Use metrics that consider the input as well as the (predicted, reference) tuple in the Trainer
### Feature request

Allow the **compute_metrics()** in the **Trainer** to take into account the **original input** in addition to the predictions and labels.

### Motivation

It is currently possible to pass a custom compute_metrics() to the Trainer for evaluation. An example is 

```
def compute_metrics(eval_preds):
    metric = evaluate.load("glue", "mrpc")
    logits, labels = eval_preds
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

```
However, the compute_metrics seems to be constrained only to receive a logit, label tuple. 

This is insufficient for some metrics that also depend on the original sentence. An example is [SARI](https://huggingface.co/spaces/evaluate-metric/sari), which is currently implemented in the evaluate library.

Being unable to use the original input in the evaluation makes it impossible to use the Trainer for some seq2seq tasks, e.g. simplification.

### Your contribution

If the request is accepted, I will try to contribute with a PR.
I just saw that the feature is actually already added. 

One can add the trainer argument --include_inputs_for_metrics
and the compute_metrics will receive the inputs as the third element of the tuple.