


'''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 		UpperCamelCase_		(					__lowerCamelCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Dict         =     DPRContextEncoderTokenizer
						UpperCAmelCase__	:       int         =     DPRContextEncoderTokenizerFast
						UpperCAmelCase__	:       str         =     True






@require_tokenizers
class 		UpperCamelCase_		(					__lowerCamelCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Union[str, Any]         =     DPRQuestionEncoderTokenizer
						UpperCAmelCase__	:       Union[str, Any]         =     DPRQuestionEncoderTokenizerFast
						UpperCAmelCase__	:       Any         =     True






@require_tokenizers
class 		UpperCamelCase_		(					__lowerCamelCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[str]         =     DPRReaderTokenizer
						UpperCAmelCase__	:       Union[str, Any]         =     DPRReaderTokenizerFast
						UpperCAmelCase__	:       Optional[Any]         =     True
						@slow
						def 					__A					( self				:					Optional[int]  )							->					Any:
										__magic_name__	  = self.tokenizer_class.from_pretrained("bert-base-uncased"  )

										__magic_name__	  = tokenizer.encode("question sequence"	,				add_special_tokens=_lowerCamelCase  )
										__magic_name__	  = tokenizer.encode("title sequence"	,				add_special_tokens=_lowerCamelCase  )
										__magic_name__	  = tokenizer.encode("text sequence " * 4	,				add_special_tokens=_lowerCamelCase  )
										__magic_name__	  = [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
										__magic_name__	  = BatchEncoding({"input_ids": input_ids}  )

										__magic_name__	  = [[0] * len(input_ids[0]  )]
										__magic_name__	  = [[0] * len(input_ids[0]  )]
										__magic_name__	  = [0]
										__magic_name__	  = DPRReaderOutput(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

										__magic_name__  ,      __magic_name__	  = 8, 9
										__magic_name__	  = 10
										__magic_name__	  = 10
										__magic_name__	  = 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 					__A					( self				:					Tuple  )							->					Optional[int]:
										__magic_name__	  = self.tokenizer_class.from_pretrained("bert-base-uncased"  )

										__magic_name__	  = tokenizer.encode("question sequence"	,				add_special_tokens=_lowerCamelCase  )
										__magic_name__	  = tokenizer.encode("title sequence"	,				add_special_tokens=_lowerCamelCase  )
										__magic_name__	  = tokenizer.encode("text sequence"	,				add_special_tokens=_lowerCamelCase  )
										__magic_name__	  = [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
										__magic_name__	  = 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  )







'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__magic_name__   :   int  							=logging.get_logger(__name__)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					List[str]	,				*_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					Optional[Any]  )							->					None:
										warnings.warn(
										    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConvNextImageProcessor instead."	,				_lowerCamelCase	,				)
										super().__init__(*_lowerCamelCase	,				**_lowerCamelCase  )



