import itertools

import numpy as np
import pandas as pd
import pytest

from xarray import DataArray, Dataset, Variable
from xarray.core import indexing, nputils

from . import IndexerMaker, ReturnItem, assert_array_equal, raises_regex

B = IndexerMaker(indexing.BasicIndexer)


class TestIndexers:
    def set_to_zero(self, x, i):
        x = x.copy()
        x[i] = 0
        return x

    def test_expanded_indexer(self):
        x = np.random.randn(10, 11, 12, 13, 14)
        y = np.arange(5)
        I = ReturnItem()  # noqa
        for i in [I[:], I[...], I[0, :, 10], I[..., 10], I[:5, ..., 0],
                  I[..., 0, :], I[y], I[y, y], I[..., y, y],
                  I[..., 0, 1, 2, 3, 4]]:
            j = indexing.expanded_indexer(i, x.ndim)
            assert_array_equal(x[i], x[j])
            assert_array_equal(self.set_to_zero(x, i),
                               self.set_to_zero(x, j))
        with raises_regex(IndexError, 'too many indices'):
            indexing.expanded_indexer(I[1, 2, 3], 2)

    def test_asarray_tuplesafe(self):
        res = indexing._asarray_tuplesafe(('a', 1))
        assert isinstance(res, np.ndarray)
        assert res.ndim == 0
        assert res.item() == ('a', 1)

        res = indexing._asarray_tuplesafe([(0,), (1,)])
        assert res.shape == (2,)
        assert res[0] == (0,)
        assert res[1] == (1,)

    def test_stacked_multiindex_min_max(self):
        data = np.random.randn(3, 23, 4)
        da = DataArray(
            data, name="value",
            dims=["replicate", "rsample", "exp"],
            coords=dict(
                replicate=[0, 1, 2],
                exp=["a", "b", "c", "d"],
                rsample=list(range(23))
            ),
        )
        da2 = da.stack(sample=("replicate", "rsample"))
        s = da2.sample
        assert_array_equal(da2.loc['a', s.max()], data[2, 22, 0])
        assert_array_equal(da2.loc['b', s.min()], data[0, 0, 1])

    def test_convert_label_indexer(self):
        # TODO: add tests that aren't just for edge cases
        index = pd.Index([1, 2, 3])
        with raises_regex(KeyError, 'not all values found'):
            indexing.convert_label_indexer(index, [0])
        with pytest.raises(KeyError):
            indexing.convert_label_indexer(index, 0)
        with raises_regex(ValueError, 'does not have a MultiIndex'):
            indexing.convert_label_indexer(index, {'one': 0})

        mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2]],
                                            names=('one', 'two'))
        with raises_regex(KeyError, 'not all values found'):
            indexing.convert_label_indexer(mindex, [0])
        with pytest.raises(KeyError):
            indexing.convert_label_indexer(mindex, 0)
        with pytest.raises(ValueError):
            indexing.convert_label_indexer(index, {'three': 0})
        with pytest.raises((KeyError, IndexError)):
            # pandas 0.21 changed this from KeyError to IndexError
            indexing.convert_label_indexer(
                mindex, (slice(None), 1, 'no_level'))

    def test_convert_unsorted_datetime_index_raises(self):
        index = pd.to_datetime(['2001', '2000', '2002'])
        with pytest.raises(KeyError):
            # pandas will try to convert this into an array indexer. We should
            # raise instead, so we can be sure the result of indexing with a
            # slice is always a view.
            indexing.convert_label_indexer(index, slice('2001', '2002'))

    def test_get_dim_indexers(self):
        mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2]],
                                            names=('one', 'two'))
        mdata = DataArray(range(4), [('x', mindex)])

        dim_indexers = indexing.get_dim_indexers(mdata, {'one': 'a', 'two': 1})
        assert dim_indexers == {'x': {'one': 'a', 'two': 1}}

        with raises_regex(ValueError, 'cannot combine'):
            indexing.get_dim_indexers(mdata, {'x': 'a', 'two': 1})

        with raises_regex(ValueError, 'do not exist'):
            indexing.get_dim_indexers(mdata, {'y': 'a'})

        with raises_regex(ValueError, 'do not exist'):
            indexing.get_dim_indexers(mdata, {'four': 1})

    def test_remap_label_indexers(self):
        def test_indexer(data, x, expected_pos, expected_idx=None):
            pos, idx = indexing.remap_label_indexers(data, {'x': x})
            assert_array_equal(pos.get('x'), expected_pos)
            assert_array_equal(idx.get('x'), expected_idx)

        data = Dataset({'x': ('x', [1, 2, 3])})
        mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]],
                                            names=('one', 'two', 'three'))
        mdata = DataArray(range(8), [('x', mindex)])

        test_indexer(data, 1, 0)
        test_indexer(data, np.int32(1), 0)
        test_indexer(data, Variable([], 1), 0)
        test_indexer(mdata, ('a', 1, -1), 0)
        test_indexer(mdata, ('a', 1),
                     [True, True, False, False, False, False, False, False],
                     [-1, -2])
        test_indexer(mdata, 'a', slice(0, 4, None),
                     pd.MultiIndex.from_product([[1, 2], [-1, -2]]))
        test_indexer(mdata, ('a',),
                     [True, True, True, True, False, False, False, False],
                     pd.MultiIndex.from_product([[1, 2], [-1, -2]]))
        test_indexer(mdata, [('a', 1, -1), ('b', 2, -2)], [0, 7])
        test_indexer(mdata, slice('a', 'b'), slice(0, 8, None))
        test_indexer(mdata, slice(('a', 1), ('b', 1)), slice(0, 6, None))
        test_indexer(mdata, {'one': 'a', 'two': 1, 'three': -1}, 0)
        test_indexer(mdata, {'one': 'a', 'two': 1},
                     [True, True, False, False, False, False, False, False],
                     [-1, -2])
        test_indexer(mdata, {'one': 'a', 'three': -1},
                     [True, False, True, False, False, False, False, False],
                     [1, 2])
        test_indexer(mdata, {'one': 'a'},
                     [True, True, True, True, False, False, False, False],
                     pd.MultiIndex.from_product([[1, 2], [-1, -2]]))


class TestLazyArray:
    def test_slice_slice(self):
        I = ReturnItem()  # noqa: E741  # allow ambiguous name
        for size in [100, 99]:
            # We test even/odd size cases
            x = np.arange(size)
            slices = [I[:3], I[:4], I[2:4], I[:1], I[:-1], I[5:-1], I[-5:-1],
                      I[::-1], I[5::-1], I[:3:-1], I[:30:-1], I[10:4:], I[::4],
                      I[4:4:4], I[:4:-4], I[::-2]]
            for i in slices:
                for j in slices:
                    expected = x[i][j]
                    new_slice = indexing.slice_slice(i, j, size=size)
                    actual = x[new_slice]
                    assert_array_equal(expected, actual)

    def test_lazily_indexed_array(self):
        original = np.random.rand(10, 20, 30)
        x = indexing.NumpyIndexingAdapter(original)
        v = Variable(['i', 'j', 'k'], original)
        lazy = indexing.LazilyOuterIndexedArray(x)
        v_lazy = Variable(['i', 'j', 'k'], lazy)
        I = ReturnItem()  # noqa: E741  # allow ambiguous name
        # test orthogonally applied indexers
        indexers = [I[:], 0, -2, I[:3], [0, 1, 2, 3], [0], np.arange(10) < 5]
        for i in indexers:
            for j in indexers:
                for k in indexers:
                    if isinstance(j, np.ndarray) and j.dtype.kind == 'b':
                        j = np.arange(20) < 5
                    if isinstance(k, np.ndarray) and k.dtype.kind == 'b':
                        k = np.arange(30) < 5
                    expected = np.asarray(v[i, j, k])
                    for actual in [v_lazy[i, j, k],
                                   v_lazy[:, j, k][i],
                                   v_lazy[:, :, k][:, j][i]]:
                        assert expected.shape == actual.shape
                        assert_array_equal(expected, actual)
                        assert isinstance(actual._data,
                                          indexing.LazilyOuterIndexedArray)

                        # make sure actual.key is appropriate type
                        if all(isinstance(k, (int, slice, ))
                               for k in v_lazy._data.key.tuple):
                            assert isinstance(v_lazy._data.key,
                                              indexing.BasicIndexer)
                        else:
                            assert isinstance(v_lazy._data.key,
                                              indexing.OuterIndexer)

        # test sequentially applied indexers
        indexers = [(3, 2), (I[:], 0), (I[:2], -1), (I[:4], [0]), ([4, 5], 0),
                    ([0, 1, 2], [0, 1]), ([0, 3, 5], I[:2])]
        for i, j in indexers:
            expected = v[i][j]
            actual = v_lazy[i][j]
            assert expected.shape == actual.shape
            assert_array_equal(expected, actual)

            # test transpose
            if actual.ndim > 1:
                order = np.random.choice(actual.ndim, actual.ndim)
                order = np.array(actual.dims)
                transposed = actual.transpose(*order)
                assert_array_equal(expected.transpose(*order), transposed)
                assert isinstance(
                    actual._data, (indexing.LazilyVectorizedIndexedArray,
                                   indexing.LazilyOuterIndexedArray))

            assert isinstance(actual._data, indexing.LazilyOuterIndexedArray)
            assert isinstance(actual._data.array,
                              indexing.NumpyIndexingAdapter)

    def test_vectorized_lazily_indexed_array(self):
        original = np.random.rand(10, 20, 30)
        x = indexing.NumpyIndexingAdapter(original)
        v_eager = Variable(['i', 'j', 'k'], x)
        lazy = indexing.LazilyOuterIndexedArray(x)
        v_lazy = Variable(['i', 'j', 'k'], lazy)
        I = ReturnItem()  # noqa: E741  # allow ambiguous name

        def check_indexing(v_eager, v_lazy, indexers):
            for indexer in indexers:
                actual = v_lazy[indexer]
                expected = v_eager[indexer]
                assert expected.shape == actual.shape
                assert isinstance(actual._data,
                                  (indexing.LazilyVectorizedIndexedArray,
                                   indexing.LazilyOuterIndexedArray))
                assert_array_equal(expected, actual)
                v_eager = expected
                v_lazy = actual

        # test orthogonal indexing
        indexers = [(I[:], 0, 1), (Variable('i', [0, 1]), )]
        check_indexing(v_eager, v_lazy, indexers)

        # vectorized indexing
        indexers = [
            (Variable('i', [0, 1]), Variable('i', [0, 1]), slice(None)),
            (slice(1, 3, 2), 0)]
        check_indexing(v_eager, v_lazy, indexers)

        indexers = [
            (slice(None, None, 2), 0, slice(None, 10)),
            (Variable('i', [3, 2, 4, 3]), Variable('i', [3, 2, 1, 0])),
            (Variable(['i', 'j'], [[0, 1], [1, 2]]), )]
        check_indexing(v_eager, v_lazy, indexers)

        indexers = [
            (Variable('i', [3, 2, 4, 3]), Variable('i', [3, 2, 1, 0])),
            (Variable(['i', 'j'], [[0, 1], [1, 2]]), )]
        check_indexing(v_eager, v_lazy, indexers)


class TestCopyOnWriteArray:
    def test_setitem(self):
        original = np.arange(10)
        wrapped = indexing.CopyOnWriteArray(original)
        wrapped[B[:]] = 0
        assert_array_equal(original, np.arange(10))
        assert_array_equal(wrapped, np.zeros(10))

    def test_sub_array(self):
        original = np.arange(10)
        wrapped = indexing.CopyOnWriteArray(original)
        child = wrapped[B[:5]]
        assert isinstance(child, indexing.CopyOnWriteArray)
        child[B[:]] = 0
        assert_array_equal(original, np.arange(10))
        assert_array_equal(wrapped, np.arange(10))
        assert_array_equal(child, np.zeros(5))

    def test_index_scalar(self):
        # regression test for GH1374
        x = indexing.CopyOnWriteArray(np.array(['foo', 'bar']))
        assert np.array(x[B[0]][B[()]]) == 'foo'


class TestMemoryCachedArray:
    def test_wrapper(self):
        original = indexing.LazilyOuterIndexedArray(np.arange(10))
        wrapped = indexing.MemoryCachedArray(original)
        assert_array_equal(wrapped, np.arange(10))
        assert isinstance(wrapped.array, indexing.NumpyIndexingAdapter)

    def test_sub_array(self):
        original = indexing.LazilyOuterIndexedArray(np.arange(10))
        wrapped = indexing.MemoryCachedArray(original)
        child = wrapped[B[:5]]
        assert isinstance(child, indexing.MemoryCachedArray)
        assert_array_equal(child, np.arange(5))
        assert isinstance(child.array, indexing.NumpyIndexingAdapter)
        assert isinstance(wrapped.array, indexing.LazilyOuterIndexedArray)

    def test_setitem(self):
        original = np.arange(10)
        wrapped = indexing.MemoryCachedArray(original)
        wrapped[B[:]] = 0
        assert_array_equal(original, np.zeros(10))

    def test_index_scalar(self):
        # regression test for GH1374
        x = indexing.MemoryCachedArray(np.array(['foo', 'bar']))
        assert np.array(x[B[0]][B[()]]) == 'foo'


def test_base_explicit_indexer():
    with pytest.raises(TypeError):
        indexing.ExplicitIndexer(())

    class Subclass(indexing.ExplicitIndexer):
        pass

    value = Subclass((1, 2, 3))
    assert value.tuple == (1, 2, 3)
    assert repr(value) == 'Subclass((1, 2, 3))'


@pytest.mark.parametrize('indexer_cls', [indexing.BasicIndexer,
                                         indexing.OuterIndexer,
                                         indexing.VectorizedIndexer])
def test_invalid_for_all(indexer_cls):
    with pytest.raises(TypeError):
        indexer_cls(None)
    with pytest.raises(TypeError):
        indexer_cls(([],))
    with pytest.raises(TypeError):
        indexer_cls((None,))
    with pytest.raises(TypeError):
        indexer_cls(('foo',))
    with pytest.raises(TypeError):
        indexer_cls((1.0,))
    with pytest.raises(TypeError):
        indexer_cls((slice('foo'),))
    with pytest.raises(TypeError):
        indexer_cls((np.array(['foo']),))


def check_integer(indexer_cls):
    value = indexer_cls((1, np.uint64(2),)).tuple
    assert all(isinstance(v, int) for v in value)
    assert value == (1, 2)


def check_slice(indexer_cls):
    (value,) = indexer_cls((slice(1, None, np.int64(2)),)).tuple
    assert value == slice(1, None, 2)
    assert isinstance(value.step, int)


def check_array1d(indexer_cls):
    (value,) = indexer_cls((np.arange(3, dtype=np.int32),)).tuple
    assert value.dtype == np.int64
    np.testing.assert_array_equal(value, [0, 1, 2])


def check_array2d(indexer_cls):
    array = np.array([[1, 2], [3, 4]], dtype=np.int64)
    (value,) = indexer_cls((array,)).tuple
    assert value.dtype == np.int64
    np.testing.assert_array_equal(value, array)


def test_basic_indexer():
    check_integer(indexing.BasicIndexer)
    check_slice(indexing.BasicIndexer)
    with pytest.raises(TypeError):
        check_array1d(indexing.BasicIndexer)
    with pytest.raises(TypeError):
        check_array2d(indexing.BasicIndexer)


def test_outer_indexer():
    check_integer(indexing.OuterIndexer)
    check_slice(indexing.OuterIndexer)
    check_array1d(indexing.OuterIndexer)
    with pytest.raises(TypeError):
        check_array2d(indexing.OuterIndexer)


def test_vectorized_indexer():
    with pytest.raises(TypeError):
        check_integer(indexing.VectorizedIndexer)
    check_slice(indexing.VectorizedIndexer)
    check_array1d(indexing.VectorizedIndexer)
    check_array2d(indexing.VectorizedIndexer)
    with raises_regex(ValueError, 'numbers of dimensions'):
        indexing.VectorizedIndexer((np.array(1, dtype=np.int64),
                                    np.arange(5, dtype=np.int64)))


class Test_vectorized_indexer:
    @pytest.fixture(autouse=True)
    def setup(self):
        self.data = indexing.NumpyIndexingAdapter(np.random.randn(10, 12, 13))
        self.indexers = [np.array([[0, 3, 2], ]),
                         np.array([[0, 3, 3], [4, 6, 7]]),
                         slice(2, -2, 2), slice(2, -2, 3), slice(None)]

    def test_arrayize_vectorized_indexer(self):
        for i, j, k in itertools.product(self.indexers, repeat=3):
            vindex = indexing.VectorizedIndexer((i, j, k))
            vindex_array = indexing._arrayize_vectorized_indexer(
                vindex, self.data.shape)
            np.testing.assert_array_equal(
                self.data[vindex], self.data[vindex_array],)

        actual = indexing._arrayize_vectorized_indexer(
            indexing.VectorizedIndexer((slice(None),)), shape=(5,))
        np.testing.assert_array_equal(actual.tuple, [np.arange(5)])

        actual = indexing._arrayize_vectorized_indexer(
            indexing.VectorizedIndexer((np.arange(5),) * 3), shape=(8, 10, 12))
        expected = np.stack([np.arange(5)] * 3)
        np.testing.assert_array_equal(np.stack(actual.tuple), expected)

        actual = indexing._arrayize_vectorized_indexer(
            indexing.VectorizedIndexer((np.arange(5), slice(None))),
            shape=(8, 10))
        a, b = actual.tuple
        np.testing.assert_array_equal(a, np.arange(5)[:, np.newaxis])
        np.testing.assert_array_equal(b, np.arange(10)[np.newaxis, :])

        actual = indexing._arrayize_vectorized_indexer(
            indexing.VectorizedIndexer((slice(None), np.arange(5))),
            shape=(8, 10))
        a, b = actual.tuple
        np.testing.assert_array_equal(a, np.arange(8)[np.newaxis, :])
        np.testing.assert_array_equal(b, np.arange(5)[:, np.newaxis])


def get_indexers(shape, mode):
    if mode == 'vectorized':
        indexed_shape = (3, 4)
        indexer = tuple(np.random.randint(0, s, size=indexed_shape)
                        for s in shape)
        return indexing.VectorizedIndexer(indexer)

    elif mode == 'outer':
        indexer = tuple(np.random.randint(0, s, s + 2) for s in shape)
        return indexing.OuterIndexer(indexer)

    elif mode == 'outer_scalar':
        indexer = (np.random.randint(0, 3, 4), 0, slice(None, None, 2))
        return indexing.OuterIndexer(indexer[:len(shape)])

    elif mode == 'outer_scalar2':
        indexer = (np.random.randint(0, 3, 4), -2, slice(None, None, 2))
        return indexing.OuterIndexer(indexer[:len(shape)])

    elif mode == 'outer1vec':
        indexer = [slice(2, -3) for s in shape]
        indexer[1] = np.random.randint(0, shape[1], shape[1] + 2)
        return indexing.OuterIndexer(tuple(indexer))

    elif mode == 'basic':  # basic indexer
        indexer = [slice(2, -3) for s in shape]
        indexer[0] = 3
        return indexing.BasicIndexer(tuple(indexer))

    elif mode == 'basic1':  # basic indexer
        return indexing.BasicIndexer((3, ))

    elif mode == 'basic2':  # basic indexer
        indexer = [0, 2, 4]
        return indexing.BasicIndexer(tuple(indexer[:len(shape)]))

    elif mode == 'basic3':  # basic indexer
        indexer = [slice(None) for s in shape]
        indexer[0] = slice(-2, 2, -2)
        indexer[1] = slice(1, -1, 2)
        return indexing.BasicIndexer(tuple(indexer[:len(shape)]))


@pytest.mark.parametrize('size', [100, 99])
@pytest.mark.parametrize('sl', [slice(1, -1, 1), slice(None, -1, 2),
                                slice(-1, 1, -1), slice(-1, 1, -2)])
def test_decompose_slice(size, sl):
    x = np.arange(size)
    slice1, slice2 = indexing._decompose_slice(sl, size)
    expected = x[sl]
    actual = x[slice1][slice2]
    assert_array_equal(expected, actual)


@pytest.mark.parametrize('shape', [(10, 5, 8), (10, 3)])
@pytest.mark.parametrize('indexer_mode',
                         ['vectorized', 'outer', 'outer_scalar',
                          'outer_scalar2', 'outer1vec',
                          'basic', 'basic1', 'basic2', 'basic3'])
@pytest.mark.parametrize('indexing_support',
                         [indexing.IndexingSupport.BASIC,
                          indexing.IndexingSupport.OUTER,
                          indexing.IndexingSupport.OUTER_1VECTOR,
                          indexing.IndexingSupport.VECTORIZED])
def test_decompose_indexers(shape, indexer_mode, indexing_support):
    data = np.random.randn(*shape)
    indexer = get_indexers(shape, indexer_mode)

    backend_ind, np_ind = indexing.decompose_indexer(
        indexer, shape, indexing_support)

    expected = indexing.NumpyIndexingAdapter(data)[indexer]
    array = indexing.NumpyIndexingAdapter(data)[backend_ind]
    if len(np_ind.tuple) > 0:
        array = indexing.NumpyIndexingAdapter(array)[np_ind]
    np.testing.assert_array_equal(expected, array)

    if not all(isinstance(k, indexing.integer_types) for k in np_ind.tuple):
        combined_ind = indexing._combine_indexers(backend_ind, shape, np_ind)
        array = indexing.NumpyIndexingAdapter(data)[combined_ind]
        np.testing.assert_array_equal(expected, array)


def test_implicit_indexing_adapter():
    array = np.arange(10, dtype=np.int64)
    implicit = indexing.ImplicitToExplicitIndexingAdapter(
        indexing.NumpyIndexingAdapter(array), indexing.BasicIndexer)
    np.testing.assert_array_equal(array, np.asarray(implicit))
    np.testing.assert_array_equal(array, implicit[:])


def test_implicit_indexing_adapter_copy_on_write():
    array = np.arange(10, dtype=np.int64)
    implicit = indexing.ImplicitToExplicitIndexingAdapter(
        indexing.CopyOnWriteArray(array))
    assert isinstance(implicit[:], indexing.ImplicitToExplicitIndexingAdapter)


def test_outer_indexer_consistency_with_broadcast_indexes_vectorized():
    def nonzero(x):
        if isinstance(x, np.ndarray) and x.dtype.kind == 'b':
            x = x.nonzero()[0]
        return x

    original = np.random.rand(10, 20, 30)
    v = Variable(['i', 'j', 'k'], original)
    I = ReturnItem()  # noqa: E741  # allow ambiguous name
    # test orthogonally applied indexers
    indexers = [I[:], 0, -2, I[:3], np.array([0, 1, 2, 3]), np.array([0]),
                np.arange(10) < 5]
    for i, j, k in itertools.product(indexers, repeat=3):

        if isinstance(j, np.ndarray) and j.dtype.kind == 'b':  # match size
            j = np.arange(20) < 4
        if isinstance(k, np.ndarray) and k.dtype.kind == 'b':
            k = np.arange(30) < 8

        _, expected, new_order = v._broadcast_indexes_vectorized((i, j, k))
        expected_data = nputils.NumpyVIndexAdapter(v.data)[expected.tuple]
        if new_order:
            old_order = range(len(new_order))
            expected_data = np.moveaxis(expected_data, old_order,
                                        new_order)

        outer_index = indexing.OuterIndexer((nonzero(i), nonzero(j),
                                             nonzero(k)))
        actual = indexing._outer_to_numpy_indexer(outer_index, v.shape)
        actual_data = v.data[actual]
        np.testing.assert_array_equal(actual_data, expected_data)


def test_create_mask_outer_indexer():
    indexer = indexing.OuterIndexer((np.array([0, -1, 2]),))
    expected = np.array([False, True, False])
    actual = indexing.create_mask(indexer, (5,))
    np.testing.assert_array_equal(expected, actual)

    indexer = indexing.OuterIndexer((1, slice(2), np.array([0, -1, 2]),))
    expected = np.array(2 * [[False, True, False]])
    actual = indexing.create_mask(indexer, (5, 5, 5,))
    np.testing.assert_array_equal(expected, actual)


def test_create_mask_vectorized_indexer():
    indexer = indexing.VectorizedIndexer(
        (np.array([0, -1, 2]), np.array([0, 1, -1])))
    expected = np.array([False, True, True])
    actual = indexing.create_mask(indexer, (5,))
    np.testing.assert_array_equal(expected, actual)

    indexer = indexing.VectorizedIndexer(
        (np.array([0, -1, 2]), slice(None), np.array([0, 1, -1])))
    expected = np.array([[False, True, True]] * 2).T
    actual = indexing.create_mask(indexer, (5, 2))
    np.testing.assert_array_equal(expected, actual)


def test_create_mask_basic_indexer():
    indexer = indexing.BasicIndexer((-1,))
    actual = indexing.create_mask(indexer, (3,))
    np.testing.assert_array_equal(True, actual)

    indexer = indexing.BasicIndexer((0,))
    actual = indexing.create_mask(indexer, (3,))
    np.testing.assert_array_equal(False, actual)


def test_create_mask_dask():
    da = pytest.importorskip('dask.array')

    indexer = indexing.OuterIndexer((1, slice(2), np.array([0, -1, 2]),))
    expected = np.array(2 * [[False, True, False]])
    actual = indexing.create_mask(indexer, (5, 5, 5,),
                                  chunks_hint=((1, 1), (2, 1)))
    assert actual.chunks == ((1, 1), (2, 1))
    np.testing.assert_array_equal(expected, actual)

    indexer = indexing.VectorizedIndexer(
        (np.array([0, -1, 2]), slice(None), np.array([0, 1, -1])))
    expected = np.array([[False, True, True]] * 2).T
    actual = indexing.create_mask(indexer, (5, 2), chunks_hint=((3,), (2,)))
    assert isinstance(actual, da.Array)
    np.testing.assert_array_equal(expected, actual)

    with pytest.raises(ValueError):
        indexing.create_mask(indexer, (5, 2), chunks_hint=())


def test_create_mask_error():
    with raises_regex(TypeError, 'unexpected key type'):
        indexing.create_mask((1, 2), (3, 4))


@pytest.mark.parametrize('indices, expected', [
    (np.arange(5), np.arange(5)),
    (np.array([0, -1, -1]), np.array([0, 0, 0])),
    (np.array([-1, 1, -1]), np.array([1, 1, 1])),
    (np.array([-1, -1, 2]), np.array([2, 2, 2])),
    (np.array([-1]), np.array([0])),
    (np.array([0, -1, 1, -1, -1]), np.array([0, 0, 1, 1, 1])),
    (np.array([0, -1, -1, -1, 1]), np.array([0, 0, 0, 0, 1])),
])
def test_posify_mask_subindexer(indices, expected):
    actual = indexing._posify_mask_subindexer(indices)
    np.testing.assert_array_equal(expected, actual)
