text: # MIT License
  #
  # Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
  # documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
  # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
  # persons to whom the Software is furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
  # Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
  # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
  # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import logging
  import os
  import unittest
  import keras
  
  from art.defences.transformer.poisoning import NeuralCleanse
  from art.utils import load_dataset
  
  from tests.utils import master_seed, get_image_classifier_kr
  
  os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
  logger = logging.getLogger(__name__)
  
  BATCH_SIZE = 100
  NB_TRAIN = 5000
  NB_TEST = 10
  
  
  class TestNeuralCleanse(unittest.TestCase):
      """
      A unittest class for testing Randomized Smoothing as a post-processing step for classifiers.
      """
  
      @classmethod
      def setUpClass(cls):
          # Get MNIST
          (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
          x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
          x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
          cls.mnist = (x_train, y_train), (x_test, y_test)
  
      def setUp(self):
          master_seed(seed=1234)
  
      def test_keras(self):
          """
          Test with a KerasClassifier.
          :return:
          """
          if keras.__version__ != "2.2.4":
              self.assertRaises(NotImplementedError)
          else:
              # Build KerasClassifier
              krc = get_image_classifier_kr()
  
              # Get MNIST
              (x_train, y_train), (x_test, y_test) = self.mnist
  
              krc.fit(x_train, y_train, nb_epochs=1)
  
              cleanse = NeuralCleanse(krc)
              defense_cleanse = cleanse(krc, steps=2)
              defense_cleanse.mitigate(x_test, y_test, mitigation_types=["filtering", "pruning", "unlearning"])
  
  
  if __name__ == "__main__":
      unittest.main()
  