





from __future__ import annotations

import math






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int					, _lowercase		:					bool					, _lowercase		:					list[int]					, _lowercase		:					float      )				->Optional[int]:






	'''simple docstring'''






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

	if len(_UpperCamelCase      ) == 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					, _UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase      )					, minimax(depth + 1					, node_index * 2 + 1					, _UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase      )					, )

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






def    _SCREAMING_SNAKE_CASE	(			)				->Any:






	'''simple docstring'''






	a   :	int			=       [90, 23, 6, 33, 21, 65, 123, 3_4423]
	a   :	Union[str, Any]			=       math.log(len(_UpperCamelCase      )					, 2      )
	print("Optimal value : "					, end=""      )
	print(minimax(0					, 0					, _UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase      )      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


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

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

a		:				Dict  			=    time.time()
a		:				Tuple  			=    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		:				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''')


