ĠÑĤ ож", + "ĠÑĤож е", + "Ġs ạch", + "ĠÑģ ÑĢок", + "Ġкли енÑĤ", + "ĠÙħØ´ رÙĪع", + "Ġalt ında", + "Ġì ·¨", + "ä¸Ń ãģ®", + "ãģķãģĽ ãĤĭ", + "ãģĻ ãģ¹", + "ãģĻãģ¹ ãģ¦", + "ê°ľ ë°ľ", + "ĠÄij êm", + "ãģªãģĦ ãģ®ãģ§", + "ì² ł", + "×¢ ×ij×ĵ", + "Ġd ấu", + "à¸Ħà¸Ļ à¸Ĺีà¹Ī", + "ĠC ách", + "تع ÙĦÙĬÙħ", + "Ġh ại", + "ãĤ» ãĥķãĥ¬", + "ĠÙĨÙģس Ùĩ", + "ĠíĨµ íķ´", + "ÑĪ ло", + "Ġнап ÑĢав", + "ĠнапÑĢав лен", + "ÑĢÑĥ Ñĩ", + "íĶ Į", + "Ġ×ijר ×Ļ×IJ", + "ãģ® ãģ¿", + "ãģ«ãģĬ ãģĦãģ¦", + "×ij ׳ק", + "ãĤ¨ ãĥ³", + "Ø«ÙĦ اث", + "Ġm ỹ", + "ĠÑģай ÑĤе", + "Ġе мÑĥ", + "ت غÙĬ", + "تغÙĬ ÙĬر", + "خص ÙĪص", + "ÑĤе ли", + "Ġ×ķ׾ ׼ף", + "פע ×Ŀ", + "Ġпо ÑįÑĤомÑĥ", + "ر اÙĨ", + "иÑĤел ей", + "пиÑģ ан", + "×¢ ×¥", + "ĠìĤ¬ ìĹħ", + "Ùħ ز", + "جÙħ ÙĬع", + "ë©´ ìĦľ", + "à¸ľà¸¥à¸´à¸ķ à¸łà¸±", + "à¸ľà¸¥à¸´à¸ķà¸łà¸± à¸ĵ", + "à¸ľà¸¥à¸´à¸ķà¸łà¸±à¸ĵ à¸ij", + "à¸ľà¸¥à¸´à¸ķà¸łà¸±à¸ĵà¸ij à¹Į", + "ĠпÑĢ имеÑĢ", + "ãĤŃ ãĥ¼", + "l â", + "Ġch Äĥm", + "缮 ãģ®", + "ãģĦ ãģĭ", + "ãģ¨è¨Ģ ãģĨ", + "×ĸ ×ķ×Ĵ", + "Ġ×ij ×ĵ×Ļ", + "Ġ×ij×ĵ×Ļ ×ķק", + "ãģĬ åºĹ", + "à¸ķà¸Ńà¸Ļ à¸Ļีà¹ī", + "Ġph á»iji", + "п ÑĤ", + "สà¸Ļ าม", + "Ø· ÙĪ", + "ص اØŃ", + "صاØŃ Ø¨", + "ĠD ü", + "ĠDü nya", + "Ġп ока", + "п ал", + "ĠÄij ảo", + "ĠاÙĦÙģ ÙĪر", + "ĠاÙĦÙģÙĪر Ùĥس", + "Ġmá u", + "кÑĢ еп", + "ĠاÙĦس اعة", + "ĠгоÑĢ ода", + "Ùģ صÙĦ", + "ай ÑĤе", + "Ġд ог", + "Ġдог овоÑĢ", + "ĠØ¥ Ø°", + "Ġ×ij׼׾ ׾", + "ÙĬ تÙĩ", + "×Ĵ ×ijר", + "Ġbir ç", + "Ġbirç ok", + "문 íĻĶ", + "ãģĿãģĨ ãģª", + "را ØŃ", + "ĠÙħ رة", + "ĠденÑĮ ги", + "f ä", + "à¸Ĥà¹īา ว", + "ĠÑģов ÑĢем", + "ĠÑģовÑĢем енн", + "׾×Ĺ ×¥", + "èī¯ ãģı", + "ĠÙģ Ø£", + "Ġ×ķ ×ĸ×Ķ", + "Ġз ани", + "Ġзани ма", + "Ġê°Ģì§Ģ ê³ł", + "Ġh Æ¡i", + "ãģªãģ® ãģĭ", + "ãĥĨ ãĥ¬ãĥĵ", + "Ġר ×ij×ķת", + "à¸ķ ี", + "Ġ×ijש ×ł×ª", + "ĠT ại", + "Ġthu áºŃn", + "Ñģ ел", + "Ñij м", + "dzi Äĩ", + "ĠÑģ ка", + "ĠÑģка Ñĩ", + "ĠÑģкаÑĩ аÑĤÑĮ", + "×ķ×ŀ ×ķ", + "г ла", + "Ġмин ÑĥÑĤ", + "åĩº ãģĻ", + "Ġ×Ĺ×Ļ ×Ļ×ij", + "Ġת ×Ĵ×ķ×ij×Ķ", + "à¸£à¸¹à¸Ľ à¹ģà¸ļà¸ļ", + "ни ÑĨа", + "ĠÄ° n", + "ĠØ£ ع", + "Ġض ÙħÙĨ", + "Ùħ ثاÙĦ", + "ĠyaÅŁ an", + "ĠìĹ° 구", + "ĠL ê", + "ש׾ ×Ĺ", + "ãģı ãģªãĤĭ", + "ìĹĨ ìĿ´", + "ĠÑĤ ÑĢи", + "ĠÑĩаÑģÑĤ о", + "Ġоб ÑĢаÑĤ", + "п ло", + "د Ø®", + "دخ ÙĪÙĦ", + "س Ùĩ", + "à¸Ń าà¸ģ", + "à¸Ńาà¸ģ าศ", + "Ġ׼ ×ĸ×Ķ", + "Ġ×Ķ×¢ סק", + "ĠاÙĦØ£ ÙĨ", + "å¹´ ãģ«", + "×¢ ש×ķ", + "Ġש ×¢×ķת", + "Ġm Ãłn", + "×IJר ×Ļ", + "sı yla", + "Ùģر ÙĤ", + "ни Ñħ", + "Ġت ست", + "è¦ĭ ãģ¦", + "ØŃا ÙĪÙĦ", + "×IJ ×Ļ׼×ķת", + "ĠbaÅŁ ladı", + "st Äħ", + "stÄħ pi", + "à¸Ĺีà¹Ī à¹Ģรา", + "ÙĤر ر", + "ج اب", + "Ġ×ijר ×ķר", + "à¹Ģà¸Ĥà¹īา à¹ĥà¸Ī", + "×ŀ׊קר", + "al ım", + "Ġס ×Ļפ×ķר", + "ãģ§ãģĤ ãĤĮãģ°", + "Ġש×ŀ ×ķר×ķת", + "Ġ×ķ ×ŀ×Ķ", + "ãģĵ ãģĿ", + "id ée", + "ä¸ĭ ãģķãģĦ", + "تÙĨا ÙĪÙĦ", + "Ġ ลà¹īาà¸Ļ", + "Ġìļ°ë¦¬ ëĬĶ", + "اÙĨ ا", + "ÑģÑĤ ой", + "б оÑĤ", + "ĠyaÅŁ am", + "kö y", + "Ø¥ ÙĦ", + "ÑĢ Ñĭв", + "기 ìĹħ", + "Ġ×Ķ×ŀ ×ĵ", + "Ġ×Ķ×ŀ×ĵ ×Ļ׳×Ķ", + "د ب", + "×¢ ×Ļ׳×Ļ", + "×ŀ ת×Ĺ", + "Ġפ ר×Ļ", + "ãĥĭ ãĥ¼", + "اÙħ ÙĬ", + "Ġnh ằm", + "ãĤĮ ãģªãģĦ", + "ت عرÙģ", + "Ġë§Ī ìĿĮ", + "ìĵ °", + "Ġh ấp", + "ר×Ĵ ×Ļ׾", + "ب Ùİ", + "Ġr Äĥng", + "gl Äħd", + "ĠÑģиÑģÑĤем Ñĭ", + "Ġkh óa", + "ãģ§ãģĻ ãĤĪãģŃ", + "大ãģį ãģı", + "기 를", + "Ġké o", + "ÙĪ Ø¡", + "ج اÙħ", + "جاÙħ ع", + "Ġ×¢ ×Ļצ×ķ×ij", + "t éri", + "Ġת ש", + "Ġ×IJ ×ij×Ļ", + "ĠCh Æ°Æ¡ng", + "à¸ļริ à¹Ģว", + "à¸ļริà¹Ģว à¸ĵ", + "ãģ¤ ãģı", + "Ġ×Ĺ ×ķ׾", + "עת ×Ļ×ĵ", + "ש ×Ļ×ŀ×Ķ", + "ëĤ ¨", + "Ġש×IJ ×Ļף", + "ĠÙĪاÙĦ Ø¥", + "ÑĦ а", + "Ġkh ám", + "Ġ×ĺ ×ķ×ij×Ķ", + "ĠвÑĭ Ñģ", + "ĠвÑĭÑģ око", + "ĠاÙĦØŃ Ø¯ÙĬØ«", + "人 ãĤĤ", + "d Ã¼ÄŁÃ¼", + "×Ļ×Ĺ ×ķ×ĵ", + "تع ÙĦÙĬ", + "تعÙĦÙĬ ÙĤ", + "l ö", + "تØŃ Ø¯ÙĬد", + "н его", + "ĠÑĥд об", + "Ġ׾ ×ŀ×Ļ", + "Ġר ×ķצ×Ļ×Ŀ", + "Ġج اء", + "Ġ×ij ×ĸ×ŀף", + "à¸Ľà¸ģ à¸ķิ", + "é«ĺ ãģı", + "à¸Ľà¸¥ า", + "Ġart ık", + "Ġbug ün", + "ק ׳×Ļ", + "Ġkho á", + "ĠÙħ رÙĥز", + "ĠìŀIJ 기", + "در جة", + "×ŀש ר×ĵ", + "Ġgi ấy", + "Ġch óng", + "ק פ", + "ÙĬب Ø©", + "ĠczÄĻ sto", + "в али", + "Ùĥ ب", + "ìŁ ģ", + "ส à¸ļาย", + "à¸Ľà¸£à¸°à¸Ĭา à¸Ĭà¸Ļ", + "×Ĵ ×ķ×£", + "ëŁ ī", + "ãģ® ãģĵãģ¨", + "ล à¸Ń", + "Ġngh á»ī", + "åŃIJ ãģ©", + "åŃIJãģ© ãĤĤ", + "à¹Ħà¸Ķ à¹īà¸Ńย", + "à¹Ħà¸Ķà¹īà¸Ńย à¹Īาà¸ĩ", + "×ĵ ×¢", + "ĠاÙĦت Ùī", + "ĠÑģов еÑĤ", + "Ġqual itÃł", + "åĩº ãģĹ", + "ĠÑĢÑĥк ов", + "ĠÑĢÑĥков од", + "ราย ละà¹Ģà¸Ńียà¸Ķ", + "ãģªãģĭ ãģªãģĭ", + "기 ê´Ģ", + "Ġ×Ĺ ×ķש", + "Ġ×Ĺ×ķש ×ij", + "л оÑĤ", + "à¸Ļะ à¸Ħรัà¸ļ", + "ק×ij ×ķצ×Ķ", + "Ġth ái", + "Ġש ×ij×Ķ", + "ĠÑĪ кол", + "ĠÙĦ ÙĥÙĦ", + "à¹ĥà¸Ļ à¸Ĭà¹Īวà¸ĩ", + "ĠÙħ ÙĥاÙĨ", + "ë ķĮ", + "Ġc ải", + "ĠCh ÃŃ", + "ÑĥÑĩ а", + "ìĿ µ", + "Ġx ảy", + "à¸Ĭà¸Ļ ิà¸Ķ", + "Ġc áºŃu", + "к ÑĢов", + "ss é", + "ĠÙĨ ÙĪع", + "ĠТ а", + "Ø® Ùħس", + "פ×ķס ×ĺ", + "Ġm ắc", + "ĠÄij em", + "à¸ģาร à¹ĥà¸Ĭà¹ī", + "ר ×ķס", + "ĠÐĽ е", + "Ġth á»Ń", + "รà¹Īาà¸ĩ à¸ģาย", + "üz ü", + "æĹ¥æľ¬ ãģ®", + "ê³¼ ìłķ", + "ש ×Ļ×IJ", + "ĠìŀĪ ê³ł", + "×ij ×ķ׾", + "ìķ ħ", + "ĠÙĪاÙĦ ا", + "ĠÐĽ и", + "ĠвÑģ Ñij", + "Ġużytk ow", + "×Ĺ ×ķ׾", + "ر Ùģض", + "Ġson uç", + "ãģĦ ãģ¾ãģĽãĤĵ", + "ìĤ¬ ìĹħ", + "ëĪ Ħ", + "ÑĤ ек", + "Ġud ziaÅĤ", + "л ез", + "Ġ×Ķ×Ļ ×Ļת×Ļ", + "ãĤīãĤĮ ãģ¦", + "Ùħس ؤÙĪÙĦ", + "ر ار", + "ÑĤ ан", + "ĠÄij Ãło", + "Ġר ×ķ×ij", + "Ġ×ijש×ij ×Ļ׾", + "ä»ĬåĽŀ ãģ¯", + "ãĤ¸ ãĥ¥", + "Ġ×¢ ×ijר", + "ãģĽ ãģ¦", + "п олÑĮ", + "ak lı", + "Ġk ÃŃnh", + "د ت", + "лож ение", + "ĠاÙĦÙħ ص", + "ĠاÙĦÙħص رÙĬ", + "à¸Īริà¸ĩ à¹Ĩ", + "ĠاÙĦشر ÙĥØ©", + "ĠÄij á»ı", + "ãĥĽ ãĥĨ", + "ãĥĽãĥĨ ãĥ«", + "Ñį кон", + "Ñįкон ом", + "ĠÙĪ عÙĨ", + "Ġת ׳", + "Ġ×ª×ł ×IJ×Ļ", + "ĠاÙĦدÙĪÙĦ ÙĬØ©", + "Ġì§Ģ ìĹŃ", + "ãģ§ãģĻ ãģĭ", + "Ġв аÑĢи", + "ĠваÑĢи анÑĤ", + "ĠاÙĦع رب", + "ел а", + "Ġt Æ°á»Ľng", + "sk Äħ", + "Ġm ặc", + "ส ัà¸ģ", + "ãĥĵ ãĥ¼", + "Ġ×ij ×Ĵ׾", + "Ġ×ij×Ĵ׾ ׾", + "ãĥķãĤ¡ ãĥ³", + "×ij ×Ļצ", + "×ij×Ļצ ×ķ×¢", + "ли ÑģÑĤ", + "à¸Ł ุ", + "à¸Łà¸¸ à¸ķ", + "à¸Łà¸¸à¸ķ à¸ļà¸Ńล", + "à¸Ŀ à¹Īาย", + "ìŀIJ ìĿĺ", + "Ġس ÙĪÙģ", + "Ġש ×Ķת", + "Ġê± ¸", + "×¢ ×ij×ķ×ĵ", + "ãģĻãĤĭ ãģĵãģ¨ãģĮ", + "ĠÑĩа ÑģÑĤÑĮ", + "ãĤ¢ ãĥ¡ãĥª", + "ãĤ¢ãĥ¡ãĥª ãĤ«", + "Ġtak ım", + "Ġs Ỽ", + "ĠsỼ m", + "שר ×Ķ", + "è¨Ģ ãģĨ", + "л ан", + "ì» ¤", + "׼ ׳×Ķ", + "ÙĪÙģ ÙĬ", + "íĹ Ī", + "lu ÄŁu", + "ĠëĮĢ íķ´", + "Ġ׾×ij ×Ļת", + "Ġ×Ķר×IJש ×ķ׳×Ķ", + "ص Ùħ", + "Ġsö yled", + "Ġsöyled i", + "à¸Ľ าà¸ģ", + "Ġard ından", + "ãģĪ ãģŁ", + "à¸Ĺัà¹Īว à¹Ħà¸Ľ", + "Ġ׳×ķס ×£", + "б олÑĮ", + "ãĤĵãģ§ãģĻ ãģijãģ©", + "ĠлиÑĪ ÑĮ", + "Ġ×ij ×IJ×Ļ", + "ĠбÑĭ ÑģÑĤÑĢо", + "ส ัà¸Ļ", + "Ġ×ij פ׳×Ļ", + "л еÑĩ", + "ĠاÙĦØ® بر", + "Ġsó c", + "Ġth ú", + "Ġп ÑıÑĤ", + "ãģĬ é¡ĺ", + "ãģĬé¡ĺ ãģĦ", + "ÑĤ ин", + "ãģ«ãģ¤ãģĦãģ¦ ãģ¯", + "פ ף", + "Ġдв ÑĥÑħ", + "à¸į ีà¹Ī", + "à¸įีà¹Ī à¸Ľ", + "à¸įีà¹Īà¸Ľ ุ", + "à¸įีà¹Īà¸Ľà¸¸ à¹Īà¸Ļ", + "оп еÑĢ", + "ĠاÙĦب شر", + "ĠاÙĦÙħ اÙĦ", + "ıyor uz", + "تØŃ ÙħÙĬÙĦ", + "à¸ģ ะ", + "éĸĵ ãģ«", + "×Ĺ ×ķש", + "ĠNg uyên", + "ãģĦãģ¦ ãģĦãĤĭ", + "дÑĥ ÑĪ", + "ש פע", + "ÑĪ Ñĥ", + "å®Ł éļĽãģ«", + "ĠÑĢай он", + "ĠCh á»ī", + "ÙĨ صر", + "Ġìļ ´", + "Ġìļ´ ìĺģ", + "Ġ×Ķ×ĵ ×Ļף", + "ØŃد د", + "ر ز", + "ĠاÙĦد Ùħ", + "ĠPh áp", + "ÑĤ ÑģÑı", + "è¦ĭ ãģĪ", + "Ġti á»ĥu", + "Ġs á»Ńa", + "а ÑİÑĤÑģÑı", + "ĠB á", + "Ġ×ķ ׼׾", + "Ð ĸ", + "ÑĪ им", + "ìĿ´ ëĬĶ", + "л ев", + "d ık", + "Ġprés ente", + "Ġara ç", + "صد ÙĤ", + "Ġпом ог", + "ĠاÙĦشر ÙĤ", + "ĠÙĪاÙĦ Ø°ÙĬ", + "رÙĬ ا", + "×ij ׳×ķת", + "Ġng á»ĵi", + "ר ×ķפ", + "ר×ķפ ×IJ", + "Ġth ấp", + "ãĤĦ ãģ¯", + "ãĤĦãģ¯ ãĤĬ", + "ĠاÙĦج دÙĬدة", + "éĿŀ常 ãģ«", + "ÙĬÙĦ ÙĬ", + "ìª ½", + "تع اÙħÙĦ", + "ãģł ãģ¨æĢĿãģĦãģ¾ãģĻ", + "Ùħ Ùħ", + "иÑĤе ли", + "ãĤµãĤ¤ ãĤº", + "اد ات", + "ĠاÙĦÙħ اÙĦÙĬØ©", + "Ùĥات ب", + "к ли", + "веÑĢ Ñħ", + "ни Ñĩ", + "Ġ×ľ×¢ ×ij×ķ×ĵ", + "׾ ×Ļ×Ķ", + "ØŃ Ùİ", + "ãĤ¤ ãĥĻ", + "ãĤ¤ãĥĻ ãĥ³ãĥĪ", + "Ġת ×Ĵ×ķ×ij×ķת", + "ÑĦ он", + "ĠдÑĢÑĥг ие", + "×IJ ×ĸ×ķר", + "Ġper ò", + "ìķ ŀ", + "åĢŁ ãĤĬ", + "ר צ×Ļ", + "×IJ ×ĸ", + "алÑĮ нÑĭÑħ", + "Ġê²ĥ ìľ¼ë¡ľ", + "ĠпÑĢав о", + "ĠاÙĦØ£ رض", + "à¹Ģà¸Ĺ à¸Ħ", + "à¹Ģà¸Ĺà¸Ħ à¹Ĥà¸Ļ", + "à¹Ģà¸Ĺà¸Ħà¹Ĥà¸Ļ à¹Ĥล", + "à¹Ģà¸Ĺà¸Ħà¹Ĥà¸Ļà¹Ĥล ย", + "à¹Ģà¸Ĺà¸Ħà¹Ĥà¸Ļà¹Ĥลย ี", + "צ ר×Ļ", + "ĠÐļ Ñĥ", + "ıl ma", + "決 ãĤģ", + "ا ÙĪ", + "Ġ×ĵ ק×ķת", + "à¸Ħร ู", + "ĠÙħست ÙĪÙī", + "à¸Ľ à¹īà¸Ńà¸ĩ", + "à¸Ľà¹īà¸Ńà¸ĩ à¸ģัà¸Ļ", + "×ĵ ×ķ×ŀ×Ķ", + "ĠÑģ егоднÑı", + "س ÙĪÙĤ", + "ר×Ĺ ×ķ×ij", + "ĠØ¥ دارة", + "Ñħ ож", + "éģİ ãģİ", + "à¸Ħ à¸Ń", + "нÑĥ л", + "×ķ׼ ×Ķ", + "ÙĪ اÙģÙĤ", + "׼׾ ׾", + "Ġ×Ķ ×ĵ×ķ", + "Ġl Ä©nh", + "Ġkh ảo", + "×IJ×ŀ צע", + "ë¨ ¸", + "Ġ׼ ×Ļצ", + "Ġ׼×Ļצ ×ĵ", + "Ġдолж нÑĭ", + "หว ัà¸ĩ", + "ãĥĩ ãĤ¶", + "ãĥĩãĤ¶ ãĤ¤ãĥ³", + "Ġng á»Ŀ", + "ä¸Ń ãģ«", + "à¸ģลัà¸ļ มา", + "جÙħ اÙĦ", + "à¸Ķัà¸ĩ à¸ģลà¹Īาว", + "س ÙĥÙĨ", + "س ÙĨ", + "Ġözellik le", + "з еÑĢ", + "rz ÄĻ", + "×ŀ ×ķר×Ķ", + "Ġl ạ", + "×ŀ ×Ļ׳×Ļ", + "ר ×Ļת", + "ãģĿãĤĮ ãģĮ", + "ãģĭ ãĤĮ", + "ĠÙĬÙħÙĥÙĨ Ùĥ", + "öff entlich", + "г ан", + "ĠاÙĦØŃ ÙĦ", + "ĠmiÄĻd zy", + "ĠÑĩа ÑģÑĤи", + "ujÄħ cy", + "ĠbaÄŁ lı", + "ĠiliÅŁ ki", + "Ùģ اء", + "ãĥª ãĥ³ãĤ°", + "Ġhã ng", + "ĠконÑĤ ÑĢ", + "ĠконÑĤÑĢ ол", + "к оп", + "ש ×Ļ×¢", + "ש×Ļ×¢ ×ķר", + "ĠÐĴ аÑĪ", + "Ġ×Ķ תק", + "ÙħÙĨ ع", + "ĠpolÃŃt ico", + "Ġг олов", + "ĠØ¥ ÙĬ", + "Ø¥ ÙĨتاج", + "à¸ļ ิ", + "Ġг овоÑĢ", + "ĠговоÑĢ иÑĤ", + "Ġph á»ķ", + "ĠÑģем ÑĮ", + "ãģ¯ ãģĤãĤĬãģ¾ãģĽãĤĵ", + "ĠÙĪ است", + "×ŀש פ×ĺ", + "з ем", + "×ŀ×ĵ ×ijר", + "Ġíģ °", + "ĠìĿ´ ë²Ī", + "ê°Ģ ëĬĶ", + "Ġì§Ģ ìĽIJ", + "Ġca ÅĤy", + "Ġgeli ÅŁtir", + "Ñģк ое", + "pos é", + "Ġkh ô", + "à¸ķิà¸Ķ à¸ķาม", + "miss ão", + "Ġ׾ ×ŀר", + "Ġ׾×ŀר ×ķת", + "Ġb ó", + "à¸ķรวà¸Ī สà¸Ńà¸ļ", + "Ġngh á»ģ", + "Ġб из", + "Ġбиз неÑģ", + "ÑģÑĤ еÑĢ", + "ÙĪ Ùİ", + "楽 ãģĹãģ", + "楽ãģĹãģ ¿", + "ãģĵãĤĮ ãģĭãĤī", + "wiÄħ zan", + "ส à¸Ńà¸Ļ", + "Ùħ ÙĪر", + "׳×ĵ ׾", + "Ġ×Ķ×IJ ×ĵ×Ŀ", + "Ġм олод", + "ØŃ Ùħا", + "ØŃÙħا ÙĬØ©", + "ÑģÑĤ ÑĢан", + "Ġbu á»ķi", + "ת×Ļ ×Ļ×Ŀ", + "abile ceÄŁi", + "L Ä°", + "à¹Ģย à¸Ńะ", + "à¸Ī ร", + "س ÙĥاÙĨ", + "à¸Ļ ัà¸Ķ", + "Ġm ấy", + "ĠÐij а", + "s ÅĤaw", + "ĠÙģ ÙĦا", + "ĠкоÑĤоÑĢ ой", + "Ġпло Ñī", + "ĠплоÑī ад", + "ãĤĤ ãģĤãĤĬ", + "sz czÄĻ", + "×Ļפ ×ķ", + "ש×ŀ ת", + "owa ÅĤa", + "Ġn ông", + "צ×ij ×IJ", + "ĠìŀĪ ìĹĪ", + "ãģ¾ ãģ¨", + "ãģ¾ãģ¨ ãĤģ", + "ÙĤÙĪ ات", + "ãģ¿ ãĤĵãģª", + "Ġ׼ ×ŀ×¢×ĺ", + "Ġx úc", + "ï¼ Ĩ", + "r ÄĻ", + "rÄĻ cz", + "×ĵ ×ŀ×Ļ", + "Ġt áºŃn", + "à¸Ķ วà¸ĩ", + "ê²½ ìłľ", + "п ÑĥÑĤ", + "Ø£ ربع", + "Ġ×ŀ שת×ŀש", + "ãĤ¿ãĤ¤ ãĥĹ", + "Ġìłľ ê°Ģ", + "Ġ׾ ׼ף", + "ĠобÑĢаз ом", + "ÙĬÙĥ ا", + "w ÅĤ", + "wÅĤ asn", + "ĠاÙĦÙĪØ·ÙĨ ÙĬØ©", + "بÙĬ ب", + "×ŀ ׾×Ļ", + "к ÑĢаÑĤ", + "기 ìĹIJ", + "ÙĤ اد", + "ĠÙĦ دÙī", + "à¸Ħวาม รูà¹ī", + "×ŀ×ĵ×Ļ׳ ×Ļ×ķת", + "ê² ¨", + "Ġíĺ Ħìŀ¬", + "ש ת×Ļ", + "м ол", + "Ġmá i", + "à¸ŀิ ม", + "à¸ŀิม à¸ŀ", + "à¸ŀิมà¸ŀ à¹Į", + "หล วà¸ĩ", + "Ġx uyên", + "×Ĺ ×¡×¨", + "رÙĪ ÙĨ", + "ãģĿãģĨ ãģĦãģĨ", + "ãģĿãĤĮ ãģŀ", + "ãģĿãĤĮãģŀ ãĤĮ", + "Ġ׼ ש×Ķ", + "ÐŁ ÑĢав", + "×ŀ×ij צע", + "ع رب", + "Ġbü yü", + "פ×Ļת ×ķ×Ĺ", + "à¸Ī à¸ļ", + "ĠØ£ Ùĥبر", + "שר ת", + "×ŀ׼ ש×Ļר", + "ĠÙĪ Ùħع", + "ãģ® ãģŁãĤģãģ«", + "à¸Ļ ัà¸ļ", + "ì° °", + "ãĥª ãĥķãĤ©", + "ãĥªãĥķãĤ© ãĥ¼ãĥł", + "Ġc Æ°á»Ŀng", + "ĠìłĢ íĿ¬", + "ÙħÙĨظ ÙħØ©", + "Ġhiç bir", + "ãģ§ãģ¯ ãģĤãĤĬãģ¾ãģĽãĤĵ", + "ร à¸Ńย", + "ëIJľ ëĭ¤", + "ãģĻãģIJ ãģ«", + "к ла", + "Ġürün ler", + "Ġki á»ĥu", + "ĠëĤĺ ëĬĶ", + "ÑĤ ки", + "Ñģ им", + "Ġchá»ī nh", + "ãĤĤ ãģªãģĦ", + "ศ รี", + "æĽ¿ ãģĪ", + "ta ÅŁ", + "Ġب ÙĥÙĦ", + "Ġ×ķ ×Ļש", + "vis ão", + "ä¼ Ŀ", + "ä¼Ŀ ãģĪ", + "ÙĦ د", + "׾ ×Ļ×ŀ", + "׾×Ļ×ŀ ×ķ×ĵ", + "t ória", + "د Ùij", + "اÙħ ر", + "Ġê·¸ëłĩ ê²Į", + "Ġmateria ÅĤ", + "à¸Ĺ รา", + "à¸Ĺรา à¸ļ", + "ãģ®æĸ¹ ãģĮ", + "ãģ¦ ãģįãģŁ", + "ض غ", + "ضغ Ø·", + "ĠÙĬ عÙĨÙĬ", + "ел о", + "×IJ×Ķ ×ij×Ķ", + "×¢ ×ŀ", + "ÅŁ ık", + "ìŀIJ ëĬĶ", + "ãĤ¿ ãĥ³", + "Ġb áºŃt", + "×ŀשפ ×Ĺ×Ķ", + "к ÑĢи", + "б ли", + "สั à¸ķ", + "สัà¸ķ วà¹Į", + "ĠسÙĨ ÙĪات", + "ĠPh Æ°Æ¡ng", + "ãģ¦ãģĹãģ¾ ãģ£ãģŁ", + "ãģª ãģľ", + "Ġ×ij×IJ ×ķ", + "Ġc án", + "س جÙĦ", + "Ġl ẽ", + "ãĤ± ãĥ¼ãĤ¹", + "Ġק ×Ļ×ij׾", + "à¸ļà¸Ĺ à¸Ħวาม", + "Ġ×ķ ׼ף", + "ĠпÑĢедÑģÑĤав лен", + "Ġn á»iji", + "Ġcoment ário", + "ени ем", + "Ġtá» ı", + "l Ãł", + "Ġש×Ķ ×Ļ×Ķ", + "Ñģл ав", + "ĠاÙĦ ÙĪÙĦا", + "ĠاÙĦÙĪÙĦا ÙĬات", + "ÙĦج ÙĨØ©", + "ק×ķר ×IJ", + "бÑĭ ÑĤ", + "Ġì ¦", + "Ġì¦ ī", + "ãģ§ãģĻ ãģĹ", + "หรืà¸Ń à¹Ħมà¹Ī", + "за ÑīиÑĤ", + "ÙģÙĦ سطÙĬÙĨ", + "Ġmi á»ħn", + "à¹Ģย à¹ĩà¸Ļ", + "ĠçalÄ±ÅŁ an", + "×Ļ×Ĵ ×Ķ", + "ĠE ÄŁ", + "ĠEÄŁ itim", + "ãĥĥãĤ· ãĥ¥", + "Ġоп Ñĭ", + "ĠопÑĭ ÑĤ", + "ر غ", + "رغ ب", + "ĠÑģво иÑħ", + "à¸Ľà¸£à¸° à¸ķ", + "à¸Ľà¸£à¸°à¸ķ ู", + "Ġ×ŀ×IJ ×ĵ", + "׼ ×ķ׳×Ļ×Ŀ", + "à¸Ļ ี", + "ĠвÑĭ Ñħод", + "ãģ®ä¸Ń ãģ«", + "פ ׾×IJ", + "ĠÙĪ ÙĦÙĬس", + "פ×ķר ס", + "פ×ķרס ×Ŀ", + "Ùħ سÙĦÙħ", + "Ġng ôi", + "×ĵ ×ŀ×ķת", + "ãĤĴ使 ãģ£ãģ¦", + "ĠпомоÑī ÑĮÑİ", + "Ø£ سر", + "бл ок", + "ÙĤ Ùĩ", + "ãģĹãģ¾ ãģĦ", + "ãģ¨ ãģĹãģŁ", + "Ġп еÑģ", + "ãĥī ãĥ«", + "×Ĺ ×Ŀ", + "ãģĹãģª ãģĮãĤī", + "ĠÐŁ ÑĢед", + "ãĥģãĤ§ ãĥĥãĤ¯", + "å¼· ãģĦ", + "ש ×Ļר×ķת", + "д аеÑĤ", + "×Ļ×ij ×ķ", + "Ġgen ç", + "ил аÑģ", + "илаÑģ ÑĮ", + "ĠبÙĦ د", + "æĤ ª", + "æĤª ãģĦ", + "Ġ×ŀ שת", + "æ§ĺ ãĢħ", + "æ§ĺãĢħ ãģª", + "à¸ĺรรม à¸Ĭาà¸ķิ", + "ĠÙĥ اÙħÙĦ", + "ĠاÙĦس Ùħ", + "×ij×ĺ ×Ļ×Ĺ", + "c á", + "g ência", + "ãĤ¹ãĤ¿ ãĥ¼", + "à¸Ĺำ à¸ģาร", + "×Ļ׾ ת", + "Ġ×Ļ ×ķצ×IJ", + "w ój", + "à¸ļุ à¸Ħ", + "à¸ļุà¸Ħ à¸Ħล", + "ع تÙħ", + "عتÙħ د", + "ãģĿãĤĮ ãģ«", + "ĠاÙĦت ارÙĬØ®", + "ÙĤر اء", + "Ġyönet im", + "ק שר", + "ĠÑģп оÑĢÑĤ", + "Ġר×IJש ×ķף", + "Ġseñ al", + "Ġch ắn", + "çĦ¡ ãģĦ", + "ĠдоÑģÑĤ аÑĤ", + "ĠдоÑģÑĤаÑĤ оÑĩно", + "Ġá gua", + "à¸ģร à¸ĵ", + "à¸ģรà¸ĵ ี", + "Ġ×ŀש ×ķ", + "Ġtr ải", + "ë² Į", + "ujÄħ cych", + "Ùģر د", + "à¹ĥ à¸ģล", + "à¹ĥà¸ģล à¹ī", + "ãĤĭ ãģ®ãģ¯", + "ר×ķ ×ķ×Ĺ", + "ÙĨ Ùĥ", + "ĠاÙĦÙĨ ÙĤ", + "ãģ®ãģ§ ãģĹãĤĩãģĨ", + "ãģ®ãģ§ãģĹãĤĩãģĨ ãģĭ", + "Ùħ عرÙģ", + "ÙħعرÙģ Ø©", + "ÑĥÑī е", + "Ġ×ij×¢ ×Ļקר", + "ت صÙĦ", + "Ġ×Ķ×IJ ר", + "Ġ×Ķ×IJר ×¥", + "ĠÅŀ i", + "à¸Ĥา à¸Ķ", + "íŀ ĺ", + "ãģªãĤĵ ãģ¨", + "ĠìĤ¬ëŀ ij", + "l Ã¼ÄŁÃ¼", + "ب اء", + "ĠاÙĦØ¢ خر", + "Ġfam ÃŃlia", + "ĠTh áng", + "Ñī ениÑı", + "ãĤ¯ ãĥŃ", + "ĠTh ứ", + "æĽ¸ ãģį", + "ен ной", + "ìŀ ¡", + "бл аг", + "благ о", + "п ов", + "à¹ģ ว", + "à¸ĩ à¸Ħà¹Į", + "à¸Ńัà¸Ļ à¸Ķัà¸ļ", + "ãģĤ ãģĴ", + "ร à¹īาย", + "ün ün", + "Ġ×Ļ׼×ķ׾ ×Ķ", + "з он", + "ĠÐľ и", + "маÑĤ еÑĢиал", + "Ġë³´ ë©´", + "ØŃÙģ ظ", + "ê Ìģ", + "ãģ« ãģĻãĤĭ", + "Ġת ×IJ", + "Ġ×Ķס ×ķ", + "ĠÑģÑĤ оÑĢ", + "ĠÑģÑĤоÑĢ он", + "ãĥĪ ãĥĥãĥĹ", + "ÅĤo ÅĽÄĩ", + "ëħ ¼", + "ëĵ Ŀ", + "ĠÙĪاÙĦ ع", + "ì¶ Ķ", + "Ġ×Ļצ ×IJ", + "ĠÑĢаз дел", + "алÑĮ наÑı", + "×IJ׳ ש×Ļ", + "spo ÅĤ", + "spoÅĤ ec", + "spoÅĤec zn", + "Ø¥ عÙĦ", + "إعÙĦ اÙĨ", + "ÙĤÙĪ Ùī", + "íķĺë©´ ìĦľ", + "تط ÙĪر", + "Ġsi êu", + "Ỽ t", + "д ви", + "дви ж", + "Ġqu ần", + "k ıl", + "ĠпÑĢи зна", + "ĠH ã", + "ĠHã y", + "ĠباÙĦ ت", + "man ın", + "ãĤ« ãĥ«", + "Ġk á»·", + "ק ׾×Ļ", + "ëIJĺ ì§Ģ", + "تعÙĦ Ùħ", + "ìĭľ ìĦ¤", + "ìĭ ¶", + "íĺ ¼", + "Ùĥ ÙĬÙģ", + "売 ãĤĬ", + "วิ à¸Ĭา", + "б ал", + "ĠØ£ ØŃ", + "Ġдолж ен", + "รา à¸ĩ", + "ราà¸ĩ วั", + "ราà¸ĩวั ล", + "Ùħ اء", + "ج ار", + "Å ļ", + "Ġ×ŀ×IJ ×ĸ", + "ר ×ŀ×Ķ", + "ãģĭãĤĤãģĹãĤĮ ãģªãģĦ", + "ét ude", + "czÄħ c", + "Ġg ór", + "×ł×¡ ×Ķ", + "Ùħ ÙĬد", + "ĠÐŁ еÑĢе", + "Ø£ خر", + "ãģĿãģ® å¾Į", + "à¹Ģà¸Ķียว à¸ģัà¸Ļ", + "×ŀ ×Ĵ×ķ", + "×ŀ×Ĵ×ķ ×ķף", + "д ов", + "mas ına", + "×¢ ׳×Ķ", + "ãĤ± ãĥĥãĥĪ", + "ס ×¢", + "סע ×Ļ×£", + "ĠT Æ°", + "Ġt óc", + "íĻľ ëıĻ", + "ĠÐŀ д", + "ĠÐŀд нако", + "Ġdol ayı", + "ؤ Ùĥد", + "ê³Ħ íļį", + "׾ ר", + "в еÑĩ", + "Ġkh ợi", + "Ġth ủy", + "×ĵ ף", + "ร à¸ģ", + "à¸ļั à¸ķร", + "à¹Ģà¸ģ à¹Īา", + "ĠاÙĦØ« اÙĦ", + "ĠاÙĦثاÙĦ Ø«", + "Ġpod rá", + "ער ×Ļ", + "ÙĨج اØŃ", + "Ġkh ắc", + "ì¸ ¡", + "Ä° M", + "ãĤ» ãĥĥãĥĪ", + "ż enia", + "Ġ׾×Ĺ ×ijר", + "er Ãł", + "ì ´Ī", + "Ġkü ç", + "Ġküç ük", + "ات ÙĩÙħ", + "à¸ĭ à¹Į", + "Ùħشار ÙĥØ©", + "ĠاÙĦ بط", + "Ġd ây", + "ен нÑĭм", + "à¸Ĺีà¹Ī à¹Ħมà¹Ī", + "ÙĤ Ùİ", + "Ġv ượt", + "Ġtr ì", + "Ġwp ÅĤyw", + "A Åŀ", + "з о", + "ĠاÙĦس ÙĬد", + "à¸Ĺะ à¹Ģล", + "ĠÑģодеÑĢж а", + "ع Ø·ÙĬ", + "ĠاÙĦع ÙĨ", + "èĢħ ãģĮ", + "à¹Ģ หà¸Ļ", + "à¹Ģหà¸Ļ ืà¸Ń", + "Ġb ÃŃ", + "Ġüzer inden", + "ĠV Å©", + "Ġnu ôi", + "ÙĨ Ùħ", + "алÑĮ ного", + "×¢ ×Ļף", + "ØŃ Ø¶Ø±", + "ĠоÑĤ дел", + "ëª ĩ", + "ìķ ¡", + "ĠÙĦدÙĬ Ùĩ", + "ìĻ ľ", + "Ġse ktör", + "Ġвозмож но", + "ĠÐĶ ж", + "Ġh ô", + "äºĭ ãģĮ", + "иÑĢов ание", + "алÑĮ ной", + "Ġ미 êµŃ", + "ر ØŃÙĦ", + "ĠÑįк Ñģ", + "пÑĢав лÑı", + "Ġnh á»Ŀ", + "ĠÄij ẩ", + "ĠÄijẩ y", + "Ùģ Ùĥر", + "ĠÙĪØ£ ضاÙģ", + "ãĥIJ ãĤ¹", + "ת×ķ׼ ׳×Ļת", + "ÑĤел ей", + "ĠØ¥ÙĦÙĬ Ùĩ", + "ãģ¨è¨Ģ ãģ£ãģ¦", + "Ġдв е", + "Ġch ấp", + "ĠL ö", + "à¸Ħล ิ", + "à¸Ħลิ à¸Ľ", + "Ġس ÙĪر", + "ĠسÙĪر ÙĬا", + "×ŀ×Ĺ ×ķ", + "st ä", + "д об", + "Ġni á»ĩm", + "ãģ® 大", + "פר×ķ ×Ļק", + "פר×ķ×Ļק ×ĺ", + "ĠCh âu", + "Ġ×ŀ×Ķ ×Ŀ", + "Ñģк им", + "ĠполÑĥÑĩ иÑĤÑĮ", + "ÙĬ ÙĪÙħ", + "Ø« ÙĪر", + "פ×ķ׾ ×Ļ×ĺ", + "פ×ķ׾×Ļ×ĺ ×Ļ", + "ĠмеÑģÑı ÑĨ", + "åħ¨ ãģ¦", + "ĠاÙĦÙħ جÙĦس", + "ĠاÙĦت اÙĦÙĬ", + "Ġ׊ר", + "åIJij ãģij", + "׼ ×ŀ×Ķ", + "б ед", + "Ø£ عض", + "أعض اء", + "ÙĪÙĦ د", + "วà¹Īา à¸Īะ", + "Ġb ánh", + "à¸Ļิ ย", + "à¸Ļิย ม", + "à¸Ľà¸£à¸° à¸ģัà¸Ļ", + "ÑģÑĤав иÑĤÑĮ", + "à¸ŀ à¸Ļัà¸Ļ", + "ĠÑį ÑĦÑĦ", + "ĠÑįÑĦÑĦ екÑĤив", + "Ġав ÑĤоÑĢ", + "ĠÄIJ Äĥng", + "Ġth Æ°á»Łng", + "ãĤĴ æĦŁãģĺ", + "à¸ģัà¸ļ à¸ģาร", + "å¾Į ãģ«", + "Ġya ÄŁ", + "ست اÙĨ", + "Ġli á»ģn", + "ãģĦ ãģ¾", + "i êu", + "à¹Ĥà¸Ķ à¸Ļ", + "ĠÙĦ Ø°ÙĦÙĥ", + "à¹Ĥรà¸ĩ à¹Ģรียà¸Ļ", + "צ ×Ļ×Ĵ", + "ĠاÙĦÙħ عÙĦÙĪÙħات", + "ç§ģ ãģŁãģ¡", + "à¸Ĺีà¹Ī à¸Ħุà¸ĵ", + "ãģ«ãģª ãģ£ãģ¦ãģĦãĤĭ", + "×ŀ×ĵ ×Ļ׳×Ķ", + "ס ׼×Ŀ", + "Ġв не", + "à¸ŀ à¸Ļัà¸ģà¸ĩาà¸Ļ", + "ÑĢ ей", + "à¹Ģà¸Īà¹īา หà¸Ļà¹īาà¸Ĺีà¹Ī", + "ĠHi á»ĩn", + "Ġméd ico", + "ĠتØŃ ÙĤÙĬÙĤ", + "ÑĮ ÑĤе", + "miÅŁ ti", + "ÙĤÙĬ ادة", + "ãĤı ãģĭãĤĬ", + "มา à¸Īาà¸ģ", + "ëħ Ģ", + "ãģ«éĸ¢ ãģĻãĤĭ", + "×IJר×Ĵ ×ķף", + "m ètre", + "Ġעצ ×ŀ×Ļ", + "ĠCh úa", + "รูà¹ī à¸Ī", + "รูà¹īà¸Ī ัà¸ģ", + "ì£ Ħ", + "ëĭ µ", + "à¹ģà¸Ĺ à¹ī", + "Ġgeç en", + "Ġlan ça", + "ĠاÙĦ بØŃØ«", + "×ĵ ×ŀ×ķ", + "ãģ¯ ãģĺ", + "ãģ¯ãģĺ ãĤģ", + "Ġdön Ã¼ÅŁ", + "è¿ij ãģı", + "à¹Ģส ม", + "à¹Ģสม à¸Ń", + "ëĿ ½", + "Ġü ç", + "á» ŀ", + "ÑĪ аÑı", + "à¸Ĺ ร", + "ØŃ ÙĤÙĬÙĤØ©", + "à¸Ĥà¸Ńà¸ĩ à¸ģาร", + "Ġ무 ìĹĩ", + "Ġ×Ķ ׼ר", + "ĠاÙĦص ÙĬÙĨ", + "ĠлÑİ Ð´Ð¸", + "à¸ķ าย", + "ب ÙĪÙĦ", + "Ġvi êm", + "Ġthi á»ĩu", + "à¸ģ à¸Ķ", + "Ġ׾ ×ĵ×ijר", + "פ ׳×Ķ", + "×IJר ×ij×¢", + "س Ùī", + "ĠاÙĦسÙĬ اس", + "ĠاÙĦسÙĬاس ÙĬØ©", + "yd ı", + "ÙĪØŃد Ø©", + "ĠдеÑıÑĤелÑĮ ноÑģÑĤи", + "Ġ×ķ×Ķ ×ŀ", + "п еÑĩ", + "пеÑĩ аÑĤ", + "иÑĢов аниÑı", + "ĠÑģ ог", + "ĠÑģог лаÑģ", + "Ġ׼ ×ĵ", + "Ġ׼×ĵ ×IJ×Ļ", + "ĠиÑģполÑĮзов аÑĤÑĮ", + "ס פ×ķר×ĺ", + "Ġil çe", + "exp érience", + "ĠTh á»Ŀi", + "Ä° K", + "à¹Ħà¸Ł à¸Łà¹īา", + "ëĵ¤ ìĹIJê²Į", + "à¸Ľà¸£à¸° à¹Ģà¸ł", + "à¸Ľà¸£à¸°à¹Ģà¸ł à¸Ĺ", + "Ġmü mk", + "Ġmümk ün", + "Ġ×IJ×ķת ׳×ķ", + "ìĦ± ìĿĦ", + "ĠìĿ´ ìľł", + "زÙĬ ارة", + "Ġolduk ça", + "r ób", + "ĠØ£ ÙĨا", + "Ġ×Ķ ×ij×Ļ", + "Ñģ ен", + "×¢ ×Ļקר", + "×Ļ×ĵ ×ķ×¢", + "d zÄħ", + "Ùħ عÙĦÙĪÙħات", + "Ø´ اب", + "Ġpar ça", + "à¸Ļะ à¸Ħะ", + "ب اس", + "ĠÑĤоÑĢ г", + "ĠÑĤоÑĢг ов", + "Ġ×Ĺ ×ĵר", + "׼ ר×ĺ", + "׼ר×ĺ ×Ļס", + "ĠA yrıca", + "ÃªÌ £", + "ìľ ¨", + "ĠÑĤак ие", + "Ġ×ŀצ ×ķ×Ļ", + "ãĥ©ãĥ³ ãĤŃãĥ³ãĤ°", + "ש×Ļ×ķ ×ķק", + "åīį ãģ®", + "ĠB ảo", + "Ñī Ñĥ", + "æĹ© ãģı", + "ĠPh òng", + "à¸ŀระ ราà¸Ĭ", + "פ ×Ĺ×ķת", + "Ġг л", + "Ġгл аз", + "à¸Ĺ à¹Īา", + "Ġd ạy", + "ÑĢ оÑģÑĤ", + "à¹Ĥà¸Ķย à¹Ģà¸īà¸ŀาะ", + "Ġqu áºŃn", + "Ġ×Ĺ×ijר ×ķת", + "m ême", + "mÄ±ÅŁ tı", + "ĠاÙĦت داÙĪÙĦ", + "Ġn ạn", + "Ġ×Ķ ×ĵ×Ļ", + "ĠاÙĦØ· رÙĬÙĤ", + "×Ĵ ×ķת", + "Ġ×Ķ ×ĵר×ļ", + "ujÄħ ce", + "Ġch ữ", + "ãĤĤãģ® ãģ®", + "ë° Ľ", + "ãģķãĤĵ ãģ¯", + "Ġyard ım", + "ĠاÙĦع Ùħ", + "Ġì§Ħ íĸī", + "Ġ×Ļ ×Ĺ", + "Ġ×Ļ×Ĺ ×¡×Ļ", + "ĠاÙĦÙħ دÙĬÙĨØ©", + "Ġc ú", + "à¸ģี ฬ", + "à¸ģีฬ า", + "Ġni ên", + "mis ión", + "׳×Ļס ×Ļ", + "׳×Ļס×Ļ ×ķף", + "Ġвоз ÑĢаÑģÑĤ", + "Ġ×¢×ķש ×Ķ", + "ĠÙħ دÙĬر", + "Ñı ÑģÑĮ", + "ØŃ Ø¬Ùħ", + "íĻĺ ê²½", + "ĠاÙĦØ£ خرÙī", + "u ÃŁer", + "ĠاÙĦعاÙĦÙħ ÙĬØ©", + "ĠNg á»įc", + "êµIJ íļĮ", + "ä¸Ĭ ãģ§", + "×Ļ×Ķ ×ķ×ĵ", + "×Ļ×Ķ×ķ×ĵ ×Ļ×Ŀ", + "Ùħس اعدة", + "Ġжиз нÑĮ", + "ĠпоÑĤ омÑĥ", + "ĠاÙĦÙħ ÙħÙĦ", + "ĠاÙĦÙħÙħÙĦ ÙĥØ©", + "ĠG ör", + "ر ÙIJ", + "×ŀק ×ķ×ŀ×ķת", + "åĩºæĿ¥ ãĤĭ", + "ÑĦ ÑĤ", + "ĠìĿ´ ìłľ", + "ĠÑĢ ем", + "ĠÑĢем онÑĤ", + "ת ×ķ×ļ", + "æĻĤ ãģ¯", + "ãĤīãĤĮ ãģªãģĦ", + "alt ı", + "家 ãģ®", + "ĠاÙĦØ¥ عÙĦاÙħ", + "리 ëĬĶ", + "ãģĭãĤī ãģ¯", + "ĠH ạ", + "ãģĤ ãģ®", + "×ĵ×Ļ ×ķף", + "رÙĬ س", + "Ġsoci etÃł", + "ĠاÙĦÙĥ بÙĬر", + "Ġ×ij ×ŀס", + "Ġ×ij×ŀס ×Ĵר", + "Ġ×ij×ŀס×Ĵר ת", + "ĠìŀĪ ìľ¼ë©°", + "Ġn ặng", + "Ùĩ Ùī", + "ĠB Ãł", + "×ŀר ×ķ", + "Ġj ÄĻ", + "ĠjÄĻ zy", + "ĠjÄĻzy k", + "Ġ׼ ×ŀ×ķ×ijף", + "×¢ ׾×Ķ", + "à¸Ĺีà¹Ī à¹Ħà¸Ķà¹ī", + "ãģ¾ ãģĹãĤĩãģĨ", + "×ŀס פר", + "Т Ðŀ", + "سÙĬاس Ø©", + "Ġкажд Ñĭй", + "ë² ł", + "t ım", + "y á»ĩn", + "ร ีà¹Ī", + "ĠдеÑĤ Ñģк", + "วิà¸ĺี à¸ģาร", + "m ówi", + "×ĺ×¢ ×Ŀ", + "×Ķצ׾ ×Ĺ×Ķ", + "ض ÙĬÙģ", + "ĠÑħоÑĤ Ñı", + "ãĤĵãģ§ ãģĦãĤĭ", + "à¸Ħา à¸Ķ", + "à¸Ħร à¸ļ", + "Ġк ÑĥÑĢÑģ", + "ĠbaÅŁ arı", + "×ijר ×ķ", + "ÙĬع Ø©", + "ĠÐĿ Ñĥ", + "à¸Ħวาม à¹Ģà¸Ľà¹ĩà¸Ļ", + "Ġ׾ ×ŀש׾", + "Ġì¢ĭ ìĿĢ", + "Ùħؤس س", + "Ùħؤسس ات", + "Ġpréc is", + "Ġth ảo", + "à¸ģà¹ĩ à¸Ħืà¸Ń", + "Ġש ׼׾", + "führ ung", + "ãģĦ ãģ§", + "à¹ģละ มี", + "à¸ģà¹ĩ มี", + "Ġש ש", + "м ел", + "Ġкни г", + "ĠباÙĦ ÙĨ", + "ĠباÙĦÙĨ سبة", + "Ġald ı", + "ÑĤ ай", + "Ġ×Ĺ×ĵ ש×Ļ×Ŀ", + "å®Ł ãģ¯", + "ع ÙĪا", + "ĠìĿĺ 미", + "из м", + "ÑĢабоÑĤ аÑĤÑĮ", + "Ùģ ص", + "Ġ×ij׳ ×ķסף", + "ãģ¨ãģĹãģ¦ ãĤĤ", + "à¹Ģà¸Ľà¹ĩà¸Ļ à¸Ĺีà¹Ī", + "ĠÑģлед ÑĥеÑĤ", + "èĢĥãģĪ ãģ¦", + "Ġ׼ ×Ļ×ķ×Ŀ", + "ÑģÑĤ Ñĭ", + "׼׾׼ ׾×Ļ", + "æµģ ãĤĮ", + "ãĤĴ ãģ¤ãģij", + "Ñĩ аÑĤ", + "×Ļ׼ ×ķף", + "×Ļר ×Ļ", + "ları yla", + "ãĤ¤ ãĥ¡", + "ãĤ¤ãĥ¡ ãĥ¼ãĤ¸", + "׳×ĸ ק", + "Ġci ò", + "Ġs ın", + "Ġsın ır", + "à¸Ļ à¸Ħร", + "к аÑĤ", + "Ġl á»Ĺi", + "ëŀ Į", + "تÙģ اص", + "تÙģاص ÙĬÙĦ", + "ëĨ ĵ", + "ĠÙħ ض", + "il miÅŁ", + "بار Ùĥ", + "ÐĿ Ðĺ", + "Ġth ẩm", + "Ġ×IJ×ķת ×ļ", + "ĠпÑĢин им", + "ĠпÑĢиним а", + "Ġyö nt", + "Ġyönt em", + "Ġ×ŀק ×ij׾", + "Ġktó rego", + "ê· Ģ", + "شر Ùģ", + "د اÙħ", + "ãģĦãĤį ãģĦãĤį", + "ĠAl ém", + "Ġgör ü", + "Ġgörü nt", + "Ġgörünt ü", + "د س", + "ÑĪ ки", + "г ÑĢад", + "Ġl ạc", + "Ġs ữa", + "ãĤīãĤĮ ãģ¾ãģĻ", + "o Ãłi", + "Ñī ен", + "ãģĭ ãģªãģĦ", + "Ġп оп", + "Ġпоп Ñĥ", + "ĠпопÑĥ лÑıÑĢ", + "ĠاÙĦÙħ ÙĪÙĤع", + "rä g", + "ï¼ ¡", + "íķ Ħ", + "ãĤĴè¦ĭ ãĤĭ", + "اÙħ ا", + "ĠاÙĦØŃ Ø±Ø¨", + "ĠÐŁ а", + "Ġ׾ ×IJתר", + "Ġt á»ijc", + "×ij ׾×Ķ", + "ر ئÙĬس", + "в Ñĥ", + "ÙĬ دÙĬ", + "каз ан", + "Ġ׊ש×ij×ķף", + "h ôtel", + "×¢ ×ķ׳×Ķ", + "ب ÙĨÙĬ", + "×ŀ ×ķ׾", + "Ġд нÑı", + "éĽ£ ãģĹãģĦ", + "вед ениÑı", + "Ġ×ķ ×ŀת", + "н апÑĢимеÑĢ", + "ÙĤ ابÙĦ", + "Ġrésult at", + "ĠÑĢазвиÑĤ иÑı", + "ر Ùij", + "ìłĦ 문", + "ĠاÙĦÙħ زÙĬد", + "ĠìľĦ íķ´ìĦľ", + "ëĨ į", + "íĻ ķ", + "ĠThi ết", + "íĮ ¨", + "malı dır", + "Ġcz ÅĤ", + "ĠczÅĤ owie", + "ĠczÅĤowie k", + "ĠÙĦ بÙĨ", + "ĠÙĦبÙĨ اÙĨ", + "üs ü", + "ãģªãĤĵ ãģł", + "Ġżyc ie", + "ĠÑħоÑĢоÑĪ о", + "æĸ¹ ãģ«", + "ëĭ¤ ë©´", + "иÑĩеÑģ каÑı", + "ער ×Ļ׼", + "ער×Ļ׼ ת", + "ãģ¾ãģĽãĤĵ ãģ§ãģĹãģŁ", + "ĠÑģоб ой", + "Ġg á»Ĺ", + "Ġдел аÑĤÑĮ", + "da Äĩ", + "аÑĢ а", + "róż ni", + "à¹Ģล ีà¹ī", + "à¹Ģลีà¹ī ย", + "à¹Ģลีà¹īย à¸ĩ", + "à¸Ŀ าà¸ģ", + "Ġت ÙĤ", + "ĠتÙĤ دÙĬ", + "ĠتÙĤدÙĬ Ùħ", + "หà¸Ļ ุà¹Īม", + "Ġmü cade", + "Ġmücade le", + "ì§Ģ 를", + "ãĤ¤ ãĤ¹", + "ĠØ£ ساس", + "jÄħce go", + "ĠÅŁ eh", + "н ÑĤеÑĢ", + "ÑĨи Ñİ", + "ï» »", + "ÑİÑī его", + "à¹Ĥà¸Ľà¸£ à¹ģ", + "à¹Ĥà¸Ľà¸£à¹ģ à¸ģรม", + "Ġmie Äĩ", + "ØŃÙĥÙĪÙħ Ø©", + "ãģ§ãģĹãģŁ ãģĮ", + "×Ļס ×Ķ", + "ãĤĤãģ® ãĤĴ", + "Ġ×ŀ ×IJת", + "สุà¸Ķ à¸Ĺà¹īาย", + "Ġc Å©", + "ÙĨ سب", + "ĠпÑĢ оÑĩ", + "Ġд ней", + "ĠÑįÑĤи Ñħ", + "׾ ×ŀת", + "нÑı Ñı", + "Ñį к", + "Ġì§Ģ ëĤľ", + "มหา วิà¸Ĺยา", + "มหาวิà¸Ĺยา ล", + "มหาวิà¸Ĺยาล ัย", + "d ão", + "ĠMá y", + "ĠêµŃ ê°Ģ", + "à¸ļุ รี", + "×Ĵ ×Ļ׾", + "ĠÑĤÑĭ ÑģÑı", + "ĠÑĤÑĭÑģÑı Ñĩ", + "Ùģ Ùĥ", + "ĠÐĺ Ñģ", + "è¡Į ãĤıãĤĮ", + "פר ×ĵ", + "ãģ¤ ãģį", + "à¸Ħร à¸Ńà¸ļ", + "à¸Ħรà¸Ńà¸ļ à¸Ħรัว", + "à¸Ĥึà¹īà¸Ļ มา", + "ä»ĬæĹ¥ ãģ¯", + "ĠìĤ¬ëŀĮ ìĿ´", + "עצ ×ŀ×Ķ", + "п оÑĢ", + "ĠK ỳ", + "Ġ Æ¡n", + "Ġth Äĥm", + "Ùģ اÙĤ", + "ãģļ ãģ«", + "Ġ׾ קר", + "Ġ׾קר ×ķ×IJ", + "اÙģ ÙĬØ©", + "Ùħ Ùİا", + "г аÑĢ", + "ص ÙĦا", + "صÙĦا Ø©", + "Ġ×ŀ ×ĸ×Ķ", + "lı ģını", + "Ġ×IJ ×Ļ׳×Ķ", + "к ÑĢо", + "Ġng Æ°Æ¡i", + "Ġв ним", + "Ġвним ание", + "jÄħ cy", + "ÙĢÙĢÙĢÙĢ ÙĢ", + "Ñģ Ñħод", + "ãģªãĤĵ ãģĭ", + "×ŀ ×Ļ׾", + "Ġ×Ķ×IJ ×Ĺ", + "ãĤı ãģªãģĦ", + "ع سÙĥر", + "ĠìĦ¸ ê³Ħ", + "ĠÑĩ его", + "ĠÑģÑĢед ÑģÑĤва", + "ĠÐł аÑģ", + "ãģª ãģģ", + "ÙĨ Ùģس", + "ר×Ļ ×ķף", + "Ñģ Ñĥд", + "ĠìĿ¸ ê°Ħ", + "ĠاÙĦÙħ ÙĤبÙĦ", + "ÙĨ عÙħ", + "تÙĪ Ùģر", + "ש ×ij×¢", + "ı lm", + "ılm Ä±ÅŁ", + "Ġ×ľ×ª ת", + "تص Ùģ", + "×Ķפ ×ķ×ļ", + "à¹ĥà¸Ļ à¸Ľà¸µ", + "ìĿ´ ê³ł", + "Ùģ ÙĪز", + "à¸ľà¸¥ à¸ĩาà¸Ļ", + "ĠGi áo", + "à¸ļà¸Ńà¸ģ วà¹Īา", + "Ġd Ä±ÅŁ", + "ĠdÄ±ÅŁ ında", + "ì£ ½", + "Ġdzie ÅĦ", + "к ÑĨии", + "и ÑĨе", + "ãģ® ä¸Ģ", + "ع Ø´", + "пÑĢ еÑģÑģ", + "หà¸Ļ à¹Īà¸Ńย", + "ลัà¸ģษ à¸ĵะ", + "Ġpossibilit Ãł", + "à¹Ħà¸Ķà¹īรัà¸ļ à¸ģาร", + "หย ุà¸Ķ", + "Ġphi ên", + "çĶŁ ãģ¾ãĤĮ", + "Ø· ÙĪÙĦ", + "ÑĦ ин", + "f ür", + "ØŃ ÙĬاة", + "íĸ ĪìĬµëĭĪëĭ¤", + "׼ ׳×ķת", + "à¸Ľà¸£à¸° ส", + "à¸Ľà¸£à¸°à¸ª à¸ļ", + "à¸Ľà¸£à¸°à¸ªà¸ļ à¸ģารà¸ĵà¹Į", + "ëIJĺ ìĹĪ", + "Ġkaż dy", + "Ġl uyá»ĩn", + "ĠоÑĢганиз аÑĨии", + "å°ij ãģªãģı", + "ÑģÑĤÑĢо ен", + "Ġtécn ico", + "ק ×Ķ׾", + "Ġ×ķ×IJ ×Ĺ", + "ĠعÙĦÙĬ Ùĥ", + "Ñī ение", + "Ġ×Ķ ×Ļ׾×ĵ×Ļ×Ŀ", + "ÙĪس ائÙĦ", + "Ġ×ķ ×Ķת", + "تÙħ ÙĬز", + "ĠÑģ казал", + "Ġпол и", + "Ġ×Ķ×ŀ ס", + "ÙĦÙij Ùİ", + "Ùħؤس سة", + "Ġ×ŀ ×Ļ×ĵ", + "ãģ£ ãģ¡", + "ĠëĦĪ 무", + "à¸ŀ ี", + "Ġt ặng", + "Ġt ấn", + "ר ש×Ŀ", + "Ġméd ica", + "Ġ×¢ ×ķ×ŀ", + "Ġ×¢×ķ×ŀ ×ĵ", + "ÑĦ оÑĢ", + "Ùħر Ø©", + "Ġvat anda", + "Ġvatanda ÅŁ", + "Ġдел о", + "à¸Ļ ม", + "ãģ¨ åIJĮãģĺ", + "Ùģ Ùī", + "Ñģ оÑĢ", + "Ġ×Ķס ר×ĺ", + "Ġép oca", + "ìłķ ì±ħ", + "ĠÑģвÑıз ан", + "ض رب", + "ĠÙĦ ÙĨا", + "Ġuży wa", + "ĠاÙĦج ÙĬØ´", + "Ñİ ÑĢ", + "×ijס ×ķ×£", + "Ġм Ñĥ", + "ĠмÑĥ зÑĭк", + "bilit é", + "Ġma ç", + "س Ùİ", + "ت ÙĦÙĥ", + "ãģ ¬", + "ÙĬ ÙĦا", + "ÑĪ ла", + "ÙĢÙĢ ÙĢ", + "Ġод ной", + "зв ан", + "ĠÑģ ÑĢаз", + "ĠÑģÑĢаз Ñĥ", + "ÙĨ ظÙħ", + "را Ùĩ", + "ĠÙĦÙĩ ذا", + "׼ ×ķר", + "Ġ×Ķש ×ij×ķ×¢", + "Ġ×Ķש ת", + "ĠQu ảng", + "ãĥ« ãĥ¼", + "ãģĪ ãģªãģĦ", + "×ĺ ×IJ", + "Ġmi á»ģn", + "ĠPh áºŃt", + "ĠاÙĦس ÙĪÙĤ", + "Ä Ĥ", + "ĠاÙĦج Ùħع", + "ĠاÙĦجÙħع Ø©", + "ÑİÑī ей", + "a ÅĤem", + "عت ÙĤد", + "Ø£ ÙĦÙħ", + "Ñģ ке", + "ĠìĿ´ íķ´", + "ÙĨس Ø®", + "è¨Ģ ãģĦ", + "д обав", + "سب ÙĤ", + "×¢×ķר ר", + "ÑĤи п", + "ãģĿãģĵ ãģ§", + "vis ión", + "عÙĪد Ø©", + "ë¨ ¹", + "×ŀ ×ĸר×Ĺ", + "ĠØ¥ ØŃ", + "Ġ׾×ij ×Ļף", + "Ġ׾צ ×IJת", + "Ġyard ı", + "Ġyardı mc", + "Ġyardımc ı", + "Ä° Z", + "ק פ×Ķ", + "tr é", + "liÄŁ ini", + "клÑİÑĩ а", + "Ġüret im", + "Ġa yrı", + "ĠkiÅŁ iler", + "à¸Ħ à¹īà¸Ļ", + "à¸Ħà¹īà¸Ļ หา", + "ĠS á»±", + "Ġ׼ ס", + "Ġ×Ľ×¡ ×£", + "ĠÑĤак иÑħ", + "ĠXu ân", + "Ġл ег", + "Ġлег ко", + "Ø«ÙĤ اÙģØ©", + "ÐĿ Ðŀ", + "ãĤ¹ãĤ¿ ãĥĥ", + "ãĤ¹ãĤ¿ãĥĥ ãĥķ", + "åIJĪ ãģĦ", + "Ġ×Ķש ×Ļ×ŀ×ķש", + "man ız", + "ĠÐĴ аÑģ", + "g ün", + "ìľĦìĽIJ íļĮ", + "Ġwsp óln", + "ĠÑģв ое", + "í ĥģ", + "à¹Ģà¸Ļ ีย", + "ÙĪب Ø©", + "в Ñıз", + "ı dır", + "ëIJĺ ìĹĪëĭ¤", + "ĠdeÄŁi ÅŁtir", + "ãĤĭ ãģĵãģ¨ãģĮ", + "Ġ×Ĺ×ĵ ש×Ķ", + "ãĤīãĤĮ ãģ¦ãģĦãĤĭ", + "×Ĺ×Ļ ×Ļ×ij", + "ĠÐļ аÑĢ", + "׳×Ļת ×ķ×Ĺ", + "Ġק×ĺ ף", + "ר ×ĸ", + "ÙĪ غ", + "èªŃ ãģ¿", + "Ġت ÙĤÙĪÙħ", + "ĠÙĥ اÙĦ", + "à¸Ŀ ึà¸ģ", + "Ġë°ľ ìĥĿ", + "ológ ico", + "ر اع", + "à¹ģà¸ģà¹ī à¹Ħà¸Ĥ", + "ĠÑĢабоÑĤ Ñĥ", + "ÙĨÙij Ùİ", + "à¸Ńยูà¹Ī à¸Ĺีà¹Ī", + "ĠاÙĦØ« اÙĨÙĬØ©", + "ĠNh ân", + "Ñħ ваÑĤ", + "ö ne", + "Ġع دة", + "à¹ģ สà¸ĩ", + "ÑĤ оп", + "пÑĥÑģ ка", + "شر اء", + "ĠÐļ ом", + "Ġפע ×ķ׾×Ķ", + "ìĤ¬ ìĿ´", + "ìĤ¬ìĿ´ íĬ¸", + "è¡Į ãģ£ãģ¦", + "Ġ×Ķ ×Ķת", + "ĠÑģÑĤ оÑĢо", + "ĠÑģÑĤоÑĢо нÑĭ", + "در س", + "à¸ĭ ู", + "à¸ķà¹Ī ำ", + "ĠØ£ بÙĬ", + "под об", + "ãģ« ãģ¦", + "ار تÙģاع", + "ĠÙħ ؤ", + "ик ов", + "ge führt", + "มืà¸Ń à¸ĸืà¸Ń", + "ĠÙĦ ÙĤد", + "ĠØ£ÙĨ Ùij", + "سÙĬ طر", + "ãģ¾ãģļ ãģ¯", + "ס ×ĵ", + "Ñģк олÑĮко", + "ãģ¿ãģŁãģĦ ãģª", + "×ĵר ×Ĵ", + "×¢ ×Ļ×ĵ", + "à¹ĥหà¹ī à¸ļริà¸ģาร", + "ĠÐĶ и", + "×ij×¢ ×Ļ×ķת", + "Ġ×Ķ×Ĺ ×ķ", + "пиÑģ ÑĮ", + "ĠاÙĦØ® ÙĦ", + "б ав", + "ĠÄ° lk", + "ĠاÙĦØ® Ùħ", + "ĠاÙĦØ®Ùħ ÙĬس", + "ĠÙĬ ÙĤÙĪÙħ", + "æĻĤ ãģ®", + "ĠsÅĤ ow", + "ĠØ£ ÙĩÙħ", + "Ø®ÙĦ ÙĤ", + "ĠØ£ صبØŃ", + "Ġchứ a", + "Ġth ác", + "Ùģ اÙĦ", + "Ġch á»Ŀ", + "ĠاÙĦØ® ار", + "ĠاÙĦخار ج", + "ĠاÙĦخارج ÙĬØ©", + "Ø· ائر", + "Ġt Ãł", + "ĠtÃł u", + "à¸ģล à¹īà¸Ńà¸ĩ", + "ĠاÙĦÙħر Ø£", + "ĠاÙĦÙħرأ Ø©", + "åħ¨ ãģı", + "ĠÃĸ n", + "çļĦ ãģ«ãģ¯", + "Ġpiè ce", + "×Ĵ ×Ļ×ij", + "ĠاÙĦ ÙĪاÙĤع", + "ä»Ĭ ãģ®", + "ĠاÙĦÙħ ÙĤ", + "cz nÄħ", + "Ùģع اÙĦ", + "ен ного", + "ĠÑĦак ÑĤ", + "ìĭł ì²Ń", + "ĠÐŀ ни", + "ĠاÙĦبÙĦ اد", + "ов иÑĩ", + "ëı Į", + "ÑĦ ÑĥнкÑĨи", + "Ġìĸ´ ëĬIJ", + "ãĥķãĤ© ãĥ¼", + "d ÃŃ", + "ил оÑģÑĮ", + "Ùħ Ùī", + "ĠاÙĦØ£ÙħرÙĬ Ùĥ", + "ĠاÙĦØ£ÙħرÙĬÙĥ ÙĬØ©", + "×ĺ ×Ļפ×ķ׾", + "íĶĦ ë¡ľê·¸", + "íĶĦë¡ľê·¸ ëŀ¨", + "Ġש ×ķ׳×ķת", + "Ø´ ÙħÙĦ", + "ĠпаÑĢ а", + "Ġ×Ķ×Ĺ ×ķק", + "ÙĪز ارة", + "ãģ¨ ãģĻãĤĭ", + "Ġqu ảng", + "ĠaÄŁ ır", + "ĠاÙĦÙĦ ج", + "ĠاÙĦÙĦج ÙĨØ©", + "ê¸ ´", + "ĠT ân", + "ج ÙħÙĦ", + "д ол", + "à¹ģà¸ŀ à¸Ĺย", + "à¹ģà¸ŀà¸Ĺย à¹Į", + "Ġר×IJ ש×Ļ", + "Ñī ей", + "Ġçev re", + "Ġкомп лекÑģ", + "Ġ×ij ×ŀש×ļ", + "Ġalt ın", + "ĠØ£ عÙħاÙĦ", + "ĠÑģво его", + "ãĤĪ ãģĦ", + "×Ĺ׾ ×Ļ×ĺ", + "×ŀ׳ ×¢", + "Ġר ×ij×Ķ", + "ĠØ£ÙĬضا Ùĭ", + "×ĸ ׾", + "ĠاÙĦسÙĬ اسÙĬ", + "æĢĿ ãģĨ", + "קר ק", + "קרק ×¢", + "ĠاÙĦÙģ رÙĬÙĤ", + "б иÑĤ", + "ק ׳×Ķ", + "ĠØ¥ ÙĨÙĩ", + "ĠÐĴ ам", + "Ðł Ðŀ", + "ãĥĪ ãĥª", + "å¿ħè¦ģ ãģª", + "Ġch âu", + "ç¶ļ ãģij", + "Ġçöz üm", + "gÅĤ ow", + "ع ÙĤÙĦ", + "売 ãĤĭ", + "i ết", + "à¸Ĭิ à¹īà¸Ļ", + "ĠØŃÙĤ ÙĪÙĤ", + "Ø·ÙĦ ع", + "ĠÄij en", + "ĠÙĥ اÙģØ©", + "ãģ® ãģĶ", + "Ġë ¬", + "Ġë¬ ¼", + "Ġ물 ë¡ł", + "Ġرس ÙĪÙĦ", + "з ам", + "зам ен", + "Ġkullan ıcı", + "×¢ ×ķ׾", + "èī² ãĢħ", + "ÑĪи ÑĢ", + "Ġ׊ש", + "Ġwy gl", + "Ġwygl Äħda", + "ש ×Ļ×ŀ×ķש", + "å¿ĺ ãĤĮ", + "×¢ ×Ļצ×ķ×ij", + "ĠاÙĦس ÙĪرÙĬ", + "å°ij ãģªãģĦ", + "Ġпо иÑģк", + "สำ à¸Ļัà¸ģà¸ĩาà¸Ļ", + "Ġ×ŀצ ×ĵ", + "Ġmü ÅŁ", + "ĠmÃ¼ÅŁ ter", + "ĠmÃ¼ÅŁter i", + "ĠÙħÙĨ ÙĩÙħ", + "à¸ķำ à¹ģ", + "à¸ķำà¹ģ หà¸Ļ", + "à¸ķำà¹ģหà¸Ļ à¹Īà¸ĩ", + "ÅĽ mie", + "Ġש ×ł×ª", + "Ġ×Ķ פ×Ļ", + "פר ש", + "×¢×ijר ×Ļת", + "สà¸Ļ ัà¸ļ", + "สà¸Ļัà¸ļ สà¸Ļุ", + "สà¸Ļัà¸ļสà¸Ļุ à¸Ļ", + "è¨Ģ ãģ£ãģ¦", + "à¸ģาร à¸Īัà¸Ķ", + "ĠMo że", + "из аÑĨии", + "ứ t", + "ĠÙĪب عد", + "ĠdeÄŁ ild", + "ĠdeÄŁild ir", + "Ġת ×ŀ", + "Ġ×ŀ×ŀ ׳×ķ", + "話 ãĤĴ", + "ĠÑĨ ена", + "Ġth úc", + "×Ļ×ŀ ×ķף", + "ĠB áo", + "ãĤĴ åıĸãĤĬ", + "å®ī ãģĦ", + "Ġ×¢×ķש ×Ļ×Ŀ", + "èĩªåĪĨ ãģĮ", + "l ée", + "ãĤĭ ãģ®ãģ§", + "иÑĢÑĥ еÑĤ", + "ãģ¦ ãĤĭ", + "ست ر", + "ĠاÙĦØŃ ÙĬ", + "×Ļ׾ ×ķת", + "Ġ×Ĺ ×ij", + "ÙĤر Ø£", + "تÙħ ÙĥÙĨ", + "س ائÙĦ", + "prü f", + "ãģĭ ãģijãģ¦", + "ĠÑģоб ÑģÑĤвенно", + "ĠìľĦ íķĺìŬ", + "׾ ×Ļ×ĺ", + "ãģĮ å¤ļãģı", + "ÙĬت Ùĩا", + "ç«ĭ ãģ¦", + "ม à¸Ńà¸ļ", + "ìĭľ ìŀ¥", + "оÑĢ а", + "Ġs avaÅŁ", + "×ĺ×Ļ×ij ×Ļ", + "×ij ׳×ķ", + "Ùħا ذا", + "기 ê°Ħ", + "ãģªãģ© ãģ§", + "Ġ×ŀ ת×Ĺ×Ļ׾", + "Ġnhi á»ħ", + "Ġnhiá»ħ m", + "ка ÑĢ", + "каÑĢ ÑĤ", + "Ġ׾×Ķ שת×ŀש", + "׳ ×Ļ×Ĺ", + "اد ÙĬØ©", + "ราย à¸ĩาà¸Ļ", + "Ġprzy kÅĤad", + "Ñī ий", + "ØŃض ÙĪر", + "Ġh ôn", + "à Ŀ", + "ת ×ķצ×IJ×ķת", + "راب Ø·", + "Ġb ếp", + "ĠполÑĥÑĩ и", + "åĩºä¼ļãģĦ ç³»", + "à¸Ľà¸¥ à¹Īà¸Ńย", + "ĠاÙĦØ´ باب", + "اÙĩ ÙĦ", + "ä»Ĭ ãģ¾ãģ§", + "رج ع", + "ãĤ¶ ãĥ¼", + "ÙĤ Ùģ", + "ĠGro ÃŁ", + "ĠíļĮ ìĽIJ", + "اج ر", + "Ġ×ij×ŀ קר×Ķ", + "Ġseg urança", + "fü hl", + "ãģ¦ ãģĦãģı", + "หม à¸Ń", + "ĠкоÑĤоÑĢ ом", + "ĠN Äĥm", + "ĠdÅĤ ugo", + "ÙħÙĨ ØŃ", + "ש×ķ ×ķ×Ļ", + "ĠØ£ÙĬ اÙħ", + "ส à¸łà¸²à¸ŀ", + "r zÄħ", + "شر Ùĥات", + "ãĤĴ èĢĥãģĪ", + "д аÑĢ", + "à¸Ľà¸£à¸° à¸Ĭุม", + "Ġ×ķ×IJ ×ĸ", + "i á»ĩn", + "Ġt Æ°Æ¡i", + "ש ×Ļ×Ĺ", + "à¸Ń à¹Īà¸Ńà¸Ļ", + "æĽ¸ ãģĦãģ¦", + "Ġng ữ", + "×ij×Ļ×ĺ ×Ĺ", + "×ij×Ļ×ĺ×Ĺ ×ķף", + "Ġs ẵ", + "Ġsẵ n", + "ì§Ģ ëıĦ", + "ĠпÑĢ еп", + "ĠпÑĢеп аÑĢаÑĤ", + "Ġна ÑĥÑĩ", + "ĠÃľ nivers", + "ĠÃľnivers ites", + "ĠÃľniversites i", + "Ġ×Ĵ×ĵ ×ķ׾×Ķ", + "Ġ×Ķ ×ł×ª", + "Ġ×Ķ×ł×ª ×ij×¢", + "ãģ§ãģĤ ãģ£ãģŁ", + "Ġmies iÄħ", + "ĠmiesiÄħ c", + "г ÑĢам", + "гÑĢам м", + "Ġبش Ø£ÙĨ", + "ĠÑħ ÑĢ", + "ק ×Ļ×ĵ", + "ק×Ļ×ĵ ×ķ×Ŀ", + "Ø´ Ùĥر", + "Ġ á»ķ", + "Ġá»ķ n", + "ãģĮãģĤ ãģ£ãģ¦", + "ãģķãĤĮ ãģ¾ãģĻ", + "Ġ×Ĺ ×ķ×ĵ", + "Ġ×Ĺ×ķ×ĵ ש×Ļ×Ŀ", + "ÙħÙĪا جÙĩ", + "ÙħÙĪاجÙĩ Ø©", + "أش خاص", + "ب غ", + "à¹Ģรียà¸Ļ รูà¹ī", + "ãģĹãģ¦ ãģĦãģı", + "Ġs ạn", + "å¿ħ ãģļ", + "׳ ×Ļ×Ĵ", + "׳×Ļ×Ĵ ×ķ×ĵ", + "باÙĦ غ", + "׊ש×ŀ", + "×Ĺש×ŀ ׾", + "Ġnap raw", + "Ġnapraw dÄĻ", + "Ø´Ùĩ اد", + "×IJ ×ķ×Ķ", + "×IJ×ķ×Ķ ×ij", + "и ÑĨÑĭ", + "Ġ×Ķ ר׼×ij", + "ëŀ ij", + "Ġת ×¢", + "Ġ×Ķ ×Ļש", + "Ġ×Ķ×Ļש ר×IJ", + "Ġ×Ķ×Ļשר×IJ ׾×Ļ", + "Ø£ ÙħÙĨ", + "ÑİÑī аÑı", + "sk ór", + "LER Ä°", + "Ġ×Ķ×IJ×Ĺר ×ķף", + "×¢ ׳ק", + "ĠÙĪ ÙĥÙĦ", + "ãģĵãģĵ ãģ§", + "Ġqu án", + "liÄŁ in", + "à¸ģà¸İ หมาย", + "Ø· Ùħ", + "Ø£ جÙĩ", + "أجÙĩ زة", + "ĠEr doÄŁan", + "ãģ§ ãģĬ", + "Ġв ÑĢа", + "ĠвÑĢа Ñĩ", + "ĠPh ó", + "à¸Ĭั à¹Īว", + "à¸Ĭัà¹Īว à¹Ĥม", + "à¸Ĭัà¹Īวà¹Ĥม à¸ĩ", + "Ġph úc", + "×Ļפ ×ķת", + "×¢×Ļ ×ķף", + "Ġduż o", + "ãĥģ ãĥ¼ãĥł", + "ĠÙĬ Ùİ", + "Ġзад аÑĩ", + "Ġ×Ĵ×ij×ķ×Ķ ×Ķ", + "Ġ׼ ׼׾", + "лож ен", + "ét at", + "Ġng Äĥn", + "èµ· ãģį", + "ĠTi ến", + "ص عب", + "Ġexperi ência", + "Ø® Ùħ", + "à¸ģาร à¸Ĺำà¸ĩาà¸Ļ", + "س ÙĬد", + "ĠD á»±", + "ĠкоÑĤоÑĢ ого", + "lad ıģı", + "Ġkh á»ķ", + "Ġê³Ħ ìĨį", + "Ñī ик", + "สà¹Īวà¸Ļ à¸ķัว", + "з оÑĢ", + "ÙĨ Ùı", + "Ġ à¸Ķัà¸ĩ", + "Ġà¸Ķัà¸ĩ à¸Ļัà¹īà¸Ļ", + "Ġc ấu", + "ĠÄij á»ijc", + "о ÑĦ", + "ĠاÙĦØ£ عÙħاÙĦ", + "ãģªãģı ãģ¦ãĤĤ", + "×ķ׼ ×Ļ×Ŀ", + "à¹ģ à¸Ľ", + "ĠB ên", + "ãĥ¯ ãĥ³", + "Ġgi ám", + "ĠÅŀ u", + "Ġd áng", + "ع ÙĦÙĬ", + "à¹Ģà¸ģ ษ", + "à¹Ģà¸ģษ à¸ķร", + "ÙĪج ب", + "н нÑĭе", + "ÙĤ ضاء", + "à¸Ħว à¸ļ", + "à¸Ħวà¸ļ à¸Ħุ", + "à¸Ħวà¸ļà¸Ħุ ม", + "ãģ¤ ãģ¤", + "ĠVi á»ĩc", + "×ŀ×ij ×ĺ", + "ש×Ļת ×ķ×£", + "Ġв едÑĮ", + "k aza", + "kaza ÅĤ", + "à¸ķำ รวà¸Ī", + "ãĤ¿ ãĥ«", + "Ġпов Ñĭ", + "ĠповÑĭ ÑĪен", + "ĠS ợ", + "ĠìĦ¤ ëªħ", + "ĠÃĩ ünkü", + "ìĥĿ íĻľ", + "Ö ¾", + "ãĤĮ ãģ¦ãģĦãĤĭ", + "Ġ×ij ר×IJש", + "ר ×ķ×Ĵ", + "Ġо ÑĦи", + "ĠоÑĦи ÑĨиалÑĮн", + "ĠÑĥ ÑģÑĤанов", + "ĠÑĥÑģÑĤанов лен", + "ĠاÙĦÙħ صر", + "ĠاÙĦÙħصر ÙĬØ©", + "ĠÐŁÐ¾ ÑįÑĤомÑĥ", + "ÙĨ صÙģ", + "ĠÙĪاÙĦ ÙĨ", + "Ġh Ãłi", + "à¸Ħ ิ", + "ĠApr ès", + "ì³ IJ", + "à¹Ģà¸ĭ ีย", + "×ĵ ×ŀ×Ķ", + "activ ité", + "à¸Ħิà¸Ķ วà¹Īา", + "ÑĤ ÑĢен", + "à¹Ģ ฮ", + "ãĥı ãĤ¤", + "ãģĮ å¢ĹãģĪ", + "ен наÑı", + "Ġìĺ¤ ëĬĺ", + "ãĥ¢ ãĥ³", + "Ġкон еÑĩно", + "ĠÙħÙĤ ابÙĦ", + "cl é", + "Ġh ü", + "Ġth ẳng", + "ìłģ ìĿ´", + "ĠÐIJ лекÑģ", + "ĠÐIJлекÑģ ан", + "ĠÐIJлекÑģан дÑĢ", + "ãĥŀãĥ³ ãĤ·ãĥ§ãĥ³", + "ãģ²ãģ¨ ãģ¤", + "ãģª ãģĬ", + "à¹Ģà¸Īà¹īา à¸Ĥà¸Ńà¸ĩ", + "ëĵľ 리", + "Ø´ اء", + "ĠsaÄŁ lık", + "ĠÅŁ imdi", + "×Ļ×IJ ׾", + "تأ Ø«ÙĬر", + "Ø£ سب", + "أسب اب", + "ĠвÑĭполн ен", + "л ок", + "ש ×Ļ×ij×Ķ", + "Ġl ắm", + "ĠTr Æ°á»Ľc", + "Ġ×Ķ×¢ ׾", + "리 를", + "ĠÑĢ еж", + "ĠÑĢеж им", + "int é", + "inté gr", + "×Ĵ ׳×Ļ", + "ĠاÙĦØ´ عر", + "Ġmil hões", + "Ġpeque ño", + "ãĤ³ ãĥ¼ãĤ¹", + "×ķ׼ ×Ĺ", + "à¹Ģà¸Ĭ à¹īา", + "شر ÙĤ", + "Ġh Æ°Æ¡ng", + "รัà¸IJ à¸ļาล", + "à¸ģล าย", + "à¸ģลาย à¹Ģà¸Ľà¹ĩà¸Ļ", + "Ġпод Ñħод", + "תש ×ķ×ij×Ķ", + "ãģıãģª ãģ£ãģ¦", + "ĠاÙĦØ£Ùħ Ùħ", + "ĠH á»įc", + "ĠwspóÅĤ pr", + "ĠwspóÅĤpr ac", + "Ñĩ Ñĥв", + "ÑĩÑĥв ÑģÑĤв", + "ÃŃst ico", + "à¹Ģà¸ģ าะ", + "ìĽ Ģ", + "Ġназ ад", + "ãĤĭ ãĤĪãģĨãģ«", + "ĠС Ш", + "ĠСШ ÐIJ", + "м он", + "ĠAs ÃŃ", + "×ķר ×Ĵ", + "полн ен", + "×ŀס ׾", + "×ŀ×¡×ľ ×ķ׾", + "à¹Ģลืà¸Ń à¸Ķ", + "à¹Ģริà¹Īม à¸ķà¹īà¸Ļ", + "ĠاÙĦØ¥ Ùħ", + "ĠاÙĦØ¥Ùħ ارات", + "צ×Ķ ר", + "ãĥ¡ãĥª ãĥĥãĥĪ", + "ĠпоÑĤ ом", + "в из", + "ĠÙģ ترة", + "å¾Į ãģ®", + "ÐĿ ÐIJ", + "×ŀס ר", + "ÙĬر ÙĬ", + "pr é", + "Ġte ÅŁek", + "ĠteÅŁek kür", + "Ġöd eme", + "د اÙĨ", + "ãģ¾ ãģĹãģ¦", + "缮 ãģ«", + "ĠÑĤ еÑĩение", + "l ard", + "lard ır", + "à¹Ģรา à¸Īะ", + "ס פ×Ļ", + "ĠÙĪÙĥ Ø°ÙĦÙĥ", + "Ġh át", + "Ġt á»Ļc", + "à¸Ħุ ย", + "Ġb ức", + "ØŃ ÙĬÙĨ", + "èģŀ ãģĦãģ¦", + "Ùħؤ شر", + "ĠNh Æ°", + "Ġмен ее", + "ละ à¸Ħร", + "Ñģ ин", + "ĠÑĢ ек", + "ĠÑĢек л", + "ĠÑĢекл ам", + "ĠÙģ ÙĩÙĪ", + "Ġ׾ ×ĸ", + "×Ļ׳ ×ķת", + "ĠÅŁ art", + "ÑģÑĤав ка", + "Ġíı¬ íķ¨", + "ãģ«è¡Į ãģı", + "ï¼ Ŀ", + "ĠпозволÑı еÑĤ", + "Ġת×ķ׼ ׾×ķ", + "ов ал", + "صÙĦ Ø©", + "Ġ׾ש ׳×ķת", + "ĠÐĺ гÑĢ", + "ÙħÙĨتج ات", + "Ġsat Ä±ÅŁ", + "Ñģ ко", + "ĠاÙĦØ«ÙĦاث اء", + "Ġ×Ķ×ĵ×ijר ×Ļ×Ŀ", + "ãģĹãģ¾ ãģĹãĤĩãģĨ", + "بÙĤ Ùī", + "åĬĽ ãĤĴ", + "ĠÃĩ ok", + "ãĥģ ãĥ¥", + "à¹Ģà¸Ĭ ืà¹īà¸Ń", + "ยุ à¸Ħ", + "ศา ล", + "Ġק×ķ×ĵ ×Ŀ", + "×ĸר ×Ļ×Ŀ", + "ãģ® åł´åIJĪ", + "ĠìķĬ ìķĺ", + "ãģĤãĤĬãģ¾ãģĻ ãģĮ", + "×IJ שר", + "è¡Į ãģı", + "ãģ» ãģĭ", + "æ°Ĺ ãģ«ãģªãĤĭ", + "й деÑĤ", + "íķĺìĺĢ ëĭ¤", + "ستÙħر ار", + "ĠÐŁÑĢ е", + "ĠÑģ боÑĢ", + "ĠìķĦ 무", + "ç§ģ ãĤĤ", + "ع ص", + "Ġн иÑĩ", + "ĠниÑĩ его", + "ĠпÑĢи ем", + "ק ×ķ×ŀ", + "ĠìĪĺ ëıĦ", + "Ġì ¡´", + "Ġì¡´ ìŀ¬", + "ĠØ£ Ø«ÙĨ", + "ĠأثÙĨ اء", + "ĠÙĪاÙĦ ØŃ", + "ãģĮ ãģ§ãģįãĤĭ", + "Ġת ×Ķ", + "Ġת×Ķ ×Ļ×Ķ", + "ר ף", + "ĠÑģвÑıз и", + "×Ĵ שת", + "Ñģп екÑĤ", + "ס ×ij×Ļ×ij", + "ס×ij×Ļ×ij ×Ķ", + "ĠíķĦìļĶ íķľ", + "ت خصص", + "Ġж ив", + "Ġжив оÑĤ", + "ĠMay ıs", + "تع ا", + "تعا ÙĪÙĨ", + "ĠعÙĨ Ùĩا", + "ów ki", + "ĠاÙĦÙģÙĦسطÙĬÙĨ ÙĬ", + "ãģłãģijãģ§ ãģªãģı", + "ìĿ¸ ì§Ģ", + "ĠاÙĦس ÙĪد", + "ĠاÙĦسÙĪد اÙĨ", + "إجراء ات", + "Ġkö tü", + "Ġ×Ļ תר", + "×Ĵ ×Ļש×Ķ", + "Ġצ ×ķר×ļ", + "รà¸ĸ ย", + "รà¸ĸย à¸Ļà¸ķà¹Į", + "Ñħ оÑĤ", + "Ðł ÐIJ", + "ÙĪ Ø·ÙĨ", + "Ġsay ısı", + "ס ×Ĺר", + "Ùħ ÙĪÙĦ", + "ãĤĴæĮģ ãģ£ãģ¦", + "ع اÙĨ", + "Ġt á»Ļi", + "ĠвÑĭ ÑĪе", + "Ġt ầm", + "ãĥĪ ãĥ¬", + "×Ļצ ×ķ", + "ม ุม", + "س ÙĪد", + "ìłĦ ìŀIJ", + "ãĤµ ãĥŃãĥ³", + "ìĤ° ìĹħ", + "ĠоÑģнов ан", + "Ø® Ùģض", + "רצ ×Ķ", + "بÙĬ ض", + "×ķÖ ¹", + "ס×Ļ ×Ļ×¢", + "Ġש ×IJ×Ļ", + "ĠاÙĦÙĤر Ø¢ÙĨ", + "ĠТак же", + "×ŀש ×ŀ×¢×ķת", + "س ÙĩÙĦ", + "Ġ×Ķ ׳×Ķ", + "ãĤĴ ãģĹãģ¦ãģĦãĤĭ", + "×Ļ ×Ļס", + "×Ķ ×ķ×IJ", + "ĠB ÃŃ", + "Ġмал о", + "ĠëĶ°ëĿ¼ ìĦľ", + "Ġר ×Ĺ×ij", + "ãģĮ é«ĺãģĦ", + "ÙĪ اس", + "ìĤ ¼", + "׳ ×¢", + "ãģ£ ãģ¡ãĤĥ", + "ĠT üm", + "à¸Ńีà¸ģ à¸Ķà¹īวย", + "ãģĹãģ¦ ãģıãģłãģķãģĦ", + "ÙĨØ´ اط", + "ãĥĹ ãĥ©ãĥ³", + "али ÑģÑĮ", + "×ĵ ×ľ×ª", + "Ġwc zeÅĽ", + "ĠwczeÅĽ niej", + "ĠÑįÑĤ им", + "Ġthá»ĭ t", + "à¸ļ ัà¸į", + "à¸ļัà¸į à¸Ĭี", + "ãģļ ãģ£ãģ¨", + "ÑĢ ин", + "Ġswo jÄħ", + "íķĺëĬĶ ëį°", + "Ġë§Įëĵ¤ ìĸ´", + "تش Ùĥ", + "تشÙĥ ÙĬÙĦ", + "ائ Ùĩ", + "Ġ׾פ ×Ĺ×ķת", + "ãĥĭ ãĥ¥", + "ãĥĭãĥ¥ ãĥ¼ãĤ¹", + "׼×IJ ף", + "ãģ§ãģį ãģŁ", + "зв он", + "Ġsta ÅĤ", + "×Ĺ×ijר ת×Ļ", + "ĠØ£ عÙĦÙĨ", + "à¹ģà¸ļà¸ļ à¸Ļีà¹ī", + "بد Ø¡", + "ãĤģ ãģŁ", + "Ġ×ŀש ×ŀ×¢×ķת", + "Ġ×ŀש×ŀ×¢×ķת ×Ļ", + "ör ü", + "Ġh ạnh", + "z ähl", + "ĠL ý", + "Ġ×ij ×Ķת", + "Ġ×ij×Ķת ×IJ×Ŀ", + "б аÑĢ", + "ì¦ Ī", + "ä»ĬåĽŀ ãģ®", + "Ġy ü", + "Ġyü ks", + "Ġyüks el", + "ãĤ½ ãĥ¼", + "ãģĤ ãĤĮ", + "ת ׾×ŀ×Ļ×ĵ", + "ãģ¤ ãģª", + "×ij ׳×Ļ×Ŀ", + "Ġx ếp", + "ĠмÑĥж Ñĩин", + "ĠاÙĦÙĥ تاب", + "׼ ×ŀ×ķת", + "Ġç e", + "Ġçe ÅŁ", + "ĠçeÅŁ it", + "ĠçeÅŁit li", + "×ĵ ×Ļר×ķת", + "à¸ļุ à¸į", + "ĠاÙĦØ¥ ÙĦÙĥ", + "ĠاÙĦØ¥ÙĦÙĥ ترÙĪ", + "ĠاÙĦØ¥ÙĦÙĥترÙĪ ÙĨÙĬ", + "ĠباÙĦØ¥ ض", + "ĠباÙĦإض اÙģØ©", + "Ġyö nel", + "Ġyönel ik", + "mys ÅĤ", + "à¸Ķà¹īวย à¸ģาร", + "à¸ģาร à¸Ĺำ", + "ов Ñĭм", + "Ø£ زÙħØ©", + "æİ¢ ãģĹ", + "íļ ¨", + "Ġ×ķ×IJ ×Ŀ", + "Ġnghi êm", + "ÑĪ ин", + "ка л", + "Ġcrian ças", + "èĩªåĪĨ ãģ§", + "Ġн ай", + "Ġнай ÑĤи", + "ĠS á»ij", + "ĠÃ¶ÄŁrenc iler", + "ãĥ¶ æľĪ", + "Ñģ ан", + "ĠJ á", + "ĠkonuÅŁ ma", + "شر Ø·", + "ëĪ Ī", + "ar rière", + "ضر ÙĪرة", + "ãĥĶ ãĥ³", + "×¢ שר", + "аÑĢ ÑĮ", + "جÙħ اع", + "Ġdé co", + "Ġ×Ļ×Ķ ×ķ×ĵ×Ļ", + "à¸ŀ ลาà¸Ķ", + "ĠÙĬ ÙĥÙĨ", + "Ġج اÙħعة", + "Ø· بÙĤ", + "Ġbo ÅŁ", + "×ķ ×ķ×IJ", + "×ŀ×ĵ ×¢", + "ק×ij×ķצ ת", + "פ ×Ļר", + "jÄħc ym", + "ÙħØ´ ا", + "Ùħشا ÙĥÙĦ", + "צ פ×ķף", + "Ø¥ ست", + "×ŀ׼ ר", + "سÙħ ع", + "Ġкак ой", + "ÑĤ воÑĢ", + "ØŃ Ø¬", + "Ùģر ض", + "пÑĢав лен", + "Ġник ак", + "Ġmi á»ĩ", + "Ġmiá»ĩ ng", + "ü ÃŁ", + "иÑĢов ал", + "׾ ×ŀ×ķת", + "次 ãģ®", + "ÙĦ Ø·", + "à¸ķ ัà¸Ļ", + "×Ķ ת×Ĺ×Ļ׾", + "Ġfoto ÄŁ", + "ĠfotoÄŁ raf", + "طر ØŃ", + "à¸Ńà¸Ńà¸ģ à¹Ħà¸Ľ", + "Ġy ên", + "Ġп ок", + "Ġпок Ñĥп", + "ĠпокÑĥп а", + "ÑĨ Ñĥ", + "Ġкомп ÑĮÑİ", + "ĠкомпÑĮÑİ ÑĤеÑĢ", + "ĠاÙĦÙĥ رÙĬÙħ", + "تص Ùħ", + "تصÙħ ÙĬÙħ", + "Ġоказ а", + "Ġzar ówn", + "Ġzarówn o", + "ëĮĢ ì¶ľ", + "ãĤ»ãĥ³ ãĤ¿ãĥ¼", + "Ġjako ÅĽci", + "æĤ ©", + "æĤ© ãģ¿", + "Ø£ÙĨ ÙĪ", + "Ø£ÙĨÙĪ اع", + "ë¹ ł", + "Ġìłķ ë§IJ", + "Ġk ẻ", + "ĠÑģай ÑĤа", + "Ġ×Ķ ער×ij", + "Ùĩ ز", + "pres ión", + "ĠÑģÑĤ ен", + "ãģ£ãģ¦ ãĤĭ", + "Ġhız lı", + "Ðļ ÐIJ", + "×ŀשפ ×Ĺת", + "ĠÙĨ Ùĩا", + "ĠÙĨÙĩا ÙĬØ©", + "ãģ¾ ãģĦ", + "о ÑħÑĢан", + "ร à¹īà¸Ńย", + "ล ึà¸ģ", + "ĠÙĪب اÙĦ", + "ãĤĤãģ® ãģĮ", + "ר׼ ×Ļ×ij", + "ãĤ¤ ãĥ¤", + "س ؤ", + "سؤ اÙĦ", + "ĠÙĦØ£ÙĨ Ùĩ", + "ĠkonuÅŁ tu", + "Ðļ ÑĥпиÑĤÑĮ", + "Ġש×IJת ×Ķ", + "ĠÙĪاÙĦ س", + "Ġmożliwo ÅĽci", + "Ġpró b", + "ëĶ °", + "ãģ© ãĤĮ", + "ĠÐľ ин", + "ĠоÑĢганиз м", + "ãģ«å¯¾ ãģĻãĤĭ", + "ĠPr é", + "Ġpriv é", + "ch è", + "ãģĦãģŁãģł ãģį", + "สà¸Ļุ à¸ģ", + "ajÄħ ce", + "ĠD zi", + "ĠDzi ÄĻki", + "ÅĤat w", + "r än", + "rän k", + "æĿ¥ ãģŁ", + "Ġ×Ķ×Ļ×Ķ ×ķ×ĵ×Ļ", + "ãĤ¬ ãĥ¼", + "ĠÑĢаР´", + "ĠÑĢад и", + "к ÑĤив", + "Ø£ Ùĩد", + "Ø£Ùĩد اÙģ", + "ש ×IJ×Ļר", + "ãģ¦ ãģĦãģªãģĦ", + "Ġfr üh", + "Ġок ол", + "Ġокол о", + "Ġreg ião", + "ĠÑĩиÑģ ле", + "Ġpon iew", + "Ġponiew aż", + "ìĦ¼ íĦ°", + "Ġb ầu", + "Ġê ·", + "Ġê· ľ", + "Ġê·ľ ìłķ", + "ĠH òa", + "ĠÑĤ оÑĤ", + "ãĤĤ å¤ļãģĦ", + "ĠاÙĦإسÙĦاÙħ ÙĬØ©", + "ãģĭ ãģĦ", + "Ñį н", + "ĠÑĥказ ан", + "ĠÑĤак ое", + "ï¼ ³", + "ëĮĢ íķĻ", + "Ġgen iÅŁ", + "ĠاÙĦØ® ÙĬ", + "ĠاÙĦØ®ÙĬ ارات", + "ãĤĴè¡Į ãģĨ", + "ש ×ŀ×Ķ", + "ĠLÃł m", + "ÙĪÙĨ ÙĬ", + "Ġ×IJ ׾×Ļ×ķ", + "Ä ĺ", + "à¹Ħมà¹Ī สามารà¸ĸ", + "人 ãģ¨", + "بر ز", + "×Ļס ×ķ×ĵ", + "×Ĵ ׾×Ļ", + "ĠÙĬ ÙĨا", + "ĠÙĬÙĨا ÙĬر", + "ĠкаÑĢÑĤ ин", + "Ġt ôn", + "à¹Ģ à¸ģร", + "à¸Ħ à¸Ķี", + "Ġ׾×IJ ×ķר×ļ", + "ãĤĤãĤī ãģĨ", + "ãģĭ ãģĭãĤĭ", + "ани и", + "Ġara ÅŁtırma", + "ÙĦاØŃ Ø¸", + "ãģĦ ãĤĦ", + "ĠT Ãłi", + "Ġ à¸Ļà¸Ńà¸ģà¸Īาà¸ģ", + "Ġà¸Ļà¸Ńà¸ģà¸Īาà¸ģ à¸Ļีà¹ī", + "ĠÄIJ ảng", + "ãģ£ãģ¦ ãģįãģŁ", + "Ġà¸ĭึà¹Īà¸ĩ à¹Ģà¸Ľà¹ĩà¸Ļ", + "Ġt ả", + "Ġmożliwo ÅĽÄĩ", + "ĠS ản", + "ĠÄ° ki", + "Ġc ắt", + "س Ø£ÙĦ", + "Ġbak ım", + "Ø´ ب", + "à¸ķ ีà¹ī", + "à¸ŀ ยาย", + "à¸ŀยาย าม", + "สั à¸Ľ", + "à¸ªà¸±à¸Ľ à¸Ķา", + "à¸ªà¸±à¸Ľà¸Ķา หà¹Į", + "ë° Ģ", + "еÑĢ Ñĭ", + "Ġc ánh", + "Ġthu ế", + "ت بع", + "ãģ«åħ¥ ãĤĮ", + "Ñİ ÑģÑĮ", + "íļĮ ìĿĺ", + "ç°¡ åį", + "ç°¡åį ĺ", + "ç°¡åįĺ ãģ«", + "Ġtr úc", + "ĠاÙĦÙĥ ÙĪÙĬ", + "ĠاÙĦÙĥÙĪÙĬ ت", + "ãĤıãģij ãģ§ãģĻ", + "ĠÑģв об", + "ĠÑģвоб од", + "ĠÑĥÑĩаÑģÑĤ ник", + "สิ à¹īà¸Ļ", + "ĠпÑĢо ÑĦеÑģÑģиона", + "ĠпÑĢоÑĦеÑģÑģиона лÑĮн", + "Ñģп оÑĢ", + "×Ĺ ×ķ×ij×Ķ", + "Ùħع ÙĨÙī", + "ĠاÙĦÙģ ترة", + "สูà¸ĩ สุà¸Ķ", + "ãĤı ãģļ", + "ĠÄij è", + "ĠÄijè n", + "æ¯Ķ ãģ¹", + "า à¸ĺิ", + "Ġmoż emy", + "à¹ģ à¸ĭ", + "à¸Īะ à¹Ħมà¹Ī", + "Ġs ắp", + "Ðļ Ðŀ", + "Ġprá ctica", + "ÙĪÙĥ اÙĦØ©", + "è¾¼ ãĤĵãģ§", + "ológ ica", + "Ġе Ñī", + "ĠеÑī Ñij", + "تع دÙĬÙĦ", + "ĠØ£ Ùĥد", + "Ġצר ×Ļ׼", + "Ġצר×Ļ׼ ×Ļ×Ŀ", + "Ø« Ùħ", + "Ġк ÑĢÑĥ", + "ĠкÑĢÑĥ п", + "×ij×Ļק ×ķרת", + "Ġì¡° ê¸Ī", + "ãģ¨ãģį ãģ¯", + "Ġb ạc", + "ĠÑĢаÑģ пол", + "ĠÑĢаÑģпол ож", + "ĠÑĢаÑģполож ен", + "ز ÙĬÙĨ", + "ĠÐļ ÑĢоме", + "ĠاÙĦÙĨ ظر", + "×Ķ ×ķ×ĵ", + "ĠاÙĦس بت", + "ãģ¨æĢĿ ãģĦ", + "Ġpa ÅĦst", + "ĠpaÅĦst w", + "ĠÙĦÙĬ ست", + "ĠбÑĥд Ñĥ", + "à¸Ĺัà¸Ļ à¸Ĺี", + "ร าม", + "ØŃ ØµÙĪÙĦ", + "ãģĹãģ¦ãģıãĤĮ ãĤĭ", + "ĠاÙĦØ¥ سرائÙĬÙĦ", + "ĠاÙĦإسرائÙĬÙĦ ÙĬ", + "ãģĵãĤĮ ãģ¾ãģ§", + "ìĤ¬ 를", + "Ġs ürü", + "à¹Ģว à¸Ńรà¹Į", + "à¹Ģà¸ĭ à¸Ńรà¹Į", + "Ġutilis é", + "ĠÑģиÑģÑĤем а", + "Ġdw ó", + "Ġdwó ch", + "Ġpróp rio", + "Ġëĵ± ìĿĦ", + "arr êt", + "ĠЧ а", + "×IJ×ŀ ׳×ķת", + "عار ض", + "à¹Ģà¸ģม สà¹Į", + "Ġ׾×Ķ ×ij×Ļף", + "Ġ׾ ×ij×Ĺ", + "Ġ׾×ij×Ĺ ×ķר", + "สา à¸Ĥา", + "ĠÐľÐ¾Ñģк ве", + "ب عد", + "ĠاÙĦÙĤر ار", + "ĠÄIJ á»ĭa", + "Ġ×Ĺ ×Ĵ", + "Ùģ تر", + "ÙĪÙĨ Ø©", + "Ġ×Ķ×ĸ ×IJת", + "å¸Ĥ ãģ®", + "ãģ» ãģĹãģĦ", + "Ġ×ij×¢ ×Ļר", + "ĠÑĤеп еÑĢÑĮ", + "ìĬµ ëĭĪê¹Į", + "à¹Ħม à¹Īว", + "à¹Ħมà¹Īว à¹Īา", + "à¹Ħมà¹Īวà¹Īา à¸Īะ", + "×ŀ ×IJ×Ķ", + "æĥħ åł±", + "æĥħåł± ãĤĴ", + "غ ÙĨ", + "Ġпо Ñı", + "ĠпоÑı ви", + "éģİ ãģĶ", + "تش غ", + "تشغ ÙĬÙĦ", + "в ел", + "Ġ×Ĺ ×ŀ", + "ãģ¨ãģªãĤĬ ãģ¾ãģĻ", + "Ġra ÄŁ", + "ĠraÄŁ men", + "ãģĭ ãģ©ãģĨ", + "ãģĭãģ©ãģĨ ãģĭ", + "ен ко", + "ì§Ģ ê³ł", + "Ġ×IJ׾ ×Ļ×Ķ", + "ĠØ£ ÙĦ", + "à¸Īำ หà¸Ļ", + "à¸Īำหà¸Ļ à¹Īาย", + "nız ı", + "Ġ׾ק ×Ĺת", + "Ø£ ÙĩÙħ", + "Ø£ÙĩÙħ ÙĬØ©", + "ت غÙĬر", + "ש ×Ĺר", + "ס×ķפ ר", + "×ĵ ×Ļר", + "èī¯ ãģĭãģ£ãģŁ", + "×ŀ׾×Ĺ ×ŀ×Ķ", + "ÑģÑĤв ие", + "ÑĤ ÑĢаÑĤ", + "ĠاÙĦØ£ Ø®", + "ĠاÙĦأخ ÙĬرة", + "ĠاÙĦØŃ ØµÙĪÙĦ", + "Ġcréd ito", + "צ ×Ļ×¢", + "ãĥ¬ ãĥĻãĥ«", + "بر ÙĬ", + "ëIJ IJ", + "ãģł ãģ£ãģ¦", + "Ġreal tÃł", + "س Ùģر", + "×ķ׳ ×ķ", + "×Ĵ ×ķ×ĵ", + "×Ĵ×ķ×ĵ ׾", + "ฮ า", + "ãģĹãģ¦ ãģĬãĤĬãģ¾ãģĻ", + "Ġg Ãł", + "Ġ׾×ij צע", + "å¼ķ è¶ĬãģĹ", + "Ġ×ŀ ×Ļ׾×Ļ", + "Ġ×ŀ×Ļ׾×Ļ ×ķף", + "Ùħ در", + "Ùħدر سة", + "פ ×ķ×ĺ", + "à¸Ļà¹īำ มัà¸Ļ", + "ëģ Ŀ", + "ع Ùĥس", + "ĠÙĤ ض", + "ĠÑĢÑĭ б", + "خط Ø·", + "×ŀ×ķס ×ĵ", + "Ġ׼׾ ׾×Ļ", + "ĠкоÑĤоÑĢ ое", + "צ×Ļ ×ķף", + "ĠмеÑģÑĤ а", + "ãģĭ ãģ¤", + "г ÑĢÑĥпп", + "׾ ×Ļ׾", + "ת ×ķ×IJר", + "ë³µ ì§Ģ", + "à¹ģà¸ľ à¹Īà¸Ļ", + "Ġ×ij×¢ ת", + "æĻĤéĸĵ ãĤĴ", + "ï¼ £", + "ãģ¨ãģĦãģĨãģĵãģ¨ ãģ§", + "Ġ׾×Ķ ק", + "Ġ׾ ×ĸ×Ķ", + "ĠìłĢ ëĬĶ", + "ĠاÙĦØ¥ رÙĩاب", + "ĠìŀĪëĬĶ ëį°", + "ĠÑĤ огда", + "Ġ×Ķ צ×Ļ", + "×ķ׾ ×ĺ", + "Ġר פ×ķ×IJ×Ļ", + "ãģĵãģ¨ ãģ§ãģĻ", + "ĠÄij ÃŃch", + "ØŃ ÙĬا", + "Ġ×Ķ×ŀש ×Ĺק", + "ãģľ ãģ²", + "Ġ×ŀ×IJ פשר", + "ãģ¿ ãģ¾ãģĹãģŁ", + "ĠاÙĦØ£ÙħÙĬر ÙĥÙĬ", + "Ùħج تÙħع", + "Ġس اب", + "Ġساب ÙĤ", + "׼ ×Ļ׾", + "Ạ¾", + "ãĥª ãĤ¹ãĥĪ", + "Ġì ĥ", + "Ġìĥ Ī", + "ĠìĥĪ ë¡ľ", + "ĠìĥĪë¡ľ ìļ´", + "ĠD á»ĭch", + "à¹Ģหมาะ สม", + "ĠاÙĦÙĨ بÙĬ", + "׾ ׾", + "ÙĨ ع", + "Ðĵ лав", + "Ðĵлав наÑı", + "Ùħر ض", + "Ġ×ķ ×ĵ", + "ت ÙĤÙĬ", + "تÙĤÙĬ ÙĬÙħ", + "Ġb ảng", + "ĠÙģ ÙĤاÙĦ", + "×¢ ×ŀ×Ļ", + "д ÑĢа", + "Ġsu á»ijt", + "سر عة", + "Ġc á»Ń", + "Ġ×Ķ ×Ļ×Ĺ×Ļ×ĵ", + "سع ÙĬد", + "à¸Ńา à¸Ĭีà¸ŀ", + "Ġس ÙĪاء", + "ãĤ½ ãĥķãĥĪ", + "Ġл иÑĩно", + "ĠÐļ оÑĢ", + "اÙĩ تÙħ", + "اÙĩتÙħ اÙħ", + "à¸Ń à¸Ķี", + "à¸Ńà¸Ķี à¸ķ", + "ãģIJ ãĤīãģĦ", + "Ġiht iya", + "Ġihtiya ç", + "ãģ¾ãģ§ ãģ®", + "ìĭľ ìĬ¤", + "ìĭľìĬ¤ íħľ", + "ÑĢÑĥ ÑĪ", + "ãĤĦ ãģ£ãģ±", + "ãĤĦãģ£ãģ± ãĤĬ", + "к еÑĢ", + "Ġ ży", + "Ġży w", + "кл он", + "Ġl ượt", + "à ¾", + "да Ñĩи", + "tür k", + "غ ÙĪ", + "ĠигÑĢ ок", + "Ġph ê", + "Ġש ×¢×ľ", + "ĠاÙĦÙħ دÙĨÙĬ", + "ĠìŬ룬 ë¶Ħ", + "ער ×Ļ×Ŀ", + "Ñħод ÑıÑĤ", + "Ġx ứ", + "ÐĹ Ð°", + "ĠÙģ رص", + "à¸Īะ à¸Ĺำà¹ĥหà¹ī", + "íģ ´", + "×¢ ×ij×ķר", + "à¹Ģหลà¹Īา à¸Ļีà¹ī", + "èĢĥãģĪ ãĤĭ", + "ÑĢ еÑģÑĤ", + "н нÑĭй", + "Ġc ầm", + "دا Ø®ÙĦ", + "ĠÙħÙĦÙĬ ار", + "ĠÐIJ л", + "ĠвÑĢем ен", + "à¸Ĭà¹Īวย à¹ĥหà¹ī", + "ר×Ļ ×ķת", + "ëĵ ¯", + "飲 ãģ¿", + "׳ ׾", + "שת ×£", + "ĠاÙĦسعÙĪد ÙĬ", + "u ÃŁ", + "ìĿ¸ ëį°", + "ĠìĿ¼ ë°ĺ", + "ÅĤ ÄĻ", + "Ġm á»iji", + "×ŀ ×Ļ׳", + "ĠاÙĦØ£ Ø·ÙģاÙĦ", + "Ġçı kan", + "é cole", + "ק ×Ļש", + "ק×Ļש ×ķר", + "ĠоÑģ ÑĥÑīеÑģÑĤв", + "ĠоÑģÑĥÑīеÑģÑĤв лÑı", + "×ij ×IJר", + "à¹Ħà¸Ľ à¸Ķà¹īวย", + "Ġ×¢ ×ķ׾×Ķ", + "à¸ģà¹ĩ à¹Ħมà¹Ī", + "ãĥ¢ ãĥĩ", + "ãĥ¢ãĥĩ ãĥ«", + "تØŃ ÙĪÙĦ", + "Ġод ного", + "ת×Ĺ×Ļ׾ ת", + "Ġت Ø®", + "Ġch cia", + "Ġchcia ÅĤ", + "ãĥIJ ãĥ³", + "èĢħ ãģ¯", + "ĠÙħ ØŃÙĦ", + "Ñģл ож", + "Ñģлож н", + "Ġt ÄĻ", + "Ġçı kt", + "Ġçıkt ı", + "ĠC Æ¡", + "à¹Ħà¸Ķà¹ī à¹Ģลย", + "ır ken", + "à¹Ģà¸Ĥà¹īา สูà¹Ī", + "ÙħØŃ Ùĥ", + "ÙħØŃÙĥ ÙħØ©", + "à¸Ħุ à¹īม", + "à¸Ļà¹Īา à¸Īะ", + "лÑİ Ð´", + "де ÑģÑı", + "деÑģÑı ÑĤ", + "ĠлÑİб ой", + "تØŃر ÙĬر", + "צע ×ĵ", + "Ġе Ñij", + "ĠاÙĦØŃ ÙĥÙħ", + "Ġص باØŃ", + "à¹Ģà¸ļ à¸Ńรà¹Į", + "Ġróż nych", + "ги б", + "ĠÑģ оÑĤ", + "ĠÑģоÑĤ ÑĢÑĥд", + "ĠÑģоÑĤÑĢÑĥд ник", + "ĠобÑĬ ем", + "פ ×ĺר", + "ãģĻãģĶ ãģı", + "ãģ«éĸ¢ ãģĹãģ¦", + "в ол", + "Ø« ÙħاÙĨ", + "Ġd ần", + "æĬ ľ", + "æĬľ ãģij", + "Ġ×¢ ש", + "Ġעש ×ķ×Ļ", + "ס ×ķף", + "ãģªãģ® ãģ§ãģĻ", + "ãģ¯ ãģ©ãģĨ", + "×ŀ×¢ ר×ij", + "ï¼ °", + "Ùħ صر", + "ÙħÙĨ اسب", + "ÙħÙĨاسب Ø©", + "ä¸Ĭ ãģ®", + "×IJ×Ļש ×ķר", + "ĠìĦ¤ ì¹ĺ", + "×ŀ×ĵ×Ļ׳ ×ķת", + "×ŀר ת", + "ãĤĭ ãģ®ãģĮ", + "د Ùİ", + "ĠاÙĦشر Ùĥات", + "ìĭľ ê°Ħ", + "ĠÑĢеÑĪ ение", + "ãģĻãĤĭ ãģ®ãģ¯", + "ĠìŀIJìĭł ìĿĺ", + "׾ ×ŀ×ķ", + "ãģ¨ãģĵãĤį ãģ§", + "Ġק צר", + "Ġmã i", + "Ġkü ltür", + "ãĥ©ãĤ¤ ãĥĸ", + "à¸ľà¸¹à¹ī หà¸įิà¸ĩ", + "æĻĤéĸĵ ãģĮ", + "клÑİÑĩ и", + "diÄŁ iniz", + "มาà¸ģ à¹Ĩ", + "تØŃ ÙħÙĦ", + "Ġh ạt", + "ãĤ¦ ãĤ£", + "п ле", + "×ŀ ׾×IJ", + "ÅĤ ó", + "Ġg á»ijc", + "Ġ×IJ ×ķ×ĵ×ķת", + "หว าà¸Ļ", + "ĠاÙĦ ÙĪز", + "ĠاÙĦÙĪز راء", + "ëĵ¤ ê³¼", + "Ġص ØŃ", + "ĠصØŃ ÙĬÙģØ©", + "Ġм м", + "تد Ø®ÙĦ", + "Ġpersön lich", + "Ġز ÙĬ", + "ĠزÙĬ ادة", + "ãĤ· ãĤ¢", + "Ġng ắn", + "à¸Ħล ิà¸ģ", + "Ġs ông", + "Ġtü ket", + "Ñį ÑĦÑĦ", + "ÑįÑĦÑĦ екÑĤ", + "ש ×Ļ×ij", + "Ġا عت", + "ت ض", + "تض ÙħÙĨ", + "ĠاÙĦÙħØ´ رÙĪع", + "Ġprodu ção", + "ĠпÑĢимен Ñı", + "ни ÑĨÑĭ", + "주 ëĬĶ", + "ر Ùı", + "Ġm Æ¡", + "Ġhayat ı", + "ëŁ ½", + "Ġü cret", + "Ġyan ında", + "Ġpr ática", + "×ij×Ļק ×ķר", + "Ãľ N", + "Ñģ оÑĤ", + "ãĤıãģij ãģ§", + "Ġдол го", + "ת ׼×ķ", + "ĠìķĦ ëĭĮ", + "ë į°ìĿ´", + "Ġç iz", + "Ġcho Äĩ", + "Ġ×Ķ ×Ļת", + "Ġ×Ķ×Ļת ר", + "Ġso át", + "׼ ×ij×ĵ", + "à¹Ģล à¹Īา", + "Ġд еÑĢ", + "ĠдеÑĢ ев", + "ãĤĴ åħ¥ãĤĮ", + "×Ĺ ×ķס", + "×Ĺ×ķס ר", + "ج ÙĬÙĨ", + "t ón", + "onn é", + "Ġпол ноÑģÑĤÑĮÑİ", + "人 ãģŁãģ¡", + "Ġpr êt", + "ëł ¸", + "Ġdéc embre", + "cı lar", + "Ġת ת", + "Ġê²½ìļ° ìĹIJëĬĶ", + "ÙĪ عد", + "è¦ĭ ãĤĭ", + "วิ à¸Īัย", + "ë ¶Ī", + "ز ÙĪا", + "زÙĪا ج", + "d ì", + "ãģ§ãģĻ ãĤĪ", + "Ġвод о", + "ĠÙĬ ÙĪجد", + "Ñģ оÑģÑĤоÑı", + "Ðŀ С", + "ĠÄIJ ó", + "׊פש", + "Ġצ ×Ļ×ij×ķר", + "ĠاÙĦÙĤ Ø·", + "ĠاÙĦÙĤØ· اع", + "Ġиме ÑİÑĤ", + "Ġph áºŃn", + "×Ľ×¡ פ×Ļ", + "полн иÑĤелÑĮ", + "éĻIJ ãĤĬ", + "ĠÑģ ÑĢав", + "ĠÑģÑĢав н", + "ÙħاÙĦ Ùĥ", + "×ĵר ×ķ×Ŀ", + "çļĨ ãģķãĤĵ", + "ØŃÙĤ ÙĤ", + "à¹ģหล à¹Īà¸ĩ", + "ĠاÙĦر سÙħÙĬ", + "оÑĩ ки", + "×ĺ ×ij×Ĺ", + "Ġcan lı", + "Ġ׾ ׾", + "Ġ׾׾ ×ŀ×ķ×ĵ", + "×ŀ×ij ×ķ", + "ת ׼", + "×ª×Ľ ׳×Ļת", + "ĠاÙĦÙħ شار", + "ĠاÙĦÙħشار ÙĥØ©", + "Ä° Åŀ", + "ĠسÙĬ اسÙĬ", + "в олÑĮ", + "ĠÑģ пÑĢав", + "æĿ¥ ãģ¦", + "פ×ķר ×ķ×Ŀ", + "สำ à¹Ģรà¹ĩ", + "สำà¹Ģรà¹ĩ à¸Ī", + "ĠÅŁ öyle", + "Ġzosta ÅĤa", + "ĠH ü", + "ר ×ķש", + "د ÙĦÙĬÙĦ", + "ÑĢи д", + "ש ף", + "×ŀק ×ķר", + "ĠÑĥ Ñĩ", + "ĠÑĥÑĩ еб", + "ĠÑį ÑĤа", + "ков а", + "à¸ķà¸Ļ à¹Ģà¸Ńà¸ĩ", + "ÙĨ ÙIJ", + "à¸Ńีà¸ģ à¸Ħรัà¹īà¸ĩ", + "ระ à¸ļุ", + "Ġd ữ", + "ĠاÙĦØŃ Ø§ÙĦÙĬ", + "׼ ×ķ׼", + "׼×ķ׼ ×ij", + "Ġ×ŀ×IJ שר", + "Ġtr ụ", + "ÑĤел ем", + "Ġв ли", + "Ġвли Ñı", + "Ġש×IJת ×Ŀ", + "Ġuw ag", + "Ġuwag ÄĻ", + "×ĺ ×Ļת", + "×IJ ×ĵ×Ŀ", + "à¸Ķ ุ", + "Ġ×Ķ×IJ ׾×Ķ", + "Ġkar Ä±ÅŁ", + "ĠÄIJ á»iji", + "да ÑİÑĤ", + "ãģªãģ® ãģ«", + "Äħ cych", + "à¹Ģà¸Ļ à¹īà¸Ļ", + "ãģĹãģ¦ ãģĹãģ¾ãģĨ", + "int érieur", + "ĠfÃŃs ica", + "ĠÐŁ ол", + "ãģĹãģ ķ", + "à¸Ĺำ à¹Ħม", + "ĠL âm", + "ĠاÙĦÙħ سÙĦÙħ", + "ĠاÙĦÙħسÙĦÙħ ÙĬÙĨ", + "ص ØŃØ©", + "ìĹ Ħ", + "à¹Ģà¸Ķà¹ĩ à¸Ķ", + "ĠÑĥ ÑĩеÑĤ", + "â Ìģ", + "Ġب ÙĦا", + "ĠاÙĦاجتÙħاع ÙĬ", + "פרס ×Ŀ", + "ãĥķ ãĥ©", + "ĠÐļ огда", + "mie ÅĽci", + "ĠبÙĬÙĨ Ùħا", + "Ġ×ŀ×IJ ×ŀר×Ļ×Ŀ", + "Ġ×ij×IJ ×ĸ×ķר", + "×ķש ×Ļ×Ŀ", + "ĠÑģдел а", + "entr ée", + "à¹Ģ à¸Ħà¹īา", + "Ñĥг л", + "ĠاÙĦÙģ ÙĨÙĬ", + "ĠÐĴ оÑĤ", + "à¸Ĺีà¹Ī มา", + "×ķצ ×Ĵ", + "ÙĤد رة", + "Ġëª ©", + "Ġ목 ìłģ", + "íıī ê°Ģ", + "ĠاÙĦØ£ ربع", + "ĠاÙĦأربع اء", + "פס ×Ļק", + "ĠÑıвлÑı ÑİÑĤÑģÑı", + "ب ÙĪÙĨ", + "ì° ¾", + "×ŀ×¢ ר׼", + "×ŀ×¢×¨×Ľ ×ķת", + "ãĤ· ãĤ§", + "ĠباÙĦ Ø£", + "íĸĪ ëįĺ", + "ĠاÙĦبر ÙĨاÙħج", + "ĠاÙĦØ£ ØŃد", + "Ġm Å©", + "ĠmÅ© i", + "п аÑĤ", + "ب Ø«", + "ĠÑĨ енÑĭ", + "Ġ×ijת ׾", + "è¨Ģ ãĤıãĤĮ", + "ĠاÙĦÙħ جاÙĦ", + "ĠìĦ¸ ìĥģ", + "Ġ×Ĵ ×ķפ", + "ĠнаÑĪ ей", + "Ġкомп аниÑı", + "б ин", + "öl ü", + "×Ļ ×Ļ×ĺ", + "Ġ×ŀס פ×Ļק", + "ยัà¸ĩ à¸Ħà¸ĩ", + "ĠЧ и", + "Ġан ÑĤи", + "ĠÑģÑĢед и", + "สà¹Īวà¸Ļ à¹ĥหà¸įà¹Ī", + "оÑĩ ка", + "íĬ¹ ë³Ħ", + "ว à¹Īาà¸ĩ", + "гоÑĢ од", + "با Ùĥ", + "à¹Ģส ีà¹Īย", + "à¹Ģสีà¹Īย à¸ĩ", + "ãĤĤãĤī ãģĦ", + "ק ×ķ×Ŀ", + "ãģĽ ãģļ", + "ĠاÙĦÙĤ اÙĩرة", + "Ġ×ij ׼×ļ", + "Ùħشار ÙĬع", + "باØŃ Ø«", + "Ġпо Ñĩ", + "ĠпоÑĩ ÑĤи", + "ĠÑĦоÑĢм а", + "S Ä°", + "Ġ×ŀצ ×Ļ×¢", + "ล ื", + "ลื ม", + "ĠÑĤ еÑĢ", + "ĠÑĤеÑĢ ÑĢиÑĤоÑĢ", + "ĠÑĤеÑĢÑĢиÑĤоÑĢ ии", + "Ġв меÑģÑĤ", + "ĠвмеÑģÑĤ е", + "dıkl arı", + "op ération", + "à¹Ĥ ห", + "ص دÙĬ", + "صدÙĬ ÙĤ", + "íĸī ìłķ", + "تج ا", + "تجا ÙĪز", + "Ġsu ç", + "Ġar ty", + "Ġarty ku", + "Ġartyku ÅĤ", + "ãĤ·ãĥ§ ãĥĥãĥĹ", + "ש פ", + "שפ ×Ļ×¢", + "Ġ×Ķש ×Ļר×ķת", + "à¹ģà¸ĸ ม", + "ë¸ Ķ", + "Ġuk ÅĤad", + "Ġ×ķ ׼×Ļ", + "หล าà¸ģ", + "หลาà¸ģ หลาย", + "æĸ¹ ãĤĤ", + "Ġpodr óż", + "ĠE ÄŁer", + "Ġком наÑĤ", + "ĠÑģам ÑĭÑħ", + "Ġв кÑĥÑģ", + "б еж", + "Ġ×ij ק×ķ", + "æİĽ ãģij", + "ãģ¿ ãĤĭãģ¨", + "ĠiliÅŁ kin", + "ĠÙĬ عÙħÙĦ", + "Ġпод аÑĢ", + "Ġyaz ılı", + "ãĤĴ å¾Ĺ", + "Ġwyst ÄĻp", + "à¸Ĺีà¹Ī à¹ĥà¸Ĭà¹ī", + "ØŃاد Ø«", + "ÙĪ ÙĬد", + "кÑĥ лÑĮÑĤ", + "кÑĥлÑĮÑĤ ÑĥÑĢ", + "à¸ģาร à¹ģà¸Ĥà¹Īà¸ĩ", + "à¸ģารà¹ģà¸Ĥà¹Īà¸ĩ à¸Ĥ", + "à¸ģารà¹ģà¸Ĥà¹Īà¸ĩà¸Ĥ ัà¸Ļ", + "ÙħÙĪ ظ", + "ÙħÙĪظ Ùģ", + "ÙĬÙħ ÙĬ", + "ãĤĵãģ§ãģĻ ãģĮ", + "diÄŁ im", + "diÄŁim iz", + "ĠÐŁ еÑĢ", + "ĠÐŁÐµÑĢ в", + "Ġm ão", + "ĠÑģ ез", + "ĠÑģез он", + "Ġ×Ķ×ŀ ×¢", + "Ùħ جÙħÙĪعة", + "ĠинÑĦоÑĢм аÑĨии", + "i ếc", + "ã ng", + "ĠÄij ấy", + "ãģĶ ç´", + "ãģĶç´ ¹", + "ãģĶç´¹ ä»ĭ", + "Ġad ım", + "à¹Ħ หล", + "Ġп ÑĢакÑĤи", + "ĠпÑĢакÑĤи Ñĩ", + "ĠпÑĢакÑĤиÑĩ еÑģ", + "ĠпÑĢакÑĤиÑĩеÑģ ки", + "ĠاÙĦÙĨ Ùģس", + "ĠÑĢабоÑĤ е", + "ÙĦÙĬ Ùģ", + "ĠاÙĦجÙĨ ÙĪب", + "Ġвод Ñĭ", + "ì¹ Ļ", + "Ġм иÑĢа", + "ĠÄij ừng", + "ĠпÑĢоÑĤив о", + "ĠÑģÑĤÑĢан Ñĭ", + "ล ู", + "ìĤ ¶", + "kre ÅĽl", + "Ġbul und", + "Ġbulund uÄŁu", + "à¹ģ สà¸Ļ", + "ãĤ± ãĤ¢", + "ת×Ĺ ×ķ×ŀ×Ļ", + "ר׼ ×Ķ", + "Ġ׾ק ×ķ×Ĺ", + "Ġ׾ק×ķ×Ĺ ×ķת", + "Ġ×Ľ×ª ×ķ×ijת", + "ĠÙĦ ÙĥÙħ", + "ب شر", + "Ġr Ãłng", + "Ġ×ŀ×Ķ ×ŀ", + "Ġ×IJ×Ĺר ×ķת", + "Ġб он", + "Ġбон ÑĥÑģ", + "ï½ Ĺ", + "à¹ģ ยà¸ģ", + "ãģĤãģªãģŁ ãģ®", + "ĠÑĥÑĩаÑģÑĤ ие", + "ĠE yl", + "ĠEyl ül", + "ĠçalÄ±ÅŁmalar ı", + "Ø® طر", + "ìĿ ½", + "à¸ģาร à¹ĥà¸Ĭà¹īà¸ĩาà¸Ļ", + "Ġана лиз", + "תק ×ij׾", + "ни ем", + "ĠÄ° ns", + "ĠÄ°ns an", + "ĠبÙĪ اس", + "ĠبÙĪاس طة", + "Ġ׳ ×Ľ×ł×¡", + "Ġ×Ķ×ŀ ×Ļ×ĵ×¢", + "Ġç o", + "Ġço ÄŁu", + "á» ĺ", + "ĠêµŃ 민", + "ãĤĤ ãģĦãģĦ", + "Ġ׼ ׾×Ļ", + "ĠÑģÑĢед не", + "g ÅĤo", + "gÅĤo ÅĽ", + "Ġneg ó", + "Ġnegó cio", + "ĠÑĢ егиÑģÑĤ", + "ĠÑĢегиÑģÑĤ ÑĢа", + "ĠÑĢегиÑģÑĤÑĢа ÑĨии", + "Ġtr á»ĵng", + "ĠпÑĢ Ñı", + "ĠпÑĢÑı мо", + "ëłĪ ìĿ´", + "Ġk ém", + "к ле", + "à¸Ļำ มา", + "ĠÑĦ ин", + "ĠÑĦин анÑģ", + "ĠÑĦинанÑģ ов", + "Ġki á»ĩm", + "ยัà¸ĩ à¹Ħ", + "ยัà¸ĩà¹Ħ à¸ĩ", + "ย ิà¸ĩ", + "à¹Ĥ à¸Ľ", + "ĠполÑĥÑĩ ил", + "×Ļ×ĸ ×Ŀ", + "à¹ģละ à¸Ħวาม", + "Ġво обÑīе", + "ص ÙĬر", + "ãĥı ãĥ³", + "ĠاÙĦÙĤ اد", + "ĠاÙĦÙĤاد Ùħ", + "Ġب دÙĪÙĨ", + "ع ظÙħ", + "ת ׳×ķ×¢", + "×ª×ł×ķ×¢ ×Ķ", + "Ø£ ÙħÙĦ", + "ãģķ ãģĪ", + "ÑĤ ем", + "ÑĤем пеÑĢ", + "ÑĤемпеÑĢ аÑĤÑĥÑĢ", + "Ġ׾ ×Ļצ×ķר", + "Ġr ÄĻk", + "ر سÙĦ", + "ìŀIJ 를", + "Ġ×Ļצ ×Ļרת", + "ÙĨ بÙĬ", + "Ñĩ наÑı", + "تØŃ ÙĦÙĬÙĦ", + "Ġм ик", + "Ġмик ÑĢо", + "ĠS öz", + "Ġfor ça", + "Ñģ он", + "ĠاÙĦع را", + "ĠاÙĦعرا ÙĤÙĬ", + "ĠH á»ĵng", + "ãģĻãĤĭ ãģŁãĤģãģ«", + "à¸Ĺีà¹Ī à¸Ńยูà¹Ī", + "Ġ×ķ×IJ ×£", + "ص ÙĬد", + "ĠìķĬ ê³ł", + "ร ัà¸ĩ", + "ĠاÙĦت ÙĪاصÙĦ", + "à¹Ģม à¸ķร", + "Ñĥ ÑģÑĤÑĢой", + "ÑĥÑģÑĤÑĢой ÑģÑĤв", + "m ıyor", + "Ġبا سÙħ", + "Ġ×ķ ׼×ķ", + "ĠG ül", + "á» IJ", + "Ãī tat", + "غ اÙĦ", + "Ø¥ ÙĨØ´", + "Ø¥ÙĨØ´ اء", + "T Ä°", + "à¸Ĥà¹īา ม", + "Ġtro ch", + "Ġtroch ÄĻ", + "Ø¥ ص", + "إص ابة", + "ĠØ« اÙĨÙĬ", + "ĠاÙĦص ØŃØ©", + "Ġ×ĸ×Ķ ×ķ", + "jÄħ cej", + "ãĥĢ ãĥ³", + "ìĿ¸ ìĿ´", + "Ġв олоÑģ", + "ëIJĺ ë©´", + "Ġzak ÅĤad", + "ãģĻ ãģĵãģ¨", + "以ä¸Ĭ ãģ®", + "Ġ×Ķ×ŀק ×ķ×Ŀ", + "ÙħØ´ اÙĩ", + "ÙħشاÙĩ دة", + "Ñĩ ив", + "ب Ø´", + "ย à¹īาย", + "Ġsür dür", + "ĠN ẵ", + "ĠNẵ ng", + "ĠигÑĢ аÑĤÑĮ", + "Ġê·¸ëŁ¬ ë©´", + "ãĥķ ãĥ«", + "ล à¹Īะ", + "Ġtend rá", + "Ġb Ãły", + "à¹Ģà¸Ľà¹ĩà¸Ļ à¸ľà¸¹à¹ī", + "Ġok o", + "Ġoko ÅĤo", + "w ÅĤa", + "wÅĤa ÅĽci", + "wÅĤaÅĽci w", + "æĢĿ ãĤı", + "ĠYa ÅŁ", + "ĠB á»ĩnh", + "íı Ń", + "بÙĬ د", + "קר ף", + "à¹Ģศ ร", + "à¹Ģศร ษ", + "à¹Ģศรษ à¸IJ", + "à¹Ģศรษà¸IJ à¸ģิà¸Ī", + "ĠاÙĦØ£ ÙĪرÙĪ", + "ĠاÙĦØ£ÙĪرÙĪ بÙĬ", + "fl äche", + "ä¹Ĺ ãĤĬ", + "Ġb á»ģn", + "Ùĩ ب", + "æľĢ ãĤĤ", + "Ġsa ç", + "à¸Ńำ à¹Ģà¸ł", + "à¸Ńำà¹Ģà¸ł à¸Ń", + "ĠØ£ ج", + "ĠاÙĦد اخÙĦ", + "ĠاÙĦداخÙĦ ÙĬØ©", + "×ĺ ×ķ×ij", + "ãĤĤ ãģªãģı", + "Ġли ÑĨа", + "à¹ģลà¹īว à¸ģà¹ĩ", + "×ĸ׼ ×Ļר", + "Ġqu Ãł", + "ĠÙĥ Ø°ÙĦÙĥ", + "صØŃ Ùģ", + "ĠÃĤ u", + "ÙĪب ا", + "à¹Ģà¸Ľà¸¥à¸µà¹Īยà¸Ļ à¹ģà¸Ľà¸¥", + "à¹Ģà¸Ľà¸¥à¸µà¹Īยà¸Ļà¹ģà¸Ľà¸¥ à¸ĩ", + "à¸ķัว à¸Ńยà¹Īาà¸ĩ", + "Ġráp ida", + "Ġtas ar", + "Ġtasar ım", + "ĠعÙĦÙĬ ÙĩÙħ", + "ס ×ķ׾", + "c ılı", + "cılı k", + "Ġر غÙħ", + "ìĭľ íĤ¤", + "Ġ×IJ׾ ק", + "Ġ×IJ׾ק ×ĺר", + "Ġ×IJ׾ק×ĺר ×ķ׳×Ļ", + "à¹ģà¸ļ à¹Īà¸ĩ", + "Ġh ạng", + "ãģ£ãģ¦ ãģıãĤĮ", + "ĠÙĨ تÙĬ", + "ĠÙĨتÙĬ جة", + "ıkl ı", + "غ اÙĨ", + "à¸Ĥà¹īà¸Ń à¸Ħวาม", + "à¸Ľà¸¥ าย", + "ĠØ£ Ùħس", + "à¸Ĺีà¹Ī à¹Ģà¸ģีà¹Īยว", + "à¸Ĺีà¹Īà¹Ģà¸ģีà¹Īยว à¸Ĥ", + "à¸Ĺีà¹Īà¹Ģà¸ģีà¹Īยวà¸Ĥ à¹īà¸Ńà¸ĩ", + "Ġdé fin", + "Ġdéfin i", + "ÙģÙĨ اد", + "ÙģÙĨاد ÙĤ", + "à¹Ħà¸Ķà¹ī วà¹Īา", + "ãģªãģĦ ãĤĪãģĨãģ«", + "Ġpróp ria", + "ĠPh át", + "ãĤĦãģĻ ãģı", + "สวย à¸ĩาม", + "ê³ł ìļĶ", + "Ñı еÑĤ", + "ãģĭãĤĤãģĹãĤĮãģ¾ãģĽãĤĵ ãģĮ", + "تر جÙħ", + "ĠкÑĢаÑģ ив", + "Ġ×ŀ ר×IJש", + "д еж", + "ĠÙĬ ÙĪÙĨ", + "ĠÙĬÙĪÙĨ ÙĬÙĪ", + "Ñģк оÑĢ", + "ĠKas ım", + "ê³Ħ ìķ½", + "к оÑģ", + "Ġна ÑĢÑĥ", + "ĠнаÑĢÑĥ ÑĪен", + "Ġdu że", + "acc ès", + "Ġh á»ĵng", + "Ġv Å©", + "ãģĦãģŁ ãģĹãģ¾ãģĻ", + "Ġ×ĺ ×Ļ", + "Ġ×ĺ×Ļ ×ķ׾", + "lıkl arı", + "Ġqu ê", + "ëħ¸ ëıĻ", + "ìķ Ķ", + "CI ÃĵN", + "Ġt ắc", + "press ão", + "ĠìŀĪ ìľ¼", + "สิà¸Ĺà¸ĺิ à¹Į", + "íĥ Ħ", + "Ġ×Ķ×ŀ ×ŀש׾×Ķ", + "å¬ī ãģĹãģĦ", + "ĠÄIJ ặc", + "ÙĨ زÙĦ", + "ĠдÑĢÑĥг ой", + "д ÑĥÑĤ", + "ìĪ Ļ", + "Ġth ụ", + "à¹Ģส ร", + "à¹Ģสร à¹ĩ", + "à¹Ģสรà¹ĩ à¸Ī", + "Ġto plant", + "Ġtoplant ı", + "×IJ×ŀ ף", + "×ķ׾ ת", + "п омн", + "Ġyo ÄŁun", + "ÅĦsk iego", + "ì° ©", + "ĠØ« ÙĦاث", + "ĠØ«ÙĦاث Ø©", + "Ġl ắng", + "ë¦ ´", + "ราà¸Ĭ à¸ģาร", + "ĠÑģлов а", + "á» Ĩ", + "à¸Ķี à¸ģวà¹Īา", + "ãģĶãģĸ ãģĦãģ¾ãģĻ", + "Ġд из", + "Ġдиз айн", + "fé rence", + "lıkl ar", + "ãģªãĤĵ ãģ§ãģĻ", + "ajÄħ cy", + "Ġëĭ¤ ìĸij", + "Ġëĭ¤ìĸij íķľ", + "ק ×Ļר", + "ØŃ Ø§Ø±", + "ส ูà¹ī", + "Ġz ro", + "Ġzro bi", + "Ġzrobi Äĩ", + "×ŀ ×Ļ׼×Ķ", + "à¸Ĭà¹Īวย à¹Ģหลืà¸Ń", + "ĠÑįÑĤ Ñĥ", + "ë´ ī", + "楽 ãģĹãģĦ", + "س ÙĪر", + "íķĺ ê±°ëĤĺ", + "Ùħؤ تÙħر", + "Ġpoc zÄħ", + "ĠpoczÄħ tk", + "ĠpoczÄħtk u", + "Ġع ربÙĬ", + "اÙĦØ£ ر", + "اÙĦأر دÙĨ", + "à¸Ķ ร", + "Åĵ uvre", + "ĠÙĪÙĥ اÙĨت", + "ĠÅĽ redni", + "Ø® ضر", + "Ġch uyến", + "н ÑĤ", + "ĠìķĮ ê³ł", + "Ġv á»Ŀi", + "Ġ×ij ×Ļ×ĵ×Ļ", + "×ŀ×ĵ ×ķ×ijר", + "ÙĪ Ùģر", + "ÙĬ Ø¡", + "׳ ×Ľ×¡", + "ĠÐĽ а", + "л он", + "Ġx ấu", + "Ùģ ÙĬÙĨ", + "Ġfé vrier", + "ĠÐŀ на", + "ĠV á»ģ", + "ĠÅŁey ler", + "ĠполÑĥÑĩ ен", + "з ад", + "Ġn ét", + "à¹Ħà¸Ľ ยัà¸ĩ", + "×Ĺש×ij ×ķ", + "à¸ļัà¸Ļ à¸Ĺ", + "à¸ļัà¸Ļà¸Ĺ ึà¸ģ", + "Ġgerçek leÅŁ", + "иÑĩеÑģк ое", + "ìĪĺ ê°Ģ", + "Ø« بت", + "ãģ¤ ãģ¾ãĤĬ", + "ĠÑĥÑģловиÑı Ñħ", + "ëĭ¤ ê°Ģ", + "ราย à¹Ħà¸Ķà¹ī", + "׼×IJ ×ij", + "à¹Ĥà¸Ľà¸£ à¹Ĥม", + "à¹Ĥà¸Ľà¸£à¹Ĥม à¸Ĭัà¹Īà¸Ļ", + "j ähr", + "jähr ige", + "ק ׳×Ļ×Ŀ", + "×ŀ ×ķק", + "×ŀ×ķק ×ĵ", + "ãģ«è¡Į ãģ£ãģ¦", + "Ø¢ ÙĦ", + "вед ение", + "Ġ׾ ×Ľ×ª×ķ×ij", + "جÙħ Ùĩ", + "جÙħÙĩ ÙĪرÙĬØ©", + "à¸ī à¸ļ", + "à¸īà¸ļ ัà¸ļ", + "ĠC òn", + "à¸ľ สม", + "ãģªãģ© ãģĮ", + "×IJ×Ķ ×ij", + "ĠдейÑģÑĤв иÑı", + "y ız", + "à¹Ħมà¹Ī à¹Ģà¸Ħย", + "ج ÙĪز", + "×Ķ×Ĺ׾×ĺ ×Ķ", + "f ällt", + "ãĥĵ ãĤ¸", + "ãĥĵãĤ¸ ãĥį", + "ãĥĵãĤ¸ãĥį ãĤ¹", + "Ġ×IJ ×Ļ׳×Ŀ", + "ĠнаÑħод иÑĤÑģÑı", + "Ġdzi ÅĽ", + "ست Ø·ÙĬع", + "׾ ×Ļף", + "Ø® ÙĦاÙģ", + "Ùĩ ÙIJ", + "Ġatr ás", + "íĺ ģ", + "ãĤĴ ãģĶ", + "Ġ×Ķ×ŀ ×ķצר", + "ĠBakan lıģı", + "ÑİÑī ее", + "ÙħÙĨ اط", + "ÙħÙĨاط ÙĤ", + "Ùģ د", + "à¸Ļำ à¹Ħà¸Ľ", + "Ġв аж", + "Ġваж но", + "Ġm ạch", + "׼ ׳×ķ", + "بع Ø«", + "lan ması", + "Ġa yr", + "Ġayr ıl", + "ìĤ¬ íļĮ", + "d ÃŃa", + "p ÅĤyw", + "اÙħ ÙĬØ©", + "íĺ ľ", + "×IJ׳ ×Ĵ׾", + "×IJ׳×Ĵ׾ ×Ļת", + "ĠìŀĪëĭ¤ ëĬĶ", + "Ġس اعة", + "ĠëĤĺ íĥĢ", + "b ö", + "à¸Ħ ัà¸Ļ", + "ĠdziaÅĤ ania", + "Ø© Ùĭ", + "Ġng Å©", + "׳צ ×Ĺ", + "ãģ¯ ãģĤãĤĭ", + "ĠyaÅŁ ında", + "st ück", + "car acter", + "caracter ÃŃsticas", + "Ġr á»Ńa", + "ĠÙħختÙĦÙģ Ø©", + "ãģ«ãģĬ ãģijãĤĭ", + "à¹ģà¸ŀ à¸ĩ", + "วิ à¹Īà¸ĩ", + "ת פ×ķ", + "سا ÙĩÙħ", + "使 ãģĨ", + "Ùĥ رÙĬ", + "×IJ פ×Ļ", + "........ .......", + "ĠÑĤак им", + "×Ļ׼ ×ķ×Ļ", + "Ø´ بÙĩ", + "ج ÙĬر", + "ãģĿãģ® ãģ¾ãģ¾", + "ac jÄĻ", + "ĠاÙĦت رÙĥ", + "ĠاÙĦترÙĥ ÙĬ", + "ĠпÑĢав илÑĮно", + "Ġت عÙħÙĦ", + "à¸ģล à¹īา", + "Ġbi ên", + "Ġ×ij׳×Ļ ×Ļת", + "Ġкл Ñĥб", + "Ġ×ŀ ש×Ķ", + "в ÑĪий", + "ãģĵãģ¨ãģĮãģ§ãģį ãĤĭ", + "à¸ŀัà¸Ļà¸ĺ ุ", + "à¸ŀัà¸Ļà¸ĺุ à¹Į", + "ר ×ķ×Ŀ", + "ĠاÙĦÙģ رÙĨ", + "ĠاÙĦÙģرÙĨ سÙĬ", + "à¹Ģà¸Ľà¹ĩà¸Ļ à¸Ħà¸Ļ", + "ãģĹãģ¦ ãģĬãĤĬ", + "Ġth ầy", + "ãĤĵ ãģłãģijãģ©", + "ìĶ ¨", + "Ùħ دÙĨ", + "ت ÙĪÙĨ", + "ĠмеÑĤ ал", + "ĠмеÑĤал л", + "Ġin ÃŃcio", + "à¸Ńà¸Ńà¸ģ à¸Īาà¸ģ", + "ëĴ ¤", + "Ġcu á»ijn", + "Ġbu á»Ļc", + "ÙĨ سÙĬ", + "ä cht", + "×ŀ ×Ļ׳×Ļ×Ŀ", + "ãģķ ãģ¦", + "ãģĮ ãģ§ãģį", + "ÑĬ ем", + "Ġtá i", + "ĠЧ ÑĤ", + "ĠЧÑĤ обÑĭ", + "à¸Ľà¸¥ ูà¸ģ", + "à¸Ĭุม à¸Ĭà¸Ļ", + "н Ñģкий", + "Ġv ững", + "Ġ×Ķ ׾×ij", + "ë le", + "Ġש ×¢×ijר", + "в аÑĤÑĮÑģÑı", + "б ой", + "ع ÙĪÙĨ", + "à¹ģà¸Ķ à¸Ļ", + "Ġספר ×Ļ×Ŀ", + "Ġt uyên", + "Ġnhi êu", + "ĠQu ý", + "Ġh uyết", + "ãĤı ãģĭãĤīãģªãģĦ", + "Ġ×ŀ ׼ף", + "Ġ×Ķ ק׾", + "Ġ׾×IJ ×ķר", + "ĠÄIJi á»ĩn", + "Ø´ ؤ", + "شؤ ÙĪÙĨ", + "Ġ×ŀ׊פש", + "ĠпоÑģÑĤоÑıн но", + "×ŀ ×Ļר", + "ìħ Ķ", + "Ðŀ Ñģ", + "ÐŀÑģ нов", + "×ĸ ×Ļת", + "ĠH á", + "ĠÑĩаÑģ ов", + "×IJ ×ķ׾×Ļ", + "Ġm át", + "Ø® رÙĪ", + "خرÙĪ ج", + "ÙĤ ضا", + "ÙĤضا ÙĬا", + "à¹Ģà¸Ľ à¸Ńรà¹Į", + "ĠÙĬ ÙĪÙĦ", + "ĠÙĬÙĪÙĦ ÙĬÙĪ", + "à¹Ĥà¸Ĺ ษ", + "׳ פ׾", + "ת ×ķש", + "ת×ķש ×ij×Ļ", + "Ġv ários", + "×ŀ ר×IJ×Ķ", + "ëĿ¼ ìĿ´", + "ÙĨ غ", + "×ij צע", + "г он", + "ĠÄIJ ược", + "ع Ùı", + "пÑĥÑģ к", + "ĠÙĪاÙĦ Ùģ", + "üc ü", + "×Ļק ×Ļ×Ŀ", + "Ġس بÙĬÙĦ", + "׾×ij ף", + "ĠاÙĦÙĤ رÙĨ", + "ס ×ķת", + "ĠQu áºŃn", + "ãģĵãĤĮ ãģĮ", + "ãĥĸ ãĥ©ãĥ³ãĥī", + "×Ĵ ×ŀר", + "Ġwarto ÅĽci", + "ĠÙĪب ÙĬÙĨ", + "Ġd ạ", + "ÐIJ в", + "ÐIJв ÑĤо", + "Ġol acaktır", + "à¸Ļ à¸Ĺà¹Į", + "Ùħ طار", + "Ġ×¢ ק×ij", + "Ġת פ", + "ãģĹãģ¦ ãģĦãģ¦", + "צ ×ŀ×Ĺ", + "à¸Ī à¸Ńà¸ĩ", + "Ġö de", + "ìį ¨", + "ÙĨ اس", + "調 ãģ¹", + "ĠогÑĢ омн", + "ë³´ íĹĺ", + "×ĺ ק", + "×ĺק ס×ĺ", + "ĠbaÅŁ v", + "ĠbaÅŁv uru", + "Ġpom ys", + "Ġpomys ÅĤ", + "ãģ« ä¹Ĺ", + "Ġש ׼ף", + "ĠاÙĦÙħس ؤÙĪÙĦ", + "Ġз ан", + "Ġзан ÑıÑĤ", + "Ġd Æ°Æ¡ng", + "ãĥĹãĥ¬ ãĤ¤", + "ล à¸ļ", + "ÑĤи ка", + "ĠAr alık", + "Ġнед о", + "Ġm á»Ļ", + "Ġor an", + "Ġoran ı", + "Ġktó r", + "Ġktór Äħ", + "Ġ×Ķ×IJ×Ĺר ×ķ׳×ķת", + "ائ ÙĨ", + "ÅĦ s", + "ÅĦs ka", + "åĽ½ ãģ®", + "×ŀ ×ĺ×Ļ", + "ĠвопÑĢоÑģ Ñĭ", + "à¸Ńà¸ĩà¸Ħà¹Į à¸ģร", + "×ŀ ×ķצ×IJ", + "Ġpó ź", + "Ġpóź niej", + "ש×ŀ ×IJ׾", + "Ġk aps", + "Ġkaps am", + "Ġkapsam ında", + "Ġmá quina", + "ĠÅĽwie cie", + "Ġho Ãłng", + "Ġöz gü", + "×Ĵ×ķר ×Ŀ", + "ãģĤ ãģŁãĤĬ", + "à¸ķัà¸Ķ สิà¸Ļ", + "à¸ķัà¸Ķสิà¸Ļ à¹ĥà¸Ī", + "б ÑĢи", + "ãģ«ãģªãĤĭ ãģ¨", + "ت ÙĥÙĪÙĨ", + "Ġ×ķ×Ķ ×Ļ×IJ", + "Ġchi ếu", + "ÑģÑĤан ав", + "ÑģÑĤанав ли", + "ÑģÑĤанавли ва", + "×ŀ ×ķ×Ĵ", + "c ité", + "ĠK örper", + "Ġש ×Ĵ×Ŀ", + "ع ظ", + "عظ ÙĬÙħ", + "Ġ×Ķ×IJ ×Ļש×Ļ", + "Ġmat ière", + "ĠÙģ ÙĪÙĤ", + "Ġk to", + "Ġkto ÅĽ", + "à¸Ļ à¹Ĥย", + "à¸Ļà¹Ĥย à¸ļาย", + "å¾ħ ãģ¡", + "à¹Ģม à¸Ļ", + "à¹Ģมà¸Ļ ู", + "A ÃĩÃĥO", + "Ġt ù", + "Ġtù y", + "ãĥĪ ãĥ³", + "ĠоÑĤ каз", + "Ġ×ŀ ×ķצר", + "ül ü", + "ãģķãĤĵ ãģ«", + "Ġ×Ĺ ×ķ×ij", + "קר ×Ļ×IJ×Ķ", + "ĠاÙĦØ® دÙħات", + "ĠÙĦÙħ دة", + "ر ؤ", + "رؤ ÙĬØ©", + "ãĤĴè¦ĭ ãģ¤ãģij", + "à¸Ł า", + "Ġréuss i", + "à¸Ļัà¸ģ à¹Ģรียà¸Ļ", + "ĠÑĩиÑģ л", + "à¸ģาร à¹Ģลà¹Īà¸Ļ", + "Ġhaz ırl", + "Ġhazırl an", + "ĠпеÑĢв Ñĭй", + "ли м", + "ĠоÑĤзÑĭв Ñĭ", + "Ġwy jÄħ", + "ĠwyjÄħ tk", + "ĠØ£ ÙĤÙĦ", + "ס ×ļ", + "Ġê²° ìłķ", + "Ġ׾×ŀ×¢ ש×Ķ", + "Ġl ắp", + "à¹ģà¸ļ ร", + "à¹ģà¸ļร à¸Ļà¸Ķà¹Į", + "วà¹Īา à¹Ģà¸Ľà¹ĩà¸Ļ", + "Ġب دا", + "Ġبدا ÙĬØ©", + "ãģ¨ãģĦãģĨ ãģ®ãģĮ", + "иÑĩеÑģк им", + "à¸ģาร à¸ŀัà¸Ĵà¸Ļา", + "Ġb Ãło", + "Ġmia ÅĤa", + "y waÄĩ", + "ĠMär z", + "ĠÙĨ سبة", + "Ġéconom ique", + "×ĸ ×ŀ", + "×ĸ×ŀ ׳×Ļ×Ŀ", + "æŃ¢ ãĤģ", + "Ġt ủ", + "íķĺ ìĭł", + "Ġkażde go", + "stra ÃŁe", + "à¸Ĭ ีà¹ī", + "à¹Ģ à¸ļา", + "ÑĢеÑģ ÑĥÑĢÑģ", + "ев ой", + "Ø´ باب", + "à¸ķà¹Īาà¸ĩ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", + "Ġ×IJ ×Ļש", + "Ġ×IJ×Ļש ×Ļת", + "×Ļ ×ķפ", + "×Ļ×ķפ ×Ļ", + "ĠìļĶ 구", + "ì¡° ìĤ¬", + "ãģ£ãģŁ ãĤī", + "׾ ×Ļק", + "миниÑģÑĤ ÑĢ", + "ãĤĤãģ® ãģ¯", + "Ġl Æ°Æ¡ng", + "Ġна и", + "Ġнаи бол", + "Ġнаибол ее", + "íİ ĺ", + "à¹ģà¸ŀ à¹ī", + "ãĤŃ ãĥ¥", + "ĠкоÑĤоÑĢ Ñĭм", + "à¹ģà¸Ĺ à¸ĩ", + "à¹ģà¸Ĺà¸ĩ à¸ļà¸Ńล", + "Ġ׳ ×Ļ×Ķ", + "Ġ׳×Ļ×Ķ ×ķ׾", + "âĤ ª", + "ĠGi ải", + "ĠиÑģполÑĮзов а", + "ëł¥ ìĿĦ", + "ãģĹãģĭ ãĤĤ", + "à¸ģà¹ĩ à¸ķà¹īà¸Ńà¸ĩ", + "ĠÑĢ еб", + "ĠÑĢеб ен", + "ĠÑĢебен ка", + "ت ÙĪاصÙĦ", + "ãĤ°ãĥ« ãĥ¼ãĥĹ", + "ãĤĦ ãĤī", + "à¹Ģà¸Ľà¸´à¸Ķ à¸ķัว", + "б ÑĢо", + "ë°ĸ ìĹIJ", + "ÙĨ Ùİا", + "×Ķ ×Ĵ", + "×Ķ×Ĵ ׳×Ķ", + "à¸Ĺ รั", + "à¸Ĺรั à¸ŀ", + "à¸Ĺรัà¸ŀ ยà¹Į", + "Ġkh á»iji", + "עצ ×ŀ×ķ", + "бол езн", + "Ġë°Ľ ìķĦ", + "ม à¸Ļ", + "มà¸Ļ ุ", + "มà¸Ļุ ษ", + "มà¸Ļุษ ยà¹Į", + "âĹ Ĩ", + "×ŀ צ׾×Ļ×Ĺ", + "Ñıв ление", + "Ùħ Ø·ÙĦ", + "ÙħØ·ÙĦ ÙĪب", + "Ø® اÙĦÙģ", + "ت ÙĪÙĤÙģ", + "ãģ§ãģį ãģ¾ãģĽãĤĵ", + "оÑģÑĤ ей", + "м еÑĩа", + "기 ëĬĶ", + "תש ×¢", + "ص ÙĬب", + "Ġ×ij×¢ ×ķ×ĵ", + "à¸Ĥà¸Ńà¸ĩ à¹Ģà¸Ĥา", + "ÑĤÑı ж", + "ĠÑĥ пÑĢав", + "ĠÑĥпÑĢав лениÑı", + "Ġgén ér", + "Ġth ÃŃ", + "פ ×ļ", + "Ġر Ùħض", + "ĠرÙħض اÙĨ", + "Ġtr uyá»ĩn", + "Ø¥ عداد", + "ãĤµ ãĥĿãĥ¼ãĥĪ", + "Ġпол но", + "Ø® اÙħ", + "ÐŁ еÑĤ", + "ÐŁÐµÑĤ еÑĢ", + "ÐŁÐµÑĤеÑĢ бÑĥÑĢ", + "ÐŁÐµÑĤеÑĢбÑĥÑĢ г", + "ÙħÙĨت دÙī", + "ãģķãĤĮ ãģ¾ãģĹãģŁ", + "ĠëĮĢ íķĺìŬ", + "à¸ľà¸¹à¹ī à¸Ĺีà¹Ī", + "Ġ×ŀ×IJ ×ķ", + "׾ ׳×ĵ", + "оÑĩ нÑĭе", + "ĠнаÑĩ ала", + "Ġ׾ ×Ļ׾×ĵ×Ļ×Ŀ", + "ов ое", + "ãģĻãĤĭãģĵãģ¨ ãģ§", + "ĠاÙĦÙĨ Ùģ", + "ĠاÙĦÙĨÙģ Ø·", + "ìŀĪ ëĬĶ", + "غ ÙĨÙĬ", + "פ ×ĵ", + "ãĤ ¾", + "ĠCr é", + "ãģ© ãģ¡ãĤī", + "Ø« اÙĨ", + "ÑĢаб аÑĤ", + "ÑĢабаÑĤ Ñĭва", + "Ġê°Ļ ëĭ¤", + "à¸Ī ั", + "à¸Īั à¸ģร", + "Ġch ụ", + "Ġchụ p", + "Ġм аÑģÑĤ", + "ĠмаÑģÑĤ еÑĢ", + "Ġn ắm", + "ĠÑģÑĤ али", + "Ġ×Ķ×IJ ×Ļר×ķ×¢", + "ãĤ½ ãĥ³", + "åĪĨ ãģĭãĤĬ", + "Ø· بع", + "بد ا", + "gr áfico", + "г еÑĢ", + "à¸Ķำà¹Ģà¸Ļิà¸Ļ à¸ģาร", + "Ġsal dır", + "Ġsaldır ı", + "в ÑĪиÑħ", + "ãģĭãģ£ãģŁ ãģ§ãģĻ", + "Ġyapı yor", + "ĠاÙĦÙģ ت", + "צר פת", + "з доÑĢов", + "×ij×¢ ׾", + "Ġ×IJ ×ŀ×Ļת×Ļ", + "Ġоб Ñĭ", + "ĠобÑĭ Ñĩ", + "ĠобÑĭÑĩ но", + "Ġ׾ ×ķ×ŀר", + "ت ÙĥÙĨ", + "تÙĥÙĨ ÙĪÙĦÙĪج", + "تÙĥÙĨÙĪÙĦÙĪج ÙĬا", + "Ġhakk ı", + "ĠÑĢаР²", + "ĠÑĢав но", + "رÙĬ Ùĥ", + "Ġ×ij ×ŀ×Ļ×ĵ", + "Ġ×ij×ŀ×Ļ×ĵ ×Ķ", + "à¹ģà¸ģ à¹īว", + "Ġìĸ ĺ", + "Ġìĸĺ 기", + "ãģĹãģ¦ ãģĦãģ¾ãģĹãģŁ", + "Ġkı sm", + "Ġkısm ı", + "ê± ¸", + "åĨħ ãģ®", + "ì§ ķ", + "à¹Ģหมืà¸Ńà¸Ļ à¸ģัà¸Ļ", + "ĠÙģ ÙIJ", + "ĠÙģÙIJ ÙĬ", + "ÙĤ اعدة", + "Ġmoż esz", + "Ùħ صاÙĦ", + "ÙħصاÙĦ ØŃ", + "ãģ¾ãģŁ ãģ¯", + "б ег", + "Ġs ıc", + "Ġsıc ak", + "Ñĩ иÑģ", + "ÑĩиÑģ лен", + "Ġн ог", + "ãĥģãĥ£ ãĥ³", + "ãĥ« ãĥī", + "Ġgi ó", + "Ġs ını", + "Ġsını f", + "ив аÑĤÑĮ", + "Ġqu ên", + "Ġì łģ", + "Ġìłģ ìļ©", + "ĠJo ão", + "Ùģ اد", + "ĠGl ück", + "à¸Ĺ à¸Ńà¸Ķ", + "Ġg ói", + "ï¼ Ĭ", + "Ġdé tail", + "ĠدÙĬ سÙħ", + "ĠدÙĬسÙħ بر", + "ë¡ľ ìĦľ", + "×ŀ ×ķ×Ĺ", + "à¹Ħ ฮ", + "ĠоÑĤ д", + "ĠоÑĤд ÑĭÑħ", + "Ġkh uyến", + "à¸Ħ à¸Ńย", + "Ġج ÙĨÙĬ", + "ĠجÙĨÙĬ Ùĩ", + "ĠاÙĦد Ùģاع", + "à¸Ļà¹īำ หà¸Ļัà¸ģ", + "ĠìĤ¬ëŀĮ ëĵ¤ìĿ´", + "Ġth ừa", + "ĠÃ¶ÄŁrenc i", + "ĠпомоÑī и", + "ĠczÄĻ ÅĽÄĩ", + "ש ×ĺר", + "ĠN hi", + "ĠNhi á»ģu", + "׳ צ×Ļ", + "ĠнаÑĪ ем", + "ĠkarÅŁÄ± laÅŁ", + "Ġ×Ķש ׳×Ļ×Ŀ", + "ĠÄIJ Æ°á»Ŀng", + "Ġtr ú", + "ĠÑĢазлиÑĩ нÑĭÑħ", + "ĠاÙĦØ´ Ùĩر", + "Ġ×ľ×¢ ×ķ׾×Ŀ", + "ØŃ Ø¬Ø±", + "ĠÄij á»ķ", + "ĠìĿĺ íķ´", + "à¸ļ à¹Īà¸Ńย", + "Ġ×Ķ ×Ļ׾×ĵ", + "ãģ¨ãģª ãģ£ãģŁ", + "Ġ×Ĺ×ķ ×ķת", + "Ġש×Ļר×ķת ×Ļ", + "Äħ cy", + "س رÙĬ", + "K Ä°", + "פ ׳×ķ", + "ÑģÑĤÑĢÑĥк ÑĤÑĥÑĢ", + "ÑĤ ÑĢÑĥд", + "Ġ×Ķ קר", + "Ġ×Ķקר ×ķ×ij", + "Ġth áºŃm", + "èģŀ ãģį", + "ÙĤÙĪ ÙĬ", + "клÑİÑĩ ен", + "ÑĤе Ñħ", + "ÑĤеÑħ нолог", + "è¡Į ãģ£ãģŁ", + "Ġ×ķ×IJ ×Ļף", + "ĠÅŁek lin", + "ĠÅŁeklin de", + "r ô", + "ÑĢ ог", + "Ġнов Ñĭе", + "Ġס ×ij×Ļ×ij", + "Ġtecn ologÃŃa", + "ס ׼", + "×¡×Ľ ×ķ×Ŀ", + "ĠÅŀ ub", + "ĠÅŀub at", + "Ġ×Ķ×ŀ ׾×IJ", + "Ġwy pos", + "Ġwypos aż", + "ãģ¯ ä½ķ", + "ãĤ¬ ãĥ³", + "ê° ĸ", + "Ġкак ие", + "Ġçocuk lar", + "Ġ׾צ ×ĵ", + "Ġkay ıt", + "ĠмеÑģÑĤ е", + "Ùħ دÙĬÙĨØ©", + "Ġ׼ ×Ĵ", + "Ġ׼×Ĵ ×ķף", + "ãģĹãģ¦ ãĤĭ", + "ĠÙħا ÙĬÙĪ", + "ãģ£ãģ¦ãģĹãģ¾ ãģ£ãģŁ", + "ĠпÑĢогÑĢамм Ñĭ", + "à¹ģล à¸Ļà¸Ķà¹Į", + "ãĥ¯ ãĤ¤", + "ער ×ķ×¥", + "Ñģ ид", + "ĠB öyle", + "Ġì²ĺ ìĿĮ", + "Ġת פק×Ļ×ĵ", + "ĠTr ên", + "íĥ Ī", + "ĠÐłÐ¾ÑģÑģ ий", + "ĠÐłÐ¾ÑģÑģий Ñģкой", + "Ġs Ãłn", + "Ġrè gle", + "ĠyaklaÅŁ ık", + "à¹Ģล ิà¸ģ", + "Ġد ائÙħ", + "Ġ×ķ ×Ĵ", + "اب ر", + "Ġb è", + "ĠاÙĦ ÙĤدÙħ", + "ĠÑĢеÑĪ ениÑı", + "hi ên", + "ÑĤи к", + "Ä Ħ", + "à¸ļรร ยาà¸ģ", + "à¸ļรรยาà¸ģ าศ", + "רצ ×ķף", + "åĭķ ãģį", + "ĠGä ste", + "Ġ기 본", + "ĠÙĬ عرÙģ", + "ĠS á»Ń", + "gÅĤ ÄĻb", + "à¹Ģà¸Ń ส", + "×IJ×ŀ ×Ļף", + "Ġп Ñĥнк", + "ĠпÑĥнк ÑĤ", + "Ġ×Ļ×ķ×ĵ ×¢×Ļ×Ŀ", + "ãĤ« ãĥ©ãĥ¼", + "Ġ×ijס ×ĵר", + "Ġbu á»ĵn", + "й ÑĤ", + "йÑĤ еÑģÑĮ", + "ãĤĴ æ±ĤãĤģ", + "Ġ×IJת ׼×Ŀ", + "Ġ모 르", + "ظ رÙĪÙģ", + "Ñĩ еÑģÑĤво", + "ìĸ´ ìĦľ", + "Ġод на", + "Ġkap ı", + "Ġëħ¸ ëł¥", + "ĠKü che", + "ĠاÙĦت Ø´", + "Ø· ÙĬب", + "ĠíĬ¹ íŀĪ", + "ĠвÑĭп ÑĥÑģ", + "ĠвÑĭпÑĥÑģ к", + "×ĵ ת×Ļ", + "Ġu ÄŁ", + "ĠuÄŁ ra", + "ائ Ùĩا", + "Ġtho át", + "ãģª ãĤĤãģ®", + "Ñij ÑĢ", + "기 ê°Ģ", + "ĠgeliÅŁ me", + "تØŃ ÙĤ", + "تØŃÙĤ ÙĤ", + "Ġоп аÑģ", + "б ÑĢоÑģ", + "ห ุ", + "หุ à¹īà¸Ļ", + "ì¼ Ģ", + "ãĤ¹ ãĥŀ", + "ãĤ¹ãĥŀ ãĥĽ", + "Ø£ Ùģر", + "Ø£Ùģر اد", + "ĠTh á»±c", + "Ġth ắ", + "ãĥªãĥ³ ãĤ¯", + "Ġni á»ģm", + "ĠHö he", + "عÙħ ار", + "ÙĥÙĪر ÙĪÙĨ", + "ÙĥÙĪرÙĪÙĨ ا", + "ĠÄIJ ến", + "ĠÑģам ом", + "ĠÑĤ еле", + "ĠÄijo án", + "à¸Ħวามà¸Ħิà¸Ķ à¹Ģหà¹ĩà¸Ļ", + "Ġд иÑģк", + "Ø£ Ø·ÙģاÙĦ", + "ม ารà¹Į", + "à¸Ĺ หาร", + "à¸Ĺ à¸Ļ", + "Ġب عÙĬد", + "ĠاÙĦÙĩ ÙĨد", + "åĩº ãģĹãģ¦", + "Ġkar de", + "Ġkarde ÅŁ", + "×Ķ×Ļס×ĺ ×ķר", + "×Ķ×Ļס×ĺ×ķר ×Ļ×Ķ", + "éģ¸ ãģ³", + "ع اÙħÙĦ", + "à¸Ĥ ยาย", + "Ġtü rl", + "Ġtürl ü", + "ĠìĿ¼ ìĿ´", + "Ġmaté ria", + "Ġ׼׾ ×ķ×ŀר", + "ãĥģãĥ£ ãĥ¼", + "جÙħ اعة", + "ĠÑģво им", + "Ø¥ÙĤ اÙħØ©", + "ä¾ĭ ãģĪãģ°", + "س اب", + "Ø¢ خر", + "ÙĤ دÙĬر", + "×IJ×ŀ ×Ļ", + "ìĸ »", + "Ġ׳×ķס פת", + "ĠÐĴ лад", + "ĠÐĴлад им", + "ĠÐĴладим иÑĢ", + "Ġest ará", + "ãģĵãģĨ ãģĦãģĨ", + "ãĤĴ 使çĶ¨", + "มา à¸ķร", + "มาà¸ķร à¸IJาà¸Ļ", + "ãģ£ãģ ½", + "Ġn ú", + "Ġnú i", + "ย าà¸ĩ", + "ĠاÙĦج ÙĨس", + "Ġüst ün", + "ëľ »", + "ãĤ» ãĥ«", + "ãģ¦ãģĦ ãģįãģ¾ãģĻ", + "Ġ×Ĺ ×ķ×ĸ", + "Ġ×Ĺ×ķ×ĸ ר", + "ĠÐĵ лав", + "à¹Ĥà¸Ĭ à¸Ħ", + "íı IJ", + "ÙĨت ظر", + "Ġ×Ĵ ×ij×Ļ", + "ع ÙĤب", + "int ér", + "intér êt", + "×ŀ פ×Ĵ", + "×ŀפ×Ĵ ש", + "Ġth ù", + "اÙģ ت", + "Ġ×ŀש פ", + "Ġ×ŀשפ ×ĺ×Ļ", + "ĠÙħ ÙĪاÙĤع", + "è¦ ļ", + "è¦ļ ãģĪ", + "×ĵ ×Ļף", + "à¹Ģรืà¹Īà¸Ńà¸ĩ ราว", + "ãģ¾ ãģĤ", + "Ġgh ế", + "иÑĢÑĥ ÑİÑĤ", + "à¸ģ ว", + "à¸ģว à¹īาà¸ĩ", + "Ġпов еÑĢ", + "ĠповеÑĢ Ñħ", + "ĠповеÑĢÑħ ноÑģÑĤ", + "׳ ×ĵר", + "Ġкон ÑĨе", + "Ġдолж на", + "Ġ×Ļש ×Ļר", + "acaģı z", + "ìĹ Ķ", + "Ġn ÃŃvel", + "Ġö r", + "Ġör nek", + "Ùĥ Ùģ", + "ĠФедеÑĢ аÑĨии", + "Ġ구 ìĦ±", + "หัว à¹ĥà¸Ī", + "ĠV áºŃy", + "м ед", + "мед и", + "меди ÑĨин", + "медиÑĨин Ñģк", + "از ÙĬ", + "×Ĵ×ij ×ķ׾", + "ÑĦ ÑĢ", + "Ġzus ätzlich", + "à¸ģ à¸ģ", + "ĠاÙĦاÙĤتصاد ÙĬØ©", + "Ġh è", + "lu ÄŁun", + "ج Ùİ", + "à¹Ħà¸Ł ลà¹Į", + "ÄIJ T", + "ãģĿãģ® ä»ĸ", + "à¸Ĺิ à¹īà¸ĩ", + "ĠاÙĦØ£ ÙĪ", + "ر سÙħ", + "æ°Ĺ ãģ¥", + "ìĿ´ ë©°", + "ÑĮ ев", + "ص Ø·", + "ĠاÙĦاست Ø«", + "ĠاÙĦاستث Ùħار", + "à¸Ńา à¸Ħาร", + "ĠÑĤоÑĩ но", + "ĠV ân", + "à¸Ń ร", + "à¸Ńร à¹Īà¸Ńย", + "ĠاÙĦس ÙĨØ©", + "Ġc Æ°á»Ľi", + "×Ļ×Ķ ף", + "íį ¼", + "話 ãģĹ", + "âĹ ĭ", + "ĠìķĬ ìĿĢ", + "ãĥ¡ ãĥ¼ãĤ", + "ãĥ¡ãĥ¼ãĤ «", + "ãĥ¡ãĥ¼ãĤ« ãĥ¼", + "ĠÑĤеп ло", + "å½¼ ãĤī", + "ĠÄ° z", + "ĠÄ°z mir", + "íĻ į", + "Ġr ượ", + "Ġrượ u", + "æĢĿãģĦ åĩº", + "ĠPh ạm", + "Ġchá u", + "צ×Ļ ×ķ×ª", + "ĠìĿ¼ 본", + "ìĤ¬ ëĬĶ", + "ĠÑģозд ан", + "Ġar acı", + "Ġ×¢ ר", + "Ġער ×Ļ׼×Ķ", + "ĠíķĺëĤĺëĭĺ ìĿĺ", + "dzi ÅĤ", + "à¸Ľà¸£à¸° à¸ĺาà¸Ļ", + "Ġser ÃŃa", + "ĠìŀĪ ëıĦë¡Ŀ", + "در ج", + "íķľëĭ¤ ëĬĶ", + "à¸Ńา à¸Ĺ", + "à¸Ńาà¸Ĺ ิà¸ķ", + "à¸Ńาà¸Ĺิà¸ķ ยà¹Į", + "ÑĤелÑĮ нÑĭй", + "ĠØ® دÙħات", + "×ŀ׳ ×ĺ", + "Ġl ược", + "ĠS Ãłi", + "ĠÙĪ اض", + "ĠÙĪاض ØŃ", + "غ از", + "ĠdoÄŁ al", + "Ġ×ijש ×Ŀ", + "Ġд лин", + "ĠØ¥ طار", + "Ġ×ijס פר", + "ãĤĴ ä¸İ", + "ãĤĴä¸İ ãģĪ", + "Ġë²ķ ë¥ł", + "ĠÑĥ вели", + "ĠÑĥвели Ñĩи", + "ส à¹Ħà¸ķ", + "สà¹Ħà¸ķ ลà¹Į", + "à¹Ħ à¸ģล", + "×ij׊ף", + "ĠìĿ´ íĽĦ", + "Ġm unic", + "Ġmunic ÃŃpio", + "تÙħ Ø«ÙĦ", + "ĠÄij áo", + "H ôtel", + "Ġl á»Ńa", + "ĠÄij ẳng", + "Ñĩ ки", + "Ø´ رÙĪ", + "شرÙĪ Ø·", + "ĠìĿ´ 를", + "ÙĬ Ùĭا", + "×ŀ׾ ×ļ", + "×ŀ×Ķ ×Ļר×ķת", + "ĠобÑıз аÑĤелÑĮ", + "ĠобÑıзаÑĤелÑĮ но", + "é nergie", + "Ġmud ança", + "Ġm ụ", + "Ġmụ n", + "Ġn º", + "ĠاÙĦت عا", + "ĠاÙĦتعا ÙĪÙĨ", + "ĠاÙĦاجتÙħاع ÙĬØ©", + "Ġп лаÑģÑĤ", + "Ġëĵ± ìĿĺ", + "ãĥIJãĤ¤ ãĤ¯", + "Ùĩج ÙĪÙħ", + "ĠSa úde", + "Ġì¤ijìļĶ íķľ", + "Ġ×Ķצ ×Ļ×ij×ķר", + "תק ף", + "ĠاÙĦعاÙĦÙħ ÙĬ", + "ĠболÑĮÑĪ ой", + "ĠÙĥ ÙĦÙħ", + "ĠÙĥÙĦÙħ Ø©", + "ãģ®ãģ§ãģ¯ãģªãģĦ ãģ§ãģĹãĤĩãģĨãģĭ", + "ĠÙħ باراة", + "Ġש×IJ ׳", + "Ġש×IJ׳ ×Ĺ׳×ķ", + "ãĤ¹ãĤ¿ ãĤ¤ãĥ«", + "ĠSa ÄŁ", + "ĠSaÄŁ lık", + "Ġh Æ°", + "׳ ×Ĺ×Ķ", + "Ġ×ij קר×ij", + "Ø· عÙħ", + "ห ิà¸Ļ", + "à¸Ĺุà¸ģ วัà¸Ļ", + "à¸Ħรัà¹īà¸ĩ à¸Ĺีà¹Ī", + "ĠlÃł nh", + "Ġdonn é", + "ãģĽ ãģĦ", + "جز ÙĬرة", + "доÑĢ ож", + "ì¼ ľ", + "تÙĨظ ÙĬÙģ", + "ãĥģ ãĥ§", + "Ġald ıģı", + "ج اج", + "ĠÑĤ омÑĥ", + "à¸Ľ ิ", + "Ġ×ijר שת", + "ãģıãģªãĤĬ ãģ¾ãģĻ", + "ĠпÑĢин ÑĨип", + "Ġ׊׾×ķ", + "ëı ¼", + "×ķ×Ĵ ש", + "س س", + "à¸Ľ ู", + "Ġh ầu", + "æĦŁãģĺ ãĤĭ", + "ï¼ ´", + "د ÙĪا", + "ĠÑģм ог", + "scri ção", + "Ġth áºŃn", + "Ġר ×ķ×IJ×Ķ", + "обÑĢаж ен", + "ĠاÙĦتج ارÙĬØ©", + "Ø· بÙĬع", + "jÄħc Äħ", + "íĸī ìľĦ", + "Ġнов Ñĭй", + "Ġ×ŀ ×Ĺ×ĵש", + "æĮ¯ ãĤĬ", + "gu é", + "Ġ×IJ ×Ļר×ķ×¢", + "Ġ×IJ×Ļר×ķ×¢ ×Ļ×Ŀ", + "ĠاÙĦ Ø°Ùĩب", + "×ĵ ×IJ", + "ت اÙĨ", + "ãģł ãģĹ", + "à¸Ńั à¸ķรา", + "à¹Ĥ à¸Ī", + "بÙĦ اد", + "×Ķ×Ļ ×Ļ׳×ķ", + "ĠÑģп е", + "ĠÑģпе ÑĨиалÑĮно", + "ĠÅĽwi ata", + "ãĤĵãģ§ãģĻ ãĤĪ", + "شر ÙĥØ©", + "ĠpÅĤ yt", + "Ġsitu é", + "Ġ׼×IJ ׾×Ķ", + "ס ×ijר", + "Ġkaż d", + "Ġkażd ym", + "ãĤĴæĮģ ãģ¤", + "׾×Ķ ׾", + "׾×Ķ׾ ף", + "ĠwÅĤ as", + "ĠwÅĤas ne", + "ĠsaÄŁ lan", + "×ŀ×¢ ׾×Ķ", + "ĠاÙĦا ÙĪÙĦ", + "ìĹIJìĦľ ëıĦ", + "×IJ×Ļר ×ķפ×Ķ", + "تÙĤ ÙĨÙĬØ©", + "Ùħ ائ", + "Ùħائ Ø©", + "Ġcompañ ÃŃa", + "Ġsü rek", + "Ġsürek li", + "ĠиÑģ кÑĥÑģ", + "ĠиÑģкÑĥÑģ ÑģÑĤв", + "ĠB ürger", + "ת ×Ĺר", + "ת×Ĺר ×ķת", + "à¸ŀรà¹īà¸Ńม à¸ģัà¸ļ", + "Ø´ Ùħ", + "à¸ĸืà¸Ń วà¹Īา", + "è¾¼ ãĤĢ", + "ä¼ij ãģ¿", + "ĠاÙĦØ£ ب", + "ĠÑģÑĤоим оÑģÑĤÑĮ", + "ĠпÑĢав а", + "may ın", + "ห วย", + "ĠاÙĦØ· بÙĬعÙĬ", + "à¸Ĺีà¹Ī à¸ŀัà¸ģ", + "ĠEst á", + "Ñĭва ÑİÑĤ", + "ب سÙĬ", + "بسÙĬ Ø·", + "Ġ×ij×¢ ×ijר", + "åı¯èĥ½ ãģ§ãģĻ", + "Ġ×ĵ ×ķ׾", + "Ġ×ĵ×ķ׾ ר", + "Ùĩ Ùİا", + "воÑĢ оÑĤ", + "ãģ¦ ãģĦãģ¾ãģĹãģŁ", + "à¹Ĥà¸Ĺร ศ", + "à¹Ĥà¸Ĺรศ ั", + "à¹Ĥà¸Ĺรศั à¸ŀ", + "à¹Ĥà¸Ĺรศัà¸ŀ à¸Ĺà¹Į", + "Ġק ׳", + "ĠاÙĦØ« ÙĨ", + "ĠاÙĦØ«ÙĨ ائÙĬØ©", + "Ġco ût", + "à¸ķิà¸Ķ à¸ķัà¹īà¸ĩ", + "Ġö rg", + "Ġörg üt", + "ĠاÙĦØ® ÙĦÙĬ", + "ĠاÙĦØ®ÙĦÙĬ ج", + "Ġb á»įn", + "×ķ׾×ķ×Ĵ ×Ļ", + "ëŀ ľ", + "ĠÐij олÑĮ", + "ĠÐijолÑĮ ÑĪ", + "×Ĵ ×ijר×Ļ×Ŀ", + "ÙĤ ÙĬد", + "×ij×Ļ×ĺ ×ķ×Ļ", + "æīĵ ãģ¡", + "Ġol muÅŁ", + "f äh", + "fäh ig", + "ล าà¸Ļ", + "ĠÙĤ طر", + "ש פ×Ķ", + "èªŃ ãĤĵãģ§", + "à¸Ĥ วา", + "Ġchi ếm", + "ãĤ¤ãĥ³ ãĤ¿", + "ãĤ¤ãĥ³ãĤ¿ ãĥ¼ãĥ", + "ãĤ¤ãĥ³ãĤ¿ãĥ¼ãĥ į", + "ãĤ¤ãĥ³ãĤ¿ãĥ¼ãĥį ãĥĥãĥĪ", + "Ġ׾ש×ŀ ×ķר", + "Ġت رÙĥ", + "ĠترÙĥ ÙĬا", + "ר ×ķ×ĺ", + "ãģ¨æĢĿ ãģĦãģ¾ãģĹãģŁ", + "ĠاÙĦت ÙĤ", + "Ġd Æ°", + "ãģ¦ãģıãĤĮ ãĤĭ", + "ãģĹãģŁ ãģĵãģ¨", + "Ġróż ne", + "ĠاÙĦØ· ÙģÙĦ", + "ĠPost é", + "Ġ×ŀש ×ķ×Ŀ", + "Ñį ÑĢ", + "ĠÑĢабоÑĤ аеÑĤ", + "ãĤ· ãĥª", + "ãĤ·ãĥª ãĥ¼ãĤº", + "Ġ×ij×Ķ ×Ĺ׾×ĺ", + "ק×Ķ ×Ļ׾×Ķ", + "ãĤ« ãĥ¡", + "ãĤ«ãĥ¡ ãĥ©", + "ï¼ ¯", + "ĠìĤ¬ ìĿ´", + "Ġk ì", + "Ġth Æ°á»Ľc", + "ض بط", + "ÙĤب ÙĪÙĦ", + "åĪ¥ ãģ®", + "Ġparticul ière", + "ĠÑģво ем", + "Ġ×¢ סק", + "Ġעסק ×Ļ×Ŀ", + "×ij×Ĺ ×Ļר×ķת", + "×ij ×Ļ׳×ķ", + "à¸ĭ à¸Ń", + "Ġ×¢ ×ķ×ijר", + "ãģłãģ£ãģŁ ãģ®ãģ§", + "ıld ıģı", + "Ùħ دار", + "Ùħدار س", + "주 ìĭľ", + "à¸Ńา ศ", + "à¸Ńาศ ัย", + "Ġt ấm", + "à¸ŀิ à¸Ī", + "à¸ŀิà¸Ī าร", + "à¸ŀิà¸Īาร à¸ĵา", + "ÑĤелÑĮ нÑĭе", + "Ñģк ÑĥÑİ", + "Ðľ Ðĺ", + "à¹Ģà¸ģ า", + "à¹Ģà¸ģา หล", + "à¹Ģà¸ģาหล ี", + "×ĵ ×Ĺ", + "à¹Ģà¸Ĭ ิà¸ĩ", + "Ġد ÙĤÙĬÙĤØ©", + "íķĻ ìĥĿ", + "Ġש×IJ ׾×Ķ", + "Ġcontr ôle", + "Ġsit uação", + "à¸Ĥà¸Ńà¸ĩ à¸ľà¸¹à¹ī", + "ÙĨ Ø·ÙĤ", + "ê³¼ íķĻ", + "หลาย à¸Ħà¸Ļ", + "Ġn ắng", + "ÙĤ Ùı", + "ì¡° ê±´", + "Ñ ķ", + "ãĥĥ ãģ¨", + "×ŀ ×Ļ׾×Ķ", + "Gr ün", + "×Ļ ×Ļ×¢", + "×Ļ×Ļ×¢ ×ķ×¥", + "×ŀ׳ ׼", + "ë ŃIJ", + "×ŀ×¢ ×ŀ×ĵ", + "สำ à¸Ļัà¸ģ", + "ج دد", + "à¸Ħ ัà¸Ķ", + "Ġ×Ķ×ŀש פ", + "Ġ×Ķ×ŀשפ ×Ĺ×Ķ", + "×ŀש ק׾", + "ÙĦ Ùı", + "Ġty tu", + "Ġtytu ÅĤ", + "ÑĪ ей", + "ĠìĿ¼ ë¶Ģ", + "ÑĪ ение", + "Ġph óng", + "ĠìĹŃ ìĤ¬", + "ãĤ« ãĥ³", + "Ġtú i", + "ĠÙĨ ÙĪÙģ", + "ĠÙĨÙĪÙģ Ùħبر", + "gr ün", + "ĠاÙĦØ´ ÙħاÙĦ", + "ÅĽwi adc", + "ÅĽwiadc zenie", + "ער ×Ķ", + "Ġ×¢ ×ķ×ij", + "Ġ×¢×ķ×ij ×ĵ×Ļ×Ŀ", + "×ĵ×ķ×Ĵ ×ŀ×IJ", + "ä»Ĭ ãģ¯", + "Ġv ão", + "ĠТ ем", + "Ñģ илÑĮ", + "Ġch ợ", + "Ùħ را", + "Ùħرا ÙĤب", + "à¹Ħมà¹Ī รูà¹ī", + "Ġر ائع", + "×IJ׳ ×Ĺ׳×ķ", + "สà¹Īà¸ĩ à¹Ģสริม", + "צ ×Ĺ", + "ĠìŀĪìĸ´ ìĦľ", + "Ġkur ulu", + "Ġkurulu ÅŁ", + "ĠÃĸ zellik", + "ĠÃĸzellik le", + "Ġת ×Ļק", + "Ġgh é", + "Ġspr zÄĻ", + "ĠsprzÄĻ t", + "ער ×ķת", + "را ØŃØ©", + "ãģ£ ãģį", + "ãģ£ãģį ãĤĬ", + "ĠìķĦ ëŀĺ", + "stit uição", + "Ġдолж но", + "×Ķ רש", + "×Ķרש ×ŀ×Ķ", + "×Ķ׾ ×ļ", + "ãģ¡ ãģª", + "ãģ¡ãģª ãģ¿", + "ãģ¡ãģªãģ¿ ãģ«", + "פ ×Ĺ×ĵ", + "ĠاÙĦج ÙħÙĬع", + "×ij×¢ ׾×Ļ", + "Ġtr ùng", + "Ġפ ת×Ĺ", + "×ŀ׾×Ĺ ×ŀת", + "ãĥĨ ãĥ¼ãĥ", + "ãĥĨãĥ¼ãĥ ŀ", + "Ùħ تاب", + "Ùħتاب عة", + "Ġ모 ìĬµ", + "ÙĬ ص", + "åIJĪ ãģĨ", + "ĠY ap", + "ĠYap ı", + "ĠÑģ казаÑĤÑĮ", + "ëª °", + "à¸Ĺีà¹Ī สำà¸Ħัà¸į", + "ĠìĹĨ ìĬµëĭĪëĭ¤", + "Ġnh ắc", + "Ġülk eler", + "Ġмног ие", + "íķĺ ìħ¨", + "มาà¸ģ à¸Ĺีà¹Īสุà¸Ķ", + "à¸ģ à¹īา", + "à¸ģà¹īา ว", + "ĠÄ° yi", + "л еж", + "леж а", + "ãĤ¸ ãĥ§", + "à¸Ĺั à¸ŀ", + "ا ÙĪر", + "Ġ×Ĺ×ijר ×Ļ", + "Ġ׾ ש×Ŀ", + "ì² «", + "ĠT á»Ń", + "×ŀ ×ķ׳×Ļ", + "ÙĤ ÙĪد", + "à¸ģระ à¹Ģà¸Ľ", + "à¸ģระà¹Ģà¸Ľ à¹ĭ", + "à¸ģระà¹Ģà¸Ľà¹ĭ า", + "ĠпÑĢоблем Ñĭ", + "Ġaç ıs", + "Ġaçıs ından", + "Ġ×Ķ×ŀ ׼", + "ĠÙħع ظÙħ", + "ÙĤÙĬ اس", + "ĠпÑĢод олж", + "ĠпÑĢодолж а", + "Ġver diÄŁi", + "ĠпÑĢед меÑĤ", + "ãģĦãģ¾ãģĻ ãģĮ", + "ĠëĶ° 른", + "ĠاÙĦ ÙĤÙĬاÙħ", + "ĠØ¥ÙĦÙĬ Ùĩا", + "Т ÐIJ", + "п оз", + "ãĤ· ãĥ¥", + "ä¸ĬãģĮ ãĤĬ", + "à¹Ģà¸Ķิม à¸ŀัà¸Ļ", + "à¸ģุ ล", + "ØŃر ÙĬØ©", + "ק×ij×ķצ ×ķת", + "ë¯ ¿", + "ĠاÙĦÙħ ÙĨا", + "ĠاÙĦÙħÙĨا Ø·ÙĤ", + "ĠвÑĭп ол", + "ĠвÑĭпол нÑı", + "ãĥĭ ãĤ¢", + "Ġê²° êµŃ", + "×Ĺ ×ķ×ŀ", + "×Ĺ×ķ×ŀ ר×Ļ×Ŀ", + "ĠУкÑĢа инÑĭ", + "ห à¸Ńม", + "ר ×Ļס", + "ĠÑħоÑĤ ел", + "ĠобÑĢаз ованиÑı", + "Ġkh ẳng", + "Ġm Æ°a", + "Ġgör me", + "Ġgüç lü", + "سع Ùī", + "มัà¹Īà¸Ļ à¹ĥà¸Ī", + "íķĺ ê²łìĬµëĭĪëĭ¤", + "Ġпол Ñĥ", + "Ġfün f", + "ãģ¨æĢĿ ãģ£ãģ¦ãģĦãģ¾ãģĻ", + "Ġê·¸ê²ĥ ìĿĢ", + "ĠdÃ¼ÅŁÃ¼n ce", + "ìŀ ł", + "ĠH Æ°á»Ľng", + "ĠTi á»ĥu", + "Ġç ift", + "ãģij ãģ°", + "à¸Īà¸Ļ à¸ĸึà¸ĩ", + "à¸Ĺำ à¹Ħà¸Ķà¹ī", + "ĠìŀIJ ì²´", + "Ġd õ", + "Ġdõ i", + "à¸Ī ัà¸Ļ", + "à¸Īัà¸Ļ à¸Ĺ", + "à¸Īัà¸Ļà¸Ĺ รà¹Į", + "ece ÄŁini", + "׳×ķ×¢ ר", + "غ ار", + "ĠاÙĦØ£ÙħرÙĬ ÙĥÙĬ", + "داع Ø´", + "ĠбезопаÑģ ноÑģÑĤи", + "Ġб Ñİ", + "ĠбÑİ Ð´Ð¶", + "ĠбÑİдж еÑĤ", + "ãĥĬ ãĤ¤", + "à¸ŀà¸ļ วà¹Īา", + "da ÄŁ", + "×IJ ×ķפף", + "íĹ Į", + "ãĥĢãĤ¤ ãĤ¨", + "ãĥĢãĤ¤ãĤ¨ ãĥĥãĥĪ", + "ĠëĮĢ íĨµ", + "ĠëĮĢíĨµ ëł¹", + "D Ä°", + "Ø£ ØŃداث", + "ĠA ÄŁ", + "ĠAÄŁ ust", + "ĠAÄŁust os", + "ØŃÙĦ ÙĪÙĦ", + "Ġw ÅĽ", + "ĠwÅĽ ród", + "ĠÑģо оÑĤвеÑĤ", + "ĠÑģооÑĤвеÑĤ ÑģÑĤв", + "ĠÑģооÑĤвеÑĤÑģÑĤв ии", + "ĠLu áºŃt", + "Ġ׼׾ פ×Ļ", + "Ġв еÑī", + "ĠвеÑī еÑģÑĤв", + "ק ×Ļ×¥", + "ĠبÙĩ ذا", + "عا Ø´", + "à¹Ģà¸Ľà¹ĩà¸Ļ à¹Ģรืà¹Īà¸Ńà¸ĩ", + "Т Ðķ", + "Ġ×ij×IJ ×Ļ׳×ĺר׳×ĺ", + "س عد", + "Ġ×Ķ×ĺ ×Ļפ×ķ׾", + "פ ×Ļס", + "à¸ĩà¹Īาย à¹Ĩ", + "ĠGer ät", + "׾ ×Ļ×ĵ×Ķ", + "ĠÑĢ иÑģк", + "׾ק ×Ĺ", + "н наÑı", + "ר ×Ļ×ĵ", + "п ÑĢакÑĤи", + "пÑĢакÑĤи к", + "à¸Ĥัà¹īà¸Ļ à¸ķà¸Ńà¸Ļ", + "à¸Ļà¹Īา รัà¸ģ", + "larınız ı", + "à¸Ńà¸Ļุ à¸įา", + "à¸Ńà¸Ļุà¸įา à¸ķ", + "ĠzdjÄĻ cia", + "Ġb ây", + "Ñģ ÑĢ", + "ÑģÑĢ оÑĩ", + "ãĥĭ ãĥ³ãĤ°", + "Ġö ner", + "Ġöner i", + "Ġнов ÑĭÑħ", + "دع ÙĪØ©", + "Ġg ắn", + "ĠاÙĦÙĦ بÙĨ", + "ĠاÙĦÙĦبÙĨ اÙĨÙĬ", + "ãĥĨãĤ£ ãĥ¼", + "Ġص ØŃÙĬØŃ", + "ем ÑĭÑħ", + "çĸ² ãĤĮ", + "ĠпÑĢо иÑģ", + "ĠпÑĢоиÑģ ÑħодиÑĤ", + "ส à¸ķิ", + "ĠT ết", + "Ġ×Ķ׾ ׾×ķ", + "à¹Ģรืà¹Īà¸Ńà¸ĩ à¸Ļีà¹ī", + "×ŀ×ij ׳×Ķ", + "Ġconte údo", + "Ġا خت", + "Ġاخت ÙĬار", + "Ùħ سÙĦ", + "ÙħسÙĦ سÙĦ", + "ëı Ī", + "Ġ׾ ×Ļ×ĵ", + "à¸ŀิ à¸ĺี", + "ĠÑģов Ñģ", + "ĠÑģовÑģ ем", + "ãģĮãģĤãĤĬ ãģ¾ãģĹãģŁ", + "Ġsó ng", + "Ø¥ صÙĦاØŃ", + "ë§ ģ", + "Ùģ ÙĬر", + "ĠJe żeli", + "ìłľ ëıĦ", + "d ÅĤug", + "ìĥģ ìĿĦ", + "Ġc áºŃn", + "Ġhá»į p", + "Ø£ ست", + "أست اذ", + "Ġ×ŀ ×Ļש×Ķ", + "Ġ×ŀ×Ļש×Ķ ×ķ", + "Ġd Ãły", + "Ġch Ãłng", + "ãģ¡ãĤĥãĤĵ ãģ¨", + "ĠÄij ám", + "Ġsw ój", + "Ġpoder á", + "ĠоÑĤлиÑĩ а", + "Ġpéri ode", + "ünd ig", + "×ĺ×¢ ף", + "ÑģÑĤÑĢо иÑĤелÑĮ", + "ר ת×Ļ", + "Ġ×Ļ×Ķ ×Ļ×ķ", + "׾ ס", + "ĠاÙĦÙħÙĨ زÙĦ", + "à¸Ļิ à¹īว", + "иÑĦ ика", + "иÑĦика ÑĨи", + "ðŁĺ ī", + "Ġad ına", + "ãĢĤãĢĤ ãĢĤ", + "×IJ ×Ļף", + "ס ×Ļר", + "ĠÙĬ عد", + "çŃĶ ãģĪ", + "اÙĦ جز", + "اÙĦجز ائر", + "енÑĮ к", + "ร ห", + "รห ัส", + "ĠTürk çe", + "ê¾ ¸", + "Ġ×Ļ ×ķ׼׾", + "Ġש ×ķ׳×Ķ", + "Ġ×ij×ŀ צ×ij", + "ĠдейÑģÑĤв иÑĤелÑĮно", + "ĠبأÙĨ Ùĩ", + "×ŀק ×ĵ", + "Ġ×Ķש ק", + "Ø®ÙĬ ارات", + "Ġf ı", + "Ġfı rs", + "Ġfırs at", + "ëij ĺ", + "ĠìĦľ ìļ¸", + "Ġ×Ķ×Ĵ ×ķ×£", + "ر عا", + "رعا ÙĬØ©", + "ĠK ết", + "к Ñģи", + "ĠÑĥÑģлÑĥг и", + "ноÑģÑĤ ей", + "ìļ´ ëıĻ", + "ĠобÑĬ Ñı", + "ĠобÑĬÑı вл", + "н еж", + "×Ķפ ×ļ", + "Ġ×ij×¢ ×Ļ׳×Ļ", + "ëĨ Ĵ", + "ĠпÑĢоÑĨ ед", + "ĠпÑĢоÑĨед ÑĥÑĢ", + "Ġiht iy", + "Ġihtiy acı", + "Ġë°Ķ ëŀį", + "Ġë°Ķëŀį ëĭĪëĭ¤", + "à¸ģล ัว", + "ĠÑģл ожно", + "ק×Ļ ×Ļ×ŀת", + "ĠÄIJ ình", + "ĠÙħ ÙĦÙģ", + "Ġà¹Ĥà¸Ķย มี", + "Ġkat kı", + "تØŃ ÙĪÙĬÙĦ", + "à¹Ħ à¸ŀ", + "ĠH á»į", + "ñ e", + "Ġдо Ñħод", + "Ġtho ải", + "íķĺìŬ ìķ¼", + "ãĤ¹ãĥĿ ãĥ¼ãĥ", + "ãĤ¹ãĥĿãĥ¼ãĥ Ħ", + "ĠG òn", + "Ġk è", + "Ġkè m", + "éĢ² ãĤģ", + "ãĤ¹ ãĥ¼ãĥ", + "ãĤ¹ãĥ¼ãĥ ij", + "ãĤ¹ãĥ¼ãĥij ãĥ¼", + "ĠgiÃł u", + "ĠØ¥ عادة", + "Ġ׾ ×ķק", + "Ġ׾×ķק ×Ĺ", + "ĠÑħоÑĩ еÑĤ", + "×ĺ ׾×ķ×ķ", + "×ĺ׾×ķ×ķ ×Ļ×ĸ", + "×ĺ׾×ķ×ķ×Ļ×ĸ ×Ļ×Ķ", + "Ġth uyết", + "ãģĿãĤĮ ãģ§", + "Ġvard ı", + "à¹Ħร à¹ī", + "ع بد", + "ĠRep ública", + "ãĥ¼ãĤ¿ ãĥ¼", + "Ġ×ŀ×IJ ×ķת", + "à¹Ħà¸Ľ à¹ģลà¹īว", + "Ġyapıl acak", + "ãĤ¹ãĤ¿ ãĥ¼ãĥĪ", + "ãģ» ãģ¼", + "Ġko ÅŁ", + "ĠмаÑĤ еÑĢи", + "Ġsiè cle", + "ĠاÙĦÙħ ختÙĦÙģ", + "ĠاÙĦÙħختÙĦÙģ Ø©", + "Ġ׾ק ר×IJ", + "Ġ׾קר×IJ ת", + "Ġ×Ķפ ×ķ×¢×ľ", + "Ġt òa", + "Ġr Æ¡i", + "åij¨ ãĤĬ", + "à¸Ŀ à¸Ļ", + "j ÅĽÄĩ", + "ĠìķĬ ìĿĦ", + "اÙĨت ÙĤاÙĦ", + "ëĸ ł", + "ив аеÑĤ", + "ãĥĪ ãĥ«", + "ĠاÙĦÙģÙĦسطÙĬÙĨ ÙĬØ©", + "à¸ģลà¹Īาว วà¹Īา", + "ا Ùĥت", + "ĠÃĸ l", + "ĠÑĢе ÑĪи", + "ĠÑĢеÑĪи л", + "Ġ׳×ķס פ×ķת", + "Ġìłķ ì¹ĺ", + "вл еÑĩен", + "Ùħر ØŃÙĦØ©", + "Ġcome ça", + "Ġy ık", + "ìĤ ´", + "à¸ĺ à¸Ļา", + "à¸ĺà¸Ļา à¸Ħาร", + "à¸Ńà¸Ļ า", + "à¸Ńà¸Ļา à¸Ħ", + "à¸Ńà¸Ļาà¸Ħ à¸ķ", + "Ġpeque ña", + "ä»ķ äºĭãĤĴ", + "Ġب Ø°ÙĦÙĥ", + "Ġнов ого", + "ãģĹãģ¦ ãģĦãģªãģĦ", + "ĠاÙĦÙħ ÙĬاÙĩ", + "à¸ģà¹ĩ à¹Ģà¸Ľà¹ĩà¸Ļ", + "Ġж ÑĥÑĢ", + "ĠжÑĥÑĢ нал", + "в еÑģ", + "خت ار", + "Ġ매 ìļ°", + "ĠM ã", + "ĠавÑĤомаÑĤ Ñĭ", + "ضع Ùģ", + "ĠاÙĦÙģ Ùĥر", + "ãģ§ãģĻ ãģ®ãģ§", + "ãĥ¡ãĥ³ ãĥIJãĥ¼", + "Ġк ÑĢÑĥг", + "ĠاÙĦسÙĦ طة", + "à¸Ħรัà¹īà¸ĩ à¹ģรà¸ģ", + "à¸ģระà¸Ĺ รว", + "à¸ģระà¸Ĺรว à¸ĩ", + "ÑĨ ов", + "éķ· ãģĦ", + "大ãģį ãģĦ", + "Ġgeç miÅŁ", + "ìĦ± ìĿ´", + "Ġצר ×Ļ׼×Ķ", + "Ġм оÑī", + "ĠмоÑī н", + "Ġק ×Ļש", + "Ġק×Ļש ×ķר×Ļ×Ŀ", + "ĠNas ıl", + "г ÑĢан", + "Ġ×ŀ ×ķצר×Ļ×Ŀ", + "Ġ×ŀס ×ķ×Ĵ", + "Ġy ür", + "Ġyür üt", + "Ġ׾׊צ×ķ", + "×ķÖ ¼", + "ĠìŀĪ ìĹĪëĭ¤", + "Ġter ör", + "ĠTh Æ°Æ¡ng", + "ĠÙĪ ÙĬÙħ", + "ĠÙĪÙĬÙħ ÙĥÙĨ", + "ج ÙĪÙĨ", + "ĠÙĪغÙĬر Ùĩا", + "×ŀ פ×ķ", + "×Ĵ×ķר ×ŀ×Ļ×Ŀ", + "׼×ij ×Ļש", + "ĠاÙĦÙĦ غ", + "ĠاÙĦÙĦغ Ø©", + "شر Ùĥ", + "ĠاÙĦر اب", + "ĠاÙĦراب ع", + "ĠпÑĢ ек", + "ĠпÑĢек ÑĢаÑģ", + "ĠпÑĢекÑĢаÑģ н", + "Ġenerg ÃŃa", + "ק×ĵ ×ŀ×Ļ", + "ãģıãģª ãģ£ãģŁ", + "ĠÄij ứ", + "ĠÄijứ a", + "Serv i", + "Servi ço", + "Ġkald ır", + "åĥį ãģį", + "Ġод еж", + "Ġодеж д", + "물 ìĿĦ", + "ãģĿãģĨ ãģ§", + "ãģĮãģĤ ãĤĮãģ°", + "ìĻ ķ", + "צ×ĵ ק", + "Ġart ır", + "Ġile ti", + "Ġileti ÅŁim", + "ãĤĪãģĨ ãģ§", + "ãĥĪ ãĥ¼", + "ãĤ¢ ãĥĭ", + "ãĤ¢ãĥĭ ãĥ¡", + "×ĺ×Ļ ×Ļ׾", + "ãĥķ ãĥªãĥ¼", + "ãĥĿ ãĥ³", + "ÐŁÑĢ о", + "Ġع اÙĦÙĬØ©", + "ĠÃ¶ÄŁ ret", + "ĠÃ¶ÄŁret men", + "ĠкаÑĩеÑģÑĤв а", + "Ġ×Ķ×ĺ ×ij×¢", + "Ġзна Ñİ", + "ãģ¦ ãģıãĤĭ", + "Ġm ừng", + "ÙħÙĪ ت", + "ש ×ķ×ŀר", + "×Ĺ׾ ×ij", + "Ġwzgl ÄĻ", + "ĠwzglÄĻ du", + "ë²Ī 째", + "Ġtá» ĵ", + "Ġtá»ĵ n", + "ãĥ¯ãĥ¼ ãĤ¯", + "Ġpo życz", + "Ġpożycz k", + "×Ļ ×ķצר×Ļ×Ŀ", + "Ùĥر Ùħ", + "Ġг аÑĢ", + "ĠгаÑĢ ан", + "ĠгаÑĢан ÑĤи", + "ล à¹īาà¸ĩ", + "Ġìĺģ íĻĶ", + "×ĺ ×Ļס", + "Ġth ẻ", + "ĠìŀĪëĭ¤ ê³ł", + "اÙĦت ز", + "اÙĦتز اÙħ", + "Ġна ÑĪи", + "is ée", + "ãģĵãĤĮ ãĤĴ", + "Ġm ẽ", + "ض ÙĦ", + "بÙĪ ت", + "Ġ׼ ׼×Ķ", + "h ợ", + "ĠاÙĦس ÙĪرÙĬØ©", + "Ġ×ľ×¢ ×ķ×ŀ", + "Ġ×ľ×¢×ķ×ŀ ת", + "ĠbaÅŁ ar", + "ĠbaÅŁar ılı", + "е ÑģÑĤÑĮ", + "à¸Ħร ี", + "à¸Ħรี ม", + "ĠìłĦ ì²´", + "ĠسÙĬ ÙĥÙĪÙĨ", + "Ġ×ŀ×ĵ ×ķ×¢", + "ĠëķĮ문 ìĿ´ëĭ¤", + "Ġc ứng", + "ger ät", + "Ġм иÑĢ", + "ĠмиÑĢ е", + "ĠÙĥÙĬÙģ ÙĬØ©", + "Ġפר ×ĺ×Ļ×Ŀ", + "Ġgo ÅĽci", + "иÑĤ еÑģÑĮ", + "ÑĥÑĪ ки", + "ؤ ÙħÙĨ", + "Ġ×IJ ׼ף", + "ĠاÙĦر جÙĦ", + "Ġl á»įc", + "à¹Ģรีย à¸ģวà¹Īา", + "ãģĵãģ® ãĤĪãģĨãģª", + "ë§Į íģ¼", + "Ġп еÑĩ", + "ÙĪÙĦ ات", + "ĠÃľ ye", + "liÄŁ inde", + "à¸Ħะ à¹ģà¸Ļ", + "à¸Ħะà¹ģà¸Ļ à¸Ļ", + "ãĤĭãģĵãģ¨ ãģ¯", + "วิ à¹Ģà¸Ħร", + "วิà¹Ģà¸Ħร าะ", + "วิà¹Ģà¸Ħราะ หà¹Į", + "Ġвозмож ноÑģÑĤи", + "ĠاÙĦÙĨ ساء", + "ãĥīãĥ© ãĥŀ", + "Ġgü c", + "Ġgüc ü", + "Ġt Æ°á»Ŀng", + "Ġacomp aña", + "ãĤ¤ ãĥ©", + "ק צ×ij", + "ĠY ö", + "ĠYö net", + "ĠYönet im", + "สัม à¸ľ", + "à¸ªà¸±à¸¡à¸ľ ัส", + "à¸Ļ าม", + "ĠÄij ợi", + "à¹ģหà¹Īà¸ĩ à¸Ĭาà¸ķิ", + "ãģĿãĤĮ ãģ§ãĤĤ", + "ät ig", + "ת ×ķ×Ŀ", + "ĠbaÅŁ lat", + "ĠвÑģ ей", + "ת ×Ļק", + "ת×Ļק ×ķף", + "ĠNg ô", + "ĠGesch ä", + "ĠGeschä fts", + "Ø£ Ùħ", + "Ø£Ùħ راض", + "à¹Ģà¸Ĺ à¸Ħà¸Ļ", + "à¹Ģà¸Ĺà¸Ħà¸Ļ ิ", + "à¹Ģà¸Ĺà¸Ħà¸Ļิ à¸Ħ", + "Ġм енÑĮ", + "ĠменÑĮ ÑĪе", + "Ġöl ç", + "Ġölç ü", + "ĠÙĬ جعÙĦ", + "ĠÄij ỡ", + "ש ×Ļ׾", + "ש×Ļ׾ ×ķ×ij", + "ĠGr Ã¶ÃŁe", + "ĠÙĩ اتÙģ", + "รà¹īาà¸Ļ à¸Ńาหาร", + "×Ķ׾ ×Ļ׼", + "×Ķ׾×Ļ׼ ×Ļ", + "иÑĢÑĥ ÑİÑī", + "èĭ¥ ãģĦ", + "ĠÃĸ zel", + "ãģĦãģŁ ãĤī", + "à¸Ħำ à¸ĸาม", + "Ġzosta ÅĤy", + "Ġ×Ķס ×Ļפ×ķר", + "×Ķ ×ķ׾", + "×Ķ×ķ׾ ×ļ", + "à¹Ģà¸Ĭà¹Īà¸Ļ à¸ģัà¸Ļ", + "à¹Ĥ à¸Ĩ", + "à¹Ĥà¸Ĩ ษ", + "à¹Ĥà¸Ĩษ à¸ĵา", + "×IJר צ×ķת", + "×Ĵר פ×Ļ", + "Ġao ût", + "ĠÙĬ رÙĬد", + "ت ÙĪج", + "تÙĪج ÙĬÙĩ", + "ĠÑįÑĤ ап", + "ãĤ¹ãĤ¿ ãĥ³", + "Ġkr ó", + "Ġkró tk", + "ãĤĴ使 ãģĨ", + "ì ·¨", + "éĸ¢ ãĤı", + "à¸Ķà¹īวย à¸Ħวาม", + "à¸Ļำ à¹Ģสà¸Ļà¸Ń", + "Ġa yrıca", + "à¸Ī à¹īาà¸ĩ", + "ĠÑĦоÑĤ огÑĢаÑĦ", + "Ġв еÑĩ", + "ĠвеÑĩ еÑĢ", + "åĩº ãģĹãģŁ", + "ĠÐ¥ о", + "Ġ×ŀ ר×Ĵ×Ļש", + "à¹ĥหà¹ī à¹Ģà¸Ľà¹ĩà¸Ļ", + "ãĤĴ 缮", + "ãĤĴ缮 æĮĩ", + "׾ ×ŀ×Ļ×Ŀ", + "nÄħ ÅĤ", + "ĠÑģÑĤ анд", + "ĠÑģÑĤанд аÑĢÑĤ", + "ĠSü d", + "ĠT âm", + "اخت بار", + "à¹Ģà¸ģ à¸Ńรà¹Į", + "Ùħس رØŃ", + "Ġbi á»ĩn", + "ب Ùı", + "Ġص اÙĦ", + "ĠصاÙĦ ØŃ", + "ĠPh ụ", + "íľ ´", + "ãĥ¬ãĥĵ ãĥ¥ãĥ¼", + "Ġbụ ng", + "Ġrég ime", + "ĠØ£ Ø´Ùĩر", + "ĠÑĢабоÑĤ ник", + "à¸Ŀ ัà¸Ļ", + "اع تÙħ", + "اعتÙħ اد", + "Ġзам еÑĤ", + "ãģ¾ ãģ£ãģ¦", + "Ġch ặt", + "æĿ¥ ãĤĭ", + "ĠاÙĦÙĤ ÙĪات", + "ãģ«åħ¥ ãģ£ãģ¦", + "تØŃ Ø§ÙĦÙģ", + "Ùħ زÙĬد", + "ĠÙĬ صÙĦ", + "ìĹ ¼", + "à¹Ģà¸Ĭ à¹ĩ", + "à¹Ģà¸Ĭà¹ĩ à¸Ħ", + "Ġk á»ĭ", + "Ġká»ĭ p", + "ĠìķĦ ì§ģ", + "×IJ׳ ×Ĵ", + "Ġобла ÑģÑĤÑĮ", + "Ġpomoc Äħ", + "Ġ×ķ ש׾", + "ëĵł ì§Ģ", + "ĠGi ám", + "ĠSt ück", + "Ġchá y", + "ĠëĤĺ ìĺ¤", + "ש ×Ļ×ĺת", + "×ŀ×ĵ ר", + "×ŀ×ĵר ×Ļ×ļ", + "Ġsüre ç", + "к ва", + "×ij׾ ×Ļ×Ŀ", + "×Ķ ת×Ļ", + "×Ķת×Ļ ×Ļ×Ĺס", + "ÙĤب اÙĦ", + "Ġס ×ķ×Ĵ", + "Ġס×ķ×Ĵ ×Ļ", + "ÑģÑĤ олÑĮ", + "ä½ķ ãĤĤ", + "×ĸ׼ ×ķר", + "è²· ãģĨ", + "å®ī ãģı", + "à¸Ħรัà¹īà¸ĩ à¸Ļีà¹ī", + "kö p", + "ĠÑģеÑĢ виÑģ", + "оÑĩ нÑĭÑħ", + "ê±° ëŀĺ", + "تأ Ùĥ", + "تأÙĥ ÙĬد", + "×ĵ ׾ק", + "Ġпо Ñĩем", + "ĠпоÑĩем Ñĥ", + "пиÑģ аÑĤÑĮ", + "×ij שר", + "ĠH Ãłng", + "ĠT ìm", + "Ġtr ừ", + "ãĤ» ãĥĥãĤ¯ãĤ¹", + "×ķ׳ ×Ĵ", + "mız da", + "п Ñģи", + "ĠìŀĪ 기", + "Ġr út", + "ز اÙĨ", + "تÙĨ ÙĪع", + "ÙħÙĤ ا", + "ÙħÙĤا ÙĪÙħØ©", + "Ġ׾צ ×ķר×ļ", + "Ġ×ij ×Ļר×ķש׾×Ļ×Ŀ", + "ãĥ´ ãĤ£", + "eb ile", + "ebile ceÄŁi", + "ãĥ¦ ãĥ¼ãĤ", + "ãĥ¦ãĥ¼ãĤ ¶", + "ãĥ¦ãĥ¼ãĤ¶ ãĥ¼", + "ãĤĴä½ľ ãĤĭ", + "Ñģ меÑĢ", + "ÑģмеÑĢ ÑĤ", + "Ġì§ ģ", + "Ġì§ģ ìłij", + "ĠÐŁ аÑĢ", + "ØŃ Ø§Ø¶", + "ØŃاض ر", + "Ùħ ÙĥاÙģ", + "ÙħÙĥاÙģ ØŃØ©", + "ล ิà¸Ļ", + "ãģ¦ ãģįãģ¦", + "ÑĢоÑģ л", + "ĠÄ°ÅŁ te", + "ÙĤص ÙĬر", + "Ġ×ij×Ĵ ×Ļ׾", + "Ġ×ŀת ×IJ×Ļ×Ŀ", + "Ġ×Ķ ×Ĺ×ĵ", + "Ġ×Ķ×Ĺ×ĵ ש×Ķ", + "ר ×ķ×¢", + "Ġprodukt ów", + "ĠÙħ صدر", + "не ÑĨ", + "ĠاÙĦعÙħÙĦ ات", + "Ġçık ma", + "Ġد بÙĬ", + "ק ×Ļף", + "ת ×IJר", + "ת×IJר ×Ļ×ļ", + "׳×Ļ ×Ļ×ĵ", + "صر اع", + "l ève", + "צ ×Ļר", + "à¸Ķ ัà¸Ļ", + "à¹ĥหà¹ī à¹Ħà¸Ķà¹ī", + "ãĤ¿ãĤ¤ ãĥł", + "Ġgi ảng", + "С ÐŁ", + "ĠاÙĦÙħ ØŃÙĦ", + "ĠاÙĦÙħØŃÙĦ ÙĬØ©", + "ĠT ất", + "׾ ×ķ×ĺ", + "h á»ķ", + "Ġam éric", + "Ġaméric ain", + "Ġ×ijש׾ ×ij", + "Ġ׾×IJ ×ķ×ŀ×Ļ", + "Ġpe ça", + "ĠÑĢаз нÑĭÑħ", + "ãģĦãĤĭ ãģ¨", + "ãĥĩ ãĥ³", + "ס קר", + "Ġ×Ķ×ŀ×Ĺ ×Ļר", + "ãģ¨ãģĦãģĨ ãĤĤãģ®", + "رت بط", + "ĠиÑģÑĤ оÑĩ", + "ĠиÑģÑĤоÑĩ ник", + "สมัà¸Ħร สมาà¸Ĭิà¸ģ", + "Ġ à¸Ĺัà¹īà¸ĩ", + "Ġà¸Ĺัà¹īà¸ĩ à¸Ļีà¹ī", + "ĠT áºŃp", + "ãģ£ãģ¦ ãģĦãģĨ", + "ĠاÙĦÙĪ صÙĪÙĦ", + "Ġdéc ada", + "Ġо ÑĦоÑĢм", + "ĠоÑĦоÑĢм лен", + "สำหรัà¸ļ à¸ģาร", + "Ġog óln", + "ãģĨãģ¡ ãģ«", + "Ġvá rias", + "ãģĻãģİ ãĤĭ", + "ÙĪ Ùĩا", + "à¹Ĥà¸Ľà¸£ à¸Ķ", + "ĠÐłÐ¾ÑģÑģ иÑı", + "人 ãĢħ", + "ãģĹãģ¦ ãģįãģŁ", + "Ġsı rasında", + "Ġng ôn", + "س ÙĨØ©", + "تÙħ تع", + "×ŀ׼ ×ij×Ļ", + "Ġnh ấn", + "×¢ ×ŀ×Ļ×ĵ", + "á» ¨", + "ж иÑĤÑĮ", + "ãĤī ãģĽ", + "gr áf", + "gráf ica", + "ĠÙĤ ÙĪÙĦ", + "ĠÙĤÙĪÙĦ Ùĩ", + "ëĭ¨ ì²´", + "ห à¹īา", + "หà¹īา ม", + "使 ãģ£ãģ¦", + "ת ×Ļ×ij", + "ת×Ļ×ij ת", + "i á»ĥu", + "à¹ģ à¸Ĭม", + "à¹ģà¸Ĭม à¸Ľ", + "à¹ģà¸Ĭà¸¡à¸Ľ à¹Į", + "Ạ¬", + "ĠëĤĺ ëĿ¼", + "ĠÙħباشر Ø©", + "Ġtr Äĥm", + "سÙĥ ÙĪ", + "ĠاÙĦØ° Ùī", + "Ġbi ç", + "Ġbiç im", + "ت راجع", + "Ġоб еÑģп", + "ĠобеÑģп еÑĩ", + "ĠобеÑģпеÑĩ ива", + "Ġвозд ÑĥÑħ", + "Ñĭв аÑĤÑĮ", + "ÙĦ ØŃÙĤ", + "ĠMü dü", + "ĠMüdü rl", + "ĠMüdürl Ã¼ÄŁÃ¼", + "Ġyapt ır", + "Ġפר ס", + "Ġפרס ×ķ×Ŀ", + "Ø· ÙĪر", + "ÑģÑĤв оваÑĤÑĮ", + "ìŀ¥ ìĿĦ", + "à¸Ĺีà¹Īà¸Ķี à¸Ĺีà¹Īสุà¸Ķ", + "à¸Ńั ล", + "ÑĢ Ñİ", + "Ùħست ÙĤبÙĦ", + "Ñģл ÑĥÑĪ", + "ÑģлÑĥÑĪ а", + "èªį ãĤģ", + "Ġ׾ ×Ļ×ŀ", + "Ġ׾×Ļ×ŀ ×ķ×ĵ×Ļ", + "ת ש×ķ×ij", + "תש×ķ×ij ×ķת", + "ĠgerçekleÅŁtir il", + "ĠاÙĦ اتÙģاÙĤ", + "ĠÑĥÑĢов не", + "ĠÑĤ ÑĢав", + "Ġ×Ķ×ŀ ×ķף", + "ØŃÙģ اظ", + "ĠÙħ ÙIJ", + "ĠÙħÙIJ ÙĨ", + "ĠÙħÙIJÙĨ ÙĴ", + "Ġdem ás", + "×ŀ×ķ×ĸ ×Ļק×Ķ", + "ש ×Ļ×Ĺ×Ķ", + "Ġb ú", + "алÑĮ нÑĭм", + "ãĤı ãģŁ", + "ãĤıãģŁ ãģĹ", + "ĠاÙĦÙħÙĪ اد", + "ת ׼׳", + "×ª×Ľ×ł ×ķף", + "ãĥŃ ãĥĥãĤ¯", + "hi ếu", + "ĠÑĥ ме", + "ÙħØŃا ÙĪÙĦØ©", + "×IJ ×ķשר", + "Ġкон кÑĥÑĢ", + "ĠконкÑĥÑĢ Ñģ", + "Ġ×ŀ ×ij×Ĺ", + "Ġ×ŀ×ij×Ĺ ×Ļ×ł×ª", + "Ġan lam", + "Ġanlam ı", + "Ġli á»ĩt", + "Ġв Ñħод", + "ĠH ình", + "ĠÙĨ ÙĬ", + "ĠÙĨÙĬ ÙĪز", + "ãĤ¸ãĥ£ ãĥ¼", + "×ij ×Ļ×¥", + "ÑĤелÑĮ нÑĭÑħ", + "à¸Ĺุà¸ģ à¸Ńยà¹Īาà¸ĩ", + "ĠkiÅŁ inin", + "Ø£ Ùĥثر", + "ĠиÑģÑĤоÑĢ ии", + "Ġë³Ģ íĻĶ", + "פ׾ ס×ĺ", + "×¤×ľ×¡×ĺ ×Ļ׳×Ļ", + "ĠÑģ еÑĤ", + "ĠÑģеÑĤ и", + "dıģ ımız", + "íķĺ ëıĦë¡Ŀ", + "×Ķ ר", + "×Ķר ×ij×Ķ", + "ãģĻãĤĭãģĵãģ¨ ãģ¯", + "Ġphi ếu", + "تØŃ Ø³ÙĬÙĨ", + "ĠÅĽ rod", + "ĠÅĽrod ow", + "ĠÅĽrodow isk", + "ĠÑĢаÑģ Ñħод", + "بر ÙĬد", + "Ġر ÙĬ", + "ĠرÙĬ اÙĦ", + "Ġ×ķ ׼×ļ", + "ì§Ģ ìļĶ", + "׼ ×ŀ×ķ", + "Ġ×¢×ľ ×Ļ×Ķ×Ŀ", + "f ÃŃcio", + "Ġkar arı", + "tıģ ını", + "ĠС ов", + "ĠСов еÑĤ", + "ãģĬéĩij ãĤĴ", + "м еждÑĥ", + "междÑĥ на", + "междÑĥна ÑĢод", + "междÑĥнаÑĢод н", + "Ġm á»Ŀi", + "ĠاÙĦØ¥ ÙĬر", + "ĠاÙĦØ¥ÙĬر اÙĨÙĬ", + "ĠاÙĦرÙĪ سÙĬ", + "ص ÙĨد", + "صÙĨد ÙĪÙĤ", + "ĠاÙĦØ¥ÙĨ ترÙĨت", + "Ġt ắm", + "ĠÑĤак ого", + "Ġ×ij ׾×ķ×Ĵ", + "Ġü crets", + "Ġücrets iz", + "×Ĺ×ĸ ×Ļר", + "ìĸ´ ìķ¼", + "ĠPh ần", + "ï¼ ľ", + "Ġ×ĺ ×ij×¢", + "Ġ×ĺ×ij×¢ ×Ļ", + "×IJ×ŀ ×IJ", + "اÙĤ ÙĦ", + "Ġcondi ções", + "ÙĤات ÙĦ", + "ĠÑĢезÑĥлÑĮÑĤаÑĤ е", + "ĠÑģво ими", + "צ×ij ×Ļ×¢", + "gé ni", + "Ġz es", + "Ġzes po", + "Ġzespo ÅĤ", + "ÑĪ ив", + "Ġפר×ĺ×Ļ ×ķת", + "Ùħست Ø´Ùģ", + "ÙħستشÙģ Ùī", + "شر ع", + "Ġko ÅĽci", + "Ġ×Ķ×IJ ×Ļ׳×ĺר׳×ĺ", + "ĠЧ еÑĢ", + "поÑĩ ÑĤ", + "Ġactiv ités", + "çŁ¥ ãģ£ãģ¦", + "Ġ×ij ×ĸ×Ķ", + "Ġyüz den", + "ãģªãĤĬ ãģ¾ãģĽãĤĵ", + "Ġíĺ ¹", + "Ġíĺ¹ ìĿĢ", + "Ġ×ŀש ׳×Ķ", + "ĠÐĴ еÑĢ", + "Ġ×ij×IJ×ķת ×ķ", + "éĿ¢ çĻ½", + "éĿ¢çĻ½ ãģĦ", + "شر ØŃ", + "gr ünde", + "Ùģ Ø´", + "ÙģØ´ ÙĦ", + "Ġsé jour", + "ë´ IJ", + "Ġr ôle", + "Ø´ عار", + "ем Ñĭе", + "ĠاÙĦج سÙħ", + "алÑĮ ное", + "Ġìĥģ íĥľ", + "ï¼ ¤", + "ë¯Ģ ë¡ľ", + "ĠÙĨ ÙĤØ·", + "ĠÙĨÙĤØ· Ø©", + "ãģĿãģĨ ãģł", + "ãģĻãĤĭ ãģ®ãģĮ", + "ห ู", + "Ġnh á»ĭ", + "Ġeconóm ica", + "ס×ĺ ×ķ×ĵ", + "ס×ĺ×ķ×ĵ ׳×ĺ", + "มี à¹Ĥà¸Ńà¸ģาส", + "Ġgest ão", + "รูà¹ī วà¹Īา", + "Ġlo ạt", + "ĠاÙĦÙħ Ùı", + "ĠاÙĦØŃ ÙħÙĦ", + "ĠاÙĦعÙħÙĦ ÙĬØ©", + "Ġê²ĥ ëıĦ", + "ĠÐľÐ¾Ñģк ва", + "ק×ĺ ×ķר", + "Ġпод ÑĢоб", + "ĠподÑĢоб н", + "Ġl Æ°ng", + "ت Ùģس", + "تÙģس ÙĬر", + "ĠاÙĦ بع", + "ĠاÙĦبع ض", + "ئ ت", + "Ðķ ÐĿ", + "ìĹ° 구", + "à¹ĥหà¹ī à¸Ħุà¸ĵ", + "ãģĤãĤĬ ãģ¾ãģĹãģŁ", + "Ġbir ka", + "Ġbirka ç", + "ĠÄ° sl", + "ĠÄ°sl am", + "çĹĽ ãģ¿", + "Ġh ảo", + "Ġм аÑı", + "ĠiÅŁ çi", + "ש ×", + "×©× ģ", + "à¸ģาร à¹Ģมืà¸Ńà¸ĩ", + "×ķ×Ķ ר", + "Ġch ó", + "ëĨ Ģ", + "Ġyan lı", + "Ġyanlı ÅŁ", + "幸 ãģĽ", + "×IJר×Ĵ ×ķ׳×Ļ", + "à¸Ńาà¸Ī าร", + "à¸Ńาà¸Īาร ยà¹Į", + "ĠинÑĦоÑĢм аÑĨиÑİ", + "Ðĵ Ðŀ", + "׳ ×Ĺש", + "ĠìķĮ ìķĦ", + "ĠÑħаÑĢакÑĤеÑĢ иÑģÑĤ", + "ĠÑħаÑĢакÑĤеÑĢиÑģÑĤ ик", + "à¸Ħุà¸ĵ สามารà¸ĸ", + "è¦ĭ ãģĪãĤĭ", + "à¸Ĭัà¸Ķ à¹Ģà¸Ī", + "à¸Ĭัà¸Ķà¹Ģà¸Ī à¸Ļ", + "ĠdziaÅĤ al", + "ĠdziaÅĤal noÅĽci", + "à¹Ĥà¸ŀ สà¸ķà¹Į", + "ĠÐļ ол", + "ĠÙģ ÙĩÙĬ", + "Ġ×ŀ פ׳×Ļ", + "Ġ×Ķק שר", + "Ùħر Ùĥ", + "ÙħرÙĥ ز", + "Ġho á", + "Ġа пп", + "Ġапп аÑĢаÑĤ", + "Ġp ami", + "Ġpami ÄĻ", + "ĠpamiÄĻ ta", + "Ġç ünkü", + "×ĵ ×ķף", + "ãģ¯ ãģĵãģ¡ãĤī", + "ĠM Ãł", + "ĠÙĬ ÙĤدÙħ", + "ĠпÑĢ ез", + "ĠпÑĢез иденÑĤ", + "à¸Ńุ à¸ķ", + "à¸Ńุà¸ķ สา", + "à¸Ńุà¸ķสา ห", + "à¸Ńุà¸ķสาห à¸ģรรม", + "ì§Ģ ìĽIJ", + "Ġ×IJפשר ×ķת", + "sch üt", + "schüt z", + "ĠTi ên", + "Ġsay ılı", + "ĠгÑĢÑĥпп Ñĭ", + "оÑĩ нÑĭй", + "Ġ×ľ×¢ ×ŀ×ķ×ĵ", + "Ġwr zeÅĽ", + "ĠwrzeÅĽ nia", + "ĠÄIJ ầu", + "à¹Ģà¸Ĥà¹īา รà¹Īวม", + "nız da", + "Ø®ÙĬ ص", + "Ġgü nc", + "Ġgünc el", + "ĠÙĦÙĩ Ø°Ùĩ", + "ĠÙĬ عتبر", + "lé gi", + "ãĤı ãģĭãĤĭ", + "Ġr ừng", + "ظ Ùĩ", + "ظÙĩ ÙĪر", + "Ġ×ŀ×ij ×Ļף", + "Ġ기 íĥĢ", + "åĪĩ ãĤĮ", + "lan mÄ±ÅŁ", + "à¸Ĺีà¹Ī มีà¸Ħวาม", + "Ġh á»ģ", + "ت ÙĪجÙĩ", + "ĠاÙĦØ¥ دارة", + "Ġú til", + "ס פ×ķ", + "à¸Ħวาม รัà¸ģ", + "à¹Ĥ ฮ", + "Ġпол иÑĤ", + "ĠполиÑĤ ик", + "Ġsat ın", + "ĠÅŀ imdi", + "×ŀ ×ķר×Ļ×Ŀ", + "ìķĺ ëĭ¤", + "×Ĺ ×ķ×ķ", + "×Ĺ×ķ×ķ ×Ļ×Ķ", + "à¸Ħà¸Ńม à¸ŀิ", + "à¸Ħà¸Ńมà¸ŀิ ว", + "à¸Ħà¸Ńมà¸ŀิว à¹Ģà¸ķà¸Ńรà¹Į", + "Ġا ذا", + "تخ اذ", + "ãĤ¨ ãĥ«", + "Ġpossibilit é", + "ยืà¸Ļ ยัà¸Ļ", + "Ġü nivers", + "Ġünivers ite", + "ĠاÙĦد ÙĪرÙĬ", + "ĠìķĬëĬĶ ëĭ¤", + "ĠìĦľ ë¡ľ", + "ØŃ Ø§ÙĦ", + "Ġë ¨", + "Ġë¨ ¼", + "Ġ먼 ìłĢ", + "à¸Ĺีà¹Ī à¸ĸูà¸ģ", + "ì§ ľ", + "Ġsk óry", + "лÑĮ ÑĨ", + "à¹ĥà¸Ĭà¹ī à¹Ģวลา", + "×ijק שת", + "ĠØ° ÙĪ", + "æĹ¥ ãĢħ", + "ĠкоÑĤоÑĢ ÑĥÑİ", + "ĠÑĥÑĢов енÑĮ", + "ê¹ ¨", + "à¹Ħ à¸Ĺ", + "ãĤµ ãĥĹãĥª", + "ãĤ¸ ãĥ§ãĥ³", + "ãģĻ ãģ¹ãģį", + "ĠG ór", + "ãĥĪ ãĤ¤", + "ãĥĪãĤ¤ ãĥ¬", + "ĠyaÅŁ ama", + "Ġdá»ĭ p", + "Ġb ữa", + "à¸ĭ ุ", + "Ġöl üm", + "ãģ£ãģ¦ ãģıãĤĭ", + "à¸ģาร à¸Ħà¹īา", + "ש ער", + "ĠÑĤип а", + "Ġг еÑĢ", + "ĠгеÑĢ о", + "רק ×¢", + "Ġu waż", + "Ġuważ a", + "ש×ŀ ף", + "Ġhast alık", + "ãĤıãĤĮ ãĤĭ", + "ba ÅŁÄ±", + "Ñĩ ÑĤо", + "Ġ×ij ×ŀר׼×ĸ", + "Ġìļ°ë¦¬ ìĿĺ", + "ĠÙĥاÙĨ ÙĪا", + "ĠØ£ بر", + "Ġأبر ÙĬÙĦ", + "ì¸ µ", + "à¹Ħà¸Ĥ à¹Ī", + "ĠÙĪ ÙĦÙĪ", + "à¸Ĺ ัว", + "à¸Ĺัว รà¹Į", + "ĠÙĪØ£ Ùĥد", + "à¸Ĭ วà¸Ļ", + "׾ ×ķק", + "æį ¨", + "æį¨ ãģ¦", + "Ġİç in", + "p éri", + "Ġy al", + "Ġyal nız", + "ÑĮÑı н", + "Ġg ắng", + "à¸ģà¹ĩ ยัà¸ĩ", + "ĠУкÑĢа ин", + "ĠÑģ ами", + "ĠпÑĢовед ен", + "à¸ķà¸ģ à¹ģà¸ķà¹Īà¸ĩ", + "ĠQu ân", + "é paration", + "ĠbaÅŁ ında", + "Ġzn ale", + "Ġznale ź", + "Ġznaleź Äĩ", + "ãĤ± ãĥ¼", + "ãĥİ ãĥ¼", + "à¸ĸูà¸ģ à¸ķà¹īà¸Ńà¸ĩ", + "ëª ¸", + "Ġëı Į", + "ĠëıĮ ìķĦ", + "ĠSch üler", + "Ġпод гоÑĤов", + "ĠподгоÑĤов к", + "ع رÙĪ", + "عرÙĪ ض", + "la ÅŁtır", + "ĠÑģоÑģÑĤав лÑıеÑĤ", + "ĠпÑĢоиз вод", + "ĠпÑĢоизвод ÑģÑĤва", + "ĠоÑģнов е", + "ĠØ´ ÙħاÙĦ", + "à¸ģร ี", + "ĠgörÃ¼ÅŁ me", + "оÑĩ ек", + "Ġ×Ĺ×ijר ×Ļ×Ŀ", + "ÙħØ® اط", + "Ùħخاط ر", + "ï¼ Ń", + "ר פ×IJ", + "ĠM ẹ", + "ยà¸Ńม รัà¸ļ", + "Ġv ết", + "Ø® Ø°", + "ĠاÙĦت Ø·", + "ĠاÙĦتط بÙĬÙĤ", + "à¸Ļ ึà¸ģ", + "Ġ×Ķ ×Ľ×ł×¡×ª", + "ĠогÑĢ ани", + "ĠогÑĢани Ñĩен", + "ĠÃĩ alÄ±ÅŁ", + "ĠاÙĦÙħÙĨت دÙī", + "à¸Īำà¸Ļวà¸Ļ มาà¸ģ", + "ĠÑĤоÑĢ ÑĢ", + "ĠÑĤоÑĢÑĢ енÑĤ", + "ĠìĤ´ ìķĦ", + "à¸ŀลัà¸ĩ à¸ĩาà¸Ļ", + "à¸Ĭ ัà¸Ļ", + "ĠÐIJн дÑĢ", + "Ġréalis é", + "×ŀש ×IJ", + "à¹ģ à¸Ĭ", + "à¹ģà¸Ĭ รà¹Į", + "Ġб ог", + "มา à¹ģลà¹īว", + "ĠاÙĦÙĨ ار", + "Ġolmad ıģı", + "×ĵ ×¢×Ķ", + "ĠÑĥ веÑĢ", + "ĠÑĥвеÑĢ ен", + "ãĤĭ ãĤĤãģ®", + "Ø£ د", + "أد ÙĪات", + "Ġ×Ķ×ĸ ×ķ×Ĵ", + "Ø¥ عÙĦاÙħ", + "h á»ı", + "ĠNä he", + "ĠÑĤ еÑģÑĤ", + "Ġ×ŀ ×ķ׼ר", + "Ġë¬¸ìłľ ê°Ģ", + "ת ×ķצ×IJ×Ķ", + "m ó", + "mó vel", + "ĠاÙĦتج ارة", + "Ġмног иÑħ", + "обÑī а", + "Ġ×¢ סק×Ļ", + "ĠEdu cação", + "ק ש×Ļ×Ŀ", + "é tabl", + "établ issement", + "Ġд еле", + "иÑĢÑĥ еÑĤÑģÑı", + "Ø¢ ثار", + "Ġ×Ķ×ŀ ר׼×ĸ×Ļ", + "ãĥIJ ãĥ«", + "ĠвÑģÑĤÑĢ еÑĩ", + "ãģĴ ãĤĭ", + "Ġci Äħ", + "ĠciÄħ gu", + "ÙĬ ست", + "à¸łà¸² ว", + "à¸łà¸²à¸§ ะ", + "Ø£ Ùħر", + "Ġо жи", + "Ġожи да", + "Ġ ủy", + "ãĥŀ ãĥ«", + "ر اس", + "оÑĩ ной", + "ת ×Ĵ×ķ×ij×ķת", + "تع رÙĬÙģ", + "ĠÑģо ÑĨиалÑĮно", + "ãĤĴ éĸĭ", + "ĠиÑģÑģлед ова", + "Ġd ú", + "Ġdú vida", + "Ġsk ÅĤ", + "ĠskÅĤ ada", + "Ġhä ufig", + "ĠвÑĭб ÑĢ", + "ĠвÑĭбÑĢ аÑĤÑĮ", + "ãģ®ãģ§ãģ¯ãģªãģĦ ãģĭ", + "ĠÑģ илÑĮно", + "ÑĤвеÑĢж ден", + "ר פ", + "רפ ×ķ×IJ×Ķ", + "æĢĿ ãģĦãģ¾ãģĻ", + "ØŃر ص", + "ש×ķת ×£", + "Ùħس جد", + "à¹Ĥà¸Ĭ วà¹Į", + "ем ÑģÑı", + "в ÑĪие", + "Ġм л", + "Ġмл н", + "Ġ׾×Ķ ×ij×Ļ×IJ", + "ĠÙĬ تعÙĦÙĤ", + "à¸ķ ูà¹ī", + "Ġп ÑĢаз", + "ĠпÑĢаз д", + "ĠпÑĢазд ник", + "Ġн ем", + "Ġнем ного", + "Ġs Ãłng", + "تÙĨ سÙĬ", + "تÙĨسÙĬ ÙĤ", + "Ġtá» Ŀ", + "Ġмед и", + "ãģ« æĪ", + "ãģ«æĪ »", + "à¸Ħว à¹īา", + "ãģĭ ãģijãĤĭ", + "×ij׾ ×ķת", + "ĠÑįк Ñģп", + "ĠÑįкÑģп еÑĢÑĤ", + "Ġдев ÑĥÑĪ", + "ĠдевÑĥÑĪ к", + "ĠØŃ Øµ", + "ÙĨØ´ Ø£", + "ãģĮãģĤãĤĭ ãģ®ãģ§", + "Ġت راÙħ", + "ĠتراÙħ ب", + "أس ÙĪاÙĤ", + "Ġ׾פ ׳×ķת", + "Ġا ï»·", + "ãģ« ãģı", + "ãģ«ãģı ãģĦ", + "ĠØ£ عÙĦÙī", + "Ġ׾×Ķ ×ŀש×Ļ×ļ", + "rä u", + "ש×ŀ ×Ļ×Ŀ", + "åĪĨ ãģij", + "ãģĻ ãģ§", + "ãģĻãģ§ ãģ«", + "×Ķ׾ ׼×Ķ", + "×Ĺ׾ ×Ļ×£", + "Ġì ±ħ", + "Ġì±ħ ìŀĦ", + "à¹Ģà¸Ī ริ", + "à¹Ģà¸Īริ à¸į", + "éģĬ ãģ³", + "ج سد", + "สา à¸ĺ", + "สาà¸ĺ าร", + "สาà¸ĺาร à¸ĵ", + "Ġbas ın", + "ÑĢаР³", + "г ад", + "Ġho ÅŁ", + "íķ µ", + "×ij×Ĺ ×Ļר×Ķ", + "×ŀס ×ļ", + "Ġìłľ íĴĪ", + "تÙħ ÙĪÙĬÙĦ", + "ĠL Æ°u", + "ë¡ľ ë¶ĢíĦ°", + "Ġп об", + "Ġпоб ед", + "ÙħÙĨ Ø°", + "常 ãģ«", + "ÙĤ س", + "ĠاÙĦÙħ صدر", + "ĠÙĪاÙĦ است", + "Ġkh ắp", + "ĠاÙĦج اÙĨب", + "Ġng uyá»ĩn", + "éĸĵ éģķãģĦ", + "ĠÑģÑĤ ÑĢа", + "ĠÑģÑĤÑĢа Ñħ", + "ĠÑģÑĤÑĢаÑħ ов", + "รี à¸ļ", + "Ġx Æ°Æ¡ng", + "Ġì° ¾", + "Ġì°¾ ìķĦ", + "Ġng ại", + "г ал", + "à¸ĭ ีà¹Ī", + "Ġ×ij פ×Ļ×Ļס×ij×ķק", + "Ц енÑĤÑĢ", + "Ġaval iação", + "Ġeconóm ico", + "×ĸ ף", + "ĠÐľ ак", + "Ġinter és", + "à¸ģล ิà¹Īà¸Ļ", + "ÑģÑĤÑĮ Ñİ", + "ĠÄij Æ°Æ¡ng", + "å¼· ãģı", + "ĠKh ách", + "à¹Ģà¸Ļืà¹īà¸Ń หา", + "ĠYaz ı", + "è²· ãģ£ãģ¦", + "Ðł Ðķ", + "à¹Ģà¸ŀิà¹Īม à¸Ĥึà¹īà¸Ļ", + "สม à¸ļู", + "สมà¸ļู รà¸ĵà¹Į", + "Ġм иÑĢов", + "×Ĵ ׳×Ļ×Ŀ", + "ĠÄij ức", + "à¸Ń ารà¹Į", + "ص اص", + "ãģĬ ãĤĪ", + "ãģĬãĤĪ ãģ³", + "ÃªÌ ī", + "ĠاÙĦÙħؤ تÙħر", + "ĠاÙĦÙħر ØŃÙĦØ©", + "สà¸Ńà¸ļ à¸ĸาม", + "Ġà¸Īาà¸ģ à¸Ļัà¹īà¸Ļ", + "Ġت عد", + "ãģĿãģ® ãģŁãĤģ", + "Ġkh áng", + "à¸Ļ ิà¸Ķ", + "ãĥĬ ãĥ³", + "ëĦ¤ ìļĶ", + "ĠاÙĦ اØŃت", + "ĠاÙĦاØŃت ÙĦاÙĦ", + "ìļ ķ", + "Ġмод ели", + "ĠпÑĢоÑĨ енÑĤ", + "à¸ŀวà¸ģ à¹Ģรา", + "Ġ×Ķצ ×ĵ", + "Ġ×Ķצ×ĵ ×ĵ×Ļ×Ŀ", + "ständ e", + "׳ ×Ĵר", + "Ġdot yc", + "Ġdotyc zÄħ", + "ĠdotyczÄħ ce", + "ĠÅĽ wiÄĻt", + "×ŀר ×Ķ", + "ãģĻãģĶ ãģĦ", + "ãĥĩãĤ£ ãĥ³ãĤ°", + "à¸ģาร สรà¹īาà¸ĩ", + "ë Ĥ¬", + "Ġì°¸ ìŬ", + "Ñģ Ñħ", + "ÑģÑħ ем", + "ÙħÙĪ س", + "Ġn ấu", + "Ġ׾×ŀ×¢ ׾×Ķ", + "à¹Ģà¸Ľ à¹īา", + "à¹Ģà¸Ľà¹īา หมาย", + "Ġmù i", + "ائ ز", + "íĽ Ī", + "×Ĺ×ij ×ķר×Ķ", + "à¸ľà¸¹à¹ī à¹ĥà¸Ĭà¹ī", + "Ġpa ź", + "Ġpaź dzi", + "Ġpaździ ern", + "Ġpaździern ika", + "ลà¸ĩ à¹Ħà¸Ľ", + "ÙĤ اع", + "Ġch áºŃm", + "Ġözellik leri", + "ĠÄIJ o", + "ĠÄIJo Ãłn", + "ж ение", + "Ġh ẳ", + "Ġhẳ n", + "ĠaÅŁ k", + "ï½ į", + "ãĥij ãĤ¹", + "×Ķ×ķר ×IJ×ķת", + "ĠÅ »", + "ĠÅ» y", + "×ŀ×ĸ ׾", + "ĠÑĥ кÑĢа", + "ĠÑĥкÑĢа ин", + "à¹Ģà¸Ĭ ิ", + "à¹Ģà¸Ĭิ à¸į", + "Ðł Ðĺ", + "ĠzwiÄħz ku", + "×Ķ×Ĺ׾×ĺ ת", + "ãĤĵãģ§ãģĻ ãĤĪãģŃ", + "ãģ¦ ãģĬãĤĬ", + "лож иÑĤÑĮ", + "×ŀ ×ķ׳×Ļ×Ŀ", + "ฮ ิ", + "ì° ¬", + "ĠاÙĦÙħØ´ ترÙĥ", + "ĠdÃ¼ÅŁ ük", + "аг енÑĤ", + "ĠاÙĦØ£ سبÙĪع", + "ĠÙĤ رÙĬب", + "ин д", + "инд ив", + "индив ид", + "индивид Ñĥ", + "индивидÑĥ алÑĮн", + "för der", + "Ġseç en", + "Ġseçen ek", + "Ġét ant", + "ĠлÑİб им", + "каз ÑĭваеÑĤ", + "ว ิà¸Ļ", + "Ġ×Ķ×ij ×IJ×Ļ×Ŀ", + "Ġд ов", + "Ġдов олÑĮ", + "ĠдоволÑĮ но", + "×¢×ĵ ×Ļ×£", + "Ġok re", + "Ġokre ÅĽ", + "ĠokreÅĽ lon", + "Ġت رÙĬد", + "à¹Ģมืà¹Īà¸Ń วัà¸Ļà¸Ĺีà¹Ī", + "ãĤĪ ãģĭãģ£ãģŁ", + "Cum h", + "Cumh ur", + "Cumhur ba", + "Cumhurba ÅŁ", + "CumhurbaÅŁ kan", + "CumhurbaÅŁkan ı", + "Ġn ợ", + "à¸ľà¸¹à¹ī à¹Ģลà¹Īà¸Ļ", + "Ġcompl ète", + "à¹Ģà¸ŀ ศ", + "د ÙIJ", + "Ġdü z", + "Ġdüz ey", + "ãģ§ãģĤãĤĭ ãģĵãģ¨", + "ext érieur", + "× ³", + "Ġinform ação", + "ãĤ¯ãĥª ãĥĭãĥĥãĤ¯", + "ĠPub li", + "ĠPubli é", + "ר ×ķ×ĵ", + "à¸Ħวาม à¸Ľà¸¥à¸Ńà¸Ķà¸łà¸±à¸¢", + "ĠØ£ÙĬ ض", + "ĠØ£ÙĬض Ùĭا", + "ت سبب", + "ãģ¤ ãĤĤãĤĬ", + "из ма", + "à¸Ĥึà¹īà¸Ļ à¹Ħà¸Ľ", + "Ùĥ ÙIJ", + "ÙĦ ÙĪÙħ", + "Ġש צר", + "Ġשצר ×Ļ×ļ", + "ãģ¯ ãĤĤãģ¡ãĤįãĤĵ", + "Ġк ан", + "Ġкан ал", + "ãģ«ãģª ãģ£ãģ¦ãģĦãģ¾ãģĻ", + "ĠاÙĦØ£ Ùĥثر", + "ت اØŃ", + "ÙĨت Ùĩ", + "ÙĨتÙĩ اء", + "ا ÙĪÙĬØ©", + "ĠBug ün", + "н Ñģкого", + "à¸Ķ à¹Īวà¸Ļ", + "é volution", + "ãģ£ãģ¦ ãģĦãģ¾ãģĹãģŁ", + "ãĤ ħ", + "ĠV Æ°Æ¡ng", + "à¸łà¸²à¸ŀ ย", + "à¸łà¸²à¸ŀย à¸Ļ", + "à¸łà¸²à¸ŀยà¸Ļ à¸ķรà¹Į", + "Ġ×Ķ צ׾×Ļ×Ĺ", + "ĠاÙĦإسÙĦاÙħ ÙĬ", + "ÙĦÙĬ ب", + "Ġed ição", + "ÑģÑĤÑĢ ел", + "Ġkh úc", + "ÙĨÙħÙĪ Ø°", + "ÙĨÙħÙĪØ° ج", + "׾ צ×Ķ", + "ÑģÑĤав ил", + "à¸ĸ า", + "สรà¹īาà¸ĩ à¸Ħวาม", + "ãģĦ ãģ£ãģ±", + "ãģĦãģ£ãģ± ãģĦ", + "ÑģÑĤав лен", + "ĠاÙĦ ÙĤدس", + "Ġng ược", + "ب Ø®", + "ส หร", + "สหร ั", + "สหรั à¸IJ", + "ĠØ£ غ", + "Ġأغ سط", + "Ġأغسط س", + "ãģĨ ãģ¾", + "ãģĨãģ¾ ãģı", + "ĠêµŃ ìłľ", + "ØŃض ار", + "Ġd ừng", + "æĬ¼ ãģĹ", + "ت ÙĪا", + "تÙĪا جد", + "ש×ŀ ×Ĺ×Ķ", + "ãģı ãĤĵ", + "Ġ×ij×¢ צ", + "Ġ×ijעצ ×Ŀ", + "×ŀ ׳×Ļ×ķת", + "×ķ ×Ļ×ĵ", + "×ķ×Ļ×ĵ ×IJ×ķ", + "à¸Ĭ ิà¸ĩ", + "Ġprac ÄĻ", + "Ġз аÑĤ", + "ĠзаÑĤ ем", + "ĠìŀIJ ìľł", + "Ġì¤ Ģ", + "Ġì¤Ģ ë¹Ħ", + "Ġb áºŃ", + "ĠbáºŃ c", + "Ġ×Ķ×ŀ צ×ij", + "ĠÙĤ ÙĬÙħØ©", + "à¹Ģà¸Ń à¹Ģà¸Ĭ", + "à¹Ģà¸Ńà¹Ģà¸Ĭ ีย", + "Ġperch è", + "ĠاÙĦع سÙĥر", + "ĠاÙĦعسÙĥر ÙĬØ©", + "ج ÙĬب", + "ëŀ µ", + "Ùħ Ùĩر", + "ÙħÙĩر جاÙĨ", + "Ùħ راÙĥ", + "ÙħراÙĥ ز", + "Ġод нако", + "à¸Ķี à¹Ĩ", + "Ġצ פ×ķ", + "Ġkullan ılan", + "Ġк ино", + "ãĥĨãĤ£ ãĥ³ãĤ°", + "ĠGi Ỽi", + "ت ÙĪز", + "تÙĪز ÙĬع", + "ย ิà¸Ļ", + "ยิà¸Ļ à¸Ķี", + "Ġc Åĵur", + "ĠiÅŁ aret", + "Ġ×ij×¢ ×ĸר", + "Ġ×ij×¢×ĸר ת", + "Ġп аÑĨи", + "ĠпаÑĨи енÑĤ", + "ãģ¿ãģŁãģĦ ãģ§ãģĻ", + "в ез", + "ли на", + "од е", + "Ġ×IJ×ķת ף", + "dıģ ınız", + "ĠÐIJ в", + "ĠÐIJв ÑĤоÑĢ", + "ï¼ ®", + "ĠC ần", + "ĠاÙĦا Ø®", + "ĠاÙĦاخ بار", + "Ġê±° ìĿĺ", + "Ġat enção", + "Ġgeld iÄŁi", + "ãĤª ãĤ¹", + "ãĤªãĤ¹ ãĤ¹", + "ãĤªãĤ¹ãĤ¹ ãĥ¡", + "ев Ñĭе", + "кÑĢÑĭ л", + "à¹Ģà¸Ĭ ียà¸ĩ", + "à¹Ģà¸Ĭียà¸ĩ à¹ĥหมà¹Ī", + "Ġmar ço", + "ĠاÙĦÙħ ادة", + "Ġг ол", + "Ġsprzeda ży", + "Ġíķ´ ê²°", + "ĠÐķ го", + "ê¹ Ģ", + "Ġ׾ק×ij׾ ת", + "ĠاÙĦÙģ ÙĨاÙĨ", + "Ġcomunic ación", + "à¹Ģสà¹īà¸Ļ à¸Ĺาà¸ĩ", + "íĺ ¹", + "à¸Ĭ ำ", + "à¸Ĭำ ระ", + "Ġ׼ ×IJ×ŀ", + "Ġ׼×IJ×ŀ ×ķר", + "à¸Ĭ à¹Īาà¸ĩ", + "ز Ùĩر", + "Ġklient ów", + "ива ÑİÑĤ", + "ан г", + "׳ ×ļ", + "Ġg á»įn", + "Ãľ R", + "ìĺģ ìĥģ", + "Ġغ زة", + "ìĿĮ ìĿĦ", + "Ġbez po", + "Ġbezpo ÅĽ", + "ĠbezpoÅĽ redni", + "ĠاÙĦÙħ ÙĪا", + "ĠاÙĦÙħÙĪا Ø·ÙĨ", + "ĠاÙĦÙħÙĪاطÙĨ ÙĬÙĨ", + "ãĤĮ ãģ¾ãģĻ", + "ĠмаÑĤ Ñĩ", + "×IJ ×ķף", + "Ġر سÙħÙĬ", + "ĠÑįк он", + "ĠÑįкон ом", + "ĠÑįконом иÑĩеÑģк", + "ãĥľ ãĥ¼", + "Ġд иÑĢ", + "ĠдиÑĢ екÑĤоÑĢ", + "ĠÑģк оÑĢо", + "à¸ļ ำ", + "à¸ļำ ร", + "à¸ļำร ุà¸ĩ", + "ĠÑĦ ÑĥÑĤ", + "ĠÑĦÑĥÑĤ бол", + "Ġ×IJ ×Ļ׾", + "Ġì¤ij êµŃ", + "ìľ ¤", + "eÄŁ e", + "à¹Ħ à¸ģà¹Ī", + "tra î", + "traî n", + "ĠÑĤ ÑĢÑĥб", + "à¹Ģà¸ļ ื", + "à¹Ģà¸ļื à¹īà¸Ńà¸ĩ", + "à¹ģม à¸Ļ", + "ĠتØŃ Ø¯ÙĬØ«", + "Ġ׼ עת", + "ØŃ Ø§Ø³Ø¨", + "lı ÄŁa", + "ק×Ļ ×Ļ×ŀ×Ļ×Ŀ", + "оÑģÑĤ ÑĮÑİ", + "à¸Ŀ ั", + "à¸Ŀั à¹Īà¸ĩ", + "Ø´ غÙĦ", + "ìĽ ¹", + "Ġкажд ого", + "Ġbölüm ü", + "หà¸Ļ ี", + "Ġistedi ÄŁi", + "Ġtr Æ°ng", + "ãĥ Į", + "ฮ à¸Ń", + "Ø£ÙĨ Ø´", + "Ø£ÙĨØ´ طة", + "ĠاÙĦÙħ سÙĬ", + "ĠاÙĦÙħسÙĬ ØŃ", + "ลัà¸ģษ à¸ĵà¹Į", + "Ġn á»Ńa", + "à¸Ĺีà¹Ī à¸ķà¹īà¸Ńà¸ĩà¸ģาร", + "ÑĪ ек", + "л Ñij", + "Ġש ×Ļ×Ķ", + "Ġש×Ļ×Ķ ×Ļ×Ķ", + "Ġkhu ôn", + "ĠÑĤÑĢеб ованиÑı", + "Ġ×ľ×¢ ×ĸ×ķר", + "ĠاÙĦع Ùħر", + "ราà¸Ħา à¸ĸูà¸ģ", + "ÙĩÙı ÙħÙĴ", + "ü st", + "üst ü", + "Ġден ег", + "Ġn ạ", + "à¸Ĥà¸Ļ ม", + "Ġбл аг", + "Ġблаг од", + "Ġблагод аÑĢ", + "ĠблагодаÑĢ Ñı", + "Ø¥ سÙĦاÙħ", + "à¸Ļิ ว", + "çŁ¥ ãĤīãģªãģĦ", + "Ø« ÙĤØ©", + "Ġг олоÑģ", + "×IJ×ķר ×Ĺ", + "Ġtr ứng", + "Ġод ном", + "ĠkoÅĦ cu", + "Ġ×ķ רק", + "Wi ÄĻ", + "WiÄĻ cej", + "Ġ×IJ ×Ļ׼×ķת", + "Ġ×IJ×Ļ׼×ķת ×Ļ", + "Ñģ оÑģ", + "Ġje żeli", + "以ä¸ĭ ãģ®", + "å°ı ãģķ", + "å°ıãģķ ãģª", + "олог ии", + "Ġоб ÑģлÑĥж", + "ĠобÑģлÑĥж ива", + "Ùĥت ابة", + "Ġê´Ģ ìĭ¬", + "×¢ ש×Ļר", + "Ġaras ındaki", + "ĠÑĢай она", + "ÙĪا جب", + "Ġ×ij×Ĺ×Ļ ×Ļ", + "íķ´ 주", + "Ġg óc", + "ай л", + "ĠT ình", + "æļ® ãĤī", + "æļ®ãĤī ãģĹ", + "æĻĤ ãģ«ãģ¯", + "ĠгоÑĢод е", + "Ġ׼×IJ ×Ļ׾", + "Ġ׼×IJ×Ļ׾ ×ķ", + "ĠC á»Ļng", + "ãģ©ãģĨ ãģĹãģ¦ãĤĤ", + "×Ĺ ×ķ×£", + "تØŃ Ø±Ùĥ", + "ĠÑģлов ам", + "à¸Īะ à¸Ĭà¹Īวย", + "ĠاÙĦÙħست ÙĤبÙĦ", + "ÙĤ ض", + "ÙĤض ÙĬ", + "×ijס ×ķפ", + "×ijס×ķפ ×ķ", + "iÄĻ Äĩ", + "ĠY ıl", + "Ø´ ÙĬØ®", + "à¸Ħุà¸ĵ à¸Īะ", + "ש×ŀ ×ķת", + "Ġت عرض", + "Ġanál ise", + "ĠÑģоб иÑĢа", + "à¹Ģà¸ŀ à¸Ĭ", + "à¹Ģà¸ŀà¸Ĭ ร", + "Ġв ели", + "Ġвели к", + "สั à¹īà¸Ļ", + "Ġpop ulação", + "รà¹Īวม à¸ģัà¸Ļ", + "×Ĺ ×ŀ", + "×Ĺ×ŀ ×Ļש×Ļ", + "ס ×Ļס", + "åĨħ ãģ§", + "Ġsob Äħ", + "ĠY ay", + "ĠYay ın", + "ãĥ¡ ãĥĭãĥ¥ãĥ¼", + "ĠпÑĢедоÑģÑĤав лÑı", + "ãģł ãģ¨æĢĿãģĨ", + "Ġê³ł ê°Ŀ", + "Ġод ним", + "à¹ĥà¸Ļ à¹Ģรืà¹Īà¸Ńà¸ĩ", + "Ġs á»ķ", + "ĠÐĹ Ð´ÐµÑģÑĮ", + "Ġизмен ениÑı", + "ĠìĿ¼ ìĿĦ", + "ãģªãģ® ãģł", + "клад Ñĭва", + "ÑĢ ма", + "Ġ×ķ×ij ׼׾", + "تأ ÙħÙĬÙĨ", + "ĠпÑĢи ÑıÑĤ", + "ĠпÑĢиÑıÑĤ н", + "Ùħ Ùħار", + "ÙħÙħار سة", + "ãģ¨ãģª ãģ£ãģ¦", + "Ġج ÙħÙĬÙĦ", + "Ġì§ Ī", + "Ġì§Ī 문", + "Ġquest ão", + "i é", + "ié ndo", + "หà¹īà¸Ńà¸ĩ à¸ŀัà¸ģ", + "ãĥij ãĥ¼ãĥĪ", + "ÑĤвеÑĢж да", + "н Ñģкой", + "з ал", + "มุ à¹Īà¸ĩ", + "á» Ĭ", + "Ġ×Ķ×IJ×Ĺר ×ķ׳×Ķ", + "ĠTh Æ°", + "주 민", + "ĠاÙĦع ب", + "év én", + "évén ement", + "ÙĤÙĪ اعد", + "د Ùı", + "ĠìķĬ ìĬµëĭĪëĭ¤", + "Ġë³´ 기", + "Ġyapıl ması", + "à¹Ģร าà¸ģ", + "à¹Ģราà¸ģ à¹ĩ", + "ØŃ Ø°Ø±", + "ÙĤ صر", + "ãģ¦ãģĹãģ¾ ãģĦãģ¾ãģĹãģŁ", + "Ġà¹Ģà¸Ľà¹ĩà¸Ļ à¸ķà¹īà¸Ļ", + "ãģ¨ ãģ«", + "ãģ¨ãģ« ãģĭ", + "ãģ¨ãģ«ãģĭ ãģı", + "н ÑĨе", + "зв Ñĥк", + "ãģĹãĤĪãģĨ ãģ¨", + "ĠاÙĦصØŃ ÙĬØ©", + "Ġש×Ķ ×Ļ×ķ", + "ĠDi ÄŁer", + "ÙĤÙĦ ÙĤ", + "ãĤ¸ãĥ£ ãĥ³", + "Ġr á»Ŀi", + "Ġл еÑĩ", + "ĠлеÑĩ ениÑı", + "تب اد", + "تباد ÙĦ", + "צ פ×Ķ", + "à¸Ħวาม à¹Ģหà¹ĩà¸Ļ", + "ĠØ´ ب", + "Ġشب ÙĥØ©", + "ר ×Ļק", + "Ùħ عد", + "Ùħعد ات", + "dıģ ında", + "Ġ×ijש ׳×Ļ×Ŀ", + "Ġ×Ķ ×Ļשר×IJ׾", + "Ġ×Ķ×Ļשר×IJ׾ ×Ļת", + "Ġsı nav", + "׳צ ×Ļ×Ĵ", + "วัà¸ķ à¸ĸุ", + "ĠاÙĦبر ÙĦÙħ", + "ĠاÙĦبرÙĦÙħ اÙĨ", + "t ivitÃł", + "ãĤĵãģł ãĤįãģĨ", + "ק×Ļ ×Ļ×ŀ", + "ÙĦÙĬ Ùĥ", + "ĠÄij ò", + "ĠÄijò i", + "ĠÐĺн ÑĤеÑĢ", + "ĠÐĺнÑĤеÑĢ неÑĤ", + "ãģ«ãģ¨ãģ£ãģ¦ ãģ¯", + "ãģ£ ãģĵ", + "ק ×ķס", + "ست ØŃÙĤ", + "æķĻ ãģĪãģ¦", + "ãĥĢ ãĥ¡", + "ĠÙħÙĨ زÙĦ", + "à¹Ģà¸ĭ à¹ĩà¸Ļ", + "使 ãģĪãĤĭ", + "è¦ĭ ç©į", + "è¦ĭç©į ãĤĤãĤĬ", + "Ø£ Ùģ", + "Ø£Ùģ Ùĥار", + "Ġиг ÑĢов", + "ĠигÑĢов Ñĭе", + "Ġm ÄĻż", + "ĠmÄĻż czy", + "ĠmÄĻżczy zn", + "ĠاÙĦØŃ ÙĤÙĬÙĤÙĬ", + "ع بر", + "׼×ķ׾ ׳×ķ", + "íĿ ¥", + "×ŀ×IJ ×ķ×Ĺר", + "خت ص", + "ãĥŀ ãĥŀ", + "Ġ×IJ×Ĺ ×ķ×ĸ", + "í ĮĢ", + "Ġr á»iji", + "Ġв ÑĤоÑĢ", + "ĠвÑĤоÑĢ ой", + "Ġl ẫn", + "пÑĢ ом", + "пÑĢом ÑĭÑĪ", + "пÑĢомÑĭÑĪ лен", + "пÑĢомÑĭÑĪлен н", + "ĠоÑĤноÑĪ ениÑı", + "Ġs ứ", + "Ġм обилÑĮ", + "ĠмобилÑĮ н", + "ĠÑįÑĤ омÑĥ", + "Ġt ạp", + "ĠìĤ¬ ê±´", + "ĠìķĮ 볤", + "Ùĥ Ùı", + "ÙĥÙı ÙħÙĴ", + "Ġק ×ķר×Ķ", + "ĠÑĦ иÑĢ", + "ĠÑĦиÑĢ м", + "Ġsık ıntı", + "׳ ׼", + "׳׼ ×ķף", + "ÙĪÙĦÙĪج ÙĬ", + "ØŃ Ø§ÙĨ", + "Ġlo ạn", + "Ġ×IJ׾ ×£", + "Ġm ắn", + "abh äng", + "abhäng ig", + "ĠÑĥÑĢов нÑı", + "Ġ׾×ij×ĵ ×ķק", + "ÙĬ ÙħÙĨ", + "lay ın", + "Ġh ải", + "Ġзав од", + "ĠìķĦ 주", + "สà¸ĸ า", + "สà¸ĸา à¸ļัà¸Ļ", + "Ġgüven lik", + "à¹Ģà¸Ķ à¹Īà¸Ļ", + "×ij×ĵ ק", + "Ġë Ī", + "ĠëĪ Ħ", + "ĠëĪĦ 구", + "éĩįè¦ģ ãģª", + "รà¸Ńà¸ĩ รัà¸ļ", + "sch lie", + "schlie ÃŁen", + "Ġìĸ ¼", + "Ġìĸ¼ ë§Ī", + "Ġìĸ¼ë§Ī ëĤĺ", + "ÑĤи ки", + "íķľëĭ¤ ê³ł", + "ãģłãģ£ãģŁ ãĤī", + "Ġ×Ķ ×Ļ×ĺ×ij", + "ãģªãģijãĤĮãģ° ãģªãĤīãģªãģĦ", + "â Ì", + "Ã¢Ì £", + "Ġph ạt", + "ak Ä±ÅŁ", + "ãģ¦ãģĹãģ¾ ãģĦãģ¾ãģĻ", + "à¹Ģà¸ĭ à¹ĩ", + "ĠС егоднÑı", + "Ġinsan ların", + "Ġdévelop pe", + "ת פר", + "תפר ×Ļ×ĺ", + "اÙĨت شار", + "ê° ij", + "Fran çois", + "Ø£ÙĦ ع", + "Ø£ÙĦع اب", + "ãĤĴ è¶ħ", + "ãĤĴè¶ħ ãģĪ", + "Ġê°Ļ ìĬµëĭĪëĭ¤", + "ãĤ³ ãĥ¬", + "ĠмеÑģÑı ÑĨев", + "íĮ ħ", + "ĠاÙĦج اÙħعة", + "ìĿ¸ íĦ°", + "ìĿ¸íĦ° ëĦ·", + "×ĵר ×ķש", + "ĠÙĪØ£ شار", + "ĠпÑĢав ила", + "ãģĿãģĵ ãģ«", + "×Ĺ ×ŀ×ĵ", + "à¹Ģหà¸ķุ à¸ģารà¸ĵà¹Į", + "Ġê²½ íĹĺ", + "ãģ¶ ãĤĬ", + "׾ ש", + "׾ש ×ķף", + "à¹Ģ à¸ĸ", + "ĠDo ÄŁu", + "ĠиÑģполÑĮзов ание", + "Ġçoc uÄŁu", + "магазин е", + "ĠÄiji á»ĥn", + "Ġas lı", + "Ġaslı nda", + "Ġdoen ça", + "Ġس اع", + "Ġساع ات", + "ĠиÑģполÑĮзов аниÑı", + "ר ×ķצ×Ļ×Ŀ", + "ĠзнаÑĩ иÑĤ", + "ĠÑĢаР¼", + "ĠÑĢам каÑħ", + "ê±° 리", + "Ġп ÑĭÑĤа", + "ãĥģ ãĥ³", + "Ġпо Ñģк", + "ĠпоÑģк олÑĮ", + "ĠпоÑģколÑĮ кÑĥ", + "Ø¥ بر", + "إبر اÙĩ", + "إبراÙĩ ÙĬÙħ", + "ĠÑĤÑĢ еÑħ", + "ĠGen ç", + "س ÙĪÙģ", + "Ġve ÃŃculo", + "ĠNg ân", + "ĠоÑĩеÑĢ едÑĮ", + "à¸Ħร ึà¹Īà¸ĩ", + "×IJ ×ij×Ļ", + "à¸ķ à¹īม", + "ãĤĴè¡Į ãģĦ", + "ĠاÙĦساب ÙĤØ©", + "на ÑĨи", + "наÑĨи она", + "наÑĨиона лÑĮн", + "Ġgest ión", + "ت ÙĤد", + "ĠاÙĦبÙĬ اÙĨ", + "ĠاÙĦبÙĬاÙĨ ات", + "ĠاÙĦ اÙĨتخاب", + "ĠاÙĦاÙĨتخاب ات", + "à¹Ģà¸Ĭ à¹Īา", + "×ĵ ×IJ×Ĵ", + "Ġ׾×Ĵ ×ŀר×Ļ", + "Ġت ØŃتاج", + "Ġth ôn", + "à¸ķ à¹īà¸Ńà¸Ļ", + "à¸ķà¹īà¸Ńà¸Ļ รัà¸ļ", + "女 ãģ®", + "女ãģ® åŃIJ", + "Ġth ợ", + "Ø· ØŃÙĨ", + "ารà¹Į à¸Ķ", + "ת ×ŀ×Ļ×ĵ", + "ĠÑģам Ñĭм", + "Ġìĭľ íĸī", + "Ø¥ صد", + "إصد ار", + "ĠNgh á»ĩ", + "ìķ ķ", + "س ئ", + "سئ ÙĦ", + "à¸Ń าร", + "à¸Ńาร ม", + "à¸Ńารม à¸ĵà¹Į", + "à¹ģ ฮ", + "׳×ĺ ׾", + "Ġì¢ĭ ìķĦ", + "×ķ׾ ׾", + "Ġ×ij ×Ľ×ª×ij", + "ãĤ« ãĥ©", + "צע ×Ļר×Ļ×Ŀ", + "تعب ÙĬر", + "Ġ×ŀ קר×Ķ", + "ĠÑĦак ÑĤоÑĢ", + "Ġت ÙħاÙħ", + "ĠتÙħاÙħ ا", + "ëį ķ", + "Ġv Æ°á»Ŀ", + "ĠvÆ°á»Ŀ n", + "Ġd Ä±ÅŁÄ±", + "ãģĦ ãģ¡", + "Ġ׾ק ׳×ķת", + "ĠاÙĦع ÙĦاÙĤات", + "п Ñĥб", + "пÑĥб ли", + "Ø¥ ÙĬÙħ", + "Ø¥ÙĬÙħ اÙĨ", + "à¸Ńำ à¸Ļา", + "à¸Ńำà¸Ļา à¸Ī", + "åIJ« ãģ¾ãĤĮ", + "ãĤĭ ãģŁãĤģãģ«", + "ס ×Ĵ", + "ס×Ĵ ׳×ķף", + "تØŃ Ø¯ÙĬ", + "Ġaup rès", + "ĠاÙĦج Ùĩا", + "ĠاÙĦجÙĩا ز", + "Ġ×ŀ ת×Ĺת", + "ен нÑĥÑİ", + "Ġз им", + "à¸ģา à¹ģà¸Ł", + "Ġ×ijת ×ķר", + "Ġngh è", + "Ġnghè o", + "ĠÐĽ Ñİ", + "ĠÐĽÑİ Ð±", + "תק צ×Ļ×ij", + "×ŀ×¢ ש×Ķ", + "ĠاÙĦبÙĬ ت", + "צ ×Ļפ", + "ĠобÑıз ан", + "ĠM á»Ĺi", + "ĠТ ÑĥÑĢ", + "ĠÙĪب اÙĦت", + "ĠÙĪباÙĦت اÙĦÙĬ", + "Ġdéc ision", + "Ġب د", + "Ġبد أت", + "Ġc ục", + "Ġb ask", + "Ġbask ı", + "Ġhat ırl", + "Ġhatırl a", + "å°ı ãģķãģĦ", + "Ġgerçek ten", + "à¸ľ ัà¸ģ", + "åı¯èĥ½ ãģª", + "×ŀ×IJ ס", + "Ġcr ÃŃtica", + "ĠìĿĺ ìĽIJ", + "عÙĤ ÙĪد", + "×ĺ ׼׳", + "×ĺ׼׳ ×ķ׾×ķ×Ĵ×Ļ×Ķ", + "è¨Ģ ãģĪãģ°", + "ĠÙĤ ÙĨا", + "ĠÙĤÙĨا Ø©", + "ĠìĿ´ê²ĥ ìĿĢ", + "ت صر", + "à¸Ł ัà¸Ļ", + "ĠÑĢе ÑĨеп", + "ĠÑĢеÑĨеп ÑĤ", + "ĠبÙĨ Ùģس", + "ÑĢо ÑĪ", + "ĠмаÑĢ ÑĤа", + "Ġson ras", + "Ġsonras ı", + "×ķ×ij ש", + "ãĥª ãĤ¹ãĤ¯", + "ĠFranç ais", + "á» ļ", + "ê° Ķ", + "Ġ×Ķ×ijר ×Ļת", + "פ ×Ļצ", + "פ×Ļצ ×ķ×Ļ", + "ĠÙĦÙħا ذا", + "ĠÐļи ев", + "ĠÑģ мÑĭÑģл", + "ê¸Ī ìľµ", + "ãĤ·ãĥ£ ãĥ«", + "ãĥ© ãĤ¤ãĥĪ", + "ìĽ ĥ", + "×ŀ ×Ĺר", + "ãĨ į", + "Ġkullan ım", + "Ġ×IJצ׾ ׳×ķ", + "Ġt Ãłn", + "ãĥı ãĥ¼", + "ãģ¨ ãģ¨ãĤĤ", + "ãģ¨ãģ¨ãĤĤ ãģ«", + "ÑĢ ег", + "ÑĢег и", + "ÑĢеги он", + "ãģªãģı ãģªãĤĭ", + "Ġch ảy", + "Ġج ÙĩØ©", + "ÅĦsk iej", + "à¸Ńี à¹Ģม", + "à¸Ńีà¹Ģม ล", + "ãģį ãģ£ãģ¨", + "ĠìĺĪ ìĤ°", + "Ġkit abı", + "Ġedu cação", + "Ġbul uÅŁ", + "олог иÑı", + "Ġкон кÑĢ", + "ĠконкÑĢ еÑĤ", + "×Ĵ ×Ļר", + "ĠпÑĢед лаг", + "ĠпÑĢедлаг аеÑĤ", + "ĠY ên", + "Ġíķľ ë²Ī", + "Ġ×ŀ ר׼×ĸ×Ļ", + "à¹Ģà¸Ľà¸´à¸Ķ à¹Ģà¸ľà¸¢", + "ÑĤвеÑĢ д", + "ĠH á»ĩ", + "ĠÐĵ ÑĢ", + "à¸Ŀ à¹īา", + "×Ķ שק", + "×Ķשק ×¢×Ķ", + "Ġна Ñĥк", + "ìłIJ ìĿĦ", + "Ġн елÑĮ", + "ĠнелÑĮ з", + "ĠнелÑĮз Ñı", + "г ин", + "ĠB öl", + "ĠBöl ge", + "Ġв ла", + "Ġвла ÑģÑĤи", + "à¹Ģà¸Ļ à¹ĩ", + "à¹Ģà¸Ļà¹ĩ à¸ķ", + "ê³ ¨", + "Ġö ld", + "Ġöld ür", + "׼׳ ×¢", + "ĠاÙĦÙĩ ÙĬئة", + "ت ارÙĬØ®", + "ĠÐij ÑĢ", + "ĠÑģ мож", + "ĠÑģмож еÑĤе", + "ĠL úc", + "à¹Ħà¸Ľ à¸ĸึà¸ĩ", + "ĠBakan ı", + "Ġerklä rt", + "ĠÐIJ на", + "Ġsc ène", + "åķı ãģĦ", + "åķıãģĦ åIJĪãĤıãģĽ", + "ÙħÙĩ ÙĨد", + "ÙħÙĩÙĨد س", + "Ġн азвание", + "ив аниÑı", + "ãĤĴ å¤īãģĪ", + "ä»ĺãģį åIJĪ", + "ãĥij ãĤ½", + "ãĥijãĤ½ ãĤ³ãĥ³", + "æĺİ ãĤī", + "æĺİãĤī ãģĭ", + "à¹Ģà¸Ńà¸ģ สาร", + "à¹Ģà¸ģิà¸Ļ à¹Ħà¸Ľ", + "л еп", + "ãģĹãģŁ ãĤĤãģ®", + "ĠC âm", + "ĠCâm ara", + "ק×ķ׾ ׳×ķ×¢", + "Ġ×ij×Ĵ ×Ļף", + "Ġoc zy", + "Ġoczy wiÅĽcie", + "att ivitÃł", + "ãĥĵ ãĥ¥ãĥ¼", + "Ġeduc ación", + "Ä° YE", + "ê¹Į ìļĶ", + "ãĤ¨ ãĥªãĤ¢", + "н еÑģÑĤи", + "Ġm óg", + "Ġmóg ÅĤ", + "Ġק×ĺ ׳×Ļ×Ŀ", + "ĠPr ä", + "Ġ×ľ×¢ ×ij×ķר", + "بÙĨ Ùī", + "з ол", + "зол оÑĤ", + "Ġwn ÄĻtr", + "ĠwnÄĻtr z", + "Ġconstr ução", + "รัà¸ļ รà¸Ńà¸ĩ", + "س جÙĨ", + "Ġק ×ķ׳", + "ס ×Ļפ×ķר", + "ĠÙħ دÙī", + "رض Ùī", + "п лав", + "ï¼ ¥", + "Ġil a", + "Ġila ç", + "ãĤĭ ãģ¹ãģį", + "ĠÙħ ÙĪÙĤÙģ", + "à¸ģร ุ", + "à¸ģรุ à¸ĵา", + "chodzÄħ c", + "ĠÑĤÑĭ Ñģ", + "Ðķ вÑĢо", + "ĠÙĬ ØŃدث", + "ãĥ¡ ãĤ¤ãĥ³", + "ĠاÙĦص ØŃÙĬ", + "ĠÐĶ ан", + "دع اء", + "ãĤ´ ãĥ¼ãĥ«", + "ש ×ł×ª×Ļ", + "×©×ł×ª×Ļ ×Ļ×Ŀ", + "à¸Ķà¹īวย à¸ģัà¸Ļ", + "Ġol acaģı", + "Ġ×ij ×ŀ×Ĺ×Ļר", + "×Ķ ק", + "×Ķק ×ŀת", + "ãĥ¢ ãĥİ", + "ĠçalÄ±ÅŁ tı", + "Ġjó venes", + "ãģĦãģı ãĤī", + "ĠÙħ عدÙĦ", + "ĠC Å©ng", + "ĠSeg ún", + "Ġdönem de", + "Ġ׾ ×Ļ×ĵ×Ļ", + "ãģį ãģ¡", + "ãģįãģ¡ ãĤĵ", + "ãģįãģ¡ãĤĵ ãģ¨", + "Ùģر ÙĨس", + "ÙģرÙĨس ا", + "åIJij ãģį", + "Ġcamp aña", + "ĠÑģам оÑģÑĤоÑı", + "ĠÑģамоÑģÑĤоÑı ÑĤелÑĮно", + "á» Ģ", + "ÙĤ ÙĪا", + "س ÙĦاØŃ", + "à¸ģระ à¹ģ", + "à¸ģระà¹ģ ส", + "ĠполÑĮз Ñĥ", + "n qu", + "nqu ête", + "รà¹Īวม à¸ģัà¸ļ", + "ëĬIJ ëĥIJ", + "à¸Ĺีม à¸Ĭาà¸ķิ", + "Ġyıll ık", + "ìĬ ¬", + "ĠØ£ صØŃاب", + "ill é", + "Ġdó la", + "Ġdóla res", + "Ġк ож", + "Ġкож и", + "ล à¹īà¸Ń", + "à¹Ģรีย à¸ļร", + "à¹Ģรียà¸ļร à¹īà¸Ńย", + "à¹Ģà¸ŀ ิ", + "à¹Ģà¸ŀิ à¹Īà¸ĩ", + "ÑĢиÑĤоÑĢ и", + "Ġí ijľ", + "Ġíijľ íĺĦ", + "ĠпеÑĢ ев", + "ĠпеÑĢев од", + "פ×Ĵ ×Ļ×¢×Ķ", + "ĠdeÄŁerlendir me", + "Ùģ ائ", + "ĠвÑĭ год", + "ınız ı", + "×ķ׼ ×Ļ×Ĺ", + "ĠдоÑģÑĤ иг", + "Ġng Ãłn", + "æĢĿ ãģ£ãģŁ", + "ĠÐķ ÑģÑĤÑĮ", + "ĠاÙĦر غÙħ", + "ĠzwiÄħz ane", + "رب Ø·", + "à¸Ļ ึà¸ĩ", + "Ġ׾×Ĺ ×ķק", + "Ġszczeg óln", + "Ġszczególn ie", + "Ġبا ستخداÙħ", + "ĠfÃŃs ico", + "×¢ ס", + "עס ×ķק", + "سÙĦ ÙĪÙĥ", + "Ġا ØŃد", + "Ñĩ ÑijÑĤ", + "×ĸ׼ ×Ķ", + "Ġl á»ĩnh", + "ĠÙĪ ØŃت", + "ĠÙĪØŃت Ùī", + "à¸Ħวาม สามารà¸ĸ", + "à¸Ńยูà¹Ī à¹ģลà¹īว", + "à¸ģาร à¹Ģà¸Ķิà¸Ļà¸Ĺาà¸ĩ", + "تخ Ø°", + "צ×Ļ ×ķ×ĵ", + "ĠاÙĦØ£ س", + "ĠاÙĦأس ÙĩÙħ", + "Ġt á»ĩ", + "ãģ£ãģ¦ ãģĦãģ¦", + "สร ุ", + "สรุ à¸Ľ", + "Ġком ÑĦ", + "ĠкомÑĦ оÑĢÑĤ", + "ìĺ¤ ëĬĶ", + "ĠÑĢаз в", + "ĠÑĢазв ива", + "л анд", + "h änge", + "ĠبÙĨ سبة", + "à¹Ģà¸Ĥ ียว", + "עצ ×Ŀ", + "Ġ׾ ×ľ×Ľ×ª", + "Ñģо ÑĨиалÑĮн", + "Ġëĭ¤ìĿĮ ê³¼", + "Ġרש ×ķ×ŀ", + "×ŀר ×Ĺ×ij", + "س ÙĤØ·", + "Ġalan ı", + "ĠÄij á»ĩ", + "é£Łãģ¹ ãĤĭ", + "à¸Ķ ึà¸ĩ", + "Ġgegen über", + "ĠبÙĩ Ø°Ùĩ", + "à¸ĸืà¸Ń à¹Ģà¸Ľà¹ĩà¸Ļ", + "ëķ ħ", + "à¸Ħà¸Ļ à¹Ħà¸Ĺย", + "ãĤ¢ ãĤ¦", + "ãĤ¢ãĤ¦ ãĥĪ", + "ศ ัà¸ģ", + "ศัà¸ģ à¸Ķิ", + "ศัà¸ģà¸Ķิ à¹Į", + "ÙĤÙĪ اÙĨ", + "ÙĤÙĪاÙĨ ÙĬÙĨ", + "Ġhá»Ļ p", + "ãģªãģıãģª ãģ£ãģ¦", + "Ġ×IJ ×ŀ׳", + "Ġ×IJ×ŀ׳ ×Ŀ", + "à¹Ģà¸ķ ืà¸Ńà¸Ļ", + "ĠзавиÑģ им", + "ĠзавиÑģим оÑģÑĤи", + "ת ×Ļ×IJ", + "ת×Ļ×IJ ×ķר", + "å§ĭãĤģ ãģŁ", + "Ġng á»į", + "Ġngá»į t", + "íĴ į", + "ê³¼ ìŀ¥", + "Ġb ại", + "ãģ§ãģį ãģ¦", + "Ġcomeç ar", + "à¸Ľà¸£ าà¸ģ", + "à¸Ľà¸£à¸²à¸ģ à¸ı", + "Ġгод Ñĭ", + "м еÑģ", + "ĠاÙĦÙħست ÙĪÙī", + "ĠÑģам Ñĭе", + "л леÑĢ", + "ãģ£ãģ¦ãģĹãģ¾ ãģĦãģ¾ãģĻ", + "ãģ¨ãģ® ãģĵãģ¨", + "bi ó", + "à¸ģล à¹Īà¸Ńà¸ĩ", + "ĠاÙĦز ÙĪج", + "ãģ«è¡Į ãģ£ãģŁ", + "à¸Ħà¹Ī à¸Ńà¸Ļ", + "à¸Ħà¹Īà¸Ńà¸Ļ à¸Ĥà¹īาà¸ĩ", + "ĠbaÄŁ l", + "ĠbaÄŁl ant", + "ĠbaÄŁlant ı", + "確 ãģĭ", + "確ãģĭ ãģ«", + "ãĥľ ãĥ¼ãĥ«", + "çµĤ ãĤıãĤĬ", + "ש ×ŀר", + "à¸Ĺีà¹Ī สามารà¸ĸ", + "ÙĦ زÙħ", + "д аеÑĤÑģÑı", + "รัà¸ļ à¸Ľà¸£à¸°", + "รัà¸ļà¸Ľà¸£à¸° à¸Ĺาà¸Ļ", + "å¤ī ãĤıãĤĬ", + "ï¼ ¢", + "ĠìĺĪìĪĺ ëĭĺ", + "ãĤĪãģĨ ãģ¨", + "มัà¸ģ à¸Īะ", + "ĠH Æ°Æ¡ng", + "ÙĨ ÙģØ°", + "×ŀ×ĵ ×ĵ", + "ĠìĿ¸ ìłķ", + "Ñħод иÑĤÑĮ", + "ĠзавиÑģ иÑĤ", + "×ķ×ĵ ×Ļ×¢", + "ãģĵãģ¨ãģĮ ãģĤãĤĬãģ¾ãģĻ", + "ع راÙĤ", + "سط ØŃ", + "à¸ģำ à¹Ħร", + "ëĵ¤ ëıĦ", + "×Ļצ ×Ļר×Ķ", + "ãģĨ ãģĵãģ¨", + "ÙĦا ØŃÙĤ", + "ãģĦ ãĤĮãģ°", + "ĠиÑģполÑĮз ÑĥÑİÑĤ", + "ĠB ợi", + "Ġשק׾ ×Ļ×Ŀ", + "ÑĨи кл", + "ÐIJ Ðŀ", + "Ġ×ijש ׳×Ķ", + "ÙĨØ´ Ø·", + "Ġש ×Ļ׳×ķ×Ļ", + "Ġש×Ļ׳×ķ×Ļ ×Ļ×Ŀ", + "Ġpobl ación", + "ĠH Æ°ng", + "ระ ว", + "ระว ัà¸ĩ", + "رÙĬاض Ø©", + "ر صد", + "تÙĤ ÙĦÙĬ", + "تÙĤÙĦÙĬ د", + "Ġülk em", + "Ġülkem iz", + "à¸Ĭ ะ", + "ãĤ¯ãĥª ãĥ¼ãĥł", + "èģŀ ãģĦãģŁ", + "Ġwa ż", + "Ġważ ne", + "ê±° ëĵł", + "ê±°ëĵł ìļĶ", + "×ŀ×IJ ×ijק", + "×Ĺ×ĵ ש×ķת", + "ĠW roc", + "ĠWroc ÅĤaw", + "ĠKü ltür", + "s ist", + "sist ência", + "×¢×ĸר ×Ķ", + "Ġg Æ°Æ¡ng", + "รà¹īาà¸Ļ à¸Ħà¹īา", + "ĠÙĪØ£ ÙĪضØŃ", + "ánd ose", + "ãĤ· ãĥ¼ãĥ³", + "×IJ׳ ר×Ĵ", + "×IJ׳ר×Ĵ ×Ļ×Ķ", + "ãģªãģĦ ãģ§ãģĻ", + "Ġkh ủng", + "Ġ문 ìĦľ", + "Ġ×ij ×ĵ×ijר", + "×ĵ ×Ļ×ķ", + "×ĵ×Ļ×ķ ×ķ×Ĺ", + "Ġré gl", + "ÙħÙĪ اد", + "об оÑĢ", + "обоÑĢ оÑĤ", + "Ġ×Ķ ×ij׾", + "Ġ×Ķ×ij׾ ×ķ×Ĵ", + "ØŃ Ø§Ùħ", + "ĠاÙĦع اص", + "ĠاÙĦعاص ÙħØ©", + "пеÑĢ аÑĤоÑĢ", + "ت Ø®ÙĦ", + "تخÙĦ ص", + "ãģŁãģł ãģĹ", + "ت سÙħ", + "à¹Ĥรà¸ĩ à¸ŀ", + "à¹Ĥรà¸ĩà¸ŀ ยา", + "à¹Ĥรà¸ĩà¸ŀยา à¸ļาล", + "ĠY ük", + "ĠYük sek", + "Ġש ׳×Ļת", + "Ġש׳×Ļת ף", + "liÄŁ e", + "Ġפ ת", + "Ġפת ×ķ×Ĺ", + "Ġbe ÄŁ", + "ĠbeÄŁ en", + "Ġ×ŀ ×ķר", + "Ġ×ŀ×ķר ׼×ij", + "Ġرس اÙĦØ©", + "íĨµ ìĭł", + "Ġaval ia", + "Ġavalia ções", + "Ġman h", + "Ġmanh ã", + "Ġìķ ŀ", + "Ġìķŀ ìľ¼ë¡ľ", + "ÙĤ تر", + "ÙĤتر ØŃ", + "à¹Ģà¸ģ ืà¸Ń", + "à¹Ģà¸ģืà¸Ń à¸ļ", + "Ġpropos é", + "Ø£ Ùħا", + "Ø£Ùħا ÙĥÙĨ", + "ĠÐŀ Ðŀ", + "ĠÐŀÐŀ Ðŀ", + "ÙħÙĤ ار", + "ÙħÙĤار ÙĨØ©", + "ëĦ IJ", + "ãģĦãģŁãģł ãģı", + "ÙĤ ÙĬÙĦ", + "Ġна ÑĪиÑħ", + "ãĤ« ãĥĥãĥĹ", + "×Ĺ׾ ת", + "Ġëĭ¤ ë§Į", + "à¸Ĺัà¹Īว à¹Ĥลà¸ģ", + "ãĥį ãĤ¿", + "ØŃس اس", + "ãģ«ãģª ãĤĮ", + "ج ائ", + "جائ زة", + "é change", + "é conom", + "économ ie", + "Т Ðĺ", + "סת ׼׾", + "à¸Ĺัà¹īà¸ĩ สà¸Ńà¸ĩ", + "ĠاÙĦØ® اÙħ", + "ĠاÙĦخاÙħ س", + "ק ×ĺ×¢", + "au waż", + "à¸ľà¸¹à¹ī à¸Ĭาย", + "à¹ģà¸Ľà¸¥ à¸ģ", + "åIJĮæĻĤ ãģ«", + "зн аниÑı", + "ãģĦãģŁãģł ãģįãģ¾ãģĹãģŁ", + "Ġ×ŀ×ij ׾×Ļ", + "à¸Ĥà¸Ń à¹ĥหà¹ī", + "ĠاÙĦت ربÙĬØ©", + "Ġdécou vert", + "Ġżyc iu", + "apr ès", + "Ġy ab", + "Ġyab anc", + "Ġyabanc ı", + "ĠbaÅŁ layan", + "ìĹĪ ëįĺ", + "Ġhes abı", + "Ġë§Į ìķ½", + "ë§ Īëĭ¤", + "ĠTh ánh", + "ãĥ´ ãĤ¡", + "à¸Ľà¸£à¸±à¸ļ à¸Ľà¸£", + "à¸Ľà¸£à¸±à¸ļà¸Ľà¸£ ุà¸ĩ", + "ĠM ặc", + "à¹Ģหà¸ķุ à¸ľà¸¥", + "ĠÐij ез", + "Ġcapac itÃł", + "ÅĤe ÅĽ", + "ĠпÑĢе им", + "ĠпÑĢеим ÑĥÑīеÑģÑĤв", + "ĠÅļ wiÄĻt", + "Ġpubli é", + "×ŀ×¢ צ×ij", + "Ùħشار Ùĥات", + "à¸łà¸² ษ", + "à¸łà¸²à¸© ี", + "Ġdeux ième", + "ĠÙħØŃ Ø§Ùģظ", + "ĠÙħØŃاÙģظ Ø©", + "ĠSch ön", + "ï½ ¤", + "Ġ×Ķ ×ij×¢", + "Ġ×Ķ×ij×¢ ×Ļ×Ķ", + "ĠÙĪاÙĦ ÙĦÙĩ", + "è¨Ģ ãģ£ãģŁ", + "à¸ķ à¹īาà¸Ļ", + "วร รà¸ĵ", + "à¸Ĺิ ศ", + "ĠbaÅŁ ına", + "Ġmog ÄĻ", + "ש ×Ļפ×ķר", + "ĠÙĪ عد", + "ĠÙĪعد Ùħ", + "Ġhistó rico", + "Ġk ısı", + "ĠìĿ´ ê²Į", + "ĠPol ÃŃtica", + "ĠÑģиÑĤÑĥ аÑĨии", + "ĠkoÅĦ ca", + "×ij×ĵ ×Ļק×Ķ", + "ĠاÙĦسÙĬ ارات", + "ãģªãĤī ãģ°", + "ãĤµ ãĥ©", + "ãĤĭãģĵãģ¨ãģĮãģ§ãģį ãĤĭ", + "Ġdecis ão", + "×ķ ×ķ×ĵ", + "lä ss", + "läss ig", + "Ġ׾ ×Ļשר×IJ׾", + "ĠÙĬ أتÙĬ", + "ר ×ķ×ĸ", + "ö ÄŁ", + "Ã¶ÄŁ ret", + "Ã¶ÄŁret im", + "Ġд ек", + "Ġдек аб", + "Ġдекаб ÑĢÑı", + "Ġש ×Ĺ×ķר", + "ãģ¦ãģıãĤĮ ãģŁ", + "عب ارة", + "Ġélect rique", + "ĠاÙĦتÙĨ ÙħÙĬØ©", + "جر Ùī", + "ĠìĪĺ íĸī", + "à¸Ĺ ู", + "ĠÑĢе алÑĮно", + "Ñģп оÑģоб", + "à¸Ħล à¹īาย", + "Ġس عÙĪد", + "ön ü", + "ĠÙģ ÙħÙĨ", + "تÙĥ ÙĪ", + "تÙĥÙĪ ÙĬÙĨ", + "ĠкаÑĩ еÑģÑĤво", + "ĠконÑĤ ак", + "ĠконÑĤак ÑĤ", + "Ġsöz leÅŁme", + "à¸Ń à¹īาà¸ĩ", + "Ġت ÙĪÙģ", + "ĠتÙĪÙģ ÙĬر", + "×Ķ×ĸ ×ĵ", + "×Ķ×ĸ×ĵ ×ŀ׳×ķת", + "ĠØ·ÙĪÙĬÙĦ Ø©", + "Ġtér mino", + "Ġ×IJ ×Ļפ×Ķ", + "ãĥĵ ãĥ«", + "ส à¹Ĥม", + "สà¹Ĥม สร", + "ĠاÙĦ اث", + "ĠاÙĦاث ÙĨÙĬÙĨ", + "ев иÑĩ", + "Ġopin ión", + "à¸Ľ วà¸Ķ", + "åı¤ ãģĦ", + "ร à¹Īา", + "ĠB iaÅĤ", + "ĠÑģÑĤ ал", + "ĠÑģÑĤал о", + "ó logo", + "ĠìķĦ ëĭĪëĭ¤", + "Ġ×IJ ×Ļת", + "Ġ×IJ×Ļת ×ķ", + "à¹Ģหà¹ĩà¸Ļ วà¹Īา", + "à¸ļ ารà¹Į", + "çĦ ¼", + "çĦ¼ ãģį", + "ĠìĿ´ìļ© ìŀIJ", + "ĠнекоÑĤоÑĢ Ñĭе", + "ks z", + "ksz taÅĤ", + "ksztaÅĤ c", + "ãĤŃãĥ£ ãĥĥãĤ·", + "ãĤŃãĥ£ãĥĥãĤ· ãĥ³ãĤ°", + "Ġro ÅĽ", + "ĠroÅĽ lin", + "ÑĢаж а", + "×ij׳×Ļ ×Ļ×Ķ", + "à¸Ľà¸£ สิ", + "à¸Ľà¸£à¸ªà¸´ à¸ķ", + "Ġgörd ü", + "×ŀ׳×Ķ ×Ļ×Ĵ", + "å¤īãĤı ãģ£ãģ¦", + "Ġ×IJ ×Ķ", + "Ġ×IJ×Ķ ×ijת×Ļ", + "à¹Ģร à¹Īà¸ĩ", + "Ġön ünde", + "Ġê·¸ ëĥ¥", + "пол иÑĤ", + "полиÑĤ иÑĩеÑģк", + "ãĥ¡ ãĥĩãĤ£", + "ãĥ¡ãĥĩãĤ£ ãĤ¢", + "ĠDet ay", + "ĠDetay lı", + "ĠاÙĦصÙģ ØŃØ©", + "à¸ģาร à¹Ģà¸ĩิà¸Ļ", + "Ġìµľ ê·¼", + "׼ ש׾", + "ï¼ ©", + "вÑĪ его", + "íķĺ ìĭ¤", + "ĠÐŃ ÑĤ", + "ĠÐŃÑĤ оÑĤ", + "ส ื", + "สื à¸ļ", + "Ġng ừng", + "ĠдокÑĥменÑĤ ов", + "дав аÑĤÑĮ", + "ĠاÙĦشخص ÙĬØ©", + "Ġצ ×¢×Ļר", + "در Ùĥ", + "س ØŃب", + "à¹Ħมà¹Ī à¸Ħà¹Īà¸Ńย", + "Ġ×Ķ×ŀק ×ķ×ŀ×Ļ", + "สัà¹Īà¸ĩ à¸ĭืà¹īà¸Ń", + "Ġê·¸ê²ĥ ìĿĦ", + "ãģĤãĤĭ ãģĦ", + "ãģĤãĤĭãģĦ ãģ¯", + "×IJ×ķ×ĺ ×ķ×ij", + "×IJ×ķ×ĺ×ķ×ij ×ķס", + "к ÑĨион", + "ĠÐľ ожно", + "ãģı ãģł", + "ãģıãģł ãģķ", + "ĠинÑĦоÑĢм аÑĨиÑı", + "ï» Ł", + "Ġìŀij ìĹħ", + "Ġ×Ļ ×ķסף", + "Ø¥ دارة", + "ĠاÙĦØŃ Ø§Ø¬", + "×ł×¡ ×Ļ×¢×Ķ", + "из аÑĨиÑı", + "×IJ׾ ×ij", + "×IJ׾×ij ×ķ×Ŀ", + "п ед", + "Ġק×ĺ ׳×Ķ", + "ĠÙĨÙģس Ùĩا", + "ĠMinist ério", + "Ġп ен", + "Ġпен Ñģи", + "ãĥIJ ãĥ©ãĥ³ãĤ¹", + "Ġ×Ķת ×ķר×Ķ", + "Ġt ạm", + "ĠìĹŃ ìĭľ", + "ï½ ¡", + "Ġth á»±", + "Ġ ısı", + "ì» ¨", + "ãģĹãģ£ãģĭãĤĬ ãģ¨", + "Ġx Æ°a", + "Ġc ặp", + "×Ĺ ×Ļ×ij×ķר", + "วัà¸Ĵà¸Ļ à¸ĺรรม", + "st är", + "stär ke", + "ĠÑģам Ñĭй", + "p isa", + "pisa Äĩ", + "ĠoluÅŁ an", + "ĠاÙĦØ¥ ÙħاÙħ", + "ĠcÄĥ ng", + "Ġgü nl", + "Ġgünl ük", + "Ġ׳ש ×IJר", + "Ġkhi á»ĥn", + "ç¶ļ ãģijãĤĭ", + "stit ución", + "Ġcapac ité", + "Ġj aki", + "Ġjaki ÅĽ", + "вÑĪ иÑģ", + "вÑĪиÑģ ÑĮ", + "פע×ķ׾ ×ķת", + "ĠØŃ ÙĬات", + "ĠØŃÙĬات Ùĩ", + "Ġник огда", + "ÐĽ Ь", + "Ġ×Ķ×¢ ×ķ×ij", + "Ġ×Ķ×¢×ķ×ij ×ĵ×Ķ", + "Ġch Ãło", + "หลาย à¹Ĩ", + "ĠÑı н", + "ĠÑıн ваÑĢ", + "ĠÑıнваÑĢ Ñı", + "à¸Īำà¹Ģà¸Ľà¹ĩà¸Ļ à¸ķà¹īà¸Ńà¸ĩ", + "Ġhö her", + "ãģķãĤĮãģ¦ ãģĦãģŁ", + "สà¸ĩ สั", + "สà¸ĩสั ย", + "ĠاÙĦ اس", + "ĠاÙĦاس ÙĦاÙħ", + "ĠاÙĦØ´ Ùħس", + "สà¸ĸาà¸Ļ ี", + "ãĤ¯ãĥ© ãĤ¹", + "à¸ŀร ร", + "à¸ŀรร à¸Ħ", + "p õ", + "põ e", + "Ġpor ém", + "à¸Ľà¸£à¸° สà¸ĩ", + "à¸Ľà¸£à¸°à¸ªà¸ĩ à¸Ħà¹Į", + "powied zie", + "powiedzie Äĩ", + "Ġмог Ñĥ", + "Ġж ел", + "Ġжел ез", + "ĠاÙĦØ« ÙĤ", + "ĠاÙĦØ«ÙĤ اÙģÙĬ", + "ĠпÑĢав ило", + "Ġgdy ż", + "פש ×ķ×ĺ", + "ÑĢабоÑĤ ка", + "ĠÙĥ رة", + "Ø´ دد", + "Ùħار Ùĥ", + "Ùħ ÙĥØ©", + "Ġпод пиÑģ", + "×ĺ×ķ ×ķ×Ĺ", + "ĠÅĽ c", + "ĠÅĽc ian", + "Ġر جاÙĦ", + "Ġ×ª×ľ ×ķ×Ļ", + "и ÑĪ", + "иÑĪ ÑĮ", + "Ġmé dec", + "Ġmédec in", + "ëįĶ ëĿ¼ëıĦ", + "ĠÑĤеб Ñı", + "Ġ׾×Ķ ×ķס×Ļ×£", + "ãģĬ 話", + "Ġà¹ģà¸ķà¹Ī à¸ģà¹ĩ", + "د اÙģ", + "داÙģ ع", + "ĠC ùng", + "ãĥ»ãĥ» ãĥ»ãĥ»", + "ê¶ ģ", + "Ġdeber ÃŃa", + "หà¸Ļà¹Īวย à¸ĩาà¸Ļ", + "Ġva ÌĢ", + "Ġעצ ×ŀ", + "Ġעצ×ŀ ×Ŀ", + "à¹Ģà¸Ĭืà¹Īà¸Ń วà¹Īา", + "שק ×¢", + "Ġ×Ķ ׼×ķ׾", + "Ġ×Ķ׼×ķ׾ ׾", + "ни бÑĥд", + "нибÑĥд ÑĮ", + "ĠëĦĪ íĿ¬", + "Ġоб ÑĢаÑī", + "ĠобÑĢаÑī а", + "Ġ×¢×ij×ķ×ĵ ת", + "ĠاÙĦÙħÙĨت خب", + "ıy ord", + "ıyord u", + "ÙĪ Ø°", + "×Ĺש ×Ļ×ij×ķת", + "Ġ×Ķ×¢ ×Ļק", + "Ġ×Ķ×¢×Ļק ר×Ļ", + "ì¢ Į", + "ยุ à¹Ĥร", + "ยุà¹Ĥร à¸Ľ", + "Ġа пÑĢ", + "ĠапÑĢ елÑı", + "sz ed", + "szed ÅĤ", + "д он", + "à¹Ģà¸ķิ à¸ļ", + "à¹Ģà¸ķิà¸ļ à¹Ĥà¸ķ", + "кол о", + "Ġkażde j", + "å¸ °", + "帰 ãĤĬ", + "Ġмил ли", + "Ġмилли он", + "ç¾İåij³ ãģĹãģĦ", + "ت ÙĤار", + "تÙĤار ÙĬر", + "ĠìĿ´ 루", + "ĠìĿ´ë£¨ ìĸ´", + "Ġsprzeda ż", + "×Ķ ×ķצ×IJ×ķת", + "ãĤ¢ãĤ¯ ãĤ»", + "ãĤ¢ãĤ¯ãĤ» ãĤ¹", + "ר ×ķ×¥", + "ĠгоÑģÑĥдаÑĢÑģÑĤв енн", + "Ø£ ØŃÙĥ", + "Ø£ØŃÙĥ اÙħ", + "ĠoluÅŁ u", + "ĠA ç", + "ĠAç ık", + "ãĤ¸ ãĥ¼", + "ç´ł æĻ´", + "ç´łæĻ´ ãĤīãģĹãģĦ", + "Ġ×ijש×ij ×ķ×¢", + "ب Ø°", + "بذ ÙĦ", + "สา à¹Ģหà¸ķุ", + "Ġpoz osta", + "Ġpozosta ÅĤ", + "ØŃر Ùħ", + "Ġimport ância", + "leÅŁtir me", + "Ġд ÑĢев", + "Ġmó vil", + "ĠA ynı", + "Ġна лог", + "Ġналог ов", + "Ġ×Ĺ ×Ļפ×Ķ", + "ĠÑĦоÑĢм Ñĥ", + "à¸Ĺà¸Ķ สà¸Ńà¸ļ", + "ĠksiÄħż ki", + "Ġma ÅĤe", + "Ùħس Ø£ÙĦ", + "ÙħسأÙĦ Ø©", + "ï¼¾ ï¼¾", + "ç ãeste", + "év iter", + "Ġкон ÑģÑĤÑĢÑĥк", + "ĠконÑģÑĤÑĢÑĥк ÑĨи", + "ï¾ ŀ", + "Ġת×ķ׼ ׳", + "ãĤ¹ãĥĪ ãĥ¬ãĤ¹", + "ĠاÙĦاÙĤتصاد ÙĬ", + "×ŀ×ĵ ×Ļ", + "Ġw ÅĤad", + "ĠwÅĤad z", + "Ø® ÙĪÙģ", + "ĠмаÑĤеÑĢиал ов", + "ãģ¨ãģ£ãģ¦ ãĤĤ", + "Ġznaj du", + "Ġznajdu jÄħ", + "Ùģ ئة", + "ãģ©ãģ® ãĤĪãģĨãģª", + "æĬij ãģĪ", + "׳ ×Ĺ׾", + "Ġdü ny", + "Ġdüny an", + "Ġdünyan ın", + "гÑĢ ани", + "гÑĢани Ñĩ", + "Ġ×Ķש׾ ×Ļש×Ļ", + "Ġ×Ķ×IJ ש", + "åıĬ ãģ³", + "ìĭŃ ìĭľ", + "ìĭŃìĭľ ìĺ¤", + "Ġдол л", + "Ġдолл аÑĢ", + "Ġпов ÑĤоÑĢ", + "Ġ×Ĺ ×Ļ׳×Ŀ", + "ת פת×Ĺ", + "Ñĥв ели", + "Ñĥвели Ñĩен", + "ãĤ« ãĥª", + "raw id", + "rawid ÅĤow", + "×ķ ×ķ׾", + "ãĥŁ ãĥ¥", + "ì½ ĺ", + "ĠBy ÅĤ", + "Ðľ ÐIJ", + "ع ÙIJ", + "ĠÑģовеÑĢ ÑĪ", + "ĠÑģовеÑĢÑĪ енно", + "Ġм ой", + "Ġ×ķ׾×IJ ×Ĺר", + "æħ £", + "æħ£ ãĤĮ", + "ØŃ Ø§Ùģظ", + "Ġ무 ë£Į", + "à¸Ħà¸ĵะ à¸ģรรม", + "à¸Ħà¸ĵะà¸ģรรม à¸ģาร", + "Ġìĸ´ ëĶĶ", + "Ġdif eren", + "Ġdiferen ça", + "ĠاÙĦØ£ ساس", + "ĠاÙĦأساس ÙĬØ©", + "Ġ׾×IJ×Ĺר ×ķ׳×Ķ", + "ê· ł", + "Ġ×Ķש׳×Ļ ×Ļ×Ķ", + "ìľĦìĽIJ ìŀ¥", + "ลุ à¸ģ", + "ç iler", + "Ġ×Ķ×IJ ׾×ķ", + "èģŀ ãģı", + "Ġ×ķ×IJ פ×Ļ׾×ķ", + "ĠÑĢе ализ", + "ĠÑĢеализ аÑĨи", + "ระยะ à¹Ģวลา", + "Ġجدا Ùĭ", + "تب اع", + "Ġveh ÃŃculo", + "Ġдол г", + "à¸Ľà¸£à¸´ มาà¸ĵ", + "ì¦ IJ", + "Ġ׾ ×ŀק×ķ×Ŀ", + "ĠìĤ¬ ì§Ħ", + "à¸Ĭ à¹īา", + "Ġ×ŀ×¢ ×ķ׾×Ķ", + "Ġgö rm", + "Ġgörm ek", + "ĠÙĪÙĩ Ø°Ùĩ", + "пеÑĢ в", + "пеÑĢв ÑĭÑħ", + "ê·¸ ëŀĺ", + "ĠاÙĦبر ÙĬØ·", + "ĠاÙĦبرÙĬØ· اÙĨÙĬ", + "ĠиÑİ Ð½Ñı", + "ĠÐĵ оÑĢ", + "Ġ׾ ש׾×Ŀ", + "ÐIJ ÐĿ", + "Ġназ наÑĩен", + "о оÑĢ", + "ооÑĢ Ñĥж", + "Ġöz elli", + "Ġözelli ÄŁi", + "Ġни же", + "ç¶ļ ãģijãģ¦", + "Ġа ÑĢенд", + "Ġkat ılı", + "Ġkatılı m", + "ĠØ¥ Ø·ÙĦاÙĤ", + "ĠÙĪØ¥ ذا", + "Ġок ÑĤÑı", + "ĠокÑĤÑı бÑĢÑı", + "à¹Ĥà¸ķ à¹", + "à¹Ĥà¸ķ๠Ĭ", + "à¹Ĥà¸ķà¹Ĭ ะ", + "Ġolduk ları", + "Ùħ ÙĪÙĤع", + "ëĤ ©", + "ãģ¨æĢĿ ãģ£ãģ¦ãģĦãĤĭ", + "Ġש ×Ļ׼×ķ׾", + "วา à¸Ķ", + "س ÙĬÙĦ", + "à¸Ĥ วั", + "à¸Ĥวั à¸į", + "تØŃ ÙĥÙħ", + "ì ĤŃ", + "Ġconna ît", + "׳ פת×Ĺ", + "Ġch ặ", + "Ġchặ n", + "ĠÙħ ØŃÙħ", + "ĠÙħØŃÙħ ÙĪد", + "ãģ ´", + "ĠпÑĢодÑĥк ÑĨии", + "зд ÑĢав", + "ãģĶ è¦", + "ãģĶè¦ §", + "×IJ×ij ×IJ", + "Ġvé ritable", + "ĠØ· ÙģÙĦ", + "ãĥĪãĥ© ãĥĸãĥ«", + "ê³ ¡", + "Ġת ×ŀ×ķ׳×Ķ", + "Ġki ên", + "ĠÙĤ ادر", + "Ø¥ÙĤ ÙĦÙĬÙħ", + "ĠпÑĢед пÑĢи", + "ĠпÑĢедпÑĢи ÑıÑĤиÑı", + "Ġb Äĥng", + "Ġay ında", + "Ġg ấp", + "еÑħ ал", + "Ġgi Ãłnh", + "Ġд ав", + "Ġдав но", + "ìĺĢ ëĭ¤", + "à¸Ļัà¸ģ à¹Ģà¸ķ", + "à¸Ļัà¸ģà¹Ģà¸ķ ะ", + "Ùħست شار", + "ست راتÙĬج", + "ستراتÙĬج ÙĬ", + "رÙħ ز", + "Ġt Ä©nh", + "ë¡ Ń", + "ĠÑĩ еÑĤ", + "ĠÑĩеÑĤ Ñĭ", + "ĠÑĩеÑĤÑĭ ÑĢе", + "ĠEnt ão", + "Ġص غ", + "Ġصغ ÙĬرة", + "×ij×Ļ×ĺ ×ķ׾", + "خط ÙĪØ·", + "ĠÑĢазвиÑĤ ие", + "Ġamacı yla", + "à¸Ĺี วี", + "Ġо ÑģÑĤ", + "ĠоÑģÑĤ алÑĮн", + "ש×ķ׾׊ף", + "Ġ׼ ׳×Ļס", + "Ġ׼׳×Ļס ×Ķ", + "Ġd áºŃy", + "ĠyaÅŁ ayan", + "Ġ×ŀ×Ķ ×ķ×ķ×Ķ", + "ĠÑĥ Ñģи", + "ĠÑĥÑģи ли", + "×ŀ פ×Ļ", + "ĠпÑĢовед ениÑı", + "Ġر ب", + "Ġرب Ùħا", + "ĠاÙĦØ£ ÙĪسط", + "Ġìľł ì§Ģ", + "Ġprac ownik", + "Ġpracownik ów", + "×ŀס ×ķרת", + "ÙĤار ب", + "à¸Ħวาม รูà¹īสึà¸ģ", + "à¹ģหล ะ", + "ĠاÙĦÙĨ ÙĤد", + "Ġ×IJ׾ פ×Ļ", + "Ùħس ئ", + "Ùħسئ ÙĪÙĦ", + "ев ÑĭÑħ", + "клÑİÑĩ ениÑı", + "×ij ×Ļ׳", + "×ij×Ļ׳ ×Ļ×Ķ×Ŀ", + "ש ×ķ×IJ×Ķ", + "ĠÅŁ ark", + "ĠÅŁark ı", + "Ġsü rec", + "Ġsürec in", + "à¹Ģà¸Ħร à¸Ķ", + "à¹Ģà¸Ħรà¸Ķ ิà¸ķ", + "ãĥIJ ãĥ¬", + "ĠØ´ Ø£ÙĨ", + "à¹Ģà¸Ńา à¹Ħวà¹ī", + "niÄĻ cie", + "רצ ×Ĺ", + "ĠaÅŁ ama", + "׳ פ×Ĵ×¢", + "Ġth á»Ŀ", + "Ġkhu ẩn", + "diÄŁ inde", + "ÑıÑī иÑħ", + "ãĥĺ ãĥ«", + "Ġüber h", + "Ġüberh aupt", + "ĠÑĤÑĢеб ова", + "ĠdÅĤ ugi", + "×ĺ ×Ļף", + "à¸Ĥà¸Ļาà¸Ķ à¹ĥหà¸įà¹Ī", + "ĠاÙĦØ£ Ùĩ", + "ĠاÙĦØ£Ùĩ ÙĦÙĬ", + "ĠMü d", + "ĠMüd ürü", + "Ġ×Ļ×Ķ ×ķ×ĵ×Ķ", + "Ñĭв аеÑĤÑģÑı", + "س اط", + "×Ķת ׳×Ķ×Ĵ", + "×Ķ×ª×ł×Ķ×Ĵ ×ķת", + "à¸ģาร à¸ľà¸¥à¸´à¸ķ", + "íĴ Ģ", + "สà¸ĸาà¸Ļ à¸ģารà¸ĵà¹Į", + "Ġо ÑĦ", + "ĠоÑĦ иÑģ", + "ĠÙĦ عبة", + "Ġstron ÄĻ", + "Ġר×IJ ×ķ×Ļ", + "×Ĺ ×ij׾", + "ĠÑĢÑĭ н", + "ĠÑĢÑĭн ке", + "Ġ׾×ŀ×¢ ף", + "اس ÙĦ", + "ห ัà¸Ļ", + "Ġ×IJ ×Ĺ×Ļ", + "ĠпÑĢод ол", + "ê°Ģ ìŀħ", + "Ġ×ijר ×Ĺ", + "Ġ×ijר×Ĺ ×ij×Ļ", + "дж еÑĢ", + "Ġ׾ ×Ĺ׾", + "Ġ׾×Ĺ׾ ×ķ×ĺ", + "Ġ׾×Ĺ׾×ķ×ĺ ×Ļף", + "ศาส à¸Ļา", + "ãĤ¢ãĤ¤ ãĥĨ", + "ãĤ¢ãĤ¤ãĥĨ ãĥł", + "Ġפר ×ķפ", + "جز اء", + "ล à¸Ńย", + "Ġc iaÅĤa", + "Ġgi ết", + "ĠзнаÑĩ иÑĤелÑĮно", + "Ġolmad ıģ", + "Ġolmadıģ ını", + "н д", + "нд екÑģ", + "تأ Ùĥد", + "Ġìĸ ¸", + "Ġìĸ¸ ìłľ", + "ay dın", + "ãĥī ãĥ¬ãĤ¹", + "Ġs ắt", + "Ġíĺ¸ íħĶ", + "Ġë¶ ģ", + "Ġë¶ģ íķľ", + "ãĥij ãĤ¤", + "Ġ×ŀש×Ĺק ×Ļ", + "à¸Ħà¸Ļ à¸Ńืà¹Īà¸Ļ", + "Ġиз гоÑĤов", + "ĠизгоÑĤов лен", + "à¹Ģà¸ģีย ร", + "à¹Ģà¸ģียร à¸ķิ", + "תק שר", + "ĠÑĢаÑģ ÑĩеÑĤ", + "ส à¹Ģà¸ķ", + "Ġl änger", + "ĠiÅŁ let", + "ĠiÅŁlet me", + "Ġع ÙĦÙĬÙĨ", + "ĠعÙĦÙĬÙĨ ا", + "é lection", + "ĠاÙĦغ ربÙĬØ©", + "íĭ Ģ", + "ãĤĤãĤī ãģĪ", + "Ġкни ги", + "Ø£ سÙħ", + "أسÙħ اء", + "Ġth á»ı", + "Ġthá»ı a", + "หà¸Ļ ู", + "Ġ×ł×¢ ש×Ķ", + "à¸łà¸²à¸¢ à¹ĥà¸ķà¹ī", + "à¸ŀื à¸Ĭ", + "رÙĬ Ø·", + "Ùģ ÙĪض", + "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĹãģŁ", + "ש ×ĵ×Ķ", + "Ġng á»±c", + "ĠÑģеÑĢ ÑĮ", + "ĠÑģеÑĢÑĮ езн", + "T ôi", + "Ġfiyat ları", + "ĠвÑģ Ñİ", + "ĠC ódigo", + "Ġ×Ķש ×IJ", + "Ġ×Ķש×IJ ׾×Ķ", + "ĠP ública", + "Ø¥ Ø®", + "إخ ÙĪاÙĨ", + "ĠзаÑıв ил", + "ãĥ¦ ãĥ¼", + "ר×IJ ×Ļת", + "vol ución", + "Ġsz ko", + "Ġszko ÅĤy", + "جرÙĬ دة", + "Ġpens é", + "ìī ¬", + "ĠBüyük ÅŁehir", + "ĠØ£Ùħ رÙĬ", + "ĠØ£ÙħرÙĬ ÙĥÙĬ", + "à¸Ļัà¸ģ ศึà¸ģษา", + "Ġtod av", + "Ġtodav ÃŃa", + "ĠС ан", + "ĠСан кÑĤ", + "íķĺ ìŀIJ", + "ØŃÙĪ اÙĦ", + "׼ ×ķשר", + "à¹Ģลย à¸Ħรัà¸ļ", + "Ġal gu", + "Ġalgu ém", + "Ùģ ز", + "Ġçek il", + "Ġ×ĵ ר׼×Ļ×Ŀ", + "ãĥIJ ãĥ©", + "à¸ģà¹ĩ สามารà¸ĸ", + "สà¹Īวà¸Ļ ลà¸Ķ", + "íı °", + "ĠP úb", + "ĠPúb lico", + "à¹ģà¸Ļว à¸Ĺาà¸ĩ", + "×IJת ×Ĵר", + "Ø´ اش", + "شاش Ø©", + "ci ÅĽni", + "ĠÃľ rün", + "ÙĦÙĪ ØŃ", + "ĠاÙĦ بÙĨ", + "ĠاÙĦبÙĨ Ùĥ", + "ì¡° ì¹ĺ", + "Ġorganiz ación", + "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĻ", + "s ätze", + "ĠÑģем ей", + "ÙĤ صد", + "ÑģÑĤв еннÑĭе", + "Ġpréc éd", + "Ġprécéd ent", + "à¸ģรุà¸ĩà¹Ģà¸Ĺà¸ŀ ฯ", + "ãģ¨è¨Ģ ãģĦ", + "×ij׳×Ļ ×Ļף", + "ĠØŃ ÙĪ", + "ĠØŃÙĪ اÙĦÙĬ", + "סק ס", + "ĠsaÄŁlam ak", + "Ġ׾ צ×Ļ×Ļף", + "ק×ĵ ש", + "Ġ×Ķ×ŀ ×¢×¨×Ľ×ª", + "Ġ׾×Ķ ×¢×ij×Ļר", + "Ġg ünd", + "Ġgünd em", + "ĠнаÑĪ его", + "à¹ĥà¸Ļ à¸ŀืà¹īà¸Ļà¸Ĺีà¹Ī", + "à¹Ģà¸Ħร ืà¸Ń", + "à¹Ģà¸Ħรืà¸Ń à¸Ĥ", + "à¹Ģà¸Ħรืà¸Ńà¸Ĥ à¹Īาย", + "ظ اÙĩرة", + "ÙħÙĨ ظÙħ", + "ÙħÙĨظÙħ ات", + "Ùħت از", + "追 ãģĦ", + "dı kt", + "dıkt an", + "ĠëįĶ ìļ±", + "ĠÐĿ апÑĢимеÑĢ", + "tw ór", + "×ŀ×ķ×¢ צ×Ķ", + "Ùĥ ÙĪÙĥ", + "Ð ©", + "×ŀ×ĺ פ׾", + "ó lica", + "訪 ãĤĮ", + "ĠëĮĢ ë¶Ģ", + "ĠëĮĢë¶Ģ ë¶Ħ", + "ãĤ¯ãĥª ãĥĥãĤ¯", + "ãĤĴ éģ¸", + "ãĤĴéģ¸ ãģ¶", + "Ġpow sta", + "Ġpowsta ÅĤ", + "Ġraz ón", + "×ij ×ķ×Ĺר", + "ĠÑģообÑī ил", + "Ġק ×ij×ķ×¢", + "r êt", + "à¸Ķี à¸Ĥึà¹īà¸Ļ", + "×ŀס ×¢×ĵ", + "×ŀסע×ĵ ×ķת", + "ĠÃĸ sterreich", + "Ġ׳ ×Ĺש×ij", + "Ùħباد رة", + "ì´ ī", + "×Ĵ ׳×ĺ×Ļ", + "ä¿¡ ãģĺ", + "du ÄŁ", + "duÄŁ unu", + "Ġph ú", + "ĠاÙĦØ£ Ø®ÙĬر", + "Ġت عتبر", + "landır ıl", + "ãģ¨ãģ¯ ãģĦ", + "ãģ¨ãģ¯ãģĦ ãģĪ", + "ĠاÙĦ Ø·ÙĦ", + "ĠاÙĦØ·ÙĦ اب", + "ĠN º", + "éģ¿ ãģij", + "اÙĦ Ùħع", + "اÙĦÙħع رÙĪÙģ", + "ส à¸łà¸²", + "éĽ¢ ãĤĮ", + "ĠпомоÑī ÑĮ", + "Ġзна еÑĤ", + "ãĥĹãĥ¬ ãĤ¼", + "ãĥĹãĥ¬ãĤ¼ ãĥ³ãĥĪ", + "Ġsup érieur", + "Ġש׾ ×Ļש×Ļ", + "ĠاÙĦÙĨ ÙĪع", + "ãĤĵãģ§ãģĻ ãģŃ", + "à¸Ńà¸ļ รม", + "Ġgi á»įng", + "Ġwzgl ÄĻd", + "ĠاÙĦÙģ ÙĤر", + "è rent", + "Ġ×ŀ×IJ ×Ĺ", + "Ġ×ŀ×IJ×Ĺ ×ķר×Ļ", + "×Ĵ ×Ĵ", + "×Ļ ×Ļ×ij", + "ÙħÙĦ اب", + "ÙħÙĦاب س", + "Ġhük ü", + "Ġhükü met", + "Ġ×ŀ×Ĵ ×Ļ×ij", + "ĠÐŀ Ñĩ", + "ĠÐŀÑĩ енÑĮ", + "æĹ© ãģĦ", + "Ġconstr ucción", + "Ġth ượng", + "ï¼ ĭ", + "Ġcor ação", + "à¹Ģหล à¹ĩà¸ģ", + "ĠBaÅŁ b", + "ĠBaÅŁb akan", + "éĢ£ ãĤĮ", + "ãģĻãĤĭ ãģĵãģ¨ãģĮãģ§ãģįãģ¾ãģĻ", + "ĠÙĤ اÙħت", + "Ġا Ùĥثر", + "Ùģاع ÙĦ", + "ĠÑĦ оÑĢ", + "ĠÑĦоÑĢ Ñĥм", + "غ Ø°ÙĬ", + "ĠiÅŁ le", + "ĠiÅŁle ml", + "ĠiÅŁleml eri", + "ĠìĤ¬ëŀĮ ìĿĢ", + "Ġìŀij ìĦ±", + "Ġë§Ī 볨", + "Ùħ جÙĦس", + "หม ู", + "д в", + "дв иг", + "двиг а", + "à¹Ģสีย à¸Ĭีวิà¸ķ", + "×Ķת פת×Ĺ", + "×Ķתפת×Ĺ ×ķת", + "ĠмеÑĤ ÑĢо", + "ĠÑģ енÑĤ", + "ĠÑģенÑĤ Ñı", + "ĠÑģенÑĤÑı бÑĢÑı", + "ê³ §", + "Ġ׾ פע", + "Ġ×ľ×¤×¢ ×ŀ×Ļ×Ŀ", + "à¹Ģà¸ļ ีย", + "詳 ãģĹãģı", + "çķ° ãģªãĤĭ", + "ĠÄ°l çe", + "ĠAt at", + "ĠAtat ür", + "ĠAtatür k", + "รุ à¹Īà¸ĩ", + "Ġkald ı", + "Ġ주 ìŀ¥", + "Ġprés ence", + "Ġн аб", + "Ġнаб лÑİ", + "ĠнаблÑİ Ð´Ð°", + "ĠÑģам ого", + "×Ĵ ×ķש", + "×ŀ×ĺ ×ķפ", + "×ŀ×ĺ×ķפ ׾", + "ĠвÑĭб иÑĢа", + "ĠìŀIJ 리", + "åĪĨ ãģĭãĤīãģªãģĦ", + "Ġз Ñĥб", + "Ġש׼ ×ijר", + "Ġد ائ", + "Ġدائ Ùħا", + "ĠпаÑĢ ÑĤи", + "ï¼ ²", + "ĠاÙĬ ضا", + "ĠÑħ оз", + "ĠÑħоз Ñı", + "ĠÑħозÑı й", + "ĠÑħозÑıй ÑģÑĤв", + "ĠاÙĦØ£ ج", + "ĠاÙĦأج ÙĨب", + "ĠاÙĦأجÙĨب ÙĬØ©", + "ĠÐĹ Ð½Ð°", + "ĠAp ós", + "ĠÑį неÑĢ", + "ĠÑįнеÑĢ ги", + "Ġy ans", + "Ġyans ı", + "ĠJust i", + "ĠJusti ça", + "Ġpré vu", + "ม วล", + "ìŀ¥ ëĭĺ", + "à¸ģระ à¸ļ", + "à¸ģระà¸ļ วà¸Ļ", + "à¸ģระà¸ļวà¸Ļ à¸ģาร", + "×ŀ ×ŀ", + "×ŀ×ŀ ×ķצע", + "Ġh ẹ", + "Ġhẹ n", + "зд ание", + "Ġak ÅŁ", + "ĠakÅŁ am", + "×ĺ ×ķפ", + "Ġgere kt", + "Ġgerekt i", + "Ġgerekti ÄŁini", + "Ġnar z", + "Ġnarz ÄĻdzi", + "é po", + "épo que", + "ĠTh ần", + "Ġwys oko", + "Ġwysoko ÅĽci", + "à¸ľà¸¹à¹ī à¸Ľ", + "à¸ľà¸¹à¹īà¸Ľ à¹Īวย", + "ĠÙĬ بدÙĪ", + "ÑĤелÑĮ ного", + "Ġвз глÑıд", + "Ġjed nÄħ", + "ĠìĿĺ 견", + "Ġ à¸Ĥà¸ĵะà¸Ĺีà¹Ī", + "פ ×Ļ×ĵ", + "ìĥģ ëĭ´", + "Ġm ỡ", + "×Ķ ×ŀ׾", + "×Ķ×ŀ׾ צ×ķת", + "ĠÑģоÑģÑĤ о", + "ĠÑģоÑģÑĤо иÑĤ", + "Ġав и", + "Ġави а", + "ĠL änder", + "تص ÙĪÙĬر", + "×ŀ×ĵ ×Ļ×Ķ", + "ìłĪ ì°¨", + "ãģ¨ ãĤĬ", + "ãģ¨ãĤĬ ãģĤ", + "ãģ¨ãĤĬãģĤ ãģĪ", + "ãģ¨ãĤĬãģĤãģĪ ãģļ", + "ĠÑĢ Ñıд", + "ĠÑĢÑıд ом", + "ĠNh ất", + "ĠاÙĦÙĥ اÙħÙĦ", + "×Ĺ׾ ׾", + "ĠGi ấy", + "צ ×ĺר", + "צ×ĺר ×£", + "Ġ׾×ij ×ĺ׾", + "Ġим еÑĤÑĮ", + "ס×ŀ ×ķ×ļ", + "Ġparticip ação", + "íķľëĭ¤ ë©´", + "ÙħÙĨت دÙĬ", + "ÙħÙĨتدÙĬ ات", + "ĠeÄŁ len", + "g änge", + "رب ØŃ", + "ãĤ® ãĥ£", + "ĠاÙĦر ÙĤÙħ", + "à¸ĭ à¹īำ", + "ĠH óa", + "×ŀר ×Ĺק", + "ØŃÙħ اÙħ", + "بÙĪ Ùĥ", + "ĠArt ÃŃculo", + "ãĥĦ ãĤ¢ãĥ¼", + "×Ķפ ׼×Ķ", + "×Ĺ׾ ×ķף", + "ĠпеÑĢе Ñħод", + "len miÅŁ", + "زر اعة", + "Ġseñ or", + "ãģ£ãģ¦ ãģįãģ¦", + "Ø¥ Ø´", + "إش ارة", + "Ġpod ÃŃa", + "ĠÃľ lke", + "н ÑģкаÑı", + "Ġadapt é", + "Ġdüzen len", + "Ġdüzenlen en", + "ĠÑģÑĤ ала", + "ĠÙĬ ØŃتاج", + "Ġn ier", + "Ġnier uch", + "Ġnieruch omo", + "Ġnieruchomo ÅĽci", + "ãģĵãģ¨ãģĮ ãģĤãĤĭ", + "ยà¸Ńà¸Ķ à¹Ģยีà¹Īยม", + "ĠÙħ ج", + "ĠÙħج اÙĨÙĬ", + "Ġз аб", + "Ġзаб ол", + "Ġзабол ев", + "Ġзаболев аниÑı", + "ĠÅĽ ro", + "ĠÅĽro dk", + "ĠÅĽrodk ów", + "Ġ×Ķ ׾×IJ×ķ×ŀ×Ļ", + "Ġdok ÅĤad", + "ĠdokÅĤad nie", + "ãģŁãģı ãģªãģĦ", + "ãģ¯ãģļ ãģ§ãģĻ", + "ãģ¨æĢĿ ãģ£ãģ¦ãģĦãģŁ", + "é cran", + "ìĹħ ì²´", + "trzym aÅĤ", + "ÑģÑĤв еннÑĭй", + "ĠNot ÃŃc", + "ĠNotÃŃc ias", + "Ùħ رÙĬ", + "ÙħرÙĬ ض", + "æ°Ĺ è»", + "æ°Ĺè» ½", + "æ°Ĺ軽 ãģ«", + "ëĵ £", + "Ġ×ĵ ×ķ×IJר", + "Ġ׾ ×ŀ׳", + "Ġ׾×ŀ׳ ×ķ×¢", + "ĠçalÄ±ÅŁ ıyor", + "ĠÅŁ idd", + "ĠÅŁidd et", + "ĠM ặt", + "Ġate ÅŁ", + "ĠполÑĥÑĩ ениÑı", + "à¹Ģà¸Ħรืà¹Īà¸Ńà¸ĩ มืà¸Ń", + "Ġgrö ÃŁer", + "د ائ", + "دائ رة", + "Ġbul un", + "Ġbulun maktadır", + "à¹Ģห ร", + "à¹Ģหร ีย", + "à¹Ģหรีย à¸į", + "à¸Ļัà¸ģ à¸Ĺà¹Īà¸Ńà¸ĩà¹Ģà¸Ĺีà¹Īยว", + "Ġalan ında", + "ĠÑĥ зна", + "Ġл еÑĩение", + "売 ãĤĮ", + "Ġçev ir", + "Ġdeste ÄŁi", + "ĠheiÃŁ t", + "âĸ ²", + "ØŃ Ø·", + "à¸Ħำ à¸ķà¸Ńà¸ļ", + "ãĤªãĥ³ ãĥ©ãĤ¤ãĥ³", + "Ġ×ij×Ĺ×Ļ ×Ļ×Ŀ", + "ãĥ¦ ãĥĭ", + "Ġdüzenle me", + "Ġmodal itÃł", + "سر Ø·", + "سرط اÙĨ", + "×ŀ׼ ×ķף", + "ĠданнÑĭ й", + "تر ت", + "ترت ÙĬب", + "à¸ļาà¸ĩ à¸Ħà¸Ļ", + "ĠÄIJ á»ĭnh", + "ม ูล", + "มูล à¸Ħà¹Īา", + "ÙĨ ÙĤص", + "à¸ģาร รัà¸ģษา", + "ĠÑĦ он", + "ĠÑĦон д", + "ãĤĪãģĨ ãģ«ãģªãģ£ãģŁ", + "Ùħع اÙĦ", + "ÙħعاÙĦ جة", + "ĠOs man", + "ĠOsman lı", + "иÑĩеÑģк ом", + "à¸Ńยาà¸ģ à¸Īะ", + "ãģķãģ¾ ãģĸ", + "ãģķãģ¾ãģĸ ãģ¾", + "ãģķãģ¾ãģĸãģ¾ ãģª", + "Ġת ×ķ׼׾", + "×¢ צ×ij", + "ĠاÙĦع سÙĥ", + "ĠاÙĦعسÙĥ رÙĬ", + "Ġvé hic", + "Ġvéhic ule", + "Ġ×Ļצ ×Ĺק", + "ĠاÙĦÙĪ ØŃ", + "ĠاÙĦÙĪØŃ ÙĬد", + "ĠاÙĦع دÙĪ", + "ĠQu ản", + "Ġê³µ ëıĻ", + "بد ÙĦ", + "ĠÄij ảng", + "Ġm á»ĩnh", + "Ġnie zb", + "Ġniezb ÄĻ", + "ĠniezbÄĻ dn", + "Ġyayın lan", + "обÑī и", + "Ġgö tür", + "צ פ", + "צפ ×ķ×Ļ", + "ĠÙĦÙĬ بÙĬ", + "ĠÙĦÙĬبÙĬ ا", + "ØŃ ÙĪا", + "Ġд об", + "Ġдоб ÑĢо", + "иÑĢÑĥ ем", + "ĠاÙĦØŃÙĥÙĪÙħ ÙĬØ©", + "m Ã¤ÃŁig", + "Ġed ición", + "влек аÑĤелÑĮ", + "влекаÑĤелÑĮ н", + "Ġת ש׾×ķ×Ŀ", + "Ġ×Ķש ×ķ׳×Ļ×Ŀ", + "มิ à¸ĸุ", + "มิà¸ĸุ à¸Ļ", + "มิà¸ĸุà¸Ļ ายà¸Ļ", + "é£Łãģ¹ ãģ¦", + "ĠìĪĺ ì§ij", + "ס ×ij×Ļ", + "ĠиÑİ Ð»Ñı", + "Ġà¹Ħà¸Ķà¹ī à¹ģà¸ģà¹Ī", + "׾×Ĺ ×Ŀ", + "tr ä", + "trä gt", + "ãģĿãĤĤ ãģĿãĤĤ", + "ÐĿ Ðķ", + "Ġв нÑĥÑĤ", + "ĠвнÑĥÑĤ ÑĢи", + "ãģ¨ ä¸Ģç·Ĵãģ«", + "ãĤ« ãĥķãĤ§", + "Ġ×ij×Ĺ ×ĵר", + "×Ĺ ×ŀש", + "ãĤ¨ ãĥį", + "ãĤ¨ãĥį ãĥ«", + "ãĤ¨ãĥįãĥ« ãĤ®", + "ãĤ¨ãĥįãĥ«ãĤ® ãĥ¼", + "à¸Ĥà¸Ńà¸ĩ à¸ķัวà¹Ģà¸Ńà¸ĩ", + "بÙĤ اء", + "פס ×Ļ׼", + "פס×Ļ׼ ×ķ׾×ķ×Ĵ", + "ãĥ¡ ãĥĥ", + "ãĥ¡ãĥĥ ãĤ»", + "ãĥ¡ãĥĥãĤ» ãĥ¼ãĤ¸", + "ÙĦ ÙĤب", + "A Äŀ", + "שק ×Ļ×¢", + "ÙĤ ساÙħ", + "×ĵ×ķ×Ĵ ×ŀ×Ķ", + "æ·± ãģĦ", + "íĸĪ ëĬĶëį°", + "ĠrozwiÄħz anie", + "à¸Ļัà¹Īà¸Ļ à¹Ģà¸Ńà¸ĩ", + "×Ļצ ×ij", + "Ġtr ông", + "à¹ĥà¸Ĭà¹ī à¸ļริà¸ģาร", + "ĠاÙĦÙħÙĪ سÙħ", + "ĠдеÑĤ и", + "ãģĹãģĭ ãģªãģĦ", + "ס ×Ļף", + "Ġréfé rence", + "à¹ģห à¹īà¸ĩ", + "ãĤĤãĤī ãģ£ãģŁ", + "Ġ׾ ר׼", + "Ġ׾ר׼ ×ķש", + "شع ÙĪر", + "ĠÐij ог", + "Ġlaz ım", + "Ġ×Ļש ׳×Ŀ", + "Ġп аÑĢÑĤ", + "ĠпаÑĢÑĤ неÑĢ", + "ĠÑĥ ника", + "ĠÑĥника лÑĮн", + "Ġmaté riel", + "×ŀר ק", + "Ġph Æ°á»Ŀng", + "Ġз ай", + "Ġзай м", + "Ùģ ÙĤد", + "Univers itÃł", + "×¢ ר׼×Ļ×Ŀ", + "Ġba ño", + "Ġн оÑı", + "ĠноÑı бÑĢÑı", + "à¸Ľ à¹īาย", + "Ġt ats", + "Ġtats äch", + "Ġtatsäch lich", + "ĠÑĤÑĢ еÑĤÑĮ", + "Ñį м", + "ãĥĻ ãĥ¼ãĤ¹", + "Ġnh á»±a", + "ìĬ¤ íģ¬", + "ĠعبداÙĦ ÙĦÙĩ", + "Ġת ×ķר×Ķ", + "أش ÙĬ", + "أشÙĬ اء", + "ĠÙĦÙĦ غا", + "ĠÙĦÙĦغا ÙĬØ©", + "Ùħ ÙĪاÙĤ", + "ÙħÙĪاÙĤ Ùģ", + "ĠgÅĤówn a", + "Ġart Ä±ÅŁ", + "Ġ×ŀק ×ķ×ŀ×Ļ", + "ãĤ¯ãĥ© ãĥĸ", + "Ġس ÙĪÙī", + "ĠìŬ ìĦ±", + "اس ر", + "اسر ائÙĬÙĦ", + "Ġ׳ ×Ľ×ª×ij", + "ย à¹īà¸Ńà¸Ļ", + "Ġdeber á", + "Ġph ẫu", + "ÑİÑī ем", + "ĠÙĦدÙĬ ÙĨا", + "×ŀ×ĺ ×Ķ", + "Ġ׳ ×ķ׾×ĵ", + "ĠвÑģÑĤÑĢ еÑĩа", + "ãĤīãĤĮ ãģ¦ãģĦãģ¾ãģĻ", + "ĠcaÅĤ ej", + "ย ึ", + "ยึ à¸Ķ", + "поÑĤ ен", + "поÑĤен ÑĨи", + "Ġл иÑĤ", + "ĠлиÑĤ еÑĢ", + "ĠлиÑĤеÑĢ аÑĤÑĥÑĢ", + "Ġкажд ом", + "ĠíĮ IJ", + "ĠíĮIJ ëĭ¨", + "à¸Ī ู", + "Ġpres ença", + "ãģªãĤĵ ãģ§", + "Ùħ ÙĬاÙĩ", + "ин ÑĦоÑĢм", + "инÑĦоÑĢм аÑĨион", + "инÑĦоÑĢмаÑĨион н", + "ĠìŀIJ ìĹ°", + "ר׼ ש", + "Ġöd ül", + "ç¶ļ ãģı", + "Ġп Ñģ", + "ĠпÑģ иÑħ", + "ĠпÑģиÑħ олог", + "ت Ø°Ùĥر", + "Ġìŀħ ìŀ¥", + "ล à¸Ķà¹Į", + "ìĦł ê±°", + "ãģ£ãģ¦ ãģĬãĤĬãģ¾ãģĻ", + "Ġ×Ļ ×¢", + "Ġ×Ļ×¢ ק×ij", + "ĠاÙĦØ· عاÙħ", + "ãĥĨ ãĤ¹ãĥĪ", + "ĠTu ấn", + "Ġparticip ación", + "×ŀ×ķ×ŀ ×Ĺ×Ķ", + "×Ĵר ס×Ķ", + "ĠاÙĦتÙĨ ÙģÙĬ", + "ĠاÙĦتÙĨÙģÙĬ Ø°ÙĬ", + "ĠбезопаÑģ н", + "ge f", + "gef ähr", + "Ø´ ÙĪر", + "Ġmy ÅĽli", + "ÙĪا Ø´ÙĨ", + "ÙĪاشÙĨ Ø·ÙĨ", + "׳×ķס ×¢", + "Ùĥ Ùĩ", + "ÙĥÙĩ رب", + "ÙĥÙĩرب اء", + "Ġmus iaÅĤ", + "ìĭ ¸", + "ãĥĸãĥ© ãĥĥãĤ¯", + "Ġcré é", + "ÙĨÙĩ ار", + "owo ÅĽÄĩ", + "ÙħØŃا ÙĥÙħ", + "ĠwÅĤa ÅĽ", + "ĠwÅĤaÅĽ c", + "ĠwÅĤaÅĽc iciel", + "ĠÙĬ ؤ", + "ĠÙĬؤ دÙĬ", + "×ŀ×¢ ×ķ׳", + "×IJ ×ij׾", + "خط Ø£", + "ĠÑħ олод", + "×ĸ ×ķ׾", + "ãģĵãĤĮ ãĤī", + "ãģĵãĤĮãĤī ãģ®", + "Ġbás ica", + "ฤ à¸Ķ", + "ฤà¸Ķ ูà¸ģ", + "ฤà¸Ķูà¸ģ า", + "ฤà¸Ķูà¸ģา ล", + "èIJ½ãģ¡ çĿĢ", + "ãģªãģĦ ãģĵãģ¨", + "ص ÙĪÙħ", + "ÙĨج ØŃ", + "׳ק ×ķ×ĵ", + "׳ק×ķ×ĵ ת", + "кл аÑģÑģ", + "íķĺìĭľ ëĬĶ", + "ëĦ ĺ", + "Ġש×IJ ×Ļ׳×ķ", + "ĠС ейÑĩаÑģ", + "may acaģı", + "Ġyap ılır", + "Ġcategor ÃŃa", + "عب اد", + "ĠТ еп", + "ĠТеп еÑĢÑĮ", + "×Ķ×Ļס×ĺ ×ķר×Ļ", + "h ế", + "ãĤ³ ãĥ¼ãĥī", + "Ġcabe ça", + "ج Ùħا", + "جÙħا Ùĩ", + "جÙħاÙĩ ÙĬر", + "ä½İ ãģĦ", + "ĠÑĤоваÑĢ ов", + "à¸Ĭาว à¸ļà¹īาà¸Ļ", + "ĠÑģÑĤан ов", + "ĠÑģÑĤанов иÑĤÑģÑı", + "ĠавÑĤом обилÑĮ", + "ĠÑģлÑĥÑĩ ай", + "à¸Ńั à¸ŀ", + "ĠG iriÅŁ", + "ĠìĿ¼ ëĭ¨", + "ĠпÑĢ оÑģ", + "ĠпÑĢоÑģ моÑĤÑĢ", + "ãģªãģıãģª ãģ£ãģŁ", + "มี à¸Ľà¸±à¸įหา", + "ïº İ", + "éc oute", + "ĠÙħ ÙĪجÙĪد", + "Ġس رÙĬع", + "ĠÙĪÙĩ ÙĨا", + "ĠÙĪÙĩÙĨا Ùĥ", + "à¸Ħุà¸ĵ สม", + "à¸Ħุà¸ĵสม à¸ļัà¸ķิ", + "Ġìļ° ìĦł", + "à¸ŀระ à¸ŀุà¸Ĺà¸ĺ", + "好 ãģ¿", + "ظ ÙĦÙħ", + "Ġм акÑģ", + "ĠмакÑģ ималÑĮ", + "ĠмакÑģималÑĮ но", + "ãĥª ãĤ¢ãĥ«", + "à¹ģมà¹ī วà¹Īา", + "ĠاÙĦØŃ ÙĪار", + "ãĥĹãĥ© ãĤ¹", + "Ġع ÙĦاÙĤØ©", + "Ġíĸī ëıĻ", + "Ġgönder il", + "Ġl ãi", + "ĠsaÄŁ lıkl", + "ĠsaÄŁlıkl ı", + "ĠÑĪ аг", + "Ġ×ij×IJר ×Ķ", + "prowadzi Äĩ", + "ãģĦãģı ãģ¤ãģĭ", + "Ġبت ارÙĬØ®", + "Ġ×ij×IJ×ķת ×Ķ", + "Ġmó c", + "ĠÐľ не", + "ãĥĹãĥ¬ ãĥ¼", + "×IJ ×ĸר×Ĺ", + "åł´åIJĪ ãģ«ãģ¯", + "使 ãģĪ", + "à¹Ģร ืà¸Ńà¸Ļ", + "ĠÐŁ еÑĤ", + "ĠÐŁÐµÑĤ ÑĢ", + "ãģ«åħ¥ ãĤĭ", + "Ùħ ادة", + "à¹Ģà¸ĩ ืà¹Īà¸Ńà¸Ļ", + "à¹Ģà¸ĩืà¹Īà¸Ńà¸Ļ à¹Ħà¸Ĥ", + "ĠÑģоÑģÑĤоÑı ние", + "ôn ica", + "ĠÑĦ ев", + "ĠÑĦев ÑĢа", + "ĠÑĦевÑĢа лÑı", + "Ġ×ķ ×ĸ", + "Ġ×ķ×ĸ ×IJת", + "à¸Ħร ิ", + "à¸Ħริ ส", + "ĠÐķ Ñīе", + "ãģ£ãģ¦ãģĹãģ¾ ãģĦãģ¾ãģĹãģŁ", + "ĠпÑĢав иÑĤелÑĮ", + "ĠпÑĢавиÑĤелÑĮ ÑģÑĤв", + "Ġtä glich", + "Ġëĭ¹ ìĭľ", + "×ŀ×ķ×¢ ×ŀ×ĵ", + "Ġдв оÑĢ", + "æī ķ", + "æīķ ãģĦ", + "ĠÑģÑĤан еÑĤ", + "Ġвозд ейÑģÑĤв", + "ĠвоздейÑģÑĤв и", + "Ġf ête", + "à¹Ģส า", + "תק ×ķ×ķ×Ķ", + "Ġu yar", + "Ġuyar ı", + "à¸ģลัà¸ļ à¹Ħà¸Ľ", + "Ġgi Æ°á»Ŀng", + "Ġв а", + "Ġва ÑĪи", + "ĠÄij áºŃu", + "ĠSpa ÃŁ", + "ĠìķĦ ë§Ī", + "à¹Ħà¸Ķà¹ī à¸ĩà¹Īาย", + "Ġ×Ķ×ŀ ×ijקש", + "æĸ° ãģŁ", + "æĸ°ãģŁ ãģª", + "ılı yor", + "пл ан", + "Ġ×Ķ×ijר ×Ļ×IJ×ķת", + "ĠaÄŁ rı", + "Ġsay gı", + "建 ãģ¦", + "Ġnaj wyż", + "Ġnajwyż sz", + "سÙĬاس ات", + "ãģĬ å¾Ĺ", + "ĠاÙĦع ÙĦÙĬ", + "ĠاÙĦعÙĦÙĬ ا", + "Ġcoraz ón", + "ì¹ĺ ë£Į", + "หัว à¸Ĥà¹īà¸Ń", + "Ġب ØŃÙĬ", + "ĠبØŃÙĬ Ø«", + "зв езд", + "بÙĪ ابة", + "ÐĽ Ðĺ", + "ÙĦا زÙħ", + "Ġroz p", + "Ġrozp oc", + "Ġrozpoc zÄĻ", + "触 ãĤĮ", + "ĠاÙĦج ÙħÙĩ", + "ĠاÙĦجÙħÙĩ ÙĪر", + "Ġsp ÄĻd", + "ĠspÄĻd z", + "วิà¸Ĺยา ศาสà¸ķรà¹Į", + "ив аеÑĤÑģÑı", + "Ġдан ной", + "Ġreprés ente", + "ĠÄij á»ĭch", + "Ġ×¢×ŀ ×ķק", + "à¸Ńัà¸Ļ à¸ķร", + "à¸Ńัà¸Ļà¸ķร าย", + "Ġestr atég", + "Ġestratég ia", + "pad ÅĤ", + "Ġв полн", + "Ġвполн е", + "ĠпÑĢедоÑģÑĤав лен", + "×Ĺ׾ ×ķק", + "×Ĺ׾×ķק ת", + "ãĤ¢ ãĥĬ", + "ĠاÙĦغ Ø°", + "ĠاÙĦغذ ائÙĬ", + "ĠÑĥ зн", + "ĠÑĥзн аÑĤÑĮ", + "à¸ĭ à¹īาย", + "å½ĵ ãģ¦", + "ØŃÙĬ اء", + "Ġbás ico", + "ק×ķ×ij ×¢", + "ĠاÙĦÙħ باراة", + "ĠاÙĦÙĩ اتÙģ", + "Ġ׼ ׳×Ĵ×ĵ", + "à¸Ľà¸£à¸° หย", + "à¸Ľà¸£à¸°à¸«à¸¢ ัà¸Ķ", + "Ðļ ак", + "à¸Ĺีà¹Ī à¸Ļà¹Īา", + "à¸Ĺีà¹Īà¸Ļà¹Īา สà¸Ļà¹ĥà¸Ī", + "ãģ¾ ãģģ", + "ï½ ¢", + "Ñģк оп", + "Ġson rasında", + "Ġur zÄħd", + "ĠurzÄħd zenia", + "׼×ķ ×ķ׳", + "׼×ķ×ķ׳ ת", + "Ġ׾×Ķת ×ŀ×ķ×ĵ", + "Ġ׾×Ķת×ŀ×ķ×ĵ ×ĵ", + "ĠÑģ ли", + "ĠÑģли ÑĪ", + "ĠÑģлиÑĪ ком", + "ĠÑģÑĤ Ñĥд", + "ĠÑģÑĤÑĥд енÑĤ", + "Ġ×Ķ ×ķ×ĵ", + "Ġ×Ķ×ķ×ĵ ×¢×Ķ", + "ë¹Ħ ìļ©", + "à¸Ńยาà¸ģ à¹ĥหà¹ī", + "Ġb á»ģ", + "ยุ à¸Ĺà¸ĺ", + "Ðĺ ÐĿ", + "س ائر", + "Ø£ صÙĪÙĦ", + "ĠاÙĦغ رÙģ", + "ãģĵãģ¨ãĤĤ ãģĤãĤĬãģ¾ãģĻ", + "è¾¼ ãģ¾ãĤĮ", + "ĠاÙĦساب ع", + "Ġc ủ", + "ãģĦãģŁãģł ãģĦãģŁ", + "ì§ ĵ", + "ìĤ¬ 무", + "powied ź", + "تÙģ Ùĥ", + "تÙģÙĥ ÙĬر", + "иÑĢов ки", + "ĠíĨµ íķ´ìĦľ", + "ãĤ¨ ãĤ¹ãĥĨ", + "ĠдеÑıÑĤелÑĮ ноÑģÑĤÑĮ", + "ĠданнÑĭ м", + "Ġ×¢ ×ķר", + "Ġ×¢×ķר ׼×Ļ", + "×ķ×ĵ עת", + "Ġhayat ını", + "Ġb Äħd", + "ĠbÄħd ź", + "obs ÅĤug", + "à¹Ģà¸ŀียà¸ĩ à¹ģà¸Ħà¹Ī", + "à¸ĭ à¹Īา", + "è²ł ãģij", + "ĠÑģÑĤÑĢ ем", + "ĠÄij á»īnh", + "ĠÐł ÑĥÑģ", + "ĠN ữ", + "Ġ׾×Ķש ×Ļ×Ĵ", + "Ġjed noc", + "Ġjednoc ze", + "Ġjednocze ÅĽnie", + "Ġ×Ķ×Ĵ ×ij×ķ×Ķ", + "أخ ÙĦاÙĤ", + "ĠнаÑģ ел", + "ĠнаÑģел ениÑı", + "ĠÙĬ ÙĨب", + "ĠÙĬÙĨب غÙĬ", + "ãģĮ ãģĭ", + "ãģĮãģĭ ãģĭ", + "×Ĵ עת", + "Ðŀ Ðł", + "ĠналиÑĩ ии", + "Ġë§Ī ì§Ģ", + "Ġë§Īì§Ģ ë§ī", + "Ġíĸī ìĤ¬", + "Ġtre ÅĽci", + "Ġê°Ģ ì¹ĺ", + "ì¦ ĺ", + "Ġана лог", + "×Ķצע ת", + "в лад", + "влад е", + "ĠÑģдел ал", + "Ġ׳ ×Ĵ×Ļש", + "Ġ׳×Ĵ×Ļש ×ķת", + "полн ение", + "à¸Ĩ à¹Īา", + "ĠD ön", + "׼׾׼ ׾×Ķ", + "×ŀ×ĸ ×Ĵ", + "Ùħ Ùģ", + "ÙħÙģ Ùĩ", + "ÙħÙģÙĩ ÙĪÙħ", + "×Ķ ×ĵ", + "×Ķ×ĵ פס", + "×Ķ×ĵפס ×Ķ", + "ãģĻãģİ ãģ¦", + "Ġг ÑĢ", + "ĠгÑĢ н", + "×ŀ×ĺ ×ķס", + "Ġ기 ìĸµ", + "ï¾ Ł", + "ĠpÅĤ yn", + "ĠGr ünde", + "ĠBü cher", + "Ġwed ÅĤug", + "ãģ¾ãģł ãģ¾ãģł", + "Ġ׳×Ķ ×ĵר", + "ĠÙĬست Ø·ÙĬع", + "ĠHi á»ĩp", + "ãĤŃãĥ£ãĥ³ ãĥļ", + "ãĤŃãĥ£ãĥ³ãĥļ ãĥ¼ãĥ³", + "Ġth á»ķ", + "Ġeuropé enne", + "à¸ļ ัà¸ĩ", + "à¸ļัà¸ĩ à¸Ħัà¸ļ", + "ĠszczegóÅĤ owo", + "׳ שק", + "ãĥķ ãĥ©ãĥ³ãĤ¹", + "×ŀ×ķ×ŀ ×Ĺ×Ļ", + "Ġcom ún", + "Ġç arp", + "ØŃت ÙĬا", + "ØŃتÙĬا ج", + "ØŃتÙĬاج ات", + "ëĭ´ ëĭ¹", + "ä½ķ 度", + "ä½ķ度 ãĤĤ", + "×ĵ ×ijק", + "ãģį ãĤĮ", + "ãģįãĤĮ ãģĦ", + "Ġк ам", + "Ġкам еÑĢ", + "ĠespecÃŃf ico", + "Ġtel éfono", + "à¸ķัà¹īà¸ĩ à¸Ńยูà¹Ī", + "I Åŀ", + "ãģ© ãĤĵãģ©", + "ãģ©ãĤĵãģ© ãĤĵ", + "עצ ×ŀ×IJ×Ļ", + "à¸Ķัà¸ĩ à¸Ļีà¹ī", + "ĠÑĦоÑĢм иÑĢов", + "ĠÑĦоÑĢмиÑĢов а", + "×ķ×ŀ ×ij", + "Ġkullan ımı", + "Ðľ Ðŀ", + "×¢ ש×Ļ", + "עש×Ļ ×Ļ×Ķ", + "Ġön lem", + "à¹Ģà¸Ń à¹ĩ", + "à¹Ģà¸Ńà¹ĩ ม", + "×ŀשק ×Ļ×¢", + "ר ×Ļ×Ĺ", + "à¸Ĥ ัà¸Ķ", + "ĠíĻ ľ", + "ĠíĻľ ìļ©", + "à¸ĭ ะ", + "ãĤĪãģĨ ãģ«ãģªãĤĬãģ¾ãģĹãģŁ", + "ĠÑĢаÑģ пÑĢ", + "ĠÑĢаÑģпÑĢ оÑģÑĤ", + "ĠÑĢаÑģпÑĢоÑģÑĤ ÑĢан", + "ĠÑĢаÑģпÑĢоÑģÑĤÑĢан ен", + "׼×Ļ ×ķף", + "ÙĤب ض", + "تص رÙĬØŃ", + "تصرÙĬØŃ Ø§Øª", + "Ġо ÑĢи", + "ĠоÑĢи г", + "ĠоÑĢиг ина", + "ĠоÑĢигина л", + "ĠاÙĦع اÙĦÙĬ", + "à¹ģหà¹Īà¸ĩ à¸Ļีà¹ī", + "ãĥķãĤ¡ ãĥ¼", + "ãģ¦ãģĦ ãģį", + "ãģ¦ãģĦãģį ãģŁãģĦ", + "פ תר", + "פתר ×ķ׳×ķת", + "Ġ×ij ×Ļ×Ĺ", + "Ġ×ij×Ļ×Ĺ ×ĵ", + "Ġod by", + "Ġodby ÅĤ", + "ĠоÑĩеÑĢ ед", + "Ġtr Æ°Æ¡ng", + "ãĤŃ ãĥ³", + "×ŀ ×ķפ", + "×ŀ×ķפ ×¢", + "ëĵľ 립", + "ëĵľë¦½ ëĭĪëĭ¤", + "à¸ŀืà¹īà¸Ļ à¸IJาà¸Ļ", + "ìŀIJ 격", + "ĠVi á»ĩn", + "ĠDes pués", + "Ġ×IJ׾ ×Ļ׳×ķ", + "Ġdur ée", + "íĩ ´", + "Ġmü zik", + "i ếu", + "ĠÑĢаз меÑīен", + "Ġк Ñĥд", + "ĠкÑĥд а", + "غ ض", + "غض ب", + "ĠTamb ém", + "à¸Īัà¸Ķ สà¹Īà¸ĩ", + "à¸ģาร à¹ģสà¸Ķà¸ĩ", + "onom ÃŃa", + "Ġан г", + "Ġанг ли", + "Ġангли й", + "Ġанглий Ñģк", + "Ġzn al", + "Ġznal az", + "Ġznalaz ÅĤ", + "תר ×Ĵ", + "תר×Ĵ ×ķ×Ŀ", + "ĠÑģ нов", + "ĠÑģнов а", + "ĠÑĩаÑģ а", + "Ġcommun auté", + "ĠespecÃŃf ica", + "ĠL á»ĭch", + "Ġli é", + "Ùģ جر", + "à¹Ģà¸ģ à¹Īà¸ĩ", + "ع اÙĦ", + "عاÙĦ ج", + "Ø£ÙĨ ظ", + "Ø£ÙĨظ ÙħØ©", + "ES Ä°", + "ĠاÙĦØŃ Ø¯ÙĬد", + "à¸ŀระ à¸Ńà¸ĩà¸Ħà¹Į", + "Ġפר שת", + "Ġдв иж", + "Ġдвиж ениÑı", + "ĠاÙĦج ارÙĬ", + "à¸ĺาà¸Ļ ี", + "неÑģ ен", + "ĠاÙĦÙĨ ÙĩائÙĬ", + "Ġб еÑĢ", + "ĠбеÑĢ ем", + "ĠбеÑĢем енн", + "Ġdépart ement", + "à¹Ģà¸Ĺ ีย", + "à¹Ģà¸Ĺีย à¸ļ", + "ĠÐľ аÑĢи", + "ĠнекоÑĤоÑĢ ÑĭÑħ", + "об еÑģп", + "обеÑģп еÑĩен", + "×Ĺ ×ķ×ĸ", + "×Ĺ×ķ×ĸ ×Ķ", + "ÙĨت ج", + "à¸Īะ à¹Ħà¸Ķà¹īรัà¸ļ", + "á» °", + "Ġél éments", + "ع Ø·", + "عط اء", + "Ġt ắt", + "i á»ĩm", + "ÑİÑīиÑħ ÑģÑı", + "ãģĹãģ °", + "ãģĹãģ° ãĤīãģı", + "Ġпом ожеÑĤ", + "à¸Ĥà¸ĵะ à¸Ļีà¹ī", + "Ġ×¢ שר×ķת", + "éģķ ãģ£ãģ¦", + "ĠпÑĢ ог", + "ĠпÑĢог н", + "ĠпÑĢогн оз", + "Ġt ÅĤ", + "ĠtÅĤ um", + "ĠtÅĤum acz", + "T ür", + "Tür kiye", + "ãģį ãģ£", + "ãģįãģ£ ãģĭãģij", + "Ġ×Ķ׳ ×ķ׼", + "Ġ×Ķ׳×ķ׼ ×Ĺ×Ļ", + "ĠìĥĿ ìĤ°", + "ĠÑĦоÑĢм Ñĭ", + "ç¾İ ãģĹãģĦ", + "à¸Ľà¸£ ึà¸ģ", + "à¸Ľà¸£à¸¶à¸ģ ษา", + "Ġlum ière", + "ãĤª ãĥ¼ãĥĹ", + "ãĤªãĥ¼ãĥĹ ãĥ³", + "à¸Ľ ืà¸Ļ", + "วั สà¸Ķ", + "วัสà¸Ķ ุ", + "еÑĢÑĤ в", + "ÙĥÙĦ Ùģ", + "ï½ £", + "à¸ĺรรม à¸Ķา", + "׳ ×ĺר", + "ĠпÑĢедÑģÑĤав лÑıеÑĤ", + "Ġanál isis", + "Ġb ãi", + "با ÙĤÙĬ", + "à¸Ľà¸£à¸° à¹Ģà¸Ķ", + "à¸Ľà¸£à¸°à¹Ģà¸Ķ à¹ĩà¸Ļ", + "ĠÑģлÑĥÑĩ аÑı", + "ĠÑģлÑĥÑĩаÑı Ñħ", + "ÐĽ ÐIJ", + "สัà¸ĩ à¹Ģà¸ģ", + "สัà¸ĩà¹Ģà¸ģ à¸ķ", + "Ġprz ec", + "Ġprzec ież", + "Ùħ صÙĦ", + "ÙħصÙĦ ØŃØ©", + "ש×ķק ×ķ׾×ĵ", + "ĠобоÑĢÑĥд ованиÑı", + "Ġtr waÅĤ", + "رÙĪ Ùħ", + "ìķĪ ëĤ´", + "ĠNgh á»ĭ", + "Ø® Ø´", + "à¸ļา à¸Ħาร", + "à¸ļาà¸Ħาร à¹Īา", + "Ġоп ÑĨион", + "ĠÑģозд аниÑı", + "ãĤ³ ãĤ¹ãĥĪ", + "Ġ×Ķ×¢ ׾×Ļ", + "Ġ×Ķ×¢×ľ×Ļ ×ķף", + "lä uft", + "ãĥĻ ãĤ¹ãĥĪ", + "Ġr ê", + "Ġrê ve", + "×IJ ×ij×Ļ×ij", + "×Ļ ×Ļ×ļ", + "ë¶ Ļ", + "ãĤ¤ãĥ³ ãĥī", + "ÅĤo ży", + "ÅĤoży Äĩ", + "ع ائÙĦ", + "عائÙĦ Ø©", + "Ø£ ÙĪر", + "Ø£ÙĪر اÙĤ", + "à¸Ĺà¹īà¸Ńà¸ĩ à¸ĸ", + "à¸Ĺà¹īà¸Ńà¸ĩà¸ĸ ิà¹Īà¸Ļ", + "Ġä hn", + "Ġähn lich", + "ãĥŁ ãĥĭ", + "à¸ľ ู", + "à¸ľà¸¹ à¹īà¸Ļ", + "à¸ľà¸¹à¹īà¸Ļ ำ", + "ĠмаÑĤеÑĢиал Ñĭ", + "Ġкап иÑĤ", + "ĠкапиÑĤ ал", + "ï¼ ¦", + "Ġseç il", + "Ġh ứng", + "Ġintéress ant", + "ãģ£ãģ¦ ãģĦãģı", + "Ġe ÄŁer", + "ëIJĺ ìĹĪìĬµëĭĪëĭ¤", + "Ġan laÅŁma", + "ãģĶ åĪ©çĶ¨", + "Ġ×ij ×ĸ׼", + "Ġ×ij×ĸ׼ ×ķת", + "ëĿ¼ ë©´", + "ĠÙĬ ÙĪس", + "ĠÙĬÙĪس Ùģ", + "أسÙĦ ØŃØ©", + "ĠGef ühl", + "ĠноÑĢм алÑĮн", + "ãĥĻ ãĥ³", + "ãģķãĤĮ ãĤĭãģĵãģ¨", + "ĠÐij еÑģ", + "ãģ¨ãģĦ ãģĪãģ°", + "ĠÙħ ÙĩÙħ", + "ĠÙħÙĩÙħ Ø©", + "ãģ§ãģĹãĤĩãģĨ ãģŃ", + "ĠêµŃ ëĤ´", + "à¹Ģม à¹ĩà¸Ķ", + "×ŀ×ij קר", + "ĠاÙĦد ÙĨÙĬ", + "ĠاÙĦدÙĨÙĬ ا", + "à¸Ĭ ู", + "к ÑĢÑĥÑĤ", + "Ġtho áng", + "Ġ׳ ×ĵר", + "Ġ׳×ĵר ש", + "ĠÑĢаÑģÑģ казал", + "ĠAu ÃŁerdem", + "פ ×IJר", + "פ×IJר ק", + "Ġ×ŀש×Ĺק ×Ļ×Ŀ", + "צ ר׼×Ļ×Ŀ", + "×ŀ×ĵ ×ķ", + "×ŀ×ĵ×ķ ×Ļק", + "èĭ¦ ãģĹ", + "ĠÑģ иг", + "ĠÑģиг нал", + "ĠM á»įi", + "Ġtr ữ", + "Ġnast ÄĻp", + "ĠnastÄĻp nie", + "Ġì¶Ķ ì§Ħ", + "ĠاÙĦÙģ ÙĨد", + "ĠاÙĦÙģÙĨد ÙĤ", + "koÅĦ czyÅĤ", + "ส ีà¹Ī", + "ק ×Ļ×ij", + "ק×Ļ×ij ×ķ×¥", + "ĠнÑĥж нÑĭ", + "大 åĪĩ", + "大åĪĩ ãģª", + "æıĽ ãģĪ", + "ת ×ķס", + "ת×ķס פת", + "ãģ£ãģ¦ ãģĦãģªãģĦ", + "Ġм Ñı", + "ĠмÑı г", + "ĠмÑıг к", + "Ġjak ie", + "Ġjakie ÅĽ", + "à¸ķำ à¸ļ", + "à¸ķำà¸ļ ล", + "ĠìŀĪ ì§Ģ", + "×ij×ĺ ×IJ", + "ĠоÑĤлиÑĩ но", + "ÙĤ ÙIJ", + "ĠавÑĤом об", + "ĠавÑĤомоб и", + "ĠавÑĤомоби лÑı", + "دÙĬÙħÙĤرا Ø·ÙĬ", + "ĠاÙĦ ÙĪا", + "ĠاÙĦÙĪا ØŃد", + "Ġس ÙĪرÙĬØ©", + "Ø£ غÙĦ", + "أغÙĦ ب", + "ĠÑįк ÑĢан", + "ãĥĹ ãĥ©ãĤ¤", + "Ġjeste ÅĽ", + "ãĥIJ ãĥª", + "Ġ×Ķ×IJ ×ķ×ķ×Ļר", + "ائ Ùĥ", + "à¸Ńยà¹Īาà¸ĩ ยิà¹Īà¸ĩ", + "ÑĢ екÑĤ", + "Ġum o", + "Ġumo ż", + "Ġumoż li", + "Ġumożli w", + "Ġumożliw ia", + "Ġnäch ste", + "ĠìŀĪ ì§Ģë§Į", + "ĠпÑĢед н", + "ĠпÑĢедн аз", + "ĠпÑĢедназ наÑĩен", + "Ġma çı", + "Ġp omi", + "Ġpomi ÄĻd", + "ĠpomiÄĻd zy", + "ĠاÙĦÙĦ ÙĤاء", + "à¹Ģà¸Ķ à¸Ńะ", + "Ġнов оÑģÑĤи", + "×ŀ׊׾×Ķ", + "رÙĬاض ÙĬ", + "à¸Ķ à¸Ļ", + "à¸Ķà¸Ļ à¸ķรี", + "ب صر", + "ìĬ¤ íĥĢ", + "scri pción", + "Ġnap isa", + "Ġnapisa ÅĤ", + "Ġ׳ש ×ŀ×¢", + "ĠاÙĦÙħØŃ ÙĦÙĬ", + "Ġhi á»ĥn", + "×IJ ×Ĺ", + "×IJ׊ר×IJ×Ļ", + "Ġг ÑĢаниÑĨ", + "æīĭ ç¶ļãģį", + "Ùĥ سب", + "Ġà¹ģà¸ķà¹Ī à¸ĸà¹īา", + "à¸Ķาว à¸Ļà¹Į", + "à¸Ķาวà¸Ļà¹Į à¹Ĥหลà¸Ķ", + "ãĤĭãģĵãģ¨ãģĮãģ§ãģį ãģ¾ãģĻ", + "åŁºæľ¬ çļĦãģ«", + "ÙĪÙĦ اد", + "rä ume", + "د Ùģاع", + "×Ļצ ×¢", + "ĠO czy", + "ĠOczy wiÅĽcie", + "ĠÅ ģ", + "ĠÅģ a", + "اÙĦÙĬ اب", + "اÙĦÙĬاب اÙĨ", + "áºł I", + "ĠBir liÄŁi", + "×Ķ ×ķצ", + "×Ķ×ķצ ×IJת", + "ĠÄij ua", + "Ġê·¸ëŁ¬ ëĭĪê¹Į", + "Ġréal ité", + "ع ÙĦاÙĤات", + "J este", + "Jeste ÅĽ", + "Ġмн ож", + "Ġмнож еÑģÑĤво", + "ï¼ «", + "ãĥĹãĥŃ ãĤ¸ãĤ§", + "ãĥĹãĥŃãĤ¸ãĤ§ ãĤ¯ãĥĪ", + "ĠÑĦ л", + "ظ ÙĨ", + "×Ĵ׾ ×Ĵ׾", + "ĠmÅĤod zie", + "ĠmÅĤodzie ż", + "à¸Ļà¹īำ à¸ķา", + "à¸Ļà¹īำà¸ķา ล", + "ÐĽ Ðķ", + "×ij ×ķ×ĺ", + "Ġ׾×Ķ ×Ĵ×Ļ×ĵ", + "ãģĵãģ¨ãĤĤ ãģĤãĤĭ", + "ز اد", + "×ŀ×Ļ×ĵ ×¢", + "ĠgÅĤówn ie", + "ãĥı ãĤ¦", + "ãĥıãĤ¦ ãĤ¹", + "б ел", + "Ġét ape", + "ðŁĺ Ģ", + "Ġмод елÑĮ", + "a ģını", + "ש ×Ĺק", + "ש×Ĺק ף", + "Ġni ño", + "à¸Ĭ à¹īาà¸ĩ", + "à¹Ģล ีย", + "ĠÑĦоÑĢм е", + "ĠاÙĦØ´ رÙĬÙģ", + "ĠÑĥд аÑĢ", + "arr iv", + "arriv ée", + "Ġmies iÄĻ", + "ĠmiesiÄĻ cy", + "ØŃ Ø±Ùĥ", + "ØŃرÙĥ ات", + "ĠDi á»ħn", + "ÐĿ Ы", + "ãģ¾ãģ£ãģŁ ãģı", + "Ġ×Ļ ר×ķק", + "еÑģÑĤ еÑģÑĤв", + "еÑģÑĤеÑģÑĤв енн", + "Ġê·¸ ëŁ¼", + "ĠاÙĦÙħ تÙĪ", + "ĠاÙĦÙħتÙĪ سط", + "Ġbéné fic", + "Ġbénéfic ie", + "Ġwy bra", + "Ġwybra Äĩ", + "ĠاÙĦز ÙħÙĨ", + "ĠпÑĢин Ñı", + "ĠпÑĢинÑı л", + "Ùģر ØŃ", + "Ġk sz", + "Ġksz taÅĤ", + "ĠksztaÅĤ t", + "ק׾ ×ĺ", + "×ij×ĵ×Ļק ת", + "Ġgi ấ", + "Ġgiấ c", + "Ġpropriet Ãł", + "деÑĢж ан", + "ĠKö ln", + "ĠGü zel", + "×Ļפ ×ķ×Ļ", + "ĠCu á»Ļc", + "ÑįÑĤ аж", + "تر ÙĥÙĬ", + "ترÙĥÙĬ ز", + "лож ений", + "Ġп Ñĥ", + "ĠпÑĥ ÑĤи", + "اخت ÙĦاÙģ", + "åĩºãģ¦ ãģıãĤĭ", + "à¸ļุ à¸ģ", + "âĿ ¤", + "ÑĦ ан", + "פש ×ĺ", + "à¸ļัà¸Ļ à¹Ģà¸Ĺ", + "à¸ļัà¸Ļà¹Ģà¸Ĺ ิà¸ĩ", + "ĠاÙĦس اد", + "ĠاÙĦساد س", + "ĠاÙĦÙĤ ÙĪÙħ", + "ĠاÙĦÙĤÙĪÙħ ÙĬ", + "Ġyönet ici", + "Ùĩ ÙĪات", + "ÙĩÙĪات Ùģ", + "Ġrespons ável", + "Ġпод деÑĢжива", + "ĠاÙĦسÙĦ Ø·", + "ĠاÙĦسÙĦØ· ات", + "ãģĹãģ¦ ãģĬãģı", + "ãĥļ ãĥĥãĥĪ", + "à¸Ľ ุà¹Īม", + "Ġogl Äħda", + "ÙĨا ÙĤ", + "ÙĨاÙĤ Ø´", + "à¸Ħà¸Ńà¸Ļ à¹Ĥà¸Ķ", + "ĠMü sl", + "ĠMüsl ü", + "ĠMüslü man", + "ĠMo ż", + "ĠMoż na", + "Ġnum érique", + "Ġv á»ı", + "ĠسÙĬ تÙħ", + "Ġyer leÅŁ", + "монÑĤ аж", + "Ġgo ût", + "ãģ¦ ãģĬãĤĬãģ¾ãģĻ", + "ĠKh ánh", + "Ġе дин", + "Ġедин ÑģÑĤв", + "اÙĨ Ø®Ùģ", + "اÙĨØ®Ùģ اض", + "ìĭľ íĹĺ", + "Ġl ặng", + "ĠÑĢ олÑĮ", + "à¸ķัว à¹ģà¸Ĺà¸Ļ", + "à¸Ħà¹Īา à¹ĥà¸Ĭà¹ī", + "à¸Ħà¹Īาà¹ĥà¸Ĭà¹ī à¸Īà¹Īาย", + "Ġver füg", + "Ġverfüg bar", + "ìĻĶ ëĭ¤", + "ãģĦ ãģļ", + "ãģĦãģļ ãĤĮ", + "ĠиÑģÑģлед ованиÑı", + "меÑī а", + "×Ķ ×Ĺ", + "×Ķ×Ĺ ×ĸר", + "à¹ģà¸Ł à¸Ĭัà¹Īà¸Ļ", + "ت صرÙģ", + "Ø¥ رÙĩاب", + "Ġexerc ÃŃcio", + "Ġé lev", + "Ġélev é", + "สัà¸įà¸įา à¸ĵ", + "Ãĸ Z", + "ãĥĹ ãĥŃãĤ°", + "ãĥĹãĥŃãĤ° ãĥ©", + "ãĥĹãĥŃãĤ°ãĥ© ãĥł", + "Ġw ewnÄĻtrzn", + "Ġhen üz", + "é£Ľ ãģ³", + "à¹Ģà¸Ķ à¸Ńรà¹Į", + "Ñģ Ñĥж", + "ÑģÑĥж ден", + "شع ÙĪب", + "ãģ²ãģ¨ ãĤĬ", + "Ġwy ÅĤÄħ", + "ĠwyÅĤÄħ cznie", + "Ġпло Ñħо", + "ÐĶ Ðķ", + "Ạ¦", + "Ùģع اÙĦÙĬ", + "ÙģعاÙĦÙĬ ات", + "ĠاÙĦع شر", + "ÑģÑĤÑĥп ил", + "Ġy arg", + "Ġyarg ı", + "нÑİ Ñİ", + "×ķ×IJ ×ij", + "Ġu ç", + "Ġuç ak", + "ë² ½", + "تÙĪ ÙĤÙĬ", + "تÙĪÙĤÙĬ ع", + "Ġì¤ij ìĭ¬", + "׳×Ļ×ķ ×ķ×ĺ", + "Ø£ ÙĥÙĦ", + "ç½® ãģĦãģ¦", + "éłĤ ãģį", + "Ġ×Ķת ×ij", + "Ġ×Ķת×ij ×Ļ×¢×Ķ", + "Ġdür fen", + "Ùħ ÙĤاÙĦ", + "ÙħÙĤاÙĦ ات", + "Ġز ÙħÙĨ", + "à¸ŀฤ ศ", + "à¸ŀฤศ à¸Ī", + "à¸ŀฤศà¸Ī ิà¸ģ", + "à¸ŀฤศà¸Īิà¸ģ ายà¸Ļ", + "ĠнеÑģк олÑĮ", + "ĠнеÑģколÑĮ ки", + "ĠнеÑģколÑĮки Ñħ", + "Ġcrian ça", + "มิ à¸ķร", + "×ŀ׼ ×Ļר×ķת", + "à¸ģาร à¸ļริหาร", + "Ġtélé charg", + "Ġ×IJ×ķ×Ķ ×ijת", + "ĠBü ro", + "ä½ľ ãģ£ãģŁ", + "ĠKi ÅŁi", + "ç¾İåij³ ãģĹ", + "à¹Ģลย à¸Ħà¹Īะ", + "à¸ŀà¸ļ à¸ģัà¸ļ", + "à¸Ī à¹īา", + "Ġç er", + "Ġçer ç", + "Ġçerç eve", + "ãĤĴä½ľ ãģ£ãģ¦", + "ĠпеÑĢв ÑĥÑİ", + "×ŀצ ר×Ļ×Ŀ", + "×IJ׾ ×ķ×Ķ", + "×IJ׾×ķ×Ķ ×Ļ×Ŀ", + "Ġagr é", + "Ġagré able", + "Ġay ır", + "Ä°L Ä°", + "ãĤ ¥", + "Ġíĺ Ħ", + "ĠíĺĦ ìĭ¤", + "ثاÙĦ Ø«", + "ת ×ĸ", + "ת×ĸ ×ķ׳×Ķ", + "ãģ¨ãģĦ ãģ£ãģ¦", + "ãģ¨ãģĦãģ£ãģ¦ ãĤĤ", + "Ġا بÙĪ", + "ĠÑģоб ак", + "é£Łãģ¹ ãģŁ", + "Ġдан ном", + "à¹Ģล ิ", + "à¹Ģลิ ศ", + "Ġí ļ", + "Ġíļ ¨", + "Ġíļ¨ ê³¼", + "ãĤĤãĤī ãģĪãĤĭ", + "׳ צ׾", + "ÑĦ ик", + "ÑĦик Ñģ", + "Ġjeste ÅĽmy", + "ת×Ĺ×ķש ×Ķ", + "à¹Ħมà¹Ī à¸Ħวร", + "ĠØŃ Ø³ÙĬÙĨ", + "à¸ģาร ลà¸ĩà¸Ĺุà¸Ļ", + "ë´ ¤", + "ĠÐĺ менно", + "à¸ļ à¸Ńรà¹Į", + "à¸ļà¸Ńรà¹Į à¸Ķ", + "ĠC ảnh", + "ìĦľ ë¹ĦìĬ¤", + "Ġпол ов", + "Ġполов ин", + "Ġзам еÑĩа", + "ãģĦãĤį ãĤĵãģª", + "Ġ×ij ×Ļק", + "Ġ×ij×Ļק ש", + "л ÑĥÑĪ", + "ãĤĴ è¿İ", + "ãĤĴè¿İ ãģĪ", + "جرÙĬ ÙħØ©", + "Ġt ây", + "ĠاÙĦÙĨ ÙĪ", + "ĠاÙĦÙĨÙĪ ÙĪÙĬ", + "ÃĤ N", + "ì¿ ł", + "หà¸Ļ าว", + "Ġ×ij׊ש×ij×ķף", + "ز ار", + "à¸Ķ าร", + "à¸Ķาร า", + "ĠÅĽ l", + "ĠÅĽl ub", + "มีà¸Ħวาม สุà¸Ĥ", + "Ġn hu", + "Ġnhu áºŃn", + "ÙħØŃ Ø·Ø©", + "à¹Ģสืà¹īà¸Ń à¸ľà¹īา", + "ĠТ олÑĮко", + "ĠÙĥ س", + "ĠÙĥس ارة", + "ÙħØ´ رÙĪع", + "niÄĻ cia", + "×¢ ׼ש×Ļ×ķ", + "ت ÙĦÙģ", + "تÙĦÙģ زÙĬ", + "تÙĦÙģزÙĬ ÙĪÙĨ", + "Ġl Æ°á»Ľi", + "ĠÐľÐ¾Ñģк вÑĭ", + "Ġré serve", + "Ġan laÅŁ", + "ĠanlaÅŁ ıl", + "Ġed eceÄŁi", + "รà¸Ńà¸ĩ à¹Ģà¸Ĺà¹īา", + "Ġب Ø·", + "Ġبط رÙĬ", + "ĠبطرÙĬ ÙĤØ©", + "ãģ¦ãģĹãģ¾ ãģ£ãģ¦", + "ãĤĤãĤī ãģ£ãģ¦", + "بر ج", + "æ± ļ", + "æ±ļ ãĤĮ", + "Ġch oc", + "Ġchoc ia", + "Ġchocia ż", + "Ġzob ac", + "Ġzobac zyÄĩ", + "пÑĢ Ñı", + "пÑĢÑı жен", + "ĠÑĨ иÑĦ", + "ĠÑĨиÑĦ ÑĢ", + "Ġм ам", + "Ġвз ÑıÑĤÑĮ", + "Ġch ạm", + "ج سÙħ", + "ØŃÙħ اس", + "à¹Ģล à¹Īม", + "à¸ŀิ ษ", + "×Ķפ ׼×ķ", + "à¸Ĭà¹Īà¸Ńà¸ĩ à¸Ĺาà¸ĩ", + "Ġв ек", + "Ġвек а", + "Æ¡ Ìģ", + "Æ¡Ìģ i", + "ĠTi á»ģn", + "Ġtr ầm", + "мÑĭ ÑĪ", + "мÑĭÑĪ л", + "ĠÑĤ Ñĥ", + "ĠÑĤÑĥ ÑĢиÑģÑĤ", + "Ġch c", + "Ġchc Äħ", + "Ġав г", + "Ġавг ÑĥÑģÑĤ", + "ĠавгÑĥÑģÑĤ а", + "ס ×IJ×ķת", + "Ġר ×Ĵ׾", + "à¸ľà¸¥ à¸ģระà¸Ĺ", + "à¸ľà¸¥à¸ģระà¸Ĺ à¸ļ", + "å¤īãĤı ãĤĭ", + "Ġ×Ķ×IJ×Ĺר ×ķ׳×Ļ×Ŀ", + "سÙģ ÙĬر", + "ĠÑĩа Ñīе", + "ãģĦ ãĤī", + "ãģĦãĤī ãģ£", + "ãģĦãĤīãģ£ ãģĹãĤĥ", + "×ķ×ŀ ׳×Ļ×Ŀ", + "Ġart tır", + "ĠCh á»ĭ", + "Ġì¡° ì§ģ", + "ĠÑĥÑģп еÑħ", + "Ġ×¢ ×ķס", + "Ġ×¢×ķס ק", + "ĠìĥĿ ëªħ", + "ÑĨ иÑĤ", + "Ġreg ión", + "Ðŀ ÐĿ", + "ĠdoÄŁ um", + "ĠyaÅŁ ad", + "ĠyaÅŁad ıģı", + "à¸Ĺà¸Ķ ลà¸Ńà¸ĩ", + "Ġgöz ü", + "ש ×Ļר×Ķ", + "дÑĥм ал", + "Ġda ģı", + "Ġdaģı t", + "à¸Ĺีม à¸ĩาà¸Ļ", + "Ġti á»ģm", + "ĠاÙĦÙĥ بر", + "ĠاÙĦÙĥبر Ùī", + "ì¹ Ń", + "ĠGü nc", + "ĠGünc elle", + "ĠGüncelle me", + "ê¹ Ĭ", + "ĠобоÑĢÑĥд ование", + "ĠÑĢеÑĪ а", + "á» ¤", + "Ġп иÑĤ", + "ĠпиÑĤ аниÑı", + "à¹Ģรีย à¸ļ", + "×Ľ×ª ×Ļ×ij×Ķ", + "Ġп он", + "Ġпон ÑĢав", + "ĠпонÑĢав и", + "Ġ×Ķ ×ķ׾×ĵ", + "Ġ×Ķ×ķ׾×ĵ ת", + "Ġê² ģ", + "Ġê²ģ ëĭĪëĭ¤", + "ĠпеÑĢв ой", + "ãĥ©ãĤ¤ ãĥķ", + "ĠÅŁi ir", + "kr ÄĻ", + "krÄĻ c", + "Ġthi á»ĥu", + "à¹Ģลย à¸Ĺี", + "à¹Ģลยà¸Ĺี à¹Ģà¸Ķียว", + "×ĺ×¢ ׳×ķת", + "ائ ÙĩÙħ", + "Ġ×IJ ס×ķר", + "ĠплаÑĤ еж", + "تر دد", + "Ġmożli we", + "Ġkh Ỽ", + "ĠkhỼ p", + "تÙģاع ÙĦ", + "ĠÑĪ колÑĮ", + "ĠÑĪколÑĮ н", + "ĠÙĤ صة", + "Ġmét ier", + "nÄĻ ÅĤa", + "หล à¹Īà¸Ń", + "Ġ ủng", + "Ġprz egl", + "Ġprzegl Äħd", + "ĠاÙĦÙħ تعÙĦ", + "ĠاÙĦÙħتعÙĦ ÙĤØ©", + "ĠÑģÑĭ н", + "Ġв олн", + "ãĥĩ ãĥ¼ãĥĪ", + "ĠÐŃ ÑĤи", + "Ġк ÑĢоме", + "à¸Ħ ารà¹Į", + "׳ק ×ķ×ĵ×Ķ", + "Ġ׾ש×ŀ ×ķ×¢", + "Ġ×ĸ ×ķ׼ר", + "ï¼ §", + "ÙĬ Ùİا", + "Ġgi á»ıi", + "åĥį ãģı", + "ĠÑģ ни", + "ĠÑģни жен", + "à¹ģà¸Ķ à¸Ķ", + "รุ à¸Ļ", + "รุà¸Ļ à¹ģรà¸ĩ", + "Ġhi á»ĩp", + "ograf ÃŃa", + "à¹Ģà¸Ī à¸Ńรà¹Į", + "Ġдв иг", + "Ġдвиг аÑĤ", + "ĠдвигаÑĤ ел", + "Ġü y", + "Ġüy eler", + "Ġüyeler i", + "Ġб Ñĥк", + "ĠбÑĥк в", + "ãĤĤ å¤ļãģı", + "Ġthi á»ĩt", + "ĠPa ÃŃs", + "ĠØ· بÙĬعÙĬ", + "à¹ģà¸Ī à¸ģ", + "ĠاÙĦص ØŃÙĬØŃ", + "Ġapp ré", + "Ġappré ci", + "Ġdecis ión", + "Ġë°ĺ ëĵľ", + "Ġë°ĺëĵľ ìĭľ", + "ĠÑĤеб е", + "ãĤ· ãĥ¼ãĤº", + "ãĤ·ãĥ¼ãĤº ãĥ³", + "Ġд алÑĮн", + "ĠìĬ ¤", + "ĠìĬ¤ ìĬ¤", + "ĠìĬ¤ìĬ¤ ë¡ľ", + "ĠTh á»ĥ", + "Ġkar ÅŁ", + "ĠkarÅŁ ıs", + "ĠkarÅŁÄ±s ında", + "ĠK ön", + "ĠKön ig", + "ив ание", + "×ij ×ķצע", + "г лаÑģ", + "Ġtw ó", + "Ġtwó rc", + "à¸Ľà¸ģ à¸Ħร", + "à¸Ľà¸ģà¸Ħร à¸Ńà¸ĩ", + "ĠG ÅĤ", + "ĠGÅĤ ówn", + "ĠUnter stüt", + "ĠUnterstüt zung", + "Ġд ÑĥÑħ", + "ĠдÑĥÑħ ов", + "Ø£ ÙħاÙĨ", + "×Ĺש ש", + "ت ظ", + "تظ اÙĩر", + "ĠлÑİб ом", + "à¸ķ าร", + "à¸ķาร าà¸ĩ", + "Ġkr ól", + "Ø£ ØŃدث", + "ì¡Į ëĭ¤", + "Ðļ ÑĥÑĢÑģ", + "ãĥĥ ãĥĦ", + "×ŀק ×ķ×ij׾", + "ĠÑģимв ол", + "Ġdés orm", + "Ġdésorm ais", + "w üns", + "wüns che", + "Ñĥ ни", + "Ñĥни ÑĨип", + "ÑĥниÑĨип алÑĮн", + "หลัà¸ģ สูà¸ķร", + "ÙĨت شر", + "Ġа л", + "Ġал к", + "Ġалк ог", + "Ġалког ол", + "ĠÑĥ ÑĩиÑĤÑĭва", + "à¸ģำ à¸ģัà¸ļ", + "Ġ׾ פע×ķ׾", + "ĠìĹ° ê²°", + "s Äħd", + "ĠاÙĦØ£ ÙĬ", + "ĠاÙĦØ£ÙĬ اÙħ", + "غÙĬ اب", + "Ġна ÑĢ", + "ĠнаÑĢ ко", + "×ŀ×ķ×ĵ ×¢", + "ĠÑģеÑĢ ии", + "пиÑģ Ñĭва", + "สิ ว", + "ç¶ļ ãģĦãģ¦", + "çĶ³ãģĹ è¾¼ãģ¿", + "Ġ׾ ×Ĵר", + "Ġ׾×Ĵר ×ķ×Ŀ", + "Ġд ем", + "Ġдем о", + "Ġë³´ ëĤ´", + "تÙĩ دÙĬد", + "ĠÙħØ´ ÙĬرا", + "Ġdu y", + "Ġduy á»ĩt", + "ĠwiÄĻks ze", + "Ùħع اÙĬ", + "ÙħعاÙĬ ÙĬر", + "ĠG da", + "ĠGda ÅĦsk", + "Ġr ah", + "Ġrah ats", + "Ġrahats ız", + "ר ×ķצ×Ķ", + "l ös", + "lös ung", + "ĠТак им", + "ÑĪ ед", + "ÑĪед ÑĪ", + "ع زÙĦ", + "Ġרש ×Ļ×ŀת", + "Ġ׾×Ķ ×Ļ׼", + "Ġ׾×Ķ×Ļ׼ ×ł×¡", + "Ġп ÑĥÑĤ", + "ĠпÑĥÑĤ еÑĪ", + "ĠпÑĥÑĤеÑĪ еÑģÑĤв", + "Ġnot ÃŃcia", + "Ġal Ä±ÅŁ", + "ĠalÄ±ÅŁ ver", + "ĠalÄ±ÅŁver iÅŁ", + "ĠwÅĤ os", + "ĠwÅĤos ów", + "Ġب غ", + "Ġبغ داد", + "Ġver öffent", + "Ġveröffent licht", + "ĠKh á", + "Ġt án", + "ëIJĺ 기", + "Ġë°© 문", + "Ùģ ÙĬÙĦ", + "à¹Ģà¸ģิà¸Ķ à¸Īาà¸ģ", + "åı¯ æĦĽ", + "åı¯æĦĽ ãģĦ", + "à¸ĸ ุà¸ĩ", + "Ġz ewnÄĻtrzn", + "à¸łà¸²à¸©à¸² à¸Ńัà¸ĩà¸ģฤษ", + "Ġmá xima", + "Ġul us", + "Ġulus lararası", + "Ġ׳×Ķ ׳", + "à¸Ĥà¹Īาว สาร", + "ĠìĿĺ ìĤ¬", + "à¹Ģหล ืà¸Ńà¸ĩ", + "Ġد ÙĤ", + "ĠدÙĤ ائÙĤ", + "สืà¹Īà¸Ń สาร", + "ë¨ ¼", + "ĠÑģоÑģÑĤоÑı нии", + "สมา à¸Ħม", + "á» Ĥ", + "ĠÐľÐ¾Ñģ ков", + "ĠÐľÐ¾Ñģков Ñģк", + "×ŀס ×ķ×Ĵ׾", + "ãģĭ ãģĭãĤĬ", + "ĠTr uyá»ģn", + "à¹ģà¸Ĥà¹ĩà¸ĩ à¹ģรà¸ĩ", + "×ŀ×Ĺ ×ĸ×Ļק", + "à¹Ĥà¸ģ à¹ī", + "ÙĬس ر", + "ìĶ ©", + "×IJ ×ķק", + "×IJ×ķק ×ĺ", + "×IJ×ķק×ĺ ×ķ×ijר", + "Ġprox imité", + "ÙħÙĨ Ùĩج", + "ĠاÙĦج ز", + "ĠاÙĦجز ائ", + "ĠاÙĦجزائ رÙĬ", + "ĠÄIJi á»ĥm", + "Ġден еж", + "Ġденеж н", + "ÙģØŃ Øµ", + "Ùģ ئ", + "ĠÐij Ñĥд", + "×Ĵ×Ļ×ĵ ×ķ׾", + "ĠÐĴ едÑĮ", + "عÙĦ اÙħØ©", + "Ġ×IJ×Ĺר ×ķ׳×ķת", + "ãģĦãģŁãģł ãģĦãģ¦", + "سÙĦ ØŃ", + "ØŃ ÙĦÙħ", + "ز ÙĪار", + "Ùĥ سر", + "×ĺ קס", + "Ġб ан", + "Ġбан ков", + "ĠпÑĢ ож", + "ĠпÑĢож ива", + "li wo", + "liwo ÅĽci", + "ĠTi ếp", + "ĠاÙĦÙħÙĨ اسب", + "ĠاÙĦØ® ÙĬار", + "ãģĬ ãģĭ", + "ãģĬãģĭ ãģĴ", + "à¸Ķà¸Ńà¸ģ à¹Ħมà¹ī", + "ä mp", + "ämp fe", + "à¸ķัà¹īà¸ĩ à¹ĥà¸Ī", + "Ġза ÑīиÑĤ", + "ĠзаÑīиÑĤ Ñĭ", + "ĠTh Æ°á»Ŀng", + "Ġص Ùģ", + "ĠصÙģ ØŃØ©", + "×Ĺ×ķר ×£", + "ãĥIJ ãĥĥãĤ°", + "Ġ×ĵ ×Ļ×Ĵ", + "Ġ×ĵ×Ļ×Ĵ ×Ļ×ĺ", + "Ġ×ĵ×Ļ×Ĵ×Ļ×ĺ ׾×Ļ", + "Ġ×Ķ×Ĺ ×ķ׾×Ļ×Ŀ", + "в еÑī", + "веÑī а", + "Ġк ÑĥлÑĮÑĤ", + "ĠкÑĥлÑĮÑĤ Ñĥ", + "ĠкÑĥлÑĮÑĤÑĥ ÑĢÑĭ", + "ĠاÙĦاÙĨ ترÙĨت", + "Ġhö ch", + "Ġhöch st", + "Ġíĺ ķ", + "Ġíĺķ íĥľ", + "Ġв ой", + "Ġвой нÑĭ", + "ÐĽ Ðŀ", + "ìĭł ìļ©", + "Ġ×ŀ×ij ×ķס", + "Ġ×ŀ×ij×ķס ס", + "×ŀ׳ ×Ļ×¢", + "Ġfiyat ı", + "ĠÑģл Ñĥж", + "ĠÑģлÑĥж бÑĭ", + "à¸Ĺั ศ", + "à¸Ĺัศ à¸Ļ", + "ãģĵãģ¨ãģĮ å¤ļãģĦ", + "Ġ×Ķ×ŀש ת", + "Ġ×Ķ×ŀשת ×ŀש", + "å¯Ħ ãģĽ", + "×ŀש׾ ×ķ×Ĺ", + "æĻĤ çĤ¹", + "æĻĤçĤ¹ ãģ§", + "à¸ŀร ี", + "à¸ŀรี à¹Ģมีย", + "à¸ŀรีà¹Ģมีย รà¹Į", + "à¸ŀรีà¹Ģมียรà¹Į ลีà¸ģ", + "Ġdiffic olt", + "Ġdifficolt Ãł", + "ãĥ¬ ãĤ¹ãĥĪ", + "ãĥ¬ãĤ¹ãĥĪ ãĥ©ãĥ³", + "สม à¹Ģà¸Ķà¹ĩ", + "สมà¹Ģà¸Ķà¹ĩ à¸Ī", + "Ġж ид", + "Ġжид к", + "Ġzu peÅĤ", + "ĠzupeÅĤ nie", + "ĠÙħ جر", + "ĠÙħجر د", + "ãģĮ å§ĭ", + "ãģĮå§ĭ ãģ¾", + "ãĤŃãĥ£ ãĥ©", + "Ġ×IJ ×ķ×ķ×Ļר", + "ãģĬ äºĴ", + "ãģĬäºĴ ãģĦ", + "Ġpot rÃł", + "ĠPa ÅĦst", + "ĠPaÅĦst wo", + "Ġب ÙĬاÙĨ", + "ĠبÙĬاÙĨ ات", + "Ġин огда", + "ĠÑĢ а", + "ĠÑĢа ÑģÑĤв", + "ĠÑĢаÑģÑĤв оÑĢ", + "Ġ×ĸ ×ŀ׳", + "ยิ à¹īม", + "Ä Ĩ", + "ãģ¾ ãģķ", + "ãģ¾ãģķ ãģ«", + "ãĥķãĤ¡ ãĤ¤ãĥ«", + "Ġgörd Ã¼ÄŁÃ¼", + "สà¸ĩ à¸Ħร", + "สà¸ĩà¸Ħร าม", + "ĠArk adaÅŁ", + "ĠrozwiÄħz ania", + "×ŀ ×ķ×ĺ", + "pi ÄĻ", + "piÄĻ t", + "ص غر", + "ส ย", + "สย าม", + "ãĤĨ ãģ£ãģıãĤĬ", + "Ġtr ần", + "Ġeconom ÃŃa", + "Ġgeh ören", + "ãĤ·ãĥ§ ãĥ¼", + "ĠsÅĤ ucha", + "à¸ŀà¸Ń à¹ĥà¸Ī", + "ĠоÑĤмеÑĤ ил", + "ÙĨت ÙĤÙĦ", + "Ġprop ósito", + "ĠваÑĪ его", + "Ġnh ắn", + "à¹ģà¸ĸ ว", + "Ġком иÑģ", + "ĠкомиÑģ Ñģи", + "waż nie", + "Ġy avaÅŁ", + "×ŀ ×Ļק", + "×ŀ×Ļק ×ķ×Ŀ", + "ש×IJ׾ ת", + "Ġyıll arda", + "ĠÐ ®", + "ĠЮ ÑĢ", + "×ł×¡ ×Ļ×ij×ķת", + "ת צ", + "תצ ×ķ×Ĵ", + "Ġод нÑĥ", + "Ġ à¸Ńยà¹Īาà¸ĩà¹Ħร", + "Ġà¸Ńยà¹Īาà¸ĩà¹Ħร à¸ģà¹ĩà¸ķาม", + "ëģ ¼", + "à¹Ħล à¹Ī", + "تس ÙĦÙĬÙħ", + "بÙĦ اغ", + "Ġì ī", + "Ġìī ½", + "Ġìī½ ê²Į", + "ãĥļ ãĥ³", + "зв ÑĥÑĩ", + "ĠW äh", + "ĠWäh rend", + "Ġ×Ļ ×Ļת", + "Ġ×Ļ×Ļת ׼ף", + "Ġkh uyên", + "Ġv ẽ", + "Ġа меÑĢ", + "ĠамеÑĢ ик", + "ĠамеÑĢик ан", + "ĠамеÑĢикан Ñģк", + "ع جب", + "ãĥĽãĥ¼ãĥł ãĥļãĥ¼ãĤ¸", + "Ġник ÑĤо", + "ĠÙĤ Ùİ", + "ĠÙĤÙİ Ø§ÙĦ", + "ĠÙĤÙİاÙĦ Ùİ", + "ÐIJ ÐĹ", + "Ùħ جÙħÙĪع", + "ÙħجÙħÙĪع ات", + "Ġnecess itÃł", + "Ġpob li", + "Ġpobli żu", + "Ġph ấn", + "ĠСо обÑī", + "ÙħÙĤ اط", + "ÙħÙĤاط ع", + "Ġ×Ķצ ×ķר×ļ", + "la ÅŁtırma", + "ว ิà¸Ķ", + "วิà¸Ķ ี", + "วิà¸Ķี à¹Ĥà¸Ń", + "Ġ그리 ìĬ¤", + "Ġ그리ìĬ¤ ëıĦ", + "ãĤ¿ãĤ¤ ãĥŁ", + "ãĤ¿ãĤ¤ãĥŁ ãĥ³ãĤ°", + "ק×ĺ ×Ĵ×ķר", + "ק×ĺ×Ĵ×ķר ×Ļ×Ķ", + "Ġ×Ĺ ×ķפ", + "Ġ×Ĺ×ķפ ש×Ļ", + "Ø£ جر", + "Ġим ени", + "ĠÑĢан ее", + "à¹Ģà¸ŀืà¹Īà¸Ńà¸Ļ à¹Ĩ", + "ĠJes ús", + "Ñģо един", + "Ñģоедин ен", + "Ġר ×Ĺ×ķק", + "à¹Ĥà¸ļ รา", + "à¹Ĥà¸ļรา à¸ĵ", + "ĠH Æ¡n", + "Ġth áºŃp", + "تع ÙĬÙĬÙĨ", + "Ġtart Ä±ÅŁ", + "ĠtartÄ±ÅŁ ma", + "ĠGes pr", + "ĠGespr äch", + "תר ×ķפ", + "תר×ķפ ×ķת", + "Ġcat égorie", + "Ġоказ Ñĭва", + "ĠналиÑĩ ие", + "Ġprésent é", + "Ġk ull", + "Ġkull and", + "Ġkulland ı", + "Ġü nl", + "Ġünl ü", + "ĠÙģ Ùĥرة", + "из аÑĤоÑĢ", + "×IJ ×ķ׳", + "×IJ×ķ׳ ×Ļ×ij", + "×IJ×ķ׳×Ļ×ij רס", + "×IJ×ķ׳×Ļ×ijרס ×Ļ×ĺת", + "ĠÑĢаÑģÑģ маÑĤ", + "ĠÑĢаÑģÑģмаÑĤ ÑĢ", + "ĠÑĢаÑģÑģмаÑĤÑĢ ива", + "تÙĥÙĦ Ùħ", + "Ùĥت رÙĪ", + "ÙĥترÙĪ ÙĨÙĬ", + "ĠÑģо ÑĩеÑĤ", + "ĠÑģоÑĩеÑĤ а", + "ãĤĴè¦ĭ ãģĽ", + "Ġng ừa", + "ĠÐł еÑģп", + "ĠÐłÐµÑģп Ñĥб", + "ĠÐłÐµÑģпÑĥб лик", + "ãĤ¦ ãĤ©", + "ãĤ¦ãĤ© ãĥ¼", + "ĠÐľ еждÑĥ", + "ĠìŀĪ ê²Į", + "Ġm â", + "ĠìļĶ ì²Ń", + "ض ار", + "ลุ à¹īà¸Ļ", + "ëĮĢ íķĻêµIJ", + "×ĸ ×Ļ׼", + "×ĸ×Ļ׼ ר×ķף", + "ãĤ¹ ãĥļ", + "ãĤ¹ãĥļ ãĥ¼ãĤ¹", + "ĠкÑĢаÑģ оÑĤ", + "ï¼ ¨", + "ê¼ Ń", + "ãĤĴ éĽĨ", + "ãĤĴéĽĨ ãĤģ", + "ë° Ŀ", + "Ġ×Ķ׳ ×IJ", + "Ġ×Ķ׳×IJ ש×Ŀ", + "Ġê°Ģ ìļ´", + "Ġê°Ģìļ´ ëį°", + "تÙĥÙĦ ÙģØ©", + "ĠØŃ ÙĤÙĬÙĤÙĬ", + "Ġh alk", + "Ġhalk ın", + "ÑİÑī ÑĥÑİ", + "ĠÑģп ин", + "סר×ĺ ף", + "ĠпеÑĢв ого", + "Ġпол ож", + "Ġполож иÑĤелÑĮн", + "Ġд л", + "Ġдл иÑĤелÑĮн", + "ĠV Ä©nh", + "ê´ ´", + "ĠÑģÑĭ ÑĢ", + "ĠíĨµ íķĺìŬ", + "ë³ij ìĽIJ", + "à¹Ĥรà¸ĩ à¸ĩาà¸Ļ", + "รัà¸ļ à¸ľà¸´à¸Ķ", + "รัà¸ļà¸ľà¸´à¸Ķ à¸Ĭà¸Ńà¸ļ", + "تج ÙĨب", + "s ÅĤ", + "sÅĤ uch", + "ãĤ¢ãĥ« ãĥIJ", + "ãĤ¢ãĥ«ãĥIJ ãĥł", + "ëī´ ìĬ¤", + "Ġpat ië", + "Ġpatië nt", + "Ġìĺ ¤í", + "Ġìĺ¤í ŀ", + "Ġìĺ¤íŀ Ī", + "Ġìĺ¤íŀĪ 볤", + "ĠDer ne", + "ĠDerne ÄŁi", + "wró ci", + "wróci Äĩ", + "Ġоб Ñī", + "ĠобÑī еÑģÑĤв", + "ĠобÑīеÑģÑĤв енно", + "ĠêµIJ ìĪĺ", + "tıģ ımız", + "Ġ×Ķ×ŀש ×Ļ×ij", + "k örper", + "Ġпозв ол", + "Ġпозвол иÑĤ", + "ĠChi ến", + "أخ ÙĪ", + "ĠAy dın", + "à¸Ķà¹īาà¸Ļ ล", + "à¸Ķà¹īาà¸Ļล à¹Īาà¸ĩ", + "Ġdr u", + "Ġdru ż", + "Ġdruż yn", + "Ġë°ľ íijľ", + "ĠTh ảo", + "جÙĩ اد", + "à¸ģระà¸Ĺ ูà¹ī", + "Ġк ÑĢов", + "ĠкÑĢов и", + "Ġiçer ik", + "Ġnad zie", + "Ġnadzie jÄĻ", + "ĠС моÑĤÑĢ", + "Ġph ức", + "ج تÙħاع", + "جتÙħاع ÙĬØ©", + "ком пон", + "компон енÑĤ", + "Ġб ил", + "Ġбил еÑĤ", + "ãĥIJ ãĥ³ãĥī", + "ĠPol ÃŃcia", + "اÙĦ تÙĩ", + "اÙĦتÙĩ اب", + "ØŃر Ùģ", + "ت خط", + "تخط ÙĬØ·", + "ãĤ³ ãĥ¼ãĥ", + "ãĤ³ãĥ¼ãĥ Ĵ", + "ãĤ³ãĥ¼ãĥĴ ãĥ¼", + "･･ ï½¥", + "à¸ĭ à¸Ńย", + "Ġcréd it", + "è²· ãģ£ãģŁ", + "ĠпоÑĢ Ñıд", + "ĠпоÑĢÑıд ке", + "Ġph ó", + "Ġw ida", + "Ġwida Äĩ", + "جر ائÙħ", + "à¸ľ ี", + "ĠbÄĻd ÄĻ", + "Ġ×ŀ פת×Ĺ", + "ãĥij ãĥ¼ãĥ", + "ãĥijãĥ¼ãĥ Ĩ", + "ãĥijãĥ¼ãĥĨ ãĤ£", + "ãĥijãĥ¼ãĥĨãĤ£ ãĥ¼", + "ĠKa ż", + "ĠKaż dy", + "ĠнеобÑħодим оÑģÑĤи", + "à¸Ł à¸Ńรà¹Į", + "à¸Łà¸Ńรà¹Į ม", + "Ġмал ÑĭÑĪ", + "Ġпл оÑĤ", + "ĠÑĥ ÑģÑĤÑĢой", + "ĠÑĥÑģÑĤÑĢой ÑģÑĤва", + "à¸ĸ à¸Ńà¸Ļ", + "ĠoluÅŁtur ul", + "ĠÅĽwi ad", + "ĠÅĽwiad om", + "Ùħع Ùĩد", + "ĠпÑĢоиз веден", + "Æ ł", + "ר ×Ļש", + "Ùħست Ø«", + "Ùħستث Ùħر", + "׳×Ļ ×Ļר", + "pa ñ", + "Ġ; -)", + "Ġë°ľ 견", + "Ġgör üyor", + "Ùħؤ ÙĦÙģ", + "ĠÄIJ á»ģ", + "ĠاÙĦÙĨ ÙĪاب", + "×Ĺק ×Ļר×Ķ", + "Ġm á»ıi", + "è¿° ãģ¹", + "ÐĿ ик", + "ìŀĸ ìķĦ", + "ìŀĸìķĦ ìļĶ", + "prowadzi ÅĤ", + "l óg", + "lóg ica", + "פס ×ĺ", + "פס×ĺ ×Ļ×ij׾", + "Ġ×ŀ ×ĵ×Ķ", + "Ġ×ŀ×ĵ×Ķ ×Ļ×Ŀ", + "ãģĵãģĵ ãģ¾ãģ§", + "×Ķ ת×Ĺ", + "×Ķת׊׾×Ķ", + "Ġפ ×ķס", + "Ġפ×ķס ×ĺ×Ļ×Ŀ", + "Ġн ев", + "Ġнев оз", + "Ġневоз можно", + "ĠdostÄĻp ny", + "Ġغ اÙĦ", + "ĠغاÙĦ ب", + "Ġbez pieczeÅĦst", + "ĠbezpieczeÅĦst wa", + "åĪĨ ãģĭãĤĭ", + "ĠF ührung", + "à¸ģ ีà¹ī", + "gem Ã¤ÃŁ", + "à¸Ĭà¹Īวà¸ĩ à¹Ģวลา", + "Ġìļ°ë¦¬ ëĤĺ", + "Ġìļ°ë¦¬ëĤĺ ëĿ¼", + "ãģ¥ ãģıãĤĬ", + "ĠاÙĦÙħ سÙĦ", + "ĠاÙĦÙħسÙĦ ØŃØ©", + "Ġlibert é", + "клÑİÑĩ ение", + "Ġzam ów", + "Ġzamów ienia", + "รà¸ĸ à¹Ħà¸Ł", + "Ø£ ÙģÙĦ", + "Ø£ÙģÙĦ اÙħ", + "Ùħ راج", + "Ùħراج عة", + "Ġë¹Ħ êµIJ", + "ĠاÙĦت اب", + "ĠاÙĦتاب عة", + "Ġë§Į ëĤĺ", + "Ġб Ñĥм", + "ĠбÑĥм аг", + "Ġgé nero", + "Ġìŀĺ 못", + "×ŀ פ×ķר×ĺ", + "è²·ãģĦ çī©", + "ĠÙĦدÙĬ Ùĥ", + "Ġ×ľ×¢ ×Ļת", + "Ġ×ľ×¢×Ļת ×Ļ×Ŀ", + "ĠsÅĤ ab", + "ĠпÑĢедÑģÑĤав лÑı", + "ãĤ¿ ãĤ¤ãĥĪ", + "ãĤ¿ãĤ¤ãĥĪ ãĥ«", + "Ùħ ص", + "Ùħص Ø·Ùģ", + "ÙħصطÙģ Ùī", + "Ġdifficult é", + "ãĥĨãĤ£ ãĥĸ", + "Ġpew noÅĽci", + "ĠpewnoÅĽci Äħ", + "Ġ무 ìĬ¨", + "Ø¥ رس", + "إرس اÙĦ", + "Ġд алÑĮ", + "ĠдалÑĮ ÑĪе", + "Ġ׾ ×ł×¡", + "Ġ×ľ×ł×¡ ×ķת", + "หมูà¹Ī à¸ļà¹īาà¸Ļ", + "×ŀס×ŀ ׼×Ļ", + "أسÙĦ ÙĪب", + "Ġzw ÅĤ", + "ĠzwÅĤ as", + "ĠzwÅĤas zc", + "ĠzwÅĤaszc za", + "ĠпÑĢ еж", + "ĠпÑĢеж де", + "ĠоÑĢганиз аÑĨиÑı", + "Ġdön emin", + "Ġdönemin de", + "Ġ Ủ", + "ĠỦ y", + "ä¸ĭ ãģĴ", + "ĠпоÑģлед ние", + "Ġgü ne", + "Ġgüne ÅŁ", + "Ġ×IJ ×ĸר", + "Ġ×IJ×ĸר ×Ĺ×Ļ", + "ãģ§ãģĤ ãĤįãģĨ", + "ĠÙĨ ÙĤ", + "ĠÙĨÙĤ اط", + "æŃ£ ãģĹãģĦ", + "ĠÑĢ ег", + "ĠÑĢег иона", + "ĠFör der", + "ê²½ ìĺģ", + "dıkl ar", + "dıklar ını", + "trzym aÄĩ", + "أش Ùĥ", + "أشÙĥ اÙĦ", + "×Ķת ×IJ", + "×Ķת×IJ ×ŀ×Ķ", + "à¸Ĺำà¹ĥหà¹ī à¹Ģà¸ģิà¸Ķ", + "ĠGeb ä", + "ĠGebä ude", + "ĠСеÑĢ г", + "ĠСеÑĢг ей", + "Ġз доÑĢов", + "ĠздоÑĢов ÑĮÑı", + "Ġr ãi", + "ĠпÑĢед ÑĥÑģ", + "ĠпÑĢедÑĥÑģ моÑĤÑĢ", + "ĠпÑĢедÑĥÑģмоÑĤÑĢ ен", + "Ġ×Ķצ ×Ļ×ij", + "Ġ×Ķצ×Ļ×ij ×ķר×Ļ", + "Ġdés ir", + "Ġн оÑĩ", + "ĠноÑĩ ÑĮ", + "möglich keiten", + "Ġ×IJ×Ĺר ×ķ׳×Ļ×Ŀ", + "Ġsoir ée", + "ĠNh áºŃn", + "Ù ª", + "à¸Ľà¸£à¸°à¸§à¸±à¸ķิ ศาสà¸ķรà¹Į", + "êµIJ íĨµ", + "ĠØ£ Ø®ÙĬ", + "Ġdé cid", + "Ġdécid é", + "Ġwy ja", + "Ġwyja ÅĽni", + "Ġ สิ", + "Ġสิ à¸ĩ", + "Ġสิà¸ĩ หา", + "Ġสิà¸ĩหา à¸Ħม", + "à¹ģ à¸Ńรà¹Į", + "หà¸Ļà¹īา à¸Īà¸Ń", + "ס תר", + "Ġê ¶", + "Ġê¶ Į", + "Ġê¶Į 리", + "pl ätze", + "ب Ø·ÙĦ", + "ê±´ ìĦ¤", + "Ġ×IJ ×Ļ×ŀ×Ļ", + "Ġ×IJ×Ļ×ŀ×Ļ ×Ļ׾", + "ãģ ½", + "تر اث", + "×IJ׾ ×Ļ×ŀ×ķת", + "Ġdispon ÃŃveis", + "Ġz ale", + "Ġzale ży", + "à¸Ľà¸£à¸°à¸Ĭา สัมà¸ŀัà¸Ļà¸ĺà¹Į", + "ĠÅļw iat", + "Ġpor ówn", + "Ġporówn a", + "Ġ׾×ĺ ×ķ×ijת", + "×Ķ×ĸ ×ŀ׳×Ķ", + "Ġ×Ľ×ª ×ķצ×IJ×Ķ", + "Ġ×ij ק׾", + "Ġ×ijק׾ ×ķת", + "ĠоÑĤ кÑĢ", + "ĠоÑĤкÑĢ Ñĭва", + "ãĥij ãĥ¯ãĥ¼", + "ë¿IJ ë§Į", + "Ġв ÑģÑı", + "ĠвÑģÑı к", + "ãģ¨ãģª ãģ£ãģ¦ãģĦãĤĭ", + "Ġgi áºŃn", + "Ġок ÑĢÑĥ", + "ĠокÑĢÑĥ жа", + "ĠокÑĢÑĥжа ÑİÑī", + "ĠUnivers ität", + "ĠÑĢ ож", + "ĠÑĢож д", + "ĠÑĢожд ениÑı", + "Ø® ÙĬÙĦ", + "Ġкомпани й", + "ĠÑĢазлиÑĩ нÑĭе", + "ĠЦ ена", + "׳×Ļ ×ķ×ĸ", + "׳×Ļ×ķ×ĸ ׾", + "׳×Ļ×ķ×ĸ׾ ×ĺר", + "Ġê³µ ê°Ħ", + "Ġê°ľ ëħIJ", + "landır ma", + "ĠÑĥдал ен", + "à¸ŀัà¸ģ à¸ľ", + "à¸ŀัà¸ģà¸ľ à¹Īà¸Ńà¸Ļ", + "Ġprote cción", + "Ġb ÅĤ", + "ĠbÅĤ ÄĻd", + "à Ī", + "Ġíĸī ë³µ", + "ĠÅŁ ü", + "ĠÅŁÃ¼ phe", + "Ġí Ķ", + "ĠíĶ ¼", + "ĠíĶ¼ íķ´", + "Ġëĭ¤ 르", + "à¹Ħมà¹Ī à¹Ģà¸ģิà¸Ļ", + "ãģ¿ ãģª", + "ãģ¿ãģª ãģķãĤĵ", + "ĠпоÑĤ ÑĢеб", + "ĠпоÑĤÑĢеб иÑĤел", + "ĠاÙĦÙĥÙĦ اÙħ", + "ìķĦ ë²Ħ", + "ìķĦë²Ħ ì§Ģ", + "ãĤĴ使 ãģ£ãģŁ", + "Ġbụ i", + "ĠпоÑĤ еÑĢ", + "ĠпоÑĤеÑĢ Ñı", + "ĠØ¢ ÙĦاÙģ", + "ĠнаÑģÑĤоÑıÑī ее", + "ãģıãģªãĤĬ ãģ¾ãģĹãģŁ", + "clus ão", + "ãĤ³ ãĥĶãĥ¼", + "צ פ×Ļ", + "צפ×Ļ ×Ļ×Ķ", + "Ø® ÙĦا", + "Ø®ÙĦا ص", + "ล à¹īำ", + "ãĥ¯ ãĤ¤ãĥ³", + "Ġมี à¸Ļา", + "Ġมีà¸Ļา à¸Ħม", + "Ø´ خص", + "شخص ÙĬات", + "Ġ×ĸ ק", + "Ġ×ĸק ×ķק", + "×Ļ ×Ļצ", + "×Ļ×Ļצ ×Ĵ", + "èĢĥãģĪ æĸ¹", + "Ġürün ü", + "ĠиÑģп ол", + "ĠиÑģпол ни", + "Ġcompañ ero", + "ק צ×Ķ", + "×ŀ×¢ ׳×Ļק", + "Ùħ ØŃÙħد", + "Ġc ámara", + "Ġп ед", + "Ġпед аг", + "Ġпедаг ог", + "м аÑĢ", + "маÑĢ к", + "×Ķת ׳×Ĵ×ĵ", + "ĠìĨĮ ê°ľ", + "Ġcom unitÃł", + "ê³ ¤", + "ĠNg Ãłi", + "สà¸ĩ à¸ļ", + "ĠmieszkaÅĦ ców", + "ĠÙĨ ÙĩائÙĬ", + "iv ité", + "Ġи де", + "Ġиде алÑĮн", + "ĠØ£ سبÙĪع", + "Ġ×Ļ ×¢×ľ", + "Ġ׾ ר×IJש", + "Ġ׾ר×IJש ×ķ׳×Ķ", + "ĠзапиÑģ и", + "ĠкоÑĢ пÑĥÑģ", + "วà¸ĩ ศ", + "วà¸ĩศ à¹Į", + "ĠÐĶ м", + "ĠÐĶм иÑĤ", + "ĠÐĶмиÑĤ ÑĢ", + "Ġkön nt", + "Ġböl ges", + "Ġbölges inde", + "׼ ×Ļ׼", + "׼×Ļ׼ ר", + "ĠاÙĦØ¥ Ø«ÙĨ", + "ĠاÙĦإثÙĨ ÙĬÙĨ", + "Ġng á»Ļ", + "ì¹ ł", + "د راج", + "Ġu da", + "Ġuda ÅĤo", + "ìº IJ", + "بر ÙĨاÙħج", + "ĠÑģÑĥд еб", + "ĠÑģÑĥдеб н", + "Ġzun ächst", + "ĠEduc ación", + "ãģ¨ãģª ãģ£ãģ¦ãģĦãģ¾ãģĻ", + "Ġ×Ķ×IJ ×ŀ×Ļת×Ļ", + "ĠÄ° nt", + "ĠÄ°nt ernet", + "ĠcaÅĤ ego", + "ãĥĹãĥª ãĥ³", + "Ø¥ بد", + "إبد اع", + "ĠпоÑĢ ÑĤал", + "à¹Ĥà¸ķ à¹ī", + "Ġ×Ķק ש×ķר", + "пл од", + "ĠÙħ د", + "ĠÙħد رÙĬد", + "×ŀסע ×ĵ×Ķ", + "ĠØ´ÙĬ ئ", + "ĠØ´ÙĬئ ا", + "à¸ģà¹Īà¸Ń สรà¹īาà¸ĩ", + "Ġì°¸ ê³ł", + "à¹Ģà¸Ĺ ร", + "à¹Ģà¸Ĺร à¸Ķ", + "Ġ×ij×ŀ קר×Ļ×Ŀ", + "Ġb ât", + "Ġbât iment", + "åij¼ ãģ³", + "ç´ł æķµ", + "ç´łæķµ ãģª", + "przedsiÄĻbior st", + "przedsiÄĻbiorst w", + "Ġ×ł×ª ×ķ׳×Ļ×Ŀ", + "×Ĺ׾ ×ķ×Ŀ", + "ร วย", + "Ùħ ÙĪضÙĪع", + "ĠÑģоб ÑĢан", + "вед ÑĥÑī", + "ĠÑĤе аÑĤ", + "ĠÑĤеаÑĤ ÑĢ", + "m eye", + "meye ceÄŁi", + "Ġpien iÄħ", + "ĠpieniÄħ d", + "ĠpieniÄħd ze", + "ÑĢез иденÑĤ", + "ØŃ ØµØ±", + "ìĺ ¥", + "à¹Ģย ืà¸Ńà¸Ļ", + "ĠÑĥ ни", + "ĠÑĥни веÑĢ", + "ĠÑĥнивеÑĢ Ñģ", + "ĠÑĥнивеÑĢÑģ иÑĤеÑĤ", + "ĠاÙĦر ØŃ", + "ĠاÙĦرØŃ ÙħÙĨ", + "ĠÑĤеÑħ нолог", + "ĠÑĤеÑħнолог ии", + "ìĹIJ ëĦĪ", + "ìĹIJëĦĪ ì§Ģ", + "Ġíķ Ń", + "ĠíķŃ ìĥģ", + "à¸ĺ า", + "à¸ĺา à¸ķุ", + "ĠEspañ ol", + "×ĵ×Ĵ ש", + "Ġêµ ī", + "Ġêµī ìŀ¥", + "Ġêµīìŀ¥ íŀĪ", + "ĠÅĤ at", + "ĠÅĤat wo", + "Ġk á»ĭch", + "Ø¥ ز", + "إز اÙĦØ©", + "ĠдейÑģÑĤв ие", + "ĠsaÄŁ layan", + "สุà¸Ķ ยà¸Ńà¸Ķ", + "Ġzosta Äĩ", + "Ġdispon ÃŃvel", + "ïº į", + "ver ständ", + "verständ lich", + "tw or", + "twor zyÄĩ", + "ع جز", + "à¹Ģà¸Ĥ à¹īม", + "ยà¹Ī à¸Ńม", + "Ġstrat ég", + "Ġstratég ie", + "à¸ľà¸¥ à¹Ħมà¹ī", + "Ġê°ģ ì¢ħ", + "ĠÙħ ÙĪا", + "ĠÙħÙĪا ض", + "ĠÙħÙĪاض ÙĬع", + "اØŃ ØªØ¬", + "اØŃتج اج", + "Ġ Ấ", + "ĠẤ n", + "×ŀ ×ŀש׾×Ķ", + "ĠÅŁek il", + "×ŀ ×Ĺ׾", + "×ŀ×Ĺ׾ ×ķת", + "Ġ à¸ĺ", + "Ġà¸ĺ ัà¸Ļ", + "Ġà¸ĺัà¸Ļ วา", + "Ġà¸ĺัà¸Ļวา à¸Ħม", + "Ġìĭ¤ ìłľ", + "Ġìĭ¤ìłľ ë¡ľ", + "ì¤ij ìķĻ", + "ëįĶ ëĿ¼", + "ĠÑĪ иÑĢ", + "ĠÑĪиÑĢ око", + "Ġsol ución", + "วาà¸ĩ à¹ģà¸ľà¸Ļ", + "×IJ×ķ×ĺ ×ķ×ŀ", + "×IJ×ķ×ĺ×ķ×ŀ ×ĺ×Ļ", + "ĠÑĢ еÑģÑĤ", + "ĠÑĢеÑģÑĤ оÑĢ", + "ĠÑĢеÑģÑĤоÑĢ ан", + "ëį ¸", + "ÑĤ ÑĢад", + "ÑĤÑĢад и", + "ÑĤÑĢади ÑĨион", + "ÑĤÑĢадиÑĨион н", + "มะ à¹Ģรà¹ĩ", + "มะà¹Ģรà¹ĩ à¸ĩ", + "à¹Ĥ ส", + "Ġol masını", + "×ŀ×ķס ר", + "ĠоÑĤноÑĪ ении", + "Ġê°ĢëĬ¥ ìĦ±", + "Ġy uk", + "Ġyuk arı", + "ìĨ Ķ", + "ĠÑģ ÑĦ", + "ĠÑģÑĦ еÑĢе", + "Ġק ×ķפ", + "ãĤ± ãĥ¼ãĤ", + "ãĤ±ãĥ¼ãĤ Ń", + "âĢķ âĢķ", + "ĠاÙĦØ£ ÙĦÙħ", + "ĠاÙĦØ£ÙĦÙħ اÙĨÙĬ", + "Ả N", + "ת×ķ׼ ׳×Ļ×ķת", + "ĠÑģÑĥÑīеÑģÑĤв ÑĥеÑĤ", + "æĪij ãĢħ", + "ĠاÙĦص ادر", + "ĠTr á»įng", + "Ġа д", + "Ġад миниÑģÑĤ", + "ĠадминиÑģÑĤ ÑĢа", + "ĠадминиÑģÑĤÑĢа ÑĨи", + "ĠдÑĢÑĥг ими", + "Ñģп еÑĪ", + "عÙĦاÙħ ات", + "Ġа б", + "Ġаб Ñģол", + "ĠабÑģол ÑİÑĤ", + "ĠабÑģолÑİÑĤ но", + "ฤ à¸Ķู", + "é tr", + "étr anger", + "нÑı ÑĤи", + "нÑıÑĤи е", + "×¢ ×ķ׳", + "×¢×ķ׳ ש", + "ĠÙĤ ائ", + "ĠÙĤائ ÙĦا", + "Ġм аÑģ", + "ĠмаÑģ ло", + "ãĥī ãĤ¤", + "ãĥīãĤ¤ ãĥĦ", + "å¿ħè¦ģ ãģĮãģĤãĤĬãģ¾ãģĻ", + "×ŀ×ķ×ĸ ×Ļ×IJ", + "×ŀ×ķ×ĸ×Ļ×IJ ×ķף", + "ĠNgo ại", + "Ġkê nh", + "à¸ģาร à¸Ńà¸Ńà¸ģà¹ģà¸ļà¸ļ", + "×ŀ פק", + "×ŀפק ×ĵ", + "ÙħÙĨ از", + "ÙħÙĨاز ÙĦ", + "ë· °", + "íĹ ¤", + "ÙħÙĩ ارات", + "Ġpropri été", + "פ×Ĵ ×Ļש×Ķ", + "Ñĩ ÑĢ", + "ÑĩÑĢ еж", + "ÑĩÑĢеж ден", + "×Ķ ×ķצ×IJ×Ķ", + "ØŃÙĥ ÙĬÙħ", + "ĠíĻ Ī", + "ĠíĻĪ íİĺìĿ´ì§Ģ", + "åİ ³", + "åݳ ãģĹãģĦ", + "×¢ ×ŀ×ĵ×Ķ", + "ĠAu ÃŁen", + "سÙĪ Ø¡", + "ë¹ Ī", + "ĠÙĪ Ø®", + "ĠÙĪØ® اصة", + "ин ÑĤеÑĢ", + "инÑĤеÑĢ еÑģ", + "èĩ´ ãģĹãģ¾ãģĻ", + "Ġhük üm", + "à¹Ħà¸Ĥ มัà¸Ļ", + "Ġdav ran", + "Ġdavran Ä±ÅŁ", + "à¹Ģà¸ķ ียà¸ĩ", + "в ÑĢем", + "вÑĢем енно", + "à¹Ģà¸Ĺศ à¸ģา", + "à¹Ģà¸Ĺศà¸ģา ล", + "å¼ķ ãģ£", + "å¼ķãģ£ è¶ĬãģĹ", + "×IJר ×ķ×Ĺ", + "×IJר×ķ×Ĺ ×ª", + "à¹Ģ วิ", + "à¹Ģวิ รà¹Į", + "à¸Ńยà¹Īาà¸ĩ รวà¸Ķà¹Ģรà¹ĩว", + "ĠìŬ íĸī", + "ĠÑĢан ÑĮ", + "ĠÑĢанÑĮ ÑĪе", + "Ġzob ow", + "Ġzobow iÄħ", + "ĠzobowiÄħ z", + "Ġ×ķ׼ ×ŀ×ķ×ijף", + "ĠاÙĦÙħ Ùĩ", + "ĠاÙĦÙħÙĩ ÙĨÙĬ", + "ãĤ¢ ãĤ¸", + "ãĤ¢ãĤ¸ ãĤ¢", + "ë°© ìĨ¡", + "à¸Ńà¸Ńà¸ģ à¸ģำลัà¸ĩ", + "à¸Ńà¸Ńà¸ģà¸ģำลัà¸ĩ à¸ģาย", + "am éli", + "améli orer", + "å½ĵãģŁãĤĬ åīį", + "Ġreg elm", + "Ġregelm Ã¤ÃŁig", + "ãģĬ åĭ", + "ãģĬåĭ §", + "ãģĬåĭ§ ãĤģ", + "Ġm Æ°á»Ŀi", + "بر Ùħج", + "ĠNat ürlich", + "ĠD Å©ng", + "ĠاÙĦر جاÙĦ", + "Ġthé p", + "Ġol muÅŁtur", + "×ŀ×ķס ×Ļק×Ķ", + "f älle", + "주 íĥĿ", + "ĠاÙĦÙģ رص", + "Ġnaj wiÄĻks", + "ĠnajwiÄĻks zy", + "Ġça ÄŁ", + "ĠçaÄŁ rı", + "ì¸ ł", + "ĠvÃŃ ct", + "ĠvÃŃct ima", + "ĠÑģовеÑĢ ÑĪен", + "×Ķ×Ļ ×Ļת×Ļ", + "à¹Ģà¸Ķ ี", + "à¹Ģà¸Ķี à¹ĭ", + "à¹Ģà¸Ķีà¹ĭ ยว", + "ü yü", + "Ġд оп", + "Ġдоп олн", + "Ġдополн иÑĤелÑĮно", + "à¹ģà¸ķà¸ģà¸ķà¹Īาà¸ĩ à¸ģัà¸Ļ", + "Ġá l", + "Ġál bum", + "à¸Ľà¸£à¸°à¸Īำ à¸Ľà¸µ", + "ĠÑĦ едеÑĢ", + "ĠÑĦедеÑĢ алÑĮн", + "Ġobs ÅĤ", + "ĠobsÅĤ ugi", + "à¹Ģร ืà¹Ī", + "à¹Ģรืà¹Ī à¸Ńย", + "à¹Ģรืà¹Īà¸Ńย à¹Ĩ", + "ëģ Į", + "Ġngh ìn", + "ĠBaÅŁkan lıģı", + "تأ سÙĬ", + "تأسÙĬ س", + "Ġ×ij×ij ×ķקר", + "Ġ×¢×ij×ķ×ĵ ×ķת", + "Ġبص ÙĪرة", + "ãĤıãģij ãģ§ãģ¯ãģªãģĦ", + "führ er", + "ãĤ¹ ãĤŃ", + "ãĤ¹ãĤŃ ãĥ«", + "ĠاÙĦÙĤ ض", + "ĠاÙĦÙĤض ÙĬØ©", + "Ġдолж ноÑģÑĤ", + "Ùģار ÙĤ", + "Ġcomeç ou", + "Ġorganis é", + "Ġxu ân", + "ĠÑģообÑī аеÑĤ", + "ĠпÑĢи д", + "ĠпÑĢид еÑĤÑģÑı", + "TÃľ RK", + "ãĥ¬ ãĥ¼ãĤ·ãĥ§ãĥ³", + "Kh ông", + "است Ùģ", + "استÙģ ادة", + "ä¸ĬãģĮ ãģ£ãģ¦", + "Ġum ie", + "Ġumie jÄĻ", + "ĠumiejÄĻ tn", + "ĠumiejÄĻtn oÅĽci", + "ëĤ ¸", + "à¹Ģà¸Ļ à¸Ńรà¹Į", + "×ĵ×ķ ×ķ×Ĺ", + "ÃŃs imo", + "I ÃĬ", + "IÃĬ N", + "Ġalcan ç", + "Ġ à¸ķุ", + "Ġà¸ķุ ลา", + "Ġà¸ķุลา à¸Ħม", + "ש׾ ×ĺ×ķף", + "Ġél è", + "Ġélè ves", + "ĠÄij u", + "ĠÄiju á»ķi", + "ĠØ£ Ùģ", + "ĠØ£Ùģ رÙĬ", + "ĠØ£ÙģرÙĬ ÙĤÙĬ", + "ĠØ£ÙģرÙĬÙĤÙĬ ا", + "ãĤĴæİ¢ ãģĻ", + "ĠпÑĢед ложениÑı", + "ج اد", + "ĠÑħоÑĤ ÑĮ", + "Ñģ ал", + "Ñģал он", + "à¸Ľà¸£à¸° à¹Ģม", + "à¸Ľà¸£à¸°à¹Ģม ิà¸Ļ", + "ãĤŃ ãĥĥãĥģ", + "ãĤŃãĥĥãĥģ ãĥ³", + "×ij×ĵ×Ļק ×ķת", + "Ġch ù", + "Ġchù a", + "ÐĴ иде", + "ÐĴиде о", + "иÑĢов ка", + "ĠÑħоÑĤ иÑĤе", + "Ġspéc ifique", + "รส à¸Ĭาà¸ķิ", + "è¾¼ ãĤĵãģł", + "伸 ãģ³", + "×Ķצ׾ ×Ĺת", + "ãģ©ãģ® ãĤĪãģĨãģ«", + "سع ادة", + "Ġл ид", + "Ġлид еÑĢ", + "ม à¸ĩ", + "มà¸ĩ à¸Ħล", + "ØŃ Ø§ÙħÙĦ", + "หล ุà¸Ķ", + "à¸Ńยà¹Īาà¸ĩ à¸ķà¹Īà¸Ń", + "à¸Ńยà¹Īาà¸ĩà¸ķà¹Īà¸Ń à¹Ģà¸Ļืà¹Īà¸Ńà¸ĩ", + "ãģķãģĽãģ¦ éłĤ", + "تس ÙĪÙĬ", + "تسÙĪÙĬ ÙĤ", + "ĠaÅŁaģı d", + "ĠaÅŁaģıd aki", + "ĠÑĨ елÑĮ", + "ĠÑĨелÑĮ Ñİ", + "ĠAra ÅŁtırma", + "à¸Ĥัà¸ļ รà¸ĸ", + "Ùĩ Ø°Ùĩ", + "ลà¸ĩ à¸Ĺะ", + "ลà¸ĩà¸Ĺะ à¹Ģà¸ļ", + "ลà¸ĩà¸Ĺะà¹Ģà¸ļ ียà¸Ļ", + "تÙĥ اÙħÙĦ", + "Ġc io", + "Ġcio è", + "ãģ¦ ãģĬãģı", + "ĠاÙĦصØŃ ÙģÙĬ", + "ĠíĬ¹ ìłķ", + "полн иÑĤÑĮ", + "ãĤĵ ãģĺãĤĥãģªãģĦ", + "ãĤĵãģĺãĤĥãģªãģĦ ãģĭ", + "ĠاÙĦج Ùĩ", + "ĠاÙĦجÙĩ ات", + "ĠÑĥÑģпеÑĪ но", + "Ġв ок", + "Ġвок ÑĢÑĥг", + "ĠÑģиÑĤÑĥ аÑĨиÑı", + "Ġ×Ķ×IJ ×ŀר", + "Ġ×Ķ×IJ×ŀר ×Ļק", + "Ġ×Ķ×IJ×ŀר×Ļק ×IJ×Ļ", + "×ŀ ×Ĵ×ĸ", + "×ŀ×Ĵ×ĸ ×Ļף", + "Ġак ÑĤÑĥ", + "ĠакÑĤÑĥ алÑĮн", + "é ta", + "éta is", + "Ġmog ÅĤa", + "ĠÑĤоÑĩ ки", + "Ġ×ŀ×Ķ ×ŀ×¢", + "Ġ×ŀ×Ķ×ŀ×¢ ×¨×Ľ×ª", + "มี à¸Ľà¸£à¸°à¸ªà¸´à¸Ĺà¸ĺà¸´à¸łà¸²à¸ŀ", + "×Ļר ×Ļ×ĵ×Ķ", + "×Ĵר ×ŀ׳", + "×Ĵר×ŀ׳ ×Ļ×Ķ", + "Ġг лав", + "Ġглав ное", + "Ġ미 ëŀĺ", + "Ġ׳׼ ×ķ׳×Ķ", + "ĠÙĪ Ø·ÙĨÙĬ", + "op port", + "opport unitÃł", + "Ġh ủy", + "ĠÙĦ تØŃ", + "ĠÙĦتØŃ ÙĤÙĬÙĤ", + "Ġó rg", + "Ġórg ão", + "ãĤ¹ ãĥĶ", + "ãĤ¹ãĥĶ ãĥ¼ãĥī", + "Ġön ü", + "Ġönü ne", + "Ùħع اÙħÙĦ", + "ש×ŀ ×Ļר×Ķ", + "ĠвеÑģÑĮ ма", + "ĠwiÄĻks zo", + "ĠwiÄĻkszo ÅĽÄĩ", + "Ġاست راتÙĬج", + "ĠاستراتÙĬج ÙĬØ©", + "ĠÙģ Ø¥", + "ĠÙģØ¥ ذا", + "à¹Ģà¸Ĭืà¹Īà¸Ń ม", + "à¹Ģà¸Ĭืà¹Īà¸Ńม à¸ķà¹Īà¸Ń", + "Ġ׾ פר", + "Ġ׾פר ×ĺ×Ļ×Ŀ", + "Ùħض ÙĬ", + "ĠGer çek", + "Ġçocuk ların", + "ÙĪØ« ائÙĤ", + "ĠÙħساء Ùĭ", + "Ġunterstüt zt", + "Ġpré st", + "Ġprést amo", + "ĠÐłÐ°Ð· меÑĢ", + "ĠÅŁ eker", + "Ġsé culo", + "×ij×Ķ ×Ļר", + "Ø´Ùĩ ÙĪر", + "Ġ à¸Ńีà¸ģ", + "Ġà¸Ńีà¸ģ à¸Ĺัà¹īà¸ĩ", + "Ġlleg ó", + "à¸¨à¸´à¸¥à¸Ľ ะ", + "æĪij ãģĮ", + "æĪijãģĮ 家", + "ع ÙĤÙĪ", + "عÙĤÙĪ بات", + "ĠF älle", + "Ġs ÅĤuż", + "ĠsÅĤuż b", + "ĠاÙĦØŃÙĤ ÙĪÙĤ", + "Ġпл иÑĤ", + "Ġи ноÑģÑĤ", + "ĠиноÑģÑĤ ÑĢан", + "ĠиноÑģÑĤÑĢан н", + "à¹ĥà¸Ļ à¸Ĥà¸ĵะà¸Ĺีà¹Ī", + "ãĤ« ãĥĨ", + "ãĤ«ãĥĨ ãĤ´", + "ãĤ«ãĥĨãĤ´ ãĥª", + "à¸Ńิ ส", + "à¸Ńิส ระ", + "à¹Ģà¸ľà¸¢ à¹ģ", + "à¹Ģà¸ľà¸¢à¹ģ à¸ŀร", + "à¹Ģà¸ľà¸¢à¹ģà¸ŀร à¹Ī", + "ãģĬ ãģĦ", + "ãģĬãģĦ ãģĹãģĦ", + "است ÙĤÙĦ", + "استÙĤÙĦ اÙĦ", + "تØŃ Ø¶", + "تØŃض ÙĬر", + "åĬ© ãģij", + "Ùħر اÙģÙĤ", + "Ġ×ĵ ×ķר", + "Ġ×ĵ×ķר ש", + "×ŀת×Ļ ×Ļ×Ĺס", + "ס ×Ļ׼", + "ס×Ļ׼ ×ķ×Ŀ", + "íĮĮ íĬ¸", + "Ġwy ÅĽ", + "ĠwyÅĽ w", + "ĠwyÅĽw iet", + "ĠwyÅĽwiet l", + "ĠاÙĦاÙĨ ساÙĨ", + "ĠStra ÃŁen", + "ï¼ ¬", + "ãģ« åŁº", + "ãģ«åŁº ãģ¥", + "Ġcap ÃŃtulo", + "ลุ ย", + "Ġ×Ķ×ŀק צ×ķ×¢×Ļ", + "ãģĤãĤĭ ç¨ĭ度", + "á» ¢", + "ĠاÙĦ ÙĦا", + "ĠاÙĦÙĦا زÙħØ©", + "æķĻ ãģĪ", + "Ġרש ×IJ×Ļ", + "з ав", + "зав иÑģ", + "завиÑģ им", + "à¸Ľà¸±à¸Ī à¸Īัย", + "à¹Ģà¸ĭ ล", + "à¹Ģà¸ĭล ลà¹Į", + "Ġdiffé rence", + "ĠAlt ın", + "Ġк ÑĢай", + "ĠкÑĢай не", + "Ġз ло", + "Ġgün ümüz", + "Ġн аÑĤÑĥÑĢ", + "ĠнаÑĤÑĥÑĢ алÑĮн", + "×Ĵ×ķ׾ ש×Ļ×Ŀ", + "Ġк аÑĤегоÑĢ", + "ĠкаÑĤегоÑĢ ии", + "Ġз нак", + "à¸ģà¹Īà¸Ńà¸Ļ หà¸Ļà¹īา", + "à¸ģà¹Īà¸Ńà¸Ļหà¸Ļà¹īา à¸Ļีà¹ī", + "ĠÙħÙĨ ت", + "ĠÙħÙĨت خب", + "ãĥĽ ãĥ¼ãĥ«", + "Ġе вÑĢо", + "ส ว", + "สว ม", + "ĠìľĦ ìĽIJ", + "ĠìľĦìĽIJ ëĭĺ", + "ĠاÙĦØŃ ÙĪØ«", + "ĠاÙĦØŃÙĪØ« ÙĬ", + "ĠÑģодеÑĢж иÑĤ", + "ãĥķãĤ¡ ãĥĥãĤ·ãĥ§ãĥ³", + "Ġ à¸ģัà¸Ļ", + "Ġà¸ģัà¸Ļ ย", + "Ġà¸ģัà¸Ļย ายà¸Ļ", + "ãĤª ãĥª", + "ãĤªãĥª ãĤ¸", + "ãĤªãĥªãĤ¸ ãĥĬãĥ«", + "Ġб ÑĢенд", + "ãĤĴæĮģ ãģ£ãģ¦ãģĦãĤĭ", + "Ġinvers ión", + "Ġê° ĸ", + "Ġê°ĸ ê³ł", + "Ġnov itÃł", + "ê´Ģ ê´ij", + "Ġà¸ŀ ฤษ", + "Ġà¸ŀฤษ à¸łà¸²", + "Ġà¸ŀà¸¤à¸©à¸łà¸² à¸Ħม", + "×ķר ×Ĺ×Ļ×Ŀ", + "׼׾ ×ķ׾", + "Ġng ạc", + "×Ļ ×Ļש", + "×Ļ×Ļש ×ķ×ij", + "f äll", + "fäll ig", + "ĠÑĤÑĢеб ÑĥеÑĤÑģÑı", + "Ġcar á", + "Ġcará cter", + "Ġprinc ÃŃpio", + "ĠÅĤ az", + "ĠÅĤaz ien", + "ĠÅĤazien k", + "Ġgi ãn", + "ÑģÑĤÑĢа ива", + "Ùħس اب", + "Ùħساب ÙĤØ©", + "à¹Ģà¸Ħรืà¹Īà¸Ńà¸ĩ à¸Ķืà¹Īม", + "ترÙĥ ÙĬب", + "vol ução", + "ĠÐŁ оÑĩ", + "ĠÐŁÐ¾Ñĩ ем", + "ĠÐŁÐ¾Ñĩем Ñĥ", + "казал оÑģÑĮ", + "ĠпÑĢимен ениÑı", + "à¹Ģà¸Ĺ ียม", + "íĮ Ķ", + "à¸Ĥà¹īà¸Ń à¹Ģสà¸Ļà¸Ń", + "à¸Ľà¸±à¸į à¸įา", + "Ġоб ÑĥÑĩ", + "ĠобÑĥÑĩ ениÑı", + "ĠÑģеÑĢ и", + "ĠÑģеÑĢи ал", + "Ġingl és", + "ĠÙĦ Ùĥرة", + "Ġ×ĺ ׾", + "Ġ×ĺ׾ פ×ķף", + "Ġìł ij", + "Ġìłij ê·¼", + "×IJ ×ķ×Ĵ", + "×IJ×ķ×Ĵ ×ķס", + "×IJ×ķ×Ĵ×ķס ×ĺ", + "ĠболÑĮÑĪ ое", + "ĠÐļон еÑĩно", + "×¢×Ļת ×ķ׳", + "×¢×Ļת×ķ׳ ×IJ×Ļ", + "Ġкноп к", + "Ġз н", + "Ġзн аÑĤÑĮ", + "ĠÄij á»±", + "ĠÄijá»± ng", + "вл аж", + "влаж н", + "×ŀ ×Ļ×ĺ×ij", + "ãĤ¬ ãĤ¤", + "ãĤ¬ãĤ¤ ãĥī", + "........ ..", + "Ġà¸ģ ุม", + "Ġà¸ģุม à¸łà¸²à¸ŀ", + "Ġà¸ģà¸¸à¸¡à¸łà¸²à¸ŀ ัà¸Ļ", + "Ġà¸ģà¸¸à¸¡à¸łà¸²à¸ŀัà¸Ļ à¸ĺ", + "Ġà¸ģà¸¸à¸¡à¸łà¸²à¸ŀัà¸Ļà¸ĺ à¹Į", + "be z", + "bez pieczeÅĦst", + "bezpieczeÅĦst w", + "ãĥijãĥij æ´»", + "ع اط", + "عاط Ùģ", + "ĠÄij áºŃm", + "Ġз ÑĢ", + "ĠзÑĢ ениÑı", + "Ġbor ç", + "Ġнед ел", + "Ġнедел Ñİ", + "Ġh á»ı", + "Ġhá»ı ng", + "ìŀ¥ ìķł", + "ìŀ¥ìķł ìĿ¸", + "ĠاÙĦع ÙĦاÙĤØ©", + "Ġíģ ¬", + "Ġíģ¬ ê²Į", + "à¹Ħร à¹Ī", + "à¸ļา à¸Ķ", + "à¸ļาà¸Ķ à¹Ģà¸Īà¹ĩà¸ļ", + "à¸Ŀ รั", + "à¸Ŀรั à¹Īà¸ĩ", + "à¸Ŀรัà¹Īà¸ĩ à¹Ģศ", + "à¸Ŀรัà¹Īà¸ĩà¹Ģศ ส", + "ר ×¢×Ļ", + "רע×Ļ ×ķ׳×ķת", + "Ġë Į", + "ĠëĮ ĵ", + "ĠëĮĵ ê¸Ģ", + "Ġnaj b", + "Ġnajb li", + "Ġnajbli ż", + "Ġnajbliż sz", + "ĠиÑģполÑĮз ÑĥеÑĤÑģÑı", + "Ġcient ÃŃf", + "ĠcientÃŃf ico", + "×¢ ×ŀק", + "Ġg ợi", + "Ø´ ØŃÙĨ", + "ĠÅĽ m", + "ĠÅĽm ier", + "ĠÅĽmier ci", + "à¸Ħาสิà¹Ĥà¸Ļ à¸Ńà¸Ńà¸Ļà¹Ħลà¸Ļà¹Į", + "×Ĺש×ij ת×Ļ", + "Ġn ingu", + "Ġningu ém", + "è¾¼ ãĤģ", + "ãģ ·", + "ĠÑĥ г", + "ĠÑĥг ол", + "ï½ °", + "פת ×Ļ×Ĺ", + "פת×Ļ×Ĺ ×ª", + "Ġ×Ķר×IJש ×ķ׳×Ļ×Ŀ", + "p ósito", + "ãĤŃ ãĥ¬ãĤ¤", + "ãģ© ãģĵãĤį", + "à¹Ģà¸Ĺà¹Īา à¹Ħ", + "à¹Ģà¸Ĺà¹Īาà¹Ħ หร", + "à¹Ģà¸Ĺà¹Īาà¹Ħหร à¹Ī", + "ĠинÑĤеÑĢ ÑĮеÑĢ", + "ĠØŃ Ø§Ø¬", + "ĠØŃاج Ø©", + "สี à¸Ĥาว", + "ìĸ ¼", + "Ġn á»Ļ", + "Ġná»Ļ p", + "ĠÃŃ nd", + "ĠÃŃnd ice", + "สำ รวà¸Ī", + "Ġкажд ой", + "Ġhot éis", + "Ġnast ÄĻ", + "ĠnastÄĻ pn", + "Ġ×Ķק ×ķ×ĵ", + "Ġ×Ķק×ķ×ĵ ×Ŀ", + "פ ×ķפ", + "פ×ķפ ×ķ׾", + "פ×ķפ×ķ׾ ר×Ļ", + "вÑĪ ей", + "ãĤ·ãĥ³ ãĥĹ", + "ãĤ·ãĥ³ãĥĹ ãĥ«", + "ĠzdjÄĻ Äĩ", + "ĠгÑĢÑĥпп а", + "Ġпом еÑī", + "ĠпомеÑī ениÑı", + "ãģ©ãģĨ ãģĦãģĨ", + "ĠиÑģп ÑĭÑĤа", + "Ġog ÅĤ", + "ĠogÅĤ os", + "ĠogÅĤos zen", + "ĠogÅĤoszen i", + "สรà¹īาà¸ĩ สรร", + "สรà¹īาà¸ĩสรร à¸Ħà¹Į", + "à¸ŀร รà¸ĵ", + "Ġçık Ä±ÅŁ", + "ĠÑĩаÑģÑĤ ноÑģÑĤи", + "Ġ×ķ ×Ļ×ķתר", + "ç¶ļãģį ãĤĴ", + "ç¶ļãģįãĤĴ èªŃ", + "ç¶ļãģįãĤĴèªŃ ãĤĢ", + "à¸ģร ั", + "à¸ģรั ม", + "г ÑĢаÑĦ", + "Ġв лад", + "Ġвлад елÑĮ", + "ĠвладелÑĮ ÑĨ", + "Ġistedi ÄŁ", + "ĠistediÄŁ iniz", + "×ij׾ ×¢", + "×ij×ľ×¢ ×ĵ×Ļ", + "ÙħÙĪ اÙģ", + "ÙħÙĪاÙģ ÙĤØ©", + "Ġ×Ļ ×ķר", + "Ġ×Ļ×ķר ק", + "ãĤ«ãĥ¼ãĥī ãĥŃãĥ¼ãĥ³", + "ĠاÙĦÙħØ´ ÙĥÙĦ", + "ĠاÙĦÙħØ´ÙĥÙĦ Ø©", + "ĠêµŃ íļĮ", + "ס פ×ĺ", + "ספ×ĺ ×ŀ", + "ספ×ĺ×ŀ ×ijר", + "Ġìĸ´ ëłµ", + "Ùĥ اÙħ", + "ÙĥاÙħ ÙĬرا", + "sch lü", + "schlü sse", + "ĠØ« ÙĨ", + "ĠØ«ÙĨ ائÙĬ", + "ìī ½", + "ĠÐŀ Ñģоб", + "ĠÐŀÑģоб енно", + "Ġин веÑģÑĤи", + "ĠинвеÑģÑĤи ÑĨи", + "اØŃ ØªÙħ", + "اØŃتÙħ اÙĦ", + "E Äŀ", + "EÄŀ Ä°", + "íķĺ ê²łëĭ¤", + "Ġ×IJ ×ijר×Ķ", + "Ġ×IJ×ijר×Ķ ×Ŀ", + "Ġ×ij×Ĺ ×Ļ׳×Ŀ", + "Ø£ ÙĪض", + "Ø£ÙĪض اع", + "Ġdé l", + "Ġdél ai", + "Ġ×IJ×ķ×Ķ ×ij×Ļ×Ŀ", + "ĠÑģо Ñħ", + "ĠÑģоÑħ ÑĢ", + "ĠÑģоÑħÑĢ ани", + "ĠдоÑģÑĤ иж", + "ĠдоÑģÑĤиж ени", + "สิà¹Īà¸ĩ à¹ģ", + "สิà¹Īà¸ĩà¹ģ วà¸Ķ", + "สิà¹Īà¸ĩà¹ģวà¸Ķ ล", + "สิà¹Īà¸ĩà¹ģวà¸Ķล à¹īà¸Ńม", + "ĠاÙĦÙħ باشر", + "ĠÑĦ иг", + "ĠÑĦиг ÑĥÑĢ", + "мож ем", + "׾×ŀ×Ļ×ĵ ×Ķ", + "Ġcin é", + "Ġciné ma", + "Ġb ada", + "Ġbada ÅĦ", + "جب ÙĩØ©", + "Ġд еп", + "Ġдеп ÑĥÑĤ", + "ĠдепÑĥÑĤ аÑĤ", + "Ġdist ância", + "ĠاÙĦÙħ عار", + "ĠاÙĦÙħعار ضة", + "thè se", + "ü nc", + "ünc ü", + "Ġдан ного", + "ĠBel gi", + "ĠBelgi ë", + "Ġ×ij ×ijק", + "Ġ×ij×ijק ש×Ķ", + "ย à¹Īาà¸Ļ", + "Ġsol ução", + "Ġ×Ķצ ×ĺר", + "Ġ×Ķצ×ĺר פ×ķ", + "ĠØ£ÙĨ ØŃ", + "ĠØ£ÙĨØŃ Ø§Ø¡", + "Ġد ÙħØ´", + "ĠدÙħØ´ ÙĤ", + "มั à¹ī", + "มัà¹ī ย", + "Ùħ غرب", + "است عÙħاÙĦ", + "ĠS ÅĤow", + "ĠëıĻ ìĭľ", + "ĠëıĻìĭľ ìĹIJ", + "ĠÑģ оÑģ", + "ĠÑģоÑģ ед", + "ì²Ń ìĨĮ", + "ì²ŃìĨĮ ëħĦ", + "Ġг ÑĢаÑĦ", + "ĠгÑĢаÑĦ ик", + "Ġìŀij ìĿĢ", + "Ġyet i", + "Ġyeti ÅŁtir", + "ĠìĿ´ê²ĥ ìĿ´", + "ห à¹Īาà¸ĩ", + "Ø¥ ÙħÙĥاÙĨ", + "Ø¥ÙħÙĥاÙĨ ÙĬØ©", + "است عراض", + "ÙħØ® در", + "ĠÑĩ ÑĥÑĤÑĮ", + "Ùħ دÙĬر", + "ÙħدÙĬر ÙĬØ©", + "Ġà¹Ģม ษ", + "Ġà¹Ģมษ ายà¸Ļ", + "Ġм еÑħ", + "ĠмеÑħ аниз", + "ĠмеÑħаниз м", + "ĠÑģ Ñĥм", + "ĠÑģÑĥм мÑĥ", + "Ġv ö", + "Ġvö ll", + "Ġvöll ig", + "Ġд ÑĢÑĥз", + "ĠдÑĢÑĥз ÑĮÑı", + "ãĤĴåĪ©çĶ¨ ãģĹãģ¦", + "à¸ļรร à¸Īุ", + "po życz", + "×ŀש ׼", + "×ŀש׼ ×ł×ª", + "×ŀ×©×Ľ×ł×ª ×IJ", + "Ġeuropé en", + "Ġpropri é", + "Ġproprié taire", + "Ġkh ấu", + "ãģĦãģŁãģł ãģijãĤĭ", + "Ġtec rü", + "Ġtecrü be", + "×Ķ ×ij", + "×Ķ×ij ׳×Ķ", + "Ġcu Ì", + "ĠcuÌ ī", + "ĠcuÌī a", + "×IJ ×ķ×ķ", + "×IJ×ķ×ķ ×Ļר×Ķ", + "Ġ׼×ķ׾ ×ķ", + "U lus", + "Ulus lararası", + "Ġ׳ ×ķת", + "Ġ׳×ķת ף", + "ãģ« åIJij", + "ãģ«åIJij ãģijãģ¦", + "ë¹ Ľ", + "à¸Ĺ ัà¸ģษ", + "à¸Ĺัà¸ģษ ะ", + "س ÙĤÙĪ", + "سÙĤÙĪ Ø·", + "Ġв н", + "Ġвн еÑĪ", + "ĠвнеÑĪ не", + "Ġur z", + "Ġurz ÄĻd", + "Ġá mb", + "Ġámb ito", + "à¸Ń à¸ĺิ", + "à¸Ńà¸ĺิ à¸ļาย", + "Ġ ÅĤad", + "ĠÅĤad n", + "ê±´ ì¶ķ", + "wód zt", + "wództ w", + "Ġquest ões", + "Ġש ק", + "Ġשק ×Ļ×ij׾", + "Ġmiejsc owoÅĽci", + "Ġв ал", + "Ġвал ÑİÑĤ", + "hä user", + "หà¸Ļ à¸Ńà¸ĩ", + "ãģ¨ åħ±", + "ãģ¨åħ± ãģ«", + "ãĥı ãĥ¼ãĥī", + "Ġê°ľ ìµľ", + "ĠоÑģнов ном", + "Ġм ÑıÑģ", + "اع ت", + "اعت ÙĤاÙĦ", + "สà¸ĸ ิ", + "สà¸ĸิ à¸ķิ", + "N gu", + "Ngu á»ĵn", + "ĠÙħ جÙĦ", + "ĠÙħجÙĦ Ø©", + "à¹ģà¸Ĥ à¸Ļ", + "ĠاÙĦÙĦÙĬ بÙĬ", + "פע×Ļ׾ ×ķ×Ļ×ķת", + "Ġ×Ķר פ×ķ×IJ×Ļ", + "פר ×ķפ", + "פר×ķפ ×Ļ׾", + "ק ׾×IJ", + "ק׾×IJ ס×Ļ", + "Ùĥت Ø´Ùģ", + "ãģ«ãģª ãģ£ãģ¦ãģĹãģ¾ãģĨ", + "à¹Ģà¸Ħล à¹ĩà¸Ķ", + "à¹Ģà¸Ħลà¹ĩà¸Ķ ลัà¸ļ", + "Ġì» ´", + "Ġì»´ íĵ¨", + "Ġì»´íĵ¨ íĦ°", + "Ġ×Ĺ×Ļ ×ķ×ij×Ļ", + "Ġnä m", + "Ġnäm lich", + "åij¼ ãģ°", + "åij¼ãģ° ãĤĮ", + "ĠÑĢ ол", + "ĠÑĢол и", + "Ġspécial isé", + "à¸Ļ วัà¸ķ", + "à¸Ļวัà¸ķ à¸ģรรม", + "ÙĨص ÙĪص", + "пеÑĢ ед", + "пеÑĢед аÑĩ", + "thè que", + "Ġר×IJ ×Ļת×Ļ", + "ãĥĢ ãĤ¦ãĥ³", + "ãĤı ãģĭ", + "ãĤıãģĭ ãģ£ãģ¦", + "беÑĢ еж", + "ĠÑģ ек", + "ĠÑģек ÑĢ", + "ĠÑģекÑĢ еÑĤ", + "ĠпоÑģÑĤоÑıн н", + "à¸Ĥà¸Ļ สà¹Īà¸ĩ", + "Ġm ük", + "Ġmük em", + "Ġmükem mel", + "еÑĤ еÑģÑĮ", + "ĠاÙĦسÙĨ ÙĪات", + "ĠìłĦ íĺĢ", + "Ġ×Ķ×ŀק ×ķר×Ļ", + "Ġmü d", + "Ġmüd ah", + "Ġmüdah ale", + "Ġwy b", + "Ġwyb ór", + "Ġtend ência", + "Ø¥ دار", + "إدار ÙĬØ©", + "Ġunterstüt zen", + "ת ×ijר", + "ת×ijר ר", + "Ġdi á", + "Ġdiá logo", + "ĠÃĸ nce", + "ĠÃĸnce ki", + "ãĤ¹ãĥĿ ãĥĥãĥĪ", + "ëĦ £", + "ĠG eli", + "ĠGeli ÅŁ", + "ãĤĴ éĢļ", + "ãĤĴéĢļ ãģĹãģ¦", + "ĠFuÃŁ ball", + "Ġsal ari", + "Ġsalari é", + "ĠпÑĢодÑĥк ÑĤов", + "صÙģ ÙĤØ©", + "รว à¸ļ", + "รวà¸ļ รวม", + "à¹ĥà¸Ļ à¸IJาà¸Ļ", + "à¹ĥà¸Ļà¸IJาà¸Ļ ะ", + "Ġkay na", + "Ġkayna ģı", + "Ġìŀij íĴĪ", + "ĠвÑĭ ÑĢаж", + "ĠвÑĭÑĢаж ен", + "ĠÑģÑĤ еп", + "ĠÑģÑĤеп ени", + "ĠاÙĦÙħ ÙĪجÙĪد", + "ĠاÙĦÙħÙĪجÙĪد Ø©", + "ล à¹īม", + "Ġnaj czÄĻ", + "ĠnajczÄĻ ÅĽcie", + "ĠnajczÄĻÅĽcie j", + "Ġz wy", + "Ġzwy k", + "Ġzwyk ÅĤ", + "Ġê·¸ëłĩ ì§Ģ", + "à¸ģระ à¸Ī", + "à¸ģระà¸Ī าย", + "Ġëĭ µ", + "Ġëĭµ ë³Ģ", + "ĠÑĢе ак", + "ĠÑĢеак ÑĨи", + "ĠÅĽwie ż", + "ĠÑģÑĤоим оÑģÑĤи", + "ÙħÙĨ اÙĤ", + "ÙħÙĨاÙĤ Ø´", + "ÙħÙĨاÙĤØ´ Ø©", + "ĠÑħоÑĩ Ñĥ", + "ãĥľ ãĥ¼ãĥī", + "Ġróż nic", + "Ġк ÑĢÑĭ", + "ĠкÑĢÑĭ ÑĪ", + "âľ ĵ", + "ãĤ³ãĥ³ ãĥĨãĥ³", + "ãĤ³ãĥ³ãĥĨãĥ³ ãĥĦ", + "ĠпÑĢед поÑĩ", + "×ŀר ×ij×Ļת", + "ĠØ´ Ùĥ", + "ĠØ´Ùĥ را", + "Ġд ал", + "Ġдал ек", + "Ġдалек о", + "بر ÙĬØ·", + "برÙĬØ· اÙĨÙĬا", + "ع ÙĨا", + "عÙĨا ÙĬØ©", + "ĠÑĢаÑģÑģ каз", + "ĠÑĢаÑģÑģказ Ñĭва", + "Ø£ ÙĦÙĪ", + "Ø£ÙĦÙĪ اÙĨ", + "æĮģ ãģ£ãģ¦", + "æĮģãģ£ãģ¦ ãģĦ", + "Ùħباد ئ", + "×Ķ ×¢×ijר", + "×Ķ×¢×ijר ת", + "Ġyay ı", + "Ġyayı ml", + "Ġyayıml a", + "m át", + "mát icos", + "à¸ģ ัà¸ĩ", + "à¸ģัà¸ĩ วล", + "Ġ׾ פת", + "Ġ×ľ×¤×ª ×ķ×Ĺ", + "à¸ŀฤ à¸ķิ", + "à¸ŀฤà¸ķิ à¸ģรรม", + "í Ĥ¬", + "Ġок ÑĢÑĥг", + "Ġ×ŀצ ×ķ×ķ×Ķ", + "ÐĽ ени", + "ÐĽÐµÐ½Ð¸ н", + "ĠTri á»ģu", + "ãĤ³ãĥŁ ãĥ¥", + "ãĤ³ãĥŁãĥ¥ ãĥĭ", + "ãĤ³ãĥŁãĥ¥ãĥĭ ãĤ±", + "ãĤ³ãĥŁãĥ¥ãĥĭãĤ± ãĥ¼ãĤ·ãĥ§ãĥ³", + "Ùĥ ÙĨÙĬ", + "ÙĥÙĨÙĬ سة", + "ãĤĴ ä¸Ńå¿ĥ", + "ãĤĴä¸Ńå¿ĥ ãģ«", + "ĠmiÄĻd z", + "ĠmiÄĻdz yn", + "ĠmiÄĻdzyn ar", + "ĠmiÄĻdzynar od", + "ĠmiÄĻdzynarod ow", + "ÙĦ ÙĨ", + "ÙĦÙĨ دا", + "بر Ø´", + "برش ÙĦÙĪÙĨ", + "برشÙĦÙĪÙĨ Ø©", + "à¸ģระ à¸ķุ", + "à¸ģระà¸ķุ à¹īà¸Ļ", + "Ġg ı", + "Ġgı da", + "à¸Ľà¸£à¸° à¸Ĺัà¸ļ", + "à¸Ľà¸£à¸°à¸Ĺัà¸ļ à¹ĥà¸Ī", + "Ġë¶Ī 구", + "Ġë¶Ī구 íķĺê³ł", + "ĠÙĨ Ø·", + "ĠÙĨØ· اÙĤ", + "ĠÐľ ожеÑĤ", + "Pr äs", + "Präs ident", + "ĠÑģк оÑĢ", + "ĠÑģкоÑĢ оÑģÑĤÑĮ", + "Ġ×Ķ×ij ×ķקר", + "еÑħ аÑĤÑĮ", + "Ġg ạo", + "Ġש×IJ ×Ļ׳×Ŀ", + "Ġ×ij׳ ×ķ×Ĵ", + "Ġ×ij׳×ķ×Ĵ ×¢", + "Ġо пиÑģание", + "Ġucz ni", + "Ġuczni ów", + "à¹Ģà¸Ń à¹ĩà¸Ļ", + "Ġت Ø´", + "Ġتش رÙĬÙĨ", + "Ġnh ãn", + "ë¹ ¨", + "Ġcaract ère", + "×¢ ׾×Ļ", + "×¢×ľ×Ļ ×Ļ×Ķ", + "楽ãģĹ ãĤģãĤĭ", + "ĠÑģ аÑħ", + "ĠÑģаÑħ аÑĢ", + "дÑĥм аÑĤÑĮ", + "ĠÐĴоз можно", + "ص ÙĬاÙĨ", + "صÙĬاÙĨ Ø©", + "öm ür", + "ส ล", + "สล à¹ĩ", + "สลà¹ĩ à¸Ń", + "สลà¹ĩà¸Ń à¸ķ", + "ë¡ ¯", + "Ġth ói", + "gr Ã¶ÃŁe", + "Ġksi ÄĻ", + "ĠksiÄĻ g", + "ĠÑĢ ом", + "ĠÑĢом ан", + "ÙĤ اسÙħ", + "×ŀ×ij ×ķ×Ĵ", + "×ŀ×ij×ķ×Ĵ ר×Ļ×Ŀ", + "bes ch", + "besch äft", + "beschäft ig", + "×Ķצע ×Ķ", + "ĠÃģ rea", + "ĠзаÑıв к", + "Ä ¹", + "ĠлÑİб ого", + "Ġ ม", + "Ġม à¸ģร", + "Ġมà¸ģร าà¸Ħม", + "ÑĦ из", + "ÑĦиз иÑĩеÑģк", + "ин ÑĦ", + "инÑĦ ек", + "инÑĦек ÑĨи", + "اÙĦ Ø·", + "اÙĦØ· ائÙģ", + "Ġкол л", + "Ġколл екÑĤив", + "ез жа", + "Ġس بØŃ", + "ĠسبØŃ Ø§ÙĨ", + "ĠسبØŃاÙĨ Ùĩ", + "sch lä", + "schlä ge", + "Ġд и", + "Ġди аг", + "Ġдиаг ноÑģÑĤ", + "ĠоÑĤмеÑĤ иÑĤÑĮ", + "Т Ь", + "ĠاÙĦ در", + "ĠاÙĦدر اسÙĬ", + "עצ ×ŀ", + "עצ×ŀ ×IJ×ķת", + "Ġdém arch", + "Ġdémarch e", + "Ġ×ĺ ×ķ×¢", + "Ġ×ĺ×ķ×¢ ף", + "Ġfuncion ários", + "á» µ", + "׾ ׼×IJ", + "׾׼×IJ ×ķר×Ķ", + "à¸ĭ à¹Ī", + "à¸ĭà¹Ī à¸Ńม", + "ĠÑĩ Ñĥв", + "ĠÑĩÑĥв ÑģÑĤво", + "âĸ ¼", + "п ÑĥÑī", + "пÑĥÑī ен", + "Ġм еÑĢ", + "ĠмеÑĢ оп", + "ĠмеÑĢоп ÑĢи", + "ĠмеÑĢопÑĢи ÑıÑĤиÑı", + "Ġu çu", + "Ġuçu ÅŁ", + "ãĤĴåĪ©çĶ¨ ãģĻãĤĭ", + "a ÄŁ", + "aÄŁ lı", + "ìĺĪ ìĪł", + "à¹ģ ยà¹Ī", + "ĠاÙĦÙĥ Ùħ", + "ĠاÙĦÙĥÙħ بÙĬ", + "ĠاÙĦÙĥÙħبÙĬ ÙĪتر", + "ت ÙĪÙĬ", + "تÙĪÙĬ تر", + "à¹Ģà¸Ĭ ีà¹Īยว", + "à¹Ģà¸Ĭีà¹Īยว à¸Ĭา", + "à¹Ģà¸Ĭีà¹Īยวà¸Ĭา à¸į", + "á» Ķ", + "Ġhi ếm", + "ذا Ùĥرة", + "Ġ×Ķ×ŀ×Ļ ×ķ×Ĺ×ĵ", + "ĠìĪ ľ", + "ĠìĪľ ê°Ħ", + "ĠK ı", + "ĠKı sa", + "Ġgele ceÄŁi", + "пÑĢо ÑĦеÑģÑģиона", + "пÑĢоÑĦеÑģÑģиона л", + "Ġog ó", + "Ġogó le", + "ĠgÅĤ ów", + "ĠgÅĤów ne", + "ĠÑģÑĤ илÑĮ", + "×IJ פ׾", + "×IJפ׾ ×Ļק", + "×IJפ׾×Ļק צ×Ļ×Ķ", + "สม ารà¹Į", + "สมารà¹Į à¸Ĺ", + "สมารà¹Įà¸Ĺ à¹Ĥà¸Ł", + "สมารà¹Įà¸Ĺà¹Ĥà¸Ł à¸Ļ", + "Ġth ánh", + "ÐŁ од", + "ÐŁÐ¾Ð´ ÑĢоб", + "ÐŁÐ¾Ð´ÑĢоб нее", + "ĠاÙĦت ÙĪÙĨ", + "ĠاÙĦتÙĪÙĨ سÙĬ", + "Ġbah çe", + "à¹ģà¸ģà¹ī à¸Ľà¸±à¸įหา", + "é ducation", + "eu rop", + "europ ä", + "europä ische", + "ĠK si", + "ĠKsi ÄĻ", + "ĠëĦ ĺ", + "ĠëĦĺ ìĸ´", + "Ġv üc", + "Ġvüc ud", + "Ġyay g", + "Ġyayg ın", + "Ġnie kt", + "Ġniekt óry", + "Ġniektóry ch", + "ãģŃ ãģĩ", + "Ġк аж", + "Ġкаж еÑĤÑģÑı", + "к аж", + "каж еÑĤ", + "ĠاÙĦ دÙĬÙħÙĤرا", + "ĠاÙĦدÙĬÙħÙĤرا Ø·", + "ĠاÙĦدÙĬÙħÙĤراط ÙĬØ©", + "æŃ ©", + "æŃ© ãģĦãģ¦", + "Ġv az", + "Ġvaz ge", + "Ġvazge ç", + "Ġмин ималÑĮ", + "ĠминималÑĮ н", + "ãĥij ãĤ¿", + "ãĥijãĤ¿ ãĥ¼ãĥ³", + "Ġë Ĭ", + "ĠëĬ IJ", + "ĠëĬIJ ëĤĮ", + "ãģ¡ ãĤĩãģĨ", + "ãģ¡ãĤĩãģĨ ãģ©", + "Ġ à¸ģร", + "Ġà¸ģร à¸ģà¸İ", + "Ġà¸ģรà¸ģà¸İ าà¸Ħม", + "تج دÙĬد", + "ĠØ´ اÙħÙĦ", + "หลัà¸ģ à¸IJาà¸Ļ", + "ĠмаÑĢ ÑĪ", + "ĠмаÑĢÑĪ ÑĢÑĥÑĤ", + "Ġv ÃŃt", + "ĠvÃŃt ima", + "Ġquiz á", + "ay gı", + "×ĵ×ijר ×Ļ×ķ", + "Ġиз д", + "Ġизд ели", + "Ġиздели Ñı", + "п ла", + "пла Ñĩ", + "плаÑĩ ива", + "ä»» ãģĽ", + "Ġéquip é", + "ä¹ħ ãģĹãģ", + "ä¹ħãģĹãģ ¶", + "ä¹ħãģĹãģ¶ ãĤĬ", + "Ġк аÑĤ", + "ĠкаÑĤ ал", + "ĠкаÑĤал ог", + "ส à¹īม", + "ĠÑĢ ей", + "ĠÑĢей ÑĤ", + "ĠÑĢейÑĤ инг", + "Ġth uyá»ģn", + "ĠاÙĦÙħ ÙĤدس", + "esp ère", + "ãģ«åħ¥ ãģ£ãģŁ", + "หมาย à¹Ģลà¸Ĥ", + "ת×Ĺ×ķש ת", + "à¸Ļ à¹Īะ", + "Ġpe ÅĤ", + "ĠpeÅĤ ne", + "Ġpé rd", + "Ġpérd ida", + "หม วà¸Ķ", + "หมวà¸Ķ หมูà¹Ī", + "иÑĩеÑģк ÑĥÑİ", + "çµĤ ãĤı", + "çµĤãĤı ãģ£ãģŁ", + "Ġ×Ĵ ×ķ×Ĵ׾", + "à¸Ĺำ à¸Ħวาม", + "à¸Ĺำà¸Ħวาม สะà¸Ńาà¸Ķ", + "Hot éis", + "Ġз аÑĢ", + "ĠзаÑĢ егиÑģÑĤ", + "ĠзаÑĢегиÑģÑĤ ÑĢи", + "ĠзаÑĢегиÑģÑĤÑĢи ÑĢова", + "ĠÑģ обÑĭÑĤи", + "ĠÑģобÑĭÑĤи Ñı", + "Ġ×ĸ ׼×IJ", + "ÙħÙĨظ ÙĪÙħØ©", + "Ġ×Ķ×ŀ צ", + "Ġ×Ķ×ŀצ ×Ļ×IJ×ķת", + "Ùħ ÙĥÙĪÙĨ", + "ÙħÙĥÙĪÙĨ ات", + "ä¸ĬãģĮ ãĤĭ", + "Ġm ÄĻ", + "ĠmÄĻ sk", + "หรืà¸Ń à¹Ģà¸Ľà¸¥à¹Īา", + "ëĤ ®", + "Ġnok tas", + "Ġnoktas ı", + "ĠболÑĮÑĪ им", + "ĠлÑĥÑĩ ÑĪиÑħ", + "Ø´Ùĩ ÙĬد", + "à¸Ńำ à¸Ļ", + "à¸Ńำà¸Ļ วย", + "à¸Ńำà¸Ļวย à¸Ħวาม", + "à¸Ńำà¸Ļวยà¸Ħวาม สะà¸Ķวà¸ģ", + "Ġе в", + "Ġев ÑĢ", + "ĠевÑĢ оп", + "ĠевÑĢоп ей", + "à¸ī าย", + "ìĦ Ń", + "Ùħ Ùģا", + "ÙħÙģا ÙĪض", + "ÙħÙģاÙĪض ات", + "ë¹ Į", + "赤 ãģ¡ãĤĥãĤĵ", + "ĠÑĥдал оÑģÑĮ", + "ĠÐ¥ оÑĤ", + "ĠХоÑĤ Ñı", + "przedsiÄĻbior c", + "ĠH ôm", + "íķĺìĺĢ ìĬµëĭĪëĭ¤", + "Ġн аг", + "Ġнаг ÑĢÑĥз", + "ĠнагÑĢÑĥз к", + "Ġ×ij×Ļ׳ ׾×IJ×ķ×ŀ×Ļ", + "Ġê°ĢëĬ¥ íķľ", + "ĠH ữu", + "à¸Ń ุà¸Ķ", + "à¸Ńุà¸Ķ ม", + "ת ×ķפ", + "ת×ķפ ×¢×Ķ", + "Ġmi ÅĤo", + "ĠmiÅĤo ÅĽci", + "ksi Äħż", + "ksiÄħż ka", + "ĠاÙĦÙĦ عبة", + "à¸ī าà¸ģ", + "สะ สม", + "×ŀ תר", + "×ŀתר ×Ĺש", + "Ġlég ère", + "Ġ׾צ פ", + "Ġ׾צפ ×Ļ×Ķ", + "ĠиÑģÑĤоÑĢ иÑı", + "Ġ ãĥĪãĥ©", + "ĠãĥĪãĥ© ãĥĥãĤ¯", + "ĠãĥĪãĥ©ãĥĥãĤ¯ ãĥIJãĥĥãĤ¯", + "Ġк а", + "Ġка ÑĦе", + "×ŀס×ŀ ×ļ", + "Ġc üm", + "Ġcüm le", + "à¹Ģà¸Ħลืà¹Īà¸Ńà¸Ļ à¹Ħหว", + "ãģĬ ãģĿ", + "ãģĬãģĿ ãĤīãģı", + "ìŀIJ ëıĻ", + "ìŀIJëıĻ ì°¨", + "à¸Ńั à¸ķ", + "à¸Ńัà¸ķ à¹Ĥà¸Ļ", + "à¸Ńัà¸ķà¹Ĥà¸Ļ มั", + "à¸Ńัà¸ķà¹Ĥà¸Ļมั à¸ķิ", + "ĠÅŁ ik", + "ĠÅŁik ay", + "ĠÅŁikay et", + "extr ême", + "kr ä", + "krä fte", + "ëĤ Ļ", + "íķ ij", + "ì² Ļ", + "íĺ Ī", + "ì° į", + "âĻ ¡", + "ìŀ Ķ", + "ë¢ °", + "íĿ Ķ", + "íĿ IJ", + "âĩ Ĵ", + "ë§ Ľ", + "ìĬ Ī", + "á» Ĵ", + "ìĺ µ", + "âĹ İ", + "í Ĥ¨", + "ê¿ Ī", + "ìĪ ¨", + "ìĽ ¨", + "ë§ ¥", + "ï½ Ģ", + "ï¼ ª", + "Ạ¨", + "ãħ İ", + "Ñ Ĺ", + "ìĦ ¬", + "ì¹ ¼", + "ï¼ ¶", + "ìĽ ł", + "ëŁ ´", + "Å ĥ", + "ëĤ ¼", + "ëĭ IJ", + "âĢ ¹", + "ë¦ Ń", + "ì§ IJ", + "âĢ ¤", + "à ħ", + "ëľ ¨", + "íĦ ¸", + "íľ ĺ", + "ê² ģ", + "ë´ ħ", + "à ĺ", + "ëŃ Ķ", + "ëĺ ij", + "âĹ ĩ", + "ìĹ ĺ", + "ï» ´", + "ë§ ¹", + "ï¾ Ŀ", + "ìĬ ·", + "íĥ ķ", + "ï¼ ł", + "ì» ´", + "ëł Į", + "ì½ ľ", + "ï» ¹", + "ãħ ł", + "ì¡ ¸", + "ëħ ¹", + "âĤ º", + "âĸ ¶", + "íĥ IJ", + "êµ ´", + "íij ¸", + "Ñ Ķ", + "íĶ ½", + "Ð ħ", + "ë° ¤", + "Ô ģ", + "ì² ¨", + "ì¶ ĺ", + "ë² Ĺ", + "ë© ¸", + "ï¼ »", + "ï¼ ½", + "ï¼ ·", + "ì° Į", + "à Ĵ", + "íı ´", + "ìĵ ¸", + "ì´ Į", + "ëģ Ķ", + "ëĶ ©", + "ëĩ Į", + "ë© Ģ", + "ë² ¨", + "ï¼ µ", + "ë§ ¡", + "ëĭ «", + "ภ¿", + "ãģ ±", + "ìĩ ¼", + "ìº ł", + "ë® ¤", + "ê± ±", + "ì» ¬", + "âĦ ĥ", + "ëĶ ±", + "ëĥ Ī", + "ìĭ ±", + "íĻ Ī", + "ëŀ IJ", + "ìħ Ģ", + "ìł ł", + "Ð Ĩ", + "ëł ī", + "ï½ ħ", + "ï½ ı", + "íĻ Ģ", + "ëĽ °", + "á» ®", + "í Ĥ¹", + "ê½ ĥ", + "ï» ¤", + "ïº Ķ", + "êº ¼", + "ìķ ī", + "âĻ ¦", + "ï½ ģ", + "ìĵ ´", + "ãĢ ī", + "ì° ®", + "ì¤ ĺ", + "á» ª", + "ëģ Ħ", + "ëIJ ¨", + "ìķ Į", + "íĿ ĺ", + "íħ IJ", + "ãĢ Ī", + "ê² ª", + "ëĭ ¥", + "ê² ¼", + "á» Į", + "ë§ ¨", + "ëģ Ĭ", + "ë² ¤", + "ëij Ķ", + "íĿ ¡", + "á» ¬", + "ë¬ ĺ", + "ãģ ī", + "ëŀ «", + "íĶ Ī", + "í ħį", + "ìŀ ĥ", + "ï½ ī", + "ìģ ľ", + "âĸ ½", + "ë¬ »", + "âĸ ³", + "ï¼ ¸", + "ìģ ĺ", + "ì¶ °", + "ìĬ ´", + "ìķ ±", + "ìĩ Ħ", + "Ạ®", + "ï´ ¿", + "ï´ ¾", + "âĤ ½", + "ëĦ ĵ", + "ë£ ©", + "ì³ ¤", + "ê´ ľ", + "à Ļ", + "á» ľ", + "ï¿ £", + "ëĵ Ń", + "ë© ĺ", + "ê» ´", + "ëł ´", + "Ð ĥ", + "ë¬ µ", + "ì§ Ŀ", + "ãģ º", + "ðŁĺ Ĥ", + "ëŀ ¬", + "ìł Ĭ", + "ê´ Ħ", + "ìŀ Ĭ", + "íŀ Į", + "ìĦ ¯", + "âĪ Ģ", + "âĸ ¡", + "ëĢ Į", + "ëŀ Ļ", + "ï½ ĥ", + "Ạ¶", + "ï¾ Ħ", + "ïº ĺ", + "ë¹ ¼", + "à Į", + "âĸ ·", + "ê¸ į", + "ë© ĭ", + "ãģ ĥ", + "ìĺ Ĩ", + "ìĺ ®", + "ëª ¬", + "ë¡ ¤", + "ëł ¬", + "ëĬ ¦", + "âĸ ª", + "ì¼ ĵ", + "ìľ Ī", + "ì§ §", + "ï½ ½", + "ëĥ ī", + "ï¾ Į", + "ëĺ IJ", + "ï¼ ĥ", + "á» Ħ", + "ì´ ¬", + "ì¶ ¤", + "ï¼ ¹", + "ï» Ń", + "âĤ «", + "ï½ ĩ", + "ìĺ ·", + "ëĸ ¨", + "âī «", + "ë¦ ¿", + "âľ ¨", + "Ù ±", + "ì¯ ¤", + "ê¹ Ķ", + "ðŁĺ Ĭ", + "ìĪ «", + "ê³ ±", + "êµ ³", + "ï½ ĭ", + "ภĮ", + "Ä ł", + "ëĶ ¸", + "ë° ij", + "ìħ ĭ", + "íİ ´", + "âľ ħ", + "íĥ ij", + "ëĪ ĩ", + "íı ¼", + "ðŁĺ į", + "ìĺ Ľ", + "ï» £", + "Ñ ĺ", + "ì© Į", + "ë¦ ħ", + "ìĿ į", + "ï½ ¸", + "ëį ľ", + "ãģ ħ", + "íİ ¼", + "ëĭ Ŀ", + "ë¿ Į", + "ì¼ °", + "ìĭ «", + "ë° ¥", + "íĽ Į", + "ì¨ Į", + "ë¹ Ļ", + "ï½ İ", + "ë´ Ħ", + "ìĦ ¹", + "ï½ ²", + "ìĮ ĵ", + "Ò ij", + "ë° į", + "ëł Ģ", + "íĨ ¤", + "ï½ ¯", + "ë¤ Ħ", + "ê½ ¤", + "ï½ Ĵ", + "ìķ ¨", + "ï½ ¼", + "ê¹ IJ", + "íģ IJ", + "âĦ ĸ", + "ë§ º", + "ïº ®", + "ëħ ģ", + "ê² ¸", + "ï» ł", + "íĬ ľ", + "Å ¹", + "ë¥ Ń", + "ëĪ ī", + "ï½ Ķ", + "íĮ ¬", + "ìŀ ĩ", + "ï ¬ģ", + "ï» ¨", + "ëij ¥", + "ëŀ Ħ", + "Ù ¬", + "íĭ ´", + "ìŀ ī", + "Ú ¾", + "ìĽ ħ", + "ï» ®", + "ëĭ ī", + "âī ª", + "âĹ Ħ", + "ëĪ Į", + "íĽ ¼", + "ì¤ į", + "Å ¸", + "ì¤ ¬", + "ì¾ Į", + "ï½ ĵ", + "ï¾ Ĭ", + "ðŁı »", + "ï¾ ī", + "Ð ģ", + "íĺ IJ", + "ï¾ Ļ", + "ê¼ ¬", + "íŀ IJ", + "âĢ ¥", + "ëŁ Ń", + "ë§ ŀ", + "ìĥ ¤", + "ïº Ĵ", + "íĭ ±", + "ë½ ij", + "à ķ", + "âĪ ļ", + "ëĤ Ħ", + "ê¹ Ŀ", + "ëĨ Ī", + "Ạº", + "ìħ Ī", + "ìĮ į", + "âĢ ¡", + "ï¼ ±", + "ìģ ¨", + "âĺ º", + "ëĴ ·", + "ìĺ ³", + "ðŁij į", + "ëª ½", + "ëĤ Ń", + "ïº Ń", + "ë© Ī", + "á» Ī", + "íķ Ģ", + "ëĭ Ļ", + "ë¦ ĩ", + "ìķ ¤", + "ìį ¼", + "ãĥ µ", + "Ñ £", + "ìľ Ĺ", + "â ŃIJ", + "ï¾ ĺ", + "íĹ ¬", + "ê¾ ¼", + "ìķ Ĺ", + "ï» Į", + "ê± ·", + "ëħ ķ", + "ë¡ ±", + "ìķ Ĭ", + "ï¾ Ģ", + "ìĩ ł", + "íĮ ©", + "ïº ª", + "ë§ Ļ", + "ï¼ ¿", + "ê¿ Ķ", + "íİ ľ", + "ë£ ¸", + "íĶ Ķ", + "ï» ³", + "ëı ķ", + "ìĭ ¼", + "á» İ", + "ë§ ĺ", + "ì¢ ĭ", + "íĨ ¡", + "ï½ ±", + "íĿ ij", + "á» ¸", + "ì¦ Į", + "ì¹ ¸", + "ëŃ ĺ", + "ï¾ Ĺ", + "ï» ĭ", + "íĬ Ģ", + "ë¥ Ļ", + "ì½ ©", + "ëģ Ĺ", + "ëį ´", + "ìħ ľ", + " ¸", + "ë» IJ", + "ìĥ µ", + "ê² IJ", + "ëĵ ¬", + "ë£ °", + "ãħ ĭ", + "ìĹ ī", + "á» ĸ", + "ëĦ Į", + "ï½ ¶", + "ë´ ĩ", + "ëĤ ³", + "ãĤ ľ", + "ëĸ »", + "íİ Ģ", + "ëį ©", + "íķ ¸", + "à ·", + "ê¼ ¼", + "ëĶ ľ", + "ë° ´", + "ë© į", + "âĹ ¯", + "ìĹ ij", + "ìĻ ¼", + "ïº ij", + "ë¶ ķ", + "ë¡ ¬", + "ï½ Į", + "íĨ ¨", + "ïº ´", + "ëł ĺ", + "ê° ¤", + "ìĪ ²", + "Ñ ĵ", + "ìħ ī", + "ï» ĵ", + "ëĪ Ķ", + "ëį §", + "âĢ ¼", + "ï» ²", + "ê° ±", + "ê¿ Ģ", + "ëĭ ·", + "Ạ¸", + "Ạª", + "Æ Ĵ", + "ëį ¤", + "ìĪ Ń", + "ï½ Ĥ", + "ï½ Ī", + "Å ł", + "ë£ ¬", + "Ñ µ", + "ëĸ ¡", + "ëĥ Ħ", + "ìĦ °", + "ëĵ Ī", + "ï¾ ĥ", + "ëĩ ¨", + "ï½ IJ", + "êµ ½", + "ìĹ ½", + "ëĤ Ģ", + "ë¬ ¶", + "ï½ ·", + "ìı Ł", + "íĺ Ķ", + "ê¼ Ī", + "ëģ Ī", + "ì¥ IJ", + "ïº Ĺ", + "Ä Į", + "ëĪ ł", + "ëĸ ¼", + "íĢ ´", + "âī ¥", + "ëĭ Ń", + "ì± Ļ", + "ê» ı", + "ë© ¤", + "ìĥ ĺ", + "ëį ®", + "ë£ ¡", + "ìĤ ½", + "ãĪ ľ", + "Ä ¨", + "âĢ §", + "ï½ º", + "Ä £", + "ì¦ ī", + "ï¼ ¼", + "Û ©", + "âĪ Ļ", + "ë° ı", + "ë¹ ħ", + "ðŁĺ Ľ", + "íĪ ´", + "ðŁĴ ķ", + "ãĢ Ĵ", + "ìŀ ĺ", + "ïº ¤", + "ï½ ĸ", + "ë© ľ", + "ë² ¼", + "ëĿ Ħ", + "ëļ ľ", + "ï» ĺ", + "ìĥ Į", + "ï½ Ħ", + "ì© Ķ", + "ï½ Ļ", + "ïº ©", + "Û ŀ", + "âĺ İ", + "ìł ¤", + "ëIJ ©", + "Å Ŀ", + "âŀ ¡", + "ï» §", + "Ð ı", + "ì« ĵ", + "ê³ ½", + "É ij", + "ãĥ ²", + "ëĤ «", + "ë¦ ī", + "ì¢ ģ", + "ë° Ń", + "ðŁĺ ģ", + "ë¹ µ", + "ì² ©", + "ì» µ", + "ðŁĺ ĺ", + "ë± ħ", + "âī Ī", + "ë¹ ļ", + "ï» ľ", + "ðŁĻ ı", + "íģ °", + "ìĦ ŀ", + "ï¾ ļ", + "ìĺ ¹", + "ë¼ Ī", + "ëĤ ¯", + "ëŀ ©", + "íļ ¡", + "ï½ ķ", + "íĥ ĵ", + "ëĿ ł", + "ê³ ģ", + "ëĵ Ģ", + "ìĹ ł", + "ï¼ º", + "ë§ ij", + "ëĭ ¿", + "ì¿ ¨", + "ãİ ¡", + "Ð Ĭ", + "íĦ ±", + "Å ¨", + "ïº ³", + "ï¾ ı", + "âĭ ħ", + "ê¼ ´", + "âī ¤", + "íĮ ģ", + "Î ©", + "ê¶ ¤", + "ìĪ į", + "âľ ¿", + "ì½ ¤", + "ëĪ ħ", + "íĨ ±", + "ãħ ľ", + "áIJ ħ", + "Å Ĵ", + "ðŁij ī", + "ï» ¦", + "Ð ª", + "ë¥ ľ", + "íķ «", + "ï¾ ĭ", + "âĻ «", + "ê¹ ľ", + "ë° ¸", + "ëĶ ĺ", + "íĿ ī", + "ï¾ ģ", + "ï¾ Ľ", + "ëł Ľ", + "ê² ¹", + "ì¿ ¼", + "ï» ¬", + "âŀ ¤", + "ðŁĻ ģ", + "ïº ł", + "ëĨ ¨", + "ë¯ ¹", + "ê¸ ĭ", + "ë» Ķ", + "ê¹ ĥ", + "ëij ij", + "íĭ ¸", + "íİ Ļ", + "âŀ ĸ", + "ãĥ ½", + "ì§ ļ", + "ï½ ¬", + "ï» ¥", + "íĮ ½", + "âĢ Ĵ", + "ì ĮĢ", + "ìŃ ī", + "ëļ ±", + "ãĤ ŀ", + "íĭ Ī", + "ãĤ IJ", + "ëī ĺ", + "Î £", + "ê³ °", + "ë¹ Ĺ", + "ï¾ İ", + "ðŁĺ Ń", + "íĿ ł", + "ìĹ ¿", + "ê° ļ", + "ì¤ Į", + "ë§ µ", + "ï½ ³", + "ãģ ¢", + "ï» Ĺ", + "âī ¦", + "Ú ¤", + "ë łģ", + "ê¼ ½", + "ï» «", + "âī §", + "ì´ Ľ", + "ìł Ŀ", + "Ạ°", + "âĻ £", + "ìº ĺ", + "âĪ ĩ", + "ê² ī", + "ë° Ł", + "ï» Ķ", + "íĸ ĩ", + "âĸ Ĵ", + "ðŁij ı", + "à ŀ", + "ðŁĺ Ĩ", + "ïº ¼", + "âĿ Ĺ", + "ìº Ķ", + "ì¹ ©", + "ëĸ ¤", + "ëĥ ħ", + "âĶ ľ", + "ï½ »", + "Î Ķ", + "áĥ ¦", + "ìŀ İ", + "âĺ Ģ", + "âĪ ¼", + "ðŁĶ ¥", + "ë° Į", + "ìł ĸ", + "íĹ Ľ", + "Î ķ", + "ïº ĥ", + "ë¶ ī", + "âĪ ŀ", + "íĥ Ń", + "à ĭ", + "âģ Ħ", + "ãħ ĩ", + "ëĦ ¥", + "ëĭ ®", + "ëł ·", + "íĮ Ŀ", + "ìº ¡", + "ë· Ķ", + "ì© į", + "íĤ ´", + "ëļ «", + "âĵ Ĵ", + "íķ į", + "âĻ Ĥ", + "ï¾ Ĩ", + "âĨ ©", + "ìį ©", + "ïº ķ", + "íĿ Ļ", + "Ñ ľ", + "íĤ ·", + "íĿ °", + "íĥ ±", + "ëķ IJ", + "ï¾ Ĵ", + "× ĥ", + "ëĮ Ħ", + "ìĺ ´", + "ìķ µ", + "ê¹ ¥", + "ëŀ Ń", + "ìª ¼", + "ãİ Ŀ", + "ðŁĺ ħ", + "ëı ĭ", + "ëª «", + "ïº ¸", + "ë® ¬", + "ë² ħ", + "ëij ł", + "ìħ °", + "ì» ·", + "ëĶ ª", + "ëħ Ķ", + "ãħ ¡", + "ìĶ »", + "íķ ı", + "ëį ±", + "ïº ¨", + "ï¾ į", + "ï½ µ", + "ì¢ Ģ", + "íİ Į", + "ï» °", + "ïº £", + "Æ £", + "ðŁ¤ £", + "ï· º", + "ëĤ ļ", + "âĭ Ĩ", + "ë³ į", + "ðŁĺ Ħ", + "ìĸ Ģ", + "ìĻ ł", + "ëĨ Ķ", + "íĹ ¨", + "ï» Ľ", + "ï» Ŀ", + "á» ¶", + "ìĸ ĺ", + "ìİ Ħ", + "Ú Ĩ", + "ï» ŀ", + "ëĢ IJ", + "ê² Ķ", + "ï» µ", + "âĹ ¦", + "íļ Ł", + "ê¹ ģ", + "ê° ĵ", + "ëĶ ´", + "ìı ĺ", + "ëļ Ŀ", + "á» ł", + "ëŀ ´", + "ëĦ ī", + "âĺ ŀ", + "ï½ ĺ", + "Å ½", + "ë¦ İ", + "âĸ ¬", + "ëŃ ī", + "âĩ Ľ", + "ìį ¬", + "ïº Ł", + "Ë ľ", + "ë¶ ĵ", + "ìĽ °", + "Å ľ", + "ëŃ ĩ", + "á» ²", + "Ë ļ", + "ëķ Ģ", + "âĺ ij", + "ðŁı ¼", + "ìĸ ½", + "âĮ Ĵ", + "Ð İ", + "É ¾", + "íĮ ¡", + "ï¾ ħ", + "ìŀ Ń", + "ï½ ¨", + "ì¹ «", + "ìľ Į", + "Ò Ľ", + "êµ ¿", + "ëĭ ¦", + "âĶ Ķ", + "ï¾ ij", + "ì§ ĸ", + "ìº Ħ", + "ãĢ ĥ", + "Ê ¼", + "ê² Ł", + "ï½ §", + "Ä ¢", + "íİ ł", + "ë§ ·", + "ê° ĩ", + "ìĭ ¹", + "ðŁĴ ¦", + "ï¾ ľ", + "ëĬ Ļ", + "ë² ¡", + "Å ¿", + "ðŁĺ ĭ", + "ðŁĴ ª", + "ì¿ Ħ", + "ë© ķ", + "ìŃ ¤", + "ëĬ Ħ", + "ðŁĮ ¸", + "ãĤ Ŀ", + "Ç İ", + "ï½ ļ", + "Ä Ĺ", + "ëģ ĵ", + "ê¶ IJ", + "áµ ī", + "ãĥ Ĥ", + "ê» į", + "ðŁĺ ¦", + "ãĢ Ŀ", + "ðŁ¤ Ĺ", + "Ñ Ł", + "ìĹ İ", + "âľ Į", + "ìī IJ", + "à Ĩ", + "íĹ IJ", + "ðŁİ ī", + "Î ij", + "ï½ Ń", + "ðŁĴ Ļ", + "ìĽ ¬", + "íĢ ĺ", + "ï» ¢", + "ðŁĺ İ", + "íij ¼", + "íĿ ©", + "ï» Ħ", + "íħ Ģ", + "ëł IJ", + "ì¥ ¬", + "Ð ĭ", + "ìĥ ·", + "ëľ ¬", + "ðŁĺ ĥ", + "ëĦ ¬", + "ë¥ ¨", + "ìĽ į", + "ï½ Ĩ", + "ï½ ´", + "ãĥ ħ", + "à ı", + "ï» ª", + "âĻ ł", + "ëĬ ¬", + "ë± Ģ", + "ë° ĭ", + "ìĥ Ģ", + "ï½ ¾", + "ëĤ ±", + "ì» ¸", + "ðŁĴ ĸ", + "ðŁij Į", + "Ñ ŀ", + "ì§ ±", + "Ë Ĩ", + "ðŁĵ ļ", + "âŃ ķ", + "ï¬ Ĥ", + "ï» ¡", + "ëij ¬", + "íĪ ¼", + "âĸ ¸", + "ê° ¯", + "ê¹ ħ", + "ï½ ®", + "ëĺ ¥", + "Ä ¡", + "íĮ Ł", + "Ð Į", + "ìĨ Ł", + "ïº ĵ", + "ï» ¼", + "à Ľ", + "ãĥ ¾", + "ëĮ ĵ", + "íĴ ĭ", + "ìķ ĵ", + "ï½ ¹", + "ëĤ ¡", + "ðŁij ĩ", + "Ạ¼", + "ãĢ Ł", + "ðŁĮ Ł", + "íĥ ł", + "ãĢ Ĩ", + "âĢ Ł", + "ë¸ IJ", + "ðŁĮ ¹", + "ìł ¼", + "ðŁĵ Į", + "ìĶ ¬", + "âĹ Ģ", + "ðŁĴ ĵ", + "ê¹ İ", + "ìĤ IJ", + "ìĶ Į", + "Ñ Ľ", + "âĶ Ī", + "ë² ³", + "ãİ ŀ", + "Õ ¡", + "íĤ µ", + "ðŁ¤ Ķ", + "ëĢ Ķ", + "ìĬ IJ", + "íĻ ī", + "âľ ¦", + "ëľ ¯", + "ìł ¯", + "ëĶ §", + "Î ¦", + "Ë Ī", + "ìī ¼", + "âĹ Ĭ", + "ëľ ©", + "ëľ °", + "ï¾ IJ", + "ë¿ Ķ", + "ìĹ ®", + "ì· Į", + "ïº §", + "Î Ĵ", + "ëµ Ļ", + "ï» Ĭ", + "ì° Ķ", + "íİ Ħ", + "ðŁĴ Ĺ", + "Ạ´", + "ì° ¢", + "íľ ¼", + "ê½ Ĥ", + "ì± Ķ", + "ìī ´", + "âĸ ¾", + "íĪ °", + "ëĭ Ľ", + "âĿ £", + "ï½ ª", + "ðŁĴ ľ", + "Ë ĺ", + "ãħ ¤", + "âĨ Ĺ", + "íĸ Ħ", + "âĻ ¬", + "ìķ °", + "ïº ľ", + "âī ¡", + "ãĢ ĵ", + "ìij ¥", + "íĮ į", + "íī ģ", + "ë» Ĺ", + "íľ ł", + "íľ ©", + "âľ Ī", + "íĢ Ħ", + "ìĸ ĩ", + "ì¢ ĩ", + "íŀ Ļ", + "ëª ¹", + "ãĤ Ľ", + "ðŁĺ ±", + "ëį Ł", + "๠ħ", + "êµ ¶", + "Ù «", + "ìĶ ģ", + "âľ ª", + "ï¾ Ī", + "ðŁĻ Į", + "âļ ¡", + "Î ļ", + "ì¼ Ī", + "ï¾ Ķ", + "ï¾ Ĥ", + "êµ ī", + "ïº »", + "ðŁĴ ĭ", + "á¹ £", + "Ó Ļ", + "ìĨ ľ", + "ìĹ £", + "âľ ©", + "ìľ Ļ", + "ïº °", + "Ạ²", + "ìŀ £", + "âĿ Į", + "âĺ ģ", + "ìķ İ", + "Ä ½", + "Û ģ", + "ãĦ ±", + "ëŁ ¿", + "íĮ ¸", + "ê½ ī", + "ìı ł", + "ðŁį Ģ", + "âĨ Ķ", + "ëŃ ¡", + "ï» ģ", + "ï¼ Ħ", + "ðŁĴ ¥", + "âĺ Ľ", + "íĹ ·", + "ëij ¡", + "Î ł", + "Î ¤", + "âĦ ĵ", + "ïº ·", + "Î Ļ", + "ëı Ķ", + "ì§ ¤", + "âĶ ĥ", + "ãĦ ·", + "Ç Ĵ", + "ðŁ¥ °", + "ëĶ ķ", + "ìļ ¥", + "ì¸ Ħ", + "íĽ Ķ", + "ïº ĩ", + "ïº ¬", + "ðŁĺ ¢", + "ë¹ ¡", + "ìĶ ¹", + "Å ³", + "Ë Ŀ", + "íİ ij", + "ï¾ ĵ", + "ðŁĴ ļ", + "ëĬ ij", + "êº ¾", + "íĨ °", + "à ¿", + "Ð Ħ", + "ëĮ IJ", + "ë½ Ģ", + "ì· Ħ", + "ðŁ ĵį", + "ðŁĻ Ī", + "âĹ Ī", + "ê¿ ĩ", + "ì¼ Ħ", + "íİ «", + "ðŁĩ ·", + "âĶ ĭ", + "âļ ł", + "ë± ī", + "ì į°", + "ìĻ Ī", + "É ª", + "ïº ĭ", + "ðŁĺ ľ", + "Î Ł", + "ðŁ ĻĤ", + "âļ ½", + "Å Ī", + "ë¹ Ķ", + "íĮ ľ", + "๠ı", + "ìĸ ¹", + "íĪ Ń", + "ðŁ¥ ĩ", + "ãĦ ´", + "ëĶ ¥", + "ìŃ Ī", + "âĪ Ĩ", + "ëĸ ³", + "ë± ĥ", + "ìŀ ¦", + "ï» IJ", + "Î ľ", + "âľ §", + "Ï į", + "ìł ĵ", + "âĹ ķ", + "ëĴ Ģ", + "ï» Ģ", + "ðŁĶ ´", + "ê½ ģ", + "ëĮ Ī", + "ëİ Į", + "ãĤ İ", + "⦠ģ", + "ì½ §", + "ï¯ ¾", + "âĿ ¯", + "ภħ", + "ðŁĻ Ħ", + "âĿ Ģ", + "ðŁĶ ¹", + "âĩ IJ", + "êµ µ", + "âĩ Ķ", + "ë¶ IJ", + "ðŁĴ Ľ", + "Î ¾", + "íĥ ¬", + "âĿ Ħ", + "Ò £", + "ãĢ °", + "âĪ ij", + "âĺ ¼", + "âī ł", + "Ò ¯", + "ïº ¯", + "ê¿ ¨", + "âľ ĸ", + "Ê ĸ", + "íĢ Ģ", + "ê¾ Ģ", + "íĹ Ŀ", + "âĶ £", + "ãİ ľ", + "ëĶ Ľ", + "ëľ ¸", + "ï º«", + "ê¿ °", + "ðŁĩ ¹", + "Ç IJ", + "Û Ĵ", + "ë£ »", + "ïº ĸ", + "Ñ ļ", + "ëĬ ł", + "Û ķ", + "ê¹ ¡", + "ë¿ ľ", + "ì² ¼", + "ï¨ ij", + "ë¥ µ", + "ìį ¸", + "íħ ħ", + "íij ¹", + "Ö Ģ", + "ï³ Į", + "ãħ £", + "ìij ¤", + "ì½ ķ", + "ëķ ł", + "ðŁĮ ¿", + "íĥ Ķ", + "ìĽ ģ", + "Î ¶", + "âŀ ľ", + "ìĬ ĺ", + "íĽ Ĺ", + "ë© §", + "ìī ĺ", + "Õ ¶", + "á¹ ĩ", + "ðŁİ ģ", + "ï½ ¿", + "ï¼ Ĥ", + "á¼ IJ", + "âľ ķ", + "âŀ ¢", + "ëĦ ¨", + "ì» «", + "ì¯ Ķ", + "ì° ľ", + "ðŁĴ °", + "íħ Ŀ", + "ãİ ı", + "ë³ ¶", + "Ò ĵ", + "âĨ ³", + "ìĥ ´", + "íģ ĺ", + "âĸ Ģ", + "ë² Ļ", + "ภĥ", + "á½ ¶", + "Ä ķ", + "⬠ĩ", + "ë¤ ĺ", + "ðŁİ µ", + "âľ ļ", + "ïº ı", + "Î ¡", + "âĹ ī", + "ðŁĴ «", + "Ð Ī", + "ìĸ Ħ", + "ì§ Ļ", + "ï» ĥ", + "ðĿij Ĵ", + "ëŃ Ħ", + "âĿ ¥", + "âĿ ĸ", + "âĺ Ŀ", + "Ê ¹", + "Ḡ¥", + "âĢ ¿", + "ãħ ħ", + "ê¸ ģ", + "ëķ ¡", + "ëį ¥", + "âĪ ©", + "ê» Ħ", + "ë® Į", + "Ò ±", + "âĪ Ĺ", + "ëł Ļ", + "ïº Į", + "Ë IJ", + "ðŁĺ ³", + "ðŁij ©", + "ðŁİ ¶", + "ì¿ µ", + "ðŁ¤ ©", + "ê· ¤", + "ëĮ Ķ", + "ïº IJ", + "Ï İ", + "ì¶ ¥", + "ï½ Ĭ", + "á¹ Ń", + "ë¤ ¼", + "âĸ «", + "ì§ ł", + "á¼ Ģ", + "ê» ij", + "ëĮ ģ", + "íĢ ¸", + "âĻ Ľ", + "ðŁĴ ŀ", + "âĸ °", + "ðĿij ĸ", + "ëĿ ¤", + "ठ¦", + "ì´ ĺ", + "ðŁĺ ĩ", + "ëĶ ¤", + "Î Ĺ", + "ðŁĻ ĩ", + "Ë Ľ", + "ì© ¡", + "âĪ §", + "Õ ¥", + "Ñ Ļ", + "ëIJ ¬", + "ëĸ Ħ", + "ðŁĮ ·", + "ìĹ Į", + "ðŁĺ ¥", + "ëĪ ´", + "ï» ļ", + "É Ľ", + "ïº Ħ", + "ï» ı", + "Å Į", + "ë² ļ", + "ìĭ £", + "ïº Ģ", + "Î ĵ", + "ðŁĺ Į", + "Ë Ļ", + "ëŀ ı", + "ðŁĶ ¸", + "ðŁĵ ·", + "ëģ ½", + "íģ ½", + "ðŁĴ ¡", + "ðŁĮ ±", + "ëº ı", + "ìģ ł", + "ìĥ IJ", + "ëı Ĺ", + "ì¸ °", + "ëĪ ķ", + "Î Ŀ", + "âģ ī", + "ðŁĮ ¼", + "íĮ ł", + "âĭ ¯", + "áĥ ĺ", + "âľ ¤", + "ê± Ķ", + "íĮ İ", + "ðŁĴ ¯", + "ìı Ļ", + "íĹ ī", + "Ù Ń", + "ì½ °", + "ïº ¿", + "ï» ±", + "ì± Į", + "âĺ ķ", + "ðŁİ Ģ", + "Ä Ŀ", + "ë° §", + "ìĤ ¿", + "áij ķ", + "ðŁį ĥ", + "âĩ ¨", + "Î Ľ", + "ë§ ´", + "ë³ ķ", + "á ijIJ", + "âĸ ĵ", + "ðĿ ijľ", + "âĻ »", + "íĤ ¥", + "Õ ¸", + "ãĪ ±", + "ëº Ģ", + "ì² ¸", + "ïº Ľ", + "ðŁı Ĩ", + "ðŁĩ ª", + "âĿ ĵ", + "Ä Ģ", + "ì½ ¥", + "ðŁĩ §", + "á½ ·", + "âľ Ĥ", + "ìŀ ¼", + "ï§ ¡", + "ðŁĵ ¸", + "âĻ ¯", + "É Ķ", + "á½ ¸", + "âĮ ª", + "ï» ĸ", + "ï¥ §", + "âļ «", + "âĶ Ĺ", + "ðŁĮ Ī", + "ï» ©", + "ðŁĵ ²", + "Ï Ī", + "ðŁĺ ¡", + "ðĿij İ", + "ìľ ½", + "ì§ ¬", + "ì§ Ĭ", + "á½ ³", + "ìĮ ¤", + "ëĤ į", + "âī Ĵ", + "ðŁij ¨", + "âĺ ĺ", + "Ó ©", + "âĤ ĵ", + "âĪ Ĥ", + "ï¹ ģ", + "ðŁĴ IJ", + "íħ ĥ", + "ðŁı ½", + "ê· Ħ", + "ðŁĺ ı", + "ðŁĮ º", + "ðŁĺ Ķ", + "ï½ «", + "âľ İ", + "ëµ Ī", + "ðŁĩ ¸", + "âĢ £", + "âŀ Ķ", + "ëĺ ĺ", + "ìĥ ¬", + "Ê ĥ", + "⬠ħ", + "ì© IJ", + "ðŁĻ Ĩ", + "ðŁİ Ħ", + "Ä ¾", + "⣠¶", + "áĥ IJ", + "âĺ »", + "ì± ķ", + "ìģ ©", + "ë½ ķ", + "ìº £", + "ðŁij Ī", + "ðŁĻ ĭ", + "ï¾ ĸ", + "Ò ļ", + "Õ «", + "ìĮ Ī", + "ë² §", + "ðŁĩ ®", + "ï½ Ŀ", + "ðŁį ģ", + "ìĹ ¥", + "Ä ³", + "ë½ IJ", + "íį ½", + "íĽ ij", + "âĤ ¹", + "ãħ ģ", + "ìĶ ½", + "ðŁĶ ģ", + "ठ¯", + "ê¾ ¹", + "ëī ľ", + "âĹ ¡", + "íķ Į", + "Î ĺ", + "ë£ ¹", + "ìĻ ĵ", + "ðŁĩ ¦", + "ðŁij Ģ", + "âĶ Į", + "á¿ ¦", + "ëĦ Ľ", + "ìĦ £", + "ìŃ Ļ", + "ï± ł", + "Î ŀ", + "Ê »", + "á¿ ¶", + "âĿ Ŀ", + "ê± Ģ", + "ëĸ ´", + "ãĦ ¹", + "ðŁĴ İ", + "Ï ¹", + "⼠ħ", + "ï» ķ", + "ãĥ ±", + "ï½ Ľ", + "ëĮ ķ", + "ë¹ ½", + "ì¥ Ķ", + "ì¿ ¤", + "ðŁĸ ¤", + "Ñ Ĵ", + "ê¹ į", + "ëİ Ģ", + "ìĭ ¯", + "ë» ¤", + "ðŁĵ ŀ", + "ðŁĵ £", + "ðŁĺ Ŀ", + "ìį ¹", + "ìĹ ¡", + "ì° IJ", + "á½ IJ", + "ï» Ī", + "âľ į", + "Ä ı", + "ðŁĮ ŀ", + "âĦ ¦", + "ê½ Ŀ", + "ë» ĺ", + "ìĪ ±", + "âĶ ĺ", + "ðŁĮ »", + "âĤ ´", + "âŀ ¨", + "íIJ ģ", + "ê ¶Ī", + "âĺ ¢", + "ðŁĺ Ī", + "ï½ ©", + "âĦ Ĺ", + "ê° Ń", + "ê° ¸", + "ë» ij", + "ì¥ ´", + "ì» ¥", + "ï¤ Ĭ", + "ï» Ĵ", + "ðŁĺ ķ", + "âĺ Ķ", + "ìĺ IJ", + "ðŁļ Ĺ", + "ëĹ Ħ", + "ë§ ı", + "Õ ½", + "âĸ »", + "⣠µ", + "ìī °", + "ï» ij", + "âĻ ©", + "Î ¥", + "ðŁĺ £", + "âĬ Ĥ", + "ãħ Ĥ", + "ìħ ¸", + "íı Ħ", + "âľ ½", + "ì¦ Ļ", + "âĸ £", + "ê± į", + "ê¿ ĭ", + "ì« Ħ", + "ìº ĩ", + "ðŁĩ µ", + "ðŁij ij", + "âľ ĺ", + "ðĿij Ľ", + "ìį ½", + "ìº ī", + "ï¬ µ", + "ðŁĶ º", + "âĦ ®", + "íĥ ¤", + "ðŁĩ º", + "ðŁĴ µ", + "íħ ¨", + "ï½ ij", + "Î ¨", + "ìĥ ¹", + "ìĸ ķ", + "ì¹ µ", + "ðŁĵ ±", + "ठµ", + "ðŁij Ĭ", + "ðŁĴ Ħ", + "ðŁĴ Ŀ", + "ãĮ Ķ", + "ìĻ ģ", + "Ð ĩ", + "à® IJ", + "âĸ ¹", + "á´ Ľ", + "âĹ ĺ", + "ëº ¨", + "íĥ ī", + "ìĸ Į", + "ðŁIJ ¶", + "ãĤ ij", + "Ë ĩ", + "Å ı", + "á½ ¹", + "ìħ §", + "ï¹ °", + "ðĿij ¡", + "ðŁĶ Ŀ", + "ðŁĺ »", + "ðŁĴ ĥ", + "ðŁ¤ ¦", + "ðŁį Ĵ", + "íĢ µ", + "âľ Ĩ", + "ë¹ ´", + "ï§ ¤", + "ï» Ļ", + "á´ Ĺ", + "ðŁĮ ´", + "Í ¾", + "ëĮ ij", + "ì¨ ĭ", + "ìµ ¸", + "ðŁİ Ī", + "ðŁı ł", + "á½ ±", + "Û Ĩ", + "á¿ ĸ", + "âĢ Ľ", + "ì° ¼", + "íķ ¥", + "íĹ ´", + "ðŁĩ ¬", + "ì° Ŀ", + "âĪ ł", + "ï¼ ĩ", + "âĬ Ļ", + "âĿ ij", + "ëĦ ĭ", + "ëŀ Ĺ", + "ë° ī", + "ìĹ Ĭ", + "ì¢ Ĩ", + "íĮ ¥", + "ï° ²", + "ðŁĵ ĸ", + "ðŁĺ ®", + "âļ ª", + "ðŁĺ ļ", + "âĿ ŀ", + "ðĿij Ł", + "ðŁİ Ĥ", + "Å ķ", + "áIJ Ī", + "êº ½", + "ì± ł", + "ïº Ŀ", + "ê¿ ī", + "áĥ ł", + "ðŁı ĥ", + "ðŁĴ ¸", + "âĿ ģ", + "âĹ ¾", + "Ú ª", + "á¹ ĥ", + "íĬ ¬", + "ðŁĩ ±", + "íİ Ń", + "ðŁĺ ŀ", + "ë¾ °", + "á¹ Ľ", + "ëĽ ¸", + "âĿ Ĥ", + "êĴ ³", + "âĶ IJ", + "íĵ °", + "âŀ ł", + "ê´ ĺ", + "ëħ ĺ", + "ë» ¥", + "ì¾ ħ", + "ðŁĺ IJ", + "âĪ ª", + "ðŁij ģ", + "âĪ ´", + "âĹ ģ", + "ëº IJ", + "ìŀ ¤", + "ì± Ĺ", + "ðŁı ¾", + "Î §", + "á½ »", + "âŀ ¥", + "ìŁ Ī", + "ï» ī", + "âĸ Į", + "ãĥ ®", + "ðŁ¤ ¤", + "âĩ ĵ", + "ì¼ ł", + "á´ ı", + "ë§ ¬", + "ë» £", + "ðŁĴ ¬", + "ðŁį ĵ", + "Ä ¸", + "Ù ¹", + "Ê ¿", + "á½ °", + "ëķ ľ", + "ì° ¡", + "ì° »", + "íİ į", + "ðŁİ ¯", + "ðŁį Ĥ", + "ðŁij §", + "âĻ ¢", + "áĨ ŀ", + "âĻ §", + "âļ ľ", + "âľ ī", + "ëĵ ¦", + "ëŃ £", + "ìĪ ı", + "ìĵ ±", + "Å Ń", + "Ê Ĭ", + "âĴ ¸", + "âĩ ©", + "ðŁĴ Ķ", + "Õ µ", + "Ð ī", + "Ò »", + "ë§ £", + "ìĽ ľ", + "ì¿ ¡", + "íĽ ħ", + "íĽ ¤", + "ïº ¢", + "âľ ĭ", + "âĪ Ī", + "ðŁĮ į", + "Ê ľ", + "ëĬ ª", + "ëĴ ¹", + "ïº ²", + "âĸ Ħ", + "ãħ Ī", + "ëļ ¤", + "íİ ©", + "âĪ ¨", + "ðŁ¤ ª", + "áĥ ļ", + "ê³ ¶", + "íĬ ķ", + "ðŁĺ ¬", + "âĪ «", + "ðŁij ĭ", + "Ò IJ", + "íĬ ¿", + "ðŁĶ µ", + "ðŁĴ ¨", + "ðŁĮ Ļ", + "ëĩ ©", + "âľ ³", + "ë¨ ģ", + "ëº Ħ", + "ìĻ ij", + "ìº ħ", + "íı Ī", + "ðĿij Ļ", + "ðŁĴ ĺ", + "ãİ ¥", + "âĿ ı", + "âľ °", + "ï¯ ¿", + "ëµ IJ", + "ì¼ IJ", + "ïº ±", + "Õ ´", + "ï¬ Ģ", + "âľ ´", + "ðŁ¤ Ń", + "ðŁij Ĩ", + "⼠Ķ", + "ê· ĵ", + "ìĮ Į", + "ðŁ¤ ·", + "Û Ķ", + "ðŁ§ ¡", + "ðŁĺ ĵ", + "Î ĸ", + "âı °", + "ê² ľ", + "ëĭ ³", + "ëİ ħ", + "ë° Ī", + "ï® IJ", + "ðŁı ¡", + "âĨ ª", + "âĵ Ķ", + "âľ Ĭ", + "Ï ²", + "Ü IJ", + "ðŁĩ ³", + "Ö Ĥ", + "âľ ı", + "ìĸ Ĺ", + "ì« Ļ", + "ðŁĺ ²", + "Ä Ń", + "âĻ Ń", + "âĶ ı", + "âĹ Į", + "ðŁĺ ¯", + "áµ Ĵ", + "íĬ ł", + "Ä ·", + "Ê ģ", + "ठŁ", + "á¹ ģ", + "á¼ °", + "á¿ Ĩ", + "â «", + "â« ¸", + "ëį «", + "ì³ ĩ", + "ì¼ ¤", + "íĽ ¨", + "ðŁĴ Ł", + "Ê Ģ", + "Ê ³", + "ëĵ IJ", + "âķ °", + "âĿ ĩ", + "Ç Ģ", + "Ç Ķ", + "É ´", + "âĺ ļ", + "âĺ ľ", + "ê¶ Ĥ", + "ì« Ĵ", + "ì± Ī", + "ðŁĩ ¨", + "ðŁİ ¥", + "ðŁĵ Ŀ", + "Ä §", + "ðĿ ijIJ", + "Û Ī", + "ठ¬", + "ì¬ IJ", + "íĹ ¥", + "âĻ ¨", + "ðŁį ´", + "ï¹ ı", + "Ë ĭ", + "ðŁ¥ º", + "âĸ ¨", + "íĻ ĭ", + "âĪ ħ", + "ëģ Ļ", + "ëŀ ł", + "ìĨ ¥", + "âĢ ĸ", + "ðŁ¤ ĺ", + "ðŁIJ »", + "áµ ķ", + "Ç Ŀ", + "âĺ ı", + "ïº ļ", + "ï» Ĥ", + "ðŁļ ©", + "ìĪ Ł", + "Ë Ĭ", + "⤠µ", + "ðŁĴ §", + "ã ħį", + "ë© ©", + "Æ ¬", + "Î ĩ", + "âĩ §", + "âĵ ļ", + "ìĤ ¯", + "ìĪ ¯", + "ëĨ ĭ", + "âľ ¯", + "ðŁļ Ģ", + "Ú ĺ", + "Ú ¨", + "âľ Ń", + "ê² ħ", + "íĮ °", + "íľ Ļ", + "ðŁĮ Ĭ", + "ðŁİ ĵ", + "ðŁĺ Ļ", + "Ë ĥ", + "ðŁĴ ģ", + "ðŁij İ", + "âĺ ¹", + "ðŁĺ «", + "ðŁĴ »", + "ëĤ µ", + "ìĿ Ĭ", + "íĮ »", + "Ò ³", + "á½ ²", + "âŀ ŀ", + "ëĤ ij", + "ëĿ Ī", + "ì£ ¤", + "ï» ¯", + "ðŁĩ ©", + "ðŁ¥ ³", + "âĴ ¼", + "ðŁ¦ ĭ", + "âĺ Ĥ", + "ðŁĺ °", + "ðŁĻ ĥ", + "ðŁĺ Ĵ", + "Û İ", + "Ï ķ", + "Ḡ¤", + "ë£ ½", + "ìĬ ¥", + "ðĿij ī", + "É IJ", + "ðŁį İ", + "âķ ¯", + "âķ ¹", + "ຠ²", + "ï¾ ł", + "ë¹ ķ", + "ïº Ĩ", + "Ê º", + "Ó §", + "âĨ ł", + "ëĥ ĩ", + "ìİ Ī", + "ìŁ ¤", + "ï± ¢", + "âķ ¬", + "âĺ ł", + "ðŁİ Ĭ", + "ãį į", + "ãİ İ", + "âĺ °", + "âľ ĥ", + "ãħ ī", + "ë¯ Ī", + "ë¹ ¤", + "ìı Ń", + "ðĿij ¢", + "ðŁIJ ¾", + "Å ĭ", + "ðŁij ¶", + "âĶ Ľ", + "ï¿ ¢", + "áĥ ¡", + "Ä ¼", + "Å Ĩ", + "Ñ IJ", + "ìĥ Ľ", + "ìĺ Į", + "ì± ¤", + "íħ ģ", + "íļ ĥ", + "ï³ Ĭ", + "ðĿij Ķ", + "ðŁĩ «", + "âĭ °", + "ðŁĺ ¨", + "âĤ ©", + "Õ ¬", + "Ḡį", + "á» ´", + "âĨ ĺ", + "âĺ ¯", + "ãħ ı", + "ìł ¬", + "âĻ Ķ", + "ðŁĶ Ķ", + "ðŁĺ ł", + "ðŁĻ Ĭ", + "à® ľ", + "á¹ ħ", + "âĹ IJ", + "âĿ Ī", + "âŀ ½", + "ìĥ ħ", + "ðĿij ł", + "Æ ¢", + "âĭ Ļ", + "ê° Ľ", + "ëĿ µ", + "ë£ Ł", + "ìı ľ", + "ïº ģ", + "ðŁĴ Ń", + "âĬ ĥ", + "ðŁIJ °", + "ãħ Į", + "Ü ĵ", + "âŀ ķ", + "á½ ģ", + "ìķ ³", + "ðĿij Ŀ", + "ðŁİ ¬", + "É ¡", + "ठĹ", + "áIJ ī", + "ì© ľ", + "ì¶ §", + "ï³ ī", + "ï» ħ", + "ðĿIJ ŀ", + "ठ¶", + "ðŁĵ ¢", + "ðŁį ĭ", + "ðŁĴ ħ", + "ï¾ ķ", + "⬠Ĩ", + "âĪ µ", + "ðŁ¤ ij", + "áĥ £", + "Æ Ħ", + "Ñ ¹", + "á¼ Ķ", + "ê° ł", + "ê´ Į", + "ê· IJ", + "ëĽ ´", + "ì± ĺ", + "ï® Ń", + "ïº ¹", + "ïº ¾", + "âľ Ĺ", + "âĿ ¦", + "ðŁij ¦", + "áĥ Ĺ", + "Ù ²", + "á½ ´", + "âĪ ı", + "âľ ®", + "ê¹ °", + "ë² µ", + "ìĦ Ģ", + "ì© Ŀ", + "ïº ŀ", + "ïº ½", + "ðŁĩ Ń", + "Ë Ĥ", + "ðŁį ij", + "ðŁį Į", + "ðŁĶ »", + "ê¹ ¬", + "ìĬ Ń", + "ìľ ·", + "ðŁĽ ij", + "Ç §", + "ë¼ Ľ", + "ïº ¡", + "ïº º", + "ðĿij ļ", + "ðŁĵ ¦", + "ðŁĶ İ", + "ðŁĹ ĵ", + "áĥ Ķ", + "âľ Ĵ", + "âľ ¡", + "ðŁĮ µ", + "âĶ ķ", + "ëĢ Ŀ", + "ðŁį Ĭ", + "âĺ ĥ", + "ìĺ ħ", + "ঠ¬", + "ðŁ¦ ģ", + "âİ ¯", + "ðŁIJ ķ", + "Ñ ¿", + "ॠ¤", + "༠ĭ", + "ê· Ī", + "ì« Į", + "ðŁĩ °", + "âĿ ī", + "ì« Ģ", + "íĿ Ħ", + "ðĿIJ ¢", + "ðŁļ ¨", + "âĻ ¤", + "ðŁĺ ©", + "ðŁį į", + "ðŁĺ ij", + "ðŁļ ļ", + "Ö Ħ", + "ë «", + "ë« ¼", + "ठı", + "á¿ ·", + "âĮ ©", + "âĺ IJ", + "âŀ £", + "ê¸ ±", + "ê¼ ¿", + "ëĦ Ŀ", + "ìı ´", + "ìļ ¤", + "ì¿ ±", + "íİ IJ", + "ðŁĴ ¢", + "ì´ IJ", + "âĩ ij", + "âĶ ĵ", + "âģ ¾", + "Ü Ŀ", + "ðŁ į°", + "â´ °", + "Æ ı", + "Ï Ł", + "Ú º", + "Û ĥ", + "áĦ Ĵ", + "âĪ Ł", + "âĿ į", + "ãĦ ²", + "ìľ ħ", + "ì¤ ı", + "ðŁĩ ²", + "êº Ħ", + "ðŁİ ¤", + "âľ £", + "⸠Ŀ", + "ï¸ µ", + "ຠ§", + "áĢ Ļ", + "âķ ł", + "Õ ¯", + "âı ©", + "ðĿij £", + "ðŁĴ £", + "Å ĺ", + "ॠIJ", + "âģ ĥ", + "âĮ ĺ", + "ê» Į", + "ìĮ Ķ", + "ðĿij ĺ", + "ðŁ¤ ĵ", + "Õ ¿", + "ठŃ", + "âĮ ļ", + "âľ Ŀ", + "ðŁIJ ¼", + "Ë Į", + "âķ ļ", + "ï¦ Ĺ", + "âĿ ķ", + "âķ £", + "ðŁIJ ±", + "à® ¤", + "Ñ ¾", + "ठļ", + "ठľ", + "ìĪ Ħ", + "ìļ ľ", + "ðŁİ ®", + "É Ĵ", + "Ú ·", + "ຠį", + "âĨ µ", + "â Īĺ", + "âĿ Ĭ", + "ë¿ į", + "ìIJ Ī", + "ìļ ĺ", + "ì¯ §", + "íĥ ¯", + "ìĸ ı", + "ï¸ °", + "ðŁĩ ¯", + "ðŁ§ ļ", + "ðŁĺ µ", + "ðŁĺ ·", + "ðŁĮ ³", + "ຠ¥", + "Ä ī", + "Ä ¥", + "âľ ¶", + "á¿ ¾", + "âĬ ±", + "âĺ ¾", + "ê° ī", + "ê¼ °", + "ëº ij", + "ðŁĶ Ĭ", + "ðŁĸ IJ", + "Å ¤", + "Ò «", + "à® ®", + "âĮ Ī", + "âĹ Ĺ", + "ëĦ µ", + "ëħ ľ", + "ëľ ¹", + "ðĿij ¥", + "ðŁĴ ¿", + "ðŁĽ Ĵ", + "Ê Ĵ", + "áŀ ĵ", + "ðŁIJ Ŀ", + "ðŁ¦ Ħ", + "ðŁį ·", + "âĺ Ł", + "ï¸ ¶", + "ðŁ¤ Ł", + "Ô ±", + "âĨ ²", + "âĪ İ", + "âľ «", + "ëĩ ½", + "ëı IJ", + "ëķ Ħ", + "ï¦ ³", + "ï§ Ŀ", + "ïº Ļ", + "ðŁij »", + "ðŁĵ º", + "êµ ¼", + "ìĮ ©", + "ðŁĮ ²", + "È ±", + "íĶ ķ", + "ðŁĺ ¤", + "ãĮ ¢", + "Ê Ķ", + "ठ¡", + "á¼ Ī", + "ëİ ĥ", + "ë© ±", + "ë® Ī", + "ðĿIJ «", + "âĬ ķ", + "ëĥ ł", + "ë» ¬", + "íĭ Ķ", + "Õ ¤", + "á¼ ±", + "âľ ¥", + "âĺ Ħ", + "âĪ ¥", + "âļ ķ", + "ðŁij Ħ", + "ðŁİ ħ", + "ຠĻ", + "âĶ ¬", + "á½ µ", + "Õ ¾", + "Ö ģ", + "âĹ Ķ", + "ê¿ į", + "ëĸ µ", + "ë© İ", + "ë® ´", + "ìķ ´", + "áĥ ľ", + "á¼ ¡", + "âĶ Ĭ", + "âķ ®", + "âĹ ¼", + "ðŁį ¾", + "ðŁĽ į", + "ðŁij Ĺ", + "ðŁ¤ ŀ", + "âľ Ħ", + "Õ Ģ", + "ঠ²", + "Ë ī", + "⣠¨", + "Ä ¯", + "Ï Ĭ", + "á´ ľ", + "ë¹ ³", + "ï³ ĭ", + "ï¿ ł", + "Ä ª", + "âĤ ¸", + "âľ ±", + "ê» IJ", + "ëĭ »", + "ë§ ¸", + "ìŀ ¿", + "ì© ¨", + "ì ŃIJ", + "ì° ¿", + "íħ Ł", + "ðĿIJ §", + "ðĿij ij", + "ðŁĮ İ", + "ðŁĵ ®", + "ðŁķ Ķ", + "âĹ Ļ", + "âĹ »", + "âŀ §", + "ìŁ Ŀ", + "âľ ¬", + "ãĥ °", + "âģ Ī", + "â ĵĺ", + "ðŁ ĴĮ", + "ï¬ ĥ", + "ຠĶ", + "ìĶ °", + "ðŁĺ ª", + "× Ģ", + "ìĥ ¨", + "ïŃ ĭ", + "ðŁį ķ", + "ðŁĺ ´", + "Ï ³", + "á¼ Ħ", + "á½ ħ", + "âĩ ¢", + "âķ Ń", + "ìĺ »", + "íĬ ¤", + "Ü ĺ", + "⤠´", + "âĹ į", + "áŀ Ł", + "ðŁį º", + "áŀ ļ", + "ðŁı Ĭ", + "ðŁIJ ·", + "Ê Į", + "á½ º", + "âģ »", + "ê½ Į", + "ëĪ Ĺ", + "ë Ĺı", + "ì¿ °", + "íĢ ¼", + "íį ħ", + "ï· ²", + "ðŁĮ ı", + "ðŁį «", + "ðŁį ³", + "ðŁİ °", + "ðŁij °", + "ðŁĴ ²", + "ᥠĻ", + "ðŁIJ Ł", + "ï¿ ¡", + "ðŁĹ £", + "ðŁį ľ", + "âľ ²", + "ãİ ¢", + "ðŁĶ °", + "á¼ ¸", + "á½ ij", + "Ä İ", + "áĦ Ģ", + "âĻ ķ", + "ëł Ŀ", + "ìĪ ´", + "ïŃ Ń", + "Ó ľ", + "Ô Ģ", + "ëĢ ľ", + "ëĥ Ķ", + "ìĬ Ľ", + "ì« ij", + "ìº ¥", + "ìº ¬", + "ðĿij ¦", + "ðŁĶ ¶", + "ì¾ ¨", + "ðĿIJ ļ", + "ðŁį »", + "ðŁĴ į", + "ðŁ¤ ¡", + "ðŁķ Ĭ", + "â½ ĩ", + "âĵ IJ", + "ðŁį Ń", + "ðŁį ª", + "ðŁĶ Ĩ", + "Ò ¡", + "á´ ĩ", + "É Ĺ", + "Ü Ķ", + "âĦ İ", + "âĿ ĥ", + "ëĹ Ģ", + "ï² Ķ", + "ïº Ī", + "ðĿIJ »", + "ðŁĴ Ĭ", + "ðŁļ «", + "Ñ °", + "Ñ ³", + "ठ·", + "âĹ ł", + "ðŁij ¤", + "ï¾ ĩ", + "âĺ ĵ", + "ðŁį µ", + "ðŁ¤ ¨", + "âĸ Ń", + "à® ´", + "Ü ¢", + "Ü ¬", + "à´ ®", + "ðŁķ º", + "Ô ¹", + "Õ £", + "à´ ¯", + "á ´Ģ", + "âĮ ī", + "âľ IJ", + "âŀ ¦", + "ê¹ ½", + "ëĮ ľ", + "ðŁı ¥", + "ðŁĵ ©", + "Ò ¹", + "Ó ĺ", + "ठħ", + "âĿ §", + "Æ Ĺ", + "âĹ ½", + "ðŁij «", + "ðŁİ §", + "ðŁij £", + "âľ »", + "ðŁĻ ħ", + "ðŁĺ ĸ", + "ðŁĴ ®", + "ຠ°", + "ðŁĶ ľ", + "ðŁį Ħ", + "ðŁ¤ Ŀ", + "á ĥĿ", + "áŀ Ģ", + "âĩ ¦", + "Ê ¾", + "Ò ®", + "Õ ¼", + "ठĨ", + "âĹ ħ", + "âļ ĵ", + "âļ ĸ", + "ê¿ ©", + "ë¯ Ħ", + "ìIJ IJ", + "ìŀ °", + "ì§ Ń", + "íĭ ĭ", + "íİ ¨", + "íĻ §", + "ï² ij", + "ðŁİ Ĺ", + "Ù ³", + "ðŁij ¸", + "ঠ®", + "ðŁij ķ", + "Ú µ", + "âĢ ¾", + "âŀ °", + "ðŁij ¯", + "ðŁİ ¼", + "ðŁı ģ", + "Ä º", + "Ê ı", + "Ú ³", + "âı ±", + "ê½ Ī", + "ëĿ Į", + "ìĮ ī", + "ìĹ ·", + "ìŀ ´", + "íĹ ¹", + "íľ ¨", + "ðĿĹ ²", + "ðŁĮ IJ", + "ðŁİ Ļ", + "ðŁı µ", + "íĽ Ļ", + "ðĿij ħ", + "ðŁĺ ¶", + "âĵ ħ", + "âķ ¥", + "ðŁį ı", + "ï¦ İ", + "Õ ©", + "ðĿIJ Ħ", + "Ó £", + "Ú ¿", + "âĻ ļ", + "ðŁĶ Ĺ", + "Ḡ«", + "âĭ ®", + "âĸ ¦", + "⼠½", + "âľ µ", + "ãħ Ĩ", + "ãħ Ĭ", + "ëĦ Ļ", + "ëĿ ¨", + "ë¥ Ħ", + "ìĦ ¦", + "ì§ °", + "ì§ ¹", + "íī Ī", + "ï§ ij", + "ï» ĩ", + "ðŁĮ ¾", + "ðŁı ĸ", + "ðŁIJ ij", + "ðŁĴ ³", + "ðŁĵ Ĩ", + "Û ĩ", + "Ü ķ", + "á½ ½", + "ëĦ ľ", + "à´ ²", + "à´ ³", + "ຠŃ", + "áĥ Ľ", + "âĿ Ķ", + "âij ħ", + "áĥ ¥", + "ðŁĵ ħ", + "âŀ ³", + "á´ µ", + "ï¹ ¡", + "ï¹ ¶", + "Î Ĩ", + "ठ¥", + "áī µ", + "âĿ Ļ", + "âĿ ±", + "ëī ł", + "ëİ ł", + "ëı Ľ", + "ë¿ ħ", + "ìĶ ¸", + "íij ¯", + "íŀ ī", + "íŀ Ľ", + "ï§ Ħ", + "ïŃ ĺ", + "ïº ¦", + "ï» ¸", + "ðĿij Ĥ", + "ðĿij ı", + "Ï ij", + "Ú ł", + "áĢ Ķ", + "áŀ Ķ", + "á¹ ¢", + "ëĦ ¸", + "ðĿIJ ¨", + "ðŁĩ ´", + "Õ °", + "ðŁij ł", + "ðŁį Ĩ", + "ðŁı Ģ", + "ðŁ ijIJ", + "ðŁį ĩ", + "ðŁIJ £", + "áĪ Ń", + "Ü ª", + "ðŁ ĮĢ", + "áŀ ĺ", + "âĩ Ħ", + "ðĿIJ Ģ", + "Ê Ļ", + "âĶ ¼", + "ðŁı ¿", + "Æ ·", + "È ł", + "Ñ ½", + "âĤ ¨", + "ê´ Ń", + "ê¹ »", + "ëĶ ¨", + "ìĪ Ģ", + "ì¾ °", + "íĨ Ī", + "ï® §", + "ï¯ ½", + "ðŁĶ ħ", + "ðŁĶ ®", + "Å ¢", + "Ê °", + "Ñ ¸", + "ठ£", + "âĬ Ĺ", + "ëª Ħ", + "ï¹ ·", + "ïº ħ", + "ðĿIJ µ", + "ðŁĮ ¶", + "ðŁĵ °", + "ðŁĶ ·", + "ðŁĸ Ĵ", + "ðŁ¤ ²", + "ëī ©", + "ðŁİ Ĩ", + "ðŁ§ IJ", + "ðŁį ®", + "âĨ º", + "âĿ ¢", + "ðŁij ª", + "ðŁij ±", + "âĨ ¡", + "áŀ ı", + "Ú ķ", + "ðŁį ¹", + "ðŁĴ Ģ", + "Ë ®", + "Ó ¨", + "Ö ħ", + "ठĩ", + "âĤ ¡", + "âĪ ķ", + "âĺ ī", + "ê¹ ¼", + "ê¼ IJ", + "ì½ ¸", + "ðĿIJ ¬", + "ðŁı ħ", + "ðŁij Ļ", + "ðŁĴ ī", + "ðŁ¤ Ļ", + "È ĺ", + "É ³", + "É ¹", + "Ù º", + "áĢ Ħ", + "á¿ ³", + "âļ ĺ", + "âĿ Ĩ", + "ëĨ ī", + "ìĸ į", + "ìĺ ĩ", + "ì¥ ĺ", + "íĸ ħ", + "íĻ ij", + "ï® Ĭ", + "ï¿ Ń", + "ðĿĴ IJ", + "ðĿĹ ¢", + "ðŁĶ ĸ", + "ðŁĶ ¨", + "ðŁļ ij", + "ðŁļ ²", + "Æ ¸", + "âĹ ¥", + "ðĿIJ Ń", + "ðŁį ½", + "âĹ ij", + "âĵ ĩ", + "ðŁĶ ±", + "âľ ¼", + "ï¹ ĥ", + "âķ ±", + "ãĢ Ĺ", + "ðŁı ĭ", + "ðŁļ ´", + "ðĿIJ ®", + "Ä ļ", + "Õ ı", + "Ä ¶", + "áĥ ij", + "á¹ ¬", + "Ä Ī", + "Ä Ĵ", + "Ò °", + "Ó ķ", + "â IJ", + "âIJ £", + "âĹ ¢", + "âļ Ļ", + "ãħ Ĺ", + "ê° ¬", + "ê³ ª", + "ê» Ģ", + "ëĦ ´", + "ëİ ģ", + "ëĿ Ķ", + "ë¬ ½", + "ëŃ į", + "ìĩ ³", + "ì° ¹", + "íĮ ¹", + "íŀ Ŀ", + "ï® ĭ", + "ï ¶Ī", + "ðĿĴ Ĥ", + "ðŁ¥ Ģ", + "ðŁ¦ ħ", + "Ê ĺ", + "á¼ ij", + "âģ İ", + "ðŁį ŀ", + "âĨ ĸ", + "âĨ Ļ", + "ðŁİ ĥ", + "âĦ ¡", + "âĭ ±", + "ðŁĶ į", + "ಠ¨", + "áµ ĥ", + "âĶ «", + "⦠¿", + "ðŁĩ »", + "Æ ¤", + "Ò ı", + "Ò ·", + "Û ī", + "à® ķ", + "Ḡ³", + "ï¬ ±", + "ðŁĨ Ķ", + "Ú Ń", + "Û ¦", + "áħ ¡", + "âĦ ¹", + "ê¿ İ", + "ëķ Ķ", + "ë¼ ī", + "ìļ §", + "ì² µ", + "ì´ ¨", + "íĬ Ī", + "íĸ IJ", + "ðĿĹ ĺ", + "ðŁĩ ¿", + "ðŁİ ĸ", + "ðŁij ħ", + "ðŁ ĵĺ", + "ðŁļ Ļ", + "ðŁĽ µ", + "ච½", + "⼠µ", + "ðĿIJ ³", + "ðĿIJ ¸", + "âļ Ķ", + "ðŁij Ń", + "Ó ij", + "âĶ ¯", + "ðŁħ ¿", + "ðŁĺ ¹", + "ï¿ «", + "â¼ ¤", + "ðŁĴ ĩ", + "ðŁĵ İ", + "ðŁĸ ĭ", + "ঠ¸", + "ðĿIJ į", + "Ä ²", + "Ï ĭ", + "Ñ ¬", + "Ú ¬", + "Ü Ĵ", + "á´ ¬", + "ï¨ Ħ", + "É £", + "Ë ij", + "Ï µ", + "Ò Ŀ", + "Û ¥", + "Ü ł", + "๠Ľ", + "áĥ ķ", + "áĬ ķ", + "á¾ ¶", + "âĤ ·", + "âĩ ¾", + "âķ ©", + "âĸ IJ", + "âĺ ª", + "âĺ ®", + "âĿ ļ", + "âĿ Ń", + "âŀ ±", + "âµ İ", + "ãı Ĭ", + "ë© ĵ", + "ìĹ ¾", + "ìª Ħ", + "íĵ Į", + "íķ ¼", + "ïŃ ¬", + "ðĿij Ĩ", + "ðĿij ŀ", + "ðĿĸ Ĭ", + "ðŁİ ¸", + "ðŁı Ħ", + "ðŁij µ", + "ðŁĴ ł", + "ðŁĶ ĺ", + "ðŁ¥ Ĥ", + "Å ª", + "à· ĥ", + "á´ ¼", + "âĬ °", + "ë³ ı", + "ë´ £", + "ï¥ ľ", + "ðŁĵ Ī", + "ðŁķ ¯", + "ðŁ§ Ģ", + "âĻ IJ", + "ðŁĨ Ĺ", + "ðŁĵ ķ", + "ðŁ§ ģ", + "Ü «", + "âĿ IJ", + "Õ ķ", + "འķ", + "âŀ Ŀ", + "ঠķ", + "ðĿIJ ¶", + "É ¢", + "Î Ħ", + "áĨ ¢", + "âĤ ±", + "Õ į", + "à¡ ķ", + "á´ °", + "Ḡ©", + "⼠·", + "âĿ ®", + "ê¡ ĵ", + "ëı ¤", + "ëĹ IJ", + "ëµ Į", + "ìij Ī", + "íı ¿", + "íĹ µ", + "ðĿIJ İ", + "ðŁĨ ĺ", + "ðŁı Ł", + "É ¥", + "Õ »", + "à¡ Ķ", + "ठĸ", + "á´ ¸", + "âİ Ļ", + "âİ ¥", + "âı ³", + "ëģ ķ", + "ëĬ ī", + "ì¡ į", + "ì¹ ¡", + "ï¦ ¶", + "ï¬ Ł", + "ï® «", + "ï® ¯", + "ï± ĥ", + "ï ·»", + "ïº µ", + "ðĿĹ Ķ", + "ðĿĹ ¡", + "ðŁİ ¨", + "ðŁĶ Ĵ", + "Ú Ľ", + "ठ§", + "âŀ ¹", + "áĢ Ģ", + "ðŁį ħ", + "âĹ ¤", + "ठł", + "ðŁIJ ¥", + "áĥ Ĵ", + "ðŁı Ŀ", + "ðŁį ¼", + "ãĮ §", + "âĿ Ľ", + "ðŁIJ Ī", + "ঠ¯", + "áĢ ŀ", + "ãĢ ĸ", + "áŀ Ļ", + "ঠª", + "Õ Ĩ", + "âĬ Ĩ", + "âľ ¾", + "ðŁIJ Ĺ", + "ï¹ ¿", + "Ä ¦", + "Ü Ł", + "ಠł", + "ಠ¥", + "áŀ ī", + "á´ ¥", + "á´ ©", + "á½ Ģ", + "á½ ¡", + "âĨ ķ", + "âŀ ¯", + "ê¡ ij", + "ëij £", + "ë± Į", + "ìĪ ij", + "ìľ Ķ", + "ìŀ ½", + "ì¨ į", + "ðĿij Ģ", + "ðŁĮ Į", + "ðŁį ¦", + "ðŁį ©", + "ðŁIJ ļ", + "ðŁĵ Ĵ", + "ðŁĵ ¹", + "ðŁ¥ ij", + "Ä ĭ", + "Ë Ĺ", + "Ñ «", + "Õ ¢", + "Ú °", + "â ĮĢ", + "âĹ Ĥ", + "âĹ £", + "âľ Ľ", + "âĿ Ĵ", + "âĿ ĺ", + "âŀ Ļ", + "âŀ ²", + "ãİ į", + "ê¡ IJ", + "ëŀ ĸ", + "ìĬ Ŀ", + "ìĽ ¤", + "ì¡ ĭ", + "ì¨ °", + "íĹ Ļ", + "ï¥ ¸", + "ï³ į", + "ï» İ", + "ðĿij ĵ", + "ðŁĵ Ĭ", + "ðŁļ ¼", + "ï¦ ģ", + "ðĿķ Ĵ", + "ðŁ ijľ", + "ðŁij ¿", + "ðŁĩ ½", + "à· Ħ", + "âĸ ´", + "ãį ī", + "âĬ ĩ", + "ðŁ§ ¸", + "Ú ¡", + "â¾ ĥ", + "ðŁĹ »", + "âĵ ij", + "ðŁ¤ ¸", + "ðŁ¤ ¯", + "êĴ °", + "ðĿIJ ĵ", + "âĶ ´", + "êĴ ±", + "áĢ ĺ", + "â ĽĦ", + "ï¹ ¹", + "Ó Ķ", + "áĥ ±", + "Ü ¡", + "ß ŀ", + "âĻ ı", + "âľ ¸", + "ìij ¨", + "ðĿIJ Ŀ", + "ðĿIJ ¥", + "ðŁį ī", + "ðŁij ¼", + "ðŁ¥ Ŀ", + "Æ Ķ", + "Ý ¬", + "ठ«", + "ຠļ", + "á´ ´", + "á½ ĸ", + "âĤ ¶", + "âİ ¢", + "âĿ ħ", + "⣠«", + "ãİ Ľ", + "ë® ¨", + "ëº Į", + "ë¼ ĺ", + "ìĨ Ŀ", + "ìľ ³", + "ìŀ Į", + "ì£ Ĺ", + "ìª ĺ", + "ì» ¹", + "ï· ¼", + "ïº Ĥ", + "ðĿIJ ´", + "ðĿIJ ¼", + "ðŁĮ ļ", + "ðŁı «", + "ðŁĴ ¤", + "ðŁĴ ¶", + "ðŁĴ ¼", + "Ê ķ", + "Ê ½", + "â² Ł", + "ãī ł", + "ê¡ Ĵ", + "ëľ Ģ", + "ìĥ ¾", + "ì¸ ¤", + "ï¥ ģ", + "ðĿļ Ĭ", + "ðŁļ ĥ", + "âŀ Ľ", + "ìħ ´", + "áĦ ĭ", + "âĩ Ĺ", + "ï§ ·", + "âĺ ĸ", + "ðŁIJ ¦", + "⸠ľ", + "ðŁĴ ´", + "ðŁ¤ ļ", + "ãĬ Ĺ", + "âĮ Ľ", + "áĪ Ľ", + "༠º", + "â½ ī", + "ðŁı ¢", + "âĵ ŀ", + "âĺ ½", + "ãĢ Ļ", + "ðŁ¤ ®", + "Å IJ", + "áĥ ¬", + "ðĿĹ »", + "ðŁį ĸ", + "Æ Ĭ", + "Ê Ł", + "ß ĭ", + "ठĭ", + "áµ Ķ", + "á¿ ĥ", + "âĦ ī", + "âĮ ĭ", + "âı ²", + "âĵ Ī", + "âĵ ¢", + "âķ Ķ", + "âļ ij", + "âĿ ĭ", + "âĿ İ", + "â µľ", + "âµ £", + "ëĴ Ī", + "ëľ ģ", + "ë¶ ĩ", + "ìį »", + "ìĺ Ń", + "ì§ ¢", + "íĹ Ģ", + "ï§ Ĭ", + "ï ¬¸", + "ï± ¡", + "ðĿIJ º", + "ðĿij §", + "ðĿĺ ¦", + "ðŁĵ ¥", + "ðŁĺ Ł", + "ðŁ¥ IJ", + "Ä ĸ", + "É ¨", + "áĢ IJ", + "áĥ ĵ", + "Ạĵ", + "á¼ ¶", + "á½ Ħ", + "âĤ ¤", + "âĮ ľ", + "âĮ Ł", + "âİ ł", + "⼠¸", + "âµ į", + "âµ ı", + "âµ ĵ", + "ãĢ ĺ", + "ë ·¸", + "íħ ¼", + "ï¦ Į", + "ïŃ Ħ", + "ïŃ İ", + "ðĿĻ ļ", + "ðĿļ ĺ", + "༠ĵ", + "ëŃ ħ", + "áIJ Ľ", + "ãİ ¾", + "ï¨ Ģ", + "ðŁĹ ½", + "âĻ ŀ", + "Ë ĸ", + "âĹ ŀ", + "ðŁ¤ «", + "ðŁĺ Ĺ", + "ï½ ¦", + "ðŁ¤ ¢", + "âģ ĩ", + "ãĢ µ", + "ðŁį Ķ", + "áĬ ł", + "ðŁĺ ¼", + "ðĿĹ ®", + "ðŁIJ ³", + "ðĿIJ ĭ", + "ðŁĨ ļ", + "ðŁĶ Ľ", + "Ñ »", + "Ü ¨", + "à® ²", + "âľ ŀ", + "âµ Ļ", + "êµ £", + "ì¸ ¨", + "ðĿ IJľ", + "ðĿĺ °", + "ðŁĶ ½", + "Ç »", + "Ç ¿", + "Ê ĩ", + "Î IJ", + "Ð Ģ", + "Ñ ¡", + "Ñ ²", + "Ò Ĵ", + "Ù ¶", + "ß ķ", + "ච±", + "áIJ ģ", + "âģ ŀ", + "âĸ §", + "⼠Ī", + "âľ ľ", + "âľ ¹", + "⣠¹", + "⤠ĩ", + "ê² Ĭ", + "ê¾ ľ", + "ë¯ IJ", + "ë³ IJ", + "ìħ ©", + "ìIJ ¬", + "ìij ¹", + "ï¤ Ķ", + "ï¦ ļ", + "ï¬ ł", + "ïŃ Ķ", + "ïº ¶", + "ðĿĴ ı", + "ðĿĸ Ĩ", + "ðĿĹ ¶", + "ðŁı Ĥ", + "ðŁIJ ½", + "ðŁĴ ©", + "ðŁĵ ½", + "ðŁĹ ¨", + "ðŁĹ º", + "ðŁĺ ¸", + "ðŁ¥ §", + "Å Ĺ", + "Ê İ", + "Ò Ļ", + "× ²", + "ठĪ", + "á¼ ´", + "á¿ ij", + "âµ ī", + "ãħ ĵ", + "ì½ ´", + "ðĿĸ ĵ", + "ðŁĵ Ĺ", + "ðŁĶ ª", + "ðŁĸ į", + "Ï Ĵ", + "ðŁij ¬", + "áĥ Ļ", + "âĨ ¬", + "âĶ ¤", + "⼠¹", + "âĻ Ł", + "ðŁļ ¶", + "ðŁij ¾", + "âĪ ĭ", + "ðŁIJ ¯", + "༠İ", + "âľ ·", + "ï¨ Ļ", + "âĶ »", + "ðŁij ¹", + "áĦ ī", + "ຠª", + "â¾ ı", + "â½ ħ", + "ãİ ĸ", + "Ñ ´", + "Õ ®", + "Ú ¼", + "áĢ ķ", + "áĨ ¼", + "ëŃ ı", + "ðŁIJ ¸", + "ðŁļ £", + "Æ Ŀ", + "Ô »", + "áĥ ¢", + "ðŁį ¯", + "É ¦", + "Õ ¦", + "âĻ ĭ", + "ï¬ «", + "ðĿĹ ¦", + "Ç ļ", + "É ±", + "ठī", + "á´ Ħ", + "âĻ ĵ", + "⼠°", + "⣠ª", + "ëĥ ĺ", + "ë¢ ¸", + "ìĤ ij", + "ï® Ķ", + "ðĿķ ĸ", + "ðĿĹ §", + "ðŁĩ ¼", + "ðŁĵ ĭ", + "ðŁļ ľ", + "ðŁ¥ ¤", + "Ä ®", + "Å ·", + "ß Ĭ", + "ॠ¥", + "à® ª", + "áŀ Ħ", + "áµ Ģ", + "Ḡħ", + "á¼ ¢", + "âĪ Ŀ", + "âĬ ¹", + "âĴ ¶", + "âķ ´", + "⼠±", + "⼠³", + "⼠º", + "âŀ Ł", + "ãı Ħ", + "ê¸ Ķ", + "ê¹ Ł", + "ëĩ °", + "ë¹ »", + "ìĤ ¥", + "ìĽ »", + "ì° Ł", + "íĥ °", + "íĨ º", + "íļ ½", + "ï¤ ´", + "ï¥ ¾", + "ï³ Ŀ", + "ðĿIJ ¦", + "ðĿĴ ľ", + "ðĿĴ Ł", + "ðĿļ Ĺ", + "ðŁİ Ń", + "ðŁı ĵ", + "ðŁı ³", + "ðŁı º", + "ðŁIJ į", + "ðŁij ĥ", + "ðŁĴ ı", + "ðŁ¤ ĸ", + "ðŁ¤ µ", + "Õ ²", + "âµ Ķ", + "ëĺ ¬", + "ï¦ £", + "Ê Ĥ", + "áĨ «", + "áŀ ij", + "ðĿĸ İ", + "ðĿĹ ĸ", + "áĦ ĥ", + "âĩ ł", + "áĢ ¡", + "འĦ", + "âŀ ¸", + "ï¦ Ļ", + "âĩ ļ", + "ðŁIJ ¬", + "ðŁIJ ¢", + "â¾ Ĵ", + "ðŁIJ ¤", + "ðŁĶ «", + "ãĢ ŀ", + "ï¸ º", + "ðŁĺ º", + "â½ ´", + "ðŁĨ ķ", + "âģ ¿", + "ðŁį ¨", + "ಠķ", + "ðŁļ ĺ", + "áŀ ħ", + "ঠħ", + "áŀ ¢", + "ਠľ", + "â ļĮ", + "ãĢ ½", + "à· ´", + "âĵ Ľ", + "áĢ ľ", + "ìĨ ¨", + "Ë ©", + "Ü Ĺ", + "âĭ ¼", + "ðŁĻ ī", + "Å Ĭ", + "É ĵ", + "Ê ²", + "Î °", + "Ñ ¼", + "Ô ¿", + "à¡ IJ", + "༠ľ", + "འ¦", + "ᶠľ", + "âĤ ²", + "âĨ ¨", + "âĬ ¥", + "âķ §", + "âĻ ľ", + "ãĭ ¡", + "ë´ ¬", + "ë¶ ij", + "ìī ¿", + "ìİ ħ", + "ìł ±", + "ì° §", + "ï² ¡", + "ðĿĴ Ľ", + "ðĿķ £", + "ðĿĹ ľ", + "ðŁį ²", + "ðŁİ ©", + "ðŁIJ IJ", + "ðŁIJ ł", + "ðŁij ½", + "ðŁĴ ij", + "ðŁĵ ľ", + "ðŁķ µ", + "ðŁ ļĮ", + "ðŁĽ £", + "Ê ĭ", + "Ó ¯", + "Ù ¸", + "ß Ķ", + "ß Ļ", + "à¡ ĵ", + "á´ į", + "Ḡ¿", + "âı º", + "âĸ ¥", + "ë¤ ½", + "íľ ij", + "ðĿIJ ¹", + "ðĿĸ Ķ", + "ðĿļ İ", + "ðŁĵ Ħ", + "ðŁ¦ ·", + "Æ ĥ", + "ঠŁ", + "âĮ Ĥ", + "âĺ Ń", + "â² ļ", + "ëĿ ķ", + "ðŁİ £", + "à® ĩ", + "འĨ", + "áħ µ", + "áĹ ľ", + "âĢ ½", + "âĮ £", + "âģ ½", + "ðŁĵ ¬", + "ðŁ¤ §", + "âĩ ª", + "â½ £", + "âĹ Ł", + "ï¨ Ĺ", + "êĴ ª", + "ðŁĽ Ģ", + "Ç Ĥ", + "ðŁ¥ ¶", + "ðŁİ į", + "ï¿ ©", + "ðŁij Ĵ", + "áµ Ī", + "ï¸ ¿", + "áħ ©", + "â¾ ¦", + "à° ¤", + "á´ ĸ", + "ਠ¬", + "ຠĹ", + "༠»", + "Ñ º", + "ਠª", + "á´ ³", + "ðĿIJ Ī", + "à» Ģ", + "á´ ¿", + "âĤ į", + "âĩ ¡", + "⼠ª", + "ðĿIJ Ĥ", + "ðĿĴ ķ", + "ðŁ IJľ", + "Ê į", + "Ñ ±", + "འĥ", + "ë® IJ", + "ìĽ ¡", + "ìľ ģ", + "ðĿIJ ¿", + "ðĿķ ł", + "ðŁij Ľ", + "Æ ª", + "Ï º", + "Ó ¬", + "Ù ¿", + "Ý £", + "ઠī", + "à® ¹", + "འij", + "áĨ ¯", + "áµ ĩ", + "âĩ ¥", + "âı ª", + "âĻ °", + "âļ Ń", + "âļ ¾", + "ãħ Ħ", + "êĢ °", + "ê° Ĺ", + "ê² ĭ", + "ê² »", + "ê¶ ľ", + "ê¼ ĩ", + "ê½ ¹", + "ëĤ Ł", + "ëħ Ī", + "ëĭ ¢", + "ë§ Ł", + "ëª Ĩ", + "ëµ Ģ", + "ì½ ±", + "íĩ ĺ", + "íľ ľ", + "ï§ ¾", + "ï± µ", + "ï² ¢", + "ï² ¤", + "ðĿĴ Ĭ", + "ðĿĺ ¯", + "ðŁį Ĺ", + "ðŁı į", + "ðŁIJ ĺ", + "ðŁĵ ¡", + "ðŁĶ ŀ", + "ðŁ¤ ³", + "ðŁ¥ ģ", + "ðŁ¥ Ĺ", + "ðŁ¦ Ĭ", + "Ä µ", + "Æ ¦", + "Ç µ", + "É ¯", + "Î ı", + "Õ Ħ", + "Ü ¥", + "འģ", + "ᨠł", + "âķ «", + "ãİ ī", + "ë· ´", + "ìĨ İ", + "ìİ Į", + "ì£ µ", + "íĽ ł", + "ï§ ª", + "ï³ ı", + "ï» 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ķ", + "ï¬ »", + "ï³ İ", + "ï¹ ¸", + "ï¹ ¾", + "ðĿIJ Ĩ", + "ðĿij ·", + "ðĿĽ ¼", + "ðŁİ ı", + "ðŁİ ŀ", + "ðŁIJ Ļ", + "ðŁij Ĥ", + "ðŁĵ ģ", + "ðŁĸ ±", + "ðŁļ į", + "ðŁļ §", + "ðŁĽ ¡", + "ðŁ¤ Ĵ", + "ðŁ¥ ŀ", + "ðŁ¥ ©", + "ðŁ¦ Ģ", + "ðŁ¦ ĸ", + "Ë ¢", + "Ü ļ", + "à® µ", + "áĢ ģ", + "áī °", + "âı Ń", + "âĻ ¿", + "ê³ ĺ", + "ëı Ŀ", + "ëķ ĥ", + "ìħ Į", + "ìĴ ¸", + "ìĽ Ł", + "íħ Ħ", + "íľ «", + "ï§ ĺ", + "ï¿ ¬", + "ðŁı ·", + "ðŁĶ §", + "ðŁ¥ Ī", + "Æ ĸ", + "áŀ ĩ", + "áŀ ĸ", + "âģ º", + "âĹ ľ", + "âŀ ©", + "ê¦ Ń", + "ëĻ ¤", + "ïŃ ¼", + "ðĿĻ ĸ", + "ðĿĻ £", + "ðĿĻ ¤", + "ðŁĮ Ŀ", + "ðŁĶ ij", + "ðŁĽ ł", + "ຠĩ", + "âĺ £", + "ãĦ ¨", + "ðĿĸ Ĺ", + "Ó ĵ", + "âĨ £", + "ðŁ¥ ī", + "ðŁĮ ł", + "ðŁĺ ½", + "ãİ ł", + "Å §", + "ðŁIJ Ĵ", + "ï§ IJ", + "ðŁĺ ¿", + "âĪ ¬", + "ðŁIJ ®", + "⣠±", + "ಠ¡", + "â¾ ¼", + "à° ²", + "Ë ¶", + "âĸ ¿", + "Õ Ī", + "áŀ İ", + "áħ ¥", + "áŀ Ĺ", + "Õ §", + "ðŁ¤ IJ", + "ðŁį ł", + "ঠ¤", + "චº", + "âĻ į", + "ìĺ Ļ", + "íĺ ĵ", + "ï¹ º", + "ðŁĽ ³", + "Å ī", + "á´ İ", + "âı ľ", + "âĶ ³", + "ê¸ ·", + "ì¡ Ķ", + "ðĿĴ Ī", + "ðĿĴ į", + "ðĿĴ ¹", + "ðĿĵ ĩ", + "ðĿķ Ł", + "ðĿĹ ¹", + "ðŁĮ ħ", + "ðŁı ´", + "Ä Ķ", + "Ä ¤", + "Å µ", + "Ç ¾", + "Ï ŀ", + "Ï ¶", + "Ô ³", + "Ü Ĩ", + "ß ©", + "à¡ Ĵ", + "ठĺ", + "චļ", + "འĸ", + "áģ Ĭ", + "áĥ ŀ", + "áĦ Ĥ", + "áĭ «", + "á´ º", + "Ḡ£", + "Ḡª", + "á¹ Ĥ", + "á¼ ·", + "á¿ ĩ", + "âĩ Į", + "âı ¬", + "âĻ Į", + "â® Ł", + "â´ »", + "âµ Ł", + "ê¦ ķ", + "ê¦ ª", + "ê¦ ®", + "ê² Ħ", + "ê¾ IJ", + "ëĥ ij", + "ëķ ĭ", + "ë¡ ¸", + "ë¬ Ģ", + "ìĩ ¤", + "ìĪ ©", + "ìľ ķ", + "ìŃ ĺ", + "ì· °", + "ì ·¸", + "íľ Ģ", + "ï¤ £", + "ï§ į", + "ï± Ħ", + "ï³ ij", + "ðĿIJ ¤", + "ðĿĴ ĵ", + "ðĿĴ ¶", + "ðĿĹ ¼", + "ðĿĻ Ĭ", + "ðŁĩ ¾", + "ðŁĮ Ľ", + "ðŁĮ ®", + "ðŁİ ĩ", + "ðŁİ ²", + "ðŁı Ľ", + "ðŁij ¥", + "ðŁij ´", + "ðŁĴ Ĩ", + "ðŁĵ Ĥ", + "ðŁĵ §", + "ðŁķ IJ", + "ðŁĸ ķ", + "ðŁĺ §", + "ðŁĻ Ģ", + "ðŁļ Ĵ", + "ðŁĽ «", + "ðŁ¤ ł", + "ðŁ¥ ļ", + "ðŁ¥ Ľ", + "ðŁ¥ £", + "Ç ¯", + "È §", + "Î Ĭ", + "Ò ²", + "× °", + "Û ij", + "áĥ ©", + "áĦ Į", + "áĪ į", + "áī ¥", + "áı Ĥ", + "âģ ±", + "âĬ ¢", + "âĹ ĵ", + "âĿ °", + "ë¿ ¡", + "ìĽ ©", + "íģ Ń", + "íĨ ³", + "íĬ Ħ", + "íĵ ¸", + "ï¥ £", + "ï¥ ´", + "ï± IJ", + "ï± ¯", + "ï³ ļ", + "ðĿĸ ĺ", + "ðĿĺ Ģ", + "ðŁIJ Ĭ", + "ðŁIJ Į", + "ðŁij ļ", + "ðŁĵ ĥ", + "ðŁļ Ľ", + "ðŁļ ª", + "ðŁ¤ °", + "Ä ´", + "áĥ ®", + "áĹ ¨", + "âĻ ®", + "â² ŀ", + "ãĪ Ķ", + "ì ħį", + "ãħ ĥ", + "ï¥ ¡", + "ຠ¡", + "Õ İ", + "Õ º", + "⬠Ľ", + "â½ ¤", + "ðĿIJ ²", + "âŀ µ", + "áĢ Ľ", + "âĶ ħ", + "âĨ Ł", + "â¼ Ĭ", + "ðŁĮ ½", + "ðŁļ ¿", + "ï¦ Ĭ", + "ãĦ £", + "⼠©", + "ï© Ľ", + "ðŁį ±", + "â¾ ¨", + "à´ ¤", + "áŀ ģ", + "ຠŀ", + "Ê ļ", + "ðĿIJ Ĵ", + "à´ ±", + "áŀ ľ", + "à® ©", + "à° Ĺ", + "à´ ļ", + "âĩ £", + "ï¦ ķ", + "Õ ħ", + "Æ ĺ", + "âĤ ¦", + "âĶ Ħ", + "ï¦ Ł", + "ï¦ «", + "ðĿIJ ģ", + "ðĿIJ ĥ", + "ðŁį ¸", + "ðŁIJ ²", + "Å ¶", + "É ĸ", + "ß ĺ", + "ภ¦", + "འĶ", + "áĨ ·", + "âģ ķ", + "âĵ Ĥ", + "âĿ ľ", + "ï¥ ¥", + "ï¬ ®", + "ðĿĹ Ŀ", + "ðĿĹ ¿", + "ðŁİ ¾", + "ðŁĹ Ŀ", + "ðŁ¦ Į", + "Æ ħ", + "Ç ª", + "Ò Ĺ", + "Ü Ľ", + "ß ł", + "à¡ ij", + "áī £", + "áĬ Ń", + "á¹ ¡", + "âŀ ¼", + "âŀ ¾", + "â´ ±", + "ãī ¡", + "ê³ ¯", + "ë½ Ī", + "ìĤ ĺ", + "ìī ij", + "ì «ĺ", + "íĮ ĥ", + "íĻ °", + "ï¤ Ĺ", + "ðŁĮ ¬", + "ðŁĮ °", + "ðŁį ¤", + "Ä »", + "Å ĩ", + "Æ ¨", + "É ķ", + "Ò ¢", + "Ò º", + "Ö į", + "× ±", + "Ú ±", + "Ú ½", + "Û IJ", + "ठĽ", + "à· Ģ", + "๠ļ", + "ຠ«", + "á´ ¹", + "á ½Ķ", + "á¾ ³", + "âĤ Ĵ", + "âĨ ´", + "âĩ Ŀ", + "âī ħ", + "â Į¨", + "âĵ ĵ", + "âĸ ¢", + "âļ ¬", + "âŀ Ń", + "â² Ĵ", + "ãİ ¿", + "ê¿ ´", + "ëĪ ±", + "ëį ¬", + "ëİ IJ", + "ëIJ «", + "ëĶ «", + "ë± ģ", + "ìĥ ¥", + "íĮ ¼", + "ïŃ ĵ", + "ï® ¥", + "ï² °", + "ðĿIJ ĩ", + "ðĿIJ ij", + "ðĿij Į", + "ðĿĵ ª", + "ðĿķ ļ", + "ðĿĺ ª", + "ðĿĺ ¼", + "ðĿļ Ľ", + "ðŁĩ ¶", + "ðŁĮ Ħ", + "ðŁĮ ķ", + "ðŁĮ ¤", + "ðŁĮ §", + "ðŁį ¬", + "ðŁİ ĭ", + "ðŁİ »", + "ðŁı ¨", + "ðŁIJ ĩ", + "ðŁij ĵ", + "ðŁĵ IJ", + "ðŁĵ Ļ", + "ðŁĶ ¼", + "ðŁķ Ĵ", + "ðŁĸ ı", + "ðŁĸ ¥", + "ðŁ¤ ¬", + "ðŁ¥ Ĭ", + "ðŁ¥ Ĵ", + "ß Į", + "ຠĦ", + "á¼ µ", + "âķ ¡", + "â² ¤", + "â´ ¼", + "âµ ¢", + "ãĪ ¯", + "ëĵ ¸", + "ëŁ ĩ", + "ëº į", + "ðĿĻ §", + "ðŁį Ī", + "ðŁĶ ¬", + "ðŁĸ Ĭ", + "ðŁ¤ ¾", + "Ë ¡", + "Ü ©", + "âĮ ¡", + "âŃ ij", + "â² ¦", + "ë© ī", + "ì¼ Ń", + "ï¿ ¤", + "ðĿĴ İ", + "ðĿĹ ¥", + "ðŁIJ µ", + "ðŁķ ¶", + "ðŁķ ¸", + "ðŁ¤ ľ", + "Õ ª", + "áĪ ĭ", + "ðŁ¥ µ", + "ï° ģ", + "áµ IJ", + "âķ ĵ", + "áĢ ĸ", + "âĭ Ī", + "É ŀ", + "âŀ ®", + "ॠ°", + "ãĨ ģ", + "ðŁĴ ±", + "ðŁı Ń", + "áĨ ¨", + "ðŁį ļ", + "ðŁ¦ IJ", + "á´ »", + "âĺ Į", + "à´ ķ", + "Õ ±", + "áħ ®", + "ðĿIJ Į", + "Å ¦", + "ຠķ", + "âľ Ļ", + "Ë ³", + "Ô µ", + "âķ Ĵ", + "ðĿĹ Ĺ", + "ðĿĹ ł", + "Ú ļ", + "ঠ§", + "âĨ Ŀ", + "âĻ ī", + "ãĮ »", + "ì¹ Ĭ", + "ðĿĹ º", + "ðŁ§ ĺ", + "ì³ £", + "ï¬ Ŀ", + "ðŁij º", + "Ç Ł", + "Î Ī", + "Î «", + "Ñ ¥", + "Ô ²", + "Õ ¨", + "Ü ¦", + "ঠĨ", + "ঠ¥", + "áIJ ¢", + "á¼ ģ", + "á¼ ĺ", + "á¼ ¦", + "âĵ Ŀ", + "ãĪ °", + "ãİ Ĺ", + "ê² ¡", + "ë¨ Ģ", + "ì£ Ķ", + "ì´ ¤", + "ìµ Ŀ", + "ï§ ´", + "ïŃ Ĭ", + "ï² Ł", + "ðĿIJ ·", + "ðĿij ĭ", + "ðĿĵ ī", + "ðĿĺ µ", + "ðŁĴ ·", + "ðŁĽ ©", + "ðŁ§ ¹", + "Å Ķ", + "Ê ŀ", + "Ë ¥", + "Î Į", + "Ñ ©", + "Ó IJ", + "Ó ł", + "Ú ij", + "Ú Ĵ", + "ß ¨", + "ઠĪ", + "áIJ ĥ", + "á¹ ¯", + "âĤ ĭ", + "âĤ µ", + "âĦ ħ", + "âĦ ł", + "âĪ £", + "âī º", + "âī »", + "âĬ Ľ", + "âĮ IJ", + "âİ ĵ", + "âĺ ¸", + "âĻ Ĵ", + "âļ Ĵ", + "âľ ĩ", + "âľ ł", + "â´ ·", + "âµ ĸ", + "ãĦ ¸", + "ãī ¢", + "ãī °", + "êĩ ´", + "ê´ ¸", + "êº ł", + "ëĤ ı", + "ëĤ ¢", + "ëIJ Ģ", + "ëº ´", + "ìĥ ľ", + "ìį ħ", + "ì¤ «", + "ì± ¦", + "ìº ij", + "ì¼ ģ", + "ì¿ ³", + "íĤ ģ", + "íħ ¡", + "íĴ Ĥ", + "íĴ ī", + "íľ Ħ", + "ïŃ ª", + "ï® ¬", + "ï¯ ¦", + "ï± ª", + "ï² ı", + "ï ´Ģ", + "ï» Ĩ", + "ï¿ ¦", + "ðĿij Ĺ", + "ðĿĸ Ļ", + "ðŁĮ ¡", + "ðŁį Ŀ", + "ðŁį §", + "ðŁİ «", + "ðŁı ĺ", + "ðŁı ª", + "ðŁIJ ĭ", + "ðŁIJ Ľ", + "ðŁIJ º", + "ðŁij ĸ", + "ðŁij ŀ", + "ðŁij ·", + "ðŁĵ Ģ", + "ðŁ ĶĦ", + "ðŁĶ Į", + "ðŁķ Ļ", + "ðŁĻ į", + "ðŁĻ İ", + "ðŁ¦ į", + "Ç °", + "É Ł", + "Ê Ĩ", + "Ô ¼", + "Ú ľ", + "ঠ¡", + "ঠ¶", + "áĴ ĥ", + "á¼ ©", + "âĵ ķ", + "â² Ī", + "ê° °", + "ê¹ ł", + "êº ħ", + "ëĦ ¹", + "ë¯ ĵ", + "íIJ Ī", + "ï§ ¶", + "ï® ij", + "ï² ¨", + "ðĿĴ ī", + "ðĿĴ Ķ", + "ðĿĹ ¨", + "ðĿĻ ŀ", + "ðĿļ Ĵ", + "ðĿļ ķ", + "ðŁIJ İ", + "ðŁ¤ ķ", + "ðŁ§ Ķ", + "Ï °", + "Ô Ŀ", + "âĮ Ĭ", + "âĴ ¾", + "ãī £", + "ïŃ ©", + "ðĿļ ŀ", + "Ê ij", + "ঠ¦", + "áĦ ĩ", + "âī ĥ", + "â² Ģ", + "ìŁ İ", + "ðĿij ¶", + "ðĿĵ ²", + "ðŁ İ·", + "ðŁļ ¹", + "ຠģ", + "áł ł", + "ãĦ ļ", + "ðŁIJ ¿", + "ἠļ", + "âķ ³", + "ðŁIJ Ń", + "âĴ ¹", + "ðĿĸ ļ", + "âĻ ĸ", + "ãĪ ²", + "âĨ ¾", + "áĦ Ĩ", + "âķ Ľ", + "ðŁ¤ į", + "â½ ¥", + "ðŁ Į¨", + "âĪ ®", + "ãĮ ĺ", + "ãį ij", + "ï¹ Ģ", + "âĵ Ĺ", + "âĬ Ħ", + "ðŁı ¹", + "Ë Ĵ", + "ðŁ¤ ±", + "ãı ľ", + "ðŁİ Į", + "ï¥ Ń", + "ঠ£", + "ðŁİ ¹", + "ãĬ Ł", + "à´ °", + "ðĿIJ Ķ", + "à´ ¨", + "འļ", + "âľ º", + "Õ ·", + "ðŁij ³", + "ঠľ", + "âĺ ĭ", + "âĻ Ĭ", + "ãĢ Ľ", + "È ĭ", + "à® °", + "áĥ ¨", + "âĦ ķ", + "íij Ģ", + "ðĿĵ ĥ", + "ðŁ¦ Ķ", + "Ä ¿", + "Å Ģ", + "Æ ³", + "É ļ", + "Ö ĥ", + "Ü £", + "ß Ł", + "ঠŃ", + "ৠ¡", + "ච»", + "ຠ£", + "འĩ", + "Ḡ¨", + "á½ Ī", + "â½ ¬", + "ê¡ Ķ", + "ì³ Ħ", + "ï¨ ī", + "ðĿIJ ¡", + "ðĿĺ ¢", + "ðŁį ¿", + "ðŁİ Ł", + "ðŁı ī", + "ðŁĶ IJ", + "ðŁļ ħ", + "ðŁ¤ ½", + "Æ į", + "Ç «", + "Ç ½", + "È ļ", + "Î ī", + "Ó ¤", + "Ó ª", + "Õ Ĭ", + "Ù ¼", + "Ú ´", + "ß Ŀ", + "චľ", + "á¼ ķ", + "á¿ ¥", + "âİ ŀ", + "ãĢ ļ", + "ãī ¤", + "ê³ ¸", + "ê· ģ", + "ëĵ Ħ", + "ëĵ ķ", + "ì¨ Ķ", + "ì± ¨", + "ðĿIJ ¾", + "ðĿij »", + "ðĿĶ ¼", + "ðĿķ Ŀ", + "ðĿĺ Ń", + "ðŁĨ Ļ", + "ðŁĵ ¤", + "ðŁĶ Ł", + "ðŁĹ ¼", + "Ä ľ", + "Æ ģ", + "Æ ¿", + "Ç ³", + "Ç ·", + "É ĥ", + "É ł", + "Ê ī", + "Ê §", + "Ë ²", + "Ï ´", + "Õ ģ", + "Õ ŀ", + "Ö ĩ", + "Û Ĥ", + "Û ĵ", + "ß Ĺ", + "ß ¦", + "ঠ¹", + "à® ³", + "à´ ¸", + "à» Ĥ", + "áĪ Ŀ", + "áĪ ª", + "áĭ µ", + "áIJ Ĭ", + "áĴ ª", + "áļ ĸ", + "áŀ Ľ", + "á´ ¢", + "áµ ı", + "áµ Ń", + "ᶠ«", + "Ḡı", + "ẠĴ", + "á¼ ¥", + "á½ ķ", + "á½ ¼", + "âĤ Ĭ", + "âĦ Ĥ", + "âĦ ©", + "âĩ ī", + "âī £", + "âĮ ł", + "âİ Ł", + "âı ®", + "âķ ĺ", + "âĹ ĸ", + "âĺ ©", + "âĻ ij", + "âĻ ²", + "âļ Ľ", + "ãĦ Ł", + "ãī ±", + "ãİ ļ", + "ê¡ ķ", + "êª ĸ", + "ê° ¹", + "ê² Ĩ", + "êµ Ħ", + "ëĩ ¬", + "ëĭ ¯", + "ëı ł", + "ëĴ ¬", + "ëĸ Ī", + "ëĸ ½", + "ëĺ Ķ", + "ëŀ ¸", + "ë¸ ħ", + "ë» ł", + "ë¿ Ł", + "ìĤ µ", + "ìĬ ī", + "ìľ °", + "ìł ĭ", + "ìł Ķ", + "ì¥ ¡", + "ìŃ Ŀ", + "ì¼ ¬", + "íĪ ĩ", + "íī ľ", + "íį Ħ", + "íĽ ¾", + "íĿ £", + "ï¤ ©", + "ï¤ ¯", + "ï¦ ľ", + "ï¦ §", + "ï§ ľ", + "ï¨ Ī", + "ï¬ ª", + "ï ¬´", + "ïŃ ½", + "ï® ī", + "ï¯ ŀ", + "ï° Ĵ", + "ï± ĩ", + "ï¿ Ħ", + "ðĿIJ ħ", + "ðĿij Ħ", + "ðĿij º", + "ðĿĴ Ĺ", + "ðĿĵ ®", + "ðĿķ Ľ", + "ðĿķ ŀ", + "ðĿĸ ij", + "ðĿĺ ģ", + "ðĿĺ Ĩ", + "ðĿĺ ¶", + "ðĿĻ ¢", + "ðĿļ ľ", + "ðŁĮ ĥ", + "ðŁĮ ¦", + "ðŁį Ł", + "ðŁİ İ", + "ðŁı Ļ", + "ðŁIJ ©", + "ðŁIJ «", + "ðŁIJ ´", + "ðŁij Ķ", + "ðŁĵ ī", + "ðŁĵ Ľ", + "ðŁĶ ī", + "ðŁĸ ¼", + "ðŁĹ ĥ", + "ðŁĹ ¯", + "ðŁļ ĩ", + "ðŁļ IJ", + "ðŁļ µ", + "ðŁ¤ ¶", + "ðŁ¥ ĭ", + "ðŁ¥ ĵ", + "ðŁ¥ ®", + "ðŁ¦ İ", + "ðŁ¦ ł", + "ðŁ§ Ĵ", + "ðŁ§ ¨", + "Æ IJ", + "Ç į", + "Ó Ģ", + "Ô Ľ", + "ಠ°", + "à´ Ļ", + "áĢ Ĵ", + "ê² Ŀ", + "ê¹ ¹", + "ë© ¥", + "ìĸ Ķ", + "ï¤ ģ", + "ï¤ ı", + "ï¦ ī", + "ï¦ ĵ", + "ï§ ī", + "ï² Ŀ", + "ðĿĹ ŀ", + "ðĿĹ ±", + "ðŁĮ ĭ", + "ðŁį ¶", + "ঠļ", + "ìķ ľ", + "ðĿIJ ¯", + "ðĿļ Ŀ", + "à° ¨", + "འĺ", + "འł", + "á¡ ¥", + "á¾ °", + "âģ į", + "âĶ °", + "⬠ľ", + "ðĿIJ ł", + "ðĿij ¯", + "ðĿĹ Ľ", + "ðĿĵ »", + "ðĿĸ Ī", + "âŀ »", + "áŀ ł", + "â¡ ±", + "â» ij", + "ðŁ§ µ", + "ï¦ ¢", + "ðŁij ĺ", + "ãĤ Ķ", + "â¼ Ł", + "ãĬ ¤", + "ï¦ Ŀ", + "ãĮ ¦", + "âĢ ¸", + "ðŁĶ Ļ", + "ã ¹", + "ã¹ ¦", + "ï¹ ħ", + "ï© Į", + "ãī ¨", + "ï¸ ½", + "âį ¥", + "ðŁļ ī", + "ðŁ¥ ľ", + "âĵ ľ", + "â» Ŀ", + "ï¨ ľ", + "ðŁĴ Ĵ", + "áĦ ij", + "â¾ ŀ", + "ï¨ ģ", + "à´ ª", + "áĦ İ", + "âŀ ´", + "ঠ·", + "áħ ¬", + "áŀ §", + "âĨ ¢", + "âķ ¦", + "âľ ij", + "Ë ¬", + "Õ IJ", + "༠Ķ", + "Ê ¤", + "Ë ¨", + "ठŀ", + "à» ĥ", + "༠ļ", + "âĵ ¥", + "âķ ľ", + "ðŁIJ ĸ", + "á¼ Ļ", + "á¼ ¤", + "ìĨ °", + "È Ĥ", + "Ê ±", + "à® ļ", + "áĥ §", + "á´ ĭ", + "á´ ®", + "âĿ ¡", + "âŀ ·", + "ëĿ ¡", + "ï§ ¢", + "ï¯ ¡", + "ðĿķ ķ", + "ðŁħ °", + "ðŁ¦ ¸", + "Ç ¸", + "Ó ŀ", + "Ô ¶", + "Ö Ĩ", + "Ú ģ", + "Û ĭ", + "áİ ¥", + "á¾ ¿", + "âĶ Ń", + "âĶ ®", + "êĢ Ģ", + "ê± ĺ", + "ëIJ Ń", + "ë½ Ħ", + "ìĶ IJ", + "ì¸ Į", + "íģ ł", + "íĻ ±", + "ï¥ ī", + "ï¨ ĸ", + "ðĿij ´", + "ðĿĸ Ĵ", + "ðĿĺ ¨", + "ðĿ ļĮ", + "ðŁIJ ¡", + "ðŁij ¢", + "ðŁĵ Ķ", + "Å ħ", + "Æ İ", + "È ©", + "Ò ª", + "Ô ĥ", + "áĥ «", + "Ḡĩ", + "⼠Ł", + "ê» Ń", + "ë¨ Ħ", + "ìŁ Ģ", + "ì¤ ´", + "íļ IJ", + "ï¤ ³", + "ðŁŁ ¢", + "Æ §", + "È ¼", + "Ê Ŀ", + "Ë Ħ", + "Ë ħ", + "Ë į", + "Ë §", + "Ò ¥", + "Õ Ķ", + "Ø ı", + "Ø ¼", + "ß IJ", + "ß ľ", + "ठĵ", + "ঠĻ", + "à® ĵ", + "ච´", + "༠į", + "༠Ĵ", + "འ£", + "áĢ Ĥ", + "áĢ Ĭ", + "áĦ Ħ", + "á Īĺ", + "áĭ Ĭ", + "áĮ į", + "áij ĭ", + "áŀ Ĥ", + "áł ¢", + "á¡ Ŀ", + "á´ ¦", + "áµ į", + "áµ ¨", + "Ḡ¡", + "Ḡ¯", + "á¼ £", + "âģ Ĥ", + "âĦ ĺ", + "âĦ ľ", + "âĦ ³", + "âĦ µ", + "âĨ ¦", + "âĩ Ĩ", + "âĪ ·", + "âĬ ļ", + "âĮ «", + "âĮ ¯", + "âİ Ľ", + "âİ ľ", + "âİ ¤", + "âİ ¦", + "âİ ®", + "âij ī", + "âĶ ī", + "âķ Ļ", + "âĸ Ĥ", + "âĹ Ń", + "âĺ Ĭ", + "âĺ į", + "âĺ Ĵ", + "âļ Ĩ", + "⼠§", + "⼠²", + "âŀ ĺ", + "⥠Ħ", + "â´ ³", + "â´ ½", + "âµ Ī", + "ãī ¯", + "ãİ ij", + "㧠¬", + "êĻ ¬", + "ê§ ģ", + "ê³ ¬", + "ê´ ŀ", + "ê» ľ", + "ëħ ĵ", + "ëĭ ¼", + "ëį ĸ", + "ëĸ ±", + "ëĿ °", + "ë¡ ¹", + "ë¢ ´", + "ë£ Ģ", + "ë¤ ł", + "ë¨ ķ", + "ëŃ ¥", + "ìĦ ¶", + "ìħ ¤", + "ìĮ ķ", + "ìį ª", + "ìı ©", + "ìĴ Ģ", + "ìĶ ¯", + "ìĿ Ķ", + "ìĿ ľ", + "ìł Ń", + "ì§ ¦", + "ì¨ ©", + "ì² ¬", + "ì³ ¥", + "ì¼ ¯", + "íĢ «", + "íĢ Ń", + "íĥ ¸", + "íĵ ģ", + "íķ ¬", + "íĹ ¸", + "íĽ ķ", + "íľ Ń", + "íĿ Ĺ", + "ï¤ Į", + "ï¤ ª", + "ï§ ¿", + "ï¬ Ħ", + "ï¬ ħ", + "ïŃ ij", + "ïŃ «", + "ïŃ º", + "ï® Ĥ", + "ï® ¢", + "ï® ¨", + "ï° İ", + "ï° ł", + "ï² £", + "ï³ IJ", + "ï³ Ĵ", + "ï³ ĺ", + "ï³ ľ", + "ï¹ ¼", + "ï¿ ¨", + "ðĿIJ ©", + "ðĿĴ ļ", + "ðĿķ Ķ", + "ðĿķ ¤", + "ðĿĸ Į", + "ðĿĹ £", + "ðĿĹ °", + "ðĿĹ ´", + "ðĿĺ Ĥ", + "ðĿĺ ¥", + "ðĿĺ ®", + "ðĿĺ ¸", + "ðĿĻ Ģ", + "ðĿĽ ¾", + "ðĿľ ı", + "ðŁĮ ģ", + "ðŁĮ ľ", + "ðŁĮ ¥", + "ðŁĮ ¯", + "ðŁį IJ", + "ðŁİ Ĵ", + "ðŁı Ķ", + "ðŁı ķ", + "ðŁı ®", + "ðŁIJ Ĥ", + "ðŁIJ ī", + "ðŁIJ ¹", + "ðŁĶ ķ", + "ðŁĶ ļ", + "ðŁķ ij", + "ðŁķ £", + "ðŁĹ ŀ", + "ðŁĹ ¡", + "ðŁĹ ¿", + "ðŁļ Ĩ", + "ðŁļ Ĭ", + "ðŁļ ĵ", + "ðŁļ ķ", + "ðŁļ ¾", + "ðŁĽ ģ", + "ðŁĽ İ", + "ðŁĽ ı", + "ðŁ¤ ´", + "ðŁ¥ ķ", + "ðŁ¥ ĸ", + "ðŁ¥ ł", + "ðŁ¥ ¥", + "ðŁ¦ Ĩ", + "ðŁ¦ ī", + "ðŁ¦ ļ", + "ðŁ§ ij", + "ðŁ§ ¥", + "ðŁ§ ¿", + "Å °", + "Æ º", + "É §", + "ઠĩ", + "à® £", + "áĪ Ī", + "áĬ ¤", + "áĭ ®", + "áĮ Ī", + "áĮ µ", + "ᥠ²", + "âĵ Ł", + "êĻ ³", + "ê° Ĭ", + "ëķ ģ", + "ëķ ¨", + "ìĬ ģ", + "ï¦ µ", + "ï¬ ²", + "ðĿĸ į", + "ðĿĺ Į", + "ðĿĺ ³", + "ðĿĻ ©", + "ðŁį Ļ", + "ðŁĸ ĸ", + "áī ³", + "áĭ ¨", + "áĸ ĩ", + "áŀ Į", + "á¹ §", + "âķ ª", + "âŀ ļ", + "â² ĺ", + "ê ķ", + "êķ ¥", + "ï¤ ·", + "ï® £", + "ï¯ ł", + "ðĿĴ ĸ", + "ðĿķ ĺ", + "ðĿĸ ĩ", + "ðĿĹ Ł", + "ðĿĹ ª", + "ðĿĹ ¯", + "ðĿĻ ł", + "ðŁĵ ı", + "ঠĹ", + "âĴ »", + "â² ł", + "ðĿĵ µ", + "Ê £", + "à° ľ", + "áĬ ¢", + "áŀ IJ", + "Ḡ·", + "âĦ Ľ", + "âĩ Ģ", + "âĩ Ĭ", + "êĴ ¦", + "ê¦ ł", + "ï® ¤", + "ðŁį Ľ", + "ðŁ¤ Ľ", + "ᨠ¾", + "âŀ º", + "áķ ¯", + "ἠı", + "âĩ Ĥ", + "âĶ ¹", + "âĻ Ĺ", + "ðŁĸ ¨", + "ê¦ ı", + "ઠ°", + "áļ ¨", + "ðŁ¤ ¥", + "ðŁ§ ¢", + "ãIJ Ĥ", + "ãĦ ¥", + "ðŁĸ Į", + "â¼ Ĵ", + "ãĬ §", + "âį ©", + "ðŁ¦ ij", + "âĶ ·", + "ï© IJ", + "ï© ¡", + "ðĵ Ī", + "ðĵĪ Ĵ", + "â» Ħ", + "ï¨ Ĵ", + "âĦ ª", + "Ò §", + "Ú Į", + "âĢ ¶", + "⺠ł", + "â» ģ", + "âĨ ¸", + "áĦ IJ", + "ãħ IJ", + "à» Ħ", + "áĹ ª", + "âĨ ¼", + "âĩ ĭ", + "âĩ ĺ", + "âĮ ij", + "âĸ ©", + "ðĿIJ Ĺ", + "Ä Ĭ", + "ঠī", + "ìī ł", + "É ¤", + "ß į", + "ß ı", + "áµ Ĺ", + "âĤ ¥", + "âĵ ī", + "âĶ ł", + "âĶ ¨", + "âķ Ħ", + "ä ¤", + "ä¤ Ģ", + "ê» ¸", + "ï® ģ", + "ðĵ Ĥ", + "ðĵĤ ĥ", + "ðŁ¦ ķ", + "Æ Ľ", + "ঠĩ", + "ãı ĺ", + "ï® ¼", + "Ú ĵ", + "Ú Ŀ", + "ঠĵ", + "ච¯", + "á´ ħ", + "á½ Ļ", + "âģ ¼", + "âĸ İ", + "â¼ ©", + "ä Ķ", + "äĶ Ģ", + "ë» ¡", + "ìĽ ½", + "íģ Ħ", + "ï¥ ¼", + "ï± ī", + "ï¹ »", + "ðĿĸ ĭ", + "ðĿĻ Ī", + "ðĿĻ ª", + "ðĿ Ļ¶", + "ðŁIJ Ħ", + "ðŁIJ Ĩ", + "áİ ¢", + "ḠĮ", + "âĿ ´", + "ðŁı ¸", + "È Ŀ", + "É ¸", + "Î ħ", + "Ï ľ", + "Ó ¢", + "Õ ¹", + "à´ ħ", + "ຠĪ", + "áĭ °", + "áij İ", + "áł µ", + "á¡ ł", + "á´ ī", + "Ḡµ", + "á¿ ´", + "âĵ £", + "âĶ ¶", + "â½ ¯", + "ê² ¥", + "ê¿ ĺ", + "ëģ İ", + "ëİ Ī", + "ëĶ ¯", + "ë² °", + "ìĺ ¯", + "ìĽ ¸", + "ìŀ Ĺ", + "ì§ ĺ", + "ì¬ ¬", + "ì· ¬", + "íģ ħ", + "íĵ Ķ", + "íĽ Ŀ", + "ï¤ ®", + "ï¤ ¹", + "ï¥ ²", + "ï¯ ĸ", + "ðĿĵ ħ", + "ðĿĻ Ħ", + "ðŁĵ ¶", + "ðŁĹ Ĵ", + "ðŁ¥ Ķ", + "ðŁ¥ Ń", + "Å ®", + "Å ´", + "Æ ī", + "Æ «", + "Ç ģ", + "Ç £", + "Ç º", + "Ç ¼", + "È į", + "È ¯", + "É ľ", + "Ê ¬", + "Ë ģ", + "Ë ¤", + "Ë µ", + "Ï Ľ", + "Ò ¤", + "Ò ¬", + "Ó ı", + "Ó Ľ", + "Ó ¡", + "Ó ³", + "Ô Į", + "Ô ¬", + "Õ ³", + "Ù »", + "Ú ī", + "Ú §", + "Ü ľ", + "ß ª", + "ठĿ", + "ঠĽ", + "ਠĨ", + "ઠķ", + "ઠ¡", + "à® İ", + "à° ¬", + "ൠ»", + "ൠ¼", + "චł", + "චŃ", + "ච¶", + "à· Ĩ", + "༠½", + "áĢ ļ", + "áħ ¢", + "áĨ ¸", + "áĪ Ģ", + "áĪ ķ", + "áĪ °", + "áī ¡", + "áī ¤", + "áĬ ¦", + "áĬ «", + "áĭ ĭ", + "áĭ į", + "áİ ¯", + "áij Ń", + "áķ Ĺ", + "ᣠĽ", + "ᥠĴ", + "á© ī", + "áŃ º", + "á´ ¡", + "áµ ĺ", + "áµ Ľ", + "ᶠł", + "Ḡģ", + "Ḡĭ", + "á¹ Ļ", + "á¹ Ŀ", + "á¹ ¦", + "Ạħ", + "á¼ Ĥ", + "á½ ĥ", + "á½ į", + "á½ §", + "á¾ ·", + "âĢ µ", + "âĤ İ", + "âĦ Ŀ", + "âħ Ģ", + "âĨ ŀ", + "âĨ §", + "âĩ ħ", + "âĪ ĥ", + "âī ı", + "âī ½", + "âĬ ŀ", + "âĬ ¡", + "âĬ §", + "â Ĭ¶", + "âĭ Ħ", + "âİ Ĵ", + "âİ ¡", + "âİ £", + "âİ ª", + "âı İ", + "âĵ ĥ", + "âĵ ĸ", + "âĵ ¨", + "âķ ĭ", + "âķ ĸ", + "âķ ¢", + "âķ ²", + "âĸ Ĩ", + "âĸ Ĭ", + "âĸ į", + "âĸ ®", + "âĺ ¡", + "âĺ ¦", + "âĺ ±", + "âĺ ¿", + "âĻ ĺ", + "âĻ Ŀ", + "âļ °", + "⼠ij", + "âŀ ª", + "⤠Ŀ", + "⤠¢", + "⤠·", + "⧠«", + "⨠Ń", + "⨠¯", + "â± £", + "â² İ", + "âµ Ľ", + "ãħ Ķ", + "ãĪ ı", + "ãī ²", + "ãī ³", + "ãĬ ij", + "ãĭ Ľ", + "ãİ IJ", + "ê² ¤", + "ê· ¿", + "ê¹ ŀ", + "ê» ¨", + "ê¼ į", + "ê¿ ¸", + "ëĥ ¬", + "ëĩ IJ", + "ëĭ ł", + "ëį ¯", + "ëĹ Į", + "ëĹ ij", + "ë¥ Ģ", + "ëª ĥ", + "ëª ¯", + "ë± ¡", + "ë³ ĵ", + "ë³ ½", + "ë µľ", + "ìĤ ³", + "ìħ ¥", + "ìĩ ½", + "ìı ¨", + "ìı ¸", + "ìķ į", + "ìĸ ĸ", + "ìŁ ¨", + "ì¢ ĥ", + "ì¢ į", + "ì¥ ij", + "ì§ ¼", + "ì© ĥ", + "ì® ľ", + "ì® ¸", + "ì³ ij", + "ì´ ¥", + "ì¾ ĥ", + "íħ ¦", + "íĪ ¿", + "íĵ ½", + "íķ ³", + "íĸ ı", + "íĹ ł", + "íĿ «", + "ï¤ ĵ", + "ï¤ ĺ", + "ï¥ İ", + "ï¥ ¶", + "ï¦ ħ", + "ï¦ ½", + "ï§ ĩ", + "ï¬ Ĩ", + "ï¬ ³", + "ï® ĩ", + "ï® Ī", + "ï® Ŀ", + "ï® ©", + "ï® ±", + "ï¯ ĺ", + "ï¯ Ļ", + "ï¯ ¢", + "ï¯ £", + "ï¯ ¤", + "ï¯ ¥", + "ï± Ĥ", + "ï² Ĩ", + "ï² ª", + "ï´ ¼", + "ïº ī", + "ïº Ĭ", + "ïº ¥", + "ðĿij ¨", + "ðĿij ©", + "ðĿij ²", + "ðĿ ĴĮ", + "ðĿĴ ª", + "ðĿĴ ®", + "ðĿĵ Ĥ", + "ðĿĵ Ī", + "ðĿĵ ¯", + "ðĿĶ ¨", + "ðĿķ Ģ", + "ðĿķ Ĩ", + "ðĿķ ¦", + "ðĿķ §", + "ðĿķ «", + "ðĿķ ·", + "ðĿĹ µ", + "ðĿĹ ¸", + "ðĿĺ Ħ", + "ðĿĺ Ļ", + "ðĿĺ ł", + "ðĿĺ ¬", + "ðĿĻ į", + "ðĿĻ ij", + "ðĿĻ ¡", + "ðĿ Ļ¨", + "ðĿĻ ·", + "ðĿļ į", + "ðĿĽ ¿", + "ðŁ ĥ", + "ðŁĥ ı", + "ðŁħ ĺ", + "ðŁ ī", + "ðŁī ij", + "ðŁİ ¡", + "ðŁİ ª", + "ðŁİ ±", + "ðŁİ ³", + "ðŁİ º", + "ðŁı İ", + "ðŁı Ĺ", + "ðŁı ļ", + "ðŁı ŀ", + "ðŁı ¦", + "ðŁı §", + "ðŁIJ ģ", + "ðŁIJ ħ", + "ðŁIJ ĵ", + "ðŁĴ Ĥ", + "ðŁĵ ij", + "ðŁĵ ĵ", + "ðŁĵ ¨", + "ðŁĵ «", + "ðŁĶ ĭ", + "ðŁĶ Ń", + "ðŁĶ ¯", + "ðŁķ Ĺ", + "ðŁļ Ĥ", + "ðŁļ ¢", + "ðŁļ ¦", + "ðŁļ ¬", + "ðŁĽ ĭ", + "ðŁĽ Į", + "ðŁĽ ¬", + "ðŁĽ ¶", + "ðŁŁ ¡", + "ðŁ¥ ĺ", + "ðŁ¥ Ł", + "ðŁ¥ ¦", + "ðŁ¦ ĩ", + "ðŁ¦ Ī", + "ðŁ§ Ĭ", + "ðŁ§ Ĺ", + "ðŁ§ ¤", + "Ê ·", + "Ë ¹", + "á¹ ļ", + "á½ ¥", + "âĦ Ł", + "ê² ¯", + "ê» «", + "ë° ·", + "ìĥ Ĩ", + "ìĽ Ŀ", + "ì¨ ī", + "ì« ı", + "ï¯ ķ", + "ðĿľ ĭ", + "É ²", + "Ò Ń", + "Ó Ī", + "འĽ", + "áĭ ĵ", + "áĻ Ń", + "áł ©", + "á¹ ®", + "âĦ Ĵ", + "âĨ »", + "âµ ĥ", + "ëĢ ¨", + "ëł §", + "ìī ¥", + "ìĮ ľ", + "ìĹ ¶", + "ì¨ Ī", + "ìª ¾", + "íı ½", + "íļ Ķ", + "íĽ µ", + "ï¤ ¸", + "ï¦ IJ", + "ï§ Ĺ", + "ï§ ļ", + "ï¬ ¯", + "ðĿIJ Ĭ", + "ðĿķ Ĺ", + "ðĿĹ ļ", + "ðĿļ ĸ", + "ðŁħ ´", + "È ĥ", + "É Ŀ", + "Ï ±", + "Ó Ĺ", + "ठ¢", + "áħ ł", + "áī ¦", + "áij Į", + "áĴ ¼", + "áŀ ¡", + "áł ¨", + "áł Ń", + "ᨠħ", + "ᨠĶ", + "á´ ĺ", + "ᶠ¦", + "Ḡİ", + "á¼ ħ", + "á¼ ¹", + "âĨ ¯", + "âĵ İ", + "ãı Į", + "ê ī", + "êī Ĥ", + "ëĨ §", + "ëĿ ±", + "ì¢ ¡", + "íĪ ½", + "ï¤ ĩ", + "ï¤ Ľ", + "ðĿIJ ķ", + "ðĿĵ ¸", + "ðĿĵ ¼", + "ðĿĹ ķ", + "ðĿĺ Ī", + "ðŁı £", + "ðŁı ¤", + "ðŁĹ Ħ", + "Ñ ·", + "Ò ł", + "áµ ĸ", + "á¼ ¨", + "ë¬ Ħ", + "ï° ´", + "âĪ ½", + "Õ Ń", + "Ú ¹", + "ॠŁ", + "áĢ Ĩ", + "áŀ Ĵ", + "ãĢ ¶", + "ê¦ «", + "ï¸ ĵ", + "ðĿIJ Ľ", + "ðĿĺ Ĺ", + "ðŁı ľ", + "ì« Ń", + "ðŁ§ ŀ", + "འĤ", + "âĨ ¿", + "âĩ ı", + "âĵ ģ", + "âĶ §", + "âķ ģ", + "âķ ¤", + "ê¦ Ĺ", + "ê¦ ¤", + "ðŁı Ī", + "áŀ ķ", + "Ô ½", + "ઠĹ", + "ଠĨ", + "âķ ķ", + "ï½ ł", + "â¼ ¦", + "â¼ ¯", + "â¾ ·", + "âĶ ĸ", + "ଠĵ", + "âĺ Ĺ", + "âį ĭ", + "ï¨ Ŀ", + "â¼ ¥", + "ï¦ ª", + "âĦ Ĭ", + "ãĢ ´", + "âį ¢", + "ð¡ Ī", + "ð¡Ī ½", + "ï© ¨", + "ãĢ »", + "ãı ĥ", + "ï¦ ¡", + "ï¨ ĺ", + "ðŁIJ ĥ", + "ðŁĨ ĸ", + "ðŁĹ ¾", + "ãĦ ĩ", + "Þ ĭ", + "â¼ ¼", + "ï¨ Ń", + "Þ Ģ", + "Þ Ħ", + "Þ Ī", + "Þ IJ", + "âĮ Ħ", + "â» ĺ", + "ãŁ ¢", + "á ħ§", + "ðIJĮ ¿", + "Ë »", + "ಠĹ", + "áĢ ĩ", + "áŀ Ĭ", + "âķ ĩ", + "ãĩ ¼", + "ãİ °", + "Õ Ĵ", + "Ü Ī", + "ß ¥", + "à¿ IJ", + "áĢ Ł", + "âĨ ¥", + "âķ Į", + "â½ Ģ", + "â½ °", + "â¾ Ĭ", + "ä Ħ", + "äĦ Ģ", + "ðĵ IJ", + "ðĵIJ į", + "ðŁİ ¦", + "âĤ ¯", + "âĬ ĺ", + "âĦ į", + "Ê µ", + "Ñ ¶", + "Ú ĥ", + "ঠĶ", + "à´ ¦", + "áİ ¶", + "áĵ ķ", + "á¹ ¨", + "âĤ ł", + "âĩ °", + "âĹ Ĵ", + "â¿ Ĭ", + "ê· ±", + "ì¹ ķ", + "íĪ ©", + "ïŃ Ģ", + "ðĿĴ ¸", + "ðĿĵ Ĭ", + "ðĿĺ ©", + "Ç ¦", + "É «", + "áĬ ¨", + "È ¹", + "Ê ¯", + "Î ª", + "Ú Ģ", + "áĮ ¸", + "áİ »", + "áı ķ", + "áı ´", + "á² Ĥ", + "á½ ¨", + "âı Ŀ", + "âĺ Ļ", + "ëĥ ¨", + "ëĦ ¼", + "ëĪ Ļ", + "ë£ ħ", + "ìĶ ¼", + "ìķ Ŀ", + "ìļ ¬", + "ìľ ±", + "ï¥ Ĥ", + "ï¦ ¹", + "ï¬ ¹", + "ïŃ ģ", + "ï³ Ī", + "ðĿĶ ħ", + "ðĿĺ ¤", + "ðĿĻ ı", + "ðĿĻ Ļ", + "ðŁķ ī", + "ðŁ§ Ļ", + "Ḡij", + "ê´ ¼", + "ëģ į", + "ëĹ ´", + "ëĿ ³", + "ë° ŀ", + "ë° ¢", + "ëµ ĺ", + "ìĤ Ķ", + "ìĦ Ħ", + "ì¼ ļ", + "íĢ ł", + "íĬ ±", + "íĮ ĸ", + "ï¤ ij", + "ï¦ ´", + "ï¦ ¸", + "ï´ į", + "ðĿĺ ·", + "Ä ¬", + "Å ¬", + "Æ Ģ", + "Æ ĭ", + "Æ ľ", + "Ç ij", + "Ç ĺ", + "Ç ŀ", + "Ç ¥", + "Ç ®", + "É °", + "É ¶", + "É ·", + "É ½", + "Ê Ī", + "Ê IJ", + "Ë İ", + "Ë Ł", + "Ë ¦", + "Ë ¯", + "Ï IJ", + "Ï ĵ", + "Ï ¢", + "Ï ¤", + "Ï ª", + "Ï Ń", + "Ï ®", + "Ï »", + "Ñ ł", + "Ñ Ń", + "Ò ¨", + "Ó Ŀ", + "Ô ¡", + "Ô ·", + "Õ ī", + "Õ ĵ", + "Õ ĸ", + "Õ ļ", + "Õ Ŀ", + "Ö İ", + "Ø ¿", + "Ú ħ", + "Ú į", + "Ú Ķ", + "Û Ĭ", + "Û ¾", + "Ü Ļ", + "Ý Ĵ", + "Ý ĺ", + "ß Ĵ", + "ß ĸ", + "ठĬ", + "ठIJ", + "ঠı", + "ঠĸ", + "ৠŁ", + "ઠ®", + "ઠ¹", + "à® ħ", + "à® Ĩ", + "à° ¡", + "à° °", + "ಠļ", + "ಠ®", + "ಠ¯", + "à´ Ł", + "à´ ·", + "ൠ¾", + "චij", + "චŀ", + "༠¼", + "འĵ", + "áĢ ĵ", + "áĤ ¦", + "áĥ ĸ", + "áĥ Ń", + "áĥ ¯", + "áħ ¨", + "áħ ª", + "áĨ °", + "áĪ ģ", + "áĪ İ", + "áĪ ĵ", + "áĪ ¥", + "áĪ ²", + "áĪ ´", + "áĪ »", + "áī ł", + "áī ²", + "áī ¶", + "áĬ £", + "áĬ ¥", + "áĬ ª", + "áĭ ĺ", + "áĭ ²", + "áĭ ¶", + "áĮ £", + "áį ¡", + "áį £", + "áİ ¬", + "áİ ¾", + "áIJ ¡", + "áķ ķ", + "áĸ ±", + "áĹ IJ", + "áĹ Ń", + "áĺ ī", + "áļ ±", + "ἠŁ", + "áŀ ¥", + "ᣠĶ", + "áł £", + "áł ª", + "áł °", + "áł ´", + "ᤠĸ", + "ᥠ£", + "á ®", + "á® ł", + "á ¯", + "ᯠĻ", + "á °", + "á° į", + "á´ Ĭ", + "á´ ¾", + "áµ ģ", + "áµ İ", + "áµ ŀ", + "áµ ¤", + "ᶠħ", + "ᶠĺ", + "ᶠŁ", + "ᶠ¢", + "ᶠ¤", + "ᶠ±", + "ᶠ»", + "Ḡī", + "Ḡŀ", + "Ḡº", + "á¹ ĵ", + "á¹ Ĺ", + "á¹ ª", + "ẠĬ", + "Ạı", + "ẠĽ", + "á¼ ĥ", + "á¼ Į", + "á¼ ¿", + "á½ Ĥ", + "á½ ĵ", + "á½ Ĺ", + "á½ ¦", + "á¾ ±", + "á¾ ´", + "á¿ ĺ", + "á¿ Ł", + "á¿ ¸", + "âģ ĺ", + "âĤ ij", + "âĤ Ľ", + "âĤ ¿", + "âĦ ĩ", + "âĦ ŀ", + "âĦ ±", + "âĩ Ł", + "âĩ ²", + "âĪ ¤", + "âĪ ¶", + "âī Ĥ", + "âī ¾", + "âĬ ¨", + "âĬ ³", + "âĬ ·", + "âĭ Į", + "âĭ ĺ", + "âĮ ķ", + "âĮ ¥", + "âĮ µ", + "âĮ º", + "âį £", + "âį ²", + "âį µ", + "âİ ĩ", + "âı ĥ", + "âı IJ", + "âı ł", + "âı ¤", + "âı ¶", + "âı ¸", + "âı ¹", + "âij Ĥ", + "âĴ ·", + "âĴ º", + "âĵ ¡", + "âĵ ¤", + "âĶ ¾", + "âĸ ĺ", + "âĸ µ", + "âĹ ª", + "âĹ ·", + "âĺ ¨", + "âĺ «", + "âĺ ²", + "âĺ ³", + "âĻ Ĩ", + "âļ ¤", + "âļ ¥", + "⼠ĵ", + "⼠´", + "⼠¾", + "âŀ «", + "âŀ ¿", + "⣠·", + "⤠ij", + "⤠«", + "⤠¶", + "⤠½", + "⧠ª", + "⨠Ģ", + "â ©½", + "⬠¡", + "⬠¢", + "⬠¤", + "â² ĸ", + "â² ª", + "âµ Ģ", + "⸠®", + "⸠½", + "ãĢ ł", + "ãĢ ·", + "ãĦ Į", + "ãĦ ĺ", + "ãħ ij", + "ãĪ İ", + "ãĪ IJ", + "ãĬ ľ", + "ãĮ ĵ", + "ãĮ ł", + "ãİ Ł", + "ãİ ¤", + "ãİ §", + "㬠®", + "ä Ī", + "äĪ Ģ", + "ä °", + "ä° Ģ", + "ê ħ", + "êħ ī", + "êĩ Ĺ", + "ê Ī", + "êĪ į", + "ê§ Ĥ", + "ê§ Ĭ", + "êª Ģ", + "ê² Ī", + "ê² į", + "ê³ Ģ", + "êµ ł", + "ê½ IJ", + "ê¾ Ī", + "ê¿ ±", + "ëĥ ı", + "ëĦ ij", + "ëħ ¤", + "ëĩ ¸", + "ëĪ ¼", + "ëī ħ", + "ëĬ £", + "ëĭ º", + "ëį ŀ", + "ëIJ Į", + "ëķ ¸", + "ëĺ ł", + "ëĻ ĩ", + "ëĻ Ī", + "ëľ ½", + "ëŀ Ķ", + "ëł ľ", + "ë£ IJ", + "ë§ Ģ", + "ë§ Ĭ", + "ëª Ģ", + "ë¬ Ń", + "ë¯ ¾", + "ë³ ľ", + "ë´ Ĭ", + "ëµ ī", + "ë· ľ", + "ë¸ Ģ", + "ë¹ ĭ", + "ìģ Ħ", + "ìĤ £", + "ìĤ »", + "ìĦ µ", + "ìħ Ĵ", + "ìī Ī", + "ìī Ķ", + "ìĬ Į", + "ìĬ Ļ", + "ìIJ ´", + "ìĵ º", + "ìķ ļ", + "ìķ º", + "ìĸ ľ", + "ìĹ ª", + "ìĺ ľ", + "ìĻ ¤", + "ìļ Ľ", + "ìļ º", + "ìĿ ħ", + "ìĿ ı", + "ìĿ Ń", + "ìĿ ¶", + "ìł Ľ", + "ì¡ Ī", + "ì¢ ī", + "ì¢ Ķ", + "ì© ł", + "ìŃ Į", + "ì¯ ©", + "ì´ £", + "ì¸ ķ", + "ì¹ Ł", + "ì¾ ¡", + "ì¿ Ļ", + "íģ ĩ", + "íģ ī", + "íĩ Ģ", + "íĪ ¶", + "íĸ ij", + "íĸ ¤", + "íĹ ħ", + "íľ ı", + "íĿ Ŀ", + "ï¤ Ĵ", + "ï¤ ķ", + "ï¤ ¬", + "ï¥ ħ", + "ï¥ ĩ", + "ï¥ ı", + "ï¥ ļ", + "ï¥ Ł", + "ï¦ Ħ", + "ï¦ Ī", + "ï¦ ¨", + "ï¦ ©", + "ï¦ ²", + "ï§ ģ", + "ï§ ĥ", + "ï§ Ķ", + "ï§ ł", + "ï§ £", + "ï§ ®", + "ï ŃIJ", + "ïŃ ĸ", + "ïŃ ¦", + "ïŃ ´", + "ïŃ µ", + "ïŃ ¶", + "ïŃ ¸", + "ï® Į", + "ï® İ", + "ï® ŀ", + "ï® Ł", + "ï® ¡", + "ï® ª", + "ï¯ Ķ", + "ï¯ Ĺ", + "ï¯ ļ", + "ï¯ Ľ", + "ï¯ Ŀ", + "ï¯ Ł", + "ï¯ §", + "ï¯ ¨", + "ï¯ «", + "ï¯ ¯", + "ï¯ °", + "ï¯ ±", + "ï¯ ²", + "ï¯ ³", + "ï¯ ´", + "ï¯ µ", + "ï¯ ¶", + "ï° Ģ", + "ï± ħ", + "ï± Ķ", + "ï± ´", + "ï² ģ", + "ï³ ķ", + "ï· ½", + "ï¸ ķ", + "ï¸ ±", + "ï¹ £", + "ï¹ ½", + "ï» į", + "ï¾ ±", + "ðĿIJ Ļ", + "ðĿIJ ½", + "ðĿij ¤", + "ðĿij ®", + "ðĿij µ", + "ðĿĴ ĥ", + "ðĿĴ Ħ", + "ðĿĵ Ń", + "ðĿĵ ·", + "ðĿĶ ĸ", + "ðĿĶ ŀ", + "ðĿĶ ¢", + "ðĿĶ ¦", + "ðĿĶ ¬", + "ðĿķ Ħ", + "ðĿķ Ĭ", + "ðĿķ İ", + "ðĿķ Ļ", + "ðĿķ ľ", + "ðĿķ Ń", + "ðĿķ ³", + "ðĿķ ¸", + "ðĿķ ¾", + "ðĿ ĸī", + "ðĿĸ ı", + "ðĿĺ ĩ", + "ðĿĺ ī", + "ðĿĺ ĸ", + "ðĿĺ Ľ", + "ðĿĺ ŀ", + "ðĿĺ «", + "ðĿĺ ¾", + "ðĿĻ ĩ", + "ðĿĻ ī", + "ðĿĻ ĭ", + "ðĿĻ İ", + "ðĿĻ ĺ", + "ðĿĻ ¥", + "ðĿļ ĥ", + "ðĿļ IJ", + "ðĿļ Ķ", + "ðĿľ ĥ", + "ðŁĦ ·", + "ðŁħ Ŀ", + "ðŁħ ¾", + "ðŁĨ Ĥ", + "ðŁĨ ĵ", + "ðŁĮ Ĥ", + "ðŁĮ Ĩ", + "ðŁĮ ī", + "ðŁĮ ij", + "ðŁĮ ĺ", + "ðŁĮ ©", + "ðŁĮ «", + "ðŁį ¢", + "ðŁį ¥", + "ðŁİ Ľ", + "ðŁİ ¢", + "ðŁİ ´", + "ðŁij ¡", + "ðŁĴ ¾", + "ðŁĵ Ń", + "ðŁĶ Ī", + "ðŁĶ ¦", + "ðŁĶ ²", + "ðŁĶ ³", + "ðŁķ ĵ", + "ðŁķ ķ", + "ðŁķ ĺ", + "ðŁķ Ł", + "ðŁķ ·", + "ðŁĹ ³", + "ðŁļ Ħ", + "ðŁļ Ķ", + "ðŁļ ĸ", + "ðŁĽ IJ", + "ðŁĽ ¤", + "ðŁĽ ¸", + "ðŁ ł", + "ðŁł ³", + "ðŁ¤ ¹", + "ðŁ¥ ĥ", + "ðŁ¥ ¨", + "ðŁ¥ ª", + "ðŁ¥ ¾", + "ðŁ¦ ĥ", + "ðŁ¦ Ĵ", + "ðŁ¦ Ļ", + "ðŁ¦ ¶", + "ðŁ§ ł", + "ðŁ§ ª", + "ðŁ§ Ń", + "ðŁ§ ²", + "𣠷", + "𣷠Ń", + "ð¦ ĺ", + "ð¦ĺ Ĵ", + "Æ ij", + "Ç Ļ", + "È ®", + "Ø ł", + "Ú Ħ", + "Ü Ģ", + "ß ¢", + "áī Ģ", + "áĬ IJ", + "áİ ł", + "Ạŀ", + "ëĪ ŀ", + "ëķ Ł", + "ë£ ģ", + "ë¤ Ĺ", + "ìĦ ¥", + "ìħ ij", + "ìĸ IJ", + "ìĽ Ľ", + "ì£ ķ", + "íİ ı", + "íĽ ĵ", + "ï¥ º", + "ï³ Ľ", + "ï´ «", + "ðĸ §", + "ðĸ§ ·", + "ðĿķ ģ", + "ðŁIJ ª", + "ðŁĴ Ī", + "ðŁĵ ł", + "ðŁķ Ľ", + "ðŁķ ´", + "Ñ Ŀ", + "Ó Ĭ", + "ॠ²", + "ઠª", + "áĥ ¤", + "áį IJ", + "ᶠ°", + "á¼ Ŀ", + "á½ ©", + "âĭ ĭ", + "âĴ ½", + "âĻ ¾", + "â ½Ķ", + "â¾ ¯", + "ãĦ Ĵ", + "ãħ ļ", + "ëIJ į", + "ë· ģ", + "ìĭ Ģ", + "ìļ Ŀ", + "ì¥ °", + "ìº ´", + "íĭ ī", + "íĿ ½", + "ï¦ Ģ", + "ï¦ ¿", + "ï§ ħ", + "ï§ ĵ", + "ïŃ ¯", + "ï® Ĩ", + "ðIJ¤ ķ", + "ðĿIJ Ł", + "ðĿĴ ħ", + "ðĿĵ ľ", + "ðĿĶ °", + "ðĿĶ »", + "ðĿĺ į", + "ðĿĻ ¯", + "ðŁĦ ½", + "ðŁħ Ĥ", + "ðŁħ Ķ", + "ðŁħ ½", + "ðŁĵ ´", + "ðŁ§ ĸ", + "Ó Ĵ", + "Ḡ²", + "ëī ¼", + "Ç ı", + "È ĵ", + "Ê ¸", + "Õ Ĥ", + "Û ħ", + "ß ¡", + "ß £", + "à® ¯", + "à° Ī", + "ಠ¸", + "ຠ®", + "༠ķ", + "áĢ İ", + "áĨ ¡", + "áIJ ĭ", + "áIJ ķ", + "áij ¯", + "áŀ Ĩ", + "ᨠķ", + "á© Ī", + "âģ ħ", + "âĨ ļ", + "âĶ İ", + "âł ©", + "â² Ĥ", + "â² Ķ", + "â² ¨", + "ãĬ ļ", + "íĵ ²", + "ðĿij Ī", + "ðĿij ¬", + "ðĿij ¹", + "ðĿĴ ¾", + "ðĿĵ ±", + "ðĿĵ ½", + "ðĿķ ¯", + "ðĿķ »", + "ðĿĺ ½", + "ðĿļ Ĩ", + "ðŁĦ °", + "ðŁIJ ¨", + "Ò ķ", + "ಠħ", + "ï¨ Ĩ", + "ðĿij °", + "ðŁĦ ¸", + "Ô İ", + "Ø į", + "Ù µ", + "ಠ¶", + "áĢ Ī", + "áĺ Ĺ", + "áł ¸", + "á¡ ¡", + "ᨠ²", + "á© ģ", + "á´ ·", + "áµ §", + "âķ ¨", + "âļ ģ", + "â¾ Ŀ", + "ãĢ ¼", + "ãĦ ı", + "êĴ «", + "ê¦ ¥", + "ê¦ ©", + "ê¦ ²", + "ìĺ ¼", + "íĵ IJ", + "ðĵ ĩ", + "ðĵĩ ¼", + "ðĿķ ¿", + "ðŁĽ ´", + "ë¨ ľ", + "ಠµ", + "à´ İ", + "༠Ģ", + "âĩ ĸ", + "ãĪ «", + "âĵ Ģ", + "áħ ´", + "áļ ¾", + "ἠŀ", + "ἠ«", + "ᥠ´", + "âĨ Ľ", + "âĨ ¶", + "âĩ ¤", + "âķ Ł", + "âĺ ·", + "âļ IJ", + "ðŁ§ ´", + "á¹ ³", + "âĶ į", + "âĶ Ĵ", + "âĶ ©", + "âĶ ¦", + "â¾ µ", + "ઠľ", + "ઠ¤", + "âĩ Ļ", + "âĶ ±", + "âķ Ģ", + "â½ Ĭ", + "ï½ Ł", + "ଠ¡", + "ðł ®", + "ðł® ·", + "âķ ĥ", + "â° Ķ", + "ãĬ ¦", + "ðŁİ IJ", + "ãĩ °", + "â¼ Ŀ", + "â¾ Ķ", + "â½ Ĵ", + "âł Ĵ", + "ï¨ ¦", + "ï© Ĵ", + "ï¨ ²", + "ï© ĸ", + "ðĵı ¸", + "ãĮ ĥ", + "ðĸ ¤", + "ðĸ¤ IJ", + "ï¦ Ń", + "âĬ ħ", + "â¾ ³", + "ä´ ¥", + "ï© ķ", + "ðŁĮ Ķ", + "áŀ ĭ", + "âļ į", + "â¼ ĭ", + "ãİ ĺ", + "ðIJĮ ²", + "É ©", + "áİ ij", + "âĨ ®", + "âĩ ĥ", + "âļ İ", + "ãĩ ±", + "ãĭ ©", + "ãĮ ¶", + "êĻ ª", + "ëİ ¬", + "ï¨ IJ", + "ï¨ Ľ", + "ï© Ĭ", + "ï© į", + "ðĵ ħ", + "ðĵħ º", + "Ï ¡", + "È ij", + "É Ĥ", + "Ô ĵ", + "ß İ", + "à´ §", + "áĢ ī", + "áĢ ĭ", + "áĢ ij", + "áĢ ł", + "áļ Ļ", + "ᨠĦ", + "ᨠ©", + "ᨠ¹", + "á© ĵ", + "ᬠľ", + "á´ Ļ", + "áµ ij", + "âĤ Ń", + "âĨ °", + "âľ ģ", + "â½ IJ", + "ãĭ ¯", + "ãĮ ½", + "íĨ ¢", + "ï¤ ¿", + "ðŁ Ĥ", + "ðŁĤ »", + "È Ĵ", + "Í º", + "Ô ¥", + "Õ ij", + "Ú ¶", + "ৠİ", + "ච®", + "ຠĸ", + "ຠľ", + "ຠ½", + "áĥ »", + "áħ ¯", + "áĭ ŀ", + "áĸ ķ", + "á ´Ī", + "ᶠĨ", + "Ḡľ", + "á¹ ¼", + "á¿ ¨", + "âĦ ĭ", + "âĦ Ń", + "âĪ ±", + "âĮ ĵ", + "âĶ ĩ", + "âĶ ¢", + "â± ®", + "â² Ħ", + "ãĩ ¾", + "ãĪ ¬", + "ë¸ ¡", + "ìIJ ī", + "íĻ Ľ", + "ðĿķ ª", + "Æ ¹", + "Í ²", + "Ó ģ", + "Û ¼", + "ঠ«", + "áħ Ł", + "áī Ĩ", + "áį Ī", + "Ạĸ", + "á½ ī", + "âĶ ¸", + "â½ ©", + "ê ľ", + "êľ ¥", + "êµ ħ", + "ëĤ Ķ", + "ëĦ ł", + "ëĩ Ĺ", + "ëĻ Ŀ", + "ìļ ¯", + "ìļ ·", + "ìŁ Ľ", + "ì· IJ", + "íŁ ¬", + "íŁ ®", + "íŁ °", + "ï¦ Ĩ", + "ï¦ ±", + "ï² ŀ", + "ï³ ¤", + "ï³ ¥", + "ðIJĮ ¸", + "ðĿĶ ı", + "ðĿķ ®", + "ðĿĺ £", + "ঠĪ", + "âı ı", + "ãĦ ĸ", + "ê² ĩ", + "ëĸ ĺ", + "ëľ ·", + "ëŀ Ĵ", + "ë¡ ĵ", + "ë¢ ī", + "ë£ ĥ", + "ë§ ĭ", + "ë² ĭ", + "ìĤ ·", + "ìĪ ķ", + "ì Į¨", + "ìĵ »", + "ìĸ Ĭ", + "ìĻ ¬", + "ìĿ »", + "ì¦ ģ", + "ìµ ¤", + "ì· ĥ", + "íĢ ľ", + "íħ ī", + "íį ł", + "íı ħ", + "íij ±", + "íķ ķ", + "íĸ ł", + "íĿ ķ", + "Æ Ļ", + "Æ ļ", + "Æ ŀ", + "Ç ĥ", + "Ç Ĭ", + "Ç ľ", + "Ç ¤", + "Ç Ń", + "Ç ¹", + "È Ģ", + "È ģ", + "È ħ", + "È ī", + "È Ĺ", + "È Ł", + "È ¤", + "È ¥", + "È ¨", + "È µ", + "È º", + "È »", + "É Į", + "É ®", + "Ê ħ", + "Ê ¥", + "Ê ¨", + "Ë ĵ", + "Ë Ķ", + "Ë ł", + "Ë £", + "Ë ¸", + "Í ´", + "Ï Ĺ", + "Ï ĺ", + "Ï Ļ", + "Ï ļ", + "Ï Ŀ", + "Ï ¨", + "Ï ¬", + "Ï ¾", + "Ï ¿", + "Ñ ª", + "Ò Ģ", + "Ò ľ", + "Ò ¼", + "Ò ½", + "Ó Ĥ", + "Ó ħ", + "Ó ĩ", + "Ó į", + "Ó ĸ", + "Ó Ł", + "Ó «", + "Ó ±", + "Ô Ĩ", + "Ô ĩ", + "Ô º", + "Õ ĭ", + "Ö ī", + "Ø Ī", + "Ø Ĭ", + "Ø ½", + "Ø ¾", + "Ù ·", + "Ú Ĥ", + "Ú Ĭ", + "Ú ĸ", + "Ú Ĺ", + "Ú £", + "Ú «", + "Ú ¸", + "Û Ģ", + "Û į", + "Û ½", + "Ü ī", + "Ü ¤", + "Ý §", + "Ý ´", + "Þ ĥ", + "Þ ¤", + "Þ ¥", + "ß ļ", + "ß Ľ", + "ß ¤", + "àł į", + "àł ĵ", + "àł ³", + "à¡ ¢", + "ॠł", + "ৠł", + "ৠº", + "ਠĬ", + "ਠIJ", + "ਠ®", + "ਠ¯", + "ਠ°", + "ਠ¸", + "ઠĨ", + "ઠ³", + "ઠµ", + "ઠ½", + "ଠĮ", + "ଠĺ", + "ଠ½", + "à® ĥ", + "à® ¸", + "à° Ĩ", + "à° ķ", + "à° ¦", + "ಠĨ", + "ಠĬ", + "ಠĮ", + "ಠIJ", + "ಠĽ", + "ಠ¤", + "ಠ¦", + "ಠª", + "ಠ²", + "ಠ¹", + "à´ Ĩ", + "à´ ı", + "à´ Ĺ", + "à´ «", + "à´ ¹", + "ൠº", + "ൠ½", + "චħ", + "චĬ", + "චĶ", + "ච§", + "ච«", + "ච°", + "༠Ħ", + "༠ħ", + "༠Ĭ", + "འĻ", + "འ¡", + "འ§", + "à¿ Ģ", + "à¿ Ļ", + "áĢ Ŀ", + "áĢ §", + "áĢ ©", + "áĢ ¿", + "áģ µ", + "áĤ ģ", + "áĤ ½", + "áĥ Ĥ", + "áĥ ª", + "áĦ Ĭ", + "áĦ ¢", + "áħ ¦", + "áħ Ń", + "áĨ ®", + "áĨ ±", + "áĨ »", + "á ĩ", + "áĩ Ĥ", + "áĪ ħ", + "áĪ ī", + "áĪ Į", + "áĪ IJ", + "áĪ Ĵ", + "áĪ Ļ", + "áĪ ļ", + "áĪ ľ", + "áĪ ŀ", + "áĪ ©", + "áĪ ³", + "áĪ º", + "áĪ ½", + "áī ħ", + "áī ¢", + "áī ±", + "áī ´", + "áĬ ĥ", + "áĬ į", + "áĬ ĸ", + "áĬ ®", + "áĬ ¸", + "áĭ Ľ", + "áĭ Ŀ", + "áĭ ³", + "áĮ ģ", + "áĮ ħ", + "áĮ ¥", + "áĮ ¦", + "á Į¨", + "áį Ĭ", + "áį į", + "áį ķ", + "áį ĸ", + "áį ¢", + "áį ¤", + "áİ Ĵ", + "áİ ª", + "áı ģ", + "áı IJ", + "áı Ł", + "áIJ Ĥ", + "áIJ ĸ", + "áIJ Ŀ", + "áIJ ŀ", + "áIJ Ł", + "áIJ ł", + "áij ĸ", + "áĴ ĭ", + "áĴ į", + "áĴ ¡", + "áĵ «", + "áĶ ķ", + "áķ ĭ", + "áķ ij", + "áķ Ļ", + "áķ ļ", + "áķ Ľ", + "áķ ¤", + "áķ ¦", + "áķ ®", + "áķ ¼", + "áĸ ĵ", + "áĹ Ĺ", + "áĹ ¢", + "áĹ ¯", + "áĹ ·", + "áĺ Ħ", + "áĺ ij", + "ἠĤ", + "ἠĻ", + "áŀ į", + "áł Ĩ", + "áł ¡", + "áł ¦", + "áł ®", + "áł ¯", + "áł ²", + "áł ·", + "á¡ į", + "á¡ ŀ", + "á¡ ¤", + "á ¡´", + "á¡ µ", + "ᤠĵ", + "ᥠĸ", + "ᥠ°", + "ᨠ¦", + "ᨠ§", + "ᨠ¨", + "ᨠª", + "ᨠ¬", + "ᨠ¯", + "ᨠ³", + "ᨠµ", + "á© ĥ", + "ᬠķ", + "áŃ £", + "á ±", + "á± ļ", + "á² ł", + "á´ ĵ", + "á´ ¶", + "áµ Ĥ", + "áµ Į", + "áµ ¥", + "áµ ´", + "ᶠĩ", + "ḠĪ", + "Ḡł", + "Ḡ§", + "Ḡ´", + "Ḡ¾", + "á¹ Ģ", + "á¹ ĸ", + "á¹ Ł", + "á¹ ł", + "á¹ «", + "á¹ ±", + "á¹ ·", + "á¹ ¿", + "ẠĦ", + "Ạį", + "Ạij", + "ẠĹ", + "á¼ ī", + "á¼ ĵ", + "á¼ Ń", + "á½ ĭ", + "á½ Ĵ", + "á½ ł", + "á½ £", + "á¾ Ħ", + "á¾ ı", + "á¾ ij", + "á¾ Ĺ", + "á¾ ¦", + "á¾ §", + "á¾ ¾", + "á¿ Ħ", + "á¿ ĵ", + "á¿ ¡", + "á¿ ¬", + "âģ ļ", + "âĤ Į", + "âĦ ģ", + "âĦ Ķ", + "âĦ £", + "âĦ §", + "âĦ ¯", + "âĦ °", + "âĦ ´", + "âħ ħ", + "âĨ ľ", + "âĨ «", + "âĨ Ń", + "âĨ ±", + "âĨ ¹", + "âĨ ½", + "âĩ ĩ", + "âĩ ľ", + "âĩ µ", + "âĪ ī", + "âĪ Ĭ", + "âĪ ĸ", + "âĪ ľ", + "âĪ ¾", + "âī Ģ", + "âī ĭ", + "âī Į", + "âī ĵ", + "âī ľ", + "âī ´", + "âī ¿", + "âĬ Ĭ", + "âĬ ĭ", + "âĬ Ķ", + "âĬ ĸ", + "âĬ £", + "âĬ ¦", + "âĭ İ", + "âĭ ª", + "âĭ ²", + "âĮ ¦", + "âĮ §", + "âį º", + "âİ Ī", + "âİ ¨", + "âİ ¬", + "âİ ³", + "âİ ¼", + "âİ ¾", + "âı Į", + "âı ļ", + "âı «", + "âı ¯", + "âı µ", + "âĴ ľ", + "âĴ Ŀ", + "âĴ «", + "âĵ Ħ", + "âĵ Ĭ", + "âĵ Ļ", + "âĵ ©", + "âĶ ij", + "âĶ Ļ", + "âĶ ļ", + "âĶ ¥", + "âķ ħ", + "âķ ī", + "âķ į", + "âķ ı", + "âķ ŀ", + "âĸ ļ", + "âĸ ¯", + "âĹ ĥ", + "âĹ ļ", + "âĹ ¬", + "âĹ ´", + "âĺ Ī", + "âĺ ¤", + "âĺ ¥", + "âĺ §", + "âĺ ¬", + "âĻ ģ", + "âĻ ±", + "âļ ĥ", + "âļ Ħ", + "âļ ħ", + "âļ ı", + "âļ ļ", + "âļ ŀ", + "âļ Ł", + "âļ ±", + "âļ ²", + "âľ Ģ", + "âľ Ł", + "âľ ¢", + "âĿ µ", + "⣠¡", + "⣠¦", + "⣠§", + "⣠³", + "⣠¾", + "⣠¿", + "âł ĩ", + "⤠Ħ", + "⤠º", + "⥠Ĥ", + "⥠¹", + "⧠ī", + "⧠¼", + "⧠½", + "⨠į", + "⬠Ĭ", + "⬠Ł", + "âŃ ŀ", + "â® ŀ", + "â® ³", + "⯠Ī", + "⯠ij", + "â± ł", + "â± ±", + "â² Ń", + "â´ ¹", + "âµ ķ", + "⸠¾", + "â º«", + "â¼ Ĩ", + "â¼ ł", + "â½ Ł", + "â½ ¼", + "â¾ Ľ", + "â¾ §", + "â¿ ĥ", + "â¿ »", + "ãĤ ķ", + "ãĤ Ł", + "ãĦ Ľ", + "ãĦ ¡", + "ãĦ ¶", + "ãĦ º", + "ãħ Ĵ", + "ãħ Ł", + "ãĨ Ģ", + "ãĩ »", + "ãĪ ij", + "ãĪ Ń", + "ãĪ ®", + "ãĪ ³", + "ãĪ ¹", + "ãī ¥", + "ãī ¦", + "ãī ¹", + "ãī ¿", + "ãĬ ŀ", + "ãĬ ¨", + "ãĭ ij", + "ãĭ ¥", + "ãĭ ´", + "ãĭ º", + "ãİ Ħ", + "ãİ ķ", + "ãİ ¯", + "ãı Ĥ", + "ãı Ī", + "ãı ĵ", + "ãı ĸ", + "ãı ±", + "ãIJ ±", + "ãŁ ģ", + "ã ¢", + "㢠¨", + "ã ¨", + "㨠³", + "ã« ª", + "ã« ´", + "㶠³", + "㺠¾", + "ä Ģ", + "äĢ Ģ", + "ä ĭ", + "äĭ Į", + "ä ĮĢ", + "äIJ Ģ", + "ä łĢ", + "ä ł", + "äł ¼", + "ä §", + "ä§ ŀ", + "ä¨ °", + "ä¨ º", + "ä ´Ģ", + "ä ·", + "ä· ħ", + "ä ·¸", + "ê Ĥ", + "êĤ «", + "ê Į", + "êĮ ¼", + "ê į", + "êį ²", + "êĴ µ", + "ê ĵ", + "êĵ ½", + "êĻ Ń", + "êĿ Ľ", + "êĿ ¥", + "ê ŀ", + "êŀ Ĭ", + "ê¦ Ĩ", + "ê¦ ĩ", + "ê¦ Ł", + "ê¦ ¨", + "ê§ Ī", + "ê ©", + "ê© Ł", + "êª ĭ", + "êª ij", + "êª ķ", + "êª Ĺ", + "êª ľ", + "êª ®", + "êª ±", + "êª »", + "êª ¼", + "ê« Ģ", + "ê« Ŀ", + "ê° ĥ", + "ê° ĺ", + "ê± ľ", + "ê² ĵ", + "ê² ļ", + "ê³ Ļ", + "ê³ ¾", + "ê´ Ĺ", + "ê´ Ļ", + "êµ Ľ", + "ê¶ ĥ", + "ê¶ ķ", + "ê¶ ¨", + "ê¸ ©", + "ê¸ ¿", + "ê ¹Ħ", + "ê¹ Ĩ", + "ê¹ ī", + "ê¹ ĵ", + "ê¹ ¢", + "ê¹ £", + "ê¹ ¸", + "êº ³", + "ê¿ ı", + "ê¿ ķ", + "ê¿ §", + "ëĢ ©", + "ëģ ħ", + "ëĥ µ", + "ëĦ ĸ", + "ëĦ Ĺ", + "ëĦ ¢", + "ëħ Ĥ", + "ëĨ IJ", + "ëĩ ľ", + "ëĪ ĭ", + "ëĪ ļ", + "ëī į", + "ëī ¨", + "ëĬ ļ", + "ëĬ ¡", + "ëĭ ľ", + "ëĭ ª", + "ëĮ ĺ", + "ëĮ ¤", + "ëĮ ¸", + "ëİ Ł", + "ëı ¨", + "ëIJ Ħ", + "ëIJ ı", + "ëIJ ´", + "ëIJ ¸", + "ëij ģ", + "ëij ¿", + "ëĴ ¨", + "ëĵ ·", + "ëĶ ®", + "ëĶ ²", + "ëķ §", + "ëĸ Ķ", + "ëĸ ª", + "ëĺ Ń", + "ëļ Ģ", + "ëļ ł", + "ëĽ Ķ", + "ëĽ ©", + "ëľ ħ", + "ëŀ ķ", + "ëŀ °", + "ëŁ IJ", + "ëł ¡", + "ë¡ ŀ", + "ë¡ £", + "ë¡ µ", + "ë£ Ħ", + "ë£ į", + "ë¤ ³", + "ë¦ į", + "ë¦ ı", + "ë¦ ³", + "ë§ Ħ", + "ë§ Ĩ", + "ë§ į", + "ë§ ľ", + "ë§ «", + "ë§ »", + "ë¨ ®", + "ë© Ĥ", + "ë© Ń", + "ëª ´", + "ë¬ ľ", + "ë¬ ł", + "ë¬ «", + "ë¬ ¾", + "ëŃ ¬", + "ë® ĺ", + "ë® ¹", + "ë¯ ķ", + "ë¯ ľ", + "ë° ¨", + "ë° ª", + "ë± Ķ", + "ë² ĺ", + "ë² Ľ", + "ë² ±", + "ë² ´", + "ë´ ½", + "ëµ ¤", + "ëµ ¨", + "ë· Ĺ", + "ë· ĺ", + "ë¸ ĵ", + "ë¸ ľ", + "ë¹ ª", + "ëº ĥ", + "ëº ĺ", + "ëº µ", + "ë» ´", + "ë¼ IJ", + "ë¾ Ķ", + "ìģ Ń", + "ìĤ ł", + "ìĤ ®", + "ìĥ ı", + "ìĥ Ļ", + "ìĦ º", + "ìħ ¢", + "ìĨ Ģ", + "ìĨ ħ", + "ìĨ ¤", + "ìĨ ¦", + "ìĨ ¬", + "ìĩ ±", + "ìĪ µ", + "ìĭ ¨", + "ìĭ ´", + "ìĮ °", + "ìį ľ", + "ìİ Ĺ", + "ìİ ĺ", + "ìİ ¼", + "ìij ī", + "ìij Ŀ", + "ìij »", + "ìĴ Ķ", + "ìĴ ¯", + "ìĵ ©", + "ìķ IJ", + "ìķ ĸ", + "ìĸ ł", + "ìĸ ¾", + "ìĹ ĥ", + "ìĹ Ĺ", + "ìĹ ľ", + "ìĹ ¨", + "ìĺ Ĥ", + "ìĺ Ħ", + "ìĺ ı", + "ìĺ ¾", + "ìĺ ¿", + "ìľ §", + "ìĿ IJ", + "ìĿ ĸ", + "ìĿ ·", + "ìŀ į", + "ìŀ ı", + "ìŀ ¨", + "ìŀ ª", + "ìŀ ³", + "ìł ¡", + "ìł ´", + "ìł ¹", + "ì¡ Ģ", + "ì¡ ª", + "ì¡ µ", + "ì¢ IJ", + "ì¢ ¨", + "ì£ Į", + "ì£ Ļ", + "ì£ ³", + "ì¦ ij", + "ì§ ¥", + "ì§ ´", + "ì§ ¾", + "ì¨ ĵ", + "ì¨ ķ", + "ì© °", + "ì© »", + "ì© ¼", + "ìª Ĺ", + "ì¬ Ķ", + "ì¬ ĺ", + "ì® ®", + "ì¯ ķ", + "ì¯ ĺ", + "ì° İ", + "ì° ¯", + "ì± ĥ", + "ì± µ", + "ì² §", + "ì² ®", + "ì² ¯", + "ì³ ¬", + "ì´ ĭ", + "ì´ ¢", + "ìµ ¥", + "ì¶ £", + "ì¸ Ī", + "ì¸ Ļ", + "ìº ¤", + "ìº Ń", + "ì» ½", + "ì¼ Ļ", + "ì½ ¬", + "ì¾ Ģ", + "ì¿ ħ", + "ì¿ ½", + "íĢ ħ", + "íģ ¦", + "íĤ ħ", + "íĥ ¶", + "íĥ ¹", + "íĦ Ķ", + "íħ £", + "íĨ Ħ", + "íĨ §", + "íĨ ¹", + "íĩ ¼", + "íī ¤", + "íĬ ½", + "íĭ Ĥ", + "íĭ ij", + "íį Ī", + "íį Ļ", + "íį ¿", + "íİ ¶", + "íIJ Ŀ", + "íĴ ľ", + "íĵ Ŀ", + "íĵ ª", + "íĵ ±", + "íĵ ·", + "íĵ ¼", + "íĶ Ļ", + "íĶ ł", + "íķ ļ", + "íķ Ľ", + "íķ ŀ", + "íķ Ł", + "íķ §", + "íķ ¶", + "íĸ Ĭ", + "íĸ ĭ", + "íĸ į", + "íĸ Ķ", + "íĸ ĺ", + "íĸ ¡", + "íĸ ¬", + "íĹ £", + "íĹ ¿", + "íĺ ĸ", + "íĺ Ń", + "íļ °", + "íĽ į", + "íĽ ½", + "íĿ Ł", + "íĿ Ń", + "íĿ ´", + "íŀ ľ", + "ï¤ ī", + "ï¤ Ń", + "ï¤ ²", + "ï¤ µ", + "ï¤ ¼", + "ï¥ Ģ", + "ï¥ ij", + "ï¥ Ĵ", + "ï¥ ķ", + "ï¥ ĺ", + "ï¥ Ļ", + "ï¥ «", + "ï¥ ¬", + "ï¥ °", + "ï ¥¿", + "ï¦ ĭ", + "ï¦ ı", + "ï¦ Ķ", + "ï¦ ĸ", + "ï¦ ĺ", + "ï¦ Ľ", + "ï¦ ł", + "ï¦ ®", + "ï¦ ¯", + "ï¦ º", + "ï¦ »", + "ï¦ ¾", + "ï§ Ĩ", + "ï§ ĸ", + "ï§ Ľ", + "ï§ ŀ", + "ï§ Ł", + "ï§ §", + "ï§ ³", + "ï§ º", + "ï§ ½", + "ï¨ ĥ", + "ï¨ ļ", + "ï¨ ¢", + "ï© Ł", + "ï¬ ¤", + "ï¬ ¬", + "ï¬ ¼", + "ïŃ Ĵ", + "ïŃ ķ", + "ïŃ Ľ", + "ïŃ Ŀ", + "ïŃ ŀ", + "ïŃ Ł", + "ïŃ ¤", + "ïŃ §", + "ïŃ ¨", + "ïŃ ®", + "ïŃ °", + "ïŃ ±", + "ïŃ ·", + "ïŃ ¹", + "ïŃ »", + "ï® Ģ", + "ï® ĥ", + "ï® Ħ", + "ï® ħ", + "ï® į", + "ï® Ĵ", + "ï® ĵ", + "ï® ķ", + "ï® ¦", + "ï® ®", + "ï® °", + "ï¯ ĵ", + "ï¯ ľ", + "ï¯ ©", + "ï¯ ª", + "ï¯ ¬", + "ï¯ Ń", + "ï¯ ®", + "ï¯ ·", + "ï¯ ¹", + "ï¯ »", + "ï¯ ¼", + "ï° ĥ", + "ï° Į", + "ï° IJ", + "ï° ĺ", + "ï° Ļ", + "ï° ľ", + "ï° ŀ", + "ï° ¢", + "ï° ®", + "ï° °", + "ï° ¼", + "ï° ¿", + "ï± Ģ", + "ï± ģ", + "ï± Ī", + "ï± ĭ", + "ï± ı", + "ï± Ń", + "ï² Ģ", + "ï² ĩ", + "ï² Ī", + "ï² ĭ", + "ï² İ", + "ï² Ĵ", + "ï² ľ", + "ï² ł", + "ï² ¬", + "ï² »", + "ï³ ĩ", + "ï³ Ķ", + "ï³ £", + "ï³ «", + "ï´ ĺ", + "ï´ °", + "ï´ ½", + "ï ¶", + "ï¶ °", + "ï¸ ĸ", + "ï¸ ´", + "ï¸ ¹", + "ï¹ į", + "ï¹ Ĺ", + "ï¹ ¢", + "ï¹ ¤", + "ï¹ ©", + "ï¹ ±", + "ï¾ °", + "ï¿ Ĥ", + "ï¿ ®", + "ðIJĮ °", + "ðIJĮ ¹", + "ðIJĮ º", + "ðIJĮ ½", + "ðIJį Ĥ", + "ðIJį ĥ", + "ðIJį Ħ", + "ðIJ İ", + "ðIJİ ¹", + "ðIJ¤ Ĥ", + "ðIJ¤ į", + "ðIJ¤ ı", + "ðIJ¤ ĵ", + "ðIJŃ ī", + "ðIJŃ į", + "ðIJ° ĩ", + "ðIJ° °", + "ðij Ĥ", + "ðijĤ Ħ", + "ðij ĺ", + "ðijĺ ģ", + "ðĴ Ģ", + "ðĴĢ ¸", + "ðĴ ģ", + "ðĴģ º", + "ðĴ Ħ", + "ðĴĦ ·", + "ðĴ Ĭ", + "ðĴĬ ij", + "ðĴ ĭ", + "ðĴĭ Ĺ", + "ð ĴĮ", + "ðĴĮ ¨", + "ðĵĥ ¢", + "ðĵĥ °", + "ðĸ ł", + "ðĸł ļ", + "ðĿĦ ĥ", + "ðĿĦ ħ", + "ðĿĦ ķ", + "ðĿĦ Ļ", + "ðĿĦ ±", + "ðĿĦ ´", + "ðĿĦ ¹", + "ðĿħ İ", + "ðĿħ ª", + "ðĿĨ £", + "ðĿĨ ³", + "ðĿĨ ¹", + "ðĿĩ Ĭ", + "ðĿĩ Ĺ", + "ðĿĩ ļ", + "ðĿĩ ľ", + "ðĿĩ ł", + "ðĿIJ ī", + "ðĿIJ ĸ", + "ðĿIJ ĺ", + "ðĿIJ £", + "ðĿIJ ±", + "ðĿij Ĭ", + "ðĿij Ń", + "ðĿij ¼", + "ðĿij ½", + "ðĿĴ °", + "ðĿĴ ·", + "ðĿĴ ¿", + "ðĿĵ ģ", + "ðĿĵ ĭ", + "ðĿĵ İ", + "ðĿĵ Ĵ", + "ðĿ ĵĺ", + "ðĿĵ ¢", + "ðĿĵ ¦", + "ðĿĵ «", + "ðĿĵ ¿", + "ðĿĶ İ", + "ðĿĶ ±", + "ðĿĶ ´", + "ðĿĶ ·", + "ðĿĶ ¸", + "ðĿĶ ½", + "ðĿķ Ĥ", + "ðĿķ ĥ", + "ðĿķ ĭ", + "ðĿķ ı", + "ðĿķ IJ", + "ðĿķ ¥", + "ðĿķ ´", + "ðĿķ º", + "ðĿĸ IJ", + "ðĿĸ Ľ", + "ðĿĸ Ŀ", + "ðĿĸ ŀ", + "ðĿĹ ©", + "ðĿĹ ³", + "ðĿĹ ½", + "ðĿĺ Ĭ", + "ðĿĺ ĭ", + "ðĿĺ Ķ", + "ðĿĺ ±", + "ðĿĺ ´", + "ðĿĺ ¿", + "ðĿĻ Ĵ", + "ðĿĻ Ŀ", + "ðĿĻ Ł", + "ðĿĻ ¬", + "ðĿĻ Ń", + "ðĿĻ »", + "ðĿĻ ¾", + "ðĿļ Ī", + "ðĿļ ĭ", + "ðĿļ ij", + "ðĿļ Ł", + "ðĿļ ł", + "ðĿļ £", + "ðĿĽ ½", + "ðĿľ Ĥ", + "ðĿľ Ķ", + "ðĿľ Ļ", + "ðŁ Ģ", + "ðŁĢ Ħ", + "ðŁĦ ²", + "ðŁĦ ¶", + "ðŁħ IJ", + "ðŁħ ĸ", + "ðŁħ ļ", + "ðŁħ Ľ", + "ðŁħ ¦", + "ðŁħ ¶", + "ðŁħ »", + "ðŁħ ¼", + "ðŁĨ ĥ", + "ðŁĨ Ĩ", + "ðŁĨ İ", + "ðŁĪ ¯", + "ðŁĪ ²", + "ðŁĪ ¹", + "ðŁĮ ĩ", + "ðŁĮ ĵ", + "ðŁį ĺ", + "ðŁİ ij", + "ðŁİ ¿", + "ðŁı ı", + "ðŁı Ĵ", + "ðŁı ©", + "ðŁı ¯", + "ðŁIJ Ģ", + "ðŁij Ŀ", + "ðŁĴ ¹", + "ðŁĴ º", + "ðŁĵ Ł", + "ðŁĵ ª", + "ðŁĵ ¼", + "ðŁĶ Ģ", + "ðŁĶ Ĥ", + "ðŁĶ ĥ", + "ðŁĶ ĩ", + "ðŁĶ ĵ", + "ðŁĶ ¢", + "ðŁĶ ¤", + "ðŁĶ ©", + "ðŁķ ĸ", + "ðŁķ ļ", + "ðŁķ ľ", + "ðŁķ Ŀ", + "ðŁķ ŀ", + "ðŁķ ł", + "ðŁķ ¢", + "ðŁķ ³", + "ðŁĸ ĩ", + "ðŁĸ ij", + "ðŁĸ ¶", + "ðŁĹ ģ", + "Ñ ¨", + "Ú İ", + "á¡ Į", + "Ḡ°", + "ẠĢ", + "á¼ ®", + "á½ Ŀ", + "âĦ ¬", + "âļ §", + "⼠¤", + "ã³ ¬", + "êĻ ĭ", + "ê¸ ij", + "ëĶ ī", + "ëĹ į", + "ë¡ ij", + "ë¯ ij", + "ë» ħ", + "ë¼ Ŀ", + "ìĦ IJ", + "ìī ¡", + "ìĭ ²", + "ìı ±", + "ìĹ ¤", + "ìĿ ©", + "ìĿ ¿", + "ìŁ Ļ", + "ìł °", + "ì¥ ī", + "íĬ Ń", + "íķ ®", + "ï® ı", + "ðŁħ ±", + "ðŁĨ Ĵ", + "ðŁķ ĭ", + "É ĺ", + "Ê ĵ", + "Õ ĥ", + "à´ ´", + "འħ", + "áĨ º", + "áĪ Ĭ", + "áĪ ¨", + "áĪ ¾", + "áī IJ", + "áĮ ĥ", + "áĮ ½", + "áĶ Ń", + "áł Ĥ", + "áł ¬", + "ᨠ¸", + "á© ĭ", + "ᶠı", + "á¾ Ķ", + "á¿ IJ", + "á¿ ļ", + "âĻ Ļ", + "âļ Ĥ", + "âļ Ĺ", + "â¡ ¢", + "⤠¦", + "ëĸ °", + "ë¤ Ĥ", + "ë§ ł", + "ë± ĭ", + "ë± IJ", + "ìĽ ¢", + "ìľ ¾", + "ì³ ħ", + "ì» ģ", + "íģ »", + "íĥ Ļ", + "íĵ ĸ", + "íĵ Ń", + "íķ ±", + "íĽ ľ", + "ï¤ ħ", + "ï¤ Ĩ", + "ï¦ ĥ", + "ï§ ©", + "ï¨ Ĥ", + "ðIJ¤ Ķ", + "ðIJŃ ĵ", + "ðIJ° ¼", + "ðĿĵ ŀ", + "ðĿĵ °", + "ðĿĻ ľ", + "ðĿļ ģ", + "ðŁħ ¢", + "ðŁı ĩ", + "È ²", + "Ê ¶", + "Ô Ī", + "Ô ij", + "Ý ĵ", + "Ý ¥", + "ठij", + "ॠ±", + "ଠī", + "à° ³", + "à° µ", + "ಠŁ", + "áĢ ı", + "áģ ¼", + "áī ¨", + "áĬ Ĵ", + "áĭ ©", + "áĮ Ħ", + "áĮ Ķ", + "áIJ §", + "á ĴĮ", + "áĶ ħ", + "áĶ Ĭ", + "áł Ħ", + "ᨠģ", + "Ḡĥ", + "Ḡ»", + "âĶ ŀ", + "âĺ µ", + "âļ £", + "â² ¢", + "ãĪ ª", + "ä¶ µ", + "ê² Ļ", + "ê² ´", + "ê³ Ĥ", + "ë¡ ¼", + "ìĨ Ĭ", + "ì¼ ĩ", + "íĭ į", + "íĵ ¬", + "íĵ ®", + "íĵ ¶", + "íĵ »", + "ï¤ ¦", + "ï¥ ł", + "ï¥ ±", + "ïŃ ²", + "ðIJŃ Ĭ", + "ðIJ ±ħ", + "ðĸ ¥", + "ðĸ¥ ¨", + "ðĿij ³", + "ðĿĵ ķ", + "ðĿĵ ¬", + "ðĿĵ ¹", + "ðĿĵ ¾", + "ðĿĶ ĵ", + "ðĿķ į", + "ðĿķ ¡", + "ðĿķ ±", + "ðĿĸ ĸ", + "ðĿĺ ı", + "ðĿĺ IJ", + "ðĿĺ ļ", + "ðĿĻ ®", + "ðĿĻ °", + "ðĿĻ ¸", + "ðĿĻ º", + "ðĿĻ ¼", + "ðĿĻ ½", + "ðĿĻ ¿", + "ðĿļ Ħ", + "ðĿļ ı", + "ðŁħ ħ", + "ðŁħ ĵ", + "Æ Ī", + "àł Į", + "áĻ ³", + "á ļĮ", + "ἠħ", + "ἠIJ", + "ᤠĬ", + "ḠĬ", + "âĶ ½", + "âķ Ĭ", + "⼠ĩ", + "⼠ı", + "âĿ ª", + "âĿ «", + "⣠°", + "ãĦ į", + "ãĦ ĵ", + "ãĦ §", + "ãħ ĸ", + "ãī «", + "ê¦ Ķ", + "ï± Ĭ", + "ຠĤ", + "áħ £", + "ᥠĶ", + "ᥠ¤", + "âĨ ¤", + "âĨ ·", + "âĩ ŀ", + "âĸ ¤", + "âŀ ¶", + "ãĪ ¼", + "ï¨ ·", + "ðĵı §", + "âĶ ²", + "âĢ ´", + "âĴ Ł", + "âĴ ¡", + "â° Ĥ", + "â° į", + "â° İ", + "â° IJ", + "â° ij", + "â° Ł", + "â° ł", + "â° ¡", + "â¼ Ń", + "ãĬ ¥", + "âĴ ł", + "â½ º", + "ãĩ º", + "ãĩ ½", + "ï¨ Ĭ", + "áķ ·", + "âį ¨", + "⺠Ł", + "â½ Ĺ" + ] + } +} \ No newline at end of file diff --git a/vendor/sd-scripts/configs/qwen3_06b/tokenizer_config.json b/vendor/sd-scripts/configs/qwen3_06b/tokenizer_config.json new file mode 100644 index 0000000..6a3829e --- /dev/null +++ b/vendor/sd-scripts/configs/qwen3_06b/tokenizer_config.json @@ -0,0 +1,239 @@ +{ + "add_bos_token": false, + "add_prefix_space": false, + "added_tokens_decoder": { + "151643": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151644": { + "content": "<|im_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151645": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151646": { + "content": "<|object_ref_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151647": { + "content": "<|object_ref_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151648": { + "content": "<|box_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151649": { + "content": "<|box_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151650": { + "content": "<|quad_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151651": { + "content": "<|quad_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151652": { + "content": "<|vision_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151653": { + "content": "<|vision_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151654": { + "content": "<|vision_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151655": { + "content": "<|image_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151656": { + "content": "<|video_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151657": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151658": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151659": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151660": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151661": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151662": { + "content": "<|fim_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151663": { + "content": "<|repo_name|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151664": { + "content": "<|file_sep|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151665": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151666": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151667": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151668": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + } + }, + "additional_special_tokens": [ + "<|im_start|>", + "<|im_end|>", + "<|object_ref_start|>", + "<|object_ref_end|>", + "<|box_start|>", + "<|box_end|>", + "<|quad_start|>", + "<|quad_end|>", + "<|vision_start|>", + "<|vision_end|>", + "<|vision_pad|>", + "<|image_pad|>", + "<|video_pad|>" + ], + "bos_token": null, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in message.content %}\n {%- set content = message.content.split('')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and 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\ No newline at end of file diff --git a/vendor/sd-scripts/library/__init__.py b/vendor/sd-scripts/library/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/vendor/sd-scripts/library/adafactor_fused.py b/vendor/sd-scripts/library/adafactor_fused.py new file mode 100644 index 0000000..b5afa23 --- /dev/null +++ b/vendor/sd-scripts/library/adafactor_fused.py @@ -0,0 +1,138 @@ +import math +import torch +from transformers import Adafactor + +# stochastic rounding for bfloat16 +# The implementation was provided by 2kpr. Thank you very much! + +def copy_stochastic_(target: torch.Tensor, source: torch.Tensor): + """ + copies source into target using stochastic rounding + + Args: + target: the target tensor with dtype=bfloat16 + source: the target tensor with dtype=float32 + """ + # create a random 16 bit integer + result = torch.randint_like(source, dtype=torch.int32, low=0, high=(1 << 16)) + + # add the random number to the lower 16 bit of the mantissa + result.add_(source.view(dtype=torch.int32)) + + # mask off the lower 16 bit of the mantissa + result.bitwise_and_(-65536) # -65536 = FFFF0000 as a signed int32 + + # copy the higher 16 bit into the target tensor + target.copy_(result.view(dtype=torch.float32)) + + del result + + +@torch.no_grad() +def adafactor_step_param(self, p, group): + if p.grad is None: + return + grad = p.grad + if grad.dtype in {torch.float16, torch.bfloat16}: + grad = grad.float() + if grad.is_sparse: + raise RuntimeError("Adafactor does not support sparse gradients.") + + state = self.state[p] + grad_shape = grad.shape + + factored, use_first_moment = Adafactor._get_options(group, grad_shape) + # State Initialization + if len(state) == 0: + state["step"] = 0 + + if use_first_moment: + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(grad) + if factored: + state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) + state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad) + else: + state["exp_avg_sq"] = torch.zeros_like(grad) + + state["RMS"] = 0 + else: + if use_first_moment: + state["exp_avg"] = state["exp_avg"].to(grad) + if factored: + state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) + state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) + else: + state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) + + p_data_fp32 = p + if p.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state["step"] += 1 + state["RMS"] = Adafactor._rms(p_data_fp32) + lr = Adafactor._get_lr(group, state) + + beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) + update = (grad**2) + group["eps"][0] + if factored: + exp_avg_sq_row = state["exp_avg_sq_row"] + exp_avg_sq_col = state["exp_avg_sq_col"] + + exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t)) + exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t)) + + # Approximation of exponential moving average of square of gradient + update = Adafactor._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) + update.mul_(grad) + else: + exp_avg_sq = state["exp_avg_sq"] + + exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t)) + update = exp_avg_sq.rsqrt().mul_(grad) + + update.div_((Adafactor._rms(update) / group["clip_threshold"]).clamp_(min=1.0)) + update.mul_(lr) + + if use_first_moment: + exp_avg = state["exp_avg"] + exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"])) + update = exp_avg + + if group["weight_decay"] != 0: + p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr)) + + p_data_fp32.add_(-update) + + # if p.dtype in {torch.float16, torch.bfloat16}: + # p.copy_(p_data_fp32) + + if p.dtype == torch.bfloat16: + copy_stochastic_(p, p_data_fp32) + elif p.dtype == torch.float16: + p.copy_(p_data_fp32) + + +@torch.no_grad() +def adafactor_step(self, closure=None): + """ + Performs a single optimization step + + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + adafactor_step_param(self, p, group) + + return loss + + +def patch_adafactor_fused(optimizer: Adafactor): + optimizer.step_param = adafactor_step_param.__get__(optimizer) + optimizer.step = adafactor_step.__get__(optimizer) diff --git a/vendor/sd-scripts/library/anima_models.py b/vendor/sd-scripts/library/anima_models.py new file mode 100644 index 0000000..ad34662 --- /dev/null +++ b/vendor/sd-scripts/library/anima_models.py @@ -0,0 +1,1671 @@ +# Anima Model Architecture +# Original code: NVIDIA CORPORATION & AFFILIATES, licensed under Apache-2.0 + +import math +from typing import Any, Optional, Tuple, Union + +import numpy as np +import torch +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +from torch import nn +import torch.nn.functional as F + +from torch.utils.checkpoint import checkpoint as torch_checkpoint + +from library import custom_offloading_utils, attention + + +def to_device(x, device): + if isinstance(x, torch.Tensor): + return x.to(device) + elif isinstance(x, (list, tuple)): + return type(x)(to_device(elem, device) for elem in x) + elif isinstance(x, dict): + return {k: to_device(v, device) for k, v in x.items()} + else: + return x + + +def to_cpu(x): + if isinstance(x, torch.Tensor): + return x.cpu() + elif isinstance(x, (list, tuple)): + return [to_cpu(elem) for elem in x] + elif isinstance(x, dict): + return {k: to_cpu(v) for k, v in x.items()} + else: + return x + + +# Unsloth Offloaded Gradient Checkpointing +# Based on Unsloth Zoo by Daniel Han-Chen & the Unsloth team +try: + from deepspeed.runtime.activation_checkpointing.checkpointing import detach_variable +except ImportError: + + def detach_variable(inputs, device=None): + """Detach tensors from computation graph, optionally moving to a device. + + Reimplementation of deepspeed.runtime.activation_checkpointing.checkpointing.detach_variable + for environments without DeepSpeed installed. + """ + if isinstance(inputs, tuple): + out = [] + for inp in inputs: + if not isinstance(inp, torch.Tensor): + out.append(inp) + continue + requires_grad = inp.requires_grad + if device is not None: + x = inp.to(device=device) + else: + x = inp + x = x.detach() + x.requires_grad = requires_grad + out.append(x) + return tuple(out) + else: + raise RuntimeError( + "Only tuple of tensors is supported. Got Unsupported input type: ", + type(inputs).__name__, + ) + + +class UnslothOffloadedGradientCheckpointer(torch.autograd.Function): + """Saves VRAM by offloading activations to CPU RAM using non-blocking transfers. + + Compared to standard cpu_offload_checkpointing which uses blocking transfers, + this uses non_blocking=True to hide CPU<->GPU transfer latency behind compute. + """ + + @staticmethod + @torch.amp.custom_fwd(device_type="cuda") + def forward(ctx, forward_function, hidden_states, *args): + # Remember the original device for backward pass (multi-GPU support) + ctx.input_device = hidden_states.device + saved_hidden_states = hidden_states.to("cpu", non_blocking=True) + with torch.no_grad(): + output = forward_function(hidden_states, *args) + ctx.save_for_backward(saved_hidden_states) + ctx.forward_function = forward_function + # NOTE: args stored directly on ctx (not via save_for_backward) because + # the training loop holds references to these tensors, preventing GC. + # Using save_for_backward for all args would add complexity for no benefit. + ctx.args = args + return output + + @staticmethod + @torch.amp.custom_bwd(device_type="cuda") + def backward(ctx, *grads): + (hidden_states,) = ctx.saved_tensors + hidden_states = hidden_states.to(ctx.input_device, non_blocking=True).detach() + hidden_states.requires_grad_(True) + args = detach_variable(ctx.args) + inputs = (hidden_states,) + args + with torch.enable_grad(): + outputs = ctx.forward_function(*inputs) + + output_tensors = [] + grad_tensors = [] + for out, grad in zip( + outputs if isinstance(outputs, tuple) else (outputs,), grads if isinstance(grads, tuple) else (grads,) + ): + if isinstance(out, torch.Tensor) and out.requires_grad: + output_tensors.append(out) + grad_tensors.append(grad) + torch.autograd.backward(output_tensors, grad_tensors) + return (None,) + tuple(inp.grad if isinstance(inp, torch.Tensor) else None for inp in inputs) + + +@torch._disable_dynamo +def unsloth_checkpoint(function, *args): + """Wrapper for UnslothOffloadedGradientCheckpointer.""" + return UnslothOffloadedGradientCheckpointer.apply(function, *args) + + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +# Utility functions: RoPE for DiT +def _rotate_half(x: torch.Tensor, interleaved: bool) -> torch.Tensor: + if not interleaved: + x1, x2 = torch.chunk(x, 2, dim=-1) + return torch.cat((-x2, x1), dim=-1) + x1 = x[:, :, :, ::2] + x2 = x[:, :, :, 1::2] + x_new = torch.stack((-x2, x1), dim=-1) + return x_new.view(x_new.shape[0], x_new.shape[1], x_new.shape[2], -1) + + +def _apply_rotary_pos_emb_base( + t: torch.Tensor, + freqs: torch.Tensor, + start_positions: torch.Tensor = None, + tensor_format: str = "sbhd", + interleaved: bool = False, +) -> torch.Tensor: + max_seq_len = freqs.shape[0] + cur_seq_len = t.shape[1] if tensor_format == "bshd" else t.shape[0] + + if start_positions is not None: + max_offset = torch.max(start_positions) + assert max_offset + cur_seq_len <= max_seq_len, f"Rotary Embeddings only supported up to {max_seq_len} sequence length!" + freqs = torch.concatenate([freqs[i : i + cur_seq_len] for i in start_positions], dim=1) + + assert cur_seq_len <= max_seq_len, f"Rotary Embeddings only supported up to {max_seq_len} sequence length!" + freqs = freqs[:cur_seq_len] + + if tensor_format == "bshd": + freqs = freqs.transpose(0, 1) + cos_ = torch.cos(freqs).to(t.dtype) + sin_ = torch.sin(freqs).to(t.dtype) + + rot_dim = freqs.shape[-1] + t, t_pass = t[..., :rot_dim], t[..., rot_dim:] + t = (t * cos_) + (_rotate_half(t, interleaved) * sin_) + return torch.cat((t, t_pass), dim=-1) + + +def apply_rotary_pos_emb( + t: torch.Tensor, + freqs: torch.Tensor, + tensor_format: str = "sbhd", + start_positions: Union[torch.Tensor, None] = None, + interleaved: bool = False, + fused: bool = False, + cu_seqlens: Union[torch.Tensor, None] = None, + cp_size: int = 1, +) -> torch.Tensor: + assert not (cp_size > 1 and start_positions is not None), "start_positions != None with CP SIZE > 1 is not supported!" + + assert tensor_format != "thd" or cu_seqlens is not None, "cu_seqlens must not be None when tensor_format is 'thd'." + + assert fused == False + + if tensor_format == "thd": + cu_seqlens = cu_seqlens // cp_size + seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() + return torch.cat( + [ + _apply_rotary_pos_emb_base( + x.unsqueeze(1), + freqs, + start_positions=(start_positions[idx : idx + 1] if start_positions is not None else None), + interleaved=interleaved, + ) + for idx, x in enumerate(torch.split(t, seqlens)) + ] + ).squeeze(1) + + if tensor_format == "sbhd": + seqlen = t.size(0) + elif tensor_format == "bshd": + seqlen = t.size(1) + else: + raise ValueError(f"Unsupported tensor_format: {tensor_format}.") + return _apply_rotary_pos_emb_base( + t, + freqs, + start_positions, + tensor_format, + interleaved=interleaved, + ) + + +# Basic building blocks +class RMSNorm(torch.nn.Module): + """RMS Normalization for DiT blocks.""" + + def __init__(self, dim: int, eps: float = 1e-5) -> None: + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def reset_parameters(self) -> None: + torch.nn.init.ones_(self.weight) + + def _norm(self, x: torch.Tensor) -> torch.Tensor: + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + with torch.autocast(device_type=x.device.type, dtype=torch.float32): + output = self._norm(x.float()).type_as(x) + return output * self.weight + + +class GPT2FeedForward(nn.Module): + """GELU feedforward network.""" + + def __init__(self, d_model: int, d_ff: int) -> None: + super().__init__() + self.activation = nn.GELU() + self.layer1 = nn.Linear(d_model, d_ff, bias=False) + self.layer2 = nn.Linear(d_ff, d_model, bias=False) + + self._layer_id = None + self._dim = d_model + self._hidden_dim = d_ff + self.init_weights() + + def init_weights(self) -> None: + std = 1.0 / math.sqrt(self._dim) + torch.nn.init.trunc_normal_(self.layer1.weight, std=std, a=-3 * std, b=3 * std) + + std = 1.0 / math.sqrt(self._hidden_dim) + if self._layer_id is not None: + std = std / math.sqrt(2 * (self._layer_id + 1)) + torch.nn.init.trunc_normal_(self.layer2.weight, std=std, a=-3 * std, b=3 * std) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer1(x) + x = self.activation(x) + x = self.layer2(x) + return x + + +# Attention module for DiT +class Attention(nn.Module): + """Multi-head attention supporting both self-attention and cross-attention. + + Uses QK-norm (RMSNorm on q/k) and optional RoPE (only for self-attention). + """ + + def __init__( + self, + query_dim: int, + context_dim: Optional[int] = None, + n_heads: int = 8, + head_dim: int = 64, + dropout: float = 0.0, + qkv_format: str = "bshd", + ) -> None: + super().__init__() + self.is_selfattn = context_dim is None + + context_dim = query_dim if context_dim is None else context_dim + inner_dim = head_dim * n_heads + + self.n_heads = n_heads + self.head_dim = head_dim + self.qkv_format = qkv_format + self.query_dim = query_dim + self.context_dim = context_dim + + self.q_proj = nn.Linear(query_dim, inner_dim, bias=False) + self.q_norm = RMSNorm(self.head_dim, eps=1e-6) + + self.k_proj = nn.Linear(context_dim, inner_dim, bias=False) + self.k_norm = RMSNorm(self.head_dim, eps=1e-6) + + self.v_proj = nn.Linear(context_dim, inner_dim, bias=False) + self.v_norm = nn.Identity() + + self.output_proj = nn.Linear(inner_dim, query_dim, bias=False) + self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity() + + self._query_dim = query_dim + self._context_dim = context_dim + self._inner_dim = inner_dim + self.init_weights() + + def init_weights(self) -> None: + std = 1.0 / math.sqrt(self._query_dim) + torch.nn.init.trunc_normal_(self.q_proj.weight, std=std, a=-3 * std, b=3 * std) + std = 1.0 / math.sqrt(self._context_dim) + torch.nn.init.trunc_normal_(self.k_proj.weight, std=std, a=-3 * std, b=3 * std) + torch.nn.init.trunc_normal_(self.v_proj.weight, std=std, a=-3 * std, b=3 * std) + + std = 1.0 / math.sqrt(self._inner_dim) + torch.nn.init.trunc_normal_(self.output_proj.weight, std=std, a=-3 * std, b=3 * std) + + for layer in self.q_norm, self.k_norm, self.v_norm: + if hasattr(layer, "reset_parameters"): + layer.reset_parameters() + + def compute_qkv( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + rope_emb: Optional[torch.Tensor] = None, + ) -> tuple: + q = self.q_proj(x) + context = x if context is None else context + k = self.k_proj(context) + v = self.v_proj(context) + q, k, v = map( + lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim), + (q, k, v), + ) + + q = self.q_norm(q) + k = self.k_norm(k) + v = self.v_norm(v) + if self.is_selfattn and rope_emb is not None: + q = apply_rotary_pos_emb(q, rope_emb, tensor_format=self.qkv_format, fused=False) + k = apply_rotary_pos_emb(k, rope_emb, tensor_format=self.qkv_format, fused=False) + + return q, k, v + + def forward( + self, + x: torch.Tensor, + attn_params: attention.AttentionParams, + context: Optional[torch.Tensor] = None, + rope_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb) + if q.dtype != v.dtype: + if (not attn_params.supports_fp32 or attn_params.requires_same_dtype) and torch.is_autocast_enabled(): + # FlashAttention requires fp16/bf16, xformers require same dtype; only cast when autocast is active. + target_dtype = v.dtype # v has fp16/bf16 dtype + q = q.to(target_dtype) + k = k.to(target_dtype) + # return self.compute_attention(q, k, v) + qkv = [q, k, v] + del q, k, v + result = attention.attention(qkv, attn_params=attn_params) + return self.output_dropout(self.output_proj(result)) + + +# Positional Embeddings +class VideoPositionEmb(nn.Module): + def __init__(self) -> None: + super().__init__() + + @property + def seq_dim(self) -> int: + return 1 + + def forward(self, x_B_T_H_W_C: torch.Tensor, fps: Optional[torch.Tensor]) -> torch.Tensor: + B_T_H_W_C = x_B_T_H_W_C.shape + embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps) + return embeddings + + def generate_embeddings(self, B_T_H_W_C: torch.Size, fps: Optional[torch.Tensor]) -> Any: + raise NotImplementedError + + +class VideoRopePosition3DEmb(VideoPositionEmb): + """3D Rotary Position Embedding for video (T, H, W) dimensions.""" + + def __init__( + self, + *, + head_dim: int, + len_h: int, + len_w: int, + len_t: int, + base_fps: int = 24, + h_extrapolation_ratio: float = 1.0, + w_extrapolation_ratio: float = 1.0, + t_extrapolation_ratio: float = 1.0, + enable_fps_modulation: bool = True, + **kwargs, + ): + del kwargs + super().__init__() + self.register_buffer("seq", torch.arange(max(len_h, len_w, len_t), dtype=torch.float)) + self.base_fps = base_fps + self.max_h = len_h + self.max_w = len_w + self.max_t = len_t + self.enable_fps_modulation = enable_fps_modulation + dim = head_dim + dim_h = dim // 6 * 2 + dim_w = dim_h + dim_t = dim - 2 * dim_h + assert dim == dim_h + dim_w + dim_t, f"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}" + self.register_buffer( + "dim_spatial_range", + torch.arange(0, dim_h, 2)[: (dim_h // 2)].float() / dim_h, + persistent=True, + ) + self.register_buffer( + "dim_temporal_range", + torch.arange(0, dim_t, 2)[: (dim_t // 2)].float() / dim_t, + persistent=True, + ) + self._dim_h = dim_h + self._dim_t = dim_t + + self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2)) + self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2)) + self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2)) + self.reset_parameters() + + def reset_parameters(self) -> None: + dim_h = self._dim_h + dim_t = self._dim_t + + self.seq = torch.arange(max(self.max_h, self.max_w, self.max_t)).float().to(self.dim_spatial_range.device) + self.dim_spatial_range = torch.arange(0, dim_h, 2)[: (dim_h // 2)].float().to(self.dim_spatial_range.device) / dim_h + self.dim_temporal_range = torch.arange(0, dim_t, 2)[: (dim_t // 2)].float().to(self.dim_spatial_range.device) / dim_t + + def generate_embeddings( + self, + B_T_H_W_C: torch.Size, + fps: Optional[torch.Tensor] = None, + h_ntk_factor: Optional[float] = None, + w_ntk_factor: Optional[float] = None, + t_ntk_factor: Optional[float] = None, + ) -> torch.Tensor: + h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor + w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor + t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor + + h_theta = 10000.0 * h_ntk_factor + w_theta = 10000.0 * w_ntk_factor + t_theta = 10000.0 * t_ntk_factor + + h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range) + w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range) + temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range) + + B, T, H, W, _ = B_T_H_W_C + assert ( + H <= self.max_h and W <= self.max_w + ), f"Input dimensions (H={H}, W={W}) exceed the maximum dimensions (max_h={self.max_h}, max_w={self.max_w})" + half_emb_h = torch.outer(self.seq[:H], h_spatial_freqs) + half_emb_w = torch.outer(self.seq[:W], w_spatial_freqs) + + if self.enable_fps_modulation: + uniform_fps = (fps is None) or (fps.min() == fps.max()) + assert ( + uniform_fps or B == 1 or T == 1 + ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1" + + if fps is None: + assert T == 1, "T should be 1 for image batch." + half_emb_t = torch.outer(self.seq[:T], temporal_freqs) + else: + half_emb_t = torch.outer(self.seq[:T] / fps[:1] * self.base_fps, temporal_freqs) + else: + half_emb_t = torch.outer(self.seq[:T], temporal_freqs) + + em_T_H_W_D = torch.cat( + [ + repeat(half_emb_t, "t d -> t h w d", h=H, w=W), + repeat(half_emb_h, "h d -> t h w d", t=T, w=W), + repeat(half_emb_w, "w d -> t h w d", t=T, h=H), + ] + * 2, + dim=-1, + ) + + return rearrange(em_T_H_W_D, "t h w d -> (t h w) 1 1 d").float() + + @property + def seq_dim(self) -> int: + return 0 + + +class LearnablePosEmbAxis(VideoPositionEmb): + """Learnable axis-decomposed positional embeddings.""" + + def __init__( + self, + *, + interpolation: str, + model_channels: int, + len_h: int, + len_w: int, + len_t: int, + **kwargs, + ): + del kwargs + super().__init__() + self.interpolation = interpolation + assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}" + self.model_channels = model_channels + + self.pos_emb_h = nn.Parameter(torch.zeros(len_h, model_channels)) + self.pos_emb_w = nn.Parameter(torch.zeros(len_w, model_channels)) + self.pos_emb_t = nn.Parameter(torch.zeros(len_t, model_channels)) + + self.reset_parameters() + + def reset_parameters(self) -> None: + std = 1.0 / math.sqrt(self.model_channels) + torch.nn.init.trunc_normal_(self.pos_emb_h, std=std, a=-3 * std, b=3 * std) + torch.nn.init.trunc_normal_(self.pos_emb_w, std=std, a=-3 * std, b=3 * std) + torch.nn.init.trunc_normal_(self.pos_emb_t, std=std, a=-3 * std, b=3 * std) + + def generate_embeddings(self, B_T_H_W_C: torch.Size, fps: Optional[torch.Tensor]) -> torch.Tensor: + B, T, H, W, _ = B_T_H_W_C + if self.interpolation == "crop": + emb_h_H = self.pos_emb_h[:H] + emb_w_W = self.pos_emb_w[:W] + emb_t_T = self.pos_emb_t[:T] + emb = ( + repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W) + + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W) + + repeat(emb_w_W, "w d-> b t h w d", b=B, t=T, h=H) + ) + assert list(emb.shape)[:4] == [B, T, H, W], f"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}" + else: + raise ValueError(f"Unknown interpolation method {self.interpolation}") + + norm = torch.linalg.vector_norm(emb, dim=-1, keepdim=True, dtype=torch.float32) + norm = torch.add(1e-6, norm, alpha=np.sqrt(norm.numel() / emb.numel())) + return emb / norm.to(emb.dtype) + + +# Timestep Embedding +class Timesteps(nn.Module): + """Sinusoidal timestep features.""" + + def __init__(self, num_channels: int): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor: + assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}" + in_dtype = timesteps_B_T.dtype + timesteps = timesteps_B_T.flatten().float() + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - 0.0) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + sin_emb = torch.sin(emb) + cos_emb = torch.cos(emb) + emb = torch.cat([cos_emb, sin_emb], dim=-1) + + return rearrange(emb.to(dtype=in_dtype), "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1]) + + +class TimestepEmbedding(nn.Module): + """Projects timestep features to model dimension, with optional AdaLN-LoRA.""" + + def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False): + super().__init__() + self.in_dim = in_features + self.out_dim = out_features + self.linear_1 = nn.Linear(in_features, out_features, bias=not use_adaln_lora) + self.activation = nn.SiLU() + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.linear_2 = nn.Linear(out_features, 3 * out_features, bias=False) + else: + self.linear_2 = nn.Linear(out_features, out_features, bias=False) + + self.init_weights() + + def init_weights(self) -> None: + std = 1.0 / math.sqrt(self.in_dim) + torch.nn.init.trunc_normal_(self.linear_1.weight, std=std, a=-3 * std, b=3 * std) + std = 1.0 / math.sqrt(self.out_dim) + torch.nn.init.trunc_normal_(self.linear_2.weight, std=std, a=-3 * std, b=3 * std) + + def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + emb = self.linear_1(sample) + emb = self.activation(emb) + emb = self.linear_2(emb) + + if self.use_adaln_lora: + adaln_lora_B_T_3D = emb + emb_B_T_D = sample + else: + adaln_lora_B_T_3D = None + emb_B_T_D = emb + + return emb_B_T_D, adaln_lora_B_T_3D + + +# Commented out Fourier Features (not used in Anima). Kept for reference. +# class FourierFeatures(nn.Module): +# """Fourier feature transform: [B] -> [B, D].""" + +# def __init__(self, num_channels: int, bandwidth: int = 1, normalize: bool = False): +# super().__init__() +# self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True) +# self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True) +# self.gain = np.sqrt(2) if normalize else 1 +# self.bandwidth = bandwidth +# self.num_channels = num_channels +# self.reset_parameters() + +# def reset_parameters(self) -> None: +# generator = torch.Generator() +# generator.manual_seed(0) +# self.freqs = 2 * np.pi * self.bandwidth * torch.randn(self.num_channels, generator=generator).to(self.freqs.device) +# self.phases = 2 * np.pi * torch.rand(self.num_channels, generator=generator).to(self.freqs.device) + +# def forward(self, x: torch.Tensor, gain: float = 1.0) -> torch.Tensor: +# in_dtype = x.dtype +# x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32)) +# x = x.cos().mul(self.gain * gain).to(in_dtype) +# return x + + +# Patch Embedding +class PatchEmbed(nn.Module): + """Patch embedding: (B, C, T, H, W) -> (B, T', H', W', D)""" + + def __init__( + self, + spatial_patch_size: int, + temporal_patch_size: int, + in_channels: int = 3, + out_channels: int = 768, + ): + super().__init__() + self.spatial_patch_size = spatial_patch_size + self.temporal_patch_size = temporal_patch_size + + self.proj = nn.Sequential( + Rearrange( + "b c (t r) (h m) (w n) -> b t h w (c r m n)", + r=temporal_patch_size, + m=spatial_patch_size, + n=spatial_patch_size, + ), + nn.Linear(in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False), + ) + self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size + + self.init_weights() + + def init_weights(self) -> None: + std = 1.0 / math.sqrt(self.dim) + torch.nn.init.trunc_normal_(self.proj[1].weight, std=std, a=-3 * std, b=3 * std) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + assert x.dim() == 5 + _, _, T, H, W = x.shape + assert ( + H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 + ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}" + assert T % self.temporal_patch_size == 0 + x = self.proj(x) + return x + + +# Final Layer +class FinalLayer(nn.Module): + """Final layer with AdaLN modulation + unpatchify.""" + + def __init__( + self, + hidden_size: int, + spatial_patch_size: int, + temporal_patch_size: int, + out_channels: int, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + ): + super().__init__() + self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear( + hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False + ) + self.hidden_size = hidden_size + self.n_adaln_chunks = 2 + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + if use_adaln_lora: + self.adaln_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, adaln_lora_dim, bias=False), + nn.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False), + ) + else: + self.adaln_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False)) + + self.init_weights() + + def init_weights(self) -> None: + std = 1.0 / math.sqrt(self.hidden_size) + torch.nn.init.trunc_normal_(self.linear.weight, std=std, a=-3 * std, b=3 * std) + if self.use_adaln_lora: + torch.nn.init.trunc_normal_(self.adaln_modulation[1].weight, std=std, a=-3 * std, b=3 * std) + torch.nn.init.zeros_(self.adaln_modulation[2].weight) + else: + torch.nn.init.zeros_(self.adaln_modulation[1].weight) + + self.layer_norm.reset_parameters() + + def forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + use_fp32: bool = False, + ): + # Compute AdaLN modulation parameters (in float32 when fp16 to avoid overflow in Linear layers) + with torch.autocast(device_type=x_B_T_H_W_D.device.type, dtype=torch.float32, enabled=use_fp32): + if self.use_adaln_lora: + assert adaln_lora_B_T_3D is not None + shift_B_T_D, scale_B_T_D = ( + self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size] + ).chunk(2, dim=-1) + else: + shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1) + + shift_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d") + scale_B_T_1_1_D = rearrange(scale_B_T_D, "b t d -> b t 1 1 d") + + x_B_T_H_W_D = self.layer_norm(x_B_T_H_W_D) * (1 + scale_B_T_1_1_D) + shift_B_T_1_1_D + x_B_T_H_W_O = self.linear(x_B_T_H_W_D) + return x_B_T_H_W_O + + +# Transformer Block (DiT Block) +class Block(nn.Module): + """Transformer block with self-attention + cross-attention + MLP, each modulated by AdaLN. + + Each sublayer: x = x + gate * sublayer(norm(x) * (1 + scale) + shift) + """ + + def __init__( + self, + x_dim: int, + context_dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + ): + super().__init__() + self.x_dim = x_dim + self.layer_norm_self_attn = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) + self.self_attn = Attention( + x_dim, + None, + num_heads, + x_dim // num_heads, + qkv_format="bshd", + ) + + self.layer_norm_cross_attn = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) + self.cross_attn = Attention( + x_dim, + context_dim, + num_heads, + x_dim // num_heads, + qkv_format="bshd", + ) + + self.layer_norm_mlp = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) + self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio)) + + self.use_adaln_lora = use_adaln_lora + if self.use_adaln_lora: + self.adaln_modulation_self_attn = nn.Sequential( + nn.SiLU(), + nn.Linear(x_dim, adaln_lora_dim, bias=False), + nn.Linear(adaln_lora_dim, 3 * x_dim, bias=False), + ) + self.adaln_modulation_cross_attn = nn.Sequential( + nn.SiLU(), + nn.Linear(x_dim, adaln_lora_dim, bias=False), + nn.Linear(adaln_lora_dim, 3 * x_dim, bias=False), + ) + self.adaln_modulation_mlp = nn.Sequential( + nn.SiLU(), + nn.Linear(x_dim, adaln_lora_dim, bias=False), + nn.Linear(adaln_lora_dim, 3 * x_dim, bias=False), + ) + else: + self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), nn.Linear(x_dim, 3 * x_dim, bias=False)) + self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), nn.Linear(x_dim, 3 * x_dim, bias=False)) + self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), nn.Linear(x_dim, 3 * x_dim, bias=False)) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + self.unsloth_offload_checkpointing = False + + def enable_gradient_checkpointing(self, cpu_offload: bool = False, unsloth_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload if not unsloth_offload else False + self.unsloth_offload_checkpointing = unsloth_offload + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + self.unsloth_offload_checkpointing = False + + def reset_parameters(self) -> None: + self.layer_norm_self_attn.reset_parameters() + self.layer_norm_cross_attn.reset_parameters() + self.layer_norm_mlp.reset_parameters() + + if self.use_adaln_lora: + std = 1.0 / math.sqrt(self.x_dim) + torch.nn.init.trunc_normal_(self.adaln_modulation_self_attn[1].weight, std=std, a=-3 * std, b=3 * std) + torch.nn.init.trunc_normal_(self.adaln_modulation_cross_attn[1].weight, std=std, a=-3 * std, b=3 * std) + torch.nn.init.trunc_normal_(self.adaln_modulation_mlp[1].weight, std=std, a=-3 * std, b=3 * std) + torch.nn.init.zeros_(self.adaln_modulation_self_attn[2].weight) + torch.nn.init.zeros_(self.adaln_modulation_cross_attn[2].weight) + torch.nn.init.zeros_(self.adaln_modulation_mlp[2].weight) + else: + torch.nn.init.zeros_(self.adaln_modulation_self_attn[1].weight) + torch.nn.init.zeros_(self.adaln_modulation_cross_attn[1].weight) + torch.nn.init.zeros_(self.adaln_modulation_mlp[1].weight) + + def init_weights(self) -> None: + self.reset_parameters() + self.self_attn.init_weights() + self.cross_attn.init_weights() + self.mlp.init_weights() + + def _forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + crossattn_emb: torch.Tensor, + attn_params: attention.AttentionParams, + use_fp32: bool = False, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + extra_per_block_pos_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if use_fp32: + # Cast to float32 for better numerical stability in residual connections. Each module will cast back to float16 by enclosing autocast context. + x_B_T_H_W_D = x_B_T_H_W_D.float() + + if extra_per_block_pos_emb is not None: + x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb + + # Compute AdaLN modulation parameters (in float32 when fp16 to avoid overflow in Linear layers) + with torch.autocast(device_type=x_B_T_H_W_D.device.type, dtype=torch.float32, enabled=use_fp32): + if self.use_adaln_lora: + shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = ( + self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = ( + self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = (self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D).chunk( + 3, dim=-1 + ) + else: + shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn( + emb_B_T_D + ).chunk(3, dim=-1) + shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn( + emb_B_T_D + ).chunk(3, dim=-1) + shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1) + + # Reshape for broadcasting: (B, T, D) -> (B, T, 1, 1, D) + shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d") + scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d") + gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d") + + shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d") + scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d") + gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d") + + shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d") + scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d") + gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d") + + B, T, H, W, D = x_B_T_H_W_D.shape + + def _adaln_fn(_x, _norm_layer, _scale, _shift): + return _norm_layer(_x) * (1 + _scale) + _shift + + # 1. Self-attention + normalized_x = _adaln_fn(x_B_T_H_W_D, self.layer_norm_self_attn, scale_self_attn_B_T_1_1_D, shift_self_attn_B_T_1_1_D) + result = rearrange( + self.self_attn( + rearrange(normalized_x, "b t h w d -> b (t h w) d"), + attn_params, + None, + rope_emb=rope_emb_L_1_1_D, + ), + "b (t h w) d -> b t h w d", + t=T, + h=H, + w=W, + ) + x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result + + # 2. Cross-attention + normalized_x = _adaln_fn(x_B_T_H_W_D, self.layer_norm_cross_attn, scale_cross_attn_B_T_1_1_D, shift_cross_attn_B_T_1_1_D) + result = rearrange( + self.cross_attn( + rearrange(normalized_x, "b t h w d -> b (t h w) d"), + attn_params, + crossattn_emb, + rope_emb=rope_emb_L_1_1_D, + ), + "b (t h w) d -> b t h w d", + t=T, + h=H, + w=W, + ) + x_B_T_H_W_D = result * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D + + # 3. MLP + normalized_x = _adaln_fn(x_B_T_H_W_D, self.layer_norm_mlp, scale_mlp_B_T_1_1_D, shift_mlp_B_T_1_1_D) + result = self.mlp(normalized_x) + x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result + + return x_B_T_H_W_D + + def forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + crossattn_emb: torch.Tensor, + attn_params: attention.AttentionParams, + use_fp32: bool = False, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + extra_per_block_pos_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if self.training and self.gradient_checkpointing: + if self.unsloth_offload_checkpointing: + # Unsloth: async non-blocking CPU RAM offload (fastest offload method) + return unsloth_checkpoint( + self._forward, + x_B_T_H_W_D, + emb_B_T_D, + crossattn_emb, + attn_params, + use_fp32, + rope_emb_L_1_1_D, + adaln_lora_B_T_3D, + extra_per_block_pos_emb, + ) + elif self.cpu_offload_checkpointing: + # Standard cpu offload: blocking transfers + def create_custom_forward(func): + def custom_forward(*inputs): + # Determine original device from first tensor input + device = next(t.device for t in inputs if isinstance(t, torch.Tensor)) + device_inputs = to_device(inputs, device) + outputs = func(*device_inputs) + return to_cpu(outputs) + + return custom_forward + + return torch_checkpoint( + create_custom_forward(self._forward), + x_B_T_H_W_D, + emb_B_T_D, + crossattn_emb, + attn_params, + use_fp32, + rope_emb_L_1_1_D, + adaln_lora_B_T_3D, + extra_per_block_pos_emb, + use_reentrant=False, + ) + else: + # Standard gradient checkpointing (no offload) + return torch_checkpoint( + self._forward, + x_B_T_H_W_D, + emb_B_T_D, + crossattn_emb, + attn_params, + use_fp32, + rope_emb_L_1_1_D, + adaln_lora_B_T_3D, + extra_per_block_pos_emb, + use_reentrant=False, + ) + else: + return self._forward( + x_B_T_H_W_D, + emb_B_T_D, + crossattn_emb, + attn_params, + use_fp32, + rope_emb_L_1_1_D, + adaln_lora_B_T_3D, + extra_per_block_pos_emb, + ) + + +# Main DiT Model: MiniTrainDIT (renamed to Anima) +class Anima(nn.Module): + """Cosmos-Predict2 DiT model for image/video generation. + + 28 transformer blocks with AdaLN-LoRA modulation, 3D RoPE, and optional LLM Adapter. + """ + + LATENT_CHANNELS = 16 + + def __init__( + self, + max_img_h: int, + max_img_w: int, + max_frames: int, + in_channels: int, + out_channels: int, + patch_spatial: int, + patch_temporal: int, + concat_padding_mask: bool = True, + model_channels: int = 768, + num_blocks: int = 10, + num_heads: int = 16, + mlp_ratio: float = 4.0, + crossattn_emb_channels: int = 1024, + pos_emb_cls: str = "sincos", + pos_emb_learnable: bool = False, + pos_emb_interpolation: str = "crop", + min_fps: int = 1, + max_fps: int = 30, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + rope_h_extrapolation_ratio: float = 1.0, + rope_w_extrapolation_ratio: float = 1.0, + rope_t_extrapolation_ratio: float = 1.0, + extra_per_block_abs_pos_emb: bool = False, + extra_h_extrapolation_ratio: float = 1.0, + extra_w_extrapolation_ratio: float = 1.0, + extra_t_extrapolation_ratio: float = 1.0, + rope_enable_fps_modulation: bool = True, + use_llm_adapter: bool = False, + attn_mode: str = "torch", + split_attn: bool = False, + ) -> None: + super().__init__() + self.max_img_h = max_img_h + self.max_img_w = max_img_w + self.max_frames = max_frames + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_spatial = patch_spatial + self.patch_temporal = patch_temporal + self.num_heads = num_heads + self.num_blocks = num_blocks + self.model_channels = model_channels + self.concat_padding_mask = concat_padding_mask + self.pos_emb_cls = pos_emb_cls + self.pos_emb_learnable = pos_emb_learnable + self.pos_emb_interpolation = pos_emb_interpolation + self.min_fps = min_fps + self.max_fps = max_fps + self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio + self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio + self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio + self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb + self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio + self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio + self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio + self.rope_enable_fps_modulation = rope_enable_fps_modulation + self.use_llm_adapter = use_llm_adapter + + self.attn_mode = attn_mode + self.split_attn = split_attn + + # Block swap support + self.blocks_to_swap = None + self.offloader: Optional[custom_offloading_utils.ModelOffloader] = None + + self.build_patch_embed() + self.build_pos_embed() + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + self.t_embedder = nn.Sequential( + Timesteps(model_channels), + TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora), + ) + + if self.use_llm_adapter: + self.llm_adapter = LLMAdapter( + source_dim=1024, + target_dim=1024, + model_dim=1024, + num_layers=6, + self_attn=True, + ) + + self.blocks = nn.ModuleList( + [ + Block( + x_dim=model_channels, + context_dim=crossattn_emb_channels, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + ) + for _ in range(num_blocks) + ] + ) + + self.final_layer = FinalLayer( + hidden_size=self.model_channels, + spatial_patch_size=self.patch_spatial, + temporal_patch_size=self.patch_temporal, + out_channels=self.out_channels, + use_adaln_lora=self.use_adaln_lora, + adaln_lora_dim=self.adaln_lora_dim, + ) + + self.t_embedding_norm = RMSNorm(model_channels, eps=1e-6) + self.init_weights() + + def init_weights(self) -> None: + self.x_embedder.init_weights() + self.pos_embedder.reset_parameters() + if self.extra_per_block_abs_pos_emb: + self.extra_pos_embedder.reset_parameters() + self.t_embedder[1].init_weights() + for block in self.blocks: + block.init_weights() + self.final_layer.init_weights() + self.t_embedding_norm.reset_parameters() + + def enable_gradient_checkpointing(self, cpu_offload: bool = False, unsloth_offload: bool = False): + for block in self.blocks: + block.enable_gradient_checkpointing(cpu_offload=cpu_offload, unsloth_offload=unsloth_offload) + + def disable_gradient_checkpointing(self): + for block in self.blocks: + block.disable_gradient_checkpointing() + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def build_patch_embed(self) -> None: + in_channels = self.in_channels + 1 if self.concat_padding_mask else self.in_channels + self.x_embedder = PatchEmbed( + spatial_patch_size=self.patch_spatial, + temporal_patch_size=self.patch_temporal, + in_channels=in_channels, + out_channels=self.model_channels, + ) + + def build_pos_embed(self) -> None: + if self.pos_emb_cls == "rope3d": + cls_type = VideoRopePosition3DEmb + else: + raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") + + kwargs = dict( + model_channels=self.model_channels, + len_h=self.max_img_h // self.patch_spatial, + len_w=self.max_img_w // self.patch_spatial, + len_t=self.max_frames // self.patch_temporal, + max_fps=self.max_fps, + min_fps=self.min_fps, + is_learnable=self.pos_emb_learnable, + interpolation=self.pos_emb_interpolation, + head_dim=self.model_channels // self.num_heads, + h_extrapolation_ratio=self.rope_h_extrapolation_ratio, + w_extrapolation_ratio=self.rope_w_extrapolation_ratio, + t_extrapolation_ratio=self.rope_t_extrapolation_ratio, + enable_fps_modulation=self.rope_enable_fps_modulation, + ) + self.pos_embedder = cls_type(**kwargs) + + if self.extra_per_block_abs_pos_emb: + kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio + kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio + kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio + self.extra_pos_embedder = LearnablePosEmbAxis(**kwargs) + + def prepare_embedded_sequence( + self, + x_B_C_T_H_W: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + from torchvision import transforms + + if self.concat_padding_mask: + padding_mask = transforms.functional.resize( + padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST + ) + x_B_C_T_H_W = torch.cat([x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1) + x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) + + if self.extra_per_block_abs_pos_emb: + extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps) + else: + extra_pos_emb = None + + if "rope" in self.pos_emb_cls.lower(): + return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps), extra_pos_emb + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D) + + return x_B_T_H_W_D, None, extra_pos_emb + + def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor: + x_B_C_Tt_Hp_Wp = rearrange( + x_B_T_H_W_M, + "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)", + p1=self.patch_spatial, + p2=self.patch_spatial, + t=self.patch_temporal, + ) + return x_B_C_Tt_Hp_Wp + + def enable_block_swap(self, num_blocks: int, device: torch.device): + self.blocks_to_swap = num_blocks + + assert ( + self.blocks_to_swap <= self.num_blocks - 2 + ), f"Cannot swap more than {self.num_blocks - 2} blocks. Requested: {self.blocks_to_swap} blocks." + + self.offloader = custom_offloading_utils.ModelOffloader(self.blocks, self.blocks_to_swap, device) + logger.info(f"Anima: Block swap enabled. Swapping {num_blocks} blocks, total blocks: {self.num_blocks}, device: {device}.") + + def move_to_device_except_swap_blocks(self, device: torch.device): + # Move all modules to device except blocks (which are managed by offloader) + if self.blocks_to_swap: + save_blocks = self.blocks + self.blocks = None # Use None to skip .to() on blocks (consistent with flux_models.py) + + self.to(device) + + if self.blocks_to_swap: + self.blocks = save_blocks + + def switch_block_swap_for_inference(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader.set_forward_only(True) + self.prepare_block_swap_before_forward() + print(f"Anima: Block swap set to forward only.") + + def switch_block_swap_for_training(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader.set_forward_only(False) + self.prepare_block_swap_before_forward() + print(f"Anima: Block swap set to forward and backward.") + + def prepare_block_swap_before_forward(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader.prepare_block_devices_before_forward(self.blocks) + + def forward_mini_train_dit( + self, + x_B_C_T_H_W: torch.Tensor, + timesteps_B_T: torch.Tensor, + crossattn_emb: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + source_attention_mask: Optional[torch.Tensor] = None, + t5_input_ids: Optional[torch.Tensor] = None, + t5_attn_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Args: + x_B_C_T_H_W: (B, C, T, H, W) noisy latents + timesteps_B_T: (B,) or (B, T) timesteps + crossattn_emb: (B, N, D) cross-attention embeddings (or raw Qwen3 prompt_embeds if t5_input_ids provided) + fps: Optional frames per second + padding_mask: Optional padding mask + source_attention_mask: Optional attention mask for Qwen3 embeddings (used with LLM adapter) + t5_input_ids: Optional T5 token IDs (triggers LLM adapter when provided) + t5_attn_mask: Optional T5 attention mask + """ + # Run LLM adapter inside forward for correct DDP gradient synchronization + if t5_input_ids is not None and self.use_llm_adapter and hasattr(self, "llm_adapter"): + crossattn_emb = self.llm_adapter( + source_hidden_states=crossattn_emb, + target_input_ids=t5_input_ids, + target_attention_mask=t5_attn_mask, + source_attention_mask=source_attention_mask, + ) + if t5_attn_mask is not None: + crossattn_emb[~t5_attn_mask.bool()] = 0 + + x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb = self.prepare_embedded_sequence( + x_B_C_T_H_W, + fps=fps, + padding_mask=padding_mask, + ) + + if timesteps_B_T.ndim == 1: + timesteps_B_T = timesteps_B_T.unsqueeze(1) + t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder(timesteps_B_T) + t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D) + + block_kwargs = { + "rope_emb_L_1_1_D": rope_emb_L_1_1_D, + "adaln_lora_B_T_3D": adaln_lora_B_T_3D, + "extra_per_block_pos_emb": extra_pos_emb, + } + + attn_params = attention.AttentionParams.create_attention_params(self.attn_mode, self.split_attn) + + # Determine whether to use float32 for block computations based on input dtype (use float32 for better stability when input is float16) + use_fp32 = x_B_T_H_W_D.dtype == torch.float16 + + for block_idx, block in enumerate(self.blocks): + if self.blocks_to_swap: + self.offloader.wait_for_block(block_idx) + + x_B_T_H_W_D = block(x_B_T_H_W_D, t_embedding_B_T_D, crossattn_emb, attn_params, use_fp32, **block_kwargs) + + if self.blocks_to_swap: + self.offloader.submit_move_blocks(self.blocks, block_idx) + + x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D, use_fp32=use_fp32) + x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O) + return x_B_C_Tt_Hp_Wp + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: Optional[torch.Tensor] = None, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + target_input_ids: Optional[torch.Tensor] = None, + target_attention_mask: Optional[torch.Tensor] = None, + source_attention_mask: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + context = self._preprocess_text_embeds(context, target_input_ids, target_attention_mask, source_attention_mask) + return self.forward_mini_train_dit(x, timesteps, context, fps=fps, padding_mask=padding_mask, **kwargs) + + def _preprocess_text_embeds( + self, source_hidden_states, target_input_ids, target_attention_mask=None, source_attention_mask=None + ): + if target_input_ids is not None: + context = self.llm_adapter( + source_hidden_states, + target_input_ids, + target_attention_mask=target_attention_mask, + source_attention_mask=source_attention_mask, + ) + context[~target_attention_mask.bool()] = 0 # zero out padding tokens + return context + else: + return source_hidden_states + + +# LLM Adapter: Bridges Qwen3 embeddings to T5-compatible space +class LLMAdapterRMSNorm(nn.Module): + """RMSNorm specifically for the LLM Adapter (T5-style, no mean subtraction).""" + + def __init__(self, hidden_size, eps=1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +def _adapter_rotate_half(x): + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def _adapter_apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1): + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + x_embed = (x * cos) + (_adapter_rotate_half(x) * sin) + return x_embed + + +class AdapterRotaryEmbedding(nn.Module): + """Rotary embedding for LLM Adapter.""" + + def __init__(self, head_dim): + super().__init__() + self.rope_theta = 10000 + inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.int64).to(dtype=torch.float) / head_dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + @torch.no_grad() + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class LLMAdapterAttention(nn.Module): + """Attention module for LLM Adapter with QK-norm and separate RoPE for query/key.""" + + def __init__(self, query_dim, context_dim, n_heads, head_dim): + super().__init__() + + inner_dim = head_dim * n_heads + self.n_heads = n_heads + self.head_dim = head_dim + self.query_dim = query_dim + self.context_dim = context_dim + + self.q_proj = nn.Linear(query_dim, inner_dim, bias=False) + self.q_norm = LLMAdapterRMSNorm(self.head_dim) + + self.k_proj = nn.Linear(context_dim, inner_dim, bias=False) + self.k_norm = LLMAdapterRMSNorm(self.head_dim) + + self.v_proj = nn.Linear(context_dim, inner_dim, bias=False) + + self.o_proj = nn.Linear(inner_dim, query_dim, bias=False) + + def forward(self, x, mask=None, context=None, position_embeddings=None, position_embeddings_context=None): + context = x if context is None else context + input_shape = x.shape[:-1] + q_shape = (*input_shape, self.n_heads, self.head_dim) + context_shape = context.shape[:-1] + kv_shape = (*context_shape, self.n_heads, self.head_dim) + + query_states = self.q_norm(self.q_proj(x).view(q_shape)).transpose(1, 2) + key_states = self.k_norm(self.k_proj(context).view(kv_shape)).transpose(1, 2) + value_states = self.v_proj(context).view(kv_shape).transpose(1, 2) + + if position_embeddings is not None: + assert position_embeddings_context is not None + cos, sin = position_embeddings + query_states = _adapter_apply_rotary_pos_emb(query_states, cos, sin) + cos, sin = position_embeddings_context + key_states = _adapter_apply_rotary_pos_emb(key_states, cos, sin) + + attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=mask) + + attn_output = attn_output.transpose(1, 2).reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output + + +class LLMAdapterTransformerBlock(nn.Module): + """Transformer block for LLM Adapter: optional self-attn + cross-attn + MLP.""" + + def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0, self_attn=False, layer_norm=False): + super().__init__() + self.has_self_attn = self_attn + + if self.has_self_attn: + self.norm_self_attn = nn.LayerNorm(model_dim) if layer_norm else LLMAdapterRMSNorm(model_dim) + self.self_attn = LLMAdapterAttention( + query_dim=model_dim, + context_dim=model_dim, + n_heads=num_heads, + head_dim=model_dim // num_heads, + ) + + self.norm_cross_attn = nn.LayerNorm(model_dim) if layer_norm else LLMAdapterRMSNorm(model_dim) + self.cross_attn = LLMAdapterAttention( + query_dim=model_dim, + context_dim=source_dim, + n_heads=num_heads, + head_dim=model_dim // num_heads, + ) + + self.norm_mlp = nn.LayerNorm(model_dim) if layer_norm else LLMAdapterRMSNorm(model_dim) + self.mlp = nn.Sequential( + nn.Linear(model_dim, int(model_dim * mlp_ratio)), nn.GELU(), nn.Linear(int(model_dim * mlp_ratio), model_dim) + ) + + def forward( + self, + x, + context, + target_attention_mask=None, + source_attention_mask=None, + position_embeddings=None, + position_embeddings_context=None, + ): + if self.has_self_attn: + # Self-attention: target_attention_mask is not expected to be all zeros + normed = self.norm_self_attn(x) + attn_out = self.self_attn( + normed, + mask=target_attention_mask, + position_embeddings=position_embeddings, + position_embeddings_context=position_embeddings, + ) + x = x + attn_out + + normed = self.norm_cross_attn(x) + attn_out = self.cross_attn( + normed, + mask=source_attention_mask, + context=context, + position_embeddings=position_embeddings, + position_embeddings_context=position_embeddings_context, + ) + x = x + attn_out + + x = x + self.mlp(self.norm_mlp(x)) + return x + + def init_weights(self): + torch.nn.init.zeros_(self.mlp[2].weight) + + +class LLMAdapter(nn.Module): + """Bridge module: Qwen3 embeddings (source) → T5-compatible space (target). + + Uses T5 token IDs as target input, embeds them, and cross-attends to Qwen3 hidden states. + """ + + def __init__( + self, source_dim, target_dim, model_dim, num_layers=6, num_heads=16, embed=None, self_attn=False, layer_norm=False + ): + super().__init__() + if embed is not None: + self.embed = nn.Embedding.from_pretrained(embed.weight) + else: + self.embed = nn.Embedding(32128, target_dim) + if model_dim != target_dim: + self.in_proj = nn.Linear(target_dim, model_dim) + else: + self.in_proj = nn.Identity() + self.rotary_emb = AdapterRotaryEmbedding(model_dim // num_heads) + self.blocks = nn.ModuleList( + [ + LLMAdapterTransformerBlock(source_dim, model_dim, num_heads=num_heads, self_attn=self_attn, layer_norm=layer_norm) + for _ in range(num_layers) + ] + ) + self.out_proj = nn.Linear(model_dim, target_dim) + self.norm = LLMAdapterRMSNorm(target_dim) + + def forward(self, source_hidden_states, target_input_ids, target_attention_mask=None, source_attention_mask=None): + if target_attention_mask is not None: + target_attention_mask = target_attention_mask.to(torch.bool) + if target_attention_mask.ndim == 2: + target_attention_mask = target_attention_mask.unsqueeze(1).unsqueeze(1) + + if source_attention_mask is not None: + source_attention_mask = source_attention_mask.to(torch.bool) + if source_attention_mask.ndim == 2: + source_attention_mask = source_attention_mask.unsqueeze(1).unsqueeze(1) + + x = self.in_proj(self.embed(target_input_ids)) + context = source_hidden_states + position_ids = torch.arange(x.shape[1], device=x.device).unsqueeze(0) + position_ids_context = torch.arange(context.shape[1], device=x.device).unsqueeze(0) + position_embeddings = self.rotary_emb(x, position_ids) + position_embeddings_context = self.rotary_emb(x, position_ids_context) + for block in self.blocks: + x = block( + x, + context, + target_attention_mask=target_attention_mask, + source_attention_mask=source_attention_mask, + position_embeddings=position_embeddings, + position_embeddings_context=position_embeddings_context, + ) + return self.norm(self.out_proj(x)) + + +# Not used currently, but kept for reference + +# def get_dit_config(state_dict, key_prefix=""): +# """Derive DiT configuration from state_dict weight shapes.""" +# dit_config = {} +# dit_config["max_img_h"] = 512 +# dit_config["max_img_w"] = 512 +# dit_config["max_frames"] = 128 +# concat_padding_mask = True +# dit_config["in_channels"] = (state_dict["{}x_embedder.proj.1.weight".format(key_prefix)].shape[1] // 4) - int( +# concat_padding_mask +# ) +# dit_config["out_channels"] = 16 +# dit_config["patch_spatial"] = 2 +# dit_config["patch_temporal"] = 1 +# dit_config["model_channels"] = state_dict["{}x_embedder.proj.1.weight".format(key_prefix)].shape[0] +# dit_config["concat_padding_mask"] = concat_padding_mask +# dit_config["crossattn_emb_channels"] = 1024 +# dit_config["pos_emb_cls"] = "rope3d" +# dit_config["pos_emb_learnable"] = True +# dit_config["pos_emb_interpolation"] = "crop" +# dit_config["min_fps"] = 1 +# dit_config["max_fps"] = 30 + +# dit_config["use_adaln_lora"] = True +# dit_config["adaln_lora_dim"] = 256 +# if dit_config["model_channels"] == 2048: +# dit_config["num_blocks"] = 28 +# dit_config["num_heads"] = 16 +# elif dit_config["model_channels"] == 5120: +# dit_config["num_blocks"] = 36 +# dit_config["num_heads"] = 40 +# elif dit_config["model_channels"] == 1280: +# dit_config["num_blocks"] = 20 +# dit_config["num_heads"] = 20 + +# if dit_config["in_channels"] == 16: +# dit_config["extra_per_block_abs_pos_emb"] = False +# dit_config["rope_h_extrapolation_ratio"] = 4.0 +# dit_config["rope_w_extrapolation_ratio"] = 4.0 +# dit_config["rope_t_extrapolation_ratio"] = 1.0 +# elif dit_config["in_channels"] == 17: +# dit_config["extra_per_block_abs_pos_emb"] = False +# dit_config["rope_h_extrapolation_ratio"] = 3.0 +# dit_config["rope_w_extrapolation_ratio"] = 3.0 +# dit_config["rope_t_extrapolation_ratio"] = 1.0 + +# dit_config["extra_h_extrapolation_ratio"] = 1.0 +# dit_config["extra_w_extrapolation_ratio"] = 1.0 +# dit_config["extra_t_extrapolation_ratio"] = 1.0 +# dit_config["rope_enable_fps_modulation"] = False + +# return dit_config diff --git a/vendor/sd-scripts/library/anima_train_utils.py b/vendor/sd-scripts/library/anima_train_utils.py new file mode 100644 index 0000000..e3b37fd --- /dev/null +++ b/vendor/sd-scripts/library/anima_train_utils.py @@ -0,0 +1,628 @@ +# Anima Training Utilities + +import argparse +import gc +import math +import os +import time +from typing import Optional + +import numpy as np +import torch +from accelerate import Accelerator +from tqdm import tqdm +from PIL import Image + +from library.device_utils import init_ipex, clean_memory_on_device, synchronize_device +from library import anima_models, anima_utils, train_util, qwen_image_autoencoder_kl + +init_ipex() + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +# Anima-specific training arguments + + +def add_anima_training_arguments(parser: argparse.ArgumentParser): + """Add Anima-specific training arguments to the parser.""" + parser.add_argument( + "--qwen3", + type=str, + default=None, + help="Path to Qwen3-0.6B model (safetensors file or directory)", + ) + parser.add_argument( + "--llm_adapter_path", + type=str, + default=None, + help="Path to separate LLM adapter weights. If None, adapter is loaded from DiT file if present", + ) + parser.add_argument( + "--llm_adapter_lr", + type=float, + default=None, + help="Learning rate for LLM adapter. None=same as base LR, 0=freeze adapter", + ) + parser.add_argument( + "--self_attn_lr", + type=float, + default=None, + help="Learning rate for self-attention layers. None=same as base LR, 0=freeze", + ) + parser.add_argument( + "--cross_attn_lr", + type=float, + default=None, + help="Learning rate for cross-attention layers. None=same as base LR, 0=freeze", + ) + parser.add_argument( + "--mlp_lr", + type=float, + default=None, + help="Learning rate for MLP layers. None=same as base LR, 0=freeze", + ) + parser.add_argument( + "--mod_lr", + type=float, + default=None, + help="Learning rate for AdaLN modulation layers. None=same as base LR, 0=freeze. Note: mod layers are not included in LoRA by default.", + ) + parser.add_argument( + "--t5_tokenizer_path", + type=str, + default=None, + help="Path to T5 tokenizer directory. If None, uses default configs/t5_old/", + ) + parser.add_argument( + "--qwen3_max_token_length", + type=int, + default=512, + help="Maximum token length for Qwen3 tokenizer (default: 512)", + ) + parser.add_argument( + "--t5_max_token_length", + type=int, + default=512, + help="Maximum token length for T5 tokenizer (default: 512)", + ) + parser.add_argument( + "--discrete_flow_shift", + type=float, + default=1.0, + help="Timestep distribution shift for rectified flow training (default: 1.0)", + ) + parser.add_argument( + "--timestep_sampling", + type=str, + default="sigmoid", + choices=["sigma", "uniform", "sigmoid", "shift", "flux_shift"], + help="Timestep sampling method (default: sigmoid (logit normal))", + ) + parser.add_argument( + "--sigmoid_scale", + type=float, + default=1.0, + help="Scale factor for sigmoid (logit_normal) timestep sampling (default: 1.0)", + ) + parser.add_argument( + "--attn_mode", + choices=["torch", "xformers", "flash", "sageattn", "sdpa"], # "sdpa" is for backward compatibility + default=None, + help="Attention implementation to use. Default is None (torch). xformers requires --split_attn. sageattn does not support training (inference only). This option overrides --xformers or --sdpa." + " / 使用するAttentionの実装。デフォルトはNone（torch）です。xformersは--split_attnの指定が必要です。sageattnはトレーニングをサポートしていません（推論のみ）。このオプションは--xformersまたは--sdpaを上書きします。", + ) + parser.add_argument( + "--split_attn", + action="store_true", + help="split attention computation to reduce memory usage / メモリ使用量を減らすためにattention時にバッチを分割する", + ) + parser.add_argument( + "--vae_chunk_size", + type=int, + default=None, + help="Spatial chunk size for VAE encoding/decoding to reduce memory usage. Must be even number. If not specified, chunking is disabled (official behavior)." + + " / メモリ使用量を減らすためのVAEエンコード/デコードの空間チャンクサイズ。偶数である必要があります。未指定の場合、チャンク処理は無効になります（公式の動作）。", + ) + parser.add_argument( + "--vae_disable_cache", + action="store_true", + help="Disable internal VAE caching mechanism to reduce memory usage. Encoding / decoding will also be faster, but this differs from official behavior." + + " / VAEのメモリ使用量を減らすために内部のキャッシュ機構を無効にします。エンコード/デコードも速くなりますが、公式の動作とは異なります。", + ) + + +# Loss weighting + + +def compute_loss_weighting_for_anima(weighting_scheme: str, sigmas: torch.Tensor) -> torch.Tensor: + """Compute loss weighting for Anima training. + + Same schemes as SD3 but can add Anima-specific ones if needed in future. + """ + if weighting_scheme == "sigma_sqrt": + weighting = (sigmas**-2.0).float() + elif weighting_scheme == "cosmap": + bot = 1 - 2 * sigmas + 2 * sigmas**2 + weighting = 2 / (math.pi * bot) + elif weighting_scheme == "none" or weighting_scheme is None: + weighting = torch.ones_like(sigmas) + else: + weighting = torch.ones_like(sigmas) + return weighting + + +# Parameter groups (6 groups with separate LRs) +def get_anima_param_groups( + dit, + base_lr: float, + self_attn_lr: Optional[float] = None, + cross_attn_lr: Optional[float] = None, + mlp_lr: Optional[float] = None, + mod_lr: Optional[float] = None, + llm_adapter_lr: Optional[float] = None, +): + """Create parameter groups for Anima training with separate learning rates. + + Args: + dit: Anima model + base_lr: Base learning rate + self_attn_lr: LR for self-attention layers (None = base_lr, 0 = freeze) + cross_attn_lr: LR for cross-attention layers + mlp_lr: LR for MLP layers + mod_lr: LR for AdaLN modulation layers + llm_adapter_lr: LR for LLM adapter + + Returns: + List of parameter group dicts for optimizer + """ + if self_attn_lr is None: + self_attn_lr = base_lr + if cross_attn_lr is None: + cross_attn_lr = base_lr + if mlp_lr is None: + mlp_lr = base_lr + if mod_lr is None: + mod_lr = base_lr + if llm_adapter_lr is None: + llm_adapter_lr = base_lr + + base_params = [] + self_attn_params = [] + cross_attn_params = [] + mlp_params = [] + mod_params = [] + llm_adapter_params = [] + + for name, p in dit.named_parameters(): + # Store original name for debugging + p.original_name = name + + if "llm_adapter" in name: + llm_adapter_params.append(p) + elif ".self_attn" in name: + self_attn_params.append(p) + elif ".cross_attn" in name: + cross_attn_params.append(p) + elif ".mlp" in name: + mlp_params.append(p) + elif ".adaln_modulation" in name: + mod_params.append(p) + else: + base_params.append(p) + + logger.info(f"Parameter groups:") + logger.info(f" base_params: {len(base_params)} (lr={base_lr})") + logger.info(f" self_attn_params: {len(self_attn_params)} (lr={self_attn_lr})") + logger.info(f" cross_attn_params: {len(cross_attn_params)} (lr={cross_attn_lr})") + logger.info(f" mlp_params: {len(mlp_params)} (lr={mlp_lr})") + logger.info(f" mod_params: {len(mod_params)} (lr={mod_lr})") + logger.info(f" llm_adapter_params: {len(llm_adapter_params)} (lr={llm_adapter_lr})") + + param_groups = [] + for lr, params, name in [ + (base_lr, base_params, "base"), + (self_attn_lr, self_attn_params, "self_attn"), + (cross_attn_lr, cross_attn_params, "cross_attn"), + (mlp_lr, mlp_params, "mlp"), + (mod_lr, mod_params, "mod"), + (llm_adapter_lr, llm_adapter_params, "llm_adapter"), + ]: + if lr == 0: + for p in params: + p.requires_grad_(False) + logger.info(f" Frozen {name} params ({len(params)} parameters)") + elif len(params) > 0: + param_groups.append({"params": params, "lr": lr}) + + total_trainable = sum(p.numel() for group in param_groups for p in group["params"] if p.requires_grad) + logger.info(f"Total trainable parameters: {total_trainable:,}") + + return param_groups + + +# Save functions +def save_anima_model_on_train_end( + args: argparse.Namespace, + save_dtype: torch.dtype, + epoch: int, + global_step: int, + dit: anima_models.Anima, +): + """Save Anima model at the end of training.""" + + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec_dataclass( + None, args, False, False, False, is_stable_diffusion_ckpt=True, anima="preview" + ).to_metadata_dict() + dit_sd = dit.state_dict() + # Save with 'net.' prefix for ComfyUI compatibility + anima_utils.save_anima_model(ckpt_file, dit_sd, sai_metadata, save_dtype) + + train_util.save_sd_model_on_train_end_common(args, True, True, epoch, global_step, sd_saver, None) + + +def save_anima_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator: Accelerator, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + dit: anima_models.Anima, +): + """Save Anima model at epoch end or specific steps.""" + + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec_dataclass( + None, args, False, False, False, is_stable_diffusion_ckpt=True, anima="preview" + ).to_metadata_dict() + dit_sd = dit.state_dict() + anima_utils.save_anima_model(ckpt_file, dit_sd, sai_metadata, save_dtype) + + train_util.save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + True, + True, + epoch, + num_train_epochs, + global_step, + sd_saver, + None, + ) + + +# Sampling (Euler discrete for rectified flow) +def do_sample( + height: int, + width: int, + seed: Optional[int], + dit: anima_models.Anima, + crossattn_emb: torch.Tensor, + steps: int, + dtype: torch.dtype, + device: torch.device, + guidance_scale: float = 1.0, + flow_shift: float = 3.0, + neg_crossattn_emb: Optional[torch.Tensor] = None, +) -> torch.Tensor: + """Generate a sample using Euler discrete sampling for rectified flow. + + Args: + height, width: Output image dimensions + seed: Random seed (None for random) + dit: Anima model + crossattn_emb: Cross-attention embeddings (B, N, D) + steps: Number of sampling steps + dtype: Compute dtype + device: Compute device + guidance_scale: CFG scale (1.0 = no guidance) + flow_shift: Flow shift parameter for rectified flow + neg_crossattn_emb: Negative cross-attention embeddings for CFG + + Returns: + Denoised latents + """ + # Latent shape: (1, 16, 1, H/8, W/8) for single image + latent_h = height // 8 + latent_w = width // 8 + latent = torch.zeros(1, 16, 1, latent_h, latent_w, device=device, dtype=dtype) + + # Generate noise + if seed is not None: + generator = torch.manual_seed(seed) + else: + generator = None + noise = torch.randn(latent.size(), dtype=torch.float32, generator=generator, device="cpu").to(dtype).to(device) + + # Timestep schedule: linear from 1.0 to 0.0 + sigmas = torch.linspace(1.0, 0.0, steps + 1, device=device, dtype=dtype) + flow_shift = float(flow_shift) + if flow_shift != 1.0: + sigmas = (sigmas * flow_shift) / (1 + (flow_shift - 1) * sigmas) + + # Start from pure noise + x = noise.clone() + + # Padding mask (zeros = no padding) — resized in prepare_embedded_sequence to match latent dims + padding_mask = torch.zeros(1, 1, latent_h, latent_w, dtype=dtype, device=device) + + use_cfg = guidance_scale > 1.0 and neg_crossattn_emb is not None + + for i in tqdm(range(steps), desc="Sampling"): + sigma = sigmas[i] + t = sigma.unsqueeze(0) # (1,) + + if use_cfg: + # CFG: two separate passes to reduce memory usage + pos_out = dit(x, t, crossattn_emb, padding_mask=padding_mask) + pos_out = pos_out.float() + neg_out = dit(x, t, neg_crossattn_emb, padding_mask=padding_mask) + neg_out = neg_out.float() + + model_output = neg_out + guidance_scale * (pos_out - neg_out) + else: + model_output = dit(x, t, crossattn_emb, padding_mask=padding_mask) + model_output = model_output.float() + + # Euler step: x_{t-1} = x_t - (sigma_t - sigma_{t-1}) * model_output + dt = sigmas[i + 1] - sigma + x = x + model_output * dt + x = x.to(dtype) + + return x + + +def sample_images( + accelerator: Accelerator, + args: argparse.Namespace, + epoch, + steps, + dit: anima_models.Anima, + vae, + text_encoder, + tokenize_strategy, + text_encoding_strategy, + sample_prompts_te_outputs=None, + prompt_replacement=None, + on_prompt_start=None, + on_prompt_end=None, +): + """Generate sample images during training. + + This is a simplified sampler for Anima - it generates images using the current model state. + + on_prompt_start / on_prompt_end: + Optional callbacks invoked around each prompt's `_sample_image_inference` call. Useful + for injecting per-prompt state into wrapper modules (e.g. ControlNet-LLLite cond image). + Signature: ``on_prompt_start(prompt_dict, accelerator)`` / ``on_prompt_end(prompt_dict)``. + """ + if steps == 0: + if not args.sample_at_first: + return + else: + if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: + return + if args.sample_every_n_epochs is not None: + if epoch is None or epoch % args.sample_every_n_epochs != 0: + return + else: + if steps % args.sample_every_n_steps != 0 or epoch is not None: + return + + logger.info(f"Generating sample images at step {steps}") + if not os.path.isfile(args.sample_prompts) and sample_prompts_te_outputs is None: + logger.error(f"No prompt file: {args.sample_prompts}") + return + + # Unwrap models + dit = accelerator.unwrap_model(dit) + if text_encoder is not None: + text_encoder = accelerator.unwrap_model(text_encoder) + + dit.switch_block_swap_for_inference() + + prompts = train_util.load_prompts(args.sample_prompts) + save_dir = os.path.join(args.output_dir, "sample") + os.makedirs(save_dir, exist_ok=True) + + # Save RNG state + rng_state = torch.get_rng_state() + cuda_rng_state = None + try: + cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None + except Exception: + pass + + with torch.no_grad(), accelerator.autocast(): + for prompt_dict in prompts: + dit.prepare_block_swap_before_forward() + if on_prompt_start is not None: + on_prompt_start(prompt_dict, accelerator) + try: + _sample_image_inference( + accelerator, + args, + dit, + text_encoder, + vae, + tokenize_strategy, + text_encoding_strategy, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, + ) + finally: + if on_prompt_end is not None: + on_prompt_end(prompt_dict) + + # Restore RNG state + torch.set_rng_state(rng_state) + if cuda_rng_state is not None: + torch.cuda.set_rng_state(cuda_rng_state) + + dit.switch_block_swap_for_training() + clean_memory_on_device(accelerator.device) + + +def _sample_image_inference( + accelerator, + args, + dit, + text_encoder, + vae: qwen_image_autoencoder_kl.AutoencoderKLQwenImage, + tokenize_strategy, + text_encoding_strategy, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, +): + """Generate a single sample image.""" + prompt = prompt_dict.get("prompt", "") + negative_prompt = prompt_dict.get("negative_prompt", "") + sample_steps = prompt_dict.get("sample_steps", 30) + width = prompt_dict.get("width", 512) + height = prompt_dict.get("height", 512) + scale = prompt_dict.get("scale", 7.5) + seed = prompt_dict.get("seed") + flow_shift = prompt_dict.get("flow_shift", 3.0) + + if prompt_replacement is not None: + prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1]) + if negative_prompt: + negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1]) + + if seed is not None: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # seed all CUDA devices for multi-GPU + + height = max(64, height - height % 16) + width = max(64, width - width % 16) + + logger.info( + f" prompt: {prompt}, size: {width}x{height}, steps: {sample_steps}, scale: {scale}, flow_shift: {flow_shift}, seed: {seed}" + ) + + # Encode prompt + def encode_prompt(prpt): + if sample_prompts_te_outputs and prpt in sample_prompts_te_outputs: + return sample_prompts_te_outputs[prpt] + if text_encoder is not None: + tokens = tokenize_strategy.tokenize(prpt) + encoded = text_encoding_strategy.encode_tokens(tokenize_strategy, [text_encoder], tokens) + return encoded + return None + + encoded = encode_prompt(prompt) + if encoded is None: + logger.warning("Cannot encode prompt, skipping sample") + return + + prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask = encoded + + # Convert to tensors if numpy + if isinstance(prompt_embeds, np.ndarray): + prompt_embeds = torch.from_numpy(prompt_embeds).unsqueeze(0) + attn_mask = torch.from_numpy(attn_mask).unsqueeze(0) + t5_input_ids = torch.from_numpy(t5_input_ids).unsqueeze(0) + t5_attn_mask = torch.from_numpy(t5_attn_mask).unsqueeze(0) + + prompt_embeds = prompt_embeds.to(accelerator.device, dtype=dit.dtype) + attn_mask = attn_mask.to(accelerator.device) + t5_input_ids = t5_input_ids.to(accelerator.device, dtype=torch.long) + t5_attn_mask = t5_attn_mask.to(accelerator.device) + + # Process through LLM adapter if available + if dit.use_llm_adapter: + crossattn_emb = dit.llm_adapter( + source_hidden_states=prompt_embeds, + target_input_ids=t5_input_ids, + target_attention_mask=t5_attn_mask, + source_attention_mask=attn_mask, + ) + crossattn_emb[~t5_attn_mask.bool()] = 0 + else: + crossattn_emb = prompt_embeds + + # Encode negative prompt for CFG + neg_crossattn_emb = None + if scale > 1.0 and negative_prompt is not None: + neg_encoded = encode_prompt(negative_prompt) + if neg_encoded is not None: + neg_pe, neg_am, neg_t5_ids, neg_t5_am = neg_encoded + if isinstance(neg_pe, np.ndarray): + neg_pe = torch.from_numpy(neg_pe).unsqueeze(0) + neg_am = torch.from_numpy(neg_am).unsqueeze(0) + neg_t5_ids = torch.from_numpy(neg_t5_ids).unsqueeze(0) + neg_t5_am = torch.from_numpy(neg_t5_am).unsqueeze(0) + + neg_pe = neg_pe.to(accelerator.device, dtype=dit.dtype) + neg_am = neg_am.to(accelerator.device) + neg_t5_ids = neg_t5_ids.to(accelerator.device, dtype=torch.long) + neg_t5_am = neg_t5_am.to(accelerator.device) + + if dit.use_llm_adapter: + neg_crossattn_emb = dit.llm_adapter( + source_hidden_states=neg_pe, + target_input_ids=neg_t5_ids, + target_attention_mask=neg_t5_am, + source_attention_mask=neg_am, + ) + neg_crossattn_emb[~neg_t5_am.bool()] = 0 + else: + neg_crossattn_emb = neg_pe + + # Generate sample + clean_memory_on_device(accelerator.device) + latents = do_sample( + height, width, seed, dit, crossattn_emb, sample_steps, dit.dtype, accelerator.device, scale, flow_shift, neg_crossattn_emb + ) + + # Decode latents + gc.collect() + synchronize_device(accelerator.device) + clean_memory_on_device(accelerator.device) + org_vae_device = vae.device + vae.to(accelerator.device) + decoded = vae.decode_to_pixels(latents) + vae.to(org_vae_device) + clean_memory_on_device(accelerator.device) + + # Convert to image + image = decoded.float() + image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)[0] + # Remove temporal dim if present + if image.ndim == 4: + image = image[:, 0, :, :] + decoded_np = 255.0 * np.moveaxis(image.cpu().numpy(), 0, 2) + decoded_np = decoded_np.astype(np.uint8) + + image = Image.fromarray(decoded_np) + + ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime()) + num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}" + seed_suffix = "" if seed is None else f"_{seed}" + i = prompt_dict.get("enum", 0) + img_filename = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png" + image.save(os.path.join(save_dir, img_filename)) + + # Log to wandb if enabled + if "wandb" in [tracker.name for tracker in accelerator.trackers]: + wandb_tracker = accelerator.get_tracker("wandb") + import wandb + + wandb_tracker.log({f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False) diff --git a/vendor/sd-scripts/library/anima_utils.py b/vendor/sd-scripts/library/anima_utils.py new file mode 100644 index 0000000..73742be --- /dev/null +++ b/vendor/sd-scripts/library/anima_utils.py @@ -0,0 +1,317 @@ +# Anima model loading/saving utilities + +import os +from typing import Dict, List, Optional, Union +import torch +from safetensors.torch import load_file, save_file +from accelerate.utils import set_module_tensor_to_device # kept for potential future use +from accelerate import init_empty_weights + +from library.fp8_optimization_utils import apply_fp8_monkey_patch +from library.lora_utils import load_safetensors_with_lora_and_fp8 +from library import anima_models +from library.safetensors_utils import WeightTransformHooks +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +# Original Anima high-precision keys. Kept for reference, but not used currently. +# # Keys that should stay in high precision (float32/bfloat16, not quantized) +# KEEP_IN_HIGH_PRECISION = ["x_embedder", "t_embedder", "t_embedding_norm", "final_layer"] + + +FP8_OPTIMIZATION_TARGET_KEYS = ["blocks", ""] +# ".embed." excludes Embedding in LLMAdapter +FP8_OPTIMIZATION_EXCLUDE_KEYS = ["_embedder", "norm", "adaln", "final_layer", ".embed."] +ANIMA_CHECKPOINT_KEY_PREFIXES = ("net.", "model.diffusion_model.", "diffusion_model.") + + +def normalize_anima_checkpoint_key(key: str) -> str: + for prefix in ANIMA_CHECKPOINT_KEY_PREFIXES: + if key.startswith(prefix): + return key[len(prefix) :] + return key + + +def load_anima_model( + device: Union[str, torch.device], + dit_path: str, + attn_mode: str, + split_attn: bool, + loading_device: Union[str, torch.device], + dit_weight_dtype: Optional[torch.dtype], + fp8_scaled: bool = False, + lora_weights_list: Optional[List[Dict[str, torch.Tensor]]] = None, + lora_multipliers: Optional[list[float]] = None, +) -> anima_models.Anima: + """ + Load Anima model from the specified checkpoint. + + Args: + device (Union[str, torch.device]): Device for optimization or merging + dit_path (str): Path to the DiT model checkpoint. + attn_mode (str): Attention mode to use, e.g., "torch", "flash", etc. + split_attn (bool): Whether to use split attention. + loading_device (Union[str, torch.device]): Device to load the model weights on. + dit_weight_dtype (Optional[torch.dtype]): Data type of the DiT weights. + If None, it will be loaded as is (same as the state_dict) or scaled for fp8. if not None, model weights will be casted to this dtype. + fp8_scaled (bool): Whether to use fp8 scaling for the model weights. + lora_weights_list (Optional[List[Dict[str, torch.Tensor]]]): LoRA weights to apply, if any. + lora_multipliers (Optional[List[float]]): LoRA multipliers for the weights, if any. + """ + # dit_weight_dtype is None for fp8_scaled + assert ( + not fp8_scaled and dit_weight_dtype is not None + ) or dit_weight_dtype is None, "dit_weight_dtype should be None when fp8_scaled is True" + + device = torch.device(device) + loading_device = torch.device(loading_device) + + # We currently support fixed DiT config for Anima models + dit_config = { + "max_img_h": 512, + "max_img_w": 512, + "max_frames": 128, + "in_channels": 16, + "out_channels": 16, + "patch_spatial": 2, + "patch_temporal": 1, + "model_channels": 2048, + "concat_padding_mask": True, + "crossattn_emb_channels": 1024, + "pos_emb_cls": "rope3d", + "pos_emb_learnable": True, + "pos_emb_interpolation": "crop", + "min_fps": 1, + "max_fps": 30, + "use_adaln_lora": True, + "adaln_lora_dim": 256, + "num_blocks": 28, + "num_heads": 16, + "extra_per_block_abs_pos_emb": False, + "rope_h_extrapolation_ratio": 4.0, + "rope_w_extrapolation_ratio": 4.0, + "rope_t_extrapolation_ratio": 1.0, + "extra_h_extrapolation_ratio": 1.0, + "extra_w_extrapolation_ratio": 1.0, + "extra_t_extrapolation_ratio": 1.0, + "rope_enable_fps_modulation": False, + "use_llm_adapter": True, + "attn_mode": attn_mode, + "split_attn": split_attn, + } + with init_empty_weights(): + model = anima_models.Anima(**dit_config) + if dit_weight_dtype is not None: + model.to(dit_weight_dtype) + + # load model weights with dynamic fp8 optimization and LoRA merging if needed + logger.info(f"Loading DiT model from {dit_path}, device={loading_device}") + rename_hooks = WeightTransformHooks(rename_hook=normalize_anima_checkpoint_key) + sd = load_safetensors_with_lora_and_fp8( + model_files=dit_path, + lora_weights_list=lora_weights_list, + lora_multipliers=lora_multipliers, + fp8_optimization=fp8_scaled, + calc_device=device, + move_to_device=(loading_device == device), + dit_weight_dtype=dit_weight_dtype, + target_keys=FP8_OPTIMIZATION_TARGET_KEYS, + exclude_keys=FP8_OPTIMIZATION_EXCLUDE_KEYS, + weight_transform_hooks=rename_hooks, + ) + + if fp8_scaled: + apply_fp8_monkey_patch(model, sd, use_scaled_mm=False) + + if loading_device.type != "cpu": + # make sure all the model weights are on the loading_device + logger.info(f"Moving weights to {loading_device}") + for key in sd.keys(): + sd[key] = sd[key].to(loading_device) + + missing, unexpected = model.load_state_dict(sd, strict=False, assign=True) + if missing: + # Filter out expected missing buffers (initialized in __init__, not saved in checkpoint) + unexpected_missing = [ + k + for k in missing + if not any(buf_name in k for buf_name in ("seq", "dim_spatial_range", "dim_temporal_range", "inv_freq")) + ] + if unexpected_missing: + # Raise error to avoid silent failures + raise RuntimeError( + f"Missing keys in checkpoint: {unexpected_missing[:10]}{'...' if len(unexpected_missing) > 10 else ''}" + ) + missing = {} # all missing keys were expected + if unexpected: + # Raise error to avoid silent failures + raise RuntimeError(f"Unexpected keys in checkpoint: {unexpected[:5]}{'...' if len(unexpected) > 5 else ''}") + logger.info(f"Loaded DiT model from {dit_path}, unexpected missing keys: {len(missing)}, unexpected keys: {len(unexpected)}") + + return model + + +def load_qwen3_tokenizer(qwen3_path: str): + """Load Qwen3 tokenizer only (without the text encoder model). + + Args: + qwen3_path: Path to either a directory with model files or a safetensors file. + If a directory, loads tokenizer from it directly. + If a file, uses configs/qwen3_06b/ for tokenizer config. + Returns: + tokenizer + """ + from transformers import AutoTokenizer + + if os.path.isdir(qwen3_path): + tokenizer = AutoTokenizer.from_pretrained(qwen3_path, local_files_only=True) + else: + config_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "configs", "qwen3_06b") + if not os.path.exists(config_dir): + raise FileNotFoundError( + f"Qwen3 config directory not found at {config_dir}. " + "Expected configs/qwen3_06b/ with config.json, tokenizer.json, etc. " + "You can download these from the Qwen3-0.6B HuggingFace repository." + ) + tokenizer = AutoTokenizer.from_pretrained(config_dir, local_files_only=True) + + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + return tokenizer + + +def load_qwen3_text_encoder( + qwen3_path: str, + dtype: torch.dtype = torch.bfloat16, + device: str = "cpu", + lora_weights: Optional[List[Dict[str, torch.Tensor]]] = None, + lora_multipliers: Optional[List[float]] = None, +): + """Load Qwen3-0.6B text encoder. + + Args: + qwen3_path: Path to either a directory with model files or a safetensors file + dtype: Model dtype + device: Device to load to + + Returns: + (text_encoder_model, tokenizer) + """ + import transformers + from transformers import AutoTokenizer + + logger.info(f"Loading Qwen3 text encoder from {qwen3_path}") + + if os.path.isdir(qwen3_path): + # Directory with full model + tokenizer = AutoTokenizer.from_pretrained(qwen3_path, local_files_only=True) + model = transformers.AutoModelForCausalLM.from_pretrained(qwen3_path, torch_dtype=dtype, local_files_only=True).model + else: + # Single safetensors file - use configs/qwen3_06b/ for config + config_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "configs", "qwen3_06b") + if not os.path.exists(config_dir): + raise FileNotFoundError( + f"Qwen3 config directory not found at {config_dir}. " + "Expected configs/qwen3_06b/ with config.json, tokenizer.json, etc. " + "You can download these from the Qwen3-0.6B HuggingFace repository." + ) + + tokenizer = AutoTokenizer.from_pretrained(config_dir, local_files_only=True) + qwen3_config = transformers.Qwen3Config.from_pretrained(config_dir, local_files_only=True) + model = transformers.Qwen3ForCausalLM(qwen3_config).model + + # Load weights + if qwen3_path.endswith(".safetensors"): + if lora_weights is None: + state_dict = load_file(qwen3_path, device="cpu") + else: + state_dict = load_safetensors_with_lora_and_fp8( + model_files=qwen3_path, + lora_weights_list=lora_weights, + lora_multipliers=lora_multipliers, + fp8_optimization=False, + calc_device=device, + move_to_device=True, + dit_weight_dtype=None, + ) + else: + assert lora_weights is None, "LoRA weights merging is only supported for safetensors checkpoints" + state_dict = torch.load(qwen3_path, map_location="cpu", weights_only=True) + + # Remove 'model.' prefix if present + new_sd = {} + for k, v in state_dict.items(): + if k.startswith("model."): + new_sd[k[len("model.") :]] = v + else: + new_sd[k] = v + + info = model.load_state_dict(new_sd, strict=False) + logger.info(f"Loaded Qwen3 state dict: {info}") + + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + model.config.use_cache = False + model = model.requires_grad_(False).to(device, dtype=dtype) + + logger.info(f"Loaded Qwen3 text encoder. Parameters: {sum(p.numel() for p in model.parameters()):,}") + return model, tokenizer + + +def load_t5_tokenizer(t5_tokenizer_path: Optional[str] = None): + """Load T5 tokenizer for LLM Adapter target tokens. + + Args: + t5_tokenizer_path: Optional path to T5 tokenizer directory. If None, uses default configs. + """ + from transformers import T5TokenizerFast + + if t5_tokenizer_path is not None: + return T5TokenizerFast.from_pretrained(t5_tokenizer_path, local_files_only=True) + + # Use bundled config + config_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "configs", "t5_old") + if os.path.exists(config_dir): + return T5TokenizerFast( + vocab_file=os.path.join(config_dir, "spiece.model"), + tokenizer_file=os.path.join(config_dir, "tokenizer.json"), + ) + + raise FileNotFoundError( + f"T5 tokenizer config directory not found at {config_dir}. " + "Expected configs/t5_old/ with spiece.model and tokenizer.json. " + "You can download these from the google/t5-v1_1-xxl HuggingFace repository." + ) + + +def save_anima_model( + save_path: str, dit_state_dict: Dict[str, torch.Tensor], metadata: Dict[str, any], dtype: Optional[torch.dtype] = None +): + """Save Anima DiT model with 'net.' prefix for ComfyUI compatibility. + + Args: + save_path: Output path (.safetensors) + dit_state_dict: State dict from dit.state_dict() + metadata: Metadata dict to include in the safetensors file + dtype: Optional dtype to cast to before saving + """ + prefixed_sd = {} + for k, v in dit_state_dict.items(): + if dtype is not None: + # v = v.to(dtype) + v = v.detach().clone().to("cpu").to(dtype) # Reduce GPU memory usage during save + prefixed_sd["net." + k] = v.contiguous() + + if metadata is None: + metadata = {} + metadata["format"] = "pt" # For compatibility with the official .safetensors file + + save_file(prefixed_sd, save_path, metadata=metadata) # safetensors.save_file cosumes a lot of memory, but Anima is small enough + logger.info(f"Saved Anima model to {save_path}") diff --git a/vendor/sd-scripts/library/attention.py b/vendor/sd-scripts/library/attention.py new file mode 100644 index 0000000..4f6a542 --- /dev/null +++ b/vendor/sd-scripts/library/attention.py @@ -0,0 +1,268 @@ +# Unified attention function supporting various implementations + +from dataclasses import dataclass +import torch +from typing import Optional, Union + +try: + import flash_attn + from flash_attn.flash_attn_interface import _flash_attn_forward + from flash_attn.flash_attn_interface import flash_attn_varlen_func + from flash_attn.flash_attn_interface import flash_attn_func +except ImportError: + flash_attn = None + flash_attn_varlen_func = None + _flash_attn_forward = None + flash_attn_func = None + +try: + from sageattention import sageattn_varlen, sageattn +except ImportError: + sageattn_varlen = None + sageattn = None + +try: + import xformers.ops as xops +except ImportError: + xops = None + + +@dataclass +class AttentionParams: + attn_mode: Optional[str] = None + split_attn: bool = False + img_len: Optional[int] = None + attention_mask: Optional[torch.Tensor] = None + seqlens: Optional[torch.Tensor] = None + cu_seqlens: Optional[torch.Tensor] = None + max_seqlen: Optional[int] = None + + @property + def supports_fp32(self) -> bool: + return self.attn_mode not in ["flash"] + + @property + def requires_same_dtype(self) -> bool: + return self.attn_mode in ["xformers"] + + @staticmethod + def create_attention_params(attn_mode: Optional[str], split_attn: bool) -> "AttentionParams": + return AttentionParams(attn_mode, split_attn) + + @staticmethod + def create_attention_params_from_mask( + attn_mode: Optional[str], split_attn: bool, img_len: Optional[int], attention_mask: Optional[torch.Tensor] + ) -> "AttentionParams": + if attention_mask is None: + # No attention mask provided: assume all tokens are valid + return AttentionParams(attn_mode, split_attn, None, None, None, None, None) + else: + # Note: attention_mask is only for text tokens, not including image tokens + seqlens = attention_mask.sum(dim=1).to(torch.int32) + img_len # [B] + max_seqlen = attention_mask.shape[1] + img_len + + if split_attn: + # cu_seqlens is not needed for split attention + return AttentionParams(attn_mode, split_attn, img_len, attention_mask, seqlens, None, max_seqlen) + + # Convert attention mask to cumulative sequence lengths for flash attention + batch_size = attention_mask.shape[0] + cu_seqlens = torch.zeros([2 * batch_size + 1], dtype=torch.int32, device=attention_mask.device) + for i in range(batch_size): + cu_seqlens[2 * i + 1] = i * max_seqlen + seqlens[i] # end of valid tokens for query + cu_seqlens[2 * i + 2] = (i + 1) * max_seqlen # end of all tokens for query + + # Expand attention mask to include image tokens + attention_mask = torch.nn.functional.pad(attention_mask, (img_len, 0), value=1) # [B, img_len + L] + + if attn_mode == "xformers": + seqlens_list = seqlens.cpu().tolist() + attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens( + seqlens_list, seqlens_list, device=attention_mask.device + ) + elif attn_mode == "torch": + attention_mask = attention_mask[:, None, None, :].to(torch.bool) # [B, 1, 1, img_len + L] + + return AttentionParams(attn_mode, split_attn, img_len, attention_mask, seqlens, cu_seqlens, max_seqlen) + + +def attention( + qkv_or_q: Union[torch.Tensor, list], + k: Optional[torch.Tensor] = None, + v: Optional[torch.Tensor] = None, + attn_params: Optional[AttentionParams] = None, + drop_rate: float = 0.0, +) -> torch.Tensor: + """ + Compute scaled dot-product attention with variable sequence lengths. + + Handles batches with different sequence lengths by splitting and + processing each sequence individually. + + Args: + qkv_or_q: Query tensor [B, L, H, D]. or list of such tensors. + k: Key tensor [B, L, H, D]. + v: Value tensor [B, L, H, D]. + attn_params: Attention parameters including mask and sequence lengths. + drop_rate: Attention dropout rate. + + Returns: + Attention output tensor [B, L, H*D]. + """ + if isinstance(qkv_or_q, list): + q, k, v = qkv_or_q + q: torch.Tensor = q + qkv_or_q.clear() + del qkv_or_q + else: + q: torch.Tensor = qkv_or_q + del qkv_or_q + assert k is not None and v is not None, "k and v must be provided if qkv_or_q is a tensor" + if attn_params is None: + attn_params = AttentionParams.create_attention_params("torch", False) + + # If split attn is False, attention mask is provided and all sequence lengths are same, we can trim the sequence + seqlen_trimmed = False + if not attn_params.split_attn and attn_params.attention_mask is not None and attn_params.seqlens is not None: + if torch.all(attn_params.seqlens == attn_params.seqlens[0]): + seqlen = attn_params.seqlens[0].item() + q = q[:, :seqlen] + k = k[:, :seqlen] + v = v[:, :seqlen] + max_seqlen = attn_params.max_seqlen + attn_params = AttentionParams.create_attention_params(attn_params.attn_mode, False) # do not in-place modify + attn_params.max_seqlen = max_seqlen # keep max_seqlen for padding + seqlen_trimmed = True + + # Determine tensor layout based on attention implementation + if attn_params.attn_mode == "torch" or ( + attn_params.attn_mode == "sageattn" and (attn_params.split_attn or attn_params.cu_seqlens is None) + ): + transpose_fn = lambda x: x.transpose(1, 2) # [B, H, L, D] for SDPA and sageattn with fixed length + # pad on sequence length dimension + pad_fn = lambda x, pad_to: torch.nn.functional.pad(x, (0, 0, 0, pad_to - x.shape[-2]), value=0) + else: + transpose_fn = lambda x: x # [B, L, H, D] for other implementations + # pad on sequence length dimension + pad_fn = lambda x, pad_to: torch.nn.functional.pad(x, (0, 0, 0, 0, 0, pad_to - x.shape[-3]), value=0) + + # Process each batch element with its valid sequence lengths + if attn_params.split_attn: + if attn_params.seqlens is None: + # If no seqlens provided, assume all tokens are valid + attn_params = AttentionParams.create_attention_params(attn_params.attn_mode, True) # do not in-place modify + attn_params.seqlens = torch.tensor([q.shape[1]] * q.shape[0], device=q.device) + attn_params.max_seqlen = q.shape[1] + q = [transpose_fn(q[i : i + 1, : attn_params.seqlens[i]]) for i in range(len(q))] + k = [transpose_fn(k[i : i + 1, : attn_params.seqlens[i]]) for i in range(len(k))] + v = [transpose_fn(v[i : i + 1, : attn_params.seqlens[i]]) for i in range(len(v))] + else: + q = transpose_fn(q) + k = transpose_fn(k) + v = transpose_fn(v) + + if attn_params.attn_mode == "torch": + if attn_params.split_attn: + x = [] + for i in range(len(q)): + x_i = torch.nn.functional.scaled_dot_product_attention(q[i], k[i], v[i], dropout_p=drop_rate) + q[i] = None + k[i] = None + v[i] = None + x.append(pad_fn(x_i, attn_params.max_seqlen)) # B, H, L, D + x = torch.cat(x, dim=0) + del q, k, v + + else: + x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_params.attention_mask, dropout_p=drop_rate) + del q, k, v + + elif attn_params.attn_mode == "xformers": + if attn_params.split_attn: + x = [] + for i in range(len(q)): + x_i = xops.memory_efficient_attention(q[i], k[i], v[i], p=drop_rate) + q[i] = None + k[i] = None + v[i] = None + x.append(pad_fn(x_i, attn_params.max_seqlen)) # B, L, H, D + x = torch.cat(x, dim=0) + del q, k, v + + else: + x = xops.memory_efficient_attention(q, k, v, attn_bias=attn_params.attention_mask, p=drop_rate) + del q, k, v + + elif attn_params.attn_mode == "sageattn": + if attn_params.split_attn: + x = [] + for i in range(len(q)): + # HND seems to cause an error + x_i = sageattn(q[i], k[i], v[i]) # B, H, L, D. No dropout support + q[i] = None + k[i] = None + v[i] = None + x.append(pad_fn(x_i, attn_params.max_seqlen)) # B, H, L, D + x = torch.cat(x, dim=0) + del q, k, v + elif attn_params.cu_seqlens is None: # all tokens are valid + x = sageattn(q, k, v) # B, L, H, D. No dropout support + del q, k, v + else: + # Reshape to [(bxs), a, d] + batch_size, seqlen = q.shape[0], q.shape[1] + q = q.view(q.shape[0] * q.shape[1], *q.shape[2:]) # [B*L, H, D] + k = k.view(k.shape[0] * k.shape[1], *k.shape[2:]) # [B*L, H, D] + v = v.view(v.shape[0] * v.shape[1], *v.shape[2:]) # [B*L, H, D] + + # Assume cu_seqlens_q == cu_seqlens_kv and max_seqlen_q == max_seqlen_kv. No dropout support + x = sageattn_varlen( + q, k, v, attn_params.cu_seqlens, attn_params.cu_seqlens, attn_params.max_seqlen, attn_params.max_seqlen + ) + del q, k, v + + # Reshape x with shape [(bxs), a, d] to [b, s, a, d] + x = x.view(batch_size, seqlen, x.shape[-2], x.shape[-1]) # B, L, H, D + + elif attn_params.attn_mode == "flash": + if attn_params.split_attn: + x = [] + for i in range(len(q)): + # HND seems to cause an error + x_i = flash_attn_func(q[i], k[i], v[i], drop_rate) # B, L, H, D + q[i] = None + k[i] = None + v[i] = None + x.append(pad_fn(x_i, attn_params.max_seqlen)) # B, L, H, D + x = torch.cat(x, dim=0) + del q, k, v + elif attn_params.cu_seqlens is None: # all tokens are valid + x = flash_attn_func(q, k, v, drop_rate) # B, L, H, D + del q, k, v + else: + # Reshape to [(bxs), a, d] + batch_size, seqlen = q.shape[0], q.shape[1] + q = q.view(q.shape[0] * q.shape[1], *q.shape[2:]) # [B*L, H, D] + k = k.view(k.shape[0] * k.shape[1], *k.shape[2:]) # [B*L, H, D] + v = v.view(v.shape[0] * v.shape[1], *v.shape[2:]) # [B*L, H, D] + + # Assume cu_seqlens_q == cu_seqlens_kv and max_seqlen_q == max_seqlen_kv + x = flash_attn_varlen_func( + q, k, v, attn_params.cu_seqlens, attn_params.cu_seqlens, attn_params.max_seqlen, attn_params.max_seqlen, drop_rate + ) + del q, k, v + + # Reshape x with shape [(bxs), a, d] to [b, s, a, d] + x = x.view(batch_size, seqlen, x.shape[-2], x.shape[-1]) # B, L, H, D + + else: + # Currently only PyTorch SDPA and xformers are implemented + raise ValueError(f"Unsupported attention mode: {attn_params.attn_mode}") + + x = transpose_fn(x) # [B, L, H, D] + x = x.reshape(x.shape[0], x.shape[1], -1) # [B, L, H*D] + + if seqlen_trimmed: + x = torch.nn.functional.pad(x, (0, 0, 0, attn_params.max_seqlen - x.shape[1]), value=0) # pad back to max_seqlen + + return x diff --git a/vendor/sd-scripts/library/attention_processors.py b/vendor/sd-scripts/library/attention_processors.py new file mode 100644 index 0000000..310c2cb --- /dev/null +++ b/vendor/sd-scripts/library/attention_processors.py @@ -0,0 +1,227 @@ +import math +from typing import Any +from einops import rearrange +import torch +from diffusers.models.attention_processor import Attention + + +# flash attention forwards and backwards + +# https://arxiv.org/abs/2205.14135 + +EPSILON = 1e-6 + + +class FlashAttentionFunction(torch.autograd.function.Function): + @staticmethod + @torch.no_grad() + def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size): + """Algorithm 2 in the paper""" + + device = q.device + dtype = q.dtype + max_neg_value = -torch.finfo(q.dtype).max + qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) + + o = torch.zeros_like(q) + all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device) + all_row_maxes = torch.full( + (*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device + ) + + scale = q.shape[-1] ** -0.5 + + if mask is None: + mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size) + else: + mask = rearrange(mask, "b n -> b 1 1 n") + mask = mask.split(q_bucket_size, dim=-1) + + row_splits = zip( + q.split(q_bucket_size, dim=-2), + o.split(q_bucket_size, dim=-2), + mask, + all_row_sums.split(q_bucket_size, dim=-2), + all_row_maxes.split(q_bucket_size, dim=-2), + ) + + for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits): + q_start_index = ind * q_bucket_size - qk_len_diff + + col_splits = zip( + k.split(k_bucket_size, dim=-2), + v.split(k_bucket_size, dim=-2), + ) + + for k_ind, (kc, vc) in enumerate(col_splits): + k_start_index = k_ind * k_bucket_size + + attn_weights = ( + torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale + ) + + if row_mask is not None: + attn_weights.masked_fill_(~row_mask, max_neg_value) + + if causal and q_start_index < (k_start_index + k_bucket_size - 1): + causal_mask = torch.ones( + (qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device + ).triu(q_start_index - k_start_index + 1) + attn_weights.masked_fill_(causal_mask, max_neg_value) + + block_row_maxes = attn_weights.amax(dim=-1, keepdims=True) + attn_weights -= block_row_maxes + exp_weights = torch.exp(attn_weights) + + if row_mask is not None: + exp_weights.masked_fill_(~row_mask, 0.0) + + block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp( + min=EPSILON + ) + + new_row_maxes = torch.maximum(block_row_maxes, row_maxes) + + exp_values = torch.einsum( + "... i j, ... j d -> ... i d", exp_weights, vc + ) + + exp_row_max_diff = torch.exp(row_maxes - new_row_maxes) + exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes) + + new_row_sums = ( + exp_row_max_diff * row_sums + + exp_block_row_max_diff * block_row_sums + ) + + oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_( + (exp_block_row_max_diff / new_row_sums) * exp_values + ) + + row_maxes.copy_(new_row_maxes) + row_sums.copy_(new_row_sums) + + ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size) + ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes) + + return o + + @staticmethod + @torch.no_grad() + def backward(ctx, do): + """Algorithm 4 in the paper""" + + causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args + q, k, v, o, l, m = ctx.saved_tensors + + device = q.device + + max_neg_value = -torch.finfo(q.dtype).max + qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) + + dq = torch.zeros_like(q) + dk = torch.zeros_like(k) + dv = torch.zeros_like(v) + + row_splits = zip( + q.split(q_bucket_size, dim=-2), + o.split(q_bucket_size, dim=-2), + do.split(q_bucket_size, dim=-2), + mask, + l.split(q_bucket_size, dim=-2), + m.split(q_bucket_size, dim=-2), + dq.split(q_bucket_size, dim=-2), + ) + + for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits): + q_start_index = ind * q_bucket_size - qk_len_diff + + col_splits = zip( + k.split(k_bucket_size, dim=-2), + v.split(k_bucket_size, dim=-2), + dk.split(k_bucket_size, dim=-2), + dv.split(k_bucket_size, dim=-2), + ) + + for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits): + k_start_index = k_ind * k_bucket_size + + attn_weights = ( + torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale + ) + + if causal and q_start_index < (k_start_index + k_bucket_size - 1): + causal_mask = torch.ones( + (qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device + ).triu(q_start_index - k_start_index + 1) + attn_weights.masked_fill_(causal_mask, max_neg_value) + + exp_attn_weights = torch.exp(attn_weights - mc) + + if row_mask is not None: + exp_attn_weights.masked_fill_(~row_mask, 0.0) + + p = exp_attn_weights / lc + + dv_chunk = torch.einsum("... i j, ... i d -> ... j d", p, doc) + dp = torch.einsum("... i d, ... j d -> ... i j", doc, vc) + + D = (doc * oc).sum(dim=-1, keepdims=True) + ds = p * scale * (dp - D) + + dq_chunk = torch.einsum("... i j, ... j d -> ... i d", ds, kc) + dk_chunk = torch.einsum("... i j, ... i d -> ... j d", ds, qc) + + dqc.add_(dq_chunk) + dkc.add_(dk_chunk) + dvc.add_(dv_chunk) + + return dq, dk, dv, None, None, None, None + + +class FlashAttnProcessor: + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + ) -> Any: + q_bucket_size = 512 + k_bucket_size = 1024 + + h = attn.heads + q = attn.to_q(hidden_states) + + encoder_hidden_states = ( + encoder_hidden_states + if encoder_hidden_states is not None + else hidden_states + ) + encoder_hidden_states = encoder_hidden_states.to(hidden_states.dtype) + + if hasattr(attn, "hypernetwork") and attn.hypernetwork is not None: + context_k, context_v = attn.hypernetwork.forward( + hidden_states, encoder_hidden_states + ) + context_k = context_k.to(hidden_states.dtype) + context_v = context_v.to(hidden_states.dtype) + else: + context_k = encoder_hidden_states + context_v = encoder_hidden_states + + k = attn.to_k(context_k) + v = attn.to_v(context_v) + del encoder_hidden_states, hidden_states + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) + + out = FlashAttentionFunction.apply( + q, k, v, attention_mask, False, q_bucket_size, k_bucket_size + ) + + out = rearrange(out, "b h n d -> b n (h d)") + + out = attn.to_out[0](out) + out = attn.to_out[1](out) + return out diff --git a/vendor/sd-scripts/library/chroma_models.py b/vendor/sd-scripts/library/chroma_models.py new file mode 100644 index 0000000..d5ac1f3 --- /dev/null +++ b/vendor/sd-scripts/library/chroma_models.py @@ -0,0 +1,744 @@ +# copy from the official repo: https://github.com/lodestone-rock/flow/blob/master/src/models/chroma/model.py +# and modified +# licensed under Apache License 2.0 + +import math +from dataclasses import dataclass + +import torch +from einops import rearrange +from torch import Tensor, nn +import torch.nn.functional as F +import torch.utils.checkpoint as ckpt + +from .flux_models import attention, rope, apply_rope, EmbedND, timestep_embedding, MLPEmbedder, RMSNorm, QKNorm, SelfAttention, Flux +from . import custom_offloading_utils + + +def distribute_modulations(tensor: torch.Tensor, depth_single_blocks, depth_double_blocks): + """ + Distributes slices of the tensor into the block_dict as ModulationOut objects. + + Args: + tensor (torch.Tensor): Input tensor with shape [batch_size, vectors, dim]. + """ + batch_size, vectors, dim = tensor.shape + + block_dict = {} + + # HARD CODED VALUES! lookup table for the generated vectors + # TODO: move this into chroma config! + # Add 38 single mod blocks + for i in range(depth_single_blocks): + key = f"single_blocks.{i}.modulation.lin" + block_dict[key] = None + + # Add 19 image double blocks + for i in range(depth_double_blocks): + key = f"double_blocks.{i}.img_mod.lin" + block_dict[key] = None + + # Add 19 text double blocks + for i in range(depth_double_blocks): + key = f"double_blocks.{i}.txt_mod.lin" + block_dict[key] = None + + # Add the final layer + block_dict["final_layer.adaLN_modulation.1"] = None + # 6.2b version + # block_dict["lite_double_blocks.4.img_mod.lin"] = None + # block_dict["lite_double_blocks.4.txt_mod.lin"] = None + + idx = 0 # Index to keep track of the vector slices + + for key in block_dict.keys(): + if "single_blocks" in key: + # Single block: 1 ModulationOut + block_dict[key] = ModulationOut( + shift=tensor[:, idx : idx + 1, :], + scale=tensor[:, idx + 1 : idx + 2, :], + gate=tensor[:, idx + 2 : idx + 3, :], + ) + idx += 3 # Advance by 3 vectors + + elif "img_mod" in key: + # Double block: List of 2 ModulationOut + double_block = [] + for _ in range(2): # Create 2 ModulationOut objects + double_block.append( + ModulationOut( + shift=tensor[:, idx : idx + 1, :], + scale=tensor[:, idx + 1 : idx + 2, :], + gate=tensor[:, idx + 2 : idx + 3, :], + ) + ) + idx += 3 # Advance by 3 vectors per ModulationOut + block_dict[key] = double_block + + elif "txt_mod" in key: + # Double block: List of 2 ModulationOut + double_block = [] + for _ in range(2): # Create 2 ModulationOut objects + double_block.append( + ModulationOut( + shift=tensor[:, idx : idx + 1, :], + scale=tensor[:, idx + 1 : idx + 2, :], + gate=tensor[:, idx + 2 : idx + 3, :], + ) + ) + idx += 3 # Advance by 3 vectors per ModulationOut + block_dict[key] = double_block + + elif "final_layer" in key: + # Final layer: 1 ModulationOut + block_dict[key] = [ + tensor[:, idx : idx + 1, :], + tensor[:, idx + 1 : idx + 2, :], + ] + idx += 2 # Advance by 3 vectors + + return block_dict + + +class Approximator(nn.Module): + def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers=4): + super().__init__() + self.in_proj = nn.Linear(in_dim, hidden_dim, bias=True) + self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim) for x in range(n_layers)]) + self.norms = nn.ModuleList([RMSNorm(hidden_dim) for x in range(n_layers)]) + self.out_proj = nn.Linear(hidden_dim, out_dim) + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def enable_gradient_checkpointing(self): + for layer in self.layers: + layer.enable_gradient_checkpointing() + + def disable_gradient_checkpointing(self): + for layer in self.layers: + layer.disable_gradient_checkpointing() + + def forward(self, x: Tensor) -> Tensor: + x = self.in_proj(x) + + for layer, norms in zip(self.layers, self.norms): + x = x + layer(norms(x)) + + x = self.out_proj(x) + + return x + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +def _modulation_shift_scale_fn(x, scale, shift): + return (1 + scale) * x + shift + + +def _modulation_gate_fn(x, gate, gate_params): + return x + gate * gate_params + + +class DoubleStreamBlock(nn.Module): + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float, + qkv_bias: bool = False, + ): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_attn = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + ) + + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_attn = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + ) + + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.gradient_checkpointing = False + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def modulation_shift_scale_fn(self, x, scale, shift): + return _modulation_shift_scale_fn(x, scale, shift) + + def modulation_gate_fn(self, x, gate, gate_params): + return _modulation_gate_fn(x, gate, gate_params) + + def enable_gradient_checkpointing(self): + self.gradient_checkpointing = True + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + + def _forward( + self, + img: Tensor, + txt: Tensor, + pe: list[Tensor], + distill_vec: list[ModulationOut], + txt_seq_len: Tensor, + ) -> tuple[Tensor, Tensor]: + (img_mod1, img_mod2), (txt_mod1, txt_mod2) = distill_vec + + # prepare image for attention + img_modulated = self.img_norm1(img) + # replaced with compiled fn + # img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_modulated = self.modulation_shift_scale_fn(img_modulated, img_mod1.scale, img_mod1.shift) + img_qkv = self.img_attn.qkv(img_modulated) + del img_modulated + + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + del img_qkv + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + # replaced with compiled fn + # txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_modulated = self.modulation_shift_scale_fn(txt_modulated, txt_mod1.scale, txt_mod1.shift) + txt_qkv = self.txt_attn.qkv(txt_modulated) + del txt_modulated + + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + del txt_qkv + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention: we split the batch into each element + max_txt_len = torch.max(txt_seq_len).item() + img_len = img_q.shape[-2] # max 64 + txt_q = list(torch.chunk(txt_q, txt_q.shape[0], dim=0)) # list of [B, H, L, D] tensors + txt_k = list(torch.chunk(txt_k, txt_k.shape[0], dim=0)) + txt_v = list(torch.chunk(txt_v, txt_v.shape[0], dim=0)) + img_q = list(torch.chunk(img_q, img_q.shape[0], dim=0)) + img_k = list(torch.chunk(img_k, img_k.shape[0], dim=0)) + img_v = list(torch.chunk(img_v, img_v.shape[0], dim=0)) + txt_attn = [] + img_attn = [] + for i in range(txt.shape[0]): + txt_q[i] = txt_q[i][:, :, : txt_seq_len[i]] + q = torch.cat((img_q[i], txt_q[i]), dim=2) + txt_q[i] = None + img_q[i] = None + + txt_k[i] = txt_k[i][:, :, : txt_seq_len[i]] + k = torch.cat((img_k[i], txt_k[i]), dim=2) + txt_k[i] = None + img_k[i] = None + + txt_v[i] = txt_v[i][:, :, : txt_seq_len[i]] + v = torch.cat((img_v[i], txt_v[i]), dim=2) + txt_v[i] = None + img_v[i] = None + + attn = attention(q, k, v, pe=pe[i : i + 1, :, : q.shape[2]], attn_mask=None) # attn = (1, L, D) + del q, k, v + img_attn_i = attn[:, :img_len, :] + txt_attn_i = torch.zeros((1, max_txt_len, attn.shape[-1]), dtype=attn.dtype, device=self.device) + txt_attn_i[:, : txt_seq_len[i], :] = attn[:, img_len:, :] + del attn + txt_attn.append(txt_attn_i) + img_attn.append(img_attn_i) + + txt_attn = torch.cat(txt_attn, dim=0) + img_attn = torch.cat(img_attn, dim=0) + + # q = torch.cat((txt_q, img_q), dim=2) + # k = torch.cat((txt_k, img_k), dim=2) + # v = torch.cat((txt_v, img_v), dim=2) + + # attn = attention(q, k, v, pe=pe, attn_mask=mask) + # txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img blocks + # replaced with compiled fn + # img = img + img_mod1.gate * self.img_attn.proj(img_attn) + # img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + img = self.modulation_gate_fn(img, img_mod1.gate, self.img_attn.proj(img_attn)) + del img_attn, img_mod1 + img = self.modulation_gate_fn( + img, + img_mod2.gate, + self.img_mlp(self.modulation_shift_scale_fn(self.img_norm2(img), img_mod2.scale, img_mod2.shift)), + ) + del img_mod2 + + # calculate the txt blocks + # replaced with compiled fn + # txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) + # txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + txt = self.modulation_gate_fn(txt, txt_mod1.gate, self.txt_attn.proj(txt_attn)) + del txt_attn, txt_mod1 + txt = self.modulation_gate_fn( + txt, + txt_mod2.gate, + self.txt_mlp(self.modulation_shift_scale_fn(self.txt_norm2(txt), txt_mod2.scale, txt_mod2.shift)), + ) + del txt_mod2 + + return img, txt + + def forward( + self, + img: Tensor, + txt: Tensor, + pe: Tensor, + distill_vec: list[ModulationOut], + txt_seq_len: Tensor, + ) -> tuple[Tensor, Tensor]: + if self.training and self.gradient_checkpointing: + return ckpt.checkpoint(self._forward, img, txt, pe, distill_vec, txt_seq_len, use_reentrant=False) + else: + return self._forward(img, txt, pe, distill_vec, txt_seq_len) + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float | None = None, + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + # proj and mlp_out + self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.norm = QKNorm(head_dim) + + self.hidden_size = hidden_size + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approximate="tanh") + + self.gradient_checkpointing = False + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def modulation_shift_scale_fn(self, x, scale, shift): + return _modulation_shift_scale_fn(x, scale, shift) + + def modulation_gate_fn(self, x, gate, gate_params): + return _modulation_gate_fn(x, gate, gate_params) + + def enable_gradient_checkpointing(self): + self.gradient_checkpointing = True + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + + def _forward(self, x: Tensor, pe: list[Tensor], distill_vec: list[ModulationOut], txt_seq_len: Tensor) -> Tensor: + mod = distill_vec + # replaced with compiled fn + # x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + x_mod = self.modulation_shift_scale_fn(self.pre_norm(x), mod.scale, mod.shift) + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + del x_mod + + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + del qkv + q, k = self.norm(q, k, v) + + # # compute attention + # attn = attention(q, k, v, pe=pe, attn_mask=mask) + + # compute attention: we split the batch into each element + max_txt_len = torch.max(txt_seq_len).item() + img_len = q.shape[-2] - max_txt_len + q = list(torch.chunk(q, q.shape[0], dim=0)) + k = list(torch.chunk(k, k.shape[0], dim=0)) + v = list(torch.chunk(v, v.shape[0], dim=0)) + attn = [] + for i in range(x.size(0)): + q[i] = q[i][:, :, : img_len + txt_seq_len[i]] + k[i] = k[i][:, :, : img_len + txt_seq_len[i]] + v[i] = v[i][:, :, : img_len + txt_seq_len[i]] + attn_trimmed = attention(q[i], k[i], v[i], pe=pe[i : i + 1, :, : img_len + txt_seq_len[i]], attn_mask=None) + q[i] = None + k[i] = None + v[i] = None + + attn_i = torch.zeros((1, x.shape[1], attn_trimmed.shape[-1]), dtype=attn_trimmed.dtype, device=self.device) + attn_i[:, : img_len + txt_seq_len[i], :] = attn_trimmed + del attn_trimmed + attn.append(attn_i) + + attn = torch.cat(attn, dim=0) + + # compute activation in mlp stream, cat again and run second linear layer + mlp = self.mlp_act(mlp) + output = self.linear2(torch.cat((attn, mlp), 2)) + del attn, mlp + # replaced with compiled fn + # return x + mod.gate * output + return self.modulation_gate_fn(x, mod.gate, output) + + def forward(self, x: Tensor, pe: Tensor, distill_vec: list[ModulationOut], txt_seq_len: Tensor) -> Tensor: + if self.training and self.gradient_checkpointing: + return ckpt.checkpoint(self._forward, x, pe, distill_vec, txt_seq_len, use_reentrant=False) + else: + return self._forward(x, pe, distill_vec, txt_seq_len) + + +class LastLayer(nn.Module): + def __init__( + self, + hidden_size: int, + patch_size: int, + out_channels: int, + ): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def modulation_shift_scale_fn(self, x, scale, shift): + return _modulation_shift_scale_fn(x, scale, shift) + + def forward(self, x: Tensor, distill_vec: list[Tensor]) -> Tensor: + shift, scale = distill_vec + shift = shift.squeeze(1) + scale = scale.squeeze(1) + # replaced with compiled fn + # x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.modulation_shift_scale_fn(self.norm_final(x), scale[:, None, :], shift[:, None, :]) + x = self.linear(x) + return x + + +@dataclass +class ChromaParams: + in_channels: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list[int] + theta: int + qkv_bias: bool + guidance_embed: bool + approximator_in_dim: int + approximator_depth: int + approximator_hidden_size: int + _use_compiled: bool + + +chroma_params = ChromaParams( + in_channels=64, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=True, + approximator_in_dim=64, + approximator_depth=5, + approximator_hidden_size=5120, + _use_compiled=False, +) + + +def modify_mask_to_attend_padding(mask, max_seq_length, num_extra_padding=8): + """ + Modifies attention mask to allow attention to a few extra padding tokens. + + Args: + mask: Original attention mask (1 for tokens to attend to, 0 for masked tokens) + max_seq_length: Maximum sequence length of the model + num_extra_padding: Number of padding tokens to unmask + + Returns: + Modified mask + """ + # Get the actual sequence length from the mask + seq_length = mask.sum(dim=-1) + batch_size = mask.shape[0] + + modified_mask = mask.clone() + + for i in range(batch_size): + current_seq_len = int(seq_length[i].item()) + + # Only add extra padding tokens if there's room + if current_seq_len < max_seq_length: + # Calculate how many padding tokens we can unmask + available_padding = max_seq_length - current_seq_len + tokens_to_unmask = min(num_extra_padding, available_padding) + + # Unmask the specified number of padding tokens right after the sequence + modified_mask[i, current_seq_len : current_seq_len + tokens_to_unmask] = 1 + + return modified_mask + + +class Chroma(Flux): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, params: ChromaParams): + nn.Module.__init__(self) + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}") + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + + # TODO: need proper mapping for this approximator output! + # currently the mapping is hardcoded in distribute_modulations function + self.distilled_guidance_layer = Approximator( + params.approximator_in_dim, + self.hidden_size, + params.approximator_hidden_size, + params.approximator_depth, + ) + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + ) + for _ in range(params.depth_single_blocks) + ] + ) + + self.final_layer = LastLayer( + self.hidden_size, + 1, + self.out_channels, + ) + + # TODO: move this hardcoded value to config + # single layer has 3 modulation vectors + # double layer has 6 modulation vectors for each expert + # final layer has 2 modulation vectors + self.mod_index_length = 3 * params.depth_single_blocks + 2 * 6 * params.depth + 2 + self.depth_single_blocks = params.depth_single_blocks + self.depth_double_blocks = params.depth + # self.mod_index = torch.tensor(list(range(self.mod_index_length)), device=0) + self.register_buffer( + "mod_index", + torch.tensor(list(range(self.mod_index_length)), device="cpu"), + persistent=False, + ) + self.approximator_in_dim = params.approximator_in_dim + + self.blocks_to_swap = None + self.offloader_double = None + self.offloader_single = None + self.num_double_blocks = len(self.double_blocks) + self.num_single_blocks = len(self.single_blocks) + + # Initialize properties required by Flux parent class + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def get_model_type(self) -> str: + return "chroma" + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + self.distilled_guidance_layer.enable_gradient_checkpointing() + for block in self.double_blocks + self.single_blocks: + block.enable_gradient_checkpointing() + + print(f"Chroma: Gradient checkpointing enabled.") + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + self.distilled_guidance_layer.disable_gradient_checkpointing() + for block in self.double_blocks + self.single_blocks: + block.disable_gradient_checkpointing() + + print("Chroma: Gradient checkpointing disabled.") + + def get_mod_vectors(self, timesteps: Tensor, guidance: Tensor | None = None, batch_size: int | None = None) -> Tensor: + # We extract this logic from forward to clarify the propagation of the gradients + # original comment: https://github.com/lodestone-rock/flow/blob/c76f63058980d0488826936025889e256a2e0458/src/models/chroma/model.py#L195 + + # print(f"Chroma get_input_vec: timesteps {timesteps}, guidance: {guidance}, batch_size: {batch_size}") + distill_timestep = timestep_embedding(timesteps, self.approximator_in_dim // 4) + # TODO: need to add toggle to omit this from schnell but that's not a priority + distil_guidance = timestep_embedding(guidance, self.approximator_in_dim // 4) + # get all modulation index + modulation_index = timestep_embedding(self.mod_index, self.approximator_in_dim // 2) + # we need to broadcast the modulation index here so each batch has all of the index + modulation_index = modulation_index.unsqueeze(0).repeat(batch_size, 1, 1) + # and we need to broadcast timestep and guidance along too + timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, self.mod_index_length, 1) + # then and only then we could concatenate it together + input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1) + + mod_vectors = self.distilled_guidance_layer(input_vec) + return mod_vectors + + def forward( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + block_controlnet_hidden_states=None, + block_controlnet_single_hidden_states=None, + guidance: Tensor | None = None, + txt_attention_mask: Tensor | None = None, + attn_padding: int = 1, + mod_vectors: Tensor | None = None, + ) -> Tensor: + # print( + # f"Chroma forward: img shape {img.shape}, txt shape {txt.shape}, img_ids shape {img_ids.shape}, txt_ids shape {txt_ids.shape}" + # ) + # print(f"input_vec shape: {input_vec.shape if input_vec is not None else 'None'}") + # print(f"timesteps: {timesteps}, guidance: {guidance}") + + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + txt = self.txt_in(txt) + + if mod_vectors is None: # fallback to the original logic + with torch.no_grad(): + mod_vectors = self.get_mod_vectors(timesteps, guidance, img.shape[0]) + mod_vectors_dict = distribute_modulations(mod_vectors, self.depth_single_blocks, self.depth_double_blocks) + + # calculate text length for each batch instead of masking + txt_emb_len = txt.shape[1] + txt_seq_len = txt_attention_mask[:, :txt_emb_len].sum(dim=-1).to(torch.int64) # (batch_size, ) + txt_seq_len = torch.clip(txt_seq_len + attn_padding, 0, txt_emb_len) + max_txt_len = torch.max(txt_seq_len).item() # max text length in the batch + # print(f"max_txt_len: {max_txt_len}, txt_seq_len: {txt_seq_len}") + + # trim txt embedding to the text length + txt = txt[:, :max_txt_len, :] + + # create positional encoding for the text and image + ids = torch.cat((img_ids, txt_ids[:, :max_txt_len]), dim=1) # reverse order of ids for faster attention + pe = self.pe_embedder(ids) # B, 1, seq_length, 64, 2, 2 + + for i, block in enumerate(self.double_blocks): + if self.blocks_to_swap: + self.offloader_double.wait_for_block(i) + + # the guidance replaced by FFN output + img_mod = mod_vectors_dict.pop(f"double_blocks.{i}.img_mod.lin") + txt_mod = mod_vectors_dict.pop(f"double_blocks.{i}.txt_mod.lin") + double_mod = [img_mod, txt_mod] + del img_mod, txt_mod + + img, txt = block(img=img, txt=txt, pe=pe, distill_vec=double_mod, txt_seq_len=txt_seq_len) + del double_mod + + if self.blocks_to_swap: + self.offloader_double.submit_move_blocks(self.double_blocks, i) + + img = torch.cat((img, txt), 1) + del txt + + for i, block in enumerate(self.single_blocks): + if self.blocks_to_swap: + self.offloader_single.wait_for_block(i) + + single_mod = mod_vectors_dict.pop(f"single_blocks.{i}.modulation.lin") + img = block(img, pe=pe, distill_vec=single_mod, txt_seq_len=txt_seq_len) + del single_mod + + if self.blocks_to_swap: + self.offloader_single.submit_move_blocks(self.single_blocks, i) + + img = img[:, :-max_txt_len, ...] + final_mod = mod_vectors_dict["final_layer.adaLN_modulation.1"] + img = self.final_layer(img, distill_vec=final_mod) # (N, T, patch_size ** 2 * out_channels) + return img diff --git a/vendor/sd-scripts/library/config_util.py b/vendor/sd-scripts/library/config_util.py new file mode 100644 index 0000000..ca6aa3e --- /dev/null +++ b/vendor/sd-scripts/library/config_util.py @@ -0,0 +1,749 @@ +import argparse +from dataclasses import ( + asdict, + dataclass, +) +import functools +import random +from textwrap import dedent, indent +import json +from pathlib import Path + +# from toolz import curry +from typing import Dict, List, Optional, Sequence, Tuple, Union + +import toml +import voluptuous +from voluptuous import ( + Any, + ExactSequence, + MultipleInvalid, + Object, + Required, + Schema, +) +from transformers import CLIPTokenizer + +from . import train_util +from .train_util import ( + DreamBoothSubset, + FineTuningSubset, + ControlNetSubset, + DreamBoothDataset, + FineTuningDataset, + ControlNetDataset, + DatasetGroup, +) +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +def add_config_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--dataset_config", type=Path, default=None, help="config file for detail settings / 詳細な設定用の設定ファイル" + ) + + +# TODO: inherit Params class in Subset, Dataset + + +@dataclass +class BaseSubsetParams: + image_dir: Optional[str] = None + num_repeats: int = 1 + shuffle_caption: bool = False + caption_separator: str = (",",) + keep_tokens: int = 0 + keep_tokens_separator: str = (None,) + secondary_separator: Optional[str] = None + enable_wildcard: bool = False + color_aug: bool = False + flip_aug: bool = False + face_crop_aug_range: Optional[Tuple[float, float]] = None + random_crop: bool = False + caption_prefix: Optional[str] = None + caption_suffix: Optional[str] = None + caption_dropout_rate: float = 0.0 + caption_dropout_every_n_epochs: int = 0 + caption_tag_dropout_rate: float = 0.0 + token_warmup_min: int = 1 + token_warmup_step: float = 0 + custom_attributes: Optional[Dict[str, Any]] = None + validation_seed: int = 0 + validation_split: float = 0.0 + resize_interpolation: Optional[str] = None + + +@dataclass +class DreamBoothSubsetParams(BaseSubsetParams): + is_reg: bool = False + class_tokens: Optional[str] = None + caption_extension: str = ".caption" + cache_info: bool = False + alpha_mask: bool = False + + +@dataclass +class FineTuningSubsetParams(BaseSubsetParams): + metadata_file: Optional[str] = None + alpha_mask: bool = False + + +@dataclass +class ControlNetSubsetParams(BaseSubsetParams): + conditioning_data_dir: str = None + caption_extension: str = ".caption" + cache_info: bool = False + + +@dataclass +class BaseDatasetParams: + resolution: Optional[Tuple[int, int]] = None + network_multiplier: float = 1.0 + train_inpainting: bool = False + debug_dataset: bool = False + validation_seed: Optional[int] = None + validation_split: float = 0.0 + resize_interpolation: Optional[str] = None + skip_image_resolution: Optional[Tuple[int, int]] = None + +@dataclass +class DreamBoothDatasetParams(BaseDatasetParams): + batch_size: int = 1 + enable_bucket: bool = False + min_bucket_reso: int = 256 + max_bucket_reso: int = 1024 + bucket_reso_steps: int = 64 + bucket_no_upscale: bool = False + prior_loss_weight: float = 1.0 + +@dataclass +class FineTuningDatasetParams(BaseDatasetParams): + batch_size: int = 1 + enable_bucket: bool = False + min_bucket_reso: int = 256 + max_bucket_reso: int = 1024 + bucket_reso_steps: int = 64 + bucket_no_upscale: bool = False + + +@dataclass +class ControlNetDatasetParams(BaseDatasetParams): + batch_size: int = 1 + enable_bucket: bool = False + min_bucket_reso: int = 256 + max_bucket_reso: int = 1024 + bucket_reso_steps: int = 64 + bucket_no_upscale: bool = False + + +@dataclass +class SubsetBlueprint: + params: Union[DreamBoothSubsetParams, FineTuningSubsetParams] + + +@dataclass +class DatasetBlueprint: + is_dreambooth: bool + is_controlnet: bool + params: Union[DreamBoothDatasetParams, FineTuningDatasetParams] + subsets: Sequence[SubsetBlueprint] + + +@dataclass +class DatasetGroupBlueprint: + datasets: Sequence[DatasetBlueprint] + + +@dataclass +class Blueprint: + dataset_group: DatasetGroupBlueprint + + +class ConfigSanitizer: + # @curry + @staticmethod + def __validate_and_convert_twodim(klass, value: Sequence) -> Tuple: + Schema(ExactSequence([klass, klass]))(value) + return tuple(value) + + # @curry + @staticmethod + def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]) -> Tuple: + Schema(Any(klass, ExactSequence([klass, klass])))(value) + try: + Schema(klass)(value) + return (value, value) + except: + return ConfigSanitizer.__validate_and_convert_twodim(klass, value) + + # subset schema + SUBSET_ASCENDABLE_SCHEMA = { + "color_aug": bool, + "face_crop_aug_range": functools.partial(__validate_and_convert_twodim.__func__, float), + "flip_aug": bool, + "num_repeats": int, + "random_crop": bool, + "shuffle_caption": bool, + "keep_tokens": int, + "keep_tokens_separator": str, + "secondary_separator": str, + "caption_separator": str, + "enable_wildcard": bool, + "token_warmup_min": int, + "token_warmup_step": Any(float, int), + "caption_prefix": str, + "caption_suffix": str, + "custom_attributes": dict, + "resize_interpolation": str, + } + # DO means DropOut + DO_SUBSET_ASCENDABLE_SCHEMA = { + "caption_dropout_every_n_epochs": int, + "caption_dropout_rate": Any(float, int), + "caption_tag_dropout_rate": Any(float, int), + } + # DB means DreamBooth + DB_SUBSET_ASCENDABLE_SCHEMA = { + "caption_extension": str, + "class_tokens": str, + "cache_info": bool, + } + DB_SUBSET_DISTINCT_SCHEMA = { + Required("image_dir"): str, + "is_reg": bool, + "alpha_mask": bool, + } + # FT means FineTuning + FT_SUBSET_DISTINCT_SCHEMA = { + Required("metadata_file"): str, + "image_dir": str, + "alpha_mask": bool, + } + CN_SUBSET_ASCENDABLE_SCHEMA = { + "caption_extension": str, + "cache_info": bool, + } + CN_SUBSET_DISTINCT_SCHEMA = { + Required("image_dir"): str, + Required("conditioning_data_dir"): str, + } + + # datasets schema + DATASET_ASCENDABLE_SCHEMA = { + "batch_size": int, + "bucket_no_upscale": bool, + "bucket_reso_steps": int, + "enable_bucket": bool, + "max_bucket_reso": int, + "min_bucket_reso": int, + "validation_seed": int, + "validation_split": float, + "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int), + "network_multiplier": float, + "resize_interpolation": str, + "skip_image_resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int), + } + + # options handled by argparse but not handled by user config + ARGPARSE_SPECIFIC_SCHEMA = { + "debug_dataset": bool, + "train_inpainting": bool, + "max_token_length": Any(None, int), + "prior_loss_weight": Any(float, int), + } + # for handling default None value of argparse + ARGPARSE_NULLABLE_OPTNAMES = [ + "face_crop_aug_range", + "resolution", + "skip_image_resolution", + ] + # prepare map because option name may differ among argparse and user config + ARGPARSE_OPTNAME_TO_CONFIG_OPTNAME = { + "train_batch_size": "batch_size", + "dataset_repeats": "num_repeats", + } + + def __init__(self, support_dreambooth: bool, support_finetuning: bool, support_controlnet: bool, support_dropout: bool) -> None: + assert support_dreambooth or support_finetuning or support_controlnet, ( + "Neither DreamBooth mode nor fine tuning mode nor controlnet mode specified. Please specify one mode or more." + + " / DreamBooth モードか fine tuning モードか controlnet モードのどれも指定されていません。1つ以上指定してください。" + ) + + self.db_subset_schema = self.__merge_dict( + self.SUBSET_ASCENDABLE_SCHEMA, + self.DB_SUBSET_DISTINCT_SCHEMA, + self.DB_SUBSET_ASCENDABLE_SCHEMA, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + ) + + self.ft_subset_schema = self.__merge_dict( + self.SUBSET_ASCENDABLE_SCHEMA, + self.FT_SUBSET_DISTINCT_SCHEMA, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + ) + + self.cn_subset_schema = self.__merge_dict( + self.SUBSET_ASCENDABLE_SCHEMA, + self.CN_SUBSET_DISTINCT_SCHEMA, + self.CN_SUBSET_ASCENDABLE_SCHEMA, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + ) + + self.db_dataset_schema = self.__merge_dict( + self.DATASET_ASCENDABLE_SCHEMA, + self.SUBSET_ASCENDABLE_SCHEMA, + self.DB_SUBSET_ASCENDABLE_SCHEMA, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + {"subsets": [self.db_subset_schema]}, + ) + + self.ft_dataset_schema = self.__merge_dict( + self.DATASET_ASCENDABLE_SCHEMA, + self.SUBSET_ASCENDABLE_SCHEMA, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + {"subsets": [self.ft_subset_schema]}, + ) + + self.cn_dataset_schema = self.__merge_dict( + self.DATASET_ASCENDABLE_SCHEMA, + self.SUBSET_ASCENDABLE_SCHEMA, + self.CN_SUBSET_ASCENDABLE_SCHEMA, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + {"subsets": [self.cn_subset_schema]}, + ) + + if support_dreambooth and support_finetuning: + + def validate_flex_dataset(dataset_config: dict): + subsets_config = dataset_config.get("subsets", []) + + if support_controlnet and all(["conditioning_data_dir" in subset for subset in subsets_config]): + return Schema(self.cn_dataset_schema)(dataset_config) + # check dataset meets FT style + # NOTE: all FT subsets should have "metadata_file" + elif all(["metadata_file" in subset for subset in subsets_config]): + return Schema(self.ft_dataset_schema)(dataset_config) + # check dataset meets DB style + # NOTE: all DB subsets should have no "metadata_file" + elif all(["metadata_file" not in subset for subset in subsets_config]): + return Schema(self.db_dataset_schema)(dataset_config) + else: + raise voluptuous.Invalid( + "DreamBooth subset and fine tuning subset cannot be mixed in the same dataset. Please split them into separate datasets. / DreamBoothのサブセットとfine tuninのサブセットを同一のデータセットに混在させることはできません。別々のデータセットに分割してください。" + ) + + self.dataset_schema = validate_flex_dataset + elif support_dreambooth: + if support_controlnet: + self.dataset_schema = self.cn_dataset_schema + else: + self.dataset_schema = self.db_dataset_schema + elif support_finetuning: + self.dataset_schema = self.ft_dataset_schema + elif support_controlnet: + self.dataset_schema = self.cn_dataset_schema + + self.general_schema = self.__merge_dict( + self.DATASET_ASCENDABLE_SCHEMA, + self.SUBSET_ASCENDABLE_SCHEMA, + self.DB_SUBSET_ASCENDABLE_SCHEMA if support_dreambooth else {}, + self.CN_SUBSET_ASCENDABLE_SCHEMA if support_controlnet else {}, + self.DO_SUBSET_ASCENDABLE_SCHEMA if support_dropout else {}, + ) + + self.user_config_validator = Schema( + { + "general": self.general_schema, + "datasets": [self.dataset_schema], + } + ) + + self.argparse_schema = self.__merge_dict( + self.general_schema, + self.ARGPARSE_SPECIFIC_SCHEMA, + {optname: Any(None, self.general_schema[optname]) for optname in self.ARGPARSE_NULLABLE_OPTNAMES}, + {a_name: self.general_schema[c_name] for a_name, c_name in self.ARGPARSE_OPTNAME_TO_CONFIG_OPTNAME.items()}, + ) + + self.argparse_config_validator = Schema(Object(self.argparse_schema), extra=voluptuous.ALLOW_EXTRA) + + def sanitize_user_config(self, user_config: dict) -> dict: + try: + return self.user_config_validator(user_config) + except MultipleInvalid: + # TODO: エラー発生時のメッセージをわかりやすくする + logger.error("Invalid user config / ユーザ設定の形式が正しくないようです") + raise + + # NOTE: In nature, argument parser result is not needed to be sanitize + # However this will help us to detect program bug + def sanitize_argparse_namespace(self, argparse_namespace: argparse.Namespace) -> argparse.Namespace: + try: + return self.argparse_config_validator(argparse_namespace) + except MultipleInvalid: + # XXX: this should be a bug + logger.error( + "Invalid cmdline parsed arguments. This should be a bug. / コマンドラインのパース結果が正しくないようです。プログラムのバグの可能性が高いです。" + ) + raise + + # NOTE: value would be overwritten by latter dict if there is already the same key + @staticmethod + def __merge_dict(*dict_list: dict) -> dict: + merged = {} + for schema in dict_list: + # merged |= schema + for k, v in schema.items(): + merged[k] = v + return merged + + +class BlueprintGenerator: + BLUEPRINT_PARAM_NAME_TO_CONFIG_OPTNAME = {} + + def __init__(self, sanitizer: ConfigSanitizer): + self.sanitizer = sanitizer + + # runtime_params is for parameters which is only configurable on runtime, such as tokenizer + def generate(self, user_config: dict, argparse_namespace: argparse.Namespace, **runtime_params) -> Blueprint: + sanitized_user_config = self.sanitizer.sanitize_user_config(user_config) + sanitized_argparse_namespace = self.sanitizer.sanitize_argparse_namespace(argparse_namespace) + + # convert argparse namespace to dict like config + # NOTE: it is ok to have extra entries in dict + optname_map = self.sanitizer.ARGPARSE_OPTNAME_TO_CONFIG_OPTNAME + argparse_config = { + optname_map.get(optname, optname): value for optname, value in vars(sanitized_argparse_namespace).items() + } + + general_config = sanitized_user_config.get("general", {}) + + dataset_blueprints = [] + for dataset_config in sanitized_user_config.get("datasets", []): + # NOTE: if subsets have no "metadata_file", these are DreamBooth datasets/subsets + subsets = dataset_config.get("subsets", []) + is_dreambooth = all(["metadata_file" not in subset for subset in subsets]) + is_controlnet = all(["conditioning_data_dir" in subset for subset in subsets]) + if is_controlnet: + subset_params_klass = ControlNetSubsetParams + dataset_params_klass = ControlNetDatasetParams + elif is_dreambooth: + subset_params_klass = DreamBoothSubsetParams + dataset_params_klass = DreamBoothDatasetParams + else: + subset_params_klass = FineTuningSubsetParams + dataset_params_klass = FineTuningDatasetParams + + subset_blueprints = [] + for subset_config in subsets: + params = self.generate_params_by_fallbacks( + subset_params_klass, [subset_config, dataset_config, general_config, argparse_config, runtime_params] + ) + subset_blueprints.append(SubsetBlueprint(params)) + + params = self.generate_params_by_fallbacks( + dataset_params_klass, [dataset_config, general_config, argparse_config, runtime_params] + ) + dataset_blueprints.append(DatasetBlueprint(is_dreambooth, is_controlnet, params, subset_blueprints)) + + dataset_group_blueprint = DatasetGroupBlueprint(dataset_blueprints) + + return Blueprint(dataset_group_blueprint) + + @staticmethod + def generate_params_by_fallbacks(param_klass, fallbacks: Sequence[dict]): + name_map = BlueprintGenerator.BLUEPRINT_PARAM_NAME_TO_CONFIG_OPTNAME + search_value = BlueprintGenerator.search_value + default_params = asdict(param_klass()) + param_names = default_params.keys() + + params = {name: search_value(name_map.get(name, name), fallbacks, default_params.get(name)) for name in param_names} + + return param_klass(**params) + + @staticmethod + def search_value(key: str, fallbacks: Sequence[dict], default_value=None): + for cand in fallbacks: + value = cand.get(key) + if value is not None: + return value + + return default_value + +def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlueprint) -> Tuple[DatasetGroup, Optional[DatasetGroup]]: + datasets: List[Union[DreamBoothDataset, FineTuningDataset, ControlNetDataset]] = [] + + for dataset_blueprint in dataset_group_blueprint.datasets: + extra_dataset_params = {} + + if dataset_blueprint.is_controlnet: + subset_klass = ControlNetSubset + dataset_klass = ControlNetDataset + elif dataset_blueprint.is_dreambooth: + subset_klass = DreamBoothSubset + dataset_klass = DreamBoothDataset + # DreamBooth datasets support splitting training and validation datasets + extra_dataset_params = {"is_training_dataset": True} + else: + subset_klass = FineTuningSubset + dataset_klass = FineTuningDataset + + subsets = [subset_klass(**asdict(subset_blueprint.params)) for subset_blueprint in dataset_blueprint.subsets] + dataset = dataset_klass(subsets=subsets, **asdict(dataset_blueprint.params), **extra_dataset_params) + datasets.append(dataset) + + val_datasets: List[Union[DreamBoothDataset, FineTuningDataset, ControlNetDataset]] = [] + for dataset_blueprint in dataset_group_blueprint.datasets: + if dataset_blueprint.params.validation_split < 0.0 or dataset_blueprint.params.validation_split > 1.0: + logging.warning(f"Dataset param `validation_split` ({dataset_blueprint.params.validation_split}) is not a valid number between 0.0 and 1.0, skipping validation split...") + continue + + # if the dataset isn't setting a validation split, there is no current validation dataset + if dataset_blueprint.params.validation_split == 0.0: + continue + + extra_dataset_params = {} + if dataset_blueprint.is_controlnet: + subset_klass = ControlNetSubset + dataset_klass = ControlNetDataset + elif dataset_blueprint.is_dreambooth: + subset_klass = DreamBoothSubset + dataset_klass = DreamBoothDataset + # DreamBooth datasets support splitting training and validation datasets + extra_dataset_params = {"is_training_dataset": False} + else: + subset_klass = FineTuningSubset + dataset_klass = FineTuningDataset + + subsets = [subset_klass(**asdict(subset_blueprint.params)) for subset_blueprint in dataset_blueprint.subsets] + dataset = dataset_klass(subsets=subsets, **asdict(dataset_blueprint.params), **extra_dataset_params) + val_datasets.append(dataset) + + def print_info(_datasets, dataset_type: str): + info = "" + for i, dataset in enumerate(_datasets): + is_dreambooth = isinstance(dataset, DreamBoothDataset) + is_controlnet = isinstance(dataset, ControlNetDataset) + info += dedent(f"""\ + [{dataset_type} {i}] + batch_size: {dataset.batch_size} + resolution: {(dataset.width, dataset.height)} + skip_image_resolution: {dataset.skip_image_resolution} + resize_interpolation: {dataset.resize_interpolation} + enable_bucket: {dataset.enable_bucket} + """) + + if dataset.enable_bucket: + info += indent(dedent(f"""\ + min_bucket_reso: {dataset.min_bucket_reso} + max_bucket_reso: {dataset.max_bucket_reso} + bucket_reso_steps: {dataset.bucket_reso_steps} + bucket_no_upscale: {dataset.bucket_no_upscale} + \n"""), " ") + else: + info += "\n" + + for j, subset in enumerate(dataset.subsets): + info += indent(dedent(f"""\ + [Subset {j} of {dataset_type} {i}] + image_dir: "{subset.image_dir}" + image_count: {subset.img_count} + num_repeats: {subset.num_repeats} + shuffle_caption: {subset.shuffle_caption} + keep_tokens: {subset.keep_tokens} + caption_dropout_rate: {subset.caption_dropout_rate} + caption_dropout_every_n_epochs: {subset.caption_dropout_every_n_epochs} + caption_tag_dropout_rate: {subset.caption_tag_dropout_rate} + caption_prefix: {subset.caption_prefix} + caption_suffix: {subset.caption_suffix} + color_aug: {subset.color_aug} + flip_aug: {subset.flip_aug} + face_crop_aug_range: {subset.face_crop_aug_range} + random_crop: {subset.random_crop} + token_warmup_min: {subset.token_warmup_min}, + token_warmup_step: {subset.token_warmup_step}, + alpha_mask: {subset.alpha_mask} + resize_interpolation: {subset.resize_interpolation} + custom_attributes: {subset.custom_attributes} + """), " ") + + if is_dreambooth: + info += indent(dedent(f"""\ + is_reg: {subset.is_reg} + class_tokens: {subset.class_tokens} + caption_extension: {subset.caption_extension} + \n"""), " ") + elif not is_controlnet: + info += indent(dedent(f"""\ + metadata_file: {subset.metadata_file} + \n"""), " ") + + logger.info(info) + + print_info(datasets, "Dataset") + + if len(val_datasets) > 0: + print_info(val_datasets, "Validation Dataset") + + # make buckets first because it determines the length of dataset + # and set the same seed for all datasets + seed = random.randint(0, 2**31) # actual seed is seed + epoch_no + + for i, dataset in enumerate(datasets): + logger.info(f"[Prepare dataset {i}]") + dataset.make_buckets() + dataset.set_seed(seed) + + for i, dataset in enumerate(val_datasets): + logger.info(f"[Prepare validation dataset {i}]") + dataset.make_buckets() + dataset.set_seed(seed) + + return ( + DatasetGroup(datasets), + DatasetGroup(val_datasets) if val_datasets else None + ) + + +def generate_dreambooth_subsets_config_by_subdirs(train_data_dir: Optional[str] = None, reg_data_dir: Optional[str] = None): + def extract_dreambooth_params(name: str) -> Tuple[int, str]: + tokens = name.split("_") + try: + n_repeats = int(tokens[0]) + except ValueError as e: + logger.warning(f"ignore directory without repeats / 繰り返し回数のないディレクトリを無視します: {name}") + return 0, "" + caption_by_folder = "_".join(tokens[1:]) + return n_repeats, caption_by_folder + + def generate(base_dir: Optional[str], is_reg: bool): + if base_dir is None: + return [] + + base_dir: Path = Path(base_dir) + if not base_dir.is_dir(): + return [] + + subsets_config = [] + for subdir in base_dir.iterdir(): + if not subdir.is_dir(): + continue + + num_repeats, class_tokens = extract_dreambooth_params(subdir.name) + if num_repeats < 1: + continue + + subset_config = {"image_dir": str(subdir), "num_repeats": num_repeats, "is_reg": is_reg, "class_tokens": class_tokens} + subsets_config.append(subset_config) + + return subsets_config + + subsets_config = [] + subsets_config += generate(train_data_dir, False) + subsets_config += generate(reg_data_dir, True) + + return subsets_config + + +def generate_controlnet_subsets_config_by_subdirs( + train_data_dir: Optional[str] = None, conditioning_data_dir: Optional[str] = None, caption_extension: str = ".txt" +): + def generate(base_dir: Optional[str]): + if base_dir is None: + return [] + + base_dir: Path = Path(base_dir) + if not base_dir.is_dir(): + return [] + + subsets_config = [] + subset_config = { + "image_dir": train_data_dir, + "conditioning_data_dir": conditioning_data_dir, + "caption_extension": caption_extension, + "num_repeats": 1, + } + subsets_config.append(subset_config) + + return subsets_config + + subsets_config = [] + subsets_config += generate(train_data_dir) + + return subsets_config + + +def load_user_config(file: str) -> dict: + file: Path = Path(file) + if not file.is_file(): + raise ValueError(f"file not found / ファイルが見つかりません: {file}") + + if file.name.lower().endswith(".json"): + try: + with open(file, "r") as f: + config = json.load(f) + except Exception: + logger.error( + f"Error on parsing JSON config file. Please check the format. / JSON 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}" + ) + raise + elif file.name.lower().endswith(".toml"): + try: + config = toml.load(file) + except Exception: + logger.error( + f"Error on parsing TOML config file. Please check the format. / TOML 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}" + ) + raise + else: + raise ValueError(f"not supported config file format / 対応していない設定ファイルの形式です: {file}") + + return config + + +# for config test +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--support_dreambooth", action="store_true") + parser.add_argument("--support_finetuning", action="store_true") + parser.add_argument("--support_controlnet", action="store_true") + parser.add_argument("--support_dropout", action="store_true") + parser.add_argument("dataset_config") + config_args, remain = parser.parse_known_args() + + parser = argparse.ArgumentParser() + train_util.add_dataset_arguments( + parser, config_args.support_dreambooth, config_args.support_finetuning, config_args.support_dropout + ) + train_util.add_training_arguments(parser, config_args.support_dreambooth) + argparse_namespace = parser.parse_args(remain) + train_util.prepare_dataset_args(argparse_namespace, config_args.support_finetuning) + + logger.info("[argparse_namespace]") + logger.info(f"{vars(argparse_namespace)}") + + user_config = load_user_config(config_args.dataset_config) + + logger.info("") + logger.info("[user_config]") + logger.info(f"{user_config}") + + sanitizer = ConfigSanitizer( + config_args.support_dreambooth, config_args.support_finetuning, config_args.support_controlnet, config_args.support_dropout + ) + sanitized_user_config = sanitizer.sanitize_user_config(user_config) + + logger.info("") + logger.info("[sanitized_user_config]") + logger.info(f"{sanitized_user_config}") + + blueprint = BlueprintGenerator(sanitizer).generate(user_config, argparse_namespace) + + logger.info("") + logger.info("[blueprint]") + logger.info(f"{blueprint}") diff --git a/vendor/sd-scripts/library/custom_offloading_utils.py b/vendor/sd-scripts/library/custom_offloading_utils.py new file mode 100644 index 0000000..883379c --- /dev/null +++ b/vendor/sd-scripts/library/custom_offloading_utils.py @@ -0,0 +1,345 @@ +from concurrent.futures import ThreadPoolExecutor +import gc +import time +from typing import Any, Optional, Union, Callable, Tuple +import torch +import torch.nn as nn + + +# Keep these functions here for portability, and private to avoid confusion with the ones in device_utils.py +def _clean_memory_on_device(device: torch.device): + r""" + Clean memory on the specified device, will be called from training scripts. + """ + gc.collect() + + # device may "cuda" or "cuda:0", so we need to check the type of device + if device.type == "cuda": + torch.cuda.empty_cache() + if device.type == "xpu": + torch.xpu.empty_cache() + if device.type == "mps": + torch.mps.empty_cache() + + +def _synchronize_device(device: torch.device): + if device.type == "cuda": + torch.cuda.synchronize() + elif device.type == "xpu": + torch.xpu.synchronize() + elif device.type == "mps": + torch.mps.synchronize() + + +def swap_weight_devices_cuda(device: torch.device, layer_to_cpu: nn.Module, layer_to_cuda: nn.Module): + assert layer_to_cpu.__class__ == layer_to_cuda.__class__ + + weight_swap_jobs: list[Tuple[nn.Module, nn.Module, torch.Tensor, torch.Tensor]] = [] + + # This is not working for all cases (e.g. SD3), so we need to find the corresponding modules + # for module_to_cpu, module_to_cuda in zip(layer_to_cpu.modules(), layer_to_cuda.modules()): + # print(module_to_cpu.__class__, module_to_cuda.__class__) + # if hasattr(module_to_cpu, "weight") and module_to_cpu.weight is not None: + # weight_swap_jobs.append((module_to_cpu, module_to_cuda, module_to_cpu.weight.data, module_to_cuda.weight.data)) + + modules_to_cpu = {k: v for k, v in layer_to_cpu.named_modules()} + for module_to_cuda_name, module_to_cuda in layer_to_cuda.named_modules(): + if hasattr(module_to_cuda, "weight") and module_to_cuda.weight is not None: + module_to_cpu = modules_to_cpu.get(module_to_cuda_name, None) + if module_to_cpu is not None and module_to_cpu.weight.shape == module_to_cuda.weight.shape: + weight_swap_jobs.append((module_to_cpu, module_to_cuda, module_to_cpu.weight.data, module_to_cuda.weight.data)) + else: + if module_to_cuda.weight.data.device.type != device.type: + # print( + # f"Module {module_to_cuda_name} not found in CPU model or shape mismatch, so not swapping and moving to device" + # ) + module_to_cuda.weight.data = module_to_cuda.weight.data.to(device) + + torch.cuda.current_stream().synchronize() # this prevents the illegal loss value + + stream = torch.Stream(device="cuda") + with torch.cuda.stream(stream): + # cuda to cpu + for module_to_cpu, module_to_cuda, cuda_data_view, cpu_data_view in weight_swap_jobs: + cuda_data_view.record_stream(stream) + module_to_cpu.weight.data = cuda_data_view.data.to("cpu", non_blocking=True) + + stream.synchronize() + + # cpu to cuda + for module_to_cpu, module_to_cuda, cuda_data_view, cpu_data_view in weight_swap_jobs: + cuda_data_view.copy_(module_to_cuda.weight.data, non_blocking=True) + module_to_cuda.weight.data = cuda_data_view + + stream.synchronize() + torch.cuda.current_stream().synchronize() # this prevents the illegal loss value + + +def swap_weight_devices_no_cuda(device: torch.device, layer_to_cpu: nn.Module, layer_to_cuda: nn.Module): + """ + not tested + """ + assert layer_to_cpu.__class__ == layer_to_cuda.__class__ + + weight_swap_jobs: list[Tuple[nn.Module, nn.Module, torch.Tensor, torch.Tensor]] = [] + for module_to_cpu, module_to_cuda in zip(layer_to_cpu.modules(), layer_to_cuda.modules()): + if hasattr(module_to_cpu, "weight") and module_to_cpu.weight is not None: + weight_swap_jobs.append((module_to_cpu, module_to_cuda, module_to_cpu.weight.data, module_to_cuda.weight.data)) + + # device to cpu + for module_to_cpu, module_to_cuda, cuda_data_view, cpu_data_view in weight_swap_jobs: + module_to_cpu.weight.data = cuda_data_view.data.to("cpu", non_blocking=True) + + _synchronize_device(device) + + # cpu to device + for module_to_cpu, module_to_cuda, cuda_data_view, cpu_data_view in weight_swap_jobs: + cuda_data_view.copy_(module_to_cuda.weight.data, non_blocking=True) + module_to_cuda.weight.data = cuda_data_view + + _synchronize_device(device) + + +def weighs_to_device(layer: nn.Module, device: torch.device): + for module in layer.modules(): + if hasattr(module, "weight") and module.weight is not None: + module.weight.data = module.weight.data.to(device, non_blocking=True) + + +class Offloader: + """ + common offloading class + """ + + def __init__(self, num_blocks: int, blocks_to_swap: int, device: torch.device, debug: bool = False): + self.num_blocks = num_blocks + self.blocks_to_swap = blocks_to_swap + self.device = device + self.debug = debug + + self.thread_pool = ThreadPoolExecutor(max_workers=1) + self.futures = {} + self.cuda_available = device.type == "cuda" + + def swap_weight_devices(self, block_to_cpu: nn.Module, block_to_cuda: nn.Module): + if self.cuda_available: + swap_weight_devices_cuda(self.device, block_to_cpu, block_to_cuda) + else: + swap_weight_devices_no_cuda(self.device, block_to_cpu, block_to_cuda) + + def _submit_move_blocks(self, blocks, block_idx_to_cpu, block_idx_to_cuda): + def move_blocks(bidx_to_cpu, block_to_cpu, bidx_to_cuda, block_to_cuda): + if self.debug: + start_time = time.perf_counter() + print(f"Move block {bidx_to_cpu} to CPU and block {bidx_to_cuda} to {'CUDA' if self.cuda_available else 'device'}") + + self.swap_weight_devices(block_to_cpu, block_to_cuda) + + if self.debug: + print(f"Moved blocks {bidx_to_cpu} and {bidx_to_cuda} in {time.perf_counter() - start_time:.2f}s") + return bidx_to_cpu, bidx_to_cuda # , event + + block_to_cpu = blocks[block_idx_to_cpu] + block_to_cuda = blocks[block_idx_to_cuda] + + self.futures[block_idx_to_cuda] = self.thread_pool.submit( + move_blocks, block_idx_to_cpu, block_to_cpu, block_idx_to_cuda, block_to_cuda + ) + + def _wait_blocks_move(self, block_idx): + if block_idx not in self.futures: + return + + if self.debug: + print(f"Wait for block {block_idx}") + start_time = time.perf_counter() + + future = self.futures.pop(block_idx) + _, bidx_to_cuda = future.result() + + assert block_idx == bidx_to_cuda, f"Block index mismatch: {block_idx} != {bidx_to_cuda}" + + if self.debug: + print(f"Waited for block {block_idx}: {time.perf_counter() - start_time:.2f}s") + + +# Gradient tensors +_grad_t = Union[tuple[torch.Tensor, ...], torch.Tensor] + + +class ModelOffloader(Offloader): + """ + supports forward offloading + """ + + def __init__( + self, + blocks: Union[list[nn.Module], nn.ModuleList], + blocks_to_swap: int, + device: torch.device, + supports_backward: bool = True, + debug: bool = False, + ): + super().__init__(len(blocks), blocks_to_swap, device, debug) + + self.supports_backward = supports_backward + self.forward_only = not supports_backward # forward only offloading: can be changed to True for inference + + if self.supports_backward: + # register backward hooks + self.remove_handles = [] + for i, block in enumerate(blocks): + hook = self.create_backward_hook(blocks, i) + if hook is not None: + handle = block.register_full_backward_hook(hook) + self.remove_handles.append(handle) + + def set_forward_only(self, forward_only: bool): + # switching must wait for all pending transfers + for block_idx in list(self.futures.keys()): + self._wait_blocks_move(block_idx) + self.forward_only = forward_only + + def __del__(self): + if self.supports_backward: + for handle in self.remove_handles: + handle.remove() + + def create_backward_hook( + self, blocks: Union[list[nn.Module], nn.ModuleList], block_index: int + ) -> Optional[Callable[[nn.Module, _grad_t, _grad_t], Union[None, _grad_t]]]: + # -1 for 0-based index + num_blocks_propagated = self.num_blocks - block_index - 1 + swapping = num_blocks_propagated > 0 and num_blocks_propagated <= self.blocks_to_swap + waiting = block_index > 0 and block_index <= self.blocks_to_swap + + if not swapping and not waiting: + return None + + # create hook + block_idx_to_cpu = self.num_blocks - num_blocks_propagated + block_idx_to_cuda = self.blocks_to_swap - num_blocks_propagated + block_idx_to_wait = block_index - 1 + + def backward_hook(module: nn.Module, grad_input: _grad_t, grad_output: _grad_t): + if self.debug: + print(f"Backward hook for block {block_index}") + + if swapping: + self._submit_move_blocks(blocks, block_idx_to_cpu, block_idx_to_cuda) + if waiting: + self._wait_blocks_move(block_idx_to_wait) + return None + + return backward_hook + + def prepare_block_devices_before_forward(self, blocks: Union[list[nn.Module], nn.ModuleList]): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + + if self.debug: + print(f"Prepare block devices before forward") + + # wait for all pending transfers + for block_idx in list(self.futures.keys()): + self._wait_blocks_move(block_idx) + + for b in blocks[0 : self.num_blocks - self.blocks_to_swap]: + b.to(self.device) + weighs_to_device(b, self.device) # make sure weights are on device + + for b in blocks[self.num_blocks - self.blocks_to_swap :]: + b.to(self.device) # move block to device first. this makes sure that buffers (non weights) are on the device + weighs_to_device(b, torch.device("cpu")) # make sure weights are on cpu + + _synchronize_device(self.device) + _clean_memory_on_device(self.device) + + def wait_for_block(self, block_idx: int): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self._wait_blocks_move(block_idx) + + def submit_move_blocks(self, blocks: Union[list[nn.Module], nn.ModuleList], block_idx: int): + # check if blocks_to_swap is enabled + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + + # if backward is enabled, we do not swap blocks in forward pass more than blocks_to_swap, because it should be on GPU + if not self.forward_only and block_idx >= self.blocks_to_swap: + return + + block_idx_to_cpu = block_idx + block_idx_to_cuda = self.num_blocks - self.blocks_to_swap + block_idx + # this works for forward-only offloading. move upstream blocks to cuda + block_idx_to_cuda = block_idx_to_cuda % self.num_blocks + self._submit_move_blocks(blocks, block_idx_to_cpu, block_idx_to_cuda) + + +# endregion + +# region cpu offload utils + + +def to_device(x: Any, device: torch.device) -> Any: + if isinstance(x, torch.Tensor): + return x.to(device) + elif isinstance(x, list): + return [to_device(elem, device) for elem in x] + elif isinstance(x, tuple): + return tuple(to_device(elem, device) for elem in x) + elif isinstance(x, dict): + return {k: to_device(v, device) for k, v in x.items()} + else: + return x + + +def to_cpu(x: Any) -> Any: + """ + Recursively moves torch.Tensor objects (and containers thereof) to CPU. + + Args: + x: A torch.Tensor, or a (possibly nested) list, tuple, or dict containing tensors. + + Returns: + The same structure as x, with all torch.Tensor objects moved to CPU. + Non-tensor objects are returned unchanged. + """ + if isinstance(x, torch.Tensor): + return x.cpu() + elif isinstance(x, list): + return [to_cpu(elem) for elem in x] + elif isinstance(x, tuple): + return tuple(to_cpu(elem) for elem in x) + elif isinstance(x, dict): + return {k: to_cpu(v) for k, v in x.items()} + else: + return x + + +def create_cpu_offloading_wrapper(func: Callable, device: torch.device) -> Callable: + """ + Create a wrapper function that offloads inputs to CPU before calling the original function + and moves outputs back to the specified device. + + Args: + func: The original function to wrap. + device: The device to move outputs back to. + + Returns: + A wrapped function that offloads inputs to CPU and moves outputs back to the specified device. + """ + + def wrapper(orig_func: Callable) -> Callable: + def custom_forward(*inputs): + nonlocal device, orig_func + cuda_inputs = to_device(inputs, device) + outputs = orig_func(*cuda_inputs) + return to_cpu(outputs) + + return custom_forward + + return wrapper(func) + + +# endregion diff --git a/vendor/sd-scripts/library/custom_train_functions.py b/vendor/sd-scripts/library/custom_train_functions.py new file mode 100644 index 0000000..4f20d48 --- /dev/null +++ b/vendor/sd-scripts/library/custom_train_functions.py @@ -0,0 +1,561 @@ +from diffusers.schedulers.scheduling_ddpm import DDPMScheduler +import torch +import argparse +import random +import re +from torch.types import Number +from typing import List, Optional, Union +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +def prepare_scheduler_for_custom_training(noise_scheduler, device): + if hasattr(noise_scheduler, "all_snr"): + return + + alphas_cumprod = noise_scheduler.alphas_cumprod + sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod) + sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod) + alpha = sqrt_alphas_cumprod + sigma = sqrt_one_minus_alphas_cumprod + all_snr = (alpha / sigma) ** 2 + + noise_scheduler.all_snr = all_snr.to(device) + + +def fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler): + # fix beta: zero terminal SNR + logger.info(f"fix noise scheduler betas: https://arxiv.org/abs/2305.08891") + + def enforce_zero_terminal_snr(betas): + # Convert betas to alphas_bar_sqrt + alphas = 1 - betas + alphas_bar = alphas.cumprod(0) + alphas_bar_sqrt = alphas_bar.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + # Shift so last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + # Scale so first timestep is back to old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 + alphas = alphas_bar[1:] / alphas_bar[:-1] + alphas = torch.cat([alphas_bar[0:1], alphas]) + betas = 1 - alphas + return betas + + betas = noise_scheduler.betas + betas = enforce_zero_terminal_snr(betas) + alphas = 1.0 - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + + # logger.info(f"original: {noise_scheduler.betas}") + # logger.info(f"fixed: {betas}") + + noise_scheduler.betas = betas + noise_scheduler.alphas = alphas + noise_scheduler.alphas_cumprod = alphas_cumprod + + +def apply_snr_weight(loss: torch.Tensor, timesteps: torch.IntTensor, noise_scheduler: DDPMScheduler, gamma: Number, v_prediction=False): + snr = torch.stack([noise_scheduler.all_snr[t] for t in timesteps]) + min_snr_gamma = torch.minimum(snr, torch.full_like(snr, gamma)) + if v_prediction: + snr_weight = torch.div(min_snr_gamma, snr + 1).float().to(loss.device) + else: + snr_weight = torch.div(min_snr_gamma, snr).float().to(loss.device) + loss = loss * snr_weight + return loss + + +def scale_v_prediction_loss_like_noise_prediction(loss: torch.Tensor, timesteps: torch.IntTensor, noise_scheduler: DDPMScheduler): + scale = get_snr_scale(timesteps, noise_scheduler) + loss = loss * scale + return loss + + +def get_snr_scale(timesteps: torch.IntTensor, noise_scheduler: DDPMScheduler): + snr_t = torch.stack([noise_scheduler.all_snr[t] for t in timesteps]) # batch_size + snr_t = torch.minimum(snr_t, torch.ones_like(snr_t) * 1000) # if timestep is 0, snr_t is inf, so limit it to 1000 + scale = snr_t / (snr_t + 1) + # # show debug info + # logger.info(f"timesteps: {timesteps}, snr_t: {snr_t}, scale: {scale}") + return scale + + +def add_v_prediction_like_loss(loss: torch.Tensor, timesteps: torch.IntTensor, noise_scheduler: DDPMScheduler, v_pred_like_loss: torch.Tensor): + scale = get_snr_scale(timesteps, noise_scheduler) + # logger.info(f"add v-prediction like loss: {v_pred_like_loss}, scale: {scale}, loss: {loss}, time: {timesteps}") + loss = loss + loss / scale * v_pred_like_loss + return loss + + +def apply_debiased_estimation(loss: torch.Tensor, timesteps: torch.IntTensor, noise_scheduler: DDPMScheduler, v_prediction=False): + snr_t = torch.stack([noise_scheduler.all_snr[t] for t in timesteps]) # batch_size + snr_t = torch.minimum(snr_t, torch.ones_like(snr_t) * 1000) # if timestep is 0, snr_t is inf, so limit it to 1000 + if v_prediction: + weight = 1 / (snr_t + 1) + else: + weight = 1 / torch.sqrt(snr_t) + loss = weight * loss + return loss + + +# TODO train_utilと分散しているのでどちらかに寄せる + + +def add_custom_train_arguments(parser: argparse.ArgumentParser, support_weighted_captions: bool = True): + parser.add_argument( + "--min_snr_gamma", + type=float, + default=None, + help="gamma for reducing the weight of high loss timesteps. Lower numbers have stronger effect. 5 is recommended by paper. / 低いタイムステップでの高いlossに対して重みを減らすためのgamma値、低いほど効果が強く、論文では5が推奨", + ) + parser.add_argument( + "--scale_v_pred_loss_like_noise_pred", + action="store_true", + help="scale v-prediction loss like noise prediction loss / v-prediction lossをnoise prediction lossと同じようにスケーリングする", + ) + parser.add_argument( + "--v_pred_like_loss", + type=float, + default=None, + help="add v-prediction like loss multiplied by this value / v-prediction lossをこの値をかけたものをlossに加算する", + ) + parser.add_argument( + "--debiased_estimation_loss", + action="store_true", + help="debiased estimation loss / debiased estimation loss", + ) + if support_weighted_captions: + parser.add_argument( + "--weighted_captions", + action="store_true", + default=False, + help="Enable weighted captions in the standard style (token:1.3). No commas inside parens, or shuffle/dropout may break the decoder. / 「[token]」、「(token)」「(token:1.3)」のような重み付きキャプションを有効にする。カンマを括弧内に入れるとシャッフルやdropoutで重みづけがおかしくなるので注意", + ) + + +re_attention = re.compile( + r""" +\\\(| +\\\)| +\\\[| +\\]| +\\\\| +\\| +\(| +\[| +:([+-]?[.\d]+)\)| +\)| +]| +[^\\()\[\]:]+| +: +""", + re.X, +) + + +def parse_prompt_attention(text): + """ + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. + Accepted tokens are: + (abc) - increases attention to abc by a multiplier of 1.1 + (abc:3.12) - increases attention to abc by a multiplier of 3.12 + [abc] - decreases attention to abc by a multiplier of 1.1 + \( - literal character '(' + \[ - literal character '[' + \) - literal character ')' + \] - literal character ']' + \\ - literal character '\' + anything else - just text + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] + """ + + res = [] + round_brackets = [] + square_brackets = [] + + round_bracket_multiplier = 1.1 + square_bracket_multiplier = 1 / 1.1 + + def multiply_range(start_position, multiplier): + for p in range(start_position, len(res)): + res[p][1] *= multiplier + + for m in re_attention.finditer(text): + text = m.group(0) + weight = m.group(1) + + if text.startswith("\\"): + res.append([text[1:], 1.0]) + elif text == "(": + round_brackets.append(len(res)) + elif text == "[": + square_brackets.append(len(res)) + elif weight is not None and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), float(weight)) + elif text == ")" and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), round_bracket_multiplier) + elif text == "]" and len(square_brackets) > 0: + multiply_range(square_brackets.pop(), square_bracket_multiplier) + else: + res.append([text, 1.0]) + + for pos in round_brackets: + multiply_range(pos, round_bracket_multiplier) + + for pos in square_brackets: + multiply_range(pos, square_bracket_multiplier) + + if len(res) == 0: + res = [["", 1.0]] + + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + + return res + + +def get_prompts_with_weights(tokenizer, prompt: List[str], max_length: int): + r""" + Tokenize a list of prompts and return its tokens with weights of each token. + + No padding, starting or ending token is included. + """ + tokens = [] + weights = [] + truncated = False + for text in prompt: + texts_and_weights = parse_prompt_attention(text) + text_token = [] + text_weight = [] + for word, weight in texts_and_weights: + # tokenize and discard the starting and the ending token + token = tokenizer(word).input_ids[1:-1] + text_token += token + # copy the weight by length of token + text_weight += [weight] * len(token) + # stop if the text is too long (longer than truncation limit) + if len(text_token) > max_length: + truncated = True + break + # truncate + if len(text_token) > max_length: + truncated = True + text_token = text_token[:max_length] + text_weight = text_weight[:max_length] + tokens.append(text_token) + weights.append(text_weight) + if truncated: + logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples") + return tokens, weights + + +def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77): + r""" + Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. + """ + max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) + weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length + for i in range(len(tokens)): + tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i])) + if no_boseos_middle: + weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) + else: + w = [] + if len(weights[i]) == 0: + w = [1.0] * weights_length + else: + for j in range(max_embeddings_multiples): + w.append(1.0) # weight for starting token in this chunk + w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))] + w.append(1.0) # weight for ending token in this chunk + w += [1.0] * (weights_length - len(w)) + weights[i] = w[:] + + return tokens, weights + + +def get_unweighted_text_embeddings( + tokenizer, + text_encoder, + text_input: torch.Tensor, + chunk_length: int, + clip_skip: int, + eos: int, + pad: int, + no_boseos_middle: Optional[bool] = True, +): + """ + When the length of tokens is a multiple of the capacity of the text encoder, + it should be split into chunks and sent to the text encoder individually. + """ + max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2) + if max_embeddings_multiples > 1: + text_embeddings = [] + for i in range(max_embeddings_multiples): + # extract the i-th chunk + text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone() + + # cover the head and the tail by the starting and the ending tokens + text_input_chunk[:, 0] = text_input[0, 0] + if pad == eos: # v1 + text_input_chunk[:, -1] = text_input[0, -1] + else: # v2 + for j in range(len(text_input_chunk)): + if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある + text_input_chunk[j, -1] = eos + if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD + text_input_chunk[j, 1] = eos + + if clip_skip is None or clip_skip == 1: + text_embedding = text_encoder(text_input_chunk)[0] + else: + enc_out = text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True) + text_embedding = enc_out["hidden_states"][-clip_skip] + text_embedding = text_encoder.text_model.final_layer_norm(text_embedding) + + if no_boseos_middle: + if i == 0: + # discard the ending token + text_embedding = text_embedding[:, :-1] + elif i == max_embeddings_multiples - 1: + # discard the starting token + text_embedding = text_embedding[:, 1:] + else: + # discard both starting and ending tokens + text_embedding = text_embedding[:, 1:-1] + + text_embeddings.append(text_embedding) + text_embeddings = torch.concat(text_embeddings, axis=1) + else: + if clip_skip is None or clip_skip == 1: + text_embeddings = text_encoder(text_input)[0] + else: + enc_out = text_encoder(text_input, output_hidden_states=True, return_dict=True) + text_embeddings = enc_out["hidden_states"][-clip_skip] + text_embeddings = text_encoder.text_model.final_layer_norm(text_embeddings) + return text_embeddings + + +def get_weighted_text_embeddings( + tokenizer, + text_encoder, + prompt: Union[str, List[str]], + device, + max_embeddings_multiples: Optional[int] = 3, + no_boseos_middle: Optional[bool] = False, + clip_skip=None, +): + r""" + Prompts can be assigned with local weights using brackets. For example, + prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful', + and the embedding tokens corresponding to the words get multiplied by a constant, 1.1. + + Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean. + + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + no_boseos_middle (`bool`, *optional*, defaults to `False`): + If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and + ending token in each of the chunk in the middle. + skip_parsing (`bool`, *optional*, defaults to `False`): + Skip the parsing of brackets. + skip_weighting (`bool`, *optional*, defaults to `False`): + Skip the weighting. When the parsing is skipped, it is forced True. + """ + max_length = (tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + if isinstance(prompt, str): + prompt = [prompt] + + prompt_tokens, prompt_weights = get_prompts_with_weights(tokenizer, prompt, max_length - 2) + + # round up the longest length of tokens to a multiple of (model_max_length - 2) + max_length = max([len(token) for token in prompt_tokens]) + + max_embeddings_multiples = min( + max_embeddings_multiples, + (max_length - 1) // (tokenizer.model_max_length - 2) + 1, + ) + max_embeddings_multiples = max(1, max_embeddings_multiples) + max_length = (tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + + # pad the length of tokens and weights + bos = tokenizer.bos_token_id + eos = tokenizer.eos_token_id + pad = tokenizer.pad_token_id + prompt_tokens, prompt_weights = pad_tokens_and_weights( + prompt_tokens, + prompt_weights, + max_length, + bos, + eos, + no_boseos_middle=no_boseos_middle, + chunk_length=tokenizer.model_max_length, + ) + prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=device) + + # get the embeddings + text_embeddings = get_unweighted_text_embeddings( + tokenizer, + text_encoder, + prompt_tokens, + tokenizer.model_max_length, + clip_skip, + eos, + pad, + no_boseos_middle=no_boseos_middle, + ) + prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=device) + + # assign weights to the prompts and normalize in the sense of mean + previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings = text_embeddings * prompt_weights.unsqueeze(-1) + current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings = text_embeddings * (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + + return text_embeddings + + +# https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2 +def pyramid_noise_like(noise, device, iterations=6, discount=0.4) -> torch.FloatTensor: + b, c, w, h = noise.shape # EDIT: w and h get over-written, rename for a different variant! + u = torch.nn.Upsample(size=(w, h), mode="bilinear").to(device) + for i in range(iterations): + r = random.random() * 2 + 2 # Rather than always going 2x, + wn, hn = max(1, int(w / (r**i))), max(1, int(h / (r**i))) + noise += u(torch.randn(b, c, wn, hn).to(device)) * discount**i + if wn == 1 or hn == 1: + break # Lowest resolution is 1x1 + return noise / noise.std() # Scaled back to roughly unit variance + + +# https://www.crosslabs.org//blog/diffusion-with-offset-noise +def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale) -> torch.FloatTensor: + if noise_offset is None: + return noise + if adaptive_noise_scale is not None: + # latent shape: (batch_size, channels, height, width) + # abs mean value for each channel + latent_mean = torch.abs(latents.mean(dim=(2, 3), keepdim=True)) + + # multiply adaptive noise scale to the mean value and add it to the noise offset + noise_offset = noise_offset + adaptive_noise_scale * latent_mean + noise_offset = torch.clamp(noise_offset, 0.0, None) # in case of adaptive noise scale is negative + + noise = noise + noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device) + return noise + + +def apply_masked_loss(loss, batch) -> torch.FloatTensor: + if "conditioning_images" in batch: + # conditioning image is -1 to 1. we need to convert it to 0 to 1 + mask_image = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1) # use R channel + mask_image = mask_image / 2 + 0.5 + # print(f"conditioning_image: {mask_image.shape}") + elif "alpha_masks" in batch and batch["alpha_masks"] is not None: + # alpha mask is 0 to 1 + mask_image = batch["alpha_masks"].to(dtype=loss.dtype).unsqueeze(1) # add channel dimension + # print(f"mask_image: {mask_image.shape}, {mask_image.mean()}") + else: + return loss + + # resize to the same size as the loss + mask_image = torch.nn.functional.interpolate(mask_image, size=loss.shape[2:], mode="area") + loss = loss * mask_image + return loss + + +""" +########################################## +# Perlin Noise +def rand_perlin_2d(device, shape, res, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3): + delta = (res[0] / shape[0], res[1] / shape[1]) + d = (shape[0] // res[0], shape[1] // res[1]) + + grid = ( + torch.stack( + torch.meshgrid(torch.arange(0, res[0], delta[0], device=device), torch.arange(0, res[1], delta[1], device=device)), + dim=-1, + ) + % 1 + ) + angles = 2 * torch.pi * torch.rand(res[0] + 1, res[1] + 1, device=device) + gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1) + + tile_grads = ( + lambda slice1, slice2: gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]] + .repeat_interleave(d[0], 0) + .repeat_interleave(d[1], 1) + ) + dot = lambda grad, shift: ( + torch.stack((grid[: shape[0], : shape[1], 0] + shift[0], grid[: shape[0], : shape[1], 1] + shift[1]), dim=-1) + * grad[: shape[0], : shape[1]] + ).sum(dim=-1) + + n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]) + n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]) + n01 = dot(tile_grads([0, -1], [1, None]), [0, -1]) + n11 = dot(tile_grads([1, None], [1, None]), [-1, -1]) + t = fade(grid[: shape[0], : shape[1]]) + return 1.414 * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]) + + +def rand_perlin_2d_octaves(device, shape, res, octaves=1, persistence=0.5): + noise = torch.zeros(shape, device=device) + frequency = 1 + amplitude = 1 + for _ in range(octaves): + noise += amplitude * rand_perlin_2d(device, shape, (frequency * res[0], frequency * res[1])) + frequency *= 2 + amplitude *= persistence + return noise + + +def perlin_noise(noise, device, octaves): + _, c, w, h = noise.shape + perlin = lambda: rand_perlin_2d_octaves(device, (w, h), (4, 4), octaves) + noise_perlin = [] + for _ in range(c): + noise_perlin.append(perlin()) + noise_perlin = torch.stack(noise_perlin).unsqueeze(0) # (1, c, w, h) + noise += noise_perlin # broadcast for each batch + return noise / noise.std() # Scaled back to roughly unit variance +""" diff --git a/vendor/sd-scripts/library/deepspeed_utils.py b/vendor/sd-scripts/library/deepspeed_utils.py new file mode 100644 index 0000000..4daeb25 --- /dev/null +++ b/vendor/sd-scripts/library/deepspeed_utils.py @@ -0,0 +1,179 @@ +import os +import argparse +import torch +from accelerate import DeepSpeedPlugin, Accelerator + +from .utils import setup_logging + +from .device_utils import get_preferred_device + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +def add_deepspeed_arguments(parser: argparse.ArgumentParser): + # DeepSpeed Arguments. https://huggingface.co/docs/accelerate/usage_guides/deepspeed + parser.add_argument("--deepspeed", action="store_true", help="enable deepspeed training") + parser.add_argument("--zero_stage", type=int, default=2, choices=[0, 1, 2, 3], help="Possible options are 0,1,2,3.") + parser.add_argument( + "--offload_optimizer_device", + type=str, + default=None, + choices=[None, "cpu", "nvme"], + help="Possible options are none|cpu|nvme. Only applicable with ZeRO Stages 2 and 3.", + ) + parser.add_argument( + "--offload_optimizer_nvme_path", + type=str, + default=None, + help="Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3.", + ) + parser.add_argument( + "--offload_param_device", + type=str, + default=None, + choices=[None, "cpu", "nvme"], + help="Possible options are none|cpu|nvme. Only applicable with ZeRO Stage 3.", + ) + parser.add_argument( + "--offload_param_nvme_path", + type=str, + default=None, + help="Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3.", + ) + parser.add_argument( + "--zero3_init_flag", + action="store_true", + help="Flag to indicate whether to enable `deepspeed.zero.Init` for constructing massive models." + "Only applicable with ZeRO Stage-3.", + ) + parser.add_argument( + "--zero3_save_16bit_model", + action="store_true", + help="Flag to indicate whether to save 16-bit model. Only applicable with ZeRO Stage-3.", + ) + parser.add_argument( + "--fp16_master_weights_and_gradients", + action="store_true", + help="fp16_master_and_gradients requires optimizer to support keeping fp16 master and gradients while keeping the optimizer states in fp32.", + ) + + +def prepare_deepspeed_args(args: argparse.Namespace): + if not args.deepspeed: + return + + # To avoid RuntimeError: DataLoader worker exited unexpectedly with exit code 1. + args.max_data_loader_n_workers = 1 + + +def prepare_deepspeed_plugin(args: argparse.Namespace): + if not args.deepspeed: + return None + + try: + import deepspeed + except ImportError as e: + logger.error( + "deepspeed is not installed. please install deepspeed in your environment with following command. DS_BUILD_OPS=0 pip install deepspeed" + ) + exit(1) + + deepspeed_plugin = DeepSpeedPlugin( + zero_stage=args.zero_stage, + gradient_accumulation_steps=args.gradient_accumulation_steps, + gradient_clipping=args.max_grad_norm, + offload_optimizer_device=args.offload_optimizer_device, + offload_optimizer_nvme_path=args.offload_optimizer_nvme_path, + offload_param_device=args.offload_param_device, + offload_param_nvme_path=args.offload_param_nvme_path, + zero3_init_flag=args.zero3_init_flag, + zero3_save_16bit_model=args.zero3_save_16bit_model, + ) + deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] = args.train_batch_size + deepspeed_plugin.deepspeed_config["train_batch_size"] = ( + args.train_batch_size * args.gradient_accumulation_steps * int(os.environ["WORLD_SIZE"]) + ) + + deepspeed_plugin.set_mixed_precision(args.mixed_precision) + if args.mixed_precision.lower() == "fp16": + deepspeed_plugin.deepspeed_config["fp16"]["initial_scale_power"] = 0 # preventing overflow. + if args.full_fp16 or args.fp16_master_weights_and_gradients: + if args.offload_optimizer_device == "cpu" and args.zero_stage == 2: + deepspeed_plugin.deepspeed_config["fp16"]["fp16_master_weights_and_grads"] = True + logger.info("[DeepSpeed] full fp16 enable.") + else: + logger.info( + "[DeepSpeed]full fp16, fp16_master_weights_and_grads currently only supported using ZeRO-Offload with DeepSpeedCPUAdam on ZeRO-2 stage." + ) + + if args.offload_optimizer_device is not None: + logger.info("[DeepSpeed] start to manually build cpu_adam.") + deepspeed.ops.op_builder.CPUAdamBuilder().load() + logger.info("[DeepSpeed] building cpu_adam done.") + + return deepspeed_plugin + + +# Accelerate library does not support multiple models for deepspeed. So, we need to wrap multiple models into a single model. +def prepare_deepspeed_model(args: argparse.Namespace, **models): + # remove None from models + models = {k: v for k, v in models.items() if v is not None} + + class DeepSpeedWrapper(torch.nn.Module): + def __init__(self, **kw_models) -> None: + super().__init__() + + self.models = torch.nn.ModuleDict() + + wrap_model_forward_with_torch_autocast = args.mixed_precision != "no" + + for key, model in kw_models.items(): + if isinstance(model, list): + model = torch.nn.ModuleList(model) + + if wrap_model_forward_with_torch_autocast: + model = self.__wrap_model_with_torch_autocast(model) + + assert isinstance( + model, torch.nn.Module + ), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}" + + self.models.update(torch.nn.ModuleDict({key: model})) + + def __wrap_model_with_torch_autocast(self, model): + if isinstance(model, torch.nn.ModuleList): + model = torch.nn.ModuleList([self.__wrap_model_forward_with_torch_autocast(m) for m in model]) + else: + model = self.__wrap_model_forward_with_torch_autocast(model) + return model + + def __wrap_model_forward_with_torch_autocast(self, model): + + assert hasattr(model, "forward"), f"model must have a forward method." + + forward_fn = model.forward + + def forward(*args, **kwargs): + try: + device_type = model.device.type + except AttributeError: + logger.warning( + "[DeepSpeed] model.device is not available. Using get_preferred_device() " + "to determine the device_type for torch.autocast()." + ) + device_type = get_preferred_device().type + + with torch.autocast(device_type=device_type): + return forward_fn(*args, **kwargs) + + model.forward = forward + return model + + def get_models(self): + return self.models + + ds_model = DeepSpeedWrapper(**models) + return ds_model diff --git a/vendor/sd-scripts/library/device_utils.py b/vendor/sd-scripts/library/device_utils.py new file mode 100644 index 0000000..2d59b64 --- /dev/null +++ b/vendor/sd-scripts/library/device_utils.py @@ -0,0 +1,108 @@ +import functools +import gc +from typing import Optional, Union + +import torch + + +try: + # intel gpu support for pytorch older than 2.5 + # ipex is not needed after pytorch 2.5 + import intel_extension_for_pytorch as ipex # noqa +except Exception: + pass + + +try: + HAS_CUDA = torch.cuda.is_available() +except Exception: + HAS_CUDA = False + +try: + HAS_MPS = torch.backends.mps.is_available() +except Exception: + HAS_MPS = False + +try: + HAS_XPU = torch.xpu.is_available() +except Exception: + HAS_XPU = False + + +def clean_memory(): + gc.collect() + if HAS_CUDA: + torch.cuda.empty_cache() + if HAS_XPU: + torch.xpu.empty_cache() + if HAS_MPS: + torch.mps.empty_cache() + + +def clean_memory_on_device(device: Optional[Union[str, torch.device]]): + r""" + Clean memory on the specified device, will be called from training scripts. + """ + gc.collect() + if device is None: + return + if isinstance(device, str): + device = torch.device(device) + # device may "cuda" or "cuda:0", so we need to check the type of device + if device.type == "cuda": + torch.cuda.empty_cache() + if device.type == "xpu": + torch.xpu.empty_cache() + if device.type == "mps": + torch.mps.empty_cache() + + +def synchronize_device(device: Optional[Union[str, torch.device]]): + if device is None: + return + if isinstance(device, str): + device = torch.device(device) + if device.type == "cuda": + torch.cuda.synchronize() + elif device.type == "xpu": + torch.xpu.synchronize() + elif device.type == "mps": + torch.mps.synchronize() + + +@functools.lru_cache(maxsize=None) +def get_preferred_device() -> torch.device: + r""" + Do not call this function from training scripts. Use accelerator.device instead. + """ + if HAS_CUDA: + device = torch.device("cuda") + elif HAS_XPU: + device = torch.device("xpu") + elif HAS_MPS: + device = torch.device("mps") + else: + device = torch.device("cpu") + print(f"get_preferred_device() -> {device}") + return device + + +def init_ipex(): + """ + Apply IPEX to CUDA hijacks using `library.ipex.ipex_init`. + + This function should run right after importing torch and before doing anything else. + + If xpu is not available, this function does nothing. + """ + try: + if HAS_XPU: + from library.ipex import ipex_init + + is_initialized, error_message = ipex_init() + if not is_initialized: + print("failed to initialize ipex:", error_message) + else: + return + except Exception as e: + print("failed to initialize ipex:", e) diff --git a/vendor/sd-scripts/library/flux_models.py b/vendor/sd-scripts/library/flux_models.py new file mode 100644 index 0000000..d2d7e06 --- /dev/null +++ b/vendor/sd-scripts/library/flux_models.py @@ -0,0 +1,1329 @@ +# copy from FLUX repo: https://github.com/black-forest-labs/flux +# license: Apache-2.0 License + + +import math +import os +import time +from concurrent.futures import Future, ThreadPoolExecutor +from dataclasses import dataclass +from typing import Dict, List, Optional, Union + +from library import utils +from library.device_utils import clean_memory_on_device, init_ipex + +init_ipex() + +import torch +from einops import rearrange +from torch import Tensor, nn +from torch.utils.checkpoint import checkpoint + +from library import custom_offloading_utils + +# USE_REENTRANT = True + + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list[int] + theta: int + qkv_bias: bool + guidance_embed: bool + + +# region autoencoder + + +@dataclass +class AutoEncoderParams: + resolution: int + in_channels: int + ch: int + out_ch: int + ch_mult: list[int] + num_res_blocks: int + z_channels: int + scale_factor: float + shift_factor: float + + +def swish(x: Tensor) -> Tensor: + return x * torch.sigmoid(x) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels + + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1) + + def attention(self, h_: Tensor) -> Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous() + k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous() + v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous() + h_ = nn.functional.scaled_dot_product_attention(q, k, v) + + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x: Tensor) -> Tensor: + return x + self.proj_out(self.attention(x)) + + +class ResnetBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h = x + h = self.norm1(h) + h = swish(h) + h = self.conv1(h) + + h = self.norm2(h) + h = swish(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h + + +class Downsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + # no asymmetric padding in torch conv, must do it ourselves + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x: Tensor): + pad = (0, 1, 0, 1) + x = nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor): + x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + return x + + +class Encoder(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + ch: int, + ch_mult: list[int], + num_res_blocks: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + # downsampling + self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + block_in = self.ch + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor) -> Tensor: + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + ch: int, + out_ch: int, + ch_mult: list[int], + num_res_blocks: int, + in_channels: int, + resolution: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.ffactor = 2 ** (self.num_resolutions - 1) + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + + # z to block_in + self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z: Tensor) -> Tensor: + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class DiagonalGaussian(nn.Module): + def __init__(self, sample: bool = True, chunk_dim: int = 1): + super().__init__() + self.sample = sample + self.chunk_dim = chunk_dim + + def forward(self, z: Tensor) -> Tensor: + mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim) + if self.sample: + std = torch.exp(0.5 * logvar) + return mean + std * torch.randn_like(mean) + else: + return mean + + +class AutoEncoder(nn.Module): + def __init__(self, params: AutoEncoderParams): + super().__init__() + self.encoder = Encoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.decoder = Decoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + out_ch=params.out_ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.reg = DiagonalGaussian() + + self.scale_factor = params.scale_factor + self.shift_factor = params.shift_factor + + @property + def device(self) -> torch.device: + return next(self.parameters()).device + + @property + def dtype(self) -> torch.dtype: + return next(self.parameters()).dtype + + def encode(self, x: Tensor) -> Tensor: + z = self.reg(self.encoder(x)) + z = self.scale_factor * (z - self.shift_factor) + return z + + def decode(self, z: Tensor) -> Tensor: + z = z / self.scale_factor + self.shift_factor + return self.decoder(z) + + def forward(self, x: Tensor) -> Tensor: + return self.decode(self.encode(x)) + + +# endregion +# region config + + +@dataclass +class ModelSpec: + params: FluxParams + ae_params: AutoEncoderParams + ckpt_path: str | None + ae_path: str | None + # repo_id: str | None + # repo_flow: str | None + # repo_ae: str | None + + +configs = { + "dev": ModelSpec( + # repo_id="black-forest-labs/FLUX.1-dev", + # repo_flow="flux1-dev.sft", + # repo_ae="ae.sft", + ckpt_path=None, # os.getenv("FLUX_DEV"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=True, + ), + ae_path=None, # os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), + "schnell": ModelSpec( + # repo_id="black-forest-labs/FLUX.1-schnell", + # repo_flow="flux1-schnell.sft", + # repo_ae="ae.sft", + ckpt_path=None, # os.getenv("FLUX_SCHNELL"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=False, + ), + ae_path=None, # os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), +} + + +# endregion + +# region math + + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Optional[Tensor] = None) -> Tensor: + q, k = apply_rope(q, k, pe) + + x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + x = rearrange(x, "B H L D -> B L (H D)") + + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos, omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.float() + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]: + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + + +# endregion + + +# region layers + + +# for cpu_offload_checkpointing + + +def to_cuda(x): + if isinstance(x, torch.Tensor): + return x.cuda() + elif isinstance(x, (list, tuple)): + return [to_cuda(elem) for elem in x] + elif isinstance(x, dict): + return {k: to_cuda(v) for k, v in x.items()} + else: + return x + + +def to_cpu(x): + if isinstance(x, torch.Tensor): + return x.cpu() + elif isinstance(x, (list, tuple)): + return [to_cpu(elem) for elem in x] + elif isinstance(x, dict): + return {k: to_cpu(v) for k, v in x.items()} + else: + return x + + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list[int]): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: Tensor) -> Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + + return emb.unsqueeze(1) + + +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int): + super().__init__() + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) + self.silu = nn.SiLU() + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) + + self.gradient_checkpointing = False + + def enable_gradient_checkpointing(self): + self.gradient_checkpointing = True + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + + def _forward(self, x: Tensor) -> Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + def forward(self, *args, **kwargs): + if self.training and self.gradient_checkpointing: + return checkpoint(self._forward, *args, use_reentrant=False, **kwargs) + else: + return self._forward(*args, **kwargs) + + # def forward(self, x): + # if self.training and self.gradient_checkpointing: + # def create_custom_forward(func): + # def custom_forward(*inputs): + # return func(*inputs) + # return custom_forward + # return torch.utils.checkpoint.checkpoint(create_custom_forward(self._forward), x, use_reentrant=USE_REENTRANT) + # else: + # return self._forward(x) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.scale = nn.Parameter(torch.ones(dim)) + + def forward(self, x: Tensor): + x_dtype = x.dtype + x = x.float() + rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) + # return (x * rrms).to(dtype=x_dtype) * self.scale + return ((x * rrms) * self.scale.float()).to(dtype=x_dtype) + + +class QKNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.query_norm = RMSNorm(dim) + self.key_norm = RMSNorm(dim) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]: + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.norm = QKNorm(head_dim) + self.proj = nn.Linear(dim, dim) + + # this is not called from DoubleStreamBlock/SingleStreamBlock because they uses attention function directly + def forward(self, x: Tensor, pe: Tensor) -> Tensor: + qkv = self.qkv(x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + x = attention(q, k, v, pe=pe) + x = self.proj(x) + return x + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) + + def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]: + out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + + return ( + ModulationOut(*out[:3]), + ModulationOut(*out[3:]) if self.is_double else None, + ) + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True) + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.txt_mod = Modulation(hidden_size, double=True) + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def _forward( + self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None + ) -> tuple[Tensor, Tensor]: + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + # make attention mask if not None + attn_mask = None + if txt_attention_mask is not None: + # F.scaled_dot_product_attention expects attn_mask to be bool for binary mask + attn_mask = txt_attention_mask.to(torch.bool) # b, seq_len + attn_mask = torch.cat( + (attn_mask, torch.ones(attn_mask.shape[0], img.shape[1], device=attn_mask.device, dtype=torch.bool)), dim=1 + ) # b, seq_len + img_len + + # broadcast attn_mask to all heads + attn_mask = attn_mask[:, None, None, :].expand(-1, q.shape[1], q.shape[2], -1) + + attn = attention(q, k, v, pe=pe, attn_mask=attn_mask) + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img blocks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt blocks + txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + return img, txt + + def forward( + self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None + ) -> tuple[Tensor, Tensor]: + if self.training and self.gradient_checkpointing: + if not self.cpu_offload_checkpointing: + return checkpoint(self._forward, img, txt, vec, pe, txt_attention_mask, use_reentrant=False) + # cpu offload checkpointing + + def create_custom_forward(func): + def custom_forward(*inputs): + cuda_inputs = to_cuda(inputs) + outputs = func(*cuda_inputs) + return to_cpu(outputs) + + return custom_forward + + return torch.utils.checkpoint.checkpoint( + create_custom_forward(self._forward), img, txt, vec, pe, txt_attention_mask, use_reentrant=False + ) + + else: + return self._forward(img, txt, vec, pe, txt_attention_mask) + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float | None = None, + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + # proj and mlp_out + self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.norm = QKNorm(head_dim) + + self.hidden_size = hidden_size + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def _forward(self, x: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None) -> Tensor: + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + + # make attention mask if not None + attn_mask = None + if txt_attention_mask is not None: + # F.scaled_dot_product_attention expects attn_mask to be bool for binary mask + attn_mask = txt_attention_mask.to(torch.bool) # b, seq_len + attn_mask = torch.cat( + ( + attn_mask, + torch.ones( + attn_mask.shape[0], x.shape[1] - txt_attention_mask.shape[1], device=attn_mask.device, dtype=torch.bool + ), + ), + dim=1, + ) # b, seq_len + img_len = x_len + + # broadcast attn_mask to all heads + attn_mask = attn_mask[:, None, None, :].expand(-1, q.shape[1], q.shape[2], -1) + + # compute attention + attn = attention(q, k, v, pe=pe, attn_mask=attn_mask) + + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + return x + mod.gate * output + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None) -> Tensor: + if self.training and self.gradient_checkpointing: + if not self.cpu_offload_checkpointing: + return checkpoint(self._forward, x, vec, pe, txt_attention_mask, use_reentrant=False) + + # cpu offload checkpointing + + def create_custom_forward(func): + def custom_forward(*inputs): + cuda_inputs = to_cuda(inputs) + outputs = func(*cuda_inputs) + return to_cpu(outputs) + + return custom_forward + + return torch.utils.checkpoint.checkpoint( + create_custom_forward(self._forward), x, vec, pe, txt_attention_mask, use_reentrant=False + ) + else: + return self._forward(x, vec, pe, txt_attention_mask) + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x + + +# endregion + + +class Flux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, params: FluxParams): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}") + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) + for _ in range(params.depth_single_blocks) + ] + ) + + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + self.blocks_to_swap = None + + self.offloader_double = None + self.offloader_single = None + self.num_double_blocks = len(self.double_blocks) + self.num_single_blocks = len(self.single_blocks) + + def get_model_type(self) -> str: + return "flux" + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + self.time_in.enable_gradient_checkpointing() + self.vector_in.enable_gradient_checkpointing() + if self.guidance_in.__class__ != nn.Identity: + self.guidance_in.enable_gradient_checkpointing() + + for block in self.double_blocks + self.single_blocks: + block.enable_gradient_checkpointing(cpu_offload=cpu_offload) + + print(f"FLUX: Gradient checkpointing enabled. CPU offload: {cpu_offload}") + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + self.time_in.disable_gradient_checkpointing() + self.vector_in.disable_gradient_checkpointing() + if self.guidance_in.__class__ != nn.Identity: + self.guidance_in.disable_gradient_checkpointing() + + for block in self.double_blocks + self.single_blocks: + block.disable_gradient_checkpointing() + + print("FLUX: Gradient checkpointing disabled.") + + def enable_block_swap(self, num_blocks: int, device: torch.device): + self.blocks_to_swap = num_blocks + double_blocks_to_swap = num_blocks // 2 + single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2 + + assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, ( + f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. " + f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks." + ) + + self.offloader_double = custom_offloading_utils.ModelOffloader( + self.double_blocks, double_blocks_to_swap, device # , debug=True + ) + self.offloader_single = custom_offloading_utils.ModelOffloader( + self.single_blocks, single_blocks_to_swap, device # , debug=True + ) + print( + f"FLUX: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}." + ) + + def move_to_device_except_swap_blocks(self, device: torch.device): + # assume model is on cpu. do not move blocks to device to reduce temporary memory usage + if self.blocks_to_swap: + save_double_blocks = self.double_blocks + save_single_blocks = self.single_blocks + self.double_blocks = None + self.single_blocks = None + + self.to(device) + + if self.blocks_to_swap: + self.double_blocks = save_double_blocks + self.single_blocks = save_single_blocks + + def prepare_block_swap_before_forward(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader_double.prepare_block_devices_before_forward(self.double_blocks) + self.offloader_single.prepare_block_devices_before_forward(self.single_blocks) + + def get_mod_vectors(self, timesteps: Tensor, guidance: Tensor | None = None, batch_size: int | None = None) -> Tensor: + return None # FLUX.1 does not use mod_vectors, but Chroma does. + + def forward( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + block_controlnet_hidden_states=None, + block_controlnet_single_hidden_states=None, + guidance: Tensor | None = None, + txt_attention_mask: Tensor | None = None, + mod_vectors: Tensor | None = None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + if block_controlnet_hidden_states is not None: + controlnet_depth = len(block_controlnet_hidden_states) + if block_controlnet_single_hidden_states is not None: + controlnet_single_depth = len(block_controlnet_single_hidden_states) + + if not self.blocks_to_swap: + for block_idx, block in enumerate(self.double_blocks): + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + if block_controlnet_hidden_states is not None and controlnet_depth > 0: + img = img + block_controlnet_hidden_states[block_idx % controlnet_depth] + + img = torch.cat((txt, img), 1) + for block_idx, block in enumerate(self.single_blocks): + img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + if block_controlnet_single_hidden_states is not None and controlnet_single_depth > 0: + img = img + block_controlnet_single_hidden_states[block_idx % controlnet_single_depth] + else: + for block_idx, block in enumerate(self.double_blocks): + self.offloader_double.wait_for_block(block_idx) + + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + if block_controlnet_hidden_states is not None and controlnet_depth > 0: + img = img + block_controlnet_hidden_states[block_idx % controlnet_depth] + + self.offloader_double.submit_move_blocks(self.double_blocks, block_idx) + + img = torch.cat((txt, img), 1) + + for block_idx, block in enumerate(self.single_blocks): + self.offloader_single.wait_for_block(block_idx) + + img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + if block_controlnet_single_hidden_states is not None and controlnet_single_depth > 0: + img = img + block_controlnet_single_hidden_states[block_idx % controlnet_single_depth] + + self.offloader_single.submit_move_blocks(self.single_blocks, block_idx) + + img = img[:, txt.shape[1] :, ...] + + if self.training and self.cpu_offload_checkpointing: + img = img.to(self.device) + vec = vec.to(self.device) + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + + return img + + +def zero_module(module): + for p in module.parameters(): + nn.init.zeros_(p) + return module + + +class ControlNetFlux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, params: FluxParams, controlnet_depth=2, controlnet_single_depth=0): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}") + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(controlnet_depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) + for _ in range(controlnet_single_depth) + ] + ) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + self.blocks_to_swap = None + + self.offloader_double = None + self.offloader_single = None + self.num_double_blocks = len(self.double_blocks) + self.num_single_blocks = len(self.single_blocks) + + # add ControlNet blocks + self.controlnet_blocks = nn.ModuleList([]) + for _ in range(controlnet_depth): + controlnet_block = nn.Linear(self.hidden_size, self.hidden_size) + controlnet_block = zero_module(controlnet_block) + self.controlnet_blocks.append(controlnet_block) + self.controlnet_blocks_for_single = nn.ModuleList([]) + for _ in range(controlnet_single_depth): + controlnet_block = nn.Linear(self.hidden_size, self.hidden_size) + controlnet_block = zero_module(controlnet_block) + self.controlnet_blocks_for_single.append(controlnet_block) + self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.gradient_checkpointing = False + self.input_hint_block = nn.Sequential( + nn.Conv2d(3, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1, stride=2), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1, stride=2), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1, stride=2), + nn.SiLU(), + zero_module(nn.Conv2d(16, 16, 3, padding=1)), + ) + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + self.time_in.enable_gradient_checkpointing() + self.vector_in.enable_gradient_checkpointing() + if self.guidance_in.__class__ != nn.Identity: + self.guidance_in.enable_gradient_checkpointing() + + for block in self.double_blocks + self.single_blocks: + block.enable_gradient_checkpointing(cpu_offload=cpu_offload) + + print(f"FLUX: Gradient checkpointing enabled. CPU offload: {cpu_offload}") + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + self.time_in.disable_gradient_checkpointing() + self.vector_in.disable_gradient_checkpointing() + if self.guidance_in.__class__ != nn.Identity: + self.guidance_in.disable_gradient_checkpointing() + + for block in self.double_blocks + self.single_blocks: + block.disable_gradient_checkpointing() + + print("FLUX: Gradient checkpointing disabled.") + + def enable_block_swap(self, num_blocks: int, device: torch.device): + self.blocks_to_swap = num_blocks + double_blocks_to_swap = num_blocks // 2 + single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2 + + assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, ( + f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. " + f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks." + ) + + self.offloader_double = custom_offloading_utils.ModelOffloader( + self.double_blocks, double_blocks_to_swap, device # , debug=True + ) + self.offloader_single = custom_offloading_utils.ModelOffloader( + self.single_blocks, single_blocks_to_swap, device # , debug=True + ) + print( + f"FLUX: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}." + ) + + def move_to_device_except_swap_blocks(self, device: torch.device): + # assume model is on cpu. do not move blocks to device to reduce temporary memory usage + if self.blocks_to_swap: + save_double_blocks = self.double_blocks + save_single_blocks = self.single_blocks + self.double_blocks = nn.ModuleList() + self.single_blocks = nn.ModuleList() + + self.to(device) + + if self.blocks_to_swap: + self.double_blocks = save_double_blocks + self.single_blocks = save_single_blocks + + def prepare_block_swap_before_forward(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader_double.prepare_block_devices_before_forward(self.double_blocks) + self.offloader_single.prepare_block_devices_before_forward(self.single_blocks) + + def forward( + self, + img: Tensor, + img_ids: Tensor, + controlnet_cond: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor | None = None, + txt_attention_mask: Tensor | None = None, + ) -> tuple[tuple[Tensor]]: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + controlnet_cond = self.input_hint_block(controlnet_cond) + controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + controlnet_cond = self.pos_embed_input(controlnet_cond) + img = img + controlnet_cond + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + block_samples = () + block_single_samples = () + if not self.blocks_to_swap: + for block in self.double_blocks: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + block_samples = block_samples + (img,) + + img = torch.cat((txt, img), 1) + for block in self.single_blocks: + img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + block_single_samples = block_single_samples + (img,) + else: + for block_idx, block in enumerate(self.double_blocks): + self.offloader_double.wait_for_block(block_idx) + + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + block_samples = block_samples + (img,) + + self.offloader_double.submit_move_blocks(self.double_blocks, block_idx) + + img = torch.cat((txt, img), 1) + + for block_idx, block in enumerate(self.single_blocks): + self.offloader_single.wait_for_block(block_idx) + + img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask) + block_single_samples = block_single_samples + (img,) + + self.offloader_single.submit_move_blocks(self.single_blocks, block_idx) + + controlnet_block_samples = () + controlnet_single_block_samples = () + for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks): + block_sample = controlnet_block(block_sample) + controlnet_block_samples = controlnet_block_samples + (block_sample,) + for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks_for_single): + block_sample = controlnet_block(block_sample) + controlnet_single_block_samples = controlnet_single_block_samples + (block_sample,) + + return controlnet_block_samples, controlnet_single_block_samples diff --git a/vendor/sd-scripts/library/flux_train_utils.py b/vendor/sd-scripts/library/flux_train_utils.py new file mode 100644 index 0000000..c96e4bb --- /dev/null +++ b/vendor/sd-scripts/library/flux_train_utils.py @@ -0,0 +1,690 @@ +import argparse +import math +import os +import numpy as np +import toml +import json +import time +from typing import Callable, Dict, List, Optional, Tuple, Union + +import torch +from accelerate import Accelerator, PartialState +from transformers import CLIPTextModel +from tqdm import tqdm +from PIL import Image +from safetensors.torch import save_file + +from library import flux_models, flux_utils, strategy_base, train_util +from library.device_utils import init_ipex, clean_memory_on_device +from library.safetensors_utils import mem_eff_save_file + +init_ipex() + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +# region sample images + + +def sample_images( + accelerator: Accelerator, + args: argparse.Namespace, + epoch, + steps, + flux, + ae, + text_encoders, + sample_prompts_te_outputs, + prompt_replacement=None, + controlnet=None, +): + if steps == 0: + if not args.sample_at_first: + return + else: + if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: + return + if args.sample_every_n_epochs is not None: + # sample_every_n_steps は無視する + if epoch is None or epoch % args.sample_every_n_epochs != 0: + return + else: + if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch + return + + logger.info("") + logger.info(f"generating sample images at step / サンプル画像生成 ステップ: {steps}") + if not os.path.isfile(args.sample_prompts) and sample_prompts_te_outputs is None: + logger.error(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}") + return + + distributed_state = PartialState() # for multi gpu distributed inference. this is a singleton, so it's safe to use it here + + # unwrap unet and text_encoder(s) + flux = accelerator.unwrap_model(flux) + if text_encoders is not None: + text_encoders = [(accelerator.unwrap_model(te) if te is not None else None) for te in text_encoders] + if controlnet is not None: + controlnet = accelerator.unwrap_model(controlnet) + # print([(te.parameters().__next__().device if te is not None else None) for te in text_encoders]) + + prompts = train_util.load_prompts(args.sample_prompts) + + save_dir = args.output_dir + "/sample" + os.makedirs(save_dir, exist_ok=True) + + # save random state to restore later + rng_state = torch.get_rng_state() + cuda_rng_state = None + try: + cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None + except Exception: + pass + + if distributed_state.num_processes <= 1: + # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts. + with torch.no_grad(), accelerator.autocast(): + for prompt_dict in prompts: + sample_image_inference( + accelerator, + args, + flux, + text_encoders, + ae, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, + controlnet, + ) + else: + # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available) + # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical. + per_process_prompts = [] # list of lists + for i in range(distributed_state.num_processes): + per_process_prompts.append(prompts[i :: distributed_state.num_processes]) + + with torch.no_grad(): + with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists: + for prompt_dict in prompt_dict_lists[0]: + sample_image_inference( + accelerator, + args, + flux, + text_encoders, + ae, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, + controlnet, + ) + + torch.set_rng_state(rng_state) + if cuda_rng_state is not None: + torch.cuda.set_rng_state(cuda_rng_state) + + clean_memory_on_device(accelerator.device) + + +def sample_image_inference( + accelerator: Accelerator, + args: argparse.Namespace, + flux: flux_models.Flux, + text_encoders: Optional[List[CLIPTextModel]], + ae: flux_models.AutoEncoder, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, + controlnet, +): + assert isinstance(prompt_dict, dict) + negative_prompt = prompt_dict.get("negative_prompt") + sample_steps = prompt_dict.get("sample_steps", 20) + width = prompt_dict.get("width", 512) + height = prompt_dict.get("height", 512) + emb_guidance_scale = prompt_dict.get("guidance_scale", 3.5) + cfg_scale = prompt_dict.get("scale", 1.0) + seed = prompt_dict.get("seed") + controlnet_image = prompt_dict.get("controlnet_image") + prompt: str = prompt_dict.get("prompt", "") + # sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler) + + if prompt_replacement is not None: + prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1]) + if negative_prompt is not None: + negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1]) + + if seed is not None: + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + else: + # True random sample image generation + torch.seed() + torch.cuda.seed() + + if negative_prompt is None: + negative_prompt = "" + height = max(64, height - height % 16) # round to divisible by 16 + width = max(64, width - width % 16) # round to divisible by 16 + logger.info(f"prompt: {prompt}") + if cfg_scale != 1.0: + logger.info(f"negative_prompt: {negative_prompt}") + elif negative_prompt != "": + logger.info(f"negative prompt is ignored because scale is 1.0") + logger.info(f"height: {height}") + logger.info(f"width: {width}") + logger.info(f"sample_steps: {sample_steps}") + logger.info(f"embedded guidance scale: {emb_guidance_scale}") + if cfg_scale != 1.0: + logger.info(f"CFG scale: {cfg_scale}") + # logger.info(f"sample_sampler: {sampler_name}") + if seed is not None: + logger.info(f"seed: {seed}") + + # encode prompts + tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() + + def encode_prompt(prpt): + text_encoder_conds = [] + if sample_prompts_te_outputs and prpt in sample_prompts_te_outputs: + text_encoder_conds = sample_prompts_te_outputs[prpt] + print(f"Using cached text encoder outputs for prompt: {prpt}") + if text_encoders is not None: + print(f"Encoding prompt: {prpt}") + tokens_and_masks = tokenize_strategy.tokenize(prpt) + # strategy has apply_t5_attn_mask option + encoded_text_encoder_conds = encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens_and_masks) + + # if text_encoder_conds is not cached, use encoded_text_encoder_conds + if len(text_encoder_conds) == 0: + text_encoder_conds = encoded_text_encoder_conds + else: + # if encoded_text_encoder_conds is not None, update cached text_encoder_conds + for i in range(len(encoded_text_encoder_conds)): + if encoded_text_encoder_conds[i] is not None: + text_encoder_conds[i] = encoded_text_encoder_conds[i] + return text_encoder_conds + + l_pooled, t5_out, txt_ids, t5_attn_mask = encode_prompt(prompt) + # encode negative prompts + if cfg_scale != 1.0: + neg_l_pooled, neg_t5_out, _, neg_t5_attn_mask = encode_prompt(negative_prompt) + neg_t5_attn_mask = ( + neg_t5_attn_mask.to(accelerator.device) if args.apply_t5_attn_mask and neg_t5_attn_mask is not None else None + ) + neg_cond = (cfg_scale, neg_l_pooled, neg_t5_out, neg_t5_attn_mask) + else: + neg_cond = None + + # sample image + weight_dtype = ae.dtype # TOFO give dtype as argument + packed_latent_height = height // 16 + packed_latent_width = width // 16 + noise = torch.randn( + 1, + packed_latent_height * packed_latent_width, + 16 * 2 * 2, + device=accelerator.device, + dtype=weight_dtype, + generator=torch.Generator(device=accelerator.device).manual_seed(seed) if seed is not None else None, + ) + timesteps = get_schedule(sample_steps, noise.shape[1], shift=True) # Chroma can use shift=True + img_ids = flux_utils.prepare_img_ids(1, packed_latent_height, packed_latent_width).to(accelerator.device, weight_dtype) + t5_attn_mask = t5_attn_mask.to(accelerator.device) if args.apply_t5_attn_mask else None + + if controlnet_image is not None: + controlnet_image = Image.open(controlnet_image).convert("RGB") + controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS) + controlnet_image = torch.from_numpy((np.array(controlnet_image) / 127.5) - 1) + controlnet_image = controlnet_image.permute(2, 0, 1).unsqueeze(0).to(weight_dtype).to(accelerator.device) + + with accelerator.autocast(), torch.no_grad(): + x = denoise( + flux, + noise, + img_ids, + t5_out, + txt_ids, + l_pooled, + timesteps=timesteps, + guidance=emb_guidance_scale, + t5_attn_mask=t5_attn_mask, + controlnet=controlnet, + controlnet_img=controlnet_image, + neg_cond=neg_cond, + ) + + x = flux_utils.unpack_latents(x, packed_latent_height, packed_latent_width) + + # latent to image + clean_memory_on_device(accelerator.device) + org_vae_device = ae.device # will be on cpu + ae.to(accelerator.device) # distributed_state.device is same as accelerator.device + with accelerator.autocast(), torch.no_grad(): + x = ae.decode(x) + ae.to(org_vae_device) + clean_memory_on_device(accelerator.device) + + x = x.clamp(-1, 1) + x = x.permute(0, 2, 3, 1) + image = Image.fromarray((127.5 * (x + 1.0)).float().cpu().numpy().astype(np.uint8)[0]) + + # adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list + # but adding 'enum' to the filename should be enough + + ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime()) + num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}" + seed_suffix = "" if seed is None else f"_{seed}" + i: int = prompt_dict["enum"] + img_filename = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png" + image.save(os.path.join(save_dir, img_filename)) + + # send images to wandb if enabled + if "wandb" in [tracker.name for tracker in accelerator.trackers]: + wandb_tracker = accelerator.get_tracker("wandb") + + import wandb + + # not to commit images to avoid inconsistency between training and logging steps + wandb_tracker.log({f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False) # positive prompt as a caption + + +def time_shift(mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + +def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]: + m = (y2 - y1) / (x2 - x1) + b = y1 - m * x1 + return lambda x: m * x + b + + +def get_schedule( + num_steps: int, + image_seq_len: int, + base_shift: float = 0.5, + max_shift: float = 1.15, + shift: bool = True, +) -> list[float]: + # extra step for zero + timesteps = torch.linspace(1, 0, num_steps + 1) + + # shifting the schedule to favor high timesteps for higher signal images + if shift: + # eastimate mu based on linear estimation between two points + mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len) + timesteps = time_shift(mu, 1.0, timesteps) + + return timesteps.tolist() + + +def denoise( + model: flux_models.Flux, + img: torch.Tensor, + img_ids: torch.Tensor, + txt: torch.Tensor, # t5_out + txt_ids: torch.Tensor, + vec: torch.Tensor, # l_pooled + timesteps: list[float], + guidance: float = 4.0, + t5_attn_mask: Optional[torch.Tensor] = None, + controlnet: Optional[flux_models.ControlNetFlux] = None, + controlnet_img: Optional[torch.Tensor] = None, + neg_cond: Optional[Tuple[float, torch.Tensor, torch.Tensor, torch.Tensor]] = None, +): + # this is ignored for schnell + guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) + do_cfg = neg_cond is not None + + for t_curr, t_prev in zip(tqdm(timesteps[:-1]), timesteps[1:]): + t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device) + model.prepare_block_swap_before_forward() + + if controlnet is not None: + block_samples, block_single_samples = controlnet( + img=img, + img_ids=img_ids, + controlnet_cond=controlnet_img, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + txt_attention_mask=t5_attn_mask, + ) + else: + block_samples = None + block_single_samples = None + + if not do_cfg: + pred = model( + img=img, + img_ids=img_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + block_controlnet_hidden_states=block_samples, + block_controlnet_single_hidden_states=block_single_samples, + timesteps=t_vec, + guidance=guidance_vec, + txt_attention_mask=t5_attn_mask, + ) + + img = img + (t_prev - t_curr) * pred + else: + cfg_scale, neg_l_pooled, neg_t5_out, neg_t5_attn_mask = neg_cond + nc_c_t5_attn_mask = None if t5_attn_mask is None else torch.cat([neg_t5_attn_mask, t5_attn_mask], dim=0) + + # TODO is it ok to use the same block samples for both cond and uncond? + block_samples = None if block_samples is None else torch.cat([block_samples, block_samples], dim=0) + block_single_samples = ( + None if block_single_samples is None else torch.cat([block_single_samples, block_single_samples], dim=0) + ) + + nc_c_pred = model( + img=torch.cat([img, img], dim=0), + img_ids=torch.cat([img_ids, img_ids], dim=0), + txt=torch.cat([neg_t5_out, txt], dim=0), + txt_ids=torch.cat([txt_ids, txt_ids], dim=0), + y=torch.cat([neg_l_pooled, vec], dim=0), + block_controlnet_hidden_states=block_samples, + block_controlnet_single_hidden_states=block_single_samples, + timesteps=t_vec.repeat(2), + guidance=guidance_vec.repeat(2), + txt_attention_mask=nc_c_t5_attn_mask, + ) + neg_pred, pred = torch.chunk(nc_c_pred, 2, dim=0) + pred = neg_pred + (pred - neg_pred) * cfg_scale + + img = img + (t_prev - t_curr) * pred + + model.prepare_block_swap_before_forward() + return img + + +# endregion + + +# region train +def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32): + sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype) + schedule_timesteps = noise_scheduler.timesteps.to(device) + timesteps = timesteps.to(device) + step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] + + sigma = sigmas[step_indices].flatten() + return sigma + + +def compute_density_for_timestep_sampling( + weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None +): + """Compute the density for sampling the timesteps when doing SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + """ + if weighting_scheme == "logit_normal": + # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$). + u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu") + u = torch.nn.functional.sigmoid(u) + elif weighting_scheme == "mode": + u = torch.rand(size=(batch_size,), device="cpu") + u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u) + else: + u = torch.rand(size=(batch_size,), device="cpu") + return u + + +def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None): + """Computes loss weighting scheme for SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + """ + if weighting_scheme == "sigma_sqrt": + weighting = (sigmas**-2.0).float() + elif weighting_scheme == "cosmap": + bot = 1 - 2 * sigmas + 2 * sigmas**2 + weighting = 2 / (math.pi * bot) + else: + weighting = torch.ones_like(sigmas) + return weighting + + +def get_noisy_model_input_and_timesteps( + args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + bsz, h, w = latents.shape[0], latents.shape[-2], latents.shape[-1] + assert bsz > 0, "Batch size not large enough" + num_timesteps = noise_scheduler.config.num_train_timesteps + if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid": + # Simple random sigma-based noise sampling + if args.timestep_sampling == "sigmoid": + # https://github.com/XLabs-AI/x-flux/tree/main + sigmas = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device)) + else: + sigmas = torch.rand((bsz,), device=device) + + timesteps = sigmas * num_timesteps + elif args.timestep_sampling == "shift": + shift = args.discrete_flow_shift + sigmas = torch.randn(bsz, device=device) + sigmas = sigmas * args.sigmoid_scale # larger scale for more uniform sampling + sigmas = sigmas.sigmoid() + sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas) + timesteps = sigmas * num_timesteps + elif args.timestep_sampling == "flux_shift": + sigmas = torch.randn(bsz, device=device) + sigmas = sigmas * args.sigmoid_scale # larger scale for more uniform sampling + sigmas = sigmas.sigmoid() + mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2)) # we are pre-packed so must adjust for packed size + sigmas = time_shift(mu, 1.0, sigmas) + timesteps = sigmas * num_timesteps + else: + # Sample a random timestep for each image + # for weighting schemes where we sample timesteps non-uniformly + u = compute_density_for_timestep_sampling( + weighting_scheme=args.weighting_scheme, + batch_size=bsz, + logit_mean=args.logit_mean, + logit_std=args.logit_std, + mode_scale=args.mode_scale, + ) + indices = (u * num_timesteps).long() + timesteps = noise_scheduler.timesteps[indices].to(device=device) + sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype) + + # Broadcast sigmas to latent shape + sigmas = sigmas.view(-1, 1, 1, 1) if latents.ndim == 4 else sigmas.view(-1, 1, 1, 1, 1) + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + if args.ip_noise_gamma: + xi = torch.randn_like(latents, device=latents.device, dtype=dtype) + if args.ip_noise_gamma_random_strength: + ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma + else: + ip_noise_gamma = args.ip_noise_gamma + noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi) + else: + noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise + + return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas + + +def apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas): + weighting = None + if args.model_prediction_type == "raw": + pass + elif args.model_prediction_type == "additive": + # add the model_pred to the noisy_model_input + model_pred = model_pred + noisy_model_input + elif args.model_prediction_type == "sigma_scaled": + # apply sigma scaling + model_pred = model_pred * (-sigmas) + noisy_model_input + + # these weighting schemes use a uniform timestep sampling + # and instead post-weight the loss + weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas) + + return model_pred, weighting + + +def save_models( + ckpt_path: str, + flux: flux_models.Flux, + sai_metadata: Optional[dict], + save_dtype: Optional[torch.dtype] = None, + use_mem_eff_save: bool = False, +): + state_dict = {} + + def update_sd(prefix, sd): + for k, v in sd.items(): + key = prefix + k + if save_dtype is not None and v.dtype != save_dtype: + v = v.detach().clone().to("cpu").to(save_dtype) + state_dict[key] = v + + update_sd("", flux.state_dict()) + + if not use_mem_eff_save: + save_file(state_dict, ckpt_path, metadata=sai_metadata) + else: + mem_eff_save_file(state_dict, ckpt_path, metadata=sai_metadata) + + +def save_flux_model_on_train_end( + args: argparse.Namespace, save_dtype: torch.dtype, epoch: int, global_step: int, flux: flux_models.Flux +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=True, flux="dev") + save_models(ckpt_file, flux, sai_metadata, save_dtype, args.mem_eff_save) + + train_util.save_sd_model_on_train_end_common(args, True, True, epoch, global_step, sd_saver, None) + + +# epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合している +# on_epoch_end: Trueならepoch終了時、Falseならstep経過時 +def save_flux_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + flux: flux_models.Flux, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=True, flux="dev") + save_models(ckpt_file, flux, sai_metadata, save_dtype, args.mem_eff_save) + + train_util.save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + True, + True, + epoch, + num_train_epochs, + global_step, + sd_saver, + None, + ) + + +# endregion + + +def add_flux_train_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--clip_l", + type=str, + help="path to clip_l (*.sft or *.safetensors), should be float16 / clip_lのパス（*.sftまたは*.safetensors）、float16が前提", + ) + parser.add_argument( + "--t5xxl", + type=str, + help="path to t5xxl (*.sft or *.safetensors), should be float16 / t5xxlのパス（*.sftまたは*.safetensors）、float16が前提", + ) + parser.add_argument("--ae", type=str, help="path to ae (*.sft or *.safetensors) / aeのパス（*.sftまたは*.safetensors）") + parser.add_argument( + "--controlnet_model_name_or_path", + type=str, + default=None, + help="path to controlnet (*.sft or *.safetensors) / controlnetのパス（*.sftまたは*.safetensors）", + ) + parser.add_argument( + "--t5xxl_max_token_length", + type=int, + default=None, + help="maximum token length for T5-XXL. if omitted, 256 for schnell and 512 for dev" + " / T5-XXLの最大トークン長。省略された場合、schnellの場合は256、devの場合は512", + ) + parser.add_argument( + "--apply_t5_attn_mask", + action="store_true", + help="apply attention mask to T5-XXL encode and FLUX double blocks / T5-XXLエンコードとFLUXダブルブロックにアテンションマスクを適用する", + ) + + parser.add_argument( + "--guidance_scale", + type=float, + default=3.5, + help="the FLUX.1 dev variant is a guidance distilled model", + ) + + parser.add_argument( + "--timestep_sampling", + choices=["sigma", "uniform", "sigmoid", "shift", "flux_shift"], + default="sigma", + help="Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal, shift of sigmoid and FLUX.1 shifting." + " / タイムステップをサンプリングする方法：sigma、random uniform、random normalのsigmoid、sigmoidのシフト、FLUX.1のシフト。", + ) + parser.add_argument( + "--sigmoid_scale", + type=float, + default=1.0, + help='Scale factor for sigmoid timestep sampling (only used when timestep-sampling is "sigmoid"). / sigmoidタイムステップサンプリングの倍率（timestep-samplingが"sigmoid"の場合のみ有効）。', + ) + parser.add_argument( + "--model_prediction_type", + choices=["raw", "additive", "sigma_scaled"], + default="sigma_scaled", + help="How to interpret and process the model prediction: " + "raw (use as is), additive (add to noisy input), sigma_scaled (apply sigma scaling)." + " / モデル予測の解釈と処理方法：" + "raw（そのまま使用）、additive（ノイズ入力に加算）、sigma_scaled（シグマスケーリングを適用）。", + ) + parser.add_argument( + "--discrete_flow_shift", + type=float, + default=3.0, + help="Discrete flow shift for the Euler Discrete Scheduler, default is 3.0. / Euler Discrete Schedulerの離散フローシフト、デフォルトは3.0。", + ) + + parser.add_argument( + "--model_type", + type=str, + choices=["flux", "chroma"], + default="flux", + help="Model type to use for training / トレーニングに使用するモデルタイプ：flux or chroma (default: flux)", + ) diff --git a/vendor/sd-scripts/library/flux_utils.py b/vendor/sd-scripts/library/flux_utils.py new file mode 100644 index 0000000..410b34c --- /dev/null +++ b/vendor/sd-scripts/library/flux_utils.py @@ -0,0 +1,563 @@ +import json +import os +from dataclasses import replace +from typing import List, Optional, Tuple, Union + +import einops +import torch +from accelerate import init_empty_weights +from safetensors import safe_open +from safetensors.torch import load_file +from transformers import CLIPConfig, CLIPTextModel, T5Config, T5EncoderModel + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +from library import flux_models +from library.safetensors_utils import load_safetensors + +MODEL_VERSION_FLUX_V1 = "flux1" +MODEL_NAME_DEV = "dev" +MODEL_NAME_SCHNELL = "schnell" +MODEL_VERSION_CHROMA = "chroma" + + +def analyze_checkpoint_state(ckpt_path: str) -> Tuple[bool, bool, Tuple[int, int], List[str]]: + """ + チェックポイントの状態を分析し、DiffusersかBFLか、devかschnellか、ブロック数を計算して返す。 + + Args: + ckpt_path (str): チェックポイントファイルまたはディレクトリのパス。 + + Returns: + Tuple[bool, bool, Tuple[int, int], List[str]]: + - bool: Diffusersかどうかを示すフラグ。 + - bool: Schnellかどうかを示すフラグ。 + - Tuple[int, int]: ダブルブロックとシングルブロックの数。 + - List[str]: チェックポイントに含まれるキーのリスト。 + """ + # check the state dict: Diffusers or BFL, dev or schnell, number of blocks + logger.info(f"Checking the state dict: Diffusers or BFL, dev or schnell") + + if os.path.isdir(ckpt_path): # if ckpt_path is a directory, it is Diffusers + ckpt_path = os.path.join(ckpt_path, "transformer", "diffusion_pytorch_model-00001-of-00003.safetensors") + if "00001-of-00003" in ckpt_path: + ckpt_paths = [ckpt_path.replace("00001-of-00003", f"0000{i}-of-00003") for i in range(1, 4)] + else: + ckpt_paths = [ckpt_path] + + keys = [] + for ckpt_path in ckpt_paths: + with safe_open(ckpt_path, framework="pt") as f: + keys.extend(f.keys()) + + # if the key has annoying prefix, remove it + if keys[0].startswith("model.diffusion_model."): + keys = [key.replace("model.diffusion_model.", "") for key in keys] + + is_diffusers = "transformer_blocks.0.attn.add_k_proj.bias" in keys + is_schnell = not ("guidance_in.in_layer.bias" in keys or "time_text_embed.guidance_embedder.linear_1.bias" in keys) + + # check number of double and single blocks + if not is_diffusers: + max_double_block_index = max( + [int(key.split(".")[1]) for key in keys if key.startswith("double_blocks.") and key.endswith(".img_attn.proj.bias")] + ) + max_single_block_index = max( + [int(key.split(".")[1]) for key in keys if key.startswith("single_blocks.") and key.endswith(".modulation.lin.bias")] + ) + else: + max_double_block_index = max( + [ + int(key.split(".")[1]) + for key in keys + if key.startswith("transformer_blocks.") and key.endswith(".attn.add_k_proj.bias") + ] + ) + max_single_block_index = max( + [ + int(key.split(".")[1]) + for key in keys + if key.startswith("single_transformer_blocks.") and key.endswith(".attn.to_k.bias") + ] + ) + + num_double_blocks = max_double_block_index + 1 + num_single_blocks = max_single_block_index + 1 + + return is_diffusers, is_schnell, (num_double_blocks, num_single_blocks), ckpt_paths + + +def load_flow_model( + ckpt_path: str, + dtype: Optional[torch.dtype], + device: Union[str, torch.device], + disable_mmap: bool = False, + model_type: str = "flux", +) -> Tuple[bool, flux_models.Flux]: + if model_type == "flux": + is_diffusers, is_schnell, (num_double_blocks, num_single_blocks), ckpt_paths = analyze_checkpoint_state(ckpt_path) + name = MODEL_NAME_DEV if not is_schnell else MODEL_NAME_SCHNELL + + # build model + logger.info(f"Building Flux model {name} from {'Diffusers' if is_diffusers else 'BFL'} checkpoint") + with torch.device("meta"): + params = flux_models.configs[name].params + + # set the number of blocks + if params.depth != num_double_blocks: + logger.info(f"Setting the number of double blocks from {params.depth} to {num_double_blocks}") + params = replace(params, depth=num_double_blocks) + if params.depth_single_blocks != num_single_blocks: + logger.info(f"Setting the number of single blocks from {params.depth_single_blocks} to {num_single_blocks}") + params = replace(params, depth_single_blocks=num_single_blocks) + + model = flux_models.Flux(params) + if dtype is not None: + model = model.to(dtype) + + # load_sft doesn't support torch.device + logger.info(f"Loading state dict from {ckpt_path}") + sd = {} + for ckpt_path in ckpt_paths: + sd.update(load_safetensors(ckpt_path, device=device, disable_mmap=disable_mmap, dtype=dtype)) + + # convert Diffusers to BFL + if is_diffusers: + logger.info("Converting Diffusers to BFL") + sd = convert_diffusers_sd_to_bfl(sd, num_double_blocks, num_single_blocks) + logger.info("Converted Diffusers to BFL") + + # if the key has annoying prefix, remove it + for key in list(sd.keys()): + new_key = key.replace("model.diffusion_model.", "") + if new_key == key: + break # the model doesn't have annoying prefix + sd[new_key] = sd.pop(key) + + info = model.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded Flux: {info}") + return is_schnell, model + + elif model_type == "chroma": + from . import chroma_models + + # build model + logger.info("Building Chroma model") + with torch.device("meta"): + model = chroma_models.Chroma(chroma_models.chroma_params) + if dtype is not None: + model = model.to(dtype) + + # load_sft doesn't support torch.device + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + + # if the key has annoying prefix, remove it + for key in list(sd.keys()): + new_key = key.replace("model.diffusion_model.", "") + if new_key == key: + break # the model doesn't have annoying prefix + sd[new_key] = sd.pop(key) + + info = model.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded Chroma: {info}") + is_schnell = False # Chroma is not schnell + return is_schnell, model + + else: + raise ValueError(f"Unsupported model_type: {model_type}. Supported types are 'flux' and 'chroma'.") + + +def load_ae( + ckpt_path: str, dtype: torch.dtype, device: Union[str, torch.device], disable_mmap: bool = False +) -> flux_models.AutoEncoder: + logger.info("Building AutoEncoder") + with torch.device("meta"): + # dev and schnell have the same AE params + ae = flux_models.AutoEncoder(flux_models.configs[MODEL_NAME_DEV].ae_params).to(dtype) + + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + info = ae.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded AE: {info}") + return ae + + +def load_controlnet( + ckpt_path: Optional[str], is_schnell: bool, dtype: torch.dtype, device: Union[str, torch.device], disable_mmap: bool = False +): + logger.info("Building ControlNet") + name = MODEL_NAME_DEV if not is_schnell else MODEL_NAME_SCHNELL + with torch.device(device): + controlnet = flux_models.ControlNetFlux(flux_models.configs[name].params).to(dtype) + + if ckpt_path is not None: + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + info = controlnet.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded ControlNet: {info}") + return controlnet + + +def dummy_clip_l() -> torch.nn.Module: + """ + Returns a dummy CLIP-L model with the output shape of (N, 77, 768). + """ + return DummyCLIPL() + + +class DummyTextModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.embeddings = torch.nn.Parameter(torch.zeros(1)) + + +class DummyCLIPL(torch.nn.Module): + def __init__(self): + super().__init__() + self.output_shape = (77, 1) # Note: The original code had (77, 768), but we use (77, 1) for the dummy output + + # dtype and device from these parameters. train_network.py accesses them + self.dummy_param = torch.nn.Parameter(torch.zeros(1)) + self.dummy_param_2 = torch.nn.Parameter(torch.zeros(1)) + self.dummy_param_3 = torch.nn.Parameter(torch.zeros(1)) + self.text_model = DummyTextModel() + + @property + def device(self): + return self.dummy_param.device + + @property + def dtype(self): + return self.dummy_param.dtype + + def forward(self, *args, **kwargs): + """ + Returns a dummy output with the shape of (N, 77, 768). + """ + batch_size = args[0].shape[0] if args else 1 + return {"pooler_output": torch.zeros(batch_size, *self.output_shape, device=self.device, dtype=self.dtype)} + + +def load_clip_l( + ckpt_path: Optional[str], + dtype: torch.dtype, + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[dict] = None, +) -> CLIPTextModel: + logger.info("Building CLIP-L") + CLIPL_CONFIG = { + "_name_or_path": "clip-vit-large-patch14/", + "architectures": ["CLIPModel"], + "initializer_factor": 1.0, + "logit_scale_init_value": 2.6592, + "model_type": "clip", + "projection_dim": 768, + # "text_config": { + "_name_or_path": "", + "add_cross_attention": False, + "architectures": None, + "attention_dropout": 0.0, + "bad_words_ids": None, + "bos_token_id": 0, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": None, + "decoder_start_token_id": None, + "diversity_penalty": 0.0, + "do_sample": False, + "dropout": 0.0, + "early_stopping": False, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": 2, + "finetuning_task": None, + "forced_bos_token_id": None, + "forced_eos_token_id": None, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "is_decoder": False, + "is_encoder_decoder": False, + "label2id": {"LABEL_0": 0, "LABEL_1": 1}, + "layer_norm_eps": 1e-05, + "length_penalty": 1.0, + "max_length": 20, + "max_position_embeddings": 77, + "min_length": 0, + "model_type": "clip_text_model", + "no_repeat_ngram_size": 0, + "num_attention_heads": 12, + "num_beam_groups": 1, + "num_beams": 1, + "num_hidden_layers": 12, + "num_return_sequences": 1, + "output_attentions": False, + "output_hidden_states": False, + "output_scores": False, + "pad_token_id": 1, + "prefix": None, + "problem_type": None, + "projection_dim": 768, + "pruned_heads": {}, + "remove_invalid_values": False, + "repetition_penalty": 1.0, + "return_dict": True, + "return_dict_in_generate": False, + "sep_token_id": None, + "task_specific_params": None, + "temperature": 1.0, + "tie_encoder_decoder": False, + "tie_word_embeddings": True, + "tokenizer_class": None, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": None, + "torchscript": False, + "transformers_version": "4.16.0.dev0", + "use_bfloat16": False, + "vocab_size": 49408, + "hidden_act": "gelu", + "hidden_size": 1280, + "intermediate_size": 5120, + "num_attention_heads": 20, + "num_hidden_layers": 32, + # }, + # "text_config_dict": { + "hidden_size": 768, + "intermediate_size": 3072, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "projection_dim": 768, + # }, + # "torch_dtype": "float32", + # "transformers_version": None, + } + config = CLIPConfig(**CLIPL_CONFIG) + with init_empty_weights(): + clip = CLIPTextModel._from_config(config) + + if state_dict is not None: + sd = state_dict + else: + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + info = clip.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded CLIP-L: {info}") + return clip + + +def load_t5xxl( + ckpt_path: str, + dtype: Optional[torch.dtype], + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[dict] = None, +) -> T5EncoderModel: + T5_CONFIG_JSON = """ +{ + "architectures": [ + "T5EncoderModel" + ], + "classifier_dropout": 0.0, + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dense_act_fn": "gelu_new", + "dropout_rate": 0.1, + "eos_token_id": 1, + "feed_forward_proj": "gated-gelu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_max_distance": 128, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "torch_dtype": "float16", + "transformers_version": "4.41.2", + "use_cache": true, + "vocab_size": 32128 +} +""" + config = json.loads(T5_CONFIG_JSON) + config = T5Config(**config) + with init_empty_weights(): + t5xxl = T5EncoderModel._from_config(config) + + if state_dict is not None: + sd = state_dict + else: + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + info = t5xxl.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded T5xxl: {info}") + return t5xxl + + +def get_t5xxl_actual_dtype(t5xxl: T5EncoderModel) -> torch.dtype: + # nn.Embedding is the first layer, but it could be casted to bfloat16 or float32 + return t5xxl.encoder.block[0].layer[0].SelfAttention.q.weight.dtype + + +def prepare_img_ids(batch_size: int, packed_latent_height: int, packed_latent_width: int): + img_ids = torch.zeros(packed_latent_height, packed_latent_width, 3) + img_ids[..., 1] = img_ids[..., 1] + torch.arange(packed_latent_height)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.arange(packed_latent_width)[None, :] + img_ids = einops.repeat(img_ids, "h w c -> b (h w) c", b=batch_size) + return img_ids + + +def unpack_latents(x: torch.Tensor, packed_latent_height: int, packed_latent_width: int) -> torch.Tensor: + """ + x: [b (h w) (c ph pw)] -> [b c (h ph) (w pw)], ph=2, pw=2 + """ + x = einops.rearrange(x, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=packed_latent_height, w=packed_latent_width, ph=2, pw=2) + return x + + +def pack_latents(x: torch.Tensor) -> torch.Tensor: + """ + x: [b c (h ph) (w pw)] -> [b (h w) (c ph pw)], ph=2, pw=2 + """ + x = einops.rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + return x + + +# region Diffusers + +NUM_DOUBLE_BLOCKS = 19 +NUM_SINGLE_BLOCKS = 38 + +BFL_TO_DIFFUSERS_MAP = { + "time_in.in_layer.weight": ["time_text_embed.timestep_embedder.linear_1.weight"], + "time_in.in_layer.bias": ["time_text_embed.timestep_embedder.linear_1.bias"], + "time_in.out_layer.weight": ["time_text_embed.timestep_embedder.linear_2.weight"], + "time_in.out_layer.bias": ["time_text_embed.timestep_embedder.linear_2.bias"], + "vector_in.in_layer.weight": ["time_text_embed.text_embedder.linear_1.weight"], + "vector_in.in_layer.bias": ["time_text_embed.text_embedder.linear_1.bias"], + "vector_in.out_layer.weight": ["time_text_embed.text_embedder.linear_2.weight"], + "vector_in.out_layer.bias": ["time_text_embed.text_embedder.linear_2.bias"], + "guidance_in.in_layer.weight": ["time_text_embed.guidance_embedder.linear_1.weight"], + "guidance_in.in_layer.bias": ["time_text_embed.guidance_embedder.linear_1.bias"], + "guidance_in.out_layer.weight": ["time_text_embed.guidance_embedder.linear_2.weight"], + "guidance_in.out_layer.bias": ["time_text_embed.guidance_embedder.linear_2.bias"], + "txt_in.weight": ["context_embedder.weight"], + "txt_in.bias": ["context_embedder.bias"], + "img_in.weight": ["x_embedder.weight"], + "img_in.bias": ["x_embedder.bias"], + "double_blocks.().img_mod.lin.weight": ["norm1.linear.weight"], + "double_blocks.().img_mod.lin.bias": ["norm1.linear.bias"], + "double_blocks.().txt_mod.lin.weight": ["norm1_context.linear.weight"], + "double_blocks.().txt_mod.lin.bias": ["norm1_context.linear.bias"], + "double_blocks.().img_attn.qkv.weight": ["attn.to_q.weight", "attn.to_k.weight", "attn.to_v.weight"], + "double_blocks.().img_attn.qkv.bias": ["attn.to_q.bias", "attn.to_k.bias", "attn.to_v.bias"], + "double_blocks.().txt_attn.qkv.weight": ["attn.add_q_proj.weight", "attn.add_k_proj.weight", "attn.add_v_proj.weight"], + "double_blocks.().txt_attn.qkv.bias": ["attn.add_q_proj.bias", "attn.add_k_proj.bias", "attn.add_v_proj.bias"], + "double_blocks.().img_attn.norm.query_norm.scale": ["attn.norm_q.weight"], + "double_blocks.().img_attn.norm.key_norm.scale": ["attn.norm_k.weight"], + "double_blocks.().txt_attn.norm.query_norm.scale": ["attn.norm_added_q.weight"], + "double_blocks.().txt_attn.norm.key_norm.scale": ["attn.norm_added_k.weight"], + "double_blocks.().img_mlp.0.weight": ["ff.net.0.proj.weight"], + "double_blocks.().img_mlp.0.bias": ["ff.net.0.proj.bias"], + "double_blocks.().img_mlp.2.weight": ["ff.net.2.weight"], + "double_blocks.().img_mlp.2.bias": ["ff.net.2.bias"], + "double_blocks.().txt_mlp.0.weight": ["ff_context.net.0.proj.weight"], + "double_blocks.().txt_mlp.0.bias": ["ff_context.net.0.proj.bias"], + "double_blocks.().txt_mlp.2.weight": ["ff_context.net.2.weight"], + "double_blocks.().txt_mlp.2.bias": ["ff_context.net.2.bias"], + "double_blocks.().img_attn.proj.weight": ["attn.to_out.0.weight"], + "double_blocks.().img_attn.proj.bias": ["attn.to_out.0.bias"], + "double_blocks.().txt_attn.proj.weight": ["attn.to_add_out.weight"], + "double_blocks.().txt_attn.proj.bias": ["attn.to_add_out.bias"], + "single_blocks.().modulation.lin.weight": ["norm.linear.weight"], + "single_blocks.().modulation.lin.bias": ["norm.linear.bias"], + "single_blocks.().linear1.weight": ["attn.to_q.weight", "attn.to_k.weight", "attn.to_v.weight", "proj_mlp.weight"], + "single_blocks.().linear1.bias": ["attn.to_q.bias", "attn.to_k.bias", "attn.to_v.bias", "proj_mlp.bias"], + "single_blocks.().linear2.weight": ["proj_out.weight"], + "single_blocks.().norm.query_norm.scale": ["attn.norm_q.weight"], + "single_blocks.().norm.key_norm.scale": ["attn.norm_k.weight"], + "single_blocks.().linear2.weight": ["proj_out.weight"], + "single_blocks.().linear2.bias": ["proj_out.bias"], + "final_layer.linear.weight": ["proj_out.weight"], + "final_layer.linear.bias": ["proj_out.bias"], + "final_layer.adaLN_modulation.1.weight": ["norm_out.linear.weight"], + "final_layer.adaLN_modulation.1.bias": ["norm_out.linear.bias"], +} + + +def make_diffusers_to_bfl_map(num_double_blocks: int, num_single_blocks: int) -> dict[str, tuple[int, str]]: + # make reverse map from diffusers map + diffusers_to_bfl_map = {} # key: diffusers_key, value: (index, bfl_key) + for b in range(num_double_blocks): + for key, weights in BFL_TO_DIFFUSERS_MAP.items(): + if key.startswith("double_blocks."): + block_prefix = f"transformer_blocks.{b}." + for i, weight in enumerate(weights): + diffusers_to_bfl_map[f"{block_prefix}{weight}"] = (i, key.replace("()", f"{b}")) + for b in range(num_single_blocks): + for key, weights in BFL_TO_DIFFUSERS_MAP.items(): + if key.startswith("single_blocks."): + block_prefix = f"single_transformer_blocks.{b}." + for i, weight in enumerate(weights): + diffusers_to_bfl_map[f"{block_prefix}{weight}"] = (i, key.replace("()", f"{b}")) + for key, weights in BFL_TO_DIFFUSERS_MAP.items(): + if not (key.startswith("double_blocks.") or key.startswith("single_blocks.")): + for i, weight in enumerate(weights): + diffusers_to_bfl_map[weight] = (i, key) + return diffusers_to_bfl_map + + +def convert_diffusers_sd_to_bfl( + diffusers_sd: dict[str, torch.Tensor], num_double_blocks: int = NUM_DOUBLE_BLOCKS, num_single_blocks: int = NUM_SINGLE_BLOCKS +) -> dict[str, torch.Tensor]: + diffusers_to_bfl_map = make_diffusers_to_bfl_map(num_double_blocks, num_single_blocks) + + # iterate over three safetensors files to reduce memory usage + flux_sd = {} + for diffusers_key, tensor in diffusers_sd.items(): + if diffusers_key in diffusers_to_bfl_map: + index, bfl_key = diffusers_to_bfl_map[diffusers_key] + if bfl_key not in flux_sd: + flux_sd[bfl_key] = [] + flux_sd[bfl_key].append((index, tensor)) + else: + logger.error(f"Error: Key not found in diffusers_to_bfl_map: {diffusers_key}") + raise KeyError(f"Key not found in diffusers_to_bfl_map: {diffusers_key}") + + # concat tensors if multiple tensors are mapped to a single key, sort by index + for key, values in flux_sd.items(): + if len(values) == 1: + flux_sd[key] = values[0][1] + else: + flux_sd[key] = torch.cat([value[1] for value in sorted(values, key=lambda x: x[0])]) + + # special case for final_layer.adaLN_modulation.1.weight and final_layer.adaLN_modulation.1.bias + def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + if "final_layer.adaLN_modulation.1.weight" in flux_sd: + flux_sd["final_layer.adaLN_modulation.1.weight"] = swap_scale_shift(flux_sd["final_layer.adaLN_modulation.1.weight"]) + if "final_layer.adaLN_modulation.1.bias" in flux_sd: + flux_sd["final_layer.adaLN_modulation.1.bias"] = swap_scale_shift(flux_sd["final_layer.adaLN_modulation.1.bias"]) + + return flux_sd + + +# endregion diff --git a/vendor/sd-scripts/library/fp8_optimization_utils.py b/vendor/sd-scripts/library/fp8_optimization_utils.py new file mode 100644 index 0000000..af35fd3 --- /dev/null +++ b/vendor/sd-scripts/library/fp8_optimization_utils.py @@ -0,0 +1,482 @@ +import os +from typing import List, Optional, Union +import torch +import torch.nn as nn +import torch.nn.functional as F + +import logging + +from tqdm import tqdm + +from library.device_utils import clean_memory_on_device +from library.safetensors_utils import MemoryEfficientSafeOpen, TensorWeightAdapter, WeightTransformHooks +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +def calculate_fp8_maxval(exp_bits=4, mantissa_bits=3, sign_bits=1): + """ + Calculate the maximum representable value in FP8 format. + Default is E4M3 format (4-bit exponent, 3-bit mantissa, 1-bit sign). Only supports E4M3 and E5M2 with sign bit. + + Args: + exp_bits (int): Number of exponent bits + mantissa_bits (int): Number of mantissa bits + sign_bits (int): Number of sign bits (0 or 1) + + Returns: + float: Maximum value representable in FP8 format + """ + assert exp_bits + mantissa_bits + sign_bits == 8, "Total bits must be 8" + if exp_bits == 4 and mantissa_bits == 3 and sign_bits == 1: + return torch.finfo(torch.float8_e4m3fn).max + elif exp_bits == 5 and mantissa_bits == 2 and sign_bits == 1: + return torch.finfo(torch.float8_e5m2).max + else: + raise ValueError(f"Unsupported FP8 format: E{exp_bits}M{mantissa_bits} with sign_bits={sign_bits}") + + +# The following is a manual calculation method (wrong implementation for E5M2), kept for reference. +""" +# Calculate exponent bias +bias = 2 ** (exp_bits - 1) - 1 + +# Calculate maximum mantissa value +mantissa_max = 1.0 +for i in range(mantissa_bits - 1): + mantissa_max += 2 ** -(i + 1) + +# Calculate maximum value +max_value = mantissa_max * (2 ** (2**exp_bits - 1 - bias)) + +return max_value +""" + + +def quantize_fp8(tensor, scale, fp8_dtype, max_value, min_value): + """ + Quantize a tensor to FP8 format using PyTorch's native FP8 dtype support. + + Args: + tensor (torch.Tensor): Tensor to quantize + scale (float or torch.Tensor): Scale factor + fp8_dtype (torch.dtype): Target FP8 dtype (torch.float8_e4m3fn or torch.float8_e5m2) + max_value (float): Maximum representable value in FP8 + min_value (float): Minimum representable value in FP8 + + Returns: + torch.Tensor: Quantized tensor in FP8 format + """ + tensor = tensor.to(torch.float32) # ensure tensor is in float32 for division + + # Create scaled tensor + tensor = torch.div(tensor, scale).nan_to_num_(0.0) # handle NaN values, equivalent to nonzero_mask in previous function + + # Clamp tensor to range + tensor = tensor.clamp_(min=min_value, max=max_value) + + # Convert to FP8 dtype + tensor = tensor.to(fp8_dtype) + + return tensor + + +def optimize_state_dict_with_fp8( + state_dict: dict, + calc_device: Union[str, torch.device], + target_layer_keys: Optional[list[str]] = None, + exclude_layer_keys: Optional[list[str]] = None, + exp_bits: int = 4, + mantissa_bits: int = 3, + move_to_device: bool = False, + quantization_mode: str = "block", + block_size: Optional[int] = 64, +): + """ + Optimize Linear layer weights in a model's state dict to FP8 format. The state dict is modified in-place. + This function is a static version of load_safetensors_with_fp8_optimization without loading from files. + + Args: + state_dict (dict): State dict to optimize, replaced in-place + calc_device (str): Device to quantize tensors on + target_layer_keys (list, optional): Layer key patterns to target (None for all Linear layers) + exclude_layer_keys (list, optional): Layer key patterns to exclude + exp_bits (int): Number of exponent bits + mantissa_bits (int): Number of mantissa bits + move_to_device (bool): Move optimized tensors to the calculating device + + Returns: + dict: FP8 optimized state dict + """ + if exp_bits == 4 and mantissa_bits == 3: + fp8_dtype = torch.float8_e4m3fn + elif exp_bits == 5 and mantissa_bits == 2: + fp8_dtype = torch.float8_e5m2 + else: + raise ValueError(f"Unsupported FP8 format: E{exp_bits}M{mantissa_bits}") + + # Calculate FP8 max value + max_value = calculate_fp8_maxval(exp_bits, mantissa_bits) + min_value = -max_value # this function supports only signed FP8 + + # Create optimized state dict + optimized_count = 0 + + # Enumerate tarket keys + target_state_dict_keys = [] + for key in state_dict.keys(): + # Check if it's a weight key and matches target patterns + is_target = (target_layer_keys is None or any(pattern in key for pattern in target_layer_keys)) and key.endswith(".weight") + is_excluded = exclude_layer_keys is not None and any(pattern in key for pattern in exclude_layer_keys) + is_target = is_target and not is_excluded + + if is_target and isinstance(state_dict[key], torch.Tensor): + target_state_dict_keys.append(key) + + # Process each key + for key in tqdm(target_state_dict_keys): + value = state_dict[key] + + # Save original device and dtype + original_device = value.device + original_dtype = value.dtype + + # Move to calculation device + if calc_device is not None: + value = value.to(calc_device) + + quantized_weight, scale_tensor = quantize_weight(key, value, fp8_dtype, max_value, min_value, quantization_mode, block_size) + + # Add to state dict using original key for weight and new key for scale + fp8_key = key # Maintain original key + scale_key = key.replace(".weight", ".scale_weight") + + if not move_to_device: + quantized_weight = quantized_weight.to(original_device) + + # keep scale shape: [1] or [out,1] or [out, num_blocks, 1]. We can determine the quantization mode from the shape of scale_weight in the patched model. + scale_tensor = scale_tensor.to(dtype=original_dtype, device=quantized_weight.device) + + state_dict[fp8_key] = quantized_weight + state_dict[scale_key] = scale_tensor + + optimized_count += 1 + + if calc_device is not None: # optimized_count % 10 == 0 and + # free memory on calculation device + clean_memory_on_device(calc_device) + + logger.info(f"Number of optimized Linear layers: {optimized_count}") + return state_dict + + +def quantize_weight( + key: str, + tensor: torch.Tensor, + fp8_dtype: torch.dtype, + max_value: float, + min_value: float, + quantization_mode: str = "block", + block_size: int = 64, +): + original_shape = tensor.shape + + # Determine quantization mode + if quantization_mode == "block": + if tensor.ndim != 2: + quantization_mode = "tensor" # fallback to per-tensor + else: + out_features, in_features = tensor.shape + if in_features % block_size != 0: + quantization_mode = "channel" # fallback to per-channel + logger.warning( + f"Layer {key} with shape {tensor.shape} is not divisible by block_size {block_size}, fallback to per-channel quantization." + ) + else: + num_blocks = in_features // block_size + tensor = tensor.contiguous().view(out_features, num_blocks, block_size) # [out, num_blocks, block_size] + elif quantization_mode == "channel": + if tensor.ndim != 2: + quantization_mode = "tensor" # fallback to per-tensor + + # Calculate scale factor (per-tensor or per-output-channel with percentile or max) + # value shape is expected to be [out_features, in_features] for Linear weights + if quantization_mode == "channel" or quantization_mode == "block": + # row-wise percentile to avoid being dominated by outliers + # result shape: [out_features, 1] or [out_features, num_blocks, 1] + scale_dim = 1 if quantization_mode == "channel" else 2 + abs_w = torch.abs(tensor) + + # shape: [out_features, 1] or [out_features, num_blocks, 1] + row_max = torch.max(abs_w, dim=scale_dim, keepdim=True).values + scale = row_max / max_value + + else: + # per-tensor + tensor_max = torch.max(torch.abs(tensor).view(-1)) + scale = tensor_max / max_value + + # print(f"Optimizing {key} with scale: {scale}") + + # numerical safety + scale = torch.clamp(scale, min=1e-8) + scale = scale.to(torch.float32) # ensure scale is in float32 for division + + # Quantize weight to FP8 (scale can be scalar or [out,1], broadcasting works) + quantized_weight = quantize_fp8(tensor, scale, fp8_dtype, max_value, min_value) + + # If block-wise, restore original shape + if quantization_mode == "block": + quantized_weight = quantized_weight.view(original_shape) # restore to original shape [out, in] + + return quantized_weight, scale + + +def load_safetensors_with_fp8_optimization( + model_files: List[str], + calc_device: Union[str, torch.device], + target_layer_keys=None, + exclude_layer_keys=None, + exp_bits=4, + mantissa_bits=3, + move_to_device=False, + weight_hook=None, + quantization_mode: str = "block", + block_size: Optional[int] = 64, + disable_numpy_memmap: bool = False, + weight_transform_hooks: Optional[WeightTransformHooks] = None, +) -> dict: + """ + Load weight tensors from safetensors files and merge LoRA weights into the state dict with explicit FP8 optimization. + + Args: + model_files (list[str]): List of model files to load + calc_device (str or torch.device): Device to quantize tensors on + target_layer_keys (list, optional): Layer key patterns to target for optimization (None for all Linear layers) + exclude_layer_keys (list, optional): Layer key patterns to exclude from optimization + exp_bits (int): Number of exponent bits + mantissa_bits (int): Number of mantissa bits + move_to_device (bool): Move optimized tensors to the calculating device + weight_hook (callable, optional): Function to apply to each weight tensor before optimization + quantization_mode (str): Quantization mode, "tensor", "channel", or "block" + block_size (int, optional): Block size for block-wise quantization (used if quantization_mode is "block") + disable_numpy_memmap (bool): Disable numpy memmap when loading safetensors + weight_transform_hooks (WeightTransformHooks, optional): Hooks for weight transformation during loading + + Returns: + dict: FP8 optimized state dict + """ + if exp_bits == 4 and mantissa_bits == 3: + fp8_dtype = torch.float8_e4m3fn + elif exp_bits == 5 and mantissa_bits == 2: + fp8_dtype = torch.float8_e5m2 + else: + raise ValueError(f"Unsupported FP8 format: E{exp_bits}M{mantissa_bits}") + + # Calculate FP8 max value + max_value = calculate_fp8_maxval(exp_bits, mantissa_bits) + min_value = -max_value # this function supports only signed FP8 + + # Define function to determine if a key is a target key. target means fp8 optimization, not for weight hook. + def is_target_key(key): + # Check if weight key matches target patterns and does not match exclude patterns + is_target = (target_layer_keys is None or any(pattern in key for pattern in target_layer_keys)) and key.endswith(".weight") + is_excluded = exclude_layer_keys is not None and any(pattern in key for pattern in exclude_layer_keys) + return is_target and not is_excluded + + # Create optimized state dict + optimized_count = 0 + + # Process each file + state_dict = {} + for model_file in model_files: + with MemoryEfficientSafeOpen(model_file, disable_numpy_memmap=disable_numpy_memmap) as original_f: + f = TensorWeightAdapter(weight_transform_hooks, original_f) if weight_transform_hooks is not None else original_f + + keys = f.keys() + for key in tqdm(keys, desc=f"Loading {os.path.basename(model_file)}", unit="key"): + value = f.get_tensor(key) + + # Save original device + original_device = value.device # usually cpu + + if weight_hook is not None: + # Apply weight hook if provided + value = weight_hook(key, value, keep_on_calc_device=(calc_device is not None)) + + if not is_target_key(key): + target_device = calc_device if (calc_device is not None and move_to_device) else original_device + value = value.to(target_device) + state_dict[key] = value + continue + + # Move to calculation device + if calc_device is not None: + value = value.to(calc_device) + + original_dtype = value.dtype + if original_dtype.itemsize == 1: + raise ValueError( + f"Layer {key} is already in {original_dtype} format. `--fp8_scaled` optimization should not be applied. Please use fp16/bf16/float32 model weights." + + f" / レイヤー {key} は既に{original_dtype}形式です。`--fp8_scaled` 最適化は適用できません。FP16/BF16/Float32のモデル重みを使用してください。" + ) + quantized_weight, scale_tensor = quantize_weight( + key, value, fp8_dtype, max_value, min_value, quantization_mode, block_size + ) + + # Add to state dict using original key for weight and new key for scale + fp8_key = key # Maintain original key + scale_key = key.replace(".weight", ".scale_weight") + assert fp8_key != scale_key, "FP8 key and scale key must be different" + + if not move_to_device: + quantized_weight = quantized_weight.to(original_device) + + # keep scale shape: [1] or [out,1] or [out, num_blocks, 1]. We can determine the quantization mode from the shape of scale_weight in the patched model. + scale_tensor = scale_tensor.to(dtype=original_dtype, device=quantized_weight.device) + + state_dict[fp8_key] = quantized_weight + state_dict[scale_key] = scale_tensor + + optimized_count += 1 + + if calc_device is not None and optimized_count % 10 == 0: + # free memory on calculation device + clean_memory_on_device(calc_device) + + logger.info(f"Number of optimized Linear layers: {optimized_count}") + return state_dict + + +def fp8_linear_forward_patch(self: nn.Linear, x, use_scaled_mm=False, max_value=None): + """ + Patched forward method for Linear layers with FP8 weights. + + Args: + self: Linear layer instance + x (torch.Tensor): Input tensor + use_scaled_mm (bool): Use scaled_mm for FP8 Linear layers, requires SM 8.9+ (RTX 40 series) + max_value (float): Maximum value for FP8 quantization. If None, no quantization is applied for input tensor. + + Returns: + torch.Tensor: Result of linear transformation + """ + if use_scaled_mm: + # **not tested** + # _scaled_mm only works for per-tensor scale for now (per-channel scale does not work in certain cases) + if self.scale_weight.ndim != 1: + raise ValueError("scaled_mm only supports per-tensor scale_weight for now.") + + input_dtype = x.dtype + original_weight_dtype = self.scale_weight.dtype + target_dtype = self.weight.dtype + # assert x.ndim == 3, "Input tensor must be 3D (batch_size, seq_len, hidden_dim)" + + if max_value is None: + # no input quantization + scale_x = torch.tensor(1.0, dtype=torch.float32, device=x.device) + else: + # calculate scale factor for input tensor + scale_x = (torch.max(torch.abs(x.flatten())) / max_value).to(torch.float32) + + # quantize input tensor to FP8: this seems to consume a lot of memory + fp8_max_value = torch.finfo(target_dtype).max + fp8_min_value = torch.finfo(target_dtype).min + x = quantize_fp8(x, scale_x, target_dtype, fp8_max_value, fp8_min_value) + + original_shape = x.shape + x = x.reshape(-1, x.shape[-1]).to(target_dtype) + + weight = self.weight.t() + scale_weight = self.scale_weight.to(torch.float32) + + if self.bias is not None: + # float32 is not supported with bias in scaled_mm + o = torch._scaled_mm(x, weight, out_dtype=original_weight_dtype, bias=self.bias, scale_a=scale_x, scale_b=scale_weight) + else: + o = torch._scaled_mm(x, weight, out_dtype=input_dtype, scale_a=scale_x, scale_b=scale_weight) + + o = o.reshape(original_shape[0], original_shape[1], -1) if len(original_shape) == 3 else o.reshape(original_shape[0], -1) + return o.to(input_dtype) + + else: + # Dequantize the weight + original_dtype = self.scale_weight.dtype + if self.scale_weight.ndim < 3: + # per-tensor or per-channel quantization, we can broadcast + dequantized_weight = self.weight.to(original_dtype) * self.scale_weight + else: + # block-wise quantization, need to reshape weight to match scale shape for broadcasting + out_features, num_blocks, _ = self.scale_weight.shape + dequantized_weight = self.weight.to(original_dtype).contiguous().view(out_features, num_blocks, -1) + dequantized_weight = dequantized_weight * self.scale_weight + dequantized_weight = dequantized_weight.view(self.weight.shape) + + # Perform linear transformation + if self.bias is not None: + output = F.linear(x, dequantized_weight, self.bias) + else: + output = F.linear(x, dequantized_weight) + + return output + + +def apply_fp8_monkey_patch(model, optimized_state_dict, use_scaled_mm=False): + """ + Apply monkey patching to a model using FP8 optimized state dict. + + Args: + model (nn.Module): Model instance to patch + optimized_state_dict (dict): FP8 optimized state dict + use_scaled_mm (bool): Use scaled_mm for FP8 Linear layers, requires SM 8.9+ (RTX 40 series) + + Returns: + nn.Module: The patched model (same instance, modified in-place) + """ + # # Calculate FP8 float8_e5m2 max value + # max_value = calculate_fp8_maxval(5, 2) + max_value = None # do not quantize input tensor + + # Find all scale keys to identify FP8-optimized layers + scale_keys = [k for k in optimized_state_dict.keys() if k.endswith(".scale_weight")] + + # Enumerate patched layers + patched_module_paths = set() + scale_shape_info = {} + for scale_key in scale_keys: + # Extract module path from scale key (remove .scale_weight) + module_path = scale_key.rsplit(".scale_weight", 1)[0] + patched_module_paths.add(module_path) + + # Store scale shape information + scale_shape_info[module_path] = optimized_state_dict[scale_key].shape + + patched_count = 0 + + # Apply monkey patch to each layer with FP8 weights + for name, module in model.named_modules(): + # Check if this module has a corresponding scale_weight + has_scale = name in patched_module_paths + + # Apply patch if it's a Linear layer with FP8 scale + if isinstance(module, nn.Linear) and has_scale: + # register the scale_weight as a buffer to load the state_dict + # module.register_buffer("scale_weight", torch.tensor(1.0, dtype=module.weight.dtype)) + scale_shape = scale_shape_info[name] + module.register_buffer("scale_weight", torch.ones(scale_shape, dtype=module.weight.dtype)) + + # Create a new forward method with the patched version. + def new_forward(self, x): + return fp8_linear_forward_patch(self, x, use_scaled_mm, max_value) + + # Bind method to module + module.forward = new_forward.__get__(module, type(module)) + + patched_count += 1 + + logger.info(f"Number of monkey-patched Linear layers: {patched_count}") + return model diff --git a/vendor/sd-scripts/library/huggingface_util.py b/vendor/sd-scripts/library/huggingface_util.py new file mode 100644 index 0000000..57b19d9 --- /dev/null +++ b/vendor/sd-scripts/library/huggingface_util.py @@ -0,0 +1,84 @@ +from typing import Union, BinaryIO +from huggingface_hub import HfApi +from pathlib import Path +import argparse +import os +from library.utils import fire_in_thread +from library.utils import setup_logging +setup_logging() +import logging +logger = logging.getLogger(__name__) + +def exists_repo(repo_id: str, repo_type: str, revision: str = "main", token: str = None): + api = HfApi( + token=token, + ) + try: + api.repo_info(repo_id=repo_id, revision=revision, repo_type=repo_type) + return True + except: + return False + + +def upload( + args: argparse.Namespace, + src: Union[str, Path, bytes, BinaryIO], + dest_suffix: str = "", + force_sync_upload: bool = False, +): + repo_id = args.huggingface_repo_id + repo_type = args.huggingface_repo_type + token = args.huggingface_token + path_in_repo = args.huggingface_path_in_repo + dest_suffix if args.huggingface_path_in_repo is not None else None + private = args.huggingface_repo_visibility is None or args.huggingface_repo_visibility != "public" + api = HfApi(token=token) + if not exists_repo(repo_id=repo_id, repo_type=repo_type, token=token): + try: + api.create_repo(repo_id=repo_id, repo_type=repo_type, private=private) + except Exception as e: # とりあえずRepositoryNotFoundErrorは確認したが他にあると困るので + logger.error("===========================================") + logger.error(f"failed to create HuggingFace repo / HuggingFaceのリポジトリの作成に失敗しました : {e}") + logger.error("===========================================") + + is_folder = (type(src) == str and os.path.isdir(src)) or (isinstance(src, Path) and src.is_dir()) + + def uploader(): + try: + if is_folder: + api.upload_folder( + repo_id=repo_id, + repo_type=repo_type, + folder_path=src, + path_in_repo=path_in_repo, + ) + else: + api.upload_file( + repo_id=repo_id, + repo_type=repo_type, + path_or_fileobj=src, + path_in_repo=path_in_repo, + ) + except Exception as e: # RuntimeErrorを確認済みだが他にあると困るので + logger.error("===========================================") + logger.error(f"failed to upload to HuggingFace / HuggingFaceへのアップロードに失敗しました : {e}") + logger.error("===========================================") + + if args.async_upload and not force_sync_upload: + fire_in_thread(uploader) + else: + uploader() + + +def list_dir( + repo_id: str, + subfolder: str, + repo_type: str, + revision: str = "main", + token: str = None, +): + api = HfApi( + token=token, + ) + repo_info = api.repo_info(repo_id=repo_id, revision=revision, repo_type=repo_type) + file_list = [file for file in repo_info.siblings if file.rfilename.startswith(subfolder)] + return file_list diff --git a/vendor/sd-scripts/library/hunyuan_image_models.py b/vendor/sd-scripts/library/hunyuan_image_models.py new file mode 100644 index 0000000..fc320df --- /dev/null +++ b/vendor/sd-scripts/library/hunyuan_image_models.py @@ -0,0 +1,489 @@ +# Original work: https://github.com/Tencent-Hunyuan/HunyuanImage-2.1 +# Re-implemented for license compliance for sd-scripts. + +from typing import Dict, Optional, Tuple, Union + +import torch +import torch.nn as nn +from accelerate import init_empty_weights + +from library import custom_offloading_utils +from library.attention import AttentionParams +from library.fp8_optimization_utils import apply_fp8_monkey_patch +from library.lora_utils import load_safetensors_with_lora_and_fp8 +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +from library.hunyuan_image_modules import ( + SingleTokenRefiner, + ByT5Mapper, + PatchEmbed2D, + TimestepEmbedder, + MMDoubleStreamBlock, + MMSingleStreamBlock, + FinalLayer, +) +from library.hunyuan_image_utils import get_nd_rotary_pos_embed + +FP8_OPTIMIZATION_TARGET_KEYS = ["double_blocks", "single_blocks"] +# FP8_OPTIMIZATION_EXCLUDE_KEYS = ["norm", "_mod", "_emb"] # , "modulation" +FP8_OPTIMIZATION_EXCLUDE_KEYS = ["norm", "_emb"] # , "modulation", "_mod" + +# full exclude 24.2GB +# norm and _emb 19.7GB +# fp8 cast 19.7GB + + +# region DiT Model +class HYImageDiffusionTransformer(nn.Module): + """ + HunyuanImage-2.1 Diffusion Transformer. + + A multimodal transformer for image generation with text conditioning, + featuring separate double-stream and single-stream processing blocks. + + Args: + attn_mode: Attention implementation mode ("torch" or "sageattn"). + """ + + def __init__(self, attn_mode: str = "torch", split_attn: bool = False): + super().__init__() + + # Fixed architecture parameters for HunyuanImage-2.1 + self.patch_size = [1, 1] # 1x1 patch size (no spatial downsampling) + self.in_channels = 64 # Input latent channels + self.out_channels = 64 # Output latent channels + self.unpatchify_channels = self.out_channels + self.guidance_embed = False # Guidance embedding disabled + self.rope_dim_list = [64, 64] # RoPE dimensions for 2D positional encoding + self.rope_theta = 256 # RoPE frequency scaling + self.use_attention_mask = True + self.text_projection = "single_refiner" + self.hidden_size = 3584 # Model dimension + self.heads_num = 28 # Number of attention heads + + # Architecture configuration + mm_double_blocks_depth = 20 # Double-stream transformer blocks + mm_single_blocks_depth = 40 # Single-stream transformer blocks + mlp_width_ratio = 4 # MLP expansion ratio + text_states_dim = 3584 # Text encoder output dimension + guidance_embed = False # No guidance embedding + + # Layer configuration + mlp_act_type: str = "gelu_tanh" # MLP activation function + qkv_bias: bool = True # Use bias in QKV projections + qk_norm: bool = True # Apply QK normalization + qk_norm_type: str = "rms" # RMS normalization type + + self.attn_mode = attn_mode + self.split_attn = split_attn + + # ByT5 character-level text encoder mapping + self.byt5_in = ByT5Mapper(in_dim=1472, out_dim=2048, hidden_dim=2048, out_dim1=self.hidden_size, use_residual=False) + + # Image latent patch embedding + self.img_in = PatchEmbed2D(self.patch_size, self.in_channels, self.hidden_size) + + # Text token refinement with cross-attention + self.txt_in = SingleTokenRefiner(text_states_dim, self.hidden_size, self.heads_num, depth=2) + + # Timestep embedding for diffusion process + self.time_in = TimestepEmbedder(self.hidden_size, nn.SiLU) + + # MeanFlow not supported in this implementation + self.time_r_in = None + + # Guidance embedding (disabled for non-distilled model) + self.guidance_in = TimestepEmbedder(self.hidden_size, nn.SiLU) if guidance_embed else None + + # Double-stream blocks: separate image and text processing + self.double_blocks = nn.ModuleList( + [ + MMDoubleStreamBlock( + self.hidden_size, + self.heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_act_type=mlp_act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + ) + for _ in range(mm_double_blocks_depth) + ] + ) + + # Single-stream blocks: joint processing of concatenated features + self.single_blocks = nn.ModuleList( + [ + MMSingleStreamBlock( + self.hidden_size, + self.heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_act_type=mlp_act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + ) + for _ in range(mm_single_blocks_depth) + ] + ) + + self.final_layer = FinalLayer(self.hidden_size, self.patch_size, self.out_channels, nn.SiLU) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + self.blocks_to_swap = None + + self.offloader_double = None + self.offloader_single = None + self.num_double_blocks = len(self.double_blocks) + self.num_single_blocks = len(self.single_blocks) + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + for block in self.double_blocks + self.single_blocks: + block.enable_gradient_checkpointing(cpu_offload=cpu_offload) + + print(f"HunyuanImage-2.1: Gradient checkpointing enabled. CPU offload: {cpu_offload}") + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + for block in self.double_blocks + self.single_blocks: + block.disable_gradient_checkpointing() + + print("HunyuanImage-2.1: Gradient checkpointing disabled.") + + def enable_block_swap(self, num_blocks: int, device: torch.device, supports_backward: bool = False): + self.blocks_to_swap = num_blocks + double_blocks_to_swap = num_blocks // 2 + single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2 + + assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, ( + f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. " + f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks." + ) + + self.offloader_double = custom_offloading_utils.ModelOffloader( + self.double_blocks, double_blocks_to_swap, device, supports_backward=supports_backward + ) + self.offloader_single = custom_offloading_utils.ModelOffloader( + self.single_blocks, single_blocks_to_swap, device, supports_backward=supports_backward + ) + # , debug=True + print( + f"HunyuanImage-2.1: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}." + ) + + def switch_block_swap_for_inference(self): + if self.blocks_to_swap: + self.offloader_double.set_forward_only(True) + self.offloader_single.set_forward_only(True) + self.prepare_block_swap_before_forward() + print(f"HunyuanImage-2.1: Block swap set to forward only.") + + def switch_block_swap_for_training(self): + if self.blocks_to_swap: + self.offloader_double.set_forward_only(False) + self.offloader_single.set_forward_only(False) + self.prepare_block_swap_before_forward() + print(f"HunyuanImage-2.1: Block swap set to forward and backward.") + + def move_to_device_except_swap_blocks(self, device: torch.device): + # assume model is on cpu. do not move blocks to device to reduce temporary memory usage + if self.blocks_to_swap: + save_double_blocks = self.double_blocks + save_single_blocks = self.single_blocks + self.double_blocks = nn.ModuleList() + self.single_blocks = nn.ModuleList() + + self.to(device) + + if self.blocks_to_swap: + self.double_blocks = save_double_blocks + self.single_blocks = save_single_blocks + + def prepare_block_swap_before_forward(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader_double.prepare_block_devices_before_forward(self.double_blocks) + self.offloader_single.prepare_block_devices_before_forward(self.single_blocks) + + def get_rotary_pos_embed(self, rope_sizes): + """ + Generate 2D rotary position embeddings for image tokens. + + Args: + rope_sizes: Tuple of (height, width) for spatial dimensions. + + Returns: + Tuple of (freqs_cos, freqs_sin) tensors for rotary position encoding. + """ + freqs_cos, freqs_sin = get_nd_rotary_pos_embed(self.rope_dim_list, rope_sizes, theta=self.rope_theta) + return freqs_cos, freqs_sin + + def reorder_txt_token( + self, byt5_txt: torch.Tensor, txt: torch.Tensor, byt5_text_mask: torch.Tensor, text_mask: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, list[int]]: + """ + Combine and reorder ByT5 character-level and word-level text embeddings. + + Concatenates valid tokens from both encoders and creates appropriate masks. + + Args: + byt5_txt: ByT5 character-level embeddings [B, L1, D]. + txt: Word-level text embeddings [B, L2, D]. + byt5_text_mask: Valid token mask for ByT5 [B, L1]. + text_mask: Valid token mask for word tokens [B, L2]. + + Returns: + Tuple of (reordered_embeddings, combined_mask, sequence_lengths). + """ + # Process each batch element separately to handle variable sequence lengths + + reorder_txt = [] + reorder_mask = [] + + txt_lens = [] + for i in range(text_mask.shape[0]): + byt5_text_mask_i = byt5_text_mask[i].bool() + text_mask_i = text_mask[i].bool() + byt5_text_length = byt5_text_mask_i.sum() + text_length = text_mask_i.sum() + assert byt5_text_length == byt5_text_mask_i[:byt5_text_length].sum() + assert text_length == text_mask_i[:text_length].sum() + + byt5_txt_i = byt5_txt[i] + txt_i = txt[i] + reorder_txt_i = torch.cat( + [byt5_txt_i[:byt5_text_length], txt_i[:text_length], byt5_txt_i[byt5_text_length:], txt_i[text_length:]], dim=0 + ) + + reorder_mask_i = torch.zeros( + byt5_text_mask_i.shape[0] + text_mask_i.shape[0], dtype=torch.bool, device=byt5_text_mask_i.device + ) + reorder_mask_i[: byt5_text_length + text_length] = True + + reorder_txt.append(reorder_txt_i) + reorder_mask.append(reorder_mask_i) + txt_lens.append(byt5_text_length + text_length) + + reorder_txt = torch.stack(reorder_txt) + reorder_mask = torch.stack(reorder_mask).to(dtype=torch.int64) + + return reorder_txt, reorder_mask, txt_lens + + def forward( + self, + hidden_states: torch.Tensor, + timestep: torch.LongTensor, + text_states: torch.Tensor, + encoder_attention_mask: torch.Tensor, + byt5_text_states: Optional[torch.Tensor] = None, + byt5_text_mask: Optional[torch.Tensor] = None, + rotary_pos_emb_cache: Optional[Dict[Tuple[int, int], Tuple[torch.Tensor, torch.Tensor]]] = None, + ) -> torch.Tensor: + """ + Forward pass through the HunyuanImage diffusion transformer. + + Args: + hidden_states: Input image latents [B, C, H, W]. + timestep: Diffusion timestep [B]. + text_states: Word-level text embeddings [B, L, D]. + encoder_attention_mask: Text attention mask [B, L]. + byt5_text_states: ByT5 character-level embeddings [B, L_byt5, D_byt5]. + byt5_text_mask: ByT5 attention mask [B, L_byt5]. + + Returns: + Tuple of (denoised_image, spatial_shape). + """ + img = x = hidden_states + text_mask = encoder_attention_mask + t = timestep + txt = text_states + + # Calculate spatial dimensions for rotary position embeddings + _, _, oh, ow = x.shape + th, tw = oh, ow # Height and width (patch_size=[1,1] means no spatial downsampling) + if rotary_pos_emb_cache is not None: + if (th, tw) in rotary_pos_emb_cache: + freqs_cis = rotary_pos_emb_cache[(th, tw)] + freqs_cis = (freqs_cis[0].to(img.device), freqs_cis[1].to(img.device)) + else: + freqs_cis = self.get_rotary_pos_embed((th, tw)) + rotary_pos_emb_cache[(th, tw)] = (freqs_cis[0].cpu(), freqs_cis[1].cpu()) + else: + freqs_cis = self.get_rotary_pos_embed((th, tw)) + + # Reshape image latents to sequence format: [B, C, H, W] -> [B, H*W, C] + img = self.img_in(img) + + # Generate timestep conditioning vector + vec = self.time_in(t) + + # MeanFlow and guidance embedding not used in this configuration + + # Process text tokens through refinement layers + txt_attn_params = AttentionParams.create_attention_params_from_mask(self.attn_mode, self.split_attn, 0, text_mask) + txt = self.txt_in(txt, t, txt_attn_params) + + # Integrate character-level ByT5 features with word-level tokens + # Use variable length sequences with sequence lengths + byt5_txt = self.byt5_in(byt5_text_states) + txt, text_mask, txt_lens = self.reorder_txt_token(byt5_txt, txt, byt5_text_mask, text_mask) + + # Trim sequences to maximum length in the batch + img_seq_len = img.shape[1] + max_txt_len = max(txt_lens) + txt = txt[:, :max_txt_len, :] + text_mask = text_mask[:, :max_txt_len] + + attn_params = AttentionParams.create_attention_params_from_mask(self.attn_mode, self.split_attn, img_seq_len, text_mask) + + input_device = img.device + + # Process through double-stream blocks (separate image/text attention) + for index, block in enumerate(self.double_blocks): + if self.blocks_to_swap: + self.offloader_double.wait_for_block(index) + img, txt = block(img, txt, vec, freqs_cis, attn_params) + if self.blocks_to_swap: + self.offloader_double.submit_move_blocks(self.double_blocks, index) + + # Concatenate image and text tokens for joint processing + x = torch.cat((img, txt), 1) + + # Process through single-stream blocks (joint attention) + for index, block in enumerate(self.single_blocks): + if self.blocks_to_swap: + self.offloader_single.wait_for_block(index) + x = block(x, vec, freqs_cis, attn_params) + if self.blocks_to_swap: + self.offloader_single.submit_move_blocks(self.single_blocks, index) + + x = x.to(input_device) + vec = vec.to(input_device) + + img = x[:, :img_seq_len, ...] + del x + + # Apply final projection to output space + img = self.final_layer(img, vec) + del vec + + # Reshape from sequence to spatial format: [B, L, C] -> [B, C, H, W] + img = self.unpatchify_2d(img, th, tw) + return img + + def unpatchify_2d(self, x, h, w): + """ + Convert sequence format back to spatial image format. + + Args: + x: Input tensor [B, H*W, C]. + h: Height dimension. + w: Width dimension. + + Returns: + Spatial tensor [B, C, H, W]. + """ + c = self.unpatchify_channels + + x = x.reshape(shape=(x.shape[0], h, w, c)) + imgs = x.permute(0, 3, 1, 2) + return imgs + + +# endregion + +# region Model Utils + + +def create_model(attn_mode: str, split_attn: bool, dtype: Optional[torch.dtype]) -> HYImageDiffusionTransformer: + with init_empty_weights(): + model = HYImageDiffusionTransformer(attn_mode=attn_mode, split_attn=split_attn) + if dtype is not None: + model.to(dtype) + return model + + +def load_hunyuan_image_model( + device: Union[str, torch.device], + dit_path: str, + attn_mode: str, + split_attn: bool, + loading_device: Union[str, torch.device], + dit_weight_dtype: Optional[torch.dtype], + fp8_scaled: bool = False, + lora_weights_list: Optional[Dict[str, torch.Tensor]] = None, + lora_multipliers: Optional[list[float]] = None, +) -> HYImageDiffusionTransformer: + """ + Load a HunyuanImage model from the specified checkpoint. + + Args: + device (Union[str, torch.device]): Device for optimization or merging + dit_path (str): Path to the DiT model checkpoint. + attn_mode (str): Attention mode to use, e.g., "torch", "flash", etc. + split_attn (bool): Whether to use split attention. + loading_device (Union[str, torch.device]): Device to load the model weights on. + dit_weight_dtype (Optional[torch.dtype]): Data type of the DiT weights. + If None, it will be loaded as is (same as the state_dict) or scaled for fp8. if not None, model weights will be casted to this dtype. + fp8_scaled (bool): Whether to use fp8 scaling for the model weights. + lora_weights_list (Optional[Dict[str, torch.Tensor]]): LoRA weights to apply, if any. + lora_multipliers (Optional[List[float]]): LoRA multipliers for the weights, if any. + """ + # dit_weight_dtype is None for fp8_scaled + assert (not fp8_scaled and dit_weight_dtype is not None) or (fp8_scaled and dit_weight_dtype is None) + + device = torch.device(device) + loading_device = torch.device(loading_device) + + model = create_model(attn_mode, split_attn, dit_weight_dtype) + + # load model weights with dynamic fp8 optimization and LoRA merging if needed + logger.info(f"Loading DiT model from {dit_path}, device={loading_device}") + + sd = load_safetensors_with_lora_and_fp8( + model_files=dit_path, + lora_weights_list=lora_weights_list, + lora_multipliers=lora_multipliers, + fp8_optimization=fp8_scaled, + calc_device=device, + move_to_device=(loading_device == device), + dit_weight_dtype=dit_weight_dtype, + target_keys=FP8_OPTIMIZATION_TARGET_KEYS, + exclude_keys=FP8_OPTIMIZATION_EXCLUDE_KEYS, + ) + + if fp8_scaled: + apply_fp8_monkey_patch(model, sd, use_scaled_mm=False) + + if loading_device.type != "cpu": + # make sure all the model weights are on the loading_device + logger.info(f"Moving weights to {loading_device}") + for key in sd.keys(): + sd[key] = sd[key].to(loading_device) + + info = model.load_state_dict(sd, strict=True, assign=True) + logger.info(f"Loaded DiT model from {dit_path}, info={info}") + + return model + + +# endregion diff --git a/vendor/sd-scripts/library/hunyuan_image_modules.py b/vendor/sd-scripts/library/hunyuan_image_modules.py new file mode 100644 index 0000000..1953a78 --- /dev/null +++ b/vendor/sd-scripts/library/hunyuan_image_modules.py @@ -0,0 +1,863 @@ +# Original work: https://github.com/Tencent-Hunyuan/HunyuanImage-2.1 +# Re-implemented for license compliance for sd-scripts. + +from typing import Tuple, Callable +import torch +import torch.nn as nn +from einops import rearrange + +from library import custom_offloading_utils +from library.attention import AttentionParams, attention +from library.hunyuan_image_utils import timestep_embedding, apply_rotary_emb, _to_tuple, apply_gate, modulate +from library.attention import attention + +# region Modules + + +class ByT5Mapper(nn.Module): + """ + Maps ByT5 character-level encoder outputs to transformer hidden space. + + Applies layer normalization, two MLP layers with GELU activation, + and optional residual connection. + + Args: + in_dim: Input dimension from ByT5 encoder (1472 for ByT5-large). + out_dim: Intermediate dimension after first projection. + hidden_dim: Hidden dimension for MLP layer. + out_dim1: Final output dimension matching transformer hidden size. + use_residual: Whether to add residual connection (requires in_dim == out_dim). + """ + + def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_residual=True): + super().__init__() + if use_residual: + assert in_dim == out_dim + self.layernorm = nn.LayerNorm(in_dim) + self.fc1 = nn.Linear(in_dim, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, out_dim) + self.fc3 = nn.Linear(out_dim, out_dim1) + self.use_residual = use_residual + self.act_fn = nn.GELU() + + def forward(self, x): + """ + Transform ByT5 embeddings to transformer space. + + Args: + x: Input ByT5 embeddings [..., in_dim]. + + Returns: + Transformed embeddings [..., out_dim1]. + """ + residual = x if self.use_residual else None + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + x = self.act_fn(x) + x = self.fc3(x) + if self.use_residual: + x = x + residual + return x + + +class PatchEmbed2D(nn.Module): + """ + 2D patch embedding layer for converting image latents to transformer tokens. + + Uses 2D convolution to project image patches to embedding space. + For HunyuanImage-2.1, patch_size=[1,1] means no spatial downsampling. + + Args: + patch_size: Spatial size of patches (int or tuple). + in_chans: Number of input channels. + embed_dim: Output embedding dimension. + """ + + def __init__(self, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + self.patch_size = tuple(patch_size) + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=True) + self.norm = nn.Identity() # No normalization layer used + + def forward(self, x): + x = self.proj(x) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + return x + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar diffusion timesteps into vector representations. + + Uses sinusoidal encoding followed by a two-layer MLP. + + Args: + hidden_size: Output embedding dimension. + act_layer: Activation function class (e.g., nn.SiLU). + frequency_embedding_size: Dimension of sinusoidal encoding. + max_period: Maximum period for sinusoidal frequencies. + out_size: Output dimension (defaults to hidden_size). + """ + + def __init__(self, hidden_size, act_layer, frequency_embedding_size=256, max_period=10000, out_size=None): + super().__init__() + self.frequency_embedding_size = frequency_embedding_size + self.max_period = max_period + if out_size is None: + out_size = hidden_size + + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), act_layer(), nn.Linear(hidden_size, out_size, bias=True) + ) + + def forward(self, t): + t_freq = timestep_embedding(t, self.frequency_embedding_size, self.max_period).type(self.mlp[0].weight.dtype) + return self.mlp(t_freq) + + +class TextProjection(nn.Module): + """ + Projects text embeddings through a two-layer MLP. + + Used for context-aware representation computation in token refinement. + + Args: + in_channels: Input feature dimension. + hidden_size: Hidden and output dimension. + act_layer: Activation function class. + """ + + def __init__(self, in_channels, hidden_size, act_layer): + super().__init__() + self.linear_1 = nn.Linear(in_features=in_channels, out_features=hidden_size, bias=True) + self.act_1 = act_layer() + self.linear_2 = nn.Linear(in_features=hidden_size, out_features=hidden_size, bias=True) + + def forward(self, caption): + hidden_states = self.linear_1(caption) + hidden_states = self.act_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +class MLP(nn.Module): + """ + Multi-layer perceptron with configurable activation and normalization. + + Standard two-layer MLP with optional dropout and intermediate normalization. + + Args: + in_channels: Input feature dimension. + hidden_channels: Hidden layer dimension (defaults to in_channels). + out_features: Output dimension (defaults to in_channels). + act_layer: Activation function class. + norm_layer: Optional normalization layer class. + bias: Whether to use bias (can be bool or tuple for each layer). + drop: Dropout rate (can be float or tuple for each layer). + use_conv: Whether to use convolution instead of linear (not supported). + """ + + def __init__( + self, + in_channels, + hidden_channels=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0.0, + use_conv=False, + ): + super().__init__() + assert not use_conv, "Convolutional MLP not supported in this implementation." + + out_features = out_features or in_channels + hidden_channels = hidden_channels or in_channels + bias = _to_tuple(bias, 2) + drop_probs = _to_tuple(drop, 2) + + self.fc1 = nn.Linear(in_channels, hidden_channels, bias=bias[0]) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.norm = norm_layer(hidden_channels) if norm_layer is not None else nn.Identity() + self.fc2 = nn.Linear(hidden_channels, out_features, bias=bias[1]) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class IndividualTokenRefinerBlock(nn.Module): + """ + Single transformer block for individual token refinement. + + Applies self-attention and MLP with adaptive layer normalization (AdaLN) + conditioned on timestep and context information. + + Args: + hidden_size: Model dimension. + heads_num: Number of attention heads. + mlp_width_ratio: MLP expansion ratio. + mlp_drop_rate: MLP dropout rate. + act_type: Activation function (only "silu" supported). + qk_norm: QK normalization flag (must be False). + qk_norm_type: QK normalization type (only "layer" supported). + qkv_bias: Use bias in QKV projections. + """ + + def __init__( + self, + hidden_size: int, + heads_num: int, + mlp_width_ratio: float = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + ): + super().__init__() + assert qk_norm_type == "layer", "Only layer normalization supported for QK norm." + assert act_type == "silu", "Only SiLU activation supported." + assert not qk_norm, "QK normalization must be disabled." + + self.heads_num = heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) + self.self_attn_qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias) + + self.self_attn_q_norm = nn.Identity() + self.self_attn_k_norm = nn.Identity() + self.self_attn_proj = nn.Linear(hidden_size, hidden_size, bias=qkv_bias) + + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) + self.mlp = MLP(in_channels=hidden_size, hidden_channels=mlp_hidden_dim, act_layer=nn.SiLU, drop=mlp_drop_rate) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True), + ) + + def forward(self, x: torch.Tensor, c: torch.Tensor, attn_params: AttentionParams) -> torch.Tensor: + """ + Apply self-attention and MLP with adaptive conditioning. + + Args: + x: Input token embeddings [B, L, C]. + c: Combined conditioning vector [B, C]. + attn_params: Attention parameters including sequence lengths. + + Returns: + Refined token embeddings [B, L, C]. + """ + gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) + norm_x = self.norm1(x) + qkv = self.self_attn_qkv(norm_x) + del norm_x + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + del qkv + q = self.self_attn_q_norm(q).to(v) + k = self.self_attn_k_norm(k).to(v) + qkv = [q, k, v] + del q, k, v + attn = attention(qkv, attn_params=attn_params) + + x = x + apply_gate(self.self_attn_proj(attn), gate_msa) + x = x + apply_gate(self.mlp(self.norm2(x)), gate_mlp) + return x + + +class IndividualTokenRefiner(nn.Module): + """ + Stack of token refinement blocks with self-attention. + + Processes tokens individually with adaptive layer normalization. + + Args: + hidden_size: Model dimension. + heads_num: Number of attention heads. + depth: Number of refinement blocks. + mlp_width_ratio: MLP expansion ratio. + mlp_drop_rate: MLP dropout rate. + act_type: Activation function type. + qk_norm: QK normalization flag. + qk_norm_type: QK normalization type. + qkv_bias: Use bias in QKV projections. + """ + + def __init__( + self, + hidden_size: int, + heads_num: int, + depth: int, + mlp_width_ratio: float = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + ): + super().__init__() + self.blocks = nn.ModuleList( + [ + IndividualTokenRefinerBlock( + hidden_size=hidden_size, + heads_num=heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + ) + for _ in range(depth) + ] + ) + + def forward(self, x: torch.Tensor, c: torch.LongTensor, attn_params: AttentionParams) -> torch.Tensor: + """ + Apply sequential token refinement. + + Args: + x: Input token embeddings [B, L, C]. + c: Combined conditioning vector [B, C]. + attn_params: Attention parameters including sequence lengths. + + Returns: + Refined token embeddings [B, L, C]. + """ + for block in self.blocks: + x = block(x, c, attn_params) + return x + + +class SingleTokenRefiner(nn.Module): + """ + Text embedding refinement with timestep and context conditioning. + + Projects input text embeddings and applies self-attention refinement + conditioned on diffusion timestep and aggregate text context. + + Args: + in_channels: Input text embedding dimension. + hidden_size: Transformer hidden dimension. + heads_num: Number of attention heads. + depth: Number of refinement blocks. + """ + + def __init__(self, in_channels: int, hidden_size: int, heads_num: int, depth: int): + # Fixed architecture parameters for HunyuanImage-2.1 + mlp_drop_rate: float = 0.0 # No MLP dropout + act_type: str = "silu" # SiLU activation + mlp_width_ratio: float = 4.0 # 4x MLP expansion + qk_norm: bool = False # No QK normalization + qk_norm_type: str = "layer" # Layer norm type (unused) + qkv_bias: bool = True # Use QKV bias + + super().__init__() + self.input_embedder = nn.Linear(in_channels, hidden_size, bias=True) + act_layer = nn.SiLU + self.t_embedder = TimestepEmbedder(hidden_size, act_layer) + self.c_embedder = TextProjection(in_channels, hidden_size, act_layer) + self.individual_token_refiner = IndividualTokenRefiner( + hidden_size=hidden_size, + heads_num=heads_num, + depth=depth, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + ) + + def forward(self, x: torch.Tensor, t: torch.LongTensor, attn_params: AttentionParams) -> torch.Tensor: + """ + Refine text embeddings with timestep conditioning. + + Args: + x: Input text embeddings [B, L, in_channels]. + t: Diffusion timestep [B]. + attn_params: Attention parameters including sequence lengths. + + Returns: + Refined embeddings [B, L, hidden_size]. + """ + timestep_aware_representations = self.t_embedder(t) + + # Compute context-aware representations by averaging valid tokens + txt_lens = attn_params.seqlens # img_len is not used for SingleTokenRefiner + context_aware_representations = torch.stack([x[i, : txt_lens[i]].mean(dim=0) for i in range(x.shape[0])], dim=0) # [B, C] + + context_aware_representations = self.c_embedder(context_aware_representations) + c = timestep_aware_representations + context_aware_representations + del timestep_aware_representations, context_aware_representations + x = self.input_embedder(x) + x = self.individual_token_refiner(x, c, attn_params) + return x + + +class FinalLayer(nn.Module): + """ + Final output projection layer with adaptive layer normalization. + + Projects transformer hidden states to output patch space with + timestep-conditioned modulation. + + Args: + hidden_size: Input hidden dimension. + patch_size: Spatial patch size for output reshaping. + out_channels: Number of output channels. + act_layer: Activation function class. + """ + + def __init__(self, hidden_size, patch_size, out_channels, act_layer): + super().__init__() + + # Layer normalization without learnable parameters + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + out_size = (patch_size[0] * patch_size[1]) * out_channels + self.linear = nn.Linear(hidden_size, out_size, bias=True) + + # Adaptive layer normalization modulation + self.adaLN_modulation = nn.Sequential( + act_layer(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True), + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift=shift, scale=scale) + del shift, scale, c + x = self.linear(x) + return x + + +class RMSNorm(nn.Module): + """ + Root Mean Square Layer Normalization. + + Normalizes input using RMS and applies learnable scaling. + More efficient than LayerNorm as it doesn't compute mean. + + Args: + dim: Input feature dimension. + eps: Small value for numerical stability. + """ + + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + """ + Apply RMS normalization. + + Args: + x: Input tensor. + + Returns: + RMS normalized tensor. + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def reset_parameters(self): + self.weight.fill_(1) + + def forward(self, x): + """ + Apply RMSNorm with learnable scaling. + + Args: + x: Input tensor. + + Returns: + Normalized and scaled tensor. + """ + output = self._norm(x.float()).type_as(x) + del x + # output = output * self.weight + # fp8 support + output = output * self.weight.to(output.dtype) + return output + + +# kept for reference, not used in current implementation +# class LinearWarpforSingle(nn.Module): +# """ +# Linear layer wrapper for concatenating and projecting two inputs. + +# Used in single-stream blocks to combine attention output with MLP features. + +# Args: +# in_dim: Input dimension (sum of both input feature dimensions). +# out_dim: Output dimension. +# bias: Whether to use bias in linear projection. +# """ + +# def __init__(self, in_dim: int, out_dim: int, bias=False): +# super().__init__() +# self.fc = nn.Linear(in_dim, out_dim, bias=bias) + +# def forward(self, x, y): +# """Concatenate inputs along feature dimension and project.""" +# x = torch.cat([x.contiguous(), y.contiguous()], dim=2).contiguous() +# return self.fc(x) + + +class ModulateDiT(nn.Module): + """ + Timestep conditioning modulation layer. + + Projects timestep embeddings to multiple modulation parameters + for adaptive layer normalization. + + Args: + hidden_size: Input conditioning dimension. + factor: Number of modulation parameters to generate. + act_layer: Activation function class. + """ + + def __init__(self, hidden_size: int, factor: int, act_layer: Callable): + super().__init__() + self.act = act_layer() + self.linear = nn.Linear(hidden_size, factor * hidden_size, bias=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.linear(self.act(x)) + + +class MMDoubleStreamBlock(nn.Module): + """ + Multimodal double-stream transformer block. + + Processes image and text tokens separately with cross-modal attention. + Each stream has its own normalization and MLP layers but shares + attention computation for cross-modal interaction. + + Args: + hidden_size: Model dimension. + heads_num: Number of attention heads. + mlp_width_ratio: MLP expansion ratio. + mlp_act_type: MLP activation function (only "gelu_tanh" supported). + qk_norm: QK normalization flag (must be True). + qk_norm_type: QK normalization type (only "rms" supported). + qkv_bias: Use bias in QKV projections. + """ + + def __init__( + self, + hidden_size: int, + heads_num: int, + mlp_width_ratio: float, + mlp_act_type: str = "gelu_tanh", + qk_norm: bool = True, + qk_norm_type: str = "rms", + qkv_bias: bool = False, + ): + super().__init__() + + assert mlp_act_type == "gelu_tanh", "Only GELU-tanh activation supported." + assert qk_norm_type == "rms", "Only RMS normalization supported." + assert qk_norm, "QK normalization must be enabled." + + self.heads_num = heads_num + head_dim = hidden_size // heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + + # Image stream processing components + self.img_mod = ModulateDiT(hidden_size, factor=6, act_layer=nn.SiLU) + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.img_attn_qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias) + + self.img_attn_q_norm = RMSNorm(head_dim, eps=1e-6) + self.img_attn_k_norm = RMSNorm(head_dim, eps=1e-6) + self.img_attn_proj = nn.Linear(hidden_size, hidden_size, bias=qkv_bias) + + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_mlp = MLP(hidden_size, mlp_hidden_dim, act_layer=lambda: nn.GELU(approximate="tanh"), bias=True) + + # Text stream processing components + self.txt_mod = ModulateDiT(hidden_size, factor=6, act_layer=nn.SiLU) + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.txt_attn_qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias) + self.txt_attn_q_norm = RMSNorm(head_dim, eps=1e-6) + self.txt_attn_k_norm = RMSNorm(head_dim, eps=1e-6) + self.txt_attn_proj = nn.Linear(hidden_size, hidden_size, bias=qkv_bias) + + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_mlp = MLP(hidden_size, mlp_hidden_dim, act_layer=lambda: nn.GELU(approximate="tanh"), bias=True) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def _forward( + self, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, freqs_cis: tuple = None, attn_params: AttentionParams = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Extract modulation parameters for image and text streams + (img_mod1_shift, img_mod1_scale, img_mod1_gate, img_mod2_shift, img_mod2_scale, img_mod2_gate) = self.img_mod(vec).chunk( + 6, dim=-1 + ) + (txt_mod1_shift, txt_mod1_scale, txt_mod1_gate, txt_mod2_shift, txt_mod2_scale, txt_mod2_gate) = self.txt_mod(vec).chunk( + 6, dim=-1 + ) + + # Process image stream for attention + img_modulated = self.img_norm1(img) + img_modulated = modulate(img_modulated, shift=img_mod1_shift, scale=img_mod1_scale) + del img_mod1_shift, img_mod1_scale + + img_qkv = self.img_attn_qkv(img_modulated) + del img_modulated + img_q, img_k, img_v = img_qkv.chunk(3, dim=-1) + del img_qkv + + img_q = rearrange(img_q, "B L (H D) -> B L H D", H=self.heads_num) + img_k = rearrange(img_k, "B L (H D) -> B L H D", H=self.heads_num) + img_v = rearrange(img_v, "B L (H D) -> B L H D", H=self.heads_num) + + # Apply QK-Norm if enabled + img_q = self.img_attn_q_norm(img_q).to(img_v) + img_k = self.img_attn_k_norm(img_k).to(img_v) + + # Apply rotary position embeddings to image tokens + if freqs_cis is not None: + img_q, img_k = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) + del freqs_cis + + # Process text stream for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = modulate(txt_modulated, shift=txt_mod1_shift, scale=txt_mod1_scale) + + txt_qkv = self.txt_attn_qkv(txt_modulated) + del txt_modulated + txt_q, txt_k, txt_v = txt_qkv.chunk(3, dim=-1) + del txt_qkv + + txt_q = rearrange(txt_q, "B L (H D) -> B L H D", H=self.heads_num) + txt_k = rearrange(txt_k, "B L (H D) -> B L H D", H=self.heads_num) + txt_v = rearrange(txt_v, "B L (H D) -> B L H D", H=self.heads_num) + + # Apply QK-Norm if enabled + txt_q = self.txt_attn_q_norm(txt_q).to(txt_v) + txt_k = self.txt_attn_k_norm(txt_k).to(txt_v) + + # Concatenate image and text tokens for joint attention + img_seq_len = img.shape[1] + q = torch.cat([img_q, txt_q], dim=1) + del img_q, txt_q + k = torch.cat([img_k, txt_k], dim=1) + del img_k, txt_k + v = torch.cat([img_v, txt_v], dim=1) + del img_v, txt_v + + qkv = [q, k, v] + del q, k, v + attn = attention(qkv, attn_params=attn_params) + del qkv + + # Split attention outputs back to separate streams + img_attn, txt_attn = (attn[:, :img_seq_len].contiguous(), attn[:, img_seq_len:].contiguous()) + del attn + + # Apply attention projection and residual connection for image stream + img = img + apply_gate(self.img_attn_proj(img_attn), gate=img_mod1_gate) + del img_attn, img_mod1_gate + + # Apply MLP and residual connection for image stream + img = img + apply_gate( + self.img_mlp(modulate(self.img_norm2(img), shift=img_mod2_shift, scale=img_mod2_scale)), + gate=img_mod2_gate, + ) + del img_mod2_shift, img_mod2_scale, img_mod2_gate + + # Apply attention projection and residual connection for text stream + txt = txt + apply_gate(self.txt_attn_proj(txt_attn), gate=txt_mod1_gate) + del txt_attn, txt_mod1_gate + + # Apply MLP and residual connection for text stream + txt = txt + apply_gate( + self.txt_mlp(modulate(self.txt_norm2(txt), shift=txt_mod2_shift, scale=txt_mod2_scale)), + gate=txt_mod2_gate, + ) + del txt_mod2_shift, txt_mod2_scale, txt_mod2_gate + + return img, txt + + def forward( + self, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, freqs_cis: tuple = None, attn_params: AttentionParams = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + if self.gradient_checkpointing and self.training: + forward_fn = self._forward + if self.cpu_offload_checkpointing: + forward_fn = custom_offloading_utils.cpu_offload_wrapper(forward_fn, self.img_attn_qkv.weight.device) + + return torch.utils.checkpoint.checkpoint(forward_fn, img, txt, vec, freqs_cis, attn_params, use_reentrant=False) + else: + return self._forward(img, txt, vec, freqs_cis, attn_params) + + +class MMSingleStreamBlock(nn.Module): + """ + Multimodal single-stream transformer block. + + Processes concatenated image and text tokens jointly with shared attention. + Uses parallel linear layers for efficiency and applies RoPE only to image tokens. + + Args: + hidden_size: Model dimension. + heads_num: Number of attention heads. + mlp_width_ratio: MLP expansion ratio. + mlp_act_type: MLP activation function (only "gelu_tanh" supported). + qk_norm: QK normalization flag (must be True). + qk_norm_type: QK normalization type (only "rms" supported). + qk_scale: Attention scaling factor (computed automatically if None). + """ + + def __init__( + self, + hidden_size: int, + heads_num: int, + mlp_width_ratio: float = 4.0, + mlp_act_type: str = "gelu_tanh", + qk_norm: bool = True, + qk_norm_type: str = "rms", + qk_scale: float = None, + ): + super().__init__() + + assert mlp_act_type == "gelu_tanh", "Only GELU-tanh activation supported." + assert qk_norm_type == "rms", "Only RMS normalization supported." + assert qk_norm, "QK normalization must be enabled." + + self.hidden_size = hidden_size + self.heads_num = heads_num + head_dim = hidden_size // heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + self.mlp_hidden_dim = mlp_hidden_dim + self.scale = qk_scale or head_dim**-0.5 + + # Parallel linear projections for efficiency + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + mlp_hidden_dim) + + # Combined output projection + # self.linear2 = LinearWarpforSingle(hidden_size + mlp_hidden_dim, hidden_size, bias=True) # for reference + self.linear2 = nn.Linear(hidden_size + mlp_hidden_dim, hidden_size, bias=True) + + # QK normalization layers + self.q_norm = RMSNorm(head_dim, eps=1e-6) + self.k_norm = RMSNorm(head_dim, eps=1e-6) + + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = ModulateDiT(hidden_size, factor=3, act_layer=nn.SiLU) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def _forward( + self, + x: torch.Tensor, + vec: torch.Tensor, + freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None, + attn_params: AttentionParams = None, + ) -> torch.Tensor: + # Extract modulation parameters + mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1) + x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale) + + # Compute Q, K, V, and MLP input + qkv_mlp = self.linear1(x_mod) + del x_mod + q, k, v, mlp = qkv_mlp.split([self.hidden_size, self.hidden_size, self.hidden_size, self.mlp_hidden_dim], dim=-1) + del qkv_mlp + + q = rearrange(q, "B L (H D) -> B L H D", H=self.heads_num) + k = rearrange(k, "B L (H D) -> B L H D", H=self.heads_num) + v = rearrange(v, "B L (H D) -> B L H D", H=self.heads_num) + + # Apply QK-Norm if enabled + q = self.q_norm(q).to(v) + k = self.k_norm(k).to(v) + + # Separate image and text tokens + img_q, txt_q = q[:, : attn_params.img_len, :, :], q[:, attn_params.img_len :, :, :] + del q + img_k, txt_k = k[:, : attn_params.img_len, :, :], k[:, attn_params.img_len :, :, :] + del k + + # Apply rotary position embeddings only to image tokens + img_q, img_k = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) + del freqs_cis + + # Recombine and compute joint attention + q = torch.cat([img_q, txt_q], dim=1) + del img_q, txt_q + k = torch.cat([img_k, txt_k], dim=1) + del img_k, txt_k + # v = torch.cat([img_v, txt_v], dim=1) + # del img_v, txt_v + qkv = [q, k, v] + del q, k, v + attn = attention(qkv, attn_params=attn_params) + del qkv + + # Combine attention and MLP outputs, apply gating + # output = self.linear2(attn, self.mlp_act(mlp)) + + mlp = self.mlp_act(mlp) + output = torch.cat([attn, mlp], dim=2).contiguous() + del attn, mlp + output = self.linear2(output) + + return x + apply_gate(output, gate=mod_gate) + + def forward( + self, + x: torch.Tensor, + vec: torch.Tensor, + freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None, + attn_params: AttentionParams = None, + ) -> torch.Tensor: + if self.gradient_checkpointing and self.training: + forward_fn = self._forward + if self.cpu_offload_checkpointing: + forward_fn = custom_offloading_utils.create_cpu_offloading_wrapper(forward_fn, self.linear1.weight.device) + + return torch.utils.checkpoint.checkpoint(forward_fn, x, vec, freqs_cis, attn_params, use_reentrant=False) + else: + return self._forward(x, vec, freqs_cis, attn_params) + + +# endregion diff --git a/vendor/sd-scripts/library/hunyuan_image_text_encoder.py b/vendor/sd-scripts/library/hunyuan_image_text_encoder.py new file mode 100644 index 0000000..b1323df --- /dev/null +++ b/vendor/sd-scripts/library/hunyuan_image_text_encoder.py @@ -0,0 +1,661 @@ +import json +import re +from typing import Tuple, Optional, Union +import torch +from transformers import ( + AutoTokenizer, + PreTrainedTokenizer, + Qwen2_5_VLConfig, + Qwen2_5_VLForConditionalGeneration, + Qwen2TokenizerFast, + T5ForConditionalGeneration, + T5Config, +) +from transformers.models.t5.modeling_t5 import T5Stack +from accelerate import init_empty_weights + +from library.safetensors_utils import load_safetensors +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +BYT5_TOKENIZER_PATH = "google/byt5-small" +QWEN_2_5_VL_IMAGE_ID = "Qwen/Qwen2.5-VL-7B-Instruct" + + +# Copy from Glyph-SDXL-V2 + +COLOR_IDX_JSON = """{"white": 0, "black": 1, "darkslategray": 2, "dimgray": 3, "darkolivegreen": 4, "midnightblue": 5, "saddlebrown": 6, "sienna": 7, "whitesmoke": 8, "darkslateblue": 9, +"indianred": 10, "linen": 11, "maroon": 12, "khaki": 13, "sandybrown": 14, "gray": 15, "gainsboro": 16, "teal": 17, "peru": 18, "gold": 19, +"snow": 20, "firebrick": 21, "crimson": 22, "chocolate": 23, "tomato": 24, "brown": 25, "goldenrod": 26, "antiquewhite": 27, "rosybrown": 28, "steelblue": 29, +"floralwhite": 30, "seashell": 31, "darkgreen": 32, "oldlace": 33, "darkkhaki": 34, "burlywood": 35, "red": 36, "darkgray": 37, "orange": 38, "royalblue": 39, +"seagreen": 40, "lightgray": 41, "tan": 42, "coral": 43, "beige": 44, "palevioletred": 45, "wheat": 46, "lavender": 47, "darkcyan": 48, "slateblue": 49, +"slategray": 50, "orangered": 51, "silver": 52, "olivedrab": 53, "forestgreen": 54, "darkgoldenrod": 55, "ivory": 56, "darkorange": 57, "yellow": 58, "hotpink": 59, +"ghostwhite": 60, "lightcoral": 61, "indigo": 62, "bisque": 63, "darkred": 64, "darksalmon": 65, "lightslategray": 66, "dodgerblue": 67, "lightpink": 68, "mistyrose": 69, +"mediumvioletred": 70, "cadetblue": 71, "deeppink": 72, "salmon": 73, "palegoldenrod": 74, "blanchedalmond": 75, "lightseagreen": 76, "cornflowerblue": 77, "yellowgreen": 78, "greenyellow": 79, +"navajowhite": 80, "papayawhip": 81, "mediumslateblue": 82, "purple": 83, "blueviolet": 84, "pink": 85, "cornsilk": 86, "lightsalmon": 87, "mediumpurple": 88, "moccasin": 89, +"turquoise": 90, "mediumseagreen": 91, "lavenderblush": 92, "mediumblue": 93, "darkseagreen": 94, "mediumturquoise": 95, "paleturquoise": 96, "skyblue": 97, "lemonchiffon": 98, "olive": 99, +"peachpuff": 100, "lightyellow": 101, "lightsteelblue": 102, "mediumorchid": 103, "plum": 104, "darkturquoise": 105, "aliceblue": 106, "mediumaquamarine": 107, "orchid": 108, "powderblue": 109, +"blue": 110, "darkorchid": 111, "violet": 112, "lightskyblue": 113, "lightcyan": 114, "lightgoldenrodyellow": 115, "navy": 116, "thistle": 117, "honeydew": 118, "mintcream": 119, +"lightblue": 120, "darkblue": 121, "darkmagenta": 122, "deepskyblue": 123, "magenta": 124, "limegreen": 125, "darkviolet": 126, "cyan": 127, "palegreen": 128, "aquamarine": 129, +"lawngreen": 130, "lightgreen": 131, "azure": 132, "chartreuse": 133, "green": 134, "mediumspringgreen": 135, "lime": 136, "springgreen": 137}""" + +MULTILINGUAL_10_LANG_IDX_JSON = """{"en-Montserrat-Regular": 0, "en-Poppins-Italic": 1, "en-GlacialIndifference-Regular": 2, "en-OpenSans-ExtraBoldItalic": 3, "en-Montserrat-Bold": 4, "en-Now-Regular": 5, "en-Garet-Regular": 6, "en-LeagueSpartan-Bold": 7, "en-DMSans-Regular": 8, "en-OpenSauceOne-Regular": 9, +"en-OpenSans-ExtraBold": 10, "en-KGPrimaryPenmanship": 11, "en-Anton-Regular": 12, "en-Aileron-BlackItalic": 13, "en-Quicksand-Light": 14, "en-Roboto-BoldItalic": 15, "en-TheSeasons-It": 16, "en-Kollektif": 17, "en-Inter-BoldItalic": 18, "en-Poppins-Medium": 19, +"en-Poppins-Light": 20, "en-RoxboroughCF-RegularItalic": 21, "en-PlayfairDisplay-SemiBold": 22, "en-Agrandir-Italic": 23, "en-Lato-Regular": 24, "en-MoreSugarRegular": 25, "en-CanvaSans-RegularItalic": 26, "en-PublicSans-Italic": 27, "en-CodePro-NormalLC": 28, "en-Belleza-Regular": 29, +"en-JosefinSans-Bold": 30, "en-HKGrotesk-Bold": 31, "en-Telegraf-Medium": 32, "en-BrittanySignatureRegular": 33, "en-Raleway-ExtraBoldItalic": 34, "en-Mont-RegularItalic": 35, "en-Arimo-BoldItalic": 36, "en-Lora-Italic": 37, "en-ArchivoBlack-Regular": 38, "en-Poppins": 39, +"en-Barlow-Black": 40, "en-CormorantGaramond-Bold": 41, "en-LibreBaskerville-Regular": 42, "en-CanvaSchoolFontRegular": 43, "en-BebasNeueBold": 44, "en-LazydogRegular": 45, "en-FredokaOne-Regular": 46, "en-Horizon-Bold": 47, "en-Nourd-Regular": 48, "en-Hatton-Regular": 49, +"en-Nunito-ExtraBoldItalic": 50, "en-CerebriSans-Regular": 51, "en-Montserrat-Light": 52, "en-TenorSans": 53, "en-Norwester-Regular": 54, "en-ClearSans-Bold": 55, "en-Cardo-Regular": 56, "en-Alice-Regular": 57, "en-Oswald-Regular": 58, "en-Gaegu-Bold": 59, +"en-Muli-Black": 60, "en-TAN-PEARL-Regular": 61, "en-CooperHewitt-Book": 62, "en-Agrandir-Grand": 63, "en-BlackMango-Thin": 64, "en-DMSerifDisplay-Regular": 65, "en-Antonio-Bold": 66, "en-Sniglet-Regular": 67, "en-BeVietnam-Regular": 68, 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"en-Garet-Bold": 139, +"en-FiraSans-Black": 140, "en-BantayogLight": 141, "en-NotoSerifDisplay-Black": 142, "en-TTChocolates-Regular": 143, "en-Ubuntu-Regular": 144, "en-Assistant-Bold": 145, "en-ABeeZee-Regular": 146, "en-LexendDeca-Regular": 147, "en-KingredSerif": 148, "en-Radley-Regular": 149, +"en-BrownSugar": 150, "en-MigraItalic-ExtraboldItalic": 151, "en-ChildosArabic-Regular": 152, "en-PeaceSans": 153, "en-LondrinaSolid-Black": 154, "en-SpaceMono-BoldItalic": 155, "en-RobotoMono-Light": 156, "en-CourierPrime-Regular": 157, "en-Alata-Regular": 158, "en-Amsterdam-One": 159, +"en-IreneFlorentina-Regular": 160, "en-CatchyMager": 161, "en-Alta_regular": 162, "en-ArticulatCF-Regular": 163, "en-Raleway-Regular": 164, "en-BrasikaDisplay": 165, "en-TANAngleton-Italic": 166, "en-NotoSerifDisplay-ExtraCondensedItalic": 167, "en-Bryndan Write": 168, "en-TTCommonsPro-It": 169, +"en-AlexBrush-Regular": 170, "en-Antic-Regular": 171, "en-TTHoves-Bold": 172, "en-DroidSerif": 173, 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"kr-SeH-CL": 99, +"kr-SeH-CM": 100, "kr-SeN-CB": 101, "kr-SeN-CBL": 102, "kr-SeN-CEB": 103, "kr-SeN-CL": 104, "kr-SeN-CM": 105, "kr-Sunflower-Bold": 106, "kr-Sunflower-Light": 107, "kr-Sunflower-Medium": 108, "kr-TTClaytoyR": 109, +"kr-TTDalpangiR": 110, "kr-TTMamablockR": 111, "kr-TTNauidongmuR": 112, "kr-TTOktapbangR": 113, "kr-UhBeeMiMi": 114, "kr-UhBeeMiMiBold": 115, "kr-UhBeeSe_hyun": 116, "kr-UhBeeSe_hyunBold": 117, "kr-UhBeenamsoyoung": 118, "kr-UhBeenamsoyoungBold": 119, +"kr-WenQuanYiMicroHei": 120, "kr-YeonSung-Regular": 121}""" + + +def add_special_token(tokenizer: PreTrainedTokenizer, text_encoder: T5Stack): + """ + Add special tokens for color and font to tokenizer and text encoder. + + Args: + tokenizer: Huggingface tokenizer. + text_encoder: Huggingface T5 encoder. + """ + idx_font_dict = json.loads(MULTILINGUAL_10_LANG_IDX_JSON) + idx_color_dict = json.loads(COLOR_IDX_JSON) + + font_token = [f"<{font_code[:2]}-font-{idx_font_dict[font_code]}>" for font_code in idx_font_dict] + color_token = [f"" for i in range(len(idx_color_dict))] + additional_special_tokens = [] + additional_special_tokens += color_token + additional_special_tokens += font_token + + tokenizer.add_tokens(additional_special_tokens, special_tokens=True) + # Set mean_resizing=False to avoid PyTorch LAPACK dependency + text_encoder.resize_token_embeddings(len(tokenizer), mean_resizing=False) + + +def load_byt5( + ckpt_path: str, + dtype: Optional[torch.dtype], + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[dict] = None, +) -> Tuple[T5Stack, PreTrainedTokenizer]: + BYT5_CONFIG_JSON = """ +{ + "_name_or_path": "/home/patrick/t5/byt5-small", + "architectures": [ + "T5ForConditionalGeneration" + ], + "d_ff": 3584, + "d_kv": 64, + "d_model": 1472, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "feed_forward_proj": "gated-gelu", + "gradient_checkpointing": false, + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 4, + "num_heads": 6, + "num_layers": 12, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "tokenizer_class": "ByT5Tokenizer", + "transformers_version": "4.7.0.dev0", + "use_cache": true, + "vocab_size": 384 + } +""" + + logger.info(f"Loading BYT5 tokenizer from {BYT5_TOKENIZER_PATH}") + byt5_tokenizer = AutoTokenizer.from_pretrained(BYT5_TOKENIZER_PATH) + + logger.info("Initializing BYT5 text encoder") + config = json.loads(BYT5_CONFIG_JSON) + config = T5Config(**config) + with init_empty_weights(): + byt5_text_encoder = T5ForConditionalGeneration._from_config(config).get_encoder() + + add_special_token(byt5_tokenizer, byt5_text_encoder) + + if state_dict is not None: + sd = state_dict + else: + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device, disable_mmap=disable_mmap, dtype=dtype) + + # remove "encoder." prefix + sd = {k[len("encoder.") :] if k.startswith("encoder.") else k: v for k, v in sd.items()} + sd["embed_tokens.weight"] = sd.pop("shared.weight") + + info = byt5_text_encoder.load_state_dict(sd, strict=True, assign=True) + byt5_text_encoder.to(device) + byt5_text_encoder.eval() + logger.info(f"BYT5 text encoder loaded with info: {info}") + + return byt5_tokenizer, byt5_text_encoder + + +def load_qwen2_5_vl( + ckpt_path: str, + dtype: Optional[torch.dtype], + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[dict] = None, +) -> tuple[Qwen2TokenizerFast, Qwen2_5_VLForConditionalGeneration]: + QWEN2_5_VL_CONFIG_JSON = """ +{ + "architectures": [ + "Qwen2_5_VLForConditionalGeneration" + ], + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 3584, + "image_token_id": 151655, + "initializer_range": 0.02, + "intermediate_size": 18944, + "max_position_embeddings": 128000, + "max_window_layers": 28, + "model_type": "qwen2_5_vl", + "num_attention_heads": 28, + "num_hidden_layers": 28, + "num_key_value_heads": 4, + "rms_norm_eps": 1e-06, + "rope_scaling": { + "mrope_section": [ + 16, + 24, + 24 + ], + "rope_type": "default", + "type": "default" + }, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "text_config": { + "architectures": [ + "Qwen2_5_VLForConditionalGeneration" + ], + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 3584, + "image_token_id": null, + "initializer_range": 0.02, + "intermediate_size": 18944, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 128000, + "max_window_layers": 28, + "model_type": "qwen2_5_vl_text", + "num_attention_heads": 28, + "num_hidden_layers": 28, + "num_key_value_heads": 4, + "rms_norm_eps": 1e-06, + "rope_scaling": { + "mrope_section": [ + 16, + 24, + 24 + ], + "rope_type": "default", + "type": "default" + }, + "rope_theta": 1000000.0, + "sliding_window": null, + "torch_dtype": "float32", + "use_cache": true, + "use_sliding_window": false, + "video_token_id": null, + "vision_end_token_id": 151653, + "vision_start_token_id": 151652, + "vision_token_id": 151654, + "vocab_size": 152064 + }, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.53.1", + "use_cache": true, + "use_sliding_window": false, + "video_token_id": 151656, + "vision_config": { + "depth": 32, + "fullatt_block_indexes": [ + 7, + 15, + 23, + 31 + ], + "hidden_act": "silu", + "hidden_size": 1280, + "in_channels": 3, + "in_chans": 3, + "initializer_range": 0.02, + "intermediate_size": 3420, + "model_type": "qwen2_5_vl", + "num_heads": 16, + "out_hidden_size": 3584, + "patch_size": 14, + "spatial_merge_size": 2, + "spatial_patch_size": 14, + "temporal_patch_size": 2, + "tokens_per_second": 2, + "torch_dtype": "float32", + "window_size": 112 + }, + "vision_end_token_id": 151653, + "vision_start_token_id": 151652, + "vision_token_id": 151654, + "vocab_size": 152064 +} +""" + config = json.loads(QWEN2_5_VL_CONFIG_JSON) + config = Qwen2_5_VLConfig(**config) + with init_empty_weights(): + qwen2_5_vl = Qwen2_5_VLForConditionalGeneration._from_config(config) + + if state_dict is not None: + sd = state_dict + else: + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device, disable_mmap=disable_mmap, dtype=dtype) + + # convert prefixes + for key in list(sd.keys()): + if key.startswith("model."): + new_key = key.replace("model.", "model.language_model.", 1) + elif key.startswith("visual."): + new_key = key.replace("visual.", "model.visual.", 1) + else: + continue + if key not in sd: + logger.warning(f"Key {key} not found in state dict, skipping.") + continue + sd[new_key] = sd.pop(key) + + info = qwen2_5_vl.load_state_dict(sd, strict=True, assign=True) + logger.info(f"Loaded Qwen2.5-VL: {info}") + qwen2_5_vl.to(device) + qwen2_5_vl.eval() + + if dtype is not None: + if dtype.itemsize == 1: # fp8 + org_dtype = torch.bfloat16 # model weight is fp8 in loading, but original dtype is bfloat16 + logger.info(f"prepare Qwen2.5-VL for fp8: set to {dtype} from {org_dtype}") + qwen2_5_vl.to(dtype) + + # prepare LLM for fp8 + def prepare_fp8(vl_model: Qwen2_5_VLForConditionalGeneration, target_dtype): + def forward_hook(module): + def forward(hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + module.variance_epsilon) + # return module.weight.to(input_dtype) * hidden_states.to(input_dtype) + return (module.weight.to(torch.float32) * hidden_states.to(torch.float32)).to(input_dtype) + + return forward + + def decoder_forward_hook(module): + def forward( + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + **kwargs, + ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: + + residual = hidden_states + + hidden_states = module.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights = module.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + input_dtype = hidden_states.dtype + hidden_states = residual.to(torch.float32) + hidden_states.to(torch.float32) + hidden_states = hidden_states.to(input_dtype) + + # Fully Connected + residual = hidden_states + hidden_states = module.post_attention_layernorm(hidden_states) + hidden_states = module.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + return outputs + + return forward + + for module in vl_model.modules(): + if module.__class__.__name__ in ["Embedding"]: + # print("set", module.__class__.__name__, "to", target_dtype) + module.to(target_dtype) + if module.__class__.__name__ in ["Qwen2RMSNorm"]: + # print("set", module.__class__.__name__, "hooks") + module.forward = forward_hook(module) + if module.__class__.__name__ in ["Qwen2_5_VLDecoderLayer"]: + # print("set", module.__class__.__name__, "hooks") + module.forward = decoder_forward_hook(module) + if module.__class__.__name__ in ["Qwen2_5_VisionRotaryEmbedding"]: + # print("set", module.__class__.__name__, "hooks") + module.to(target_dtype) + + prepare_fp8(qwen2_5_vl, org_dtype) + + else: + logger.info(f"Setting Qwen2.5-VL to dtype: {dtype}") + qwen2_5_vl.to(dtype) + + # Load tokenizer + logger.info(f"Loading tokenizer from {QWEN_2_5_VL_IMAGE_ID}") + tokenizer = Qwen2TokenizerFast.from_pretrained(QWEN_2_5_VL_IMAGE_ID) + return tokenizer, qwen2_5_vl + + +TOKENIZER_MAX_LENGTH = 1024 +PROMPT_TEMPLATE_ENCODE_START_IDX = 34 + + +def get_qwen_prompt_embeds( + tokenizer: Qwen2TokenizerFast, vlm: Qwen2_5_VLForConditionalGeneration, prompt: Union[str, list[str]] = None +) -> Tuple[torch.Tensor, torch.Tensor]: + input_ids, mask = get_qwen_tokens(tokenizer, prompt) + return get_qwen_prompt_embeds_from_tokens(vlm, input_ids, mask) + + +def get_qwen_tokens(tokenizer: Qwen2TokenizerFast, prompt: Union[str, list[str]] = None) -> Tuple[torch.Tensor, torch.Tensor]: + tokenizer_max_length = TOKENIZER_MAX_LENGTH + + # HunyuanImage-2.1 does not use "<|im_start|>assistant\n" in the prompt template + prompt_template_encode = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>" + # \n<|im_start|>assistant\n" + prompt_template_encode_start_idx = PROMPT_TEMPLATE_ENCODE_START_IDX + # default_sample_size = 128 + + prompt = [prompt] if isinstance(prompt, str) else prompt + + template = prompt_template_encode + drop_idx = prompt_template_encode_start_idx + txt = [template.format(e) for e in prompt] + txt_tokens = tokenizer(txt, max_length=tokenizer_max_length + drop_idx, padding=True, truncation=True, return_tensors="pt") + return txt_tokens.input_ids, txt_tokens.attention_mask + + +def get_qwen_prompt_embeds_from_tokens( + vlm: Qwen2_5_VLForConditionalGeneration, input_ids: torch.Tensor, attention_mask: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + tokenizer_max_length = TOKENIZER_MAX_LENGTH + drop_idx = PROMPT_TEMPLATE_ENCODE_START_IDX + + device = vlm.device + dtype = vlm.dtype + + input_ids = input_ids.to(device=device) + attention_mask = attention_mask.to(device=device) + + if dtype.itemsize == 1: # fp8 + with torch.no_grad(), torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=True): + encoder_hidden_states = vlm(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True) + else: + with torch.no_grad(), torch.autocast(device_type=device.type, dtype=dtype, enabled=True): + encoder_hidden_states = vlm(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True) + + hidden_states = encoder_hidden_states.hidden_states[-3] # use the 3rd last layer's hidden states for HunyuanImage-2.1 + if hidden_states.shape[1] > tokenizer_max_length + drop_idx: + logger.warning(f"Hidden states shape {hidden_states.shape} exceeds max length {tokenizer_max_length + drop_idx}") + + # --- Unnecessary complicated processing, keep for reference --- + # split_hidden_states = extract_masked_hidden(hidden_states, txt_tokens.attention_mask) + # split_hidden_states = [e[drop_idx:] for e in split_hidden_states] + # attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states] + # max_seq_len = max([e.size(0) for e in split_hidden_states]) + # prompt_embeds = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]) + # encoder_attention_mask = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]) + # ---------------------------------------------------------- + + prompt_embeds = hidden_states[:, drop_idx:, :] + encoder_attention_mask = attention_mask[:, drop_idx:] + prompt_embeds = prompt_embeds.to(device=device) + + return prompt_embeds, encoder_attention_mask + + +def format_prompt(texts, styles): + """ + Text "{text}" in {color}, {type}. + """ + + prompt = "" + for text, style in zip(texts, styles): + # color and style are always None in official implementation, so we only use text + text_prompt = f'Text "{text}"' + text_prompt += ". " + prompt = prompt + text_prompt + return prompt + + +BYT5_MAX_LENGTH = 128 + + +def get_glyph_prompt_embeds( + tokenizer: PreTrainedTokenizer, text_encoder: T5Stack, prompt: Optional[str] = None +) -> Tuple[list[bool], torch.Tensor, torch.Tensor]: + byt5_tokens, byt5_text_mask = get_byt5_text_tokens(tokenizer, prompt) + return get_byt5_prompt_embeds_from_tokens(text_encoder, byt5_tokens, byt5_text_mask) + + +def get_byt5_prompt_embeds_from_tokens( + text_encoder: T5Stack, byt5_text_ids: Optional[torch.Tensor], byt5_text_mask: Optional[torch.Tensor] +) -> Tuple[list[bool], torch.Tensor, torch.Tensor]: + byt5_max_length = BYT5_MAX_LENGTH + + if byt5_text_ids is None or byt5_text_mask is None or byt5_text_mask.sum() == 0: + return ( + [False], + torch.zeros((1, byt5_max_length, 1472), device=text_encoder.device), + torch.zeros((1, byt5_max_length), device=text_encoder.device, dtype=torch.int64), + ) + + byt5_text_ids = byt5_text_ids.to(device=text_encoder.device) + byt5_text_mask = byt5_text_mask.to(device=text_encoder.device) + + with torch.no_grad(), torch.autocast(device_type=text_encoder.device.type, dtype=text_encoder.dtype, enabled=True): + byt5_prompt_embeds = text_encoder(byt5_text_ids, attention_mask=byt5_text_mask.float()) + byt5_emb = byt5_prompt_embeds[0] + + return [True], byt5_emb, byt5_text_mask + + +def get_byt5_text_tokens(tokenizer, prompt): + if not prompt: + return None, None + + try: + text_prompt_texts = [] + # pattern_quote_single = r"\'(.*?)\'" + pattern_quote_double = r"\"(.*?)\"" + pattern_quote_chinese_single = r"‘(.*?)’" + pattern_quote_chinese_double = r"“(.*?)”" + + # matches_quote_single = re.findall(pattern_quote_single, prompt) + matches_quote_double = re.findall(pattern_quote_double, prompt) + matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, prompt) + matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, prompt) + + # text_prompt_texts.extend(matches_quote_single) + text_prompt_texts.extend(matches_quote_double) + text_prompt_texts.extend(matches_quote_chinese_single) + text_prompt_texts.extend(matches_quote_chinese_double) + + if not text_prompt_texts: + return None, None + + text_prompt_style_list = [{"color": None, "font-family": None} for _ in range(len(text_prompt_texts))] + glyph_text_formatted = format_prompt(text_prompt_texts, text_prompt_style_list) + logger.info(f"Glyph text formatted: {glyph_text_formatted}") + + byt5_text_inputs = tokenizer( + glyph_text_formatted, + padding="max_length", + max_length=BYT5_MAX_LENGTH, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + + byt5_text_ids = byt5_text_inputs.input_ids + byt5_text_mask = byt5_text_inputs.attention_mask + + return byt5_text_ids, byt5_text_mask + + except Exception as e: + logger.warning(f"Warning: Error in glyph encoding, using fallback: {e}") + return None, None diff --git a/vendor/sd-scripts/library/hunyuan_image_utils.py b/vendor/sd-scripts/library/hunyuan_image_utils.py new file mode 100644 index 0000000..8e95925 --- /dev/null +++ b/vendor/sd-scripts/library/hunyuan_image_utils.py @@ -0,0 +1,525 @@ +# Original work: https://github.com/Tencent-Hunyuan/HunyuanImage-2.1 +# Re-implemented for license compliance for sd-scripts. + +import math +from typing import Tuple, Union, Optional +import torch + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +MODEL_VERSION_2_1 = "hunyuan-image-2.1" + +# region model + + +def _to_tuple(x, dim=2): + """ + Convert int or sequence to tuple of specified dimension. + + Args: + x: Int or sequence to convert. + dim: Target dimension for tuple. + + Returns: + Tuple of length dim. + """ + if isinstance(x, int) or isinstance(x, float): + return (x,) * dim + elif len(x) == dim: + return x + else: + raise ValueError(f"Expected length {dim} or int, but got {x}") + + +def get_meshgrid_nd(start, dim=2): + """ + Generate n-dimensional coordinate meshgrid from 0 to grid_size. + + Creates coordinate grids for each spatial dimension, useful for + generating position embeddings. + + Args: + start: Grid size for each dimension (int or tuple). + dim: Number of spatial dimensions. + + Returns: + Coordinate grid tensor [dim, *grid_size]. + """ + # Convert start to grid sizes + num = _to_tuple(start, dim=dim) + start = (0,) * dim + stop = num + + # Generate coordinate arrays for each dimension + axis_grid = [] + for i in range(dim): + a, b, n = start[i], stop[i], num[i] + g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n] + axis_grid.append(g) + grid = torch.meshgrid(*axis_grid, indexing="ij") # dim x [W, H, D] + grid = torch.stack(grid, dim=0) # [dim, W, H, D] + + return grid + + +def get_nd_rotary_pos_embed(rope_dim_list, start, theta=10000.0): + """ + Generate n-dimensional rotary position embeddings for spatial tokens. + + Creates RoPE embeddings for multi-dimensional positional encoding, + distributing head dimensions across spatial dimensions. + + Args: + rope_dim_list: Dimensions allocated to each spatial axis (should sum to head_dim). + start: Spatial grid size for each dimension. + theta: Base frequency for RoPE computation. + + Returns: + Tuple of (cos_freqs, sin_freqs) for rotary embedding [H*W, D/2]. + """ + + grid = get_meshgrid_nd(start, dim=len(rope_dim_list)) # [3, W, H, D] / [2, W, H] + + # Generate RoPE embeddings for each spatial dimension + embs = [] + for i in range(len(rope_dim_list)): + emb = get_1d_rotary_pos_embed(rope_dim_list[i], grid[i].reshape(-1), theta) # 2 x [WHD, rope_dim_list[i]] + embs.append(emb) + + cos = torch.cat([emb[0] for emb in embs], dim=1) # (WHD, D/2) + sin = torch.cat([emb[1] for emb in embs], dim=1) # (WHD, D/2) + return cos, sin + + +def get_1d_rotary_pos_embed( + dim: int, pos: Union[torch.FloatTensor, int], theta: float = 10000.0 +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Generate 1D rotary position embeddings. + + Args: + dim: Embedding dimension (must be even). + pos: Position indices [S] or scalar for sequence length. + theta: Base frequency for sinusoidal encoding. + + Returns: + Tuple of (cos_freqs, sin_freqs) tensors [S, D]. + """ + if isinstance(pos, int): + pos = torch.arange(pos).float() + + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + freqs = torch.outer(pos, freqs) # [S, D/2] + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + + +def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings for diffusion models. + + Converts scalar timesteps to high-dimensional embeddings using + sinusoidal encoding at different frequencies. + + Args: + t: Timestep tensor [N]. + dim: Output embedding dimension. + max_period: Maximum period for frequency computation. + + Returns: + Timestep embeddings [N, dim]. + """ + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +def modulate(x, shift=None, scale=None): + """ + Apply adaptive layer normalization modulation. + + Applies scale and shift transformations for conditioning + in adaptive layer normalization. + + Args: + x: Input tensor to modulate. + shift: Additive shift parameter (optional). + scale: Multiplicative scale parameter (optional). + + Returns: + Modulated tensor x * (1 + scale) + shift. + """ + if scale is None and shift is None: + return x + elif shift is None: + return x * (1 + scale.unsqueeze(1)) + elif scale is None: + return x + shift.unsqueeze(1) + else: + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def apply_gate(x, gate=None, tanh=False): + """ + Apply gating mechanism to tensor. + + Multiplies input by gate values, optionally applying tanh activation. + Used in residual connections for adaptive control. + + Args: + x: Input tensor to gate. + gate: Gating values (optional). + tanh: Whether to apply tanh to gate values. + + Returns: + Gated tensor x * gate (with optional tanh). + """ + if gate is None: + return x + if tanh: + return x * gate.unsqueeze(1).tanh() + else: + return x * gate.unsqueeze(1) + + +def reshape_for_broadcast( + freqs_cis: Tuple[torch.Tensor, torch.Tensor], + x: torch.Tensor, + head_first=False, +): + """ + Reshape RoPE frequency tensors for broadcasting with attention tensors. + + Args: + freqs_cis: Tuple of (cos_freqs, sin_freqs) tensors. + x: Target tensor for broadcasting compatibility. + head_first: Must be False (only supported layout). + + Returns: + Reshaped (cos_freqs, sin_freqs) tensors ready for broadcasting. + """ + assert not head_first, "Only head_first=False layout supported." + assert isinstance(freqs_cis, tuple), "Expected tuple of (cos, sin) frequency tensors." + assert x.ndim > 1, f"x should have at least 2 dimensions, but got {x.ndim}" + + # Validate frequency tensor dimensions match target tensor + assert freqs_cis[0].shape == ( + x.shape[1], + x.shape[-1], + ), f"Frequency tensor shape {freqs_cis[0].shape} incompatible with target shape {x.shape}" + + shape = [d if i == 1 or i == x.ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + + +def rotate_half(x): + """ + Rotate half the dimensions for RoPE computation. + + Splits the last dimension in half and applies a 90-degree rotation + by swapping and negating components. + + Args: + x: Input tensor [..., D] where D is even. + + Returns: + Rotated tensor with same shape as input. + """ + x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, xk: torch.Tensor, freqs_cis: Tuple[torch.Tensor, torch.Tensor], head_first: bool = False +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary position embeddings to query and key tensors. + + Args: + xq: Query tensor [B, S, H, D]. + xk: Key tensor [B, S, H, D]. + freqs_cis: Tuple of (cos_freqs, sin_freqs) for rotation. + head_first: Whether head dimension precedes sequence dimension. + + Returns: + Tuple of rotated (query, key) tensors. + """ + device = xq.device + dtype = xq.dtype + + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) + cos, sin = cos.to(device), sin.to(device) + + # Apply rotation: x' = x * cos + rotate_half(x) * sin + xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).to(dtype) + xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).to(dtype) + + return xq_out, xk_out + + +# endregion + +# region inference + + +def get_timesteps_sigmas(sampling_steps: int, shift: float, device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Generate timesteps and sigmas for diffusion sampling. + + Args: + sampling_steps: Number of sampling steps. + shift: Sigma shift parameter for schedule modification. + device: Target device for tensors. + + Returns: + Tuple of (timesteps, sigmas) tensors. + """ + sigmas = torch.linspace(1, 0, sampling_steps + 1) + sigmas = (shift * sigmas) / (1 + (shift - 1) * sigmas) + sigmas = sigmas.to(torch.float32) + timesteps = (sigmas[:-1] * 1000).to(dtype=torch.float32, device=device) + return timesteps, sigmas + + +def step(latents, noise_pred, sigmas, step_i): + """ + Perform a single diffusion sampling step. + + Args: + latents: Current latent state. + noise_pred: Predicted noise. + sigmas: Noise schedule sigmas. + step_i: Current step index. + + Returns: + Updated latents after the step. + """ + return latents.float() - (sigmas[step_i] - sigmas[step_i + 1]) * noise_pred.float() + + +# endregion + + +# region AdaptiveProjectedGuidance + + +class MomentumBuffer: + """ + Exponential moving average buffer for APG momentum. + """ + + def __init__(self, momentum: float): + self.momentum = momentum + self.running_average = 0 + + def update(self, update_value: torch.Tensor): + new_average = self.momentum * self.running_average + self.running_average = update_value + new_average + + +def normalized_guidance_apg( + pred_cond: torch.Tensor, + pred_uncond: torch.Tensor, + guidance_scale: float, + momentum_buffer: Optional[MomentumBuffer] = None, + eta: float = 1.0, + norm_threshold: float = 0.0, + use_original_formulation: bool = False, +): + """ + Apply normalized adaptive projected guidance. + + Projects the guidance vector to reduce over-saturation while maintaining + directional control by decomposing into parallel and orthogonal components. + + Args: + pred_cond: Conditional prediction. + pred_uncond: Unconditional prediction. + guidance_scale: Guidance scale factor. + momentum_buffer: Optional momentum buffer for temporal smoothing. + eta: Scaling factor for parallel component. + norm_threshold: Maximum norm for guidance vector clipping. + use_original_formulation: Whether to use original APG formulation. + + Returns: + Guided prediction tensor. + """ + diff = pred_cond - pred_uncond + dim = [-i for i in range(1, len(diff.shape))] # All dimensions except batch + + # Apply momentum smoothing if available + if momentum_buffer is not None: + momentum_buffer.update(diff) + diff = momentum_buffer.running_average + + # Apply norm clipping if threshold is set + if norm_threshold > 0: + diff_norm = diff.norm(p=2, dim=dim, keepdim=True) + scale_factor = torch.minimum(torch.ones_like(diff_norm), norm_threshold / diff_norm) + diff = diff * scale_factor + + # Project guidance vector into parallel and orthogonal components + v0, v1 = diff.double(), pred_cond.double() + v1 = torch.nn.functional.normalize(v1, dim=dim) + v0_parallel = (v0 * v1).sum(dim=dim, keepdim=True) * v1 + v0_orthogonal = v0 - v0_parallel + diff_parallel, diff_orthogonal = v0_parallel.type_as(diff), v0_orthogonal.type_as(diff) + + # Combine components with different scaling + normalized_update = diff_orthogonal + eta * diff_parallel + pred = pred_cond if use_original_formulation else pred_uncond + pred = pred + guidance_scale * normalized_update + + return pred + + +class AdaptiveProjectedGuidance: + """ + Adaptive Projected Guidance for classifier-free guidance. + + Implements APG which projects the guidance vector to reduce over-saturation + while maintaining directional control. + """ + + def __init__( + self, + guidance_scale: float = 7.5, + adaptive_projected_guidance_momentum: Optional[float] = None, + adaptive_projected_guidance_rescale: float = 15.0, + eta: float = 0.0, + guidance_rescale: float = 0.0, + use_original_formulation: bool = False, + ): + self.guidance_scale = guidance_scale + self.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum + self.adaptive_projected_guidance_rescale = adaptive_projected_guidance_rescale + self.eta = eta + self.guidance_rescale = guidance_rescale + self.use_original_formulation = use_original_formulation + self.momentum_buffer = None + + def __call__(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None, step=None) -> torch.Tensor: + if step == 0 and self.adaptive_projected_guidance_momentum is not None: + self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum) + + pred = normalized_guidance_apg( + pred_cond, + pred_uncond, + self.guidance_scale, + self.momentum_buffer, + self.eta, + self.adaptive_projected_guidance_rescale, + self.use_original_formulation, + ) + + if self.guidance_rescale > 0.0: + pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale) + + return pred + + +def rescale_noise_cfg(guided_noise, conditional_noise, rescale_factor=0.0): + """ + Rescale guided noise prediction to prevent overexposure and improve image quality. + + This implementation addresses the overexposure issue described in "Common Diffusion Noise + Schedules and Sample Steps are Flawed" (https://arxiv.org/pdf/2305.08891.pdf) (Section 3.4). + The rescaling preserves the statistical properties of the conditional prediction while reducing artifacts. + + Args: + guided_noise (torch.Tensor): Noise prediction from classifier-free guidance. + conditional_noise (torch.Tensor): Noise prediction from conditional model. + rescale_factor (float): Interpolation factor between original and rescaled predictions. + 0.0 = no rescaling, 1.0 = full rescaling. + + Returns: + torch.Tensor: Rescaled noise prediction with reduced overexposure. + """ + if rescale_factor == 0.0: + return guided_noise + + # Calculate standard deviation across spatial dimensions for both predictions + spatial_dims = list(range(1, conditional_noise.ndim)) + conditional_std = conditional_noise.std(dim=spatial_dims, keepdim=True) + guided_std = guided_noise.std(dim=spatial_dims, keepdim=True) + + # Rescale guided noise to match conditional noise statistics + std_ratio = conditional_std / guided_std + rescaled_prediction = guided_noise * std_ratio + + # Interpolate between original and rescaled predictions + final_prediction = rescale_factor * rescaled_prediction + (1.0 - rescale_factor) * guided_noise + + return final_prediction + + +def apply_classifier_free_guidance( + noise_pred_text: torch.Tensor, + noise_pred_uncond: torch.Tensor, + is_ocr: bool, + guidance_scale: float, + step: int, + apg_start_step_ocr: int = 38, + apg_start_step_general: int = 5, + cfg_guider_ocr: AdaptiveProjectedGuidance = None, + cfg_guider_general: AdaptiveProjectedGuidance = None, + guidance_rescale: float = 0.0, +): + """ + Apply classifier-free guidance with OCR-aware APG for batch_size=1. + + Args: + noise_pred_text: Conditional noise prediction tensor [1, ...]. + noise_pred_uncond: Unconditional noise prediction tensor [1, ...]. + is_ocr: Whether this sample requires OCR-specific guidance. + guidance_scale: Guidance scale for CFG. + step: Current diffusion step index. + apg_start_step_ocr: Step to start APG for OCR regions. + apg_start_step_general: Step to start APG for general regions. + cfg_guider_ocr: APG guider for OCR regions. + cfg_guider_general: APG guider for general regions. + + Returns: + Guided noise prediction tensor [1, ...]. + """ + if guidance_scale == 1.0: + return noise_pred_text + + # Select appropriate guider and start step based on OCR requirement + if is_ocr: + cfg_guider = cfg_guider_ocr + apg_start_step = apg_start_step_ocr + else: + cfg_guider = cfg_guider_general + apg_start_step = apg_start_step_general + + # Apply standard CFG or APG based on current step + if step <= apg_start_step: + # Standard classifier-free guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + if guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale) + + # Initialize APG guider state + _ = cfg_guider(noise_pred_text, noise_pred_uncond, step=step) + else: + # Use APG for guidance + noise_pred = cfg_guider(noise_pred_text, noise_pred_uncond, step=step) + + return noise_pred + + +# endregion diff --git a/vendor/sd-scripts/library/hunyuan_image_vae.py b/vendor/sd-scripts/library/hunyuan_image_vae.py new file mode 100644 index 0000000..a6ed1e8 --- /dev/null +++ b/vendor/sd-scripts/library/hunyuan_image_vae.py @@ -0,0 +1,755 @@ +from typing import Optional, Tuple + +from einops import rearrange +import numpy as np +import torch +from torch import Tensor, nn +from torch.nn import Conv2d +from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution + +from library.safetensors_utils import load_safetensors +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +VAE_SCALE_FACTOR = 32 # 32x spatial compression + +LATENT_SCALING_FACTOR = 0.75289 # Latent scaling factor for Hunyuan Image-2.1 + + +def swish(x: Tensor) -> Tensor: + """Swish activation function: x * sigmoid(x).""" + return x * torch.sigmoid(x) + + +class AttnBlock(nn.Module): + """Self-attention block using scaled dot-product attention.""" + + def __init__(self, in_channels: int, chunk_size: Optional[int] = None): + super().__init__() + self.in_channels = in_channels + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if chunk_size is None or chunk_size <= 0: + self.q = Conv2d(in_channels, in_channels, kernel_size=1) + self.k = Conv2d(in_channels, in_channels, kernel_size=1) + self.v = Conv2d(in_channels, in_channels, kernel_size=1) + self.proj_out = Conv2d(in_channels, in_channels, kernel_size=1) + else: + self.q = ChunkedConv2d(in_channels, in_channels, kernel_size=1, chunk_size=chunk_size) + self.k = ChunkedConv2d(in_channels, in_channels, kernel_size=1, chunk_size=chunk_size) + self.v = ChunkedConv2d(in_channels, in_channels, kernel_size=1, chunk_size=chunk_size) + self.proj_out = ChunkedConv2d(in_channels, in_channels, kernel_size=1, chunk_size=chunk_size) + + def attention(self, x: Tensor) -> Tensor: + x = self.norm(x) + q = self.q(x) + k = self.k(x) + v = self.v(x) + + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b (h w) c").contiguous() + k = rearrange(k, "b c h w -> b (h w) c").contiguous() + v = rearrange(v, "b c h w -> b (h w) c").contiguous() + + x = nn.functional.scaled_dot_product_attention(q, k, v) + return rearrange(x, "b (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x: Tensor) -> Tensor: + return x + self.proj_out(self.attention(x)) + + +class ChunkedConv2d(nn.Conv2d): + """ + Convolutional layer that processes input in chunks to reduce memory usage. + + Parameters + ---------- + chunk_size : int, optional + Size of chunks to process at a time. Default is 64. + """ + + def __init__(self, *args, **kwargs): + if "chunk_size" in kwargs: + self.chunk_size = kwargs.pop("chunk_size", 64) + super().__init__(*args, **kwargs) + assert self.padding_mode == "zeros", "Only 'zeros' padding mode is supported." + assert self.dilation == (1, 1) and self.stride == (1, 1), "Only dilation=1 and stride=1 are supported." + assert self.groups == 1, "Only groups=1 is supported." + assert self.kernel_size[0] == self.kernel_size[1], "Only square kernels are supported." + assert ( + self.padding[0] == self.padding[1] and self.padding[0] == self.kernel_size[0] // 2 + ), "Only kernel_size//2 padding is supported." + self.original_padding = self.padding + self.padding = (0, 0) # We handle padding manually in forward + + def forward(self, x: Tensor) -> Tensor: + # If chunking is not needed, process normally. We chunk only along height dimension. + if self.chunk_size is None or x.shape[1] <= self.chunk_size: + self.padding = self.original_padding + x = super().forward(x) + self.padding = (0, 0) + if torch.cuda.is_available(): + torch.cuda.empty_cache() + return x + + # Process input in chunks to reduce memory usage + org_shape = x.shape + + # If kernel size is not 1, we need to use overlapping chunks + overlap = self.kernel_size[0] // 2 # 1 for kernel size 3 + step = self.chunk_size - overlap + y = torch.zeros((org_shape[0], self.out_channels, org_shape[2], org_shape[3]), dtype=x.dtype, device=x.device) + yi = 0 + i = 0 + while i < org_shape[2]: + si = i if i == 0 else i - overlap + ei = i + self.chunk_size + + # Check last chunk. If remaining part is small, include it in last chunk + if ei > org_shape[2] or ei + step // 4 > org_shape[2]: + ei = org_shape[2] + + chunk = x[:, :, : ei - si, :] + x = x[:, :, ei - si - overlap * 2 :, :] + + # Pad chunk if needed: This is as the original Conv2d with padding + if i == 0: # First chunk + # Pad except bottom + chunk = torch.nn.functional.pad(chunk, (overlap, overlap, overlap, 0), mode="constant", value=0) + elif ei == org_shape[2]: # Last chunk + # Pad except top + chunk = torch.nn.functional.pad(chunk, (overlap, overlap, 0, overlap), mode="constant", value=0) + else: + # Pad left and right only + chunk = torch.nn.functional.pad(chunk, (overlap, overlap), mode="constant", value=0) + + chunk = super().forward(chunk) + y[:, :, yi : yi + chunk.shape[2], :] = chunk + yi += chunk.shape[2] + del chunk + + if ei == org_shape[2]: + break + i += step + + assert yi == org_shape[2], f"yi={yi}, org_shape[2]={org_shape[2]}" + + if torch.cuda.is_available(): + torch.cuda.empty_cache() # This helps reduce peak memory usage, but slows down a bit + return y + + +class ResnetBlock(nn.Module): + """ + Residual block with two convolutions, group normalization, and swish activation. + Includes skip connection with optional channel dimension matching. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + """ + + def __init__(self, in_channels: int, out_channels: int, chunk_size: Optional[int] = None): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) + if chunk_size is None or chunk_size <= 0: + self.conv1 = Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + # Skip connection projection for channel dimension mismatch + if self.in_channels != self.out_channels: + self.nin_shortcut = Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + else: + self.conv1 = ChunkedConv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size) + self.conv2 = ChunkedConv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size) + + # Skip connection projection for channel dimension mismatch + if self.in_channels != self.out_channels: + self.nin_shortcut = ChunkedConv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0, chunk_size=chunk_size + ) + + def forward(self, x: Tensor) -> Tensor: + h = x + # First convolution block + h = self.norm1(h) + h = swish(h) + h = self.conv1(h) + # Second convolution block + h = self.norm2(h) + h = swish(h) + h = self.conv2(h) + + # Apply skip connection with optional projection + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + return x + h + + +class Downsample(nn.Module): + """ + Spatial downsampling block that reduces resolution by 2x using convolution followed by + pixel rearrangement. Includes skip connection with grouped averaging. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels (must be divisible by 4). + """ + + def __init__(self, in_channels: int, out_channels: int, chunk_size: Optional[int] = None): + super().__init__() + factor = 4 # 2x2 spatial reduction factor + assert out_channels % factor == 0 + + if chunk_size is None or chunk_size <= 0: + self.conv = Conv2d(in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1) + else: + self.conv = ChunkedConv2d( + in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size + ) + self.group_size = factor * in_channels // out_channels + + def forward(self, x: Tensor) -> Tensor: + # Apply convolution and rearrange pixels for 2x downsampling + h = self.conv(x) + h = rearrange(h, "b c (h r1) (w r2) -> b (r1 r2 c) h w", r1=2, r2=2) + + # Create skip connection with pixel rearrangement + shortcut = rearrange(x, "b c (h r1) (w r2) -> b (r1 r2 c) h w", r1=2, r2=2) + B, C, H, W = shortcut.shape + shortcut = shortcut.view(B, h.shape[1], self.group_size, H, W).mean(dim=2) + + return h + shortcut + + +class Upsample(nn.Module): + """ + Spatial upsampling block that increases resolution by 2x using convolution followed by + pixel rearrangement. Includes skip connection with channel repetition. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + """ + + def __init__(self, in_channels: int, out_channels: int, chunk_size: Optional[int] = None): + super().__init__() + factor = 4 # 2x2 spatial expansion factor + + if chunk_size is None or chunk_size <= 0: + self.conv = Conv2d(in_channels, out_channels * factor, kernel_size=3, stride=1, padding=1) + else: + self.conv = ChunkedConv2d(in_channels, out_channels * factor, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size) + + self.repeats = factor * out_channels // in_channels + + def forward(self, x: Tensor) -> Tensor: + # Apply convolution and rearrange pixels for 2x upsampling + h = self.conv(x) + h = rearrange(h, "b (r1 r2 c) h w -> b c (h r1) (w r2)", r1=2, r2=2) + + # Create skip connection with channel repetition + shortcut = x.repeat_interleave(repeats=self.repeats, dim=1) + shortcut = rearrange(shortcut, "b (r1 r2 c) h w -> b c (h r1) (w r2)", r1=2, r2=2) + + return h + shortcut + + +class Encoder(nn.Module): + """ + VAE encoder that progressively downsamples input images to a latent representation. + Uses residual blocks, attention, and spatial downsampling. + + Parameters + ---------- + in_channels : int + Number of input image channels (e.g., 3 for RGB). + z_channels : int + Number of latent channels in the output. + block_out_channels : Tuple[int, ...] + Output channels for each downsampling block. + num_res_blocks : int + Number of residual blocks per downsampling stage. + ffactor_spatial : int + Total spatial downsampling factor (e.g., 32 for 32x compression). + """ + + def __init__( + self, + in_channels: int, + z_channels: int, + block_out_channels: Tuple[int, ...], + num_res_blocks: int, + ffactor_spatial: int, + chunk_size: Optional[int] = None, + ): + super().__init__() + assert block_out_channels[-1] % (2 * z_channels) == 0 + + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + + if chunk_size is None or chunk_size <= 0: + self.conv_in = Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1) + else: + self.conv_in = ChunkedConv2d( + in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1, chunk_size=chunk_size + ) + + self.down = nn.ModuleList() + block_in = block_out_channels[0] + + # Build downsampling blocks + for i_level, ch in enumerate(block_out_channels): + block = nn.ModuleList() + block_out = ch + + # Add residual blocks for this level + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, chunk_size=chunk_size)) + block_in = block_out + + down = nn.Module() + down.block = block + + # Add spatial downsampling if needed + add_spatial_downsample = bool(i_level < np.log2(ffactor_spatial)) + if add_spatial_downsample: + assert i_level < len(block_out_channels) - 1 + block_out = block_out_channels[i_level + 1] + down.downsample = Downsample(block_in, block_out, chunk_size=chunk_size) + block_in = block_out + + self.down.append(down) + + # Middle blocks with attention + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, chunk_size=chunk_size) + self.mid.attn_1 = AttnBlock(block_in, chunk_size=chunk_size) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, chunk_size=chunk_size) + + # Output layers + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + if chunk_size is None or chunk_size <= 0: + self.conv_out = Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1) + else: + self.conv_out = ChunkedConv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size) + + def forward(self, x: Tensor) -> Tensor: + # Initial convolution + h = self.conv_in(x) + + # Progressive downsampling through blocks + for i_level in range(len(self.block_out_channels)): + # Apply residual blocks at this level + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h) + # Apply spatial downsampling if available + if hasattr(self.down[i_level], "downsample"): + h = self.down[i_level].downsample(h) + + # Middle processing with attention + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # Final output layers with skip connection + group_size = self.block_out_channels[-1] // (2 * self.z_channels) + shortcut = rearrange(h, "b (c r) h w -> b c r h w", r=group_size).mean(dim=2) + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + h += shortcut + return h + + +class Decoder(nn.Module): + """ + VAE decoder that progressively upsamples latent representations back to images. + Uses residual blocks, attention, and spatial upsampling. + + Parameters + ---------- + z_channels : int + Number of latent channels in the input. + out_channels : int + Number of output image channels (e.g., 3 for RGB). + block_out_channels : Tuple[int, ...] + Output channels for each upsampling block. + num_res_blocks : int + Number of residual blocks per upsampling stage. + ffactor_spatial : int + Total spatial upsampling factor (e.g., 32 for 32x expansion). + """ + + def __init__( + self, + z_channels: int, + out_channels: int, + block_out_channels: Tuple[int, ...], + num_res_blocks: int, + ffactor_spatial: int, + chunk_size: Optional[int] = None, + ): + super().__init__() + assert block_out_channels[0] % z_channels == 0 + + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + + block_in = block_out_channels[0] + if chunk_size is None or chunk_size <= 0: + self.conv_in = Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + else: + self.conv_in = ChunkedConv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size) + + # Middle blocks with attention + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, chunk_size=chunk_size) + self.mid.attn_1 = AttnBlock(block_in, chunk_size=chunk_size) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, chunk_size=chunk_size) + + # Build upsampling blocks + self.up = nn.ModuleList() + for i_level, ch in enumerate(block_out_channels): + block = nn.ModuleList() + block_out = ch + + # Add residual blocks for this level (extra block for decoder) + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, chunk_size=chunk_size)) + block_in = block_out + + up = nn.Module() + up.block = block + + # Add spatial upsampling if needed + add_spatial_upsample = bool(i_level < np.log2(ffactor_spatial)) + if add_spatial_upsample: + assert i_level < len(block_out_channels) - 1 + block_out = block_out_channels[i_level + 1] + up.upsample = Upsample(block_in, block_out, chunk_size=chunk_size) + block_in = block_out + + self.up.append(up) + + # Output layers + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + if chunk_size is None or chunk_size <= 0: + self.conv_out = Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) + else: + self.conv_out = ChunkedConv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1, chunk_size=chunk_size) + + def forward(self, z: Tensor) -> Tensor: + # Initial processing with skip connection + repeats = self.block_out_channels[0] // self.z_channels + h = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1) + + # Middle processing with attention + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # Progressive upsampling through blocks + for i_level in range(len(self.block_out_channels)): + # Apply residual blocks at this level + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + # Apply spatial upsampling if available + if hasattr(self.up[i_level], "upsample"): + h = self.up[i_level].upsample(h) + + # Final output layers + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class HunyuanVAE2D(nn.Module): + """ + VAE model for Hunyuan Image-2.1 with spatial tiling support. + + This VAE uses a fixed architecture optimized for the Hunyuan Image-2.1 model, + with 32x spatial compression and optional memory-efficient tiling for large images. + """ + + def __init__(self, chunk_size: Optional[int] = None): + super().__init__() + + # Fixed configuration for Hunyuan Image-2.1 + block_out_channels = (128, 256, 512, 512, 1024, 1024) + in_channels = 3 # RGB input + out_channels = 3 # RGB output + latent_channels = 64 + layers_per_block = 2 + ffactor_spatial = 32 # 32x spatial compression + sample_size = 384 # Minimum sample size for tiling + scaling_factor = LATENT_SCALING_FACTOR # 0.75289 # Latent scaling factor + + self.ffactor_spatial = ffactor_spatial + self.scaling_factor = scaling_factor + + self.encoder = Encoder( + in_channels=in_channels, + z_channels=latent_channels, + block_out_channels=block_out_channels, + num_res_blocks=layers_per_block, + ffactor_spatial=ffactor_spatial, + chunk_size=chunk_size, + ) + + self.decoder = Decoder( + z_channels=latent_channels, + out_channels=out_channels, + block_out_channels=list(reversed(block_out_channels)), + num_res_blocks=layers_per_block, + ffactor_spatial=ffactor_spatial, + chunk_size=chunk_size, + ) + + # Spatial tiling configuration for memory efficiency + self.use_spatial_tiling = False + self.tile_sample_min_size = sample_size + self.tile_latent_min_size = sample_size // ffactor_spatial + self.tile_overlap_factor = 0.25 # 25% overlap between tiles + + @property + def dtype(self): + """Get the data type of the model parameters.""" + return next(self.encoder.parameters()).dtype + + @property + def device(self): + """Get the device of the model parameters.""" + return next(self.encoder.parameters()).device + + def enable_spatial_tiling(self, use_tiling: bool = True): + """Enable or disable spatial tiling.""" + self.use_spatial_tiling = use_tiling + + def disable_spatial_tiling(self): + """Disable spatial tiling.""" + self.use_spatial_tiling = False + + def enable_tiling(self, use_tiling: bool = True): + """Enable or disable spatial tiling (alias for enable_spatial_tiling).""" + self.enable_spatial_tiling(use_tiling) + + def disable_tiling(self): + """Disable spatial tiling (alias for disable_spatial_tiling).""" + self.disable_spatial_tiling() + + def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: + """ + Blend two tensors horizontally with smooth transition. + + Parameters + ---------- + a : torch.Tensor + Left tensor. + b : torch.Tensor + Right tensor. + blend_extent : int + Number of columns to blend. + """ + blend_extent = min(a.shape[-1], b.shape[-1], blend_extent) + for x in range(blend_extent): + b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent) + return b + + def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: + """ + Blend two tensors vertically with smooth transition. + + Parameters + ---------- + a : torch.Tensor + Top tensor. + b : torch.Tensor + Bottom tensor. + blend_extent : int + Number of rows to blend. + """ + blend_extent = min(a.shape[-2], b.shape[-2], blend_extent) + for y in range(blend_extent): + b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent) + return b + + def spatial_tiled_encode(self, x: torch.Tensor) -> torch.Tensor: + """ + Encode large images using spatial tiling to reduce memory usage. + Tiles are processed independently and blended at boundaries. + + Parameters + ---------- + x : torch.Tensor + Input tensor of shape (B, C, T, H, W) or (B, C, H, W). + """ + # Handle 5D input (B, C, T, H, W) by removing time dimension + original_ndim = x.ndim + if original_ndim == 5: + x = x.squeeze(2) + + B, C, H, W = x.shape + overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor) + row_limit = self.tile_latent_min_size - blend_extent + + rows = [] + for i in range(0, H, overlap_size): + row = [] + for j in range(0, W, overlap_size): + tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size] + tile = self.encoder(tile) + row.append(tile) + rows.append(row) + + result_rows = [] + for i, row in enumerate(rows): + result_row = [] + for j, tile in enumerate(row): + if i > 0: + tile = self.blend_v(rows[i - 1][j], tile, blend_extent) + if j > 0: + tile = self.blend_h(row[j - 1], tile, blend_extent) + result_row.append(tile[:, :, :row_limit, :row_limit]) + result_rows.append(torch.cat(result_row, dim=-1)) + + moments = torch.cat(result_rows, dim=-2) + return moments + + def spatial_tiled_decode(self, z: torch.Tensor) -> torch.Tensor: + """ + Decode large latents using spatial tiling to reduce memory usage. + Tiles are processed independently and blended at boundaries. + + Parameters + ---------- + z : torch.Tensor + Latent tensor of shape (B, C, H, W). + """ + B, C, H, W = z.shape + overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) + row_limit = self.tile_sample_min_size - blend_extent + + rows = [] + for i in range(0, H, overlap_size): + row = [] + for j in range(0, W, overlap_size): + tile = z[:, :, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size] + decoded = self.decoder(tile) + row.append(decoded) + rows.append(row) + + result_rows = [] + for i, row in enumerate(rows): + result_row = [] + for j, tile in enumerate(row): + if i > 0: + tile = self.blend_v(rows[i - 1][j], tile, blend_extent) + if j > 0: + tile = self.blend_h(row[j - 1], tile, blend_extent) + result_row.append(tile[:, :, :, :row_limit, :row_limit]) + result_rows.append(torch.cat(result_row, dim=-1)) + + dec = torch.cat(result_rows, dim=-2) + return dec + + def encode(self, x: Tensor) -> DiagonalGaussianDistribution: + """ + Encode input images to latent representation. + Uses spatial tiling for large images if enabled. + + Parameters + ---------- + x : Tensor + Input image tensor of shape (B, C, H, W) or (B, C, T, H, W). + + Returns + ------- + DiagonalGaussianDistribution + Latent distribution with mean and logvar. + """ + # Handle 5D input (B, C, T, H, W) by removing time dimension + original_ndim = x.ndim + if original_ndim == 5: + x = x.squeeze(2) + + # Use tiling for large images to reduce memory usage + if self.use_spatial_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size): + h = self.spatial_tiled_encode(x) + else: + h = self.encoder(x) + + # Restore time dimension if input was 5D + if original_ndim == 5: + h = h.unsqueeze(2) + + posterior = DiagonalGaussianDistribution(h) + return posterior + + def decode(self, z: Tensor): + """ + Decode latent representation back to images. + Uses spatial tiling for large latents if enabled. + + Parameters + ---------- + z : Tensor + Latent tensor of shape (B, C, H, W) or (B, C, T, H, W). + + Returns + ------- + Tensor + Decoded image tensor. + """ + # Handle 5D input (B, C, T, H, W) by removing time dimension + original_ndim = z.ndim + if original_ndim == 5: + z = z.squeeze(2) + + # Use tiling for large latents to reduce memory usage + if self.use_spatial_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size): + decoded = self.spatial_tiled_decode(z) + else: + decoded = self.decoder(z) + + # Restore time dimension if input was 5D + if original_ndim == 5: + decoded = decoded.unsqueeze(2) + + return decoded + + +def load_vae(vae_path: str, device: torch.device, disable_mmap: bool = False, chunk_size: Optional[int] = None) -> HunyuanVAE2D: + logger.info(f"Initializing VAE with chunk_size={chunk_size}") + vae = HunyuanVAE2D(chunk_size=chunk_size) + + logger.info(f"Loading VAE from {vae_path}") + state_dict = load_safetensors(vae_path, device=device, disable_mmap=disable_mmap) + info = vae.load_state_dict(state_dict, strict=True, assign=True) + logger.info(f"Loaded VAE: {info}") + + vae.to(device) + return vae diff --git a/vendor/sd-scripts/library/hypernetwork.py b/vendor/sd-scripts/library/hypernetwork.py new file mode 100644 index 0000000..fbd3fb2 --- /dev/null +++ b/vendor/sd-scripts/library/hypernetwork.py @@ -0,0 +1,223 @@ +import torch +import torch.nn.functional as F +from diffusers.models.attention_processor import ( + Attention, + AttnProcessor2_0, + SlicedAttnProcessor, + XFormersAttnProcessor +) + +try: + import xformers.ops +except: + xformers = None + + +loaded_networks = [] + + +def apply_single_hypernetwork( + hypernetwork, hidden_states, encoder_hidden_states +): + context_k, context_v = hypernetwork.forward(hidden_states, encoder_hidden_states) + return context_k, context_v + + +def apply_hypernetworks(context_k, context_v, layer=None): + if len(loaded_networks) == 0: + return context_v, context_v + for hypernetwork in loaded_networks: + context_k, context_v = hypernetwork.forward(context_k, context_v) + + context_k = context_k.to(dtype=context_k.dtype) + context_v = context_v.to(dtype=context_k.dtype) + + return context_k, context_v + + + +def xformers_forward( + self: XFormersAttnProcessor, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor = None, + attention_mask: torch.Tensor = None, +): + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + context_k, context_v = apply_hypernetworks(hidden_states, encoder_hidden_states) + + key = attn.to_k(context_k) + value = attn.to_v(context_v) + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention( + query, + key, + value, + attn_bias=attention_mask, + op=self.attention_op, + scale=attn.scale, + ) + hidden_states = hidden_states.to(query.dtype) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + return hidden_states + + +def sliced_attn_forward( + self: SlicedAttnProcessor, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor = None, + attention_mask: torch.Tensor = None, +): + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + + query = attn.to_q(hidden_states) + dim = query.shape[-1] + query = attn.head_to_batch_dim(query) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + context_k, context_v = apply_hypernetworks(hidden_states, encoder_hidden_states) + + key = attn.to_k(context_k) + value = attn.to_v(context_v) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + batch_size_attention, query_tokens, _ = query.shape + hidden_states = torch.zeros( + (batch_size_attention, query_tokens, dim // attn.heads), + device=query.device, + dtype=query.dtype, + ) + + for i in range(batch_size_attention // self.slice_size): + start_idx = i * self.slice_size + end_idx = (i + 1) * self.slice_size + + query_slice = query[start_idx:end_idx] + key_slice = key[start_idx:end_idx] + attn_mask_slice = ( + attention_mask[start_idx:end_idx] if attention_mask is not None else None + ) + + attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) + + attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) + + hidden_states[start_idx:end_idx] = attn_slice + + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + return hidden_states + + +def v2_0_forward( + self: AttnProcessor2_0, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, +): + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + inner_dim = hidden_states.shape[-1] + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view( + batch_size, attn.heads, -1, attention_mask.shape[-1] + ) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + context_k, context_v = apply_hypernetworks(hidden_states, encoder_hidden_states) + + key = attn.to_k(context_k) + value = attn.to_v(context_v) + + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape( + batch_size, -1, attn.heads * head_dim + ) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + return hidden_states + + +def replace_attentions_for_hypernetwork(): + import diffusers.models.attention_processor + + diffusers.models.attention_processor.XFormersAttnProcessor.__call__ = ( + xformers_forward + ) + diffusers.models.attention_processor.SlicedAttnProcessor.__call__ = ( + sliced_attn_forward + ) + diffusers.models.attention_processor.AttnProcessor2_0.__call__ = v2_0_forward diff --git a/vendor/sd-scripts/library/ipex/__init__.py b/vendor/sd-scripts/library/ipex/__init__.py new file mode 100644 index 0000000..92a88e2 --- /dev/null +++ b/vendor/sd-scripts/library/ipex/__init__.py @@ -0,0 +1,207 @@ +import os +import sys +import torch +from packaging import version +try: + import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import + has_ipex = True +except Exception: + has_ipex = False +from .hijacks import ipex_hijacks + +torch_version = version.parse(torch.__version__) + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +def ipex_init(): # pylint: disable=too-many-statements + try: + if hasattr(torch, "cuda") and hasattr(torch.cuda, "is_xpu_hijacked") and torch.cuda.is_xpu_hijacked: + return True, "Skipping IPEX hijack" + else: + try: + # force xpu device on torch compile and triton + # import inductor utils to get around lazy import + from torch._inductor import utils as torch_inductor_utils # pylint: disable=import-error, unused-import # noqa: F401 + torch._inductor.utils.GPU_TYPES = ["xpu"] + torch._inductor.utils.get_gpu_type = lambda *args, **kwargs: "xpu" + from triton import backends as triton_backends # pylint: disable=import-error + triton_backends.backends["nvidia"].driver.is_active = lambda *args, **kwargs: False + except Exception: + pass + # Replace cuda with xpu: + torch.cuda.current_device = torch.xpu.current_device + torch.cuda.current_stream = torch.xpu.current_stream + torch.cuda.device = torch.xpu.device + torch.cuda.device_count = torch.xpu.device_count + torch.cuda.device_of = torch.xpu.device_of + torch.cuda.get_device_name = torch.xpu.get_device_name + torch.cuda.get_device_properties = torch.xpu.get_device_properties + torch.cuda.init = torch.xpu.init + torch.cuda.is_available = torch.xpu.is_available + torch.cuda.is_initialized = torch.xpu.is_initialized + torch.cuda.is_current_stream_capturing = lambda: False + torch.cuda.stream = torch.xpu.stream + torch.cuda.Event = torch.xpu.Event + torch.cuda.Stream = torch.xpu.Stream + torch.Tensor.cuda = torch.Tensor.xpu + torch.Tensor.is_cuda = torch.Tensor.is_xpu + torch.nn.Module.cuda = torch.nn.Module.xpu + torch.cuda.Optional = torch.xpu.Optional + torch.cuda.__cached__ = torch.xpu.__cached__ + torch.cuda.__loader__ = torch.xpu.__loader__ + torch.cuda.streams = torch.xpu.streams + torch.cuda.Any = torch.xpu.Any + torch.cuda.__doc__ = torch.xpu.__doc__ + torch.cuda.default_generators = torch.xpu.default_generators + torch.cuda._get_device_index = torch.xpu._get_device_index + torch.cuda.__path__ = torch.xpu.__path__ + torch.cuda.set_stream = torch.xpu.set_stream + torch.cuda.torch = torch.xpu.torch + torch.cuda.__annotations__ = torch.xpu.__annotations__ + torch.cuda.__package__ = torch.xpu.__package__ + torch.cuda.__builtins__ = torch.xpu.__builtins__ + torch.cuda._lazy_init = torch.xpu._lazy_init + torch.cuda.StreamContext = torch.xpu.StreamContext + torch.cuda._lazy_call = torch.xpu._lazy_call + torch.cuda.random = torch.xpu.random + torch.cuda.__name__ = torch.xpu.__name__ + torch.cuda.__spec__ = torch.xpu.__spec__ + torch.cuda.__file__ = torch.xpu.__file__ + # torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing + + if torch_version < version.parse("2.3"): + torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock + torch.cuda._initialized = torch.xpu.lazy_init._initialized + torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork + torch.cuda._lazy_seed_tracker = torch.xpu.lazy_init._lazy_seed_tracker + torch.cuda._queued_calls = torch.xpu.lazy_init._queued_calls + torch.cuda._tls = torch.xpu.lazy_init._tls + torch.cuda.threading = torch.xpu.lazy_init.threading + torch.cuda.traceback = torch.xpu.lazy_init.traceback + torch.cuda._lazy_new = torch.xpu._lazy_new + + torch.cuda.FloatTensor = torch.xpu.FloatTensor + torch.cuda.FloatStorage = torch.xpu.FloatStorage + torch.cuda.BFloat16Tensor = torch.xpu.BFloat16Tensor + torch.cuda.BFloat16Storage = torch.xpu.BFloat16Storage + torch.cuda.HalfTensor = torch.xpu.HalfTensor + torch.cuda.HalfStorage = torch.xpu.HalfStorage + torch.cuda.ByteTensor = torch.xpu.ByteTensor + torch.cuda.ByteStorage = torch.xpu.ByteStorage + torch.cuda.DoubleTensor = torch.xpu.DoubleTensor + torch.cuda.DoubleStorage = torch.xpu.DoubleStorage + torch.cuda.ShortTensor = torch.xpu.ShortTensor + torch.cuda.ShortStorage = torch.xpu.ShortStorage + torch.cuda.LongTensor = torch.xpu.LongTensor + torch.cuda.LongStorage = torch.xpu.LongStorage + torch.cuda.IntTensor = torch.xpu.IntTensor + torch.cuda.IntStorage = torch.xpu.IntStorage + torch.cuda.CharTensor = torch.xpu.CharTensor + torch.cuda.CharStorage = torch.xpu.CharStorage + torch.cuda.BoolTensor = torch.xpu.BoolTensor + torch.cuda.BoolStorage = torch.xpu.BoolStorage + torch.cuda.ComplexFloatStorage = torch.xpu.ComplexFloatStorage + torch.cuda.ComplexDoubleStorage = torch.xpu.ComplexDoubleStorage + else: + torch.cuda._initialization_lock = torch.xpu._initialization_lock + torch.cuda._initialized = torch.xpu._initialized + torch.cuda._is_in_bad_fork = torch.xpu._is_in_bad_fork + torch.cuda._lazy_seed_tracker = torch.xpu._lazy_seed_tracker + torch.cuda._queued_calls = torch.xpu._queued_calls + torch.cuda._tls = torch.xpu._tls + torch.cuda.threading = torch.xpu.threading + torch.cuda.traceback = torch.xpu.traceback + + if torch_version < version.parse("2.5"): + torch.cuda.os = torch.xpu.os + torch.cuda.Device = torch.xpu.Device + torch.cuda.warnings = torch.xpu.warnings + torch.cuda.classproperty = torch.xpu.classproperty + torch.UntypedStorage.cuda = torch.UntypedStorage.xpu + + if torch_version < version.parse("2.7"): + torch.cuda.Tuple = torch.xpu.Tuple + torch.cuda.List = torch.xpu.List + + if torch_version < version.parse("2.11"): + torch.cuda._device_t = torch.xpu._device_t + torch.cuda._device = torch.xpu._device + torch.cuda.Union = torch.xpu.Union + + + # Memory: + if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read(): + torch.xpu.empty_cache = lambda: None + torch.cuda.empty_cache = torch.xpu.empty_cache + + if has_ipex: + torch.cuda.memory_summary = torch.xpu.memory_summary + torch.cuda.memory_snapshot = torch.xpu.memory_snapshot + torch.cuda.memory = torch.xpu.memory + torch.cuda.memory_stats = torch.xpu.memory_stats + torch.cuda.memory_allocated = torch.xpu.memory_allocated + torch.cuda.max_memory_allocated = torch.xpu.max_memory_allocated + torch.cuda.memory_reserved = torch.xpu.memory_reserved + torch.cuda.memory_cached = torch.xpu.memory_reserved + torch.cuda.max_memory_reserved = torch.xpu.max_memory_reserved + torch.cuda.max_memory_cached = torch.xpu.max_memory_reserved + torch.cuda.reset_peak_memory_stats = torch.xpu.reset_peak_memory_stats + torch.cuda.reset_max_memory_cached = torch.xpu.reset_peak_memory_stats + torch.cuda.reset_max_memory_allocated = torch.xpu.reset_peak_memory_stats + torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict + torch.cuda.reset_accumulated_memory_stats = torch.xpu.reset_accumulated_memory_stats + + # RNG: + torch.cuda.get_rng_state = torch.xpu.get_rng_state + torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all + torch.cuda.set_rng_state = torch.xpu.set_rng_state + torch.cuda.set_rng_state_all = torch.xpu.set_rng_state_all + torch.cuda.manual_seed = torch.xpu.manual_seed + torch.cuda.manual_seed_all = torch.xpu.manual_seed_all + torch.cuda.seed = torch.xpu.seed + torch.cuda.seed_all = torch.xpu.seed_all + torch.cuda.initial_seed = torch.xpu.initial_seed + + # C + if torch_version < version.parse("2.3"): + torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentRawStream + ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_subslice_count + ipex._C._DeviceProperties.major = 12 + ipex._C._DeviceProperties.minor = 1 + ipex._C._DeviceProperties.L2_cache_size = 16*1024*1024 # A770 and A750 + else: + torch._C._cuda_getCurrentRawStream = torch._C._xpu_getCurrentRawStream + torch._C._XpuDeviceProperties.multi_processor_count = torch._C._XpuDeviceProperties.gpu_subslice_count + torch._C._XpuDeviceProperties.major = 12 + torch._C._XpuDeviceProperties.minor = 1 + torch._C._XpuDeviceProperties.L2_cache_size = 16*1024*1024 # A770 and A750 + + # Fix functions with ipex: + # torch.xpu.mem_get_info always returns the total memory as free memory + torch.xpu.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_reserved(device)), torch.xpu.get_device_properties(device).total_memory] + torch.cuda.mem_get_info = torch.xpu.mem_get_info + torch._utils._get_available_device_type = lambda: "xpu" + torch.has_cuda = True + torch.cuda.has_half = True + torch.cuda.is_bf16_supported = getattr(torch.xpu, "is_bf16_supported", lambda *args, **kwargs: True) + torch.cuda.is_fp16_supported = lambda *args, **kwargs: True + torch.backends.cuda.is_built = lambda *args, **kwargs: True + torch.version.cuda = "12.1" + torch.cuda.get_arch_list = getattr(torch.xpu, "get_arch_list", lambda: ["pvc", "dg2", "ats-m150"]) + torch.cuda.get_device_capability = lambda *args, **kwargs: (12,1) + torch.cuda.get_device_properties.major = 12 + torch.cuda.get_device_properties.minor = 1 + torch.cuda.get_device_properties.L2_cache_size = 16*1024*1024 # A770 and A750 + torch.cuda.ipc_collect = lambda *args, **kwargs: None + torch.cuda.utilization = lambda *args, **kwargs: 0 + + device_supports_fp64 = ipex_hijacks() + try: + from .diffusers import ipex_diffusers + ipex_diffusers(device_supports_fp64=device_supports_fp64) + except Exception: # pylint: disable=broad-exception-caught + pass + torch.cuda.is_xpu_hijacked = True + except Exception as e: + return False, e + return True, None diff --git a/vendor/sd-scripts/library/ipex/attention.py b/vendor/sd-scripts/library/ipex/attention.py new file mode 100644 index 0000000..177f5bc --- /dev/null +++ b/vendor/sd-scripts/library/ipex/attention.py @@ -0,0 +1,119 @@ +import os +import torch +from functools import cache, wraps + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +# ARC GPUs can't allocate more than 4GB to a single block so we slice the attention layers + +sdpa_slice_trigger_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 1)) +attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 0.5)) + +# Find something divisible with the input_tokens +@cache +def find_split_size(original_size, slice_block_size, slice_rate=2): + split_size = original_size + while True: + if (split_size * slice_block_size) <= slice_rate and original_size % split_size == 0: + return split_size + split_size = split_size - 1 + if split_size <= 1: + return 1 + return split_size + + +# Find slice sizes for SDPA +@cache +def find_sdpa_slice_sizes(query_shape, key_shape, query_element_size, slice_rate=2, trigger_rate=3): + batch_size, attn_heads, query_len, _ = query_shape + _, _, key_len, _ = key_shape + + slice_batch_size = attn_heads * (query_len * key_len) * query_element_size / 1024 / 1024 / 1024 + + split_batch_size = batch_size + split_head_size = attn_heads + split_query_size = query_len + + do_batch_split = False + do_head_split = False + do_query_split = False + + if batch_size * slice_batch_size >= trigger_rate: + do_batch_split = True + split_batch_size = find_split_size(batch_size, slice_batch_size, slice_rate=slice_rate) + + if split_batch_size * slice_batch_size > slice_rate: + slice_head_size = split_batch_size * (query_len * key_len) * query_element_size / 1024 / 1024 / 1024 + do_head_split = True + split_head_size = find_split_size(attn_heads, slice_head_size, slice_rate=slice_rate) + + if split_head_size * slice_head_size > slice_rate: + slice_query_size = split_batch_size * split_head_size * (key_len) * query_element_size / 1024 / 1024 / 1024 + do_query_split = True + split_query_size = find_split_size(query_len, slice_query_size, slice_rate=slice_rate) + + return do_batch_split, do_head_split, do_query_split, split_batch_size, split_head_size, split_query_size + + +original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention +@wraps(torch.nn.functional.scaled_dot_product_attention) +def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs): + if query.device.type != "xpu": + return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) + is_unsqueezed = False + if query.dim() == 3: + query = query.unsqueeze(0) + is_unsqueezed = True + if key.dim() == 3: + key = key.unsqueeze(0) + if value.dim() == 3: + value = value.unsqueeze(0) + do_batch_split, do_head_split, do_query_split, split_batch_size, split_head_size, split_query_size = find_sdpa_slice_sizes(query.shape, key.shape, query.element_size(), slice_rate=attention_slice_rate, trigger_rate=sdpa_slice_trigger_rate) + + # Slice SDPA + if do_batch_split: + batch_size, attn_heads, query_len, _ = query.shape + _, _, _, head_dim = value.shape + hidden_states = torch.zeros((batch_size, attn_heads, query_len, head_dim), device=query.device, dtype=query.dtype) + if attn_mask is not None: + attn_mask = attn_mask.expand((query.shape[0], query.shape[1], query.shape[2], key.shape[-2])) + for ib in range(batch_size // split_batch_size): + start_idx = ib * split_batch_size + end_idx = (ib + 1) * split_batch_size + if do_head_split: + for ih in range(attn_heads // split_head_size): # pylint: disable=invalid-name + start_idx_h = ih * split_head_size + end_idx_h = (ih + 1) * split_head_size + if do_query_split: + for iq in range(query_len // split_query_size): # pylint: disable=invalid-name + start_idx_q = iq * split_query_size + end_idx_q = (iq + 1) * split_query_size + hidden_states[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :] = original_scaled_dot_product_attention( + query[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :], + key[start_idx:end_idx, start_idx_h:end_idx_h, :, :], + value[start_idx:end_idx, start_idx_h:end_idx_h, :, :], + attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal, **kwargs + ) + else: + hidden_states[start_idx:end_idx, start_idx_h:end_idx_h, :, :] = original_scaled_dot_product_attention( + query[start_idx:end_idx, start_idx_h:end_idx_h, :, :], + key[start_idx:end_idx, start_idx_h:end_idx_h, :, :], + value[start_idx:end_idx, start_idx_h:end_idx_h, :, :], + attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, :, :] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal, **kwargs + ) + else: + hidden_states[start_idx:end_idx, :, :, :] = original_scaled_dot_product_attention( + query[start_idx:end_idx, :, :, :], + key[start_idx:end_idx, :, :, :], + value[start_idx:end_idx, :, :, :], + attn_mask=attn_mask[start_idx:end_idx, :, :, :] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal, **kwargs + ) + torch.xpu.synchronize(query.device) + else: + hidden_states = original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) + if is_unsqueezed: + hidden_states = hidden_states.squeeze(0) + return hidden_states diff --git a/vendor/sd-scripts/library/ipex/diffusers.py b/vendor/sd-scripts/library/ipex/diffusers.py new file mode 100644 index 0000000..d3487fe --- /dev/null +++ b/vendor/sd-scripts/library/ipex/diffusers.py @@ -0,0 +1,126 @@ +from functools import wraps +import torch +import diffusers # pylint: disable=import-error +from diffusers.utils import torch_utils # pylint: disable=import-error, unused-import # noqa: F401 + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + + +# Diffusers FreeU +# Diffusers is imported before ipex hijacks so fourier_filter needs hijacking too +original_fourier_filter = diffusers.utils.torch_utils.fourier_filter +@wraps(diffusers.utils.torch_utils.fourier_filter) +def fourier_filter(x_in, threshold, scale): + return_dtype = x_in.dtype + return original_fourier_filter(x_in.to(dtype=torch.float32), threshold, scale).to(dtype=return_dtype) + + +# fp64 error +class FluxPosEmbed(torch.nn.Module): + def __init__(self, theta: int, axes_dim): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: torch.Tensor) -> torch.Tensor: + n_axes = ids.shape[-1] + cos_out = [] + sin_out = [] + pos = ids.float() + for i in range(n_axes): + cos, sin = diffusers.models.embeddings.get_1d_rotary_pos_embed( + self.axes_dim[i], + pos[:, i], + theta=self.theta, + repeat_interleave_real=True, + use_real=True, + freqs_dtype=torch.float32, + ) + cos_out.append(cos) + sin_out.append(sin) + freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device) + freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device) + return freqs_cos, freqs_sin + + +def hidream_rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: + assert dim % 2 == 0, "The dimension must be even." + return_device = pos.device + pos = pos.to("cpu") + + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim + omega = 1.0 / (theta**scale) + + batch_size, seq_length = pos.shape + out = torch.einsum("...n,d->...nd", pos, omega) + cos_out = torch.cos(out) + sin_out = torch.sin(out) + + stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1) + out = stacked_out.view(batch_size, -1, dim // 2, 2, 2) + return out.to(return_device, dtype=torch.float32) + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, output_type="np"): + if output_type == "np": + return diffusers.models.embeddings.get_1d_sincos_pos_embed_from_grid_np(embed_dim=embed_dim, pos=pos) + if embed_dim % 2 != 0: + raise ValueError("embed_dim must be divisible by 2") + + omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float32) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = torch.outer(pos, omega) # (M, D/2), outer product + + emb_sin = torch.sin(out) # (M, D/2) + emb_cos = torch.cos(out) # (M, D/2) + + emb = torch.concat([emb_sin, emb_cos], dim=1) # (M, D) + return emb + + +def apply_rotary_emb(x, freqs_cis, use_real: bool = True, use_real_unbind_dim: int = -1): + if use_real: + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + if use_real_unbind_dim == -1: + # Used for flux, cogvideox, hunyuan-dit + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + elif use_real_unbind_dim == -2: + # Used for Stable Audio, OmniGen, CogView4 and Cosmos + x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2) # [B, S, H, D//2] + x_rotated = torch.cat([-x_imag, x_real], dim=-1) + else: + raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.") + + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + return out + else: + # used for lumina + # force cpu with Alchemist + x_rotated = torch.view_as_complex(x.to("cpu").float().reshape(*x.shape[:-1], -1, 2)) + freqs_cis = freqs_cis.to("cpu").unsqueeze(2) + x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3) + return x_out.type_as(x).to(x.device) + + +def ipex_diffusers(device_supports_fp64=False): + diffusers.utils.torch_utils.fourier_filter = fourier_filter + if not device_supports_fp64: + # get around lazy imports + from diffusers.models import embeddings as diffusers_embeddings # pylint: disable=import-error, unused-import # noqa: F401 + from diffusers.models import transformers as diffusers_transformers # pylint: disable=import-error, unused-import # noqa: F401 + from diffusers.models import controlnets as diffusers_controlnets # pylint: disable=import-error, unused-import # noqa: F401 + diffusers.models.embeddings.get_1d_sincos_pos_embed_from_grid = get_1d_sincos_pos_embed_from_grid + diffusers.models.embeddings.FluxPosEmbed = FluxPosEmbed + diffusers.models.embeddings.apply_rotary_emb = apply_rotary_emb + diffusers.models.transformers.transformer_flux.FluxPosEmbed = FluxPosEmbed + diffusers.models.transformers.transformer_lumina2.apply_rotary_emb = apply_rotary_emb + diffusers.models.controlnets.controlnet_flux.FluxPosEmbed = FluxPosEmbed + diffusers.models.transformers.transformer_hidream_image.rope = hidream_rope diff --git a/vendor/sd-scripts/library/ipex/hijacks.py b/vendor/sd-scripts/library/ipex/hijacks.py new file mode 100644 index 0000000..29df78e --- /dev/null +++ b/vendor/sd-scripts/library/ipex/hijacks.py @@ -0,0 +1,466 @@ +import os +from functools import wraps +from contextlib import nullcontext +import torch +import numpy as np + +torch_version = float(torch.__version__[:3]) +current_xpu_device = f"xpu:{torch.xpu.current_device()}" +device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp64_dtype") else torch.xpu.get_device_properties(current_xpu_device).has_fp64 + +if os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '0': + if (torch.xpu.get_device_properties(current_xpu_device).total_memory / 1024 / 1024 / 1024) > 4.1: + try: + x = torch.ones((33000,33000), dtype=torch.float32, device=current_xpu_device) + del x + torch.xpu.empty_cache() + use_dynamic_attention = False + except Exception: + use_dynamic_attention = True + else: + use_dynamic_attention = True +else: + use_dynamic_attention = bool(os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '1') + +# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return + +class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods + def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument + if isinstance(device_ids, list) and len(device_ids) > 1: + print("IPEX backend doesn't support DataParallel on multiple XPU devices") + return module.to(f"xpu:{torch.xpu.current_device()}") + +def return_null_context(*args, **kwargs): # pylint: disable=unused-argument + return nullcontext() + +@property +def is_cuda(self): + return self.device.type == "xpu" or self.device.type == "cuda" + +def check_device_type(device, device_type: str) -> bool: + if device is None or type(device) not in {str, int, torch.device}: + return False + else: + return bool(torch.device(device).type == device_type) + +def check_cuda(device) -> bool: + return bool(isinstance(device, int) or check_device_type(device, "cuda")) + +def return_xpu(device): # keep the device instance type, aka return string if the input is string + return f"xpu:{torch.xpu.current_device()}" if device is None else f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device(f"xpu:{device.index}" if device.index is not None else "xpu") if isinstance(device, torch.device) else "xpu" + + +# Autocast +original_autocast_init = torch.amp.autocast_mode.autocast.__init__ +@wraps(torch.amp.autocast_mode.autocast.__init__) +def autocast_init(self, device_type=None, dtype=None, enabled=True, cache_enabled=None): + if device_type is None or check_cuda(device_type): + return original_autocast_init(self, device_type="xpu", dtype=dtype, enabled=enabled, cache_enabled=cache_enabled) + else: + return original_autocast_init(self, device_type=device_type, dtype=dtype, enabled=enabled, cache_enabled=cache_enabled) + + +original_grad_scaler_init = torch.amp.grad_scaler.GradScaler.__init__ +@wraps(torch.amp.grad_scaler.GradScaler.__init__) +def GradScaler_init(self, device: str = None, init_scale: float = 2.0**16, growth_factor: float = 2.0, backoff_factor: float = 0.5, growth_interval: int = 2000, enabled: bool = True): + if device is None or check_cuda(device): + return original_grad_scaler_init(self, device=return_xpu(device), init_scale=init_scale, growth_factor=growth_factor, backoff_factor=backoff_factor, growth_interval=growth_interval, enabled=enabled) + else: + return original_grad_scaler_init(self, device=device, init_scale=init_scale, growth_factor=growth_factor, backoff_factor=backoff_factor, growth_interval=growth_interval, enabled=enabled) + + +original_is_autocast_enabled = torch.is_autocast_enabled +@wraps(torch.is_autocast_enabled) +def torch_is_autocast_enabled(device_type=None): + if device_type is None or check_cuda(device_type): + return original_is_autocast_enabled(return_xpu(device_type)) + else: + return original_is_autocast_enabled(device_type) + + +original_get_autocast_dtype = torch.get_autocast_dtype +@wraps(torch.get_autocast_dtype) +def torch_get_autocast_dtype(device_type=None): + if device_type is None or check_cuda(device_type) or check_device_type(device_type, "xpu"): + return torch.bfloat16 + else: + return original_get_autocast_dtype(device_type) + + +# Latent Antialias CPU Offload: +# IPEX 2.5 and above has partial support but doesn't really work most of the time. +original_interpolate = torch.nn.functional.interpolate +@wraps(torch.nn.functional.interpolate) +def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments + if mode in {'bicubic', 'bilinear'}: + return_device = tensor.device + return_dtype = tensor.dtype + return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype) + else: + return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias) + + +# Diffusers Float64 (Alchemist GPUs doesn't support 64 bit): +original_from_numpy = torch.from_numpy +@wraps(torch.from_numpy) +def from_numpy(ndarray): + if ndarray.dtype == float: + return original_from_numpy(ndarray.astype("float32")) + else: + return original_from_numpy(ndarray) + +original_as_tensor = torch.as_tensor +@wraps(torch.as_tensor) +def as_tensor(data, dtype=None, device=None): + if check_cuda(device): + device = return_xpu(device) + if isinstance(data, np.ndarray) and data.dtype == float and not check_device_type(device, "cpu"): + return original_as_tensor(data, dtype=torch.float32, device=device) + else: + return original_as_tensor(data, dtype=dtype, device=device) + + +if not use_dynamic_attention: + original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention +else: + # 32 bit attention workarounds for Alchemist: + try: + from .attention import dynamic_scaled_dot_product_attention as original_scaled_dot_product_attention + except Exception: # pylint: disable=broad-exception-caught + original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention + +@wraps(torch.nn.functional.scaled_dot_product_attention) +def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs): + if query.dtype != key.dtype: + key = key.to(dtype=query.dtype) + if query.dtype != value.dtype: + value = value.to(dtype=query.dtype) + if attn_mask is not None and query.dtype != attn_mask.dtype: + attn_mask = attn_mask.to(dtype=query.dtype) + return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) + +# Data Type Errors: +original_torch_bmm = torch.bmm +@wraps(torch.bmm) +def torch_bmm(input, mat2, *, out=None): + if input.dtype != mat2.dtype: + mat2 = mat2.to(dtype=input.dtype) + return original_torch_bmm(input, mat2, out=out) + +# Diffusers FreeU +original_fft_fftn = torch.fft.fftn +@wraps(torch.fft.fftn) +def fft_fftn(input, s=None, dim=None, norm=None, *, out=None): + return_dtype = input.dtype + return original_fft_fftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype) + +# Diffusers FreeU +original_fft_ifftn = torch.fft.ifftn +@wraps(torch.fft.ifftn) +def fft_ifftn(input, s=None, dim=None, norm=None, *, out=None): + return_dtype = input.dtype + return original_fft_ifftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype) + +# A1111 FP16 +original_functional_group_norm = torch.nn.functional.group_norm +@wraps(torch.nn.functional.group_norm) +def functional_group_norm(input, num_groups, weight=None, bias=None, eps=1e-05): + if weight is not None and input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_group_norm(input, num_groups, weight=weight, bias=bias, eps=eps) + +# A1111 BF16 +original_functional_layer_norm = torch.nn.functional.layer_norm +@wraps(torch.nn.functional.layer_norm) +def functional_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05): + if weight is not None and input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_layer_norm(input, normalized_shape, weight=weight, bias=bias, eps=eps) + +# Training +original_functional_linear = torch.nn.functional.linear +@wraps(torch.nn.functional.linear) +def functional_linear(input, weight, bias=None): + if input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_linear(input, weight, bias=bias) + +original_functional_conv1d = torch.nn.functional.conv1d +@wraps(torch.nn.functional.conv1d) +def functional_conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_conv1d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +original_functional_conv2d = torch.nn.functional.conv2d +@wraps(torch.nn.functional.conv2d) +def functional_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +# LTX Video +original_functional_conv3d = torch.nn.functional.conv3d +@wraps(torch.nn.functional.conv3d) +def functional_conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_conv3d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +# SwinIR BF16: +original_functional_pad = torch.nn.functional.pad +@wraps(torch.nn.functional.pad) +def functional_pad(input, pad, mode='constant', value=None): + if mode == 'reflect' and input.dtype == torch.bfloat16: + return original_functional_pad(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16) + else: + return original_functional_pad(input, pad, mode=mode, value=value) + + +original_torch_tensor = torch.tensor +@wraps(torch.tensor) +def torch_tensor(data, *args, dtype=None, device=None, **kwargs): + global device_supports_fp64 + if check_cuda(device): + device = return_xpu(device) + if not device_supports_fp64: + if check_device_type(device, "xpu"): + if dtype == torch.float64: + dtype = torch.float32 + elif dtype is None and (hasattr(data, "dtype") and (data.dtype == torch.float64 or data.dtype == float)): + dtype = torch.float32 + return original_torch_tensor(data, *args, dtype=dtype, device=device, **kwargs) + +torch.Tensor.original_Tensor_to = torch.Tensor.to +@wraps(torch.Tensor.to) +def Tensor_to(self, device=None, *args, **kwargs): + if check_cuda(device): + return self.original_Tensor_to(return_xpu(device), *args, **kwargs) + else: + return self.original_Tensor_to(device, *args, **kwargs) + +original_Tensor_cuda = torch.Tensor.cuda +@wraps(torch.Tensor.cuda) +def Tensor_cuda(self, device=None, *args, **kwargs): + if device is None or check_cuda(device): + return self.to(return_xpu(device), *args, **kwargs) + else: + return original_Tensor_cuda(self, device, *args, **kwargs) + +original_Tensor_pin_memory = torch.Tensor.pin_memory +@wraps(torch.Tensor.pin_memory) +def Tensor_pin_memory(self, device=None, *args, **kwargs): + if device is None or check_cuda(device): + return original_Tensor_pin_memory(self, return_xpu(device), *args, **kwargs) + else: + return original_Tensor_pin_memory(self, device, *args, **kwargs) + +original_UntypedStorage_init = torch.UntypedStorage.__init__ +@wraps(torch.UntypedStorage.__init__) +def UntypedStorage_init(*args, device=None, **kwargs): + if check_cuda(device): + return original_UntypedStorage_init(*args, device=return_xpu(device), **kwargs) + else: + return original_UntypedStorage_init(*args, device=device, **kwargs) + +if torch_version >= 2.4: + original_UntypedStorage_to = torch.UntypedStorage.to + @wraps(torch.UntypedStorage.to) + def UntypedStorage_to(self, *args, device=None, **kwargs): + if check_cuda(device): + return original_UntypedStorage_to(self, *args, device=return_xpu(device), **kwargs) + else: + return original_UntypedStorage_to(self, *args, device=device, **kwargs) + + original_UntypedStorage_cuda = torch.UntypedStorage.cuda + @wraps(torch.UntypedStorage.cuda) + def UntypedStorage_cuda(self, device=None, non_blocking=False, **kwargs): + if device is None or check_cuda(device): + return self.to(device=return_xpu(device), non_blocking=non_blocking, **kwargs) + else: + return original_UntypedStorage_cuda(self, device=device, non_blocking=non_blocking, **kwargs) + +original_torch_empty = torch.empty +@wraps(torch.empty) +def torch_empty(*args, device=None, **kwargs): + if check_cuda(device): + return original_torch_empty(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_empty(*args, device=device, **kwargs) + +original_torch_randn = torch.randn +@wraps(torch.randn) +def torch_randn(*args, device=None, dtype=None, **kwargs): + if dtype is bytes: + dtype = None + if check_cuda(device): + return original_torch_randn(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_randn(*args, device=device, **kwargs) + +original_torch_ones = torch.ones +@wraps(torch.ones) +def torch_ones(*args, device=None, **kwargs): + if check_cuda(device): + return original_torch_ones(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_ones(*args, device=device, **kwargs) + +original_torch_zeros = torch.zeros +@wraps(torch.zeros) +def torch_zeros(*args, device=None, **kwargs): + if check_cuda(device): + return original_torch_zeros(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_zeros(*args, device=device, **kwargs) + +original_torch_full = torch.full +@wraps(torch.full) +def torch_full(*args, device=None, **kwargs): + if check_cuda(device): + return original_torch_full(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_full(*args, device=device, **kwargs) + +original_torch_linspace = torch.linspace +@wraps(torch.linspace) +def torch_linspace(*args, device=None, **kwargs): + if check_cuda(device): + return original_torch_linspace(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_linspace(*args, device=device, **kwargs) + +original_torch_eye = torch.eye +@wraps(torch.eye) +def torch_eye(*args, device=None, **kwargs): + if check_cuda(device): + return original_torch_eye(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_eye(*args, device=device, **kwargs) + +original_torch_load = torch.load +@wraps(torch.load) +def torch_load(f, map_location=None, *args, **kwargs): + if map_location is None or check_cuda(map_location): + return original_torch_load(f, *args, map_location=return_xpu(map_location), **kwargs) + else: + return original_torch_load(f, *args, map_location=map_location, **kwargs) + +@wraps(torch.cuda.synchronize) +def torch_cuda_synchronize(device=None): + if check_cuda(device): + return torch.xpu.synchronize(return_xpu(device)) + else: + return torch.xpu.synchronize(device) + +@wraps(torch.cuda.device) +def torch_cuda_device(device): + if check_cuda(device): + return torch.xpu.device(return_xpu(device)) + else: + return torch.xpu.device(device) + +@wraps(torch.cuda.set_device) +def torch_cuda_set_device(device): + if check_cuda(device): + torch.xpu.set_device(return_xpu(device)) + else: + torch.xpu.set_device(device) + +# torch.Generator has to be a class for isinstance checks +original_torch_Generator = torch.Generator +class torch_Generator(original_torch_Generator): + def __new__(self, device=None): + # can't hijack __init__ because of C override so use return super().__new__ + if check_cuda(device): + return super().__new__(self, return_xpu(device)) + else: + return super().__new__(self, device) + + +# Hijack Functions: +def ipex_hijacks(): + global device_supports_fp64 + if torch_version >= 2.4: + torch.UntypedStorage.cuda = UntypedStorage_cuda + torch.UntypedStorage.to = UntypedStorage_to + torch.tensor = torch_tensor + torch.Tensor.to = Tensor_to + torch.Tensor.cuda = Tensor_cuda + torch.Tensor.pin_memory = Tensor_pin_memory + torch.UntypedStorage.__init__ = UntypedStorage_init + torch.empty = torch_empty + torch.randn = torch_randn + torch.ones = torch_ones + torch.zeros = torch_zeros + torch.full = torch_full + torch.linspace = torch_linspace + torch.eye = torch_eye + torch.load = torch_load + torch.cuda.synchronize = torch_cuda_synchronize + torch.cuda.device = torch_cuda_device + torch.cuda.set_device = torch_cuda_set_device + + torch.Generator = torch_Generator + torch._C.Generator = torch_Generator + + torch.backends.cuda.sdp_kernel = return_null_context + torch.nn.DataParallel = DummyDataParallel + torch.UntypedStorage.is_cuda = is_cuda + torch.amp.autocast_mode.autocast.__init__ = autocast_init + + torch.nn.functional.interpolate = interpolate + torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention + torch.nn.functional.group_norm = functional_group_norm + torch.nn.functional.layer_norm = functional_layer_norm + torch.nn.functional.linear = functional_linear + torch.nn.functional.conv1d = functional_conv1d + torch.nn.functional.conv2d = functional_conv2d + torch.nn.functional.conv3d = functional_conv3d + torch.nn.functional.pad = functional_pad + + torch.bmm = torch_bmm + torch.fft.fftn = fft_fftn + torch.fft.ifftn = fft_ifftn + if not device_supports_fp64: + torch.from_numpy = from_numpy + torch.as_tensor = as_tensor + + # AMP: + torch.amp.grad_scaler.GradScaler.__init__ = GradScaler_init + torch.is_autocast_enabled = torch_is_autocast_enabled + torch.get_autocast_gpu_dtype = torch_get_autocast_dtype + torch.get_autocast_dtype = torch_get_autocast_dtype + + if hasattr(torch.xpu, "amp"): + if not hasattr(torch.xpu.amp, "custom_fwd"): + torch.xpu.amp.custom_fwd = torch.cuda.amp.custom_fwd + torch.xpu.amp.custom_bwd = torch.cuda.amp.custom_bwd + if not hasattr(torch.xpu.amp, "GradScaler"): + torch.xpu.amp.GradScaler = torch.amp.grad_scaler.GradScaler + torch.cuda.amp = torch.xpu.amp + else: + if not hasattr(torch.amp, "custom_fwd"): + torch.amp.custom_fwd = torch.cuda.amp.custom_fwd + torch.amp.custom_bwd = torch.cuda.amp.custom_bwd + torch.cuda.amp = torch.amp + + if not hasattr(torch.cuda.amp, "common"): + torch.cuda.amp.common = nullcontext() + torch.cuda.amp.common.amp_definitely_not_available = lambda: False + + return device_supports_fp64 diff --git a/vendor/sd-scripts/library/jpeg_xl_util.py b/vendor/sd-scripts/library/jpeg_xl_util.py new file mode 100644 index 0000000..c2e3a39 --- /dev/null +++ b/vendor/sd-scripts/library/jpeg_xl_util.py @@ -0,0 +1,186 @@ +# Modified from https://github.com/Fraetor/jxl_decode Original license: MIT +# Added partial read support for up to 200x speedup + +import os +from typing import List, Tuple + +class JXLBitstream: + """ + A stream of bits with methods for easy handling. + """ + + def __init__(self, file, offset: int = 0, offsets: List[List[int]] = None): + self.shift = 0 + self.bitstream = bytearray() + self.file = file + self.offset = offset + self.offsets = offsets + if self.offsets: + self.offset = self.offsets[0][1] + self.previous_data_len = 0 + self.index = 0 + self.file.seek(self.offset) + + def get_bits(self, length: int = 1) -> int: + if self.offsets and self.shift + length > self.previous_data_len + self.offsets[self.index][2]: + self.partial_to_read_length = length + if self.shift < self.previous_data_len + self.offsets[self.index][2]: + self.partial_read(0, length) + self.bitstream.extend(self.file.read(self.partial_to_read_length)) + else: + self.bitstream.extend(self.file.read(length)) + bitmask = 2**length - 1 + bits = (int.from_bytes(self.bitstream, "little") >> self.shift) & bitmask + self.shift += length + return bits + + def partial_read(self, current_length: int, length: int) -> None: + self.previous_data_len += self.offsets[self.index][2] + to_read_length = self.previous_data_len - (self.shift + current_length) + self.bitstream.extend(self.file.read(to_read_length)) + current_length += to_read_length + self.partial_to_read_length -= to_read_length + self.index += 1 + self.file.seek(self.offsets[self.index][1]) + if self.shift + length > self.previous_data_len + self.offsets[self.index][2]: + self.partial_read(current_length, length) + + +def decode_codestream(file, offset: int = 0, offsets: List[List[int]] = None) -> Tuple[int,int]: + """ + Decodes the actual codestream. + JXL codestream specification: http://www-internal/2022/18181-1 + """ + + # Convert codestream to int within an object to get some handy methods. + codestream = JXLBitstream(file, offset=offset, offsets=offsets) + + # Skip signature + codestream.get_bits(16) + + # SizeHeader + div8 = codestream.get_bits(1) + if div8: + height = 8 * (1 + codestream.get_bits(5)) + else: + distribution = codestream.get_bits(2) + match distribution: + case 0: + height = 1 + codestream.get_bits(9) + case 1: + height = 1 + codestream.get_bits(13) + case 2: + height = 1 + codestream.get_bits(18) + case 3: + height = 1 + codestream.get_bits(30) + ratio = codestream.get_bits(3) + if div8 and not ratio: + width = 8 * (1 + codestream.get_bits(5)) + elif not ratio: + distribution = codestream.get_bits(2) + match distribution: + case 0: + width = 1 + codestream.get_bits(9) + case 1: + width = 1 + codestream.get_bits(13) + case 2: + width = 1 + codestream.get_bits(18) + case 3: + width = 1 + codestream.get_bits(30) + else: + match ratio: + case 1: + width = height + case 2: + width = (height * 12) // 10 + case 3: + width = (height * 4) // 3 + case 4: + width = (height * 3) // 2 + case 5: + width = (height * 16) // 9 + case 6: + width = (height * 5) // 4 + case 7: + width = (height * 2) // 1 + return width, height + + +def decode_container(file) -> Tuple[int,int]: + """ + Parses the ISOBMFF container, extracts the codestream, and decodes it. + JXL container specification: http://www-internal/2022/18181-2 + """ + + def parse_box(file, file_start: int) -> dict: + file.seek(file_start) + LBox = int.from_bytes(file.read(4), "big") + XLBox = None + if 1 < LBox <= 8: + raise ValueError(f"Invalid LBox at byte {file_start}.") + if LBox == 1: + file.seek(file_start + 8) + XLBox = int.from_bytes(file.read(8), "big") + if XLBox <= 16: + raise ValueError(f"Invalid XLBox at byte {file_start}.") + if XLBox: + header_length = 16 + box_length = XLBox + else: + header_length = 8 + if LBox == 0: + box_length = os.fstat(file.fileno()).st_size - file_start + else: + box_length = LBox + file.seek(file_start + 4) + box_type = file.read(4) + file.seek(file_start) + return { + "length": box_length, + "type": box_type, + "offset": header_length, + } + + file.seek(0) + # Reject files missing required boxes. These two boxes are required to be at + # the start and contain no values, so we can manually check there presence. + # Signature box. (Redundant as has already been checked.) + if file.read(12) != bytes.fromhex("0000000C 4A584C20 0D0A870A"): + raise ValueError("Invalid signature box.") + # File Type box. + if file.read(20) != bytes.fromhex( + "00000014 66747970 6A786C20 00000000 6A786C20" + ): + raise ValueError("Invalid file type box.") + + offset = 0 + offsets = [] + data_offset_not_found = True + container_pointer = 32 + file_size = os.fstat(file.fileno()).st_size + while data_offset_not_found: + box = parse_box(file, container_pointer) + match box["type"]: + case b"jxlc": + offset = container_pointer + box["offset"] + data_offset_not_found = False + case b"jxlp": + file.seek(container_pointer + box["offset"]) + index = int.from_bytes(file.read(4), "big") + offsets.append([index, container_pointer + box["offset"] + 4, box["length"] - box["offset"] - 4]) + container_pointer += box["length"] + if container_pointer >= file_size: + data_offset_not_found = False + + if offsets: + offsets.sort(key=lambda i: i[0]) + file.seek(0) + + return decode_codestream(file, offset=offset, offsets=offsets) + + +def get_jxl_size(path: str) -> Tuple[int,int]: + with open(path, "rb") as file: + if file.read(2) == bytes.fromhex("FF0A"): + return decode_codestream(file) + return decode_container(file) diff --git a/vendor/sd-scripts/library/leco_train_util.py b/vendor/sd-scripts/library/leco_train_util.py new file mode 100644 index 0000000..5e95c16 --- /dev/null +++ b/vendor/sd-scripts/library/leco_train_util.py @@ -0,0 +1,522 @@ +import argparse +import json +import math +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union + +import torch +import toml +from torch.utils.checkpoint import checkpoint + +from library import train_util + +import logging + +logger = logging.getLogger(__name__) + + +def build_network_kwargs(args: argparse.Namespace) -> Dict[str, str]: + kwargs = {} + if args.network_args: + for net_arg in args.network_args: + key, value = net_arg.split("=", 1) + kwargs[key] = value + if "dropout" not in kwargs: + kwargs["dropout"] = args.network_dropout + return kwargs + + +def get_save_extension(args: argparse.Namespace) -> str: + if args.save_model_as == "ckpt": + return ".ckpt" + if args.save_model_as == "pt": + return ".pt" + return ".safetensors" + + +def save_weights( + accelerator, + network, + args: argparse.Namespace, + save_dtype, + prompt_settings, + global_step: int, + last: bool = False, + extra_metadata: Optional[Dict[str, str]] = None, +) -> None: + os.makedirs(args.output_dir, exist_ok=True) + ext = get_save_extension(args) + ckpt_name = train_util.get_last_ckpt_name(args, ext) if last else train_util.get_step_ckpt_name(args, ext, global_step) + ckpt_file = os.path.join(args.output_dir, ckpt_name) + + metadata = None + if not args.no_metadata: + metadata = { + "ss_network_module": args.network_module, + "ss_network_dim": str(args.network_dim), + "ss_network_alpha": str(args.network_alpha), + "ss_leco_prompt_count": str(len(prompt_settings)), + "ss_leco_prompts_file": os.path.basename(args.prompts_file), + } + if extra_metadata: + metadata.update(extra_metadata) + if args.training_comment: + metadata["ss_training_comment"] = args.training_comment + metadata["ss_leco_preview"] = json.dumps( + [ + { + "target": p.target, + "positive": p.positive, + "unconditional": p.unconditional, + "neutral": p.neutral, + "action": p.action, + "multiplier": p.multiplier, + "weight": p.weight, + } + for p in prompt_settings[:16] + ], + ensure_ascii=False, + ) + + unwrapped = accelerator.unwrap_model(network) + unwrapped.save_weights(ckpt_file, save_dtype, metadata) + logger.info(f"saved model to: {ckpt_file}") + + + +ResolutionValue = Union[int, Tuple[int, int]] + + +@dataclass +class PromptEmbedsXL: + text_embeds: torch.Tensor + pooled_embeds: torch.Tensor + + +class PromptEmbedsCache: + def __init__(self): + self.prompts: dict[str, Any] = {} + + def __setitem__(self, name: str, value: Any) -> None: + self.prompts[name] = value + + def __getitem__(self, name: str) -> Any: + return self.prompts[name] + + +@dataclass +class PromptSettings: + target: str + positive: Optional[str] = None + unconditional: str = "" + neutral: Optional[str] = None + action: str = "erase" + guidance_scale: float = 1.0 + resolution: ResolutionValue = 512 + dynamic_resolution: bool = False + batch_size: int = 1 + dynamic_crops: bool = False + multiplier: float = 1.0 + weight: float = 1.0 + + def __post_init__(self): + if self.positive is None: + self.positive = self.target + if self.neutral is None: + self.neutral = self.unconditional + if self.action not in ("erase", "enhance"): + raise ValueError(f"Invalid action: {self.action}") + + self.guidance_scale = float(self.guidance_scale) + self.batch_size = int(self.batch_size) + self.multiplier = float(self.multiplier) + self.weight = float(self.weight) + self.dynamic_resolution = bool(self.dynamic_resolution) + self.dynamic_crops = bool(self.dynamic_crops) + self.resolution = normalize_resolution(self.resolution) + + def get_resolution(self) -> Tuple[int, int]: + if isinstance(self.resolution, tuple): + return self.resolution + return (self.resolution, self.resolution) + + def build_target(self, positive_latents, neutral_latents, unconditional_latents): + offset = self.guidance_scale * (positive_latents - unconditional_latents) + if self.action == "erase": + return neutral_latents - offset + return neutral_latents + offset + + +def normalize_resolution(value: Any) -> ResolutionValue: + if isinstance(value, tuple): + if len(value) != 2: + raise ValueError(f"resolution tuple must have 2 items: {value}") + return (int(value[0]), int(value[1])) + if isinstance(value, list): + if len(value) == 2 and all(isinstance(v, (int, float)) for v in value): + return (int(value[0]), int(value[1])) + raise ValueError(f"resolution list must have 2 numeric items: {value}") + return int(value) + + +def _read_non_empty_lines(path: Union[str, Path]) -> List[str]: + with open(path, "r", encoding="utf-8") as f: + return [line.strip() for line in f.readlines() if line.strip()] + + +def _recognized_prompt_keys() -> set[str]: + return { + "target", + "positive", + "unconditional", + "neutral", + "action", + "guidance_scale", + "resolution", + "dynamic_resolution", + "batch_size", + "dynamic_crops", + "multiplier", + "weight", + } + + +def _recognized_slider_keys() -> set[str]: + return { + "target_class", + "positive", + "negative", + "neutral", + "guidance_scale", + "resolution", + "resolutions", + "dynamic_resolution", + "batch_size", + "dynamic_crops", + "multiplier", + "weight", + } + + +def _merge_known_defaults(defaults: dict[str, Any], item: dict[str, Any], known_keys: Iterable[str]) -> dict[str, Any]: + merged = {k: v for k, v in defaults.items() if k in known_keys} + merged.update(item) + return merged + + +def _normalize_resolution_values(value: Any) -> List[ResolutionValue]: + if value is None: + return [512] + if isinstance(value, list) and value and isinstance(value[0], (list, tuple)): + return [normalize_resolution(v) for v in value] + return [normalize_resolution(value)] + + +def _expand_slider_target(target: dict[str, Any], neutral: str) -> List[PromptSettings]: + target_class = str(target.get("target_class", "")) + positive = str(target.get("positive", "") or "") + negative = str(target.get("negative", "") or "") + multiplier = target.get("multiplier", 1.0) + resolutions = _normalize_resolution_values(target.get("resolutions", target.get("resolution", 512))) + + if not positive.strip() and not negative.strip(): + raise ValueError("slider target requires either positive or negative prompt") + + base = dict( + target=target_class, + neutral=neutral, + guidance_scale=target.get("guidance_scale", 1.0), + dynamic_resolution=target.get("dynamic_resolution", False), + batch_size=target.get("batch_size", 1), + dynamic_crops=target.get("dynamic_crops", False), + weight=target.get("weight", 1.0), + ) + + # Build bidirectional (positive_prompt, unconditional_prompt, action, multiplier_sign) pairs. + # With both positive and negative: 4 pairs; with only one: 2 pairs. + pairs: list[tuple[str, str, str, float]] = [] + if positive.strip() and negative.strip(): + pairs = [ + (negative, positive, "erase", multiplier), + (positive, negative, "enhance", multiplier), + (positive, negative, "erase", -multiplier), + (negative, positive, "enhance", -multiplier), + ] + elif negative.strip(): + pairs = [ + (negative, "", "erase", multiplier), + (negative, "", "enhance", -multiplier), + ] + else: + pairs = [ + (positive, "", "enhance", multiplier), + (positive, "", "erase", -multiplier), + ] + + prompt_settings: List[PromptSettings] = [] + for resolution in resolutions: + for pos, uncond, action, mult in pairs: + prompt_settings.append( + PromptSettings(**base, positive=pos, unconditional=uncond, action=action, resolution=resolution, multiplier=mult) + ) + + return prompt_settings + + +def load_prompt_settings(path: Union[str, Path]) -> List[PromptSettings]: + path = Path(path) + with open(path, "r", encoding="utf-8") as f: + data = toml.load(f) + + if not data: + raise ValueError("prompt file is empty") + + default_prompt_values = { + "guidance_scale": 1.0, + "resolution": 512, + "dynamic_resolution": False, + "batch_size": 1, + "dynamic_crops": False, + "multiplier": 1.0, + "weight": 1.0, + } + + prompt_settings: List[PromptSettings] = [] + + def append_prompt_item(item: dict[str, Any], defaults: dict[str, Any]) -> None: + merged = _merge_known_defaults(defaults, item, _recognized_prompt_keys()) + prompt_settings.append(PromptSettings(**merged)) + + def append_slider_item(item: dict[str, Any], defaults: dict[str, Any], neutral_values: Sequence[str]) -> None: + merged = _merge_known_defaults(defaults, item, _recognized_slider_keys()) + if not neutral_values: + neutral_values = [str(merged.get("neutral", "") or "")] + for neutral in neutral_values: + prompt_settings.extend(_expand_slider_target(merged, neutral)) + + if "prompts" in data: + defaults = {**default_prompt_values, **{k: v for k, v in data.items() if k in _recognized_prompt_keys()}} + for item in data["prompts"]: + if "target_class" in item: + append_slider_item(item, defaults, [str(item.get("neutral", "") or "")]) + else: + append_prompt_item(item, defaults) + else: + slider_config = data.get("slider", data) + targets = slider_config.get("targets") + if targets is None: + if "target_class" in slider_config: + targets = [slider_config] + elif "target" in slider_config: + targets = [slider_config] + else: + raise ValueError("prompt file does not contain prompts or slider targets") + if len(targets) == 0: + raise ValueError("prompt file contains an empty targets list") + + if "target" in targets[0]: + defaults = {**default_prompt_values, **{k: v for k, v in slider_config.items() if k in _recognized_prompt_keys()}} + for item in targets: + append_prompt_item(item, defaults) + else: + defaults = {**default_prompt_values, **{k: v for k, v in slider_config.items() if k in _recognized_slider_keys()}} + neutral_values: List[str] = [] + if "neutrals" in slider_config: + neutral_values.extend(str(v) for v in slider_config["neutrals"]) + if "neutral_prompt_file" in slider_config: + neutral_values.extend(_read_non_empty_lines(path.parent / slider_config["neutral_prompt_file"])) + if "prompt_file" in slider_config: + neutral_values.extend(_read_non_empty_lines(path.parent / slider_config["prompt_file"])) + if not neutral_values: + neutral_values = [str(slider_config.get("neutral", "") or "")] + + for item in targets: + item_neutrals = neutral_values + if "neutrals" in item: + item_neutrals = [str(v) for v in item["neutrals"]] + elif "neutral_prompt_file" in item: + item_neutrals = _read_non_empty_lines(path.parent / item["neutral_prompt_file"]) + elif "prompt_file" in item: + item_neutrals = _read_non_empty_lines(path.parent / item["prompt_file"]) + elif "neutral" in item: + item_neutrals = [str(item["neutral"] or "")] + + append_slider_item(item, defaults, item_neutrals) + + if not prompt_settings: + raise ValueError("no prompt settings found") + + return prompt_settings + + +def encode_prompt_sd(tokenize_strategy, text_encoding_strategy, text_encoder, prompt: str) -> torch.Tensor: + tokens = tokenize_strategy.tokenize(prompt) + return text_encoding_strategy.encode_tokens(tokenize_strategy, [text_encoder], tokens)[0] + + +def encode_prompt_sdxl(tokenize_strategy, text_encoding_strategy, text_encoders, prompt: str) -> PromptEmbedsXL: + tokens = tokenize_strategy.tokenize(prompt) + hidden1, hidden2, pool2 = text_encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens) + return PromptEmbedsXL(torch.cat([hidden1, hidden2], dim=2), pool2) + + +def apply_noise_offset(latents: torch.Tensor, noise_offset: Optional[float]) -> torch.Tensor: + if noise_offset is None: + return latents + noise = torch.randn((latents.shape[0], latents.shape[1], 1, 1), dtype=torch.float32, device="cpu") + noise = noise.to(dtype=latents.dtype, device=latents.device) + return latents + noise_offset * noise + + +def get_initial_latents(scheduler, batch_size: int, height: int, width: int, n_prompts: int = 1) -> torch.Tensor: + noise = torch.randn( + (batch_size, 4, height // 8, width // 8), + device="cpu", + ).repeat(n_prompts, 1, 1, 1) + return noise * scheduler.init_noise_sigma + + +def concat_embeddings(unconditional: torch.Tensor, conditional: torch.Tensor, batch_size: int) -> torch.Tensor: + return torch.cat([unconditional, conditional], dim=0).repeat_interleave(batch_size, dim=0) + + +def concat_embeddings_xl(unconditional: PromptEmbedsXL, conditional: PromptEmbedsXL, batch_size: int) -> PromptEmbedsXL: + text_embeds = torch.cat([unconditional.text_embeds, conditional.text_embeds], dim=0).repeat_interleave(batch_size, dim=0) + pooled_embeds = torch.cat([unconditional.pooled_embeds, conditional.pooled_embeds], dim=0).repeat_interleave(batch_size, dim=0) + return PromptEmbedsXL(text_embeds=text_embeds, pooled_embeds=pooled_embeds) + + +def batch_add_time_ids(add_time_ids: torch.Tensor, batch_size: int) -> torch.Tensor: + """Duplicate add_time_ids for CFG (unconditional + conditional) and repeat for the batch.""" + return torch.cat([add_time_ids, add_time_ids], dim=0).repeat_interleave(batch_size, dim=0) + + +def _run_with_checkpoint(function, *args): + if torch.is_grad_enabled(): + return checkpoint(function, *args, use_reentrant=False) + return function(*args) + + +def predict_noise(unet, scheduler, timestep, latents: torch.Tensor, text_embeddings: torch.Tensor, guidance_scale: float = 1.0): + latent_model_input = torch.cat([latents] * 2) + latent_model_input = scheduler.scale_model_input(latent_model_input, timestep) + + def run_unet(model_input, encoder_hidden_states): + return unet(model_input, timestep, encoder_hidden_states=encoder_hidden_states).sample + + noise_pred = _run_with_checkpoint(run_unet, latent_model_input, text_embeddings) + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + return noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + +def diffusion( + unet, + scheduler, + latents: torch.Tensor, + text_embeddings: torch.Tensor, + total_timesteps: int, + start_timesteps: int = 0, + guidance_scale: float = 3.0, +): + for timestep in scheduler.timesteps[start_timesteps:total_timesteps]: + noise_pred = predict_noise(unet, scheduler, timestep, latents, text_embeddings, guidance_scale=guidance_scale) + latents = scheduler.step(noise_pred, timestep, latents).prev_sample + return latents + + +def get_add_time_ids( + height: int, + width: int, + dynamic_crops: bool = False, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +) -> torch.Tensor: + if dynamic_crops: + random_scale = torch.rand(1).item() * 2 + 1 + original_size = (int(height * random_scale), int(width * random_scale)) + crops_coords_top_left = ( + torch.randint(0, max(original_size[0] - height, 1), (1,)).item(), + torch.randint(0, max(original_size[1] - width, 1), (1,)).item(), + ) + target_size = (height, width) + else: + original_size = (height, width) + crops_coords_top_left = (0, 0) + target_size = (height, width) + + add_time_ids = torch.tensor([list(original_size + crops_coords_top_left + target_size)], dtype=dtype) + if device is not None: + add_time_ids = add_time_ids.to(device) + return add_time_ids + + +def predict_noise_xl( + unet, + scheduler, + timestep, + latents: torch.Tensor, + prompt_embeds: PromptEmbedsXL, + add_time_ids: torch.Tensor, + guidance_scale: float = 1.0, +): + latent_model_input = torch.cat([latents] * 2) + latent_model_input = scheduler.scale_model_input(latent_model_input, timestep) + + orig_size = add_time_ids[:, :2] + crop_size = add_time_ids[:, 2:4] + target_size = add_time_ids[:, 4:6] + from library import sdxl_train_util + + size_embeddings = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, latent_model_input.device) + vector_embedding = torch.cat([prompt_embeds.pooled_embeds, size_embeddings.to(prompt_embeds.pooled_embeds.dtype)], dim=1) + + def run_unet(model_input, text_embeds, vector_embeds): + return unet(model_input, timestep, text_embeds, vector_embeds) + + noise_pred = _run_with_checkpoint(run_unet, latent_model_input, prompt_embeds.text_embeds, vector_embedding) + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + return noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + +def diffusion_xl( + unet, + scheduler, + latents: torch.Tensor, + prompt_embeds: PromptEmbedsXL, + add_time_ids: torch.Tensor, + total_timesteps: int, + start_timesteps: int = 0, + guidance_scale: float = 3.0, +): + for timestep in scheduler.timesteps[start_timesteps:total_timesteps]: + noise_pred = predict_noise_xl( + unet, + scheduler, + timestep, + latents, + prompt_embeds=prompt_embeds, + add_time_ids=add_time_ids, + guidance_scale=guidance_scale, + ) + latents = scheduler.step(noise_pred, timestep, latents).prev_sample + return latents + + +def get_random_resolution_in_bucket(bucket_resolution: int = 512) -> Tuple[int, int]: + max_resolution = bucket_resolution + min_resolution = bucket_resolution // 2 + step = 64 + min_step = min_resolution // step + max_step = max_resolution // step + height = torch.randint(min_step, max_step + 1, (1,)).item() * step + width = torch.randint(min_step, max_step + 1, (1,)).item() * step + return height, width + + +def get_random_resolution(prompt: PromptSettings) -> Tuple[int, int]: + height, width = prompt.get_resolution() + if prompt.dynamic_resolution and height == width: + return get_random_resolution_in_bucket(height) + return height, width diff --git a/vendor/sd-scripts/library/lora_utils.py b/vendor/sd-scripts/library/lora_utils.py new file mode 100644 index 0000000..00692cd --- /dev/null +++ b/vendor/sd-scripts/library/lora_utils.py @@ -0,0 +1,287 @@ +import os +import re +from typing import Dict, List, Optional, Union +import torch +from tqdm import tqdm +from library.device_utils import synchronize_device +from library.fp8_optimization_utils import load_safetensors_with_fp8_optimization +from library.safetensors_utils import MemoryEfficientSafeOpen, TensorWeightAdapter, WeightTransformHooks, get_split_weight_filenames +from networks.loha import merge_weights_to_tensor as loha_merge +from networks.lokr import merge_weights_to_tensor as lokr_merge + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +def filter_lora_state_dict( + weights_sd: Dict[str, torch.Tensor], + include_pattern: Optional[str] = None, + exclude_pattern: Optional[str] = None, +) -> Dict[str, torch.Tensor]: + # apply include/exclude patterns + original_key_count = len(weights_sd.keys()) + if include_pattern is not None: + regex_include = re.compile(include_pattern) + weights_sd = {k: v for k, v in weights_sd.items() if regex_include.search(k)} + logger.info(f"Filtered keys with include pattern {include_pattern}: {original_key_count} -> {len(weights_sd.keys())}") + + if exclude_pattern is not None: + original_key_count_ex = len(weights_sd.keys()) + regex_exclude = re.compile(exclude_pattern) + weights_sd = {k: v for k, v in weights_sd.items() if not regex_exclude.search(k)} + logger.info(f"Filtered keys with exclude pattern {exclude_pattern}: {original_key_count_ex} -> {len(weights_sd.keys())}") + + if len(weights_sd) != original_key_count: + remaining_keys = list(set([k.split(".", 1)[0] for k in weights_sd.keys()])) + remaining_keys.sort() + logger.info(f"Remaining LoRA modules after filtering: {remaining_keys}") + if len(weights_sd) == 0: + logger.warning("No keys left after filtering.") + + return weights_sd + + +def load_safetensors_with_lora_and_fp8( + model_files: Union[str, List[str]], + lora_weights_list: Optional[List[Dict[str, torch.Tensor]]], + lora_multipliers: Optional[List[float]], + fp8_optimization: bool, + calc_device: torch.device, + move_to_device: bool = False, + dit_weight_dtype: Optional[torch.dtype] = None, + target_keys: Optional[List[str]] = None, + exclude_keys: Optional[List[str]] = None, + disable_numpy_memmap: bool = False, + weight_transform_hooks: Optional[WeightTransformHooks] = None, +) -> dict[str, torch.Tensor]: + """ + Merge LoRA weights into the state dict of a model with fp8 optimization if needed. + + Args: + model_files (Union[str, List[str]]): Path to the model file or list of paths. If the path matches a pattern like `00001-of-00004`, it will load all files with the same prefix. + lora_weights_list (Optional[List[Dict[str, torch.Tensor]]]): List of dictionaries of LoRA weight tensors to load. + lora_multipliers (Optional[List[float]]): List of multipliers for LoRA weights. + fp8_optimization (bool): Whether to apply FP8 optimization. + calc_device (torch.device): Device to calculate on. + move_to_device (bool): Whether to move tensors to the calculation device after loading. + dit_weight_dtype (Optional[torch.dtype]): Dtype to load weights in when not using FP8 optimization. + target_keys (Optional[List[str]]): Keys to target for optimization. + exclude_keys (Optional[List[str]]): Keys to exclude from optimization. + disable_numpy_memmap (bool): Whether to disable numpy memmap when loading safetensors. + weight_transform_hooks (Optional[WeightTransformHooks]): Hooks for transforming weights during loading. + """ + + # if the file name ends with 00001-of-00004 etc, we need to load the files with the same prefix + if isinstance(model_files, str): + model_files = [model_files] + + extended_model_files = [] + for model_file in model_files: + split_filenames = get_split_weight_filenames(model_file) + if split_filenames is not None: + extended_model_files.extend(split_filenames) + else: + extended_model_files.append(model_file) + model_files = extended_model_files + logger.info(f"Loading model files: {model_files}") + + # load LoRA weights + weight_hook = None + if lora_weights_list is None or len(lora_weights_list) == 0: + lora_weights_list = [] + lora_multipliers = [] + list_of_lora_weight_keys = [] + else: + list_of_lora_weight_keys = [] + for lora_sd in lora_weights_list: + lora_weight_keys = set(lora_sd.keys()) + list_of_lora_weight_keys.append(lora_weight_keys) + + if lora_multipliers is None: + lora_multipliers = [1.0] * len(lora_weights_list) + while len(lora_multipliers) < len(lora_weights_list): + lora_multipliers.append(1.0) + if len(lora_multipliers) > len(lora_weights_list): + lora_multipliers = lora_multipliers[: len(lora_weights_list)] + + # Merge LoRA weights into the state dict + logger.info(f"Merging LoRA weights into state dict. multipliers: {lora_multipliers}") + + # make hook for LoRA merging + def weight_hook_func(model_weight_key, model_weight: torch.Tensor, keep_on_calc_device=False): + nonlocal list_of_lora_weight_keys, lora_weights_list, lora_multipliers, calc_device + + if not model_weight_key.endswith(".weight"): + return model_weight + + original_device = model_weight.device + if original_device != calc_device: + model_weight = model_weight.to(calc_device) # to make calculation faster + + for lora_weight_keys, lora_sd, multiplier in zip(list_of_lora_weight_keys, lora_weights_list, lora_multipliers): + # check if this weight has LoRA weights + lora_name_without_prefix = model_weight_key.rsplit(".", 1)[0] # remove trailing ".weight" + found = False + for prefix in ["lora_unet_", ""]: + lora_name = prefix + lora_name_without_prefix.replace(".", "_") + down_key = lora_name + ".lora_down.weight" + up_key = lora_name + ".lora_up.weight" + alpha_key = lora_name + ".alpha" + if down_key in lora_weight_keys and up_key in lora_weight_keys: + found = True + break + + if found: + # Standard LoRA merge + # get LoRA weights + down_weight = lora_sd[down_key] + up_weight = lora_sd[up_key] + + dim = down_weight.size()[0] + alpha = lora_sd.get(alpha_key, dim) + scale = alpha / dim + + down_weight = down_weight.to(calc_device) + up_weight = up_weight.to(calc_device) + + original_dtype = model_weight.dtype + if original_dtype.itemsize == 1: # fp8 + # temporarily convert to float16 for calculation + model_weight = model_weight.to(torch.float16) + down_weight = down_weight.to(torch.float16) + up_weight = up_weight.to(torch.float16) + + # W <- W + U * D + if len(model_weight.size()) == 2: + # linear + if len(up_weight.size()) == 4: # use linear projection mismatch + up_weight = up_weight.squeeze(3).squeeze(2) + down_weight = down_weight.squeeze(3).squeeze(2) + model_weight = model_weight + multiplier * (up_weight @ down_weight) * scale + elif down_weight.size()[2:4] == (1, 1): + # conv2d 1x1 + model_weight = ( + model_weight + + multiplier + * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) + * scale + ) + else: + # conv2d 3x3 + conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3) + # logger.info(conved.size(), weight.size(), module.stride, module.padding) + model_weight = model_weight + multiplier * conved * scale + + if original_dtype.itemsize == 1: # fp8 + model_weight = model_weight.to(original_dtype) # convert back to original dtype + + # remove LoRA keys from set + lora_weight_keys.remove(down_key) + lora_weight_keys.remove(up_key) + if alpha_key in lora_weight_keys: + lora_weight_keys.remove(alpha_key) + continue + + # Check for LoHa/LoKr weights with same prefix search + for prefix in ["lora_unet_", ""]: + lora_name = prefix + lora_name_without_prefix.replace(".", "_") + hada_key = lora_name + ".hada_w1_a" + lokr_key = lora_name + ".lokr_w1" + + if hada_key in lora_weight_keys: + # LoHa merge + model_weight = loha_merge(model_weight, lora_name, lora_sd, lora_weight_keys, multiplier, calc_device) + break + elif lokr_key in lora_weight_keys: + # LoKr merge + model_weight = lokr_merge(model_weight, lora_name, lora_sd, lora_weight_keys, multiplier, calc_device) + break + + if not keep_on_calc_device and original_device != calc_device: + model_weight = model_weight.to(original_device) # move back to original device + return model_weight + + weight_hook = weight_hook_func + + state_dict = load_safetensors_with_fp8_optimization_and_hook( + model_files, + fp8_optimization, + calc_device, + move_to_device, + dit_weight_dtype, + target_keys, + exclude_keys, + weight_hook=weight_hook, + disable_numpy_memmap=disable_numpy_memmap, + weight_transform_hooks=weight_transform_hooks, + ) + + for lora_weight_keys in list_of_lora_weight_keys: + # check if all LoRA keys are used + if len(lora_weight_keys) > 0: + # if there are still LoRA keys left, it means they are not used in the model + # this is a warning, not an error + logger.warning(f"Warning: not all LoRA keys are used: {', '.join(lora_weight_keys)}") + + return state_dict + + +def load_safetensors_with_fp8_optimization_and_hook( + model_files: list[str], + fp8_optimization: bool, + calc_device: torch.device, + move_to_device: bool = False, + dit_weight_dtype: Optional[torch.dtype] = None, + target_keys: Optional[List[str]] = None, + exclude_keys: Optional[List[str]] = None, + weight_hook: callable = None, + disable_numpy_memmap: bool = False, + weight_transform_hooks: Optional[WeightTransformHooks] = None, +) -> dict[str, torch.Tensor]: + """ + Load state dict from safetensors files and merge LoRA weights into the state dict with fp8 optimization if needed. + """ + if fp8_optimization: + logger.info( + f"Loading state dict with FP8 optimization. Dtype of weight: {dit_weight_dtype}, hook enabled: {weight_hook is not None}" + ) + # dit_weight_dtype is not used because we use fp8 optimization + state_dict = load_safetensors_with_fp8_optimization( + model_files, + calc_device, + target_keys, + exclude_keys, + move_to_device=move_to_device, + weight_hook=weight_hook, + disable_numpy_memmap=disable_numpy_memmap, + weight_transform_hooks=weight_transform_hooks, + ) + else: + logger.info( + f"Loading state dict without FP8 optimization. Dtype of weight: {dit_weight_dtype}, hook enabled: {weight_hook is not None}" + ) + state_dict = {} + for model_file in model_files: + with MemoryEfficientSafeOpen(model_file, disable_numpy_memmap=disable_numpy_memmap) as original_f: + f = TensorWeightAdapter(weight_transform_hooks, original_f) if weight_transform_hooks is not None else original_f + for key in tqdm(f.keys(), desc=f"Loading {os.path.basename(model_file)}", leave=False): + if weight_hook is None and move_to_device: + value = f.get_tensor(key, device=calc_device, dtype=dit_weight_dtype) + else: + value = f.get_tensor(key) # we cannot directly load to device because get_tensor does non-blocking transfer + if weight_hook is not None: + value = weight_hook(key, value, keep_on_calc_device=move_to_device) + if move_to_device: + value = value.to(calc_device, dtype=dit_weight_dtype, non_blocking=True) + elif dit_weight_dtype is not None: + value = value.to(dit_weight_dtype) + + state_dict[key] = value + if move_to_device: + synchronize_device(calc_device) + + return state_dict diff --git a/vendor/sd-scripts/library/lpw_stable_diffusion.py b/vendor/sd-scripts/library/lpw_stable_diffusion.py new file mode 100644 index 0000000..e3b912a --- /dev/null +++ b/vendor/sd-scripts/library/lpw_stable_diffusion.py @@ -0,0 +1,1271 @@ +# copy from https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py +# and modify to support SD2.x + +import inspect +import re +from typing import Callable, List, Optional, Union + +import numpy as np +import PIL.Image +import torch +from packaging import version +from transformers import CLIPImageProcessor as CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection + +import diffusers +from diffusers import SchedulerMixin, StableDiffusionPipeline +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker +from diffusers.utils import logging + +try: + from diffusers.utils import PIL_INTERPOLATION +except ImportError: + if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): + PIL_INTERPOLATION = { + "linear": PIL.Image.Resampling.BILINEAR, + "bilinear": PIL.Image.Resampling.BILINEAR, + "bicubic": PIL.Image.Resampling.BICUBIC, + "lanczos": PIL.Image.Resampling.LANCZOS, + "nearest": PIL.Image.Resampling.NEAREST, + } + else: + PIL_INTERPOLATION = { + "linear": PIL.Image.LINEAR, + "bilinear": PIL.Image.BILINEAR, + "bicubic": PIL.Image.BICUBIC, + "lanczos": PIL.Image.LANCZOS, + "nearest": PIL.Image.NEAREST, + } +# ------------------------------------------------------------------------------ + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +re_attention = re.compile( + r""" +\\\(| +\\\)| +\\\[| +\\]| +\\\\| +\\| +\(| +\[| +:([+-]?[.\d]+)\)| +\)| +]| +[^\\()\[\]:]+| +: +""", + re.X, +) + + +def parse_prompt_attention(text): + """ + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. + Accepted tokens are: + (abc) - increases attention to abc by a multiplier of 1.1 + (abc:3.12) - increases attention to abc by a multiplier of 3.12 + [abc] - decreases attention to abc by a multiplier of 1.1 + \( - literal character '(' + \[ - literal character '[' + \) - literal character ')' + \] - literal character ']' + \\ - literal character '\' + anything else - just text + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] + """ + + res = [] + round_brackets = [] + square_brackets = [] + + round_bracket_multiplier = 1.1 + square_bracket_multiplier = 1 / 1.1 + + def multiply_range(start_position, multiplier): + for p in range(start_position, len(res)): + res[p][1] *= multiplier + + for m in re_attention.finditer(text): + text = m.group(0) + weight = m.group(1) + + if text.startswith("\\"): + res.append([text[1:], 1.0]) + elif text == "(": + round_brackets.append(len(res)) + elif text == "[": + square_brackets.append(len(res)) + elif weight is not None and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), float(weight)) + elif text == ")" and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), round_bracket_multiplier) + elif text == "]" and len(square_brackets) > 0: + multiply_range(square_brackets.pop(), square_bracket_multiplier) + else: + res.append([text, 1.0]) + + for pos in round_brackets: + multiply_range(pos, round_bracket_multiplier) + + for pos in square_brackets: + multiply_range(pos, square_bracket_multiplier) + + if len(res) == 0: + res = [["", 1.0]] + + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + + return res + + +def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], max_length: int): + r""" + Tokenize a list of prompts and return its tokens with weights of each token. + + No padding, starting or ending token is included. + """ + tokens = [] + weights = [] + truncated = False + for text in prompt: + texts_and_weights = parse_prompt_attention(text) + text_token = [] + text_weight = [] + for word, weight in texts_and_weights: + # tokenize and discard the starting and the ending token + token = pipe.tokenizer(word).input_ids[1:-1] + text_token += token + # copy the weight by length of token + text_weight += [weight] * len(token) + # stop if the text is too long (longer than truncation limit) + if len(text_token) > max_length: + truncated = True + break + # truncate + if len(text_token) > max_length: + truncated = True + text_token = text_token[:max_length] + text_weight = text_weight[:max_length] + tokens.append(text_token) + weights.append(text_weight) + if truncated: + logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples") + return tokens, weights + + +def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77): + r""" + Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. + """ + max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) + weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length + for i in range(len(tokens)): + tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i])) + if no_boseos_middle: + weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) + else: + w = [] + if len(weights[i]) == 0: + w = [1.0] * weights_length + else: + for j in range(max_embeddings_multiples): + w.append(1.0) # weight for starting token in this chunk + w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))] + w.append(1.0) # weight for ending token in this chunk + w += [1.0] * (weights_length - len(w)) + weights[i] = w[:] + + return tokens, weights + + +def get_unweighted_text_embeddings( + pipe: StableDiffusionPipeline, + text_input: torch.Tensor, + chunk_length: int, + clip_skip: int, + eos: int, + pad: int, + no_boseos_middle: Optional[bool] = True, +): + """ + When the length of tokens is a multiple of the capacity of the text encoder, + it should be split into chunks and sent to the text encoder individually. + """ + max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2) + if max_embeddings_multiples > 1: + text_embeddings = [] + for i in range(max_embeddings_multiples): + # extract the i-th chunk + text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone() + + # cover the head and the tail by the starting and the ending tokens + text_input_chunk[:, 0] = text_input[0, 0] + if pad == eos: # v1 + text_input_chunk[:, -1] = text_input[0, -1] + else: # v2 + for j in range(len(text_input_chunk)): + if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある + text_input_chunk[j, -1] = eos + if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD + text_input_chunk[j, 1] = eos + + if clip_skip is None or clip_skip == 1: + text_embedding = pipe.text_encoder(text_input_chunk)[0] + else: + enc_out = pipe.text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True) + text_embedding = enc_out["hidden_states"][-clip_skip] + text_embedding = pipe.text_encoder.text_model.final_layer_norm(text_embedding) + + if no_boseos_middle: + if i == 0: + # discard the ending token + text_embedding = text_embedding[:, :-1] + elif i == max_embeddings_multiples - 1: + # discard the starting token + text_embedding = text_embedding[:, 1:] + else: + # discard both starting and ending tokens + text_embedding = text_embedding[:, 1:-1] + + text_embeddings.append(text_embedding) + text_embeddings = torch.concat(text_embeddings, axis=1) + else: + if clip_skip is None or clip_skip == 1: + text_embeddings = pipe.text_encoder(text_input)[0] + else: + enc_out = pipe.text_encoder(text_input, output_hidden_states=True, return_dict=True) + text_embeddings = enc_out["hidden_states"][-clip_skip] + text_embeddings = pipe.text_encoder.text_model.final_layer_norm(text_embeddings) + return text_embeddings + + +def get_weighted_text_embeddings( + pipe: StableDiffusionPipeline, + prompt: Union[str, List[str]], + uncond_prompt: Optional[Union[str, List[str]]] = None, + max_embeddings_multiples: Optional[int] = 3, + no_boseos_middle: Optional[bool] = False, + skip_parsing: Optional[bool] = False, + skip_weighting: Optional[bool] = False, + clip_skip=None, +): + r""" + Prompts can be assigned with local weights using brackets. For example, + prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful', + and the embedding tokens corresponding to the words get multiplied by a constant, 1.1. + + Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean. + + Args: + pipe (`StableDiffusionPipeline`): + Pipe to provide access to the tokenizer and the text encoder. + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + uncond_prompt (`str` or `List[str]`): + The unconditional prompt or prompts for guide the image generation. If unconditional prompt + is provided, the embeddings of prompt and uncond_prompt are concatenated. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + no_boseos_middle (`bool`, *optional*, defaults to `False`): + If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and + ending token in each of the chunk in the middle. + skip_parsing (`bool`, *optional*, defaults to `False`): + Skip the parsing of brackets. + skip_weighting (`bool`, *optional*, defaults to `False`): + Skip the weighting. When the parsing is skipped, it is forced True. + """ + max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + if isinstance(prompt, str): + prompt = [prompt] + + if not skip_parsing: + prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2) + if uncond_prompt is not None: + if isinstance(uncond_prompt, str): + uncond_prompt = [uncond_prompt] + uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2) + else: + prompt_tokens = [token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids] + prompt_weights = [[1.0] * len(token) for token in prompt_tokens] + if uncond_prompt is not None: + if isinstance(uncond_prompt, str): + uncond_prompt = [uncond_prompt] + uncond_tokens = [ + token[1:-1] for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids + ] + uncond_weights = [[1.0] * len(token) for token in uncond_tokens] + + # round up the longest length of tokens to a multiple of (model_max_length - 2) + max_length = max([len(token) for token in prompt_tokens]) + if uncond_prompt is not None: + max_length = max(max_length, max([len(token) for token in uncond_tokens])) + + max_embeddings_multiples = min( + max_embeddings_multiples, + (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1, + ) + max_embeddings_multiples = max(1, max_embeddings_multiples) + max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + + # pad the length of tokens and weights + bos = pipe.tokenizer.bos_token_id + eos = pipe.tokenizer.eos_token_id + pad = pipe.tokenizer.pad_token_id + prompt_tokens, prompt_weights = pad_tokens_and_weights( + prompt_tokens, + prompt_weights, + max_length, + bos, + eos, + no_boseos_middle=no_boseos_middle, + chunk_length=pipe.tokenizer.model_max_length, + ) + prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device) + if uncond_prompt is not None: + uncond_tokens, uncond_weights = pad_tokens_and_weights( + uncond_tokens, + uncond_weights, + max_length, + bos, + eos, + no_boseos_middle=no_boseos_middle, + chunk_length=pipe.tokenizer.model_max_length, + ) + uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device) + + # get the embeddings + text_embeddings = get_unweighted_text_embeddings( + pipe, + prompt_tokens, + pipe.tokenizer.model_max_length, + clip_skip, + eos, + pad, + no_boseos_middle=no_boseos_middle, + ) + prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device) + if uncond_prompt is not None: + uncond_embeddings = get_unweighted_text_embeddings( + pipe, + uncond_tokens, + pipe.tokenizer.model_max_length, + clip_skip, + eos, + pad, + no_boseos_middle=no_boseos_middle, + ) + uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device) + + # assign weights to the prompts and normalize in the sense of mean + # TODO: should we normalize by chunk or in a whole (current implementation)? + if (not skip_parsing) and (not skip_weighting): + previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings *= prompt_weights.unsqueeze(-1) + current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + if uncond_prompt is not None: + previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) + uncond_embeddings *= uncond_weights.unsqueeze(-1) + current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) + uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + + if uncond_prompt is not None: + return text_embeddings, uncond_embeddings + return text_embeddings, None + + +def preprocess_image(image): + w, h = image.size + w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) + image = np.array(image).astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + return 2.0 * image - 1.0 + + +def preprocess_mask(mask, scale_factor=8): + mask = mask.convert("L") + w, h = mask.size + w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]) + mask = np.array(mask).astype(np.float32) / 255.0 + mask = np.tile(mask, (4, 1, 1)) + mask = mask[None].transpose(0, 1, 2, 3) # what does this step do? + mask = 1 - mask # repaint white, keep black + mask = torch.from_numpy(mask) + return mask + + +def prepare_controlnet_image( + image: PIL.Image.Image, + width: int, + height: int, + batch_size: int, + num_images_per_prompt: int, + device: torch.device, + dtype: torch.dtype, + do_classifier_free_guidance: bool = False, + guess_mode: bool = False, +): + if not isinstance(image, torch.Tensor): + if isinstance(image, PIL.Image.Image): + image = [image] + + if isinstance(image[0], PIL.Image.Image): + images = [] + + for image_ in image: + image_ = image_.convert("RGB") + image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]) + image_ = np.array(image_) + image_ = image_[None, :] + images.append(image_) + + image = images + + image = np.concatenate(image, axis=0) + image = np.array(image).astype(np.float32) / 255.0 + image = image.transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + elif isinstance(image[0], torch.Tensor): + image = torch.cat(image, dim=0) + + image_batch_size = image.shape[0] + + if image_batch_size == 1: + repeat_by = batch_size + else: + # image batch size is the same as prompt batch size + repeat_by = num_images_per_prompt + + image = image.repeat_interleave(repeat_by, dim=0) + + image = image.to(device=device, dtype=dtype) + + if do_classifier_free_guidance and not guess_mode: + image = torch.cat([image] * 2) + + return image + + +class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline): + r""" + Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing + weighting in prompt. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + + # if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"): + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: SchedulerMixin, + # clip_skip: int, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + requires_safety_checker: bool = True, + image_encoder: CLIPVisionModelWithProjection = None, + clip_skip: int = 1, + ): + super().__init__( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + requires_safety_checker=requires_safety_checker, + image_encoder=image_encoder, + ) + self.custom_clip_skip = clip_skip + self.__init__additional__() + + def __init__additional__(self): + if not hasattr(self, "vae_scale_factor"): + setattr(self, "vae_scale_factor", 2 ** (len(self.vae.config.block_out_channels) - 1)) + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + max_embeddings_multiples, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `list(int)`): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + """ + batch_size = len(prompt) if isinstance(prompt, list) else 1 + + if negative_prompt is None: + negative_prompt = [""] * batch_size + elif isinstance(negative_prompt, str): + negative_prompt = [negative_prompt] * batch_size + if batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + + text_embeddings, uncond_embeddings = get_weighted_text_embeddings( + pipe=self, + prompt=prompt, + uncond_prompt=negative_prompt if do_classifier_free_guidance else None, + max_embeddings_multiples=max_embeddings_multiples, + clip_skip=self.custom_clip_skip, + ) + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) + text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) + + if do_classifier_free_guidance: + bs_embed, seq_len, _ = uncond_embeddings.shape + uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1) + uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def check_inputs(self, prompt, height, width, strength, callback_steps): + if not isinstance(prompt, str) and not isinstance(prompt, list): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if strength < 0 or strength > 1: + raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") + + if height % 8 != 0 or width % 8 != 0: + logger.info(f'{height} {width}') + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." + ) + + def get_timesteps(self, num_inference_steps, strength, device, is_text2img): + if is_text2img: + return self.scheduler.timesteps.to(device), num_inference_steps + else: + # get the original timestep using init_timestep + offset = self.scheduler.config.get("steps_offset", 0) + init_timestep = int(num_inference_steps * strength) + offset + init_timestep = min(init_timestep, num_inference_steps) + + t_start = max(num_inference_steps - init_timestep + offset, 0) + timesteps = self.scheduler.timesteps[t_start:].to(device) + return timesteps, num_inference_steps - t_start + + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is not None: + safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values.to(dtype)) + else: + has_nsfw_concept = None + return image, has_nsfw_concept + + def decode_latents(self, latents): + latents = 1 / 0.18215 * latents + image = self.vae.decode(latents).sample + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents(self, image, timestep, batch_size, height, width, dtype, device, generator, latents=None): + if image is None: + shape = ( + batch_size, + self.vae.config.latent_channels, # always 4; unet.in_channels may be 9 for inpainting + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + + if latents is None: + if device.type == "mps": + # randn does not work reproducibly on mps + latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device) + else: + latents = torch.randn(shape, generator=generator, device=device, dtype=dtype) + else: + if latents.shape != shape: + raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents, None, None + else: + init_latent_dist = self.vae.encode(image).latent_dist + init_latents = init_latent_dist.sample(generator=generator) + init_latents = 0.18215 * init_latents + init_latents = torch.cat([init_latents] * batch_size, dim=0) + init_latents_orig = init_latents + shape = init_latents.shape + + # add noise to latents using the timesteps + if device.type == "mps": + noise = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device) + else: + noise = torch.randn(shape, generator=generator, device=device, dtype=dtype) + latents = self.scheduler.add_noise(init_latents, noise, timestep) + return latents, init_latents_orig, noise + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + image: Union[torch.FloatTensor, PIL.Image.Image] = None, + mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None, + height: int = 512, + width: int = 512, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + strength: float = 0.8, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[torch.Generator] = None, + latents: Optional[torch.FloatTensor] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + controlnet=None, + controlnet_image=None, + inpaint_image: PIL.Image.Image = None, + inpaint_mask: PIL.Image.Image = None, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. + mask_image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be + replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a + PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should + contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. + height (`int`, *optional*, defaults to 512): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to 512): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. + `image` will be used as a starting point, adding more noise to it the larger the `strength`. The + number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added + noise will be maximum and the denoising process will run for the full number of iterations specified in + `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + controlnet (`diffusers.ControlNetModel`, *optional*): + A controlnet model to be used for the inference. If not provided, controlnet will be disabled. + controlnet_image (`torch.FloatTensor` or `PIL.Image.Image`, *optional*): + `Image`, or tensor representing an image batch, to be used as the starting point for the controlnet + inference. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + + Returns: + `None` if cancelled by `is_cancelled_callback`, + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + if controlnet is not None and controlnet_image is None: + raise ValueError("controlnet_image must be provided if controlnet is not None.") + + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs(prompt, height, width, strength, callback_steps) + + # 2. Define call parameters + batch_size = 1 if isinstance(prompt, str) else len(prompt) + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + text_embeddings = self._encode_prompt( + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + max_embeddings_multiples, + ) + dtype = text_embeddings.dtype + + # 4. Preprocess image and mask + if isinstance(image, PIL.Image.Image): + image = preprocess_image(image) + if image is not None: + image = image.to(device=self.device, dtype=dtype) + if isinstance(mask_image, PIL.Image.Image): + mask_image = preprocess_mask(mask_image, self.vae_scale_factor) + if mask_image is not None: + mask = mask_image.to(device=self.device, dtype=dtype) + mask = torch.cat([mask] * batch_size * num_images_per_prompt) + else: + mask = None + + if controlnet_image is not None: + controlnet_image = prepare_controlnet_image( + controlnet_image, width, height, batch_size, 1, self.device, controlnet.dtype, do_classifier_free_guidance, False + ) + + # 5. set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None) + latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) + + # 6. Prepare latent variables + latents, init_latents_orig, noise = self.prepare_latents( + image, + latent_timestep, + batch_size * num_images_per_prompt, + height, + width, + dtype, + device, + generator, + latents, + ) + + # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Prepare 9-channel inpainting inputs (mask + masked image latents) if supplied. + # The scheduler and latents always stay 4-channel; concatenation only happens + # on the UNet input (latent_model_input) inside the denoising loop. + inpaint_mask_latent = None + inpaint_masked_image_latent = None + if inpaint_image is not None and inpaint_mask is not None: + import numpy as np + SD15_VAE_SCALE_FACTOR = 0.18215 + # Cast inpaint inputs to self.vae.dtype rather than the UNet dtype, so + # that VAE encode operates in its own precision regardless of mixed + # precision settings (mirrors the existing decode path). + img_np = np.array(inpaint_image.convert("RGB")).astype(np.float32) / 127.5 - 1.0 + img_t = torch.from_numpy(img_np).permute(2, 0, 1)[None].to(device=device, dtype=self.vae.dtype) + mask_np = np.array(inpaint_mask.convert("L")).astype(np.float32) / 255.0 + mask_np = (mask_np >= 0.5).astype(np.float32) + mask_t = torch.from_numpy(mask_np)[None, None].to(device=device, dtype=self.vae.dtype) + masked_img_t = img_t * (1.0 - mask_t) + # Disable autocast: under fp16 autocast (set by accelerator.autocast() in + # the caller), conv kernels run in fp16 even when weights/inputs are fp32, + # which can produce NaN in the VAE encode. Mirrors how decode_latents() + # runs outside the autocast block in the caller. + with torch.autocast(device_type=device.type, enabled=False): + inpaint_masked_image_latent = ( + self.vae.encode(masked_img_t).latent_dist.sample() * SD15_VAE_SCALE_FACTOR + ) + inpaint_mask_latent = torch.nn.functional.interpolate( + mask_t, size=(height // 8, width // 8) + ) + + # 8. Denoising loop + for i, t in enumerate(self.progress_bar(timesteps)): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # For 9-channel inpainting UNet: concatenate mask and masked-image latents. + if inpaint_mask_latent is not None: + n = latent_model_input.shape[0] // latents.shape[0] # 2 if CFG, else 1 + mask_in = inpaint_mask_latent.repeat(n, 1, 1, 1).to(latent_model_input.dtype) + masked_in = inpaint_masked_image_latent.repeat(n, 1, 1, 1).to(latent_model_input.dtype) + latent_model_input = torch.cat([latent_model_input, mask_in, masked_in], dim=1) + + unet_additional_args = {} + if controlnet is not None: + down_block_res_samples, mid_block_res_sample = controlnet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings, + controlnet_cond=controlnet_image, + conditioning_scale=1.0, + guess_mode=False, + return_dict=False, + ) + unet_additional_args["down_block_additional_residuals"] = down_block_res_samples + unet_additional_args["mid_block_additional_residual"] = mid_block_res_sample + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings, **unet_additional_args).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + if mask is not None: + # masking + init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t])) + latents = (init_latents_proper * mask) + (latents * (1 - mask)) + + # call the callback, if provided + if i % callback_steps == 0: + if callback is not None: + callback(i, t, latents) + if is_cancelled_callback is not None and is_cancelled_callback(): + return None + + return latents + + def latents_to_image(self, latents): + # 9. Post-processing + image = self.decode_latents(latents.to(self.vae.dtype)) + image = self.numpy_to_pil(image) + return image + + def text2img( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + height: int = 512, + width: int = 512, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[torch.Generator] = None, + latents: Optional[torch.FloatTensor] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function for text-to-image generation. + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + height (`int`, *optional*, defaults to 512): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to 512): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + return self.__call__( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + num_images_per_prompt=num_images_per_prompt, + eta=eta, + generator=generator, + latents=latents, + max_embeddings_multiples=max_embeddings_multiples, + output_type=output_type, + return_dict=return_dict, + callback=callback, + is_cancelled_callback=is_cancelled_callback, + callback_steps=callback_steps, + ) + + def img2img( + self, + image: Union[torch.FloatTensor, PIL.Image.Image], + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + strength: float = 0.8, + num_inference_steps: Optional[int] = 50, + guidance_scale: Optional[float] = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: Optional[float] = 0.0, + generator: Optional[torch.Generator] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function for image-to-image generation. + Args: + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. + `image` will be used as a starting point, adding more noise to it the larger the `strength`. The + number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added + noise will be maximum and the denoising process will run for the full number of iterations specified in + `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. This parameter will be modulated by `strength`. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + return self.__call__( + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + strength=strength, + num_images_per_prompt=num_images_per_prompt, + eta=eta, + generator=generator, + max_embeddings_multiples=max_embeddings_multiples, + output_type=output_type, + return_dict=return_dict, + callback=callback, + is_cancelled_callback=is_cancelled_callback, + callback_steps=callback_steps, + ) + + def inpaint( + self, + image: Union[torch.FloatTensor, PIL.Image.Image], + mask_image: Union[torch.FloatTensor, PIL.Image.Image], + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + strength: float = 0.8, + num_inference_steps: Optional[int] = 50, + guidance_scale: Optional[float] = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: Optional[float] = 0.0, + generator: Optional[torch.Generator] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function for inpaint. + Args: + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. This is the image whose masked region will be inpainted. + mask_image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be + replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a + PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should + contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength` + is 1, the denoising process will be run on the masked area for the full number of iterations specified + in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more + noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur. + num_inference_steps (`int`, *optional*, defaults to 50): + The reference number of denoising steps. More denoising steps usually lead to a higher quality image at + the expense of slower inference. This parameter will be modulated by `strength`, as explained above. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + return self.__call__( + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + mask_image=mask_image, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + strength=strength, + num_images_per_prompt=num_images_per_prompt, + eta=eta, + generator=generator, + max_embeddings_multiples=max_embeddings_multiples, + output_type=output_type, + return_dict=return_dict, + callback=callback, + is_cancelled_callback=is_cancelled_callback, + callback_steps=callback_steps, + ) diff --git a/vendor/sd-scripts/library/lumina_models.py b/vendor/sd-scripts/library/lumina_models.py new file mode 100644 index 0000000..c51c900 --- /dev/null +++ b/vendor/sd-scripts/library/lumina_models.py @@ -0,0 +1,1387 @@ +# Copyright Alpha VLLM/Lumina Image 2.0 and contributors +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- + +import math +from typing import List, Optional, Tuple +from dataclasses import dataclass + +import torch +from torch import Tensor +from torch.utils.checkpoint import checkpoint +import torch.nn as nn +import torch.nn.functional as F + +from library import custom_offloading_utils + +try: + from flash_attn import flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +except ImportError: + # flash_attn may not be available but it is not required + pass + +try: + from sageattention import sageattn +except ImportError: + pass + +try: + from apex.normalization import FusedRMSNorm as RMSNorm +except ImportError: + import warnings + + warnings.warn("Cannot import apex RMSNorm, switch to vanilla implementation") + + ############################################################################# + # RMSNorm # + ############################################################################# + + class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x) -> Tensor: + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x: Tensor): + """ + Apply RMSNorm to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + """ + x_dtype = x.dtype + # To handle float8 we need to convert the tensor to float + x = x.float() + rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps) + return ((x * rrms) * self.weight.float()).to(dtype=x_dtype) + + + +@dataclass +class LuminaParams: + """Parameters for Lumina model configuration""" + + patch_size: int = 2 + in_channels: int = 4 + dim: int = 4096 + n_layers: int = 30 + n_refiner_layers: int = 2 + n_heads: int = 24 + n_kv_heads: int = 8 + multiple_of: int = 256 + axes_dims: List[int] = None + axes_lens: List[int] = None + qk_norm: bool = False + ffn_dim_multiplier: Optional[float] = None + norm_eps: float = 1e-5 + scaling_factor: float = 1.0 + cap_feat_dim: int = 32 + + def __post_init__(self): + if self.axes_dims is None: + self.axes_dims = [36, 36, 36] + if self.axes_lens is None: + self.axes_lens = [300, 512, 512] + + @classmethod + def get_2b_config(cls) -> "LuminaParams": + """Returns the configuration for the 2B parameter model""" + return cls( + patch_size=2, + in_channels=16, # VAE channels + dim=2304, + n_layers=26, + n_heads=24, + n_kv_heads=8, + axes_dims=[32, 32, 32], + axes_lens=[300, 512, 512], + qk_norm=True, + cap_feat_dim=2304, # Gemma 2 hidden_size + ) + + @classmethod + def get_7b_config(cls) -> "LuminaParams": + """Returns the configuration for the 7B parameter model""" + return cls( + patch_size=2, + dim=4096, + n_layers=32, + n_heads=32, + n_kv_heads=8, + axes_dims=[64, 64, 64], + axes_lens=[300, 512, 512], + ) + + +class GradientCheckpointMixin(nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + + def disable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = False + + def forward(self, *args, **kwargs): + if self.training and self.gradient_checkpointing: + return checkpoint(self._forward, *args, use_reentrant=False, **kwargs) + else: + return self._forward(*args, **kwargs) + + + +def modulate(x, scale): + return x * (1 + scale.unsqueeze(1)) + + +############################################################################# +# Embedding Layers for Timesteps and Class Labels # +############################################################################# + + +class TimestepEmbedder(GradientCheckpointMixin): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear( + frequency_embedding_size, + hidden_size, + bias=True, + ), + nn.SiLU(), + nn.Linear( + hidden_size, + hidden_size, + bias=True, + ), + ) + nn.init.normal_(self.mlp[0].weight, std=0.02) + nn.init.zeros_(self.mlp[0].bias) + nn.init.normal_(self.mlp[2].weight, std=0.02) + nn.init.zeros_(self.mlp[2].bias) + + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def _forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) + return t_emb + + +def to_cuda(x): + if isinstance(x, torch.Tensor): + return x.cuda() + elif isinstance(x, (list, tuple)): + return [to_cuda(elem) for elem in x] + elif isinstance(x, dict): + return {k: to_cuda(v) for k, v in x.items()} + else: + return x + + +def to_cpu(x): + if isinstance(x, torch.Tensor): + return x.cpu() + elif isinstance(x, (list, tuple)): + return [to_cpu(elem) for elem in x] + elif isinstance(x, dict): + return {k: to_cpu(v) for k, v in x.items()} + else: + return x + + +############################################################################# +# Core NextDiT Model # +############################################################################# + + +class JointAttention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + use_flash_attn=False, + use_sage_attn=False, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + qk_norm (bool): Whether to use normalization for queries and keys. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_local_heads = n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = dim // n_heads + + self.qkv = nn.Linear( + dim, + (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.qkv.weight) + + self.out = nn.Linear( + n_heads * self.head_dim, + dim, + bias=False, + ) + nn.init.xavier_uniform_(self.out.weight) + + if qk_norm: + self.q_norm = RMSNorm(self.head_dim) + self.k_norm = RMSNorm(self.head_dim) + else: + self.q_norm = self.k_norm = nn.Identity() + + self.use_flash_attn = use_flash_attn + self.use_sage_attn = use_sage_attn + + if use_sage_attn : + self.attention_processor = self.sage_attn + else: + # self.attention_processor = xformers.ops.memory_efficient_attention + self.attention_processor = F.scaled_dot_product_attention + + def set_attention_processor(self, attention_processor): + self.attention_processor = attention_processor + + def get_attention_processor(self): + return self.attention_processor + + def forward( + self, + x: Tensor, + x_mask: Tensor, + freqs_cis: Tensor, + ) -> Tensor: + """ + Args: + x: + x_mask: + freqs_cis: + """ + bsz, seqlen, _ = x.shape + dtype = x.dtype + + xq, xk, xv = torch.split( + self.qkv(x), + [ + self.n_local_heads * self.head_dim, + self.n_local_kv_heads * self.head_dim, + self.n_local_kv_heads * self.head_dim, + ], + dim=-1, + ) + xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) + xq = self.q_norm(xq) + xk = self.k_norm(xk) + xq = apply_rope(xq, freqs_cis=freqs_cis) + xk = apply_rope(xk, freqs_cis=freqs_cis) + xq, xk = xq.to(dtype), xk.to(dtype) + + softmax_scale = math.sqrt(1 / self.head_dim) + + if self.use_sage_attn: + # Handle GQA (Grouped Query Attention) if needed + n_rep = self.n_local_heads // self.n_local_kv_heads + if n_rep > 1: + xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + + output = self.sage_attn(xq, xk, xv, x_mask, softmax_scale) + elif self.use_flash_attn: + output = self.flash_attn(xq, xk, xv, x_mask, softmax_scale) + else: + n_rep = self.n_local_heads // self.n_local_kv_heads + if n_rep > 1: + xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + + output = ( + self.attention_processor( + xq.permute(0, 2, 1, 3), + xk.permute(0, 2, 1, 3), + xv.permute(0, 2, 1, 3), + attn_mask=x_mask.bool().view(bsz, 1, 1, seqlen).expand(-1, self.n_local_heads, seqlen, -1), + scale=softmax_scale, + ) + .permute(0, 2, 1, 3) + .to(dtype) + ) + + output = output.flatten(-2) + return self.out(output) + + # copied from huggingface modeling_llama.py + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.n_local_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + def sage_attn(self, q: Tensor, k: Tensor, v: Tensor, x_mask: Tensor, softmax_scale: float): + try: + bsz = q.shape[0] + seqlen = q.shape[1] + + # Transpose to SageAttention's expected HND layout: [batch, heads, seq_len, head_dim] + q_transposed = q.permute(0, 2, 1, 3) + k_transposed = k.permute(0, 2, 1, 3) + v_transposed = v.permute(0, 2, 1, 3) + + # Fast path: if all tokens are valid, run batched SageAttention directly + if x_mask.all(): + output = sageattn( + q_transposed, k_transposed, v_transposed, + tensor_layout="HND", is_causal=False, sm_scale=softmax_scale, + ) + # output: [batch, heads, seq_len, head_dim] -> [batch, seq_len, heads, head_dim] + output = output.permute(0, 2, 1, 3) + else: + # Slow path: per-batch loop to handle variable-length masking + # SageAttention does not support attention masks natively + outputs = [] + for b in range(bsz): + valid_indices = x_mask[b].nonzero(as_tuple=True)[0] + if valid_indices.numel() == 0: + outputs.append(torch.zeros( + seqlen, self.n_local_heads, self.head_dim, + device=q.device, dtype=q.dtype, + )) + continue + + batch_output_valid = sageattn( + q_transposed[b:b+1, :, valid_indices, :], + k_transposed[b:b+1, :, valid_indices, :], + v_transposed[b:b+1, :, valid_indices, :], + tensor_layout="HND", is_causal=False, sm_scale=softmax_scale, + ) + + batch_output = torch.zeros( + seqlen, self.n_local_heads, self.head_dim, + device=q.device, dtype=q.dtype, + ) + batch_output[valid_indices] = batch_output_valid.squeeze(0).permute(1, 0, 2) + outputs.append(batch_output) + + output = torch.stack(outputs, dim=0) + except NameError as e: + raise RuntimeError( + f"Could not load Sage Attention. Please install https://github.com/thu-ml/SageAttention. / Sage Attention を読み込めませんでした。https://github.com/thu-ml/SageAttention をインストールしてください。 / {e}" + ) + + return output + + def flash_attn( + self, + q: Tensor, + k: Tensor, + v: Tensor, + x_mask: Tensor, + softmax_scale, + ) -> Tensor: + bsz, seqlen, _, _ = q.shape + + try: + # begin var_len flash attn + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(q, k, v, x_mask, seqlen) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=0.0, + causal=False, + softmax_scale=softmax_scale, + ) + output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) + # end var_len_flash_attn + + return output + except NameError as e: + raise RuntimeError( + f"Could not load flash attention. Please install flash_attn. / フラッシュアテンションを読み込めませんでした。flash_attn をインストールしてください。 / {e}" + ) + + +def apply_rope( + x_in: torch.Tensor, + freqs_cis: torch.Tensor, +) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + with torch.autocast("cuda", enabled=False): + x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) + freqs_cis = freqs_cis.unsqueeze(2) + x_out = torch.view_as_real(x * freqs_cis).flatten(3) + + return x_out.type_as(x_in) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + """ + super().__init__() + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear( + dim, + hidden_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.w1.weight) + self.w2 = nn.Linear( + hidden_dim, + dim, + bias=False, + ) + nn.init.xavier_uniform_(self.w2.weight) + self.w3 = nn.Linear( + dim, + hidden_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.w3.weight) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class JointTransformerBlock(GradientCheckpointMixin): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + multiple_of: int, + ffn_dim_multiplier: Optional[float], + norm_eps: float, + qk_norm: bool, + modulation=True, + use_flash_attn=False, + use_sage_attn=False, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): Number of multiple of the hidden dimension. + ffn_dim_multiplier (Optional[float]): Dimension multiplier for the + feedforward layer. + norm_eps (float): Epsilon value for normalization. + qk_norm (bool): Whether to use normalization for queries and keys. + modulation (bool): Whether to use modulation for the attention + layer. + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, use_flash_attn=use_flash_attn, use_sage_attn=use_sage_attn) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + ) + self.layer_id = layer_id + self.attention_norm1 = RMSNorm(dim, eps=norm_eps) + self.ffn_norm1 = RMSNorm(dim, eps=norm_eps) + + self.attention_norm2 = RMSNorm(dim, eps=norm_eps) + self.ffn_norm2 = RMSNorm(dim, eps=norm_eps) + + self.modulation = modulation + if modulation: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear( + min(dim, 1024), + 4 * dim, + bias=True, + ), + ) + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def _forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + pe: torch.Tensor, + adaln_input: Optional[torch.Tensor] = None, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (Tensor): Input tensor. + pe (Tensor): Rope position embedding. + + Returns: + Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if self.modulation: + assert adaln_input is not None + scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) + + x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( + self.attention( + modulate(self.attention_norm1(x), scale_msa), + x_mask, + pe, + ) + ) + x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( + self.feed_forward( + modulate(self.ffn_norm1(x), scale_mlp), + ) + ) + else: + assert adaln_input is None + x = x + self.attention_norm2( + self.attention( + self.attention_norm1(x), + x_mask, + pe, + ) + ) + x = x + self.ffn_norm2( + self.feed_forward( + self.ffn_norm1(x), + ) + ) + return x + + +class FinalLayer(GradientCheckpointMixin): + """ + The final layer of NextDiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + """ + Initialize the FinalLayer. + + Args: + hidden_size (int): Hidden size of the input features. + patch_size (int): Patch size of the input features. + out_channels (int): Number of output channels. + """ + super().__init__() + self.norm_final = nn.LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + ) + self.linear = nn.Linear( + hidden_size, + patch_size * patch_size * out_channels, + bias=True, + ) + nn.init.zeros_(self.linear.weight) + nn.init.zeros_(self.linear.bias) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear( + min(hidden_size, 1024), + hidden_size, + bias=True, + ), + ) + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def forward(self, x, c): + scale = self.adaLN_modulation(c) + x = modulate(self.norm_final(x), scale) + x = self.linear(x) + return x + + +class RopeEmbedder: + def __init__( + self, + theta: float = 10000.0, + axes_dims: List[int] = [16, 56, 56], + axes_lens: List[int] = [1, 512, 512], + ): + super().__init__() + self.theta = theta + self.axes_dims = axes_dims + self.axes_lens = axes_lens + self.freqs_cis = NextDiT.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta) + + def __call__(self, ids: torch.Tensor): + device = ids.device + self.freqs_cis = [freqs_cis.to(ids.device) for freqs_cis in self.freqs_cis] + result = [] + for i in range(len(self.axes_dims)): + freqs = self.freqs_cis[i].to(ids.device) + index = ids[:, :, i : i + 1].repeat(1, 1, freqs.shape[-1]).to(torch.int64) + result.append(torch.gather(freqs.unsqueeze(0).repeat(index.shape[0], 1, 1), dim=1, index=index)) + return torch.cat(result, dim=-1) + + +class NextDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_refiner_layers: int = 2, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + axes_dims: List[int] = [16, 56, 56], + axes_lens: List[int] = [1, 512, 512], + use_flash_attn=False, + use_sage_attn=False, + ) -> None: + """ + Initialize the NextDiT model. + + Args: + patch_size (int): Patch size of the input features. + in_channels (int): Number of input channels. + dim (int): Hidden size of the input features. + n_layers (int): Number of Transformer layers. + n_refiner_layers (int): Number of refiner layers. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): Multiple of the hidden size. + ffn_dim_multiplier (Optional[float]): Dimension multiplier for the + feedforward layer. + norm_eps (float): Epsilon value for normalization. + qk_norm (bool): Whether to use query key normalization. + cap_feat_dim (int): Dimension of the caption features. + axes_dims (List[int]): List of dimensions for the axes. + axes_lens (List[int]): List of lengths for the axes. + use_flash_attn (bool): Whether to use Flash Attention. + use_sage_attn (bool): Whether to use Sage Attention. Sage Attention only supports inference. + + Returns: + None + """ + super().__init__() + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + + self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.cap_embedder = nn.Sequential( + RMSNorm(cap_feat_dim, eps=norm_eps), + nn.Linear( + cap_feat_dim, + dim, + bias=True, + ), + ) + + nn.init.trunc_normal_(self.cap_embedder[1].weight, std=0.02) + nn.init.zeros_(self.cap_embedder[1].bias) + + self.context_refiner = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + modulation=False, + ) + for layer_id in range(n_refiner_layers) + ] + ) + + self.x_embedder = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + ) + nn.init.xavier_uniform_(self.x_embedder.weight) + nn.init.constant_(self.x_embedder.bias, 0.0) + + self.noise_refiner = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + modulation=True, + ) + for layer_id in range(n_refiner_layers) + ] + ) + + + self.layers = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + use_flash_attn=use_flash_attn, + use_sage_attn=use_sage_attn, + ) + for layer_id in range(n_layers) + ] + ) + self.norm_final = RMSNorm(dim, eps=norm_eps) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + + assert (dim // n_heads) == sum(axes_dims) + self.axes_dims = axes_dims + self.axes_lens = axes_lens + self.rope_embedder = RopeEmbedder(axes_dims=axes_dims, axes_lens=axes_lens) + self.dim = dim + self.n_heads = n_heads + + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False # TODO: not yet supported + self.blocks_to_swap = None # TODO: not yet supported + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def enable_gradient_checkpointing(self, cpu_offload: bool = False): + self.gradient_checkpointing = True + self.cpu_offload_checkpointing = cpu_offload + + self.t_embedder.enable_gradient_checkpointing() + + for block in self.layers + self.context_refiner + self.noise_refiner: + block.enable_gradient_checkpointing(cpu_offload=cpu_offload) + + self.final_layer.enable_gradient_checkpointing() + + print(f"Lumina: Gradient checkpointing enabled. CPU offload: {cpu_offload}") + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.cpu_offload_checkpointing = False + + self.t_embedder.disable_gradient_checkpointing() + + for block in self.layers + self.context_refiner + self.noise_refiner: + block.disable_gradient_checkpointing() + + self.final_layer.disable_gradient_checkpointing() + + print("Lumina: Gradient checkpointing disabled.") + + def unpatchify( + self, + x: Tensor, + width: int, + height: int, + encoder_seq_lengths: List[int], + seq_lengths: List[int], + ) -> Tensor: + """ + Unpatchify the input tensor and embed the caption features. + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + + Args: + x (Tensor): Input tensor. + width (int): Width of the input tensor. + height (int): Height of the input tensor. + encoder_seq_lengths (List[int]): List of encoder sequence lengths. + seq_lengths (List[int]): List of sequence lengths + + Returns: + output: (N, C, H, W) + """ + pH = pW = self.patch_size + + output = [] + for i, (encoder_seq_len, seq_len) in enumerate(zip(encoder_seq_lengths, seq_lengths)): + output.append( + x[i][encoder_seq_len:seq_len] + .view(height // pH, width // pW, pH, pW, self.out_channels) + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + output = torch.stack(output, dim=0) + + return output + + def patchify_and_embed( + self, + x: Tensor, + cap_feats: Tensor, + cap_mask: Tensor, + t: Tensor, + ) -> Tuple[Tensor, Tensor, Tensor, List[int], List[int]]: + """ + Patchify and embed the input image and caption features. + + Args: + x: (N, C, H, W) image latents + cap_feats: (N, C, D) caption features + cap_mask: (N, C, D) caption attention mask + t: (N), T timesteps + + Returns: + Tuple[Tensor, Tensor, Tensor, List[int], List[int]]: + + return x, attention_mask, freqs_cis, l_effective_cap_len, seq_lengths + """ + bsz, channels, height, width = x.shape + pH = pW = self.patch_size + device = x.device + + l_effective_cap_len = cap_mask.sum(dim=1).tolist() + encoder_seq_len = cap_mask.shape[1] + image_seq_len = (height // self.patch_size) * (width // self.patch_size) + + seq_lengths = [cap_seq_len + image_seq_len for cap_seq_len in l_effective_cap_len] + max_seq_len = max(seq_lengths) + + position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device) + + for i, (cap_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): + H_tokens, W_tokens = height // pH, width // pW + + position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device) + position_ids[i, cap_len:seq_len, 0] = cap_len + + row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten() + col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten() + + position_ids[i, cap_len:seq_len, 1] = row_ids + position_ids[i, cap_len:seq_len, 2] = col_ids + + # Get combined rotary embeddings + freqs_cis = self.rope_embedder(position_ids) + + # Create separate rotary embeddings for captions and images + cap_freqs_cis = torch.zeros( + bsz, + encoder_seq_len, + freqs_cis.shape[-1], + device=device, + dtype=freqs_cis.dtype, + ) + img_freqs_cis = torch.zeros( + bsz, + image_seq_len, + freqs_cis.shape[-1], + device=device, + dtype=freqs_cis.dtype, + ) + + for i, (cap_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): + cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len] + img_freqs_cis[i, :image_seq_len] = freqs_cis[i, cap_len:seq_len] + + # Refine caption context + for layer in self.context_refiner: + cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis) + + x = x.view(bsz, channels, height // pH, pH, width // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2) + + # x.shape[1] == image_seq_len after patchify, so this was assigning to itself. + # The mask can be set without a loop since all samples have the same image_seq_len. + x_mask = torch.ones(bsz, image_seq_len, dtype=torch.bool, device=device) + + x = self.x_embedder(x) + + # Refine image context + for layer in self.noise_refiner: + x = layer(x, x_mask, img_freqs_cis, t) + + joint_hidden_states = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x.dtype) + attention_mask = torch.zeros(bsz, max_seq_len, dtype=torch.bool, device=device) + for i, (cap_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): + attention_mask[i, :seq_len] = True + joint_hidden_states[i, :cap_len] = cap_feats[i, :cap_len] + joint_hidden_states[i, cap_len:seq_len] = x[i] + + x = joint_hidden_states + + return x, attention_mask, freqs_cis, l_effective_cap_len, seq_lengths + + def forward(self, x: Tensor, t: Tensor, cap_feats: Tensor, cap_mask: Tensor) -> Tensor: + """ + Forward pass of NextDiT. + Args: + x: (N, C, H, W) image latents + t: (N,) tensor of diffusion timesteps + cap_feats: (N, L, D) caption features + cap_mask: (N, L) caption attention mask + + Returns: + x: (N, C, H, W) denoised latents + """ + _, _, height, width = x.shape # B, C, H, W + t = self.t_embedder(t) # (N, D) + cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute + + x, mask, freqs_cis, l_effective_cap_len, seq_lengths = self.patchify_and_embed(x, cap_feats, cap_mask, t) + + if not self.blocks_to_swap: + for layer in self.layers: + x = layer(x, mask, freqs_cis, t) + else: + for block_idx, layer in enumerate(self.layers): + self.offloader_main.wait_for_block(block_idx) + + x = layer(x, mask, freqs_cis, t) + + self.offloader_main.submit_move_blocks(self.layers, block_idx) + + x = self.final_layer(x, t) + x = self.unpatchify(x, width, height, l_effective_cap_len, seq_lengths) + + return x + + def forward_with_cfg( + self, + x: Tensor, + t: Tensor, + cap_feats: Tensor, + cap_mask: Tensor, + cfg_scale: float, + cfg_trunc: float = 0.25, + renorm_cfg: float = 1.0, + ): + """ + Forward pass of NextDiT, but also batches the unconditional forward pass + for classifier-free guidance. + """ + # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + half = x[: len(x) // 2] + if t[0] < cfg_trunc: + combined = torch.cat([half, half], dim=0) # [2, 16, 128, 128] + assert ( + cap_mask.shape[0] == combined.shape[0] + ), f"caption attention mask shape: {cap_mask.shape[0]} latents shape: {combined.shape[0]}" + model_out = self.forward(x, t, cap_feats, cap_mask) # [2, 16, 128, 128] + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + eps, rest = ( + model_out[:, : self.in_channels], + model_out[:, self.in_channels :], + ) + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + if float(renorm_cfg) > 0.0: + ori_pos_norm = torch.linalg.vector_norm(cond_eps, dim=tuple(range(1, len(cond_eps.shape))), keepdim=True) + max_new_norm = ori_pos_norm * float(renorm_cfg) + new_pos_norm = torch.linalg.vector_norm(half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True) + if new_pos_norm >= max_new_norm: + half_eps = half_eps * (max_new_norm / new_pos_norm) + else: + combined = half + model_out = self.forward( + combined, + t[: len(x) // 2], + cap_feats[: len(x) // 2], + cap_mask[: len(x) // 2], + ) + eps, rest = ( + model_out[:, : self.in_channels], + model_out[:, self.in_channels :], + ) + half_eps = eps + + output = torch.cat([half_eps, half_eps], dim=0) + return output + + @staticmethod + def precompute_freqs_cis( + dim: List[int], + end: List[int], + theta: float = 10000.0, + ) -> List[Tensor]: + """ + Precompute the frequency tensor for complex exponentials (cis) with + given dimensions. + + This function calculates a frequency tensor with complex exponentials + using the given dimension 'dim' and the end index 'end'. The 'theta' + parameter scales the frequencies. The returned tensor contains complex + values in complex64 data type. + + Args: + dim (list): Dimension of the frequency tensor. + end (list): End index for precomputing frequencies. + theta (float, optional): Scaling factor for frequency computation. + Defaults to 10000.0. + + Returns: + List[torch.Tensor]: Precomputed frequency tensor with complex + exponentials. + """ + freqs_cis = [] + freqs_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64 + + for i, (d, e) in enumerate(zip(dim, end)): + pos = torch.arange(e, dtype=freqs_dtype, device="cpu") + freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=freqs_dtype, device="cpu") / d)) + freqs = torch.outer(pos, freqs) + freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs) # [S, D/2] + freqs_cis.append(freqs_cis_i) + + return freqs_cis + + def parameter_count(self) -> int: + total_params = 0 + + def _recursive_count_params(module): + nonlocal total_params + for param in module.parameters(recurse=False): + total_params += param.numel() + for submodule in module.children(): + _recursive_count_params(submodule) + + _recursive_count_params(self) + return total_params + + def get_fsdp_wrap_module_list(self) -> List[nn.Module]: + return list(self.layers) + + def get_checkpointing_wrap_module_list(self) -> List[nn.Module]: + return list(self.layers) + + def enable_block_swap(self, blocks_to_swap: int, device: torch.device): + """ + Enable block swapping to reduce memory usage during inference. + + Args: + num_blocks (int): Number of blocks to swap between CPU and device + device (torch.device): Device to use for computation + """ + self.blocks_to_swap = blocks_to_swap + + # Calculate how many blocks to swap from main layers + + assert blocks_to_swap <= len(self.layers) - 2, ( + f"Cannot swap more than {len(self.layers) - 2} main blocks. " + f"Requested {blocks_to_swap} blocks." + ) + + self.offloader_main = custom_offloading_utils.ModelOffloader( + self.layers, blocks_to_swap, device, debug=False + ) + + def move_to_device_except_swap_blocks(self, device: torch.device): + """ + Move the model to the device except for blocks that will be swapped. + This reduces temporary memory usage during model loading. + + Args: + device (torch.device): Device to move the model to + """ + if self.blocks_to_swap: + save_layers = self.layers + self.layers = nn.ModuleList([]) + + self.to(device) + + if self.blocks_to_swap: + self.layers = save_layers + + def prepare_block_swap_before_forward(self): + """ + Prepare blocks for swapping before forward pass. + """ + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + + self.offloader_main.prepare_block_devices_before_forward(self.layers) + + +############################################################################# +# NextDiT Configs # +############################################################################# + + +def NextDiT_2B_GQA_patch2_Adaln_Refiner(params: Optional[LuminaParams] = None, **kwargs): + if params is None: + params = LuminaParams.get_2b_config() + + return NextDiT( + patch_size=params.patch_size, + in_channels=params.in_channels, + dim=params.dim, + n_layers=params.n_layers, + n_heads=params.n_heads, + n_kv_heads=params.n_kv_heads, + axes_dims=params.axes_dims, + axes_lens=params.axes_lens, + qk_norm=params.qk_norm, + ffn_dim_multiplier=params.ffn_dim_multiplier, + norm_eps=params.norm_eps, + cap_feat_dim=params.cap_feat_dim, + **kwargs, + ) + + +def NextDiT_3B_GQA_patch2_Adaln_Refiner(**kwargs): + return NextDiT( + patch_size=2, + dim=2592, + n_layers=30, + n_heads=24, + n_kv_heads=8, + axes_dims=[36, 36, 36], + axes_lens=[300, 512, 512], + **kwargs, + ) + + +def NextDiT_4B_GQA_patch2_Adaln_Refiner(**kwargs): + return NextDiT( + patch_size=2, + dim=2880, + n_layers=32, + n_heads=24, + n_kv_heads=8, + axes_dims=[40, 40, 40], + axes_lens=[300, 512, 512], + **kwargs, + ) + + +def NextDiT_7B_GQA_patch2_Adaln_Refiner(**kwargs): + return NextDiT( + patch_size=2, + dim=3840, + n_layers=32, + n_heads=32, + n_kv_heads=8, + axes_dims=[40, 40, 40], + axes_lens=[300, 512, 512], + **kwargs, + ) \ No newline at end of file diff --git a/vendor/sd-scripts/library/lumina_train_util.py b/vendor/sd-scripts/library/lumina_train_util.py new file mode 100644 index 0000000..afbfc24 --- /dev/null +++ b/vendor/sd-scripts/library/lumina_train_util.py @@ -0,0 +1,1093 @@ +import inspect +import argparse +import math +import os +import numpy as np +import time +from typing import Callable, Dict, List, Optional, Tuple, Any, Union, Generator + +import torch +from torch import Tensor +from accelerate import Accelerator, PartialState +from transformers import Gemma2Model +from tqdm import tqdm +from PIL import Image +from safetensors.torch import save_file + +from library import lumina_models, strategy_base, strategy_lumina, train_util +from library.flux_models import AutoEncoder +from library.device_utils import init_ipex, clean_memory_on_device +from library.sd3_train_utils import FlowMatchEulerDiscreteScheduler +from library.safetensors_utils import mem_eff_save_file + +init_ipex() + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +# region sample images + + +def batchify( + prompt_dicts, batch_size=None +) -> Generator[list[dict[str, str]], None, None]: + """ + Group prompt dictionaries into batches with configurable batch size. + + Args: + prompt_dicts (list): List of dictionaries containing prompt parameters. + batch_size (int, optional): Number of prompts per batch. Defaults to None. + + Yields: + list[dict[str, str]]: Batch of prompts. + """ + # Validate batch_size + if batch_size is not None: + if not isinstance(batch_size, int) or batch_size <= 0: + raise ValueError("batch_size must be a positive integer or None") + + # Group prompts by their parameters + batches = {} + for prompt_dict in prompt_dicts: + # Extract parameters + width = int(prompt_dict.get("width", 1024)) + height = int(prompt_dict.get("height", 1024)) + height = max(64, height - height % 8) # round to divisible by 8 + width = max(64, width - width % 8) # round to divisible by 8 + guidance_scale = float(prompt_dict.get("scale", 3.5)) + sample_steps = int(prompt_dict.get("sample_steps", 38)) + cfg_trunc_ratio = float(prompt_dict.get("cfg_trunc_ratio", 0.25)) + renorm_cfg = float(prompt_dict.get("renorm_cfg", 1.0)) + seed = prompt_dict.get("seed", None) + seed = int(seed) if seed is not None else None + + # Create a key based on the parameters + key = ( + width, + height, + guidance_scale, + seed, + sample_steps, + cfg_trunc_ratio, + renorm_cfg, + ) + + # Add the prompt_dict to the corresponding batch + if key not in batches: + batches[key] = [] + batches[key].append(prompt_dict) + + # Yield each batch with its parameters + for key in batches: + prompts = batches[key] + if batch_size is None: + # Yield the entire group as a single batch + yield prompts + else: + # Split the group into batches of size `batch_size` + start = 0 + while start < len(prompts): + end = start + batch_size + batch = prompts[start:end] + yield batch + start = end + + +@torch.no_grad() +def sample_images( + accelerator: Accelerator, + args: argparse.Namespace, + epoch: int, + global_step: int, + nextdit: lumina_models.NextDiT, + vae: AutoEncoder, + gemma2_model: Gemma2Model, + sample_prompts_gemma2_outputs: dict[str, Tuple[Tensor, Tensor, Tensor]], + prompt_replacement: Optional[Tuple[str, str]] = None, + controlnet=None, +): + """ + Generate sample images using the NextDiT model. + + Args: + accelerator (Accelerator): Accelerator instance. + args (argparse.Namespace): Command-line arguments. + epoch (int): Current epoch number. + global_step (int): Current global step number. + nextdit (lumina_models.NextDiT): The NextDiT model instance. + vae (AutoEncoder): The VAE module. + gemma2_model (Gemma2Model): The Gemma2 model instance. + sample_prompts_gemma2_outputs (dict[str, Tuple[Tensor, Tensor, Tensor]]): + Dictionary of tuples containing the encoded prompts, text masks, and timestep for each sample. + prompt_replacement (Optional[Tuple[str, str]], optional): + Tuple containing the prompt and negative prompt replacements. Defaults to None. + controlnet (): ControlNet model, not yet supported + + Returns: + None + """ + if global_step == 0: + if not args.sample_at_first: + return + else: + if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: + return + if args.sample_every_n_epochs is not None: + # sample_every_n_steps は無視する + if epoch is None or epoch % args.sample_every_n_epochs != 0: + return + else: + if ( + global_step % args.sample_every_n_steps != 0 or epoch is not None + ): # steps is not divisible or end of epoch + return + + assert ( + args.sample_prompts is not None + ), "No sample prompts found. Provide `--sample_prompts` / サンプルプロンプトが見つかりません。`--sample_prompts` を指定してください" + + logger.info("") + logger.info( + f"generating sample images at step / サンプル画像生成 ステップ: {global_step}" + ) + if ( + not os.path.isfile(args.sample_prompts) + and sample_prompts_gemma2_outputs is None + ): + logger.error( + f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}" + ) + return + + distributed_state = ( + PartialState() + ) # for multi gpu distributed inference. this is a singleton, so it's safe to use it here + + # unwrap nextdit and gemma2_model + nextdit = accelerator.unwrap_model(nextdit) + if gemma2_model is not None: + gemma2_model = accelerator.unwrap_model(gemma2_model) + # if controlnet is not None: + # controlnet = accelerator.unwrap_model(controlnet) + # print([(te.parameters().__next__().device if te is not None else None) for te in text_encoders]) + + prompts = train_util.load_prompts(args.sample_prompts) + + save_dir = args.output_dir + "/sample" + os.makedirs(save_dir, exist_ok=True) + + # save random state to restore later + rng_state = torch.get_rng_state() + cuda_rng_state = None + try: + cuda_rng_state = ( + torch.cuda.get_rng_state() if torch.cuda.is_available() else None + ) + except Exception: + pass + + batch_size = args.sample_batch_size or args.train_batch_size or 1 + + if distributed_state.num_processes <= 1: + # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts. + # TODO: batch prompts together with buckets of image sizes + for prompt_dicts in batchify(prompts, batch_size): + sample_image_inference( + accelerator, + args, + nextdit, + gemma2_model, + vae, + save_dir, + prompt_dicts, + epoch, + global_step, + sample_prompts_gemma2_outputs, + prompt_replacement, + controlnet, + ) + else: + # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available) + # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical. + per_process_prompts = [] # list of lists + for i in range(distributed_state.num_processes): + per_process_prompts.append(prompts[i :: distributed_state.num_processes]) + + with distributed_state.split_between_processes( + per_process_prompts + ) as prompt_dict_lists: + # TODO: batch prompts together with buckets of image sizes + for prompt_dicts in batchify(prompt_dict_lists[0], batch_size): + sample_image_inference( + accelerator, + args, + nextdit, + gemma2_model, + vae, + save_dir, + prompt_dicts, + epoch, + global_step, + sample_prompts_gemma2_outputs, + prompt_replacement, + controlnet, + ) + + torch.set_rng_state(rng_state) + if cuda_rng_state is not None: + torch.cuda.set_rng_state(cuda_rng_state) + + clean_memory_on_device(accelerator.device) + + +@torch.no_grad() +def sample_image_inference( + accelerator: Accelerator, + args: argparse.Namespace, + nextdit: lumina_models.NextDiT, + gemma2_model: list[Gemma2Model], + vae: AutoEncoder, + save_dir: str, + prompt_dicts: list[Dict[str, str]], + epoch: int, + global_step: int, + sample_prompts_gemma2_outputs: dict[str, Tuple[Tensor, Tensor, Tensor]], + prompt_replacement: Optional[Tuple[str, str]] = None, + controlnet=None, +): + """ + Generates sample images + + Args: + accelerator (Accelerator): Accelerator object + args (argparse.Namespace): Arguments object + nextdit (lumina_models.NextDiT): NextDiT model + gemma2_model (list[Gemma2Model]): Gemma2 model + vae (AutoEncoder): VAE model + save_dir (str): Directory to save images + prompt_dict (Dict[str, str]): Prompt dictionary + epoch (int): Epoch number + steps (int): Number of steps to run + sample_prompts_gemma2_outputs (List[Tuple[Tensor, Tensor, Tensor]]): List of tuples containing Gemma 2 outputs + prompt_replacement (Optional[Tuple[str, str]], optional): Replacement for positive and negative prompt. Defaults to None. + + Returns: + None + """ + + # encode prompts + tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() + + assert isinstance(tokenize_strategy, strategy_lumina.LuminaTokenizeStrategy) + assert isinstance(encoding_strategy, strategy_lumina.LuminaTextEncodingStrategy) + + text_conds = [] + + # assuming seed, width, height, sample steps, guidance are the same + width = int(prompt_dicts[0].get("width", 1024)) + height = int(prompt_dicts[0].get("height", 1024)) + height = max(64, height - height % 8) # round to divisible by 8 + width = max(64, width - width % 8) # round to divisible by 8 + + guidance_scale = float(prompt_dicts[0].get("scale", 3.5)) + cfg_trunc_ratio = float(prompt_dicts[0].get("cfg_trunc_ratio", 0.25)) + renorm_cfg = float(prompt_dicts[0].get("renorm_cfg", 1.0)) + sample_steps = int(prompt_dicts[0].get("sample_steps", 36)) + seed = prompt_dicts[0].get("seed", None) + seed = int(seed) if seed is not None else None + assert seed is None or seed > 0, f"Invalid seed {seed}" + generator = torch.Generator(device=accelerator.device) + if seed is not None: + generator.manual_seed(seed) + + for prompt_dict in prompt_dicts: + controlnet_image = prompt_dict.get("controlnet_image") + prompt: str = prompt_dict.get("prompt", "") + negative_prompt = prompt_dict.get("negative_prompt", "") + # sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler) + + if prompt_replacement is not None: + prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1]) + if negative_prompt is not None: + negative_prompt = negative_prompt.replace( + prompt_replacement[0], prompt_replacement[1] + ) + + if negative_prompt is None: + negative_prompt = "" + logger.info(f"prompt: {prompt}") + logger.info(f"negative_prompt: {negative_prompt}") + logger.info(f"height: {height}") + logger.info(f"width: {width}") + logger.info(f"sample_steps: {sample_steps}") + logger.info(f"scale: {guidance_scale}") + logger.info(f"trunc: {cfg_trunc_ratio}") + logger.info(f"renorm: {renorm_cfg}") + # logger.info(f"sample_sampler: {sampler_name}") + + + # No need to add system prompt here, as it has been handled in the tokenize_strategy + + # Get sample prompts from cache, fallback to live encoding + gemma2_conds = None + neg_gemma2_conds = None + + if sample_prompts_gemma2_outputs and prompt in sample_prompts_gemma2_outputs: + gemma2_conds = sample_prompts_gemma2_outputs[prompt] + logger.info(f"Using cached Gemma2 outputs for prompt: {prompt}") + + if sample_prompts_gemma2_outputs and negative_prompt in sample_prompts_gemma2_outputs: + neg_gemma2_conds = sample_prompts_gemma2_outputs[negative_prompt] + logger.info(f"Using cached Gemma2 outputs for negative prompt: {negative_prompt}") + + # Only encode if not found in cache + if gemma2_conds is None and gemma2_model is not None: + tokens_and_masks = tokenize_strategy.tokenize(prompt) + gemma2_conds = encoding_strategy.encode_tokens( + tokenize_strategy, gemma2_model, tokens_and_masks + ) + + if neg_gemma2_conds is None and gemma2_model is not None: + tokens_and_masks = tokenize_strategy.tokenize(negative_prompt, is_negative=True) + neg_gemma2_conds = encoding_strategy.encode_tokens( + tokenize_strategy, gemma2_model, tokens_and_masks + ) + + if gemma2_conds is None or neg_gemma2_conds is None: + logger.error(f"Cannot generate sample: no cached outputs and no text encoder available for prompt: {prompt}") + continue + + # Unpack Gemma2 outputs + gemma2_hidden_states, _, gemma2_attn_mask = gemma2_conds + neg_gemma2_hidden_states, _, neg_gemma2_attn_mask = neg_gemma2_conds + + text_conds.append( + ( + gemma2_hidden_states.squeeze(0), + gemma2_attn_mask.squeeze(0), + neg_gemma2_hidden_states.squeeze(0), + neg_gemma2_attn_mask.squeeze(0), + ) + ) + + # Stack conditioning + cond_hidden_states = torch.stack([text_cond[0] for text_cond in text_conds]).to( + accelerator.device + ) + cond_attn_masks = torch.stack([text_cond[1] for text_cond in text_conds]).to( + accelerator.device + ) + uncond_hidden_states = torch.stack([text_cond[2] for text_cond in text_conds]).to( + accelerator.device + ) + uncond_attn_masks = torch.stack([text_cond[3] for text_cond in text_conds]).to( + accelerator.device + ) + + # sample image + weight_dtype = vae.dtype # TOFO give dtype as argument + latent_height = height // 8 + latent_width = width // 8 + latent_channels = 16 + noise = torch.randn( + 1, + latent_channels, + latent_height, + latent_width, + device=accelerator.device, + dtype=weight_dtype, + generator=generator, + ) + noise = noise.repeat(cond_hidden_states.shape[0], 1, 1, 1) + + scheduler = FlowMatchEulerDiscreteScheduler(shift=6.0) + timesteps, num_inference_steps = retrieve_timesteps( + scheduler, num_inference_steps=sample_steps + ) + + # if controlnet_image is not None: + # controlnet_image = Image.open(controlnet_image).convert("RGB") + # controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS) + # controlnet_image = torch.from_numpy((np.array(controlnet_image) / 127.5) - 1) + # controlnet_image = controlnet_image.permute(2, 0, 1).unsqueeze(0).to(weight_dtype).to(accelerator.device) + + with accelerator.autocast(): + x = denoise( + scheduler, + nextdit, + noise, + cond_hidden_states, + cond_attn_masks, + uncond_hidden_states, + uncond_attn_masks, + timesteps=timesteps, + guidance_scale=guidance_scale, + cfg_trunc_ratio=cfg_trunc_ratio, + renorm_cfg=renorm_cfg, + ) + + # Latent to image + clean_memory_on_device(accelerator.device) + org_vae_device = vae.device # will be on cpu + vae.to(accelerator.device) # distributed_state.device is same as accelerator.device + for img, prompt_dict in zip(x, prompt_dicts): + + img = (img / vae.scale_factor) + vae.shift_factor + + with accelerator.autocast(): + # Add a single batch image for the VAE to decode + img = vae.decode(img.unsqueeze(0)) + + img = img.clamp(-1, 1) + img = img.permute(0, 2, 3, 1) # B, H, W, C + # Scale images back to 0 to 255 + img = (127.5 * (img + 1.0)).float().cpu().numpy().astype(np.uint8) + + # Get single image + image = Image.fromarray(img[0]) + + # adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list + # but adding 'enum' to the filename should be enough + + ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime()) + num_suffix = f"e{epoch:06d}" if epoch is not None else f"{global_step:06d}" + seed_suffix = "" if seed is None else f"_{seed}" + i: int = int(prompt_dict.get("enum", 0)) + img_filename = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png" + image.save(os.path.join(save_dir, img_filename)) + + # send images to wandb if enabled + if "wandb" in [tracker.name for tracker in accelerator.trackers]: + wandb_tracker = accelerator.get_tracker("wandb") + + import wandb + + # not to commit images to avoid inconsistency between training and logging steps + wandb_tracker.log( + {f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False + ) # positive prompt as a caption + + vae.to(org_vae_device) + clean_memory_on_device(accelerator.device) + + +def time_shift(mu: float, sigma: float, t: torch.Tensor): + """Apply time shifting to timesteps.""" + t = math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + return t + + +def get_lin_function( + x1: float = 256, x2: float = 4096, y1: float = 0.5, y2: float = 1.15 +) -> Callable[[float], float]: + """ + Get linear function for resolution-dependent shifting. + + Args: + image_seq_len, + x1 base_seq_len: int = 256, + y2 max_seq_len: int = 4096, + y1 base_shift: float = 0.5, + y2 max_shift: float = 1.15, + + Return: + Callable[[float], float]: linear function + """ + m = (y2 - y1) / (x2 - x1) + b = y1 - m * x1 + return lambda x: m * x + b + + +def get_schedule( + num_steps: int, + image_seq_len: int, + base_shift: float = 0.5, + max_shift: float = 1.15, + shift: bool = True, +) -> list[float]: + """ + Get timesteps schedule + + Args: + num_steps (int): Number of steps in the schedule. + image_seq_len (int): Sequence length of the image. + base_shift (float, optional): Base shift value. Defaults to 0.5. + max_shift (float, optional): Maximum shift value. Defaults to 1.15. + shift (bool, optional): Whether to shift the schedule. Defaults to True. + + Return: + List[float]: timesteps schedule + """ + timesteps = torch.linspace(1, 1 / num_steps, num_steps) + + # shifting the schedule to favor high timesteps for higher signal images + if shift: + # eastimate mu based on linear estimation between two points + mu = get_lin_function(y1=base_shift, y2=max_shift, x1=256, x2=4096)( + image_seq_len + ) + timesteps = torch.clamp(timesteps, min=1e-7).to(timesteps.device) + timesteps = time_shift(mu, 1.0, timesteps) + + return timesteps.tolist() + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +) -> Tuple[torch.Tensor, int]: + r""" + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) + if timesteps is not None: + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + +def denoise( + scheduler, + model: lumina_models.NextDiT, + img: Tensor, + txt: Tensor, + txt_mask: Tensor, + neg_txt: Tensor, + neg_txt_mask: Tensor, + timesteps: Union[List[float], torch.Tensor], + guidance_scale: float = 4.0, + cfg_trunc_ratio: float = 0.25, + renorm_cfg: float = 1.0, +): + """ + Denoise an image using the NextDiT model. + + Args: + scheduler (): + Noise scheduler + model (lumina_models.NextDiT): The NextDiT model instance. + img (Tensor): + The input image latent tensor. + txt (Tensor): + The input text tensor. + txt_mask (Tensor): + The input text mask tensor. + neg_txt (Tensor): + The negative input txt tensor + neg_txt_mask (Tensor): + The negative input text mask tensor. + timesteps (List[Union[float, torch.FloatTensor]]): + A list of timesteps for the denoising process. + guidance_scale (float, optional): + The guidance scale for the denoising process. Defaults to 4.0. + cfg_trunc_ratio (float, optional): + The ratio of the timestep interval to apply normalization-based guidance scale. + renorm_cfg (float, optional): + The factor to limit the maximum norm after guidance. Default: 1.0 + Returns: + img (Tensor): Denoised latent tensor + """ + + for i, t in enumerate(tqdm(timesteps)): + model.prepare_block_swap_before_forward() + + # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image + current_timestep = 1 - t / scheduler.config.num_train_timesteps + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + current_timestep = current_timestep * torch.ones( + img.shape[0], device=img.device + ) + + noise_pred_cond = model( + img, + current_timestep, + cap_feats=txt, # Gemma2的hidden states作为caption features + cap_mask=txt_mask.to(dtype=torch.int32), # Gemma2的attention mask + ) + + # compute whether to apply classifier-free guidance based on current timestep + if current_timestep[0] < cfg_trunc_ratio: + model.prepare_block_swap_before_forward() + noise_pred_uncond = model( + img, + current_timestep, + cap_feats=neg_txt, # Gemma2的hidden states作为caption features + cap_mask=neg_txt_mask.to(dtype=torch.int32), # Gemma2的attention mask + ) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_cond - noise_pred_uncond + ) + # apply normalization after classifier-free guidance + if float(renorm_cfg) > 0.0: + cond_norm = torch.linalg.vector_norm( + noise_pred_cond, + dim=tuple(range(1, len(noise_pred_cond.shape))), + keepdim=True, + ) + max_new_norms = cond_norm * float(renorm_cfg) + noise_norms = torch.linalg.vector_norm( + noise_pred, dim=tuple(range(1, len(noise_pred.shape))), keepdim=True + ) + # Iterate through batch + for i, (noise_norm, max_new_norm) in enumerate(zip(noise_norms, max_new_norms)): + if noise_norm >= max_new_norm: + noise_pred[i] = noise_pred[i] * (max_new_norm / noise_norm) + else: + noise_pred = noise_pred_cond + + img_dtype = img.dtype + + # compute the previous noisy sample x_t -> x_t-1 + noise_pred = -noise_pred + img = scheduler.step(noise_pred, t, img, return_dict=False)[0] + + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + if img.dtype != img_dtype: + if torch.backends.mps.is_available(): + img = img.to(img_dtype) + + model.prepare_block_swap_before_forward() + return img + + +# endregion + + +# region train +def get_sigmas( + noise_scheduler: FlowMatchEulerDiscreteScheduler, + timesteps: Tensor, + device: torch.device, + n_dim=4, + dtype=torch.float32, +) -> Tensor: + """ + Get sigmas for timesteps + + Args: + noise_scheduler (FlowMatchEulerDiscreteScheduler): The noise scheduler instance. + timesteps (Tensor): A tensor of timesteps for the denoising process. + device (torch.device): The device on which the tensors are stored. + n_dim (int, optional): The number of dimensions for the output tensor. Defaults to 4. + dtype (torch.dtype, optional): The data type for the output tensor. Defaults to torch.float32. + + Returns: + sigmas (Tensor): The sigmas tensor. + """ + sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype) + schedule_timesteps = noise_scheduler.timesteps.to(device) + timesteps = timesteps.to(device) + step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < n_dim: + sigma = sigma.unsqueeze(-1) + return sigma + + +def compute_density_for_timestep_sampling( + weighting_scheme: str, + batch_size: int, + logit_mean: float = None, + logit_std: float = None, + mode_scale: float = None, +): + """ + Compute the density for sampling the timesteps when doing SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + + Args: + weighting_scheme (str): The weighting scheme to use. + batch_size (int): The batch size for the sampling process. + logit_mean (float, optional): The mean of the logit distribution. Defaults to None. + logit_std (float, optional): The standard deviation of the logit distribution. Defaults to None. + mode_scale (float, optional): The mode scale for the mode weighting scheme. Defaults to None. + + Returns: + u (Tensor): The sampled timesteps. + """ + if weighting_scheme == "logit_normal": + # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$). + u = torch.normal( + mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu" + ) + u = torch.nn.functional.sigmoid(u) + elif weighting_scheme == "mode": + u = torch.rand(size=(batch_size,), device="cpu") + u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u) + else: + u = torch.rand(size=(batch_size,), device="cpu") + return u + + +def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None) -> Tensor: + """Computes loss weighting scheme for SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + + Args: + weighting_scheme (str): The weighting scheme to use. + sigmas (Tensor, optional): The sigmas tensor. Defaults to None. + + Returns: + u (Tensor): The sampled timesteps. + """ + if weighting_scheme == "sigma_sqrt": + weighting = (sigmas**-2.0).float() + elif weighting_scheme == "cosmap": + bot = 1 - 2 * sigmas + 2 * sigmas**2 + weighting = 2 / (math.pi * bot) + else: + weighting = torch.ones_like(sigmas) + return weighting + +# mainly copied from flux_train_utils.get_noisy_model_input_and_timesteps +def get_noisy_model_input_and_timesteps( + args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + bsz, _, h, w = latents.shape + assert bsz > 0, "Batch size not large enough" + num_timesteps = noise_scheduler.config.num_train_timesteps + if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid": + # Simple random sigma-based noise sampling + if args.timestep_sampling == "sigmoid": + # https://github.com/XLabs-AI/x-flux/tree/main + sigmas = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device)) + else: + sigmas = torch.rand((bsz,), device=device) + + timesteps = sigmas * num_timesteps + elif args.timestep_sampling == "shift": + shift = args.discrete_flow_shift + sigmas = torch.randn(bsz, device=device) + sigmas = sigmas * args.sigmoid_scale # larger scale for more uniform sampling + sigmas = sigmas.sigmoid() + sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas) + timesteps = sigmas * num_timesteps + elif args.timestep_sampling == "nextdit_shift": + sigmas = torch.rand((bsz,), device=device) + sigmas = torch.clamp(sigmas, min=1e-7).to(device) + mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2)) + sigmas = time_shift(mu, 1.0, sigmas) + + timesteps = sigmas * num_timesteps + elif args.timestep_sampling == "flux_shift": + sigmas = torch.randn(bsz, device=device) + sigmas = sigmas * args.sigmoid_scale # larger scale for more uniform sampling + sigmas = sigmas.sigmoid() + sigmas = torch.clamp(sigmas, min=1e-7).to(device) + mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2)) # we are pre-packed so must adjust for packed size + sigmas = time_shift(mu, 1.0, sigmas) + timesteps = sigmas * num_timesteps + else: + # Sample a random timestep for each image + # for weighting schemes where we sample timesteps non-uniformly + u = compute_density_for_timestep_sampling( + weighting_scheme=args.weighting_scheme, + batch_size=bsz, + logit_mean=args.logit_mean, + logit_std=args.logit_std, + mode_scale=args.mode_scale, + ) + indices = (u * num_timesteps).long() + timesteps = noise_scheduler.timesteps[indices].to(device=device) + sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype) + + # Broadcast sigmas to latent shape + sigmas = sigmas.view(-1, 1, 1, 1) + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + if args.ip_noise_gamma: + xi = torch.randn_like(latents, device=latents.device, dtype=dtype) + if args.ip_noise_gamma_random_strength: + ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma + else: + ip_noise_gamma = args.ip_noise_gamma + noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi) + else: + noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise + + return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas + + +def apply_model_prediction_type( + args, model_pred: Tensor, noisy_model_input: Tensor, sigmas: Tensor +) -> Tuple[Tensor, Optional[Tensor]]: + """ + Apply model prediction type to the model prediction and the sigmas. + + Args: + args (argparse.Namespace): Arguments. + model_pred (Tensor): Model prediction. + noisy_model_input (Tensor): Noisy model input. + sigmas (Tensor): Sigmas. + + Return: + Tuple[Tensor, Optional[Tensor]]: + """ + weighting = None + if args.model_prediction_type == "raw": + pass + elif args.model_prediction_type == "additive": + # add the model_pred to the noisy_model_input + model_pred = model_pred + noisy_model_input + elif args.model_prediction_type == "sigma_scaled": + # apply sigma scaling + model_pred = model_pred * (-sigmas) + noisy_model_input + + # these weighting schemes use a uniform timestep sampling + # and instead post-weight the loss + weighting = compute_loss_weighting_for_sd3( + weighting_scheme=args.weighting_scheme, sigmas=sigmas + ) + + return model_pred, weighting + + +def save_models( + ckpt_path: str, + lumina: lumina_models.NextDiT, + sai_metadata: Dict[str, Any], + save_dtype: Optional[torch.dtype] = None, + use_mem_eff_save: bool = False, +): + """ + Save the model to the checkpoint path. + + Args: + ckpt_path (str): Path to the checkpoint. + lumina (lumina_models.NextDiT): NextDIT model. + sai_metadata (Optional[dict]): Metadata for the SAI model. + save_dtype (Optional[torch.dtype]): Data + + Return: + None + """ + state_dict = {} + + def update_sd(prefix, sd): + for k, v in sd.items(): + key = prefix + k + if save_dtype is not None and v.dtype != save_dtype: + v = v.detach().clone().to("cpu").to(save_dtype) + state_dict[key] = v + + update_sd("", lumina.state_dict()) + + if not use_mem_eff_save: + save_file(state_dict, ckpt_path, metadata=sai_metadata) + else: + mem_eff_save_file(state_dict, ckpt_path, metadata=sai_metadata) + + +def save_lumina_model_on_train_end( + args: argparse.Namespace, + save_dtype: torch.dtype, + epoch: int, + global_step: int, + lumina: lumina_models.NextDiT, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec( + None, + args, + False, + False, + False, + is_stable_diffusion_ckpt=True, + lumina="lumina2", + ) + save_models(ckpt_file, lumina, sai_metadata, save_dtype, args.mem_eff_save) + + train_util.save_sd_model_on_train_end_common( + args, True, True, epoch, global_step, sd_saver, None + ) + + +# epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合してている +# on_epoch_end: Trueならepoch終了時、Falseならstep経過時 +def save_lumina_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator: Accelerator, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + lumina: lumina_models.NextDiT, +): + """ + Save the model to the checkpoint path. + + Args: + args (argparse.Namespace): Arguments. + save_dtype (torch.dtype): Data type. + epoch (int): Epoch. + global_step (int): Global step. + lumina (lumina_models.NextDiT): NextDIT model. + + Return: + None + """ + + def sd_saver(ckpt_file: str, epoch_no: int, global_step: int): + sai_metadata = train_util.get_sai_model_spec( + {}, + args, + False, + False, + False, + is_stable_diffusion_ckpt=True, + lumina="lumina2", + ) + save_models(ckpt_file, lumina, sai_metadata, save_dtype, args.mem_eff_save) + + train_util.save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + True, + True, + epoch, + num_train_epochs, + global_step, + sd_saver, + None, + ) + + +# endregion + + +def add_lumina_train_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--gemma2", + type=str, + help="path to gemma2 model (*.sft or *.safetensors), should be float16 / gemma2のパス（*.sftまたは*.safetensors）、float16が前提", + ) + parser.add_argument( + "--ae", + type=str, + help="path to ae (*.sft or *.safetensors) / aeのパス（*.sftまたは*.safetensors）", + ) + parser.add_argument( + "--gemma2_max_token_length", + type=int, + default=None, + help="maximum token length for Gemma2. if omitted, 256" + " / Gemma2の最大トークン長。省略された場合、256になります", + ) + + parser.add_argument( + "--timestep_sampling", + choices=["sigma", "uniform", "sigmoid", "shift", "nextdit_shift", "flux_shift"], + default="shift", + help="Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal, shift of sigmoid, Flux.1 and NextDIT.1 shifting. Default is 'shift'." + " / タイムステップをサンプリングする方法：sigma、random uniform、random normalのsigmoid、sigmoidのシフト、Flux.1、NextDIT.1のシフト。デフォルトは'shift'です。", + ) + parser.add_argument( + "--sigmoid_scale", + type=float, + default=1.0, + help='Scale factor for sigmoid timestep sampling (only used when timestep-sampling is "sigmoid"). / sigmoidタイムステップサンプリングの倍率（timestep-samplingが"sigmoid"の場合のみ有効）。', + ) + parser.add_argument( + "--model_prediction_type", + choices=["raw", "additive", "sigma_scaled"], + default="raw", + help="How to interpret and process the model prediction: " + "raw (use as is), additive (add to noisy input), sigma_scaled (apply sigma scaling)." + " / モデル予測の解釈と処理方法：" + "raw（そのまま使用）、additive（ノイズ入力に加算）、sigma_scaled（シグマスケーリングを適用）。", + ) + parser.add_argument( + "--discrete_flow_shift", + type=float, + default=6.0, + help="Discrete flow shift for the Euler Discrete Scheduler, default is 6.0 / Euler Discrete Schedulerの離散フローシフト、デフォルトは6.0", + ) + parser.add_argument( + "--use_flash_attn", + action="store_true", + help="Use Flash Attention for the model / モデルにFlash Attentionを使用する", + ) + parser.add_argument( + "--use_sage_attn", + action="store_true", + help="Use Sage Attention for the model / モデルにSage Attentionを使用する", + ) + parser.add_argument( + "--system_prompt", + type=str, + default="", + help="System prompt to add to the prompt / プロンプトに追加するシステムプロンプト", + ) + parser.add_argument( + "--sample_batch_size", + type=int, + default=None, + help="Batch size to use for sampling, defaults to --training_batch_size value. Sample batches are bucketed by width, height, guidance scale, and seed / サンプリングに使用するバッチサイズ。デフォルトは --training_batch_size の値です。サンプルバッチは、幅、高さ、ガイダンススケール、シードによってバケット化されます", + ) diff --git a/vendor/sd-scripts/library/lumina_util.py b/vendor/sd-scripts/library/lumina_util.py new file mode 100644 index 0000000..f7f3c82 --- /dev/null +++ b/vendor/sd-scripts/library/lumina_util.py @@ -0,0 +1,259 @@ +import json +import os +from dataclasses import replace +from typing import List, Optional, Tuple, Union + +import einops +import torch +from accelerate import init_empty_weights +from safetensors import safe_open +from safetensors.torch import load_file +from transformers import Gemma2Config, Gemma2Model + +from library.utils import setup_logging +from library import lumina_models, flux_models +from library.safetensors_utils import load_safetensors +import logging + +setup_logging() +logger = logging.getLogger(__name__) + +MODEL_VERSION_LUMINA_V2 = "lumina2" + + +def load_lumina_model( + ckpt_path: str, + dtype: Optional[torch.dtype], + device: torch.device, + disable_mmap: bool = False, + use_flash_attn: bool = False, + use_sage_attn: bool = False, +): + """ + Load the Lumina model from the checkpoint path. + + Args: + ckpt_path (str): Path to the checkpoint. + dtype (torch.dtype): The data type for the model. + device (torch.device): The device to load the model on. + disable_mmap (bool, optional): Whether to disable mmap. Defaults to False. + use_flash_attn (bool, optional): Whether to use flash attention. Defaults to False. + + Returns: + model (lumina_models.NextDiT): The loaded model. + """ + logger.info("Building Lumina") + with torch.device("meta"): + model = lumina_models.NextDiT_2B_GQA_patch2_Adaln_Refiner(use_flash_attn=use_flash_attn, use_sage_attn=use_sage_attn).to( + dtype + ) + + logger.info(f"Loading state dict from {ckpt_path}") + state_dict = load_safetensors(ckpt_path, device=device, disable_mmap=disable_mmap, dtype=dtype) + + # Neta-Lumina support + if "model.diffusion_model.cap_embedder.0.weight" in state_dict: + # remove "model.diffusion_model." prefix + filtered_state_dict = { + k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if k.startswith("model.diffusion_model.") + } + state_dict = filtered_state_dict + + info = model.load_state_dict(state_dict, strict=False, assign=True) + logger.info(f"Loaded Lumina: {info}") + return model + + +def load_ae( + ckpt_path: str, + dtype: torch.dtype, + device: Union[str, torch.device], + disable_mmap: bool = False, +) -> flux_models.AutoEncoder: + """ + Load the AutoEncoder model from the checkpoint path. + + Args: + ckpt_path (str): Path to the checkpoint. + dtype (torch.dtype): The data type for the model. + device (Union[str, torch.device]): The device to load the model on. + disable_mmap (bool, optional): Whether to disable mmap. Defaults to False. + + Returns: + ae (flux_models.AutoEncoder): The loaded model. + """ + logger.info("Building AutoEncoder") + with torch.device("meta"): + # dev and schnell have the same AE params + ae = flux_models.AutoEncoder(flux_models.configs["schnell"].ae_params).to(dtype) + + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=device, disable_mmap=disable_mmap, dtype=dtype) + + # Neta-Lumina support + if "vae.decoder.conv_in.bias" in sd: + # remove "vae." prefix + filtered_sd = {k.replace("vae.", ""): v for k, v in sd.items() if k.startswith("vae.")} + sd = filtered_sd + + info = ae.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded AE: {info}") + return ae + + +def load_gemma2( + ckpt_path: Optional[str], + dtype: torch.dtype, + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[dict] = None, +) -> Gemma2Model: + """ + Load the Gemma2 model from the checkpoint path. + + Args: + ckpt_path (str): Path to the checkpoint. + dtype (torch.dtype): The data type for the model. + device (Union[str, torch.device]): The device to load the model on. + disable_mmap (bool, optional): Whether to disable mmap. Defaults to False. + state_dict (Optional[dict], optional): The state dict to load. Defaults to None. + + Returns: + gemma2 (Gemma2Model): The loaded model + """ + logger.info("Building Gemma2") + GEMMA2_CONFIG = { + "_name_or_path": "google/gemma-2-2b", + "architectures": ["Gemma2Model"], + "attention_bias": False, + "attention_dropout": 0.0, + "attn_logit_softcapping": 50.0, + "bos_token_id": 2, + "cache_implementation": "hybrid", + "eos_token_id": 1, + "final_logit_softcapping": 30.0, + "head_dim": 256, + "hidden_act": "gelu_pytorch_tanh", + "hidden_activation": "gelu_pytorch_tanh", + "hidden_size": 2304, + "initializer_range": 0.02, + "intermediate_size": 9216, + "max_position_embeddings": 8192, + "model_type": "gemma2", + "num_attention_heads": 8, + "num_hidden_layers": 26, + "num_key_value_heads": 4, + "pad_token_id": 0, + "query_pre_attn_scalar": 256, + "rms_norm_eps": 1e-06, + "rope_theta": 10000.0, + "sliding_window": 4096, + "torch_dtype": "float32", + "transformers_version": "4.44.2", + "use_cache": True, + "vocab_size": 256000, + } + + config = Gemma2Config(**GEMMA2_CONFIG) + with init_empty_weights(): + gemma2 = Gemma2Model._from_config(config) + + if state_dict is not None: + sd = state_dict + else: + logger.info(f"Loading state dict from {ckpt_path}") + sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + + for key in list(sd.keys()): + new_key = key.replace("model.", "") + if new_key == key: + break # the model doesn't have annoying prefix + sd[new_key] = sd.pop(key) + + # Neta-Lumina support + if "text_encoders.gemma2_2b.logit_scale" in sd: + # remove "text_encoders.gemma2_2b.transformer.model." prefix + filtered_sd = { + k.replace("text_encoders.gemma2_2b.transformer.model.", ""): v + for k, v in sd.items() + if k.startswith("text_encoders.gemma2_2b.transformer.model.") + } + sd = filtered_sd + + info = gemma2.load_state_dict(sd, strict=False, assign=True) + logger.info(f"Loaded Gemma2: {info}") + return gemma2 + + +def unpack_latents(x: torch.Tensor, packed_latent_height: int, packed_latent_width: int) -> torch.Tensor: + """ + x: [b (h w) (c ph pw)] -> [b c (h ph) (w pw)], ph=2, pw=2 + """ + x = einops.rearrange(x, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=packed_latent_height, w=packed_latent_width, ph=2, pw=2) + return x + + +def pack_latents(x: torch.Tensor) -> torch.Tensor: + """ + x: [b c (h ph) (w pw)] -> [b (h w) (c ph pw)], ph=2, pw=2 + """ + x = einops.rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + return x + + +DIFFUSERS_TO_ALPHA_VLLM_MAP: dict[str, str] = { + # Embedding layers + "time_caption_embed.caption_embedder.0.weight": "cap_embedder.0.weight", + "time_caption_embed.caption_embedder.1.weight": "cap_embedder.1.weight", + "text_embedder.1.bias": "cap_embedder.1.bias", + "patch_embedder.proj.weight": "x_embedder.weight", + "patch_embedder.proj.bias": "x_embedder.bias", + # Attention modulation + "transformer_blocks.().adaln_modulation.1.weight": "layers.().adaLN_modulation.1.weight", + "transformer_blocks.().adaln_modulation.1.bias": "layers.().adaLN_modulation.1.bias", + # Final layers + "final_adaln_modulation.1.weight": "final_layer.adaLN_modulation.1.weight", + "final_adaln_modulation.1.bias": "final_layer.adaLN_modulation.1.bias", + "final_linear.weight": "final_layer.linear.weight", + "final_linear.bias": "final_layer.linear.bias", + # Noise refiner + "single_transformer_blocks.().adaln_modulation.1.weight": "noise_refiner.().adaLN_modulation.1.weight", + "single_transformer_blocks.().adaln_modulation.1.bias": "noise_refiner.().adaLN_modulation.1.bias", + "single_transformer_blocks.().attn.to_qkv.weight": "noise_refiner.().attention.qkv.weight", + "single_transformer_blocks.().attn.to_out.0.weight": "noise_refiner.().attention.out.weight", + # Normalization + "transformer_blocks.().norm1.weight": "layers.().attention_norm1.weight", + "transformer_blocks.().norm2.weight": "layers.().attention_norm2.weight", + # FFN + "transformer_blocks.().ff.net.0.proj.weight": "layers.().feed_forward.w1.weight", + "transformer_blocks.().ff.net.2.weight": "layers.().feed_forward.w2.weight", + "transformer_blocks.().ff.net.4.weight": "layers.().feed_forward.w3.weight", +} + + +def convert_diffusers_sd_to_alpha_vllm(sd: dict, num_double_blocks: int) -> dict: + """Convert Diffusers checkpoint to Alpha-VLLM format""" + logger.info("Converting Diffusers checkpoint to Alpha-VLLM format") + new_sd = sd.copy() # Preserve original keys + + for diff_key, alpha_key in DIFFUSERS_TO_ALPHA_VLLM_MAP.items(): + # Handle block-specific patterns + if "()." in diff_key: + for block_idx in range(num_double_blocks): + block_alpha_key = alpha_key.replace("().", f"{block_idx}.") + block_diff_key = diff_key.replace("().", f"{block_idx}.") + + # Search for and convert block-specific keys + for input_key, value in list(sd.items()): + if input_key == block_diff_key: + new_sd[block_alpha_key] = value + else: + # Handle static keys + if diff_key in sd: + print(f"Replacing {diff_key} with {alpha_key}") + new_sd[alpha_key] = sd[diff_key] + else: + print(f"Not found: {diff_key}") + + logger.info(f"Converted {len(new_sd)} keys to Alpha-VLLM format") + return new_sd diff --git a/vendor/sd-scripts/library/mask_generator.py b/vendor/sd-scripts/library/mask_generator.py new file mode 100644 index 0000000..1dcaeca --- /dev/null +++ b/vendor/sd-scripts/library/mask_generator.py @@ -0,0 +1,354 @@ +""" +Procedural mask generation for inpainting training. + +Provides three mask types that can be combined: + - cloud : fractional Brownian motion turbulence (layered smooth noise) + - polygon : random convex or irregular polygon + - shape : original rectangle / ellipse (kept for backwards compat) + +All functions return a PIL Image in mode "L" (0 = keep, 255 = mask/inpaint). + +| Function | Description | +|---|---| +| `cloud_mask(w, h, ...)` | Organic blob shapes via fractional Brownian motion (layered noise) | +| `polygon_mask(w, h, ...)` | Random convex polygons | +| `shape_mask(w, h, ...)` | Axis-aligned rectangles and ellipses | +| `combine_masks(*masks)` | Union or intersection of any of the above | +| `random_mask(w, h, ...)` | Randomly selects and optionally combines mask types | + +To visually inspect mask output, run: + +python3 tests/visualize_masks.py --data-dir /path/to/images --out-dir ./mask_viz + +This produces gallery PNG files for each mask type, showing the raw mask alongside the mask applied to +source images. If no `--data-dir` is provided (or the directory is empty), synthetic test images +are used as the background. + +""" + +import math +import random +from typing import Optional, Tuple + +import cv2 +import numpy as np +from PIL import Image, ImageDraw + + +# --------------------------------------------------------------------------- +# Cloud / turbulence masks +# --------------------------------------------------------------------------- + +def _smooth_noise(w: int, h: int, scale: float, rng: np.random.Generator) -> np.ndarray: + """Generate a single octave of smooth noise by upsampling small random values.""" + small_w = max(2, int(w / scale)) + small_h = max(2, int(h / scale)) + small = rng.random((small_h, small_w)).astype(np.float32) + return cv2.resize(small, (w, h), interpolation=cv2.INTER_CUBIC) + + +def cloud_mask( + width: int, + height: int, + octaves: int = 4, + persistence: float = 0.5, + base_scale: float = 16.0, + threshold: float = 0.2, + seed: Optional[int] = None, +) -> Image.Image: + """ + Fractional Brownian Motion cloud mask. + + Parameters + ---------- + width, height : output size in pixels + octaves : number of noise layers; more = finer detail, more = grainier + persistence : amplitude falloff per octave (0..1); lower = smoother / fewer details + base_scale : spatial scale of the coarsest octave — higher = larger blobs + threshold : fraction of image area to mask (0..1); keep in ~0.1–0.3 for realistic patches + seed : optional RNG seed for reproducibility + """ + rng = np.random.default_rng(seed) + + noise = np.zeros((height, width), dtype=np.float32) + amplitude = 1.0 + total_amplitude = 0.0 + scale = base_scale + + for _ in range(octaves): + noise += amplitude * _smooth_noise(width, height, scale, rng) + total_amplitude += amplitude + amplitude *= persistence + scale *= 2.0 + + noise /= total_amplitude # normalise to [0, 1] + cutoff = np.quantile(noise, 1.0 - threshold) + binary = (noise >= cutoff).astype(np.uint8) * 255 + + return Image.fromarray(binary, mode="L") + + +# --------------------------------------------------------------------------- +# Polygon masks +# --------------------------------------------------------------------------- + +def _random_convex_polygon( + cx: float, cy: float, radius: float, n_points: int, irregularity: float, rng: random.Random +) -> list: + """Return vertices of a roughly convex polygon centred at (cx, cy).""" + angles = sorted(rng.uniform(0, 2 * math.pi) for _ in range(n_points)) + points = [] + for angle in angles: + r = radius * rng.uniform(1.0 - irregularity, 1.0) + points.append((cx + r * math.cos(angle), cy + r * math.sin(angle))) + return points + + +def polygon_mask( + width: int, + height: int, + n_points: int = 6, + min_coverage: float = 0.1, + max_coverage: float = 0.6, + irregularity: float = 0.4, + n_polygons: int = 1, + seed: Optional[int] = None, +) -> Image.Image: + """ + Random polygon mask. + + Parameters + ---------- + width, height : output size in pixels + n_points : number of vertices per polygon + min_coverage : minimum fraction of image area each polygon can cover + max_coverage : maximum fraction of image area each polygon can cover + irregularity : how irregular the polygon is (0 = circle, 1 = very jagged) + n_polygons : how many polygons to draw + seed : optional RNG seed + """ + rng = random.Random(seed) + mask = Image.new("L", (width, height), 0) + draw = ImageDraw.Draw(mask) + + min_r = math.sqrt(min_coverage * width * height / math.pi) + max_r = math.sqrt(max_coverage * width * height / math.pi) + + for _ in range(n_polygons): + radius = rng.uniform(min_r, max_r) + cx = rng.uniform(radius * 0.2, width - radius * 0.2) + cy = rng.uniform(radius * 0.2, height - radius * 0.2) + pts = _random_convex_polygon(cx, cy, radius, n_points, irregularity, rng) + draw.polygon(pts, fill=255) + + return mask + + +# --------------------------------------------------------------------------- +# Shape masks (rectangles / ellipses — original behaviour) +# --------------------------------------------------------------------------- + +def shape_mask( + width: int, + height: int, + min_coverage: float = 0.1, + max_coverage: float = 0.4, + shape: str = "random", + seed: Optional[int] = None, +) -> Image.Image: + """ + Rectangle or ellipse mask (mirrors the original random_mask behaviour). + + Parameters + ---------- + shape : "rectangle", "ellipse", or "random" + """ + rng = random.Random(seed) + mask = Image.new("L", (width, height), 0) + draw = ImageDraw.Draw(mask) + + area = width * height + size_w = rng.randint(int(width * min_coverage ** 0.5), int(width * max_coverage ** 0.5)) + size_h = rng.randint(int(height * min_coverage ** 0.5), int(height * max_coverage ** 0.5)) + + x1 = rng.randint(0, width - size_w) + y1 = rng.randint(0, height - size_h) + x2, y2 = x1 + size_w, y1 + size_h + + use_ellipse = (shape == "ellipse") or (shape == "random" and rng.random() < 0.5) + if use_ellipse: + draw.ellipse([x1, y1, x2, y2], fill=255) + else: + draw.rectangle([x1, y1, x2, y2], fill=255) + + return mask + + +# --------------------------------------------------------------------------- +# Wobbly ellipse mask (fBm radius variation — good default for sampling) +# --------------------------------------------------------------------------- + +def wobbly_ellipse_mask( + width: int, + height: int, + coverage: float = 0.2, + wobble_scale: float = 0.5, + octaves: int = 4, + persistence: float = 0.5, + n_points: int = 128, + seed: Optional[int] = None, +) -> Image.Image: + """ + Ellipse mask whose radius varies along the circumference using periodic + fractional Brownian motion noise, producing organic blob-like shapes. + + Unlike cloud masks (which can produce large, fragmented regions), this + always yields a single connected region, making it well-suited as a + default mask for inpainting sampling and inference. + + Parameters + ---------- + width, height : output size in pixels + coverage : approximate fraction of the image area to mask (0..1) + wobble_scale : radius variation relative to the base radius. + Default 0.25 means radius varies ±25% around the base. + octaves : number of harmonic layers in the radius noise + persistence : amplitude falloff per octave (0..1) + n_points : number of polygon vertices used to approximate the shape + seed : optional RNG seed for reproducibility + """ + rng = np.random.RandomState(seed if seed is not None else np.random.randint(0, 2**31)) + + # Center — biased toward image interior so the ellipse stays mostly inside + cx = rng.uniform(0.25, 0.75) * width + cy = rng.uniform(0.25, 0.75) * height + + # Ellipse aspect ratio and rotation + aspect = rng.uniform(0.6, 1.4) # ratio of semi-major to semi-minor + rotation = rng.uniform(0, math.pi) + + # Semi-axes derived from target coverage area (≈ π·a·b) + area_target = coverage * width * height + r_base = math.sqrt(area_target / (math.pi * aspect)) + a = r_base * math.sqrt(aspect) # semi-major axis + b = r_base / math.sqrt(aspect) # semi-minor axis + + # Periodic fBm using harmonic cosines so the noise wraps seamlessly at 0 = 2π + thetas = np.linspace(0, 2 * math.pi, n_points, endpoint=False) + noise = np.zeros(n_points, dtype=np.float32) + amplitude = 1.0 + total_amplitude = 0.0 + for k in range(1, octaves + 1): + phase = rng.uniform(0, 2 * math.pi) + noise += amplitude * np.cos(k * thetas + phase) + total_amplitude += amplitude + amplitude *= persistence + noise /= total_amplitude + std = noise.std() + if std > 1e-6: + noise = noise / std * wobble_scale # normalise so ±wobble_scale bounds hold approximately + + # Build polygon: for each sample angle, compute the ellipse radius at that + # angle (accounting for rotation), then scale by the noise modulation. + points = [] + for i, theta in enumerate(thetas): + t_rot = theta - rotation + r = (a * b) / math.sqrt((b * math.cos(t_rot)) ** 2 + (a * math.sin(t_rot)) ** 2) + r_wobbled = max(r * (1.0 + noise[i]), 1.0) + points.append((cx + r_wobbled * math.cos(theta), cy + r_wobbled * math.sin(theta))) + + img = Image.new("L", (width, height), 0) + ImageDraw.Draw(img).polygon(points, fill=255) + return img + + +# --------------------------------------------------------------------------- +# Combiner +# --------------------------------------------------------------------------- + +def combine_masks(*masks: Image.Image, mode: str = "union") -> Image.Image: + """ + Combine multiple "L" mode masks. + + mode : "union" — pixel is masked if ANY mask covers it (logical OR) + "intersection" — pixel is masked if ALL masks cover it (logical AND) + """ + arrays = [np.array(m, dtype=np.uint8) for m in masks] + if mode == "union": + result = arrays[0].copy() + for a in arrays[1:]: + result = np.maximum(result, a) + else: + result = arrays[0].copy() + for a in arrays[1:]: + result = np.minimum(result, a) + return Image.fromarray(result, mode="L") + + +# --------------------------------------------------------------------------- +# High-level entry point used by BaseDataset.random_mask +# --------------------------------------------------------------------------- + +# Probability weights for mask type selection +_TYPE_WEIGHTS = { + "cloud": 0.40, + "polygon": 0.35, + "shape": 0.25, +} + +def random_mask( + width: int, + height: int, + cloud_octaves: int = 4, + cloud_persistence: float = 0.5, + cloud_threshold: float = 0.2, + polygon_n_points: int = 6, + polygon_irregularity: float = 0.4, + polygon_n_polygons: int = 1, + min_coverage: float = 0.1, + max_coverage: float = 0.6, + combine_cloud_and_shape: bool = True, + seed: Optional[int] = None, +) -> Image.Image: + """ + Generate a random inpainting mask using a randomly chosen (or combined) strategy. + + With combine_cloud_and_shape=True (default) there is a 33% chance the result + is the union of a cloud mask and a polygon/shape mask, which produces more + varied and naturalistic training masks. + """ + rng = random.Random(seed) + np_seed = rng.randint(0, 2**31) + poly_seed = rng.randint(0, 2**31) + + mask_type = rng.choices( + list(_TYPE_WEIGHTS.keys()), weights=list(_TYPE_WEIGHTS.values()) + )[0] + + if mask_type == "cloud": + threshold = rng.uniform(max(0.05, cloud_threshold - 0.1), min(0.4, cloud_threshold + 0.1)) + m = cloud_mask(width, height, octaves=cloud_octaves, persistence=cloud_persistence, + threshold=threshold, seed=np_seed) + if combine_cloud_and_shape and rng.random() < 0.33: + shape_type = rng.choice(["polygon", "shape"]) + if shape_type == "polygon": + m2 = polygon_mask(width, height, n_points=polygon_n_points, + min_coverage=min_coverage, max_coverage=max_coverage, + irregularity=polygon_irregularity, seed=poly_seed) + else: + m2 = shape_mask(width, height, min_coverage=min_coverage, + max_coverage=max_coverage, seed=poly_seed) + m = combine_masks(m, m2) + + elif mask_type == "polygon": + n_poly = rng.randint(1, max(1, polygon_n_polygons * 2)) + m = polygon_mask(width, height, n_points=polygon_n_points, + min_coverage=min_coverage, max_coverage=max_coverage, + irregularity=polygon_irregularity, n_polygons=n_poly, + seed=poly_seed) + + else: # shape + m = shape_mask(width, height, min_coverage=min_coverage, + max_coverage=max_coverage, seed=poly_seed) + + return m diff --git a/vendor/sd-scripts/library/model_util.py b/vendor/sd-scripts/library/model_util.py new file mode 100644 index 0000000..385f29b --- /dev/null +++ b/vendor/sd-scripts/library/model_util.py @@ -0,0 +1,1430 @@ +# v1: split from train_db_fixed.py. +# v2: support safetensors + +import math +import os + +import torch +from library.device_utils import init_ipex + +init_ipex() + +import diffusers +from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging +from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline # , UNet2DConditionModel +from safetensors.torch import load_file, save_file +from library.original_unet import UNet2DConditionModel +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +# DiffUsers版StableDiffusionのモデルパラメータ +NUM_TRAIN_TIMESTEPS = 1000 +BETA_START = 0.00085 +BETA_END = 0.0120 + +UNET_PARAMS_MODEL_CHANNELS = 320 +UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4] +UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1] +UNET_PARAMS_IMAGE_SIZE = 64 # fixed from old invalid value `32` +UNET_PARAMS_IN_CHANNELS = 4 +UNET_PARAMS_OUT_CHANNELS = 4 +UNET_PARAMS_NUM_RES_BLOCKS = 2 +UNET_PARAMS_CONTEXT_DIM = 768 +UNET_PARAMS_NUM_HEADS = 8 +# UNET_PARAMS_USE_LINEAR_PROJECTION = False + +VAE_PARAMS_Z_CHANNELS = 4 +VAE_PARAMS_RESOLUTION = 256 +VAE_PARAMS_IN_CHANNELS = 3 +VAE_PARAMS_OUT_CH = 3 +VAE_PARAMS_CH = 128 +VAE_PARAMS_CH_MULT = [1, 2, 4, 4] +VAE_PARAMS_NUM_RES_BLOCKS = 2 + +# V2 +V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20] +V2_UNET_PARAMS_CONTEXT_DIM = 1024 +# V2_UNET_PARAMS_USE_LINEAR_PROJECTION = True + +# Diffusersの設定を読み込むための参照モデル +DIFFUSERS_REF_MODEL_ID_V1 = "runwayml/stable-diffusion-v1-5" +DIFFUSERS_REF_MODEL_ID_V2 = "stabilityai/stable-diffusion-2-1" + + +# region StableDiffusion->Diffusersの変換コード +# convert_original_stable_diffusion_to_diffusers をコピーして修正している（ASL 2.0） + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + if diffusers.__version__ < "0.17.0": + new_item = new_item.replace("q.weight", "query.weight") + new_item = new_item.replace("q.bias", "query.bias") + + new_item = new_item.replace("k.weight", "key.weight") + new_item = new_item.replace("k.bias", "key.bias") + + new_item = new_item.replace("v.weight", "value.weight") + new_item = new_item.replace("v.bias", "value.bias") + + new_item = new_item.replace("proj_out.weight", "proj_attn.weight") + new_item = new_item.replace("proj_out.bias", "proj_attn.bias") + else: + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming + to them. It splits attention layers, and takes into account additional replacements + that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + reshaping = False + if diffusers.__version__ < "0.17.0": + if "proj_attn.weight" in new_path: + reshaping = True + else: + if ".attentions." in new_path and ".0.to_" in new_path and old_checkpoint[path["old"]].ndim > 2: + reshaping = True + + if reshaping: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def linear_transformer_to_conv(checkpoint): + keys = list(checkpoint.keys()) + tf_keys = ["proj_in.weight", "proj_out.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in tf_keys: + if checkpoint[key].ndim == 2: + checkpoint[key] = checkpoint[key].unsqueeze(2).unsqueeze(2) + + +def convert_ldm_unet_checkpoint(v2, checkpoint, config): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + # extract state_dict for UNet + unet_state_dict = {} + unet_key = "model.diffusion_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}." in key] for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias") + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + + # オリジナル： + # if ["conv.weight", "conv.bias"] in output_block_list.values(): + # index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) + + # biasとweightの順番に依存しないようにする：もっといいやり方がありそうだが + for l in output_block_list.values(): + l.sort() + + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + # SDのv2では1*1のconv2dがlinearに変わっている + # 誤って Diffusers 側を conv2d のままにしてしまったので、変換必要 + if v2 and not config.get("use_linear_projection", False): + linear_transformer_to_conv(new_checkpoint) + + return new_checkpoint + + +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + # if len(vae_state_dict) == 0: + # # 渡されたcheckpointは.ckptから読み込んだcheckpointではなくvaeのstate_dict + # vae_state_dict = checkpoint + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = {layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)} + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = {layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +def create_unet_diffusers_config(v2, use_linear_projection_in_v2=False): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + # unet_params = original_config.model.params.unet_config.params + + block_out_channels = [UNET_PARAMS_MODEL_CHANNELS * mult for mult in UNET_PARAMS_CHANNEL_MULT] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + config = dict( + sample_size=UNET_PARAMS_IMAGE_SIZE, + in_channels=UNET_PARAMS_IN_CHANNELS, + out_channels=UNET_PARAMS_OUT_CHANNELS, + down_block_types=tuple(down_block_types), + up_block_types=tuple(up_block_types), + block_out_channels=tuple(block_out_channels), + layers_per_block=UNET_PARAMS_NUM_RES_BLOCKS, + cross_attention_dim=UNET_PARAMS_CONTEXT_DIM if not v2 else V2_UNET_PARAMS_CONTEXT_DIM, + attention_head_dim=UNET_PARAMS_NUM_HEADS if not v2 else V2_UNET_PARAMS_ATTENTION_HEAD_DIM, + # use_linear_projection=UNET_PARAMS_USE_LINEAR_PROJECTION if not v2 else V2_UNET_PARAMS_USE_LINEAR_PROJECTION, + ) + if v2 and use_linear_projection_in_v2: + config["use_linear_projection"] = True + + return config + + +def create_vae_diffusers_config(): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + # vae_params = original_config.model.params.first_stage_config.params.ddconfig + # _ = original_config.model.params.first_stage_config.params.embed_dim + block_out_channels = [VAE_PARAMS_CH * mult for mult in VAE_PARAMS_CH_MULT] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + config = dict( + sample_size=VAE_PARAMS_RESOLUTION, + in_channels=VAE_PARAMS_IN_CHANNELS, + out_channels=VAE_PARAMS_OUT_CH, + down_block_types=tuple(down_block_types), + up_block_types=tuple(up_block_types), + block_out_channels=tuple(block_out_channels), + latent_channels=VAE_PARAMS_Z_CHANNELS, + layers_per_block=VAE_PARAMS_NUM_RES_BLOCKS, + ) + return config + + +def convert_ldm_clip_checkpoint_v1(checkpoint): + keys = list(checkpoint.keys()) + text_model_dict = {} + for key in keys: + if key.startswith("cond_stage_model.transformer"): + text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key] + + # remove position_ids for newer transformer, which causes error :( + if "text_model.embeddings.position_ids" in text_model_dict: + text_model_dict.pop("text_model.embeddings.position_ids") + + return text_model_dict + + +def convert_ldm_clip_checkpoint_v2(checkpoint, max_length): + # 嫌になるくらい違うぞ！ + def convert_key(key): + if not key.startswith("cond_stage_model"): + return None + + # common conversion + key = key.replace("cond_stage_model.model.transformer.", "text_model.encoder.") + key = key.replace("cond_stage_model.model.", "text_model.") + + if "resblocks" in key: + # resblocks conversion + key = key.replace(".resblocks.", ".layers.") + if ".ln_" in key: + key = key.replace(".ln_", ".layer_norm") + elif ".mlp." in key: + key = key.replace(".c_fc.", ".fc1.") + key = key.replace(".c_proj.", ".fc2.") + elif ".attn.out_proj" in key: + key = key.replace(".attn.out_proj.", ".self_attn.out_proj.") + elif ".attn.in_proj" in key: + key = None # 特殊なので後で処理する + else: + raise ValueError(f"unexpected key in SD: {key}") + elif ".positional_embedding" in key: + key = key.replace(".positional_embedding", ".embeddings.position_embedding.weight") + elif ".text_projection" in key: + key = None # 使われない??? + elif ".logit_scale" in key: + key = None # 使われない??? + elif ".token_embedding" in key: + key = key.replace(".token_embedding.weight", ".embeddings.token_embedding.weight") + elif ".ln_final" in key: + key = key.replace(".ln_final", ".final_layer_norm") + return key + + keys = list(checkpoint.keys()) + new_sd = {} + for key in keys: + # remove resblocks 23 + if ".resblocks.23." in key: + continue + new_key = convert_key(key) + if new_key is None: + continue + new_sd[new_key] = checkpoint[key] + + # attnの変換 + for key in keys: + if ".resblocks.23." in key: + continue + if ".resblocks" in key and ".attn.in_proj_" in key: + # 三つに分割 + values = torch.chunk(checkpoint[key], 3) + + key_suffix = ".weight" if "weight" in key else ".bias" + key_pfx = key.replace("cond_stage_model.model.transformer.resblocks.", "text_model.encoder.layers.") + key_pfx = key_pfx.replace("_weight", "") + key_pfx = key_pfx.replace("_bias", "") + key_pfx = key_pfx.replace(".attn.in_proj", ".self_attn.") + new_sd[key_pfx + "q_proj" + key_suffix] = values[0] + new_sd[key_pfx + "k_proj" + key_suffix] = values[1] + new_sd[key_pfx + "v_proj" + key_suffix] = values[2] + + # remove position_ids for newer transformer, which causes error :( + ANOTHER_POSITION_IDS_KEY = "text_model.encoder.text_model.embeddings.position_ids" + if ANOTHER_POSITION_IDS_KEY in new_sd: + # waifu diffusion v1.4 + del new_sd[ANOTHER_POSITION_IDS_KEY] + + if "text_model.embeddings.position_ids" in new_sd: + del new_sd["text_model.embeddings.position_ids"] + + return new_sd + + +# endregion + + +# region Diffusers->StableDiffusion の変換コード +# convert_diffusers_to_original_stable_diffusion をコピーして修正している（ASL 2.0） + + +def conv_transformer_to_linear(checkpoint): + keys = list(checkpoint.keys()) + tf_keys = ["proj_in.weight", "proj_out.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in tf_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + + +def convert_unet_state_dict_to_sd(v2, unet_state_dict): + unet_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + ] + + unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), + ] + + unet_conversion_map_layer = [] + for i in range(4): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3*i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + + hf_mid_atn_prefix = "mid_block.attentions.0." + sd_mid_atn_prefix = "middle_block.1." + unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + + for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2*j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} + + if v2: + conv_transformer_to_linear(new_state_dict) + + return new_state_dict + + +def controlnet_conversion_map(): + unet_conversion_map = [ + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("middle_block_out.0.weight", "controlnet_mid_block.weight"), + ("middle_block_out.0.bias", "controlnet_mid_block.bias"), + ] + + unet_conversion_map_resnet = [ + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), + ] + + unet_conversion_map_layer = [] + for i in range(4): + for j in range(2): + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_mid_atn_prefix = "mid_block.attentions.0." + sd_mid_atn_prefix = "middle_block.1." + unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + + for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2*j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + controlnet_cond_embedding_names = ["conv_in"] + [f"blocks.{i}" for i in range(6)] + ["conv_out"] + for i, hf_prefix in enumerate(controlnet_cond_embedding_names): + hf_prefix = f"controlnet_cond_embedding.{hf_prefix}." + sd_prefix = f"input_hint_block.{i*2}." + unet_conversion_map_layer.append((sd_prefix, hf_prefix)) + + for i in range(12): + hf_prefix = f"controlnet_down_blocks.{i}." + sd_prefix = f"zero_convs.{i}.0." + unet_conversion_map_layer.append((sd_prefix, hf_prefix)) + + return unet_conversion_map, unet_conversion_map_resnet, unet_conversion_map_layer + + +def convert_controlnet_state_dict_to_sd(controlnet_state_dict): + unet_conversion_map, unet_conversion_map_resnet, unet_conversion_map_layer = controlnet_conversion_map() + + mapping = {k: k for k in controlnet_state_dict.keys()} + for sd_name, diffusers_name in unet_conversion_map: + mapping[diffusers_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, diffusers_part in unet_conversion_map_resnet: + v = v.replace(diffusers_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, diffusers_part in unet_conversion_map_layer: + v = v.replace(diffusers_part, sd_part) + mapping[k] = v + new_state_dict = {v: controlnet_state_dict[k] for k, v in mapping.items()} + return new_state_dict + + +def convert_controlnet_state_dict_to_diffusers(controlnet_state_dict): + unet_conversion_map, unet_conversion_map_resnet, unet_conversion_map_layer = controlnet_conversion_map() + + mapping = {k: k for k in controlnet_state_dict.keys()} + for sd_name, diffusers_name in unet_conversion_map: + mapping[sd_name] = diffusers_name + for k, v in mapping.items(): + for sd_part, diffusers_part in unet_conversion_map_layer: + v = v.replace(sd_part, diffusers_part) + mapping[k] = v + for k, v in mapping.items(): + if "resnets" in v: + for sd_part, diffusers_part in unet_conversion_map_resnet: + v = v.replace(sd_part, diffusers_part) + mapping[k] = v + new_state_dict = {v: controlnet_state_dict[k] for k, v in mapping.items()} + return new_state_dict + + +# ================# +# VAE Conversion # +# ================# + + +def reshape_weight_for_sd(w): + # convert HF linear weights to SD conv2d weights + return w.reshape(*w.shape, 1, 1) + + +def convert_vae_state_dict(vae_state_dict): + vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), + ] + + for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3-i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3-i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + + # this part accounts for mid blocks in both the encoder and the decoder + for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i+1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + if diffusers.__version__ < "0.17.0": + vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "query."), + ("k.", "key."), + ("v.", "value."), + ("proj_out.", "proj_attn."), + ] + else: + vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), + ] + + mapping = {k: k for k in vae_state_dict.keys()} + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + # logger.info(f"Reshaping {k} for SD format: shape {v.shape} -> {v.shape} x 1 x 1") + new_state_dict[k] = reshape_weight_for_sd(v) + + return new_state_dict + + +# endregion + +# region 自作のモデル読み書きなど + + +def is_safetensors(path): + return os.path.splitext(path)[1].lower() == ".safetensors" + + +def load_checkpoint_with_text_encoder_conversion(ckpt_path, device="cpu"): + # text encoderの格納形式が違うモデルに対応する ('text_model'がない) + TEXT_ENCODER_KEY_REPLACEMENTS = [ + ("cond_stage_model.transformer.embeddings.", "cond_stage_model.transformer.text_model.embeddings."), + ("cond_stage_model.transformer.encoder.", "cond_stage_model.transformer.text_model.encoder."), + ("cond_stage_model.transformer.final_layer_norm.", "cond_stage_model.transformer.text_model.final_layer_norm."), + ] + + if is_safetensors(ckpt_path): + checkpoint = None + state_dict = load_file(ckpt_path) # , device) # may causes error + else: + checkpoint = torch.load(ckpt_path, map_location=device, weights_only=False) + if "state_dict" in checkpoint: + state_dict = checkpoint["state_dict"] + else: + state_dict = checkpoint + checkpoint = None + + key_reps = [] + for rep_from, rep_to in TEXT_ENCODER_KEY_REPLACEMENTS: + for key in state_dict.keys(): + if key.startswith(rep_from): + new_key = rep_to + key[len(rep_from) :] + key_reps.append((key, new_key)) + + for key, new_key in key_reps: + state_dict[new_key] = state_dict[key] + del state_dict[key] + + return checkpoint, state_dict + + +# TODO dtype指定の動作が怪しいので確認する text_encoderを指定形式で作れるか未確認 +def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, device="cpu", dtype=None, unet_use_linear_projection_in_v2=True): + _, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path, device) + + # Convert the UNet2DConditionModel model. + unet_config = create_unet_diffusers_config(v2, unet_use_linear_projection_in_v2) + converted_unet_checkpoint = convert_ldm_unet_checkpoint(v2, state_dict, unet_config) + + # Inpainting checkpoints have conv_in with 9 input channels instead of 4. + # Detect this from the converted weights and update the config accordingly. + actual_in_channels = converted_unet_checkpoint["conv_in.weight"].shape[1] + if actual_in_channels != UNET_PARAMS_IN_CHANNELS: + unet_config["in_channels"] = actual_in_channels + + unet = UNet2DConditionModel(**unet_config).to(device) + info = unet.load_state_dict(converted_unet_checkpoint) + logger.info(f"loading u-net: {info}") + + # Convert the VAE model. + vae_config = create_vae_diffusers_config() + converted_vae_checkpoint = convert_ldm_vae_checkpoint(state_dict, vae_config) + + vae = AutoencoderKL(**vae_config).to(device) + info = vae.load_state_dict(converted_vae_checkpoint) + logger.info(f"loading vae: {info}") + + # convert text_model + if v2: + converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v2(state_dict, 77) + cfg = CLIPTextConfig( + vocab_size=49408, + hidden_size=1024, + intermediate_size=4096, + num_hidden_layers=23, + num_attention_heads=16, + max_position_embeddings=77, + hidden_act="gelu", + layer_norm_eps=1e-05, + dropout=0.0, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + model_type="clip_text_model", + projection_dim=512, + torch_dtype="float32", + transformers_version="4.25.0.dev0", + ) + text_model = CLIPTextModel._from_config(cfg) + info = text_model.load_state_dict(converted_text_encoder_checkpoint) + else: + converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v1(state_dict) + + # logging.set_verbosity_error() # don't show annoying warning + # text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(device) + # logging.set_verbosity_warning() + # logger.info(f"config: {text_model.config}") + cfg = CLIPTextConfig( + vocab_size=49408, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + max_position_embeddings=77, + hidden_act="quick_gelu", + layer_norm_eps=1e-05, + dropout=0.0, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + model_type="clip_text_model", + projection_dim=768, + torch_dtype="float32", + ) + text_model = CLIPTextModel._from_config(cfg) + info = text_model.load_state_dict(converted_text_encoder_checkpoint) + logger.info(f"loading text encoder: {info}") + + return text_model, vae, unet + + +def get_model_version_str_for_sd1_sd2(v2, v_parameterization): + # only for reference + version_str = "sd" + if v2: + version_str += "_v2" + else: + version_str += "_v1" + if v_parameterization: + version_str += "_v" + return version_str + + +def convert_text_encoder_state_dict_to_sd_v2(checkpoint, make_dummy_weights=False): + def convert_key(key): + # position_idsの除去 + if ".position_ids" in key: + return None + + # common + key = key.replace("text_model.encoder.", "transformer.") + key = key.replace("text_model.", "") + if "layers" in key: + # resblocks conversion + key = key.replace(".layers.", ".resblocks.") + if ".layer_norm" in key: + key = key.replace(".layer_norm", ".ln_") + elif ".mlp." in key: + key = key.replace(".fc1.", ".c_fc.") + key = key.replace(".fc2.", ".c_proj.") + elif ".self_attn.out_proj" in key: + key = key.replace(".self_attn.out_proj.", ".attn.out_proj.") + elif ".self_attn." in key: + key = None # 特殊なので後で処理する + else: + raise ValueError(f"unexpected key in DiffUsers model: {key}") + elif ".position_embedding" in key: + key = key.replace("embeddings.position_embedding.weight", "positional_embedding") + elif ".token_embedding" in key: + key = key.replace("embeddings.token_embedding.weight", "token_embedding.weight") + elif "final_layer_norm" in key: + key = key.replace("final_layer_norm", "ln_final") + return key + + keys = list(checkpoint.keys()) + new_sd = {} + for key in keys: + new_key = convert_key(key) + if new_key is None: + continue + new_sd[new_key] = checkpoint[key] + + # attnの変換 + for key in keys: + if "layers" in key and "q_proj" in key: + # 三つを結合 + key_q = key + key_k = key.replace("q_proj", "k_proj") + key_v = key.replace("q_proj", "v_proj") + + value_q = checkpoint[key_q] + value_k = checkpoint[key_k] + value_v = checkpoint[key_v] + value = torch.cat([value_q, value_k, value_v]) + + new_key = key.replace("text_model.encoder.layers.", "transformer.resblocks.") + new_key = new_key.replace(".self_attn.q_proj.", ".attn.in_proj_") + new_sd[new_key] = value + + # 最後の層などを捏造するか + if make_dummy_weights: + logger.info("make dummy weights for resblock.23, text_projection and logit scale.") + keys = list(new_sd.keys()) + for key in keys: + if key.startswith("transformer.resblocks.22."): + new_sd[key.replace(".22.", ".23.")] = new_sd[key].clone() # copyしないとsafetensorsの保存で落ちる + + # Diffusersに含まれない重みを作っておく + new_sd["text_projection"] = torch.ones((1024, 1024), dtype=new_sd[keys[0]].dtype, device=new_sd[keys[0]].device) + new_sd["logit_scale"] = torch.tensor(1) + + return new_sd + + +def save_stable_diffusion_checkpoint( + v2, output_file, text_encoder, unet, ckpt_path, epochs, steps, metadata, save_dtype=None, vae=None +): + if ckpt_path is not None: + # epoch/stepを参照する。またVAEがメモリ上にないときなど、もう一度VAEを含めて読み込む + checkpoint, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path) + if checkpoint is None: # safetensors または state_dictのckpt + checkpoint = {} + strict = False + else: + strict = True + if "state_dict" in state_dict: + del state_dict["state_dict"] + else: + # 新しく作る + assert vae is not None, "VAE is required to save a checkpoint without a given checkpoint" + checkpoint = {} + state_dict = {} + strict = False + + def update_sd(prefix, sd): + for k, v in sd.items(): + key = prefix + k + assert not strict or key in state_dict, f"Illegal key in save SD: {key}" + if save_dtype is not None: + v = v.detach().clone().to("cpu").to(save_dtype) + state_dict[key] = v + + # Convert the UNet model + unet_state_dict = convert_unet_state_dict_to_sd(v2, unet.state_dict()) + update_sd("model.diffusion_model.", unet_state_dict) + + # Convert the text encoder model + if v2: + make_dummy = ckpt_path is None # 参照元のcheckpointがない場合は最後の層を前の層から複製して作るなどダミーの重みを入れる + text_enc_dict = convert_text_encoder_state_dict_to_sd_v2(text_encoder.state_dict(), make_dummy) + update_sd("cond_stage_model.model.", text_enc_dict) + else: + text_enc_dict = text_encoder.state_dict() + update_sd("cond_stage_model.transformer.", text_enc_dict) + + # Convert the VAE + if vae is not None: + vae_dict = convert_vae_state_dict(vae.state_dict()) + update_sd("first_stage_model.", vae_dict) + + # Put together new checkpoint + key_count = len(state_dict.keys()) + new_ckpt = {"state_dict": state_dict} + + # epoch and global_step are sometimes not int + try: + if "epoch" in checkpoint: + epochs += checkpoint["epoch"] + if "global_step" in checkpoint: + steps += checkpoint["global_step"] + except: + pass + + new_ckpt["epoch"] = epochs + new_ckpt["global_step"] = steps + + if is_safetensors(output_file): + # TODO Tensor以外のdictの値を削除したほうがいいか + save_file(state_dict, output_file, metadata) + else: + torch.save(new_ckpt, output_file) + + return key_count + + +def save_diffusers_checkpoint(v2, output_dir, text_encoder, unet, pretrained_model_name_or_path, vae=None, use_safetensors=False): + if pretrained_model_name_or_path is None: + # load default settings for v1/v2 + if v2: + pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_V2 + else: + pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_V1 + + scheduler = DDIMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler") + tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer") + if vae is None: + vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae") + + # original U-Net cannot be saved, so we need to convert it to the Diffusers version + # TODO this consumes a lot of memory + diffusers_unet = diffusers.UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet") + diffusers_unet.load_state_dict(unet.state_dict()) + + pipeline = StableDiffusionPipeline( + unet=diffusers_unet, + text_encoder=text_encoder, + vae=vae, + scheduler=scheduler, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=None, + requires_safety_checker=None, + ) + pipeline.save_pretrained(output_dir, safe_serialization=use_safetensors) + + +VAE_PREFIX = "first_stage_model." + + +def load_vae(vae_id, dtype): + logger.info(f"load VAE: {vae_id}") + if os.path.isdir(vae_id) or not os.path.isfile(vae_id): + # Diffusers local/remote + try: + vae = AutoencoderKL.from_pretrained(vae_id, subfolder=None, torch_dtype=dtype) + except EnvironmentError as e: + logger.error(f"exception occurs in loading vae: {e}") + logger.error("retry with subfolder='vae'") + vae = AutoencoderKL.from_pretrained(vae_id, subfolder="vae", torch_dtype=dtype) + return vae + + # local + vae_config = create_vae_diffusers_config() + + if vae_id.endswith(".bin"): + # SD 1.5 VAE on Huggingface + converted_vae_checkpoint = torch.load(vae_id, map_location="cpu") + else: + # StableDiffusion + vae_model = load_file(vae_id, "cpu") if is_safetensors(vae_id) else torch.load(vae_id, map_location="cpu") + vae_sd = vae_model["state_dict"] if "state_dict" in vae_model else vae_model + + # vae only or full model + full_model = False + for vae_key in vae_sd: + if vae_key.startswith(VAE_PREFIX): + full_model = True + break + if not full_model: + sd = {} + for key, value in vae_sd.items(): + sd[VAE_PREFIX + key] = value + vae_sd = sd + del sd + + # Convert the VAE model. + converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_sd, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + return vae + + +# endregion + + +def expand_unet_to_inpainting(unet) -> None: + """ + Expand a standard 4-channel UNet input conv to 9 channels for inpainting training. + + Inpainting UNets expect 9 input channels: 4 noisy latents + 1 mask + 4 masked-image + latents. When starting from a standard (non-inpainting) checkpoint the input conv + weight has shape [out, 4, kH, kW]. This function replaces it with a new Conv2d whose + weight has shape [out, 9, kH, kW], copying the original 4-channel weights into the + first 4 channels and zero-initialising the remaining 5. The bias (if present) is + copied as-is. + + Supports both diffusers-style UNets (unet.conv_in, unet.config) and the custom SDXL + UNet (unet.input_blocks[0][0], unet.in_channels). The replacement is done in-place. + """ + import torch.nn as nn + + # Locate the first conv and choose how to replace it + if hasattr(unet, "conv_in"): + # Diffusers-style UNet (SD1.5 / SD2) + old_conv = unet.conv_in + use_conv_in = True + elif hasattr(unet, "input_blocks"): + # Custom SDXL original UNet + old_conv = unet.input_blocks[0][0] + use_conv_in = False + else: + raise AttributeError("Cannot locate input conv on UNet — unknown architecture") + + if old_conv.in_channels == 9: + return # already expanded — nothing to do + + if old_conv.in_channels != 4: + raise ValueError( + f"Expected input conv to have 4 or 9 input channels, got {old_conv.in_channels}" + ) + + device = old_conv.weight.device + dtype = old_conv.weight.dtype + + new_conv = nn.Conv2d( + 9, + old_conv.out_channels, + kernel_size=old_conv.kernel_size, + stride=old_conv.stride, + padding=old_conv.padding, + bias=old_conv.bias is not None, + ).to(device=device, dtype=dtype) + + with torch.no_grad(): + # Zero-initialise all 9 channels, then copy the original 4 + new_conv.weight.zero_() + new_conv.weight[:, :4] = old_conv.weight + if old_conv.bias is not None: + new_conv.bias.copy_(old_conv.bias) + + if use_conv_in: + unet.conv_in = new_conv + # diffusers' UNet stores config in a FrozenDict; mutating it directly raises, + # so use the register_to_config API to keep unet.config in sync. + if hasattr(unet, "register_to_config"): + unet.register_to_config(in_channels=9) + else: + unet.input_blocks[0][0] = new_conv + unet.in_channels = 9 + + +def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64): + max_width, max_height = max_reso + max_area = max_width * max_height + + resos = set() + + width = int(math.sqrt(max_area) // divisible) * divisible + resos.add((width, width)) + + width = min_size + while width <= max_size: + height = min(max_size, int((max_area // width) // divisible) * divisible) + if height >= min_size: + resos.add((width, height)) + resos.add((height, width)) + + # # make additional resos + # if width >= height and width - divisible >= min_size: + # resos.add((width - divisible, height)) + # resos.add((height, width - divisible)) + # if height >= width and height - divisible >= min_size: + # resos.add((width, height - divisible)) + # resos.add((height - divisible, width)) + + width += divisible + + resos = list(resos) + resos.sort() + return resos + + +if __name__ == "__main__": + resos = make_bucket_resolutions((512, 768)) + logger.info(f"{len(resos)}") + logger.info(f"{resos}") + aspect_ratios = [w / h for w, h in resos] + logger.info(f"{aspect_ratios}") + + ars = set() + for ar in aspect_ratios: + if ar in ars: + logger.error(f"error! duplicate ar: {ar}") + ars.add(ar) diff --git a/vendor/sd-scripts/library/optimizers/__init__.py b/vendor/sd-scripts/library/optimizers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/vendor/sd-scripts/library/optimizers/automagic.py b/vendor/sd-scripts/library/optimizers/automagic.py new file mode 100644 index 0000000..73d2bab --- /dev/null +++ b/vendor/sd-scripts/library/optimizers/automagic.py @@ -0,0 +1,291 @@ +# Ported from ostris/ai-toolkit (MIT License) +# https://github.com/ostris/ai-toolkit/blob/main/toolkit/optimizers/automagic.py +# Copyright (c) ostris. Used under MIT License. +from typing import List +import torch +from library.optimizers.optimizer_utils import Auto8bitTensor, copy_stochastic, stochastic_grad_accummulation + +try: + from optimum.quanto import QBytesTensor + _QUANTO_AVAILABLE = True +except ImportError: + QBytesTensor = None + _QUANTO_AVAILABLE = False + + +class Automagic(torch.optim.Optimizer): + def __init__( + self, + params, + lr=1e-6, + min_lr=1e-7, + max_lr=1e-3, + lr_bump=1e-6, + eps=(1e-30, 1e-3), + clip_threshold=1.0, + beta2=0.999, + weight_decay=0.0, + do_paramiter_swapping=False, + paramiter_swapping_factor=0.1, + ): + self.lr = lr + if self.lr > 1e-3: + print(f"Warning! Start lr is very high: {self.lr}. Forcing to 1e-6.") + self.lr = 1e-6 + self.min_lr = min_lr + self.max_lr = max_lr + self.lr_bump = lr_bump + + defaults = { + "lr": lr, + "eps": eps, + "clip_threshold": clip_threshold, + "beta2": beta2, + "weight_decay": weight_decay, + } + super().__init__(params, defaults) + + self.base_lrs: List[float] = [lr for group in self.param_groups] + self.is_stochastic_rounding_accumulation = False + + for group in self.param_groups: + for param in group['params']: + if param.requires_grad and param.dtype != torch.float32: + self.is_stochastic_rounding_accumulation = True + param.register_post_accumulate_grad_hook(stochastic_grad_accummulation) + + self.do_paramiter_swapping = do_paramiter_swapping + self.paramiter_swapping_factor = paramiter_swapping_factor + self._total_paramiter_size = sum( + torch.numel(p) for group in self.param_groups for p in group['params'] + ) + print(f"Automagic: total training parameters: {self._total_paramiter_size:,}") + + if self.do_paramiter_swapping: + self.enable_paramiter_swapping(self.paramiter_swapping_factor) + + def enable_paramiter_swapping(self, paramiter_swapping_factor=0.1): + self.do_paramiter_swapping = True + self.paramiter_swapping_factor = paramiter_swapping_factor + self.swap_paramiters() + + def swap_paramiters(self): + import random + all_params = [] + for group in self.param_groups: + for param in group['params']: + param.requires_grad_(False) + param.grad = None + all_params.append(param) + random.shuffle(all_params) + + target = int(self._total_paramiter_size * self.paramiter_swapping_factor) + total = 0 + for param in all_params: + total += torch.numel(param) + if total >= target: + break + else: + param.requires_grad_(True) + + @staticmethod + def _get_lr(param_group, param_state): + return param_state.get("avg_lr", 0.0) + + def _get_group_lr(self, group): + lrs = [self._get_lr(group, self.state[p]) for p in group["params"]] + return sum(lrs) / len(lrs) if lrs else self.lr + + @staticmethod + def _rms(tensor): + return tensor.norm(2) / (tensor.numel() ** 0.5) + + @staticmethod + def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col): + r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) + c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() + return torch.mul(r_factor, c_factor) + + def step_hook(self): + if not self.is_stochastic_rounding_accumulation: + return + for group in self.param_groups: + for param in group['params']: + if param.requires_grad and hasattr(param, "_accum_grad"): + param.grad = param._accum_grad + del param._accum_grad + + def get_learning_rates(self): + lrs = [self._get_group_lr(group) for group in self.param_groups] + return lrs if lrs else self.base_lrs + + def get_avg_learning_rate(self): + lrs = self.get_learning_rates() + return sum(lrs) / len(lrs) + + @torch.no_grad() + def step(self, closure=None): + self.step_hook() + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None or not p.requires_grad: + continue + + grad = p.grad + if grad.dtype != torch.float32: + grad = grad.to(torch.float32) + if grad.is_sparse: + raise RuntimeError("Automagic does not support sparse gradients.") + + state = self.state[p] + grad_shape = grad.shape + factored = len(grad_shape) >= 2 + + if len(state) == 0: + self.initialize_state(p) + else: + if factored: + if "exp_avg_sq_row" not in state or "exp_avg_sq_col" not in state: + state["exp_avg_sq_row"] = torch.zeros(p.shape[:-1]).to(grad) + state["exp_avg_sq_col"] = torch.zeros(p.shape[:-2] + p.shape[-1:]).to(grad) + else: + state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) + state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) + else: + if "exp_avg_sq" not in state: + state["exp_avg_sq"] = torch.zeros_like(grad) + else: + state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) + + p_data_fp32 = p + if _QUANTO_AVAILABLE and isinstance(p_data_fp32, QBytesTensor): + p_data_fp32 = p_data_fp32.dequantize() + if p.dtype != torch.float32: + p_data_fp32 = p_data_fp32.clone().float() + + if "step" not in state: + state["step"] = 0 + state["step"] += 1 + state["RMS"] = self._rms(p_data_fp32) + + beta2 = group["beta2"] + eps = group["eps"] + if isinstance(eps, (tuple, list)): + eps = eps[0] + + update = (grad ** 2) + eps + if factored: + exp_avg_sq_row = state["exp_avg_sq_row"] + exp_avg_sq_col = state["exp_avg_sq_col"] + exp_avg_sq_row.mul_(beta2).add_(update.mean(dim=-1), alpha=(1.0 - beta2)) + exp_avg_sq_col.mul_(beta2).add_(update.mean(dim=-2), alpha=(1.0 - beta2)) + update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) + update.mul_(grad) + else: + exp_avg_sq = state["exp_avg_sq"] + exp_avg_sq.mul_(beta2).add_(update, alpha=(1.0 - beta2)) + update = exp_avg_sq.rsqrt().mul_(grad) + + update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0)) + + if 'last_polarity' not in state or 'lr_mask' not in state: + self.initialize_state(p) + + last_polarity = state['last_polarity'] + current_polarity = (update > 0).to(torch.bool) + sign_agreement = torch.where(last_polarity == current_polarity, 1, -1) + state['last_polarity'] = current_polarity + + lr_mask = state['lr_mask'].to(torch.float32) + new_lr = torch.where( + sign_agreement > 0, + lr_mask + self.lr_bump, + lr_mask - self.lr_bump, + ) + new_lr = torch.clamp(new_lr, min=self.min_lr, max=self.max_lr) + update.mul_(new_lr) + + state['lr_mask'] = Auto8bitTensor(new_lr) + state['avg_lr'] = torch.mean(new_lr) + + if group["weight_decay"] != 0: + weight_decay_update = p_data_fp32 * (-group["weight_decay"]) * new_lr + p_data_fp32.add_(weight_decay_update) + + p_data_fp32.add_(-update) + + if p.dtype != torch.float32: + copy_stochastic(p, p_data_fp32) + + return loss + + def initialize_state(self, p): + state = self.state[p] + state["step"] = 0 + if 'lr_mask' not in state: + state['lr_mask'] = Auto8bitTensor( + torch.ones(p.shape).to(p.device, dtype=torch.float32) * self.lr + ) + state['avg_lr'] = torch.mean(state['lr_mask'].to(torch.float32)) + if 'last_polarity' not in state: + state['last_polarity'] = torch.zeros(p.shape, dtype=torch.bool, device=p.device) + factored = len(p.shape) >= 2 + if factored: + state["exp_avg_sq_row"] = torch.zeros(p.shape[:-1]).to(p) + state["exp_avg_sq_col"] = torch.zeros(p.shape[:-2] + p.shape[-1:]).to(p) + else: + state["exp_avg_sq"] = torch.zeros_like(p) + state["RMS"] = 0 + + def state_dict(self, *args, **kwargs): + orig = super().state_dict(*args, **kwargs) + new_state = {} + for p, state in orig['state'].items(): + s = {k: v for k, v in state.items() if k != 'lr_mask'} + if 'lr_mask' in state: + s['lr_mask'] = state['lr_mask'].state_dict() + new_state[p] = s + orig['state'] = new_state + return orig + + def load_state_dict(self, state_dict, strict=True): + is_valid = any( + 'lr_mask' in pstate + for pstate in state_dict.get('state', {}).values() + if isinstance(pstate, dict) + ) + if not is_valid: + return + + stripped = { + 'state': {pid: {k: v for k, v in ps.items() if k != 'lr_mask'} + for pid, ps in state_dict['state'].items()}, + 'param_groups': state_dict['param_groups'], + } + super().load_state_dict(stripped) + + current_params = [p for g in self.param_groups for p in g['params'] if p.requires_grad] + saved_ids = list(state_dict['state'].keys()) + for i, param in enumerate(current_params): + if i >= len(saved_ids): + break + saved = state_dict['state'][saved_ids[i]] + if 'lr_mask' not in saved: + continue + if param not in self.state: + self.initialize_state(param) + try: + lm = saved['lr_mask'] + if 'quantized' in lm and lm['quantized'].shape == param.shape: + self.state[param]['lr_mask'] = Auto8bitTensor(lm) + else: + raise ValueError("shape mismatch") + except Exception as e: + print(f"Automagic: reinit lr_mask for param {i}: {e}") + self.state[param]['lr_mask'] = Auto8bitTensor( + torch.ones(param.shape).to(param.device, dtype=torch.float32) * self.lr + ) diff --git a/vendor/sd-scripts/library/optimizers/emosens.py b/vendor/sd-scripts/library/optimizers/emosens.py new file mode 100644 index 0000000..c39e5a5 --- /dev/null +++ b/vendor/sd-scripts/library/optimizers/emosens.py @@ -0,0 +1,243 @@ +# Ported from muooon/EmoSens (Apache License 2.0) +# https://github.com/muooon/EmoSens +# Copyright (c) muooon. Used under Apache License 2.0. +# Original: EmoSens v3.9.1 (260520) Standard Edition ECC +import torch +from torch.optim import Optimizer +import math + +""" +EmoSens v3.9.1 (260520) Standard Edition ECC版(CPU-GPUデータ転送対応含む) +shadow-system v3.1 -moment v3.1 emoPulse v3.9 FFT-Swap-Aware dNR-converge +|学習率推奨値| LoRA:1.0 |FFT/Full-Fine-Tuning| Transformer:0.01, UNET:0.1, etc... +全層同一LRのため Transformer 等では発散しやすい(FFTは難しい) 事前学習やLoRA等が望ましいです +これまでの emo系 v3.7～3.8 継承、早期停止関連の効率化やコード修正やコメント最適化等を実施 +Early Stop 判定通知の動的最適化、dNR活用で収束点をユーザー任意で明確化できる(stopcoef) +### FFT適応 cuDNN 等でデータ配置を求める仕様により中間テンソル(コピー)生じる(VRAM負荷増) ### +""" + +# ECC - emo closure capture (Loss-Bypass) +if not hasattr(torch.optim.Optimizer, "_manual_loss"): + torch.optim.Optimizer._manual_loss = 0.0 + + # backward-cap (一度だけ実行されるようにする) + _old_backward = torch.Tensor.backward + def _new_backward(self, *args, **kwargs): + if self.ndim == 0: + try: + torch.optim.Optimizer._manual_loss = self.item() + except: + pass + return _old_backward(self, *args, **kwargs) + torch.Tensor.backward = _new_backward + print("🚩 emo-optim success ecc system ...") + +class EmoSens(Optimizer): # クラス定義＆初期化 + def __init__(self, params, + lr=1.0, + eps=1e-8, + betas=(0.9, 0.995), + weight_decay=0.01, + stopcoef=0.04, + use_shadow:bool=False, + notify:bool=True): + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + super().__init__(params, defaults) + self._init_lr = lr + self.notify = notify # 収束･安定の通知切替 + self.should_stop = False # 停止フラグの初期化 + self.stopcoef = stopcoef # 収束目標値(ユーザー指定可) + self.use_shadow = use_shadow # 🔸shadow(通常 False) + self.emoScope = lr # 動的学習率の調和とリズム + + # shadow は solver 等の特殊用途時に必要かもしれない (optimizerとしては通常不要) + # use_shadow 緊急時モデル保護：通常 False (将来の特殊アーキテクチャへの保護機能) + # notify 収束通知の切替え：通常 True (通知不要な場合は False にできる) + # stopcoef 収束目標Loss：通常 0.04[予兆] (ユーザーの好みで仕上げる) + + self.base_scale, self.max_lim, self.min_lim = 1e-4, 3e-3, 1e-8 + self.dNR_hist, self.noise_est, self.d_est, self.c_est = 1.0, 1.0, 0.02, 0.0 + + # 学習の引き継ぎ可能(状態保存対応)／収束を深めたい場合に役立つ + def state_dict(self): + state_dict = super().state_dict() + state_dict['emo_internal'] = { + 'emoScope': self.emoScope, + 'dNR_hist': self.dNR_hist, + 'noise_est': self.noise_est, + 'd_est': self.d_est, + 'c_est': self.c_est, + 'should_stop': self.should_stop, + 'stopcoef': self.stopcoef, + } + return state_dict + + def load_state_dict(self, state_dict): + emo_internal = state_dict.pop('emo_internal', None) + if emo_internal: + self.emoScope = emo_internal.get('emoScope', self._init_lr) + self.dNR_hist = emo_internal.get('dNR_hist', 1.0) + self.noise_est = emo_internal.get('noise_est', 1.0) + self.d_est = emo_internal.get('d_est', 0.02) + self.c_est = emo_internal.get('c_est', 0.0) + self.should_stop = emo_internal.get('should_stop', False) + self.stopcoef = emo_internal.get('stopcoef', self.stopcoef) + super().load_state_dict(state_dict) + + # 感情EMA更新(緊張と安静)／３次４次５次モーメント近似相当(感覚神経系) + # MLにおいて勾配emaを１次２次相当とするならlossは３次相当以上とみなせる + def _update_ema(self, state, loss_val): + ema = state.setdefault('ema', {}) + ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val) + ema['medium'] = 0.05 * loss_val + 0.95 * ema.get('medium', loss_val) + ema['long'] = 0.01 * loss_val + 0.99 * ema.get('long', loss_val) + return ema + + # 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh(diff) は ±1.0 で有界性)(内分泌系) + # scale_base：Loss値とema値の乖離を修正(分母 ema(long)「改善率」共通化/アーキ非依存) + # 1e-5(デフォルト)／1e-6(感度向上)／1e-4(安定性向上)：分母を０にせず安定(６次近似相当) + def _compute_scalar(self, ema): + scale_base_l = max(ema['long'], 1e-5) + scale_base_m = max(ema['medium'], 1e-5) + diff_base = ema['long'] - ema['short'] + diff_l = diff_base / scale_base_l + diff_m = diff_base / scale_base_m + # longが十分静かなら、常にlongを優先 + if abs(diff_l) < 0.05: + res_scalar = math.tanh(diff_l) + # longが静かでない時のみ、mediumの静けさを条件付きで採用 + elif abs(diff_m) * scale_base_m < abs(diff_l) * scale_base_l: + res_scalar = math.tanh(diff_m) + else: + res_scalar = math.tanh(diff_l) + # scalar と scale_base_m をタプルで返す + return res_scalar, scale_base_m + + # (重要)全機能は use_shadow=False で成立／通常VRAM負荷は shadow を考慮外(無視できる) + # emoPulse機構によるダンパー制動はODE縮約を助けるのでshadowは未知のアーキテクチャへの保険(免疫系) + # Shadow混合比 ３段階構成 タスクに応じ調整可、以下を参考に 開始値・範囲量･変化幅を調整 + # return 開始値 + ((scalar) - 閾値) / 範囲量 * 変化幅 も可能(特殊用途向け) + def _decide_ratio(self, scalar): + if not self.use_shadow: + return 0.0 # 🔸use_shadow = False のとき常に比率を 0 にする + if abs(scalar) > 0.625: + return 1.0 - abs(scalar) # 急変｜強抑制｜tanh 0.73(0.27) + else: + return 0.0 # return<0 の場合は leap 専用(書き戻しはしないが履歴更新のみ) + + # 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ) + # closure への対応は loss.backward() と optimizer.step() の間に + # optimizer._manual_loss = loss.item() を記述する (通常は ECC に任せる) + @torch.no_grad() + def step(self, closure=None): + loss = torch.enable_grad()(closure)() if closure is not None else None + loss_val = loss.item() if loss is not None else getattr(self, '_manual_loss', 0.0) + + # EMA更新・スカラー生成(EMA差分からスカラーを生成しスパイク比率等を決定) + ema = self._update_ema(self.state, loss_val) + scalar, scale_base_m = self._compute_scalar(ema) + ratio = self._decide_ratio(scalar) + trust = math.copysign((1.0 - abs(scalar)), scalar) + + # --- Start emoPulse (完全自動LR生成) --- + # emoPulse (loss 時系列から制振ダンパーとしてLRを生成)(循環器系) + # 時間的D推定：loss-LR-lossの閉循環／ML的にこの差分は６次近似相当とみなせる + self.noise_est = 0.97 * self.noise_est + 0.03 * abs(scalar) + self.d_est = 0.97 * self.d_est + 0.03 * abs(trust) + self.c_est = 0.7 * self.c_est + 0.3 * scalar + noise = max(self.noise_est, 1e-10) # max:1e-12程度(変更後：要アーリーストップ見直し) + d = self.d_est + # 瞬間的D推定：(scalar、trust、差分)各時間軸の確度推定(疑念と信頼の綱引き) + Noise_base = abs(scalar - trust) + 0.1 + d_base = abs(noise - d) + 0.1 + # 異なる時間的確度比率から更新力を導出し２乗で出力最大化(心拍)７次近似相当 + dNR_now_val = (d_base / Noise_base) ** 2 + # 最大値の成長率の増減と履歴化でLRの微調整を担う(非対称性により減衰側を優勢とする) + if dNR_now_val >= self.dNR_hist and trust >= 0.5: + # 加速：どんなに dNR が高くても、1.50倍という｢歩幅｣の成長制限 + self.dNR_hist = min(dNR_now_val, self.dNR_hist * 1.50) + elif -0.5 <= trust <= 0.5: + # 減速：怪しい時は即座に比率を下げる(確実に信頼できない場合に下げ圧力を溜める) + self.dNR_hist = dNR_now_val * 0.80 + # 基礎倍率 100^c_est (-1.0 ～ 1.0) max() で 1e-3以下を無視 (100～0.001変動) + # 0.0：100^0 = 1.0倍, 1.0：100^1 = 100.0倍, -1.0：100^-1 = 0.001倍 + emoChain = self.emoScope * max((100.0 ** self.c_est), 1e-3) + # emoPulse 最終決定： emoScorp によるユーザー意思の反映と安全値による制限 + emoPulse = float(max(min(self.dNR_hist * (emoChain * self.base_scale), + self.emoScope * self.max_lim), self.min_lim)) + # --- End emoPulse (完全自動LR生成) --- + + for group in self.param_groups: + beta1, beta2 = group['betas'] + for p in group['params']: + if p.grad is None: + continue + + grad = p.grad + state = self.state[p] + + # 動的学習率補正により shadow 形成を信頼度で調整(trustは正値化(負にならない)) + # shadow：必要時のみ(スパイクp部分に現在値を最大10%追従させる動的履歴更新) + # 混合比率：スカラーが閾値を超える場合にのみ計算される(信頼できる感情信号かどうかの選別) + # 急変時は感情機構による shadow 混合で強く抑制する(急制動による安定性の確保) + # 機械学習optimizerとしては不要／物理solver的な用途でつかえるかもしれない + if self.use_shadow : + if 'shadow' not in state: # 🔸shadow = False (デフォルト) + state['shadow'] = p.clone() + if ratio > 0: # 書き戻しと履歴更新(急変時の強い抑制と弱めの履歴更新) + p.mul_(1-ratio).add_(state['shadow'], alpha=abs(trust)) + else: # 書き戻しせず履歴更新のみ：10%×trust + leap_ratio = 0.1 * abs(trust) + state['shadow'].lerp_(p, leap_ratio) + + # --- Start Gradient Update Logic --- + # 1次・2次モーメントを使った勾配補正(decoupled weight decay) + if 'exp_avg' not in state: + exp_avg = state.setdefault('exp_avg', torch.zeros_like(p)) + exp_avg_sq = state.setdefault('exp_avg_sq', torch.zeros_like(p)) + + exp_avg = state['exp_avg'] + exp_avg_sq = state['exp_avg_sq'] + + exp_avg.mul_(beta1).add_(grad.to(exp_avg.device), alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad.to(exp_avg_sq.device), grad.to(exp_avg_sq.device), value=1 - beta2) + + denom = exp_avg_sq.sqrt().add_(group['eps']) + + # FFT版と通常版を統合した分岐(デバイス状態判定へ更新) + # device 一致の場合のみ sign_() を使い高速化 + if p.device != exp_avg.device: + # 節約モード：デバイス間の計算を同じ場所へ統一 + update = exp_avg.to(p.device) + else: + # 通常モード：同じ場所の場合は負荷軽減 + update = exp_avg + + if group['weight_decay']: + p.mul_(1.0 - group['weight_decay'] * emoPulse) + p.addcdiv_(update, denom.to(p.device), value=-emoPulse) + # --- End Gradient Update Logic --- + + # ユーザー指定初期LRを実効値(emoPulse)で可視化する(PyTorch標準) + for group in self.param_groups: + group['lr'] = emoPulse + + # 感情機構の穏やかさ"安定状態"を外部伝達する(自動停止ではない) + # Early Stop：誤判定防止をしないのは点灯頻度で停止準備(予兆)にするため + self.stop_base = self.d_est - self.noise_est + if self.stop_base >= 0.3 and scale_base_m <= self.stopcoef: + self.should_stop = True # 💡 外部からこれを見て判断可 + if self.notify: # 💡 収束・安定の「お知らせ」 + print(f"✨[READY TO STOP]✨") + else: + self.should_stop = False # 💡 誤判定などの取り消し + + #print(f"Loss: {loss_val:.4f} | Pulse: {emoPulse:.4e}") + + return + +""" + https://github.com/muooon/EmoSens + An emotion-driven optimizer that feels loss and navigates accordingly. + Don't think. Feel. Don't stop. Keep running. Believe in what's beyond. +""" diff --git a/vendor/sd-scripts/library/optimizers/optimizer_utils.py b/vendor/sd-scripts/library/optimizers/optimizer_utils.py new file mode 100644 index 0000000..65f1f73 --- /dev/null +++ b/vendor/sd-scripts/library/optimizers/optimizer_utils.py @@ -0,0 +1,151 @@ +# Ported from ostris/ai-toolkit (MIT License) +# https://github.com/ostris/ai-toolkit/blob/main/toolkit/optimizers/optimizer_utils.py +import torch +from torch import Tensor +from typing import Optional + +try: + from optimum.quanto import QBytesTensor + _QUANTO_AVAILABLE = True +except ImportError: + QBytesTensor = None + _QUANTO_AVAILABLE = False + + +def compute_scale_for_dtype(tensor, dtype): + if dtype == torch.int8: + abs_max = torch.max(torch.abs(tensor)) + return abs_max / 127.0 if abs_max > 0 else 1.0 + elif dtype == torch.uint8: + max_val = torch.max(tensor) + min_val = torch.min(tensor) + range_val = max_val - min_val + return range_val / 255.0 if range_val > 0 else 1.0 + elif dtype in (torch.float8_e4m3fn, torch.float8_e5m2): + abs_max = torch.max(torch.abs(tensor)) + max_representable = 448.0 if dtype == torch.float8_e4m3fn else 57344.0 + return abs_max / max_representable if abs_max > 0 else 1.0 + else: + raise ValueError(f"Unsupported dtype for quantization: {dtype}") + + +def quantize_tensor(tensor, dtype): + scale = compute_scale_for_dtype(tensor, dtype) + if dtype == torch.int8: + quantized_data = torch.clamp(torch.round(tensor / scale), -128, 127).to(dtype) + elif dtype == torch.uint8: + quantized_data = torch.clamp(torch.round(tensor / scale), 0, 255).to(dtype) + elif dtype in (torch.float8_e4m3fn, torch.float8_e5m2): + quantized_data = (tensor / scale).to(dtype) + else: + raise ValueError(f"Unsupported dtype for quantization: {dtype}") + return quantized_data, scale + + +def update_parameter(target, result_float): + if _QUANTO_AVAILABLE and isinstance(target, QBytesTensor): + target_dtype = target._data.dtype + device = target._data.device + result_float = result_float.to(device) + quantized_data, new_scale = quantize_tensor(result_float, target_dtype) + target._data.copy_(quantized_data) + target._scale.copy_(new_scale) + else: + target.copy_(result_float) + + +def get_format_params(dtype: torch.dtype): + if dtype == torch.float32: + return 23, 32 + elif dtype == torch.bfloat16: + return 7, 16 + elif dtype == torch.float16: + return 10, 16 + elif dtype == torch.float8_e4m3fn: + return 3, 8 + elif dtype == torch.float8_e5m2: + return 2, 8 + elif dtype == torch.int8: + return 0, 8 + else: + raise ValueError(f"Unsupported dtype: {dtype}") + + +def copy_stochastic_bf16(target: torch.Tensor, source: torch.Tensor): + result = torch.randint_like(source, dtype=torch.int32, low=0, high=(1 << 16)) + result.add_(source.view(dtype=torch.int32)) + result.bitwise_and_(-65536) + target.copy_(result.view(dtype=torch.float32)) + del result + + +def copy_stochastic(target: torch.Tensor, source: torch.Tensor, eps: Optional[float] = None) -> None: + with torch.no_grad(): + assert target.device.type != 'cpu', "Target is on cpu!" + assert source.device.type != 'cpu', "Source is on cpu!" + if target.dtype == torch.float32: + target.copy_(source) + return + if target.dtype == torch.bfloat16: + copy_stochastic_bf16(target, source) + return + mantissa_bits, _ = get_format_params(target.dtype) + round_factor = 2 ** (23 - mantissa_bits) + noise = torch.rand_like(source, device=source.device) - 0.5 + rounded = torch.round(source * round_factor + noise) + result_float = rounded / round_factor + if target.dtype == torch.float8_e4m3fn: + result_float.clamp_(-448.0, 448.0) + elif target.dtype == torch.float8_e5m2: + result_float.clamp_(-57344.0, 57344.0) + update_parameter(target, result_float) + + +class Auto8bitTensor: + def __init__(self, data: Tensor, *args, **kwargs): + if isinstance(data, dict): + self._load_from_state_dict(data) + else: + abs_max = data.abs().max().item() + scale = abs_max / 127.0 if abs_max > 0 else 1.0 + self.quantized = (data / scale).round().clamp(-127, 127).to(torch.int8) + self.scale = scale + self.orig_dtype = data.dtype + + def dequantize(self) -> Tensor: + return self.quantized.to(dtype=torch.float32) * self.scale + + def to(self, *args, **kwargs): + dtype = None + if args and isinstance(args[0], torch.dtype): + dtype = args[0] + args = args[1:] + elif 'dtype' in kwargs: + dtype = kwargs['dtype'] + del kwargs['dtype'] + if dtype is not None: + return self.dequantize().to(dtype=dtype, *args, **kwargs) + return self.dequantize().to(*args, **kwargs) + + def state_dict(self): + return {'quantized': self.quantized, 'scale': self.scale, 'orig_dtype': self.orig_dtype} + + def _load_from_state_dict(self, state_dict): + self.quantized = state_dict['quantized'] + self.scale = state_dict['scale'] + self.orig_dtype = state_dict['orig_dtype'] + + def __str__(self): + return f"Auto8bitTensor({self.dequantize()})" + + +def stochastic_grad_accummulation(param): + if hasattr(param, "_accum_grad"): + grad_fp32 = param._accum_grad.clone().to(torch.float32) + grad_fp32.add_(param.grad.to(torch.float32)) + copy_stochastic(param._accum_grad, grad_fp32) + del grad_fp32 + del param.grad + else: + param._accum_grad = param.grad.clone() + del param.grad diff --git a/vendor/sd-scripts/library/original_unet.py b/vendor/sd-scripts/library/original_unet.py new file mode 100644 index 0000000..6dbb9fb --- /dev/null +++ b/vendor/sd-scripts/library/original_unet.py @@ -0,0 +1,1928 @@ +# Diffusers 0.10.2からStable Diffusionに必要な部分だけを持ってくる +# 条件分岐等で不要な部分は削除している +# コードの多くはDiffusersからコピーしている +# 制約として、モデルのstate_dictがDiffusers 0.10.2のものと同じ形式である必要がある + +# Copy from Diffusers 0.10.2 for Stable Diffusion. Most of the code is copied from Diffusers. +# Unnecessary parts are deleted by condition branching. +# As a constraint, the state_dict of the model must be in the same format as that of Diffusers 0.10.2 + +""" +v1.5とv2.1の相違点は +- attention_head_dimがintかlist[int]か +- cross_attention_dimが768か1024か +- use_linear_projection: trueがない（=False, 1.5）かあるか +- upcast_attentionがFalse(1.5)かTrue(2.1)か +- （以下は多分無視していい） +- sample_sizeが64か96か +- dual_cross_attentionがあるかないか +- num_class_embedsがあるかないか +- only_cross_attentionがあるかないか + +v1.5 +{ + "_class_name": "UNet2DConditionModel", + "_diffusers_version": "0.6.0", + "act_fn": "silu", + "attention_head_dim": 8, + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "center_input_sample": false, + "cross_attention_dim": 768, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "norm_eps": 1e-05, + "norm_num_groups": 32, + "out_channels": 4, + "sample_size": 64, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ] +} + +v2.1 +{ + "_class_name": "UNet2DConditionModel", + "_diffusers_version": "0.10.0.dev0", + "act_fn": "silu", + "attention_head_dim": [ + 5, + 10, + 20, + 20 + ], + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "center_input_sample": false, + "cross_attention_dim": 1024, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "dual_cross_attention": false, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_class_embeds": null, + "only_cross_attention": false, + "out_channels": 4, + "sample_size": 96, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "use_linear_projection": true, + "upcast_attention": true +} +""" + +import math +from types import SimpleNamespace +from typing import Dict, Optional, Tuple, Union +import torch +from torch import nn +from torch.nn import functional as F +from einops import rearrange +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +BLOCK_OUT_CHANNELS: Tuple[int] = (320, 640, 1280, 1280) +TIMESTEP_INPUT_DIM = BLOCK_OUT_CHANNELS[0] +TIME_EMBED_DIM = BLOCK_OUT_CHANNELS[0] * 4 +IN_CHANNELS: int = 4 +OUT_CHANNELS: int = 4 +LAYERS_PER_BLOCK: int = 2 +LAYERS_PER_BLOCK_UP: int = LAYERS_PER_BLOCK + 1 +TIME_EMBED_FLIP_SIN_TO_COS: bool = True +TIME_EMBED_FREQ_SHIFT: int = 0 +NORM_GROUPS: int = 32 +NORM_EPS: float = 1e-5 +TRANSFORMER_NORM_NUM_GROUPS = 32 + +DOWN_BLOCK_TYPES = ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"] +UP_BLOCK_TYPES = ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"] + + +# region memory efficient attention + +# FlashAttentionを使うCrossAttention +# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py +# LICENSE MIT https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/LICENSE + +# constants + +EPSILON = 1e-6 + +# helper functions + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +# flash attention forwards and backwards + +# https://arxiv.org/abs/2205.14135 + + +class FlashAttentionFunction(torch.autograd.Function): + @staticmethod + @torch.no_grad() + def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size): + """Algorithm 2 in the paper""" + + device = q.device + dtype = q.dtype + max_neg_value = -torch.finfo(q.dtype).max + qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) + + o = torch.zeros_like(q) + all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device) + all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device) + + scale = q.shape[-1] ** -0.5 + + if not exists(mask): + mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size) + else: + mask = rearrange(mask, "b n -> b 1 1 n") + mask = mask.split(q_bucket_size, dim=-1) + + row_splits = zip( + q.split(q_bucket_size, dim=-2), + o.split(q_bucket_size, dim=-2), + mask, + all_row_sums.split(q_bucket_size, dim=-2), + all_row_maxes.split(q_bucket_size, dim=-2), + ) + + for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits): + q_start_index = ind * q_bucket_size - qk_len_diff + + col_splits = zip( + k.split(k_bucket_size, dim=-2), + v.split(k_bucket_size, dim=-2), + ) + + for k_ind, (kc, vc) in enumerate(col_splits): + k_start_index = k_ind * k_bucket_size + + attn_weights = torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale + + if exists(row_mask): + attn_weights.masked_fill_(~row_mask, max_neg_value) + + if causal and q_start_index < (k_start_index + k_bucket_size - 1): + causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device).triu( + q_start_index - k_start_index + 1 + ) + attn_weights.masked_fill_(causal_mask, max_neg_value) + + block_row_maxes = attn_weights.amax(dim=-1, keepdims=True) + attn_weights -= block_row_maxes + exp_weights = torch.exp(attn_weights) + + if exists(row_mask): + exp_weights.masked_fill_(~row_mask, 0.0) + + block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(min=EPSILON) + + new_row_maxes = torch.maximum(block_row_maxes, row_maxes) + + exp_values = torch.einsum("... i j, ... j d -> ... i d", exp_weights, vc) + + exp_row_max_diff = torch.exp(row_maxes - new_row_maxes) + exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes) + + new_row_sums = exp_row_max_diff * row_sums + exp_block_row_max_diff * block_row_sums + + oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_((exp_block_row_max_diff / new_row_sums) * exp_values) + + row_maxes.copy_(new_row_maxes) + row_sums.copy_(new_row_sums) + + ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size) + ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes) + + return o + + @staticmethod + @torch.no_grad() + def backward(ctx, do): + """Algorithm 4 in the paper""" + + causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args + q, k, v, o, l, m = ctx.saved_tensors + + device = q.device + + max_neg_value = -torch.finfo(q.dtype).max + qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) + + dq = torch.zeros_like(q) + dk = torch.zeros_like(k) + dv = torch.zeros_like(v) + + row_splits = zip( + q.split(q_bucket_size, dim=-2), + o.split(q_bucket_size, dim=-2), + do.split(q_bucket_size, dim=-2), + mask, + l.split(q_bucket_size, dim=-2), + m.split(q_bucket_size, dim=-2), + dq.split(q_bucket_size, dim=-2), + ) + + for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits): + q_start_index = ind * q_bucket_size - qk_len_diff + + col_splits = zip( + k.split(k_bucket_size, dim=-2), + v.split(k_bucket_size, dim=-2), + dk.split(k_bucket_size, dim=-2), + dv.split(k_bucket_size, dim=-2), + ) + + for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits): + k_start_index = k_ind * k_bucket_size + + attn_weights = torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale + + if causal and q_start_index < (k_start_index + k_bucket_size - 1): + causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device).triu( + q_start_index - k_start_index + 1 + ) + attn_weights.masked_fill_(causal_mask, max_neg_value) + + exp_attn_weights = torch.exp(attn_weights - mc) + + if exists(row_mask): + exp_attn_weights.masked_fill_(~row_mask, 0.0) + + p = exp_attn_weights / lc + + dv_chunk = torch.einsum("... i j, ... i d -> ... j d", p, doc) + dp = torch.einsum("... i d, ... j d -> ... i j", doc, vc) + + D = (doc * oc).sum(dim=-1, keepdims=True) + ds = p * scale * (dp - D) + + dq_chunk = torch.einsum("... i j, ... j d -> ... i d", ds, kc) + dk_chunk = torch.einsum("... i j, ... i d -> ... j d", ds, qc) + + dqc.add_(dq_chunk) + dkc.add_(dk_chunk) + dvc.add_(dv_chunk) + + return dq, dk, dv, None, None, None, None + + +# endregion + + +def get_parameter_dtype(parameter: torch.nn.Module): + return next(parameter.parameters()).dtype + + +def get_parameter_device(parameter: torch.nn.Module): + return next(parameter.parameters()).device + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +# Deep Shrink: We do not common this function, because minimize dependencies. +def resize_like(x, target, mode="bicubic", align_corners=False): + org_dtype = x.dtype + if org_dtype == torch.bfloat16: + x = x.to(torch.float32) + + if x.shape[-2:] != target.shape[-2:]: + if mode == "nearest": + x = F.interpolate(x, size=target.shape[-2:], mode=mode) + else: + x = F.interpolate(x, size=target.shape[-2:], mode=mode, align_corners=align_corners) + + if org_dtype == torch.bfloat16: + x = x.to(org_dtype) + return x + + +class SampleOutput: + def __init__(self, sample): + self.sample = sample + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_channels: int, time_embed_dim: int, act_fn: str = "silu", out_dim: int = None): + super().__init__() + + self.linear_1 = nn.Linear(in_channels, time_embed_dim) + self.act = None + if act_fn == "silu": + self.act = nn.SiLU() + elif act_fn == "mish": + self.act = nn.Mish() + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out) + + def forward(self, sample): + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + return sample + + +class Timesteps(nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + ) + return t_emb + + +class ResnetBlock2D(nn.Module): + def __init__( + self, + in_channels, + out_channels, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + self.norm1 = torch.nn.GroupNorm(num_groups=NORM_GROUPS, num_channels=in_channels, eps=NORM_EPS, affine=True) + + self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.time_emb_proj = torch.nn.Linear(TIME_EMBED_DIM, out_channels) + + self.norm2 = torch.nn.GroupNorm(num_groups=NORM_GROUPS, num_channels=out_channels, eps=NORM_EPS, affine=True) + self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + # if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + + self.use_in_shortcut = self.in_channels != self.out_channels + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = input_tensor + hidden_states + + return output_tensor + + +class DownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + add_downsample=True, + ): + super().__init__() + + self.has_cross_attention = False + resnets = [] + + for i in range(LAYERS_PER_BLOCK): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + ) + ) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = [Downsample2D(out_channels, out_channels=out_channels)] + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + pass + + def set_use_sdpa(self, sdpa): + pass + + def forward(self, hidden_states, temb=None): + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = resnet(hidden_states, temb) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class Downsample2D(nn.Module): + def __init__(self, channels, out_channels): + super().__init__() + + self.channels = channels + self.out_channels = out_channels + + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=2, padding=1) + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class CrossAttention(nn.Module): + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + dim_head: int = 64, + upcast_attention: bool = False, + ): + super().__init__() + inner_dim = dim_head * heads + cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.upcast_attention = upcast_attention + + self.scale = dim_head**-0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=False) + self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=False) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(inner_dim, query_dim)) + # no dropout here + + self.use_memory_efficient_attention_xformers = False + self.use_memory_efficient_attention_mem_eff = False + self.use_sdpa = False + + # Attention processor + self.processor = None + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + self.use_memory_efficient_attention_xformers = xformers + self.use_memory_efficient_attention_mem_eff = mem_eff + + def set_use_sdpa(self, sdpa): + self.use_sdpa = sdpa + + def reshape_heads_to_batch_dim(self, tensor): + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) + tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size) + return tensor + + def reshape_batch_dim_to_heads(self, tensor): + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) + tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size) + return tensor + + def set_processor(self): + return self.processor + + def get_processor(self): + return self.processor + + def forward(self, hidden_states, context=None, mask=None, **kwargs): + if self.processor is not None: + ( + hidden_states, + encoder_hidden_states, + attention_mask, + ) = translate_attention_names_from_diffusers(hidden_states=hidden_states, context=context, mask=mask, **kwargs) + return self.processor( + attn=self, hidden_states=hidden_states, encoder_hidden_states=context, attention_mask=mask, **kwargs + ) + if self.use_memory_efficient_attention_xformers: + return self.forward_memory_efficient_xformers(hidden_states, context, mask) + if self.use_memory_efficient_attention_mem_eff: + return self.forward_memory_efficient_mem_eff(hidden_states, context, mask) + if self.use_sdpa: + return self.forward_sdpa(hidden_states, context, mask) + + query = self.to_q(hidden_states) + context = context if context is not None else hidden_states + key = self.to_k(context) + value = self.to_v(context) + + query = self.reshape_heads_to_batch_dim(query) + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + hidden_states = self._attention(query, key, value) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + # hidden_states = self.to_out[1](hidden_states) # no dropout + return hidden_states + + def _attention(self, query, key, value): + if self.upcast_attention: + query = query.float() + key = key.float() + + attention_scores = torch.baddbmm( + torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device), + query, + key.transpose(-1, -2), + beta=0, + alpha=self.scale, + ) + attention_probs = attention_scores.softmax(dim=-1) + + # cast back to the original dtype + attention_probs = attention_probs.to(value.dtype) + + # compute attention output + hidden_states = torch.bmm(attention_probs, value) + + # reshape hidden_states + hidden_states = self.reshape_batch_dim_to_heads(hidden_states) + return hidden_states + + # TODO support Hypernetworks + def forward_memory_efficient_xformers(self, x, context=None, mask=None): + import xformers.ops + + h = self.heads + q_in = self.to_q(x) + context = context if context is not None else x + context = context.to(x.dtype) + k_in = self.to_k(context) + v_in = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b n h d", h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + q = q.contiguous() + k = k.contiguous() + v = v.contiguous() + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) # 最適なのを選んでくれる + + out = rearrange(out, "b n h d -> b n (h d)", h=h) + + out = self.to_out[0](out) + return out + + def forward_memory_efficient_mem_eff(self, x, context=None, mask=None): + flash_func = FlashAttentionFunction + + q_bucket_size = 512 + k_bucket_size = 1024 + + h = self.heads + q = self.to_q(x) + context = context if context is not None else x + context = context.to(x.dtype) + k = self.to_k(context) + v = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) + + out = flash_func.apply(q, k, v, mask, False, q_bucket_size, k_bucket_size) + + out = rearrange(out, "b h n d -> b n (h d)") + + out = self.to_out[0](out) + return out + + def forward_sdpa(self, x, context=None, mask=None): + h = self.heads + q_in = self.to_q(x) + context = context if context is not None else x + context = context.to(x.dtype) + k_in = self.to_k(context) + v_in = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + + out = rearrange(out, "b h n d -> b n (h d)", h=h) + + out = self.to_out[0](out) + return out + + +def translate_attention_names_from_diffusers( + hidden_states: torch.FloatTensor, + context: Optional[torch.FloatTensor] = None, + mask: Optional[torch.FloatTensor] = None, + # HF naming + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, +): + # translate from hugging face diffusers + context = context if context is not None else encoder_hidden_states + + # translate from hugging face diffusers + mask = mask if mask is not None else attention_mask + + return hidden_states, context, mask + + +# feedforward +class GEGLU(nn.Module): + r""" + A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def gelu(self, gate): + if gate.device.type != "mps": + return F.gelu(gate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype) + + def forward(self, hidden_states): + hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) + return hidden_states * self.gelu(gate) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + ): + super().__init__() + inner_dim = int(dim * 4) # mult is always 4 + + self.net = nn.ModuleList([]) + # project in + self.net.append(GEGLU(dim, inner_dim)) + # project dropout + self.net.append(nn.Identity()) # nn.Dropout(0)) # dummy for dropout with 0 + # project out + self.net.append(nn.Linear(inner_dim, dim)) + + def forward(self, hidden_states): + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, dim: int, num_attention_heads: int, attention_head_dim: int, cross_attention_dim: int, upcast_attention: bool = False + ): + super().__init__() + + # 1. Self-Attn + self.attn1 = CrossAttention( + query_dim=dim, + cross_attention_dim=None, + heads=num_attention_heads, + dim_head=attention_head_dim, + upcast_attention=upcast_attention, + ) + self.ff = FeedForward(dim) + + # 2. Cross-Attn + self.attn2 = CrossAttention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + upcast_attention=upcast_attention, + ) + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim) + + def set_use_memory_efficient_attention(self, xformers: bool, mem_eff: bool): + self.attn1.set_use_memory_efficient_attention(xformers, mem_eff) + self.attn2.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa: bool): + self.attn1.set_use_sdpa(sdpa) + self.attn2.set_use_sdpa(sdpa) + + def forward(self, hidden_states, context=None, timestep=None): + # 1. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + + hidden_states = self.attn1(norm_hidden_states) + hidden_states + + # 2. Cross-Attention + norm_hidden_states = self.norm2(hidden_states) + hidden_states = self.attn2(norm_hidden_states, context=context) + hidden_states + + # 3. Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + return hidden_states + + +class Transformer2DModel(nn.Module): + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + use_linear_projection: bool = False, + upcast_attention: bool = False, + ): + super().__init__() + self.in_channels = in_channels + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + self.use_linear_projection = use_linear_projection + + self.norm = torch.nn.GroupNorm(num_groups=TRANSFORMER_NORM_NUM_GROUPS, num_channels=in_channels, eps=1e-6, affine=True) + + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + cross_attention_dim=cross_attention_dim, + upcast_attention=upcast_attention, + ) + ] + ) + + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + for transformer in self.transformer_blocks: + transformer.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa): + for transformer in self.transformer_blocks: + transformer.set_use_sdpa(sdpa) + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True): + # 1. Input + batch, _, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block(hidden_states, context=encoder_hidden_states, timestep=timestep) + + # 3. Output + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + + if not return_dict: + return (output,) + + return SampleOutput(sample=output) + + +class CrossAttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + add_downsample=True, + cross_attention_dim=1280, + attn_num_head_channels=1, + use_linear_projection=False, + upcast_attention=False, + ): + super().__init__() + self.has_cross_attention = True + resnets = [] + attentions = [] + + self.attn_num_head_channels = attn_num_head_channels + + for i in range(LAYERS_PER_BLOCK): + in_channels = in_channels if i == 0 else out_channels + + resnets.append(ResnetBlock2D(in_channels=in_channels, out_channels=out_channels)) + attentions.append( + Transformer2DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + cross_attention_dim=cross_attention_dim, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList([Downsample2D(out_channels, out_channels)]) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + for attn in self.attentions: + attn.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa): + for attn in self.attentions: + attn.set_use_sdpa(sdpa) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None): + output_states = () + + for resnet, attn in zip(self.resnets, self.attentions): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, use_reentrant=False + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class UNetMidBlock2DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + attn_num_head_channels=1, + cross_attention_dim=1280, + use_linear_projection=False, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + # Middle block has two resnets and one attention + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + ), + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + ), + ] + attentions = [ + Transformer2DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + cross_attention_dim=cross_attention_dim, + use_linear_projection=use_linear_projection, + ) + ] + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + for attn in self.attentions: + attn.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa): + for attn in self.attentions: + attn.set_use_sdpa(sdpa) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None): + for i, resnet in enumerate(self.resnets): + attn = None if i == 0 else self.attentions[i - 1] + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + if attn is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, use_reentrant=False + )[0] + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + if attn is not None: + hidden_states = attn(hidden_states, encoder_hidden_states).sample + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class Upsample2D(nn.Module): + def __init__(self, channels, out_channels): + super().__init__() + self.channels = channels + self.out_channels = out_channels + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, hidden_states, output_size): + assert hidden_states.shape[1] == self.channels + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch + # https://github.com/pytorch/pytorch/issues/86679 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class UpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + add_upsample=True, + ): + super().__init__() + + self.has_cross_attention = False + resnets = [] + + for i in range(LAYERS_PER_BLOCK_UP): + res_skip_channels = in_channels if (i == LAYERS_PER_BLOCK_UP - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + pass + + def set_use_sdpa(self, sdpa): + pass + + def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = resnet(hidden_states, temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class CrossAttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + attn_num_head_channels=1, + cross_attention_dim=1280, + add_upsample=True, + use_linear_projection=False, + upcast_attention=False, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(LAYERS_PER_BLOCK_UP): + res_skip_channels = in_channels if (i == LAYERS_PER_BLOCK_UP - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + ) + ) + attentions.append( + Transformer2DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + cross_attention_dim=cross_attention_dim, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + for attn in self.attentions: + attn.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa): + for attn in self.attentions: + attn.set_use_sdpa(sdpa) + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + ): + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, use_reentrant=False + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +def get_down_block( + down_block_type, + in_channels, + out_channels, + add_downsample, + attn_num_head_channels, + cross_attention_dim, + use_linear_projection, + upcast_attention, +): + if down_block_type == "DownBlock2D": + return DownBlock2D( + in_channels=in_channels, + out_channels=out_channels, + add_downsample=add_downsample, + ) + elif down_block_type == "CrossAttnDownBlock2D": + return CrossAttnDownBlock2D( + in_channels=in_channels, + out_channels=out_channels, + add_downsample=add_downsample, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + + +def get_up_block( + up_block_type, + in_channels, + out_channels, + prev_output_channel, + add_upsample, + attn_num_head_channels, + cross_attention_dim=None, + use_linear_projection=False, + upcast_attention=False, +): + if up_block_type == "UpBlock2D": + return UpBlock2D( + in_channels=in_channels, + prev_output_channel=prev_output_channel, + out_channels=out_channels, + add_upsample=add_upsample, + ) + elif up_block_type == "CrossAttnUpBlock2D": + return CrossAttnUpBlock2D( + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + attn_num_head_channels=attn_num_head_channels, + cross_attention_dim=cross_attention_dim, + add_upsample=add_upsample, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + + +class UNet2DConditionModel(nn.Module): + _supports_gradient_checkpointing = True + + def __init__( + self, + sample_size: Optional[int] = None, + attention_head_dim: Union[int, Tuple[int]] = 8, + cross_attention_dim: int = 1280, + use_linear_projection: bool = False, + upcast_attention: bool = False, + in_channels: int = IN_CHANNELS, + **kwargs, + ): + super().__init__() + assert sample_size is not None, "sample_size must be specified" + logger.info( + f"UNet2DConditionModel: {sample_size}, {attention_head_dim}, {cross_attention_dim}, {use_linear_projection}, {upcast_attention}" + ) + + # 外部からの参照用に定義しておく + self.in_channels = in_channels + self.out_channels = OUT_CHANNELS + + self.sample_size = sample_size + self.prepare_config(sample_size=sample_size) + + # state_dictの書式が変わるのでmoduleの持ち方は変えられない + + # input + self.conv_in = nn.Conv2d(in_channels, BLOCK_OUT_CHANNELS[0], kernel_size=3, padding=(1, 1)) + + # time + self.time_proj = Timesteps(BLOCK_OUT_CHANNELS[0], TIME_EMBED_FLIP_SIN_TO_COS, TIME_EMBED_FREQ_SHIFT) + + self.time_embedding = TimestepEmbedding(TIMESTEP_INPUT_DIM, TIME_EMBED_DIM) + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * 4 + + # down + output_channel = BLOCK_OUT_CHANNELS[0] + for i, down_block_type in enumerate(DOWN_BLOCK_TYPES): + input_channel = output_channel + output_channel = BLOCK_OUT_CHANNELS[i] + is_final_block = i == len(BLOCK_OUT_CHANNELS) - 1 + + down_block = get_down_block( + down_block_type, + in_channels=input_channel, + out_channels=output_channel, + add_downsample=not is_final_block, + attn_num_head_channels=attention_head_dim[i], + cross_attention_dim=cross_attention_dim, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlock2DCrossAttn( + in_channels=BLOCK_OUT_CHANNELS[-1], + attn_num_head_channels=attention_head_dim[-1], + cross_attention_dim=cross_attention_dim, + use_linear_projection=use_linear_projection, + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(BLOCK_OUT_CHANNELS)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(UP_BLOCK_TYPES): + is_final_block = i == len(BLOCK_OUT_CHANNELS) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(BLOCK_OUT_CHANNELS) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + add_upsample=add_upsample, + attn_num_head_channels=reversed_attention_head_dim[i], + cross_attention_dim=cross_attention_dim, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=BLOCK_OUT_CHANNELS[0], num_groups=NORM_GROUPS, eps=NORM_EPS) + self.conv_act = nn.SiLU() + self.conv_out = nn.Conv2d(BLOCK_OUT_CHANNELS[0], OUT_CHANNELS, kernel_size=3, padding=1) + + # region diffusers compatibility + def prepare_config(self, *args, **kwargs): + self.config = SimpleNamespace(**kwargs) + + @property + def dtype(self) -> torch.dtype: + # `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype). + return get_parameter_dtype(self) + + @property + def device(self) -> torch.device: + # `torch.device`: The device on which the module is (assuming that all the module parameters are on the same device). + return get_parameter_device(self) + + def set_attention_slice(self, slice_size): + raise NotImplementedError("Attention slicing is not supported for this model.") + + def is_gradient_checkpointing(self) -> bool: + return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules()) + + def enable_gradient_checkpointing(self): + self.set_gradient_checkpointing(value=True) + + def disable_gradient_checkpointing(self): + self.set_gradient_checkpointing(value=False) + + def set_use_memory_efficient_attention(self, xformers: bool, mem_eff: bool) -> None: + modules = self.down_blocks + [self.mid_block] + self.up_blocks + for module in modules: + module.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa: bool) -> None: + modules = self.down_blocks + [self.mid_block] + self.up_blocks + for module in modules: + module.set_use_sdpa(sdpa) + + def set_gradient_checkpointing(self, value=False): + modules = self.down_blocks + [self.mid_block] + self.up_blocks + for module in modules: + logger.info(f"{module.__class__.__name__} {module.gradient_checkpointing} -> {value}") + module.gradient_checkpointing = value + + # endregion + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + return_dict: bool = True, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + ) -> Union[Dict, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a dict instead of a plain tuple. + + Returns: + `SampleOutput` or `tuple`: + `SampleOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + # デフォルトではサンプルは「2^アップサンプルの数」、つまり64の倍数である必要がある + # ただそれ以外のサイズにも対応できるように、必要ならアップサンプルのサイズを変更する + # 多分画質が悪くなるので、64で割り切れるようにしておくのが良い + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + # 64で割り切れないときはupsamplerにサイズを伝える + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + # logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # 1. time + timesteps = timestep + timesteps = self.handle_unusual_timesteps(sample, timesteps) # 変な時だけ処理 + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + # timestepsは重みを含まないので常にfloat32のテンソルを返す + # しかしtime_embeddingはfp16で動いているかもしれないので、ここでキャストする必要がある + # time_projでキャストしておけばいいんじゃね？ + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + # 2. pre-process + sample = self.conv_in(sample) + + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + # downblockはforwardで必ずencoder_hidden_statesを受け取るようにしても良さそうだけど、 + # まあこちらのほうがわかりやすいかもしれない + if downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + down_block_res_samples += res_samples + + # skip connectionにControlNetの出力を追加する + if down_block_additional_residuals is not None: + down_block_res_samples = list(down_block_res_samples) + for i in range(len(down_block_res_samples)): + down_block_res_samples[i] += down_block_additional_residuals[i] + down_block_res_samples = tuple(down_block_res_samples) + + # 4. mid + sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states) + + # ControlNetの出力を追加する + if mid_block_additional_residual is not None: + sample += mid_block_additional_residual + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # skip connection + + # if we have not reached the final block and need to forward the upsample size, we do it here + # 前述のように最後のブロック以外ではupsample_sizeを伝える + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + + # 6. post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return SampleOutput(sample=sample) + + def handle_unusual_timesteps(self, sample, timesteps): + r""" + timestampsがTensorでない場合、Tensorに変換する。またOnnx/Core MLと互換性のあるようにbatchサイズまでbroadcastする。 + """ + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timesteps, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + return timesteps + + +class InferUNet2DConditionModel: + def __init__(self, original_unet: UNet2DConditionModel): + self.delegate = original_unet + + # override original model's forward method: because forward is not called by `__call__` + # overriding `__call__` is not enough, because nn.Module.forward has a special handling + self.delegate.forward = self.forward + + # override original model's up blocks' forward method + for up_block in self.delegate.up_blocks: + if up_block.__class__.__name__ == "UpBlock2D": + + def resnet_wrapper(func, block): + def forward(*args, **kwargs): + return func(block, *args, **kwargs) + + return forward + + up_block.forward = resnet_wrapper(self.up_block_forward, up_block) + + elif up_block.__class__.__name__ == "CrossAttnUpBlock2D": + + def cross_attn_up_wrapper(func, block): + def forward(*args, **kwargs): + return func(block, *args, **kwargs) + + return forward + + up_block.forward = cross_attn_up_wrapper(self.cross_attn_up_block_forward, up_block) + + # Deep Shrink + self.ds_depth_1 = None + self.ds_depth_2 = None + self.ds_timesteps_1 = None + self.ds_timesteps_2 = None + self.ds_ratio = None + + # call original model's methods + def __getattr__(self, name): + return getattr(self.delegate, name) + + def __call__(self, *args, **kwargs): + return self.delegate(*args, **kwargs) + + def set_deep_shrink(self, ds_depth_1, ds_timesteps_1=650, ds_depth_2=None, ds_timesteps_2=None, ds_ratio=0.5): + if ds_depth_1 is None: + logger.info("Deep Shrink is disabled.") + self.ds_depth_1 = None + self.ds_timesteps_1 = None + self.ds_depth_2 = None + self.ds_timesteps_2 = None + self.ds_ratio = None + else: + logger.info( + f"Deep Shrink is enabled: [depth={ds_depth_1}/{ds_depth_2}, timesteps={ds_timesteps_1}/{ds_timesteps_2}, ratio={ds_ratio}]" + ) + self.ds_depth_1 = ds_depth_1 + self.ds_timesteps_1 = ds_timesteps_1 + self.ds_depth_2 = ds_depth_2 if ds_depth_2 is not None else -1 + self.ds_timesteps_2 = ds_timesteps_2 if ds_timesteps_2 is not None else 1000 + self.ds_ratio = ds_ratio + + def up_block_forward(self, _self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): + for resnet in _self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # Deep Shrink + if res_hidden_states.shape[-2:] != hidden_states.shape[-2:]: + hidden_states = resize_like(hidden_states, res_hidden_states) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + hidden_states = resnet(hidden_states, temb) + + if _self.upsamplers is not None: + for upsampler in _self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + def cross_attn_up_block_forward( + self, + _self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + ): + for resnet, attn in zip(_self.resnets, _self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # Deep Shrink + if res_hidden_states.shape[-2:] != hidden_states.shape[-2:]: + hidden_states = resize_like(hidden_states, res_hidden_states) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + if _self.upsamplers is not None: + for upsampler in _self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + return_dict: bool = True, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + ) -> Union[Dict, Tuple]: + r""" + current implementation is a copy of `UNet2DConditionModel.forward()` with Deep Shrink. + """ + + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a dict instead of a plain tuple. + + Returns: + `SampleOutput` or `tuple`: + `SampleOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. + """ + + _self = self.delegate + + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + # デフォルトではサンプルは「2^アップサンプルの数」、つまり64の倍数である必要がある + # ただそれ以外のサイズにも対応できるように、必要ならアップサンプルのサイズを変更する + # 多分画質が悪くなるので、64で割り切れるようにしておくのが良い + default_overall_up_factor = 2**_self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + # 64で割り切れないときはupsamplerにサイズを伝える + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + # logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # 1. time + timesteps = timestep + timesteps = _self.handle_unusual_timesteps(sample, timesteps) # 変な時だけ処理 + + t_emb = _self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + # timestepsは重みを含まないので常にfloat32のテンソルを返す + # しかしtime_embeddingはfp16で動いているかもしれないので、ここでキャストする必要がある + # time_projでキャストしておけばいいんじゃね？ + t_emb = t_emb.to(dtype=_self.dtype) + emb = _self.time_embedding(t_emb) + + # 2. pre-process + sample = _self.conv_in(sample) + + down_block_res_samples = (sample,) + for depth, downsample_block in enumerate(_self.down_blocks): + # Deep Shrink + if self.ds_depth_1 is not None: + if (depth == self.ds_depth_1 and timesteps[0] >= self.ds_timesteps_1) or ( + self.ds_depth_2 is not None + and depth == self.ds_depth_2 + and timesteps[0] < self.ds_timesteps_1 + and timesteps[0] >= self.ds_timesteps_2 + ): + org_dtype = sample.dtype + if org_dtype == torch.bfloat16: + sample = sample.to(torch.float32) + sample = F.interpolate(sample, scale_factor=self.ds_ratio, mode="bicubic", align_corners=False).to(org_dtype) + + # downblockはforwardで必ずencoder_hidden_statesを受け取るようにしても良さそうだけど、 + # まあこちらのほうがわかりやすいかもしれない + if downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + down_block_res_samples += res_samples + + # skip connectionにControlNetの出力を追加する + if down_block_additional_residuals is not None: + down_block_res_samples = list(down_block_res_samples) + for i in range(len(down_block_res_samples)): + down_block_res_samples[i] += down_block_additional_residuals[i] + down_block_res_samples = tuple(down_block_res_samples) + + # 4. mid + sample = _self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states) + + # ControlNetの出力を追加する + if mid_block_additional_residual is not None: + sample += mid_block_additional_residual + + # 5. up + for i, upsample_block in enumerate(_self.up_blocks): + is_final_block = i == len(_self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # skip connection + + # if we have not reached the final block and need to forward the upsample size, we do it here + # 前述のように最後のブロック以外ではupsample_sizeを伝える + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + + # 6. post-process + sample = _self.conv_norm_out(sample) + sample = _self.conv_act(sample) + sample = _self.conv_out(sample) + + if not return_dict: + return (sample,) + + return SampleOutput(sample=sample) diff --git a/vendor/sd-scripts/library/qwen_image_autoencoder_kl.py b/vendor/sd-scripts/library/qwen_image_autoencoder_kl.py new file mode 100644 index 0000000..ab65e3b --- /dev/null +++ b/vendor/sd-scripts/library/qwen_image_autoencoder_kl.py @@ -0,0 +1,1735 @@ +# Copied and modified from Diffusers (via Musubi-Tuner). Original copyright notice follows. + +# Copyright 2025 The Qwen-Image Team, Wan Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We gratefully acknowledge the Wan Team for their outstanding contributions. +# QwenImageVAE is further fine-tuned from the Wan Video VAE to achieve improved performance. +# For more information about the Wan VAE, please refer to: +# - GitHub: https://github.com/Wan-Video/Wan2.1 +# - arXiv: https://arxiv.org/abs/2503.20314 + +import json +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from library.safetensors_utils import load_safetensors + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +CACHE_T = 2 + +SCALE_FACTOR = 8 # VAE downsampling factor + + +# region diffusers-vae + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters: torch.Tensor, deterministic: bool = False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device, dtype=self.parameters.dtype) + + def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor: + # make sure sample is on the same device as the parameters and has same dtype + if generator is not None and generator.device.type != self.parameters.device.type: + rand_device = generator.device + else: + rand_device = self.parameters.device + sample = torch.randn(self.mean.shape, generator=generator, device=rand_device, dtype=self.parameters.dtype).to( + self.parameters.device + ) + x = self.mean + self.std * sample + return x + + def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor: + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self) -> torch.Tensor: + return self.mean + + +# endregion diffusers-vae + + +class ChunkedConv2d(nn.Conv2d): + """ + Convolutional layer that processes input in chunks to reduce memory usage. + + Parameters + ---------- + spatial_chunk_size : int, optional + Size of chunks to process at a time. Default is None, which means no chunking. + + TODO: Commonize with similar implementation in hunyuan_image_vae.py + """ + + def __init__(self, *args, **kwargs): + if "spatial_chunk_size" in kwargs: + self.spatial_chunk_size = kwargs.pop("spatial_chunk_size", None) + else: + self.spatial_chunk_size = None + super().__init__(*args, **kwargs) + assert self.padding_mode == "zeros", "Only 'zeros' padding mode is supported." + assert self.dilation == (1, 1), "Only dilation=1 is supported." + assert self.groups == 1, "Only groups=1 is supported." + assert self.kernel_size[0] == self.kernel_size[1], "Only square kernels are supported." + assert self.stride[0] == self.stride[1], "Only equal strides are supported." + self.original_padding = self.padding + self.padding = (0, 0) # We handle padding manually in forward + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # If chunking is not needed, process normally. We chunk only along height dimension. + if ( + self.spatial_chunk_size is None + or x.shape[2] <= self.spatial_chunk_size + self.kernel_size[0] + self.spatial_chunk_size // 4 + ): + self.padding = self.original_padding + x = super().forward(x) + self.padding = (0, 0) + return x + + # Process input in chunks to reduce memory usage + org_shape = x.shape + + # If kernel size is not 1, we need to use overlapping chunks + overlap = self.kernel_size[0] // 2 # 1 for kernel size 3 + if self.original_padding[0] == 0: + overlap = 0 + + # If stride > 1, QwenImageVAE pads manually with zeros before convolution, so we do not need to consider it here + y_height = org_shape[2] // self.stride[0] + y_width = org_shape[3] // self.stride[1] + y = torch.zeros((org_shape[0], self.out_channels, y_height, y_width), dtype=x.dtype, device=x.device) + yi = 0 + i = 0 + while i < org_shape[2]: + si = i if i == 0 else i - overlap + ei = i + self.spatial_chunk_size + overlap + self.stride[0] - 1 + + # Check last chunk. If remaining part is small, include it in last chunk + if ei > org_shape[2] or ei + self.spatial_chunk_size // 4 > org_shape[2]: + ei = org_shape[2] + + chunk = x[:, :, si:ei, :] + + # Pad chunk if needed: This is as the original Conv2d with padding + if i == 0 and overlap > 0: # First chunk + # Pad except bottom + chunk = torch.nn.functional.pad(chunk, (overlap, overlap, overlap, 0), mode="constant", value=0) + elif ei == org_shape[2] and overlap > 0: # Last chunk + # Pad except top + chunk = torch.nn.functional.pad(chunk, (overlap, overlap, 0, overlap), mode="constant", value=0) + elif overlap > 0: # Middle chunks + # Pad left and right only + chunk = torch.nn.functional.pad(chunk, (overlap, overlap), mode="constant", value=0) + + # print(f"Processing chunk: org_shape={org_shape}, si={si}, ei={ei}, chunk.shape={chunk.shape}, overlap={overlap}") + chunk = super().forward(chunk) + # print(f" -> chunk after conv shape: {chunk.shape}") + y[:, :, yi : yi + chunk.shape[2], :] = chunk + yi += chunk.shape[2] + del chunk + + if ei == org_shape[2]: + break + i += self.spatial_chunk_size + + assert yi == y_height, f"yi={yi}, y_height={y_height}" + + return y + + +class QwenImageCausalConv3d(nn.Conv3d): + r""" + A custom 3D causal convolution layer with feature caching support. + + This layer extends the standard Conv3D layer by ensuring causality in the time dimension and handling feature + caching for efficient inference. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0 + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int, int]], + stride: Union[int, Tuple[int, int, int]] = 1, + padding: Union[int, Tuple[int, int, int]] = 0, + spatial_chunk_size: Optional[int] = None, + ) -> None: + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ) + + # Set up causal padding + self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + self.spatial_chunk_size = spatial_chunk_size + self._supports_spatial_chunking = ( + self.groups == 1 and self.dilation[1] == 1 and self.dilation[2] == 1 and self.stride[1] == 1 and self.stride[2] == 1 + ) + + def _forward_chunked_height(self, x: torch.Tensor) -> torch.Tensor: + chunk_size = self.spatial_chunk_size + if chunk_size is None or chunk_size <= 0: + return super().forward(x) + if not self._supports_spatial_chunking: + return super().forward(x) + + kernel_h = self.kernel_size[1] + if kernel_h <= 1 or x.shape[3] <= chunk_size: + return super().forward(x) + + receptive_h = kernel_h + out_h = x.shape[3] - receptive_h + 1 + if out_h <= 0: + return super().forward(x) + + y0 = 0 + out = None + while y0 < out_h: + y1 = min(y0 + chunk_size, out_h) + in0 = y0 + in1 = y1 + receptive_h - 1 + out_chunk = super().forward(x[:, :, :, in0:in1, :]) + if out is None: + out_shape = list(out_chunk.shape) + out_shape[3] = out_h + out = out_chunk.new_empty(out_shape) + out[:, :, :, y0:y1, :] = out_chunk + y0 = y1 + + return out + + def forward(self, x, cache_x=None): + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + x = F.pad(x, padding) + return self._forward_chunked_height(x) + + +class QwenImageRMS_norm(nn.Module): + r""" + A custom RMS normalization layer. + + Args: + dim (int): The number of dimensions to normalize over. + channel_first (bool, optional): Whether the input tensor has channels as the first dimension. + Default is True. + images (bool, optional): Whether the input represents image data. Default is True. + bias (bool, optional): Whether to include a learnable bias term. Default is False. + """ + + def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None: + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0 + + def forward(self, x): + return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias + + +class QwenImageUpsample(nn.Upsample): + r""" + Perform upsampling while ensuring the output tensor has the same data type as the input. + + Args: + x (torch.Tensor): Input tensor to be upsampled. + + Returns: + torch.Tensor: Upsampled tensor with the same data type as the input. + """ + + def forward(self, x): + return super().forward(x.float()).type_as(x) + + +class QwenImageResample(nn.Module): + r""" + A custom resampling module for 2D and 3D data. + + Args: + dim (int): The number of input/output channels. + mode (str): The resampling mode. Must be one of: + - 'none': No resampling (identity operation). + - 'upsample2d': 2D upsampling with nearest-exact interpolation and convolution. + - 'upsample3d': 3D upsampling with nearest-exact interpolation, convolution, and causal 3D convolution. + - 'downsample2d': 2D downsampling with zero-padding and convolution. + - 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution. + """ + + def __init__(self, dim: int, mode: str) -> None: + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == "upsample2d": + self.resample = nn.Sequential( + QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), + ChunkedConv2d(dim, dim // 2, 3, padding=1), + ) + elif mode == "upsample3d": + self.resample = nn.Sequential( + QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), + ChunkedConv2d(dim, dim // 2, 3, padding=1), + ) + self.time_conv = QwenImageCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + + elif mode == "downsample2d": + self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), ChunkedConv2d(dim, dim, 3, stride=(2, 2))) + elif mode == "downsample3d": + self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), ChunkedConv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = QwenImageCausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == "upsample3d": + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = "Rep" + feat_idx[0] += 1 + else: + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep": + # cache last frame of last two chunk + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep": + cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2) + if feat_cache[idx] == "Rep": + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) + x = self.resample(x) + x = x.view(b, t, x.size(1), x.size(2), x.size(3)).permute(0, 2, 1, 3, 4) + + if self.mode == "downsample3d": + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + cache_x = x[:, :, -1:, :, :].clone() + x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + +class QwenImageResidualBlock(nn.Module): + r""" + A custom residual block module. + + Args: + in_dim (int): Number of input channels. + out_dim (int): Number of output channels. + dropout (float, optional): Dropout rate for the dropout layer. Default is 0.0. + non_linearity (str, optional): Type of non-linearity to use. Default is "silu". + """ + + def __init__( + self, + in_dim: int, + out_dim: int, + dropout: float = 0.0, + non_linearity: str = "silu", + ) -> None: + assert non_linearity in ["silu"], "Only 'silu' non-linearity is supported currently." + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + self.nonlinearity = nn.SiLU() # get_activation(non_linearity) + + # layers + self.norm1 = QwenImageRMS_norm(in_dim, images=False) + self.conv1 = QwenImageCausalConv3d(in_dim, out_dim, 3, padding=1) + self.norm2 = QwenImageRMS_norm(out_dim, images=False) + self.dropout = nn.Dropout(dropout) + self.conv2 = QwenImageCausalConv3d(out_dim, out_dim, 3, padding=1) + self.conv_shortcut = QwenImageCausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + # Apply shortcut connection + h = self.conv_shortcut(x) + + # First normalization and activation + x = self.norm1(x) + x = self.nonlinearity(x) + + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + # Second normalization and activation + x = self.norm2(x) + x = self.nonlinearity(x) + + # Dropout + x = self.dropout(x) + + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + + x = self.conv2(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv2(x) + + # Add residual connection + return x + h + + +class QwenImageAttentionBlock(nn.Module): + r""" + Causal self-attention with a single head. + + Args: + dim (int): The number of channels in the input tensor. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = QwenImageRMS_norm(dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + self.proj = nn.Conv2d(dim, dim, 1) + + def forward(self, x): + identity = x + batch_size, channels, time, height, width = x.size() + + x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * time, channels, height, width) + x = self.norm(x) + + # compute query, key, value + qkv = self.to_qkv(x) + qkv = qkv.reshape(batch_size * time, 1, channels * 3, -1) + qkv = qkv.permute(0, 1, 3, 2).contiguous() + q, k, v = qkv.chunk(3, dim=-1) + + # apply attention + x = F.scaled_dot_product_attention(q, k, v) + + x = x.squeeze(1).permute(0, 2, 1).reshape(batch_size * time, channels, height, width) + + # output projection + x = self.proj(x) + + # Reshape back: [(b*t), c, h, w] -> [b, c, t, h, w] + x = x.view(batch_size, time, channels, height, width) + x = x.permute(0, 2, 1, 3, 4) + + return x + identity + + +class QwenImageMidBlock(nn.Module): + """ + Middle block for QwenImageVAE encoder and decoder. + + Args: + dim (int): Number of input/output channels. + dropout (float): Dropout rate. + non_linearity (str): Type of non-linearity to use. + """ + + def __init__(self, dim: int, dropout: float = 0.0, non_linearity: str = "silu", num_layers: int = 1): + super().__init__() + self.dim = dim + + # Create the components + resnets = [QwenImageResidualBlock(dim, dim, dropout, non_linearity)] + attentions = [] + for _ in range(num_layers): + attentions.append(QwenImageAttentionBlock(dim)) + resnets.append(QwenImageResidualBlock(dim, dim, dropout, non_linearity)) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def forward(self, x, feat_cache=None, feat_idx=[0]): + # First residual block + x = self.resnets[0](x, feat_cache, feat_idx) + + # Process through attention and residual blocks + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if attn is not None: + x = attn(x) + + x = resnet(x, feat_cache, feat_idx) + + return x + + +class QwenImageEncoder3d(nn.Module): + r""" + A 3D encoder module. + + Args: + dim (int): The base number of channels in the first layer. + z_dim (int): The dimensionality of the latent space. + dim_mult (list of int): Multipliers for the number of channels in each block. + num_res_blocks (int): Number of residual blocks in each block. + attn_scales (list of float): Scales at which to apply attention mechanisms. + temperal_downsample (list of bool): Whether to downsample temporally in each block. + dropout (float): Dropout rate for the dropout layers. + input_channels (int): Number of input channels. + non_linearity (str): Type of non-linearity to use. + """ + + def __init__( + self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0, + input_channels: int = 3, + non_linearity: str = "silu", + ): + super().__init__() + assert non_linearity in ["silu"], "Only 'silu' non-linearity is supported currently." + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.nonlinearity = nn.SiLU() # get_activation(non_linearity) + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv_in = QwenImageCausalConv3d(input_channels, dims[0], 3, padding=1) + + # downsample blocks + self.down_blocks = nn.ModuleList([]) + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + self.down_blocks.append(QwenImageResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + self.down_blocks.append(QwenImageAttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = "downsample3d" if temperal_downsample[i] else "downsample2d" + self.down_blocks.append(QwenImageResample(out_dim, mode=mode)) + scale /= 2.0 + + # middle blocks + self.mid_block = QwenImageMidBlock(out_dim, dropout, non_linearity, num_layers=1) + + # output blocks + self.norm_out = QwenImageRMS_norm(out_dim, images=False) + self.conv_out = QwenImageCausalConv3d(out_dim, z_dim, 3, padding=1) + + self.gradient_checkpointing = False + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + x = self.conv_in(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv_in(x) + + ## downsamples + for layer in self.down_blocks: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + x = self.mid_block(x, feat_cache, feat_idx) + + ## head + x = self.norm_out(x) + x = self.nonlinearity(x) + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + x = self.conv_out(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv_out(x) + return x + + +class QwenImageUpBlock(nn.Module): + """ + A block that handles upsampling for the QwenImageVAE decoder. + + Args: + in_dim (int): Input dimension + out_dim (int): Output dimension + num_res_blocks (int): Number of residual blocks + dropout (float): Dropout rate + upsample_mode (str, optional): Mode for upsampling ('upsample2d' or 'upsample3d') + non_linearity (str): Type of non-linearity to use + """ + + def __init__( + self, + in_dim: int, + out_dim: int, + num_res_blocks: int, + dropout: float = 0.0, + upsample_mode: Optional[str] = None, + non_linearity: str = "silu", + ): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # Create layers list + resnets = [] + # Add residual blocks and attention if needed + current_dim = in_dim + for _ in range(num_res_blocks + 1): + resnets.append(QwenImageResidualBlock(current_dim, out_dim, dropout, non_linearity)) + current_dim = out_dim + + self.resnets = nn.ModuleList(resnets) + + # Add upsampling layer if needed + self.upsamplers = None + if upsample_mode is not None: + self.upsamplers = nn.ModuleList([QwenImageResample(out_dim, mode=upsample_mode)]) + + self.gradient_checkpointing = False + + def forward(self, x, feat_cache=None, feat_idx=[0]): + """ + Forward pass through the upsampling block. + + Args: + x (torch.Tensor): Input tensor + feat_cache (list, optional): Feature cache for causal convolutions + feat_idx (list, optional): Feature index for cache management + + Returns: + torch.Tensor: Output tensor + """ + for resnet in self.resnets: + if feat_cache is not None: + x = resnet(x, feat_cache, feat_idx) + else: + x = resnet(x) + + if self.upsamplers is not None: + if feat_cache is not None: + x = self.upsamplers[0](x, feat_cache, feat_idx) + else: + x = self.upsamplers[0](x) + return x + + +class QwenImageDecoder3d(nn.Module): + r""" + A 3D decoder module. + + Args: + dim (int): The base number of channels in the first layer. + z_dim (int): The dimensionality of the latent space. + dim_mult (list of int): Multipliers for the number of channels in each block. + num_res_blocks (int): Number of residual blocks in each block. + attn_scales (list of float): Scales at which to apply attention mechanisms. + temperal_upsample (list of bool): Whether to upsample temporally in each block. + dropout (float): Dropout rate for the dropout layers. + output_channels (int): Number of output channels. + non_linearity (str): Type of non-linearity to use. + """ + + def __init__( + self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0, + output_channels: int = 3, + non_linearity: str = "silu", + ): + super().__init__() + assert non_linearity in ["silu"], "Only 'silu' non-linearity is supported currently." + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + self.nonlinearity = nn.SiLU() # get_activation(non_linearity) + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2 ** (len(dim_mult) - 2) + + # init block + self.conv_in = QwenImageCausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.mid_block = QwenImageMidBlock(dims[0], dropout, non_linearity, num_layers=1) + + # upsample blocks + self.up_blocks = nn.ModuleList([]) + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i > 0: + in_dim = in_dim // 2 + + # Determine if we need upsampling + upsample_mode = None + if i != len(dim_mult) - 1: + upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d" + + # Create and add the upsampling block + up_block = QwenImageUpBlock( + in_dim=in_dim, + out_dim=out_dim, + num_res_blocks=num_res_blocks, + dropout=dropout, + upsample_mode=upsample_mode, + non_linearity=non_linearity, + ) + self.up_blocks.append(up_block) + + # Update scale for next iteration + if upsample_mode is not None: + scale *= 2.0 + + # output blocks + self.norm_out = QwenImageRMS_norm(out_dim, images=False) + self.conv_out = QwenImageCausalConv3d(out_dim, output_channels, 3, padding=1) + + self.gradient_checkpointing = False + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + x = self.conv_in(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv_in(x) + + ## middle + x = self.mid_block(x, feat_cache, feat_idx) + + ## upsamples + for up_block in self.up_blocks: + x = up_block(x, feat_cache, feat_idx) + + ## head + x = self.norm_out(x) + x = self.nonlinearity(x) + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + x = self.conv_out(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv_out(x) + return x + + +class AutoencoderKLQwenImage(nn.Module): # ModelMixin, ConfigMixin, FromOriginalModelMixin): + r""" + A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + """ + + _supports_gradient_checkpointing = False + + # @register_to_config + def __init__( + self, + base_dim: int = 96, + z_dim: int = 16, + dim_mult: Tuple[int] = [1, 2, 4, 4], + num_res_blocks: int = 2, + attn_scales: List[float] = [], + temperal_downsample: List[bool] = [False, True, True], + dropout: float = 0.0, + latents_mean: List[float] = [ + -0.7571, + -0.7089, + -0.9113, + 0.1075, + -0.1745, + 0.9653, + -0.1517, + 1.5508, + 0.4134, + -0.0715, + 0.5517, + -0.3632, + -0.1922, + -0.9497, + 0.2503, + -0.2921, + ], + latents_std: List[float] = [ + 2.8184, + 1.4541, + 2.3275, + 2.6558, + 1.2196, + 1.7708, + 2.6052, + 2.0743, + 3.2687, + 2.1526, + 2.8652, + 1.5579, + 1.6382, + 1.1253, + 2.8251, + 1.9160, + ], + input_channels: int = 3, + spatial_chunk_size: Optional[int] = None, + disable_cache: bool = False, + ) -> None: + super().__init__() + + self.z_dim = z_dim + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + self.latents_mean = latents_mean + self.latents_std = latents_std + + self.encoder = QwenImageEncoder3d( + base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout, input_channels + ) + self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1) + self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1) + + self.decoder = QwenImageDecoder3d( + base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout, input_channels + ) + + self.spatial_compression_ratio = 2 ** len(self.temperal_downsample) + + # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension + # to perform decoding of a single video latent at a time. + self.use_slicing = False + + # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent + # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the + # intermediate tiles together, the memory requirement can be lowered. + self.use_tiling = False + + # The minimal tile height and width for spatial tiling to be used + self.tile_sample_min_height = 256 + self.tile_sample_min_width = 256 + + # The minimal distance between two spatial tiles + self.tile_sample_stride_height = 192 + self.tile_sample_stride_width = 192 + + # Precompute and cache conv counts for encoder and decoder for clear_cache speedup + self._cached_conv_counts = { + "decoder": sum(isinstance(m, QwenImageCausalConv3d) for m in self.decoder.modules()) if self.decoder is not None else 0, + "encoder": sum(isinstance(m, QwenImageCausalConv3d) for m in self.encoder.modules()) if self.encoder is not None else 0, + } + + self.spatial_chunk_size = None + if spatial_chunk_size is not None and spatial_chunk_size > 0: + self.enable_spatial_chunking(spatial_chunk_size) + + self.cache_disabled = False + if disable_cache: + self.disable_cache() + + @property + def dtype(self): + return self.encoder.parameters().__next__().dtype + + @property + def device(self): + return self.encoder.parameters().__next__().device + + def enable_tiling( + self, + tile_sample_min_height: Optional[int] = None, + tile_sample_min_width: Optional[int] = None, + tile_sample_stride_height: Optional[float] = None, + tile_sample_stride_width: Optional[float] = None, + ) -> None: + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger images. + + Args: + tile_sample_min_height (`int`, *optional*): + The minimum height required for a sample to be separated into tiles across the height dimension. + tile_sample_min_width (`int`, *optional*): + The minimum width required for a sample to be separated into tiles across the width dimension. + tile_sample_stride_height (`int`, *optional*): + The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are + no tiling artifacts produced across the height dimension. + tile_sample_stride_width (`int`, *optional*): + The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling + artifacts produced across the width dimension. + """ + self.use_tiling = True + self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height + self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width + self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height + self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width + + def disable_tiling(self) -> None: + r""" + Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing + decoding in one step. + """ + self.use_tiling = False + + def enable_slicing(self) -> None: + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.use_slicing = True + + def disable_slicing(self) -> None: + r""" + Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing + decoding in one step. + """ + self.use_slicing = False + + def enable_spatial_chunking(self, spatial_chunk_size: int) -> None: + r""" + Enable memory-efficient convolution by chunking all causal Conv3d layers only along height. + """ + if spatial_chunk_size is None or spatial_chunk_size <= 0: + raise ValueError(f"`spatial_chunk_size` must be a positive integer, got {spatial_chunk_size}.") + self.spatial_chunk_size = int(spatial_chunk_size) + for module in self.modules(): + if isinstance(module, QwenImageCausalConv3d): + module.spatial_chunk_size = self.spatial_chunk_size + elif isinstance(module, ChunkedConv2d): + module.spatial_chunk_size = self.spatial_chunk_size + + def disable_spatial_chunking(self) -> None: + r""" + Disable memory-efficient convolution chunking on all causal Conv3d layers. + """ + self.spatial_chunk_size = None + for module in self.modules(): + if isinstance(module, QwenImageCausalConv3d): + module.spatial_chunk_size = None + elif isinstance(module, ChunkedConv2d): + module.spatial_chunk_size = None + + def disable_cache(self) -> None: + r""" + Disable caching mechanism in encoder and decoder. + """ + self.cache_disabled = True + self.clear_cache = lambda: None + self._feat_map = None # Disable decoder cache + self._enc_feat_map = None # Disable encoder cache + + def clear_cache(self): + def _count_conv3d(model): + count = 0 + for m in model.modules(): + if isinstance(m, QwenImageCausalConv3d): + count += 1 + return count + + self._conv_num = _count_conv3d(self.decoder) + self._conv_idx = [0] + self._feat_map = [None] * self._conv_num + # cache encode + self._enc_conv_num = _count_conv3d(self.encoder) + self._enc_conv_idx = [0] + self._enc_feat_map = [None] * self._enc_conv_num + + def _encode(self, x: torch.Tensor): + _, _, num_frame, height, width = x.shape + assert num_frame == 1 or not self.cache_disabled, "Caching must be enabled for encoding multiple frames." + + if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height): + return self.tiled_encode(x) + + self.clear_cache() + iter_ = 1 + (num_frame - 1) // 4 + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder(x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx) + else: + out_ = self.encoder( + x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx, + ) + out = torch.cat([out, out_], 2) + + enc = self.quant_conv(out) + self.clear_cache() + return enc + + # @apply_forward_hook + def encode( + self, x: torch.Tensor, return_dict: bool = True + ) -> Union[Dict[str, torch.Tensor], Tuple[DiagonalGaussianDistribution]]: + r""" + Encode a batch of images into latents. + + Args: + x (`torch.Tensor`): Input batch of images. + return_dict (`bool`, *optional*, defaults to `True`): + Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. + + Returns: + The latent representations of the encoded videos. If `return_dict` is True, a dictionary is returned, otherwise a plain `tuple` is returned. + """ + if self.use_slicing and x.shape[0] > 1: + encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)] + h = torch.cat(encoded_slices) + else: + h = self._encode(x) + posterior = DiagonalGaussianDistribution(h) + + if not return_dict: + return (posterior,) + return {"latent_dist": posterior} + + def _decode(self, z: torch.Tensor, return_dict: bool = True): + _, _, num_frame, height, width = z.shape + assert num_frame == 1 or not self.cache_disabled, "Caching must be enabled for encoding multiple frames." + tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio + tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio + + if self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height): + return self.tiled_decode(z, return_dict=return_dict) + + self.clear_cache() + x = self.post_quant_conv(z) + for i in range(num_frame): + self._conv_idx = [0] + if i == 0: + out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx) + else: + out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) + + out = torch.clamp(out, min=-1.0, max=1.0) + self.clear_cache() + if not return_dict: + return (out,) + + return {"sample": out} + + # @apply_forward_hook + def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[Dict[str, torch.Tensor], torch.Tensor]: + r""" + Decode a batch of images. + + Args: + z (`torch.Tensor`): Input batch of latent vectors. + return_dict (`bool`, *optional*, defaults to `True`): + Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. + + Returns: + [`~models.vae.DecoderOutput`] or `tuple`: + If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is + returned. + """ + if self.use_slicing and z.shape[0] > 1: + decoded_slices = [self._decode(z_slice)["sample"] for z_slice in z.split(1)] + decoded = torch.cat(decoded_slices) + else: + decoded = self._decode(z)["sample"] + + if not return_dict: + return (decoded,) + return {"sample": decoded} + + def decode_to_pixels(self, latents: torch.Tensor) -> torch.Tensor: + is_4d = latents.dim() == 4 + if is_4d: + latents = latents.unsqueeze(2) # [B, C, H, W] -> [B, C, 1, H, W] + + latents = latents.to(self.dtype) + latents_mean = torch.tensor(self.latents_mean).view(1, self.z_dim, 1, 1, 1).to(latents.device, latents.dtype) + latents_std = 1.0 / torch.tensor(self.latents_std).view(1, self.z_dim, 1, 1, 1).to(latents.device, latents.dtype) + latents = latents / latents_std + latents_mean + + image = self.decode(latents, return_dict=False)[0] # -1 to 1 + if is_4d: + image = image.squeeze(2) # [B, C, 1, H, W] -> [B, C, H, W] + + return image.clamp(-1.0, 1.0) + + def encode_pixels_to_latents(self, pixels: torch.Tensor) -> torch.Tensor: + """ + Convert pixel values to latents and apply normalization using mean/std. + + Args: + pixels (torch.Tensor): Input pixels in [0, 1] range with shape [B, C, H, W] or [B, C, T, H, W] + + Returns: + torch.Tensor: Normalized latents + """ + # # Convert from [0, 1] to [-1, 1] range + # pixels = (pixels * 2.0 - 1.0).clamp(-1.0, 1.0) + + # Handle 2D input by adding temporal dimension + is_4d = pixels.dim() == 4 + if is_4d: + pixels = pixels.unsqueeze(2) # [B, C, H, W] -> [B, C, 1, H, W] + + pixels = pixels.to(self.dtype) + + # Encode to latent space + posterior = self.encode(pixels, return_dict=False)[0] + latents = posterior.mode() # Use mode instead of sampling for deterministic results + # latents = posterior.sample() + + # Apply normalization using mean/std + latents_mean = torch.tensor(self.latents_mean).view(1, self.z_dim, 1, 1, 1).to(latents.device, latents.dtype) + latents_std = 1.0 / torch.tensor(self.latents_std).view(1, self.z_dim, 1, 1, 1).to(latents.device, latents.dtype) + latents = (latents - latents_mean) * latents_std + + if is_4d: + latents = latents.squeeze(2) # [B, C, 1, H, W] -> [B, C, H, W] + + return latents + + def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: + blend_extent = min(a.shape[-2], b.shape[-2], blend_extent) + for y in range(blend_extent): + b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent) + return b + + def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: + blend_extent = min(a.shape[-1], b.shape[-1], blend_extent) + for x in range(blend_extent): + b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent) + return b + + def tiled_encode(self, x: torch.Tensor) -> torch.Tensor: + r"""Encode a batch of images using a tiled encoder. + + Args: + x (`torch.Tensor`): Input batch of videos. + + Returns: + `torch.Tensor`: + The latent representation of the encoded videos. + """ + _, _, num_frames, height, width = x.shape + latent_height = height // self.spatial_compression_ratio + latent_width = width // self.spatial_compression_ratio + + tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio + tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio + tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio + tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio + + blend_height = tile_latent_min_height - tile_latent_stride_height + blend_width = tile_latent_min_width - tile_latent_stride_width + + # Split x into overlapping tiles and encode them separately. + # The tiles have an overlap to avoid seams between tiles. + rows = [] + for i in range(0, height, self.tile_sample_stride_height): + row = [] + for j in range(0, width, self.tile_sample_stride_width): + self.clear_cache() + time = [] + frame_range = 1 + (num_frames - 1) // 4 + for k in range(frame_range): + self._enc_conv_idx = [0] + if k == 0: + tile = x[:, :, :1, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width] + else: + tile = x[ + :, + :, + 1 + 4 * (k - 1) : 1 + 4 * k, + i : i + self.tile_sample_min_height, + j : j + self.tile_sample_min_width, + ] + tile = self.encoder(tile, feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx) + tile = self.quant_conv(tile) + time.append(tile) + row.append(torch.cat(time, dim=2)) + rows.append(row) + self.clear_cache() + + result_rows = [] + for i, row in enumerate(rows): + result_row = [] + for j, tile in enumerate(row): + # blend the above tile and the left tile + # to the current tile and add the current tile to the result row + if i > 0: + tile = self.blend_v(rows[i - 1][j], tile, blend_height) + if j > 0: + tile = self.blend_h(row[j - 1], tile, blend_width) + result_row.append(tile[:, :, :, :tile_latent_stride_height, :tile_latent_stride_width]) + result_rows.append(torch.cat(result_row, dim=-1)) + + enc = torch.cat(result_rows, dim=3)[:, :, :, :latent_height, :latent_width] + return enc + + def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[Dict[str, torch.Tensor], torch.Tensor]: + r""" + Decode a batch of images using a tiled decoder. + + Args: + z (`torch.Tensor`): Input batch of latent vectors. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a dictionary instead of a plain tuple. + + Returns: + `dict` or `tuple`: + If return_dict is True, a dictionary is returned, otherwise a plain `tuple` is + returned. + """ + _, _, num_frames, height, width = z.shape + sample_height = height * self.spatial_compression_ratio + sample_width = width * self.spatial_compression_ratio + + tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio + tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio + tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio + tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio + + blend_height = self.tile_sample_min_height - self.tile_sample_stride_height + blend_width = self.tile_sample_min_width - self.tile_sample_stride_width + + # Split z into overlapping tiles and decode them separately. + # The tiles have an overlap to avoid seams between tiles. + rows = [] + for i in range(0, height, tile_latent_stride_height): + row = [] + for j in range(0, width, tile_latent_stride_width): + self.clear_cache() + time = [] + for k in range(num_frames): + self._conv_idx = [0] + tile = z[:, :, k : k + 1, i : i + tile_latent_min_height, j : j + tile_latent_min_width] + tile = self.post_quant_conv(tile) + decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx) + time.append(decoded) + row.append(torch.cat(time, dim=2)) + rows.append(row) + self.clear_cache() + + result_rows = [] + for i, row in enumerate(rows): + result_row = [] + for j, tile in enumerate(row): + # blend the above tile and the left tile + # to the current tile and add the current tile to the result row + if i > 0: + tile = self.blend_v(rows[i - 1][j], tile, blend_height) + if j > 0: + tile = self.blend_h(row[j - 1], tile, blend_width) + result_row.append(tile[:, :, :, : self.tile_sample_stride_height, : self.tile_sample_stride_width]) + result_rows.append(torch.cat(result_row, dim=-1)) + + dec = torch.cat(result_rows, dim=3)[:, :, :, :sample_height, :sample_width] + + if not return_dict: + return (dec,) + return {"sample": dec} + + def forward( + self, + sample: torch.Tensor, + sample_posterior: bool = False, + return_dict: bool = True, + generator: Optional[torch.Generator] = None, + ) -> Union[Dict[str, torch.Tensor], torch.Tensor]: + """ + Args: + sample (`torch.Tensor`): Input sample. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`Dict[str, torch.Tensor]`] instead of a plain tuple. + """ + x = sample + posterior = self.encode(x).latent_dist + if sample_posterior: + z = posterior.sample(generator=generator) + else: + z = posterior.mode() + dec = self.decode(z, return_dict=return_dict) + return dec + + +# region utils + +# This region is not included in the original implementation. Added for musubi-tuner/sd-scripts. + + +# Convert ComfyUI keys to standard keys if necessary +def convert_comfyui_state_dict(sd): + if "conv1.bias" not in sd: + return sd + + # Key mapping from ComfyUI VAE to official VAE, auto-generated by a script + key_map = { + "conv1": "quant_conv", + "conv2": "post_quant_conv", + "decoder.conv1": "decoder.conv_in", + "decoder.head.0": "decoder.norm_out", + "decoder.head.2": "decoder.conv_out", + "decoder.middle.0.residual.0": "decoder.mid_block.resnets.0.norm1", + "decoder.middle.0.residual.2": "decoder.mid_block.resnets.0.conv1", + "decoder.middle.0.residual.3": "decoder.mid_block.resnets.0.norm2", + "decoder.middle.0.residual.6": "decoder.mid_block.resnets.0.conv2", + "decoder.middle.1.norm": "decoder.mid_block.attentions.0.norm", + "decoder.middle.1.proj": "decoder.mid_block.attentions.0.proj", + "decoder.middle.1.to_qkv": "decoder.mid_block.attentions.0.to_qkv", + "decoder.middle.2.residual.0": "decoder.mid_block.resnets.1.norm1", + "decoder.middle.2.residual.2": "decoder.mid_block.resnets.1.conv1", + "decoder.middle.2.residual.3": "decoder.mid_block.resnets.1.norm2", + "decoder.middle.2.residual.6": "decoder.mid_block.resnets.1.conv2", + "decoder.upsamples.0.residual.0": "decoder.up_blocks.0.resnets.0.norm1", + "decoder.upsamples.0.residual.2": "decoder.up_blocks.0.resnets.0.conv1", + "decoder.upsamples.0.residual.3": "decoder.up_blocks.0.resnets.0.norm2", + "decoder.upsamples.0.residual.6": "decoder.up_blocks.0.resnets.0.conv2", + "decoder.upsamples.1.residual.0": "decoder.up_blocks.0.resnets.1.norm1", + "decoder.upsamples.1.residual.2": "decoder.up_blocks.0.resnets.1.conv1", + "decoder.upsamples.1.residual.3": "decoder.up_blocks.0.resnets.1.norm2", + "decoder.upsamples.1.residual.6": "decoder.up_blocks.0.resnets.1.conv2", + "decoder.upsamples.10.residual.0": "decoder.up_blocks.2.resnets.2.norm1", + "decoder.upsamples.10.residual.2": "decoder.up_blocks.2.resnets.2.conv1", + "decoder.upsamples.10.residual.3": "decoder.up_blocks.2.resnets.2.norm2", + "decoder.upsamples.10.residual.6": "decoder.up_blocks.2.resnets.2.conv2", + "decoder.upsamples.11.resample.1": "decoder.up_blocks.2.upsamplers.0.resample.1", + "decoder.upsamples.12.residual.0": "decoder.up_blocks.3.resnets.0.norm1", + "decoder.upsamples.12.residual.2": "decoder.up_blocks.3.resnets.0.conv1", + "decoder.upsamples.12.residual.3": "decoder.up_blocks.3.resnets.0.norm2", + "decoder.upsamples.12.residual.6": "decoder.up_blocks.3.resnets.0.conv2", + "decoder.upsamples.13.residual.0": "decoder.up_blocks.3.resnets.1.norm1", + "decoder.upsamples.13.residual.2": "decoder.up_blocks.3.resnets.1.conv1", + "decoder.upsamples.13.residual.3": "decoder.up_blocks.3.resnets.1.norm2", + "decoder.upsamples.13.residual.6": "decoder.up_blocks.3.resnets.1.conv2", + "decoder.upsamples.14.residual.0": "decoder.up_blocks.3.resnets.2.norm1", + "decoder.upsamples.14.residual.2": "decoder.up_blocks.3.resnets.2.conv1", + "decoder.upsamples.14.residual.3": "decoder.up_blocks.3.resnets.2.norm2", + "decoder.upsamples.14.residual.6": "decoder.up_blocks.3.resnets.2.conv2", + "decoder.upsamples.2.residual.0": "decoder.up_blocks.0.resnets.2.norm1", + "decoder.upsamples.2.residual.2": "decoder.up_blocks.0.resnets.2.conv1", + "decoder.upsamples.2.residual.3": "decoder.up_blocks.0.resnets.2.norm2", + "decoder.upsamples.2.residual.6": "decoder.up_blocks.0.resnets.2.conv2", + "decoder.upsamples.3.resample.1": "decoder.up_blocks.0.upsamplers.0.resample.1", + "decoder.upsamples.3.time_conv": "decoder.up_blocks.0.upsamplers.0.time_conv", + "decoder.upsamples.4.residual.0": "decoder.up_blocks.1.resnets.0.norm1", + "decoder.upsamples.4.residual.2": "decoder.up_blocks.1.resnets.0.conv1", + "decoder.upsamples.4.residual.3": "decoder.up_blocks.1.resnets.0.norm2", + "decoder.upsamples.4.residual.6": "decoder.up_blocks.1.resnets.0.conv2", + "decoder.upsamples.4.shortcut": "decoder.up_blocks.1.resnets.0.conv_shortcut", + "decoder.upsamples.5.residual.0": "decoder.up_blocks.1.resnets.1.norm1", + "decoder.upsamples.5.residual.2": "decoder.up_blocks.1.resnets.1.conv1", + "decoder.upsamples.5.residual.3": "decoder.up_blocks.1.resnets.1.norm2", + "decoder.upsamples.5.residual.6": "decoder.up_blocks.1.resnets.1.conv2", + "decoder.upsamples.6.residual.0": "decoder.up_blocks.1.resnets.2.norm1", + "decoder.upsamples.6.residual.2": "decoder.up_blocks.1.resnets.2.conv1", + "decoder.upsamples.6.residual.3": "decoder.up_blocks.1.resnets.2.norm2", + "decoder.upsamples.6.residual.6": "decoder.up_blocks.1.resnets.2.conv2", + "decoder.upsamples.7.resample.1": "decoder.up_blocks.1.upsamplers.0.resample.1", + "decoder.upsamples.7.time_conv": "decoder.up_blocks.1.upsamplers.0.time_conv", + "decoder.upsamples.8.residual.0": "decoder.up_blocks.2.resnets.0.norm1", + "decoder.upsamples.8.residual.2": "decoder.up_blocks.2.resnets.0.conv1", + "decoder.upsamples.8.residual.3": "decoder.up_blocks.2.resnets.0.norm2", + "decoder.upsamples.8.residual.6": "decoder.up_blocks.2.resnets.0.conv2", + "decoder.upsamples.9.residual.0": "decoder.up_blocks.2.resnets.1.norm1", + "decoder.upsamples.9.residual.2": "decoder.up_blocks.2.resnets.1.conv1", + "decoder.upsamples.9.residual.3": "decoder.up_blocks.2.resnets.1.norm2", + "decoder.upsamples.9.residual.6": "decoder.up_blocks.2.resnets.1.conv2", + "encoder.conv1": "encoder.conv_in", + "encoder.downsamples.0.residual.0": "encoder.down_blocks.0.norm1", + "encoder.downsamples.0.residual.2": "encoder.down_blocks.0.conv1", + "encoder.downsamples.0.residual.3": "encoder.down_blocks.0.norm2", + "encoder.downsamples.0.residual.6": "encoder.down_blocks.0.conv2", + "encoder.downsamples.1.residual.0": "encoder.down_blocks.1.norm1", + "encoder.downsamples.1.residual.2": "encoder.down_blocks.1.conv1", + "encoder.downsamples.1.residual.3": "encoder.down_blocks.1.norm2", + "encoder.downsamples.1.residual.6": "encoder.down_blocks.1.conv2", + "encoder.downsamples.10.residual.0": "encoder.down_blocks.10.norm1", + "encoder.downsamples.10.residual.2": "encoder.down_blocks.10.conv1", + "encoder.downsamples.10.residual.3": "encoder.down_blocks.10.norm2", + "encoder.downsamples.10.residual.6": "encoder.down_blocks.10.conv2", + "encoder.downsamples.2.resample.1": "encoder.down_blocks.2.resample.1", + "encoder.downsamples.3.residual.0": "encoder.down_blocks.3.norm1", + "encoder.downsamples.3.residual.2": "encoder.down_blocks.3.conv1", + "encoder.downsamples.3.residual.3": "encoder.down_blocks.3.norm2", + "encoder.downsamples.3.residual.6": "encoder.down_blocks.3.conv2", + "encoder.downsamples.3.shortcut": "encoder.down_blocks.3.conv_shortcut", + "encoder.downsamples.4.residual.0": "encoder.down_blocks.4.norm1", + "encoder.downsamples.4.residual.2": "encoder.down_blocks.4.conv1", + "encoder.downsamples.4.residual.3": "encoder.down_blocks.4.norm2", + "encoder.downsamples.4.residual.6": "encoder.down_blocks.4.conv2", + "encoder.downsamples.5.resample.1": "encoder.down_blocks.5.resample.1", + "encoder.downsamples.5.time_conv": "encoder.down_blocks.5.time_conv", + "encoder.downsamples.6.residual.0": "encoder.down_blocks.6.norm1", + "encoder.downsamples.6.residual.2": "encoder.down_blocks.6.conv1", + "encoder.downsamples.6.residual.3": "encoder.down_blocks.6.norm2", + "encoder.downsamples.6.residual.6": "encoder.down_blocks.6.conv2", + "encoder.downsamples.6.shortcut": "encoder.down_blocks.6.conv_shortcut", + "encoder.downsamples.7.residual.0": "encoder.down_blocks.7.norm1", + "encoder.downsamples.7.residual.2": "encoder.down_blocks.7.conv1", + "encoder.downsamples.7.residual.3": "encoder.down_blocks.7.norm2", + "encoder.downsamples.7.residual.6": "encoder.down_blocks.7.conv2", + "encoder.downsamples.8.resample.1": "encoder.down_blocks.8.resample.1", + "encoder.downsamples.8.time_conv": "encoder.down_blocks.8.time_conv", + "encoder.downsamples.9.residual.0": "encoder.down_blocks.9.norm1", + "encoder.downsamples.9.residual.2": "encoder.down_blocks.9.conv1", + "encoder.downsamples.9.residual.3": "encoder.down_blocks.9.norm2", + "encoder.downsamples.9.residual.6": "encoder.down_blocks.9.conv2", + "encoder.head.0": "encoder.norm_out", + "encoder.head.2": "encoder.conv_out", + "encoder.middle.0.residual.0": "encoder.mid_block.resnets.0.norm1", + "encoder.middle.0.residual.2": "encoder.mid_block.resnets.0.conv1", + "encoder.middle.0.residual.3": "encoder.mid_block.resnets.0.norm2", + "encoder.middle.0.residual.6": "encoder.mid_block.resnets.0.conv2", + "encoder.middle.1.norm": "encoder.mid_block.attentions.0.norm", + "encoder.middle.1.proj": "encoder.mid_block.attentions.0.proj", + "encoder.middle.1.to_qkv": "encoder.mid_block.attentions.0.to_qkv", + "encoder.middle.2.residual.0": "encoder.mid_block.resnets.1.norm1", + "encoder.middle.2.residual.2": "encoder.mid_block.resnets.1.conv1", + "encoder.middle.2.residual.3": "encoder.mid_block.resnets.1.norm2", + "encoder.middle.2.residual.6": "encoder.mid_block.resnets.1.conv2", + } + + new_state_dict = {} + for key in sd.keys(): + new_key = key + key_without_suffix = key.rsplit(".", 1)[0] + if key_without_suffix in key_map: + new_key = key.replace(key_without_suffix, key_map[key_without_suffix]) + new_state_dict[new_key] = sd[key] + + logger.info("Converted ComfyUI AutoencoderKL state dict keys to official format") + return new_state_dict + + +def load_vae( + vae_path: str, + input_channels: int = 3, + device: Union[str, torch.device] = "cpu", + disable_mmap: bool = False, + spatial_chunk_size: Optional[int] = None, + disable_cache: bool = False, +) -> AutoencoderKLQwenImage: + """Load VAE from a given path.""" + VAE_CONFIG_JSON = """ +{ + "_class_name": "AutoencoderKLQwenImage", + "_diffusers_version": "0.34.0.dev0", + "attn_scales": [], + "base_dim": 96, + "dim_mult": [ + 1, + 2, + 4, + 4 + ], + "dropout": 0.0, + "latents_mean": [ + -0.7571, + -0.7089, + -0.9113, + 0.1075, + -0.1745, + 0.9653, + -0.1517, + 1.5508, + 0.4134, + -0.0715, + 0.5517, + -0.3632, + -0.1922, + -0.9497, + 0.2503, + -0.2921 + ], + "latents_std": [ + 2.8184, + 1.4541, + 2.3275, + 2.6558, + 1.2196, + 1.7708, + 2.6052, + 2.0743, + 3.2687, + 2.1526, + 2.8652, + 1.5579, + 1.6382, + 1.1253, + 2.8251, + 1.916 + ], + "num_res_blocks": 2, + "temperal_downsample": [ + false, + true, + true + ], + "z_dim": 16 +} +""" + logger.info("Initializing VAE") + + if spatial_chunk_size is not None and spatial_chunk_size % 2 != 0: + spatial_chunk_size += 1 + logger.warning(f"Adjusted spatial_chunk_size to the next even number: {spatial_chunk_size}") + + config = json.loads(VAE_CONFIG_JSON) + vae = AutoencoderKLQwenImage( + base_dim=config["base_dim"], + z_dim=config["z_dim"], + dim_mult=config["dim_mult"], + num_res_blocks=config["num_res_blocks"], + attn_scales=config["attn_scales"], + temperal_downsample=config["temperal_downsample"], + dropout=config["dropout"], + latents_mean=config["latents_mean"], + latents_std=config["latents_std"], + input_channels=input_channels, + spatial_chunk_size=spatial_chunk_size, + disable_cache=disable_cache, + ) + + logger.info(f"Loading VAE from {vae_path}") + state_dict = load_safetensors(vae_path, device=device, disable_mmap=disable_mmap) + + # Convert ComfyUI VAE keys to official VAE keys + state_dict = convert_comfyui_state_dict(state_dict) + + info = vae.load_state_dict(state_dict, strict=True, assign=True) + logger.info(f"Loaded VAE: {info}") + + vae.to(device) + return vae + + +if __name__ == "__main__": + # Debugging / testing code + import argparse + import glob + import os + import time + + from PIL import Image + + from library.device_utils import get_preferred_device, synchronize_device + + parser = argparse.ArgumentParser() + parser.add_argument("--vae", type=str, required=True, help="Path to the VAE model file.") + parser.add_argument("--input_image_dir", type=str, required=True, help="Path to the input image directory.") + parser.add_argument("--output_image_dir", type=str, required=True, help="Path to the output image directory.") + args = parser.parse_args() + + # Load VAE + vae = load_vae(args.vae, device=get_preferred_device()) + + # Process images + def encode_decode_image(image_path, output_path): + image = Image.open(image_path).convert("RGB") + + # Crop to multiple of 8 + width, height = image.size + new_width = (width // 8) * 8 + new_height = (height // 8) * 8 + if new_width != width or new_height != height: + image = image.crop((0, 0, new_width, new_height)) + + image_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0 * 2 - 1 + image_tensor = image_tensor.to(vae.dtype).to(vae.device) + + with torch.no_grad(): + latents = vae.encode_pixels_to_latents(image_tensor) + reconstructed = vae.decode_to_pixels(latents) + + diff = (image_tensor - reconstructed).abs().mean().item() + print(f"Processed {image_path} (size: {image.size}), reconstruction diff: {diff}") + + reconstructed_image = ((reconstructed.squeeze(0).permute(1, 2, 0).float().cpu().numpy() + 1) / 2 * 255).astype(np.uint8) + Image.fromarray(reconstructed_image).save(output_path) + + def process_directory(input_dir, output_dir): + if get_preferred_device().type == "cuda": + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() + + synchronize_device(get_preferred_device()) + start_time = time.perf_counter() + + os.makedirs(output_dir, exist_ok=True) + image_paths = glob.glob(os.path.join(input_dir, "*.jpg")) + glob.glob(os.path.join(input_dir, "*.png")) + for image_path in image_paths: + filename = os.path.basename(image_path) + output_path = os.path.join(output_dir, filename) + encode_decode_image(image_path, output_path) + + if get_preferred_device().type == "cuda": + max_mem = torch.cuda.max_memory_allocated() / (1024**3) + print(f"Max GPU memory allocated: {max_mem:.2f} GB") + + synchronize_device(get_preferred_device()) + end_time = time.perf_counter() + print(f"Processing time: {end_time - start_time:.2f} seconds") + + print("Starting image processing with default settings...") + process_directory(args.input_image_dir, args.output_image_dir) + + print("Starting image processing with spatial chunking enabled with chunk size 64...") + vae.enable_spatial_chunking(64) + process_directory(args.input_image_dir, args.output_image_dir + "_chunked_64") + + print("Starting image processing with spatial chunking enabled with chunk size 16...") + vae.enable_spatial_chunking(16) + process_directory(args.input_image_dir, args.output_image_dir + "_chunked_16") + + print("Starting image processing without caching and chunking enabled with chunk size 64...") + vae.enable_spatial_chunking(64) + vae.disable_cache() + process_directory(args.input_image_dir, args.output_image_dir + "_no_cache_chunked_64") + + print("Starting image processing without caching and chunking enabled with chunk size 16...") + vae.disable_cache() + process_directory(args.input_image_dir, args.output_image_dir + "_no_cache_chunked_16") + + print("Starting image processing without caching and chunking disabled...") + vae.disable_spatial_chunking() + process_directory(args.input_image_dir, args.output_image_dir + "_no_cache") + + print("Processing completed.") diff --git a/vendor/sd-scripts/library/safetensors_utils.py b/vendor/sd-scripts/library/safetensors_utils.py new file mode 100644 index 0000000..c7a3bdd --- /dev/null +++ b/vendor/sd-scripts/library/safetensors_utils.py @@ -0,0 +1,478 @@ +from dataclasses import dataclass +import os +import re +import numpy as np +import torch +import json +import struct +from typing import Dict, Any, Union, Optional + +from safetensors.torch import load_file + +from library.device_utils import synchronize_device + + +def mem_eff_save_file(tensors: Dict[str, torch.Tensor], filename: str, metadata: Dict[str, Any] = None): + """ + memory efficient save file + """ + + _TYPES = { + torch.float64: "F64", + torch.float32: "F32", + torch.float16: "F16", + torch.bfloat16: "BF16", + torch.int64: "I64", + torch.int32: "I32", + torch.int16: "I16", + torch.int8: "I8", + torch.uint8: "U8", + torch.bool: "BOOL", + getattr(torch, "float8_e5m2", None): "F8_E5M2", + getattr(torch, "float8_e4m3fn", None): "F8_E4M3", + } + _ALIGN = 256 + + def validate_metadata(metadata: Dict[str, Any]) -> Dict[str, str]: + validated = {} + for key, value in metadata.items(): + if not isinstance(key, str): + raise ValueError(f"Metadata key must be a string, got {type(key)}") + if not isinstance(value, str): + print(f"Warning: Metadata value for key '{key}' is not a string. Converting to string.") + validated[key] = str(value) + else: + validated[key] = value + return validated + + # print(f"Using memory efficient save file: {filename}") + + header = {} + offset = 0 + if metadata: + header["__metadata__"] = validate_metadata(metadata) + for k, v in tensors.items(): + if v.numel() == 0: # empty tensor + header[k] = {"dtype": _TYPES[v.dtype], "shape": list(v.shape), "data_offsets": [offset, offset]} + else: + size = v.numel() * v.element_size() + header[k] = {"dtype": _TYPES[v.dtype], "shape": list(v.shape), "data_offsets": [offset, offset + size]} + offset += size + + hjson = json.dumps(header).encode("utf-8") + hjson += b" " * (-(len(hjson) + 8) % _ALIGN) + + with open(filename, "wb") as f: + f.write(struct.pack(" Dict[str, str]: + """Get metadata from the file. + + Returns: + Dict[str, str]: Metadata dictionary. + """ + return self.header.get("__metadata__", {}) + + def _read_header(self): + """Read and parse the header from the safetensors file. + + Returns: + tuple: (header_dict, header_size) containing parsed header and its size. + """ + # Read header size (8 bytes, little-endian unsigned long long) + header_size = struct.unpack("10MB) and the target device is CUDA, memory mapping with numpy.memmap is used to avoid intermediate copies. + + Args: + key (str): Name of the tensor to load. + device (Optional[torch.device]): Target device for the tensor. + dtype (Optional[torch.dtype]): Target dtype for the tensor. + + Returns: + torch.Tensor: The loaded tensor. + + Raises: + KeyError: If the tensor key is not found in the file. + """ + if key not in self.header: + raise KeyError(f"Tensor '{key}' not found in the file") + + metadata = self.header[key] + offset_start, offset_end = metadata["data_offsets"] + num_bytes = offset_end - offset_start + + original_dtype = self._get_torch_dtype(metadata["dtype"]) + target_dtype = dtype if dtype is not None else original_dtype + + # Handle empty tensors + if num_bytes == 0: + return torch.empty(metadata["shape"], dtype=target_dtype, device=device) + + # Determine if we should use pinned memory for GPU transfer + non_blocking = device is not None and device.type == "cuda" + + # Calculate absolute file offset + tensor_offset = self.header_size + 8 + offset_start # adjust offset by header size + + # Memory mapping strategy for large tensors to GPU + # Use memmap for large tensors to avoid intermediate copies. + # If device is cpu, tensor is not copied to gpu, so using memmap locks the file, which is not desired. + # So we only use memmap if device is not cpu. + # If disable_numpy_memmap is True, skip numpy memory mapping to load with standard file read. + if not self.disable_numpy_memmap and num_bytes > 10 * 1024 * 1024 and device is not None and device.type != "cpu": + # Create memory map for zero-copy reading + mm = np.memmap(self.filename, mode="c", dtype=np.uint8, offset=tensor_offset, shape=(num_bytes,)) + byte_tensor = torch.from_numpy(mm) # zero copy + del mm + + # Deserialize tensor (view and reshape) + cpu_tensor = self._deserialize_tensor(byte_tensor, metadata) # view and reshape + del byte_tensor + + # Transfer to target device and dtype + gpu_tensor = cpu_tensor.to(device=device, dtype=target_dtype, non_blocking=non_blocking) + del cpu_tensor + return gpu_tensor + + # Standard file reading strategy for smaller tensors or CPU target + # seek to the specified position + self.file.seek(tensor_offset) + + # read directly into a numpy array by numpy.fromfile without intermediate copy + numpy_array = np.fromfile(self.file, dtype=np.uint8, count=num_bytes) + byte_tensor = torch.from_numpy(numpy_array) + del numpy_array + + # deserialize (view and reshape) + deserialized_tensor = self._deserialize_tensor(byte_tensor, metadata) + del byte_tensor + + # cast to target dtype and move to device + return deserialized_tensor.to(device=device, dtype=target_dtype, non_blocking=non_blocking) + + def _deserialize_tensor(self, byte_tensor: torch.Tensor, metadata: Dict): + """Deserialize byte tensor to the correct shape and dtype. + + Args: + byte_tensor (torch.Tensor): Raw byte tensor from file. + metadata (Dict): Tensor metadata containing dtype and shape info. + + Returns: + torch.Tensor: Deserialized tensor with correct shape and dtype. + """ + dtype = self._get_torch_dtype(metadata["dtype"]) + shape = metadata["shape"] + + # Handle special float8 types + if metadata["dtype"] in ["F8_E5M2", "F8_E4M3"]: + return self._convert_float8(byte_tensor, metadata["dtype"], shape) + + # Standard conversion: view as target dtype and reshape + return byte_tensor.view(dtype).reshape(shape) + + @staticmethod + def _get_torch_dtype(dtype_str): + """Convert string dtype to PyTorch dtype. + + Args: + dtype_str (str): String representation of the dtype. + + Returns: + torch.dtype: Corresponding PyTorch dtype. + """ + # Standard dtype mappings + dtype_map = { + "F64": torch.float64, + "F32": torch.float32, + "F16": torch.float16, + "BF16": torch.bfloat16, + "I64": torch.int64, + "I32": torch.int32, + "I16": torch.int16, + "I8": torch.int8, + "U8": torch.uint8, + "BOOL": torch.bool, + } + # Add float8 types if available in PyTorch version + if hasattr(torch, "float8_e5m2"): + dtype_map["F8_E5M2"] = torch.float8_e5m2 + if hasattr(torch, "float8_e4m3fn"): + dtype_map["F8_E4M3"] = torch.float8_e4m3fn + return dtype_map.get(dtype_str) + + @staticmethod + def _convert_float8(byte_tensor, dtype_str, shape): + """Convert byte tensor to float8 format if supported. + + Args: + byte_tensor (torch.Tensor): Raw byte tensor. + dtype_str (str): Float8 dtype string ("F8_E5M2" or "F8_E4M3"). + shape (tuple): Target tensor shape. + + Returns: + torch.Tensor: Tensor with float8 dtype. + + Raises: + ValueError: If float8 type is not supported in current PyTorch version. + """ + # Convert to specific float8 types if available + if dtype_str == "F8_E5M2" and hasattr(torch, "float8_e5m2"): + return byte_tensor.view(torch.float8_e5m2).reshape(shape) + elif dtype_str == "F8_E4M3" and hasattr(torch, "float8_e4m3fn"): + return byte_tensor.view(torch.float8_e4m3fn).reshape(shape) + else: + # Float8 not supported in this PyTorch version + raise ValueError(f"Unsupported float8 type: {dtype_str} (upgrade PyTorch to support float8 types)") + + +def load_safetensors( + path: str, + device: Union[str, torch.device], + disable_mmap: bool = False, + dtype: Optional[torch.dtype] = None, + disable_numpy_memmap: bool = False, +) -> dict[str, torch.Tensor]: + if disable_mmap: + # return safetensors.torch.load(open(path, "rb").read()) + # use experimental loader + # logger.info(f"Loading without mmap (experimental)") + state_dict = {} + device = torch.device(device) if device is not None else None + with MemoryEfficientSafeOpen(path, disable_numpy_memmap=disable_numpy_memmap) as f: + for key in f.keys(): + state_dict[key] = f.get_tensor(key, device=device, dtype=dtype) + synchronize_device(device) + return state_dict + else: + try: + state_dict = load_file(path, device=device) + except: + state_dict = load_file(path) # prevent device invalid Error + if dtype is not None: + for key in state_dict.keys(): + state_dict[key] = state_dict[key].to(dtype=dtype) + return state_dict + + +def get_split_weight_filenames(file_path: str) -> Optional[list[str]]: + """ + Get the list of split weight filenames (full paths) if the file name ends with 00001-of-00004 etc. + Returns None if the file is not split. + """ + basename = os.path.basename(file_path) + match = re.match(r"^(.*?)(\d+)-of-(\d+)\.safetensors$", basename) + if match: + prefix = basename[: match.start(2)] + count = int(match.group(3)) + filenames = [] + for i in range(count): + filename = f"{prefix}{i + 1:05d}-of-{count:05d}.safetensors" + filepath = os.path.join(os.path.dirname(file_path), filename) + if os.path.exists(filepath): + filenames.append(filepath) + else: + raise FileNotFoundError(f"File {filepath} not found") + return filenames + else: + return None + + +def load_split_weights( + file_path: str, device: Union[str, torch.device] = "cpu", disable_mmap: bool = False, dtype: Optional[torch.dtype] = None +) -> Dict[str, torch.Tensor]: + """ + Load split weights from a file. If the file name ends with 00001-of-00004 etc, it will load all files with the same prefix. + dtype is as is, no conversion is done. + """ + device = torch.device(device) + + # if the file name ends with 00001-of-00004 etc, we need to load the files with the same prefix + split_filenames = get_split_weight_filenames(file_path) + if split_filenames is not None: + state_dict = {} + for filename in split_filenames: + state_dict.update(load_safetensors(filename, device=device, disable_mmap=disable_mmap, dtype=dtype)) + else: + state_dict = load_safetensors(file_path, device=device, disable_mmap=disable_mmap, dtype=dtype) + return state_dict + + +def find_key(safetensors_file: str, starts_with: Optional[str] = None, ends_with: Optional[str] = None) -> Optional[str]: + """ + Find a key in a safetensors file that starts with `starts_with` and ends with `ends_with`. + If `starts_with` is None, it will match any key. + If `ends_with` is None, it will match any key. + Returns the first matching key or None if no key matches. + """ + with MemoryEfficientSafeOpen(safetensors_file) as f: + for key in f.keys(): + if (starts_with is None or key.startswith(starts_with)) and (ends_with is None or key.endswith(ends_with)): + return key + return None + + +@dataclass +class WeightTransformHooks: + split_hook: Optional[callable] = None + concat_hook: Optional[callable] = None + rename_hook: Optional[callable] = None + + +class TensorWeightAdapter: + """ + A wrapper for weight conversion hooks (split and concat) to be used with MemoryEfficientSafeOpen. + This wrapper adapts the original MemoryEfficientSafeOpen to apply the provided split and concat hooks + when loading tensors. + + split_hook: A callable that takes (original_key: str, original_tensor: torch.Tensor) and returns (new_keys: list[str], new_tensors: list[torch.Tensor]). + concat_hook: A callable that takes (original_key: str, tensors: dict[str, torch.Tensor]) and returns (new_key: str, concatenated_tensor: torch.Tensor). + rename_hook: A callable that takes (original_key: str) and returns (new_key: str). + + If tensors is None, the hook should return only the new keys (for split) or new key (for concat), without tensors. + + No need to implement __enter__ and __exit__ methods, as they are handled by the original MemoryEfficientSafeOpen. + Do not use this wrapper as a context manager directly, like `with WeightConvertHookWrapper(...) as f:`. + + **concat_hook is not tested yet.** + """ + + def __init__(self, weight_convert_hook: WeightTransformHooks, original_f: MemoryEfficientSafeOpen): + self.original_f = original_f + self.new_key_to_original_key_map: dict[str, Union[str, list[str]]] = ( + {} + ) # for split: new_key -> original_key; for concat: new_key -> list of original_keys; for direct mapping: new_key -> original_key + self.concat_key_set = set() # set of concatenated keys + self.split_key_set = set() # set of split keys + self.new_keys = [] + self.tensor_cache = {} # cache for split tensors + self.split_hook = weight_convert_hook.split_hook + self.concat_hook = weight_convert_hook.concat_hook + self.rename_hook = weight_convert_hook.rename_hook + + for key in self.original_f.keys(): + if self.split_hook is not None: + converted_keys, _ = self.split_hook(key, None) # get new keys only + if converted_keys is not None: + for converted_key in converted_keys: + self.new_key_to_original_key_map[converted_key] = key + self.split_key_set.add(converted_key) + self.new_keys.extend(converted_keys) + continue # skip concat_hook if split_hook is applied + + if self.concat_hook is not None: + converted_key, _ = self.concat_hook(key, None) # get new key only + if converted_key is not None: + if converted_key not in self.concat_key_set: # first time seeing this concatenated key + self.concat_key_set.add(converted_key) + self.new_key_to_original_key_map[converted_key] = [] + self.new_keys.append(converted_key) + + # multiple original keys map to the same concatenated key + self.new_key_to_original_key_map[converted_key].append(key) + continue # skip to next key + + # direct mapping + if self.rename_hook is not None: + new_key = self.rename_hook(key) + self.new_key_to_original_key_map[new_key] = key + else: + new_key = key + + self.new_keys.append(new_key) + + def keys(self) -> list[str]: + return self.new_keys + + def get_tensor(self, new_key: str, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> torch.Tensor: + # load tensor by new_key, applying split or concat hooks as needed + if new_key not in self.new_key_to_original_key_map: + # direct mapping + return self.original_f.get_tensor(new_key, device=device, dtype=dtype) + + elif new_key in self.split_key_set: + # split hook: split key is requested multiple times, so we cache the result + original_key = self.new_key_to_original_key_map[new_key] + if original_key not in self.tensor_cache: # not yet split + original_tensor = self.original_f.get_tensor(original_key, device=device, dtype=dtype) + new_keys, new_tensors = self.split_hook(original_key, original_tensor) # apply split hook + for k, t in zip(new_keys, new_tensors): + self.tensor_cache[k] = t + return self.tensor_cache.pop(new_key) # return and remove from cache + + elif new_key in self.concat_key_set: + # concat hook: concatenated key is requested only once, so we do not cache the result + tensors = {} + for original_key in self.new_key_to_original_key_map[new_key]: + tensor = self.original_f.get_tensor(original_key, device=device, dtype=dtype) + tensors[original_key] = tensor + _, concatenated_tensors = self.concat_hook(self.new_key_to_original_key_map[new_key][0], tensors) # apply concat hook + return concatenated_tensors + + else: + # direct mapping + original_key = self.new_key_to_original_key_map[new_key] + return self.original_f.get_tensor(original_key, device=device, dtype=dtype) diff --git a/vendor/sd-scripts/library/sai_model_spec.py b/vendor/sd-scripts/library/sai_model_spec.py new file mode 100644 index 0000000..0ac9b3b --- /dev/null +++ b/vendor/sd-scripts/library/sai_model_spec.py @@ -0,0 +1,693 @@ +# based on https://github.com/Stability-AI/ModelSpec +import datetime +import hashlib +import argparse +import base64 +import logging +import mimetypes +import subprocess +from dataclasses import dataclass, field +from io import BytesIO +import os +from typing import Union +import safetensors +from library.utils import setup_logging + +setup_logging() + +logger = logging.getLogger(__name__) + +r""" +# Metadata Example +metadata = { + # === Must === + "modelspec.sai_model_spec": "1.0.0", # Required version ID for the spec + "modelspec.architecture": "stable-diffusion-xl-v1-base", # Architecture, reference the ID of the original model of the arch to match the ID + "modelspec.implementation": "sgm", + "modelspec.title": "Example Model Version 1.0", # Clean, human-readable title. May use your own phrasing/language/etc + # === Should === + "modelspec.author": "Example Corp", # Your name or company name + "modelspec.description": "This is my example model to show you how to do it!", # Describe the model in your own words/language/etc. Focus on what users need to know + "modelspec.date": "2023-07-20", # ISO-8601 compliant date of when the model was created + # === Can === + "modelspec.license": "ExampleLicense-1.0", # eg CreativeML Open RAIL, etc. + "modelspec.usage_hint": "Use keyword 'example'" # In your own language, very short hints about how the user should use the model +} +""" + +BASE_METADATA = { + # === MUST === + "modelspec.sai_model_spec": "1.0.1", + "modelspec.architecture": None, + "modelspec.implementation": None, + "modelspec.title": None, + "modelspec.resolution": None, + # === SHOULD === + "modelspec.description": None, + "modelspec.author": None, + "modelspec.date": None, + "modelspec.hash_sha256": None, + # === CAN=== + "modelspec.implementation_version": None, + "modelspec.license": None, + "modelspec.usage_hint": None, + "modelspec.thumbnail": None, + "modelspec.tags": None, + "modelspec.merged_from": None, + "modelspec.trigger_phrase": None, + "modelspec.prediction_type": None, + "modelspec.timestep_range": None, + "modelspec.encoder_layer": None, + "modelspec.preprocessor": None, + "modelspec.is_negative_embedding": None, + "modelspec.unet_dtype": None, + "modelspec.vae_dtype": None, +} + +# 別に使うやつだけ定義 +MODELSPEC_TITLE = "modelspec.title" + +ARCH_SD_V1 = "stable-diffusion-v1" +ARCH_SD_V2_512 = "stable-diffusion-v2-512" +ARCH_SD_V2_768_V = "stable-diffusion-v2-768-v" +ARCH_SD_XL_V1_BASE = "stable-diffusion-xl-v1-base" +ARCH_SD3_M = "stable-diffusion-3" # may be followed by "-m" or "-5-large" etc. +# ARCH_SD3_UNKNOWN = "stable-diffusion-3" +ARCH_FLUX_1_DEV = "flux-1-dev" +ARCH_FLUX_1_SCHNELL = "flux-1-schnell" +ARCH_FLUX_1_CHROMA = "chroma" # for Flux Chroma +ARCH_FLUX_1_UNKNOWN = "flux-1" +ARCH_LUMINA_2 = "lumina-2" +ARCH_LUMINA_UNKNOWN = "lumina" +ARCH_HUNYUAN_IMAGE_2_1 = "hunyuan-image-2.1" +ARCH_HUNYUAN_IMAGE_UNKNOWN = "hunyuan-image" +ARCH_ANIMA_PREVIEW = "anima-preview" +ARCH_ANIMA_UNKNOWN = "anima-unknown" + +ADAPTER_LORA = "lora" +ADAPTER_TEXTUAL_INVERSION = "textual-inversion" + +IMPL_STABILITY_AI = "https://github.com/Stability-AI/generative-models" +IMPL_COMFY_UI = "https://github.com/comfyanonymous/ComfyUI" +IMPL_DIFFUSERS = "diffusers" +IMPL_FLUX = "https://github.com/black-forest-labs/flux" +IMPL_CHROMA = "https://huggingface.co/lodestones/Chroma" +IMPL_LUMINA = "https://github.com/Alpha-VLLM/Lumina-Image-2.0" +IMPL_HUNYUAN_IMAGE = "https://github.com/Tencent-Hunyuan/HunyuanImage-2.1" +IMPL_ANIMA = "https://huggingface.co/circlestone-labs/Anima" + +PRED_TYPE_EPSILON = "epsilon" +PRED_TYPE_V = "v" + + +@dataclass +class ModelSpecMetadata: + """ + ModelSpec 1.0.1 compliant metadata for safetensors models. + All fields correspond to modelspec.* keys in the final metadata. + """ + + # === MUST === + architecture: str + implementation: str + title: str + resolution: str + sai_model_spec: str = "1.0.1" + + # === SHOULD === + description: str | None = None + author: str | None = None + date: str | None = None + hash_sha256: str | None = None + + # === CAN === + implementation_version: str | None = None + license: str | None = None + usage_hint: str | None = None + thumbnail: str | None = None + tags: str | None = None + merged_from: str | None = None + trigger_phrase: str | None = None + prediction_type: str | None = None + timestep_range: str | None = None + encoder_layer: str | None = None + preprocessor: str | None = None + is_negative_embedding: str | None = None + unet_dtype: str | None = None + vae_dtype: str | None = None + + # === Additional metadata === + additional_fields: dict[str, str] = field(default_factory=dict) + + def to_metadata_dict(self) -> dict[str, str]: + """Convert dataclass to metadata dictionary with modelspec. prefixes.""" + metadata = {} + + # Add all non-None fields with modelspec prefix + for field_name, value in self.__dict__.items(): + if field_name == "additional_fields": + # Handle additional fields separately + for key, val in value.items(): + if key.startswith("modelspec."): + metadata[key] = val + else: + metadata[f"modelspec.{key}"] = val + elif value is not None: + metadata[f"modelspec.{field_name}"] = value + + return metadata + + @classmethod + def from_args(cls, args, **kwargs) -> "ModelSpecMetadata": + """Create ModelSpecMetadata from argparse Namespace, extracting metadata_* fields.""" + metadata_fields = {} + + # Extract all metadata_* attributes from args + for attr_name in dir(args): + if attr_name.startswith("metadata_") and not attr_name.startswith("metadata___"): + value = getattr(args, attr_name, None) + if value is not None: + # Remove metadata_ prefix + field_name = attr_name[9:] # len("metadata_") = 9 + metadata_fields[field_name] = value + + # Handle known standard fields + standard_fields = { + "author": metadata_fields.pop("author", None), + "description": metadata_fields.pop("description", None), + "license": metadata_fields.pop("license", None), + "tags": metadata_fields.pop("tags", None), + } + + # Remove None values + standard_fields = {k: v for k, v in standard_fields.items() if v is not None} + + # Merge with kwargs and remaining metadata fields + all_fields = {**standard_fields, **kwargs} + if metadata_fields: + all_fields["additional_fields"] = metadata_fields + + return cls(**all_fields) + + +def determine_architecture( + v2: bool, v_parameterization: bool, sdxl: bool, lora: bool, textual_inversion: bool, model_config: dict[str, str] | None = None +) -> str: + """Determine model architecture string from parameters.""" + + model_config = model_config or {} + + if sdxl: + arch = ARCH_SD_XL_V1_BASE + elif "sd3" in model_config: + arch = ARCH_SD3_M + "-" + model_config["sd3"] + elif "flux" in model_config: + flux_type = model_config["flux"] + if flux_type == "dev": + arch = ARCH_FLUX_1_DEV + elif flux_type == "schnell": + arch = ARCH_FLUX_1_SCHNELL + elif flux_type == "chroma": + arch = ARCH_FLUX_1_CHROMA + else: + arch = ARCH_FLUX_1_UNKNOWN + elif "lumina" in model_config: + lumina_type = model_config["lumina"] + if lumina_type == "lumina2": + arch = ARCH_LUMINA_2 + else: + arch = ARCH_LUMINA_UNKNOWN + elif "hunyuan_image" in model_config: + hunyuan_image_type = model_config["hunyuan_image"] + if hunyuan_image_type == "2.1": + arch = ARCH_HUNYUAN_IMAGE_2_1 + else: + arch = ARCH_HUNYUAN_IMAGE_UNKNOWN + elif "anima" in model_config: + anima_type = model_config["anima"] + if anima_type == "preview": + arch = ARCH_ANIMA_PREVIEW + else: + arch = ARCH_ANIMA_UNKNOWN + elif v2: + arch = ARCH_SD_V2_768_V if v_parameterization else ARCH_SD_V2_512 + else: + arch = ARCH_SD_V1 + + # Add adapter suffix + if lora: + arch += f"/{ADAPTER_LORA}" + elif textual_inversion: + arch += f"/{ADAPTER_TEXTUAL_INVERSION}" + + return arch + + +def determine_implementation( + lora: bool, + textual_inversion: bool, + sdxl: bool, + model_config: dict[str, str] | None = None, + is_stable_diffusion_ckpt: bool | None = None, +) -> str: + """Determine implementation string from parameters.""" + + model_config = model_config or {} + + if "flux" in model_config: + if model_config["flux"] == "chroma": + return IMPL_CHROMA + else: + return IMPL_FLUX + elif "lumina" in model_config: + return IMPL_LUMINA + elif "anima" in model_config: + return IMPL_ANIMA + elif (lora and sdxl) or textual_inversion or is_stable_diffusion_ckpt: + return IMPL_STABILITY_AI + else: + return IMPL_DIFFUSERS + + +def get_implementation_version() -> str: + """Get the current implementation version as sd-scripts/{commit_hash}.""" + try: + # Get the git commit hash + result = subprocess.run( + ["git", "rev-parse", "HEAD"], + capture_output=True, + text=True, + cwd=os.path.dirname(os.path.dirname(__file__)), # Go up to sd-scripts root + timeout=5, + ) + + if result.returncode == 0: + commit_hash = result.stdout.strip() + return f"sd-scripts/{commit_hash}" + else: + logger.warning("Failed to get git commit hash, using fallback") + return "sd-scripts/unknown" + + except (subprocess.TimeoutExpired, subprocess.SubprocessError, FileNotFoundError) as e: + logger.warning(f"Could not determine git commit: {e}") + return "sd-scripts/unknown" + + +def file_to_data_url(file_path: str) -> str: + """Convert a file path to a data URL for embedding in metadata.""" + if not os.path.exists(file_path): + raise FileNotFoundError(f"File not found: {file_path}") + + # Get MIME type + mime_type, _ = mimetypes.guess_type(file_path) + if mime_type is None: + # Default to binary if we can't detect + mime_type = "application/octet-stream" + + # Read file and encode as base64 + with open(file_path, "rb") as f: + file_data = f.read() + + encoded_data = base64.b64encode(file_data).decode("ascii") + + return f"data:{mime_type};base64,{encoded_data}" + + +def determine_resolution( + reso: Union[int, tuple[int, int]] | None = None, + sdxl: bool = False, + model_config: dict[str, str] | None = None, + v2: bool = False, + v_parameterization: bool = False, +) -> str: + """Determine resolution string from parameters.""" + + model_config = model_config or {} + + if reso is not None: + # Handle comma separated string + if isinstance(reso, str): + reso = tuple(map(int, reso.split(","))) + # Handle single int + if isinstance(reso, int): + reso = (reso, reso) + # Handle single-element tuple + if len(reso) == 1: + reso = (reso[0], reso[0]) + else: + # Determine default resolution based on model type + if sdxl or "sd3" in model_config or "flux" in model_config or "lumina" in model_config or "anima" in model_config: + reso = (1024, 1024) + elif v2 and v_parameterization: + reso = (768, 768) + else: + reso = (512, 512) + + return f"{reso[0]}x{reso[1]}" + + +def load_bytes_in_safetensors(tensors): + bytes = safetensors.torch.save(tensors) + b = BytesIO(bytes) + + b.seek(0) + header = b.read(8) + n = int.from_bytes(header, "little") + + offset = n + 8 + b.seek(offset) + + return b.read() + + +def precalculate_safetensors_hashes(state_dict): + # calculate each tensor one by one to reduce memory usage + hash_sha256 = hashlib.sha256() + for tensor in state_dict.values(): + single_tensor_sd = {"tensor": tensor} + bytes_for_tensor = load_bytes_in_safetensors(single_tensor_sd) + hash_sha256.update(bytes_for_tensor) + + return f"0x{hash_sha256.hexdigest()}" + + +def update_hash_sha256(metadata: dict, state_dict: dict): + raise NotImplementedError + + +def build_metadata_dataclass( + state_dict: dict | None, + v2: bool, + v_parameterization: bool, + sdxl: bool, + lora: bool, + textual_inversion: bool, + timestamp: float, + title: str | None = None, + reso: int | tuple[int, int] | None = None, + is_stable_diffusion_ckpt: bool | None = None, + author: str | None = None, + description: str | None = None, + license: str | None = None, + tags: str | None = None, + merged_from: str | None = None, + timesteps: tuple[int, int] | None = None, + clip_skip: int | None = None, + model_config: dict | None = None, + optional_metadata: dict | None = None, +) -> ModelSpecMetadata: + """ + Build ModelSpec 1.0.1 compliant metadata dataclass. + + Args: + model_config: Dict containing model type info, e.g. {"flux": "dev"}, {"sd3": "large"} + optional_metadata: Dict of additional metadata fields to include + """ + + # Use helper functions for complex logic + architecture = determine_architecture(v2, v_parameterization, sdxl, lora, textual_inversion, model_config) + + if not lora and not textual_inversion and is_stable_diffusion_ckpt is None: + is_stable_diffusion_ckpt = True # default is stable diffusion ckpt if not lora and not textual_inversion + + implementation = determine_implementation(lora, textual_inversion, sdxl, model_config, is_stable_diffusion_ckpt) + + if title is None: + if lora: + title = "LoRA" + elif textual_inversion: + title = "TextualInversion" + else: + title = "Checkpoint" + title += f"@{timestamp}" + + # remove microsecond from time + int_ts = int(timestamp) + # time to iso-8601 compliant date + date = datetime.datetime.fromtimestamp(int_ts).isoformat() + + # Use helper function for resolution + resolution = determine_resolution(reso, sdxl, model_config, v2, v_parameterization) + + # Handle prediction type - Flux models don't use prediction_type + model_config = model_config or {} + prediction_type = None + if "flux" not in model_config: + if v_parameterization: + prediction_type = PRED_TYPE_V + else: + prediction_type = PRED_TYPE_EPSILON + + # Handle timesteps + timestep_range = None + if timesteps is not None: + if isinstance(timesteps, str) or isinstance(timesteps, int): + timesteps = (timesteps, timesteps) + if len(timesteps) == 1: + timesteps = (timesteps[0], timesteps[0]) + timestep_range = f"{timesteps[0]},{timesteps[1]}" + + # Handle encoder layer (clip skip) + encoder_layer = None + if clip_skip is not None: + encoder_layer = f"{clip_skip}" + + # TODO: Implement hash calculation when memory-efficient method is available + # hash_sha256 = None + # if state_dict is not None: + # hash_sha256 = precalculate_safetensors_hashes(state_dict) + + # Process thumbnail - convert file path to data URL if needed + processed_optional_metadata = optional_metadata.copy() if optional_metadata else {} + if "thumbnail" in processed_optional_metadata: + thumbnail_value = processed_optional_metadata["thumbnail"] + # Check if it's already a data URL or if it's a file path + if thumbnail_value and not thumbnail_value.startswith("data:"): + try: + processed_optional_metadata["thumbnail"] = file_to_data_url(thumbnail_value) + logger.info(f"Converted thumbnail file {thumbnail_value} to data URL") + except FileNotFoundError as e: + logger.warning(f"Thumbnail file not found, skipping: {e}") + del processed_optional_metadata["thumbnail"] + except Exception as e: + logger.warning(f"Failed to convert thumbnail to data URL: {e}") + del processed_optional_metadata["thumbnail"] + + # Automatically set implementation version if not provided + if "implementation_version" not in processed_optional_metadata: + processed_optional_metadata["implementation_version"] = get_implementation_version() + + # Create the dataclass + metadata = ModelSpecMetadata( + architecture=architecture, + implementation=implementation, + title=title, + description=description, + author=author, + date=date, + license=license, + tags=tags, + merged_from=merged_from, + resolution=resolution, + prediction_type=prediction_type, + timestep_range=timestep_range, + encoder_layer=encoder_layer, + additional_fields=processed_optional_metadata, + ) + + return metadata + + +def build_metadata( + state_dict: dict | None, + v2: bool, + v_parameterization: bool, + sdxl: bool, + lora: bool, + textual_inversion: bool, + timestamp: float, + title: str | None = None, + reso: int | tuple[int, int] | None = None, + is_stable_diffusion_ckpt: bool | None = None, + author: str | None = None, + description: str | None = None, + license: str | None = None, + tags: str | None = None, + merged_from: str | None = None, + timesteps: tuple[int, int] | None = None, + clip_skip: int | None = None, + model_config: dict | None = None, + optional_metadata: dict | None = None, +) -> dict[str, str]: + """ + Build ModelSpec 1.0.1 compliant metadata for safetensors models. + Legacy function that returns dict - prefer build_metadata_dataclass for new code. + + Args: + model_config: Dict containing model type info, e.g. {"flux": "dev"}, {"sd3": "large"} + optional_metadata: Dict of additional metadata fields to include + """ + # Use the dataclass function and convert to dict + metadata_obj = build_metadata_dataclass( + state_dict=state_dict, + v2=v2, + v_parameterization=v_parameterization, + sdxl=sdxl, + lora=lora, + textual_inversion=textual_inversion, + timestamp=timestamp, + title=title, + reso=reso, + is_stable_diffusion_ckpt=is_stable_diffusion_ckpt, + author=author, + description=description, + license=license, + tags=tags, + merged_from=merged_from, + timesteps=timesteps, + clip_skip=clip_skip, + model_config=model_config, + optional_metadata=optional_metadata, + ) + + return metadata_obj.to_metadata_dict() + + +# region utils + + +def get_title(metadata: dict) -> str | None: + return metadata.get(MODELSPEC_TITLE, None) + + +def load_metadata_from_safetensors(model: str) -> dict: + if not model.endswith(".safetensors"): + return {} + + with safetensors.safe_open(model, framework="pt") as f: + metadata = f.metadata() + if metadata is None: + metadata = {} + return metadata + + +def build_merged_from(models: list[str]) -> str: + def get_title(model: str): + metadata = load_metadata_from_safetensors(model) + title = metadata.get(MODELSPEC_TITLE, None) + if title is None: + title = os.path.splitext(os.path.basename(model))[0] # use filename + return title + + titles = [get_title(model) for model in models] + return ", ".join(titles) + + +def add_model_spec_arguments(parser: argparse.ArgumentParser): + """Add all ModelSpec metadata arguments to the parser.""" + + parser.add_argument( + "--metadata_title", + type=str, + default=None, + help="title for model metadata (default is output_name) / メタデータに書き込まれるモデルタイトル、省略時はoutput_name", + ) + parser.add_argument( + "--metadata_author", + type=str, + default=None, + help="author name for model metadata / メタデータに書き込まれるモデル作者名", + ) + parser.add_argument( + "--metadata_description", + type=str, + default=None, + help="description for model metadata / メタデータに書き込まれるモデル説明", + ) + parser.add_argument( + "--metadata_license", + type=str, + default=None, + help="license for model metadata / メタデータに書き込まれるモデルライセンス", + ) + parser.add_argument( + "--metadata_tags", + type=str, + default=None, + help="tags for model metadata, separated by comma / メタデータに書き込まれるモデルタグ、カンマ区切り", + ) + parser.add_argument( + "--metadata_usage_hint", + type=str, + default=None, + help="usage hint for model metadata / メタデータに書き込まれる使用方法のヒント", + ) + parser.add_argument( + "--metadata_thumbnail", + type=str, + default=None, + help="thumbnail image as data URL or file path (will be converted to data URL) for model metadata / メタデータに書き込まれるサムネイル画像（データURLまたはファイルパス、ファイルパスの場合はデータURLに変換されます）", + ) + parser.add_argument( + "--metadata_merged_from", + type=str, + default=None, + help="source models for merged model metadata / メタデータに書き込まれるマージ元モデル名", + ) + parser.add_argument( + "--metadata_trigger_phrase", + type=str, + default=None, + help="trigger phrase for model metadata / メタデータに書き込まれるトリガーフレーズ", + ) + parser.add_argument( + "--metadata_preprocessor", + type=str, + default=None, + help="preprocessor used for model metadata / メタデータに書き込まれる前処理手法", + ) + parser.add_argument( + "--metadata_is_negative_embedding", + type=str, + default=None, + help="whether this is a negative embedding for model metadata / メタデータに書き込まれるネガティブ埋め込みかどうか", + ) + + +# endregion + + +r""" +if __name__ == "__main__": + import argparse + import torch + from safetensors.torch import load_file + from library import train_util + + parser = argparse.ArgumentParser() + parser.add_argument("--ckpt", type=str, required=True) + args = parser.parse_args() + + print(f"Loading {args.ckpt}") + state_dict = load_file(args.ckpt) + + print(f"Calculating metadata") + metadata = get(state_dict, False, False, False, False, "sgm", False, False, "title", "date", 256, 1000, 0) + print(metadata) + del state_dict + + # by reference implementation + with open(args.ckpt, mode="rb") as file_data: + file_hash = hashlib.sha256() + head_len = struct.unpack("Q", file_data.read(8)) # int64 header length prefix + header = json.loads(file_data.read(head_len[0])) # header itself, json string + content = ( + file_data.read() + ) # All other content is tightly packed tensors. Copy to RAM for simplicity, but you can avoid this read with a more careful FS-dependent impl. + file_hash.update(content) + # ===== Update the hash for modelspec ===== + by_ref = f"0x{file_hash.hexdigest()}" + print(by_ref) + print("is same?", by_ref == metadata["modelspec.hash_sha256"]) + +""" diff --git a/vendor/sd-scripts/library/sd3_models.py b/vendor/sd-scripts/library/sd3_models.py new file mode 100644 index 0000000..996f819 --- /dev/null +++ b/vendor/sd-scripts/library/sd3_models.py @@ -0,0 +1,1428 @@ +# some modules/classes are copied and modified from https://github.com/mcmonkey4eva/sd3-ref +# the original code is licensed under the MIT License + +# and some module/classes are contributed from KohakuBlueleaf. Thanks for the contribution! + +from ast import Tuple +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass +from functools import partial +import math +from types import SimpleNamespace +from typing import Dict, List, Optional, Union +import einops +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.checkpoint import checkpoint +from transformers import CLIPTokenizer, T5TokenizerFast + +from library import custom_offloading_utils +from library.device_utils import clean_memory_on_device + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +memory_efficient_attention = None +try: + import xformers +except: + pass + +try: + from xformers.ops import memory_efficient_attention +except: + memory_efficient_attention = None + + +# region mmdit + + +@dataclass +class SD3Params: + patch_size: int + depth: int + num_patches: int + pos_embed_max_size: int + adm_in_channels: int + qk_norm: Optional[str] + x_block_self_attn_layers: list[int] + context_embedder_in_features: int + context_embedder_out_features: int + model_type: str + + +def get_2d_sincos_pos_embed( + embed_dim, + grid_size, + scaling_factor=None, + offset=None, +): + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + if scaling_factor is not None: + grid = grid / scaling_factor + if offset is not None: + grid = grid - offset + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_scaled_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, sample_size=64, base_size=16): + """ + This function is contributed by KohakuBlueleaf. Thanks for the contribution! + + Creates scaled 2D sinusoidal positional embeddings that maintain consistent relative positions + when the resolution differs from the training resolution. + + Args: + embed_dim (int): Dimension of the positional embedding. + grid_size (int or tuple): Size of the position grid (H, W). If int, assumes square grid. + cls_token (bool): Whether to include class token. Defaults to False. + extra_tokens (int): Number of extra tokens (e.g., cls_token). Defaults to 0. + sample_size (int): Reference resolution (typically training resolution). Defaults to 64. + base_size (int): Base grid size used during training. Defaults to 16. + + Returns: + numpy.ndarray: Positional embeddings of shape (H*W, embed_dim) or + (H*W + extra_tokens, embed_dim) if cls_token is True. + """ + # Convert grid_size to tuple if it's an integer + if isinstance(grid_size, int): + grid_size = (grid_size, grid_size) + + # Create normalized grid coordinates (0 to 1) + grid_h = np.arange(grid_size[0], dtype=np.float32) / grid_size[0] + grid_w = np.arange(grid_size[1], dtype=np.float32) / grid_size[1] + + # Calculate scaling factors for height and width + # This ensures that the central region matches the original resolution's embeddings + scale_h = base_size * grid_size[0] / (sample_size) + scale_w = base_size * grid_size[1] / (sample_size) + + # Calculate shift values to center the original resolution's embedding region + # This ensures that the central sample_size x sample_size region has similar + # positional embeddings to the original resolution + shift_h = 1 * scale_h * (grid_size[0] - sample_size) / (2 * grid_size[0]) + shift_w = 1 * scale_w * (grid_size[1] - sample_size) / (2 * grid_size[1]) + + # Apply scaling and shifting to create the final grid coordinates + grid_h = grid_h * scale_h - shift_h + grid_w = grid_w * scale_w - shift_w + + # Create 2D grid using meshgrid (note: w goes first) + grid = np.meshgrid(grid_w, grid_h) + grid = np.stack(grid, axis=0) + + # # Calculate the starting indices for the central region + # # This is used for debugging/visualization of the central region + # st_h = (grid_size[0] - sample_size) // 2 + # st_w = (grid_size[1] - sample_size) // 2 + # print(grid[:, st_h : st_h + sample_size, st_w : st_w + sample_size]) + + # Reshape grid for positional embedding calculation + grid = grid.reshape([2, 1, grid_size[1], grid_size[0]]) + + # Generate the sinusoidal positional embeddings + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + + # Add zeros for extra tokens (e.g., [CLS] token) if required + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + + return pos_embed + + +# if __name__ == "__main__": +# # This is what you get when you load SD3.5 state dict +# pos_emb = torch.from_numpy(get_scaled_2d_sincos_pos_embed( +# 1536, [384, 384], sample_size=64, base_size=16 +# )).float().unsqueeze(0) + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid_torch( + embed_dim, + pos, + device=None, + dtype=torch.float32, +): + omega = torch.arange(embed_dim // 2, device=device, dtype=dtype) + omega *= 2.0 / embed_dim + omega = 1.0 / 10000**omega + out = torch.outer(pos.reshape(-1), omega) + emb = torch.cat([out.sin(), out.cos()], dim=1) + return emb + + +def get_2d_sincos_pos_embed_torch( + embed_dim, + w, + h, + val_center=7.5, + val_magnitude=7.5, + device=None, + dtype=torch.float32, +): + small = min(h, w) + val_h = (h / small) * val_magnitude + val_w = (w / small) * val_magnitude + grid_h, grid_w = torch.meshgrid( + torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), + torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), + indexing="ij", + ) + emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) + emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) + emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) + return emb + + +def modulate(x, shift, scale): + if shift is None: + shift = torch.zeros_like(scale) + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def default(x, default_value): + if x is None: + return default_value + return x + + +def timestep_embedding(t, dim, max_period=10000): + half = dim // 2 + # freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + # device=t.device, dtype=t.dtype + # ) + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(dtype=t.dtype) + return embedding + + +class PatchEmbed(nn.Module): + def __init__( + self, + img_size=256, + patch_size=4, + in_channels=3, + embed_dim=512, + norm_layer=None, + flatten=True, + bias=True, + strict_img_size=True, + dynamic_img_pad=False, + ): + # dynamic_img_pad and norm is omitted in SD3.5 + super().__init__() + self.patch_size = patch_size + self.flatten = flatten + self.strict_img_size = strict_img_size + self.dynamic_img_pad = dynamic_img_pad + if img_size is not None: + self.img_size = img_size + self.grid_size = img_size // patch_size + self.num_patches = self.grid_size**2 + else: + self.img_size = None + self.grid_size = None + self.num_patches = None + + self.proj = nn.Conv2d(in_channels, embed_dim, patch_size, patch_size, bias=bias) + self.norm = nn.Identity() if norm_layer is None else norm_layer(embed_dim) + + def forward(self, x): + B, C, H, W = x.shape + + if self.dynamic_img_pad: + # Pad input so we won't have partial patch + pad_h = (self.patch_size - H % self.patch_size) % self.patch_size + pad_w = (self.patch_size - W % self.patch_size) % self.patch_size + x = nn.functional.pad(x, (0, pad_w, 0, pad_h), mode="reflect") + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + return x + + +# FinalLayer in mmdit.py +class UnPatch(nn.Module): + def __init__(self, hidden_size=512, patch_size=4, out_channels=3): + super().__init__() + self.patch_size = patch_size + self.c = out_channels + + # eps is default in mmdit.py + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size**2 * out_channels) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size), + ) + + def forward(self, x: torch.Tensor, cmod, H=None, W=None): + b, n, _ = x.shape + p = self.patch_size + c = self.c + if H is None and W is None: + w = h = int(n**0.5) + assert h * w == n + else: + h = H // p if H else n // (W // p) + w = W // p if W else n // h + assert h * w == n + + shift, scale = self.adaLN_modulation(cmod).chunk(2, dim=-1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + + x = x.view(b, h, w, p, p, c) + x = x.permute(0, 5, 1, 3, 2, 4).contiguous() + x = x.view(b, c, h * p, w * p) + return x + + +class MLP(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=lambda: nn.GELU(), + norm_layer=None, + bias=True, + use_conv=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.use_conv = use_conv + + layer = partial(nn.Conv1d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = layer(in_features, hidden_features, bias=bias) + self.fc2 = layer(hidden_features, out_features, bias=bias) + self.act = act_layer() + self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity() + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.norm(x) + x = self.fc2(x) + return x + + +class TimestepEmbedding(nn.Module): + def __init__(self, hidden_size, freq_embed_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(freq_embed_size, hidden_size), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size), + ) + self.freq_embed_size = freq_embed_size + + def forward(self, t, dtype=None, **kwargs): + t_freq = timestep_embedding(t, self.freq_embed_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class Embedder(nn.Module): + def __init__(self, input_dim, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(input_dim, hidden_size), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size), + ) + + def forward(self, x): + return self.mlp(x) + + +def rmsnorm(x, eps=1e-6): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps) + + +class RMSNorm(torch.nn.Module): + def __init__( + self, + dim: int, + elementwise_affine: bool = False, + eps: float = 1e-6, + device=None, + dtype=None, + ): + """ + Initialize the RMSNorm normalization layer. + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + """ + super().__init__() + self.eps = eps + self.learnable_scale = elementwise_affine + if self.learnable_scale: + self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + else: + self.register_parameter("weight", None) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + Args: + x (torch.Tensor): The input tensor. + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + """ + x = rmsnorm(x, eps=self.eps) + if self.learnable_scale: + return x * self.weight.to(device=x.device, dtype=x.dtype) + else: + return x + + +class SwiGLUFeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: float = None, + ): + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + def forward(self, x): + return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) + + +# Linears for SelfAttention in mmdit.py +class AttentionLinears(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + pre_only: bool = False, + qk_norm: Optional[str] = None, + ): + super().__init__() + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + if not pre_only: + self.proj = nn.Linear(dim, dim) + self.pre_only = pre_only + + if qk_norm == "rms": + self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6) + self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6) + elif qk_norm == "ln": + self.ln_q = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6) + self.ln_k = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6) + elif qk_norm is None: + self.ln_q = nn.Identity() + self.ln_k = nn.Identity() + else: + raise ValueError(qk_norm) + + def pre_attention(self, x: torch.Tensor) -> torch.Tensor: + """ + output: + q, k, v: [B, L, D] + """ + B, L, C = x.shape + qkv: torch.Tensor = self.qkv(x) + q, k, v = qkv.reshape(B, L, -1, self.head_dim).chunk(3, dim=2) + q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) + k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) + return (q, k, v) + + def post_attention(self, x: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + x = self.proj(x) + return x + + +MEMORY_LAYOUTS = { + "torch": ( + lambda x, head_dim: x.reshape(x.shape[0], x.shape[1], -1, head_dim).transpose(1, 2), + lambda x: x.transpose(1, 2).reshape(x.shape[0], x.shape[2], -1), + lambda x: (1, x, 1, 1), + ), + "xformers": ( + lambda x, head_dim: x.reshape(x.shape[0], x.shape[1], -1, head_dim), + lambda x: x.reshape(x.shape[0], x.shape[1], -1), + lambda x: (1, 1, x, 1), + ), + "math": ( + lambda x, head_dim: x.reshape(x.shape[0], x.shape[1], -1, head_dim).transpose(1, 2), + lambda x: x.transpose(1, 2).reshape(x.shape[0], x.shape[2], -1), + lambda x: (1, x, 1, 1), + ), +} +# ATTN_FUNCTION = { +# "torch": F.scaled_dot_product_attention, +# "xformers": memory_efficient_attention, +# } + + +def vanilla_attention(q, k, v, mask, scale=None): + if scale is None: + scale = math.sqrt(q.size(-1)) + scores = torch.bmm(q, k.transpose(-1, -2)) / scale + if mask is not None: + mask = einops.rearrange(mask, "b ... -> b (...)") + max_neg_value = -torch.finfo(scores.dtype).max + mask = einops.repeat(mask, "b j -> (b h) j", h=q.size(-3)) + scores = scores.masked_fill(~mask, max_neg_value) + p_attn = F.softmax(scores, dim=-1) + return torch.bmm(p_attn, v) + + +def attention(q, k, v, head_dim, mask=None, scale=None, mode="xformers"): + """ + q, k, v: [B, L, D] + """ + pre_attn_layout = MEMORY_LAYOUTS[mode][0] + post_attn_layout = MEMORY_LAYOUTS[mode][1] + q = pre_attn_layout(q, head_dim) + k = pre_attn_layout(k, head_dim) + v = pre_attn_layout(v, head_dim) + + # scores = ATTN_FUNCTION[mode](q, k.to(q), v.to(q), mask, scale=scale) + if mode == "torch": + assert scale is None + scores = F.scaled_dot_product_attention(q, k.to(q), v.to(q), mask) # , scale=scale) + elif mode == "xformers": + scores = memory_efficient_attention(q, k.to(q), v.to(q), mask, scale=scale) + else: + scores = vanilla_attention(q, k.to(q), v.to(q), mask, scale=scale) + + scores = post_attn_layout(scores) + return scores + + +# DismantledBlock in mmdit.py +class SingleDiTBlock(nn.Module): + """ + A DiT block with gated adaptive layer norm (adaLN) conditioning. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: str = "xformers", + qkv_bias: bool = False, + pre_only: bool = False, + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + qk_norm: Optional[str] = None, + x_block_self_attn: bool = False, + **block_kwargs, + ): + super().__init__() + assert attn_mode in MEMORY_LAYOUTS + self.attn_mode = attn_mode + if not rmsnorm: + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + else: + self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = AttentionLinears(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, pre_only=pre_only, qk_norm=qk_norm) + + self.x_block_self_attn = x_block_self_attn + if self.x_block_self_attn: + assert not pre_only + assert not scale_mod_only + self.attn2 = AttentionLinears(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, pre_only=False, qk_norm=qk_norm) + + if not pre_only: + if not rmsnorm: + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + else: + self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + if not pre_only: + if not swiglu: + self.mlp = MLP( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=lambda: nn.GELU(approximate="tanh"), + ) + else: + self.mlp = SwiGLUFeedForward( + dim=hidden_size, + hidden_dim=mlp_hidden_dim, + multiple_of=256, + ) + self.scale_mod_only = scale_mod_only + if self.x_block_self_attn: + n_mods = 9 + elif not scale_mod_only: + n_mods = 6 if not pre_only else 2 + else: + n_mods = 4 if not pre_only else 1 + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, n_mods * hidden_size)) + self.pre_only = pre_only + + def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + if not self.pre_only: + if not self.scale_mod_only: + (shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp) = self.adaLN_modulation(c).chunk(6, dim=-1) + else: + shift_msa = None + shift_mlp = None + (scale_msa, gate_msa, scale_mlp, gate_mlp) = self.adaLN_modulation(c).chunk(4, dim=-1) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp) + else: + if not self.scale_mod_only: + (shift_msa, scale_msa) = self.adaLN_modulation(c).chunk(2, dim=-1) + else: + shift_msa = None + scale_msa = self.adaLN_modulation(c) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, None + + def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + assert self.x_block_self_attn + (shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2) = self.adaLN_modulation( + c + ).chunk(9, dim=1) + x_norm = self.norm1(x) + qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa)) + qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2)) + return qkv, qkv2, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2) + + def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): + assert not self.pre_only + x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2, attn1_dropout: float = 0.0): + assert not self.pre_only + if attn1_dropout > 0.0: + # Use torch.bernoulli to implement dropout, only dropout the batch dimension + attn1_dropout = torch.bernoulli(torch.full((attn.size(0), 1, 1), 1 - attn1_dropout, device=attn.device)) + attn_ = gate_msa.unsqueeze(1) * self.attn.post_attention(attn) * attn1_dropout + else: + attn_ = gate_msa.unsqueeze(1) * self.attn.post_attention(attn) + x = x + attn_ + attn2_ = gate_msa2.unsqueeze(1) * self.attn2.post_attention(attn2) + x = x + attn2_ + mlp_ = gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + x = x + mlp_ + return x + + +# JointBlock + block_mixing in mmdit.py +class MMDiTBlock(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + pre_only = kwargs.pop("pre_only") + x_block_self_attn = kwargs.pop("x_block_self_attn") + + self.context_block = SingleDiTBlock(*args, pre_only=pre_only, **kwargs) + self.x_block = SingleDiTBlock(*args, pre_only=False, x_block_self_attn=x_block_self_attn, **kwargs) + + self.head_dim = self.x_block.attn.head_dim + self.mode = self.x_block.attn_mode + self.gradient_checkpointing = False + + def enable_gradient_checkpointing(self): + self.gradient_checkpointing = True + + def _forward(self, context, x, c): + ctx_qkv, ctx_intermediate = self.context_block.pre_attention(context, c) + + if self.x_block.x_block_self_attn: + x_qkv, x_qkv2, x_intermediates = self.x_block.pre_attention_x(x, c) + else: + x_qkv, x_intermediates = self.x_block.pre_attention(x, c) + + ctx_len = ctx_qkv[0].size(1) + + q = torch.concat((ctx_qkv[0], x_qkv[0]), dim=1) + k = torch.concat((ctx_qkv[1], x_qkv[1]), dim=1) + v = torch.concat((ctx_qkv[2], x_qkv[2]), dim=1) + + attn = attention(q, k, v, head_dim=self.head_dim, mode=self.mode) + ctx_attn_out = attn[:, :ctx_len] + x_attn_out = attn[:, ctx_len:] + + if self.x_block.x_block_self_attn: + x_q2, x_k2, x_v2 = x_qkv2 + attn2 = attention(x_q2, x_k2, x_v2, self.x_block.attn2.num_heads, mode=self.mode) + x = self.x_block.post_attention_x(x_attn_out, attn2, *x_intermediates) + else: + x = self.x_block.post_attention(x_attn_out, *x_intermediates) + + if not self.context_block.pre_only: + context = self.context_block.post_attention(ctx_attn_out, *ctx_intermediate) + else: + context = None + + return context, x + + def forward(self, *args, **kwargs): + if self.training and self.gradient_checkpointing: + return checkpoint(self._forward, *args, use_reentrant=False, **kwargs) + else: + return self._forward(*args, **kwargs) + + +class MMDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + # prepare pos_embed for latent size * 2 + POS_EMBED_MAX_RATIO = 1.5 + + def __init__( + self, + input_size: int = 32, + patch_size: int = 2, + in_channels: int = 4, + depth: int = 28, + # hidden_size: Optional[int] = None, + # num_heads: Optional[int] = None, + mlp_ratio: float = 4.0, + learn_sigma: bool = False, + adm_in_channels: Optional[int] = None, + context_embedder_in_features: Optional[int] = None, + context_embedder_out_features: Optional[int] = None, + use_checkpoint: bool = False, + register_length: int = 0, + attn_mode: str = "torch", + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + out_channels: Optional[int] = None, + pos_embed_scaling_factor: Optional[float] = None, + pos_embed_offset: Optional[float] = None, + pos_embed_max_size: Optional[int] = None, + num_patches=None, + qk_norm: Optional[str] = None, + x_block_self_attn_layers: Optional[list[int]] = [], + qkv_bias: bool = True, + pos_emb_random_crop_rate: float = 0.0, + use_scaled_pos_embed: bool = False, + pos_embed_latent_sizes: Optional[list[int]] = None, + model_type: str = "sd3m", + ): + super().__init__() + self._model_type = model_type + self.learn_sigma = learn_sigma + self.in_channels = in_channels + default_out_channels = in_channels * 2 if learn_sigma else in_channels + self.out_channels = default(out_channels, default_out_channels) + self.patch_size = patch_size + self.pos_embed_scaling_factor = pos_embed_scaling_factor + self.pos_embed_offset = pos_embed_offset + self.pos_embed_max_size = pos_embed_max_size + self.x_block_self_attn_layers = x_block_self_attn_layers + self.pos_emb_random_crop_rate = pos_emb_random_crop_rate + self.gradient_checkpointing = use_checkpoint + + # hidden_size = default(hidden_size, 64 * depth) + # num_heads = default(num_heads, hidden_size // 64) + + # apply magic --> this defines a head_size of 64 + self.hidden_size = 64 * depth + num_heads = depth + + self.num_heads = num_heads + + self.enable_scaled_pos_embed(use_scaled_pos_embed, pos_embed_latent_sizes) + + self.x_embedder = PatchEmbed( + input_size, + patch_size, + in_channels, + self.hidden_size, + bias=True, + strict_img_size=self.pos_embed_max_size is None, + ) + self.t_embedder = TimestepEmbedding(self.hidden_size) + + self.y_embedder = None + if adm_in_channels is not None: + assert isinstance(adm_in_channels, int) + self.y_embedder = Embedder(adm_in_channels, self.hidden_size) + + if context_embedder_in_features is not None: + self.context_embedder = nn.Linear(context_embedder_in_features, context_embedder_out_features) + else: + self.context_embedder = nn.Identity() + + self.register_length = register_length + if self.register_length > 0: + self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size)) + + # num_patches = self.x_embedder.num_patches + # Will use fixed sin-cos embedding: + # just use a buffer already + if num_patches is not None: + self.register_buffer( + "pos_embed", + torch.empty(1, num_patches, self.hidden_size), + ) + else: + self.pos_embed = None + + self.use_checkpoint = use_checkpoint + self.joint_blocks = nn.ModuleList( + [ + MMDiTBlock( + self.hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + attn_mode=attn_mode, + qkv_bias=qkv_bias, + pre_only=i == depth - 1, + rmsnorm=rmsnorm, + scale_mod_only=scale_mod_only, + swiglu=swiglu, + qk_norm=qk_norm, + x_block_self_attn=(i in self.x_block_self_attn_layers), + ) + for i in range(depth) + ] + ) + for block in self.joint_blocks: + block.gradient_checkpointing = use_checkpoint + + self.final_layer = UnPatch(self.hidden_size, patch_size, self.out_channels) + # self.initialize_weights() + + self.blocks_to_swap = None + self.offloader = None + self.num_blocks = len(self.joint_blocks) + + def enable_scaled_pos_embed(self, use_scaled_pos_embed: bool, latent_sizes: Optional[list[int]]): + self.use_scaled_pos_embed = use_scaled_pos_embed + + if self.use_scaled_pos_embed: + # # remove pos_embed to free up memory up to 0.4 GB -> this causes error because pos_embed is not saved + # self.pos_embed = None + # move pos_embed to CPU to free up memory up to 0.4 GB + self.pos_embed = self.pos_embed.cpu() + + # remove duplicates and sort latent sizes in ascending order + latent_sizes = list(set(latent_sizes)) + latent_sizes = sorted(latent_sizes) + + patched_sizes = [latent_size // self.patch_size for latent_size in latent_sizes] + + # calculate value range for each latent area: this is used to determine the pos_emb size from the latent shape + max_areas = [] + for i in range(1, len(patched_sizes)): + prev_area = patched_sizes[i - 1] ** 2 + area = patched_sizes[i] ** 2 + max_areas.append((prev_area + area) // 2) + + # area of the last latent size, if the latent size exceeds this, error will be raised + max_areas.append(int((patched_sizes[-1] * MMDiT.POS_EMBED_MAX_RATIO) ** 2)) + # print("max_areas", max_areas) + + self.resolution_area_to_latent_size = [(area, latent_size) for area, latent_size in zip(max_areas, patched_sizes)] + + self.resolution_pos_embeds = {} + for patched_size in patched_sizes: + grid_size = int(patched_size * MMDiT.POS_EMBED_MAX_RATIO) + pos_embed = get_scaled_2d_sincos_pos_embed(self.hidden_size, grid_size, sample_size=patched_size) + pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0) + self.resolution_pos_embeds[patched_size] = pos_embed + # print(f"pos_embed for {patched_size}x{patched_size} latent size: {pos_embed.shape}") + + else: + self.resolution_area_to_latent_size = None + self.resolution_pos_embeds = None + + @property + def model_type(self): + return self._model_type + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def enable_gradient_checkpointing(self): + self.gradient_checkpointing = True + for block in self.joint_blocks: + block.enable_gradient_checkpointing() + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + for block in self.joint_blocks: + block.disable_gradient_checkpointing() + + def initialize_weights(self): + # TODO: Init context_embedder? + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + # Initialize (and freeze) pos_embed by sin-cos embedding + if self.pos_embed is not None: + pos_embed = get_2d_sincos_pos_embed( + self.pos_embed.shape[-1], + int(self.pos_embed.shape[-2] ** 0.5), + scaling_factor=self.pos_embed_scaling_factor, + ) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.x_embedder.proj.weight.data + nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj.bias, 0) + + if getattr(self, "y_embedder", None) is not None: + nn.init.normal_(self.y_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.y_embedder.mlp[2].weight, std=0.02) + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers in DiT blocks: + for block in self.joint_blocks: + nn.init.constant_(block.x_block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.x_block.adaLN_modulation[-1].bias, 0) + nn.init.constant_(block.context_block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.context_block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def set_pos_emb_random_crop_rate(self, rate: float): + self.pos_emb_random_crop_rate = rate + + def cropped_pos_embed(self, h, w, device=None, random_crop: bool = False): + p = self.x_embedder.patch_size + # patched size + h = (h + 1) // p + w = (w + 1) // p + if self.pos_embed is None: # should not happen + return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) + assert self.pos_embed_max_size is not None + assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) + assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) + + if not random_crop: + top = (self.pos_embed_max_size - h) // 2 + left = (self.pos_embed_max_size - w) // 2 + else: + top = torch.randint(0, self.pos_embed_max_size - h + 1, (1,)).item() + left = torch.randint(0, self.pos_embed_max_size - w + 1, (1,)).item() + + spatial_pos_embed = self.pos_embed.reshape( + 1, + self.pos_embed_max_size, + self.pos_embed_max_size, + self.pos_embed.shape[-1], + ) + spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] + spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1]) + return spatial_pos_embed + + def cropped_scaled_pos_embed(self, h, w, device=None, dtype=None, random_crop: bool = False): + p = self.x_embedder.patch_size + # patched size + h = (h + 1) // p + w = (w + 1) // p + + # select pos_embed size based on area + area = h * w + patched_size = None + for area_, patched_size_ in self.resolution_area_to_latent_size: + if area <= area_: + patched_size = patched_size_ + break + if patched_size is None: + # raise ValueError(f"Area {area} is too large for the given latent sizes {self.resolution_area_to_latent_size}.") + # use largest latent size + patched_size = self.resolution_area_to_latent_size[-1][1] + + pos_embed = self.resolution_pos_embeds[patched_size] + pos_embed_size = round(math.sqrt(pos_embed.shape[1])) # max size, patched_size * POS_EMBED_MAX_RATIO + if h > pos_embed_size or w > pos_embed_size: + # # fallback to normal pos_embed + # return self.cropped_pos_embed(h * p, w * p, device=device, random_crop=random_crop) + # extend pos_embed size + logger.warning( + f"Add new pos_embed for size {h}x{w} as it exceeds the scaled pos_embed size {pos_embed_size}. Image is too tall or wide." + ) + patched_size = max(h, w) + grid_size = int(patched_size * MMDiT.POS_EMBED_MAX_RATIO) + pos_embed_size = grid_size + pos_embed = get_scaled_2d_sincos_pos_embed(self.hidden_size, grid_size, sample_size=patched_size) + pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0) + self.resolution_pos_embeds[patched_size] = pos_embed + logger.info(f"Added pos_embed for size {patched_size}x{patched_size}") + + # print(torch.allclose(pos_embed.to(torch.float32).cpu(), self.pos_embed.to(torch.float32).cpu(), atol=5e-2)) + # diff = pos_embed.to(torch.float32).cpu() - self.pos_embed.to(torch.float32).cpu() + # print(diff.abs().max(), diff.abs().mean()) + + # insert to resolution_area_to_latent_size, by adding and sorting + area = pos_embed_size**2 + self.resolution_area_to_latent_size.append((area, patched_size)) + self.resolution_area_to_latent_size = sorted(self.resolution_area_to_latent_size) + + if not random_crop: + top = (pos_embed_size - h) // 2 + left = (pos_embed_size - w) // 2 + else: + top = torch.randint(0, pos_embed_size - h + 1, (1,)).item() + left = torch.randint(0, pos_embed_size - w + 1, (1,)).item() + + if pos_embed.device != device: + pos_embed = pos_embed.to(device) + # which is better to update device, or transfer every time to device? -> 64x64 emb is 96*96*1536*4=56MB. It's okay to update device. + self.resolution_pos_embeds[patched_size] = pos_embed # update device + if pos_embed.dtype != dtype: + pos_embed = pos_embed.to(dtype) + self.resolution_pos_embeds[patched_size] = pos_embed # update dtype + + spatial_pos_embed = pos_embed.reshape(1, pos_embed_size, pos_embed_size, pos_embed.shape[-1]) + spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] + spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1]) + # print( + # f"patched size: {h}x{w}, pos_embed size: {pos_embed_size}, pos_embed shape: {pos_embed.shape}, top: {top}, left: {left}" + # ) + return spatial_pos_embed + + def enable_block_swap(self, num_blocks: int, device: torch.device): + self.blocks_to_swap = num_blocks + + assert ( + self.blocks_to_swap <= self.num_blocks - 2 + ), f"Cannot swap more than {self.num_blocks - 2} blocks. Requested: {self.blocks_to_swap} blocks." + + self.offloader = custom_offloading_utils.ModelOffloader( + self.joint_blocks, self.blocks_to_swap, device # , debug=True + ) + print(f"SD3: Block swap enabled. Swapping {num_blocks} blocks, total blocks: {self.num_blocks}, device: {device}.") + + def move_to_device_except_swap_blocks(self, device: torch.device): + # assume model is on cpu. do not move blocks to device to reduce temporary memory usage + if self.blocks_to_swap: + save_blocks = self.joint_blocks + self.joint_blocks = nn.ModuleList() + + self.to(device) + + if self.blocks_to_swap: + self.joint_blocks = save_blocks + + def prepare_block_swap_before_forward(self): + if self.blocks_to_swap is None or self.blocks_to_swap == 0: + return + self.offloader.prepare_block_devices_before_forward(self.joint_blocks) + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Forward pass of DiT. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, D) tensor of class labels + """ + pos_emb_random_crop = ( + False if self.pos_emb_random_crop_rate == 0.0 else torch.rand(1).item() < self.pos_emb_random_crop_rate + ) + + B, C, H, W = x.shape + + # x = self.x_embedder(x) + self.cropped_pos_embed(H, W, device=x.device, random_crop=pos_emb_random_crop).to(dtype=x.dtype) + if not self.use_scaled_pos_embed: + pos_embed = self.cropped_pos_embed(H, W, device=x.device, random_crop=pos_emb_random_crop).to(dtype=x.dtype) + else: + # print(f"Using scaled pos_embed for size {H}x{W}") + pos_embed = self.cropped_scaled_pos_embed(H, W, device=x.device, dtype=x.dtype, random_crop=pos_emb_random_crop) + x = self.x_embedder(x) + pos_embed + del pos_embed + + c = self.t_embedder(t, dtype=x.dtype) # (N, D) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) # (N, D) + c = c + y # (N, D) + + if context is not None: + context = self.context_embedder(context) + + if self.register_length > 0: + context = torch.cat( + (einops.repeat(self.register, "1 ... -> b ...", b=x.shape[0]), default(context, torch.Tensor([]).type_as(x))), 1 + ) + + if not self.blocks_to_swap: + for block in self.joint_blocks: + context, x = block(context, x, c) + else: + for block_idx, block in enumerate(self.joint_blocks): + self.offloader.wait_for_block(block_idx) + + context, x = block(context, x, c) + + self.offloader.submit_move_blocks(self.joint_blocks, block_idx) + + x = self.final_layer(x, c, H, W) # Our final layer combined UnPatchify + return x[:, :, :H, :W] + + +def create_sd3_mmdit(params: SD3Params, attn_mode: str = "torch") -> MMDiT: + mmdit = MMDiT( + input_size=None, + pos_embed_max_size=params.pos_embed_max_size, + patch_size=params.patch_size, + in_channels=16, + adm_in_channels=params.adm_in_channels, + context_embedder_in_features=params.context_embedder_in_features, + context_embedder_out_features=params.context_embedder_out_features, + depth=params.depth, + mlp_ratio=4, + qk_norm=params.qk_norm, + x_block_self_attn_layers=params.x_block_self_attn_layers, + num_patches=params.num_patches, + attn_mode=attn_mode, + model_type=params.model_type, + ) + return mmdit + + +# endregion + +# region VAE + +VAE_SCALE_FACTOR = 1.5305 +VAE_SHIFT_FACTOR = 0.0609 + + +def Normalize(in_channels, num_groups=32, dtype=torch.float32, device=None): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + + +class ResnetBlock(torch.nn.Module): + def __init__(self, *, in_channels, out_channels=None, dtype=torch.float32, device=None): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = Normalize(in_channels, dtype=dtype, device=device) + self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + self.norm2 = Normalize(out_channels, dtype=dtype, device=device) + self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + if self.in_channels != self.out_channels: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device + ) + else: + self.nin_shortcut = None + self.swish = torch.nn.SiLU(inplace=True) + + def forward(self, x): + hidden = x + hidden = self.norm1(hidden) + hidden = self.swish(hidden) + hidden = self.conv1(hidden) + hidden = self.norm2(hidden) + hidden = self.swish(hidden) + hidden = self.conv2(hidden) + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + return x + hidden + + +class AttnBlock(torch.nn.Module): + def __init__(self, in_channels, dtype=torch.float32, device=None): + super().__init__() + self.norm = Normalize(in_channels, dtype=dtype, device=device) + self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) + self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) + self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) + self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) + + def forward(self, x): + hidden = self.norm(x) + q = self.q(hidden) + k = self.k(hidden) + v = self.v(hidden) + b, c, h, w = q.shape + q, k, v = map(lambda x: einops.rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v)) + hidden = torch.nn.functional.scaled_dot_product_attention(q, k, v) # scale is dim ** -0.5 per default + hidden = einops.rearrange(hidden, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + hidden = self.proj_out(hidden) + return x + hidden + + +class Downsample(torch.nn.Module): + def __init__(self, in_channels, dtype=torch.float32, device=None): + super().__init__() + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0, dtype=dtype, device=device) + + def forward(self, x): + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(torch.nn.Module): + def __init__(self, in_channels, dtype=torch.float32, device=None): + super().__init__() + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + + def forward(self, x): + org_dtype = x.dtype + if x.dtype == torch.bfloat16: + x = x.to(torch.float32) + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if x.dtype != org_dtype: + x = x.to(org_dtype) + x = self.conv(x) + return x + + +class VAEEncoder(torch.nn.Module): + def __init__( + self, ch=128, ch_mult=(1, 2, 4, 4), num_res_blocks=2, in_channels=3, z_channels=16, dtype=torch.float32, device=None + ): + super().__init__() + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, ch, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = torch.nn.ModuleList() + for i_level in range(self.num_resolutions): + block = torch.nn.ModuleList() + attn = torch.nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device)) + block_in = block_out + down = torch.nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, dtype=dtype, device=device) + self.down.append(down) + # middle + self.mid = torch.nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device) + self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device) + # end + self.norm_out = Normalize(block_in, dtype=dtype, device=device) + self.conv_out = torch.nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + self.swish = torch.nn.SiLU(inplace=True) + + def forward(self, x): + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + # end + h = self.norm_out(h) + h = self.swish(h) + h = self.conv_out(h) + return h + + +class VAEDecoder(torch.nn.Module): + def __init__( + self, + ch=128, + out_ch=3, + ch_mult=(1, 2, 4, 4), + num_res_blocks=2, + resolution=256, + z_channels=16, + dtype=torch.float32, + device=None, + ): + super().__init__() + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + # middle + self.mid = torch.nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device) + self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device) + # upsampling + self.up = torch.nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = torch.nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device)) + block_in = block_out + up = torch.nn.Module() + up.block = block + if i_level != 0: + up.upsample = Upsample(block_in, dtype=dtype, device=device) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + # end + self.norm_out = Normalize(block_in, dtype=dtype, device=device) + self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) + self.swish = torch.nn.SiLU(inplace=True) + + def forward(self, z): + # z to block_in + hidden = self.conv_in(z) + # middle + hidden = self.mid.block_1(hidden) + hidden = self.mid.attn_1(hidden) + hidden = self.mid.block_2(hidden) + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + hidden = self.up[i_level].block[i_block](hidden) + if i_level != 0: + hidden = self.up[i_level].upsample(hidden) + # end + hidden = self.norm_out(hidden) + hidden = self.swish(hidden) + hidden = self.conv_out(hidden) + return hidden + + +class SDVAE(torch.nn.Module): + def __init__(self, dtype=torch.float32, device=None): + super().__init__() + self.encoder = VAEEncoder(dtype=dtype, device=device) + self.decoder = VAEDecoder(dtype=dtype, device=device) + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + # @torch.autocast("cuda", dtype=torch.float16) + def decode(self, latent): + return self.decoder(latent) + + # @torch.autocast("cuda", dtype=torch.float16) + def encode(self, image): + hidden = self.encoder(image) + mean, logvar = torch.chunk(hidden, 2, dim=1) + logvar = torch.clamp(logvar, -30.0, 20.0) + std = torch.exp(0.5 * logvar) + return mean + std * torch.randn_like(mean) + + @staticmethod + def process_in(latent): + return (latent - VAE_SHIFT_FACTOR) * VAE_SCALE_FACTOR + + @staticmethod + def process_out(latent): + return (latent / VAE_SCALE_FACTOR) + VAE_SHIFT_FACTOR + + +# endregion diff --git a/vendor/sd-scripts/library/sd3_train_utils.py b/vendor/sd-scripts/library/sd3_train_utils.py new file mode 100644 index 0000000..c407988 --- /dev/null +++ b/vendor/sd-scripts/library/sd3_train_utils.py @@ -0,0 +1,945 @@ +import argparse +import math +import os +import toml +import json +import time +from typing import Dict, List, Optional, Tuple, Union + +import torch +from safetensors.torch import save_file +from accelerate import Accelerator, PartialState +from tqdm import tqdm +from PIL import Image +from transformers import CLIPTextModelWithProjection, T5EncoderModel + +from library.device_utils import init_ipex, clean_memory_on_device + +init_ipex() + +# from transformers import CLIPTokenizer +# from library import model_util +# , sdxl_model_util, train_util, sdxl_original_unet +# from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +from library import sd3_models, sd3_utils, strategy_base, train_util + + +def save_models( + ckpt_path: str, + mmdit: Optional[sd3_models.MMDiT], + vae: Optional[sd3_models.SDVAE], + clip_l: Optional[CLIPTextModelWithProjection], + clip_g: Optional[CLIPTextModelWithProjection], + t5xxl: Optional[T5EncoderModel], + sai_metadata: Optional[dict], + save_dtype: Optional[torch.dtype] = None, +): + r""" + Save models to checkpoint file. Only supports unified checkpoint format. + """ + + state_dict = {} + + def update_sd(prefix, sd): + for k, v in sd.items(): + key = prefix + k + if save_dtype is not None: + v = v.detach().clone().to("cpu").to(save_dtype) + state_dict[key] = v + + update_sd("model.diffusion_model.", mmdit.state_dict()) + update_sd("first_stage_model.", vae.state_dict()) + + # do not support unified checkpoint format for now + # if clip_l is not None: + # update_sd("text_encoders.clip_l.", clip_l.state_dict()) + # if clip_g is not None: + # update_sd("text_encoders.clip_g.", clip_g.state_dict()) + # if t5xxl is not None: + # update_sd("text_encoders.t5xxl.", t5xxl.state_dict()) + + save_file(state_dict, ckpt_path, metadata=sai_metadata) + + if clip_l is not None: + clip_l_path = ckpt_path.replace(".safetensors", "_clip_l.safetensors") + save_file(clip_l.state_dict(), clip_l_path) + if clip_g is not None: + clip_g_path = ckpt_path.replace(".safetensors", "_clip_g.safetensors") + save_file(clip_g.state_dict(), clip_g_path) + if t5xxl is not None: + t5xxl_path = ckpt_path.replace(".safetensors", "_t5xxl.safetensors") + t5xxl_state_dict = t5xxl.state_dict() + + # replace "shared.weight" with copy of it to avoid annoying shared tensor error on safetensors.save_file + shared_weight = t5xxl_state_dict["shared.weight"] + shared_weight_copy = shared_weight.detach().clone() + t5xxl_state_dict["shared.weight"] = shared_weight_copy + + save_file(t5xxl_state_dict, t5xxl_path) + + +def save_sd3_model_on_train_end( + args: argparse.Namespace, + save_dtype: torch.dtype, + epoch: int, + global_step: int, + clip_l: Optional[CLIPTextModelWithProjection], + clip_g: Optional[CLIPTextModelWithProjection], + t5xxl: Optional[T5EncoderModel], + mmdit: sd3_models.MMDiT, + vae: sd3_models.SDVAE, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec( + None, args, False, False, False, is_stable_diffusion_ckpt=True, sd3=mmdit.model_type + ) + save_models(ckpt_file, mmdit, vae, clip_l, clip_g, t5xxl, sai_metadata, save_dtype) + + train_util.save_sd_model_on_train_end_common(args, True, True, epoch, global_step, sd_saver, None) + + +# epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合している +# on_epoch_end: Trueならepoch終了時、Falseならstep経過時 +def save_sd3_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + clip_l: Optional[CLIPTextModelWithProjection], + clip_g: Optional[CLIPTextModelWithProjection], + t5xxl: Optional[T5EncoderModel], + mmdit: sd3_models.MMDiT, + vae: sd3_models.SDVAE, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec( + None, args, False, False, False, is_stable_diffusion_ckpt=True, sd3=mmdit.model_type + ) + save_models(ckpt_file, mmdit, vae, clip_l, clip_g, t5xxl, sai_metadata, save_dtype) + + train_util.save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + True, + True, + epoch, + num_train_epochs, + global_step, + sd_saver, + None, + ) + + +def add_sd3_training_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--clip_l", + type=str, + required=False, + help="CLIP-L model path. if not specified, use ckpt's state_dict / CLIP-Lモデルのパス。指定しない場合はckptのstate_dictを使用", + ) + parser.add_argument( + "--clip_g", + type=str, + required=False, + help="CLIP-G model path. if not specified, use ckpt's state_dict / CLIP-Gモデルのパス。指定しない場合はckptのstate_dictを使用", + ) + parser.add_argument( + "--t5xxl", + type=str, + required=False, + help="T5-XXL model path. if not specified, use ckpt's state_dict / T5-XXLモデルのパス。指定しない場合はckptのstate_dictを使用", + ) + parser.add_argument( + "--save_clip", + action="store_true", + help="[DOES NOT WORK] unified checkpoint is not supported / 統合チェックポイントはまだサポートされていません", + ) + parser.add_argument( + "--save_t5xxl", + action="store_true", + help="[DOES NOT WORK] unified checkpoint is not supported / 統合チェックポイントはまだサポートされていません", + ) + + parser.add_argument( + "--t5xxl_device", + type=str, + default=None, + help="[DOES NOT WORK] not supported yet. T5-XXL device. if not specified, use accelerator's device / T5-XXLデバイス。指定しない場合はacceleratorのデバイスを使用", + ) + parser.add_argument( + "--t5xxl_dtype", + type=str, + default=None, + help="[DOES NOT WORK] not supported yet. T5-XXL dtype. if not specified, use default dtype (from mixed precision) / T5-XXL dtype。指定しない場合はデフォルトのdtype（mixed precisionから）を使用", + ) + + parser.add_argument( + "--t5xxl_max_token_length", + type=int, + default=256, + help="maximum token length for T5-XXL. 256 is the default value / T5-XXLの最大トークン長。デフォルトは256", + ) + parser.add_argument( + "--apply_lg_attn_mask", + action="store_true", + help="apply attention mask (zero embs) to CLIP-L and G / CLIP-LとGにアテンションマスク（ゼロ埋め）を適用する", + ) + parser.add_argument( + "--apply_t5_attn_mask", + action="store_true", + help="apply attention mask (zero embs) to T5-XXL / T5-XXLにアテンションマスク（ゼロ埋め）を適用する", + ) + parser.add_argument( + "--clip_l_dropout_rate", + type=float, + default=0.0, + help="Dropout rate for CLIP-L encoder, default is 0.0 / CLIP-Lエンコーダのドロップアウト率、デフォルトは0.0", + ) + parser.add_argument( + "--clip_g_dropout_rate", + type=float, + default=0.0, + help="Dropout rate for CLIP-G encoder, default is 0.0 / CLIP-Gエンコーダのドロップアウト率、デフォルトは0.0", + ) + parser.add_argument( + "--t5_dropout_rate", + type=float, + default=0.0, + help="Dropout rate for T5 encoder, default is 0.0 / T5エンコーダのドロップアウト率、デフォルトは0.0", + ) + parser.add_argument( + "--pos_emb_random_crop_rate", + type=float, + default=0.0, + help="Random crop rate for positional embeddings, default is 0.0. Only for SD3.5M" + " / 位置埋め込みのランダムクロップ率、デフォルトは0.0。SD3.5M以外では予期しない動作になります", + ) + parser.add_argument( + "--enable_scaled_pos_embed", + action="store_true", + help="Scale position embeddings for each resolution during multi-resolution training. Only for SD3.5M" + " / 複数解像度学習時に解像度ごとに位置埋め込みをスケーリングする。SD3.5M以外では予期しない動作になります", + ) + + # Dependencies of Diffusers noise sampler has been removed for clarity in training + + parser.add_argument( + "--training_shift", + type=float, + default=1.0, + help="Discrete flow shift for training timestep distribution adjustment, applied in addition to the weighting scheme, default is 1.0. /タイムステップ分布のための離散フローシフト、重み付けスキームの上に適用される、デフォルトは1.0。", + ) + + +def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCaching: bool = True): + assert not args.v2, "v2 cannot be enabled in SDXL training / SDXL学習ではv2を有効にすることはできません" + if args.v_parameterization: + logger.warning("v_parameterization will be unexpected / SDXL学習ではv_parameterizationは想定外の動作になります") + + if args.clip_skip is not None: + logger.warning("clip_skip will be unexpected / SDXL学習ではclip_skipは動作しません") + + # if args.multires_noise_iterations: + # logger.info( + # f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET}, but noise_offset is disabled due to multires_noise_iterations / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されていますが、multires_noise_iterationsが有効になっているためnoise_offsetは無効になります" + # ) + # else: + # if args.noise_offset is None: + # args.noise_offset = DEFAULT_NOISE_OFFSET + # elif args.noise_offset != DEFAULT_NOISE_OFFSET: + # logger.info( + # f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET} / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されています" + # ) + # logger.info(f"noise_offset is set to {args.noise_offset} / noise_offsetが{args.noise_offset}に設定されました") + + assert ( + not hasattr(args, "weighted_captions") or not args.weighted_captions + ), "weighted_captions cannot be enabled in SDXL training currently / SDXL学習では今のところweighted_captionsを有効にすることはできません" + + if supportTextEncoderCaching: + if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs: + args.cache_text_encoder_outputs = True + logger.warning( + "cache_text_encoder_outputs is enabled because cache_text_encoder_outputs_to_disk is enabled / " + + "cache_text_encoder_outputs_to_diskが有効になっているためcache_text_encoder_outputsが有効になりました" + ) + + +# temporary copied from sd3_minimal_inferece.py + + +def get_all_sigmas(sampling: sd3_utils.ModelSamplingDiscreteFlow, steps): + start = sampling.timestep(sampling.sigma_max) + end = sampling.timestep(sampling.sigma_min) + timesteps = torch.linspace(start, end, steps) + sigs = [] + for x in range(len(timesteps)): + ts = timesteps[x] + sigs.append(sampling.sigma(ts)) + sigs += [0.0] + return torch.FloatTensor(sigs) + + +def max_denoise(model_sampling, sigmas): + max_sigma = float(model_sampling.sigma_max) + sigma = float(sigmas[0]) + return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma + + +def do_sample( + height: int, + width: int, + seed: int, + cond: Tuple[torch.Tensor, torch.Tensor], + neg_cond: Tuple[torch.Tensor, torch.Tensor], + mmdit: sd3_models.MMDiT, + steps: int, + guidance_scale: float, + dtype: torch.dtype, + device: str, +): + latent = torch.zeros(1, 16, height // 8, width // 8, device=device) + latent = latent.to(dtype).to(device) + + # noise = get_noise(seed, latent).to(device) + if seed is not None: + generator = torch.manual_seed(seed) + else: + generator = None + noise = ( + torch.randn(latent.size(), dtype=torch.float32, layout=latent.layout, generator=generator, device="cpu") + .to(latent.dtype) + .to(device) + ) + + model_sampling = sd3_utils.ModelSamplingDiscreteFlow(shift=3.0) # 3.0 is for SD3 + + sigmas = get_all_sigmas(model_sampling, steps).to(device) + + noise_scaled = model_sampling.noise_scaling(sigmas[0], noise, latent, max_denoise(model_sampling, sigmas)) + + c_crossattn = torch.cat([cond[0], neg_cond[0]]).to(device).to(dtype) + y = torch.cat([cond[1], neg_cond[1]]).to(device).to(dtype) + + x = noise_scaled.to(device).to(dtype) + # print(x.shape) + + # with torch.no_grad(): + for i in tqdm(range(len(sigmas) - 1)): + sigma_hat = sigmas[i] + + timestep = model_sampling.timestep(sigma_hat).float() + timestep = torch.FloatTensor([timestep, timestep]).to(device) + + x_c_nc = torch.cat([x, x], dim=0) + # print(x_c_nc.shape, timestep.shape, c_crossattn.shape, y.shape) + + mmdit.prepare_block_swap_before_forward() + model_output = mmdit(x_c_nc, timestep, context=c_crossattn, y=y) + model_output = model_output.float() + batched = model_sampling.calculate_denoised(sigma_hat, model_output, x) + + pos_out, neg_out = batched.chunk(2) + denoised = neg_out + (pos_out - neg_out) * guidance_scale + # print(denoised.shape) + + # d = to_d(x, sigma_hat, denoised) + dims_to_append = x.ndim - sigma_hat.ndim + sigma_hat_dims = sigma_hat[(...,) + (None,) * dims_to_append] + # print(dims_to_append, x.shape, sigma_hat.shape, denoised.shape, sigma_hat_dims.shape) + """Converts a denoiser output to a Karras ODE derivative.""" + d = (x - denoised) / sigma_hat_dims + + dt = sigmas[i + 1] - sigma_hat + + # Euler method + x = x + d * dt + x = x.to(dtype) + + mmdit.prepare_block_swap_before_forward() + return x + + +def sample_images( + accelerator: Accelerator, + args: argparse.Namespace, + epoch, + steps, + mmdit, + vae, + text_encoders, + sample_prompts_te_outputs, + prompt_replacement=None, +): + if steps == 0: + if not args.sample_at_first: + return + else: + if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: + return + if args.sample_every_n_epochs is not None: + # sample_every_n_steps は無視する + if epoch is None or epoch % args.sample_every_n_epochs != 0: + return + else: + if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch + return + + logger.info("") + logger.info(f"generating sample images at step / サンプル画像生成 ステップ: {steps}") + if not os.path.isfile(args.sample_prompts) and sample_prompts_te_outputs is None: + logger.error(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}") + return + + distributed_state = PartialState() # for multi gpu distributed inference. this is a singleton, so it's safe to use it here + + # unwrap unet and text_encoder(s) + mmdit = accelerator.unwrap_model(mmdit) + text_encoders = None if text_encoders is None else [accelerator.unwrap_model(te) for te in text_encoders] + # print([(te.parameters().__next__().device if te is not None else None) for te in text_encoders]) + + prompts = train_util.load_prompts(args.sample_prompts) + + save_dir = args.output_dir + "/sample" + os.makedirs(save_dir, exist_ok=True) + + # save random state to restore later + rng_state = torch.get_rng_state() + cuda_rng_state = None + try: + cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None + except Exception: + pass + + if distributed_state.num_processes <= 1: + # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts. + with torch.no_grad(), accelerator.autocast(): + for prompt_dict in prompts: + sample_image_inference( + accelerator, + args, + mmdit, + text_encoders, + vae, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, + ) + else: + # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available) + # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical. + per_process_prompts = [] # list of lists + for i in range(distributed_state.num_processes): + per_process_prompts.append(prompts[i :: distributed_state.num_processes]) + + with torch.no_grad(): + with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists: + for prompt_dict in prompt_dict_lists[0]: + sample_image_inference( + accelerator, + args, + mmdit, + text_encoders, + vae, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, + ) + + torch.set_rng_state(rng_state) + if cuda_rng_state is not None: + torch.cuda.set_rng_state(cuda_rng_state) + + clean_memory_on_device(accelerator.device) + + +def sample_image_inference( + accelerator: Accelerator, + args: argparse.Namespace, + mmdit: sd3_models.MMDiT, + text_encoders: List[Union[CLIPTextModelWithProjection, T5EncoderModel]], + vae: sd3_models.SDVAE, + save_dir, + prompt_dict, + epoch, + steps, + sample_prompts_te_outputs, + prompt_replacement, +): + assert isinstance(prompt_dict, dict) + negative_prompt = prompt_dict.get("negative_prompt") + sample_steps = prompt_dict.get("sample_steps", 30) + width = prompt_dict.get("width", 512) + height = prompt_dict.get("height", 512) + scale = prompt_dict.get("scale", 7.5) + seed = prompt_dict.get("seed") + # controlnet_image = prompt_dict.get("controlnet_image") + prompt: str = prompt_dict.get("prompt", "") + # sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler) + + if prompt_replacement is not None: + prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1]) + if negative_prompt is not None: + negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1]) + + if seed is not None: + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + else: + # True random sample image generation + torch.seed() + torch.cuda.seed() + + if negative_prompt is None: + negative_prompt = "" + + height = max(64, height - height % 8) # round to divisible by 8 + width = max(64, width - width % 8) # round to divisible by 8 + logger.info(f"prompt: {prompt}") + logger.info(f"negative_prompt: {negative_prompt}") + logger.info(f"height: {height}") + logger.info(f"width: {width}") + logger.info(f"sample_steps: {sample_steps}") + logger.info(f"scale: {scale}") + # logger.info(f"sample_sampler: {sampler_name}") + if seed is not None: + logger.info(f"seed: {seed}") + + # encode prompts + tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() + + def encode_prompt(prpt): + text_encoder_conds = [] + if sample_prompts_te_outputs and prpt in sample_prompts_te_outputs: + text_encoder_conds = sample_prompts_te_outputs[prpt] + print(f"Using cached text encoder outputs for prompt: {prpt}") + if text_encoders is not None: + print(f"Encoding prompt: {prpt}") + tokens_and_masks = tokenize_strategy.tokenize(prpt) + # strategy has apply_t5_attn_mask option + encoded_text_encoder_conds = encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens_and_masks) + + # if text_encoder_conds is not cached, use encoded_text_encoder_conds + if len(text_encoder_conds) == 0: + text_encoder_conds = encoded_text_encoder_conds + else: + # if encoded_text_encoder_conds is not None, update cached text_encoder_conds + for i in range(len(encoded_text_encoder_conds)): + if encoded_text_encoder_conds[i] is not None: + text_encoder_conds[i] = encoded_text_encoder_conds[i] + return text_encoder_conds + + lg_out, t5_out, pooled, l_attn_mask, g_attn_mask, t5_attn_mask = encode_prompt(prompt) + cond = encoding_strategy.concat_encodings(lg_out, t5_out, pooled) + + # encode negative prompts + lg_out, t5_out, pooled, l_attn_mask, g_attn_mask, t5_attn_mask = encode_prompt(negative_prompt) + neg_cond = encoding_strategy.concat_encodings(lg_out, t5_out, pooled) + + # sample image + clean_memory_on_device(accelerator.device) + with accelerator.autocast(), torch.no_grad(): + # mmdit may be fp8, so we need weight_dtype here. vae is always in that dtype. + latents = do_sample(height, width, seed, cond, neg_cond, mmdit, sample_steps, scale, vae.dtype, accelerator.device) + + # latent to image + clean_memory_on_device(accelerator.device) + org_vae_device = vae.device # will be on cpu + vae.to(accelerator.device) + latents = vae.process_out(latents.to(vae.device, dtype=vae.dtype)) + image = vae.decode(latents) + vae.to(org_vae_device) + clean_memory_on_device(accelerator.device) + + image = image.float() + image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)[0] + decoded_np = 255.0 * np.moveaxis(image.cpu().numpy(), 0, 2) + decoded_np = decoded_np.astype(np.uint8) + + image = Image.fromarray(decoded_np) + # adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list + # but adding 'enum' to the filename should be enough + + ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime()) + num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}" + seed_suffix = "" if seed is None else f"_{seed}" + i: int = prompt_dict["enum"] + img_filename = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png" + image.save(os.path.join(save_dir, img_filename)) + + # send images to wandb if enabled + if "wandb" in [tracker.name for tracker in accelerator.trackers]: + wandb_tracker = accelerator.get_tracker("wandb") + + import wandb + + # not to commit images to avoid inconsistency between training and logging steps + wandb_tracker.log({f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False) # positive prompt as a caption + + +# region Diffusers + + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import SchedulerMixin +from diffusers.utils.torch_utils import randn_tensor +from diffusers.utils import BaseOutput + + +@dataclass +class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): + """ + Euler scheduler. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + ): + timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy() + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) + + sigmas = timesteps / num_train_timesteps + sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) + + self.timesteps = sigmas * num_train_timesteps + + self._step_index = None + self._begin_index = None + + self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + if self.step_index is None: + self._init_step_index(timestep) + + sigma = self.sigmas[self.step_index] + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + self.num_inference_steps = num_inference_steps + + timesteps = np.linspace(self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps) + + sigmas = timesteps / self.config.num_train_timesteps + sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) + + timesteps = sigmas * self.config.num_train_timesteps + self.timesteps = timesteps.to(device=device) + self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) + + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + s_churn: float = 0.0, + s_tmin: float = 0.0, + s_tmax: float = float("inf"), + s_noise: float = 1.0, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + s_churn (`float`): + s_tmin (`float`): + s_tmax (`float`): + s_noise (`float`, defaults to 1.0): + Scaling factor for noise added to the sample. + generator (`torch.Generator`, *optional*): + A random number generator. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or + tuple. + + Returns: + [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is + returned, otherwise a tuple is returned where the first element is the sample tensor. + """ + + if isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + sigma = self.sigmas[self.step_index] + + gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0 + + noise = randn_tensor(model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator) + + eps = noise * s_noise + sigma_hat = sigma * (gamma + 1) + + if gamma > 0: + sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5 + + # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise + # NOTE: "original_sample" should not be an expected prediction_type but is left in for + # backwards compatibility + + # if self.config.prediction_type == "vector_field": + + denoised = sample - model_output * sigma + # 2. Convert to an ODE derivative + derivative = (sample - denoised) / sigma_hat + + dt = self.sigmas[self.step_index + 1] - sigma_hat + + prev_sample = sample + derivative * dt + # Cast sample back to model compatible dtype + prev_sample = prev_sample.to(model_output.dtype) + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample) + + def __len__(self): + return self.config.num_train_timesteps + + +def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32): + sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype) + schedule_timesteps = noise_scheduler.timesteps.to(device) + timesteps = timesteps.to(device) + step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < n_dim: + sigma = sigma.unsqueeze(-1) + return sigma + + +def compute_density_for_timestep_sampling( + weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None +): + """Compute the density for sampling the timesteps when doing SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + """ + if weighting_scheme == "logit_normal": + # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$). + u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu") + u = torch.nn.functional.sigmoid(u) + elif weighting_scheme == "mode": + u = torch.rand(size=(batch_size,), device="cpu") + u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u) + else: + u = torch.rand(size=(batch_size,), device="cpu") + return u + + +def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None): + """Computes loss weighting scheme for SD3 training. + + Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. + + SD3 paper reference: https://arxiv.org/abs/2403.03206v1. + """ + if weighting_scheme == "sigma_sqrt": + weighting = (sigmas**-2.0).float() + elif weighting_scheme == "cosmap": + bot = 1 - 2 * sigmas + 2 * sigmas**2 + weighting = 2 / (math.pi * bot) + else: + weighting = torch.ones_like(sigmas) + return weighting + + +# endregion + + +def get_noisy_model_input_and_timesteps(args, latents, noise, device, dtype) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + bsz = latents.shape[0] + + # Sample a random timestep for each image + # for weighting schemes where we sample timesteps non-uniformly + u = compute_density_for_timestep_sampling( + weighting_scheme=args.weighting_scheme, + batch_size=bsz, + logit_mean=args.logit_mean, + logit_std=args.logit_std, + mode_scale=args.mode_scale, + ) + t_min = args.min_timestep if args.min_timestep is not None else 0 + t_max = args.max_timestep if args.max_timestep is not None else 1000 + shift = args.training_shift + + # weighting shift, value >1 will shift distribution to noisy side (focus more on overall structure), value <1 will shift towards less-noisy side (focus more on details) + u = (u * shift) / (1 + (shift - 1) * u) + + indices = (u * (t_max - t_min) + t_min).long() + timesteps = indices.to(device=device, dtype=dtype) + + # sigmas according to flowmatching + sigmas = timesteps / 1000 + sigmas = sigmas.view(-1, 1, 1, 1) + noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents + + return noisy_model_input, timesteps, sigmas diff --git a/vendor/sd-scripts/library/sd3_utils.py b/vendor/sd-scripts/library/sd3_utils.py new file mode 100644 index 0000000..5fbaa4c --- /dev/null +++ b/vendor/sd-scripts/library/sd3_utils.py @@ -0,0 +1,302 @@ +from dataclasses import dataclass +import math +import re +from typing import Dict, List, Optional, Union +import torch +import safetensors +from safetensors.torch import load_file +from accelerate import init_empty_weights +from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPConfig, CLIPTextConfig + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +from library import sd3_models + +# TODO move some of functions to model_util.py +from library import sdxl_model_util + +# region models + +# TODO remove dependency on flux_utils +from library.safetensors_utils import load_safetensors +from library.flux_utils import load_t5xxl as flux_utils_load_t5xxl + + +def analyze_state_dict_state(state_dict: Dict, prefix: str = ""): + logger.info(f"Analyzing state dict state...") + + # analyze configs + patch_size = state_dict[f"{prefix}x_embedder.proj.weight"].shape[2] + depth = state_dict[f"{prefix}x_embedder.proj.weight"].shape[0] // 64 + num_patches = state_dict[f"{prefix}pos_embed"].shape[1] + pos_embed_max_size = round(math.sqrt(num_patches)) + adm_in_channels = state_dict[f"{prefix}y_embedder.mlp.0.weight"].shape[1] + context_shape = state_dict[f"{prefix}context_embedder.weight"].shape + qk_norm = "rms" if f"{prefix}joint_blocks.0.context_block.attn.ln_k.weight" in state_dict.keys() else None + + # x_block_self_attn_layers.append(int(key.split(".x_block.attn2.ln_k.weight")[0].split(".")[-1])) + x_block_self_attn_layers = [] + re_attn = re.compile(r"\.(\d+)\.x_block\.attn2\.ln_k\.weight") + for key in list(state_dict.keys()): + m = re_attn.search(key) + if m: + x_block_self_attn_layers.append(int(m.group(1))) + + context_embedder_in_features = context_shape[1] + context_embedder_out_features = context_shape[0] + + # only supports 3-5-large, medium or 3-medium. This is added after `stable-diffusion-3-`. + if qk_norm is not None: + if len(x_block_self_attn_layers) == 0: + model_type = "5-large" + else: + model_type = "5-medium" + else: + model_type = "medium" + + params = sd3_models.SD3Params( + patch_size=patch_size, + depth=depth, + num_patches=num_patches, + pos_embed_max_size=pos_embed_max_size, + adm_in_channels=adm_in_channels, + qk_norm=qk_norm, + x_block_self_attn_layers=x_block_self_attn_layers, + context_embedder_in_features=context_embedder_in_features, + context_embedder_out_features=context_embedder_out_features, + model_type=model_type, + ) + logger.info(f"Analyzed state dict state: {params}") + return params + + +def load_mmdit( + state_dict: Dict, dtype: Optional[Union[str, torch.dtype]], device: Union[str, torch.device], attn_mode: str = "torch" +) -> sd3_models.MMDiT: + mmdit_sd = {} + + mmdit_prefix = "model.diffusion_model." + for k in list(state_dict.keys()): + if k.startswith(mmdit_prefix): + mmdit_sd[k[len(mmdit_prefix) :]] = state_dict.pop(k) + + # load MMDiT + logger.info("Building MMDit") + params = analyze_state_dict_state(mmdit_sd) + with init_empty_weights(): + mmdit = sd3_models.create_sd3_mmdit(params, attn_mode) + + logger.info("Loading state dict...") + info = mmdit.load_state_dict(mmdit_sd, strict=False, assign=True) + logger.info(f"Loaded MMDiT: {info}") + return mmdit + + +def load_clip_l( + clip_l_path: Optional[str], + dtype: Optional[Union[str, torch.dtype]], + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[Dict] = None, +): + clip_l_sd = None + if clip_l_path is None: + if "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight" in state_dict: + # found clip_l: remove prefix "text_encoders.clip_l." + logger.info("clip_l is included in the checkpoint") + clip_l_sd = {} + prefix = "text_encoders.clip_l." + for k in list(state_dict.keys()): + if k.startswith(prefix): + clip_l_sd[k[len(prefix) :]] = state_dict.pop(k) + elif clip_l_path is None: + logger.info("clip_l is not included in the checkpoint and clip_l_path is not provided") + return None + + # load clip_l + logger.info("Building CLIP-L") + config = CLIPTextConfig( + vocab_size=49408, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + max_position_embeddings=77, + hidden_act="quick_gelu", + layer_norm_eps=1e-05, + dropout=0.0, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + model_type="clip_text_model", + projection_dim=768, + # torch_dtype="float32", + # transformers_version="4.25.0.dev0", + ) + with init_empty_weights(): + clip = CLIPTextModelWithProjection(config) + + if clip_l_sd is None: + logger.info(f"Loading state dict from {clip_l_path}") + clip_l_sd = load_safetensors(clip_l_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + + if "text_projection.weight" not in clip_l_sd: + logger.info("Adding text_projection.weight to clip_l_sd") + clip_l_sd["text_projection.weight"] = torch.eye(768, dtype=dtype, device=device) + + info = clip.load_state_dict(clip_l_sd, strict=False, assign=True) + logger.info(f"Loaded CLIP-L: {info}") + return clip + + +def load_clip_g( + clip_g_path: Optional[str], + dtype: Optional[Union[str, torch.dtype]], + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[Dict] = None, +): + clip_g_sd = None + if state_dict is not None: + if "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight" in state_dict: + # found clip_g: remove prefix "text_encoders.clip_g." + logger.info("clip_g is included in the checkpoint") + clip_g_sd = {} + prefix = "text_encoders.clip_g." + for k in list(state_dict.keys()): + if k.startswith(prefix): + clip_g_sd[k[len(prefix) :]] = state_dict.pop(k) + elif clip_g_path is None: + logger.info("clip_g is not included in the checkpoint and clip_g_path is not provided") + return None + + # load clip_g + logger.info("Building CLIP-G") + config = CLIPTextConfig( + vocab_size=49408, + hidden_size=1280, + intermediate_size=5120, + num_hidden_layers=32, + num_attention_heads=20, + max_position_embeddings=77, + hidden_act="gelu", + layer_norm_eps=1e-05, + dropout=0.0, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + model_type="clip_text_model", + projection_dim=1280, + # torch_dtype="float32", + # transformers_version="4.25.0.dev0", + ) + with init_empty_weights(): + clip = CLIPTextModelWithProjection(config) + + if clip_g_sd is None: + logger.info(f"Loading state dict from {clip_g_path}") + clip_g_sd = load_safetensors(clip_g_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype) + info = clip.load_state_dict(clip_g_sd, strict=False, assign=True) + logger.info(f"Loaded CLIP-G: {info}") + return clip + + +def load_t5xxl( + t5xxl_path: Optional[str], + dtype: Optional[Union[str, torch.dtype]], + device: Union[str, torch.device], + disable_mmap: bool = False, + state_dict: Optional[Dict] = None, +): + t5xxl_sd = None + if state_dict is not None: + if "text_encoders.t5xxl.transformer.encoder.block.0.layer.0.SelfAttention.k.weight" in state_dict: + # found t5xxl: remove prefix "text_encoders.t5xxl." + logger.info("t5xxl is included in the checkpoint") + t5xxl_sd = {} + prefix = "text_encoders.t5xxl." + for k in list(state_dict.keys()): + if k.startswith(prefix): + t5xxl_sd[k[len(prefix) :]] = state_dict.pop(k) + elif t5xxl_path is None: + logger.info("t5xxl is not included in the checkpoint and t5xxl_path is not provided") + return None + + return flux_utils_load_t5xxl(t5xxl_path, dtype, device, disable_mmap, state_dict=t5xxl_sd) + + +def load_vae( + vae_path: Optional[str], + vae_dtype: Optional[Union[str, torch.dtype]], + device: Optional[Union[str, torch.device]], + disable_mmap: bool = False, + state_dict: Optional[Dict] = None, +): + vae_sd = {} + if vae_path: + logger.info(f"Loading VAE from {vae_path}...") + vae_sd = load_safetensors(vae_path, device, disable_mmap, dtype=vae_dtype) + else: + # remove prefix "first_stage_model." + vae_sd = {} + vae_prefix = "first_stage_model." + for k in list(state_dict.keys()): + if k.startswith(vae_prefix): + vae_sd[k[len(vae_prefix) :]] = state_dict.pop(k) + + logger.info("Building VAE") + vae = sd3_models.SDVAE(vae_dtype, device) + logger.info("Loading state dict...") + info = vae.load_state_dict(vae_sd) + logger.info(f"Loaded VAE: {info}") + vae.to(device=device, dtype=vae_dtype) # make sure it's in the right device and dtype + return vae + + +# endregion + + +class ModelSamplingDiscreteFlow: + """Helper for sampler scheduling (ie timestep/sigma calculations) for Discrete Flow models""" + + def __init__(self, shift=1.0): + self.shift = shift + timesteps = 1000 + self.sigmas = self.sigma(torch.arange(1, timesteps + 1, 1)) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return sigma * 1000 + + def sigma(self, timestep: torch.Tensor): + timestep = timestep / 1000.0 + if self.shift == 1.0: + return timestep + return self.shift * timestep / (1 + (self.shift - 1) * timestep) + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + # assert max_denoise is False, "max_denoise not implemented" + # max_denoise is always True, I'm not sure why it's there + return sigma * noise + (1.0 - sigma) * latent_image diff --git a/vendor/sd-scripts/library/sdxl_lpw_stable_diffusion.py b/vendor/sd-scripts/library/sdxl_lpw_stable_diffusion.py new file mode 100644 index 0000000..a6e0c93 --- /dev/null +++ b/vendor/sd-scripts/library/sdxl_lpw_stable_diffusion.py @@ -0,0 +1,1311 @@ +# copy from https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py +# and modify to support SD2.x + +import inspect +import re +from typing import Callable, List, Optional, Union + +import numpy as np +import PIL.Image +import torch +from packaging import version +from tqdm import tqdm +from transformers import CLIPImageProcessor as CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from diffusers import SchedulerMixin, StableDiffusionPipeline +from diffusers.models import AutoencoderKL +from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker +from diffusers.utils import logging +from PIL import Image + +from library import ( + sdxl_model_util, + sdxl_train_util, + strategy_base, + strategy_sdxl, + train_util, + sdxl_original_unet, + sdxl_original_control_net, +) + + +try: + from diffusers.utils import PIL_INTERPOLATION +except ImportError: + if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): + PIL_INTERPOLATION = { + "linear": PIL.Image.Resampling.BILINEAR, + "bilinear": PIL.Image.Resampling.BILINEAR, + "bicubic": PIL.Image.Resampling.BICUBIC, + "lanczos": PIL.Image.Resampling.LANCZOS, + "nearest": PIL.Image.Resampling.NEAREST, + } + else: + PIL_INTERPOLATION = { + "linear": PIL.Image.LINEAR, + "bilinear": PIL.Image.BILINEAR, + "bicubic": PIL.Image.BICUBIC, + "lanczos": PIL.Image.LANCZOS, + "nearest": PIL.Image.NEAREST, + } +# ------------------------------------------------------------------------------ + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +re_attention = re.compile( + r""" +\\\(| +\\\)| +\\\[| +\\]| +\\\\| +\\| +\(| +\[| +:([+-]?[.\d]+)\)| +\)| +]| +[^\\()\[\]:]+| +: +""", + re.X, +) + + +def parse_prompt_attention(text): + """ + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. + Accepted tokens are: + (abc) - increases attention to abc by a multiplier of 1.1 + (abc:3.12) - increases attention to abc by a multiplier of 3.12 + [abc] - decreases attention to abc by a multiplier of 1.1 + \( - literal character '(' + \[ - literal character '[' + \) - literal character ')' + \] - literal character ']' + \\ - literal character '\' + anything else - just text + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] + """ + + res = [] + round_brackets = [] + square_brackets = [] + + round_bracket_multiplier = 1.1 + square_bracket_multiplier = 1 / 1.1 + + def multiply_range(start_position, multiplier): + for p in range(start_position, len(res)): + res[p][1] *= multiplier + + for m in re_attention.finditer(text): + text = m.group(0) + weight = m.group(1) + + if text.startswith("\\"): + res.append([text[1:], 1.0]) + elif text == "(": + round_brackets.append(len(res)) + elif text == "[": + square_brackets.append(len(res)) + elif weight is not None and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), float(weight)) + elif text == ")" and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), round_bracket_multiplier) + elif text == "]" and len(square_brackets) > 0: + multiply_range(square_brackets.pop(), square_bracket_multiplier) + else: + res.append([text, 1.0]) + + for pos in round_brackets: + multiply_range(pos, round_bracket_multiplier) + + for pos in square_brackets: + multiply_range(pos, square_bracket_multiplier) + + if len(res) == 0: + res = [["", 1.0]] + + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + + return res + + +def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], max_length: int): + r""" + Tokenize a list of prompts and return its tokens with weights of each token. + + No padding, starting or ending token is included. + """ + tokens = [] + weights = [] + truncated = False + for text in prompt: + texts_and_weights = parse_prompt_attention(text) + text_token = [] + text_weight = [] + for word, weight in texts_and_weights: + # tokenize and discard the starting and the ending token + token = pipe.tokenizer(word).input_ids[1:-1] + text_token += token + # copy the weight by length of token + text_weight += [weight] * len(token) + # stop if the text is too long (longer than truncation limit) + if len(text_token) > max_length: + truncated = True + break + # truncate + if len(text_token) > max_length: + truncated = True + text_token = text_token[:max_length] + text_weight = text_weight[:max_length] + tokens.append(text_token) + weights.append(text_weight) + if truncated: + logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples") + return tokens, weights + + +def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77): + r""" + Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. + """ + max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) + weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length + for i in range(len(tokens)): + tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (max_length - 2 - len(tokens[i])) + if no_boseos_middle: + weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) + else: + w = [] + if len(weights[i]) == 0: + w = [1.0] * weights_length + else: + for j in range(max_embeddings_multiples): + w.append(1.0) # weight for starting token in this chunk + w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))] + w.append(1.0) # weight for ending token in this chunk + w += [1.0] * (weights_length - len(w)) + weights[i] = w[:] + + return tokens, weights + + +def get_hidden_states(text_encoder, input_ids, is_sdxl_text_encoder2: bool, eos_token_id, device): + if not is_sdxl_text_encoder2: + # text_encoder1: same as SD1/2 + enc_out = text_encoder(input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=True) + hidden_states = enc_out["hidden_states"][11] + pool = None + else: + # text_encoder2 + enc_out = text_encoder(input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=True) + hidden_states = enc_out["hidden_states"][-2] # penuultimate layer + # pool = enc_out["text_embeds"] + pool = train_util.pool_workaround(text_encoder, enc_out["last_hidden_state"], input_ids, eos_token_id) + hidden_states = hidden_states.to(device) + if pool is not None: + pool = pool.to(device) + return hidden_states, pool + + +def get_unweighted_text_embeddings( + pipe: StableDiffusionPipeline, + text_input: torch.Tensor, + chunk_length: int, + clip_skip: int, + eos: int, + pad: int, + is_sdxl_text_encoder2: bool, + no_boseos_middle: Optional[bool] = True, +): + """ + When the length of tokens is a multiple of the capacity of the text encoder, + it should be split into chunks and sent to the text encoder individually. + """ + max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2) + text_pool = None + if max_embeddings_multiples > 1: + text_embeddings = [] + for i in range(max_embeddings_multiples): + # extract the i-th chunk + text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone() + + # cover the head and the tail by the starting and the ending tokens + text_input_chunk[:, 0] = text_input[0, 0] + if pad == eos: # v1 + text_input_chunk[:, -1] = text_input[0, -1] + else: # v2 + for j in range(len(text_input_chunk)): + if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある + text_input_chunk[j, -1] = eos + if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD + text_input_chunk[j, 1] = eos + + text_embedding, current_text_pool = get_hidden_states( + pipe.text_encoder, text_input_chunk, is_sdxl_text_encoder2, eos, pipe.device + ) + if text_pool is None: + text_pool = current_text_pool + + if no_boseos_middle: + if i == 0: + # discard the ending token + text_embedding = text_embedding[:, :-1] + elif i == max_embeddings_multiples - 1: + # discard the starting token + text_embedding = text_embedding[:, 1:] + else: + # discard both starting and ending tokens + text_embedding = text_embedding[:, 1:-1] + + text_embeddings.append(text_embedding) + text_embeddings = torch.concat(text_embeddings, axis=1) + else: + text_embeddings, text_pool = get_hidden_states(pipe.text_encoder, text_input, is_sdxl_text_encoder2, eos, pipe.device) + return text_embeddings, text_pool + + +def get_weighted_text_embeddings( + pipe, # : SdxlStableDiffusionLongPromptWeightingPipeline, + prompt: Union[str, List[str]], + uncond_prompt: Optional[Union[str, List[str]]] = None, + max_embeddings_multiples: Optional[int] = 3, + no_boseos_middle: Optional[bool] = False, + skip_parsing: Optional[bool] = False, + skip_weighting: Optional[bool] = False, + clip_skip=None, + is_sdxl_text_encoder2=False, +): + r""" + Prompts can be assigned with local weights using brackets. For example, + prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful', + and the embedding tokens corresponding to the words get multiplied by a constant, 1.1. + + Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean. + + Args: + pipe (`StableDiffusionPipeline`): + Pipe to provide access to the tokenizer and the text encoder. + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + uncond_prompt (`str` or `List[str]`): + The unconditional prompt or prompts for guide the image generation. If unconditional prompt + is provided, the embeddings of prompt and uncond_prompt are concatenated. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + no_boseos_middle (`bool`, *optional*, defaults to `False`): + If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and + ending token in each of the chunk in the middle. + skip_parsing (`bool`, *optional*, defaults to `False`): + Skip the parsing of brackets. + skip_weighting (`bool`, *optional*, defaults to `False`): + Skip the weighting. When the parsing is skipped, it is forced True. + """ + max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + if isinstance(prompt, str): + prompt = [prompt] + + if not skip_parsing: + prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2) + if uncond_prompt is not None: + if isinstance(uncond_prompt, str): + uncond_prompt = [uncond_prompt] + uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2) + else: + prompt_tokens = [token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids] + prompt_weights = [[1.0] * len(token) for token in prompt_tokens] + if uncond_prompt is not None: + if isinstance(uncond_prompt, str): + uncond_prompt = [uncond_prompt] + uncond_tokens = [ + token[1:-1] for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids + ] + uncond_weights = [[1.0] * len(token) for token in uncond_tokens] + + # round up the longest length of tokens to a multiple of (model_max_length - 2) + max_length = max([len(token) for token in prompt_tokens]) + if uncond_prompt is not None: + max_length = max(max_length, max([len(token) for token in uncond_tokens])) + + max_embeddings_multiples = min( + max_embeddings_multiples, + (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1, + ) + max_embeddings_multiples = max(1, max_embeddings_multiples) + max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + + # pad the length of tokens and weights + bos = pipe.tokenizer.bos_token_id + eos = pipe.tokenizer.eos_token_id + pad = pipe.tokenizer.pad_token_id + prompt_tokens, prompt_weights = pad_tokens_and_weights( + prompt_tokens, + prompt_weights, + max_length, + bos, + eos, + pad, + no_boseos_middle=no_boseos_middle, + chunk_length=pipe.tokenizer.model_max_length, + ) + prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device) + if uncond_prompt is not None: + uncond_tokens, uncond_weights = pad_tokens_and_weights( + uncond_tokens, + uncond_weights, + max_length, + bos, + eos, + pad, + no_boseos_middle=no_boseos_middle, + chunk_length=pipe.tokenizer.model_max_length, + ) + uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device) + + # get the embeddings + text_embeddings, text_pool = get_unweighted_text_embeddings( + pipe, + prompt_tokens, + pipe.tokenizer.model_max_length, + clip_skip, + eos, + pad, + is_sdxl_text_encoder2, + no_boseos_middle=no_boseos_middle, + ) + prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device) + + if uncond_prompt is not None: + uncond_embeddings, uncond_pool = get_unweighted_text_embeddings( + pipe, + uncond_tokens, + pipe.tokenizer.model_max_length, + clip_skip, + eos, + pad, + is_sdxl_text_encoder2, + no_boseos_middle=no_boseos_middle, + ) + uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device) + + # assign weights to the prompts and normalize in the sense of mean + # TODO: should we normalize by chunk or in a whole (current implementation)? + if (not skip_parsing) and (not skip_weighting): + previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings *= prompt_weights.unsqueeze(-1) + current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + if uncond_prompt is not None: + previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) + uncond_embeddings *= uncond_weights.unsqueeze(-1) + current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) + uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + + if uncond_prompt is not None: + return text_embeddings, text_pool, uncond_embeddings, uncond_pool + return text_embeddings, text_pool, None, None + + +def preprocess_image(image): + w, h = image.size + w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) + image = np.array(image).astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + return 2.0 * image - 1.0 + + +def preprocess_mask(mask, scale_factor=8): + mask = mask.convert("L") + w, h = mask.size + w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]) + mask = np.array(mask).astype(np.float32) / 255.0 + mask = np.tile(mask, (4, 1, 1)) + mask = mask[None].transpose(0, 1, 2, 3) # what does this step do? + mask = 1 - mask # repaint white, keep black + mask = torch.from_numpy(mask) + return mask + + +def prepare_controlnet_image( + image: PIL.Image.Image, + width: int, + height: int, + batch_size: int, + num_images_per_prompt: int, + device: torch.device, + dtype: torch.dtype, + do_classifier_free_guidance: bool = False, + guess_mode: bool = False, +): + if not isinstance(image, torch.Tensor): + if isinstance(image, PIL.Image.Image): + image = [image] + + if isinstance(image[0], PIL.Image.Image): + images = [] + + for image_ in image: + image_ = image_.convert("RGB") + image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]) + image_ = np.array(image_) + image_ = image_[None, :] + images.append(image_) + + image = images + + image = np.concatenate(image, axis=0) + image = np.array(image).astype(np.float32) / 255.0 + image = image.transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + elif isinstance(image[0], torch.Tensor): + image = torch.cat(image, dim=0) + + image_batch_size = image.shape[0] + + if image_batch_size == 1: + repeat_by = batch_size + else: + # image batch size is the same as prompt batch size + repeat_by = num_images_per_prompt + + image = image.repeat_interleave(repeat_by, dim=0) + + image = image.to(device=device, dtype=dtype) + + if do_classifier_free_guidance and not guess_mode: + image = torch.cat([image] * 2) + + return image + + +class SdxlStableDiffusionLongPromptWeightingPipeline: + r""" + Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing + weighting in prompt. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + + # if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"): + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: List[CLIPTextModel], + tokenizer: List[CLIPTokenizer], + unet: Union[sdxl_original_unet.SdxlUNet2DConditionModel, sdxl_original_control_net.SdxlControlledUNet], + scheduler: SchedulerMixin, + # clip_skip: int, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + requires_safety_checker: bool = True, + clip_skip: int = 1, + ): + # clip skip is ignored currently + self.tokenizer = tokenizer[0] + self.text_encoder = text_encoder[0] + self.unet = unet + self.scheduler = scheduler + self.safety_checker = safety_checker + self.feature_extractor = feature_extractor + self.requires_safety_checker = requires_safety_checker + self.vae = vae + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.progress_bar = lambda x: tqdm(x, leave=False) + + self.clip_skip = clip_skip + self.tokenizers = tokenizer + self.text_encoders = text_encoder + + # self.__init__additional__() + + # def __init__additional__(self): + # if not hasattr(self, "vae_scale_factor"): + # setattr(self, "vae_scale_factor", 2 ** (len(self.vae.config.block_out_channels) - 1)) + + def to(self, device=None, dtype=None): + if device is not None: + self.device = device + # self.vae.to(device=self.device) + if dtype is not None: + self.dtype = dtype + + # do not move Text Encoders to device, because Text Encoder should be on CPU + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def check_inputs(self, prompt, height, width, strength, callback_steps): + if not isinstance(prompt, str) and not isinstance(prompt, list): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if strength < 0 or strength > 1: + raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." + ) + + def get_timesteps(self, num_inference_steps, strength, device, is_text2img): + if is_text2img: + return self.scheduler.timesteps.to(device), num_inference_steps + else: + # get the original timestep using init_timestep + offset = self.scheduler.config.get("steps_offset", 0) + init_timestep = int(num_inference_steps * strength) + offset + init_timestep = min(init_timestep, num_inference_steps) + + t_start = max(num_inference_steps - init_timestep + offset, 0) + timesteps = self.scheduler.timesteps[t_start:].to(device) + return timesteps, num_inference_steps - t_start + + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is not None: + safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values.to(dtype)) + else: + has_nsfw_concept = None + return image, has_nsfw_concept + + def decode_latents(self, latents): + with torch.no_grad(): + latents = 1 / sdxl_model_util.VAE_SCALE_FACTOR * latents + + # print("post_quant_conv dtype:", self.vae.post_quant_conv.weight.dtype) # torch.float32 + # x = torch.nn.functional.conv2d(latents, self.vae.post_quant_conv.weight.detach(), stride=1, padding=0) + # print("latents dtype:", latents.dtype, "x dtype:", x.dtype) # torch.float32, torch.float16 + # self.vae.to("cpu") + # self.vae.set_use_memory_efficient_attention_xformers(False) + # image = self.vae.decode(latents.to("cpu")).sample + + image = self.vae.decode(latents.to(self.vae.dtype)).sample + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents(self, image, timestep, batch_size, height, width, dtype, device, generator, latents=None): + if image is None: + shape = ( + batch_size, + self.vae.config.latent_channels, # always 4; unet.in_channels may be 9 for inpainting + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + + if latents is None: + if device.type == "mps": + # randn does not work reproducibly on mps + latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device) + else: + latents = torch.randn(shape, generator=generator, device=device, dtype=dtype) + else: + if latents.shape != shape: + raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents, None, None + else: + init_latent_dist = self.vae.encode(image).latent_dist + init_latents = init_latent_dist.sample(generator=generator) + init_latents = sdxl_model_util.VAE_SCALE_FACTOR * init_latents + init_latents = torch.cat([init_latents] * batch_size, dim=0) + init_latents_orig = init_latents + shape = init_latents.shape + + # add noise to latents using the timesteps + if device.type == "mps": + noise = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device) + else: + noise = torch.randn(shape, generator=generator, device=device, dtype=dtype) + latents = self.scheduler.add_noise(init_latents, noise, timestep) + return latents, init_latents_orig, noise + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + image: Union[torch.FloatTensor, PIL.Image.Image] = None, + mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None, + height: int = 512, + width: int = 512, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + strength: float = 0.8, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[torch.Generator] = None, + latents: Optional[torch.FloatTensor] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + controlnet: sdxl_original_control_net.SdxlControlNet = None, + controlnet_image=None, + inpaint_image: PIL.Image.Image = None, + inpaint_mask: PIL.Image.Image = None, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. + mask_image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be + replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a + PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should + contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. + height (`int`, *optional*, defaults to 512): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to 512): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. + `image` will be used as a starting point, adding more noise to it the larger the `strength`. The + number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added + noise will be maximum and the denoising process will run for the full number of iterations specified in + `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + controlnet (`diffusers.ControlNetModel`, *optional*): + A controlnet model to be used for the inference. If not provided, controlnet will be disabled. + controlnet_image (`torch.FloatTensor` or `PIL.Image.Image`, *optional*): + `Image`, or tensor representing an image batch, to be used as the starting point for the controlnet + inference. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + + Returns: + `None` if cancelled by `is_cancelled_callback`, + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + if controlnet is not None and controlnet_image is None: + raise ValueError("controlnet_image must be provided if controlnet is not None.") + + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs(prompt, height, width, strength, callback_steps) + + # 2. Define call parameters + batch_size = 1 if isinstance(prompt, str) else len(prompt) + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + tokenize_strategy: strategy_sdxl.SdxlTokenizeStrategy = strategy_base.TokenizeStrategy.get_strategy() + encoding_strategy: strategy_sdxl.SdxlTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy() + + text_input_ids, text_weights = tokenize_strategy.tokenize_with_weights(prompt) + hidden_states_1, hidden_states_2, text_pool = encoding_strategy.encode_tokens_with_weights( + tokenize_strategy, self.text_encoders, text_input_ids, text_weights + ) + text_embeddings = torch.cat([hidden_states_1, hidden_states_2], dim=-1) + + if do_classifier_free_guidance: + input_ids, weights = tokenize_strategy.tokenize_with_weights(negative_prompt or "") + hidden_states_1, hidden_states_2, uncond_pool = encoding_strategy.encode_tokens_with_weights( + tokenize_strategy, self.text_encoders, input_ids, weights + ) + uncond_embeddings = torch.cat([hidden_states_1, hidden_states_2], dim=-1) + else: + uncond_embeddings = None + uncond_pool = None + + unet_dtype = self.unet.dtype + dtype = unet_dtype + if hasattr(dtype, "itemsize") and dtype.itemsize == 1: # fp8 + dtype = torch.float16 + self.unet.to(dtype) + + # 4. Preprocess image and mask + if isinstance(image, PIL.Image.Image): + image = preprocess_image(image) + if image is not None: + image = image.to(device=self.device, dtype=dtype) + if isinstance(mask_image, PIL.Image.Image): + mask_image = preprocess_mask(mask_image, self.vae_scale_factor) + if mask_image is not None: + mask = mask_image.to(device=self.device, dtype=dtype) + mask = torch.cat([mask] * batch_size * num_images_per_prompt) + else: + mask = None + + # ControlNet is not working yet in SDXL, but keep the code here for future use + if controlnet_image is not None: + controlnet_image = prepare_controlnet_image( + controlnet_image, width, height, batch_size, 1, self.device, controlnet.dtype, do_classifier_free_guidance, False + ) + + # 5. set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None) + latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) + + # 6. Prepare latent variables + latents, init_latents_orig, noise = self.prepare_latents( + image, + latent_timestep, + batch_size * num_images_per_prompt, + height, + width, + dtype, + device, + generator, + latents, + ) + + # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # create size embs and concat embeddings for SDXL + orig_size = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1).to(device, dtype) + crop_size = torch.zeros_like(orig_size) + target_size = orig_size + embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, device).to(device, dtype) + + # make conditionings + text_pool = text_pool.to(device, dtype) + if do_classifier_free_guidance: + text_embedding = torch.cat([uncond_embeddings, text_embeddings]).to(device, dtype) + + uncond_pool = uncond_pool.to(device, dtype) + cond_vector = torch.cat([text_pool, embs], dim=1).to(dtype) + uncond_vector = torch.cat([uncond_pool, embs], dim=1).to(dtype) + vector_embedding = torch.cat([uncond_vector, cond_vector]) + else: + text_embedding = text_embeddings.to(device, dtype) + vector_embedding = torch.cat([text_pool, embs], dim=1) + + # Prepare 9-channel inpainting inputs (mask + masked image latents) if supplied. + # The scheduler and latents always stay 4-channel; concatenation only happens + # on the UNet input (latent_model_input) inside the denoising loop. + inpaint_mask_latent = None + inpaint_masked_image_latent = None + if inpaint_image is not None and inpaint_mask is not None: + import numpy as np + # Encode masked image: image * (1 - mask). + # The VAE may run in a different dtype than the UNet (e.g. fp32 with + # --no_half_vae, or bf16 to avoid SDXL's fp16 VAE NaN issue), so cast + # to self.vae.dtype here — mirroring the existing decode_latents path + # (`self.vae.decode(latents.to(self.vae.dtype))`). + img_np = np.array(inpaint_image.convert("RGB")).astype(np.float32) / 127.5 - 1.0 + img_t = torch.from_numpy(img_np).permute(2, 0, 1)[None].to(device=device, dtype=self.vae.dtype) + mask_np = np.array(inpaint_mask.convert("L")).astype(np.float32) / 255.0 + mask_np = (mask_np >= 0.5).astype(np.float32) + mask_t = torch.from_numpy(mask_np)[None, None].to(device=device, dtype=self.vae.dtype) + masked_img_t = img_t * (1.0 - mask_t) + # Disable autocast: under fp16 autocast (set by accelerator.autocast() in + # train_util.sample_image_inference), conv kernels run in fp16 even when + # weights/inputs are fp32 — and SDXL VAE produces NaN in fp16. This mirrors + # how decode_latents() runs *outside* the autocast block in the caller. + with torch.autocast(device_type=device.type, enabled=False): + inpaint_masked_image_latent = ( + self.vae.encode(masked_img_t).latent_dist.sample() * sdxl_model_util.VAE_SCALE_FACTOR + ) + inpaint_mask_latent = torch.nn.functional.interpolate( + mask_t, size=(height // 8, width // 8) + ) + + # 8. Denoising loop + for i, t in enumerate(self.progress_bar(timesteps)): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # For 9-channel inpainting UNet: concatenate mask and masked-image latents. + # Duplicate for CFG the same way latent_model_input was duplicated. + if inpaint_mask_latent is not None: + n = latent_model_input.shape[0] // latents.shape[0] # 2 if CFG, else 1 + mask_in = inpaint_mask_latent.repeat(n, 1, 1, 1).to(latent_model_input.dtype) + masked_in = inpaint_masked_image_latent.repeat(n, 1, 1, 1).to(latent_model_input.dtype) + latent_model_input = torch.cat([latent_model_input, mask_in, masked_in], dim=1) + + # FIXME SD1 ControlNet is not working + + # predict the noise residual + if controlnet is not None: + input_resi_add, mid_add = controlnet(latent_model_input, t, text_embedding, vector_embedding, controlnet_image) + noise_pred = self.unet(latent_model_input, t, text_embedding, vector_embedding, input_resi_add, mid_add) + else: + noise_pred = self.unet(latent_model_input, t, text_embedding, vector_embedding) + noise_pred = noise_pred.to(dtype) # U-Net changes dtype in LoRA training + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + if mask is not None: + # masking + init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t])) + latents = (init_latents_proper * mask) + (latents * (1 - mask)) + + # call the callback, if provided + if i % callback_steps == 0: + if callback is not None: + callback(i, t, latents) + if is_cancelled_callback is not None and is_cancelled_callback(): + return None + + self.unet.to(unet_dtype) + return latents + + def latents_to_image(self, latents): + # 9. Post-processing + image = self.decode_latents(latents.to(self.vae.dtype)) + image = self.numpy_to_pil(image) + return image + + # copy from pil_utils.py + def numpy_to_pil(self, images: np.ndarray) -> Image.Image: + """ + Convert a numpy image or a batch of images to a PIL image. + """ + if images.ndim == 3: + images = images[None, ...] + images = (images * 255).round().astype("uint8") + if images.shape[-1] == 1: + # special case for grayscale (single channel) images + pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images] + else: + pil_images = [Image.fromarray(image) for image in images] + + return pil_images + + def text2img( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + height: int = 512, + width: int = 512, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[torch.Generator] = None, + latents: Optional[torch.FloatTensor] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function for text-to-image generation. + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + height (`int`, *optional*, defaults to 512): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to 512): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + return self.__call__( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + num_images_per_prompt=num_images_per_prompt, + eta=eta, + generator=generator, + latents=latents, + max_embeddings_multiples=max_embeddings_multiples, + output_type=output_type, + return_dict=return_dict, + callback=callback, + is_cancelled_callback=is_cancelled_callback, + callback_steps=callback_steps, + ) + + def img2img( + self, + image: Union[torch.FloatTensor, PIL.Image.Image], + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + strength: float = 0.8, + num_inference_steps: Optional[int] = 50, + guidance_scale: Optional[float] = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: Optional[float] = 0.0, + generator: Optional[torch.Generator] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function for image-to-image generation. + Args: + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. + `image` will be used as a starting point, adding more noise to it the larger the `strength`. The + number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added + noise will be maximum and the denoising process will run for the full number of iterations specified in + `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. This parameter will be modulated by `strength`. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + return self.__call__( + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + strength=strength, + num_images_per_prompt=num_images_per_prompt, + eta=eta, + generator=generator, + max_embeddings_multiples=max_embeddings_multiples, + output_type=output_type, + return_dict=return_dict, + callback=callback, + is_cancelled_callback=is_cancelled_callback, + callback_steps=callback_steps, + ) + + def inpaint( + self, + image: Union[torch.FloatTensor, PIL.Image.Image], + mask_image: Union[torch.FloatTensor, PIL.Image.Image], + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + strength: float = 0.8, + num_inference_steps: Optional[int] = 50, + guidance_scale: Optional[float] = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: Optional[float] = 0.0, + generator: Optional[torch.Generator] = None, + max_embeddings_multiples: Optional[int] = 3, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + is_cancelled_callback: Optional[Callable[[], bool]] = None, + callback_steps: int = 1, + ): + r""" + Function for inpaint. + Args: + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. This is the image whose masked region will be inpainted. + mask_image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be + replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a + PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should + contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength` + is 1, the denoising process will be run on the masked area for the full number of iterations specified + in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more + noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur. + num_inference_steps (`int`, *optional*, defaults to 50): + The reference number of denoising steps. More denoising steps usually lead to a higher quality image at + the expense of slower inference. This parameter will be modulated by `strength`, as explained above. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + is_cancelled_callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. If the function returns + `True`, the inference will be cancelled. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + return self.__call__( + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + mask_image=mask_image, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + strength=strength, + num_images_per_prompt=num_images_per_prompt, + eta=eta, + generator=generator, + max_embeddings_multiples=max_embeddings_multiples, + output_type=output_type, + return_dict=return_dict, + callback=callback, + is_cancelled_callback=is_cancelled_callback, + callback_steps=callback_steps, + ) diff --git a/vendor/sd-scripts/library/sdxl_model_util.py b/vendor/sd-scripts/library/sdxl_model_util.py new file mode 100644 index 0000000..0613153 --- /dev/null +++ b/vendor/sd-scripts/library/sdxl_model_util.py @@ -0,0 +1,586 @@ +import torch +import safetensors +from accelerate import init_empty_weights +from accelerate.utils.modeling import set_module_tensor_to_device +from safetensors.torch import load_file, save_file +from transformers import CLIPTextModel, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer +from typing import List +from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel +from library import model_util +from library import sdxl_original_unet +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +VAE_SCALE_FACTOR = 0.13025 +MODEL_VERSION_SDXL_BASE_V1_0 = "sdxl_base_v1-0" + +# Diffusersの設定を読み込むための参照モデル +DIFFUSERS_REF_MODEL_ID_SDXL = "stabilityai/stable-diffusion-xl-base-1.0" + +DIFFUSERS_SDXL_UNET_CONFIG = { + "act_fn": "silu", + "addition_embed_type": "text_time", + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": 256, + "attention_head_dim": [5, 10, 20], + "block_out_channels": [320, 640, 1280], + "center_input_sample": False, + "class_embed_type": None, + "class_embeddings_concat": False, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 2048, + "cross_attention_norm": None, + "down_block_types": ["DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"], + "downsample_padding": 1, + "dual_cross_attention": False, + "encoder_hid_dim": None, + "encoder_hid_dim_type": None, + "flip_sin_to_cos": True, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_only_cross_attention": None, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": None, + "num_class_embeds": None, + "only_cross_attention": False, + "out_channels": 4, + "projection_class_embeddings_input_dim": 2816, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": False, + "resnet_time_scale_shift": "default", + "sample_size": 128, + "time_cond_proj_dim": None, + "time_embedding_act_fn": None, + "time_embedding_dim": None, + "time_embedding_type": "positional", + "timestep_post_act": None, + "transformer_layers_per_block": [1, 2, 10], + "up_block_types": ["CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"], + "upcast_attention": False, + "use_linear_projection": True, +} + + +def convert_sdxl_text_encoder_2_checkpoint(checkpoint, max_length): + SDXL_KEY_PREFIX = "conditioner.embedders.1.model." + + # SD2のと、基本的には同じ。logit_scaleを後で使うので、それを追加で返す + # logit_scaleはcheckpointの保存時に使用する + def convert_key(key): + # common conversion + key = key.replace(SDXL_KEY_PREFIX + "transformer.", "text_model.encoder.") + key = key.replace(SDXL_KEY_PREFIX, "text_model.") + + if "resblocks" in key: + # resblocks conversion + key = key.replace(".resblocks.", ".layers.") + if ".ln_" in key: + key = key.replace(".ln_", ".layer_norm") + elif ".mlp." in key: + key = key.replace(".c_fc.", ".fc1.") + key = key.replace(".c_proj.", ".fc2.") + elif ".attn.out_proj" in key: + key = key.replace(".attn.out_proj.", ".self_attn.out_proj.") + elif ".attn.in_proj" in key: + key = None # 特殊なので後で処理する + else: + raise ValueError(f"unexpected key in SD: {key}") + elif ".positional_embedding" in key: + key = key.replace(".positional_embedding", ".embeddings.position_embedding.weight") + elif ".text_projection" in key: + key = key.replace("text_model.text_projection", "text_projection.weight") + elif ".logit_scale" in key: + key = None # 後で処理する + elif ".token_embedding" in key: + key = key.replace(".token_embedding.weight", ".embeddings.token_embedding.weight") + elif ".ln_final" in key: + key = key.replace(".ln_final", ".final_layer_norm") + # ckpt from comfy has this key: text_model.encoder.text_model.embeddings.position_ids + elif ".embeddings.position_ids" in key: + key = None # remove this key: position_ids is not used in newer transformers + return key + + keys = list(checkpoint.keys()) + new_sd = {} + for key in keys: + new_key = convert_key(key) + if new_key is None: + continue + new_sd[new_key] = checkpoint[key] + + # attnの変換 + for key in keys: + if ".resblocks" in key and ".attn.in_proj_" in key: + # 三つに分割 + values = torch.chunk(checkpoint[key], 3) + + key_suffix = ".weight" if "weight" in key else ".bias" + key_pfx = key.replace(SDXL_KEY_PREFIX + "transformer.resblocks.", "text_model.encoder.layers.") + key_pfx = key_pfx.replace("_weight", "") + key_pfx = key_pfx.replace("_bias", "") + key_pfx = key_pfx.replace(".attn.in_proj", ".self_attn.") + new_sd[key_pfx + "q_proj" + key_suffix] = values[0] + new_sd[key_pfx + "k_proj" + key_suffix] = values[1] + new_sd[key_pfx + "v_proj" + key_suffix] = values[2] + + # logit_scale はDiffusersには含まれないが、保存時に戻したいので別途返す + logit_scale = checkpoint.get(SDXL_KEY_PREFIX + "logit_scale", None) + + # temporary workaround for text_projection.weight.weight for Playground-v2 + if "text_projection.weight.weight" in new_sd: + logger.info("convert_sdxl_text_encoder_2_checkpoint: convert text_projection.weight.weight to text_projection.weight") + new_sd["text_projection.weight"] = new_sd["text_projection.weight.weight"] + del new_sd["text_projection.weight.weight"] + + return new_sd, logit_scale + + +# load state_dict without allocating new tensors +def _load_state_dict_on_device(model, state_dict, device, dtype=None): + # dtype will use fp32 as default + missing_keys = list(model.state_dict().keys() - state_dict.keys()) + unexpected_keys = list(state_dict.keys() - model.state_dict().keys()) + + # similar to model.load_state_dict() + if not missing_keys and not unexpected_keys: + for k in list(state_dict.keys()): + set_module_tensor_to_device(model, k, device, value=state_dict.pop(k), dtype=dtype) + return "" + + # error_msgs + error_msgs: List[str] = [] + if missing_keys: + error_msgs.insert(0, "Missing key(s) in state_dict: {}. ".format(", ".join('"{}"'.format(k) for k in missing_keys))) + if unexpected_keys: + error_msgs.insert(0, "Unexpected key(s) in state_dict: {}. ".format(", ".join('"{}"'.format(k) for k in unexpected_keys))) + + raise RuntimeError("Error(s) in loading state_dict for {}:\n\t{}".format(model.__class__.__name__, "\n\t".join(error_msgs))) + + +def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None, disable_mmap=False): + # model_version is reserved for future use + # dtype is used for full_fp16/bf16 integration. Text Encoder will remain fp32, because it runs on CPU when caching + + # Load the state dict + if model_util.is_safetensors(ckpt_path): + checkpoint = None + if disable_mmap: + state_dict = safetensors.torch.load(open(ckpt_path, "rb").read()) + else: + try: + state_dict = load_file(ckpt_path, device=map_location) + except: + state_dict = load_file(ckpt_path) # prevent device invalid Error + epoch = None + global_step = None + else: + checkpoint = torch.load(ckpt_path, map_location=map_location) + if "state_dict" in checkpoint: + state_dict = checkpoint["state_dict"] + epoch = checkpoint.get("epoch", 0) + global_step = checkpoint.get("global_step", 0) + else: + state_dict = checkpoint + epoch = 0 + global_step = 0 + checkpoint = None + + # U-Net + logger.info("building U-Net") + unet_sd = {} + for k in list(state_dict.keys()): + if k.startswith("model.diffusion_model."): + unet_sd[k.replace("model.diffusion_model.", "")] = state_dict.pop(k) + + # Inpainting checkpoints have input_blocks.0.0.weight with 9 in_channels instead of 4. + actual_in_channels = unet_sd["input_blocks.0.0.weight"].shape[1] + with init_empty_weights(): + unet = sdxl_original_unet.SdxlUNet2DConditionModel(in_channels=actual_in_channels) + + logger.info("loading U-Net from checkpoint") + info = _load_state_dict_on_device(unet, unet_sd, device=map_location, dtype=dtype) + logger.info(f"U-Net: {info}") + + # Text Encoders + logger.info("building text encoders") + + # Text Encoder 1 is same to Stability AI's SDXL + text_model1_cfg = CLIPTextConfig( + vocab_size=49408, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + max_position_embeddings=77, + hidden_act="quick_gelu", + layer_norm_eps=1e-05, + dropout=0.0, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + model_type="clip_text_model", + projection_dim=768, + # torch_dtype="float32", + # transformers_version="4.25.0.dev0", + ) + with init_empty_weights(): + text_model1 = CLIPTextModel._from_config(text_model1_cfg) + + # Text Encoder 2 is different from Stability AI's SDXL. SDXL uses open clip, but we use the model from HuggingFace. + # Note: Tokenizer from HuggingFace is different from SDXL. We must use open clip's tokenizer. + text_model2_cfg = CLIPTextConfig( + vocab_size=49408, + hidden_size=1280, + intermediate_size=5120, + num_hidden_layers=32, + num_attention_heads=20, + max_position_embeddings=77, + hidden_act="gelu", + layer_norm_eps=1e-05, + dropout=0.0, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + model_type="clip_text_model", + projection_dim=1280, + # torch_dtype="float32", + # transformers_version="4.25.0.dev0", + ) + with init_empty_weights(): + text_model2 = CLIPTextModelWithProjection(text_model2_cfg) + + logger.info("loading text encoders from checkpoint") + te1_sd = {} + te2_sd = {} + for k in list(state_dict.keys()): + if k.startswith("conditioner.embedders.0.transformer."): + te1_sd[k.replace("conditioner.embedders.0.transformer.", "")] = state_dict.pop(k) + elif k.startswith("conditioner.embedders.1.model."): + te2_sd[k] = state_dict.pop(k) + + # 最新の transformers では position_ids を含むとエラーになるので削除 / remove position_ids for latest transformers + if "text_model.embeddings.position_ids" in te1_sd: + te1_sd.pop("text_model.embeddings.position_ids") + + info1 = _load_state_dict_on_device(text_model1, te1_sd, device=map_location) # remain fp32 + logger.info(f"text encoder 1: {info1}") + + converted_sd, logit_scale = convert_sdxl_text_encoder_2_checkpoint(te2_sd, max_length=77) + info2 = _load_state_dict_on_device(text_model2, converted_sd, device=map_location) # remain fp32 + logger.info(f"text encoder 2: {info2}") + + # prepare vae + logger.info("building VAE") + vae_config = model_util.create_vae_diffusers_config() + with init_empty_weights(): + vae = AutoencoderKL(**vae_config) + + logger.info("loading VAE from checkpoint") + converted_vae_checkpoint = model_util.convert_ldm_vae_checkpoint(state_dict, vae_config) + info = _load_state_dict_on_device(vae, converted_vae_checkpoint, device=map_location, dtype=dtype) + logger.info(f"VAE: {info}") + + ckpt_info = (epoch, global_step) if epoch is not None else None + return text_model1, text_model2, vae, unet, logit_scale, ckpt_info + + +def make_unet_conversion_map(): + unet_conversion_map_layer = [] + + for i in range(3): # num_blocks is 3 in sdxl + # loop over downblocks/upblocks + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3*i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + # if i > 0: commentout for sdxl + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}." # change for sdxl + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + + hf_mid_atn_prefix = "mid_block.attentions.0." + sd_mid_atn_prefix = "middle_block.1." + unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + + for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2*j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0.", "norm1."), + ("in_layers.2.", "conv1."), + ("out_layers.0.", "norm2."), + ("out_layers.3.", "conv2."), + ("emb_layers.1.", "time_emb_proj."), + ("skip_connection.", "conv_shortcut."), + ] + + unet_conversion_map = [] + for sd, hf in unet_conversion_map_layer: + if "resnets" in hf: + for sd_res, hf_res in unet_conversion_map_resnet: + unet_conversion_map.append((sd + sd_res, hf + hf_res)) + else: + unet_conversion_map.append((sd, hf)) + + for j in range(2): + hf_time_embed_prefix = f"time_embedding.linear_{j+1}." + sd_time_embed_prefix = f"time_embed.{j*2}." + unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix)) + + for j in range(2): + hf_label_embed_prefix = f"add_embedding.linear_{j+1}." + sd_label_embed_prefix = f"label_emb.0.{j*2}." + unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix)) + + unet_conversion_map.append(("input_blocks.0.0.", "conv_in.")) + unet_conversion_map.append(("out.0.", "conv_norm_out.")) + unet_conversion_map.append(("out.2.", "conv_out.")) + + return unet_conversion_map + + +def convert_diffusers_unet_state_dict_to_sdxl(du_sd): + unet_conversion_map = make_unet_conversion_map() + + conversion_map = {hf: sd for sd, hf in unet_conversion_map} + return convert_unet_state_dict(du_sd, conversion_map) + + +def convert_unet_state_dict(src_sd, conversion_map): + converted_sd = {} + for src_key, value in src_sd.items(): + # さすがに全部回すのは時間がかかるので右から要素を削りつつprefixを探す + src_key_fragments = src_key.split(".")[:-1] # remove weight/bias + while len(src_key_fragments) > 0: + src_key_prefix = ".".join(src_key_fragments) + "." + if src_key_prefix in conversion_map: + converted_prefix = conversion_map[src_key_prefix] + converted_key = converted_prefix + src_key[len(src_key_prefix) :] + converted_sd[converted_key] = value + break + src_key_fragments.pop(-1) + assert len(src_key_fragments) > 0, f"key {src_key} not found in conversion map" + + return converted_sd + + +def convert_sdxl_unet_state_dict_to_diffusers(sd): + unet_conversion_map = make_unet_conversion_map() + + conversion_dict = {sd: hf for sd, hf in unet_conversion_map} + return convert_unet_state_dict(sd, conversion_dict) + + +def convert_text_encoder_2_state_dict_to_sdxl(checkpoint, logit_scale): + def convert_key(key): + # position_idsの除去 + if ".position_ids" in key: + return None + + # common + key = key.replace("text_model.encoder.", "transformer.") + key = key.replace("text_model.", "") + if "layers" in key: + # resblocks conversion + key = key.replace(".layers.", ".resblocks.") + if ".layer_norm" in key: + key = key.replace(".layer_norm", ".ln_") + elif ".mlp." in key: + key = key.replace(".fc1.", ".c_fc.") + key = key.replace(".fc2.", ".c_proj.") + elif ".self_attn.out_proj" in key: + key = key.replace(".self_attn.out_proj.", ".attn.out_proj.") + elif ".self_attn." in key: + key = None # 特殊なので後で処理する + else: + raise ValueError(f"unexpected key in DiffUsers model: {key}") + elif ".position_embedding" in key: + key = key.replace("embeddings.position_embedding.weight", "positional_embedding") + elif ".token_embedding" in key: + key = key.replace("embeddings.token_embedding.weight", "token_embedding.weight") + elif "text_projection" in key: # no dot in key + key = key.replace("text_projection.weight", "text_projection") + elif "final_layer_norm" in key: + key = key.replace("final_layer_norm", "ln_final") + return key + + keys = list(checkpoint.keys()) + new_sd = {} + for key in keys: + new_key = convert_key(key) + if new_key is None: + continue + new_sd[new_key] = checkpoint[key] + + # attnの変換 + for key in keys: + if "layers" in key and "q_proj" in key: + # 三つを結合 + key_q = key + key_k = key.replace("q_proj", "k_proj") + key_v = key.replace("q_proj", "v_proj") + + value_q = checkpoint[key_q] + value_k = checkpoint[key_k] + value_v = checkpoint[key_v] + value = torch.cat([value_q, value_k, value_v]) + + new_key = key.replace("text_model.encoder.layers.", "transformer.resblocks.") + new_key = new_key.replace(".self_attn.q_proj.", ".attn.in_proj_") + new_sd[new_key] = value + + if logit_scale is not None: + new_sd["logit_scale"] = logit_scale + + return new_sd + + +def save_stable_diffusion_checkpoint( + output_file, + text_encoder1, + text_encoder2, + unet, + epochs, + steps, + ckpt_info, + vae, + logit_scale, + metadata, + save_dtype=None, +): + state_dict = {} + + def update_sd(prefix, sd): + for k, v in sd.items(): + key = prefix + k + if save_dtype is not None: + v = v.detach().clone().to("cpu").to(save_dtype) + state_dict[key] = v + + # Convert the UNet model + update_sd("model.diffusion_model.", unet.state_dict()) + + # Convert the text encoders + update_sd("conditioner.embedders.0.transformer.", text_encoder1.state_dict()) + + text_enc2_dict = convert_text_encoder_2_state_dict_to_sdxl(text_encoder2.state_dict(), logit_scale) + update_sd("conditioner.embedders.1.model.", text_enc2_dict) + + # Convert the VAE + vae_dict = model_util.convert_vae_state_dict(vae.state_dict()) + update_sd("first_stage_model.", vae_dict) + + # Put together new checkpoint + key_count = len(state_dict.keys()) + new_ckpt = {"state_dict": state_dict} + + # epoch and global_step are sometimes not int + if ckpt_info is not None: + epochs += ckpt_info[0] + steps += ckpt_info[1] + + new_ckpt["epoch"] = epochs + new_ckpt["global_step"] = steps + + if model_util.is_safetensors(output_file): + save_file(state_dict, output_file, metadata) + else: + torch.save(new_ckpt, output_file) + + return key_count + + +def save_diffusers_checkpoint( + output_dir, text_encoder1, text_encoder2, unet, pretrained_model_name_or_path, vae=None, use_safetensors=False, save_dtype=None +): + from diffusers import StableDiffusionXLPipeline + + # convert U-Net + unet_sd = unet.state_dict() + du_unet_sd = convert_sdxl_unet_state_dict_to_diffusers(unet_sd) + + diffusers_unet = UNet2DConditionModel(**DIFFUSERS_SDXL_UNET_CONFIG) + if save_dtype is not None: + diffusers_unet.to(save_dtype) + diffusers_unet.load_state_dict(du_unet_sd) + + # create pipeline to save + if pretrained_model_name_or_path is None: + pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_SDXL + + scheduler = EulerDiscreteScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler") + tokenizer1 = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer") + tokenizer2 = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer_2") + if vae is None: + vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae") + + # prevent local path from being saved + def remove_name_or_path(model): + if hasattr(model, "config"): + model.config._name_or_path = None + model.config._name_or_path = None + + remove_name_or_path(diffusers_unet) + remove_name_or_path(text_encoder1) + remove_name_or_path(text_encoder2) + remove_name_or_path(scheduler) + remove_name_or_path(tokenizer1) + remove_name_or_path(tokenizer2) + remove_name_or_path(vae) + + pipeline = StableDiffusionXLPipeline( + unet=diffusers_unet, + text_encoder=text_encoder1, + text_encoder_2=text_encoder2, + vae=vae, + scheduler=scheduler, + tokenizer=tokenizer1, + tokenizer_2=tokenizer2, + ) + if save_dtype is not None: + pipeline.to(None, save_dtype) + pipeline.save_pretrained(output_dir, safe_serialization=use_safetensors) diff --git a/vendor/sd-scripts/library/sdxl_original_control_net.py b/vendor/sd-scripts/library/sdxl_original_control_net.py new file mode 100644 index 0000000..3af45f4 --- /dev/null +++ b/vendor/sd-scripts/library/sdxl_original_control_net.py @@ -0,0 +1,272 @@ +# some parts are modified from Diffusers library (Apache License 2.0) + +import math +from types import SimpleNamespace +from typing import Any, Optional +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import functional as F +from einops import rearrange +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +from library import sdxl_original_unet +from library.sdxl_model_util import convert_sdxl_unet_state_dict_to_diffusers, convert_diffusers_unet_state_dict_to_sdxl + + +class ControlNetConditioningEmbedding(nn.Module): + def __init__(self): + super().__init__() + + dims = [16, 32, 96, 256] + + self.conv_in = nn.Conv2d(3, dims[0], kernel_size=3, padding=1) + self.blocks = nn.ModuleList([]) + + for i in range(len(dims) - 1): + channel_in = dims[i] + channel_out = dims[i + 1] + self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) + self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) + + self.conv_out = nn.Conv2d(dims[-1], 320, kernel_size=3, padding=1) + nn.init.zeros_(self.conv_out.weight) # zero module weight + nn.init.zeros_(self.conv_out.bias) # zero module bias + + def forward(self, x): + x = self.conv_in(x) + x = F.silu(x) + for block in self.blocks: + x = block(x) + x = F.silu(x) + x = self.conv_out(x) + return x + + +class SdxlControlNet(sdxl_original_unet.SdxlUNet2DConditionModel): + def __init__(self, multiplier: Optional[float] = None, **kwargs): + super().__init__(**kwargs) + self.multiplier = multiplier + + # remove unet layers + self.output_blocks = nn.ModuleList([]) + del self.out + + self.controlnet_cond_embedding = ControlNetConditioningEmbedding() + + dims = [320, 320, 320, 320, 640, 640, 640, 1280, 1280] + self.controlnet_down_blocks = nn.ModuleList([]) + for dim in dims: + self.controlnet_down_blocks.append(nn.Conv2d(dim, dim, kernel_size=1)) + nn.init.zeros_(self.controlnet_down_blocks[-1].weight) # zero module weight + nn.init.zeros_(self.controlnet_down_blocks[-1].bias) # zero module bias + + self.controlnet_mid_block = nn.Conv2d(1280, 1280, kernel_size=1) + nn.init.zeros_(self.controlnet_mid_block.weight) # zero module weight + nn.init.zeros_(self.controlnet_mid_block.bias) # zero module bias + + def init_from_unet(self, unet: sdxl_original_unet.SdxlUNet2DConditionModel): + unet_sd = unet.state_dict() + unet_sd = {k: v for k, v in unet_sd.items() if not k.startswith("out")} + sd = super().state_dict() + sd.update(unet_sd) + info = super().load_state_dict(sd, strict=True, assign=True) + return info + + def load_state_dict(self, state_dict: dict, strict: bool = True, assign: bool = True) -> Any: + # convert state_dict to SAI format + unet_sd = {} + for k in list(state_dict.keys()): + if not k.startswith("controlnet_"): + unet_sd[k] = state_dict.pop(k) + unet_sd = convert_diffusers_unet_state_dict_to_sdxl(unet_sd) + state_dict.update(unet_sd) + super().load_state_dict(state_dict, strict=strict, assign=assign) + + def state_dict(self, destination=None, prefix="", keep_vars=False): + # convert state_dict to Diffusers format + state_dict = super().state_dict(destination, prefix, keep_vars) + control_net_sd = {} + for k in list(state_dict.keys()): + if k.startswith("controlnet_"): + control_net_sd[k] = state_dict.pop(k) + state_dict = convert_sdxl_unet_state_dict_to_diffusers(state_dict) + state_dict.update(control_net_sd) + return state_dict + + def forward( + self, + x: torch.Tensor, + timesteps: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + cond_image: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + # broadcast timesteps to batch dimension + timesteps = timesteps.expand(x.shape[0]) + + t_emb = sdxl_original_unet.get_timestep_embedding(timesteps, self.model_channels, downscale_freq_shift=0) + t_emb = t_emb.to(x.dtype) + emb = self.time_embed(t_emb) + + assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}" + assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}" + emb = emb + self.label_emb(y) + + def call_module(module, h, emb, context): + x = h + for layer in module: + if isinstance(layer, sdxl_original_unet.ResnetBlock2D): + x = layer(x, emb) + elif isinstance(layer, sdxl_original_unet.Transformer2DModel): + x = layer(x, context) + else: + x = layer(x) + return x + + h = x + multiplier = self.multiplier if self.multiplier is not None else 1.0 + hs = [] + for i, module in enumerate(self.input_blocks): + h = call_module(module, h, emb, context) + if i == 0: + h = self.controlnet_cond_embedding(cond_image) + h + hs.append(self.controlnet_down_blocks[i](h) * multiplier) + + h = call_module(self.middle_block, h, emb, context) + h = self.controlnet_mid_block(h) * multiplier + + return hs, h + + +class SdxlControlledUNet(sdxl_original_unet.SdxlUNet2DConditionModel): + """ + This class is for training purpose only. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def forward(self, x, timesteps=None, context=None, y=None, input_resi_add=None, mid_add=None, **kwargs): + # broadcast timesteps to batch dimension + timesteps = timesteps.expand(x.shape[0]) + + hs = [] + t_emb = sdxl_original_unet.get_timestep_embedding(timesteps, self.model_channels, downscale_freq_shift=0) + t_emb = t_emb.to(x.dtype) + emb = self.time_embed(t_emb) + + assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}" + assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}" + emb = emb + self.label_emb(y) + + def call_module(module, h, emb, context): + x = h + for layer in module: + if isinstance(layer, sdxl_original_unet.ResnetBlock2D): + x = layer(x, emb) + elif isinstance(layer, sdxl_original_unet.Transformer2DModel): + x = layer(x, context) + else: + x = layer(x) + return x + + h = x + for module in self.input_blocks: + h = call_module(module, h, emb, context) + hs.append(h) + + h = call_module(self.middle_block, h, emb, context) + h = h + mid_add + + for module in self.output_blocks: + resi = hs.pop() + input_resi_add.pop() + h = torch.cat([h, resi], dim=1) + h = call_module(module, h, emb, context) + + h = h.type(x.dtype) + h = call_module(self.out, h, emb, context) + + return h + + +if __name__ == "__main__": + import time + + logger.info("create unet") + unet = SdxlControlledUNet() + unet.to("cuda", torch.bfloat16) + unet.set_use_sdpa(True) + unet.set_gradient_checkpointing(True) + unet.train() + + logger.info("create control_net") + control_net = SdxlControlNet() + control_net.to("cuda") + control_net.set_use_sdpa(True) + control_net.set_gradient_checkpointing(True) + control_net.train() + + logger.info("Initialize control_net from unet") + control_net.init_from_unet(unet) + + unet.requires_grad_(False) + control_net.requires_grad_(True) + + # 使用メモリ量確認用の疑似学習ループ + logger.info("preparing optimizer") + + # optimizer = torch.optim.SGD(unet.parameters(), lr=1e-3, nesterov=True, momentum=0.9) # not working + + import bitsandbytes + + optimizer = bitsandbytes.adam.Adam8bit(control_net.parameters(), lr=1e-3) # not working + # optimizer = bitsandbytes.optim.RMSprop8bit(unet.parameters(), lr=1e-3) # working at 23.5 GB with torch2 + # optimizer=bitsandbytes.optim.Adagrad8bit(unet.parameters(), lr=1e-3) # working at 23.5 GB with torch2 + + # import transformers + # optimizer = transformers.optimization.Adafactor(unet.parameters(), relative_step=True) # working at 22.2GB with torch2 + + scaler = torch.cuda.amp.GradScaler(enabled=True) + + logger.info("start training") + steps = 10 + batch_size = 1 + + for step in range(steps): + logger.info(f"step {step}") + if step == 1: + time_start = time.perf_counter() + + x = torch.randn(batch_size, 4, 128, 128).cuda() # 1024x1024 + t = torch.randint(low=0, high=1000, size=(batch_size,), device="cuda") + txt = torch.randn(batch_size, 77, 2048).cuda() + vector = torch.randn(batch_size, sdxl_original_unet.ADM_IN_CHANNELS).cuda() + cond_img = torch.rand(batch_size, 3, 1024, 1024).cuda() + + with torch.cuda.amp.autocast(enabled=True, dtype=torch.bfloat16): + input_resi_add, mid_add = control_net(x, t, txt, vector, cond_img) + output = unet(x, t, txt, vector, input_resi_add, mid_add) + target = torch.randn_like(output) + loss = torch.nn.functional.mse_loss(output, target) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad(set_to_none=True) + + time_end = time.perf_counter() + logger.info(f"elapsed time: {time_end - time_start} [sec] for last {steps - 1} steps") + + logger.info("finish training") + sd = control_net.state_dict() + + from safetensors.torch import save_file + + save_file(sd, r"E:\Work\SD\Tmp\sdxl\ctrl\control_net.safetensors") diff --git a/vendor/sd-scripts/library/sdxl_original_unet.py b/vendor/sd-scripts/library/sdxl_original_unet.py new file mode 100644 index 0000000..6839b3f --- /dev/null +++ b/vendor/sd-scripts/library/sdxl_original_unet.py @@ -0,0 +1,1293 @@ +# Diffusersのコードをベースとした sd_xl_baseのU-Net +# state dictの形式をSDXLに合わせてある + +""" + target: sgm.modules.diffusionmodules.openaimodel.UNetModel + params: + adm_in_channels: 2816 + num_classes: sequential + use_checkpoint: True + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [4, 2] + num_res_blocks: 2 + channel_mult: [1, 2, 4] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + spatial_transformer_attn_type: softmax-xformers + legacy: False +""" + +import math +from types import SimpleNamespace +from typing import Any, Optional +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import functional as F +from einops import rearrange +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +IN_CHANNELS: int = 4 +OUT_CHANNELS: int = 4 +ADM_IN_CHANNELS: int = 2816 +CONTEXT_DIM: int = 2048 +MODEL_CHANNELS: int = 320 +TIME_EMBED_DIM = 320 * 4 + +USE_REENTRANT = True + +# region memory efficient attention + +# FlashAttentionを使うCrossAttention +# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py +# LICENSE MIT https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/LICENSE + +# constants + +EPSILON = 1e-6 + +# helper functions + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +# flash attention forwards and backwards + +# https://arxiv.org/abs/2205.14135 + + +class FlashAttentionFunction(torch.autograd.Function): + @staticmethod + @torch.no_grad() + def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size): + """Algorithm 2 in the paper""" + + device = q.device + dtype = q.dtype + max_neg_value = -torch.finfo(q.dtype).max + qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) + + o = torch.zeros_like(q) + all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device) + all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device) + + scale = q.shape[-1] ** -0.5 + + if not exists(mask): + mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size) + else: + mask = rearrange(mask, "b n -> b 1 1 n") + mask = mask.split(q_bucket_size, dim=-1) + + row_splits = zip( + q.split(q_bucket_size, dim=-2), + o.split(q_bucket_size, dim=-2), + mask, + all_row_sums.split(q_bucket_size, dim=-2), + all_row_maxes.split(q_bucket_size, dim=-2), + ) + + for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits): + q_start_index = ind * q_bucket_size - qk_len_diff + + col_splits = zip( + k.split(k_bucket_size, dim=-2), + v.split(k_bucket_size, dim=-2), + ) + + for k_ind, (kc, vc) in enumerate(col_splits): + k_start_index = k_ind * k_bucket_size + + attn_weights = torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale + + if exists(row_mask): + attn_weights.masked_fill_(~row_mask, max_neg_value) + + if causal and q_start_index < (k_start_index + k_bucket_size - 1): + causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device).triu( + q_start_index - k_start_index + 1 + ) + attn_weights.masked_fill_(causal_mask, max_neg_value) + + block_row_maxes = attn_weights.amax(dim=-1, keepdims=True) + attn_weights -= block_row_maxes + exp_weights = torch.exp(attn_weights) + + if exists(row_mask): + exp_weights.masked_fill_(~row_mask, 0.0) + + block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(min=EPSILON) + + new_row_maxes = torch.maximum(block_row_maxes, row_maxes) + + exp_values = torch.einsum("... i j, ... j d -> ... i d", exp_weights, vc) + + exp_row_max_diff = torch.exp(row_maxes - new_row_maxes) + exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes) + + new_row_sums = exp_row_max_diff * row_sums + exp_block_row_max_diff * block_row_sums + + oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_((exp_block_row_max_diff / new_row_sums) * exp_values) + + row_maxes.copy_(new_row_maxes) + row_sums.copy_(new_row_sums) + + ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size) + ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes) + + return o + + @staticmethod + @torch.no_grad() + def backward(ctx, do): + """Algorithm 4 in the paper""" + + causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args + q, k, v, o, l, m = ctx.saved_tensors + + device = q.device + + max_neg_value = -torch.finfo(q.dtype).max + qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) + + dq = torch.zeros_like(q) + dk = torch.zeros_like(k) + dv = torch.zeros_like(v) + + row_splits = zip( + q.split(q_bucket_size, dim=-2), + o.split(q_bucket_size, dim=-2), + do.split(q_bucket_size, dim=-2), + mask, + l.split(q_bucket_size, dim=-2), + m.split(q_bucket_size, dim=-2), + dq.split(q_bucket_size, dim=-2), + ) + + for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits): + q_start_index = ind * q_bucket_size - qk_len_diff + + col_splits = zip( + k.split(k_bucket_size, dim=-2), + v.split(k_bucket_size, dim=-2), + dk.split(k_bucket_size, dim=-2), + dv.split(k_bucket_size, dim=-2), + ) + + for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits): + k_start_index = k_ind * k_bucket_size + + attn_weights = torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale + + if causal and q_start_index < (k_start_index + k_bucket_size - 1): + causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device).triu( + q_start_index - k_start_index + 1 + ) + attn_weights.masked_fill_(causal_mask, max_neg_value) + + exp_attn_weights = torch.exp(attn_weights - mc) + + if exists(row_mask): + exp_attn_weights.masked_fill_(~row_mask, 0.0) + + p = exp_attn_weights / lc + + dv_chunk = torch.einsum("... i j, ... i d -> ... j d", p, doc) + dp = torch.einsum("... i d, ... j d -> ... i j", doc, vc) + + D = (doc * oc).sum(dim=-1, keepdims=True) + ds = p * scale * (dp - D) + + dq_chunk = torch.einsum("... i j, ... j d -> ... i d", ds, kc) + dk_chunk = torch.einsum("... i j, ... i d -> ... j d", ds, qc) + + dqc.add_(dq_chunk) + dkc.add_(dk_chunk) + dvc.add_(dv_chunk) + + return dq, dk, dv, None, None, None, None + + +# endregion + + +def get_parameter_dtype(parameter: torch.nn.Module): + return next(parameter.parameters()).dtype + + +def get_parameter_device(parameter: torch.nn.Module): + return next(parameter.parameters()).device + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings: flipped from Diffusers original ver because always flip_sin_to_cos=True + emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +# Deep Shrink: We do not common this function, because minimize dependencies. +def resize_like(x, target, mode="bicubic", align_corners=False): + org_dtype = x.dtype + if org_dtype == torch.bfloat16: + x = x.to(torch.float32) + + if x.shape[-2:] != target.shape[-2:]: + if mode == "nearest": + x = F.interpolate(x, size=target.shape[-2:], mode=mode) + else: + x = F.interpolate(x, size=target.shape[-2:], mode=mode, align_corners=align_corners) + + if org_dtype == torch.bfloat16: + x = x.to(org_dtype) + return x + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + if self.weight.dtype != torch.float32: + return super().forward(x) + return super().forward(x.float()).type(x.dtype) + + +class ResnetBlock2D(nn.Module): + def __init__( + self, + in_channels, + out_channels, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + self.in_layers = nn.Sequential( + GroupNorm32(32, in_channels), + nn.SiLU(), + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1), + ) + + self.emb_layers = nn.Sequential(nn.SiLU(), nn.Linear(TIME_EMBED_DIM, out_channels)) + + self.out_layers = nn.Sequential( + GroupNorm32(32, out_channels), + nn.SiLU(), + nn.Identity(), # to make state_dict compatible with original model + nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1), + ) + + if in_channels != out_channels: + self.skip_connection = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + else: + self.skip_connection = nn.Identity() + + self.gradient_checkpointing = False + + def forward_body(self, x, emb): + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + h = h + emb_out[:, :, None, None] + h = self.out_layers(h) + x = self.skip_connection(x) + return x + h + + def forward(self, x, emb): + if self.training and self.gradient_checkpointing: + # logger.info("ResnetBlock2D: gradient_checkpointing") + + def create_custom_forward(func): + def custom_forward(*inputs): + return func(*inputs) + + return custom_forward + + x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.forward_body), x, emb, use_reentrant=USE_REENTRANT) + else: + x = self.forward_body(x, emb) + + return x + + +class Downsample2D(nn.Module): + def __init__(self, channels, out_channels): + super().__init__() + + self.channels = channels + self.out_channels = out_channels + + self.op = nn.Conv2d(self.channels, self.out_channels, 3, stride=2, padding=1) + + self.gradient_checkpointing = False + + def forward_body(self, hidden_states): + assert hidden_states.shape[1] == self.channels + hidden_states = self.op(hidden_states) + + return hidden_states + + def forward(self, hidden_states): + if self.training and self.gradient_checkpointing: + # logger.info("Downsample2D: gradient_checkpointing") + + def create_custom_forward(func): + def custom_forward(*inputs): + return func(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.forward_body), hidden_states, use_reentrant=USE_REENTRANT + ) + else: + hidden_states = self.forward_body(hidden_states) + + return hidden_states + + +class CrossAttention(nn.Module): + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + dim_head: int = 64, + upcast_attention: bool = False, + ): + super().__init__() + inner_dim = dim_head * heads + cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.upcast_attention = upcast_attention + + self.scale = dim_head**-0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=False) + self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=False) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(inner_dim, query_dim)) + # no dropout here + + self.use_memory_efficient_attention_xformers = False + self.use_memory_efficient_attention_mem_eff = False + self.use_sdpa = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + self.use_memory_efficient_attention_xformers = xformers + self.use_memory_efficient_attention_mem_eff = mem_eff + + def set_use_sdpa(self, sdpa): + self.use_sdpa = sdpa + + def reshape_heads_to_batch_dim(self, tensor): + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) + tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size) + return tensor + + def reshape_batch_dim_to_heads(self, tensor): + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) + tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size) + return tensor + + def forward(self, hidden_states, context=None, mask=None): + if self.use_memory_efficient_attention_xformers: + return self.forward_memory_efficient_xformers(hidden_states, context, mask) + if self.use_memory_efficient_attention_mem_eff: + return self.forward_memory_efficient_mem_eff(hidden_states, context, mask) + if self.use_sdpa: + return self.forward_sdpa(hidden_states, context, mask) + + query = self.to_q(hidden_states) + context = context if context is not None else hidden_states + key = self.to_k(context) + value = self.to_v(context) + + query = self.reshape_heads_to_batch_dim(query) + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + hidden_states = self._attention(query, key, value) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + # hidden_states = self.to_out[1](hidden_states) # no dropout + return hidden_states + + def _attention(self, query, key, value): + if self.upcast_attention: + query = query.float() + key = key.float() + + attention_scores = torch.baddbmm( + torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device), + query, + key.transpose(-1, -2), + beta=0, + alpha=self.scale, + ) + attention_probs = attention_scores.softmax(dim=-1) + + # cast back to the original dtype + attention_probs = attention_probs.to(value.dtype) + + # compute attention output + hidden_states = torch.bmm(attention_probs, value) + + # reshape hidden_states + hidden_states = self.reshape_batch_dim_to_heads(hidden_states) + return hidden_states + + # TODO support Hypernetworks + def forward_memory_efficient_xformers(self, x, context=None, mask=None): + import xformers.ops + + h = self.heads + q_in = self.to_q(x) + context = context if context is not None else x + context = context.to(x.dtype) + k_in = self.to_k(context) + v_in = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b n h d", h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + q = q.contiguous() + k = k.contiguous() + v = v.contiguous() + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) # 最適なのを選んでくれる + del q, k, v + + out = rearrange(out, "b n h d -> b n (h d)", h=h) + + out = self.to_out[0](out) + return out + + def forward_memory_efficient_mem_eff(self, x, context=None, mask=None): + flash_func = FlashAttentionFunction + + q_bucket_size = 512 + k_bucket_size = 1024 + + h = self.heads + q = self.to_q(x) + context = context if context is not None else x + context = context.to(x.dtype) + k = self.to_k(context) + v = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) + + out = flash_func.apply(q, k, v, mask, False, q_bucket_size, k_bucket_size) + + out = rearrange(out, "b h n d -> b n (h d)") + + out = self.to_out[0](out) + return out + + def forward_sdpa(self, x, context=None, mask=None): + h = self.heads + q_in = self.to_q(x) + context = context if context is not None else x + context = context.to(x.dtype) + k_in = self.to_k(context) + v_in = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + + out = rearrange(out, "b h n d -> b n (h d)", h=h) + + out = self.to_out[0](out) + return out + + +# feedforward +class GEGLU(nn.Module): + r""" + A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def gelu(self, gate): + if gate.device.type != "mps": + return F.gelu(gate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype) + + def forward(self, hidden_states): + hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) + return hidden_states * self.gelu(gate) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + ): + super().__init__() + inner_dim = int(dim * 4) # mult is always 4 + + self.net = nn.ModuleList([]) + # project in + self.net.append(GEGLU(dim, inner_dim)) + # project dropout + self.net.append(nn.Identity()) # nn.Dropout(0)) # dummy for dropout with 0 + # project out + self.net.append(nn.Linear(inner_dim, dim)) + + def forward(self, hidden_states): + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, dim: int, num_attention_heads: int, attention_head_dim: int, cross_attention_dim: int, upcast_attention: bool = False + ): + super().__init__() + + self.gradient_checkpointing = False + + # 1. Self-Attn + self.attn1 = CrossAttention( + query_dim=dim, + cross_attention_dim=None, + heads=num_attention_heads, + dim_head=attention_head_dim, + upcast_attention=upcast_attention, + ) + self.ff = FeedForward(dim) + + # 2. Cross-Attn + self.attn2 = CrossAttention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + upcast_attention=upcast_attention, + ) + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim) + + def set_use_memory_efficient_attention(self, xformers: bool, mem_eff: bool): + self.attn1.set_use_memory_efficient_attention(xformers, mem_eff) + self.attn2.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa: bool): + self.attn1.set_use_sdpa(sdpa) + self.attn2.set_use_sdpa(sdpa) + + def forward_body(self, hidden_states, context=None, timestep=None): + # 1. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + + hidden_states = self.attn1(norm_hidden_states) + hidden_states + + # 2. Cross-Attention + norm_hidden_states = self.norm2(hidden_states) + hidden_states = self.attn2(norm_hidden_states, context=context) + hidden_states + + # 3. Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + return hidden_states + + def forward(self, hidden_states, context=None, timestep=None): + if self.training and self.gradient_checkpointing: + # logger.info("BasicTransformerBlock: checkpointing") + + def create_custom_forward(func): + def custom_forward(*inputs): + return func(*inputs) + + return custom_forward + + output = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.forward_body), hidden_states, context, timestep, use_reentrant=USE_REENTRANT + ) + else: + output = self.forward_body(hidden_states, context, timestep) + + return output + + +class Transformer2DModel(nn.Module): + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + use_linear_projection: bool = False, + upcast_attention: bool = False, + num_transformer_layers: int = 1, + ): + super().__init__() + self.in_channels = in_channels + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + self.use_linear_projection = use_linear_projection + + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + # self.norm = GroupNorm32(32, in_channels, eps=1e-6, affine=True) + + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + + blocks = [] + for _ in range(num_transformer_layers): + blocks.append( + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + cross_attention_dim=cross_attention_dim, + upcast_attention=upcast_attention, + ) + ) + + self.transformer_blocks = nn.ModuleList(blocks) + + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + + self.gradient_checkpointing = False + + def set_use_memory_efficient_attention(self, xformers, mem_eff): + for transformer in self.transformer_blocks: + transformer.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa): + for transformer in self.transformer_blocks: + transformer.set_use_sdpa(sdpa) + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None): + # 1. Input + batch, _, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block(hidden_states, context=encoder_hidden_states, timestep=timestep) + + # 3. Output + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + + return output + + +class Upsample2D(nn.Module): + def __init__(self, channels, out_channels): + super().__init__() + self.channels = channels + self.out_channels = out_channels + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) + + self.gradient_checkpointing = False + + def forward_body(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch + # https://github.com/pytorch/pytorch/issues/86679 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + hidden_states = self.conv(hidden_states) + + return hidden_states + + def forward(self, hidden_states, output_size=None): + if self.training and self.gradient_checkpointing: + # logger.info("Upsample2D: gradient_checkpointing") + + def create_custom_forward(func): + def custom_forward(*inputs): + return func(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.forward_body), hidden_states, output_size, use_reentrant=USE_REENTRANT + ) + else: + hidden_states = self.forward_body(hidden_states, output_size) + + return hidden_states + + +class SdxlUNet2DConditionModel(nn.Module): + _supports_gradient_checkpointing = True + + def __init__( + self, + in_channels: int = IN_CHANNELS, + **kwargs, + ): + super().__init__() + + self.in_channels = in_channels + self.out_channels = OUT_CHANNELS + self.model_channels = MODEL_CHANNELS + self.time_embed_dim = TIME_EMBED_DIM + self.adm_in_channels = ADM_IN_CHANNELS + + self.gradient_checkpointing = False + # self.sample_size = sample_size + + # time embedding + self.time_embed = nn.Sequential( + nn.Linear(self.model_channels, self.time_embed_dim), + nn.SiLU(), + nn.Linear(self.time_embed_dim, self.time_embed_dim), + ) + + # label embedding + self.label_emb = nn.Sequential( + nn.Sequential( + nn.Linear(self.adm_in_channels, self.time_embed_dim), + nn.SiLU(), + nn.Linear(self.time_embed_dim, self.time_embed_dim), + ) + ) + + # input + self.input_blocks = nn.ModuleList( + [ + nn.Sequential( + nn.Conv2d(self.in_channels, self.model_channels, kernel_size=3, padding=(1, 1)), + ) + ] + ) + + # level 0 + for i in range(2): + layers = [ + ResnetBlock2D( + in_channels=1 * self.model_channels, + out_channels=1 * self.model_channels, + ), + ] + self.input_blocks.append(nn.ModuleList(layers)) + + self.input_blocks.append( + nn.Sequential( + Downsample2D( + channels=1 * self.model_channels, + out_channels=1 * self.model_channels, + ), + ) + ) + + # level 1 + for i in range(2): + layers = [ + ResnetBlock2D( + in_channels=(1 if i == 0 else 2) * self.model_channels, + out_channels=2 * self.model_channels, + ), + Transformer2DModel( + num_attention_heads=2 * self.model_channels // 64, + attention_head_dim=64, + in_channels=2 * self.model_channels, + num_transformer_layers=2, + use_linear_projection=True, + cross_attention_dim=2048, + ), + ] + self.input_blocks.append(nn.ModuleList(layers)) + + self.input_blocks.append( + nn.Sequential( + Downsample2D( + channels=2 * self.model_channels, + out_channels=2 * self.model_channels, + ), + ) + ) + + # level 2 + for i in range(2): + layers = [ + ResnetBlock2D( + in_channels=(2 if i == 0 else 4) * self.model_channels, + out_channels=4 * self.model_channels, + ), + Transformer2DModel( + num_attention_heads=4 * self.model_channels // 64, + attention_head_dim=64, + in_channels=4 * self.model_channels, + num_transformer_layers=10, + use_linear_projection=True, + cross_attention_dim=2048, + ), + ] + self.input_blocks.append(nn.ModuleList(layers)) + + # mid + self.middle_block = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=4 * self.model_channels, + out_channels=4 * self.model_channels, + ), + Transformer2DModel( + num_attention_heads=4 * self.model_channels // 64, + attention_head_dim=64, + in_channels=4 * self.model_channels, + num_transformer_layers=10, + use_linear_projection=True, + cross_attention_dim=2048, + ), + ResnetBlock2D( + in_channels=4 * self.model_channels, + out_channels=4 * self.model_channels, + ), + ] + ) + + # output + self.output_blocks = nn.ModuleList([]) + + # level 2 + for i in range(3): + layers = [ + ResnetBlock2D( + in_channels=4 * self.model_channels + (4 if i <= 1 else 2) * self.model_channels, + out_channels=4 * self.model_channels, + ), + Transformer2DModel( + num_attention_heads=4 * self.model_channels // 64, + attention_head_dim=64, + in_channels=4 * self.model_channels, + num_transformer_layers=10, + use_linear_projection=True, + cross_attention_dim=2048, + ), + ] + if i == 2: + layers.append( + Upsample2D( + channels=4 * self.model_channels, + out_channels=4 * self.model_channels, + ) + ) + + self.output_blocks.append(nn.ModuleList(layers)) + + # level 1 + for i in range(3): + layers = [ + ResnetBlock2D( + in_channels=2 * self.model_channels + (4 if i == 0 else (2 if i == 1 else 1)) * self.model_channels, + out_channels=2 * self.model_channels, + ), + Transformer2DModel( + num_attention_heads=2 * self.model_channels // 64, + attention_head_dim=64, + in_channels=2 * self.model_channels, + num_transformer_layers=2, + use_linear_projection=True, + cross_attention_dim=2048, + ), + ] + if i == 2: + layers.append( + Upsample2D( + channels=2 * self.model_channels, + out_channels=2 * self.model_channels, + ) + ) + + self.output_blocks.append(nn.ModuleList(layers)) + + # level 0 + for i in range(3): + layers = [ + ResnetBlock2D( + in_channels=1 * self.model_channels + (2 if i == 0 else 1) * self.model_channels, + out_channels=1 * self.model_channels, + ), + ] + + self.output_blocks.append(nn.ModuleList(layers)) + + # output + self.out = nn.ModuleList( + [GroupNorm32(32, self.model_channels), nn.SiLU(), nn.Conv2d(self.model_channels, self.out_channels, 3, padding=1)] + ) + + # region diffusers compatibility + def prepare_config(self): + self.config = SimpleNamespace() + + @property + def dtype(self) -> torch.dtype: + # `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype). + return get_parameter_dtype(self) + + @property + def device(self) -> torch.device: + # `torch.device`: The device on which the module is (assuming that all the module parameters are on the same device). + return get_parameter_device(self) + + def set_attention_slice(self, slice_size): + raise NotImplementedError("Attention slicing is not supported for this model.") + + def is_gradient_checkpointing(self) -> bool: + return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules()) + + def enable_gradient_checkpointing(self): + self.gradient_checkpointing = True + self.set_gradient_checkpointing(value=True) + + def disable_gradient_checkpointing(self): + self.gradient_checkpointing = False + self.set_gradient_checkpointing(value=False) + + def set_use_memory_efficient_attention(self, xformers: bool, mem_eff: bool) -> None: + blocks = self.input_blocks + [self.middle_block] + self.output_blocks + for block in blocks: + for module in block: + if hasattr(module, "set_use_memory_efficient_attention"): + # logger.info(module.__class__.__name__) + module.set_use_memory_efficient_attention(xformers, mem_eff) + + def set_use_sdpa(self, sdpa: bool) -> None: + blocks = self.input_blocks + [self.middle_block] + self.output_blocks + for block in blocks: + for module in block: + if hasattr(module, "set_use_sdpa"): + module.set_use_sdpa(sdpa) + + def set_gradient_checkpointing(self, value=False): + blocks = self.input_blocks + [self.middle_block] + self.output_blocks + for block in blocks: + for module in block.modules(): + if hasattr(module, "gradient_checkpointing"): + # logger.info(f{module.__class__.__name__} {module.gradient_checkpointing} -> {value}") + module.gradient_checkpointing = value + + # endregion + + def forward(self, x, timesteps=None, context=None, y=None, **kwargs): + # broadcast timesteps to batch dimension + timesteps = timesteps.expand(x.shape[0]) + + hs = [] + t_emb = get_timestep_embedding(timesteps, self.model_channels, downscale_freq_shift=0) # , repeat_only=False) + t_emb = t_emb.to(x.dtype) + emb = self.time_embed(t_emb) + + assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}" + assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}" + # assert x.dtype == self.dtype + emb = emb + self.label_emb(y) + + def call_module(module, h, emb, context): + x = h + for layer in module: + # logger.info(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if context is not None else None) + if isinstance(layer, ResnetBlock2D): + x = layer(x, emb) + elif isinstance(layer, Transformer2DModel): + x = layer(x, context) + else: + x = layer(x) + return x + + # h = x.type(self.dtype) + h = x + + for module in self.input_blocks: + h = call_module(module, h, emb, context) + hs.append(h) + + h = call_module(self.middle_block, h, emb, context) + + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = call_module(module, h, emb, context) + + h = h.type(x.dtype) + h = call_module(self.out, h, emb, context) + + return h + + +class InferSdxlUNet2DConditionModel: + def __init__(self, original_unet: SdxlUNet2DConditionModel, **kwargs): + self.delegate = original_unet + + # override original model's forward method: because forward is not called by `__call__` + # overriding `__call__` is not enough, because nn.Module.forward has a special handling + self.delegate.forward = self.forward + + # Deep Shrink + self.ds_depth_1 = None + self.ds_depth_2 = None + self.ds_timesteps_1 = None + self.ds_timesteps_2 = None + self.ds_ratio = None + + # call original model's methods + def __getattr__(self, name): + return getattr(self.delegate, name) + + def __call__(self, *args, **kwargs): + return self.delegate(*args, **kwargs) + + def set_deep_shrink(self, ds_depth_1, ds_timesteps_1=650, ds_depth_2=None, ds_timesteps_2=None, ds_ratio=0.5): + if ds_depth_1 is None: + logger.info("Deep Shrink is disabled.") + self.ds_depth_1 = None + self.ds_timesteps_1 = None + self.ds_depth_2 = None + self.ds_timesteps_2 = None + self.ds_ratio = None + else: + logger.info( + f"Deep Shrink is enabled: [depth={ds_depth_1}/{ds_depth_2}, timesteps={ds_timesteps_1}/{ds_timesteps_2}, ratio={ds_ratio}]" + ) + self.ds_depth_1 = ds_depth_1 + self.ds_timesteps_1 = ds_timesteps_1 + self.ds_depth_2 = ds_depth_2 if ds_depth_2 is not None else -1 + self.ds_timesteps_2 = ds_timesteps_2 if ds_timesteps_2 is not None else 1000 + self.ds_ratio = ds_ratio + + def forward(self, x, timesteps=None, context=None, y=None, input_resi_add=None, mid_add=None, **kwargs): + r""" + current implementation is a copy of `SdxlUNet2DConditionModel.forward()` with Deep Shrink and ControlNet. + """ + _self = self.delegate + + # broadcast timesteps to batch dimension + timesteps = timesteps.expand(x.shape[0]) + + hs = [] + t_emb = get_timestep_embedding(timesteps, _self.model_channels, downscale_freq_shift=0) # , repeat_only=False) + t_emb = t_emb.to(x.dtype) + emb = _self.time_embed(t_emb) + + assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}" + assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}" + # assert x.dtype == _self.dtype + emb = emb + _self.label_emb(y) + + def call_module(module, h, emb, context): + x = h + for layer in module: + # print(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if context is not None else None) + if isinstance(layer, ResnetBlock2D): + x = layer(x, emb) + elif isinstance(layer, Transformer2DModel): + x = layer(x, context) + else: + x = layer(x) + return x + + # h = x.type(self.dtype) + h = x + + for depth, module in enumerate(_self.input_blocks): + # Deep Shrink + if self.ds_depth_1 is not None: + if (depth == self.ds_depth_1 and timesteps[0] >= self.ds_timesteps_1) or ( + self.ds_depth_2 is not None + and depth == self.ds_depth_2 + and timesteps[0] < self.ds_timesteps_1 + and timesteps[0] >= self.ds_timesteps_2 + ): + # print("downsample", h.shape, self.ds_ratio) + org_dtype = h.dtype + if org_dtype == torch.bfloat16: + h = h.to(torch.float32) + h = F.interpolate(h, scale_factor=self.ds_ratio, mode="bicubic", align_corners=False).to(org_dtype) + + h = call_module(module, h, emb, context) + hs.append(h) + + h = call_module(_self.middle_block, h, emb, context) + if mid_add is not None: + h = h + mid_add + + for module in _self.output_blocks: + # Deep Shrink + if self.ds_depth_1 is not None: + if hs[-1].shape[-2:] != h.shape[-2:]: + # print("upsample", h.shape, hs[-1].shape) + h = resize_like(h, hs[-1]) + + resi = hs.pop() + if input_resi_add is not None: + resi = resi + input_resi_add.pop() + + h = torch.cat([h, resi], dim=1) + h = call_module(module, h, emb, context) + + # Deep Shrink: in case of depth 0 + if self.ds_depth_1 == 0 and h.shape[-2:] != x.shape[-2:]: + # print("upsample", h.shape, x.shape) + h = resize_like(h, x) + + h = h.type(x.dtype) + h = call_module(_self.out, h, emb, context) + + return h + + +if __name__ == "__main__": + import time + + logger.info("create unet") + unet = SdxlUNet2DConditionModel() + + unet.to("cuda") + unet.set_use_memory_efficient_attention(True, False) + unet.set_gradient_checkpointing(True) + unet.train() + + # 使用メモリ量確認用の疑似学習ループ + logger.info("preparing optimizer") + + # optimizer = torch.optim.SGD(unet.parameters(), lr=1e-3, nesterov=True, momentum=0.9) # not working + + # import bitsandbytes + # optimizer = bitsandbytes.adam.Adam8bit(unet.parameters(), lr=1e-3) # not working + # optimizer = bitsandbytes.optim.RMSprop8bit(unet.parameters(), lr=1e-3) # working at 23.5 GB with torch2 + # optimizer=bitsandbytes.optim.Adagrad8bit(unet.parameters(), lr=1e-3) # working at 23.5 GB with torch2 + + import transformers + + optimizer = transformers.optimization.Adafactor(unet.parameters(), relative_step=True) # working at 22.2GB with torch2 + + scaler = torch.cuda.amp.GradScaler(enabled=True) + + logger.info("start training") + steps = 10 + batch_size = 1 + + for step in range(steps): + logger.info(f"step {step}") + if step == 1: + time_start = time.perf_counter() + + x = torch.randn(batch_size, 4, 128, 128).cuda() # 1024x1024 + t = torch.randint(low=0, high=10, size=(batch_size,), device="cuda") + ctx = torch.randn(batch_size, 77, 2048).cuda() + y = torch.randn(batch_size, ADM_IN_CHANNELS).cuda() + + with torch.cuda.amp.autocast(enabled=True): + output = unet(x, t, ctx, y) + target = torch.randn_like(output) + loss = torch.nn.functional.mse_loss(output, target) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad(set_to_none=True) + + time_end = time.perf_counter() + logger.info(f"elapsed time: {time_end - time_start} [sec] for last {steps - 1} steps") diff --git a/vendor/sd-scripts/library/sdxl_train_util.py b/vendor/sd-scripts/library/sdxl_train_util.py new file mode 100644 index 0000000..86eb9c3 --- /dev/null +++ b/vendor/sd-scripts/library/sdxl_train_util.py @@ -0,0 +1,393 @@ +import argparse +import math +import os +from typing import Optional + +import torch +from library.device_utils import init_ipex, clean_memory_on_device + +init_ipex() + +from accelerate import init_empty_weights +from tqdm import tqdm +from transformers import CLIPTokenizer +from library import model_util, sdxl_model_util, train_util, sdxl_original_unet +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +TOKENIZER1_PATH = "openai/clip-vit-large-patch14" +TOKENIZER2_PATH = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" + +# DEFAULT_NOISE_OFFSET = 0.0357 + + +def load_target_model(args, accelerator, model_version: str, weight_dtype): + model_dtype = match_mixed_precision(args, weight_dtype) # prepare fp16/bf16 + for pi in range(accelerator.state.num_processes): + if pi == accelerator.state.local_process_index: + logger.info(f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}") + + ( + load_stable_diffusion_format, + text_encoder1, + text_encoder2, + vae, + unet, + logit_scale, + ckpt_info, + ) = _load_target_model( + args.pretrained_model_name_or_path, + args.vae, + model_version, + weight_dtype, + accelerator.device if args.lowram else "cpu", + model_dtype, + args.disable_mmap_load_safetensors, + ) + + # Expand 4-channel conv_in to 9 channels when training inpainting from a + # standard (non-inpainting) checkpoint. + if getattr(args, "train_inpainting", False) and getattr(unet, "in_channels", 4) == 4: + logger.info( + "train_inpainting: expanding UNet conv_in from 4 to 9 channels " + "(standard checkpoint → inpainting training from scratch)" + ) + model_util.expand_unet_to_inpainting(unet) + + # work on low-ram device + if args.lowram: + text_encoder1.to(accelerator.device) + text_encoder2.to(accelerator.device) + unet.to(accelerator.device) + vae.to(accelerator.device) + + clean_memory_on_device(accelerator.device) + accelerator.wait_for_everyone() + + return load_stable_diffusion_format, text_encoder1, text_encoder2, vae, unet, logit_scale, ckpt_info + + +def _load_target_model( + name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None, disable_mmap=False +): + # model_dtype only work with full fp16/bf16 + name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path + load_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers + + if load_stable_diffusion_format: + logger.info(f"load StableDiffusion checkpoint: {name_or_path}") + ( + text_encoder1, + text_encoder2, + vae, + unet, + logit_scale, + ckpt_info, + ) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device, model_dtype, disable_mmap) + else: + # Diffusers model is loaded to CPU + from diffusers import StableDiffusionXLPipeline + + variant = "fp16" if weight_dtype == torch.float16 else None + logger.info(f"load Diffusers pretrained models: {name_or_path}, variant={variant}") + try: + try: + pipe = StableDiffusionXLPipeline.from_pretrained( + name_or_path, torch_dtype=model_dtype, variant=variant, tokenizer=None + ) + except EnvironmentError as ex: + if variant is not None: + logger.info("try to load fp32 model") + pipe = StableDiffusionXLPipeline.from_pretrained(name_or_path, variant=None, tokenizer=None) + else: + raise ex + except EnvironmentError as ex: + logger.error( + f"model is not found as a file or in Hugging Face, perhaps file name is wrong? / 指定したモデル名のファイル、またはHugging Faceのモデルが見つかりません。ファイル名が誤っているかもしれません: {name_or_path}" + ) + raise ex + + text_encoder1 = pipe.text_encoder + text_encoder2 = pipe.text_encoder_2 + + # convert to fp32 for cache text_encoders outputs + if text_encoder1.dtype != torch.float32: + text_encoder1 = text_encoder1.to(dtype=torch.float32) + if text_encoder2.dtype != torch.float32: + text_encoder2 = text_encoder2.to(dtype=torch.float32) + + vae = pipe.vae + unet = pipe.unet + del pipe + + # Diffusers U-Net to original U-Net + state_dict = sdxl_model_util.convert_diffusers_unet_state_dict_to_sdxl(unet.state_dict()) + actual_in_channels = state_dict["input_blocks.0.0.weight"].shape[1] + with init_empty_weights(): + unet = sdxl_original_unet.SdxlUNet2DConditionModel(in_channels=actual_in_channels) # overwrite unet + sdxl_model_util._load_state_dict_on_device(unet, state_dict, device=device, dtype=model_dtype) + logger.info("U-Net converted to original U-Net") + + logit_scale = None + ckpt_info = None + + # VAEを読み込む + if vae_path is not None: + vae = model_util.load_vae(vae_path, weight_dtype) + logger.info("additional VAE loaded") + + return load_stable_diffusion_format, text_encoder1, text_encoder2, vae, unet, logit_scale, ckpt_info + + +def load_tokenizers(args: argparse.Namespace): + logger.info("prepare tokenizers") + + original_paths = [TOKENIZER1_PATH, TOKENIZER2_PATH] + tokeniers = [] + for i, original_path in enumerate(original_paths): + tokenizer: CLIPTokenizer = None + if args.tokenizer_cache_dir: + local_tokenizer_path = os.path.join(args.tokenizer_cache_dir, original_path.replace("/", "_")) + if os.path.exists(local_tokenizer_path): + logger.info(f"load tokenizer from cache: {local_tokenizer_path}") + tokenizer = CLIPTokenizer.from_pretrained(local_tokenizer_path) + + if tokenizer is None: + tokenizer = CLIPTokenizer.from_pretrained(original_path) + + if args.tokenizer_cache_dir and not os.path.exists(local_tokenizer_path): + logger.info(f"save Tokenizer to cache: {local_tokenizer_path}") + tokenizer.save_pretrained(local_tokenizer_path) + + if i == 1: + tokenizer.pad_token_id = 0 # fix pad token id to make same as open clip tokenizer + + tokeniers.append(tokenizer) + + if hasattr(args, "max_token_length") and args.max_token_length is not None: + logger.info(f"update token length: {args.max_token_length}") + + return tokeniers + + +def match_mixed_precision(args, weight_dtype): + if args.full_fp16: + assert ( + weight_dtype == torch.float16 + ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + return weight_dtype + elif args.full_bf16: + assert ( + weight_dtype == torch.bfloat16 + ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" + return weight_dtype + else: + return None + + +def timestep_embedding(timesteps, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + device=timesteps.device + ) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +def get_timestep_embedding(x, outdim): + assert len(x.shape) == 2 + b, dims = x.shape[0], x.shape[1] + x = torch.flatten(x) + emb = timestep_embedding(x, outdim) + emb = torch.reshape(emb, (b, dims * outdim)) + return emb + + +def get_size_embeddings(orig_size, crop_size, target_size, device): + emb1 = get_timestep_embedding(orig_size, 256) + emb2 = get_timestep_embedding(crop_size, 256) + emb3 = get_timestep_embedding(target_size, 256) + vector = torch.cat([emb1, emb2, emb3], dim=1).to(device) + return vector + + +def save_sd_model_on_train_end( + args: argparse.Namespace, + src_path: str, + save_stable_diffusion_format: bool, + use_safetensors: bool, + save_dtype: torch.dtype, + epoch: int, + global_step: int, + text_encoder1, + text_encoder2, + unet, + vae, + logit_scale, + ckpt_info, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec(None, args, True, False, False, is_stable_diffusion_ckpt=True) + sdxl_model_util.save_stable_diffusion_checkpoint( + ckpt_file, + text_encoder1, + text_encoder2, + unet, + epoch_no, + global_step, + ckpt_info, + vae, + logit_scale, + sai_metadata, + save_dtype, + ) + + def diffusers_saver(out_dir): + sdxl_model_util.save_diffusers_checkpoint( + out_dir, + text_encoder1, + text_encoder2, + unet, + src_path, + vae, + use_safetensors=use_safetensors, + save_dtype=save_dtype, + ) + + train_util.save_sd_model_on_train_end_common( + args, save_stable_diffusion_format, use_safetensors, epoch, global_step, sd_saver, diffusers_saver + ) + + +# epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合している +# on_epoch_end: Trueならepoch終了時、Falseならstep経過時 +def save_sd_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator, + src_path, + save_stable_diffusion_format: bool, + use_safetensors: bool, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + text_encoder1, + text_encoder2, + unet, + vae, + logit_scale, + ckpt_info, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec(None, args, True, False, False, is_stable_diffusion_ckpt=True) + sdxl_model_util.save_stable_diffusion_checkpoint( + ckpt_file, + text_encoder1, + text_encoder2, + unet, + epoch_no, + global_step, + ckpt_info, + vae, + logit_scale, + sai_metadata, + save_dtype, + ) + + def diffusers_saver(out_dir): + sdxl_model_util.save_diffusers_checkpoint( + out_dir, + text_encoder1, + text_encoder2, + unet, + src_path, + vae, + use_safetensors=use_safetensors, + save_dtype=save_dtype, + ) + + train_util.save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + save_stable_diffusion_format, + use_safetensors, + epoch, + num_train_epochs, + global_step, + sd_saver, + diffusers_saver, + ) + + +def add_sdxl_training_arguments(parser: argparse.ArgumentParser, support_text_encoder_caching: bool = True): + if support_text_encoder_caching: + parser.add_argument( + "--cache_text_encoder_outputs", + action="store_true", + help="cache text encoder outputs / text encoderの出力をキャッシュする", + ) + parser.add_argument( + "--cache_text_encoder_outputs_to_disk", + action="store_true", + help="cache text encoder outputs to disk / text encoderの出力をディスクにキャッシュする", + ) + parser.add_argument( + "--disable_mmap_load_safetensors", + action="store_true", + help="disable mmap load for safetensors. Speed up model loading in WSL environment / safetensorsのmmapロードを無効にする。WSL環境等でモデル読み込みを高速化できる", + ) + + +def verify_sdxl_training_args(args: argparse.Namespace, support_text_encoder_caching: bool = True): + assert not args.v2, "v2 cannot be enabled in SDXL training / SDXL学習ではv2を有効にすることはできません" + + if args.clip_skip is not None: + logger.warning("clip_skip will be unexpected / SDXL学習ではclip_skipは動作しません") + + # if args.multires_noise_iterations: + # logger.info( + # f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET}, but noise_offset is disabled due to multires_noise_iterations / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されていますが、multires_noise_iterationsが有効になっているためnoise_offsetは無効になります" + # ) + # else: + # if args.noise_offset is None: + # args.noise_offset = DEFAULT_NOISE_OFFSET + # elif args.noise_offset != DEFAULT_NOISE_OFFSET: + # logger.info( + # f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET} / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されています" + # ) + # logger.info(f"noise_offset is set to {args.noise_offset} / noise_offsetが{args.noise_offset}に設定されました") + + # assert ( + # not hasattr(args, "weighted_captions") or not args.weighted_captions + # ), "weighted_captions cannot be enabled in SDXL training currently / SDXL学習では今のところweighted_captionsを有効にすることはできません" + + if support_text_encoder_caching: + if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs: + args.cache_text_encoder_outputs = True + logger.warning( + "cache_text_encoder_outputs is enabled because cache_text_encoder_outputs_to_disk is enabled / " + + "cache_text_encoder_outputs_to_diskが有効になっているためcache_text_encoder_outputsが有効になりました" + ) + + +def sample_images(*args, **kwargs): + from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline + + return train_util.sample_images_common(SdxlStableDiffusionLongPromptWeightingPipeline, *args, **kwargs) diff --git a/vendor/sd-scripts/library/slicing_vae.py b/vendor/sd-scripts/library/slicing_vae.py new file mode 100644 index 0000000..ea76534 --- /dev/null +++ b/vendor/sd-scripts/library/slicing_vae.py @@ -0,0 +1,682 @@ +# Modified from Diffusers to reduce VRAM usage + +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn as nn + + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block +from diffusers.models.vae import DecoderOutput, DiagonalGaussianDistribution +from diffusers.models.autoencoder_kl import AutoencoderKLOutput +from .utils import setup_logging +setup_logging() +import logging +logger = logging.getLogger(__name__) + +def slice_h(x, num_slices): + # slice with pad 1 both sides: to eliminate side effect of padding of conv2d + # Conv2dのpaddingの副作用を排除するために、両側にpad 1しながらHをスライスする + # NCHWでもNHWCでもどちらでも動く + size = (x.shape[2] + num_slices - 1) // num_slices + sliced = [] + for i in range(num_slices): + if i == 0: + sliced.append(x[:, :, : size + 1, :]) + else: + end = size * (i + 1) + 1 + if x.shape[2] - end < 3: # if the last slice is too small, use the rest of the tensor 最後が細すぎるとconv2dできないので全部使う + end = x.shape[2] + sliced.append(x[:, :, size * i - 1 : end, :]) + if end >= x.shape[2]: + break + return sliced + + +def cat_h(sliced): + # padding分を除いて結合する + cat = [] + for i, x in enumerate(sliced): + if i == 0: + cat.append(x[:, :, :-1, :]) + elif i == len(sliced) - 1: + cat.append(x[:, :, 1:, :]) + else: + cat.append(x[:, :, 1:-1, :]) + del x + x = torch.cat(cat, dim=2) + return x + + +def resblock_forward(_self, num_slices, input_tensor, temb, **kwargs): + assert _self.upsample is None and _self.downsample is None + assert _self.norm1.num_groups == _self.norm2.num_groups + assert temb is None + + # make sure norms are on cpu + org_device = input_tensor.device + cpu_device = torch.device("cpu") + _self.norm1.to(cpu_device) + _self.norm2.to(cpu_device) + + # GroupNormがCPUでfp16で動かない対策 + org_dtype = input_tensor.dtype + if org_dtype == torch.float16: + _self.norm1.to(torch.float32) + _self.norm2.to(torch.float32) + + # すべてのテンソルをCPUに移動する + input_tensor = input_tensor.to(cpu_device) + hidden_states = input_tensor + + # どうもこれは結果が異なるようだ…… + # def sliced_norm1(norm, x): + # num_div = 4 if up_block_idx <= 2 else x.shape[1] // norm.num_groups + # sliced_tensor = torch.chunk(x, num_div, dim=1) + # sliced_weight = torch.chunk(norm.weight, num_div, dim=0) + # sliced_bias = torch.chunk(norm.bias, num_div, dim=0) + # logger.info(sliced_tensor[0].shape, num_div, sliced_weight[0].shape, sliced_bias[0].shape) + # normed_tensor = [] + # for i in range(num_div): + # n = torch.group_norm(sliced_tensor[i], norm.num_groups, sliced_weight[i], sliced_bias[i], norm.eps) + # normed_tensor.append(n) + # del n + # x = torch.cat(normed_tensor, dim=1) + # return num_div, x + + # normを分割すると結果が変わるので、ここだけは分割しない。GPUで計算するとVRAMが足りなくなるので、CPUで計算する。幸いCPUでもそこまで遅くない + if org_dtype == torch.float16: + hidden_states = hidden_states.to(torch.float32) + hidden_states = _self.norm1(hidden_states) # run on cpu + if org_dtype == torch.float16: + hidden_states = hidden_states.to(torch.float16) + + sliced = slice_h(hidden_states, num_slices) + del hidden_states + + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + # 計算する部分だけGPUに移動する、以下同様 + x = x.to(org_device) + x = _self.nonlinearity(x) + x = _self.conv1(x) + x = x.to(cpu_device) + sliced[i] = x + del x + + hidden_states = cat_h(sliced) + del sliced + + if org_dtype == torch.float16: + hidden_states = hidden_states.to(torch.float32) + hidden_states = _self.norm2(hidden_states) # run on cpu + if org_dtype == torch.float16: + hidden_states = hidden_states.to(torch.float16) + + sliced = slice_h(hidden_states, num_slices) + del hidden_states + + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + x = x.to(org_device) + x = _self.nonlinearity(x) + x = _self.dropout(x) + x = _self.conv2(x) + x = x.to(cpu_device) + sliced[i] = x + del x + + hidden_states = cat_h(sliced) + del sliced + + # make shortcut + if _self.conv_shortcut is not None: + sliced = list(torch.chunk(input_tensor, num_slices, dim=2)) # no padding in conv_shortcut パディングがないので普通にスライスする + del input_tensor + + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + x = x.to(org_device) + x = _self.conv_shortcut(x) + x = x.to(cpu_device) + sliced[i] = x + del x + + input_tensor = torch.cat(sliced, dim=2) + del sliced + + output_tensor = (input_tensor + hidden_states) / _self.output_scale_factor + + output_tensor = output_tensor.to(org_device) # 次のレイヤーがGPUで計算する + return output_tensor + + +class SlicingEncoder(nn.Module): + def __init__( + self, + in_channels=3, + out_channels=3, + down_block_types=("DownEncoderBlock2D",), + block_out_channels=(64,), + layers_per_block=2, + norm_num_groups=32, + act_fn="silu", + double_z=True, + num_slices=2, + ): + super().__init__() + self.layers_per_block = layers_per_block + + self.conv_in = torch.nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1) + + self.mid_block = None + self.down_blocks = nn.ModuleList([]) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=self.layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + add_downsample=not is_final_block, + resnet_eps=1e-6, + downsample_padding=0, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + attention_head_dim=output_channel, + temb_channels=None, + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlock2D( + in_channels=block_out_channels[-1], + resnet_eps=1e-6, + resnet_act_fn=act_fn, + output_scale_factor=1, + resnet_time_scale_shift="default", + attention_head_dim=block_out_channels[-1], + resnet_groups=norm_num_groups, + temb_channels=None, + ) + self.mid_block.attentions[0].set_use_memory_efficient_attention_xformers(True) # とりあえずDiffusersのxformersを使う + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) + self.conv_act = nn.SiLU() + + conv_out_channels = 2 * out_channels if double_z else out_channels + self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1) + + # replace forward of ResBlocks + def wrapper(func, module, num_slices): + def forward(*args, **kwargs): + return func(module, num_slices, *args, **kwargs) + + return forward + + self.num_slices = num_slices + div = num_slices / (2 ** (len(self.down_blocks) - 1)) # 深い層はそこまで分割しなくていいので適宜減らす + # logger.info(f"initial divisor: {div}") + if div >= 2: + div = int(div) + for resnet in self.mid_block.resnets: + resnet.forward = wrapper(resblock_forward, resnet, div) + # midblock doesn't have downsample + + for i, down_block in enumerate(self.down_blocks[::-1]): + if div >= 2: + div = int(div) + # logger.info(f"down block: {i} divisor: {div}") + for resnet in down_block.resnets: + resnet.forward = wrapper(resblock_forward, resnet, div) + if down_block.downsamplers is not None: + # logger.info("has downsample") + for downsample in down_block.downsamplers: + downsample.forward = wrapper(self.downsample_forward, downsample, div * 2) + div *= 2 + + def forward(self, x): + sample = x + del x + + org_device = sample.device + cpu_device = torch.device("cpu") + + # sample = self.conv_in(sample) + sample = sample.to(cpu_device) + sliced = slice_h(sample, self.num_slices) + del sample + + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + x = x.to(org_device) + x = self.conv_in(x) + x = x.to(cpu_device) + sliced[i] = x + del x + + sample = cat_h(sliced) + del sliced + + sample = sample.to(org_device) + + # down + for down_block in self.down_blocks: + sample = down_block(sample) + + # middle + sample = self.mid_block(sample) + + # post-process + # ここも省メモリ化したいが、恐らくそこまでメモリを食わないので省略 + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + return sample + + def downsample_forward(self, _self, num_slices, hidden_states): + assert hidden_states.shape[1] == _self.channels + assert _self.use_conv and _self.padding == 0 + logger.info(f"downsample forward {num_slices} {hidden_states.shape}") + + org_device = hidden_states.device + cpu_device = torch.device("cpu") + + hidden_states = hidden_states.to(cpu_device) + pad = (0, 1, 0, 1) + hidden_states = torch.nn.functional.pad(hidden_states, pad, mode="constant", value=0) + + # slice with even number because of stride 2 + # strideが2なので偶数でスライスする + # slice with pad 1 both sides: to eliminate side effect of padding of conv2d + size = (hidden_states.shape[2] + num_slices - 1) // num_slices + size = size + 1 if size % 2 == 1 else size + + sliced = [] + for i in range(num_slices): + if i == 0: + sliced.append(hidden_states[:, :, : size + 1, :]) + else: + end = size * (i + 1) + 1 + if hidden_states.shape[2] - end < 4: # if the last slice is too small, use the rest of the tensor + end = hidden_states.shape[2] + sliced.append(hidden_states[:, :, size * i - 1 : end, :]) + if end >= hidden_states.shape[2]: + break + del hidden_states + + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + x = x.to(org_device) + x = _self.conv(x) + x = x.to(cpu_device) + + # ここだけ雰囲気が違うのはCopilotのせい + if i == 0: + hidden_states = x + else: + hidden_states = torch.cat([hidden_states, x], dim=2) + + hidden_states = hidden_states.to(org_device) + # logger.info(f"downsample forward done {hidden_states.shape}") + return hidden_states + + +class SlicingDecoder(nn.Module): + def __init__( + self, + in_channels=3, + out_channels=3, + up_block_types=("UpDecoderBlock2D",), + block_out_channels=(64,), + layers_per_block=2, + norm_num_groups=32, + act_fn="silu", + num_slices=2, + ): + super().__init__() + self.layers_per_block = layers_per_block + + self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1) + + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + # mid + self.mid_block = UNetMidBlock2D( + in_channels=block_out_channels[-1], + resnet_eps=1e-6, + resnet_act_fn=act_fn, + output_scale_factor=1, + resnet_time_scale_shift="default", + attention_head_dim=block_out_channels[-1], + resnet_groups=norm_num_groups, + temb_channels=None, + ) + self.mid_block.attentions[0].set_use_memory_efficient_attention_xformers(True) # とりあえずDiffusersのxformersを使う + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + + is_final_block = i == len(block_out_channels) - 1 + + up_block = get_up_block( + up_block_type, + num_layers=self.layers_per_block + 1, + in_channels=prev_output_channel, + out_channels=output_channel, + prev_output_channel=None, + add_upsample=not is_final_block, + resnet_eps=1e-6, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + attention_head_dim=output_channel, + temb_channels=None, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) + self.conv_act = nn.SiLU() + self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) + + # replace forward of ResBlocks + def wrapper(func, module, num_slices): + def forward(*args, **kwargs): + return func(module, num_slices, *args, **kwargs) + + return forward + + self.num_slices = num_slices + div = num_slices / (2 ** (len(self.up_blocks) - 1)) + logger.info(f"initial divisor: {div}") + if div >= 2: + div = int(div) + for resnet in self.mid_block.resnets: + resnet.forward = wrapper(resblock_forward, resnet, div) + # midblock doesn't have upsample + + for i, up_block in enumerate(self.up_blocks): + if div >= 2: + div = int(div) + # logger.info(f"up block: {i} divisor: {div}") + for resnet in up_block.resnets: + resnet.forward = wrapper(resblock_forward, resnet, div) + if up_block.upsamplers is not None: + # logger.info("has upsample") + for upsample in up_block.upsamplers: + upsample.forward = wrapper(self.upsample_forward, upsample, div * 2) + div *= 2 + + def forward(self, z): + sample = z + del z + sample = self.conv_in(sample) + + # middle + sample = self.mid_block(sample) + + # up + for i, up_block in enumerate(self.up_blocks): + sample = up_block(sample) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + + # conv_out with slicing because of VRAM usage + # conv_outはとてもVRAM使うのでスライスして対応 + org_device = sample.device + cpu_device = torch.device("cpu") + sample = sample.to(cpu_device) + + sliced = slice_h(sample, self.num_slices) + del sample + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + x = x.to(org_device) + x = self.conv_out(x) + x = x.to(cpu_device) + sliced[i] = x + sample = cat_h(sliced) + del sliced + + sample = sample.to(org_device) + return sample + + def upsample_forward(self, _self, num_slices, hidden_states, output_size=None): + assert hidden_states.shape[1] == _self.channels + assert _self.use_conv_transpose == False and _self.use_conv + + org_dtype = hidden_states.dtype + org_device = hidden_states.device + cpu_device = torch.device("cpu") + + hidden_states = hidden_states.to(cpu_device) + sliced = slice_h(hidden_states, num_slices) + del hidden_states + + for i in range(len(sliced)): + x = sliced[i] + sliced[i] = None + + x = x.to(org_device) + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch + # https://github.com/pytorch/pytorch/issues/86679 + # PyTorch 2で直らないかね…… + if org_dtype == torch.bfloat16: + x = x.to(torch.float32) + + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + + if org_dtype == torch.bfloat16: + x = x.to(org_dtype) + + x = _self.conv(x) + + # upsampleされてるのでpadは2になる + if i == 0: + x = x[:, :, :-2, :] + elif i == num_slices - 1: + x = x[:, :, 2:, :] + else: + x = x[:, :, 2:-2, :] + + x = x.to(cpu_device) + sliced[i] = x + del x + + hidden_states = torch.cat(sliced, dim=2) + # logger.info(f"us hidden_states {hidden_states.shape}") + del sliced + + hidden_states = hidden_states.to(org_device) + return hidden_states + + +class SlicingAutoencoderKL(ModelMixin, ConfigMixin): + r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma + and Max Welling. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library + implements for all the model (such as downloading or saving, etc.) + + Parameters: + in_channels (int, *optional*, defaults to 3): Number of channels in the input image. + out_channels (int, *optional*, defaults to 3): Number of channels in the output. + down_block_types (`Tuple[str]`, *optional*, defaults to : + obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types. + up_block_types (`Tuple[str]`, *optional*, defaults to : + obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types. + block_out_channels (`Tuple[int]`, *optional*, defaults to : + obj:`(64,)`): Tuple of block output channels. + act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. + latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space. + sample_size (`int`, *optional*, defaults to `32`): TODO + """ + + @register_to_config + def __init__( + self, + in_channels: int = 3, + out_channels: int = 3, + down_block_types: Tuple[str] = ("DownEncoderBlock2D",), + up_block_types: Tuple[str] = ("UpDecoderBlock2D",), + block_out_channels: Tuple[int] = (64,), + layers_per_block: int = 1, + act_fn: str = "silu", + latent_channels: int = 4, + norm_num_groups: int = 32, + sample_size: int = 32, + num_slices: int = 16, + ): + super().__init__() + + # pass init params to Encoder + self.encoder = SlicingEncoder( + in_channels=in_channels, + out_channels=latent_channels, + down_block_types=down_block_types, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + act_fn=act_fn, + norm_num_groups=norm_num_groups, + double_z=True, + num_slices=num_slices, + ) + + # pass init params to Decoder + self.decoder = SlicingDecoder( + in_channels=latent_channels, + out_channels=out_channels, + up_block_types=up_block_types, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + norm_num_groups=norm_num_groups, + act_fn=act_fn, + num_slices=num_slices, + ) + + self.quant_conv = torch.nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) + self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1) + self.use_slicing = False + + def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput: + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + + if not return_dict: + return (posterior,) + + return AutoencoderKLOutput(latent_dist=posterior) + + def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: + z = self.post_quant_conv(z) + dec = self.decoder(z) + + if not return_dict: + return (dec,) + + return DecoderOutput(sample=dec) + + # これはバッチ方向のスライシング　紛らわしい + def enable_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.use_slicing = True + + def disable_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing + decoding in one step. + """ + self.use_slicing = False + + def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: + if self.use_slicing and z.shape[0] > 1: + decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] + decoded = torch.cat(decoded_slices) + else: + decoded = self._decode(z).sample + + if not return_dict: + return (decoded,) + + return DecoderOutput(sample=decoded) + + def forward( + self, + sample: torch.FloatTensor, + sample_posterior: bool = False, + return_dict: bool = True, + generator: Optional[torch.Generator] = None, + ) -> Union[DecoderOutput, torch.FloatTensor]: + r""" + Args: + sample (`torch.FloatTensor`): Input sample. + sample_posterior (`bool`, *optional*, defaults to `False`): + Whether to sample from the posterior. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`DecoderOutput`] instead of a plain tuple. + """ + x = sample + posterior = self.encode(x).latent_dist + if sample_posterior: + z = posterior.sample(generator=generator) + else: + z = posterior.mode() + dec = self.decode(z).sample + + if not return_dict: + return (dec,) + + return DecoderOutput(sample=dec) diff --git a/vendor/sd-scripts/library/strategy_anima.py b/vendor/sd-scripts/library/strategy_anima.py new file mode 100644 index 0000000..ec135be --- /dev/null +++ b/vendor/sd-scripts/library/strategy_anima.py @@ -0,0 +1,302 @@ +# Anima Strategy Classes + +import os +import random +from typing import Any, List, Optional, Tuple, Union + +import numpy as np +import torch + +from library import anima_utils, train_util +from library.strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy +from library import qwen_image_autoencoder_kl + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +class AnimaTokenizeStrategy(TokenizeStrategy): + """Tokenize strategy for Anima: dual tokenization with Qwen3 + T5. + + Qwen3 tokens are used for the text encoder. + T5 tokens are used as target input IDs for the LLM Adapter (NOT encoded by T5). + + Can be initialized with either pre-loaded tokenizer objects or paths to load from. + """ + + def __init__( + self, + qwen3_tokenizer=None, + t5_tokenizer=None, + qwen3_max_length: int = 512, + t5_max_length: int = 512, + qwen3_path: Optional[str] = None, + t5_tokenizer_path: Optional[str] = None, + ) -> None: + # Load tokenizers from paths if not provided directly + if qwen3_tokenizer is None: + if qwen3_path is None: + raise ValueError("Either qwen3_tokenizer or qwen3_path must be provided") + qwen3_tokenizer = anima_utils.load_qwen3_tokenizer(qwen3_path) + if t5_tokenizer is None: + t5_tokenizer = anima_utils.load_t5_tokenizer(t5_tokenizer_path) + + self.qwen3_tokenizer = qwen3_tokenizer + self.qwen3_max_length = qwen3_max_length + self.t5_tokenizer = t5_tokenizer + self.t5_max_length = t5_max_length + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + text = [text] if isinstance(text, str) else text + + # Tokenize with Qwen3 + qwen3_encoding = self.qwen3_tokenizer( + text, return_tensors="pt", truncation=True, padding="max_length", max_length=self.qwen3_max_length + ) + qwen3_input_ids = qwen3_encoding["input_ids"] + qwen3_attn_mask = qwen3_encoding["attention_mask"] + + # Tokenize with T5 (for LLM Adapter target tokens) + t5_encoding = self.t5_tokenizer( + text, return_tensors="pt", truncation=True, padding="max_length", max_length=self.t5_max_length + ) + t5_input_ids = t5_encoding["input_ids"] + t5_attn_mask = t5_encoding["attention_mask"] + return [qwen3_input_ids, qwen3_attn_mask, t5_input_ids, t5_attn_mask] + + +class AnimaTextEncodingStrategy(TextEncodingStrategy): + """Text encoding strategy for Anima. + + Encodes Qwen3 tokens through the Qwen3 text encoder to get hidden states. + T5 tokens are passed through unchanged (only used by LLM Adapter). + """ + + def __init__(self) -> None: + super().__init__() + + def encode_tokens( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] + ) -> List[torch.Tensor]: + """Encode Qwen3 tokens and return embeddings + T5 token IDs. + + Args: + models: [qwen3_text_encoder] + tokens: [qwen3_input_ids, qwen3_attn_mask, t5_input_ids, t5_attn_mask] + + Returns: + [prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask] + """ + # Do not handle dropout here; handled dataset-side or in drop_cached_text_encoder_outputs() + + qwen3_text_encoder = models[0] + qwen3_input_ids, qwen3_attn_mask, t5_input_ids, t5_attn_mask = tokens + + encoder_device = qwen3_text_encoder.device + + qwen3_input_ids = qwen3_input_ids.to(encoder_device) + qwen3_attn_mask = qwen3_attn_mask.to(encoder_device) + outputs = qwen3_text_encoder(input_ids=qwen3_input_ids, attention_mask=qwen3_attn_mask) + prompt_embeds = outputs.last_hidden_state + prompt_embeds[~qwen3_attn_mask.bool()] = 0 + + return [prompt_embeds, qwen3_attn_mask, t5_input_ids, t5_attn_mask] + + def drop_cached_text_encoder_outputs( + self, + prompt_embeds: torch.Tensor, + attn_mask: torch.Tensor, + t5_input_ids: torch.Tensor, + t5_attn_mask: torch.Tensor, + caption_dropout_rates: Optional[torch.Tensor] = None, + ) -> List[torch.Tensor]: + """Apply dropout to cached text encoder outputs. + + Called during training when using cached outputs. + Replaces dropped items with pre-cached unconditional embeddings (from encoding "") + to match diffusion-pipe-main behavior. + """ + if caption_dropout_rates is None or torch.all(caption_dropout_rates == 0.0).item(): + return [prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask] + + # Clone to avoid in-place modification of cached tensors + prompt_embeds = prompt_embeds.clone() + if attn_mask is not None: + attn_mask = attn_mask.clone() + if t5_input_ids is not None: + t5_input_ids = t5_input_ids.clone() + if t5_attn_mask is not None: + t5_attn_mask = t5_attn_mask.clone() + + for i in range(prompt_embeds.shape[0]): + if random.random() < caption_dropout_rates[i].item(): + # Use pre-cached unconditional embeddings + prompt_embeds[i] = 0 + if attn_mask is not None: + attn_mask[i] = 0 + if t5_input_ids is not None: + t5_input_ids[i, 0] = 1 # Set to token ID + t5_input_ids[i, 1:] = 0 + if t5_attn_mask is not None: + t5_attn_mask[i, 0] = 1 + t5_attn_mask[i, 1:] = 0 + + return [prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask] + + +class AnimaTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): + """Caching strategy for Anima text encoder outputs. + + Caches: prompt_embeds (float), attn_mask (int), t5_input_ids (int), t5_attn_mask (int) + """ + + ANIMA_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_anima_te.npz" + + def __init__( + self, + cache_to_disk: bool, + batch_size: int, + skip_disk_cache_validity_check: bool, + is_partial: bool = False, + ) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial) + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + return os.path.splitext(image_abs_path)[0] + self.ANIMA_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX + + def is_disk_cached_outputs_expected(self, npz_path: str) -> bool: + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + try: + npz = np.load(npz_path) + if "prompt_embeds" not in npz: + return False + if "attn_mask" not in npz: + return False + if "t5_input_ids" not in npz: + return False + if "t5_attn_mask" not in npz: + return False + if "caption_dropout_rate" not in npz: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + data = np.load(npz_path) + prompt_embeds = data["prompt_embeds"] + attn_mask = data["attn_mask"] + t5_input_ids = data["t5_input_ids"] + t5_attn_mask = data["t5_attn_mask"] + caption_dropout_rate = data["caption_dropout_rate"] + return [prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask, caption_dropout_rate] + + def cache_batch_outputs( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + text_encoding_strategy: TextEncodingStrategy, + infos: List, + ): + anima_text_encoding_strategy: AnimaTextEncodingStrategy = text_encoding_strategy + captions = [info.caption for info in infos] + + tokens_and_masks = tokenize_strategy.tokenize(captions) + with torch.no_grad(): + prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask = anima_text_encoding_strategy.encode_tokens( + tokenize_strategy, models, tokens_and_masks + ) + + # Convert to numpy for caching + if prompt_embeds.dtype == torch.bfloat16: + prompt_embeds = prompt_embeds.float() + prompt_embeds = prompt_embeds.cpu().numpy() + attn_mask = attn_mask.cpu().numpy() + t5_input_ids = t5_input_ids.cpu().numpy().astype(np.int32) + t5_attn_mask = t5_attn_mask.cpu().numpy().astype(np.int32) + + for i, info in enumerate(infos): + prompt_embeds_i = prompt_embeds[i] + attn_mask_i = attn_mask[i] + t5_input_ids_i = t5_input_ids[i] + t5_attn_mask_i = t5_attn_mask[i] + caption_dropout_rate = torch.tensor(info.caption_dropout_rate, dtype=torch.float32) + + if self.cache_to_disk: + np.savez( + info.text_encoder_outputs_npz, + prompt_embeds=prompt_embeds_i, + attn_mask=attn_mask_i, + t5_input_ids=t5_input_ids_i, + t5_attn_mask=t5_attn_mask_i, + caption_dropout_rate=caption_dropout_rate, + ) + else: + info.text_encoder_outputs = (prompt_embeds_i, attn_mask_i, t5_input_ids_i, t5_attn_mask_i, caption_dropout_rate) + + +class AnimaLatentsCachingStrategy(LatentsCachingStrategy): + """Latent caching strategy for Anima using WanVAE. + + WanVAE produces 16-channel latents with spatial downscale 8x. + Latent shape for images: (B, 16, 1, H/8, W/8) + """ + + ANIMA_LATENTS_NPZ_SUFFIX = "_anima.npz" + + def __init__(self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) + + @property + def cache_suffix(self) -> str: + return self.ANIMA_LATENTS_NPZ_SUFFIX + + def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: + return os.path.splitext(absolute_path)[0] + f"_{image_size[0]:04d}x{image_size[1]:04d}" + self.ANIMA_LATENTS_NPZ_SUFFIX + + def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool): + return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True) + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + return self._default_load_latents_from_disk(8, npz_path, bucket_reso) + + def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool): + """Cache batch of latents using Qwen Image VAE. + + vae is expected to be the Qwen Image VAE (AutoencoderKLQwenImage). + The encoding function handles the mean/std normalization. + """ + vae: qwen_image_autoencoder_kl.AutoencoderKLQwenImage = vae + vae_device = vae.device + vae_dtype = vae.dtype + + def encode_by_vae(img_tensor): + """Encode image tensor to latents. + + img_tensor: (B, C, H, W) in [-1, 1] range (already normalized by IMAGE_TRANSFORMS) + Qwen Image VAE accepts inputs in (B, C, H, W) or (B, C, 1, H, W) shape. + Returns latents in (B, 16, 1, H/8, W/8) shape on CPU. + """ + latents = vae.encode_pixels_to_latents(img_tensor) # Keep 4D for input/output + return latents.to("cpu") + + self._default_cache_batch_latents( + encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True + ) + + if not train_util.HIGH_VRAM: + train_util.clean_memory_on_device(vae_device) diff --git a/vendor/sd-scripts/library/strategy_base.py b/vendor/sd-scripts/library/strategy_base.py new file mode 100644 index 0000000..5a04334 --- /dev/null +++ b/vendor/sd-scripts/library/strategy_base.py @@ -0,0 +1,651 @@ +# base class for platform strategies. this file defines the interface for strategies + +import os +import re +from typing import Any, List, Optional, Tuple, Union, Callable + +import numpy as np +import torch +from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection + + +# TODO remove circular import by moving ImageInfo to a separate file +# from library.train_util import ImageInfo + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +class TokenizeStrategy: + _strategy = None # strategy instance: actual strategy class + + _re_attention = re.compile( + r"""\\\(| +\\\)| +\\\[| +\\]| +\\\\| +\\| +\(| +\[| +:([+-]?[.\d]+)\)| +\)| +]| +[^\\()\[\]:]+| +: +""", + re.X, + ) + + @classmethod + def set_strategy(cls, strategy): + if cls._strategy is not None: + raise RuntimeError(f"Internal error. {cls.__name__} strategy is already set") + cls._strategy = strategy + + @classmethod + def get_strategy(cls) -> Optional["TokenizeStrategy"]: + return cls._strategy + + def _load_tokenizer( + self, model_class: Any, model_id: str, subfolder: Optional[str] = None, tokenizer_cache_dir: Optional[str] = None + ) -> Any: + tokenizer = None + if tokenizer_cache_dir: + local_tokenizer_path = os.path.join(tokenizer_cache_dir, model_id.replace("/", "_")) + if os.path.exists(local_tokenizer_path): + logger.info(f"load tokenizer from cache: {local_tokenizer_path}") + tokenizer = model_class.from_pretrained(local_tokenizer_path) # same for v1 and v2 + + if tokenizer is None: + tokenizer = model_class.from_pretrained(model_id, subfolder=subfolder) + + if tokenizer_cache_dir and not os.path.exists(local_tokenizer_path): + logger.info(f"save Tokenizer to cache: {local_tokenizer_path}") + tokenizer.save_pretrained(local_tokenizer_path) + + return tokenizer + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + raise NotImplementedError + + def tokenize_with_weights(self, text: Union[str, List[str]]) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """ + returns: [tokens1, tokens2, ...], [weights1, weights2, ...] + """ + raise NotImplementedError + + def _get_weighted_input_ids( + self, tokenizer: CLIPTokenizer, text: str, max_length: Optional[int] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + max_length includes starting and ending tokens. + """ + + def parse_prompt_attention(text): + """ + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. + Accepted tokens are: + (abc) - increases attention to abc by a multiplier of 1.1 + (abc:3.12) - increases attention to abc by a multiplier of 3.12 + [abc] - decreases attention to abc by a multiplier of 1.1 + \( - literal character '(' + \[ - literal character '[' + \) - literal character ')' + \] - literal character ']' + \\ - literal character '\' + anything else - just text + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] + """ + + res = [] + round_brackets = [] + square_brackets = [] + + round_bracket_multiplier = 1.1 + square_bracket_multiplier = 1 / 1.1 + + def multiply_range(start_position, multiplier): + for p in range(start_position, len(res)): + res[p][1] *= multiplier + + for m in TokenizeStrategy._re_attention.finditer(text): + text = m.group(0) + weight = m.group(1) + + if text.startswith("\\"): + res.append([text[1:], 1.0]) + elif text == "(": + round_brackets.append(len(res)) + elif text == "[": + square_brackets.append(len(res)) + elif weight is not None and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), float(weight)) + elif text == ")" and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), round_bracket_multiplier) + elif text == "]" and len(square_brackets) > 0: + multiply_range(square_brackets.pop(), square_bracket_multiplier) + else: + res.append([text, 1.0]) + + for pos in round_brackets: + multiply_range(pos, round_bracket_multiplier) + + for pos in square_brackets: + multiply_range(pos, square_bracket_multiplier) + + if len(res) == 0: + res = [["", 1.0]] + + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + + return res + + def get_prompts_with_weights(text: str, max_length: int): + r""" + Tokenize a list of prompts and return its tokens with weights of each token. max_length does not include starting and ending token. + + No padding, starting or ending token is included. + """ + truncated = False + + texts_and_weights = parse_prompt_attention(text) + tokens = [] + weights = [] + for word, weight in texts_and_weights: + # tokenize and discard the starting and the ending token + token = tokenizer(word).input_ids[1:-1] + tokens += token + # copy the weight by length of token + weights += [weight] * len(token) + # stop if the text is too long (longer than truncation limit) + if len(tokens) > max_length: + truncated = True + break + # truncate + if len(tokens) > max_length: + truncated = True + tokens = tokens[:max_length] + weights = weights[:max_length] + if truncated: + logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples") + return tokens, weights + + def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad): + r""" + Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. + """ + tokens = [bos] + tokens + [eos] + [pad] * (max_length - 2 - len(tokens)) + weights = [1.0] + weights + [1.0] * (max_length - 1 - len(weights)) + return tokens, weights + + if max_length is None: + max_length = tokenizer.model_max_length + + tokens, weights = get_prompts_with_weights(text, max_length - 2) + tokens, weights = pad_tokens_and_weights( + tokens, weights, max_length, tokenizer.bos_token_id, tokenizer.eos_token_id, tokenizer.pad_token_id + ) + return torch.tensor(tokens).unsqueeze(0), torch.tensor(weights).unsqueeze(0) + + def _get_input_ids( + self, tokenizer: CLIPTokenizer, text: str, max_length: Optional[int] = None, weighted: bool = False + ) -> torch.Tensor: + """ + for SD1.5/2.0/SDXL + TODO support batch input + """ + if max_length is None: + max_length = tokenizer.model_max_length - 2 + + if weighted: + input_ids, weights = self._get_weighted_input_ids(tokenizer, text, max_length) + else: + input_ids = tokenizer(text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt").input_ids + + if max_length > tokenizer.model_max_length: + input_ids = input_ids.squeeze(0) + iids_list = [] + if tokenizer.pad_token_id == tokenizer.eos_token_id: + # v1 + # 77以上の時は " .... " でトータル227とかになっているので、"..."の三連に変換する + # 1111氏のやつは , で区切る、とかしているようだが　とりあえず単純に + for i in range(1, max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2): # (1, 152, 75) + ids_chunk = ( + input_ids[0].unsqueeze(0), + input_ids[i : i + tokenizer.model_max_length - 2], + input_ids[-1].unsqueeze(0), + ) + ids_chunk = torch.cat(ids_chunk) + iids_list.append(ids_chunk) + else: + # v2 or SDXL + # 77以上の時は " .... ..." でトータル227とかになっているので、"... ..."の三連に変換する + for i in range(1, max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2): + ids_chunk = ( + input_ids[0].unsqueeze(0), # BOS + input_ids[i : i + tokenizer.model_max_length - 2], + input_ids[-1].unsqueeze(0), + ) # PAD or EOS + ids_chunk = torch.cat(ids_chunk) + + # 末尾が または の場合は、何もしなくてよい + # 末尾が x の場合は末尾を に変える（x なら結果的に変化なし） + if ids_chunk[-2] != tokenizer.eos_token_id and ids_chunk[-2] != tokenizer.pad_token_id: + ids_chunk[-1] = tokenizer.eos_token_id + # 先頭が ... の場合は ... に変える + if ids_chunk[1] == tokenizer.pad_token_id: + ids_chunk[1] = tokenizer.eos_token_id + + iids_list.append(ids_chunk) + + input_ids = torch.stack(iids_list) # 3,77 + + if weighted: + weights = weights.squeeze(0) + new_weights = torch.ones(input_ids.shape) + for i in range(1, max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2): + b = i // (tokenizer.model_max_length - 2) + new_weights[b, 1 : 1 + tokenizer.model_max_length - 2] = weights[i : i + tokenizer.model_max_length - 2] + weights = new_weights + + if weighted: + return input_ids, weights + return input_ids + + +class TextEncodingStrategy: + _strategy = None # strategy instance: actual strategy class + + @classmethod + def set_strategy(cls, strategy): + if cls._strategy is not None: + raise RuntimeError(f"Internal error. {cls.__name__} strategy is already set") + cls._strategy = strategy + + @classmethod + def get_strategy(cls) -> Optional["TextEncodingStrategy"]: + return cls._strategy + + def encode_tokens( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] + ) -> List[torch.Tensor]: + """ + Encode tokens into embeddings and outputs. + :param tokens: list of token tensors for each TextModel + :return: list of output embeddings for each architecture + """ + raise NotImplementedError + + def encode_tokens_with_weights( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor], weights: List[torch.Tensor] + ) -> List[torch.Tensor]: + """ + Encode tokens into embeddings and outputs. + :param tokens: list of token tensors for each TextModel + :param weights: list of weight tensors for each TextModel + :return: list of output embeddings for each architecture + """ + raise NotImplementedError + + +class TextEncoderOutputsCachingStrategy: + _strategy = None # strategy instance: actual strategy class + + def __init__( + self, + cache_to_disk: bool, + batch_size: Optional[int], + skip_disk_cache_validity_check: bool, + is_partial: bool = False, + is_weighted: bool = False, + ) -> None: + self._cache_to_disk = cache_to_disk + self._batch_size = batch_size + self.skip_disk_cache_validity_check = skip_disk_cache_validity_check + self._is_partial = is_partial + self._is_weighted = is_weighted + + @classmethod + def set_strategy(cls, strategy): + if cls._strategy is not None: + raise RuntimeError(f"Internal error. {cls.__name__} strategy is already set") + cls._strategy = strategy + + @classmethod + def get_strategy(cls) -> Optional["TextEncoderOutputsCachingStrategy"]: + return cls._strategy + + @property + def cache_to_disk(self): + return self._cache_to_disk + + @property + def batch_size(self): + return self._batch_size + + @property + def is_partial(self): + return self._is_partial + + @property + def is_weighted(self): + return self._is_weighted + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + raise NotImplementedError + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + raise NotImplementedError + + def is_disk_cached_outputs_expected(self, npz_path: str) -> bool: + raise NotImplementedError + + def cache_batch_outputs( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, batch: List + ): + raise NotImplementedError + + +class LatentsCachingStrategy: + # TODO commonize utillity functions to this class, such as npz handling etc. + + _strategy = None # strategy instance: actual strategy class + + _warned_fallback_to_old_npz = False # to avoid spamming logs about fallback + + def __init__(self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: + self._cache_to_disk = cache_to_disk + self._batch_size = batch_size + self.skip_disk_cache_validity_check = skip_disk_cache_validity_check + + @classmethod + def set_strategy(cls, strategy): + if cls._strategy is not None: + raise RuntimeError(f"Internal error. {cls.__name__} strategy is already set") + cls._strategy = strategy + + @classmethod + def get_strategy(cls) -> Optional["LatentsCachingStrategy"]: + return cls._strategy + + @property + def cache_to_disk(self): + return self._cache_to_disk + + @property + def batch_size(self): + return self._batch_size + + @property + def cache_suffix(self): + raise NotImplementedError + + def get_image_size_from_disk_cache_path(self, absolute_path: str, npz_path: str) -> Tuple[Optional[int], Optional[int]]: + w, h = os.path.splitext(npz_path)[0].split("_")[-2].split("x") + return int(w), int(h) + + def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: + raise NotImplementedError + + def is_disk_cached_latents_expected( + self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool + ) -> bool: + raise NotImplementedError + + def cache_batch_latents(self, model: Any, batch: List, flip_aug: bool, alpha_mask: bool, random_crop: bool): + raise NotImplementedError + + def _default_is_disk_cached_latents_expected( + self, + latents_stride: int, + bucket_reso: Tuple[int, int], + npz_path: str, + flip_aug: bool, + apply_alpha_mask: bool, + multi_resolution: bool = False, + ) -> bool: + """ + Args: + latents_stride: stride of latents + bucket_reso: resolution of the bucket + npz_path: path to the npz file + flip_aug: whether to flip images + apply_alpha_mask: whether to apply alpha mask + multi_resolution: whether to use multi-resolution latents + + Returns: + bool + """ + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + expected_latents_size = (bucket_reso[1] // latents_stride, bucket_reso[0] // latents_stride) # bucket_reso is (W, H) + + # e.g. "_32x64", HxW + key_reso_suffix = f"_{expected_latents_size[0]}x{expected_latents_size[1]}" if multi_resolution else "" + + try: + npz = np.load(npz_path) + + # In old SD/SDXL npz files, if the actual latents shape does not match the expected shape, it doesn't raise an error as long as "latents" key exists (backward compatibility) + # In non-SD/SDXL npz files (multi-resolution support), the latents key always has the resolution suffix, and no latents key without suffix exists, so it raises an error if the expected resolution suffix key is not found (this doesn't change the behavior for non-SD/SDXL npz files). + if "latents" + key_reso_suffix not in npz and "latents" not in npz: + return False + if flip_aug and ("latents_flipped" + key_reso_suffix not in npz and "latents_flipped" not in npz): + return False + if apply_alpha_mask and ("alpha_mask" + key_reso_suffix not in npz and "alpha_mask" not in npz): + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + # TODO remove circular dependency for ImageInfo + def _default_cache_batch_latents( + self, + encode_by_vae: Callable, + vae_device: torch.device, + vae_dtype: torch.dtype, + image_infos: List, + flip_aug: bool, + apply_alpha_mask: bool, + random_crop: bool, + multi_resolution: bool = False, + ): + """ + Default implementation for cache_batch_latents. Image loading, VAE, flipping, alpha mask handling are common. + + Args: + encode_by_vae: function to encode images by VAE + vae_device: device to use for VAE + vae_dtype: dtype to use for VAE + image_infos: list of ImageInfo + flip_aug: whether to flip images + apply_alpha_mask: whether to apply alpha mask + random_crop: whether to random crop images + multi_resolution: whether to use multi-resolution latents + + Returns: + None + """ + from library import train_util # import here to avoid circular import + + img_tensor, alpha_masks, original_sizes, crop_ltrbs = train_util.load_images_and_masks_for_caching( + image_infos, apply_alpha_mask, random_crop + ) + img_tensor = img_tensor.to(device=vae_device, dtype=vae_dtype) + + with torch.no_grad(): + latents_tensors = encode_by_vae(img_tensor).to("cpu") + if flip_aug: + img_tensor = torch.flip(img_tensor, dims=[3]) + with torch.no_grad(): + flipped_latents = encode_by_vae(img_tensor).to("cpu") + else: + flipped_latents = [None] * len(latents_tensors) + + # for info, latents, flipped_latent, alpha_mask in zip(image_infos, latents_tensors, flipped_latents, alpha_masks): + for i in range(len(image_infos)): + info = image_infos[i] + latents = latents_tensors[i] + flipped_latent = flipped_latents[i] + alpha_mask = alpha_masks[i] + original_size = original_sizes[i] + crop_ltrb = crop_ltrbs[i] + + latents_size = latents.shape[-2:] # H, W (supports both 4D and 5D latents) + key_reso_suffix = f"_{latents_size[0]}x{latents_size[1]}" if multi_resolution else "" # e.g. "_32x64", HxW + + if self.cache_to_disk: + self.save_latents_to_disk( + info.latents_npz, latents, original_size, crop_ltrb, flipped_latent, alpha_mask, key_reso_suffix + ) + else: + info.latents_original_size = original_size + info.latents_crop_ltrb = crop_ltrb + info.latents = latents + if flip_aug: + info.latents_flipped = flipped_latent + info.alpha_mask = alpha_mask + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + """ + For single resolution architectures (currently no architecture is single resolution specific). Kept for reference. + + Args: + npz_path (str): Path to the npz file. + bucket_reso (Tuple[int, int]): The resolution of the bucket. + + Returns: + Tuple[ + Optional[np.ndarray], + Optional[List[int]], + Optional[List[int]], + Optional[np.ndarray], + Optional[np.ndarray] + ]: Latent np tensors, original size, crop (left top, right bottom), flipped latents, alpha mask + """ + return self._default_load_latents_from_disk(None, npz_path, bucket_reso) + + def _default_load_latents_from_disk( + self, latents_stride: Optional[int], npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + """ + Args: + latents_stride (Optional[int]): Stride for latents. If None, load all latents. + npz_path (str): Path to the npz file. + bucket_reso (Tuple[int, int]): The resolution of the bucket. + + Returns: + Tuple[ + Optional[np.ndarray], + Optional[List[int]], + Optional[List[int]], + Optional[np.ndarray], + Optional[np.ndarray] + ]: Latent np tensors, original size, crop (left top, right bottom), flipped latents, alpha mask + """ + if latents_stride is None: + key_reso_suffix = "" + else: + expected_latents_size = (bucket_reso[1] // latents_stride, bucket_reso[0] // latents_stride) # bucket_reso is (W, H) + key_reso_suffix = f"_{expected_latents_size[0]}x{expected_latents_size[1]}" # e.g. "_32x64", HxW + + npz = np.load(npz_path) + if "latents" + key_reso_suffix not in npz: + # raise ValueError(f"latents{key_reso_suffix} not found in {npz_path}") + # Fallback to old npz without resolution suffix + if "latents" not in npz: + raise ValueError(f"latents not found in {npz_path} (either with or without resolution suffix: {key_reso_suffix})") + if not self._warned_fallback_to_old_npz: + logger.warning( + f"latents{key_reso_suffix} not found in {npz_path}. Falling back to latents without resolution suffix (old npz). This warning will only be shown once. To avoid this warning, please re-cache the latents with the latest version." + ) + self._warned_fallback_to_old_npz = True + key_reso_suffix = "" + + latents = npz["latents" + key_reso_suffix] + original_size = npz["original_size" + key_reso_suffix].tolist() + crop_ltrb = npz["crop_ltrb" + key_reso_suffix].tolist() + flipped_latents = npz["latents_flipped" + key_reso_suffix] if "latents_flipped" + key_reso_suffix in npz else None + alpha_mask = npz["alpha_mask" + key_reso_suffix] if "alpha_mask" + key_reso_suffix in npz else None + return latents, original_size, crop_ltrb, flipped_latents, alpha_mask + + def save_latents_to_disk( + self, + npz_path, + latents_tensor, + original_size, + crop_ltrb, + flipped_latents_tensor=None, + alpha_mask=None, + key_reso_suffix="", + ): + """ + Args: + npz_path (str): Path to the npz file. + latents_tensor (torch.Tensor): Latent tensor + original_size (List[int]): Original size of the image + crop_ltrb (List[int]): Crop left top right bottom + flipped_latents_tensor (Optional[torch.Tensor]): Flipped latent tensor + alpha_mask (Optional[torch.Tensor]): Alpha mask + key_reso_suffix (str): Key resolution suffix + + Returns: + None + """ + kwargs = {} + + if os.path.exists(npz_path): + # load existing npz and update it + npz = np.load(npz_path) + for key in npz.files: + kwargs[key] = npz[key] + + # TODO float() is needed if vae is in bfloat16. Remove it if vae is float16. + kwargs["latents" + key_reso_suffix] = latents_tensor.float().cpu().numpy() + kwargs["original_size" + key_reso_suffix] = np.array(original_size) + kwargs["crop_ltrb" + key_reso_suffix] = np.array(crop_ltrb) + if flipped_latents_tensor is not None: + kwargs["latents_flipped" + key_reso_suffix] = flipped_latents_tensor.float().cpu().numpy() + if alpha_mask is not None: + kwargs["alpha_mask" + key_reso_suffix] = alpha_mask.float().cpu().numpy() + np.savez(npz_path, **kwargs) diff --git a/vendor/sd-scripts/library/strategy_flux.py b/vendor/sd-scripts/library/strategy_flux.py new file mode 100644 index 0000000..5e65927 --- /dev/null +++ b/vendor/sd-scripts/library/strategy_flux.py @@ -0,0 +1,271 @@ +import os +import glob +from typing import Any, List, Optional, Tuple, Union +import torch +import numpy as np +from transformers import CLIPTokenizer, T5TokenizerFast + +from library import flux_utils, train_util +from library.strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +CLIP_L_TOKENIZER_ID = "openai/clip-vit-large-patch14" +T5_XXL_TOKENIZER_ID = "google/t5-v1_1-xxl" + + +class FluxTokenizeStrategy(TokenizeStrategy): + def __init__(self, t5xxl_max_length: int = 512, tokenizer_cache_dir: Optional[str] = None) -> None: + self.t5xxl_max_length = t5xxl_max_length + self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) + self.t5xxl = self._load_tokenizer(T5TokenizerFast, T5_XXL_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + text = [text] if isinstance(text, str) else text + + l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt") + t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt") + + t5_attn_mask = t5_tokens["attention_mask"] + l_tokens = l_tokens["input_ids"] + t5_tokens = t5_tokens["input_ids"] + + return [l_tokens, t5_tokens, t5_attn_mask] + + +class FluxTextEncodingStrategy(TextEncodingStrategy): + def __init__(self, apply_t5_attn_mask: Optional[bool] = None) -> None: + """ + Args: + apply_t5_attn_mask: Default value for apply_t5_attn_mask. + """ + self.apply_t5_attn_mask = apply_t5_attn_mask + + def encode_tokens( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + tokens: List[torch.Tensor], + apply_t5_attn_mask: Optional[bool] = None, + ) -> List[torch.Tensor]: + # supports single model inference + + if apply_t5_attn_mask is None: + apply_t5_attn_mask = self.apply_t5_attn_mask + + clip_l, t5xxl = models if len(models) == 2 else (models[0], None) + l_tokens, t5_tokens = tokens[:2] + t5_attn_mask = tokens[2] if len(tokens) > 2 else None + + # clip_l is None when using T5 only + if clip_l is not None and l_tokens is not None: + l_pooled = clip_l(l_tokens.to(clip_l.device))["pooler_output"] + else: + l_pooled = None + + # t5xxl is None when using CLIP only + if t5xxl is not None and t5_tokens is not None: + # t5_out is [b, max length, 4096] + attention_mask = None if not apply_t5_attn_mask else t5_attn_mask.to(t5xxl.device) + t5_out, _ = t5xxl(t5_tokens.to(t5xxl.device), attention_mask, return_dict=False, output_hidden_states=True) + # if zero_pad_t5_output: + # t5_out = t5_out * t5_attn_mask.to(t5_out.device).unsqueeze(-1) + txt_ids = torch.zeros(t5_out.shape[0], t5_out.shape[1], 3, device=t5_out.device) + else: + t5_out = None + txt_ids = None + t5_attn_mask = None # caption may be dropped/shuffled, so t5_attn_mask should not be used to make sure the mask is same as the cached one + + return [l_pooled, t5_out, txt_ids, t5_attn_mask] # returns t5_attn_mask for attention mask in transformer + + +class FluxTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): + FLUX_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_flux_te.npz" + + def __init__( + self, + cache_to_disk: bool, + batch_size: int, + skip_disk_cache_validity_check: bool, + is_partial: bool = False, + apply_t5_attn_mask: bool = False, + ) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial) + self.apply_t5_attn_mask = apply_t5_attn_mask + + self.warn_fp8_weights = False + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + return os.path.splitext(image_abs_path)[0] + FluxTextEncoderOutputsCachingStrategy.FLUX_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX + + def is_disk_cached_outputs_expected(self, npz_path: str): + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + try: + npz = np.load(npz_path) + if "l_pooled" not in npz: + return False + if "t5_out" not in npz: + return False + if "txt_ids" not in npz: + return False + if "t5_attn_mask" not in npz: + return False + if "apply_t5_attn_mask" not in npz: + return False + npz_apply_t5_attn_mask = npz["apply_t5_attn_mask"] + if npz_apply_t5_attn_mask != self.apply_t5_attn_mask: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + data = np.load(npz_path) + l_pooled = data["l_pooled"] + t5_out = data["t5_out"] + txt_ids = data["txt_ids"] + t5_attn_mask = data["t5_attn_mask"] + # apply_t5_attn_mask should be same as self.apply_t5_attn_mask + return [l_pooled, t5_out, txt_ids, t5_attn_mask] + + def cache_batch_outputs( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List + ): + if not self.warn_fp8_weights: + if flux_utils.get_t5xxl_actual_dtype(models[1]) == torch.float8_e4m3fn: + logger.warning( + "T5 model is using fp8 weights for caching. This may affect the quality of the cached outputs." + " / T5モデルはfp8の重みを使用しています。これはキャッシュの品質に影響を与える可能性があります。" + ) + self.warn_fp8_weights = True + + flux_text_encoding_strategy: FluxTextEncodingStrategy = text_encoding_strategy + captions = [info.caption for info in infos] + + tokens_and_masks = tokenize_strategy.tokenize(captions) + with torch.no_grad(): + # attn_mask is applied in text_encoding_strategy.encode_tokens if apply_t5_attn_mask is True + l_pooled, t5_out, txt_ids, _ = flux_text_encoding_strategy.encode_tokens(tokenize_strategy, models, tokens_and_masks) + + if l_pooled.dtype == torch.bfloat16: + l_pooled = l_pooled.float() + if t5_out.dtype == torch.bfloat16: + t5_out = t5_out.float() + if txt_ids.dtype == torch.bfloat16: + txt_ids = txt_ids.float() + + l_pooled = l_pooled.cpu().numpy() + t5_out = t5_out.cpu().numpy() + txt_ids = txt_ids.cpu().numpy() + t5_attn_mask = tokens_and_masks[2].cpu().numpy() + + for i, info in enumerate(infos): + l_pooled_i = l_pooled[i] + t5_out_i = t5_out[i] + txt_ids_i = txt_ids[i] + t5_attn_mask_i = t5_attn_mask[i] + apply_t5_attn_mask_i = self.apply_t5_attn_mask + + if self.cache_to_disk: + np.savez( + info.text_encoder_outputs_npz, + l_pooled=l_pooled_i, + t5_out=t5_out_i, + txt_ids=txt_ids_i, + t5_attn_mask=t5_attn_mask_i, + apply_t5_attn_mask=apply_t5_attn_mask_i, + ) + else: + # it's fine that attn mask is not None. it's overwritten before calling the model if necessary + info.text_encoder_outputs = (l_pooled_i, t5_out_i, txt_ids_i, t5_attn_mask_i) + + +class FluxLatentsCachingStrategy(LatentsCachingStrategy): + FLUX_LATENTS_NPZ_SUFFIX = "_flux.npz" + + def __init__(self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) + + @property + def cache_suffix(self) -> str: + return FluxLatentsCachingStrategy.FLUX_LATENTS_NPZ_SUFFIX + + def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: + return ( + os.path.splitext(absolute_path)[0] + + f"_{image_size[0]:04d}x{image_size[1]:04d}" + + FluxLatentsCachingStrategy.FLUX_LATENTS_NPZ_SUFFIX + ) + + def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool): + return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True) + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + return self._default_load_latents_from_disk(8, npz_path, bucket_reso) # support multi-resolution + + # TODO remove circular dependency for ImageInfo + def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool): + encode_by_vae = lambda img_tensor: vae.encode(img_tensor).to("cpu") + vae_device = vae.device + vae_dtype = vae.dtype + + self._default_cache_batch_latents( + encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True + ) + + if not train_util.HIGH_VRAM: + train_util.clean_memory_on_device(vae.device) + + +if __name__ == "__main__": + # test code for FluxTokenizeStrategy + # tokenizer = sd3_models.SD3Tokenizer() + strategy = FluxTokenizeStrategy(256) + text = "hello world" + + l_tokens, g_tokens, t5_tokens = strategy.tokenize(text) + # print(l_tokens.shape) + print(l_tokens) + print(g_tokens) + print(t5_tokens) + + texts = ["hello world", "the quick brown fox jumps over the lazy dog"] + l_tokens_2 = strategy.clip_l(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt") + g_tokens_2 = strategy.clip_g(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt") + t5_tokens_2 = strategy.t5xxl( + texts, max_length=strategy.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt" + ) + print(l_tokens_2) + print(g_tokens_2) + print(t5_tokens_2) + + # compare + print(torch.allclose(l_tokens, l_tokens_2["input_ids"][0])) + print(torch.allclose(g_tokens, g_tokens_2["input_ids"][0])) + print(torch.allclose(t5_tokens, t5_tokens_2["input_ids"][0])) + + text = ",".join(["hello world! this is long text"] * 50) + l_tokens, g_tokens, t5_tokens = strategy.tokenize(text) + print(l_tokens) + print(g_tokens) + print(t5_tokens) + + print(f"model max length l: {strategy.clip_l.model_max_length}") + print(f"model max length g: {strategy.clip_g.model_max_length}") + print(f"model max length t5: {strategy.t5xxl.model_max_length}") diff --git a/vendor/sd-scripts/library/strategy_hunyuan_image.py b/vendor/sd-scripts/library/strategy_hunyuan_image.py new file mode 100644 index 0000000..05a3208 --- /dev/null +++ b/vendor/sd-scripts/library/strategy_hunyuan_image.py @@ -0,0 +1,218 @@ +import os +from typing import Any, List, Optional, Tuple, Union +import torch +import numpy as np +from transformers import AutoTokenizer, Qwen2TokenizerFast + +from library import hunyuan_image_text_encoder, hunyuan_image_vae, train_util +from library.strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +class HunyuanImageTokenizeStrategy(TokenizeStrategy): + def __init__(self, tokenizer_cache_dir: Optional[str] = None) -> None: + self.vlm_tokenizer = self._load_tokenizer( + Qwen2TokenizerFast, hunyuan_image_text_encoder.QWEN_2_5_VL_IMAGE_ID, tokenizer_cache_dir=tokenizer_cache_dir + ) + self.byt5_tokenizer = self._load_tokenizer( + AutoTokenizer, hunyuan_image_text_encoder.BYT5_TOKENIZER_PATH, subfolder="", tokenizer_cache_dir=tokenizer_cache_dir + ) + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + text = [text] if isinstance(text, str) else text + + vlm_tokens, vlm_mask = hunyuan_image_text_encoder.get_qwen_tokens(self.vlm_tokenizer, text) + + # byt5_tokens, byt5_mask = hunyuan_image_text_encoder.get_byt5_text_tokens(self.byt5_tokenizer, text) + byt5_tokens = [] + byt5_mask = [] + for t in text: + tokens, mask = hunyuan_image_text_encoder.get_byt5_text_tokens(self.byt5_tokenizer, t) + if tokens is None: + tokens = torch.zeros((1, 1), dtype=torch.long) + mask = torch.zeros((1, 1), dtype=torch.long) + byt5_tokens.append(tokens) + byt5_mask.append(mask) + max_len = max([m.shape[1] for m in byt5_mask]) + byt5_tokens = torch.cat([torch.nn.functional.pad(t, (0, max_len - t.shape[1]), value=0) for t in byt5_tokens], dim=0) + byt5_mask = torch.cat([torch.nn.functional.pad(m, (0, max_len - m.shape[1]), value=0) for m in byt5_mask], dim=0) + + return [vlm_tokens, vlm_mask, byt5_tokens, byt5_mask] + + +class HunyuanImageTextEncodingStrategy(TextEncodingStrategy): + def __init__(self) -> None: + pass + + def encode_tokens( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] + ) -> List[torch.Tensor]: + vlm_tokens, vlm_mask, byt5_tokens, byt5_mask = tokens + + qwen2vlm, byt5 = models + + # autocast and no_grad are handled in hunyuan_image_text_encoder + vlm_embed, vlm_mask = hunyuan_image_text_encoder.get_qwen_prompt_embeds_from_tokens(qwen2vlm, vlm_tokens, vlm_mask) + + # ocr_mask, byt5_embed, byt5_mask = hunyuan_image_text_encoder.get_byt5_prompt_embeds_from_tokens( + # byt5, byt5_tokens, byt5_mask + # ) + ocr_mask, byt5_embed, byt5_updated_mask = [], [], [] + for i in range(byt5_tokens.shape[0]): + ocr_m, byt5_e, byt5_m = hunyuan_image_text_encoder.get_byt5_prompt_embeds_from_tokens( + byt5, byt5_tokens[i : i + 1], byt5_mask[i : i + 1] + ) + ocr_mask.append(torch.zeros((1,), dtype=torch.long) + (1 if ocr_m[0] else 0)) # 1 or 0 + byt5_embed.append(byt5_e) + byt5_updated_mask.append(byt5_m) + + ocr_mask = torch.cat(ocr_mask, dim=0).to(torch.bool) # [B] + byt5_embed = torch.cat(byt5_embed, dim=0) + byt5_updated_mask = torch.cat(byt5_updated_mask, dim=0) + + return [vlm_embed, vlm_mask, byt5_embed, byt5_updated_mask, ocr_mask] + + +class HunyuanImageTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): + HUNYUAN_IMAGE_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_hi_te.npz" + + def __init__( + self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool, is_partial: bool = False + ) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial) + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + return ( + os.path.splitext(image_abs_path)[0] + + HunyuanImageTextEncoderOutputsCachingStrategy.HUNYUAN_IMAGE_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX + ) + + def is_disk_cached_outputs_expected(self, npz_path: str): + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + try: + npz = np.load(npz_path) + if "vlm_embed" not in npz: + return False + if "vlm_mask" not in npz: + return False + if "byt5_embed" not in npz: + return False + if "byt5_mask" not in npz: + return False + if "ocr_mask" not in npz: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + data = np.load(npz_path) + vln_embed = data["vlm_embed"] + vlm_mask = data["vlm_mask"] + byt5_embed = data["byt5_embed"] + byt5_mask = data["byt5_mask"] + ocr_mask = data["ocr_mask"] + return [vln_embed, vlm_mask, byt5_embed, byt5_mask, ocr_mask] + + def cache_batch_outputs( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List + ): + huyuan_image_text_encoding_strategy: HunyuanImageTextEncodingStrategy = text_encoding_strategy + captions = [info.caption for info in infos] + + tokens_and_masks = tokenize_strategy.tokenize(captions) + with torch.no_grad(): + vlm_embed, vlm_mask, byt5_embed, byt5_mask, ocr_mask = huyuan_image_text_encoding_strategy.encode_tokens( + tokenize_strategy, models, tokens_and_masks + ) + + if vlm_embed.dtype == torch.bfloat16: + vlm_embed = vlm_embed.float() + if byt5_embed.dtype == torch.bfloat16: + byt5_embed = byt5_embed.float() + + vlm_embed = vlm_embed.cpu().numpy() + vlm_mask = vlm_mask.cpu().numpy() + byt5_embed = byt5_embed.cpu().numpy() + byt5_mask = byt5_mask.cpu().numpy() + ocr_mask = ocr_mask.cpu().numpy() + + for i, info in enumerate(infos): + vlm_embed_i = vlm_embed[i] + vlm_mask_i = vlm_mask[i] + byt5_embed_i = byt5_embed[i] + byt5_mask_i = byt5_mask[i] + ocr_mask_i = ocr_mask[i] + + if self.cache_to_disk: + np.savez( + info.text_encoder_outputs_npz, + vlm_embed=vlm_embed_i, + vlm_mask=vlm_mask_i, + byt5_embed=byt5_embed_i, + byt5_mask=byt5_mask_i, + ocr_mask=ocr_mask_i, + ) + else: + info.text_encoder_outputs = (vlm_embed_i, vlm_mask_i, byt5_embed_i, byt5_mask_i, ocr_mask_i) + + +class HunyuanImageLatentsCachingStrategy(LatentsCachingStrategy): + HUNYUAN_IMAGE_LATENTS_NPZ_SUFFIX = "_hi.npz" + + def __init__(self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) + + @property + def cache_suffix(self) -> str: + return HunyuanImageLatentsCachingStrategy.HUNYUAN_IMAGE_LATENTS_NPZ_SUFFIX + + def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: + return ( + os.path.splitext(absolute_path)[0] + + f"_{image_size[0]:04d}x{image_size[1]:04d}" + + HunyuanImageLatentsCachingStrategy.HUNYUAN_IMAGE_LATENTS_NPZ_SUFFIX + ) + + def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool): + return self._default_is_disk_cached_latents_expected(32, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True) + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + return self._default_load_latents_from_disk(32, npz_path, bucket_reso) # support multi-resolution + + # TODO remove circular dependency for ImageInfo + def cache_batch_latents( + self, vae: hunyuan_image_vae.HunyuanVAE2D, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool + ): + # encode_by_vae = lambda img_tensor: vae.encode(img_tensor).sample() + def encode_by_vae(img_tensor): + # no_grad is handled in _default_cache_batch_latents + nonlocal vae + with torch.autocast(device_type=vae.device.type, dtype=vae.dtype): + return vae.encode(img_tensor).sample() + + vae_device = vae.device + vae_dtype = vae.dtype + + self._default_cache_batch_latents( + encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True + ) + + if not train_util.HIGH_VRAM: + train_util.clean_memory_on_device(vae.device) diff --git a/vendor/sd-scripts/library/strategy_lumina.py b/vendor/sd-scripts/library/strategy_lumina.py new file mode 100644 index 0000000..964d9f7 --- /dev/null +++ b/vendor/sd-scripts/library/strategy_lumina.py @@ -0,0 +1,375 @@ +import glob +import os +from typing import Any, List, Optional, Tuple, Union + +import torch +from transformers import AutoTokenizer, AutoModel, Gemma2Model, GemmaTokenizerFast +from library import train_util +from library.strategy_base import ( + LatentsCachingStrategy, + TokenizeStrategy, + TextEncodingStrategy, + TextEncoderOutputsCachingStrategy, +) +import numpy as np +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +GEMMA_ID = "google/gemma-2-2b" + + +class LuminaTokenizeStrategy(TokenizeStrategy): + def __init__( + self, system_prompt:str, max_length: Optional[int], tokenizer_cache_dir: Optional[str] = None + ) -> None: + self.tokenizer: GemmaTokenizerFast = AutoTokenizer.from_pretrained( + GEMMA_ID, cache_dir=tokenizer_cache_dir + ) + self.tokenizer.padding_side = "right" + + if system_prompt is None: + system_prompt = "" + system_prompt_special_token = "" + system_prompt = f"{system_prompt} {system_prompt_special_token} " if system_prompt else "" + self.system_prompt = system_prompt + + if max_length is None: + self.max_length = 256 + else: + self.max_length = max_length + + def tokenize( + self, text: Union[str, List[str]], is_negative: bool = False + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + text (Union[str, List[str]]): Text to tokenize + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + token input ids, attention_masks + """ + text = [text] if isinstance(text, str) else text + + # In training, we always add system prompt (is_negative=False) + if not is_negative: + # Add system prompt to the beginning of each text + text = [self.system_prompt + t for t in text] + + encodings = self.tokenizer( + text, + max_length=self.max_length, + return_tensors="pt", + padding="max_length", + truncation=True, + pad_to_multiple_of=8, + ) + return (encodings.input_ids, encodings.attention_mask) + + def tokenize_with_weights( + self, text: str | List[str] + ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: + """ + Args: + text (Union[str, List[str]]): Text to tokenize + + Returns: + Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: + token input ids, attention_masks, weights + """ + # Gemma doesn't support weighted prompts, return uniform weights + tokens, attention_masks = self.tokenize(text) + weights = [torch.ones_like(t) for t in tokens] + return tokens, attention_masks, weights + + +class LuminaTextEncodingStrategy(TextEncodingStrategy): + def __init__(self) -> None: + super().__init__() + + def encode_tokens( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + tokens: Tuple[torch.Tensor, torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Args: + tokenize_strategy (LuminaTokenizeStrategy): Tokenize strategy + models (List[Any]): Text encoders + tokens (Tuple[torch.Tensor, torch.Tensor]): tokens, attention_masks + + Returns: + Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + hidden_states, input_ids, attention_masks + """ + text_encoder = models[0] + # Check model or torch dynamo OptimizedModule + assert isinstance(text_encoder, Gemma2Model) or isinstance(text_encoder._orig_mod, Gemma2Model), f"text encoder is not Gemma2Model {text_encoder.__class__.__name__}" + input_ids, attention_masks = tokens + + outputs = text_encoder( + input_ids=input_ids.to(text_encoder.device), + attention_mask=attention_masks.to(text_encoder.device), + output_hidden_states=True, + return_dict=True, + ) + + return outputs.hidden_states[-2], input_ids, attention_masks + + def encode_tokens_with_weights( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + tokens: Tuple[torch.Tensor, torch.Tensor], + weights: List[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Args: + tokenize_strategy (LuminaTokenizeStrategy): Tokenize strategy + models (List[Any]): Text encoders + tokens (Tuple[torch.Tensor, torch.Tensor]): tokens, attention_masks + weights_list (List[torch.Tensor]): Currently unused + + Returns: + Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + hidden_states, input_ids, attention_masks + """ + # For simplicity, use uniform weighting + return self.encode_tokens(tokenize_strategy, models, tokens) + + +class LuminaTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): + LUMINA_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_lumina_te.npz" + + def __init__( + self, + cache_to_disk: bool, + batch_size: int, + skip_disk_cache_validity_check: bool, + is_partial: bool = False, + ) -> None: + super().__init__( + cache_to_disk, + batch_size, + skip_disk_cache_validity_check, + is_partial, + ) + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + return ( + os.path.splitext(image_abs_path)[0] + + LuminaTextEncoderOutputsCachingStrategy.LUMINA_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX + ) + + def is_disk_cached_outputs_expected(self, npz_path: str) -> bool: + """ + Args: + npz_path (str): Path to the npz file. + + Returns: + bool: True if the npz file is expected to be cached. + """ + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + try: + npz = np.load(npz_path) + if "hidden_state" not in npz: + return False + if "attention_mask" not in npz: + return False + if "input_ids" not in npz: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + """ + Load outputs from a npz file + + Returns: + List[np.ndarray]: hidden_state, input_ids, attention_mask + """ + data = np.load(npz_path) + hidden_state = data["hidden_state"] + attention_mask = data["attention_mask"] + input_ids = data["input_ids"] + return [hidden_state, input_ids, attention_mask] + + @torch.no_grad() + def cache_batch_outputs( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + text_encoding_strategy: TextEncodingStrategy, + batch: List[train_util.ImageInfo], + ) -> None: + """ + Args: + tokenize_strategy (LuminaTokenizeStrategy): Tokenize strategy + models (List[Any]): Text encoders + text_encoding_strategy (LuminaTextEncodingStrategy): + infos (List): List of ImageInfo + + Returns: + None + """ + assert isinstance(text_encoding_strategy, LuminaTextEncodingStrategy) + assert isinstance(tokenize_strategy, LuminaTokenizeStrategy) + + captions = [info.caption for info in batch] + + if self.is_weighted: + tokens, attention_masks, weights_list = ( + tokenize_strategy.tokenize_with_weights(captions) + ) + hidden_state, input_ids, attention_masks = ( + text_encoding_strategy.encode_tokens_with_weights( + tokenize_strategy, + models, + (tokens, attention_masks), + weights_list, + ) + ) + else: + tokens = tokenize_strategy.tokenize(captions) + hidden_state, input_ids, attention_masks = ( + text_encoding_strategy.encode_tokens( + tokenize_strategy, models, tokens + ) + ) + + if hidden_state.dtype != torch.float32: + hidden_state = hidden_state.float() + + hidden_state = hidden_state.cpu().numpy() + attention_mask = attention_masks.cpu().numpy() # (B, S) + input_ids = input_ids.cpu().numpy() # (B, S) + + + for i, info in enumerate(batch): + hidden_state_i = hidden_state[i] + attention_mask_i = attention_mask[i] + input_ids_i = input_ids[i] + + if self.cache_to_disk: + assert info.text_encoder_outputs_npz is not None, f"Text encoder cache outputs to disk not found for image {info.image_key}" + np.savez( + info.text_encoder_outputs_npz, + hidden_state=hidden_state_i, + attention_mask=attention_mask_i, + input_ids=input_ids_i, + ) + else: + info.text_encoder_outputs = [ + hidden_state_i, + input_ids_i, + attention_mask_i, + ] + + +class LuminaLatentsCachingStrategy(LatentsCachingStrategy): + LUMINA_LATENTS_NPZ_SUFFIX = "_lumina.npz" + + def __init__( + self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool + ) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) + + @property + def cache_suffix(self) -> str: + return LuminaLatentsCachingStrategy.LUMINA_LATENTS_NPZ_SUFFIX + + def get_latents_npz_path( + self, absolute_path: str, image_size: Tuple[int, int] + ) -> str: + return ( + os.path.splitext(absolute_path)[0] + + f"_{image_size[0]:04d}x{image_size[1]:04d}" + + LuminaLatentsCachingStrategy.LUMINA_LATENTS_NPZ_SUFFIX + ) + + def is_disk_cached_latents_expected( + self, + bucket_reso: Tuple[int, int], + npz_path: str, + flip_aug: bool, + alpha_mask: bool, + ) -> bool: + """ + Args: + bucket_reso (Tuple[int, int]): The resolution of the bucket. + npz_path (str): Path to the npz file. + flip_aug (bool): Whether to flip the image. + alpha_mask (bool): Whether to apply + """ + return self._default_is_disk_cached_latents_expected( + 8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True + ) + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[ + Optional[np.ndarray], + Optional[List[int]], + Optional[List[int]], + Optional[np.ndarray], + Optional[np.ndarray], + ]: + """ + Args: + npz_path (str): Path to the npz file. + bucket_reso (Tuple[int, int]): The resolution of the bucket. + + Returns: + Tuple[ + Optional[np.ndarray], + Optional[List[int]], + Optional[List[int]], + Optional[np.ndarray], + Optional[np.ndarray], + ]: Tuple of latent tensors, attention_mask, input_ids, latents, latents_unet + """ + return self._default_load_latents_from_disk( + 8, npz_path, bucket_reso + ) # support multi-resolution + + # TODO remove circular dependency for ImageInfo + def cache_batch_latents( + self, + model, + batch: List, + flip_aug: bool, + alpha_mask: bool, + random_crop: bool, + ): + encode_by_vae = lambda img_tensor: model.encode(img_tensor).to("cpu") + vae_device = model.device + vae_dtype = model.dtype + + self._default_cache_batch_latents( + encode_by_vae, + vae_device, + vae_dtype, + batch, + flip_aug, + alpha_mask, + random_crop, + multi_resolution=True, + ) + + if not train_util.HIGH_VRAM: + train_util.clean_memory_on_device(model.device) diff --git a/vendor/sd-scripts/library/strategy_sd.py b/vendor/sd-scripts/library/strategy_sd.py new file mode 100644 index 0000000..4521ae8 --- /dev/null +++ b/vendor/sd-scripts/library/strategy_sd.py @@ -0,0 +1,179 @@ +import glob +import os +from typing import Any, List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import CLIPTokenizer +from library import train_util +from library.strategy_base import LatentsCachingStrategy, TokenizeStrategy, TextEncodingStrategy +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +TOKENIZER_ID = "openai/clip-vit-large-patch14" +V2_STABLE_DIFFUSION_ID = "stabilityai/stable-diffusion-2" # ここからtokenizerだけ使う v2とv2.1はtokenizer仕様は同じ + + +class SdTokenizeStrategy(TokenizeStrategy): + def __init__(self, v2: bool, max_length: Optional[int], tokenizer_cache_dir: Optional[str] = None) -> None: + """ + max_length does not include and (None, 75, 150, 225) + """ + logger.info(f"Using {'v2' if v2 else 'v1'} tokenizer") + if v2: + self.tokenizer = self._load_tokenizer( + CLIPTokenizer, V2_STABLE_DIFFUSION_ID, subfolder="tokenizer", tokenizer_cache_dir=tokenizer_cache_dir + ) + else: + self.tokenizer = self._load_tokenizer(CLIPTokenizer, TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) + + if max_length is None: + self.max_length = self.tokenizer.model_max_length + else: + self.max_length = max_length + 2 + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + text = [text] if isinstance(text, str) else text + return [torch.stack([self._get_input_ids(self.tokenizer, t, self.max_length) for t in text], dim=0)] + + def tokenize_with_weights(self, text: str | List[str]) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + text = [text] if isinstance(text, str) else text + tokens_list = [] + weights_list = [] + for t in text: + tokens, weights = self._get_input_ids(self.tokenizer, t, self.max_length, weighted=True) + tokens_list.append(tokens) + weights_list.append(weights) + return [torch.stack(tokens_list, dim=0)], [torch.stack(weights_list, dim=0)] + + +class SdTextEncodingStrategy(TextEncodingStrategy): + def __init__(self, clip_skip: Optional[int] = None) -> None: + self.clip_skip = clip_skip + + def encode_tokens( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] + ) -> List[torch.Tensor]: + text_encoder = models[0] + tokens = tokens[0] + sd_tokenize_strategy = tokenize_strategy # type: SdTokenizeStrategy + + # tokens: b,n,77 + b_size = tokens.size()[0] + max_token_length = tokens.size()[1] * tokens.size()[2] + model_max_length = sd_tokenize_strategy.tokenizer.model_max_length + tokens = tokens.reshape((-1, model_max_length)) # batch_size*3, 77 + + tokens = tokens.to(text_encoder.device) + + if self.clip_skip is None: + encoder_hidden_states = text_encoder(tokens)[0] + else: + enc_out = text_encoder(tokens, output_hidden_states=True, return_dict=True) + encoder_hidden_states = enc_out["hidden_states"][-self.clip_skip] + encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states) + + # bs*3, 77, 768 or 1024 + encoder_hidden_states = encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])) + + if max_token_length != model_max_length: + v1 = sd_tokenize_strategy.tokenizer.pad_token_id == sd_tokenize_strategy.tokenizer.eos_token_id + if not v1: + # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん + states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # + for i in range(1, max_token_length, model_max_length): + chunk = encoder_hidden_states[:, i : i + model_max_length - 2] # の後から 最後の前まで + if i > 0: + for j in range(len(chunk)): + if tokens[j, 1] == sd_tokenize_strategy.tokenizer.eos_token: + # 空、つまり ...のパターン + chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする + states_list.append(chunk) # の後から の前まで + states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # か のどちらか + encoder_hidden_states = torch.cat(states_list, dim=1) + else: + # v1: ... の三連を ... へ戻す + states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # + for i in range(1, max_token_length, model_max_length): + states_list.append(encoder_hidden_states[:, i : i + model_max_length - 2]) # の後から の前まで + states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # + encoder_hidden_states = torch.cat(states_list, dim=1) + + return [encoder_hidden_states] + + def encode_tokens_with_weights( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + tokens_list: List[torch.Tensor], + weights_list: List[torch.Tensor], + ) -> List[torch.Tensor]: + encoder_hidden_states = self.encode_tokens(tokenize_strategy, models, tokens_list)[0] + + weights = weights_list[0].to(encoder_hidden_states.device) + + # apply weights + if weights.shape[1] == 1: # no max_token_length + # weights: ((b, 1, 77), (b, 1, 77)), hidden_states: (b, 77, 768), (b, 77, 768) + encoder_hidden_states = encoder_hidden_states * weights.squeeze(1).unsqueeze(2) + else: + # weights: ((b, n, 77), (b, n, 77)), hidden_states: (b, n*75+2, 768), (b, n*75+2, 768) + for i in range(weights.shape[1]): + encoder_hidden_states[:, i * 75 + 1 : i * 75 + 76] = encoder_hidden_states[:, i * 75 + 1 : i * 75 + 76] * weights[ + :, i, 1:-1 + ].unsqueeze(-1) + + return [encoder_hidden_states] + + +class SdSdxlLatentsCachingStrategy(LatentsCachingStrategy): + # sd and sdxl share the same strategy. we can make them separate, but the difference is only the suffix. + # and we keep the old npz for the backward compatibility. + + SD_OLD_LATENTS_NPZ_SUFFIX = ".npz" + SD_LATENTS_NPZ_SUFFIX = "_sd.npz" + SDXL_LATENTS_NPZ_SUFFIX = "_sdxl.npz" + + def __init__(self, sd: bool, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) + self.sd = sd + self.suffix = ( + SdSdxlLatentsCachingStrategy.SD_LATENTS_NPZ_SUFFIX if sd else SdSdxlLatentsCachingStrategy.SDXL_LATENTS_NPZ_SUFFIX + ) + + @property + def cache_suffix(self) -> str: + return self.suffix + + def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: + # support old .npz + old_npz_file = os.path.splitext(absolute_path)[0] + SdSdxlLatentsCachingStrategy.SD_OLD_LATENTS_NPZ_SUFFIX + if os.path.exists(old_npz_file): + return old_npz_file + return os.path.splitext(absolute_path)[0] + f"_{image_size[0]:04d}x{image_size[1]:04d}" + self.suffix + + def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool): + return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True) + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + return self._default_load_latents_from_disk(8, npz_path, bucket_reso) + + # TODO remove circular dependency for ImageInfo + def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool): + encode_by_vae = lambda img_tensor: vae.encode(img_tensor).latent_dist.sample() + vae_device = vae.device + vae_dtype = vae.dtype + + self._default_cache_batch_latents( + encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True + ) + + if not train_util.HIGH_VRAM: + train_util.clean_memory_on_device(vae.device) diff --git a/vendor/sd-scripts/library/strategy_sd3.py b/vendor/sd-scripts/library/strategy_sd3.py new file mode 100644 index 0000000..1d55fe2 --- /dev/null +++ b/vendor/sd-scripts/library/strategy_sd3.py @@ -0,0 +1,420 @@ +import os +import glob +import random +from typing import Any, List, Optional, Tuple, Union +import torch +import numpy as np +from transformers import CLIPTokenizer, T5TokenizerFast, CLIPTextModel, CLIPTextModelWithProjection, T5EncoderModel + +from library import sd3_utils, train_util +from library import sd3_models +from library.strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +CLIP_L_TOKENIZER_ID = "openai/clip-vit-large-patch14" +CLIP_G_TOKENIZER_ID = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" +T5_XXL_TOKENIZER_ID = "google/t5-v1_1-xxl" + + +class Sd3TokenizeStrategy(TokenizeStrategy): + def __init__(self, t5xxl_max_length: int = 256, tokenizer_cache_dir: Optional[str] = None) -> None: + self.t5xxl_max_length = t5xxl_max_length + self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) + self.clip_g = self._load_tokenizer(CLIPTokenizer, CLIP_G_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) + self.t5xxl = self._load_tokenizer(T5TokenizerFast, T5_XXL_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) + self.clip_g.pad_token_id = 0 # use 0 as pad token for clip_g + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + text = [text] if isinstance(text, str) else text + + l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt") + g_tokens = self.clip_g(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt") + t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt") + + l_attn_mask = l_tokens["attention_mask"] + g_attn_mask = g_tokens["attention_mask"] + t5_attn_mask = t5_tokens["attention_mask"] + l_tokens = l_tokens["input_ids"] + g_tokens = g_tokens["input_ids"] + t5_tokens = t5_tokens["input_ids"] + + return [l_tokens, g_tokens, t5_tokens, l_attn_mask, g_attn_mask, t5_attn_mask] + + +class Sd3TextEncodingStrategy(TextEncodingStrategy): + def __init__( + self, + apply_lg_attn_mask: Optional[bool] = None, + apply_t5_attn_mask: Optional[bool] = None, + l_dropout_rate: float = 0.0, + g_dropout_rate: float = 0.0, + t5_dropout_rate: float = 0.0, + ) -> None: + """ + Args: + apply_t5_attn_mask: Default value for apply_t5_attn_mask. + """ + self.apply_lg_attn_mask = apply_lg_attn_mask + self.apply_t5_attn_mask = apply_t5_attn_mask + self.l_dropout_rate = l_dropout_rate + self.g_dropout_rate = g_dropout_rate + self.t5_dropout_rate = t5_dropout_rate + + def encode_tokens( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + tokens: List[torch.Tensor], + apply_lg_attn_mask: Optional[bool] = False, + apply_t5_attn_mask: Optional[bool] = False, + enable_dropout: bool = True, + ) -> List[torch.Tensor]: + """ + returned embeddings are not masked + """ + clip_l, clip_g, t5xxl = models + clip_l: Optional[CLIPTextModel] + clip_g: Optional[CLIPTextModelWithProjection] + t5xxl: Optional[T5EncoderModel] + + if apply_lg_attn_mask is None: + apply_lg_attn_mask = self.apply_lg_attn_mask + if apply_t5_attn_mask is None: + apply_t5_attn_mask = self.apply_t5_attn_mask + + l_tokens, g_tokens, t5_tokens, l_attn_mask, g_attn_mask, t5_attn_mask = tokens + + # dropout: if enable_dropout is False, dropout is not applied. dropout means zeroing out embeddings + + if l_tokens is None or clip_l is None: + assert g_tokens is None, "g_tokens must be None if l_tokens is None" + lg_out = None + lg_pooled = None + l_attn_mask = None + g_attn_mask = None + else: + assert g_tokens is not None, "g_tokens must not be None if l_tokens is not None" + + # drop some members of the batch: we do not call clip_l and clip_g for dropped members + batch_size, l_seq_len = l_tokens.shape + g_seq_len = g_tokens.shape[1] + + non_drop_l_indices = [] + non_drop_g_indices = [] + for i in range(l_tokens.shape[0]): + drop_l = enable_dropout and (self.l_dropout_rate > 0.0 and random.random() < self.l_dropout_rate) + drop_g = enable_dropout and (self.g_dropout_rate > 0.0 and random.random() < self.g_dropout_rate) + if not drop_l: + non_drop_l_indices.append(i) + if not drop_g: + non_drop_g_indices.append(i) + + # filter out dropped members + if len(non_drop_l_indices) > 0 and len(non_drop_l_indices) < batch_size: + l_tokens = l_tokens[non_drop_l_indices] + l_attn_mask = l_attn_mask[non_drop_l_indices] + if len(non_drop_g_indices) > 0 and len(non_drop_g_indices) < batch_size: + g_tokens = g_tokens[non_drop_g_indices] + g_attn_mask = g_attn_mask[non_drop_g_indices] + + # call clip_l for non-dropped members + if len(non_drop_l_indices) > 0: + nd_l_attn_mask = l_attn_mask.to(clip_l.device) + prompt_embeds = clip_l( + l_tokens.to(clip_l.device), nd_l_attn_mask if apply_lg_attn_mask else None, output_hidden_states=True + ) + nd_l_pooled = prompt_embeds[0] + nd_l_out = prompt_embeds.hidden_states[-2] + if len(non_drop_g_indices) > 0: + nd_g_attn_mask = g_attn_mask.to(clip_g.device) + prompt_embeds = clip_g( + g_tokens.to(clip_g.device), nd_g_attn_mask if apply_lg_attn_mask else None, output_hidden_states=True + ) + nd_g_pooled = prompt_embeds[0] + nd_g_out = prompt_embeds.hidden_states[-2] + + # fill in the dropped members + if len(non_drop_l_indices) == batch_size: + l_pooled = nd_l_pooled + l_out = nd_l_out + else: + # model output is always float32 because of the models are wrapped with Accelerator + l_pooled = torch.zeros((batch_size, 768), device=clip_l.device, dtype=torch.float32) + l_out = torch.zeros((batch_size, l_seq_len, 768), device=clip_l.device, dtype=torch.float32) + l_attn_mask = torch.zeros((batch_size, l_seq_len), device=clip_l.device, dtype=l_attn_mask.dtype) + if len(non_drop_l_indices) > 0: + l_pooled[non_drop_l_indices] = nd_l_pooled + l_out[non_drop_l_indices] = nd_l_out + l_attn_mask[non_drop_l_indices] = nd_l_attn_mask + + if len(non_drop_g_indices) == batch_size: + g_pooled = nd_g_pooled + g_out = nd_g_out + else: + g_pooled = torch.zeros((batch_size, 1280), device=clip_g.device, dtype=torch.float32) + g_out = torch.zeros((batch_size, g_seq_len, 1280), device=clip_g.device, dtype=torch.float32) + g_attn_mask = torch.zeros((batch_size, g_seq_len), device=clip_g.device, dtype=g_attn_mask.dtype) + if len(non_drop_g_indices) > 0: + g_pooled[non_drop_g_indices] = nd_g_pooled + g_out[non_drop_g_indices] = nd_g_out + g_attn_mask[non_drop_g_indices] = nd_g_attn_mask + + lg_pooled = torch.cat((l_pooled, g_pooled), dim=-1) + lg_out = torch.cat([l_out, g_out], dim=-1) + + if t5xxl is None or t5_tokens is None: + t5_out = None + t5_attn_mask = None + else: + # drop some members of the batch: we do not call t5xxl for dropped members + batch_size, t5_seq_len = t5_tokens.shape + non_drop_t5_indices = [] + for i in range(t5_tokens.shape[0]): + drop_t5 = enable_dropout and (self.t5_dropout_rate > 0.0 and random.random() < self.t5_dropout_rate) + if not drop_t5: + non_drop_t5_indices.append(i) + + # filter out dropped members + if len(non_drop_t5_indices) > 0 and len(non_drop_t5_indices) < batch_size: + t5_tokens = t5_tokens[non_drop_t5_indices] + t5_attn_mask = t5_attn_mask[non_drop_t5_indices] + + # call t5xxl for non-dropped members + if len(non_drop_t5_indices) > 0: + nd_t5_attn_mask = t5_attn_mask.to(t5xxl.device) + nd_t5_out, _ = t5xxl( + t5_tokens.to(t5xxl.device), + nd_t5_attn_mask if apply_t5_attn_mask else None, + return_dict=False, + output_hidden_states=True, + ) + + # fill in the dropped members + if len(non_drop_t5_indices) == batch_size: + t5_out = nd_t5_out + else: + t5_out = torch.zeros((batch_size, t5_seq_len, 4096), device=t5xxl.device, dtype=torch.float32) + t5_attn_mask = torch.zeros((batch_size, t5_seq_len), device=t5xxl.device, dtype=t5_attn_mask.dtype) + if len(non_drop_t5_indices) > 0: + t5_out[non_drop_t5_indices] = nd_t5_out + t5_attn_mask[non_drop_t5_indices] = nd_t5_attn_mask + + # masks are used for attention masking in transformer + return [lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask] + + def drop_cached_text_encoder_outputs( + self, + lg_out: torch.Tensor, + t5_out: torch.Tensor, + lg_pooled: torch.Tensor, + l_attn_mask: torch.Tensor, + g_attn_mask: torch.Tensor, + t5_attn_mask: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + # dropout: if enable_dropout is True, dropout is not applied. dropout means zeroing out embeddings + if lg_out is not None: + for i in range(lg_out.shape[0]): + drop_l = self.l_dropout_rate > 0.0 and random.random() < self.l_dropout_rate + if drop_l: + lg_out[i, :, :768] = torch.zeros_like(lg_out[i, :, :768]) + lg_pooled[i, :768] = torch.zeros_like(lg_pooled[i, :768]) + if l_attn_mask is not None: + l_attn_mask[i] = torch.zeros_like(l_attn_mask[i]) + drop_g = self.g_dropout_rate > 0.0 and random.random() < self.g_dropout_rate + if drop_g: + lg_out[i, :, 768:] = torch.zeros_like(lg_out[i, :, 768:]) + lg_pooled[i, 768:] = torch.zeros_like(lg_pooled[i, 768:]) + if g_attn_mask is not None: + g_attn_mask[i] = torch.zeros_like(g_attn_mask[i]) + + if t5_out is not None: + for i in range(t5_out.shape[0]): + drop_t5 = self.t5_dropout_rate > 0.0 and random.random() < self.t5_dropout_rate + if drop_t5: + t5_out[i] = torch.zeros_like(t5_out[i]) + if t5_attn_mask is not None: + t5_attn_mask[i] = torch.zeros_like(t5_attn_mask[i]) + + return [lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask] + + def concat_encodings( + self, lg_out: torch.Tensor, t5_out: Optional[torch.Tensor], lg_pooled: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) + if t5_out is None: + t5_out = torch.zeros((lg_out.shape[0], 77, 4096), device=lg_out.device, dtype=lg_out.dtype) + return torch.cat([lg_out, t5_out], dim=-2), lg_pooled + + +class Sd3TextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): + SD3_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_sd3_te.npz" + + def __init__( + self, + cache_to_disk: bool, + batch_size: int, + skip_disk_cache_validity_check: bool, + is_partial: bool = False, + apply_lg_attn_mask: bool = False, + apply_t5_attn_mask: bool = False, + ) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial) + self.apply_lg_attn_mask = apply_lg_attn_mask + self.apply_t5_attn_mask = apply_t5_attn_mask + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + return os.path.splitext(image_abs_path)[0] + Sd3TextEncoderOutputsCachingStrategy.SD3_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX + + def is_disk_cached_outputs_expected(self, npz_path: str): + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + try: + npz = np.load(npz_path) + if "lg_out" not in npz: + return False + if "lg_pooled" not in npz: + return False + if "clip_l_attn_mask" not in npz or "clip_g_attn_mask" not in npz: # necessary even if not used + return False + if "apply_lg_attn_mask" not in npz: + return False + if "t5_out" not in npz: + return False + if "t5_attn_mask" not in npz: + return False + npz_apply_lg_attn_mask = npz["apply_lg_attn_mask"] + if npz_apply_lg_attn_mask != self.apply_lg_attn_mask: + return False + if "apply_t5_attn_mask" not in npz: + return False + npz_apply_t5_attn_mask = npz["apply_t5_attn_mask"] + if npz_apply_t5_attn_mask != self.apply_t5_attn_mask: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + data = np.load(npz_path) + lg_out = data["lg_out"] + lg_pooled = data["lg_pooled"] + t5_out = data["t5_out"] + + l_attn_mask = data["clip_l_attn_mask"] + g_attn_mask = data["clip_g_attn_mask"] + t5_attn_mask = data["t5_attn_mask"] + + # apply_t5_attn_mask and apply_lg_attn_mask are same as self.apply_t5_attn_mask and self.apply_lg_attn_mask + return [lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask] + + def cache_batch_outputs( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List + ): + sd3_text_encoding_strategy: Sd3TextEncodingStrategy = text_encoding_strategy + captions = [info.caption for info in infos] + + tokens_and_masks = tokenize_strategy.tokenize(captions) + with torch.no_grad(): + # always disable dropout during caching + lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask = sd3_text_encoding_strategy.encode_tokens( + tokenize_strategy, + models, + tokens_and_masks, + apply_lg_attn_mask=self.apply_lg_attn_mask, + apply_t5_attn_mask=self.apply_t5_attn_mask, + enable_dropout=False, + ) + + if lg_out.dtype == torch.bfloat16: + lg_out = lg_out.float() + if lg_pooled.dtype == torch.bfloat16: + lg_pooled = lg_pooled.float() + if t5_out.dtype == torch.bfloat16: + t5_out = t5_out.float() + + lg_out = lg_out.cpu().numpy() + lg_pooled = lg_pooled.cpu().numpy() + t5_out = t5_out.cpu().numpy() + + l_attn_mask = tokens_and_masks[3].cpu().numpy() + g_attn_mask = tokens_and_masks[4].cpu().numpy() + t5_attn_mask = tokens_and_masks[5].cpu().numpy() + + for i, info in enumerate(infos): + lg_out_i = lg_out[i] + t5_out_i = t5_out[i] + lg_pooled_i = lg_pooled[i] + l_attn_mask_i = l_attn_mask[i] + g_attn_mask_i = g_attn_mask[i] + t5_attn_mask_i = t5_attn_mask[i] + apply_lg_attn_mask = self.apply_lg_attn_mask + apply_t5_attn_mask = self.apply_t5_attn_mask + + if self.cache_to_disk: + np.savez( + info.text_encoder_outputs_npz, + lg_out=lg_out_i, + lg_pooled=lg_pooled_i, + t5_out=t5_out_i, + clip_l_attn_mask=l_attn_mask_i, + clip_g_attn_mask=g_attn_mask_i, + t5_attn_mask=t5_attn_mask_i, + apply_lg_attn_mask=apply_lg_attn_mask, + apply_t5_attn_mask=apply_t5_attn_mask, + ) + else: + # it's fine that attn mask is not None. it's overwritten before calling the model if necessary + info.text_encoder_outputs = (lg_out_i, t5_out_i, lg_pooled_i, l_attn_mask_i, g_attn_mask_i, t5_attn_mask_i) + + +class Sd3LatentsCachingStrategy(LatentsCachingStrategy): + SD3_LATENTS_NPZ_SUFFIX = "_sd3.npz" + + def __init__(self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) + + @property + def cache_suffix(self) -> str: + return Sd3LatentsCachingStrategy.SD3_LATENTS_NPZ_SUFFIX + + def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: + return ( + os.path.splitext(absolute_path)[0] + + f"_{image_size[0]:04d}x{image_size[1]:04d}" + + Sd3LatentsCachingStrategy.SD3_LATENTS_NPZ_SUFFIX + ) + + def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool): + return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True) + + def load_latents_from_disk( + self, npz_path: str, bucket_reso: Tuple[int, int] + ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: + return self._default_load_latents_from_disk(8, npz_path, bucket_reso) # support multi-resolution + + # TODO remove circular dependency for ImageInfo + def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool): + encode_by_vae = lambda img_tensor: vae.encode(img_tensor).to("cpu") + vae_device = vae.device + vae_dtype = vae.dtype + + self._default_cache_batch_latents( + encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True + ) + + if not train_util.HIGH_VRAM: + train_util.clean_memory_on_device(vae.device) diff --git a/vendor/sd-scripts/library/strategy_sdxl.py b/vendor/sd-scripts/library/strategy_sdxl.py new file mode 100644 index 0000000..6b3e2af --- /dev/null +++ b/vendor/sd-scripts/library/strategy_sdxl.py @@ -0,0 +1,306 @@ +import os +from typing import Any, List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection +from library.strategy_base import TokenizeStrategy, TextEncodingStrategy, TextEncoderOutputsCachingStrategy + + +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +TOKENIZER1_PATH = "openai/clip-vit-large-patch14" +TOKENIZER2_PATH = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" + + +class SdxlTokenizeStrategy(TokenizeStrategy): + def __init__(self, max_length: Optional[int], tokenizer_cache_dir: Optional[str] = None) -> None: + self.tokenizer1 = self._load_tokenizer(CLIPTokenizer, TOKENIZER1_PATH, tokenizer_cache_dir=tokenizer_cache_dir) + self.tokenizer2 = self._load_tokenizer(CLIPTokenizer, TOKENIZER2_PATH, tokenizer_cache_dir=tokenizer_cache_dir) + self.tokenizer2.pad_token_id = 0 # use 0 as pad token for tokenizer2 + + if max_length is None: + self.max_length = self.tokenizer1.model_max_length + else: + self.max_length = max_length + 2 + + def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: + text = [text] if isinstance(text, str) else text + return ( + torch.stack([self._get_input_ids(self.tokenizer1, t, self.max_length) for t in text], dim=0), + torch.stack([self._get_input_ids(self.tokenizer2, t, self.max_length) for t in text], dim=0), + ) + + def tokenize_with_weights(self, text: str | List[str]) -> Tuple[List[torch.Tensor]]: + text = [text] if isinstance(text, str) else text + tokens1_list, tokens2_list = [], [] + weights1_list, weights2_list = [], [] + for t in text: + tokens1, weights1 = self._get_input_ids(self.tokenizer1, t, self.max_length, weighted=True) + tokens2, weights2 = self._get_input_ids(self.tokenizer2, t, self.max_length, weighted=True) + tokens1_list.append(tokens1) + tokens2_list.append(tokens2) + weights1_list.append(weights1) + weights2_list.append(weights2) + return [torch.stack(tokens1_list, dim=0), torch.stack(tokens2_list, dim=0)], [ + torch.stack(weights1_list, dim=0), + torch.stack(weights2_list, dim=0), + ] + + +class SdxlTextEncodingStrategy(TextEncodingStrategy): + def __init__(self) -> None: + pass + + def _pool_workaround( + self, text_encoder: CLIPTextModelWithProjection, last_hidden_state: torch.Tensor, input_ids: torch.Tensor, eos_token_id: int + ): + r""" + workaround for CLIP's pooling bug: it returns the hidden states for the max token id as the pooled output + instead of the hidden states for the EOS token + If we use Textual Inversion, we need to use the hidden states for the EOS token as the pooled output + + Original code from CLIP's pooling function: + + \# text_embeds.shape = [batch_size, sequence_length, transformer.width] + \# take features from the eot embedding (eot_token is the highest number in each sequence) + \# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 + pooled_output = last_hidden_state[ + torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), + input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1), + ] + """ + + # input_ids: b*n,77 + # find index for EOS token + + # Following code is not working if one of the input_ids has multiple EOS tokens (very odd case) + # eos_token_index = torch.where(input_ids == eos_token_id)[1] + # eos_token_index = eos_token_index.to(device=last_hidden_state.device) + + # Create a mask where the EOS tokens are + eos_token_mask = (input_ids == eos_token_id).int() + + # Use argmax to find the last index of the EOS token for each element in the batch + eos_token_index = torch.argmax(eos_token_mask, dim=1) # this will be 0 if there is no EOS token, it's fine + eos_token_index = eos_token_index.to(device=last_hidden_state.device) + + # get hidden states for EOS token + pooled_output = last_hidden_state[ + torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), eos_token_index + ] + + # apply projection: projection may be of different dtype than last_hidden_state + pooled_output = text_encoder.text_projection(pooled_output.to(text_encoder.text_projection.weight.dtype)) + pooled_output = pooled_output.to(last_hidden_state.dtype) + + return pooled_output + + def _get_hidden_states_sdxl( + self, + input_ids1: torch.Tensor, + input_ids2: torch.Tensor, + tokenizer1: CLIPTokenizer, + tokenizer2: CLIPTokenizer, + text_encoder1: Union[CLIPTextModel, torch.nn.Module], + text_encoder2: Union[CLIPTextModelWithProjection, torch.nn.Module], + unwrapped_text_encoder2: Optional[CLIPTextModelWithProjection] = None, + ): + # input_ids: b,n,77 -> b*n, 77 + b_size = input_ids1.size()[0] + if input_ids1.size()[1] == 1: + max_token_length = None + else: + max_token_length = input_ids1.size()[1] * input_ids1.size()[2] + input_ids1 = input_ids1.reshape((-1, tokenizer1.model_max_length)) # batch_size*n, 77 + input_ids2 = input_ids2.reshape((-1, tokenizer2.model_max_length)) # batch_size*n, 77 + input_ids1 = input_ids1.to(text_encoder1.device) + input_ids2 = input_ids2.to(text_encoder2.device) + + # text_encoder1 + enc_out = text_encoder1(input_ids1, output_hidden_states=True, return_dict=True) + hidden_states1 = enc_out["hidden_states"][11] + + # text_encoder2 + enc_out = text_encoder2(input_ids2, output_hidden_states=True, return_dict=True) + hidden_states2 = enc_out["hidden_states"][-2] # penuultimate layer + + # pool2 = enc_out["text_embeds"] + unwrapped_text_encoder2 = unwrapped_text_encoder2 or text_encoder2 + pool2 = self._pool_workaround(unwrapped_text_encoder2, enc_out["last_hidden_state"], input_ids2, tokenizer2.eos_token_id) + + # b*n, 77, 768 or 1280 -> b, n*77, 768 or 1280 + n_size = 1 if max_token_length is None else max_token_length // 75 + hidden_states1 = hidden_states1.reshape((b_size, -1, hidden_states1.shape[-1])) + hidden_states2 = hidden_states2.reshape((b_size, -1, hidden_states2.shape[-1])) + + if max_token_length is not None: + # bs*3, 77, 768 or 1024 + # encoder1: ... の三連を ... へ戻す + states_list = [hidden_states1[:, 0].unsqueeze(1)] # + for i in range(1, max_token_length, tokenizer1.model_max_length): + states_list.append(hidden_states1[:, i : i + tokenizer1.model_max_length - 2]) # の後から の前まで + states_list.append(hidden_states1[:, -1].unsqueeze(1)) # + hidden_states1 = torch.cat(states_list, dim=1) + + # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん + states_list = [hidden_states2[:, 0].unsqueeze(1)] # + for i in range(1, max_token_length, tokenizer2.model_max_length): + chunk = hidden_states2[:, i : i + tokenizer2.model_max_length - 2] # の後から 最後の前まで + # this causes an error: + # RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation + # if i > 1: + # for j in range(len(chunk)): # batch_size + # if input_ids2[n_index + j * n_size, 1] == tokenizer2.eos_token_id: # 空、つまり ...のパターン + # chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする + states_list.append(chunk) # の後から の前まで + states_list.append(hidden_states2[:, -1].unsqueeze(1)) # か のどちらか + hidden_states2 = torch.cat(states_list, dim=1) + + # pool はnの最初のものを使う + pool2 = pool2[::n_size] + + return hidden_states1, hidden_states2, pool2 + + def encode_tokens( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] + ) -> List[torch.Tensor]: + """ + Args: + tokenize_strategy: TokenizeStrategy + models: List of models, [text_encoder1, text_encoder2, unwrapped text_encoder2 (optional)]. + If text_encoder2 is wrapped by accelerate, unwrapped_text_encoder2 is required + tokens: List of tokens, for text_encoder1 and text_encoder2 + """ + if len(models) == 2: + text_encoder1, text_encoder2 = models + unwrapped_text_encoder2 = None + else: + text_encoder1, text_encoder2, unwrapped_text_encoder2 = models + tokens1, tokens2 = tokens + sdxl_tokenize_strategy = tokenize_strategy # type: SdxlTokenizeStrategy + tokenizer1, tokenizer2 = sdxl_tokenize_strategy.tokenizer1, sdxl_tokenize_strategy.tokenizer2 + + hidden_states1, hidden_states2, pool2 = self._get_hidden_states_sdxl( + tokens1, tokens2, tokenizer1, tokenizer2, text_encoder1, text_encoder2, unwrapped_text_encoder2 + ) + return [hidden_states1, hidden_states2, pool2] + + def encode_tokens_with_weights( + self, + tokenize_strategy: TokenizeStrategy, + models: List[Any], + tokens_list: List[torch.Tensor], + weights_list: List[torch.Tensor], + ) -> List[torch.Tensor]: + hidden_states1, hidden_states2, pool2 = self.encode_tokens(tokenize_strategy, models, tokens_list) + + weights_list = [weights.to(hidden_states1.device) for weights in weights_list] + + # apply weights + if weights_list[0].shape[1] == 1: # no max_token_length + # weights: ((b, 1, 77), (b, 1, 77)), hidden_states: (b, 77, 768), (b, 77, 768) + hidden_states1 = hidden_states1 * weights_list[0].squeeze(1).unsqueeze(2) + hidden_states2 = hidden_states2 * weights_list[1].squeeze(1).unsqueeze(2) + else: + # weights: ((b, n, 77), (b, n, 77)), hidden_states: (b, n*75+2, 768), (b, n*75+2, 768) + for weight, hidden_states in zip(weights_list, [hidden_states1, hidden_states2]): + for i in range(weight.shape[1]): + hidden_states[:, i * 75 + 1 : i * 75 + 76] = hidden_states[:, i * 75 + 1 : i * 75 + 76] * weight[ + :, i, 1:-1 + ].unsqueeze(-1) + + return [hidden_states1, hidden_states2, pool2] + + +class SdxlTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): + SDXL_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_te_outputs.npz" + + def __init__( + self, + cache_to_disk: bool, + batch_size: int, + skip_disk_cache_validity_check: bool, + is_partial: bool = False, + is_weighted: bool = False, + ) -> None: + super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial, is_weighted) + + def get_outputs_npz_path(self, image_abs_path: str) -> str: + return os.path.splitext(image_abs_path)[0] + SdxlTextEncoderOutputsCachingStrategy.SDXL_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX + + def is_disk_cached_outputs_expected(self, npz_path: str): + if not self.cache_to_disk: + return False + if not os.path.exists(npz_path): + return False + if self.skip_disk_cache_validity_check: + return True + + try: + npz = np.load(npz_path) + if "hidden_state1" not in npz or "hidden_state2" not in npz or "pool2" not in npz: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: + data = np.load(npz_path) + hidden_state1 = data["hidden_state1"] + hidden_state2 = data["hidden_state2"] + pool2 = data["pool2"] + return [hidden_state1, hidden_state2, pool2] + + def cache_batch_outputs( + self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List + ): + sdxl_text_encoding_strategy = text_encoding_strategy # type: SdxlTextEncodingStrategy + captions = [info.caption for info in infos] + + if self.is_weighted: + tokens_list, weights_list = tokenize_strategy.tokenize_with_weights(captions) + with torch.no_grad(): + hidden_state1, hidden_state2, pool2 = sdxl_text_encoding_strategy.encode_tokens_with_weights( + tokenize_strategy, models, tokens_list, weights_list + ) + else: + tokens1, tokens2 = tokenize_strategy.tokenize(captions) + with torch.no_grad(): + hidden_state1, hidden_state2, pool2 = sdxl_text_encoding_strategy.encode_tokens( + tokenize_strategy, models, [tokens1, tokens2] + ) + + if hidden_state1.dtype == torch.bfloat16: + hidden_state1 = hidden_state1.float() + if hidden_state2.dtype == torch.bfloat16: + hidden_state2 = hidden_state2.float() + if pool2.dtype == torch.bfloat16: + pool2 = pool2.float() + + hidden_state1 = hidden_state1.cpu().numpy() + hidden_state2 = hidden_state2.cpu().numpy() + pool2 = pool2.cpu().numpy() + + for i, info in enumerate(infos): + hidden_state1_i = hidden_state1[i] + hidden_state2_i = hidden_state2[i] + pool2_i = pool2[i] + + if self.cache_to_disk: + np.savez( + info.text_encoder_outputs_npz, + hidden_state1=hidden_state1_i, + hidden_state2=hidden_state2_i, + pool2=pool2_i, + ) + else: + info.text_encoder_outputs = [hidden_state1_i, hidden_state2_i, pool2_i] diff --git a/vendor/sd-scripts/library/train_util.py b/vendor/sd-scripts/library/train_util.py new file mode 100644 index 0000000..f807572 --- /dev/null +++ b/vendor/sd-scripts/library/train_util.py @@ -0,0 +1,6933 @@ +# common functions for training + +import argparse +import ast +import asyncio +from concurrent.futures import Future, ThreadPoolExecutor +import datetime +import importlib +import json +import logging +import pathlib +import re +import shutil +import time +import typing +from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union +from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs, PartialState +import glob +import math +import os +import random +import hashlib +import subprocess +import traceback +from io import BytesIO +import toml + +# from concurrent.futures import ThreadPoolExecutor, as_completed + +from tqdm import tqdm +from packaging.version import Version + +import torch +from library.device_utils import init_ipex, clean_memory_on_device +from library.strategy_base import LatentsCachingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy, TextEncodingStrategy + +init_ipex() + +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import Optimizer +from torchvision import transforms +from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection +import transformers +from diffusers.optimization import ( + SchedulerType as DiffusersSchedulerType, + TYPE_TO_SCHEDULER_FUNCTION as DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION, +) +from transformers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION +from diffusers import ( + StableDiffusionPipeline, + DDPMScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + DPMSolverSinglestepScheduler, + LMSDiscreteScheduler, + PNDMScheduler, + DDIMScheduler, + EulerDiscreteScheduler, + HeunDiscreteScheduler, + KDPM2DiscreteScheduler, + KDPM2AncestralDiscreteScheduler, + AutoencoderKL, +) +from library import custom_train_functions, sd3_utils +from library.original_unet import UNet2DConditionModel +from huggingface_hub import hf_hub_download +import numpy as np +from PIL import Image +import imagesize +import cv2 +import safetensors.torch +from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline +from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline +import library.model_util as model_util +import library.huggingface_util as huggingface_util +import library.sai_model_spec as sai_model_spec +import library.deepspeed_utils as deepspeed_utils +from library.utils import setup_logging, resize_image, validate_interpolation_fn + +setup_logging() +import logging + +logger = logging.getLogger(__name__) +# from library.attention_processors import FlashAttnProcessor +# from library.hypernetwork import replace_attentions_for_hypernetwork +from library.original_unet import UNet2DConditionModel + +HIGH_VRAM = False + +# checkpointファイル名 +EPOCH_STATE_NAME = "{}-{:06d}-state" +EPOCH_FILE_NAME = "{}-{:06d}" +EPOCH_DIFFUSERS_DIR_NAME = "{}-{:06d}" +LAST_STATE_NAME = "{}-state" +DEFAULT_EPOCH_NAME = "epoch" +DEFAULT_LAST_OUTPUT_NAME = "last" + +DEFAULT_STEP_NAME = "at" +STEP_STATE_NAME = "{}-step{:08d}-state" +STEP_FILE_NAME = "{}-step{:08d}" +STEP_DIFFUSERS_DIR_NAME = "{}-step{:08d}" + +# region dataset + +IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".PNG", ".JPG", ".JPEG", ".WEBP", ".BMP"] + +try: + import pillow_avif + + IMAGE_EXTENSIONS.extend([".avif", ".AVIF"]) +except: + pass + +# JPEG-XL on Linux +try: + from jxlpy import JXLImagePlugin + from library.jpeg_xl_util import get_jxl_size + + IMAGE_EXTENSIONS.extend([".jxl", ".JXL"]) +except: + pass + +# JPEG-XL on Linux and Windows +try: + import pillow_jxl + from library.jpeg_xl_util import get_jxl_size + + IMAGE_EXTENSIONS.extend([".jxl", ".JXL"]) +except: + pass + +IMAGE_TRANSFORMS = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] +) + +TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX = "_te_outputs.npz" +TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX_SD3 = "_sd3_te.npz" + + +def split_train_val( + paths: List[str], + sizes: List[Optional[Tuple[int, int]]], + is_training_dataset: bool, + validation_split: float, + validation_seed: int | None, +) -> Tuple[List[str], List[Optional[Tuple[int, int]]]]: + """ + Split the dataset into train and validation + + Shuffle the dataset based on the validation_seed or the current random seed. + For example if the split of 0.2 of 100 images. + [0:80] = 80 training images + [80:] = 20 validation images + """ + dataset = list(zip(paths, sizes)) + if validation_seed is not None: + logging.info(f"Using validation seed: {validation_seed}") + prevstate = random.getstate() + random.seed(validation_seed) + random.shuffle(dataset) + random.setstate(prevstate) + else: + random.shuffle(dataset) + + paths, sizes = zip(*dataset) + paths = list(paths) + sizes = list(sizes) + # Split the dataset between training and validation + if is_training_dataset: + # Training dataset we split to the first part + split = math.ceil(len(paths) * (1 - validation_split)) + return paths[0:split], sizes[0:split] + else: + # Validation dataset we split to the second part + split = len(paths) - round(len(paths) * validation_split) + return paths[split:], sizes[split:] + + +class ImageInfo: + def __init__( + self, image_key: str, num_repeats: int, caption: str, is_reg: bool, absolute_path: str, caption_dropout_rate: float = 0.0 + ) -> None: + self.image_key: str = image_key + self.num_repeats: int = num_repeats + self.caption: str = caption + self.is_reg: bool = is_reg + self.absolute_path: str = absolute_path + self.caption_dropout_rate: float = caption_dropout_rate + self.image_size: Tuple[int, int] = None + self.resized_size: Tuple[int, int] = None + self.bucket_reso: Tuple[int, int] = None + self.latents: Optional[torch.Tensor] = None + self.latents_flipped: Optional[torch.Tensor] = None + self.latents_npz: Optional[str] = None # set in cache_latents + self.latents_original_size: Optional[Tuple[int, int]] = None # original image size, not latents size + self.latents_crop_ltrb: Optional[Tuple[int, int]] = ( + None # crop left top right bottom in original pixel size, not latents size + ) + self.cond_img_path: Optional[str] = None + self.image: Optional[Image.Image] = None # optional, original PIL Image + self.text_encoder_outputs_npz: Optional[str] = None # filename. set in cache_text_encoder_outputs + + # new + self.text_encoder_outputs: Optional[List[torch.Tensor]] = None + # old + self.text_encoder_outputs1: Optional[torch.Tensor] = None + self.text_encoder_outputs2: Optional[torch.Tensor] = None + self.text_encoder_pool2: Optional[torch.Tensor] = None + + self.alpha_mask: Optional[torch.Tensor] = None # alpha mask can be flipped in runtime + self.resize_interpolation: Optional[str] = None + + +class BucketManager: + def __init__(self, no_upscale, max_reso, min_size, max_size, reso_steps) -> None: + if max_size is not None: + if max_reso is not None: + assert max_size >= max_reso[0], "the max_size should be larger than the width of max_reso" + assert max_size >= max_reso[1], "the max_size should be larger than the height of max_reso" + if min_size is not None: + assert max_size >= min_size, "the max_size should be larger than the min_size" + + self.no_upscale = no_upscale + if max_reso is None: + self.max_reso = None + self.max_area = None + else: + self.max_reso = max_reso + self.max_area = max_reso[0] * max_reso[1] + self.min_size = min_size + self.max_size = max_size + self.reso_steps = reso_steps + + self.resos = [] + self.reso_to_id = {} + self.buckets = [] # 前処理時は (image_key, image, original size, crop left/top)、学習時は image_key + + def add_image(self, reso, image_or_info): + bucket_id = self.reso_to_id[reso] + self.buckets[bucket_id].append(image_or_info) + + def shuffle(self): + for bucket in self.buckets: + random.shuffle(bucket) + + def sort(self): + # 解像度順にソートする（表示時、メタデータ格納時の見栄えをよくするためだけ）。bucketsも入れ替えてreso_to_idも振り直す + sorted_resos = self.resos.copy() + sorted_resos.sort() + + sorted_buckets = [] + sorted_reso_to_id = {} + for i, reso in enumerate(sorted_resos): + bucket_id = self.reso_to_id[reso] + sorted_buckets.append(self.buckets[bucket_id]) + sorted_reso_to_id[reso] = i + + self.resos = sorted_resos + self.buckets = sorted_buckets + self.reso_to_id = sorted_reso_to_id + + def make_buckets(self): + resos = model_util.make_bucket_resolutions(self.max_reso, self.min_size, self.max_size, self.reso_steps) + self.set_predefined_resos(resos) + + def set_predefined_resos(self, resos): + # 規定サイズから選ぶ場合の解像度、aspect ratioの情報を格納しておく + self.predefined_resos = resos.copy() + self.predefined_resos_set = set(resos) + self.predefined_aspect_ratios = np.array([w / h for w, h in resos]) + + def add_if_new_reso(self, reso): + if reso not in self.reso_to_id: + bucket_id = len(self.resos) + self.reso_to_id[reso] = bucket_id + self.resos.append(reso) + self.buckets.append([]) + # logger.info(reso, bucket_id, len(self.buckets)) + + def round_to_steps(self, x): + x = int(x + 0.5) + return x - x % self.reso_steps + + def select_bucket(self, image_width, image_height): + aspect_ratio = image_width / image_height + if not self.no_upscale: + # 拡大および縮小を行う + # 同じaspect ratioがあるかもしれないので（fine tuningで、no_upscale=Trueで前処理した場合）、解像度が同じものを優先する + reso = (image_width, image_height) + if reso in self.predefined_resos_set: + pass + else: + ar_errors = self.predefined_aspect_ratios - aspect_ratio + predefined_bucket_id = np.abs(ar_errors).argmin() # 当該解像度以外でaspect ratio errorが最も少ないもの + reso = self.predefined_resos[predefined_bucket_id] + + ar_reso = reso[0] / reso[1] + if aspect_ratio > ar_reso: # 横が長い→縦を合わせる + scale = reso[1] / image_height + else: + scale = reso[0] / image_width + + resized_size = (int(image_width * scale + 0.5), int(image_height * scale + 0.5)) + # logger.info(f"use predef, {image_width}, {image_height}, {reso}, {resized_size}") + else: + # 縮小のみを行う + if image_width * image_height > self.max_area: + # 画像が大きすぎるのでアスペクト比を保ったまま縮小することを前提にbucketを決める + resized_width = math.sqrt(self.max_area * aspect_ratio) + resized_height = self.max_area / resized_width + assert abs(resized_width / resized_height - aspect_ratio) < 1e-2, "aspect is illegal" + + # リサイズ後の短辺または長辺をreso_steps単位にする：aspect ratioの差が少ないほうを選ぶ + # 元のbucketingと同じロジック + b_width_rounded = self.round_to_steps(resized_width) + b_height_in_wr = self.round_to_steps(b_width_rounded / aspect_ratio) + ar_width_rounded = b_width_rounded / b_height_in_wr + + b_height_rounded = self.round_to_steps(resized_height) + b_width_in_hr = self.round_to_steps(b_height_rounded * aspect_ratio) + ar_height_rounded = b_width_in_hr / b_height_rounded + + # logger.info(b_width_rounded, b_height_in_wr, ar_width_rounded) + # logger.info(b_width_in_hr, b_height_rounded, ar_height_rounded) + + if abs(ar_width_rounded - aspect_ratio) < abs(ar_height_rounded - aspect_ratio): + resized_size = (b_width_rounded, int(b_width_rounded / aspect_ratio + 0.5)) + else: + resized_size = (int(b_height_rounded * aspect_ratio + 0.5), b_height_rounded) + # logger.info(resized_size) + else: + resized_size = (image_width, image_height) # リサイズは不要 + + # 画像のサイズ未満をbucketのサイズとする（paddingせずにcroppingする） + bucket_width = resized_size[0] - resized_size[0] % self.reso_steps + bucket_height = resized_size[1] - resized_size[1] % self.reso_steps + # logger.info(f"use arbitrary {image_width}, {image_height}, {resized_size}, {bucket_width}, {bucket_height}") + + reso = (bucket_width, bucket_height) + + self.add_if_new_reso(reso) + + ar_error = (reso[0] / reso[1]) - aspect_ratio + return reso, resized_size, ar_error + + @staticmethod + def get_crop_ltrb(bucket_reso: Tuple[int, int], image_size: Tuple[int, int]): + # Stability AIの前処理に合わせてcrop left/topを計算する。crop rightはflipのaugmentationのために求める + # Calculate crop left/top according to the preprocessing of Stability AI. Crop right is calculated for flip augmentation. + + bucket_ar = bucket_reso[0] / bucket_reso[1] + image_ar = image_size[0] / image_size[1] + if bucket_ar > image_ar: + # bucketのほうが横長→縦を合わせる + resized_width = bucket_reso[1] * image_ar + resized_height = bucket_reso[1] + else: + resized_width = bucket_reso[0] + resized_height = bucket_reso[0] / image_ar + crop_left = (bucket_reso[0] - resized_width) // 2 + crop_top = (bucket_reso[1] - resized_height) // 2 + crop_right = crop_left + resized_width + crop_bottom = crop_top + resized_height + return crop_left, crop_top, crop_right, crop_bottom + + +class BucketBatchIndex(NamedTuple): + bucket_index: int + bucket_batch_size: int + batch_index: int + + +class AugHelper: + # albumentationsへの依存をなくしたがとりあえず同じinterfaceを持たせる + + def __init__(self): + pass + + def color_aug(self, image: np.ndarray): + # self.color_aug_method = albu.OneOf( + # [ + # albu.HueSaturationValue(8, 0, 0, p=0.5), + # albu.RandomGamma((95, 105), p=0.5), + # ], + # p=0.33, + # ) + hue_shift_limit = 8 + + # remove dependency to albumentations + if random.random() <= 0.33: + if random.random() > 0.5: + # hue shift + hsv_img = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + hue_shift = random.uniform(-hue_shift_limit, hue_shift_limit) + if hue_shift < 0: + hue_shift = 180 + hue_shift + hsv_img[:, :, 0] = (hsv_img[:, :, 0] + hue_shift) % 180 + image = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR) + else: + # random gamma + gamma = random.uniform(0.95, 1.05) + image = np.clip(image**gamma, 0, 255).astype(np.uint8) + + return {"image": image} + + def get_augmentor(self, use_color_aug: bool): # -> Optional[Callable[[np.ndarray], Dict[str, np.ndarray]]]: + return self.color_aug if use_color_aug else None + + +class BaseSubset: + def __init__( + self, + image_dir: Optional[str], + alpha_mask: Optional[bool], + num_repeats: int, + shuffle_caption: bool, + caption_separator: str, + keep_tokens: int, + keep_tokens_separator: str, + secondary_separator: Optional[str], + enable_wildcard: bool, + color_aug: bool, + flip_aug: bool, + face_crop_aug_range: Optional[Tuple[float, float]], + random_crop: bool, + caption_dropout_rate: float, + caption_dropout_every_n_epochs: int, + caption_tag_dropout_rate: float, + caption_prefix: Optional[str], + caption_suffix: Optional[str], + token_warmup_min: int, + token_warmup_step: Union[float, int], + custom_attributes: Optional[Dict[str, Any]] = None, + validation_seed: Optional[int] = None, + validation_split: Optional[float] = 0.0, + resize_interpolation: Optional[str] = None, + ) -> None: + self.image_dir = image_dir + self.alpha_mask = alpha_mask if alpha_mask is not None else False + self.num_repeats = num_repeats + self.shuffle_caption = shuffle_caption + self.caption_separator = caption_separator + self.keep_tokens = keep_tokens + self.keep_tokens_separator = keep_tokens_separator + self.secondary_separator = secondary_separator + self.enable_wildcard = enable_wildcard + self.color_aug = color_aug + self.flip_aug = flip_aug + self.face_crop_aug_range = face_crop_aug_range + self.random_crop = random_crop + self.caption_dropout_rate = caption_dropout_rate + self.caption_dropout_every_n_epochs = caption_dropout_every_n_epochs + self.caption_tag_dropout_rate = caption_tag_dropout_rate + self.caption_prefix = caption_prefix + self.caption_suffix = caption_suffix + + self.token_warmup_min = token_warmup_min # step=0におけるタグの数 + self.token_warmup_step = token_warmup_step # N（N<1ならN*max_train_steps）ステップ目でタグの数が最大になる + + self.custom_attributes = custom_attributes if custom_attributes is not None else {} + + self.img_count = 0 + + self.validation_seed = validation_seed + self.validation_split = validation_split + + self.resize_interpolation = resize_interpolation + + +class DreamBoothSubset(BaseSubset): + def __init__( + self, + image_dir: str, + is_reg: bool, + class_tokens: Optional[str], + caption_extension: str, + cache_info: bool, + alpha_mask: bool, + num_repeats, + shuffle_caption, + caption_separator: str, + keep_tokens, + keep_tokens_separator, + secondary_separator, + enable_wildcard, + color_aug, + flip_aug, + face_crop_aug_range, + random_crop, + caption_dropout_rate, + caption_dropout_every_n_epochs, + caption_tag_dropout_rate, + caption_prefix, + caption_suffix, + token_warmup_min, + token_warmup_step, + custom_attributes: Optional[Dict[str, Any]] = None, + validation_seed: Optional[int] = None, + validation_split: Optional[float] = 0.0, + resize_interpolation: Optional[str] = None, + ) -> None: + assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です" + + super().__init__( + image_dir, + alpha_mask, + num_repeats, + shuffle_caption, + caption_separator, + keep_tokens, + keep_tokens_separator, + secondary_separator, + enable_wildcard, + color_aug, + flip_aug, + face_crop_aug_range, + random_crop, + caption_dropout_rate, + caption_dropout_every_n_epochs, + caption_tag_dropout_rate, + caption_prefix, + caption_suffix, + token_warmup_min, + token_warmup_step, + custom_attributes=custom_attributes, + validation_seed=validation_seed, + validation_split=validation_split, + resize_interpolation=resize_interpolation, + ) + + self.is_reg = is_reg + self.class_tokens = class_tokens + self.caption_extension = caption_extension + if self.caption_extension and not self.caption_extension.startswith("."): + self.caption_extension = "." + self.caption_extension + self.cache_info = cache_info + + def __eq__(self, other) -> bool: + if not isinstance(other, DreamBoothSubset): + return NotImplemented + return self.image_dir == other.image_dir + + +class FineTuningSubset(BaseSubset): + def __init__( + self, + image_dir, + metadata_file: str, + alpha_mask: bool, + num_repeats, + shuffle_caption, + caption_separator, + keep_tokens, + keep_tokens_separator, + secondary_separator, + enable_wildcard, + color_aug, + flip_aug, + face_crop_aug_range, + random_crop, + caption_dropout_rate, + caption_dropout_every_n_epochs, + caption_tag_dropout_rate, + caption_prefix, + caption_suffix, + token_warmup_min, + token_warmup_step, + custom_attributes: Optional[Dict[str, Any]] = None, + validation_seed: Optional[int] = None, + validation_split: Optional[float] = 0.0, + resize_interpolation: Optional[str] = None, + ) -> None: + assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です" + + super().__init__( + image_dir, + alpha_mask, + num_repeats, + shuffle_caption, + caption_separator, + keep_tokens, + keep_tokens_separator, + secondary_separator, + enable_wildcard, + color_aug, + flip_aug, + face_crop_aug_range, + random_crop, + caption_dropout_rate, + caption_dropout_every_n_epochs, + caption_tag_dropout_rate, + caption_prefix, + caption_suffix, + token_warmup_min, + token_warmup_step, + custom_attributes=custom_attributes, + validation_seed=validation_seed, + validation_split=validation_split, + resize_interpolation=resize_interpolation, + ) + + self.metadata_file = metadata_file + + def __eq__(self, other) -> bool: + if not isinstance(other, FineTuningSubset): + return NotImplemented + return self.metadata_file == other.metadata_file + + +class ControlNetSubset(BaseSubset): + def __init__( + self, + image_dir: str, + conditioning_data_dir: str, + caption_extension: str, + cache_info: bool, + num_repeats, + shuffle_caption, + caption_separator, + keep_tokens, + keep_tokens_separator, + secondary_separator, + enable_wildcard, + color_aug, + flip_aug, + face_crop_aug_range, + random_crop, + caption_dropout_rate, + caption_dropout_every_n_epochs, + caption_tag_dropout_rate, + caption_prefix, + caption_suffix, + token_warmup_min, + token_warmup_step, + custom_attributes: Optional[Dict[str, Any]] = None, + validation_seed: Optional[int] = None, + validation_split: Optional[float] = 0.0, + resize_interpolation: Optional[str] = None, + ) -> None: + assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です" + + super().__init__( + image_dir, + False, # alpha_mask + num_repeats, + shuffle_caption, + caption_separator, + keep_tokens, + keep_tokens_separator, + secondary_separator, + enable_wildcard, + color_aug, + flip_aug, + face_crop_aug_range, + random_crop, + caption_dropout_rate, + caption_dropout_every_n_epochs, + caption_tag_dropout_rate, + caption_prefix, + caption_suffix, + token_warmup_min, + token_warmup_step, + custom_attributes=custom_attributes, + validation_seed=validation_seed, + validation_split=validation_split, + resize_interpolation=resize_interpolation, + ) + + self.conditioning_data_dir = conditioning_data_dir + self.caption_extension = caption_extension + if self.caption_extension and not self.caption_extension.startswith("."): + self.caption_extension = "." + self.caption_extension + self.cache_info = cache_info + + def __eq__(self, other) -> bool: + if not isinstance(other, ControlNetSubset): + return NotImplemented + return self.image_dir == other.image_dir and self.conditioning_data_dir == other.conditioning_data_dir + + +class BaseDataset(torch.utils.data.Dataset): + def __init__( + self, + resolution: Optional[Tuple[int, int]], + network_multiplier: float, + train_inpainting: bool, + debug_dataset: bool, + resize_interpolation: Optional[str] = None, + skip_image_resolution: Optional[Tuple[int, int]] = None, + ) -> None: + super().__init__() + + # width/height is used when enable_bucket==False + self.width, self.height = (None, None) if resolution is None else resolution + self.network_multiplier = network_multiplier + self.debug_dataset = debug_dataset + + self.subsets: List[Union[DreamBoothSubset, FineTuningSubset]] = [] + + self.token_padding_disabled = False + self.tag_frequency = {} + self.XTI_layers = None + self.token_strings = None + + self.enable_bucket = False + self.bucket_manager: BucketManager = None # not initialized + self.min_bucket_reso = None + self.max_bucket_reso = None + self.bucket_reso_steps = None + self.bucket_no_upscale = None + self.bucket_info = None # for metadata + + self.current_epoch: int = 0 # インスタンスがepochごとに新しく作られるようなので外側から渡さないとダメ + + self.current_step: int = 0 + self.max_train_steps: int = 0 + self.seed: int = 0 + + # inpainting + self.train_inpainting = train_inpainting + + # augmentation + self.aug_helper = AugHelper() + + self.image_transforms = IMAGE_TRANSFORMS + + if resize_interpolation is not None: + assert validate_interpolation_fn( + resize_interpolation + ), f'Resize interpolation "{resize_interpolation}" is not a valid interpolation' + self.resize_interpolation = resize_interpolation + + self.skip_image_resolution = skip_image_resolution + + self.image_data: Dict[str, ImageInfo] = {} + self.image_to_subset: Dict[str, Union[DreamBoothSubset, FineTuningSubset]] = {} + + self.replacements = {} + + # caching + self.caching_mode = None # None, 'latents', 'text' + + self.tokenize_strategy = None + self.text_encoder_output_caching_strategy = None + self.latents_caching_strategy = None + + def set_current_strategies(self): + self.tokenize_strategy = TokenizeStrategy.get_strategy() + self.text_encoder_output_caching_strategy = TextEncoderOutputsCachingStrategy.get_strategy() + self.latents_caching_strategy = LatentsCachingStrategy.get_strategy() + + def adjust_min_max_bucket_reso_by_steps( + self, resolution: Tuple[int, int], min_bucket_reso: int, max_bucket_reso: int, bucket_reso_steps: int + ) -> Tuple[int, int]: + # make min/max bucket reso to be multiple of bucket_reso_steps + if min_bucket_reso % bucket_reso_steps != 0: + adjusted_min_bucket_reso = min_bucket_reso - min_bucket_reso % bucket_reso_steps + logger.warning( + f"min_bucket_reso is adjusted to be multiple of bucket_reso_steps" + f" / min_bucket_resoがbucket_reso_stepsの倍数になるように調整されました: {min_bucket_reso} -> {adjusted_min_bucket_reso}" + ) + min_bucket_reso = adjusted_min_bucket_reso + if max_bucket_reso % bucket_reso_steps != 0: + adjusted_max_bucket_reso = max_bucket_reso + bucket_reso_steps - max_bucket_reso % bucket_reso_steps + logger.warning( + f"max_bucket_reso is adjusted to be multiple of bucket_reso_steps" + f" / max_bucket_resoがbucket_reso_stepsの倍数になるように調整されました: {max_bucket_reso} -> {adjusted_max_bucket_reso}" + ) + max_bucket_reso = adjusted_max_bucket_reso + + assert ( + min(resolution) >= min_bucket_reso + ), f"min_bucket_reso must be equal or less than resolution / min_bucket_resoは最小解像度より大きくできません。解像度を大きくするかmin_bucket_resoを小さくしてください" + assert ( + max(resolution) <= max_bucket_reso + ), f"max_bucket_reso must be equal or greater than resolution / max_bucket_resoは最大解像度より小さくできません。解像度を小さくするかmin_bucket_resoを大きくしてください" + + return min_bucket_reso, max_bucket_reso + + def set_seed(self, seed): + self.seed = seed + + def set_caching_mode(self, mode): + self.caching_mode = mode + + def set_current_epoch(self, epoch): + if not self.current_epoch == epoch: # epochが切り替わったらバケツをシャッフルする + if epoch > self.current_epoch: + logger.info("epoch is incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch)) + num_epochs = epoch - self.current_epoch + for _ in range(num_epochs): + self.current_epoch += 1 + self.shuffle_buckets() + # self.current_epoch seem to be set to 0 again in the next epoch. it may be caused by skipped_dataloader? + else: + logger.warning("epoch is not incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch)) + self.current_epoch = epoch + + def set_current_step(self, step): + self.current_step = step + + def set_max_train_steps(self, max_train_steps): + self.max_train_steps = max_train_steps + + def set_tag_frequency(self, dir_name, captions): + frequency_for_dir = self.tag_frequency.get(dir_name, {}) + self.tag_frequency[dir_name] = frequency_for_dir + for caption in captions: + for tag in caption.split(","): + tag = tag.strip() + if tag: + tag = tag.lower() + frequency = frequency_for_dir.get(tag, 0) + frequency_for_dir[tag] = frequency + 1 + + def disable_token_padding(self): + self.token_padding_disabled = True + + def enable_XTI(self, layers=None, token_strings=None): + self.XTI_layers = layers + self.token_strings = token_strings + + def add_replacement(self, str_from, str_to): + self.replacements[str_from] = str_to + + def process_caption(self, subset: BaseSubset, caption): + # caption に prefix/suffix を付ける + if subset.caption_prefix: + caption = subset.caption_prefix + " " + caption + if subset.caption_suffix: + caption = caption + " " + subset.caption_suffix + + # dropoutの決定：tag dropがこのメソッド内にあるのでここで行うのが良い + is_drop_out = subset.caption_dropout_rate > 0 and random.random() < subset.caption_dropout_rate + is_drop_out = ( + is_drop_out + or subset.caption_dropout_every_n_epochs > 0 + and self.current_epoch % subset.caption_dropout_every_n_epochs == 0 + ) + + if is_drop_out: + caption = "" + else: + # process wildcards + if subset.enable_wildcard: + # if caption is multiline, random choice one line + if "\n" in caption: + caption = random.choice(caption.split("\n")) + + # wildcard is like '{aaa|bbb|ccc...}' + # escape the curly braces like {{ or }} + replacer1 = "⦅" + replacer2 = "⦆" + while replacer1 in caption or replacer2 in caption: + replacer1 += "⦅" + replacer2 += "⦆" + + caption = caption.replace("{{", replacer1).replace("}}", replacer2) + + # replace the wildcard + def replace_wildcard(match): + return random.choice(match.group(1).split("|")) + + caption = re.sub(r"\{([^}]+)\}", replace_wildcard, caption) + + # unescape the curly braces + caption = caption.replace(replacer1, "{").replace(replacer2, "}") + else: + # if caption is multiline, use the first line + caption = caption.split("\n")[0] + + if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0: + fixed_tokens = [] + flex_tokens = [] + fixed_suffix_tokens = [] + if ( + hasattr(subset, "keep_tokens_separator") + and subset.keep_tokens_separator + and subset.keep_tokens_separator in caption + ): + fixed_part, flex_part = caption.split(subset.keep_tokens_separator, 1) + if subset.keep_tokens_separator in flex_part: + flex_part, fixed_suffix_part = flex_part.split(subset.keep_tokens_separator, 1) + fixed_suffix_tokens = [t.strip() for t in fixed_suffix_part.split(subset.caption_separator) if t.strip()] + + fixed_tokens = [t.strip() for t in fixed_part.split(subset.caption_separator) if t.strip()] + flex_tokens = [t.strip() for t in flex_part.split(subset.caption_separator) if t.strip()] + else: + tokens = [t.strip() for t in caption.strip().split(subset.caption_separator)] + flex_tokens = tokens[:] + if subset.keep_tokens > 0: + fixed_tokens = flex_tokens[: subset.keep_tokens] + flex_tokens = tokens[subset.keep_tokens :] + + if subset.token_warmup_step < 1: # 初回に上書きする + subset.token_warmup_step = math.floor(subset.token_warmup_step * self.max_train_steps) + if subset.token_warmup_step and self.current_step < subset.token_warmup_step: + tokens_len = ( + math.floor( + (self.current_step) * ((len(flex_tokens) - subset.token_warmup_min) / (subset.token_warmup_step)) + ) + + subset.token_warmup_min + ) + flex_tokens = flex_tokens[:tokens_len] + + def dropout_tags(tokens): + if subset.caption_tag_dropout_rate <= 0: + return tokens + l = [] + for token in tokens: + if random.random() >= subset.caption_tag_dropout_rate: + l.append(token) + return l + + if subset.shuffle_caption: + random.shuffle(flex_tokens) + + flex_tokens = dropout_tags(flex_tokens) + + caption = ", ".join(fixed_tokens + flex_tokens + fixed_suffix_tokens) + + # process secondary separator + if subset.secondary_separator: + caption = caption.replace(subset.secondary_separator, subset.caption_separator) + + # textual inversion対応 + for str_from, str_to in self.replacements.items(): + if str_from == "": + # replace all + if type(str_to) == list: + caption = random.choice(str_to) + else: + caption = str_to + else: + caption = caption.replace(str_from, str_to) + + return caption + + def get_input_ids(self, caption, tokenizer=None): + if tokenizer is None: + tokenizer = self.tokenizers[0] + + input_ids = tokenizer( + caption, padding="max_length", truncation=True, max_length=self.tokenizer_max_length, return_tensors="pt" + ).input_ids + + if self.tokenizer_max_length > tokenizer.model_max_length: + input_ids = input_ids.squeeze(0) + iids_list = [] + if tokenizer.pad_token_id == tokenizer.eos_token_id: + # v1 + # 77以上の時は " .... " でトータル227とかになっているので、"..."の三連に変換する + # 1111氏のやつは , で区切る、とかしているようだが　とりあえず単純に + for i in range( + 1, self.tokenizer_max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2 + ): # (1, 152, 75) + ids_chunk = ( + input_ids[0].unsqueeze(0), + input_ids[i : i + tokenizer.model_max_length - 2], + input_ids[-1].unsqueeze(0), + ) + ids_chunk = torch.cat(ids_chunk) + iids_list.append(ids_chunk) + else: + # v2 or SDXL + # 77以上の時は " .... ..." でトータル227とかになっているので、"... ..."の三連に変換する + for i in range(1, self.tokenizer_max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2): + ids_chunk = ( + input_ids[0].unsqueeze(0), # BOS + input_ids[i : i + tokenizer.model_max_length - 2], + input_ids[-1].unsqueeze(0), + ) # PAD or EOS + ids_chunk = torch.cat(ids_chunk) + + # 末尾が または の場合は、何もしなくてよい + # 末尾が x の場合は末尾を に変える（x なら結果的に変化なし） + if ids_chunk[-2] != tokenizer.eos_token_id and ids_chunk[-2] != tokenizer.pad_token_id: + ids_chunk[-1] = tokenizer.eos_token_id + # 先頭が ... の場合は ... に変える + if ids_chunk[1] == tokenizer.pad_token_id: + ids_chunk[1] = tokenizer.eos_token_id + + iids_list.append(ids_chunk) + + input_ids = torch.stack(iids_list) # 3,77 + return input_ids + + def register_image(self, info: ImageInfo, subset: BaseSubset): + self.image_data[info.image_key] = info + self.image_to_subset[info.image_key] = subset + + def make_buckets(self): + """ + bucketingを行わない場合も呼び出し必須（ひとつだけbucketを作る） + min_size and max_size are ignored when enable_bucket is False + """ + logger.info("loading image sizes.") + for info in tqdm(self.image_data.values()): + if info.image_size is None: + info.image_size = self.get_image_size(info.absolute_path) + + # # run in parallel + # max_workers = min(os.cpu_count(), len(self.image_data)) # TODO consider multi-gpu (processes) + # with ThreadPoolExecutor(max_workers) as executor: + # futures = [] + # for info in tqdm(self.image_data.values(), desc="loading image sizes"): + # if info.image_size is None: + # def get_and_set_image_size(info): + # info.image_size = self.get_image_size(info.absolute_path) + # futures.append(executor.submit(get_and_set_image_size, info)) + # # consume futures to reduce memory usage and prevent Ctrl-C hang + # if len(futures) >= max_workers: + # for future in futures: + # future.result() + # futures = [] + # for future in futures: + # future.result() + + if self.enable_bucket: + logger.info("make buckets") + else: + logger.info("prepare dataset") + + # bucketを作成し、画像をbucketに振り分ける + if self.enable_bucket: + if self.bucket_manager is None: # fine tuningの場合でmetadataに定義がある場合は、すでに初期化済み + self.bucket_manager = BucketManager( + self.bucket_no_upscale, + (self.width, self.height), + self.min_bucket_reso, + self.max_bucket_reso, + self.bucket_reso_steps, + ) + if not self.bucket_no_upscale: + self.bucket_manager.make_buckets() + else: + logger.warning( + "min_bucket_reso and max_bucket_reso are ignored if bucket_no_upscale is set, because bucket reso is defined by image size automatically / bucket_no_upscaleが指定された場合は、bucketの解像度は画像サイズから自動計算されるため、min_bucket_resoとmax_bucket_resoは無視されます" + ) + + img_ar_errors = [] + for image_info in self.image_data.values(): + image_width, image_height = image_info.image_size + image_info.bucket_reso, image_info.resized_size, ar_error = self.bucket_manager.select_bucket( + image_width, image_height + ) + + # logger.info(image_info.image_key, image_info.bucket_reso) + img_ar_errors.append(abs(ar_error)) + + self.bucket_manager.sort() + else: + self.bucket_manager = BucketManager(False, (self.width, self.height), None, None, None) + self.bucket_manager.set_predefined_resos([(self.width, self.height)]) # ひとつの固定サイズbucketのみ + for image_info in self.image_data.values(): + image_width, image_height = image_info.image_size + image_info.bucket_reso, image_info.resized_size, _ = self.bucket_manager.select_bucket(image_width, image_height) + + for image_info in self.image_data.values(): + for _ in range(image_info.num_repeats): + self.bucket_manager.add_image(image_info.bucket_reso, image_info.image_key) + + # bucket情報を表示、格納する + if self.enable_bucket: + self.bucket_info = {"buckets": {}} + logger.info("number of images (including repeats) / 各bucketの画像枚数（繰り返し回数を含む）") + for i, (reso, bucket) in enumerate(zip(self.bucket_manager.resos, self.bucket_manager.buckets)): + count = len(bucket) + if count > 0: + self.bucket_info["buckets"][i] = {"resolution": reso, "count": len(bucket)} + logger.info(f"bucket {i}: resolution {reso}, count: {len(bucket)}") + + if len(img_ar_errors) == 0: + mean_img_ar_error = 0 # avoid NaN + else: + img_ar_errors = np.array(img_ar_errors) + mean_img_ar_error = np.mean(np.abs(img_ar_errors)) + self.bucket_info["mean_img_ar_error"] = mean_img_ar_error + logger.info(f"mean ar error (without repeats): {mean_img_ar_error}") + + # データ参照用indexを作る。このindexはdatasetのshuffleに用いられる + self.buckets_indices: List[BucketBatchIndex] = [] + for bucket_index, bucket in enumerate(self.bucket_manager.buckets): + batch_count = int(math.ceil(len(bucket) / self.batch_size)) + for batch_index in range(batch_count): + self.buckets_indices.append(BucketBatchIndex(bucket_index, self.batch_size, batch_index)) + + self.shuffle_buckets() + self._length = len(self.buckets_indices) + + def shuffle_buckets(self): + # set random seed for this epoch + random.seed(self.seed + self.current_epoch) + + random.shuffle(self.buckets_indices) + self.bucket_manager.shuffle() + + def verify_bucket_reso_steps(self, min_steps: int): + assert self.bucket_reso_steps is None or self.bucket_reso_steps % min_steps == 0, ( + f"bucket_reso_steps is {self.bucket_reso_steps}. it must be divisible by {min_steps}.\n" + + f"bucket_reso_stepsが{self.bucket_reso_steps}です。{min_steps}で割り切れる必要があります" + ) + + def is_latent_cacheable(self): + return all([not subset.color_aug and not subset.random_crop for subset in self.subsets]) + + def is_text_encoder_output_cacheable(self, cache_supports_dropout: bool = False): + return all( + [ + not ( + subset.caption_dropout_rate > 0 + and not cache_supports_dropout + or subset.shuffle_caption + or subset.token_warmup_step > 0 + or subset.caption_tag_dropout_rate > 0 + ) + for subset in self.subsets + ] + ) + + def new_cache_latents(self, model: Any, accelerator: Accelerator): + r""" + a brand new method to cache latents. This method caches latents with caching strategy. + normal cache_latents method is used by default, but this method is used when caching strategy is specified. + """ + logger.info("caching latents with caching strategy.") + caching_strategy = LatentsCachingStrategy.get_strategy() + image_infos = list(self.image_data.values()) + + # sort by resolution + image_infos.sort(key=lambda info: info.bucket_reso[0] * info.bucket_reso[1]) + + # split by resolution and some conditions + class Condition: + def __init__(self, reso, flip_aug, alpha_mask, random_crop): + self.reso = reso + self.flip_aug = flip_aug + self.alpha_mask = alpha_mask + self.random_crop = random_crop + + def __eq__(self, other): + return ( + other is not None + and self.reso == other.reso + and self.flip_aug == other.flip_aug + and self.alpha_mask == other.alpha_mask + and self.random_crop == other.random_crop + ) + + batch: List[ImageInfo] = [] + current_condition = None + + # support multiple-gpus + num_processes = accelerator.num_processes + process_index = accelerator.process_index + + # define a function to submit a batch to cache + def submit_batch(batch, cond): + for info in batch: + if info.image is not None and isinstance(info.image, Future): + info.image = info.image.result() # future to image + caching_strategy.cache_batch_latents(model, batch, cond.flip_aug, cond.alpha_mask, cond.random_crop) + + # remove image from memory + for info in batch: + info.image = None + + # define ThreadPoolExecutor to load images in parallel + max_workers = min(os.cpu_count(), len(image_infos)) + max_workers = max(1, max_workers // num_processes) # consider multi-gpu + max_workers = min(max_workers, caching_strategy.batch_size) # max_workers should be less than batch_size + executor = ThreadPoolExecutor(max_workers) + + try: + # iterate images + logger.info("caching latents...") + for i, info in enumerate(tqdm(image_infos)): + subset = self.image_to_subset[info.image_key] + + if info.latents_npz is not None: # fine tuning dataset + continue + + # check disk cache exists and size of latents + if caching_strategy.cache_to_disk: + # info.latents_npz = os.path.splitext(info.absolute_path)[0] + file_suffix + info.latents_npz = caching_strategy.get_latents_npz_path(info.absolute_path, info.image_size) + + # if the modulo of num_processes is not equal to process_index, skip caching + # this makes each process cache different latents + if i % num_processes != process_index: + continue + + # print(f"{process_index}/{num_processes} {i}/{len(image_infos)} {info.latents_npz}") + + cache_available = caching_strategy.is_disk_cached_latents_expected( + info.bucket_reso, info.latents_npz, subset.flip_aug, subset.alpha_mask + ) + if cache_available: # do not add to batch + continue + + # if batch is not empty and condition is changed, flush the batch. Note that current_condition is not None if batch is not empty + condition = Condition(info.bucket_reso, subset.flip_aug, subset.alpha_mask, subset.random_crop) + if len(batch) > 0 and current_condition != condition: + submit_batch(batch, current_condition) + batch = [] + if condition != current_condition and HIGH_VRAM: # even with high VRAM, if shape is changed + clean_memory_on_device(accelerator.device) + + if info.image is None: + # load image in parallel + info.image = executor.submit(load_image, info.absolute_path, condition.alpha_mask) + + batch.append(info) + current_condition = condition + + # if number of data in batch is enough, flush the batch + if len(batch) >= caching_strategy.batch_size: + submit_batch(batch, current_condition) + batch = [] + # current_condition = None # keep current_condition to avoid next `clean_memory_on_device` call + + if len(batch) > 0: + submit_batch(batch, current_condition) + + finally: + executor.shutdown() + + def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True, file_suffix=".npz"): + # マルチGPUには対応していないので、そちらはtools/cache_latents.pyを使うこと + logger.info("caching latents.") + + image_infos = list(self.image_data.values()) + + # sort by resolution + image_infos.sort(key=lambda info: info.bucket_reso[0] * info.bucket_reso[1]) + + # split by resolution and some conditions + class Condition: + def __init__(self, reso, flip_aug, alpha_mask, random_crop): + self.reso = reso + self.flip_aug = flip_aug + self.alpha_mask = alpha_mask + self.random_crop = random_crop + + def __eq__(self, other): + return ( + self.reso == other.reso + and self.flip_aug == other.flip_aug + and self.alpha_mask == other.alpha_mask + and self.random_crop == other.random_crop + ) + + batches: List[Tuple[Condition, List[ImageInfo]]] = [] + batch: List[ImageInfo] = [] + current_condition = None + + logger.info("checking cache validity...") + for info in tqdm(image_infos): + subset = self.image_to_subset[info.image_key] + + if info.latents_npz is not None: # fine tuning dataset + continue + + # check disk cache exists and size of latents + if cache_to_disk: + info.latents_npz = os.path.splitext(info.absolute_path)[0] + file_suffix + if not is_main_process: # store to info only + continue + + cache_available = is_disk_cached_latents_is_expected( + info.bucket_reso, info.latents_npz, subset.flip_aug, subset.alpha_mask + ) + + if cache_available: # do not add to batch + continue + + # if batch is not empty and condition is changed, flush the batch. Note that current_condition is not None if batch is not empty + condition = Condition(info.bucket_reso, subset.flip_aug, subset.alpha_mask, subset.random_crop) + if len(batch) > 0 and current_condition != condition: + batches.append((current_condition, batch)) + batch = [] + + batch.append(info) + current_condition = condition + + # if number of data in batch is enough, flush the batch + if len(batch) >= vae_batch_size: + batches.append((current_condition, batch)) + batch = [] + current_condition = None + + if len(batch) > 0: + batches.append((current_condition, batch)) + + if cache_to_disk and not is_main_process: # if cache to disk, don't cache latents in non-main process, set to info only + return + + # iterate batches: batch doesn't have image, image will be loaded in cache_batch_latents and discarded + logger.info("caching latents...") + for condition, batch in tqdm(batches, smoothing=1, total=len(batches)): + cache_batch_latents(vae, cache_to_disk, batch, condition.flip_aug, condition.alpha_mask, condition.random_crop) + + def new_cache_text_encoder_outputs(self, models: List[Any], accelerator: Accelerator): + r""" + a brand new method to cache text encoder outputs. This method caches text encoder outputs with caching strategy. + """ + tokenize_strategy = TokenizeStrategy.get_strategy() + text_encoding_strategy = TextEncodingStrategy.get_strategy() + caching_strategy = TextEncoderOutputsCachingStrategy.get_strategy() + batch_size = caching_strategy.batch_size or self.batch_size + + logger.info("caching Text Encoder outputs with caching strategy.") + image_infos = list(self.image_data.values()) + + # split by resolution + batches = [] + batch = [] + + # support multiple-gpus + num_processes = accelerator.num_processes + process_index = accelerator.process_index + + logger.info("checking cache validity...") + for i, info in enumerate(tqdm(image_infos)): + # check disk cache exists and size of text encoder outputs + if caching_strategy.cache_to_disk: + te_out_npz = caching_strategy.get_outputs_npz_path(info.absolute_path) + info.text_encoder_outputs_npz = te_out_npz # set npz filename regardless of cache availability + + # if the modulo of num_processes is not equal to process_index, skip caching + # this makes each process cache different text encoder outputs + if i % num_processes != process_index: + continue + + cache_available = caching_strategy.is_disk_cached_outputs_expected(te_out_npz) + if cache_available: # do not add to batch + continue + + batch.append(info) + + # if number of data in batch is enough, flush the batch + if len(batch) >= batch_size: + batches.append(batch) + batch = [] + + if len(batch) > 0: + batches.append(batch) + + if len(batches) == 0: + logger.info("no Text Encoder outputs to cache") + return + + # iterate batches + logger.info("caching Text Encoder outputs...") + for batch in tqdm(batches, smoothing=1, total=len(batches)): + # cache_batch_latents(vae, cache_to_disk, batch, subset.flip_aug, subset.alpha_mask, subset.random_crop) + caching_strategy.cache_batch_outputs(tokenize_strategy, models, text_encoding_strategy, batch) + + # if weight_dtype is specified, Text Encoder itself and output will be converted to the dtype + # this method is only for SDXL, but it should be implemented here because it needs to be a method of dataset + # to support SD1/2, it needs a flag for v2, but it is postponed + def cache_text_encoder_outputs( + self, tokenizers, text_encoders, device, output_dtype, cache_to_disk=False, is_main_process=True + ): + assert len(tokenizers) == 2, "only support SDXL" + return self.cache_text_encoder_outputs_common( + tokenizers, text_encoders, [device, device], output_dtype, [output_dtype], cache_to_disk, is_main_process + ) + + # same as above, but for SD3 + def cache_text_encoder_outputs_sd3( + self, tokenizer, text_encoders, devices, output_dtype, te_dtypes, cache_to_disk=False, is_main_process=True, batch_size=None + ): + return self.cache_text_encoder_outputs_common( + [tokenizer], + text_encoders, + devices, + output_dtype, + te_dtypes, + cache_to_disk, + is_main_process, + TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX_SD3, + batch_size, + ) + + def cache_text_encoder_outputs_common( + self, + tokenizers, + text_encoders, + devices, + output_dtype, + te_dtypes, + cache_to_disk=False, + is_main_process=True, + file_suffix=TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX, + batch_size=None, + ): + # latentsのキャッシュと同様に、ディスクへのキャッシュに対応する + # またマルチGPUには対応していないので、そちらはtools/cache_latents.pyを使うこと + logger.info("caching text encoder outputs.") + + tokenize_strategy = TokenizeStrategy.get_strategy() + + if batch_size is None: + batch_size = self.batch_size + + image_infos = list(self.image_data.values()) + + logger.info("checking cache existence...") + image_infos_to_cache = [] + for info in tqdm(image_infos): + # subset = self.image_to_subset[info.image_key] + if cache_to_disk: + te_out_npz = os.path.splitext(info.absolute_path)[0] + file_suffix + info.text_encoder_outputs_npz = te_out_npz + + if not is_main_process: # store to info only + continue + + if os.path.exists(te_out_npz): + # TODO check varidity of cache here + continue + + image_infos_to_cache.append(info) + + if cache_to_disk and not is_main_process: # if cache to disk, don't cache latents in non-main process, set to info only + return + + # prepare tokenizers and text encoders + for text_encoder, device, te_dtype in zip(text_encoders, devices, te_dtypes): + text_encoder.to(device) + if te_dtype is not None: + text_encoder.to(dtype=te_dtype) + + # create batch + is_sd3 = len(tokenizers) == 1 + batch = [] + batches = [] + for info in image_infos_to_cache: + if not is_sd3: + input_ids1 = self.get_input_ids(info.caption, tokenizers[0]) + input_ids2 = self.get_input_ids(info.caption, tokenizers[1]) + batch.append((info, input_ids1, input_ids2)) + else: + l_tokens, g_tokens, t5_tokens = tokenize_strategy.tokenize(info.caption) + batch.append((info, l_tokens, g_tokens, t5_tokens)) + + if len(batch) >= batch_size: + batches.append(batch) + batch = [] + + if len(batch) > 0: + batches.append(batch) + + # iterate batches: call text encoder and cache outputs for memory or disk + logger.info("caching text encoder outputs...") + if not is_sd3: + for batch in tqdm(batches): + infos, input_ids1, input_ids2 = zip(*batch) + input_ids1 = torch.stack(input_ids1, dim=0) + input_ids2 = torch.stack(input_ids2, dim=0) + cache_batch_text_encoder_outputs( + infos, tokenizers, text_encoders, self.max_token_length, cache_to_disk, input_ids1, input_ids2, output_dtype + ) + else: + for batch in tqdm(batches): + infos, l_tokens, g_tokens, t5_tokens = zip(*batch) + + # stack tokens + # l_tokens = [tokens[0] for tokens in l_tokens] + # g_tokens = [tokens[0] for tokens in g_tokens] + # t5_tokens = [tokens[0] for tokens in t5_tokens] + + cache_batch_text_encoder_outputs_sd3( + infos, + tokenizers[0], + text_encoders, + self.max_token_length, + cache_to_disk, + (l_tokens, g_tokens, t5_tokens), + output_dtype, + ) + + def get_image_size(self, image_path): + if image_path.endswith(".jxl") or image_path.endswith(".JXL"): + return get_jxl_size(image_path) + # return imagesize.get(image_path) + image_size = imagesize.get(image_path) + if image_size[0] <= 0: + # imagesize doesn't work for some images, so use PIL as a fallback + try: + with Image.open(image_path) as img: + image_size = img.size + except Exception as e: + logger.warning(f"failed to get image size: {image_path}, error: {e}") + image_size = (0, 0) + return image_size + + def load_image_with_face_info(self, subset: BaseSubset, image_path: str, alpha_mask=False): + img = load_image(image_path, alpha_mask) + + face_cx = face_cy = face_w = face_h = 0 + if subset.face_crop_aug_range is not None: + tokens = os.path.splitext(os.path.basename(image_path))[0].split("_") + if len(tokens) >= 5: + face_cx = int(tokens[-4]) + face_cy = int(tokens[-3]) + face_w = int(tokens[-2]) + face_h = int(tokens[-1]) + + return img, face_cx, face_cy, face_w, face_h + + # いい感じに切り出す + def crop_target(self, subset: BaseSubset, image, face_cx, face_cy, face_w, face_h): + height, width = image.shape[0:2] + if height == self.height and width == self.width: + return image + + # 画像サイズはsizeより大きいのでリサイズする + face_size = max(face_w, face_h) + size = min(self.height, self.width) # 短いほう + min_scale = max(self.height / height, self.width / width) # 画像がモデル入力サイズぴったりになる倍率（最小の倍率） + min_scale = min(1.0, max(min_scale, size / (face_size * subset.face_crop_aug_range[1]))) # 指定した顔最小サイズ + max_scale = min(1.0, max(min_scale, size / (face_size * subset.face_crop_aug_range[0]))) # 指定した顔最大サイズ + if min_scale >= max_scale: # range指定がmin==max + scale = min_scale + else: + scale = random.uniform(min_scale, max_scale) + + nh = int(height * scale + 0.5) + nw = int(width * scale + 0.5) + assert nh >= self.height and nw >= self.width, f"internal error. small scale {scale}, {width}*{height}" + image = resize_image(image, width, height, nw, nh, subset.resize_interpolation) + face_cx = int(face_cx * scale + 0.5) + face_cy = int(face_cy * scale + 0.5) + height, width = nh, nw + + # 顔を中心として448*640とかへ切り出す + for axis, (target_size, length, face_p) in enumerate(zip((self.height, self.width), (height, width), (face_cy, face_cx))): + p1 = face_p - target_size // 2 # 顔を中心に持ってくるための切り出し位置 + + if subset.random_crop: + # 背景も含めるために顔を中心に置く確率を高めつつずらす + range = max(length - face_p, face_p) # 画像の端から顔中心までの距離の長いほう + p1 = p1 + (random.randint(0, range) + random.randint(0, range)) - range # -range ~ +range までのいい感じの乱数 + else: + # range指定があるときのみ、すこしだけランダムに（わりと適当） + if subset.face_crop_aug_range[0] != subset.face_crop_aug_range[1]: + if face_size > size // 10 and face_size >= 40: + p1 = p1 + random.randint(-face_size // 20, +face_size // 20) + + p1 = max(0, min(p1, length - target_size)) + + if axis == 0: + image = image[p1 : p1 + target_size, :] + else: + image = image[:, p1 : p1 + target_size] + + return image + + def __len__(self): + return self._length + + def __getitem__(self, index): + bucket = self.bucket_manager.buckets[self.buckets_indices[index].bucket_index] + bucket_batch_size = self.buckets_indices[index].bucket_batch_size + image_index = self.buckets_indices[index].batch_index * bucket_batch_size + + if self.caching_mode is not None: # return batch for latents/text encoder outputs caching + return self.get_item_for_caching(bucket, bucket_batch_size, image_index) + + loss_weights = [] + captions = [] + input_ids_list = [] + latents_list = [] + alpha_mask_list = [] + images = [] + original_sizes_hw = [] + crop_top_lefts = [] + target_sizes_hw = [] + flippeds = [] # 変数名が微妙 + text_encoder_outputs_list = [] + custom_attributes = [] + masks = [] + masked_images = [] + + for image_key in bucket[image_index : image_index + bucket_batch_size]: + image_info = self.image_data[image_key] + subset = self.image_to_subset[image_key] + + custom_attributes.append(subset.custom_attributes) + + # in case of fine tuning, is_reg is always False + loss_weights.append(self.prior_loss_weight if image_info.is_reg else 1.0) + + flipped = subset.flip_aug and random.random() < 0.5 # not flipped or flipped with 50% chance + + # image/latentsを処理する + if image_info.latents is not None: # cache_latents=Trueの場合 + original_size = image_info.latents_original_size + crop_ltrb = image_info.latents_crop_ltrb # calc values later if flipped + if not flipped: + latents = image_info.latents + alpha_mask = image_info.alpha_mask + else: + latents = image_info.latents_flipped + alpha_mask = None if image_info.alpha_mask is None else torch.flip(image_info.alpha_mask, [1]) + + image = None + elif image_info.latents_npz is not None: # FineTuningDatasetまたはcache_latents_to_disk=Trueの場合 + latents, original_size, crop_ltrb, flipped_latents, alpha_mask = ( + self.latents_caching_strategy.load_latents_from_disk(image_info.latents_npz, image_info.bucket_reso) + ) + if flipped: + latents = flipped_latents + alpha_mask = None if alpha_mask is None else alpha_mask[:, ::-1].copy() # copy to avoid negative stride problem + del flipped_latents + latents = torch.FloatTensor(latents) + if alpha_mask is not None: + alpha_mask = torch.FloatTensor(alpha_mask) + + image = None + else: + # 画像を読み込み、必要ならcropする + img, face_cx, face_cy, face_w, face_h = self.load_image_with_face_info( + subset, image_info.absolute_path, subset.alpha_mask + ) + im_h, im_w = img.shape[0:2] + + if self.enable_bucket: + img, original_size, crop_ltrb = trim_and_resize_if_required( + subset.random_crop, + img, + image_info.bucket_reso, + image_info.resized_size, + resize_interpolation=image_info.resize_interpolation, + ) + else: + if face_cx > 0: # 顔位置情報あり + img = self.crop_target(subset, img, face_cx, face_cy, face_w, face_h) + elif im_h > self.height or im_w > self.width: + assert ( + subset.random_crop + ), f"image too large, but cropping and bucketing are disabled / 画像サイズが大きいのでface_crop_aug_rangeかrandom_crop、またはbucketを有効にしてください: {image_info.absolute_path}" + if im_h > self.height: + p = random.randint(0, im_h - self.height) + img = img[p : p + self.height] + if im_w > self.width: + p = random.randint(0, im_w - self.width) + img = img[:, p : p + self.width] + + im_h, im_w = img.shape[0:2] + assert ( + im_h == self.height and im_w == self.width + ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}" + + original_size = [im_w, im_h] + crop_ltrb = (0, 0, 0, 0) + + # augmentation + aug = self.aug_helper.get_augmentor(subset.color_aug) + if aug is not None: + # augment RGB channels only + img_rgb = img[:, :, :3] + img_rgb = aug(image=img_rgb)["image"] + img[:, :, :3] = img_rgb + + if flipped: + img = img[:, ::-1, :].copy() # copy to avoid negative stride problem + + if subset.alpha_mask: + if img.shape[2] == 4: + alpha_mask = img[:, :, 3] # [H,W] + alpha_mask = alpha_mask.astype(np.float32) / 255.0 # 0.0~1.0 + alpha_mask = torch.FloatTensor(alpha_mask) + else: + alpha_mask = torch.ones((img.shape[0], img.shape[1]), dtype=torch.float32) + else: + alpha_mask = None + + img = img[:, :, :3] # remove alpha channel + + if self.train_inpainting: + pil_image = transforms.functional.to_pil_image(img) + mask = self.random_mask(pil_image.size) + mask, masked_image = self.prepare_mask_and_masked_image(pil_image, mask) + + masks.append(mask) + masked_images.append(masked_image) + + latents = None + image = self.image_transforms(img) # -1.0~1.0のtorch.Tensorになる + del img + + images.append(image) + latents_list.append(latents) + alpha_mask_list.append(alpha_mask) + + target_size = (image.shape[2], image.shape[1]) if image is not None else (latents.shape[2] * 8, latents.shape[1] * 8) + + if not flipped: + crop_left_top = (crop_ltrb[0], crop_ltrb[1]) + else: + # crop_ltrb[2] is right, so target_size[0] - crop_ltrb[2] is left in flipped image + crop_left_top = (target_size[0] - crop_ltrb[2], crop_ltrb[1]) + + original_sizes_hw.append((int(original_size[1]), int(original_size[0]))) + crop_top_lefts.append((int(crop_left_top[1]), int(crop_left_top[0]))) + target_sizes_hw.append((int(target_size[1]), int(target_size[0]))) + flippeds.append(flipped) + + # captionとtext encoder outputを処理する + caption = image_info.caption # default + + tokenization_required = ( + self.text_encoder_output_caching_strategy is None or self.text_encoder_output_caching_strategy.is_partial + ) + text_encoder_outputs = None + input_ids = None + + if image_info.text_encoder_outputs is not None: + # cached + text_encoder_outputs = image_info.text_encoder_outputs + elif image_info.text_encoder_outputs_npz is not None: + # on disk + text_encoder_outputs = self.text_encoder_output_caching_strategy.load_outputs_npz( + image_info.text_encoder_outputs_npz + ) + else: + tokenization_required = True + text_encoder_outputs_list.append(text_encoder_outputs) + + if tokenization_required: + caption = self.process_caption(subset, image_info.caption) + input_ids = [ids[0] for ids in self.tokenize_strategy.tokenize(caption)] # remove batch dimension + # if self.XTI_layers: + # caption_layer = [] + # for layer in self.XTI_layers: + # token_strings_from = " ".join(self.token_strings) + # token_strings_to = " ".join([f"{x}_{layer}" for x in self.token_strings]) + # caption_ = caption.replace(token_strings_from, token_strings_to) + # caption_layer.append(caption_) + # captions.append(caption_layer) + # else: + # captions.append(caption) + + # if not self.token_padding_disabled: # this option might be omitted in future + # # TODO get_input_ids must support SD3 + # if self.XTI_layers: + # token_caption = self.get_input_ids(caption_layer, self.tokenizers[0]) + # else: + # token_caption = self.get_input_ids(caption, self.tokenizers[0]) + # input_ids_list.append(token_caption) + + # if len(self.tokenizers) > 1: + # if self.XTI_layers: + # token_caption2 = self.get_input_ids(caption_layer, self.tokenizers[1]) + # else: + # token_caption2 = self.get_input_ids(caption, self.tokenizers[1]) + # input_ids2_list.append(token_caption2) + + input_ids_list.append(input_ids) + captions.append(caption) + + def none_or_stack_elements(tensors_list, converter): + # [[clip_l, clip_g, t5xxl], [clip_l, clip_g, t5xxl], ...] -> [torch.stack(clip_l), torch.stack(clip_g), torch.stack(t5xxl)] + if len(tensors_list) == 0 or tensors_list[0] == None or len(tensors_list[0]) == 0 or tensors_list[0][0] is None: + return None + + # old implementation without padding: all elements must have same length + # return [torch.stack([converter(x[i]) for x in tensors_list]) for i in range(len(tensors_list[0]))] + + # new implementation with padding support + result = [] + for i in range(len(tensors_list[0])): + tensors = [x[i] for x in tensors_list] + if tensors[0].ndim == 0: + # scalar value: e.g. ocr mask + result.append(torch.stack([converter(x[i]) for x in tensors_list])) + continue + + min_len = min([len(x) for x in tensors]) + max_len = max([len(x) for x in tensors]) + + if min_len == max_len: + # no padding + result.append(torch.stack([converter(x) for x in tensors])) + else: + # padding + tensors = [converter(x) for x in tensors] + if tensors[0].ndim == 1: + # input_ids or mask + result.append(torch.stack([(torch.nn.functional.pad(x, (0, max_len - x.shape[0]))) for x in tensors])) + else: + # text encoder outputs + result.append(torch.stack([(torch.nn.functional.pad(x, (0, 0, 0, max_len - x.shape[0]))) for x in tensors])) + return result + + # set example + example = {} + example["custom_attributes"] = custom_attributes # may be list of empty dict + example["loss_weights"] = torch.FloatTensor(loss_weights) + example["text_encoder_outputs_list"] = none_or_stack_elements(text_encoder_outputs_list, torch.FloatTensor) + example["input_ids_list"] = none_or_stack_elements(input_ids_list, lambda x: x) + + # if one of alpha_masks is not None, we need to replace None with ones + none_or_not = [x is None for x in alpha_mask_list] + if all(none_or_not): + example["alpha_masks"] = None + elif any(none_or_not): + for i in range(len(alpha_mask_list)): + if alpha_mask_list[i] is None: + if images[i] is not None: + alpha_mask_list[i] = torch.ones((images[i].shape[1], images[i].shape[2]), dtype=torch.float32) + else: + alpha_mask_list[i] = torch.ones( + (latents_list[i].shape[1] * 8, latents_list[i].shape[2] * 8), dtype=torch.float32 + ) + example["alpha_masks"] = torch.stack(alpha_mask_list) + else: + example["alpha_masks"] = torch.stack(alpha_mask_list) + + if images[0] is not None: + images = torch.stack(images) + images = images.to(memory_format=torch.contiguous_format).float() + else: + images = None + example["images"] = images + + example["masks"] = torch.stack(masks) if masks else None + example["masked_images"] = torch.stack(masked_images) if masked_images else None + + example["latents"] = torch.stack(latents_list) if latents_list[0] is not None else None + example["captions"] = captions + + example["original_sizes_hw"] = torch.stack([torch.LongTensor(x) for x in original_sizes_hw]) + example["crop_top_lefts"] = torch.stack([torch.LongTensor(x) for x in crop_top_lefts]) + example["target_sizes_hw"] = torch.stack([torch.LongTensor(x) for x in target_sizes_hw]) + example["flippeds"] = flippeds + + example["network_multipliers"] = torch.FloatTensor([self.network_multiplier] * len(captions)) + + if self.debug_dataset: + example["image_keys"] = bucket[image_index : image_index + self.batch_size] + return example + + def get_item_for_caching(self, bucket, bucket_batch_size, image_index): + captions = [] + images = [] + input_ids1_list = [] + input_ids2_list = [] + absolute_paths = [] + resized_sizes = [] + bucket_reso = None + flip_aug = None + alpha_mask = None + random_crop = None + + for image_key in bucket[image_index : image_index + bucket_batch_size]: + image_info = self.image_data[image_key] + subset = self.image_to_subset[image_key] + + if flip_aug is None: + flip_aug = subset.flip_aug + alpha_mask = subset.alpha_mask + random_crop = subset.random_crop + bucket_reso = image_info.bucket_reso + else: + # TODO そもそも混在してても動くようにしたほうがいい + assert flip_aug == subset.flip_aug, "flip_aug must be same in a batch" + assert alpha_mask == subset.alpha_mask, "alpha_mask must be same in a batch" + assert random_crop == subset.random_crop, "random_crop must be same in a batch" + assert bucket_reso == image_info.bucket_reso, "bucket_reso must be same in a batch" + + caption = image_info.caption # TODO cache some patterns of dropping, shuffling, etc. + + if self.caching_mode == "latents": + image = load_image(image_info.absolute_path) + else: + image = None + + if self.caching_mode == "text": + input_ids1 = self.get_input_ids(caption, self.tokenizers[0]) + input_ids2 = self.get_input_ids(caption, self.tokenizers[1]) + else: + input_ids1 = None + input_ids2 = None + + captions.append(caption) + images.append(image) + input_ids1_list.append(input_ids1) + input_ids2_list.append(input_ids2) + absolute_paths.append(image_info.absolute_path) + resized_sizes.append(image_info.resized_size) + + example = {} + + if images[0] is None: + images = None + example["images"] = images + + example["captions"] = captions + example["input_ids1_list"] = input_ids1_list + example["input_ids2_list"] = input_ids2_list + example["absolute_paths"] = absolute_paths + example["resized_sizes"] = resized_sizes + example["flip_aug"] = flip_aug + example["alpha_mask"] = alpha_mask + example["random_crop"] = random_crop + example["bucket_reso"] = bucket_reso + return example + + @staticmethod + def prepare_mask_and_masked_image(image, mask): + image = np.array(image.convert("RGB")) + image = image.transpose(2, 0, 1) # HWC -> CHW + image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 + + mask = np.array(mask.convert("L")) + mask = mask.astype(np.float32) / 255.0 + mask = mask[None] # 1,H,W + mask[mask < 0.5] = 0 + mask[mask >= 0.5] = 1 + mask = torch.from_numpy(mask) + + masked_image = image * (mask < 0.5) + + return mask, masked_image + + # generate random masks + @staticmethod + def random_mask(im_shape): + from library.mask_generator import random_mask as _random_mask + + w, h = im_shape + return _random_mask(w, h) + + +class DreamBoothDataset(BaseDataset): + IMAGE_INFO_CACHE_FILE = "metadata_cache.json" + + # The is_training_dataset defines the type of dataset, training or validation + # if is_training_dataset is True -> training dataset + # if is_training_dataset is False -> validation dataset + def __init__( + self, + subsets: Sequence[DreamBoothSubset], + is_training_dataset: bool, + batch_size: int, + resolution, + network_multiplier: float, + enable_bucket: bool, + min_bucket_reso: int, + max_bucket_reso: int, + bucket_reso_steps: int, + bucket_no_upscale: bool, + prior_loss_weight: float, + train_inpainting: bool, + debug_dataset: bool, + validation_split: float, + validation_seed: Optional[int], + resize_interpolation: Optional[str], + skip_image_resolution: Optional[Tuple[int, int]] = None, + ) -> None: + super().__init__( + resolution, + network_multiplier, + train_inpainting, + debug_dataset, + resize_interpolation, + skip_image_resolution, + ) + + assert resolution is not None, f"resolution is required / resolution（解像度）指定は必須です" + + self.batch_size = batch_size + self.size = min(self.width, self.height) # 短いほう + self.prior_loss_weight = prior_loss_weight + self.latents_cache = None + self.is_training_dataset = is_training_dataset + self.validation_seed = validation_seed + self.validation_split = validation_split + + self.enable_bucket = enable_bucket + if self.enable_bucket: + min_bucket_reso, max_bucket_reso = self.adjust_min_max_bucket_reso_by_steps( + resolution, min_bucket_reso, max_bucket_reso, bucket_reso_steps + ) + self.min_bucket_reso = min_bucket_reso + self.max_bucket_reso = max_bucket_reso + self.bucket_reso_steps = bucket_reso_steps + self.bucket_no_upscale = bucket_no_upscale + else: + self.min_bucket_reso = None + self.max_bucket_reso = None + self.bucket_reso_steps = None # この情報は使われない + self.bucket_no_upscale = False + + def read_caption(img_path, caption_extension, enable_wildcard): + # captionの候補ファイル名を作る + base_name = os.path.splitext(img_path)[0] + base_name_face_det = base_name + tokens = base_name.split("_") + if len(tokens) >= 5: + base_name_face_det = "_".join(tokens[:-4]) + cap_paths = [base_name + caption_extension, base_name_face_det + caption_extension] + + caption = None + for cap_path in cap_paths: + if os.path.isfile(cap_path): + with open(cap_path, "rt", encoding="utf-8") as f: + try: + lines = f.readlines() + except UnicodeDecodeError as e: + logger.error(f"illegal char in file (not UTF-8) / ファイルにUTF-8以外の文字があります: {cap_path}") + raise e + assert len(lines) > 0, f"caption file is empty / キャプションファイルが空です: {cap_path}" + if enable_wildcard: + caption = "\n".join([line.strip() for line in lines if line.strip() != ""]) # 空行を除く、改行で連結 + else: + caption = lines[0].strip() + break + return caption + + def load_dreambooth_dir(subset: DreamBoothSubset): + if not os.path.isdir(subset.image_dir): + logger.warning(f"not directory: {subset.image_dir}") + return [], [], [] + + info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE) + use_cached_info_for_subset = subset.cache_info + if use_cached_info_for_subset: + logger.info( + f"using cached image info for this subset / このサブセットで、キャッシュされた画像情報を使います: {info_cache_file}" + ) + if not os.path.isfile(info_cache_file): + logger.warning( + f"image info file not found. You can ignore this warning if this is the first time to use this subset" + + " / キャッシュファイルが見つかりませんでした。初回実行時はこの警告を無視してください: {metadata_file}" + ) + use_cached_info_for_subset = False + + if use_cached_info_for_subset: + # json: {`img_path`:{"caption": "caption...", "resolution": [width, height]}, ...} + with open(info_cache_file, "r", encoding="utf-8") as f: + metas = json.load(f) + img_paths = list(metas.keys()) + sizes: List[Optional[Tuple[int, int]]] = [meta["resolution"] for meta in metas.values()] + + # we may need to check image size and existence of image files, but it takes time, so user should check it before training + else: + img_paths = glob_images(subset.image_dir, "*") + sizes: List[Optional[Tuple[int, int]]] = [None] * len(img_paths) + + # new caching: get image size from cache files + strategy = LatentsCachingStrategy.get_strategy() + if strategy is not None: + logger.info("get image size from name of cache files") + + # make image path to npz path mapping + npz_paths = glob.glob(os.path.join(subset.image_dir, "*" + strategy.cache_suffix)) + npz_paths.sort( + key=lambda item: item.rsplit("_", maxsplit=2)[0] + ) # sort by name excluding resolution and cache_suffix + npz_path_index = 0 + + size_set_count = 0 + for i, img_path in enumerate(tqdm(img_paths)): + l = len(os.path.splitext(img_path)[0]) # remove extension + found = False + while npz_path_index < len(npz_paths): # until found or end of npz_paths + # npz_paths are sorted, so if npz_path > img_path, img_path is not found + if npz_paths[npz_path_index][:l] > img_path[:l]: + break + if npz_paths[npz_path_index][:l] == img_path[:l]: # found + found = True + break + npz_path_index += 1 # next npz_path + + if found: + w, h = strategy.get_image_size_from_disk_cache_path(img_path, npz_paths[npz_path_index]) + else: + w, h = None, None + + if w is not None and h is not None: + sizes[i] = (w, h) + size_set_count += 1 + logger.info(f"set image size from cache files: {size_set_count}/{len(img_paths)}") + + if self.skip_image_resolution is not None: + filtered_img_paths = [] + filtered_sizes = [] + skip_image_area = self.skip_image_resolution[0] * self.skip_image_resolution[1] + for img_path, size in zip(img_paths, sizes): + if size is None: # no latents cache file, get image size by reading image file (slow) + size = self.get_image_size(img_path) + if size[0] * size[1] <= skip_image_area: + continue + filtered_img_paths.append(img_path) + filtered_sizes.append(size) + if len(filtered_img_paths) < len(img_paths): + logger.info( + f"filtered {len(img_paths) - len(filtered_img_paths)} images by original resolution from {subset.image_dir}" + ) + img_paths = filtered_img_paths + sizes = filtered_sizes + + # We want to create a training and validation split. This should be improved in the future + # to allow a clearer distinction between training and validation. This can be seen as a + # short-term solution to limit what is necessary to implement validation datasets + # + # We split the dataset for the subset based on if we are doing a validation split + # The self.is_training_dataset defines the type of dataset, training or validation + # if self.is_training_dataset is True -> training dataset + # if self.is_training_dataset is False -> validation dataset + if self.validation_split > 0.0: + # For regularization images we do not want to split this dataset. + if subset.is_reg is True: + # Skip any validation dataset for regularization images + if self.is_training_dataset is False: + img_paths = [] + sizes = [] + # Otherwise the img_paths remain as original img_paths and no split + # required for training images dataset of regularization images + else: + img_paths, sizes = split_train_val( + img_paths, sizes, self.is_training_dataset, self.validation_split, self.validation_seed + ) + + logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files") + + if use_cached_info_for_subset: + captions = [metas[img_path]["caption"] for img_path in img_paths] + missing_captions = [img_path for img_path, caption in zip(img_paths, captions) if caption is None or caption == ""] + else: + # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う + captions = [] + missing_captions = [] + for img_path in tqdm(img_paths, desc="read caption"): + cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard) + if cap_for_img is None and subset.class_tokens is None: + logger.warning( + f"neither caption file nor class tokens are found. use empty caption for {img_path} / キャプションファイルもclass tokenも見つかりませんでした。空のキャプションを使用します: {img_path}" + ) + captions.append("") + missing_captions.append(img_path) + else: + if cap_for_img is None: + captions.append(subset.class_tokens) + missing_captions.append(img_path) + else: + captions.append(cap_for_img) + + self.set_tag_frequency(os.path.basename(subset.image_dir), captions) # タグ頻度を記録 + + if missing_captions: + number_of_missing_captions = len(missing_captions) + number_of_missing_captions_to_show = 5 + remaining_missing_captions = number_of_missing_captions - number_of_missing_captions_to_show + + logger.warning( + f"No caption file found for {number_of_missing_captions} images. Training will continue without captions for these images. If class token exists, it will be used. / {number_of_missing_captions}枚の画像にキャプションファイルが見つかりませんでした。これらの画像についてはキャプションなしで学習を続行します。class tokenが存在する場合はそれを使います。" + ) + for i, missing_caption in enumerate(missing_captions): + if i >= number_of_missing_captions_to_show: + logger.warning(missing_caption + f"... and {remaining_missing_captions} more") + break + logger.warning(missing_caption) + + if not use_cached_info_for_subset and subset.cache_info: + logger.info(f"cache image info for / 画像情報をキャッシュします : {info_cache_file}") + sizes = [self.get_image_size(img_path) for img_path in tqdm(img_paths, desc="get image size")] + matas = {} + for img_path, caption, size in zip(img_paths, captions, sizes): + matas[img_path] = {"caption": caption, "resolution": list(size)} + with open(info_cache_file, "w", encoding="utf-8") as f: + json.dump(matas, f, ensure_ascii=False, indent=2) + logger.info(f"cache image info done for / 画像情報を出力しました : {info_cache_file}") + + # if sizes are not set, image size will be read in make_buckets + return img_paths, captions, sizes + + logger.info("prepare images.") + num_train_images = 0 + num_reg_images = 0 + reg_infos: List[Tuple[ImageInfo, DreamBoothSubset]] = [] + for subset in subsets: + num_repeats = subset.num_repeats if self.is_training_dataset else 1 + if num_repeats < 1: + logger.warning( + f"ignore subset with image_dir='{subset.image_dir}': num_repeats is less than 1 / num_repeatsが1を下回っているためサブセットを無視します: {num_repeats}" + ) + continue + + if subset in self.subsets: + logger.warning( + f"ignore duplicated subset with image_dir='{subset.image_dir}': use the first one / 既にサブセットが登録されているため、重複した後発のサブセットを無視します" + ) + continue + + img_paths, captions, sizes = load_dreambooth_dir(subset) + if len(img_paths) < 1: + logger.warning( + f"ignore subset with image_dir='{subset.image_dir}': no images found / 画像が見つからないためサブセットを無視します" + ) + continue + + if subset.is_reg: + num_reg_images += num_repeats * len(img_paths) + else: + num_train_images += num_repeats * len(img_paths) + + for img_path, caption, size in zip(img_paths, captions, sizes): + info = ImageInfo(img_path, num_repeats, caption, subset.is_reg, img_path, subset.caption_dropout_rate) + info.resize_interpolation = ( + subset.resize_interpolation if subset.resize_interpolation is not None else self.resize_interpolation + ) + if size is not None: + info.image_size = size + if subset.is_reg: + reg_infos.append((info, subset)) + else: + self.register_image(info, subset) + + subset.img_count = len(img_paths) + self.subsets.append(subset) + + images_split_name = "train" if self.is_training_dataset else "validation" + logger.info(f"{num_train_images} {images_split_name} images with repeats.") + + self.num_train_images = num_train_images + + logger.info(f"{num_reg_images} reg images with repeats.") + if num_train_images < num_reg_images: + logger.warning("some of reg images are not used / 正則化画像の数が多いので、一部使用されない正則化画像があります") + + if num_reg_images == 0: + logger.warning("no regularization images / 正則化画像が見つかりませんでした") + else: + # num_repeatsを計算する：どうせ大した数ではないのでループで処理する + n = 0 + first_loop = True + while n < num_train_images: + for info, subset in reg_infos: + if first_loop: + self.register_image(info, subset) + n += info.num_repeats + else: + info.num_repeats += 1 # rewrite registered info + n += 1 + if n >= num_train_images: + break + first_loop = False + + self.num_reg_images = num_reg_images + + +class FineTuningDataset(BaseDataset): + def __init__( + self, + subsets: Sequence[FineTuningSubset], + batch_size: int, + resolution, + network_multiplier: float, + enable_bucket: bool, + min_bucket_reso: int, + max_bucket_reso: int, + bucket_reso_steps: int, + bucket_no_upscale: bool, + train_inpainting: bool, + debug_dataset: bool, + validation_seed: int, + validation_split: float, + resize_interpolation: Optional[str], + skip_image_resolution: Optional[Tuple[int, int]] = None, + ) -> None: + super().__init__( + resolution, + network_multiplier, + train_inpainting, + debug_dataset, + resize_interpolation, + skip_image_resolution, + ) + + self.batch_size = batch_size + self.size = min(self.width, self.height) # 短いほう + self.latents_cache = None + + self.enable_bucket = enable_bucket + if self.enable_bucket: + min_bucket_reso, max_bucket_reso = self.adjust_min_max_bucket_reso_by_steps( + resolution, min_bucket_reso, max_bucket_reso, bucket_reso_steps + ) + self.min_bucket_reso = min_bucket_reso + self.max_bucket_reso = max_bucket_reso + self.bucket_reso_steps = bucket_reso_steps + self.bucket_no_upscale = bucket_no_upscale + else: + self.min_bucket_reso = None + self.max_bucket_reso = None + self.bucket_reso_steps = None # この情報は使われない + self.bucket_no_upscale = False + + self.num_train_images = 0 + self.num_reg_images = 0 + + for subset in subsets: + if subset.num_repeats < 1: + logger.warning( + f"ignore subset with metadata_file='{subset.metadata_file}': num_repeats is less than 1 / num_repeatsが1を下回っているためサブセットを無視します: {subset.num_repeats}" + ) + continue + + if subset in self.subsets: + logger.warning( + f"ignore duplicated subset with metadata_file='{subset.metadata_file}': use the first one / 既にサブセットが登録されているため、重複した後発のサブセットを無視します" + ) + continue + + # メタデータを読み込む + if os.path.exists(subset.metadata_file): + if subset.metadata_file.endswith(".jsonl"): + logger.info(f"loading existing JSOL metadata: {subset.metadata_file}") + # optional JSONL format + # {"image_path": "/path/to/image1.jpg", "caption": "A caption for image1", "image_size": [width, height]} + metadata = {} + with open(subset.metadata_file, "rt", encoding="utf-8") as f: + for line in f: + line_md = json.loads(line) + image_md = {"caption": line_md.get("caption", "")} + if "image_size" in line_md: + image_md["image_size"] = line_md["image_size"] + if "tags" in line_md: + image_md["tags"] = line_md["tags"] + metadata[line_md["image_path"]] = image_md + else: + # standard JSON format + logger.info(f"loading existing metadata: {subset.metadata_file}") + with open(subset.metadata_file, "rt", encoding="utf-8") as f: + metadata = json.load(f) + else: + raise ValueError(f"no metadata / メタデータファイルがありません: {subset.metadata_file}") + + if len(metadata) < 1: + logger.warning( + f"ignore subset with '{subset.metadata_file}': no image entries found / 画像に関するデータが見つからないためサブセットを無視します" + ) + continue + + # Add full path for image + image_dirs = set() + if subset.image_dir is not None: + image_dirs.add(subset.image_dir) + for image_key in metadata.keys(): + if not os.path.isabs(image_key): + assert ( + subset.image_dir is not None + ), f"image_dir is required when image paths are relative / 画像パスが相対パスの場合、image_dirの指定が必要です: {image_key}" + abs_path = os.path.join(subset.image_dir, image_key) + else: + abs_path = image_key + image_dirs.add(os.path.dirname(abs_path)) + + # if image_key does not have extension, try to find image file with supported extensions + if not os.path.splitext(image_key)[1] or not os.path.exists(abs_path): # no extension or file does not exist + paths = glob_images(os.path.dirname(abs_path), os.path.basename(image_key)) + if len(paths) > 0: + abs_path = paths[0] + # If no file is found, we use *.npz file to get image size and for training + + metadata[image_key]["abs_path"] = abs_path + + # Enumerate existing npz files + strategy = LatentsCachingStrategy.get_strategy() + npz_paths = [] + for image_dir in image_dirs: + npz_paths.extend(glob.glob(os.path.join(image_dir, "*" + strategy.cache_suffix))) + npz_paths = sorted(npz_paths, key=lambda item: len(os.path.basename(item)), reverse=True) # longer paths first + + # Match image filename longer to shorter because some images share same prefix + image_keys_sorted_by_length_desc = sorted(metadata.keys(), key=len, reverse=True) + + # Collect tags and sizes + tags_list = [] + size_set_from_metadata = 0 + size_set_from_cache_filename = 0 + num_filtered = 0 + for image_key in image_keys_sorted_by_length_desc: + img_md = metadata[image_key] + caption = img_md.get("caption") + tags = img_md.get("tags") + image_size = img_md.get("image_size") + abs_path = img_md.get("abs_path") + + # search npz if image_size is not given + npz_path = None + if image_size is None: + image_without_ext = os.path.splitext(image_key)[0] + for candidate in npz_paths: + if candidate.startswith(image_without_ext): + npz_path = candidate + break + if npz_path is not None: + npz_paths.remove(npz_path) # remove to avoid matching same file (share prefix) + abs_path = npz_path + + if caption is None: + caption = "" + + if subset.enable_wildcard: + # tags must be single line (split by caption separator) + if tags is not None: + tags = tags.replace("\n", subset.caption_separator) + + # add tags to each line of caption + if tags is not None: + caption = "\n".join( + [f"{line}{subset.caption_separator}{tags}" for line in caption.split("\n") if line.strip() != ""] + ) + tags_list.append(tags) + else: + # use as is + if tags is not None and len(tags) > 0: + if len(caption) > 0: + caption = caption + subset.caption_separator + caption = caption + tags + tags_list.append(tags) + + if caption is None: + caption = "" + + image_info = ImageInfo(image_key, subset.num_repeats, caption, False, abs_path, subset.caption_dropout_rate) + image_info.resize_interpolation = ( + subset.resize_interpolation if subset.resize_interpolation is not None else self.resize_interpolation + ) + + if image_size is not None: + image_info.image_size = tuple(image_size) # width, height + size_set_from_metadata += 1 + elif npz_path is not None: + # get image size from npz filename + w, h = strategy.get_image_size_from_disk_cache_path(abs_path, npz_path) + image_info.image_size = (w, h) + size_set_from_cache_filename += 1 + + if self.skip_image_resolution is not None: + size = image_info.image_size + if size is None: # no image size in metadata or latents cache file, get image size by reading image file (slow) + size = self.get_image_size(abs_path) + image_info.image_size = size + skip_image_area = self.skip_image_resolution[0] * self.skip_image_resolution[1] + if size[0] * size[1] <= skip_image_area: + num_filtered += 1 + continue + + self.register_image(image_info, subset) + + if size_set_from_cache_filename > 0: + logger.info( + f"set image size from cache files: {size_set_from_cache_filename}/{len(image_keys_sorted_by_length_desc)}" + ) + if size_set_from_metadata > 0: + logger.info(f"set image size from metadata: {size_set_from_metadata}/{len(image_keys_sorted_by_length_desc)}") + if num_filtered > 0: + logger.info(f"filtered {num_filtered} images by original resolution from {subset.metadata_file}") + self.num_train_images += len(metadata) * subset.num_repeats + + # TODO do not record tag freq when no tag + self.set_tag_frequency(os.path.basename(subset.metadata_file), tags_list) + subset.img_count = len(metadata) + self.subsets.append(subset) + + +class ControlNetDataset(BaseDataset): + def __init__( + self, + subsets: Sequence[ControlNetSubset], + batch_size: int, + resolution, + network_multiplier: float, + enable_bucket: bool, + min_bucket_reso: int, + max_bucket_reso: int, + bucket_reso_steps: int, + bucket_no_upscale: bool, + train_inpainting: bool, + debug_dataset: bool, + validation_split: float, + validation_seed: Optional[int], + resize_interpolation: Optional[str] = None, + skip_image_resolution: Optional[Tuple[int, int]] = None, + ) -> None: + super().__init__( + resolution, + network_multiplier, + train_inpainting, + debug_dataset, + resize_interpolation, + skip_image_resolution, + ) + + db_subsets = [] + for subset in subsets: + assert ( + not subset.random_crop + ), "random_crop is not supported in ControlNetDataset / random_cropはControlNetDatasetではサポートされていません" + db_subset = DreamBoothSubset( + subset.image_dir, + False, + None, + subset.caption_extension, + subset.cache_info, + False, + subset.num_repeats, + subset.shuffle_caption, + subset.caption_separator, + subset.keep_tokens, + subset.keep_tokens_separator, + subset.secondary_separator, + subset.enable_wildcard, + subset.color_aug, + subset.flip_aug, + subset.face_crop_aug_range, + subset.random_crop, + subset.caption_dropout_rate, + subset.caption_dropout_every_n_epochs, + subset.caption_tag_dropout_rate, + subset.caption_prefix, + subset.caption_suffix, + subset.token_warmup_min, + subset.token_warmup_step, + resize_interpolation=subset.resize_interpolation, + ) + db_subsets.append(db_subset) + + self.dreambooth_dataset_delegate = DreamBoothDataset( + db_subsets, + True, + batch_size, + resolution, + network_multiplier, + enable_bucket, + min_bucket_reso, + max_bucket_reso, + bucket_reso_steps, + bucket_no_upscale, + 1.0, + train_inpainting, + debug_dataset, + validation_split, + validation_seed, + resize_interpolation, + skip_image_resolution, + ) + + # config_util等から参照される値をいれておく（若干微妙なのでなんとかしたい） + self.image_data = self.dreambooth_dataset_delegate.image_data + self.batch_size = batch_size + self.num_train_images = self.dreambooth_dataset_delegate.num_train_images + self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images + self.validation_split = validation_split + self.validation_seed = validation_seed + self.resize_interpolation = resize_interpolation + + # assert all conditioning data exists + missing_imgs = [] + cond_imgs_with_pair = set() + for image_key, info in self.dreambooth_dataset_delegate.image_data.items(): + db_subset = self.dreambooth_dataset_delegate.image_to_subset[image_key] + subset = None + for s in subsets: + if s.image_dir == db_subset.image_dir: + subset = s + break + assert subset is not None, "internal error: subset not found" + + if not os.path.isdir(subset.conditioning_data_dir): + logger.warning(f"not directory: {subset.conditioning_data_dir}") + continue + + img_basename = os.path.splitext(os.path.basename(info.absolute_path))[0] + ctrl_img_path = glob_images(subset.conditioning_data_dir, img_basename) + if len(ctrl_img_path) < 1: + missing_imgs.append(img_basename) + continue + ctrl_img_path = ctrl_img_path[0] + ctrl_img_path = os.path.abspath(ctrl_img_path) # normalize path + + info.cond_img_path = ctrl_img_path + cond_imgs_with_pair.add(os.path.splitext(ctrl_img_path)[0]) # remove extension because Windows is case insensitive + + extra_imgs = [] + for subset in subsets: + conditioning_img_paths = glob_images(subset.conditioning_data_dir, "*") + conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths] # normalize path + extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair]) + + assert ( + len(missing_imgs) == 0 + ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}" + if len(extra_imgs) > 0: + logger.warning(f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}") + + self.conditioning_image_transforms = IMAGE_TRANSFORMS + + def set_current_strategies(self): + return self.dreambooth_dataset_delegate.set_current_strategies() + + def make_buckets(self): + self.dreambooth_dataset_delegate.make_buckets() + self.bucket_manager = self.dreambooth_dataset_delegate.bucket_manager + self.buckets_indices = self.dreambooth_dataset_delegate.buckets_indices + + def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True): + return self.dreambooth_dataset_delegate.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process) + + def new_cache_latents(self, model: Any, accelerator: Accelerator): + return self.dreambooth_dataset_delegate.new_cache_latents(model, accelerator) + + def new_cache_text_encoder_outputs(self, models: List[Any], is_main_process: bool): + return self.dreambooth_dataset_delegate.new_cache_text_encoder_outputs(models, is_main_process) + + def __len__(self): + return self.dreambooth_dataset_delegate.__len__() + + def __getitem__(self, index): + example = self.dreambooth_dataset_delegate[index] + + bucket = self.dreambooth_dataset_delegate.bucket_manager.buckets[ + self.dreambooth_dataset_delegate.buckets_indices[index].bucket_index + ] + bucket_batch_size = self.dreambooth_dataset_delegate.buckets_indices[index].bucket_batch_size + image_index = self.dreambooth_dataset_delegate.buckets_indices[index].batch_index * bucket_batch_size + + conditioning_images = [] + + for i, image_key in enumerate(bucket[image_index : image_index + bucket_batch_size]): + image_info = self.dreambooth_dataset_delegate.image_data[image_key] + + target_size_hw = example["target_sizes_hw"][i] + original_size_hw = example["original_sizes_hw"][i] + crop_top_left = example["crop_top_lefts"][i] + flipped = example["flippeds"][i] + cond_img = load_image(image_info.cond_img_path) + + if self.dreambooth_dataset_delegate.enable_bucket: + assert ( + cond_img.shape[0] == original_size_hw[0] and cond_img.shape[1] == original_size_hw[1] + ), f"size of conditioning image is not match / 画像サイズが合いません: {image_info.absolute_path}" + + cond_img = resize_image( + cond_img, + original_size_hw[1], + original_size_hw[0], + target_size_hw[1], + target_size_hw[0], + self.resize_interpolation, + ) + + # TODO support random crop + # 現在サポートしているcropはrandomではなく中央のみ + h, w = target_size_hw + ct = (cond_img.shape[0] - h) // 2 + cl = (cond_img.shape[1] - w) // 2 + cond_img = cond_img[ct : ct + h, cl : cl + w] + else: + # assert ( + # cond_img.shape[0] == self.height and cond_img.shape[1] == self.width + # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}" + # resize to target + if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]: + cond_img = resize_image( + cond_img, + cond_img.shape[0], + cond_img.shape[1], + target_size_hw[1], + target_size_hw[0], + self.resize_interpolation, + ) + + if flipped: + cond_img = cond_img[:, ::-1, :].copy() # copy to avoid negative stride + + cond_img = self.conditioning_image_transforms(cond_img) + conditioning_images.append(cond_img) + + example["conditioning_images"] = torch.stack(conditioning_images).to(memory_format=torch.contiguous_format).float() + + return example + + +# behave as Dataset mock +class DatasetGroup(torch.utils.data.ConcatDataset): + def __init__(self, datasets: Sequence[Union[DreamBoothDataset, FineTuningDataset]]): + self.datasets: List[Union[DreamBoothDataset, FineTuningDataset]] + + super().__init__(datasets) + + self.image_data = {} + self.num_train_images = 0 + self.num_reg_images = 0 + + # simply concat together + # TODO: handling image_data key duplication among dataset + # In practical, this is not the big issue because image_data is accessed from outside of dataset only for debug_dataset. + for dataset in datasets: + self.image_data.update(dataset.image_data) + self.num_train_images += dataset.num_train_images + self.num_reg_images += dataset.num_reg_images + + def add_replacement(self, str_from, str_to): + for dataset in self.datasets: + dataset.add_replacement(str_from, str_to) + + # def make_buckets(self): + # for dataset in self.datasets: + # dataset.make_buckets() + + def set_text_encoder_output_caching_strategy(self, strategy: TextEncoderOutputsCachingStrategy): + """ + DataLoader is run in multiple processes, so we need to set the strategy manually. + """ + for dataset in self.datasets: + dataset.set_text_encoder_output_caching_strategy(strategy) + + def enable_XTI(self, *args, **kwargs): + for dataset in self.datasets: + dataset.enable_XTI(*args, **kwargs) + + def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True, file_suffix=".npz"): + for i, dataset in enumerate(self.datasets): + logger.info(f"[Dataset {i}]") + dataset.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process, file_suffix) + + def new_cache_latents(self, model: Any, accelerator: Accelerator): + for i, dataset in enumerate(self.datasets): + logger.info(f"[Dataset {i}]") + dataset.new_cache_latents(model, accelerator) + accelerator.wait_for_everyone() + + def cache_text_encoder_outputs( + self, tokenizers, text_encoders, device, weight_dtype, cache_to_disk=False, is_main_process=True + ): + for i, dataset in enumerate(self.datasets): + logger.info(f"[Dataset {i}]") + dataset.cache_text_encoder_outputs(tokenizers, text_encoders, device, weight_dtype, cache_to_disk, is_main_process) + + def cache_text_encoder_outputs_sd3( + self, tokenizer, text_encoders, device, output_dtype, te_dtypes, cache_to_disk=False, is_main_process=True, batch_size=None + ): + for i, dataset in enumerate(self.datasets): + logger.info(f"[Dataset {i}]") + dataset.cache_text_encoder_outputs_sd3( + tokenizer, text_encoders, device, output_dtype, te_dtypes, cache_to_disk, is_main_process, batch_size + ) + + def new_cache_text_encoder_outputs(self, models: List[Any], accelerator: Accelerator): + for i, dataset in enumerate(self.datasets): + logger.info(f"[Dataset {i}]") + dataset.new_cache_text_encoder_outputs(models, accelerator) + accelerator.wait_for_everyone() + + def set_caching_mode(self, caching_mode): + for dataset in self.datasets: + dataset.set_caching_mode(caching_mode) + + def verify_bucket_reso_steps(self, min_steps: int): + for dataset in self.datasets: + dataset.verify_bucket_reso_steps(min_steps) + + def get_resolutions(self) -> List[Tuple[int, int]]: + return [(dataset.width, dataset.height) for dataset in self.datasets] + + def is_latent_cacheable(self) -> bool: + return all([dataset.is_latent_cacheable() for dataset in self.datasets]) + + def is_text_encoder_output_cacheable(self, cache_supports_dropout: bool = False) -> bool: + return all([dataset.is_text_encoder_output_cacheable(cache_supports_dropout) for dataset in self.datasets]) + + def set_current_strategies(self): + for dataset in self.datasets: + dataset.set_current_strategies() + + def set_current_epoch(self, epoch): + for dataset in self.datasets: + dataset.set_current_epoch(epoch) + + def set_current_step(self, step): + for dataset in self.datasets: + dataset.set_current_step(step) + + def set_max_train_steps(self, max_train_steps): + for dataset in self.datasets: + dataset.set_max_train_steps(max_train_steps) + + def disable_token_padding(self): + for dataset in self.datasets: + dataset.disable_token_padding() + + +def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool, alpha_mask: bool): + expected_latents_size = (reso[1] // 8, reso[0] // 8) # bucket_resoはWxHなので注意 + + if not os.path.exists(npz_path): + return False + + try: + npz = np.load(npz_path) + if "latents" not in npz or "original_size" not in npz or "crop_ltrb" not in npz: # old ver? + return False + if npz["latents"].shape[1:3] != expected_latents_size: + return False + + if flip_aug: + if "latents_flipped" not in npz: + return False + if npz["latents_flipped"].shape[1:3] != expected_latents_size: + return False + + if alpha_mask: + if "alpha_mask" not in npz: + return False + if (npz["alpha_mask"].shape[1], npz["alpha_mask"].shape[0]) != reso: # HxW => WxH != reso + return False + else: + if "alpha_mask" in npz: + return False + except Exception as e: + logger.error(f"Error loading file: {npz_path}") + raise e + + return True + + +# 戻り値は、latents_tensor, (original_size width, original_size height), (crop left, crop top) +# TODO update to use CachingStrategy +# def load_latents_from_disk( +# npz_path, +# ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: +# npz = np.load(npz_path) +# if "latents" not in npz: +# raise ValueError(f"error: npz is old format. please re-generate {npz_path}") + +# latents = npz["latents"] +# original_size = npz["original_size"].tolist() +# crop_ltrb = npz["crop_ltrb"].tolist() +# flipped_latents = npz["latents_flipped"] if "latents_flipped" in npz else None +# alpha_mask = npz["alpha_mask"] if "alpha_mask" in npz else None +# return latents, original_size, crop_ltrb, flipped_latents, alpha_mask + + +# def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_ltrb, flipped_latents_tensor=None, alpha_mask=None): +# kwargs = {} +# if flipped_latents_tensor is not None: +# kwargs["latents_flipped"] = flipped_latents_tensor.float().cpu().numpy() +# if alpha_mask is not None: +# kwargs["alpha_mask"] = alpha_mask.float().cpu().numpy() +# np.savez( +# npz_path, +# latents=latents_tensor.float().cpu().numpy(), +# original_size=np.array(original_size), +# crop_ltrb=np.array(crop_ltrb), +# **kwargs, +# ) + + +def debug_dataset(train_dataset, show_input_ids=False): + logger.info(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}") + logger.info( + "`S` for next step, `E` for next epoch no. , Escape for exit. / Sキーで次のステップ、Eキーで次のエポック、Escキーで中断、終了します" + ) + + epoch = 1 + while True: + logger.info(f"") + logger.info(f"epoch: {epoch}") + + steps = (epoch - 1) * len(train_dataset) + 1 + indices = list(range(len(train_dataset))) + random.shuffle(indices) + + k = 0 + for i, idx in enumerate(indices): + train_dataset.set_current_epoch(epoch) + train_dataset.set_current_step(steps) + logger.info(f"steps: {steps} ({i + 1}/{len(train_dataset)})") + + example = train_dataset[idx] + if example["latents"] is not None: + logger.info(f"sample has latents from npz file: {example['latents'].size()}") + for j, (ik, cap, lw, orgsz, crptl, trgsz, flpdz) in enumerate( + zip( + example["image_keys"], + example["captions"], + example["loss_weights"], + # example["input_ids"], + example["original_sizes_hw"], + example["crop_top_lefts"], + example["target_sizes_hw"], + example["flippeds"], + ) + ): + logger.info( + f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}", original size: {orgsz}, crop top left: {crptl}, target size: {trgsz}, flipped: {flpdz}' + ) + if "network_multipliers" in example: + logger.info(f"network multiplier: {example['network_multipliers'][j]}") + if "custom_attributes" in example: + logger.info(f"custom attributes: {example['custom_attributes'][j]}") + + # if show_input_ids: + # logger.info(f"input ids: {iid}") + # if "input_ids2" in example: + # logger.info(f"input ids2: {example['input_ids2'][j]}") + if example["images"] is not None: + im = example["images"][j] + logger.info(f"image size: {im.size()}") + im = ((im.numpy() + 1.0) * 127.5).astype(np.uint8) + im = np.transpose(im, (1, 2, 0)) # c,H,W -> H,W,c + im = im[:, :, ::-1] # RGB -> BGR (OpenCV) + + if "conditioning_images" in example: + cond_img = example["conditioning_images"][j] + logger.info(f"conditioning image size: {cond_img.size()}") + cond_img = ((cond_img.numpy() + 1.0) * 127.5).astype(np.uint8) + cond_img = np.transpose(cond_img, (1, 2, 0)) + cond_img = cond_img[:, :, ::-1] + if os.name == "nt": + cv2.imshow("cond_img", cond_img) + + if "alpha_masks" in example and example["alpha_masks"] is not None: + alpha_mask = example["alpha_masks"][j] + logger.info(f"alpha mask size: {alpha_mask.size()}") + alpha_mask = (alpha_mask.numpy() * 255.0).astype(np.uint8) + if os.name == "nt": + cv2.imshow("alpha_mask", alpha_mask) + + if os.name == "nt": # only windows + cv2.imshow("img", im) + k = cv2.waitKey() + cv2.destroyAllWindows() + if k == 27 or k == ord("s") or k == ord("e"): + break + steps += 1 + + if k == ord("e"): + break + if k == 27 or (example["images"] is None and i >= 8): + k = 27 + break + if k == 27: + break + + epoch += 1 + + +def glob_images(directory, base="*"): + img_paths = [] + for ext in IMAGE_EXTENSIONS: + if base == "*": + img_paths.extend(glob.glob(os.path.join(glob.escape(directory), base + ext))) + else: + img_paths.extend(glob.glob(glob.escape(os.path.join(directory, base + ext)))) + img_paths = list(set(img_paths)) # 重複を排除 + img_paths.sort() + return img_paths + + +def glob_images_pathlib(dir_path, recursive): + image_paths = [] + if recursive: + for ext in IMAGE_EXTENSIONS: + image_paths += list(dir_path.rglob("*" + ext)) + else: + for ext in IMAGE_EXTENSIONS: + image_paths += list(dir_path.glob("*" + ext)) + image_paths = list(set(image_paths)) # 重複を排除 + image_paths.sort() + return image_paths + + +class MinimalDataset(BaseDataset): + def __init__(self, resolution, network_multiplier, train_inpainting=False, debug_dataset=False): + super().__init__(resolution, network_multiplier, train_inpainting, debug_dataset) + + self.num_train_images = 0 # update in subclass + self.num_reg_images = 0 # update in subclass + self.datasets = [self] + self.batch_size = 1 # update in subclass + + self.subsets = [self] + self.num_repeats = 1 # update in subclass if needed + self.img_count = 1 # update in subclass if needed + self.bucket_info = {} + self.is_reg = False + self.image_dir = "dummy" # for metadata + + def verify_bucket_reso_steps(self, min_steps: int): + pass + + def is_latent_cacheable(self) -> bool: + return False + + def __len__(self): + raise NotImplementedError + + # override to avoid shuffling buckets + def set_current_epoch(self, epoch): + self.current_epoch = epoch + + def __getitem__(self, idx): + r""" + The subclass may have image_data for debug_dataset, which is a dict of ImageInfo objects. + + Returns: example like this: + + for i in range(batch_size): + image_key = ... # whatever hashable + image_keys.append(image_key) + + image = ... # PIL Image + img_tensor = self.image_transforms(img) + images.append(img_tensor) + + caption = ... # str + input_ids = self.get_input_ids(caption) + input_ids_list.append(input_ids) + + captions.append(caption) + + images = torch.stack(images, dim=0) + input_ids_list = torch.stack(input_ids_list, dim=0) + example = { + "images": images, + "input_ids": input_ids_list, + "captions": captions, # for debug_dataset + "latents": None, + "image_keys": image_keys, # for debug_dataset + "loss_weights": torch.ones(batch_size, dtype=torch.float32), + } + return example + """ + raise NotImplementedError + + def get_resolutions(self) -> List[Tuple[int, int]]: + return [] + + +def load_arbitrary_dataset(args, tokenizer=None) -> MinimalDataset: + module = ".".join(args.dataset_class.split(".")[:-1]) + dataset_class = args.dataset_class.split(".")[-1] + module = importlib.import_module(module) + dataset_class = getattr(module, dataset_class) + train_dataset_group: MinimalDataset = dataset_class(tokenizer, args.max_token_length, args.resolution, args.debug_dataset) + return train_dataset_group + + +def load_image(image_path, alpha=False): + try: + with Image.open(image_path) as image: + if alpha: + if not image.mode == "RGBA": + image = image.convert("RGBA") + else: + if not image.mode == "RGB": + image = image.convert("RGB") + img = np.array(image, np.uint8) + return img + except (IOError, OSError) as e: + logger.error(f"Error loading file: {image_path}") + raise e + + +# 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top, crop right, crop bottom) +def trim_and_resize_if_required( + random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int], resize_interpolation: Optional[str] = None +) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int, int, int]]: + image_height, image_width = image.shape[0:2] + original_size = (image_width, image_height) # size before resize + + if image_width != resized_size[0] or image_height != resized_size[1]: + image = resize_image(image, image_width, image_height, resized_size[0], resized_size[1], resize_interpolation) + + image_height, image_width = image.shape[0:2] + + if image_width > reso[0]: + trim_size = image_width - reso[0] + p = trim_size // 2 if not random_crop else random.randint(0, trim_size) + # logger.info(f"w {trim_size} {p}") + image = image[:, p : p + reso[0]] + if image_height > reso[1]: + trim_size = image_height - reso[1] + p = trim_size // 2 if not random_crop else random.randint(0, trim_size) + # logger.info(f"h {trim_size} {p}) + image = image[p : p + reso[1]] + + # random cropの場合のcropされた値をどうcrop left/topに反映するべきか全くアイデアがない + # I have no idea how to reflect the cropped value in crop left/top in the case of random crop + + crop_ltrb = BucketManager.get_crop_ltrb(reso, original_size) + + assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}" + return image, original_size, crop_ltrb + + +# for new_cache_latents +def load_images_and_masks_for_caching( + image_infos: List[ImageInfo], use_alpha_mask: bool, random_crop: bool +) -> Tuple[torch.Tensor, List[np.ndarray], List[Tuple[int, int]], List[Tuple[int, int, int, int]]]: + r""" + requires image_infos to have: [absolute_path or image], bucket_reso, resized_size + + returns: image_tensor, alpha_masks, original_sizes, crop_ltrbs + + image_tensor: torch.Tensor = torch.Size([B, 3, H, W]), ...], normalized to [-1, 1] + alpha_masks: List[np.ndarray] = [np.ndarray([H, W]), ...], normalized to [0, 1] + original_sizes: List[Tuple[int, int]] = [(W, H), ...] + crop_ltrbs: List[Tuple[int, int, int, int]] = [(L, T, R, B), ...] + """ + images: List[torch.Tensor] = [] + alpha_masks: List[np.ndarray] = [] + original_sizes: List[Tuple[int, int]] = [] + crop_ltrbs: List[Tuple[int, int, int, int]] = [] + for info in image_infos: + image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8) + # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要 + image, original_size, crop_ltrb = trim_and_resize_if_required( + random_crop, image, info.bucket_reso, info.resized_size, resize_interpolation=info.resize_interpolation + ) + + original_sizes.append(original_size) + crop_ltrbs.append(crop_ltrb) + + if use_alpha_mask: + if image.shape[2] == 4: + alpha_mask = image[:, :, 3] # [H,W] + alpha_mask = alpha_mask.astype(np.float32) / 255.0 + alpha_mask = torch.FloatTensor(alpha_mask) # [H,W] + else: + alpha_mask = torch.ones_like(image[:, :, 0], dtype=torch.float32) # [H,W] + else: + alpha_mask = None + alpha_masks.append(alpha_mask) + + image = image[:, :, :3] # remove alpha channel if exists + image = IMAGE_TRANSFORMS(image) + images.append(image) + + img_tensor = torch.stack(images, dim=0) + return img_tensor, alpha_masks, original_sizes, crop_ltrbs + + +def cache_batch_latents( + vae: AutoencoderKL, cache_to_disk: bool, image_infos: List[ImageInfo], flip_aug: bool, use_alpha_mask: bool, random_crop: bool +) -> None: + r""" + requires image_infos to have: absolute_path, bucket_reso, resized_size, latents_npz + optionally requires image_infos to have: image + if cache_to_disk is True, set info.latents_npz + flipped latents is also saved if flip_aug is True + if cache_to_disk is False, set info.latents + latents_flipped is also set if flip_aug is True + latents_original_size and latents_crop_ltrb are also set + """ + images = [] + alpha_masks: List[np.ndarray] = [] + for info in image_infos: + image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8) + # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要 + image, original_size, crop_ltrb = trim_and_resize_if_required( + random_crop, image, info.bucket_reso, info.resized_size, resize_interpolation=info.resize_interpolation + ) + + info.latents_original_size = original_size + info.latents_crop_ltrb = crop_ltrb + + if use_alpha_mask: + if image.shape[2] == 4: + alpha_mask = image[:, :, 3] # [H,W] + alpha_mask = alpha_mask.astype(np.float32) / 255.0 + alpha_mask = torch.FloatTensor(alpha_mask) # [H,W] + else: + alpha_mask = torch.ones_like(image[:, :, 0], dtype=torch.float32) # [H,W] + else: + alpha_mask = None + alpha_masks.append(alpha_mask) + + image = image[:, :, :3] # remove alpha channel if exists + image = IMAGE_TRANSFORMS(image) + images.append(image) + + img_tensors = torch.stack(images, dim=0) + img_tensors = img_tensors.to(device=vae.device, dtype=vae.dtype) + + with torch.no_grad(): + latents = vae.encode(img_tensors).latent_dist.sample().to("cpu") + + if flip_aug: + img_tensors = torch.flip(img_tensors, dims=[3]) + with torch.no_grad(): + flipped_latents = vae.encode(img_tensors).latent_dist.sample().to("cpu") + else: + flipped_latents = [None] * len(latents) + + for info, latent, flipped_latent, alpha_mask in zip(image_infos, latents, flipped_latents, alpha_masks): + # check NaN + if torch.isnan(latents).any() or (flipped_latent is not None and torch.isnan(flipped_latent).any()): + raise RuntimeError(f"NaN detected in latents: {info.absolute_path}") + + if cache_to_disk: + # save_latents_to_disk( + # info.latents_npz, + # latent, + # info.latents_original_size, + # info.latents_crop_ltrb, + # flipped_latent, + # alpha_mask, + # ) + pass + else: + info.latents = latent + if flip_aug: + info.latents_flipped = flipped_latent + info.alpha_mask = alpha_mask + + if not HIGH_VRAM: + clean_memory_on_device(vae.device) + + +def cache_batch_text_encoder_outputs( + image_infos, tokenizers, text_encoders, max_token_length, cache_to_disk, input_ids1, input_ids2, dtype +): + input_ids1 = input_ids1.to(text_encoders[0].device) + input_ids2 = input_ids2.to(text_encoders[1].device) + + with torch.no_grad(): + b_hidden_state1, b_hidden_state2, b_pool2 = get_hidden_states_sdxl( + max_token_length, + input_ids1, + input_ids2, + tokenizers[0], + tokenizers[1], + text_encoders[0], + text_encoders[1], + dtype, + ) + + # ここでcpuに移動しておかないと、上書きされてしまう + b_hidden_state1 = b_hidden_state1.detach().to("cpu") # b,n*75+2,768 + b_hidden_state2 = b_hidden_state2.detach().to("cpu") # b,n*75+2,1280 + b_pool2 = b_pool2.detach().to("cpu") # b,1280 + + for info, hidden_state1, hidden_state2, pool2 in zip(image_infos, b_hidden_state1, b_hidden_state2, b_pool2): + if cache_to_disk: + save_text_encoder_outputs_to_disk(info.text_encoder_outputs_npz, hidden_state1, hidden_state2, pool2) + else: + info.text_encoder_outputs1 = hidden_state1 + info.text_encoder_outputs2 = hidden_state2 + info.text_encoder_pool2 = pool2 + + +def cache_batch_text_encoder_outputs_sd3( + image_infos, tokenizer, text_encoders, max_token_length, cache_to_disk, input_ids, output_dtype +): + # make input_ids for each text encoder + l_tokens, g_tokens, t5_tokens = input_ids + + clip_l, clip_g, t5xxl = text_encoders + with torch.no_grad(): + b_lg_out, b_t5_out, b_pool = sd3_utils.get_cond_from_tokens( + l_tokens, g_tokens, t5_tokens, clip_l, clip_g, t5xxl, "cpu", output_dtype + ) + b_lg_out = b_lg_out.detach() + b_t5_out = b_t5_out.detach() + b_pool = b_pool.detach() + + for info, lg_out, t5_out, pool in zip(image_infos, b_lg_out, b_t5_out, b_pool): + # debug: NaN check + if torch.isnan(lg_out).any() or torch.isnan(t5_out).any() or torch.isnan(pool).any(): + raise RuntimeError(f"NaN detected in text encoder outputs: {info.absolute_path}") + + if cache_to_disk: + save_text_encoder_outputs_to_disk(info.text_encoder_outputs_npz, lg_out, t5_out, pool) + else: + info.text_encoder_outputs1 = lg_out + info.text_encoder_outputs2 = t5_out + info.text_encoder_pool2 = pool + + +def save_text_encoder_outputs_to_disk(npz_path, hidden_state1, hidden_state2, pool2): + np.savez( + npz_path, + hidden_state1=hidden_state1.cpu().float().numpy(), + hidden_state2=hidden_state2.cpu().float().numpy(), + pool2=pool2.cpu().float().numpy(), + ) + + +def load_text_encoder_outputs_from_disk(npz_path): + with np.load(npz_path) as f: + hidden_state1 = torch.from_numpy(f["hidden_state1"]) + hidden_state2 = torch.from_numpy(f["hidden_state2"]) if "hidden_state2" in f else None + pool2 = torch.from_numpy(f["pool2"]) if "pool2" in f else None + return hidden_state1, hidden_state2, pool2 + + +# endregion + +# region モジュール入れ替え部 +""" +高速化のためのモジュール入れ替え +""" + +# FlashAttentionを使うCrossAttention +# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py +# LICENSE MIT https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/LICENSE + +# constants + +EPSILON = 1e-6 + +# helper functions + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def model_hash(filename): + """Old model hash used by stable-diffusion-webui""" + try: + with open(filename, "rb") as file: + m = hashlib.sha256() + + file.seek(0x100000) + m.update(file.read(0x10000)) + return m.hexdigest()[0:8] + except FileNotFoundError: + return "NOFILE" + except IsADirectoryError: # Linux? + return "IsADirectory" + except PermissionError: # Windows + return "IsADirectory" + + +def calculate_sha256(filename): + """New model hash used by stable-diffusion-webui""" + try: + hash_sha256 = hashlib.sha256() + blksize = 1024 * 1024 + + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(blksize), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + except FileNotFoundError: + return "NOFILE" + except IsADirectoryError: # Linux? + return "IsADirectory" + except PermissionError: # Windows + return "IsADirectory" + + +def precalculate_safetensors_hashes(tensors, metadata): + """Precalculate the model hashes needed by sd-webui-additional-networks to + save time on indexing the model later.""" + + # Because writing user metadata to the file can change the result of + # sd_models.model_hash(), only retain the training metadata for purposes of + # calculating the hash, as they are meant to be immutable + metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")} + + bytes = safetensors.torch.save(tensors, metadata) + b = BytesIO(bytes) + + model_hash = addnet_hash_safetensors(b) + legacy_hash = addnet_hash_legacy(b) + return model_hash, legacy_hash + + +def addnet_hash_legacy(b): + """Old model hash used by sd-webui-additional-networks for .safetensors format files""" + m = hashlib.sha256() + + b.seek(0x100000) + m.update(b.read(0x10000)) + return m.hexdigest()[0:8] + + +def addnet_hash_safetensors(b): + """New model hash used by sd-webui-additional-networks for .safetensors format files""" + hash_sha256 = hashlib.sha256() + blksize = 1024 * 1024 + + b.seek(0) + header = b.read(8) + n = int.from_bytes(header, "little") + + offset = n + 8 + b.seek(offset) + for chunk in iter(lambda: b.read(blksize), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + + +def get_git_revision_hash() -> str: + try: + return subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=os.path.dirname(__file__)).decode("ascii").strip() + except: + return "(unknown)" + + +# def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers): +# replace_attentions_for_hypernetwork() +# # unet is not used currently, but it is here for future use +# unet.enable_xformers_memory_efficient_attention() +# return +# if mem_eff_attn: +# unet.set_attn_processor(FlashAttnProcessor()) +# elif xformers: +# unet.enable_xformers_memory_efficient_attention() + + +# def replace_unet_cross_attn_to_xformers(): +# logger.info("CrossAttention.forward has been replaced to enable xformers.") +# try: +# import xformers.ops +# except ImportError: +# raise ImportError("No xformers / xformersがインストールされていないようです") + +# def forward_xformers(self, x, context=None, mask=None): +# h = self.heads +# q_in = self.to_q(x) + +# context = default(context, x) +# context = context.to(x.dtype) + +# if hasattr(self, "hypernetwork") and self.hypernetwork is not None: +# context_k, context_v = self.hypernetwork.forward(x, context) +# context_k = context_k.to(x.dtype) +# context_v = context_v.to(x.dtype) +# else: +# context_k = context +# context_v = context + +# k_in = self.to_k(context_k) +# v_in = self.to_v(context_v) + +# q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b n h d", h=h), (q_in, k_in, v_in)) +# del q_in, k_in, v_in + +# q = q.contiguous() +# k = k.contiguous() +# v = v.contiguous() +# out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) # 最適なのを選んでくれる + +# out = rearrange(out, "b n h d -> b n (h d)", h=h) + +# # diffusers 0.7.0~ +# out = self.to_out[0](out) +# out = self.to_out[1](out) +# return out + + +# diffusers.models.attention.CrossAttention.forward = forward_xformers +def replace_unet_modules(unet: UNet2DConditionModel, mem_eff_attn, xformers, sdpa): + if mem_eff_attn: + logger.info("Enable memory efficient attention for U-Net") + unet.set_use_memory_efficient_attention(False, True) + elif xformers: + logger.info("Enable xformers for U-Net") + try: + import xformers.ops + except ImportError: + raise ImportError("No xformers / xformersがインストールされていないようです") + + unet.set_use_memory_efficient_attention(True, False) + elif sdpa: + logger.info("Enable SDPA for U-Net") + unet.set_use_sdpa(True) + + +""" +def replace_vae_modules(vae: diffusers.models.AutoencoderKL, mem_eff_attn, xformers): + # vae is not used currently, but it is here for future use + if mem_eff_attn: + replace_vae_attn_to_memory_efficient() + elif xformers: + # とりあえずDiffusersのxformersを使う。AttentionがあるのはMidBlockのみ + logger.info("Use Diffusers xformers for VAE") + vae.encoder.mid_block.attentions[0].set_use_memory_efficient_attention_xformers(True) + vae.decoder.mid_block.attentions[0].set_use_memory_efficient_attention_xformers(True) + + +def replace_vae_attn_to_memory_efficient(): + logger.info("AttentionBlock.forward has been replaced to FlashAttention (not xformers)") + flash_func = FlashAttentionFunction + + def forward_flash_attn(self, hidden_states): + logger.info("forward_flash_attn") + q_bucket_size = 512 + k_bucket_size = 1024 + + residual = hidden_states + batch, channel, height, width = hidden_states.shape + + # norm + hidden_states = self.group_norm(hidden_states) + + hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2) + + # proj to q, k, v + query_proj = self.query(hidden_states) + key_proj = self.key(hidden_states) + value_proj = self.value(hidden_states) + + query_proj, key_proj, value_proj = map( + lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.num_heads), (query_proj, key_proj, value_proj) + ) + + out = flash_func.apply(query_proj, key_proj, value_proj, None, False, q_bucket_size, k_bucket_size) + + out = rearrange(out, "b h n d -> b n (h d)") + + # compute next hidden_states + hidden_states = self.proj_attn(hidden_states) + hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width) + + # res connect and rescale + hidden_states = (hidden_states + residual) / self.rescale_output_factor + return hidden_states + + diffusers.models.attention.AttentionBlock.forward = forward_flash_attn +""" + + +# endregion + + +# region arguments + + +def load_metadata_from_safetensors(safetensors_file: str) -> dict: + """r + This method locks the file. see https://github.com/huggingface/safetensors/issues/164 + If the file isn't .safetensors or doesn't have metadata, return empty dict. + """ + if os.path.splitext(safetensors_file)[1] != ".safetensors": + return {} + + with safetensors.safe_open(safetensors_file, framework="pt", device="cpu") as f: + metadata = f.metadata() + if metadata is None: + metadata = {} + return metadata + + +# this metadata is referred from train_network and various scripts, so we wrote here +SS_METADATA_KEY_V2 = "ss_v2" +SS_METADATA_KEY_BASE_MODEL_VERSION = "ss_base_model_version" +SS_METADATA_KEY_NETWORK_MODULE = "ss_network_module" +SS_METADATA_KEY_NETWORK_DIM = "ss_network_dim" +SS_METADATA_KEY_NETWORK_ALPHA = "ss_network_alpha" +SS_METADATA_KEY_NETWORK_ARGS = "ss_network_args" + +SS_METADATA_MINIMUM_KEYS = [ + SS_METADATA_KEY_V2, + SS_METADATA_KEY_BASE_MODEL_VERSION, + SS_METADATA_KEY_NETWORK_MODULE, + SS_METADATA_KEY_NETWORK_DIM, + SS_METADATA_KEY_NETWORK_ALPHA, + SS_METADATA_KEY_NETWORK_ARGS, +] + + +def build_minimum_network_metadata( + v2: Optional[str], + base_model: Optional[str], + network_module: str, + network_dim: str, + network_alpha: str, + network_args: Optional[dict], +): + # old LoRA doesn't have base_model + metadata = { + SS_METADATA_KEY_NETWORK_MODULE: network_module, + SS_METADATA_KEY_NETWORK_DIM: network_dim, + SS_METADATA_KEY_NETWORK_ALPHA: network_alpha, + } + if v2 is not None: + metadata[SS_METADATA_KEY_V2] = v2 + if base_model is not None: + metadata[SS_METADATA_KEY_BASE_MODEL_VERSION] = base_model + if network_args is not None: + metadata[SS_METADATA_KEY_NETWORK_ARGS] = json.dumps(network_args) + return metadata + + +def get_sai_model_spec( + state_dict: dict, + args: argparse.Namespace, + sdxl: bool, + lora: bool, + textual_inversion: bool, + is_stable_diffusion_ckpt: Optional[bool] = None, # None for TI and LoRA + sd3: str = None, + flux: str = None, # "dev", "schnell" or "chroma" + lumina: str = None, + optional_metadata: dict[str, str] | None = None, +): + timestamp = time.time() + + v2 = args.v2 + v_parameterization = args.v_parameterization + reso = args.resolution + + title = args.metadata_title if args.metadata_title is not None else args.output_name + + if args.min_timestep is not None or args.max_timestep is not None: + min_time_step = args.min_timestep if args.min_timestep is not None else 0 + max_time_step = args.max_timestep if args.max_timestep is not None else 1000 + timesteps = (min_time_step, max_time_step) + else: + timesteps = None + + # Convert individual model parameters to model_config dict + # TODO: Update calls to this function to pass in the model config + model_config = {} + if sd3 is not None: + model_config["sd3"] = sd3 + if flux is not None: + model_config["flux"] = flux + if lumina is not None: + model_config["lumina"] = lumina + + # Extract metadata_* fields from args and merge with optional_metadata + extracted_metadata = {} + + # Extract all metadata_* attributes from args + for attr_name in dir(args): + if attr_name.startswith("metadata_") and not attr_name.startswith("metadata___"): + value = getattr(args, attr_name, None) + if value is not None: + # Remove metadata_ prefix and exclude already handled fields + field_name = attr_name[9:] # len("metadata_") = 9 + if field_name not in ["title", "author", "description", "license", "tags"]: + extracted_metadata[field_name] = value + + # Merge extracted metadata with provided optional_metadata + all_optional_metadata = {**extracted_metadata} + if optional_metadata: + all_optional_metadata.update(optional_metadata) + + metadata = sai_model_spec.build_metadata( + state_dict, + v2, + v_parameterization, + sdxl, + lora, + textual_inversion, + timestamp, + title=title, + reso=reso, + is_stable_diffusion_ckpt=is_stable_diffusion_ckpt, + author=args.metadata_author, + description=args.metadata_description, + license=args.metadata_license, + tags=args.metadata_tags, + timesteps=timesteps, + clip_skip=args.clip_skip, # None or int + model_config=model_config, + optional_metadata=all_optional_metadata if all_optional_metadata else None, + ) + return metadata + + +def get_sai_model_spec_dataclass( + state_dict: dict, + args: argparse.Namespace, + sdxl: bool, + lora: bool, + textual_inversion: bool, + is_stable_diffusion_ckpt: Optional[bool] = None, + sd3: str = None, + flux: str = None, + lumina: str = None, + hunyuan_image: str = None, + anima: str = None, + optional_metadata: dict[str, str] | None = None, +) -> sai_model_spec.ModelSpecMetadata: + """ + Get ModelSpec metadata as a dataclass - preferred for new code. + Automatically extracts metadata_* fields from args. + """ + timestamp = time.time() + + v2 = args.v2 + v_parameterization = args.v_parameterization + reso = args.resolution + + title = args.metadata_title if args.metadata_title is not None else args.output_name + + if args.min_timestep is not None or args.max_timestep is not None: + min_time_step = args.min_timestep if args.min_timestep is not None else 0 + max_time_step = args.max_timestep if args.max_timestep is not None else 1000 + timesteps = (min_time_step, max_time_step) + else: + timesteps = None + + # Convert individual model parameters to model_config dict + model_config = {} + if sd3 is not None: + model_config["sd3"] = sd3 + if flux is not None: + model_config["flux"] = flux + if lumina is not None: + model_config["lumina"] = lumina + if hunyuan_image is not None: + model_config["hunyuan_image"] = hunyuan_image + if anima is not None: + model_config["anima"] = anima + # Use the dataclass function directly + return sai_model_spec.build_metadata_dataclass( + state_dict, + v2, + v_parameterization, + sdxl, + lora, + textual_inversion, + timestamp, + title=title, + reso=reso, + is_stable_diffusion_ckpt=is_stable_diffusion_ckpt, + author=args.metadata_author, + description=args.metadata_description, + license=args.metadata_license, + tags=args.metadata_tags, + timesteps=timesteps, + clip_skip=args.clip_skip, + model_config=model_config, + optional_metadata=optional_metadata, + ) + + +def add_sd_models_arguments(parser: argparse.ArgumentParser): + # for pretrained models + parser.add_argument( + "--v2", action="store_true", help="load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む" + ) + parser.add_argument( + "--v_parameterization", action="store_true", help="enable v-parameterization training / v-parameterization学習を有効にする" + ) + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + help="pretrained model to train, directory to Diffusers model or StableDiffusion checkpoint / 学習元モデル、Diffusers形式モデルのディレクトリまたはStableDiffusionのckptファイル", + ) + parser.add_argument( + "--tokenizer_cache_dir", + type=str, + default=None, + help="directory for caching Tokenizer (for offline training) / Tokenizerをキャッシュするディレクトリ（ネット接続なしでの学習のため）", + ) + + +def add_optimizer_arguments(parser: argparse.ArgumentParser): + def int_or_float(value): + if value.endswith("%"): + try: + return float(value[:-1]) / 100.0 + except ValueError: + raise argparse.ArgumentTypeError(f"Value '{value}' is not a valid percentage") + try: + float_value = float(value) + if float_value >= 1: + return int(value) + return float(value) + except ValueError: + raise argparse.ArgumentTypeError(f"'{value}' is not an int or float") + + parser.add_argument( + "--optimizer_type", + type=str, + default="", + help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, " + "Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, " + "DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, " + "AdaFactor. " + "Also, you can use any optimizer by specifying the full path to the class, like 'bitsandbytes.optim.AdEMAMix8bit' or 'bitsandbytes.optim.PagedAdEMAMix8bit'.", + ) + + # backward compatibility + parser.add_argument( + "--use_8bit_adam", + action="store_true", + help="use 8bit AdamW optimizer (requires bitsandbytes) / 8bit Adamオプティマイザを使う（bitsandbytesのインストールが必要）", + ) + parser.add_argument( + "--use_lion_optimizer", + action="store_true", + help="use Lion optimizer (requires lion-pytorch) / Lionオプティマイザを使う（ lion-pytorch のインストールが必要）", + ) + + parser.add_argument("--learning_rate", type=float, default=2.0e-6, help="learning rate / 学習率") + parser.add_argument( + "--max_grad_norm", + default=1.0, + type=float, + help="Max gradient norm, 0 for no clipping / 勾配正規化の最大norm、0でclippingを行わない", + ) + + parser.add_argument( + "--optimizer_args", + type=str, + default=None, + nargs="*", + help='additional arguments for optimizer (like "weight_decay=0.01 betas=0.9,0.999 ...") / オプティマイザの追加引数（例： "weight_decay=0.01 betas=0.9,0.999 ..."）', + ) + + # parser.add_argument( + # "--optimizer_schedulefree_wrapper", + # action="store_true", + # help="use schedulefree_wrapper any optimizer / 任意のオプティマイザにschedulefree_wrapperを使用", + # ) + + # parser.add_argument( + # "--schedulefree_wrapper_args", + # type=str, + # default=None, + # nargs="*", + # help='additional arguments for schedulefree_wrapper (like "momentum=0.9 weight_decay_at_y=0.1 ...") / オプティマイザの追加引数（例： "momentum=0.9 weight_decay_at_y=0.1 ..."）', + # ) + + parser.add_argument("--lr_scheduler_type", type=str, default="", help="custom scheduler module / 使用するスケジューラ") + parser.add_argument( + "--lr_scheduler_args", + type=str, + default=None, + nargs="*", + help='additional arguments for scheduler (like "T_max=100") / スケジューラの追加引数（例： "T_max100"）', + ) + + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help="scheduler to use for learning rate / 学習率のスケジューラ: linear, cosine, cosine_with_restarts, polynomial, constant (default), constant_with_warmup, adafactor", + ) + parser.add_argument( + "--lr_warmup_steps", + type=int_or_float, + default=0, + help="Int number of steps for the warmup in the lr scheduler (default is 0) or float with ratio of train steps" + " / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）、または学習ステップの比率（1未満のfloat値の場合）", + ) + parser.add_argument( + "--lr_decay_steps", + type=int_or_float, + default=0, + help="Int number of steps for the decay in the lr scheduler (default is 0) or float (<1) with ratio of train steps" + " / 学習率のスケジューラを減衰させるステップ数（デフォルト0）、または学習ステップの比率（1未満のfloat値の場合）", + ) + parser.add_argument( + "--lr_scheduler_num_cycles", + type=int, + default=1, + help="Number of restarts for cosine scheduler with restarts / cosine with restartsスケジューラでのリスタート回数", + ) + parser.add_argument( + "--lr_scheduler_power", + type=float, + default=1, + help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power", + ) + parser.add_argument( + "--fused_backward_pass", + action="store_true", + help="Combines backward pass and optimizer step to reduce VRAM usage. Only available in SDXL, SD3 and FLUX" + " / バックワードパスとオプティマイザステップを組み合わせてVRAMの使用量を削減します。SDXL、SD3、FLUXでのみ利用可能", + ) + parser.add_argument( + "--lr_scheduler_timescale", + type=int, + default=None, + help="Inverse sqrt timescale for inverse sqrt scheduler,defaults to `num_warmup_steps`" + + " / 逆平方根スケジューラのタイムスケール、デフォルトは`num_warmup_steps`", + ) + parser.add_argument( + "--lr_scheduler_min_lr_ratio", + type=float, + default=None, + help="The minimum learning rate as a ratio of the initial learning rate for cosine with min lr scheduler and warmup decay scheduler" + + " / 初期学習率の比率としての最小学習率を指定する、cosine with min lr と warmup decay スケジューラ で有効", + ) + + +def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool): + parser.add_argument( + "--output_dir", type=str, default=None, help="directory to output trained model / 学習後のモデル出力先ディレクトリ" + ) + parser.add_argument( + "--output_name", type=str, default=None, help="base name of trained model file / 学習後のモデルの拡張子を除くファイル名" + ) + parser.add_argument( + "--huggingface_repo_id", + type=str, + default=None, + help="huggingface repo name to upload / huggingfaceにアップロードするリポジトリ名", + ) + parser.add_argument( + "--huggingface_repo_type", + type=str, + default=None, + help="huggingface repo type to upload / huggingfaceにアップロードするリポジトリの種類", + ) + parser.add_argument( + "--huggingface_path_in_repo", + type=str, + default=None, + help="huggingface model path to upload files / huggingfaceにアップロードするファイルのパス", + ) + parser.add_argument("--huggingface_token", type=str, default=None, help="huggingface token / huggingfaceのトークン") + parser.add_argument( + "--huggingface_repo_visibility", + type=str, + default=None, + help="huggingface repository visibility ('public' for public, 'private' or None for private) / huggingfaceにアップロードするリポジトリの公開設定（'public'で公開、'private'またはNoneで非公開）", + ) + parser.add_argument( + "--save_state_to_huggingface", action="store_true", help="save state to huggingface / huggingfaceにstateを保存する" + ) + parser.add_argument( + "--resume_from_huggingface", + action="store_true", + help="resume from huggingface (ex: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type}) / huggingfaceから学習を再開する(例: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type})", + ) + parser.add_argument( + "--async_upload", + action="store_true", + help="upload to huggingface asynchronously / huggingfaceに非同期でアップロードする", + ) + parser.add_argument( + "--save_precision", + type=str, + default=None, + choices=[None, "float", "fp16", "bf16"], + help="precision in saving / 保存時に精度を変更して保存する", + ) + parser.add_argument( + "--save_every_n_epochs", + type=int, + default=None, + help="save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する", + ) + parser.add_argument( + "--save_every_n_steps", + type=int, + default=None, + help="save checkpoint every N steps / 学習中のモデルを指定ステップごとに保存する", + ) + parser.add_argument( + "--save_n_epoch_ratio", + type=int, + default=None, + help="save checkpoint N epoch ratio (for example 5 means save at least 5 files total) / 学習中のモデルを指定のエポック割合で保存する（たとえば5を指定すると最低5個のファイルが保存される）", + ) + parser.add_argument( + "--save_last_n_epochs", + type=int, + default=None, + help="save last N checkpoints when saving every N epochs (remove older checkpoints) / 指定エポックごとにモデルを保存するとき最大Nエポック保存する（古いチェックポイントは削除する）", + ) + parser.add_argument( + "--save_last_n_epochs_state", + type=int, + default=None, + help="save last N checkpoints of state (overrides the value of --save_last_n_epochs)/ 最大Nエポックstateを保存する（--save_last_n_epochsの指定を上書きする）", + ) + parser.add_argument( + "--save_last_n_steps", + type=int, + default=None, + help="save checkpoints until N steps elapsed (remove older checkpoints if N steps elapsed) / 指定ステップごとにモデルを保存するとき、このステップ数経過するまで保存する（このステップ数経過したら削除する）", + ) + parser.add_argument( + "--save_last_n_steps_state", + type=int, + default=None, + help="save states until N steps elapsed (remove older states if N steps elapsed, overrides --save_last_n_steps) / 指定ステップごとにstateを保存するとき、このステップ数経過するまで保存する（このステップ数経過したら削除する。--save_last_n_stepsを上書きする）", + ) + parser.add_argument( + "--save_state", + action="store_true", + help="save training state additionally (including optimizer states etc.) when saving model / optimizerなど学習状態も含めたstateをモデル保存時に追加で保存する", + ) + parser.add_argument( + "--save_state_on_train_end", + action="store_true", + help="save training state (including optimizer states etc.) on train end / optimizerなど学習状態も含めたstateを学習完了時に保存する", + ) + parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate") + + parser.add_argument("--train_batch_size", type=int, default=1, help="batch size for training / 学習時のバッチサイズ") + parser.add_argument( + "--max_token_length", + type=int, + default=None, + choices=[None, 150, 225], + help="max token length of text encoder (default for 75, 150 or 225) / text encoderのトークンの最大長（未指定で75、150または225が指定可）", + ) + parser.add_argument( + "--mem_eff_attn", + action="store_true", + help="use memory efficient attention for CrossAttention / CrossAttentionに省メモリ版attentionを使う", + ) + parser.add_argument( + "--torch_compile", action="store_true", help="use torch.compile (requires PyTorch 2.0) / torch.compile を使う" + ) + parser.add_argument( + "--dynamo_backend", + type=str, + default="inductor", + # available backends: + # https://github.com/huggingface/accelerate/blob/d1abd59114ada8ba673e1214218cb2878c13b82d/src/accelerate/utils/dataclasses.py#L376-L388C5 + # https://pytorch.org/docs/stable/torch.compiler.html + choices=[ + "eager", + "aot_eager", + "inductor", + "aot_ts_nvfuser", + "nvprims_nvfuser", + "cudagraphs", + "ofi", + "fx2trt", + "onnxrt", + "tensort", + "ipex", + "tvm", + ], + help="dynamo backend type (default is inductor) / dynamoのbackendの種類（デフォルトは inductor）", + ) + parser.add_argument("--xformers", action="store_true", help="use xformers for CrossAttention / CrossAttentionにxformersを使う") + parser.add_argument( + "--sdpa", + action="store_true", + help="use sdpa for CrossAttention (requires PyTorch 2.0) / CrossAttentionにsdpaを使う（PyTorch 2.0が必要）", + ) + parser.add_argument( + "--vae", + type=str, + default=None, + help="path to checkpoint of vae to replace / VAEを入れ替える場合、VAEのcheckpointファイルまたはディレクトリ", + ) + + parser.add_argument("--max_train_steps", type=int, default=1600, help="training steps / 学習ステップ数") + parser.add_argument( + "--max_train_epochs", + type=int, + default=None, + help="training epochs (overrides max_train_steps) / 学習エポック数（max_train_stepsを上書きします）", + ) + parser.add_argument( + "--max_data_loader_n_workers", + type=int, + default=8, + help="max num workers for DataLoader (lower is less main RAM usage, faster epoch start and slower data loading) / DataLoaderの最大プロセス数（小さい値ではメインメモリの使用量が減りエポック間の待ち時間が減りますが、データ読み込みは遅くなります）", + ) + parser.add_argument( + "--persistent_data_loader_workers", + action="store_true", + help="persistent DataLoader workers (useful for reduce time gap between epoch, but may use more memory) / DataLoader のワーカーを持続させる (エポック間の時間差を少なくするのに有効だが、より多くのメモリを消費する可能性がある)", + ) + parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed") + parser.add_argument( + "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / gradient checkpointingを有効にする" + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass / 学習時に逆伝播をする前に勾配を合計するステップ数", + ) + parser.add_argument( + "--mixed_precision", + type=str, + default="no", + choices=["no", "fp16", "bf16"], + help="use mixed precision / 混合精度を使う場合、その精度", + ) + parser.add_argument( + "--full_fp16", + action="store_true", + help="fp16 training including gradients, some models are not supported / 勾配も含めてfp16で学習する、一部のモデルではサポートされていません", + ) + parser.add_argument( + "--full_bf16", + action="store_true", + help="bf16 training including gradients, some models are not supported / 勾配も含めてbf16で学習する、一部のモデルではサポートされていません", + ) # TODO move to SDXL training, because it is not supported by SD1/2 + parser.add_argument( + "--fp8_base", + action="store_true", + help="use fp8 for base model, some models are not supported / base modelにfp8を使う、一部のモデルではサポートされていません", + ) + + parser.add_argument( + "--ddp_timeout", + type=int, + default=None, + help="DDP timeout (min, None for default of accelerate) / DDPのタイムアウト（分、Noneでaccelerateのデフォルト）", + ) + parser.add_argument( + "--ddp_gradient_as_bucket_view", + action="store_true", + help="enable gradient_as_bucket_view for DDP / DDPでgradient_as_bucket_viewを有効にする", + ) + parser.add_argument( + "--ddp_static_graph", + action="store_true", + help="enable static_graph for DDP / DDPでstatic_graphを有効にする", + ) + parser.add_argument( + "--clip_skip", + type=int, + default=None, + help="use output of nth layer from back of text encoder (n>=1) / text encoderの後ろからn番目の層の出力を用いる（nは1以上）", + ) + parser.add_argument( + "--logging_dir", + type=str, + default=None, + help="enable logging and output TensorBoard log to this directory / ログ出力を有効にしてこのディレクトリにTensorBoard用のログを出力する", + ) + parser.add_argument( + "--log_with", + type=str, + default=None, + choices=["tensorboard", "wandb", "all"], + help="what logging tool(s) to use (if 'all', TensorBoard and WandB are both used) / ログ出力に使用するツール (allを指定するとTensorBoardとWandBの両方が使用される)", + ) + parser.add_argument( + "--log_prefix", type=str, default=None, help="add prefix for each log directory / ログディレクトリ名の先頭に追加する文字列" + ) + parser.add_argument( + "--log_tracker_name", + type=str, + default=None, + help="name of tracker to use for logging, default is script-specific default name / ログ出力に使用するtrackerの名前、省略時はスクリプトごとのデフォルト名", + ) + parser.add_argument( + "--wandb_run_name", + type=str, + default=None, + help="The name of the specific wandb session / wandb ログに表示される特定の実行の名前", + ) + parser.add_argument( + "--log_tracker_config", + type=str, + default=None, + help="path to tracker config file to use for logging / ログ出力に使用するtrackerの設定ファイルのパス", + ) + parser.add_argument( + "--wandb_api_key", + type=str, + default=None, + help="specify WandB API key to log in before starting training (optional). / WandB APIキーを指定して学習開始前にログインする（オプション）", + ) + parser.add_argument("--log_config", action="store_true", help="log training configuration / 学習設定をログに出力する") + + parser.add_argument( + "--noise_offset", + type=float, + default=None, + help="enable noise offset with this value (if enabled, around 0.1 is recommended) / Noise offsetを有効にしてこの値を設定する（有効にする場合は0.1程度を推奨）", + ) + parser.add_argument( + "--noise_offset_random_strength", + action="store_true", + help="use random strength between 0~noise_offset for noise offset. / noise offsetにおいて、0からnoise_offsetの間でランダムな強度を使用します。", + ) + parser.add_argument( + "--multires_noise_iterations", + type=int, + default=None, + help="enable multires noise with this number of iterations (if enabled, around 6-10 is recommended) / Multires noiseを有効にしてこのイテレーション数を設定する（有効にする場合は6-10程度を推奨）", + ) + parser.add_argument( + "--ip_noise_gamma", + type=float, + default=None, + help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) " + + "/ input perturbation noiseを有効にする。正則化に使用される。推奨値: 0.1程度 (arxiv.org/abs/2301.11706 より)", + ) + parser.add_argument( + "--ip_noise_gamma_random_strength", + action="store_true", + help="Use random strength between 0~ip_noise_gamma for input perturbation noise." + + "/ input perturbation noiseにおいて、0からip_noise_gammaの間でランダムな強度を使用します。", + ) + # parser.add_argument( + # "--perlin_noise", + # type=int, + # default=None, + # help="enable perlin noise and set the octaves / perlin noiseを有効にしてoctavesをこの値に設定する", + # ) + parser.add_argument( + "--multires_noise_discount", + type=float, + default=0.3, + help="set discount value for multires noise (has no effect without --multires_noise_iterations) / Multires noiseのdiscount値を設定する（--multires_noise_iterations指定時のみ有効）", + ) + parser.add_argument( + "--adaptive_noise_scale", + type=float, + default=None, + help="add `latent mean absolute value * this value` to noise_offset (disabled if None, default) / latentの平均値の絶対値 * この値をnoise_offsetに加算する（Noneの場合は無効、デフォルト）", + ) + parser.add_argument( + "--zero_terminal_snr", + action="store_true", + help="fix noise scheduler betas to enforce zero terminal SNR / noise schedulerのbetasを修正して、zero terminal SNRを強制する", + ) + parser.add_argument( + "--min_timestep", + type=int, + default=None, + help="set minimum time step for U-Net training (0~999, default is 0) / U-Net学習時のtime stepの最小値を設定する（0~999で指定、省略時はデフォルト値(0)） ", + ) + parser.add_argument( + "--max_timestep", + type=int, + default=None, + help="set maximum time step for U-Net training (1~1000, default is 1000) / U-Net学習時のtime stepの最大値を設定する（1~1000で指定、省略時はデフォルト値(1000)）", + ) + parser.add_argument( + "--loss_type", + type=str, + default="l2", + choices=["l1", "l2", "huber", "smooth_l1"], + help="The type of loss function to use (L1, L2, Huber, or smooth L1), default is L2 / 使用する損失関数の種類（L1、L2、Huber、またはsmooth L1）、デフォルトはL2", + ) + parser.add_argument( + "--huber_schedule", + type=str, + default="snr", + choices=["constant", "exponential", "snr"], + help="The scheduling method for Huber loss (constant, exponential, or SNR-based). Only used when loss_type is 'huber' or 'smooth_l1'. default is snr" + + " / Huber損失のスケジューリング方法（constant、exponential、またはSNRベース）。loss_typeが'huber'または'smooth_l1'の場合に有効、デフォルトは snr", + ) + parser.add_argument( + "--huber_c", + type=float, + default=0.1, + help="The Huber loss decay parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 0.1" + " / Huber損失の減衰パラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは0.1", + ) + + parser.add_argument( + "--huber_scale", + type=float, + default=1.0, + help="The Huber loss scale parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 1.0" + " / Huber損失のスケールパラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは1.0", + ) + + parser.add_argument( + "--lowram", + action="store_true", + help="enable low RAM optimization. e.g. load models to VRAM instead of RAM (for machines which have bigger VRAM than RAM such as Colab and Kaggle) / メインメモリが少ない環境向け最適化を有効にする。たとえばVRAMにモデルを読み込む等（ColabやKaggleなどRAMに比べてVRAMが多い環境向け）", + ) + parser.add_argument( + "--highvram", + action="store_true", + help="disable low VRAM optimization. e.g. do not clear CUDA cache after each latent caching (for machines which have bigger VRAM) " + + "/ VRAMが少ない環境向け最適化を無効にする。たとえば各latentのキャッシュ後のCUDAキャッシュクリアを行わない等（VRAMが多い環境向け）", + ) + + parser.add_argument( + "--sample_every_n_steps", + type=int, + default=None, + help="generate sample images every N steps / 学習中のモデルで指定ステップごとにサンプル出力する", + ) + parser.add_argument( + "--sample_at_first", action="store_true", help="generate sample images before training / 学習前にサンプル出力する" + ) + parser.add_argument( + "--sample_every_n_epochs", + type=int, + default=None, + help="generate sample images every N epochs (overwrites n_steps) / 学習中のモデルで指定エポックごとにサンプル出力する（ステップ数指定を上書きします）", + ) + parser.add_argument( + "--sample_prompts", + type=str, + default=None, + help="file for prompts to generate sample images / 学習中モデルのサンプル出力用プロンプトのファイル", + ) + parser.add_argument( + "--sample_sampler", + type=str, + default="ddim", + choices=[ + "ddim", + "pndm", + "lms", + "euler", + "euler_a", + "heun", + "dpm_2", + "dpm_2_a", + "dpmsolver", + "dpmsolver++", + "dpmsingle", + "k_lms", + "k_euler", + "k_euler_a", + "k_dpm_2", + "k_dpm_2_a", + ], + help=f"sampler (scheduler) type for sample images / サンプル出力時のサンプラー（スケジューラ）の種類", + ) + + parser.add_argument( + "--config_file", + type=str, + default=None, + help="using .toml instead of args to pass hyperparameter / ハイパーパラメータを引数ではなく.tomlファイルで渡す", + ) + parser.add_argument( + "--output_config", action="store_true", help="output command line args to given .toml file / 引数を.tomlファイルに出力する" + ) + if support_dreambooth: + # DreamBooth training + parser.add_argument( + "--prior_loss_weight", type=float, default=1.0, help="loss weight for regularization images / 正則化画像のlossの重み" + ) + + +def add_masked_loss_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--conditioning_data_dir", + type=str, + default=None, + help="conditioning data directory / 条件付けデータのディレクトリ", + ) + parser.add_argument( + "--masked_loss", + action="store_true", + help="apply mask for calculating loss. conditioning_data_dir is required for dataset. / 損失計算時にマスクを適用する。datasetにはconditioning_data_dirが必要", + ) + + +def add_dit_training_arguments(parser: argparse.ArgumentParser): + # Text encoder related arguments + parser.add_argument( + "--cache_text_encoder_outputs", action="store_true", help="cache text encoder outputs / text encoderの出力をキャッシュする" + ) + parser.add_argument( + "--cache_text_encoder_outputs_to_disk", + action="store_true", + help="cache text encoder outputs to disk / text encoderの出力をディスクにキャッシュする", + ) + parser.add_argument( + "--text_encoder_batch_size", + type=int, + default=None, + help="text encoder batch size (default: None, use dataset's batch size)" + + " / text encoderのバッチサイズ（デフォルト: None, データセットのバッチサイズを使用）", + ) + + # Model loading optimization + parser.add_argument( + "--disable_mmap_load_safetensors", + action="store_true", + help="disable mmap load for safetensors. Speed up model loading in WSL environment / safetensorsのmmapロードを無効にする。WSL環境等でモデル読み込みを高速化できる", + ) + + # Training arguments. partial copy from Diffusers + parser.add_argument( + "--weighting_scheme", + type=str, + default="uniform", + choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none", "uniform"], + help="weighting scheme for timestep distribution. Default is uniform, uniform and none are the same behavior" + " / タイムステップ分布の重み付けスキーム、デフォルトはuniform、uniform と none は同じ挙動", + ) + parser.add_argument( + "--logit_mean", + type=float, + default=0.0, + help="mean to use when using the `'logit_normal'` weighting scheme / `'logit_normal'`重み付けスキームを使用する場合の平均", + ) + parser.add_argument( + "--logit_std", + type=float, + default=1.0, + help="std to use when using the `'logit_normal'` weighting scheme / `'logit_normal'`重み付けスキームを使用する場合のstd", + ) + parser.add_argument( + "--mode_scale", + type=float, + default=1.29, + help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme` / モード重み付けスキームのスケール", + ) + + # offloading + parser.add_argument( + "--blocks_to_swap", + type=int, + default=None, + help="[EXPERIMENTAL] " + "Sets the number of blocks to swap during the forward and backward passes." + "Increasing this number lowers the overall VRAM used during training at the expense of training speed (s/it)." + " / 順伝播および逆伝播中にスワップするブロックの数を設定します。" + "この数を増やすと、トレーニング中のVRAM使用量が減りますが、トレーニング速度（s/it）も低下します。", + ) + + +def get_sanitized_config_or_none(args: argparse.Namespace): + # if `--log_config` is enabled, return args for logging. if not, return None. + # when `--log_config is enabled, filter out sensitive values from args + # if wandb is not enabled, the log is not exposed to the public, but it is fine to filter out sensitive values to be safe + + if not args.log_config: + return None + + sensitive_args = ["wandb_api_key", "huggingface_token"] + sensitive_path_args = [ + "pretrained_model_name_or_path", + "vae", + "tokenizer_cache_dir", + "train_data_dir", + "conditioning_data_dir", + "reg_data_dir", + "output_dir", + "logging_dir", + ] + filtered_args = {} + for k, v in vars(args).items(): + # filter out sensitive values and convert to string if necessary + if k not in sensitive_args + sensitive_path_args: + # Accelerate values need to have type `bool`,`str`, `float`, `int`, or `None`. + if v is None or isinstance(v, bool) or isinstance(v, str) or isinstance(v, float) or isinstance(v, int): + filtered_args[k] = v + # accelerate does not support lists + elif isinstance(v, list): + filtered_args[k] = f"{v}" + # accelerate does not support objects + elif isinstance(v, object): + filtered_args[k] = f"{v}" + + return filtered_args + + +# verify command line args for training +def verify_command_line_training_args(args: argparse.Namespace): + # if wandb is enabled, the command line is exposed to the public + # check whether sensitive options are included in the command line arguments + # if so, warn or inform the user to move them to the configuration file + # wandbが有効な場合、コマンドラインが公開される + # 学習用のコマンドライン引数に敏感なオプションが含まれているかどうかを確認し、 + # 含まれている場合は設定ファイルに移動するようにユーザーに警告または通知する + + wandb_enabled = args.log_with is not None and args.log_with != "tensorboard" # "all" or "wandb" + if not wandb_enabled: + return + + sensitive_args = ["wandb_api_key", "huggingface_token"] + sensitive_path_args = [ + "pretrained_model_name_or_path", + "vae", + "tokenizer_cache_dir", + "train_data_dir", + "conditioning_data_dir", + "reg_data_dir", + "output_dir", + "logging_dir", + ] + + for arg in sensitive_args: + if getattr(args, arg, None) is not None: + logger.warning( + f"wandb is enabled, but option `{arg}` is included in the command line. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file." + + f" / wandbが有効で、かつオプション `{arg}` がコマンドラインに含まれています。コマンドラインは公開されるため、`.toml`ファイルに移動することをお勧めします。" + ) + + # if path is absolute, it may include sensitive information + for arg in sensitive_path_args: + if getattr(args, arg, None) is not None and os.path.isabs(getattr(args, arg)): + logger.info( + f"wandb is enabled, but option `{arg}` is included in the command line and it is an absolute path. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file or use relative path." + + f" / wandbが有効で、かつオプション `{arg}` がコマンドラインに含まれており、絶対パスです。コマンドラインは公開されるため、`.toml`ファイルに移動するか、相対パスを使用することをお勧めします。" + ) + + if getattr(args, "config_file", None) is not None: + logger.info( + f"wandb is enabled, but option `config_file` is included in the command line. Because the command line is exposed to the public, please be careful about the information included in the path." + + f" / wandbが有効で、かつオプション `config_file` がコマンドラインに含まれています。コマンドラインは公開されるため、パスに含まれる情報にご注意ください。" + ) + + # other sensitive options + if args.huggingface_repo_id is not None and args.huggingface_repo_visibility != "public": + logger.info( + f"wandb is enabled, but option huggingface_repo_id is included in the command line and huggingface_repo_visibility is not 'public'. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file." + + f" / wandbが有効で、かつオプション huggingface_repo_id がコマンドラインに含まれており、huggingface_repo_visibility が 'public' ではありません。コマンドラインは公開されるため、`.toml`ファイルに移動することをお勧めします。" + ) + + +def enable_high_vram(args: argparse.Namespace): + if args.highvram: + logger.info("highvram is enabled / highvramが有効です") + global HIGH_VRAM + HIGH_VRAM = True + + +def verify_training_args(args: argparse.Namespace): + r""" + Verify training arguments. Also reflect highvram option to global variable + 学習用引数を検証する。あわせて highvram オプションの指定をグローバル変数に反映する + """ + enable_high_vram(args) + + if args.v2 and args.clip_skip is not None: + logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません") + + if args.cache_latents_to_disk and not args.cache_latents: + args.cache_latents = True + logger.warning( + "cache_latents_to_disk is enabled, so cache_latents is also enabled / cache_latents_to_diskが有効なため、cache_latentsを有効にします" + ) + + if getattr(args, "train_inpainting", False) and getattr(args, "cache_latents", False): + raise ValueError( + "train_inpainting and cache_latents cannot be used together. " + "Inpainting masks are generated randomly per step from the original image, " + "so the image must be read on every step. " + "Disable cache_latents (and cache_latents_to_disk) when using --train_inpainting." + ) + + # noise_offset, perlin_noise, multires_noise_iterations cannot be enabled at the same time + # # Listを使って数えてもいいけど並べてしまえ + # if args.noise_offset is not None and args.multires_noise_iterations is not None: + # raise ValueError( + # "noise_offset and multires_noise_iterations cannot be enabled at the same time / noise_offsetとmultires_noise_iterationsを同時に有効にできません" + # ) + # if args.noise_offset is not None and args.perlin_noise is not None: + # raise ValueError("noise_offset and perlin_noise cannot be enabled at the same time / noise_offsetとperlin_noiseは同時に有効にできません") + # if args.perlin_noise is not None and args.multires_noise_iterations is not None: + # raise ValueError( + # "perlin_noise and multires_noise_iterations cannot be enabled at the same time / perlin_noiseとmultires_noise_iterationsを同時に有効にできません" + # ) + + if args.adaptive_noise_scale is not None and args.noise_offset is None: + raise ValueError("adaptive_noise_scale requires noise_offset / adaptive_noise_scaleを使用するにはnoise_offsetが必要です") + + if args.scale_v_pred_loss_like_noise_pred and not args.v_parameterization: + raise ValueError( + "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます" + ) + + if args.v_pred_like_loss and args.v_parameterization: + raise ValueError( + "v_pred_like_loss cannot be enabled with v_parameterization / v_pred_like_lossはv_parameterizationが有効なときには有効にできません" + ) + + if args.zero_terminal_snr and not args.v_parameterization: + logger.warning( + f"zero_terminal_snr is enabled, but v_parameterization is not enabled. training will be unexpected" + + " / zero_terminal_snrが有効ですが、v_parameterizationが有効ではありません。学習結果は想定外になる可能性があります" + ) + + if args.sample_every_n_epochs is not None and args.sample_every_n_epochs <= 0: + logger.warning( + "sample_every_n_epochs is less than or equal to 0, so it will be disabled / sample_every_n_epochsに0以下の値が指定されたため無効になります" + ) + args.sample_every_n_epochs = None + + if args.sample_every_n_steps is not None and args.sample_every_n_steps <= 0: + logger.warning( + "sample_every_n_steps is less than or equal to 0, so it will be disabled / sample_every_n_stepsに0以下の値が指定されたため無効になります" + ) + args.sample_every_n_steps = None + + +def add_dataset_arguments( + parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool, support_caption_dropout: bool +): + # dataset common + parser.add_argument( + "--train_data_dir", type=str, default=None, help="directory for train images / 学習画像データのディレクトリ" + ) + parser.add_argument( + "--cache_info", + action="store_true", + help="cache meta information (caption and image size) for faster dataset loading. only available for DreamBooth" + + " / メタ情報（キャプションとサイズ）をキャッシュしてデータセット読み込みを高速化する。DreamBooth方式のみ有効", + ) + parser.add_argument( + "--shuffle_caption", action="store_true", help="shuffle separated caption / 区切られたcaptionの各要素をshuffleする" + ) + parser.add_argument("--caption_separator", type=str, default=",", help="separator for caption / captionの区切り文字") + parser.add_argument( + "--caption_extension", type=str, default=".caption", help="extension of caption files / 読み込むcaptionファイルの拡張子" + ) + parser.add_argument( + "--caption_extention", + type=str, + default=None, + help="extension of caption files (backward compatibility) / 読み込むcaptionファイルの拡張子（スペルミスを残してあります）", + ) + parser.add_argument( + "--keep_tokens", + type=int, + default=0, + help="keep heading N tokens when shuffling caption tokens (token means comma separated strings) / captionのシャッフル時に、先頭からこの個数のトークンをシャッフルしないで残す（トークンはカンマ区切りの各部分を意味する）", + ) + parser.add_argument( + "--keep_tokens_separator", + type=str, + default="", + help="A custom separator to divide the caption into fixed and flexible parts. Tokens before this separator will not be shuffled. If not specified, '--keep_tokens' will be used to determine the fixed number of tokens." + + " / captionを固定部分と可変部分に分けるためのカスタム区切り文字。この区切り文字より前のトークンはシャッフルされない。指定しない場合、'--keep_tokens'が固定部分のトークン数として使用される。", + ) + parser.add_argument( + "--secondary_separator", + type=str, + default=None, + help="a secondary separator for caption. This separator is replaced to caption_separator after dropping/shuffling caption" + + " / captionのセカンダリ区切り文字。この区切り文字はcaptionのドロップやシャッフル後にcaption_separatorに置き換えられる", + ) + parser.add_argument( + "--enable_wildcard", + action="store_true", + help="enable wildcard for caption (e.g. '{image|picture|rendition}') / captionのワイルドカードを有効にする（例：'{image|picture|rendition}'）", + ) + parser.add_argument( + "--caption_prefix", + type=str, + default=None, + help="prefix for caption text / captionのテキストの先頭に付ける文字列", + ) + parser.add_argument( + "--caption_suffix", + type=str, + default=None, + help="suffix for caption text / captionのテキストの末尾に付ける文字列", + ) + parser.add_argument( + "--color_aug", action="store_true", help="enable weak color augmentation / 学習時に色合いのaugmentationを有効にする" + ) + parser.add_argument( + "--flip_aug", action="store_true", help="enable horizontal flip augmentation / 学習時に左右反転のaugmentationを有効にする" + ) + parser.add_argument( + "--face_crop_aug_range", + type=str, + default=None, + help="enable face-centered crop augmentation and its range (e.g. 2.0,4.0) / 学習時に顔を中心とした切り出しaugmentationを有効にするときは倍率を指定する（例：2.0,4.0）", + ) + parser.add_argument( + "--random_crop", + action="store_true", + help="enable random crop (for style training in face-centered crop augmentation) / ランダムな切り出しを有効にする（顔を中心としたaugmentationを行うときに画風の学習用に指定する）", + ) + parser.add_argument( + "--debug_dataset", + action="store_true", + help="show images for debugging (do not train) / デバッグ用に学習データを画面表示する（学習は行わない）", + ) + parser.add_argument( + "--resolution", + type=str, + default=None, + help="resolution in training ('size' or 'width,height') / 学習時の画像解像度（'サイズ'指定、または'幅,高さ'指定）", + ) + parser.add_argument( + "--cache_latents", + action="store_true", + help="cache latents to main memory to reduce VRAM usage (augmentations must be disabled) / VRAM削減のためにlatentをメインメモリにcacheする（augmentationは使用不可） ", + ) + parser.add_argument( + "--vae_batch_size", type=int, default=1, help="batch size for caching latents / latentのcache時のバッチサイズ" + ) + parser.add_argument( + "--cache_latents_to_disk", + action="store_true", + help="cache latents to disk to reduce VRAM usage (augmentations must be disabled) / VRAM削減のためにlatentをディスクにcacheする（augmentationは使用不可）", + ) + parser.add_argument( + "--skip_cache_check", + action="store_true", + help="skip the content validation of cache (latent and text encoder output). Cache file existence check is always performed, and cache processing is performed if the file does not exist" + " / cacheの内容の検証をスキップする（latentとテキストエンコーダの出力）。キャッシュファイルの存在確認は常に行われ、ファイルがなければキャッシュ処理が行われる", + ) + parser.add_argument( + "--enable_bucket", + action="store_true", + help="enable buckets for multi aspect ratio training / 複数解像度学習のためのbucketを有効にする", + ) + parser.add_argument( + "--min_bucket_reso", + type=int, + default=256, + help="minimum resolution for buckets, must be divisible by bucket_reso_steps " + " / bucketの最小解像度、bucket_reso_stepsで割り切れる必要があります", + ) + parser.add_argument( + "--max_bucket_reso", + type=int, + default=1024, + help="maximum resolution for buckets, must be divisible by bucket_reso_steps " + " / bucketの最大解像度、bucket_reso_stepsで割り切れる必要があります", + ) + parser.add_argument( + "--skip_image_resolution", + type=str, + default=None, + help="images not larger than this resolution will be skipped ('size' or 'width,height')" + " / この解像度以下の画像はスキップされます（'サイズ'指定、または'幅,高さ'指定）", + ) + parser.add_argument( + "--bucket_reso_steps", + type=int, + default=64, + help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します", + ) + parser.add_argument( + "--bucket_no_upscale", + action="store_true", + help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します", + ) + parser.add_argument( + "--resize_interpolation", + type=str, + default=None, + choices=["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area"], + help="Resize interpolation when required. Default: area Options: lanczos, nearest, bilinear, bicubic, area / 必要に応じてサイズ補間を変更します。デフォルト: area オプション: lanczos, nearest, bilinear, bicubic, area", + ) + parser.add_argument( + "--token_warmup_min", + type=int, + default=1, + help="start learning at N tags (token means comma separated strinfloatgs) / タグ数をN個から増やしながら学習する", + ) + parser.add_argument( + "--token_warmup_step", + type=float, + default=0, + help="tag length reaches maximum on N steps (or N*max_train_steps if N<1) / N（N<1ならN*max_train_steps）ステップでタグ長が最大になる。デフォルトは0（最初から最大）", + ) + parser.add_argument( + "--alpha_mask", + action="store_true", + help="use alpha channel as mask for training / 画像のアルファチャンネルをlossのマスクに使用する", + ) + + parser.add_argument( + "--dataset_class", + type=str, + default=None, + help="dataset class for arbitrary dataset (package.module.Class) / 任意のデータセットを用いるときのクラス名 (package.module.Class)", + ) + + parser.add_argument( + "--train_inpainting", + action="store_true", + help="train an inpainting model / インペイントモデルを学習する", + ) + + if support_caption_dropout: + # Textual Inversion はcaptionのdropoutをsupportしない + # いわゆるtensorのDropoutと紛らわしいのでprefixにcaptionを付けておく　every_n_epochsは他と平仄を合わせてdefault Noneに + parser.add_argument( + "--caption_dropout_rate", type=float, default=0.0, help="Rate out dropout caption(0.0~1.0) / captionをdropoutする割合" + ) + parser.add_argument( + "--caption_dropout_every_n_epochs", + type=int, + default=0, + help="Dropout all captions every N epochs / captionを指定エポックごとにdropoutする", + ) + parser.add_argument( + "--caption_tag_dropout_rate", + type=float, + default=0.0, + help="Rate out dropout comma separated tokens(0.0~1.0) / カンマ区切りのタグをdropoutする割合", + ) + + if support_dreambooth: + # DreamBooth dataset + parser.add_argument( + "--reg_data_dir", type=str, default=None, help="directory for regularization images / 正則化画像データのディレクトリ" + ) + + if support_caption: + # caption dataset + parser.add_argument( + "--in_json", type=str, default=None, help="json metadata for dataset / データセットのmetadataのjsonファイル" + ) + parser.add_argument( + "--dataset_repeats", + type=int, + default=1, + help="repeat dataset when training with captions / キャプションでの学習時にデータセットを繰り返す回数", + ) + + +def add_sd_saving_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--save_model_as", + type=str, + default=None, + choices=[None, "ckpt", "safetensors", "diffusers", "diffusers_safetensors"], + help="format to save the model (default is same to original) / モデル保存時の形式（未指定時は元モデルと同じ）", + ) + parser.add_argument( + "--use_safetensors", + action="store_true", + help="use safetensors format to save (if save_model_as is not specified) / checkpoint、モデルをsafetensors形式で保存する（save_model_as未指定時）", + ) + + +def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentParser): + if not args.config_file: + return args + + config_path = args.config_file + ".toml" if not args.config_file.endswith(".toml") else args.config_file + + if args.output_config: + # check if config file exists + if os.path.exists(config_path): + logger.error(f"Config file already exists. Aborting... / 出力先の設定ファイルが既に存在します: {config_path}") + exit(1) + + # convert args to dictionary + args_dict = vars(args) + + # remove unnecessary keys + for key in ["config_file", "output_config", "wandb_api_key"]: + if key in args_dict: + del args_dict[key] + + # get default args from parser + default_args = vars(parser.parse_args([])) + + # remove default values: cannot use args_dict.items directly because it will be changed during iteration + for key, value in list(args_dict.items()): + if key in default_args and value == default_args[key]: + del args_dict[key] + + # convert Path to str in dictionary + for key, value in args_dict.items(): + if isinstance(value, pathlib.Path): + args_dict[key] = str(value) + + # convert to toml and output to file + with open(config_path, "w") as f: + toml.dump(args_dict, f) + + logger.info(f"Saved config file / 設定ファイルを保存しました: {config_path}") + exit(0) + + if not os.path.exists(config_path): + logger.info(f"{config_path} not found.") + exit(1) + + logger.info(f"Loading settings from {config_path}...") + with open(config_path, "r", encoding="utf-8") as f: + config_dict = toml.load(f) + + # combine all sections into one + ignore_nesting_dict = {} + for section_name, section_dict in config_dict.items(): + # if value is not dict, save key and value as is + if not isinstance(section_dict, dict): + ignore_nesting_dict[section_name] = section_dict + continue + + # if value is dict, save all key and value into one dict + for key, value in section_dict.items(): + ignore_nesting_dict[key] = value + + config_args = argparse.Namespace(**ignore_nesting_dict) + args = parser.parse_args(namespace=config_args) + args.config_file = os.path.splitext(args.config_file)[0] + + return args + + +# endregion + +# region utils + + +def _infer_step_from_state_dir(state_dir: str) -> int: + match = re.search(r"-step(\d+)-state$", os.path.basename(os.path.normpath(state_dir))) + return int(match.group(1)) if match else 0 + + +def _is_accelerate_missing_step_error(exc: KeyError) -> bool: + if exc.args != ("step",): + return False + for frame in traceback.extract_tb(exc.__traceback__): + filename = frame.filename.replace("\\", "/") + if filename.endswith("accelerate/accelerator.py") and frame.name == "load_state": + return True + return False + + +def _load_accelerate_state_with_step_fallback(accelerator, state_dir: str): + try: + accelerator.load_state(state_dir) + except KeyError as exc: + if not _is_accelerate_missing_step_error(exc): + raise + step = _infer_step_from_state_dir(state_dir) + accelerator.step = step + logger.warning( + "Accelerate state is missing internal 'step' metadata; " + f"continuing resume with inferred step={step}. " + "This is usually caused by accelerate checkpoint compatibility." + ) + + +def resume_from_local_or_hf_if_specified(accelerator, args): + if not args.resume: + return + + if not args.resume_from_huggingface: + logger.info(f"resume training from local state: {args.resume}") + _load_accelerate_state_with_step_fallback(accelerator, args.resume) + return + + logger.info(f"resume training from huggingface state: {args.resume}") + repo_id = args.resume.split("/")[0] + "/" + args.resume.split("/")[1] + path_in_repo = "/".join(args.resume.split("/")[2:]) + revision = None + repo_type = None + if ":" in path_in_repo: + divided = path_in_repo.split(":") + if len(divided) == 2: + path_in_repo, revision = divided + repo_type = "model" + else: + path_in_repo, revision, repo_type = divided + logger.info(f"Downloading state from huggingface: {repo_id}/{path_in_repo}@{revision}") + + list_files = huggingface_util.list_dir( + repo_id=repo_id, + subfolder=path_in_repo, + revision=revision, + token=args.huggingface_token, + repo_type=repo_type, + ) + + async def download(filename) -> str: + def task(): + return hf_hub_download( + repo_id=repo_id, + filename=filename, + revision=revision, + repo_type=repo_type, + token=args.huggingface_token, + ) + + return await asyncio.get_event_loop().run_in_executor(None, task) + + loop = asyncio.get_event_loop() + results = loop.run_until_complete(asyncio.gather(*[download(filename=filename.rfilename) for filename in list_files])) + if len(results) == 0: + raise ValueError( + "No files found in the specified repo id/path/revision / 指定されたリポジトリID/パス/リビジョンにファイルが見つかりませんでした" + ) + dirname = os.path.dirname(results[0]) + _load_accelerate_state_with_step_fallback(accelerator, dirname) + + +def get_optimizer(args, trainable_params) -> tuple[str, str, object]: + # "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, AdEMAMix8bit, PagedAdEMAMix8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, Adafactor" + + optimizer_type = args.optimizer_type + if args.use_8bit_adam: + assert ( + not args.use_lion_optimizer + ), "both option use_8bit_adam and use_lion_optimizer are specified / use_8bit_adamとuse_lion_optimizerの両方のオプションが指定されています" + assert ( + optimizer_type is None or optimizer_type == "" + ), "both option use_8bit_adam and optimizer_type are specified / use_8bit_adamとoptimizer_typeの両方のオプションが指定されています" + optimizer_type = "AdamW8bit" + + elif args.use_lion_optimizer: + assert ( + optimizer_type is None or optimizer_type == "" + ), "both option use_lion_optimizer and optimizer_type are specified / use_lion_optimizerとoptimizer_typeの両方のオプションが指定されています" + optimizer_type = "Lion" + + if optimizer_type is None or optimizer_type == "": + optimizer_type = "AdamW" + optimizer_type = optimizer_type.lower() + + if args.fused_backward_pass: + assert ( + optimizer_type == "Adafactor".lower() + ), "fused_backward_pass currently only works with optimizer_type Adafactor / fused_backward_passは現在optimizer_type Adafactorでのみ機能します" + assert ( + args.gradient_accumulation_steps == 1 + ), "fused_backward_pass does not work with gradient_accumulation_steps > 1 / fused_backward_passはgradient_accumulation_steps>1では機能しません" + + # 引数を分解する + optimizer_kwargs = {} + if args.optimizer_args is not None and len(args.optimizer_args) > 0: + for arg in args.optimizer_args: + key, value = arg.split("=") + value = ast.literal_eval(value) + + # value = value.split(",") + # for i in range(len(value)): + # if value[i].lower() == "true" or value[i].lower() == "false": + # value[i] = value[i].lower() == "true" + # else: + # value[i] = ast.float(value[i]) + # if len(value) == 1: + # value = value[0] + # else: + # value = tuple(value) + + optimizer_kwargs[key] = value + # logger.info(f"optkwargs {optimizer}_{kwargs}") + + lr = args.learning_rate + optimizer = None + optimizer_class = None + + if optimizer_type == "Lion".lower(): + try: + import lion_pytorch + except ImportError: + raise ImportError("No lion_pytorch / lion_pytorch がインストールされていないようです") + logger.info(f"use Lion optimizer | {optimizer_kwargs}") + optimizer_class = lion_pytorch.Lion + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type.endswith("8bit".lower()): + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです") + + if optimizer_type == "AdamW8bit".lower(): + logger.info(f"use 8-bit AdamW optimizer | {optimizer_kwargs}") + optimizer_class = bnb.optim.AdamW8bit + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type == "SGDNesterov8bit".lower(): + logger.info(f"use 8-bit SGD with Nesterov optimizer | {optimizer_kwargs}") + if "momentum" not in optimizer_kwargs: + logger.warning( + f"8-bit SGD with Nesterov must be with momentum, set momentum to 0.9 / 8-bit SGD with Nesterovはmomentum指定が必須のため0.9に設定します" + ) + optimizer_kwargs["momentum"] = 0.9 + + optimizer_class = bnb.optim.SGD8bit + optimizer = optimizer_class(trainable_params, lr=lr, nesterov=True, **optimizer_kwargs) + + elif optimizer_type == "Lion8bit".lower(): + logger.info(f"use 8-bit Lion optimizer | {optimizer_kwargs}") + try: + optimizer_class = bnb.optim.Lion8bit + except AttributeError: + raise AttributeError( + "No Lion8bit. The version of bitsandbytes installed seems to be old. Please install 0.38.0 or later. / Lion8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.38.0以上をインストールしてください" + ) + elif optimizer_type == "PagedAdamW8bit".lower(): + logger.info(f"use 8-bit PagedAdamW optimizer | {optimizer_kwargs}") + try: + optimizer_class = bnb.optim.PagedAdamW8bit + except AttributeError: + raise AttributeError( + "No PagedAdamW8bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamW8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" + ) + elif optimizer_type == "PagedLion8bit".lower(): + logger.info(f"use 8-bit Paged Lion optimizer | {optimizer_kwargs}") + try: + optimizer_class = bnb.optim.PagedLion8bit + except AttributeError: + raise AttributeError( + "No PagedLion8bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedLion8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" + ) + + if optimizer_class is not None: + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type == "PagedAdamW".lower(): + logger.info(f"use PagedAdamW optimizer | {optimizer_kwargs}") + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです") + try: + optimizer_class = bnb.optim.PagedAdamW + except AttributeError: + raise AttributeError( + "No PagedAdamW. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamWが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" + ) + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type == "PagedAdamW32bit".lower(): + logger.info(f"use 32-bit PagedAdamW optimizer | {optimizer_kwargs}") + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです") + try: + optimizer_class = bnb.optim.PagedAdamW32bit + except AttributeError: + raise AttributeError( + "No PagedAdamW32bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamW32bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" + ) + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type == "SGDNesterov".lower(): + logger.info(f"use SGD with Nesterov optimizer | {optimizer_kwargs}") + if "momentum" not in optimizer_kwargs: + logger.info( + f"SGD with Nesterov must be with momentum, set momentum to 0.9 / SGD with Nesterovはmomentum指定が必須のため0.9に設定します" + ) + optimizer_kwargs["momentum"] = 0.9 + + optimizer_class = torch.optim.SGD + optimizer = optimizer_class(trainable_params, lr=lr, nesterov=True, **optimizer_kwargs) + + elif optimizer_type.startswith("DAdapt".lower()) or optimizer_type == "Prodigy".lower(): + # check lr and lr_count, and logger.info warning + actual_lr = lr + lr_count = 1 + if type(trainable_params) == list and type(trainable_params[0]) == dict: + lrs = set() + actual_lr = trainable_params[0].get("lr", actual_lr) + for group in trainable_params: + lrs.add(group.get("lr", actual_lr)) + lr_count = len(lrs) + + if actual_lr <= 0.1: + logger.warning( + f"learning rate is too low. If using D-Adaptation or Prodigy, set learning rate around 1.0 / 学習率が低すぎるようです。D-AdaptationまたはProdigyの使用時は1.0前後の値を指定してください: lr={actual_lr}" + ) + logger.warning("recommend option: lr=1.0 / 推奨は1.0です") + if lr_count > 1: + logger.warning( + f"when multiple learning rates are specified with dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-AdaptationまたはProdigyで複数の学習率を指定した場合（Text EncoderとU-Netなど）、最初の学習率のみが有効になります: lr={actual_lr}" + ) + + if optimizer_type.startswith("DAdapt".lower()): + # DAdaptation family + # check dadaptation is installed + try: + import dadaptation + import dadaptation.experimental as experimental + except ImportError: + raise ImportError("No dadaptation / dadaptation がインストールされていないようです") + + # set optimizer + if optimizer_type == "DAdaptation".lower() or optimizer_type == "DAdaptAdamPreprint".lower(): + optimizer_class = experimental.DAdaptAdamPreprint + logger.info(f"use D-Adaptation AdamPreprint optimizer | {optimizer_kwargs}") + elif optimizer_type == "DAdaptAdaGrad".lower(): + optimizer_class = dadaptation.DAdaptAdaGrad + logger.info(f"use D-Adaptation AdaGrad optimizer | {optimizer_kwargs}") + elif optimizer_type == "DAdaptAdam".lower(): + optimizer_class = dadaptation.DAdaptAdam + logger.info(f"use D-Adaptation Adam optimizer | {optimizer_kwargs}") + elif optimizer_type == "DAdaptAdan".lower(): + optimizer_class = dadaptation.DAdaptAdan + logger.info(f"use D-Adaptation Adan optimizer | {optimizer_kwargs}") + elif optimizer_type == "DAdaptAdanIP".lower(): + optimizer_class = experimental.DAdaptAdanIP + logger.info(f"use D-Adaptation AdanIP optimizer | {optimizer_kwargs}") + elif optimizer_type == "DAdaptLion".lower(): + optimizer_class = dadaptation.DAdaptLion + logger.info(f"use D-Adaptation Lion optimizer | {optimizer_kwargs}") + elif optimizer_type == "DAdaptSGD".lower(): + optimizer_class = dadaptation.DAdaptSGD + logger.info(f"use D-Adaptation SGD optimizer | {optimizer_kwargs}") + else: + raise ValueError(f"Unknown optimizer type: {optimizer_type}") + + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + else: + # Prodigy + # check Prodigy is installed + try: + import prodigyopt + except ImportError: + raise ImportError("No Prodigy / Prodigy がインストールされていないようです") + + logger.info(f"use Prodigy optimizer | {optimizer_kwargs}") + optimizer_class = prodigyopt.Prodigy + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type == "Adafactor".lower(): + # 引数を確認して適宜補正する + if "relative_step" not in optimizer_kwargs: + optimizer_kwargs["relative_step"] = True # default + if not optimizer_kwargs["relative_step"] and optimizer_kwargs.get("warmup_init", False): + logger.info( + f"set relative_step to True because warmup_init is True / warmup_initがTrueのためrelative_stepをTrueにします" + ) + optimizer_kwargs["relative_step"] = True + logger.info(f"use Adafactor optimizer | {optimizer_kwargs}") + + if optimizer_kwargs["relative_step"]: + logger.info(f"relative_step is true / relative_stepがtrueです") + if lr != 0.0: + logger.warning(f"learning rate is used as initial_lr / 指定したlearning rateはinitial_lrとして使用されます") + args.learning_rate = None + + # trainable_paramsがgroupだった時の処理：lrを削除する + if type(trainable_params) == list and type(trainable_params[0]) == dict: + has_group_lr = False + for group in trainable_params: + p = group.pop("lr", None) + has_group_lr = has_group_lr or (p is not None) + + if has_group_lr: + # 一応argsを無効にしておく TODO 依存関係が逆転してるのであまり望ましくない + logger.warning(f"unet_lr and text_encoder_lr are ignored / unet_lrとtext_encoder_lrは無視されます") + args.unet_lr = None + args.text_encoder_lr = None + + if args.lr_scheduler != "adafactor": + logger.info(f"use adafactor_scheduler / スケジューラにadafactor_schedulerを使用します") + args.lr_scheduler = f"adafactor:{lr}" # ちょっと微妙だけど + + lr = None + else: + if args.max_grad_norm != 0.0: + logger.warning( + f"because max_grad_norm is set, clip_grad_norm is enabled. consider set to 0 / max_grad_normが設定されているためclip_grad_normが有効になります。0に設定して無効にしたほうがいいかもしれません" + ) + if args.lr_scheduler != "constant_with_warmup": + logger.warning(f"constant_with_warmup will be good / スケジューラはconstant_with_warmupが良いかもしれません") + if optimizer_kwargs.get("clip_threshold", 1.0) != 1.0: + logger.warning(f"clip_threshold=1.0 will be good / clip_thresholdは1.0が良いかもしれません") + + optimizer_class = transformers.optimization.Adafactor + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type == "Automagic".lower(): + try: + from library.optimizers.automagic import Automagic + except ImportError: + raise ImportError("Automagic optimizer requires 'optimum-quanto'. Run: pip install optimum-quanto") + logger.info(f"use Automagic optimizer (ostris/ai-toolkit, MIT) | {optimizer_kwargs}") + optimizer_class = Automagic + # Automagic manages its own lr; pass lr as the starting point + optimizer = optimizer_class(trainable_params, lr=lr if lr is not None else 1e-6, **optimizer_kwargs) + + elif optimizer_type == "EmoSens".lower(): + from library.optimizers.emosens import EmoSens + logger.info(f"use EmoSens optimizer (muooon/EmoSens, Apache-2.0) | {optimizer_kwargs}") + optimizer_class = EmoSens + # EmoSens generates LR autonomously via emoPulse; lr=1.0 is recommended + optimizer = optimizer_class(trainable_params, lr=lr if lr is not None else 1.0, **optimizer_kwargs) + + elif optimizer_type == "AdamW".lower(): + logger.info(f"use AdamW optimizer | {optimizer_kwargs}") + optimizer_class = torch.optim.AdamW + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + elif optimizer_type.endswith("schedulefree".lower()): + try: + import schedulefree as sf + except ImportError: + raise ImportError("No schedulefree / schedulefreeがインストールされていないようです") + + if optimizer_type == "RAdamScheduleFree".lower(): + optimizer_class = sf.RAdamScheduleFree + logger.info(f"use RAdamScheduleFree optimizer | {optimizer_kwargs}") + elif optimizer_type == "AdamWScheduleFree".lower(): + optimizer_class = sf.AdamWScheduleFree + logger.info(f"use AdamWScheduleFree optimizer | {optimizer_kwargs}") + elif optimizer_type == "SGDScheduleFree".lower(): + optimizer_class = sf.SGDScheduleFree + logger.info(f"use SGDScheduleFree optimizer | {optimizer_kwargs}") + else: + optimizer_class = None + + if optimizer_class is not None: + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + if optimizer is None: + # 任意のoptimizerを使う + case_sensitive_optimizer_type = args.optimizer_type # not lower + logger.info(f"use {case_sensitive_optimizer_type} | {optimizer_kwargs}") + + if "." not in case_sensitive_optimizer_type: # from torch.optim + optimizer_module = torch.optim + else: # from other library + values = case_sensitive_optimizer_type.split(".") + optimizer_module = importlib.import_module(".".join(values[:-1])) + case_sensitive_optimizer_type = values[-1] + + optimizer_class = getattr(optimizer_module, case_sensitive_optimizer_type) + optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) + + """ + # wrap any of above optimizer with schedulefree, if optimizer is not schedulefree + if args.optimizer_schedulefree_wrapper and not optimizer_type.endswith("schedulefree".lower()): + try: + import schedulefree as sf + except ImportError: + raise ImportError("No schedulefree / schedulefreeがインストールされていないようです") + + schedulefree_wrapper_kwargs = {} + if args.schedulefree_wrapper_args is not None and len(args.schedulefree_wrapper_args) > 0: + for arg in args.schedulefree_wrapper_args: + key, value = arg.split("=") + value = ast.literal_eval(value) + schedulefree_wrapper_kwargs[key] = value + + sf_wrapper = sf.ScheduleFreeWrapper(optimizer, **schedulefree_wrapper_kwargs) + sf_wrapper.train() # make optimizer as train mode + + # we need to make optimizer as a subclass of torch.optim.Optimizer, we make another Proxy class over SFWrapper + class OptimizerProxy(torch.optim.Optimizer): + def __init__(self, sf_wrapper): + self._sf_wrapper = sf_wrapper + + def __getattr__(self, name): + return getattr(self._sf_wrapper, name) + + # override properties + @property + def state(self): + return self._sf_wrapper.state + + @state.setter + def state(self, state): + self._sf_wrapper.state = state + + @property + def param_groups(self): + return self._sf_wrapper.param_groups + + @param_groups.setter + def param_groups(self, param_groups): + self._sf_wrapper.param_groups = param_groups + + @property + def defaults(self): + return self._sf_wrapper.defaults + + @defaults.setter + def defaults(self, defaults): + self._sf_wrapper.defaults = defaults + + def add_param_group(self, param_group): + self._sf_wrapper.add_param_group(param_group) + + def load_state_dict(self, state_dict): + self._sf_wrapper.load_state_dict(state_dict) + + def state_dict(self): + return self._sf_wrapper.state_dict() + + def zero_grad(self): + self._sf_wrapper.zero_grad() + + def step(self, closure=None): + self._sf_wrapper.step(closure) + + def train(self): + self._sf_wrapper.train() + + def eval(self): + self._sf_wrapper.eval() + + # isinstance チェックをパスするためのメソッド + def __instancecheck__(self, instance): + return isinstance(instance, (type(self), Optimizer)) + + optimizer = OptimizerProxy(sf_wrapper) + + logger.info(f"wrap optimizer with ScheduleFreeWrapper | {schedulefree_wrapper_kwargs}") + """ + + # for logging + optimizer_name = optimizer_class.__module__ + "." + optimizer_class.__name__ + optimizer_args = ",".join([f"{k}={v}" for k, v in optimizer_kwargs.items()]) + + if hasattr(optimizer, "train") and callable(optimizer.train): + # make optimizer as train mode before training for schedulefree optimizer. the optimizer will be in eval mode in sampling and saving. + optimizer.train() + + return optimizer_name, optimizer_args, optimizer + + +def get_optimizer_train_eval_fn(optimizer: Optimizer, args: argparse.Namespace) -> Tuple[Callable, Callable]: + if not is_schedulefree_optimizer(optimizer, args): + # return dummy func + return lambda: None, lambda: None + + # get train and eval functions from optimizer + train_fn = optimizer.train + eval_fn = optimizer.eval + + return train_fn, eval_fn + + +def is_schedulefree_optimizer(optimizer: Optimizer, args: argparse.Namespace) -> bool: + return args.optimizer_type.lower().endswith("schedulefree".lower()) # or args.optimizer_schedulefree_wrapper + + +def get_dummy_scheduler(optimizer: Optimizer) -> Any: + # dummy scheduler for schedulefree optimizer. supports only empty step(), get_last_lr() and optimizers. + # this scheduler is used for logging only. + # this isn't be wrapped by accelerator because of this class is not a subclass of torch.optim.lr_scheduler._LRScheduler + class DummyScheduler: + def __init__(self, optimizer: Optimizer): + self.optimizer = optimizer + + def step(self): + pass + + def get_last_lr(self): + return [group["lr"] for group in self.optimizer.param_groups] + + return DummyScheduler(optimizer) + + +# Modified version of get_scheduler() function from diffusers.optimizer.get_scheduler +# Add some checking and features to the original function. + + +def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int): + """ + Unified API to get any scheduler from its name. + """ + # if schedulefree optimizer, return dummy scheduler + if is_schedulefree_optimizer(optimizer, args): + return get_dummy_scheduler(optimizer) + + name = args.lr_scheduler + num_training_steps = args.max_train_steps * num_processes # * args.gradient_accumulation_steps + num_warmup_steps: Optional[int] = ( + int(args.lr_warmup_steps * num_training_steps) if isinstance(args.lr_warmup_steps, float) else args.lr_warmup_steps + ) + num_decay_steps: Optional[int] = ( + int(args.lr_decay_steps * num_training_steps) if isinstance(args.lr_decay_steps, float) else args.lr_decay_steps + ) + num_stable_steps = num_training_steps - num_warmup_steps - num_decay_steps + num_cycles = args.lr_scheduler_num_cycles + power = args.lr_scheduler_power + timescale = args.lr_scheduler_timescale + min_lr_ratio = args.lr_scheduler_min_lr_ratio + + lr_scheduler_kwargs = {} # get custom lr_scheduler kwargs + if args.lr_scheduler_args is not None and len(args.lr_scheduler_args) > 0: + for arg in args.lr_scheduler_args: + key, value = arg.split("=") + value = ast.literal_eval(value) + lr_scheduler_kwargs[key] = value + + def wrap_check_needless_num_warmup_steps(return_vals): + if num_warmup_steps is not None and num_warmup_steps != 0: + raise ValueError(f"{name} does not require `num_warmup_steps`. Set None or 0.") + return return_vals + + # using any lr_scheduler from other library + if args.lr_scheduler_type: + lr_scheduler_type = args.lr_scheduler_type + logger.info(f"use {lr_scheduler_type} | {lr_scheduler_kwargs} as lr_scheduler") + if "." not in lr_scheduler_type: # default to use torch.optim + lr_scheduler_module = torch.optim.lr_scheduler + else: + values = lr_scheduler_type.split(".") + lr_scheduler_module = importlib.import_module(".".join(values[:-1])) + lr_scheduler_type = values[-1] + lr_scheduler_class = getattr(lr_scheduler_module, lr_scheduler_type) + lr_scheduler = lr_scheduler_class(optimizer, **lr_scheduler_kwargs) + return wrap_check_needless_num_warmup_steps(lr_scheduler) + + if name.startswith("adafactor"): + assert ( + type(optimizer) == transformers.optimization.Adafactor + ), f"adafactor scheduler must be used with Adafactor optimizer / adafactor schedulerはAdafactorオプティマイザと同時に使ってください" + initial_lr = float(name.split(":")[1]) + # logger.info(f"adafactor scheduler init lr {initial_lr}") + return wrap_check_needless_num_warmup_steps(transformers.optimization.AdafactorSchedule(optimizer, initial_lr)) + + if name == DiffusersSchedulerType.PIECEWISE_CONSTANT.value: + name = DiffusersSchedulerType(name) + schedule_func = DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION[name] + return schedule_func(optimizer, **lr_scheduler_kwargs) # step_rules and last_epoch are given as kwargs + + name = SchedulerType(name) + schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] + + if name == SchedulerType.CONSTANT: + return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs)) + + # All other schedulers require `num_warmup_steps` + if num_warmup_steps is None: + raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.") + + if name == SchedulerType.CONSTANT_WITH_WARMUP: + return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **lr_scheduler_kwargs) + + if name == SchedulerType.INVERSE_SQRT: + return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, timescale=timescale, **lr_scheduler_kwargs) + + # All other schedulers require `num_training_steps` + if num_training_steps is None: + raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.") + + if name == SchedulerType.COSINE_WITH_RESTARTS: + return schedule_func( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=num_training_steps, + num_cycles=num_cycles, + **lr_scheduler_kwargs, + ) + + if name == SchedulerType.POLYNOMIAL: + return schedule_func( + optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power, **lr_scheduler_kwargs + ) + + if name == SchedulerType.COSINE_WITH_MIN_LR: + return schedule_func( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=num_training_steps, + num_cycles=num_cycles / 2, + min_lr_rate=min_lr_ratio, + **lr_scheduler_kwargs, + ) + + # these schedulers do not require `num_decay_steps` + if name == SchedulerType.LINEAR or name == SchedulerType.COSINE: + return schedule_func( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=num_training_steps, + **lr_scheduler_kwargs, + ) + + # All other schedulers require `num_decay_steps` + if num_decay_steps is None: + raise ValueError(f"{name} requires `num_decay_steps`, please provide that argument.") + if name == SchedulerType.WARMUP_STABLE_DECAY: + return schedule_func( + optimizer, + num_warmup_steps=num_warmup_steps, + num_stable_steps=num_stable_steps, + num_decay_steps=num_decay_steps, + num_cycles=num_cycles / 2, + min_lr_ratio=min_lr_ratio if min_lr_ratio is not None else 0.0, + **lr_scheduler_kwargs, + ) + + return schedule_func( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=num_training_steps, + num_decay_steps=num_decay_steps, + **lr_scheduler_kwargs, + ) + + +def prepare_dataset_args(args: argparse.Namespace, support_metadata: bool): + # backward compatibility + if args.caption_extention is not None: + args.caption_extension = args.caption_extention + args.caption_extention = None + + # assert args.resolution is not None, f"resolution is required / resolution（解像度）を指定してください" + if args.resolution is not None: + args.resolution = tuple([int(r) for r in args.resolution.split(",")]) + if len(args.resolution) == 1: + args.resolution = (args.resolution[0], args.resolution[0]) + assert ( + len(args.resolution) == 2 + ), f"resolution must be 'size' or 'width,height' / resolution（解像度）は'サイズ'または'幅','高さ'で指定してください: {args.resolution}" + + if args.skip_image_resolution is not None: + args.skip_image_resolution = tuple([int(r) for r in args.skip_image_resolution.split(",")]) + if len(args.skip_image_resolution) == 1: + args.skip_image_resolution = (args.skip_image_resolution[0], args.skip_image_resolution[0]) + assert ( + len(args.skip_image_resolution) == 2 + ), f"skip_image_resolution must be 'size' or 'width,height' / skip_image_resolutionは'サイズ'または'幅','高さ'で指定してください: {args.skip_image_resolution}" + + if args.face_crop_aug_range is not None: + args.face_crop_aug_range = tuple([float(r) for r in args.face_crop_aug_range.split(",")]) + assert ( + len(args.face_crop_aug_range) == 2 and args.face_crop_aug_range[0] <= args.face_crop_aug_range[1] + ), f"face_crop_aug_range must be two floats / face_crop_aug_rangeは'下限,上限'で指定してください: {args.face_crop_aug_range}" + else: + args.face_crop_aug_range = None + + if support_metadata: + if args.in_json is not None and (args.color_aug or args.random_crop): + logger.warning( + f"latents in npz is ignored when color_aug or random_crop is True / color_augまたはrandom_cropを有効にした場合、npzファイルのlatentsは無視されます" + ) + + +def prepare_accelerator(args: argparse.Namespace): + """ + this function also prepares deepspeed plugin + """ + + if args.logging_dir is None: + logging_dir = None + else: + log_prefix = "" if args.log_prefix is None else args.log_prefix + logging_dir = args.logging_dir + "/" + log_prefix + time.strftime("%Y%m%d%H%M%S", time.localtime()) + + if args.log_with is None: + if logging_dir is not None: + log_with = "tensorboard" + else: + log_with = None + else: + log_with = args.log_with + if log_with in ["tensorboard", "all"]: + if logging_dir is None: + raise ValueError( + "logging_dir is required when log_with is tensorboard / Tensorboardを使う場合、logging_dirを指定してください" + ) + if log_with in ["wandb", "all"]: + try: + import wandb + except ImportError: + raise ImportError("No wandb / wandb がインストールされていないようです") + if logging_dir is not None: + os.makedirs(logging_dir, exist_ok=True) + os.environ["WANDB_DIR"] = logging_dir + if args.wandb_api_key is not None: + wandb.login(key=args.wandb_api_key) + + # torch.compile のオプション。 NO の場合は torch.compile は使わない + dynamo_backend = "NO" + if args.torch_compile: + dynamo_backend = args.dynamo_backend + + kwargs_handlers = [ + ( + InitProcessGroupKwargs( + backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl", + init_method=( + "env://?use_libuv=False" if os.name == "nt" and Version(torch.__version__) >= Version("2.4.0") else None + ), + timeout=datetime.timedelta(minutes=args.ddp_timeout) if args.ddp_timeout else None, + ) + if torch.cuda.device_count() > 1 + else None + ), + ( + DistributedDataParallelKwargs( + gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, static_graph=args.ddp_static_graph + ) + if args.ddp_gradient_as_bucket_view or args.ddp_static_graph + else None + ), + ] + kwargs_handlers = [i for i in kwargs_handlers if i is not None] + deepspeed_plugin = deepspeed_utils.prepare_deepspeed_plugin(args) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with=log_with, + project_dir=logging_dir, + kwargs_handlers=kwargs_handlers, + dynamo_backend=dynamo_backend, + deepspeed_plugin=deepspeed_plugin, + ) + print("accelerator device:", accelerator.device) + return accelerator + + +def prepare_dtype(args: argparse.Namespace): + weight_dtype = torch.float32 + if args.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif args.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + save_dtype = None + if args.save_precision == "fp16": + save_dtype = torch.float16 + elif args.save_precision == "bf16": + save_dtype = torch.bfloat16 + elif args.save_precision == "float": + save_dtype = torch.float32 + + return weight_dtype, save_dtype + + +def _load_target_model(args: argparse.Namespace, weight_dtype, device="cpu", unet_use_linear_projection_in_v2=False): + name_or_path = args.pretrained_model_name_or_path + name_or_path = os.path.realpath(name_or_path) if os.path.islink(name_or_path) else name_or_path + load_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers + if load_stable_diffusion_format: + logger.info(f"load StableDiffusion checkpoint: {name_or_path}") + text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint( + args.v2, name_or_path, device, unet_use_linear_projection_in_v2=unet_use_linear_projection_in_v2 + ) + else: + # Diffusers model is loaded to CPU + logger.info(f"load Diffusers pretrained models: {name_or_path}") + try: + pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None) + except EnvironmentError as ex: + logger.error( + f"model is not found as a file or in Hugging Face, perhaps file name is wrong? / 指定したモデル名のファイル、またはHugging Faceのモデルが見つかりません。ファイル名が誤っているかもしれません: {name_or_path}" + ) + raise ex + text_encoder = pipe.text_encoder + vae = pipe.vae + unet = pipe.unet + del pipe + + # Diffusers U-Net to original U-Net + # TODO *.ckpt/*.safetensorsのv2と同じ形式にここで変換すると良さそう + # logger.info(f"unet config: {unet.config}") + original_unet = UNet2DConditionModel( + unet.config.sample_size, + unet.config.attention_head_dim, + unet.config.cross_attention_dim, + unet.config.use_linear_projection, + unet.config.upcast_attention, + ) + original_unet.load_state_dict(unet.state_dict()) + unet = original_unet + logger.info("U-Net converted to original U-Net") + + # VAEを読み込む + if args.vae is not None: + vae = model_util.load_vae(args.vae, weight_dtype) + logger.info("additional VAE loaded") + + return text_encoder, vae, unet, load_stable_diffusion_format + + +def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=False): + for pi in range(accelerator.state.num_processes): + if pi == accelerator.state.local_process_index: + logger.info(f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}") + + text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model( + args, + weight_dtype, + accelerator.device if args.lowram else "cpu", + unet_use_linear_projection_in_v2=unet_use_linear_projection_in_v2, + ) + + # Expand 4-channel conv_in to 9 channels when training inpainting from a + # standard (non-inpainting) checkpoint. + if getattr(args, "train_inpainting", False) and getattr(unet, "in_channels", 4) == 4: + logger.info( + "train_inpainting: expanding UNet conv_in from 4 to 9 channels " + "(standard checkpoint → inpainting training from scratch)" + ) + model_util.expand_unet_to_inpainting(unet) + + # work on low-ram device + if args.lowram: + text_encoder.to(accelerator.device) + unet.to(accelerator.device) + vae.to(accelerator.device) + + clean_memory_on_device(accelerator.device) + accelerator.wait_for_everyone() + return text_encoder, vae, unet, load_stable_diffusion_format + + +def patch_accelerator_for_fp16_training(accelerator): + + from accelerate import DistributedType + + if accelerator.distributed_type == DistributedType.DEEPSPEED: + return + + org_unscale_grads = accelerator.scaler._unscale_grads_ + + def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16): + return org_unscale_grads(optimizer, inv_scale, found_inf, True) + + accelerator.scaler._unscale_grads_ = _unscale_grads_replacer + + +def get_hidden_states(args: argparse.Namespace, input_ids, tokenizer, text_encoder, weight_dtype=None): + # with no_token_padding, the length is not max length, return result immediately + if input_ids.size()[-1] != tokenizer.model_max_length: + return text_encoder(input_ids)[0] + + # input_ids: b,n,77 + b_size = input_ids.size()[0] + input_ids = input_ids.reshape((-1, tokenizer.model_max_length)) # batch_size*3, 77 + + if args.clip_skip is None: + encoder_hidden_states = text_encoder(input_ids)[0] + else: + enc_out = text_encoder(input_ids, output_hidden_states=True, return_dict=True) + encoder_hidden_states = enc_out["hidden_states"][-args.clip_skip] + encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states) + + # bs*3, 77, 768 or 1024 + encoder_hidden_states = encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])) + + if args.max_token_length is not None: + if args.v2: + # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん + states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # + for i in range(1, args.max_token_length, tokenizer.model_max_length): + chunk = encoder_hidden_states[:, i : i + tokenizer.model_max_length - 2] # の後から 最後の前まで + if i > 0: + for j in range(len(chunk)): + if input_ids[j, 1] == tokenizer.eos_token: # 空、つまり ...のパターン + chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする + states_list.append(chunk) # の後から の前まで + states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # か のどちらか + encoder_hidden_states = torch.cat(states_list, dim=1) + else: + # v1: ... の三連を ... へ戻す + states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # + for i in range(1, args.max_token_length, tokenizer.model_max_length): + states_list.append( + encoder_hidden_states[:, i : i + tokenizer.model_max_length - 2] + ) # の後から の前まで + states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # + encoder_hidden_states = torch.cat(states_list, dim=1) + + if weight_dtype is not None: + # this is required for additional network training + encoder_hidden_states = encoder_hidden_states.to(weight_dtype) + + return encoder_hidden_states + + +def pool_workaround( + text_encoder: CLIPTextModelWithProjection, last_hidden_state: torch.Tensor, input_ids: torch.Tensor, eos_token_id: int +): + r""" + workaround for CLIP's pooling bug: it returns the hidden states for the max token id as the pooled output + instead of the hidden states for the EOS token + If we use Textual Inversion, we need to use the hidden states for the EOS token as the pooled output + + Original code from CLIP's pooling function: + + \# text_embeds.shape = [batch_size, sequence_length, transformer.width] + \# take features from the eot embedding (eot_token is the highest number in each sequence) + \# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 + pooled_output = last_hidden_state[ + torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), + input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1), + ] + """ + + # input_ids: b*n,77 + # find index for EOS token + + # Following code is not working if one of the input_ids has multiple EOS tokens (very odd case) + # eos_token_index = torch.where(input_ids == eos_token_id)[1] + # eos_token_index = eos_token_index.to(device=last_hidden_state.device) + + # Create a mask where the EOS tokens are + eos_token_mask = (input_ids == eos_token_id).int() + + # Use argmax to find the last index of the EOS token for each element in the batch + eos_token_index = torch.argmax(eos_token_mask, dim=1) # this will be 0 if there is no EOS token, it's fine + eos_token_index = eos_token_index.to(device=last_hidden_state.device) + + # get hidden states for EOS token + pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), eos_token_index] + + # apply projection: projection may be of different dtype than last_hidden_state + pooled_output = text_encoder.text_projection(pooled_output.to(text_encoder.text_projection.weight.dtype)) + pooled_output = pooled_output.to(last_hidden_state.dtype) + + return pooled_output + + +def get_hidden_states_sdxl( + max_token_length: int, + input_ids1: torch.Tensor, + input_ids2: torch.Tensor, + tokenizer1: CLIPTokenizer, + tokenizer2: CLIPTokenizer, + text_encoder1: CLIPTextModel, + text_encoder2: CLIPTextModelWithProjection, + weight_dtype: Optional[str] = None, + accelerator: Optional[Accelerator] = None, +): + # input_ids: b,n,77 -> b*n, 77 + b_size = input_ids1.size()[0] + input_ids1 = input_ids1.reshape((-1, tokenizer1.model_max_length)) # batch_size*n, 77 + input_ids2 = input_ids2.reshape((-1, tokenizer2.model_max_length)) # batch_size*n, 77 + + # text_encoder1 + enc_out = text_encoder1(input_ids1, output_hidden_states=True, return_dict=True) + hidden_states1 = enc_out["hidden_states"][11] + + # text_encoder2 + enc_out = text_encoder2(input_ids2, output_hidden_states=True, return_dict=True) + hidden_states2 = enc_out["hidden_states"][-2] # penuultimate layer + + # pool2 = enc_out["text_embeds"] + unwrapped_text_encoder2 = text_encoder2 if accelerator is None else accelerator.unwrap_model(text_encoder2) + pool2 = pool_workaround(unwrapped_text_encoder2, enc_out["last_hidden_state"], input_ids2, tokenizer2.eos_token_id) + + # b*n, 77, 768 or 1280 -> b, n*77, 768 or 1280 + n_size = 1 if max_token_length is None else max_token_length // 75 + hidden_states1 = hidden_states1.reshape((b_size, -1, hidden_states1.shape[-1])) + hidden_states2 = hidden_states2.reshape((b_size, -1, hidden_states2.shape[-1])) + + if max_token_length is not None: + # bs*3, 77, 768 or 1024 + # encoder1: ... の三連を ... へ戻す + states_list = [hidden_states1[:, 0].unsqueeze(1)] # + for i in range(1, max_token_length, tokenizer1.model_max_length): + states_list.append(hidden_states1[:, i : i + tokenizer1.model_max_length - 2]) # の後から の前まで + states_list.append(hidden_states1[:, -1].unsqueeze(1)) # + hidden_states1 = torch.cat(states_list, dim=1) + + # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん + states_list = [hidden_states2[:, 0].unsqueeze(1)] # + for i in range(1, max_token_length, tokenizer2.model_max_length): + chunk = hidden_states2[:, i : i + tokenizer2.model_max_length - 2] # の後から 最後の前まで + # this causes an error: + # RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation + # if i > 1: + # for j in range(len(chunk)): # batch_size + # if input_ids2[n_index + j * n_size, 1] == tokenizer2.eos_token_id: # 空、つまり ...のパターン + # chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする + states_list.append(chunk) # の後から の前まで + states_list.append(hidden_states2[:, -1].unsqueeze(1)) # か のどちらか + hidden_states2 = torch.cat(states_list, dim=1) + + # pool はnの最初のものを使う + pool2 = pool2[::n_size] + + if weight_dtype is not None: + # this is required for additional network training + hidden_states1 = hidden_states1.to(weight_dtype) + hidden_states2 = hidden_states2.to(weight_dtype) + + return hidden_states1, hidden_states2, pool2 + + +def default_if_none(value, default): + return default if value is None else value + + +def get_epoch_ckpt_name(args: argparse.Namespace, ext: str, epoch_no: int): + model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME) + return EPOCH_FILE_NAME.format(model_name, epoch_no) + ext + + +def get_step_ckpt_name(args: argparse.Namespace, ext: str, step_no: int): + model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) + return STEP_FILE_NAME.format(model_name, step_no) + ext + + +def get_last_ckpt_name(args: argparse.Namespace, ext: str): + model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) + return model_name + ext + + +def get_remove_epoch_no(args: argparse.Namespace, epoch_no: int): + if args.save_last_n_epochs is None: + return None + + remove_epoch_no = epoch_no - args.save_every_n_epochs * args.save_last_n_epochs + if remove_epoch_no < 0: + return None + return remove_epoch_no + + +def get_remove_step_no(args: argparse.Namespace, step_no: int): + if args.save_last_n_steps is None: + return None + + # last_n_steps前のstep_noから、save_every_n_stepsの倍数のstep_noを計算して削除する + # save_every_n_steps=10, save_last_n_steps=30の場合、50step目には30step分残し、10step目を削除する + remove_step_no = step_no - args.save_last_n_steps - 1 + remove_step_no = remove_step_no - (remove_step_no % args.save_every_n_steps) + if remove_step_no < 0: + return None + return remove_step_no + + +# epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合している +# on_epoch_end: Trueならepoch終了時、Falseならstep経過時 +def save_sd_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator, + src_path: str, + save_stable_diffusion_format: bool, + use_safetensors: bool, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + text_encoder, + unet, + vae, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=True) + model_util.save_stable_diffusion_checkpoint( + args.v2, ckpt_file, text_encoder, unet, src_path, epoch_no, global_step, sai_metadata, save_dtype, vae + ) + + def diffusers_saver(out_dir): + model_util.save_diffusers_checkpoint( + args.v2, out_dir, text_encoder, unet, src_path, vae=vae, use_safetensors=use_safetensors + ) + + save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + save_stable_diffusion_format, + use_safetensors, + epoch, + num_train_epochs, + global_step, + sd_saver, + diffusers_saver, + ) + + +def save_sd_model_on_epoch_end_or_stepwise_common( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator, + save_stable_diffusion_format: bool, + use_safetensors: bool, + epoch: int, + num_train_epochs: int, + global_step: int, + sd_saver, + diffusers_saver, +): + if on_epoch_end: + epoch_no = epoch + 1 + saving = epoch_no % args.save_every_n_epochs == 0 and epoch_no < num_train_epochs + if not saving: + return + + model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME) + remove_no = get_remove_epoch_no(args, epoch_no) + else: + # 保存するか否かは呼び出し側で判断済み + + model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) + epoch_no = epoch # 例: 最初のepochの途中で保存したら0になる、SDモデルに保存される + remove_no = get_remove_step_no(args, global_step) + + os.makedirs(args.output_dir, exist_ok=True) + if save_stable_diffusion_format: + ext = ".safetensors" if use_safetensors else ".ckpt" + + if on_epoch_end: + ckpt_name = get_epoch_ckpt_name(args, ext, epoch_no) + else: + ckpt_name = get_step_ckpt_name(args, ext, global_step) + + ckpt_file = os.path.join(args.output_dir, ckpt_name) + logger.info("") + logger.info(f"saving checkpoint: {ckpt_file}") + sd_saver(ckpt_file, epoch_no, global_step) + + if args.huggingface_repo_id is not None: + huggingface_util.upload(args, ckpt_file, "/" + ckpt_name) + + # remove older checkpoints + if remove_no is not None: + if on_epoch_end: + remove_ckpt_name = get_epoch_ckpt_name(args, ext, remove_no) + else: + remove_ckpt_name = get_step_ckpt_name(args, ext, remove_no) + + remove_ckpt_file = os.path.join(args.output_dir, remove_ckpt_name) + if os.path.exists(remove_ckpt_file): + logger.info(f"removing old checkpoint: {remove_ckpt_file}") + os.remove(remove_ckpt_file) + + else: + if on_epoch_end: + out_dir = os.path.join(args.output_dir, EPOCH_DIFFUSERS_DIR_NAME.format(model_name, epoch_no)) + else: + out_dir = os.path.join(args.output_dir, STEP_DIFFUSERS_DIR_NAME.format(model_name, global_step)) + + logger.info("") + logger.info(f"saving model: {out_dir}") + diffusers_saver(out_dir) + + if args.huggingface_repo_id is not None: + huggingface_util.upload(args, out_dir, "/" + model_name) + + # remove older checkpoints + if remove_no is not None: + if on_epoch_end: + remove_out_dir = os.path.join(args.output_dir, EPOCH_DIFFUSERS_DIR_NAME.format(model_name, remove_no)) + else: + remove_out_dir = os.path.join(args.output_dir, STEP_DIFFUSERS_DIR_NAME.format(model_name, remove_no)) + + if os.path.exists(remove_out_dir): + logger.info(f"removing old model: {remove_out_dir}") + shutil.rmtree(remove_out_dir) + + if args.save_state: + if on_epoch_end: + save_and_remove_state_on_epoch_end(args, accelerator, epoch_no) + else: + save_and_remove_state_stepwise(args, accelerator, global_step) + + +def save_and_remove_state_on_epoch_end(args: argparse.Namespace, accelerator, epoch_no): + model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME) + + logger.info("") + logger.info(f"saving state at epoch {epoch_no}") + os.makedirs(args.output_dir, exist_ok=True) + + state_dir = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, epoch_no)) + accelerator.save_state(state_dir) + if args.save_state_to_huggingface: + logger.info("uploading state to huggingface.") + huggingface_util.upload(args, state_dir, "/" + EPOCH_STATE_NAME.format(model_name, epoch_no)) + + last_n_epochs = args.save_last_n_epochs_state if args.save_last_n_epochs_state else args.save_last_n_epochs + if last_n_epochs is not None: + remove_epoch_no = epoch_no - args.save_every_n_epochs * last_n_epochs + state_dir_old = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, remove_epoch_no)) + if os.path.exists(state_dir_old): + logger.info(f"removing old state: {state_dir_old}") + shutil.rmtree(state_dir_old) + + +def save_and_remove_state_stepwise(args: argparse.Namespace, accelerator, step_no): + model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) + + logger.info("") + logger.info(f"saving state at step {step_no}") + os.makedirs(args.output_dir, exist_ok=True) + + state_dir = os.path.join(args.output_dir, STEP_STATE_NAME.format(model_name, step_no)) + accelerator.save_state(state_dir) + if args.save_state_to_huggingface: + logger.info("uploading state to huggingface.") + huggingface_util.upload(args, state_dir, "/" + STEP_STATE_NAME.format(model_name, step_no)) + + last_n_steps = args.save_last_n_steps_state if args.save_last_n_steps_state else args.save_last_n_steps + if last_n_steps is not None: + # last_n_steps前のstep_noから、save_every_n_stepsの倍数のstep_noを計算して削除する + remove_step_no = step_no - last_n_steps - 1 + remove_step_no = remove_step_no - (remove_step_no % args.save_every_n_steps) + + if remove_step_no > 0: + state_dir_old = os.path.join(args.output_dir, STEP_STATE_NAME.format(model_name, remove_step_no)) + if os.path.exists(state_dir_old): + logger.info(f"removing old state: {state_dir_old}") + shutil.rmtree(state_dir_old) + + +def save_state_on_train_end(args: argparse.Namespace, accelerator): + model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) + + logger.info("") + logger.info("saving last state.") + os.makedirs(args.output_dir, exist_ok=True) + + state_dir = os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name)) + accelerator.save_state(state_dir) + + if args.save_state_to_huggingface: + logger.info("uploading last state to huggingface.") + huggingface_util.upload(args, state_dir, "/" + LAST_STATE_NAME.format(model_name)) + + +def save_sd_model_on_train_end( + args: argparse.Namespace, + src_path: str, + save_stable_diffusion_format: bool, + use_safetensors: bool, + save_dtype: torch.dtype, + epoch: int, + global_step: int, + text_encoder, + unet, + vae, +): + def sd_saver(ckpt_file, epoch_no, global_step): + sai_metadata = get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=True) + model_util.save_stable_diffusion_checkpoint( + args.v2, ckpt_file, text_encoder, unet, src_path, epoch_no, global_step, sai_metadata, save_dtype, vae + ) + + def diffusers_saver(out_dir): + model_util.save_diffusers_checkpoint( + args.v2, out_dir, text_encoder, unet, src_path, vae=vae, use_safetensors=use_safetensors + ) + + save_sd_model_on_train_end_common( + args, save_stable_diffusion_format, use_safetensors, epoch, global_step, sd_saver, diffusers_saver + ) + + +def save_sd_model_on_train_end_common( + args: argparse.Namespace, + save_stable_diffusion_format: bool, + use_safetensors: bool, + epoch: int, + global_step: int, + sd_saver, + diffusers_saver, +): + model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) + + if save_stable_diffusion_format: + os.makedirs(args.output_dir, exist_ok=True) + + ckpt_name = model_name + (".safetensors" if use_safetensors else ".ckpt") + ckpt_file = os.path.join(args.output_dir, ckpt_name) + + logger.info(f"save trained model as StableDiffusion checkpoint to {ckpt_file}") + sd_saver(ckpt_file, epoch, global_step) + + if args.huggingface_repo_id is not None: + huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=True) + else: + out_dir = os.path.join(args.output_dir, model_name) + os.makedirs(out_dir, exist_ok=True) + + logger.info(f"save trained model as Diffusers to {out_dir}") + diffusers_saver(out_dir) + + if args.huggingface_repo_id is not None: + huggingface_util.upload(args, out_dir, "/" + model_name, force_sync_upload=True) + + +def get_timesteps(min_timestep: int, max_timestep: int, b_size: int, device: torch.device) -> torch.Tensor: + if min_timestep < max_timestep: + timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu") + else: + timesteps = torch.full((b_size,), max_timestep, device="cpu") + timesteps = timesteps.long().to(device) + return timesteps + + +def get_noise_noisy_latents_and_timesteps( + args, noise_scheduler, latents: torch.FloatTensor +) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.IntTensor]: + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents, device=latents.device) + if args.noise_offset: + if args.noise_offset_random_strength: + noise_offset = torch.rand(1, device=latents.device) * args.noise_offset + else: + noise_offset = args.noise_offset + noise = custom_train_functions.apply_noise_offset(latents, noise, noise_offset, args.adaptive_noise_scale) + if args.multires_noise_iterations: + noise = custom_train_functions.pyramid_noise_like( + noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount + ) + + # Sample a random timestep for each image + b_size = latents.shape[0] + min_timestep = 0 if args.min_timestep is None else args.min_timestep + max_timestep = noise_scheduler.config.num_train_timesteps if args.max_timestep is None else args.max_timestep + timesteps = get_timesteps(min_timestep, max_timestep, b_size, latents.device) + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + if args.ip_noise_gamma: + if args.ip_noise_gamma_random_strength: + strength = torch.rand(1, device=latents.device) * args.ip_noise_gamma + else: + strength = args.ip_noise_gamma + noisy_latents = noise_scheduler.add_noise(latents, noise + strength * torch.randn_like(latents), timesteps) + else: + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # This moves the alphas_cumprod back to the CPU after it is moved in noise_scheduler.add_noise + noise_scheduler.alphas_cumprod = noise_scheduler.alphas_cumprod.cpu() + + return noise, noisy_latents, timesteps + + +def get_huber_threshold_if_needed(args, timesteps: torch.Tensor, noise_scheduler) -> Optional[torch.Tensor]: + if not (args.loss_type == "huber" or args.loss_type == "smooth_l1"): + return None + + b_size = timesteps.shape[0] + if args.huber_schedule == "exponential": + alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps + result = torch.exp(-alpha * timesteps) * args.huber_scale + elif args.huber_schedule == "snr": + if not hasattr(noise_scheduler, "alphas_cumprod"): + raise NotImplementedError("Huber schedule 'snr' is not supported with the current model.") + alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps.cpu()) + sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5 + result = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c + result = result.to(timesteps.device) + elif args.huber_schedule == "constant": + result = torch.full((b_size,), args.huber_c * args.huber_scale, device=timesteps.device) + else: + raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!") + + return result + + +def conditional_loss( + model_pred: torch.Tensor, target: torch.Tensor, loss_type: str, reduction: str, huber_c: Optional[torch.Tensor] = None +): + """ + NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already + """ + if loss_type == "l2": + loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction) + elif loss_type == "l1": + loss = torch.nn.functional.l1_loss(model_pred, target, reduction=reduction) + elif loss_type == "huber": + if huber_c is None: + raise NotImplementedError("huber_c not implemented correctly") + # Reshape huber_c to broadcast with model_pred (supports 4D and 5D tensors) + huber_c = huber_c.view(-1, *([1] * (model_pred.ndim - 1))) + loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c) + if reduction == "mean": + loss = torch.mean(loss) + elif reduction == "sum": + loss = torch.sum(loss) + elif loss_type == "smooth_l1": + if huber_c is None: + raise NotImplementedError("huber_c not implemented correctly") + # Reshape huber_c to broadcast with model_pred (supports 4D and 5D tensors) + huber_c = huber_c.view(-1, *([1] * (model_pred.ndim - 1))) + loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c) + if reduction == "mean": + loss = torch.mean(loss) + elif reduction == "sum": + loss = torch.sum(loss) + else: + raise NotImplementedError(f"Unsupported Loss Type: {loss_type}") + return loss + + +def append_lr_to_logs(logs, lr_scheduler, optimizer_type, including_unet=True): + names = [] + if including_unet: + names.append("unet") + names.append("text_encoder1") + names.append("text_encoder2") + names.append("text_encoder3") # SD3 + + append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names) + + +def append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names): + lrs = lr_scheduler.get_last_lr() + + for lr_index in range(len(lrs)): + name = names[lr_index] + logs["lr/" + name] = float(lrs[lr_index]) + + if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower().startswith("Prodigy".lower()): + logs["lr/d*lr/" + name] = ( + lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["lr"] + ) + if "effective_lr" in lr_scheduler.optimizers[-1].param_groups[lr_index]: + logs["lr/d*eff_lr/" + name] = ( + lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] + * lr_scheduler.optimizers[-1].param_groups[lr_index]["effective_lr"] + ) + + +# scheduler: +SCHEDULER_LINEAR_START = 0.00085 +SCHEDULER_LINEAR_END = 0.0120 +SCHEDULER_TIMESTEPS = 1000 +SCHEDLER_SCHEDULE = "scaled_linear" + + +def get_my_scheduler( + *, + sample_sampler: str, + v_parameterization: bool, +): + sched_init_args = {} + if sample_sampler == "ddim": + scheduler_cls = DDIMScheduler + elif sample_sampler == "ddpm": # ddpmはおかしくなるのでoptionから外してある + scheduler_cls = DDPMScheduler + elif sample_sampler == "pndm": + scheduler_cls = PNDMScheduler + elif sample_sampler == "lms" or sample_sampler == "k_lms": + scheduler_cls = LMSDiscreteScheduler + elif sample_sampler == "euler" or sample_sampler == "k_euler": + scheduler_cls = EulerDiscreteScheduler + elif sample_sampler == "euler_a" or sample_sampler == "k_euler_a": + scheduler_cls = EulerAncestralDiscreteScheduler + elif sample_sampler == "dpmsolver" or sample_sampler == "dpmsolver++": + scheduler_cls = DPMSolverMultistepScheduler + sched_init_args["algorithm_type"] = sample_sampler + elif sample_sampler == "dpmsingle": + scheduler_cls = DPMSolverSinglestepScheduler + elif sample_sampler == "heun": + scheduler_cls = HeunDiscreteScheduler + elif sample_sampler == "dpm_2" or sample_sampler == "k_dpm_2": + scheduler_cls = KDPM2DiscreteScheduler + elif sample_sampler == "dpm_2_a" or sample_sampler == "k_dpm_2_a": + scheduler_cls = KDPM2AncestralDiscreteScheduler + else: + scheduler_cls = DDIMScheduler + + if v_parameterization: + sched_init_args["prediction_type"] = "v_prediction" + + scheduler = scheduler_cls( + num_train_timesteps=SCHEDULER_TIMESTEPS, + beta_start=SCHEDULER_LINEAR_START, + beta_end=SCHEDULER_LINEAR_END, + beta_schedule=SCHEDLER_SCHEDULE, + steps_offset=1, + **sched_init_args, + ) + + # clip_sample=Trueにする + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False: + # logger.info("set clip_sample to True") + scheduler.config.clip_sample = True + + return scheduler + + +def sample_images(*args, **kwargs): + return sample_images_common(StableDiffusionLongPromptWeightingPipeline, *args, **kwargs) + + +def line_to_prompt_dict(line: str) -> dict: + # subset of gen_img_diffusers + prompt_args = line.split(" --") + prompt_dict = {} + prompt_dict["prompt"] = prompt_args[0] + + for parg in prompt_args: + try: + m = re.match(r"w (\d+)", parg, re.IGNORECASE) + if m: + prompt_dict["width"] = int(m.group(1)) + continue + + m = re.match(r"h (\d+)", parg, re.IGNORECASE) + if m: + prompt_dict["height"] = int(m.group(1)) + continue + + m = re.match(r"d (\d+)", parg, re.IGNORECASE) + if m: + prompt_dict["seed"] = int(m.group(1)) + continue + + m = re.match(r"s (\d+)", parg, re.IGNORECASE) + if m: # steps + prompt_dict["sample_steps"] = max(1, min(1000, int(m.group(1)))) + continue + + m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE) + if m: # scale + prompt_dict["scale"] = float(m.group(1)) + continue + + m = re.match(r"g ([\d\.]+)", parg, re.IGNORECASE) + if m: # guidance scale + prompt_dict["guidance_scale"] = float(m.group(1)) + continue + + m = re.match(r"n (.+)", parg, re.IGNORECASE) + if m: # negative prompt + prompt_dict["negative_prompt"] = m.group(1) + continue + + m = re.match(r"ss (.+)", parg, re.IGNORECASE) + if m: + prompt_dict["sample_sampler"] = m.group(1) + continue + + m = re.match(r"cn (.+)", parg, re.IGNORECASE) + if m: + prompt_dict["controlnet_image"] = m.group(1) + continue + + m = re.match(r"i (.+)", parg, re.IGNORECASE) + if m: + prompt_dict["image"] = m.group(1).strip() + continue + + m = re.match(r"ctr (.+)", parg, re.IGNORECASE) + if m: + prompt_dict["cfg_trunc_ratio"] = float(m.group(1)) + continue + + m = re.match(r"rcfg (.+)", parg, re.IGNORECASE) + if m: + prompt_dict["renorm_cfg"] = float(m.group(1)) + continue + + m = re.match(r"fs (.+)", parg, re.IGNORECASE) + if m: + prompt_dict["flow_shift"] = m.group(1) + continue + + m = re.match(r"am ([\d\.\-,]+)", parg, re.IGNORECASE) + if m: # additional network multiplier(s) — comma-separated list, same as gen_img.py + prompt_dict["additional_network_multiplier"] = [float(v) for v in m.group(1).split(",")] + continue + + except ValueError as ex: + logger.error(f"Exception in parsing / 解析エラー: {parg}") + logger.error(ex) + + return prompt_dict + + +def load_prompts(prompt_file: str) -> List[Dict]: + # read prompts + if prompt_file.endswith(".txt"): + with open(prompt_file, "r", encoding="utf-8") as f: + lines = f.readlines() + prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"] + elif prompt_file.endswith(".toml"): + with open(prompt_file, "r", encoding="utf-8") as f: + data = toml.load(f) + prompts = [dict(**data["prompt"], **subset) for subset in data["prompt"]["subset"]] + elif prompt_file.endswith(".json"): + with open(prompt_file, "r", encoding="utf-8") as f: + prompts = json.load(f) + + # preprocess prompts + for i in range(len(prompts)): + prompt_dict = prompts[i] + if isinstance(prompt_dict, str): + from library.train_util import line_to_prompt_dict + + prompt_dict = line_to_prompt_dict(prompt_dict) + prompts[i] = prompt_dict + assert isinstance(prompt_dict, dict) + + # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict. + prompt_dict["enum"] = i + prompt_dict.pop("subset", None) + + return prompts + + +def sample_images_common( + pipe_class, + accelerator: Accelerator, + args: argparse.Namespace, + epoch: int, + steps: int, + device, + vae, + tokenizer, + text_encoder, + unet_wrapped, + prompt_replacement=None, + controlnet=None, +): + """ + StableDiffusionLongPromptWeightingPipelineの改造版を使うようにしたので、clip skipおよびプロンプトの重みづけに対応した + TODO Use strategies here + """ + + if steps == 0: + if not args.sample_at_first: + return + else: + if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: + return + if args.sample_every_n_epochs is not None: + # sample_every_n_steps は無視する + if epoch is None or epoch % args.sample_every_n_epochs != 0: + return + else: + if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch + return + + logger.info("") + logger.info(f"generating sample images at step / サンプル画像生成 ステップ: {steps}") + if not os.path.isfile(args.sample_prompts): + logger.error(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}") + return + + distributed_state = PartialState() # for multi gpu distributed inference. this is a singleton, so it's safe to use it here + + org_vae_device = vae.device # CPUにいるはず + vae.to(distributed_state.device) # distributed_state.device is same as accelerator.device + + # unwrap unet and text_encoder(s) + unet = accelerator.unwrap_model(unet_wrapped) + if isinstance(text_encoder, (list, tuple)): + text_encoder = [accelerator.unwrap_model(te) for te in text_encoder] + else: + text_encoder = accelerator.unwrap_model(text_encoder) + + # read prompts + if args.sample_prompts.endswith(".txt"): + with open(args.sample_prompts, "r", encoding="utf-8") as f: + lines = f.readlines() + prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"] + elif args.sample_prompts.endswith(".toml"): + with open(args.sample_prompts, "r", encoding="utf-8") as f: + data = toml.load(f) + prompts = [dict(**data["prompt"], **subset) for subset in data["prompt"]["subset"]] + elif args.sample_prompts.endswith(".json"): + with open(args.sample_prompts, "r", encoding="utf-8") as f: + prompts = json.load(f) + + default_scheduler = get_my_scheduler(sample_sampler=args.sample_sampler, v_parameterization=args.v_parameterization) + + pipeline = pipe_class( + text_encoder=text_encoder, + vae=vae, + unet=unet, + tokenizer=tokenizer, + scheduler=default_scheduler, + safety_checker=None, + feature_extractor=None, + requires_safety_checker=False, + clip_skip=args.clip_skip, + ) + pipeline.to(distributed_state.device) + save_dir = args.output_dir + "/sample" + os.makedirs(save_dir, exist_ok=True) + + # preprocess prompts + for i in range(len(prompts)): + prompt_dict = prompts[i] + if isinstance(prompt_dict, str): + prompt_dict = line_to_prompt_dict(prompt_dict) + prompts[i] = prompt_dict + assert isinstance(prompt_dict, dict) + + # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict. + prompt_dict["enum"] = i + prompt_dict.pop("subset", None) + + # save random state to restore later + rng_state = torch.get_rng_state() + cuda_rng_state = None + try: + cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None + except Exception: + pass + + if distributed_state.num_processes <= 1: + # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts. + with torch.no_grad(): + for prompt_dict in prompts: + sample_image_inference( + accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet + ) + else: + # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available) + # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical. + per_process_prompts = [] # list of lists + for i in range(distributed_state.num_processes): + per_process_prompts.append(prompts[i :: distributed_state.num_processes]) + + with torch.no_grad(): + with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists: + for prompt_dict in prompt_dict_lists[0]: + sample_image_inference( + accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet + ) + + # clear pipeline and cache to reduce vram usage + del pipeline + + torch.set_rng_state(rng_state) + if torch.cuda.is_available() and cuda_rng_state is not None: + torch.cuda.set_rng_state(cuda_rng_state) + vae.to(org_vae_device) + + clean_memory_on_device(accelerator.device) + + +def sample_image_inference( + accelerator: Accelerator, + args: argparse.Namespace, + pipeline: Union[StableDiffusionLongPromptWeightingPipeline, SdxlStableDiffusionLongPromptWeightingPipeline], + save_dir, + prompt_dict, + epoch, + steps, + prompt_replacement, + controlnet=None, +): + assert isinstance(prompt_dict, dict) + negative_prompt = prompt_dict.get("negative_prompt") + sample_steps = prompt_dict.get("sample_steps", 30) + width = prompt_dict.get("width", 512) + height = prompt_dict.get("height", 512) + scale = prompt_dict.get("scale", 7.5) + seed = prompt_dict.get("seed") + controlnet_image = prompt_dict.get("controlnet_image") + prompt: str = prompt_dict.get("prompt", "") + sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler) + + if prompt_replacement is not None: + prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1]) + if negative_prompt is not None: + negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1]) + + if seed is not None: + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + else: + # True random sample image generation + torch.seed() + if torch.cuda.is_available(): + torch.cuda.seed() + + scheduler = get_my_scheduler( + sample_sampler=sampler_name, + v_parameterization=args.v_parameterization, + ) + pipeline.scheduler = scheduler + + if controlnet_image is not None: + controlnet_image = Image.open(controlnet_image).convert("RGB") + controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS) + + # Round down so the latent shape is divisible by the UNet's largest stride: + # SDXL has 2 downsamples (latent /4 → image /32); SD1.5/2.x has 3 (latent /8 → image /64). + divisor = 32 if isinstance(pipeline, SdxlStableDiffusionLongPromptWeightingPipeline) else 64 + height = max(divisor, height - height % divisor) + width = max(divisor, width - width % divisor) + logger.info(f"prompt: {prompt}") + logger.info(f"negative_prompt: {negative_prompt}") + logger.info(f"height: {height}") + logger.info(f"width: {width}") + logger.info(f"sample_steps: {sample_steps}") + logger.info(f"scale: {scale}") + logger.info(f"sample_sampler: {sampler_name}") + if seed is not None: + logger.info(f"seed: {seed}") + + # Prepare inpainting source image and mask when training an inpainting model. + # The prompt line should include "--i /path/to/image.jpg". + # The mask is generated reproducibly from the prompt seed (or randomly if no seed). + inpaint_image = None + inpaint_mask = None + if getattr(args, "train_inpainting", False): + image_path = prompt_dict.get("image") + if image_path: + from library.mask_generator import wobbly_ellipse_mask as _gen_mask + + if not os.path.exists(image_path): + logger.warning(f"inpaint image not found, skipping sample: {image_path}") + return + inpaint_image = Image.open(image_path).convert("RGB").resize((width, height), Image.LANCZOS) + inpaint_mask = _gen_mask(width, height, seed=seed) + logger.info(f"inpaint image: {image_path}") + else: + logger.warning( + "train_inpainting is set but no source image specified in the prompt line; " + "skipping sample. Add '--i /path/to/image.jpg' to the prompt to enable inpainting sampling." + ) + return + + with accelerator.autocast(), torch.no_grad(): + latents = pipeline( + prompt=prompt, + height=height, + width=width, + num_inference_steps=sample_steps, + guidance_scale=scale, + negative_prompt=negative_prompt, + controlnet=controlnet, + controlnet_image=controlnet_image, + inpaint_image=inpaint_image, + inpaint_mask=inpaint_mask, + ) + + if torch.cuda.is_available(): + with torch.cuda.device(torch.cuda.current_device()): + torch.cuda.empty_cache() + + image = pipeline.latents_to_image(latents)[0] + + # adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list + # but adding 'enum' to the filename should be enough + + ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime()) + num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}" + seed_suffix = "" if seed is None else f"_{seed}" + i: int = prompt_dict["enum"] + img_filename = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png" + image.save(os.path.join(save_dir, img_filename)) + + # send images to wandb if enabled + if "wandb" in [tracker.name for tracker in accelerator.trackers]: + wandb_tracker = accelerator.get_tracker("wandb") + + import wandb + + # not to commit images to avoid inconsistency between training and logging steps + wandb_tracker.log({f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False) # positive prompt as a caption + + +def init_trackers(accelerator: Accelerator, args: argparse.Namespace, default_tracker_name: str): + """ + Initialize experiment trackers with tracker specific behaviors + """ + if accelerator.is_main_process: + init_kwargs = {} + if args.wandb_run_name: + init_kwargs["wandb"] = {"name": args.wandb_run_name} + if args.log_tracker_config is not None: + init_kwargs = toml.load(args.log_tracker_config) + accelerator.init_trackers( + default_tracker_name if args.log_tracker_name is None else args.log_tracker_name, + config=get_sanitized_config_or_none(args), + init_kwargs=init_kwargs, + ) + + if "wandb" in [tracker.name for tracker in accelerator.trackers]: + import wandb + + wandb_tracker = accelerator.get_tracker("wandb", unwrap=True) + + # Define specific metrics to handle validation and epochs "steps" + wandb_tracker.define_metric("epoch", hidden=True) + wandb_tracker.define_metric("val_step", hidden=True) + + wandb_tracker.define_metric("global_step", hidden=True) + + +# endregion + + +# region 前処理用 + + +class ImageLoadingDataset(torch.utils.data.Dataset): + def __init__(self, image_paths): + self.images = image_paths + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + img_path = self.images[idx] + + try: + image = Image.open(img_path).convert("RGB") + # convert to tensor temporarily so dataloader will accept it + tensor_pil = transforms.functional.pil_to_tensor(image) + except Exception as e: + logger.error(f"Could not load image path / 画像を読み込めません: {img_path}, error: {e}") + return None + + return (tensor_pil, img_path) + + +# endregion + + +# collate_fn用 epoch,stepはmultiprocessing.Value +class collator_class: + def __init__(self, epoch, step, dataset): + self.current_epoch = epoch + self.current_step = step + self.dataset = dataset # not used if worker_info is not None, in case of multiprocessing + + def __call__(self, examples): + worker_info = torch.utils.data.get_worker_info() + # worker_info is None in the main process + if worker_info is not None: + dataset = worker_info.dataset + else: + dataset = self.dataset + + # set epoch and step + dataset.set_current_epoch(self.current_epoch.value) + dataset.set_current_step(self.current_step.value) + return examples[0] + + +class LossRecorder: + def __init__(self): + self.loss_list: List[float] = [] + self.loss_total: float = 0.0 + + def add(self, *, epoch: int, step: int, loss: float) -> None: + if epoch == 0: + self.loss_list.append(loss) + else: + while len(self.loss_list) <= step: + self.loss_list.append(0.0) + self.loss_total -= self.loss_list[step] + self.loss_list[step] = loss + self.loss_total += loss + + @property + def moving_average(self) -> float: + losses = len(self.loss_list) + if losses == 0: + return 0 + return self.loss_total / losses diff --git a/vendor/sd-scripts/library/utils.py b/vendor/sd-scripts/library/utils.py new file mode 100644 index 0000000..296fc41 --- /dev/null +++ b/vendor/sd-scripts/library/utils.py @@ -0,0 +1,522 @@ +import logging +import sys +import threading +from typing import * + +import torch +import torch.nn as nn +from torchvision import transforms +from diffusers import EulerAncestralDiscreteScheduler +import diffusers.schedulers.scheduling_euler_ancestral_discrete +from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteSchedulerOutput +import cv2 +from PIL import Image +import numpy as np + + +def fire_in_thread(f, *args, **kwargs): + threading.Thread(target=f, args=args, kwargs=kwargs).start() + + +# region Logging + + +def add_logging_arguments(parser): + parser.add_argument( + "--console_log_level", + type=str, + default=None, + choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], + help="Set the logging level, default is INFO / ログレベルを設定する。デフォルトはINFO", + ) + parser.add_argument( + "--console_log_file", + type=str, + default=None, + help="Log to a file instead of stderr / 標準エラー出力ではなくファイルにログを出力する", + ) + parser.add_argument("--console_log_simple", action="store_true", help="Simple log output / シンプルなログ出力") + + +def setup_logging(args=None, log_level=None, reset=False): + if logging.root.handlers: + if reset: + # remove all handlers + for handler in logging.root.handlers[:]: + logging.root.removeHandler(handler) + else: + return + + # log_level can be set by the caller or by the args, the caller has priority. If not set, use INFO + if log_level is None and args is not None: + log_level = args.console_log_level + if log_level is None: + log_level = "INFO" + log_level = getattr(logging, log_level) + + msg_init = None + if args is not None and args.console_log_file: + handler = logging.FileHandler(args.console_log_file, mode="w") + else: + handler = None + if not args or not args.console_log_simple: + try: + from rich.logging import RichHandler + from rich.console import Console + from rich.logging import RichHandler + + handler = RichHandler(console=Console(stderr=True)) + except ImportError: + # print("rich is not installed, using basic logging") + msg_init = "rich is not installed, using basic logging" + + if handler is None: + handler = logging.StreamHandler(sys.stdout) # same as print + handler.propagate = False + + formatter = logging.Formatter( + fmt="%(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + handler.setFormatter(formatter) + logging.root.setLevel(log_level) + logging.root.addHandler(handler) + + if msg_init is not None: + logger = logging.getLogger(__name__) + logger.info(msg_init) + + +setup_logging() +logger = logging.getLogger(__name__) + +# endregion + +# region PyTorch utils + + +def swap_weight_devices(layer_to_cpu: nn.Module, layer_to_cuda: nn.Module): + assert layer_to_cpu.__class__ == layer_to_cuda.__class__ + + weight_swap_jobs = [] + for module_to_cpu, module_to_cuda in zip(layer_to_cpu.modules(), layer_to_cuda.modules()): + if hasattr(module_to_cpu, "weight") and module_to_cpu.weight is not None: + weight_swap_jobs.append((module_to_cpu, module_to_cuda, module_to_cpu.weight.data, module_to_cuda.weight.data)) + + torch.cuda.current_stream().synchronize() # this prevents the illegal loss value + + stream = torch.cuda.Stream() + with torch.cuda.stream(stream): + # cuda to cpu + for module_to_cpu, module_to_cuda, cuda_data_view, cpu_data_view in weight_swap_jobs: + cuda_data_view.record_stream(stream) + module_to_cpu.weight.data = cuda_data_view.data.to("cpu", non_blocking=True) + + stream.synchronize() + + # cpu to cuda + for module_to_cpu, module_to_cuda, cuda_data_view, cpu_data_view in weight_swap_jobs: + cuda_data_view.copy_(module_to_cuda.weight.data, non_blocking=True) + module_to_cuda.weight.data = cuda_data_view + + stream.synchronize() + torch.cuda.current_stream().synchronize() # this prevents the illegal loss value + + +def weighs_to_device(layer: nn.Module, device: torch.device): + for module in layer.modules(): + if hasattr(module, "weight") and module.weight is not None: + module.weight.data = module.weight.data.to(device, non_blocking=True) + + +def str_to_dtype(s: Optional[str], default_dtype: Optional[torch.dtype] = None) -> torch.dtype: + """ + Convert a string to a torch.dtype + + Args: + s: string representation of the dtype + default_dtype: default dtype to return if s is None + + Returns: + torch.dtype: the corresponding torch.dtype + + Raises: + ValueError: if the dtype is not supported + + Examples: + >>> str_to_dtype("float32") + torch.float32 + >>> str_to_dtype("fp32") + torch.float32 + >>> str_to_dtype("float16") + torch.float16 + >>> str_to_dtype("fp16") + torch.float16 + >>> str_to_dtype("bfloat16") + torch.bfloat16 + >>> str_to_dtype("bf16") + torch.bfloat16 + >>> str_to_dtype("fp8") + torch.float8_e4m3fn + >>> str_to_dtype("fp8_e4m3fn") + torch.float8_e4m3fn + >>> str_to_dtype("fp8_e4m3fnuz") + torch.float8_e4m3fnuz + >>> str_to_dtype("fp8_e5m2") + torch.float8_e5m2 + >>> str_to_dtype("fp8_e5m2fnuz") + torch.float8_e5m2fnuz + """ + if s is None: + return default_dtype + if s in ["bf16", "bfloat16"]: + return torch.bfloat16 + elif s in ["fp16", "float16"]: + return torch.float16 + elif s in ["fp32", "float32", "float"]: + return torch.float32 + elif s in ["fp8_e4m3fn", "e4m3fn", "float8_e4m3fn"]: + return torch.float8_e4m3fn + elif s in ["fp8_e4m3fnuz", "e4m3fnuz", "float8_e4m3fnuz"]: + return torch.float8_e4m3fnuz + elif s in ["fp8_e5m2", "e5m2", "float8_e5m2"]: + return torch.float8_e5m2 + elif s in ["fp8_e5m2fnuz", "e5m2fnuz", "float8_e5m2fnuz"]: + return torch.float8_e5m2fnuz + elif s in ["fp8", "float8"]: + return torch.float8_e4m3fn # default fp8 + else: + raise ValueError(f"Unsupported dtype: {s}") + + +# endregion + +# region Image utils + + +def pil_resize(image, size, interpolation): + has_alpha = image.shape[2] == 4 if len(image.shape) == 3 else False + + if has_alpha: + pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)) + else: + pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + + resized_pil = pil_image.resize(size, resample=interpolation) + + # Convert back to cv2 format + if has_alpha: + resized_cv2 = cv2.cvtColor(np.array(resized_pil), cv2.COLOR_RGBA2BGRA) + else: + resized_cv2 = cv2.cvtColor(np.array(resized_pil), cv2.COLOR_RGB2BGR) + + return resized_cv2 + + +def resize_image( + image: np.ndarray, + width: int, + height: int, + resized_width: int, + resized_height: int, + resize_interpolation: Optional[str] = None, +): + """ + Resize image with resize interpolation. Default interpolation to AREA if image is smaller, else LANCZOS. + + Args: + image: numpy.ndarray + width: int Original image width + height: int Original image height + resized_width: int Resized image width + resized_height: int Resized image height + resize_interpolation: Optional[str] Resize interpolation method "lanczos", "area", "bilinear", "bicubic", "nearest", "box" + + Returns: + image + """ + + # Ensure all size parameters are actual integers + width = int(width) + height = int(height) + resized_width = int(resized_width) + resized_height = int(resized_height) + + if resize_interpolation is None: + if width >= resized_width and height >= resized_height: + resize_interpolation = "area" + else: + resize_interpolation = "lanczos" + + # we use PIL for lanczos (for backward compatibility) and box, cv2 for others + use_pil = resize_interpolation in ["lanczos", "lanczos4", "box"] + + resized_size = (resized_width, resized_height) + if use_pil: + interpolation = get_pil_interpolation(resize_interpolation) + image = pil_resize(image, resized_size, interpolation=interpolation) + logger.debug(f"resize image using {resize_interpolation} (PIL)") + else: + interpolation = get_cv2_interpolation(resize_interpolation) + image = cv2.resize(image, resized_size, interpolation=interpolation) + logger.debug(f"resize image using {resize_interpolation} (cv2)") + + return image + + +def get_cv2_interpolation(interpolation: Optional[str]) -> Optional[int]: + """ + Convert interpolation value to cv2 interpolation integer + + https://docs.opencv.org/3.4/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121 + """ + if interpolation is None: + return None + + if interpolation == "lanczos" or interpolation == "lanczos4": + # Lanczos interpolation over 8x8 neighborhood + return cv2.INTER_LANCZOS4 + elif interpolation == "nearest": + # Bit exact nearest neighbor interpolation. This will produce same results as the nearest neighbor method in PIL, scikit-image or Matlab. + return cv2.INTER_NEAREST_EXACT + elif interpolation == "bilinear" or interpolation == "linear": + # bilinear interpolation + return cv2.INTER_LINEAR + elif interpolation == "bicubic" or interpolation == "cubic": + # bicubic interpolation + return cv2.INTER_CUBIC + elif interpolation == "area": + # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. + return cv2.INTER_AREA + elif interpolation == "box": + # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. + return cv2.INTER_AREA + else: + return None + + +def get_pil_interpolation(interpolation: Optional[str]) -> Optional[Image.Resampling]: + """ + Convert interpolation value to PIL interpolation + + https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-filters + """ + if interpolation is None: + return None + + if interpolation == "lanczos": + return Image.Resampling.LANCZOS + elif interpolation == "nearest": + # Pick one nearest pixel from the input image. Ignore all other input pixels. + return Image.Resampling.NEAREST + elif interpolation == "bilinear" or interpolation == "linear": + # For resize calculate the output pixel value using linear interpolation on all pixels that may contribute to the output value. For other transformations linear interpolation over a 2x2 environment in the input image is used. + return Image.Resampling.BILINEAR + elif interpolation == "bicubic" or interpolation == "cubic": + # For resize calculate the output pixel value using cubic interpolation on all pixels that may contribute to the output value. For other transformations cubic interpolation over a 4x4 environment in the input image is used. + return Image.Resampling.BICUBIC + elif interpolation == "area": + # Image.Resampling.BOX may be more appropriate if upscaling + # Area interpolation is related to cv2.INTER_AREA + # Produces a sharper image than Resampling.BILINEAR, doesn’t have dislocations on local level like with Resampling.BOX. + return Image.Resampling.HAMMING + elif interpolation == "box": + # Each pixel of source image contributes to one pixel of the destination image with identical weights. For upscaling is equivalent of Resampling.NEAREST. + return Image.Resampling.BOX + else: + return None + + +def validate_interpolation_fn(interpolation_str: str) -> bool: + """ + Check if a interpolation function is supported + """ + return interpolation_str in ["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area", "box"] + + +# endregion + +# TODO make inf_utils.py +# region Gradual Latent hires fix + + +class GradualLatent: + def __init__( + self, + ratio, + start_timesteps, + every_n_steps, + ratio_step, + s_noise=1.0, + gaussian_blur_ksize=None, + gaussian_blur_sigma=0.5, + gaussian_blur_strength=0.5, + unsharp_target_x=True, + ): + self.ratio = ratio + self.start_timesteps = start_timesteps + self.every_n_steps = every_n_steps + self.ratio_step = ratio_step + self.s_noise = s_noise + self.gaussian_blur_ksize = gaussian_blur_ksize + self.gaussian_blur_sigma = gaussian_blur_sigma + self.gaussian_blur_strength = gaussian_blur_strength + self.unsharp_target_x = unsharp_target_x + + def __str__(self) -> str: + return ( + f"GradualLatent(ratio={self.ratio}, start_timesteps={self.start_timesteps}, " + + f"every_n_steps={self.every_n_steps}, ratio_step={self.ratio_step}, s_noise={self.s_noise}, " + + f"gaussian_blur_ksize={self.gaussian_blur_ksize}, gaussian_blur_sigma={self.gaussian_blur_sigma}, gaussian_blur_strength={self.gaussian_blur_strength}, " + + f"unsharp_target_x={self.unsharp_target_x})" + ) + + def apply_unshark_mask(self, x: torch.Tensor): + if self.gaussian_blur_ksize is None: + return x + blurred = transforms.functional.gaussian_blur(x, self.gaussian_blur_ksize, self.gaussian_blur_sigma) + # mask = torch.sigmoid((x - blurred) * self.gaussian_blur_strength) + mask = (x - blurred) * self.gaussian_blur_strength + sharpened = x + mask + return sharpened + + def interpolate(self, x: torch.Tensor, resized_size, unsharp=True): + org_dtype = x.dtype + if org_dtype == torch.bfloat16: + x = x.float() + + x = torch.nn.functional.interpolate(x, size=resized_size, mode="bicubic", align_corners=False).to(dtype=org_dtype) + + # apply unsharp mask / アンシャープマスクを適用する + if unsharp and self.gaussian_blur_ksize: + x = self.apply_unshark_mask(x) + + return x + + +class EulerAncestralDiscreteSchedulerGL(EulerAncestralDiscreteScheduler): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.resized_size = None + self.gradual_latent = None + + def set_gradual_latent_params(self, size, gradual_latent: GradualLatent): + self.resized_size = size + self.gradual_latent = gradual_latent + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + generator (`torch.Generator`, *optional*): + A random number generator. + return_dict (`bool`): + Whether or not to return a + [`~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput`] or tuple. + + Returns: + [`~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, + [`~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput`] is returned, + otherwise a tuple is returned where the first element is the sample tensor. + + """ + + if isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if not self.is_scale_input_called: + # logger.warning( + print( + "The `scale_model_input` function should be called before `step` to ensure correct denoising. " + "See `StableDiffusionPipeline` for a usage example." + ) + + if self.step_index is None: + self._init_step_index(timestep) + + sigma = self.sigmas[self.step_index] + + # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise + if self.config.prediction_type == "epsilon": + pred_original_sample = sample - sigma * model_output + elif self.config.prediction_type == "v_prediction": + # * c_out + input * c_skip + pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1)) + elif self.config.prediction_type == "sample": + raise NotImplementedError("prediction_type not implemented yet: sample") + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" + ) + + sigma_from = self.sigmas[self.step_index] + sigma_to = self.sigmas[self.step_index + 1] + sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5 + sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 + + # 2. Convert to an ODE derivative + derivative = (sample - pred_original_sample) / sigma + + dt = sigma_down - sigma + + device = model_output.device + if self.resized_size is None: + prev_sample = sample + derivative * dt + + noise = diffusers.schedulers.scheduling_euler_ancestral_discrete.randn_tensor( + model_output.shape, dtype=model_output.dtype, device=device, generator=generator + ) + s_noise = 1.0 + else: + print("resized_size", self.resized_size, "model_output.shape", model_output.shape, "sample.shape", sample.shape) + s_noise = self.gradual_latent.s_noise + + if self.gradual_latent.unsharp_target_x: + prev_sample = sample + derivative * dt + prev_sample = self.gradual_latent.interpolate(prev_sample, self.resized_size) + else: + sample = self.gradual_latent.interpolate(sample, self.resized_size) + derivative = self.gradual_latent.interpolate(derivative, self.resized_size, unsharp=False) + prev_sample = sample + derivative * dt + + noise = diffusers.schedulers.scheduling_euler_ancestral_discrete.randn_tensor( + (model_output.shape[0], model_output.shape[1], self.resized_size[0], self.resized_size[1]), + dtype=model_output.dtype, + device=device, + generator=generator, + ) + + prev_sample = prev_sample + noise * sigma_up * s_noise + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return EulerAncestralDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) + + +# endregion diff --git a/vendor/sd-scripts/networks/__init__.py b/vendor/sd-scripts/networks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/vendor/sd-scripts/networks/loha.py b/vendor/sd-scripts/networks/loha.py new file mode 100644 index 0000000..305c8a5 --- /dev/null +++ b/vendor/sd-scripts/networks/loha.py @@ -0,0 +1,643 @@ +# LoHa (Low-rank Hadamard Product) network module +# Reference: https://arxiv.org/abs/2108.06098 +# +# Based on the LyCORIS project by KohakuBlueleaf +# https://github.com/KohakuBlueleaf/LyCORIS + +import ast +import os +import logging +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .network_base import ArchConfig, AdditionalNetwork, detect_arch_config, _parse_kv_pairs +from library.utils import setup_logging + +setup_logging() +logger = logging.getLogger(__name__) + + +class HadaWeight(torch.autograd.Function): + """Efficient Hadamard product forward/backward for LoHa. + + Computes ((w1a @ w1b) * (w2a @ w2b)) * scale with custom backward + that recomputes intermediates instead of storing them. + """ + + @staticmethod + def forward(ctx, w1a, w1b, w2a, w2b, scale=None): + if scale is None: + scale = torch.tensor(1, device=w1a.device, dtype=w1a.dtype) + ctx.save_for_backward(w1a, w1b, w2a, w2b, scale) + diff_weight = ((w1a @ w1b) * (w2a @ w2b)) * scale + return diff_weight + + @staticmethod + def backward(ctx, grad_out): + (w1a, w1b, w2a, w2b, scale) = ctx.saved_tensors + grad_out = grad_out * scale + temp = grad_out * (w2a @ w2b) + grad_w1a = temp @ w1b.T + grad_w1b = w1a.T @ temp + + temp = grad_out * (w1a @ w1b) + grad_w2a = temp @ w2b.T + grad_w2b = w2a.T @ temp + + del temp + return grad_w1a, grad_w1b, grad_w2a, grad_w2b, None + + +class HadaWeightTucker(torch.autograd.Function): + """Tucker-decomposed Hadamard product forward/backward for LoHa Conv2d 3x3+. + + Computes (rebuild(t1, w1b, w1a) * rebuild(t2, w2b, w2a)) * scale + where rebuild = einsum("i j ..., j r, i p -> p r ...", t, wb, wa). + Compatible with LyCORIS parameter naming convention. + """ + + @staticmethod + def forward(ctx, t1, w1b, w1a, t2, w2b, w2a, scale=None): + if scale is None: + scale = torch.tensor(1, device=t1.device, dtype=t1.dtype) + ctx.save_for_backward(t1, w1b, w1a, t2, w2b, w2a, scale) + + rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1b, w1a) + rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2b, w2a) + + return rebuild1 * rebuild2 * scale + + @staticmethod + def backward(ctx, grad_out): + (t1, w1b, w1a, t2, w2b, w2a, scale) = ctx.saved_tensors + grad_out = grad_out * scale + + # Gradients for w1a, w1b, t1 (using rebuild2) + temp = torch.einsum("i j ..., j r -> i r ...", t2, w2b) + rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w2a) + + grad_w = rebuild * grad_out + del rebuild + + grad_w1a = torch.einsum("r j ..., i j ... -> r i", temp, grad_w) + grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w1a.T) + del grad_w, temp + + grad_w1b = torch.einsum("i r ..., i j ... -> r j", t1, grad_temp) + grad_t1 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w1b.T) + del grad_temp + + # Gradients for w2a, w2b, t2 (using rebuild1) + temp = torch.einsum("i j ..., j r -> i r ...", t1, w1b) + rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w1a) + + grad_w = rebuild * grad_out + del rebuild + + grad_w2a = torch.einsum("r j ..., i j ... -> r i", temp, grad_w) + grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w2a.T) + del grad_w, temp + + grad_w2b = torch.einsum("i r ..., i j ... -> r j", t2, grad_temp) + grad_t2 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w2b.T) + del grad_temp + + return grad_t1, grad_w1b, grad_w1a, grad_t2, grad_w2b, grad_w2a, None + + +class LoHaModule(torch.nn.Module): + """LoHa module for training. Replaces forward method of the original Linear/Conv2d.""" + + def __init__( + self, + lora_name, + org_module: torch.nn.Module, + multiplier=1.0, + lora_dim=4, + alpha=1, + dropout=None, + rank_dropout=None, + module_dropout=None, + use_tucker=False, + **kwargs, + ): + super().__init__() + self.lora_name = lora_name + self.lora_dim = lora_dim + + is_conv2d = org_module.__class__.__name__ == "Conv2d" + if is_conv2d: + in_dim = org_module.in_channels + out_dim = org_module.out_channels + kernel_size = org_module.kernel_size + self.is_conv = True + self.stride = org_module.stride + self.padding = org_module.padding + self.dilation = org_module.dilation + self.groups = org_module.groups + self.kernel_size = kernel_size + + self.tucker = use_tucker and any(k != 1 for k in kernel_size) + + if kernel_size == (1, 1): + self.conv_mode = "1x1" + elif self.tucker: + self.conv_mode = "tucker" + else: + self.conv_mode = "flat" + else: + in_dim = org_module.in_features + out_dim = org_module.out_features + self.is_conv = False + self.tucker = False + self.conv_mode = None + self.kernel_size = None + + self.in_dim = in_dim + self.out_dim = out_dim + + # Create parameters based on mode + if self.conv_mode == "tucker": + # Tucker decomposition for Conv2d 3x3+ + # Shapes follow LyCORIS convention: w_a = (rank, out_dim), w_b = (rank, in_dim) + self.hada_t1 = nn.Parameter(torch.empty(lora_dim, lora_dim, *kernel_size)) + self.hada_w1_a = nn.Parameter(torch.empty(lora_dim, out_dim)) + self.hada_w1_b = nn.Parameter(torch.empty(lora_dim, in_dim)) + self.hada_t2 = nn.Parameter(torch.empty(lora_dim, lora_dim, *kernel_size)) + self.hada_w2_a = nn.Parameter(torch.empty(lora_dim, out_dim)) + self.hada_w2_b = nn.Parameter(torch.empty(lora_dim, in_dim)) + + # LyCORIS init: w1_a = 0 (ensures ΔW=0), t1/t2 normal(0.1) + torch.nn.init.normal_(self.hada_t1, std=0.1) + torch.nn.init.normal_(self.hada_t2, std=0.1) + torch.nn.init.normal_(self.hada_w1_b, std=1.0) + torch.nn.init.constant_(self.hada_w1_a, 0) + torch.nn.init.normal_(self.hada_w2_b, std=1.0) + torch.nn.init.normal_(self.hada_w2_a, std=0.1) + elif self.conv_mode == "flat": + # Non-Tucker Conv2d 3x3+: flatten kernel into in_dim + k_prod = 1 + for k in kernel_size: + k_prod *= k + flat_in = in_dim * k_prod + + self.hada_w1_a = nn.Parameter(torch.empty(out_dim, lora_dim)) + self.hada_w1_b = nn.Parameter(torch.empty(lora_dim, flat_in)) + self.hada_w2_a = nn.Parameter(torch.empty(out_dim, lora_dim)) + self.hada_w2_b = nn.Parameter(torch.empty(lora_dim, flat_in)) + + torch.nn.init.normal_(self.hada_w1_a, std=0.1) + torch.nn.init.normal_(self.hada_w1_b, std=1.0) + torch.nn.init.constant_(self.hada_w2_a, 0) + torch.nn.init.normal_(self.hada_w2_b, std=1.0) + else: + # Linear or Conv2d 1x1 + self.hada_w1_a = nn.Parameter(torch.empty(out_dim, lora_dim)) + self.hada_w1_b = nn.Parameter(torch.empty(lora_dim, in_dim)) + self.hada_w2_a = nn.Parameter(torch.empty(out_dim, lora_dim)) + self.hada_w2_b = nn.Parameter(torch.empty(lora_dim, in_dim)) + + torch.nn.init.normal_(self.hada_w1_a, std=0.1) + torch.nn.init.normal_(self.hada_w1_b, std=1.0) + torch.nn.init.constant_(self.hada_w2_a, 0) + torch.nn.init.normal_(self.hada_w2_b, std=1.0) + + if type(alpha) == torch.Tensor: + alpha = alpha.detach().float().numpy() + alpha = lora_dim if alpha is None or alpha == 0 else alpha + self.scale = alpha / self.lora_dim + self.register_buffer("alpha", torch.tensor(alpha)) + + self.multiplier = multiplier + self.org_module = org_module # remove in applying + self.dropout = dropout + self.rank_dropout = rank_dropout + self.module_dropout = module_dropout + + def apply_to(self): + self.org_forward = self.org_module.forward + self.org_module.forward = self.forward + del self.org_module + + def get_diff_weight(self): + """Return materialized weight delta. + + Returns: + - Linear: 2D tensor (out_dim, in_dim) + - Conv2d 1x1: 2D tensor (out_dim, in_dim) — caller should unsqueeze for F.conv2d + - Conv2d 3x3+ Tucker: 4D tensor (out_dim, in_dim, k1, k2) + - Conv2d 3x3+ flat: 4D tensor (out_dim, in_dim, k1, k2) + """ + if self.tucker: + scale = torch.tensor(self.scale, dtype=self.hada_t1.dtype, device=self.hada_t1.device) + return HadaWeightTucker.apply( + self.hada_t1, self.hada_w1_b, self.hada_w1_a, + self.hada_t2, self.hada_w2_b, self.hada_w2_a, scale + ) + elif self.conv_mode == "flat": + scale = torch.tensor(self.scale, dtype=self.hada_w1_a.dtype, device=self.hada_w1_a.device) + diff = HadaWeight.apply(self.hada_w1_a, self.hada_w1_b, self.hada_w2_a, self.hada_w2_b, scale) + return diff.reshape(self.out_dim, self.in_dim, *self.kernel_size) + else: + scale = torch.tensor(self.scale, dtype=self.hada_w1_a.dtype, device=self.hada_w1_a.device) + return HadaWeight.apply(self.hada_w1_a, self.hada_w1_b, self.hada_w2_a, self.hada_w2_b, scale) + + def forward(self, x): + org_forwarded = self.org_forward(x) + + # module dropout + if self.module_dropout is not None and self.training: + if torch.rand(1) < self.module_dropout: + return org_forwarded + + diff_weight = self.get_diff_weight() + + # rank dropout (applied on output dimension) + if self.rank_dropout is not None and self.training: + drop = (torch.rand(diff_weight.size(0), device=diff_weight.device) > self.rank_dropout).to(diff_weight.dtype) + drop = drop.view(-1, *([1] * (diff_weight.dim() - 1))) + diff_weight = diff_weight * drop + scale = 1.0 / (1.0 - self.rank_dropout) + else: + scale = 1.0 + + if self.is_conv: + if self.conv_mode == "1x1": + diff_weight = diff_weight.unsqueeze(2).unsqueeze(3) + return org_forwarded + F.conv2d( + x, diff_weight, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups + ) * self.multiplier * scale + else: + # Conv2d 3x3+: diff_weight is already 4D from get_diff_weight + return org_forwarded + F.conv2d( + x, diff_weight, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups + ) * self.multiplier * scale + else: + return org_forwarded + F.linear(x, diff_weight) * self.multiplier * scale + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + +class LoHaInfModule(LoHaModule): + """LoHa module for inference. Supports merge_to and get_weight.""" + + def __init__( + self, + lora_name, + org_module: torch.nn.Module, + multiplier=1.0, + lora_dim=4, + alpha=1, + **kwargs, + ): + # no dropout for inference; pass use_tucker from kwargs + use_tucker = kwargs.pop("use_tucker", False) + super().__init__(lora_name, org_module, multiplier, lora_dim, alpha, use_tucker=use_tucker) + + self.org_module_ref = [org_module] + self.enabled = True + self.network: AdditionalNetwork = None + + def set_network(self, network): + self.network = network + + def merge_to(self, sd, dtype, device): + # extract weight from org_module + org_sd = self.org_module.state_dict() + weight = org_sd["weight"] + org_dtype = weight.dtype + org_device = weight.device + weight = weight.to(torch.float) + + if dtype is None: + dtype = org_dtype + if device is None: + device = org_device + + # get LoHa weights + w1a = sd["hada_w1_a"].to(torch.float).to(device) + w1b = sd["hada_w1_b"].to(torch.float).to(device) + w2a = sd["hada_w2_a"].to(torch.float).to(device) + w2b = sd["hada_w2_b"].to(torch.float).to(device) + + if self.tucker: + # Tucker mode + t1 = sd["hada_t1"].to(torch.float).to(device) + t2 = sd["hada_t2"].to(torch.float).to(device) + rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1b, w1a) + rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2b, w2a) + diff_weight = rebuild1 * rebuild2 * self.scale + else: + diff_weight = ((w1a @ w1b) * (w2a @ w2b)) * self.scale + # reshape diff_weight to match original weight shape if needed + if diff_weight.shape != weight.shape: + diff_weight = diff_weight.reshape(weight.shape) + + weight = weight.to(device) + self.multiplier * diff_weight + + org_sd["weight"] = weight.to(dtype) + self.org_module.load_state_dict(org_sd) + + def get_weight(self, multiplier=None): + if multiplier is None: + multiplier = self.multiplier + + if self.tucker: + t1 = self.hada_t1.to(torch.float) + w1a = self.hada_w1_a.to(torch.float) + w1b = self.hada_w1_b.to(torch.float) + t2 = self.hada_t2.to(torch.float) + w2a = self.hada_w2_a.to(torch.float) + w2b = self.hada_w2_b.to(torch.float) + rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1b, w1a) + rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2b, w2a) + weight = rebuild1 * rebuild2 * self.scale * multiplier + else: + w1a = self.hada_w1_a.to(torch.float) + w1b = self.hada_w1_b.to(torch.float) + w2a = self.hada_w2_a.to(torch.float) + w2b = self.hada_w2_b.to(torch.float) + weight = ((w1a @ w1b) * (w2a @ w2b)) * self.scale * multiplier + + if self.is_conv: + if self.conv_mode == "1x1": + weight = weight.unsqueeze(2).unsqueeze(3) + elif self.conv_mode == "flat": + weight = weight.reshape(self.out_dim, self.in_dim, *self.kernel_size) + + return weight + + def default_forward(self, x): + diff_weight = self.get_diff_weight() + if self.is_conv: + if self.conv_mode == "1x1": + diff_weight = diff_weight.unsqueeze(2).unsqueeze(3) + return self.org_forward(x) + F.conv2d( + x, diff_weight, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups + ) * self.multiplier + else: + return self.org_forward(x) + F.linear(x, diff_weight) * self.multiplier + + def forward(self, x): + if not self.enabled: + return self.org_forward(x) + return self.default_forward(x) + + +def create_network( + multiplier: float, + network_dim: Optional[int], + network_alpha: Optional[float], + vae, + text_encoder, + unet, + neuron_dropout: Optional[float] = None, + **kwargs, +): + """Create a LoHa network. Called by train_network.py via network_module.create_network().""" + if network_dim is None: + network_dim = 4 + if network_alpha is None: + network_alpha = 1.0 + + # handle text_encoder as list + text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] + + # detect architecture + arch_config = detect_arch_config(unet, text_encoders) + + # train LLM adapter + train_llm_adapter = kwargs.get("train_llm_adapter", "false") + if train_llm_adapter is not None: + train_llm_adapter = True if str(train_llm_adapter).lower() == "true" else False + + # exclude patterns + exclude_patterns = kwargs.get("exclude_patterns", None) + if exclude_patterns is None: + exclude_patterns = [] + else: + exclude_patterns = ast.literal_eval(exclude_patterns) + if not isinstance(exclude_patterns, list): + exclude_patterns = [exclude_patterns] + + # add default exclude patterns from arch config + exclude_patterns.extend(arch_config.default_excludes) + + # include patterns + include_patterns = kwargs.get("include_patterns", None) + if include_patterns is not None: + include_patterns = ast.literal_eval(include_patterns) + if not isinstance(include_patterns, list): + include_patterns = [include_patterns] + + # rank/module dropout + rank_dropout = kwargs.get("rank_dropout", None) + if rank_dropout is not None: + rank_dropout = float(rank_dropout) + module_dropout = kwargs.get("module_dropout", None) + if module_dropout is not None: + module_dropout = float(module_dropout) + + # conv dim/alpha for Conv2d 3x3 + conv_lora_dim = kwargs.get("conv_dim", None) + conv_alpha = kwargs.get("conv_alpha", None) + if conv_lora_dim is not None: + conv_lora_dim = int(conv_lora_dim) + if conv_alpha is None: + conv_alpha = 1.0 + else: + conv_alpha = float(conv_alpha) + + # Tucker decomposition for Conv2d 3x3 + use_tucker = kwargs.get("use_tucker", "false") + if use_tucker is not None: + use_tucker = True if str(use_tucker).lower() == "true" else False + + # verbose + verbose = kwargs.get("verbose", "false") + if verbose is not None: + verbose = True if str(verbose).lower() == "true" else False + + # regex-specific learning rates / dimensions + network_reg_lrs = kwargs.get("network_reg_lrs", None) + reg_lrs = _parse_kv_pairs(network_reg_lrs, is_int=False) if network_reg_lrs is not None else None + + network_reg_dims = kwargs.get("network_reg_dims", None) + reg_dims = _parse_kv_pairs(network_reg_dims, is_int=True) if network_reg_dims is not None else None + + network = AdditionalNetwork( + text_encoders, + unet, + arch_config=arch_config, + multiplier=multiplier, + lora_dim=network_dim, + alpha=network_alpha, + dropout=neuron_dropout, + rank_dropout=rank_dropout, + module_dropout=module_dropout, + module_class=LoHaModule, + module_kwargs={"use_tucker": use_tucker}, + conv_lora_dim=conv_lora_dim, + conv_alpha=conv_alpha, + train_llm_adapter=train_llm_adapter, + exclude_patterns=exclude_patterns, + include_patterns=include_patterns, + reg_dims=reg_dims, + reg_lrs=reg_lrs, + verbose=verbose, + ) + + # LoRA+ support + loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None) + loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None) + loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None) + loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None + loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None + loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None + if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None: + network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio) + + return network + + +def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs): + """Create a LoHa network from saved weights. Called by train_network.py.""" + if weights_sd is None: + if os.path.splitext(file)[1] == ".safetensors": + from safetensors.torch import load_file + + weights_sd = load_file(file) + else: + weights_sd = torch.load(file, map_location="cpu") + + # detect dim/alpha from weights + modules_dim = {} + modules_alpha = {} + train_llm_adapter = False + for key, value in weights_sd.items(): + if "." not in key: + continue + + lora_name = key.split(".")[0] + if "alpha" in key: + modules_alpha[lora_name] = value + elif "hada_w1_b" in key: + dim = value.shape[0] + modules_dim[lora_name] = dim + + if "llm_adapter" in lora_name: + train_llm_adapter = True + + # detect Tucker mode from weights + use_tucker = any("hada_t1" in key for key in weights_sd.keys()) + + # handle text_encoder as list + text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] + + # detect architecture + arch_config = detect_arch_config(unet, text_encoders) + + module_class = LoHaInfModule if for_inference else LoHaModule + module_kwargs = {"use_tucker": use_tucker} + + network = AdditionalNetwork( + text_encoders, + unet, + arch_config=arch_config, + multiplier=multiplier, + modules_dim=modules_dim, + modules_alpha=modules_alpha, + module_class=module_class, + module_kwargs=module_kwargs, + train_llm_adapter=train_llm_adapter, + ) + return network, weights_sd + + +def merge_weights_to_tensor( + model_weight: torch.Tensor, + lora_name: str, + lora_sd: Dict[str, torch.Tensor], + lora_weight_keys: set, + multiplier: float, + calc_device: torch.device, +) -> torch.Tensor: + """Merge LoHa weights directly into a model weight tensor. + + Supports standard LoHa, non-Tucker Conv2d 3x3, and Tucker Conv2d 3x3. + No Module/Network creation needed. Consumed keys are removed from lora_weight_keys. + Returns model_weight unchanged if no matching LoHa keys found. + """ + w1a_key = lora_name + ".hada_w1_a" + w1b_key = lora_name + ".hada_w1_b" + w2a_key = lora_name + ".hada_w2_a" + w2b_key = lora_name + ".hada_w2_b" + t1_key = lora_name + ".hada_t1" + t2_key = lora_name + ".hada_t2" + alpha_key = lora_name + ".alpha" + + if w1a_key not in lora_weight_keys: + return model_weight + + w1a = lora_sd[w1a_key].to(calc_device) + w1b = lora_sd[w1b_key].to(calc_device) + w2a = lora_sd[w2a_key].to(calc_device) + w2b = lora_sd[w2b_key].to(calc_device) + + has_tucker = t1_key in lora_weight_keys + + dim = w1b.shape[0] + alpha = lora_sd.get(alpha_key, torch.tensor(dim)) + if isinstance(alpha, torch.Tensor): + alpha = alpha.item() + scale = alpha / dim + + original_dtype = model_weight.dtype + if original_dtype.itemsize == 1: # fp8 + model_weight = model_weight.to(torch.float16) + w1a, w1b = w1a.to(torch.float16), w1b.to(torch.float16) + w2a, w2b = w2a.to(torch.float16), w2b.to(torch.float16) + + if has_tucker: + # Tucker decomposition: rebuild via einsum + t1 = lora_sd[t1_key].to(calc_device) + t2 = lora_sd[t2_key].to(calc_device) + if original_dtype.itemsize == 1: + t1, t2 = t1.to(torch.float16), t2.to(torch.float16) + rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1b, w1a) + rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2b, w2a) + diff_weight = rebuild1 * rebuild2 * scale + else: + # Standard LoHa: ΔW = ((w1a @ w1b) * (w2a @ w2b)) * scale + diff_weight = ((w1a @ w1b) * (w2a @ w2b)) * scale + + # Reshape diff_weight to match model_weight shape if needed + # (handles Conv2d 1x1 unsqueeze, Conv2d 3x3 non-Tucker reshape, etc.) + if diff_weight.shape != model_weight.shape: + diff_weight = diff_weight.reshape(model_weight.shape) + + model_weight = model_weight + multiplier * diff_weight + + if original_dtype.itemsize == 1: + model_weight = model_weight.to(original_dtype) + + # remove consumed keys + consumed = [w1a_key, w1b_key, w2a_key, w2b_key, alpha_key] + if has_tucker: + consumed.extend([t1_key, t2_key]) + for key in consumed: + lora_weight_keys.discard(key) + + return model_weight diff --git a/vendor/sd-scripts/networks/lokr.py b/vendor/sd-scripts/networks/lokr.py new file mode 100644 index 0000000..515858e --- /dev/null +++ b/vendor/sd-scripts/networks/lokr.py @@ -0,0 +1,683 @@ +# LoKr (Low-rank Kronecker Product) network module +# Reference: https://arxiv.org/abs/2309.14859 +# +# Based on the LyCORIS project by KohakuBlueleaf +# https://github.com/KohakuBlueleaf/LyCORIS + +import ast +import math +import os +import logging +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .network_base import ArchConfig, AdditionalNetwork, detect_arch_config, _parse_kv_pairs +from library.utils import setup_logging + +setup_logging() +logger = logging.getLogger(__name__) + + +def factorization(dimension: int, factor: int = -1) -> tuple: + """Return a tuple of two values whose product equals dimension, + optimized for balanced factors. + + In LoKr, the first value is for the weight scale (smaller), + and the second value is for the weight (larger). + + Examples: + factor=-1: 128 -> (8, 16), 512 -> (16, 32), 1024 -> (32, 32) + factor=4: 128 -> (4, 32), 512 -> (4, 128) + """ + if factor > 0 and (dimension % factor) == 0: + m = factor + n = dimension // factor + if m > n: + n, m = m, n + return m, n + if factor < 0: + factor = dimension + m, n = 1, dimension + length = m + n + while m < n: + new_m = m + 1 + while dimension % new_m != 0: + new_m += 1 + new_n = dimension // new_m + if new_m + new_n > length or new_m > factor: + break + else: + m, n = new_m, new_n + if m > n: + n, m = m, n + return m, n + + +def make_kron(w1, w2, scale): + """Compute Kronecker product of w1 and w2, scaled by scale.""" + if w1.dim() != w2.dim(): + for _ in range(w2.dim() - w1.dim()): + w1 = w1.unsqueeze(-1) + w2 = w2.contiguous() + rebuild = torch.kron(w1, w2) + if scale != 1: + rebuild = rebuild * scale + return rebuild + + +def rebuild_tucker(t, wa, wb): + """Rebuild weight from Tucker decomposition: einsum("i j ..., i p, j r -> p r ...", t, wa, wb). + + Compatible with LyCORIS convention. + """ + return torch.einsum("i j ..., i p, j r -> p r ...", t, wa, wb) + + +class LoKrModule(torch.nn.Module): + """LoKr module for training. Replaces forward method of the original Linear/Conv2d.""" + + def __init__( + self, + lora_name, + org_module: torch.nn.Module, + multiplier=1.0, + lora_dim=4, + alpha=1, + dropout=None, + rank_dropout=None, + module_dropout=None, + factor=-1, + use_tucker=False, + **kwargs, + ): + super().__init__() + self.lora_name = lora_name + self.lora_dim = lora_dim + + is_conv2d = org_module.__class__.__name__ == "Conv2d" + if is_conv2d: + in_dim = org_module.in_channels + out_dim = org_module.out_channels + kernel_size = org_module.kernel_size + self.is_conv = True + self.stride = org_module.stride + self.padding = org_module.padding + self.dilation = org_module.dilation + self.groups = org_module.groups + self.kernel_size = kernel_size + + self.tucker = use_tucker and any(k != 1 for k in kernel_size) + + if kernel_size == (1, 1): + self.conv_mode = "1x1" + elif self.tucker: + self.conv_mode = "tucker" + else: + self.conv_mode = "flat" + else: + in_dim = org_module.in_features + out_dim = org_module.out_features + self.is_conv = False + self.tucker = False + self.conv_mode = None + self.kernel_size = None + + self.in_dim = in_dim + self.out_dim = out_dim + + factor = int(factor) + self.use_w2 = False + + # Factorize dimensions + in_m, in_n = factorization(in_dim, factor) + out_l, out_k = factorization(out_dim, factor) + + # w1 is always a full matrix (the "scale" factor, small) + self.lokr_w1 = nn.Parameter(torch.empty(out_l, in_m)) + + # w2: depends on mode + if self.conv_mode in ("tucker", "flat"): + # Conv2d 3x3+ modes + k_size = kernel_size + + if lora_dim >= max(out_k, in_n) / 2: + # Full matrix mode (includes kernel dimensions) + self.use_w2 = True + self.lokr_w2 = nn.Parameter(torch.empty(out_k, in_n, *k_size)) + logger.warning( + f"LoKr: lora_dim {lora_dim} is large for dim={max(in_dim, out_dim)} " + f"and factor={factor}, using full matrix mode for Conv2d." + ) + elif self.tucker: + # Tucker mode: separate kernel into t2 tensor + self.lokr_t2 = nn.Parameter(torch.empty(lora_dim, lora_dim, *k_size)) + self.lokr_w2_a = nn.Parameter(torch.empty(lora_dim, out_k)) + self.lokr_w2_b = nn.Parameter(torch.empty(lora_dim, in_n)) + else: + # Non-Tucker: flatten kernel into w2_b + k_prod = 1 + for k in k_size: + k_prod *= k + self.lokr_w2_a = nn.Parameter(torch.empty(out_k, lora_dim)) + self.lokr_w2_b = nn.Parameter(torch.empty(lora_dim, in_n * k_prod)) + else: + # Linear or Conv2d 1x1 + if lora_dim < max(out_k, in_n) / 2: + self.lokr_w2_a = nn.Parameter(torch.empty(out_k, lora_dim)) + self.lokr_w2_b = nn.Parameter(torch.empty(lora_dim, in_n)) + else: + self.use_w2 = True + self.lokr_w2 = nn.Parameter(torch.empty(out_k, in_n)) + if lora_dim >= max(out_k, in_n) / 2: + logger.warning( + f"LoKr: lora_dim {lora_dim} is large for dim={max(in_dim, out_dim)} " + f"and factor={factor}, using full matrix mode." + ) + + if type(alpha) == torch.Tensor: + alpha = alpha.detach().float().numpy() + alpha = lora_dim if alpha is None or alpha == 0 else alpha + # if both w1 and w2 are full matrices, use scale = 1 + if self.use_w2: + alpha = lora_dim + self.scale = alpha / self.lora_dim + self.register_buffer("alpha", torch.tensor(alpha)) + + # Initialization + torch.nn.init.kaiming_uniform_(self.lokr_w1, a=math.sqrt(5)) + if self.use_w2: + torch.nn.init.constant_(self.lokr_w2, 0) + else: + if self.tucker: + torch.nn.init.kaiming_uniform_(self.lokr_t2, a=math.sqrt(5)) + torch.nn.init.kaiming_uniform_(self.lokr_w2_a, a=math.sqrt(5)) + torch.nn.init.constant_(self.lokr_w2_b, 0) + # Ensures ΔW = kron(w1, 0) = 0 at init + + self.multiplier = multiplier + self.org_module = org_module # remove in applying + self.dropout = dropout + self.rank_dropout = rank_dropout + self.module_dropout = module_dropout + + def apply_to(self): + self.org_forward = self.org_module.forward + self.org_module.forward = self.forward + del self.org_module + + def get_diff_weight(self): + """Return materialized weight delta. + + Returns: + - Linear: 2D tensor (out_dim, in_dim) + - Conv2d 1x1: 2D tensor (out_dim, in_dim) — caller should unsqueeze for F.conv2d + - Conv2d 3x3+ Tucker/full: 4D tensor (out_dim, in_dim, k1, k2) + - Conv2d 3x3+ flat: 4D tensor (out_dim, in_dim, k1, k2) — reshaped from 2D + """ + w1 = self.lokr_w1 + + if self.use_w2: + w2 = self.lokr_w2 + elif self.tucker: + w2 = rebuild_tucker(self.lokr_t2, self.lokr_w2_a, self.lokr_w2_b) + else: + w2 = self.lokr_w2_a @ self.lokr_w2_b + + result = make_kron(w1, w2, self.scale) + + # For non-Tucker Conv2d 3x3+, result is 2D; reshape to 4D + if self.conv_mode == "flat" and result.dim() == 2: + result = result.reshape(self.out_dim, self.in_dim, *self.kernel_size) + + return result + + def forward(self, x): + org_forwarded = self.org_forward(x) + + # module dropout + if self.module_dropout is not None and self.training: + if torch.rand(1) < self.module_dropout: + return org_forwarded + + diff_weight = self.get_diff_weight() + + # rank dropout + if self.rank_dropout is not None and self.training: + drop = (torch.rand(diff_weight.size(0), device=diff_weight.device) > self.rank_dropout).to(diff_weight.dtype) + drop = drop.view(-1, *([1] * (diff_weight.dim() - 1))) + diff_weight = diff_weight * drop + scale = 1.0 / (1.0 - self.rank_dropout) + else: + scale = 1.0 + + if self.is_conv: + if self.conv_mode == "1x1": + diff_weight = diff_weight.unsqueeze(2).unsqueeze(3) + return org_forwarded + F.conv2d( + x, diff_weight, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups + ) * self.multiplier * scale + else: + # Conv2d 3x3+: diff_weight is already 4D from get_diff_weight + return org_forwarded + F.conv2d( + x, diff_weight, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups + ) * self.multiplier * scale + else: + return org_forwarded + F.linear(x, diff_weight) * self.multiplier * scale + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + +class LoKrInfModule(LoKrModule): + """LoKr module for inference. Supports merge_to and get_weight.""" + + def __init__( + self, + lora_name, + org_module: torch.nn.Module, + multiplier=1.0, + lora_dim=4, + alpha=1, + **kwargs, + ): + # no dropout for inference; pass factor and use_tucker from kwargs + factor = kwargs.pop("factor", -1) + use_tucker = kwargs.pop("use_tucker", False) + super().__init__(lora_name, org_module, multiplier, lora_dim, alpha, factor=factor, use_tucker=use_tucker) + + self.org_module_ref = [org_module] + self.enabled = True + self.network: AdditionalNetwork = None + + def set_network(self, network): + self.network = network + + def merge_to(self, sd, dtype, device): + # extract weight from org_module + org_sd = self.org_module.state_dict() + weight = org_sd["weight"] + org_dtype = weight.dtype + org_device = weight.device + weight = weight.to(torch.float) + + if dtype is None: + dtype = org_dtype + if device is None: + device = org_device + + # get LoKr weights + w1 = sd["lokr_w1"].to(torch.float).to(device) + + if "lokr_w2" in sd: + w2 = sd["lokr_w2"].to(torch.float).to(device) + elif "lokr_t2" in sd: + # Tucker mode + t2 = sd["lokr_t2"].to(torch.float).to(device) + w2a = sd["lokr_w2_a"].to(torch.float).to(device) + w2b = sd["lokr_w2_b"].to(torch.float).to(device) + w2 = rebuild_tucker(t2, w2a, w2b) + else: + w2a = sd["lokr_w2_a"].to(torch.float).to(device) + w2b = sd["lokr_w2_b"].to(torch.float).to(device) + w2 = w2a @ w2b + + # compute ΔW via Kronecker product + diff_weight = make_kron(w1, w2, self.scale) + + # reshape diff_weight to match original weight shape if needed + if diff_weight.shape != weight.shape: + diff_weight = diff_weight.reshape(weight.shape) + + weight = weight.to(device) + self.multiplier * diff_weight + + org_sd["weight"] = weight.to(dtype) + self.org_module.load_state_dict(org_sd) + + def get_weight(self, multiplier=None): + if multiplier is None: + multiplier = self.multiplier + + w1 = self.lokr_w1.to(torch.float) + + if self.use_w2: + w2 = self.lokr_w2.to(torch.float) + elif self.tucker: + w2 = rebuild_tucker( + self.lokr_t2.to(torch.float), + self.lokr_w2_a.to(torch.float), + self.lokr_w2_b.to(torch.float), + ) + else: + w2 = (self.lokr_w2_a @ self.lokr_w2_b).to(torch.float) + + weight = make_kron(w1, w2, self.scale) * multiplier + + # reshape to match original weight shape if needed + if self.is_conv: + if self.conv_mode == "1x1": + weight = weight.unsqueeze(2).unsqueeze(3) + elif self.conv_mode == "flat" and weight.dim() == 2: + weight = weight.reshape(self.out_dim, self.in_dim, *self.kernel_size) + # Tucker and full matrix modes: already 4D from kron + + return weight + + def default_forward(self, x): + diff_weight = self.get_diff_weight() + if self.is_conv: + if self.conv_mode == "1x1": + diff_weight = diff_weight.unsqueeze(2).unsqueeze(3) + return self.org_forward(x) + F.conv2d( + x, diff_weight, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups + ) * self.multiplier + else: + return self.org_forward(x) + F.linear(x, diff_weight) * self.multiplier + + def forward(self, x): + if not self.enabled: + return self.org_forward(x) + return self.default_forward(x) + + +def create_network( + multiplier: float, + network_dim: Optional[int], + network_alpha: Optional[float], + vae, + text_encoder, + unet, + neuron_dropout: Optional[float] = None, + **kwargs, +): + """Create a LoKr network. Called by train_network.py via network_module.create_network().""" + if network_dim is None: + network_dim = 4 + if network_alpha is None: + network_alpha = 1.0 + + # handle text_encoder as list + text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] + + # detect architecture + arch_config = detect_arch_config(unet, text_encoders) + + # train LLM adapter + train_llm_adapter = kwargs.get("train_llm_adapter", "false") + if train_llm_adapter is not None: + train_llm_adapter = True if str(train_llm_adapter).lower() == "true" else False + + # exclude patterns + exclude_patterns = kwargs.get("exclude_patterns", None) + if exclude_patterns is None: + exclude_patterns = [] + else: + exclude_patterns = ast.literal_eval(exclude_patterns) + if not isinstance(exclude_patterns, list): + exclude_patterns = [exclude_patterns] + + # add default exclude patterns from arch config + exclude_patterns.extend(arch_config.default_excludes) + + # include patterns + include_patterns = kwargs.get("include_patterns", None) + if include_patterns is not None: + include_patterns = ast.literal_eval(include_patterns) + if not isinstance(include_patterns, list): + include_patterns = [include_patterns] + + # rank/module dropout + rank_dropout = kwargs.get("rank_dropout", None) + if rank_dropout is not None: + rank_dropout = float(rank_dropout) + module_dropout = kwargs.get("module_dropout", None) + if module_dropout is not None: + module_dropout = float(module_dropout) + + # conv dim/alpha for Conv2d 3x3 + conv_lora_dim = kwargs.get("conv_dim", None) + conv_alpha = kwargs.get("conv_alpha", None) + if conv_lora_dim is not None: + conv_lora_dim = int(conv_lora_dim) + if conv_alpha is None: + conv_alpha = 1.0 + else: + conv_alpha = float(conv_alpha) + + # Tucker decomposition for Conv2d 3x3 + use_tucker = kwargs.get("use_tucker", "false") + if use_tucker is not None: + use_tucker = True if str(use_tucker).lower() == "true" else False + + # factor for LoKr + factor = int(kwargs.get("factor", -1)) + + # verbose + verbose = kwargs.get("verbose", "false") + if verbose is not None: + verbose = True if str(verbose).lower() == "true" else False + + # regex-specific learning rates / dimensions + network_reg_lrs = kwargs.get("network_reg_lrs", None) + reg_lrs = _parse_kv_pairs(network_reg_lrs, is_int=False) if network_reg_lrs is not None else None + + network_reg_dims = kwargs.get("network_reg_dims", None) + reg_dims = _parse_kv_pairs(network_reg_dims, is_int=True) if network_reg_dims is not None else None + + network = AdditionalNetwork( + text_encoders, + unet, + arch_config=arch_config, + multiplier=multiplier, + lora_dim=network_dim, + alpha=network_alpha, + dropout=neuron_dropout, + rank_dropout=rank_dropout, + module_dropout=module_dropout, + module_class=LoKrModule, + module_kwargs={"factor": factor, "use_tucker": use_tucker}, + conv_lora_dim=conv_lora_dim, + conv_alpha=conv_alpha, + train_llm_adapter=train_llm_adapter, + exclude_patterns=exclude_patterns, + include_patterns=include_patterns, + reg_dims=reg_dims, + reg_lrs=reg_lrs, + verbose=verbose, + ) + + # LoRA+ support + loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None) + loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None) + loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None) + loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None + loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None + loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None + if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None: + network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio) + + return network + + +def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs): + """Create a LoKr network from saved weights. Called by train_network.py.""" + if weights_sd is None: + if os.path.splitext(file)[1] == ".safetensors": + from safetensors.torch import load_file + + weights_sd = load_file(file) + else: + weights_sd = torch.load(file, map_location="cpu") + + # detect dim/alpha from weights + modules_dim = {} + modules_alpha = {} + train_llm_adapter = False + use_tucker = False + for key, value in weights_sd.items(): + if "." not in key: + continue + + lora_name = key.split(".")[0] + if "alpha" in key: + modules_alpha[lora_name] = value + elif "lokr_w2_a" in key: + # low-rank mode: dim detection depends on Tucker vs non-Tucker + if "lokr_t2" in key.replace("lokr_w2_a", "lokr_t2") and lora_name + ".lokr_t2" in weights_sd: + # Tucker: w2_a = (rank, out_k) → dim = w2_a.shape[0] + dim = value.shape[0] + else: + # Non-Tucker: w2_a = (out_k, rank) → dim = w2_a.shape[1] + dim = value.shape[1] + modules_dim[lora_name] = dim + elif "lokr_w2" in key and "lokr_w2_a" not in key and "lokr_w2_b" not in key: + # full matrix mode: set dim large enough to trigger full-matrix path + if lora_name not in modules_dim: + modules_dim[lora_name] = max(value.shape[0], value.shape[1]) + + if "lokr_t2" in key: + use_tucker = True + + if "llm_adapter" in lora_name: + train_llm_adapter = True + + # handle text_encoder as list + text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] + + # detect architecture + arch_config = detect_arch_config(unet, text_encoders) + + # extract factor for LoKr + factor = int(kwargs.get("factor", -1)) + + module_class = LoKrInfModule if for_inference else LoKrModule + module_kwargs = {"factor": factor, "use_tucker": use_tucker} + + network = AdditionalNetwork( + text_encoders, + unet, + arch_config=arch_config, + multiplier=multiplier, + modules_dim=modules_dim, + modules_alpha=modules_alpha, + module_class=module_class, + module_kwargs=module_kwargs, + train_llm_adapter=train_llm_adapter, + ) + return network, weights_sd + + +def merge_weights_to_tensor( + model_weight: torch.Tensor, + lora_name: str, + lora_sd: Dict[str, torch.Tensor], + lora_weight_keys: set, + multiplier: float, + calc_device: torch.device, +) -> torch.Tensor: + """Merge LoKr weights directly into a model weight tensor. + + Supports standard LoKr, non-Tucker Conv2d 3x3, and Tucker Conv2d 3x3. + No Module/Network creation needed. Consumed keys are removed from lora_weight_keys. + Returns model_weight unchanged if no matching LoKr keys found. + """ + w1_key = lora_name + ".lokr_w1" + w2_key = lora_name + ".lokr_w2" + w2a_key = lora_name + ".lokr_w2_a" + w2b_key = lora_name + ".lokr_w2_b" + t2_key = lora_name + ".lokr_t2" + alpha_key = lora_name + ".alpha" + + if w1_key not in lora_weight_keys: + return model_weight + + w1 = lora_sd[w1_key].to(calc_device) + + # determine mode: full matrix vs Tucker vs low-rank + has_tucker = t2_key in lora_weight_keys + + if w2a_key in lora_weight_keys: + w2a = lora_sd[w2a_key].to(calc_device) + w2b = lora_sd[w2b_key].to(calc_device) + + if has_tucker: + # Tucker: w2a = (rank, out_k), dim = rank + dim = w2a.shape[0] + else: + # Non-Tucker low-rank: w2a = (out_k, rank), dim = rank + dim = w2a.shape[1] + + consumed_keys = [w1_key, w2a_key, w2b_key, alpha_key] + if has_tucker: + consumed_keys.append(t2_key) + elif w2_key in lora_weight_keys: + # full matrix mode + w2a = None + w2b = None + dim = None + consumed_keys = [w1_key, w2_key, alpha_key] + else: + return model_weight + + alpha = lora_sd.get(alpha_key, None) + if alpha is not None and isinstance(alpha, torch.Tensor): + alpha = alpha.item() + + # compute scale + if w2a is not None: + if alpha is None: + alpha = dim + scale = alpha / dim + else: + # full matrix mode: scale = 1.0 + scale = 1.0 + + original_dtype = model_weight.dtype + if original_dtype.itemsize == 1: # fp8 + model_weight = model_weight.to(torch.float16) + w1 = w1.to(torch.float16) + if w2a is not None: + w2a, w2b = w2a.to(torch.float16), w2b.to(torch.float16) + + # compute w2 + if w2a is not None: + if has_tucker: + t2 = lora_sd[t2_key].to(calc_device) + if original_dtype.itemsize == 1: + t2 = t2.to(torch.float16) + w2 = rebuild_tucker(t2, w2a, w2b) + else: + w2 = w2a @ w2b + else: + w2 = lora_sd[w2_key].to(calc_device) + if original_dtype.itemsize == 1: + w2 = w2.to(torch.float16) + + # ΔW = kron(w1, w2) * scale + diff_weight = make_kron(w1, w2, scale) + + # Reshape diff_weight to match model_weight shape if needed + # (handles Conv2d 1x1 unsqueeze, Conv2d 3x3 non-Tucker reshape, etc.) + if diff_weight.shape != model_weight.shape: + diff_weight = diff_weight.reshape(model_weight.shape) + + model_weight = model_weight + multiplier * diff_weight + + if original_dtype.itemsize == 1: + model_weight = model_weight.to(original_dtype) + + # remove consumed keys + for key in consumed_keys: + lora_weight_keys.discard(key) + + return model_weight diff --git a/vendor/sd-scripts/networks/network_base.py b/vendor/sd-scripts/networks/network_base.py new file mode 100644 index 0000000..d526060 --- /dev/null +++ b/vendor/sd-scripts/networks/network_base.py @@ -0,0 +1,545 @@ +# Shared network base for additional network modules (like LyCORIS-family modules: LoHa, LoKr, etc). +# Provides architecture detection and a generic AdditionalNetwork class. + +import os +import re +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple, Type, Union + +import torch +from library.sdxl_original_unet import InferSdxlUNet2DConditionModel +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +@dataclass +class ArchConfig: + unet_target_modules: List[str] + te_target_modules: List[str] + unet_prefix: str + te_prefixes: List[str] + default_excludes: List[str] = field(default_factory=list) + adapter_target_modules: List[str] = field(default_factory=list) + unet_conv_target_modules: List[str] = field(default_factory=list) + + +def detect_arch_config(unet, text_encoders) -> ArchConfig: + """Detect architecture from model structure and return ArchConfig.""" + from library.sdxl_original_unet import SdxlUNet2DConditionModel + + # Check SDXL first + if unet is not None and ( + issubclass(unet.__class__, SdxlUNet2DConditionModel) or issubclass(unet.__class__, InferSdxlUNet2DConditionModel) + ): + return ArchConfig( + unet_target_modules=["Transformer2DModel"], + te_target_modules=["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"], + unet_prefix="lora_unet", + te_prefixes=["lora_te1", "lora_te2"], + default_excludes=[], + unet_conv_target_modules=["ResnetBlock2D", "Downsample2D", "Upsample2D"], + ) + + # Check Anima: look for Block class in named_modules + module_class_names = set() + if unet is not None: + for module in unet.modules(): + module_class_names.add(type(module).__name__) + + if "Block" in module_class_names: + return ArchConfig( + unet_target_modules=["Block", "PatchEmbed", "TimestepEmbedding", "FinalLayer"], + te_target_modules=["Qwen3Attention", "Qwen3MLP", "Qwen3SdpaAttention", "Qwen3FlashAttention2"], + unet_prefix="lora_unet", + te_prefixes=["lora_te"], + default_excludes=[r".*(_modulation|_norm|_embedder|final_layer).*"], + adapter_target_modules=["LLMAdapterTransformerBlock"], + ) + + raise ValueError(f"Cannot auto-detect architecture for LyCORIS. Module classes found: {sorted(module_class_names)}") + + +def _parse_kv_pairs(kv_pair_str: str, is_int: bool) -> Dict[str, Union[int, float]]: + """Parse a string of key-value pairs separated by commas.""" + pairs = {} + for pair in kv_pair_str.split(","): + pair = pair.strip() + if not pair: + continue + if "=" not in pair: + logger.warning(f"Invalid format: {pair}, expected 'key=value'") + continue + key, value = pair.split("=", 1) + key = key.strip() + value = value.strip() + try: + pairs[key] = int(value) if is_int else float(value) + except ValueError: + logger.warning(f"Invalid value for {key}: {value}") + return pairs + + +class AdditionalNetwork(torch.nn.Module): + """Generic Additional network that supports LoHa, LoKr, and similar module types. + + Constructed with a module_class parameter to inject the specific module type. + Based on the lora_anima.py LoRANetwork, generalized for multiple architectures. + """ + + def __init__( + self, + text_encoders: list, + unet, + arch_config: ArchConfig, + multiplier: float = 1.0, + lora_dim: int = 4, + alpha: float = 1, + dropout: Optional[float] = None, + rank_dropout: Optional[float] = None, + module_dropout: Optional[float] = None, + module_class: Type[torch.nn.Module] = None, + module_kwargs: Optional[Dict] = None, + modules_dim: Optional[Dict[str, int]] = None, + modules_alpha: Optional[Dict[str, int]] = None, + conv_lora_dim: Optional[int] = None, + conv_alpha: Optional[float] = None, + exclude_patterns: Optional[List[str]] = None, + include_patterns: Optional[List[str]] = None, + reg_dims: Optional[Dict[str, int]] = None, + reg_lrs: Optional[Dict[str, float]] = None, + train_llm_adapter: bool = False, + verbose: bool = False, + ) -> None: + super().__init__() + assert module_class is not None, "module_class must be specified" + + self.multiplier = multiplier + self.lora_dim = lora_dim + self.alpha = alpha + self.dropout = dropout + self.rank_dropout = rank_dropout + self.module_dropout = module_dropout + self.conv_lora_dim = conv_lora_dim + self.conv_alpha = conv_alpha + self.train_llm_adapter = train_llm_adapter + self.reg_dims = reg_dims + self.reg_lrs = reg_lrs + self.arch_config = arch_config + + self.loraplus_lr_ratio = None + self.loraplus_unet_lr_ratio = None + self.loraplus_text_encoder_lr_ratio = None + + if module_kwargs is None: + module_kwargs = {} + + if modules_dim is not None: + logger.info(f"create {module_class.__name__} network from weights") + else: + logger.info(f"create {module_class.__name__} network. base dim (rank): {lora_dim}, alpha: {alpha}") + logger.info( + f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}" + ) + + # compile regular expressions + def str_to_re_patterns(patterns: Optional[List[str]]) -> List[re.Pattern]: + re_patterns = [] + if patterns is not None: + for pattern in patterns: + try: + re_pattern = re.compile(pattern) + except re.error as e: + logger.error(f"Invalid pattern '{pattern}': {e}") + continue + re_patterns.append(re_pattern) + return re_patterns + + exclude_re_patterns = str_to_re_patterns(exclude_patterns) + include_re_patterns = str_to_re_patterns(include_patterns) + + # create module instances + def create_modules( + prefix: str, + root_module: torch.nn.Module, + target_replace_modules: List[str], + default_dim: Optional[int] = None, + ) -> Tuple[List[torch.nn.Module], List[str]]: + loras = [] + skipped = [] + for name, module in root_module.named_modules(): + if target_replace_modules is None or module.__class__.__name__ in target_replace_modules: + if target_replace_modules is None: + module = root_module + + for child_name, child_module in module.named_modules(): + is_linear = child_module.__class__.__name__ == "Linear" + is_conv2d = child_module.__class__.__name__ == "Conv2d" + is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1) + + if is_linear or is_conv2d: + original_name = (name + "." if name else "") + child_name + lora_name = f"{prefix}.{original_name}".replace(".", "_") + + # exclude/include filter + excluded = any(pattern.fullmatch(original_name) for pattern in exclude_re_patterns) + included = any(pattern.fullmatch(original_name) for pattern in include_re_patterns) + if excluded and not included: + if verbose: + logger.info(f"exclude: {original_name}") + continue + + dim = None + alpha_val = None + + if modules_dim is not None: + if lora_name in modules_dim: + dim = modules_dim[lora_name] + alpha_val = modules_alpha[lora_name] + else: + if self.reg_dims is not None: + for reg, d in self.reg_dims.items(): + if re.fullmatch(reg, original_name): + dim = d + alpha_val = self.alpha + logger.info(f"Module {original_name} matched with regex '{reg}' -> dim: {dim}") + break + # fallback to default dim + if dim is None: + if is_linear or is_conv2d_1x1: + dim = default_dim if default_dim is not None else self.lora_dim + alpha_val = self.alpha + elif is_conv2d and self.conv_lora_dim is not None: + dim = self.conv_lora_dim + alpha_val = self.conv_alpha + + if dim is None or dim == 0: + if is_linear or is_conv2d_1x1: + skipped.append(lora_name) + continue + + lora = module_class( + lora_name, + child_module, + self.multiplier, + dim, + alpha_val, + dropout=dropout, + rank_dropout=rank_dropout, + module_dropout=module_dropout, + **module_kwargs, + ) + lora.original_name = original_name + loras.append(lora) + + if target_replace_modules is None: + break + return loras, skipped + + # Create modules for text encoders + self.text_encoder_loras: List[torch.nn.Module] = [] + skipped_te = [] + if text_encoders is not None: + for i, text_encoder in enumerate(text_encoders): + if text_encoder is None: + continue + + # Determine prefix for this text encoder + if i < len(arch_config.te_prefixes): + te_prefix = arch_config.te_prefixes[i] + else: + te_prefix = arch_config.te_prefixes[0] + + logger.info(f"create {module_class.__name__} for Text Encoder {i+1} (prefix={te_prefix}):") + te_loras, te_skipped = create_modules(te_prefix, text_encoder, arch_config.te_target_modules) + logger.info(f"create {module_class.__name__} for Text Encoder {i+1}: {len(te_loras)} modules.") + self.text_encoder_loras.extend(te_loras) + skipped_te += te_skipped + + # Create modules for UNet/DiT + target_modules = list(arch_config.unet_target_modules) + if modules_dim is not None or conv_lora_dim is not None: + target_modules.extend(arch_config.unet_conv_target_modules) + if train_llm_adapter and arch_config.adapter_target_modules: + target_modules.extend(arch_config.adapter_target_modules) + + self.unet_loras: List[torch.nn.Module] + self.unet_loras, skipped_un = create_modules(arch_config.unet_prefix, unet, target_modules) + logger.info(f"create {module_class.__name__} for UNet/DiT: {len(self.unet_loras)} modules.") + + if verbose: + for lora in self.unet_loras: + logger.info(f"\t{lora.lora_name:60} {lora.lora_dim}, {lora.alpha}") + + skipped = skipped_te + skipped_un + if verbose and len(skipped) > 0: + logger.warning(f"dim (rank) is 0, {len(skipped)} modules are skipped:") + for name in skipped: + logger.info(f"\t{name}") + + # assertion: no duplicate names + names = set() + for lora in self.text_encoder_loras + self.unet_loras: + assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}" + names.add(lora.lora_name) + + def set_multiplier(self, multiplier): + self.multiplier = multiplier + for lora in self.text_encoder_loras + self.unet_loras: + lora.multiplier = self.multiplier + + def set_enabled(self, is_enabled): + for lora in self.text_encoder_loras + self.unet_loras: + lora.enabled = is_enabled + + def load_weights(self, file): + if os.path.splitext(file)[1] == ".safetensors": + from safetensors.torch import load_file + + weights_sd = load_file(file) + else: + weights_sd = torch.load(file, map_location="cpu") + + info = self.load_state_dict(weights_sd, False) + return info + + def apply_to(self, text_encoders, unet, apply_text_encoder=True, apply_unet=True): + if apply_text_encoder: + logger.info(f"enable modules for text encoder: {len(self.text_encoder_loras)} modules") + else: + self.text_encoder_loras = [] + + if apply_unet: + logger.info(f"enable modules for UNet/DiT: {len(self.unet_loras)} modules") + else: + self.unet_loras = [] + + for lora in self.text_encoder_loras + self.unet_loras: + lora.apply_to() + self.add_module(lora.lora_name, lora) + + def is_mergeable(self): + return True + + def merge_to(self, text_encoders, unet, weights_sd, dtype=None, device=None): + apply_text_encoder = apply_unet = False + te_prefixes = self.arch_config.te_prefixes + unet_prefix = self.arch_config.unet_prefix + + for key in weights_sd.keys(): + if any(key.startswith(p) for p in te_prefixes): + apply_text_encoder = True + elif key.startswith(unet_prefix): + apply_unet = True + + if apply_text_encoder: + logger.info("enable modules for text encoder") + else: + self.text_encoder_loras = [] + + if apply_unet: + logger.info("enable modules for UNet/DiT") + else: + self.unet_loras = [] + + for lora in self.text_encoder_loras + self.unet_loras: + sd_for_lora = {} + for key in weights_sd.keys(): + if key.startswith(lora.lora_name): + sd_for_lora[key[len(lora.lora_name) + 1 :]] = weights_sd[key] + lora.merge_to(sd_for_lora, dtype, device) + + logger.info("weights are merged") + + def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio): + self.loraplus_lr_ratio = loraplus_lr_ratio + self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio + self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio + + logger.info(f"LoRA+ UNet LR Ratio: {self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio}") + logger.info(f"LoRA+ Text Encoder LR Ratio: {self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio}") + + def prepare_optimizer_params_with_multiple_te_lrs(self, text_encoder_lr, unet_lr, default_lr): + if text_encoder_lr is None or (isinstance(text_encoder_lr, list) and len(text_encoder_lr) == 0): + text_encoder_lr = [default_lr] + elif isinstance(text_encoder_lr, float) or isinstance(text_encoder_lr, int): + text_encoder_lr = [float(text_encoder_lr)] + elif len(text_encoder_lr) == 1: + pass # already a list with one element + + self.requires_grad_(True) + + all_params = [] + lr_descriptions = [] + + def assemble_params(loras, lr, loraplus_ratio): + param_groups = {"lora": {}, "plus": {}} + reg_groups = {} + reg_lrs_list = list(self.reg_lrs.items()) if self.reg_lrs is not None else [] + + for lora in loras: + matched_reg_lr = None + for i, (regex_str, reg_lr) in enumerate(reg_lrs_list): + if re.fullmatch(regex_str, lora.original_name): + matched_reg_lr = (i, reg_lr) + logger.info(f"Module {lora.original_name} matched regex '{regex_str}' -> LR {reg_lr}") + break + + for name, param in lora.named_parameters(): + if matched_reg_lr is not None: + reg_idx, reg_lr = matched_reg_lr + group_key = f"reg_lr_{reg_idx}" + if group_key not in reg_groups: + reg_groups[group_key] = {"lora": {}, "plus": {}, "lr": reg_lr} + # LoRA+ detection: check for "up" weight parameters + if loraplus_ratio is not None and self._is_plus_param(name): + reg_groups[group_key]["plus"][f"{lora.lora_name}.{name}"] = param + else: + reg_groups[group_key]["lora"][f"{lora.lora_name}.{name}"] = param + continue + + if loraplus_ratio is not None and self._is_plus_param(name): + param_groups["plus"][f"{lora.lora_name}.{name}"] = param + else: + param_groups["lora"][f"{lora.lora_name}.{name}"] = param + + params = [] + descriptions = [] + for group_key, group in reg_groups.items(): + reg_lr = group["lr"] + for key in ("lora", "plus"): + param_data = {"params": group[key].values()} + if len(param_data["params"]) == 0: + continue + if key == "plus": + param_data["lr"] = reg_lr * loraplus_ratio if loraplus_ratio is not None else reg_lr + else: + param_data["lr"] = reg_lr + if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None: + logger.info("NO LR skipping!") + continue + params.append(param_data) + desc = f"reg_lr_{group_key.split('_')[-1]}" + descriptions.append(desc + (" plus" if key == "plus" else "")) + + for key in param_groups.keys(): + param_data = {"params": param_groups[key].values()} + if len(param_data["params"]) == 0: + continue + if lr is not None: + if key == "plus": + param_data["lr"] = lr * loraplus_ratio + else: + param_data["lr"] = lr + if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None: + logger.info("NO LR skipping!") + continue + params.append(param_data) + descriptions.append("plus" if key == "plus" else "") + return params, descriptions + + if self.text_encoder_loras: + loraplus_ratio = self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio + # Group TE loras by prefix + for te_idx, te_prefix in enumerate(self.arch_config.te_prefixes): + te_loras = [lora for lora in self.text_encoder_loras if lora.lora_name.startswith(te_prefix)] + if len(te_loras) > 0: + te_lr = text_encoder_lr[te_idx] if te_idx < len(text_encoder_lr) else text_encoder_lr[0] + logger.info(f"Text Encoder {te_idx+1} ({te_prefix}): {len(te_loras)} modules, LR {te_lr}") + params, descriptions = assemble_params(te_loras, te_lr, loraplus_ratio) + all_params.extend(params) + lr_descriptions.extend([f"textencoder {te_idx+1}" + (" " + d if d else "") for d in descriptions]) + + if self.unet_loras: + params, descriptions = assemble_params( + self.unet_loras, + unet_lr if unet_lr is not None else default_lr, + self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio, + ) + all_params.extend(params) + lr_descriptions.extend(["unet" + (" " + d if d else "") for d in descriptions]) + + return all_params, lr_descriptions + + def _is_plus_param(self, name: str) -> bool: + """Check if a parameter name corresponds to a 'plus' (higher LR) param for LoRA+. + + For LoRA: lora_up. For LoHa: hada_w2_a (the second pair). For LoKr: lokr_w1 (the scale factor). + Override in subclass if needed. Default: check for common 'up' patterns. + """ + return "lora_up" in name or "hada_w2_a" in name or "lokr_w1" in name + + def enable_gradient_checkpointing(self): + pass # not supported + + def prepare_grad_etc(self, text_encoder, unet): + self.requires_grad_(True) + + def on_epoch_start(self, text_encoder, unet): + self.train() + + def get_trainable_params(self): + return self.parameters() + + def save_weights(self, file, dtype, metadata): + if metadata is not None and len(metadata) == 0: + metadata = None + + state_dict = self.state_dict() + + if dtype is not None: + for key in list(state_dict.keys()): + v = state_dict[key] + v = v.detach().clone().to("cpu").to(dtype) + state_dict[key] = v + + if os.path.splitext(file)[1] == ".safetensors": + from safetensors.torch import save_file + from library import train_util + + if metadata is None: + metadata = {} + model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata) + metadata["sshs_model_hash"] = model_hash + metadata["sshs_legacy_hash"] = legacy_hash + + save_file(state_dict, file, metadata) + else: + torch.save(state_dict, file) + + def backup_weights(self): + loras = self.text_encoder_loras + self.unet_loras + for lora in loras: + org_module = lora.org_module_ref[0] + if not hasattr(org_module, "_lora_org_weight"): + sd = org_module.state_dict() + org_module._lora_org_weight = sd["weight"].detach().clone() + org_module._lora_restored = True + + def restore_weights(self): + loras = self.text_encoder_loras + self.unet_loras + for lora in loras: + org_module = lora.org_module_ref[0] + if not org_module._lora_restored: + sd = org_module.state_dict() + sd["weight"] = org_module._lora_org_weight + org_module.load_state_dict(sd) + org_module._lora_restored = True + + def pre_calculation(self): + loras = self.text_encoder_loras + self.unet_loras + for lora in loras: + org_module = lora.org_module_ref[0] + sd = org_module.state_dict() + + org_weight = sd["weight"] + lora_weight = lora.get_weight().to(org_weight.device, dtype=org_weight.dtype) + sd["weight"] = org_weight + lora_weight + assert sd["weight"].shape == org_weight.shape + org_module.load_state_dict(sd) + + org_module._lora_restored = False + lora.enabled = False diff --git a/vendor/sd-scripts/requirements.txt b/vendor/sd-scripts/requirements.txt new file mode 100644 index 0000000..bb5d2cd --- /dev/null +++ b/vendor/sd-scripts/requirements.txt @@ -0,0 +1,49 @@ +accelerate==1.6.0 +transformers==4.54.1 +diffusers[torch]==0.32.1 +ftfy==6.3.1 +# albumentations==1.3.0 +opencv-python==4.10.0.84 +einops==0.7.0 +# pytorch-lightning==1.9.0 +bitsandbytes +lion-pytorch==0.2.3 +schedulefree==1.4 +pytorch-optimizer==3.10.0 +prodigy-plus-schedule-free==1.9.2 +prodigyopt==1.1.2 +tensorboard +safetensors==0.4.5 +# gradio==3.16.2 +# altair==4.2.2 +# easygui==0.98.3 +toml==0.10.2 +voluptuous==0.15.2 +huggingface-hub==0.34.3 +# for Image utils +imagesize==1.4.1 +numpy +# <=2.0 +# for BLIP captioning +# requests==2.28.2 +# timm==0.6.12 +# fairscale==0.4.13 +# for WD14 captioning (tensorflow) +# tensorflow==2.10.1 +# for WD14 captioning (onnx) +# onnx==1.15.0 +# onnxruntime-gpu==1.17.1 +# onnxruntime==1.17.1 +# for cuda 12.1(default 11.8) +# onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ + +# this is for onnx: +# protobuf==3.20.3 +# open clip for SDXL +# open-clip-torch==2.20.0 +# For logging +rich==14.1.0 +# for T5XXL tokenizer (SD3/FLUX) +sentencepiece==0.2.1 +# for kohya_ss library +-e . diff --git a/vendor/sd-scripts/setup.py b/vendor/sd-scripts/setup.py new file mode 100644 index 0000000..7bf5483 --- /dev/null +++ b/vendor/sd-scripts/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup, find_packages + +setup(name = "library", packages = find_packages()) \ No newline at end of file