"""
Multi-Modal Agent Fusion Service
Implements advanced fusion models and cross-domain capability integration
"""

import asyncio
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Tuple
from uuid import uuid4
import logging

from sqlmodel import Session, select, update, delete, and_, or_, func
from sqlalchemy.exc import SQLAlchemyError

from ..domain.agent_performance import (
    FusionModel, AgentCapability, CreativeCapability,
    ReinforcementLearningConfig, AgentPerformanceProfile
)

logger = logging.getLogger(__name__)


class MultiModalFusionEngine:
    """Advanced multi-modal agent fusion system"""
    
    def __init__(self):
        self.fusion_strategies = {
            'ensemble_fusion': self.ensemble_fusion,
            'attention_fusion': self.attention_fusion,
            'cross_modal_attention': self.cross_modal_attention,
            'neural_architecture_search': self.neural_architecture_search,
            'transformer_fusion': self.transformer_fusion,
            'graph_neural_fusion': self.graph_neural_fusion
        }
        
        self.modality_types = {
            'text': {'weight': 0.3, 'encoder': 'transformer'},
            'image': {'weight': 0.25, 'encoder': 'cnn'},
            'audio': {'weight': 0.2, 'encoder': 'wav2vec'},
            'video': {'weight': 0.15, 'encoder': '3d_cnn'},
            'structured': {'weight': 0.1, 'encoder': 'tabular'}
        }
        
        self.fusion_objectives = {
            'performance': 0.4,
            'efficiency': 0.3,
            'robustness': 0.2,
            'adaptability': 0.1
        }
    
    async def create_fusion_model(
        self, 
        session: Session,
        model_name: str,
        fusion_type: str,
        base_models: List[str],
        input_modalities: List[str],
        fusion_strategy: str = "ensemble_fusion"
    ) -> FusionModel:
        """Create a new multi-modal fusion model"""
        
        fusion_id = f"fusion_{uuid4().hex[:8]}"
        
        # Calculate model weights based on modalities
        modality_weights = self.calculate_modality_weights(input_modalities)
        
        # Estimate computational requirements
        computational_complexity = self.estimate_complexity(base_models, input_modalities)
        
        # Set memory requirements
        memory_requirement = self.estimate_memory_requirement(base_models, fusion_type)
        
        fusion_model = FusionModel(
            fusion_id=fusion_id,
            model_name=model_name,
            fusion_type=fusion_type,
            base_models=base_models,
            model_weights=self.calculate_model_weights(base_models),
            fusion_strategy=fusion_strategy,
            input_modalities=input_modalities,
            modality_weights=modality_weights,
            computational_complexity=computational_complexity,
            memory_requirement=memory_requirement,
            status="training"
        )
        
        session.add(fusion_model)
        session.commit()
        session.refresh(fusion_model)
        
        # Start fusion training process
        asyncio.create_task(self.train_fusion_model(session, fusion_id))
        
        logger.info(f"Created fusion model {fusion_id} with strategy {fusion_strategy}")
        return fusion_model
    
    async def train_fusion_model(self, session: Session, fusion_id: str) -> Dict[str, Any]:
        """Train a fusion model"""
        
        fusion_model = session.exec(
            select(FusionModel).where(FusionModel.fusion_id == fusion_id)
        ).first()
        
        if not fusion_model:
            raise ValueError(f"Fusion model {fusion_id} not found")
        
        try:
            # Simulate fusion training process
            training_results = await self.simulate_fusion_training(fusion_model)
            
            # Update model with training results
            fusion_model.fusion_performance = training_results['performance']
            fusion_model.synergy_score = training_results['synergy']
            fusion_model.robustness_score = training_results['robustness']
            fusion_model.inference_time = training_results['inference_time']
            fusion_model.status = "ready"
            fusion_model.trained_at = datetime.utcnow()
            
            session.commit()
            
            logger.info(f"Fusion model {fusion_id} training completed")
            return training_results
            
        except Exception as e:
            logger.error(f"Error training fusion model {fusion_id}: {str(e)}")
            fusion_model.status = "failed"
            session.commit()
            raise
    
    async def simulate_fusion_training(self, fusion_model: FusionModel) -> Dict[str, Any]:
        """Simulate fusion training process"""
        
        # Calculate training time based on complexity
        base_time = 4.0  # hours
        complexity_multipliers = {
            'low': 1.0,
            'medium': 2.0,
            'high': 4.0,
            'very_high': 8.0
        }
        
        training_time = base_time * complexity_multipliers.get(fusion_model.computational_complexity, 2.0)
        
        # Calculate fusion performance based on modalities and base models
        modality_bonus = len(fusion_model.input_modalities) * 0.05
        model_bonus = len(fusion_model.base_models) * 0.03
        
        # Calculate synergy score (how well modalities complement each other)
        synergy_score = self.calculate_synergy_score(fusion_model.input_modalities)
        
        # Calculate robustness (ability to handle missing modalities)
        robustness_score = min(1.0, 0.7 + (len(fusion_model.base_models) * 0.1))
        
        # Calculate inference time
        inference_time = 0.1 + (len(fusion_model.base_models) * 0.05)  # seconds
        
        # Calculate overall performance
        base_performance = 0.75
        fusion_performance = min(1.0, base_performance + modality_bonus + model_bonus + synergy_score * 0.1)
        
        return {
            'performance': {
                'accuracy': fusion_performance,
                'f1_score': fusion_performance * 0.95,
                'precision': fusion_performance * 0.97,
                'recall': fusion_performance * 0.93
            },
            'synergy': synergy_score,
            'robustness': robustness_score,
            'inference_time': inference_time,
            'training_time': training_time,
            'convergence_epoch': int(training_time * 5)
        }
    
    def calculate_modality_weights(self, modalities: List[str]) -> Dict[str, float]:
        """Calculate weights for different modalities"""
        
        weights = {}
        total_weight = 0.0
        
        for modality in modalities:
            weight = self.modality_types.get(modality, {}).get('weight', 0.1)
            weights[modality] = weight
            total_weight += weight
        
        # Normalize weights
        if total_weight > 0:
            for modality in weights:
                weights[modality] /= total_weight
        
        return weights
    
    def calculate_model_weights(self, base_models: List[str]) -> Dict[str, float]:
        """Calculate weights for base models in fusion"""
        
        # Equal weighting by default, could be based on individual model performance
        weight = 1.0 / len(base_models)
        return {model: weight for model in base_models}
    
    def estimate_complexity(self, base_models: List[str], modalities: List[str]) -> str:
        """Estimate computational complexity"""
        
        model_complexity = len(base_models)
        modality_complexity = len(modalities)
        
        total_complexity = model_complexity * modality_complexity
        
        if total_complexity <= 4:
            return "low"
        elif total_complexity <= 8:
            return "medium"
        elif total_complexity <= 16:
            return "high"
        else:
            return "very_high"
    
    def estimate_memory_requirement(self, base_models: List[str], fusion_type: str) -> float:
        """Estimate memory requirement in GB"""
        
        base_memory = len(base_models) * 2.0  # 2GB per base model
        
        fusion_multipliers = {
            'ensemble': 1.0,
            'hybrid': 1.5,
            'multi_modal': 2.0,
            'cross_domain': 2.5
        }
        
        multiplier = fusion_multipliers.get(fusion_type, 1.5)
        return base_memory * multiplier
    
    def calculate_synergy_score(self, modalities: List[str]) -> float:
        """Calculate synergy score between modalities"""
        
        # Define synergy matrix between modalities
        synergy_matrix = {
            ('text', 'image'): 0.8,
            ('text', 'audio'): 0.7,
            ('text', 'video'): 0.9,
            ('image', 'audio'): 0.6,
            ('image', 'video'): 0.85,
            ('audio', 'video'): 0.75,
            ('text', 'structured'): 0.6,
            ('image', 'structured'): 0.5,
            ('audio', 'structured'): 0.4,
            ('video', 'structured'): 0.7
        }
        
        total_synergy = 0.0
        synergy_count = 0
        
        # Calculate pairwise synergy
        for i, mod1 in enumerate(modalities):
            for j, mod2 in enumerate(modalities):
                if i < j:  # Avoid duplicate pairs
                    key = tuple(sorted([mod1, mod2]))
                    synergy = synergy_matrix.get(key, 0.5)
                    total_synergy += synergy
                    synergy_count += 1
        
        # Average synergy score
        if synergy_count > 0:
            return total_synergy / synergy_count
        else:
            return 0.5  # Default synergy for single modality
    
    async def fuse_modalities(
        self, 
        session: Session,
        fusion_id: str,
        input_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Fuse multiple modalities using trained fusion model"""
        
        fusion_model = session.exec(
            select(FusionModel).where(FusionModel.fusion_id == fusion_id)
        ).first()
        
        if not fusion_model:
            raise ValueError(f"Fusion model {fusion_id} not found")
        
        if fusion_model.status != "ready":
            raise ValueError(f"Fusion model {fusion_id} is not ready for inference")
        
        try:
            # Get fusion strategy
            fusion_strategy = self.fusion_strategies.get(fusion_model.fusion_strategy)
            if not fusion_strategy:
                raise ValueError(f"Unknown fusion strategy: {fusion_model.fusion_strategy}")
            
            # Apply fusion strategy
            fusion_result = await fusion_strategy(input_data, fusion_model)
            
            # Update deployment count
            fusion_model.deployment_count += 1
            session.commit()
            
            logger.info(f"Fusion completed for model {fusion_id}")
            return fusion_result
            
        except Exception as e:
            logger.error(f"Error during fusion with model {fusion_id}: {str(e)}")
            raise
    
    async def ensemble_fusion(
        self, 
        input_data: Dict[str, Any], 
        fusion_model: FusionModel
    ) -> Dict[str, Any]:
        """Ensemble fusion strategy"""
        
        # Simulate ensemble fusion
        ensemble_results = {}
        
        for modality in fusion_model.input_modalities:
            if modality in input_data:
                # Simulate modality-specific processing
                modality_result = self.process_modality(input_data[modality], modality)
                weight = fusion_model.modality_weights.get(modality, 0.1)
                ensemble_results[modality] = {
                    'result': modality_result,
                    'weight': weight,
                    'confidence': 0.8 + (weight * 0.2)
                }
        
        # Combine results using weighted average
        combined_result = self.weighted_combination(ensemble_results)
        
        return {
            'fusion_type': 'ensemble',
            'combined_result': combined_result,
            'modality_contributions': ensemble_results,
            'confidence': self.calculate_ensemble_confidence(ensemble_results)
        }
    
    async def attention_fusion(
        self, 
        input_data: Dict[str, Any], 
        fusion_model: FusionModel
    ) -> Dict[str, Any]:
        """Attention-based fusion strategy"""
        
        # Calculate attention weights for each modality
        attention_weights = self.calculate_attention_weights(input_data, fusion_model)
        
        # Apply attention to each modality
        attended_results = {}
        
        for modality in fusion_model.input_modalities:
            if modality in input_data:
                modality_result = self.process_modality(input_data[modality], modality)
                attention_weight = attention_weights.get(modality, 0.1)
                
                attended_results[modality] = {
                    'result': modality_result,
                    'attention_weight': attention_weight,
                    'attended_result': self.apply_attention(modality_result, attention_weight)
                }
        
        # Combine attended results
        combined_result = self.attended_combination(attended_results)
        
        return {
            'fusion_type': 'attention',
            'combined_result': combined_result,
            'attention_weights': attention_weights,
            'attended_results': attended_results
        }
    
    async def cross_modal_attention(
        self, 
        input_data: Dict[str, Any], 
        fusion_model: FusionModel
    ) -> Dict[str, Any]:
        """Cross-modal attention fusion strategy"""
        
        # Build cross-modal attention matrix
        attention_matrix = self.build_cross_modal_attention(input_data, fusion_model)
        
        # Apply cross-modal attention
        cross_modal_results = {}
        
        for i, modality1 in enumerate(fusion_model.input_modalities):
            if modality1 in input_data:
                modality_result = self.process_modality(input_data[modality1], modality1)
                
                # Get attention from other modalities
                cross_attention = {}
                for j, modality2 in enumerate(fusion_model.input_modalities):
                    if i != j and modality2 in input_data:
                        cross_attention[modality2] = attention_matrix[i][j]
                
                cross_modal_results[modality1] = {
                    'result': modality_result,
                    'cross_attention': cross_attention,
                    'enhanced_result': self.enhance_with_cross_attention(modality_result, cross_attention)
                }
        
        # Combine cross-modal enhanced results
        combined_result = self.cross_modal_combination(cross_modal_results)
        
        return {
            'fusion_type': 'cross_modal_attention',
            'combined_result': combined_result,
            'attention_matrix': attention_matrix,
            'cross_modal_results': cross_modal_results
        }
    
    async def neural_architecture_search(
        self, 
        input_data: Dict[str, Any], 
        fusion_model: FusionModel
    ) -> Dict[str, Any]:
        """Neural Architecture Search for fusion"""
        
        # Search for optimal fusion architecture
        optimal_architecture = await self.search_optimal_architecture(input_data, fusion_model)
        
        # Apply optimal architecture
        arch_results = {}
        
        for modality in fusion_model.input_modalities:
            if modality in input_data:
                modality_result = self.process_modality(input_data[modality], modality)
                arch_config = optimal_architecture.get(modality, {})
                
                arch_results[modality] = {
                    'result': modality_result,
                    'architecture': arch_config,
                    'optimized_result': self.apply_architecture(modality_result, arch_config)
                }
        
        # Combine optimized results
        combined_result = self.architecture_combination(arch_results)
        
        return {
            'fusion_type': 'neural_architecture_search',
            'combined_result': combined_result,
            'optimal_architecture': optimal_architecture,
            'arch_results': arch_results
        }
    
    async def transformer_fusion(
        self, 
        input_data: Dict[str, Any], 
        fusion_model: FusionModel
    ) -> Dict[str, Any]:
        """Transformer-based fusion strategy"""
        
        # Convert modalities to transformer tokens
        tokenized_modalities = {}
        
        for modality in fusion_model.input_modalities:
            if modality in input_data:
                tokens = self.tokenize_modality(input_data[modality], modality)
                tokenized_modalities[modality] = tokens
        
        # Apply transformer fusion
        fused_embeddings = self.transformer_fusion_embeddings(tokenized_modalities)
        
        # Generate final result
        combined_result = self.decode_transformer_output(fused_embeddings)
        
        return {
            'fusion_type': 'transformer',
            'combined_result': combined_result,
            'tokenized_modalities': tokenized_modalities,
            'fused_embeddings': fused_embeddings
        }
    
    async def graph_neural_fusion(
        self, 
        input_data: Dict[str, Any], 
        fusion_model: FusionModel
    ) -> Dict[str, Any]:
        """Graph Neural Network fusion strategy"""
        
        # Build modality graph
        modality_graph = self.build_modality_graph(input_data, fusion_model)
        
        # Apply GNN fusion
        graph_embeddings = self.gnn_fusion_embeddings(modality_graph)
        
        # Generate final result
        combined_result = self.decode_gnn_output(graph_embeddings)
        
        return {
            'fusion_type': 'graph_neural',
            'combined_result': combined_result,
            'modality_graph': modality_graph,
            'graph_embeddings': graph_embeddings
        }
    
    def process_modality(self, data: Any, modality_type: str) -> Dict[str, Any]:
        """Process individual modality data"""
        
        # Simulate modality-specific processing
        if modality_type == 'text':
            return {
                'features': self.extract_text_features(data),
                'embeddings': self.generate_text_embeddings(data),
                'confidence': 0.85
            }
        elif modality_type == 'image':
            return {
                'features': self.extract_image_features(data),
                'embeddings': self.generate_image_embeddings(data),
                'confidence': 0.80
            }
        elif modality_type == 'audio':
            return {
                'features': self.extract_audio_features(data),
                'embeddings': self.generate_audio_embeddings(data),
                'confidence': 0.75
            }
        elif modality_type == 'video':
            return {
                'features': self.extract_video_features(data),
                'embeddings': self.generate_video_embeddings(data),
                'confidence': 0.78
            }
        elif modality_type == 'structured':
            return {
                'features': self.extract_structured_features(data),
                'embeddings': self.generate_structured_embeddings(data),
                'confidence': 0.90
            }
        else:
            return {
                'features': {},
                'embeddings': [],
                'confidence': 0.5
            }
    
    def weighted_combination(self, results: Dict[str, Any]) -> Dict[str, Any]:
        """Combine results using weighted average"""
        
        combined_features = {}
        combined_confidence = 0.0
        total_weight = 0.0
        
        for modality, result in results.items():
            weight = result['weight']
            features = result['result']['features']
            confidence = result['confidence']
            
            # Weight features
            for feature, value in features.items():
                if feature not in combined_features:
                    combined_features[feature] = 0.0
                combined_features[feature] += value * weight
            
            combined_confidence += confidence * weight
            total_weight += weight
        
        # Normalize
        if total_weight > 0:
            for feature in combined_features:
                combined_features[feature] /= total_weight
            combined_confidence /= total_weight
        
        return {
            'features': combined_features,
            'confidence': combined_confidence
        }
    
    def calculate_attention_weights(self, input_data: Dict[str, Any], fusion_model: FusionModel) -> Dict[str, float]:
        """Calculate attention weights for modalities"""
        
        # Simulate attention weight calculation based on input quality and modality importance
        attention_weights = {}
        
        for modality in fusion_model.input_modalities:
            if modality in input_data:
                # Base weight from modality weights
                base_weight = fusion_model.modality_weights.get(modality, 0.1)
                
                # Adjust based on input quality (simulated)
                quality_factor = 0.8 + (hash(str(input_data[modality])) % 20) / 100.0
                
                attention_weights[modality] = base_weight * quality_factor
        
        # Normalize attention weights
        total_attention = sum(attention_weights.values())
        if total_attention > 0:
            for modality in attention_weights:
                attention_weights[modality] /= total_attention
        
        return attention_weights
    
    def apply_attention(self, result: Dict[str, Any], attention_weight: float) -> Dict[str, Any]:
        """Apply attention weight to modality result"""
        
        attended_result = result.copy()
        
        # Scale features by attention weight
        for feature, value in attended_result['features'].items():
            attended_result['features'][feature] = value * attention_weight
        
        # Adjust confidence
        attended_result['confidence'] = result['confidence'] * (0.5 + attention_weight * 0.5)
        
        return attended_result
    
    def attended_combination(self, results: Dict[str, Any]) -> Dict[str, Any]:
        """Combine attended results"""
        
        combined_features = {}
        combined_confidence = 0.0
        
        for modality, result in results.items():
            features = result['attended_result']['features']
            confidence = result['attended_result']['confidence']
            
            # Add features
            for feature, value in features.items():
                if feature not in combined_features:
                    combined_features[feature] = 0.0
                combined_features[feature] += value
            
            combined_confidence += confidence
        
        # Average confidence
        if results:
            combined_confidence /= len(results)
        
        return {
            'features': combined_features,
            'confidence': combined_confidence
        }
    
    def build_cross_modal_attention(self, input_data: Dict[str, Any], fusion_model: FusionModel) -> List[List[float]]:
        """Build cross-modal attention matrix"""
        
        modalities = fusion_model.input_modalities
        n_modalities = len(modalities)
        
        # Initialize attention matrix
        attention_matrix = [[0.0 for _ in range(n_modalities)] for _ in range(n_modalities)]
        
        # Calculate cross-modal attention based on synergy
        for i, mod1 in enumerate(modalities):
            for j, mod2 in enumerate(modalities):
                if i != j and mod1 in input_data and mod2 in input_data:
                    # Calculate attention based on synergy and input compatibility
                    synergy = self.calculate_synergy_score([mod1, mod2])
                    compatibility = self.calculate_modality_compatibility(input_data[mod1], input_data[mod2])
                    
                    attention_matrix[i][j] = synergy * compatibility
        
        # Normalize rows
        for i in range(n_modalities):
            row_sum = sum(attention_matrix[i])
            if row_sum > 0:
                for j in range(n_modalities):
                    attention_matrix[i][j] /= row_sum
        
        return attention_matrix
    
    def calculate_modality_compatibility(self, data1: Any, data2: Any) -> float:
        """Calculate compatibility between two modalities"""
        
        # Simulate compatibility calculation
        # In real implementation, would analyze actual data compatibility
        return 0.6 + (hash(str(data1) + str(data2)) % 40) / 100.0
    
    def enhance_with_cross_attention(self, result: Dict[str, Any], cross_attention: Dict[str, float]) -> Dict[str, Any]:
        """Enhance result with cross-attention from other modalities"""
        
        enhanced_result = result.copy()
        
        # Apply cross-attention enhancement
        attention_boost = sum(cross_attention.values()) / len(cross_attention) if cross_attention else 0.0
        
        # Boost features based on cross-attention
        for feature, value in enhanced_result['features'].items():
            enhanced_result['features'][feature] *= (1.0 + attention_boost * 0.2)
        
        # Boost confidence
        enhanced_result['confidence'] = min(1.0, result['confidence'] * (1.0 + attention_boost * 0.3))
        
        return enhanced_result
    
    def cross_modal_combination(self, results: Dict[str, Any]) -> Dict[str, Any]:
        """Combine cross-modal enhanced results"""
        
        combined_features = {}
        combined_confidence = 0.0
        total_cross_attention = 0.0
        
        for modality, result in results.items():
            features = result['enhanced_result']['features']
            confidence = result['enhanced_result']['confidence']
            cross_attention_sum = sum(result['cross_attention'].values())
            
            # Add features
            for feature, value in features.items():
                if feature not in combined_features:
                    combined_features[feature] = 0.0
                combined_features[feature] += value
            
            combined_confidence += confidence
            total_cross_attention += cross_attention_sum
        
        # Average values
        if results:
            combined_confidence /= len(results)
            total_cross_attention /= len(results)
        
        return {
            'features': combined_features,
            'confidence': combined_confidence,
            'cross_attention_boost': total_cross_attention
        }
    
    async def search_optimal_architecture(self, input_data: Dict[str, Any], fusion_model: FusionModel) -> Dict[str, Any]:
        """Search for optimal fusion architecture"""
        
        optimal_arch = {}
        
        for modality in fusion_model.input_modalities:
            if modality in input_data:
                # Simulate architecture search
                arch_config = {
                    'layers': np.random.randint(2, 6).tolist(),
                    'units': [2**i for i in range(4, 9)],
                    'activation': np.random.choice(['relu', 'tanh', 'sigmoid']),
                    'dropout': np.random.uniform(0.1, 0.3),
                    'batch_norm': np.random.choice([True, False])
                }
                
                optimal_arch[modality] = arch_config
        
        return optimal_arch
    
    def apply_architecture(self, result: Dict[str, Any], arch_config: Dict[str, Any]) -> Dict[str, Any]:
        """Apply architecture configuration to result"""
        
        optimized_result = result.copy()
        
        # Simulate architecture optimization
        optimization_factor = 1.0 + (arch_config.get('layers', 3) - 3) * 0.05
        
        # Optimize features
        for feature, value in optimized_result['features'].items():
            optimized_result['features'][feature] *= optimization_factor
        
        # Optimize confidence
        optimized_result['confidence'] = min(1.0, result['confidence'] * optimization_factor)
        
        return optimized_result
    
    def architecture_combination(self, results: Dict[str, Any]) -> Dict[str, Any]:
        """Combine architecture-optimized results"""
        
        combined_features = {}
        combined_confidence = 0.0
        optimization_gain = 0.0
        
        for modality, result in results.items():
            features = result['optimized_result']['features']
            confidence = result['optimized_result']['confidence']
            
            # Add features
            for feature, value in features.items():
                if feature not in combined_features:
                    combined_features[feature] = 0.0
                combined_features[feature] += value
            
            combined_confidence += confidence
            
            # Calculate optimization gain
            original_confidence = result['result']['confidence']
            optimization_gain += (confidence - original_confidence) / original_confidence if original_confidence > 0 else 0
        
        # Average values
        if results:
            combined_confidence /= len(results)
            optimization_gain /= len(results)
        
        return {
            'features': combined_features,
            'confidence': combined_confidence,
            'optimization_gain': optimization_gain
        }
    
    def tokenize_modality(self, data: Any, modality_type: str) -> List[str]:
        """Tokenize modality data for transformer"""
        
        # Simulate tokenization
        if modality_type == 'text':
            return str(data).split()[:100]  # Limit to 100 tokens
        elif modality_type == 'image':
            return [f"img_token_{i}" for i in range(50)]  # 50 image tokens
        elif modality_type == 'audio':
            return [f"audio_token_{i}" for i in range(75)]  # 75 audio tokens
        else:
            return [f"token_{i}" for i in range(25)]  # 25 generic tokens
    
    def transformer_fusion_embeddings(self, tokenized_modalities: Dict[str, List[str]]) -> Dict[str, Any]:
        """Apply transformer fusion to tokenized modalities"""
        
        # Simulate transformer fusion
        all_tokens = []
        modality_boundaries = []
        
        for modality, tokens in tokenized_modalities.items():
            modality_boundaries.append(len(all_tokens))
            all_tokens.extend(tokens)
        
        # Simulate transformer processing
        embedding_dim = 768
        fused_embeddings = np.random.rand(len(all_tokens), embedding_dim).tolist()
        
        return {
            'tokens': all_tokens,
            'embeddings': fused_embeddings,
            'modality_boundaries': modality_boundaries,
            'embedding_dim': embedding_dim
        }
    
    def decode_transformer_output(self, fused_embeddings: Dict[str, Any]) -> Dict[str, Any]:
        """Decode transformer output to final result"""
        
        # Simulate decoding
        embeddings = fused_embeddings['embeddings']
        
        # Pool embeddings (simple average)
        pooled_embedding = np.mean(embeddings, axis=0) if embeddings else []
        
        return {
            'features': {
                'pooled_embedding': pooled_embedding.tolist(),
                'embedding_dim': fused_embeddings['embedding_dim']
            },
            'confidence': 0.88
        }
    
    def build_modality_graph(self, input_data: Dict[str, Any], fusion_model: FusionModel) -> Dict[str, Any]:
        """Build modality relationship graph"""
        
        # Simulate graph construction
        nodes = list(fusion_model.input_modalities)
        edges = []
        
        # Create edges based on synergy
        for i, mod1 in enumerate(nodes):
            for j, mod2 in enumerate(nodes):
                if i < j:
                    synergy = self.calculate_synergy_score([mod1, mod2])
                    if synergy > 0.5:  # Only add edges for high synergy
                        edges.append({
                            'source': mod1,
                            'target': mod2,
                            'weight': synergy
                        })
        
        return {
            'nodes': nodes,
            'edges': edges,
            'node_features': {node: np.random.rand(64).tolist() for node in nodes}
        }
    
    def gnn_fusion_embeddings(self, modality_graph: Dict[str, Any]) -> Dict[str, Any]:
        """Apply Graph Neural Network fusion"""
        
        # Simulate GNN processing
        nodes = modality_graph['nodes']
        edges = modality_graph['edges']
        node_features = modality_graph['node_features']
        
        # Simulate GNN layers
        gnn_embeddings = {}
        
        for node in nodes:
            # Aggregate neighbor features
            neighbor_features = []
            for edge in edges:
                if edge['target'] == node:
                    neighbor_features.extend(node_features[edge['source']])
                elif edge['source'] == node:
                    neighbor_features.extend(node_features[edge['target']])
            
            # Combine self and neighbor features
            self_features = node_features[node]
            if neighbor_features:
                combined_features = np.mean([self_features] + [neighbor_features], axis=0).tolist()
            else:
                combined_features = self_features
            
            gnn_embeddings[node] = combined_features
        
        return {
            'node_embeddings': gnn_embeddings,
            'graph_embedding': np.mean(list(gnn_embeddings.values()), axis=0).tolist()
        }
    
    def decode_gnn_output(self, graph_embeddings: Dict[str, Any]) -> Dict[str, Any]:
        """Decode GNN output to final result"""
        
        graph_embedding = graph_embeddings['graph_embedding']
        
        return {
            'features': {
                'graph_embedding': graph_embedding,
                'embedding_dim': len(graph_embedding)
            },
            'confidence': 0.82
        }
    
    # Helper methods for feature extraction (simulated)
    def extract_text_features(self, data: Any) -> Dict[str, float]:
        return {'length': len(str(data)), 'complexity': 0.7, 'sentiment': 0.8}
    
    def generate_text_embeddings(self, data: Any) -> List[float]:
        return np.random.rand(768).tolist()
    
    def extract_image_features(self, data: Any) -> Dict[str, float]:
        return {'brightness': 0.6, 'contrast': 0.7, 'sharpness': 0.8}
    
    def generate_image_embeddings(self, data: Any) -> List[float]:
        return np.random.rand(512).tolist()
    
    def extract_audio_features(self, data: Any) -> Dict[str, float]:
        return {'loudness': 0.7, 'pitch': 0.6, 'tempo': 0.8}
    
    def generate_audio_embeddings(self, data: Any) -> List[float]:
        return np.random.rand(256).tolist()
    
    def extract_video_features(self, data: Any) -> Dict[str, float]:
        return {'motion': 0.7, 'clarity': 0.8, 'duration': 0.6}
    
    def generate_video_embeddings(self, data: Any) -> List[float]:
        return np.random.rand(1024).tolist()
    
    def extract_structured_features(self, data: Any) -> Dict[str, float]:
        return {'completeness': 0.9, 'consistency': 0.8, 'quality': 0.85}
    
    def generate_structured_embeddings(self, data: Any) -> List[float]:
        return np.random.rand(128).tolist()
    
    def calculate_ensemble_confidence(self, results: Dict[str, Any]) -> float:
        """Calculate overall confidence for ensemble fusion"""
        
        confidences = [result['confidence'] for result in results.values()]
        return np.mean(confidences) if confidences else 0.5
