move progress bar test to correct test folder (#5667)

CHANGELOG.md

@@ -206,7 +206,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed FileNotFoundError for best checkpoint when using DDP with Hydra ([#5629](https://github.com/PyTorchLightning/pytorch-lightning/pull/5629))
 - Fixed an error when logging a progress bar metric with a reserved name ([#5620](https://github.com/PyTorchLightning/pytorch-lightning/pull/5620))
 - Fixed `Metric`'s `state_dict` not included when child modules ([#5614](https://github.com/PyTorchLightning/pytorch-lightning/pull/5614))
-- Fixed Neptune logger creating multiple experiments when GPUs > 1 ([#3256](https://github.com/PyTorchLightning/pytorch-lightning/pull/3256)) 
+- Fixed Neptune logger creating multiple experiments when GPUs > 1 ([#3256](https://github.com/PyTorchLightning/pytorch-lightning/pull/3256))
 - Fixed duplicate logs appearing in console when using the python logging module ([#5509](https://github.com/PyTorchLightning/pytorch-lightning/pull/5509))
 - Fixed tensor printing in `trainer.test()` ([#5138](https://github.com/PyTorchLightning/pytorch-lightning/pull/5138))
 - Fixed not using dataloader when `hparams` present ([#4559](https://github.com/PyTorchLightning/pytorch-lightning/pull/4559))

tests/trainer/logging_/test_progress_bar_logging.py

@@ -0,0 +1,22 @@
+import pytest
+
+from pytorch_lightning import Trainer
+from tests.base import BoringModel
+
+
+def test_logging_to_progress_bar_with_reserved_key(tmpdir):
+    """ Test that logging a metric with a reserved name to the progress bar raises a warning. """
+    class TestModel(BoringModel):
+
+        def training_step(self, *args, **kwargs):
+            output = super().training_step(*args, **kwargs)
+            self.log("loss", output["loss"], prog_bar=True)
+            return output
+
+    model = TestModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=2,
+    )
+    with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
+        trainer.fit(model)