use super::*;
use burn_tensor::{Bool, Tensor, TensorData};

#[test]
fn should_diff_nonzero() {
    let data_1 = TensorData::from([[1.0, 2.0], [3.0, 4.0]]);
    let data_2 = TensorData::from([-1.0, 1.0]);
    let mask = TensorData::from([[false, true], [true, false]]);

    let device = Default::default();
    let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
    let tensor_2 = TestAutodiffTensor::<1>::from_data(data_2, &device).require_grad();

    // Multi-dimensional tensor indexing isn't really supported yet so the easiest way to do
    // this is to flatten the mask and tensor to get proper indexing. Anyway the returned tensor would
    // have dimensions different from the input, so this is somewhat equivalent.
    let mask = Tensor::<TestAutodiffBackend, 2, Bool>::from_bool(mask, &device).flatten::<1>(0, 1);
    let indices = mask.nonzero();
    let tensor_3 = tensor_1
        .clone()
        .flatten::<1>(0, 1)
        .select(0, indices[0].clone());

    // Vector dot product not supported (only 2D matmuls) so unsqueeze for test purposes
    let tensor_4 = tensor_2
        .clone()
        .unsqueeze_dim::<2>(0)
        .matmul(tensor_3.unsqueeze_dim(1));

    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([[0.0, -1.0], [1.0, 0.0]]), false);
    grad_2
        .to_data()
        .assert_eq(&TensorData::from([2.0, 3.0]), false);
}