





'''simple docstring'''






from __future__ import annotations

import math
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : bool    ,       UpperCamelCase__    : list[int]    ,       UpperCamelCase__    : float		)		->  Any:


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

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

    return min(
        minimax(depth + 1    ,       node_index * 2    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase		)    ,       minimax(depth + 1    ,       node_index * 2 + 1    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase		)    ,       )
def 							_lowerCamelCase(  )		->  Any:
    A       :       Union[str, Any]							=						[90, 23, 6, 33, 21, 65, 123, 34_423]
    A       :       List[Any]							=						math.log(len(_UpperCamelCase		)    ,       2		)
    print('''Optimal value : '''    ,       end=''''''		)
    print(minimax(0    ,       0    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase		)		)


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


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """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

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
