In [1]: import xarray as xr
   ...: import cupy
   ...: import dask.array as da
   ...: 
   ...: arr = xr.DataArray(cupy.arange(4), dims="x")
   ...: mask = xr.DataArray(cupy.array([False, True, True, False]), dims="x")

In [2]: arr.where(mask)
Out[2]: 
<xarray.DataArray (x: 4)>
array([nan,  1.,  2., nan])
Dimensions without coordinates: x

In [4]: arr.chunk().where(mask).compute()
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[4], line 1
----> 1 arr.chunk().where(mask).compute()

File ~/repos/xarray/xarray/core/dataarray.py:1095, in DataArray.compute(self, **kwargs)
   1076 """Manually trigger loading of this array's data from disk or a
   1077 remote source into memory and return a new array. The original is
   1078 left unaltered.
   (...)
   1092 dask.compute
   1093 """
   1094 new = self.copy(deep=False)
-> 1095 return new.load(**kwargs)

File ~/repos/xarray/xarray/core/dataarray.py:1069, in DataArray.load(self, **kwargs)
   1051 def load(self: T_DataArray, **kwargs) -> T_DataArray:
   1052     """Manually trigger loading of this array's data from disk or a
   1053     remote source into memory and return this array.
   1054 
   (...)
   1067     dask.compute
   1068     """
-> 1069     ds = self._to_temp_dataset().load(**kwargs)
   1070     new = self._from_temp_dataset(ds)
   1071     self._variable = new._variable

File ~/repos/xarray/xarray/core/dataset.py:752, in Dataset.load(self, **kwargs)
    749 import dask.array as da
    751 # evaluate all the dask arrays simultaneously
--> 752 evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    754 for k, data in zip(lazy_data, evaluated_data):
    755     self.variables[k].data = data

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/base.py:600, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    597     keys.append(x.__dask_keys__())
    598     postcomputes.append(x.__dask_postcompute__())
--> 600 results = schedule(dsk, keys, **kwargs)
    601 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, **kwargs)
     86     elif isinstance(pool, multiprocessing.pool.Pool):
     87         pool = MultiprocessingPoolExecutor(pool)
---> 89 results = get_async(
     90     pool.submit,
     91     pool._max_workers,
     92     dsk,
     93     keys,
     94     cache=cache,
     95     get_id=_thread_get_id,
     96     pack_exception=pack_exception,
     97     **kwargs,
     98 )
    100 # Cleanup pools associated to dead threads
    101 with pools_lock:

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
    509         _execute_task(task, data)  # Re-execute locally
    510     else:
--> 511         raise_exception(exc, tb)
    512 res, worker_id = loads(res_info)
    513 state["cache"][key] = res

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/local.py:319, in reraise(exc, tb)
    317 if exc.__traceback__ is not tb:
    318     raise exc.with_traceback(tb)
--> 319 raise exc

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    222 try:
    223     task, data = loads(task_info)
--> 224     result = _execute_task(task, data)
    225     id = get_id()
    226     result = dumps((result, id))

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/optimization.py:990, in SubgraphCallable.__call__(self, *args)
    988 if not len(args) == len(self.inkeys):
    989     raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 990 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/core.py:149, in get(dsk, out, cache)
    147 for key in toposort(dsk):
    148     task = dsk[key]
--> 149     result = _execute_task(task, cache)
    150     cache[key] = result
    151 result = _execute_task(out, cache)

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File <__array_function__ internals>:180, in where(*args, **kwargs)

File cupy/_core/core.pyx:1723, in cupy._core.core._ndarray_base.__array_function__()

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/cupy/_sorting/search.py:211, in where(condition, x, y)
    209 if fusion._is_fusing():
    210     return fusion._call_ufunc(_where_ufunc, condition, x, y)
--> 211 return _where_ufunc(condition.astype('?'), x, y)

File cupy/_core/_kernel.pyx:1287, in cupy._core._kernel.ufunc.__call__()

File cupy/_core/_kernel.pyx:160, in cupy._core._kernel._preprocess_args()

File cupy/_core/_kernel.pyx:146, in cupy._core._kernel._preprocess_arg()

TypeError: Unsupported type <class 'numpy.ndarray'>

In [7]: arr.chunk().where(mask.chunk(), cupy.array(cupy.nan)).compute()
Out[7]: 
<xarray.DataArray (x: 4)>
array([nan,  1.,  2., nan])
Dimensions without coordinates: x