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       a__      (					a=1_0							)		->	Union[str, Any]:







       for _ in tqdm(range(0  ,   args.n_examples  ,   a							)							):
              yield [next(a							)[args.text_column] for _ in range(a							)]


# Configuration
_lowerCAmelCase        =       HfArgumentParser(TokenizerTrainingArguments)
_lowerCAmelCase        =       parser.parse_args()

# Base tokenizer
_lowerCAmelCase        =       AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCAmelCase        =       list(bytes_to_unicode().values())

# Load dataset
_lowerCAmelCase        =       load_dataset(args.dataset_name, split='train', streaming=True)
_lowerCAmelCase        =       iter(dataset)


# Training and saving
_lowerCAmelCase        =       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)









from __future__ import annotations
def  __UpperCamelCase    (						snake_case      )    ->		bool:


   '''simple docstring'''







   __A    =  42
   __A    =  list(snake_case      )
   __A   ,		__A    =  _vector[:-1], int(_vector[-1]      )
   __A    =  [int(snake_case      ) for digit in __vector]

   vector.reverse()
   for i, digit in enumerate(snake_case      ):
      if i & 1 == 0:
         __A    =  digit * 2
         if doubled > 9:
            doubled -= 9
         check_digit += doubled
      else:
         check_digit += digit

   return check_digit % 1_0 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    assert is_luhn("""79927398713""")
    assert not is_luhn("""79927398714""")

