"""Reproducible v0.2 multi-zone supplement; preserves v0.1 artifacts."""
import os
os.environ.setdefault('OPENBLAS_NUM_THREADS','1')
os.environ.setdefault('OMP_NUM_THREADS','1')
from pathlib import Path
import json,gzip,time
import numpy as np
import torch
from torch import nn
from safetensors.torch import save_file,load_file
from rod import Rod,Controls,serial

ROOT=Path(__file__).resolve().parents[1]
OUT=ROOT/'release/dataset/multizone-v02'
MODEL=ROOT/'release/model/multizone-v02'
def features(ks,chi,fx,fy):
    field=np.asarray(ks)[np.floor((np.arange(48)+.5)*len(ks)/48).astype(int)]
    return np.r_[field,fx/chi,fy/chi,np.log10(chi)],np.cumsum(field)/48
def network():
    return nn.Sequential(nn.Linear(51,96),nn.Tanh(),nn.Linear(96,96),nn.Tanh(),nn.Linear(96,48))
def xy(z,fx,fy):
    angle=(np.c_[np.zeros(len(z)),z[:,:-1]]+z)/2
    stretch=1+fx[:,None]*np.cos(angle)+fy[:,None]*np.sin(angle)
    ds=stretch[:,:,None]*np.stack([np.cos(angle),np.sin(angle)],axis=2)/48
    return np.concatenate([np.zeros((len(z),1,2)),np.cumsum(ds,axis=1)],axis=1)
def main():
    start=time.time();OUT.mkdir(parents=True,exist_ok=True);MODEL.mkdir(parents=True,exist_ok=True);(ROOT/'reports').mkdir(parents=True,exist_ok=True)
    rng=np.random.default_rng(20261015);rod=Rod(48);fine=Rod(96);rows=[];mesh=[];rejected=[]
    for m in [3,6,12,8]:
        total=200 if m!=8 else 80
        order=rng.permutation(total);splits={int(j):('train' if i<140 else 'validation' if i<170 else 'test') for i,j in enumerate(order)}
        for j in range(total):
            split=splits[j] if m!=8 else 'zone_ood';ks=rng.uniform(-2.4,2.4,m);chi=float(10**rng.uniform(-4,-2.5));fx=float(chi*rng.uniform(0,.6));fy=float(chi*rng.uniform(-.6,.6))
            for step,t in enumerate(np.linspace(0,1,6)):
                k=ks*t;c=Controls(chi=chi,fx=fx*t,fy=fy*t,curvatures=tuple(k));_,seed=features(k,chi,c.fx,c.fy);s=rod.solve(c,seed)
                row=dict(id=f'z{m}_{j:03d}_{step}',scene_id=f'z{m}_{j:03d}',split=split,zones=m,t=float(t),chi=chi,fx=c.fx,fy=c.fy,curvatures=k.tolist(),**serial(s))
                if not s['accepted'] or s['stability']!='stable':rejected.append(row);continue
                rows.append(row)
                if step==5 and j%20==0:
                    field=k[np.floor((np.arange(96)+.5)*m/96).astype(int)];f=fine.solve(c,np.cumsum(field)/96)
                    assert f['accepted'] and f['stability']=='stable'
                    mesh.append(dict(id=row['id'],tip_difference_over_L=float(np.linalg.norm(f['tip']-s['tip'])),centerline_rmse_over_L=float(np.sqrt(np.mean(np.sum((f['centerline']-s['centerline'])**2,axis=1))))))
        print('Generated zones',m,'states',len(rows),flush=True)
    for split in ['train','validation','test','zone_ood']:
        with gzip.open(OUT/f'{split}.jsonl.gz','wt') as f:
            for row in rows:
                if row['split']==split:f.write(json.dumps(row)+'\n')
    (OUT/'rejected.json').write_text(json.dumps(rejected))
    (OUT/'mesh-checks.json').write_text(json.dumps(mesh,indent=2))
    items=[features(r['curvatures'],r['chi'],r['fx'],r['fy']) for r in rows]
    x=np.array([a for a,b in items],np.float32);seed=np.array([b for a,b in items],np.float32);z=np.array([r['z'] for r in rows],np.float32)
    ix={s:np.array([i for i,r in enumerate(rows) if r['split']==s]) for s in ['train','validation','test','zone_ood']}
    mean=x[ix['train']].mean(0);std=x[ix['train']].std(0);std[std<1e-6]=1
    X=torch.from_numpy((x-mean)/std);Y=torch.from_numpy((z-seed)/.1)
    torch.set_num_threads(2);torch.manual_seed(15);net=network();opt=torch.optim.Adam(net.parameters(),lr=.0015);best=float('inf');best_epoch=0
    for epoch in range(201):
        net.train()
        for batch in np.array_split(rng.permutation(ix['train']),12):
            opt.zero_grad();loss=(net(X[batch])-Y[batch]).square().mean();loss.backward();opt.step()
        net.eval()
        with torch.no_grad():val=float((net(X[ix['validation']])-Y[ix['validation']]).square().mean())
        if val<best:best=val;best_epoch=epoch;weights={k:v.clone().contiguous() for k,v in net.state_dict().items()}
        if epoch%50==0:print('Epoch',epoch,'validation',val,flush=True)
    save_file(weights,str(MODEL/'residual.safetensors'));net.load_state_dict(load_file(str(MODEL/'residual.safetensors')))
    with torch.no_grad():pred=seed+.1*net(X).numpy()
    fx=np.array([r['fx'] for r in rows]);fy=np.array([r['fy'] for r in rows]);truth=xy(z,fx,fy)
    metrics={}
    for split,idx in ix.items():
        metrics[split]={}
        for name,zz in [('intrinsic_shape',seed),('learned_residual',pred)]:
            err=np.linalg.norm(xy(zz,fx,fy)[idx]-truth[idx],axis=2)
            metrics[split][name]={'centerline_rmse_over_L':float(np.sqrt(np.mean(err**2))),'tip_mean_over_L':float(err[:,-1].mean()),'tip_p95_over_L':float(np.quantile(err[:,-1],.95))}
            nonzero=np.array([rows[i]['t']>0 for i in idx]);metrics[split][name]['nonzero_loading_rmse_over_L']=float(np.sqrt(np.mean(err[nonzero]**2)))
    correction=[]
    for i in ix['test'][5::36]:
        r=rows[i];c=Controls(chi=r['chi'],fx=r['fx'],fy=r['fy'],curvatures=tuple(r['curvatures']))
        corrected=rod.solve(c,pred[i]);assert corrected['accepted']
        correction.append(dict(id=r['id'],residual=corrected['residual'],tip_error_over_L=float(np.linalg.norm(corrected['tip']-r['tip']))))
    report=dict(version='0.2',states=len(rows),scenes=len(set(r['scene_id'] for r in rows)),counts={s:len(i) for s,i in ix.items()},zones_id=[3,6,12],zones_ood=[8],rejected=len(rejected),mesh_checks=len(mesh),max_mesh_tip_difference_over_L=max(r['tip_difference_over_L'] for r in mesh),best_epoch=best_epoch,metrics=metrics,seconds=time.time()-start)
    report['mechanics_correction_checks']=correction
    (MODEL/'config.json').write_text(json.dumps(dict(mean=mean.tolist(),std=std.tolist(),output_scale=.1,n=48,architecture=[51,96,96,48],features='48 piecewise intrinsic curvatures, fx/chi, fy/chi, log10(chi)',prediction='cumulative intrinsic angle + output_scale * network(standardized features)'),indent=2))
    for p in [MODEL/'evaluation.json',OUT/'validation.json',ROOT/'reports/multizone-v02.json']:p.write_text(json.dumps(report,indent=2))
    print(json.dumps(report,indent=2),flush=True)
if __name__=='__main__':main()