Fix Array API tests for Numba backend.

Author: hameerabbasi

ci/Numba-array-api-xfails.txt

@@ -42,8 +42,6 @@ array_api_tests/test_has_names.py::test_has_names[array_method-__dlpack_device__
 array_api_tests/test_has_names.py::test_has_names[array_method-__setitem__]
 array_api_tests/test_indexing_functions.py::test_take
 array_api_tests/test_linalg.py::test_vecdot
-array_api_tests/test_operators_and_elementwise_functions.py::test_ceil
-array_api_tests/test_operators_and_elementwise_functions.py::test_trunc
 array_api_tests/test_set_functions.py::test_unique_all
 array_api_tests/test_set_functions.py::test_unique_inverse
 array_api_tests/test_signatures.py::test_func_signature[unique_all]

ci/test_array_api.sh

@@ -6,4 +6,4 @@ source ci/clone_array_api_tests.sh
 if [ "${SPARSE_BACKEND}" = "Finch" ]; then
     python -c 'import finch'
 fi
-ARRAY_API_TESTS_MODULE="sparse" pytest "$ARRAY_API_TESTS_DIR/array_api_tests/" -v -c "$ARRAY_API_TESTS_DIR/pytest.ini" --ci --max-examples=2 --derandomize --disable-deadline -o xfail_strict=True -n auto --xfails-file ../sparse/ci/${SPARSE_BACKEND}-array-api-xfails.txt --skips-file ../sparse/ci/${SPARSE_BACKEND}-array-api-skips.txt
+ARRAY_API_TESTS_MODULE="sparse" pytest "$ARRAY_API_TESTS_DIR/array_api_tests/" -v -c "$ARRAY_API_TESTS_DIR/pytest.ini" --ci --max-examples=2 --derandomize --disable-deadline --disable-warnings -o xfail_strict=True -n auto --xfails-file ../sparse/ci/${SPARSE_BACKEND}-array-api-xfails.txt --skips-file ../sparse/ci/${SPARSE_BACKEND}-array-api-skips.txt

sparse/numba_backend/__init__.py

@@ -4,7 +4,6 @@
     bitwise_not,
     bitwise_or,
     bitwise_xor,
-    can_cast,
     ceil,
     complex64,
     complex128,
@@ -86,6 +85,7 @@
     astype,
     broadcast_arrays,
     broadcast_to,
+    can_cast,
     concat,
     concatenate,
     dot,

sparse/numba_backend/_common.py

@@ -1601,6 +1601,13 @@ def eye(N, M=None, k=0, dtype=float, format="coo", *, device=None, **kwargs):
     k = int(k)
 
     data_length = builtins.min(N, M)
+    if k > 0:
+        data_length = builtins.max(builtins.min(data_length, M - k), 0)
+    elif k < 0:
+        data_length = builtins.max(builtins.min(data_length, N + k), 0)
+
+    if data_length == 0:
+        return zeros((N, M), dtype=dtype, format=format, device=device)
 
     if k > 0:
         data_length = builtins.max(builtins.min(data_length, M - k), 0)
@@ -1854,6 +1861,41 @@ def empty_like(a, dtype=None, shape=None, format=None, *, device=None, **kwargs)
 empty_like.__doc__ = zeros_like.__doc__
 
 
+def can_cast(from_: SparseArray, to: np.dtype, /, *, casting: str = "safe") -> bool:
+    """Determines if one data type can be cast to another data type
+
+    Parameters
+    ----------
+    from_ : dtype or SparseArray
+        Source array or dtype.
+    to : dtype
+        Destination dtype.
+    casting: str
+        Casting kind
+
+    Returns
+    -------
+    out : bool
+        Whether or not a cast is possible.
+
+    Examples
+    --------
+    >>> x = sparse.ones((2, 3), dtype=sparse.int8)
+    >>> sparse.can_cast(x, sparse.float64)
+    True
+
+    See Also
+    --------
+    - [`numpy.can_cast`][] : NumPy equivalent function
+    """
+    try:
+        from_ = np.dtype(from_)
+    except TypeError:
+        from_ = from_.dtype
+
+    return np.can_cast(from_, to, casting=casting)
+
+
 def outer(a, b, out=None):
     """
     Return outer product of two sparse arrays.