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 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict		):
         # Initialise PyTorch model
         _A   : str       					=				RoFormerConfig.from_json_file(UpperCamelCase__		)
         print(f"Building PyTorch model from configuration: {config}"		)
         _A   : Optional[int]       					=				RoFormerForMaskedLM(UpperCamelCase__		)
  
         # Load weights from tf checkpoint
         load_tf_weights_in_roformer(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         # Save pytorch-model
         print(f"Save PyTorch model to {pytorch_dump_path}"		)
         torch.save(model.state_dict()       ,							UpperCamelCase__       ,							_use_new_zipfile_serialization=UpperCamelCase__		)
  
  
  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: 
  
  
  lowerCAmelCase__   	=				'\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
  
  lowerCAmelCase__   	=				[{'type': 'code', 'content': INSTALL_CONTENT}]
  lowerCAmelCase__   	=				{
      '{processor_class}': 'FakeProcessorClass',
      '{model_class}': 'FakeModelClass',
      '{object_class}': 'FakeObjectClass',
  }
  