'''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 lowercase_					(		UpperCAmelCase__ ):







   """simple docstring"""

   __lowerCAmelCase    						=      SqueezeBertTokenizer
   __lowerCAmelCase    						=      SqueezeBertTokenizerFast
   __lowerCAmelCase    						=      True
   def   __UpperCAmelCase      (			self			: List[str],   **UpperCamelCase__			: Dict    )     -> List[str]:
        return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,   **lowerCamelCase__    )

   @slow
   def   __UpperCAmelCase      (			self			: Any    )     -> str:
        _A								=     SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'    )

        _A								=     tokenizer.encode('sequence builders',   add_special_tokens=lowerCamelCase__    )
        _A								=     tokenizer.encode('multi-sequence build',   add_special_tokens=lowerCamelCase__    )

        _A								=     tokenizer.build_inputs_with_special_tokens(lowerCamelCase__    )
        _A								=     tokenizer.build_inputs_with_special_tokens(lowerCamelCase__,   lowerCamelCase__    )

        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
        ]




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



