run_summarization requires a dataset_name or train_file or validation_file in all cases
### System Info

In the latest version of run_summarization on line 264 it requires dataset_name or train_file or validation_file. I was trying to perform a "do_predict" with the parameter "test_file" set, but the validation will not let me proceed. Looking at the code, do_predict only uses the test dataset anyway, so this appears to be a bug.

See the specific file
https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization.py

### Who can help?

@sgugger Unable to "do_predict" due to validation not checking for "train_file". As a workaround I set "validation_file".

### Information

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

### Tasks

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

### Reproduction

in a Colab notebook:
%%shell
/content/transformers/examples/pytorch/summarization/run_summarization.py \
    --model_name_or_path /content/gs/models/Clinical-T5-Large/ \
    --do_predict \
    --test_file "/content/gs/models/Clinical-T5-Large/validation.csv" \
    --dataset_config "3.0.0" \
    --source_prefix "summarize: " \
    --output_dir /content/gs/models/Clinical-T5-Large \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --predict_with_generate \
    --max_source_length=1024

You get the error "Need either a dataset name or a training/validation file."

### Expected behavior

Expected normal prediction output like below:

Running tokenizer on prediction dataset:   0% 0/5040 [00:00<?, ? examples/s]03/20/2023 15:40:20 - INFO - datasets.arrow_dataset - Caching processed dataset at /root/.cache/huggingface/datasets/csv/default-73bac40c02a24f34/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317/cache-1e88c7213311bd88.arrow
Downloading builder script: 100% 6.27k/6.27k [00:00<00:00, 3.59MB/s]
03/20/2023 15:40:52 - INFO - __main__ - *** Predict ***
[INFO|trainer.py:3066] 2023-03-20 15:40:52,611 >> ***** Running Prediction *****
[INFO|trainer.py:3068] 2023-03-20 15:40:52,611 >>   Num examples = 5040
[INFO|trainer.py:3071] 2023-03-20 15:40:52,611 >>   Batch size = 4
[WARNING|logging.py:280] 2023-03-20 15:40:52,624 >> You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
[INFO|configuration_utils.py:575] 2023-03-20 15:40:52,636 >> Generate config GenerationConfig {
  "_from_model_config": true,
  "decoder_start_token_id": 0,
  "eos_token_id": 1,
  "pad_token_id": 0,
  "transformers_version": "4.28.0.dev0"
}
Indeed. Would you like to open a PR to fix this?