T5 & mT5
# What does this PR do?

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This PR adds T5v1.1 and MT5.

I was really unsure whether I should make a new model file `modeling_5_v1_1.py` or not. I finally decided (also after discussions in https://github.com/huggingface/transformers/issues/6285) that in this case, it is better to add the few new T5 features to the existing `modeling_t5.py` file.

The context is the following:
The T5 team released weights for a **T5v1.1** model: https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md. These model architecture of T5v1.1 is equivalent to T5 besides two changes:
1) input and output word embedding of decoder is not shared anymore
2) different feed forward layer is used
In addition, the **mT5** model checkpoints were also released and are fully based on T5v1_1: https://github.com/google-research/multilingual-t5#released-model-checkpoints .

Now the philosophy of the library is to create a new model class if the architecture slightly differs from a previously integrated model, but I'd argue that in this case it is better to add a new config param called "feed_forward_proj" which defines whether a "relu" (T5) or a "gated-gelu" (T5v1.1) should be used. The arguments for not creating a new model class are the following:

1) Both T5 and T5v1.1 are "officially" the same model and both belong to the same code base: https://github.com/google-research/text-to-text-transfer-transformer
2) T5v1.1 has no official paper and it's quite difficult to find a good name as discussed in https://github.com/huggingface/transformers/issues/6285 => `T5v11` is a very cryptic name IMO and `T5v2` is not ideal either.
3) One could argue that it makes sense to create a mixture of T5 and T5v1v1 by sharing input and output word embedding of the decoder but using the "gated-gelu" feed-forward layer. This design would allow a user to define a model whereas creating a new T5v11 model  file would make this impossible
4) `"feed-forward-proj"` is less of a model-specific architecture configuration than `"do_blenderbot_90_layernorm"` IMO

The obvious disadvantage is that I am adding new architecture code to an existing model to make a new model (or new model version) work, which is kinda what we did not want to do. 

It's a tough decision here I think, so I'd be very happy for your feedback on this @LysandreJik @sgugger @thomwolf . I'd also understand if you guys think we should make a new model file here.

I already uploaded `google/t5-v1_1-small` and `google/mt5-small`. If you guys are ok with this PR, I'll add the TF code, add the new model to README.md and we're good for merge.
I'm in favor of this solution and happy you selected it :)

Our philosophy of simple to use and modify standalone code is made not to be a hard requirement but a guide for user-experience and in this case I also think the best is to adapt T5 as you do.<|||||>Big thanks for integrating mT5! I checked out your PR and there seems to be a problem with extra_ids. I guess, sentencepiece vocab already has <extra_id> tokens but T5Tokenizer goes on to add it's own.  E.g., real id of <external_id_0> is 250099 but T5Tokenizer translates it into 250199<|||||>> Big thanks for integrating mT5! I checked out your PR and there seems to be a problem with extra_ids. I guess, sentencepiece vocab already has <extra_id> tokens but T5Tokenizer goes on to add it's own. E.g., real id of <external_id_0> is 250099 but T5Tokenizer translates it into 250199

You're 100% correct - thanks a lot for letting me know! I corrected the behavior - should be good now: https://huggingface.co/google/mt5-small/tree/main<|||||>Ran the slow tests & added multiple new slow tests & checked the docs => good to merge.