"""
Data Source Evolution Manager for Calendar Generation Framework

Manages the evolution of data sources without architectural changes,
providing version management, enhancement planning, and evolution tracking.
"""

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

from .registry import DataSourceRegistry

logger = logging.getLogger(__name__)


class DataSourceEvolutionManager:
    """
    Manages the evolution of data sources without architectural changes.
    
    Provides comprehensive evolution management including version tracking,
    enhancement planning, implementation steps, and evolution monitoring.
    """
    
    def __init__(self, registry: DataSourceRegistry):
        """
        Initialize the data source evolution manager.
        
        Args:
            registry: Data source registry to manage
        """
        self.registry = registry
        self.evolution_configs = self._load_evolution_configs()
        self.evolution_history = {}
        
        logger.info("Initialized DataSourceEvolutionManager")
    
    def _load_evolution_configs(self) -> Dict[str, Dict[str, Any]]:
        """
        Load evolution configurations for data sources.
        
        Returns:
            Dictionary of evolution configurations
        """
        return {
            "content_strategy": {
                "current_version": "2.0.0",
                "target_version": "2.5.0",
                "enhancement_plan": [
                    "AI-powered strategy optimization",
                    "Real-time strategy adaptation",
                    "Advanced audience segmentation",
                    "Predictive strategy recommendations"
                ],
                "implementation_steps": [
                    "Implement AI strategy optimization algorithms",
                    "Add real-time strategy adaptation capabilities",
                    "Enhance audience segmentation with ML",
                    "Integrate predictive analytics for strategy recommendations"
                ],
                "priority": "high",
                "estimated_effort": "medium"
            },
            "gap_analysis": {
                "current_version": "1.5.0",
                "target_version": "2.0.0",
                "enhancement_plan": [
                    "AI-powered gap identification",
                    "Competitor analysis integration",
                    "Market trend analysis",
                    "Content opportunity scoring"
                ],
                "implementation_steps": [
                    "Enhance data collection methods",
                    "Add AI analysis capabilities",
                    "Integrate competitor data sources",
                    "Implement opportunity scoring algorithms"
                ],
                "priority": "high",
                "estimated_effort": "medium"
            },
            "keywords": {
                "current_version": "1.5.0",
                "target_version": "2.0.0",
                "enhancement_plan": [
                    "Dynamic keyword research",
                    "Trending keywords integration",
                    "Competitor keyword analysis",
                    "Keyword difficulty scoring"
                ],
                "implementation_steps": [
                    "Add dynamic research capabilities",
                    "Integrate trending data sources",
                    "Implement competitor analysis",
                    "Add difficulty scoring algorithms"
                ],
                "priority": "medium",
                "estimated_effort": "medium"
            },
            "content_pillars": {
                "current_version": "1.5.0",
                "target_version": "2.0.0",
                "enhancement_plan": [
                    "AI-generated dynamic pillars",
                    "Market-based pillar optimization",
                    "Performance-based pillar adjustment",
                    "Audience preference integration"
                ],
                "implementation_steps": [
                    "Implement AI pillar generation",
                    "Add market analysis integration",
                    "Create performance tracking",
                    "Integrate audience feedback"
                ],
                "priority": "medium",
                "estimated_effort": "medium"
            },
            "performance_data": {
                "current_version": "1.0.0",
                "target_version": "1.5.0",
                "enhancement_plan": [
                    "Real-time performance tracking",
                    "Conversion rate analysis",
                    "Engagement metrics integration",
                    "ROI calculation and optimization"
                ],
                "implementation_steps": [
                    "Build performance tracking system",
                    "Implement conversion tracking",
                    "Add engagement analytics",
                    "Create ROI optimization algorithms"
                ],
                "priority": "high",
                "estimated_effort": "high"
            },
            "ai_analysis": {
                "current_version": "2.0.0",
                "target_version": "2.5.0",
                "enhancement_plan": [
                    "Advanced predictive analytics",
                    "Real-time market intelligence",
                    "Automated competitive analysis",
                    "Strategic recommendation engine"
                ],
                "implementation_steps": [
                    "Enhance predictive analytics capabilities",
                    "Add real-time market data integration",
                    "Implement automated competitive analysis",
                    "Build strategic recommendation engine"
                ],
                "priority": "high",
                "estimated_effort": "high"
            }
        }
    
    async def evolve_data_source(self, source_id: str, target_version: str) -> bool:
        """
        Evolve a data source to a target version.
        
        Args:
            source_id: ID of the source to evolve
            target_version: Target version to evolve to
            
        Returns:
            True if evolution successful, False otherwise
        """
        source = self.registry.get_source(source_id)
        if not source:
            logger.error(f"Data source not found for evolution: {source_id}")
            return False
        
        config = self.evolution_configs.get(source_id)
        if not config:
            logger.error(f"Evolution config not found for: {source_id}")
            return False
        
        try:
            logger.info(f"Starting evolution of {source_id} to version {target_version}")
            
            # Record evolution start
            evolution_record = {
                "source_id": source_id,
                "from_version": source.version,
                "to_version": target_version,
                "started_at": datetime.utcnow().isoformat(),
                "status": "in_progress",
                "steps_completed": [],
                "steps_failed": []
            }
            
            # Implement evolution steps
            implementation_steps = config.get("implementation_steps", [])
            for step in implementation_steps:
                try:
                    await self._implement_evolution_step(source_id, step)
                    evolution_record["steps_completed"].append(step)
                    logger.info(f"Completed evolution step for {source_id}: {step}")
                except Exception as e:
                    evolution_record["steps_failed"].append({"step": step, "error": str(e)})
                    logger.error(f"Failed evolution step for {source_id}: {step} - {e}")
            
            # Update source version
            source.version = target_version
            
            # Record evolution completion
            evolution_record["completed_at"] = datetime.utcnow().isoformat()
            evolution_record["status"] = "completed" if not evolution_record["steps_failed"] else "partial"
            
            # Store evolution history
            if source_id not in self.evolution_history:
                self.evolution_history[source_id] = []
            self.evolution_history[source_id].append(evolution_record)
            
            logger.info(f"✅ Successfully evolved {source_id} to version {target_version}")
            return True
            
        except Exception as e:
            logger.error(f"Error evolving data source {source_id}: {e}")
            return False
    
    async def _implement_evolution_step(self, source_id: str, step: str):
        """
        Implement a specific evolution step.
        
        Args:
            source_id: ID of the source
            step: Step to implement
            
        Raises:
            Exception: If step implementation fails
        """
        # This is a simplified implementation
        # In a real implementation, this would contain actual evolution logic
        
        logger.info(f"Implementing evolution step for {source_id}: {step}")
        
        # Simulate step implementation
        # In reality, this would contain actual code to enhance the data source
        await self._simulate_evolution_step(source_id, step)
    
    async def _simulate_evolution_step(self, source_id: str, step: str):
        """
        Simulate evolution step implementation.
        
        Args:
            source_id: ID of the source
            step: Step to simulate
            
        Raises:
            Exception: If simulation fails
        """
        # Simulate processing time
        import asyncio
        await asyncio.sleep(0.1)
        
        # Simulate potential failure (10% chance)
        import random
        if random.random() < 0.1:
            raise Exception(f"Simulated failure in evolution step: {step}")
    
    def get_evolution_status(self) -> Dict[str, Dict[str, Any]]:
        """
        Get evolution status for all data sources.
        
        Returns:
            Dictionary containing evolution status for all sources
        """
        status = {}
        
        for source_id, config in self.evolution_configs.items():
            source = self.registry.get_source(source_id)
            evolution_history = self.evolution_history.get(source_id, [])
            
            status[source_id] = {
                "current_version": getattr(source, 'version', '1.0.0') if source else config["current_version"],
                "target_version": config["target_version"],
                "enhancement_plan": config["enhancement_plan"],
                "implementation_steps": config["implementation_steps"],
                "priority": config.get("priority", "medium"),
                "estimated_effort": config.get("estimated_effort", "medium"),
                "is_active": source.is_active if source else False,
                "evolution_history": evolution_history,
                "last_evolution": evolution_history[-1] if evolution_history else None,
                "evolution_status": self._get_evolution_status_for_source(source_id, config, source)
            }
        
        return status
    
    def _get_evolution_status_for_source(self, source_id: str, config: Dict[str, Any], source) -> str:
        """
        Get evolution status for a specific source.
        
        Args:
            source_id: ID of the source
            config: Evolution configuration
            source: Data source object
            
        Returns:
            Evolution status string
        """
        if not source:
            return "not_registered"
        
        current_version = getattr(source, 'version', config["current_version"])
        target_version = config["target_version"]
        
        if current_version == target_version:
            return "up_to_date"
        elif current_version < target_version:
            return "needs_evolution"
        else:
            return "ahead_of_target"
    
    def get_evolution_plan(self, source_id: str) -> Dict[str, Any]:
        """
        Get evolution plan for a specific source.
        
        Args:
            source_id: ID of the source
            
        Returns:
            Evolution plan dictionary
        """
        config = self.evolution_configs.get(source_id, {})
        source = self.registry.get_source(source_id)
        
        plan = {
            "source_id": source_id,
            "current_version": getattr(source, 'version', '1.0.0') if source else config.get("current_version", "1.0.0"),
            "target_version": config.get("target_version", "1.0.0"),
            "enhancement_plan": config.get("enhancement_plan", []),
            "implementation_steps": config.get("implementation_steps", []),
            "priority": config.get("priority", "medium"),
            "estimated_effort": config.get("estimated_effort", "medium"),
            "is_ready_for_evolution": self._is_ready_for_evolution(source_id),
            "dependencies": self._get_evolution_dependencies(source_id)
        }
        
        return plan
    
    def _is_ready_for_evolution(self, source_id: str) -> bool:
        """
        Check if a source is ready for evolution.
        
        Args:
            source_id: ID of the source
            
        Returns:
            True if ready for evolution, False otherwise
        """
        source = self.registry.get_source(source_id)
        if not source:
            return False
        
        # Check if source is active
        if not source.is_active:
            return False
        
        # Check if evolution is needed
        config = self.evolution_configs.get(source_id, {})
        current_version = getattr(source, 'version', config.get("current_version", "1.0.0"))
        target_version = config.get("target_version", "1.0.0")
        
        return current_version < target_version
    
    def _get_evolution_dependencies(self, source_id: str) -> List[str]:
        """
        Get evolution dependencies for a source.
        
        Args:
            source_id: ID of the source
            
        Returns:
            List of dependency source IDs
        """
        # Simplified dependency mapping
        # In a real implementation, this would be more sophisticated
        dependencies = {
            "gap_analysis": ["content_strategy"],
            "keywords": ["content_strategy", "gap_analysis"],
            "content_pillars": ["content_strategy", "gap_analysis"],
            "performance_data": ["content_strategy", "gap_analysis"],
            "ai_analysis": ["content_strategy", "gap_analysis", "keywords"]
        }
        
        return dependencies.get(source_id, [])
    
    def add_evolution_config(self, source_id: str, config: Dict[str, Any]) -> bool:
        """
        Add evolution configuration for a data source.
        
        Args:
            source_id: ID of the source
            config: Evolution configuration
            
        Returns:
            True if added successfully, False otherwise
        """
        try:
            if source_id in self.evolution_configs:
                logger.warning(f"Evolution config already exists for: {source_id}")
                return False
            
            # Validate required fields
            required_fields = ["current_version", "target_version", "enhancement_plan", "implementation_steps"]
            for field in required_fields:
                if field not in config:
                    logger.error(f"Missing required field for evolution config {source_id}: {field}")
                    return False
            
            self.evolution_configs[source_id] = config
            logger.info(f"Added evolution config for: {source_id}")
            return True
            
        except Exception as e:
            logger.error(f"Error adding evolution config for {source_id}: {e}")
            return False
    
    def update_evolution_config(self, source_id: str, config: Dict[str, Any]) -> bool:
        """
        Update evolution configuration for a data source.
        
        Args:
            source_id: ID of the source
            config: Updated evolution configuration
            
        Returns:
            True if updated successfully, False otherwise
        """
        try:
            if source_id not in self.evolution_configs:
                logger.error(f"Evolution config not found for: {source_id}")
                return False
            
            # Update configuration
            self.evolution_configs[source_id].update(config)
            logger.info(f"Updated evolution config for: {source_id}")
            return True
            
        except Exception as e:
            logger.error(f"Error updating evolution config for {source_id}: {e}")
            return False
    
    def get_evolution_summary(self) -> Dict[str, Any]:
        """
        Get comprehensive evolution summary.
        
        Returns:
            Evolution summary dictionary
        """
        summary = {
            "total_sources": len(self.evolution_configs),
            "sources_needing_evolution": 0,
            "sources_up_to_date": 0,
            "evolution_priority": {
                "high": 0,
                "medium": 0,
                "low": 0
            },
            "evolution_effort": {
                "high": 0,
                "medium": 0,
                "low": 0
            },
            "recent_evolutions": [],
            "evolution_recommendations": []
        }
        
        for source_id, config in self.evolution_configs.items():
            source = self.registry.get_source(source_id)
            if source:
                status = self._get_evolution_status_for_source(source_id, config, source)
                if status == "needs_evolution":
                    summary["sources_needing_evolution"] += 1
                elif status == "up_to_date":
                    summary["sources_up_to_date"] += 1
                
                # Count priorities and efforts
                priority = config.get("priority", "medium")
                effort = config.get("estimated_effort", "medium")
                summary["evolution_priority"][priority] += 1
                summary["evolution_effort"][effort] += 1
        
        # Get recent evolutions
        for source_id, history in self.evolution_history.items():
            if history:
                latest = history[-1]
                if latest.get("status") == "completed":
                    summary["recent_evolutions"].append({
                        "source_id": source_id,
                        "from_version": latest.get("from_version"),
                        "to_version": latest.get("to_version"),
                        "completed_at": latest.get("completed_at")
                    })
        
        # Generate recommendations
        for source_id, config in self.evolution_configs.items():
            if self._is_ready_for_evolution(source_id):
                summary["evolution_recommendations"].append({
                    "source_id": source_id,
                    "priority": config.get("priority", "medium"),
                    "effort": config.get("estimated_effort", "medium"),
                    "target_version": config.get("target_version")
                })
        
        return summary
    
    def __str__(self) -> str:
        """String representation of the evolution manager."""
        return f"DataSourceEvolutionManager(sources={len(self.evolution_configs)}, registry={self.registry})"
    
    def __repr__(self) -> str:
        """Detailed string representation of the evolution manager."""
        return f"DataSourceEvolutionManager(configs={list(self.evolution_configs.keys())}, history={list(self.evolution_history.keys())})"
