






"""simple docstring"""



from __future__ import annotations




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int]      )		->Optional[int]:
      A__						:  List[Any]		  =  [True] * limit
      A__						:  str		  =  False
      A__						:  Any		  =  False
      A__						:  Union[str, Any]		  =  True

      for i in range(3,						int(limit**0.5 + 1      ),						2      ):
            A__						:  Optional[Any]		  =  i * 2
            while index < limit:
                  A__						:  Dict		  =  False
                  A__						:  str		  =  index + i

      A__						:  Union[str, Any]		  =  [2]

      for i in range(3,						UpperCAmelCase__,						2      ):
            if is_prime[i]:
                  primes.append(UpperCAmelCase__      )

      return primes




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any = 1_0_0_0_0_0_0      )		->str:
      A__						:  Union[str, Any]		  =  prime_sieve(UpperCAmelCase__      )
      A__						:  int		  =  0
      A__						:  Tuple		  =  0

      for i in range(len(UpperCAmelCase__      )      ):
            for j in range(i + length,						len(UpperCAmelCase__      )      ):
                  A__						:  List[str]		  =  sum(primes[i:j]      )
                  if sol >= ceiling:
                        break

                  if sol in primes:
                        A__						:  List[Any]		  =  j - i
                        A__						:  Any		  =  sol

      return largest


if __name__ == "__main__":
      print(F'{solution() = }')














"""simple docstring"""



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->str:
						A__						:  Tuple		  =  {}
						A__						:  Union[str, Any]		  =  tokenizer(example["""content"""],						truncation=UpperCAmelCase__      )["""input_ids"""]
						A__						:  Any		  =  len(example["""content"""]      ) / len(output["""input_ids"""]      )
						return output


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

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

A_    =							time.time()
A_    =							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')

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

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






