REF: DatetimeIndex constructor fixups

pandas/_libs/tslibs/conversion.pyx

@@ -866,8 +866,8 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None,
         - bool if True, treat all vals as DST. If False, treat them as non-DST
         - 'NaT' will return NaT where there are ambiguous times
 
-    nonexistent : str
-        If arraylike, must have the same length as vals
+    nonexistent : {None, "NaT", "shift", "raise"}
+        How to handle non-existent times when converting wall times to UTC
 
         .. versionadded:: 0.24.0
 

pandas/core/arrays/datetimes.py

@@ -183,7 +183,7 @@ class DatetimeArrayMixin(dtl.DatetimeLikeArrayMixin):
     _freq = None
 
     @classmethod
-    def _simple_new(cls, values, freq=None, tz=None, **kwargs):
+    def _simple_new(cls, values, freq=None, tz=None):
         """
         we require the we have a dtype compat for the values
         if we are passed a non-dtype compat, then coerce using the constructor

pandas/core/indexes/datetimes.py

@@ -317,17 +317,15 @@ def __new__(cls, data=None,
         return subarr._deepcopy_if_needed(ref_to_data, copy)
 
     @classmethod
-    def _simple_new(cls, values, name=None, freq=None, tz=None,
-                    dtype=None, **kwargs):
+    def _simple_new(cls, values, name=None, freq=None, tz=None, dtype=None):
         """
         we require the we have a dtype compat for the values
         if we are passed a non-dtype compat, then coerce using the constructor
         """
         # DatetimeArray._simple_new will accept either i8 or M8[ns] dtypes
         assert isinstance(values, np.ndarray), type(values)
 
-        result = super(DatetimeIndex, cls)._simple_new(values, freq, tz,
-                                                       **kwargs)
+        result = super(DatetimeIndex, cls)._simple_new(values, freq, tz)
         result.name = name
         result._reset_identity()
         return result

pandas/tests/scalar/timedelta/test_timedelta.py

@@ -550,7 +550,7 @@ def test_overflow(self):
 
         # mean
         result = (s - s.min()).mean()
-        expected = pd.Timedelta((pd.DatetimeIndex((s - s.min())).asi8 / len(s)
+        expected = pd.Timedelta((pd.TimedeltaIndex((s - s.min())).asi8 / len(s)
                                  ).sum())
 
         # the computation is converted to float so