VideoMAEForVideoClassification does not support `device_map='auto'` yet.
### Feature request

Support for `device_map = 'auto'` so that the VideoMAE models can be run with Int8 mixed precision. For reproducibility, here is what I get when I run the command in a collab notebook (w/ GPU) with accelerate and bitsandbytes installed:

```
from transformers import AutoModelForVideoClassification

model_name = 'MCG-NJU/videomae-base-finetuned-ssv2 #Example checkpoint
model = AutoModelForVideoClassification.from_pretrained(model_name,load_in_8bit=True,device_map='auto')
```

Which gives the following error message:

```
Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 4>:4                                                                              │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:471 in          │
│ from_pretrained                                                                                  │
│                                                                                                  │
│   468 │   │   │   )                                                                              │
│   469 │   │   elif type(config) in cls._model_mapping.keys():                                    │
│   470 │   │   │   model_class = _get_model_class(config, cls._model_mapping)                     │
│ ❱ 471 │   │   │   return model_class.from_pretrained(                                            │
│   472 │   │   │   │   pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs,   │
│   473 │   │   │   )                                                                              │
│   474 │   │   raise ValueError(                                                                  │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:2703 in from_pretrained   │
│                                                                                                  │
│   2700 │   │   │   )                                                                             │
│   2701 │   │   │                                                                                 │
│   2702 │   │   │   if model._no_split_modules is None:                                           │
│ ❱ 2703 │   │   │   │   raise ValueError(f"{model.__class__.__name__} does not support `device_m  │
│   2704 │   │   │   no_split_modules = model._no_split_modules                                    │
│   2705 │   │   │   if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:    │
│   2706 │   │   │   │   raise ValueError(                                                         │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: VideoMAEForVideoClassification does not support `device_map='auto'` yet.
```




### Motivation

I saw a similar issue #22018 which got resolved really quickly. Hoping that this won't be a lot of work to incorperate into the VideoMAE models  :slightly_smiling_face: 

### Your contribution

Would prefer if someone more familiar with the repo did this instead (it doesn't appear to be much work if the update is like #22207 but I didn't understand what the change did and don't currently have time to study the codebase)
cc @alaradirik and @amyeroberts <|||||>Hi, thanks for the commitment. I tested with this change, but there is still a bug which made me very confusing: 
```python
RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemmStridedBatched( handle, opa, opb, m, n, k, &alpha, a, lda, stridea, b, ldb, strideb, &beta, c, ldc, stridec, num_batches)`
``` 
My codes can run on one GPU, and I met this error when I run it on two GPUs with this change. 
I already checked the compatibility of CUDA, pytorch etc...Also did small tests about training on other small dataset with two GPUs by simple pytorch codes. They all worked. I even set the batch_size=1, the error is still there...
If you have any idea about this error, I am really appreciated. 
