




class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: int ,       snake_case					: Any ,       snake_case					: Any ,       snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						graph

          self._normalize_graph(snake_case ,       snake_case				)
          __UpperCamelCase  		=						len(snake_case				)
          __UpperCamelCase  		=						None







      def       snake_case	(     self					: str ,       snake_case					: Optional[int] ,       snake_case					: int				):
          if sources is int:
              __UpperCamelCase  		=						[sources]
          if sinks is int:
              __UpperCamelCase  		=						[sinks]

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

          __UpperCamelCase  		=						sources[0]
          __UpperCamelCase  		=						sinks[0]

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

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

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







      def       snake_case	(     self					: Dict				):
          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       snake_case	(     self					: List[Any] ,       snake_case					: List[str]				):
          __UpperCamelCase  		=						algorithm(self				)







class  _lowerCamelCase						:

      """simple docstring"""

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







      def       snake_case	(     self					: List[str]				):
          if not self.executed:
              self._algorithm()
              __UpperCamelCase  		=						True







      def       snake_case	(     self					: Optional[int]				):
          pass







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Tuple ,       snake_case					: Optional[int]				):
          super().__init__(snake_case				)
          # use this to save your result
          __UpperCamelCase  		=						-1







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

          return self.maximum_flow







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Optional[int] ,       snake_case					: Tuple				):
          super().__init__(snake_case				)

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

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







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						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  		=						[
              i
              for i in range(self.verticies_count				)
              if i != self.source_index and i != self.sink_index
          ]

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

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







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: 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       snake_case	(     self					: Union[str, Any] ,       snake_case					: List[str] ,       snake_case					: int				):
          __UpperCamelCase  		=						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       snake_case	(     self					: Optional[Any] ,       snake_case					: Dict				):
          __UpperCamelCase  		=						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  		=						self.heights[to_index]

          if min_height is not None:
              __UpperCamelCase  		=						min_height + 1


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

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

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








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


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					"mctct"







      def __init__(     self					: List[Any] ,       snake_case					: Optional[Any]=8065 ,       snake_case					: Optional[int]=1536 ,       snake_case					: Any=36 ,       snake_case					: List[str]=6144 ,       snake_case					: Dict=4 ,       snake_case					: str=384 ,       snake_case					: List[str]=920 ,       snake_case					: Dict=1E-5 ,       snake_case					: Union[str, Any]=0.3 ,       snake_case					: Optional[Any]="relu" ,       snake_case					: str=0.02 ,       snake_case					: Optional[int]=0.3 ,       snake_case					: int=0.3 ,       snake_case					: Any=1 ,       snake_case					: int=0 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: List[Any]=1 ,       snake_case					: Dict=0.3 ,       snake_case					: int=1 ,       snake_case					: Optional[int]=(7,) ,       snake_case					: List[Any]=(3,) ,       snake_case					: Optional[int]=80 ,       snake_case					: List[str]=1 ,       snake_case					: int=None ,       snake_case					: List[str]="sum" ,       snake_case					: Tuple=False ,       **snake_case					: List[str] ,       ):
          super().__init__(**snake_case ,       pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case				)
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						attention_head_dim
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						layerdrop
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						pad_token_id
          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id
          __UpperCamelCase  		=						conv_glu_dim
          __UpperCamelCase  		=						conv_dropout
          __UpperCamelCase  		=						num_conv_layers
          __UpperCamelCase  		=						input_feat_per_channel
          __UpperCamelCase  		=						input_channels
          __UpperCamelCase  		=						conv_channels
          __UpperCamelCase  		=						ctc_loss_reduction
          __UpperCamelCase  		=						ctc_zero_infinity

          # prevents config testing fail with exporting to json
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)

          if len(self.conv_kernel				) != self.num_conv_layers:
              raise ValueError(
                  '''Configuration for convolutional module is incorrect. '''
                  '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
                  F"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
                  F"`config.num_conv_layers = {self.num_conv_layers}`."				)






