


class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							=None
         __UpperCamelCase   							=None
         __UpperCamelCase   							=graph

         self._normalize_graph(A_ ,       A_   )
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=None






    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         if sources is int:
              __UpperCamelCase   							=[sources]
         if sinks is int:
              __UpperCamelCase   							=[sinks]

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

         __UpperCamelCase   							=sources[0]
         __UpperCamelCase   							=sinks[0]

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

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

              __UpperCamelCase   							=len(self.graph   ) + 1
              for room in self.graph:
                   room.append(0   )
              self.graph.append([0] * size   )
              for i in sinks:
                   __UpperCamelCase   							=max_input_flow
              __UpperCamelCase   							=size - 1






    def _a (      self   )  ->	Optional[Any]:
         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 _a (      self ,       A_   )  ->	Optional[int]:
         __UpperCamelCase   							=algorithm(self   )






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Any:
         __UpperCamelCase   							=flow_network
         __UpperCamelCase   							=flow_network.verticesCount
         __UpperCamelCase   							=flow_network.sourceIndex
         __UpperCamelCase   							=flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         __UpperCamelCase   							=flow_network.graph
         __UpperCamelCase   							=False






    def _a (      self   )  ->	Optional[int]:
         if not self.executed:
              self._algorithm()
              __UpperCamelCase   							=True






    def _a (      self   )  ->	Tuple:
         pass






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Optional[int]:
         super().__init__(A_   )
         # use this to save your result
         __UpperCamelCase   							=-1






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

         return self.maximum_flow






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	int:
         super().__init__(A_   )

         __UpperCamelCase   							=[[0] * self.verticies_count for i in range(self.verticies_count   )]

         __UpperCamelCase   							=[0] * self.verticies_count
         __UpperCamelCase   							=[0] * self.verticies_count






    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=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
         __UpperCamelCase   							=[
             i
             for i in range(self.verticies_count   )
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         __UpperCamelCase   							=0
         while i < len(A_   ):
              __UpperCamelCase   							=vertices_list[i]
              __UpperCamelCase   							=self.heights[vertex_index]
              self.process_vertex(A_   )
              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(A_   )   )
                   __UpperCamelCase   							=0
              else:
                   i += 1

         __UpperCamelCase   							=sum(self.preflow[self.source_index]   )






    def _a (      self ,       A_   )  ->	List[str]:
         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(A_ ,       A_   )

              self.relabel(A_   )






    def _a (      self ,       A_ ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=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 _a (      self ,       A_   )  ->	str:
         __UpperCamelCase   							=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):
                   __UpperCamelCase   							=self.heights[to_index]

         if min_height is not None:
              __UpperCamelCase   							=min_height + 1


if __name__ == "__main__":
   _A			=						[0]
   _A			=						[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],
   # ]
   _A			=						[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

   print(f"""maximum flow is {maximum_flow}""")









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





