


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			:						List[Any]        =  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
A			:						Optional[int]        =  {
    "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			:						Dict        =  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)



def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase     ,	lowercase         =      [], []
				while len(__magic_name__						) > 1:
								lowercase     ,	lowercase         =      min(__magic_name__						), max(__magic_name__						)
								start.append(__magic_name__						)
								end.append(__magic_name__						)
								collection.remove(__magic_name__						)
								collection.remove(__magic_name__						)
				end.reverse()
				return start + collection + end


if __name__ == "__main__":
						_snake_case      :							int     	=    input("Enter numbers separated by a comma:\n").strip()
						_snake_case      :							Dict     	=    [int(item) for item in user_input.split(",")]
						print(*merge_sort(unsorted), sep=",")
