TF port of the Segment Anything Model (SAM)

[Rocketknight1]

This is a first draft of the SAM port - will update this PR as I port tests and make sure everything is working okay. It's also a first proof-of-concept for full GPT-4 auto-translation from PyTorch: The entire modeling_tf_sam.py file was converted from PyTorch by GPT-4 with the exception of the imports at the top, because I haven't written a prompt for those yet.

Update: I checked over all of the code and fixed the issues in the GPT port. Equivalence tests all look good! This is almost ready to merge, but there are a few small issues left:

• [ ] Get saved model creation working and re-enable tests (problem with the serving signature)
• [ ] Check for duplication in the processor files - I can probably refactor and simplify things a bit
• [ ] Refactor convolutions - channels_first doesn't actually work on CPU in TF

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[Rocketknight1]

This is now almost ready to go and the code should be ready for review! Remaining issues:

• I added a tol parameter to the TF-PT equivalence test - 1e-5 is too low for SAM (errors are more like 1e-4, but I used 5e-4 in the test to avoid flakiness). This will require a couple of minor tweaks in other models that are calling that test.
• Cleanup/refactor in the processor, there's probably some code duplication that I can remove.

[Rocketknight1]

Thanks for the review - about half of the comments relate to the processor code, which is definitely in need of a refactor, yes. Working on that now!

[Rocketknight1]

@amyeroberts @sgugger I refactored all the changes to the common tests, and just overrode check_pt_tf_outputs to change the tol in the tests instead - this is much cleaner and resolves most of the issues there. I also refactored the processor, removing the duplicated files and merging methods where appropriate. I think all comments have now been addressed!

[Rocketknight1]

@gante I think all comments are now addressed, and I added training wherever it touched a layer that had training-specific behaviour (which is literally one dropout call)

All comments from @amyeroberts and @sgugger should be addressed too - are you okay with going ahead and merging now once tests pass?

[Rocketknight1]

I think comments are addressed now - are we okay to merge?

[Rocketknight1]

I'm treating silence as agreement, merging!