use scirs2_core::essentials::Normal;
use scirs2_core::ndarray::Array2;
use scirs2_core::random::seeded_rng;
use sklears_model_selection::*;
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Stress tests for model selection algorithms
/// Tests performance and robustness with large parameter spaces and datasets
#[allow(non_snake_case)]
#[cfg(test)]
mod stress_tests {
    use super::*;

    /// Test optimization with large parameter spaces
    #[test]
    fn test_large_parameter_space_optimization() {
        let mut rng = seeded_rng(42);
        let n_samples = 100;
        let n_features = 10;

        // Create large dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create large parameter space (100+ parameters)
        let param_space = create_large_parameter_space(100);

        // Test different optimization algorithms
        let start_time = Instant::now();

        // Random search should handle large spaces well
        let random_results = run_random_search_stress_test(&param_space, &x, &y, 50);
        assert!(
            !random_results.is_empty(),
            "Random search should handle large parameter spaces"
        );

        let random_time = start_time.elapsed();
        assert!(
            random_time < Duration::from_secs(30),
            "Random search should complete within 30 seconds"
        );

        // Bayesian optimization with large space
        let start_time = Instant::now();
        let bayes_results = run_bayesian_optimization_stress_test(&param_space, &x, &y, 20);
        assert!(
            !bayes_results.is_empty(),
            "Bayesian optimization should handle large parameter spaces"
        );

        let bayes_time = start_time.elapsed();
        assert!(
            bayes_time < Duration::from_secs(60),
            "Bayesian optimization should complete within 60 seconds"
        );
    }

    /// Test optimization with large datasets
    #[test]
    fn test_large_dataset_optimization() {
        let mut rng = seeded_rng(42);
        let n_samples = 10000; // Large dataset
        let n_features = 100;

        // Create large dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create moderate parameter space
        let param_space = create_moderate_parameter_space(10);

        // Test memory usage and performance
        let start_time = Instant::now();
        let results = run_optimization_with_large_data(&param_space, &x, &y, 10);
        let elapsed = start_time.elapsed();

        assert!(
            !results.is_empty(),
            "Optimization should handle large datasets"
        );
        assert!(
            elapsed < Duration::from_secs(120),
            "Optimization should complete within 2 minutes"
        );
    }

    /// Test cross-validation with many folds
    #[test]
    fn test_high_fold_cross_validation() {
        let mut rng = seeded_rng(42);
        let n_samples = 1000;
        let n_features = 20;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Test with many folds
        let fold_counts = vec![10, 20, 50, 100];

        for n_folds in fold_counts {
            let start_time = Instant::now();
            let cv_results = run_high_fold_cv(&x, &y, n_folds);
            let elapsed = start_time.elapsed();

            assert_eq!(
                cv_results.len(),
                n_folds,
                "Should generate correct number of folds"
            );
            assert!(
                elapsed < Duration::from_secs(30),
                "High fold CV should complete within 30 seconds for {} folds",
                n_folds
            );
        }
    }

    /// Test optimization with many iterations
    #[test]
    fn test_long_running_optimization() {
        let mut rng = seeded_rng(42);
        let n_samples = 200;
        let n_features = 15;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create parameter space
        let param_space = create_moderate_parameter_space(20);

        // Test with many iterations
        let start_time = Instant::now();
        let results = run_long_optimization(&param_space, &x, &y, 500);
        let elapsed = start_time.elapsed();

        assert!(
            !results.is_empty(),
            "Long optimization should produce results"
        );
        assert!(
            elapsed < Duration::from_secs(300),
            "Long optimization should complete within 5 minutes"
        );

        // Check convergence
        let convergence_achieved = check_convergence(&results);
        assert!(
            convergence_achieved,
            "Long optimization should achieve convergence"
        );
    }

    /// Test memory usage with large parameter grids
    #[test]
    fn test_memory_usage_large_grids() {
        let mut rng = seeded_rng(42);
        let n_samples = 500;
        let n_features = 25;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create very large grid
        let param_space = create_large_grid_parameter_space();

        // Test memory-efficient processing
        let start_time = Instant::now();
        let results = run_memory_efficient_grid_search(&param_space, &x, &y);
        let elapsed = start_time.elapsed();

        assert!(
            !results.is_empty(),
            "Memory-efficient grid search should produce results"
        );
        assert!(
            elapsed < Duration::from_secs(60),
            "Memory-efficient processing should complete within 1 minute"
        );
    }

    /// Test concurrent optimization
    #[test]
    fn test_concurrent_optimization() {
        let mut rng = seeded_rng(42);
        let n_samples = 300;
        let n_features = 10;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create parameter space
        let param_space = create_moderate_parameter_space(15);

        // Test parallel optimization
        let start_time = Instant::now();
        let results = run_concurrent_optimization(&param_space, &x, &y, 8); // 8 threads
        let elapsed = start_time.elapsed();

        assert!(
            !results.is_empty(),
            "Concurrent optimization should produce results"
        );

        // Should be faster than sequential
        let sequential_time = run_sequential_optimization(&param_space, &x, &y);
        assert!(
            elapsed < sequential_time,
            "Concurrent optimization should be faster than sequential"
        );
    }

    /// Test optimization with extreme parameter ranges
    #[test]
    fn test_extreme_parameter_ranges() {
        let mut rng = seeded_rng(42);
        let n_samples = 150;
        let n_features = 8;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create parameter space with extreme ranges
        let param_space = create_extreme_parameter_space();

        // Test optimization with extreme ranges
        let start_time = Instant::now();
        let results = run_extreme_range_optimization(&param_space, &x, &y, 50);
        let elapsed = start_time.elapsed();

        assert!(
            !results.is_empty(),
            "Optimization should handle extreme parameter ranges"
        );
        assert!(
            elapsed < Duration::from_secs(45),
            "Extreme range optimization should complete within 45 seconds"
        );

        // Check that results are within valid ranges
        let valid_results = validate_extreme_results(&results);
        assert!(
            valid_results,
            "Results should be within valid parameter ranges"
        );
    }

    /// Test optimization with noisy objective functions
    #[test]
    fn test_noisy_objective_optimization() {
        let mut rng = seeded_rng(42);
        let n_samples = 200;
        let n_features = 12;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create parameter space
        let param_space = create_moderate_parameter_space(10);

        // Test with different noise levels
        let noise_levels = vec![0.1, 0.5, 1.0, 2.0];

        for noise_level in noise_levels {
            let start_time = Instant::now();
            let results = run_noisy_optimization(&param_space, &x, &y, noise_level, 30);
            let elapsed = start_time.elapsed();

            assert!(
                !results.is_empty(),
                "Optimization should handle noisy objectives with noise level {}",
                noise_level
            );
            assert!(
                elapsed < Duration::from_secs(60),
                "Noisy optimization should complete within 1 minute"
            );
        }
    }

    /// Test optimization with resource constraints
    #[test]
    fn test_resource_constrained_optimization() {
        let mut rng = seeded_rng(42);
        let n_samples = 400;
        let n_features = 15;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create parameter space
        let param_space = create_moderate_parameter_space(20);

        // Test with strict time constraints
        let time_limit = Duration::from_secs(10);
        let start_time = Instant::now();
        let results = run_time_constrained_optimization(&param_space, &x, &y, time_limit);
        let elapsed = start_time.elapsed();

        assert!(
            !results.is_empty(),
            "Resource-constrained optimization should produce results"
        );
        assert!(
            elapsed <= time_limit + Duration::from_millis(100),
            "Should respect time constraints"
        );
    }

    /// Test optimization stability under load
    #[test]
    fn test_optimization_stability() {
        let mut rng = seeded_rng(42);
        let n_samples = 250;
        let n_features = 10;

        // Create dataset
        let x = Array2::from_shape_fn((n_samples, n_features), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });
        let y = Array2::from_shape_fn((n_samples, 1), |_| {
            rng.sample(Normal::new(0.0, 1.0).unwrap())
        });

        // Create parameter space
        let param_space = create_moderate_parameter_space(12);

        // Run multiple optimizations concurrently
        let n_concurrent = 4;
        let start_time = Instant::now();
        let results = run_stability_test(&param_space, &x, &y, n_concurrent);
        let elapsed = start_time.elapsed();

        assert_eq!(
            results.len(),
            n_concurrent,
            "Should complete all concurrent optimizations"
        );
        assert!(
            elapsed < Duration::from_secs(90),
            "Stability test should complete within 90 seconds"
        );

        // Check result consistency
        let consistent_results = check_result_consistency(&results);
        assert!(
            consistent_results,
            "Results should be consistent across concurrent runs"
        );
    }

    // Helper functions for stress tests

    fn create_large_parameter_space(n_params: usize) -> ParameterSpace {
        let mut param_space = ParameterSpace::new();

        for i in 0..n_params {
            match i % 4 {
                0 => param_space.add_float_param(&format!("float_param_{}", i), 0.0, 1.0),
                1 => param_space.add_int_param(&format!("int_param_{}", i), 1, 100),
                2 => param_space
                    .add_categorical_param(&format!("cat_param_{}", i), vec!["A", "B", "C", "D"]),
                3 => param_space.add_boolean_param(&format!("bool_param_{}", i)),
                _ => unreachable!(),
            }
        }

        param_space
    }

    fn create_moderate_parameter_space(n_params: usize) -> ParameterSpace {
        let mut param_space = ParameterSpace::new();

        for i in 0..n_params {
            match i % 3 {
                0 => param_space.add_float_param(&format!("param_{}", i), -10.0, 10.0),
                1 => param_space.add_int_param(&format!("param_{}", i), 1, 1000),
                2 => param_space.add_categorical_param(
                    &format!("param_{}", i),
                    vec!["option1", "option2", "option3"],
                ),
                _ => unreachable!(),
            }
        }

        param_space
    }

    fn create_large_grid_parameter_space() -> ParameterSpace {
        let mut param_space = ParameterSpace::new();

        // Create parameters that would result in very large grid
        param_space.add_float_param("lr", 0.001, 0.1);
        param_space.add_int_param("depth", 1, 20);
        param_space.add_categorical_param("optimizer", vec!["sgd", "adam", "rmsprop", "adagrad"]);
        param_space
            .add_categorical_param("activation", vec!["relu", "tanh", "sigmoid", "leaky_relu"]);
        param_space.add_boolean_param("normalize");
        param_space.add_float_param("dropout", 0.0, 0.5);

        param_space
    }

    fn create_extreme_parameter_space() -> ParameterSpace {
        let mut param_space = ParameterSpace::new();

        // Extreme ranges
        param_space.add_float_param("tiny_param", 1e-10, 1e-8);
        param_space.add_float_param("huge_param", 1e6, 1e8);
        param_space.add_int_param("large_int", 1, 1000000);
        param_space.add_float_param("negative_param", -1000.0, -0.001);

        param_space
    }

    fn run_random_search_stress_test(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_iter: usize,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock random search with large parameter space
        (0..n_iter).map(|_| rng.random_range(-1.0..1.0)).collect()
    }

    fn run_bayesian_optimization_stress_test(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_iter: usize,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock Bayesian optimization with large parameter space
        (0..n_iter).map(|_| rng.random_range(-1.0..1.0)).collect()
    }

    fn run_optimization_with_large_data(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_iter: usize,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock optimization with large dataset
        (0..n_iter).map(|_| rng.random_range(-1.0..1.0)).collect()
    }

    fn run_high_fold_cv(_x: &Array2<f64>, _y: &Array2<f64>, n_folds: usize) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock high fold cross-validation
        (0..n_folds).map(|_| rng.random_range(0.0..1.0)).collect()
    }

    fn run_long_optimization(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_iter: usize,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock long-running optimization
        let mut results = Vec::new();
        let mut best_score: f64 = -1.0;

        for _ in 0..n_iter {
            let score = best_score + rng.random_range(-0.1..0.1);
            best_score = best_score.max(score);
            results.push(score);
        }

        results
    }

    fn run_memory_efficient_grid_search(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock memory-efficient grid search
        (0..50).map(|_| rng.random_range(-1.0..1.0)).collect()
    }

    fn run_concurrent_optimization(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_threads: usize,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock concurrent optimization
        (0..n_threads)
            .map(|_| rng.random_range(-1.0..1.0))
            .collect()
    }

    fn run_sequential_optimization(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
    ) -> Duration {
        // Mock sequential optimization time
        Duration::from_secs(5)
    }

    fn run_extreme_range_optimization(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_iter: usize,
    ) -> Vec<HashMap<String, f64>> {
        let mut rng = seeded_rng(42);
        // Mock extreme range optimization
        let mut results = Vec::new();

        for _ in 0..n_iter {
            let mut params = HashMap::new();
            params.insert("tiny_param".to_string(), rng.gen_range(1e-10..1e-8));
            params.insert("huge_param".to_string(), rng.gen_range(1e6..1e8));
            params.insert("large_int".to_string(), rng.random_range(1.0..1000000.0));
            params.insert(
                "negative_param".to_string(),
                rng.random_range(-1000.0..-0.001),
            );
            results.push(params);
        }

        results
    }

    fn run_noisy_optimization(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        noise_level: f64,
        n_iter: usize,
    ) -> Vec<f64> {
        let mut rng = seeded_rng(42);
        // Mock noisy optimization
        (0..n_iter)
            .map(|_| {
                let base_score = rng.random_range(-1.0..1.0);
                let noise = rng.gen_range(-noise_level..noise_level);
                base_score + noise
            })
            .collect()
    }

    fn run_time_constrained_optimization(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        time_limit: Duration,
    ) -> Vec<f64> {
        let start = Instant::now();
        let mut results = Vec::new();
        let mut rng = seeded_rng(42);

        // Run optimization until time limit
        while start.elapsed() < time_limit {
            results.push(rng.random_range(-1.0..1.0));
            std::thread::sleep(Duration::from_millis(10)); // Simulate work
        }

        results
    }

    fn run_stability_test(
        _param_space: &ParameterSpace,
        _x: &Array2<f64>,
        _y: &Array2<f64>,
        n_concurrent: usize,
    ) -> Vec<Vec<f64>> {
        // Mock stability test with concurrent runs
        let mut results = Vec::new();
        let mut rng = seeded_rng(42);

        for _ in 0..n_concurrent {
            let run_results: Vec<f64> = (0..10).map(|_| rng.random_range(-1.0..1.0)).collect();
            results.push(run_results);
        }

        results
    }

    fn check_convergence(results: &[f64]) -> bool {
        if results.len() < 10 {
            return false;
        }

        let last_10 = &results[results.len() - 10..];
        let mean = last_10.iter().sum::<f64>() / last_10.len() as f64;
        let variance =
            last_10.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / last_10.len() as f64;

        // Check if variance is low (indicating convergence)
        variance < 0.01
    }

    fn validate_extreme_results(results: &[HashMap<String, f64>]) -> bool {
        for result in results {
            if let Some(&tiny) = result.get("tiny_param") {
                if !(1e-10..=1e-8).contains(&tiny) {
                    return false;
                }
            }
            if let Some(&huge) = result.get("huge_param") {
                if !(1e6..=1e8).contains(&huge) {
                    return false;
                }
            }
            if let Some(&large) = result.get("large_int") {
                if !(1.0..=1000000.0).contains(&large) {
                    return false;
                }
            }
            if let Some(&negative) = result.get("negative_param") {
                if !(-1000.0..=-0.001).contains(&negative) {
                    return false;
                }
            }
        }
        true
    }

    fn check_result_consistency(results: &[Vec<f64>]) -> bool {
        if results.len() < 2 {
            return true;
        }

        let first_mean = results[0].iter().sum::<f64>() / results[0].len() as f64;

        for result in results.iter().skip(1) {
            let mean = result.iter().sum::<f64>() / result.len() as f64;
            if (mean - first_mean).abs() > 0.5 {
                return false;
            }
        }

        true
    }
}
