from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
      from .pipeline_unclip import UnCLIPPipeline
      from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
      from .text_proj import UnCLIPTextProjModel





"""simple docstring"""

class  lowerCamelCase__    :
      def __init__(		self						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str   ,							A_						:						Any				):






       '''simple docstring'''


       __lowercase						=						None
       __lowercase						=						None
       __lowercase						=						graph

       self._normalize_graph(A_   ,							A_				)
       __lowercase						=						len(A_				)
       __lowercase						=						None
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str   ,							A_						:						List[Any]				):






       '''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						:						Dict				):






       '''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[str]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						algorithm(self				)






class  lowerCamelCase__    :
      def __init__(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''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						:						Dict				):






       '''simple docstring'''


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






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






       '''simple docstring'''


       pass






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Union[str, Any]   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       # use this to save your result
       __lowercase						=						-1






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


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

       return self.maximum_flow






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						List[str]   ,							A_						:						Tuple				):






       '''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						:						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						:						Optional[int]   ,							A_						:						Optional[int]				):






       '''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[Any]   ,							A_						:						Union[str, Any]   ,							A_						:						List[str]				):






       '''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_						:						Optional[Any]				):






       '''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__":
       UpperCAmelCase__					=[0]
       UpperCAmelCase__					=[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__					=[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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