code: 
  
  
  '''simple docstring'''
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				A ) -> Optional[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if target == 0:
                answer.append(path.copy() )
         else:
                for index in range(__A						,				len(__A ) ):
                       if target >= candidates[index]:
                              path.append(candidates[index] )
                              backtrack(__A						,				__A						,				__A						,				target - candidates[index]						,				__A )
                              path.pop(len(__A ) - 1 )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				[]  # type: list[int]
         lowercase__       =				[]  # type: list[int]
         backtrack(__A						,				__A						,				__A						,				__A						,				0 )
         return answer
  
  def _SCREAMING_SNAKE_CASE   () -> Any:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         print(combination_sum([-8, 2.3, 0]						,				1 ) )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
   main()
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[int]								=			[
       'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTV2ForImageClassification',
       'MobileViTV2ForSemanticSegmentation',
       'MobileViTV2Model',
       'MobileViTV2PreTrainedModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  