from pathlib import Path
import sys,os,numpy as np,torch;import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
R=Path(__file__).resolve().parents[1];sys.path.insert(0,str(R));from model.grace_seda import *
c=cfg(R);rank=int(os.getenv('RANK',0));world=int(os.getenv('WORLD_SIZE',1));ddp=world>1
if ddp:dist.init_process_group('gloo')
d=np.load(R/c['data']['path']);states=[];hist=[]
for member in range(5):
 torch.manual_seed(c['seed']+member);base=GRACESEDA(**c['model']);m=DDP(base) if ddp else base;opt=torch.optim.Adam(m.parameters(),lr=c['train']['learning_rate']);ls=[]
 for i in range(rank,20,world):x=torch.tensor(d['input'][i:i+1]);loss=loss_fn(m(x),x);opt.zero_grad();loss.backward();opt.step();ls.append(float(loss))
 if rank==0:states.append(base.state_dict());hist.append(float(np.mean(ls)))
p=R/c['paths']['checkpoint']
if rank==0:p.parent.mkdir(parents=True,exist_ok=True);torch.save({'states':states,'model_config':c['model']},p);write(R/c['paths']['training_metrics'],{'ensemble_losses':hist,'world_size':world});print(p)
if ddp:dist.destroy_process_group()