"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__lowercase		:		Union[str, Any]														=							HfArgumentParser(InitializationArguments)
__lowercase		:		str														=							parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__lowercase		:		Optional[int]														=							AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__lowercase		:		Any														=							{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
__lowercase		:		str														=							AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__lowercase		:		Tuple														=							AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
"""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."""
       )