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, 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:	List[str]   ,       snake_case__:	int   ,       snake_case__:	int = 3   ,       snake_case__:	int = 1   ,       snake_case__:	str = "relu"   ,       **snake_case__:	Union[str, Any]	)  ->		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:	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:	List[str]   ,       snake_case__:	ResNetConfig   ,       **snake_case__:	Tuple	)  ->		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:	Tuple   ,       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__:	Optional[Any]	)  ->		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:	List[Any]   ,       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:	Any   ,       snake_case__:	int   ,       snake_case__:	int   ,       snake_case__:	int = 1   ,       snake_case__:	str = "relu"   ,       **snake_case__:	List[Any]	)  ->		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:	Tuple   ,       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:	Optional[int]   ,       snake_case__:	int   ,       snake_case__:	int   ,       snake_case__:	int = 1   ,       snake_case__:	str = "relu"   ,       snake_case__:	int = 4   ,       **snake_case__:	str   ,       )  ->		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:	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.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:	Optional[int]   ,       snake_case__:	ResNetConfig   ,       snake_case__:	int   ,       snake_case__:	int   ,       snake_case__:	int = 2   ,       snake_case__:	int = 2   ,       **snake_case__:	Tuple	)  ->		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:	Tuple   ,       snake_case__:	ResNetConfig   ,       **snake_case__:	Optional[int]	)  ->		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:	Optional[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:	Dict	)  ->		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:	str   ,       snake_case__:	ResNetConfig   ,       **snake_case__:	List[Any]	)  ->		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:	List[Any]   ,       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:	List[Any]   ,       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:	List[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
  
         _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:	List[str]   ,       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:	Any   ,       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:	List[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	)
  
style_context: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  ['''UtteranceLevel''']
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )
  
      _lowercase     =	checkpoint['Downstream']
  
      _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
      _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
      _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
          _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )
  
      if hf_congfig.use_weighted_layer_sum:
          _lowercase     =	checkpoint['Featurizer']['weights']
  
      _lowercase     =	downstream_dict['projector.weight']
      _lowercase     =	downstream_dict['projector.bias']
      _lowercase     =	downstream_dict['model.post_net.linear.weight']
      _lowercase     =	downstream_dict['model.post_net.linear.bias']
  
      hf_feature_extractor.save_pretrained(_lowercase      )
      hf_model.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     lowerCamelCase										=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  