Data augmentation does not replace balancing.
When you apply augmentation techniques to your data samples, you would apply e.g. random transformations to your images. While this randomizes the data domain, it does not change the target in a classification use case.
If you are applying the WeightedRandomSampler, each batch will be created using the passed weights to the sampler, so that in a balancing use case, each batch should contain approx. the same amount of samples for each class.
Since both approaches do not interfere with each other, you could apply them together.