ViT modeling file is missing drop path present in PyTorch image models
### System Info

- `transformers` version: 4.20.1
- Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.8.1
- PyTorch version (GPU?): 1.10.1+cu113 (True)
- Tensorflow version (GPU?): 2.9.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@rwightman @NielsRogge

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

1. Vision transformer modeling file from Pytorch image models uses [drop path ](https://github.com/rwightman/pytorch-image-models/blob/324a4e58b6b0365d16dcf8f93739be8f74cd7d37/timm/models/vision_transformer.py#L234) after attention and feedforward layers. It is missing in the current Huggingface modeling_vit.py file. We found that on VTAB benchmark, having drop path significantly boost the performance of ViTB model. 
2. The epsilon value of layer norm used in pretrained ViTConfig is 1e-12 whereas vision_transformer.py file in  pytorch-image-models uses epsilon value of 1e-6. Because of this, google/vit-base-patch16-224 checkpoint performs slightly different with modeling_vit.py from Huggingface and vision_transformer.py from pytorch-image-models in the test mode. 

### Expected behavior

1. Given that Drop path is shown to perform well on VTAB benchmark , it can be added to current modeling file of Huggingface. 
2. Epsilon value in layer norm of the transformer can be made consistent to pytorch-image-models. 
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