OverflowError: out of range integral type conversion attempted for run_summarization.py script using t5-small
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 
- Platform: Mac OS
- Python version: 3.8.5
- PyTorch version (GPU?):  No GPU , >- 1.3
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help

Models:

- t5: @patrickvonplaten, @patil-suraj

Library:

- tokenizers: @LysandreJik
Looks like this is an issue with the t5Tokenizer possibly? - Seems related to this old github issue as well https://github.com/huggingface/transformers/pull/10046.  


- maintained examples (not research project or legacy): @sgugger, @patil-suraj

 -->

## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
* [X ] the official example scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [X] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Run the run_summarization.py file on the multi_news dataset using t5-small with do_predict and predict_with_generate options set to true
2. In the prediction step, the decode step highlighted in the error stack below gives an OverFlow error and the prediction stops

```
File "run_summarization.py", line 674, in <module>
    main()
  File "run_summarization.py", line 628, in main
    predict_results = trainer.predict(
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/trainer_seq2seq.py", line 125, in predict
    return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/trainer.py", line 2133, in predict
    output = eval_loop(
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/trainer.py", line 2235, in evaluation_loop
    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/trainer_seq2seq.py", line 180, in prediction_step
    print(self.tokenizer_t5.batch_decode(inputs["labels"], skip_special_tokens=True, clean_up_tokenization_spaces=True))
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/tokenization_utils_base.py", line 3047, in batch_decode
    return [
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/tokenization_utils_base.py", line 3048, in <listcomp>
    self.decode(
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/tokenization_utils_base.py", line 3086, in decode
    return self._decode(
  File "/Users/aiswarya.s/Desktop/transformers_fudge/src/transformers/tokenization_utils_fast.py", line 507, in _decode
    text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
OverflowError: out of range integral type conversion attempted```
```


## Expected behavior

Should generate the tokenizer.decode outputs

@aiswaryasankar - could you attach a **minimal** reproducible code snippet (also in google colab form) that allows us to quickly spot the error? Thank you :-)<|||||>from the stack-trace it looks like you are decoding `labels` in the `prediction_step` method. When 
`--ignore_pad_token_for_loss` argument is set, the `labels` will still have -100 in `prediction_step`, so -100 should be replaced by pad token before decoding. The `run_summarization.py` script does that in the `compute_metrics` function which is called after the `prediction_step` method. 
https://github.com/huggingface/transformers/blob/c02cd95c56249e9bd38ecb3e4ebcce6d9eebd4a4/examples/pytorch/summarization/run_summarization.py#L509<|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.