Pix2Struct: wrong attention mask broadcasting in the visual encoder
### System Info

- `transformers` version: 4.29.2
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.10
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- Safetensors version: not installed
- PyTorch version (GPU?): 2.0.1+cu117 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): 0.6.10 (cpu)
- Jax version: 0.4.10
- JaxLib version: 0.4.10
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

### Who can help?

@ArthurZucker @younesbelkada @amyeroberts

### Information

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

### Tasks

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

### Reproduction

The Pix2Struct visual attention layer masks the attention scores (logits) by adding negative infinities from the attention mask.
https://github.com/huggingface/transformers/blob/539e2281cd97c35ef4122757f26c88f44115fa94/src/transformers/models/pix2struct/modeling_pix2struct.py#L200-L220

And the below line is to broadcast the boolean attention mask of which shape is `[batch_size, seq_len]` to make a shape of `[batch_size, num_heads, query_len, key_len]`. But it seems the mask tensor is broadcasted on wrong axes.
https://github.com/huggingface/transformers/blob/539e2281cd97c35ef4122757f26c88f44115fa94/src/transformers/models/pix2struct/modeling_pix2struct.py#L213

Here is the common way to extend the mask:

https://github.com/huggingface/transformers/blob/539e2281cd97c35ef4122757f26c88f44115fa94/src/transformers/modeling_utils.py#L896

Because of that, the model will attend mask tokens as well while the original implementation uses `flaxformer` as follows:
https://github.com/google/flaxformer/blob/9adaa4467cf17703949b9f537c3566b99de1b416/flaxformer/components/attention/dense_attention.py#L2019


### Expected behavior

Fix the order of new axes on the attention mask.