




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				:      str	   = logging.get_logger(__name__)

lowercase__				:      List[str]	   = {}




class       a__	( UpperCamelCase__				):
     a		:      Union[str, Any]		 =     "llama"
     a		:      Union[str, Any]		 =     ["past_key_values"]







     def __init__(     self  ,		A=32000  ,		A=4096  ,		A=11008  ,		A=32  ,		A=32  ,		A=None  ,		A="silu"  ,		A=2048  ,		A=0.0_2  ,		A=1e-6  ,		A=True  ,		A=0  ,		A=1  ,		A=2  ,		A=1  ,		A=False  ,		A=None  ,		**A  ,		)  ->   int:



          '''simple docstring'''



          a     =				vocab_size
          a     =				max_position_embeddings
          a     =				hidden_size
          a     =				intermediate_size
          a     =				num_hidden_layers
          a     =				num_attention_heads

          # for backward compatibility
          if num_key_value_heads is None:
               a     =				num_attention_heads

          a     =				num_key_value_heads
          a     =				hidden_act
          a     =				initializer_range
          a     =				rms_norm_eps
          a     =				pretraining_tp
          a     =				use_cache
          a     =				rope_scaling
          self._rope_scaling_validation()

          super().__init__(
              pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		tie_word_embeddings=A  ,		**A  ,		)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          if self.rope_scaling is None:
               return

          if not isinstance(self.rope_scaling  ,		A			) or len(self.rope_scaling			) != 2:
               raise ValueError(
                   "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                   F'''got {self.rope_scaling}'''			)
          a     =				self.rope_scaling.get("type"  ,		A			)
          a     =				self.rope_scaling.get("factor"  ,		A			)
          if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
               raise ValueError(
                   F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''			)
          if rope_scaling_factor is None or not isinstance(A  ,		A			) or rope_scaling_factor <= 1.0:
               raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''			)











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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     a     =				0
     a     =				number
     while duplicate > 0:
          a  ,				a     =				divmod(__UpperCamelCase		, 10)
          fact_sum += factorial(__UpperCamelCase)
     return fact_sum == number


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

