Add TensorFlow implementation of  EfficientFormer
# What does this PR do?

* Adding EfficientFormer computer vision model TensorFlow port (not a llm port). 
* Fixes some minor typos and a couple of differences in the PyTorch model code: 1) the non-dict / tuple return was not returning last hidden state but the state before last stage. The dict and tuple return of the encoder should be equivalent, as seen in other [models](https://github.com/huggingface/transformers/blob/main/src/transformers/models/poolformer/modeling_poolformer.py#L258). 2) Two layernorms were not using the config eps (assuming that the config is the ground truth). Let me know how you think about this.


Ran tests (CPU-only, all pass) with:
`NVIDIA_TF32_OVERRIDE=1 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 py.test -vv -rA tests/models/efficientformer/test_modeling_tf_efficientformer.py `

Double checked pt and tf architecture codes with the "[EfficientFormer: Vision Transformers at MobileNet Speed](https://proceedings.neurips.cc/paper_files/paper/2022/hash/5452ad8ee6ea6e7dc41db1cbd31ba0b8-Abstract-Conference.html)" paper.

Verified on example image shapes and diffs in hidden states:
```
from transformers import EfficientFormerImageProcessor
from src.transformers.models.efficientformer.modeling_tf_efficientformer import TFEfficientFormerModel
from src.transformers.models.efficientformer.modeling_efficientformer import EfficientFormerModel 

model_tf = TFEfficientFormerModel.from_pretrained("snap-research/efficientformer-l1-300", from_pt=True)
model_pt = EfficientFormerModel.from_pretrained("snap-research/efficientformer-l1-300")

image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
proc =  EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
inputstf = proc(images=image, return_tensors="tf")
inputspt = proc(images=image, return_tensors="pt")
       
outtf = model_tf(**inputstf, output_hidden_states=True, training=False)
with torch.no_grad():
    outpt = model_pt(**inputspt, output_hidden_states=True)

max_diff = np.amax(np.abs(outtf[0].numpy() - outpt[0].numpy()))
print(f"last hidden diff shape: {outtf[0].shape}, last hidden diff: {max_diff}, last hidden <= 1e-4, {max_diff <= 1e-4}")
for i in range(7):
    max_diff = np.amax(np.abs(outtf[1][i].numpy() - outpt[1][i].numpy()))
    print(f"hidden state {i} shape: {outtf[1][i].shape}, diff: {max_diff}, max_diff <= 1e-4: {max_diff <= 1e-4}")
```
which gives:
```
last hidden diff shape: (1, 49, 448), last hidden diff: 2.1457672119140625e-05, last hidden <= 1e-4, True
hidden state 0 shape: (1, 48, 56, 56), diff: 7.271766662597656e-06, max_diff <= 1e-4: True
hidden state 1 shape: (1, 48, 56, 56), diff: 5.054473876953125e-05, max_diff <= 1e-4: True
hidden state 2 shape: (1, 96, 28, 28), diff: 2.9087066650390625e-05, max_diff <= 1e-4: True
hidden state 3 shape: (1, 96, 28, 28), diff: 2.3603439331054688e-05, max_diff <= 1e-4: True
hidden state 4 shape: (1, 224, 14, 14), diff: 1.6689300537109375e-05, max_diff <= 1e-4: True
hidden state 5 shape: (1, 224, 14, 14), diff: 4.1961669921875e-05, max_diff <= 1e-4: True
hidden state 6 shape: (1, 448, 7, 7), diff: 1.9550323486328125e-05, max_diff <= 1e-4: True
```

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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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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: @stas00, 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

 -->

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_22620). All of your documentation changes will be reflected on that endpoint.<|||||>cc @Rocketknight1 <|||||>Hi @D-Roberts, just letting you know the TF team at Hugging Face is aware of this and definitely interested in the port! Please ping me or @gante whenever it's ready for review, or if you run into any issues while porting.<|||||>@Rocketknight1 @gante This PR is now ready for review.<|||||>cc @amyeroberts for core maintainer review as well<|||||>@Rocketknight1 @amyeroberts I addressed your comments and also submitted two PRs for the l1 and l3 weights (and tagged Rocketknight1). Let me know what's next!<|||||>@D-Roberts  - that's great! 

For the CI - it seems there is an issue with your CircleCI permissions, as the tests won't run.
Could you try refreshing your permissions as shown [here](https://support.circleci.com/hc/en-us/articles/360048210711-How-to-Refresh-User-Permissions-)? Once all the tests are green, we'll be ready for final reviews :) <|||||>@amyeroberts Thanks for pointing out the circle ci fix. It appears that one doc test which (rightly) can't find tf weights is failing for now. I added back the `from_pt` in the model tests  for the sake of ci tests until the tf weights get merged.<|||||>@D-Roberts Just to let you know, we've reached out to the team at Snap to ask them to merge your PRs on the EfficientFormer checkpoints. Sorry for the delay!<|||||>@D-Roberts the checkpoint PRs should be merged now. Thank you to @alanspike for the quick response!<|||||>@amyeroberts @Rocketknight1 All local tests pass with the new tf weights. The CI gets this documentation tests failing; the pt version also predicts 281 which maps to label_281 in config.<|||||>@D-roberts I think it's fine to swap those tests for just checking the actual argmax index rather than the `id2label` string value. Obviously the repository config doesn't actually have the `id2label` values set, so fixing that would require another PR to the repos.<|||||>@Rocketknight1 @amyeroberts Alright. :)<|||||>LGTM now - I'm happy to merge as soon as you and @amyeroberts are!<|||||>Hi @Rocketknight1 , I've addressed the last comments from @amyeroberts and had all tests pass. I am ready for merge whenever you are. I've just rebased to upstream and there are some unrelated ci tests failing, though last night everything was green.<|||||>@Rocketknight1 All green again. :)
<|||||>@sgugger @amyeroberts @Rocketknight1 I was wondering - when do you plan a transformers release that includes this code? <|||||>@D-Roberts We release [roughly once a month](https://github.com/huggingface/transformers/releases)  and are planning on releasing 4.30 later this week. If you need it right now, it's possible to [install from source](https://huggingface.co/docs/transformers/installation#install-from-source) to have the `main` version too. 