[Tests] Fix slow video tensor creation from list of numpy arrays in SmolVLM

[Defalt-Meh]

What does this PR do?

While running SmolVLM tests I noticed this warning in the output:

tests/test_video_processing_common.py:57: UserWarning: Creating a tensor from a list of
numpy.ndarrays is extremely slow. Please consider converting the list to a single
numpy.ndarray with numpy.array() before converting to a tensor.

Tracked it down to prepare_video() in tests/test_video_processing_common.py — it builds a list of numpy arrays (one per frame), then passes that list straight to torch.tensor(). PyTorch really doesn't like this; it ends up iterating over each array one by one instead of doing a bulk conversion.

The fix is simple — consolidate into a single numpy array first, then hand it to torch:

# before
video = torch.tensor(video).permute(0, 3, 1, 2)
 
# after
video = torch.from_numpy(np.array(video)).permute(0, 3, 1, 2)

np.array() was already imported. Output is bit-identical (verified with torch.equal()), warning is gone.

Quick benchmark on Apple M-series, 500 iterations:

Scenario	Before	After	Speedup
What the tests actually use (8 frames, 10x10)	0.37 ms	0.002 ms	~190x
More realistic frames (32 frames, 224x224)	711 ms	0.06 ms	~11,000x

All SmolVLM tests pass (302 passed, 186 skipped, 0 failures). No other instances of this pattern in tests/*.py.

Fixes # (issue)

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Who can review?

@yonigozlan @zucchini-nlp

[github-actions]

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CI recap

Dashboard: View test results in Grafana
Latest run: 29311436569:2
Result: success | Jobs: 15 | Tests: 170,983 | Failures: 0 | Duration: 15h 25m

[zucchini-nlp]

With the test setting and 8 frames, not much win at the end. But yeah, why not, let's merge it an make our tests slightly faster :)

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