"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int = 10    ,							UpperCAmelCase_					:       int = 22						)							->    int:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				range(1    ,							UpperCAmelCase_						)
	__snake_case    :							str   	=				range(1    ,							UpperCAmelCase_						)
	return sum(
	    1 for power in powers for base in bases if len(str(base**power						)						) == power						)


if __name__ == "__main__":
	print(f'''{solution(10, 22) = }''')






"""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)





