"""Shared loader for the sae_embed nanobind module (SCRFD + ArcFace).

sae_embed.FaceEmbedder loads both ONNX sessions once and exposes an
embed(path) -> FaceResult method, avoiding the per-process model reload cost
of spawning the embed_faces CLI binary for every image.
"""

import sys
from pathlib import Path


def load_embedder(build_dir: str, models_dir: str, arcface: str | None = None,
                   conf: float = 0.5, nms: float = 0.4, max_side: int = 500):
    """Import sae_embed from build_dir and construct a FaceEmbedder.

    Exits with a clear error if the module or models are missing — there is
    no subprocess fallback.
    """
    build_path = Path(build_dir).resolve()
    sys.path.insert(0, str(build_path))
    try:
        import sae_embed
    except ImportError as e:
        sys.exit(
            f"sae_embed module not found in {build_path}: {e}\n"
            f"Build it first: cmake --build {build_dir} --target sae_embed"
        )

    models_path = Path(models_dir)
    detector_path = str(models_path / "scrfd_500m_bnkps.onnx")
    arcface_path = arcface if arcface else str(models_path / "arcface_w600k_r50.onnx")
    for model, name in [(detector_path, "SCRFD"), (arcface_path, "ArcFace")]:
        if not Path(model).is_file():
            sys.exit(f"{name} model not found: {model}\nRun: bash scripts/download_models.sh")

    # A TRT-backend build cannot load .onnx; it needs pre-built engines from
    # scripts/build_trt_engines.sh. Pass them when present (ignored by ORT).
    trt = Path(models_path).parent / "trt_cache"
    det_engine = trt / "scrfd.scrfd_500m_bnkps.640.fp16.engine"
    arc_engine = trt / f"arcface.{Path(arcface_path).stem}.b4.fp16.engine"

    return sae_embed.FaceEmbedder(
        detector_path, arcface_path, conf, nms, max_side,
        str(det_engine) if det_engine.is_file() else "",
        str(arc_engine) if arc_engine.is_file() else "",
    )