




'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Any   							=					['''image_processor''', '''tokenizer''']
      __magic_name__  :       List[Any]   							=					'''CLIPImageProcessor'''
      __magic_name__  :       List[Any]   							=					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






      def __init__( self   :  int						,						lowercase__   :  Dict=None						,						lowercase__   :  Any=None						,						**lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							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."""						,						lowercase__						,						)
                a_     :							int     						=  kwargs.pop("""feature_extractor"""       )

           a_     :							List[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__(lowercase__						,						lowercase__       )






      def __call__( self   :  str						,						lowercase__   :  Union[str, Any]=None						,						lowercase__   :  List[str]=None						,						lowercase__   :  Optional[Any]=None						,						**lowercase__   :  Any       ):


           '''simple docstring'''

           if text is None and images is None:
                raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

           if text is not None:
                a_     :							str     						=  self.tokenizer(lowercase__						,						return_tensors=lowercase__						,						**lowercase__       )

           if images is not None:
                a_     :							Tuple     						=  self.image_processor(lowercase__						,						return_tensors=lowercase__						,						**lowercase__       )

           if text is not None and images is not None:
                a_     :							Optional[int]     						=  image_features.pixel_values
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**lowercase__       )						,						tensor_type=lowercase__       )






      def 							lowercase_						( self   :  Tuple						,						*lowercase__   :  List[str]						,						**lowercase__   :  str       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Dict						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

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






      @property
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tokenizer.model_input_names
           a_     :							List[Any]     						=  self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






'''simple docstring'''


from ..utils import DummyObject, requires_backends







class      SCREAMING_SNAKE_CASE    (						metaclass=snake_case_				):
      __magic_name__  :       Dict   							=					['''transformers''', '''torch''', '''note_seq''']






      def __init__( self   :  List[str]						,						*lowercase__   :  Dict						,						**lowercase__   :  int       ):


           '''simple docstring'''

           requires_backends(self						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						*lowercase__   :  List[str]						,						**lowercase__   :  str       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  str						,						*lowercase__   :  Optional[int]						,						**lowercase__   :  Any       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )
