from __future__ import annotations
import sys,time,json
from pathlib import Path
import numpy as np,pandas as pd
from numba import njit,prange,set_num_threads
sys.path.insert(0,'/mnt/data')
import msmarco_best_tail_core as b
import msmarco_full_search_uniform1m as m
ROOT=m.ROOT; WORK=m.WORK; idx=b.idx; M=m.M; P=2000
OUT=WORK/'structural_fusion'; OUT.mkdir(exist_ok=True); set_num_threads(5)

@njit(parallel=True,cache=False)
def selected_support_extra(cand_docs,ip,ids,lexvec,dl,avgdl):
 n=len(cand_docs); rawlex=np.zeros(n,np.float32); qcount=np.zeros(n,np.float32); lenfac=np.zeros(n,np.float32)
 for z in prange(n):
  d=int(cand_docs[z]); a=int(ip[d]); bb=int(ip[d+1]); raw=0.0; cnt=0.0
  for k in range(a,bb):
   t=int(ids[k]); v=lexvec[t]
   if v>0: raw+=v; cnt+=1.0
  rawlex[z]=raw; qcount[z]=cnt; lenfac[z]=(1.0-m.LENGTH_B)+m.LENGTH_B*(float(dl[d])/avgdl)
 return rawlex,qcount,lenfac

def topk(score,k):
 n=len(score); k=min(k,n)
 if n<=k:return np.argsort(score)[::-1]
 ii=np.argpartition(score,-k)[-k:]; return ii[np.argsort(score[ii])[::-1]]

tr=pd.read_csv(ROOT/'train.tsv',sep='\t',usecols=['query-id']); uq=np.unique(tr['query-id'].to_numpy()); del tr
rng=np.random.default_rng(20260815); qids=[str(x) for x in rng.choice(uq,size=1000,replace=False)]; texts=m.load_query_texts(qids)
shape=(len(qids),P); docsO=np.zeros(shape,np.uint32); valid=np.zeros(len(qids),np.int32); qterms=np.zeros(len(qids),np.int16)
fnames=['geom','cons','tail','lex','rawlex','sem','qcount','lenfac','branch_count']
feat={k:np.zeros(shape,np.float32) for k in fnames}; prep=[]
_=selected_support_extra(np.array([0],np.uint32),idx.sup_ip,idx.sup_ids,np.zeros(M,np.float32),idx.dl,idx.avgdl)
for qi,qid in enumerate(qids):
 t0=time.perf_counter(); q=idx.query_vec(texts[qid]); qd=np.zeros(M,np.float32); qd[q.indices]=q.data; qterms[qi]=len(q.indices); rterms,rd=idx.route(q)
 spans=[(int(j),int(idx.offs[j]),int(idx.offs[j+1])) for j in rterms if idx.offs[j+1]>idx.offs[j]]
 if not spans: continue
 dmem=np.concatenate([np.asarray(idx.pd[a:bb]) for j,a,bb in spans]).astype(np.uint32,copy=False); mm=np.concatenate([np.asarray(idx.pm[a:bb]) for j,a,bb in spans]).astype(np.float32,copy=False); rt=np.concatenate([np.asarray(idx.pr[a:bb]) for j,a,bb in spans]).astype(np.uint16,copy=False); sb=np.concatenate([np.asarray(idx.ps[a:bb]) for j,a,bb in spans]).astype(np.uint16,copy=False)
 nr=len(spans); cent=np.zeros((nr,M),np.float32); rel=np.zeros((nr,M),np.float32); rho=np.empty(nr,np.float32)
 for u,(j,a,bb) in enumerate(spans):
  rowt=np.asarray(idx.ct[j]); ok=rowt!=65535; tids=rowt[ok].astype(np.int32,copy=False); cent[u,tids]=np.asarray(idx.cv[j])[ok]; ra=int(idx.rp[j]); rb=int(idx.rp[j+1]); rel[u,np.asarray(idx.ri[ra:rb],np.int32)]=np.asarray(idx.rv[ra:rb]); rho[u]=rd[j]
 rslot=np.concatenate([np.full(bb-a,u,dtype=np.uint8) for u,(j,a,bb) in enumerate(spans)]); base,sig,consmem=b.score_components(rslot,mm,rt,sb,qd,rho,cent,rel)
 ud,inv=np.unique(dmem,return_inverse=True); geom=np.bincount(inv,weights=base*np.power(sig,b.GAMMA,dtype=np.float32),minlength=len(ud)).astype(np.float32); cons=np.bincount(inv,weights=consmem,minlength=len(ud)).astype(np.float32); tail=geom+b.LAM*cons; bc=np.bincount(inv,minlength=len(ud)).astype(np.float32)
 lexvec=np.zeros(M,np.float32); lexvec[q.indices]=idx.idf[q.indices]; semvec=np.zeros(M,np.float32)
 for tt,amp in zip(q.indices,q.data):
  a,bb=idx.A.indptr[tt],idx.A.indptr[tt+1]; nb=idx.A.indices[a:bb][:m.SEMK]; sv=idx.A.data[a:bb][:m.SEMK]; semvec[nb]+=float(amp)*sv*idx.idf[nb]
 lx,sm=m.score_support_pool(ud,idx.sup_ip,idx.sup_ids,lexvec,semvec,idx.dl,idx.avgdl)
 sel=topk(m.zscore(tail)+m.zscore(lx),P); k=len(sel); valid[qi]=k; dd=ud[sel]; docsO[qi,:k]=dd
 raw,qc,lf=selected_support_extra(dd,idx.sup_ip,idx.sup_ids,lexvec,idx.dl,idx.avgdl)
 vals={'geom':geom[sel],'cons':cons[sel],'tail':tail[sel],'lex':lx[sel],'rawlex':raw,'sem':sm[sel],'qcount':qc,'lenfac':lf,'branch_count':bc[sel]}
 for nm,v in vals.items(): feat[nm][qi,:k]=v
 prep.append((time.perf_counter()-t0)*1000)
 if (qi+1)%100==0: print('q',qi+1,'median',float(np.median(prep)),flush=True)
np.savez_compressed(OUT/'fixed_eta1_structural_features.npz',qids=np.asarray(qids),valid=valid,docs=docsO,qterms=qterms,**feat)
meta={'protocol':'same deterministic 1000 TRAIN validation, eta=1 P=2000 pools; index-only features','features':fnames,'median_ms':float(np.median(prep)),'p95_ms':float(np.percentile(prep,95))}; json.dump(meta,open(OUT/'structural_feature_meta.json','w'),indent=2); print('DONE',meta,flush=True)