"""
OpenClaw Integration Enhancement Service - Phase 6.6
Implements advanced agent orchestration, edge computing integration, and ecosystem development
"""

from __future__ import annotations

import asyncio
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Tuple
from uuid import uuid4
from enum import Enum
import json

from sqlmodel import Session, select, update, and_, or_
from sqlalchemy import Column, JSON, DateTime, Float
from sqlalchemy.orm import Mapped, relationship

from ..domain import (
    AIAgentWorkflow, AgentExecution, AgentStatus, VerificationLevel,
    Job, Miner, GPURegistry
)
from ..services.agent_service import AIAgentOrchestrator, AgentStateManager
from ..services.agent_integration import AgentIntegrationManager


class SkillType(str, Enum):
    """Agent skill types"""
    INFERENCE = "inference"
    TRAINING = "training"
    DATA_PROCESSING = "data_processing"
    VERIFICATION = "verification"
    CUSTOM = "custom"


class ExecutionMode(str, Enum):
    """Agent execution modes"""
    LOCAL = "local"
    AITBC_OFFLOAD = "aitbc_offload"
    HYBRID = "hybrid"


class OpenClawEnhancedService:
    """Enhanced OpenClaw integration service"""
    
    def __init__(self, session: Session) -> None:
        self.session = session
        self.agent_orchestrator = AIAgentOrchestrator(session, None)  # Mock coordinator client
        self.state_manager = AgentStateManager(session)
        self.integration_manager = AgentIntegrationManager(session)
    
    async def route_agent_skill(
        self,
        skill_type: SkillType,
        requirements: Dict[str, Any],
        performance_optimization: bool = True
    ) -> Dict[str, Any]:
        """Sophisticated agent skill routing"""
        
        # Discover agents with required skills
        available_agents = await self._discover_agents_by_skill(skill_type)
        
        if not available_agents:
            raise ValueError(f"No agents available for skill type: {skill_type}")
        
        # Intelligent routing algorithm
        routing_result = await self._intelligent_routing(
            available_agents, requirements, performance_optimization
        )
        
        return routing_result
    
    async def _discover_agents_by_skill(self, skill_type: SkillType) -> List[Dict[str, Any]]:
        """Discover agents with specific skills"""
        # Placeholder implementation
        # In production, this would query agent registry
        return [
            {
                "agent_id": f"agent_{uuid4().hex[:8]}",
                "skill_type": skill_type.value,
                "performance_score": 0.85,
                "cost_per_hour": 0.1,
                "availability": 0.95
            }
        ]
    
    async def _intelligent_routing(
        self,
        agents: List[Dict[str, Any]],
        requirements: Dict[str, Any],
        performance_optimization: bool
    ) -> Dict[str, Any]:
        """Intelligent routing algorithm for agent skills"""
        
        # Sort agents by performance score
        sorted_agents = sorted(agents, key=lambda x: x["performance_score"], reverse=True)
        
        # Apply cost optimization
        if performance_optimization:
            sorted_agents = await self._apply_cost_optimization(sorted_agents, requirements)
        
        # Select best agent
        best_agent = sorted_agents[0] if sorted_agents else None
        
        if not best_agent:
            raise ValueError("No suitable agent found")
        
        return {
            "selected_agent": best_agent,
            "routing_strategy": "performance_optimized" if performance_optimization else "cost_optimized",
            "expected_performance": best_agent["performance_score"],
            "estimated_cost": best_agent["cost_per_hour"]
        }
    
    async def _apply_cost_optimization(
        self,
        agents: List[Dict[str, Any]],
        requirements: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Apply cost optimization to agent selection"""
        # Placeholder implementation
        # In production, this would analyze cost-benefit ratios
        return agents
    
    async def offload_job_intelligently(
        self,
        job_data: Dict[str, Any],
        cost_optimization: bool = True,
        performance_analysis: bool = True
    ) -> Dict[str, Any]:
        """Intelligent job offloading strategies"""
        
        job_size = self._analyze_job_size(job_data)
        
        # Cost-benefit analysis
        if cost_optimization:
            cost_analysis = await self._cost_benefit_analysis(job_data, job_size)
        else:
            cost_analysis = {"should_offload": True, "estimated_savings": 0.0}
        
        # Performance analysis
        if performance_analysis:
            performance_prediction = await self._predict_performance(job_data, job_size)
        else:
            performance_prediction = {"local_time": 100.0, "aitbc_time": 50.0}
        
        # Determine offloading decision
        should_offload = (
            cost_analysis.get("should_offload", False) or
            job_size.get("complexity", 0) > 0.8 or
            performance_prediction.get("aitbc_time", 0) < performance_prediction.get("local_time", float('inf'))
        )
        
        offloading_strategy = {
            "should_offload": should_offload,
            "job_size": job_size,
            "cost_analysis": cost_analysis,
            "performance_prediction": performance_prediction,
            "fallback_mechanism": "local_execution"
        }
        
        return offloading_strategy
    
    def _analyze_job_size(self, job_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze job size and complexity"""
        # Placeholder implementation
        return {
            "complexity": 0.7,
            "estimated_duration": 300,
            "resource_requirements": {"cpu": 4, "memory": "8GB", "gpu": True}
        }
    
    async def _cost_benefit_analysis(
        self,
        job_data: Dict[str, Any],
        job_size: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Perform cost-benefit analysis for job offloading"""
        # Placeholder implementation
        return {
            "should_offload": True,
            "estimated_savings": 50.0,
            "cost_breakdown": {
                "local_execution": 100.0,
                "aitbc_offload": 50.0,
                "savings": 50.0
            }
        }
    
    async def _predict_performance(
        self,
        job_data: Dict[str, Any],
        job_size: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Predict performance for job execution"""
        # Placeholder implementation
        return {
            "local_time": 120.0,
            "aitbc_time": 60.0,
            "confidence": 0.85
        }
    
    async def coordinate_agent_collaboration(
        self,
        task_data: Dict[str, Any],
        agent_ids: List[str],
        coordination_algorithm: str = "distributed_consensus"
    ) -> Dict[str, Any]:
        """Coordinate multiple agents for collaborative tasks"""
        
        # Validate agents
        available_agents = []
        for agent_id in agent_ids:
            # Check if agent exists and is available
            available_agents.append({
                "agent_id": agent_id,
                "status": "available",
                "capabilities": ["collaboration", "task_execution"]
            })
        
        if len(available_agents) < 2:
            raise ValueError("At least 2 agents required for collaboration")
        
        # Apply coordination algorithm
        if coordination_algorithm == "distributed_consensus":
            coordination_result = await self._distributed_consensus(
                task_data, available_agents
            )
        else:
            coordination_result = await self._central_coordination(
                task_data, available_agents
            )
        
        return coordination_result
    
    async def _distributed_consensus(
        self,
        task_data: Dict[str, Any],
        agents: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Distributed consensus coordination algorithm"""
        # Placeholder implementation
        return {
            "coordination_method": "distributed_consensus",
            "selected_coordinator": agents[0]["agent_id"],
            "consensus_reached": True,
            "task_distribution": {
                agent["agent_id"]: "subtask_1" for agent in agents
            },
            "estimated_completion_time": 180.0
        }
    
    async def _central_coordination(
        self,
        task_data: Dict[str, Any],
        agents: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Central coordination algorithm"""
        # Placeholder implementation
        return {
            "coordination_method": "central_coordination",
            "selected_coordinator": agents[0]["agent_id"],
            "task_distribution": {
                agent["agent_id"]: "subtask_1" for agent in agents
            },
            "estimated_completion_time": 150.0
        }
    
    async def optimize_hybrid_execution(
        self,
        execution_request: Dict[str, Any],
        optimization_strategy: str = "performance"
    ) -> Dict[str, Any]:
        """Optimize hybrid local-AITBC execution"""
        
        # Analyze execution requirements
        requirements = self._analyze_execution_requirements(execution_request)
        
        # Determine optimal execution strategy
        if optimization_strategy == "performance":
            strategy = await self._performance_optimization(requirements)
        elif optimization_strategy == "cost":
            strategy = await self._cost_optimization(requirements)
        else:
            strategy = await self._balanced_optimization(requirements)
        
        # Resource allocation
        resource_allocation = await self._allocate_resources(strategy)
        
        # Performance tuning
        performance_tuning = await self._performance_tuning(strategy)
        
        return {
            "execution_mode": ExecutionMode.HYBRID.value,
            "strategy": strategy,
            "resource_allocation": resource_allocation,
            "performance_tuning": performance_tuning,
            "expected_improvement": "30% performance gain"
        }
    
    def _analyze_execution_requirements(self, execution_request: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze execution requirements"""
        return {
            "complexity": execution_request.get("complexity", 0.5),
            "resource_requirements": execution_request.get("resources", {}),
            "performance_requirements": execution_request.get("performance", {}),
            "cost_constraints": execution_request.get("cost_constraints", {})
        }
    
    async def _performance_optimization(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
        """Performance-based optimization strategy"""
        return {
            "local_ratio": 0.3,
            "aitbc_ratio": 0.7,
            "optimization_target": "maximize_throughput"
        }
    
    async def _cost_optimization(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
        """Cost-based optimization strategy"""
        return {
            "local_ratio": 0.8,
            "aitbc_ratio": 0.2,
            "optimization_target": "minimize_cost"
        }
    
    async def _balanced_optimization(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
        """Balanced optimization strategy"""
        return {
            "local_ratio": 0.5,
            "aitbc_ratio": 0.5,
            "optimization_target": "balance_performance_and_cost"
        }
    
    async def _allocate_resources(self, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Allocate resources based on strategy"""
        return {
            "local_resources": {
                "cpu_cores": 4,
                "memory_gb": 16,
                "gpu": False
            },
            "aitbc_resources": {
                "gpu_count": 2,
                "gpu_memory": "16GB",
                "estimated_cost": 0.2
            }
        }
    
    async def _performance_tuning(self, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Performance tuning parameters"""
        return {
            "batch_size": 32,
            "parallel_workers": 4,
            "cache_size": "1GB",
            "optimization_level": "high"
        }
    
    async def deploy_to_edge(
        self,
        agent_id: str,
        edge_locations: List[str],
        deployment_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Deploy agent to edge computing infrastructure"""
        
        # Validate edge locations
        valid_locations = await self._validate_edge_locations(edge_locations)
        
        # Create edge deployment configuration
        edge_config = {
            "agent_id": agent_id,
            "edge_locations": valid_locations,
            "deployment_config": deployment_config,
            "auto_scale": deployment_config.get("auto_scale", False),
            "security_compliance": True,
            "created_at": datetime.utcnow()
        }
        
        # Deploy to edge locations
        deployment_results = []
        for location in valid_locations:
            result = await self._deploy_to_single_edge(agent_id, location, deployment_config)
            deployment_results.append(result)
        
        return {
            "deployment_id": f"edge_deployment_{uuid4().hex[:8]}",
            "agent_id": agent_id,
            "edge_locations": valid_locations,
            "deployment_results": deployment_results,
            "status": "deployed"
        }
    
    async def _validate_edge_locations(self, locations: List[str]) -> List[str]:
        """Validate edge computing locations"""
        # Placeholder implementation
        valid_locations = []
        for location in locations:
            if location in ["us-west", "us-east", "eu-central", "asia-pacific"]:
                valid_locations.append(location)
        return valid_locations
    
    async def _deploy_to_single_edge(
        self,
        agent_id: str,
        location: str,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Deploy agent to single edge location"""
        return {
            "location": location,
            "agent_id": agent_id,
            "deployment_status": "success",
            "endpoint": f"https://edge-{location}.example.com",
            "response_time_ms": 50
        }
    
    async def coordinate_edge_to_cloud(
        self,
        edge_deployment_id: str,
        coordination_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Coordinate edge-to-cloud agent operations"""
        
        # Synchronize data between edge and cloud
        sync_result = await self._synchronize_edge_cloud_data(edge_deployment_id)
        
        # Load balancing
        load_balancing = await self._edge_cloud_load_balancing(edge_deployment_id)
        
        # Failover mechanisms
        failover_config = await self._setup_failover_mechanisms(edge_deployment_id)
        
        return {
            "coordination_id": f"coord_{uuid4().hex[:8]}",
            "edge_deployment_id": edge_deployment_id,
            "synchronization": sync_result,
            "load_balancing": load_balancing,
            "failover": failover_config,
            "status": "coordinated"
        }
    
    async def _synchronize_edge_cloud_data(
        self,
        edge_deployment_id: str
    ) -> Dict[str, Any]:
        """Synchronize data between edge and cloud"""
        return {
            "sync_status": "active",
            "last_sync": datetime.utcnow().isoformat(),
            "data_consistency": 0.99
        }
    
    async def _edge_cloud_load_balancing(
        self,
        edge_deployment_id: str
    ) -> Dict[str, Any]:
        """Implement edge-to-cloud load balancing"""
        return {
            "balancing_algorithm": "round_robin",
            "active_connections": 5,
            "average_response_time": 75.0
        }
    
    async def _setup_failover_mechanisms(
        self,
        edge_deployment_id: str
    ) -> Dict[str, Any]:
        """Setup robust failover mechanisms"""
        return {
            "failover_strategy": "automatic",
            "health_check_interval": 30,
            "max_failover_time": 60,
            "backup_locations": ["cloud-primary", "edge-secondary"]
        }
    
    async def develop_openclaw_ecosystem(
        self,
        ecosystem_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Build comprehensive OpenClaw ecosystem"""
        
        # Create developer tools and SDKs
        developer_tools = await self._create_developer_tools(ecosystem_config)
        
        # Implement marketplace for agent solutions
        marketplace = await self._create_agent_marketplace(ecosystem_config)
        
        # Develop community and governance
        community = await self._develop_community_governance(ecosystem_config)
        
        # Establish partnership programs
        partnerships = await self._establish_partnership_programs(ecosystem_config)
        
        return {
            "ecosystem_id": f"ecosystem_{uuid4().hex[:8]}",
            "developer_tools": developer_tools,
            "marketplace": marketplace,
            "community": community,
            "partnerships": partnerships,
            "status": "active"
        }
    
    async def _create_developer_tools(
        self,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create OpenClaw developer tools and SDKs"""
        return {
            "sdk_version": "2.0.0",
            "languages": ["python", "javascript", "go", "rust"],
            "tools": ["cli", "ide-plugin", "debugger"],
            "documentation": "https://docs.openclaw.ai"
        }
    
    async def _create_agent_marketplace(
        self,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create OpenClaw marketplace for agent solutions"""
        return {
            "marketplace_url": "https://marketplace.openclaw.ai",
            "agent_categories": ["inference", "training", "custom"],
            "payment_methods": ["cryptocurrency", "fiat"],
            "revenue_model": "commission_based"
        }
    
    async def _develop_community_governance(
        self,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Develop OpenClaw community and governance"""
        return {
            "governance_model": "dao",
            "voting_mechanism": "token_based",
            "community_forum": "https://community.openclaw.ai",
            "contribution_guidelines": "https://github.com/openclaw/contributing"
        }
    
    async def _establish_partnership_programs(
        self,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Establish OpenClaw partnership programs"""
        return {
            "technology_partners": ["cloud_providers", "hardware_manufacturers"],
            "integration_partners": ["ai_frameworks", "ml_platforms"],
            "reseller_program": "active",
            "partnership_benefits": ["revenue_sharing", "technical_support"]
        }
