REF: avoid sanitize_to_nanoseconds (#49009)

* REF: avoid sanitize_to_nanoseconds

* fix test

Author: jbrockmendel

pandas/core/construction.py

@@ -45,7 +45,6 @@
     maybe_convert_platform,
     maybe_infer_to_datetimelike,
     maybe_upcast,
-    sanitize_to_nanoseconds,
 )
 from pandas.core.dtypes.common import (
     is_datetime64_ns_dtype,
@@ -782,7 +781,9 @@ def _try_cast(
         if is_ndarray:
             arr = cast(np.ndarray, arr)
             if arr.dtype != object:
-                return sanitize_to_nanoseconds(arr, copy=copy)
+                if copy:
+                    return arr.copy()
+                return arr
 
             out = maybe_infer_to_datetimelike(arr)
             if out is arr and copy:

pandas/core/dtypes/cast.py

@@ -1423,12 +1423,7 @@ def maybe_cast_to_datetime(
             return astype_nansafe(value, dtype)  # type: ignore[arg-type]
 
     elif isinstance(value, np.ndarray):
-        if value.dtype.kind in ["M", "m"]:
-            # catch a datetime/timedelta that is not of ns variety
-            # and no coercion specified
-            value = sanitize_to_nanoseconds(value)
-
-        elif value.dtype == _dtype_obj:
+        if value.dtype == _dtype_obj:
             value = maybe_infer_to_datetimelike(value)
 
     elif isinstance(value, list):

pandas/core/groupby/grouper.py

@@ -26,7 +26,6 @@
 from pandas.util._decorators import cache_readonly
 from pandas.util._exceptions import find_stack_level
 
-from pandas.core.dtypes.cast import sanitize_to_nanoseconds
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     is_list_like,
@@ -558,9 +557,12 @@ def __init__(
                 raise AssertionError(errmsg)
 
         if isinstance(self.grouping_vector, np.ndarray):
-            # if we have a date/time-like grouper, make sure that we have
-            # Timestamps like
-            self.grouping_vector = sanitize_to_nanoseconds(self.grouping_vector)
+            if self.grouping_vector.dtype.kind in ["m", "M"]:
+                # if we have a date/time-like grouper, make sure that we have
+                # Timestamps like
+                # TODO 2022-10-08 we only have one test that gets here and
+                #  values are already in nanoseconds in that case.
+                self.grouping_vector = Series(self.grouping_vector).to_numpy()
 
     def __repr__(self) -> str:
         return f"Grouping({self.name})"