
import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def 				a						(							snake_case__:					List[str]					):


				'''simple docstring'''
				lowercase_       =					{}
				lowercase_       =					tokenizer(example['''content''']						,   truncation=snake_case__					)['''input_ids''']
				lowercase_       =					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")






import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


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




