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


UpperCamelCase			 =	logging.get_logger(__name__)

UpperCamelCase			 =	{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}


class _a   (       __SCREAMING_SNAKE_CASE					):

	'''simple docstring'''



	lowerCamelCase_    :			Optional[Any]	  =       """qdqbert"""

	def __init__(   self							, __UpperCAmelCase=30_522							, __UpperCAmelCase=768							, __UpperCAmelCase=12							, __UpperCAmelCase=12							, __UpperCAmelCase=3_072							, __UpperCAmelCase="gelu"							, __UpperCAmelCase=0.1							, __UpperCAmelCase=0.1							, __UpperCAmelCase=512							, __UpperCAmelCase=2							, __UpperCAmelCase=0.02							, __UpperCAmelCase=1e-12							, __UpperCAmelCase=True							, __UpperCAmelCase=1							, __UpperCAmelCase=0							, __UpperCAmelCase=2							, **__UpperCAmelCase							, ):
			super().__init__(pad_token_id=lowerCAmelCase__							, bos_token_id=lowerCAmelCase__							, eos_token_id=lowerCAmelCase__							, **lowerCAmelCase__    )

			__A							:    Dict         =      vocab_size
			__A							:    int         =      max_position_embeddings
			__A							:    int         =      hidden_size
			__A							:    Optional[int]         =      num_hidden_layers
			__A							:    Dict         =      num_attention_heads
			__A							:    int         =      intermediate_size
			__A							:    Any         =      hidden_act
			__A							:    Dict         =      hidden_dropout_prob
			__A							:    List[str]         =      attention_probs_dropout_prob
			__A							:    Tuple         =      initializer_range
			__A							:    int         =      type_vocab_size
			__A							:    Optional[int]         =      layer_norm_eps
			__A							:    int         =      use_cache









def 		a__       (							A__ = 2_0							):

			try:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= int(A__							)
			except (TypeError, ValueError):
						raise TypeError('Parameter n must be int or castable to int.'							)
			if n <= 0:
						raise ValueError('Parameter n must be greater than or equal to one.'							)
			SCREAMING_SNAKE_CASE_						:			int       						= 0
			while 1:
						i += n * (n - 1)
						SCREAMING_SNAKE_CASE_						:			List[str]       						= 0
						for j in range(2,	A__							):
									if i % j != 0:
												SCREAMING_SNAKE_CASE_						:			str       						= 1
												break
						if nfound == 0:
									if i == 0:
												SCREAMING_SNAKE_CASE_						:			Tuple       						= 1
									return i
			return None


if __name__ == "__main__":
				print(F"""{solution() = }""")





