from __future__ import annotations

import math







def 						A__  (					__A   , __A   , __A   , __A   , __A	):







    '''simple docstring'''

    if depth < 0:
        raise ValueError("""Depth cannot be less than 0"""	)

    if len(__A	) == 0:
        raise ValueError("""Scores cannot be empty"""	)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1   , node_index * 2   , __A   , __A   , __A	)   , minimax(depth + 1   , node_index * 2 + 1   , __A   , __A   , __A	)   , )

    return min(
        minimax(depth + 1   , node_index * 2   , __A   , __A   , __A	)   , minimax(depth + 1   , node_index * 2 + 1   , __A   , __A   , __A	)   , )







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						[90, 23, 6, 33, 21, 65, 123, 34_423]
    _lowerCamelCase     :		List[Any] 						=						math.log(len(__A	)   , 2	)
    print("""Optimal value : """   , end=""""""	)
    print(minimax(0   , 0   , __A   , __A   , __A	)	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





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""")




