


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings









"""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))





