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 classifier `MXClassifier` for MXNet Gluon models.
  """
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import logging
  import os
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  
  import numpy as np
  import six
  
  from art import config
  from art.estimators.mxnet import MXEstimator
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.utils import check_and_transform_label_format
  
  if TYPE_CHECKING:
      # pylint: disable=C0412
      import mxnet as mx
  
      from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
      from art.data_generators import DataGenerator
      from art.defences.preprocessor import Preprocessor
      from art.defences.postprocessor import Postprocessor
  
  logger = logging.getLogger(__name__)
  
  
  class MXClassifier(ClassGradientsMixin, ClassifierMixin, MXEstimator):  # lgtm [py/missing-call-to-init]
      """
      Wrapper class for importing MXNet Gluon models.
      """
  
      estimator_params = (
          MXEstimator.estimator_params
          + ClassifierMixin.estimator_params
          + [
              "loss",
              "input_shape",
              "nb_classes",
              "optimizer",
              "ctx",
              "channels_first",
          ]
      )
  
      def __init__(
          self,
          model: "mx.gluon.Block",
          loss: Union["mx.nd.loss", "mx.gluon.loss"],
          input_shape: Tuple[int, ...],
          nb_classes: int,
          optimizer: Optional["mx.gluon.Trainer"] = None,
          ctx: Optional["mx.context.Context"] = None,
          channels_first: bool = True,
          clip_values: Optional["CLIP_VALUES_TYPE"] = None,
          preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
          postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
          preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
      ) -> None:
          """
          Initialize an `MXClassifier` object. Assumes the `model` passed as parameter is a Gluon model.
  
          :param model: The Gluon model. The output of the model can be logits, probabilities or anything else. Logits
                 output should be preferred where possible to ensure attack efficiency.
          :param loss: The loss function for which to compute gradients for training.
          :param input_shape: The shape of one input instance.
          :param nb_classes: The number of classes of the model.
          :param optimizer: The optimizer used to train the classifier. This parameter is only required if fitting will
                            be done with method fit.
          :param ctx: The device on which the model runs (CPU or GPU). If not provided, CPU is assumed.
          :param channels_first: Set channels first or last.
          :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
                 maximum values allowed for features. If floats are provided, these will be used as the range of all
                 features. If arrays are provided, each value will be considered the bound for a feature, thus
                 the shape of clip values needs to match the total number of features.
          :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
          :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
          :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
                 used for data preprocessing. The first value will be subtracted from the input. The input will then
                 be divided by the second one.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
  
          super().__init__(
              model=model,
              clip_values=clip_values,
              channels_first=channels_first,
              preprocessing_defences=preprocessing_defences,
              postprocessing_defences=postprocessing_defences,
              preprocessing=preprocessing,
          )
  
          self._loss = loss
          self._nb_classes = nb_classes
          self._input_shape = input_shape
          self._device = ctx
          self._optimizer = optimizer
  
          if ctx is None:
              self._ctx = mx.cpu()
          else:
              self._ctx = ctx
  
          # Get the internal layer
          self._layer_names = self._get_layers()
  
      @property
      def input_shape(self) -> Tuple[int, ...]:
          """
          Return the shape of one input sample.
  
          :return: Shape of one input sample.
          """
          return self._input_shape  # type: ignore
  
      @property
      def loss(self) -> Union["mx.nd.loss", "mx.gluon.loss"]:
          """
          Return the loss function.
  
          :return: The loss function.
          """
          return self._loss  # type: ignore
  
      @property
      def optimizer(self) -> "mx.gluon.Trainer":
          """
          Return the optimizer used to train the classifier.
  
          :return: The optimizer used to train the classifier.
          """
          return self._optimizer  # type: ignore
  
      @property
      def ctx(self) -> "mx.context.Context":
          """
          Return the device on which the model runs.
  
          :return: The device on which the model runs (CPU or GPU).
          """
          return self._ctx  # type: ignore
  
      def fit(
          self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs
      ) -> None:  # pragma: no cover
          """
          Fit the classifier on the training set `(inputs, outputs)`.
  
          :param x: Training data.
          :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
                    shape (nb_samples,).
          :param batch_size: Size of batches.
          :param nb_epochs: Number of epochs to use for training.
          :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for MXNet
                 and providing it takes no effect.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
  
          if self._optimizer is None:
              raise ValueError("An MXNet optimizer is required for fitting the model.")
  
          training_mode = True
  
          y = check_and_transform_label_format(y, self.nb_classes)
  
          # Apply preprocessing
          x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
          y_preprocessed = np.argmax(y_preprocessed, axis=1)
          nb_batch = int(np.ceil(len(x_preprocessed) / batch_size))
          ind = np.arange(len(x_preprocessed))
  
          for _ in range(nb_epochs):
              # Shuffle the examples
              np.random.shuffle(ind)
  
              # Train for one epoch
              for m in range(nb_batch):
                  x_batch = mx.nd.array(
                      x_preprocessed[ind[m * batch_size : (m + 1) * batch_size]].astype(config.ART_NUMPY_DTYPE)
                  ).as_in_context(self._ctx)
                  y_batch = mx.nd.array(y_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]).as_in_context(
                      self._ctx
                  )
  
                  with mx.autograd.record(train_mode=training_mode):
                      # Perform prediction
                      preds = self._model(x_batch)
  
                      # Apply postprocessing
                      preds = self._apply_postprocessing(preds=preds, fit=True)
  
                      # Form the loss function
                      loss = self._loss(preds, y_batch)
  
                  loss.backward()
  
                  # Update parameters
                  self._optimizer.step(batch_size)
  
      def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:  # pragma: no cover
          """
          Fit the classifier using the generator that yields batches as specified.
  
          :param generator: Batch generator providing `(x, y)` for each epoch.
          :param nb_epochs: Number of epochs to use for training.
          :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for MXNet
                 and providing it takes no effect.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
          from art.data_generators import MXDataGenerator
  
          if self._optimizer is None:
              raise ValueError("An MXNet optimizer is required for fitting the model.")
  
          training_mode = True
  
          if (
              isinstance(generator, MXDataGenerator)
              and (self.preprocessing is None or self.preprocessing == [])
              and self.preprocessing == (0, 1)
          ):
              # Train directly in MXNet
              for _ in range(nb_epochs):
                  for x_batch, y_batch in generator.iterator:
                      x_batch = mx.nd.array(x_batch.astype(config.ART_NUMPY_DTYPE)).as_in_context(self._ctx)
                      y_batch = mx.nd.argmax(y_batch, axis=1)
                      y_batch = mx.nd.array(y_batch).as_in_context(self._ctx)
  
                      with mx.autograd.record(train_mode=training_mode):
                          # Perform prediction
                          preds = self._model(x_batch)
  
                          # Form the loss function
                          loss = self._loss(preds, y_batch)
  
                      loss.backward()
  
                      # Update parameters
                      self._optimizer.step(x_batch.shape[0])
          else:
              # Fit a generic data generator through the API
              super().fit_generator(generator, nb_epochs=nb_epochs)
  
      def predict(  # pylint: disable=W0221
          self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs
      ) -> np.ndarray:
          """
          Perform prediction for a batch of inputs.
  
          :param x: Input samples.
          :param batch_size: Size of batches.
          :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
          :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
  
          # Apply preprocessing
          x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
  
          # Run prediction with batch processing
          results = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=np.float32)
          num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
          for m in range(num_batch):
              # Batch indexes
              begin, end = (
                  m * batch_size,
                  min((m + 1) * batch_size, x_preprocessed.shape[0]),
              )
  
              # Predict
              x_batch = mx.nd.array(x_preprocessed[begin:end].astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
              x_batch.attach_grad()
              with mx.autograd.record(train_mode=training_mode):
                  preds = self._model(x_batch)
  
              results[begin:end] = preds.asnumpy()
  
          # Apply postprocessing
          predictions = self._apply_postprocessing(preds=results, fit=False)
  
          return predictions
  
      def class_gradient(  # pylint: disable=W0221
          self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
      ) -> np.ndarray:
          """
          Compute per-class derivatives w.r.t. `x`.
  
          :param x: Sample input with shape as expected by the model.
          :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
                        output is computed for all samples. If multiple values as provided, the first dimension should
                        match the batch size of `x`, and each value will be used as target for its corresponding sample in
                        `x`. If `None`, then gradients for all classes will be computed for each sample.
          :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
          :return: Array of gradients of input features w.r.t. each class in the form
                   `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
                   `(batch_size, 1, input_shape)` when `label` parameter is specified.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
  
          # Check value of label for computing gradients
          if not (
              label is None
              or (isinstance(label, (int, np.integer)) and label in range(self.nb_classes))
              or (
                  isinstance(label, np.ndarray)
                  and len(label.shape) == 1
                  and (label < self.nb_classes).all()
                  and label.shape[0] == x.shape[0]
              )
          ):
              raise ValueError("Label %s is out of range." % str(label))
  
          # Apply preprocessing
          x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
          x_preprocessed = mx.nd.array(x_preprocessed.astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
          x_preprocessed.attach_grad()
  
          if label is None:
              with mx.autograd.record(train_mode=False):
                  preds = self._model(x_preprocessed)
                  class_slices = [preds[:, i] for i in range(self.nb_classes)]
  
              grads = []
              for slice_ in class_slices:
                  slice_.backward(retain_graph=True)
                  grad = x_preprocessed.grad.asnumpy()
                  grads.append(grad)
              grads = np.swapaxes(np.array(grads), 0, 1)
          elif isinstance(label, (int, np.integer)):
              with mx.autograd.record(train_mode=training_mode):
                  preds = self._model(x_preprocessed)
                  class_slice = preds[:, label]
  
              class_slice.backward()
              grads = np.expand_dims(x_preprocessed.grad.asnumpy(), axis=1)
          else:
              unique_labels = list(np.unique(label))
  
              with mx.autograd.record(train_mode=training_mode):
                  preds = self._model(x_preprocessed)
                  class_slices = [preds[:, i] for i in unique_labels]
  
              grads = []
              for slice_ in class_slices:
                  slice_.backward(retain_graph=True)
                  grad = x_preprocessed.grad.asnumpy()
                  grads.append(grad)
  
              grads = np.swapaxes(np.array(grads), 0, 1)
              lst = [unique_labels.index(i) for i in label]
              grads = grads[np.arange(len(grads)), lst]
              grads = np.expand_dims(grads, axis=1)
  
          grads = self._apply_preprocessing_gradient(x, grads)
  
          return grads
  
      def loss_gradient(  # pylint: disable=W0221
          self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs
      ) -> np.ndarray:
          """
          Compute the gradient of the loss function w.r.t. `x`.
  
          :param x: Sample input with shape as expected by the model.
          :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
                    `(nb_samples,)`.
          :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
          :return: Array of gradients of the same shape as `x`.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
  
          # Apply preprocessing
          x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
          y_preprocessed = mx.nd.array([np.argmax(y_preprocessed, axis=1)], ctx=self._ctx).T
          x_preprocessed = mx.nd.array(x_preprocessed.astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
          x_preprocessed.attach_grad()
  
          with mx.autograd.record(train_mode=training_mode):
              preds = self._model(x_preprocessed)
              loss = self._loss(preds, y_preprocessed)
  
          loss.backward()
  
          # Compute gradients
          grads = x_preprocessed.grad.asnumpy()
          grads = self._apply_preprocessing_gradient(x, grads)
          assert grads.shape == x.shape
  
          return grads
  
      def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
          """
          Compute the loss of the neural network for samples `x`.
  
          :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
                    nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
          :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
                    of shape `(nb_samples,)`.
          :return: Loss values.
          :rtype: Format as expected by the `model`
          """
          raise NotImplementedError
  
      @property
      def layer_names(self) -> List[str]:
          """
          Return the hidden layers in the model, if applicable.
  
          :return: The hidden layers in the model, input and output layers excluded.
  
          .. warning:: `layer_names` tries to infer the internal structure of the model.
                       This feature comes with no guarantees on the correctness of the result.
                       The intended order of the layers tries to match their order in the model, but this is not
                       guaranteed either.
          """
          return self._layer_names
  
      def get_activations(
          self, x: np.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False
      ) -> np.ndarray:
          """
          Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
          `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
          calling `layer_names`.
  
          :param x: Input for computing the activations.
          :param layer: Layer for computing the activations
          :param batch_size: Size of batches.
          :param framework: If true, return the intermediate tensor representation of the activation.
          :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
          """
          import mxnet as mx  # lgtm [py/repeated-import]
  
          if isinstance(layer, six.string_types):
              if layer not in self._layer_names:
                  raise ValueError("Layer name %s is not part of the model." % layer)
              layer_ind = self._layer_names.index(layer)
          elif isinstance(layer, int):
              if layer < 0 or layer >= len(self._layer_names):
                  raise ValueError(
                      "Layer index %d is outside of range (0 to %d included)." % (layer, len(self._layer_names) - 1)
                  )
              layer_ind = layer
          else:
              raise TypeError("Layer must be of type `str` or `int`.")
  
          # Apply preprocessing and defences
          if x.shape == self.input_shape:
              x_expanded = np.expand_dims(x, 0)
          else:
              x_expanded = x
  
          x_preprocessed, _ = self._apply_preprocessing(x=x_expanded, y=None, fit=False)
  
          if framework:
              return self._model[layer_ind]
  
          # Compute activations with batching
          activations = []
          nb_batches = int(np.ceil(len(x_preprocessed) / float(batch_size)))
          for batch_index in range(nb_batches):
              # Batch indexes
              begin, end = (
                  batch_index * batch_size,
                  min((batch_index + 1) * batch_size, x_preprocessed.shape[0]),
              )
  
              # Predict
              x_batch = mx.nd.array(x_preprocessed[begin:end].astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
              x_batch.attach_grad()
              with mx.autograd.record(train_mode=False):
                  preds = self._model[layer_ind](x_batch)
  
              activations.append(preds.asnumpy())
  
          activations = np.vstack(activations)
          return activations
  
      def save(self, filename: str, path: Optional[str] = None) -> None:
          """
          Save a model to file in the format specific to the backend framework. For Gluon, only parameters are saved in
          file with name `<filename>.params` at the specified path. To load the saved model, the original model code needs
          to be run before calling `load_parameters` on the generated Gluon model.
  
          :param filename: Name of the file where to store the model.
          :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
                       the default data location of the library `ART_DATA_PATH`.
          """
          if path is None:
              full_path = os.path.join(config.ART_DATA_PATH, filename)
          else:
              full_path = os.path.join(path, filename)
          folder = os.path.split(full_path)[0]
          if not os.path.exists(folder):
              os.makedirs(folder)
  
          self._model.save_parameters(full_path + ".params")
          logger.info("Model parameters saved in path: %s.params.", full_path)
  
      def __repr__(self):
          repr_ = (
              "%s(model=%r, loss=%r, input_shape=%r, nb_classes=%r, optimizer=%r, ctx=%r, "
              " channels_first=%r, clip_values=%r, preprocessing=%r, postprocessing_defences=%r,"
              " preprocessing=%r)"
              % (
                  self.__module__ + "." + self.__class__.__name__,
                  self._model,
                  self._loss,
                  self.input_shape,
                  self.nb_classes,
                  self._optimizer,
                  self._ctx,
                  self.channels_first,
                  self.clip_values,
                  self.preprocessing,
                  self.postprocessing_defences,
                  self.preprocessing,
              )
          )
  
          return repr_
  
      def _get_layers(self) -> list:
          """
          Return the hidden layers in the model, if applicable.
  
          :return: The hidden layers in the model, input and output layers excluded.
          """
          import mxnet
  
          if isinstance(self._model, mxnet.gluon.nn.Sequential):
              layer_names = [layer.name for layer in self._model[:-1]]
              logger.info("Inferred %i hidden layers on MXNet classifier.", len(layer_names))
          else:
              layer_names = []
  
          return layer_names
  