


def     UpperCamelCase    (      _a       ,		_a = 0		)					->							list:
     '''simple docstring'''





     lowercase_       :str     =       length or len(_a		)
     lowercase_       :str     =       False
     for i in range(length - 1		):
          if list_data[i] > list_data[i + 1]:
               lowercase_    ,      lowercase_       :Dict     =       list_data[i + 1], list_data[i]
               lowercase_       :List[Any]     =       True

     return list_data if not swapped else bubble_sort(_a       ,		length - 1		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





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)

