






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


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[int]									=							"lilt"







			def __init__(     self:  List[Any]				,     UpperCAmelCase_:  Union[str, Any]=30_522				,     UpperCAmelCase_:  str=768				,     UpperCAmelCase_:  int=12				,     UpperCAmelCase_:  Optional[Any]=12				,     UpperCAmelCase_:  Any=3_072				,     UpperCAmelCase_:  Tuple="gelu"				,     UpperCAmelCase_:  List[str]=0.1				,     UpperCAmelCase_:  List[str]=0.1				,     UpperCAmelCase_:  Dict=512				,     UpperCAmelCase_:  int=2				,     UpperCAmelCase_:  Dict=0.02				,     UpperCAmelCase_:  Tuple=1E-12				,     UpperCAmelCase_:  Optional[Any]=0				,     UpperCAmelCase_:  Any="absolute"				,     UpperCAmelCase_:  Optional[int]=None				,     UpperCAmelCase_:  List[Any]=4				,     UpperCAmelCase_:  List[Any]=1_024				,     **UpperCAmelCase_:  List[str]				,     ):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE     				=  vocab_size
									_SCREAMING_SNAKE_CASE     				=  hidden_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     				=  position_embedding_type
									_SCREAMING_SNAKE_CASE     				=  classifier_dropout
									_SCREAMING_SNAKE_CASE     				=  channel_shrink_ratio
									_SCREAMING_SNAKE_CASE     				=  max_ad_position_embeddings









from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  prime_factors(snake_case__		)
						if is_square_free(snake_case__		):
												return -1 if len(snake_case__		) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

