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.
  """
  Genetic-Attack.
  """
  import numpy as np
  from scipy.special import softmax
  
  from mindarmour.utils.logger import LogUtil
  from mindarmour.utils._check_param import check_numpy_param, check_model, \
      check_pair_numpy_param, check_param_type, check_value_positive, \
      check_int_positive, check_detection_inputs, check_value_non_negative, check_param_multi_types
  from mindarmour.adv_robustness.attacks.attack import Attack
  from .black_model import BlackModel
  
  LOGGER = LogUtil.get_instance()
  TAG = 'GeneticAttack'
  
  
  class GeneticAttack(Attack):
      """
      The Genetic Attack represents the black-box attack based on the genetic algorithm,
      which belongs to differential evolution algorithms.
  
      This attack was proposed by Moustafa Alzantot et al. (2018).
  
      References: `Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty,
      "GeneticAttack: Practical Black-box Attacks with
      Gradient-FreeOptimization" <https://arxiv.org/abs/1805.11090>`_
  
      Args:
          model (BlackModel): Target model.
          model_type (str): The type of targeted model. 'classification' and 'detection' are supported now.
              default: 'classification'.
          targeted (bool): If True, turns on the targeted attack. If False,
              turns on untargeted attack. It should be noted that only untargeted attack
              is supproted for model_type='detection', Default: True.
          reserve_ratio (Union[int, float]): The percentage of objects that can be detected after attacks,
              specifically for model_type='detection'. Reserve_ratio should be in the range of (0, 1). Default: 0.3.
          pop_size (int): The number of particles, which should be greater than
              zero. Default: 6.
          mutation_rate (Union[int, float]): The probability of mutations, which should be in the range of (0, 1).
              Default: 0.005.
          per_bounds (Union[int, float]): Maximum L_inf distance.
          max_steps (int): The maximum round of iteration for each adversarial
              example. Default: 1000.
          step_size (Union[int, float]): Attack step size. Default: 0.2.
          temp (Union[int, float]): Sampling temperature for selection. Default: 0.3.
              The greater the temp, the greater the differences between individuals'
              selecting probabilities.
          bounds (Union[tuple, list, None]): Upper and lower bounds of data. In form
              of (clip_min, clip_max). Default: (0, 1.0).
          adaptive (bool): If True, turns on dynamic scaling of mutation
              parameters. If false, turns on static mutation parameters.
              Default: False.
          sparse (bool): If True, input labels are sparse-encoded. If False,
              input labels are one-hot-encoded. Default: True.
          c (Union[int, float]): Weight of perturbation loss. Default: 0.1.
  
      Examples:
          >>> attack = GeneticAttack(model)
      """
      def __init__(self, model, model_type='classification', targeted=True, reserve_ratio=0.3, sparse=True,
                   pop_size=6, mutation_rate=0.005, per_bounds=0.15, max_steps=1000, step_size=0.20, temp=0.3,
                   bounds=(0, 1.0), adaptive=False, c=0.1):
          super(GeneticAttack, self).__init__()
          self._model = check_model('model', model, BlackModel)
          self._model_type = check_param_type('model_type', model_type, str)
          if self._model_type not in ('classification', 'detection'):
              msg = "Only 'classification' or 'detection' is supported now, but got {}.".format(self._model_type)
              LOGGER.error(TAG, msg)
              raise ValueError(msg)
          self._targeted = check_param_type('targeted', targeted, bool)
          self._reserve_ratio = check_value_non_negative('reserve_ratio', reserve_ratio)
          if self._reserve_ratio > 1:
              msg = "reserve_ratio should not be greater than 1.0, but got {}.".format(self._reserve_ratio)
              LOGGER.error(TAG, msg)
              raise ValueError(msg)
          self._sparse = check_param_type('sparse', sparse, bool)
          self._per_bounds = check_value_positive('per_bounds', per_bounds)
          self._pop_size = check_int_positive('pop_size', pop_size)
          self._step_size = check_value_positive('step_size', step_size)
          self._temp = check_value_positive('temp', temp)
          self._max_steps = check_int_positive('max_steps', max_steps)
          self._mutation_rate = check_value_non_negative('mutation_rate', mutation_rate)
          if self._mutation_rate > 1:
              msg = "mutation_rate should not be greater than 1.0, but got {}.".format(self._mutation_rate)
              LOGGER.error(TAG, msg)
              raise ValueError(msg)
          self._adaptive = check_param_type('adaptive', adaptive, bool)
          # initial global optimum fitness value
          self._best_fit = -np.inf
          # count times of no progress
          self._plateau_times = 0
          # count times of changing attack step_size
          self._adap_times = 0
          self._bounds = bounds
          if self._bounds is not None:
              self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
              for b in self._bounds:
                  _ = check_param_multi_types('bound', b, [int, float])
          self._c = check_value_positive('c', c)
  
      def _mutation(self, cur_pop, step_noise=0.01, prob=0.005):
          """
          Generate mutation samples in genetic_attack.
  
          Args:
              cur_pop (numpy.ndarray): Samples before mutation.
              step_noise (float): Noise range. Default: 0.01.
              prob (float): Mutation probability. Default: 0.005.
  
          Returns:
              numpy.ndarray, samples after mutation operation in genetic_attack.
  
          Examples:
              >>> mul_pop = self._mutation_op([0.2, 0.3, 0.4], step_noise=0.03,
              >>> prob=0.01)
          """
          cur_pop = check_numpy_param('cur_pop', cur_pop)
          perturb_noise = np.clip(np.random.random(cur_pop.shape) - 0.5,
                                  -step_noise, step_noise)*(self._bounds[1] - self._bounds[0])
          mutated_pop = perturb_noise*(
              np.random.random(cur_pop.shape) < prob) + cur_pop
          return mutated_pop
  
      def generate(self, inputs, labels):
          """
          Generate adversarial examples based on input data and targeted
          labels (or ground_truth labels).
  
          Args:
              inputs (Union[numpy.ndarray, tuple]): Input samples. The format of inputs should be numpy.ndarray if
                  model_type='classification'. The format of inputs can be (input1, input2, ...) or only one array if
                  model_type='detection'.
              labels (Union[numpy.ndarray, tuple]): Targeted labels or ground-truth labels. The format of labels should
                  be numpy.ndarray if model_type='classification'. The format of labels should be (gt_boxes, gt_labels)
                  if model_type='detection'.
  
          Returns:
              - numpy.ndarray, bool values for each attack result.
  
              - numpy.ndarray, generated adversarial examples.
  
              - numpy.ndarray, query times for each sample.
  
          Examples:
              >>> advs = attack.generate([[0.2, 0.3, 0.4],
              >>>                         [0.3, 0.3, 0.2]],
              >>>                        [1, 2])
          """
          if self._model_type == 'classification':
              inputs, labels = check_pair_numpy_param('inputs', inputs,
                                                      'labels', labels)
              if self._sparse:
                  if labels.size > 1:
                      label_squ = np.squeeze(labels)
                  else:
                      label_squ = labels
                  if len(label_squ.shape) >= 2 or label_squ.shape[0] != inputs.shape[0]:
                      msg = "The parameter 'sparse' of GeneticAttack is True, but the input labels is not sparse style " \
                            "and got its shape as {}.".format(labels.shape)
                      LOGGER.error(TAG, msg)
                      raise ValueError(msg)
              else:
                  labels = np.argmax(labels, axis=1)
              images = inputs
          elif self._model_type == 'detection':
              images, auxiliary_inputs, gt_boxes, gt_labels = check_detection_inputs(inputs, labels)
  
          adv_list = []
          success_list = []
          query_times_list = []
          for i in range(images.shape[0]):
              is_success = False
              x_ori = images[i]
              if not self._bounds:
                  self._bounds = [np.min(x_ori), np.max(x_ori)]
              pixel_deep = self._bounds[1] - self._bounds[0]
  
              if self._model_type == 'classification':
                  label_i = labels[i]
              elif self._model_type == 'detection':
                  auxiliary_input_i = tuple()
                  for item in auxiliary_inputs:
                      auxiliary_input_i += (np.expand_dims(item[i], axis=0),)
                  gt_boxes_i, gt_labels_i = np.expand_dims(gt_boxes[i], axis=0), np.expand_dims(gt_labels[i], axis=0)
                  inputs_i = (images[i],) + auxiliary_input_i
                  confi_ori, gt_object_num = self._detection_scores(inputs_i, gt_boxes_i, gt_labels_i, model=self._model)
                  LOGGER.info(TAG, 'The number of ground-truth objects is %s', gt_object_num[0])
  
              # generate particles
              ori_copies = np.repeat(x_ori[np.newaxis, :], self._pop_size, axis=0)
              # initial perturbations
              cur_pert = np.random.uniform(self._bounds[0], self._bounds[1], ori_copies.shape)
              cur_pop = ori_copies + cur_pert
              query_times = 0
              iters = 0
  
              while iters < self._max_steps:
                  iters += 1
                  cur_pop = np.clip(np.clip(cur_pop,
                                            ori_copies - pixel_deep*self._per_bounds,
                                            ori_copies + pixel_deep*self._per_bounds),
                                    self._bounds[0], self._bounds[1])
  
                  if self._model_type == 'classification':
                      pop_preds = self._model.predict(cur_pop)
                      query_times += cur_pop.shape[0]
                      all_preds = np.argmax(pop_preds, axis=1)
                      if self._targeted:
                          success_pop = np.equal(label_i, all_preds).astype(np.int32)
                      else:
                          success_pop = np.not_equal(label_i, all_preds).astype(np.int32)
                      is_success = max(success_pop)
                      best_idx = np.argmax(success_pop)
                      target_preds = pop_preds[:, label_i]
                      others_preds_sum = np.sum(pop_preds, axis=1) - target_preds
                      if self._targeted:
                          fit_vals = target_preds - others_preds_sum
                      else:
                          fit_vals = others_preds_sum - target_preds
  
                  elif self._model_type == 'detection':
                      confi_adv, correct_nums_adv = self._detection_scores(
                          (cur_pop,) + auxiliary_input_i, gt_boxes_i, gt_labels_i, model=self._model)
                      LOGGER.info(TAG, 'The number of correctly detected objects in adversarial image is %s',
                                  np.min(correct_nums_adv))
                      query_times += self._pop_size
                      fit_vals = abs(
                          confi_ori - confi_adv) - self._c / self._pop_size * np.linalg.norm(
                              (cur_pop - x_ori).reshape(cur_pop.shape[0], -1), axis=1)
  
                      if np.max(fit_vals) < 0:
                          self._c /= 2
  
                      if np.max(fit_vals) < -2:
                          LOGGER.debug(TAG,
                                       'best fitness value is %s, which is too small. We recommend that you decrease '
                                       'the value of the initialization parameter c.', np.max(fit_vals))
                      if iters < 3 and np.max(fit_vals) > 100:
                          LOGGER.debug(TAG,
                                       'best fitness value is %s, which is too large. We recommend that you increase '
                                       'the value of the initialization parameter c.', np.max(fit_vals))
  
                      if np.min(correct_nums_adv) <= int(gt_object_num*self._reserve_ratio):
                          is_success = True
                          best_idx = np.argmin(correct_nums_adv)
  
                  if is_success:
                      LOGGER.debug(TAG, 'successfully find one adversarial sample '
                                        'and start Reduction process.')
                      final_adv = cur_pop[best_idx]
                      if self._model_type == 'classification':
                          final_adv, query_times = self._reduction(x_ori, query_times, label_i, final_adv,
                                                                   model=self._model, targeted_attack=self._targeted)
                      break
  
                  best_fit = max(fit_vals)
  
                  if best_fit > self._best_fit:
                      self._best_fit = best_fit
                      self._plateau_times = 0
                  else:
                      self._plateau_times += 1
                  adap_threshold = (lambda z: 100 if z > -0.4 else 300)(best_fit)
                  if self._plateau_times > adap_threshold:
                      self._adap_times += 1
                      self._plateau_times = 0
                  if self._adaptive:
                      step_noise = max(self._step_size, 0.4*(0.9**self._adap_times))
                      step_p = max(self._mutation_rate, 0.5*(0.9**self._adap_times))
                  else:
                      step_noise = self._step_size
                      step_p = self._mutation_rate
                  step_temp = self._temp
                  elite = cur_pop[np.argmax(fit_vals)]
                  select_probs = softmax(fit_vals/step_temp)
                  select_args = np.arange(self._pop_size)
                  parents_arg = np.random.choice(
                      a=select_args, size=2*(self._pop_size - 1),
                      replace=True, p=select_probs)
                  parent1 = cur_pop[parents_arg[:self._pop_size - 1]]
                  parent2 = cur_pop[parents_arg[self._pop_size - 1:]]
                  parent1_probs = select_probs[parents_arg[:self._pop_size - 1]]
                  parent2_probs = select_probs[parents_arg[self._pop_size - 1:]]
                  parent2_probs = parent2_probs / (parent1_probs + parent2_probs)
                  # duplicate the probabilities to all features of each particle.
                  dims = len(x_ori.shape)
                  for _ in range(dims):
                      parent2_probs = parent2_probs[:, np.newaxis]
                  parent2_probs = np.tile(parent2_probs, ((1,) + x_ori.shape))
                  cross_probs = (np.random.random(parent1.shape) >
                                 parent2_probs).astype(np.int32)
                  childs = parent1*cross_probs + parent2*(1 - cross_probs)
                  mutated_childs = self._mutation(
                      childs, step_noise=self._per_bounds*step_noise,
                      prob=step_p)
                  cur_pop = np.concatenate((mutated_childs, elite[np.newaxis, :]))
  
              if not is_success:
                  LOGGER.debug(TAG, 'fail to find adversarial sample.')
                  final_adv = elite
              if self._model_type == 'detection':
                  final_adv, query_times = self._fast_reduction(
                      x_ori, final_adv, query_times, auxiliary_input_i, gt_boxes_i, gt_labels_i, model=self._model)
              adv_list.append(final_adv)
  
              LOGGER.debug(TAG,
                           'iteration times is: %d and query times is: %d',
                           iters,
                           query_times)
              success_list.append(is_success)
              query_times_list.append(query_times)
              del ori_copies, cur_pert, cur_pop
          return np.asarray(success_list), \
                 np.asarray(adv_list), \
                 np.asarray(query_times_list)
  