"""Windowed Master prediction: positive overlap weights, global phase0 output."""
import torch
from .master import window_starts

@torch.inference_mode()
def predict_features(model,features,device):
    tokens=len(features)//20
    if tokens==0:return torch.empty(0,3)
    raw=features[:tokens*20].reshape(tokens,20,12).transpose(1,2)
    total=torch.zeros(tokens,3);weight=torch.zeros(tokens)
    for start in window_starts(tokens):
        value=raw[start:start+640][None].to(device);n=value.shape[1]
        with torch.autocast(device_type=device.type,dtype=torch.bfloat16,enabled=device.type=='cuda'):
            pred=model(value,torch.tensor([n],device=device),torch.tensor([start],device=device))[0].float().cpu()
        i=torch.arange(n);w=torch.minimum(torch.minimum(i+1,n-i),torch.full((n,),160)).float()/160
        total[start:start+n]+=pred*w[:,None];weight[start:start+n]+=w
    if not (weight>0).all():raise ValueError('uncovered inference tokens')
    total=total/weight[:,None]
    windows=tokens//10
    return total[:windows*10].reshape(windows,10,3).mean(1)