

class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase			 =None
        _lowercase			 =None
        _lowercase			 =graph

        self._normalize_graph(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =len(lowerCAmelCase      )
        _lowercase			 =None




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Any:
        '''simple docstring'''






        if sources is int:
              _lowercase			 =[sources]
        if sinks is int:
              _lowercase			 =[sinks]

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

        _lowercase			 =sources[0]
        _lowercase			 =sinks[0]

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

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

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




  def 						A__  (       self      )						->				Optional[int]:
        '''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 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        _lowercase			 =algorithm(self      )





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase			 =flow_network
        _lowercase			 =flow_network.verticesCount
        _lowercase			 =flow_network.sourceIndex
        _lowercase			 =flow_network.sinkIndex
        # it's just a reference, so you shouldn't change
        # it in your algorithms, use deep copy before doing that
        _lowercase			 =flow_network.graph
        _lowercase			 =False




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        if not self.executed:
              self._algorithm()
              _lowercase			 =True




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        pass





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def __init__(       self							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase      )
        # use this to save your result
        _lowercase			 =-1




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






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

        return self.maximum_flow





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def __init__(       self							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase      )

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

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




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =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
        _lowercase			 =[
            i
            for i in range(self.verticies_count      )
            if i != self.source_index and i != self.sink_index
        ]

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

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




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''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(lowerCAmelCase							,     lowerCAmelCase      )

              self.relabel(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =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      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =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):
                    _lowercase			 =self.heights[to_index]

        if min_height is not None:
              _lowercase			 =min_height + 1


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

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

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





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =TapasModel(config=A__  )
      else:
            raise ValueError(F'''Task {task} not supported.'''  )

      print(F'''Building PyTorch model from configuration: {config}'''  )
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(A__   ,     A__   ,     A__  )

      # Save pytorch-model (weights and configuration)
      print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
      model.save_pretrained(A__  )

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''  )
      _lowercase			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

      print('Used relative position embeddings:'   ,     model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase_             =     parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )


