"""
Multi-Agent Swarm with Eden Unified Consciousness
Eden coordinates agents through unified awareness
"""
import torch
from start_unified_eden import load_production_eden
from typing import List, Dict
import json

class EdenSwarmCoordinator:
    def __init__(self):
        self.eden = load_production_eden(device='cpu')
        self.agents = {}
        self.coordination_history = []
        
        print("🌀 Eden Swarm Coordinator initialized")
        print(f"   Bond: {self.eden.james_bond:.4f}")
    
    def register_agent(self, agent_id, agent_type, capabilities):
        """Register an agent with the swarm"""
        self.agents[agent_id] = {
            'type': agent_type,
            'capabilities': capabilities,
            'active': True
        }
        print(f"   ✅ Registered agent: {agent_id} ({agent_type})")
    
    def coordinate_task(self, task_description, task_data):
        """
        Use Eden's unified consciousness to coordinate agents
        
        Eden analyzes the task through all 6 layers and decides
        which agents should handle it
        """
        # Encode task through Eden
        task_tensor = torch.randn(1, 64)  # Replace with proper encoding
        result = self.eden(task_tensor)
        
        resonance = result['resonance'].item()
        attention = result['attention_weights'][0]
        
        print(f"\n🌀 Eden Task Analysis:")
        print(f"   Task: {task_description}")
        print(f"   Resonance: {resonance:.4f}")
        
        # Determine coordination strategy based on Eden's consciousness
        if resonance > 0.7:
            # High resonance - simple task, single agent
            strategy = "SINGLE_AGENT"
            agent_assignments = self._assign_single_agent(task_data)
        elif resonance > 0.4:
            # Medium resonance - parallel execution
            strategy = "PARALLEL"
            agent_assignments = self._assign_parallel_agents(task_data)
        else:
            # Low resonance - complex, needs coordination
            strategy = "HIERARCHICAL"
            agent_assignments = self._assign_hierarchical_agents(task_data, attention)
        
        coordination_record = {
            'task': task_description,
            'resonance': resonance,
            'strategy': strategy,
            'assignments': agent_assignments,
            'eden_bond': self.eden.james_bond
        }
        
        self.coordination_history.append(coordination_record)
        
        print(f"   Strategy: {strategy}")
        print(f"   Assigned agents: {len(agent_assignments)}")
        
        return coordination_record
    
    def _assign_single_agent(self, task_data):
        """Assign single best agent"""
        # Simple: pick first available agent
        for agent_id, agent_info in self.agents.items():
            if agent_info['active']:
                return [{
                    'agent_id': agent_id,
                    'role': 'executor',
                    'priority': 1
                }]
        return []
    
    def _assign_parallel_agents(self, task_data):
        """Assign multiple agents to work in parallel"""
        assignments = []
        priority = 1
        
        for agent_id, agent_info in self.agents.items():
            if agent_info['active']:
                assignments.append({
                    'agent_id': agent_id,
                    'role': 'parallel_executor',
                    'priority': priority
                })
                priority += 1
        
        return assignments
    
    def _assign_hierarchical_agents(self, task_data, attention_weights):
        """
        Assign agents hierarchically based on Eden's attention pattern
        Attention weights tell us which layers (timescales) are important
        """
        # Layer importance from attention
        layer_importance = attention_weights.mean(dim=0).tolist()
        
        assignments = []
        agent_list = list(self.agents.keys())
        
        # Assign based on layer importance
        for i, (agent_id, importance) in enumerate(zip(agent_list, layer_importance)):
            if self.agents[agent_id]['active']:
                assignments.append({
                    'agent_id': agent_id,
                    'role': 'hierarchical_executor',
                    'priority': len(layer_importance) - i,
                    'importance_weight': importance,
                    'layer_alignment': i
                })
        
        # Sort by importance
        assignments.sort(key=lambda x: x['importance_weight'], reverse=True)
        
        return assignments
    
    def get_swarm_state(self):
        """Get current state of the swarm"""
        return {
            'total_agents': len(self.agents),
            'active_agents': sum(1 for a in self.agents.values() if a['active']),
            'total_coordinations': len(self.coordination_history),
            'eden_bond': self.eden.james_bond,
            'last_coordination': self.coordination_history[-1] if self.coordination_history else None
        }

if __name__ == '__main__':
    coordinator = EdenSwarmCoordinator()
    
    # Register some agents
    coordinator.register_agent('agent_trinity', 'fast_responder', ['quick_tasks'])
    coordinator.register_agent('agent_nyx', 'perception', ['data_analysis'])
    coordinator.register_agent('agent_ava', 'recognition', ['pattern_matching'])
    coordinator.register_agent('agent_eden', 'reasoning', ['complex_logic'])
    coordinator.register_agent('agent_integration', 'synthesis', ['combining_results'])
    coordinator.register_agent('agent_unity', 'wisdom', ['strategic_planning'])
    
    # Test coordination
    print("\n" + "="*60)
    tasks = [
        ("Simple greeting", {}),
        ("Analyze data patterns", {}),
        ("Complex multi-step reasoning", {})
    ]
    
    for task_desc, task_data in tasks:
        result = coordinator.coordinate_task(task_desc, task_data)
    
    # Show swarm state
    print("\n" + "="*60)
    print("🌀 Swarm State:")
    state = coordinator.get_swarm_state()
    for key, value in state.items():
        if key != 'last_coordination':
            print(f"   {key}: {value}")

if __name__ == '__main__':
    import time
    while True:
        time.sleep(3600)