"""
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
# Removed click dependency


class DROIDConverter:
    """
    Converter class for transforming robot datasets to DROID format.
    
    DROID is designed for distributed robot learning with visual observations
    and continuous action spaces.
    """
    
    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_dir: 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_dir}")
            print(f"    Selected videos: {', '.join(selected_videos)}")
        
        try:
            # TODO: Implement actual DROID conversion logic
            # This would include:
            # 1. Load robot trajectory data from input_dir
            # 2. Convert to DROID's action space representation
            #    - Normalize continuous actions
            #    - Handle different robot morphologies
            #    - Convert coordinate systems
            # 3. Handle observation encoding for DROID's visual processing
            #    - Resize and normalize images
            #    - Extract relevant camera views
            #    - Apply DROID-specific preprocessing
            # 4. Structure episodes for DROID training
            #    - Create trajectory segments
            #    - Add task identifiers
            #    - Include success/failure labels
            # 5. Save in DROID-compatible format
            #    - Use DROID's data format specification
            #    - Include required metadata
            #    - Maintain episode structure
            
            # Placeholder implementation
            for video_stream in selected_videos:
                if self.verbose:
                    print(f"      Processing video stream: {video_stream}")
                
                # Simulate conversion steps
                self._process_video_stream(video_stream, input_dir, output_dir)
            
            # Create output directory if it doesn't exist
            output_dir.parent.mkdir(parents=True, exist_ok=True)
            
            # Simulate successful conversion
            conversion_result = {
                'status': 'success',
                'repo_id': repo_id,
                'format': self.format_name,
                'output_file': str(output_dir),
                '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_dir),
                '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) -> None:
        """
        Process a single video stream for DROID conversion.
        
        Args:
            video_stream: Name of the video stream (e.g., 'observation.images.front')
            input_dir: Input directory containing the dataset
            output_file: Output file path
        """
        # TODO: Implement video stream processing
        # This would include:
        # - Loading video frames from the input dataset
        # - Applying DROID-specific preprocessing
        # - Converting to DROID's observation format
        # - Handling multi-camera setups
        
        if self.verbose:
            print(f"        - Extracting frames from {video_stream}")
            print(f"        - Applying DROID preprocessing")
            print(f"        - Converting to DROID observation format")
            print(f"        - Handling action space conversion")
    
    def _get_placeholder_episode_count(self) -> int:
        """Get placeholder episode count for demonstration."""
        # TODO: Replace with actual episode counting logic
        return 75
    
    def get_supported_video_streams(self) -> List[str]:
        """
        Get list of supported video stream types for DROID.
        
        Returns:
            List of supported video stream patterns
        """
        return [
            "observation.images.*",
            "observation.videos.*",
            "observation.camera.*"
        ]
    
    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
        # - Validate action space compatibility
        
        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 