




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   (   _SCREAMING_SNAKE_CASE=10					)			->				Optional[int]:
       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
SCREAMING_SNAKE_CASE__ :   Optional[int]						=     HfArgumentParser(TokenizerTrainingArguments)
SCREAMING_SNAKE_CASE__ :   str						=     parser.parse_args()

# Base tokenizer
SCREAMING_SNAKE_CASE__ :   List[str]						=     AutoTokenizer.from_pretrained(args.base_tokenizer)
SCREAMING_SNAKE_CASE__ :   Tuple						=     list(bytes_to_unicode().values())

# Load dataset
SCREAMING_SNAKE_CASE__ :   Tuple						=     load_dataset(args.dataset_name, split='train', streaming=True)
SCREAMING_SNAKE_CASE__ :   Optional[int]						=     iter(dataset)


# Training and saving
SCREAMING_SNAKE_CASE__ :   Optional[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)












from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) and mask > 0
       ), f'''mask needs to be positive integer, your input {mask}'''
       lowerCamelCase     :   str    				=      []
       lowerCamelCase     :   Any    				=      mask

       while submask:
              all_submasks.append(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      (submask - 1) & mask

       return all_submasks


if __name__ == "__main__":
       import doctest

       doctest.testmod()






