
"""simple docstring"""





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def       __a	(						__lowerCamelCase=None,						__lowerCamelCase=None    ):
   return field(default_factory=lambda: default,						metadata=__lowerCamelCase    )


@dataclass
class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """The csv file to plot."""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Disable logarithmic scale when plotting"""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={
          """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
      }							,)
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}							,)
  SCREAMING_SNAKE_CASE__     :		Optional[List[str]]    	=						list_field(
      default=lowercase__							,metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""} )







def       __a	(						__lowerCamelCase    ):
   try:
      int(__lowerCamelCase    )
      return True
   except ValueError:
      return False







def       __a	(						__lowerCamelCase    ):
   try:
      float(__lowerCamelCase    )
      return True
   except ValueError:
      return False


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       args
     UpperCAmelCase_   :   Optional[Any]     	=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

     with open(self.args.csv_file							,  newline=""    ) as csv_file:
        UpperCAmelCase_   :   Dict     	=       csv.DictReader(lowercase_    )
        for row in reader:
           UpperCAmelCase_   :   Dict     	=       row["model"]
           self.result_dict[model_name]["bsz"].append(int(row["batch_size"]    )    )
           self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]    )    )
           if can_convert_to_int(row["result"]    ):
              # value is not None
              UpperCAmelCase_   :   Union[str, Any]     	=       int(row["result"]    )
           elif can_convert_to_float(row["result"]    ):
              # value is not None
              UpperCAmelCase_   :   int     	=       float(row["result"]    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       plt.subplots()
     UpperCAmelCase_   :   List[Any]     	=       "Time usage" if self.args.is_time else "Memory usage"
     UpperCAmelCase_   :   int     	=       title_str + " for training" if self.args.is_train else title_str + " for inference"

     if not self.args.no_log_scale:
        # set logarithm scales
        ax.set_xscale("log"    )
        ax.set_yscale("log"    )

     for axis in [ax.xaxis, ax.yaxis]:
        axis.set_major_formatter(ScalarFormatter()    )

     for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       sorted(set(self.result_dict[model_name]["bsz"]    )    )
        UpperCAmelCase_   :   Any     	=       sorted(set(self.result_dict[model_name]["seq_len"]    )    )
        UpperCAmelCase_   :   Optional[int]     	=       self.result_dict[model_name]["result"]

        ((UpperCAmelCase_)	,  (UpperCAmelCase_))   :   Dict     	=       (
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        UpperCAmelCase_   :   Optional[int]     	=       (
            model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
        )

        for inner_loop_value in inner_loop_array:
           if self.args.plot_along_batch:
              UpperCAmelCase_   :   Union[str, Any]     	=       np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,  dtype=lowercase_							,  )
           else:
              UpperCAmelCase_   :   List[Any]     	=       np.asarray(
                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,  dtype=np.floataa							,  )

           ((UpperCAmelCase_)	,  (UpperCAmelCase_))   :   int     	=       (
               ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
           )

           UpperCAmelCase_   :   Dict     	=       np.asarray(lowercase_							,  lowercase_    )[: len(lowercase_    )]
           plt.scatter(
               lowercase_							,  lowercase_							,  label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""    )
           plt.plot(lowercase_							,  lowercase_							,  "--"    )

        title_str += F""" {label_model_name} vs."""

     UpperCAmelCase_   :   Optional[int]     	=       title_str[:-4]
     UpperCAmelCase_   :   int     	=       "Time in s" if self.args.is_time else "Memory in MB"

     # plot
     plt.title(lowercase_    )
     plt.xlabel(lowercase_    )
     plt.ylabel(lowercase_    )
     plt.legend()

     if self.args.figure_png_file is not None:
        plt.savefig(self.args.figure_png_file    )
     else:
        plt.show()







def       __a	(						):
   UpperCAmelCase_   :   List[str]     	=       HfArgumentParser(__lowerCamelCase    )
   UpperCAmelCase_   :   Any     	=       parser.parse_args_into_dataclasses()[0]
   UpperCAmelCase_   :   Any     	=       Plot(args=__lowerCamelCase    )
   plot.plot()


if __name__ == "__main__":
    main()










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, 2048, 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        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int = 3 ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						int						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						kernel_size // 2
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=snake_case__ ,							strides=snake_case__ ,							padding='valid' ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)
       _lowercase     			=						ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						_lowercase     			=						(self.pad_value, self.pad_value)
       _lowercase     			=						tf.pad(snake_case__ ,							[(0, 0), height_pad, width_pad, (0, 0)]						)
       _lowercase     			=						self.conv(snake_case__						)
       return hidden_state

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						TFResNetConvLayer(
           config.embedding_size ,							kernel_size=7 ,							stride=2 ,							activation=config.hidden_act ,							name='embedder' ,							)
       _lowercase     			=						tf.keras.layers.MaxPoolaD(pool_size=3 ,							strides=2 ,							padding='valid' ,							name='pooler'						)
       _lowercase     			=						config.num_channels

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase			,     _lowercase			,     _lowercase			,     _lowercase     			=						shape_list(snake_case__						)
       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.'						)
       _lowercase     			=						pixel_values
       _lowercase     			=						self.embedder(snake_case__						)
       _lowercase     			=						tf.pad(snake_case__ ,							[[0, 0], [1, 1], [1, 1], [0, 0]]						)
       _lowercase     			=						self.pooler(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						List[str] ,							snake_case__:						int ,							snake_case__:						int = 2 ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=1 ,							strides=snake_case__ ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						x
       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							activation=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							snake_case__:						int = 4 ,							**snake_case__:						Optional[Any] ,							)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						out_channels // reduction
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							activation=snake_case__ ,							name='layer.2'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						ResNetConfig ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 2 ,							snake_case__:						int = 2 ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)

       _lowercase     			=						TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

       _lowercase     			=						[layer(snake_case__ ,							snake_case__ ,							stride=snake_case__ ,							activation=config.hidden_act ,							name='layers.0'						)]
       layers += [
           layer(snake_case__ ,							snake_case__ ,							activation=config.hidden_act ,							name=f'''layers.{i + 1}'''						)
           for i in range(depth - 1						)
       ]
       _lowercase     			=						layers

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       for layer in self.stage_layers:
           _lowercase     			=						layer(snake_case__ ,							training=snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Optional[Any] ,							snake_case__:						ResNetConfig ,							**snake_case__:						Union[str, Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       _lowercase     			=						[
           TFResNetStage(
               snake_case__ ,							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(snake_case__ ,							snake_case__ ,							snake_case__ ,							depth=snake_case__ ,							name=f'''stages.{i + 1}'''						)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False ,							snake_case__:						bool = True ,							snake_case__:						bool = False ,							)	->		TFBaseModelOutputWithNoAttention:


       """simple docstring"""



       _lowercase     			=						() if output_hidden_states else None

       for stage_module in self.stages:
           if output_hidden_states:
               _lowercase     			=						hidden_states + (hidden_state,)

           _lowercase     			=						stage_module(snake_case__ ,							training=snake_case__						)

       if output_hidden_states:
           _lowercase     			=						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=snake_case__ ,							hidden_states=snake_case__						)






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig
   __lowerCAmelCase  =    """resnet"""
   __lowerCAmelCase  =    """pixel_values"""

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Dict:


       """simple docstring"""



       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) ,							dtype=tf.floataa						)}






lowerCamelCase					    =	r'''
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''


lowerCamelCase					    =	r'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

@keras_serializable
class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig

   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Dict						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						config
       _lowercase     			=						TFResNetEmbeddings(snake_case__ ,							name='embedder'						)
       _lowercase     			=						TFResNetEncoder(snake_case__ ,							name='encoder'						)
       _lowercase     			=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__						)

   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



       _lowercase     			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowercase     			=						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)
       _lowercase     			=						tf.transpose(snake_case__ ,							perm=[0, 2, 3, 1]						)
       _lowercase     			=						self.embedder(snake_case__ ,							training=snake_case__						)

       _lowercase     			=						self.encoder(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

       _lowercase     			=						encoder_outputs[0]

       _lowercase     			=						self.pooler(snake_case__						)

       # Transpose all the outputs to the NCHW format
       # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
       _lowercase     			=						tf.transpose(snake_case__ ,							(0, 3, 1, 2)						)
       _lowercase     			=						tf.transpose(snake_case__ ,							(0, 3, 1, 2)						)
       _lowercase     			=						()
       for hidden_state in encoder_outputs[1:]:
           _lowercase     			=						hidden_states + tuple(tf.transpose(snake_case__ ,							(0, 3, 1, 2)						) for h in hidden_state						)

       if not return_dict:
           return (last_hidden_state, pooled_output) + hidden_states

       _lowercase     			=						hidden_states if output_hidden_states else None

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=snake_case__ ,							pooler_output=snake_case__ ,							hidden_states=snake_case__ ,							)






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						TFResNetMainLayer(config=snake_case__ ,							name='resnet'						)

   @add_start_docstrings_to_model_forward(snake_case__						)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC ,							output_type=snake_case__ ,							config_class=_CONFIG_FOR_DOC ,							modality='vision' ,							expected_output=_EXPECTED_OUTPUT_SHAPE ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



       _lowercase     			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           pixel_values=snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__ ,							)
       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.
    """   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ,      lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Any						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						config.num_labels
       _lowercase     			=						TFResNetMainLayer(snake_case__ ,							name='resnet'						)
       # classification head
       _lowercase     			=						(
           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       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						tf.keras.layers.Flatten()(snake_case__						)
       _lowercase     			=						self.classifier_layer(snake_case__						)
       return logits

   @add_start_docstrings_to_model_forward(snake_case__						)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT ,							output_type=snake_case__ ,							config_class=_CONFIG_FOR_DOC ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor = None ,							snake_case__:						tf.Tensor = None ,							snake_case__:						bool = None ,							snake_case__:						bool = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:


       """simple docstring"""



       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

       _lowercase     			=						outputs.pooler_output if return_dict else outputs[1]

       _lowercase     			=						self.classifier(snake_case__						)

       _lowercase     			=						None if labels is None else self.hf_compute_loss(snake_case__ ,							snake_case__						)

       if not return_dict:
           _lowercase     			=						(logits,) + outputs[2:]
           return (loss,) + output if loss is not None else output

       return TFImageClassifierOutputWithNoAttention(loss=snake_case__ ,							logits=snake_case__ ,							hidden_states=outputs.hidden_states						)
