#!/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

__golden__ = {
  	"kernel": {
  	    "exp": "exp_golden"
  	}
}
  	
def exp_golden(x,
               base: float=-1.0, scale: float=1.0, shift: float=0.0,
               **kwargs):
    '''
    Kernel golden for exp.
    All the parameters follow @exp_def.cpp without outputs.
    All the input Tensors are numpy.ndarray.
    kwargs may contain: short_soc_version, input_ori_shapes, output_ori_shapes, 
  	    input_formats, output_formats, input_ori_formats, output_ori_formats,
  	    input_dtypes, output_dtypes.
    '''
    import torch

    x_dtype = x.dtype
    if x_dtype.name == "bfloat16" or x_dtype.name == "float16":
        x = torch.from_numpy(x.astype(np.float32))
    else:
        x = torch.from_numpy(x)
    if scale == 1 and shift == 0:
        if base == -1:
            output = torch.exp(x)
        else:
            output = torch.exp((scale * x + shift)*np.log(base))
    elif base == -1:
        output = torch.exp(scale * x + shift)
    else:
        output = torch.exp((scale * x + shift)*np.log(base))

    return output.numpy().astype(x_dtype, copy=False)