text: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # 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.
  # ==============================================================================
  
  """NASNet-A models for Keras.
  
  NASNet refers to Neural Architecture Search Network, a family of models
  that were designed automatically by learning the model architectures
  directly on the dataset of interest.
  
  Here we consider NASNet-A, the highest performance model that was found
  for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset,
  obtaining state of the art performance on CIFAR-10 and ImageNet 2012.
  Only the NASNet-A models, and their respective weights, which are suited
  for ImageNet 2012 are provided.
  
  The below table describes the performance on ImageNet 2012:
  --------------------------------------------------------------------------------
        Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
  --------------------------------------------------------------------------------
  |   NASNet-A (4 @ 1056)  |   74.0 %  |   91.6 %  |       564 M    |     5.3    |
  |   NASNet-A (6 @ 4032)  |   82.7 %  |   96.2 %  |      23.8 B    |    88.9    |
  --------------------------------------------------------------------------------
  
  Reference:
    - [Learning Transferable Architectures for Scalable Image Recognition](
        https://arxiv.org/abs/1707.07012) (CVPR 2018)
  """
  
  import tensorflow.compat.v2 as tf
  
  from keras import backend
  from keras.applications import imagenet_utils
  from keras.engine import training
  from keras.layers import VersionAwareLayers
  from keras.utils import data_utils
  from keras.utils import layer_utils
  
  # isort: off
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.util.tf_export import keras_export
  
  BASE_WEIGHTS_PATH = (
      "https://storage.googleapis.com/tensorflow/" "keras-applications/nasnet/"
  )
  NASNET_MOBILE_WEIGHT_PATH = BASE_WEIGHTS_PATH + "NASNet-mobile.h5"
  NASNET_MOBILE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + "NASNet-mobile-no-top.h5"
  NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + "NASNet-large.h5"
  NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + "NASNet-large-no-top.h5"
  
  layers = VersionAwareLayers()
  
  
  def NASNet(
      input_shape=None,
      penultimate_filters=4032,
      num_blocks=6,
      stem_block_filters=96,
      skip_reduction=True,
      filter_multiplier=2,
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      pooling=None,
      classes=1000,
      default_size=None,
      classifier_activation="softmax",
  ):
      """Instantiates a NASNet model.
  
      Reference:
      - [Learning Transferable Architectures for Scalable Image Recognition](
          https://arxiv.org/abs/1707.07012) (CVPR 2018)
  
      For image classification use cases, see
      [this page for detailed examples](
        https://keras.io/api/applications/#usage-examples-for-image-classification-models).
  
      For transfer learning use cases, make sure to read the
      [guide to transfer learning & fine-tuning](
        https://keras.io/guides/transfer_learning/).
  
      Note: each Keras Application expects a specific kind of input preprocessing.
      For NasNet, call `tf.keras.applications.nasnet.preprocess_input`
      on your inputs before passing them to the model.
      `nasnet.preprocess_input` will scale input pixels between -1 and 1.
  
      Args:
        input_shape: Optional shape tuple, the input shape
          is by default `(331, 331, 3)` for NASNetLarge and
          `(224, 224, 3)` for NASNetMobile.
          It should have exactly 3 input channels,
          and width and height should be no smaller than 32.
          E.g. `(224, 224, 3)` would be one valid value.
        penultimate_filters: Number of filters in the penultimate layer.
          NASNet models use the notation `NASNet (N @ P)`, where:
              -   N is the number of blocks
              -   P is the number of penultimate filters
        num_blocks: Number of repeated blocks of the NASNet model.
          NASNet models use the notation `NASNet (N @ P)`, where:
              -   N is the number of blocks
              -   P is the number of penultimate filters
        stem_block_filters: Number of filters in the initial stem block
        skip_reduction: Whether to skip the reduction step at the tail
          end of the network.
        filter_multiplier: Controls the width of the network.
          - If `filter_multiplier` < 1.0, proportionally decreases the number
              of filters in each layer.
          - If `filter_multiplier` > 1.0, proportionally increases the number
              of filters in each layer.
          - If `filter_multiplier` = 1, default number of filters from the
               paper are used at each layer.
        include_top: Whether to include the fully-connected
          layer at the top of the network.
        weights: `None` (random initialization) or
            `imagenet` (ImageNet weights)
        input_tensor: Optional Keras tensor (i.e. output of
          `layers.Input()`)
          to use as image input for the model.
        pooling: Optional pooling mode for feature extraction
          when `include_top` is `False`.
          - `None` means that the output of the model
              will be the 4D tensor output of the
              last convolutional block.
          - `avg` means that global average pooling
              will be applied to the output of the
              last convolutional block, and thus
              the output of the model will be a
              2D tensor.
          - `max` means that global max pooling will
              be applied.
        classes: Optional number of classes to classify images
          into, only to be specified if `include_top` is True, and
          if no `weights` argument is specified.
        default_size: Specifies the default image size of the model
        classifier_activation: A `str` or callable. The activation function to use
          on the "top" layer. Ignored unless `include_top=True`. Set
          `classifier_activation=None` to return the logits of the "top" layer.
          When loading pretrained weights, `classifier_activation` can only
          be `None` or `"softmax"`.
  
      Returns:
        A `keras.Model` instance.
      """
      if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
          raise ValueError(
              "The `weights` argument should be either "
              "`None` (random initialization), `imagenet` "
              "(pre-training on ImageNet), "
              "or the path to the weights file to be loaded."
          )
  
      if weights == "imagenet" and include_top and classes != 1000:
          raise ValueError(
              'If using `weights` as `"imagenet"` with `include_top` '
              "as true, `classes` should be 1000"
          )
  
      if (
          isinstance(input_shape, tuple)
          and None in input_shape
          and weights == "imagenet"
      ):
          raise ValueError(
              "When specifying the input shape of a NASNet"
              " and loading `ImageNet` weights, "
              "the input_shape argument must be static "
              "(no None entries). Got: `input_shape=" + str(input_shape) + "`."
          )
  
      if default_size is None:
          default_size = 331
  
      # Determine proper input shape and default size.
      input_shape = imagenet_utils.obtain_input_shape(
          input_shape,
          default_size=default_size,
          min_size=32,
          data_format=backend.image_data_format(),
          require_flatten=True,
          weights=weights,
      )
  
      if backend.image_data_format() != "channels_last":
          logging.warning(
              "The NASNet family of models is only available "
              'for the input data format "channels_last" '
              "(width, height, channels). "
              "However your settings specify the default "
              'data format "channels_first" (channels, width, height).'
              ' You should set `image_data_format="channels_last"` '
              "in your Keras config located at ~/.keras/keras.json. "
              "The model being returned right now will expect inputs "
              'to follow the "channels_last" data format.'
          )
          backend.set_image_data_format("channels_last")
          old_data_format = "channels_first"
      else:
          old_data_format = None
  
      if input_tensor is None:
          img_input = layers.Input(shape=input_shape)
      else:
          if not backend.is_keras_tensor(input_tensor):
              img_input = layers.Input(tensor=input_tensor, shape=input_shape)
          else:
              img_input = input_tensor
  
      if penultimate_filters % (24 * (filter_multiplier**2)) != 0:
          raise ValueError(
              "For NASNet-A models, the `penultimate_filters` must be a multiple "
              "of 24 * (`filter_multiplier` ** 2). Current value: %d"
              % penultimate_filters
          )
  
      channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
      filters = penultimate_filters // 24
  
      x = layers.Conv2D(
          stem_block_filters,
          (3, 3),
          strides=(2, 2),
          padding="valid",
          use_bias=False,
          name="stem_conv1",
          kernel_initializer="he_normal",
      )(img_input)
  
      x = layers.BatchNormalization(
          axis=channel_dim, momentum=0.9997, epsilon=1e-3, name="stem_bn1"
      )(x)
  
      p = None
      x, p = _reduction_a_cell(
          x, p, filters // (filter_multiplier**2), block_id="stem_1"
      )
      x, p = _reduction_a_cell(
          x, p, filters // filter_multiplier, block_id="stem_2"
      )
  
      for i in range(num_blocks):
          x, p = _normal_a_cell(x, p, filters, block_id="%d" % (i))
  
      x, p0 = _reduction_a_cell(
          x, p, filters * filter_multiplier, block_id="reduce_%d" % (num_blocks)
      )
  
      p = p0 if not skip_reduction else p
  
      for i in range(num_blocks):
          x, p = _normal_a_cell(
              x,
              p,
              filters * filter_multiplier,
              block_id="%d" % (num_blocks + i + 1),
          )
  
      x, p0 = _reduction_a_cell(
          x,
          p,
          filters * filter_multiplier**2,
          block_id="reduce_%d" % (2 * num_blocks),
      )
  
      p = p0 if not skip_reduction else p
  
      for i in range(num_blocks):
          x, p = _normal_a_cell(
              x,
              p,
              filters * filter_multiplier**2,
              block_id="%d" % (2 * num_blocks + i + 1),
          )
  
      x = layers.Activation("relu")(x)
  
      if include_top:
          x = layers.GlobalAveragePooling2D()(x)
          imagenet_utils.validate_activation(classifier_activation, weights)
          x = layers.Dense(
              classes, activation=classifier_activation, name="predictions"
          )(x)
      else:
          if pooling == "avg":
              x = layers.GlobalAveragePooling2D()(x)
          elif pooling == "max":
              x = layers.GlobalMaxPooling2D()(x)
  
      # Ensure that the model takes into account
      # any potential predecessors of `input_tensor`.
      if input_tensor is not None:
          inputs = layer_utils.get_source_inputs(input_tensor)
      else:
          inputs = img_input
  
      model = training.Model(inputs, x, name="NASNet")
  
      # Load weights.
      if weights == "imagenet":
          if default_size == 224:  # mobile version
              if include_top:
                  weights_path = data_utils.get_file(
                      "nasnet_mobile.h5",
                      NASNET_MOBILE_WEIGHT_PATH,
                      cache_subdir="models",
                      file_hash="020fb642bf7360b370c678b08e0adf61",
                  )
              else:
                  weights_path = data_utils.get_file(
                      "nasnet_mobile_no_top.h5",
                      NASNET_MOBILE_WEIGHT_PATH_NO_TOP,
                      cache_subdir="models",
                      file_hash="1ed92395b5b598bdda52abe5c0dbfd63",
                  )
              model.load_weights(weights_path)
          elif default_size == 331:  # large version
              if include_top:
                  weights_path = data_utils.get_file(
                      "nasnet_large.h5",
                      NASNET_LARGE_WEIGHT_PATH,
                      cache_subdir="models",
                      file_hash="11577c9a518f0070763c2b964a382f17",
                  )
              else:
                  weights_path = data_utils.get_file(
                      "nasnet_large_no_top.h5",
                      NASNET_LARGE_WEIGHT_PATH_NO_TOP,
                      cache_subdir="models",
                      file_hash="d81d89dc07e6e56530c4e77faddd61b5",
                  )
              model.load_weights(weights_path)
          else:
              raise ValueError(
                  "ImageNet weights can only be loaded with NASNetLarge"
                  " or NASNetMobile"
              )
      elif weights is not None:
          model.load_weights(weights)
  
      if old_data_format:
          backend.set_image_data_format(old_data_format)
  
      return model
  
  
  @keras_export(
      "keras.applications.nasnet.NASNetMobile", "keras.applications.NASNetMobile"
  )
  def NASNetMobile(
      input_shape=None,
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates a Mobile NASNet model in ImageNet mode.
  
      Reference:
      - [Learning Transferable Architectures for Scalable Image Recognition](
          https://arxiv.org/abs/1707.07012) (CVPR 2018)
  
      Optionally loads weights pre-trained on ImageNet.
      Note that the data format convention used by the model is
      the one specified in your Keras config at `~/.keras/keras.json`.
  
      Note: each Keras Application expects a specific kind of input preprocessing.
      For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
      inputs before passing them to the model.
  
      Args:
          input_shape: Optional shape tuple, only to be specified
              if `include_top` is False (otherwise the input shape
              has to be `(224, 224, 3)` for NASNetMobile
              It should have exactly 3 inputs channels,
              and width and height should be no smaller than 32.
              E.g. `(224, 224, 3)` would be one valid value.
          include_top: Whether to include the fully-connected
              layer at the top of the network.
          weights: `None` (random initialization) or
              `imagenet` (ImageNet weights). For loading `imagenet` weights,
              `input_shape` should be (224, 224, 3)
          input_tensor: Optional Keras tensor (i.e. output of
              `layers.Input()`)
              to use as image input for the model.
          pooling: Optional pooling mode for feature extraction
              when `include_top` is `False`.
              - `None` means that the output of the model
                  will be the 4D tensor output of the
                  last convolutional layer.
              - `avg` means that global average pooling
                  will be applied to the output of the
                  last convolutional layer, and thus
                  the output of the model will be a
                  2D tensor.
              - `max` means that global max pooling will
                  be applied.
          classes: Optional number of classes to classify images
              into, only to be specified if `include_top` is True, and
              if no `weights` argument is specified.
          classifier_activation: A `str` or callable. The activation function to
              use on the "top" layer. Ignored unless `include_top=True`. Set
              `classifier_activation=None` to return the logits of the "top"
              layer.  When loading pretrained weights, `classifier_activation` can
              only be `None` or `"softmax"`.
  
      Returns:
          A Keras model instance.
  
      Raises:
          ValueError: In case of invalid argument for `weights`,
              or invalid input shape.
          RuntimeError: If attempting to run this model with a
              backend that does not support separable convolutions.
      """
      return NASNet(
          input_shape,
          penultimate_filters=1056,
          num_blocks=4,
          stem_block_filters=32,
          skip_reduction=False,
          filter_multiplier=2,
          include_top=include_top,
          weights=weights,
          input_tensor=input_tensor,
          pooling=pooling,
          classes=classes,
          default_size=224,
          classifier_activation=classifier_activation,
      )
  
  
  @keras_export(
      "keras.applications.nasnet.NASNetLarge", "keras.applications.NASNetLarge"
  )
  def NASNetLarge(
      input_shape=None,
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates a NASNet model in ImageNet mode.
  
      Reference:
      - [Learning Transferable Architectures for Scalable Image Recognition](
          https://arxiv.org/abs/1707.07012) (CVPR 2018)
  
      Optionally loads weights pre-trained on ImageNet.
      Note that the data format convention used by the model is
      the one specified in your Keras config at `~/.keras/keras.json`.
  
      Note: each Keras Application expects a specific kind of input preprocessing.
      For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
      inputs before passing them to the model.
  
      Args:
          input_shape: Optional shape tuple, only to be specified
              if `include_top` is False (otherwise the input shape
              has to be `(331, 331, 3)` for NASNetLarge.
              It should have exactly 3 inputs channels,
              and width and height should be no smaller than 32.
              E.g. `(224, 224, 3)` would be one valid value.
          include_top: Whether to include the fully-connected
              layer at the top of the network.
          weights: `None` (random initialization) or
              `imagenet` (ImageNet weights).  For loading `imagenet` weights,
              `input_shape` should be (331, 331, 3)
          input_tensor: Optional Keras tensor (i.e. output of
              `layers.Input()`)
              to use as image input for the model.
          pooling: Optional pooling mode for feature extraction
              when `include_top` is `False`.
              - `None` means that the output of the model
                  will be the 4D tensor output of the
                  last convolutional layer.
              - `avg` means that global average pooling
                  will be applied to the output of the
                  last convolutional layer, and thus
                  the output of the model will be a
                  2D tensor.
              - `max` means that global max pooling will
                  be applied.
          classes: Optional number of classes to classify images
              into, only to be specified if `include_top` is True, and
              if no `weights` argument is specified.
          classifier_activation: A `str` or callable. The activation function to
              use on the "top" layer. Ignored unless `include_top=True`. Set
              `classifier_activation=None` to return the logits of the "top"
              layer.  When loading pretrained weights, `classifier_activation` can
              only be `None` or `"softmax"`.
  
      Returns:
          A Keras model instance.
  
      Raises:
          ValueError: in case of invalid argument for `weights`,
              or invalid input shape.
          RuntimeError: If attempting to run this model with a
              backend that does not support separable convolutions.
      """
      return NASNet(
          input_shape,
          penultimate_filters=4032,
          num_blocks=6,
          stem_block_filters=96,
          skip_reduction=True,
          filter_multiplier=2,
          include_top=include_top,
          weights=weights,
          input_tensor=input_tensor,
          pooling=pooling,
          classes=classes,
          default_size=331,
          classifier_activation=classifier_activation,
      )
  
  
  def _separable_conv_block(
      ip, filters, kernel_size=(3, 3), strides=(1, 1), block_id=None
  ):
      """Adds 2 blocks of [relu-separable conv-batchnorm].
  
      Args:
          ip: Input tensor
          filters: Number of output filters per layer
          kernel_size: Kernel size of separable convolutions
          strides: Strided convolution for downsampling
          block_id: String block_id
  
      Returns:
          A Keras tensor
      """
      channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
  
      with backend.name_scope("separable_conv_block_%s" % block_id):
          x = layers.Activation("relu")(ip)
          if strides == (2, 2):
              x = layers.ZeroPadding2D(
                  padding=imagenet_utils.correct_pad(x, kernel_size),
                  name="separable_conv_1_pad_%s" % block_id,
              )(x)
              conv_pad = "valid"
          else:
              conv_pad = "same"
          x = layers.SeparableConv2D(
              filters,
              kernel_size,
              strides=strides,
              name="separable_conv_1_%s" % block_id,
              padding=conv_pad,
              use_bias=False,
              kernel_initializer="he_normal",
          )(x)
          x = layers.BatchNormalization(
              axis=channel_dim,
              momentum=0.9997,
              epsilon=1e-3,
              name="separable_conv_1_bn_%s" % (block_id),
          )(x)
          x = layers.Activation("relu")(x)
          x = layers.SeparableConv2D(
              filters,
              kernel_size,
              name="separable_conv_2_%s" % block_id,
              padding="same",
              use_bias=False,
              kernel_initializer="he_normal",
          )(x)
          x = layers.BatchNormalization(
              axis=channel_dim,
              momentum=0.9997,
              epsilon=1e-3,
              name="separable_conv_2_bn_%s" % (block_id),
          )(x)
      return x
  
  
  def _adjust_block(p, ip, filters, block_id=None):
      """Adjusts the input `previous path` to match the shape of the `input`.
  
      Used in situations where the output number of filters needs to be changed.
  
      Args:
          p: Input tensor which needs to be modified
          ip: Input tensor whose shape needs to be matched
          filters: Number of output filters to be matched
          block_id: String block_id
  
      Returns:
          Adjusted Keras tensor
      """
      channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
      img_dim = 2 if backend.image_data_format() == "channels_first" else -2
  
      ip_shape = backend.int_shape(ip)
  
      if p is not None:
          p_shape = backend.int_shape(p)
  
      with backend.name_scope("adjust_block"):
          if p is None:
              p = ip
  
          elif p_shape[img_dim] != ip_shape[img_dim]:
              with backend.name_scope("adjust_reduction_block_%s" % block_id):
                  p = layers.Activation(
                      "relu", name="adjust_relu_1_%s" % block_id
                  )(p)
                  p1 = layers.AveragePooling2D(
                      (1, 1),
                      strides=(2, 2),
                      padding="valid",
                      name="adjust_avg_pool_1_%s" % block_id,
                  )(p)
                  p1 = layers.Conv2D(
                      filters // 2,
                      (1, 1),
                      padding="same",
                      use_bias=False,
                      name="adjust_conv_1_%s" % block_id,
                      kernel_initializer="he_normal",
                  )(p1)
  
                  p2 = layers.ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
                  p2 = layers.Cropping2D(cropping=((1, 0), (1, 0)))(p2)
                  p2 = layers.AveragePooling2D(
                      (1, 1),
                      strides=(2, 2),
                      padding="valid",
                      name="adjust_avg_pool_2_%s" % block_id,
                  )(p2)
                  p2 = layers.Conv2D(
                      filters // 2,
                      (1, 1),
                      padding="same",
                      use_bias=False,
                      name="adjust_conv_2_%s" % block_id,
                      kernel_initializer="he_normal",
                  )(p2)
  
                  p = layers.concatenate([p1, p2], axis=channel_dim)
                  p = layers.BatchNormalization(
                      axis=channel_dim,
                      momentum=0.9997,
                      epsilon=1e-3,
                      name="adjust_bn_%s" % block_id,
                  )(p)
  
          elif p_shape[channel_dim] != filters:
              with backend.name_scope("adjust_projection_block_%s" % block_id):
                  p = layers.Activation("relu")(p)
                  p = layers.Conv2D(
                      filters,
                      (1, 1),
                      strides=(1, 1),
                      padding="same",
                      name="adjust_conv_projection_%s" % block_id,
                      use_bias=False,
                      kernel_initializer="he_normal",
                  )(p)
                  p = layers.BatchNormalization(
                      axis=channel_dim,
                      momentum=0.9997,
                      epsilon=1e-3,
                      name="adjust_bn_%s" % block_id,
                  )(p)
      return p
  
  
  def _normal_a_cell(ip, p, filters, block_id=None):
      """Adds a Normal cell for NASNet-A (Fig. 4 in the paper).
  
      Args:
          ip: Input tensor `x`
          p: Input tensor `p`
          filters: Number of output filters
          block_id: String block_id
  
      Returns:
          A Keras tensor
      """
      channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
  
      with backend.name_scope("normal_A_block_%s" % block_id):
          p = _adjust_block(p, ip, filters, block_id)
  
          h = layers.Activation("relu")(ip)
          h = layers.Conv2D(
              filters,
              (1, 1),
              strides=(1, 1),
              padding="same",
              name="normal_conv_1_%s" % block_id,
              use_bias=False,
              kernel_initializer="he_normal",
          )(h)
          h = layers.BatchNormalization(
              axis=channel_dim,
              momentum=0.9997,
              epsilon=1e-3,
              name="normal_bn_1_%s" % block_id,
          )(h)
  
          with backend.name_scope("block_1"):
              x1_1 = _separable_conv_block(
                  h,
                  filters,
                  kernel_size=(5, 5),
                  block_id="normal_left1_%s" % block_id,
              )
              x1_2 = _separable_conv_block(
                  p, filters, block_id="normal_right1_%s" % block_id
              )
              x1 = layers.add([x1_1, x1_2], name="normal_add_1_%s" % block_id)
  
          with backend.name_scope("block_2"):
              x2_1 = _separable_conv_block(
                  p, filters, (5, 5), block_id="normal_left2_%s" % block_id
              )
              x2_2 = _separable_conv_block(
                  p, filters, (3, 3), block_id="normal_right2_%s" % block_id
              )
              x2 = layers.add([x2_1, x2_2], name="normal_add_2_%s" % block_id)
  
          with backend.name_scope("block_3"):
              x3 = layers.AveragePooling2D(
                  (3, 3),
                  strides=(1, 1),
                  padding="same",
                  name="normal_left3_%s" % (block_id),
              )(h)
              x3 = layers.add([x3, p], name="normal_add_3_%s" % block_id)
  
          with backend.name_scope("block_4"):
              x4_1 = layers.AveragePooling2D(
                  (3, 3),
                  strides=(1, 1),
                  padding="same",
                  name="normal_left4_%s" % (block_id),
              )(p)
              x4_2 = layers.AveragePooling2D(
                  (3, 3),
                  strides=(1, 1),
                  padding="same",
                  name="normal_right4_%s" % (block_id),
              )(p)
              x4 = layers.add([x4_1, x4_2], name="normal_add_4_%s" % block_id)
  
          with backend.name_scope("block_5"):
              x5 = _separable_conv_block(
                  h, filters, block_id="normal_left5_%s" % block_id
              )
              x5 = layers.add([x5, h], name="normal_add_5_%s" % block_id)
  
          x = layers.concatenate(
              [p, x1, x2, x3, x4, x5],
              axis=channel_dim,
              name="normal_concat_%s" % block_id,
          )
      return x, ip
  
  
  def _reduction_a_cell(ip, p, filters, block_id=None):
      """Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).
  
      Args:
        ip: Input tensor `x`
        p: Input tensor `p`
        filters: Number of output filters
        block_id: String block_id
  
      Returns:
        A Keras tensor
      """
      channel_dim = 1 if backend.image_data_format() == "channels_first" else -1
  
      with backend.name_scope("reduction_A_block_%s" % block_id):
          p = _adjust_block(p, ip, filters, block_id)
  
          h = layers.Activation("relu")(ip)
          h = layers.Conv2D(
              filters,
              (1, 1),
              strides=(1, 1),
              padding="same",
              name="reduction_conv_1_%s" % block_id,
              use_bias=False,
              kernel_initializer="he_normal",
          )(h)
          h = layers.BatchNormalization(
              axis=channel_dim,
              momentum=0.9997,
              epsilon=1e-3,
              name="reduction_bn_1_%s" % block_id,
          )(h)
          h3 = layers.ZeroPadding2D(
              padding=imagenet_utils.correct_pad(h, 3),
              name="reduction_pad_1_%s" % block_id,
          )(h)
  
          with backend.name_scope("block_1"):
              x1_1 = _separable_conv_block(
                  h,
                  filters,
                  (5, 5),
                  strides=(2, 2),
                  block_id="reduction_left1_%s" % block_id,
              )
              x1_2 = _separable_conv_block(
                  p,
                  filters,
                  (7, 7),
                  strides=(2, 2),
                  block_id="reduction_right1_%s" % block_id,
              )
              x1 = layers.add([x1_1, x1_2], name="reduction_add_1_%s" % block_id)
  
          with backend.name_scope("block_2"):
              x2_1 = layers.MaxPooling2D(
                  (3, 3),
                  strides=(2, 2),
                  padding="valid",
                  name="reduction_left2_%s" % block_id,
              )(h3)
              x2_2 = _separable_conv_block(
                  p,
                  filters,
                  (7, 7),
                  strides=(2, 2),
                  block_id="reduction_right2_%s" % block_id,
              )
              x2 = layers.add([x2_1, x2_2], name="reduction_add_2_%s" % block_id)
  
          with backend.name_scope("block_3"):
              x3_1 = layers.AveragePooling2D(
                  (3, 3),
                  strides=(2, 2),
                  padding="valid",
                  name="reduction_left3_%s" % block_id,
              )(h3)
              x3_2 = _separable_conv_block(
                  p,
                  filters,
                  (5, 5),
                  strides=(2, 2),
                  block_id="reduction_right3_%s" % block_id,
              )
              x3 = layers.add([x3_1, x3_2], name="reduction_add3_%s" % block_id)
  
          with backend.name_scope("block_4"):
              x4 = layers.AveragePooling2D(
                  (3, 3),
                  strides=(1, 1),
                  padding="same",
                  name="reduction_left4_%s" % block_id,
              )(x1)
              x4 = layers.add([x2, x4])
  
          with backend.name_scope("block_5"):
              x5_1 = _separable_conv_block(
                  x1, filters, (3, 3), block_id="reduction_left4_%s" % block_id
              )
              x5_2 = layers.MaxPooling2D(
                  (3, 3),
                  strides=(2, 2),
                  padding="valid",
                  name="reduction_right5_%s" % block_id,
              )(h3)
              x5 = layers.add([x5_1, x5_2], name="reduction_add4_%s" % block_id)
  
          x = layers.concatenate(
              [x2, x3, x4, x5],
              axis=channel_dim,
              name="reduction_concat_%s" % block_id,
          )
          return x, ip
  
  
  @keras_export("keras.applications.nasnet.preprocess_input")
  def preprocess_input(x, data_format=None):
      return imagenet_utils.preprocess_input(
          x, data_format=data_format, mode="tf"
      )
  
  
  @keras_export("keras.applications.nasnet.decode_predictions")
  def decode_predictions(preds, top=5):
      return imagenet_utils.decode_predictions(preds, top=top)
  
  
  preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
      mode="",
      ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
      error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
  )
  decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
  