Add how to preprocess mask for finetuning with SAM
### Feature request

The [SAM image processor](https://github.com/huggingface/transformers/blob/main/src/transformers/models/sam/image_processing_sam.py) takes images as input and resizes them so that the longest edge is 1024 (using default values). This is the size expect as input fo the SAM model. 
For inference, this works fine as only the images need resizing but for fine-tuning as per [this tutorial](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb), you need to resize both your images and your masks as the SAM model produces `pred_masks` with size 256x256. If I don't resize my masks I get `ground truth has different shape (torch.Size([2, 1, 768, 1024])) from input (torch.Size([2, 1, 256, 256]))` when trying to calculate loss.

To fix this, I've currently written a resize and pad function into my code:

```
from PIL import Image

def resize_mask(image):
    longest_edge = 256
    
    # get new size
    w, h = image.size
    scale = longest_edge * 1.0 / max(h, w)
    new_h, new_w = h * scale, w * scale
    new_h = int(new_h + 0.5)
    new_w = int(new_w + 0.5)

    resized_image = image.resize((new_w, new_h), resample=Image.Resampling.BILINEAR)
    return resized_image

def pad_mask(image):
    pad_height = 256 - image.height
    pad_width = 256 - image.width

    padding = ((0, pad_height), (0, pad_width))
    padded_image = np.pad(image, padding, mode="constant")
    return padded_image

def process_mask(image):
    resized_mask = resize_mask(image)
    padded_mask = pad_mask(resized_mask)
    return padded_mask
```

and then have added this to my definition of SAMDataset:

```
class SAMDataset(Dataset):
    def __init__(self, dataset, processor, transform = None):
        self.dataset = dataset
        self.processor = processor
        self.transform = transform

    def __len__(self):
        return len(self.dataset)

    def __getitem__(self, idx):
        item = self.dataset[idx]
        
        if self.transform:
            image = self.transform(item["pixel_values"])
        else:
            image = item["pixel_values"]
        
        # get bounding box prompt
        padded_mask = process_mask(item["label"])
        prompt = get_bounding_box(padded_mask)

        # prepare image and prompt for the model
        inputs = self.processor(image, input_boxes=[[prompt]], return_tensors="pt")

        # remove batch dimension which the processor adds by default
        inputs = {k:v.squeeze(0) for k,v in inputs.items()}

        # add ground truth segmentation
        inputs["ground_truth_mask"] = padded_mask

        return inputs
```

This seems to work fine. 

What I think would be good is to allow input of masks in the SAM image processor. For example, the [Segformer image processor](https://github.com/huggingface/transformers/blob/v4.35.0/src/transformers/models/segformer/image_processing_segformer.py#L305) takes images and masks as inputs and resizes both to the size expected by the Segformer model. 

I have also seen there is a 'post_process_mask' method in the SAM image processor but I am unsure how to implement this in the tutorial I'm following. If you think this is a better way vs. what I am suggesting then please could you explain where I would add this in the code from the tutorial notebook.

### Motivation

Easier fine tuning of SAM model.

### Your contribution

I could try write a PR for this and/or make a PR to update the [notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) instead .