"""Model wrappers for LightX2V components."""

from typing import Any, Dict, List, Optional, Union
import torch
from abc import ABC, abstractmethod

from .config import EncoderConfig, ModelConfig, VideoConfig, TeaCacheConfig


class BaseModel(ABC):
    """Base class for all LightX2V models."""

    def __init__(self, config: Union[EncoderConfig, ModelConfig]):
        self.config = config

    @abstractmethod
    def to(self, device: torch.device) -> "BaseModel":
        """Move model to device."""
        pass


class LightX2VT5Encoder(BaseModel):
    """Wrapper for T5 text encoder."""

    def __init__(self, t5_model: Any, config: EncoderConfig):
        super().__init__(config)
        self._model = t5_model

    def encode(self, prompts: List[str]) -> Dict[str, torch.Tensor]:
        """Encode text prompts."""
        context = self._model.infer(prompts)
        return {"context": context}

    def encode_with_negative(self, prompt: str, negative_prompt: Optional[str] = None) -> Dict[str, torch.Tensor]:
        """Encode prompt with negative prompt."""
        context = self._model.infer([prompt])
        context_null = self._model.infer([negative_prompt if negative_prompt else ""])
        return {"context": context, "context_null": context_null}

    def to(self, device: torch.device) -> "LightX2VT5Encoder":
        """Move encoder to device."""
        # T5 model handles device internally
        return self

    @property
    def device(self) -> torch.device:
        """Get current device."""
        return self.config.device


class LightX2VClipVisionEncoder(BaseModel):
    """Wrapper for CLIP vision encoder."""

    def __init__(self, clip_model: Any, config: EncoderConfig):
        super().__init__(config)
        self._model = clip_model

    def encode(self, images: torch.Tensor, video_config: Optional[VideoConfig] = None) -> torch.Tensor:
        """Encode images with CLIP."""
        if video_config:
            # Convert VideoConfig to dict format expected by CLIP
            config_dict = {
                "target_height": video_config.target_height,
                "target_width": video_config.target_width,
                "target_video_length": video_config.target_video_length,
                "vae_stride": video_config.vae_stride,
                "patch_size": video_config.patch_size,
            }
        else:
            config_dict = {}

        # Ensure images are in correct format [B, C, T, H, W]
        if images.dim() == 3:  # [C, H, W]
            images = images.unsqueeze(0).unsqueeze(2)  # [1, C, 1, H, W]
        elif images.dim() == 4:  # [B, C, H, W]
            images = images.unsqueeze(2)  # [B, C, 1, H, W]

        return self._model.visual(images, config_dict)

    def to(self, device: torch.device) -> "LightX2VClipVisionEncoder":
        """Move encoder to device."""
        # CLIP model handles device internally
        return self

    @property
    def device(self) -> torch.device:
        """Get current device."""
        return self.config.device


class LightX2VVae(BaseModel):
    """Wrapper for VAE."""

    def __init__(self, vae_model: Any, config: EncoderConfig):
        super().__init__(config)
        self._model = vae_model

    def encode(self, videos: List[torch.Tensor], video_config: Optional[VideoConfig] = None, cpu_offload: bool = False) -> List[torch.Tensor]:
        """Encode videos to latent space."""
        config_dict = {"cpu_offload": cpu_offload}

        if video_config:
            config_dict.update(
                {
                    "target_height": video_config.target_height,
                    "target_width": video_config.target_width,
                    "target_video_length": video_config.target_video_length,
                    "vae_stride": video_config.vae_stride,
                    "patch_size": video_config.patch_size,
                }
            )

        from easydict import EasyDict

        return self._model.encode(videos, EasyDict(config_dict))

    def decode(self, latents: torch.Tensor, generator: Optional[torch.Generator] = None, cpu_offload: bool = False) -> torch.Tensor:
        """Decode latents to video."""
        from easydict import EasyDict

        config = EasyDict({"cpu_offload": cpu_offload})

        return self._model.decode(latents, generator=generator, config=config)

    def to(self, device: torch.device) -> "LightX2VVae":
        """Move VAE to device."""
        # VAE model handles device internally
        return self

    @property
    def device(self) -> torch.device:
        """Get current device."""
        return self.config.device


class LightX2VModel(BaseModel):
    """Wrapper for main LightX2V model."""

    def __init__(self, wan_model: Any, config: ModelConfig, easydict_config: Any):
        super().__init__(config)
        self._model = wan_model
        self._easydict_config = easydict_config
        self._scheduler = None

    def set_scheduler(self, scheduler: Any):
        """Set the scheduler for the model."""
        self._scheduler = scheduler
        self._model.set_scheduler(scheduler)

    def infer(self, inputs: Dict[str, Any]):
        """Run inference."""
        return self._model.infer(inputs)

    def prepare_inputs(
        self,
        text_embeddings: Dict[str, torch.Tensor],
        image_embeddings: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """Prepare inputs for inference."""
        inputs = {
            "text_encoder_output": text_embeddings,
            "image_encoder_output": image_embeddings or {},
        }
        return inputs

    def to(self, device: torch.device) -> "LightX2VModel":
        """Move model to device."""
        # Model handles device internally
        return self

    @property
    def device(self) -> torch.device:
        """Get current device."""
        return self.config.to_device()

    @property
    def easydict_config(self) -> Any:
        """Get EasyDict config for compatibility."""
        return self._easydict_config