code: 
  
  
  
  
  
  
  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    _lowerCamelCase	(	__lowerCamelCase=10					)   -> Tuple:
  
       '''simple docstring'''
       for _ in tqdm(range(0   ,       args.n_examples   ,       __lowerCamelCase					)					):
            yield [next(__lowerCamelCase					)[args.text_column] for _ in range(__lowerCamelCase					)]
  
  
  # Configuration
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							HfArgumentParser(TokenizerTrainingArguments)
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							parser.parse_args()
  
  # Base tokenizer
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							list(bytes_to_unicode().values())
  
  # Load dataset
  SCREAMING_SNAKE_CASE__		:       Dict   							=							load_dataset(args.dataset_name, split="""train""", streaming=True)
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							iter(dataset)
  
  
  # Training and saving
  SCREAMING_SNAKE_CASE__		:       List[Any]   							=							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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import random
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Union[str, Any]										= x
            UpperCAmelCase__			:     Union[str, Any]										= y
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            return (self.x**2 + self.y**2) <= 1
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls		):
            return cls(x=random.random()  ,	y=random.random()		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> float:
  
       '''simple docstring'''
       if number_of_simulations < 1:
            raise ValueError("""At least one simulation is necessary to estimate PI."""					)
  
       UpperCAmelCase__			:     Dict										= 0
       for _ in range(__lowerCamelCase					):
            UpperCAmelCase__			:     Union[str, Any]										= Point.random_unit_square()
  
            if random_point.is_in_unit_circle():
                 number_in_unit_circle += 1
  
       return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
        # import doctest
  
        # doctest.testmod()
        from math import pi
  
        SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""Please enter the desired number of Monte Carlo simulations: """
        SCREAMING_SNAKE_CASE__		:       int   							=							estimate_pi(int(input(prompt).strip()))
        print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')
  
  
  
  
  
  
  