code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  A_			  =    "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
  
  A_			  =    "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"
  
  
  A_			  =    "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION       )
  class 						__lowerCAmelCase   (			datasets.Metric       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		Union[str, Any]					):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(self._get_feature_types()					)				,			reference_urls=[
               "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
           ]				,			)
    def 					UpperCamelCase__   (      self:		Dict					):
       if self.config_name == "multilist":
          return {
              "predictions": datasets.Sequence(datasets.Value("float"					)					),
              "references": datasets.Sequence(datasets.Value("float"					)					),
          }
       else:
          return {
              "predictions": datasets.Value("float"					),
              "references": datasets.Value("float"					),
          }
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		List[Any]				,			UpperCamelCase_:		Tuple				,			UpperCamelCase_:		Union[str, Any]				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		int="uniform_average"					):
       UpperCamelCase_     =mean_absolute_error(UpperCamelCase_				,			UpperCamelCase_				,			sample_weight=UpperCamelCase_				,			multioutput=UpperCamelCase_					)
  
       return {"mae": mae_score}
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  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
  
  
  A_			  =    logging.get_logger(__name__)
  
  # General docstring
  A_			  =    "ResNetConfig"
  
  # Base docstring
  A_			  =    "microsoft/resnet-50"
  A_			  =    [1, 2048, 7, 7]
  
  # Image classification docstring
  A_			  =    "microsoft/resnet-50"
  A_			  =    "tiger cat"
  
  A_			  =    [
      "microsoft/resnet-50",
      # See all resnet models at https://huggingface.co/models?filter=resnet
  ]
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Dict				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int = 3				,			UpperCamelCase_:		int = 1				,			UpperCamelCase_:		str = "relu"				,			**UpperCamelCase_:		List[Any]					):
       super().__init__(**UpperCamelCase_					)
       UpperCamelCase_     =kernel_size // 2
       UpperCamelCase_     =tf.keras.layers.ConvaD(
           UpperCamelCase_				,			kernel_size=UpperCamelCase_				,			strides=UpperCamelCase_				,			padding="valid"				,			use_bias=UpperCamelCase_				,			name="convolution"					)
       # Use same default momentum and epsilon as PyTorch equivalent
       UpperCamelCase_     =tf.keras.layers.BatchNormalization(epsilon=1e-5				,			momentum=0.9				,			name="normalization"					)
       UpperCamelCase_     =ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"					)
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		tf.Tensor					):
       # Pad to match that done in the PyTorch Conv2D model
       UpperCamelCase_     =UpperCamelCase_     =(self.pad_value, self.pad_value)
       UpperCamelCase_     =tf.pad(UpperCamelCase_				,			[(0, 0), height_pad, width_pad, (0, 0)]					)
       UpperCamelCase_     =self.conv(UpperCamelCase_					)
       return hidden_state
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[int]				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False					):
       UpperCamelCase_     =self.convolution(UpperCamelCase_					)
       UpperCamelCase_     =self.normalization(UpperCamelCase_				,			training=UpperCamelCase_					)
       UpperCamelCase_     =self.activation(UpperCamelCase_					)
       return hidden_state
  
  
  
  
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Optional[int]				,			UpperCamelCase_:		ResNetConfig				,			**UpperCamelCase_:		Dict					):
       super().__init__(**UpperCamelCase_					)
       UpperCamelCase_     =TFResNetConvLayer(
           config.embedding_size				,			kernel_size=7				,			stride=2				,			activation=config.hidden_act				,			name="embedder"				,			)
       UpperCamelCase_     =tf.keras.layers.MaxPoolaD(pool_size=3				,			strides=2				,			padding="valid"				,			name="pooler"					)
       UpperCamelCase_     =config.num_channels
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Dict				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False					):
       UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =shape_list(UpperCamelCase_					)
       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."					)
       UpperCamelCase_     =pixel_values
       UpperCamelCase_     =self.embedder(UpperCamelCase_					)
       UpperCamelCase_     =tf.pad(UpperCamelCase_				,			[[0, 0], [1, 1], [1, 1], [0, 0]]					)
       UpperCamelCase_     =self.pooler(UpperCamelCase_					)
       return hidden_state
  
  
  
  
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Optional[int]				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int = 2				,			**UpperCamelCase_:		Tuple					):
       super().__init__(**UpperCamelCase_					)
       UpperCamelCase_     =tf.keras.layers.ConvaD(
           UpperCamelCase_				,			kernel_size=1				,			strides=UpperCamelCase_				,			use_bias=UpperCamelCase_				,			name="convolution"					)
       # Use same default momentum and epsilon as PyTorch equivalent
       UpperCamelCase_     =tf.keras.layers.BatchNormalization(epsilon=1e-5				,			momentum=0.9				,			name="normalization"					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[int]				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False					):
       UpperCamelCase_     =x
       UpperCamelCase_     =self.convolution(UpperCamelCase_					)
       UpperCamelCase_     =self.normalization(UpperCamelCase_				,			training=UpperCamelCase_					)
       return hidden_state
  
  
  
  
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Optional[Any]				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int = 1				,			UpperCamelCase_:		str = "relu"				,			**UpperCamelCase_:		Dict					):
       super().__init__(**UpperCamelCase_					)
       UpperCamelCase_     =in_channels != out_channels or stride != 1
       UpperCamelCase_     =TFResNetConvLayer(UpperCamelCase_				,			stride=UpperCamelCase_				,			name="layer.0"					)
       UpperCamelCase_     =TFResNetConvLayer(UpperCamelCase_				,			activation=UpperCamelCase_				,			name="layer.1"					)
       UpperCamelCase_     =(
           TFResNetShortCut(UpperCamelCase_				,			stride=UpperCamelCase_				,			name="shortcut"					)
           if should_apply_shortcut
           else tf.keras.layers.Activation("linear"				,			name="shortcut"					)
       )
       UpperCamelCase_     =ACTaFN[activation]
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False					):
       UpperCamelCase_     =hidden_state
       UpperCamelCase_     =self.conva(UpperCamelCase_				,			training=UpperCamelCase_					)
       UpperCamelCase_     =self.conva(UpperCamelCase_				,			training=UpperCamelCase_					)
       UpperCamelCase_     =self.shortcut(UpperCamelCase_				,			training=UpperCamelCase_					)
       hidden_state += residual
       UpperCamelCase_     =self.activation(UpperCamelCase_					)
       return hidden_state
  
  
  
  
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Dict				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int = 1				,			UpperCamelCase_:		str = "relu"				,			UpperCamelCase_:		int = 4				,			**UpperCamelCase_:		Union[str, Any]				,			):
       super().__init__(**UpperCamelCase_					)
       UpperCamelCase_     =in_channels != out_channels or stride != 1
       UpperCamelCase_     =out_channels // reduction
       UpperCamelCase_     =TFResNetConvLayer(UpperCamelCase_				,			kernel_size=1				,			name="layer.0"					)
       UpperCamelCase_     =TFResNetConvLayer(UpperCamelCase_				,			stride=UpperCamelCase_				,			name="layer.1"					)
       UpperCamelCase_     =TFResNetConvLayer(UpperCamelCase_				,			kernel_size=1				,			activation=UpperCamelCase_				,			name="layer.2"					)
       UpperCamelCase_     =(
           TFResNetShortCut(UpperCamelCase_				,			stride=UpperCamelCase_				,			name="shortcut"					)
           if should_apply_shortcut
           else tf.keras.layers.Activation("linear"				,			name="shortcut"					)
       )
       UpperCamelCase_     =ACTaFN[activation]
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False					):
       UpperCamelCase_     =hidden_state
       UpperCamelCase_     =self.conva(UpperCamelCase_				,			training=UpperCamelCase_					)
       UpperCamelCase_     =self.conva(UpperCamelCase_				,			training=UpperCamelCase_					)
       UpperCamelCase_     =self.conva(UpperCamelCase_				,			training=UpperCamelCase_					)
       UpperCamelCase_     =self.shortcut(UpperCamelCase_				,			training=UpperCamelCase_					)
       hidden_state += residual
       UpperCamelCase_     =self.activation(UpperCamelCase_					)
       return hidden_state
  
  
  
  
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Optional[int]				,			UpperCamelCase_:		ResNetConfig				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int = 2				,			UpperCamelCase_:		int = 2				,			**UpperCamelCase_:		Optional[Any]					):
       super().__init__(**UpperCamelCase_					)
  
       UpperCamelCase_     =TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer
  
       UpperCamelCase_     =[layer(UpperCamelCase_				,			UpperCamelCase_				,			stride=UpperCamelCase_				,			activation=config.hidden_act				,			name="layers.0"					)]
       layers += [
           layer(UpperCamelCase_				,			UpperCamelCase_				,			activation=config.hidden_act				,			name=f"""layers.{i + 1}"""					)
           for i in range(depth - 1					)
       ]
       UpperCamelCase_     =layers
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False					):
       for layer in self.stage_layers:
          UpperCamelCase_     =layer(UpperCamelCase_				,			training=UpperCamelCase_					)
       return hidden_state
  
  
  
  
  class 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Dict				,			UpperCamelCase_:		ResNetConfig				,			**UpperCamelCase_:		Any					):
       super().__init__(**UpperCamelCase_					)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       UpperCamelCase_     =[
           TFResNetStage(
               UpperCamelCase_				,			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(UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_				,			depth=UpperCamelCase_				,			name=f"""stages.{i + 1}"""					)					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		bool = False				,			UpperCamelCase_:		bool = True				,			UpperCamelCase_:		bool = False				,			):
       UpperCamelCase_     =() if output_hidden_states else None
  
       for stage_module in self.stages:
          if output_hidden_states:
             UpperCamelCase_     =hidden_states + (hidden_state,)
  
          UpperCamelCase_     =stage_module(UpperCamelCase_				,			training=UpperCamelCase_					)
  
       if output_hidden_states:
          UpperCamelCase_     =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=UpperCamelCase_				,			hidden_states=UpperCamelCase_					)
  
  
  
  
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Any     	=			ResNetConfig
    __lowerCamelCase       :    Dict     	=			"resnet"
    __lowerCamelCase       :    str     	=			"pixel_values"
    @property
    def 					UpperCamelCase__   (      self:		List[str]					):
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)				,			dtype=tf.floataa					)}
  
  
  
  
  A_			  =    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"
  
  
  A_			  =    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 						__lowerCAmelCase   (			tf.keras.layers.Layer       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    str     	=			ResNetConfig
    def __init__(      self:		str				,			UpperCamelCase_:		ResNetConfig				,			**UpperCamelCase_:		Any					):
       super().__init__(**UpperCamelCase_					)
       UpperCamelCase_     =config
       UpperCamelCase_     =TFResNetEmbeddings(UpperCamelCase_				,			name="embedder"					)
       UpperCamelCase_     =TFResNetEncoder(UpperCamelCase_				,			name="encoder"					)
       UpperCamelCase_     =tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase_					)
  
  
  
  
  
    @unpack_inputs
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		Optional[bool] = None				,			UpperCamelCase_:		Optional[bool] = None				,			UpperCamelCase_:		bool = False				,			):
       UpperCamelCase_     =(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase_     =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)
       UpperCamelCase_     =tf.transpose(UpperCamelCase_				,			perm=[0, 2, 3, 1]					)
       UpperCamelCase_     =self.embedder(UpperCamelCase_				,			training=UpperCamelCase_					)
  
       UpperCamelCase_     =self.encoder(
           UpperCamelCase_				,			output_hidden_states=UpperCamelCase_				,			return_dict=UpperCamelCase_				,			training=UpperCamelCase_					)
  
       UpperCamelCase_     =encoder_outputs[0]
  
       UpperCamelCase_     =self.pooler(UpperCamelCase_					)
  
       # Transpose all the outputs to the NCHW format
       # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
       UpperCamelCase_     =tf.transpose(UpperCamelCase_				,			(0, 3, 1, 2)					)
       UpperCamelCase_     =tf.transpose(UpperCamelCase_				,			(0, 3, 1, 2)					)
       UpperCamelCase_     =()
       for hidden_state in encoder_outputs[1:]:
          UpperCamelCase_     =hidden_states + tuple(tf.transpose(UpperCamelCase_				,			(0, 3, 1, 2)					) for h in hidden_state					)
  
       if not return_dict:
          return (last_hidden_state, pooled_output) + hidden_states
  
       UpperCamelCase_     =hidden_states if output_hidden_states else None
  
       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=UpperCamelCase_				,			pooler_output=UpperCamelCase_				,			hidden_states=UpperCamelCase_				,			)
  
  
  
  
  @add_start_docstrings(
      "The bare ResNet model outputting raw features without any specific head on top."       ,   UpperCAmelCase       ,   )
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Tuple				,			UpperCamelCase_:		ResNetConfig				,			**UpperCamelCase_:		int					):
       super().__init__(UpperCamelCase_				,			**UpperCamelCase_					)
       UpperCamelCase_     =TFResNetMainLayer(config=UpperCamelCase_				,			name="resnet"					)
  
  
  
  
  
    @add_start_docstrings_to_model_forward(UpperCamelCase_					)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC				,			output_type=UpperCamelCase_				,			config_class=_CONFIG_FOR_DOC				,			modality="vision"				,			expected_output=_EXPECTED_OUTPUT_SHAPE				,			)
    @unpack_inputs
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		tf.Tensor				,			UpperCamelCase_:		Optional[bool] = None				,			UpperCamelCase_:		Optional[bool] = None				,			UpperCamelCase_:		bool = False				,			):
       UpperCamelCase_     =(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase_     =return_dict if return_dict is not None else self.config.use_return_dict
  
       UpperCamelCase_     =self.resnet(
           pixel_values=UpperCamelCase_				,			output_hidden_states=UpperCamelCase_				,			return_dict=UpperCamelCase_				,			training=UpperCamelCase_				,			)
       return resnet_outputs
  
  
  
  
  @add_start_docstrings(
      "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "       ,   UpperCAmelCase       ,   )
  class 						__lowerCAmelCase   (			UpperCAmelCase       ,   UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		List[Any]				,			UpperCamelCase_:		ResNetConfig				,			**UpperCamelCase_:		List[str]					):
       super().__init__(UpperCamelCase_				,			**UpperCamelCase_					)
       UpperCamelCase_     =config.num_labels
       UpperCamelCase_     =TFResNetMainLayer(UpperCamelCase_				,			name="resnet"					)
       # classification head
       UpperCamelCase_     =(
           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 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		tf.Tensor					):
       UpperCamelCase_     =tf.keras.layers.Flatten()(UpperCamelCase_					)
       UpperCamelCase_     =self.classifier_layer(UpperCamelCase_					)
       return logits
  
  
  
  
  
    @add_start_docstrings_to_model_forward(UpperCamelCase_					)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT				,			output_type=UpperCamelCase_				,			config_class=_CONFIG_FOR_DOC				,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,			)
    @unpack_inputs
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		tf.Tensor = None				,			UpperCamelCase_:		tf.Tensor = None				,			UpperCamelCase_:		bool = None				,			UpperCamelCase_:		bool = None				,			UpperCamelCase_:		bool = False				,			):
       UpperCamelCase_     =return_dict if return_dict is not None else self.config.use_return_dict
  
       UpperCamelCase_     =self.resnet(
           UpperCamelCase_				,			output_hidden_states=UpperCamelCase_				,			return_dict=UpperCamelCase_				,			training=UpperCamelCase_					)
  
       UpperCamelCase_     =outputs.pooler_output if return_dict else outputs[1]
  
       UpperCamelCase_     =self.classifier(UpperCamelCase_					)
  
       UpperCamelCase_     =None if labels is None else self.hf_compute_loss(UpperCamelCase_				,			UpperCamelCase_					)
  
       if not return_dict:
          UpperCamelCase_     =(logits,) + outputs[2:]
          return (loss,) + output if loss is not None else output
  
       return TFImageClassifierOutputWithNoAttention(loss=UpperCamelCase_				,			logits=UpperCamelCase_				,			hidden_states=outputs.hidden_states					)
  