

def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> None:



    '''simple docstring'''






    if target == 0:
        answer.append(path.copy()				)
    else:
        for index in range(_UpperCAmelCase,      len(_UpperCAmelCase				)				):
            if target >= candidates[index]:
                path.append(candidates[index]				)
                backtrack(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      target - candidates[index],      _UpperCAmelCase				)
                path.pop(len(_UpperCAmelCase				) - 1				)
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> list:



    '''simple docstring'''






    lowerCAmelCase				:			Any    			=      []  # type: list[int]
    lowerCAmelCase				:			Dict    			=      []  # type: list[int]
    backtrack(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      0				)
    return answer
def 		SCREAMING_SNAKE_CASE__				(							)					-> None:



    '''simple docstring'''






    print(combination_sum([-8, 2.3, 0],      1				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()








from typing import TYPE_CHECKING

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


__A   :     Optional[int]      =			{
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Optional[int]      =			['''SplinterTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Union[str, Any]      =			[
     '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SplinterForQuestionAnswering''',
     '''SplinterForPreTraining''',
     '''SplinterLayer''',
     '''SplinterModel''',
     '''SplinterPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
 from .tokenization_splinter import SplinterTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_splinter_fast import SplinterTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_splinter import (
      SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SplinterForPreTraining,
      SplinterForQuestionAnswering,
      SplinterLayer,
      SplinterModel,
      SplinterPreTrainedModel,
  )


else:
 import sys

 __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





