"""
Emergent Reasoning Module
------------------------
Implements emergent reasoning capabilities that arise from the interaction
of multiple reasoning strategies and patterns.
"""

from typing import Dict, Any, List, Optional
from .base import ReasoningStrategy
from .meta_learning import MetaLearningStrategy
from .chain_of_thought import ChainOfThoughtStrategy
from .tree_of_thoughts import TreeOfThoughtsStrategy

class EmergentReasoning(ReasoningStrategy):
    """
    A reasoning strategy that combines multiple approaches to discover
    emergent patterns and solutions.
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize emergent reasoning with component strategies."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
        
        # Initialize component strategies with shared config
        strategy_config = {
            'min_confidence': self.min_confidence,
            'parallel_threshold': self.parallel_threshold,
            'learning_rate': self.learning_rate,
            'strategy_weights': self.strategy_weights
        }
        
        self.meta_learner = MetaLearningStrategy(strategy_config)
        self.chain_of_thought = ChainOfThoughtStrategy(strategy_config)
        self.tree_of_thoughts = TreeOfThoughtsStrategy(strategy_config)
        
        # Configure weights for strategy combination
        self.weights = self.config.get('combination_weights', {
            'meta': 0.4,
            'chain': 0.3,
            'tree': 0.3
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Apply emergent reasoning by combining multiple strategies and
        identifying patterns that emerge from their interaction.
        
        Args:
            query: The input query to reason about
            context: Additional context and parameters
            
        Returns:
            Dict containing reasoning results and confidence scores
        """
        try:
            # Get results from each strategy
            meta_result = await self.meta_learner.reason(query, context)
            chain_result = await self.chain_of_thought.reason(query, context)
            tree_result = await self.tree_of_thoughts.reason(query, context)
            
            # Combine results with weighted averaging
            combined_answer = self._combine_results([
                (meta_result.get('answer', ''), self.weights['meta']),
                (chain_result.get('answer', ''), self.weights['chain']), 
                (tree_result.get('answer', ''), self.weights['tree'])
            ])
            
            # Calculate overall confidence
            confidence = (
                meta_result.get('confidence', 0) * self.weights['meta'] +
                chain_result.get('confidence', 0) * self.weights['chain'] +
                tree_result.get('confidence', 0) * self.weights['tree']
            )
            
            return {
                'answer': combined_answer,
                'confidence': confidence,
                'reasoning_path': {
                    'meta': meta_result.get('reasoning_path'),
                    'chain': chain_result.get('reasoning_path'),
                    'tree': tree_result.get('reasoning_path')
                },
                'emergent_patterns': self._identify_patterns([
                    meta_result, chain_result, tree_result
                ])
            }
            
        except Exception as e:
            return {
                'error': f"Emergent reasoning failed: {str(e)}",
                'confidence': 0.0
            }
    
    def _combine_results(self, weighted_results: List[tuple[str, float]]) -> str:
        """Combine multiple reasoning results with weights."""
        if not weighted_results:
            return ""
            
        # For now, use the highest weighted result
        return max(weighted_results, key=lambda x: x[1])[0]
    
    def _identify_patterns(self, results: List[Dict[str, Any]]) -> List[str]:
        """Identify common patterns across different reasoning strategies."""
        patterns = []
        
        # Extract common themes or conclusions
        answers = [r.get('answer', '') for r in results if r.get('answer')]
        if len(set(answers)) == 1:
            patterns.append("All strategies reached the same conclusion")
        elif len(set(answers)) < len(answers):
            patterns.append("Some strategies converged on similar conclusions")
            
        # Look for common confidence patterns
        confidences = [r.get('confidence', 0) for r in results]
        avg_confidence = sum(confidences) / len(confidences) if confidences else 0
        if avg_confidence > 0.8:
            patterns.append("High confidence across all strategies")
        elif avg_confidence < 0.3:
            patterns.append("Low confidence across strategies")
            
        return patterns