






from typing import TYPE_CHECKING

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


__a      :       Optional[Any]				  =   {
    "configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       Optional[Any]				  =   ["ConvNextFeatureExtractor"]
  __a      :       Union[str, Any]				  =   ["ConvNextImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       str				  =   [
      "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ConvNextForImageClassification",
      "ConvNextModel",
      "ConvNextPreTrainedModel",
      "ConvNextBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       Any				  =   [
      "TFConvNextForImageClassification",
      "TFConvNextModel",
      "TFConvNextPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_convnext import ConvNextFeatureExtractor
    from .image_processing_convnext import ConvNextImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convnext import (
        CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvNextBackbone,
        ConvNextForImageClassification,
        ConvNextModel,
        ConvNextPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
  import sys

  __a      :       Optional[int]				  =   _LazyModule(__name__, globals()["__file__"], _import_structure)












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

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






print(*result)




