






"""simple docstring"""




from __future__ import annotations






def        lowerCAmelCase (      UpperCamelCase_:							list[int]     , UpperCamelCase_:							int					)       ->							list[list[int]]:







  '''simple docstring'''







  _a							=	[]
  _a							=	[]
  _a							=	0
  _a							=	sum(UpperCamelCase_					)
  create_state_space_tree(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
  return result






def        lowerCAmelCase (      UpperCamelCase_:							list[int]     , UpperCamelCase_:							int     , UpperCamelCase_:							int     , UpperCamelCase_:							list[int]     , UpperCamelCase_:							list[list[int]]     , UpperCamelCase_:							int     , )       ->							None:







  '''simple docstring'''







  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]     , )
UpperCamelCase							       =    [3, 34, 4, 12, 5, 2]
UpperCamelCase							       =    9
UpperCamelCase							       =    generate_sum_of_subsets_soln(nums, max_sum)




print(*result)










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase							       =    {
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

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


