




'''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'''






class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [0, 1]





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if (difference := index - (len(self.sequence ) - 2)) >= 1:
               for _ in range(A ):
                      self.sequence.append(self.sequence[-1] + self.sequence[-2] )
        return self.sequence[:index]






def 		a_( )				->None:






       '''simple docstring'''




       print(
           '''Fibonacci Series Using Dynamic Programming\n'''   ,				'''Enter the index of the Fibonacci number you want to calculate '''   ,				'''in the prompt below. (To exit enter exit or Ctrl-C)\n'''   ,				sep=''''''   ,				)
       lowercase  :		Optional[int]						=      Fibonacci()

       while True:
              lowercase  :		str						=      input('''>> '''      )
              if prompt in {"exit", "quit"}:
                     break

              try:
                     lowercase  :		int						=      int(lowerCamelCase      )
              except ValueError:
                     print('''Enter a number or \'exit\''''      )
                     continue

              print(fibonacci.get(lowerCamelCase      )      )


if __name__ == "__main__":
      main()




