code: 
  
  
  
  '''simple docstring'''
  
  
  
  from ..utils import DummyObject, requires_backends
  class        UpperCAmelCase__  (    metaclass=UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				['''torch''', '''transformers''', '''k_diffusion''']
  
  
  
   def __init__(  self		,	*lowercase		,	**lowercase			)  ->							Union[str, Any]:
    requires_backends(self		,	["""torch""", """transformers""", """k_diffusion"""]			)
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls		,	*lowercase		,	**lowercase			)  ->							Optional[Any]:
    requires_backends(cls		,	["""torch""", """transformers""", """k_diffusion"""]			)
  
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls		,	*lowercase		,	**lowercase			)  ->							Optional[int]:
    requires_backends(cls		,	["""torch""", """transformers""", """k_diffusion"""]			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  