text: # Copyright 2019-2021 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 tests.common.tensorio import compare_tensor
  from akg.utils import kernel_exec as utils
  from tests.common.test_op.ascend import mean_square
  from akg.utils.dsl_create import get_reduce_out_shape
  from tests.common.gen_random import random_gaussian
  
  def mean_square_run(shape, reduce_axis, keepdims, dtype, attrs):
      op_attrs = [reduce_axis, keepdims]
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(mean_square.mean_square, [shape], [dtype], op_attrs, kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, input1, output = gen_data(dtype, keepdims, reduce_axis, shape)
              return mod, expect, (input1, output)
          else:
              return mod
      else:
          mod = utils.op_build_test(mean_square.mean_square, [shape], [dtype], op_attrs, attrs=attrs)
          expect, input1, output = gen_data(dtype, keepdims, reduce_axis, shape)
          output = utils.mod_launch(mod, (input1, output), expect=expect)
          return input1, output, expect, compare_tensor(output, expect, rtol=5e-3, atol=5e-3, equal_nan=True)
  
  
  def gen_data(dtype, keepdims, reduce_axis, shape):
      input1 = random_gaussian(shape, miu=1, sigma=0.1)
      input1 = input1.astype(dtype)
      input1_square = np.square(input1)
      expect = np.mean(input1_square, axis=reduce_axis, keepdims=keepdims)
      out_shape = get_reduce_out_shape(shape, axis=reduce_axis, keepdims=keepdims)
      output = np.full(out_shape, np.nan, dtype)
      return expect, input1, output
  