



import warnings

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







class 		snake_case    (						__UpperCamelCase	):



   '''simple docstring'''






   snake_case_       :			str        =		["""image_processor""", """tokenizer"""]
   snake_case_       :			Optional[int]        =		"""ViTImageProcessor"""
   snake_case_       :			List[Any]        =		("""CLIPTokenizer""", """CLIPTokenizerFast""")

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

    """simple docstring"""







    _snake_case     :	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."""					,		__UpperCAmelCase					,		)
     _snake_case     :	Dict											=       kwargs.pop("""feature_extractor""")

    _snake_case     :	Optional[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__(__UpperCAmelCase					,		__UpperCAmelCase)

   def __call__(       self				:      Union[str, Any]					,		lowerCAmelCase				:      Any=None					,		lowerCAmelCase				:      Tuple=None					,		lowerCAmelCase				:      Optional[Any]=None					,		lowerCAmelCase				:      Optional[Any]=None					,		**lowerCAmelCase				:      Tuple)					->					Union[str, Any]:

    """simple docstring"""







    if text is None and visual_prompt is None and images is None:
     raise ValueError("""You have to specify either text, visual prompt or images.""")

    if text is not None and visual_prompt is not None:
     raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt.""")

    if text is not None:
     _snake_case     :	int											=       self.tokenizer(__UpperCAmelCase					,		return_tensors=__UpperCAmelCase					,		**__UpperCAmelCase)

    if visual_prompt is not None:
     _snake_case     :	int											=       self.image_processor(__UpperCAmelCase					,		return_tensors=__UpperCAmelCase					,		**__UpperCAmelCase)

    if images is not None:
     _snake_case     :	Optional[int]											=       self.image_processor(__UpperCAmelCase					,		return_tensors=__UpperCAmelCase					,		**__UpperCAmelCase)

    if visual_prompt is not None and images is not None:
     _snake_case     :	str											=       {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
    elif text is not None and images is not None:
     _snake_case     :	Tuple											=       image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    elif visual_prompt is not None:
     _snake_case     :	List[Any]											=       {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
    else:
     return BatchEncoding(data=dict(**__UpperCAmelCase)					,		tensor_type=__UpperCAmelCase)

   def   UpperCamelCase_	(       self				:      int					,		*lowerCAmelCase				:      List[Any]					,		**lowerCAmelCase				:      List[Any])					->					Any:

    """simple docstring"""







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

   def   UpperCamelCase_	(       self				:      Optional[int]					,		*lowerCAmelCase				:      Any					,		**lowerCAmelCase				:      List[str])					->					Dict:

    """simple docstring"""







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

   @property
   def   UpperCamelCase_	(       self				:      Any)					->					Optional[int]:

    """simple docstring"""







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


   @property
   def   UpperCamelCase_	(       self				:      Optional[int])					->					str:

    """simple docstring"""







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








from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@staticmethod
						@abstractmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					ArgumentParser   )    ->     Tuple:




											'''simple docstring'''



											raise NotImplementedError()



						@abstractmethod
						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											raise NotImplementedError()





