"""
Advanced Agent Performance Service
Implements meta-learning, resource optimization, and performance enhancement for OpenClaw agents
"""

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 (
    AgentPerformanceProfile, MetaLearningModel, ResourceAllocation,
    PerformanceOptimization, AgentCapability, FusionModel,
    ReinforcementLearningConfig, CreativeCapability,
    LearningStrategy, PerformanceMetric, ResourceType,
    OptimizationTarget
)

logger = logging.getLogger(__name__)


class MetaLearningEngine:
    """Advanced meta-learning system for rapid skill acquisition"""
    
    def __init__(self):
        self.meta_algorithms = {
            'model_agnostic_meta_learning': self.maml_algorithm,
            'reptile': self.reptile_algorithm,
            'meta_sgd': self.meta_sgd_algorithm,
            'prototypical_networks': self.prototypical_algorithm
        }
        
        self.adaptation_strategies = {
            'fast_adaptation': self.fast_adaptation,
            'gradual_adaptation': self.gradual_adaptation,
            'transfer_adaptation': self.transfer_adaptation,
            'multi_task_adaptation': self.multi_task_adaptation
        }
        
        self.performance_metrics = [
            PerformanceMetric.ACCURACY,
            PerformanceMetric.ADAPTATION_SPEED,
            PerformanceMetric.GENERALIZATION,
            PerformanceMetric.RESOURCE_EFFICIENCY
        ]
    
    async def create_meta_learning_model(
        self, 
        session: Session,
        model_name: str,
        base_algorithms: List[str],
        meta_strategy: LearningStrategy,
        adaptation_targets: List[str]
    ) -> MetaLearningModel:
        """Create a new meta-learning model"""
        
        model_id = f"meta_{uuid4().hex[:8]}"
        
        # Initialize meta-features based on adaptation targets
        meta_features = self.generate_meta_features(adaptation_targets)
        
        # Set up task distributions for meta-training
        task_distributions = self.setup_task_distributions(adaptation_targets)
        
        model = MetaLearningModel(
            model_id=model_id,
            model_name=model_name,
            base_algorithms=base_algorithms,
            meta_strategy=meta_strategy,
            adaptation_targets=adaptation_targets,
            meta_features=meta_features,
            task_distributions=task_distributions,
            status="training"
        )
        
        session.add(model)
        session.commit()
        session.refresh(model)
        
        # Start meta-training process
        asyncio.create_task(self.train_meta_model(session, model_id))
        
        logger.info(f"Created meta-learning model {model_id} with strategy {meta_strategy.value}")
        return model
    
    async def train_meta_model(self, session: Session, model_id: str) -> Dict[str, Any]:
        """Train a meta-learning model"""
        
        model = session.exec(
            select(MetaLearningModel).where(MetaLearningModel.model_id == model_id)
        ).first()
        
        if not model:
            raise ValueError(f"Meta-learning model {model_id} not found")
        
        try:
            # Simulate meta-training process
            training_results = await self.simulate_meta_training(model)
            
            # Update model with training results
            model.meta_accuracy = training_results['accuracy']
            model.adaptation_speed = training_results['adaptation_speed']
            model.generalization_ability = training_results['generalization']
            model.training_time = training_results['training_time']
            model.computational_cost = training_results['computational_cost']
            model.status = "ready"
            model.trained_at = datetime.utcnow()
            
            session.commit()
            
            logger.info(f"Meta-learning model {model_id} training completed")
            return training_results
            
        except Exception as e:
            logger.error(f"Error training meta-model {model_id}: {str(e)}")
            model.status = "failed"
            session.commit()
            raise
    
    async def simulate_meta_training(self, model: MetaLearningModel) -> Dict[str, Any]:
        """Simulate meta-training process"""
        
        # Simulate training time based on complexity
        base_time = 2.0  # hours
        complexity_multiplier = len(model.base_algorithms) * 0.5
        training_time = base_time * complexity_multiplier
        
        # Simulate computational cost
        computational_cost = training_time * 10.0  # cost units
        
        # Simulate performance metrics
        meta_accuracy = 0.75 + (len(model.adaptation_targets) * 0.05)
        adaptation_speed = 0.8 + (len(model.meta_features) * 0.02)
        generalization = 0.7 + (len(model.task_distributions) * 0.03)
        
        # Cap values at 1.0
        meta_accuracy = min(1.0, meta_accuracy)
        adaptation_speed = min(1.0, adaptation_speed)
        generalization = min(1.0, generalization)
        
        return {
            'accuracy': meta_accuracy,
            'adaptation_speed': adaptation_speed,
            'generalization': generalization,
            'training_time': training_time,
            'computational_cost': computational_cost,
            'convergence_epoch': int(training_time * 10)
        }
    
    def generate_meta_features(self, adaptation_targets: List[str]) -> List[str]:
        """Generate meta-features for adaptation targets"""
        
        meta_features = []
        
        for target in adaptation_targets:
            if target == "text_generation":
                meta_features.extend(["text_length", "complexity", "domain", "style"])
            elif target == "image_generation":
                meta_features.extend(["resolution", "style", "content_type", "complexity"])
            elif target == "reasoning":
                meta_features.extend(["logic_type", "complexity", "domain", "step_count"])
            elif target == "classification":
                meta_features.extend(["feature_count", "class_count", "data_type", "imbalance"])
            else:
                meta_features.extend(["complexity", "domain", "data_size", "quality"])
        
        return list(set(meta_features))
    
    def setup_task_distributions(self, adaptation_targets: List[str]) -> Dict[str, float]:
        """Set up task distributions for meta-training"""
        
        distributions = {}
        total_targets = len(adaptation_targets)
        
        for i, target in enumerate(adaptation_targets):
            # Distribute weights evenly with slight variations
            base_weight = 1.0 / total_targets
            variation = (i - total_targets / 2) * 0.1
            distributions[target] = max(0.1, base_weight + variation)
        
        return distributions
    
    async def adapt_to_new_task(
        self, 
        session: Session,
        model_id: str,
        task_data: Dict[str, Any],
        adaptation_steps: int = 10
    ) -> Dict[str, Any]:
        """Adapt meta-learning model to new task"""
        
        model = session.exec(
            select(MetaLearningModel).where(MetaLearningModel.model_id == model_id)
        ).first()
        
        if not model:
            raise ValueError(f"Meta-learning model {model_id} not found")
        
        if model.status != "ready":
            raise ValueError(f"Model {model_id} is not ready for adaptation")
        
        try:
            # Simulate adaptation process
            adaptation_results = await self.simulate_adaptation(model, task_data, adaptation_steps)
            
            # Update deployment count and success rate
            model.deployment_count += 1
            model.success_rate = (model.success_rate * (model.deployment_count - 1) + adaptation_results['success']) / model.deployment_count
            
            session.commit()
            
            logger.info(f"Model {model_id} adapted to new task with success rate {adaptation_results['success']:.2f}")
            return adaptation_results
            
        except Exception as e:
            logger.error(f"Error adapting model {model_id}: {str(e)}")
            raise
    
    async def simulate_adaptation(
        self, 
        model: MetaLearningModel, 
        task_data: Dict[str, Any], 
        steps: int
    ) -> Dict[str, Any]:
        """Simulate adaptation to new task"""
        
        # Calculate adaptation success based on model capabilities
        base_success = model.meta_accuracy * model.adaptation_speed
        
        # Factor in task similarity (simplified)
        task_similarity = 0.8  # Would calculate based on meta-features
        
        # Calculate adaptation success
        adaptation_success = base_success * task_similarity * (1.0 - (0.1 / steps))
        
        # Calculate adaptation time
        adaptation_time = steps * 0.1  # seconds per step
        
        return {
            'success': adaptation_success,
            'adaptation_time': adaptation_time,
            'steps_used': steps,
            'final_performance': adaptation_success * 0.9,  # Slight degradation
            'convergence_achieved': adaptation_success > 0.7
        }
    
    def maml_algorithm(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Model-Agnostic Meta-Learning algorithm"""
        
        # Simplified MAML implementation
        return {
            'algorithm': 'MAML',
            'inner_learning_rate': 0.01,
            'outer_learning_rate': 0.001,
            'inner_steps': 5,
            'meta_batch_size': 32
        }
    
    def reptile_algorithm(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Reptile algorithm implementation"""
        
        return {
            'algorithm': 'Reptile',
            'inner_learning_rate': 0.1,
            'meta_batch_size': 20,
            'inner_steps': 1,
            'epsilon': 1.0
        }
    
    def meta_sgd_algorithm(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Meta-SGD algorithm implementation"""
        
        return {
            'algorithm': 'Meta-SGD',
            'learning_rate': 0.01,
            'momentum': 0.9,
            'weight_decay': 0.0001
        }
    
    def prototypical_algorithm(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Prototypical Networks algorithm"""
        
        return {
            'algorithm': 'Prototypical',
            'embedding_size': 128,
            'distance_metric': 'euclidean',
            'support_shots': 5,
            'query_shots': 10
        }
    
    def fast_adaptation(self, model: MetaLearningModel, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Fast adaptation strategy"""
        
        return {
            'strategy': 'fast_adaptation',
            'learning_rate': 0.01,
            'steps': 5,
            'adaptation_speed': 0.9
        }
    
    def gradual_adaptation(self, model: MetaLearningModel, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Gradual adaptation strategy"""
        
        return {
            'strategy': 'gradual_adaptation',
            'learning_rate': 0.005,
            'steps': 20,
            'adaptation_speed': 0.7
        }
    
    def transfer_adaptation(self, model: MetaLearningModel, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Transfer learning adaptation"""
        
        return {
            'strategy': 'transfer_adaptation',
            'source_tasks': model.adaptation_targets,
            'transfer_rate': 0.8,
            'fine_tuning_steps': 10
        }
    
    def multi_task_adaptation(self, model: MetaLearningModel, task_data: Dict[str, Any]) -> Dict[str, Any]:
        """Multi-task adaptation"""
        
        return {
            'strategy': 'multi_task_adaptation',
            'task_weights': model.task_distributions,
            'shared_layers': 3,
            'task_specific_layers': 2
        }


class ResourceManager:
    """Self-optimizing resource management system"""
    
    def __init__(self):
        self.optimization_algorithms = {
            'genetic_algorithm': self.genetic_optimization,
            'simulated_annealing': self.simulated_annealing,
            'gradient_descent': self.gradient_optimization,
            'bayesian_optimization': self.bayesian_optimization
        }
        
        self.resource_constraints = {
            ResourceType.CPU: {'min': 0.5, 'max': 16.0, 'step': 0.5},
            ResourceType.MEMORY: {'min': 1.0, 'max': 64.0, 'step': 1.0},
            ResourceType.GPU: {'min': 0.0, 'max': 8.0, 'step': 1.0},
            ResourceType.STORAGE: {'min': 10.0, 'max': 1000.0, 'step': 10.0},
            ResourceType.NETWORK: {'min': 10.0, 'max': 1000.0, 'step': 10.0}
        }
    
    async def allocate_resources(
        self, 
        session: Session,
        agent_id: str,
        task_requirements: Dict[str, Any],
        optimization_target: OptimizationTarget = OptimizationTarget.EFFICIENCY
    ) -> ResourceAllocation:
        """Allocate and optimize resources for agent task"""
        
        allocation_id = f"alloc_{uuid4().hex[:8]}"
        
        # Calculate initial resource requirements
        initial_allocation = self.calculate_initial_allocation(task_requirements)
        
        # Optimize allocation based on target
        optimized_allocation = await self.optimize_allocation(
            initial_allocation, task_requirements, optimization_target
        )
        
        allocation = ResourceAllocation(
            allocation_id=allocation_id,
            agent_id=agent_id,
            cpu_cores=optimized_allocation[ResourceType.CPU],
            memory_gb=optimized_allocation[ResourceType.MEMORY],
            gpu_count=optimized_allocation[ResourceType.GPU],
            gpu_memory_gb=optimized_allocation.get('gpu_memory', 0.0),
            storage_gb=optimized_allocation[ResourceType.STORAGE],
            network_bandwidth=optimized_allocation[ResourceType.NETWORK],
            optimization_target=optimization_target,
            status="allocated",
            allocated_at=datetime.utcnow()
        )
        
        session.add(allocation)
        session.commit()
        session.refresh(allocation)
        
        logger.info(f"Allocated resources for agent {agent_id} with target {optimization_target.value}")
        return allocation
    
    def calculate_initial_allocation(self, task_requirements: Dict[str, Any]) -> Dict[ResourceType, float]:
        """Calculate initial resource allocation based on task requirements"""
        
        allocation = {
            ResourceType.CPU: 2.0,
            ResourceType.MEMORY: 4.0,
            ResourceType.GPU: 0.0,
            ResourceType.STORAGE: 50.0,
            ResourceType.NETWORK: 100.0
        }
        
        # Adjust based on task type
        task_type = task_requirements.get('task_type', 'general')
        
        if task_type == 'inference':
            allocation[ResourceType.CPU] = 4.0
            allocation[ResourceType.MEMORY] = 8.0
            allocation[ResourceType.GPU] = 1.0 if task_requirements.get('model_size') == 'large' else 0.0
            allocation[ResourceType.NETWORK] = 200.0
            
        elif task_type == 'training':
            allocation[ResourceType.CPU] = 8.0
            allocation[ResourceType.MEMORY] = 16.0
            allocation[ResourceType.GPU] = 2.0
            allocation[ResourceType.STORAGE] = 200.0
            allocation[ResourceType.NETWORK] = 500.0
            
        elif task_type == 'text_generation':
            allocation[ResourceType.CPU] = 2.0
            allocation[ResourceType.MEMORY] = 6.0
            allocation[ResourceType.GPU] = 0.0
            allocation[ResourceType.NETWORK] = 50.0
            
        elif task_type == 'image_generation':
            allocation[ResourceType.CPU] = 4.0
            allocation[ResourceType.MEMORY] = 12.0
            allocation[ResourceType.GPU] = 1.0
            allocation[ResourceType.STORAGE] = 100.0
            allocation[ResourceType.NETWORK] = 100.0
        
        # Adjust based on workload size
        workload_factor = task_requirements.get('workload_factor', 1.0)
        for resource_type in allocation:
            allocation[resource_type] *= workload_factor
        
        return allocation
    
    async def optimize_allocation(
        self, 
        initial_allocation: Dict[ResourceType, float],
        task_requirements: Dict[str, Any],
        target: OptimizationTarget
    ) -> Dict[ResourceType, float]:
        """Optimize resource allocation based on target"""
        
        if target == OptimizationTarget.SPEED:
            return await self.optimize_for_speed(initial_allocation, task_requirements)
        elif target == OptimizationTarget.ACCURACY:
            return await self.optimize_for_accuracy(initial_allocation, task_requirements)
        elif target == OptimizationTarget.EFFICIENCY:
            return await self.optimize_for_efficiency(initial_allocation, task_requirements)
        elif target == OptimizationTarget.COST:
            return await self.optimize_for_cost(initial_allocation, task_requirements)
        else:
            return initial_allocation
    
    async def optimize_for_speed(
        self, 
        allocation: Dict[ResourceType, float], 
        task_requirements: Dict[str, Any]
    ) -> Dict[ResourceType, float]:
        """Optimize allocation for speed"""
        
        optimized = allocation.copy()
        
        # Increase CPU and memory for faster processing
        optimized[ResourceType.CPU] = min(
            self.resource_constraints[ResourceType.CPU]['max'],
            optimized[ResourceType.CPU] * 1.5
        )
        optimized[ResourceType.MEMORY] = min(
            self.resource_constraints[ResourceType.MEMORY]['max'],
            optimized[ResourceType.MEMORY] * 1.3
        )
        
        # Add GPU if available and beneficial
        if task_requirements.get('task_type') in ['inference', 'image_generation']:
            optimized[ResourceType.GPU] = min(
                self.resource_constraints[ResourceType.GPU]['max'],
                max(optimized[ResourceType.GPU], 1.0)
            )
        
        return optimized
    
    async def optimize_for_accuracy(
        self, 
        allocation: Dict[ResourceType, float], 
        task_requirements: Dict[str, Any]
    ) -> Dict[ResourceType, float]:
        """Optimize allocation for accuracy"""
        
        optimized = allocation.copy()
        
        # Increase memory for larger models
        optimized[ResourceType.MEMORY] = min(
            self.resource_constraints[ResourceType.MEMORY]['max'],
            optimized[ResourceType.MEMORY] * 2.0
        )
        
        # Add GPU for compute-intensive tasks
        if task_requirements.get('task_type') in ['training', 'inference']:
            optimized[ResourceType.GPU] = min(
                self.resource_constraints[ResourceType.GPU]['max'],
                max(optimized[ResourceType.GPU], 2.0)
            )
            optimized[ResourceType.GPU_MEMORY_GB] = optimized[ResourceType.GPU] * 8.0
        
        return optimized
    
    async def optimize_for_efficiency(
        self, 
        allocation: Dict[ResourceType, float], 
        task_requirements: Dict[str, Any]
    ) -> Dict[ResourceType, float]:
        """Optimize allocation for efficiency"""
        
        optimized = allocation.copy()
        
        # Find optimal balance between resources
        task_type = task_requirements.get('task_type', 'general')
        
        if task_type == 'text_generation':
            # Text generation is CPU-efficient
            optimized[ResourceType.CPU] = max(
                self.resource_constraints[ResourceType.CPU]['min'],
                optimized[ResourceType.CPU] * 0.8
            )
            optimized[ResourceType.GPU] = 0.0
            
        elif task_type == 'inference':
            # Moderate GPU usage for inference
            optimized[ResourceType.GPU] = min(
                self.resource_constraints[ResourceType.GPU]['max'],
                max(0.5, optimized[ResourceType.GPU] * 0.7)
            )
        
        return optimized
    
    async def optimize_for_cost(
        self, 
        allocation: Dict[ResourceType, float], 
        task_requirements: Dict[str, Any]
    ) -> Dict[ResourceType, float]:
        """Optimize allocation for cost"""
        
        optimized = allocation.copy()
        
        # Minimize expensive resources
        optimized[ResourceType.GPU] = 0.0
        optimized[ResourceType.CPU] = max(
            self.resource_constraints[ResourceType.CPU]['min'],
            optimized[ResourceType.CPU] * 0.5
        )
        optimized[ResourceType.MEMORY] = max(
            self.resource_constraints[ResourceType.MEMORY]['min'],
            optimized[ResourceType.MEMORY] * 0.7
        )
        
        return optimized
    
    def genetic_optimization(self, allocation: Dict[ResourceType, float]) -> Dict[str, Any]:
        """Genetic algorithm for resource optimization"""
        
        return {
            'algorithm': 'genetic_algorithm',
            'population_size': 50,
            'generations': 100,
            'mutation_rate': 0.1,
            'crossover_rate': 0.8
        }
    
    def simulated_annealing(self, allocation: Dict[ResourceType, float]) -> Dict[str, Any]:
        """Simulated annealing optimization"""
        
        return {
            'algorithm': 'simulated_annealing',
            'initial_temperature': 100.0,
            'cooling_rate': 0.95,
            'iterations': 1000
        }
    
    def gradient_optimization(self, allocation: Dict[ResourceType, float]) -> Dict[str, Any]:
        """Gradient descent optimization"""
        
        return {
            'algorithm': 'gradient_descent',
            'learning_rate': 0.01,
            'iterations': 500,
            'momentum': 0.9
        }
    
    def bayesian_optimization(self, allocation: Dict[ResourceType, float]) -> Dict[str, Any]:
        """Bayesian optimization"""
        
        return {
            'algorithm': 'bayesian_optimization',
            'acquisition_function': 'expected_improvement',
            'iterations': 50,
            'exploration_weight': 0.1
        }


class PerformanceOptimizer:
    """Advanced performance optimization system"""
    
    def __init__(self):
        self.optimization_techniques = {
            'hyperparameter_tuning': self.tune_hyperparameters,
            'architecture_optimization': self.optimize_architecture,
            'algorithm_selection': self.select_algorithm,
            'data_optimization': self.optimize_data_pipeline
        }
        
        self.performance_targets = {
            PerformanceMetric.ACCURACY: {'weight': 0.3, 'target': 0.95},
            PerformanceMetric.LATENCY: {'weight': 0.25, 'target': 100.0},  # ms
            PerformanceMetric.THROUGHPUT: {'weight': 0.2, 'target': 100.0},
            PerformanceMetric.RESOURCE_EFFICIENCY: {'weight': 0.15, 'target': 0.8},
            PerformanceMetric.COST_EFFICIENCY: {'weight': 0.1, 'target': 0.9}
        }
    
    async def optimize_agent_performance(
        self, 
        session: Session,
        agent_id: str,
        target_metric: PerformanceMetric,
        current_performance: Dict[str, float]
    ) -> PerformanceOptimization:
        """Optimize agent performance for specific metric"""
        
        optimization_id = f"opt_{uuid4().hex[:8]}"
        
        # Create optimization record
        optimization = PerformanceOptimization(
            optimization_id=optimization_id,
            agent_id=agent_id,
            optimization_type="comprehensive",
            target_metric=target_metric,
            baseline_performance=current_performance,
            baseline_cost=self.calculate_cost(current_performance),
            status="running"
        )
        
        session.add(optimization)
        session.commit()
        session.refresh(optimization)
        
        try:
            # Run optimization process
            optimization_results = await self.run_optimization_process(
                agent_id, target_metric, current_performance
            )
            
            # Update optimization with results
            optimization.optimized_performance = optimization_results['performance']
            optimization.optimized_resources = optimization_results['resources']
            optimization.optimized_cost = optimization_results['cost']
            optimization.performance_improvement = optimization_results['improvement']
            optimization.resource_savings = optimization_results['savings']
            optimization.cost_savings = optimization_results['cost_savings']
            optimization.overall_efficiency_gain = optimization_results['efficiency_gain']
            optimization.optimization_duration = optimization_results['duration']
            optimization.iterations_required = optimization_results['iterations']
            optimization.convergence_achieved = optimization_results['converged']
            optimization.optimization_applied = True
            optimization.status = "completed"
            optimization.completed_at = datetime.utcnow()
            
            session.commit()
            
            logger.info(f"Performance optimization {optimization_id} completed for agent {agent_id}")
            return optimization
            
        except Exception as e:
            logger.error(f"Error optimizing performance for agent {agent_id}: {str(e)}")
            optimization.status = "failed"
            session.commit()
            raise
    
    async def run_optimization_process(
        self, 
        agent_id: str, 
        target_metric: PerformanceMetric,
        current_performance: Dict[str, float]
    ) -> Dict[str, Any]:
        """Run comprehensive optimization process"""
        
        start_time = datetime.utcnow()
        
        # Step 1: Analyze current performance
        analysis_results = self.analyze_current_performance(current_performance, target_metric)
        
        # Step 2: Generate optimization candidates
        candidates = await self.generate_optimization_candidates(target_metric, analysis_results)
        
        # Step 3: Evaluate candidates
        best_candidate = await self.evaluate_candidates(candidates, target_metric)
        
        # Step 4: Apply optimization
        applied_performance = await self.apply_optimization(best_candidate)
        
        # Step 5: Calculate improvements
        improvements = self.calculate_improvements(current_performance, applied_performance)
        
        end_time = datetime.utcnow()
        duration = (end_time - start_time).total_seconds()
        
        return {
            'performance': applied_performance,
            'resources': best_candidate.get('resources', {}),
            'cost': self.calculate_cost(applied_performance),
            'improvement': improvements['overall'],
            'savings': improvements['resource'],
            'cost_savings': improvements['cost'],
            'efficiency_gain': improvements['efficiency'],
            'duration': duration,
            'iterations': len(candidates),
            'converged': improvements['overall'] > 0.05
        }
    
    def analyze_current_performance(
        self, 
        current_performance: Dict[str, float], 
        target_metric: PerformanceMetric
    ) -> Dict[str, Any]:
        """Analyze current performance to identify bottlenecks"""
        
        analysis = {
            'current_value': current_performance.get(target_metric.value, 0.0),
            'target_value': self.performance_targets[target_metric]['target'],
            'gap': 0.0,
            'bottlenecks': [],
            'improvement_potential': 0.0
        }
        
        # Calculate performance gap
        current_value = analysis['current_value']
        target_value = analysis['target_value']
        
        if target_metric == PerformanceMetric.ACCURACY:
            analysis['gap'] = target_value - current_value
            analysis['improvement_potential'] = min(1.0, analysis['gap'] / target_value)
        elif target_metric == PerformanceMetric.LATENCY:
            analysis['gap'] = current_value - target_value
            analysis['improvement_potential'] = min(1.0, analysis['gap'] / current_value)
        else:
            # For other metrics, calculate relative improvement
            analysis['gap'] = target_value - current_value
            analysis['improvement_potential'] = min(1.0, analysis['gap'] / target_value)
        
        # Identify bottlenecks
        if current_performance.get('cpu_utilization', 0) > 0.9:
            analysis['bottlenecks'].append('cpu')
        if current_performance.get('memory_utilization', 0) > 0.9:
            analysis['bottlenecks'].append('memory')
        if current_performance.get('gpu_utilization', 0) > 0.9:
            analysis['bottlenecks'].append('gpu')
        
        return analysis
    
    async def generate_optimization_candidates(
        self, 
        target_metric: PerformanceMetric, 
        analysis: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Generate optimization candidates"""
        
        candidates = []
        
        # Hyperparameter tuning candidate
        hp_candidate = await self.tune_hyperparameters(target_metric, analysis)
        candidates.append(hp_candidate)
        
        # Architecture optimization candidate
        arch_candidate = await self.optimize_architecture(target_metric, analysis)
        candidates.append(arch_candidate)
        
        # Algorithm selection candidate
        algo_candidate = await self.select_algorithm(target_metric, analysis)
        candidates.append(algo_candidate)
        
        # Data optimization candidate
        data_candidate = await self.optimize_data_pipeline(target_metric, analysis)
        candidates.append(data_candidate)
        
        return candidates
    
    async def evaluate_candidates(
        self, 
        candidates: List[Dict[str, Any]], 
        target_metric: PerformanceMetric
    ) -> Dict[str, Any]:
        """Evaluate optimization candidates and select best"""
        
        best_candidate = None
        best_score = 0.0
        
        for candidate in candidates:
            # Calculate expected performance improvement
            expected_improvement = candidate.get('expected_improvement', 0.0)
            resource_cost = candidate.get('resource_cost', 1.0)
            implementation_complexity = candidate.get('complexity', 0.5)
            
            # Calculate overall score
            score = (expected_improvement * 0.6 - 
                     resource_cost * 0.2 - 
                     implementation_complexity * 0.2)
            
            if score > best_score:
                best_score = score
                best_candidate = candidate
        
        return best_candidate or {}
    
    async def apply_optimization(self, candidate: Dict[str, Any]) -> Dict[str, float]:
        """Apply optimization and return expected performance"""
        
        # Simulate applying optimization
        base_performance = candidate.get('base_performance', {})
        improvement_factor = candidate.get('expected_improvement', 0.0)
        
        applied_performance = {}
        for metric, value in base_performance.items():
            if metric == candidate.get('target_metric'):
                applied_performance[metric] = value * (1.0 + improvement_factor)
            else:
                # Other metrics may change slightly
                applied_performance[metric] = value * (1.0 + improvement_factor * 0.1)
        
        return applied_performance
    
    def calculate_improvements(
        self, 
        baseline: Dict[str, float], 
        optimized: Dict[str, float]
    ) -> Dict[str, float]:
        """Calculate performance improvements"""
        
        improvements = {
            'overall': 0.0,
            'resource': 0.0,
            'cost': 0.0,
            'efficiency': 0.0
        }
        
        # Calculate overall improvement
        baseline_total = sum(baseline.values())
        optimized_total = sum(optimized.values())
        improvements['overall'] = (optimized_total - baseline_total) / baseline_total if baseline_total > 0 else 0.0
        
        # Calculate resource savings (simplified)
        baseline_resources = baseline.get('cpu_cores', 1.0) + baseline.get('memory_gb', 2.0)
        optimized_resources = optimized.get('cpu_cores', 1.0) + optimized.get('memory_gb', 2.0)
        improvements['resource'] = (baseline_resources - optimized_resources) / baseline_resources if baseline_resources > 0 else 0.0
        
        # Calculate cost savings
        baseline_cost = self.calculate_cost(baseline)
        optimized_cost = self.calculate_cost(optimized)
        improvements['cost'] = (baseline_cost - optimized_cost) / baseline_cost if baseline_cost > 0 else 0.0
        
        # Calculate efficiency gain
        improvements['efficiency'] = improvements['overall'] + improvements['resource'] + improvements['cost']
        
        return improvements
    
    def calculate_cost(self, performance: Dict[str, float]) -> float:
        """Calculate cost based on resource usage"""
        
        cpu_cost = performance.get('cpu_cores', 1.0) * 10.0  # $10 per core
        memory_cost = performance.get('memory_gb', 2.0) * 2.0  # $2 per GB
        gpu_cost = performance.get('gpu_count', 0.0) * 100.0  # $100 per GPU
        storage_cost = performance.get('storage_gb', 50.0) * 0.1  # $0.1 per GB
        
        return cpu_cost + memory_cost + gpu_cost + storage_cost
    
    async def tune_hyperparameters(
        self, 
        target_metric: PerformanceMetric, 
        analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Tune hyperparameters for performance optimization"""
        
        return {
            'technique': 'hyperparameter_tuning',
            'target_metric': target_metric.value,
            'parameters': {
                'learning_rate': 0.001,
                'batch_size': 64,
                'dropout_rate': 0.1,
                'weight_decay': 0.0001
            },
            'expected_improvement': 0.15,
            'resource_cost': 0.1,
            'complexity': 0.3
        }
    
    async def optimize_architecture(
        self, 
        target_metric: PerformanceMetric, 
        analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Optimize model architecture"""
        
        return {
            'technique': 'architecture_optimization',
            'target_metric': target_metric.value,
            'architecture': {
                'layers': [256, 128, 64],
                'activations': ['relu', 'relu', 'tanh'],
                'normalization': 'batch_norm'
            },
            'expected_improvement': 0.25,
            'resource_cost': 0.2,
            'complexity': 0.7
        }
    
    async def select_algorithm(
        self, 
        target_metric: PerformanceMetric, 
        analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Select optimal algorithm"""
        
        return {
            'technique': 'algorithm_selection',
            'target_metric': target_metric.value,
            'algorithm': 'transformer',
            'expected_improvement': 0.20,
            'resource_cost': 0.3,
            'complexity': 0.5
        }
    
    async def optimize_data_pipeline(
        self, 
        target_metric: PerformanceMetric, 
        analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Optimize data processing pipeline"""
        
        return {
            'technique': 'data_optimization',
            'target_metric': target_metric.value,
            'optimizations': {
                'data_augmentation': True,
                'batch_normalization': True,
                'early_stopping': True
            },
            'expected_improvement': 0.10,
            'resource_cost': 0.05,
            'complexity': 0.2
        }


class AgentPerformanceService:
    """Main service for advanced agent performance management"""
    
    def __init__(self, session: Session):
        self.session = session
        self.meta_learning_engine = MetaLearningEngine()
        self.resource_manager = ResourceManager()
        self.performance_optimizer = PerformanceOptimizer()
    
    async def create_performance_profile(
        self, 
        agent_id: str,
        agent_type: str = "openclaw",
        initial_metrics: Optional[Dict[str, float]] = None
    ) -> AgentPerformanceProfile:
        """Create comprehensive agent performance profile"""
        
        profile_id = f"perf_{uuid4().hex[:8]}"
        
        profile = AgentPerformanceProfile(
            profile_id=profile_id,
            agent_id=agent_id,
            agent_type=agent_type,
            performance_metrics=initial_metrics or {},
            learning_strategies=["meta_learning", "transfer_learning"],
            specialization_areas=["general"],
            expertise_levels={},
            performance_history=[],
            benchmark_scores={},
            created_at=datetime.utcnow()
        )
        
        self.session.add(profile)
        self.session.commit()
        self.session.refresh(profile)
        
        logger.info(f"Created performance profile {profile_id} for agent {agent_id}")
        return profile
    
    async def update_performance_metrics(
        self, 
        agent_id: str,
        new_metrics: Dict[str, float],
        task_context: Optional[Dict[str, Any]] = None
    ) -> AgentPerformanceProfile:
        """Update agent performance metrics"""
        
        profile = self.session.exec(
            select(AgentPerformanceProfile).where(AgentPerformanceProfile.agent_id == agent_id)
        ).first()
        
        if not profile:
            # Create profile if it doesn't exist
            profile = await self.create_performance_profile(agent_id, "openclaw", new_metrics)
        else:
            # Update existing profile
            profile.performance_metrics.update(new_metrics)
            
            # Add to performance history
            history_entry = {
                'timestamp': datetime.utcnow().isoformat(),
                'metrics': new_metrics,
                'context': task_context or {}
            }
            profile.performance_history.append(history_entry)
            
            # Calculate overall score
            profile.overall_score = self.calculate_overall_score(profile.performance_metrics)
            
            # Update trends
            profile.improvement_trends = self.calculate_improvement_trends(profile.performance_history)
            
            profile.updated_at = datetime.utcnow()
            profile.last_assessed = datetime.utcnow()
            
            self.session.commit()
        
        return profile
    
    def calculate_overall_score(self, metrics: Dict[str, float]) -> float:
        """Calculate overall performance score"""
        
        if not metrics:
            return 0.0
        
        # Weight different metrics
        weights = {
            'accuracy': 0.3,
            'latency': -0.2,  # Lower is better
            'throughput': 0.2,
            'efficiency': 0.15,
            'cost_efficiency': 0.15
        }
        
        score = 0.0
        total_weight = 0.0
        
        for metric, value in metrics.items():
            weight = weights.get(metric, 0.1)
            score += value * weight
            total_weight += weight
        
        return score / total_weight if total_weight > 0 else 0.0
    
    def calculate_improvement_trends(self, history: List[Dict[str, Any]]) -> Dict[str, float]:
        """Calculate performance improvement trends"""
        
        if len(history) < 2:
            return {}
        
        trends = {}
        
        # Get latest and previous metrics
        latest_metrics = history[-1]['metrics']
        previous_metrics = history[-2]['metrics']
        
        for metric in latest_metrics:
            if metric in previous_metrics:
                latest_value = latest_metrics[metric]
                previous_value = previous_metrics[metric]
                
                if previous_value != 0:
                    change = (latest_value - previous_value) / abs(previous_value)
                    trends[metric] = change
        
        return trends
    
    async def get_comprehensive_profile(
        self, 
        agent_id: str
    ) -> Dict[str, Any]:
        """Get comprehensive agent performance profile"""
        
        profile = self.session.exec(
            select(AgentPerformanceProfile).where(AgentPerformanceProfile.agent_id == agent_id)
        ).first()
        
        if not profile:
            return {'error': 'Profile not found'}
        
        return {
            'profile_id': profile.profile_id,
            'agent_id': profile.agent_id,
            'agent_type': profile.agent_type,
            'overall_score': profile.overall_score,
            'performance_metrics': profile.performance_metrics,
            'learning_strategies': profile.learning_strategies,
            'specialization_areas': profile.specialization_areas,
            'expertise_levels': profile.expertise_levels,
            'resource_efficiency': profile.resource_efficiency,
            'cost_per_task': profile.cost_per_task,
            'throughput': profile.throughput,
            'average_latency': profile.average_latency,
            'performance_history': profile.performance_history,
            'improvement_trends': profile.improvement_trends,
            'benchmark_scores': profile.benchmark_scores,
            'ranking_position': profile.ranking_position,
            'percentile_rank': profile.percentile_rank,
            'last_assessed': profile.last_assessed.isoformat() if profile.last_assessed else None
        }
