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# pylint: disable=invalid-name,unused-argument
"""Loss functions definitions."""

from . import cpp


def nll_loss(predictions, targets, weights, reduction, ignore_index):
    """Negative log likelihood loss on the input data.

    output{n, i_1, i_2, ..., i_k} = -p * w
      where t = target{n, i_1, i_2, ..., i_k}
            p = predictions{n, t, i_1, i_2, i_k}
            w = weights{n, i_1, i_2, ..., i_k} if t != ignore_index else 0

    result = reduction(output)

    Parameters
    ----------
    predictions : tvm.te.Tensor
        (k+2)-D with shape (N, C, d_1, d_2, ..., d_k),
        where C is the number of target classes

    targets : tvm.te.Tensor
        (k+1)-D with shape (N, d_1, d_2, ..., d_k)
        The target value of the input.

    weights : tvm.te.Tensor
        1-D with shape (C,)
        The weight of each target value.

    reduction : string
        The reduction method to apply to output.
        Can be "mean", "sum" or "none".

    ignore_index : int
        The target value to ignore.

    Returns
    -------
    output : tvm.te.Tensor
        a scalar if the reduction type is "mean" or "sum",
        otherwise the same shape as `target`.
    """
    return cpp.nn.nll_loss(predictions, targets, weights, reduction, ignore_index)