
from __future__ import annotations


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   list[list[int]]					=				[]
      UpperCamelCase__    :   list[int]					=				[]
      UpperCamelCase__    :   List[Any]					=				0
      UpperCamelCase__    :   str					=				sum(UpperCamelCase__		)
      create_state_space_tree(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
      return result


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		):
      if sum(UpperCamelCase__		) > max_sum or (remaining_nums_sum + sum(UpperCamelCase__		)) < max_sum:
            return
      if sum(UpperCamelCase__		) == max_sum:
            result.append(UpperCamelCase__		)
            return
      for index in range(UpperCamelCase__ ,		len(UpperCamelCase__		)		):
            create_state_space_tree(
                UpperCamelCase__ ,		UpperCamelCase__ ,		index + 1 ,		[*path, nums[index]] ,		UpperCamelCase__ ,		remaining_nums_sum - nums[index] ,		)
lowerCamelCase      =[3, 3_4, 4, 1_2, 5, 2]
lowerCamelCase      =9
lowerCamelCase      =generate_sum_of_subsets_soln(nums, max_sum)
print(*result)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      ={
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CLIPSegModel",
        "CLIPSegPreTrainedModel",
        "CLIPSegTextModel",
        "CLIPSegVisionModel",
        "CLIPSegForImageSegmentation",
    ]

if TYPE_CHECKING:
    from .configuration_clipseg import (
        CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPSegConfig,
        CLIPSegTextConfig,
        CLIPSegVisionConfig,
    )
    from .processing_clipseg import CLIPSegProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clipseg import (
            CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPSegForImageSegmentation,
            CLIPSegModel,
            CLIPSegPreTrainedModel,
            CLIPSegTextModel,
            CLIPSegVisionModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


