"""``transformers`` wrapper of encoder-tvsd: ``AutoModel.from_pretrained(...)`` returns this class,
which runs the plain-PyTorch model in ``modeling.py``."""

from transformers import PreTrainedModel

from .configuration_encoder_tvsd import EncoderTVSDConfig
from .modeling import EncoderTVSD, variant_config


class EncoderTVSDModel(PreTrainedModel):
    config_class = EncoderTVSDConfig
    base_model_prefix = "encoder"
    main_input_name = "images"

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *args, variant=None, **kwargs):
        """``variant`` selects a variant (default: the repository's ``default_variant``).
        ``transformers`` then loads only that variant's weights, ``model.<variant>.safetensors``
        (the default variant's are ``model.safetensors``)."""

        config = kwargs.get("config")
        if not isinstance(config, EncoderTVSDConfig):
            hub = ("revision", "cache_dir", "token", "local_files_only", "force_download")
            config = cls.config_class.from_pretrained(
                pretrained_model_name_or_path, **{k: kwargs[k] for k in hub if k in kwargs}
            )
            kwargs["config"] = config
        name = variant or config.default_variant
        if name not in config.variants:
            raise ValueError(f"unknown variant {name!r}; available: {', '.join(config.variants)}")
        config.selected_variant = name
        weights = None if name == config.default_variant else name
        return super().from_pretrained(
            pretrained_model_name_or_path, *args, variant=weights, **kwargs
        )

    def __init__(self, config: EncoderTVSDConfig):
        super().__init__(config)
        selected = getattr(config, "selected_variant", None)
        self.encoder = EncoderTVSD(variant_config(config.to_dict(), selected))
        self.post_init()
        self.eval()

    @property
    def variant(self) -> str:
        return self.encoder.variant

    def _init_weights(self, module):
        """All weights come from the checkpoint."""

    @property
    def monkeys(self) -> list[str]:
        return self.encoder.monkeys

    @property
    def areas(self) -> list[str]:
        return list(self.encoder.areas)

    def features(self, images, area: str, batch_size: int = 32):
        """See ``EncoderTVSD.features``: the readout input of ``area``."""

        return self.encoder.features(images, area, batch_size=batch_size)

    def forward(self, images, areas=None, batch_size: int = 32):
        """See ``EncoderTVSD.forward``: ``{monkey: {area: (images, electrodes)}}``."""

        return self.encoder(images, areas=areas, batch_size=batch_size)


__all__ = ["EncoderTVSDModel"]