chore: import upstream snapshot with attribution

tests/entrypoints/pooling/test_utils.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import importlib
+import importlib.util
+import json
+import warnings
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+
+from vllm.entrypoints.pooling.utils import encode_pooling_output_float_or_ndarray
+
+
+def _pooling_output(data):
+    return SimpleNamespace(outputs=SimpleNamespace(data=data))
+
+
+def test_encode_pooling_output_float_or_ndarray_returns_numpy_array():
+    output = _pooling_output(torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32))
+
+    encoded = encode_pooling_output_float_or_ndarray(output)
+
+    assert isinstance(encoded, np.ndarray)
+    np.testing.assert_allclose(encoded, [1.0, 2.0, 3.0])
+
+
+@pytest.mark.skipif(
+    importlib.util.find_spec("orjson") is None,
+    reason="orjson is not installed",
+)
+def test_orjson_serializes_numpy_array():
+    from fastapi.responses import ORJSONResponse
+
+    output = _pooling_output(torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32))
+    encoded = encode_pooling_output_float_or_ndarray(output)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", DeprecationWarning)
+        response = ORJSONResponse(content={"embedding": encoded})
+    assert json.loads(response.body)["embedding"] == pytest.approx([1.0, 2.0, 3.0])
+
+
+def test_encode_pooling_output_float_or_ndarray_falls_back_to_list():
+    class DataWithUnsupportedNumpy:
+        def is_contiguous(self):
+            return True
+
+        def numpy(self):
+            raise TypeError("unsupported dtype")
+
+        def tolist(self):
+            return [1.0, 2.0, 3.0]
+
+    output = _pooling_output(DataWithUnsupportedNumpy())
+
+    assert encode_pooling_output_float_or_ndarray(output) == [1.0, 2.0, 3.0]