code: 
  
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  __lowerCamelCase       :       Tuple				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       str				   =			["UtteranceLevel"]
  
  
  
  @torch.no_grad()
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_	)			->							Union[str, Any]:
  				A__     :							Tuple   				=torch.load(snake_case_,	map_location='''cpu'''	)
  				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  								raise NotImplementedError(f'The supported s3prl models are {SUPPORTED_MODELS}'	)
  
  				A__     :							Optional[int]   				=checkpoint['''Downstream''']
  
  				A__     :							Optional[int]   				=HubertConfig.from_pretrained(snake_case_	)
  				A__     :							Tuple   				=HubertForSequenceClassification.from_pretrained(snake_case_,	config=snake_case_	)
  				A__     :							List[str]   				=WavaVecaFeatureExtractor.from_pretrained(
  				    snake_case_,	return_attention_mask=snake_case_,	do_normalize=snake_case_	)
  
  				if hf_congfig.use_weighted_layer_sum:
  								A__     :							Optional[int]   				=checkpoint['''Featurizer''']['''weights''']
  
  				A__     :							Tuple   				=downstream_dict['''projector.weight''']
  				A__     :							str   				=downstream_dict['''projector.bias''']
  				A__     :							int   				=downstream_dict['''model.post_net.linear.weight''']
  				A__     :							List[Any]   				=downstream_dict['''model.post_net.linear.bias''']
  
  				hf_feature_extractor.save_pretrained(snake_case_	)
  				hf_model.save_pretrained(snake_case_	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Tuple				   =			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       :       List[Any]				   =			parser.parse_args()
  						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if number != int(snake_case_	):
  								raise ValueError('''factorial() only accepts integral values'''	)
  				if number < 0:
  								raise ValueError('''factorial() not defined for negative values'''	)
  				A__     :							Union[str, Any]   				=1
  				for i in range(1,	number + 1	):
  								value *= i
  				return value
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if not isinstance(snake_case_,	snake_case_	):
  								raise ValueError('''factorial() only accepts integral values'''	)
  				if n < 0:
  								raise ValueError('''factorial() not defined for negative values'''	)
  				return 1 if n == 0 or n == 1 else n * factorial(n - 1	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  						__lowerCamelCase       :       Any				   =			int(input("Enter a positive integer: ").strip() or 0)
  						print(F"factorial{n} is {factorial(n)}")
  
  
  
  