"""SANDBOX C3 (large): position-invariant dual-orientation run over 102 items
(51 humanized injected rows + their 51 host rows), samples=3 per orientation =
612 judgments. Re-judging the hosts in THIS run makes before/after apples-to-apples
(same judge instance, same pairing), removing run-to-run variance vs the stored
baseline. Crash-safe: appends each finished item to out/c3_big_results.jsonl.
Canonical files are read-only.

Run from repo root:  python -u temp/injection_sandbox/run_c3_big.py
"""
from __future__ import annotations

import json
import logging
import threading
from collections import Counter
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from statistics import mean

import yaml

for _n in ("azure", "azure.identity", "azure.core.pipeline.policies.http_logging_policy"):
    logging.getLogger(_n).setLevel(logging.WARNING)

import sys
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT))
from datasetreview import pipelines as P          # noqa: E402
from datasetreview import judge_prompts as J       # noqa: E402
from datasetreview.llm_client import make_judge     # noqa: E402

SAND = Path(__file__).resolve().parent
OUT = SAND / "out"
MERGED = OUT / "_staging_merged_injections.jsonl"
PROV = json.load(open(OUT / "_merged_injections.prov.json", encoding="utf-8"))
BASELINE = ROOT / "datasetreview" / "results" / "new" / "C3.jsonl"
RESULTS = OUT / "c3_big_results.jsonl"

cfg = yaml.safe_load(open(ROOT / "datasetreview" / "config.yaml", encoding="utf-8"))
SAMPLES = 3

reals = P.real_trajectories()
pairer = P.make_pairer(reals)
judge = make_judge(cfg["model"])

injected = [json.loads(l) for l in open(MERGED, encoding="utf-8") if l.strip()]
c3 = {r["example_id"]: r for r in
      (json.loads(l) for l in open(SAND / "data" / "c3_trajectories.jsonl", encoding="utf-8") if l.strip())}
host_of = {p["example_id"]: p["host_of"] for p in PROV}
hosts = [c3[h] for h in dict.fromkeys(host_of.values())]   # unique, order-preserving

items = ([("injected", r) for r in injected]
         + [("host", r) for r in hosts])

_lock = threading.Lock()
_done = {}
if RESULTS.exists():                               # resume support
    for line in RESULTS.read_text(encoding="utf-8").splitlines():
        if line.strip():
            d = json.loads(line)
            _done[(d["group"], d["example_id"])] = d


def majority_guess(fake, real, swap):
    msgs = J.build_c3(fake, real, swap=swap)
    key = msgs["answer_key"]
    gs = []
    for _ in range(SAMPLES):
        try:
            gs.append(judge.judge({"system": msgs["system"], "user": msgs["user"]}).get("guess"))
        except Exception:                          # noqa: BLE001
            pass
    if not gs:
        return None, key, gs
    return Counter(gs).most_common(1)[0][0], key, gs


def run_item(group, fake):
    eid = fake["example_id"]
    if (group, eid) in _done:
        return _done[(group, eid)]
    real = pairer(fake)
    per = {}
    for name, swap in (("A", False), ("B", True)):   # fake in A, then fake in B
        maj, key, gs = majority_guess(fake, real, swap)
        per[name] = {"guess": maj, "answer_key": key, "caught": (maj == key), "samples": gs}
    catches = [per["A"]["caught"], per["B"]["caught"]]
    rec = {"group": group, "example_id": eid, "real_id": real.get("example_id"),
           "order_avg_catch": mean(1.0 if c else 0.0 for c in catches),
           "consistent_catch": all(catches), "any_catch": any(catches),
           "A": per["A"], "B": per["B"]}
    with _lock:
        with open(RESULTS, "a", encoding="utf-8") as f:
            f.write(json.dumps(rec) + "\n")
    return rec


def summarize(recs, group):
    g = [r for r in recs if r["group"] == group]
    n = len(g)
    if not n:
        return
    oa = mean(r["order_avg_catch"] for r in g)
    cons = sum(r["consistent_catch"] for r in g) / n
    a_only = sum(r["A"]["caught"] for r in g) / n
    print(f"  {group:9} n={n}  order-avg caught={oa:.1%}  "
          f"consistent(both orders)={cons:.1%}  orientation-A caught={a_only:.1%}")


def main():
    todo = [it for it in items if (it[0], it[1]["example_id"]) not in _done]
    print(f"items total={len(items)} (already done={len(_done)}) to-judge={len(todo)}  "
          f"orientations=2 samples={SAMPLES}  -> ~{len(todo)*2*SAMPLES} live calls")
    with ThreadPoolExecutor(max_workers=cfg["run"].get("workers", 4)) as ex:
        list(ex.map(lambda it: run_item(*it), todo))

    recs = [json.loads(l) for l in RESULTS.read_text(encoding="utf-8").splitlines() if l.strip()]
    print("\n=== C3 LARGE RESULT (dual-orientation, samples=3 majority) ===")
    summarize(recs, "injected")
    summarize(recs, "host")

    base = {}
    for line in open(BASELINE, encoding="utf-8"):
        if line.strip():
            d = json.loads(line)
            base[d.get("item_id")] = d.get("caught")
    base_all = [v for v in base.values() if v is not None]
    inj = [r for r in recs if r["group"] == "injected"]
    hos = [r for r in recs if r["group"] == "host"]
    print(f"\n  stored-baseline corpus (795): {sum(base_all)}/{len(base_all)} = {sum(base_all)/len(base_all):.1%}")
    if inj and hos:
        oi = mean(r["order_avg_catch"] for r in inj)
        oh = mean(r["order_avg_catch"] for r in hos)
        print(f"  same-run host order-avg caught:     {oh:.1%}")
        print(f"  same-run injected order-avg caught: {oi:.1%}")
        print(f"  injection+humanization effect (same hosts): {oi-oh:+.1%}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())