


"""simple docstring"""





import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def   _snake_case    (    _snake_case					:      Union[str, Any]			, _snake_case					:      Tuple			, _snake_case					:      List[Any]      ):
      lowerCAmelCase       :						List[str]							= json.load(open(_snake_case			, '''r'''      )      )
      lowerCAmelCase       :						str							= GPTNeoConfig(
          hidden_size=config_json['''n_embd''']			, num_layers=config_json['''n_layer''']			, num_heads=config_json['''n_head''']			, attention_types=config_json['''attention_types''']			, max_position_embeddings=config_json['''n_positions''']			, resid_dropout=config_json['''res_dropout''']			, embed_dropout=config_json['''embed_dropout''']			, attention_dropout=config_json['''attn_dropout''']			, )
      print(f'''Building PyTorch model from configuration: {config}'''      )
      lowerCAmelCase       :						Dict							= GPTNeoForCausalLM(_snake_case      )

      # Load weights from tf checkpoint
      load_tf_weights_in_gpt_neo(_snake_case			, _snake_case			, _snake_case      )

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''      )
      model.save_pretrained(_snake_case      )


if __name__ == "__main__":
 snake_case__		:  Any     					=					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 mesh-tf model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 snake_case__		:  Union[str, Any]     					=					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








import requests

try:
							from django.contrib.auth import authenticate, login
							from django.shortcuts import redirect, render
except ImportError:
							UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		print






def    snake_case   (				A     )				->				List[str]:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				'secretKey'
	lowerCamelCase_				:						str				  =				'https://www.google.com/recaptcha/api/siteverify'

	# when method is not POST, direct user to login page
	if request.method != "POST":
		return render(A		,  'login.html'     )

	# from the frontend, get username, password, and client_key
	lowerCamelCase_				:						int				  =				request.POST.get('username'     )
	lowerCamelCase_				:						str				  =				request.POST.get('password'     )
	lowerCamelCase_				:						List[Any]				  =				request.POST.get('g-recaptcha-response'     )

	# post recaptcha response to Google's recaptcha api
	lowerCamelCase_				:						Union[str, Any]				  =				requests.post(A		,  data={'secret': secret_key, 'response': client_key}     )
	# if the recaptcha api verified our keys
	if response.json().get('success'		,  A     ):
		# authenticate the user
		lowerCamelCase_				:						int				  =				authenticate(A		,  username=A		,  password=A     )
		if user_in_database:
			login(A		,  A     )
			return redirect('/your-webpage'     )
	return render(A		,  'login.html'     )
