chore: import upstream snapshot with attribution

doc/external/test_hashes.py

@@ -0,0 +1,47 @@
+import hashlib
+import sys
+from typing import TypedDict
+import os
+
+import runfiles
+import pytest
+
+_REPO_NAME = "io_ray"
+
+_runfiles = runfiles.Create()
+
+
+class ExternalDoc(TypedDict):
+    file: str
+    digest: str
+    ref: str
+
+
+# Files here are referenced on external pages as examples, and are tested
+# to make sure exteranl referenced Ray examples are working with latest version
+# of Ray. If you need to make changes, make sure to update the external examples
+# too, and then update the digests here as a confirmation.
+docs = [
+    ExternalDoc(
+        file="pytorch_tutorials_hyperparameter_tuning_tutorial.py",
+        digest="04f8bab9fda98bceaf541984482faacab7bd8d35d6e5850ae610bfea08709743",
+        ref="https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html"
+    ),
+]
+
+
+def test_hashes():
+    for doc in docs:
+        path = os.path.join(_REPO_NAME, "doc", "external", doc["file"])
+        runfile = _runfiles.Rlocation(path)
+        with open(runfile, "rb") as f:
+            content = f.read()
+        want = doc["digest"]
+        got = hashlib.sha256(content).hexdigest()
+        name = doc["file"]
+        ref = doc["ref"]
+        assert got == want, f"{name} ({ref}) has sha256 {got}, want {want}"
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main(["-v", __file__]))