text: # Copyright 2022 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
  import pytest
  import mindspore.context as context
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.ops import operations as P
  
  
  class RandomChoiceWithMaskNet(nn.Cell):
      def __init__(self):
          super(RandomChoiceWithMaskNet, self).__init__()
          self.random_choice_with_mask = P.RandomChoiceWithMask(count=4, seed=1)
          self.random_choice_with_mask.add_prim_attr("cust_aicpu", "mindspore_aicpu_kernels")
  
      def construct(self, x):
          return self.random_choice_with_mask(x)
  
  
  @pytest.mark.level0
  @pytest.mark.platform_arm_ascend_training
  @pytest.mark.platform_x86_ascend_training
  @pytest.mark.env_onecard
  def test_random_choice_with_mask_graph():
      """
      Feature: Custom aicpu feature.
      Description: Test random_choice_with_mask kernel in graph mode.
      Expectation: No exception.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_tensor = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1],
                                      [0, 0, 0, 1]]).astype(np.bool))
      expect1 = (4, 2)
      expect2 = (4,)
      net = RandomChoiceWithMaskNet()
      output1, output2 = net(input_tensor)
      assert output1.shape == expect1
      assert output2.shape == expect2
  
  
  @pytest.mark.level0
  @pytest.mark.platform_arm_ascend_training
  @pytest.mark.platform_x86_ascend_training
  @pytest.mark.env_onecard
  def test_random_choice_with_mask_pynative():
      """
      Feature: Custom aicpu feature.
      Description: Test random_choice_with_mask kernel in pynative mode.
      Expectation: No exception.
      """
      context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
      input_tensor = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1],
                                      [0, 0, 0, 1]]).astype(np.bool))
      expect1 = (4, 2)
      expect2 = (4,)
      net = RandomChoiceWithMaskNet()
      output1, output2 = net(input_tensor)
      assert output1.shape == expect1
      assert output2.shape == expect2
  