"""Image-paired summaries and calibration with explicit finite-sample limits.

The bound concerns ADDITIONAL false abstention from an already nonempty CACP
mask. It does not bound total false-empty errors and does not survive arbitrary
train-to-test distribution shift. All tested thresholds share a Bonferroni bound.
"""
from pathlib import Path
import argparse,json,collections,csv,gzip,hashlib
import numpy as np
from scipy.optimize import minimize
from scipy.special import expit
from scipy.stats import beta
ROOT=Path(__file__).resolve().parents[1];OUT=ROOT/'results/analysis'
SEEDS=[11,23,37];GRID=np.r_[np.arange(.025,1.001,.025),1.001]
FAMILY_SIZE=40*2*3*2 # thresholds x positive strata x fits x backbones

def fit_logistic(rows,seed):
    x=np.array([r['features'] for r in rows]);y=np.array([r['target_count']==0 for r in rows],float)
    rng=np.random.RandomState(seed);idx=np.concatenate([rng.choice(np.flatnonzero(y==v),int((y==v).sum()),replace=True) for v in [0,1]])
    x=x[idx];y=y[idx];mu=x.mean(0);sd=x.std(0);sd[sd<1e-8]=1.;z=np.c_[(x-mu)/sd,np.ones(len(x))]
    w=np.where(y==1,.5/max((y==1).mean(),1e-6),.5/max((y==0).mean(),1e-6))
    def objective(b):
        v=z@b;loss=np.mean(w*(np.logaddexp(0,v)-y*v))+.01*np.dot(b[:-1],b[:-1])
        grad=z.T@(w*(expit(v)-y))/len(y);grad[:-1]+=.02*b[:-1]
        return loss,grad
    opt=minimize(objective,np.zeros(z.shape[1]),jac=True,method='L-BFGS-B',options={'maxiter':1000,'ftol':1e-12})
    if not opt.success:raise RuntimeError('Logistic fit failed: '+opt.message)
    return dict(mean=mu.tolist(),std=sd.tolist(),coef=opt.x.tolist(),n=len(y),seed=seed,objective=float(opt.fun))

def probability(rows,fit):
    x=np.array([r['features'] for r in rows]);return expit(np.c_[(x-np.array(fit['mean']))/np.array(fit['std']),np.ones(len(x))]@np.array(fit['coef']))

def cp_upper(k,n,alpha):
    if n==0:return 1.
    return 1. if k==n else float(beta.ppf(1-alpha,k+1,n-k))

def operating_table(rows,fit):
    iid_images=len({r['scene_id'] for r in rows})==len(rows)
    p=probability(rows,fit);y=np.array([r['target_count']==0 for r in rows]);base=np.array([r['scores']['cacp']['empty'] for r in rows]);counts=np.array([r['target_count'] for r in rows]);out=[]
    for t in GRID:
        reject=p>=t;empty=base|reject;additional=reject&~base;groups={}
        for name,sel in [('one',counts==1),('multi',counts>1)]:
            n=int(sel.sum());k=int((additional&sel).sum())
            groups[name]={'n':n,'k':k,'upper':(0. if t>1 else cp_upper(k,n,.05/FAMILY_SIZE)) if iid_images else None}
        acc0=float(empty[y].mean()) if y.any() else 0.;far=float(empty[~y].mean()) if (~y).any() else 0.
        out.append({'threshold':float(t),'no_target_accuracy':acc0,'false_empty':far,'additional_false_abstention':float(additional[~y].mean()),'balanced_accuracy':.5*(acc0+1-far),'groups':groups,'worst_upper':max(g['upper'] for g in groups.values()) if iid_images else None})
    return out

def add_calibration(rows):
    params={};curve=[]
    for seed in SEEDS:
        for domain in ['controlled','natural']:
            train=[r for r in rows if r['domain']==domain and r['split']=='fit' and r['mode']!='action']
            cal=[r for r in rows if r['domain']==domain and r['split']=='cal' and r['mode']!='action']
            fit=fit_logistic(train,seed);table=operating_table(cal,fit)
            uncon=max(table,key=lambda x:(x['balanced_accuracy'],x['threshold']))
            variants={('source_unconstrained' if domain=='controlled' else 'target_unconstrained'):uncon}
            if domain=='natural':
                for eps in [.01,.025,.05,.10]:
                    allowed=[x for x in table if x['worst_upper']<=eps]
                    variants[f'constrained_{eps:g}']=max(allowed,key=lambda x:(x['no_target_accuracy'],-x['additional_false_abstention'],x['threshold']))
                curve.extend(dict(seed=seed,**x) for x in table)
            scores=probability(rows,fit)
            for variant,point in variants.items():
                name=f'{variant}_s{seed}';t=point['threshold'];params[name]={'fit':fit,'operating_point':point,'calibration_domain':domain}
                for r,pr in zip(rows,scores):
                    base=r['scores']['cacp'];reject=bool(pr>=t)
                    # Explicit action semantics are already handled by CACP.
                    val=dict(base)
                    if reject:val.update(iou=1. if r['target_count']==0 else 0.,intersection=0,union=base['gt_pixels'],pred_pixels=0,empty=True)
                    val['additional_abstention']=reject and not base['empty'];r['scores'][name]=val
                    if domain=='natural' and seed==11:r['absence_probability']=float(pr)
    return params,curve

def cluster_ci(values,keys,strata=None,reps=2000):
    vals=np.asarray(values,float);groups=collections.defaultdict(list)
    for i,k in enumerate(keys):groups[k].append(i)
    ids=list(groups);arr=np.array([vals[groups[k]].mean(0) for k in ids])
    if strata is None:labels=np.zeros(len(ids),int)
    else:labels=np.array([str(strata[groups[k][0]]) for k in ids])
    rng=np.random.RandomState(20260906);boot=np.zeros((reps,)+arr.shape[1:]);n=0
    for label in sorted(set(labels)):
        a=arr[labels==label];draw=rng.randint(0,len(a),size=(reps,len(a)));boot+=a[draw].sum(axis=1);n+=len(a)
    boot/=n
    return arr.mean(axis=0),np.quantile(boot,.025,axis=0),np.quantile(boot,.975,axis=0),len(arr)

def summarize(rows,methods):
    summaries=[]
    def emit(name,rr,metric,threshold=.5):
        if not rr:return
        if metric=='pc':
            pairs=collections.defaultdict(list)
            for r in rr:pairs[r['pair_id']].append(r)
            assert all(len(x)==2 for x in pairs.values())
            data=[];keys=[];strata=[]
            for pp in pairs.values():
                data.append([float(all(r['scores'][m]['iou']>=threshold for r in pp)) for m in methods]);keys.append(pp[0]['scene_id']);strata.append(pp[0]['seed'])
        else:
            data=[];keys=[];strata=[]
            for r in rr:
                data.append([float(r['scores'][m]['empty']) if metric in ['no_target_accuracy','false_empty'] else float(r['scores'][m].get('additional_abstention',False)) if metric=='additional_false_abstention' else r['scores'][m]['iou'] for m in methods]);keys.append(r['scene_id']);strata.append(r['seed'] if r['domain']=='controlled' else r['split']+'_'+r['mode'])
        data=np.array(data);mean,lo,hi,n=cluster_ci(data,keys,strata)
        delta,dlo,dhi,_=cluster_ci(data-data[:,[methods.index('frozen')]],keys,strata)
        for i,m in enumerate(methods):summaries.append(dict(population=name,metric=('PC50' if threshold==.5 else 'PC70') if metric=='pc' else metric,method=m,estimate=float(mean[i]),lo=float(lo[i]),hi=float(hi[i]),gain=float(delta[i]),gain_lo=float(dlo[i]),gain_hi=float(dhi[i]),clusters=n,units=len(data)))
    controlled=[r for r in rows if r['domain']=='controlled' and r['split']=='test']
    clean=[r for r in controlled if r['corruption']=='clean']
    primary=[r for r in clean if r['mode'] in ['attribute','relation','quantifier','absence']]
    emit('controlled_primary',primary,'pc');emit('controlled_primary',primary,'pc',.7)
    for family in ['attribute','relation','quantifier','action','absence','paraphrase']:
        emit('family_'+family,[r for r in clean if r['mode']==family],'pc')
    for template in ['seen','held']:emit('template_'+template,[r for r in primary if r['template']==template],'pc')
    stress_ids={r['scene_id'] for r in controlled if r['corruption']!='clean'}
    for corruption in ['clean','blur','noise','dim']:
        rr=[r for r in controlled if r['corruption']==corruption and r['scene_id'] in stress_ids and r['mode'] in ['attribute','relation','quantifier','absence']]
        emit('stress_'+corruption,rr,'pc')
    for name,rr in [('controlled_clean',clean),('natural_test',[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB']])]+[(s,[r for r in rows if r['domain']=='natural' and r['split']==s]) for s in ['val','testA','testB']]:
        emit(name,rr,'gIoU');emit(name,[r for r in rr if r['target_count']>0],'positive_mIoU');emit(name,[r for r in rr if r['target_count']==0],'no_target_accuracy');emit(name,[r for r in rr if r['target_count']>0],'false_empty');emit(name,[r for r in rr if r['target_count']>0],'additional_false_abstention')
    for mode in ['zero','one','multi']:
        rr=[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB'] and r['mode']==mode]
        emit('natural_'+mode,rr,'gIoU')
    return summaries

def main():
    a=argparse.ArgumentParser();a.add_argument('--model',choices=['clipseg','groundedsam']);args=a.parse_args();OUT.mkdir(parents=True,exist_ok=True)
    models=[args.model] if args.model else ['clipseg','groundedsam'];allsummary=[];allparam={};allcurves=[];metadata={}
    main_methods=['frozen','action_only','largest','global_direction','target_only','anchor_nogate','anchor_gate','counterfactual_nogate','cacp','source_unconstrained_s11','target_unconstrained_s11','constrained_0.05_s11']
    for model in models:
        shards=2 if model=='clipseg' else 8
        files=sorted((ROOT/'results').glob(model+f'_main_*of{shards}.jsonl'));metas=sorted((ROOT/'results').glob(model+f'_main_*of{shards}_meta.json'))
        if len(files)!=shards or len(metas)!=shards:raise RuntimeError('Model runs are incomplete: '+model)
        rows=[json.loads(l) for f in files for l in f.read_text().splitlines()];expected=json.loads((ROOT/'data/dataset_summary.json').read_text())['records']
        assert len(rows)==expected and len({r['id'] for r in rows})==expected
        assert all(json.loads(m.read_text())['status']=='COMPLETE' for m in metas)
        params,curves=add_calibration(rows);allparam[model]=params;allcurves.extend(dict(model=model,**c) for c in curves)
        ss=summarize(rows,main_methods);allsummary.extend(dict(model=model,**s) for s in ss)
        # Seed/tolerance sensitivity is a separate table, not independent evidence.
        sensitivity=[]
        for seed in SEEDS:
            for eps in [.01,.025,.05,.1]:
                name=f'constrained_{eps:g}_s{seed}'
                for pop,sel in [('natural_test',[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB']]),('val',[r for r in rows if r['domain']=='natural' and r['split']=='val'])]:
                    pos=[r for r in sel if r['target_count']>0];neg=[r for r in sel if r['target_count']==0]
                    sensitivity.append(dict(model=model,seed=seed,tolerance=eps,population=pop,threshold=params[name]['operating_point']['threshold'],cal_upper=params[name]['operating_point']['worst_upper'],positive_iou=float(np.mean([r['scores'][name]['iou'] for r in pos])),no_target_accuracy=float(np.mean([r['scores'][name]['empty'] for r in neg])),false_empty=float(np.mean([r['scores'][name]['empty'] for r in pos])),additional_false_abstention=float(np.mean([r['scores'][name]['additional_abstention'] for r in pos]))))
        (OUT/f'{model}_sensitivity.json').write_text(json.dumps(sensitivity,indent=2))
        # Oracle anchor substitution: diagnostic, not a guaranteed upper bound.
        oracle_rows=[r for r in rows if r['domain']=='controlled'];os=summarize(oracle_rows,['frozen','cacp','oracle_anchor'])
        (OUT/f'{model}_oracle.json').write_text(json.dumps(os,indent=2))
        reasons=collections.Counter(r['flags']['reason'] for r in rows if r['split'] in ['test','testA','testB','val']);bydomain={}
        for domain in ['controlled','natural']:
            rr=[r for r in rows if r['domain']==domain and r['split'] in ['test','testA','testB','val'] and r['corruption']=='clean']
            bydomain[domain]={'n':len(rr),'supported':sum(r['flags']['supported'] for r in rr),'changed':sum(r['flags']['changed'] for r in rr),'harm_iou':sum(r['scores']['cacp']['iou']<r['scores']['frozen']['iou'] for r in rr),'help_iou':sum(r['scores']['cacp']['iou']>r['scores']['frozen']['iou'] for r in rr)}
        metadata[model]={'runs':[json.loads(m.read_text()) for m in metas],'reason_counts':dict(reasons),'coverage':bydomain}
        with gzip.open(OUT/f'{model}_evaluated.jsonl.gz','wt') as f:
            for r in rows:f.write(json.dumps(r)+'\n')
        # Test trade-off curves use the fixed calibration-trained score; no tuning.
        test=[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB']]
        curve_test=operating_table(test,params['target_unconstrained_s11']['fit'])
        (OUT/f'{model}_test_curve.json').write_text(json.dumps(curve_test,indent=2))
        print('ANALYZED',model,flush=True)
    (OUT/'summary.json').write_text(json.dumps(allsummary,indent=2))
    with (OUT/'summary.csv').open('w') as f:
        writer=csv.DictWriter(f,fieldnames=list(allsummary[0]));writer.writeheader();writer.writerows(allsummary)
    (OUT/'calibration.json').write_text(json.dumps(allparam,indent=2));(OUT/'calibration_curves.json').write_text(json.dumps(allcurves,indent=2));(OUT/'run_summary.json').write_text(json.dumps(metadata,indent=2))
    (OUT/'statistical_notes.json').write_text(json.dumps({'bootstrap_replicates':2000,'interval':'95% percentile, paired image/scene clusters, stratified by seed or official split and target stratum','multiplicity':'Confidence intervals are descriptive and unadjusted; ablations are not independent replications','calibration_family_size':FAMILY_SIZE,'calibration_alpha':.05,'calibration_scope':'Simultaneous stratum-wise Clopper-Pearson bounds for additional false abstention under independent, identically distributed Bernoulli sampling within each stratum; official train/test shifts are evaluated empirically, not guaranteed','minimum_n_zero_failures':{str(eps):int(np.ceil(np.log(.05/FAMILY_SIZE)/np.log(1-eps))) for eps in [.01,.025,.05,.1]}},indent=2))
if __name__=='__main__':main()