'TrainingArguments' object has no attribute 'parallel_mode' when running mBart test

## How to reproduce

```python
python ./tests/transformers/models/mbart/test_training.py
```

## Environment

- OS : CentOS 7.9
- Python version : 3.9
- Transformers version : 4.21.2
- Whether to use Docker:
- Misc.:

```
python ./tests/transformers/models/mbart/test_training.py Reusing dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|███████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 682.67it/s]
100%|██████████████████████████████████████████████████████████████████████████████| 68/68 [00:01<00:00, 52.15ba/s]
100%|████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 43.15ba/s]
100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 42.94ba/s]
You are using a model of type bart to instantiate a model of type mbart. This is not supported for all configurations of models and can yield errors.
Some weights of MBartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-base and are newly initialized: ['encoder.layer_norm.bias', 'decoder.layer_norm.weight', 'encoder.layer_norm.weight', 'decoder.layer_norm.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
You are using a model of type bart to instantiate a model of type mbart. This is not supported for all configurations of models and can yield errors.
Some weights of MBartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-base and are newly initialized: ['encoder.layer_norm.bias', 'decoder.layer_norm.weight', 'encoder.layer_norm.weight', 'decoder.layer_norm.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
PyTorch: setting up devices
Traceback (most recent call last):
  File "./tests/transformers/models/mbart/test_training.py", line 94, in
    fp16=False,
  File "./tests/transformers/models/mbart/test_training.py", line 44, in train
    eval_dataset=dataset["validation"],
  File "/opt/conda/lib/python3.7/site-packages/oslo_core-3.0.0-py3.7.egg/oslo/transformers/trainer.py", line 186, in __init__
    if len(args.parallel_mode) > 0:
AttributeError: 'TrainingArguments' object has no attribute 'parallel_mode'

```

The problem seems to be the `parallel_mode` property in `training_args.py` is commented, line 989

`
# @property
# def parallel_mode(self):
# """
# The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:
#
# - `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
# - `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`).
# - `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses
# `torch.nn.DistributedDataParallel`).
# - `ParallelMode.TPU`: several TPU cores.
# """
# # if is_torch_tpu_available():
# # return ParallelMode.TPU
# # elif is_sagemaker_mp_enabled():
# # return ParallelMode.SAGEMAKER_MODEL_PARALLEL
# # elif is_sagemaker_dp_enabled():
# # return ParallelMode.SAGEMAKER_DATA_PARALLEL
# if self.local_rank != -1:
# return ParallelMode.DISTRIBUTED
# elif self.n_gpu > 1:
# return ParallelMode.NOT_DISTRIBUTED
# else:
# return ParallelMode.NOT_PARALLEL
`