"""Generate embeddings AND breed predictions for a dataset in a SINGLE model pass per image.

This is the batch path to use going forward: when the embedder and breed classifier are the same
HF model (``EMBEDDER=hf`` + ``BREED_CLASSIFIER=hf`` on the same repo), each image forwards through
the model exactly once, producing both the re-ID embedding and the breed softmax. It replaces
running ``scripts.embed_dataset`` and a separate breed pass (which forward every image through the
model twice). Idempotent: skips pictures that already have both, so it's safe to re-run / resume.

Usage (from the backend dir):
    python -m scripts.process_dataset --dataset-id 3      # one dataset
    python -m scripts.process_dataset --all               # every dataset
    python -m scripts.process_dataset --all --limit 5     # quick smoke (first 5 pictures each)
"""
from __future__ import annotations

import argparse
import time

from sqlalchemy import select

from app.db import SessionLocal, engine
from app.ml import get_breed_classifier, get_embedder, hf_breed_name_version
from app.models import Base, Dataset, Picture
from app.services.datasets import _known_ids, _picture_ids, _unknown_ids
from app.services.images import _same_hf_model, embed_and_breed_picture

BATCH = 100


def run(dataset_id: int | None, do_all: bool, limit: int | None) -> None:
    Base.metadata.create_all(bind=engine)
    db = SessionLocal()
    try:
        embedder = get_embedder()
        print(f"Active embedder: {embedder.name}/{embedder.version} (dim {embedder.dim})")
        if _same_hf_model():
            bn, bv = hf_breed_name_version()
            print(f"Single-pass mode: one forward per image -> embedding + breed ({bn}/{bv}).")
        else:
            c = get_breed_classifier()
            print(
                f"Two-model mode: embedder + separate breed classifier {c.name}/{c.version} "
                "(embedder and breed are not the same HF model)."
            )

        if do_all:
            datasets = db.execute(select(Dataset).order_by(Dataset.id)).scalars().all()
        elif dataset_id is not None:
            d = db.get(Dataset, dataset_id)
            datasets = [d] if d else []
            if not d:
                print(f"Dataset #{dataset_id} not found.")
        else:
            print("Pass --dataset-id N or --all.")
            return

        for ds in datasets:
            pic_ids = _picture_ids(db, _known_ids(db, ds.id), _unknown_ids(db, ds.id))
            if limit:
                pic_ids = pic_ids[:limit]
            print(f"\nDataset #{ds.id} {ds.name!r}: {len(pic_ids)} picture(s) to consider")
            embedded = breeds = skipped = errors = 0
            start = time.time()
            for i, pid in enumerate(pic_ids, 1):
                pic = db.get(Picture, pid)
                if pic is None:
                    continue
                try:
                    emb, breed = embed_and_breed_picture(db, pic, skip_if_exists=True)
                    embedded += int(emb)
                    breeds += int(breed)
                    skipped += int(not emb and not breed)
                except Exception as exc:  # noqa: BLE001
                    errors += 1
                    print(f"  ! picture {pid}: {exc}")
                if i % BATCH == 0:
                    db.commit()
                    rate = i / max(time.time() - start, 1e-6)
                    print(
                        f"  {i}/{len(pic_ids)}  embedded={embedded} breeds={breeds} "
                        f"skipped={skipped} errors={errors}  ({rate:.1f} img/s)"
                    )
            db.commit()
            print(
                f"  done: embedded={embedded} breeds={breeds} skipped={skipped} errors={errors} "
                f"in {time.time() - start:.0f}s"
            )
    finally:
        db.close()


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Embed + predict breeds for a dataset in one model pass per image."
    )
    parser.add_argument("--dataset-id", type=int, default=None)
    parser.add_argument("--all", action="store_true", help="Process every dataset")
    parser.add_argument("--limit", type=int, default=None, help="Only the first N pictures (smoke test)")
    args = parser.parse_args()
    run(args.dataset_id, args.all, args.limit)


if __name__ == "__main__":
    main()