



'''simple docstring'''






from ...processing_utils import ProcessorMixin

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """SpeechT5FeatureExtractor"""
	lowerCamelCase__    				= """SpeechT5Tokenizer"""







	def __init__(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Tuple	)    ->	List[str]:
								super().__init__(__snake_case	,    __snake_case	)







	def __call__(		self    :  str	,    *__snake_case    :  int	,    **__snake_case    :  str	)    ->	str:
								UpperCAmelCase				: Optional[Any]											=	kwargs.pop('''audio'''	,    __snake_case	)
								UpperCAmelCase				: List[Any]											=	kwargs.pop('''text'''	,    __snake_case	)
								UpperCAmelCase				: Tuple											=	kwargs.pop('''text_target'''	,    __snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	kwargs.pop('''audio_target'''	,    __snake_case	)
								UpperCAmelCase				: List[str]											=	kwargs.pop('''sampling_rate'''	,    __snake_case	)

								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:
															UpperCAmelCase				: List[Any]											=	self.feature_extractor(__snake_case	,    *__snake_case	,    sampling_rate=__snake_case	,    **__snake_case	)
								elif text is not None:
															UpperCAmelCase				: int											=	self.tokenizer(__snake_case	,    **__snake_case	)
								else:
															UpperCAmelCase				: Any											=	None

								if audio_target is not None:
															UpperCAmelCase				: Tuple											=	self.feature_extractor(audio_target=__snake_case	,    *__snake_case	,    sampling_rate=__snake_case	,    **__snake_case	)
															UpperCAmelCase				: Optional[int]											=	targets['''input_values''']
								elif text_target is not None:
															UpperCAmelCase				: Optional[int]											=	self.tokenizer(__snake_case	,    **__snake_case	)
															UpperCAmelCase				: List[Any]											=	targets['''input_ids''']
								else:
															UpperCAmelCase				: Any											=	None

								if inputs is None:
															return targets

								if targets is not None:
															UpperCAmelCase				: Union[str, Any]											=	labels

															UpperCAmelCase				: Union[str, Any]											=	targets.get('''attention_mask'''	)
															if decoder_attention_mask is not None:
																						UpperCAmelCase				: List[str]											=	decoder_attention_mask

								return inputs







	def   A			(		self    :  Optional[Any]	,    *__snake_case    :  Union[str, Any]	,    **__snake_case    :  Optional[int]	)    ->	Optional[int]:
								UpperCAmelCase				: Any											=	kwargs.pop('''input_values'''	,    __snake_case	)
								UpperCAmelCase				: str											=	kwargs.pop('''input_ids'''	,    __snake_case	)
								UpperCAmelCase				: List[Any]											=	kwargs.pop('''labels'''	,    __snake_case	)

								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:
															UpperCAmelCase				: List[Any]											=	self.feature_extractor.pad(__snake_case	,    *__snake_case	,    **__snake_case	)
								elif input_ids is not None:
															UpperCAmelCase				: Optional[int]											=	self.tokenizer.pad(__snake_case	,    **__snake_case	)
								else:
															UpperCAmelCase				: Dict											=	None

								if labels is not None:
															if "input_ids" in labels or (isinstance(__snake_case	,    __snake_case	) and "input_ids" in labels[0]):
																						UpperCAmelCase				: Dict											=	self.tokenizer.pad(__snake_case	,    **__snake_case	)
																						UpperCAmelCase				: List[Any]											=	targets['''input_ids''']
															else:
																						UpperCAmelCase				: int											=	self.feature_extractor.feature_size
																						UpperCAmelCase				: Optional[int]											=	self.feature_extractor.num_mel_bins
																						UpperCAmelCase				: int											=	self.feature_extractor.pad(__snake_case	,    *__snake_case	,    **__snake_case	)
																						UpperCAmelCase				: str											=	feature_size_hack
																						UpperCAmelCase				: str											=	targets['''input_values''']
								else:
															UpperCAmelCase				: Optional[int]											=	None

								if inputs is None:
															return targets

								if targets is not None:
															UpperCAmelCase				: Optional[Any]											=	labels

															UpperCAmelCase				: int											=	targets.get('''attention_mask'''	)
															if decoder_attention_mask is not None:
																						UpperCAmelCase				: Any											=	decoder_attention_mask

								return inputs







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







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










'''simple docstring'''






def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     Union[str, Any]:
							# Return True if there is node that has not iterated.
							UpperCAmelCase				: Optional[Any]											=	[False] * len(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	[]
							queue.append(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	True

							while queue:
														UpperCAmelCase				: Optional[int]											=	queue.pop(0						)
														for ind in range(len(graph[u]						)						):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(_lowerCAmelCase						)
																												UpperCAmelCase				: Union[str, Any]											=	True
																												UpperCAmelCase				: Optional[int]											=	u

							return visited[t]


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]						)   ->     Any:
							# This array is filled by BFS and to store path
							UpperCAmelCase				: Dict											=	[-1] * (len(_lowerCAmelCase						))
							UpperCAmelCase				: Any											=	0
							while bfs(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: List[str]											=	float('''Inf'''						)
														UpperCAmelCase				: str											=	sink

														while s != source:
																					# Find the minimum value in select path
																					UpperCAmelCase				: int											=	min(_lowerCAmelCase     ,   graph[parent[s]][s]						)
																					UpperCAmelCase				: Tuple											=	parent[s]

														max_flow += path_flow
														UpperCAmelCase				: Optional[Any]											=	sink

														while v != source:
																					UpperCAmelCase				: Dict											=	parent[v]
																					graph[u][v] -= path_flow
																					graph[v][u] += path_flow
																					UpperCAmelCase				: Dict											=	parent[v]
							return max_flow


UpperCamelCase__:     Tuple										=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCamelCase__			,      UpperCamelCase__:     int										=    0, 5




print(ford_fulkerson(graph, source, sink))





