



def      lowerCAmelCase__		(   lowerCamelCase_		:		float	,lowerCamelCase_		:		int):





				'''simple docstring'''

				if digit_amount > 0:
								return round(number - int(lowercase_)	,lowercase_)
				return number - int(lowercase_)


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))






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

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

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

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

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

