from pydantic.dataclasses import dataclass

from multivae.models.base import BaseMultiVAEConfig


@dataclass
class MHVAEConfig(BaseMultiVAEConfig):
    """This is the configuration class for the Conditional Variational Autoencoder model.

    Args:
        n_modalities (int): The number of modalities. Default: None.
        latent_dim (int): The dimension of the latent space. Default: None.
        input_dims (dict[str,tuple]) : The modalities'names (str) and input shapes (tuple).
        uses_likelihood_rescaling (bool): To mitigate modality collapse, it is possible to use likelihood rescaling.
            (see : https://proceedings.mlr.press/v162/javaloy22a.html).
            The inputs_dim must be provided to compute the likelihoods rescalings. It is used in a number of models
            which is why we include it here. Default to False.
        rescale_factors (dict[str, float]): The reconstruction rescaling factors per modality.
            If None is provided but uses_likelihood_rescaling is True, a default value proportional to the input modality
            size is computed. Default to None.
        decoders_dist (Dict[str, Union[function, str]]). The decoder distributions to use per modality.
            Per modality, you can provide a string in ['normal','bernoulli','laplace']. For Bernoulli distribution,
            the decoder is expected to output **logits**. If None is provided, a normal distribution is used for each modality.
        decoder_dist_params (Dict[str,dict]) : Parameters for the output decoder distributions, for
            computing the log-probability.
            For instance, with normal or laplace distribution, you can pass the scale in this dictionary with
            :code:`decoder_dist_params =  {'mod1' : {"scale" : 0.75}}`.

        n_latent (int) : the number of latent variables.
        beta (float) : the weight for the KL divergence.

    """

    n_latent: int = 3
    beta: float = 1.0
