

lowerCAmelCase   :      List[Any]     =     0  # The first color of the flag.
lowerCAmelCase   :      str     =     1  # The second color of the flag.
lowerCAmelCase   :      str     =     2  # The third color of the flag.
lowerCAmelCase   :      Tuple     =     (red, white, blue)

def       A_     (							a     ):

      """simple docstring"""



      if not sequence:
            return []
      if len(_UpperCAmelCase     ) == 1:
            return list(_UpperCAmelCase     )
      SCREAMING_SNAKE_CASE_ :     str							=   0
      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   len(_UpperCAmelCase     ) - 1
      SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  SCREAMING_SNAKE_CASE_ :     str							=   sequence[high], sequence[mid]
                  high -= 1
            else:
                  SCREAMING_SNAKE_CASE_ :     int							=   f"The elements inside the sequence must contains only {colors} values"
                  raise ValueError(_UpperCAmelCase     )
      return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase   :      Dict     =     input('Enter numbers separated by commas:\n').strip()
    lowerCAmelCase   :      List[str]     =     [int(item.strip()) for item in user_input.split(',')]
    print(F'{dutch_national_flag_sort(unsorted)}')













"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

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

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

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