





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	TaConfig.from_json_file(_lowerCAmelCase     )
   print(f"""Building PyTorch model from configuration: {config}"""     )
   A_     :				int			      =	TaForConditionalGeneration(_lowerCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_ta(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
   model.save_pretrained(_lowerCAmelCase     )


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(
          """--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."""
      )
      _lowerCAmelCase 				=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)













from typing import List

from .keymap import KEYMAP, get_character



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				List[Any]			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += [key]
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator



def     _lowerCAmelCase		(  *_lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				Tuple			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += keys
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __new__(		cls      ,       a__      ,       a__      ,       a__       ):
         A_     :				Any			      =	super().__new__(cls      ,       a__      ,       a__      ,       a__       )
         if not hasattr(a__      ,       """key_handler"""       ):
            setattr(a__      ,       """key_handler"""      ,       {}       )
         setattr(a__      ,       """handle_input"""      ,       KeyHandler.handle_input       )

         for value in attrs.values():
            A_     :				Tuple			      =	getattr(a__      ,       """handle_key"""      ,       []       )
            for key in handled_keys:
               A_     :				Optional[Any]			      =	value
         return new_cls






      @staticmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	get_character()
         if char != KEYMAP["undefined"]:
            A_     :				str			      =	ord(a__       )
         A_     :				List[str]			      =	cls.key_handler.get(a__       )
         if handler:
            A_     :				Optional[int]			      =	char
            return handler(cls       )
         else:
            return None






def     _lowerCAmelCase		(  cls     ):
   '''simple docstring'''


   return KeyHandler(cls.__name__  ,cls.__bases__  ,cls.__dict__.copy()     )






