






from math import factorial


def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int   ) ->   int:
						"""simple docstring"""







						if n < k or k < 0:
												raise ValueError("Please enter positive integers for n and k where n >= k"   )
						return factorial(UpperCamelCase_   ) // (factorial(UpperCamelCase_   ) * 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_torch_available


a_							=    {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''IBertForMaskedLM''',
	    '''IBertForMultipleChoice''',
	    '''IBertForQuestionAnswering''',
	    '''IBertForSequenceClassification''',
	    '''IBertForTokenClassification''',
	    '''IBertModel''',
	    '''IBertPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

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






