# -*- coding: utf-8 -*-
"""Audit 5: precision held-out des regles de TRAIT D'UNION + confusion matrix point final."""
import csv, json, os
from collections import Counter

OUT = []
def P(*a):
    s = " ".join(str(x) for x in a); OUT.append(s); print(s)

sub = {}
with open("/root/sub_LMA06.csv", encoding="utf-8") as f:
    for row in csv.DictReader(f): sub[row["ID"]] = row["Target"]
lang = json.load(open("/root/test_lang.json", encoding="utf-8"))
hyp = {"lin": [], "sna": []}
for k, v in sub.items():
    if lang.get(k) in hyp: hyp[lang[k]].append(v)
train, val = {"lin": [], "sna": []}, {"lin": [], "sna": []}
for L in ("lin", "sna"):
    for sp, st in (("train", train), ("validation", val)):
        with open("/scratch/prep/manifests/waxal_%s_%s.jsonl" % (L, sp), encoding="utf-8") as f:
            for line in f:
                if line.strip(): st[L].append(json.loads(line)["text"])
tot_w = sum(len(t.split()) for L in hyp for t in hyp[L])
tot_c = sum(len(t) for L in hyp for t in hyp[L])
W_ERR, C_ERR = 0.5/tot_w, 0.5/tot_c
PUNCT = ".,;:!?\"()«»"
def toks(t): return [x for x in (w.strip(PUNCT) for w in t.split()) if x]

P("=== TRAIT D'UNION : la regle 'a b' -> 'a-b' est-elle fiable ? (VAL held-out) ===")
P("1 fix correct = -2 err-mot -1 err-car ; 1 fix errone = +2 err-mot +1 err-car")
UNIT = 2*W_ERR + C_ERR
P("valeur d'un fix = %.3e score ; ecart au 2e = %.1f fix nets" % (UNIT, 0.000199/UNIT))
for L in ("lin", "sna"):
    def counts(texts):
        u = Counter(); b = Counter()
        for t in texts:
            ws = [w.lower() for w in toks(t)]
            u.update(ws); b.update(zip(ws, ws[1:]))
        return u, b
    u_tr, b_tr = counts(train[L])
    u_va, b_va = counts(val[L])
    hyp_bi = Counter()
    for t in hyp[L]:
        ws = [w.lower() for w in toks(t)]; hyp_bi.update(zip(ws, ws[1:]))

    P("")
    P("--- %s ---" % L)
    P("   %-30s %6s %6s %7s | %6s %6s %7s | %5s" % ("regle a b -> a-b", "trHyph", "trSep", "trPrec", "vaHyph", "vaSep", "vaPrec", "hyp"))
    tot_fire = 0; tot_tp = 0; tot_fp = 0
    rows = []
    for (a, b), c in hyp_bi.items():
        h = a + "-" + b
        th, ts = u_tr.get(h, 0), b_tr.get((a, b), 0)
        vh, vs = u_va.get(h, 0), b_va.get((a, b), 0)
        if th + vh < 3: continue
        rows.append((c, a, b, th, ts, vh, vs))
    rows.sort(reverse=True)
    for c, a, b, th, ts, vh, vs in rows:
        tp = th/max(th+ts, 1); vp = vh/max(vh+vs, 1) if (vh+vs) else float("nan")
        P("   %-30s %6d %6d %6.1f%% | %6d %6d %6s | %5d"
          % (a+" "+b, th, ts, 100*tp, vh, vs, ("%.1f%%" % (100*vp)) if (vh+vs) else "n/a", c))
        tot_fire += c; tot_tp += vh; tot_fp += vs
    prec = tot_tp/max(tot_tp+tot_fp, 1)
    P("   TOTAL: %d declenchements | precision VAL agregee = %.2f%% (%d/%d)" % (tot_fire, 100*prec, tot_tp, tot_tp+tot_fp))
    net = tot_fire*(2*prec-1)
    P("   GAIN = %d x (2p-1)=%.3f = %.1f fix nets = %+.5f score" % (tot_fire, 2*prec-1, net, net*UNIT))
    # variante conservatrice: seulement les regles a precision train >= 90%
    ff = tt = fpp = 0
    for c, a, b, th, ts, vh, vs in rows:
        if th/max(th+ts,1) >= 0.90:
            ff += c; tt += vh; fpp += vs
    if ff:
        pr2 = tt/max(tt+fpp,1); net2 = ff*(2*pr2-1)
        P("   CONSERVATEUR (precision train>=90%%): %d declench., precision VAL=%.2f%% -> %.1f fix nets = %+.5f score"
          % (ff, 100*pr2, net2, net2*UNIT))
    # combien de traits d'union produisons-nous deja ?
    nh = sum(1 for t in hyp[L] for w in toks(t) if "-" in w)
    ntr = sum(1 for t in train[L] for w in toks(t) if "-" in w)
    P("   mots contenant '-' : hyp=%d (%.3f%% des mots) | train=%d (%.3f%%)"
      % (nh, 100.0*nh/sum(len(toks(t)) for t in hyp[L]), ntr, 100.0*ntr/sum(len(toks(t)) for t in train[L])))

# ===== point final : matrice de confusion complete =====
P("")
P("=== POINT FINAL : matrice de confusion devhard-lin (joint_cont_best) ===")
allh = json.load(open("/root/devhard_allhyps.json", encoding="utf-8"))
refs = {}
for fn in os.listdir("/root/devhard"):
    with open("/root/devhard/"+fn, encoding="utf-8") as f:
        for line in f:
            if line.strip():
                d = json.loads(line); refs[d["id"]] = d.get("text", "")
hyps = allh["joint_cont_best"]
pairs = [(h.strip().endswith("."), refs[i].strip().endswith("."))
         for i, h in hyps.items() if i.startswith("lin_") and i in refs and refs[i].strip()]
n11 = sum(1 for a,b in pairs if a and b); n10 = sum(1 for a,b in pairs if a and not b)
n01 = sum(1 for a,b in pairs if not a and b); n00 = sum(1 for a,b in pairs if not a and not b)
P("n=%d  n11=%d n10=%d n01=%d n00=%d" % (len(pairs), n11, n10, n01, n00))
P("P(ref='.' | nous emettons '.')      = %.3f" % (n11/max(n11+n10,1)))
P("P(ref='.' | nous n'emettons PAS)    = %.3f   <- si > 0.5, forcer '.' partout gagne" % (n01/max(n01+n00,1)))
ntest_no = sum(1 for t in hyp["lin"] if not t.strip().endswith("."))
p = n01/max(n01+n00,1)
P("clips lingala du TEST sans point final = %d" % ntest_no)
P("gain si on force le point: %d x (2p-1)=%.3f = %.1f clips nets = %+.5f score"
  % (ntest_no, 2*p-1, ntest_no*(2*p-1), ntest_no*(2*p-1)*(W_ERR+C_ERR)))
ntest_no_s = sum(1 for t in hyp["sna"] if not t.strip().endswith("."))
P("clips shona du TEST sans point final = %d (marge negligeable)" % ntest_no_s)

with open("/root/audit_STATSLING5_out.txt","w",encoding="utf-8") as f: f.write("\n".join(OUT))
print("\n[OK] /root/audit_STATSLING5_out.txt")