





from string import ascii_uppercase

_SCREAMING_SNAKE_CASE      	=		{str(ord(c) - 55): c for c in ascii_uppercase}


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if isinstance(__a   , __a   ):
		raise TypeError('int() can\'t convert non-string with explicit base'   )
	if num < 0:
		raise ValueError('parameter must be positive int'   )
	if isinstance(__a   , __a   ):
		raise TypeError('\'str\' object cannot be interpreted as an integer'   )
	if isinstance(__a   , __a   ):
		raise TypeError('\'float\' object cannot be interpreted as an integer'   )
	if base in (0, 1):
		raise ValueError('base must be >= 2'   )
	if base > 36:
		raise ValueError('base must be <= 36'   )
	snake_case_		:			Union[str, Any]											=	''
	snake_case_		:			Optional[int]											=	0
	snake_case_		:			List[str]											=	0
	while div != 1:
		snake_case_						,snake_case_		:			Optional[int]											=	divmod(__a   , __a   )
		if base >= 11 and 9 < mod < 36:
			snake_case_		:			Dict											=	ALPHABET_VALUES[str(__a   )]
		else:
			snake_case_		:			str											=	str(__a   )
		new_value += actual_value
		snake_case_		:			List[str]											=	num // base
		snake_case_		:			Any											=	div
		if div == 0:
			return str(new_value[::-1]   )
		elif div == 1:
			new_value += str(__a   )
			return str(new_value[::-1]   )

	return new_value[::-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for base in range(2, 37):
												for num in range(10_00):
																		assert int(decimal_to_any(num, base), base) == num, (
																		    num,
																		    base,
																		    decimal_to_any(num, base),
																		    int(decimal_to_any(num, base), base),
																		)








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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "lilt"






						def __init__(      self      :      Any		,   _A      :      Dict=30522		,   _A      :      List[Any]=768		,   _A      :      str=12		,   _A      :      int=12		,   _A      :      Optional[int]=3072		,   _A      :      Optional[Any]="gelu"		,   _A      :      Dict=0.1		,   _A      :      Tuple=0.1		,   _A      :      str=512		,   _A      :      Union[str, Any]=2		,   _A      :      Union[str, Any]=0.0_2		,   _A      :      Optional[Any]=1E-12		,   _A      :      Optional[int]=0		,   _A      :      Dict="absolute"		,   _A      :      Any=None		,   _A      :      Optional[Any]=4		,   _A      :      Any=1024		,   **_A      :      Union[str, Any]		,   )							->    List[str]:







							"""simple docstring"""


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

							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			Dict											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			Tuple											=	hidden_act
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			int											=	hidden_dropout_prob
							snake_case_		:			Tuple											=	attention_probs_dropout_prob
							snake_case_		:			Dict											=	max_position_embeddings
							snake_case_		:			Any											=	type_vocab_size
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			Optional[int]											=	layer_norm_eps
							snake_case_		:			List[str]											=	position_embedding_type
							snake_case_		:			Optional[int]											=	classifier_dropout
							snake_case_		:			Dict											=	channel_shrink_ratio
							snake_case_		:			Tuple											=	max_ad_position_embeddings

