⬆️🔒️ Lock file maintenance (#516)

This PR contains the following updates:

| Update | Change |
|---|---|
| lockFileMaintenance | All locks refreshed |

🔧 This Pull Request updates lock files to use the latest dependency
versions.

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Author: 3 people

tests/conftest.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2023 - 2025 Chair for Design Automation, TUM
+# Copyright (c) 2025 Munich Quantum Software Company GmbH
+# All rights reserved.
+#
+# SPDX-License-Identifier: MIT
+#
+# Licensed under the MIT License
+
+"""Clean-up fixtures for the tests."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pytest
+
+from mqt.predictor.ml.helper import get_path_training_data as ml_get_path_training_data
+from mqt.predictor.rl.helper import get_path_trained_model as rl_get_path_trained_model
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+@pytest.fixture(scope="session", autouse=True)
+def clean_rl_models() -> Generator[None]:
+    """Clean up trained RL models after test session."""
+    yield
+
+    for model in rl_get_path_trained_model().glob("*.zip"):
+        model.unlink()
+
+
+@pytest.fixture(scope="session", autouse=True)
+def clean_ml_models() -> Generator[None]:
+    """Clean up trained ML models after test session."""
+    yield
+
+    for model in (ml_get_path_training_data() / "trained_model").glob("*.joblib"):
+        model.unlink()
+
+    for data in (ml_get_path_training_data() / "training_data_aggregated").glob("*.npy"):
+        data.unlink()

tests/hellinger_distance/test_estimated_hellinger_distance.py

@@ -249,33 +249,6 @@ def test_train_and_qcompile_with_hellinger_model(source_path: Path, target_path:
         assert predicted_dev.description in get_available_device_names()
 
 
-def test_remove_files(source_path: Path, target_path: Path) -> None:
-    """Remove files created during testing."""
-    if source_path.exists():
-        for file in source_path.iterdir():
-            if file.suffix == ".qasm":
-                file.unlink()
-        source_path.rmdir()
-
-    if target_path.exists():
-        for file in target_path.iterdir():
-            if file.suffix == ".qasm":
-                file.unlink()
-        target_path.rmdir()
-
-    data_path = get_path_training_data() / "training_data_aggregated"
-    if data_path.exists():
-        for file in data_path.iterdir():
-            if file.suffix == ".npy":
-                file.unlink()
-
-    model_path = get_path_training_data() / "trained_model"
-    if model_path.exists():
-        for file in model_path.iterdir():
-            if file.suffix == ".joblib":
-                file.unlink()
-
-
 def test_predict_device_for_estimated_hellinger_distance_no_device_provided() -> None:
     """Test the error handling of the device selection predictor when no device is provided for the Hellinger distance model."""
     rng = np.random.default_rng()