


class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Any:
             a_					:		str							=  None
             a_					:		Tuple							=  None
             a_					:		Union[str, Any]							=  graph

             self._normalize_graph(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  len(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[Any]							=  None
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[Any]:
             if sources is int:
                    a_					:		str							=  [sources]
             if sinks is int:
                    a_					:		str							=  [sinks]

             if len(SCREAMING_SNAKE_CASE__     ) == 0 or len(SCREAMING_SNAKE_CASE__     ) == 0:
                    return

             a_					:		Tuple							=  sources[0]
             a_					:		Optional[int]							=  sinks[0]

             # make fake vertex if there are more
             # than one source or sink
             if len(SCREAMING_SNAKE_CASE__     ) > 1 or len(SCREAMING_SNAKE_CASE__     ) > 1:
                    a_					:		Tuple							=  0
                    for i in sources:
                           max_input_flow += sum(self.graph[i]     )

                    a_					:		Optional[Any]							=  len(self.graph     ) + 1
                    for room in self.graph:
                           room.insert(0     ,							0     )
                    self.graph.insert(0     ,							[0] * size     )
                    for i in sources:
                           a_					:		Optional[int]							=  max_input_flow
                    a_					:		int							=  0

                    a_					:		List[str]							=  len(self.graph     ) + 1
                    for room in self.graph:
                           room.append(0     )
                    self.graph.append([0] * size     )
                    for i in sinks:
                           a_					:		Any							=  max_input_flow
                    a_					:		Union[str, Any]							=  size - 1
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			str:
             if self.maximum_flow_algorithm is None:
                    raise Exception('You need to set maximum flow algorithm before.'     )
             if self.source_index is None or self.sink_index is None:
                    return 0

             self.maximum_flow_algorithm.execute()
             return self.maximum_flow_algorithm.getMaximumFlow()

      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			List[Any]:
             a_					:		Union[str, Any]							=  algorithm(self     )
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			int:
             a_					:		Dict							=  flow_network
             a_					:		Optional[Any]							=  flow_network.verticesCount
             a_					:		int							=  flow_network.sourceIndex
             a_					:		Optional[Any]							=  flow_network.sinkIndex
             # it's just a reference, so you shouldn't change
             # it in your algorithms, use deep copy before doing that
             a_					:		List[Any]							=  flow_network.graph
             a_					:		Optional[int]							=  False
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             if not self.executed:
                    self._algorithm()
                    a_					:		Any							=  True

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Dict:
             pass
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Any:
             super().__init__(SCREAMING_SNAKE_CASE__     )
             # use this to save your result
             a_					:		int							=  -1

      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Any:
             if not self.executed:
                    raise Exception('You should execute algorithm before using its result!'     )

             return self.maximum_flow
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Tuple:
             super().__init__(SCREAMING_SNAKE_CASE__     )

             a_					:		Dict							=  [[0] * self.verticies_count for i in range(self.verticies_count     )]

             a_					:		Optional[Any]							=  [0] * self.verticies_count
             a_					:		Union[str, Any]							=  [0] * self.verticies_count
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Dict:
             a_					:		List[str]							=  self.verticies_count

             # push some substance to graph
             for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]     ):
                    self.preflow[self.source_index][nextvertex_index] += bandwidth
                    self.preflow[nextvertex_index][self.source_index] -= bandwidth
                    self.excesses[nextvertex_index] += bandwidth

             # Relabel-to-front selection rule
             a_					:		str							=  [
                 i
                 for i in range(self.verticies_count     )
                 if i != self.source_index and i != self.sink_index
             ]

             # move through list
             a_					:		List[str]							=  0
             while i < len(SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  vertices_list[i]
                    a_					:		Dict							=  self.heights[vertex_index]
                    self.process_vertex(SCREAMING_SNAKE_CASE__     )
                    if self.heights[vertex_index] > previous_height:
                           # if it was relabeled, swap elements
                           # and start from 0 index
                           vertices_list.insert(0     ,							vertices_list.pop(SCREAMING_SNAKE_CASE__     )     )
                           a_					:		List[Any]							=  0
                    else:
                           i += 1

             a_					:		Union[str, Any]							=  sum(self.preflow[self.source_index]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			int:
             while self.excesses[vertex_index] > 0:
                    for neighbour_index in range(self.verticies_count     ):
                           # if it's neighbour and current vertex is higher
                           if (
                               self.graph[vertex_index][neighbour_index]
                               - self.preflow[vertex_index][neighbour_index]
                               > 0
                               and self.heights[vertex_index] > self.heights[neighbour_index]
                           ):
                                  self.push(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

                    self.relabel(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Any:
             a_					:		str							=  min(
                 self.excesses[from_index]     ,							self.graph[from_index][to_index] - self.preflow[from_index][to_index]     ,							)
             self.preflow[from_index][to_index] += preflow_delta
             self.preflow[to_index][from_index] -= preflow_delta
             self.excesses[from_index] -= preflow_delta
             self.excesses[to_index] += preflow_delta

      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			str:
             a_					:		Optional[Any]							=  None
             for to_index in range(self.verticies_count     ):
                    if (
                        self.graph[vertex_index][to_index]
                        - self.preflow[vertex_index][to_index]
                        > 0
                    ) and (min_height is None or self.heights[to_index] < min_height):
                           a_					:		Optional[Any]							=  self.heights[to_index]

             if min_height is not None:
                    a_					:		List[Any]							=  min_height + 1


if __name__ == "__main__":
   UpperCAmelCase_      : Any  					=       [0]
   UpperCAmelCase_      : Any  					=       [3]
   # graph = [
   #     [0, 0, 4, 6, 0, 0],
   #     [0, 0, 5, 2, 0, 0],
   #     [0, 0, 0, 0, 4, 4],
   #     [0, 0, 0, 0, 6, 6],
   #     [0, 0, 0, 0, 0, 0],
   #     [0, 0, 0, 0, 0, 0],
   # ]
   UpperCAmelCase_      : Any  					=       [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

   # prepare our network
   UpperCAmelCase_      : Tuple  					=       FlowNetwork(graph, entrances, exits)
   # set algorithm
   flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
   # and calculate
   UpperCAmelCase_      : int  					=       flow_network.find_maximum_flow()

   print(F'maximum flow is {maximum_flow}')






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Tuple        =						['''image_processor''', '''tokenizer''']
      snake_case__				:     Union[str, Any]        =						'''CLIPImageProcessor'''
      snake_case__				:     Dict        =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							**SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             a_					:		List[Any]							=  None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
                    a_					:		Tuple							=  kwargs.pop('feature_extractor'     )

             a_					:		Tuple							=  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__(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Optional[Any]:

             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:
                    a_					:		List[str]							=  self.tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if images is not None:
                    a_					:		Dict							=  self.image_processor(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if text is not None and images is not None:
                    a_					:		Dict							=  image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__     )     ,							tensor_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		str							=  self.tokenizer.model_input_names
             a_					:		Tuple							=  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor_class

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor


