import torch
from safetensors.torch import load_file

def load_model(path='model.safetensors'):
    return load_file(path)

def priority_encode(i3, i2, i1, i0, weights):
    """4-to-2 priority encoder. Returns (y1, y0, valid)."""
    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
    v = int((inp @ weights['v.weight'].T + weights['v.bias'] >= 0).item())
    return y1, y0, v

if __name__ == '__main__':
    w = load_model()
    print('Priority Encoder 4')
    print('i3 i2 i1 i0 | y1 y0 v | highest')
    print('-' * 35)
    for val in range(16):
        i3, i2, i1, i0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
        y1, y0, v = priority_encode(i3, i2, i1, i0, w)
        highest = 'none' if v == 0 else f'i{2*y1 + y0}'
        print(f' {i3}  {i2}  {i1}  {i0}  |  {y1}  {y0} {v} | {highest}')