[pre-commit.ci] pre-commit suggestions (#20968)

* [pre-commit.ci] pre-commit suggestions

updates:
- [github.com/PyCQA/docformatter: 06907d0267368b49b9180eed423fae5697c1e909 → v1.7.7](PyCQA/docformatter@06907d0...v1.7.7)
- [github.com/astral-sh/ruff-pre-commit: v0.11.4 → v0.12.2](astral-sh/ruff-pre-commit@v0.11.4...v0.12.2)
- [github.com/pre-commit/mirrors-prettier: v3.1.0 → v4.0.0-alpha.8](pre-commit/mirrors-prettier@v3.1.0...v4.0.0-alpha.8)

* Apply suggestions from code review

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <[email protected]>
Co-authored-by: Jirka B <[email protected]>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -58,7 +58,7 @@ repos:
         #args: ["--write-changes"] # uncomment if you want to get automatic fixing
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: 06907d0267368b49b9180eed423fae5697c1e909 # todo: fix for docformatter after last 1.7.5
+    rev: v1.7.7
     hooks:
       - id: docformatter
         additional_dependencies: [tomli]
@@ -70,7 +70,7 @@ repos:
       - id: sphinx-lint
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.4
+    rev: v0.12.2
     hooks:
       # try to fix what is possible
       - id: ruff

src/lightning/pytorch/core/optimizer.py

@@ -274,7 +274,7 @@ def _configure_schedulers_automatic_opt(schedulers: list, monitor: Optional[str]
             scheduler["reduce_on_plateau"] = scheduler.get(
                 "reduce_on_plateau", isinstance(scheduler["scheduler"], optim.lr_scheduler.ReduceLROnPlateau)
             )
-            if scheduler["reduce_on_plateau"] and scheduler.get("monitor", None) is None:
+            if scheduler["reduce_on_plateau"] and scheduler.get("monitor") is None:
                 raise MisconfigurationException(
                     "The lr scheduler dict must include a monitor when a `ReduceLROnPlateau` scheduler is used."
                     ' For example: {"optimizer": optimizer, "lr_scheduler":'

tests/tests_pytorch/callbacks/test_finetuning_callback.py

@@ -109,8 +109,8 @@ def configure_optimizers(self):
     model.validation_step = None
     callback = TestBackboneFinetuningWarningCallback(unfreeze_backbone_at_epoch=3, verbose=False)
 
+    trainer = Trainer(limit_train_batches=1, default_root_dir=tmp_path, callbacks=[callback, chk], max_epochs=2)
     with pytest.warns(UserWarning, match="Did you init your optimizer in"):
-        trainer = Trainer(limit_train_batches=1, default_root_dir=tmp_path, callbacks=[callback, chk], max_epochs=2)
         trainer.fit(model)
 
     assert model.backbone.has_been_used

tests/tests_pytorch/profilers/test_profiler.py

@@ -73,9 +73,9 @@ def test_simple_profiler_durations(simple_profiler, action: str, expected: list)
     np.testing.assert_allclose(simple_profiler.recorded_durations[action], expected, rtol=0.2)
 
 
-def test_simple_profiler_overhead(simple_profiler, n_iter=5):
+def test_simple_profiler_overhead(simple_profiler):
     """Ensure that the profiler doesn't introduce too much overhead during training."""
-    for _ in range(n_iter):
+    for _ in range(5):
         with simple_profiler.profile("no-op"):
             pass
 
@@ -284,8 +284,9 @@ def test_advanced_profiler_durations(advanced_profiler, action: str, expected: l
 
 
 @pytest.mark.flaky(reruns=3)
-def test_advanced_profiler_overhead(advanced_profiler, n_iter=5):
+def test_advanced_profiler_overhead(advanced_profiler):
     """Ensure that the profiler doesn't introduce too much overhead during training."""
+    n_iter = 5
     for _ in range(n_iter):
         with advanced_profiler.profile("no-op"):
             pass
@@ -620,8 +621,8 @@ def test_pytorch_profiler_raises_warning_for_limited_steps(tmp_path, trainer_con
     warning_cache.clear()
     with pytest.warns(UserWarning, match="not enough steps to properly record traces"):
         getattr(trainer, trainer_fn)(model)
-        assert trainer.profiler._schedule is None
-        warning_cache.clear()
+    assert trainer.profiler._schedule is None
+    warning_cache.clear()
 
 
 def test_profile_callbacks(tmp_path):

tests/tests_pytorch/trainer/logging_/test_eval_loop_logging.py

@@ -234,9 +234,9 @@ def on_test_epoch_end(self):
 
 
 @pytest.mark.parametrize("suffix", [False, True])
-def test_multi_dataloaders_add_suffix_properly(tmp_path, suffix):
+def test_multi_dataloaders_add_suffix_properly(suffix, tmp_path):
     class TestModel(BoringModel):
-        def test_step(self, batch, batch_idx, dataloader_idx=0):
+        def test_step(self, batch, batch_idx, dataloader_idx=0):  # noqa: PT028
             out = super().test_step(batch, batch_idx)
             self.log("test_loss", out["y"], on_step=True, on_epoch=True)
             return out
@@ -441,7 +441,7 @@ def on_test_epoch_end(self, _, pl_module):
     class TestModel(BoringModel):
         seen_losses = {i: [] for i in range(num_dataloaders)}
 
-        def test_step(self, batch, batch_idx, dataloader_idx=0):
+        def test_step(self, batch, batch_idx, dataloader_idx=0):  # noqa: PT028
             loss = super().test_step(batch, batch_idx)["y"]
             self.log("test_loss", loss)
             self.seen_losses[dataloader_idx].append(loss)

tests/tests_pytorch/trainer/test_config_validator.py

@@ -16,7 +16,6 @@
 import pytest
 import torch
 
-from lightning.fabric.utilities.warnings import PossibleUserWarning
 from lightning.pytorch import LightningDataModule, LightningModule, Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from lightning.pytorch.trainer.configuration_validator import (
@@ -46,20 +45,19 @@ def test_wrong_configure_optimizers(tmp_path):
         trainer.fit(model)
 
 
-def test_fit_val_loop_config(tmp_path):
+@pytest.mark.parametrize("model_attrib", ["validation_step", "val_dataloader"])
+def test_fit_val_loop_config(model_attrib, tmp_path):
     """When either val loop or val data are missing raise warning."""
     trainer = Trainer(default_root_dir=tmp_path, max_epochs=1)
 
-    # no val data has val loop
-    with pytest.warns(UserWarning, match=r"You passed in a `val_dataloader` but have no `validation_step`"):
-        model = BoringModel()
-        model.validation_step = None
-        trainer.fit(model)
-
-    # has val loop but no val data
-    with pytest.warns(PossibleUserWarning, match=r"You defined a `validation_step` but have no `val_dataloader`"):
-        model = BoringModel()
-        model.val_dataloader = None
+    model = BoringModel()
+    setattr(model, model_attrib, None)
+    match_msg = (
+        r"You passed in a `val_dataloader` but have no `validation_step`"
+        if model_attrib == "validation_step"
+        else "You defined a `validation_step` but have no `val_dataloader`"
+    )
+    with pytest.warns(UserWarning, match=match_msg):
         trainer.fit(model)
 
 

tests/tests_pytorch/trainer/test_dataloaders.py

@@ -545,13 +545,14 @@ def test_warning_with_few_workers(_, tmp_path, ckpt_path, stage):
 
     trainer = Trainer(default_root_dir=tmp_path, max_epochs=1, limit_val_batches=0.1, limit_train_batches=0.2)
 
-    with pytest.warns(UserWarning, match=f"The '{stage}_dataloader' does not have many workers"):
-        if stage == "test":
-            if ckpt_path in ("specific", "best"):
-                trainer.fit(model, train_dataloaders=train_dl, val_dataloaders=val_dl)
-            ckpt_path = trainer.checkpoint_callback.best_model_path if ckpt_path == "specific" else ckpt_path
+    if stage == "test":
+        if ckpt_path in ("specific", "best"):
+            trainer.fit(model, train_dataloaders=train_dl, val_dataloaders=val_dl)
+        ckpt_path = trainer.checkpoint_callback.best_model_path if ckpt_path == "specific" else ckpt_path
+        with pytest.warns(UserWarning, match=f"The '{stage}_dataloader' does not have many workers"):
             trainer.test(model, dataloaders=train_dl, ckpt_path=ckpt_path)
-        else:
+    else:
+        with pytest.warns(UserWarning, match=f"The '{stage}_dataloader' does not have many workers"):
             trainer.fit(model, train_dataloaders=train_dl, val_dataloaders=val_dl)
 
 
@@ -579,16 +580,15 @@ def training_step(self, batch, batch_idx):
 
     trainer = Trainer(default_root_dir=tmp_path, max_epochs=1, limit_val_batches=0.1, limit_train_batches=0.2)
 
-    with pytest.warns(
-        UserWarning,
-        match=f"The '{stage}_dataloader' does not have many workers",
-    ):
-        if stage == "test":
-            if ckpt_path in ("specific", "best"):
-                trainer.fit(model, train_dataloaders=train_multi_dl, val_dataloaders=val_multi_dl)
-            ckpt_path = trainer.checkpoint_callback.best_model_path if ckpt_path == "specific" else ckpt_path
+    if stage == "test":
+        if ckpt_path in ("specific", "best"):
+            trainer.fit(model, train_dataloaders=train_multi_dl, val_dataloaders=val_multi_dl)
+        ckpt_path = trainer.checkpoint_callback.best_model_path if ckpt_path == "specific" else ckpt_path
+
+        with pytest.warns(UserWarning, match=f"The '{stage}_dataloader' does not have many workers"):
             trainer.test(model, dataloaders=test_multi_dl, ckpt_path=ckpt_path)
-        else:
+    else:
+        with pytest.warns(UserWarning, match=f"The '{stage}_dataloader' does not have many workers"):
             trainer.fit(model, train_dataloaders=train_multi_dl, val_dataloaders=val_multi_dl)
 
 
@@ -669,28 +669,35 @@ def test_auto_add_worker_init_fn_distributed(tmp_path, monkeypatch):
     trainer.fit(model, train_dataloaders=dataloader)
 
 
-def test_warning_with_small_dataloader_and_logging_interval(tmp_path):
+@pytest.mark.parametrize("log_interval", [2, 11])
+def test_warning_with_small_dataloader_and_logging_interval(log_interval, tmp_path):
     """Test that a warning message is shown if the dataloader length is too short for the chosen logging interval."""
     model = BoringModel()
     dataloader = DataLoader(RandomDataset(32, length=10))
     model.train_dataloader = lambda: dataloader
 
-    with pytest.warns(UserWarning, match=r"The number of training batches \(10\) is smaller than the logging interval"):
-        trainer = Trainer(default_root_dir=tmp_path, max_epochs=1, log_every_n_steps=11, logger=CSVLogger(tmp_path))
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        max_epochs=1,
+        log_every_n_steps=log_interval,
+        limit_train_batches=1 if log_interval < 10 else None,
+        logger=CSVLogger(tmp_path),
+    )
+    with pytest.warns(
+        UserWarning,
+        match=rf"The number of training batches \({log_interval - 1}\) is smaller than the logging interval",
+    ):
         trainer.fit(model)
 
-    with pytest.warns(UserWarning, match=r"The number of training batches \(1\) is smaller than the logging interval"):
-        trainer = Trainer(
-            default_root_dir=tmp_path,
-            max_epochs=1,
-            log_every_n_steps=2,
-            limit_train_batches=1,
-            logger=CSVLogger(tmp_path),
-        )
-        trainer.fit(model)
 
+def test_warning_with_small_dataloader_and_fast_dev_run(tmp_path):
+    """Test that a warning message is shown if the dataloader length is too short for the chosen logging interval."""
+    model = BoringModel()
+    dataloader = DataLoader(RandomDataset(32, length=10))
+    model.train_dataloader = lambda: dataloader
+
+    trainer = Trainer(default_root_dir=tmp_path, fast_dev_run=True, log_every_n_steps=2)
     with no_warning_call(UserWarning, match="The number of training batches"):
-        trainer = Trainer(default_root_dir=tmp_path, fast_dev_run=True, log_every_n_steps=2)
         trainer.fit(model)
 
 

tests/tests_pytorch/utilities/test_model_summary.py

@@ -18,6 +18,7 @@
 import pytest
 import torch
 import torch.nn as nn
+from lightning_utilities.test.warning import no_warning_call
 
 from lightning.pytorch import LightningModule, Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
@@ -348,7 +349,7 @@ def test_model_size_warning_on_unsupported_precision(tmp_path):
 
     with pytest.warns(UserWarning, match="Precision .* is not supported by the model summary.*"):
         summary = summarize(model)
-        assert model.pre_calculated_model_size == summary.model_size
+    assert model.pre_calculated_model_size == summary.model_size
 
 
 def test_lazy_model_summary():
@@ -358,6 +359,7 @@ def test_lazy_model_summary():
 
     with pytest.warns(UserWarning, match="The total number of parameters detected may be inaccurate."):
         assert summary.total_parameters == 0
+    with no_warning_call():
         assert summary.trainable_parameters == 0