

"""simple docstring"""




class      __lowercase			:

  def __init__(  self		:			int					,A		:			List[Any]					,A		:			List[str]					,A		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					None
   UpperCAmelCase__					:  Tuple					   =					None
   UpperCAmelCase__					:  List[Any]					   =					graph

   self._normalize_graph(A					,A    )
   UpperCAmelCase__					:  Tuple					   =					len(A    )
   UpperCAmelCase__					:  Tuple					   =					None

  def    __lowercase       (  self		:			str					,A		:			Union[str, Any]					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   if sources is int:
    UpperCAmelCase__					:  int					   =					[sources]
   if sinks is int:
    UpperCAmelCase__					:  Optional[Any]					   =					[sinks]

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

   UpperCAmelCase__					:  List[Any]					   =					sources[0]
   UpperCAmelCase__					:  Dict					   =					sinks[0]

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

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

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

  def    __lowercase       (  self		:			Tuple    ):






   '''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    __lowercase       (  self		:			List[Any]					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					algorithm(self    )







class      __lowercase			:

  def __init__(  self		:			Dict					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					flow_network
   UpperCAmelCase__					:  Dict					   =					flow_network.verticesCount
   UpperCAmelCase__					:  List[str]					   =					flow_network.sourceIndex
   UpperCAmelCase__					:  Union[str, 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__					:  Union[str, Any]					   =					flow_network.graph
   UpperCAmelCase__					:  Any					   =					False

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   if not self.executed:
    self._algorithm()
    UpperCAmelCase__					:  Optional[int]					   =					True




  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   pass







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			Dict					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   super().__init__(A    )
   # use this to save your result
   UpperCAmelCase__					:  Tuple					   =					-1




  def    __lowercase       (  self		:			Dict    ):






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

   return self.maximum_flow







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			List[str]					,A		:			Dict    ):






   '''simple docstring'''
   super().__init__(A    )

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

   UpperCAmelCase__					:  Optional[int]					   =					[0] * self.verticies_count
   UpperCAmelCase__					:  Optional[int]					   =					[0] * self.verticies_count

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  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__					:  Any					   =					0
   while i < len(A    ):
    UpperCAmelCase__					:  Any					   =					vertices_list[i]
    UpperCAmelCase__					:  Optional[int]					   =					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    )    )
     UpperCAmelCase__					:  str					   =					0
    else:
     i += 1

   UpperCAmelCase__					:  Optional[int]					   =					sum(self.preflow[self.source_index]    )

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]    ):






   '''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    __lowercase       (  self		:			List[str]					,A		:			Tuple					,A		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					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]					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					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__					:  Optional[Any]					   =					self.heights[to_index]

   if min_height is not None:
    UpperCAmelCase__					:  int					   =					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}")








"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 if "model" in orig_key:
  UpperCAmelCase__					:  List[str]					   =					orig_key.replace("""model."""							,    """"""      )
 if "norm1" in orig_key:
  UpperCAmelCase__					:  List[Any]					   =					orig_key.replace("""norm1"""							,    """attention.output.LayerNorm"""      )
 if "norm2" in orig_key:
  UpperCAmelCase__					:  Optional[Any]					   =					orig_key.replace("""norm2"""							,    """output.LayerNorm"""      )
 if "norm" in orig_key:
  UpperCAmelCase__					:  int					   =					orig_key.replace("""norm"""							,    """LayerNorm"""      )
 if "transformer" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.split("""."""      )[0].split("""_"""      )[-1]
  UpperCAmelCase__					:  Dict					   =					orig_key.replace(F"transformer_{layer_num}"							,    F"encoder.layer.{layer_num}"      )
 if "mha.attn" in orig_key:
  UpperCAmelCase__					:  Tuple					   =					orig_key.replace("""mha.attn"""							,    """attention.self"""      )
 if "mha" in orig_key:
  UpperCAmelCase__					:  Dict					   =					orig_key.replace("""mha"""							,    """attention"""      )
 if "W_q" in orig_key:
  UpperCAmelCase__					:  str					   =					orig_key.replace("""W_q"""							,    """self.query"""      )
 if "W_k" in orig_key:
  UpperCAmelCase__					:  List[Any]					   =					orig_key.replace("""W_k"""							,    """self.key"""      )
 if "W_v" in orig_key:
  UpperCAmelCase__					:  str					   =					orig_key.replace("""W_v"""							,    """self.value"""      )
 if "ff1" in orig_key:
  UpperCAmelCase__					:  Optional[int]					   =					orig_key.replace("""ff1"""							,    """intermediate.dense"""      )
 if "ff2" in orig_key:
  UpperCAmelCase__					:  Dict					   =					orig_key.replace("""ff2"""							,    """output.dense"""      )
 if "ff" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.replace("""ff"""							,    """output.dense"""      )
 if "mlm_class" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.replace("""mlm.mlm_class"""							,    """cls.predictions.decoder"""      )
 if "mlm" in orig_key:
  UpperCAmelCase__					:  Optional[Any]					   =					orig_key.replace("""mlm"""							,    """cls.predictions.transform"""      )
 if "cls" not in orig_key:
  UpperCAmelCase__					:  Dict					   =					"""yoso.""" + orig_key

 return orig_key
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__					:  Optional[Any]					   =					orig_state_dict.pop(__UpperCamelCase      )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   UpperCAmelCase__					:  Dict					   =					val

 UpperCAmelCase__					:  Dict					   =					orig_state_dict["""cls.predictions.decoder.bias"""]
 UpperCAmelCase__					:  Optional[Any]					   =					torch.arange(__UpperCamelCase      ).expand((1, -1)      ) + 2

 return orig_state_dict
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					torch.load(__UpperCamelCase							,    map_location="""cpu"""      )["""model_state_dict"""]
 UpperCAmelCase__					:  List[str]					   =					YosoConfig.from_json_file(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					YosoForMaskedLM(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					convert_checkpoint_helper(config.max_position_embeddings							,    __UpperCamelCase      )

 print(model.load_state_dict(__UpperCamelCase      )      )
 model.eval()
 model.save_pretrained(__UpperCamelCase      )

 print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The json file for YOSO model config.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





