BUG: infer_dtype with ArrowDtype (pandas-dev#53023)

* BUG: infer_dtype with ArrowDtype

* xfail

Author: jbrockmendel

pandas/_libs/lib.pyx

@@ -13,7 +13,11 @@ from cpython.datetime cimport (
     PyDateTime_Check,
     PyDelta_Check,
     PyTime_Check,
+    date,
+    datetime,
     import_datetime,
+    time,
+    timedelta,
 )
 from cpython.iterator cimport PyIter_Check
 from cpython.number cimport PyNumber_Check
@@ -1204,6 +1208,12 @@ _TYPE_MAP = {
     "m": "timedelta64",
     "interval": "interval",
     Period: "period",
+    datetime: "datetime64",
+    date: "date",
+    time: "time",
+    timedelta: "timedelta64",
+    Decimal: "decimal",
+    bytes: "bytes",
 }
 
 # types only exist on certain platform
@@ -1373,7 +1383,8 @@ cdef object _try_infer_map(object dtype):
     cdef:
         object val
         str attr
-    for attr in ["kind", "name", "base", "type"]:
+    for attr in ["type", "kind", "name", "base"]:
+        # Checking type before kind matters for ArrowDtype cases
         val = getattr(dtype, attr, None)
         if val in _TYPE_MAP:
             return _TYPE_MAP[val]

pandas/tests/extension/decimal/test_decimal.py

@@ -6,7 +6,6 @@
 
 import pandas as pd
 import pandas._testing as tm
-from pandas.api.types import infer_dtype
 from pandas.tests.extension import base
 from pandas.tests.extension.decimal.array import (
     DecimalArray,
@@ -70,15 +69,7 @@ def data_for_grouping():
 
 
 class TestDtype(base.BaseDtypeTests):
-    def test_hashable(self, dtype):
-        pass
-
-    @pytest.mark.parametrize("skipna", [True, False])
-    def test_infer_dtype(self, data, data_missing, skipna):
-        # here overriding base test to ensure we fall back to return
-        # "unknown-array" for an EA pandas doesn't know
-        assert infer_dtype(data, skipna=skipna) == "unknown-array"
-        assert infer_dtype(data_missing, skipna=skipna) == "unknown-array"
+    pass
 
 
 class TestInterface(base.BaseInterfaceTests):

pandas/tests/extension/test_arrow.py

@@ -2905,3 +2905,17 @@ def test_duration_overflow_from_ndarray_containing_nat():
     result = ser_ts + ser_td
     expected = pd.Series([2, None], dtype=ArrowDtype(pa.timestamp("ns")))
     tm.assert_series_equal(result, expected)
+
+
+def test_infer_dtype_pyarrow_dtype(data, request):
+    res = lib.infer_dtype(data)
+    assert res != "unknown-array"
+
+    if data._hasna and res in ["floating", "datetime64", "timedelta64"]:
+        mark = pytest.mark.xfail(
+            reason="in infer_dtype pd.NA is not ignored in these cases "
+            "even with skipna=True in the list(data) check below"
+        )
+        request.node.add_marker(mark)
+
+    assert res == lib.infer_dtype(list(data), skipna=True)