



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_lowercase	  =		HfArgumentParser(InitializationArguments)
_lowercase	  =		parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_lowercase	  =		AutoTokenizer.from_pretrained(args.tokenizer_name)

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

# Initialize new model with config
_lowercase	  =		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_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
