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# Licensed under the Apache License, Version 2.0 (the "License");
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import nemo_run as run
from nemo.collections import llm
from nemo.collections.diffusion.vae.train_vae import train_vae


@run.cli.factory(target=llm.validate)
def validate_vae() -> run.Partial:
    """
    Create a partial function for validating a VAE (Variational Autoencoder) model.

    This function uses the training recipe defined in `train_vae()` to set up
    the model, data, trainer, logging, and optimization configurations for
    validation. It returns a Partial object that can be used by the NeMo run CLI
    to execute the validation procedure on the provided model and data.

    Returns:
        run.Partial: A partial object configured with llm.validate target
        and all necessary arguments extracted from the VAE training recipe.
    """
    recipe = train_vae()
    return run.Partial(
        llm.validate,
        model=recipe.model,
        data=recipe.data,
        trainer=recipe.trainer,
        log=recipe.log,
        optim=recipe.optim,
        tokenizer=None,
        resume=recipe.resume,
        model_transform=None,
    )


if __name__ == "__main__":
    run.cli.main(llm.validate, default_factory=validate_vae)