




"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

# Initialize new model with config
SCREAMING_SNAKE_CASE				=       AutoModelForCausalLM.from_config(config)

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






'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					list )			->      list:
							if any(not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ) or x < 0 for x in sequence ):
														raise TypeError("""Sequence must be list of non-negative integers""" )
							for _ in range(len(UpperCAmelCase__ ) ):
														for i, (rod_upper, rod_lower) in enumerate(zip(UpperCAmelCase__      ,      sequence[1:] ) ):
																					if rod_upper > rod_lower:
																												sequence[i] -= rod_upper - rod_lower
																												sequence[i + 1] += rod_upper - rod_lower
							return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





