From a1c200ea58fbb3a0ab70b909e40f96e56b18c9a1 Mon Sep 17 00:00:00 2001
From: Patrick Hoefler <patrick_hoefler@gmx.net>
Date: Tue, 25 Apr 2023 23:08:52 +0200
Subject: [PATCH] CLN: Simplify to_masked implementation

---
 pandas/core/arrays/arrow/array.py | 35 ++++++-------------------------
 1 file changed, 6 insertions(+), 29 deletions(-)

diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 55c6b74e495c0..6e19f0fce316d 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -69,6 +69,7 @@
 )
 from pandas.core.strings.base import BaseStringArrayMethods
 
+from pandas.io._util import _arrow_dtype_mapping
 from pandas.tseries.frequencies import to_offset
 
 if not pa_version_under7p0:
@@ -1719,42 +1720,18 @@ def _replace_with_mask(
 
     def _to_masked(self):
         pa_dtype = self._pa_array.type
-        na_value = 1
-        from pandas.core.arrays import (
-            BooleanArray,
-            FloatingArray,
-            IntegerArray,
-        )
-
-        arr_cls: type[FloatingArray | IntegerArray | BooleanArray]
-        if pa.types.is_floating(pa_dtype):
-            nbits = pa_dtype.bit_width
-            dtype = f"Float{nbits}"
-            np_dtype = dtype.lower()
-            arr_cls = FloatingArray
-        elif pa.types.is_unsigned_integer(pa_dtype):
-            nbits = pa_dtype.bit_width
-            dtype = f"UInt{nbits}"
-            np_dtype = dtype.lower()
-            arr_cls = IntegerArray
-
-        elif pa.types.is_signed_integer(pa_dtype):
-            nbits = pa_dtype.bit_width
-            dtype = f"Int{nbits}"
-            np_dtype = dtype.lower()
-            arr_cls = IntegerArray
 
+        if pa.types.is_floating(pa_dtype) or pa.types.is_integer(pa_dtype):
+            na_value = 1
         elif pa.types.is_boolean(pa_dtype):
-            dtype = "boolean"
-            np_dtype = "bool"
             na_value = True
-            arr_cls = BooleanArray
         else:
             raise NotImplementedError
 
+        dtype = _arrow_dtype_mapping()[pa_dtype]
         mask = self.isna()
-        arr = self.to_numpy(dtype=np_dtype, na_value=na_value)
-        return arr_cls(arr, mask)
+        arr = self.to_numpy(dtype=dtype.numpy_dtype, na_value=na_value)
+        return dtype.construct_array_type()(arr, mask)
 
     def _groupby_op(
         self,