"""simple docstring"""



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Union[str, Any]							=					['''image_processor''', '''tokenizer''']
    UpperCamelCase_	:					str							=					'''BridgeTowerImageProcessor'''
    UpperCamelCase_	:					Dict							=					('''RobertaTokenizer''', '''RobertaTokenizerFast''')
    def __init__(		self							:     List[Any]	,      A_							:     str	,      A_							:     str    )    ->       Union[str, Any]:
         super().__init__(A_	,      A_    )
    def __call__(		self							:     Any	,      A_							:     str	,      A_							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,      A_							:     bool = True	,      A_							:     Union[bool, str, PaddingStrategy] = False	,      A_							:     Union[bool, str, TruncationStrategy] = None	,      A_							:     Optional[int] = None	,      A_							:     int = 0	,      A_							:     Optional[int] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[bool] = None	,      A_							:     bool = False	,      A_							:     bool = False	,      A_							:     bool = False	,      A_							:     bool = False	,      A_							:     bool = True	,      A_							:     Optional[Union[str, TensorType]] = None	,      **A_							:     Union[str, Any]	,      )    ->       BatchEncoding:
         __snake_case				=							self.tokenizer(
             text=A_	,      add_special_tokens=A_	,      padding=A_	,      truncation=A_	,      max_length=A_	,      stride=A_	,      pad_to_multiple_of=A_	,      return_token_type_ids=A_	,      return_attention_mask=A_	,      return_overflowing_tokens=A_	,      return_special_tokens_mask=A_	,      return_offsets_mapping=A_	,      return_length=A_	,      verbose=A_	,      return_tensors=A_	,      **A_	,      )
         # add pixel_values + pixel_mask
         __snake_case				=							self.image_processor(
             A_	,      return_tensors=A_	,      do_normalize=A_	,      do_center_crop=A_	,      **A_    )
         encoding.update(A_    )

         return encoding
    def  lowercase					(		self							:     Optional[Any]	,      *A_							:     List[str]	,      **A_							:     int    )    ->       int:
         return self.tokenizer.batch_decode(*A_	,      **A_    )
    def  lowercase					(		self							:     Optional[Any]	,      *A_							:     Optional[int]	,      **A_							:     Optional[Any]    )    ->       Any:
         return self.tokenizer.decode(*A_	,      **A_    )







    @property
    def  lowercase					(		self							:     str    )    ->       Dict:
         __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    )    )
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     return 1 if digit in (0, 1) else (digit * factorial(digit - 1))





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							0
     __snake_case				=							number
     while duplicate > 0:
          __snake_case		,	__snake_case				=							divmod(snake_case,      10)
          fact_sum += factorial(snake_case)
     return fact_sum == number


if __name__ == "__main__":
       print("Program to check whether a number is a Krisnamurthy Number or not.")
       __lowercase		:		Optional[int]														=							int(input("Enter number: ").strip())
       print(
           F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
       )