"""
GGUF Plugin for LM Studio Models

Supports LM Studio's GGUF format for model validation.
"""

from typing import Dict, Any
import struct
import json


class GGUFPlugin:
    """Plugin for LM Studio GGUF format."""
    
    def extract_metadata(self, model_path: str) -> Dict[str, Any]:
        """
        Extract metadata from GGUF file.
        
        GGUF format structure:
        - Magic number (4 bytes)
        - Version (4 bytes)
        - Metadata (key-value pairs)
        """
        metadata = {}
        
        try:
            with open(model_path, 'rb') as f:
                # Read magic number
                magic = f.read(4)
                if magic != b'GGUF':
                    return {'error': 'Invalid GGUF file'}
                
                # Read version
                version = struct.unpack('<I', f.read(4))[0]
                metadata['gguf_version'] = version
                
                # Read tensor count and metadata count
                tensor_count = struct.unpack('<Q', f.read(8))[0]
                metadata_count = struct.unpack('<Q', f.read(8))[0]
                
                metadata['tensor_count'] = tensor_count
                metadata['metadata_fields'] = metadata_count
                
                # Extract key metadata fields
                # (Simplified - full GGUF parsing is complex)
                metadata['format'] = 'gguf'
                metadata['vendor'] = 'lm_studio'
                
        except Exception as e:
            metadata['error'] = str(e)
        
        return metadata
    
    def analyze_weights(self, model_path: str) -> Dict[str, Any]:
        """
        Analyze weight distributions in GGUF model.
        
        Returns:
            Analysis results including anomaly detection
        """
        analysis = {
            'has_anomalies': False,
            'format': 'gguf'
        }
        
        try:
            # Get file size
            import os
            file_size = os.path.getsize(model_path)
            analysis['size_mb'] = file_size / (1024 * 1024)
            
            # Basic sanity checks
            if file_size < 1024 * 1024:  # Less than 1MB
                analysis['has_anomalies'] = True
                analysis['anomaly_description'] = 'Model file suspiciously small'
            elif file_size > 100 * 1024 * 1024 * 1024:  # More than 100GB
                analysis['has_anomalies'] = True
                analysis['anomaly_description'] = 'Model file suspiciously large'
            
        except Exception as e:
            analysis['error'] = str(e)
        
        return analysis
