REF: avoid importing pd.NA in tslibs

pandas/_libs/missing.pxd

@@ -6,6 +6,7 @@ cpdef ndarray[uint8_t] isnaobj(ndarray arr)
 
 cdef bint is_null_datetime64(v)
 cdef bint is_null_timedelta64(v)
+cdef bint checknull_with_nat_and_na(object obj)
 
 cdef class C_NAType:
     pass

pandas/_libs/missing.pyx

@@ -279,6 +279,11 @@ cdef inline bint is_null_timedelta64(v):
     return False
 
 
+cdef bint checknull_with_nat_and_na(object obj):
+    # See GH#32214
+    return checknull_with_nat(obj) or obj is C_NA
+
+
 # -----------------------------------------------------------------------------
 # Implementation of NA singleton
 

pandas/_libs/tslib.pyx

@@ -51,8 +51,7 @@ from pandas._libs.tslibs.conversion cimport (
     get_datetime64_nanos)
 
 from pandas._libs.tslibs.nattype import nat_strings
-from pandas._libs.tslibs.nattype cimport (
-    checknull_with_nat, NPY_NAT, c_NaT as NaT)
+from pandas._libs.tslibs.nattype cimport NPY_NAT, c_NaT as NaT
 
 from pandas._libs.tslibs.offsets cimport to_offset
 
@@ -64,6 +63,9 @@ from pandas._libs.tslibs.tzconversion cimport (
     tz_convert_utc_to_tzlocal,
 )
 
+# Note: this is the only non-tslibs intra-pandas dependency here
+from pandas._libs.missing cimport checknull_with_nat_and_na
+
 
 cdef inline object create_datetime_from_ts(
     int64_t value,
@@ -438,7 +440,7 @@ def array_with_unit_to_datetime(
         for i in range(n):
             val = values[i]
 
-            if checknull_with_nat(val):
+            if checknull_with_nat_and_na(val):
                 iresult[i] = NPY_NAT
 
             elif is_integer_object(val) or is_float_object(val):
@@ -505,7 +507,7 @@ def array_with_unit_to_datetime(
     for i in range(n):
         val = values[i]
 
-        if checknull_with_nat(val):
+        if checknull_with_nat_and_na(val):
             oresult[i] = <object>NaT
         elif is_integer_object(val) or is_float_object(val):
 
@@ -602,7 +604,7 @@ cpdef array_to_datetime(
             val = values[i]
 
             try:
-                if checknull_with_nat(val):
+                if checknull_with_nat_and_na(val):
                     iresult[i] = NPY_NAT
 
                 elif PyDateTime_Check(val):
@@ -812,7 +814,7 @@ cdef ignore_errors_out_of_bounds_fallback(ndarray[object] values):
         val = values[i]
 
         # set as nan except if its a NaT
-        if checknull_with_nat(val):
+        if checknull_with_nat_and_na(val):
             if isinstance(val, float):
                 oresult[i] = np.nan
             else:
@@ -874,7 +876,7 @@ cdef array_to_datetime_object(
     # 2) datetime strings, which we return as datetime.datetime
     for i in range(n):
         val = values[i]
-        if checknull_with_nat(val) or PyDateTime_Check(val):
+        if checknull_with_nat_and_na(val) or PyDateTime_Check(val):
             # GH 25978. No need to parse NaT-like or datetime-like vals
             oresult[i] = val
         elif isinstance(val, str):

pandas/_libs/tslibs/nattype.pyx

@@ -31,8 +31,6 @@ from pandas._libs.tslibs.np_datetime cimport (
 )
 cimport pandas._libs.tslibs.util as util
 
-from pandas._libs.missing cimport C_NA
-
 
 # ----------------------------------------------------------------------
 # Constants
@@ -809,7 +807,7 @@ cdef inline bint checknull_with_nat(object val):
     """
     Utility to check if a value is a nat or not.
     """
-    return val is None or util.is_nan(val) or val is c_NaT or val is C_NA
+    return val is None or util.is_nan(val) or val is c_NaT
 
 
 cpdef bint is_null_datetimelike(object val, bint inat_is_null=True):