def       _snake_case	(   lowerCAmelCase							:   int = 3   , lowerCAmelCase							:   int = 7   , lowerCAmelCase							:   int = 1_0_0_0_0_0_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    1

		for current_denominator in range(1   , limit + 1	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    current_denominator * numerator // denominator
				if current_denominator % denominator == 0:
						current_numerator -= 1
				if current_numerator * max_denominator > current_denominator * max_numerator:
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    current_numerator
						SCREAMING_SNAKE_CASE_  :		int				      =    current_denominator
		return max_numerator


if __name__ == "__main__":
		print(solution(numerator=3, denominator=7, limit=1_00_00_00))




'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


__snake_case    						=HfArgumentParser(PretokenizationArguments)
__snake_case    						=parser.parse_args()
if args.num_workers is None:
       __snake_case    						=multiprocessing.cpu_count()
__snake_case    						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

__snake_case    						=time.time()
__snake_case    						=load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
__snake_case    						=ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





