diff --git a/examples/pytorch/summarization/run_summarization_no_trainer.py b/examples/pytorch/summarization/run_summarization_no_trainer.py
index eb809ada99..594d468330 100644
--- a/examples/pytorch/summarization/run_summarization_no_trainer.py
+++ b/examples/pytorch/summarization/run_summarization_no_trainer.py
@@ -669,7 +669,6 @@ def main():
             "max_length": args.val_max_target_length if args is not None else config.max_length,
             "num_beams": args.num_beams,
         }
-        samples_seen = 0
         for step, batch in enumerate(eval_dataloader):
             with torch.no_grad():
                 generated_tokens = accelerator.unwrap_model(model).generate(
@@ -686,7 +685,7 @@ def main():
                     # If we did not pad to max length, we need to pad the labels too
                     labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id)
 
-                generated_tokens, labels = accelerator.gather((generated_tokens, labels))
+                generated_tokens, labels = accelerator.gather_for_metrics(generated_tokens, labels)
                 generated_tokens = generated_tokens.cpu().numpy()
                 labels = labels.cpu().numpy()
 
@@ -699,14 +698,8 @@ def main():
                 decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
 
                 decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
-                # If we are in a multiprocess environment, the last batch has duplicates
-                if accelerator.num_processes > 1:
-                    if step == len(eval_dataloader) - 1:
-                        decoded_preds = decoded_preds[: len(eval_dataloader.dataset) - samples_seen]
-                        decoded_labels = decoded_labels[: len(eval_dataloader.dataset) - samples_seen]
-                    else:
-                        samples_seen += len(decoded_labels)
 
+                decoded_preds, decoded_labels = accelerator.gather_for_metrics(decoded_preds, decoded_labels)
                 metric.add_batch(
                     predictions=decoded_preds,
                     references=decoded_labels,