


def 						_A						(						_lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase      ) ->      List[str]:


     """simple docstring"""


     if index == r:
          for j in range(_A      ):
               print(data[j] ,   end=' '      )
          print(' '      )
          return
     #  When no more elements are there to put in data[]
     if i >= n:
          return
     # current is included, put next at next location
     __UpperCamelCase      =       arr[i]
     combination_util(_A ,   _A ,   _A ,   index + 1 ,   _A ,   i + 1      )
     # current is excluded, replace it with
     # next (Note that i+1 is passed, but
     # index is not changed)
     combination_util(_A ,   _A ,   _A ,   _A ,   _A ,   i + 1      )
     # The main function that prints all combinations
     # of size r in arr[] of size n. This function
     # mainly uses combinationUtil()






def 						_A						(						_lowercase ,   _lowercase ,   _lowercase      ) ->      Tuple:


     """simple docstring"""


     __UpperCamelCase      =       [0] * r
     # Print all combination using temporary array 'data[]'
     combination_util(_A ,   _A ,   _A ,   0 ,   _A ,   0      )


if __name__ == "__main__":
    # Driver code to check the function above
    __snake_case									=		[1_0, 2_0, 3_0, 4_0, 5_0]
    print_combination(arr, len(arr), 3)
    # This code is contributed by Ambuj sahu



'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowercase          =  {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''NezhaForNextSentencePrediction''',
						    '''NezhaForMaskedLM''',
						    '''NezhaForPreTraining''',
						    '''NezhaForMultipleChoice''',
						    '''NezhaForQuestionAnswering''',
						    '''NezhaForSequenceClassification''',
						    '''NezhaForTokenClassification''',
						    '''NezhaModel''',
						    '''NezhaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

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




