"""
CollaborativeAI Component for Codette
Handles multi-agent collaboration and consensus building
"""

import logging
from typing import Dict, List, Any, Optional
from datetime import datetime
import asyncio

try:
    import numpy as np
except Exception:
    np = None

logger = logging.getLogger(__name__)

class CollaborativeAI:
    """Manages collaborative AI processes for Codette"""
    
    def __init__(self,
                 consensus_threshold: float = 0.7,
                 max_rounds: int = 5,
                 min_agents: int = 2):
        """Initialize the collaborative AI system"""
        self.consensus_threshold = consensus_threshold
        self.max_rounds = max_rounds
        self.min_agents = min_agents
        
        # Initialize state
        self.active_agents = {}
        self.collaboration_history = []
        self.current_state = {
            "round": 0,
            "consensus_level": 0.0,
            "active_collaborations": 0,
            "agent_states": {}
        }
        
        logger.info("CollaborativeAI system initialized")
        
    async def collaborate(self,
                        task: Dict[str, Any],
                        agents: List[str],
                        context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Coordinate collaboration between multiple agents"""
        try:
            if len(agents) < self.min_agents:
                return {
                    "status": "error",
                    "message": f"Need at least {self.min_agents} agents"
                }
                
            # Initialize collaboration
            collab_id = self._init_collaboration(task, agents)
            
            # Run collaboration rounds
            final_result = await self._run_collaboration_rounds(collab_id, task, agents, context)
            
            # Update history
            self._update_history(collab_id, final_result)
            
            return final_result
            
        except Exception as e:
            logger.error(f"Error in collaboration: {e}")
            return {"status": "error", "message": str(e)}
            
    def _init_collaboration(self,
                          task: Dict[str, Any],
                          agents: List[str]) -> str:
        """Initialize a new collaboration session"""
        try:
            # Generate collaboration ID
            collab_id = f"collab_{datetime.now().timestamp()}"
            
            # Initialize agent states
            for agent in agents:
                self.active_agents[agent] = {
                    "status": "ready",
                    "contributions": [],
                    "consensus_votes": {},
                    "last_update": datetime.now().isoformat()
                }
                
            # Reset current state
            self.current_state.update({
                "round": 0,
                "consensus_level": 0.0,
                "active_collaborations": len(self.active_agents),
                "agent_states": {
                    agent: "ready" for agent in agents
                }
            })
            
            logger.info(f"Initialized collaboration {collab_id}")
            return collab_id
            
        except Exception as e:
            logger.error(f"Error initializing collaboration: {e}")
            raise
            
    async def _run_collaboration_rounds(self,
                                      collab_id: str,
                                      task: Dict[str, Any],
                                      agents: List[str],
                                      context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Run multiple rounds of collaboration"""
        try:
            round_results = []
            consensus_reached = False
            
            for round_num in range(self.max_rounds):
                self.current_state["round"] = round_num + 1
                
                # Collect individual contributions
                contributions = await self._collect_contributions(task, agents, context)
                
                # Synthesize contributions
                synthesis = self._synthesize_contributions(contributions)
                
                # Check for consensus
                consensus_level = await self._evaluate_consensus(synthesis, agents)
                self.current_state["consensus_level"] = consensus_level
                
                round_results.append({
                    "round": round_num + 1,
                    "contributions": contributions,
                    "synthesis": synthesis,
                    "consensus_level": consensus_level
                })
                
                if consensus_level >= self.consensus_threshold:
                    consensus_reached = True
                    break
                    
                # Update task with synthesis for next round
                task = self._update_task_with_synthesis(task, synthesis)
                
            return self._prepare_final_result(
                collab_id,
                round_results,
                consensus_reached
            )
            
        except Exception as e:
            logger.error(f"Error in collaboration rounds: {e}")
            return {"status": "error", "message": str(e)}
            
    async def _collect_contributions(self,
                                   task: Dict[str, Any],
                                   agents: List[str],
                                   context: Optional[Dict[str, Any]] = None) -> List[Dict[str, Any]]:
        """Collect contributions from all agents"""
        contributions = []
        try:
            # Create tasks for each agent
            agent_tasks = [
                self._get_agent_contribution(agent, task, context)
                for agent in agents
            ]
            
            # Gather contributions asynchronously
            results = await asyncio.gather(*agent_tasks, return_exceptions=True)
            
            for agent, result in zip(agents, results):
                if isinstance(result, Exception):
                    logger.error(f"Error getting contribution from {agent}: {result}")
                    continue
                    
                contributions.append({
                    "agent": agent,
                    "contribution": result,
                    "timestamp": datetime.now().isoformat()
                })
                
            return contributions
            
        except Exception as e:
            logger.error(f"Error collecting contributions: {e}")
            return []
            
    async def _get_agent_contribution(self,
                                    agent: str,
                                    task: Dict[str, Any],
                                    context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Get contribution from a single agent"""
        try:
            # Update agent state
            self.active_agents[agent]["status"] = "working"
            
            # Simulate agent processing
            # In real implementation, this would call the actual agent's processing
            await asyncio.sleep(0.1)  # Simulate processing time
            
            contribution = {
                "task_id": task.get("id"),
                "agent_id": agent,
                "content": self._generate_contribution(task, agent),
                "confidence": float(np.random.uniform(0.5, 1.0)) if np is not None else float(0.75),
                "timestamp": datetime.now().isoformat()
            }
            
            # Store contribution
            self.active_agents[agent]["contributions"].append(contribution)
            self.active_agents[agent]["status"] = "contributed"
            
            return contribution
            
        except Exception as e:
            logger.error(f"Error getting agent contribution: {e}")
            raise
            
    def _synthesize_contributions(self,
                                contributions: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Synthesize multiple contributions into a single result"""
        try:
            if not contributions:
                return {"status": "error", "message": "No contributions to synthesize"}
                
            # Extract content and confidence
            contents = []
            confidences = []
            
            for contrib in contributions:
                content = contrib.get("contribution", {}).get("content")
                confidence = contrib.get("contribution", {}).get("confidence", 0.5)
                
                if content:
                    contents.append(content)
                    confidences.append(confidence)
                    
            # Weight contents by confidence
            if not contents:
                return {"status": "error", "message": "No valid content to synthesize"}
                
            # Simple weighted combination for demonstration
            # In real implementation, this would use more sophisticated methods
            synthesis = {
                "content": self._combine_contents(contents, confidences),
                "confidence_level": float(np.mean(confidences)) if np is not None else float(sum(confidences)/len(confidences)) if confidences else 0.0,
                "num_contributors": len(contents),
                "timestamp": datetime.now().isoformat()
            }
            
            return synthesis
            
        except Exception as e:
            logger.error(f"Error synthesizing contributions: {e}")
            return {"status": "error", "message": str(e)}
            
    async def _evaluate_consensus(self,
                                synthesis: Dict[str, Any],
                                agents: List[str]) -> float:
        """Evaluate consensus level among agents"""
        try:
            # Collect votes from agents
            votes = await self._collect_consensus_votes(synthesis, agents)
            
            if not votes:
                return 0.0
                
            # Calculate consensus level
            positive_votes = sum(1 for vote in votes if vote.get("agreement", 0) > 0.5)
            consensus_level = positive_votes / len(votes)
            
            return consensus_level
            
        except Exception as e:
            logger.error(f"Error evaluating consensus: {e}")
            return 0.0
            
    async def _collect_consensus_votes(self,
                                     synthesis: Dict[str, Any],
                                     agents: List[str]) -> List[Dict[str, Any]]:
        """Collect consensus votes from all agents"""
        votes = []
        try:
            # Create voting tasks
            vote_tasks = [
                self._get_agent_vote(agent, synthesis)
                for agent in agents
            ]
            
            # Collect votes asynchronously
            results = await asyncio.gather(*vote_tasks, return_exceptions=True)
            
            for agent, result in zip(agents, results):
                if isinstance(result, Exception):
                    logger.error(f"Error getting vote from {agent}: {result}")
                    continue
                    
                votes.append(result)
                
            return votes
            
        except Exception as e:
            logger.error(f"Error collecting votes: {e}")
            return []
            
    async def _get_agent_vote(self,
                             agent: str,
                             synthesis: Dict[str, Any]) -> Dict[str, Any]:
        """Get consensus vote from a single agent"""
        try:
            # Simulate agent voting
            # In real implementation, this would use actual agent evaluation
            await asyncio.sleep(0.1)  # Simulate processing time
            
            agreement = np.random.uniform(0.5, 1.0)  # Simulate agreement level
            
            vote = {
                "agent": agent,
                "agreement": agreement,
                "timestamp": datetime.now().isoformat()
            }
            
            # Store vote
            self.active_agents[agent]["consensus_votes"] = vote
            
            return vote
            
        except Exception as e:
            logger.error(f"Error getting agent vote: {e}")
            raise
            
    def _update_task_with_synthesis(self,
                                  task: Dict[str, Any],
                                  synthesis: Dict[str, Any]) -> Dict[str, Any]:
        """Update task for next round using synthesis results"""
        try:
            updated_task = task.copy()
            
            # Add synthesis results to task context
            if "context" not in updated_task:
                updated_task["context"] = {}
                
            updated_task["context"]["previous_synthesis"] = synthesis
            updated_task["context"]["round"] = self.current_state["round"]
            
            return updated_task
            
        except Exception as e:
            logger.error(f"Error updating task: {e}")
            return task
            
    def _prepare_final_result(self,
                            collab_id: str,
                            round_results: List[Dict[str, Any]],
                            consensus_reached: bool) -> Dict[str, Any]:
        """Prepare final collaboration result"""
        try:
            final_synthesis = round_results[-1]["synthesis"] if round_results else {}
            
            return {
                "status": "success" if consensus_reached else "partial",
                "collaboration_id": collab_id,
                "rounds_completed": len(round_results),
                "consensus_reached": consensus_reached,
                "final_consensus_level": self.current_state["consensus_level"],
                "final_result": final_synthesis,
                "round_history": round_results,
                "metrics": {
                    "total_contributions": sum(
                        len(r["contributions"]) for r in round_results
                    ),
                    "average_consensus": float(np.mean([
                        r["consensus_level"] for r in round_results
                    ])) if np is not None and round_results else float(sum(r["consensus_level"] for r in round_results)/len(round_results)) if round_results else 0.0,
                    "collaboration_duration": len(round_results)
                },
                "timestamp": datetime.now().isoformat()
            }
            
        except Exception as e:
            logger.error(f"Error preparing final result: {e}")
            return {"status": "error", "message": str(e)}
            
    def _generate_contribution(self,
                             task: Dict[str, Any],
                             agent: str) -> Dict[str, Any]:
        """Generate a contribution for an agent"""
        try:
            # This is a placeholder implementation
            # In real system, this would use the agent's actual processing
            return {
                "type": "contribution",
                "agent": agent,
                "task_id": task.get("id"),
                "content": f"Contribution from {agent} for task {task.get('id')}",
                "timestamp": datetime.now().isoformat()
            }
            
        except Exception as e:
            logger.error(f"Error generating contribution: {e}")
            return {}
            
    def _combine_contents(self,
                        contents: List[Dict[str, Any]],
                        weights: List[float]) -> Dict[str, Any]:
        """Combine multiple contents with weights"""
        try:
            # This is a placeholder implementation
            # In real system, this would use more sophisticated combination methods
            return {
                "type": "synthesis",
                "components": len(contents),
                "weighted_combination": "Combined result",
                "timestamp": datetime.now().isoformat()
            }
            
        except Exception as e:
            logger.error(f"Error combining contents: {e}")
            return {}
            
    def _update_history(self, collab_id: str, result: Dict[str, Any]):
        """Update collaboration history"""
        try:
            self.collaboration_history.append({
                "id": collab_id,
                "result": result,
                "timestamp": datetime.now().isoformat()
            })
            
        except Exception as e:
            logger.error(f"Error updating history: {e}")
            
    def get_state(self) -> Dict[str, Any]:
        """Get current state of the collaborative system"""
        return self.current_state.copy()
        
    def get_history(self) -> List[Dict[str, Any]]:
        """Get collaboration history"""
        return self.collaboration_history.copy()
        
    def get_agent_state(self, agent: str) -> Optional[Dict[str, Any]]:
        """Get state of a specific agent"""
        return self.active_agents.get(agent)