Add `accelerate` support for `ViT` family
# What does this PR do?

This PR adds `accelerate` support for ViT models, therefore these models can be loaded in 8-bit as follows:
```
# pip install accelerate bitsandbytes
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

model_8bit = ViTForImageClassification.from_pretrained('google/vit-large-patch32-384', device_map="auto", load_in_8bit=True)
outputs_8bit = model_8bit(**inputs)
logits = outputs_8bit.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model_8bit.config.id2label[predicted_class_idx])
``` 
This PR introduces the first 8-bit compatible vision model.
The same script works for `deit` too

Putting the PR as a draft as I have few questions!

cc @NielsRogge @sgugger 
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_20174). All of your documentation changes will be reflected on that endpoint.<|||||>The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_20174). All of your documentation changes will be reflected on that endpoint.<|||||>The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_20174). All of your documentation changes will be reflected on that endpoint.<|||||>The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_20174). All of your documentation changes will be reflected on that endpoint.