#[burn_tensor_testgen::testgen(ad_mask)]
mod tests {
    use super::*;
    use burn_tensor::{Bool, Tensor, TensorData};

    #[test]
    fn should_diff_mask_fill() {
        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 mask = TensorData::from([[true, false], [false, true]]);

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

        let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
        let tensor_4 = tensor_3.mask_fill(mask, 2.0);
        let grads = tensor_4.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([[7.0, 3.0], [4.0, 2.0]]), false);
        grad_2
            .to_data()
            .assert_eq(&TensorData::from([[2.0, 1.0], [3.0, 7.0]]), false);
    }

    #[test]
    fn should_diff_mask_where() {
        let device = Default::default();
        let tensor_1 =
            TestAutodiffTensor::from_data([[1.0, 7.0], [2.0, 3.0]], &device).require_grad();
        let tensor_2 =
            TestAutodiffTensor::from_data([[4.0, 7.0], [2.0, 3.0]], &device).require_grad();
        let tensor_3 =
            TestAutodiffTensor::from_data([[8.8, 9.8], [10.8, 11.8]], &device).require_grad();
        let mask = Tensor::<TestAutodiffBackend, 2, Bool>::from_data(
            [[true, false], [false, true]],
            &device,
        );

        let tensor_4 = tensor_1.clone().matmul(tensor_2.clone());
        let tensor_5 = tensor_4.clone().matmul(tensor_3.clone());
        let tensor_6 = tensor_5.mask_where(mask, tensor_3.clone());
        let grads = tensor_6.backward();

        let grad_1 = tensor_1.grad(&grads).unwrap();
        let grad_2 = tensor_2.grad(&grads).unwrap();
        let grad_3 = tensor_3.grad(&grads).unwrap();

        let expected = TensorData::from([[121.8, 55.0], [110.8, 50.0]]);
        grad_1.into_data().assert_approx_eq(&expected, 3);

        let expected = TensorData::from([[27.4, 33.4], [95.0, 115.0]]);
        grad_2.into_data().assert_approx_eq(&expected, 3);

        let expected = TensorData::from([[15., 18.], [23., 29.]]);
        grad_3.into_data().assert_approx_eq(&expected, 3);
    }
}
