implement min_scalar_type

torch_np/_funcs.py

@@ -61,4 +61,5 @@ def __getitem__(self, item):
 index_exp = IndexExpression(maketuple=True)
 s_ = IndexExpression(maketuple=False)
 
+
 __all__ += ["index_exp", "s_"]

torch_np/_funcs_impl.py

@@ -1656,3 +1656,43 @@ def histogram(
         b = b.long()
 
     return h, b
+
+
+# ### odds and ends
+
+
+def min_scalar_type(a: ArrayLike, /):
+    # https://github.com/numpy/numpy/blob/maintenance/1.24.x/numpy/core/src/multiarray/convert_datatype.c#L1288
+
+    from ._dtypes import DType
+
+    if a.numel() > 1:
+        # numpy docs: "For non-scalar array a, returns the vector’s dtype unmodified."
+        return DType(a.dtype)
+
+    if a.dtype == torch.bool:
+        dtype = torch.bool
+
+    elif a.dtype.is_complex:
+        fi = torch.finfo(torch.float32)
+        fits_in_single = a.dtype == torch.complex64 or (
+            fi.min <= a.real <= fi.max and fi.min <= a.imag <= fi.max
+        )
+        dtype = torch.complex64 if fits_in_single else torch.complex128
+
+    elif a.dtype.is_floating_point:
+        for dt in [torch.float16, torch.float32, torch.float64]:
+            fi = torch.finfo(dt)
+            if fi.min <= a <= fi.max:
+                dtype = dt
+                break
+    else:
+        # must be integer
+        for dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]:
+            # Prefer unsigned int where possible, as numpy does.
+            ii = torch.iinfo(dt)
+            if ii.min <= a <= ii.max:
+                dtype = dt
+                break
+
+    return DType(dtype)

torch_np/tests/numpy_tests/core/test_multiarray.py

@@ -6124,14 +6124,26 @@ def test_complex_warning(self):
             assert_equal(x, [1, 2])
 
 
-@pytest.mark.xfail(reason='TODO')
 class TestMinScalarType:
 
     def test_usigned_shortshort(self):
         dt = np.min_scalar_type(2**8-1)
         wanted = np.dtype('uint8')
         assert_equal(wanted, dt)
 
+    # three tests below are added based on what numpy does
+    def test_complex(self):
+        dt = np.min_scalar_type(0+0j)
+        assert dt == np.dtype('complex64')
+
+    def test_float(self):
+        dt = np.min_scalar_type(0.1)
+        assert dt == np.dtype('float16')
+
+    def test_nonscalar(self):
+        dt = np.min_scalar_type([0, 1, 2])
+        assert dt == np.dtype('int64')
+
 
 from numpy.core._internal import _dtype_from_pep3118