




__lowerCAmelCase	:       List[Any]     =	{
    'km/h': 1.0,
    'm/s': 3.6,
    'mph': 1.6_0_9_3_4_4,
    'knot': 1.8_5_2,
}

__lowerCAmelCase	:       Union[str, Any]     =	{
    'km/h': 1.0,
    'm/s': 0.2_7_7_7_7_7_7_7_8,
    'mph': 0.6_2_1_3_7_1_1_9_2,
    'knot': 0.5_3_9_9_5_6_8_0_3,
}




def     __magic_name__       (				A	:      List[str],						A	:      Union[str, Any],						A	:      int     ):






			'''simple docstring'''







			if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
						a					=    (
						    F"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
						    F"""Valid values are: {", ".join(__UpperCAmelCase     )}"""
						)
						raise ValueError(__UpperCAmelCase     )
			return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to],						3     )


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''')






