




from __future__ import annotations

import math


def 						lowerCamelCase__    (		_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 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     [90, 23, 6, 33, 21, 65, 123, 34423]
	snake_case_		    =     math.log(len(A_							)     ,	2							)
	print(f"Optimal value : {minimax(0     ,	0     ,	A_     ,	A_     ,	A_							)}"							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




