


'''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 						_A					(lowerCAmelCase__	:Tuple=10		) ->	Union[str, Any]:




     '''simple docstring'''



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


# Configuration
a_       :     Optional[Any] 							=     HfArgumentParser(TokenizerTrainingArguments)
a_       :     Union[str, Any] 							=     parser.parse_args()

# Base tokenizer
a_       :     Optional[int] 							=     AutoTokenizer.from_pretrained(args.base_tokenizer)
a_       :     Optional[int] 							=     list(bytes_to_unicode().values())

# Load dataset
a_       :     Union[str, Any] 							=     load_dataset(args.dataset_name, split="train", streaming=True)
a_       :     Union[str, Any] 							=     iter(dataset)


# Training and saving
a_       :     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)









"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :int ):







  '''simple docstring'''

  assert (
      isinstance(A_      ,     A_ ) and mask > 0
  ), f'''mask needs to be positive integer, your input {mask}'''
  a	:     Any							=  []
  a	:     Union[str, Any]							=  mask

  while submask:
    all_submasks.append(A_ )
    a	:     Any							=  (submask - 1) & mask

  return all_submasks


if __name__ == "__main__":
   import doctest

   doctest.testmod()




