





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Any									=       ['image_processor', 'tokenizer']
       _snake_case      :	Optional[Any]									=       'ChineseCLIPImageProcessor'
       _snake_case      :	List[str]									=       ('BertTokenizer', 'BertTokenizerFast')





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

            '''simple docstring'''




            _UpperCamelCase							       =			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__			,					)
                 _UpperCamelCase							       =			kwargs.pop('''feature_extractor'''	)

            _UpperCamelCase							       =			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__	)
            _UpperCamelCase							       =			self.image_processor





       def __call__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]=None			,					lowerCAmelCase__  :						List[Any]=None			,					lowerCAmelCase__  :						List[str]=None			,					**lowerCAmelCase__  :						Optional[Any]	)       ->	Union[str, 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:
                 _UpperCamelCase							       =			self.tokenizer(lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					**lowerCAmelCase__	)

            if images is not None:
                 _UpperCamelCase							       =			self.image_processor(lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					**lowerCAmelCase__	)

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





       def snake_case__		(				self  :						Optional[int]			,					*lowerCAmelCase__  :						str			,					**lowerCAmelCase__  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						int			,					*lowerCAmelCase__  :						Union[str, Any]			,					**lowerCAmelCase__  :						int	)       ->	str:

            '''simple docstring'''




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





       @property
       def snake_case__		(				self  :						str	)       ->	List[str]:

            '''simple docstring'''




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







       @property
       def snake_case__		(				self  :						List[str]	)       ->	Tuple:

            '''simple docstring'''




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










'''simple docstring'''

def    a__							(							lowercase   :	str )						->						int:





     """simple docstring"""

     assert column_title.isupper()
     _UpperCamelCase							       =			0
     _UpperCamelCase							       =			len(lowercase ) - 1
     _UpperCamelCase							       =			0

     while index >= 0:
          _UpperCamelCase							       =			(ord(column_title[index] ) - 64) * pow(26,   lowercase )
          answer += value
          power += 1
          index -= 1

     return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()



