"""simple docstring"""




class __snake_case    :




   def __init__(   self      :    Dict	,     _lowercase      :    List[str]	,     _lowercase      :    Tuple	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				None
     SCREAMING_SNAKE_CASE__      							=				None
     SCREAMING_SNAKE_CASE__      							=				graph

     self._normalize_graph(__snake_case	,     __snake_case				)
     SCREAMING_SNAKE_CASE__      							=				len(__snake_case				)
     SCREAMING_SNAKE_CASE__      							=				None




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    str	,     _lowercase      :    List[Any]				):



     """simple docstring"""






     if sources is int:
       SCREAMING_SNAKE_CASE__      							=				[sources]
     if sinks is int:
       SCREAMING_SNAKE_CASE__      							=				[sinks]

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

     SCREAMING_SNAKE_CASE__      							=				sources[0]
     SCREAMING_SNAKE_CASE__      							=				sinks[0]

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

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

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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     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      :    int	,     _lowercase      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				algorithm(self				)







class __snake_case    :




   def __init__(   self      :    List[str]	,     _lowercase      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				flow_network
     SCREAMING_SNAKE_CASE__      							=				flow_network.verticesCount
     SCREAMING_SNAKE_CASE__      							=				flow_network.sourceIndex
     SCREAMING_SNAKE_CASE__      							=				flow_network.sinkIndex
     # it's just a reference, so you shouldn't change
     # it in your algorithms, use deep copy before doing that
     SCREAMING_SNAKE_CASE__      							=				flow_network.graph
     SCREAMING_SNAKE_CASE__      							=				False




   def      __a						(   self      :    str				):



     """simple docstring"""






     if not self.executed:
       self._algorithm()
       SCREAMING_SNAKE_CASE__      							=				True




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     pass







class __snake_case    (						lowerCamelCase__		):




   def __init__(   self      :    Optional[int]	,     _lowercase      :    Any				):



     """simple docstring"""






     super().__init__(__snake_case				)
     # use this to save your result
     SCREAMING_SNAKE_CASE__      							=				-1




   def      __a						(   self      :    Any				):



     """simple docstring"""






     if not self.executed:
       raise Exception("""You should execute algorithm before using its result!"""				)

     return self.maximum_flow







class __snake_case    (						lowerCamelCase__		):




   def __init__(   self      :    Dict	,     _lowercase      :    int				):



     """simple docstring"""






     super().__init__(__snake_case				)

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

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




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				[
         i
         for i in range(self.verticies_count				)
         if i != self.source_index and i != self.sink_index
     ]

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

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




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    List[Any]				):



     """simple docstring"""






     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(__snake_case	,     __snake_case				)

       self.relabel(__snake_case				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Tuple	,     _lowercase      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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      :    Optional[Any]	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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):
         SCREAMING_SNAKE_CASE__      							=				self.heights[to_index]

     if min_height is not None:
       SCREAMING_SNAKE_CASE__      							=				min_height + 1


if __name__ == "__main__":
   __lowerCamelCase      :			Any							       =		[0]
   __lowerCamelCase      :			Optional[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],
   # ]
   __lowerCamelCase      :			Optional[Any]							       =		[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

   # prepare our network
   __lowerCamelCase      :			Optional[Any]							       =		FlowNetwork(graph, entrances, exits)
   # set algorithm
   flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
   # and calculate
   __lowerCamelCase      :			int							       =		flow_network.find_maximum_flow()

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

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
   __lowerCamelCase      :			Optional[Any]							       =		{
       '''linear''': PIL.Image.Resampling.BILINEAR,
       '''bilinear''': PIL.Image.Resampling.BILINEAR,
       '''bicubic''': PIL.Image.Resampling.BICUBIC,
       '''lanczos''': PIL.Image.Resampling.LANCZOS,
       '''nearest''': PIL.Image.Resampling.NEAREST,
   }
else:
   __lowerCamelCase      :			int							       =		{
       '''linear''': PIL.Image.LINEAR,
       '''bilinear''': PIL.Image.BILINEAR,
       '''bicubic''': PIL.Image.BICUBIC,
       '''lanczos''': PIL.Image.LANCZOS,
       '''nearest''': PIL.Image.NEAREST,
   }



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				(images / 2 + 0.5).clamp(0			,					1     )
  SCREAMING_SNAKE_CASE__      							=				images.cpu().permute(0			,					2			,					3			,					1     ).float().numpy()
  SCREAMING_SNAKE_CASE__      							=				numpy_to_pil(__UpperCamelCase     )
  return images






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> int:







  """simple docstring"""





  if images.ndim == 3:
    SCREAMING_SNAKE_CASE__      							=				images[None, ...]
  SCREAMING_SNAKE_CASE__      							=				(images * 2_55).round().astype("""uint8"""     )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(image.squeeze()			,					mode="""L"""     ) for image in images]
  else:
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(__UpperCamelCase     ) for image in images]

  return pil_images
