"""HuggingFace config for the packaged SkinMap multi-teacher embedding model."""

from transformers import PretrainedConfig


class SkinMapConfig(PretrainedConfig):
    """Minimal config for SkinMap.

    SkinMap is not a single set of HF weights but an ensemble of teacher encoders
    plus a trained projector, whose loading is driven by a bundled pipeline-config
    JSON. This config only points at that JSON and records the output
    dimensionality. The heavy lifting happens in `SkinMapModel`.
    """

    model_type = "skinmap"

    def __init__(
        self,
        embedding_dim: int = 1024,
        pipeline_config: str = "weights/embedding_pipeline_config.json",
        probes_dir: str = "probes",
        **kwargs,
    ):
        self.embedding_dim = embedding_dim
        # Paths are relative to the repo snapshot root.
        self.pipeline_config = pipeline_config
        # Directory of bundled metadata probes (enables predict_meta); may be
        # absent in an embedding-only package.
        self.probes_dir = probes_dir
        super().__init__(**kwargs)