[WIP] add the tokenizer for SMALL100 model
Author: alirezamshi-zz
Pull request: yes

# What does this PR do?

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We propose SMaLL-100, which is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M-100 while being much smaller and faster. It is introduced in [this paper](https://arxiv.org/abs/2210.11621)(accepted to EMNLP 2022), and initially released in [this repository](https://github.com/alirezamshi/small100). 

The model architecture and config are the same as [M2M-100](https://huggingface.co/facebook/m2m100_418M/tree/main) implementation, but the tokenizer is modified to adjust language codes. Comparing to M2M-100, target language code is added to the beginning of source sequence (instead of source language code), and target language code is removed from the target side.

I've added the usage instruction in [model hub](https://huggingface.co/alirezamsh/small100). Adding this model to transformers, will help NMT community especially for low-resource languages.


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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_20140). All of your documentation changes will be reflected on that endpoint.

Since the only difference is in the tokenization code, maybe it would be more beneficial to add the custom code directly in the model repo (see [documentation here](https://huggingface.co/docs/transformers/custom_models#sharing-custom-models) ) and not add a new model to the library?

@sgugger Thanks for the comment. I currently put the model and tokenization code [here](https://huggingface.co/alirezamsh/small100) in model hub. Is it the standard way? as users have to download the code too. 
Another alternative is to add new options to [m2m-100 tokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/m2m_100/tokenization_m2m_100.py)

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.