def 				_snake_case     (       lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       List[Any]       ,			lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       Any     ):
		"""simple docstring"""







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




def 				_snake_case     (       lowerCAmelCase							:       Optional[int]       ,			lowerCAmelCase							:       str     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   []  # type: list[int]
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   []  # type: list[int]
		backtrack(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			0     )
		return answer




def 				_snake_case     (       ):
		"""simple docstring"""







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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  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(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
