

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Any														=					"""roberta-prelayernorm"""





    def __init__(				self						,       a=5_0265						,       a=768						,       a=12						,       a=12						,       a=3072						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=2						,       a=0.02						,       a=1e-12						,       a=1						,       a=0						,       a=2						,       a="absolute"						,       a=True						,       a=None						,       **a						,       ):
         super().__init__(pad_token_id=a						,       bos_token_id=a						,       eos_token_id=a						,       **a)

         lowercase__   :       int				=	vocab_size
         lowercase__   :       Union[str, Any]				=	hidden_size
         lowercase__   :       Optional[int]				=	num_hidden_layers
         lowercase__   :       Dict				=	num_attention_heads
         lowercase__   :       str				=	hidden_act
         lowercase__   :       Union[str, Any]				=	intermediate_size
         lowercase__   :       int				=	hidden_dropout_prob
         lowercase__   :       Optional[Any]				=	attention_probs_dropout_prob
         lowercase__   :       Union[str, Any]				=	max_position_embeddings
         lowercase__   :       Optional[int]				=	type_vocab_size
         lowercase__   :       int				=	initializer_range
         lowercase__   :       Tuple				=	layer_norm_eps
         lowercase__   :       Optional[Any]				=	position_embedding_type
         lowercase__   :       Union[str, Any]				=	use_cache
         lowercase__   :       Optional[int]				=	classifier_dropout



class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    @property
    def 				snake_case_    (				self):
         if self.task == "multiple-choice":
              lowercase__   :       List[Any]				=	{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              lowercase__   :       List[Any]				=	{0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return 1 if digit in (0, 1) else (digit * factorial(digit - 1      ))

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	0
     lowercase__   :       Optional[int]				=	number
     while duplicate > 0:
          lowercase__				,			lowercase__   :       Tuple				=	divmod(SCREAMING_SNAKE_CASE_     ,					10      )
          fact_sum += factorial(SCREAMING_SNAKE_CASE_      )
     return fact_sum == number


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

