import logging

import numpy as np
import torch
from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
from lerobot.common.utils.utils import init_logging
from torchvision import transforms

from .base_inference import BaseInferenceEngine

logger = logging.getLogger(__name__)


class DiffusionInferenceEngine(BaseInferenceEngine):
    """
    Diffusion Policy inference engine.

    Handles image preprocessing, joint normalization, and action prediction
    for Diffusion Policy models using diffusion-based action generation.
    """

    def __init__(
        self,
        policy_path: str,
        camera_names: list[str],
        device: str | None = None,
    ):
        super().__init__(policy_path, camera_names, device)

        # Diffusion-specific configuration
        self.num_inference_steps = 10  # Number of diffusion steps
        self.supports_language = (
            False  # Diffusion policies typically don't use language
        )

    async def load_policy(self):
        """Load the Diffusion policy from the specified path."""
        logger.info(f"Loading Diffusion policy from: {self.policy_path}")

        # Initialize hydra config for LeRobot
        init_logging()

        # Load the Diffusion policy
        self.policy = DiffusionPolicy.from_pretrained(self.policy_path)
        self.policy.to(self.device)
        self.policy.eval()

        # Set up image transforms based on policy config
        if hasattr(self.policy, "config"):
            self._setup_image_transforms()

        self.is_loaded = True
        logger.info(f"Diffusion policy loaded successfully on {self.device}")

    def _setup_image_transforms(self):
        """Set up image transforms based on the policy configuration."""
        # Get image size from policy config
        config = self.policy.config
        image_size = getattr(config, "image_size", 224)

        # Create transforms for each camera
        for camera_name in self.camera_names:
            # Use policy-specific transforms if available
            if hasattr(self.policy, "image_processor"):
                self.image_transforms[camera_name] = self.policy.image_processor
            else:
                # Fall back to default transform
                self.image_transforms[camera_name] = transforms.Compose([
                    transforms.Resize((image_size, image_size)),
                    transforms.ToTensor(),
                    transforms.Normalize(
                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                    ),
                ])

    async def predict(
        self, images: dict[str, np.ndarray], joint_positions: np.ndarray, **kwargs
    ) -> np.ndarray:
        """
        Run Diffusion policy inference to predict actions.

        Args:
            images: Dictionary of {camera_name: rgb_image_array}
            joint_positions: Current joint positions in LeRobot standard order
            **kwargs: Additional arguments (unused for Diffusion)

        Returns:
            Array of predicted actions

        """
        if not self.is_loaded:
            msg = "Policy not loaded. Call load_policy() first."
            raise RuntimeError(msg)

        # Preprocess inputs
        processed_images = self.preprocess_images(images)
        processed_joints = self.preprocess_joint_positions(joint_positions)

        # Prepare batch inputs for Diffusion policy
        batch = self._prepare_batch(processed_images, processed_joints)

        # Run inference
        with torch.no_grad():
            action = self.policy.predict(batch)

            # Convert to numpy
            if isinstance(action, torch.Tensor):
                action = action.cpu().numpy()

            return action

    def _prepare_batch(
        self, images: dict[str, torch.Tensor], joints: torch.Tensor
    ) -> dict:
        """
        Prepare batch inputs for Diffusion policy model.

        Args:
            images: Preprocessed images
            joints: Preprocessed joint positions

        Returns:
            Batch dictionary for Diffusion policy model

        """
        batch = {}

        # Add images to batch
        for camera_name, image_tensor in images.items():
            # Add batch dimension if needed
            if len(image_tensor.shape) == 3:
                image_tensor = image_tensor.unsqueeze(0)
            batch[f"observation.images.{camera_name}"] = image_tensor

        # Add joint positions
        if len(joints.shape) == 1:
            joints = joints.unsqueeze(0)
        batch["observation.state"] = joints

        return batch