report_to flag does not work with TFTrainer
Author: tomy0000000
Pull request: no

## Environment info

- `transformers` version: 4.6.1
- Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.27
- Python version: 3.8.10
- PyTorch version (GPU?): 1.7.1+cu101 (True)
- Tensorflow version (GPU?): 2.5.0 (False)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: Yes

### Who can help

@sgugger @LysandreJik @Rocketknight1 

## Information

The problem arises when using my own modified scripts: [CoLA from BLUE on TF version BERT](https://gist.github.com/tomy0000000/af06394aa00a8b0fffb992e5bf444adf)

My modified script only run the CoLA task, which is minimized from the official [tf_glue.py](https://github.com/huggingface/transformers/blob/master/examples/tensorflow/text-classification/run_tf_glue.py)

I've setup comet.ml and wandb for other project, but don't want to use them in this one.
However `report_to` flag don't seems to work in `TFTrainingArguments`.
More specific in the [trainer_tf.py](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py#L113-L127)

## To reproduce

Just run the notebook in jupyter, error pops in the 8th cell when `TFTrainer` is initialized.

Stacktrace:

```python
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-9-308135373fed> in <module>
----> 1 trainer = TFTrainer(
      2     model=model,
      3     args=TFTrainingArguments(output_dir=".", report_to="tensorboard"),
      4     train_dataset=train_dataset,
      5     eval_dataset=eval_dataset,

~/.local/lib/python3.8/site-packages/transformers/trainer_tf.py in __init__(self, model, args, train_dataset, eval_dataset, compute_metrics, tb_writer, optimizers)
    120 
    121         if is_comet_available():
--> 122             self.setup_comet()
    123         elif os.environ.get("COMET_MODE") != "DISABLED":
    124             logger.info(

~/.local/lib/python3.8/site-packages/transformers/trainer_tf.py in setup_comet(self)
    274         experiment = None
    275         if comet_mode == "ONLINE":
--> 276             experiment = comet_ml.Experiment(**args)
    277             logger.info("Automatic Comet.ml online logging enabled")
    278         elif comet_mode == "OFFLINE":

~/.local/lib/python3.8/site-packages/comet_ml/__init__.py in __init__(self, api_key, project_name, workspace, log_code, log_graph, auto_param_logging, auto_metric_logging, parse_args, auto_output_logging, log_env_details, log_git_metadata, log_git_patch, disabled, log_env_gpu, log_env_host, display_summary, log_env_cpu, display_summary_level, optimizer_data, auto_weight_logging, auto_log_co2, auto_metric_step_rate, auto_histogram_tensorboard_logging, auto_histogram_epoch_rate, auto_histogram_weight_logging, auto_histogram_gradient_logging, auto_histogram_activation_logging)
    239             )
    240 
--> 241         super(Experiment, self).__init__(
    242             project_name=project_name,
    243             workspace=workspace,

~/.local/lib/python3.8/site-packages/comet_ml/experiment.py in __init__(self, project_name, workspace, log_code, log_graph, auto_param_logging, auto_metric_logging, parse_args, auto_output_logging, log_env_details, log_git_metadata, log_git_patch, disabled, log_env_gpu, log_env_host, display_summary, log_env_cpu, display_summary_level, optimizer_data, auto_weight_logging, auto_log_co2, auto_metric_step_rate, auto_histogram_tensorboard_logging, auto_histogram_epoch_rate, auto_histogram_weight_logging, auto_histogram_gradient_logging, auto_histogram_activation_logging)
    458                     ALREADY_IMPORTED_MODULES
    459                 )
--> 460                 raise ImportError(msg)
    461 
    462         # Generate a unique identifier for this experiment.

ImportError: You must import Comet before these modules: tensorflow, torch, tensorboard
```

## Expected behavior

`TFTrainer` should respect the `report_to` arguments provided


Comments:
No this is not implemented for the `TFTrainer`. More generally, we are moving away from `TFTrainer` to go to pure Keras for the training loop.

Well, does that means `trainer_tf.py` is getting a huge rewrite? If not, I'm willing to work on a PR solving this particular issue. Let me know if anyone have thoughts about how this should be implemented.

No it's just going to disappear and we will use the Keras methods (fit etc.) instead.