Install all libraries in the Test CI job

.github/workflows/tests.yml

@@ -32,20 +32,20 @@ jobs:
           python -m pip install --upgrade pip
           python -m pip install pytest
 
+          # Don't `pip install .[dev]` as it would pull in the whole torch cuda stack
+          python -m pip install array-api-strict
+          python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
+
           if [ "${{ matrix.numpy-version }}" == "dev" ]; then
             python -m pip install numpy --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
+            python -m pip install dask[array] jax[cpu] sparse ndonnx
           elif [ "${{ matrix.numpy-version }}" == "1.22" ]; then
             python -m pip install 'numpy==1.22.*'
           elif [ "${{ matrix.numpy-version }}" == "1.26" ]; then
             python -m pip install 'numpy==1.26.*'
           else
-            # Don't `pip install .[dev]` as it would pull in the whole torch cuda stack
-            python -m pip install array-api-strict dask[array] jax[cpu] numpy sparse
-            python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
-            if [ "${{ matrix.python-version }}" != "3.13" ]; then
-              # onnx wheels are not available on Python 3.13 at the moment of writing
-              python -m pip install ndonnx
-            fi
+            python -m pip install numpy
+            python -m pip install dask[array] jax[cpu] sparse ndonnx
           fi
 
       - name: Dump pip environment

tests/test_array_namespace.py

@@ -23,7 +23,9 @@ def test_array_namespace(library, api_version, use_compat):
     if library == "ndonnx" and api_version in ("2021.12", "2022.12"):
         pytest.skip("Unsupported API version")
 
-    namespace = array_namespace(array, api_version=api_version, use_compat=use_compat)
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', UserWarning)
+        namespace = array_namespace(array, api_version=api_version, use_compat=use_compat)
 
     if use_compat is False or use_compat is None and library not in wrapped_libraries:
         if library == "jax.numpy" and use_compat is None:
@@ -45,10 +47,13 @@ def test_array_namespace(library, api_version, use_compat):
 
     if library == "numpy":
         # check that the same namespace is returned for NumPy scalars
-        scalar_namespace = array_namespace(
-            xp.float64(0.0), api_version=api_version, use_compat=use_compat
-        )
-        assert scalar_namespace == namespace
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', UserWarning)
+
+            scalar_namespace = array_namespace(
+                xp.float64(0.0), api_version=api_version, use_compat=use_compat
+            )
+            assert scalar_namespace == namespace
 
     # Check that array_namespace works even if jax.experimental.array_api
     # hasn't been imported yet (it monkeypatches __array_namespace__
@@ -97,7 +102,9 @@ def test_api_version_torch():
     torch = import_("torch")
     x = torch.asarray([1, 2])
     torch_ = import_("torch", wrapper=True)
-    assert array_namespace(x, api_version="2023.12") == torch_
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', UserWarning)
+        assert array_namespace(x, api_version="2023.12") == torch_
     assert array_namespace(x, api_version=None) == torch_
     assert array_namespace(x) == torch_
     # Should issue a warning

tests/test_common.py

@@ -195,6 +195,9 @@ def test_device_to_device(library, request):
         xfail(request, reason="Stub raises ValueError")
     if library == "sparse":
         xfail(request, reason="No __array_namespace_info__()")
+    if library == "array_api_strict":
+        if np.__version__ < "2":
+            xfail(request, reason="no copy argument of np.asarray")
 
     xp = import_(library, wrapper=True)
     devices = xp.__array_namespace_info__().devices()