from transformers import PretrainedConfig


class AutextificationMTLConfig(PretrainedConfig):
    model_type = "custom-text-classifier"

    def __init__(
        self,
        transformer_name: str = "xlm-roberta-base",
        hidden_nodes: int = 64,
        threshold: float = 0.9919,
        **kwargs,
    ):
        if hidden_nodes <= 0:
            raise ValueError(
                f"`hidden_size` must be a positive number, got {hidden_nodes}."
            )

        self.transformer_name = transformer_name
        self.hidden_nodes = hidden_nodes
        self.threshold = threshold
        super().__init__(**kwargs)