TYP: use type annotations in vectorized.pyi

pandas/_libs/tslibs/vectorized.pyi

@@ -2,11 +2,9 @@
 For cython types that cannot be represented precisely, closest-available
 python equivalents are used, and the precise types kept as adjacent comments.
 """
+from __future__ import annotations
+
 from datetime import tzinfo
-from typing import (
-    Optional,
-    Union,
-)
 
 import numpy as np
 
@@ -16,32 +14,24 @@ from pandas._libs.tslibs.offsets import BaseOffset
 def dt64arr_to_periodarr(
     stamps: np.ndarray,  # const int64_t[:]
     freq: int,
-    tz: Optional[tzinfo],
+    tz: tzinfo | None,
 ) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=1]
-
-
 def is_date_array_normalized(
     stamps: np.ndarray,  # const int64_t[:]
-    tz: Optional[tzinfo] = None,
+    tz: tzinfo | None = None,
 ) -> bool: ...
-
-
 def normalize_i8_timestamps(
     stamps: np.ndarray,  # const int64_t[:]
-    tz: Optional[tzinfo],
+    tz: tzinfo | None,
 ) -> np.ndarray: ...  # np.ndarray[np.int64]
-
-
 def get_resolution(
     stamps: np.ndarray,  # const int64_t[:]
-    tz: Optional[tzinfo] = None,
+    tz: tzinfo | None = None,
 ) -> Resolution: ...
-
-
 def ints_to_pydatetime(
     arr: np.ndarray,  # const int64_t[:}]
-    tz: Optional[tzinfo] = None,
-    freq: Optional[Union[str, BaseOffset]] = None,
+    tz: tzinfo | None = None,
+    freq: str | BaseOffset | None = None,
     fold: bool = False,
     box: str = "datetime",
 ) -> np.ndarray: ...  # np.ndarray[object]