code: 
  import argparse
  
  import torch
  
  from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  def       a			(   a		,	a		,	a  )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   RoFormerConfig.from_json_file(a  )
  			print(F"""Building PyTorch model from configuration: {config}"""  )
  			SCREAMING_SNAKE_CASE    	=   RoFormerForMaskedLM(a  )
  
  			# Load weights from tf checkpoint
  			load_tf_weights_in_roformer(a		,	a		,	a  )
  
  			# Save pytorch-model
  			print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
  			torch.save(model.state_dict()		,	a		,	_use_new_zipfile_serialization=a  )
  
  
  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(
  							    '--bert_config_file',
  							    default=None,
  							    type=str,
  							    required=True,
  							    help=(
  							        'The config json file corresponding to the pre-trained BERT 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.'
  							)
  							__lowerCAmelCase				      =					parser.parse_args()
  							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
style_context: 
  import json
  
  import requests
  
  from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info
  
  
  def       a			(   a  )    ->Tuple:
  
  
  
  
  			'''simple docstring'''
  
  
  			class 						lowerCamelCase			:
  
  
  
  
  
  
  
  				def __init__(    self			:int      ,    lowercase			:List[str] )		->			None:
  
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  							assert isinstance(lowercase      ,    (bytes, str) )
  							SCREAMING_SNAKE_CASE    	=   content
  
  
  
  
  
  
  
  				def    snake_case__    (    self			:Optional[Any] )		->			Optional[int]:
  
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  							return json.loads(self.content )
  
  			def mock_response(*a		,	**a  ):
  						assert args[0] == AUTHENTICATED_USER_ENDPOINT
  						assert "Authorization" in kwargs["headers"]
  						assert kwargs["headers"]["Authorization"].startswith('''token '''  )
  						assert "Accept" in kwargs["headers"]
  						return FakeResponse(B'''{"login":"test","id":1}'''  )
  
  			monkeypatch.setattr(a		,	'''get'''		,	a  )
  			SCREAMING_SNAKE_CASE    	=   fetch_github_info('''token'''  )
  			assert result["login"] == "test"
  			assert result["id"] == 1