code: 
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
  
  
  lowerCamelCase				  =  logging.get_logger(__name__)
  
  lowerCamelCase				  =  {
      '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
      # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
  }
  
  class 					snake_case_							(       __SCREAMING_SNAKE_CASE      ,			__SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""convnext"""
  
     def __init__(    self:	Dict   ,       snake_case__:	Optional[int]=3   ,       snake_case__:	List[str]=4   ,       snake_case__:	List[Any]=4   ,       snake_case__:	str=None   ,       snake_case__:	Optional[int]=None   ,       snake_case__:	Tuple="gelu"   ,       snake_case__:	List[Any]=0.02   ,       snake_case__:	Tuple=1E-12   ,       snake_case__:	Tuple=1E-6   ,       snake_case__:	int=0.0   ,       snake_case__:	Optional[Any]=224   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	int=None   ,       **snake_case__:	List[Any]   ,       )  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**UpperCamelCase__	)
  
         _lowercase     =	num_channels
         _lowercase     =	patch_size
         _lowercase     =	num_stages
         _lowercase     =	[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
         _lowercase     =	[3, 3, 9, 3] if depths is None else depths
         _lowercase     =	hidden_act
         _lowercase     =	initializer_range
         _lowercase     =	layer_norm_eps
         _lowercase     =	layer_scale_init_value
         _lowercase     =	drop_path_rate
         _lowercase     =	image_size
         _lowercase     =	['''stem'''] + [f'''stage{idx}''' for idx in range(1   ,       len(self.depths	) + 1	)]
         _lowercase     =	get_aligned_output_features_output_indices(
             out_features=UpperCamelCase__   ,       out_indices=UpperCamelCase__   ,       stage_names=self.stage_names	)
  
  
  
  
  
  
  class 					snake_case_							(       __SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						version.parse("""1.11"""      )
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Mapping[str, Mapping[int, str]]:
  
  
         """simple docstring"""
  
  
  
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]	)
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		float:
  
  
         """simple docstring"""
  
  
  
         return 1E-5
  
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)
  