text: # MIT License
  #
  # Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
  #
  # 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.
  """
  This module implements the label smoothing defence in `LabelSmoothing`. It computes a vector of smooth labels from a
  vector of hard labels.
  
  | Paper link: https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf
  
  | Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
      see https://arxiv.org/abs/1902.06705  .
  """
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import logging
  from typing import Optional, Tuple
  
  import numpy as np
  
  from art.defences.preprocessor.preprocessor import Preprocessor
  
  logger = logging.getLogger(__name__)
  
  
  class LabelSmoothing(Preprocessor):
      """
      Computes a vector of smooth labels from a vector of hard ones. The hard labels have to contain ones for the
      correct classes and zeros for all the others. The remaining probability mass between `max_value` and 1 is
      distributed uniformly between the incorrect classes for each instance.
  
  
      | Paper link: https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf
  
      | Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
          see https://arxiv.org/abs/1902.06705  .
      """
  
      params = ["max_value"]
  
      def __init__(self, max_value: float = 0.9, apply_fit: bool = True, apply_predict: bool = False,) -> None:
          """
          Create an instance of label smoothing.
  
          :param max_value: Value to affect to correct label
          :param apply_fit: True if applied during fitting/training.
          :param apply_predict: True if applied during predicting.
          """
          super(LabelSmoothing, self).__init__()
          self._is_fitted = True
          self._apply_fit = apply_fit
          self._apply_predict = apply_predict
          self.max_value = max_value
          self._check_params()
  
      @property
      def apply_fit(self) -> bool:
          return self._apply_fit
  
      @property
      def apply_predict(self) -> bool:
          return self._apply_predict
  
      def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
          """
          Apply label smoothing.
  
          :param x: Input data, will not be modified by this method.
          :param y: Original vector of label probabilities (one-vs-rest).
          :return: Unmodified input data and the vector of smooth probabilities as correct labels.
          :raises `ValueError`: If no labels are provided.
          """
          if y is None:
              raise ValueError("Labels `y` cannot be None.")
  
          min_value = (1 - self.max_value) / (y.shape[1] - 1)
          assert self.max_value >= min_value
  
          smooth_y = y.copy()
          smooth_y[smooth_y == 1.0] = self.max_value
          smooth_y[smooth_y == 0.0] = min_value
          return x, smooth_y
  
      def estimate_gradient(self, x: np.ndarray, grad: np.ndarray) -> np.ndarray:
          return grad
  
      def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
          """
          No parameters to learn for this method; do nothing.
          """
          pass
  
      def _check_params(self) -> None:
          if self.max_value <= 0 or self.max_value > 1:
              raise ValueError("The maximum value for correct labels must be between 0 and 1.")
  