



"""simple docstring"""


from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class 	_a       (    __lowercase):
			"""simple docstring"""



			def __lt__(						self   :			Optional[int] ,      __UpperCamelCase   :			List[str]      )->List[Any]:
							return self[-1] < other[-1]



			def __eq__(						self   :			str ,      __UpperCamelCase   :			List[str]      )->Tuple:
							return self[-1] == other[-1]







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  list ):






				'''simple docstring'''



				_UpperCAmelCase							=			[]
				# sort into stacks
				for element in collection:
								_UpperCAmelCase							=			Stack([element] )
								_UpperCAmelCase							=			bisect_left(_SCREAMING_SNAKE_CASE    ,   _SCREAMING_SNAKE_CASE )
								if i != len(_SCREAMING_SNAKE_CASE ):
												stacks[i].append(_SCREAMING_SNAKE_CASE )
								else:
												stacks.append(_SCREAMING_SNAKE_CASE )

    # use a heap-based merge to merge stack efficiently
				_UpperCAmelCase							=			merge(*(reversed(_SCREAMING_SNAKE_CASE ) for stack in stacks) )
				return collection


if __name__ == "__main__":
					__A      :							Any   =      input("Enter numbers separated by a comma:\n").strip()
					__A      :							Optional[int]   =      [int(item) for item in user_input.split(",")]
					print(patience_sort(unsorted))







'''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''')





