Generate: slow assisted generation test
# What does this PR do?

`test_assisted_decoding_matches_greedy_search` fails once in a while, which blocks development. This PR removes the blocker by moving it to a slow test. 

Why a slow test (and not redesign the test or add the flaky decorator)?
1. It is impossible to remove at 100% the non-determinism in this test. Some form of masking has to be used by design, which means that there is always a chance for the generations to diverge. When the generated sequences do diverge, the scores should be very similar at the step they diverge, as they are caused by the very small values within the numerical attention masks.
2. I've tried to add the check above (when the sequences diverge, the scores should be similar)... but some models still failed that check quite hard when the sequences didn't match. Some well-established models can run it without observed failures (e.g. 10k runs on GPT2 = 0 sequence mismatches). Others, especially roberta-based models, fail at a high rate. This means I should explore this further.
3. Since there is something to be explored, I believe the slow decorator is more appropriate: we can track failures without risking red CI on PRs.

_The documentation is not available anymore as the PR was closed or merged._<|||||>> Maybe the test will be less flaky if done on a pretrained checkpoint

Definitely! However, I think there is a deeper problem here, the logits diverge way more than I'd expect on some models, and it's odd that those models rely on the same base code (roberta). After I finish preparing the release for assisted generation, I'll get back to sorting related bugs