#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# ----------------------------------------------------------------------------
# Copyright (c) 2026 Huawei Technologies Co., Ltd.
# This program is free software, you can redistribute it and/or modify it under the terms and conditions of
# CANN Open Software License Agreement Version 2.0 (the "License").
# Please refer to the License for details. You may not use this file except in compliance with the License.
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
# See LICENSE in the root of the software repository for the full text of the License.
# ----------------------------------------------------------------------------

import numpy as np

__input__ = {"kernel": {"swish_grad": "swish_grad_input"}}


def swish_grad_input(grad, x, y, *, scale, **kwargs):
    '''
    Input function for swish_grad.
    All the parameters (names and order) follow swish_grad_def.cpp without outputs.
    All the input Tensors are numpy.ndarray.

    Args:
        **kwargs: {input,output}_{dtypes,ori_shapes,formats,ori_formats},
                  input_ranges, full_soc_version, short_soc_version, testcase_name

    Returns:
        List of input tensors (length must match Input count in _def.cpp)
    '''
    y_new = grad / (1 + np.exp(-1 * scale * grad))
    return [grad, x, y_new]