code: 
  
  
  
  
  
  from typing import Optional, Tuple, Union
  
  import tensorflow as tf
  
  from ...activations_tf import ACTaFN
  from ...modeling_tf_outputs import (
      TFBaseModelOutputWithNoAttention,
      TFBaseModelOutputWithPoolingAndNoAttention,
      TFImageClassifierOutputWithNoAttention,
  )
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_resnet import ResNetConfig
  
  
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  # General docstring
  lowerCAmelCase_   =   "ResNetConfig"
  
  # Base docstring
  lowerCAmelCase_   =   "microsoft/resnet-50"
  lowerCAmelCase_   =   [1, 2_0_4_8, 7, 7]
  
  # Image classification docstring
  lowerCAmelCase_   =   "microsoft/resnet-50"
  lowerCAmelCase_   =   "tiger cat"
  
  lowerCAmelCase_   =   [
      "microsoft/resnet-50",
      # See all resnet models at https://huggingface.co/models?filter=resnet
  ]
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_ = 3     ,  lowercase_ = 1     ,  lowercase_ = "relu"     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               _snake_case      :							List[Any]     				=						kernel_size // 2
               _snake_case      :							Tuple     				=						tf.keras.layers.ConvaD(
                   lowercase_     ,  kernel_size=lowercase_     ,  strides=lowercase_     ,  padding='''valid'''     ,  use_bias=lowercase_     ,  name='''convolution'''					)
               # Use same default momentum and epsilon as PyTorch equivalent
               _snake_case      :							Optional[int]     				=						tf.keras.layers.BatchNormalization(epsilon=1E-5     ,  momentum=0.9     ,  name='''normalization'''					)
               _snake_case      :							List[Any]     				=						ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						(self.pad_value, self.pad_value)
               _snake_case      :							Tuple     				=						tf.pad(lowercase_     ,  [(0, 0), height_pad, width_pad, (0, 0)]					)
               _snake_case      :							Union[str, Any]     				=						self.conv(lowercase_					)
               return hidden_state
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.convolution(lowercase_					)
               _snake_case      :							Dict     				=						self.normalization(lowercase_     ,  training=lowercase_					)
               _snake_case      :							Any     				=						self.activation(lowercase_					)
               return hidden_state
  
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               _snake_case      :							List[Any]     				=						TFResNetConvLayer(
                   config.embedding_size     ,  kernel_size=7     ,  stride=2     ,  activation=config.hidden_act     ,  name='''embedder'''     ,  )
               _snake_case      :							Any     				=						tf.keras.layers.MaxPoolaD(pool_size=3     ,  strides=2     ,  padding='''valid'''     ,  name='''pooler'''					)
               _snake_case      :							Optional[Any]     				=						config.num_channels
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						shape_list(lowercase_					)
               if tf.executing_eagerly() and num_channels != self.num_channels:
                      raise ValueError(
                          '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''					)
               _snake_case      :							Optional[Any]     				=						pixel_values
               _snake_case      :							Union[str, Any]     				=						self.embedder(lowercase_					)
               _snake_case      :							str     				=						tf.pad(lowercase_     ,  [[0, 0], [1, 1], [1, 1], [0, 0]]					)
               _snake_case      :							Dict     				=						self.pooler(lowercase_					)
               return hidden_state
  
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_ = 2     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               _snake_case      :							Optional[int]     				=						tf.keras.layers.ConvaD(
                   lowercase_     ,  kernel_size=1     ,  strides=lowercase_     ,  use_bias=lowercase_     ,  name='''convolution'''					)
               # Use same default momentum and epsilon as PyTorch equivalent
               _snake_case      :							Optional[Any]     				=						tf.keras.layers.BatchNormalization(epsilon=1E-5     ,  momentum=0.9     ,  name='''normalization'''					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						x
               _snake_case      :							Any     				=						self.convolution(lowercase_					)
               _snake_case      :							Optional[int]     				=						self.normalization(lowercase_     ,  training=lowercase_					)
               return hidden_state
  
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = 1     ,  lowercase_ = "relu"     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               _snake_case      :							Union[str, Any]     				=						in_channels != out_channels or stride != 1
               _snake_case      :							List[str]     				=						TFResNetConvLayer(lowercase_     ,  stride=lowercase_     ,  name='''layer.0'''					)
               _snake_case      :							str     				=						TFResNetConvLayer(lowercase_     ,  activation=lowercase_     ,  name='''layer.1'''					)
               _snake_case      :							Tuple     				=						(
                   TFResNetShortCut(lowercase_     ,  stride=lowercase_     ,  name='''shortcut'''					)
                   if should_apply_shortcut
                   else tf.keras.layers.Activation('''linear'''     ,  name='''shortcut'''					)
               )
               _snake_case      :							Tuple     				=						ACTaFN[activation]
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						hidden_state
               _snake_case      :							Tuple     				=						self.conva(lowercase_     ,  training=lowercase_					)
               _snake_case      :							Optional[Any]     				=						self.conva(lowercase_     ,  training=lowercase_					)
               _snake_case      :							Dict     				=						self.shortcut(lowercase_     ,  training=lowercase_					)
               hidden_state += residual
               _snake_case      :							Dict     				=						self.activation(lowercase_					)
               return hidden_state
  
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = 1     ,  lowercase_ = "relu"     ,  lowercase_ = 4     ,  **lowercase_     ,  )      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               _snake_case      :							List[str]     				=						in_channels != out_channels or stride != 1
               _snake_case      :							int     				=						out_channels // reduction
               _snake_case      :							List[Any]     				=						TFResNetConvLayer(lowercase_     ,  kernel_size=1     ,  name='''layer.0'''					)
               _snake_case      :							str     				=						TFResNetConvLayer(lowercase_     ,  stride=lowercase_     ,  name='''layer.1'''					)
               _snake_case      :							Optional[int]     				=						TFResNetConvLayer(lowercase_     ,  kernel_size=1     ,  activation=lowercase_     ,  name='''layer.2'''					)
               _snake_case      :							Optional[int]     				=						(
                   TFResNetShortCut(lowercase_     ,  stride=lowercase_     ,  name='''shortcut'''					)
                   if should_apply_shortcut
                   else tf.keras.layers.Activation('''linear'''     ,  name='''shortcut'''					)
               )
               _snake_case      :							Union[str, Any]     				=						ACTaFN[activation]
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						hidden_state
               _snake_case      :							Dict     				=						self.conva(lowercase_     ,  training=lowercase_					)
               _snake_case      :							List[str]     				=						self.conva(lowercase_     ,  training=lowercase_					)
               _snake_case      :							str     				=						self.conva(lowercase_     ,  training=lowercase_					)
               _snake_case      :							Dict     				=						self.shortcut(lowercase_     ,  training=lowercase_					)
               hidden_state += residual
               _snake_case      :							str     				=						self.activation(lowercase_					)
               return hidden_state
  
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_ = 2     ,  lowercase_ = 2     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
  
               _snake_case      :							List[str]     				=						TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer
  
               _snake_case      :							int     				=						[layer(lowercase_     ,  lowercase_     ,  stride=lowercase_     ,  activation=config.hidden_act     ,  name='''layers.0'''					)]
               layers += [
                   layer(lowercase_     ,  lowercase_     ,  activation=config.hidden_act     ,  name=F'''layers.{i + 1}'''					)
                   for i in range(depth - 1					)
               ]
               _snake_case      :							Tuple     				=						layers
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               for layer in self.stage_layers:
                      _snake_case      :							List[Any]     				=						layer(lowercase_     ,  training=lowercase_					)
               return hidden_state
  
  
  class      A    (tf.keras.layers.Layer				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
               _snake_case      :							Any     				=						[
                   TFResNetStage(
                       lowercase_     ,  config.embedding_size     ,  config.hidden_sizes[0]     ,  stride=2 if config.downsample_in_first_stage else 1     ,  depth=config.depths[0]     ,  name='''stages.0'''     ,  )
               ]
               for i, (in_channels, out_channels, depth) in enumerate(
                   zip(config.hidden_sizes     ,  config.hidden_sizes[1:]     ,  config.depths[1:]					)					):
                      self.stages.append(TFResNetStage(lowercase_     ,  lowercase_     ,  lowercase_     ,  depth=lowercase_     ,  name=F'''stages.{i + 1}'''					)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = False     ,  lowercase_ = True     ,  lowercase_ = False     ,  )      ->   TFBaseModelOutputWithNoAttention:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						() if output_hidden_states else None
  
               for stage_module in self.stages:
                      if output_hidden_states:
                             _snake_case      :							Optional[int]     				=						hidden_states + (hidden_state,)
  
                      _snake_case      :							Dict     				=						stage_module(lowercase_     ,  training=lowercase_					)
  
               if output_hidden_states:
                      _snake_case      :							Tuple     				=						hidden_states + (hidden_state,)
  
               if not return_dict:
                      return tuple(v for v in [hidden_state, hidden_states] if v is not None					)
  
               return TFBaseModelOutputWithNoAttention(last_hidden_state=lowercase_     ,  hidden_states=lowercase_					)
  
  
  class      A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							ResNetConfig
        _SCREAMING_SNAKE_CASE					=							"""resnet"""
        _SCREAMING_SNAKE_CASE					=							"""pixel_values"""
  
  
  
        @property
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)     ,  dtype=tf.floataa					)}
  
  
  lowerCAmelCase_   =   R"\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"
  
  
  lowerCAmelCase_   =   R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
  
  @keras_serializable
  class      A    (tf.keras.layers.Layer				):
        _SCREAMING_SNAKE_CASE					=							ResNetConfig
  
  
  
        def __init__(					self     ,  lowercase_     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(**lowercase_					)
               _snake_case      :							Optional[int]     				=						config
               _snake_case      :							int     				=						TFResNetEmbeddings(lowercase_     ,  name='''embedder'''					)
               _snake_case      :							Union[str, Any]     				=						TFResNetEncoder(lowercase_     ,  name='''encoder'''					)
               _snake_case      :							str     				=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowercase_					)
  
  
  
        @unpack_inputs
        def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = False     ,  )      ->   Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						(
                   output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
               )
               _snake_case      :							str     				=						return_dict if return_dict is not None else self.config.use_return_dict
  
               # TF 2.0 image layers can't use NCHW format when running on CPU.
               # We transpose to NHWC format and then transpose back after the full forward pass.
               # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
               _snake_case      :							List[Any]     				=						tf.transpose(lowercase_     ,  perm=[0, 2, 3, 1]					)
               _snake_case      :							Optional[Any]     				=						self.embedder(lowercase_     ,  training=lowercase_					)
  
               _snake_case      :							int     				=						self.encoder(
                   lowercase_     ,  output_hidden_states=lowercase_     ,  return_dict=lowercase_     ,  training=lowercase_					)
  
               _snake_case      :							Union[str, Any]     				=						encoder_outputs[0]
  
               _snake_case      :							str     				=						self.pooler(lowercase_					)
  
               # Transpose all the outputs to the NCHW format
               # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
               _snake_case      :							Optional[Any]     				=						tf.transpose(lowercase_     ,  (0, 3, 1, 2)					)
               _snake_case      :							Tuple     				=						tf.transpose(lowercase_     ,  (0, 3, 1, 2)					)
               _snake_case      :							Dict     				=						()
               for hidden_state in encoder_outputs[1:]:
                      _snake_case      :							Any     				=						hidden_states + tuple(tf.transpose(lowercase_     ,  (0, 3, 1, 2)					) for h in hidden_state					)
  
               if not return_dict:
                      return (last_hidden_state, pooled_output) + hidden_states
  
               _snake_case      :							Tuple     				=						hidden_states if output_hidden_states else None
  
               return TFBaseModelOutputWithPoolingAndNoAttention(
                   last_hidden_state=lowercase_     ,  pooler_output=lowercase_     ,  hidden_states=lowercase_     ,  )
  
  
  @add_start_docstrings(
      """The bare ResNet model outputting raw features without any specific head on top."""       ,__UpperCAmelCase       ,)
  class      A    (__UpperCAmelCase				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(lowercase_     ,  **lowercase_					)
               _snake_case      :							str     				=						TFResNetMainLayer(config=lowercase_     ,  name='''resnet'''					)
  
  
  
        @add_start_docstrings_to_model_forward(lowercase_					)
        @add_code_sample_docstrings(
            checkpoint=_CHECKPOINT_FOR_DOC     ,  output_type=lowercase_     ,  config_class=_CONFIG_FOR_DOC     ,  modality='''vision'''     ,  expected_output=_EXPECTED_OUTPUT_SHAPE     ,  )
        @unpack_inputs
        def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = False     ,  )      ->   Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						(
                   output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
               )
               _snake_case      :							List[Any]     				=						return_dict if return_dict is not None else self.config.use_return_dict
  
               _snake_case      :							Union[str, Any]     				=						self.resnet(
                   pixel_values=lowercase_     ,  output_hidden_states=lowercase_     ,  return_dict=lowercase_     ,  training=lowercase_     ,  )
               return resnet_outputs
  
  
  @add_start_docstrings(
      """
      ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
      ImageNet.
      """       ,__UpperCAmelCase       ,)
  class      A    (__UpperCAmelCase       ,__UpperCAmelCase				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  **lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               super().__init__(lowercase_     ,  **lowercase_					)
               _snake_case      :							Optional[int]     				=						config.num_labels
               _snake_case      :							Optional[Any]     				=						TFResNetMainLayer(lowercase_     ,  name='''resnet'''					)
               # classification head
               _snake_case      :							Dict     				=						(
                   tf.keras.layers.Dense(config.num_labels     ,  name='''classifier.1'''					)
                   if config.num_labels > 0
                   else tf.keras.layers.Activation('''linear'''     ,  name='''classifier.1'''					)
               )
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   tf.Tensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						tf.keras.layers.Flatten()(lowercase_					)
               _snake_case      :							int     				=						self.classifier_layer(lowercase_					)
               return logits
  
  
  
        @add_start_docstrings_to_model_forward(lowercase_					)
        @add_code_sample_docstrings(
            checkpoint=_IMAGE_CLASS_CHECKPOINT     ,  output_type=lowercase_     ,  config_class=_CONFIG_FOR_DOC     ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,  )
        @unpack_inputs
        def    __a (					self     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = False     ,  )      ->   Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						return_dict if return_dict is not None else self.config.use_return_dict
  
               _snake_case      :							Tuple     				=						self.resnet(
                   lowercase_     ,  output_hidden_states=lowercase_     ,  return_dict=lowercase_     ,  training=lowercase_					)
  
               _snake_case      :							Tuple     				=						outputs.pooler_output if return_dict else outputs[1]
  
               _snake_case      :							List[str]     				=						self.classifier(lowercase_					)
  
               _snake_case      :							Tuple     				=						None if labels is None else self.hf_compute_loss(lowercase_     ,  lowercase_					)
  
               if not return_dict:
                      _snake_case      :							Optional[int]     				=						(logits,) + outputs[2:]
                      return (loss,) + output if loss is not None else output
  
               return TFImageClassifierOutputWithNoAttention(loss=lowercase_     ,  logits=lowercase_     ,  hidden_states=outputs.hidden_states					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_  )			->						Optional[Any]:
         _snake_case      :							Union[str, Any]     				=						np.exp(lowercase_  )
  
         # Add up the all the exponentials
         _snake_case      :							List[str]     				=						np.sum(lowercase_  )
  
         # Divide every exponent by the sum of all exponents
         _snake_case      :							Optional[int]     				=						exponent_vector / sum_of_exponents
  
         return softmax_vector
  
  
  if __name__ == "__main__":
     print(softmax((0,)))
  
  
  
  
  
  
  