PRETRAINED_INIT_CONFIGURATION for local model path
Tokenizers have a special dict `PRETRAINED_INIT_CONFIGURATION`, which tells the tokenization_utils_base, which extra args to pass to the tokenizer's `__init__`, except it doesn't work for the local model, as the hash is for online s3 models.

I have:

```
PRETRAINED_INIT_CONFIGURATION = {
    "stas/fsmt-wmt19-ru-en": {
        "langs": ["ru", "en"],
    },
    "stas/fsmt-wmt19-en-ru": {
        "langs": ["en", "ru"],
    },
    "stas/fsmt-wmt19-de-en": {
        "langs": ["de", "en"],
    },
    "stas/fsmt-wmt19-en-de": {
        "langs": ["en", "de"],
    },
}

```

So in my own code I use

```
   if LOCAL:
        path = "/code/huggingface/transformers-fair-wmt/data/fsmt-wmt19-ru-en/"
        mname = path
        mname_tok = f"stas/fsmt-wmt19-{src}-{tgt}"
        tokenizer = FSMTTokenizer.from_pretrained(mname_tok)
        model = FSMTForConditionalGeneration.from_pretrained(mname)
    else:
        # # s3 uploaded model
        mname = f"stas/fsmt-wmt19-{src}-{tgt}"
        tokenizer = FSMTTokenizer.from_pretrained(mname)
        model = FSMTForConditionalGeneration.from_pretrained(mname)
```

So `mname_tok` overrides to look up the dict above, since it fails to find that entry using local path.

This, however, doesn't work in tools that aren't under my control, `run_eval.py` in seq2seq for example.

**edit**, more to it - it doesn't pass `PRETRAINED_VOCAB_FILES_MAP` args either for the same reason - it fails to look up those entries for local path. I need to change them all.

Any suggestions on how to fix this problem?
I found a sort of band-aid, I added this code in the model's tokenization code, right after  init of `PRETRAINED_INIT_CONFIGURATION, PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES`:

```
LOCALIZE=1
if LOCALIZE:
    old, new  = ("stas/", "/mnt/nvme1/code/huggingface/transformers-fair-wmt/data/")

    def localize(buf): return buf.replace(old, new)

    for d in [PRETRAINED_INIT_CONFIGURATION, PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES]:
        for k, v in d.copy().items():
            d[localize(k)] = v

    for d in [PRETRAINED_VOCAB_FILES_MAP]:
        for tk, tv in d.items():
            for k, v in tv.copy().items():
                tv[localize(k)] = v
```

It's still not great, since now I can't commit this file to repo as this leads to a problem of committing other changes in this file. But at least I can move forward.<|||||>I dug dipper and found how to solve it - needed to create `model_dir/tokenizer_config.json` and put the special init params there. And do several more tweaks to make the vocab files to not include the language names in the filename, but use the generic 'vocab-src.txt', 'vocab-tgt.txt'.