






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


a_		:    Dict   							=     logging.get_logger(__name__)

a_		:    List[str]   							=     {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}

class 		_snake_case	(	A__       ):
			_lowercase		:     str        =				'''luke'''






			def __init__(							self		,       a=5_0267		,       a=50_0000		,       a=768		,       a=256		,       a=12		,       a=12		,       a=3072		,       a="gelu"		,       a=0.1		,       a=0.1		,       a=512		,       a=2		,       a=0.02		,       a=1E-12		,       a=True		,       a=None		,       a=1		,       a=0		,       a=2		,       **a		,       )   ->							Dict:
						super().__init__(pad_token_id=a		,       bos_token_id=a		,       eos_token_id=a		,       **a)

						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			entity_vocab_size
						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			entity_emb_size
						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			hidden_act
						SCREAMING_SNAKE_CASE							=			intermediate_size
						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			max_position_embeddings
						SCREAMING_SNAKE_CASE							=			type_vocab_size
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			layer_norm_eps
						SCREAMING_SNAKE_CASE							=			use_entity_aware_attention
						SCREAMING_SNAKE_CASE							=			classifier_dropout










def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Optional[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      [0 for i in range(r + 1  )]
			# nc0 = 1
			SCREAMING_SNAKE_CASE_   : Dict					  =      1
			for i in range(1  ,							n + 1  ):
						# to compute current row from previous row.
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      min(lowerCamelCase_  ,							lowerCamelCase_  )
						while j > 0:
									c[j] += c[j - 1]
									j -= 1
			return c[r]




print(binomial_coefficient(n=10, r=5))



