mBART tokenizer not following expected target token order
## Environment info
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- `transformers` version: 4.17
- Platform: linux
- Python version: 3.8.12
- PyTorch version (GPU?): 1.10.2+cu113
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help
Maybe  @patil-suraj @SaulLu @patrickvonplaten ?
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## Information

Model I am using (Bert, XLNet ...): mBART

The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce & Expected behavior

```
from transformers import MBartTokenizerFast, MBartTokenizer
tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro')
tokenizer.src_lang = 'en_XX'
tokenizer.tgt_lang = 'ro_RO'

print(tokenizer('haha'))
with tokenizer.as_target_tokenizer():
    print(tokenizer('haha'))
```

which returns 
```
{'input_ids': [22010, 2, 250004], 'attention_mask': [1, 1, 1]}
{'input_ids': [22010, 2, 250020], 'attention_mask': [1, 1, 1]}
```

However, per the [official doc](https://huggingface.co/transformers/v4.1.1/model_doc/mbart.html#training), the target sequence should follow the order of `[tgt_lang_code] X [eos]`, which is `[250020, 2, 22010]` in the case above.

Hey @zijwang ! Thank you for reporting this.

Here actually the doc is wrong. Here the target tokens actually refer to  `decoder_input_ids` for which the format should be
`[tgt_lang_code] x [eos]`

But it's not required to directly pass `decoder_input_ids`, we can instead just pass `labels` which is of the format `x [eos][tgt_lang_code]`. And the `decoder_input_ids` are prepared inside the model by shifting `labels` to the right, so we get the correct format for `decoder_input_ids` i.e `[tgt_lang_code] x [eos]`.


The `with tokenizer.as_target_tokenizer():` is used to prepare `labels`, so the order is correct.  I will fix the docs and add better examples. 
<|||||>Thanks, @patil-suraj . I see your point here. Having the docs updated will be helpful :)
Meanwhile, do we still need to set `decoder_start_token_id` (something like https://github.com/huggingface/transformers/blob/master/examples/pytorch/translation/run_translation_no_trainer.py#L365-L372) given `decoder_input_ids` is right-shifted input?<|||||>>  do we still need to set `decoder_start_token_id`

It's not required to set it, if you pass `labels` those will be created inside the model.<|||||>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.