






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)














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)}''')






