#!/usr/bin/env python3
"""
Swarm Coordinator
Central coordination system for the Organic AI swarm.

This class manages:
- Agent registration and lifecycle
- Situation processing and coordination
- Conflict resolution and decision fusion
- Performance monitoring and optimization
"""

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

from swarm_agent_base import SwarmAgent
class SwarmCoordinator:
    """
    Central coordinator for the swarm agent system.
    
    This class manages all swarm agents, coordinates their actions,
    resolves conflicts, and optimizes overall swarm performance.
    """
    
    def __init__(self):
        self.agents = {}
        self.current_situation = {}
        self.decision_log = []
        self.performance_metrics = {}
        self.model_adapters = {}
        self.conflict_resolver = ConflictResolver()
        self.performance_optimizer = PerformanceOptimizer()
        self.adaptive_learner = AdaptiveLearner()
        
    def register_agent(self, agent_config: Dict[str, Any]) -> SwarmAgent:
        """
        Register a new swarm agent.
        
        Args:
            agent_config: Agent configuration
            
        Returns:
            Registered agent instance
        """
        # Create agent
        agent = SwarmAgent(
            agent_id=agent_config["id"],
            agent_type=agent_config["type"],
            priority=agent_config["priority"]
        )
        
        # Set up model adapter if specified
        if "model_config" in agent_config:
            model_adapter = self.create_model_adapter(
                agent_config["model_config"]["type"],
                agent_config["model_config"]["params"]
            )
            agent.set_model_adapter(model_adapter)
            
        self.agents[agent.agent_id] = agent
        return agent
        
    def update_situation(self, situation: Dict[str, Any]) -> Dict[str, Any]:
        """
        Update global situation and coordinate agents.
        
        Args:
            situation: Current situation data
            
        Returns:
            Coordinated actions
        """
        self.current_situation = situation
        return self.coordinate_agent_actions()
        
    def coordinate_agent_actions(self) -> Dict[str, Any]:
        """
        Coordinate actions from all agents.
        
        Returns:
            Coordinated actions
        """
        # Get recommendations from all agents
        agent_recommendations = self.get_agent_recommendations()
        
        # Resolve conflicts and fuse recommendations
        fused_recommendations = self.fuse_agent_recommendations(agent_recommendations)
        
        # Generate final coordinated actions
        final_actions = self.generate_final_actions(fused_recommendations)
        
        # Log coordination for learning
        self.log_coordination_event(final_actions)
        
        return {
            "coordinated_actions": final_actions,
            "agent_recommendations": agent_recommendations,
            "coordination_efficiency": self.calculate_coordination_efficiency()
        }
        
    def get_agent_recommendations(self) -> Dict[str, Any]:
        """
        Get recommendations from all agents.
        
        Returns:
            Dictionary mapping agent IDs to recommendations
        """
        recommendations = {}
        
        for agent_id, agent in self.agents.items():
            try:
                agent_recommendations = agent.process_input(self.current_situation)
                recommendations[agent_id] = agent_recommendations
            except Exception as e:
                # Handle agent errors gracefully
                recommendations[agent_id] = {
                    "agent_id": agent_id,
                    "recommendations": [],
                    "confidence": 0.0,
                    "alternative_actions": [],
                    "expertise_level": 0,
                    "reasoning": {"error": str(e)}
                }
                
        return recommendations
        
    def fuse_agent_recommendations(self, recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """
        Fuse recommendations from all agents.
        
        Args:
            recommendations: Agent recommendations
            
        Returns:
            Fused recommendations
        """
        # Analyze action patterns across agents
        action_patterns = self.analyze_action_patterns(recommendations)
        
        # Identify strongly agreed-upon actions
        consensus_actions = self.identify_consensus_actions(action_patterns)
        
        # Resolve conflicts between actions
        conflict_resolution = self.conflict_resolver.resolve_conflicts(
            recommendations, action_patterns
        )
        
        # Generate final fused recommendations
        fused_recommendations = {
            "primary_actions": consensus_actions,
            "conflict_resolution": conflict_resolution,
            "action_confidence": self.calculate_action_confidence(action_patterns),
            "agent_contributions": self.analyze_agent_contributions(recommendations)
        }
        
        return fused_recommendations
        
    def generate_final_actions(self, fused_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Generate final coordinated actions.
        
        Args:
            fused_recommendations: Fused recommendations
            
        Returns:
            List of final actions
        """
        primary_actions = fused_recommendations["primary_actions"]
        conflict_resolution = fused_recommendations["conflict_resolution"]
        
        # Apply conflict resolution to primary actions
        final_actions = self.apply_conflict_resolution(
            primary_actions, conflict_resolution
        )
        
        return final_actions
        
    def create_model_adapter(self, model_type: str, model_params: Dict[str, Any]):
        """
        Create model adapter for the specified type.
        
        Args:
            model_type: Type of model adapter
            model_params: Model parameters
            
        Returns:
            Model adapter instance
        """
        if model_type == "vanilla":
            return VanillaAIAdapter(model_params)
        elif model_type == "cp":
            return CommunityPatchAIAdapter(model_params)
        elif model_type == "rice":
            return RICEAIAdapter(model_params)
        elif model_type == "organic":
            return QyvarianAIAdapter(model_params)
        else:
            raise ValueError(f"Unknown model type: {model_type}")
        
    def analyze_action_patterns(self, recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze action patterns across agents.
        
        Args:
            recommendations: Agent recommendations
            
        Returns:
            Action patterns analysis
        """
        action_patterns = defaultdict(list)
        
        for agent_id, recommendation in recommendations.items():
            actions = recommendation.get("recommendations", [])
            for action in actions:
                action_patterns[action["action"]].append(agent_id)
                
        return dict(action_patterns)
        
    def identify_consensus_actions(self, action_patterns: Dict[str, Any]) -> List[str]:
        """
        Identify actions that have consensus across agents.
        
        args:
            action_patterns: Action patterns
            
        Returns:
            List of consensus actions
        """
        consensus_actions = []
        threshold = 0.6  # 60% agreement threshold
        
        total_agents = len(self.agents)
        
        for action, supporting_agents in action_patterns.items():
            agreement_ratio = len(supporting_agents) / total_agents
            if agreement_ratio >= threshold:
                consensus_actions.append(action)
                
        return consensus_actions
        
    def calculate_action_confidence(self, action_patterns: Dict[str, Any]) -> Dict[str, float]:
        """
        Calculate confidence for each action.
        
        Args:
            action_patterns: Action patterns
            
        Returns:
            Dictionary mapping actions to confidence scores
        """
        action_confidence = {}
        total_agents = len(self.agents)
        
        for action, supporting_agents in action_patterns.items():
            confidence = len(supporting_agents) / total_agents
            action_confidence[action] = confidence
            
        return action_confidence
        
    def analyze_agent_contributions(self, recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze contributions from individual agents.
        
        Args:
            recommendations: Agent recommendations
            
        Returns:
            Agent contribution analysis
        """
        contributions = {}
        
        for agent_id, recommendation in recommendations.items():
            confidence = recommendation.get("confidence", 0.0)
            expertise = recommendation.get("expertise_level", 0)
            
            contribution_score = (confidence + expertise) / 2.0
            
            contributions[agent_id] = {
                "confidence": confidence,
                "expertise": expertise,
                "contribution_score": contribution_score
            }
            
        return contributions
        
    def apply_conflict_resolution(self, actions: List[str], conflict_resolution: Dict[str, Any]) -> List[str]:
        """
        Apply conflict resolution to actions.
n        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
        
    def calculate_coordination_efficiency(self) -> float:
        """
        Calculate coordination efficiency.
        
        Returns:
            Coordination efficiency score (0.0 to 1.0)
        """
        if not self.agents:
            return 0.0
            
        total_agents = len(self.agents)
        active_agents = sum(1 for agent in self.agents.values() if agent.is_active)
        
        return active_agents / total_agents
        
    def log_coordination_event(self, actions: List[Dict[str, Any]]):
        """
        Log coordination event for learning.
        
        Args:
            actions: Coordinated actions
        """
        self.decision_log.append({
            "timestamp": time.time(),
            "actions": actions,
            "situation": self.current_situation
        })
        
        # Keep only recent log entries
        if len(self.decision_log) > 1000:
            self.decision_log = self.decision_log[-1000:]
class ConflictResolver:
    """
    Resolve conflicts between agent recommendations.
    """
    
    def resolve_conflicts(self, recommendations: Dict[str, Any], action_patterns: Dict[str, Any]) -> Dict[str, Any]:
        """
        Resolve conflicts between agent recommendations.
        
        Args:
            recommendations: Agent recommendations
            action_patterns: Action patterns
            
        Returns:
            Conflict resolution information
        """
        conflicts = []
        
        for agent_id, recommendation in recommendations.items():
            agent_actions = recommendation.get("recommendations", [])
            for action in agent_actions:
                if self.is_action_conflict(action, agent_id, recommendations):
                    conflicts.append({
                        "action": action,
                        "conflicting_agent": agent_id,
                        "conflicting_actions": self.get_conflicting_actions(action, recommendations),
                        "resolution_strategy": self.determine_resolution_strategy(action, agent_id, recommendations)
                    })
                    
        return {
            "conflicts": conflicts,
            "resolution_summary": self.summarize_conflict_resolution(conflicts)
        }
        
    def is_action_conflict(self, action: Dict[str, Any], agent_id: str, recommendations: Dict[str, Any]) -> bool:
        """
        Check if an action conflicts with other agent recommendations.
        
        Args:
            action: Action to check
            agent_id: Agent ID
            recommendations: All agent recommendations
            
        Returns:
            True if action conflicts, False otherwise
        """
        # Implementation depends on specific conflict detection logic
        return False
        
    def get_conflicting_actions(self, action: Dict[str, Any], recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Get actions that conflict with the specified action.
        
        Args:
            action: Action to check
            recommendations: All agent recommendations
            
        Returns:
            List of conflicting actions
        """
        conflicting_actions = []
        
        for agent_id, recommendation in recommendations.items():
            if agent_id == action.get("source_agent"):
                continue
                
            agent_actions = recommendation.get("recommendations", [])
            for other_action in agent_actions:
                if self.actions_conflict(action, other_action):
                    conflicting_actions.append(other_action)
                    
        return conflicting_actions
        
    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
        """
        return "consensus"
        
    def actions_conflict(self, action1: Dict[str, Any], action2: Dict[str, Any]) -> bool:
        """
        Check if two actions conflict with each other.
        
        Args:
            action1: First action
            action2: Second action
            
        Returns:
            True if actions conflict, False otherwise
        """
        return False
        
    def summarize_conflict_resolution(self, conflicts: List[Dict[str, Any]]) -> Dict[str, Any]:
        """
        Summarize conflict resolution.
        
        Args:
            conflicts: List of conflicts
            
        Returns:
            Conflict resolution summary
        """
        return {
            "total_conflicts": len(conflicts),
            "resolved_conflicts": len([c for c in conflicts if c.get("resolved", False)]),
            "conflict_types": self.categorize_conflicts(conflicts)
        }
        
    def categorize_conflicts(self, conflicts: List[Dict[str, Any]]) -> Dict[str, int]:
        """
        Categorize conflicts by type.
        
        Args:
            conflicts: List admiredial conflicts
            
        Returns:
            Dictionary mapping conflict types to counts
        """
        return {}
class PerformanceOptimizer:
    """
    Optimize swarm performance.
    """
    
    def analyze(self, performance_metrics: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze performance metrics.
        
        Args:
            performance_metrics: Performance metrics
            
        Returns:
            Analysis results
        """
        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
        """
        return []
        
    def identify_inefficiencies(self, performance_metrics: Dict[str, Any]) -> List[str]:
        """
        Identify inefficiencies.
n        args:
            performance_metrics: Performance metrics
            
        Returns:
            List of inefficiency identifiers
        """
        return []
        
    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
        """
        return []
class AdaptiveLearner:
    """
    Learn from swarm performance and adapt.
    """
    
    def __init__(self):
        self.learned_strategies = {}
        self.performance_history = []
        
    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
# Import model adapters (these would be defined in separate files)
from .model_adapters import (
    VanillaAIAdapter,
    CommunityPatchAIAdapter,
    RICEAIAdapter,
    QyvarianAIAdapter,
    DefaultAIAdapter
)
# Import other helper classes
from .helper_classes import (
    ConflictResolver,
    PerformanceOptimizer,
    AdaptiveLearner,
    ResourceManager,
    LoadBalancer,
    ErrorHandler,
    TestingFramework,
    ValidationSystem,
    MonitoringSystem,
    ExperienceDatabase,
    PatternRecognizer,
    EvolutionEngine,
    PerformanceMonitor
)