


'''simple docstring'''






import warnings

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




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""image_processor""", """tokenizer"""]
 _lowerCAmelCase	   =		"""CLIPImageProcessor"""
 _lowerCAmelCase	   =		("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(	self ,			__magic_name__=None ,			__magic_name__=None ,			**__magic_name__  )   ->							Optional[Any]:
      _a      =							None
      if "feature_extractor" in kwargs:
           warnings.warn(
               'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
               ' instead.' ,			__magic_name__ ,			)
           _a      =							kwargs.pop('feature_extractor'  )

      _a      =							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__(__magic_name__ ,			__magic_name__  )
 def __call__(	self ,			__magic_name__=None ,			__magic_name__=None ,			__magic_name__=None ,			**__magic_name__  )   ->							str:

      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      =							self.tokenizer(__magic_name__ ,			return_tensors=__magic_name__ ,			**__magic_name__  )

      if images is not None:
           _a      =							self.image_processor(__magic_name__ ,			return_tensors=__magic_name__ ,			**__magic_name__  )

      if text is not None and images is not None:
           _a      =							image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__magic_name__  ) ,			tensor_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							Dict:
      return self.tokenizer.batch_decode(*__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							int:
      return self.tokenizer.decode(*__magic_name__ ,			**__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.tokenizer.model_input_names
      _a      =							self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,			__magic_name__ ,			)
      return self.image_processor_class




 @property
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,			__magic_name__ ,			)
      return self.image_processor





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

