AssertionError: internal model should be a reference to self.model
## Environment info
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- `transformers` version: 4.2.2 
- Platform: Linux
- Python version: 3.6
- PyTorch version (GPU?): 1.7 CPU
- Tensorflow version (GPU?):
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help

@sgugger 

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## Information

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

The problem arises when using:
* [ ] the official example scripts: (give details below)
When I run `trainer.train()` for the second time in Jyputer, it throws error:
```
AssertionError                            Traceback (most recent call last)
<ipython-input-7-3435b262f1ae> in <module>
----> 1 trainer.train()

~/data/apps/anaconda3/lib/python3.7/site-packages/transformers/trainer.py in train(self, model_path, trial)
    933 
    934             self.control = self.callback_handler.on_epoch_end(self.args, self.state, self.control)
--> 935             self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)
    936 
    937             if self.args.tpu_metrics_debug or self.args.debug:

~/data/apps/anaconda3/lib/python3.7/site-packages/transformers/trainer.py in _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch)
   1006 
   1007         if self.control.should_save:
-> 1008             self._save_checkpoint(model, trial, metrics=metrics)
   1009             self.control = self.callback_handler.on_save(self.args, self.state, self.control)
   1010 

~/data/apps/anaconda3/lib/python3.7/site-packages/transformers/trainer.py in _save_checkpoint(self, model, trial, metrics)
   1012         # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
   1013         # want to save.
-> 1014         assert _model_unwrap(model) is self.model, "internal model should be a reference to self.model"
   1015 
   1016         # Save model checkpoint

AssertionError: internal model should be a reference to self.model

```
https://huggingface.co/transformers/training.html

The tasks I am working on is:
* [ ] sequence classification
* [ ] my own task

## To reproduce

Steps to reproduce the behavior:

1. run `trainer.train()`
2. run `trainer.train()` again.

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## Expected behavior

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

You should update your version of Transformers to solve this issue.<|||||>thanks<|||||>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.

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