






'''simple docstring'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[int]       ,   __A			:							int      )							->   int:



 """simple docstring"""




 if len(__A      ) < k or k < 0:
  raise ValueError('''Invalid Input'''      )
 lowercase       : List[Any]						=sum(array[:k]      )
 for i in range(len(__A      ) - k      ):
  lowercase       : str						=current_sum - array[i] + array[i + k]
  lowercase       : int						=max(__A       ,   __A      )
 return max_sum


if __name__ == "__main__":
      from doctest import testmod
      from random import randint

      testmod()
      SCREAMING_SNAKE_CASE							=						[randint(-1_000, 1_000) for i in range(100)]
      SCREAMING_SNAKE_CASE							=						randint(0, 110)
      print(f"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")






'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


UpperCAmelCase					:			Tuple       =	HfArgumentParser(PretokenizationArguments)
UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase					:			List[Any]       =	multiprocessing.cpu_count()
UpperCAmelCase					:			str       =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase					:			Optional[int]       =	time.time()
UpperCAmelCase					:			Any       =	load_dataset(args.dataset_name, split='train')
print(f"""Dataset loaded in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[str]       =	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""")

UpperCAmelCase					:			List[Any]       =	time.time()
ds.push_to_hub(args.tokenized_data_repo)



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






