






"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 		a_      (	__UpperCAmelCase ):







      '''simple docstring'''

      lowerCamelCase__       :				int =	['image_processor', 'tokenizer']
      lowerCamelCase__       :				List[str] =	'AutoImageProcessor'
      lowerCamelCase__       :				Tuple =	'AutoTokenizer'




      def __init__(self,     lowerCamelCase_=None,     lowerCamelCase_=None,     **lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						None
       if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.',     UpperCAmelCase_,     )
        lowerCamelCase__     :			List[str]     				=						kwargs.pop('feature_extractor'					)

       lowerCamelCase__     :			Any     				=						image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'					)
       if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'					)

       super().__init__(UpperCAmelCase_,     UpperCAmelCase_					)
       lowerCamelCase__     :			List[str]     				=						self.image_processor
       lowerCamelCase__     :			Union[str, Any]     				=						False




      def __call__(self,     *lowerCamelCase_,     **lowerCamelCase_					):

       '''simple docstring'''



       if self._in_target_context_manager:
        return self.current_processor(*UpperCAmelCase_,     **UpperCAmelCase_					)

       lowerCamelCase__     :			Dict     				=						kwargs.pop('images',     UpperCAmelCase_					)
       lowerCamelCase__     :			List[str]     				=						kwargs.pop('text',     UpperCAmelCase_					)
       if len(UpperCAmelCase_					) > 0:
        lowerCamelCase__     :			Any     				=						args[0]
        lowerCamelCase__     :			Tuple     				=						args[1:]

       if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'					)

       if images is not None:
        lowerCamelCase__     :			Dict     				=						self.image_processor(UpperCAmelCase_,     *UpperCAmelCase_,     **UpperCAmelCase_					)
       if text is not None:
        lowerCamelCase__     :			Optional[Any]     				=						self.tokenizer(UpperCAmelCase_,     **UpperCAmelCase_					)

       if text is None:
        return inputs
       elif images is None:
        return encodings
       else:
        lowerCamelCase__     :			Optional[Any]     				=						encodings['input_ids']
        return inputs




      def 			a__							(self,     *lowerCamelCase_,     **lowerCamelCase_					):

       '''simple docstring'''



       return self.tokenizer.batch_decode(*UpperCAmelCase_,     **UpperCAmelCase_					)




      def 			a__							(self,     *lowerCamelCase_,     **lowerCamelCase_					):

       '''simple docstring'''



       return self.tokenizer.decode(*UpperCAmelCase_,     **UpperCAmelCase_					)




      @contextmanager
      def 			a__							(self					):

       '''simple docstring'''



       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your images inputs, or in a separate call.'					)
       lowerCamelCase__     :			List[Any]     				=						True
       lowerCamelCase__     :			Dict     				=						self.tokenizer
       yield
       lowerCamelCase__     :			str     				=						self.image_processor
       lowerCamelCase__     :			List[Any]     				=						False




      @property
      def 			a__							(self					):

       '''simple docstring'''



       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',     UpperCAmelCase_,     )
       return self.image_processor_class





      @property
      def 			a__							(self					):

       '''simple docstring'''



       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',     UpperCAmelCase_,     )
       return self.image_processor



"""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        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRContextEncoderTokenizer
	lowercase_        =	DPRContextEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRQuestionEncoderTokenizer
	lowercase_        =	DPRQuestionEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRReaderTokenizer
	lowercase_        =	DPRReaderTokenizerFast
	lowercase_        =	True




	@slow
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			int		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    tokenizer.encode('text sequence ' * 4		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    [[101] + text_a + [102] + text_a + [102] + text_a]
			lowerCamelCase     :			List[str]		=    BatchEncoding({'input_ids': input_ids}					)

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

			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    8, 9
			lowerCamelCase     :			Optional[int]		=    10
			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			List[Any]		=    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					)		->							List[Any]:
			lowerCamelCase     :			Union[str, Any]		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			List[str]		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('text sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    [101] + text_a + [102] + text_a + [102] + text_a
			lowerCamelCase     :			Optional[Any]		=    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_					)






