import os
import json
import time
import hashlib
from pathlib import Path
from typing import Any, Optional, Dict, Union
import pickle
import shutil
from datetime import datetime, timedelta

class ModelCache:
    """Manages caching for AI models and generated content"""
    
    def __init__(self, cache_dir: Optional[Union[str, Path]] = None):
        if cache_dir is None:
            # Use HuggingFace Spaces persistent storage if available
            if os.path.exists("/data"):
                cache_dir = "/data/cache"
            else:
                cache_dir = Path.home() / ".cache" / "digipal"
        
        self.cache_dir = Path(cache_dir)
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        
        # Cache subdirectories
        self.model_cache_dir = self.cache_dir / "models"
        self.generation_cache_dir = self.cache_dir / "generations"
        self.audio_cache_dir = self.cache_dir / "audio"
        
        for dir_path in [self.model_cache_dir, self.generation_cache_dir, self.audio_cache_dir]:
            dir_path.mkdir(exist_ok=True)
        
        # Cache settings
        self.max_cache_size_gb = 10  # Maximum cache size in GB
        self.cache_expiry_days = 7   # Cache expiry in days
        self.generation_cache_enabled = True
        
        # In-memory cache for fast access
        self.memory_cache = {}
        self.cache_stats = self._load_cache_stats()
    
    def cache_model_weights(self, model_id: str, model_data: Any) -> bool:
        """Cache model weights to disk"""
        try:
            model_hash = self._get_hash(model_id)
            cache_path = self.model_cache_dir / f"{model_hash}.pkl"
            
            with open(cache_path, 'wb') as f:
                pickle.dump(model_data, f)
            
            # Update cache stats
            self._update_cache_stats('model', model_id, cache_path.stat().st_size)
            
            return True
        except Exception as e:
            print(f"Failed to cache model {model_id}: {e}")
            return False
    
    def get_cached_model(self, model_id: str) -> Optional[Any]:
        """Retrieve cached model weights"""
        try:
            model_hash = self._get_hash(model_id)
            cache_path = self.model_cache_dir / f"{model_hash}.pkl"
            
            if cache_path.exists():
                # Check if cache is still valid
                if self._is_cache_valid(cache_path):
                    with open(cache_path, 'rb') as f:
                        return pickle.load(f)
            
            return None
        except Exception as e:
            print(f"Failed to load cached model {model_id}: {e}")
            return None
    
    def cache_generation(self, prompt: str, result: Dict[str, Any], 
                        generation_type: str = "monster") -> str:
        """Cache generation results"""
        if not self.generation_cache_enabled:
            return ""
        
        try:
            # Create unique key for this generation
            cache_key = self._get_generation_key(prompt, generation_type)
            cache_dir = self.generation_cache_dir / generation_type / cache_key[:2]
            cache_dir.mkdir(parents=True, exist_ok=True)
            
            cache_file = cache_dir / f"{cache_key}.json"
            
            # Prepare cache data
            cache_data = {
                'prompt': prompt,
                'type': generation_type,
                'timestamp': datetime.now().isoformat(),
                'result': result
            }
            
            # Handle file paths in results
            if 'image' in result and hasattr(result['image'], 'save'):
                image_path = cache_dir / f"{cache_key}_image.png"
                result['image'].save(image_path)
                cache_data['result']['image'] = str(image_path)
            
            if 'model_3d' in result and isinstance(result['model_3d'], str):
                # Copy 3D model to cache
                model_ext = Path(result['model_3d']).suffix
                model_cache_path = cache_dir / f"{cache_key}_model{model_ext}"
                shutil.copy2(result['model_3d'], model_cache_path)
                cache_data['result']['model_3d'] = str(model_cache_path)
            
            # Save cache data
            with open(cache_file, 'w') as f:
                json.dump(cache_data, f, indent=2)
            
            # Update stats
            self._update_cache_stats('generation', cache_key, cache_file.stat().st_size)
            
            return cache_key
            
        except Exception as e:
            print(f"Failed to cache generation: {e}")
            return ""
    
    def get_cached_generation(self, prompt: str, generation_type: str = "monster") -> Optional[Dict[str, Any]]:
        """Retrieve cached generation if available"""
        if not self.generation_cache_enabled:
            return None
        
        try:
            cache_key = self._get_generation_key(prompt, generation_type)
            cache_file = self.generation_cache_dir / generation_type / cache_key[:2] / f"{cache_key}.json"
            
            if cache_file.exists() and self._is_cache_valid(cache_file):
                with open(cache_file, 'r') as f:
                    cache_data = json.load(f)
                
                # Load associated files
                result = cache_data['result']
                
                if 'image' in result and isinstance(result['image'], str):
                    from PIL import Image
                    if os.path.exists(result['image']):
                        result['image'] = Image.open(result['image'])
                
                return result
            
            return None
            
        except Exception as e:
            print(f"Failed to load cached generation: {e}")
            return None
    
    def cache_audio_transcription(self, audio_path: str, transcription: str) -> bool:
        """Cache audio transcription results"""
        try:
            # Get audio file hash
            with open(audio_path, 'rb') as f:
                audio_hash = hashlib.md5(f.read()).hexdigest()
            
            cache_file = self.audio_cache_dir / f"{audio_hash}.json"
            
            cache_data = {
                'audio_path': audio_path,
                'transcription': transcription,
                'timestamp': datetime.now().isoformat()
            }
            
            with open(cache_file, 'w') as f:
                json.dump(cache_data, f)
            
            return True
            
        except Exception as e:
            print(f"Failed to cache audio transcription: {e}")
            return False
    
    def get_cached_transcription(self, audio_path: str) -> Optional[str]:
        """Get cached audio transcription"""
        try:
            with open(audio_path, 'rb') as f:
                audio_hash = hashlib.md5(f.read()).hexdigest()
            
            cache_file = self.audio_cache_dir / f"{audio_hash}.json"
            
            if cache_file.exists() and self._is_cache_valid(cache_file):
                with open(cache_file, 'r') as f:
                    cache_data = json.load(f)
                return cache_data['transcription']
            
            return None
            
        except Exception as e:
            print(f"Failed to load cached transcription: {e}")
            return None
    
    def add_to_memory_cache(self, key: str, value: Any, ttl_seconds: int = 300):
        """Add item to in-memory cache with TTL"""
        expiry_time = time.time() + ttl_seconds
        self.memory_cache[key] = {
            'value': value,
            'expiry': expiry_time
        }
    
    def get_from_memory_cache(self, key: str) -> Optional[Any]:
        """Get item from in-memory cache"""
        if key in self.memory_cache:
            cache_item = self.memory_cache[key]
            if time.time() < cache_item['expiry']:
                return cache_item['value']
            else:
                # Remove expired item
                del self.memory_cache[key]
        return None
    
    def clear_expired_cache(self):
        """Clear expired cache entries"""
        current_time = datetime.now()
        cleared_size = 0
        
        # Clear file cache
        for cache_type in [self.model_cache_dir, self.generation_cache_dir, self.audio_cache_dir]:
            for file_path in cache_type.rglob('*'):
                if file_path.is_file():
                    file_age = current_time - datetime.fromtimestamp(file_path.stat().st_mtime)
                    if file_age > timedelta(days=self.cache_expiry_days):
                        file_size = file_path.stat().st_size
                        file_path.unlink()
                        cleared_size += file_size
        
        # Clear memory cache
        expired_keys = [
            key for key, item in self.memory_cache.items()
            if time.time() > item['expiry']
        ]
        for key in expired_keys:
            del self.memory_cache[key]
        
        print(f"Cleared {cleared_size / (1024**2):.2f} MB of expired cache")
        
        return cleared_size
    
    def get_cache_size(self) -> Dict[str, float]:
        """Get current cache size in MB"""
        sizes = {
            'models': 0,
            'generations': 0,
            'audio': 0,
            'total': 0
        }
        
        # Calculate directory sizes
        for file_path in self.model_cache_dir.rglob('*'):
            if file_path.is_file():
                sizes['models'] += file_path.stat().st_size
        
        for file_path in self.generation_cache_dir.rglob('*'):
            if file_path.is_file():
                sizes['generations'] += file_path.stat().st_size
        
        for file_path in self.audio_cache_dir.rglob('*'):
            if file_path.is_file():
                sizes['audio'] += file_path.stat().st_size
        
        # Convert to MB
        for key in sizes:
            sizes[key] = sizes[key] / (1024 ** 2)
        
        sizes['total'] = sizes['models'] + sizes['generations'] + sizes['audio']
        
        return sizes
    
    def enforce_size_limit(self):
        """Enforce cache size limit by removing oldest entries"""
        cache_size = self.get_cache_size()
        
        if cache_size['total'] > self.max_cache_size_gb * 1024:  # Convert GB to MB
            # Get all cache files with timestamps
            all_files = []
            
            for cache_dir in [self.model_cache_dir, self.generation_cache_dir, self.audio_cache_dir]:
                for file_path in cache_dir.rglob('*'):
                    if file_path.is_file():
                        all_files.append({
                            'path': file_path,
                            'size': file_path.stat().st_size,
                            'mtime': file_path.stat().st_mtime
                        })
            
            # Sort by modification time (oldest first)
            all_files.sort(key=lambda x: x['mtime'])
            
            # Remove files until under limit
            current_size = cache_size['total'] * (1024 ** 2)  # Convert to bytes
            target_size = self.max_cache_size_gb * (1024 ** 3) * 0.8  # 80% of limit
            
            for file_info in all_files:
                if current_size <= target_size:
                    break
                
                file_info['path'].unlink()
                current_size -= file_info['size']
                print(f"Removed {file_info['path'].name} to enforce cache limit")
    
    def _get_hash(self, text: str) -> str:
        """Get MD5 hash of text"""
        return hashlib.md5(text.encode()).hexdigest()
    
    def _get_generation_key(self, prompt: str, generation_type: str) -> str:
        """Get unique key for generation cache"""
        combined = f"{generation_type}:{prompt}"
        return self._get_hash(combined)
    
    def _is_cache_valid(self, cache_path: Path) -> bool:
        """Check if cache file is still valid"""
        if not cache_path.exists():
            return False
        
        file_age = datetime.now() - datetime.fromtimestamp(cache_path.stat().st_mtime)
        return file_age < timedelta(days=self.cache_expiry_days)
    
    def _load_cache_stats(self) -> Dict[str, Any]:
        """Load cache statistics"""
        stats_file = self.cache_dir / "cache_stats.json"
        
        if stats_file.exists():
            with open(stats_file, 'r') as f:
                return json.load(f)
        
        return {
            'total_hits': 0,
            'total_misses': 0,
            'last_cleanup': datetime.now().isoformat(),
            'entries': {}
        }
    
    def _update_cache_stats(self, cache_type: str, key: str, size: int):
        """Update cache statistics"""
        self.cache_stats['entries'][key] = {
            'type': cache_type,
            'size': size,
            'timestamp': datetime.now().isoformat()
        }
        
        # Save stats
        stats_file = self.cache_dir / "cache_stats.json"
        with open(stats_file, 'w') as f:
            json.dump(self.cache_stats, f, indent=2)
    
    def get_cache_info(self) -> Dict[str, Any]:
        """Get cache information and statistics"""
        sizes = self.get_cache_size()
        
        return {
            'sizes': sizes,
            'stats': self.cache_stats,
            'memory_cache_items': len(self.memory_cache),
            'cache_dir': str(self.cache_dir),
            'max_size_gb': self.max_cache_size_gb,
            'expiry_days': self.cache_expiry_days
        }