lint: switch from Black to ruff-format (#2388)

* lint: switch from Black to `ruff-format`
* manual fixes
* mypy

---------

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

.pre-commit-config.yaml

@@ -67,12 +67,6 @@ repos:
         additional_dependencies: [tomli]
         args: ["--in-place"]
 
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
-    hooks:
-      - id: black
-        name: Format code
-
   - repo: https://github.com/executablebooks/mdformat
     rev: 0.7.17
     hooks:
@@ -130,7 +124,9 @@ repos:
       - id: text-unicode-replacement-char
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.9
+    rev: v0.2.0
     hooks:
+      - id: ruff-format
+        args: ["--preview"]
       - id: ruff
         args: ["--fix"]

docs/source/conf.py

@@ -18,12 +18,10 @@
 import sys
 
 import lai_sphinx_theme
+import torchmetrics
 from lightning_utilities.docs import fetch_external_assets
 from lightning_utilities.docs.formatting import _transform_changelog
 
-import torchmetrics
-
-
 _PATH_HERE = os.path.abspath(os.path.dirname(__file__))
 _PATH_ROOT = os.path.realpath(os.path.join(_PATH_HERE, "..", ".."))
 sys.path.insert(0, os.path.abspath(_PATH_ROOT))

docs/source/pyplots/binary_accuracy.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

docs/source/pyplots/binary_accuracy_multistep.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

docs/source/pyplots/collection_binary.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

docs/source/pyplots/collection_binary_together.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

docs/source/pyplots/confusion_matrix.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

docs/source/pyplots/multiclass_accuracy.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

docs/source/pyplots/tracker_binary.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import torch
-
 import torchmetrics
 
 N = 10

examples/bert_score-own_model.py

@@ -79,15 +79,13 @@ def __call__(self, sentences: Union[str, List[str]], max_len: int = _MAX_LEN) ->
             sentence.lower().split()[:max_len] + [self.PAD_TOKEN] * (max_len - len(sentence.lower().split()))
             for sentence in sentences
         ]
-        output_dict["input_ids"] = torch.cat(
-            [torch.cat([self.word2vec[word] for word in sentence]).unsqueeze(0) for sentence in tokenized_sentences]
-        )
-        output_dict["attention_mask"] = torch.cat(
-            [
-                torch.tensor([1 if word != self.PAD_TOKEN else 0 for word in sentence]).unsqueeze(0)
-                for sentence in tokenized_sentences
-            ]
-        ).long()
+        output_dict["input_ids"] = torch.cat([
+            torch.cat([self.word2vec[word] for word in sentence]).unsqueeze(0) for sentence in tokenized_sentences
+        ])
+        output_dict["attention_mask"] = torch.cat([
+            torch.tensor([1 if word != self.PAD_TOKEN else 0 for word in sentence]).unsqueeze(0)
+            for sentence in tokenized_sentences
+        ]).long()
 
         return output_dict
 

pyproject.toml

@@ -33,11 +33,6 @@ concurrency = "thread"
 relative_files = true
 
 
-[tool.black]
-# https://github.com/psf/black
-line-length = 120
-exclude = "(.eggs|.git|.hg|.mypy_cache|.venv|_build|buck-out|build|dist)"
-
 [tool.docformatter]
 recursive = true
 # some docstring start with r"""
@@ -85,7 +80,7 @@ wil = "wil"
 target-version = "py38"
 line-length = 120
 # Enable Pyflakes `E` and `F` codes by default.
-select = [
+lint.select = [
     "E",
     "W", # see: https://pypi.org/project/pycodestyle
     "F", # see: https://pypi.org/project/pyflakes
@@ -94,7 +89,7 @@ select = [
     "N", # see: https://pypi.org/project/pep8-naming
     "S", # see: https://pypi.org/project/flake8-bandit
 ]
-extend-select = [
+lint.extend-select = [
     "A",    # see: https://pypi.org/project/flake8-builtins
     "B",    # see: https://pypi.org/project/flake8-bugbear
     "C4",   # see: https://pypi.org/project/flake8-comprehensions
@@ -114,7 +109,7 @@ extend-select = [
     "PERF", # see: https://pypi.org/project/perflint/
     "PYI",  # see: https://pypi.org/project/flake8-pyi/
 ]
-ignore = [
+lint.ignore = [
     "E731",   # Do not assign a lambda expression, use a def
     "D100",   # todo: Missing docstring in public module
     "D104",   # todo: Missing docstring in public package
@@ -136,22 +131,22 @@ exclude = [
     "dist",
     "docs",
 ]
-ignore-init-module-imports = true
-unfixable = ["F401"]
+lint.ignore-init-module-imports = true
+lint.unfixable = ["F401"]
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "setup.py" = ["ANN202", "ANN401"]
 "src/**" = ["ANN401"]
 "tests/**" = ["S101", "ANN001", "ANN201", "ANN202", "ANN401"]
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 # Use Google-style docstrings.
 convention = "google"
 
 #[tool.ruff.pycodestyle]
 #ignore-overlong-task-comments = true
 
-[tool.ruff.mccabe]
+[tool.ruff.lint.mccabe]
 # Unlike Flake8, default to a complexity level of 10.
 max-complexity = 10
 

src/torchmetrics/__init__.py

@@ -1,4 +1,5 @@
 r"""Root package info."""
+
 import logging as __logging
 import os
 

src/torchmetrics/audio/snr.py

@@ -68,6 +68,7 @@ class SignalNoiseRatio(Metric):
         tensor(16.1805)
 
     """
+
     full_state_update: bool = False
     is_differentiable: bool = True
     higher_is_better: bool = True

src/torchmetrics/audio/stoi.py

@@ -73,6 +73,7 @@ class ShortTimeObjectiveIntelligibility(Metric):
         tensor(-0.0100)
 
     """
+
     sum_stoi: Tensor
     total: Tensor
     full_state_update: bool = False

src/torchmetrics/classification/accuracy.py

@@ -93,6 +93,7 @@ class BinaryAccuracy(BinaryStatScores):
         tensor([0.3333, 0.1667])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -244,6 +245,7 @@ class MulticlassAccuracy(MulticlassStatScores):
                 [0.0000, 0.3333, 0.5000]])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -396,6 +398,7 @@ class MultilabelAccuracy(MultilabelStatScores):
                 [0.0000, 0.0000, 0.5000]])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -499,9 +502,11 @@ def __new__(  # type: ignore[misc]
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
 
-        kwargs.update(
-            {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
-        )
+        kwargs.update({
+            "multidim_average": multidim_average,
+            "ignore_index": ignore_index,
+            "validate_args": validate_args,
+        })
 
         if task == ClassificationTask.BINARY:
             return BinaryAccuracy(threshold, **kwargs)

src/torchmetrics/classification/auroc.py

@@ -98,6 +98,7 @@ class BinaryAUROC(BinaryPrecisionRecallCurve):
         tensor(0.5000)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -273,7 +274,10 @@ def compute(self) -> Tensor:  # type: ignore[override]
         """Compute metric."""
         state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
         return _multiclass_auroc_compute(
-            state, self.num_classes, self.average, self.thresholds  # type: ignore[arg-type]
+            state,
+            self.num_classes,
+            self.average,  # type: ignore[arg-type]
+            self.thresholds,
         )
 
     def plot(  # type: ignore[override]
@@ -396,6 +400,7 @@ class MultilabelAUROC(MultilabelPrecisionRecallCurve):
         tensor([0.6250, 0.5000, 0.8333])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False

src/torchmetrics/classification/average_precision.py

@@ -105,6 +105,7 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
         tensor(0.6667)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -271,7 +272,10 @@ def compute(self) -> Tensor:  # type: ignore[override]
         """Compute metric."""
         state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
         return _multiclass_average_precision_compute(
-            state, self.num_classes, self.average, self.thresholds  # type: ignore[arg-type]
+            state,
+            self.num_classes,
+            self.average,  # type: ignore[arg-type]
+            self.thresholds,
         )
 
     def plot(  # type: ignore[override]
@@ -399,6 +403,7 @@ class MultilabelAveragePrecision(MultilabelPrecisionRecallCurve):
         tensor([0.7500, 0.6667, 0.9167])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False

src/torchmetrics/classification/calibration_error.py

@@ -98,6 +98,7 @@ class BinaryCalibrationError(Metric):
         tensor(0.3167)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = False
     full_state_update: bool = False
@@ -249,6 +250,7 @@ class MulticlassCalibrationError(Metric):
         tensor(0.2333)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = False
     full_state_update: bool = False

src/torchmetrics/classification/cohen_kappa.py

@@ -89,6 +89,7 @@ class labels.
         tensor(0.5000)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -216,6 +217,7 @@ class labels.
         tensor(0.6364)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False

src/torchmetrics/classification/confusion_matrix.py

@@ -108,6 +108,7 @@ class BinaryConfusionMatrix(Metric):
                 [1, 1]])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: Optional[bool] = None
     full_state_update: bool = False
@@ -248,6 +249,7 @@ class MulticlassConfusionMatrix(Metric):
                 [0, 0, 1]])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: Optional[bool] = None
     full_state_update: bool = False
@@ -387,6 +389,7 @@ class MultilabelConfusionMatrix(Metric):
                 [[0, 1], [0, 1]]])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: Optional[bool] = None
     full_state_update: bool = False

src/torchmetrics/classification/dice.py

@@ -134,6 +134,7 @@ class Dice(Metric):
         tensor(0.2500)
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False

src/torchmetrics/classification/exact_match.py

@@ -96,6 +96,7 @@ class MulticlassExactMatch(Metric):
         tensor([1., 0.])
 
     """
+
     is_differentiable: bool = False
     higher_is_better: bool = True
     full_state_update: bool = False
@@ -405,9 +406,11 @@ def __new__(
     ) -> Metric:
         """Initialize task metric."""
         task = ClassificationTaskNoBinary.from_str(task)
-        kwargs.update(
-            {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
-        )
+        kwargs.update({
+            "multidim_average": multidim_average,
+            "ignore_index": ignore_index,
+            "validate_args": validate_args,
+        })
         if task == ClassificationTaskNoBinary.MULTICLASS:
             if not isinstance(num_classes, int):
                 raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")