





from __future__ import annotations




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       list[list[int]]:
  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  0
  lowercase__           =  sum(_SCREAMING_SNAKE_CASE		)
  create_state_space_tree(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  return result




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


print(*result)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_          =		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		["""EfficientFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EfficientFormerForImageClassification""",
          """EfficientFormerForImageClassificationWithTeacher""",
          """EfficientFormerModel""",
          """EfficientFormerPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEfficientFormerForImageClassification""",
          """TFEfficientFormerForImageClassificationWithTeacher""",
          """TFEfficientFormerModel""",
          """TFEfficientFormerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientformer import EfficientFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientformer import (
                EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientFormerForImageClassification,
                EfficientFormerForImageClassificationWithTeacher,
                EfficientFormerModel,
                EfficientFormerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_efficientformer import (
                TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
                TFEfficientFormerModel,
                TFEfficientFormerPreTrainedModel,
            )

else:
      import sys

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


