



from __future__ import annotations

import math



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  List[Any]:

    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 		lowerCAmelCase__    (					)->  Any:
    A__		 =					[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    A__		 =					math.log(len(__lowerCAmelCase	) ,       2	)
    print('''Optimal value : ''' ,       end=''''''	)
    print(minimax(0 ,       0 ,       __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase	)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()







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


_lowercase						  =		logging.get_logger(__name__)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''timm_backbone'''




     def __init__(				self ,						a__=None ,						a__=3 ,						a__=True ,						a__=True ,						a__=None ,						**a__ ,						):
         super().__init__(**a__)
         A__		 =					backbone
         A__		 =					num_channels
         A__		 =					features_only
         A__		 =					use_pretrained_backbone
         A__		 =					True
         A__		 =					out_indices if out_indices is not None else (-1,)
