Fix categorical conversion from chunked arrow arrays (#15886)

The current logic for converting arrow dictionary arrays to cudf doesn't properly uniquify categories across chunks of chunked arrays. This PR implements the simplest fix by having arrow combine chunks when this case is encountered.

Resolves #6828

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #15886

python/cudf/cudf/core/frame.py

@@ -897,6 +897,13 @@ def from_arrow(cls, data: pa.Table) -> Self:
         # so handling indices and dictionary as two different columns.
         # This needs be removed once we have hooked libcudf dictionary32
         # with categorical.
+        if any(
+            isinstance(x.type, pa.DictionaryType)
+            and isinstance(x, pa.ChunkedArray)
+            for x in data
+        ):
+            data = data.combine_chunks()
+
         dict_indices = {}
         dict_dictionaries = {}
         dict_ordered = {}

python/cudf/cudf/tests/test_dataframe.py

@@ -1984,6 +1984,18 @@ def test_from_arrow(nelem, data_type):
     np.testing.assert_array_equal(s.to_pandas(), gs.to_numpy())
 
 
+def test_from_arrow_chunked_categories():
+    # Verify that categories are properly deduplicated across chunked arrays.
+    indices = pa.array([0, 1, 0, 1, 2, 0, None, 2])
+    dictionary = pa.array(["foo", "bar", "baz"])
+    dict_array = pa.DictionaryArray.from_arrays(indices, dictionary)
+    chunked_array = pa.chunked_array([dict_array, dict_array])
+    table = pa.table({"a": chunked_array})
+    df = cudf.DataFrame.from_arrow(table)
+    final_dictionary = df["a"].dtype.categories.to_arrow().to_pylist()
+    assert sorted(final_dictionary) == sorted(dictionary.to_pylist())
+
+
 @pytest.mark.parametrize("nelem", [0, 2, 3, 100, 1000])
 @pytest.mark.parametrize("data_type", dtypes)
 def test_to_arrow(nelem, data_type):