Split checkpoint from model_name_or_path in examples
# What does this PR do?

There is currently a problem in the way we handle resuming from checkpoint in our PyTorch example scripts with `Trainer`: if the user wants to resume form a specific checkpoint, they have to pass `--model_name_or_path checkpoint_folder` which is a bit counter-intuitive but also poses the problem that sometimes the user is passing `--model_name_or_path local_pretrained_model`.

This has caused multiple issues that we tried to patch a bit as they came:
- in text classification or token classification, the `local_pretrained_model` might be pretrained with a different number of labels which made the loading fail (shape mismatch)
- more recently since we're not using `from_pretrained` anymore when loading the checkpoint (to keep the model as provided by the user, with potential frozen layers), the state dict of `local_pretrained_model` generally won't match: if the task is different, its head will have weights incompatible with the model at end.

This PR cleans things up by adding a new training argument called `--resume_from_checkpoint`. To resume training from an explicit checkpoint, the user now has to pass `--resume_from_checkpoint checkpoint_folder` as passing `--model_name_or_path some_local_folder` now only loads the model inside the local folder as a pretrained model and doesn't look for a checkpoint.
It's slightly breaking (not in the library, just the commands to run the examples) but cleaner IMO.

The training argument `resume_from_checkpoint` is used as a default  for the argument of the same name in the `train` method.

Might fix #11485
Hi guys, just one comment/question from my side: instead of introducing new commandline options (allowing to resume from a checkpoint), what do you think about the following workflow: 

a) load the model (with AutoModel)
b) check if the loaded model is from type "TokenClassification"

:thinking: <|||||>> I'm also thinking - if we know we are going to resume from checkpoint for sure, then replace from_pretrained with from_config and save the double loading of the weights?

I'm leaving this to your PoC on one of the seq2seq scripts if you don't mind? This PR is also kind of an urgent bug fix.<|||||>Oh, of course, I had no idea about the urgency. I will do it in another PR then.