SAM example code does not work 
### System Info

- `transformers` version: 4.29.0.dev0
- Platform: Linux-3.10.0-957.12.2.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.3
- Huggingface_hub version: 0.13.4
- Safetensors version: not installed
- PyTorch version (GPU?): 1.5.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

### Who can help?

_No response_

### 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


img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]]  # 2D location of a window in the image

inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
outputs = model(**inputs)

masks = processor.image_processor.post_process_masks(
    outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores

### Expected behavior

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-5-abdc2d7068b8> in <module>
      4 
      5 inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
----> 6 outputs = model(**inputs)
      7 
      8 masks = processor.image_processor.post_process_masks(

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/transformers/models/sam/modeling_sam.py in forward(self, pixel_values, input_points, input_labels, input_boxes, input_masks, image_embeddings, multimask_output, output_attentions, output_hidden_states, return_dict, **kwargs)
   1331             )
   1332 
-> 1333         sparse_embeddings, dense_embeddings = self.prompt_encoder(
   1334             input_points=input_points,
   1335             input_labels=input_labels,

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/transformers/models/sam/modeling_sam.py in forward(self, input_points, input_labels, input_boxes, input_masks)
    669             if input_labels is None:
    670                 raise ValueError("If points are provided, labels must also be provided.")
--> 671             point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None))
    672             sparse_embeddings = torch.empty((batch_size, point_batch_size, 0, self.hidden_size), device=target_device)
    673             sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=2)

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/transformers/models/sam/modeling_sam.py in _embed_points(self, points, labels, pad)
    619             padding_point = torch.zeros(target_point_shape, device=points.device)
    620             padding_label = -torch.ones(target_labels_shape, device=labels.device)
--> 621             points = torch.cat([points, padding_point], dim=2)
    622             labels = torch.cat([labels, padding_label], dim=2)
    623         input_shape = (self.input_image_size, self.input_image_size)

RuntimeError: Expected object of scalar type double but got scalar type float for sequence element 1.
Hello @YubinXie 
Thanks for the issue! 
I did not managed to reproduce your issue with `torch==1.13.1`, and here is the snippet I used:
```python
from PIL import Image
import requests
import torch

from transformers import AutoModel, AutoProcessor

device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModel.from_pretrained("facebook/sam-vit-base").to(device)
processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]]  # 2D location of a window in the image

inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
with torch.no_grad():
    outputs = model(**inputs)
```
I can see that you are using `torch==1.5.x`. Note that  `transformers` has a minimum required version of `1.9` for `torch`: https://github.com/huggingface/transformers/blob/main/setup.py#L180 - hence I have tried to run that script with `torch==1.9.1` and did not encountered the issue. I strongly recommend you to install a greater version of `torch` (i.e. use at least the version `1.9`). Could you try to update `torch` and let us know if you still face the issue?<|||||>> Hello @YubinXie Thanks for the issue! I did not managed to reproduce your issue with `torch==1.13.1`, and here is the snippet I used:
> 
> ```python
> from PIL import Image
> import requests
> import torch
> 
> from transformers import AutoModel, AutoProcessor
> 
> device = "cuda" if torch.cuda.is_available() else "cpu"
> 
> model = AutoModel.from_pretrained("facebook/sam-vit-base").to(device)
> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")
> 
> img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
> input_points = [[[450, 600]]]  # 2D location of a window in the image
> 
> inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
> with torch.no_grad():
>     outputs = model(**inputs)
> ```
> 
> I can see that you are using `torch==1.5.x`. Note that `transformers` has a minimum required version of `1.9` for `torch`: https://github.com/huggingface/transformers/blob/main/setup.py#L180 - hence I have tried to run that script with `torch==1.9.1` and did not encountered the issue. I strongly recommend you to install a greater version of `torch` (i.e. use at least the version `1.9`). Could you try to update `torch` and let us know if you still face the issue?

Hi @younesbelkada Thank you for your response. I updated my torch and now the model works!
However, I got another error the the post process:
```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-abdc2d7068b8> in <module>
      6 outputs = model(**inputs)
      7 
----> 8 masks = processor.image_processor.post_process_masks(
      9     outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
     10 )

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/transformers/models/sam/image_processing_sam.py in post_process_masks(self, masks, original_sizes, reshaped_input_sizes, mask_threshold, binarize, pad_size)
    404             interpolated_mask = F.interpolate(masks[i], target_image_size, mode="bilinear", align_corners=False)
    405             interpolated_mask = interpolated_mask[..., : reshaped_input_sizes[i][0], : reshaped_input_sizes[i][1]]
--> 406             interpolated_mask = F.interpolate(interpolated_mask, original_size, mode="bilinear", align_corners=False)
    407             if binarize:
    408                 interpolated_mask = interpolated_mask > mask_threshold

~/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/functional.py in interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias)
   3957         if antialias:
   3958             return torch._C._nn._upsample_bilinear2d_aa(input, output_size, align_corners, scale_factors)
-> 3959         return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, scale_factors)
   3960     if input.dim() == 5 and mode == "trilinear":
   3961         assert align_corners is not None

TypeError: upsample_bilinear2d() received an invalid combination of arguments - got (Tensor, list, bool, NoneType), but expected one of:
 * (Tensor input, tuple of ints output_size, bool align_corners, tuple of floats scale_factors)
      didn't match because some of the arguments have invalid types: (Tensor, list of [Tensor, Tensor], bool, NoneType)
 * (Tensor input, tuple of ints output_size, bool align_corners, float scales_h, float scales_w, *, Tensor out)
```

The code is from hugging face SAM page. I wonder if it is code issue or, other package issue.
<|||||>Hi @YubinXie 
Thanks for iterating, it seems that this is a duplicate of https://github.com/huggingface/transformers/issues/22904 
Could you try to uninstall `transformers` and re-install it from source? <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.