






from __future__ import annotations







def 						A__  (					__A	):  # This function is recursive







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						len(__A	)
    # If the array contains only one element, we return it (it's the stop condition of
    # recursion)
    if array_length <= 1:
        return array
        # Else
    _lowerCamelCase     :		Optional[Any] 						=						array[0]
    _lowerCamelCase     :		List[str] 						=						False
    _lowerCamelCase     :		Dict 						=						1
    _lowerCamelCase     :		list[int] 						=						[]
    while not is_found and i < array_length:
        if array[i] < pivot:
            _lowerCamelCase     :		Dict 						=						True
            _lowerCamelCase     :		int 						=						[element for element in array[i:] if element >= array[i]]
            _lowerCamelCase     :		Tuple 						=						longest_subsequence(__A	)
            if len(__A	) > len(__A	):
                _lowerCamelCase     :		Optional[int] 						=						temp_array
        else:
            i += 1

    _lowerCamelCase     :		Optional[Any] 						=						[element for element in array[1:] if element >= pivot]
    _lowerCamelCase     :		Tuple 						=						[pivot, *longest_subsequence(__A	)]
    if len(__A	) > len(__A	):
        return temp_array
    else:
        return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							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""")

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




