



'''simple docstring'''






class 							a__:







    def __init__(				self			:  Dict						,      __snake_case			:  List[str]						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[Any]     ):
      a						: Union[str, Any]					       =					None
      a						: Union[str, Any]					       =					None
      a						: Tuple					       =					graph

      self._normalize_graph(__snake_case						,      __snake_case     )
      a						: List[str]					       =					len(__snake_case     )
      a						: Union[str, Any]					       =					None







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str						,      __snake_case			:  List[Any]     ):
      if sources is int:
        a						: List[str]					       =					[sources]
      if sinks is int:
        a						: Any					       =					[sinks]

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

      a						: Optional[int]					       =					sources[0]
      a						: List[Any]					       =					sinks[0]

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

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

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







    def       lowercase_	(				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       lowercase_	(				self			:  int						,      __snake_case			:  Tuple     ):
      a						: int					       =					algorithm(self     )

class 							a__:







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







    def       lowercase_	(				self			:  str     ):
      if not self.executed:
        self._algorithm()
        a						: Any					       =					True







    def       lowercase_	(				self			:  List[Any]     ):
      pass

class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  Optional[int]						,      __snake_case			:  Any     ):
      super().__init__(__snake_case     )
      # use this to save your result
      a						: Optional[int]					       =					-1







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

      return self.maximum_flow

class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  Dict						,      __snake_case			:  int     ):
      super().__init__(__snake_case     )

      a						: Any					       =					[[0] * self.verticies_count for i in range(self.verticies_count     )]

      a						: Union[str, Any]					       =					[0] * self.verticies_count
      a						: int					       =					[0] * self.verticies_count







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Tuple					       =					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
      a						: Union[str, Any]					       =					[
          i
          for i in range(self.verticies_count     )
          if i != self.source_index and i != self.sink_index
      ]

      # move through list
      a						: Tuple					       =					0
      while i < len(__snake_case     ):
        a						: Any					       =					vertices_list[i]
        a						: List[Any]					       =					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     )     )
          a						: int					       =					0
        else:
          i += 1

      a						: Union[str, Any]					       =					sum(self.preflow[self.source_index]     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[Any]     ):
      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       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Tuple						,      __snake_case			:  str     ):
      a						: 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       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[int]     ):
      a						: Optional[int]					       =					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):
          a						: Tuple					       =					self.heights[to_index]

      if min_height is not None:
        a						: Dict					       =					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}")




'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output