





from __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[list[int]]:
 UpperCamelCase     : list[list[int]]	     =       []
 UpperCamelCase     : list[int]	     =       []
 UpperCamelCase     : List[Any]	     =       0
 UpperCamelCase     : str	     =       sum(_lowerCAmelCase					)
 create_state_space_tree(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
 return result



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			None:
 if sum(_lowerCAmelCase					) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase					)) < max_sum:
  return
 if sum(_lowerCAmelCase					) == max_sum:
  result.append(_lowerCAmelCase					)
  return
 for index in range(_lowerCAmelCase							,       len(_lowerCAmelCase					)					):
  create_state_space_tree(
      _lowerCAmelCase							,       _lowerCAmelCase							,       index + 1							,       [*path, nums[index]]							,       _lowerCAmelCase							,       remaining_nums_sum - nums[index]							,       )
__lowerCamelCase	:					Dict			  =					[3, 34, 4, 12, 5, 2]
__lowerCamelCase	:					int			  =					9
__lowerCamelCase	:					Any			  =					generate_sum_of_subsets_soln(nums, max_sum)


print(*result)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase	:					Tuple			  =					logging.get_logger(__name__)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :List[Any]						   =       'timm_backbone'





      def __init__(			self	,							A_=None	,							A_=3	,							A_=True	,							A_=True	,							A_=None	,							**A_	,							):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : Tuple	     =       backbone
       UpperCamelCase     : Dict	     =       num_channels
       UpperCamelCase     : Tuple	     =       features_only
       UpperCamelCase     : Optional[int]	     =       use_pretrained_backbone
       UpperCamelCase     : Dict	     =       True
       UpperCamelCase     : List[str]	     =       out_indices if out_indices is not None else (-1,)
