






"""simple docstring"""







from __future__ import annotations





def 						lowercase__(A ,		A							)    ->list[list[int]]:




						"""simple docstring"""



						lowercase__    :				list[list[int]]=	[]
						lowercase__    :				list[int]=	[]
						lowercase__    :				List[Any]=	0
						lowercase__    :				Optional[Any]=	sum(_snake_case							)
						create_state_space_tree(_snake_case ,		_snake_case ,		_snake_case ,		_snake_case ,		_snake_case ,		_snake_case							)
						return result





def 						lowercase__(A ,		A ,		A ,		A ,		A ,		A ,		)    ->None:




						"""simple docstring"""



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







print(*result)














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    List[str]				 =		{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    List[str]				 =		[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			a			:    List[str]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






