//! Simple Integration Tests for CUDA Performance Optimization
//!
//! This test suite provides basic integration tests for the CUDA performance
//! optimization systems with minimal dependencies and straightforward test cases.
//!
//! NOTE: This test is currently disabled because the performance_optimization_coordinator
//! and other related modules are not yet exported from the CUDA module.

#![allow(unexpected_cfgs)]
#![cfg(all(cuda_available, feature = "simple_integration_tests"))]

use std::time::{Duration, SystemTime};

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

    /// Test basic performance coordinator creation and initialization
    #[tokio::test]
    async fn test_performance_coordinator_basic() {
        use torsh_backend::cuda::{
            CudaPerformanceOptimizationCoordinator, PerformanceCoordinatorConfig,
        };

        let config = PerformanceCoordinatorConfig::default();
        let coordinator = CudaPerformanceOptimizationCoordinator::new(config);

        // Test basic initialization
        let init_result = coordinator.initialize().await;
        assert!(
            init_result.is_ok(),
            "Performance coordinator should initialize successfully"
        );

        // Test getting status
        let status = coordinator.get_comprehensive_status();
        assert_eq!(status.total_operations_coordinated, 0);
        assert!(status.success_rate >= 0.0);
    }

    /// Test memory optimizer basic functionality
    #[test]
    fn test_memory_optimizer_basic() {
        use torsh_backend::cuda::memory::optimization::advanced_memory_optimizer::{
            AdvancedMemoryConfig, AdvancedMemoryOptimizer,
        };

        let config = AdvancedMemoryConfig::default();
        let optimizer = AdvancedMemoryOptimizer::new(config);

        // Test initialization
        let init_result = optimizer.initialize();
        assert!(
            init_result.is_ok(),
            "Memory optimizer should initialize successfully"
        );

        // Test getting status
        let status = optimizer.get_optimization_status();
        assert_eq!(status.total_optimizations, 0);
        assert!(status.performance_improvement >= 0.0);
    }

    /// Test kernel fusion optimizer basic functionality
    #[test]
    fn test_kernel_fusion_optimizer_basic() {
        use torsh_backend::cuda::{AdvancedKernelFusionOptimizer, KernelFusionConfig};

        let config = KernelFusionConfig::default();
        let optimizer = AdvancedKernelFusionOptimizer::new(config);

        // Test initialization
        let init_result = optimizer.initialize();
        assert!(
            init_result.is_ok(),
            "Kernel fusion optimizer should initialize successfully"
        );

        // Test getting status
        let status = optimizer.get_optimization_status();
        assert_eq!(status.total_fusions, 0);
        assert!(status.success_rate >= 0.0);
    }

    /// Test intelligent task scheduler basic functionality
    #[test]
    fn test_task_scheduler_basic() {
        use torsh_backend::cuda::{IntelligentSchedulingConfig, IntelligentTaskScheduler};

        let config = IntelligentSchedulingConfig::default();
        let scheduler = IntelligentTaskScheduler::new(config);

        // Test initialization
        let init_result = scheduler.initialize();
        assert!(
            init_result.is_ok(),
            "Task scheduler should initialize successfully"
        );

        // Test getting status
        let status = scheduler.get_scheduling_status();
        assert_eq!(status.total_tasks_scheduled, 0);
        assert!(status.success_rate >= 0.0);
    }

    /// Test configuration objects can be created
    #[test]
    fn test_configuration_objects() {
        use torsh_backend::cuda::memory::optimization::advanced_memory_optimizer::AdvancedMemoryConfig;
        use torsh_backend::cuda::{
            IntelligentSchedulingConfig, KernelFusionConfig, PerformanceCoordinatorConfig,
        };

        // Test default configurations
        let perf_config = PerformanceCoordinatorConfig::default();
        assert!(perf_config.enable_memory_optimization);
        assert!(perf_config.enable_kernel_fusion);

        let memory_config = AdvancedMemoryConfig::default();
        assert!(memory_config.enable_predictive_pooling);

        let fusion_config = KernelFusionConfig::default();
        assert!(fusion_config.enable_aggressive_fusion);

        let scheduling_config = IntelligentSchedulingConfig::default();
        assert!(scheduling_config.enable_dynamic_priority);
    }

    /// Test error handling for invalid configurations
    #[test]
    fn test_error_handling() {
        use torsh_backend::cuda::{
            CudaPerformanceOptimizationCoordinator, PerformanceCoordinatorConfig,
        };

        // Test with various configuration settings
        let config = PerformanceCoordinatorConfig {
            enable_memory_optimization: false,
            enable_kernel_fusion: false,
            enable_intelligent_scheduling: false,
            ..Default::default()
        };

        let coordinator = CudaPerformanceOptimizationCoordinator::new(config);
        let status = coordinator.get_comprehensive_status();

        // Should handle configuration gracefully
        assert!(status.total_operations_coordinated >= 0);
    }

    /// Test component integration at basic level
    #[tokio::test]
    async fn test_basic_component_integration() {
        use torsh_backend::cuda::{
            CudaPerformanceOptimizationCoordinator, PerformanceCoordinatorConfig,
        };

        let config = PerformanceCoordinatorConfig {
            enable_memory_optimization: true,
            enable_kernel_fusion: true,
            enable_intelligent_scheduling: true,
            min_performance_improvement: 1.0, // Low threshold for testing
            ..Default::default()
        };

        let coordinator = CudaPerformanceOptimizationCoordinator::new(config);

        // Test initialization with all components enabled
        let init_result = coordinator.initialize().await;
        assert!(
            init_result.is_ok() || init_result.is_err(),
            "Initialization should complete"
        );

        // Test status retrieval
        let status = coordinator.get_comprehensive_status();
        assert!(!status.active_optimizations.is_empty());
    }

    /// Test performance metrics collection
    #[test]
    fn test_performance_metrics() {
        use torsh_backend::cuda::PerformanceMetrics;

        let metrics = PerformanceMetrics::default();
        assert!(metrics.throughput > 0.0);
        assert!(metrics.memory_utilization >= 0.0 && metrics.memory_utilization <= 1.0);
        assert!(metrics.compute_utilization >= 0.0 && metrics.compute_utilization <= 1.0);
        assert!(metrics.cache_hit_ratio >= 0.0 && metrics.cache_hit_ratio <= 1.0);
    }

    /// Test operation request creation
    #[test]
    fn test_operation_request_creation() {
        use torsh_backend::cuda::{
            performance_optimization_coordinator::{
                CudaOperationType, OptimizationHints, PerformanceRequirements, RequestPriority,
                ResourceRequirements, TensorOperation,
            },
            CudaOperationRequest,
        };

        let request = CudaOperationRequest {
            request_id: "test_request".to_string(),
            operation_type: CudaOperationType::TensorComputation,
            tensor_operations: vec![TensorOperation::default()],
            resource_requirements: ResourceRequirements::default(),
            performance_requirements: PerformanceRequirements::default(),
            optimization_hints: OptimizationHints::default(),
            deadline: Some(SystemTime::now() + Duration::from_secs(10)),
            priority: RequestPriority::Medium,
            submission_time: SystemTime::now(),
        };

        assert_eq!(request.request_id, "test_request");
        assert_eq!(request.operation_type, CudaOperationType::TensorComputation);
        assert_eq!(request.priority, RequestPriority::Medium);
    }

    /// Test fusion operation creation
    #[test]
    fn test_fusion_operation_creation() {
        use std::collections::HashMap;
        use torsh_backend::cuda::{FusionOperation, OperationType};

        let operation = FusionOperation {
            operation_id: "test_op".to_string(),
            operation_type: OperationType::ElementWiseAdd,
            input_tensors: Vec::new(),
            output_tensors: Vec::new(),
            parameters: HashMap::new(),
            memory_requirements: Default::default(),
            compute_requirements: Default::default(),
            dependencies: Vec::new(),
            execution_order: 0,
        };

        assert_eq!(operation.operation_id, "test_op");
        assert_eq!(operation.operation_type, OperationType::ElementWiseAdd);
        assert_eq!(operation.execution_order, 0);
    }

    /// Test schedulable task creation
    #[test]
    fn test_schedulable_task_creation() {
        use torsh_backend::cuda::{
            intelligent_task_scheduler::{ResourceRequirements, SchedulingConstraints, TaskData},
            SchedulableTask, TaskPriority, TaskType,
        };

        let task = SchedulableTask {
            task_id: "test_task".to_string(),
            task_type: TaskType::TensorOperation,
            priority: TaskPriority {
                base_priority: 100,
                dynamic_adjustment: 0,
                aging_bonus: 0,
                performance_bonus: 0,
                deadline_urgency: 0,
            },
            resource_requirements: ResourceRequirements {
                gpu_memory: 1024 * 1024 * 256, // 256MB
                compute_units: 16,
                bandwidth_requirements: 100.0,
                shared_memory: 48 * 1024,
                register_count: 64,
                device_capabilities: Vec::new(),
                affinity_preferences: Default::default(),
            },
            dependencies: Vec::new(),
            estimated_execution_time: Duration::from_millis(100),
            deadline: None,
            submission_time: SystemTime::now(),
            task_data: TaskData::default(),
            scheduling_constraints: SchedulingConstraints::default(),
        };

        assert_eq!(task.task_id, "test_task");
        assert_eq!(task.task_type, TaskType::TensorOperation);
        assert_eq!(task.priority.base_priority, 100);
    }
}

/// Basic performance characterization tests
#[cfg(all(test, cuda_available))]
mod performance_tests {
    use super::*;
    use std::time::Instant;

    #[test]
    fn test_coordinator_creation_performance() {
        use torsh_backend::cuda::{
            CudaPerformanceOptimizationCoordinator, PerformanceCoordinatorConfig,
        };

        let start = Instant::now();

        for _ in 0..10 {
            let config = PerformanceCoordinatorConfig::default();
            let _coordinator = CudaPerformanceOptimizationCoordinator::new(config);
        }

        let creation_time = start.elapsed();
        println!("Created 10 coordinators in {:?}", creation_time);

        // Coordinator creation should be fast
        assert!(
            creation_time < Duration::from_millis(100),
            "Coordinator creation should be fast"
        );
    }

    #[test]
    fn test_memory_optimizer_creation_performance() {
        use torsh_backend::cuda::memory::optimization::advanced_memory_optimizer::{
            AdvancedMemoryConfig, AdvancedMemoryOptimizer,
        };

        let start = Instant::now();

        for _ in 0..10 {
            let config = AdvancedMemoryConfig::default();
            let _optimizer = AdvancedMemoryOptimizer::new(config);
        }

        let creation_time = start.elapsed();
        println!("Created 10 memory optimizers in {:?}", creation_time);

        // Memory optimizer creation should be fast
        assert!(
            creation_time < Duration::from_millis(100),
            "Memory optimizer creation should be fast"
        );
    }

    #[test]
    fn test_status_query_performance() {
        use torsh_backend::cuda::{
            CudaPerformanceOptimizationCoordinator, PerformanceCoordinatorConfig,
        };

        let config = PerformanceCoordinatorConfig::default();
        let coordinator = CudaPerformanceOptimizationCoordinator::new(config);

        let start = Instant::now();

        for _ in 0..100 {
            let _status = coordinator.get_comprehensive_status();
        }

        let query_time = start.elapsed();
        println!("Performed 100 status queries in {:?}", query_time);

        // Status queries should be very fast
        assert!(
            query_time < Duration::from_millis(100),
            "Status queries should be fast"
        );
    }
}
