

from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCAmelCase		:       Union[str, 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)
_UpperCAmelCase		:       Union[str, Any]						    =	AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCAmelCase		:       Optional[int]						    =	AutoModelForCausalLM.from_config(config)

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







def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   1
							snake_case_          =   2
							while i * i <= n:
														snake_case_          =   0
														while n % i == 0:
																					n //= i
																					multiplicity += 1
														n_divisors *= multiplicity + 1
														i += 1
							if n > 1:
														n_divisors *= 2
							return n_divisors





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   1
							snake_case_          =   1

							while True:
														i += 1
														t_num += i

														if count_divisors(UpperCamelCase__				) > 500:
																					break

							return t_num


if __name__ == "__main__":
					print(solution())




