


from __future__ import annotations


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				list[list[int]]		     =					[]
  A_      :				list[int]		     =					[]
  A_      :				Optional[Any]		     =					0
  A_      :				Any		     =					sum(SCREAMING_SNAKE_CASE       )
  create_state_space_tree(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  return result


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			):
  if sum(SCREAMING_SNAKE_CASE       ) > max_sum or (remaining_nums_sum + sum(SCREAMING_SNAKE_CASE       )) < max_sum:
    return
  if sum(SCREAMING_SNAKE_CASE       ) == max_sum:
    result.append(SCREAMING_SNAKE_CASE       )
    return
  for index in range(SCREAMING_SNAKE_CASE ,			len(SCREAMING_SNAKE_CASE       )       ):
    create_state_space_tree(
        SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			index + 1 ,			[*path, nums[index]] ,			SCREAMING_SNAKE_CASE ,			remaining_nums_sum - nums[index] ,			)
UpperCamelCase								=	[3, 34, 4, 12, 5, 2]
UpperCamelCase								=	9
UpperCamelCase								=	generate_sum_of_subsets_soln(nums, max_sum)







print(*result)








from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




