





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  List[str]:






   """simple docstring"""







   _UpperCAmelCase           =   {}
   _UpperCAmelCase           =   tokenizer(example['''content''']				,	truncation=SCREAMING_SNAKE_CASE_						)['''input_ids''']
   _UpperCAmelCase           =   len(example['''content''']						) / len(output['''input_ids''']						)
   return output


UpperCAmelCase_           =	HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_           =	parser.parse_args()
if args.num_workers is None:
   UpperCAmelCase_           =	multiprocessing.cpu_count()
UpperCAmelCase_           =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

UpperCAmelCase_           =	time.time()
UpperCAmelCase_           =	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''')

UpperCAmelCase_           =	time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')



SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





