mBART Large-50 MMT provides incorrect translation when the source and target language are the same
mBART Large-50 MMT provides incorrect translation when the source and target language are the same, e.g. when translating from "en_XX" to "en_XX"


## 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: 4.4.0.dev0
- Platform: Linux-4.19.112+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyTorch version (GPU?): 1.8.0+cu101 (False)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No


### Who can help

@patrickvonplaten, @patil-suraj

## Information

Model I am using (Bert, XLNet ...): 
`facebook/mbart-large-50-one-to-many-mmt`

The problem arises when using:
* [ ] 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)
* [ ] my own task or dataset: (give details below)

## To reproduce

```python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
article_en = "The head of the United Nations says there is no military solution in Syria"
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", src_lang="en_XX")
model_inputs = tokenizer(article_en, return_tensors="pt")
generated_tokens = model.generate(
    **model_inputs,
    forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"]
)
decoded = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(decoded)
```

Returns:
```
['Şeful Naţiunilor Unite declară că nu există o soluţie militară în Siria']
```

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
Should return something in english, preferably the same content as the original input
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/master/CONTRIBUTING.md) are likely to be ignored.<|||||>@patil-suraj Was this resolved? If so I'll close this issue<|||||>HI @xhlulu 

I don't think if this is an issue with the model implementation, also the model is not really expected to do well on paraphrasing (which English-English), I've seen few other issues where models output text in the wrong language but it's the same with original model in `fairseq` as well. From my experience, multilingual models tend to do this in few cases.<|||||>Thanks, that makes sense! Glad you clarified it 😊