Generate: handle text conditioning with multimodal encoder-decoder models
# What does this PR do?

Consolidates `decoder_input_ids` preparation changes in a single place, for all future multimodal encoder-decoder models on PT and TF.

In a nutshell, this PR generalizes the following use cases:
1. The user passes `decoder_input_ids`, but it is missing the BOS token (some tokenizers, like the T5 tokenizer, do not prepend a BOS token). In this case, a BOS token is prepended.
2. The user passes `input_ids`, but the encoder has no `input_ids` input. In this case, `input_ids` is handled just like `decoder_input_ids`.

Slow tests were run on T5, Pix2Struct, BLIP, and BLIP2.
cc @younesbelkada @NielsRogge FYI -- this PR consolidates your recent changes regarding text conditioning on multimodal models. The next models should be easier to add :) <|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>Thanks a lot @gante! 🙏 