"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__lowerCAmelCase    :							str													=						HfArgumentParser(InitializationArguments)
__lowerCAmelCase    :							str													=						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    :							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)
__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)



"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__ = 50     ):





		"""simple docstring"""




		lowerCAmelCase__        =   [[0] * 3 for _ in range(length + 1     )]

		for row_length in range(length + 1     ):
				for tile_length in range(2			,  5     ):
						for tile_start in range(row_length - tile_length + 1     ):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]     )


if __name__ == "__main__":
			print(F"{solution() = }")




