Fix rich progress bar crashing on empty val dataloader sanity checking

src/lightning/pytorch/CHANGELOG.md

@@ -39,6 +39,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed misalignment column while using rich model summary in `DeepSpeedstrategy` ([#21100](https://github.com/Lightning-AI/pytorch-lightning/pull/21100))
 
+
+- Fixed `RichProgressBar` crashing when sanity checking using val dataloader with 0 len ([#21108](https://github.com/Lightning-AI/pytorch-lightning/pull/21108))
+
 ---
 
 ## [2.5.3] - 2025-08-13

src/lightning/pytorch/callbacks/progress/rich_progress.py

@@ -390,8 +390,7 @@ def on_sanity_check_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningM
 
     @override
     def on_sanity_check_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        if self.progress is not None:
-            assert self.val_sanity_progress_bar_id is not None
+        if self.progress is not None and self.val_sanity_progress_bar_id is not None:
             self.progress.update(self.val_sanity_progress_bar_id, advance=0, visible=False)
         self.refresh()
 

tests/tests_pytorch/callbacks/progress/test_rich_progress_bar.py

@@ -577,3 +577,31 @@ def test_rich_progress_bar_metrics_theme_update(*_):
     theme = RichProgressBar(theme=RichProgressBarTheme(metrics_format=".3e", metrics_text_delimiter="\n")).theme
     assert theme.metrics_format == ".3e"
     assert theme.metrics_text_delimiter == "\n"
+
+
+@RunIf(rich=True)
+def test_rich_progress_bar_empty_val_dataloader_model(tmp_path):
+    """Test that RichProgressBar doesn't crash with empty val_dataloader list from model."""
+
+    class EmptyListModel(BoringModel):
+        def train_dataloader(self):
+            return DataLoader(RandomDataset(32, 64), batch_size=2)
+
+        def val_dataloader(self):
+            return []
+
+    model = EmptyListModel()
+    progress_bar = RichProgressBar()
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        max_epochs=1,
+        num_sanity_val_steps=1,
+        callbacks=[progress_bar],
+        limit_train_batches=2,
+        enable_checkpointing=False,
+        logger=False,
+    )
+
+    # This should not raise an AssertionError
+    trainer.fit(model)