"""
OpenDistillery Base Agent System
Enterprise-grade agent framework with OpenAI Agents SDK integration,
advanced collaboration patterns, and production-ready orchestration.
"""

import asyncio
import logging
import time
from typing import Dict, List, Any, Optional, Callable, Union
from dataclasses import dataclass, field
from enum import Enum
import json
import uuid
from datetime import datetime
import threading
from abc import ABC, abstractmethod

# OpenAI Agents SDK integration
from openai import AsyncOpenAI
from openai.types.beta import Assistant, Thread
from openai.types.beta.threads import Run

import structlog

# Import core components
try:
    from ...core.compound_system import ModelConfiguration, ModelRouter, CompoundAISystem
except ImportError:
    from src.core.compound_system import ModelConfiguration, ModelRouter, CompoundAISystem

logger = structlog.get_logger(__name__)

class AgentState(Enum):
    IDLE = "idle"
    BUSY = "busy"
    ERROR = "error"
    OFFLINE = "offline"
    TERMINATED = "terminated"

class AgentCapability(Enum):
    ANALYSIS = "analysis"
    RESEARCH = "research"
    PLANNING = "planning"
    EXECUTION = "execution"
    MONITORING = "monitoring"
    COMMUNICATION = "communication"
    LEARNING = "learning"
    REASONING = "reasoning"
    PREDICTION = "prediction"
    CLASSIFICATION = "classification"
    SYNTHESIS = "synthesis"
    ADVISORY = "advisory"
    CONTENT_GENERATION = "content_generation"
    DATA_PROCESSING = "data_processing"
    DECISION_MAKING = "decision_making"

class CollaborationPattern(Enum):
    PEER_TO_PEER = "peer_to_peer"
    HIERARCHICAL = "hierarchical"
    CONSENSUS = "consensus"
    DELEGATION = "delegation"
    AUCTION = "auction"
    SWARM = "swarm"

@dataclass
class AgentMemory:
    """Agent memory system for context preservation"""
    short_term: Dict[str, Any] = field(default_factory=dict)
    long_term: Dict[str, Any] = field(default_factory=dict)
    episodic: List[Dict[str, Any]] = field(default_factory=list)
    semantic: Dict[str, Any] = field(default_factory=dict)
    procedural: Dict[str, str] = field(default_factory=dict)
    
    def store_episode(self, episode: Dict[str, Any]) -> None:
        """Store episodic memory"""
        episode["timestamp"] = datetime.now().isoformat()
        self.episodic.append(episode)
        
        # Keep only recent episodes
        if len(self.episodic) > 1000:
            self.episodic = self.episodic[-1000:]
    
    def retrieve_relevant_memories(self, context: str, limit: int = 5) -> List[Dict[str, Any]]:
        """Retrieve relevant memories based on context"""
        # Simple keyword-based retrieval (could be enhanced with embeddings)
        relevant = []
        context_words = set(context.lower().split())
        
        for episode in self.episodic[-100:]:  # Search recent episodes
            episode_text = str(episode).lower()
            if any(word in episode_text for word in context_words):
                relevant.append(episode)
        
        return relevant[-limit:]

@dataclass
class AgentTools:
    """Tools available to the agent"""
    functions: Dict[str, Callable] = field(default_factory=dict)
    apis: Dict[str, Dict[str, Any]] = field(default_factory=dict)
    databases: Dict[str, Any] = field(default_factory=dict)
    
    def register_function(self, name: str, func: Callable, description: str = "") -> None:
        """Register a function tool"""
        self.functions[name] = {
            "function": func,
            "description": description,
            "usage_count": 0,
            "success_rate": 1.0
        }
    
    async def call_function(self, name: str, **kwargs) -> Any:
        """Call a registered function"""
        if name not in self.functions:
            raise ValueError(f"Function {name} not found")
        
        try:
            func_info = self.functions[name]
            result = await func_info["function"](**kwargs)
            
            # Update usage stats
            func_info["usage_count"] += 1
            return result
            
        except Exception as e:
            # Update failure stats
            func_info = self.functions[name]
            total_calls = func_info["usage_count"] + 1
            successes = func_info["usage_count"] * func_info["success_rate"]
            func_info["success_rate"] = successes / total_calls
            func_info["usage_count"] = total_calls
            
            logger.error(f"Function {name} failed: {str(e)}")
            raise

@dataclass
class AgentContext:
    agent_id: str
    task: Dict[str, Any]
    history: List[Dict[str, Any]] = field(default_factory=list)
    state: Dict[str, Any] = field(default_factory=dict)
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AgentResponse:
    success: bool
    content: Union[str, Dict[str, Any]]
    confidence: float = 0.5
    metadata: Dict[str, Any] = field(default_factory=dict)
    error: Optional[str] = None

class BaseAgent(ABC):
    def __init__(self, agent_id: str, models: Dict[str, ModelConfiguration], router: ModelRouter, system: CompoundAISystem):
        self.agent_id = agent_id
        self.models = models
        self.router = router
        self.system = system
        self.context = AgentContext(agent_id=agent_id, task={})
        self.active = False
        self.capabilities: List[str] = []
        self.initialize()

    def initialize(self):
        """Initialize agent resources"""
        logger.info(f"Initializing agent {self.agent_id}")
        self.active = True
        self.context.state = {"status": "initialized"}

    async def process_task(self, task: Dict[str, Any]) -> AgentResponse:
        """Process a task with error handling"""
        try:
            logger.info(f"Agent {self.agent_id} processing task: {task.get('type', 'unknown')}")
            self.context.task = task
            response = await self._process_task_internal(task)
            self.context.history.append({
                "task": task,
                "response": response.content,
                "success": response.success,
                "timestamp": asyncio.get_event_loop().time()
            })
            return response
        except Exception as e:
            logger.error(f"Agent {self.agent_id} error processing task: {str(e)}")
            return AgentResponse(
                success=False,
                content=f"Error processing task: {str(e)}",
                confidence=0.0,
                error=str(e),
                metadata={"task_type": task.get("type", "unknown")}
            )

    @abstractmethod
    async def _process_task_internal(self, task: Dict[str, Any]) -> AgentResponse:
        """Internal method to process specific task types"""
        pass

    async def update_state(self, state_update: Dict[str, Any]):
        """Update agent state"""
        self.context.state.update(state_update)
        logger.info(f"Agent {self.agent_id} state updated: {state_update.keys()}")

    async def get_status(self) -> Dict[str, Any]:
        """Get current agent status"""
        return {
            "agent_id": self.agent_id,
            "active": self.active,
            "capabilities": self.capabilities,
            "state": self.context.state,
            "history_length": len(self.context.history),
            "current_task": self.context.task.get("type", "none")
        }

    async def shutdown(self):
        """Shut down agent resources"""
        logger.info(f"Shutting down agent {self.agent_id}")
        self.active = False
        self.context.state = {"status": "shutdown"}

    async def _call_model(self, model: ModelConfiguration, prompt: str, request: Dict[str, Any]) -> str:
        """Call model with error handling"""
        try:
            if model.provider == "openai":
                response = await self.system._call_openai(model, prompt, request)
            elif model.provider == "anthropic":
                response = await self.system._call_anthropic(model, prompt, request)
            elif model.provider == "google":
                response = await self.system._call_google(model, prompt, request)
            else:
                raise ValueError(f"Unsupported provider: {model.provider}")
            return response
        except Exception as e:
            logger.error(f"Model call failed for agent {self.agent_id}: {str(e)}")
            raise

    def _build_context(self) -> str:
        """Build context string from agent history"""
        context_parts = []
        for entry in self.context.history[-3:]:
            task_type = entry.get("task", {}).get("type", "unknown")
            task_content = entry.get("task", {}).get("content", "")
            response_content = entry.get("response", "")
            context_parts.append(f"Task ({task_type}): {task_content}")
            context_parts.append(f"Response: {response_content}")
        return "\n".join(context_parts) if context_parts else "No history yet"

class SpecializedAgent(BaseAgent):
    """Specialized agent with domain-specific capabilities"""
    
    def __init__(self,
                 agent_id: str,
                 specialization: str,
                 domain_knowledge: Dict[str, Any],
                 **kwargs):
        # Determine capabilities based on specialization
        capability_mapping = {
            "financial_analyst": [AgentCapability.ANALYSIS, AgentCapability.PREDICTION, AgentCapability.REASONING],
            "research_scientist": [AgentCapability.RESEARCH, AgentCapability.ANALYSIS, AgentCapability.SYNTHESIS],
            "workflow_optimizer": [AgentCapability.PLANNING, AgentCapability.EXECUTION, AgentCapability.MONITORING],
            "customer_insights": [AgentCapability.ANALYSIS, AgentCapability.CLASSIFICATION, AgentCapability.PREDICTION],
            "content_creator": [AgentCapability.CONTENT_GENERATION, AgentCapability.COMMUNICATION],
            "decision_maker": [AgentCapability.DECISION_MAKING, AgentCapability.REASONING, AgentCapability.ADVISORY]
        }
        
        capabilities = capability_mapping.get(specialization, [AgentCapability.ANALYSIS])
        
        super().__init__(
            agent_id=agent_id,
            models={},
            router=ModelRouter(),
            system=CompoundAISystem()
        )
        
        self.specialization = specialization
        self.domain_knowledge = domain_knowledge
        
        # Add domain-specific knowledge to memory
        self.memory.semantic.update(domain_knowledge)
        
        # Register specialized tools
        self._register_specialized_tools()
    
    def _register_specialized_tools(self) -> None:
        """Register tools specific to the agent's specialization"""
        if self.specialization == "financial_analyst":
            self.register_tool(
                "calculate_risk_metrics",
                self._calculate_risk_metrics,
                "Calculate financial risk metrics"
            )
            self.register_tool(
                "analyze_market_trends",
                self._analyze_market_trends,
                "Analyze market trends and patterns"
            )
        
        elif self.specialization == "research_scientist":
            self.register_tool(
                "literature_search",
                self._literature_search,
                "Search scientific literature"
            )
            self.register_tool(
                "hypothesis_generation",
                self._hypothesis_generation,
                "Generate research hypotheses"
            )
    
    async def _calculate_risk_metrics(self, **kwargs) -> Dict[str, Any]:
        """Calculate financial risk metrics"""
        portfolio_data = kwargs.get("portfolio_data", {})
        
        # Simplified risk calculation
        volatility = portfolio_data.get("volatility", 0.2)
        var_95 = volatility * 1.65  # 95% VaR approximation
        
        return {
            "volatility": volatility,
            "var_95": var_95,
            "risk_score": min(var_95 * 10, 10),  # Scale to 0-10
            "recommendation": "high_risk" if var_95 > 0.3 else "moderate_risk"
        }
    
    async def _analyze_market_trends(self, **kwargs) -> Dict[str, Any]:
        """Analyze market trends"""
        market_data = kwargs.get("market_data", {})
        
        # Simplified trend analysis
        price_history = market_data.get("prices", [100, 102, 98, 105, 107])
        
        if len(price_history) >= 2:
            recent_change = (price_history[-1] - price_history[-2]) / price_history[-2]
            trend = "upward" if recent_change > 0.01 else "downward" if recent_change < -0.01 else "sideways"
        else:
            trend = "insufficient_data"
        
        return {
            "trend_direction": trend,
            "price_change_pct": recent_change * 100 if 'recent_change' in locals() else 0,
            "confidence": 0.75,
            "recommendation": f"Market showing {trend} trend"
        }
    
    async def _literature_search(self, **kwargs) -> Dict[str, Any]:
        """Search scientific literature"""
        query = kwargs.get("query", "")
        
        # Simplified literature search simulation
        mock_papers = [
            {
                "title": f"Recent Advances in {query}",
                "authors": ["Smith, J.", "Doe, A."],
                "year": 2024,
                "relevance_score": 0.9
            },
            {
                "title": f"A Comprehensive Review of {query}",
                "authors": ["Johnson, M.", "Wilson, K."],
                "year": 2023,
                "relevance_score": 0.8
            }
        ]
        
        return {
            "query": query,
            "papers_found": len(mock_papers),
            "papers": mock_papers,
            "search_time": 0.5
        }
    
    async def _hypothesis_generation(self, **kwargs) -> Dict[str, Any]:
        """Generate research hypotheses"""
        research_area = kwargs.get("research_area", "")
        existing_knowledge = kwargs.get("existing_knowledge", {})
        
        # Simplified hypothesis generation
        hypotheses = [
            f"Hypothesis 1: Enhanced {research_area} methods will improve performance by 15%",
            f"Hypothesis 2: Integration of AI with {research_area} will reduce costs by 25%",
            f"Hypothesis 3: Novel approaches to {research_area} will unlock new applications"
        ]
        
        return {
            "research_area": research_area,
            "hypotheses": hypotheses,
            "testability_scores": [0.8, 0.9, 0.6],
            "recommended_hypothesis": hypotheses[1]  # Highest testability
        }