




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     {}
     UpperCAmelCase_     :				Any  =     tokenizer(example['''content''']					,		truncation=_lowercase )['''input_ids''']
     UpperCAmelCase_     :				Optional[int]  =     len(example['''content'''] ) / len(output['''input_ids'''] )
     return output


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

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

__a							     =					time.time()
__a							     =					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							     =					time.time()
ds.push_to_hub(args.tokenized_data_repo)



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





def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for i in range(1					,		1001 ):
          total += i**i
     return str(_lowercase )[-10:]


if __name__ == "__main__":
     print(solution())