text: # MIT License
  #
  # Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
  #
  # 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 numpy as np
  import pytest
  
  from art.attacks.evasion import FastGradientMethod
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  
  from tests.attacks.utils import backend_check_adverse_values, backend_test_defended_images
  from tests.attacks.utils import backend_test_random_initialisation_images, backend_targeted_images
  from tests.attacks.utils import backend_targeted_tabular, backend_untargeted_tabular, backend_masked_images
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ExpectedValue, ARTTestException
  
  logger = logging.getLogger(__name__)
  
  
  @pytest.fixture()
  def fix_get_mnist_subset(get_mnist_dataset):
      (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
      n_train = 100
      n_test = 11
      yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
  
  
  # currently NOT setting this test as framework_agnostic since no tensorflow implementation
  # of the defended classifier exists
  def test_classifier_defended_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod, defended=True)
          attack = FastGradientMethod(classifier, eps=1.0, batch_size=128)
          backend_test_defended_images(attack, fix_get_mnist_subset)
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.framework_agnostic
  def test_random_initialisation_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
          attack = FastGradientMethod(classifier, num_random_init=3)
          backend_test_random_initialisation_images(attack, fix_get_mnist_subset)
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.framework_agnostic
  def test_targeted_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
          attack = FastGradientMethod(classifier, eps=1.0, targeted=True)
          attack_params = {"minimal": True, "eps_step": 0.01, "eps": 1.0}
          attack.set_params(**attack_params)
  
          backend_targeted_images(attack, fix_get_mnist_subset)
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.framework_agnostic
  def test_masked_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
          attack = FastGradientMethod(classifier, eps=1.0, num_random_init=1)
          backend_masked_images(attack, fix_get_mnist_subset)
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.framework_agnostic
  def test_minimal_perturbations_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
  
          expected_values = {
              "x_test_mean": ExpectedValue(0.03896513, 0.01),
              "x_test_min": ExpectedValue(-0.30000000, 0.00001),
              "x_test_max": ExpectedValue(0.30000000, 0.00001),
              "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 2, 4, 7, 0, 4, 7, 2, 0, 7, 0]), 2),
          }
  
          attack = FastGradientMethod(classifier, eps=1.0, batch_size=11)
  
          # Test eps of float type
          attack_params = {"minimal": True, "eps_step": 0.1, "eps": 5.0}
          attack.set_params(**attack_params)
  
          backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
  
          # Test eps of array type 1
          (_, _, x_test_mnist, _) = fix_get_mnist_subset
          eps = np.ones(shape=x_test_mnist.shape) * 5.0
          eps_step = np.ones_like(eps) * 0.1
  
          attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
          attack.set_params(**attack_params)
  
          backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
  
          # Test eps of array type 2
          eps = np.ones(shape=x_test_mnist.shape[1:]) * 5.0
          eps_step = np.ones_like(eps) * 0.1
  
          attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
          attack.set_params(**attack_params)
  
          backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
  
          # Test eps of array type 3
          eps = np.ones(shape=x_test_mnist.shape[2:]) * 5.0
          eps_step = np.ones_like(eps) * 0.1
  
          attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
          attack.set_params(**attack_params)
  
          backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
  
          # Test eps of array type 4
          eps = np.ones(shape=x_test_mnist.shape[3:]) * 5.0
          eps_step = np.ones_like(eps) * 0.1
  
          attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
          attack.set_params(**attack_params)
  
          backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
  
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.parametrize("norm", [np.inf, 1, 2])
  @pytest.mark.skipMlFramework("pytorch")  # temporarily skipping for pytorch until find bug fix in bounded test
  @pytest.mark.framework_agnostic
  def test_norm_images(art_warning, norm, fix_get_mnist_subset, image_dl_estimator_for_attack):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
  
          if norm == np.inf:
              expected_values = {
                  "x_test_mean": ExpectedValue(0.2346725, 0.002),
                  "x_test_min": ExpectedValue(-1.0, 0.00001),
                  "x_test_max": ExpectedValue(1.0, 0.00001),
                  "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 4, 4, 7, 7, 4, 7, 2, 2, 3, 0]), 2),
              }
  
          elif norm == 1:
              expected_values = {
                  "x_test_mean": ExpectedValue(0.00051374, 0.002),
                  "x_test_min": ExpectedValue(-0.01486498, 0.001),
                  "x_test_max": ExpectedValue(0.014761963, 0.001),
                  "y_test_pred_adv_expected": ExpectedValue(np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4, 4]), 4),
              }
          elif norm == 2:
              expected_values = {
                  "x_test_mean": ExpectedValue(0.007636416, 0.001),
                  "x_test_min": ExpectedValue(-0.211054801, 0.001),
                  "x_test_max": ExpectedValue(0.209592223, 0.001),
                  "y_test_pred_adv_expected": ExpectedValue(np.asarray([7, 2, 4, 4, 4, 7, 7, 4, 0, 4, 4]), 2),
              }
  
          attack = FastGradientMethod(classifier, eps=1.0, norm=norm, batch_size=128)
  
          backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.skipMlFramework("scikitlearn")  # temporarily skipping for scikitlearn until find bug fix in bounded test
  @pytest.mark.parametrize("targeted, clipped", [(True, True), (True, False), (False, True), (False, False)])
  @pytest.mark.framework_agnostic
  def test_tabular(art_warning, tabular_dl_estimator, framework, get_iris_dataset, targeted, clipped):
      try:
          classifier = tabular_dl_estimator(clipped=clipped)
  
          if targeted:
              attack = FastGradientMethod(classifier, targeted=True, eps=0.1, batch_size=128)
              backend_targeted_tabular(attack, get_iris_dataset)
          else:
              attack = FastGradientMethod(classifier, eps=0.1)
              backend_untargeted_tabular(attack, get_iris_dataset, clipped=clipped)
      except ARTTestException as e:
          art_warning(e)
  
  
  @pytest.mark.framework_agnostic
  def test_classifier_type_check_fail(art_warning):
      try:
          backend_test_classifier_type_check_fail(FastGradientMethod, [BaseEstimator, LossGradientsMixin])
      except ARTTestException as e:
          art_warning(e)
  