Fix AutoConfig and AutoModel support for Llava-Next-Video
Author: TKONIY
Pull request: yes

# What does this PR do?
Bug fix: Llava-Next-Video model cannot be loaded by AutoConfig and AutoModel.
* Support AutoConfig: Unify the `model_type` with the config.json in hugging face.
* Support AutoModel: Add llava* models to MODEL_MAPPING.

## 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.
- [x] 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?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@zucchini-nlp 

<!-- 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
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc

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

 -->


Comments:
Oh, I see now. I changed hub models to underscore naming but didn't know transformers had it this way. IMO the best way here is to modify files on the hub to use `model_type=llava-next-video`. Feel free to open a PR on the hub :)

For `AutoModel` we don't support VLMs because they have no base model (i.e. without lm_head) and the ConditionalGeneration models should be loaded with `AutoModelForVision2Seq`. 

> Oh, I see now. I changed hub models to underscore naming but didn't know transformers had it this way. IMO the best way here is to modify files on the hub to use `model_type=llava-next-video`. Feel free to open a PR on the hub :)
> 
> For `AutoModel` we don't support VLMs because they have no base model (i.e. without lm_head) and the ConditionalGeneration models should be loaded with `AutoModelForVision2Seq`.

Thanks! I have removed the AutoModel comit.

But I think that model_type="llava_next_video" would be better because the "llava_next" model type name is also underscore. And the model_type in the implementation files were also "llava_next_video", which need to be consistent.

https://github.com/huggingface/transformers/blob/5fd7ca7bc99371b6bb7524acb482741b17fdc91d/src/transformers/models/llava_next_video/configuration_llava_next_video.py#L95

https://github.com/huggingface/transformers/blob/5fd7ca7bc99371b6bb7524acb482741b17fdc91d/src/transformers/models/llava_next_video/diff_llava_next_video.py#L104

@TKONIY fair enough. Can you run `make fix-copies` to make th CI happy?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_32844). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Dear @amyeroberts, do you know when this commit will be merged into a released version of Transformers? It is important since [vLLM's video support](https://github.com/vllm-project/vllm/pull/7559) can be merged into the main branch only when Transformers can correctly load Llava-Next-Video by AutoConfig, which is fixed by this commit. And the pending video support also blocks other [PRs](https://github.com/vllm-project/vllm/pull/7905). Therefore, it would be great if you could help us by adding this fixing to the next release. Thank you!

@TKONIY All commits currently merged into main will be part of the next minor release (but not necessarily a patch release). Our release schedule is monthly, so v4.45 will probably be this week or the next 

@amyeroberts Thanks for the information. Really appreciate your work!