
'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= ['image_processor', 'tokenizer']
   A		= 'AutoImageProcessor'
   A		= 'AutoTokenizer'






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,**__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		None
    if "feature_extractor" in kwargs:
     warnings.warn(
         'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
         ' instead.'    ,__SCREAMING_SNAKE_CASE    ,)
     SCREAMING_SNAKE_CASE  :  List[Any]								=		kwargs.pop('feature_extractor'      )

    SCREAMING_SNAKE_CASE  :  int								=		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__(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.image_processor
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		False






   def __call__(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    # For backward compatibility
    if self._in_target_context_manager:
     return self.current_processor(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		kwargs.pop('images'    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		kwargs.pop('text'    ,__SCREAMING_SNAKE_CASE      )
    if len(__SCREAMING_SNAKE_CASE      ) > 0:
     SCREAMING_SNAKE_CASE  :  List[str]								=		args[0]
     SCREAMING_SNAKE_CASE  :  List[Any]								=		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:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.image_processor(__SCREAMING_SNAKE_CASE    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )
    if text is not None:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.tokenizer(__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )

    if text is None:
     return inputs
    elif images is None:
     return encodings
    else:
     SCREAMING_SNAKE_CASE  :  List[str]								=		encodings['input_ids']
     return inputs






   def 					__a			(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )






   @contextmanager
   def 					__a			(       self      ):
    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.'      )
    SCREAMING_SNAKE_CASE  :  Dict								=		True
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.tokenizer
    yield
    SCREAMING_SNAKE_CASE  :  Dict								=		self.image_processor
    SCREAMING_SNAKE_CASE  :  int								=		False






   @property
   def 					__a			(       self      ):
    warnings.warn(
        '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,__SCREAMING_SNAKE_CASE    ,)
    return self.image_processor_class






   @property
   def 					__a			(       self      ):
    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,__SCREAMING_SNAKE_CASE    ,)
    return self.image_processor




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


