Resampling for imbalaced datasets: should testing set also be resampled?
You are right! As you said, the final goal of the model is to work with real life data distribution, therefor the test data has to stay unchanged.  

The goal of resampling is not to have the same number of objects from all classes, but to fit model to work with your data. So an intuitive example might be: if you have k objects of the first class and 5*k of the second, but your model learn data well, then resampling is not necessary. It's the reason, why we shouldn't use it during test (we have already trained model) 

One more thing that could be useful to remember is that F1 score could be calculate in multiclass task in different ways (it could account label imbalance), here is an example from sklearn https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html