'''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    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Dict  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = ElectraConfig.from_json_file(lowerCamelCase_  )
     print(F'''Building PyTorch model from configuration: {config}'''  )

     if discriminator_or_generator == "discriminator":
          UpperCAmelCase_				:  Union[str, Any]  = ElectraForPreTraining(lowerCamelCase_  )
     elif discriminator_or_generator == "generator":
          UpperCAmelCase_				:  int  = 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__":
 snake_case__	:     Tuple      =    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\'.'''
     ),
 )
 snake_case__	:     List[Any]      =    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__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

