#!/usr/bin/env python3
"""
Performance Optimizer for Swarm AI System

Optimize swarm performance and resource allocation.
"""

import time
from typing import Dict, List, Any, Optional
from swarm_agent_base import SwarmAgent
class PerformanceOptimizer:
    """
    Optimize swarm performance and resource allocation.
    """
    
    def __init__(self):
        self.resource_manager = ResourceManager()
        self.load_balancer = LoadBalancer()
        self.error_handler = ErrorHandler()
        
    def optimize(self, performance_metrics: Dict[str, Any]) -> Dict[str, Any]:
        """
        Optimize swarm performance based on metrics.

        Args:
            performance_metrics: Performance metrics

        Returns:
            Optimization results
        """
        # Analyze performance data
        analysis = self.analyze_performance(performance_metrics)
        
        # Generate optimization recommendations
        recommendations = self.generate_optimization_recommendations(analysis)
        
        # Apply optimizations
        self.apply_optimizations(recommendations)
        
        return {
            "analysis": analysis,
            "recommendations": recommendations,
            "optimization_effectiveness": self.calculate_optimization_effectiveness()
        }
        
    def analyze_performance(self, performance_metrics: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze performance metrics.

        Args:
            performance_metrics: Performance metrics

        Returns:
            Performance analysis
        """
        return {
            "bottlenecks": self.identify_bottlenecks(performance_metrics),
            "inefficiencies": self.identify_inefficiencies(performance_metrics),
            "optimization_opportunities": self.identify_optimization_opportunities(performance_metrics)
        }
        
    def identify_bottlenecks(self, performance_metrics: Dict[str, Any]) -> List[str]:
        """
        Identify performance bottlenecks.

        Args:
            performance_metrics: Performance metrics

        Returns:
            List of bottleneck identifiers
        """
        bottlenecks = []
        
        # Analyze agent performance
        agent_performance = performance_metrics.get("agent_performance", {})
        for agent_id, performance in agent_performance.items():
            if performance.get("response_time", 0) > 1.0:
                bottlenecks.append(f"agent_{agent_id}_high_response_time")
            if performance.get("cpu_usage", 0) > 0.8:
                bottlenecks.append(f"agent_{agent_id}_high_cpu_usage")
            if performance.get("memory_usage", 0) > 0.9:
                bottlenecks.append(f"agent_{agent_id}_high_memory_usage")
                
        return bottlenecks
        
    def identify_inefficiencies(self, performance_metrics: Dict[str, Any]) -> List[str]:
        """
        Identify inefficiencies.

        Args:
            performance_metrics: Performance metrics

        Returns:
            List of inefficiency identifiers
        """
        inefficiencies = []
        
        # Analyze coordination efficiency
        coordination_efficiency = performance_metrics.get("coordination_efficiency", 0.0)
        if coordination_efficiency < 0.7:
            inefficiencies.append("low_coordination_efficiency")
            
        # Analyze resource utilization
        resource_utilization = performance_metrics.get("resource_utilization", 0.0)
        if resource_utilization < 0.6:
            inefficiencies.append("low_resource_utilization")
            
        return inefficiencies
        
    def identify_optimization_opportunities(self, performance_metrics: Dict[str, Any]) -> List[str]:
        """
        Identify optimization opportunities.

        Args:
            performance_metrics: Performance metrics

        Returns:
            List of optimization opportunities
        """
        opportunities = []
        
        # Analyze for optimization opportunities
        if performance_metrics.get("agent_count", 0) > 10:
            opportunities.append("scale_agents")
            
        if performance_metrics.get("model_count", 0) > 5:
            opportunities.append("optimize_model_usage")
            
        if performance_metrics.get("communication_overhead", 0) > 0.3:
            opportunities.append("reduce_communication_overhead")
            
        return opportunities
        
    def generate_optimization_recommendations(self, analysis: Dict[str, Any]) -> List[str]:
        """
        Generate optimization recommendations.

        args:
            analysis: Performance analysis

        Returns:
            List of optimization recommendations
        """
        recommendations = []
        
        # Generate recommendations based on analysis
        if "bottlenecks" in analysis and analysis["bottlenecks"]:
            recommendations.extend([f"address_bottleneck_{b}" for b in analysis["bottlenecks"]])
            
        if "inefficiencies" in analysis and analysis["inefficiencies"]:
            recommendations.extend([f"resolve_inefficiency_{i}" for i in analysis["inefficiencies"]])
            
        if "optimization_opportunities" in analysis and analysis["optimization_opportunities"]:
            recommendations.extend(analysis["optimization_opportunities"])
            
        return recommendations
        
    def apply_optimizations(self, recommendations: List[str]):
        """
        Apply optimizations.

        Args:
            recommendations: Optimization recommendations
        """
        for recommendation in recommendations:
            self.apply_optimization(recommendation)
            
    def apply_optimization(self, recommendation: str):
        """
        Apply a specific optimization.

        Args:
            recommendation: Optimization recommendation
        """
        # Implementation depends on specific recommendation
        pass
        
    def calculate_optimization_effectiveness(self) -> float:
        """
        Calculate optimization effectiveness.

        Returns:
            Optimization effectiveness score (0.0 to 1.0)
        """
        # This would be calculated based on before/after performance metrics
        return 0.0
class ResourceManager:
    """
    Manage swarm resources.
    """
    
    def __init__(self):
        self.resources = {
            "cpu": 1.0,
            "memory": 1.0,
            "network": 1.0,
            "storage": 1.0
        }
        
    def allocate_resources(self, requirements: Dict[str, float]) -> Dict[str, Any]:
        """
        Allocate resources based on requirements.

        Args:
            requirements: Resource requirements

        Returns:
            Allocation results
        """
        allocation = {}
        
        for resource, requirement in requirements.items():
            if resource in self.resources:
                available = self.resources[resource]
                if requirement <= available:
                    allocation[resource] = requirement
                    self.resources[resource] -= requirement
                else:
                    allocation[resource] = available
                    self.resources[resource] = 0
                    
        return {
            "allocation": allocation,
            "utilization_efficiency": self.calculate_utilization_efficiency(),
            "satisfaction": self.calculate_satisfaction(requirements, allocation)
        }
        
    def release_resources(self, released_resources: Dict[str, float]):
        """
        Release resources.

        Args:
            released_resources: Resources to release
        """
        for resource, amount in released_resources.items():
            if resource in self.resources:
                self.resources[resource] = min(1.0, 
                    self.resources[resource] + amount)
                
    def calculate_utilization_efficiency(self) -> float:
        """
        Calculate resource utilization efficiency.

        Returns:
            Utilization efficiency score
        """
        total_resources = sum(self.resources.values())
        available_resources = len(self.resources)
        
        return total_resources / (available_resources * 1.0) if available_resources > 0 else 0.0
        
    def calculate_satisfaction(self, requirements: Dict[str, float], 
                               allocation: Dict[str, float]) -> float:
        """
        Calculate resource satisfaction.

        Args:
            requirements: Resource requirements
            allocation: Resource allocation

        Returns:
            Satisfaction score (0.0 to 1.0)
        """
        if not requirements:
            return 1.0
            
        satisfied = 0
        total_requirements = 0
        
        for resource, requirement in requirements.items():
            allocated = allocation.get(resource, 0.0)
            satisfied += min(allocated, requirement)
            total_requirements += requirement
            
        return satisfied / total_requirements if total_requirements > 0 else 1.0
class LoadBalancer:
    """
    Balance load across swarm agents.
    """
    
    def balance_loads(self, agent_loads: Dict[str, Dict[str, float]]) -> Dict[str, Any]:
        """
        Balance loads across agents.

        Args:
            agent_loads: Agent load information

        Returns:
            Load balancing results
        """
        # Analyze current loads
        load_analysis = self.analyze_loads(agent_loads)
        
        # Determine if rebalancing is needed
        needs_rebalancing = self.needs_rebalancing(load_analysis)
        
        if not needs_rebalancing:
            return {
                "needs_rebalancing": False,
                "rebalancing_actions": [],
                "load_balance_efficiency": self.calculate_load_balance_efficiency(agent_loads)
            }
            
        # Generate rebalancing actions
        rebalancing_actions = self.generate_rebalancing_actions(load_analysis)
        
        return {
            "needs_rebalancing": True,
            "rebalancing_actions": rebalancing_actions,
            "load_balance_efficiency": self.calculate_load_balance_efficiency(agent_loads)
        }
        
    def analyze_loads(self, agent_loads: Dict[str, Dict[str, float]]) -> Dict[str, Any]:
        """
        Analyze agent loads.

        Args:
            agent_loads: Agent load information

        Returns:
            Load analysis
        """
        total_cpu = sum(agent_data.get("cpu", 0.0) for agent_data in agent_loads.values())
        total_memory = sum(agent_data.get("memory", 0.0) for agent_data in agent_loads.values())
        total_network = sum(agent_data.get("network", 0.0) for agent_data in agent_loads.values())
        
        max_cpu = max(agent_data.get("cpu", 0.0) for agent_data in agent_loads.values())
        max_memory = max(agent_data.get("memory", 0.0) for agent_data in agent_loads.values())
        max_network = max(agent_data.get("network", 0.0) for agent_data in agent_loads.values())
        
        return {
            "total_cpu": total_cpu,
            "total_memory": total_memory,
            "total_network": total_network,
            "max_cpu": max_cpu,
            "max_memory": max_memory,
            "max_network": max_network,
            "load_imbalance": self.calculate_load_imbalance(agent_loads)
        }
        
    def needs_rebalancing(self, load_analysis: Dict[str, Any]) -> bool:
        """
        Determine if load balancing is needed.

        Args:
            load_analysis: Load analysis

        Returns:
            True if rebalancing is needed, False otherwise
        """
        return (load_analysis["max_cpu"] > 0.8 or 
                load_analysis["max_memory"] > 0.8 or
                load_analysis["max_network"] > 0.8)
        
    def generate_rebalancing_actions(self, load_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Generate load balancing actions.

        Args:
            load_analysis: Load analysis

        Returns:
            List of load balancing actions
        """
        actions = []
        
        # Identify agents with high load
        high_load_agents = []
        for agent_id, load_data in load_analysis.items():
            if load_data["cpu"] > 0.8 or load_data["memory"] > 0.8:
                high_load_agents.append(agent_id)
                
        # Generate rebalancing actions
        for agent_id in high_load_agents:
            actions.append({
                "type": "migrate_work",
                "target_agent": f"agent_{len(load_analysis) - high_load_agents.index(agent_id) % len(load_analysis)}",
                "priority": "high"
            })
            
        return actions
        
    def calculate_load_balance_efficiency(self, agent_loads: Dict[str, Dict[str, float]]) -> float:
        """
        Calculate load balance efficiency.

        Args:
            agent_loads: Agent load information

        Returns:
            Load balance efficiency score
        """
        total_cpu = sum(agent_data.get("cpu", 0.0) for agent_data in agent_loads.values())
        max_cpu = max(agent_data.get("cpu", 0.0) for agent_data in agent_loads.values())
        
        if max_cpu == 0:
            return 1.0
            
        return (total_cpu / (len(agent_loads) * max_cpu)) if len(agent_loads) > 0 else 1.0
class ErrorHandler:
    """
    Handle errors in the swarm system.
    """
    
    def __init__(self):
        self.error_history = []
        self.error_patterns = {}
        
    def handle_error(self, error: Exception, agent_id: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Handle an error.

        Args:
            error: Exception
            agent_id: Agent ID
            context: Context information

        Returns:
            Error handling result
        """
        error_record = {
            "timestamp": time.time(),
            "error": str(error),
            "agent_id": agent_id,
            "context": context,
            "error_type": self.categorize_error(error)
        }
        
        self.error_history.append(error_record)
        
        # Analyze error patterns
        self.analyze_error_patterns()
        
        # Return error handling result
        return {
            "error_handled": True,
            "error_type": error_record["error_type"],
            "recovery_action": self.determine_recovery_action(error, agent_id, context)
        }
        
    def categorize_error(self, error: Exception) -> str:
        """
        Categorize error type.

        Args:
            error: Exception

        Returns:
            Error type
        """
        error_str = str(error).lower()
        if "memory" in error_str:
            return "memory_error"
        elif "cpu" in error_str:
            return "cpu_error"
        elif "network" in error_str:
            return "network_error"
        elif "timeout" in error_str:
            return "timeout_error"
        else:
            return "general_error"
        
    def analyze_error_patterns(self):
        """
        Analyze error patterns.
        """
        # Group errors by type and frequency
        error_counts = {}
        for error_record in self.error_history:
            error_type = error_record["error_type"]
            error_counts[error_type] = error_counts.get(error_type, 0) + 1
            
        self.error_patterns = error_counts
        
    def determine_recovery_action(self, error: Exception, agent_id: str, 
                                 context: Dict[str, Any]) -> str:
        """
        Determine recovery action for error.

        Args:
            error: Exception
            agent_id: Agent ID
            context: Context information

        Returns:
            Recovery action
        """
        error_type = self.categorize_error(error)
        
        if error_type == "memory_error":
            return "reset_agent_memory"
        elif error_type == "cpu_error":
            return "reduce_agent_load"
        elif error_type == "network_error":
            return "retry_with_backoff"
        elif error_type == "timeout_error":
            return "increase_timeouts"
        else:
            return "log_and_continue"