"""Analyses added for the paper: cross-engine agreement, trivial baselines, top-1 and its chance level,
per-engine and per-query means, position combination, overlap check. Usage: python code/analyses.py <archive root>.
Writes results/analyses/extra_analyses.json.
Reuses reproduce_tables.py (same Spearman, same query-clustered bootstrap, seed 88)."""
import collections, glob, json, os, sys
import numpy as np
A = sys.argv[1]
OUT = A + "/results/analyses/extra_analyses.json"
PREV = json.load(open(OUT)) if os.path.exists(OUT) else {}
sys.path.insert(0, A + "/code")
import reproduce_tables as R

lab = R.labels()
P = A + "/benchmark/predictions/"
SC = {"jev_listwise": "judges/jev_1.13_listwise", "jev_pointwise": "judges/jev_1.13_pointwise",
      "jev27b": "open_decision_models/jev27b_listwise", "pplx27b": "open_decision_models/pplx27b_listwise",
      "jev9b": "open_decision_models/jev9b_listwise", "student4b_s42": "students/student_4b_s42",
      "student4b_ens3": "students/student_4b_ensemble3", "student06_s42": "students/student_0.6b_s42",
      "qwen_rr4b_zs": "zero_shot/qwen3_reranker_4b", "nim_zs": "zero_shot/nim_reranker_by_group"}
f = {k: R.load_scorer(P + v + ".json") for k, v in SC.items()}
rho = {k: R.per_group(lab, s) for k, s in f.items()}
out = {}
r3 = lambda t: [round(x, 4) for x in t[:3]] + [t[3]]

# 1. Cross-engine agreement: other engines' mean label on the same query
byq = collections.defaultdict(dict)
for g, l in lab.items():
    e, q = g.split("::", 1); byq[q][e] = l
def loo(g, s):
    e, q = g.split("::", 1)
    o = [byq[q][x][s] for x in byq[q] if x != e]
    return sum(o) / len(o) if o else None
cons = R.per_group(lab, loo)
out["consensus"] = r3(R.boot(cons))
for k in ("jev_listwise", "jev27b", "student4b_s42", "student4b_ens3"):
    sub = {g: rho[k][g] for g in cons}
    out[f"{k}_on_consensus_groups"] = r3(R.boot(sub))
    out[f"consensus_minus_{k}"] = r3(R.paired(cons, rho[k]))

# 2. Trivial baselines
txt = {}
for l in open(A + "/benchmark/passages.jsonl"):
    r = json.loads(l); txt[(r["query"], r["src_idx"])] = r["text"].lower().split()
N = len(txt); avg = sum(map(len, txt.values())) / N
df = collections.Counter(w for d in txt.values() for w in set(d))
def bm25(g, s, k1=1.2, b=0.75):
    q = g.split("::", 1)[1]; d = txt[(q, s)]; tf = collections.Counter(d); sc = 0.0
    for w in set(q.lower().split()):
        if w in tf:
            sc += np.log(1 + (N - df[w] + .5) / (df[w] + .5)) * tf[w] * (k1 + 1) / (tf[w] + k1 * (1 - b + b * len(d) / avg))
    return sc
base = {"position": lambda g, s: -s, "bm25": bm25, "length": lambda g, s: len(txt[(g.split("::", 1)[1], s)])}
for k, fn in base.items():
    out[f"baseline_{k}"] = r3(R.boot(R.per_group(lab, fn)))
pos_mean = collections.defaultdict(list)
for g, l in lab.items():
    for s, v in l.items(): pos_mean[s].append(v)
out["mean_label_by_position"] = {s: round(float(np.mean(v)), 4) for s, v in sorted(pos_mean.items())}

# 3. Top-1 accuracy (prediction's top source is a most-cited source)
def top1(fn):
    h = {}
    for g, l in lab.items():
        y = [l[s] for s in sorted(l)]
        if max(y) == min(y): continue
        p = [fn(g, s) for s in sorted(l)]
        if any(v is None for v in p): continue
        h[g] = float(y[int(np.argmax(p))] == max(y))
    return h
out["top1"] = {k: r3(R.boot(top1(fn))) for k, fn in list(f.items()) + [("position", base["position"])]}

# 4. Per-engine macro average
eng = collections.defaultdict(lambda: collections.defaultdict(list))
for k in SC:
    for g, v in rho[k].items(): eng[k][g.split("::")[0]].append(v)
out["per_engine"] = {k: {e: [round(float(np.mean(v)), 4), len(v)] for e, v in sorted(d.items())} for k, d in eng.items()}
out["macro_over_engines"] = {k: round(float(np.mean([np.mean(v) for v in d.values()])), 4) for k, d in eng.items()}

# 5. Score + source position, weight fitted on four folds, applied to the fifth
folds = json.load(open(A + "/benchmark/folds_query.json"))
def ranks5(x):
    return R.ranks(x)
def combo(fn, w):
    def g_(g, s):
        sis = sorted(lab[g]); p = [fn(g, t) for t in sis]
        if any(v is None for v in p): return None
        return ranks5(p)[sis.index(s)] + w * (4 - s)
    return g_
W = [0, .1, .2, .3, .5, .75, 1, 1.5, 2, 3]
for k in ("student4b_s42", "jev_listwise"):
    pre = {w: R.per_group(lab, combo(f[k], w)) for w in W}
    oof, chosen = {}, []
    for fo in range(5):
        tr = lambda g: folds[g.split("::", 1)[1]] != fo
        best = max(W, key=lambda w: np.mean([v for g, v in pre[w].items() if tr(g)]))
        chosen.append(best)
        oof.update({g: v for g, v in pre[best].items() if not tr(g)})
    out[f"{k}_plus_position_oof"] = {"rho": r3(R.boot(oof)), "weights": chosen,
                                     "gain": r3(R.paired(oof, rho[k]))}

# 7. Cross-engine agreement excluding engines of the same vendor
VEND = {"gpt": "openai", "openai": "openai", "gemini": "google", "google": "google", "mistralai": "mistral",
        "qwen": "qwen", "meta": "meta", "nex-agi": "nex"}
vend = lambda e: VEND[e.split("/")[0] if "/" in e else e.split("-")[0]]
def loo_v(g, s):
    e, q = g.split("::", 1)
    o = [byq[q][x][s] for x in byq[q] if vend(x) != vend(e)]
    return sum(o) / len(o) if o else None
cv = R.per_group(lab, loo_v)
out["consensus_other_vendors"] = r3(R.boot(cv))
out["consensus_other_vendors_minus_jev_listwise"] = r3(R.paired(cv, rho["jev_listwise"]))
out["consensus_other_vendors_minus_student4b_s42"] = r3(R.paired(cv, rho["student4b_s42"]))
json.dump(out, open(OUT, "w"), indent=1)
print({k: out[k] for k in out if k.startswith("consensus_other")})

# 8. Agreement between the two teachers (0.759): computed on teacher labels, which are not redistributed
out["teacher_agreement"] = PREV.get("teacher_agreement")

# 9. Per-query mean (each query weighted once) and chance level of top-1
pq = {}
for k in SC:
    byq_r = collections.defaultdict(list)
    for g, v in rho[k].items():
        byq_r[g.split("::", 1)[1]].append(v)
    pq[k] = round(float(np.mean([np.mean(v) for v in byq_r.values()])), 4)
out["per_query_mean"] = pq
ch = [sum(1 for v in l.values() if v == max(l.values())) / 5 for g, l in lab.items() if len(set(l.values())) > 1]
out["top1_chance"] = round(float(np.mean(ch)), 4)

# 10. Overlap: evaluation passages whose first 40 words also open a training source
key40 = lambda t: " ".join(t.split()[:40])
train_txt = {key40(json.loads(l)["text"]) for l in open(A + "/benchmark/training_groups.jsonl")}
shared = {(r["query"], r["src_idx"]) for r in map(json.loads, open(A + "/benchmark/passages.jsonl")) if key40(r["text"]) in train_txt}
touched = {g for g, l in lab.items() if any((g.split("::", 1)[1], s) in shared for s in l)}
allp = {os.path.relpath(p, A + "/benchmark/predictions")[:-5]: R.per_group(lab, R.load_scorer(p))
        for p in glob.glob(A + "/benchmark/predictions/**/*.json", recursive=True)}
shift = {k: abs(np.mean(list(v.values())) - np.mean([x for g, x in v.items() if g not in touched])) for k, v in allp.items()}
out["overlap"] = {"shared_passages": len(shared), "groups_touched": len([g for g in touched if g in rho["student4b_s42"]]),
                  "max_abs_shift": round(float(max(shift.values())), 4), "argmax": max(shift, key=shift.get),
                  "n_configurations": len(shift),
                  "max_abs_shift_students": round(float(max(v for k, v in shift.items() if k.startswith("students/"))), 4),
                  "signed_shift_student4b_s42": round(float(np.mean([x for g, x in allp["students/student_4b_s42"].items() if g not in touched])
                                                        - np.mean(list(allp["students/student_4b_s42"].values()))), 4)}
json.dump(out, open(OUT, "w"), indent=1)
print(out["per_query_mean"], out["top1_chance"], out["overlap"])