
"""simple docstring"""

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		DPRContextEncoderTokenizer
     lowercase		  =		DPRContextEncoderTokenizerFast
     lowercase		  =		True


@require_tokenizers
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		DPRQuestionEncoderTokenizer
     lowercase		  =		DPRQuestionEncoderTokenizerFast
     lowercase		  =		True


@require_tokenizers
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		DPRReaderTokenizer
     lowercase		  =		DPRReaderTokenizerFast
     lowercase		  =		True





     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.tokenizer_class.from_pretrained('bert-base-uncased' )

      __UpperCamelCase							    =			tokenizer.encode('question sequence'      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer.encode('title sequence'      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer.encode('text sequence ' * 4      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			[[101] + text_a + [102] + text_a + [102] + text_a]
      __UpperCamelCase							    =			BatchEncoding({'input_ids': input_ids} )

      __UpperCamelCase							    =			[[0] * len(input_ids[0] )]
      __UpperCamelCase							    =			[[0] * len(input_ids[0] )]
      __UpperCamelCase							    =			[0]
      __UpperCamelCase							    =			DPRReaderOutput(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )

      __UpperCamelCase    ,      __UpperCamelCase							    =			8, 9
      __UpperCamelCase							    =			10
      __UpperCamelCase							    =			10
      __UpperCamelCase							    =			tokenizer.decode_best_spans(__UpperCAmelCase      ,	__UpperCAmelCase )
      self.assertEqual(predicted_spans[0].start_index      ,	__UpperCAmelCase )
      self.assertEqual(predicted_spans[0].end_index      ,	__UpperCAmelCase )
      self.assertEqual(predicted_spans[0].doc_id      ,	0 )







     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.tokenizer_class.from_pretrained('bert-base-uncased' )

      __UpperCamelCase							    =			tokenizer.encode('question sequence'      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer.encode('title sequence'      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer.encode('text sequence'      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			[101] + text_a + [102] + text_a + [102] + text_a
      __UpperCamelCase							    =			tokenizer(questions=['question sequence']      ,	titles=['title sequence']      ,	texts=['text sequence'] )
      self.assertIn('input_ids'      ,	__UpperCAmelCase )
      self.assertIn('attention_mask'      ,	__UpperCAmelCase )
      self.assertListEqual(encoded_input['input_ids'][0]      ,	__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()



