code: 
  
  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 		__magic_name__    (							SCREAMING_SNAKE_CASE__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			str												=     DPRContextEncoderTokenizer
     lowerCamelCase__				:			Any												=     DPRContextEncoderTokenizerFast
     lowerCamelCase__				:			Optional[int]												=     True
  
  
  
  
  
  
  
  @require_tokenizers
  class 		__magic_name__    (							SCREAMING_SNAKE_CASE__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Tuple												=     DPRQuestionEncoderTokenizer
     lowerCamelCase__				:			Dict												=     DPRQuestionEncoderTokenizerFast
     lowerCamelCase__				:			List[Any]												=     True
  
  
  
  
  
  
  
  @require_tokenizers
  class 		__magic_name__    (							SCREAMING_SNAKE_CASE__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[str, Any]												=     DPRReaderTokenizer
     lowerCamelCase__				:			int												=     DPRReaderTokenizerFast
     lowerCamelCase__				:			Any												=     True
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.tokenizer_class.from_pretrained('''bert-base-uncased''' )
  
       a__						=tokenizer.encode('''question sequence''',					add_special_tokens=lowercase_ )
       a__						=tokenizer.encode('''title sequence''',					add_special_tokens=lowercase_ )
       a__						=tokenizer.encode('''text sequence ''' * 4,					add_special_tokens=lowercase_ )
       a__						=[[101] + text_a + [102] + text_a + [102] + text_a]
       a__						=BatchEncoding({'''input_ids''': input_ids} )
  
       a__						=[[0] * len(input_ids[0] )]
       a__						=[[0] * len(input_ids[0] )]
       a__						=[0]
       a__						=DPRReaderOutput(lowercase_,					lowercase_,					lowercase_ )
  
       a__,   a__						=8, 9
       a__						=10
       a__						=10
       a__						=tokenizer.decode_best_spans(lowercase_,					lowercase_ )
       self.assertEqual(predicted_spans[0].start_index,					lowercase_ )
       self.assertEqual(predicted_spans[0].end_index,					lowercase_ )
       self.assertEqual(predicted_spans[0].doc_id,					0 )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.tokenizer_class.from_pretrained('''bert-base-uncased''' )
  
       a__						=tokenizer.encode('''question sequence''',					add_special_tokens=lowercase_ )
       a__						=tokenizer.encode('''title sequence''',					add_special_tokens=lowercase_ )
       a__						=tokenizer.encode('''text sequence''',					add_special_tokens=lowercase_ )
       a__						=[101] + text_a + [102] + text_a + [102] + text_a
       a__						=tokenizer(questions=['''question sequence'''],					titles=['''title sequence'''],					texts=['''text sequence'''] )
       self.assertIn('''input_ids''',					lowercase_ )
       self.assertIn('''attention_mask''',					lowercase_ )
       self.assertListEqual(encoded_input['''input_ids'''][0],					lowercase_ )
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  