


from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				AutoTokenizer.from_pretrained(args.tokenizer_name)

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

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

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






def        UpperCamelCase_(   lowerCamelCase_				)					->		int:
    assert (
        isinstance(lowerCamelCase_							,				lowerCamelCase_				) and number_of_steps > 0
    ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
    if number_of_steps == 1:
        return 1
    _lowercase	,       _lowercase :							Dict   	=	1, 1
    for _ in range(number_of_steps - 1				):
        _lowercase	,       _lowercase :							Tuple   	=	current + previous, current
    return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()


