


'''simple docstring'''
from __future__ import annotations

import math


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Optional[int]:



					'''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     a   (    )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				[90, 23, 6, 33, 21, 65, 123, 34423]
					UpperCamelCase__     :int							      =				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'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'Speech2TextFeatureExtractor'
		_a									=       'Speech2TextTokenizer'




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.feature_extractor
							UpperCamelCase__     :Optional[Any]							      =				False




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor(*UpperCamelCase_					,     **UpperCamelCase_							)

							if "raw_speech" in kwargs:
												warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
												UpperCamelCase__     :Optional[Any]							      =				kwargs.pop('''raw_speech'''							)
							else:
												UpperCamelCase__     :List[Any]							      =				kwargs.pop('''audio'''					,     UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				kwargs.pop('''sampling_rate'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				kwargs.pop('''text'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :Tuple							      =				args[0]
												UpperCamelCase__     :Optional[int]							      =				args[1:]

							if audio is None and text is None:
												raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

							if audio is not None:
												UpperCamelCase__     :Any							      =				self.feature_extractor(UpperCamelCase_					,     *UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     **UpperCamelCase_							)
							if text is not None:
												UpperCamelCase__     :Dict							      =				self.tokenizer(UpperCamelCase_					,     **UpperCamelCase_							)

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												UpperCamelCase__     :Dict							      =				encodings['''input_ids''']
												return inputs




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		@contextmanager
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							warnings.warn(
							    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
							    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
							    '''your audio inputs, or in a separate call.'''							)
							UpperCamelCase__     :Optional[Any]							      =				True
							UpperCamelCase__     :Any							      =				self.tokenizer
							yield
							UpperCamelCase__     :Any							      =				self.feature_extractor
							UpperCamelCase__     :Any							      =				False