




def     __magic_name__       (				A	:      list,						A	:      list     ):






			'''simple docstring'''







			_validate_point(A     )
			_validate_point(A     )
			if len(A     ) != len(A     ):
						raise ValueError("Both points must be in the same n-dimensional space"     )

			return float(sum(abs(a - b     ) for a, b in zip(A,						A     )     )     )




def     __magic_name__       (				A	:      list[float]     ):






			'''simple docstring'''







			if point:
						if isinstance(A,						A     ):
									for item in point:
												if not isinstance(A,						(int, float)     ):
															a					=    (
															    "Expected a list of numbers as input, found "
															    F"""{type(A     ).__name__}"""
															)
															raise TypeError(A     )
						else:
									a					=    F"""Expected a list of numbers as input, found {type(A     ).__name__}"""
									raise TypeError(A     )
			else:
						raise ValueError("Missing an input"     )




def     __magic_name__       (				A	:      list,						A	:      list     ):






			'''simple docstring'''







			_validate_point(A     )
			_validate_point(A     )
			if len(A     ) != len(A     ):
						raise ValueError("Both points must be in the same n-dimensional space"     )

			return float(sum(abs(x - y     ) for x, y in zip(A,						A     )     )     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







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


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

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[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''')

__lowerCAmelCase	:       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''')






