




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						       =						'''ViltImageProcessor'''
 UpperCamelCase						       =						('''BertTokenizer''', '''BertTokenizerFast''')

 def __init__(    self :  Any						,							A_ :  Optional[int]=None						,							A_ :  int=None						,							**A_ :  List[Any]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'						,							A_						,							)
             lowerCamelCase_							=   kwargs.pop('feature_extractor'  )

       lowerCamelCase_							=   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__(A_						,							A_  )
       lowerCamelCase_							=   self.image_processor

 def __call__(    self :  Dict						,							A_ :  List[Any]						,							A_ :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,							A_ :  bool = True						,							A_ :  Union[bool, str, PaddingStrategy] = False						,							A_ :  Union[bool, str, TruncationStrategy] = None						,							A_ :  Optional[int] = None						,							A_ :  int = 0						,							A_ :  Optional[int] = None						,							A_ :  Optional[bool] = None						,							A_ :  Optional[bool] = None						,							A_ :  bool = False						,							A_ :  bool = False						,							A_ :  bool = False						,							A_ :  bool = False						,							A_ :  bool = True						,							A_ :  Optional[Union[str, TensorType]] = None						,							**A_ :  Union[str, Any]						,							)       ->     BatchEncoding:

       """simple docstring"""






       lowerCamelCase_							=   self.tokenizer(
           text=A_						,							add_special_tokens=A_						,							padding=A_						,							truncation=A_						,							max_length=A_						,							stride=A_						,							pad_to_multiple_of=A_						,							return_token_type_ids=A_						,							return_attention_mask=A_						,							return_overflowing_tokens=A_						,							return_special_tokens_mask=A_						,							return_offsets_mapping=A_						,							return_length=A_						,							verbose=A_						,							return_tensors=A_						,							**A_						,							)
       # add pixel_values + pixel_mask
       lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_  )
       encoding.update(A_  )

       return encoding

 def     a__			(    self :  int						,							*A_ :  List[str]						,							**A_ :  str  )       ->     Tuple:

       """simple docstring"""






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

 def     a__			(    self :  Any						,							*A_ :  Optional[Any]						,							**A_ :  Optional[Any]  )       ->     int:

       """simple docstring"""






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

 @property
 def     a__			(    self :  Union[str, Any]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.tokenizer.model_input_names
       lowerCamelCase_							=   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

 @property
 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






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





 @property
 def     a__			(    self :  List[str]  )       ->     Tuple:

       """simple docstring"""






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









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''keras_nlp''']

 def __init__(    self :  Optional[int]						,							*A_ :  Any						,							**A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       requires_backends(self						,							['keras_nlp']  )



