


"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


UpperCamelCase						:				Any             =      logging.get_logger(__name__)



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						def __init__(							self   :    Optional[Any]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[Any]		):
								warnings.warn(
								    'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
								    ' Please use DeformableDetrImageProcessor instead.'			,			_lowercase			,			)
								super().__init__(*_lowercase			,			**_lowercase		)









"""simple docstring"""


from math import factorial







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		float:







		"""simple docstring"""





		if successes > trials:
				raise ValueError('successes must be lower or equal to trials'	)
		if trials < 0 or successes < 0:
				raise ValueError('the function is defined for non-negative integers'	)
		if not isinstance(UpperCamelCase__ ,			UpperCamelCase__	) or not isinstance(UpperCamelCase__ ,			UpperCamelCase__	):
				raise ValueError('the function is defined for non-negative integers'	)
		if not 0 < prob < 1:
				raise ValueError('prob has to be in range of 1 - 0'	)
		A					       = (prob**successes) * ((1 - prob) ** (trials - successes))
		# Calculate the binomial coefficient: n! / k!(n-k)!
		A					       = float(factorial(UpperCamelCase__	)	)
		coefficient /= factorial(UpperCamelCase__	) * factorial(trials - successes	)
		return probability * coefficient


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

	testmod()
	print("Probability of 2 successes out of 4 trails")
	print("with probability of 0.75 is:", end=" ")
	print(binomial_distribution(2, 4, 0.75))





