
'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__UpperCAmelCase				,__UpperCAmelCase    ):
    _snake_case		    = """maskformer-swin"""

    _snake_case		    = {
        """num_attention_heads""": """num_heads""",
        """num_hidden_layers""": """num_layers""",
    }




    def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Dict=2_24,    SCREAMING_SNAKE_CASE_	:				List[Any]=4,    SCREAMING_SNAKE_CASE_	:				int=3,    SCREAMING_SNAKE_CASE_	:				Dict=96,    SCREAMING_SNAKE_CASE_	:				Optional[int]=[2, 2, 6, 2],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=[3, 6, 12, 24],    SCREAMING_SNAKE_CASE_	:				Any=7,    SCREAMING_SNAKE_CASE_	:				str=4.0,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=True,    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.0,    SCREAMING_SNAKE_CASE_	:				Tuple=0.0,    SCREAMING_SNAKE_CASE_	:				Any=0.1,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="gelu",    SCREAMING_SNAKE_CASE_	:				Tuple=False,    SCREAMING_SNAKE_CASE_	:				str=0.02,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=1e-5,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				int=None,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
          super().__init__(**UpperCAmelCase_      )

          snake_case       :     Tuple =    image_size
          snake_case       :     Dict =    patch_size
          snake_case       :     Any =    num_channels
          snake_case       :     str =    embed_dim
          snake_case       :     str =    depths
          snake_case       :     Union[str, Any] =    len(UpperCAmelCase_      )
          snake_case       :     Dict =    num_heads
          snake_case       :     Optional[Any] =    window_size
          snake_case       :     Optional[Any] =    mlp_ratio
          snake_case       :     List[str] =    qkv_bias
          snake_case       :     int =    hidden_dropout_prob
          snake_case       :     Dict =    attention_probs_dropout_prob
          snake_case       :     int =    drop_path_rate
          snake_case       :     Optional[Any] =    hidden_act
          snake_case       :     Tuple =    use_absolute_embeddings
          snake_case       :     Any =    layer_norm_eps
          snake_case       :     Optional[int] =    initializer_range
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case       :     List[str] =    int(embed_dim * 2 ** (len(UpperCAmelCase_      ) - 1)      )
          snake_case       :     Optional[int] =    ['''stem'''] + [F"""stage{idx}""" for idx in range(1,    len(UpperCAmelCase_      ) + 1      )]
          snake_case,					snake_case       :     Union[str, Any] =    get_aligned_output_features_output_indices(
              out_features=UpperCAmelCase_,    out_indices=UpperCAmelCase_,    stage_names=self.stage_names      )



'''simple docstring'''





from __future__ import annotations

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						if sum(A_       ) > max_sum or (remaining_nums_sum + sum(A_       )) < max_sum:
												return
						if sum(A_       ) == max_sum:
												result.append(A_       )
												return
						for index in range(A_    ,							len(A_       )       ):
												create_state_space_tree(
												    A_    ,							A_    ,							index + 1    ,							[*path, nums[index]]    ,							A_    ,							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)

