



"""simple docstring"""




import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	Optional[Any]							):
   # Initialize PyTorch model
   __lowerCamelCase  							=      CanineConfig()
   __lowerCamelCase  							=      CanineModel(_UpperCamelCase							)
   model.eval()

   print(F"""Building PyTorch model from configuration: {config}"""							)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
   model.save_pretrained(_UpperCamelCase							)

   # Save tokenizer files
   __lowerCamelCase  							=      CanineTokenizer()
   print(F"""Save tokenizer files to {pytorch_dump_path}"""							)
   tokenizer.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
  a_								=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
  )
  parser.add_argument(
      """--pytorch_dump_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to a folder where the PyTorch model will be placed.""",
  )
  a_								=   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)











def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	int	,_UpperCamelCase     :	int							):
   if target == 0:
      answer.append(path.copy()							)
   else:
      for index in range(_UpperCamelCase	,len(_UpperCamelCase							)							):
         if target >= candidates[index]:
            path.append(candidates[index]							)
            backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,target - candidates[index]	,_UpperCamelCase							)
            path.pop(len(_UpperCamelCase							) - 1							)





def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	int							):
   __lowerCamelCase  							=      []  # type: list[int]
   __lowerCamelCase  							=      []  # type: list[int]
   backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,0							)
   return answer





def 						a__     (						):
   print(combination_sum([-8, 2.3, 0]	,1							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






