




import warnings

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





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   ["image_processor", "tokenizer"]
 lowercase_       =   "CLIPImageProcessor"
 lowercase_       =   ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")



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

       SCREAMING_SNAKE_CASE_     						=      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__(_lowerCAmelCase    ,    _lowerCAmelCase						)



 def __call__(							self  :		List[str]    ,    _lowerCAmelCase  :		Any=None    ,    _lowerCAmelCase  :		Dict=None    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    **_lowerCAmelCase  :		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:
             SCREAMING_SNAKE_CASE_     						=      self.tokenizer(_lowerCAmelCase    ,    return_tensors=_lowerCAmelCase    ,    **_lowerCAmelCase						)

       if images is not None:
             SCREAMING_SNAKE_CASE_     						=      self.image_processor(_lowerCAmelCase    ,    return_tensors=_lowerCAmelCase    ,    **_lowerCAmelCase						)

       if text is not None and images is not None:
             SCREAMING_SNAKE_CASE_     						=      image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCAmelCase						)    ,    tensor_type=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    *_lowerCAmelCase  :		Dict    ,    **_lowerCAmelCase  :		str						):
       return self.tokenizer.batch_decode(*_lowerCAmelCase    ,    **_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		str    ,    *_lowerCAmelCase  :		List[Any]    ,    **_lowerCAmelCase  :		List[Any]						):
       return self.tokenizer.decode(*_lowerCAmelCase    ,    **_lowerCAmelCase						)




 @property
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE_     						=      self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
   import accelerate

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			int:
      if not is_accelerate_available():
            return method
      SCREAMING_SNAKE_CASE_     						=      version.parse(accelerate.__version__							).base_version
      if version.parse(__UpperCAmelCase							) < version.parse('0.17.0'							):
            return method

      def wrapper(self						:     Optional[int]      ,      *__UpperCAmelCase						:     Optional[Any]      ,      **__UpperCAmelCase						:     Optional[Any]							):
            if hasattr(self      ,      '_hf_hook'							) and hasattr(self._hf_hook      ,      'pre_forward'							):
                  self._hf_hook.pre_forward(self							)
            return method(self      ,      *__UpperCAmelCase      ,      **__UpperCAmelCase							)

      return wrapper