"""
Same spike as match_spike.py, but using DINOv2 instead of CLIP.

DINOv2 is trained with a self-supervised image-only objective specifically
aimed at instance/fine-grained visual similarity, which is a better fit for
"is this query photo the same object as this reference photo" than CLIP
(CLIP is trained for text-image alignment and tends to cluster images by
generic scene/style rather than object identity).

Usage: .venv\\Scripts\\python.exe match_spike_dinov2.py
"""

import sys
from pathlib import Path

import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel

HERE = Path(__file__).parent
REF_DIR = HERE / "images" / (sys.argv[1] if len(sys.argv) > 1 else "reference")
QUERY_DIR = HERE / "images" / "query"

MODEL_NAME = "facebook/dinov2-base"


def load_model():
    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
    model = AutoModel.from_pretrained(MODEL_NAME)
    model.eval()
    return model, processor


def embed_image(model, processor, path: Path) -> torch.Tensor:
    image = Image.open(path).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        features = outputs.last_hidden_state[:, 0, :]  # CLS token
        features = features / features.norm(dim=-1, keepdim=True)
    return features.squeeze(0)


def main():
    print(f"Loading {MODEL_NAME}...")
    model, processor = load_model()

    ref_paths = (
        sorted(REF_DIR.glob("*.jpg"))
        + sorted(REF_DIR.glob("*.webp"))
        + sorted(REF_DIR.glob("*.png"))
    )
    query_paths = (
        sorted(QUERY_DIR.glob("*.jpg"))
        + sorted(QUERY_DIR.glob("*.webp"))
        + sorted(QUERY_DIR.glob("*.png"))
    )

    print(f"Embedding {len(ref_paths)} reference images...")
    ref_names = [p.stem for p in ref_paths]
    ref_embeds = torch.stack([embed_image(model, processor, p) for p in ref_paths])

    print(f"Embedding {len(query_paths)} query images...\n")
    correct = 0
    for qpath in query_paths:
        qembed = embed_image(model, processor, qpath)
        sims = ref_embeds @ qembed
        ranked = sorted(zip(ref_names, sims.tolist()), key=lambda x: -x[1])
        top_name, top_sim = ranked[0]
        expected = qpath.stem.split("_")[0]
        is_correct = top_name == expected
        correct += is_correct
        marker = "OK  " if is_correct else "MISS"
        print(f"[{marker}] query={qpath.stem:<18} -> best={top_name:<10} sim={top_sim:.4f}")
        runner_up = ", ".join(f"{n}={s:.3f}" for n, s in ranked[1:4])
        print(f"         runner-up: {runner_up}")

    print(f"\n{correct}/{len(query_paths)} correct top-1 matches")


if __name__ == "__main__":
    main()