"""simple docstring"""


from __future__ import annotations

import math







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      str:

     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 					UpperCAmelCase    (		)				->      Union[str, Any]:
     snake_case_					       =					[90, 23, 6, 33, 21, 65, 123, 34423]
     snake_case_					       =					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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

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


