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

#[test]
fn should_diff_expand() {
    // Python code to generate the test case values
    // import torch
    // x1 = torch.tensor([4.0, 7.0, 2.0, 3.0], requires_grad=True)
    // x2 = torch.tensor([2.0, 4.5, 7.0, 3.0], requires_grad=True)
    // y = x1.expand(4, 4)
    // z = (x2 * y).sum()
    // z.backward()
    // print("x1", x1.grad)
    // print("x2", x2.grad)

    let device = Default::default();

    let data_1 = TensorData::from([4.0, 7.0, 2.0, 3.0]);
    let tensor_1 = TestAutodiffTensor::<1>::from_data(data_1, &device).require_grad();

    let data_2 = TensorData::from([2.0, 4.5, 7.0, 3.0]);
    let tensor_2 = TestAutodiffTensor::<1>::from_data(data_2, &device).require_grad();

    let tensor_3 = tensor_1.clone().expand([4, 4]);

    // Use unsqueeze to make tensor_2 have the same shape as tensor_3
    let tensor_4 = tensor_2.clone().unsqueeze().mul(tensor_3).sum();
    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([8., 18., 28., 12.]), false);
    grad_2
        .to_data()
        .assert_eq(&TensorData::from([16., 28., 8., 12.]), false);
}