Question about resum_from_checkpoint in run_translation_no_trainer.py
### System Info

- `transformers` version: 4.29.0.dev0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.6
- Huggingface_hub version: 0.14.1
- Safetensors version: not installed
- PyTorch version (GPU?): 1.12.1+cpu (False)
- Tensorflow version (GPU?): 2.10.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no


### Who can help?

@sgugger

### Information

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

### Tasks

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

### Reproduction

-

### Expected behavior

Hi, i stumbled across this function, while i was debugging my own code which is a changed version of the no_trainer version.
While my code crashes with a cuda error, i found this part interesting as it would help me in getting faster to the evaluation process (where my error occurs).

However it seems a bit weird as i was reading this function
[https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py#LL594C4-L614C66](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py#LL594C4-L614C66)

What seems a bit wird to me is, that first "resume_from_checkpoint" could be either None (default) or a checkpoint like "epoch_5" or "step_1000". The parameter Definition says it should be of Type String or daults to None [https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py#L266-L271](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py#L266-L271)

The first check is if the argument was set, to go into the resume block. 
```
if args.resume_from_checkpoint:
```
From my understanding the following values would work:
"step_1234" or any other string,
"" an empty string.
True

Now the next check will look if it is not none or not empty
```
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
```
So everything in the if block will be executed in these instances:
is: True/False
is: "step_1000" or any other non empty string.
But if it is True, the later part can not work here as it searches either for "epoch_*" or "step_*", in one of my testruns it whowed when using True it will search for the folder "True" which then fails. While it might be perfectly finde to set it to "" i also found some examples about using the normal trainer, where resume was simply set to true like so:
```trainer.train(resume_from_checkpoint = True)```

My suggestion here would be to first check if it is not None, then if it is not True:
```
if args.resume_from_checkpoint:
    args.resume_from_checkpoint != "" and args.resume_from_checkpoint is not True
```
So if it is set to True or an empty string, it will search for a folder with either "step_" or "epoch_" as a name and use this folder.

Another difficulty i found in my project was, that i wanted to save the checkpoints into a subfolder, saving it was not the problem but loading it here, because the script will only replace "epoch_" or "step_" to fetch the step or epoch number. In Order to find the folders in the subfolder i changed the check like so:
```
                dirs = [os.path.join(args.checkpoint_dir, f.name) for f in os.scandir(f"{args.checkpoint_dir}") if f.is_dir()]
...
            import re
            # First the correct epoch is detected here, later the process will skip training until reaching the correct step.
            if "epoch" in training_difference:
                repl = re.search(r'epoch_(\d+)', training_difference).group()
                starting_epoch = int(repl.replace("epoch_", "")) + 1
```
Same check would have to be done in the "step_" part.

Maybe the args.checkpoint_dir can be omitted, so the user can specify the path when setting the argument (True or "" would then not work anymore)

At last i have a question about this part:
```
accelerator.load_state(checkpoint)
```
This is only defined in the block when ```args.resume_from_checkpoint``` is not empty, is it not needed to let the accelerator always load the appropriate checkpoint? In the example it would not use accelerator.load_state when loading it from the latest chekpoint which it discovered by itself.

Edit:
fixed file links
cc @muellerzr <|||||>@danielDigitalArt it's not needed unless we resume from a checkpoint, yes. If you would like to open a PR with your suggestions, that would be welcome as they do make sense to me as an inclusion. Very keen observation<|||||>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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