BUG: __from_arrow__ inconsistent strictness (#44891)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -672,6 +672,8 @@ Conversion
 - Bug in :meth:`DataFrame.convert_dtypes` not returning the correct type when a subclass does not overload :meth:`_constructor_sliced` (:issue:`43201`)
 - Bug in :meth:`DataFrame.astype` not propagating ``attrs`` from the original :class:`DataFrame` (:issue:`44414`)
 - Bug in :meth:`DataFrame.convert_dtypes` result losing ``columns.names`` (:issue:`41435`)
+- Bug in constructing a ``IntegerArray`` from pyarrow data failing to validate dtypes (:issue:`44891`)
+-
 
 Strings
 ^^^^^^^

pandas/core/arrays/numeric.py

@@ -21,6 +21,7 @@
     is_integer,
     is_integer_dtype,
     is_list_like,
+    pandas_dtype,
 )
 
 from pandas.core.arrays.masked import (
@@ -49,6 +50,16 @@ def __from_arrow__(
 
         pyarrow_type = pyarrow.from_numpy_dtype(self.type)
         if not array.type.equals(pyarrow_type):
+            # test_from_arrow_type_error raise for string, but allow
+            #  through itemsize conversion GH#31896
+            rt_dtype = pandas_dtype(array.type.to_pandas_dtype())
+            if rt_dtype.kind not in ["i", "u", "f"]:
+                # Could allow "c" or potentially disallow float<->int conversion,
+                #  but at the moment we specifically test that uint<->int works
+                raise TypeError(
+                    f"Expected array of {self} type, got {array.type} instead"
+                )
+
             array = array.cast(pyarrow_type)
 
         if isinstance(array, pyarrow.Array):

pandas/tests/arrays/masked/test_arrow_compat.py

@@ -176,15 +176,9 @@ def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
     tm.assert_numpy_array_equal(mask, mask_expected_empty)
 
 
-def test_from_arrow_type_error(request, data):
+def test_from_arrow_type_error(data):
     # ensure that __from_arrow__ returns a TypeError when getting a wrong
     # array type
-    if data.dtype != "boolean":
-        # TODO numeric dtypes cast any incoming array to the correct dtype
-        # instead of erroring
-        request.node.add_marker(
-            pytest.mark.xfail(raises=None, reason="numeric dtypes don't error but cast")
-        )
 
     arr = pa.array(data).cast("string")
     with pytest.raises(TypeError, match=None):