chore: import upstream snapshot with attribution

haystack/testing/test_utils.py

@@ -0,0 +1,36 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import random
+
+from haystack import logging
+
+logger = logging.getLogger(__name__)
+
+
+def set_all_seeds(seed: int, deterministic_cudnn: bool = False) -> None:
+    """
+    Setting multiple seeds to make runs reproducible.
+
+    Important: Enabling `deterministic_cudnn` gives you full reproducibility with CUDA,
+    but might slow down your training (see https://pytorch.org/docs/stable/notes/randomness.html#cudnn) !
+
+    :param seed:number to use as seed
+    :param deterministic_cudnn: Enable for full reproducibility when using CUDA. Caution: might slow down training.
+    """
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+
+    try:
+        import torch
+
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        if deterministic_cudnn:
+            torch.backends.cudnn.deterministic = True
+            torch.backends.cudnn.benchmark = False
+
+    except (ImportError, ModuleNotFoundError) as exc:
+        logger.info("Could not set PyTorch seed because torch is not installed. Exception: {exception}", exception=exc)