#!/usr/bin/env python3
"""CyberData 2: cut the nested releases Small (15,000) ⊂ Medium (25,000) ⊂ Large (65,000) ⊂ Full (everything) from build/assembled_all.jsonl.

Selection (deterministic): rows of each SOURCE are ordered by a hash of their group (so code-fix triples etc. stay together), and a tier takes a PREFIX of that
order per source. Prefix lengths grow with the tier, so the smaller tier is always inside the larger one. How big each source's prefix is: proportional to
a goal-driven target SHARE per source (see SHARES), capped at what exists, with a floor of the previous tier's prefix. The train/valid split of a row is fixed (by group hash) and identical in every tier."""
import collections, csv, json, math, os, sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from lib import *

BUILD, OUT = os.path.join(HERE, "build"), os.path.join(HERE, "out")
TIERS = [("small", 15000), ("medium", 25000), ("large", 65000), ("full", None)]
# Goal-driven target shares for Small / Medium / Large (Full takes everything). Agentic coding ~29%, code writing + vulnerability finding ~37%, cyber reasoning ~9%,
# security knowledge ~21%. A source is capped at what exists; the surplus is redistributed in proportion to the other shares.
SHARES = {"swe-hero": 0.115, "openswe": 0.115, "Humanlearning/CyberSecurity_OWASP-sft-dataset": 0.05, "agentforge": 0.01,
          "primevul": 0.085, "hitoshura25/cvefixes": 0.115, "sigmahq/sigma": 0.075, "CyberNative/Code_Vulnerability_Security_DPO": 0.04, "jcordon5/cybersecurity-rules": 0.01, "mitre/cwe": 0.045,
          "Leopo1d/OpenVul-rejection-sampling": 0.09,
          "mitre/attack": 0.04, "mitre/capec": 0.01, "cisa/kev": 0.018, "reloading0101/threat-intelligence-dataset": 0.055, "mrmoor/cyber-threat-intelligence": 0.02,
          "nuhmanpk/cybersecurity-controls-instructions": 0.015, "Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset": 0.03, "AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1": 0.02}
meta = [json.loads(l) for l in open(os.path.join(BUILD, "assembled_meta.jsonl"), encoding="utf-8")]
by_src = collections.defaultdict(list)
for m in meta: by_src[m["source"]].append(m)
for s in by_src: by_src[s].sort(key=lambda m: (prio(m["group"], "tier"), m["id"]))
avail = {s: len(v) for s, v in by_src.items()}
missing = set(by_src) - set(SHARES); assert not missing, f"sources without a share: {missing}"
tot = sum(SHARES[s] for s in by_src); weight = {s: SHARES[s] / tot for s in by_src}
print("pool", sum(avail.values()), {s: avail[s] for s in avail}, flush=True)

take = {}                       # tier -> {source: prefix length}
prev = {s: 0 for s in by_src}
for name, target in TIERS:
    if target is None:
        take[name] = dict(avail); prev = dict(avail); continue
    def k(lam): return {s: min(avail[s], max(prev[s], round(lam * weight[s]))) for s in by_src}   # lam = tier size scale; share-proportional with caps and nesting floors
    lo, hi = 0.0, 1e6
    for _ in range(200):
        mid = (lo + hi) / 2
        if sum(k(mid).values()) < target: lo = mid
        else: hi = mid
    t = k(hi)
    extra = sum(t.values()) - target                   # trim overshoot from the sources with the most headroom above the floor
    while extra > 0:
        s = max((s for s in t if t[s] > prev[s]), key=lambda s: t[s] - prev[s]); t[s] -= 1; extra -= 1
    short = target - sum(t.values())
    while short > 0:
        s = max((s for s in t if t[s] < avail[s]), key=lambda s: weight[s] - t[s]); t[s] += 1; short -= 1
    assert sum(t.values()) == target, (name, sum(t.values()), target)
    take[name] = t; prev = t
    print(f"{name}: {target} rows ->", {s: t[s] for s in sorted(t)}, flush=True)

tier_of = {}                    # row id -> smallest tier index that contains it
for ti, (name, _) in enumerate(TIERS):
    for s, k in take[name].items():
        for m in by_src[s][:k]:
            tier_of.setdefault(m["id"], ti)
for name, _ in TIERS: os.makedirs(os.path.join(OUT, f"cyberdata-2-{name}"), exist_ok=True)
fh = {}
for name, _ in TIERS:
    d = os.path.join(OUT, f"cyberdata-2-{name}")
    fh[name] = {"train": open(os.path.join(d, "train.jsonl"), "w", encoding="utf-8"), "valid": open(os.path.join(d, "valid.jsonl"), "w", encoding="utf-8")}
index = {name: [] for name, _ in TIERS}
n = 0
with open(os.path.join(BUILD, "assembled_all.jsonl"), encoding="utf-8") as f:
    for line in f:
        r = json.loads(line); ti = tier_of.get(r["id"])
        if ti is None: continue
        out = json.dumps({"messages": r["messages"]}, ensure_ascii=False) + "\n"
        for tj in range(ti, len(TIERS)):
            name = TIERS[tj][0]; fh[name][r["split"]].write(out)
            index[name].append((r["id"], r["source"], r["task"], r["provenance"], r["split"], r["group"]))
        n += 1
for d in fh.values():
    for h in d.values(): h.close()
for name, target in TIERS:
    d = os.path.join(OUT, f"cyberdata-2-{name}"); ix = index[name]
    with open(os.path.join(d, "row_index.csv"), "w", newline="") as f:
        w = csv.writer(f); w.writerow(["id", "source", "task", "provenance", "split", "group"]); w.writerows(sorted(ix))
    st = {"tier": name, "rows": len(ix), "train": sum(1 for r in ix if r[4] == "train"), "valid": sum(1 for r in ix if r[4] == "valid"),
          "by_provenance": dict(collections.Counter(r[3] for r in ix)), "by_source": dict(collections.Counter(r[1] for r in ix)), "by_task": dict(collections.Counter(r[2] for r in ix)),
          "bytes": {x: os.path.getsize(os.path.join(d, f"{x}.jsonl")) for x in ("train", "valid")}}
    json.dump(st, open(os.path.join(d, "stats.json"), "w"), indent=1)
    assert target is None or len(ix) == target, (name, len(ix), target)
    print(f"{name:7s} rows {st['rows']:>7} train {st['train']:>7} valid {st['valid']:>6} | " + ", ".join(f"{k} {v}" for k, v in sorted(st['by_provenance'].items(), key=lambda x: -x[1])))
print("SIZES_DONE")