import torch
from safetensors.torch import load_file

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

def binary_to_onehot(a1, a0, weights):
    """Convert 2-bit binary to 4-bit one-hot encoding."""
    inp = torch.tensor([float(a1), float(a0)])
    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
    y2 = int((inp @ weights['y2.weight'].T + weights['y2.bias'] >= 0).item())
    y3 = int((inp @ weights['y3.weight'].T + weights['y3.bias'] >= 0).item())
    return y0, y1, y2, y3

if __name__ == '__main__':
    w = load_model()
    print('Binary to One-Hot Encoder (2-to-4):')
    print('a1 a0 | y0 y1 y2 y3 | value')
    print('------+-------------+------')
    for val in range(4):
        a1, a0 = (val >> 1) & 1, val & 1
        y0, y1, y2, y3 = binary_to_onehot(a1, a0, w)
        print(f' {a1}  {a0} |  {y0}  {y1}  {y2}  {y3} |   {val}')