⬆️🪝 Update pre-commit hook astral-sh/ruff-pre-commit to v0.8.4 (#345)

This PR contains the following updates:

| Package | Type | Update | Change |
|---|---|---|---|
|
[astral-sh/ruff-pre-commit](https://redirect.github.com/astral-sh/ruff-pre-commit)
| repository | patch | `v0.8.2` -> `v0.8.4` |

Note: The `pre-commit` manager in Renovate is not supported by the
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if you have any questions.

---

### Release Notes

<details>
<summary>astral-sh/ruff-pre-commit (astral-sh/ruff-pre-commit)</summary>

###
[`v0.8.4`](https://redirect.github.com/astral-sh/ruff-pre-commit/releases/tag/v0.8.4)

[Compare
Source](https://redirect.github.com/astral-sh/ruff-pre-commit/compare/v0.8.3...v0.8.4)

See: https://github.com/astral-sh/ruff/releases/tag/0.8.4

###
[`v0.8.3`](https://redirect.github.com/astral-sh/ruff-pre-commit/releases/tag/v0.8.3)

[Compare
Source](https://redirect.github.com/astral-sh/ruff-pre-commit/compare/v0.8.2...v0.8.3)

See: https://github.com/astral-sh/ruff/releases/tag/0.8.3

</details>

---

### Configuration

📅 **Schedule**: Branch creation - "on the first day of the month" (UTC),
Automerge - At any time (no schedule defined).

🚦 **Automerge**: Enabled.

♻ **Rebasing**: Whenever PR is behind base branch, or you tick the
rebase/retry checkbox.

🔕 **Ignore**: Close this PR and you won't be reminded about this update
again.

---

- [ ] <!-- rebase-check -->If you want to rebase/retry this PR, check
this box

---

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View the [repository job
log](https://developer.mend.io/github/cda-tum/mqt-predictor).

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---------

Signed-off-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: Nils Quetschlich <nils.quetschlich@tum.de>

Author: renovate[bot]

.pre-commit-config.yaml

@@ -57,7 +57,7 @@ repos:
 
   # Python linting and formatting using ruff
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+    rev: v0.8.4
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]

tests/compilation/test_predictor_rl.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import re
 from pathlib import Path
 
 import pytest
@@ -23,7 +24,7 @@ def test_predictor_env_reset_from_string() -> None:
 
 def test_predictor_env_esp_error() -> None:
     """Test the predictor environment with ESP as figure of merit and missing calibration data."""
-    with pytest.raises(ValueError, match="Missing calibration data for ESP calculation on ibm_montreal."):
+    with pytest.raises(ValueError, match=re.escape("Missing calibration data for ESP calculation on ibm_montreal.")):
         rl.Predictor(figure_of_merit="estimated_success_probability", device_name="ibm_montreal")
 
 
@@ -42,7 +43,8 @@ def test_qcompile_with_newly_trained_models() -> None:
     model_path = Path(rl.helper.get_path_trained_model() / (model_name + ".zip"))
     if not model_path.exists():
         with pytest.raises(
-            FileNotFoundError, match="The RL model is not trained yet. Please train the model before using it."
+            FileNotFoundError,
+            match=re.escape("The RL model is not trained yet. Please train the model before using it."),
         ):
             rl.qcompile(qc, figure_of_merit=figure_of_merit, device_name=device)
 
@@ -61,7 +63,7 @@ def test_qcompile_with_newly_trained_models() -> None:
 def test_qcompile_with_false_input() -> None:
     """Test the qcompile function with false input."""
     qc = get_benchmark("dj", 1, 5)
-    with pytest.raises(ValueError, match="figure_of_merit must not be None if predictor_singleton is None."):
+    with pytest.raises(ValueError, match=re.escape("figure_of_merit must not be None if predictor_singleton is None.")):
         rl.helper.qcompile(qc, None, "quantinuum_h2")
-    with pytest.raises(ValueError, match="device_name must not be None if predictor_singleton is None."):
+    with pytest.raises(ValueError, match=re.escape("device_name must not be None if predictor_singleton is None.")):
         rl.helper.qcompile(qc, "expected_fidelity", None)

tests/device_selection/test_helper_ml.py

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+import re
+
 import pytest
 
 from mqt.bench import benchmark_generator
@@ -10,7 +12,9 @@
 
 def test_load_and_save_training_data() -> None:
     """Test the loading and saving of the training data."""
-    with pytest.raises(FileNotFoundError, match="Training data not found. Please run the training script first."):
+    with pytest.raises(
+        FileNotFoundError, match=re.escape("Training data not found. Please run the training script first.")
+    ):
         ml.helper.load_training_data("false_input")  # type: ignore[arg-type]
 
     training_data, names_list, scores_list = ml.helper.load_training_data()

tests/device_selection/test_predictor_ml.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import re
 import sys
 from pathlib import Path
 from typing import Literal
@@ -39,7 +40,7 @@ def test_train_and_predictor_random_forest_classifier() -> None:
         assert 0 <= elem <= 1
 
     with pytest.raises(
-        FileNotFoundError, match="The ML model is not trained yet. Please train the model before using it."
+        FileNotFoundError, match=re.escape("The ML model is not trained yet. Please train the model before using it.")
     ):
         ml.helper.predict_device_for_figure_of_merit(qc, "false_input")  # type: ignore[arg-type]
 
@@ -99,7 +100,7 @@ def test_compile_all_circuits_for_dev_and_fom() -> None:
         dump(qc, f)
 
     if sys.platform == "win32":
-        with pytest.warns(RuntimeWarning, match="Timeout is not supported on Windows."):
+        with pytest.warns(RuntimeWarning, match=re.escape("Timeout is not supported on Windows.")):
             predictor.compile_all_circuits_devicewise(
                 device_name="ionq_harmony",
                 timeout=100,