transformers.image_transforms.normalize documents and checks for the wrong type for std and mean arguments

[CalOmnie]

System Info

• transformers version: 4.49.0.dev0
• Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.39
• Python version: 3.11.11
• Huggingface_hub version: 0.27.1
• Safetensors version: 0.5.1
• Accelerate version: 1.2.1
• Accelerate config: not found
• PyTorch version (GPU?): 2.5.1+cu124 (True)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using distributed or parallel set-up in script?: Neither
• Using GPU in script?: No
• GPU type: NVIDIA GeForce GTX 1060 6GB

Who can help?

No response

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

import numpy as np
from transformers.image_transforms import normalize

mock_img = np.random.rand(3, 10, 10)

# Simulating getting the std and mean from text
std = map(float, "1.5 0. 2.".split())
mean = map(float, "1.5 0. 2.".split())
# According to the doc, this setup should work, but it does not since "map" does not implement `len`

normalize(mock_img, std, mean)

Expected behavior

Use the Sequence type instead of Iterable to let users know that len needs to be implemented.

[CalOmnie]

Question: Am I following the right process to submit very small fix that do not impact the runtime (e.g. typing and documentations)? I noticed a few other mistypes while reading the code and I'd like to do it properly going forward.

[Rocketknight1]

Hi @CalOmnie, yes, this was good! For small fixes when the issue is clear, like bugs or obviously incorrect types, you can just submit a PR. We recommend opening an issue first when the change might be controversial or need discussion (for example, adding a whole new model class, or changing the behaviour of existing tools)