


'''simple docstring'''
from math import factorial


def       a				(      __a			, __a			, __a       )  ->     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(__a			, __a       ) or not isinstance(__a			, __a       ):
										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'''       )
					UpperCamelCase__       :Optional[Any]							 =			(prob**successes) * ((1 - prob) ** (trials - successes))
					# Calculate the binomial coefficient: n! / k!(n-k)!
					UpperCamelCase__       :str							 =			float(factorial(__a       )       )
					coefficient /= factorial(__a       ) * 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))



'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case											=					logging.getLogger(__name__)
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    'masked_bert'




		def __init__(		self   ,       UpperCamelCase_=30522   ,       UpperCamelCase_=768   ,       UpperCamelCase_=12   ,       UpperCamelCase_=12   ,       UpperCamelCase_=3072   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=512   ,       UpperCamelCase_=2   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=1e-12   ,       UpperCamelCase_=0   ,       UpperCamelCase_="topK"   ,       UpperCamelCase_="constant"   ,       UpperCamelCase_=0.0   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_   ,       **UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			vocab_size
							UpperCamelCase__       :Dict							 =			hidden_size
							UpperCamelCase__       :Optional[int]							 =			num_hidden_layers
							UpperCamelCase__       :Any							 =			num_attention_heads
							UpperCamelCase__       :Optional[Any]							 =			hidden_act
							UpperCamelCase__       :str							 =			intermediate_size
							UpperCamelCase__       :List[Any]							 =			hidden_dropout_prob
							UpperCamelCase__       :List[str]							 =			attention_probs_dropout_prob
							UpperCamelCase__       :List[Any]							 =			max_position_embeddings
							UpperCamelCase__       :int							 =			type_vocab_size
							UpperCamelCase__       :List[Any]							 =			initializer_range
							UpperCamelCase__       :Any							 =			layer_norm_eps
							UpperCamelCase__       :str							 =			pruning_method
							UpperCamelCase__       :int							 =			mask_init
							UpperCamelCase__       :Optional[Any]							 =			mask_scale