
def     UpperCamelCase    (      _a		)					->							Any:
     '''simple docstring'''





     lowercase_       :int     =       [], []
     while len(_a		) > 1:
          lowercase_       :List[str]     =       min(_a		), max(_a		)
          start.append(_a		)
          end.append(_a		)
          collection.remove(_a		)
          collection.remove(_a		)
     end.reverse()
     return start + collection + end


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





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		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)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

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

