"""Prove the Space's embeddings match the corpus before anything ships.

Run inside an environment that has laion_clap (audio-brief's venv locally,
the Space's own env when run there), from a *staged* directory — i.e. the
exact set of files that gets uploaded:

    python verify.py --stage DIR --parity AUDIO --parity-ref VECTOR.npy \
                     [--analyse AUDIO] [--out RESULT.json]

`--parity` re-embeds a track whose vector is already in the corpus (the
canonical path being audio-brief's clap_worker via WorkerBackend) and reports
the cosine against the stored vector. Anything below 0.9999 means this code
has drifted from the worker and every similarity the app shows would be
quietly wrong.

`--analyse` runs a full headless analysis so the market ranking, snippets and
tags can be eyeballed without a browser.
"""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

import numpy as np

PARITY_FLOOR = 0.9999


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--stage", required=True)
    ap.add_argument("--parity", default=None)
    ap.add_argument("--parity-ref", default=None)
    ap.add_argument("--analyse", default=None)
    ap.add_argument("--out", default=None)
    args = ap.parse_args()

    sys.path.insert(0, str(Path(args.stage).resolve()))
    import analysis  # noqa: E402
    import clap_embed  # noqa: E402
    import livematch  # noqa: E402
    import tags as tagmod  # noqa: E402

    corpus = livematch.Corpus.load(args.stage)
    emb = clap_embed.Embedder()
    result: dict = {"corpus": {"week": corpus.week, "ckpt": corpus.ckpt,
                               "sounds": len(corpus.sounds),
                               "regions": len(corpus.regions)}}

    if args.parity:
        v = emb.embed_track(clap_embed.load_audio(args.parity))
        ref = np.load(args.parity_ref).astype(np.float32).ravel()
        ref = ref / np.linalg.norm(ref)
        cos = float(np.dot(v, ref))
        result["parity"] = {"file": args.parity, "cosine": round(cos, 7),
                            "floor": PARITY_FLOOR, "passed": cos >= PARITY_FLOOR}

    vocab = tagmod.TagVocab(emb)
    if args.analyse:
        res = analysis.analyse_track(args.analyse, emb, corpus, vocab=vocab)
        result["zero_shot_check"] = tagmod.discrimination_check(
            vocab, res["track_vec"],
            expect_above=["afrobeats", "afro pop", "afro fusion"],
            expect_below=["techno", "country", "k-pop", "ambient"])
        res.pop("track_vec", None)
        result["analysis"] = {
            "duration_s": res["duration_s"], "week": res["week"],
            "beat": res["beat"], "window_count": res["window_count"],
            "markets": [{"iso": i, "name": res["regions"][i]["name"],
                         "best": res["regions"][i]["best"],
                         "band": livematch.band(res["regions"][i]["best"]),
                         "matched": res["regions"][i]["matched"],
                         "pool": res["regions"][i]["pool"],
                         "top": res["regions"][i]["top"][:3]}
                        for i in res["shown_markets"]],
            "snippets": res["snippets"],
            "tags": {
                "by_group": {g: rows for g, rows
                             in (res["tags"]["model"]["by_group"].items()
                                 if res["tags"]["model"] else [])},
                "trend": res["tags"]["trend"],
                "copy_line": res["tags"]["copy_line"],
            },
        }

    text = json.dumps(result, indent=2, default=str)
    if args.out:
        Path(args.out).write_text(text)
    print(text)
    parity = result.get("parity")
    return 0 if (parity is None or parity["passed"]) else 4


if __name__ == "__main__":
    sys.exit(main())