


from math import factorial


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):

  # If either of the conditions are true, the function is being asked
  # to calculate a factorial of a negative number, which is not possible
  if n < k or k < 0:
    raise ValueError('''Please enter positive integers for n and k where n >= k'''       )
  return factorial(SCREAMING_SNAKE_CASE       ) // (factorial(SCREAMING_SNAKE_CASE       ) * factorial(n - k       ))


if __name__ == "__main__":
     print(
         """The number of five-card hands possible from a standard""",
         F'''fifty-two card deck is: {combinations(52, 5)}\n''',
     )

     print(
         """If a class of 40 students must be arranged into groups of""",
         F'''4 for group projects, there are {combinations(40, 4)} ways''',
         """to arrange them.\n""",
     )

     print(
         """If 10 teams are competing in a Formula One race, there""",
         F'''are {combinations(10, 3)} ways that first, second and''',
         """third place can be awarded.""",
     )








from typing import TYPE_CHECKING

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


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """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

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




