



'''simple docstring'''






def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						str  )      ->   List[str]:
							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					UpperCAmelCase :       str 							=		mf_knapsack(i - 1   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														else:
																					UpperCAmelCase :       int 							=		max(
																					    mf_knapsack(i - 1   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )   ,      mf_knapsack(i - 1   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      j - wt[i - 1]  ) + val[i - 1]   ,      )
														UpperCAmelCase :       List[str] 							=		val
							return f[i][j]


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Dict  )      ->   List[str]:
							UpperCAmelCase :       Any 							=		[[0] * (w + 1) for _ in range(n + 1  )]

							for i in range(1   ,      n + 1  ):
														for w_ in range(1   ,      w + 1  ):
																					if wt[i - 1] <= w_:
																												UpperCAmelCase :       int 							=		max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   ,      dp[i - 1][w_]  )
																					else:
																												UpperCAmelCase :       Union[str, Any] 							=		dp[i - 1][w_]

							return dp[n][w_], dp


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						list   ,      _lowerCAmelCase  :						list  )      ->   Dict:
							if not (isinstance(_lowerCAmelCase   ,      (list, tuple)  ) and isinstance(_lowerCAmelCase   ,      (list, tuple)  )):
														raise ValueError(
														    '''Both the weights and values vectors must be either lists or tuples'''  )

							UpperCAmelCase :       int 							=		len(_lowerCAmelCase  )
							if num_items != len(_lowerCAmelCase  ):
														UpperCAmelCase :       str 							=		(
														    '''The number of weights must be the same as the number of values.\n'''
														    f"""But got {num_items} weights and {len(_lowerCAmelCase  )} values"""
														)
														raise ValueError(_lowerCAmelCase  )
							for i in range(_lowerCAmelCase  ):
														if not isinstance(wt[i]   ,      _lowerCAmelCase  ):
																					UpperCAmelCase :       Any 							=		(
																					    '''All weights must be integers but got weight of '''
																					    f"""type {type(wt[i]  )} at index {i}"""
																					)
																					raise TypeError(_lowerCAmelCase  )

							UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		knapsack(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							UpperCAmelCase :       set 							=		set()
							_construct_solution(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							return optimal_val, example_optional_set


def  snake_case_     (					_lowerCAmelCase  :						list   ,      _lowerCAmelCase  :						list   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						set  )      ->   int:
							# for the current item i at a maximum weight j to be part of an optimal subset,
							# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
							# where i - 1 means considering only the previous items at the given maximum weight
							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(_lowerCAmelCase   ,      _lowerCAmelCase   ,      i - 1   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														else:
																					optimal_set.add(_lowerCAmelCase  )
																					_construct_solution(_lowerCAmelCase   ,      _lowerCAmelCase   ,      i - 1   ,      j - wt[i - 1]   ,      _lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   Optional[int]								=			[3, 2, 4, 4]
						UpperCamelCase__:   List[str]								=			[4, 3, 2, 3]
						UpperCamelCase__:   List[Any]								=			4
						UpperCamelCase__:   Tuple								=			6
						UpperCamelCase__:   Optional[Any]								=			[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
						UpperCamelCase__	,					UpperCamelCase__:   Union[str, Any]								=			knapsack(w, wt, val, n)
						print(optimal_solution)
						print(mf_knapsack(n, wt, val, w))  # switched the n and w

						# testing the dynamic programming problem with example
						# the optimal subset for the above example are items 3 and 4
						UpperCamelCase__	,					UpperCamelCase__:   Tuple								=			knapsack_with_example_solution(w, wt, val)
						assert optimal_solution == 8
						assert optimal_subset == {3, 4}
						print("optimal_value = ", optimal_solution)
						print("An optimal subset corresponding to the optimal value", optimal_subset)










'''simple docstring'''






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       """MCTCTFeatureExtractor"""
	lowerCamelCase__			 =       """AutoTokenizer"""







	def __init__(						self			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		)			->		str:
								super().__init__(__snake_case		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		self.feature_extractor
								UpperCAmelCase :       Union[str, Any] 							=		False







	def __call__(						self			:     Any		,			*__snake_case			:     List[str]		,			**__snake_case			:     Any		)			->		List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor(*__snake_case		,			**__snake_case		)

								if "raw_speech" in kwargs:
															warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''		)
															UpperCAmelCase :       int 							=		kwargs.pop('''raw_speech'''		)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		kwargs.pop('''audio'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''sampling_rate'''		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		kwargs.pop('''text'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       Any 							=		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 :       List[str] 							=		self.feature_extractor(__snake_case		,			*__snake_case		,			sampling_rate=__snake_case		,			**__snake_case		)
								if text is not None:
															UpperCAmelCase :       int 							=		self.tokenizer(__snake_case		,			**__snake_case		)

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







	def      A	(						self			:     List[Any]		,			*__snake_case			:     List[Any]		,			**__snake_case			:     List[Any]		)			->		str:
								return self.tokenizer.batch_decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			*__snake_case			:     int		,			**__snake_case			:     Optional[int]		)			->		Any:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor.pad(*__snake_case		,			**__snake_case		)

								UpperCAmelCase :       List[Any] 							=		kwargs.pop('''input_features'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''labels'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       List[str] 							=		args[0]
															UpperCAmelCase :       List[Any] 							=		args[1:]

								if input_features is not None:
															UpperCAmelCase :       Tuple 							=		self.feature_extractor.pad(__snake_case		,			*__snake_case		,			**__snake_case		)
								if labels is not None:
															UpperCAmelCase :       Optional[int] 							=		self.tokenizer.pad(__snake_case		,			**__snake_case		)

								if labels is None:
															return input_features
								elif input_features is None:
															return labels
								else:
															UpperCAmelCase :       List[str] 							=		labels['''input_ids''']
															return input_features







	def      A	(						self			:     Union[str, Any]		,			*__snake_case			:     Optional[Any]		,			**__snake_case			:     Optional[int]		)			->		Optional[Any]:
								return self.tokenizer.decode(*__snake_case		,			**__snake_case		)







	@contextmanager
	def      A	(						self			:     Any		)			->		Optional[int]:
								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 :       Dict 							=		True
								UpperCAmelCase :       List[Any] 							=		self.tokenizer
								yield
								UpperCAmelCase :       Tuple 							=		self.feature_extractor
								UpperCAmelCase :       List[Any] 							=		False





