def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__a		 =       True

					while queue:
										__a		 =       queue.pop(0						)
										for ind in range(len(graph[u]						)						):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(__lowerCamelCase						)
																				__a		 =       True
																				__a		 =       u

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

										while s != source:
															# Find the minimum value in select path
															__a		 =       min(__lowerCamelCase				,    graph[parent[s]][s]						)
															__a		 =       parent[s]

										max_flow += path_flow
										__a		 =       sink

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


lowerCamelCase_    :		str							=	[
    [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],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								try:
													return super().from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								except OSError:
													warnings.warn(
													    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
													    ' include a `tokenizer_class` attribute is deprecated and will be '
													    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
													    ' attribute to either your `config.json` or `tokenizer_config.json` '
													    'file to suppress this warning: '				,						UpperCAmelCase				,						)

													__a		 =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
													__a		 =       WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )

													return cls(feature_extractor=UpperCAmelCase				,						tokenizer=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								# For backward compatibility
								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.'   )
													__a		 =       kwargs.pop('raw_speech'   )
								else:
													__a		 =       kwargs.pop('audio'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('sampling_rate'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('text'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       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:
													__a		 =       self.feature_extractor(UpperCAmelCase				,						*UpperCAmelCase				,						sampling_rate=UpperCAmelCase				,						**UpperCAmelCase   )
								if text is not None:
													__a		 =       self.tokenizer(UpperCAmelCase				,						**UpperCAmelCase   )

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



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor.pad(*UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       kwargs.pop('input_features'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('labels'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if input_features is not None:
													__a		 =       self.feature_extractor.pad(UpperCAmelCase				,						*UpperCAmelCase				,						**UpperCAmelCase   )
								if labels is not None:
													__a		 =       self.tokenizer.pad(UpperCAmelCase				,						**UpperCAmelCase   )

								if labels is None:
													return input_features
								elif input_features is None:
													return labels
								else:
													__a		 =       labels['input_ids']
													return input_features



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.tokenizer.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return self.tokenizer.decode(*UpperCAmelCase				,						**UpperCAmelCase   )







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								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.'   )
								__a		 =       True
								__a		 =       self.tokenizer
								yield
								__a		 =       self.feature_extractor
								__a		 =       False




