

def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







     global f  # a global dp table for knapsack
     if f[i][j] < 0:
          if j < wt[i - 1]:
               SCREAMING_SNAKE_CASE      						=mf_knapsack(i - 1,    _lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase			)
          else:
               SCREAMING_SNAKE_CASE      						=max(
                   mf_knapsack(i - 1,    _lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase			),    mf_knapsack(i - 1,    _lowerCAmelCase,    _lowerCAmelCase,    j - wt[i - 1]			) + val[i - 1],    )
          SCREAMING_SNAKE_CASE      						=val
     return f[i][j]







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE      						=[[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_:
                    SCREAMING_SNAKE_CASE      						=max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],    dp[i - 1][w_]			)
               else:
                    SCREAMING_SNAKE_CASE      						=dp[i - 1][w_]

     return dp[n][w_], dp







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







     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'			)

     SCREAMING_SNAKE_CASE      						=len(_lowerCAmelCase			)
     if num_items != len(_lowerCAmelCase			):
          SCREAMING_SNAKE_CASE      						=(
              '''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			):
               SCREAMING_SNAKE_CASE      						=(
                   '''All weights must be integers but got weight of '''
                   F'type {type(wt[i]			)} at index {i}'
               )
               raise TypeError(_lowerCAmelCase			)

     SCREAMING_SNAKE_CASE      						=knapsack(_lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase			)
     SCREAMING_SNAKE_CASE      						=set()
     _construct_solution(_lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase,    _lowerCAmelCase			)

     return optimal_val, example_optional_set







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







     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__":
 _lowerCamelCase							       =[3, 2, 4, 4]
 _lowerCamelCase							       =[4, 3, 2, 3]
 _lowerCamelCase							       =4
 _lowerCamelCase							       =6
 _lowerCamelCase							       =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 _lowerCamelCase							       =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
 _lowerCamelCase							       =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





