Modify testsuite to add sparse support.

tests/_helpers.py

@@ -3,7 +3,7 @@
 import pytest
 
 wrapped_libraries = ["cupy", "torch", "dask.array"]
-all_libraries = wrapped_libraries + ["numpy", "jax.numpy"]
+all_libraries = wrapped_libraries + ["numpy", "jax.numpy", "sparse"]
 import numpy as np
 if np.__version__[0] == '1':
     wrapped_libraries.append("numpy")
@@ -14,6 +14,8 @@ def import_(library, wrapper=False):
     if wrapper:
         if 'jax' in library:
             library = 'jax.experimental.array_api'
+        elif library.startswith('sparse'):
+            library = 'sparse'
         else:
             library = 'array_api_compat.' + library
 

tests/test_array_namespace.py

@@ -19,7 +19,7 @@ def test_array_namespace(library, api_version, use_compat):
     xp = import_(library)
 
     array = xp.asarray([1.0, 2.0, 3.0])
-    if use_compat is True and library in ['array_api_strict', 'jax.numpy']:
+    if use_compat is True and library in {'array_api_strict', 'jax.numpy', 'sparse'}:
         pytest.raises(ValueError, lambda: array_namespace(array, use_compat=use_compat))
         return
     namespace = array_api_compat.array_namespace(array, api_version=api_version, use_compat=use_compat)

tests/test_common.py

@@ -1,5 +1,5 @@
 from array_api_compat import (is_numpy_array, is_cupy_array, is_torch_array, # noqa: F401
-                              is_dask_array, is_jax_array)
+                              is_dask_array, is_jax_array, is_pydata_sparse)
 
 from array_api_compat import is_array_api_obj, device, to_device
 
@@ -16,6 +16,7 @@
     'torch': 'is_torch_array',
     'dask.array': 'is_dask_array',
     'jax.numpy': 'is_jax_array',
+    'sparse': 'is_pydata_sparse',
 }
 
 @pytest.mark.parametrize('library', is_functions.keys())
@@ -76,6 +77,8 @@ def test_asarray_cross_library(source_library, target_library, request):
     if source_library == "cupy" and target_library != "cupy":
         # cupy explicitly disallows implicit conversions to CPU
         pytest.skip(reason="cupy does not support implicit conversion to CPU")
+    elif source_library == "sparse" and target_library != "sparse":
+        pytest.skip(reason="`sparse` does not allow implicit densification")
     src_lib = import_(source_library, wrapper=True)
     tgt_lib = import_(target_library, wrapper=True)
     is_tgt_type = globals()[is_functions[target_library]]

tests/test_no_dependencies.py

@@ -33,7 +33,7 @@ def _test_dependency(mod):
 
     # array-api-strict is an example of an array API library that isn't
     # wrapped by array-api-compat.
-    if "strict" not in mod:
+    if "strict" not in mod and mod != "sparse":
         is_mod_array = getattr(array_api_compat, f"is_{mod.split('.')[0]}_array")
         assert not is_mod_array(a)
         assert mod not in sys.modules
@@ -50,7 +50,7 @@ def _test_dependency(mod):
 # Y (except most array libraries actually do themselves depend on numpy).
 
 @pytest.mark.parametrize("library", ["cupy", "numpy", "torch", "dask.array",
-                                     "jax.numpy", "array_api_strict"])
+                                     "jax.numpy", "sparse", "array_api_strict"])
 def test_numpy_dependency(library):
     # This import is here because it imports numpy
     from ._helpers import import_