Update versions for pre-commit hooks

.pre-commit-config.yaml

@@ -16,13 +16,13 @@ repos:
       - id: check-toml
 
   - repo: https://github.com/python-poetry/poetry
-    rev: 1.8.4
+    rev: 2.0.1
     hooks:
       - id: poetry-check
         args: [--lock]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.2
+    rev: v0.9.2
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
@@ -31,13 +31,13 @@ repos:
         types_or: [ python, pyi, jupyter ]
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1.27.0
+    rev: v1.29.4
     hooks:
       - id: typos
         args: []
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.13.0
+    rev: v1.14.1
     hooks:
       - id: mypy
         entry: mypy
@@ -46,7 +46,7 @@ repos:
         exclude: tests|projects
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.9.0
+    rev: 1.9.1
     hooks:
       - id: nbqa-ruff
         args: [--fix, --exit-non-zero-on-fix]

mmlearn/cli/_instantiators.py

@@ -103,9 +103,9 @@ def instantiate_sampler(
             kwargs.update(distributed_sampler_kwargs)
 
         sampler = hydra.utils.instantiate(cfg, **kwargs)
-        assert isinstance(
-            sampler, Sampler
-        ), f"Expected a `torch.utils.data.Sampler` object but got {type(sampler)}."
+        assert isinstance(sampler, Sampler), (
+            f"Expected a `torch.utils.data.Sampler` object but got {type(sampler)}."
+        )
 
     if sampler is None and requires_distributed_sampler:
         sampler = DistributedSampler(dataset, **distributed_sampler_kwargs)

mmlearn/cli/run.py

@@ -56,9 +56,9 @@ def main(cfg: MMLearnConf) -> None:  # noqa: PLR0912
     trainer: Trainer = hydra.utils.instantiate(
         cfg.trainer, callbacks=callbacks, logger=loggers, _convert_="all"
     )
-    assert isinstance(
-        trainer, Trainer
-    ), "Trainer must be an instance of `lightning.pytorch.trainer.Trainer`"
+    assert isinstance(trainer, Trainer), (
+        "Trainer must be an instance of `lightning.pytorch.trainer.Trainer`"
+    )
 
     if rank_zero_only.rank == 0 and loggers is not None:  # update wandb config
         for trainer_logger in loggers:
@@ -79,9 +79,9 @@ def main(cfg: MMLearnConf) -> None:  # noqa: PLR0912
     # prepare dataloaders
     if cfg.job_type == JobType.train:
         train_dataset = instantiate_datasets(cfg.datasets.train)
-        assert (
-            train_dataset is not None
-        ), "Train dataset (`cfg.datasets.train`) is required for training."
+        assert train_dataset is not None, (
+            "Train dataset (`cfg.datasets.train`) is required for training."
+        )
 
         train_sampler = instantiate_sampler(
             cfg.dataloader.train.get("sampler"),
@@ -109,9 +109,9 @@ def main(cfg: MMLearnConf) -> None:  # noqa: PLR0912
             )
     else:
         test_dataset = instantiate_datasets(cfg.datasets.test)
-        assert (
-            test_dataset is not None
-        ), "Test dataset (`cfg.datasets.test`) is required for evaluation."
+        assert test_dataset is not None, (
+            "Test dataset (`cfg.datasets.test`) is required for evaluation."
+        )
 
         test_sampler = instantiate_sampler(
             cfg.dataloader.test.get("sampler"),

mmlearn/datasets/chexpert.py

@@ -66,12 +66,12 @@ def __init__(
         transform: Optional[Callable[[Image.Image], torch.Tensor]] = None,
     ) -> None:
         assert split in ["train", "valid"], f"split {split} is not available."
-        assert (
-            labeler in ["chexpert", "chexbert", "vchexbert"] or labeler is None
-        ), f"labeler {labeler} is not available."
-        assert (
-            callable(transform) or transform is None
-        ), "transform is not callable or None."
+        assert labeler in ["chexpert", "chexbert", "vchexbert"] or labeler is None, (
+            f"labeler {labeler} is not available."
+        )
+        assert callable(transform) or transform is None, (
+            "transform is not callable or None."
+        )
 
         if split == "valid":
             data_file = f"{split}_data.json"

mmlearn/datasets/core/data_collator.py

@@ -103,7 +103,7 @@ def _merge_examples(examples: list[Example]) -> dict[str, Any]:
             else:
                 merged_examples[key] = [example[key]]
 
-    for key in merged_examples:
+    for key in merged_examples:  # noqa: PLC0206
         if isinstance(merged_examples[key][0], Example):
             merged_examples[key] = _merge_examples(merged_examples[key])
 

mmlearn/datasets/librispeech.py

@@ -107,9 +107,9 @@ def __len__(self) -> int:
     def __getitem__(self, idx: int) -> Example:
         """Return an example from the dataset."""
         waveform, sample_rate, transcript, _, _, _ = self.dataset[idx]
-        assert (
-            sample_rate == SAMPLE_RATE
-        ), f"Expected sample rate to be `16000`, got {sample_rate}."
+        assert sample_rate == SAMPLE_RATE, (
+            f"Expected sample rate to be `16000`, got {sample_rate}."
+        )
         waveform = pad_or_trim(waveform.flatten())
 
         return Example(

mmlearn/datasets/nihcxr.py

@@ -62,9 +62,9 @@ def __init__(
         transform: Optional[Callable[[Image.Image], torch.Tensor]] = None,
     ) -> None:
         assert split in ["train", "test", "bbox"], f"split {split} is not available."
-        assert (
-            callable(transform) or transform is None
-        ), "transform is not callable or None."
+        assert callable(transform) or transform is None, (
+            "transform is not callable or None."
+        )
 
         data_path = os.path.join(root_dir, split + "_data.json")
 

mmlearn/modules/encoders/vision.py

@@ -512,9 +512,9 @@ def forward(
         masks: Union[torch.Tensor, list[torch.Tensor]],
     ) -> torch.Tensor:
         """Forward pass through the Vision Transformer Predictor."""
-        assert (masks is not None) and (
-            masks_x is not None
-        ), "Cannot run predictor without mask indices"
+        assert (masks is not None) and (masks_x is not None), (
+            "Cannot run predictor without mask indices"
+        )
 
         if not isinstance(masks_x, list):
             masks_x = [masks_x]

mmlearn/tasks/contrastive_pretraining.py

@@ -277,9 +277,9 @@ def __init__(  # noqa: PLR0912, PLR0915
             Modalities.get_modality(modality_key)
             for modality_key in modality_encoder_mapping
         ]
-        assert (
-            len(self._available_modalities) >= 2
-        ), "Expected at least two modalities to be available. "
+        assert len(self._available_modalities) >= 2, (
+            "Expected at least two modalities to be available. "
+        )
 
         #: A :py:class:`~torch.nn.ModuleDict`, where the keys are the names of the
         #: modalities and the values are the encoder modules.

projects/med_benchmarking/datasets/mimiciv_cxr.py

@@ -160,9 +160,9 @@ def __getitem__(self, idx: int) -> Example:
             )
             if tokens is not None:
                 if isinstance(tokens, dict):  # output of HFTokenizer
-                    assert (
-                        Modalities.TEXT.name in tokens
-                    ), f"Missing key `{Modalities.TEXT.name}` in tokens."
+                    assert Modalities.TEXT.name in tokens, (
+                        f"Missing key `{Modalities.TEXT.name}` in tokens."
+                    )
                     example.update(tokens)
                 else:
                     example[Modalities.TEXT.name] = tokens

projects/med_benchmarking/datasets/pmcoa.py

@@ -123,9 +123,9 @@ def __getitem__(self, idx: int) -> Example:
         tokens = self.tokenizer(caption) if self.tokenizer is not None else None
         if tokens is not None:
             if isinstance(tokens, dict):  # output of HFTokenizer
-                assert (
-                    Modalities.TEXT.name in tokens
-                ), f"Missing key `{Modalities.TEXT.name}` in tokens."
+                assert Modalities.TEXT.name in tokens, (
+                    f"Missing key `{Modalities.TEXT.name}` in tokens."
+                )
                 example.update(tokens)
             else:
                 example[Modalities.TEXT.name] = tokens

projects/med_benchmarking/datasets/quilt.py

@@ -158,9 +158,9 @@ def __getitem__(self, idx: int) -> Example:
 
         if tokens is not None:
             if isinstance(tokens, dict):  # output of HFTokenizer
-                assert (
-                    Modalities.TEXT.name in tokens
-                ), f"Missing key `{Modalities.TEXT.name}` in tokens."
+                assert Modalities.TEXT.name in tokens, (
+                    f"Missing key `{Modalities.TEXT.name}` in tokens."
+                )
                 example.update(tokens)
             else:
                 example[Modalities.TEXT.name] = tokens

projects/med_benchmarking/datasets/roco.py

@@ -100,9 +100,9 @@ def __getitem__(self, idx: int) -> Example:
 
         if tokens is not None:
             if isinstance(tokens, dict):  # output of HFTokenizer
-                assert (
-                    Modalities.TEXT.name in tokens
-                ), f"Missing key `{Modalities.TEXT.name}` in tokens."
+                assert Modalities.TEXT.name in tokens, (
+                    f"Missing key `{Modalities.TEXT.name}` in tokens."
+                )
                 example.update(tokens)
             else:
                 example[Modalities.TEXT.name] = tokens

tests/datasets/test_example.py

@@ -97,7 +97,7 @@ def test_collate_example_list():
     result = DefaultDataCollator()(
         [img_class_example, img_text_pair, audio_text_pair, nested_example],
     )
-    for key in expected_result:
+    for key in expected_result:  # noqa: PLC0206
         assert key in result
         if isinstance(expected_result[key], torch.Tensor):
             assert torch.equal(result[key], expected_result[key])