code: 
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		float       )						->     float:
  
  
  
  
  
       '''simple docstring'''
  
       return -num if num < 0 else num
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) == 0:
            raise ValueError("abs_min() arg is an empty sequence"       )
       _UpperCAmelCase    :			int              =					x[0]
       for i in x:
            if abs_val(SCREAMING_SNAKE_CASE__       ) < abs_val(SCREAMING_SNAKE_CASE__       ):
                 _UpperCAmelCase    :			List[str]              =					i
       return j
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) == 0:
            raise ValueError("abs_max() arg is an empty sequence"       )
       _UpperCAmelCase    :			int              =					x[0]
       for i in x:
            if abs(SCREAMING_SNAKE_CASE__       ) > abs(SCREAMING_SNAKE_CASE__       ):
                 _UpperCAmelCase    :			Tuple              =					i
       return j
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) == 0:
            raise ValueError("abs_max_sort() arg is an empty sequence"       )
       return sorted(SCREAMING_SNAKE_CASE__		,						key=SCREAMING_SNAKE_CASE__       )[-1]
  
  def 		__snake_case		(   )						->     Optional[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       assert abs_val(0       ) == 0
       assert abs_val(34       ) == 34
       assert abs_val(-100_000_000_000       ) == 100_000_000_000
  
       _UpperCAmelCase    :			List[str]              =					[-3, -1, 2, -11]
       assert abs_max(SCREAMING_SNAKE_CASE__       ) == -11
       assert abs_max_sort(SCREAMING_SNAKE_CASE__       ) == -11
       assert abs_min(SCREAMING_SNAKE_CASE__       ) == -1
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
   test_abs_val()
   print(abs_val(-34))  # --> 34
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  