"""Deep Usage Optimizer Component

This module implements the DeepUsageOptimizer component that analyzes user behavior
patterns and optimizes GUI automation strategies for better efficiency and user experience.
"""

from typing import List, Dict, Any, Optional, Tuple, Set
from pydantic import BaseModel, Field
from datetime import datetime, timedelta, UTC
from collections import defaultdict, Counter
from dataclasses import dataclass
import json
import statistics
from enum import Enum

from agenticx.core.component import Component
from agenticx.memory.component import MemoryComponent
from agenticx.embodiment.core.models import GUITask


class OptimizationType(str, Enum):
    """Types of optimizations that can be applied."""
    SPEED = "speed"
    ACCURACY = "accuracy"
    USER_EXPERIENCE = "user_experience"
    RESOURCE_EFFICIENCY = "resource_efficiency"
    ERROR_REDUCTION = "error_reduction"


class UsagePattern(BaseModel):
    """Represents a user usage pattern."""
    
    pattern_id: str = Field(description="Unique identifier for the pattern")
    user_id: Optional[str] = Field(default=None, description="User identifier")
    application: str = Field(description="Target application")
    task_category: str = Field(description="Category of tasks")
    frequency: int = Field(default=1, description="How often this pattern occurs")
    avg_execution_time: float = Field(default=0.0, description="Average execution time in seconds")
    success_rate: float = Field(default=1.0, description="Success rate of the pattern")
    error_types: List[str] = Field(default_factory=list, description="Common error types")
    peak_usage_hours: List[int] = Field(default_factory=list, description="Hours when pattern is most used")
    context_factors: Dict[str, Any] = Field(default_factory=dict, description="Contextual factors affecting usage")
    optimization_opportunities: List[str] = Field(default_factory=list, description="Identified optimization opportunities")
    user_preferences: Dict[str, Any] = Field(default_factory=dict, description="User-specific preferences")
    created_at: datetime = Field(default_factory=datetime.now)
    last_updated: datetime = Field(default_factory=datetime.now)


class OptimizationRecommendation(BaseModel):
    """Represents an optimization recommendation."""
    
    recommendation_id: str = Field(description="Unique identifier")
    pattern_id: str = Field(description="Related usage pattern ID")
    optimization_type: OptimizationType = Field(description="Type of optimization")
    title: str = Field(description="Short title for the recommendation")
    description: str = Field(description="Detailed description")
    expected_improvement: Dict[str, float] = Field(
        default_factory=dict,
        description="Expected improvements (e.g., {'speed': 0.3, 'accuracy': 0.1})"
    )
    implementation_complexity: str = Field(
        default="medium",
        description="Implementation complexity: low, medium, high"
    )
    priority_score: float = Field(default=0.5, description="Priority score (0-1)")
    prerequisites: List[str] = Field(default_factory=list, description="Prerequisites for implementation")
    estimated_impact: str = Field(default="medium", description="Estimated impact: low, medium, high")
    implementation_steps: List[str] = Field(default_factory=list, description="Steps to implement")
    metrics_to_track: List[str] = Field(default_factory=list, description="Metrics to track after implementation")
    created_at: datetime = Field(default_factory=datetime.now)


class PerformanceMetrics(BaseModel):
    """Performance metrics for optimization analysis."""
    
    metric_id: str = Field(description="Unique identifier")
    pattern_id: str = Field(description="Related pattern ID")
    execution_times: List[float] = Field(default_factory=list, description="Execution times")
    success_count: int = Field(default=0, description="Number of successful executions")
    failure_count: int = Field(default=0, description="Number of failed executions")
    error_details: List[Dict[str, Any]] = Field(default_factory=list, description="Error details")
    user_satisfaction_scores: List[float] = Field(default_factory=list, description="User satisfaction scores")
    resource_usage: Dict[str, List[float]] = Field(
        default_factory=dict,
        description="Resource usage metrics (CPU, memory, etc.)"
    )
    timestamp: datetime = Field(default_factory=datetime.now)


class OptimizationConfig(BaseModel):
    """Configuration for the optimization engine."""
    
    min_pattern_frequency: int = Field(default=5, description="Minimum frequency to analyze a pattern")
    analysis_time_window_days: int = Field(default=30, description="Time window for analysis in days")
    optimization_threshold: float = Field(default=0.1, description="Minimum improvement threshold")
    max_recommendations_per_pattern: int = Field(default=3, description="Maximum recommendations per pattern")
    enable_real_time_optimization: bool = Field(default=True, description="Enable real-time optimization")
    enable_predictive_optimization: bool = Field(default=True, description="Enable predictive optimization")
    user_feedback_weight: float = Field(default=0.3, description="Weight of user feedback in optimization")
    performance_weight: float = Field(default=0.7, description="Weight of performance metrics in optimization")


class DeepUsageOptimizer(Component):
    """Deep Usage Optimizer Component
    
    Analyzes user behavior patterns and provides intelligent optimization
    recommendations for GUI automation tasks.
    """
    
    def __init__(self, name: Optional[str] = None, **kwargs):
        """Initialize the DeepUsageOptimizer.
        
        Args:
            name: Optional component name
            **kwargs: Additional configuration options
        """
        super().__init__(name=name, **kwargs)
        self._config = OptimizationConfig(**kwargs.get('optimization_config', {}))
        self._usage_patterns: Dict[str, UsagePattern] = {}
        self._performance_metrics: Dict[str, PerformanceMetrics] = {}
        self._recommendations: Dict[str, OptimizationRecommendation] = {}
        self._optimization_history: List[Dict[str, Any]] = []
    
    async def analyze_usage_patterns(self, 
                                   memory: MemoryComponent,
                                   user_id: Optional[str] = None,
                                   application: Optional[str] = None) -> List[UsagePattern]:
        """Analyze usage patterns from memory data.
        
        Args:
            memory: Memory component containing usage data
            user_id: Optional user ID to filter analysis
            application: Optional application to filter analysis
            
        Returns:
            List of identified usage patterns
        """
        # Build search query
        query_parts = ["task execution", "user interaction", "performance"]
        if application:
            query_parts.append(application)
        
        search_query = " ".join(query_parts)
        
        # Build metadata filter
        metadata_filter = {}
        if user_id:
            metadata_filter['user_id'] = user_id
        if application:
            metadata_filter['application'] = application
        
        # Search for relevant memories
        memories = await memory.search_across_memories(
            query=search_query,
            limit=1000,
            metadata_filter=metadata_filter if metadata_filter else None
        )
        
        # Filter by time window
        cutoff_date = datetime.now() - timedelta(days=self._config.analysis_time_window_days)
        recent_memories = [
            mem for mem in memories
            if self._is_recent_memory(mem.record.metadata.get('timestamp'), cutoff_date)
        ]
        
        # Group memories by patterns
        pattern_groups = await self._group_memories_by_pattern(recent_memories)
        
        # Analyze each pattern group
        analyzed_patterns = []
        for group_key, group_memories in pattern_groups.items():
            if len(group_memories) >= self._config.min_pattern_frequency:
                pattern = await self._analyze_pattern_group(group_key, group_memories)
                if pattern:
                    analyzed_patterns.append(pattern)
                    self._usage_patterns[pattern.pattern_id] = pattern
        
        return analyzed_patterns
    
    async def generate_optimization_recommendations(self, 
                                                  patterns: Optional[List[UsagePattern]] = None) -> List[OptimizationRecommendation]:
        """Generate optimization recommendations based on usage patterns.
        
        Args:
            patterns: Optional list of patterns to analyze. If None, uses all known patterns.
            
        Returns:
            List of optimization recommendations
        """
        if patterns is None:
            patterns = list(self._usage_patterns.values())
        
        recommendations = []
        
        for pattern in patterns:
            pattern_recommendations = await self._generate_pattern_recommendations(pattern)
            recommendations.extend(pattern_recommendations)
        
        # Sort by priority score
        recommendations.sort(key=lambda r: r.priority_score, reverse=True)
        
        # Store recommendations
        for rec in recommendations:
            self._recommendations[rec.recommendation_id] = rec
        
        return recommendations
    
    async def optimize_task_execution(self, 
                                    task: GUITask,
                                    context: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize task execution based on learned patterns.
        
        Args:
            task: The GUI task to optimize
            context: Current execution context
            
        Returns:
            Optimization suggestions and parameters
        """
        # Find relevant patterns
        relevant_patterns = await self._find_relevant_patterns(task, context)
        
        if not relevant_patterns:
            return {'optimizations': [], 'confidence': 0.0}
        
        # Generate optimizations
        optimizations = []
        
        for pattern in relevant_patterns:
            pattern_optimizations = await self._generate_task_optimizations(task, pattern, context)
            optimizations.extend(pattern_optimizations)
        
        # Calculate overall confidence
        confidence = await self._calculate_optimization_confidence(optimizations, relevant_patterns)
        
        return {
            'optimizations': optimizations,
            'confidence': confidence,
            'relevant_patterns': [p.pattern_id for p in relevant_patterns]
        }
    
    async def track_performance_metrics(self, 
                                      pattern_id: str,
                                      execution_time: float,
                                      success: bool,
                                      error_details: Optional[Dict[str, Any]] = None,
                                      user_satisfaction: Optional[float] = None,
                                      resource_usage: Optional[Dict[str, float]] = None):
        """Track performance metrics for a pattern.
        
        Args:
            pattern_id: ID of the pattern
            execution_time: Time taken to execute
            success: Whether execution was successful
            error_details: Details about any errors
            user_satisfaction: User satisfaction score (0-1)
            resource_usage: Resource usage metrics
        """
        if pattern_id not in self._performance_metrics:
            self._performance_metrics[pattern_id] = PerformanceMetrics(
                metric_id=f"metrics_{pattern_id}",
                pattern_id=pattern_id
            )
        
        metrics = self._performance_metrics[pattern_id]
        
        # Update metrics
        metrics.execution_times.append(execution_time)
        
        if success:
            metrics.success_count += 1
        else:
            metrics.failure_count += 1
            if error_details:
                metrics.error_details.append(error_details)
        
        if user_satisfaction is not None:
            metrics.user_satisfaction_scores.append(user_satisfaction)
        
        if resource_usage:
            for resource, value in resource_usage.items():
                if resource not in metrics.resource_usage:
                    metrics.resource_usage[resource] = []
                metrics.resource_usage[resource].append(value)
        
        metrics.timestamp = datetime.now()
        
        # Trigger real-time optimization if enabled
        if self._config.enable_real_time_optimization:
            await self._check_real_time_optimization(pattern_id)
    
    async def apply_optimization(self, 
                               recommendation_id: str,
                               feedback: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Apply an optimization recommendation.
        
        Args:
            recommendation_id: ID of the recommendation to apply
            feedback: Optional user feedback about the recommendation
            
        Returns:
            Result of applying the optimization
        """
        if recommendation_id not in self._recommendations:
            return {'success': False, 'error': 'Recommendation not found'}
        
        recommendation = self._recommendations[recommendation_id]
        
        # Record optimization attempt
        optimization_record = {
            'recommendation_id': recommendation_id,
            'pattern_id': recommendation.pattern_id,
            'optimization_type': recommendation.optimization_type,
            'applied_at': datetime.now(),
            'feedback': feedback
        }
        
        try:
            # Apply the optimization based on type
            result = await self._apply_optimization_by_type(recommendation)
            
            optimization_record['success'] = result.get('success', False)
            optimization_record['details'] = result
            
            # Update pattern with optimization
            if recommendation.pattern_id in self._usage_patterns:
                await self._update_pattern_with_optimization(
                    recommendation.pattern_id, 
                    recommendation,
                    result
                )
            
            return result
        
        except Exception as e:
            optimization_record['success'] = False
            optimization_record['error'] = str(e)
            return {'success': False, 'error': str(e)}
        
        finally:
            self._optimization_history.append(optimization_record)
    
    async def get_optimization_insights(self, 
                                      pattern_id: Optional[str] = None,
                                      time_range: Optional[Tuple[datetime, datetime]] = None) -> Dict[str, Any]:
        """Get insights about optimizations and performance.
        
        Args:
            pattern_id: Optional pattern ID to filter insights
            time_range: Optional time range for analysis
            
        Returns:
            Dictionary containing optimization insights
        """
        insights = {
            'summary': {},
            'patterns': {},
            'recommendations': {},
            'performance_trends': {},
            'optimization_impact': {}
        }
        
        # Filter data based on parameters
        patterns_to_analyze = [self._usage_patterns[pattern_id]] if pattern_id and pattern_id in self._usage_patterns else list(self._usage_patterns.values())
        
        if time_range:
            start_time, end_time = time_range
            patterns_to_analyze = [
                p for p in patterns_to_analyze
                if start_time <= p.last_updated <= end_time
            ]
        
        # Generate summary insights
        insights['summary'] = await self._generate_summary_insights(patterns_to_analyze)
        
        # Analyze individual patterns
        for pattern in patterns_to_analyze:
            pattern_insights = await self._generate_pattern_insights(pattern)
            insights['patterns'][pattern.pattern_id] = pattern_insights
        
        # Analyze recommendations
        relevant_recommendations = [
            rec for rec in self._recommendations.values()
            if not pattern_id or rec.pattern_id == pattern_id
        ]
        insights['recommendations'] = await self._analyze_recommendations(relevant_recommendations)
        
        # Generate performance trends
        insights['performance_trends'] = await self._generate_performance_trends(patterns_to_analyze)
        
        # Analyze optimization impact
        insights['optimization_impact'] = await self._analyze_optimization_impact()
        
        return insights
    
    async def _group_memories_by_pattern(self, memories) -> Dict[str, List]:
        """Group memories by usage patterns."""
        pattern_groups = defaultdict(list)
        
        for memory in memories:
            metadata = memory.record.metadata
            
            # Create pattern key based on task characteristics
            app_name = metadata.get('application', 'unknown')
            task_type = metadata.get('task_type', 'unknown')
            user_id = metadata.get('user_id', 'anonymous')
            
            pattern_key = f"{user_id}_{app_name}_{task_type}"
            pattern_groups[pattern_key].append(memory)
        
        return dict(pattern_groups)
    
    async def _analyze_pattern_group(self, group_key: str, memories) -> Optional[UsagePattern]:
        """Analyze a group of memories to create a usage pattern."""
        if not memories:
            return None
        
        # Extract pattern information
        user_id, app_name, task_category = group_key.split('_', 2)
        
        # Calculate metrics
        execution_times = []
        success_count = 0
        error_types = []
        usage_hours = []
        
        for memory in memories:
            metadata = memory.record.metadata
            
            # Execution time
            if 'execution_time' in metadata:
                execution_times.append(float(metadata['execution_time']))
            
            # Success rate
            if metadata.get('success', True):
                success_count += 1
            else:
                error_type = metadata.get('error_type', 'unknown')
                error_types.append(error_type)
            
            # Usage time
            timestamp_str = metadata.get('timestamp')
            if timestamp_str:
                try:
                    timestamp = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00'))
                    usage_hours.append(timestamp.hour)
                except (ValueError, TypeError):
                    pass
        
        # Calculate averages and patterns
        avg_execution_time = statistics.mean(execution_times) if execution_times else 0.0
        success_rate = success_count / len(memories) if memories else 0.0
        peak_hours = [hour for hour, count in Counter(usage_hours).most_common(3)]
        common_errors = [error for error, count in Counter(error_types).most_common(5)]
        
        # Identify optimization opportunities
        optimization_opportunities = await self._identify_optimization_opportunities(
            avg_execution_time, success_rate, common_errors, execution_times
        )
        
        pattern = UsagePattern(
            pattern_id=f"pattern_{hash(group_key) % 1000000}",
            user_id=user_id if user_id != 'anonymous' else None,
            application=app_name,
            task_category=task_category,
            frequency=len(memories),
            avg_execution_time=avg_execution_time,
            success_rate=success_rate,
            error_types=common_errors,
            peak_usage_hours=peak_hours,
            optimization_opportunities=optimization_opportunities
        )
        
        return pattern
    
    async def _identify_optimization_opportunities(self, 
                                                 avg_time: float, 
                                                 success_rate: float, 
                                                 errors: List[str],
                                                 execution_times: List[float]) -> List[str]:
        """Identify optimization opportunities based on metrics."""
        opportunities = []
        
        # Speed optimization
        if avg_time > 10.0:  # More than 10 seconds
            opportunities.append("speed_optimization")
        
        if execution_times and statistics.stdev(execution_times) > avg_time * 0.5:
            opportunities.append("consistency_improvement")
        
        # Accuracy optimization
        if success_rate < 0.9:
            opportunities.append("accuracy_improvement")
        
        # Error reduction
        if errors:
            opportunities.append("error_reduction")
        
        # Resource optimization
        if avg_time > 5.0:
            opportunities.append("resource_optimization")
        
        return opportunities
    
    async def _generate_pattern_recommendations(self, pattern: UsagePattern) -> List[OptimizationRecommendation]:
        """Generate optimization recommendations for a specific pattern."""
        recommendations = []
        
        for opportunity in pattern.optimization_opportunities:
            if opportunity == "speed_optimization":
                rec = await self._create_speed_optimization_recommendation(pattern)
                if rec:
                    recommendations.append(rec)
            
            elif opportunity == "accuracy_improvement":
                rec = await self._create_accuracy_optimization_recommendation(pattern)
                if rec:
                    recommendations.append(rec)
            
            elif opportunity == "error_reduction":
                rec = await self._create_error_reduction_recommendation(pattern)
                if rec:
                    recommendations.append(rec)
            
            elif opportunity == "resource_optimization":
                rec = await self._create_resource_optimization_recommendation(pattern)
                if rec:
                    recommendations.append(rec)
        
        # Limit recommendations per pattern
        recommendations = recommendations[:self._config.max_recommendations_per_pattern]
        
        return recommendations
    
    async def _create_speed_optimization_recommendation(self, pattern: UsagePattern) -> Optional[OptimizationRecommendation]:
        """Create a speed optimization recommendation."""
        return OptimizationRecommendation(
            recommendation_id=f"speed_{pattern.pattern_id}_{datetime.now().timestamp()}",
            pattern_id=pattern.pattern_id,
            optimization_type=OptimizationType.SPEED,
            title="Optimize Task Execution Speed",
            description=f"Reduce average execution time from {pattern.avg_execution_time:.1f}s by implementing parallel processing and caching.",
            expected_improvement={'speed': 0.3, 'user_experience': 0.2},
            implementation_complexity="medium",
            priority_score=0.8,
            implementation_steps=[
                "Analyze task steps for parallelization opportunities",
                "Implement element caching for frequently accessed UI elements",
                "Optimize wait times and polling intervals",
                "Add smart retry mechanisms"
            ],
            metrics_to_track=['execution_time', 'success_rate', 'user_satisfaction']
        )
    
    async def _create_accuracy_optimization_recommendation(self, pattern: UsagePattern) -> Optional[OptimizationRecommendation]:
        """Create an accuracy optimization recommendation."""
        return OptimizationRecommendation(
            recommendation_id=f"accuracy_{pattern.pattern_id}_{datetime.now().timestamp()}",
            pattern_id=pattern.pattern_id,
            optimization_type=OptimizationType.ACCURACY,
            title="Improve Task Accuracy",
            description=f"Increase success rate from {pattern.success_rate:.1%} by enhancing element detection and validation.",
            expected_improvement={'accuracy': 0.15, 'error_reduction': 0.25},
            implementation_complexity="medium",
            priority_score=0.9,
            implementation_steps=[
                "Implement more robust element selectors",
                "Add pre-execution validation checks",
                "Enhance error detection and recovery",
                "Implement adaptive waiting strategies"
            ],
            metrics_to_track=['success_rate', 'error_count', 'error_types']
        )
    
    async def _create_error_reduction_recommendation(self, pattern: UsagePattern) -> Optional[OptimizationRecommendation]:
        """Create an error reduction recommendation."""
        common_errors = ", ".join(pattern.error_types[:3])
        
        return OptimizationRecommendation(
            recommendation_id=f"error_reduction_{pattern.pattern_id}_{datetime.now().timestamp()}",
            pattern_id=pattern.pattern_id,
            optimization_type=OptimizationType.ERROR_REDUCTION,
            title="Reduce Common Errors",
            description=f"Address common errors: {common_errors} through improved error handling and prevention.",
            expected_improvement={'error_reduction': 0.4, 'accuracy': 0.1},
            implementation_complexity="low",
            priority_score=0.7,
            implementation_steps=[
                "Analyze error patterns and root causes",
                "Implement specific error prevention measures",
                "Add graceful error recovery mechanisms",
                "Enhance error logging and reporting"
            ],
            metrics_to_track=['error_count', 'error_types', 'recovery_success_rate']
        )
    
    async def _create_resource_optimization_recommendation(self, pattern: UsagePattern) -> Optional[OptimizationRecommendation]:
        """Create a resource optimization recommendation."""
        return OptimizationRecommendation(
            recommendation_id=f"resource_{pattern.pattern_id}_{datetime.now().timestamp()}",
            pattern_id=pattern.pattern_id,
            optimization_type=OptimizationType.RESOURCE_EFFICIENCY,
            title="Optimize Resource Usage",
            description="Reduce CPU and memory usage through efficient algorithms and resource management.",
            expected_improvement={'resource_efficiency': 0.25, 'speed': 0.1},
            implementation_complexity="high",
            priority_score=0.6,
            implementation_steps=[
                "Profile resource usage during task execution",
                "Optimize memory allocation and cleanup",
                "Implement efficient data structures",
                "Add resource monitoring and throttling"
            ],
            metrics_to_track=['cpu_usage', 'memory_usage', 'execution_time']
        )
    
    async def _find_relevant_patterns(self, task: GUITask, context: Dict[str, Any]) -> List[UsagePattern]:
        """Find usage patterns relevant to a task."""
        relevant_patterns = []
        
        for pattern in self._usage_patterns.values():
            # Check application match
            if pattern.application == task.target_application:
                relevance_score = await self._calculate_pattern_relevance(pattern, task, context)
                if relevance_score > 0.5:
                    relevant_patterns.append(pattern)
        
        # Sort by relevance
        relevant_patterns.sort(
            key=lambda p: p.success_rate * p.frequency,
            reverse=True
        )
        
        return relevant_patterns[:5]  # Top 5 most relevant
    
    async def _calculate_pattern_relevance(self, pattern: UsagePattern, task: GUITask, context: Dict[str, Any]) -> float:
        """Calculate how relevant a pattern is to a task."""
        relevance = 0.0
        
        # Application match
        if pattern.application == task.target_application:
            relevance += 0.4
        
        # Task category match - Fixed: use a proper way to determine task category
        # Since GUITask doesn't have task_type attribute, we'll derive category from task description or steps
        task_category = self._derive_task_category(task)
        if pattern.task_category == task_category:
            relevance += 0.3
        
        # Context similarity
        context_similarity = await self._calculate_context_similarity(pattern.context_factors, context)
        relevance += context_similarity * 0.3
        
        return min(relevance, 1.0)
    
    def _derive_task_category(self, task: GUITask) -> str:
        """Derive task category from task properties.
        
        Since GUITask doesn't have a task_type attribute, we derive it from:
        1. The first step's action if available
        2. Keywords in the task description
        3. Default to 'general' if nothing specific found
        """
        # Try to derive from steps if available
        if task.steps:
            first_step = task.steps[0] if task.steps else {}
            action = first_step.get('action', '').lower()
            if action:
                # Map common actions to categories
                action_to_category = {
                    'click': 'click',
                    'type': 'input',
                    'fill': 'input',
                    'submit': 'form',
                    'navigate': 'navigation',
                    'search': 'search',
                    'select': 'selection',
                    'drag': 'drag_drop',
                    'drop': 'drag_drop',
                    'scroll': 'navigation',
                    'login': 'authentication',
                    'logout': 'authentication'
                }
                # Ensure we always return a string, even if action is not in the mapping
                category = action_to_category.get(action, action)
                return category if category is not None else 'general'
        
        # Try to derive from description keywords
        description = task.description.lower()
        if 'login' in description or 'sign in' in description:
            return 'authentication'
        elif 'search' in description:
            return 'search'
        elif 'form' in description or 'submit' in description:
            return 'form'
        elif 'navigate' in description or 'go to' in description:
            return 'navigation'
        
        # Default category
        return 'general'
    
    async def _calculate_context_similarity(self, pattern_context: Dict[str, Any], current_context: Dict[str, Any]) -> float:
        """Calculate similarity between contexts."""
        if not pattern_context or not current_context:
            return 0.0
        
        common_keys = set(pattern_context.keys()) & set(current_context.keys())
        if not common_keys:
            return 0.0
        
        matches = sum(
            1 for key in common_keys
            if pattern_context[key] == current_context[key]
        )
        
        return matches / len(common_keys)
    
    async def _generate_task_optimizations(self, task: GUITask, pattern: UsagePattern, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate specific optimizations for a task based on a pattern."""
        optimizations = []
        
        # Speed optimizations
        if "speed_optimization" in pattern.optimization_opportunities:
            optimizations.append({
                'type': 'speed',
                'action': 'reduce_wait_times',
                'parameters': {'max_wait': pattern.avg_execution_time * 0.5},
                'confidence': 0.8
            })
        
        # Accuracy optimizations
        if "accuracy_improvement" in pattern.optimization_opportunities:
            optimizations.append({
                'type': 'accuracy',
                'action': 'enhance_selectors',
                'parameters': {'use_multiple_selectors': True},
                'confidence': 0.7
            })
        
        # Error reduction optimizations
        if "error_reduction" in pattern.optimization_opportunities:
            optimizations.append({
                'type': 'error_reduction',
                'action': 'add_retry_logic',
                'parameters': {'max_retries': 3, 'backoff_factor': 1.5},
                'confidence': 0.9
            })
        
        return optimizations
    
    async def _calculate_optimization_confidence(self, optimizations: List[Dict[str, Any]], patterns: List[UsagePattern]) -> float:
        """Calculate confidence in the optimization suggestions."""
        if not optimizations or not patterns:
            return 0.0
        
        # Base confidence on pattern quality
        pattern_scores = [
            p.success_rate * min(p.frequency / 10.0, 1.0)
            for p in patterns
        ]
        pattern_confidence = statistics.mean(pattern_scores) if pattern_scores else 0.0
        
        # Adjust based on optimization confidence
        opt_scores = [opt.get('confidence', 0.5) for opt in optimizations]
        optimization_confidence = statistics.mean(opt_scores) if opt_scores else 0.0
        
        return (pattern_confidence * 0.6) + (optimization_confidence * 0.4)
    
    def _is_recent_memory(self, timestamp_str: Optional[str], cutoff_date: datetime) -> bool:
        """Check if a memory is recent enough for analysis."""
        if not timestamp_str:
            return False
        
        try:
            timestamp = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00'))
            return timestamp >= cutoff_date
        except (ValueError, TypeError):
            return False
    
    async def _check_real_time_optimization(self, pattern_id: str):
        """Check if real-time optimization should be triggered."""
        if pattern_id not in self._performance_metrics:
            return
        
        metrics = self._performance_metrics[pattern_id]
        
        # Check if recent performance has degraded
        if len(metrics.execution_times) >= 5:
            recent_times = metrics.execution_times[-5:]
            older_times = metrics.execution_times[:-5] if len(metrics.execution_times) > 5 else []
            
            if older_times and recent_times:
                recent_avg = statistics.mean(recent_times) if recent_times else 0.0
                older_avg = statistics.mean(older_times) if older_times else 0.0
                
                # If recent performance is significantly worse
                if recent_avg > older_avg * 1.2:
                    await self._trigger_real_time_optimization(pattern_id)
    
    async def _trigger_real_time_optimization(self, pattern_id: str):
        """Trigger real-time optimization for a pattern."""
        # This would implement real-time optimization logic
        # For now, just log the event
        optimization_event = {
            'type': 'real_time_optimization',
            'pattern_id': pattern_id,
            'timestamp': datetime.now(),
            'trigger': 'performance_degradation'
        }
        self._optimization_history.append(optimization_event)
    
    async def _apply_optimization_by_type(self, recommendation: OptimizationRecommendation) -> Dict[str, Any]:
        """Apply optimization based on its type."""
        # This is a simplified implementation
        # In practice, this would implement specific optimization logic
        
        result = {
            'success': True,
            'optimization_type': recommendation.optimization_type,
            'applied_at': datetime.now(),
            'details': f"Applied {recommendation.optimization_type} optimization"
        }
        
        return result
    
    async def _update_pattern_with_optimization(self, pattern_id: str, recommendation: OptimizationRecommendation, result: Dict[str, Any]):
        """Update a pattern with applied optimization."""
        if pattern_id in self._usage_patterns:
            pattern = self._usage_patterns[pattern_id]
            
            # Remove the addressed optimization opportunity
            if recommendation.optimization_type.value in pattern.optimization_opportunities:
                pattern.optimization_opportunities.remove(recommendation.optimization_type.value)
            
            pattern.last_updated = datetime.now()
    
    async def _generate_summary_insights(self, patterns: List[UsagePattern]) -> Dict[str, Any]:
        """Generate summary insights from patterns."""
        if not patterns:
            return {}
        
        success_rates = [p.success_rate for p in patterns]
        execution_times = [p.avg_execution_time for p in patterns]
        
        return {
            'total_patterns': len(patterns),
            'avg_success_rate': statistics.mean(success_rates) if success_rates else 0.0,
            'avg_execution_time': statistics.mean(execution_times) if execution_times else 0.0,
            'most_common_apps': [app for app, _ in Counter([p.application for p in patterns]).most_common(5)],
            'optimization_opportunities': sum(len(p.optimization_opportunities) for p in patterns)
        }
    
    async def _generate_pattern_insights(self, pattern: UsagePattern) -> Dict[str, Any]:
        """Generate insights for a specific pattern."""
        insights = {
            'pattern_id': pattern.pattern_id,
            'performance_score': pattern.success_rate * (1 / max(pattern.avg_execution_time, 1)),
            'optimization_potential': len(pattern.optimization_opportunities),
            'usage_trend': 'stable'  # Would calculate from historical data
        }
        
        return insights
    
    async def _analyze_recommendations(self, recommendations: List[OptimizationRecommendation]) -> Dict[str, Any]:
        """Analyze optimization recommendations."""
        if not recommendations:
            return {}
        
        priority_scores = [r.priority_score for r in recommendations]
        
        return {
            'total_recommendations': len(recommendations),
            'by_type': dict(Counter([r.optimization_type for r in recommendations])),
            'avg_priority': statistics.mean(priority_scores) if priority_scores else 0.0,
            'high_priority_count': sum(1 for r in recommendations if r.priority_score > 0.7)
        }
    
    async def _generate_performance_trends(self, patterns: List[UsagePattern]) -> Dict[str, Any]:
        """Generate performance trends analysis."""
        # This would analyze historical performance data
        # For now, return a simplified structure
        return {
            'execution_time_trend': 'improving',
            'success_rate_trend': 'stable',
            'error_rate_trend': 'decreasing'
        }
    
    async def _analyze_optimization_impact(self) -> Dict[str, Any]:
        """Analyze the impact of applied optimizations."""
        successful_optimizations = [
            opt for opt in self._optimization_history
            if opt.get('success', False)
        ]
        
        return {
            'total_optimizations_applied': len(successful_optimizations),
            'success_rate': len(successful_optimizations) / max(len(self._optimization_history), 1),
            'most_effective_types': []  # Would analyze which types had best impact
        }