import os

import numpy as np
import torch
from diffusers import (DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from omegaconf import OmegaConf
from PIL import Image
from transformers import (BertModel, BertTokenizer, CLIPImageProcessor,
                          CLIPVisionModelWithProjection,
                          T5EncoderModel, T5Tokenizer)

from easyanimate.models import (name_to_autoencoder_magvit,
                                name_to_transformer3d)
from easyanimate.pipeline.pipeline_easyanimate_inpaint import \
    EasyAnimateInpaintPipeline
from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import \
    EasyAnimatePipeline_Multi_Text_Encoder_Inpaint
from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
from easyanimate.utils.utils import (get_video_to_video_latent,
                                     save_videos_grid)
from easyanimate.utils.fp8_optimization import convert_weight_dtype_wrapper

# GPU memory mode, which can be choosen in [model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
# 
# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use, 
# and the transformer model has been quantized to float8, which can save more GPU memory. 
# 
# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use, 
# resulting in slower speeds but saving a large amount of GPU memory.
GPU_memory_mode     = "model_cpu_offload"

# Config and model path
config_path         = "config/easyanimate_video_v5_magvit_multi_text_encoder.yaml"
model_name          = "models/Diffusion_Transformer/EasyAnimateV5-12b-zh-InP"

# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
# EasyAnimateV1, V2 and V3 cannot use DDIM.
# EasyAnimateV4 and V5 support DDIM.
sampler_name        = "DDIM"

# Load pretrained model if need
transformer_path    = None
# Only V1 does need a motion module
motion_module_path  = None
vae_path            = None
lora_path           = None

# Other params
sample_size         = [384, 672]
# In EasyAnimateV1, the video_length of video is 40 ~ 80.
# In EasyAnimateV2, V3, V4, the video_length of video is 1 ~ 144. 
# In EasyAnimateV5, the video_length of video is 1 ~ 49.
# If u want to generate a image, please set the video_length = 1.
video_length        = 49
fps                 = 8

# Use torch.float16 if GPU does not support torch.bfloat16
# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
weight_dtype            = torch.bfloat16
# If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
validation_video        = "asset/1.mp4"
denoise_strength        = 0.70

# prompts
# # EasyAnimateV1, V2 and V3 support English.
# EasyAnimateV4 and V5 support English and Chinese.
prompt                  = "一只猫正在弹吉他。"
negative_prompt         = "模糊，突变，变形，失真，画面暗，画面固定，连环画，漫画，线稿，没有主体。"
# prompt                  = "A cute cat is playing the guitar. "
# negative_prompt         = "Blurring, mutation, deformation, distortion, dark and solid, comics."
guidance_scale          = 6.0
seed                    = 43
num_inference_steps     = 50
lora_weight             = 0.55
save_path               = "samples/easyanimate-videos_v2v"

config = OmegaConf.load(config_path)

# Get Transformer
Choosen_Transformer3DModel = name_to_transformer3d[
    config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel')
]

transformer_additional_kwargs = OmegaConf.to_container(config['transformer_additional_kwargs'])
if weight_dtype == torch.float16:
    transformer_additional_kwargs["upcast_attention"] = True

transformer = Choosen_Transformer3DModel.from_pretrained_2d(
    model_name, 
    subfolder="transformer",
    transformer_additional_kwargs=transformer_additional_kwargs
).to(weight_dtype)

if transformer_path is not None:
    print(f"From checkpoint: {transformer_path}")
    if transformer_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(transformer_path)
    else:
        state_dict = torch.load(transformer_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

if motion_module_path is not None:
    print(f"From Motion Module: {motion_module_path}")
    if motion_module_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(motion_module_path)
    else:
        state_dict = torch.load(motion_module_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}, {u}")

# Get Vae
Choosen_AutoencoderKL = name_to_autoencoder_magvit[
    config['vae_kwargs'].get('vae_type', 'AutoencoderKL')
]
vae = Choosen_AutoencoderKL.from_pretrained(
    model_name, 
    subfolder="vae",
    vae_additional_kwargs=OmegaConf.to_container(config['vae_kwargs'])
).to(weight_dtype)
if config['vae_kwargs'].get('vae_type', 'AutoencoderKL') == 'AutoencoderKLMagvit' and weight_dtype == torch.float16:
    vae.upcast_vae = True

if vae_path is not None:
    print(f"From checkpoint: {vae_path}")
    if vae_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(vae_path)
    else:
        state_dict = torch.load(vae_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = vae.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
    tokenizer = BertTokenizer.from_pretrained(
        model_name, subfolder="tokenizer"
    )
    tokenizer_2 = T5Tokenizer.from_pretrained(
        model_name, subfolder="tokenizer_2"
    )
else:
    tokenizer = T5Tokenizer.from_pretrained(
        model_name, subfolder="tokenizer"
    )
    tokenizer_2 = None

if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
    text_encoder = BertModel.from_pretrained(
        model_name, subfolder="text_encoder", torch_dtype=weight_dtype
    )
    text_encoder_2 = T5EncoderModel.from_pretrained(
        model_name, subfolder="text_encoder_2", torch_dtype=weight_dtype
    )
else:
    text_encoder = T5EncoderModel.from_pretrained(
        model_name, subfolder="text_encoder", torch_dtype=weight_dtype
    )
    text_encoder_2 = None

if transformer.config.in_channels != vae.config.latent_channels and config['transformer_additional_kwargs'].get('enable_clip_in_inpaint', True):
    clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
        model_name, subfolder="image_encoder"
    ).to("cuda", weight_dtype)
    clip_image_processor = CLIPImageProcessor.from_pretrained(
        model_name, subfolder="image_encoder"
    )
else:
    clip_image_encoder = None
    clip_image_processor = None

# Get Scheduler
Choosen_Scheduler = scheduler_dict = {
    "Euler": EulerDiscreteScheduler,
    "Euler A": EulerAncestralDiscreteScheduler,
    "DPM++": DPMSolverMultistepScheduler, 
    "PNDM": PNDMScheduler,
    "DDIM": DDIMScheduler,
}[sampler_name]

scheduler = Choosen_Scheduler.from_pretrained(
    model_name, 
    subfolder="scheduler"
)
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
    pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.from_pretrained(
        model_name,
        text_encoder=text_encoder,
        text_encoder_2=text_encoder_2,
        tokenizer=tokenizer,
        tokenizer_2=tokenizer_2,
        vae=vae,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype,
        clip_image_encoder=clip_image_encoder,
        clip_image_processor=clip_image_processor,
    )
else:
    pipeline = EasyAnimateInpaintPipeline.from_pretrained(
        model_name,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        vae=vae,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype,
        clip_image_encoder=clip_image_encoder,
        clip_image_processor=clip_image_processor,
    )

if GPU_memory_mode == "sequential_cpu_offload":
    pipeline.enable_sequential_cpu_offload()
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
    pipeline.enable_model_cpu_offload()
    pipeline.enable_autocast_float8_transformer()
    convert_weight_dtype_wrapper(pipeline.transformer, weight_dtype)
else:
    pipeline.enable_model_cpu_offload()

generator = torch.Generator(device="cuda").manual_seed(seed)

if lora_path is not None:
    pipeline = merge_lora(pipeline, lora_path, lora_weight, "cuda")

if vae.cache_mag_vae:
    video_length = int((video_length - 1) // vae.mini_batch_encoder * vae.mini_batch_encoder) + 1 if video_length != 1 else 1
else:
    video_length = int(video_length // vae.mini_batch_encoder * vae.mini_batch_encoder) if video_length != 1 else 1
input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, video_length=video_length, fps=fps, sample_size=sample_size)

with torch.no_grad():
    sample = pipeline(
        prompt, 
        video_length = video_length,
        negative_prompt = negative_prompt,
        height      = sample_size[0],
        width       = sample_size[1],
        generator   = generator,
        guidance_scale = guidance_scale,
        num_inference_steps = num_inference_steps,

        video        = input_video,
        mask_video   = input_video_mask,
        clip_image   = clip_image, 
        strength     = denoise_strength
    ).videos

if lora_path is not None:
    pipeline = unmerge_lora(pipeline, lora_path, lora_weight, "cuda")
    
if not os.path.exists(save_path):
    os.makedirs(save_path, exist_ok=True)

index = len([path for path in os.listdir(save_path)]) + 1
prefix = str(index).zfill(8)
    
if video_length == 1:
    save_sample_path = os.path.join(save_path, prefix + f".png")

    image = sample[0, :, 0]
    image = image.transpose(0, 1).transpose(1, 2)
    image = (image * 255).numpy().astype(np.uint8)
    image = Image.fromarray(image)
    image.save(save_sample_path)
else:
    video_path = os.path.join(save_path, prefix + ".mp4")
    save_videos_grid(sample, video_path, fps=fps)