code: 
  
  
  '''simple docstring'''
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				Any   )		->					Union[str, Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						TaConfig.from_json_file(_UpperCamelCase   )
     print(F'Building PyTorch model from configuration: {config}'   )
     UpperCamelCase__ 						=						TaForConditionalGeneration(_UpperCamelCase   )
  
     # Load weights from tf checkpoint
     load_tf_weights_in_ta(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'   )
     model.save_pretrained(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
         __lowercase:		List[str]							=     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 T5 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."
         )
         __lowercase:		List[str]							=     parser.parse_args()
         convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  '''simple docstring'''
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
   _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
   _lowerCamelCase :					int    					=					True
  
   def 		lowercase_						( self :	List[str],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)
  
   @slow
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)
  
      UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
      UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)
  
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)
  
      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]