CLN: remove rebox_native

pandas/core/arrays/datetimelike.py

@@ -151,7 +151,9 @@ def _rebox_native(cls, value: int) -> Union[int, np.datetime64, np.timedelta64]:
         """
         raise AbstractMethodError(cls)
 
-    def _unbox_scalar(self, value: DTScalarOrNaT, setitem: bool = False) -> int:
+    def _unbox_scalar(
+        self, value: DTScalarOrNaT, setitem: bool = False
+    ) -> Union[np.int64, np.datetime64, np.timedelta64]:
         """
         Unbox the integer value of a scalar `value`.
 
@@ -636,7 +638,6 @@ def _unbox(
         """
         if lib.is_scalar(other):
             other = self._unbox_scalar(other, setitem=setitem)
-            other = self._rebox_native(other)
         else:
             # same type as self
             self._check_compatible_with(other, setitem=setitem)

pandas/core/arrays/datetimes.py

@@ -454,16 +454,13 @@ def _generate_range(
     # -----------------------------------------------------------------
     # DatetimeLike Interface
 
-    @classmethod
-    def _rebox_native(cls, value: int) -> np.datetime64:
-        return np.int64(value).view("M8[ns]")
-
-    def _unbox_scalar(self, value, setitem: bool = False):
+    def _unbox_scalar(self, value, setitem: bool = False) -> np.datetime64:
         if not isinstance(value, self._scalar_type) and value is not NaT:
             raise ValueError("'value' should be a Timestamp.")
         if not isna(value):
             self._check_compatible_with(value, setitem=setitem)
-        return value.value
+            return value.asm8
+        return np.datetime64(value.value, "ns")
 
     def _scalar_from_string(self, value):
         return Timestamp(value, tz=self.tz)

pandas/core/arrays/period.py

@@ -260,18 +260,14 @@ def _generate_range(cls, start, end, periods, freq, fields):
     # -----------------------------------------------------------------
     # DatetimeLike Interface
 
-    @classmethod
-    def _rebox_native(cls, value: int) -> np.int64:
-        return np.int64(value)
-
     def _unbox_scalar(
         self, value: Union[Period, NaTType], setitem: bool = False
     ) -> int:
         if value is NaT:
-            return value.value
+            return np.int64(value.value)
         elif isinstance(value, self._scalar_type):
             self._check_compatible_with(value, setitem=setitem)
-            return value.ordinal
+            return np.int64(value.ordinal)
         else:
             raise ValueError(f"'value' should be a Period. Got '{value}' instead.")
 

pandas/core/arrays/timedeltas.py

@@ -301,15 +301,11 @@ def _generate_range(cls, start, end, periods, freq, closed=None):
     # ----------------------------------------------------------------
     # DatetimeLike Interface
 
-    @classmethod
-    def _rebox_native(cls, value: int) -> np.timedelta64:
-        return np.int64(value).view("m8[ns]")
-
-    def _unbox_scalar(self, value, setitem: bool = False):
+    def _unbox_scalar(self, value, setitem: bool = False) -> np.timedelta64:
         if not isinstance(value, self._scalar_type) and value is not NaT:
             raise ValueError("'value' should be a Timedelta.")
         self._check_compatible_with(value, setitem=setitem)
-        return value.value
+        return np.timedelta64(value.value, "ns")
 
     def _scalar_from_string(self, value):
         return Timedelta(value)

pandas/tests/arrays/test_datetimelike.py

@@ -191,10 +191,11 @@ def test_unbox_scalar(self):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
         arr = self.array_cls(data, freq="D")
         result = arr._unbox_scalar(arr[0])
-        assert isinstance(result, int)
+        expected = arr._data.dtype.type
+        assert isinstance(result, expected)
 
         result = arr._unbox_scalar(pd.NaT)
-        assert isinstance(result, int)
+        assert isinstance(result, expected)
 
         msg = f"'value' should be a {self.dtype.__name__}."
         with pytest.raises(ValueError, match=msg):