



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

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

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







"""simple docstring"""






import re
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       str:

							'''simple docstring'''



							if len(re.findall("""[ATCG]"""       ,			_UpperCamelCase							)							) != len(_UpperCamelCase							):
														raise ValueError("""Invalid Strand"""							)

							return dna.translate(dna.maketrans("""ATCG"""       ,			"""TAGC"""							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
