


"""simple docstring"""
def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      Dict						, __snake_case							:      int						, __snake_case							:      int   ):




   """simple docstring"""







   global f  # a global dp table for knapsack
   if f[i][j] < 0:
      if j < wt[i - 1]:
         _lowerCamelCase	:					Dict   =		mf_knapsack(i - 1						, __snake_case						, __snake_case						, __snake_case   )
      else:
         _lowerCamelCase	:					Union[str, Any]   =		max(
             mf_knapsack(i - 1						, __snake_case						, __snake_case						, __snake_case   )						, mf_knapsack(i - 1						, __snake_case						, __snake_case						, j - wt[i - 1]   ) + val[i - 1]						, )
      _lowerCamelCase	:					List[Any]   =		val
   return f[i][j]

def 				_snake_case      (   __snake_case							:      Dict						, __snake_case							:      Tuple						, __snake_case							:      str						, __snake_case							:      List[Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		[[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_:
            _lowerCamelCase	:					List[str]   =		max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]						, dp[i - 1][w_]   )
         else:
            _lowerCamelCase	:					Union[str, Any]   =		dp[i - 1][w_]

   return dp[n][w_], dp

def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      list						, __snake_case							:      list   ):




   """simple docstring"""







   if not (isinstance(__snake_case						, (list, tuple)   ) and isinstance(__snake_case						, (list, tuple)   )):
      raise ValueError(
          """Both the weights and values vectors must be either lists or tuples"""   )

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

   _lowerCamelCase   ,    _lowerCamelCase	:					int   =		knapsack(__snake_case						, __snake_case						, __snake_case						, __snake_case   )
   _lowerCamelCase	:					set   =		set()
   _construct_solution(__snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case   )

   return optimal_val, example_optional_set

def 				_snake_case      (   __snake_case							:      list						, __snake_case							:      list						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      set   ):




   """simple docstring"""







   if i > 0 and j > 0:
      if dp[i - 1][j] == dp[i][j]:
         _construct_solution(__snake_case						, __snake_case						, i - 1						, __snake_case						, __snake_case   )
      else:
         optimal_set.add(__snake_case   )
         _construct_solution(__snake_case						, __snake_case						, i - 1						, j - wt[i - 1]						, __snake_case   )


if __name__ == "__main__":
 UpperCAmelCase				=						[3, 2, 4, 4]
 UpperCAmelCase				=						[4, 3, 2, 3]
 UpperCAmelCase				=						4
 UpperCAmelCase				=						6
 UpperCAmelCase				=						[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 UpperCAmelCase			,  UpperCAmelCase				=						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				=						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)



from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""sampling_rate"""				,			__UpperCamelCase  )

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""  )
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""  )
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""  )

									if audio is not None:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if audio_target is not None:
															_lowercase					       =					self.feature_extractor(audio_target=__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_values"""]
									elif text_target is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_ids"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""labels"""				,			__UpperCamelCase  )

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""  )
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""  )

									if input_values is not None:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if labels is not None:
															if "input_ids" in labels or (isinstance(__UpperCamelCase				,			__UpperCamelCase  ) and "input_ids" in labels[0]):
																					_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					targets["""input_ids"""]
															else:
																					_lowercase					       =					self.feature_extractor.feature_size
																					_lowercase					       =					self.feature_extractor.num_mel_bins
																					_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					feature_size_hack
																					_lowercase					       =					targets["""input_values"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )