





"""simple docstring"""
def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				None:
   if target == 0:
      answer.append(path.copy()    )
   else:
      for index in range(__A ,len(__A    )    ):
         if target >= candidates[index]:
            path.append(candidates[index]    )
            backtrack(__A ,__A ,__A ,target - candidates[index] ,__A    )
            path.pop(len(__A    ) - 1    )




def 			__snake_case						(      __A ,__A    )		->				list:
   lowercase    :			Tuple  =       []  # type: list[int]
   lowercase    :			int  =       []  # type: list[int]
   backtrack(__A ,__A ,__A ,__A ,0    )
   return answer




def 			__snake_case						(      )		->				None:
   print(combination_sum([-8, 2.3, 0] ,1    )    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


