code: 
  
  
  
  import os
  from string import ascii_letters
  
  __lowerCamelCase  				=					ascii_letters + """ \t\n"""
  
  
  
  
  def     UpperCAmelCase__ (							)   -> dict[str, None]:
    _A           =    os.path.split(os.path.realpath(__snake_case							)							)
    _A           =    {}
    with open(path[0] + '''/dictionary.txt'''							) as dictionary_file:
      for word in dictionary_file.read().split('''\n'''							):
        _A           =    None
    return english_words
  
  
  __lowerCamelCase  				=					load_dictionary()
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> float:
    _A           =    message.upper()
    _A           =    remove_non_letters(__snake_case							)
    _A           =    message.split()
    _A           =    len([word for word in possible_words if word in ENGLISH_WORDS]							)
    return float(__snake_case							) / len(__snake_case							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> str:
    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case = 20						,				__snake_case = 85							)   -> bool:
    _A           =    get_english_count(__snake_case							) * 100 >= word_percentage
    _A           =    len(remove_non_letters(__snake_case							)							)
    _A           =    (float(__snake_case							) / len(__snake_case							)) * 100
    _A           =    message_letters_percentage >= letter_percentage
    return words_match and letters_match
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
style_context: 
  
  
  
  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__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
    # Initialise PyTorch model
    _A           =    ElectraConfig.from_json_file(__snake_case							)
    print(F'''Building PyTorch model from configuration: {config}'''							)
  
    if discriminator_or_generator == "discriminator":
      _A           =    ElectraForPreTraining(__snake_case							)
    elif discriminator_or_generator == "generator":
      _A           =    ElectraForMaskedLM(__snake_case							)
    else:
      raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''							)
  
    # Load weights from tf checkpoint
    load_tf_weights_in_electra(
        __snake_case						,				__snake_case						,				__snake_case						,				discriminator_or_generator=__snake_case							)
  
    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
    torch.save(model.state_dict()						,				__snake_case							)
  
  
  if __name__ == "__main__":
      __lowerCamelCase  				=					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'."""
          ),
      )
      __lowerCamelCase  				=					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
      )