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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Any    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = RoFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_    )
       print(f"""Building PyTorch model from configuration: {config}"""    )
       lowercase__					:     Union[str, Any]   = RoFormerForMaskedLM(SCREAMING_SNAKE_CASE_    )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_roformer(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
       torch.save(model.state_dict()			,  SCREAMING_SNAKE_CASE_			,  _use_new_zipfile_serialization=SCREAMING_SNAKE_CASE_    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				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.'''
         )
         snake_case_    						=				parser.parse_args()
         convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
  
  
style_context: 
  
  from transformers.models.cpm.tokenization_cpm import CpmTokenizer
  from transformers.testing_utils import custom_tokenizers
  
  from ..xlnet.test_modeling_xlnet import XLNetModelTest
  
  
  @custom_tokenizers
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
           return True
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
           lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
           lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
           lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()
  
           lowercase__					:     Any   = tokenizer.tokenize(a)
           self.assertListEqual(a		,     a)
  
           lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]
  
           lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)
  
           lowercase__					:     Optional[Any]   = tokenizer.decode(a)
           self.assertEqual(a		,     a)
  
  