



from __future__ import annotations

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str       )						->     bool:





     '''simple docstring'''

     _UpperCAmelCase    :			int
     _UpperCAmelCase    :			list[str]              =					list(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Dict              =					_vector[:-1], int(_vector[-1]       )
     _UpperCAmelCase    :			list[int]              =					[int(SCREAMING_SNAKE_CASE__       ) for digit in __vector]

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

     return check_digit % 10 == 0


if __name__ == "__main__":
 import doctest

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





"""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)
