def 							__UpperCamelCase   (				__UpperCAmelCase     :				str )       ->  str:
  lowercase							:       Any											=      word.upper()
  lowercase							:       List[str]											=      0
  lowercase							:       List[Any]											=      {}
  lowercase							:       str											=      []

  for letter in word:
    if letter not in letter_nums:
      lowercase							:       Tuple											=      str(__UpperCAmelCase )
      next_num += 1
    word_pattern.append(letter_nums[letter] )
  return ".".join(__UpperCAmelCase )


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

 _A		:			Union[str, Any]	      =time.time()
 with open('''dictionary.txt''') as in_file:
  _A		:			Any	      =in_file.read().splitlines()

 _A		:			dict	      ={}
 for word in word_list:
  _A		:			Tuple	      =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		:			Dict	      =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.
'''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
 )