pre-commit auto updates

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
       - id: docformatter
         args: [--in-place, --make-summary-multi-line, --close-quotes-on-newline, --wrap-summaries, "88"]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.7
+    rev: v0.6.3
     hooks:
       - id: ruff
       - id: ruff-format
@@ -19,7 +19,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/quintenroets/package-dev-tools
-    rev: v0.6.3
+    rev: v0.6.8
     hooks:
       - id: infer-missing-annotations
   - repo: local
@@ -39,7 +39,7 @@ repos:
         always_run: true
         fail_fast: true
   - repo: https://github.com/quintenroets/package-dev-tools
-    rev: v0.6.3
+    rev: v0.6.8
     hooks:
       - id: check-coverage
       - id: check-shields

tests/conftest.py

@@ -2,6 +2,7 @@
 from unittest.mock import PropertyMock, patch
 
 import pytest
+
 from revnets.context import context as context_
 from revnets.context.context import Context
 from revnets.models import Config, Evaluation, HyperParameters, Path
@@ -22,7 +23,7 @@ def _mocked_assets_path() -> Iterator[None]:
         yield
 
 
-@pytest.fixture()
+@pytest.fixture
 def test_context(
     context: Context,
     _mocked_assets_path: None,  # noqa: PT019

tests/evaluations/test_evaluations.py

@@ -4,14 +4,14 @@
 from typing import cast
 
 import pytest
+from torch.nn import Sequential
+
 from revnets import evaluations, pipelines, reconstructions
 from revnets.context import Context
 from revnets.evaluations import analysis, attack, outputs, weights
 from revnets.evaluations.evaluate import format_percentage
 from revnets.networks import mininet
 from revnets.pipelines import Pipeline
-from torch.nn import Sequential
-
 from tests.evaluations import verifier
 from tests.evaluations.verifier import Standardization
 
@@ -40,7 +40,7 @@
 )
 
 
-@pytest.fixture()
+@pytest.fixture
 def pipeline() -> Pipeline:
     return pipelines.mininet.Pipeline()
 

tests/evaluations/test_standardize.py

@@ -1,9 +1,9 @@
 from types import ModuleType
 
 import pytest
+
 from revnets import networks
 from revnets.models import Activation
-
 from tests.evaluations.verifier import Standardization, Verifier
 
 standardizations = (

tests/evaluations/test_utils.py

@@ -1,13 +1,14 @@
 from typing import cast
 
 from hypothesis import given, strategies
+from torch import nn
+
 from revnets.standardization.utils import (
     extract_layer,
     extract_rnn_layers,
     extract_scale_isomorphism_after_max_pool,
 )
 from revnets.standardization.weights import rnn
-from torch import nn
 
 
 @given(

tests/evaluations/verifier.py

@@ -6,6 +6,8 @@
 from typing import cast
 
 import torch
+from torch.nn import Module, Sequential
+
 from revnets import standardization
 from revnets.context import context
 from revnets.models import Activation
@@ -18,7 +20,6 @@
     extract_internal_layers,
 )
 from revnets.standardization.weights import feedforward
-from torch.nn import Module, Sequential
 
 
 class Standardization(Enum):

tests/test_cli_entry_points.py

@@ -1,6 +1,7 @@
 from unittest.mock import MagicMock, patch
 
 from package_dev_utils.tests.args import no_cli_args
+
 from revnets.cli import entry_point, launch
 
 

tests/test_data.py

@@ -2,6 +2,7 @@
 from typing import TYPE_CHECKING
 
 import pytest
+
 from revnets import data
 
 if TYPE_CHECKING:

tests/test_main.py

@@ -1,5 +1,6 @@
 import pytest
 import torch
+
 from revnets.main.main import Experiment
 
 gpu_available = torch.cuda.is_available()

tests/test_pipelines.py

@@ -2,6 +2,7 @@
 from typing import TYPE_CHECKING
 
 import pytest
+
 from revnets import pipelines
 
 if TYPE_CHECKING:

tests/test_reconstructions.py

@@ -2,12 +2,13 @@
 
 import pytest
 from pytorch_lightning.core.optimizer import LightningOptimizer
+from torch import nn
+
 from revnets import reconstructions
 from revnets.pipelines.mininet import Pipeline
 from revnets.reconstructions import Reconstructor, queries
 from revnets.training.reconstructions import Network
 from revnets.training.reconstructions.callbacks import LearningRateScheduler
-from torch import nn
 
 reconstruction_modules = (
     reconstructions.empty,