



def      lowerCAmelCase__		(   lowerCamelCase_		:		list):





				'''simple docstring'''

				if len(lowerCamelCase_) < 2:
								return collection

				def circle_sort_util(lowerCamelCase_		:		list	,lowerCamelCase_		:		int	,lowerCamelCase_		:		int) -> bool:
								lowerCAmelCase__					:    Optional[int]												=					False

								if low == high:
												return swapped

								lowerCAmelCase__					:    str												=					low
								lowerCAmelCase__					:    Dict												=					high

								while left < right:
												if collection[left] > collection[right]:
																lowerCAmelCase__					:    Optional[int]												=					(
																    collection[right],
																    collection[left],
																)
																lowerCAmelCase__					:    Union[str, Any]												=					True

												left += 1
												right -= 1

								if left == right and collection[left] > collection[right + 1]:
												lowerCAmelCase__					:    Tuple												=					(
												    collection[right + 1],
												    collection[left],
												)

												lowerCAmelCase__					:    Optional[Any]												=					True

								lowerCAmelCase__					:    Any												=					low + int((high - low) / 2)
								lowerCAmelCase__					:    str												=					circle_sort_util(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
								lowerCAmelCase__					:    int												=					circle_sort_util(lowerCamelCase_	,mid + 1	,lowerCamelCase_)

								return swapped or left_swap or right_swap

				lowerCAmelCase__					:    Union[str, Any]												=					True

				while is_not_sorted is True:
								lowerCAmelCase__					:    Dict												=					circle_sort_util(lowerCamelCase_	,0	,len(lowerCamelCase_) - 1)

				return collection


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






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)

