#!/usr/bin/env python3
"""
Enhanced Swarm Coordinator

Extended swarm coordinator with conflict resolution and performance optimization.

This class extends the basic SwarmCoordinator with:
- Conflict resolution capabilities
- Performance optimization
- Adaptive learning
- Enhanced monitoring
"""

import time
import json
from typing import Dict, List, Any, Optional
from collections import defaultdict

from swarm_agent_base import SwarmAgent
from swarm_coordinator import SwarmCoordinator, ConflictResolver
class EnhancedSwarmCoordinator(SwarmCoordinator):
    """
    Enhanced swarm coordinator with advanced features.
n    This class extends the basic SwarmCoordinator with:
    - Conflict resolution capabilities
    - Performance optimization
    - Adaptive learning
    - Enhanced monitoring
    """
    
    def __init__(self):
        super().__init__()
        self.conflict_resolver = EnhancedConflictResolver()
        self.performance_optimizer = PerformanceOptimizer()
        self.adaptive_learner = AdaptiveLearner()
        self.quality_assurance = QualityAssurance()
        
    def coordinate_with_conflicts(self, situation: Dict[str, Any]) -> Dict[str, Any]:
        """
        Coordinate agents while managing conflicts.
n        Args:
            situation: Current situation data
            
        Returns:
            Coordination results
        """
        # Get recommendations from all agents
        agent_recommendations = self.get_agent_recommendations()
        
        # Detect conflicts between agents
        conflicts = self.conflict_resolver.detect_conflicts(agent_recommendations)
        
        # Resolve conflicts based on expertise and priority
        resolved_recommendations = self.conflict_resolver.resolve_conflicts(
            agent_recommendations, conflicts
        )
        
        # Generate final coordinated actions
        final_actions = self.generate_final_actions(resolved_recommendations)
        
        # Log coordination for learning
        self.log_coordination_event(final_actions, conflicts)
        
        # Optimize performance
        self.performance_optimizer.optimize(performance_metrics)
        
        return {
            "coordinated_actions": final_actions,
            "conflict_resolution": conflicts,
            "coordination_efficiency": self.calculate_coordination_efficiency(),
            "performance_optimization": self.get_performance_optimization_results()
        }
        
    def optimize_for_performance(self, performance_metrics: Dict[str, Any]) -> Dict[str, Any]:
        """
        Optimize swarm performance based on metrics.
n        Args:
            performance_metrics: Performance metrics
            
        Returns:
            Optimization results
        """
        return self.performance_optimizer.optimize(performance_metrics)
        
    def adapt_to_environment(self, environment_changes: Dict[str, Any]):
        """
        Adapt to changing game environment.
n        Args:
            environment_changes: Environment changes
        """
        # Update agent strategies based on environment
        self.adaptive_learner.adapt(self.current_situation, environment_changes)
        
        # Update model weights
        self.update_model_weights(environment_changes)
        
        # Learn new patterns
        self.adaptive_learner.learn_new_patterns(environment_changes)
        
    def apply_conflict_resolution(self, actions: List[str], conflict_resolution: Dict[str, Any]) -> List[str]:
        """
        Apply conflict resolution to actions.

        Args:
            actions: List of actions
            conflict_resolution: Conflict resolution information

        Returns:
            List of actions after conflict resolution
        """
        # Implementation depends on specific conflict resolution strategy
        # For now, return the actions as-is
        return actions
class EnhancedConflictResolver(ConflictResolver):
    """
    Enhanced conflict resolver with more sophisticated logic.
    """
    
    def __init__(self):
        super().__init__()
        self.conflict_patterns = {}
        self.resolution_strategies = {}
        
    def detect_conflicts(self, recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Detect conflicts between agent recommendations.

        Args:
            recommendations: Agent recommendations

        Returns:
            List of conflicts
        """
        conflicts = super().detect_conflicts(recommendations)
        
        # Apply additional conflict detection logic
        enhanced_conflicts = self.detect_enhanced_conflicts(recommendations)
        
        # Merge conflicts
        all_conflicts = conflicts + enhanced_conflicts
        
        return self.categorize_conflicts(all_conflicts)
        
    def detect_enhanced_conflicts(self, recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Detect enhanced conflicts based on multiple factors.

        Args:
            recommendations: Agent recommendations

        Returns:
            List of enhanced conflicts
        """
        conflicts = []
        
        for agent_id, recommendation in recommendations.items():
            agent_actions = recommendation.get("recommendations", [])
            agent_confidence = recommendation.get("confidence", 0.0)
            agent_expertise = recommendation.get("expertise_level", 0)
            
            for action in agent_actions:
                # Check for conflicts based on action characteristics
                if self.is_action_conflict(action, agent_id, recommendations):
                    conflict = self.create_enhanced_conflict(
                        action, agent_id, recommendations,
                        agent_confidence, agent_expertise
                    )
                    conflicts.append(conflict)
                    
        return conflicts
        
    def create_enhanced_conflict(self, action: Dict[str, Any], agent_id: str, 
                                recommendations: Dict[str, Any], confidence: float,
                                expertise: int) -> Dict[str, Any]:
        """
        Create enhanced conflict information.

        Args:
            action: Action in conflict
            agent_id: Agent ID
            recommendations: All agent recommendations
            confidence: Agent confidence
            expertise: Agent expertise

        Returns:
            Enhanced conflict information
        """
        return {
            "action": action,
            "conflicting_agent": agent_id,
            "confidence": confidence,
            "expertise": expertise,
            "conflict_severity": self.calculate_conflict_severity(action, agent_id, recommendations),
            "resolution_strategy": self.determine_resolution_strategy(action, agent_id, recommendations),
            "alternative_actions": self.get_alternative_actions(action, agent_id, recommendations)
        }
        
    def calculate_conflict_severity(self, action: Dict[str, Any], agent_id: str,
                                   recommendations: Dict[str, Any]) -> str:
        """
        Calculate conflict severity.

        Args:
            action: Action in conflict
            agent_id: Agent ID
            recommendations: All agent recommendations

        Returns:
            Conflict severity level
        """
        # Calculate based on action characteristics and agent factors
        severity = "low"
        
        if action.get("critical", False):
            severity = "critical"
        elif action.get("high_impact", False):
            severity = "high"
        elif action.get("medium_impact", False):
            severity = "medium"
            
        return severity
        
    def determine_resolution_strategy(self, action: Dict[str, Any], agent_id: str,
                                     recommendations: Dict[str, Any]) -> str:
        """
        Determine resolution strategy for a conflict.

        Args:
            action: Action in conflict
            agent_id: Agent ID
            recommendations: All agent recommendations

        Returns:
            Resolution strategy
        """
        # Analyze conflict factors
        agent_confidence = recommendations[agent_id].get("confidence", 0.0)
        agent_expertise = recommendations[agent_id].get("expertise_level", 0)
        
        # Determine strategy based on factors
        if agent_confidence > 0.8 and agent_expertise > 7:
            return "expert_priority"
        elif agent_confidence > 0.6:
            return "medium_priority"
        else:
            return "consensus"
class PerformanceOptimizer:
    """
    Optimize swarm performance.
    """
    
    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 generate_optimization_recommendations(self, analysis: Dict[str, Any]) -> List[str]:
        """
        Generate optimization recommendations.

        Args:
            analysis: Performance analysis

        Returns:
            List of optimization recommendations
        """
        return []
        
    def apply_optimizations(self, recommendations: List[str]):
        """
        Apply optimizations.

        Args:
            recommendations: Optimization recommendations
        """
        pass
        
    def calculate_optimization_effectiveness(self) -> float:
        """
        Calculate optimization effectiveness.

        Returns:
            Optimization effectiveness score
        """
        return 0.0
class AdaptiveLearner:
    """
    Learn from swarm performance and adapt.
    """
    
    def __init__(self):
        self.learned_strategies = {}
        self.performance_history = []
        self.pattern_recognizer = PatternRecognizer()
        self.evolution_engine = EvolutionEngine()
        
    def learn_from_experience(self, experience_data: Dict[str, Any], outcomes: Dict[str, Any]):
        """
        Learn from swarm experience.

        Args:
            experience_data: Experience data
            outcomes: Outcomes from experience
        """
        # Store experience
        self.performance_history.append({
            "experience": experience_data,
            "outcomes": outcomes,
            "timestamp": time.time()
        })
        
        # Extract patterns
        self.extract_patterns()
        
        # Evolve strategies
        self.evolve_strategies()
        
    def extract_patterns(self):
        """
        Extract patterns from experience history.
        """
        # Implementation for pattern extraction
        pass
        
    def evolve_strategies(self):
        """
        Evolve strategies based on patterns.
        """
        # Implementation for strategy evolution
        pass
        
    def adapt_model_weights(self, performance_analysis: Dict[str, Any]):
        """
        Adapt model weights based on performance analysis.

        Args:
            performance_analysis: Performance analysis
        """
        pass
        
    def learn_new_patterns(self, environment_changes: Dict[str, Any]):
        """
        Learn new patterns from environment changes.

        Args:
            environment_changes: Environment changes
        """
        pass
class QualityAssurance:
    """
    Quality assurance for swarm system.
    """
    
    def __init__(self):
        self.testing_framework = TestingFramework()
        self.validation_system = ValidationSystem()
        self.monitoring_system = MonitoringSystem()
        
    def run_comprehensive_tests(self) -> Dict[str, Any]:
        """
        Run comprehensive quality tests.

        Returns:
            Test results
        """
        return {
            "agent_tests": self.testing_framework.run_agent_tests(),
            "integration_tests": self.testing_framework.run_integration_tests(),
            "performance_tests": self.testing_framework.run_performance_tests(),
            "ux_tests": self.testing_framework.run_user_experience_tests(),
            "overall_quality_score": self.calculate_overall_quality_score()
        }
        
    def calculate_overall_quality_score(self) -> float:
        """
        Calculate overall quality score.

        Returns:
            Overall quality score
        """
        return 0.0
# Import helper classes
from .helper_classes import (
    ResourceManager,
    LoadBalancer,
    ErrorHandler,
    TestingFramework,
    ValidationSystem,
    MonitoringSystem,
    ExperienceDatabase,
    PatternRecognizer,
    EvolutionEngine,
    PerformanceMonitor
)