code: 
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  def   _A (	SCREAMING_SNAKE_CASE  :     int , SCREAMING_SNAKE_CASE  :     List[str] , SCREAMING_SNAKE_CASE  :     Any		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      List[str]            =RoFormerConfig.from_json_file(lowerCamelCase__		)
    print(f'''Building PyTorch model from configuration: {config}'''		)
    a__			:      Optional[int]            =RoFormerForMaskedLM(lowerCamelCase__		)
  
    # Load weights from tf checkpoint
    load_tf_weights_in_roformer(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__		)
  
    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}'''		)
    torch.save(model.state_dict() , lowerCamelCase__ , _use_new_zipfile_serialization=lowerCamelCase__		)
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			int	  = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--bert_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       UpperCAmelCase			:			Optional[Any]	  = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    barcode //= 10  # exclude the last digit
    a__			:      int            =False
    a__			:      List[str]            =0
  
    # extract and check each digit
    while barcode != 0:
      a__			:      Optional[Any]            =1 if checker else 3
      s += mult * (barcode % 10)
      barcode //= 10
      a__			:      Union[str, Any]            =not checker
  
    return (10 - (s % 10)) % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    return len(str(SCREAMING_SNAKE_CASE		)		) == 13 and get_check_digit(SCREAMING_SNAKE_CASE		) == barcode % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if str(SCREAMING_SNAKE_CASE		).isalpha():
      a__			:      Tuple            =f'''Barcode \'{barcode}\' has alphabetic characters.'''
      raise ValueError(SCREAMING_SNAKE_CASE		)
    elif int(SCREAMING_SNAKE_CASE		) < 0:
      raise ValueError("The entered barcode has a negative value. Try again."		)
    else:
      return int(SCREAMING_SNAKE_CASE		)
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
       UpperCAmelCase			:			List[str]	  = get_barcode(input("""Barcode: """).strip())
  
       if is_valid(barcode):
            print(F"""'{barcode}' is a valid barcode.""")
       else:
            print(F"""'{barcode}' is NOT a valid barcode.""")
  
  
  
  
  
  
  