torsh-utils 0.2.0

//! Integration tests for torsh-utils

use std::collections::HashMap;
use tempfile::TempDir;
use torsh_tensor::Tensor;

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

    /// Test complete mobile optimization workflow
    #[test]
    fn test_mobile_optimization_workflow() {
        let _temp_dir = TempDir::new().unwrap();

        // 1. Create a test model
        let test_model = create_test_model();

        // 2. Run optimization pipeline
        let optimization_result = run_mobile_optimization_pipeline(&test_model);
        assert!(optimization_result.is_ok());

        // 3. Benchmark optimized model
        let benchmark_result = run_mobile_benchmarking(&optimization_result.unwrap());
        assert!(benchmark_result.is_ok());

        // 4. Validate performance
        let validation_result = validate_mobile_performance(&benchmark_result.unwrap());
        assert!(validation_result.is_ok());
    }

    /// Test profiling and optimization integration
    #[test]
    fn test_profiling_optimization_integration() {
        let _temp_dir = TempDir::new().unwrap();

        // 1. Create test model
        let test_model = create_test_model();

        // 2. Profile model to identify bottlenecks
        let profiling_result = profile_model_bottlenecks(&test_model);
        assert!(profiling_result.is_ok());

        let bottlenecks = profiling_result.unwrap();

        // 3. Apply targeted optimizations based on profiling
        let optimization_suggestions = generate_optimization_suggestions(&bottlenecks);
        assert!(!optimization_suggestions.is_empty());

        // 4. Apply optimizations
        let optimized_model = apply_targeted_optimizations(&test_model, &optimization_suggestions);
        assert!(optimized_model.is_ok());

        // 5. Verify improvements
        let improved_profile = profile_model_bottlenecks(&optimized_model.unwrap());
        assert!(improved_profile.is_ok());
    }

    /// Test TensorBoard logging integration
    #[test]
    fn test_tensorboard_integration() {
        let temp_dir = TempDir::new().unwrap();

        // 1. Create TensorBoard writer
        let tensorboard_result = create_tensorboard_writer(temp_dir.path());
        assert!(tensorboard_result.is_ok());

        let mut writer = tensorboard_result.unwrap();

        // 2. Log model architecture
        let test_model = create_test_model();
        let graph_result = log_model_graph(&mut writer, &test_model);
        assert!(graph_result.is_ok());

        // 3. Log profiling data
        let profiling_data = create_test_profiling_data();
        let profiling_log_result = log_profiling_data(&mut writer, &profiling_data);
        assert!(profiling_log_result.is_ok());

        // 4. Log benchmark results
        let benchmark_data = create_test_benchmark_data();
        let benchmark_log_result = log_benchmark_data(&mut writer, &benchmark_data);
        assert!(benchmark_log_result.is_ok());

        // 5. Log mobile optimization results
        let mobile_data = create_test_mobile_data();
        let mobile_log_result = log_mobile_optimization_data(&mut writer, &mobile_data);
        assert!(mobile_log_result.is_ok());

        // 6. Verify files were created
        verify_tensorboard_files(temp_dir.path());
    }

    /// Test end-to-end mobile deployment workflow
    #[test]
    fn test_mobile_deployment_workflow() {
        let _temp_dir = TempDir::new().unwrap();

        // 1. Start with a baseline model
        let baseline_model = create_baseline_model();

        // 2. Profile baseline performance
        let baseline_profile = profile_model_bottlenecks(&baseline_model).unwrap();

        // 3. Apply mobile-specific optimizations
        let mobile_optimized = optimize_for_mobile_deployment(&baseline_model).unwrap();

        // 4. Benchmark mobile performance
        let mobile_benchmark = benchmark_mobile_model(&mobile_optimized).unwrap();

        // 5. Validate against mobile requirements
        let validation = validate_mobile_requirements(&mobile_benchmark).unwrap();

        // 6. Generate deployment report
        let deployment_report =
            generate_mobile_deployment_report(&baseline_profile, &mobile_benchmark, &validation);

        assert!(deployment_report.is_ok());
        assert!(validation.meets_latency_requirements);
        assert!(validation.meets_memory_requirements);
    }

    /// Test cross-platform compatibility
    #[test]
    fn test_cross_platform_compatibility() {
        let test_model = create_test_model();

        // Test iOS optimization
        let ios_result = optimize_for_ios_deployment(&test_model);
        assert!(ios_result.is_ok());

        // Test Android optimization
        let android_result = optimize_for_android_deployment(&test_model);
        assert!(android_result.is_ok());

        // Test generic mobile optimization
        let generic_result = optimize_for_generic_mobile(&test_model);
        assert!(generic_result.is_ok());

        // Verify all optimizations produce valid models
        let ios_model = ios_result.unwrap();
        let android_model = android_result.unwrap();
        let generic_model = generic_result.unwrap();

        assert!(validate_model_correctness(&ios_model));
        assert!(validate_model_correctness(&android_model));
        assert!(validate_model_correctness(&generic_model));
    }

    /// Test performance regression detection across optimizations
    #[test]
    fn test_performance_regression_detection() {
        let baseline_model = create_baseline_model();

        // Establish baseline performance
        let baseline_metrics = benchmark_model_performance(&baseline_model).unwrap();

        // Apply various optimizations
        let quantized_model = apply_quantization(&baseline_model).unwrap();
        let pruned_model = apply_pruning(&baseline_model).unwrap();
        let compressed_model = apply_compression(&baseline_model).unwrap();

        // Benchmark optimized models
        let quantized_metrics = benchmark_model_performance(&quantized_model).unwrap();
        let pruned_metrics = benchmark_model_performance(&pruned_model).unwrap();
        let compressed_metrics = benchmark_model_performance(&compressed_model).unwrap();

        // Detect regressions
        let quantization_regression = detect_regression(&baseline_metrics, &quantized_metrics);
        let pruning_regression = detect_regression(&baseline_metrics, &pruned_metrics);
        let compression_regression = detect_regression(&baseline_metrics, &compressed_metrics);

        // Verify regression detection
        assert!(!quantization_regression.has_critical_regression);
        assert!(!pruning_regression.has_critical_regression);
        assert!(!compression_regression.has_critical_regression);

        // All optimizations should maintain acceptable performance
        assert!(quantized_metrics.accuracy_drop < 0.05); // < 5% accuracy drop
        assert!(pruned_metrics.latency_increase < 0.1); // < 10% latency increase
        assert!(compressed_metrics.memory_reduction > 0.2); // > 20% memory reduction
    }

    /// Test memory leak detection across utilities
    #[test]
    fn test_memory_leak_detection() {
        let _temp_dir = TempDir::new().unwrap();

        // Run memory-intensive operations
        let test_model = create_large_test_model();

        // Enable memory tracking
        let memory_tracker = enable_memory_tracking().unwrap();

        // Run optimization pipeline (potential source of leaks)
        let _optimization_result = run_extensive_optimization_pipeline(&test_model);

        // Run benchmarking (potential source of leaks)
        let _benchmark_result = run_extensive_benchmarking(&test_model);

        // Run profiling (potential source of leaks)
        let _profiling_result = run_extensive_profiling(&test_model);

        // Check for memory leaks
        let leak_report = memory_tracker.generate_leak_report();
        assert!(leak_report.is_ok());

        let leaks = leak_report.unwrap();

        // Should not have significant memory leaks
        assert!(leaks.total_leaked_mb < 10.0); // Less than 10MB leaked
        assert!(leaks.leak_count < 5); // Less than 5 leak sites

        // Clean up tracking
        memory_tracker.cleanup();
    }

    /// Test error handling and recovery
    #[test]
    fn test_error_handling_recovery() {
        // Test with invalid inputs
        let invalid_model = create_invalid_model();

        // Optimization should handle invalid model gracefully
        let optimization_result = run_mobile_optimization_pipeline(&invalid_model);
        assert!(optimization_result.is_err());

        // Error should be descriptive
        let error = optimization_result.unwrap_err();
        assert!(error.to_string().contains("invalid"));

        // Test with corrupted tensor data
        let corrupted_tensor = create_corrupted_tensor();
        let tensor_processing_result = process_tensor_for_mobile(&corrupted_tensor);
        assert!(tensor_processing_result.is_err());

        // Test recovery from optimization failures
        let problematic_model = create_problematic_model();
        let recovery_result = run_optimization_with_recovery(&problematic_model);
        assert!(recovery_result.is_ok());

        // Should fall back to safe optimization settings
        let recovered_model = recovery_result.unwrap();
        assert!(validate_model_correctness(&recovered_model));
    }

    /// Test concurrent operations and thread safety
    #[test]
    fn test_concurrent_operations() {
        use std::sync::Arc;
        use std::thread;

        let test_model = Arc::new(create_test_model());
        let num_threads = 4;
        let mut handles = Vec::new();

        // Run concurrent optimizations
        for i in 0..num_threads {
            let model_clone = Arc::clone(&test_model);
            let handle = thread::spawn(move || {
                let thread_id = i;
                let optimization_result = run_threaded_optimization(&*model_clone, thread_id);
                optimization_result
            });
            handles.push(handle);
        }

        // Wait for all threads to complete
        let mut results = Vec::new();
        for handle in handles {
            let result = handle.join().unwrap();
            results.push(result);
        }

        // Verify all operations completed successfully
        for result in results {
            assert!(result.is_ok());
        }

        // Test concurrent benchmarking
        let concurrent_benchmark_result = run_concurrent_benchmarks(&*test_model, num_threads);
        assert!(concurrent_benchmark_result.is_ok());
    }

    // Helper functions and mock implementations

    fn create_test_model() -> TestModel {
        TestModel {
            id: "test_model_1".to_string(),
            parameters: create_test_parameters(),
            architecture: "ResNet18".to_string(),
            input_shape: vec![1, 3, 224, 224],
        }
    }

    fn create_baseline_model() -> TestModel {
        TestModel {
            id: "baseline_model".to_string(),
            parameters: create_baseline_parameters(),
            architecture: "MobileNetV2".to_string(),
            input_shape: vec![1, 3, 224, 224],
        }
    }

    fn create_large_test_model() -> TestModel {
        TestModel {
            id: "large_test_model".to_string(),
            parameters: create_large_parameters(),
            architecture: "ResNet152".to_string(),
            input_shape: vec![1, 3, 224, 224],
        }
    }

    fn create_invalid_model() -> TestModel {
        TestModel {
            id: "invalid_model".to_string(),
            parameters: HashMap::new(), // Empty parameters - invalid
            architecture: "InvalidArch".to_string(),
            input_shape: vec![], // Empty shape - invalid
        }
    }

    fn create_problematic_model() -> TestModel {
        TestModel {
            id: "problematic_model".to_string(),
            parameters: create_problematic_parameters(),
            architecture: "ProblematicNet".to_string(),
            input_shape: vec![1, 3, 224, 224],
        }
    }

    fn create_test_parameters() -> HashMap<String, Tensor> {
        let mut params = HashMap::new();
        let weight_data: Vec<f32> = (0..1000).map(|i| i as f32 / 1000.0).collect();
        let weight_tensor = Tensor::from_vec(weight_data, &[10, 100]).unwrap();
        params.insert("conv1.weight".to_string(), weight_tensor);
        params
    }

    fn create_baseline_parameters() -> HashMap<String, Tensor> {
        let mut params = HashMap::new();
        let weight_data: Vec<f32> = (0..5000).map(|i| (i as f32 / 5000.0) * 2.0 - 1.0).collect();
        let weight_tensor = Tensor::from_vec(weight_data, &[50, 100]).unwrap();
        params.insert("conv1.weight".to_string(), weight_tensor);
        params
    }

    fn create_large_parameters() -> HashMap<String, Tensor> {
        let mut params = HashMap::new();
        let weight_data: Vec<f32> = (0..100000)
            .map(|i| (i as f32 / 100000.0) * 2.0 - 1.0)
            .collect();
        let weight_tensor = Tensor::from_vec(weight_data, &[100, 1000]).unwrap();
        params.insert("conv1.weight".to_string(), weight_tensor);
        params
    }

    fn create_problematic_parameters() -> HashMap<String, Tensor> {
        let mut params = HashMap::new();
        // Create tensor with extreme values that might cause optimization issues
        let weight_data: Vec<f32> = (0..1000)
            .map(|i| {
                if i % 100 == 0 {
                    f32::INFINITY // Problematic infinite values
                } else {
                    (i as f32 / 1000.0) * 1000.0 // Very large values
                }
            })
            .collect();
        let weight_tensor = Tensor::from_vec(weight_data, &[10, 100]).unwrap();
        params.insert("conv1.weight".to_string(), weight_tensor);
        params
    }

    fn create_corrupted_tensor() -> Tensor {
        let corrupted_data: Vec<f32> = vec![f32::NAN; 1000]; // All NaN values
        Tensor::from_vec(corrupted_data, &[10, 100]).unwrap()
    }

    // Mock function implementations

    fn run_mobile_optimization_pipeline(model: &TestModel) -> Result<OptimizedTestModel, String> {
        // Check for invalid model conditions
        if model.id.contains("invalid") {
            return Err("Model has invalid configuration".to_string());
        }
        if model.parameters.is_empty() {
            return Err("Model has no parameters - invalid model".to_string());
        }
        if model.input_shape.is_empty() {
            return Err("Model has invalid input shape".to_string());
        }

        Ok(OptimizedTestModel {
            original_model: model.id.clone(),
            optimizations_applied: vec!["quantization".to_string(), "pruning".to_string()],
            compression_ratio: 0.75,
            estimated_speedup: 1.5,
        })
    }

    fn run_mobile_benchmarking(_model: &OptimizedTestModel) -> Result<MobileBenchmarkData, String> {
        Ok(MobileBenchmarkData {
            latency_ms: 15.0,
            throughput_fps: 66.7,
            memory_usage_mb: 128.0,
            power_consumption_mw: 2000.0,
            thermal_state: "Normal".to_string(),
        })
    }

    fn validate_mobile_performance(
        benchmark: &MobileBenchmarkData,
    ) -> Result<MobileValidationResult, String> {
        Ok(MobileValidationResult {
            meets_latency_requirements: benchmark.latency_ms <= 16.67, // 60 FPS
            meets_memory_requirements: benchmark.memory_usage_mb <= 256.0,
            meets_power_requirements: benchmark.power_consumption_mw <= 5000.0,
            overall_score: 0.85,
        })
    }

    fn profile_model_bottlenecks(_model: &TestModel) -> Result<ProfilingData, String> {
        Ok(ProfilingData {
            total_time_ms: 25.0,
            layer_timings: vec![
                LayerProfile {
                    name: "conv1".to_string(),
                    time_ms: 10.0,
                    percentage: 40.0,
                },
                LayerProfile {
                    name: "conv2".to_string(),
                    time_ms: 8.0,
                    percentage: 32.0,
                },
            ],
            memory_usage_mb: 256.0,
            bottlenecks: vec!["conv1".to_string()],
        })
    }

    fn generate_optimization_suggestions(_profile: &ProfilingData) -> Vec<OptimizationSuggestion> {
        vec![OptimizationSuggestion {
            target: "conv1".to_string(),
            optimization_type: "quantization".to_string(),
            expected_improvement: 0.3,
            difficulty: "Medium".to_string(),
        }]
    }

    fn apply_targeted_optimizations(
        _model: &TestModel,
        _suggestions: &[OptimizationSuggestion],
    ) -> Result<TestModel, String> {
        Ok(create_test_model()) // Return optimized version
    }

    fn create_tensorboard_writer(
        _log_dir: &std::path::Path,
    ) -> Result<MockTensorBoardWriter, String> {
        Ok(MockTensorBoardWriter {
            log_dir: _log_dir.to_path_buf(),
            entries: Vec::new(),
        })
    }

    fn log_model_graph(
        _writer: &mut MockTensorBoardWriter,
        _model: &TestModel,
    ) -> Result<(), String> {
        _writer.entries.push("model_graph".to_string());
        Ok(())
    }

    fn log_profiling_data(
        _writer: &mut MockTensorBoardWriter,
        _data: &ProfilingData,
    ) -> Result<(), String> {
        _writer.entries.push("profiling_data".to_string());
        Ok(())
    }

    fn log_benchmark_data(
        _writer: &mut MockTensorBoardWriter,
        _data: &MobileBenchmarkData,
    ) -> Result<(), String> {
        _writer.entries.push("benchmark_data".to_string());
        Ok(())
    }

    fn log_mobile_optimization_data(
        _writer: &mut MockTensorBoardWriter,
        _data: &MobileOptimizationData,
    ) -> Result<(), String> {
        _writer.entries.push("mobile_optimization_data".to_string());
        Ok(())
    }

    fn verify_tensorboard_files(_log_dir: &std::path::Path) {
        // In a real implementation, would check for actual files
        assert!(_log_dir.exists());
    }

    fn create_test_profiling_data() -> ProfilingData {
        ProfilingData {
            total_time_ms: 25.0,
            layer_timings: vec![],
            memory_usage_mb: 256.0,
            bottlenecks: vec![],
        }
    }

    fn create_test_benchmark_data() -> MobileBenchmarkData {
        MobileBenchmarkData {
            latency_ms: 15.0,
            throughput_fps: 66.7,
            memory_usage_mb: 128.0,
            power_consumption_mw: 2000.0,
            thermal_state: "Normal".to_string(),
        }
    }

    fn create_test_mobile_data() -> MobileOptimizationData {
        MobileOptimizationData {
            original_size_mb: 10.0,
            optimized_size_mb: 7.5,
            compression_ratio: 0.75,
            optimizations: vec!["quantization".to_string()],
        }
    }

    fn optimize_for_mobile_deployment(_model: &TestModel) -> Result<OptimizedTestModel, String> {
        Ok(OptimizedTestModel {
            original_model: _model.id.clone(),
            optimizations_applied: vec!["mobile_optimization".to_string()],
            compression_ratio: 0.8,
            estimated_speedup: 1.3,
        })
    }

    fn benchmark_mobile_model(_model: &OptimizedTestModel) -> Result<MobileBenchmarkData, String> {
        Ok(MobileBenchmarkData {
            latency_ms: 12.0,
            throughput_fps: 83.3,
            memory_usage_mb: 96.0,
            power_consumption_mw: 1800.0,
            thermal_state: "Normal".to_string(),
        })
    }

    fn validate_mobile_requirements(
        _benchmark: &MobileBenchmarkData,
    ) -> Result<MobileValidationResult, String> {
        Ok(MobileValidationResult {
            meets_latency_requirements: true,
            meets_memory_requirements: true,
            meets_power_requirements: true,
            overall_score: 0.92,
        })
    }

    fn generate_mobile_deployment_report(
        _baseline: &ProfilingData,
        _mobile_benchmark: &MobileBenchmarkData,
        _validation: &MobileValidationResult,
    ) -> Result<DeploymentReport, String> {
        Ok(DeploymentReport {
            ready_for_deployment: true,
            optimization_summary: "Successfully optimized for mobile deployment".to_string(),
            performance_improvements: vec![
                "25% reduction in latency".to_string(),
                "20% reduction in memory usage".to_string(),
            ],
            recommendations: vec!["Deploy to production".to_string()],
        })
    }

    fn optimize_for_ios_deployment(_model: &TestModel) -> Result<TestModel, String> {
        Ok(_model.clone())
    }

    fn optimize_for_android_deployment(_model: &TestModel) -> Result<TestModel, String> {
        Ok(_model.clone())
    }

    fn optimize_for_generic_mobile(_model: &TestModel) -> Result<TestModel, String> {
        Ok(_model.clone())
    }

    fn validate_model_correctness(_model: &TestModel) -> bool {
        !_model.parameters.is_empty() && !_model.input_shape.is_empty()
    }

    fn benchmark_model_performance(model: &TestModel) -> Result<PerformanceMetrics, String> {
        // Return different metrics based on model ID to simulate different optimizations
        let (accuracy_drop, latency_increase, memory_reduction) = if model.id.contains("quantized")
        {
            (0.02, 0.0, 0.1) // Quantization: slight accuracy drop, small memory reduction
        } else if model.id.contains("pruned") {
            (0.01, 0.05, 0.15) // Pruning: minimal accuracy drop, slight latency increase
        } else if model.id.contains("compressed") {
            (0.015, 0.02, 0.25) // Compression: good memory reduction
        } else {
            (0.0, 0.0, 0.0) // Baseline model
        };

        Ok(PerformanceMetrics {
            latency_ms: 20.0,
            throughput_fps: 50.0,
            memory_usage_mb: 200.0,
            accuracy_drop,
            latency_increase,
            memory_reduction,
        })
    }

    fn apply_quantization(model: &TestModel) -> Result<TestModel, String> {
        let mut quantized_model = model.clone();
        quantized_model.id = format!("{}_quantized", model.id);
        Ok(quantized_model)
    }

    fn apply_pruning(model: &TestModel) -> Result<TestModel, String> {
        let mut pruned_model = model.clone();
        pruned_model.id = format!("{}_pruned", model.id);
        Ok(pruned_model)
    }

    fn apply_compression(model: &TestModel) -> Result<TestModel, String> {
        let mut compressed_model = model.clone();
        compressed_model.id = format!("{}_compressed", model.id);
        Ok(compressed_model)
    }

    fn detect_regression(
        _baseline: &PerformanceMetrics,
        _current: &PerformanceMetrics,
    ) -> RegressionResult {
        RegressionResult {
            has_critical_regression: false,
            latency_regression: 0.0,
            memory_regression: 0.0,
            accuracy_regression: 0.0,
        }
    }

    fn enable_memory_tracking() -> Result<MockMemoryTracker, String> {
        Ok(MockMemoryTracker {
            start_memory: 100.0,
            current_memory: 105.0,
            peak_memory: 110.0,
        })
    }

    fn run_extensive_optimization_pipeline(
        _model: &TestModel,
    ) -> Result<OptimizedTestModel, String> {
        Ok(OptimizedTestModel {
            original_model: _model.id.clone(),
            optimizations_applied: vec!["extensive_optimization".to_string()],
            compression_ratio: 0.6,
            estimated_speedup: 2.0,
        })
    }

    fn run_extensive_benchmarking(_model: &TestModel) -> Result<MobileBenchmarkData, String> {
        Ok(MobileBenchmarkData {
            latency_ms: 10.0,
            throughput_fps: 100.0,
            memory_usage_mb: 80.0,
            power_consumption_mw: 1500.0,
            thermal_state: "Normal".to_string(),
        })
    }

    fn run_extensive_profiling(_model: &TestModel) -> Result<ProfilingData, String> {
        Ok(ProfilingData {
            total_time_ms: 10.0,
            layer_timings: vec![],
            memory_usage_mb: 80.0,
            bottlenecks: vec![],
        })
    }

    fn process_tensor_for_mobile(_tensor: &Tensor) -> Result<Tensor, String> {
        Err("Corrupted tensor data detected".to_string())
    }

    fn run_optimization_with_recovery(_model: &TestModel) -> Result<TestModel, String> {
        // Simulate recovery from optimization failure
        Ok(_model.clone())
    }

    fn run_threaded_optimization(
        _model: &TestModel,
        _thread_id: usize,
    ) -> Result<OptimizedTestModel, String> {
        Ok(OptimizedTestModel {
            original_model: _model.id.clone(),
            optimizations_applied: vec![format!("thread_{}_optimization", _thread_id)],
            compression_ratio: 0.8,
            estimated_speedup: 1.2,
        })
    }

    fn run_concurrent_benchmarks(
        _model: &TestModel,
        _num_threads: usize,
    ) -> Result<Vec<MobileBenchmarkData>, String> {
        Ok(vec![create_test_benchmark_data(); _num_threads])
    }

    // Test data structures

    #[derive(Debug, Clone)]
    #[allow(dead_code)]
    struct TestModel {
        id: String,
        parameters: HashMap<String, Tensor>,
        architecture: String,
        input_shape: Vec<usize>,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct OptimizedTestModel {
        original_model: String,
        optimizations_applied: Vec<String>,
        compression_ratio: f32,
        estimated_speedup: f32,
    }

    #[derive(Debug, Clone)]
    #[allow(dead_code)]
    struct MobileBenchmarkData {
        latency_ms: f32,
        throughput_fps: f32,
        memory_usage_mb: f32,
        power_consumption_mw: f32,
        thermal_state: String,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct MobileValidationResult {
        meets_latency_requirements: bool,
        meets_memory_requirements: bool,
        meets_power_requirements: bool,
        overall_score: f32,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct ProfilingData {
        total_time_ms: f32,
        layer_timings: Vec<LayerProfile>,
        memory_usage_mb: f32,
        bottlenecks: Vec<String>,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct LayerProfile {
        name: String,
        time_ms: f32,
        percentage: f32,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct OptimizationSuggestion {
        target: String,
        optimization_type: String,
        expected_improvement: f32,
        difficulty: String,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct MobileOptimizationData {
        original_size_mb: f32,
        optimized_size_mb: f32,
        compression_ratio: f32,
        optimizations: Vec<String>,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct DeploymentReport {
        ready_for_deployment: bool,
        optimization_summary: String,
        performance_improvements: Vec<String>,
        recommendations: Vec<String>,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct PerformanceMetrics {
        latency_ms: f32,
        throughput_fps: f32,
        memory_usage_mb: f32,
        accuracy_drop: f32,
        latency_increase: f32,
        memory_reduction: f32,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct RegressionResult {
        has_critical_regression: bool,
        latency_regression: f32,
        memory_regression: f32,
        accuracy_regression: f32,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct MockTensorBoardWriter {
        log_dir: std::path::PathBuf,
        entries: Vec<String>,
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct MockMemoryTracker {
        start_memory: f32,
        current_memory: f32,
        peak_memory: f32,
    }

    impl MockMemoryTracker {
        fn generate_leak_report(&self) -> Result<MemoryLeakReport, String> {
            Ok(MemoryLeakReport {
                total_leaked_mb: self.current_memory - self.start_memory,
                leak_count: 1,
                leak_sites: vec!["test_allocation".to_string()],
            })
        }

        fn cleanup(&self) {
            // Cleanup tracking resources
        }
    }

    #[derive(Debug)]
    #[allow(dead_code)]
    struct MemoryLeakReport {
        total_leaked_mb: f32,
        leak_count: usize,
        leak_sites: Vec<String>,
    }
}