
'''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 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       lowercase  :		str						=      ElectraConfig.from_json_file(lowerCamelCase      )
       print(f"""Building PyTorch model from configuration: {config}"""      )

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

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

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


if __name__ == "__main__":
      lowercase	:      int				=       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\'.'''
          ),
      )
      lowercase	:      Dict				=       parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
      )










'''simple docstring'''






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->list[tuple[int, int, int]]:






       '''simple docstring'''




       lowercase  :		Dict						=      sorted(lowerCamelCase   ,				key=lambda lowerCamelCase      : edge[2]      )

       lowercase  :		str						=      list(range(lowerCamelCase      )      )

       def find_parent(lowerCamelCase      ):
              if i != parent[i]:
                     lowercase  :		Optional[int]						=      find_parent(parent[i]      )
              return parent[i]

       lowercase  :		Union[str, Any]						=      0
       lowercase  :		Any						=      []

       for edge in edges:
              lowercase  :		Optional[Any]						=      find_parent(edge[0]      )
              lowercase  :		Any						=      find_parent(edge[1]      )
              if parent_a != parent_b:
                     minimum_spanning_tree_cost += edge[2]
                     minimum_spanning_tree.append(lowerCamelCase      )
                     lowercase  :		Optional[int]						=      parent_b

       return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
      lowercase	,	lowercase	:      List[Any]				=       list(map(int, input().strip().split()))
      lowercase	:      List[Any]				=       []

      for _ in range(num_edges):
            lowercase	,	lowercase	,	lowercase	:      Optional[Any]				=       (int(x) for x in input().strip().split())
            edges.append((nodea, nodea, cost))

      kruskal(num_nodes, edges)




