






'''simple docstring'''






from __future__ import annotations

import math




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       str  ,		A  :       str  ,		A  :       List[str]  ,		A  :       List[str]  )    -> str:







   """simple docstring"""

   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        _SCREAMING_SNAKE_CASE  (				)    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
   __snake_case				:      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)