code: 
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
      # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""qdqbert"""
  
     def __init__(    self:	Tuple   ,       snake_case__:	Union[str, Any]=30_522   ,       snake_case__:	Optional[Any]=768   ,       snake_case__:	int=12   ,       snake_case__:	Tuple=12   ,       snake_case__:	List[Any]=3_072   ,       snake_case__:	str="gelu"   ,       snake_case__:	Tuple=0.1   ,       snake_case__:	str=0.1   ,       snake_case__:	str=512   ,       snake_case__:	Any=2   ,       snake_case__:	Optional[int]=0.02   ,       snake_case__:	List[str]=1E-12   ,       snake_case__:	Optional[int]=True   ,       snake_case__:	Union[str, Any]=1   ,       snake_case__:	int=0   ,       snake_case__:	Union[str, Any]=2   ,       **snake_case__:	Optional[Any]   ,       )  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         super().__init__(pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__   ,       **snake_case__	)
  
         _lowercase     =	vocab_size
         _lowercase     =	max_position_embeddings
         _lowercase     =	hidden_size
         _lowercase     =	num_hidden_layers
         _lowercase     =	num_attention_heads
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	hidden_dropout_prob
         _lowercase     =	attention_probs_dropout_prob
         _lowercase     =	initializer_range
         _lowercase     =	type_vocab_size
         _lowercase     =	layer_norm_eps
         _lowercase     =	use_cache
  
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)
  