Let vindex accept a Dask Array indexer under certain conditions

dask/array/core.py

@@ -2037,10 +2037,19 @@ def _vindex(self, key):
                 "Use normal slicing instead when only using slices. Got: {}".format(key)
             )
         elif any(is_dask_collection(k) for k in key):
-            raise IndexError(
-                "vindex does not support indexing with dask objects. Call compute "
-                "on the indexer first to get an evalurated array. Got: {}".format(key)
-            )
+            if math.prod(self.numblocks) == 1 and len(key) == 1 and self.ndim == 1:
+                idxr = key[0]
+                # we can broadcast in this case
+                return idxr.map_blocks(
+                    _numpy_vindex, self, dtype=self.dtype, chunks=idxr.chunks
+                )
+            else:
+                raise IndexError(
+                    "vindex does not support indexing with dask objects. Call compute "
+                    "on the indexer first to get an evalurated array. Got: {}".format(
+                        key
+                    )
+                )
         return _vindex(self, *key)
 
     @property
@@ -6075,4 +6084,8 @@ def ravel(self) -> list[Array]:
         return [self[idx] for idx in np.ndindex(self.shape)]
 
 
+def _numpy_vindex(indexer, arr):
+    return arr[indexer]
+
+
 from dask.array.blockwise import blockwise

dask/array/tests/test_slicing.py

@@ -1111,3 +1111,12 @@ def test_minimal_dtype_doesnt_overflow():
     ib = np.zeros(1980, dtype=bool)
     ib[1560:1860] = True
     assert_eq(dx[ib], x[ib])
+
+
+def test_vindex_with_dask_array():
+    arr = np.array([0.2, 0.4, 0.6])
+    darr = da.from_array(arr, chunks=-1)
+
+    indexer = np.random.randint(0, 3, 8).reshape(4, 2).astype(int)
+    dindexer = da.from_array(indexer, chunks=(2, 2))
+    assert_eq(darr.vindex[dindexer], arr[indexer])