






import math

from numpy import inf
from scipy.integrate import quad


def        _a  (     UpperCamelCase_					:				Optional[int]   ) ->   float:
						"""simple docstring"""







						if num <= 0:
												raise ValueError("math domain error"   )

						return quad(UpperCamelCase__    ,   0    ,   UpperCamelCase__    ,   args=(UpperCamelCase__)   )[0]


def        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Optional[Any]   ) ->   float:
						"""simple docstring"""







						return math.pow(UpperCamelCase__    ,   z - 1   ) * math.exp(-x   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()














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__)






