"""
Meta-Learning System
------------------
Implements meta-learning capabilities for improved learning and adaptation.
"""

from typing import Dict, Any, List, Optional, Tuple
import numpy as np
from dataclasses import dataclass, field
import logging
from datetime import datetime
from enum import Enum
import json
from quantum_learning import QuantumLearningSystem, Pattern, PatternType

class LearningStrategy(Enum):
    GRADIENT_BASED = "gradient_based"
    MEMORY_BASED = "memory_based"
    EVOLUTIONARY = "evolutionary"
    REINFORCEMENT = "reinforcement"
    QUANTUM = "quantum"

@dataclass
class MetaParameters:
    """Meta-parameters for learning strategies"""
    learning_rate: float = 0.01
    memory_size: int = 1000
    evolution_rate: float = 0.1
    exploration_rate: float = 0.2
    quantum_interference: float = 0.5
    adaptation_threshold: float = 0.7

@dataclass
class LearningMetrics:
    """Metrics for learning performance"""
    accuracy: float
    convergence_rate: float
    adaptation_speed: float
    resource_usage: float
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

class MetaLearningSystem:
    """Meta-learning system for optimizing learning strategies"""
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        self.logger = logging.getLogger(__name__)
        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 quantum system with shared config
        quantum_config = {
            'min_confidence': self.min_confidence,
            'parallel_threshold': self.parallel_threshold,
            'learning_rate': self.learning_rate,
            'strategy_weights': self.strategy_weights,
            'num_qubits': self.config.get('num_qubits', 8),
            'entanglement_strength': self.config.get('entanglement_strength', 0.5),
            'interference_threshold': self.config.get('interference_threshold', 0.3),
            'tunneling_rate': self.config.get('tunneling_rate', 0.1),
            'annealing_schedule': self.config.get('annealing_schedule', {
                'initial_temp': 1.0,
                'final_temp': 0.01,
                'steps': 100,
                'cooling_rate': 0.95
            })
        }
        self.quantum_system = QuantumLearningSystem(quantum_config)
        self.strategies = {}
        self.performance_history = []
        self.meta_parameters = MetaParameters()
        
    async def optimize_learning(
        self,
        observation: Dict[str, Any],
        current_strategy: LearningStrategy
    ) -> Tuple[Dict[str, Any], LearningMetrics]:
        """Optimize learning strategy based on observation"""
        try:
            # Process with quantum system
            quantum_result = await self.quantum_system.process_observation(observation)
            
            # Evaluate current strategy
            current_metrics = self._evaluate_strategy(
                current_strategy,
                observation,
                quantum_result
            )
            
            # Update performance history
            self._update_performance_history(current_metrics)
            
            # Adapt meta-parameters
            self._adapt_meta_parameters(current_metrics)
            
            # Select optimal strategy
            optimal_strategy = self._select_optimal_strategy(
                observation,
                current_metrics
            )
            
            # Apply selected strategy
            result = await self._apply_strategy(
                optimal_strategy,
                observation,
                quantum_result
            )
            
            return result, current_metrics
            
        except Exception as e:
            self.logger.error(f"Failed to optimize learning: {str(e)}")
            raise
            
    def _evaluate_strategy(
        self,
        strategy: LearningStrategy,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> LearningMetrics:
        """Evaluate performance of current learning strategy"""
        # Calculate accuracy
        accuracy = self._calculate_accuracy(
            strategy,
            observation,
            quantum_result
        )
        
        # Calculate convergence rate
        convergence_rate = self._calculate_convergence_rate(
            strategy,
            self.performance_history
        )
        
        # Calculate adaptation speed
        adaptation_speed = self._calculate_adaptation_speed(
            strategy,
            observation
        )
        
        # Calculate resource usage
        resource_usage = self._calculate_resource_usage(strategy)
        
        return LearningMetrics(
            accuracy=accuracy,
            convergence_rate=convergence_rate,
            adaptation_speed=adaptation_speed,
            resource_usage=resource_usage
        )
        
    def _update_performance_history(
        self,
        metrics: LearningMetrics
    ) -> None:
        """Update performance history with new metrics"""
        self.performance_history.append(metrics)
        
        # Maintain history size
        if len(self.performance_history) > self.meta_parameters.memory_size:
            self.performance_history.pop(0)
            
    def _adapt_meta_parameters(
        self,
        metrics: LearningMetrics
    ) -> None:
        """Adapt meta-parameters based on performance metrics"""
        # Adjust learning rate
        if metrics.convergence_rate < self.meta_parameters.adaptation_threshold:
            self.meta_parameters.learning_rate *= 0.9
        else:
            self.meta_parameters.learning_rate *= 1.1
            
        # Adjust memory size
        if metrics.resource_usage > 0.8:
            self.meta_parameters.memory_size = int(
                self.meta_parameters.memory_size * 0.9
            )
        elif metrics.resource_usage < 0.2:
            self.meta_parameters.memory_size = int(
                self.meta_parameters.memory_size * 1.1
            )
            
        # Adjust evolution rate
        if metrics.adaptation_speed < self.meta_parameters.adaptation_threshold:
            self.meta_parameters.evolution_rate *= 1.1
        else:
            self.meta_parameters.evolution_rate *= 0.9
            
        # Adjust exploration rate
        if metrics.accuracy < self.meta_parameters.adaptation_threshold:
            self.meta_parameters.exploration_rate *= 1.1
        else:
            self.meta_parameters.exploration_rate *= 0.9
            
        # Adjust quantum interference
        if metrics.accuracy > 0.8:
            self.meta_parameters.quantum_interference *= 1.1
        else:
            self.meta_parameters.quantum_interference *= 0.9
            
        # Ensure parameters stay within reasonable bounds
        self._normalize_parameters()
        
    def _normalize_parameters(self) -> None:
        """Normalize meta-parameters to stay within bounds"""
        self.meta_parameters.learning_rate = np.clip(
            self.meta_parameters.learning_rate,
            0.001,
            0.1
        )
        self.meta_parameters.memory_size = np.clip(
            self.meta_parameters.memory_size,
            100,
            10000
        )
        self.meta_parameters.evolution_rate = np.clip(
            self.meta_parameters.evolution_rate,
            0.01,
            0.5
        )
        self.meta_parameters.exploration_rate = np.clip(
            self.meta_parameters.exploration_rate,
            0.1,
            0.9
        )
        self.meta_parameters.quantum_interference = np.clip(
            self.meta_parameters.quantum_interference,
            0.1,
            0.9
        )
        
    def _select_optimal_strategy(
        self,
        observation: Dict[str, Any],
        metrics: LearningMetrics
    ) -> LearningStrategy:
        """Select optimal learning strategy"""
        strategies = list(LearningStrategy)
        scores = []
        
        for strategy in strategies:
            # Calculate strategy score
            score = self._calculate_strategy_score(
                strategy,
                observation,
                metrics
            )
            scores.append((strategy, score))
            
        # Select strategy with highest score
        optimal_strategy = max(scores, key=lambda x: x[1])[0]
        
        return optimal_strategy
        
    async def _apply_strategy(
        self,
        strategy: LearningStrategy,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply selected learning strategy"""
        if strategy == LearningStrategy.GRADIENT_BASED:
            return await self._apply_gradient_strategy(
                observation,
                quantum_result
            )
        elif strategy == LearningStrategy.MEMORY_BASED:
            return await self._apply_memory_strategy(
                observation,
                quantum_result
            )
        elif strategy == LearningStrategy.EVOLUTIONARY:
            return await self._apply_evolutionary_strategy(
                observation,
                quantum_result
            )
        elif strategy == LearningStrategy.REINFORCEMENT:
            return await self._apply_reinforcement_strategy(
                observation,
                quantum_result
            )
        else:  # QUANTUM
            return quantum_result
            
    def _calculate_accuracy(
        self,
        strategy: LearningStrategy,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> float:
        """Calculate accuracy of learning strategy"""
        if "patterns" not in quantum_result:
            return 0.0
            
        patterns = quantum_result["patterns"]
        if not patterns:
            return 0.0
            
        # Calculate pattern confidence
        confidence_sum = sum(pattern.confidence for pattern in patterns)
        return confidence_sum / len(patterns)
        
    def _calculate_convergence_rate(
        self,
        strategy: LearningStrategy,
        history: List[LearningMetrics]
    ) -> float:
        """Calculate convergence rate of learning strategy"""
        if not history:
            return 0.0
            
        # Calculate rate of improvement
        accuracies = [metrics.accuracy for metrics in history[-10:]]
        if len(accuracies) < 2:
            return 0.0
            
        differences = np.diff(accuracies)
        return float(np.mean(differences > 0))
        
    def _calculate_adaptation_speed(
        self,
        strategy: LearningStrategy,
        observation: Dict[str, Any]
    ) -> float:
        """Calculate adaptation speed of learning strategy"""
        if not self.performance_history:
            return 0.0
            
        # Calculate time to reach adaptation threshold
        threshold = self.meta_parameters.adaptation_threshold
        for i, metrics in enumerate(self.performance_history):
            if metrics.accuracy >= threshold:
                return 1.0 / (i + 1)
                
        return 0.0
        
    def _calculate_resource_usage(
        self,
        strategy: LearningStrategy
    ) -> float:
        """Calculate resource usage of learning strategy"""
        # Simulate resource usage based on strategy
        base_usage = {
            LearningStrategy.GRADIENT_BASED: 0.4,
            LearningStrategy.MEMORY_BASED: 0.6,
            LearningStrategy.EVOLUTIONARY: 0.7,
            LearningStrategy.REINFORCEMENT: 0.5,
            LearningStrategy.QUANTUM: 0.8
        }
        
        return base_usage[strategy]
        
    def _calculate_strategy_score(
        self,
        strategy: LearningStrategy,
        observation: Dict[str, Any],
        metrics: LearningMetrics
    ) -> float:
        """Calculate score for learning strategy"""
        # Weight different factors
        weights = {
            "accuracy": 0.4,
            "convergence": 0.2,
            "adaptation": 0.2,
            "resources": 0.2
        }
        
        score = (
            weights["accuracy"] * metrics.accuracy +
            weights["convergence"] * metrics.convergence_rate +
            weights["adaptation"] * metrics.adaptation_speed +
            weights["resources"] * (1 - metrics.resource_usage)
        )
        
        # Add exploration bonus
        if np.random.random() < self.meta_parameters.exploration_rate:
            score += 0.1
            
        return score
        
    async def _apply_gradient_strategy(
        self,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply gradient-based learning strategy"""
        return {
            "result": "gradient_optimization",
            "quantum_enhanced": quantum_result,
            "meta_parameters": self.meta_parameters.__dict__
        }
        
    async def _apply_memory_strategy(
        self,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply memory-based learning strategy"""
        return {
            "result": "memory_optimization",
            "quantum_enhanced": quantum_result,
            "meta_parameters": self.meta_parameters.__dict__
        }
        
    async def _apply_evolutionary_strategy(
        self,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply evolutionary learning strategy"""
        return {
            "result": "evolutionary_optimization",
            "quantum_enhanced": quantum_result,
            "meta_parameters": self.meta_parameters.__dict__
        }
        
    async def _apply_reinforcement_strategy(
        self,
        observation: Dict[str, Any],
        quantum_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply reinforcement learning strategy"""
        return {
            "result": "reinforcement_optimization",
            "quantum_enhanced": quantum_result,
            "meta_parameters": self.meta_parameters.__dict__
        }