"""Budget-bounded architecture/curriculum screening. Sources pinned by bootstrap."""
import os,sys,json,time,math,random,hashlib,shutil,traceback,copy,io
from pathlib import Path
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from PIL import Image,ImageDraw
from skimage.color import rgb2lab
import cv2,pyarrow as pa,pyarrow.parquet as pq
from huggingface_hub import HfApi,hf_hub_download
from safetensors.torch import load_file
from semantic_model import load_semantic
from decision_model import DecisionColorizer,initialize_from_v3,save_decision,load_decision
import persistence
import r7 as r
from data import decode_image

REPO='User-2468/mini-unet-colorizer'
BASE='1a9eb8af2754ad2329a24cfe50d388cb559441d0'
ARTIFACTS='12dfd8111622f692a7f69d0a961ce9b65f044c01'
CACHE_REV='7b10485ee9d205ce2f7b660dfc6a90012d78b1cb'
COCO_REV='26ddc382fe75dfc2a0655b5977e296ea10efebce'
PREFIX=os.environ.get('EXPERIMENT_PREFIX','experiments/final-20260929')
SEED=int(os.environ.get('SEED','9292026'))
STEPS=int(os.environ.get('STEPS','24000'));BATCH=24
START=time.monotonic();TRAIN_LIMIT=float(os.environ.get('TRAIN_LIMIT_SECONDS','3000'))
OUT=Path('/work/results');OUT.mkdir(exist_ok=True,parents=True)
persistence.PREFIX=PREFIX;r.OUT=OUT;r.ev.OUT=OUT;r.DEVICE='cuda';r.ev.DEVICE='cuda';r.ev.deadline=lambda:None
api=HfApi();DURABLE=None
def write(n,obj):
    p=OUT/n;p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(obj,indent=2));return obj
def dl(f,rev=ARTIFACTS):return Path(hf_hub_download(REPO,f,revision=rev))
def seed(s):torch.manual_seed(s);np.random.seed(s);random.seed(s)

def objective(details,target,step):
    allout=details['all'];b,k=allout.shape[:2]
    losses=r.losses(allout.flatten(0,1),target[:,None].expand(-1,k,-1,-1,-1).flatten(0,1)).reshape(b,k)
    if k==1:return losses.mean(),losses.argmin(1),losses
    # One winning complete image, never a per-pixel best-of-K patchwork.
    winner=losses.detach().argmin(1)
    selected=losses.gather(1,winner[:,None]).mean()
    # Small nonwinner term prevents completely untrained branches; no saturation reward.
    loss=.95*selected+.05*losses.mean()+.1*F.cross_entropy(details['scores'],winner)
    return loss,winner,losses

class Batches:
    def __init__(self,rgb,teacher):
        self.rgb=torch.from_numpy(rgb).cuda().permute(0,3,1,2)
        self.teacher=torch.from_numpy(teacher).cuda().float()
        self.n=len(rgb)
    @staticmethod
    def light(g):
        y=torch.where(g<=.04045,g/12.92,((g+.055)/1.055).pow(2.4))
        l=torch.where(y>.008856,116*y.clamp_min(1e-9).pow(1/3)-16,903.296296*y)
        return l/50-1
    def batch(self,step,recipe,original,curriculum_step=None):
        phase=step if curriculum_step is None else curriculum_step
        gen=torch.Generator(device='cuda').manual_seed(SEED*100000+step)
        ids=torch.randint(self.n,(BATCH,),generator=gen,device='cuda')
        rgb=self.rgb[ids].float()/255;t=self.teacher[ids];o=original[ids]
        g=(rgb*rgb.new_tensor([.299,.587,.114])[None,:,None,None]).sum(1,keepdim=True)
        # Emulate standard 8-bit PIL grayscale conversion before Lab conversion.
        g=(g*255).round()/255
        L=self.light(g)
        if recipe=='staged' and phase>STEPS*2/3:
            take=torch.rand(BATCH,1,1,1,generator=gen,device='cuda')<.35
            weights=torch.rand(BATCH,3,1,1,generator=gen,device='cuda')+rgb.new_tensor([.8,1.2,.2])[None,:,None,None]
            weights/=weights.sum(1,keepdim=True)
            film=(rgb*weights).sum(1,keepdim=True)
            gamma=.85+.4*torch.rand(BATCH,1,1,1,generator=gen,device='cuda')
            film=(film.pow(gamma)-.5)*(.8+.3*torch.rand(BATCH,1,1,1,generator=gen,device='cuda'))+.5
            film=film+.005*torch.randn(film.shape,generator=gen,device='cuda')
            L=torch.where(take,self.light(film.clamp(0,1)),L)
        else:
            gain=.9+.2*torch.rand(BATCH,1,1,1,generator=gen,device='cuda')
            L=(L*gain).clamp(-1,1)
        flips=torch.rand(BATCH,1,1,1,generator=gen,device='cuda')<.5
        L=torch.where(flips,L.flip(-1),L);t=torch.where(flips,t.flip(-1),t);o=torch.where(flips,o.flip(-1),o)
        prob=0 if recipe=='teacher' else .5
        if recipe=='staged':prob*=min(max((phase/STEPS-.15)/.35,0),1)
        pick=torch.rand(BATCH,1,1,1,generator=gen,device='cuda')<prob
        # Stochastic full targets: never choose the easier one, never average hues.
        target=torch.where(pick,o,t)
        return L,target,ids,float(pick.float().mean())

def summary(result):return {s:result[s]['summary'] for s in ['gray','film']}
def score(result,baseline):
    val=0;okay=True
    for style in ['gray','film']:
        s=result[style]['summary'];b=baseline[style]['summary']
        okay &= s['missed_color']<=b['missed_color']+.015 and s['neutral_spill']<=b['neutral_spill']+.015 and s['color_coverage']>=.95*b['color_coverage'] and s['ab_error']<=b['ab_error']*1.10
        val+=s['patch_excess']/max(b['patch_excess'],1e-6)+.2*s['ab_error']/b['ab_error']+2*s['missed_color']+s['neutral_spill']
    return val/2,bool(okay)

@torch.inference_mode()
def diagnostics(model,table,ids):
    model.eval();rows=[]
    for idx in ids:
        L,t,_=r.ev.gray_arrays(decode_image(table,idx));x=torch.from_numpy(L)[None].cuda()
        v=model(x,True);a=v['all'];sel=int(v['scores'].argmax(1))
        resized=model(F.interpolate(x,(224,224),mode='bilinear',align_corners=False),True)
        p=F.interpolate(resized['output'],(256,256),mode='bilinear',align_corners=False)
        flip=model(x.flip(-1),True)
        colors=a[0].mean((-1,-2));k=model.modes
        diff=(a[:,:,None]-a[:,None]).abs().mean((0,3,4,5))
        target=torch.from_numpy(t)[None].cuda();target64=F.avg_pool2d(target,4)
        losses=r.losses(a.flatten(0,1),target64.expand(k,-1,-1,-1))
        mask=v['weights'].clamp_min(1e-8)
        rows.append({'index':int(idx),'selected_mode':sel,'resized_mode':int(resized['scores'].argmax(1)),
            'flip_mode':int(flip['scores'].argmax(1)),'resize_ab_change':float((p-v['output']).abs().mean()),
            'flip_ab_change':float((flip['output'].flip(-1)-v['output']).abs().mean()),
            'mode_diversity':float(diff.sum()/max(k*(k-1),1)),
            'default_target_loss':float(losses[sel]),'oracle_target_loss':float(losses.min()),
            'mask_entropy':float(-(mask*mask.log()).sum(1).mean()),
            'palette_chroma':float(v['palette'].norm(dim=-1).mean()),'output_chroma':float(v['output'].norm(dim=1).mean())})
    keys=['resize_ab_change','flip_ab_change','mode_diversity','default_target_loss','oracle_target_loss','mask_entropy','palette_chroma','output_chroma']
    return {'rows':rows,'summary':{k:float(np.mean([x[k] for x in rows])) for k in keys},
        'mode_frequency':np.bincount([x['selected_mode'] for x in rows],minlength=model.modes).tolist(),
        'resize_switch_fraction':float(np.mean([x['selected_mode']!=x['resized_mode'] for x in rows])),
        'note':'Oracle uses unknown original only for diagnosis. Default mode is selected by the learned image-only score, never by ground truth.'}

def paired_bootstrap(result,base):
    rng=np.random.default_rng(937);out={}
    for style in ['gray','film']:
        br={z['index']:z for z in base[style]['per_image']};rows=[z for z in result[style]['per_image'] if z['target_chroma']>=5]
        out[style]={}
        for key in ['patch_excess','raw_patch_excess','missed_color','neutral_spill','color_coverage','ab_error']:
            d=np.array([z[key]-br[z['index']][key] for z in rows]);boot=d[rng.integers(len(d),size=(3000,len(d)))].mean(1)
            out[style][key]={'delta':float(d.mean()),'ci95':np.quantile(boot,[.025,.975]).tolist()}
    return out

@torch.inference_mode()
def pipeline_grid(models,table,ids,label):
    from inference import colorize
    from PIL import ImageOps
    names=list(models);order=np.random.default_rng(9184).permutation(len(names))
    key={chr(65+j):names[int(i)] for j,i in enumerate(order)};write(label+'_key.json',key)
    for start in range(0,min(32,len(ids)),4):
        canvas=Image.new('RGB',(320*(len(names)+1),344*len(ids[start:start+4])),'white');d=ImageDraw.Draw(canvas)
        for row,idx in enumerate(ids[start:start+4]):
            original=decode_image(table,idx).convert('L').convert('RGB')
            original.thumbnail((800,800))
            d.text((2,row*344),f'{idx}: grayscale | '+' | '.join(key),fill='black')
            canvas.paste(ImageOps.pad(original,(320,320),color='white'),(0,row*344+24))
            for col,j in enumerate(order):
                output=colorize(models[names[int(j)]].eval(),original,size=256,radius=8)
                canvas.paste(ImageOps.pad(output,(320,320),color='white'),((col+1)*320,row*344+24))
        canvas.save(OUT/f'{label}_{start//4}.jpg',quality=93)

@torch.inference_mode()
def resolution_grid(model,table,records):
    from inference import colorize
    from PIL import ImageOps
    for start in range(0,len(table),3):
        count=min(3,len(table)-start)
        canvas=Image.new('RGB',(1280,344*count),'white');d=ImageDraw.Draw(canvas)
        for row,idx in enumerate(range(start,start+count)):
            im=decode_image(table,idx).convert('L').convert('RGB');im.thumbnail((1000,1000))
            d.text((2,row*344),records[idx]['name']+' | input | 256 | 384 | 512',fill='black')
            canvas.paste(ImageOps.pad(im,(320,320),color='white'),(0,row*344+24))
            for col,size in enumerate([256,384,512],1):
                pred=colorize(model,im,size=size,radius=8)
                canvas.paste(ImageOps.pad(pred,(320,320),color='white'),(col*320,row*344+24))
        canvas.save(OUT/f'v3_resolution_{start//3}.jpg',quality=94)

def main():
    global DURABLE
    assert api.whoami()['name']=='User-2468'
    DURABLE=persistence.DurableRun(OUT)
    shutil.copytree('/work/source',OUT/'source',dirs_exist_ok=True,ignore=shutil.ignore_patterns('__pycache__'))
    shutil.copy2('/work/PROTOCOL.md',OUT/'PROTOCOL.md')
    if Path('/work/REVIEW_HANDOFF.md').exists():shutil.copy2('/work/REVIEW_HANDOFF.md',OUT/'REVIEW_HANDOFF.md')
    if Path('/work/supplement').exists():shutil.copytree('/work/supplement',OUT/'supplement',dirs_exist_ok=True)
    write('status.json',{'status':'preflight','seed':SEED,'production_approved':False})
    DURABLE.sync('source and write verification before training')
    torch.set_num_threads(6);seed(SEED)
    torch.backends.cuda.matmul.allow_tf32=False;torch.backends.cudnn.allow_tf32=False
    initial=Path('/work/initial');initial.mkdir(exist_ok=True)
    for f in ['model.safetensors','config.json']:shutil.copy2(dl(f,BASE),initial/f)
    baseline=load_semantic(initial,'cuda').requires_grad_(False)
    counts={};smokes={}
    encoder_rev=None
    write('encoder_provenance.json',{'backbone':'MobileNetV3 Large','initial_revision':ARTIFACTS,'initial_subfolder':'experiments/decision-20260928-v2/multi_mix/candidate'})
    constructors=[('multi',4,'mobilenet')]
    for name,k,backbone in constructors:
        seed(SEED);m=DecisionColorizer(modes=k,backbone=backbone,pretrained=backbone=='mobilevit',encoder_revision=encoder_rev if backbone=='mobilevit' else None).cuda()
        copied=initialize_from_v3(m,baseline)
        seedpath=Path('/work/pilot');seedpath.mkdir(exist_ok=True)
        for f in ['config.json','model.safetensors']:shutil.copy2(dl('experiments/decision-20260928-v2/multi_mix/candidate/'+f),seedpath/f)
        m=load_decision(seedpath,'cuda');m.eval();count=sum(p.numel() for p in m.parameters());assert count<4_000_000
        counts[name]=count
        x=torch.rand(2,1,97,113,device='cuda')*2-1
        with torch.no_grad():
            v=m(x,True);assert v['all'].shape==(2,k,2,97,113) and torch.isfinite(v['all']).all()
            parity=float((v['output']-baseline(x)).abs().max()) if name=='single' else None
            if parity is not None:assert parity<1e-3,parity
        loss,_,_=objective(m(x,True),torch.randn(2,2,64,64,device='cuda'),1)
        loss.backward();assert torch.isfinite(loss) and all(torch.isfinite(p.grad).all() for p in m.parameters() if p.grad is not None)
        m.zero_grad(set_to_none=True);save_decision(m,OUT/'initial'/name)
        re=load_decision(OUT/'initial'/name,'cuda')
        with torch.no_grad():assert torch.allclose(m(x),re(x),atol=1e-4)
        smokes[name]={'parameters':count,'copied_tensors':len(copied),'odd_shape':True,'finite_backward':True,'reload':True,'baseline_max_abs':parity}
        del m,re
    write('smoke.json',smokes);DURABLE.sync('all three architectures pass preflight')
    torch.backends.cuda.matmul.allow_tf32=True;torch.backends.cudnn.allow_tf32=True;torch.backends.cudnn.benchmark=True
    tables=[]
    for repo,rev,files in [('johnowhitaker/imagenette2-320','771c1310a2487e8076ede6b7d6307244aa8400af',['default/train/0000.parquet']),('detection-datasets/coco',COCO_REV,['default/train/0000.parquet','default/train/0001.parquet'])]:
        for f in files:
            print('PREP downloading',repo,f,flush=True)
            tables.append(pq.read_table(hf_hub_download(repo,f,repo_type='dataset',revision=rev),columns=['image']))
            print('PREP loaded',repo,f,len(tables[-1]),flush=True)
    table=pa.concat_tables(tables)
    full_manifest=json.loads(dl('experiments/region-coherence-20260928/source/previous_manifest.json').read_text())
    full_ids=full_manifest['train']
    # Same fixed training subset for every arm, independent of new dev/test.
    ids=list(full_ids);pos={i:j for j,i in enumerate(ids)}
    teacher=np.empty((len(ids),2,64,64),np.float16)
    sums=json.loads(dl('experiments/round6-20260928/SHA256SUMS.json',CACHE_REV).read_text())
    for start in range(0,len(full_ids),2048):
        f=f'teacher_cache/{start:05d}.npz';p=dl('experiments/round6-20260928/'+f,CACHE_REV)
        assert hashlib.sha256(p.read_bytes()).hexdigest()==sums[f]
        with np.load(p) as z:
            # NpzFile indexing decompresses a member on every access. Materialize once.
            cache_ids=z['indices'];cache_targets=z['target']
            assert cache_ids.tolist()==full_ids[start:start+len(cache_ids)]
            src=[j for j,i in enumerate(cache_ids) if int(i) in pos]
            dst=[pos[int(cache_ids[j])] for j in src]
            teacher[dst]=cache_targets[src]
            print('PREP cache',start,'selected',len(src),flush=True)
    print('PREP decoding training images',len(ids),flush=True)
    rgb=np.stack([np.asarray(decode_image(table,i).resize((256,256),Image.Resampling.BILINEAR)) for i in ids])
    original=np.stack([cv2.resize(rgb2lab(im.astype(np.float32)/255)[...,1:],(64,64),interpolation=cv2.INTER_AREA).transpose(2,0,1) for im in rgb]).astype(np.float32)
    batcher=Batches(rgb,teacher);original=torch.from_numpy(original).cuda();del rgb,teacher
    print('PREP training tensors ready',flush=True)
    fresh=pq.read_table(hf_hub_download('detection-datasets/coco','default/val/0001.parquet',repo_type='dataset',revision=COCO_REV),columns=['image'])
    exclude=set()
    for path,keys in [('decision-20260928-v2',['development','test']),('round7-20260928',['development','heldout']),('region-coherence-20260928',['development_regression','heldout_regression','new_holdout']),('recurrent-refinement-20260928',['fresh_holdout','development_regression'])]:
        manifest=json.loads(dl('experiments/'+path+'/manifest.json').read_text())
        for key in keys:exclude.update(manifest[key])
    hashes={hashlib.sha256(table['image'][i].as_py()['bytes']).digest() for i in full_ids}
    freshids=[int(i) for i in np.random.default_rng(SEED).permutation(len(fresh)) if int(i) not in exclude and hashlib.sha256(fresh['image'][int(i)].as_py()['bytes']).digest() not in hashes][:288]
    assert len(freshids)==288
    dev,test=freshids[:128],freshids[128:]
    write('manifest.json',{'train':ids,'development':dev,'test':test,'prior_eval_excluded':sorted(exclude),'coco_revision':COCO_REV,'fresh_file':'default/val/0001.parquet','teacher_revision':CACHE_REV,'base_revision':BASE,'seed':SEED,'near_duplicates_and_upstream_overlap_not_excluded':True})
    base_dev=r.ev.evaluate(baseline,fresh,dev);write('baseline_development.json',base_dev)
    DURABLE.sync('dataset checks and unseen development/test split frozen')
    arms=[('multi','mix')]
    if os.environ.get('ARMS'):arms=[tuple(x.split(':')) for x in os.environ['ARMS'].split(',')]
    selected={};histories={};dev_results={};begin_train=time.monotonic()
    for head,recipe in arms:
        name=head+'_'+recipe;seed(SEED)
        m=load_decision(OUT/'initial'/head,'cuda')
        # New encoder gets a separate alignment warmup, explicitly outside matched six arms.
        warmup=1000 if head=='mobilevit' else 0
        groups=[{'params':[p for n,p in m.named_parameters() if n.startswith('encoder.')],'lr':2e-5 if head=='mobilevit' else 2e-6},
                {'params':[p for n,p in m.named_parameters() if not n.startswith('encoder.')],'lr':2e-4 if head=='mobilevit' else 2e-5}]
        opt=torch.optim.AdamW(groups,weight_decay=.01)
        for g in opt.param_groups:g['base_lr']=g['lr']
        history=[];beststep=0;last=0;arm_start=time.monotonic();mode_counts=np.zeros(m.modes,np.int64);stale=0
        initial_res=r.ev.evaluate(m,fresh,dev);val,okay=score(initial_res,base_dev);best=val+(0 if okay else 100)
        save_decision(m,OUT/name/'candidate');dev_results[name]=initial_res
        write(name+'/development_00000.json',initial_res);DURABLE.sync('retain initial pilot candidate before continuation')
        for s in range(1,STEPS+warmup+1):
            if time.monotonic()-START>TRAIN_LIMIT:break
            step=max(1,s-warmup);active_recipe='teacher' if s<=warmup else recipe
            L,target,_,fraction=batcher.batch(s,active_recipe,original,curriculum_step=step)
            m.train()
            for layer in m.encoder.modules():
                if isinstance(layer,nn.BatchNorm2d):layer.eval()
            opt.zero_grad(set_to_none=True)
            factor=min(s/150,1)*(.15+.85*(1+math.cos(math.pi*max(0,s-warmup)/STEPS))/2)
            for g in opt.param_groups:g['lr']=g['base_lr']*factor
            with torch.autocast('cuda',dtype=torch.bfloat16):
                details=m(L,True)
                loss,winners,terms=objective(details,target,step)
                consistency=loss.new_zeros(())
                if active_recipe=='staged' and step>STEPS*2/3 and s%4==0:
                    small=m(F.interpolate(L,(192,192),mode='bilinear',align_corners=False),True)
                    a=F.adaptive_avg_pool2d(small['all'].flatten(0,1).float(),(64,64))
                    b=F.adaptive_avg_pool2d(details['all'].flatten(0,1).detach().float(),(64,64))
                    consistency=F.l1_loss(a/20,b/20)
                    if m.modes>1:consistency+=.1*F.kl_div(small['scores'].log_softmax(1),details['scores'].detach().softmax(1),reduction='batchmean')
                    loss=loss+.4*consistency # every fourth step -> mean weight .1
            assert torch.isfinite(loss),name
            loss.backward();torch.nn.utils.clip_grad_norm_(m.parameters(),1,error_if_nonfinite=True);opt.step();last=s
            mode_counts+=np.bincount(winners.detach().cpu().numpy(),minlength=m.modes)
            if s%100==0:
                row={'step':s,'loss':float(loss.detach()),'reconstruction':float(terms.mean().detach()),'consistency':float(consistency.detach()),'photo_fraction_this_batch':fraction,'winner_counts':mode_counts.tolist(),'seconds':time.monotonic()-arm_start}
                history.append(row);print('STEP',name,s,round(row['loss'],4),flush=True)
            if s%2000==0 or s==STEPS+warmup:
                m.eval();res=r.ev.evaluate(m,fresh,dev);val,okay=score(res,base_dev)
                write(name+f'/development_{s:05d}.json',res)
                write(name+'/history.json',history)
                save_decision(m,OUT/name/f'step_{s:05d}')
                # Ineligible models still retained for diagnosis, ranked after eligible.
                ranked=val+(0 if okay else 100)
                if ranked<best-.002:
                    stale=0;best=ranked;beststep=s;save_decision(m,OUT/name/'candidate');dev_results[name]=res
                if ranked>=best-.002 and beststep!=s:stale+=1
                write('status.json',{'status':'training','arm':name,'step':s,'elapsed_seconds':time.monotonic()-START,'production_approved':False})
                DURABLE.sync(name+' checkpoint '+str(s))
                if stale>=6:
                    print('EARLY_STOP development score unchanged for six evaluations',flush=True);break
        if not last:raise RuntimeError('Budget exhausted before any final training update')
        write(name+'/history.json',history)
        torch.save({'optimizer':opt.state_dict(),'step':last,'seed':SEED,'torch_rng':torch.get_rng_state(),'cuda_rng':torch.cuda.get_rng_state_all(),'exact_resume':False},OUT/name/'optimizer.pt')
        selected[name]={'step':beststep,'rank_score':best,'parameters':counts[head],'steps_completed':last,'warmup_steps':warmup,'seconds':time.monotonic()-arm_start}
        write('candidates.json',selected);DURABLE.sync(name+' selected checkpoint on development only')
        del m,opt;torch.cuda.empty_cache()
        if last<STEPS+warmup:break
    # Test is opened only after all checkpoint choices have been frozen.
    write('candidates.json',selected);DURABLE.sync('freeze candidates before opening final test')
    models={'v3':baseline};test_results={}
    base_test=r.ev.evaluate(baseline,fresh,test);write('v3_test.json',base_test);test_results['v3']=base_test
    for name in selected:
        m=load_decision(OUT/name/'candidate','cuda');models[name]=m
        res=r.ev.evaluate(m,fresh,test);test_results[name]=res
        write(name+'_test.json',res);write(name+'_test_paired.json',paired_bootstrap(res,base_test))
        write(name+'_diagnostics.json',diagnostics(m,fresh,test[:48]))
        r.ev.probes(m,name+'_probes')
        x=torch.zeros(1,1,256,256,device='cuda')
        with torch.inference_mode():
            for _ in range(5):m(x)
            torch.cuda.synchronize();t0=time.monotonic()
            for _ in range(30):m(x)
            torch.cuda.synchronize()
        write(name+'_latency.json',{'milliseconds':1000*(time.monotonic()-t0)/30,'hardware':torch.cuda.get_device_name(),'size':256,'precision':'FP32'})
        DURABLE.sync(name+' final test and diagnostic outputs')
    r.ev.probes(baseline,'v3_probes')
    # Smaller comparison groups preserve enough image detail to inspect.
    for head in ['single','multi']:
        group={n:m for n,m in models.items() if n=='v3' or n.startswith(head+'_')}
        r.grid(group,fresh,test,head+'_test')
    if 'mobilevit_staged' in models:r.grid({'v3':baseline,'single_staged':models['single_staged'],'mobilevit_staged':models['mobilevit_staged']},fresh,test,'encoder_test')
    # Historical originals deliberately never serve as neutral-color supervision.
    archive=json.loads(Path('/work/archive_sources.json').read_text())
    import requests
    records=[];images=[]
    for entry in archive:
        try:
            response=requests.get(entry['image_url'],timeout=20);response.raise_for_status()
            im=Image.open(io.BytesIO(response.content)).convert('RGB');im.load()
            images.append({'bytes':response.content,'path':None});records.append(entry|{'sha256':hashlib.sha256(response.content).hexdigest()})
        except Exception as e:records.append(entry|{'failed':type(e).__name__})
    if images:
        ar=pa.table({'image':images})
        resolution_grid(baseline,ar,[x for x in records if 'failed' not in x])
        for head in ['single','multi']:
            r.grid({n:m for n,m in models.items() if n=='v3' or n.startswith(head+'_')},ar,list(range(len(images))),head+'_archive')
        if 'mobilevit_staged' in models:r.grid({'v3':baseline,'mobilevit_staged':models['mobilevit_staged']},ar,list(range(len(images))),'encoder_archive')
        pipeline_grid({'v3':baseline,**{n:m for n,m in models.items() if n!='v3'}},ar,list(range(len(images))),'production_archive')
    pipeline_grid({'v3':baseline,**{n:m for n,m in models.items() if n!='v3'}},fresh,test[:16],'production_test')
    write('archive_sources.json',records)
    write('summary.json',{'parameters':counts,'candidates':selected,'test':{n:summary(v) for n,v in test_results.items()},'all_arms_completed':len(selected)==len(arms),'planned_max_steps':STEPS,'elapsed_seconds':time.monotonic()-START,'estimated_compute_usd':(time.monotonic()-START)/3600*.8,'warning':'No metric proves realism. All outputs need visual review. Candidate can be the retained step0 pilot; selection does not automatically approve production deployment.'})
    write('status.json',{'status':'completed','all_arms_completed':len(selected)==len(arms),'planned_max_steps':STEPS,'elapsed_seconds':time.monotonic()-START,'production_approved':False})
    DURABLE.sync('architecture and curriculum suite completed')

if __name__=='__main__':
    try:main()
    except BaseException:
        msg=traceback.format_exc();token=os.environ.get('HF_TOKEN')
        if token:msg=msg.replace(token,'[REDACTED]')
        write('failure.json',{'traceback':msg});write('status.json',{'status':'failed','elapsed_seconds':time.monotonic()-START,'production_approved':False})
        if DURABLE:
            try:DURABLE.sync('failure report and saved checkpoints')
            except Exception:pass
        print(msg,flush=True);raise SystemExit(1)