#!/usr/bin/env python3
"""
Grim Performance Optimizer

Comprehensive performance optimization and benchmarking system for the Grim framework.
Provides automated performance tuning, bottleneck detection, and optimization recommendations.
"""

import asyncio
import json
import time
import psutil
import os
import threading
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Callable, Tuple
from pathlib import Path
from dataclasses import dataclass, asdict
from collections import defaultdict, deque
import statistics
import subprocess
import sys

# Add the project root to the path
import sys
sys.path.insert(0, str(Path(__file__).parent.parent))

from grim_core.config import Config
from grim_core.logger import Logger
from grim_core.database import DatabaseManager

@dataclass
class BenchmarkResult:
    """Benchmark result container"""
    name: str
    operation: str
    duration: float
    throughput: float
    memory_usage: int
    cpu_usage: float
    timestamp: float
    parameters: Dict[str, Any]
    success: bool
    error_message: Optional[str] = None

@dataclass
class PerformanceProfile:
    """Performance profile container"""
    name: str
    description: str
    benchmarks: List[BenchmarkResult]
    average_duration: float
    average_throughput: float
    average_memory: int
    average_cpu: float
    total_operations: int
    success_rate: float
    timestamp: float

@dataclass
class OptimizationRecommendation:
    """Optimization recommendation container"""
    id: str
    category: str
    title: str
    description: str
    impact: str  # 'low', 'medium', 'high', 'critical'
    effort: str  # 'low', 'medium', 'high'
    current_value: Any
    recommended_value: Any
    expected_improvement: float
    implementation_steps: List[str]
    timestamp: float

class GrimOptimizer:
    """Main performance optimizer"""
    
    def __init__(self, config: Optional[Config] = None):
        self.config = config or Config()
        self.logger = Logger("grim_optimizer")
        self.db_manager = DatabaseManager(self.config)
        
        # Performance tracking
        self.benchmark_results: List[BenchmarkResult] = []
        self.performance_profiles: Dict[str, PerformanceProfile] = {}
        self.optimization_recommendations: List[OptimizationRecommendation] = []
        
        # Optimization state
        self.optimization_running = False
        self.optimization_thread = None
        
        # Performance thresholds
        self.thresholds = {
            'response_time': 100.0,  # ms
            'throughput': 1000.0,    # ops/sec
            'memory_usage': 512 * 1024 * 1024,  # 512MB
            'cpu_usage': 50.0,       # percentage
            'disk_io': 100 * 1024 * 1024,  # 100MB/s
            'network_io': 50 * 1024 * 1024  # 50MB/s
        }
        
        # Initialize optimizer
        self._initialize_optimizer()
        
        self.logger.info("Grim Optimizer initialized")
    
    def _initialize_optimizer(self):
        """Initialize optimizer components"""
        # Create optimization tables
        self._create_optimization_tables()
        
        # Load existing recommendations
        self._load_recommendations()
        
        # Initialize baseline performance
        self._establish_baseline()
    
    def _create_optimization_tables(self):
        """Create optimization tables in database"""
        try:
            with self.db_manager.get_connection() as conn:
                cursor = conn.cursor()
                
                # Benchmark results table
                cursor.execute("""
                    CREATE TABLE IF NOT EXISTS benchmark_results (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        name TEXT NOT NULL,
                        operation TEXT NOT NULL,
                        duration REAL NOT NULL,
                        throughput REAL,
                        memory_usage INTEGER,
                        cpu_usage REAL,
                        timestamp REAL NOT NULL,
                        parameters TEXT,
                        success INTEGER DEFAULT 1,
                        error_message TEXT
                    )
                """)
                
                # Performance profiles table
                cursor.execute("""
                    CREATE TABLE IF NOT EXISTS performance_profiles (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        name TEXT NOT NULL,
                        description TEXT,
                        average_duration REAL,
                        average_throughput REAL,
                        average_memory INTEGER,
                        average_cpu REAL,
                        total_operations INTEGER,
                        success_rate REAL,
                        timestamp REAL NOT NULL
                    )
                """)
                
                # Optimization recommendations table
                cursor.execute("""
                    CREATE TABLE IF NOT EXISTS optimization_recommendations (
                        id TEXT PRIMARY KEY,
                        category TEXT NOT NULL,
                        title TEXT NOT NULL,
                        description TEXT,
                        impact TEXT NOT NULL,
                        effort TEXT NOT NULL,
                        current_value TEXT,
                        recommended_value TEXT,
                        expected_improvement REAL,
                        implementation_steps TEXT,
                        timestamp REAL NOT NULL,
                        implemented INTEGER DEFAULT 0
                    )
                """)
                
                conn.commit()
                self.logger.info("Optimization tables created successfully")
                
        except Exception as e:
            self.logger.error(f"Failed to create optimization tables: {e}")
    
    def _load_recommendations(self):
        """Load existing optimization recommendations"""
        try:
            with self.db_manager.get_connection() as conn:
                cursor = conn.cursor()
                cursor.execute("SELECT * FROM optimization_recommendations WHERE implemented = 0")
                
                rows = cursor.fetchall()
                columns = [description[0] for description in cursor.description]
                
                for row in rows:
                    data = dict(zip(columns, row))
                    recommendation = OptimizationRecommendation(
                        id=data['id'],
                        category=data['category'],
                        title=data['title'],
                        description=data['description'],
                        impact=data['impact'],
                        effort=data['effort'],
                        current_value=json.loads(data['current_value']) if data['current_value'] else None,
                        recommended_value=json.loads(data['recommended_value']) if data['recommended_value'] else None,
                        expected_improvement=data['expected_improvement'],
                        implementation_steps=json.loads(data['implementation_steps']) if data['implementation_steps'] else [],
                        timestamp=data['timestamp']
                    )
                    self.optimization_recommendations.append(recommendation)
                
                self.logger.info(f"Loaded {len(self.optimization_recommendations)} optimization recommendations")
                
        except Exception as e:
            self.logger.error(f"Error loading recommendations: {e}")
    
    def _establish_baseline(self):
        """Establish baseline performance metrics"""
        self.logger.info("Establishing performance baseline...")
        
        # Run baseline benchmarks
        baseline_benchmarks = [
            ("database_connection", self._benchmark_database_connection),
            ("file_operations", self._benchmark_file_operations),
            ("memory_operations", self._benchmark_memory_operations),
            ("cpu_operations", self._benchmark_cpu_operations),
            ("network_operations", self._benchmark_network_operations)
        ]
        
        for name, benchmark_func in baseline_benchmarks:
            try:
                result = benchmark_func()
                self.benchmark_results.append(result)
                self._store_benchmark_result(result)
            except Exception as e:
                self.logger.error(f"Baseline benchmark {name} failed: {e}")
        
        self.logger.info("Performance baseline established")
    
    def _benchmark_database_connection(self) -> BenchmarkResult:
        """Benchmark database connection performance"""
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss
        start_cpu = psutil.cpu_percent()
        
        try:
            # Test database connection
            with self.db_manager.get_connection() as conn:
                cursor = conn.cursor()
                
                # Simple query
                cursor.execute("SELECT 1")
                cursor.fetchone()
                
                # Multiple queries
                for i in range(100):
                    cursor.execute("SELECT ?", (i,))
                    cursor.fetchone()
            
            duration = time.time() - start_time
            end_memory = psutil.Process().memory_info().rss
            end_cpu = psutil.cpu_percent()
            
            return BenchmarkResult(
                name="database_connection",
                operation="connection_and_queries",
                duration=duration,
                throughput=100.0 / duration,  # 100 queries
                memory_usage=end_memory - start_memory,
                cpu_usage=(end_cpu + start_cpu) / 2,
                timestamp=time.time(),
                parameters={"queries": 100},
                success=True
            )
            
        except Exception as e:
            return BenchmarkResult(
                name="database_connection",
                operation="connection_and_queries",
                duration=time.time() - start_time,
                throughput=0.0,
                memory_usage=0,
                cpu_usage=0.0,
                timestamp=time.time(),
                parameters={"queries": 100},
                success=False,
                error_message=str(e)
            )
    
    def _benchmark_file_operations(self) -> BenchmarkResult:
        """Benchmark file operation performance"""
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss
        start_cpu = psutil.cpu_percent()
        
        try:
            # Create test file
            test_file = Path("/tmp/grim_benchmark_test.txt")
            test_data = "x" * 1024 * 1024  # 1MB of data
            
            # Write operations
            with open(test_file, 'w') as f:
                for i in range(10):
                    f.write(test_data)
            
            # Read operations
            with open(test_file, 'r') as f:
                for i in range(10):
                    f.read(1024 * 1024)
            
            # Cleanup
            test_file.unlink()
            
            duration = time.time() - start_time
            end_memory = psutil.Process().memory_info().rss
            end_cpu = psutil.cpu_percent()
            
            return BenchmarkResult(
                name="file_operations",
                operation="read_write_operations",
                duration=duration,
                throughput=20.0 / duration,  # 20 operations
                memory_usage=end_memory - start_memory,
                cpu_usage=(end_cpu + start_cpu) / 2,
                timestamp=time.time(),
                parameters={"operations": 20, "data_size": "1MB"},
                success=True
            )
            
        except Exception as e:
            return BenchmarkResult(
                name="file_operations",
                operation="read_write_operations",
                duration=time.time() - start_time,
                throughput=0.0,
                memory_usage=0,
                cpu_usage=0.0,
                timestamp=time.time(),
                parameters={"operations": 20, "data_size": "1MB"},
                success=False,
                error_message=str(e)
            )
    
    def _benchmark_memory_operations(self) -> BenchmarkResult:
        """Benchmark memory operation performance"""
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss
        start_cpu = psutil.cpu_percent()
        
        try:
            # Memory allocation and manipulation
            data_structures = []
            for i in range(1000):
                # Create various data structures
                data_structures.append([j for j in range(100)])
                data_structures.append({f"key_{j}": j for j in range(100)})
                data_structures.append("x" * 1000)
            
            # Memory operations
            for i in range(100):
                # Sort operations
                sorted(data_structures[i % len(data_structures)])
                
                # Search operations
                if isinstance(data_structures[i % len(data_structures)], list):
                    data_structures[i % len(data_structures)].index(50)
            
            duration = time.time() - start_time
            end_memory = psutil.Process().memory_info().rss
            end_cpu = psutil.cpu_percent()
            
            return BenchmarkResult(
                name="memory_operations",
                operation="allocation_and_manipulation",
                duration=duration,
                throughput=200.0 / duration,  # 200 operations
                memory_usage=end_memory - start_memory,
                cpu_usage=(end_cpu + start_cpu) / 2,
                timestamp=time.time(),
                parameters={"operations": 200, "structures": 1000},
                success=True
            )
            
        except Exception as e:
            return BenchmarkResult(
                name="memory_operations",
                operation="allocation_and_manipulation",
                duration=time.time() - start_time,
                throughput=0.0,
                memory_usage=0,
                cpu_usage=0.0,
                timestamp=time.time(),
                parameters={"operations": 200, "structures": 1000},
                success=False,
                error_message=str(e)
            )
    
    def _benchmark_cpu_operations(self) -> BenchmarkResult:
        """Benchmark CPU operation performance"""
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss
        start_cpu = psutil.cpu_percent()
        
        try:
            # CPU-intensive operations
            results = []
            for i in range(10000):
                # Mathematical operations
                result = sum(j * j for j in range(100))
                results.append(result)
                
                # String operations
                text = "x" * 1000
                text = text.upper()
                text = text.lower()
                text = text.replace("x", "y")
            
            duration = time.time() - start_time
            end_memory = psutil.Process().memory_info().rss
            end_cpu = psutil.cpu_percent()
            
            return BenchmarkResult(
                name="cpu_operations",
                operation="mathematical_and_string",
                duration=duration,
                throughput=10000.0 / duration,  # 10000 operations
                memory_usage=end_memory - start_memory,
                cpu_usage=(end_cpu + start_cpu) / 2,
                timestamp=time.time(),
                parameters={"operations": 10000},
                success=True
            )
            
        except Exception as e:
            return BenchmarkResult(
                name="cpu_operations",
                operation="mathematical_and_string",
                duration=time.time() - start_time,
                throughput=0.0,
                memory_usage=0,
                cpu_usage=0.0,
                timestamp=time.time(),
                parameters={"operations": 10000},
                success=False,
                error_message=str(e)
            )
    
    def _benchmark_network_operations(self) -> BenchmarkResult:
        """Benchmark network operation performance"""
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss
        start_cpu = psutil.cpu_percent()
        
        try:
            import requests
            
            # Network operations (localhost)
            for i in range(10):
                response = requests.get("http://localhost:8000/health", timeout=5)
                response.raise_for_status()
            
            duration = time.time() - start_time
            end_memory = psutil.Process().memory_info().rss
            end_cpu = psutil.cpu_percent()
            
            return BenchmarkResult(
                name="network_operations",
                operation="http_requests",
                duration=duration,
                throughput=10.0 / duration,  # 10 requests
                memory_usage=end_memory - start_memory,
                cpu_usage=(end_cpu + start_cpu) / 2,
                timestamp=time.time(),
                parameters={"requests": 10, "endpoint": "/health"},
                success=True
            )
            
        except Exception as e:
            return BenchmarkResult(
                name="network_operations",
                operation="http_requests",
                duration=time.time() - start_time,
                throughput=0.0,
                memory_usage=0,
                cpu_usage=0.0,
                timestamp=time.time(),
                parameters={"requests": 10, "endpoint": "/health"},
                success=False,
                error_message=str(e)
            )
    
    def _store_benchmark_result(self, result: BenchmarkResult):
        """Store benchmark result in database"""
        try:
            with self.db_manager.get_connection() as conn:
                cursor = conn.cursor()
                cursor.execute("""
                    INSERT INTO benchmark_results (
                        name, operation, duration, throughput, memory_usage, cpu_usage,
                        timestamp, parameters, success, error_message
                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                """, (
                    result.name, result.operation, result.duration, result.throughput,
                    result.memory_usage, result.cpu_usage, result.timestamp,
                    json.dumps(result.parameters), 1 if result.success else 0,
                    result.error_message
                ))
                conn.commit()
                
        except Exception as e:
            self.logger.error(f"Error storing benchmark result: {e}")
    
    def run_performance_analysis(self) -> Dict[str, Any]:
        """Run comprehensive performance analysis"""
        self.logger.info("Starting performance analysis...")
        
        # Run all benchmarks
        benchmarks = [
            ("database_connection", self._benchmark_database_connection),
            ("file_operations", self._benchmark_file_operations),
            ("memory_operations", self._benchmark_memory_operations),
            ("cpu_operations", self._benchmark_cpu_operations),
            ("network_operations", self._benchmark_network_operations)
        ]
        
        results = []
        for name, benchmark_func in benchmarks:
            try:
                result = benchmark_func()
                results.append(result)
                self._store_benchmark_result(result)
            except Exception as e:
                self.logger.error(f"Benchmark {name} failed: {e}")
        
        # Analyze results
        analysis = self._analyze_benchmark_results(results)
        
        # Generate recommendations
        recommendations = self._generate_recommendations(analysis)
        
        # Store recommendations
        for recommendation in recommendations:
            self._store_recommendation(recommendation)
        
        self.logger.info("Performance analysis completed")
        
        return {
            "analysis": analysis,
            "recommendations": [asdict(r) for r in recommendations],
            "benchmark_count": len(results)
        }
    
    def _analyze_benchmark_results(self, results: List[BenchmarkResult]) -> Dict[str, Any]:
        """Analyze benchmark results and identify issues"""
        analysis = {
            "overall_score": 0.0,
            "categories": {},
            "issues": [],
            "strengths": []
        }
        
        if not results:
            return analysis
        
        # Calculate overall score
        successful_results = [r for r in results if r.success]
        if successful_results:
            # Score based on throughput and resource usage
            throughput_scores = []
            memory_scores = []
            cpu_scores = []
            
            for result in successful_results:
                # Normalize scores (higher is better)
                if result.throughput > 0:
                    throughput_scores.append(min(result.throughput / 1000.0, 1.0))
                
                if result.memory_usage > 0:
                    memory_scores.append(max(0, 1.0 - (result.memory_usage / (100 * 1024 * 1024))))
                
                if result.cpu_usage > 0:
                    cpu_scores.append(max(0, 1.0 - (result.cpu_usage / 100.0)))
            
            if throughput_scores:
                analysis["overall_score"] = (
                    statistics.mean(throughput_scores) * 0.4 +
                    statistics.mean(memory_scores) * 0.3 +
                    statistics.mean(cpu_scores) * 0.3
                ) * 100
        
        # Analyze by category
        categories = defaultdict(list)
        for result in results:
            categories[result.name].append(result)
        
        for category, category_results in categories.items():
            successful = [r for r in category_results if r.success]
            
            if successful:
                analysis["categories"][category] = {
                    "average_duration": statistics.mean([r.duration for r in successful]),
                    "average_throughput": statistics.mean([r.throughput for r in successful]),
                    "average_memory": statistics.mean([r.memory_usage for r in successful]),
                    "average_cpu": statistics.mean([r.cpu_usage for r in successful]),
                    "success_rate": len(successful) / len(category_results),
                    "issues": []
                }
                
                # Check for issues
                category_data = analysis["categories"][category]
                
                if category_data["average_duration"] > self.thresholds["response_time"] / 1000:
                    category_data["issues"].append("Slow response time")
                    analysis["issues"].append(f"{category}: Slow response time")
                
                if category_data["average_throughput"] < self.thresholds["throughput"]:
                    category_data["issues"].append("Low throughput")
                    analysis["issues"].append(f"{category}: Low throughput")
                
                if category_data["average_memory"] > self.thresholds["memory_usage"]:
                    category_data["issues"].append("High memory usage")
                    analysis["issues"].append(f"{category}: High memory usage")
                
                if category_data["average_cpu"] > self.thresholds["cpu_usage"]:
                    category_data["issues"].append("High CPU usage")
                    analysis["issues"].append(f"{category}: High CPU usage")
                
                # Identify strengths
                if not category_data["issues"]:
                    analysis["strengths"].append(f"{category}: Good performance")
        
        return analysis
    
    def _generate_recommendations(self, analysis: Dict[str, Any]) -> List[OptimizationRecommendation]:
        """Generate optimization recommendations based on analysis"""
        recommendations = []
        
        # Database recommendations
        if "database_connection" in analysis["categories"]:
            db_data = analysis["categories"]["database_connection"]
            
            if db_data["average_duration"] > 0.1:  # 100ms
                recommendations.append(OptimizationRecommendation(
                    id=f"db_connection_{int(time.time())}",
                    category="database",
                    title="Optimize Database Connection Pooling",
                    description="Database connections are taking too long to establish",
                    impact="high",
                    effort="medium",
                    current_value=db_data["average_duration"],
                    recommended_value=0.05,  # 50ms
                    expected_improvement=50.0,
                    implementation_steps=[
                        "Increase connection pool size",
                        "Enable connection pooling",
                        "Optimize database queries",
                        "Add database indexes"
                    ],
                    timestamp=time.time()
                ))
        
        # Memory recommendations
        memory_issues = [issue for issue in analysis["issues"] if "memory" in issue.lower()]
        if memory_issues:
            recommendations.append(OptimizationRecommendation(
                id=f"memory_optimization_{int(time.time())}",
                category="memory",
                title="Optimize Memory Usage",
                description="High memory usage detected across multiple operations",
                impact="high",
                effort="high",
                current_value="High memory usage",
                recommended_value="Optimized memory usage",
                expected_improvement=30.0,
                implementation_steps=[
                    "Implement object pooling",
                    "Use generators for large datasets",
                    "Optimize data structures",
                    "Add memory monitoring",
                    "Implement garbage collection tuning"
                ],
                timestamp=time.time()
            ))
        
        # CPU recommendations
        cpu_issues = [issue for issue in analysis["issues"] if "cpu" in issue.lower()]
        if cpu_issues:
            recommendations.append(OptimizationRecommendation(
                id=f"cpu_optimization_{int(time.time())}",
                category="cpu",
                title="Optimize CPU Usage",
                description="High CPU usage detected in operations",
                impact="medium",
                effort="medium",
                current_value="High CPU usage",
                recommended_value="Optimized CPU usage",
                expected_improvement=25.0,
                implementation_steps=[
                    "Implement caching strategies",
                    "Use async/await for I/O operations",
                    "Optimize algorithms",
                    "Add CPU profiling",
                    "Implement task scheduling"
                ],
                timestamp=time.time()
            ))
        
        # General recommendations
        if analysis["overall_score"] < 70:
            recommendations.append(OptimizationRecommendation(
                id=f"general_optimization_{int(time.time())}",
                category="general",
                title="General Performance Optimization",
                description="Overall performance score is below optimal levels",
                impact="high",
                effort="high",
                current_value=analysis["overall_score"],
                recommended_value=85.0,
                expected_improvement=20.0,
                implementation_steps=[
                    "Review and optimize critical paths",
                    "Implement caching at multiple levels",
                    "Add performance monitoring",
                    "Optimize resource usage",
                    "Implement load balancing"
                ],
                timestamp=time.time()
            ))
        
        return recommendations
    
    def _store_recommendation(self, recommendation: OptimizationRecommendation):
        """Store optimization recommendation in database"""
        try:
            with self.db_manager.get_connection() as conn:
                cursor = conn.cursor()
                cursor.execute("""
                    INSERT OR REPLACE INTO optimization_recommendations (
                        id, category, title, description, impact, effort,
                        current_value, recommended_value, expected_improvement,
                        implementation_steps, timestamp, implemented
                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                """, (
                    recommendation.id, recommendation.category, recommendation.title,
                    recommendation.description, recommendation.impact, recommendation.effort,
                    json.dumps(recommendation.current_value),
                    json.dumps(recommendation.recommended_value),
                    recommendation.expected_improvement,
                    json.dumps(recommendation.implementation_steps),
                    recommendation.timestamp, 0
                ))
                conn.commit()
                
        except Exception as e:
            self.logger.error(f"Error storing recommendation: {e}")
    
    def get_recommendations(self, category: Optional[str] = None) -> List[OptimizationRecommendation]:
        """Get optimization recommendations"""
        if category:
            return [r for r in self.optimization_recommendations if r.category == category]
        return self.optimization_recommendations
    
    def implement_recommendation(self, recommendation_id: str) -> bool:
        """Implement an optimization recommendation"""
        recommendation = next((r for r in self.optimization_recommendations if r.id == recommendation_id), None)
        
        if not recommendation:
            self.logger.error(f"Recommendation {recommendation_id} not found")
            return False
        
        try:
            self.logger.info(f"Implementing recommendation: {recommendation.title}")
            
            # Implementation logic based on category
            if recommendation.category == "database":
                success = self._implement_database_optimization(recommendation)
            elif recommendation.category == "memory":
                success = self._implement_memory_optimization(recommendation)
            elif recommendation.category == "cpu":
                success = self._implement_cpu_optimization(recommendation)
            else:
                success = self._implement_general_optimization(recommendation)
            
            if success:
                # Mark as implemented
                self._mark_recommendation_implemented(recommendation_id)
                self.logger.info(f"Successfully implemented recommendation: {recommendation.title}")
            
            return success
            
        except Exception as e:
            self.logger.error(f"Error implementing recommendation {recommendation_id}: {e}")
            return False
    
    def _implement_database_optimization(self, recommendation: OptimizationRecommendation) -> bool:
        """Implement database optimization"""
        try:
            # Update database configuration
            self.config.set("database", "pool_size", 20)
            self.config.set("database", "max_overflow", 30)
            self.config.set("database", "pool_timeout", 30)
            self.config.set("database", "pool_recycle", 3600)
            
            # Save configuration
            self.config.save()
            
            return True
            
        except Exception as e:
            self.logger.error(f"Database optimization failed: {e}")
            return False
    
    def _implement_memory_optimization(self, recommendation: OptimizationRecommendation) -> bool:
        """Implement memory optimization"""
        try:
            # Update memory-related configurations
            self.config.set("performance", "enable_object_pooling", True)
            self.config.set("performance", "max_memory_usage", "512MB")
            self.config.set("performance", "garbage_collection_threshold", 0.8)
            
            # Save configuration
            self.config.save()
            
            return True
            
        except Exception as e:
            self.logger.error(f"Memory optimization failed: {e}")
            return False
    
    def _implement_cpu_optimization(self, recommendation: OptimizationRecommendation) -> bool:
        """Implement CPU optimization"""
        try:
            # Update CPU-related configurations
            self.config.set("performance", "enable_caching", True)
            self.config.set("performance", "cache_ttl", 3600)
            self.config.set("performance", "max_workers", psutil.cpu_count())
            self.config.set("performance", "enable_async", True)
            
            # Save configuration
            self.config.save()
            
            return True
            
        except Exception as e:
            self.logger.error(f"CPU optimization failed: {e}")
            return False
    
    def _implement_general_optimization(self, recommendation: OptimizationRecommendation) -> bool:
        """Implement general optimization"""
        try:
            # Update general performance configurations
            self.config.set("performance", "enable_monitoring", True)
            self.config.set("performance", "enable_profiling", True)
            self.config.set("performance", "optimization_level", "high")
            
            # Save configuration
            self.config.save()
            
            return True
            
        except Exception as e:
            self.logger.error(f"General optimization failed: {e}")
            return False
    
    def _mark_recommendation_implemented(self, recommendation_id: str):
        """Mark recommendation as implemented"""
        try:
            with self.db_manager.get_connection() as conn:
                cursor = conn.cursor()
                cursor.execute("""
                    UPDATE optimization_recommendations 
                    SET implemented = 1 
                    WHERE id = ?
                """, (recommendation_id,))
                conn.commit()
                
        except Exception as e:
            self.logger.error(f"Error marking recommendation implemented: {e}")
    
    def get_performance_summary(self) -> Dict[str, Any]:
        """Get performance summary"""
        return {
            "total_benchmarks": len(self.benchmark_results),
            "total_recommendations": len(self.optimization_recommendations),
            "implemented_recommendations": len([r for r in self.optimization_recommendations if hasattr(r, 'implemented') and r.implemented]),
            "pending_recommendations": len([r for r in self.optimization_recommendations if not hasattr(r, 'implemented') or not r.implemented]),
            "last_analysis": max([r.timestamp for r in self.benchmark_results]) if self.benchmark_results else 0
        }

def main():
    """Main entry point"""
    import argparse
    
    parser = argparse.ArgumentParser(description="Grim Performance Optimizer")
    parser.add_argument("--analyze", action="store_true", help="Run performance analysis")
    parser.add_argument("--implement", help="Implement recommendation by ID")
    parser.add_argument("--list", action="store_true", help="List recommendations")
    parser.add_argument("--summary", action="store_true", help="Show performance summary")
    
    args = parser.parse_args()
    
    # Create optimizer
    optimizer = GrimOptimizer()
    
    if args.analyze:
        # Run performance analysis
        results = optimizer.run_performance_analysis()
        print(json.dumps(results, indent=2))
    
    elif args.implement:
        # Implement recommendation
        success = optimizer.implement_recommendation(args.implement)
        print(f"Implementation {'successful' if success else 'failed'}")
    
    elif args.list:
        # List recommendations
        recommendations = optimizer.get_recommendations()
        for rec in recommendations:
            print(f"[{rec.impact.upper()}] {rec.title}")
            print(f"  Category: {rec.category}")
            print(f"  Effort: {rec.effort}")
            print(f"  Expected improvement: {rec.expected_improvement}%")
            print(f"  ID: {rec.id}")
            print()
    
    elif args.summary:
        # Show summary
        summary = optimizer.get_performance_summary()
        print(json.dumps(summary, indent=2))
    
    else:
        # Default: run analysis
        results = optimizer.run_performance_analysis()
        print(json.dumps(results, indent=2))

if __name__ == "__main__":
    main() 