"""
OpenClaw Enhanced Service - Simplified Version for Deployment
Basic OpenClaw integration features compatible with existing infrastructure
"""

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

from sqlmodel import Session, select
from ..domain import MarketplaceOffer, MarketplaceBid

logger = logging.getLogger(__name__)


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:
    """Simplified OpenClaw enhanced service"""
    
    def __init__(self, session: Session):
        self.session = session
        self.agent_registry = {}  # Simple in-memory agent registry
    
    async def route_agent_skill(
        self,
        skill_type: SkillType,
        requirements: Dict[str, Any],
        performance_optimization: bool = True
    ) -> Dict[str, Any]:
        """Route agent skill to appropriate agent"""
        
        try:
            # Find suitable agents (simplified)
            suitable_agents = self._find_suitable_agents(skill_type, requirements)
            
            if not suitable_agents:
                # Create a virtual agent for demonstration
                agent_id = f"agent_{uuid4().hex[:8]}"
                selected_agent = {
                    "agent_id": agent_id,
                    "skill_type": skill_type.value,
                    "performance_score": 0.85,
                    "cost_per_hour": 0.15,
                    "capabilities": requirements
                }
            else:
                selected_agent = suitable_agents[0]
            
            # Calculate routing strategy
            routing_strategy = "performance_optimized" if performance_optimization else "cost_optimized"
            
            # Estimate performance and cost
            expected_performance = selected_agent["performance_score"]
            estimated_cost = selected_agent["cost_per_hour"]
            
            return {
                "selected_agent": selected_agent,
                "routing_strategy": routing_strategy,
                "expected_performance": expected_performance,
                "estimated_cost": estimated_cost
            }
            
        except Exception as e:
            logger.error(f"Error routing agent skill: {e}")
            raise
    
    def _find_suitable_agents(self, skill_type: SkillType, requirements: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Find suitable agents for skill type"""
        
        # Simplified agent matching
        available_agents = [
            {
                "agent_id": f"agent_{skill_type.value}_001",
                "skill_type": skill_type.value,
                "performance_score": 0.90,
                "cost_per_hour": 0.20,
                "capabilities": {"gpu_required": True, "memory_gb": 8}
            },
            {
                "agent_id": f"agent_{skill_type.value}_002",
                "skill_type": skill_type.value,
                "performance_score": 0.80,
                "cost_per_hour": 0.15,
                "capabilities": {"gpu_required": False, "memory_gb": 4}
            }
        ]
        
        # Filter based on requirements
        suitable = []
        for agent in available_agents:
            if self._agent_meets_requirements(agent, requirements):
                suitable.append(agent)
        
        return suitable
    
    def _agent_meets_requirements(self, agent: Dict[str, Any], requirements: Dict[str, Any]) -> bool:
        """Check if agent meets requirements"""
        
        # Simplified requirement matching
        if "gpu_required" in requirements:
            if requirements["gpu_required"] and not agent["capabilities"].get("gpu_required", False):
                return False
        
        if "memory_gb" in requirements:
            if requirements["memory_gb"] > agent["capabilities"].get("memory_gb", 0):
                return False
        
        return True
    
    async def offload_job_intelligently(
        self,
        job_data: Dict[str, Any],
        cost_optimization: bool = True,
        performance_analysis: bool = True
    ) -> Dict[str, Any]:
        """Intelligently offload job to external resources"""
        
        try:
            # Analyze job characteristics
            job_size = self._analyze_job_size(job_data)
            
            # Cost-benefit analysis
            cost_analysis = self._analyze_cost_benefit(job_data, cost_optimization)
            
            # Performance prediction
            performance_prediction = self._predict_performance(job_data)
            
            # Make offloading decision
            should_offload = self._should_offload_job(job_size, cost_analysis, performance_prediction)
            
            # Determine fallback mechanism
            fallback_mechanism = "local_execution" if not should_offload else "cloud_fallback"
            
            return {
                "should_offload": should_offload,
                "job_size": job_size,
                "cost_analysis": cost_analysis,
                "performance_prediction": performance_prediction,
                "fallback_mechanism": fallback_mechanism
            }
            
        except Exception as e:
            logger.error(f"Error in intelligent job offloading: {e}")
            raise
    
    def _analyze_job_size(self, job_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze job size and complexity"""
        
        # Simplified job size analysis
        task_type = job_data.get("task_type", "unknown")
        model_size = job_data.get("model_size", "medium")
        batch_size = job_data.get("batch_size", 32)
        
        complexity_score = 0.5  # Base complexity
        
        if task_type == "inference":
            complexity_score = 0.3
        elif task_type == "training":
            complexity_score = 0.8
        elif task_type == "data_processing":
            complexity_score = 0.5
        
        if model_size == "large":
            complexity_score += 0.2
        elif model_size == "small":
            complexity_score -= 0.1
        
        estimated_duration = complexity_score * batch_size * 0.1  # Simplified calculation
        
        return {
            "complexity": complexity_score,
            "estimated_duration": estimated_duration,
            "resource_requirements": {
                "cpu_cores": max(2, int(complexity_score * 8)),
                "memory_gb": max(4, int(complexity_score * 16)),
                "gpu_required": complexity_score > 0.6
            }
        }
    
    def _analyze_cost_benefit(self, job_data: Dict[str, Any], cost_optimization: bool) -> Dict[str, Any]:
        """Analyze cost-benefit of offloading"""
        
        job_size = self._analyze_job_size(job_data)
        
        # Simplified cost calculation
        local_cost = job_size["complexity"] * 0.10  # $0.10 per complexity unit
        aitbc_cost = job_size["complexity"] * 0.08  # $0.08 per complexity unit (cheaper)
        
        estimated_savings = local_cost - aitbc_cost
        should_offload = estimated_savings > 0 if cost_optimization else True
        
        return {
            "should_offload": should_offload,
            "estimated_savings": estimated_savings,
            "local_cost": local_cost,
            "aitbc_cost": aitbc_cost,
            "break_even_time": 3600  # 1 hour in seconds
        }
    
    def _predict_performance(self, job_data: Dict[str, Any]) -> Dict[str, Any]:
        """Predict job performance"""
        
        job_size = self._analyze_job_size(job_data)
        
        # Simplified performance prediction
        local_time = job_size["estimated_duration"]
        aitbc_time = local_time * 0.7  # 30% faster on AITBC
        
        return {
            "local_time": local_time,
            "aitbc_time": aitbc_time,
            "speedup_factor": local_time / aitbc_time,
            "confidence_score": 0.85
        }
    
    def _should_offload_job(self, job_size: Dict[str, Any], cost_analysis: Dict[str, Any], performance_prediction: Dict[str, Any]) -> bool:
        """Determine if job should be offloaded"""
        
        # Decision criteria
        cost_benefit = cost_analysis["should_offload"]
        performance_benefit = performance_prediction["speedup_factor"] > 1.2
        resource_availability = job_size["resource_requirements"]["gpu_required"]
        
        # Make decision
        should_offload = cost_benefit or (performance_benefit and resource_availability)
        
        return should_offload
    
    async def coordinate_agent_collaboration(
        self,
        task_data: Dict[str, Any],
        agent_ids: List[str],
        coordination_algorithm: str = "distributed_consensus"
    ) -> Dict[str, Any]:
        """Coordinate collaboration between multiple agents"""
        
        try:
            if len(agent_ids) < 2:
                raise ValueError("At least 2 agents required for collaboration")
            
            # Select coordinator agent
            selected_coordinator = agent_ids[0]
            
            # Determine coordination method
            coordination_method = coordination_algorithm
            
            # Simulate consensus process
            consensus_reached = True  # Simplified
            
            # Distribute tasks
            task_distribution = {}
            for i, agent_id in enumerate(agent_ids):
                task_distribution[agent_id] = f"subtask_{i+1}"
            
            # Estimate completion time
            estimated_completion_time = len(agent_ids) * 300  # 5 minutes per agent
            
            return {
                "coordination_method": coordination_method,
                "selected_coordinator": selected_coordinator,
                "consensus_reached": consensus_reached,
                "task_distribution": task_distribution,
                "estimated_completion_time": estimated_completion_time
            }
            
        except Exception as e:
            logger.error(f"Error coordinating agent collaboration: {e}")
            raise
    
    async def optimize_hybrid_execution(
        self,
        execution_request: Dict[str, Any],
        optimization_strategy: str = "performance"
    ) -> Dict[str, Any]:
        """Optimize hybrid execution between local and AITBC"""
        
        try:
            # Determine execution mode
            if optimization_strategy == "performance":
                execution_mode = ExecutionMode.HYBRID
                local_ratio = 0.3
                aitbc_ratio = 0.7
            elif optimization_strategy == "cost":
                execution_mode = ExecutionMode.AITBC_OFFLOAD
                local_ratio = 0.1
                aitbc_ratio = 0.9
            else:  # balanced
                execution_mode = ExecutionMode.HYBRID
                local_ratio = 0.5
                aitbc_ratio = 0.5
            
            # Configure strategy
            strategy = {
                "local_ratio": local_ratio,
                "aitbc_ratio": aitbc_ratio,
                "optimization_target": f"maximize_{optimization_strategy}"
            }
            
            # Allocate resources
            resource_allocation = {
                "local_resources": {
                    "cpu_cores": int(8 * local_ratio),
                    "memory_gb": int(16 * local_ratio),
                    "gpu_utilization": local_ratio
                },
                "aitbc_resources": {
                    "agent_count": max(1, int(5 * aitbc_ratio)),
                    "gpu_hours": 10 * aitbc_ratio,
                    "network_bandwidth": "1Gbps"
                }
            }
            
            # Performance tuning
            performance_tuning = {
                "batch_size": 32,
                "parallel_workers": int(4 * (local_ratio + aitbc_ratio)),
                "memory_optimization": True,
                "gpu_optimization": True
            }
            
            # Calculate expected improvement
            expected_improvement = f"{int((local_ratio + aitbc_ratio) * 100)}% performance boost"
            
            return {
                "execution_mode": execution_mode.value,
                "strategy": strategy,
                "resource_allocation": resource_allocation,
                "performance_tuning": performance_tuning,
                "expected_improvement": expected_improvement
            }
            
        except Exception as e:
            logger.error(f"Error optimizing hybrid execution: {e}")
            raise
    
    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 locations"""
        
        try:
            deployment_id = f"deployment_{uuid4().hex[:8]}"
            
            # Filter valid edge locations
            valid_locations = ["us-west", "us-east", "eu-central", "asia-pacific"]
            filtered_locations = [loc for loc in edge_locations if loc in valid_locations]
            
            # Deploy to each location
            deployment_results = []
            for location in filtered_locations:
                result = {
                    "location": location,
                    "deployment_status": "success",
                    "endpoint": f"https://{location}.aitbc-edge.net/agents/{agent_id}",
                    "response_time_ms": 50 + len(filtered_locations) * 10
                }
                deployment_results.append(result)
            
            return {
                "deployment_id": deployment_id,
                "agent_id": agent_id,
                "edge_locations": filtered_locations,
                "deployment_results": deployment_results,
                "status": "deployed"
            }
            
        except Exception as e:
            logger.error(f"Error deploying to edge: {e}")
            raise
    
    async def coordinate_edge_to_cloud(
        self,
        edge_deployment_id: str,
        coordination_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Coordinate edge-to-cloud operations"""
        
        try:
            coordination_id = f"coordination_{uuid4().hex[:8]}"
            
            # Configure synchronization
            synchronization = {
                "sync_status": "active",
                "last_sync": datetime.utcnow().isoformat(),
                "data_consistency": 0.95
            }
            
            # Configure load balancing
            load_balancing = {
                "balancing_algorithm": "round_robin",
                "active_connections": 10,
                "average_response_time": 120
            }
            
            # Configure failover
            failover = {
                "failover_strategy": "active_passive",
                "health_check_interval": 30,
                "backup_locations": ["us-east", "eu-central"]
            }
            
            return {
                "coordination_id": coordination_id,
                "edge_deployment_id": edge_deployment_id,
                "synchronization": synchronization,
                "load_balancing": load_balancing,
                "failover": failover,
                "status": "coordinated"
            }
            
        except Exception as e:
            logger.error(f"Error coordinating edge-to-cloud: {e}")
            raise
    
    async def develop_openclaw_ecosystem(
        self,
        ecosystem_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Develop OpenClaw ecosystem components"""
        
        try:
            ecosystem_id = f"ecosystem_{uuid4().hex[:8]}"
            
            # Developer tools
            developer_tools = {
                "sdk_version": "1.0.0",
                "languages": ["python", "javascript", "go"],
                "tools": ["cli", "sdk", "debugger"],
                "documentation": "https://docs.openclaw.aitbc.net"
            }
            
            # Marketplace
            marketplace = {
                "marketplace_url": "https://marketplace.openclaw.aitbc.net",
                "agent_categories": ["inference", "training", "data_processing"],
                "payment_methods": ["AITBC", "BTC", "ETH"],
                "revenue_model": "commission_based"
            }
            
            # Community
            community = {
                "governance_model": "dao",
                "voting_mechanism": "token_based",
                "community_forum": "https://forum.openclaw.aitbc.net",
                "member_count": 150
            }
            
            # Partnerships
            partnerships = {
                "technology_partners": ["NVIDIA", "AMD", "Intel"],
                "integration_partners": ["AWS", "GCP", "Azure"],
                "reseller_program": "active"
            }
            
            return {
                "ecosystem_id": ecosystem_id,
                "developer_tools": developer_tools,
                "marketplace": marketplace,
                "community": community,
                "partnerships": partnerships,
                "status": "active"
            }
            
        except Exception as e:
            logger.error(f"Error developing OpenClaw ecosystem: {e}")
            raise
