


from math import pi, sqrt






def 	__lowerCamelCase						(							lowerCamelCase__		:						float      ):





			'''simple docstring'''




			if num <= 0:
						raise ValueError("""math domain error"""      )
			if num > 1_7_1.5:
						raise OverflowError("""math range error"""      )
			elif num - int(lowerCamelCase__      ) not in (0, 0.5):
						raise NotImplementedError("""num must be an integer or a half-integer"""      )
			elif num == 0.5:
						return sqrt(lowerCamelCase__      )
			else:
						return 1.0 if num == 1 else (num - 1) * gamma(num - 1      )






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			assert gamma(0.5      ) == sqrt(lowerCamelCase__      )
			assert gamma(1      ) == 1.0
			assert gamma(2      ) == 1.0


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

		testmod()
		UpperCAmelCase   :    Dict     =		1.0
		while num:
				UpperCAmelCase   :    Dict     =		float(input("Gamma of: "))
				print(f"""gamma({num}) = {gamma(num)}""")
				print("\nEnter 0 to exit...")





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


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

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







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"lilt"







		def __init__( self		,      A=3_05_22		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=2		,      A=0.02		,      A=1e-1_2		,      A=0		,      A="absolute"		,      A=None		,      A=4		,      A=10_24		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







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

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

