

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 					A		(				__SCREAMING_SNAKE_CASE					):







    """simple docstring"""





    lowerCamelCase						      =				DPRContextEncoderTokenizer
    lowerCamelCase						      =				DPRContextEncoderTokenizerFast
    lowerCamelCase						      =				True




@require_tokenizers
class 					A		(				__SCREAMING_SNAKE_CASE					):







    """simple docstring"""





    lowerCamelCase						      =				DPRQuestionEncoderTokenizer
    lowerCamelCase						      =				DPRQuestionEncoderTokenizerFast
    lowerCamelCase						      =				True




@require_tokenizers
class 					A		(				__SCREAMING_SNAKE_CASE					):







    """simple docstring"""





    lowerCamelCase						      =				DPRReaderTokenizer
    lowerCamelCase						      =				DPRReaderTokenizerFast
    lowerCamelCase						      =				True


    @slow
    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







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

        A__        =  tokenizer.encode('question sequence',add_special_tokens=__UpperCAmelCase						)
        A__        =  tokenizer.encode('title sequence',add_special_tokens=__UpperCAmelCase						)
        A__        =  tokenizer.encode('text sequence ' * 4,add_special_tokens=__UpperCAmelCase						)
        A__        =  [[1_0_1] + text_a + [1_0_2] + text_a + [1_0_2] + text_a]
        A__        =  BatchEncoding({'input_ids': input_ids}						)

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

        A__ ,		A__        =  8, 9
        A__        =  1_0
        A__        =  1_0
        A__        =  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 		snake_case__	( self					:			Optional[Any]						)->       Optional[Any]:





        '''simple docstring'''







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

        A__        =  tokenizer.encode('question sequence',add_special_tokens=__UpperCAmelCase						)
        A__        =  tokenizer.encode('title sequence',add_special_tokens=__UpperCAmelCase						)
        A__        =  tokenizer.encode('text sequence',add_special_tokens=__UpperCAmelCase						)
        A__        =  [1_0_1] + text_a + [1_0_2] + text_a + [1_0_2] + text_a
        A__        =  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()



