Does BERT use GLoVE?
BERT cannot use GloVe embeddings, simply because it uses a different input segmentation. GloVe works with the traditional word-like tokens, whereas BERT segments its input into subword units called word-pieces. On one hand, it ensures there are no out-of-vocabulary tokens, on the other hand, totally unknown words get split into characters and BERT probably cannot make much sense of them either. 

Anyway, BERT learns its custom word-piece embeddings jointly with the entire model. They cannot carry the same type of semantic information as word2vec or GloVe because they are often only word fragments and BERT needs to make sense of them in the later layers.

You might say that inputs are one-hot vectors if you want, but as almost always, it is just a useful didactic abstraction. All modern deep learning frameworks implement embedding lookup just by direct indexing, multiplying the embedding matrix with a one-hot-vector would be just wasteful.