






"""simple docstring"""







__a							:						List[str]   							=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

__a							:						Optional[Any]   							=			[{'type': 'code', 'content': INSTALL_CONTENT}]
__a							:						Any   							=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}










"""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


