






"""simple docstring"""






import string



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	""
    for i in sequence:
        SCREAMING_SNAKE_CASE_   :						int							=	ord(snake_case_							)
        if 65 <= extract <= 90:
            output += chr(1_55 - extract							)
        elif 97 <= extract <= 1_22:
            output += chr(2_19 - extract							)
        else:
            output += i
    return output



def       _A    (__a							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	string.ascii_letters
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
    return "".join(
        letters_reversed[letters.index(snake_case_							)] if c in letters else c for c in sequence							)



def       _A    ()				-> Optional[int]:
    """simple docstring"""






    from timeit import timeit

    print('''Running performance benchmarks...'''							)
    SCREAMING_SNAKE_CASE_   :						str							=	"from string import printable ; from __main__ import atbash, atbash_slow"
    print(f'> atbash_slow(): {timeit("atbash_slow(printable)"					,       setup=snake_case_							)} seconds'							)
    print(f'>      atbash(): {timeit("atbash(printable)"					,       setup=snake_case_							)} seconds'							)


if __name__ == "__main__":
     for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
          print(f'''{example} encrypted in atbash: {atbash(example)}''')
     benchmark()










"""simple docstring"""






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


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

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

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				Dict         =				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_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


