from __future__ import annotations

import math



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 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		(      ):



 """simple docstring"""


 __a							=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
 __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 typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


