Fixing issue where generic model types wouldn't load properly with the pipeline
Author: Narsil
Pull request: yes

# What does this PR do?

When this occurs https://github.com/huggingface/transformers/issues/17929
we can provide a better error message since this is detectable at load time
and the fix should happen within `transformers`.

Found out 3 odd cases which have been dealt with differently:

- `translation` actually uses `translation_XX_to_YY` and also relies on `task_specific_params` for some model configs.
  I tried cleaning that up and using `task_specific_params` only once, but the rabbit hole is deep, and it would have meant more
  code changes that this PR should hold. Waiting for a subsequent PR.
  The issue is that `translation_XX_to_YY` is not a normalized task name and is not within `NO_TOKENIZER_TASKS` nor `NO_FEATURE_EXTRACTION_TASKS` so the configuration on wether we should load or not doesn't work.
- `feature-extraction`. That one is extremely special, since ALL models could in theory use that pipeline, and so we cannot enforce or detect anything statically on what should be loaded or not.
- `automatic-speech-recognition` has this `speech-encoder-decoder` type of model, which do not define any `tokenizer` class, so the `type(config)` is NOT within `TOKENIZER_MAPPING` (correctly), but the first version of the check would fail when deciding staticly if we should load the tokenizer or not. The fix was to check if the user passed a tokenizer or not (if tokenizer is passed we should never try to do anything anyway)

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Fixes # (issue)


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Comments:
_The documentation is not available anymore as the PR was closed or merged._

@sgugger 

It seems I had completely misunderstood what was going on there. I thought it was misconfiguration while it's more of a normal state of things (Wasn't aware we had added those generic models for vision too).

My new proposed PR then actually fixes the underlying issue initially created #17929 . 

The way I did it is keep some manual bookkeeping for these "multi model" configurations (is the name right) ?

Then if we are actually using one of these models, attempt to load the `Tokenizer`/`feature_Extractor` looking exclusively at whether or not the task requires one.
This should fix the original issue, and it so happens we had a test that could easily be updated to support those use cases.

What do you think of this approach ? 

If a user created a model and forgot to upload either one of the necessary components, the pipeline will simply fail to load attempting to load one of them. I think that sort of failure mode should be OK to understand and users should be able to recover on their own. So no need for error messages now.

I am still keeping the regular way to detect if we need the tokenizer for other types of configs, but then we will still fail if the AutoTokenizer/FeatureExtractor is not correctly configured. I think maybe switching entirely to `NO_TOKENIZER_TASKS` detection seems easier in the long run but I didn't want to do such a change in a small PR. (`feature-extraction` will still be a corner case)