"""
Agora Integration & Coordination Protocol
Defines how the AI Prompt Engineer Agent integrates with Agora v5 supervisors
"""

from typing import Dict, List, Optional, Any, Protocol, Union
from dataclasses import dataclass, field
from enum import Enum, auto
import asyncio
import json
from datetime import datetime
import uuid


class MessagePriority(Enum):
    """Message priority levels for Agora communication"""
    CRITICAL = auto()  # System failures, security breaches
    HIGH = auto()      # User-initiated requests
    NORMAL = auto()    # Standard operations
    LOW = auto()       # Background tasks, analytics


class SupervisorType(Enum):
    """Agora supervisor types"""
    AI_MEDIATOR = "ai_mediator_supervisor"
    SYNTHESIS = "synthesis_orchestrator"
    RESEARCH = "research_coordinator"
    PROMPT_ENGINEER = "prompt_engineer_agent"


class AgentCapability(Enum):
    """Capabilities that agents can advertise"""
    QUERY_ENHANCEMENT = "query_enhancement"
    CONTEXT_BUILDING = "context_building"
    SECURITY_SCREENING = "security_screening"
    OFFLINE_PROCESSING = "offline_processing"
    MULTI_AGENT_COORDINATION = "multi_agent_coordination"
    REAL_TIME_ADAPTATION = "real_time_adaptation"


@dataclass
class AgoraMessage:
    """Standard message format for Agora inter-agent communication"""
    id: str = field(default_factory=lambda: str(uuid.uuid4()))
    source: str = ""
    destination: str = ""
    message_type: str = ""
    payload: Dict[str, Any] = field(default_factory=dict)
    priority: MessagePriority = MessagePriority.NORMAL
    timestamp: datetime = field(default_factory=datetime.utcnow)
    correlation_id: Optional[str] = None
    requires_response: bool = False
    timeout_seconds: int = 30
    security_classification: str = "UNCLASSIFIED"
    
    def to_json(self) -> str:
        """Serialize message to JSON"""
        return json.dumps({
            "id": self.id,
            "source": self.source,
            "destination": self.destination,
            "message_type": self.message_type,
            "payload": self.payload,
            "priority": self.priority.name,
            "timestamp": self.timestamp.isoformat(),
            "correlation_id": self.correlation_id,
            "requires_response": self.requires_response,
            "timeout_seconds": self.timeout_seconds,
            "security_classification": self.security_classification
        })


@dataclass
class AgentRegistration:
    """Agent registration information for Agora directory"""
    agent_id: str
    agent_type: str
    capabilities: List[AgentCapability]
    performance_metrics: Dict[str, float]
    availability_status: str  # "online", "offline", "degraded"
    max_concurrent_requests: int
    average_response_time_ms: float
    supported_message_types: List[str]
    security_clearance: str


class AgoraIntegrationProtocol:
    """
    Core integration protocol for AI Prompt Engineer Agent within Agora ecosystem
    """
    
    def __init__(self, agent_id: str = "prompt_engineer_agent_001"):
        self.agent_id = agent_id
        self.message_bus = MessageBus()
        self.coordinator = CoordinationManager()
        self.health_monitor = HealthMonitor()
        self.routing_table = RoutingTable()
        
        # Register with Agora
        self._register_agent()
        
    def _register_agent(self):
        """Register this agent with Agora's directory service"""
        registration = AgentRegistration(
            agent_id=self.agent_id,
            agent_type=SupervisorType.PROMPT_ENGINEER.value,
            capabilities=[
                AgentCapability.QUERY_ENHANCEMENT,
                AgentCapability.CONTEXT_BUILDING,
                AgentCapability.SECURITY_SCREENING,
                AgentCapability.OFFLINE_PROCESSING,
                AgentCapability.REAL_TIME_ADAPTATION
            ],
            performance_metrics={
                "enhancement_accuracy": 0.95,
                "average_latency_ms": 250,
                "offline_capability": 1.0,
                "security_compliance": 1.0
            },
            availability_status="online",
            max_concurrent_requests=100,
            average_response_time_ms=250,
            supported_message_types=[
                "ENHANCE_QUERY",
                "BUILD_CONTEXT",
                "SECURITY_CHECK",
                "STATUS_REQUEST",
                "CAPABILITY_QUERY"
            ],
            security_clearance="TOP_SECRET"
        )
        
        # Broadcast registration
        self.message_bus.broadcast(AgoraMessage(
            source=self.agent_id,
            destination="agora.directory",
            message_type="AGENT_REGISTRATION",
            payload=registration.__dict__,
            priority=MessagePriority.HIGH
        ))
    
    async def handle_incoming_query(self, message: AgoraMessage) -> AgoraMessage:
        """
        Main entry point for handling queries from other Agora components
        """
        try:
            # Extract query and context
            query = message.payload.get("query", "")
            context = message.payload.get("context", {})
            requirements = message.payload.get("requirements", {})
            
            # Determine routing strategy
            routing_decision = self.routing_table.determine_route(
                query, context, message.source
            )
            
            # Process based on routing decision
            if routing_decision.requires_mediation:
                return await self._coordinate_with_mediator(message, routing_decision)
            elif routing_decision.requires_synthesis:
                return await self._coordinate_with_synthesis(message, routing_decision)
            else:
                return await self._process_standalone(message)
                
        except Exception as e:
            return self._create_error_response(message, str(e))
    
    async def _coordinate_with_mediator(self, message: AgoraMessage, 
                                       routing: 'RoutingDecision') -> AgoraMessage:
        """
        Coordinate with AI Mediator Supervisor for complex queries
        """
        # Enhance query first
        enhanced_query = await self._enhance_query_internal(
            message.payload["query"],
            message.payload.get("context", {})
        )
        
        # Create mediation request
        mediation_request = AgoraMessage(
            source=self.agent_id,
            destination=SupervisorType.AI_MEDIATOR.value,
            message_type="MEDIATION_REQUEST",
            payload={
                "original_query": message.payload["query"],
                "enhanced_query": enhanced_query,
                "enhancement_metadata": {
                    "confidence": 0.95,
                    "techniques_applied": ["deep_analysis", "context_injection"],
                    "security_cleared": True
                },
                "routing_hints": routing.hints
            },
            correlation_id=message.id,
            priority=MessagePriority.HIGH,
            requires_response=True
        )
        
        # Send and await response
        response = await self.message_bus.send_and_wait(mediation_request)
        
        # Package response back to original requester
        return AgoraMessage(
            source=self.agent_id,
            destination=message.source,
            message_type="ENHANCED_QUERY_RESPONSE",
            payload={
                "enhanced_query": enhanced_query,
                "mediation_result": response.payload,
                "processing_trace": self._get_processing_trace()
            },
            correlation_id=message.id
        )
    
    async def _coordinate_with_synthesis(self, message: AgoraMessage,
                                       routing: 'RoutingDecision') -> AgoraMessage:
        """
        Coordinate with Synthesis Orchestrator for multi-source queries
        """
        # Enhance and prepare for synthesis
        enhanced_query = await self._enhance_query_internal(
            message.payload["query"],
            message.payload.get("context", {})
        )
        
        # Identify required sources
        sources = self._identify_synthesis_sources(enhanced_query, routing)
        
        # Create synthesis request
        synthesis_request = AgoraMessage(
            source=self.agent_id,
            destination=SupervisorType.SYNTHESIS.value,
            message_type="SYNTHESIS_REQUEST",
            payload={
                "enhanced_query": enhanced_query,
                "required_sources": sources,
                "synthesis_strategy": routing.hints.get("synthesis_strategy", "comprehensive"),
                "quality_requirements": {
                    "min_confidence": 0.9,
                    "require_citations": True,
                    "max_hallucination_rate": 0.03
                }
            },
            correlation_id=message.id,
            priority=MessagePriority.HIGH,
            requires_response=True,
            timeout_seconds=60  # Longer timeout for synthesis
        )
        
        # Send and await response
        response = await self.message_bus.send_and_wait(synthesis_request)
        
        # Post-process synthesis result
        processed_result = await self._post_process_synthesis(response.payload)
        
        return AgoraMessage(
            source=self.agent_id,
            destination=message.source,
            message_type="SYNTHESIZED_RESPONSE",
            payload={
                "original_query": message.payload["query"],
                "enhanced_query": enhanced_query,
                "synthesis_result": processed_result,
                "quality_metrics": self._calculate_quality_metrics(processed_result)
            },
            correlation_id=message.id
        )
    
    async def _process_standalone(self, message: AgoraMessage) -> AgoraMessage:
        """
        Process query independently without coordination
        """
        # Direct enhancement
        enhanced_query = await self._enhance_query_internal(
            message.payload["query"],
            message.payload.get("context", {})
        )
        
        return AgoraMessage(
            source=self.agent_id,
            destination=message.source,
            message_type="ENHANCED_QUERY_RESPONSE",
            payload={
                "enhanced_query": enhanced_query,
                "processing_mode": "standalone",
                "confidence": 0.92,
                "processing_time_ms": 150
            },
            correlation_id=message.id
        )
    
    async def participate_in_swarm(self, swarm_id: str, role: str) -> None:
        """
        Participate in a multi-agent swarm for complex tasks
        """
        swarm_config = await self._get_swarm_configuration(swarm_id)
        
        while True:
            # Get next task from swarm coordinator
            task = await self._get_swarm_task(swarm_id)
            
            if task is None:
                break
                
            # Process task based on role
            if role == "enhancement_specialist":
                result = await self._process_enhancement_task(task)
            elif role == "context_builder":
                result = await self._process_context_task(task)
            elif role == "quality_validator":
                result = await self._process_validation_task(task)
            else:
                result = {"error": f"Unknown role: {role}"}
            
            # Submit result back to swarm
            await self._submit_swarm_result(swarm_id, task.id, result)
    
    def establish_feedback_loop(self, target_agent: str) -> 'FeedbackChannel':
        """
        Establish a feedback loop with another agent for continuous improvement
        """
        channel = FeedbackChannel(
            source=self.agent_id,
            target=target_agent,
            feedback_types=["quality", "performance", "accuracy"]
        )
        
        # Register feedback handler
        self.message_bus.register_handler(
            f"FEEDBACK_{target_agent}",
            self._handle_feedback
        )
        
        return channel
    
    async def _handle_feedback(self, feedback_message: AgoraMessage) -> None:
        """
        Process feedback from other agents to improve performance
        """
        feedback_type = feedback_message.payload.get("feedback_type")
        feedback_data = feedback_message.payload.get("data")
        
        if feedback_type == "quality":
            # Adjust enhancement strategies based on quality feedback
            await self._adjust_quality_parameters(feedback_data)
        elif feedback_type == "performance":
            # Optimize processing based on performance feedback
            await self._optimize_performance(feedback_data)
        elif feedback_type == "accuracy":
            # Update models or rules based on accuracy feedback
            await self._improve_accuracy(feedback_data)
    
    async def handle_emergency_protocol(self, emergency_type: str) -> None:
        """
        Handle emergency protocols (e.g., security breach, system failure)
        """
        if emergency_type == "SECURITY_BREACH":
            # Switch to maximum security mode
            await self._enable_maximum_security()
            # Notify all connected agents
            self.message_bus.broadcast(AgoraMessage(
                source=self.agent_id,
                destination="*",
                message_type="SECURITY_ALERT",
                payload={"alert_type": "BREACH", "timestamp": datetime.utcnow()},
                priority=MessagePriority.CRITICAL
            ))
        elif emergency_type == "NETWORK_ISOLATION":
            # Switch to offline mode
            await self._switch_to_offline_mode()
        elif emergency_type == "RESOURCE_EXHAUSTION":
            # Implement resource conservation
            await self._enable_resource_conservation()


class MessageBus:
    """Internal message bus for Agora communication"""
    
    def __init__(self):
        self.handlers: Dict[str, Any] = {}
        self.pending_responses: Dict[str, asyncio.Future] = {}
        
    async def send_and_wait(self, message: AgoraMessage) -> AgoraMessage:
        """Send a message and wait for response"""
        if not message.requires_response:
            raise ValueError("Message does not require response")
            
        # Create future for response
        future = asyncio.Future()
        self.pending_responses[message.id] = future
        
        # Send message
        await self._send(message)
        
        # Wait for response with timeout
        try:
            response = await asyncio.wait_for(
                future, 
                timeout=message.timeout_seconds
            )
            return response
        finally:
            # Clean up
            self.pending_responses.pop(message.id, None)
    
    def broadcast(self, message: AgoraMessage) -> None:
        """Broadcast a message to all agents"""
        # Implementation would use actual message queue/broker
        pass
    
    async def _send(self, message: AgoraMessage) -> None:
        """Send a message to specific destination"""
        # Implementation would use actual message queue/broker
        pass


class CoordinationManager:
    """Manages coordination between multiple agents"""
    
    def __init__(self):
        self.active_coordinations: Dict[str, 'CoordinationSession'] = {}
        
    async def initiate_coordination(self, agents: List[str], 
                                  task: Dict[str, Any]) -> str:
        """Initiate a coordination session between multiple agents"""
        session_id = str(uuid.uuid4())
        session = CoordinationSession(
            session_id=session_id,
            participating_agents=agents,
            task=task,
            start_time=datetime.utcnow()
        )
        
        self.active_coordinations[session_id] = session
        
        # Notify all agents
        for agent in agents:
            await self._notify_agent_of_coordination(agent, session)
            
        return session_id
    
    async def _notify_agent_of_coordination(self, agent: str, 
                                          session: 'CoordinationSession') -> None:
        """Notify an agent about a coordination session"""
        # Implementation would send actual notification
        pass


@dataclass
class CoordinationSession:
    """Represents an active coordination session"""
    session_id: str
    participating_agents: List[str]
    task: Dict[str, Any]
    start_time: datetime
    status: str = "active"
    results: Dict[str, Any] = field(default_factory=dict)


class RoutingTable:
    """Intelligent routing decisions for queries"""
    
    def determine_route(self, query: str, context: Dict[str, Any], 
                       source: str) -> 'RoutingDecision':
        """Determine optimal routing for a query"""
        decision = RoutingDecision()
        
        # Complex multi-domain queries need mediation
        if self._is_complex_multidomain(query):
            decision.requires_mediation = True
            decision.hints["complexity"] = "high"
            
        # Research or synthesis queries need synthesis orchestrator
        if self._requires_synthesis(query, context):
            decision.requires_synthesis = True
            decision.hints["synthesis_strategy"] = "comprehensive"
            
        # Security-sensitive queries need special handling
        if self._is_security_sensitive(query, context):
            decision.requires_security_review = True
            decision.hints["security_level"] = "high"
            
        return decision
    
    def _is_complex_multidomain(self, query: str) -> bool:
        """Check if query spans multiple domains"""
        domains = ["technical", "legal", "financial", "operational", "strategic"]
        domain_count = sum(1 for domain in domains if domain in query.lower())
        return domain_count >= 2
    
    def _requires_synthesis(self, query: str, context: Dict[str, Any]) -> bool:
        """Check if query requires synthesis from multiple sources"""
        synthesis_indicators = ["compare", "analyze", "synthesize", "combine", "integrate"]
        return any(indicator in query.lower() for indicator in synthesis_indicators)
    
    def _is_security_sensitive(self, query: str, context: Dict[str, Any]) -> bool:
        """Check if query contains security-sensitive content"""
        return context.get("security_classification", "").upper() in ["SECRET", "TOP_SECRET"]


@dataclass
class RoutingDecision:
    """Routing decision for query processing"""
    requires_mediation: bool = False
    requires_synthesis: bool = False
    requires_security_review: bool = False
    target_agents: List[str] = field(default_factory=list)
    hints: Dict[str, Any] = field(default_factory=dict)


class HealthMonitor:
    """Monitor agent health and performance"""
    
    def __init__(self):
        self.metrics: Dict[str, float] = {
            "cpu_usage": 0.0,
            "memory_usage": 0.0,
            "request_rate": 0.0,
            "error_rate": 0.0,
            "average_latency_ms": 0.0
        }
        
    async def report_health(self) -> Dict[str, Any]:
        """Report current health status"""
        return {
            "status": self._calculate_health_status(),
            "metrics": self.metrics,
            "timestamp": datetime.utcnow().isoformat()
        }
    
    def _calculate_health_status(self) -> str:
        """Calculate overall health status"""
        if self.metrics["error_rate"] > 0.1:
            return "degraded"
        elif self.metrics["cpu_usage"] > 0.9:
            return "overloaded"
        else:
            return "healthy"


@dataclass
class FeedbackChannel:
    """Bidirectional feedback channel between agents"""
    source: str
    target: str
    feedback_types: List[str]
    established_at: datetime = field(default_factory=datetime.utcnow)
    
    async def send_feedback(self, feedback_type: str, data: Dict[str, Any]) -> None:
        """Send feedback to target agent"""
        # Implementation would send actual feedback
        pass


# Example integration scenarios
async def demonstrate_integration():
    """Demonstrate integration scenarios"""
    
    # Initialize integration protocol
    integration = AgoraIntegrationProtocol("prompt_engineer_001")
    
    # Scenario 1: Handle incoming query from UI layer
    ui_query = AgoraMessage(
        source="ui_gateway",
        destination="prompt_engineer_001",
        message_type="ENHANCE_QUERY",
        payload={
            "query": "Design a secure microservices architecture for a fintech startup",
            "context": {
                "user_role": "CTO",
                "company_size": "startup",
                "industry": "fintech",
                "security_requirements": "PCI-DSS compliant"
            },
            "requirements": {
                "detail_level": "comprehensive",
                "include_examples": True,
                "time_constraint": "1 week implementation"
            }
        },
        priority=MessagePriority.HIGH,
        requires_response=True
    )
    
    # Process query
    response = await integration.handle_incoming_query(ui_query)
    print(f"Response type: {response.message_type}")
    print(f"Enhanced query provided: {'enhanced_query' in response.payload}")
    
    # Scenario 2: Participate in multi-agent swarm
    await integration.participate_in_swarm(
        swarm_id="research_swarm_001",
        role="enhancement_specialist"
    )
    
    # Scenario 3: Establish feedback loop
    feedback_channel = integration.establish_feedback_loop("synthesis_orchestrator")
    await feedback_channel.send_feedback(
        "quality",
        {"enhancement_quality": 0.95, "suggestions": ["Add more context about scalability"]}
    )
    
    # Scenario 4: Handle emergency
    await integration.handle_emergency_protocol("NETWORK_ISOLATION")


if __name__ == "__main__":
    asyncio.run(demonstrate_integration())
