"""Local model registry for GPT-2 LLM2Vec integration."""

from __future__ import annotations

from typing import Type

from transformers import PreTrainedModel

from gpt2_llm2vec.models.bidirectional_gpt2 import GPT2BiForMNTP


MODEL_REGISTRY: dict[str, Type[PreTrainedModel]] = {
    "gpt2": GPT2BiForMNTP,
    "gpt2-medium": GPT2BiForMNTP,
    "gpt2-large": GPT2BiForMNTP,
    "gpt2-xl": GPT2BiForMNTP,
}


def get_model_class(model_name_or_path: str) -> Type[PreTrainedModel]:
    """Return a registered model class for a given model name/path."""
    key = model_name_or_path.lower()
    if key in MODEL_REGISTRY:
        return MODEL_REGISTRY[key]

    # Fallback: if user passes a local path/name containing "gpt2"
    if "gpt2" in key:
        return GPT2BiForMNTP

    raise ValueError(
        f"Unsupported model '{model_name_or_path}'. "
        f"Supported keys: {sorted(MODEL_REGISTRY.keys())}"
    )