#!/usr/bin/env python3
"""Discover new identities in /mnt/x/src/osrc and emit them as facesets.

Workflow (mirrors the shape of build_folders.py, but identities are
discovered by clustering rather than asserted by folder):

  1. Load cache; restrict to face records whose canonical or alias path
     lies under /mnt/x/src/osrc/.
  2. Build centroids of the existing 19 canonical identities in
     facesets_swap_ready/faceset_001..019. Drop any osrc face whose
     nearest-existing-identity cos-dist <= EXISTING_MATCH_THRESHOLD;
     those are already covered by `extend` and shouldn't seed new
     facesets.
  3. Cluster the remaining osrc faces among themselves at
     INITIAL_THRESHOLD (matches `extend`'s new_cluster_threshold default).
  4. Per cluster, apply refine-equivalent gates: face_short >= MIN_SHORT,
     blur >= MIN_BLUR, det_score >= MIN_DET_SCORE; for clusters >= 4,
     drop faces with cos-dist > OUTLIER_THRESHOLD from the cluster
     centroid.
  5. Keep clusters whose surviving unique source-path count is >= MIN_FACES.
  6. Number kept clusters faceset_020, 021, ... (past the highest existing
     in facesets_swap_ready, which is 019). Order by descending size.
  7. Synthesize a refine_manifest.json and call cmd_export_swap on it,
     emitting into a temp dir. Move new dirs into facesets_swap_ready/.
  8. Append new entries to the top-level facesets_swap_ready/manifest.json
     (preserving existing facesets / thin_eras).
"""

from __future__ import annotations

import json
import shutil
import sys
from pathlib import Path

import numpy as np

REPO = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(REPO))

from sort_faces import (  # noqa: E402
    _cluster_embeddings,
    cmd_export_swap,
    load_cache,
)

# ---- config -------------------------------------------------------------- #

CACHE = REPO / "work" / "cache" / "nl_full.npz"
SWAP_READY = Path("/mnt/e/temp_things/fcswp/nl_sorted/facesets_swap_ready")
OUT_TMP = Path("/mnt/e/temp_things/fcswp/nl_sorted/facesets_swap_ready_osrc_new")
SYNTH_MANIFEST = REPO / "work" / "synthetic_osrc_manifest.json"

OSRC_DIR = Path("/mnt/x/src/osrc")
START_NNN = 20  # facesets_swap_ready max is 019; pick up here.

# Existing-identity exclusion: drop osrc faces whose nearest existing
# identity centroid is within this cosine distance. 0.45 matches the
# build_folders.py OSRC_THRESHOLD: at this cutoff the face is already
# routed to an existing identity by extend / build_folders.py.
EXISTING_MATCH_THRESHOLD = 0.45

# Cluster the unmatched.
INITIAL_THRESHOLD = 0.55

# Refine-equivalent gates (per the user's request: drop min_faces to 6).
MIN_FACES = 6
MIN_SHORT = 90
MIN_BLUR = 40.0
MIN_DET_SCORE = 0.6
OUTLIER_THRESHOLD = 0.55  # only applied if cluster >= 4

# export-swap params (defaults from sort_faces.py).
TOP_N = 30
EXPORT_OUTLIER_THRESHOLD = 0.45
PAD_RATIO = 0.5
OUT_SIZE = 512
EXPORT_MIN_FACE_SHORT = 100


# ---- helpers ------------------------------------------------------------- #

def _normalize(v: np.ndarray) -> np.ndarray:
    n = np.linalg.norm(v)
    return v / n if n > 0 else v


def _under(folder: Path, p: str) -> bool:
    fs = str(folder).rstrip("/") + "/"
    return p == str(folder) or p.startswith(fs)


def _record_in_folder(rec: dict, folder: Path, path_aliases: dict[str, list[str]]) -> bool:
    if _under(folder, rec["path"]):
        return True
    for alias in path_aliases.get(rec["path"], []):
        if _under(folder, alias):
            return True
    return False


def _existing_identity_centroids(
    emb: np.ndarray, face_records: list[dict]
) -> tuple[np.ndarray, list[str]]:
    """Build a (n_identities, 512) matrix of L2-normalized centroids and a parallel name list,
    drawn from the canonical faceset_001..019 manifests in facesets_swap_ready/."""
    bbox_idx: dict[tuple[str, tuple], int] = {
        (m["path"], tuple(m.get("bbox") or ())): i for i, m in enumerate(face_records)
    }
    centroids: list[np.ndarray] = []
    names: list[str] = []
    for n in range(1, 20):
        d = SWAP_READY / f"faceset_{n:03d}"
        man_path = d / "manifest.json"
        if not man_path.exists():
            continue
        man = json.loads(man_path.read_text())
        keys = [(f["source"], tuple(f.get("bbox") or ())) for f in man.get("faces", [])]
        idxs = [bbox_idx[k] for k in keys if k in bbox_idx]
        if not idxs:
            continue
        centroids.append(_normalize(emb[idxs].mean(axis=0)))
        names.append(d.name)
    return np.stack(centroids), names


# ---- phase 1: identify new osrc clusters --------------------------------- #

def discover_new_clusters() -> tuple[dict, list[dict]]:
    emb, meta, _src_root, _proc, path_aliases = load_cache(CACHE)
    face_records = [m for m in meta if not m.get("noface")]
    if len(face_records) != len(emb):
        raise SystemExit(f"meta/embedding mismatch: {len(face_records)} vs {len(emb)}")
    print(f"Cache: {len(face_records)} face records.")

    # Step 1: filter to osrc.
    osrc_idx = [
        i for i, m in enumerate(face_records)
        if _record_in_folder(m, OSRC_DIR, path_aliases)
    ]
    print(f"osrc face records: {len(osrc_idx)}")

    # Step 2: drop those already matching an existing identity.
    cents, cent_names = _existing_identity_centroids(emb, face_records)
    osrc_emb = emb[osrc_idx]
    sims = osrc_emb @ cents.T
    nearest_d = 1.0 - sims.max(axis=1)
    nearest_id = sims.argmax(axis=1)
    covered_mask = nearest_d <= EXISTING_MATCH_THRESHOLD
    n_covered = int(covered_mask.sum())
    print(
        f"Already covered by existing 19 identities at cos-dist <= "
        f"{EXISTING_MATCH_THRESHOLD}: {n_covered}/{len(osrc_idx)}"
    )
    # Per-identity coverage breakdown (for logging only).
    for j, name in enumerate(cent_names):
        c = int(((nearest_id == j) & covered_mask).sum())
        if c:
            print(f"  -> {name}: {c}")

    new_idx = [osrc_idx[k] for k in range(len(osrc_idx)) if not covered_mask[k]]
    print(f"\nUnmatched osrc faces to cluster: {len(new_idx)}")

    # Step 3: cluster the unmatched among themselves.
    new_emb = emb[new_idx]
    if len(new_idx) <= 1:
        labels = np.zeros(len(new_idx), dtype=int)
    else:
        labels = _cluster_embeddings(new_emb, INITIAL_THRESHOLD)
    n_clusters = len(set(int(l) for l in labels))
    print(
        f"Initial clusters at threshold {INITIAL_THRESHOLD}: {n_clusters} "
        f"(top sizes: {sorted([int((labels==l).sum()) for l in set(labels)], reverse=True)[:10]})"
    )

    # Step 4 + 5: per-cluster refine gates + min_faces.
    clusters: dict[int, list[int]] = {}
    for k, lab in enumerate(labels):
        clusters.setdefault(int(lab), []).append(new_idx[k])

    kept_clusters: list[dict] = []
    drop_quality_total = 0
    drop_outlier_total = 0
    for cid, idxs in clusters.items():
        # Per-face quality gate.
        good: list[int] = []
        for i in idxs:
            r = face_records[i]
            if r.get("face_short", 0) < MIN_SHORT:
                drop_quality_total += 1
                continue
            if r.get("blur", 0.0) < MIN_BLUR:
                drop_quality_total += 1
                continue
            if r.get("det_score", 0.0) < MIN_DET_SCORE:
                drop_quality_total += 1
                continue
            good.append(i)
        if not good:
            continue

        # Outlier rejection (only if cluster >= 4).
        if len(good) >= 4:
            cent = _normalize(emb[good].mean(axis=0))
            d = 1.0 - emb[good] @ cent
            tight = [good[k] for k, dist in enumerate(d) if dist <= OUTLIER_THRESHOLD]
            drop_outlier_total += len(good) - len(tight)
            good = tight
        if not good:
            continue

        unique_paths = sorted({face_records[i]["path"] for i in good})
        if len(unique_paths) < MIN_FACES:
            continue

        kept_clusters.append({
            "indices": good,
            "unique_paths": unique_paths,
            "size_face": len(good),
            "size_paths": len(unique_paths),
        })

    kept_clusters.sort(key=lambda c: -c["size_paths"])
    print(
        f"\nAfter quality gate ({drop_quality_total} dropped) + outlier "
        f"rejection ({drop_outlier_total} dropped) + min_faces={MIN_FACES}: "
        f"{len(kept_clusters)} clusters kept"
    )
    for rank, c in enumerate(kept_clusters, start=START_NNN):
        print(
            f"  faceset_{rank:03d}: faces={c['size_face']:3d}  "
            f"unique_paths={c['size_paths']:3d}"
        )

    # Build synthetic refine_manifest.json compatible with cmd_export_swap.
    facesets = [
        {
            "name": f"faceset_{rank:03d}",
            "image_count": c["size_paths"],
            "face_count": c["size_face"],
            "images": c["unique_paths"],
        }
        for rank, c in enumerate(kept_clusters, start=START_NNN)
    ]
    manifest = {
        "params": {
            "existing_match_threshold": EXISTING_MATCH_THRESHOLD,
            "initial_threshold": INITIAL_THRESHOLD,
            "outlier_threshold": OUTLIER_THRESHOLD,
            "min_faces": MIN_FACES,
            "min_short": MIN_SHORT,
            "min_blur": MIN_BLUR,
            "min_det_score": MIN_DET_SCORE,
            "source_root": str(OSRC_DIR),
        },
        "facesets": facesets,
    }
    SYNTH_MANIFEST.write_text(json.dumps(manifest, indent=2))
    print(f"\nSynthetic manifest -> {SYNTH_MANIFEST}")
    return manifest, kept_clusters


# ---- phase 2: export + relocate + merge top-level manifest -------------- #

def export_and_relocate(manifest: dict) -> None:
    if OUT_TMP.exists():
        shutil.rmtree(OUT_TMP)
    OUT_TMP.mkdir(parents=True)

    print(f"\nRunning cmd_export_swap -> {OUT_TMP}")
    cmd_export_swap(
        cache_path=CACHE,
        refine_manifest_path=SYNTH_MANIFEST,
        raw_manifest_path=None,
        out_dir=OUT_TMP,
        top_n=TOP_N,
        outlier_threshold=EXPORT_OUTLIER_THRESHOLD,
        pad_ratio=PAD_RATIO,
        out_size=OUT_SIZE,
        include_candidates=False,
        candidate_match_threshold=0.55,
        candidate_min_score=0.40,
        min_face_short=EXPORT_MIN_FACE_SHORT,
    )

    new_top = json.loads((OUT_TMP / "manifest.json").read_text())
    new_entries = new_top.get("facesets", [])

    moved = 0
    for fs_meta in new_entries:
        name = fs_meta["name"]
        src_dir = OUT_TMP / name
        if not src_dir.exists():
            print(f"[{name}] export dir missing; skipping")
            continue
        dst_dir = SWAP_READY / name
        if dst_dir.exists():
            print(f"[{name}] {dst_dir} already exists; refusing to overwrite")
            continue
        # Add a marker file so the source provenance is obvious.
        (src_dir / "osrc.txt").write_text(
            f"{name}\n\nSource: osrc cluster (auto-discovered, {OSRC_DIR}).\n"
        )
        shutil.move(str(src_dir), str(dst_dir))
        moved += 1
        print(f"[{name}] -> {dst_dir}")

    # Merge top-level manifest, preserving facesets / thin_eras / etc.
    final_manifest_path = SWAP_READY / "manifest.json"
    if final_manifest_path.exists():
        existing = json.loads(final_manifest_path.read_text())
    else:
        existing = {"facesets": []}
    existing.setdefault("facesets", [])

    existing_names = {fs["name"] for fs in existing["facesets"]}
    appended = 0
    for entry in new_entries:
        if entry["name"] in existing_names:
            print(f"[manifest] {entry['name']} already present; not duplicating")
            continue
        existing["facesets"].append(entry)
        appended += 1

    final_manifest_path.write_text(json.dumps(existing, indent=2))
    print(f"\nMerged manifest: appended {appended} entries -> {final_manifest_path}")
    print(f"Moved {moved} faceset directories into {SWAP_READY}")

    # Clean up temp dir if empty.
    if OUT_TMP.exists():
        leftover = list(OUT_TMP.iterdir())
        if not leftover:
            OUT_TMP.rmdir()


# ---- main ---------------------------------------------------------------- #

def main() -> None:
    dry = "--dry-run" in sys.argv
    manifest, kept = discover_new_clusters()
    if dry:
        print("\n--dry-run: stopping after cluster discovery (no exports written).")
        return
    if not manifest.get("facesets"):
        print("No new facesets to build; nothing to do.")
        return
    export_and_relocate(manifest)
    print("\nDone.")


if __name__ == "__main__":
    main()