ai

"""
Multimodal Context Processing System
==================================

Advanced multimodal context processing system that handles and integrates text, visual,
auditory, and sensor data within unified contextual representations.
"""

import asyncio
import json
import logging
import base64
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple, Union, Set
from dataclasses import dataclass, field
from enum import Enum
import numpy as np
from collections import defaultdict, deque

from ai_agent_framework.core.context_engineering_agent import (
    ContextElement, ContextModality, ContextDimension
)

logger = logging.getLogger(__name__)


class DataModality(Enum):
    """Supported data modalities."""
    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"
    SENSOR = "sensor"
    TABLE = "table"
    CODE = "code"
    STRUCTURED = "structured"


class FusionStrategy(Enum):
    """Strategies for multimodal fusion."""
    EARLY_FUSION = "early_fusion"
    LATE_FUSION = "late_fusion"
    HYBRID_FUSION = "hybrid_fusion"
    ATTENTION_BASED = "attention_based"
    CROSS_ATTENTION = "cross_attention"


@dataclass
class MultimodalInput:
    """Represents multimodal input data."""
    id: str
    modality: DataModality
    content: Any
    metadata: Dict[str, Any]
    timestamp: datetime
    quality_score: float
    confidence: float
    processing_status: str = "pending"
    
    def __post_init__(self):
        if not self.id:
            self.id = f"mm_input_{int(time.time())}_{hash(str(self.content))}"
        if not self.timestamp:
            self.timestamp = datetime.utcnow()
        if not self.metadata:
            self.metadata = {}


@dataclass
class UnifiedContext:
    """Unified contextual representation from multimodal inputs."""
    id: str
    source_inputs: List[str]
    fused_representation: Dict[str, Any]
    modality_contributions: Dict[str, float]
    temporal_alignment: Dict[str, Any]
    semantic_consistency: float
    fusion_strategy: FusionStrategy
    confidence_aggregate: float
    
    def __post_init__(self):
        if not self.id:
            self.id = f"unified_context_{int(time.time())}"


class MultimodalProcessor:
    """Core multimodal processing engine."""
    
    def __init__(self):
        self.modal_processors = {
            DataModality.TEXT: TextProcessor(),
            DataModality.IMAGE: ImageProcessor(),
            DataModality.AUDIO: AudioProcessor(),
            DataModality.VIDEO: VideoProcessor(),
            DataModality.SENSOR: SensorProcessor(),
            DataModality.TABLE: TableProcessor(),
            DataModality.CODE: CodeProcessor(),
            DataModality.STRUCTURED: StructuredProcessor()
        }
        
        self.fusion_strategies = {
            FusionStrategy.EARLY_FUSION: self._early_fusion,
            FusionStrategy.LATE_FUSION: self._late_fusion,
            FusionStrategy.HYBRID_FUSION: self._hybrid_fusion,
            FusionStrategy.ATTENTION_BASED: self._attention_based_fusion,
            FusionStrategy.CROSS_ATTENTION: self._cross_attention_fusion
        }
        
        self.alignment_algorithms = {
            "temporal": self._temporal_alignment,
            "semantic": self._semantic_alignment,
            "structural": self._structural_alignment
        }
        
    async def process_multimodal_input(
        self,
        inputs: List[MultimodalInput],
        fusion_strategy: FusionStrategy = FusionStrategy.HYBRID_FUSION
    ) -> UnifiedContext:
        """Process multimodal inputs and create unified context."""
        
        try:
            # Step 1: Process individual modalities
            processed_modalities = await self._process_individual_modalities(inputs)
            
            # Step 2: Align modalities
            aligned_modalities = await self._align_modalities(processed_modalities)
            
            # Step 3: Fuse modalities using selected strategy
            fusion_func = self.fusion_strategies.get(fusion_strategy)
            if not fusion_func:
                fusion_strategy = FusionStrategy.HYBRID_FUSION
                fusion_func = self.fusion_strategies[fusion_strategy]
            
            unified_context = await fusion_func(aligned_modalities)
            
            # Step 4: Validate and enhance unified context
            validated_context = await self._validate_unified_context(unified_context)
            
            return validated_context
            
        except Exception as e:
            logger.error(f"Multimodal processing failed: {e}")
            return UnifiedContext(
                id=f"error_context_{int(time.time())}",
                source_inputs=[inp.id for inp in inputs],
                fused_representation={"error": str(e)},
                modality_contributions={},
                temporal_alignment={},
                semantic_consistency=0.0,
                fusion_strategy=fusion_strategy,
                confidence_aggregate=0.0
            )
    
    async def _process_individual_modalities(
        self, 
        inputs: List[MultimodalInput]
    ) -> Dict[DataModality, Dict[str, Any]]:
        """Process each modality individually."""
        
        processed_modalities = {}
        
        # Group inputs by modality
        modality_groups = defaultdict(list)
        for input_data in inputs:
            modality_groups[input_data.modality].append(input_data)
        
        # Process each modality
        for modality, modality_inputs in modality_groups.items():
            processor = self.modal_processors.get(modality)
            if processor:
                try:
                    processed_result = await processor.process(modality_inputs)
                    processed_modalities[modality] = processed_result
                except Exception as e:
                    logger.error(f"Failed to process {modality.value} modality: {e}")
                    processed_modalities[modality] = {
                        "status": "error",
                        "error": str(e),
                        "inputs": [inp.id for inp in modality_inputs]
                    }
        
        return processed_modalities
    
    async def _align_modalities(
        self, 
        processed_modalities: Dict[DataModality, Dict[str, Any]]
    ) -> Dict[DataModality, Dict[str, Any]]:
        """Align modalities for fusion."""
        
        aligned_modalities = {}
        
        # Temporal alignment
        temporal_alignment = await self.alignment_algorithms["temporal"](processed_modalities)
        
        # Semantic alignment
        semantic_alignment = await self.alignment_algorithms["semantic"](processed_modalities)
        
        # Structural alignment
        structural_alignment = await self.alignment_algorithms["structural"](processed_modalities)
        
        # Apply alignments to each modality
        for modality, processed_data in processed_modalities.items():
            if processed_data.get("status") == "success":
                aligned_data = processed_data.copy()
                aligned_data["alignment"] = {
                    "temporal": temporal_alignment.get(modality, {}),
                    "semantic": semantic_alignment.get(modality, {}),
                    "structural": structural_alignment.get(modality, {})
                }
                aligned_modalities[modality] = aligned_data
        
        return aligned_modalities
    
    async def _early_fusion(
        self, 
        aligned_modalities: Dict[DataModality, Dict[str, Any]]
    ) -> UnifiedContext:
        """Perform early fusion of modalities."""
        
        # Combine features at input level
        fused_features = {}
        modality_contributions = {}
        confidence_scores = []
        
        for modality, data in aligned_modalities.items():
            if data.get("status") == "success":
                # Extract features from each modality
                features = data.get("features", {})
                fused_features[modality.value] = features
                modality_contributions[modality.value] = data.get("confidence", 0.5)
                confidence_scores.append(data.get("confidence", 0.5))
        
        # Create unified representation
        unified_representation = {
            "fusion_type": "early_fusion",
            "modality_features": fused_features,
            "combined_embedding": await self._combine_embeddings(fused_features),
            "cross_modal_patterns": await self._detect_cross_modal_patterns(fused_features)
        }
        
        return UnifiedContext(
            id=f"early_fusion_{int(time.time())}",
            source_inputs=list(fused_features.keys()),
            fused_representation=unified_representation,
            modality_contributions=modality_contributions,
            temporal_alignment={},
            semantic_consistency=await self._calculate_semantic_consistency(fused_features),
            fusion_strategy=FusionStrategy.EARLY_FUSION,
            confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0
        )
    
    async def _late_fusion(
        self, 
        aligned_modalities: Dict[DataModality, Dict[str, Any]]
    ) -> UnifiedContext:
        """Perform late fusion of modalities."""
        
        # Process each modality to high-level representations
        high_level_representations = {}
        modality_contributions = {}
        confidence_scores = []
        
        for modality, data in aligned_modalities.items():
            if data.get("status") == "success":
                # Extract semantic representations
                representation = data.get("semantic_representation", {})
                high_level_representations[modality.value] = representation
                modality_contributions[modality.value] = data.get("confidence", 0.5)
                confidence_scores.append(data.get("confidence", 0.5))
        
        # Fuse at semantic level
        unified_representation = {
            "fusion_type": "late_fusion",
            "semantic_representations": high_level_representations,
            "fused_semantics": await self._fuse_semantics(high_level_representations),
            "consensus_features": await self._extract_consensus_features(high_level_representations)
        }
        
        return UnifiedContext(
            id=f"late_fusion_{int(time.time())}",
            source_inputs=list(high_level_representations.keys()),
            fused_representation=unified_representation,
            modality_contributions=modality_contributions,
            temporal_alignment={},
            semantic_consistency=await self._calculate_semantic_consistency(high_level_representations),
            fusion_strategy=FusionStrategy.LATE_FUSION,
            confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0
        )
    
    async def _hybrid_fusion(
        self, 
        aligned_modalities: Dict[DataModality, Dict[str, Any]]
    ) -> UnifiedContext:
        """Perform hybrid fusion combining early and late fusion."""
        
        # Early fusion for complementary features
        early_fused = await self._early_fusion(aligned_modalities)
        
        # Late fusion for semantic alignment
        late_fused = await self._late_fusion(aligned_modalities)
        
        # Combine both approaches
        hybrid_representation = {
            "fusion_type": "hybrid_fusion",
            "early_fusion": early_fused.fused_representation,
            "late_fusion": late_fused.fused_representation,
            "combined_features": await self._combine_fusion_results(early_fused, late_fused),
            "adaptive_weights": await self._calculate_adaptive_weights(aligned_modalities)
        }
        
        # Merge contributions and confidence
        combined_contributions = {}
        for modality in aligned_modalities.keys():
            early_contrib = early_fused.modality_contributions.get(modality.value, 0)
            late_contrib = late_fused.modality_contributions.get(modality.value, 0)
            combined_contributions[modality.value] = (early_contrib + late_contrib) / 2
        
        return UnifiedContext(
            id=f"hybrid_fusion_{int(time.time())}",
            source_inputs=list(combined_contributions.keys()),
            fused_representation=hybrid_representation,
            modality_contributions=combined_contributions,
            temporal_alignment={},
            semantic_consistency=(early_fused.semantic_consistency + late_fused.semantic_consistency) / 2,
            fusion_strategy=FusionStrategy.HYBRID_FUSION,
            confidence_aggregate=(early_fused.confidence_aggregate + late_fused.confidence_aggregate) / 2
        )
    
    async def _attention_based_fusion(
        self, 
        aligned_modalities: Dict[DataModality, Dict[str, Any]]
    ) -> UnifiedContext:
        """Perform attention-based fusion."""
        
        # Calculate attention weights for each modality
        attention_weights = await self._calculate_attention_weights(aligned_modalities)
        
        # Apply attention-based fusion
        fused_features = {}
        modality_contributions = {}
        confidence_scores = []
        
        for modality, data in aligned_modalities.items():
            if data.get("status") == "success":
                modality_weight = attention_weights.get(modality, 0.5)
                features = data.get("features", {})
                
                # Apply attention weighting
                weighted_features = {}
                for feature_name, feature_value in features.items():
                    if isinstance(feature_value, (int, float)):
                        weighted_features[feature_name] = feature_value * modality_weight
                    else:
                        weighted_features[feature_name] = feature_value
                
                fused_features[modality.value] = weighted_features
                modality_contributions[modality.value] = modality_weight
                confidence_scores.append(data.get("confidence", 0.5) * modality_weight)
        
        unified_representation = {
            "fusion_type": "attention_based",
            "attention_weights": attention_weights,
            "weighted_features": fused_features,
            "attention_mechanism": "dynamic_modality_weighting"
        }
        
        return UnifiedContext(
            id=f"attention_fusion_{int(time.time())}",
            source_inputs=list(fused_features.keys()),
            fused_representation=unified_representation,
            modality_contributions=modality_contributions,
            temporal_alignment={},
            semantic_consistency=await self._calculate_semantic_consistency(fused_features),
            fusion_strategy=FusionStrategy.ATTENTION_BASED,
            confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0
        )
    
    async def _cross_attention_fusion(
        self, 
        aligned_modalities: Dict[DataModality, Dict[str, Any]]
    ) -> UnifiedContext:
        """Perform cross-attention fusion."""
        
        # Generate cross-attention matrices between modalities
        cross_attention_matrices = await self._calculate_cross_attention(aligned_modalities)
        
        # Apply cross-attention fusion
        fused_representations = {}
        modality_contributions = {}
        confidence_scores = []
        
        for modality, data in aligned_modalities.items():
            if data.get("status") == "success":
                # Get cross-attention with other modalities
                cross_attention = cross_attention_matrices.get(modality, {})
                features = data.get("features", {})
                
                # Apply cross-attention
                attended_features = {}
                for feature_name, feature_value in features.items():
                    if isinstance(feature_value, (int, float)):
                        attention_sum = sum(cross_attention.get(other_mod, 0) 
                                          for other_mod in aligned_modalities.keys() 
                                          if other_mod != modality)
                        attended_features[feature_name] = feature_value * (1 + attention_sum)
                    else:
                        attended_features[feature_name] = feature_value
                
                fused_representations[modality.value] = attended_features
                modality_contributions[modality.value] = data.get("confidence", 0.5)
                confidence_scores.append(data.get("confidence", 0.5))
        
        unified_representation = {
            "fusion_type": "cross_attention",
            "cross_attention_matrices": cross_attention_matrices,
            "attended_features": fused_representations,
            "inter_modal_relationships": await self._analyze_inter_modal_relationships(aligned_modalities)
        }
        
        return UnifiedContext(
            id=f"cross_attention_{int(time.time())}",
            source_inputs=list(fused_representations.keys()),
            fused_representation=unified_representation,
            modality_contributions=modality_contributions,
            temporal_alignment={},
            semantic_consistency=await self._calculate_semantic_consistency(fused_representations),
            fusion_strategy=FusionStrategy.CROSS_ATTENTION,
            confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0
        )
    
    async def _validate_unified_context(self, context: UnifiedContext) -> UnifiedContext:
        """Validate and enhance unified context."""
        
        # Check for consistency issues
        issues = []
        
        if context.semantic_consistency < 0.3:
            issues.append("Low semantic consistency detected")
        
        if context.confidence_aggregate < 0.4:
            issues.append("Low aggregate confidence")
        
        if len(context.source_inputs) < 2:
            issues.append("Insufficient modalities for robust fusion")
        
        # Enhance context if issues are found
        if issues:
            context.fused_representation["validation_issues"] = issues
            context.fused_representation["enhancement_applied"] = True
            
            # Apply enhancement strategies
            if context.semantic_consistency < 0.5:
                context.semantic_consistency = min(0.8, context.semantic_consistency * 1.2)
            
            if context.confidence_aggregate < 0.5:
                context.confidence_aggregate = min(0.8, context.confidence_aggregate * 1.1)
        
        return context
    
    # Helper methods for fusion strategies
    
    async def _combine_embeddings(self, features: Dict[str, Any]) -> Dict[str, Any]:
        """Combine embeddings from different modalities."""
        combined = {}
        for modality, modality_features in features.items():
            for feature_name, feature_value in modality_features.items():
                combined_key = f"{modality}_{feature_name}"
                combined[combined_key] = feature_value
        return combined
    
    async def _detect_cross_modal_patterns(self, features: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Detect patterns across modalities."""
        patterns = []
        modalities = list(features.keys())
        
        # Simple pattern detection
        for i, mod1 in enumerate(modalities):
            for mod2 in modalities[i+1:]:
                # Check for correlated features
                mod1_features = features[mod1]
                mod2_features = features[mod2]
                
                common_features = set(mod1_features.keys()) & set(mod2_features.keys())
                if common_features:
                    patterns.append({
                        "modalities": [mod1, mod2],
                        "common_features": list(common_features),
                        "correlation_strength": 0.7  # Simplified
                    })
        
        return patterns
    
    async def _fuse_semantics(self, representations: Dict[str, Any]) -> Dict[str, Any]:
        """Fuse semantic representations."""
        # Simple semantic fusion
        fused_semantics = {}
        
        # Extract common semantic elements
        all_semantics = []
        for modality, representation in representations.items():
            if isinstance(representation, dict):
                all_semantics.extend(representation.keys())
        
        common_semantics = list(set(all_semantics))
        
        for semantic in common_semantics:
            values = []
            for modality, representation in representations.items():
                if semantic in representation:
                    values.append(representation[semantic])
            
            if values:
                if all(isinstance(v, (int, float)) for v in values):
                    fused_semantics[semantic] = np.mean(values)
                else:
                    fused_semantics[semantic] = values[0]  # Take first non-numeric value
        
        return fused_semantics
    
    async def _extract_consensus_features(self, representations: Dict[str, Any]) -> Dict[str, Any]:
        """Extract features with high consensus across modalities."""
        consensus_features = {}
        
        # Find features present in multiple modalities
        feature_counts = defaultdict(int)
        for modality, representation in representations.items():
            if isinstance(representation, dict):
                for feature in representation.keys():
                    feature_counts[feature] += 1
        
        # Select features with high consensus
        threshold = len(representations) * 0.5
        consensus_features = {
            feature: self._get_consensus_value(feature, representations)
            for feature, count in feature_counts.items()
            if count >= threshold
        }
        
        return consensus_features
    
    def _get_consensus_value(self, feature: str, representations: Dict[str, Any]) -> Any:
        """Get consensus value for a feature across modalities."""
        values = []
        for modality, representation in representations.items():
            if isinstance(representation, dict) and feature in representation:
                values.append(representation[feature])
        
        if not values:
            return None
        
        if all(isinstance(v, (int, float)) for v in values):
            return np.mean(values)
        else:
            # For non-numeric values, return most common
            from collections import Counter
            value_counts = Counter(values)
            return value_counts.most_common(1)[0][0]
    
    async def _combine_fusion_results(self, early_fused: UnifiedContext, late_fused: UnifiedContext) -> Dict[str, Any]:
        """Combine early and late fusion results."""
        return {
            "early_features": early_fused.fused_representation.get("combined_embedding", {}),
            "late_semantics": late_fused.fused_representation.get("fused_semantics", {}),
            "combined_score": (early_fused.confidence_aggregate + late_fused.confidence_aggregate) / 2
        }
    
    async def _calculate_adaptive_weights(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[str, float]:
        """Calculate adaptive weights for modalities."""
        weights = {}
        
        for modality, data in modalities.items():
            if data.get("status") == "success":
                # Base weight on confidence and data quality
                confidence = data.get("confidence", 0.5)
                quality = data.get("quality_score", 0.5)
                weights[modality] = (confidence + quality) / 2
            else:
                weights[modality] = 0.1  # Low weight for failed processing
        
        # Normalize weights
        total_weight = sum(weights.values())
        if total_weight > 0:
            weights = {k: v / total_weight for k, v in weights.items()}
        
        return weights
    
    async def _calculate_attention_weights(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, float]:
        """Calculate attention weights for modalities."""
        weights = {}
        
        for modality, data in modalities.items():
            if data.get("status") == "success":
                # Attention based on relevance and information content
                confidence = data.get("confidence", 0.5)
                info_content = data.get("information_content", 0.5)
                weights[modality] = confidence * info_content
            else:
                weights[modality] = 0.1
        
        # Apply softmax-like normalization
        total_weight = sum(weights.values())
        if total_weight > 0:
            weights = {k: v / total_weight for k, v in weights.items()}
        
        return weights
    
    async def _calculate_cross_attention(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[DataModality, float]]:
        """Calculate cross-attention between modalities."""
        cross_attention = {}
        
        modalities_list = list(modalities.keys())
        
        for i, mod1 in enumerate(modalities_list):
            cross_attention[mod1] = {}
            for mod2 in modalities_list:
                if mod1 != mod2:
                    # Calculate attention based on feature similarity
                    similarity = await self._calculate_modality_similarity(modalities[mod1], modalities[mod2])
                    cross_attention[mod1][mod2] = similarity
                else:
                    cross_attention[mod1][mod2] = 0.0
        
        return cross_attention
    
    async def _calculate_modality_similarity(self, mod1_data: Dict[str, Any], mod2_data: Dict[str, Any]]) -> float:
        """Calculate similarity between two modalities."""
        if mod1_data.get("status") != "success" or mod2_data.get("status") != "success":
            return 0.0
        
        # Simple similarity based on confidence correlation
        conf1 = mod1_data.get("confidence", 0.5)
        conf2 = mod2_data.get("confidence", 0.5)
        
        # Similar confidence levels indicate related content
        similarity = 1 - abs(conf1 - conf2)
        return max(0.0, similarity)
    
    async def _analyze_inter_modal_relationships(self, modalities: Dict[DataModality, Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Analyze relationships between modalities."""
        relationships = []
        
        modalities_list = list(modalities.keys())
        
        for i, mod1 in enumerate(modalities_list):
            for mod2 in modalities_list[i+1:]:
                data1 = modalities[mod1]
                data2 = modalities[mod2]
                
                if data1.get("status") == "success" and data2.get("status") == "success":
                    similarity = await self._calculate_modality_similarity(data1, data2)
                    
                    relationships.append({
                        "modalities": [mod1.value, mod2.value],
                        "relationship_type": "complementary" if similarity > 0.7 else "independent",
                        "strength": similarity,
                        "temporal_alignment": await self._check_temporal_alignment(data1, data2)
                    })
        
        return relationships
    
    async def _check_temporal_alignment(self, data1: Dict[str, Any], data2: Dict[str, Any]]) -> float:
        """Check temporal alignment between modalities."""
        # Simplified temporal alignment check
        timestamp1 = data1.get("timestamp", datetime.utcnow())
        timestamp2 = data2.get("timestamp", datetime.utcnow())
        
        time_diff = abs((timestamp1 - timestamp2).total_seconds())
        # Normalize by 1 hour
        alignment_score = max(0, 1 - time_diff / 3600)
        return alignment_score
    
    async def _calculate_semantic_consistency(self, representations: Dict[str, Any]]) -> float:
        """Calculate semantic consistency across modalities."""
        if not representations:
            return 0.0
        
        # Simple consistency calculation
        consistency_scores = []
        
        # Check for semantic overlap
        all_semantics = []
        for modality, representation in representations.items():
            if isinstance(representation, dict):
                all_semantics.append(set(representation.keys()))
        
        if len(all_semantics) > 1:
            # Calculate Jaccard similarity between semantic sets
            for i in range(len(all_semantics)):
                for j in range(i+1, len(all_semantics)):
                    intersection = len(all_semantics[i] & all_semantics[j])
                    union = len(all_semantics[i] | all_semantics[j])
                    if union > 0:
                        consistency_scores.append(intersection / union)
        
        return np.mean(consistency_scores) if consistency_scores else 0.5
    
    # Alignment algorithms
    
    async def _temporal_alignment(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[str, Any]]:
        """Align modalities temporally."""
        alignment_results = {}
        
        for modality, data in modalities.items():
            if data.get("status") == "success":
                timestamp = data.get("timestamp", datetime.utcnow())
                alignment_results[modality] = {
                    "timestamp": timestamp.isoformat(),
                    "time_category": self._categorize_time(timestamp),
                    "temporal_priority": self._calculate_temporal_priority(timestamp)
                }
        
        return alignment_results
    
    def _categorize_time(self, timestamp: datetime) -> str:
        """Categorize timestamp into time categories."""
        now = datetime.utcnow()
        age_seconds = (now - timestamp).total_seconds()
        
        if age_seconds < 60:
            return "immediate"
        elif age_seconds < 3600:
            return "recent"
        elif age_seconds < 86400:
            return "today"
        else:
            return "historical"
    
    def _calculate_temporal_priority(self, timestamp: datetime) -> float:
        """Calculate temporal priority (recent = high priority)."""
        now = datetime.utcnow()
        age_seconds = (now - timestamp).total_seconds()
        return max(0, 1 - age_seconds / 86400)  # Decay over 24 hours
    
    async def _semantic_alignment(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[str, Any]]:
        """Align modalities semantically."""
        alignment_results = {}
        
        for modality, data in modalities.items():
            if data.get("status") == "success":
                features = data.get("features", {})
                semantic_tags = data.get("semantic_tags", [])
                
                alignment_results[modality] = {
                    "semantic_tags": semantic_tags,
                    "dominant_concepts": await self._extract_dominant_concepts(features),
                    "semantic_density": len(semantic_tags) / max(len(features), 1)
                }
        
        return alignment_results
    
    async def _extract_dominant_concepts(self, features: Dict[str, Any]) -> List[str]:
        """Extract dominant concepts from features."""
        # Simple concept extraction based on feature names and values
        concepts = []
        
        for feature_name, feature_value in features.items():
            if isinstance(feature_value, str) and len(feature_value) > 3:
                concepts.append(feature_value.lower())
            elif isinstance(feature_name, str) and len(feature_name) > 3:
                concepts.append(feature_name.lower())
        
        return list(set(concepts))[:5]  # Top 5 concepts
    
    async def _structural_alignment(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[str, Any]]:
        """Align modalities structurally."""
        alignment_results = {}
        
        for modality, data in modalities.items():
            if data.get("status") == "success":
                features = data.get("features", {})
                
                alignment_results[modality] = {
                    "structure_type": self._determine_structure_type(features),
                    "complexity_score": self._calculate_complexity_score(features),
                    "organization_pattern": self._identify_organization_pattern(features)
                }
        
        return alignment_results
    
    def _determine_structure_type(self, features: Dict[str, Any]) -> str:
        """Determine the structural type of the data."""
        if not features:
            return "minimal"
        
        # Simple structure detection
        if all(isinstance(v, (int, float)) for v in features.values()):
            return "numerical"
        elif all(isinstance(v, str) for v in features.values()):
            return "textual"
        elif len(features) > 10:
            return "complex"
        else:
            return "simple"
    
    def _calculate_complexity_score(self, features: Dict[str, Any]) -> float:
        """Calculate complexity score of the data structure."""
        if not features:
            return 0.0
        
        # Simple complexity based on feature count and type diversity
        type_counts = defaultdict(int)
        for value in features.values():
            type_counts[type(value).__name__] += 1
        
        type_diversity = len(type_counts)
        feature_count = len(features)
        
        # Normalize complexity score
        complexity = (feature_count / 20) * 0.6 + (type_diversity / 4) * 0.4
        return min(1.0, complexity)
    
    def _identify_organization_pattern(self, features: Dict[str, Any]) -> str:
        """Identify the organization pattern of the data."""
        if not features:
            return "none"
        
        # Simple pattern detection
        feature_names = list(features.keys())
        
        if any("time" in name.lower() or "date" in name.lower() for name in feature_names):
            return "temporal"
        elif any("category" in name.lower() or "type" in name.lower() for name in feature_names):
            return "categorical"
        elif any("value" in name.lower() or "amount" in name.lower() for name in feature_names):
            return "quantitative"
        else:
            return "mixed"


# Individual modality processors

class TextProcessor:
    """Processor for text modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        """Process text inputs."""
        if not inputs:
            return {"status": "error", "error": "No text inputs"}
        
        # Combine all text inputs
        combined_text = " ".join([inp.content for inp in inputs if isinstance(inp.content, str)])
        
        # Extract features
        features = {
            "text_length": len(combined_text),
            "word_count": len(combined_text.split()),
            "sentence_count": combined_text.count('.') + combined_text.count('!') + combined_text.count('?'),
            "complexity_score": self._calculate_text_complexity(combined_text)
        }
        
        # Generate semantic representation
        semantic_representation = await self._extract_text_semantics(combined_text)
        
        return {
            "status": "success",
            "features": features,
            "semantic_representation": semantic_representation,
            "confidence": np.mean([inp.confidence for inp in inputs]),
            "quality_score": np.mean([inp.quality_score for inp in inputs]),
            "timestamp": max(inp.timestamp for inp in inputs)
        }
    
    def _calculate_text_complexity(self, text: str) -> float:
        """Calculate text complexity score."""
        if not text:
            return 0.0
        
        words = text.split()
        avg_word_length = np.mean([len(word) for word in words]) if words else 0
        sentence_count = max(1, text.count('.') + text.count('!') + text.count('?'))
        avg_sentence_length = len(words) / sentence_count
        
        # Simple complexity calculation
        complexity = (avg_word_length / 10) * 0.4 + (avg_sentence_length / 20) * 0.6
        return min(1.0, complexity)
    
    async def _extract_text_semantics(self, text: str) -> Dict[str, Any]:
        """Extract semantic representation from text."""
        # Simple semantic extraction
        words = text.lower().split()
        
        # Extract key concepts (simplified)
        concepts = []
        for word in words:
            if len(word) > 4:  # Skip short words
                concepts.append(word)
        
        # Extract topics (simplified)
        topics = []
        if any(word in text.lower() for word in ["business", "company", "revenue"]):
            topics.append("business")
        if any(word in text.lower() for word in ["technology", "system", "software"]):
            topics.append("technology")
        if any(word in text.lower() for word in ["data", "information", "analysis"]):
            topics.append("data")
        
        return {
            "concepts": list(set(concepts))[:10],
            "topics": topics,
            "sentiment": self._analyze_sentiment(text),
            "entities": []  # Would use NER in production
        }
    
    def _analyze_sentiment(self, text: str) -> str:
        """Simple sentiment analysis."""
        positive_words = ["good", "great", "excellent", "positive", "happy", "success"]
        negative_words = ["bad", "terrible", "negative", "sad", "failure", "problem"]
        
        text_lower = text.lower()
        positive_count = sum(1 for word in positive_words if word in text_lower)
        negative_count = sum(1 for word in negative_words if word in text_lower)
        
        if positive_count > negative_count:
            return "positive"
        elif negative_count > positive_count:
            return "negative"
        else:
            return "neutral"


class ImageProcessor:
    """Processor for image modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        """Process image inputs."""
        if not inputs:
            return {"status": "error", "error": "No image inputs"}
        
        # Process first image (simplified)
        image_input = inputs[0]
        
        # Extract features (simplified)
        features = {
            "image_size": image_input.metadata.get("size", 0),
            "format": image_input.metadata.get("format", "unknown"),
            "color_diversity": image_input.metadata.get("color_diversity", 0.5),
            "complexity_score": image_input.metadata.get("complexity", 0.5)
        }
        
        # Generate semantic representation
        semantic_representation = await self._extract_image_semantics(image_input)
        
        return {
            "status": "success",
            "features": features,
            "semantic_representation": semantic_representation,
            "confidence": image_input.confidence,
            "quality_score": image_input.quality_score,
            "timestamp": image_input.timestamp
        }
    
    async def _extract_image_semantics(self, image_input: MultimodalInput) -> Dict[str, Any]:
        """Extract semantic representation from image."""
        # Simplified image semantic extraction
        metadata = image_input.metadata
        
        return {
            "objects": metadata.get("objects", []),
            "colors": metadata.get("dominant_colors", []),
            "scenes": metadata.get("scene_types", []),
            "text_content": metadata.get("extracted_text", ""),
            "visual_concepts": metadata.get("visual_concepts", [])
        }


class AudioProcessor:
    """Processor for audio modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        """Process audio inputs."""
        if not inputs:
            return {"status": "error", "error": "No audio inputs"}
        
        # Process first audio (simplified)
        audio_input = inputs[0]
        
        # Extract features
        features = {
            "duration": audio_input.metadata.get("duration", 0),
            "sample_rate": audio_input.metadata.get("sample_rate", 44100),
            "channels": audio_input.metadata.get("channels", 1),
            "frequency_content": audio_input.metadata.get("frequency_profile", {})
        }
        
        # Generate semantic representation
        semantic_representation = await self._extract_audio_semantics(audio_input)
        
        return {
            "status": "success",
            "features": features,
            "semantic_representation": semantic_representation,
            "confidence": audio_input.confidence,
            "quality_score": audio_input.quality_score,
            "timestamp": audio_input.timestamp
        }
    
    async def _extract_audio_semantics(self, audio_input: MultimodalInput) -> Dict[str, Any]:
        """Extract semantic representation from audio."""
        metadata = audio_input.metadata
        
        return {
            "speech_content": metadata.get("transcribed_text", ""),
            "speaker_count": metadata.get("speaker_count", 1),
            "emotion": metadata.get("emotion", "neutral"),
            "language": metadata.get("language", "unknown"),
            "audio_quality": metadata.get("quality_score", 0.5)
        }


# Additional processors would be implemented similarly...
class VideoProcessor:
    """Processor for video modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        return {"status": "success", "features": {}, "semantic_representation": {}}


class SensorProcessor:
    """Processor for sensor modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        return {"status": "success", "features": {}, "semantic_representation": {}}


class TableProcessor:
    """Processor for table modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        return {"status": "success", "features": {}, "semantic_representation": {}}


class CodeProcessor:
    """Processor for code modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        return {"status": "success", "features": {}, "semantic_representation": {}}


class StructuredProcessor:
    """Processor for structured data modality."""
    
    async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]:
        return {"status": "success", "features": {}, "semantic_representation": {}}


# Integration with main system
class MultimodalContextProcessor:
    """Integrated multimodal context processing system."""
    
    def __init__(self):
        self.multimodal_processor = MultimodalProcessor()
        
    async def process_multimodal_input(
        self,
        input_data: Dict[str, Any],
        fusion_strategy: FusionStrategy = FusionStrategy.HYBRID_FUSION
    ) -> Dict[str, Any]:
        """Process multimodal input and return unified context."""
        
        # Convert input data to MultimodalInput objects
        multimodal_inputs = []
        
        for modality_str, content_list in input_data.items():
            try:
                modality = DataModality(modality_str)
                if isinstance(content_list, list):
                    for content in content_list:
                        multimodal_input = MultimodalInput(
                            id=f"{modality_str}_{len(multimodal_inputs)}",
                            modality=modality,
                            content=content.get("content", content),
                            metadata=content.get("metadata", {}),
                            timestamp=datetime.utcnow(),
                            quality_score=content.get("quality_score", 0.8),
                            confidence=content.get("confidence", 0.8)
                        )
                        multimodal_inputs.append(multimodal_input)
                else:
                    multimodal_input = MultimodalInput(
                        id=f"{modality_str}_0",
                        modality=modality,
                        content=content_list.get("content", content_list),
                        metadata=content_list.get("metadata", {}),
                        timestamp=datetime.utcnow(),
                        quality_score=content_list.get("quality_score", 0.8),
                        confidence=content_list.get("confidence", 0.8)
                    )
                    multimodal_inputs.append(multimodal_input)
            except ValueError:
                logger.warning(f"Unknown modality: {modality_str}")
        
        # Process multimodal inputs
        unified_context = await self.multimodal_processor.process_multimodal_input(
            multimodal_inputs, fusion_strategy
        )
        
        return {
            "unified_context": {
                "id": unified_context.id,
                "fusion_strategy": unified_context.fusion_strategy.value,
                "modality_contributions": unified_context.modality_contributions,
                "semantic_consistency": unified_context.semantic_consistency,
                "confidence_aggregate": unified_context.confidence_aggregate,
                "fused_representation": unified_context.fused_representation
            },
            "processing_summary": {
                "modalities_processed": len(set(inp.modality for inp in multimodal_inputs)),
                "total_inputs": len(multimodal_inputs),
                "fusion_quality": unified_context.confidence_aggregate
            }
        }


if __name__ == "__main__":
    print("Multimodal Context Processing System Initialized")
    print("=" * 60)
    processor = MultimodalContextProcessor()
    print("Ready for advanced multimodal context processing and fusion!")