Sampling from a set according to unnormalized log-probabilities in NumPy

I have a 1-D np.ndarray filled with unnormalized log-probabilities that define a categorical distribution. I would like to sample an integer index from this distribution. Since many of the probabilities are small, normalizing and exponentiating the log-probabilities introduces significant numerical error, therefore I cannot use np.random.choice. Effectively, I am looking for a NumPy equivalent to TensorFlow's tf.random.categorical, which works on unnormalized log-probabilities.

If there is not a function in NumPy that achieves this directly, what is an efficient manner to implement such sampling?