Encoder - Decoder loading wrong weights and missing decoders
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...): Roberta as encoder and Bert as decoder

Language I am using the model on (English, Chinese ...): English

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

The tasks I am working on is:
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. https://github.com/ThilinaRajapakse/simpletransformers/pull/360

## Expected behavior
Predicting the same string before saving and after loading the saved model are completely different.
Expected that i get the same results both times

Furthermore your tutorial on medium is not working, caused missing decoder models.
Link to medium: https://medium.com/huggingface/encoder-decoders-in-transformers-a-hybrid-pre-trained-architecture-for-seq2seq-af4d7bf14bb8
There is missing an argument in the forward function:
`TypeError: forward() got an unexpected keyword argument 'encoder_hidden_states'`

## 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:
- Platform: Linux
- Python version: 3.7
- PyTorch version (GPU?): GPU 1.4.0
- Tensorflow version (GPU?):
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
