transformers - 💡(How to fix) Fix [RT-DETRv2] MPS crash: build_2d_sinusoidal_position_embedding hardcodes torch.float64, breaking Apple Silicon / MPS inference

- `transformers` version: 5.9.0
- Platform: macOS (Apple Silicon, MPS backend)
- Python version: 3.13
- PyTorch version: (MPS-enabled build)
- `docling` version: 2.95.0
- `docling-ibm-models` version: 3.13.2

---

import torch
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor
from PIL import Image
import requests

device = torch.device("mps")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd").to(device)

inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)  # crashes here

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TypeError: Cannot convert a MPS Tensor to float64 dtype as the MPS framework doesn't support float64. Please use float32 instead.

---

omega = torch.arange(pos_dim, dtype=torch.float64, device=device) / pos_dim