🚀 Speed up Image Processing for CV models
Author: yaswanth19
Pull request: yes

# What does this PR do?

This speeds up image processing by converting the data to numpy array before sending to BatchFeature.


Queries:
* Currently I am checking if all the images are of same size or not before converting the whole batch to  ndarray; for that I am utilizing the arguments like `do_resize`,`do_pad` etc.. but is it necessary to do ti this way or we can write a helper function which checks whether every sub array is uniform or not and if so then convert the whole array to ndarray. WDYT? 
<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes #31205  

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @ylacombe, @eustlb
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->
@yonigozlan Please review this first draft PR where I have applied the logic only on some model and once approach is finalised, same changes would be made to all other models

Comments:
@yonigozlan @amyeroberts  A gentle ping to address my query so that I can implement it fully

@yonigozlan @amyeroberts A gentle ping again

@amyeroberts @yonigozlan Please do a first review and address my query

@yonigozlan Can you please address my below query

> Currently I am checking if all the images are of same size or not before converting the whole batch to ndarray; for that I am utilizing the arguments like do_resize,do_pad etc.. but is it necessary to do ti this way or we can write a helper function which checks whether every sub array is uniform or not and if so then convert the whole array to ndarray. WDYT?

Also LMK we can use a better way or modify something in a common class.

Hi @yaswanth19 , sorry for the delay, and thank you for working on this. However, as we are moving to fast image processors, which implement similar changes to the ones in this PR, this shouldn't be needed in slow image processors. The focus for now for the slow image processors is backward compatibility during this transition, and these changes could cause some issues on that front

Okay NP, closing this PR then :hugs: