Fix a bunch of slow tests
This PR fixes a bunch of slow tests.

DPR had a few issues, which this PR fixes. There was an issue where the `DPRReader` object was not using `token_type_ids`, but for some unknown reason the interaction with the underlying `TFBertMainLayer` that requires those crashed when using the oh-so-terrifying `tf.saved_model.save`.

I chose to add the `token_type_ids` to that model, as imo an additional feature is not a bad idea, even if it wasn't in the original model. I can imagine a bunch of reasons why users might want to have `token_type_ids` in that model even though it doesn't exist now. 

All in all, after an hour of debugging I feel that this is the only way to have the slow `tf.saved_model.save` test passing on TFDPR. @patrickvonplaten @lhoestq please tell me what you think.
Good for me - thanks a lot for taking care of it! 

It would probably all save us a lot of time to find once and for all a good solution for the `test_saved_model_with_attentions_output` and `test_saved_model_with_hidden_states_output` functions. I've spent way too much time trying to fix those for TFT5 as well and without finding a good solution. If you have a good idea of how to deal with this functionality/test in the future let me know @LysandreJik :-) 

@sgugger - not sure where the `MODIFY` statements are coming from...I think we can delete it along with `return_dict=True` now<|||||>@patrickvonplaten I tried but then test failed ;-)<|||||>> @patrickvonplaten I tried but then test failed ;-)

Hmm maybe @lhoestq has an idea<|||||>The test_modeling_dpr was added recently in #8203 
Maybe @ratthachat knows why the `# MODIFY` are there ?
We should indeed remove them

Also I'm ok with adding token_type_ids since it's a common additional input to models based on bert<|||||>Hi guys, first of all I apologize if there's a problem at the `MODIFY` tag which is about `return_dict` argument.

I translated `test_modeling_tf_dpr` from the Pytorch's one.  
If I remember correctly, I found out that there's some tests in `test_modeling_tf_common.py` 
need `return_dict=False` argument. 
(and when I looked at the tests, I judged that all tests just need to ensure the correct values of output,
not about `return_dict` argument.) 
That's why I changed the config to `return_dict=False` as default, and left the `MODIFY` comments 
just to note that this part was modified from the Pytorch's one. 
(Again, I thought the main tests are on outputs' values)

It's my first time to write this kind of test file here, so I apologize again if I made something wrong!<|||||>> Hi guys, first of all I apologize if there's a problem at the `MODIFY` tag which is about `return_dict` argument.
> 
> I translated `test_modeling_tf_dpr` from the Pytorch's one.
> If I remember correctly, I found out that there's some tests in `test_modeling_tf_common.py`
> need `return_dict=False` argument.
> (and when I looked at the tests, I judged that all tests just need to ensure the correct values of output,
> not about `return_dict` argument.)
> That's why I changed the config to `return_dict=False` as default, and left the `MODIFY` comments
> just to note that this part was modified from the Pytorch's one.
> (Again, I thought the main tests are on outputs' values)
> 
> It's my first time to write this kind of test file here, so I apologize again if I made something wrong!

Absolutely no problem! I should have been more careful when reviewing your PR -> don't worry at all :-) 
We also have some difficulties with those `test_compile_tf_model` tests in TF, so I only understand it too well why you added those `return_dict=False/True` statements ;-) 

If you run into similar problems with TF compilation/ TF graph tests when integrating TFRAG, you can just point it out to us. It's more important to have TFRag fully work in eager mode in the beginning and then we are more then happy to help you out if you encounter problems with graph mode / compilation<|||||>Thanks again for your kind help @patrickvonplaten !!
Yes, as you predicted, there are similar (many more) hacks I did to make TFRag works at the moment.

When submitting PR I will make sure to list everything to you guys :)<|||||>Thanks for your reviews/comments/fixes!