"""
Multimodal reasoning capabilities for SE-AGI
Handles reasoning across text, images, audio, and other modalities
"""

import asyncio
import logging
from typing import Dict, List, Optional, Any, Union, Tuple
from dataclasses import dataclass
from datetime import datetime
import json
import base64


@dataclass
class ModalityData:
    """Data structure for different modalities"""
    modality_type: str  # text, image, audio, video, etc.
    content: Any
    metadata: Dict[str, Any]
    confidence: float = 1.0
    timestamp: datetime = None
    
    def __post_init__(self):
        if self.timestamp is None:
            self.timestamp = datetime.now()


@dataclass
class ReasoningResult:
    """Result of multimodal reasoning"""
    conclusion: str
    confidence: float
    evidence: List[str]
    modality_contributions: Dict[str, float]
    reasoning_chain: List[str]
    metadata: Dict[str, Any]
    timestamp: datetime = None
    
    def __post_init__(self):
        if self.timestamp is None:
            self.timestamp = datetime.now()


class MultiModalReasoner:
    """
    Multimodal reasoning engine that can process and reason across
    different types of input modalities
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize multimodal reasoner"""
        self.config = config or {}
        self.logger = logging.getLogger(__name__)
        
        # Supported modalities
        self.supported_modalities = {
            'text', 'image', 'audio', 'video', 'structured_data'
        }
        
        # Reasoning strategies
        self.reasoning_strategies = {
            'logical': self._logical_reasoning,
            'analogical': self._analogical_reasoning,
            'causal': self._causal_reasoning,
            'temporal': self._temporal_reasoning,
            'spatial': self._spatial_reasoning,
            'cross_modal': self._cross_modal_reasoning
        }
        
        # Context and memory
        self.reasoning_context: List[ReasoningResult] = []
        self.modality_cache: Dict[str, ModalityData] = {}
        
        self.logger.info("MultiModalReasoner initialized")
    
    async def reason(self, 
                    inputs: List[ModalityData], 
                    strategy: str = 'logical',
                    context: Optional[Dict[str, Any]] = None) -> ReasoningResult:
        """
        Perform multimodal reasoning on given inputs
        
        Args:
            inputs: List of modality data to reason over
            strategy: Reasoning strategy to use
            context: Additional context for reasoning
            
        Returns:
            ReasoningResult with conclusions and evidence
        """
        self.logger.info(f"Starting multimodal reasoning with strategy: {strategy}")
        
        try:
            # Validate inputs
            validated_inputs = await self._validate_inputs(inputs)
            
            # Extract features from each modality
            features = await self._extract_features(validated_inputs)
            
            # Apply reasoning strategy
            if strategy in self.reasoning_strategies:
                result = await self.reasoning_strategies[strategy](features, context)
            else:
                raise ValueError(f"Unknown reasoning strategy: {strategy}")
            
            # Store result in context
            self.reasoning_context.append(result)
            
            self.logger.info("Multimodal reasoning completed successfully")
            return result
            
        except Exception as e:
            self.logger.error(f"Error in multimodal reasoning: {str(e)}")
            return ReasoningResult(
                conclusion=f"Reasoning failed: {str(e)}",
                confidence=0.0,
                evidence=[],
                modality_contributions={},
                reasoning_chain=[f"Error: {str(e)}"],
                metadata={'error': True}
            )
    
    async def _validate_inputs(self, inputs: List[ModalityData]) -> List[ModalityData]:
        """Validate and preprocess inputs"""
        validated = []
        
        for input_data in inputs:
            if input_data.modality_type not in self.supported_modalities:
                self.logger.warning(f"Unsupported modality: {input_data.modality_type}")
                continue
                
            # Validate content based on modality
            if await self._validate_modality_content(input_data):
                validated.append(input_data)
            else:
                self.logger.warning(f"Invalid content for modality: {input_data.modality_type}")
        
        return validated
    
    async def _validate_modality_content(self, data: ModalityData) -> bool:
        """Validate content for specific modality"""
        try:
            if data.modality_type == 'text':
                return isinstance(data.content, str) and len(data.content.strip()) > 0
            elif data.modality_type == 'image':
                # Basic validation for image data
                return data.content is not None
            elif data.modality_type == 'audio':
                return data.content is not None
            elif data.modality_type == 'structured_data':
                return isinstance(data.content, (dict, list))
            else:
                return True
        except Exception:
            return False
    
    async def _extract_features(self, inputs: List[ModalityData]) -> Dict[str, Any]:
        """Extract features from each modality"""
        features = {
            'text_features': [],
            'image_features': [],
            'audio_features': [],
            'structured_features': [],
            'temporal_features': [],
            'spatial_features': []
        }
        
        for input_data in inputs:
            if input_data.modality_type == 'text':
                text_features = await self._extract_text_features(input_data)
                features['text_features'].append(text_features)
                
            elif input_data.modality_type == 'image':
                image_features = await self._extract_image_features(input_data)
                features['image_features'].append(image_features)
                
            elif input_data.modality_type == 'audio':
                audio_features = await self._extract_audio_features(input_data)
                features['audio_features'].append(audio_features)
                
            elif input_data.modality_type == 'structured_data':
                struct_features = await self._extract_structured_features(input_data)
                features['structured_features'].append(struct_features)
        
        return features
    
    async def _extract_text_features(self, data: ModalityData) -> Dict[str, Any]:
        """Extract features from text data"""
        content = data.content
        
        features = {
            'length': len(content),
            'word_count': len(content.split()),
            'sentences': content.count('.') + content.count('!') + content.count('?'),
            'keywords': await self._extract_keywords(content),
            'entities': await self._extract_entities(content),
            'sentiment': await self._analyze_sentiment(content),
            'topics': await self._extract_topics(content)
        }
        
        return features
    
    async def _extract_image_features(self, data: ModalityData) -> Dict[str, Any]:
        """Extract features from image data"""
        # Placeholder for image analysis
        features = {
            'objects': [],
            'colors': [],
            'composition': {},
            'text_in_image': "",
            'visual_concepts': []
        }
        
        # In a real implementation, this would use computer vision models
        self.logger.info("Image feature extraction (placeholder)")
        
        return features
    
    async def _extract_audio_features(self, data: ModalityData) -> Dict[str, Any]:
        """Extract features from audio data"""
        # Placeholder for audio analysis
        features = {
            'duration': 0.0,
            'transcription': "",
            'speaker_info': {},
            'audio_quality': {},
            'emotions': []
        }
        
        # In a real implementation, this would use speech recognition and audio analysis
        self.logger.info("Audio feature extraction (placeholder)")
        
        return features
    
    async def _extract_structured_features(self, data: ModalityData) -> Dict[str, Any]:
        """Extract features from structured data"""
        content = data.content
        
        features = {
            'data_type': type(content).__name__,
            'size': len(content) if hasattr(content, '__len__') else 1,
            'keys': list(content.keys()) if isinstance(content, dict) else [],
            'values_types': self._analyze_value_types(content),
            'patterns': await self._detect_patterns(content)
        }
        
        return features
    
    async def _logical_reasoning(self, features: Dict[str, Any], context: Optional[Dict[str, Any]]) -> ReasoningResult:
        """Perform logical reasoning"""
        reasoning_chain = ["Starting logical reasoning"]
        evidence = []
        modality_contributions = {}
        
        # Analyze text features for logical content
        if features['text_features']:
            for text_feat in features['text_features']:
                if 'keywords' in text_feat:
                    logical_indicators = [kw for kw in text_feat['keywords'] 
                                        if kw.lower() in ['therefore', 'because', 'since', 'thus', 'hence']]
                    if logical_indicators:
                        evidence.append(f"Logical indicators found: {logical_indicators}")
                        reasoning_chain.append(f"Detected logical structure: {logical_indicators}")
            
            modality_contributions['text'] = 0.8
        
        # Combine evidence from all modalities
        if features['structured_features']:
            reasoning_chain.append("Analyzing structured data patterns")
            modality_contributions['structured'] = 0.6
        
        # Generate conclusion
        if evidence:
            conclusion = f"Logical analysis indicates: {'; '.join(evidence)}"
            confidence = min(sum(modality_contributions.values()) / len(modality_contributions), 1.0)
        else:
            conclusion = "No clear logical patterns detected"
            confidence = 0.3
        
        return ReasoningResult(
            conclusion=conclusion,
            confidence=confidence,
            evidence=evidence,
            modality_contributions=modality_contributions,
            reasoning_chain=reasoning_chain,
            metadata={'strategy': 'logical'}
        )
    
    async def _analogical_reasoning(self, features: Dict[str, Any], context: Optional[Dict[str, Any]]) -> ReasoningResult:
        """Perform analogical reasoning"""
        reasoning_chain = ["Starting analogical reasoning"]
        evidence = []
        modality_contributions = {}
        
        # Look for analogies in text
        if features['text_features']:
            for text_feat in features['text_features']:
                analogy_indicators = ['like', 'similar to', 'as if', 'resembles', 'analogous']
                # Simple pattern matching for analogies
                evidence.append("Potential analogical structures detected")
                reasoning_chain.append("Searching for analogical patterns")
            
            modality_contributions['text'] = 0.7
        
        conclusion = "Analogical reasoning completed"
        confidence = 0.6
        
        return ReasoningResult(
            conclusion=conclusion,
            confidence=confidence,
            evidence=evidence,
            modality_contributions=modality_contributions,
            reasoning_chain=reasoning_chain,
            metadata={'strategy': 'analogical'}
        )
    
    async def _causal_reasoning(self, features: Dict[str, Any], context: Optional[Dict[str, Any]]) -> ReasoningResult:
        """Perform causal reasoning"""
        reasoning_chain = ["Starting causal reasoning"]
        evidence = []
        modality_contributions = {}
        
        # Look for causal relationships
        if features['text_features']:
            causal_indicators = ['causes', 'leads to', 'results in', 'because of', 'due to']
            reasoning_chain.append("Analyzing causal relationships")
            modality_contributions['text'] = 0.8
        
        conclusion = "Causal analysis completed"
        confidence = 0.5
        
        return ReasoningResult(
            conclusion=conclusion,
            confidence=confidence,
            evidence=evidence,
            modality_contributions=modality_contributions,
            reasoning_chain=reasoning_chain,
            metadata={'strategy': 'causal'}
        )
    
    async def _temporal_reasoning(self, features: Dict[str, Any], context: Optional[Dict[str, Any]]) -> ReasoningResult:
        """Perform temporal reasoning"""
        reasoning_chain = ["Starting temporal reasoning"]
        evidence = []
        modality_contributions = {}
        
        # Analyze temporal aspects
        reasoning_chain.append("Analyzing temporal relationships")
        conclusion = "Temporal analysis completed"
        confidence = 0.5
        
        return ReasoningResult(
            conclusion=conclusion,
            confidence=confidence,
            evidence=evidence,
            modality_contributions=modality_contributions,
            reasoning_chain=reasoning_chain,
            metadata={'strategy': 'temporal'}
        )
    
    async def _spatial_reasoning(self, features: Dict[str, Any], context: Optional[Dict[str, Any]]) -> ReasoningResult:
        """Perform spatial reasoning"""
        reasoning_chain = ["Starting spatial reasoning"]
        evidence = []
        modality_contributions = {}
        
        # Analyze spatial relationships
        if features['image_features']:
            reasoning_chain.append("Analyzing spatial relationships in images")
            modality_contributions['image'] = 0.9
        
        conclusion = "Spatial analysis completed"
        confidence = 0.5
        
        return ReasoningResult(
            conclusion=conclusion,
            confidence=confidence,
            evidence=evidence,
            modality_contributions=modality_contributions,
            reasoning_chain=reasoning_chain,
            metadata={'strategy': 'spatial'}
        )
    
    async def _cross_modal_reasoning(self, features: Dict[str, Any], context: Optional[Dict[str, Any]]) -> ReasoningResult:
        """Perform cross-modal reasoning"""
        reasoning_chain = ["Starting cross-modal reasoning"]
        evidence = []
        modality_contributions = {}
        
        # Analyze relationships between modalities
        active_modalities = [k for k, v in features.items() if v]
        reasoning_chain.append(f"Analyzing relationships between: {active_modalities}")
        
        for modality in active_modalities:
            if features[modality]:
                modality_contributions[modality.replace('_features', '')] = 0.6
        
        conclusion = f"Cross-modal analysis of {len(active_modalities)} modalities completed"
        confidence = min(len(active_modalities) * 0.2, 1.0)
        
        return ReasoningResult(
            conclusion=conclusion,
            confidence=confidence,
            evidence=evidence,
            modality_contributions=modality_contributions,
            reasoning_chain=reasoning_chain,
            metadata={'strategy': 'cross_modal'}
        )
    
    # Helper methods for feature extraction
    async def _extract_keywords(self, text: str) -> List[str]:
        """Extract keywords from text"""
        # Simple keyword extraction
        words = text.lower().split()
        # Filter out common words (simple stopword removal)
        stopwords = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'is', 'are', 'was', 'were'}
        keywords = [word.strip('.,!?;:') for word in words if word not in stopwords and len(word) > 3]
        return list(set(keywords))[:10]  # Return top 10 unique keywords
    
    async def _extract_entities(self, text: str) -> List[str]:
        """Extract named entities from text"""
        # Placeholder for named entity recognition
        # In a real implementation, this would use NLP libraries
        return []
    
    async def _analyze_sentiment(self, text: str) -> Dict[str, float]:
        """Analyze sentiment of text"""
        # Simple sentiment analysis placeholder
        positive_words = ['good', 'great', 'excellent', 'amazing', 'wonderful', 'fantastic']
        negative_words = ['bad', 'terrible', 'awful', 'horrible', 'disgusting', 'hate']
        
        words = text.lower().split()
        positive_score = sum(1 for word in words if word in positive_words)
        negative_score = sum(1 for word in words if word in negative_words)
        
        total = positive_score + negative_score
        if total == 0:
            return {'positive': 0.5, 'negative': 0.5, 'neutral': 0.0}
        
        return {
            'positive': positive_score / total,
            'negative': negative_score / total,
            'neutral': 1.0 - (positive_score + negative_score) / len(words)
        }
    
    async def _extract_topics(self, text: str) -> List[str]:
        """Extract topics from text"""
        # Simple topic extraction placeholder
        keywords = await self._extract_keywords(text)
        return keywords[:5]  # Return top 5 as topics
    
    def _analyze_value_types(self, data: Any) -> Dict[str, int]:
        """Analyze types of values in structured data"""
        types_count = {}
        
        if isinstance(data, dict):
            for value in data.values():
                type_name = type(value).__name__
                types_count[type_name] = types_count.get(type_name, 0) + 1
        elif isinstance(data, list):
            for item in data:
                type_name = type(item).__name__
                types_count[type_name] = types_count.get(type_name, 0) + 1
        
        return types_count
    
    async def _detect_patterns(self, data: Any) -> List[str]:
        """Detect patterns in structured data"""
        patterns = []
        
        if isinstance(data, dict):
            patterns.append(f"Dictionary with {len(data)} keys")
            if data:
                patterns.append(f"Key types: {set(type(k).__name__ for k in data.keys())}")
        elif isinstance(data, list):
            patterns.append(f"List with {len(data)} items")
            if data:
                patterns.append(f"Item types: {set(type(item).__name__ for item in data)}")
        
        return patterns
    
    def get_reasoning_history(self) -> List[ReasoningResult]:
        """Get history of reasoning results"""
        return self.reasoning_context.copy()
    
    def clear_context(self) -> None:
        """Clear reasoning context"""
        self.reasoning_context.clear()
        self.modality_cache.clear()
        self.logger.info("Reasoning context cleared")
