#!/usr/bin/env python3
"""WS3 (г) verification, part 2 — emit a seed-delta (§C2-7) from the persisted exp16
tables (A3 candidates + banknote dst), which exp16 itself never emitted (only sidecars),
then DRY-LINT it against a faithful Python replication of the memseed.loadGlossarySeed
fail-louds (the (д) after-build spec runs the real Go loadGlossarySeed for byte parity).

Emission rules (research/20 §C2-7, memseed.go:30-57 seedTerm schema, miner NEVER writes approved):
  - non-seed A3 candidate WITH a banknote dst  -> status: draft, dst=<majority banknote dst>
  - non-seed A3 candidate WITHOUT a dst        -> status: auto  (no dst, inert in automaton)
  - since_ch = first chapter of occurrence (auto, by source); until_ch left 0 (owner only)

$0: deterministic, no LLM, no network. Reuses eval/exp16 miner + banknote artifacts.
Writes seed-delta YAML + lint report to /home/ubuntu/books/gu-zhenren/design11/ (OUT of git).
"""
import json, os, sys, glob, re, collections
import yaml

EVAL = "/home/ubuntu/projects/textmachine/eval"
sys.path.insert(0, f"{EVAL}/exp15")
sys.path.insert(0, f"{EVAL}/exp16")
import exp16_common as X
import arms as A
import mem_select as MS  # normalize_source_key, significant_len (byte-verified port of memnorm.go)

A3 = "/home/ubuntu/books/gu-zhenren/exp16/A3_candidates_full.json"
BANK = "/home/ubuntu/books/gu-zhenren/exp16/coldstart/banknote/*.banknote.json"
OUT_YAML = "/home/ubuntu/books/gu-zhenren/design11/ws3_seed_delta.yaml"
OUT_LINT = "/home/ubuntu/books/gu-zhenren/design11/ws3_seeddelta_lint.json"

MIN_KEY_HAN, MIN_KEY_PHON = 2, 3
def min_key_len(nk):
    return MIN_KEY_HAN if any('㐀' <= c <= '鿿' or '豈' <= c <= '﫿' for c in nk) else MIN_KEY_PHON

# ---------- faithful replication of memseed.loadGlossarySeed fail-louds ----------
def lint_seed(terms):
    problems = []
    seen = {}
    for i, t in enumerate(terms):
        src = (t.get("src") or "").strip()
        sense = (t.get("sense") or "").strip()
        dst = (t.get("dst") or "").strip()
        if not src:
            problems.append(f"term {i}: src is required"); continue
        status = t.get("status") or "approved"
        if status not in ("auto", "draft", "approved"):
            problems.append(f"term {src!r}: status must be auto|draft|approved, got {status!r}"); continue
        if status in ("approved", "draft") and not dst:
            problems.append(f"term {src!r}: a {status} term must have a non-empty dst (silently inert)"); continue
        key = (src, sense, t.get("since_ch", 0), t.get("until_ch", 0))
        if key in seen:
            problems.append(f"term {src!r}: duplicate (src,sense,window)"); continue
        seen[key] = i
    # spoiler-window overlap same src different dst (D16.1) + shared-key collision (approved only)
    def win_overlap(s1, u1, s2, u2):
        s1 = s1 or 1; s2 = s2 or 1; u1 = u1 or (1 << 62); u2 = u2 or (1 << 62)
        return max(s1, s2) <= min(u1, u2)
    for i in range(len(terms)):
        for j in range(i):
            a, b = terms[i], terms[j]
            if a.get("src") != b.get("src"):
                continue
            if (a.get("dst") or "") == (b.get("dst") or ""):
                continue
            if win_overlap(a.get("since_ch", 0), a.get("until_ch", 0), b.get("since_ch", 0), b.get("until_ch", 0)):
                problems.append(f"term {a['src']!r}: overlapping spoiler windows with different dst")
    return problems

def is_fragment(src, gt_norm, particles="的了在是和就也都不心面前后上下今少多大小与之其"):
    if len(src) >= 2 and MS.normalize_source_key(src[:-1]) in gt_norm and src[-1] in particles:
        return True
    return False

def main():
    chunks = X.load_chunks()
    gt = X.load_gt()
    gt_norm = set()
    for e in gt:
        for s in e.norm_surfaces:
            gt_norm.add(s)

    a3 = json.load(open(A3))
    # banknote dst: majority per src; strip trailing [type] annotations the model added
    bn = collections.defaultdict(collections.Counter)
    for f in glob.glob(BANK):
        d = json.load(open(f))
        # chapter from filename e.g. 4.0.banknote.json -> chapter 4
        ch = int(os.path.basename(f).split(".")[0])
        for e in d.get("entries", []):
            dst = re.sub(r"\[[^\]]*\]", "", e["dst"]).strip()
            bn[MS.normalize_source_key(e["src"])][(dst, e.get("type", "term"))] += 1
    bn_first_ch = {}
    for f in glob.glob(BANK):
        ch = int(os.path.basename(f).split(".")[0])
        d = json.load(open(f))
        for e in d.get("entries", []):
            k = MS.normalize_source_key(e["src"])
            bn_first_ch[k] = min(bn_first_ch.get(k, 1 << 30), ch)

    # candidate chapters helper (first chapter of occurrence)
    def first_chapter(src_norm):
        for c in chunks:
            if src_norm in c.nsource:
                return c.chapter
        return 0

    delta = []
    seen_src = set()
    n_draft = n_auto = 0
    # (1) non-seed candidates WITH a banknote dst -> draft
    for cand in a3:
        srcn = MS.normalize_source_key(cand["src"])
        if srcn in gt_norm or srcn in seen_src:
            continue
        if is_fragment(cand["src"], gt_norm):
            continue
        typ = (cand["types"] or ["term"])[0]
        if srcn in bn:
            (dst, bt) = bn[srcn].most_common(1)[0][0], bn[srcn].most_common(1)[0]
            dstv = bn[srcn].most_common(1)[0][0][0]
            if not dstv:
                continue
            delta.append({"src": cand["src"], "dst": dstv, "type": typ, "status": "draft",
                          "since_ch": bn_first_ch.get(srcn) or first_chapter(srcn),
                          "note": "mined draft (banknote dst); ⟨проверить⟩ — canon proposal, owner signs"})
            seen_src.add(srcn); n_draft += 1
    # (2) top non-seed WHICH-only candidates WITHOUT dst -> auto (inert), a bounded sample.
    # Apply the miner's operating point (top_k=90 rank window) + single-key ban + fragment filter
    # so the owner review-sheet is clean (raw 13618-dump would carry particle/fragment noise).
    a3_top = a3[: A.FROZEN["top_k"] * 3]  # generous rank window; only pattern-typed or freq-anchored survive filters
    for cand in a3_top:
        if n_auto >= 40:
            break
        srcn = MS.normalize_source_key(cand["src"])
        if srcn in gt_norm or srcn in seen_src:
            continue
        if is_fragment(cand["src"], gt_norm):
            continue
        if MS.significant_len(srcn) < min_key_len(srcn):
            continue  # single-key ban (A3): a too-short key never fires / is not an entity
        if cand["freq"] < X.FREQ_FLOOR and not cand["from_pattern"]:
            continue
        typ = (cand["types"] or ["term"])[0]
        if typ not in ("name", "place", "title", "term", "nickname"):
            typ = "term"
        delta.append({"src": cand["src"], "dst": "", "type": typ, "status": "auto",
                      "since_ch": first_chapter(srcn),
                      "note": "mined WHICH-only auto-candidate (no dst; inert until owner assigns)"})
        seen_src.add(srcn); n_auto += 1

    seed_doc = {"terms": delta}
    os.makedirs(os.path.dirname(OUT_YAML), exist_ok=True)
    with open(OUT_YAML, "w") as fh:
        yaml.safe_dump(seed_doc, fh, allow_unicode=True, sort_keys=False)

    # round-trip: re-parse the emitted YAML and lint it (as loadGlossarySeed would)
    reparsed = yaml.safe_load(open(OUT_YAML))["terms"]
    problems = lint_seed(reparsed)

    report = {
        "n_delta_terms": len(delta), "n_draft_with_banknote_dst": n_draft, "n_auto_no_dst": n_auto,
        "lint_problems": problems, "lint_pass": len(problems) == 0,
        "sample_draft": [t for t in delta if t["status"] == "draft"][:8],
        "sample_auto": [t for t in delta if t["status"] == "auto"][:8],
    }
    json.dump(report, open(OUT_LINT, "w"), ensure_ascii=False, indent=2)
    print(json.dumps({k: report[k] for k in ("n_delta_terms", "n_draft_with_banknote_dst", "n_auto_no_dst", "lint_pass", "lint_problems")}, ensure_ascii=False, indent=2))
    print("\nsample draft (mined dst):", [(t["src"], t["dst"], t["type"]) for t in report["sample_draft"]])
    print("sample auto (which-only):", [(t["src"], t["type"]) for t in report["sample_auto"]])
    print(f"\nVERDICT: seed-delta of {len(delta)} terms ({n_draft} draft+dst / {n_auto} auto) "
          f"{'PASSES' if report['lint_pass'] else 'FAILS'} memseed fail-loud dry-lint.")

if __name__ == "__main__":
    main()