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					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	DPRContextEncoderTokenizer
 _lowerCamelCase  :		Any			      =	DPRContextEncoderTokenizerFast
 _lowerCamelCase  :		List[str]			      =	True







@require_tokenizers
class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Any			      =	DPRQuestionEncoderTokenizer
 _lowerCamelCase  :		Tuple			      =	DPRQuestionEncoderTokenizerFast
 _lowerCamelCase  :		Optional[Any]			      =	True







@require_tokenizers
class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int]			      =	DPRReaderTokenizer
 _lowerCamelCase  :		Tuple			      =	DPRReaderTokenizerFast
 _lowerCamelCase  :		Tuple			      =	True

 @slow
 def    __snake_case   (     self    :						List[Any]					)	->							Optional[int]:
        _UpperCamelCase       :    Dict					  =					self.tokenizer_class.from_pretrained('bert-base-uncased'					)

        _UpperCamelCase       :    Tuple					  =					tokenizer.encode('question sequence'    ,     add_special_tokens=lowerCamelCase					)
        _UpperCamelCase       :    Optional[int]					  =					tokenizer.encode('title sequence'    ,     add_special_tokens=lowerCamelCase					)
        _UpperCamelCase       :    Union[str, Any]					  =					tokenizer.encode('text sequence ' * 4    ,     add_special_tokens=lowerCamelCase					)
        _UpperCamelCase       :    int					  =					[[1_0_1] + text_a + [1_0_2] + text_a + [1_0_2] + text_a]
        _UpperCamelCase       :    Any					  =					BatchEncoding({'input_ids': input_ids}					)

        _UpperCamelCase       :    List[str]					  =					[[0] * len(input_ids[0]					)]
        _UpperCamelCase       :    Any					  =					[[0] * len(input_ids[0]					)]
        _UpperCamelCase       :    Optional[Any]					  =					[0]
        _UpperCamelCase       :    List[str]					  =					DPRReaderOutput(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)

        _UpperCamelCase  , _UpperCamelCase       :    Tuple					  =					8, 9
        _UpperCamelCase       :    str					  =					1_0
        _UpperCamelCase       :    List[Any]					  =					1_0
        _UpperCamelCase       :    Union[str, Any]					  =					tokenizer.decode_best_spans(lowerCamelCase    ,     lowerCamelCase					)
        self.assertEqual(predicted_spans[0].start_index    ,     lowerCamelCase					)
        self.assertEqual(predicted_spans[0].end_index    ,     lowerCamelCase					)
        self.assertEqual(predicted_spans[0].doc_id    ,     0					)

 @slow
 def    __snake_case   (     self    :						Dict					)	->							Tuple:
        _UpperCamelCase       :    str					  =					self.tokenizer_class.from_pretrained('bert-base-uncased'					)

        _UpperCamelCase       :    int					  =					tokenizer.encode('question sequence'    ,     add_special_tokens=lowerCamelCase					)
        _UpperCamelCase       :    Dict					  =					tokenizer.encode('title sequence'    ,     add_special_tokens=lowerCamelCase					)
        _UpperCamelCase       :    Optional[int]					  =					tokenizer.encode('text sequence'    ,     add_special_tokens=lowerCamelCase					)
        _UpperCamelCase       :    Optional[Any]					  =					[1_0_1] + text_a + [1_0_2] + text_a + [1_0_2] + text_a
        _UpperCamelCase       :    str					  =					tokenizer(questions=['question sequence']    ,     titles=['title sequence']    ,     texts=['text sequence']					)
        self.assertIn('input_ids'    ,     lowerCamelCase					)
        self.assertIn('attention_mask'    ,     lowerCamelCase					)
        self.assertListEqual(encoded_input['input_ids'][0]    ,     lowerCamelCase					)




def SCREAMING_SNAKE_CASE			(       lowercase_:		int = 4000000			):
       _UpperCamelCase       :    str					  =					1
       _UpperCamelCase       :    str					  =					2
       _UpperCamelCase       :    Tuple					  =					0
       while j <= n:
              if j % 2 == 0:
                     total += j
              _UpperCamelCase  , _UpperCamelCase       :    int					  =					j, i + j

       return total


if __name__ == "__main__":
 print(f'''{solution() = }''')



