

"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						['''image_processor''', '''tokenizer''']
			SCREAMING_SNAKE_CASE__     				=						'''CLIPImageProcessor'''
			SCREAMING_SNAKE_CASE__     				=						('''CLIPTokenizer''', '''CLIPTokenizerFast''')







			def __init__(			self			: Union[str, Any]			,  lowercase			: str=None			,  lowercase			: List[str]=None			,  **lowercase			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."			,  lowercase			,  )
													UpperCAmelCase			:   str      =				kwargs.pop("feature_extractor"  )

								UpperCAmelCase			:   str      =				image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
													raise ValueError("You need to specify an `image_processor`."  )
								if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."  )

								super().__init__(lowercase			,  lowercase  )







			def __call__(			self			: Union[str, Any]			,  lowercase			: Union[str, Any]=None			,  lowercase			: Tuple=None			,  lowercase			: str=None			,  **lowercase			: int  ):





								'''simple docstring'''



								if text is None and images is None:
													raise ValueError("You have to specify either text or images. Both cannot be none."  )

								if text is not None:
													UpperCAmelCase			:   Union[str, Any]      =				self.tokenizer(lowercase			,  return_tensors=lowercase			,  **lowercase  )

								if images is not None:
													UpperCAmelCase			:   Any      =				self.image_processor(lowercase			,  return_tensors=lowercase			,  **lowercase  )

								if text is not None and images is not None:
													UpperCAmelCase			:   Optional[int]      =				image_features.pixel_values
													return encoding
								elif text is not None:
													return encoding
								else:
													return BatchEncoding(data=dict(**lowercase  )			,  tensor_type=lowercase  )







			def 			__lowerCAmelCase				(			self			: Any			,  *lowercase			: Any			,  **lowercase			: Optional[Any]  ):





								'''simple docstring'''



								return self.tokenizer.batch_decode(*lowercase			,  **lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  *lowercase			: Optional[int]			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



								return self.tokenizer.decode(*lowercase			,  **lowercase  )







			@property
			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.tokenizer.model_input_names
								UpperCAmelCase			:   Tuple      =				self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







			@property
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,  lowercase			,  )
								return self.image_processor_class







			@property
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,  lowercase			,  )
								return self.image_processor





"""simple docstring"""






def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Tuple							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				[False] * len(_lowercase	)
					UpperCAmelCase			:   List[str]      =				[]
					queue.append(_lowercase	)
					UpperCAmelCase			:   List[Any]      =				True

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

					return visited[t]



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Dict							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				[-1] * (len(_lowercase	))
					UpperCAmelCase			:   List[Any]      =				0
					while bfs(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				float("Inf"	)
										UpperCAmelCase			:   str      =				sink

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

										max_flow += path_flow
										UpperCAmelCase			:   List[str]      =				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


snake_case_   :   Optional[Any]        =       [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

snake_case_     ,      snake_case_   :   str        =       0, 5




print(ford_fulkerson(graph, source, sink))


