Does MBartTokenizer remove the parameter decoder_input_ids?
## Environment info
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- `transformers` version:3.4.0
- Platform:Google Colab
- Python version:3.7
- PyTorch version (GPU?):1.7.0+cu101
- Tensorflow version (GPU?):2.x
- Using GPU in script?: no
- Using distributed or parallel set-up in script?:no

### Who can help
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 tokenizers: @mfuntowicz
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 Summarization: @sshleifer
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
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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
```python
example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
batch = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25').prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
input_ids = batch["input_ids"]
target_ids = batch["decoder_input_ids"]
```
Steps to reproduce the behavior:
```python
KeyError                                  Traceback (most recent call last)
<ipython-input-11-b3eedaf10c3e> in <module>()
      3 batch = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro').prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
      4 input_ids = batch["input_ids"]
----> 5 target_ids = batch["decoder_input_ids"]

/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in __getitem__(self, item)
    232         """
    233         if isinstance(item, str):
--> 234             return self.data[item]
    235         elif self._encodings is not None:
    236             return self._encodings[item]

KeyError: 'decoder_input_ids'
```

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<!-- A clear and concise description of what you would expect to happen. -->

The docs are incorrect, sorry about that.

Try
```python
    from transformers import MBartForConditionalGeneration, MBartTokenizer
    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
    tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
    article = "UN Chief Says There Is No Military Solution in Syria"
    batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
    translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
    translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
    assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"
```<|||||>> The docs are incorrect, sorry about that.
> 
> Try
> 
> ```python
>     from transformers import MBartForConditionalGeneration, MBartTokenizer
>     model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
>     tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
>     article = "UN Chief Says There Is No Military Solution in Syria"
>     batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
>     translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
>     translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
>     assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"
> ```

thank you for your reply, If I don't want to generate, I just want to train. How should I change it?

```python
example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
input_ids = batch["input_ids"]
target_ids = batch["decoder_input_ids"] # Error
decoder_input_ids = target_ids[:, :-1].contiguous()
labels = target_ids[:, 1:].clone()
model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, labels=labels) #forward
```<|||||>See this https://github.com/huggingface/transformers/blob/master/examples/seq2seq/finetune.py#L138

the `batch` argument to that fn is the same as your `batch` (the output of `prepare_seq2seq_batch`)