Avoid floating point math for ceil operations

[xenova]

What does this PR do?

This PR introduces a helper utility function, int_div_ceil, which performs math.ceil(a / b) for non-negative integer operands. This is necessary as the current approach is both error-prone and imprecise (especially when exporting models via torch dynamo, or similar).

This bug was originally discovered when exporting granite-speech to ONNX (microsoft/onnxruntime#27661), where the current approach will require casting integers to floats then back to integers, causing precision issues

  CPU: Ceil(165/15) = 11 (PASS)
  GPU: Ceil(165/15) = 12 (FAIL)

A similar issue seems to have been in the library for the fuyu processor:

math.ceil(torch.tensor(300).cuda() / 30) == 11

whereas

int_div_ceil(torch.tensor(300).cuda(), 30) == 10

operates as expected (because operations are performed as integers).

Fixes # (issue)

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@ArthurZucker

[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: bit, chmv2, dac, depth_pro, dpt, encodec, eomt, falcon_mamba, fuyu, gemma3, granite_speech, higgs_audio_v2_tokenizer, idefics3, jamba, layoutlmv2, lfm2_vl

[xenova]

ci formatting issue seems to be unrelated 🤷

[HuggingFaceDocBuilderDev]

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