




from __future__ import annotations

import math




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase						,lowercase					)  ->       int:


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

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

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




def 						SCREAMING_SNAKE_CASE__ (   )  ->       None:
   snake_case				:    List[str]           =	[90, 23, 6, 33, 21, 65, 123, 34423]
   snake_case				:    Optional[Any]           =	math.log(len(SCREAMING_SNAKE_CASE__					)						,2					)
   print("""Optimal value : """						,end=""""""					)
   print(minimax(0						,0						,SCREAMING_SNAKE_CASE__						,SCREAMING_SNAKE_CASE__						,SCREAMING_SNAKE_CASE__					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:	List[str]				=   {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[Any]				=   [
           'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
           'Swinv2ForImageClassification',
           'Swinv2ForMaskedImageModeling',
           'Swinv2Model',
           'Swinv2PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swinva import (
                  SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinvaForImageClassification,
                  SwinvaForMaskedImageModeling,
                  SwinvaModel,
                  SwinvaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





