





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]    )						->      Tuple:





    """simple docstring"""

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


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

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

__a      :       Dict				  =   time.time()
__a      :       List[Any]				  =   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      :       int				  =   time.time()
ds.push_to_hub(args.tokenized_data_repo)






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












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




