


'''simple docstring'''

from transformers import ProcessorMixin







class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Dict    					=					'AutoFeatureExtractor'
 _lowerCamelCase :					str    					=					'AutoTokenizer'



'''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  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
 _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
 _lowerCamelCase :					int    					=					True

 def 		lowercase_						( self :	List[str],					**a_ :	str					):





    """simple docstring"""



    return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)

 @slow
 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)

    UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
    UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)

    UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
    UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)

    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
    ]