



def        __a     (    lowerCAmelCase_   :					list			,lowerCAmelCase_   :					list			,lowerCAmelCase_   :					list			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int				)      -> None:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					list			,lowerCAmelCase_   :					int				)      -> list:






						'''simple docstring'''



						UpperCAmelCase_=   []  # type: list[int]
						UpperCAmelCase_=   []  # type: list[int]
						backtrack(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,0				)
						return answer



def        __a     (    )      -> 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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				    =						{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MPNetForMaskedLM''',
	    '''MPNetForMultipleChoice''',
	    '''MPNetForQuestionAnswering''',
	    '''MPNetForSequenceClassification''',
	    '''MPNetForTokenClassification''',
	    '''MPNetLayer''',
	    '''MPNetModel''',
	    '''MPNetPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFMPNetEmbeddings''',
	    '''TFMPNetForMaskedLM''',
	    '''TFMPNetForMultipleChoice''',
	    '''TFMPNetForQuestionAnswering''',
	    '''TFMPNetForSequenceClassification''',
	    '''TFMPNetForTokenClassification''',
	    '''TFMPNetMainLayer''',
	    '''TFMPNetModel''',
	    '''TFMPNetPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
	from .tokenization_mpnet import MPNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mpnet_fast import MPNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mpnet import (
		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetLayer,
		    MPNetModel,
		    MPNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mpnet import (
		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMPNetEmbeddings,
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetMainLayer,
		    TFMPNetModel,
		    TFMPNetPreTrainedModel,
		)

else:
	import sys

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




