



from __future__ import annotations

import math


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:


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

			if not scores:
						raise ValueError('Scores cannot be empty'       )

			if depth == height:
						return scores[node_index]

			return (
			    max(
			        minimax(depth + 1			,						node_index * 2			,						_A			,						_A			,						_A       )			,						minimax(depth + 1			,						node_index * 2 + 1			,						_A			,						_A			,						_A       )			,						)
			    if is_max
			    else min(
			        minimax(depth + 1			,						node_index * 2			,						_A			,						_A			,						_A       )			,						minimax(depth + 1			,						node_index * 2 + 1			,						_A			,						_A			,						_A       )			,						)
			)


def A    (							)    ->   Optional[int]:
			A__	  =     [90, 23, 6, 33, 21, 65, 123, 34_423]
			A__	  =     math.log(len(_A       )			,						2       )
			print(f'''Optimal value : {minimax(0			,						0			,						_A			,						_A			,						_A       )}'''       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()





from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""T5Config"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig


