Pipeline testing - using tiny models on Hub
# What does this PR do?

Pipeline testing - using tiny models on Hub.

A few comments:
  - This PR moves the tiny model creation from `PipelineTestCaseMeta` (where done dynamically during testing) to `utils/create_dummy_models.py` (where the tiny models are created once and live on the Hub):
    - The logic is still large, but at least it is done once rather than being created dynamically 

  - When a new model is added in `transformers`, it would **NOT** be used in pipeline testing **UNTIL** we create & upload tiny models for the new model type.
    - even if we upload the new tiny models (or re-create the existing one), we also have to **UPDATE** [this repo](https://huggingface.co/datasets/hf-internal-testing/tiny-random-model-summary), see comments below

  - While `pytest` collects the tests to run, the collection is done **in each process** (if we specify `-n N` with `N > 1`):
    - If we use `from_pretrained` during test collection, there will be too many calls, and the server refuses the requests at some point: the tests being collected will **vary each time and being incomplete**
    - So I upload a file [processor_classes.json](https://huggingface.co/datasets/hf-internal-testing/tiny-random-model-summary/blob/main/processor_classes.json) containing necessary information to call `gen_test`. The `from_pretrained` will only be called when the test is actually running.
  - Some tests are just not working (yet), and an important subset of those failed tests is not tested in the current `main` branch
    - for example, on `main`, all pipeline tests use fast tokenizers
    - we probably need to check (and possibly fix) some of them, but depends on `impact` and `usage`, we will leave some of them skipped for now
_The documentation is not available anymore as the PR was closed or merged._<|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>Gently pining @LysandreJik for review at their convenience. We discussed last time offline the pipeline testing will eventually avoid using metaclass - I will work on it in a future PR. I think it's better to have progressive changes toward our ultimate goal 😊🙏<|||||>Thank you for the ping, I'll have a look!<|||||>Hi @Narsil

In this PR, commit [3d46ed81](https://github.com/huggingface/transformers/pull/20426/commits/3d46ed81a7b688b13a85114b9f4168242e24e902), I revert some changes in your (merged) PR #20851.

In short: this `def get_test_pipeline(self, model, tokenizer, feature_extractor, image_processor):`  is changed to `get_test_pipeline(self, model, tokenizer, processor):`

Before you PR, it was `get_test_pipeline(self, model, tokenizer, feature_extractor):`

More context:
- This PR leverages the uploaded checkpoints on the Hub for pipeline testing.
- In a follow-up PR, we plan to remove the usage of  `PipelineTestCaseMeta`
  - (therefore, this particular change will be just short-lived)

Let me know if you have any question or comment 🙏
<|||||>This change was necessary to get some tests running.

Namely testing that oneformer and the like are actually working. These models **do not** have a feature extractor, only a `ImageProcessor`. So how can you make it work?

Since you're using tiny models maybe that function could be bypassed entirely?

Also for the network issue (too many from pretrained iiuc) isn't there a way to download all tiny models once and keep them on the runner so we could run the tests in offline mode? So no network calls? Maybe running network mode if there's a failure? (so downloading the new model) <|||||>@Narsil 

> These models do not have a feature extractor, only a ImageProcessor. So how can you make it work?

- The creation and upload of tiny models (which is done in another script) should create the tokenizers and/or processors (feature extractors or image processor). During pipeline testing, we just load them. I don't see any problem here, but let me know if I miss any detail.
- (however, the tiny model creation should be run in a regular basis (or triggered by some conditions) in order to make the tiny checkpoints for newly added models available on the hub)
  - this is not done yet, but I will work on it too
  -  `oneformer` doesn't have a tiny model checkpoint yet, so not tested by this PR
  - but for other models, even they only have image processors, the tests could pass already

> Also for the network issue (too many from pretrained iiuc) isn't there a way to download all tiny models once and keep them on the runner

On our hosted runners, it's fine (i.e. cached). But what I mentioned is for pull request CI - which runs on `CircleCI`. So far I haven't looked into how to do similar things on it.