


'''simple docstring'''




def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     str				):

   def get_matched_characters(lowerCamelCase	:     str	, lowerCamelCase	:     str				) -> str:
      lowerCAmelCase     =			[]
      lowerCAmelCase     =			min(len(_stra				)	, len(_stra				)				) // 2
      for i, l in enumerate(_stra				):
         lowerCAmelCase     =			int(max(0	, i - limit				)				)
         lowerCAmelCase     =			int(min(i + limit + 1	, len(_stra				)				)				)
         if l in _stra[left:right]:
            matched.append(lowerCamelCase				)
            lowerCAmelCase     =			f'''{_stra[0:_stra.index(lowerCamelCase				)]} {_stra[_stra.index(lowerCamelCase				) + 1:]}'''

      return "".join(lowerCamelCase				)

   # matching characters
   lowerCAmelCase     =			get_matched_characters(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			get_matched_characters(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			len(lowerCamelCase				)

   # transposition
   lowerCAmelCase     =			(
       len([(ca, ca) for ca, ca in zip(lowerCamelCase	, lowerCamelCase				) if ca != ca]				) // 2
   )

   if not match_count:
      lowerCAmelCase     =			0.0
   else:
      lowerCAmelCase     =			(
          1
          / 3
          * (
              match_count / len(lowerCamelCase				)
              + match_count / len(lowerCamelCase				)
              + (match_count - transpositions) / match_count
          )
      )

   # common prefix up to 4 characters
   lowerCAmelCase     =			0
   for ca, ca in zip(stra[:4]	, stra[:4]				):
      if ca == ca:
         prefix_len += 1
      else:
         break

   return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("""hello""", """world"""))









'''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''')





