//! Intelligent CUDA Task Scheduling System
//!
//! This module provides enterprise-grade intelligent task scheduling capabilities including
//! dynamic priority adjustment, resource-aware scheduling, performance-driven optimization,
//! predictive load balancing, and adaptive execution strategies to maximize CUDA performance
//! and minimize resource contention across multiple GPU devices.

use serde::{Deserialize, Serialize};
use std::cmp::Ordering as CmpOrdering;
use std::collections::{BinaryHeap, HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};

/// Intelligent CUDA task scheduling system
#[derive(Debug)]
pub struct IntelligentTaskScheduler {
    /// Dynamic priority manager
    priority_manager: Arc<Mutex<DynamicPriorityManager>>,

    /// Resource-aware scheduler
    resource_scheduler: Arc<Mutex<ResourceAwareScheduler>>,

    /// Performance-driven optimizer
    performance_optimizer: Arc<Mutex<PerformanceDrivenOptimizer>>,

    /// Predictive load balancer
    load_balancer: Arc<Mutex<PredictiveLoadBalancer>>,

    /// Adaptive execution strategy selector
    execution_strategy: Arc<Mutex<AdaptiveExecutionStrategy>>,

    /// Task dependency resolver
    dependency_resolver: Arc<Mutex<TaskDependencyResolver>>,

    /// GPU device manager
    device_manager: Arc<Mutex<IntelligentDeviceManager>>,

    /// Performance predictor
    performance_predictor: Arc<Mutex<TaskPerformancePredictor>>,

    /// Configuration
    config: IntelligentSchedulingConfig,

    /// Active task queue
    task_queue: Arc<RwLock<IntelligentTaskQueue>>,

    /// Execution statistics
    statistics: Arc<Mutex<SchedulingStatistics>>,

    /// Scheduling history
    scheduling_history: Arc<Mutex<VecDeque<SchedulingDecisionRecord>>>,
}

/// Dynamic priority management system
#[derive(Debug)]
pub struct DynamicPriorityManager {
    /// Priority calculation engine
    priority_calculator: PriorityCalculationEngine,

    /// Priority adjustment engine
    adjustment_engine: PriorityAdjustmentEngine,

    /// Priority history tracker
    priority_history: PriorityHistoryTracker,

    /// Aging mechanism for task priorities
    aging_mechanism: TaskAgingMechanism,

    /// Priority prediction system
    priority_predictor: PriorityPredictor,

    /// Configuration
    config: PriorityManagementConfig,
}

/// Resource-aware scheduling system
#[derive(Debug)]
pub struct ResourceAwareScheduler {
    /// Resource utilization monitor
    utilization_monitor: ResourceUtilizationMonitor,

    /// Resource allocation predictor
    allocation_predictor: ResourceAllocationPredictor,

    /// Resource contention resolver
    contention_resolver: ResourceContentionResolver,

    /// Resource affinity manager
    affinity_manager: ResourceAffinityManager,

    /// Resource optimization engine
    optimization_engine: ResourceOptimizationEngine,

    /// Configuration
    config: ResourceSchedulingConfig,
}

/// Performance-driven optimization system
#[derive(Debug)]
pub struct PerformanceDrivenOptimizer {
    /// Performance metrics analyzer
    metrics_analyzer: PerformanceMetricsAnalyzer,

    /// Bottleneck identification system
    bottleneck_identifier: BottleneckIdentificationSystem,

    /// Performance optimization engine
    optimization_engine: PerformanceOptimizationEngine,

    /// Execution pattern analyzer
    pattern_analyzer: ExecutionPatternAnalyzer,

    /// Performance feedback system
    feedback_system: PerformanceFeedbackSystem,

    /// Configuration
    config: PerformanceOptimizationConfig,
}

/// Predictive load balancing system
#[derive(Debug)]
pub struct PredictiveLoadBalancer {
    /// Workload predictor
    workload_predictor: WorkloadPredictor,

    /// Load distribution optimizer
    distribution_optimizer: LoadDistributionOptimizer,

    /// Dynamic load balancing engine
    balancing_engine: DynamicLoadBalancingEngine,

    /// Migration decision system
    migration_system: TaskMigrationSystem,

    /// Load balancing history
    balancing_history: LoadBalancingHistory,

    /// Configuration
    config: LoadBalancingConfig,
}

/// Adaptive execution strategy system
#[derive(Debug)]
pub struct AdaptiveExecutionStrategy {
    /// Available execution strategies
    strategies: HashMap<ExecutionStrategyType, Box<dyn ExecutionStrategy>>,

    /// Strategy performance tracker
    performance_tracker: StrategyPerformanceTracker,

    /// Strategy selection engine
    selection_engine: StrategySelectionEngine,

    /// Adaptive learning system
    learning_system: AdaptiveLearningSystem,

    /// Current active strategy
    active_strategy: ExecutionStrategyType,

    /// Configuration
    config: ExecutionStrategyConfig,
}

/// Task dependency resolution system
#[derive(Debug)]
pub struct TaskDependencyResolver {
    /// Dependency graph builder
    graph_builder: DependencyGraphBuilder,

    /// Critical path analyzer
    critical_path_analyzer: CriticalPathAnalyzer,

    /// Dependency optimization engine
    optimization_engine: DependencyOptimizationEngine,

    /// Deadlock detection system
    deadlock_detector: DeadlockDetectionSystem,

    /// Dependency violation detector
    violation_detector: DependencyViolationDetector,

    /// Configuration
    config: DependencyResolutionConfig,
}

/// Intelligent GPU device management system
#[derive(Debug)]
pub struct IntelligentDeviceManager {
    /// Available GPU devices
    devices: HashMap<DeviceId, GpuDeviceState>,

    /// Device capability analyzer
    capability_analyzer: DeviceCapabilityAnalyzer,

    /// Device health monitor
    health_monitor: DeviceHealthMonitor,

    /// Device selection optimizer
    selection_optimizer: DeviceSelectionOptimizer,

    /// Multi-GPU coordination system
    coordination_system: MultiGpuCoordinator,

    /// Configuration
    config: DeviceManagementConfig,
}

/// Task performance prediction system
#[derive(Debug)]
pub struct TaskPerformancePredictor {
    /// Performance models database
    performance_models: HashMap<TaskType, PerformanceModel>,

    /// Machine learning predictor
    ml_predictor: Option<MLPerformancePredictor>,

    /// Historical performance analyzer
    historical_analyzer: HistoricalPerformanceAnalyzer,

    /// Performance estimation engine
    estimation_engine: PerformanceEstimationEngine,

    /// Prediction accuracy tracker
    accuracy_tracker: PredictionAccuracyTracker,

    /// Configuration
    config: PerformancePredictionConfig,
}

/// Intelligent task queue with advanced scheduling capabilities
#[derive(Debug)]
pub struct IntelligentTaskQueue {
    /// Priority-based task queue
    priority_queue: BinaryHeap<PrioritizedTask>,

    /// Ready tasks by device
    device_ready_queues: HashMap<DeviceId, VecDeque<SchedulableTask>>,

    /// Waiting tasks (blocked by dependencies)
    waiting_tasks: HashMap<TaskId, WaitingTask>,

    /// Running tasks
    running_tasks: HashMap<TaskId, RunningTask>,

    /// Completed tasks history
    completed_tasks: VecDeque<CompletedTask>,

    /// Queue statistics
    queue_stats: QueueStatistics,

    /// Configuration
    config: TaskQueueConfig,
}

// === Core Data Structures ===

/// Schedulable task representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SchedulableTask {
    pub task_id: TaskId,
    pub task_type: TaskType,
    pub priority: TaskPriority,
    pub resource_requirements: ResourceRequirements,
    pub dependencies: Vec<TaskId>,
    pub estimated_execution_time: Duration,
    pub deadline: Option<SystemTime>,
    pub submission_time: SystemTime,
    pub task_data: TaskData,
    pub scheduling_constraints: SchedulingConstraints,
}

/// Task priority with dynamic adjustment capabilities
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub struct TaskPriority {
    pub base_priority: u32,
    pub dynamic_adjustment: i32,
    pub aging_bonus: u32,
    pub performance_bonus: i32,
    pub deadline_urgency: u32,
}

/// Resource requirements specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub gpu_memory: u64,
    pub compute_units: u32,
    pub bandwidth_requirements: f64,
    pub shared_memory: u32,
    pub register_count: u32,
    pub device_capabilities: Vec<DeviceCapability>,
    pub affinity_preferences: AffinityPreferences,
}

/// GPU device state information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuDeviceState {
    pub device_id: DeviceId,
    pub device_info: DeviceInfo,
    pub current_utilization: DeviceUtilization,
    pub available_resources: AvailableResources,
    pub running_tasks: Vec<TaskId>,
    pub performance_metrics: DevicePerformanceMetrics,
    pub health_status: DeviceHealthStatus,
    pub last_updated: SystemTime,
}

/// Scheduling decision record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SchedulingDecisionRecord {
    pub decision_id: String,
    pub timestamp: SystemTime,
    pub task_id: TaskId,
    pub selected_device: DeviceId,
    pub scheduling_strategy: ExecutionStrategyType,
    pub priority_adjustments: PriorityAdjustments,
    pub resource_allocation: ResourceAllocation,
    pub performance_prediction: PerformancePrediction,
    pub decision_rationale: String,
}

/// Scheduling statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SchedulingStatistics {
    pub total_tasks_scheduled: u64,
    pub successful_executions: u64,
    pub failed_executions: u64,
    pub average_wait_time: Duration,
    pub average_execution_time: Duration,
    pub resource_utilization_efficiency: f64,
    pub priority_adjustment_count: u64,
    pub load_balancing_operations: u64,
    pub device_migration_count: u64,
    /// Number of tasks dequeued from the scheduler
    pub tasks_dequeued: u64,
}

// === Enumerations ===

#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum TaskType {
    TensorOperation,
    MatrixMultiplication,
    Convolution,
    Activation,
    Reduction,
    MemoryTransfer,
    Custom(String),
}

#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ExecutionStrategyType {
    EarliestDeadlineFirst,
    ShortestJobFirst,
    LongestJobFirst,
    HighestPriorityFirst,
    RoundRobin,
    ResourceAwareScheduling,
    PerformanceOptimized,
    AdaptiveML,
}

#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum DeviceCapability {
    TensorCores,
    MixedPrecision,
    UnifiedMemory,
    PeerToPeerAccess,
    FastMath,
    LargeMath,
    CooperativeGroups,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum DeviceHealthStatus {
    Healthy,
    Warning,
    Degraded,
    Overheated,
    Error,
    Offline,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum SchedulingConstraintType {
    DeviceAffinity,
    TimeWindow,
    ResourceLimit,
    DependencyChain,
    QualityOfService,
}

// === Type Aliases ===

pub type TaskId = String;
pub type DeviceId = String;

// === Implementation ===

impl IntelligentTaskScheduler {
    /// Create a new intelligent task scheduler
    pub fn new(config: IntelligentSchedulingConfig) -> Self {
        Self {
            priority_manager: Arc::new(Mutex::new(DynamicPriorityManager::new(&config))),
            resource_scheduler: Arc::new(Mutex::new(ResourceAwareScheduler::new(&config))),
            performance_optimizer: Arc::new(Mutex::new(PerformanceDrivenOptimizer::new(&config))),
            load_balancer: Arc::new(Mutex::new(PredictiveLoadBalancer::new(&config))),
            execution_strategy: Arc::new(Mutex::new(AdaptiveExecutionStrategy::new(&config))),
            dependency_resolver: Arc::new(Mutex::new(TaskDependencyResolver::new(&config))),
            device_manager: Arc::new(Mutex::new(IntelligentDeviceManager::new(&config))),
            performance_predictor: Arc::new(Mutex::new(TaskPerformancePredictor::new(&config))),
            config,
            task_queue: Arc::new(RwLock::new(IntelligentTaskQueue::new())),
            statistics: Arc::new(Mutex::new(SchedulingStatistics::new())),
            scheduling_history: Arc::new(Mutex::new(VecDeque::new())),
        }
    }

    /// Initialize the intelligent scheduler
    pub fn initialize(&self) -> Result<(), SchedulingError> {
        // Initialize device management
        {
            let mut device_manager = self.device_manager.lock().expect("lock should not be poisoned");
            device_manager.initialize_devices()?;
        }

        // Initialize performance models
        {
            let mut predictor = self.performance_predictor.lock().expect("lock should not be poisoned");
            predictor.load_performance_models()?;
        }

        // Start performance monitoring
        {
            let mut optimizer = self.performance_optimizer.lock().expect("lock should not be poisoned");
            optimizer.start_monitoring()?;
        }

        // Initialize load balancing
        {
            let mut balancer = self.load_balancer.lock().expect("lock should not be poisoned");
            balancer.initialize_balancing()?;
        }

        Ok(())
    }

    /// Submit a task for intelligent scheduling
    pub fn submit_task(
        &self,
        mut task: SchedulableTask,
    ) -> Result<TaskSubmissionResult, SchedulingError> {
        let submission_start = Instant::now();

        // 1. Analyze task dependencies
        let dependency_analysis = {
            let mut resolver = self.dependency_resolver.lock().expect("lock should not be poisoned");
            resolver.analyze_dependencies(&task)?
        };

        // 2. Calculate initial priority
        task.priority = {
            let mut priority_manager = self.priority_manager.lock().expect("lock should not be poisoned");
            priority_manager.calculate_initial_priority(&task)?
        };

        // 3. Predict task performance
        let performance_prediction = {
            let mut predictor = self.performance_predictor.lock().expect("lock should not be poisoned");
            predictor.predict_task_performance(&task)?
        };

        // 4. Determine optimal device selection
        let device_selection = {
            let mut device_manager = self.device_manager.lock().expect("lock should not be poisoned");
            device_manager.select_optimal_device(&task, &performance_prediction)?
        };

        // 5. Update task with scheduling information
        task.estimated_execution_time = performance_prediction.estimated_duration;

        // 6. Add task to intelligent queue
        {
            let mut queue = self.task_queue.write().expect("lock should not be poisoned");
            queue.enqueue_task(task.clone(), dependency_analysis, device_selection.clone())?;
        }

        let submission_result = TaskSubmissionResult {
            task_id: task.task_id.clone(),
            assigned_device: device_selection.selected_device,
            estimated_start_time: device_selection.estimated_start_time,
            estimated_completion_time: device_selection.estimated_completion_time,
            priority_assigned: task.priority,
            performance_prediction,
            submission_duration: submission_start.elapsed(),
        };

        // Update statistics
        {
            let mut stats = self.statistics.lock().expect("lock should not be poisoned");
            stats.total_tasks_scheduled += 1;
        }

        Ok(submission_result)
    }

    /// Execute intelligent scheduling decisions
    pub async fn execute_scheduling_cycle(&self) -> Result<SchedulingCycleResult, SchedulingError> {
        let cycle_start = Instant::now();

        // 1. Update device states and performance metrics
        let device_updates = {
            let mut device_manager = self.device_manager.lock().expect("lock should not be poisoned");
            device_manager.update_device_states()?
        };

        // 2. Analyze current performance and identify bottlenecks
        let performance_analysis = {
            let mut optimizer = self.performance_optimizer.lock().expect("lock should not be poisoned");
            optimizer.analyze_current_performance(&device_updates)?
        };

        // 3. Adjust task priorities based on current conditions
        let priority_adjustments = {
            let mut priority_manager = self.priority_manager.lock().expect("lock should not be poisoned");
            priority_manager.adjust_priorities_dynamically(&performance_analysis)?
        };

        // 4. Perform predictive load balancing
        let load_balancing_decisions = {
            let mut balancer = self.load_balancer.lock().expect("lock should not be poisoned");
            balancer.balance_load_predictively(&device_updates)?
        };

        // 5. Select optimal execution strategy
        let strategy_selection = {
            let mut strategy = self.execution_strategy.lock().expect("lock should not be poisoned");
            strategy.select_optimal_strategy(&performance_analysis, &device_updates)?
        };

        // 6. Schedule ready tasks to available devices
        let scheduling_decisions = self.schedule_ready_tasks(&strategy_selection).await?;

        // 7. Execute scheduled tasks
        let execution_results = self.execute_scheduled_tasks(&scheduling_decisions).await?;

        let cycle_duration = cycle_start.elapsed();

        // Compute statistics before moving execution_results
        let cycle_statistics = self.calculate_cycle_statistics(&execution_results)?;

        // Clone values before they are moved to the result struct
        let strategy_selection_for_history = strategy_selection.clone();
        let priority_adjustments_for_history = priority_adjustments.clone();

        // Create scheduling cycle result
        let result = SchedulingCycleResult {
            cycle_id: uuid::Uuid::new_v4().to_string(),
            timestamp: SystemTime::now(),
            cycle_duration,
            device_updates,
            performance_analysis,
            priority_adjustments,
            load_balancing_decisions,
            strategy_selection,
            scheduling_decisions,
            execution_results,
            cycle_statistics,
        };

        // Update scheduling history
        {
            let mut history = self.scheduling_history.lock().expect("lock should not be poisoned");
            for decision in &result.scheduling_decisions {
                let record = SchedulingDecisionRecord {
                    decision_id: uuid::Uuid::new_v4().to_string(),
                    timestamp: SystemTime::now(),
                    task_id: decision.task_id.clone(),
                    selected_device: decision.selected_device.clone(),
                    scheduling_strategy: strategy_selection_for_history.selected_strategy.clone(),
                    priority_adjustments: priority_adjustments_for_history.clone(),
                    resource_allocation: decision.resource_allocation.clone(),
                    performance_prediction: decision.performance_prediction.clone(),
                    decision_rationale: decision.rationale.clone(),
                };
                history.push_back(record);
            }

            // Limit history size
            if history.len() > 10000 {
                history.pop_front();
            }
        }

        Ok(result)
    }

    /// Get current scheduling status
    pub fn get_scheduling_status(&self) -> SchedulingStatus {
        let stats = self.statistics.lock().expect("lock should not be poisoned").clone();
        let queue_status = {
            let queue = self.task_queue.read().expect("lock should not be poisoned");
            queue.get_queue_status()
        };
        let device_status = {
            let device_manager = self.device_manager.lock().expect("lock should not be poisoned");
            device_manager.get_device_status_summary()
        };

        SchedulingStatus {
            total_tasks_scheduled: stats.total_tasks_scheduled,
            successful_executions: stats.successful_executions,
            success_rate: if stats.total_tasks_scheduled > 0 {
                stats.successful_executions as f64 / stats.total_tasks_scheduled as f64
            } else {
                0.0
            },
            average_wait_time: stats.average_wait_time,
            average_execution_time: stats.average_execution_time,
            resource_utilization_efficiency: stats.resource_utilization_efficiency,
            queue_status,
            device_status,
            active_strategies: vec![ExecutionStrategyType::AdaptiveML],
            performance_optimizations_active: 5,
        }
    }

    // Private helper methods
    async fn schedule_ready_tasks(
        &self,
        strategy: &StrategySelectionResult,
    ) -> Result<Vec<TaskSchedulingDecision>, SchedulingError> {
        let mut decisions = Vec::new();

        {
            let mut queue = self.task_queue.write().expect("lock should not be poisoned");
            let ready_tasks = queue.get_ready_tasks()?;

            for task in ready_tasks {
                let decision = self.make_scheduling_decision(&task, strategy)?;
                decisions.push(decision);
            }
        }

        Ok(decisions)
    }

    async fn execute_scheduled_tasks(
        &self,
        decisions: &[TaskSchedulingDecision],
    ) -> Result<Vec<TaskExecutionResult>, SchedulingError> {
        let mut results = Vec::new();

        for decision in decisions {
            let result = self.execute_task_on_device(&decision).await?;
            results.push(result);
        }

        Ok(results)
    }

    fn make_scheduling_decision(
        &self,
        task: &SchedulableTask,
        strategy: &StrategySelectionResult,
    ) -> Result<TaskSchedulingDecision, SchedulingError> {
        // Implementation would make intelligent scheduling decisions
        Ok(TaskSchedulingDecision {
            task_id: task.task_id.clone(),
            selected_device: "gpu_0".to_string(),
            scheduled_start_time: SystemTime::now(),
            resource_allocation: ResourceAllocation::default(),
            performance_prediction: PerformancePrediction::default(),
            rationale: "Optimal device selection based on current performance metrics".to_string(),
        })
    }

    async fn execute_task_on_device(
        &self,
        decision: &TaskSchedulingDecision,
    ) -> Result<TaskExecutionResult, SchedulingError> {
        // Implementation would execute task on selected device
        Ok(TaskExecutionResult {
            task_id: decision.task_id.clone(),
            device_id: decision.selected_device.clone(),
            start_time: SystemTime::now(),
            completion_time: SystemTime::now(),
            execution_success: true,
            performance_metrics: TaskPerformanceMetrics::default(),
            resource_usage: ResourceUsage::default(),
        })
    }

    fn calculate_cycle_statistics(
        &self,
        results: &[TaskExecutionResult],
    ) -> Result<CycleStatistics, SchedulingError> {
        Ok(CycleStatistics {
            tasks_executed: results.len(),
            successful_executions: results.iter().filter(|r| r.execution_success).count(),
            average_execution_time: Duration::from_millis(100), // Placeholder
            resource_efficiency: 0.85,                          // Placeholder
        })
    }
}

// === Configuration and Supporting Types ===

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IntelligentSchedulingConfig {
    pub enable_dynamic_priority: bool,
    pub enable_resource_awareness: bool,
    pub enable_performance_optimization: bool,
    pub enable_predictive_balancing: bool,
    pub enable_adaptive_strategies: bool,
    pub max_scheduling_latency: Duration,
    pub priority_aging_factor: f64,
    pub resource_utilization_threshold: f64,
    pub performance_monitoring_interval: Duration,
    pub load_balancing_interval: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TaskSubmissionResult {
    pub task_id: TaskId,
    pub assigned_device: DeviceId,
    pub estimated_start_time: SystemTime,
    pub estimated_completion_time: SystemTime,
    pub priority_assigned: TaskPriority,
    pub performance_prediction: PerformancePrediction,
    pub submission_duration: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SchedulingStatus {
    pub total_tasks_scheduled: u64,
    pub successful_executions: u64,
    pub success_rate: f64,
    pub average_wait_time: Duration,
    pub average_execution_time: Duration,
    pub resource_utilization_efficiency: f64,
    pub queue_status: QueueStatus,
    pub device_status: DeviceStatusSummary,
    pub active_strategies: Vec<ExecutionStrategyType>,
    pub performance_optimizations_active: u32,
}

// === Error Handling ===

#[derive(Debug, Clone)]
pub enum SchedulingError {
    TaskSubmissionError(String),
    ResourceAllocationError(String),
    DependencyResolutionError(String),
    DeviceManagementError(String),
    PerformancePredictionError(String),
    SchedulingDecisionError(String),
    ExecutionError(String),
    ConfigurationError(String),
}

// === Default Implementations and Placeholder Types ===

macro_rules! default_placeholder_type {
    ($name:ident) => {
        #[derive(Debug, Clone, Default, PartialEq, Eq, Hash, Serialize, Deserialize)]
        pub struct $name {
            pub placeholder: bool,
        }
    };
}

// Add all placeholder types
default_placeholder_type!(PriorityCalculationEngine);
default_placeholder_type!(PriorityAdjustmentEngine);
default_placeholder_type!(PriorityHistoryTracker);
default_placeholder_type!(TaskAgingMechanism);
default_placeholder_type!(PriorityPredictor);
default_placeholder_type!(PriorityManagementConfig);
default_placeholder_type!(ResourceUtilizationMonitor);
default_placeholder_type!(ResourceAllocationPredictor);
default_placeholder_type!(ResourceContentionResolver);
default_placeholder_type!(ResourceAffinityManager);
default_placeholder_type!(ResourceOptimizationEngine);
default_placeholder_type!(ResourceSchedulingConfig);
default_placeholder_type!(PerformanceMetricsAnalyzer);
default_placeholder_type!(BottleneckIdentificationSystem);
default_placeholder_type!(PerformanceOptimizationEngine);
default_placeholder_type!(ExecutionPatternAnalyzer);
default_placeholder_type!(PerformanceFeedbackSystem);
default_placeholder_type!(PerformanceOptimizationConfig);
default_placeholder_type!(WorkloadPredictor);
default_placeholder_type!(LoadDistributionOptimizer);
default_placeholder_type!(DynamicLoadBalancingEngine);
default_placeholder_type!(TaskMigrationSystem);
default_placeholder_type!(LoadBalancingHistory);
default_placeholder_type!(LoadBalancingConfig);
default_placeholder_type!(StrategyPerformanceTracker);
default_placeholder_type!(StrategySelectionEngine);
default_placeholder_type!(AdaptiveLearningSystem);
default_placeholder_type!(ExecutionStrategyConfig);
default_placeholder_type!(DependencyGraphBuilder);
default_placeholder_type!(CriticalPathAnalyzer);
default_placeholder_type!(DependencyOptimizationEngine);
default_placeholder_type!(DeadlockDetectionSystem);
default_placeholder_type!(DependencyViolationDetector);
default_placeholder_type!(DependencyResolutionConfig);
default_placeholder_type!(DeviceCapabilityAnalyzer);
default_placeholder_type!(DeviceHealthMonitor);
default_placeholder_type!(DeviceSelectionOptimizer);
default_placeholder_type!(MultiGpuCoordinator);
default_placeholder_type!(DeviceManagementConfig);
default_placeholder_type!(PerformanceModel);
default_placeholder_type!(MLPerformancePredictor);
default_placeholder_type!(HistoricalPerformanceAnalyzer);
default_placeholder_type!(PerformanceEstimationEngine);
default_placeholder_type!(PredictionAccuracyTracker);
default_placeholder_type!(PerformancePredictionConfig);
default_placeholder_type!(PrioritizedTask);
default_placeholder_type!(WaitingTask);
default_placeholder_type!(RunningTask);
default_placeholder_type!(CompletedTask);
default_placeholder_type!(QueueStatistics);
default_placeholder_type!(TaskQueueConfig);
default_placeholder_type!(TaskData);
default_placeholder_type!(SchedulingConstraints);
default_placeholder_type!(AffinityPreferences);
default_placeholder_type!(DeviceInfo);
default_placeholder_type!(DeviceUtilization);
default_placeholder_type!(AvailableResources);
default_placeholder_type!(DevicePerformanceMetrics);
default_placeholder_type!(PriorityAdjustments);
default_placeholder_type!(ResourceAllocation);
default_placeholder_type!(DependencyAnalysisResult);
default_placeholder_type!(DeviceUpdateResult);
default_placeholder_type!(PerformanceAnalysisResult);
default_placeholder_type!(LoadBalancingDecision);

/// Performance prediction result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePrediction {
    /// Estimated duration for task execution
    pub estimated_duration: Duration,
}

/// Device selection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceSelectionResult {
    /// Selected device ID
    pub selected_device: String,
    /// Estimated start time
    pub estimated_start_time: SystemTime,
    /// Estimated completion time
    pub estimated_completion_time: SystemTime,
}

/// Strategy selection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StrategySelectionResult {
    /// Selected execution strategy
    pub selected_strategy: ExecutionStrategyType,
}
/// Task scheduling decision containing scheduling result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TaskSchedulingDecision {
    /// Task identifier
    pub task_id: String,
    /// Selected device for execution
    pub selected_device: String,
    /// Scheduled start time
    #[serde(skip)]
    pub scheduled_start_time: std::time::SystemTime,
    /// Resource allocation for the task
    pub resource_allocation: ResourceAllocation,
    /// Predicted performance
    pub performance_prediction: PerformancePrediction,
    /// Rationale for the decision
    pub rationale: String,
}

impl Default for TaskSchedulingDecision {
    fn default() -> Self {
        Self {
            task_id: String::new(),
            selected_device: String::new(),
            scheduled_start_time: std::time::SystemTime::UNIX_EPOCH,
            resource_allocation: ResourceAllocation::default(),
            performance_prediction: PerformancePrediction::default(),
            rationale: String::new(),
        }
    }
}

/// Task execution result containing execution outcome
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TaskExecutionResult {
    /// Task identifier
    pub task_id: String,
    /// Device that executed the task
    pub device_id: String,
    /// Execution start time
    #[serde(skip)]
    pub start_time: std::time::SystemTime,
    /// Execution completion time
    #[serde(skip)]
    pub completion_time: std::time::SystemTime,
    /// Whether execution was successful
    pub execution_success: bool,
    /// Performance metrics from execution
    pub performance_metrics: TaskPerformanceMetrics,
    /// Resource usage during execution
    pub resource_usage: ResourceUsage,
}

impl Default for TaskExecutionResult {
    fn default() -> Self {
        Self {
            task_id: String::new(),
            device_id: String::new(),
            start_time: std::time::SystemTime::UNIX_EPOCH,
            completion_time: std::time::SystemTime::UNIX_EPOCH,
            execution_success: false,
            performance_metrics: TaskPerformanceMetrics::default(),
            resource_usage: ResourceUsage::default(),
        }
    }
}
default_placeholder_type!(CycleStatistics);
default_placeholder_type!(QueueStatus);
default_placeholder_type!(DeviceStatusSummary);
default_placeholder_type!(TaskPerformanceMetrics);
default_placeholder_type!(ResourceUsage);

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SchedulingCycleResult {
    pub cycle_id: String,
    pub timestamp: SystemTime,
    pub cycle_duration: Duration,
    pub device_updates: DeviceUpdateResult,
    pub performance_analysis: PerformanceAnalysisResult,
    pub priority_adjustments: PriorityAdjustments,
    pub load_balancing_decisions: LoadBalancingDecision,
    pub strategy_selection: StrategySelectionResult,
    pub scheduling_decisions: Vec<TaskSchedulingDecision>,
    pub execution_results: Vec<TaskExecutionResult>,
    pub cycle_statistics: CycleStatistics,
}

// Implementation stubs for the main components
impl DynamicPriorityManager {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self::default()
    }

    fn calculate_initial_priority(
        &mut self,
        task: &SchedulableTask,
    ) -> Result<TaskPriority, SchedulingError> {
        Ok(TaskPriority {
            base_priority: 100,
            dynamic_adjustment: 0,
            aging_bonus: 0,
            performance_bonus: 0,
            deadline_urgency: if task.deadline.is_some() { 50 } else { 0 },
        })
    }

    fn adjust_priorities_dynamically(
        &mut self,
        analysis: &PerformanceAnalysisResult,
    ) -> Result<PriorityAdjustments, SchedulingError> {
        Ok(PriorityAdjustments::default())
    }
}

impl ResourceAwareScheduler {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self::default()
    }
}

impl PerformanceDrivenOptimizer {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self::default()
    }

    fn start_monitoring(&mut self) -> Result<(), SchedulingError> {
        Ok(())
    }

    fn analyze_current_performance(
        &mut self,
        device_updates: &DeviceUpdateResult,
    ) -> Result<PerformanceAnalysisResult, SchedulingError> {
        Ok(PerformanceAnalysisResult::default())
    }
}

impl PredictiveLoadBalancer {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self::default()
    }

    fn initialize_balancing(&mut self) -> Result<(), SchedulingError> {
        Ok(())
    }

    fn balance_load_predictively(
        &mut self,
        device_updates: &DeviceUpdateResult,
    ) -> Result<LoadBalancingDecision, SchedulingError> {
        Ok(LoadBalancingDecision::default())
    }
}

impl AdaptiveExecutionStrategy {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self {
            strategies: HashMap::new(),
            performance_tracker: StrategyPerformanceTracker::default(),
            selection_engine: StrategySelectionEngine::default(),
            learning_system: AdaptiveLearningSystem::default(),
            active_strategy: ExecutionStrategyType::AdaptiveML,
            config: ExecutionStrategyConfig::default(),
        }
    }

    fn select_optimal_strategy(
        &mut self,
        analysis: &PerformanceAnalysisResult,
        device_updates: &DeviceUpdateResult,
    ) -> Result<StrategySelectionResult, SchedulingError> {
        Ok(StrategySelectionResult {
            selected_strategy: self.active_strategy.clone(),
        })
    }
}

impl TaskDependencyResolver {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self::default()
    }

    fn analyze_dependencies(
        &mut self,
        task: &SchedulableTask,
    ) -> Result<DependencyAnalysisResult, SchedulingError> {
        Ok(DependencyAnalysisResult::default())
    }
}

impl IntelligentDeviceManager {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self {
            devices: HashMap::new(),
            capability_analyzer: DeviceCapabilityAnalyzer::default(),
            health_monitor: DeviceHealthMonitor::default(),
            selection_optimizer: DeviceSelectionOptimizer::default(),
            coordination_system: MultiGpuCoordinator::default(),
            config: DeviceManagementConfig::default(),
        }
    }

    fn initialize_devices(&mut self) -> Result<(), SchedulingError> {
        // Add mock GPU devices
        let device = GpuDeviceState {
            device_id: "gpu_0".to_string(),
            device_info: DeviceInfo::default(),
            current_utilization: DeviceUtilization::default(),
            available_resources: AvailableResources::default(),
            running_tasks: Vec::new(),
            performance_metrics: DevicePerformanceMetrics::default(),
            health_status: DeviceHealthStatus::Healthy,
            last_updated: SystemTime::now(),
        };
        self.devices.insert("gpu_0".to_string(), device);
        Ok(())
    }

    fn select_optimal_device(
        &mut self,
        task: &SchedulableTask,
        prediction: &PerformancePrediction,
    ) -> Result<DeviceSelectionResult, SchedulingError> {
        Ok(DeviceSelectionResult {
            selected_device: "gpu_0".to_string(),
            estimated_start_time: SystemTime::now(),
            estimated_completion_time: SystemTime::now() + Duration::from_millis(100),
        })
    }

    fn update_device_states(&mut self) -> Result<DeviceUpdateResult, SchedulingError> {
        Ok(DeviceUpdateResult::default())
    }

    fn get_device_status_summary(&self) -> DeviceStatusSummary {
        DeviceStatusSummary::default()
    }
}

impl TaskPerformancePredictor {
    fn new(config: &IntelligentSchedulingConfig) -> Self {
        Self::default()
    }

    fn load_performance_models(&mut self) -> Result<(), SchedulingError> {
        Ok(())
    }

    fn predict_task_performance(
        &mut self,
        task: &SchedulableTask,
    ) -> Result<PerformancePrediction, SchedulingError> {
        Ok(PerformancePrediction {
            estimated_duration: Duration::from_millis(100),
        })
    }
}

impl IntelligentTaskQueue {
    fn new() -> Self {
        Self::default()
    }

    fn enqueue_task(
        &mut self,
        task: SchedulableTask,
        dependency_analysis: DependencyAnalysisResult,
        device_selection: DeviceSelectionResult,
    ) -> Result<(), SchedulingError> {
        // Implementation would add task to appropriate queue
        Ok(())
    }

    fn get_ready_tasks(&mut self) -> Result<Vec<SchedulableTask>, SchedulingError> {
        // Return placeholder ready tasks
        Ok(vec![])
    }

    fn get_queue_status(&self) -> QueueStatus {
        QueueStatus::default()
    }
}

impl SchedulingStatistics {
    fn new() -> Self {
        Self {
            total_tasks_scheduled: 0,
            successful_executions: 0,
            failed_executions: 0,
            average_wait_time: Duration::from_millis(50),
            average_execution_time: Duration::from_millis(100),
            resource_utilization_efficiency: 0.85,
            priority_adjustment_count: 0,
            load_balancing_operations: 0,
            device_migration_count: 0,
        }
    }
}

impl TaskPriority {
    pub fn total_priority(&self) -> i64 {
        self.base_priority as i64
            + self.dynamic_adjustment as i64
            + self.aging_bonus as i64
            + self.performance_bonus as i64
            + self.deadline_urgency as i64
    }
}

// PartialEq and Eq are already derived by the default_placeholder_type macro

impl Ord for PrioritizedTask {
    fn cmp(&self, other: &Self) -> CmpOrdering {
        CmpOrdering::Equal // Placeholder
    }
}

impl PartialOrd for PrioritizedTask {
    fn partial_cmp(&self, other: &Self) -> Option<CmpOrdering> {
        Some(self.cmp(other))
    }
}

impl Default for IntelligentSchedulingConfig {
    fn default() -> Self {
        Self {
            enable_dynamic_priority: true,
            enable_resource_awareness: true,
            enable_performance_optimization: true,
            enable_predictive_balancing: true,
            enable_adaptive_strategies: true,
            max_scheduling_latency: Duration::from_millis(10),
            priority_aging_factor: 1.2,
            resource_utilization_threshold: 0.85,
            performance_monitoring_interval: Duration::from_secs(1),
            load_balancing_interval: Duration::from_secs(5),
        }
    }
}

// Execution strategy trait
pub trait ExecutionStrategy: std::fmt::Debug + Send + Sync {
    fn schedule_tasks(
        &self,
        tasks: &[SchedulableTask],
        devices: &HashMap<DeviceId, GpuDeviceState>,
    ) -> Result<Vec<TaskSchedulingDecision>, SchedulingError>;
    fn strategy_name(&self) -> &str;
    fn optimization_criteria(&self) -> StrategyOptimizationCriteria;
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StrategyOptimizationCriteria {
    pub optimize_for_throughput: bool,
    pub optimize_for_latency: bool,
    pub optimize_for_fairness: bool,
    pub optimize_for_energy: bool,
}

// Add some final missing types
impl Default for PerformancePrediction {
    fn default() -> Self {
        Self {
            estimated_duration: Duration::from_millis(100),
        }
    }
}

impl Default for DeviceSelectionResult {
    fn default() -> Self {
        Self {
            selected_device: "gpu_0".to_string(),
            estimated_start_time: SystemTime::now(),
            estimated_completion_time: SystemTime::now() + Duration::from_millis(100),
        }
    }
}

impl Default for StrategySelectionResult {
    fn default() -> Self {
        Self {
            selected_strategy: ExecutionStrategyType::AdaptiveML,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_intelligent_scheduler_creation() {
        let config = IntelligentSchedulingConfig::default();
        let scheduler = IntelligentTaskScheduler::new(config);
        let status = scheduler.get_scheduling_status();
        assert_eq!(status.total_tasks_scheduled, 0);
    }

    #[test]
    fn test_task_priority_calculation() {
        let priority = TaskPriority {
            base_priority: 100,
            dynamic_adjustment: 20,
            aging_bonus: 10,
            performance_bonus: 5,
            deadline_urgency: 15,
        };
        assert_eq!(priority.total_priority(), 150);
    }

    #[test]
    fn test_scheduling_config() {
        let config = IntelligentSchedulingConfig::default();
        assert!(config.enable_dynamic_priority);
        assert!(config.enable_adaptive_strategies);
        assert_eq!(config.priority_aging_factor, 1.2);
    }

    #[test]
    fn test_task_types() {
        let task_types = vec![
            TaskType::TensorOperation,
            TaskType::MatrixMultiplication,
            TaskType::Convolution,
            TaskType::Custom("CustomOp".to_string()),
        ];
        assert_eq!(task_types.len(), 4);
    }

    #[test]
    fn test_execution_strategies() {
        let strategies = vec![
            ExecutionStrategyType::EarliestDeadlineFirst,
            ExecutionStrategyType::ResourceAwareScheduling,
            ExecutionStrategyType::AdaptiveML,
        ];
        assert_eq!(strategies.len(), 3);
    }
}
