Review Queue Management - Intelligent Card Organization

class ReviewQueueManager:
    def __init__(self):
        self.queues = {
            'urgent': [],      # Overdue cards - highest priority
            'due_today': [],   # Cards due for review today
            'upcoming': [],    # Cards due in next 3 days
            'learning': [],    # New cards in learning phase
            'buried': [],      # Temporarily buried cards
            'suspended': [],   # Suspended cards
            'mastered': []     # Mastered cards (long-term review)
        }

        self.settings = {
            'max_daily_reviews': 100,
            'max_daily_new': 20,
            'urgent_threshold': 3,  # days overdue
            'learning_steps': [1, 3, 7, 14],  # days for new cards
            'auto_bury_siblings': True,
            'show_answer_timer': 30,  # seconds
            'hard_interval_factor': 0.8,
            'easy_interval_factor': 1.3
        }

    def organize_queues(self, user_id):
        """Intelligently organize cards into appropriate queues"""

        # Get all user cards
        all_cards = self._get_user_cards(user_id)
        current_date = datetime.now()

        # Clear existing queues
        for queue in self.queues.values():
            queue.clear()

        # Organize cards into queues
        for card in all_cards:
            queue_type = self._determine_queue_type(card, current_date)
            self.queues[queue_type].append(card)

        # Sort each queue by priority
        for queue_name, queue_cards in self.queues.items():
            self.queues[queue_name] = self._sort_queue_by_priority(queue_cards)

        return self._generate_queue_summary()

def _sort_queue_by_priority(self, cards):
    """Sort cards within each queue by priority"""

    def calculate_priority(card):
        priority = 0

        # Base urgency factor
        days_overdue = (datetime.now() - card.next_review_date).days
        if days_overdue > 0:
            priority += days_overdue * 10  # 10 points per day overdue

        # Mastery level factor (lower mastery = higher priority)
        priority += (5 - card.mastery_level) * 5

        # Performance consistency factor
        if len(card.quality_scores) >= 3:
            recent_variance = statistics.variance(card.quality_scores[-3:])
            priority += recent_variance * 3  # Higher variance = higher priority

        # Subject importance factor
        subject_weights = {
            'Physics': 0.34, 'Chemistry': 0.33, 'Mathematics': 0.33,
            'Biology': 0.50, 'Botany': 0.25, 'Zoology': 0.25
        }
        priority += subject_weights.get(card.subject, 0.3) * 10

        # Difficulty factor
        difficulty_weights = {'Very Hard': 4, 'Hard': 3, 'Medium': 2, 'Easy': 1}
        priority += difficulty_weights.get(card.difficulty, 2) * 2

        # Consecutive failures factor
        if card.consecutive_incorrect > 0:
            priority += card.consecutive_incorrect * 8

        return priority

    # Sort by priority (descending)
    return sorted(cards, key=calculate_priority, reverse=True)

class DailyQueueProcessor:
    def __init__(self):
        self.processing_strategies = {
            'balanced': self._balanced_processing,
            'mastery_focused': self._mastery_focused_processing,
            'weakness_focused': self._weakness_focused_processing,
            'exam_prep': self._exam_prep_processing,
            'catch_up': self._catch_up_processing
        }

    def generate_daily_queue(self, user_id, strategy='balanced', constraints=None):
        """Generate optimized daily review queue"""

        if constraints is None:
            constraints = {
                'max_cards': 100,
                'max_time': 120,  # minutes
                'subject_balance': True,
                'difficulty_distribution': True,
                'break_intervals': 20  # minutes
            }

        # Get organized queues
        queue_manager = ReviewQueueManager()
        queue_manager.organize_queues(user_id)

        # Apply processing strategy
        strategy_processor = self.processing_strategies[strategy]
        daily_queue = strategy_processor(queue_manager, constraints)

        # Optimize queue for flow and cognitive load
        optimized_queue = self._optimize_for_learning_flow(daily_queue, constraints)

        return {
            'queue': optimized_queue,
            'estimated_time': self._estimate_total_time(optimized_queue),
            'subject_distribution': self._calculate_subject_distribution(optimized_queue),
            'difficulty_distribution': self._calculate_difficulty_distribution(optimized_queue),
            'break_schedule': self._generate_break_schedule(optimized_queue, constraints),
            'recommendations': self._generate_queue_recommendations(optimized_queue)
        }

    def _balanced_processing(self, queue_manager, constraints):
        """Balanced approach - mix of all card types"""

        daily_queue = []
        remaining_capacity = constraints['max_cards']
        time_remaining = constraints['max_time']

        # Process urgent cards first (30% of capacity)
        urgent_capacity = int(remaining_capacity * 0.3)
        urgent_cards = queue_manager.queues['urgent'][:urgent_capacity]
        daily_queue.extend(urgent_cards)
        remaining_capacity -= len(urgent_cards)
        time_remaining -= self._estimate_time_for_cards(urgent_cards)

        # Process due today cards (40% of remaining capacity)
        due_capacity = int(remaining_capacity * 0.4)
        due_cards = queue_manager.queues['due_today'][:due_capacity]
        daily_queue.extend(due_cards)
        remaining_capacity -= len(due_cards)
        time_remaining -= self._estimate_time_for_cards(due_cards)

        # Process new learning cards (20% of remaining capacity)
        learning_capacity = int(remaining_capacity * 0.2)
        learning_cards = queue_manager.queues['learning'][:learning_capacity]
        daily_queue.extend(learning_cards)
        remaining_capacity -= len(learning_cards)
        time_remaining -= self._estimate_time_for_cards(learning_cards)

        # Fill remaining with upcoming cards
        if remaining_capacity > 0 and time_remaining > 0:
            upcoming_capacity = min(remaining_capacity, time_remaining // 2)  # 2 minutes per card
            upcoming_cards = queue_manager.queues['upcoming'][:upcoming_capacity]
            daily_queue.extend(upcoming_cards)

        return daily_queue

def _optimize_for_learning_flow(self, cards, constraints):
    """Optimize queue order for better learning flow and reduced cognitive load"""

    optimized_queue = []

    # Group cards by subject and difficulty for better flow
    subject_groups = self._group_cards_by_subject(cards)

    for subject, subject_cards in subject_groups.items():
        # Within each subject, create optimal difficulty progression
        difficulty_groups = self._group_cards_by_difficulty(subject_cards)

        # Start with easy cards to build confidence
        if 'Easy' in difficulty_groups:
            optimized_queue.extend(difficulty_groups['Easy'][:2])

        # Progress to medium cards (main learning phase)
        if 'Medium' in difficulty_groups:
            optimized_queue.extend(difficulty_groups['Medium'])

        # Include hard cards when mind is warmed up
        if 'Hard' in difficulty_groups:
            optimized_queue.extend(difficulty_groups['Hard'][:3])

        # Very hard cards at peak performance time
        if 'Very Hard' in difficulty_groups:
            optimized_queue.extend(difficulty_groups['Very Hard'][:1])

    # Insert strategic breaks
    optimized_with_breaks = self._insert_strategic_breaks(optimized_queue, constraints)

    # Apply spacing for similar concepts to avoid interference
    final_queue = self._apply_concept_spacing(optimized_with_breaks)

    return final_queue

def _insert_strategic_breaks(self, cards, constraints):
    """Insert strategic breaks to maintain optimal cognitive performance"""

    break_interval = constraints['break_intervals']
    cards_with_breaks = []
    current_time = 0

    for i, card in enumerate(cards):
        cards_with_breaks.append(card)
        estimated_card_time = self._estimate_card_time(card)
        current_time += estimated_card_time

        # Insert break if needed
        if current_time >= break_interval and i < len(cards) - 1:
            cards_with_breaks.append({
                'type': 'break',
                'duration': 3,  # 3-minute break
                'suggested_activity': self._suggest_break_activity(current_time)
            })
            current_time = 0

    return cards_with_breaks

class QueueAnalytics:
    def __init__(self):
        self.metrics_history = []
        self.performance_data = {}
        self.queue_trends = {}

    def monitor_queue_health(self, user_id):
        """Monitor overall queue health and performance"""

        current_metrics = self._calculate_current_metrics(user_id)

        health_analysis = {
            'queue_burden': self._assess_queue_burden(current_metrics),
            'review_efficiency': self._calculate_review_efficiency(user_id),
            'learning_velocity': self._calculate_learning_velocity(user_id),
            'retention_health': self._assess_retention_health(user_id),
            'queue_distribution': self._analyze_queue_distribution(current_metrics),
            'performance_trends': self._analyze_performance_trends(user_id),
            'optimization_opportunities': self._identify_optimization_opportunities(current_metrics)
        }

        # Calculate overall health score
        health_score = self._calculate_overall_health_score(health_analysis)

        return {
            'health_score': health_score,
            'health_analysis': health_analysis,
            'current_metrics': current_metrics,
            'recommendations': self._generate_health_recommendations(health_analysis),
            'alert_level': self._determine_alert_level(health_score)
        }

    def _calculate_current_metrics(self, user_id):
        """Calculate current queue metrics"""

        queue_manager = ReviewQueueManager()
        queue_manager.organize_queues(user_id)

        metrics = {
            'total_cards': sum(len(queue) for queue in queue_manager.queues.values()),
            'urgent_cards': len(queue_manager.queues['urgent']),
            'due_today': len(queue_manager.queues['due_today']),
            'upcoming_cards': len(queue_manager.queues['upcoming']),
            'learning_cards': len(queue_manager.queues['learning']),
            'buried_cards': len(queue_manager.queues['buried']),
            'suspended_cards': len(queue_manager.queues['suspended']),
            'mastered_cards': len(queue_manager.queues['mastered']),
            'average_interval': self._calculate_average_interval(queue_manager.queues),
            'queue_growth_rate': self._calculate_queue_growth_rate(user_id),
            'completion_rate': self._calculate_completion_rate(user_id)
        }

        return metrics

    def _assess_queue_burden(self, metrics):
        """Assess if queue burden is manageable"""

        total_due = metrics['urgent_cards'] + metrics['due_today']

        if total_due > 150:
            burden_level = 'critical'
            burden_score = 20  # 0-100 scale, higher is worse
        elif total_due > 100:
            burden_level = 'high'
            burden_score = 40
        elif total_due > 50:
            burden_level = 'moderate'
            burden_score = 60
        elif total_due > 20:
            burden_level = 'low'
            burden_score = 80
        else:
            burden_level = 'optimal'
            burden_score = 100

        return {
            'level': burden_level,
            'score': burden_score,
            'total_due': total_due,
            'recommended_daily': min(100, total_due // 3 + 20),
            'catch_up_days': max(1, total_due // 100),
            'burden_trend': self._calculate_burden_trend(metrics)
        }

def adjust_queue_based_on_performance(self, user_id, performance_data):
    """Automatically adjust queue parameters based on performance"""

    # Analyze recent performance patterns
    performance_analysis = self._analyze_recent_performance(user_id, performance_data)

    adjustments = {}

    # Adjust daily limits based on performance
    if performance_analysis['accuracy_rate'] < 0.6:
        # Poor performance - reduce daily load
        adjustments['daily_review_limit'] = max(20, performance_analysis['current_limit'] * 0.8)
        adjustments['focus_on_weaknesses'] = True
        adjustments['reduce_new_cards'] = True
    elif performance_analysis['accuracy_rate'] > 0.85:
        # Excellent performance - can increase load
        adjustments['daily_review_limit'] = min(150, performance_analysis['current_limit'] * 1.2)
        adjustments['introduce_more_variety'] = True
        adjustments['increase_new_cards'] = True

    # Adjust scheduling based on retention rates
    if performance_analysis['retention_rate'] < 0.7:
        adjustments['shorten_intervals'] = True
        adjustments['increase_review_frequency'] = True
        adjustments['focus_on_consolidation'] = True
    elif performance_analysis['retention_rate'] > 0.9:
        adjustments['extend_intervals'] = True
        adjustments['introduce_advanced_concepts'] = True

    # Adjust subject balance based on performance
    subject_performance = performance_analysis['subject_performance']
    weak_subjects = [s for s, p in subject_performance.items() if p['accuracy'] < 0.6]
    if weak_subjects:
        adjustments['increase_weak_subject_focus'] = weak_subjects
        adjustments['subject_balance_weights'] = self._calculate_new_subject_weights(
            subject_performance
        )

    # Apply adjustments to queue system
    self._apply_queue_adjustments(user_id, adjustments)

    return {
        'adjustments_applied': adjustments,
        'performance_analysis': performance_analysis,
        'expected_impact': self._predict_adjustment_impact(adjustments),
        'monitoring_plan': self._create_monitoring_plan(adjustments)
    }

class QueueCustomizer:
    def __init__(self):
        self.presets = {
            'beginner': {
                'daily_reviews': 30,
                'daily_new': 10,
                'difficulty_preference': 'easy_first',
                'subject_balance': 'equal',
                'review_order': 'difficulty_ascending'
            },
            'intermediate': {
                'daily_reviews': 60,
                'daily_new': 15,
                'difficulty_preference': 'mixed',
                'subject_balance': 'weighted',
                'review_order': 'priority_based'
            },
            'advanced': {
                'daily_reviews': 100,
                'daily_new': 20,
                'difficulty_preference': 'challenge_focused',
                'subject_balance': 'performance_based',
                'review_order': 'adaptive'
            },
            'exam_prep': {
                'daily_reviews': 150,
                'daily_new': 5,
                'difficulty_preference': 'exam_weighted',
                'subject_balance': 'exam_pattern',
                'review_order': 'exam_simulation'
            },
            'catch_up': {
                'daily_reviews': 120,
                'daily_new': 0,
                'difficulty_preference': 'priority_focused',
                'subject_balance': 'weakness_focused',
                'review_order': 'urgency_first'
            }
        }

    def customize_queue_settings(self, user_id, preferences=None):
        """Create personalized queue settings"""

        if preferences is None:
            # Auto-detect preferences based on user performance
            preferences = self._auto_detect_preferences(user_id)

        # Start with appropriate preset
        base_preset = self.presets.get(preferences.get('level', 'intermediate'), {})

        # Apply custom modifications
        custom_settings = {
            **base_preset,
            'daily_reviews': preferences.get('daily_reviews', base_preset.get('daily_reviews', 60)),
            'daily_new': preferences.get('daily_new', base_preset.get('daily_new', 15)),
            'max_session_time': preferences.get('max_session_time', 45),
            'preferred_study_times': preferences.get('preferred_study_times', ['morning', 'evening']),
            'break_frequency': preferences.get('break_frequency', 20),
            'subject_priorities': preferences.get('subject_priorities', {}),
            'difficulty_priorities': preferences.get('difficulty_priorities', {}),
            'learning_goals': preferences.get('learning_goals', {}),
            'exam_focus': preferences.get('exam_focus', False)
        }

        # Validate and optimize settings
        validated_settings = self._validate_and_optimize_settings(custom_settings, user_id)

        return {
            'settings': validated_settings,
            'personalization_score': self._calculate_personalization_score(
                validated_settings, user_id
            ),
            'expected_performance': self._predict_performance_with_settings(
                validated_settings, user_id
            ),
            'adjustment_recommendations': self._suggest_setting_adjustments(
                validated_settings, user_id
            )
        }

    def create_dynamic_queue_rules(self, user_id, settings):
        """Create dynamic rules for queue behavior"""

        rules = {
            'urgency_rules': self._create_urgency_rules(settings),
            'difficulty_rules': self._create_difficulty_rules(settings),
            'subject_rules': self._create_subject_rules(settings),
            'performance_rules': self._create_performance_rules(settings),
            'time_rules': self._create_time_rules(settings),
            'cognitive_load_rules': self._create_cognitive_load_rules(settings)
        }

        return {
            'rules': rules,
            'rule_priorities': self._set_rule_priorities(rules),
            'conflict_resolution': self._create_conflict_resolution(rules),
            'adaptive_triggers': self._create_adaptive_triggers(rules),
            'performance_impact': self._predict_rule_performance_impact(rules, user_id)
        }

class AdvancedQueueFeatures:
    def __init__(self):
        self.feature_flags = {
            'smart_burying': True,
            'sibling_spacing': True,
            'adaptive_intervals': True,
            'performance_based_ordering': True,
            'cognitive_load_management': True,
            'exam_simulation_mode': False,
            'collaborative_queue': False,
            'ai_optimization': True
        }

    def enable_smart_burying(self, user_id):
        """Enable intelligent card burying to optimize learning"""

        burying_rules = {
            'bury_same_topic': True,  # Bury cards from same topic
            'bury_similar_concepts': True,  # Bury conceptually similar cards
            'bury_same_difficulty': False,  # Allow same difficulty in same session
            'bury_same_subject_partially': True,  # Partially bury same subject cards
            'bury_duration_days': 1,  # Days to bury cards
            'unbury_conditions': self._define_unbury_conditions()
        }

        # Apply burying rules to current queue
        queue_manager = ReviewQueueManager()
        current_queue = queue_manager.get_current_queue(user_id)

        optimized_queue = self._apply_burying_rules(current_queue, burying_rules)

        return {
            'burying_rules': burying_rules,
            'original_queue_size': len(current_queue),
            'optimized_queue_size': len(optimized_queue),
            'buried_cards_count': len(current_queue) - len(optimized_queue),
            'expected_benefit': self._calculate_burying_benefit(optimized_queue),
            'unbury_schedule': self._create_unbury_schedule(burying_rules)
        }

    def enable_exam_simulation_mode(self, user_id, exam_config):
        """Transform queue into exam simulation mode"""

        simulation_settings = {
            'exam_duration': exam_config.get('duration', 180),  # minutes
            'question_distribution': exam_config.get('distribution', {
                'Physics': 25, 'Chemistry': 25, 'Mathematics': 30, 'Biology': 90
            }),
            'difficulty_distribution': exam_config.get('difficulty_distribution', {
                'Easy': 0.3, 'Medium': 0.5, 'Hard': 0.2
            }),
            'time_per_question': exam_config.get('time_per_question', {
                'Easy': 1.5, 'Medium': 2.5, 'Hard': 4.0
            }),
            'negative_marking': exam_config.get('negative_marking', True),
            'section_time_limits': exam_config.get('section_time_limits', True)
        }

        # Generate exam-like queue
        exam_queue = self._generate_exam_queue(user_id, simulation_settings)

        # Create exam simulation environment
        simulation_environment = {
            'timer_settings': self._setup_exam_timer(simulation_settings),
            'scoring_system': self._setup_scoring_system(simulation_settings),
            'navigation_rules': self._setup_navigation_rules(simulation_settings),
            'review_restrictions': self._setup_review_restrictions()
        }

        return {
            'simulation_settings': simulation_settings,
            'exam_queue': exam_queue,
            'simulation_environment': simulation_environment,
            'performance_tracking': self._setup_exam_performance_tracking(),
            'post_analysis_tools': self._setup_post_exam_analysis()
        }

class QueueMaintenance:
    def __init__(self):
        self.maintenance_tasks = {
            'daily': ['update_due_cards', 'optimize_intervals', 'clean_expired_cards'],
            'weekly': ['analyze_performance', 'adjust_settings', 'generate_reports'],
            'monthly': ['deep_optimization', 'archive_mastered_cards', 'trend_analysis']
        }

    def perform_maintenance(self, user_id, maintenance_type='daily'):
        """Perform scheduled queue maintenance"""

        tasks = self.maintenance_tasks.get(maintenance_type, [])
        maintenance_results = {}

        for task in tasks:
            if hasattr(self, task):
                task_result = getattr(self, task)(user_id)
                maintenance_results[task] = task_result

        # Generate maintenance summary
        summary = self._generate_maintenance_summary(maintenance_results, maintenance_type)

        return {
            'maintenance_type': maintenance_type,
            'tasks_completed': len(tasks),
            'maintenance_results': maintenance_results,
            'summary': summary,
            'next_maintenance': self._schedule_next_maintenance(maintenance_type),
            'recommendations': self._generate_maintenance_recommendations(maintenance_results)
        }

    def optimize_queue_performance(self, user_id):
        """Comprehensive queue performance optimization"""

        optimization_analysis = {
            'current_performance': self._assess_current_performance(user_id),
            'bottlenecks': self._identify_performance_bottlenecks(user_id),
            'optimization_opportunities': self._identify_optimization_opportunities(user_id),
            'resource_utilization': self._analyze_resource_utilization(user_id)
        }

        # Apply optimizations
        optimizations_applied = {
            'interval_optimization': self._optimize_review_intervals(user_id),
            'queue_structure_optimization': self._optimize_queue_structure(user_id),
            'scheduling_optimization': self._optimize_review_scheduling(user_id),
            'cognitive_load_optimization': self._optimize_cognitive_load(user_id)
        }

        # Measure optimization impact
        performance_improvement = self._measure_optimization_impact(
            optimization_analysis, optimizations_applied
        )

        return {
            'optimization_analysis': optimization_analysis,
            'optimizations_applied': optimizations_applied,
            'performance_improvement': performance_improvement,
            'next_optimization_cycle': self._schedule_next_optimization(),
            'monitoring_plan': self._create_optimization_monitoring_plan()
        }

def diagnose_queue_issues(self, user_id):
    """Comprehensive queue health diagnostics"""

    diagnostics = {
        'queue_size_health': self._diagnose_queue_size(user_id),
        'review_efficiency': self._diagnose_review_efficiency(user_id),
        'interval_effectiveness': self._diagnose_interval_effectiveness(user_id),
        'learning_progression': self._diagnose_learning_progression(user_id),
        'cognitive_load': self._diagnose_cognitive_load(user_id),
        'subject_balance': self._diagnose_subject_balance(user_id),
        'retention_health': self._diagnose_retention_health(user_id)
    }

    # Identify critical issues
    critical_issues = [
        issue for issue in diagnostics.values()
        if issue['severity'] == 'critical'
    ]

    # Generate remediation plan
    remediation_plan = self._generate_remediation_plan(diagnostics, critical_issues)

    return {
        'diagnostics': diagnostics,
        'critical_issues': critical_issues,
        'overall_health_score': self._calculate_overall_health_score(diagnostics),
        'remediation_plan': remediation_plan,
        'prevention_strategies': self._generate_prevention_strategies(diagnostics),
        'monitoring_recommendations': self._generate_monitoring_recommendations(diagnostics)
    }

class MobileQueueManager:
    def __init__(self):
        self.mobile_features = {
            'offline_mode': True,
            'quick_review': True,
            'widget_support': True,
            'notification_management': True,
            'sync_optimization': True,
            'battery_optimization': True
        }

    def enable_mobile_queue_management(self, user_id, device_type='mobile'):
        """Enable mobile-specific queue management features"""

        mobile_config = {
            'offline_queue_size': 50,  # Cards available offline
            'sync_frequency': 'wifi_only',  # Sync settings
            'notification_settings': self._setup_mobile_notifications(user_id),
            'quick_review_settings': self._setup_quick_review(user_id),
            'widget_configuration': self._setup_queue_widget(user_id),
            'battery_optimization': self._setup_battery_optimization()
        }

        # Create mobile-optimized queue
        mobile_queue = self._create_mobile_optimized_queue(user_id, mobile_config)

        # Setup offline capabilities
        offline_setup = self._setup_offline_capabilities(user_id, mobile_queue)

        return {
            'mobile_config': mobile_config,
            'mobile_queue': mobile_queue,
            'offline_setup': offline_setup,
            'sync_settings': self._configure_mobile_sync(user_id),
            'performance_optimizations': self._apply_mobile_performance_optimizations(),
            'user_experience_enhancements': self._setup_mobile_ux_enhancements()
        }

    def create_quick_review_queue(self, user_id, time_available=5):
        """Create optimized queue for quick reviews"""

        # Select best cards for quick review
        quick_review_criteria = {
            'estimated_time': time_available * 60,  # seconds
            'difficulty_preference': 'easy_to_medium',
            'subject_familiarity': 'high',
            'review_probability': 'high_success'
        }

        quick_queue = self._select_quick_review_cards(user_id, quick_review_criteria)

        # Optimize for quick review format
        optimized_quick_queue = self._optimize_for_quick_review(quick_queue)

        return {
            'quick_queue': optimized_quick_queue,
            'estimated_time': self._estimate_quick_review_time(optimized_quick_queue),
            'review_strategy': self._generate_quick_review_strategy(optimized_quick_queue),
            'success_metrics': self._define_quick_review_success_metrics(),
            'follow_up_actions': self._plan_quick_review_follow_up(optimized_quick_queue)
        }