Expand test coverage and remove unnecessary lines.

Author: hameerabbasi

sparse/numba_backend/_common.py

@@ -1610,11 +1610,9 @@ def eye(N, M=None, k=0, dtype=float, format="coo", *, device=None, **kwargs):
         return zeros((N, M), dtype=dtype, format=format, device=device)
 
     if k > 0:
-        data_length = builtins.max(builtins.min(data_length, M - k), 0)
         n_coords = np.arange(data_length, dtype=np.intp)
         m_coords = n_coords + k
     elif k < 0:
-        data_length = builtins.max(builtins.min(data_length, N + k), 0)
         m_coords = np.arange(data_length, dtype=np.intp)
         n_coords = m_coords - k
     else:
@@ -1888,10 +1886,7 @@ def can_cast(from_: SparseArray, to: np.dtype, /, *, casting: str = "safe") -> b
     --------
     - [`numpy.can_cast`][] : NumPy equivalent function
     """
-    try:
-        from_ = np.dtype(from_)
-    except TypeError:
-        from_ = from_.dtype
+    from_ = np.dtype(from_)
 
     return np.can_cast(from_, to, casting=casting)
 

sparse/numba_backend/tests/test_coo.py

@@ -1203,11 +1203,21 @@ def test_initialization(ndim, rng):
 @pytest.mark.parametrize("N, M", [(4, None), (4, 10), (10, 4), (0, 10)])
 def test_eye(N, M):
     m = M or N
-    for k in [0, N - 2, N + 2, m - 2, m + 2]:
+    for k in [0, N - 2, N + 2, m - 2, m + 2, np.iinfo(np.intp).min]:
         assert_eq(sparse.eye(N, M=M, k=k), np.eye(N, M=M, k=k))
         assert_eq(sparse.eye(N, M=M, k=k, dtype="i4"), np.eye(N, M=M, k=k, dtype="i4"))
 
 
+@pytest.mark.parametrize("from_", [np.int8, np.int64, np.float32, np.float64, np.complex64, np.complex128])
+@pytest.mark.parametrize("to", [np.int8, np.int64, np.float32, np.float64, np.complex64, np.complex128])
+@pytest.mark.parametrize("casting", ["no", "safe", "same_kind"])
+def test_can_cast(from_, to, casting):
+    assert sparse.can_cast(sparse.zeros((2, 2), dtype=from_), to, casting=casting) == np.can_cast(
+        np.zeros((2, 2), dtype=from_), to, casting=casting
+    )
+    assert sparse.can_cast(from_, to, casting=casting) == np.can_cast(from_, to, casting=casting)
+
+
 @pytest.mark.parametrize("funcname", ["ones", "zeros"])
 def test_ones_zeros(funcname):
     sp_func = getattr(sparse, funcname)