"""
Evolution Engine

This module contains the core evolution engine that drives the self-improvement
process of ELLMa.
"""

import os
import json
import time
import tempfile
import importlib
import subprocess
import shutil
import logging
from datetime import datetime, timedelta
from pathlib import Path
from typing import Dict, List, Any, Optional, Tuple, Set, Callable, Type

from rich.console import Console
from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
from rich.table import Table
from rich.panel import Panel

from ellma.core.evolution.config import EvolutionConfig
from ellma.core.evolution.exceptions import (
    EvolutionError, ResourceLimitExceeded, EvolutionTimeout,
    InvalidEvolutionState, ModuleGenerationError, TestFailure
)
from ellma.core.module_generator import ModuleGenerator
from ellma.utils.evolution_utils import (
    setup_evolution_environment,
    check_system_resources,
    cleanup_resources,
    log_evolution_result
)
from ellma.utils.logger import get_logger
from ellma.core.error_logger import log_error

logger = get_logger(__name__)


class EvolutionEngine:
    """
    Core Evolution Engine for ELLMa
    
    Handles the self-improvement cycle:
    1. Analysis - Examine current capabilities and performance
    2. Identification - Find improvement opportunities  
    3. Generation - Create new modules and enhancements
    4. Testing - Validate new capabilities
    5. Integration - Deploy improvements
    """
    
    def __init__(self, config: Optional[EvolutionConfig] = None):
        """Initialize the evolution engine.
        
        Args:
            config: Configuration for the evolution process
        """
        self.config = config or EvolutionConfig()
        self.module_generator = ModuleGenerator()
        self.console = Console()
        self.start_time = datetime.now()
        self.generation = 0
        self.best_fitness = float('-inf')
        self.best_candidate: Dict[str, Any] = {}
        self.population: List[Dict[str, Any]] = []
        self.history: List[Dict[str, Any]] = []
        
        # Setup working directory
        self.working_dir = Path(self.config.working_dir)
        self.working_dir.mkdir(parents=True, exist_ok=True)
        
        # Setup logging
        self.log_file = self.working_dir / 'evolution.log'
        self.setup_logging()
    
    def setup_logging(self) -> None:
        """Configure logging for the evolution process."""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler(self.log_file),
                logging.StreamHandler()
            ]
        )
    
    def run(self) -> Dict[str, Any]:
        """Run the evolution process.
        
        Returns:
            Dict containing results of the evolution process
            
        Raises:
            EvolutionError: If evolution fails
        """
        try:
            self._validate_environment()
            self._initialize_population()
            
            with Progress(
                SpinnerColumn(),
                TextColumn("[progress.description]{task.description}"),
                BarColumn(),
                transient=True,
            ) as progress:
                task = progress.add_task("Evolving...", total=self.config.max_generations)
                
                while self.generation < self.config.max_generations:
                    if self._check_timeout():
                        raise EvolutionTimeout("Evolution timed out")
                    
                    self._evaluate_population()
                    self._update_best()
                    self._log_progress()
                    
                    if self._should_terminate():
                        break
                        
                    self._next_generation()
                    progress.update(task, advance=1)
            
            return self._finalize()
            
        except Exception as e:
            self._handle_error(e)
            raise
        finally:
            self.cleanup()
    
    def _validate_environment(self) -> None:
        """Validate the environment before starting evolution."""
        if not check_system_resources(self.config):
            raise ResourceLimitExceeded("Insufficient system resources")
        
        if not setup_evolution_environment(self.working_dir):
            raise EvolutionError("Failed to setup evolution environment")
    
    def _initialize_population(self) -> None:
        """Initialize the initial population of candidates."""
        self.population = [
            self._generate_candidate() 
            for _ in range(self.config.population_size)
        ]
        self.generation = 0
    
    def _evaluate_population(self) -> None:
        """Evaluate all candidates in the current population."""
        for candidate in self.population:
            if 'fitness' not in candidate:
                candidate['fitness'] = self._evaluate_candidate(candidate)
    
    def _update_best(self) -> None:
        """Update the best candidate found so far."""
        current_best = max(self.population, key=lambda x: x['fitness'])
        if current_best['fitness'] > self.best_fitness:
            self.best_fitness = current_best['fitness']
            self.best_candidate = current_best.copy()
    
    def _should_terminate(self) -> bool:
        """Check if evolution should terminate."""
        # Add termination conditions here
        return False
    
    def _next_generation(self) -> None:
        """Generate the next generation of candidates."""
        # Implementation of selection, crossover, and mutation
        self.generation += 1
    
    def _finalize(self) -> Dict[str, Any]:
        """Finalize the evolution process and return results."""
        return {
            'best_fitness': self.best_fitness,
            'best_candidate': self.best_candidate,
            'generations': self.generation,
            'duration': str(datetime.now() - self.start_time)
        }
    
    def _generate_candidate(self) -> Dict[str, Any]:
        """Generate a new candidate solution."""
        # Implementation for generating a new candidate
        return {}
    
    def _evaluate_candidate(self, candidate: Dict[str, Any]) -> float:
        """Evaluate a candidate solution and return its fitness."""
        # Implementation for evaluating a candidate
        return 0.0
    
    def _check_timeout(self) -> bool:
        """Check if the evolution has timed out."""
        return (datetime.now() - self.start_time) > self.config.timeout
    
    def _log_progress(self) -> None:
        """Log the current progress of evolution."""
        avg_fitness = sum(c['fitness'] for c in self.population) / len(self.population)
        logger.info(
            f"Generation {self.generation}: "
            f"Best={self.best_fitness:.4f}, "
            f"Avg={avg_fitness:.4f}"
        )
    
    def _handle_error(self, error: Exception) -> None:
        """Handle errors during evolution."""
        logger.error(f"Evolution failed: {error}", exc_info=True)
        log_error(error, "evolution_error")
    
    def cleanup(self) -> None:
        """Clean up resources used during evolution."""
        cleanup_resources()
        
        # Log final results
        log_evolution_result({
            'generation': self.generation,
            'best_fitness': self.best_fitness,
            'best_candidate': self.best_candidate,
            'duration': str(datetime.now() - self.start_time)
        })
