"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any






class 							__snake_case:


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		data
         __A					:					int								=		None

    def __str__(  self	):




         '''simple docstring'''



         return F'{self.data}'







class 							__snake_case:


    '''simple docstring'''





    def __init__(  self	):




         '''simple docstring'''



         __A					:					Any								=		None
         __A					:					Tuple								=		None
    def __iter__(  self	):




         '''simple docstring'''



         __A					:					int								=		self.front
         while node:
              yield node.data
              __A					:					Any								=		node.next
    def __len__(  self	):




         '''simple docstring'''



         return len(tuple(iter(self	)	)	)
    def __str__(  self	):




         '''simple docstring'''



         return " <- ".join(str(__A	) for item in self	)
    def 						_a							(  self	):




         '''simple docstring'''



         return len(self	) == 0
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					str								=		Node(__A	)
         if self.is_empty():
              __A					:					Dict								=		node
         else:
              assert isinstance(self.rear					,       __A	)
              __A					:					List[str]								=		node
              __A					:					Optional[int]								=		node
    def 						_a							(  self	):




         '''simple docstring'''



         if self.is_empty():
              raise IndexError('dequeue from empty queue'	)
         assert isinstance(self.front					,       __A	)
         __A					:					Tuple								=		self.front
         __A					:					Optional[Any]								=		self.front.next
         if self.front is None:
              __A					:					Optional[int]								=		None
         return node.data

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Any								=		None


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

       testmod()











"""simple docstring"""







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


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

__a							:						Optional[Any]   							=			{
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     ='splinter'




							def __init__( self:  Dict					,		__A:  List[Any]=30522					,		__A:  str=768					,		__A:  int=12					,		__A:  str=12					,		__A:  Tuple=3072					,		__A:  Dict="gelu"					,		__A:  Optional[int]=0.1					,		__A:  List[str]=0.1					,		__A:  str=512					,		__A:  List[Any]=2					,		__A:  Any=0.0_2					,		__A:  Any=1e-12					,		__A:  Tuple=True					,		__A:  List[Any]=0					,		__A:  List[str]=104					,		**__A:  Union[str, Any]					,		):

												'''simple docstring'''

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

												a__ 							=	vocab_size
												a__ 							=	max_position_embeddings
												a__ 							=	hidden_size
												a__ 							=	num_hidden_layers
												a__ 							=	num_attention_heads
												a__ 							=	intermediate_size
												a__ 							=	hidden_act
												a__ 							=	hidden_dropout_prob
												a__ 							=	attention_probs_dropout_prob
												a__ 							=	initializer_range
												a__ 							=	type_vocab_size
												a__ 							=	layer_norm_eps
												a__ 							=	use_cache
												a__ 							=	question_token_id


