import logging

import numpy as np
import torch
from lerobot.common.policies.act.modeling_act import ACTPolicy
from lerobot.common.utils.utils import init_logging
from torchvision import transforms

from .base_inference import BaseInferenceEngine

logger = logging.getLogger(__name__)


class ACTInferenceEngine(BaseInferenceEngine):
    """
    ACT (Action Chunking Transformer) inference engine.

    Handles image preprocessing, joint normalization, and action prediction
    for ACT models with proper action chunking.
    """

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

        # ACT-specific configuration
        self.chunk_size = 10  # Default chunk size for ACT
        self.action_history = []  # Store recent actions for chunking

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

        # Initialize hydra config for LeRobot
        init_logging()

        # Load the ACT policy
        self.policy = ACTPolicy.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"ACT 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"):
                # Use the policy's image processor
                self.image_transforms[camera_name] = self.policy.image_processor
            else:
                # Fall back to default transform with correct size
                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 ACT 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 ACT)

        Returns:
            Array of predicted actions (chunk of actions for ACT)

        """
        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 ACT
        batch = self._prepare_batch(processed_images, processed_joints)

        # Run inference
        with torch.no_grad():
            # ACT returns a chunk of actions
            action_chunk = self.policy.predict(batch)

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

            # Store in action history
            self.action_history.append(action_chunk)
            if len(self.action_history) > 10:  # Keep last 10 chunks
                self.action_history.pop(0)

            return action_chunk

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

        Args:
            images: Preprocessed images
            joints: Preprocessed joint positions

        Returns:
            Batch dictionary for ACT 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

    def reset(self):
        """Reset ACT-specific state."""
        super().reset()
        self.action_history = []

        # Reset ACT model state if it has one
        if self.policy and hasattr(self.policy, "reset"):
            self.policy.reset()