



'''simple docstring'''

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


__lowerCAmelCase		    =     logging.get_logger(__name__)

__lowerCAmelCase		    =     {
    '''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 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					List[Any]            =      'splinter'







							def __init__(							self      : str			,_UpperCAmelCase      : Tuple=30522			,_UpperCAmelCase      : Optional[Any]=768			,_UpperCAmelCase      : Dict=12			,_UpperCAmelCase      : List[str]=12			,_UpperCAmelCase      : Union[str, Any]=3072			,_UpperCAmelCase      : int="gelu"			,_UpperCAmelCase      : Tuple=0.1			,_UpperCAmelCase      : Union[str, Any]=0.1			,_UpperCAmelCase      : Dict=512			,_UpperCAmelCase      : Dict=2			,_UpperCAmelCase      : Optional[int]=0.02			,_UpperCAmelCase      : Optional[Any]=1E-12			,_UpperCAmelCase      : Any=True			,_UpperCAmelCase      : int=0			,_UpperCAmelCase      : Any=104			,**_UpperCAmelCase      : List[str]			,):
									super().__init__(pad_token_id=_UpperCAmelCase			,**_UpperCAmelCase	)

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









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




