
class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					None
          snake_case__    =					None
          snake_case__    =					graph

          self._normalize_graph(lowerCamelCase		,						lowerCamelCase			)
          snake_case__    =					len(lowerCamelCase			)
          snake_case__    =					None
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          if sources is int:
              snake_case__    =					[sources]
          if sinks is int:
              snake_case__    =					[sinks]

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

          snake_case__    =					sources[0]
          snake_case__    =					sinks[0]

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

              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:
                  snake_case__    =					max_input_flow
              snake_case__    =					0

              snake_case__    =					len(self.graph			) + 1
              for room in self.graph:
                  room.append(0			)
              self.graph.append([0] * size			)
              for i in sinks:
                  snake_case__    =					max_input_flow
              snake_case__    =					size - 1
      def A_      (      self			):
          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		,						lowerCamelCase			):
          snake_case__    =					algorithm(self			)





class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					flow_network
          snake_case__    =					flow_network.verticesCount
          snake_case__    =					flow_network.sourceIndex
          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
          snake_case__    =					flow_network.graph
          snake_case__    =					False
      def A_      (      self			):
          if not self.executed:
              self._algorithm()
              snake_case__    =					True



      def A_      (      self			):
          pass





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      def __init__(      self		,						lowerCamelCase			):
          super().__init__(lowerCamelCase			)
          # use this to save your result
          snake_case__    =					-1



      def A_      (      self			):
          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		,						lowerCamelCase			):
          super().__init__(lowerCamelCase			)

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

          snake_case__    =					[0] * self.verticies_count
          snake_case__    =					[0] * self.verticies_count
      def A_      (      self			):
          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
          snake_case__    =					[
              i
              for i in range(self.verticies_count			)
              if i != self.source_index and i != self.sink_index
          ]

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

          snake_case__    =					sum(self.preflow[self.source_index]			)
      def A_      (      self		,						lowerCamelCase			):
          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(lowerCamelCase		,						lowerCamelCase			)

              self.relabel(lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          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		,						lowerCamelCase			):
          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):
                  snake_case__    =					self.heights[to_index]

          if min_height is not None:
              snake_case__    =					min_height + 1


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

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

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





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


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



