Tokenizers: setting bos_token_id = 0 and adding language_pair_codes
I am unable to set bos_token_id=0 for a new SentencePiece tokenizer (MBART).
Here is what I'm doing?

```bash
wget https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/sentence.bpe.model
```

```python
from transformers import T5Tokenizer
vocab_file = 'sentence.bpe.model'
t2 = T5Tokenizer(vocab_file, bos_token='<s>', bos_token_id=0)
t2.bos_token_id # => 1
```

The following also returns 1
```python
t2 = T5Tokenizer(vocab_file, bos_token='<s>', bos_token_id=0,
                 additional_special_tokens=['<s>'])
t2.bos_token_id
```

Help much appreciated!
You can't set the ids, they are set automatically from the sentence piece model.
But (1) why are you using the T5Tokenizer for a Bart checkpoint and (2) why do you want to tweak the id?<|||||>(1) I used the `T5Tokenizer` in order to make a runnable example that did not require checking out my `mbart` branch.

(2) Fairseq's MBART logic is split into two stages:
- use `spm_encode --model sentence.bpe.model` to preprocess. (this is like encode_as_pieces in python).
- use a `vocab.json` style lookup to convert each token to an ID.

I'm trying to do that in one step, using `sp_model.encode_as_ids`, but my ids are off by 1, because the special tokens (sp_model.bos_token, etc) are different than fairseq's dictionary object:


![image](https://user-images.githubusercontent.com/6045025/78171826-0bde0b00-7423-11ea-80ee-8e8d84890f71.png)

So I need to either manipulate the sp_model, retrain it with correct control codes, or try a different approach.

<|||||>Yes you can check how we do these token index offset stuff (it’s specific to fairseq + sentencepiece) in Camembert and XLMRoberta tokenizers.<|||||>Extremely helpful! Mbart also adds a language code like en_XX and ro_RO to the end of the source and target sentences. So the sentences are like `[tokens]+[<eos>, <language_id>]`

Do we have any tokenizers that do that? <|||||>can't find an easy way to generate examples like
```python
input_ids = [src_tokens]+[<eos>, <src_language_id>]
decoder_input_ids = [tgt_tokens]+[<eos>, <tgt_language_id>]
```
where the special tokens depend on the language.

My best idea is to add a method 
```python
def prepare_language_pair_batch(self, source_sentences, source_lang, target_sentences=None,  target_lang=None):
	# encode source sentence
	# if target_sentence is None ignore it else process it
    return {input_ids=encoded_source_ids, attention_mask=attention_mask, decoder_input_ids=processed_target}
```
(Could also overwrite `prepare_inputs_for_model` and add arguments.)


Two other ideas that don't quite work:
- Try to stuff the language codes into the string as text in `prepare_text_for_tokenization`. The problem is this would go before EOS.
- Try to do the magic in `build_inputs_with_special_tokens`. the problem is that you still can't use `prepare_for_model` because it doesn't pass kwargs to  `build_inputs_with_special_tokens`.

We could also instantiate two tokenizers with different special tokens, but that feels wasteful.

@LysandreJik @patrickvonplaten <|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>> Yes you can check how we do these token index offset stuff (it’s specific to fairseq + sentencepiece) in Camembert and XLMRoberta tokenizers.

For posterity, I think Thomas means this:
```
https://huggingface.co/transformers/v4.6.0/_modules/transformers/models/camembert/tokenization_camembert.html
https://huggingface.co/transformers/v3.5.1/_modules/transformers/tokenization_xlm_roberta.html
```
