





class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Union[str, Any]      ,					a      :    Union[str, Any]      ,					a      :    str      ,					a      :    Dict	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  None
     lowercase__           =  None
     lowercase__           =  graph

     self._normalize_graph(a      ,					a	)
     lowercase__           =  len(a	)
     lowercase__           =  None


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    List[str]      ,					a      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







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

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

     lowercase__           =  sources[0]
     lowercase__           =  sinks[0]

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

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Union[str, 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 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Tuple	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  algorithm(self	)






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[int]      ,					a      :    List[Any]	)->       Optional[Any]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Any:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Dict:





     """simple docstring"""







     pass






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def __init__(   self      :    List[Any]      ,					a      :    List[str]	)->       Tuple:





     """simple docstring"""







     super().__init__(a	)
     # use this to save your result
     lowercase__           =  -1


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Union[str, Any]:





     """simple docstring"""







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

     return self.maximum_flow






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def __init__(   self      :    Optional[int]      ,					a      :    Tuple	)->       int:





     """simple docstring"""







     super().__init__(a	)

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Dict:





     """simple docstring"""







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

     # move through list
     lowercase__           =  0
     while i < len(a	):
       lowercase__           =  vertices_list[i]
       lowercase__           =  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	)	)
         lowercase__           =  0
       else:
         i += 1

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    Optional[Any]	)->       str:





     """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(a      ,					a	)

       self.relabel(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Tuple      ,					a      :    List[str]	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  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      :    Optional[int]      ,					a      :    Any	)->       List[str]:





     """simple docstring"""







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

     if min_height is not None:
       lowercase__           =  min_height + 1


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

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

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









from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowercase_          =		{
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
      from .config import (
          EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
          OnnxConfig,
          OnnxConfigWithPast,
          OnnxSeqaSeqConfigWithPast,
          PatchingSpec,
      )
      from .convert import export, validate_model_outputs
      from .features import FeaturesManager
      from .utils import ParameterFormat, compute_serialized_parameters_size

else:
      import sys

      lowercase_          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


