'''simple docstring'''
_A		:			Optional[Any]	      =float('''inf''')




class _lowerCAmelCase :







      def __init__(     self     ,						lowercase    ):
        lowercase							:       Optional[Any]											=      [0] * n
        lowercase							:       str											=      [0] * n
        lowercase							:       Union[str, Any]											=      [0] * n
        lowercase							:       Optional[int]											=      [[] for _ in range(lowercase    )]







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase=0    ):
        self.adj[a].append([b, len(self.adj[b]    ), c, 0]    )
        self.adj[b].append([a, len(self.adj[a]    ) - 1, rcap, 0]    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase    ):
        if vertex == sink or not flow:
          return flow

        for i in range(self.ptr[vertex]     ,						len(self.adj[vertex]    )    ):
          lowercase							:       Optional[Any]											=      self.adj[vertex][i]
          if self.lvl[e[0]] == self.lvl[vertex] + 1:
            lowercase							:       Optional[Any]											=      self.depth_first_search(e[0]     ,						lowercase     ,						min(lowercase     ,						e[2] - e[3]    )    )
            if p:
              self.adj[vertex][i][3] += p
              self.adj[e[0]][e[1]][3] -= p
              return p
          lowercase							:       int											=      self.ptr[vertex] + 1
        return 0







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase    ):
        lowercase							,			lowercase							:       str											=      0, source
        for l in range(31    ):  # noqa: E741  l = 30 maybe faster for random data
          while True:
            lowercase							,			lowercase							:       Any											=      [0] * len(self.q    ), [0] * len(self.q    )
            lowercase							,			lowercase							,			lowercase							:       Dict											=      0, 1, 1
            while qi < qe and not self.lvl[sink]:
              lowercase							:       Union[str, Any]											=      self.q[qi]
              qi += 1
              for e in self.adj[v]:
                if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                  lowercase							:       Dict											=      e[0]
                  qe += 1
                  lowercase							:       List[Any]											=      self.lvl[v] + 1

            lowercase							:       str											=      self.depth_first_search(lowercase     ,						lowercase     ,						lowercase    )
            while p:
              flow += p
              lowercase							:       Dict											=      self.depth_first_search(lowercase     ,						lowercase     ,						lowercase    )

            if not self.lvl[sink]:
              break

        return flow
_A		:			Any	      =Dinic(1_0)
_A		:			Optional[Any]	      =0
_A		:			Optional[Any]	      =9
for vertex in range(1, 5):
 graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
 graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
 graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))
'''simple docstring'''
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[str] )       ->  List[str]:
  # Initialise PyTorch model
  lowercase							:       Any											=      ElectraConfig.from_json_file(__UpperCAmelCase )
  print(f"""Building PyTorch model from configuration: {config}""" )

  if discriminator_or_generator == "discriminator":
    lowercase							:       Union[str, Any]											=      ElectraForPreTraining(__UpperCAmelCase )
  elif discriminator_or_generator == "generator":
    lowercase							:       Tuple											=      ElectraForMaskedLM(__UpperCAmelCase )
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase   ,      discriminator_or_generator=__UpperCAmelCase )

  # Save pytorch-model
  print(f"""Save PyTorch model to {pytorch_dump_path}""" )
  torch.save(model.state_dict()   ,      __UpperCAmelCase )


if __name__ == "__main__":
 _A		:			str	      =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--discriminator_or_generator''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
         '''\'generator\'.'''
     ),
 )
 _A		:			Any	      =parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )