TST: simplify pyarrow tests, make mode work with temporal dtypes

pandas/core/arrays/arrow/array.py

@@ -657,12 +657,11 @@ def factorize(
         pa_type = self._data.type
         if pa.types.is_duration(pa_type):
             # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
-            arr = cast(ArrowExtensionArray, self.astype("int64[pyarrow]"))
-            indices, uniques = arr.factorize(use_na_sentinel=use_na_sentinel)
-            uniques = uniques.astype(self.dtype)
-            return indices, uniques
+            data = self._data.cast(pa.int64())
+        else:
+            data = self._data
 
-        encoded = self._data.dictionary_encode(null_encoding=null_encoding)
+        encoded = data.dictionary_encode(null_encoding=null_encoding)
         if encoded.length() == 0:
             indices = np.array([], dtype=np.intp)
             uniques = type(self)(pa.chunked_array([], type=encoded.type.value_type))
@@ -674,6 +673,9 @@ def factorize(
                 np.intp, copy=False
             )
             uniques = type(self)(encoded.chunk(0).dictionary)
+
+        if pa.types.is_duration(pa_type):
+            uniques = cast(ArrowExtensionArray, uniques.astype(self.dtype))
         return indices, uniques
 
     def reshape(self, *args, **kwargs):
@@ -858,13 +860,20 @@ def unique(self: ArrowExtensionArrayT) -> ArrowExtensionArrayT:
         -------
         ArrowExtensionArray
         """
-        if pa.types.is_duration(self._data.type):
+        pa_type = self._data.type
+
+        if pa.types.is_duration(pa_type):
             # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
-            arr = cast(ArrowExtensionArrayT, self.astype("int64[pyarrow]"))
-            result = arr.unique()
-            return cast(ArrowExtensionArrayT, result.astype(self.dtype))
+            data = self._data.cast(pa.int64())
+        else:
+            data = self._data
+
+        pa_result = pc.unique(data)
 
-        return type(self)(pc.unique(self._data))
+        if pa.types.is_duration(pa_type):
+            pa_result = pa_result.cast(pa_type)
+
+        return type(self)(pa_result)
 
     def value_counts(self, dropna: bool = True) -> Series:
         """
@@ -883,27 +892,30 @@ def value_counts(self, dropna: bool = True) -> Series:
         --------
         Series.value_counts
         """
-        if pa.types.is_duration(self._data.type):
+        pa_type = self._data.type
+        if pa.types.is_duration(pa_type):
             # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
-            arr = cast(ArrowExtensionArray, self.astype("int64[pyarrow]"))
-            result = arr.value_counts()
-            result.index = result.index.astype(self.dtype)
-            return result
+            data = self._data.cast(pa.int64())
+        else:
+            data = self._data
 
         from pandas import (
             Index,
             Series,
         )
 
-        vc = self._data.value_counts()
+        vc = data.value_counts()
 
         values = vc.field(0)
         counts = vc.field(1)
-        if dropna and self._data.null_count > 0:
+        if dropna and data.null_count > 0:
             mask = values.is_valid()
             values = values.filter(mask)
             counts = counts.filter(mask)
 
+        if pa.types.is_duration(pa_type):
+            values = values.cast(pa_type)
+
         # No missing values so we can adhere to the interface and return a numpy array.
         counts = np.array(counts)
 
@@ -1231,12 +1243,29 @@ def _mode(self: ArrowExtensionArrayT, dropna: bool = True) -> ArrowExtensionArra
         """
         if pa_version_under6p0:
             raise NotImplementedError("mode only supported for pyarrow version >= 6.0")
-        modes = pc.mode(self._data, pc.count_distinct(self._data).as_py())
+
+        pa_type = self._data.type
+        if pa.types.is_temporal(pa_type):
+            nbits = pa_type.bit_width
+            if nbits == 32:
+                data = self._data.cast(pa.int32())
+            elif nbits == 64:
+                data = self._data.cast(pa.int64())
+            else:
+                raise NotImplementedError(pa_type)
+        else:
+            data = self._data
+
+        modes = pc.mode(data, pc.count_distinct(data).as_py())
         values = modes.field(0)
         counts = modes.field(1)
         # counts sorted descending i.e counts[0] = max
         mask = pc.equal(counts, counts[0])
         most_common = values.filter(mask)
+
+        if pa.types.is_temporal(pa_type):
+            most_common = most_common.cast(pa_type)
+
         return type(self)(most_common)
 
     def _maybe_convert_setitem_value(self, value):