API: Use `ResultType` in `PyArray_ConvertToCommonType`

This slightly modifies the behaviour of `np.choose` (practically a
bug fix) and the public function itself. The function is not used within
e.g. SciPy, so the small performance hit of this implementation
is probably insignificant.

The change should help clean up dtypes a bit, since the whole "scalar
cast" logic is brittle and should be deprecated/removed, and this is
probably one of the few places actually using it.

The choose change means that:

```
np.choose([0], (1000, np.array([1], dtype=np.uint8)))
```
will actually return a value of 1000 (the dtype not being uint8).

4 files changed, 59 insertions, 85 deletions

diff --git a/doc/release/upcoming_changes/14933.compatibility.rst b/doc/release/upcoming_changes/14933.compatibility.rst
new file mode 100644
index 000000000..b939fec7f
--- /dev/null
+++ b/doc/release/upcoming_changes/14933.compatibility.rst
@@ -0,0 +1,10 @@
+Scalar promotion in ``PyArray_ConvertToCommonType``
+---------------------------------------------------
+
+The promotion of mixed scalars and arrays in ``PyArray_ConvertToCommonType``
+has been changed to adhere to those used by ``np.result_type``.
+This means that input such as ``(1000, np.array([1], dtype=np.uint8)))``
+will now return ``uint16`` dtypes. In most cases the behaviour is unchanged.
+Note that the use of this C-API function is generally discouarged.
+This also fixes ``np.choose`` to behave the same way as the rest of NumPy
+in this respect.
diff --git a/doc/source/reference/c-api/array.rst b/doc/source/reference/c-api/array.rst
index 0530a5747..c910efa60 100644
--- a/doc/source/reference/c-api/array.rst
+++ b/doc/source/reference/c-api/array.rst
@@ -1255,14 +1255,18 @@ Converting data types
 
     Convert a sequence of Python objects contained in *op* to an array
     of ndarrays each having the same data type. The type is selected
-    based on the typenumber (larger type number is chosen over a
-    smaller one) ignoring objects that are only scalars. The length of
-    the sequence is returned in *n*, and an *n* -length array of
-    :c:type:`PyArrayObject` pointers is the return value (or ``NULL`` if an
-    error occurs). The returned array must be freed by the caller of
-    this routine (using :c:func:`PyDataMem_FREE` ) and all the array objects
-    in it ``DECREF`` 'd or a memory-leak will occur. The example
-    template-code below shows a typically usage:
+    in the same way as `PyArray_ResultType`. The length of the sequence is
+    returned in *n*, and an *n* -length array of :c:type:`PyArrayObject`
+    pointers is the return value (or ``NULL`` if an error occurs).
+    The returned array must be freed by the caller of this routine
+    (using :c:func:`PyDataMem_FREE` ) and all the array objects in it
+    ``DECREF`` 'd or a memory-leak will occur. The example template-code
+    below shows a typically usage:
+
+    .. versionchanged:: 1.18.0
+       A mix of scalars and zero-dimensional arrays now produces a type
+       capable of holding the scalar value.
+       Previously priority was given to the dtype of the arrays.
 
     .. code-block:: c
 
diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c
index 025c66013..11609092e 100644
--- a/numpy/core/src/multiarray/convert_datatype.c
+++ b/numpy/core/src/multiarray/convert_datatype.c
@@ -2115,15 +2115,19 @@ PyArray_ObjectType(PyObject *op, int minimum_type)
 
 /* Raises error when len(op) == 0 */
 
-/*NUMPY_API*/
+/*NUMPY_API
+ *
+ * This function is only used in one place within NumPy and should
+ * generally be avoided. It is provided mainly for backward compatibility.
+ *
+ * The user of the function has to free the returned array.
+ */
 NPY_NO_EXPORT PyArrayObject **
 PyArray_ConvertToCommonType(PyObject *op, int *retn)
 {
-    int i, n, allscalars = 0;
+    int i, n;
+    PyArray_Descr *common_descr = NULL;
     PyArrayObject **mps = NULL;
-    PyArray_Descr *intype = NULL, *stype = NULL;
-    PyArray_Descr *newtype = NULL;
-    NPY_SCALARKIND scalarkind = NPY_NOSCALAR, intypekind = NPY_NOSCALAR;
 
     *retn = n = PySequence_Length(op);
     if (n == 0) {
@@ -2159,94 +2163,41 @@ PyArray_ConvertToCommonType(PyObject *op, int *retn)
     }
 
     for (i = 0; i < n; i++) {
-        PyObject *otmp = PySequence_GetItem(op, i);
-        if (otmp == NULL) {
+        /* Convert everything to an array, this could be optimized away */
+        PyObject *tmp = PySequence_GetItem(op, i);
+        if (tmp == NULL) {
             goto fail;
         }
-        if (!PyArray_CheckAnyScalar(otmp)) {
-            newtype = PyArray_DescrFromObject(otmp, intype);
-            Py_DECREF(otmp);
-            Py_XDECREF(intype);
-            if (newtype == NULL) {
-                goto fail;
-            }
-            intype = newtype;
-            intypekind = PyArray_ScalarKind(intype->type_num, NULL);
-        }
-        else {
-            newtype = PyArray_DescrFromObject(otmp, stype);
-            Py_DECREF(otmp);
-            Py_XDECREF(stype);
-            if (newtype == NULL) {
-                goto fail;
-            }
-            stype = newtype;
-            scalarkind = PyArray_ScalarKind(newtype->type_num, NULL);
-            mps[i] = (PyArrayObject *)Py_None;
-            Py_INCREF(Py_None);
-        }
-    }
-    if (intype == NULL) {
-        /* all scalars */
-        allscalars = 1;
-        intype = stype;
-        Py_INCREF(intype);
-        for (i = 0; i < n; i++) {
-            Py_XDECREF(mps[i]);
-            mps[i] = NULL;
-        }
-    }
-    else if ((stype != NULL) && (intypekind != scalarkind)) {
-        /*
-         * we need to upconvert to type that
-         * handles both intype and stype
-         * also don't forcecast the scalars.
-         */
-        if (!PyArray_CanCoerceScalar(stype->type_num,
-                                     intype->type_num,
-                                     scalarkind)) {
-            newtype = PyArray_PromoteTypes(intype, stype);
-            Py_XDECREF(intype);
-            intype = newtype;
-            if (newtype == NULL) {
-                goto fail;
-            }
-        }
-        for (i = 0; i < n; i++) {
-            Py_XDECREF(mps[i]);
-            mps[i] = NULL;
+
+        mps[i] = (PyArrayObject *)PyArray_FROM_O(tmp);
+        Py_DECREF(tmp);
+        if (mps[i] == NULL) {
+            goto fail;
         }
     }
 
+    common_descr = PyArray_ResultType(n, mps, 0, NULL);
+    if (common_descr == NULL) {
+        goto fail;
+    }
 
-    /* Make sure all arrays are actual array objects. */
+    /* Make sure all arrays are contiguous and have the correct dtype. */
     for (i = 0; i < n; i++) {
         int flags = NPY_ARRAY_CARRAY;
-        PyObject *otmp = PySequence_GetItem(op, i);
+        PyArrayObject *tmp = mps[i];
 
-        if (otmp == NULL) {
-            goto fail;
-        }
-        if (!allscalars && ((PyObject *)(mps[i]) == Py_None)) {
-            /* forcecast scalars */
-            flags |= NPY_ARRAY_FORCECAST;
-            Py_DECREF(Py_None);
-        }
-        Py_INCREF(intype);
-        mps[i] = (PyArrayObject*)PyArray_FromAny(otmp, intype, 0, 0,
-                                                 flags, NULL);
-        Py_DECREF(otmp);
+        Py_INCREF(common_descr);
+        mps[i] = (PyArrayObject *)PyArray_FromArray(tmp, common_descr, flags);
+        Py_DECREF(tmp);
         if (mps[i] == NULL) {
             goto fail;
         }
     }
-    Py_DECREF(intype);
-    Py_XDECREF(stype);
+    Py_DECREF(common_descr);
     return mps;
 
  fail:
-    Py_XDECREF(intype);
-    Py_XDECREF(stype);
+    Py_XDECREF(common_descr);
     *retn = 0;
     for (i = 0; i < n; i++) {
         Py_XDECREF(mps[i]);
diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py
index 218106a63..6c496a028 100644
--- a/numpy/core/tests/test_multiarray.py
+++ b/numpy/core/tests/test_multiarray.py
@@ -6488,6 +6488,15 @@ class TestChoose(object):
         A = np.choose(self.ind, (self.x, self.y2))
         assert_equal(A, [[2, 2, 3], [2, 2, 3]])
 
+    @pytest.mark.parametrize("ops",
+        [(1000, np.array([1], dtype=np.uint8)),
+         (-1, np.array([1], dtype=np.uint8)),
+         (1., np.float32(3)),
+         (1., np.array([3], dtype=np.float32))],)
+    def test_output_dtype(self, ops):
+        expected_dt = np.result_type(*ops)
+        assert(np.choose([0], ops).dtype == expected_dt)
+
 
 class TestRepeat(object):
     def setup(self):