use super::*;
use burn_tensor::{TensorData, Tolerance};

#[test]
fn should_diff_erf() {
    let data_1 = TensorData::from([[0.0, 1.0], [3.0, 4.0]]);
    let data_2 = TensorData::from([[6.0, 7.0], [9.0, 10.0]]);

    let device = Default::default();
    let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
    let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();

    let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().erf());
    let tensor_4 = tensor_3.matmul(tensor_2.clone());
    let grads = tensor_4.backward();

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

    let expected = TensorData::from([[32.0, 32.0], [32.0, 32.0]]);
    grad_1
        .to_data()
        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());

    let expected = TensorData::from([[8.0, 8.0], [8.0, 8.0]]);
    grad_2
        .to_data()
        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}