
'''simple docstring'''



from typing import List

from .keymap import KEYMAP, get_character


def A (__lowerCamelCase :str						):

						def decorator(__lowerCamelCase :Optional[int]						):
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      """handle_key"""					,      []						)
												handle += [key]
												setattr(__lowerCamelCase					,      """handle_key"""					,      __lowerCamelCase						)
												return func

						return decorator


def A (*__lowerCamelCase :List[str]						):

						def decorator(__lowerCamelCase :Optional[Any]						):
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      """handle_key"""					,      []						)
												handle += keys
												setattr(__lowerCamelCase					,      """handle_key"""					,      __lowerCamelCase						)
												return func

						return decorator




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __new__(   cls       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					super().__new__(cls       ,  _lowercase       ,  _lowercase       ,  _lowercase )
												if not hasattr(_lowercase       ,  """key_handler""" ):
																		setattr(_lowercase       ,  """key_handler"""       ,  {} )
												setattr(_lowercase       ,  """handle_input"""       ,  KeyHandler.handle_input )

												for value in attrs.values():
																		_lowerCAmelCase    		=					getattr(_lowercase       ,  """handle_key"""       ,  [] )
																		for key in handled_keys:
																								_lowerCAmelCase    		=					value
												return new_cls




						@staticmethod
						def    _lowercase      (   cls ):

												"""simple docstring"""



												_lowerCAmelCase    		=					get_character()
												if char != KEYMAP["undefined"]:
																		_lowerCAmelCase    		=					ord(_lowercase )
												_lowerCAmelCase    		=					cls.key_handler.get(_lowercase )
												if handler:
																		_lowerCAmelCase    		=					char
																		return handler(cls )
												else:
																		return None






def A (cls :Any						):
						return KeyHandler(cls.__name__					,      cls.__bases__					,      cls.__dict__.copy()						)




'''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"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							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__":
       lowerCAmelCase     :  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."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

