use criterion::{Criterion, black_box, criterion_group, criterion_main};
use rtx_automeasure::{AutoMLAgent, AutoMLConfig, TaskType};
use rtx_tensor::Tensor;

fn benchmark_automl_fit(c: &mut Criterion) {
    let rt = tokio::runtime::Runtime::new().unwrap();

    c.bench_function("automl_basic_fit", |b| {
        b.iter(|| {
            rt.block_on(async {
                let config = AutoMLConfig::new()
                    .with_task_type(TaskType::Classification)
                    .with_time_budget(10); // Very short for benchmark

                let mut agent = AutoMLAgent::new(config).unwrap();
                let x_train = Tensor::randn(&[50, 5], rtx_tensor::DType::F32);
                let y_train = Tensor::zeros(&[50], rtx_tensor::DType::I64);

                let _pipeline = agent.fit(&x_train, &y_train).await.unwrap();
            })
        })
    });
}

fn benchmark_model_selection(c: &mut Criterion) {
    let rt = tokio::runtime::Runtime::new().unwrap();

    c.bench_function("model_selector_recommend", |b| {
        b.iter(|| {
            rt.block_on(async {
                // This will be implemented when we create the actual modules
                // For now, just a placeholder
                black_box(());
            })
        })
    });
}

criterion_group!(benches, benchmark_automl_fit, benchmark_model_selection);
criterion_main!(benches);