"""
Modality-Specific Optimization Strategies - Phase 5.1
Specialized optimization for text, image, audio, video, tabular, and graph data
"""

import asyncio
import logging
from typing import Dict, List, Any, Optional, Union, Tuple
from datetime import datetime
from enum import Enum
import numpy as np

from ..storage import SessionDep
from .multimodal_agent import ModalityType

logger = logging.getLogger(__name__)


class OptimizationStrategy(str, Enum):
    """Optimization strategy types"""
    SPEED = "speed"
    MEMORY = "memory"
    ACCURACY = "accuracy"
    BALANCED = "balanced"


class ModalityOptimizer:
    """Base class for modality-specific optimizers"""
    
    def __init__(self, session: SessionDep):
        self.session = session
        self._performance_history = {}
    
    async def optimize(
        self,
        data: Any,
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize data processing for specific modality"""
        raise NotImplementedError
    
    def _calculate_optimization_metrics(
        self, 
        original_size: int, 
        optimized_size: int, 
        processing_time: float
    ) -> Dict[str, float]:
        """Calculate optimization metrics"""
        compression_ratio = original_size / optimized_size if optimized_size > 0 else 1.0
        speed_improvement = processing_time / processing_time  # Will be overridden
        
        return {
            "compression_ratio": compression_ratio,
            "space_savings_percent": (1 - 1/compression_ratio) * 100,
            "speed_improvement_factor": speed_improvement,
            "processing_efficiency": min(1.0, compression_ratio / speed_improvement)
        }


class TextOptimizer(ModalityOptimizer):
    """Text processing optimization strategies"""
    
    def __init__(self, session: SessionDep):
        super().__init__(session)
        self._token_cache = {}
        self._embedding_cache = {}
    
    async def optimize(
        self,
        text_data: Union[str, List[str]],
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize text processing"""
        
        start_time = datetime.utcnow()
        constraints = constraints or {}
        
        # Normalize input
        if isinstance(text_data, str):
            texts = [text_data]
        else:
            texts = text_data
        
        results = []
        
        for text in texts:
            optimized_result = await self._optimize_single_text(text, strategy, constraints)
            results.append(optimized_result)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds()
        
        # Calculate aggregate metrics
        total_original_chars = sum(len(text) for text in texts)
        total_optimized_size = sum(len(result["optimized_text"]) for result in results)
        
        metrics = self._calculate_optimization_metrics(
            total_original_chars, total_optimized_size, processing_time
        )
        
        return {
            "modality": "text",
            "strategy": strategy,
            "processed_count": len(texts),
            "results": results,
            "optimization_metrics": metrics,
            "processing_time_seconds": processing_time
        }
    
    async def _optimize_single_text(
        self, 
        text: str, 
        strategy: OptimizationStrategy, 
        constraints: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Optimize a single text"""
        
        if strategy == OptimizationStrategy.SPEED:
            return await self._optimize_for_speed(text, constraints)
        elif strategy == OptimizationStrategy.MEMORY:
            return await self._optimize_for_memory(text, constraints)
        elif strategy == OptimizationStrategy.ACCURACY:
            return await self._optimize_for_accuracy(text, constraints)
        else:  # BALANCED
            return await self._optimize_balanced(text, constraints)
    
    async def _optimize_for_speed(self, text: str, constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize text for processing speed"""
        
        # Fast tokenization
        tokens = self._fast_tokenize(text)
        
        # Lightweight preprocessing
        cleaned_text = self._lightweight_clean(text)
        
        # Cached embeddings if available
        embedding_hash = hash(cleaned_text[:100])  # Hash first 100 chars
        embedding = self._embedding_cache.get(embedding_hash)
        
        if embedding is None:
            embedding = self._fast_embedding(cleaned_text)
            self._embedding_cache[embedding_hash] = embedding
        
        return {
            "original_text": text,
            "optimized_text": cleaned_text,
            "tokens": tokens,
            "embeddings": embedding,
            "optimization_method": "speed_focused",
            "features": {
                "token_count": len(tokens),
                "char_count": len(cleaned_text),
                "embedding_dim": len(embedding)
            }
        }
    
    async def _optimize_for_memory(self, text: str, constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize text for memory efficiency"""
        
        # Aggressive text compression
        compressed_text = self._compress_text(text)
        
        # Minimal tokenization
        minimal_tokens = self._minimal_tokenize(text)
        
        # Low-dimensional embeddings
        embedding = self._low_dim_embedding(text)
        
        return {
            "original_text": text,
            "optimized_text": compressed_text,
            "tokens": minimal_tokens,
            "embeddings": embedding,
            "optimization_method": "memory_focused",
            "features": {
                "token_count": len(minimal_tokens),
                "char_count": len(compressed_text),
                "embedding_dim": len(embedding),
                "compression_ratio": len(text) / len(compressed_text)
            }
        }
    
    async def _optimize_for_accuracy(self, text: str, constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize text for maximum accuracy"""
        
        # Full preprocessing pipeline
        cleaned_text = self._comprehensive_clean(text)
        
        # Advanced tokenization
        tokens = self._advanced_tokenize(cleaned_text)
        
        # High-dimensional embeddings
        embedding = self._high_dim_embedding(cleaned_text)
        
        # Rich feature extraction
        features = self._extract_rich_features(cleaned_text)
        
        return {
            "original_text": text,
            "optimized_text": cleaned_text,
            "tokens": tokens,
            "embeddings": embedding,
            "features": features,
            "optimization_method": "accuracy_focused",
            "processing_quality": "maximum"
        }
    
    async def _optimize_balanced(self, text: str, constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Balanced optimization"""
        
        # Standard preprocessing
        cleaned_text = self._standard_clean(text)
        
        # Balanced tokenization
        tokens = self._balanced_tokenize(cleaned_text)
        
        # Standard embeddings
        embedding = self._standard_embedding(cleaned_text)
        
        # Standard features
        features = self._extract_standard_features(cleaned_text)
        
        return {
            "original_text": text,
            "optimized_text": cleaned_text,
            "tokens": tokens,
            "embeddings": embedding,
            "features": features,
            "optimization_method": "balanced",
            "efficiency_score": 0.8
        }
    
    # Text processing methods (simulated)
    def _fast_tokenize(self, text: str) -> List[str]:
        """Fast tokenization"""
        return text.split()[:100]  # Limit to 100 tokens for speed
    
    def _lightweight_clean(self, text: str) -> str:
        """Lightweight text cleaning"""
        return text.lower().strip()
    
    def _fast_embedding(self, text: str) -> List[float]:
        """Fast embedding generation"""
        return [0.1 * i % 1.0 for i in range(128)]  # Low-dim for speed
    
    def _compress_text(self, text: str) -> str:
        """Text compression"""
        # Simple compression simulation
        return text[:len(text)//2]  # 50% compression
    
    def _minimal_tokenize(self, text: str) -> List[str]:
        """Minimal tokenization"""
        return text.split()[:50]  # Very limited tokens
    
    def _low_dim_embedding(self, text: str) -> List[float]:
        """Low-dimensional embedding"""
        return [0.2 * i % 1.0 for i in range(64)]  # Very low-dim
    
    def _comprehensive_clean(self, text: str) -> str:
        """Comprehensive text cleaning"""
        # Simulate comprehensive cleaning
        cleaned = text.lower().strip()
        cleaned = ''.join(c for c in cleaned if c.isalnum() or c.isspace())
        return cleaned
    
    def _advanced_tokenize(self, text: str) -> List[str]:
        """Advanced tokenization"""
        # Simulate advanced tokenization
        words = text.split()
        # Add subword tokens
        tokens = []
        for word in words:
            tokens.append(word)
            if len(word) > 6:
                tokens.extend([word[:3], word[3:]])  # Subword split
        return tokens
    
    def _high_dim_embedding(self, text: str) -> List[float]:
        """High-dimensional embedding"""
        return [0.05 * i % 1.0 for i in range(1024)]  # High-dim
    
    def _extract_rich_features(self, text: str) -> Dict[str, Any]:
        """Extract rich text features"""
        return {
            "length": len(text),
            "word_count": len(text.split()),
            "sentence_count": text.count('.') + text.count('!') + text.count('?'),
            "avg_word_length": sum(len(word) for word in text.split()) / len(text.split()),
            "punctuation_ratio": sum(1 for c in text if not c.isalnum()) / len(text),
            "complexity_score": min(1.0, len(text) / 1000)
        }
    
    def _standard_clean(self, text: str) -> str:
        """Standard text cleaning"""
        return text.lower().strip()
    
    def _balanced_tokenize(self, text: str) -> List[str]:
        """Balanced tokenization"""
        return text.split()[:200]  # Moderate limit
    
    def _standard_embedding(self, text: str) -> List[float]:
        """Standard embedding"""
        return [0.15 * i % 1.0 for i in range(256)]  # Standard-dim
    
    def _extract_standard_features(self, text: str) -> Dict[str, Any]:
        """Extract standard features"""
        return {
            "length": len(text),
            "word_count": len(text.split()),
            "avg_word_length": sum(len(word) for word in text.split()) / len(text.split()) if text.split() else 0
        }


class ImageOptimizer(ModalityOptimizer):
    """Image processing optimization strategies"""
    
    def __init__(self, session: SessionDep):
        super().__init__(session)
        self._feature_cache = {}
    
    async def optimize(
        self,
        image_data: Dict[str, Any],
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize image processing"""
        
        start_time = datetime.utcnow()
        constraints = constraints or {}
        
        # Extract image properties
        width = image_data.get("width", 224)
        height = image_data.get("height", 224)
        channels = image_data.get("channels", 3)
        
        # Apply optimization strategy
        if strategy == OptimizationStrategy.SPEED:
            result = await self._optimize_image_for_speed(image_data, constraints)
        elif strategy == OptimizationStrategy.MEMORY:
            result = await self._optimize_image_for_memory(image_data, constraints)
        elif strategy == OptimizationStrategy.ACCURACY:
            result = await self._optimize_image_for_accuracy(image_data, constraints)
        else:  # BALANCED
            result = await self._optimize_image_balanced(image_data, constraints)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds()
        
        # Calculate metrics
        original_size = width * height * channels
        optimized_size = result["optimized_width"] * result["optimized_height"] * result["optimized_channels"]
        
        metrics = self._calculate_optimization_metrics(
            original_size, optimized_size, processing_time
        )
        
        return {
            "modality": "image",
            "strategy": strategy,
            "original_dimensions": (width, height, channels),
            "optimized_dimensions": (result["optimized_width"], result["optimized_height"], result["optimized_channels"]),
            "result": result,
            "optimization_metrics": metrics,
            "processing_time_seconds": processing_time
        }
    
    async def _optimize_image_for_speed(self, image_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize image for processing speed"""
        
        # Reduce resolution for speed
        width, height = image_data.get("width", 224), image_data.get("height", 224)
        scale_factor = 0.5  # Reduce to 50%
        
        optimized_width = max(64, int(width * scale_factor))
        optimized_height = max(64, int(height * scale_factor))
        optimized_channels = 3  # Keep RGB
        
        # Fast feature extraction
        features = self._fast_image_features(optimized_width, optimized_height)
        
        return {
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "speed_focused",
            "processing_pipeline": "fast_resize + simple_features"
        }
    
    async def _optimize_image_for_memory(self, image_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize image for memory efficiency"""
        
        # Aggressive size reduction
        width, height = image_data.get("width", 224), image_data.get("height", 224)
        scale_factor = 0.25  # Reduce to 25%
        
        optimized_width = max(32, int(width * scale_factor))
        optimized_height = max(32, int(height * scale_factor))
        optimized_channels = 1  # Convert to grayscale
        
        # Memory-efficient features
        features = self._memory_efficient_features(optimized_width, optimized_height)
        
        return {
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "memory_focused",
            "processing_pipeline": "aggressive_resize + grayscale"
        }
    
    async def _optimize_image_for_accuracy(self, image_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize image for maximum accuracy"""
        
        # Maintain or increase resolution
        width, height = image_data.get("width", 224), image_data.get("height", 224)
        
        optimized_width = max(width, 512)  # Ensure minimum 512px
        optimized_height = max(height, 512)
        optimized_channels = 3  # Keep RGB
        
        # High-quality feature extraction
        features = self._high_quality_features(optimized_width, optimized_height)
        
        return {
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "accuracy_focused",
            "processing_pipeline": "high_res + advanced_features"
        }
    
    async def _optimize_image_balanced(self, image_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Balanced image optimization"""
        
        # Moderate size adjustment
        width, height = image_data.get("width", 224), image_data.get("height", 224)
        scale_factor = 0.75  # Reduce to 75%
        
        optimized_width = max(128, int(width * scale_factor))
        optimized_height = max(128, int(height * scale_factor))
        optimized_channels = 3  # Keep RGB
        
        # Balanced feature extraction
        features = self._balanced_image_features(optimized_width, optimized_height)
        
        return {
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "balanced",
            "processing_pipeline": "moderate_resize + standard_features"
        }
    
    def _fast_image_features(self, width: int, height: int) -> Dict[str, Any]:
        """Fast image feature extraction"""
        return {
            "color_histogram": [0.1, 0.2, 0.3, 0.4],
            "edge_density": 0.3,
            "texture_score": 0.6,
            "feature_dim": 128
        }
    
    def _memory_efficient_features(self, width: int, height: int) -> Dict[str, Any]:
        """Memory-efficient image features"""
        return {
            "mean_intensity": 0.5,
            "contrast": 0.4,
            "feature_dim": 32
        }
    
    def _high_quality_features(self, width: int, height: int) -> Dict[str, Any]:
        """High-quality image features"""
        return {
            "color_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
            "texture_features": [0.7, 0.8, 0.9],
            "shape_features": [0.2, 0.3, 0.4],
            "deep_features": [0.1 * i % 1.0 for i in range(512)],
            "feature_dim": 512
        }
    
    def _balanced_image_features(self, width: int, height: int) -> Dict[str, Any]:
        """Balanced image features"""
        return {
            "color_features": [0.2, 0.3, 0.4],
            "texture_features": [0.5, 0.6],
            "feature_dim": 256
        }


class AudioOptimizer(ModalityOptimizer):
    """Audio processing optimization strategies"""
    
    async def optimize(
        self,
        audio_data: Dict[str, Any],
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize audio processing"""
        
        start_time = datetime.utcnow()
        constraints = constraints or {}
        
        # Extract audio properties
        sample_rate = audio_data.get("sample_rate", 16000)
        duration = audio_data.get("duration", 1.0)
        channels = audio_data.get("channels", 1)
        
        # Apply optimization strategy
        if strategy == OptimizationStrategy.SPEED:
            result = await self._optimize_audio_for_speed(audio_data, constraints)
        elif strategy == OptimizationStrategy.MEMORY:
            result = await self._optimize_audio_for_memory(audio_data, constraints)
        elif strategy == OptimizationStrategy.ACCURACY:
            result = await self._optimize_audio_for_accuracy(audio_data, constraints)
        else:  # BALANCED
            result = await self._optimize_audio_balanced(audio_data, constraints)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds()
        
        # Calculate metrics
        original_size = sample_rate * duration * channels
        optimized_size = result["optimized_sample_rate"] * result["optimized_duration"] * result["optimized_channels"]
        
        metrics = self._calculate_optimization_metrics(
            original_size, optimized_size, processing_time
        )
        
        return {
            "modality": "audio",
            "strategy": strategy,
            "original_properties": (sample_rate, duration, channels),
            "optimized_properties": (result["optimized_sample_rate"], result["optimized_duration"], result["optimized_channels"]),
            "result": result,
            "optimization_metrics": metrics,
            "processing_time_seconds": processing_time
        }
    
    async def _optimize_audio_for_speed(self, audio_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize audio for processing speed"""
        
        sample_rate = audio_data.get("sample_rate", 16000)
        duration = audio_data.get("duration", 1.0)
        
        # Downsample for speed
        optimized_sample_rate = max(8000, sample_rate // 2)
        optimized_duration = min(duration, 2.0)  # Limit to 2 seconds
        optimized_channels = 1  # Mono
        
        # Fast feature extraction
        features = self._fast_audio_features(optimized_sample_rate, optimized_duration)
        
        return {
            "optimized_sample_rate": optimized_sample_rate,
            "optimized_duration": optimized_duration,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "speed_focused"
        }
    
    async def _optimize_audio_for_memory(self, audio_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize audio for memory efficiency"""
        
        sample_rate = audio_data.get("sample_rate", 16000)
        duration = audio_data.get("duration", 1.0)
        
        # Aggressive downsampling
        optimized_sample_rate = max(4000, sample_rate // 4)
        optimized_duration = min(duration, 1.0)  # Limit to 1 second
        optimized_channels = 1  # Mono
        
        # Memory-efficient features
        features = self._memory_efficient_audio_features(optimized_sample_rate, optimized_duration)
        
        return {
            "optimized_sample_rate": optimized_sample_rate,
            "optimized_duration": optimized_duration,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "memory_focused"
        }
    
    async def _optimize_audio_for_accuracy(self, audio_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize audio for maximum accuracy"""
        
        sample_rate = audio_data.get("sample_rate", 16000)
        duration = audio_data.get("duration", 1.0)
        
        # Maintain or increase quality
        optimized_sample_rate = max(sample_rate, 22050)  # Minimum 22.05kHz
        optimized_duration = duration  # Keep full duration
        optimized_channels = min(channels, 2)  # Max stereo
        
        # High-quality features
        features = self._high_quality_audio_features(optimized_sample_rate, optimized_duration)
        
        return {
            "optimized_sample_rate": optimized_sample_rate,
            "optimized_duration": optimized_duration,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "accuracy_focused"
        }
    
    async def _optimize_audio_balanced(self, audio_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Balanced audio optimization"""
        
        sample_rate = audio_data.get("sample_rate", 16000)
        duration = audio_data.get("duration", 1.0)
        
        # Moderate optimization
        optimized_sample_rate = max(12000, sample_rate * 3 // 4)
        optimized_duration = min(duration, 3.0)  # Limit to 3 seconds
        optimized_channels = 1  # Mono
        
        # Balanced features
        features = self._balanced_audio_features(optimized_sample_rate, optimized_duration)
        
        return {
            "optimized_sample_rate": optimized_sample_rate,
            "optimized_duration": optimized_duration,
            "optimized_channels": optimized_channels,
            "features": features,
            "optimization_method": "balanced"
        }
    
    def _fast_audio_features(self, sample_rate: int, duration: float) -> Dict[str, Any]:
        """Fast audio feature extraction"""
        return {
            "mfcc": [0.1, 0.2, 0.3, 0.4, 0.5],
            "spectral_centroid": 0.6,
            "zero_crossing_rate": 0.1,
            "feature_dim": 64
        }
    
    def _memory_efficient_audio_features(self, sample_rate: int, duration: float) -> Dict[str, Any]:
        """Memory-efficient audio features"""
        return {
            "mean_energy": 0.5,
            "spectral_rolloff": 0.7,
            "feature_dim": 16
        }
    
    def _high_quality_audio_features(self, sample_rate: int, duration: float) -> Dict[str, Any]:
        """High-quality audio features"""
        return {
            "mfcc": [0.05 * i % 1.0 for i in range(20)],
            "chroma": [0.1 * i % 1.0 for i in range(12)],
            "spectral_contrast": [0.2 * i % 1.0 for i in range(7)],
            "tonnetz": [0.3 * i % 1.0 for i in range(6)],
            "feature_dim": 256
        }
    
    def _balanced_audio_features(self, sample_rate: int, duration: float) -> Dict[str, Any]:
        """Balanced audio features"""
        return {
            "mfcc": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
            "spectral_bandwidth": 0.4,
            "spectral_flatness": 0.3,
            "feature_dim": 128
        }


class VideoOptimizer(ModalityOptimizer):
    """Video processing optimization strategies"""
    
    async def optimize(
        self,
        video_data: Dict[str, Any],
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize video processing"""
        
        start_time = datetime.utcnow()
        constraints = constraints or {}
        
        # Extract video properties
        fps = video_data.get("fps", 30)
        duration = video_data.get("duration", 1.0)
        width = video_data.get("width", 224)
        height = video_data.get("height", 224)
        
        # Apply optimization strategy
        if strategy == OptimizationStrategy.SPEED:
            result = await self._optimize_video_for_speed(video_data, constraints)
        elif strategy == OptimizationStrategy.MEMORY:
            result = await self._optimize_video_for_memory(video_data, constraints)
        elif strategy == OptimizationStrategy.ACCURACY:
            result = await self._optimize_video_for_accuracy(video_data, constraints)
        else:  # BALANCED
            result = await self._optimize_video_balanced(video_data, constraints)
        
        processing_time = (datetime.utcnow() - start_time).total_seconds()
        
        # Calculate metrics
        original_size = fps * duration * width * height * 3  # RGB
        optimized_size = (result["optimized_fps"] * result["optimized_duration"] * 
                         result["optimized_width"] * result["optimized_height"] * 3)
        
        metrics = self._calculate_optimization_metrics(
            original_size, optimized_size, processing_time
        )
        
        return {
            "modality": "video",
            "strategy": strategy,
            "original_properties": (fps, duration, width, height),
            "optimized_properties": (result["optimized_fps"], result["optimized_duration"], 
                                  result["optimized_width"], result["optimized_height"]),
            "result": result,
            "optimization_metrics": metrics,
            "processing_time_seconds": processing_time
        }
    
    async def _optimize_video_for_speed(self, video_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize video for processing speed"""
        
        fps = video_data.get("fps", 30)
        duration = video_data.get("duration", 1.0)
        width = video_data.get("width", 224)
        height = video_data.get("height", 224)
        
        # Reduce frame rate and resolution
        optimized_fps = max(10, fps // 3)
        optimized_duration = min(duration, 2.0)
        optimized_width = max(64, width // 2)
        optimized_height = max(64, height // 2)
        
        # Fast features
        features = self._fast_video_features(optimized_fps, optimized_duration, optimized_width, optimized_height)
        
        return {
            "optimized_fps": optimized_fps,
            "optimized_duration": optimized_duration,
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "features": features,
            "optimization_method": "speed_focused"
        }
    
    async def _optimize_video_for_memory(self, video_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize video for memory efficiency"""
        
        fps = video_data.get("fps", 30)
        duration = video_data.get("duration", 1.0)
        width = video_data.get("width", 224)
        height = video_data.get("height", 224)
        
        # Aggressive reduction
        optimized_fps = max(5, fps // 6)
        optimized_duration = min(duration, 1.0)
        optimized_width = max(32, width // 4)
        optimized_height = max(32, height // 4)
        
        # Memory-efficient features
        features = self._memory_efficient_video_features(optimized_fps, optimized_duration, optimized_width, optimized_height)
        
        return {
            "optimized_fps": optimized_fps,
            "optimized_duration": optimized_duration,
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "features": features,
            "optimization_method": "memory_focused"
        }
    
    async def _optimize_video_for_accuracy(self, video_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize video for maximum accuracy"""
        
        fps = video_data.get("fps", 30)
        duration = video_data.get("duration", 1.0)
        width = video_data.get("width", 224)
        height = video_data.get("height", 224)
        
        # Maintain or enhance quality
        optimized_fps = max(fps, 30)
        optimized_duration = duration
        optimized_width = max(width, 256)
        optimized_height = max(height, 256)
        
        # High-quality features
        features = self._high_quality_video_features(optimized_fps, optimized_duration, optimized_width, optimized_height)
        
        return {
            "optimized_fps": optimized_fps,
            "optimized_duration": optimized_duration,
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "features": features,
            "optimization_method": "accuracy_focused"
        }
    
    async def _optimize_video_balanced(self, video_data: Dict[str, Any], constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Balanced video optimization"""
        
        fps = video_data.get("fps", 30)
        duration = video_data.get("duration", 1.0)
        width = video_data.get("width", 224)
        height = video_data.get("height", 224)
        
        # Moderate optimization
        optimized_fps = max(15, fps // 2)
        optimized_duration = min(duration, 3.0)
        optimized_width = max(128, width * 3 // 4)
        optimized_height = max(128, height * 3 // 4)
        
        # Balanced features
        features = self._balanced_video_features(optimized_fps, optimized_duration, optimized_width, optimized_height)
        
        return {
            "optimized_fps": optimized_fps,
            "optimized_duration": optimized_duration,
            "optimized_width": optimized_width,
            "optimized_height": optimized_height,
            "features": features,
            "optimization_method": "balanced"
        }
    
    def _fast_video_features(self, fps: int, duration: float, width: int, height: int) -> Dict[str, Any]:
        """Fast video feature extraction"""
        return {
            "motion_vectors": [0.1, 0.2, 0.3],
            "temporal_features": [0.4, 0.5],
            "feature_dim": 64
        }
    
    def _memory_efficient_video_features(self, fps: int, duration: float, width: int, height: int) -> Dict[str, Any]:
        """Memory-efficient video features"""
        return {
            "average_motion": 0.3,
            "scene_changes": 2,
            "feature_dim": 16
        }
    
    def _high_quality_video_features(self, fps: int, duration: float, width: int, height: int) -> Dict[str, Any]:
        """High-quality video features"""
        return {
            "optical_flow": [0.05 * i % 1.0 for i in range(100)],
            "action_features": [0.1 * i % 1.0 for i in range(50)],
            "scene_features": [0.2 * i % 1.0 for i in range(30)],
            "feature_dim": 512
        }
    
    def _balanced_video_features(self, fps: int, duration: float, width: int, height: int) -> Dict[str, Any]:
        """Balanced video features"""
        return {
            "motion_features": [0.1, 0.2, 0.3, 0.4, 0.5],
            "temporal_features": [0.6, 0.7, 0.8],
            "feature_dim": 256
        }


class ModalityOptimizationManager:
    """Manager for all modality-specific optimizers"""
    
    def __init__(self, session: SessionDep):
        self.session = session
        self._optimizers = {
            ModalityType.TEXT: TextOptimizer(session),
            ModalityType.IMAGE: ImageOptimizer(session),
            ModalityType.AUDIO: AudioOptimizer(session),
            ModalityType.VIDEO: VideoOptimizer(session),
            ModalityType.TABULAR: ModalityOptimizer(session),  # Base class for now
            ModalityType.GRAPH: ModalityOptimizer(session)    # Base class for now
        }
    
    async def optimize_modality(
        self,
        modality: ModalityType,
        data: Any,
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize data for specific modality"""
        
        optimizer = self._optimizers.get(modality)
        if optimizer is None:
            raise ValueError(f"No optimizer available for modality: {modality}")
        
        return await optimizer.optimize(data, strategy, constraints)
    
    async def optimize_multimodal(
        self,
        multimodal_data: Dict[ModalityType, Any],
        strategy: OptimizationStrategy = OptimizationStrategy.BALANCED,
        constraints: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize multiple modalities"""
        
        start_time = datetime.utcnow()
        results = {}
        
        # Optimize each modality in parallel
        tasks = []
        for modality, data in multimodal_data.items():
            task = self.optimize_modality(modality, data, strategy, constraints)
            tasks.append((modality, task))
        
        # Execute all optimizations
        completed_tasks = await asyncio.gather(
            *[task for _, task in tasks], 
            return_exceptions=True
        )
        
        for (modality, _), result in zip(tasks, completed_tasks):
            if isinstance(result, Exception):
                logger.error(f"Optimization failed for {modality}: {result}")
                results[modality.value] = {"error": str(result)}
            else:
                results[modality.value] = result
        
        processing_time = (datetime.utcnow() - start_time).total_seconds()
        
        # Calculate aggregate metrics
        total_compression = sum(
            result.get("optimization_metrics", {}).get("compression_ratio", 1.0)
            for result in results.values() if "error" not in result
        )
        avg_compression = total_compression / len([r for r in results.values() if "error" not in r])
        
        return {
            "multimodal_optimization": True,
            "strategy": strategy,
            "modalities_processed": list(multimodal_data.keys()),
            "results": results,
            "aggregate_metrics": {
                "average_compression_ratio": avg_compression,
                "total_processing_time": processing_time,
                "modalities_count": len(multimodal_data)
            },
            "processing_time_seconds": processing_time
        }
