mBART: incorrect <mask> token id
# 🐛 Bug

## Information

Model I am using: mBART

## To reproduce

```
from transformers import MBartTokenizer
tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
print(tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']))
```
The output for the above code is `[250001, 250001]`  - two different special tokens are mapped to the same id.

## Expected behavior

As far as I can tell, `<mask>` token should be mapped to id 250026.

I've checked [fairseq implementation](https://github.com/pytorch/fairseq/blob/master/fairseq/tasks/multilingual_denoising.py) and it seems that `<mask>` token is added after all the language codes, so it should be the last token in the vocab.

Currently, when I try to use mBART to denoise text with `<mask>` tokens, it mostly just ignores them, but if I replace mask ids with 250026, the model actually generates new text in place of `<mask>` tokens:

```
from transformers import MBartTokenizer, BartForConditionalGeneration

tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
model = BartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')

text = 'I highly recommend <mask> - it is one of the best <mask> ever read!'
inputs = tokenizer.prepare_translation_batch([text], src_lang='en_XX')

outputs = model.generate(inputs['input_ids'], decoder_start_token_id=tokenizer.lang_code_to_id['en_XX'], 
                         num_beams=5)
print(tokenizer.batch_decode(outputs)[0])
```

The output is:
```
en_XX<s> highly recommend - it is one of the best ever read!
```
Replacing mask ids:

```
where = (inputs['input_ids'] == 250001)
inputs['input_ids'][where] = 250026

outputs = model.generate(inputs['input_ids'], decoder_start_token_id=tokenizer.lang_code_to_id['en_XX'], 
                         num_beams=5)
print(tokenizer.batch_decode(outputs)[0])
```

The output is:
```
en_XX<s> highly recommend this book - it is one of the best books I have ever read!
```

(In both cases, the model also skips the first input token when generating output, as discussed in #5755.)

I've also noticed that fairseq is using [language code tokens](https://github.com/pytorch/fairseq/blob/108bb2560b1ec01524ba723bc7c69186875afa0a/fairseq/tasks/multilingual_denoising.py#L62) of the form `[en_XX]` rather than just `en_XX`, which can lead to different tokenization if words like  `en_XX` appear in the text, but that's a rather contrived case.


## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 3.0.2


@sshleifer

great catch and incredibly detailed description, I'll fix this one! Thanks!