Temporary fix for RoBERTa's mismatch of vocab size and embedding size - issue #1091
I added an optional input argument so you can pass the starting index when adding new tokens.
# [Codecov](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1096?src=pr&el=h1) Report
> Merging [#1096](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1096?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/pytorch-transformers/commit/df9d6effae43e92761eb92540bc45fac846789ee?src=pr&el=desc) will **increase** coverage by `<.01%`.
> The diff coverage is `85.71%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1096/graphs/tree.svg?width=650&token=9qOlN6Hb1c&height=150&src=pr)](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1096?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #1096      +/-   ##
==========================================
+ Coverage   79.61%   79.62%   +<.01%     
==========================================
  Files          42       42              
  Lines        6898     6900       +2     
==========================================
+ Hits         5492     5494       +2     
  Misses       1406     1406
```


| [Impacted Files](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1096?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pytorch\_transformers/tokenization\_utils.py](https://codecov.io/gh/huggingface/pytorch-transformers/pull/1096/diff?src=pr&el=tree#diff-cHl0b3JjaF90cmFuc2Zvcm1lcnMvdG9rZW5pemF0aW9uX3V0aWxzLnB5) | `86.31% <85.71%> (+0.08%)` | :arrow_up: |

------

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<|||||>Thanks @amirsaffari. We actually don't need this fix anymore.

@LysandreJik has updated the vocabulary on the AWS S3 bucket to include the missing (unused) tokens (called `makeupword0000`, `makeupword0001` and `makeupword0002`). So that the vocabular now has the same length as the last token index. We're adding a test as well.

If you have the latest release you can delete your cached vocabulary to download the updated version. If you have installed from master, you can just force the download and overwriting of the new vocabulary with `tokenizer = RobertaTokenizer.from_pretrained('your-model', force_download=True)` <|||||>👍 