
class 				_lowerCamelCase :





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				None
           UpperCamelCase__    :   Union[str, Any]					=				None
           UpperCamelCase__    :   List[str]					=				graph

           self._normalize_graph(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_  )
           UpperCamelCase__    :   Dict					=				len(SCREAMING_SNAKE_CASE_  )
           UpperCamelCase__    :   str					=				None






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           if sources is int:
                 UpperCamelCase__    :   Union[str, Any]					=				[sources]
           if sinks is int:
                 UpperCamelCase__    :   int					=				[sinks]

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

           UpperCamelCase__    :   Dict					=				sources[0]
           UpperCamelCase__    :   Optional[Any]					=				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:
                 UpperCamelCase__    :   List[str]					=				0
                 for i in sources:
                       max_input_flow += sum(self.graph[i]  )

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

                 UpperCamelCase__    :   Union[str, Any]					=				len(self.graph  ) + 1
                 for room in self.graph:
                       room.append(0  )
                 self.graph.append([0] * size  )
                 for i in sinks:
                       UpperCamelCase__    :   int					=				max_input_flow
                 UpperCamelCase__    :   Optional[int]					=				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						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				algorithm(self  )




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				flow_network
           UpperCamelCase__    :   List[Any]					=				flow_network.verticesCount
           UpperCamelCase__    :   Dict					=				flow_network.sourceIndex
           UpperCamelCase__    :   List[Any]					=				flow_network.sinkIndex
           # it's just a reference, so you shouldn't change
           # it in your algorithms, use deep copy before doing that
           UpperCamelCase__    :   Optional[int]					=				flow_network.graph
           UpperCamelCase__    :   Optional[int]					=				False






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           if not self.executed:
                 self._algorithm()
                 UpperCamelCase__    :   Union[str, Any]					=				True






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           pass




class 				_lowerCamelCase (      __lowerCamelCase ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           super().__init__(SCREAMING_SNAKE_CASE_  )
           # use this to save your result
           UpperCamelCase__    :   Any					=				-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 (      __lowerCamelCase ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           super().__init__(SCREAMING_SNAKE_CASE_  )

           UpperCamelCase__    :   Tuple					=				[[0] * self.verticies_count for i in range(self.verticies_count  )]

           UpperCamelCase__    :   Tuple					=				[0] * self.verticies_count
           UpperCamelCase__    :   Any					=				[0] * self.verticies_count






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				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__    :   Optional[int]					=				[
               i
               for i in range(self.verticies_count  )
               if i != self.source_index and i != self.sink_index
           ]

           # move through list
           UpperCamelCase__    :   List[Any]					=				0
           while i < len(SCREAMING_SNAKE_CASE_  ):
                 UpperCamelCase__    :   Optional[Any]					=				vertices_list[i]
                 UpperCamelCase__    :   Optional[int]					=				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_  )  )
                       UpperCamelCase__    :   Dict					=				0
                 else:
                       i += 1

           UpperCamelCase__    :   Dict					=				sum(self.preflow[self.source_index]  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """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(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_  )

                 self.relabel(SCREAMING_SNAKE_CASE_  )






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




           """simple docstring"""







           UpperCamelCase__    :   List[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						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, 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):
                       UpperCamelCase__    :   Tuple					=				self.heights[to_index]

           if min_height is not None:
                 UpperCamelCase__    :   Optional[Any]					=				min_height + 1


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

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

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase      ={
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["BlenderbotTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BlenderbotForCausalLM",
        "BlenderbotForConditionalGeneration",
        "BlenderbotModel",
        "BlenderbotPreTrainedModel",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TFBlenderbotForConditionalGeneration",
        "TFBlenderbotModel",
        "TFBlenderbotPreTrainedModel",
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "FlaxBlenderbotForConditionalGeneration",
        "FlaxBlenderbotModel",
        "FlaxBlenderbotPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_blenderbot import (
        BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotConfig,
        BlenderbotOnnxConfig,
    )
    from .tokenization_blenderbot import BlenderbotTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blenderbot import (
            BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlenderbotForCausalLM,
            BlenderbotForConditionalGeneration,
            BlenderbotModel,
            BlenderbotPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blenderbot import (
            TFBlenderbotForConditionalGeneration,
            TFBlenderbotModel,
            TFBlenderbotPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_blenderbot import (
            FlaxBlenderbotForConditionalGeneration,
            FlaxBlenderbotModel,
            FlaxBlenderbotPreTrainedModel,
        )

else:
    import sys

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


