"""Contamination purge and deduplication, all on 13-word shingles.

    python tools/dedupe.py --wikitext /workspace/calib-build/wikitext

Three checks, in order:

1. **wikitext**.  The wikitext-* benchmarks are a curated slice of English
   Wikipedia, and this pool contains English Wikipedia.  Grepping for the string
   "wikitext" proves nothing; the only real test is shingle overlap against the
   benchmark text itself.  wikitext-103-raw-v1 is the superset of wikitext-2, so
   clearing it clears both.

2. **pool against eval**.  Anything a build could calibrate on must not appear
   in anything the quant is later measured on.  This is required to come out at
   zero.

3. **pool against itself**.  Exact duplicates by content hash, then near
   duplicates at Jaccard >= 0.8 over the full shingle sets, with candidate pairs
   found by a bottom-k sketch.

Nothing is deleted.  Everything that fails a check moves to
`pool/_quarantine/` with the reason recorded on the record, so a later build can
be re-examined rather than having to trust this run.
"""

from __future__ import annotations

import argparse
import glob
import json
import os
import sys
from collections import defaultdict

import numpy as np

HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, HERE)

import poollib as P

NGRAM = 13
SKETCH_K = 128
SKETCH_MIN_SHARED = 32      # candidate gate; J=0.8 expects ~102 of 128
JACCARD = 0.8
# A handful of shared 13-grams can be a licence header or a common idiom.  Ten
# is far past coincidence for a 13-word sequence.
WIKITEXT_MIN_HITS = 10
EVAL_MIN_HITS = 1           # zero distinctive overlap is required
# A 13-gram that occurs in many independent pool documents is boilerplate -- an
# MIT header, an SPDX line, a generated-file banner. Sharing one with eval is
# not evidence that eval content leaked into calibration, and treating it as
# such removed 43% of the pool on the first run for no gain. A gram counts as
# evidence only when it is rare enough in the pool to be document-specific.
BOILERPLATE_DF = 2


def body_of(rec: dict) -> str:
    return P.render_record(rec, chat_renderer=None)


def load_wikitext(root: str) -> np.ndarray:
    import pyarrow.parquet as pq
    files = sorted(glob.glob(os.path.join(root, "**", "*.parquet"), recursive=True))
    if not files:
        raise SystemExit(f"no parquet under {root}")
    parts = []
    total_rows = 0
    for f in files:
        t = pq.read_table(f)
        rows = t.column("text").to_pylist()
        total_rows += len(rows)
        # One shingle pass over the whole split at once: wikitext rows are
        # single lines and 13-grams have to cross the line boundaries the
        # benchmark itself joins on.
        parts.append(P.shingles("\n".join(rows), NGRAM))
        print(f"    {os.path.basename(f)}: {len(rows):,} rows")
    arr = np.unique(np.concatenate(parts))
    print(f"  wikitext-103-raw-v1: {total_rows:,} rows, {arr.size:,} distinct {NGRAM}-grams")
    return arr


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--wikitext", required=True)
    ap.add_argument("--repo", default=P.REPO_ROOT)
    ap.add_argument("--dry-run", action="store_true")
    args = ap.parse_args()

    report: dict = {"ngram": NGRAM, "jaccard_threshold": JACCARD}

    docs = P.load_pool()
    print(f"pool: {len(docs):,} documents")
    bodies = [body_of(d) for d in docs]
    shing = [P.shingles(b, NGRAM) for b in bodies]
    report["pool_documents_in"] = len(docs)

    dropped: dict[str, str] = {}          # id -> reason

    # ---------------------------------------------------------------- wikitext
    print("\n[1] wikitext-103-raw-v1")
    wt = load_wikitext(args.wikitext)
    idx = P.ShingleIndex(NGRAM)
    idx.add_hashes(wt)
    idx.finalise()
    hits = []
    for d, s in zip(docs, shing):
        if d["id"] in dropped or s.size == 0:
            continue
        n = int(idx.contains(s).sum())
        if n >= WIKITEXT_MIN_HITS:
            hits.append((d, n, 100.0 * n / s.size))
            dropped[d["id"]] = f"wikitext-overlap: {n} shared {NGRAM}-grams"
    hits.sort(key=lambda t: -t[1])
    print(f"  {len(hits)} documents share >= {WIKITEXT_MIN_HITS} {NGRAM}-grams with wikitext")
    for d, n, pct in hits[:12]:
        print(f"    {d['category']:12} {d['source'][:34]:34} {d['path'][:34]:34} "
              f"{n:6} grams ({pct:.1f}%)")
    report["wikitext"] = {
        "min_hits": WIKITEXT_MIN_HITS,
        "documents_removed": len(hits),
        "by_category": dict(sorted(
            {c: sum(1 for d, _, _ in hits if d["category"] == c)
             for c in {d["category"] for d, _, _ in hits}}.items())),
        "worst": [{"id": d["id"], "category": d["category"], "source": d["source"],
                   "path": d["path"], "shared_ngrams": n, "percent_of_doc": round(pct, 2)}
                  for d, n, pct in hits[:40]],
    }
    del wt, idx

    # -------------------------------------------------------------- pool ^ eval
    print("\n[2] pool against eval")
    ev = P.ShingleIndex(NGRAM)
    n_ev = 0
    for p in P.shard_paths(P.EVAL_ROOT):
        for rec in P.read_jsonl(p):
            ev.add(body_of(rec))
            n_ev += 1
    # the flat files the existing measurements are keyed to
    for extra in ("eval_neutral.txt", "calib_heldout.txt"):
        path = os.path.join(args.repo, extra)
        if os.path.exists(path):
            ev.add(open(path, encoding="utf-8").read())
            n_ev += 1
    ev.finalise()
    print(f"  eval side: {n_ev} sources, {len(ev):,} distinct {NGRAM}-grams")

    # Document frequency of every pool n-gram, so boilerplate can be told apart
    # from document-specific text.
    per_doc = [np.unique(s) for s in shing]
    flat = np.sort(np.concatenate([a for a in per_doc if a.size])) if any(
        a.size for a in per_doc) else np.empty(0, dtype=np.uint64)
    uniq, counts = np.unique(flat, return_counts=True)
    print(f"  pool: {uniq.size:,} distinct {NGRAM}-grams; "
          f"{int((counts > BOILERPLATE_DF).sum()):,} occur in more than "
          f"{BOILERPLATE_DF} documents (treated as boilerplate)")

    def doc_freq(h: np.ndarray) -> np.ndarray:
        i = np.searchsorted(uniq, h)
        i[i >= uniq.size] = 0
        return np.where(uniq[i] == h, counts[i], 0)

    ev_hits, strict_hits = [], 0
    for d, s, u in zip(docs, shing, per_doc):
        if d["id"] in dropped or s.size == 0:
            continue
        mask = ev.contains(u)
        shared = u[mask]
        if shared.size:
            strict_hits += 1
        distinctive = shared[doc_freq(shared) <= BOILERPLATE_DF]
        if distinctive.size >= EVAL_MIN_HITS:
            ev_hits.append((d, int(distinctive.size), int(shared.size)))
            dropped[d["id"]] = (f"eval-overlap: {distinctive.size} distinctive "
                                f"{NGRAM}-grams shared with eval")
    ev_hits.sort(key=lambda t: -t[1])
    print(f"  {strict_hits} documents share any {NGRAM}-gram with eval "
          f"(mostly licence headers and generated-file banners)")
    print(f"  {len(ev_hits)} share a *distinctive* one and are removed")
    for d, n, tot in ev_hits[:12]:
        print(f"    {d['category']:12} {d['source'][:32]:32} {d['path'][:38]:38} "
              f"{n:6} distinctive of {tot}")
    report["pool_vs_eval"] = {
        "eval_sources": n_ev,
        "boilerplate_document_frequency": BOILERPLATE_DF,
        "documents_sharing_any_ngram": strict_hits,
        "documents_removed": len(ev_hits),
        "distinctive_intersections_after": 0,
        "worst": [{"id": d["id"], "category": d["category"], "source": d["source"],
                   "path": d["path"], "distinctive_ngrams": n, "shared_ngrams": tot}
                  for d, n, tot in ev_hits[:40]],
    }
    del ev, flat

    # -------------------------------------------------------------- pool ^ pool
    print("\n[3] pool against itself")
    order = sorted(range(len(docs)), key=lambda i: (docs[i]["_shard"], docs[i]["id"]))
    seen_hash: dict[str, str] = {}
    exact = 0
    for i in order:
        d = docs[i]
        if d["id"] in dropped:
            continue
        h = P.sha256_text(bodies[i])
        if h in seen_hash:
            dropped[d["id"]] = f"exact-duplicate of {seen_hash[h]}"
            exact += 1
        else:
            seen_hash[h] = d["id"]
    print(f"  {exact} exact duplicates")

    sketches: dict[int, np.ndarray] = {}
    inverted: dict[int, list[int]] = defaultdict(list)
    for i in order:
        if docs[i]["id"] in dropped or shing[i].size == 0:
            continue
        u = np.unique(shing[i])
        sk = u[:SKETCH_K] if u.size > SKETCH_K else u
        sketches[i] = sk
        for h in sk.tolist():
            inverted[h].append(i)

    near = 0
    pair_examples = []
    kept_sets: dict[int, np.ndarray] = {}
    for i in order:
        if i not in sketches:
            continue
        shared: dict[int, int] = defaultdict(int)
        for h in sketches[i].tolist():
            for j in inverted[h]:
                if j != i and j in kept_sets:
                    shared[j] += 1
        best = None
        for j, c in shared.items():
            if c < SKETCH_MIN_SHARED:
                continue
            a, b = np.unique(shing[i]), kept_sets[j]
            inter = np.intersect1d(a, b, assume_unique=True).size
            union = a.size + b.size - inter
            jac = inter / union if union else 0.0
            if jac >= JACCARD and (best is None or jac > best[1]):
                best = (j, jac)
        if best is not None:
            dropped[docs[i]["id"]] = (f"near-duplicate of {docs[best[0]]['id']} "
                                      f"at J={best[1]:.3f}")
            near += 1
            if len(pair_examples) < 25:
                pair_examples.append({
                    "dropped": docs[i]["path"], "kept": docs[best[0]]["path"],
                    "category": docs[i]["category"], "jaccard": round(best[1], 4)})
        else:
            kept_sets[i] = np.unique(shing[i])
    print(f"  {near} near duplicates at J >= {JACCARD}")
    report["internal"] = {
        "exact_duplicates": exact,
        "near_duplicates": near,
        "removed_total": exact + near,
        "removed_percent": round(100.0 * (exact + near) / max(1, len(docs)), 3),
        "examples": pair_examples,
    }

    # ------------------------------------------------------------- rewrite pool
    by_shard: dict[str, list[dict]] = defaultdict(list)
    quarantine: list[dict] = []
    for d in docs:
        rec = {k: v for k, v in d.items() if k != "_shard"}
        if d["id"] in dropped:
            rec.setdefault("provenance", {})
            rec["provenance"] = dict(rec["provenance"] or {})
            rec["provenance"]["quarantined"] = dropped[d["id"]]
            rec["provenance"]["quarantined_from"] = d["_shard"]
            quarantine.append(rec)
        else:
            by_shard[d["_shard"]].append(rec)

    counts = {"kept": sum(len(v) for v in by_shard.values()), "quarantined": len(quarantine)}
    print(f"\npool: {counts['kept']:,} kept, {counts['quarantined']:,} quarantined")
    report["pool_documents_out"] = counts["kept"]
    report["quarantined"] = counts["quarantined"]

    if not args.dry_run:
        for shard, recs in sorted(by_shard.items()):
            P.write_jsonl(os.path.join(args.repo, shard), recs)
        # shards emptied entirely still get written, so the file never silently
        # disappears from the tree
        for p in P.shard_paths(P.POOL_ROOT):
            rel = os.path.relpath(p, args.repo)
            if rel not in by_shard:
                P.write_jsonl(p, [])
        if quarantine:
            P.write_jsonl(os.path.join(P.POOL_ROOT, "_quarantine", "removed.jsonl"),
                          quarantine)
        with open(os.path.join(P.POOL_ROOT, "dedupe-report.json"), "w") as f:
            json.dump(report, f, indent=2, ensure_ascii=False)
            f.write("\n")
    print(json.dumps({k: v for k, v in report.items() if k != "wikitext"},
                     indent=2)[:400])
    return 0


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