





def   __lowerCamelCase   (				__a  :list       )						->							list:






		"""simple docstring"""

		for i in range(len(__a       ) - 1       ,	0       ,	-1       ):
				A__     =  False

				for j in range(__a       ,	0       ,	-1       ):
						if unsorted[j] < unsorted[j - 1]:
								A__ ,     A__     =  unsorted[j - 1], unsorted[j]
								A__     =  True

				for j in range(__a       ):
						if unsorted[j] > unsorted[j + 1]:
								A__ ,     A__     =  unsorted[j + 1], unsorted[j]
								A__     =  True

				if not swapped:
						break
		return unsorted


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					A       :   str					= input('''Enter numbers separated by a comma:\n''').strip()
					A       :   Tuple					= [int(item) for item in user_input.split(''',''')]
					print(F'''{cocktail_shaker_sort(unsorted) = }''')










import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

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


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

A       :   Optional[Any]					= time.time()
A       :   int					= 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       :   Tuple					= time.time()
ds.push_to_hub(args.tokenized_data_repo)






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



