add axis==None condition for zero-size array

Author: vtavana

dpnp/dpnp_iface_statistics.py

@@ -413,14 +413,14 @@ def max(a, axis=None, out=None, keepdims=False, initial=None, where=True):
             # TODO: get rid of this if condition when dpctl supports it
             for i in range(a.ndim):
                 if a.shape[i] == 0:
-                    if i not in axis:
-                        indices = [i for i in range(a.ndim) if i not in axis]
-                        res_shape = tuple([a.shape[i] for i in indices])
-                        result = dpnp.empty(res_shape, dtype=a.dtype)
-                    else:
+                    if axis is None or i in axis:
                         raise ValueError(
                             "reduction does not support zero-size arrays"
                         )
+                    else:
+                        indices = [i for i in range(a.ndim) if i not in axis]
+                        res_shape = tuple([a.shape[i] for i in indices])
+                        result = dpnp.empty(res_shape, dtype=a.dtype)
         else:
             result = dpnp_array._create_from_usm_ndarray(
                 dpt.max(dpt_array, axis=axis, keepdims=keepdims)
@@ -657,14 +657,14 @@ def min(a, axis=None, out=None, keepdims=False, initial=None, where=True):
             # TODO: get rid of this if condition when dpctl supports it
             for i in range(a.ndim):
                 if a.shape[i] == 0:
-                    if i not in axis:
-                        indices = [i for i in range(a.ndim) if i not in axis]
-                        res_shape = tuple([a.shape[i] for i in indices])
-                        result = dpnp.empty(res_shape, dtype=a.dtype)
-                    else:
+                    if axis is None or i in axis:
                         raise ValueError(
                             "reduction does not support zero-size arrays"
                         )
+                    else:
+                        indices = [i for i in range(a.ndim) if i not in axis]
+                        res_shape = tuple([a.shape[i] for i in indices])
+                        result = dpnp.empty(res_shape, dtype=a.dtype)
         else:
             result = dpnp_array._create_from_usm_ndarray(
                 dpt.min(dpt_array, axis=axis, keepdims=keepdims)