code: 
  
  
  
  import requests
  
  try:
      from django.contrib.auth import authenticate, login
      from django.shortcuts import redirect, render
  except ImportError:
      __lowerCamelCase  				=					__lowerCamelCase  				=					__lowerCamelCase  				=					__lowerCamelCase  				=					print
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> int:
    _A           =    '''secretKey'''
    _A           =    '''https://www.google.com/recaptcha/api/siteverify'''
  
    # when method is not POST, direct user to login page
    if request.method != "POST":
      return render(lowerCamelCase__						,				'''login.html'''							)
  
    # from the frontend, get username, password, and client_key
    _A           =    request.POST.get('''username'''							)
    _A           =    request.POST.get('''password'''							)
    _A           =    request.POST.get('''g-recaptcha-response'''							)
  
    # post recaptcha response to Google's recaptcha api
    _A           =    requests.post(lowerCamelCase__						,				data={'''secret''': secret_key, '''response''': client_key}							)
    # if the recaptcha api verified our keys
    if response.json().get('''success'''						,				lowerCamelCase__							):
      # authenticate the user
      _A           =    authenticate(lowerCamelCase__						,				username=lowerCamelCase__						,				password=lowerCamelCase__							)
      if user_in_database:
        login(lowerCamelCase__						,				lowerCamelCase__							)
        return redirect('''/your-webpage'''							)
    return render(lowerCamelCase__						,				'''login.html'''							)
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
      )