AssertionError: Padding_idx must be within num_embeddings  MarianModel
### System Info

- `transformers` version: 4.22.2
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.14
- Huggingface_hub version: 0.10.0
- PyTorch version (GPU?): 1.12.1+cu113 (False)
- Tensorflow version (GPU?): 2.8.2 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?

### Who can help?

@LysandreJik

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

The code is taken from official huggingface documentation [here](https://huggingface.co/docs/transformers/model_doc/marian#transformers.MarianConfig.example)

from transformers import MarianModel, MarianConfig

#### Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
configuration = MarianConfig()

#### Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
model = MarianModel(configuration)

#### Accessing the model configuration
configuration = model.config

### Expected behavior

I am trying to initialize MarianModel without using pretrained weights. So instead of this code
```
model_name = "penpen/novel-zh-en"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
```` 
I wan to use it with out using pretrained model. Instead I want it to be trained from scratch.  So i used MarianConfig, but it throw the error. 
```
AssertionError: Padding_idx must be within num_embeddings
```

Hi @talhaanwarch 👋 This seems to be an error in our default value for the size of the embeddings. Thank you for raising it!

Meanwhile, you should be able to run
```python
from transformers import MarianModel, MarianConfig

# Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
configuration = MarianConfig()
configuration.vocab_size = configuration.pad_token_id + 1

# Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
model = MarianModel(configuration)
```