





"""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 	__lowercase  (     snake_case_						:						int=10		)     ->Optional[int]:



    '''simple docstring'''







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


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

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

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


# Training and saving
a_				=    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"""



import decimal







def 	__lowercase  (     )     ->int:



    '''simple docstring'''







    __A					:		List[str]						=  0
    __A					:		Tuple						=  decimal.Context(prec=105		)
    for i in range(2       ,100		):
        __A					:		int						=  decimal.Decimal(snake_case_		)
        __A					:		str						=  number.sqrt(snake_case_		)
        if len(str(snake_case_		)		) > 1:
            answer += int(str(snake_case_		)[0]		)
            __A					:		Optional[Any]						=  str(snake_case_		)[2:101]
            answer += sum(int(snake_case_		) for x in sqrt_number_str		)
    return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f'''{solution() = }''')




