Save the tokenizer and image preprocessor after training a model with the contrastive image-text example
Author: regisss
Pull request: yes

# What does this PR do?

<!--
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 -->

When training a model with the contrastive image-text example, only the model is saved (see [here](https://github.com/huggingface/transformers/blob/88399476c3892435395618ed37993176dbb0de73/examples/pytorch/contrastive-image-text/run_clip.py#L512)). As a consequence, when using the trained model to only perform inference with the same script, an error will be raised [here](https://github.com/huggingface/transformers/blob/88399476c3892435395618ed37993176dbb0de73/examples/pytorch/contrastive-image-text/run_clip.py#L324) and [there](https://github.com/huggingface/transformers/blob/88399476c3892435395618ed37993176dbb0de73/examples/pytorch/contrastive-image-text/run_clip.py#L335) because the checkpoint doesn't contain a tokenizer nor a preprocessor.

This PR fixes this issue by saving the tokenizer and the image preprocessor at the end of the training.

To reproduce it, after creating a model following [this section](https://github.com/huggingface/transformers/tree/main/examples/pytorch/contrastive-image-text#create-a-model-from-a-vision-encoder-model-and-a-text-encoder-model), run:
```bash
python examples/pytorch/contrastive-image-text/run_clip.py \
    --output_dir ./clip-roberta-finetuned \
    --model_name_or_path ./clip-roberta \
    --data_dir $PWD/data \
    --dataset_name ydshieh/coco_dataset_script \
    --dataset_config_name=2017 \
    --image_column image_path \
    --caption_column caption \
    --remove_unused_columns=False \
    --do_train \
    --per_device_train_batch_size="64" \
    --learning_rate="5e-5" \
    --overwrite_output_dir
```

And then run:
```bash
python examples/pytorch/contrastive-image-text/run_clip.py \
    --output_dir ./clip-roberta-finetuned \
    --model_name_or_path ./clip-roberta-finetuned \
    --data_dir $PWD/data \
    --dataset_name ydshieh/coco_dataset_script \
    --dataset_config_name=2017 \
    --image_column image_path \
    --caption_column caption \
    --remove_unused_columns=False \
    --do_eval \
    --per_device_eval_batch_size="64" \
    --overwrite_output_dir
```
which raises the following error:
```
Traceback (most recent call last):
  File "run_clip.py", line 540, in <module>
    main()
  File "run_clip.py", line 325, in main
    tokenizer = AutoTokenizer.from_pretrained(
  File "/home/ubuntu/workspace/venv/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py", line 718, in from_pretrained
    tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)]
  File "/home/ubuntu/workspace/venv/lib/python3.8/site-packages/transformers/models/auto/auto_factory.py", line 610, in __getitem__
    raise KeyError(key)
KeyError: <class 'transformers.models.vision_text_dual_encoder.configuration_vision_text_dual_encoder.VisionTextDualEncoderConfig'>
```


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      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?

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.

<!-- 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 and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

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: @sgugger
- TensorFlow: @Rocketknight1

 -->


Comments:
_The documentation is not available anymore as the PR was closed or merged._

@sgugger All tests passed, so I think this one can be merged :slightly_smiling_face: