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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Functions for performing operations with broadcasting to the right axis
#
# Example
# input1: tensor of size (N1, N2)
# input2: tensor of size (N1, N2, N3, N4)
# batch_mul(input1, input2) = input1[:, :, None, None] * input2
#
# If the common dimensions don't match, we raise an assertion error.

from torch import Tensor


def common_broadcast(x: Tensor, y: Tensor) -> tuple[Tensor, Tensor]:
    ndims1 = x.ndim
    ndims2 = y.ndim

    common_ndims = min(ndims1, ndims2)
    for axis in range(common_ndims):
        assert x.shape[axis] == y.shape[axis], "Dimensions not equal at axis {}".format(axis)

    if ndims1 < ndims2:
        x = x.reshape(x.shape + (1,) * (ndims2 - ndims1))
    elif ndims2 < ndims1:
        y = y.reshape(y.shape + (1,) * (ndims1 - ndims2))

    return x, y


def batch_mul(x: Tensor, y: Tensor) -> Tensor:
    x, y = common_broadcast(x, y)
    return x * y