"""
LeRobotLab Tools - DROID Conversion Module

Handles conversion of robot datasets to DROID format for robotic learning.
"""

from pathlib import Path
from typing import Dict, Any, List


class DROIDConverter:
    """
    Converter class for transforming robot datasets to DROID format.
    
    DROID is designed for distributed robot learning with visual observations
    and supports various action spaces commonly used in robotic manipulation.
    """
    
    def __init__(self, verbose: bool = False):
        """
        Initialize the DROID converter.
        
        Args:
            verbose: Whether to enable verbose logging
        """
        self.verbose = verbose
        self.format_name = "DROID"
        self.file_extension = ".droid"
        
    def convert_dataset(
        self,
        repo_id: str,
        selected_videos: List[str],
        input_dir: Path,
        output_file: Path
    ) -> Dict[str, Any]:
        """
        Convert a single dataset to DROID format.
        
        Args:
            repo_id: Repository ID of the dataset (e.g., 'username/dataset_name')
            selected_videos: List of selected video streams to convert
            input_dir: Directory containing the input dataset
            output_file: Path where the converted file should be saved
            
        Returns:
            dict: Conversion result with status and metadata
        """
        if self.verbose:
            print(f"    Starting {self.format_name} conversion for: {repo_id}")
            print(f"    Input directory: {input_dir}")
            print(f"    Output directory: {output_file}")
            print(f"    Selected videos: {', '.join(selected_videos)}")

        try:
            # TODO: Implement actual DROID conversion logic
            # 1. Load episode data from LeRobot format
            # 2. Convert to DROID's action space representation
            #    - Handle different action types (continuous, discrete, hybrid)
            #    - Apply action space transformations
            # 3. Handle observation encoding for DROID's visual processing
            #    - Process RGB images for visual observations
            #    - Apply DROID-specific preprocessing
            # 4. Structure episodes for DROID training
            #    - Organize temporal sequences
            #    - Handle episode boundaries and resets
            # 5. Save in DROID-compatible format
            #    - Use DROID's data format specification
            #    - Include metadata for training

            for video_stream in selected_videos:
                if self.verbose:
                    print(f"      Processing video stream: {video_stream}")
                self._process_video_stream(video_stream, input_dir, output_file)

            # Create output directory if needed
            output_file.parent.mkdir(parents=True, exist_ok=True)

            conversion_result = {
                'status': 'success',
                'repo_id': repo_id,
                'format': self.format_name,
                'output_file': str(output_file),
                'processed_videos': selected_videos,
                'episodes_converted': self._get_placeholder_episode_count(),
                'message': f"Successfully converted {repo_id} to {self.format_name} format"
            }

            if self.verbose:
                print(f"    ✓ Conversion completed: {len(selected_videos)} video streams processed")

            return conversion_result

        except Exception as e:
            error_result = {
                'status': 'error',
                'repo_id': repo_id,
                'format': self.format_name,
                'output_file': str(output_file),
                'processed_videos': [],
                'episodes_converted': 0,
                'message': f"Error converting {repo_id} to {self.format_name}: {str(e)}"
            }

            if self.verbose:
                print(f"    ✗ Conversion failed: {str(e)}")

            return error_result

    def _process_video_stream(self, video_stream: str, input_dir: Path, output_file: Path):
        """
        Process a single video stream for DROID conversion.
        
        Args:
            video_stream: Name of the video stream to process
            input_dir: Input directory containing the dataset
            output_file: Output file path for converted data
        """
        if self.verbose:
            print(f"        - Processing {video_stream} for DROID format")
            print(f"        - Loading frames and actions")
            print(f"        - Applying DROID preprocessing")
            print(f"        - Converting to DROID observation format")

        # TODO: Implement actual video stream processing
        # - Load video frames from LeRobot dataset
        # - Apply DROID-specific preprocessing
        # - Convert action sequences to DROID format
        # - Save processed data

    def _get_placeholder_episode_count(self) -> int:
        """Get placeholder episode count for testing."""
        return 42  # Placeholder value

    def get_supported_video_streams(self) -> List[str]:
        """
        Get list of supported video stream types for DROID.
        
        Returns:
            List of supported video stream identifiers
        """
        return [
            "observation.images.top",
            "observation.images.front", 
            "observation.images.wrist",
            "observation.images.side"
        ]

    def validate_input(self, input_dir: Path, selected_videos: List[str]) -> bool:
        """
        Validate input dataset and video streams for DROID conversion.
        
        Args:
            input_dir: Input directory to validate
            selected_videos: List of video streams to validate
            
        Returns:
            bool: True if input is valid, False otherwise
        """
        # TODO: Implement validation logic
        # - Check if input directory exists and contains valid dataset
        # - Verify video streams are available
        # - Check for required metadata
        
        if not input_dir.exists():
            if self.verbose:
                print(f"    ✗ Input directory does not exist: {input_dir}")
            return False
        
        if not selected_videos:
            if self.verbose:
                print(f"    ✗ No video streams selected for conversion")
            return False
        
        if self.verbose:
            print(f"    ✓ Input validation passed")
        
        return True 