



'''simple docstring'''






from __future__ import annotations

import math


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		bool     ,   _lowerCAmelCase						:		list[int]     ,   _lowerCAmelCase						:		float						)   ->     int:

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

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

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


def 				snake_case_  (      )   ->     None:
							UpperCAmelCase				: Tuple											=	[90, 23, 6, 33, 21, 65, 123, 34423]
							UpperCAmelCase				: Union[str, Any]											=	math.log(len(_lowerCAmelCase						)     ,   2						)
							print('''Optimal value : '''     ,   end=''''''						)
							print(minimax(0     ,   0     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

UpperCamelCase__:     int										=    {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """megatron-bert"""







	def __init__(		self    :  int	,    __snake_case    :  List[Any]=29056	,    __snake_case    :  Dict=1024	,    __snake_case    :  Tuple=24	,    __snake_case    :  Any=16	,    __snake_case    :  Tuple=4096	,    __snake_case    :  int="gelu"	,    __snake_case    :  Dict=0.1	,    __snake_case    :  str=0.1	,    __snake_case    :  Optional[Any]=512	,    __snake_case    :  Tuple=2	,    __snake_case    :  Tuple=0.02	,    __snake_case    :  int=1E-12	,    __snake_case    :  Union[str, Any]=0	,    __snake_case    :  int="absolute"	,    __snake_case    :  str=True	,    **__snake_case    :  Tuple	,    )    ->	Union[str, Any]:
								super().__init__(pad_token_id=__snake_case	,    **__snake_case	)

								UpperCAmelCase				: Any											=	vocab_size
								UpperCAmelCase				: Tuple											=	hidden_size
								UpperCAmelCase				: List[Any]											=	num_hidden_layers
								UpperCAmelCase				: str											=	num_attention_heads
								UpperCAmelCase				: Optional[int]											=	hidden_act
								UpperCAmelCase				: Optional[int]											=	intermediate_size
								UpperCAmelCase				: Dict											=	hidden_dropout_prob
								UpperCAmelCase				: List[Any]											=	attention_probs_dropout_prob
								UpperCAmelCase				: Dict											=	max_position_embeddings
								UpperCAmelCase				: Any											=	type_vocab_size
								UpperCAmelCase				: str											=	initializer_range
								UpperCAmelCase				: str											=	layer_norm_eps
								UpperCAmelCase				: Optional[Any]											=	position_embedding_type
								UpperCAmelCase				: str											=	use_cache





