#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""recall_planted.py — قياس استدعاء مولّد المرشحين (v7: مؤشر معكوس على أندر الكلمات)
بمكررات مزروعة: 500 جملة تدريب + تشوهات (تنقيط/تشكيل/تطبيع/كلمة-اثنتين…)، ثم:
  gen_v7 : هل توجد مرساة من مراسي P داخل الجملة المصدر S؟ (هذا ما يجعل S يجلب P)
  gen_v6 : فلتر simhash القديم (Hamming≤4) — للمقارنة
  e2e    : gen ∧ معيار التشابه (jaccard≥0.45 ∧ containment≥0.65)
"""
import json, random, hashlib, re, sys
import numpy as np
sys.path.insert(0,"/home/user/lhc/recon")
exec(open('/home/user/lhc/recon/build_masks_stream.py').read().split('# ---------------- مجموعات التقييم')[0])
sys.path.insert(0,"/home/user/lhc/code")
from phase_w1_data import download_corpus

N=500; SEED=7
DMAX=60; NANCHOR=4; J_TH=0.45; CT_TH=0.65
rng=random.Random(SEED)
# جمل تدريب حقيقية
src=[]
with open("/var/tmp/lhc_w4/pairs.jsonl", encoding="utf-8") as f:
    for i,line in enumerate(f):
        if i>=200000: break
        if i%397==0:
            o=json.loads(line)
            if o.get("ar") and 8<=len(o["ar"].split())<=40: src.append(o["ar"])
        if len(src)>=N: break
print(f"جمل التدريب المصدر: {len(src)}")
# قاموس df شبيه بجانب الاستعلامات (نستخدم مصادر متاحة: نصوص المصدر نفسها كتقدير)
toks_all=[tokens(norm_ar(s)) for s in src]
df={}
for tk in toks_all: 
    for t in set(tk): df[t]=df.get(t,0)+1

_HARAKAT=["\u064E","\u064F","\u0650","\u0652","\u0651","\u064B","\u064C","\u064D"]
def pert_punct(t,nrs):
    s=t
    for a,b in (("\"","«"),("\"","»"),(","," ، "),("."," ."),(":"," :"),(";"," ؛"),("?"," ؟"),("!"," !")):
        s=s.replace(a,b)
    if nrs.rand()<0.5: s="« "+s+" »"
    return s
def pert_tashkeel(t,nrs):
    out=[]
    for ch in t:
        out.append(ch)
        if re.match(r"[\u0621-\u064A]",ch) and nrs.rand()<0.30: out.append(_HARAKAT[int(nrs.randint(len(_HARAKAT)))])
    return "".join(out)
def pert_norm(t,nrs):
    for a,b in (("أ","ا"),("إ","ا"),("آ","ا"),("ى","ي"),("ة","ه")):
        t=t.replace(a,b)
    return t.translate(str.maketrans("0123456789","٠١٢٣٤٥٦٧٨٩"))
def pert_words(t,k,nrs):
    ws=t.split(" ")
    idxs=[i for i,w in enumerate(ws) if len(w)>3]
    if len(idxs)<=k: return t
    for i in nrs.choice(idxs, size=k, replace=False):
        ws[i]=rng.choice(["الجديد","أيضا","فعلا","ربما","تماما","جدا","هنا","الآن","كذلك","بشكل"])
    return " ".join(ws)

variants=[
 ("control",       lambda t,nrs: t),
 ("punct",         pert_punct),
 ("tashkeel-full", pert_tashkeel),
 ("normalize",     pert_norm),
 ("word-1",        lambda t,nrs: pert_words(t,1,nrs)),
 ("word-2",        lambda t,nrs: pert_words(t,2,nrs)),
 ("mixed",         lambda t,nrs: pert_norm(pert_punct(t,nrs),nrs)),
]
out={}
for name,fn in variants:
    g7=0; g6=0; e2e=0; e2e_wide=0; no_anchor=0
    for si,S in enumerate(src):
        nrs=np.random.RandomState(SEED+si)
        P=fn(S,nrs)
        aq=set(tokens(norm_ar(P))); sq=set(tokens(norm_ar(S)))
        # مراسي P حسب استراتيجية v7 (أندر الكلمات بشرط df≤DMAX، حد NANCHOR)
        rare=sorted(aq, key=lambda t: df.get(t,0))
        anchors=[t for t in rare if df.get(t,0)<=DMAX][:NANCHOR]
        if not anchors: no_anchor+=1
        gen7 = any(a in sq for a in anchors)
        # فلتر v6 القديم
        h=0
        if aq and sq:
            sh_p=simhash64(tokens(norm_ar(P))); sh_s=simhash64(tokens(norm_ar(S)))
            h=(sh_p^sh_s).bit_count()
        gen6 = (h<=4)
        # المعيار
        x=len(aq&sq); j=x/(len(aq|sq)) if (aq or sq) else 0; ct=x/min(len(aq),len(sq)) if (aq and sq) else 0
        strong = (j>=J_TH and ct>=CT_TH)
        wide = (ct>=CT_TH)
        g7+=int(gen7); g6+=int(gen6)
        e2e+=int(gen7 and strong); e2e_wide+=int(gen7 and wide)
    n=len(src)
    out[name]={"n":n,"gen_v7_pct":round(100*g7/n,1),"gen_v6_pct":round(100*g6/n,1),
               "e2e_strong_pct":round(100*e2e/n,1),"e2e_wide_pct":round(100*e2e_wide/n,1),
               "no_anchor":no_anchor}
    print(f"[{name:13s}] استدعاء-مولّد v7={100*g7/n:5.1f}% | v6(simhash≤4)={100*g6/n:5.1f}% | شامل(قوي)={100*e2e/n:5.1f}% | شامل(واسع)={100*e2e_wide/n:5.1f}% | بلا مرساة={no_anchor}")
json.dump({"config":{"N":N,"seed":SEED,"DMAX":DMAX,"NANCHOR":NANCHOR,"J":J_TH,"CT":CT_TH},
           "results":out}, open("/home/user/lhc/results/recall_planted.json","w"), ensure_ascii=False, indent=1)
print("SAVED results/recall_planted.json")