"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str] = 10_00						)							->    List[str]:

							'''simple docstring'''





							__snake_case    :							List[Any]   	=				1, 1
							__snake_case    :							Optional[Any]   	=				2
							while True:
														__snake_case    :							str   	=				0
														__snake_case    :							List[str]   	=				fa + fa
														__snake_case    :							Optional[Any]   	=				fa, f
														index += 1
														for _ in str(lowerCamelCase__						):
																					i += 1
														if i == n:
																					break
							return index


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[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)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

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





