#![cfg(feature = "incomplete-benchmarks")]

use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
use ndarray::{Array1, Array2};
use rand_chacha::ChaCha8Rng;
use sklears_model_selection::{
    cv::basic_cv::KFold,
    grid_search::GridSearchCV,
    parallel_optimization::{ParallelOptimizer, ParallelStrategy},
    parameter_space::{Parameter, ParameterSpace, ParameterValue},
};
use std::time::Duration;

// High-performance mock estimator for stress testing
#[derive(Clone)]
struct StressMockEstimator {
    params: HashMap<String, ParameterValue>,
    computation_factor: f64,
}

impl StressMockEstimator {
    fn new() -> Self {
        Self {
            params: HashMap::new(),
            computation_factor: 1.0,
        }
    }

    fn set_params(&mut self, params: HashMap<String, ParameterValue>) {
        self.params = params;

        // Adjust computation factor based on parameters
        self.computation_factor = self
            .params
            .values()
            .map(|v| match v {
                ParameterValue::Float(f) => *f,
                ParameterValue::Integer(i) => *i as f64,
                _ => 1.0,
            })
            .sum::<f64>()
            / self.params.len().max(1) as f64;
    }

    fn fit(&mut self, x: &Array2<f64>, y: &Array1<f64>) {
        // Simulate computation based on data size and parameter complexity
        let iterations = (x.nrows() as f64 * self.computation_factor / 1000.0) as usize;
        let mut result = 0.0;

        for _ in 0..iterations {
            result += x.sum() + y.sum();
        }

        black_box(result);
    }

    fn score(&mut self, x: &Array2<f64>, y: &Array1<f64>) -> f64 {
        self.fit(x, y);

        // Return a score that depends on parameters and data
        let data_score = (x.sum() + y.sum()) * 1e-6;
        let param_score = self.computation_factor * 0.1;

        0.8 + data_score + param_score
    }
}

fn generate_large_test_data(n_samples: usize, n_features: usize) -> (Array2<f64>, Array1<f64>) {
    let mut rng = ChaCha8Rng::seed_from_u64(42);
    let x = Array2::random_using(
        (n_samples, n_features),
        rand_distr::StandardNormal,
        &mut rng,
    );
    let y = Array1::random_using(n_samples, rand_distr::StandardNormal, &mut rng);
    (x, y)
}

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

    for i in 0..n_params {
        if i % 2 == 0 {
            space.add_parameter(
                &format!("float_param_{}", i),
                Parameter::Float {
                    low: 0.0,
                    high: 10.0,
                    log: false,
                },
            );
        } else {
            space.add_parameter(
                &format!("int_param_{}", i),
                Parameter::Integer { low: 1, high: 20 },
            );
        }
    }

    space
}

fn bench_large_dataset_cv(c: &mut Criterion) {
    let mut group = c.benchmark_group("Large Dataset Cross-Validation");
    group.measurement_time(Duration::from_secs(30));
    group.sample_size(10);

    for &n_samples in &[1000, 5000, 10000, 20000] {
        let (x, y) = generate_large_test_data(n_samples, 50);

        group.bench_with_input(
            BenchmarkId::new("KFold-Large", n_samples),
            &n_samples,
            |b, _| {
                b.iter(|| {
                    let cv = KFold::new(5).unwrap();
                    let mut estimator = StressMockEstimator::new();
                    let mut scores = Vec::new();

                    for (train_idx, test_idx) in cv.split(&x, Some(&y), None) {
                        let x_train = x.select(ndarray::Axis(0), &train_idx);
                        let y_train = y.select(ndarray::Axis(0), &train_idx);

                        let score = estimator.score(black_box(&x_train), black_box(&y_train));
                        scores.push(score);
                    }

                    black_box(scores)
                })
            },
        );
    }

    group.finish();
}

fn bench_high_dimensional_cv(c: &mut Criterion) {
    let mut group = c.benchmark_group("High-Dimensional Cross-Validation");
    group.measurement_time(Duration::from_secs(20));

    for &n_features in &[100, 500, 1000, 5000] {
        let (x, y) = generate_large_test_data(1000, n_features);

        group.bench_with_input(
            BenchmarkId::new("HighDim-KFold", n_features),
            &n_features,
            |b, _| {
                b.iter(|| {
                    let cv = KFold::new(5).unwrap();
                    let mut estimator = StressMockEstimator::new();
                    let mut scores = Vec::new();

                    for (train_idx, test_idx) in cv.split(&x, Some(&y), None) {
                        let x_train = x.select(ndarray::Axis(0), &train_idx);
                        let y_train = y.select(ndarray::Axis(0), &train_idx);

                        let score = estimator.score(black_box(&x_train), black_box(&y_train));
                        scores.push(score);
                    }

                    black_box(scores)
                })
            },
        );
    }

    group.finish();
}

fn bench_large_parameter_space_optimization(c: &mut Criterion) {
    let mut group = c.benchmark_group("Large Parameter Space Optimization");
    group.measurement_time(Duration::from_secs(25));
    group.sample_size(10);

    let (x, y) = generate_large_test_data(500, 20);

    for &n_params in &[5, 10, 20, 50] {
        let parameter_space = create_large_parameter_space(n_params);

        group.bench_with_input(
            BenchmarkId::new("RandomSearch-LargeSpace", n_params),
            &n_params,
            |b, _| {
                b.iter(|| {
                    let mut rng = ChaCha8Rng::seed_from_u64(42);
                    let mut estimator = StressMockEstimator::new();
                    let mut best_score = f64::NEG_INFINITY;
                    let mut best_params = HashMap::new();

                    for _ in 0..50 {
                        let params = parameter_space.sample(&mut rng);
                        estimator.set_params(params.clone());
                        let score = estimator.score(black_box(&x), black_box(&y));

                        if score > best_score {
                            best_score = score;
                            best_params = params;
                        }
                    }

                    black_box((best_score, best_params))
                })
            },
        );
    }

    group.finish();
}

fn bench_parallel_optimization_stress(c: &mut Criterion) {
    let mut group = c.benchmark_group("Parallel Optimization Stress Test");
    group.measurement_time(Duration::from_secs(30));
    group.sample_size(10);

    let (x, y) = generate_large_test_data(1000, 30);
    let parameter_space = create_large_parameter_space(10);

    for &n_workers in &[1, 2, 4, 8] {
        group.bench_with_input(
            BenchmarkId::new("ParallelOptimization", n_workers),
            &n_workers,
            |b, _| {
                b.iter(|| {
                    let mut optimizer = ParallelOptimizer::new(
                        ParallelStrategy::ParallelRandomSearch { n_workers },
                        parameter_space.clone(),
                    );

                    let mut estimator = StressMockEstimator::new();
                    let mut best_score = f64::NEG_INFINITY;
                    let mut best_params = HashMap::new();

                    let objective = |params: HashMap<String, ParameterValue>| -> f64 {
                        let mut est = estimator.clone();
                        est.set_params(params);
                        est.score(&x, &y)
                    };

                    for _ in 0..20 {
                        let suggestions = optimizer.suggest_batch(n_workers);
                        for params in suggestions {
                            let score = objective(params.clone());
                            optimizer.update(params.clone(), score);

                            if score > best_score {
                                best_score = score;
                                best_params = params;
                            }
                        }
                    }

                    black_box((best_score, best_params))
                })
            },
        );
    }

    group.finish();
}

fn bench_memory_usage_stress(c: &mut Criterion) {
    let mut group = c.benchmark_group("Memory Usage Stress Test");
    group.measurement_time(Duration::from_secs(15));

    for &n_folds in &[5, 10, 20, 50] {
        let (x, y) = generate_large_test_data(2000, 100);

        group.bench_with_input(
            BenchmarkId::new("HighFoldCount", n_folds),
            &n_folds,
            |b, _| {
                b.iter(|| {
                    let cv = KFold::new(n_folds).unwrap();
                    let mut total_indices = 0;

                    for (train_idx, test_idx) in cv.split(&x, Some(&y), None) {
                        // Only count indices to test memory efficiency
                        total_indices += train_idx.len() + test_idx.len();

                        // Simulate minimal computation
                        black_box(train_idx.len() * test_idx.len());
                    }

                    black_box(total_indices)
                })
            },
        );
    }

    group.finish();
}

fn bench_concurrent_cv_operations(c: &mut Criterion) {
    let mut group = c.benchmark_group("Concurrent CV Operations");
    group.measurement_time(Duration::from_secs(20));

    let (x, y) = generate_large_test_data(1000, 50);

    group.bench_function("Sequential-CV", |b| {
        b.iter(|| {
            let mut results = Vec::new();

            for fold_count in [3, 5, 10] {
                let cv = KFold::new(fold_count).unwrap();
                let mut estimator = StressMockEstimator::new();
                let mut scores = Vec::new();

                for (train_idx, test_idx) in cv.split(&x, Some(&y), None) {
                    let x_train = x.select(ndarray::Axis(0), &train_idx);
                    let y_train = y.select(ndarray::Axis(0), &train_idx);

                    let score = estimator.score(&x_train, &y_train);
                    scores.push(score);
                }

                results.push(scores);
            }

            black_box(results)
        })
    });

    group.bench_function("Parallel-CV", |b| {
        b.iter(|| {
            use rayon::prelude::*;

            let results: Vec<Vec<f64>> = [3, 5, 10]
                .par_iter()
                .map(|&fold_count| {
                    let cv = KFold::new(fold_count).unwrap();
                    let splits: Vec<_> = cv.split(&x, Some(&y), None).collect();

                    splits
                        .par_iter()
                        .map(|(train_idx, test_idx)| {
                            let mut estimator = StressMockEstimator::new();
                            let x_train = x.select(ndarray::Axis(0), train_idx);
                            let y_train = y.select(ndarray::Axis(0), train_idx);

                            estimator.score(&x_train, &y_train)
                        })
                        .collect()
                })
                .collect();

            black_box(results)
        })
    });

    group.finish();
}

fn bench_optimization_convergence_stress(c: &mut Criterion) {
    let mut group = c.benchmark_group("Optimization Convergence Stress");
    group.measurement_time(Duration::from_secs(25));
    group.sample_size(10);

    let (x, y) = generate_large_test_data(500, 20);

    for &max_iterations in &[50, 100, 200, 500] {
        group.bench_with_input(
            BenchmarkId::new("ConvergenceStress", max_iterations),
            &max_iterations,
            |b, _| {
                b.iter(|| {
                    let parameter_space = create_large_parameter_space(8);
                    let mut rng = ChaCha8Rng::seed_from_u64(42);
                    let mut estimator = StressMockEstimator::new();

                    let mut scores_history = Vec::new();
                    let mut best_score = f64::NEG_INFINITY;

                    for iteration in 0..max_iterations {
                        let params = parameter_space.sample(&mut rng);
                        estimator.set_params(params);
                        let score = estimator.score(black_box(&x), black_box(&y));

                        scores_history.push(score);
                        if score > best_score {
                            best_score = score;
                        }

                        // Simulate early stopping check
                        if iteration > 20 && scores_history.len() > 10 {
                            let recent_mean = scores_history[scores_history.len() - 10..]
                                .iter()
                                .sum::<f64>()
                                / 10.0;
                            if (best_score - recent_mean).abs() < 1e-6 {
                                break;
                            }
                        }
                    }

                    black_box((best_score, scores_history.len()))
                })
            },
        );
    }

    group.finish();
}

fn bench_resource_constrained_optimization(c: &mut Criterion) {
    let mut group = c.benchmark_group("Resource Constrained Optimization");
    group.measurement_time(Duration::from_secs(20));

    // Simulate optimization under different resource constraints
    for &budget_factor in &[1, 2, 5, 10] {
        let (x, y) = generate_large_test_data(200 * budget_factor, 10);

        group.bench_with_input(
            BenchmarkId::new("ResourceConstrained", budget_factor),
            &budget_factor,
            |b, _| {
                b.iter(|| {
                    let parameter_space = create_large_parameter_space(5);
                    let mut rng = ChaCha8Rng::seed_from_u64(42);
                    let mut estimator = StressMockEstimator::new();

                    let max_evaluations = 50 / budget_factor.max(1); // Fewer evaluations with larger data
                    let mut best_score = f64::NEG_INFINITY;
                    let mut best_params = HashMap::new();

                    for _ in 0..max_evaluations {
                        let params = parameter_space.sample(&mut rng);
                        estimator.set_params(params.clone());
                        let score = estimator.score(black_box(&x), black_box(&y));

                        if score > best_score {
                            best_score = score;
                            best_params = params;
                        }
                    }

                    black_box((best_score, best_params))
                })
            },
        );
    }

    group.finish();
}

criterion_group!(
    benches,
    bench_large_dataset_cv,
    bench_high_dimensional_cv,
    bench_large_parameter_space_optimization,
    bench_parallel_optimization_stress,
    bench_memory_usage_stress,
    bench_concurrent_cv_operations,
    bench_optimization_convergence_stress,
    bench_resource_constrained_optimization
);

criterion_main!(benches);
