Aria: get_number_of_image_patches returns wrong count with max_image_size=980 when best resolution has odd 490 multiples
Author: arnavkewalram
Pull request: no

## System Info

```
transformers version: main (latest)
Python: 3.10
torch: 2.6
```

## Who can help?

@zucchini-nlp @yonigozlan (Aria model maintainers)

## Information

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

## Tasks

- [ ] An officially supported task in the `examples` folder
- [x] My own task or dataset (multi-modal / vision)

## Reproduction

```python
from transformers import AriaProcessor

processor = AriaProcessor.from_pretrained("rhymes-ai/Aria")

# max_image_size=980 is the default; split_image=True triggers the bug
images_kwargs = {"max_image_size": 980, "split_image": True}

# Image size where select_best_resolution picks [490, 980]
# (a portrait image, best matched to the (1,2) grid slot = [490, 980])
height, width = 300, 600  # aspect ratio ~1:2

num_patches = processor.image_processor.get_number_of_image_patches(
    height, width, images_kwargs
)
print(f"Reported num_patches: {num_patches}")  # prints 0 — WRONG, should be 1

# Confirm what divide_to_patches actually produces:
import torch
from transformers.image_transforms import divide_to_patches

dummy = torch.zeros(3, 490, 980)  # the padded image at best_resolution
actual_patches = divide_to_patches(dummy, patch_size=980)
print(f"Actual patches: {len(actual_patches)}")  # prints 1
```

Similarly for resolution `[980, 490]` (landscape image matching the (2,1) grid):

```python
# resized_height=980, resized_width=490, max_image_size=980
# 980 // 980 * 490 // 980 = 1 * 0 = 0   ← wrong, should be 1
```

And for resolution `[980, 1470]` (2×3 grid):

```python
# resized_height=980, resized_width=1470, max_image_size=980
# 980 // 980 * 1470 // 980 = 1 * 1 = 1  ← wrong, should be 1*2=2
```

## Expected behavior

`get_number_of_image_patches` should return the same count as the number of patches `get_image_patches` actually produces. Internally, `get_image_patches` calls `divide_to_patches(padded_image, patch_size=max_image_size)`, which iterates `range(0, height, patch_size)` — equivalent to `ceil(height / patch_size)` patches per dimension.

Expected formula:

```python
import math
num_patches = math.ceil(resized_height / max_image_size) * math.ceil(resized_width / max_image_size)
```

## Actual behavior

The current code uses integer floor division:

```python
num_patches = 1 if not split_image else resized_height // max_image_size * resized_width // max_image_size
```

`//` truncates toward zero, so when `resized_height < max_image_size` (e.g., `490 // 980 = 0`) the result is wrong. More broadly, any time `resized_height` or `resized_width` is not an exact multiple of `max_image_size`, the count is too low.

With the default `max_image_size=980`, the `split_resolutions` are `el[0]*490, el[1]*490` for small integers `el`. Any resolution with an **odd** `el` value (1, 3, 5, 7) produces a dimension that is not a multiple of 980:

| Best resolution | Floor result | Correct (ceil) |
|-----------------|-------------|-----------------|
| [490, 980]      | 0           | 1               |
| [980, 490]      | 0           | 1               |
| [490, 1470]     | 0           | 2               |
| [1470, 490]     | 0           | 2               |
| [980, 1470]     | 1           | 2               |
| [1470, 980]     | 1           | 2               |
| [1470, 1470]    | 1           | 4               |

When `num_patches` is wrong, `get_multimodal_data` computes the wrong `num_image_tokens`, causing visual embeddings to be misaligned with text token positions.

## Affected files

The same bug appears in three places (the latter two are auto-generated from the first):

- `src/transformers/models/aria/modular_aria.py` (line 514) ← fix here
- `src/transformers/models/aria/image_processing_aria.py` (line 222) ← auto-generated
- `src/transformers/models/aria/image_processing_pil_aria.py` (line 226) ← auto-generated

Comments:
I would like to work on a fix for this.