





"""simple docstring"""


import warnings

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



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Dict											=     ["""image_processor""", """tokenizer"""]
       snake_case__				:							Tuple											=     """AutoImageProcessor"""
       snake_case__				:							Tuple											=     """AutoTokenizer"""

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        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."   ,					__SCREAMING_SNAKE_CASE   ,					)
         snake_case__    							=				kwargs.pop("feature_extractor"     )

        snake_case__    							=				image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
         raise ValueError("You have to specify an image_processor."     )
        if tokenizer is None:
         raise ValueError("You have to specify a tokenizer."     )

        super().__init__(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				self.image_processor

       def __call__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''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:
         snake_case__    							=				self.tokenizer(__SCREAMING_SNAKE_CASE   ,					return_tensors=__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     )

        if images is not None:
         snake_case__    							=				self.image_processor(__SCREAMING_SNAKE_CASE   ,					return_tensors=__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     )

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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








"""simple docstring"""


def 							a__			(  _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 return int(_UpperCAmelCase	) if x - int(_UpperCAmelCase	) <= 0 else int(_UpperCAmelCase	) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

