text: # Copyright 2019 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import numpy as np
  from tensorio import compare_tensor
  from akg.utils import kernel_exec as utils
  from test_op import cos_ad
  from gen_random import random_gaussian
  from base import get_rtol_atol
  
  def cos_ad_run(shape, dtype, attrs):
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          expect, head_np, inputs = get_input_data(dtype, shape)
          mod = utils.op_build_test(cos_ad.cos_ad, [expect.shape, shape], [dtype, dtype], kernel_name=kernel_name,
                                    attrs=attrs, tuning=t)
          if t:
              # inputs and output to hold the data
              output = np.full(expect.shape, np.nan, dtype)
              return mod, expect, (head_np, inputs, output)
          else:
              return mod
      else:
          expect, head_np, inputs = get_input_data(dtype, shape)
          mod = utils.op_build_test(cos_ad.cos_ad, [expect.shape, shape], [dtype, dtype],
                                    kernel_name='cos_ad', attrs=attrs)
          # inputs and output to hold the data
          output = np.full(expect.shape, np.nan, dtype)
          output = utils.mod_launch(mod, (head_np, inputs, output), expect=expect)
          # compare result
          rtol, atol = get_rtol_atol("cos_ad", dtype)
          TestCase_Result = compare_tensor(output, expect, rtol=rtol, atol=atol, equal_nan=False)
          return (head_np, inputs), output, expect, TestCase_Result
  
  
  def get_input_data(dtype, shape):
      # Generate data for testing the op
      inputs = random_gaussian(shape, miu=0, sigma=0.1).astype(dtype)
      head_np = random_gaussian(shape, miu=0, sigma=0.1).astype(dtype)
      expect = -np.sin(inputs) * head_np
      return expect, head_np, inputs
  