Add Compact Convolutional Transformer model (CCT)
Author: rishabbala
Pull request: yes
Labels: name: Model on the Hub

# What does this PR do?

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Fixes #20133 (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- [x] Did you write any new necessary tests?


## Who can review?

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@amyeroberts @ArthurZucker 

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

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Comments:
Could you let me know how to fix the failed test. The issue apparently is that PIL.Image.LINEAR does not exist

Rebase on main, a fix has been merge! 😉 

Done. Could you let me know if there are any further changes

Hey! Sorry I forgot to mention this in my previous comment, we are trying to push models to the hub as often as possible, to make it a LOT easier for contributors! Then we share the repo on the hub where everything is supported 🤗 
I would recommend following [this tutorial](https://huggingface.co/docs/transformers/custom_models), and sharing here the uploaded model! Tell me if that sounds good to you! 

Sorry if I misunderstood, but I followed this [tutorial](https://huggingface.co/docs/transformers/add_new_model#514-port-brandnewbert-to-transformers), and it looks similar to the tutorial you shared. Could you quickly point out the difference between the two, or what additionally I must do?

Sure, adding the model to the hub rather than with a PR to transformers would give a model usable out of the box, without having to fix all of the CI, while keeping your code and no need for reviews etc. Mostly you would have to add MAPPINGS as explain in the tutorial, and users will just need to use `trust_remote_code = True` when doing `class.from_pretrained`! 

I've made the changes, and uploaded the models: [cct_224](https://huggingface.co/rishabbala/cct_14_7x2_224) and [cct_384](https://huggingface.co/rishabbala/cct_14_7x2_384). Let me know if this looks ok, and if I should go ahead and close the PR. Thanks for the help :)

Look good to me thanks a lot! Would suggest you to add a model card for people who don't really know the model and might want a quick way to use it!