use super::*;
use burn_tensor::TensorData;

#[test]
fn should_diff_matmul() {
    let data_1 = TensorData::from([[1.0, 7.0], [2.0, 3.0]]);
    let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);

    let device = AutodiffDevice::new();
    let tensor_1 = TestTensor::<2>::from_data(data_1, &device).require_grad();
    let tensor_2 = TestTensor::from_data(data_2, &device).require_grad();

    let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
    let grads = tensor_3.backward();

    let grad_1 = tensor_1.grad(&grads).unwrap();
    let grad_2 = tensor_2.grad(&grads).unwrap();

    grad_1
        .to_data()
        .assert_eq(&TensorData::from([[11.0, 5.0], [11.0, 5.0]]), false);
    grad_2
        .to_data()
        .assert_eq(&TensorData::from([[3.0, 3.0], [10.0, 10.0]]), false);
    tensor_3
        .to_data()
        .assert_eq(&TensorData::from([[18.0, 28.0], [14.0, 23.0]]), false);
}

#[test]
fn test_matmul_complex_1() {
    let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
    let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
    let data_3 = TensorData::from([[2.0, 2.0], [2.0, 2.0]]);

    let device = AutodiffDevice::new();
    let tensor_1 = TestTensor::<2>::from_data(data_1, &device).require_grad();
    let tensor_2 = TestTensor::from_data(data_2, &device).require_grad();
    let tensor_3 = TestTensor::from_data(data_3, &device).require_grad();

    let tensor_4 = tensor_1.clone().matmul(tensor_2.clone());
    let tensor_5 = tensor_4.matmul(tensor_3);

    let grads = tensor_5.backward();

    let grad_1 = tensor_1.grad(&grads).unwrap();
    let grad_2 = tensor_2.grad(&grads).unwrap();

    grad_1
        .to_data()
        .assert_eq(&TensorData::from([[44.0, 20.0], [44.0, 20.0]]), false);
    grad_2
        .to_data()
        .assert_eq(&TensorData::from([[56.0, 56.0], [16.0, 16.0]]), false);
}

#[test]
fn test_matmul_complex_2() {
    let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
    let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
    let data_3 = TensorData::from([[2.0, 2.0], [2.0, 2.0]]);

    let device = AutodiffDevice::new();
    let tensor_1 = TestTensor::<2>::from_data(data_1, &device).require_grad();
    let tensor_2 = TestTensor::from_data(data_2, &device).require_grad();
    let tensor_3 = TestTensor::from_data(data_3, &device).require_grad();

    let tensor_4 = tensor_1.clone().matmul(tensor_2.clone());
    let tensor_5 = tensor_4.matmul(tensor_3.clone());
    let tensor_6 = tensor_1.clone().matmul(tensor_5);

    let grads = tensor_6.backward();

    let grad_1 = tensor_1.grad(&grads).unwrap();
    let grad_2 = tensor_2.grad(&grads).unwrap();

    grad_1
        .to_data()
        .assert_eq(&TensorData::from([[800.0, 792.0], [360.0, 592.0]]), false);
    grad_2
        .to_data()
        .assert_eq(&TensorData::from([[264., 264.0], [344.0, 344.0]]), false);
}
