Only resize embeddings when necessary
# What does this PR do?

As seen in #19959, using our examples with models where the embedding size is larger than the tokenizer for padding reasons (to get embedding dim a multiple of a give number like 8 or 128), the fine-tuned models become incompatible with the original model.

This has confused a lot of users, just to support a full pretraining example. This is why this PR proposes to only resize the embeddings when the tokenizer has more tokens. This might in turn confuse users that do a full pretraining on a small vocab and expect the model to have a smaller embedding size, which is why a comment is added.

Fixes #19959
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