"""
Functions for applying functions that act on arrays to xarray's labeled data.
"""

from __future__ import annotations

import functools
import itertools
import operator
import warnings
from collections import Counter
from collections.abc import (
    Callable,
    Hashable,
    Iterable,
    Iterator,
    Mapping,
    Sequence,
    Set,
)
from typing import TYPE_CHECKING, Any, Literal, TypeVar, Union

import numpy as np

_T = TypeVar("_T", bound=Union["Dataset", "DataArray"])
_U = TypeVar("_U", bound=Union["Dataset", "DataArray"])
_V = TypeVar("_V", bound=Union["Dataset", "DataArray"])

from xarray.core import duck_array_ops, utils
from xarray.core.formatting import limit_lines
from xarray.core.indexes import Index, filter_indexes_from_coords
from xarray.core.options import _get_keep_attrs
from xarray.core.utils import (
    is_dict_like,
    result_name,
)
from xarray.core.variable import Variable
from xarray.namedarray.parallelcompat import get_chunked_array_type
from xarray.namedarray.pycompat import is_chunked_array
from xarray.structure.alignment import deep_align
from xarray.structure.merge import merge_attrs, merge_coordinates_without_align

if TYPE_CHECKING:
    from xarray.core.coordinates import Coordinates
    from xarray.core.dataarray import DataArray
    from xarray.core.dataset import Dataset
    from xarray.core.types import CombineAttrsOptions, JoinOptions

    MissingCoreDimOptions = Literal["raise", "copy", "drop"]

_NO_FILL_VALUE = utils.ReprObject("<no-fill-value>")
_JOINS_WITHOUT_FILL_VALUES = frozenset({"inner", "exact"})


def _first_of_type(args, kind):
    """Return either first object of type 'kind' or raise if not found."""
    for arg in args:
        if isinstance(arg, kind):
            return arg

    raise ValueError("This should be unreachable.")


def _all_of_type(args, kind):
    """Return all objects of type 'kind'"""
    return [arg for arg in args if isinstance(arg, kind)]


class _UFuncSignature:
    """Core dimensions signature for a given function.

    Based on the signature provided by generalized ufuncs in NumPy.

    Attributes
    ----------
    input_core_dims : tuple[tuple, ...]
        Core dimension names on each input variable.
    output_core_dims : tuple[tuple, ...]
        Core dimension names on each output variable.
    """

    __slots__ = (
        "_all_core_dims",
        "_all_input_core_dims",
        "_all_output_core_dims",
        "input_core_dims",
        "output_core_dims",
    )

    def __init__(self, input_core_dims, output_core_dims=((),)):
        self.input_core_dims = tuple(tuple(a) for a in input_core_dims)
        self.output_core_dims = tuple(tuple(a) for a in output_core_dims)
        self._all_input_core_dims = None
        self._all_output_core_dims = None
        self._all_core_dims = None

    @property
    def all_input_core_dims(self):
        if self._all_input_core_dims is None:
            self._all_input_core_dims = frozenset(
                dim for dims in self.input_core_dims for dim in dims
            )
        return self._all_input_core_dims

    @property
    def all_output_core_dims(self):
        if self._all_output_core_dims is None:
            self._all_output_core_dims = frozenset(
                dim for dims in self.output_core_dims for dim in dims
            )
        return self._all_output_core_dims

    @property
    def all_core_dims(self):
        if self._all_core_dims is None:
            self._all_core_dims = self.all_input_core_dims | self.all_output_core_dims
        return self._all_core_dims

    @property
    def dims_map(self):
        return {
            core_dim: f"dim{n}" for n, core_dim in enumerate(sorted(self.all_core_dims))
        }

    @property
    def num_inputs(self):
        return len(self.input_core_dims)

    @property
    def num_outputs(self):
        return len(self.output_core_dims)

    def __eq__(self, other):
        try:
            return (
                self.input_core_dims == other.input_core_dims
                and self.output_core_dims == other.output_core_dims
            )
        except AttributeError:
            return False

    def __ne__(self, other):
        return not self == other

    def __repr__(self):
        return f"{type(self).__name__}({list(self.input_core_dims)!r}, {list(self.output_core_dims)!r})"

    def __str__(self):
        lhs = ",".join("({})".format(",".join(dims)) for dims in self.input_core_dims)
        rhs = ",".join("({})".format(",".join(dims)) for dims in self.output_core_dims)
        return f"{lhs}->{rhs}"

    def to_gufunc_string(self, exclude_dims=frozenset()):
        """Create an equivalent signature string for a NumPy gufunc.

        Unlike __str__, handles dimensions that don't map to Python
        identifiers.

        Also creates unique names for input_core_dims contained in exclude_dims.
        """
        input_core_dims = [
            [self.dims_map[dim] for dim in core_dims]
            for core_dims in self.input_core_dims
        ]
        output_core_dims = [
            [self.dims_map[dim] for dim in core_dims]
            for core_dims in self.output_core_dims
        ]

        # enumerate input_core_dims contained in exclude_dims to make them unique
        if exclude_dims:
            exclude_dims = [self.dims_map[dim] for dim in exclude_dims]

            counter: Counter = Counter()

            def _enumerate(dim):
                if dim in exclude_dims:
                    n = counter[dim]
                    counter.update([dim])
                    dim = f"{dim}_{n}"
                return dim

            input_core_dims = [
                [_enumerate(dim) for dim in arg] for arg in input_core_dims
            ]

        alt_signature = type(self)(input_core_dims, output_core_dims)
        return str(alt_signature)


def _get_coords_list(args: Iterable[Any]) -> list[Coordinates]:
    coords_list = []
    for arg in args:
        try:
            coords = arg.coords
        except AttributeError:
            pass  # skip this argument
        else:
            coords_list.append(coords)
    return coords_list


def build_output_coords_and_indexes(
    args: Iterable[Any],
    signature: _UFuncSignature,
    exclude_dims: Set = frozenset(),
    combine_attrs: CombineAttrsOptions = "override",
) -> tuple[list[dict[Any, Variable]], list[dict[Any, Index]]]:
    """Build output coordinates and indexes for an operation.

    Parameters
    ----------
    args : Iterable
        List of raw operation arguments. Any valid types for xarray operations
        are OK, e.g., scalars, Variable, DataArray, Dataset.
    signature : _UfuncSignature
        Core dimensions signature for the operation.
    exclude_dims : set, optional
        Dimensions excluded from the operation. Coordinates along these
        dimensions are dropped.
    combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", \
                     "override"} or callable, default: "drop"
        A callable or a string indicating how to combine attrs of the objects being
        merged:

        - "drop": empty attrs on returned Dataset.
        - "identical": all attrs must be the same on every object.
        - "no_conflicts": attrs from all objects are combined, any that have
          the same name must also have the same value.
        - "drop_conflicts": attrs from all objects are combined, any that have
          the same name but different values are dropped.
        - "override": skip comparing and copy attrs from the first dataset to
          the result.

        If a callable, it must expect a sequence of ``attrs`` dicts and a context object
        as its only parameters.

    Returns
    -------
    Dictionaries of Variable and Index objects with merged coordinates.
    """
    coords_list = _get_coords_list(args)

    if len(coords_list) == 1 and not exclude_dims:
        # we can skip the expensive merge
        (unpacked_coords,) = coords_list
        merged_vars = dict(unpacked_coords.variables)
        merged_indexes = dict(unpacked_coords.xindexes)
    else:
        merged_vars, merged_indexes = merge_coordinates_without_align(
            coords_list, exclude_dims=exclude_dims, combine_attrs=combine_attrs
        )

    output_coords = []
    output_indexes = []
    for output_dims in signature.output_core_dims:
        dropped_dims = signature.all_input_core_dims - set(output_dims)
        if dropped_dims:
            filtered_coords = {
                k: v for k, v in merged_vars.items() if dropped_dims.isdisjoint(v.dims)
            }
            filtered_indexes = filter_indexes_from_coords(
                merged_indexes, set(filtered_coords)
            )
        else:
            filtered_coords = merged_vars
            filtered_indexes = merged_indexes
        output_coords.append(filtered_coords)
        output_indexes.append(filtered_indexes)

    return output_coords, output_indexes


def apply_dataarray_vfunc(
    func,
    *args,
    signature: _UFuncSignature,
    join: JoinOptions = "inner",
    exclude_dims=frozenset(),
    keep_attrs="override",
) -> tuple[DataArray, ...] | DataArray:
    """Apply a variable level function over DataArray, Variable and/or ndarray
    objects.
    """
    from xarray.core.dataarray import DataArray

    if len(args) > 1:
        args = tuple(
            deep_align(
                args,
                join=join,
                copy=False,
                exclude=exclude_dims,
                raise_on_invalid=False,
            )
        )

    objs = _all_of_type(args, DataArray)

    if keep_attrs == "drop":
        name = result_name(args)
    else:
        first_obj = _first_of_type(args, DataArray)
        name = first_obj.name
    result_coords, result_indexes = build_output_coords_and_indexes(
        args, signature, exclude_dims, combine_attrs=keep_attrs
    )

    data_vars = [getattr(a, "variable", a) for a in args]
    result_var = func(*data_vars)

    out: tuple[DataArray, ...] | DataArray
    if signature.num_outputs > 1:
        out = tuple(
            DataArray(
                variable, coords=coords, indexes=indexes, name=name, fastpath=True
            )
            for variable, coords, indexes in zip(
                result_var, result_coords, result_indexes, strict=True
            )
        )
    else:
        (coords,) = result_coords
        (indexes,) = result_indexes
        out = DataArray(
            result_var, coords=coords, indexes=indexes, name=name, fastpath=True
        )

    attrs = merge_attrs([x.attrs for x in objs], combine_attrs=keep_attrs)
    if isinstance(out, tuple):
        for da in out:
            da.attrs = attrs
    else:
        out.attrs = attrs

    return out


def ordered_set_union(all_keys: list[Iterable]) -> Iterable:
    return {key: None for keys in all_keys for key in keys}.keys()


def ordered_set_intersection(all_keys: list[Iterable]) -> Iterable:
    intersection = set(all_keys[0])
    for keys in all_keys[1:]:
        intersection.intersection_update(keys)
    return [key for key in all_keys[0] if key in intersection]


def assert_and_return_exact_match(all_keys):
    first_keys = all_keys[0]
    for keys in all_keys[1:]:
        if keys != first_keys:
            raise ValueError(
                "exact match required for all data variable names, "
                f"but {list(keys)} != {list(first_keys)}: {set(keys) ^ set(first_keys)} are not in both."
            )
    return first_keys


_JOINERS: dict[str, Callable] = {
    "inner": ordered_set_intersection,
    "outer": ordered_set_union,
    "left": operator.itemgetter(0),
    "right": operator.itemgetter(-1),
    "exact": assert_and_return_exact_match,
}


def join_dict_keys(objects: Iterable[Mapping | Any], how: str = "inner") -> Iterable:
    joiner = _JOINERS[how]
    all_keys = [obj.keys() for obj in objects if hasattr(obj, "keys")]
    return joiner(all_keys)


def collect_dict_values(
    objects: Iterable[Mapping | Any], keys: Iterable, fill_value: object = None
) -> list[list]:
    return [
        [obj.get(key, fill_value) if is_dict_like(obj) else obj for obj in objects]
        for key in keys
    ]


def _as_variables_or_variable(arg) -> Variable | tuple[Variable]:
    try:
        return arg.variables
    except AttributeError:
        try:
            return arg.variable
        except AttributeError:
            return arg


def _unpack_dict_tuples(
    result_vars: Mapping[Any, tuple[Variable, ...]], num_outputs: int
) -> tuple[dict[Hashable, Variable], ...]:
    out: tuple[dict[Hashable, Variable], ...] = tuple({} for _ in range(num_outputs))
    for name, values in result_vars.items():
        for value, results_dict in zip(values, out, strict=True):
            results_dict[name] = value
    return out


def _check_core_dims(signature, variable_args, name):
    """
    Check if an arg has all the core dims required by the signature.

    Slightly awkward design, of returning the error message. But we want to
    give a detailed error message, which requires inspecting the variable in
    the inner loop.
    """
    missing = []
    for i, (core_dims, variable_arg) in enumerate(
        zip(signature.input_core_dims, variable_args, strict=True)
    ):
        # Check whether all the dims are on the variable. Note that we need the
        # `hasattr` to check for a dims property, to protect against the case where
        # a numpy array is passed in.
        if hasattr(variable_arg, "dims") and set(core_dims) - set(variable_arg.dims):
            missing += [[i, variable_arg, core_dims]]
    if missing:
        message = ""
        for i, variable_arg, core_dims in missing:
            message += f"Missing core dims {set(core_dims) - set(variable_arg.dims)} from arg number {i + 1} on a variable named `{name}`:\n{variable_arg}\n\n"
        message += "Either add the core dimension, or if passing a dataset alternatively pass `on_missing_core_dim` as `copy` or `drop`. "
        return message
    return True


def apply_dict_of_variables_vfunc(
    func,
    *args,
    signature: _UFuncSignature,
    join="inner",
    fill_value=None,
    on_missing_core_dim: MissingCoreDimOptions = "raise",
):
    """Apply a variable level function over dicts of DataArray, DataArray,
    Variable and ndarray objects.
    """
    args = tuple(_as_variables_or_variable(arg) for arg in args)
    names = join_dict_keys(args, how=join)
    grouped_by_name = collect_dict_values(args, names, fill_value)

    result_vars = {}
    for name, variable_args in zip(names, grouped_by_name, strict=True):
        core_dim_present = _check_core_dims(signature, variable_args, name)
        if core_dim_present is True:
            result_vars[name] = func(*variable_args)
        else:
            if on_missing_core_dim == "raise":
                raise ValueError(core_dim_present)
            elif on_missing_core_dim == "copy":
                result_vars[name] = variable_args[0]
            elif on_missing_core_dim == "drop":
                pass
            else:
                raise ValueError(
                    f"Invalid value for `on_missing_core_dim`: {on_missing_core_dim!r}"
                )

    if signature.num_outputs > 1:
        return _unpack_dict_tuples(result_vars, signature.num_outputs)
    else:
        return result_vars


def _fast_dataset(
    variables: dict[Hashable, Variable],
    coord_variables: Mapping[Hashable, Variable],
    indexes: dict[Hashable, Index],
) -> Dataset:
    """Create a dataset as quickly as possible.

    Beware: the `variables` dict is modified INPLACE.
    """
    from xarray.core.dataset import Dataset

    variables.update(coord_variables)
    coord_names = set(coord_variables)
    return Dataset._construct_direct(variables, coord_names, indexes=indexes)


def apply_dataset_vfunc(
    func,
    *args,
    signature: _UFuncSignature,
    join="inner",
    dataset_join="exact",
    fill_value=_NO_FILL_VALUE,
    exclude_dims=frozenset(),
    keep_attrs="override",
    on_missing_core_dim: MissingCoreDimOptions = "raise",
) -> Dataset | tuple[Dataset, ...]:
    """Apply a variable level function over Dataset, dict of DataArray,
    DataArray, Variable and/or ndarray objects.
    """
    from xarray.core.dataset import Dataset

    if dataset_join not in _JOINS_WITHOUT_FILL_VALUES and fill_value is _NO_FILL_VALUE:
        raise TypeError(
            "to apply an operation to datasets with different "
            "data variables with apply_ufunc, you must supply the "
            "dataset_fill_value argument."
        )

    objs = _all_of_type(args, Dataset)

    if len(args) > 1:
        args = tuple(
            deep_align(
                args,
                join=join,
                copy=False,
                exclude=exclude_dims,
                raise_on_invalid=False,
            )
        )

    list_of_coords, list_of_indexes = build_output_coords_and_indexes(
        args, signature, exclude_dims, combine_attrs=keep_attrs
    )
    args = tuple(getattr(arg, "data_vars", arg) for arg in args)

    result_vars = apply_dict_of_variables_vfunc(
        func,
        *args,
        signature=signature,
        join=dataset_join,
        fill_value=fill_value,
        on_missing_core_dim=on_missing_core_dim,
    )

    out: Dataset | tuple[Dataset, ...]
    if signature.num_outputs > 1:
        out = tuple(
            _fast_dataset(*args)
            for args in zip(result_vars, list_of_coords, list_of_indexes, strict=True)
        )
    else:
        (coord_vars,) = list_of_coords
        (indexes,) = list_of_indexes
        out = _fast_dataset(result_vars, coord_vars, indexes=indexes)

    attrs = merge_attrs([x.attrs for x in objs], combine_attrs=keep_attrs)
    if isinstance(out, tuple):
        for ds in out:
            ds.attrs = attrs
    else:
        out.attrs = attrs

    return out


def _iter_over_selections(obj, dim, values):
    """Iterate over selections of an xarray object in the provided order."""
    from xarray.core.groupby import _dummy_copy

    dummy = None
    for value in values:
        try:
            obj_sel = obj.sel(**{dim: value})
        except (KeyError, IndexError):
            if dummy is None:
                dummy = _dummy_copy(obj)
            obj_sel = dummy
        yield obj_sel


def apply_groupby_func(func, *args):
    """Apply a dataset or datarray level function over GroupBy, Dataset,
    DataArray, Variable and/or ndarray objects.
    """
    from xarray.core.groupby import GroupBy, peek_at
    from xarray.core.variable import Variable

    groupbys = [arg for arg in args if isinstance(arg, GroupBy)]
    assert groupbys, "must have at least one groupby to iterate over"
    first_groupby = groupbys[0]
    (grouper,) = first_groupby.groupers
    if any(not grouper.group.equals(gb.groupers[0].group) for gb in groupbys[1:]):  # type: ignore[union-attr]
        raise ValueError(
            "apply_ufunc can only perform operations over "
            "multiple GroupBy objects at once if they are all "
            "grouped the same way"
        )

    grouped_dim = grouper.name
    unique_values = grouper.unique_coord.values

    iterators = []
    for arg in args:
        iterator: Iterator[Any]
        if isinstance(arg, GroupBy):
            iterator = (value for _, value in arg)
        elif hasattr(arg, "dims") and grouped_dim in arg.dims:
            if isinstance(arg, Variable):
                raise ValueError(
                    "groupby operations cannot be performed with "
                    "xarray.Variable objects that share a dimension with "
                    "the grouped dimension"
                )
            iterator = _iter_over_selections(arg, grouped_dim, unique_values)
        else:
            iterator = itertools.repeat(arg)
        iterators.append(iterator)

    applied: Iterator = (
        func(*zipped_args) for zipped_args in zip(*iterators, strict=False)
    )
    applied_example, applied = peek_at(applied)
    combine = first_groupby._combine  # type: ignore[attr-defined]
    if isinstance(applied_example, tuple):
        combined = tuple(combine(output) for output in zip(*applied, strict=True))
    else:
        combined = combine(applied)
    return combined


def unified_dim_sizes(
    variables: Iterable[Variable], exclude_dims: Set = frozenset()
) -> dict[Hashable, int]:
    dim_sizes: dict[Hashable, int] = {}

    for var in variables:
        if len(set(var.dims)) < len(var.dims):
            raise ValueError(
                "broadcasting cannot handle duplicate "
                f"dimensions on a variable: {list(var.dims)}"
            )
        for dim, size in zip(var.dims, var.shape, strict=True):
            if dim not in exclude_dims:
                if dim not in dim_sizes:
                    dim_sizes[dim] = size
                elif dim_sizes[dim] != size:
                    raise ValueError(
                        "operands cannot be broadcast together "
                        "with mismatched lengths for dimension "
                        f"{dim}: {dim_sizes[dim]} vs {size}"
                    )
    return dim_sizes


SLICE_NONE = slice(None)


def broadcast_compat_data(
    variable: Variable,
    broadcast_dims: tuple[Hashable, ...],
    core_dims: tuple[Hashable, ...],
) -> Any:
    data = variable.data

    old_dims = variable.dims
    new_dims = broadcast_dims + core_dims

    if new_dims == old_dims:
        # optimize for the typical case
        return data

    set_old_dims = set(old_dims)
    set_new_dims = set(new_dims)
    unexpected_dims = [d for d in old_dims if d not in set_new_dims]

    if unexpected_dims:
        raise ValueError(
            "operand to apply_ufunc encountered unexpected "
            f"dimensions {unexpected_dims!r} on an input variable: these are core "
            "dimensions on other input or output variables"
        )

    # for consistency with numpy, keep broadcast dimensions to the left
    old_broadcast_dims = tuple(d for d in broadcast_dims if d in set_old_dims)
    reordered_dims = old_broadcast_dims + core_dims
    if reordered_dims != old_dims:
        order = tuple(old_dims.index(d) for d in reordered_dims)
        data = duck_array_ops.transpose(data, order)

    if new_dims != reordered_dims:
        key_parts: list[slice | None] = []
        for dim in new_dims:
            if dim in set_old_dims:
                key_parts.append(SLICE_NONE)
            elif key_parts:
                # no need to insert new axes at the beginning that are already
                # handled by broadcasting
                key_parts.append(np.newaxis)
        data = data[tuple(key_parts)]

    return data


def _vectorize(func, signature, output_dtypes, exclude_dims):
    if signature.all_core_dims:
        func = np.vectorize(
            func,
            otypes=output_dtypes,
            signature=signature.to_gufunc_string(exclude_dims),
        )
    else:
        func = np.vectorize(func, otypes=output_dtypes)

    return func


def apply_variable_ufunc(
    func,
    *args,
    signature: _UFuncSignature,
    exclude_dims=frozenset(),
    dask="forbidden",
    output_dtypes=None,
    vectorize=False,
    keep_attrs="override",
    dask_gufunc_kwargs=None,
) -> Variable | tuple[Variable, ...]:
    """Apply a ndarray level function over Variable and/or ndarray objects."""
    from xarray.core.formatting import short_array_repr
    from xarray.core.variable import Variable, as_compatible_data

    dim_sizes = unified_dim_sizes(
        (a for a in args if hasattr(a, "dims")), exclude_dims=exclude_dims
    )
    broadcast_dims = tuple(
        dim for dim in dim_sizes if dim not in signature.all_core_dims
    )
    output_dims = [broadcast_dims + out for out in signature.output_core_dims]

    input_data = [
        (
            broadcast_compat_data(arg, broadcast_dims, core_dims)
            if isinstance(arg, Variable)
            else arg
        )
        for arg, core_dims in zip(args, signature.input_core_dims, strict=True)
    ]

    if any(is_chunked_array(array) for array in input_data):
        if dask == "forbidden":
            raise ValueError(
                "apply_ufunc encountered a chunked array on an "
                "argument, but handling for chunked arrays has not "
                "been enabled. Either set the ``dask`` argument "
                "or load your data into memory first with "
                "``.load()`` or ``.compute()``"
            )
        elif dask == "parallelized":
            chunkmanager = get_chunked_array_type(*input_data)

            numpy_func = func

            if dask_gufunc_kwargs is None:
                dask_gufunc_kwargs = {}
            else:
                dask_gufunc_kwargs = dask_gufunc_kwargs.copy()

            allow_rechunk = dask_gufunc_kwargs.get("allow_rechunk", None)
            if allow_rechunk is None:
                for n, (data, core_dims) in enumerate(
                    zip(input_data, signature.input_core_dims, strict=True)
                ):
                    if is_chunked_array(data):
                        # core dimensions cannot span multiple chunks
                        for axis, dim in enumerate(core_dims, start=-len(core_dims)):
                            if len(data.chunks[axis]) != 1:
                                raise ValueError(
                                    f"dimension {dim} on {n}th function argument to "
                                    "apply_ufunc with dask='parallelized' consists of "
                                    "multiple chunks, but is also a core dimension. To "
                                    "fix, either rechunk into a single array chunk along "
                                    f"this dimension, i.e., ``.chunk(dict({dim}=-1))``, or "
                                    "pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` "
                                    "but beware that this may significantly increase memory usage."
                                )
                dask_gufunc_kwargs["allow_rechunk"] = True

            output_sizes = dask_gufunc_kwargs.pop("output_sizes", {})
            if output_sizes:
                output_sizes_renamed = {}
                for key, value in output_sizes.items():
                    if key not in signature.all_output_core_dims:
                        raise ValueError(
                            f"dimension '{key}' in 'output_sizes' must correspond to output_core_dims"
                        )
                    output_sizes_renamed[signature.dims_map[key]] = value
                dask_gufunc_kwargs["output_sizes"] = output_sizes_renamed

            for key in signature.all_output_core_dims:
                if (
                    key not in signature.all_input_core_dims or key in exclude_dims
                ) and key not in output_sizes:
                    raise ValueError(
                        f"dimension '{key}' in 'output_core_dims' needs corresponding (dim, size) in 'output_sizes'"
                    )

            def func(*arrays):
                res = chunkmanager.apply_gufunc(
                    numpy_func,
                    signature.to_gufunc_string(exclude_dims),
                    *arrays,
                    vectorize=vectorize,
                    output_dtypes=output_dtypes,
                    **dask_gufunc_kwargs,
                )

                return res

        elif dask == "allowed":
            pass
        else:
            raise ValueError(
                f"unknown setting for chunked array handling in apply_ufunc: {dask}"
            )
    else:
        if vectorize:
            func = _vectorize(
                func, signature, output_dtypes=output_dtypes, exclude_dims=exclude_dims
            )

    result_data = func(*input_data)

    if signature.num_outputs == 1:
        result_data = (result_data,)
    elif (
        not isinstance(result_data, tuple) or len(result_data) != signature.num_outputs
    ):
        raise ValueError(
            f"applied function does not have the number of "
            f"outputs specified in the ufunc signature. "
            f"Received a {type(result_data)} with {len(result_data)} elements. "
            f"Expected a tuple of {signature.num_outputs} elements:\n\n"
            f"{limit_lines(repr(result_data), limit=10)}"
        )

    objs = _all_of_type(args, Variable)
    attrs = merge_attrs(
        [obj.attrs for obj in objs],
        combine_attrs=keep_attrs,
    )

    output: list[Variable] = []
    for dims, data in zip(output_dims, result_data, strict=True):
        data = as_compatible_data(data)
        if data.ndim != len(dims):
            raise ValueError(
                "applied function returned data with an unexpected "
                f"number of dimensions. Received {data.ndim} dimension(s) but "
                f"expected {len(dims)} dimensions with names {dims!r}, from:\n\n"
                f"{short_array_repr(data)}"
            )

        var = Variable(dims, data, fastpath=True)
        for dim, new_size in var.sizes.items():
            if dim in dim_sizes and new_size != dim_sizes[dim]:
                raise ValueError(
                    f"size of dimension '{dim}' on inputs was unexpectedly "
                    f"changed by applied function from {dim_sizes[dim]} to {new_size}. Only "
                    "dimensions specified in ``exclude_dims`` with "
                    "xarray.apply_ufunc are allowed to change size. "
                    "The data returned was:\n\n"
                    f"{short_array_repr(data)}"
                )

        var.attrs = attrs
        output.append(var)

    if signature.num_outputs == 1:
        return output[0]
    else:
        return tuple(output)


def apply_array_ufunc(func, *args, dask="forbidden"):
    """Apply a ndarray level function over ndarray objects."""
    if any(is_chunked_array(arg) for arg in args):
        if dask == "forbidden":
            raise ValueError(
                "apply_ufunc encountered a dask array on an "
                "argument, but handling for dask arrays has not "
                "been enabled. Either set the ``dask`` argument "
                "or load your data into memory first with "
                "``.load()`` or ``.compute()``"
            )
        elif dask == "parallelized":
            raise ValueError(
                "cannot use dask='parallelized' for apply_ufunc "
                "unless at least one input is an xarray object"
            )
        elif dask == "allowed":
            pass
        else:
            raise ValueError(f"unknown setting for dask array handling: {dask}")
    return func(*args)


def apply_ufunc(
    func: Callable,
    *args: Any,
    input_core_dims: Sequence[Sequence] | None = None,
    output_core_dims: Sequence[Sequence] | None = ((),),
    exclude_dims: Set = frozenset(),
    vectorize: bool = False,
    join: JoinOptions = "exact",
    dataset_join: str = "exact",
    dataset_fill_value: object = _NO_FILL_VALUE,
    keep_attrs: bool | str | None = None,
    kwargs: Mapping | None = None,
    dask: Literal["forbidden", "allowed", "parallelized"] = "forbidden",
    output_dtypes: Sequence | None = None,
    output_sizes: Mapping[Any, int] | None = None,
    meta: Any = None,
    dask_gufunc_kwargs: dict[str, Any] | None = None,
    on_missing_core_dim: MissingCoreDimOptions = "raise",
) -> Any:
    """Apply a vectorized function for unlabeled arrays on xarray objects.

    The function will be mapped over the data variable(s) of the input
    arguments using xarray's standard rules for labeled computation, including
    alignment, broadcasting, looping over GroupBy/Dataset variables, and
    merging of coordinates.

    Parameters
    ----------
    func : callable
        Function to call like ``func(*args, **kwargs)`` on unlabeled arrays
        (``.data``) that returns an array or tuple of arrays. If multiple
        arguments with non-matching dimensions are supplied, this function is
        expected to vectorize (broadcast) over axes of positional arguments in
        the style of NumPy universal functions [1]_ (if this is not the case,
        set ``vectorize=True``). If this function returns multiple outputs, you
        must set ``output_core_dims`` as well.
    *args : Dataset, DataArray, DataArrayGroupBy, DatasetGroupBy, Variable, \
        numpy.ndarray, dask.array.Array or scalar
        Mix of labeled and/or unlabeled arrays to which to apply the function.
    input_core_dims : sequence of sequence, optional
        List of the same length as ``args`` giving the list of core dimensions
        on each input argument that should not be broadcast. By default, we
        assume there are no core dimensions on any input arguments.

        For example, ``input_core_dims=[[], ['time']]`` indicates that all
        dimensions on the first argument and all dimensions other than 'time'
        on the second argument should be broadcast.

        Core dimensions are automatically moved to the last axes of input
        variables before applying ``func``, which facilitates using NumPy style
        generalized ufuncs [2]_.
    output_core_dims : list of tuple, optional
        List of the same length as the number of output arguments from
        ``func``, giving the list of core dimensions on each output that were
        not broadcast on the inputs. By default, we assume that ``func``
        outputs exactly one array, with axes corresponding to each broadcast
        dimension.

        Core dimensions are assumed to appear as the last dimensions of each
        output in the provided order.
    exclude_dims : set, optional
        Core dimensions on the inputs to exclude from alignment and
        broadcasting entirely. Any input coordinates along these dimensions
        will be dropped. Each excluded dimension must also appear in
        ``input_core_dims`` for at least one argument. Only dimensions listed
        here are allowed to change size between input and output objects.
    vectorize : bool, optional
        If True, then assume ``func`` only takes arrays defined over core
        dimensions as input and vectorize it automatically with
        :py:func:`numpy.vectorize`. This option exists for convenience, but is
        almost always slower than supplying a pre-vectorized function.
    join : {"outer", "inner", "left", "right", "exact"}, default: "exact"
        Method for joining the indexes of the passed objects along each
        dimension, and the variables of Dataset objects with mismatched
        data variables:

        - 'outer': use the union of object indexes
        - 'inner': use the intersection of object indexes
        - 'left': use indexes from the first object with each dimension
        - 'right': use indexes from the last object with each dimension
        - 'exact': raise `ValueError` instead of aligning when indexes to be
          aligned are not equal
    dataset_join : {"outer", "inner", "left", "right", "exact"}, default: "exact"
        Method for joining variables of Dataset objects with mismatched
        data variables.

        - 'outer': take variables from both Dataset objects
        - 'inner': take only overlapped variables
        - 'left': take only variables from the first object
        - 'right': take only variables from the last object
        - 'exact': data variables on all Dataset objects must match exactly
    dataset_fill_value : optional
        Value used in place of missing variables on Dataset inputs when the
        datasets do not share the exact same ``data_vars``. Required if
        ``dataset_join not in {'inner', 'exact'}``, otherwise ignored.
    keep_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", "override"} or bool, optional
        - 'drop' or False: empty attrs on returned xarray object.
        - 'identical': all attrs must be the same on every object.
        - 'no_conflicts': attrs from all objects are combined, any that have the same name must also have the same value.
        - 'drop_conflicts': attrs from all objects are combined, any that have the same name but different values are dropped.
        - 'override' or True: skip comparing and copy attrs from the first object to the result.
    kwargs : dict, optional
        Optional keyword arguments passed directly on to call ``func``.
    dask : {"forbidden", "allowed", "parallelized"}, default: "forbidden"
        How to handle applying to objects containing lazy data in the form of
        dask arrays:

        - 'forbidden' (default): raise an error if a dask array is encountered.
        - 'allowed': pass dask arrays directly on to ``func``. Prefer this option if
          ``func`` natively supports dask arrays.
        - 'parallelized': automatically parallelize ``func`` if any of the
          inputs are a dask array by using :py:func:`dask.array.apply_gufunc`. Multiple output
          arguments are supported. Only use this option if ``func`` does not natively
          support dask arrays (e.g. converts them to numpy arrays).
    dask_gufunc_kwargs : dict, optional
        Optional keyword arguments passed to :py:func:`dask.array.apply_gufunc` if
        dask='parallelized'. Possible keywords are ``output_sizes``, ``allow_rechunk``
        and ``meta``.
    output_dtypes : list of dtype, optional
        Optional list of output dtypes. Only used if ``dask='parallelized'`` or
        ``vectorize=True``.
    output_sizes : dict, optional
        Optional mapping from dimension names to sizes for outputs. Only used
        if dask='parallelized' and new dimensions (not found on inputs) appear
        on outputs. ``output_sizes`` should be given in the ``dask_gufunc_kwargs``
        parameter. It will be removed as direct parameter in a future version.
    meta : optional
        Size-0 object representing the type of array wrapped by dask array. Passed on to
        :py:func:`dask.array.apply_gufunc`. ``meta`` should be given in the
        ``dask_gufunc_kwargs`` parameter . It will be removed as direct parameter
        a future version.
    on_missing_core_dim : {"raise", "copy", "drop"}, default: "raise"
        How to handle missing core dimensions on input variables.

    Returns
    -------
    Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or
    numpy.ndarray, the first type on that list to appear on an input.

    Notes
    -----
    This function is designed for the more common case where ``func`` can work on numpy
    arrays. If ``func`` needs to manipulate a whole xarray object subset to each block
    it is possible to use :py:func:`xarray.map_blocks`.

    Note that due to the overhead :py:func:`xarray.map_blocks` is considerably slower than ``apply_ufunc``.

    Examples
    --------
    Calculate the vector magnitude of two arguments:

    >>> def magnitude(a, b):
    ...     func = lambda x, y: np.sqrt(x**2 + y**2)
    ...     return xr.apply_ufunc(func, a, b)
    ...

    You can now apply ``magnitude()`` to :py:class:`DataArray` and :py:class:`Dataset`
    objects, with automatically preserved dimensions and coordinates, e.g.,

    >>> array = xr.DataArray([1, 2, 3], coords=[("x", [0.1, 0.2, 0.3])])
    >>> magnitude(array, -array)
    <xarray.DataArray (x: 3)> Size: 24B
    array([1.41421356, 2.82842712, 4.24264069])
    Coordinates:
      * x        (x) float64 24B 0.1 0.2 0.3

    Plain scalars, numpy arrays and a mix of these with xarray objects is also
    supported:

    >>> magnitude(3, 4)
    np.float64(5.0)
    >>> magnitude(3, np.array([0, 4]))
    array([3., 5.])
    >>> magnitude(array, 0)
    <xarray.DataArray (x: 3)> Size: 24B
    array([1., 2., 3.])
    Coordinates:
      * x        (x) float64 24B 0.1 0.2 0.3

    Other examples of how you could use ``apply_ufunc`` to write functions to
    (very nearly) replicate existing xarray functionality:

    Compute the mean (``.mean``) over one dimension:

    >>> def mean(obj, dim):
    ...     # note: apply always moves core dimensions to the end
    ...     return apply_ufunc(
    ...         np.mean, obj, input_core_dims=[[dim]], kwargs={"axis": -1}
    ...     )
    ...

    Inner product over a specific dimension (like :py:func:`dot`):

    >>> def _inner(x, y):
    ...     result = np.matmul(x[..., np.newaxis, :], y[..., :, np.newaxis])
    ...     return result[..., 0, 0]
    ...
    >>> def inner_product(a, b, dim):
    ...     return apply_ufunc(_inner, a, b, input_core_dims=[[dim], [dim]])
    ...

    Stack objects along a new dimension (like :py:func:`concat`):

    >>> def stack(objects, dim, new_coord):
    ...     # note: this version does not stack coordinates
    ...     func = lambda *x: np.stack(x, axis=-1)
    ...     result = apply_ufunc(
    ...         func,
    ...         *objects,
    ...         output_core_dims=[[dim]],
    ...         join="outer",
    ...         dataset_fill_value=np.nan
    ...     )
    ...     result[dim] = new_coord
    ...     return result
    ...

    If your function is not vectorized but can be applied only to core
    dimensions, you can use ``vectorize=True`` to turn into a vectorized
    function. This wraps :py:func:`numpy.vectorize`, so the operation isn't
    terribly fast. Here we'll use it to calculate the distance between
    empirical samples from two probability distributions, using a scipy
    function that needs to be applied to vectors:

    >>> import scipy.stats
    >>> def earth_mover_distance(first_samples, second_samples, dim="ensemble"):
    ...     return apply_ufunc(
    ...         scipy.stats.wasserstein_distance,
    ...         first_samples,
    ...         second_samples,
    ...         input_core_dims=[[dim], [dim]],
    ...         vectorize=True,
    ...     )
    ...

    Most of NumPy's builtin functions already broadcast their inputs
    appropriately for use in ``apply_ufunc``. You may find helper functions such as
    :py:func:`numpy.broadcast_arrays` helpful in writing your function. ``apply_ufunc`` also
    works well with :py:func:`numba.vectorize` and :py:func:`numba.guvectorize`.

    See Also
    --------
    numpy.broadcast_arrays
    numba.vectorize
    numba.guvectorize
    dask.array.apply_gufunc
    xarray.map_blocks

    Notes
    -----
    :ref:`dask.automatic-parallelization`
        User guide describing :py:func:`apply_ufunc` and :py:func:`map_blocks`.

    :doc:`xarray-tutorial:advanced/apply_ufunc/apply_ufunc`
        Advanced Tutorial on applying numpy function using :py:func:`apply_ufunc`

    References
    ----------
    .. [1] https://numpy.org/doc/stable/reference/ufuncs.html
    .. [2] https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html
    """
    from xarray.core.dataarray import DataArray
    from xarray.core.groupby import GroupBy
    from xarray.core.variable import Variable

    if input_core_dims is None:
        input_core_dims = ((),) * (len(args))
    elif len(input_core_dims) != len(args):
        raise ValueError(
            f"input_core_dims must be None or a tuple with the length same to "
            f"the number of arguments. "
            f"Given {len(input_core_dims)} input_core_dims: {input_core_dims}, "
            f" but number of args is {len(args)}."
        )

    if kwargs is None:
        kwargs = {}

    signature = _UFuncSignature(input_core_dims, output_core_dims)

    if exclude_dims:
        if not isinstance(exclude_dims, set):
            raise TypeError(
                f"Expected exclude_dims to be a 'set'. Received '{type(exclude_dims).__name__}' instead."
            )
        if not exclude_dims <= signature.all_core_dims:
            raise ValueError(
                f"each dimension in `exclude_dims` must also be a "
                f"core dimension in the function signature. "
                f"Please make {(exclude_dims - signature.all_core_dims)} a core dimension"
            )

    # handle dask_gufunc_kwargs
    if dask == "parallelized":
        if dask_gufunc_kwargs is None:
            dask_gufunc_kwargs = {}
        else:
            dask_gufunc_kwargs = dask_gufunc_kwargs.copy()
        # todo: remove warnings after deprecation cycle
        if meta is not None:
            warnings.warn(
                "``meta`` should be given in the ``dask_gufunc_kwargs`` parameter."
                " It will be removed as direct parameter in a future version.",
                FutureWarning,
                stacklevel=2,
            )
            dask_gufunc_kwargs.setdefault("meta", meta)
        if output_sizes is not None:
            warnings.warn(
                "``output_sizes`` should be given in the ``dask_gufunc_kwargs`` "
                "parameter. It will be removed as direct parameter in a future "
                "version.",
                FutureWarning,
                stacklevel=2,
            )
            dask_gufunc_kwargs.setdefault("output_sizes", output_sizes)

    if kwargs:
        func = functools.partial(func, **kwargs)

    if keep_attrs is None:
        keep_attrs = _get_keep_attrs(default=False)

    if isinstance(keep_attrs, bool):
        keep_attrs = "override" if keep_attrs else "drop"

    variables_vfunc = functools.partial(
        apply_variable_ufunc,
        func,
        signature=signature,
        exclude_dims=exclude_dims,
        keep_attrs=keep_attrs,
        dask=dask,
        vectorize=vectorize,
        output_dtypes=output_dtypes,
        dask_gufunc_kwargs=dask_gufunc_kwargs,
    )

    # feed groupby-apply_ufunc through apply_groupby_func
    if any(isinstance(a, GroupBy) for a in args):
        this_apply = functools.partial(
            apply_ufunc,
            func,
            input_core_dims=input_core_dims,
            output_core_dims=output_core_dims,
            exclude_dims=exclude_dims,
            join=join,
            dataset_join=dataset_join,
            dataset_fill_value=dataset_fill_value,
            keep_attrs=keep_attrs,
            dask=dask,
            vectorize=vectorize,
            output_dtypes=output_dtypes,
            dask_gufunc_kwargs=dask_gufunc_kwargs,
        )
        return apply_groupby_func(this_apply, *args)
    # feed datasets apply_variable_ufunc through apply_dataset_vfunc
    elif any(is_dict_like(a) for a in args):
        return apply_dataset_vfunc(
            variables_vfunc,
            *args,
            signature=signature,
            join=join,
            exclude_dims=exclude_dims,
            dataset_join=dataset_join,
            fill_value=dataset_fill_value,
            keep_attrs=keep_attrs,
            on_missing_core_dim=on_missing_core_dim,
        )
    # feed DataArray apply_variable_ufunc through apply_dataarray_vfunc
    elif any(isinstance(a, DataArray) for a in args):
        return apply_dataarray_vfunc(
            variables_vfunc,
            *args,
            signature=signature,
            join=join,
            exclude_dims=exclude_dims,
            keep_attrs=keep_attrs,
        )
    # feed Variables directly through apply_variable_ufunc
    elif any(isinstance(a, Variable) for a in args):
        return variables_vfunc(*args)
    else:
        # feed anything else through apply_array_ufunc
        return apply_array_ufunc(func, *args, dask=dask)
