






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				str						    =      ['image_processor', 'tokenizer']
    __lowerCamelCase:				Optional[Any]						    =      'AutoImageProcessor'
    __lowerCamelCase:				Tuple						    =      'AutoTokenizer'




    def __init__(     self					:    Tuple					,       a					:    Tuple=None					,       a					:    str=None					,       **a					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Optional[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."					,       a					,       )
          lowercase_		:      Dict          =       kwargs.pop("feature_extractor"     )

       lowercase_		:      str          =       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__(a					,       a     )
       lowercase_		:      Optional[int]          =       self.image_processor
       lowercase_		:      List[str]          =       False




    def __call__(     self					:    Dict					,       *a					:    List[Any]					,       **a					:    Optional[Any]     ):






       '''simple docstring'''






       if self._in_target_context_manager:
          return self.current_processor(*a					,       **a     )

       lowercase_		:      Optional[Any]          =       kwargs.pop("images"					,       a     )
       lowercase_		:      List[str]          =       kwargs.pop("text"					,       a     )
       if len(a     ) > 0:
          lowercase_		:      Union[str, Any]          =       args[0]
          lowercase_		:      int          =       args[1:]

       if images is None and text is None:
          raise ValueError("You need to specify either an `images` or `text` input to process."     )

       if images is not None:
          lowercase_		:      Union[str, Any]          =       self.image_processor(a					,       *a					,       **a     )
       if text is not None:
          lowercase_		:      Optional[int]          =       self.tokenizer(a					,       **a     )

       if text is None:
          return inputs
       elif images is None:
          return encodings
       else:
          lowercase_		:      int          =       encodings["input_ids"]
          return inputs




    def 			lowerCAmelCase__			(     self					:    str					,       *a					:    int					,       **a					:    int     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Dict					,       *a					:    Union[str, Any]					,       **a					:    List[str]     ):






       '''simple docstring'''






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




    @contextmanager
    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       warnings.warn(
           "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
           "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
           "your images inputs, or in a separate call."     )
       lowercase_		:      List[Any]          =       True
       lowercase_		:      Optional[int]          =       self.tokenizer
       yield
       lowercase_		:      Optional[Any]          =       self.image_processor
       lowercase_		:      Any          =       False




    @property
    def 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






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





    @property
    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






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












'''simple docstring'''
UpperCamelCase__	=     (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
UpperCamelCase__	=     (-153, -1, 0, 1.2, 200, "A", [], {}, None)




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False

   # Initialization of sum and number of digits.
   lowercase_		:      Optional[int]          =       0
   lowercase_		:      Any          =       0
   lowercase_		:      Optional[Any]          =       n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   lowercase_		:      int          =       n
   while temp > 0:
      lowercase_		:      Any          =       temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False

   # Init a "histogram" of the digits
   lowercase_		:      Optional[Any]          =       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   lowercase_		:      Tuple          =       0
   lowercase_		:      Any          =       0
   lowercase_		:      Tuple          =       n
   while temp > 0:
      lowercase_      ,						lowercase_		:      Optional[Any]          =       divmod(_UpperCamelCase  ,						10							)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(_UpperCamelCase  ,						range(len(_UpperCamelCase							)							)							):
      total += cnt * i**digit_total

   return n == total




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False
   lowercase_		:      List[str]          =       len(str(_UpperCamelCase							)							)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(_UpperCamelCase							) ** expo for i in str(_UpperCamelCase							)							)




def 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Dict          =       int(input("Enter an integer to see if it is an Armstrong number: "							).strip()							)
   print(F"""{num} is {"" if armstrong_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)
   print(F"""{num} is {"" if narcissistic_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)
   print(F"""{num} is {"" if pluperfect_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()




