
"""simple docstring"""

from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest

@custom_tokenizers
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      return True







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate' )
      __UpperCamelCase							    =			'Hugging Face大法好，谁用谁知道。'
      __UpperCamelCase							    =			'Hugging Face大法好,谁用谁知道。<unk>'
      __UpperCamelCase							    =			'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

      __UpperCamelCase							    =			tokenizer.tokenize(__UpperCAmelCase )
      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )

      __UpperCamelCase							    =			tokens + [tokenizer.unk_token]

      __UpperCamelCase							    =			[1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase )      ,	__UpperCAmelCase )

      __UpperCamelCase							    =			tokenizer.decode(__UpperCAmelCase )
      self.assertEqual(__UpperCAmelCase      ,	__UpperCAmelCase )





"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



