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.
  """
  LBFGS-Attack test.
  """
  import os
  
  import numpy as np
  import pytest
  from mindspore import context
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  
  from mindarmour.adv_robustness.attacks import LBFGS
  from mindarmour.utils.logger import LogUtil
  
  from tests.ut.python.utils.mock_net import Net
  
  context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
  
  
  LOGGER = LogUtil.get_instance()
  TAG = 'LBFGS_Test'
  LOGGER.set_level('DEBUG')
  
  
  @pytest.mark.level0
  @pytest.mark.platform_arm_ascend_training
  @pytest.mark.platform_x86_ascend_training
  @pytest.mark.env_card
  @pytest.mark.component_mindarmour
  def test_lbfgs_attack():
      """
      LBFGS-Attack test
      """
      np.random.seed(123)
      # upload trained network
      current_dir = os.path.dirname(os.path.abspath(__file__))
      ckpt_path = os.path.join(current_dir,
                               '../../dataset/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt')
      net = Net()
      load_dict = load_checkpoint(ckpt_path)
      load_param_into_net(net, load_dict)
  
      # get one mnist image
      input_np = np.load(os.path.join(current_dir,
                                      '../../dataset/test_images.npy'))[:1]
      label_np = np.load(os.path.join(current_dir,
                                      '../../dataset/test_labels.npy'))[:1]
      LOGGER.debug(TAG, 'true label is :{}'.format(label_np[0]))
      classes = 10
      target_np = np.random.randint(0, classes, 1)
      while target_np == label_np[0]:
          target_np = np.random.randint(0, classes)
      target_np = np.eye(10)[target_np].astype(np.float32)
  
      attack = LBFGS(net, is_targeted=True)
      LOGGER.debug(TAG, 'target_np is :{}'.format(target_np[0]))
      _ = attack.generate(input_np, target_np)
  