




'''simple docstring'''







def 						snake_case_							(_a	:							int     ):
    if not isinstance(_a    ,   _a     ):
        raise ValueError('''Input must be an integer'''     )
    if input_num <= 0:
        raise ValueError('''Input must be positive'''     )
    return sum(
        divisor for divisor in range(1    ,   input_num // 2 + 1     ) if input_num % divisor == 0     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()










'''simple docstring'''







import warnings

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


class 			_a  (	__a ):
       __a				:     int   				=					["""image_processor""", """tokenizer"""]
       __a				:     Union[str, Any]   				=					"""ChineseCLIPImageProcessor"""
       __a				:     List[Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Dict   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Dict=None   ,				**lowercase						:	Optional[Any]				):



           '''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.'''   ,				lowercase   ,				)
               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__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	int=None   ,				**lowercase						:	Dict				):



           '''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(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           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(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	int   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''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     A      (	self						:	List[Any]				):



           '''simple docstring'''

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




