





"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterator


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   )			->				None:
								a   :	Dict			=       value
								a   :	Node | None			=       None
								a   :	Node | None			=       None


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   )			->				None:
								a   :	int			=       tree






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
								if node is None:
									return 0
								return node.value + (
								    self.depth_first_search(node.left   ) + self.depth_first_search(node.right   )
								)






							def __iter__(     self   )			->				Iterator[int]:
								yield self.depth_first_search(self.tree   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

a		:				Dict  			=    time.time()
a		:				Tuple  			=    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''')


