# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE

from __future__ import absolute_import

import awkward as ak

np = ak.nplike.NumpyMetadata.instance()


def softmax(x, axis=None, keepdims=False, mask_identity=False):
    pass


#     """
#     Args:
#         x: the data on which to compute the softmax.
#         weight: data that can be broadcasted to `x` to give each value a
#             weight. Weighting values equally is the same as no weights;
#             weighting some values higher increases the significance of those
#             values. Weights can be zero or negative.
#         axis (None or int): If None, combine all values from the array into
#             a single scalar result; if an int, group by that axis: `0` is the
#             outermost, `1` is the first level of nested lists, etc., and
#             negative `axis` counts from the innermost: `-1` is the innermost,
#             `-2` is the next level up, etc.
#         keepdims (bool): If False, this function decreases the number of
#             dimensions by 1; if True, the output values are wrapped in a new
#             length-1 dimension so that the result of this operation may be
#             broadcasted with the original array.
#         mask_identity (bool): If True, the application of this function on
#             empty lists results in None (an option type); otherwise, the
#             calculation is followed through with the reducers' identities,
#             usually resulting in floating-point `nan`.

#     Computes the softmax in each group of elements from `x` (many
#     types supported, including all Awkward Arrays and Records). The grouping
#     is performed the same way as for reducers, though this operation is not a
#     reducer and has no identity.

#     This function has no NumPy equivalent.

#     Passing all arguments to the reducers, the softmax is calculated as

#         np.exp(x) / ak.sum(np.exp(x))

#     See #ak.sum for a complete description of handling nested lists and
#     missing values (None) in reducers, and #ak.mean for an example with another
#     non-reducer.
#     """
#     with np.errstate(invalid="ignore"):
#         nplike = ak.nplike.of(x)
#         expx = nplike.exp(x)
#         denom = sum(expx, axis=axis, keepdims=keepdims, mask_identity=mask_identity)
#         return nplike.true_divide(expx, denom)
