# Owner(s): ["module: tests"]

import torch
import numpy as np

import random
from torch._six import nan
from itertools import permutations, product

from torch.testing import make_tensor
from torch.testing._internal.common_dtype import all_types, all_types_and, floating_types_and
from torch.testing._internal.common_utils import \
    (TestCase, run_tests, slowTest)
from torch.testing._internal.common_device_type import \
    (instantiate_device_type_tests, dtypes, onlyNativeDeviceTypes,
     onlyCUDA, dtypesIfCUDA, dtypesIfCPU, onlyCPU, largeTensorTest)

# TODO: remove this
SIZE = 100

class TestSortAndSelect(TestCase):

    def assertIsOrdered(self, order, x, mxx, ixx, task):
        SIZE = x.size(1)
        if order == 'descending':
            def check_order(a, b):
                # `a != a` because we put NaNs
                # at the end of ascending sorted lists,
                # and the beginning of descending ones.
                return ((a != a) | (a >= b)).all().item()
        elif order == 'ascending':
            def check_order(a, b):
                # see above
                return ((b != b) | (a <= b)).all().item()
        else:
            error('unknown order "{}", must be "ascending" or "descending"'.format(order))

        are_ordered = True
        for k in range(1, SIZE):
            self.assertTrue(check_order(mxx[:, k - 1], mxx[:, k]),
                            'torch.sort ({}) values unordered for {}'.format(order, task))

        seen = set()
        indicesCorrect = True
        size0 = x.size(0)
        size = x.size(x.dim() - 1)
        x = x.tolist()
        mxx = mxx.tolist()
        ixx = ixx.tolist()
        for k in range(size0):
            seen.clear()
            for j in range(size):
                self.assertEqual(x[k][ixx[k][j]], mxx[k][j],
                                 msg='torch.sort ({}) indices wrong for {}'.format(order, task))
                seen.add(ixx[k][j])
            self.assertEqual(len(seen), size)

    def test_sort(self, device):
        # on CUDA 2048 vs >2048 have different code path for the dim being sorted
        for SIZE in (4, 2049):
            x = torch.rand(4, SIZE, device=device)
            res1val, res1ind = torch.sort(x)

            # Test inplace
            y = x.clone()
            y_inds = torch.tensor((), dtype=torch.int64, device=device)
            torch.sort(y, out=(y, y_inds))
            x_vals, x_inds = torch.sort(x)
            self.assertEqual(x_vals, y)
            self.assertEqual(x_inds, y_inds)

            # Test use of result tensor
            res2val = torch.tensor((), device=device)
            res2ind = torch.tensor((), device=device, dtype=torch.long)
            torch.sort(x, out=(res2val, res2ind))
            self.assertEqual(res1val, res2val, atol=0, rtol=0)
            self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
            self.assertEqual(torch.argsort(x), res1ind)
            self.assertEqual(x.argsort(), res1ind)

            # Test sorting of random numbers
            self.assertIsOrdered('ascending', x, res2val, res2ind, 'random')

            # Test simple sort
            self.assertEqual(
                torch.sort(torch.tensor((50, 40, 30, 20, 10), device=device))[0],
                torch.tensor((10, 20, 30, 40, 50), device=device),
                atol=0, rtol=0
            )

            # Test that we still have proper sorting with duplicate keys
            x = torch.floor(torch.rand(4, SIZE, device=device) * 10)
            torch.sort(x, out=(res2val, res2ind))
            self.assertIsOrdered('ascending', x, res2val, res2ind, 'random with duplicate keys')

            # DESCENDING SORT
            x = torch.rand(4, SIZE, device=device)
            res1val, res1ind = torch.sort(x, x.dim() - 1, True)

            # Test use of result tensor
            res2val = torch.tensor((), device=device)
            res2ind = torch.tensor((), device=device, dtype=torch.long)
            torch.sort(x, x.dim() - 1, True, out=(res2val, res2ind))
            self.assertEqual(res1val, res2val, atol=0, rtol=0)
            self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
            self.assertEqual(torch.argsort(x, x.dim() - 1, True), res1ind)
            self.assertEqual(x.argsort(x.dim() - 1, True), res1ind)

            # Test sorting of random numbers
            self.assertIsOrdered('descending', x, res2val, res2ind, 'random')

            # Test simple sort task
            self.assertEqual(
                torch.sort(torch.tensor((10, 20, 30, 40, 50), device=device), 0, True)[0],
                torch.tensor((50, 40, 30, 20, 10), device=device),
                atol=0, rtol=0
            )

            # Test that we still have proper sorting with duplicate keys
            self.assertIsOrdered('descending', x, res2val, res2ind, 'random with duplicate keys')

            # Test argument sorting with and without stable
            x = torch.tensor([1, 10, 2, 2, 3, 7, 7, 8, 9, 9] * 3)
            self.assertEqual(torch.argsort(x, stable=True), torch.sort(x, stable=True).indices)
            self.assertEqual(torch.argsort(x, stable=False), torch.sort(x, stable=False).indices)
            self.assertEqual(torch.argsort(x), torch.sort(x).indices)


            # Test sorting with NaNs
            x = torch.rand(4, SIZE, device=device)
            x[1][2] = float('NaN')
            x[3][0] = float('NaN')
            torch.sort(x, out=(res2val, res2ind))
            self.assertIsOrdered('ascending', x, res2val, res2ind,
                                 'random with NaNs')
            torch.sort(x, out=(res2val, res2ind), descending=True)
            self.assertIsOrdered('descending', x, res2val, res2ind,
                                 'random with NaNs')

    @onlyCUDA
    def test_sort_large_slice(self, device):
        # tests direct cub path
        x = torch.randn(4, 1024000, device=device)
        res1val, res1ind = torch.sort(x, stable=True)
        torch.cuda.synchronize()
        # assertIsOrdered is too slow, so just compare to cpu
        res1val_cpu, res1ind_cpu = torch.sort(x.cpu(), stable=True)
        self.assertEqual(res1val, res1val_cpu.cuda())
        self.assertEqual(res1ind, res1ind_cpu.cuda())
        res1val, res1ind = torch.sort(x, descending=True, stable=True)
        torch.cuda.synchronize()
        res1val_cpu, res1ind_cpu = torch.sort(x.cpu(), descending=True, stable=True)
        self.assertEqual(res1val, res1val_cpu.cuda())
        self.assertEqual(res1ind, res1ind_cpu.cuda())

    # FIXME: remove torch.bool from unsupported types once support is added for cub sort
    @dtypes(*all_types_and(torch.half, torch.bfloat16))
    def test_stable_sort(self, device, dtype):
        sizes = (100, 1000, 10000)
        for ncopies in sizes:
            x = torch.tensor([0, 1] * ncopies, dtype=dtype, device=device)
            _, idx = x.sort(stable=True)
            self.assertEqual(
                idx[:ncopies],
                torch.arange(start=0, end=2 * ncopies, step=2, device=device)
            )
            self.assertEqual(
                idx[ncopies:],
                torch.arange(start=1, end=2 * ncopies, step=2, device=device)
            )

    @onlyCUDA
    @dtypes(torch.uint8)
    @largeTensorTest('200GB')  # Unfortunately 80GB A100 is not large enough
    def test_sort_large(self, device, dtype):
        t0 = torch.randperm(8192, device=device).to(dtype)
        t = t0.view(1, 8192).expand(2 ** 18 + 1, -1).contiguous()
        v, i = t.sort()
        del t
        iv, im = i.var_mean(dim=0)
        del i
        vv, vm = v.var_mean(dim=0)
        del v
        self.assertEqual(vv, torch.zeros_like(vv))
        self.assertEqual(iv, torch.zeros_like(iv))
        self.assertEqual(vm, torch.arange(255, dtype=dtype, device=device))
        self.assertEqual(im, t0.sort().indices)


    @dtypes(torch.float32)
    def test_sort_restride(self, device, dtype):
        # Input: non-contiguous (stride: 5) 3-element array
        tensor = torch.randn((3, 5), dtype=dtype, device=device)[:, 0]
        # Outputs: 0-dim tensors
        # They will need to be resized, which means they will also be
        # restrided with the input tensor's strides as base.
        values = torch.tensor(0, dtype=dtype, device=device)
        indices = torch.tensor(0, dtype=torch.long, device=device)
        torch.sort(tensor, out=(values, indices))
        # Check: outputs were restrided to dense strides
        self.assertEqual(values.stride(), (1,))
        self.assertEqual(indices.stride(), (1,))
        # Check: 'tensor'  indexed by 'indices' is equal to 'values'
        self.assertEqual(tensor[indices], values)

    def _test_sort_discontiguous(self, device, dtype):
        # on CUDA 2048 vs >2048 have different code path for the dim being sorted
        sizes = (5, 7, 2049)
        for shape in permutations(sizes):
            for perm in permutations((0, 1, 2)):
                for dim in range(3):
                    t = torch.randn(shape, device=device, dtype=dtype).permute(perm)
                    r1 = t.sort(dim=dim)
                    r2 = t.contiguous().sort(dim=dim)
                    self.assertEqual(r1, r2)
                    n = t.size(dim)

                    # assert ordered
                    self.assertTrue((r1.values.narrow(dim, 1, n - 1) >= r1.values.narrow(dim, 0, n - 1)).all())

                    # assert that different segments does not mix, which can easily happen
                    # if the stride is not handled correctly
                    self.assertTrue((t.unsqueeze(-1).transpose(dim, -1) == r1.values.unsqueeze(-1)).any(dim=dim).any(dim=-1).all())

                    # assert stride is preserved
                    if self.device_type == 'cuda':
                        # FIXME: this behavior should be true for all cases, not
                        # just the one specified in if condition
                        self.assertEqual(r1.values.stride(), t.stride())
                        self.assertEqual(r1.indices.stride(), t.stride())

    @onlyCUDA
    @dtypes(torch.float32)
    def test_sort_discontiguous(self, device, dtype):
        self._test_sort_discontiguous(device, dtype)

    @slowTest  # this test is slow on CPU, but not on CUDA
    @onlyCPU
    @dtypes(torch.float32)
    def test_sort_discontiguous_slow(self, device, dtype):
        self._test_sort_discontiguous(device, dtype)

    @dtypes(torch.float32)
    def test_sort_1d_output_discontiguous(self, device, dtype):
        tensor = torch.randn(12, device=device, dtype=dtype)[:6]
        values = torch.empty_like(tensor)[::2]
        indices = torch.empty(18, device=device, dtype=torch.long)[::3]
        torch.sort(tensor, out=(values, indices))
        values_cont, indices_cont = tensor.sort()
        self.assertEqual(indices, indices_cont)
        self.assertEqual(values, values_cont)

    @dtypes(torch.float32)
    def test_topk_1d_output_discontiguous(self, device, dtype):
        tensor = torch.randn(12, device=device, dtype=dtype)
        values = torch.empty_like(tensor)[::2]
        indices = torch.empty(18, device=device, dtype=torch.long)[::3]
        for sorted in (True, False):
            # outputs of `sorted=False` test are not guaranteed to be the same,
            # but with current implementation they are
            torch.topk(tensor, 6, sorted=sorted, out=(values, indices))
            values_cont, indices_cont = tensor.topk(6, sorted=sorted)
            self.assertEqual(indices, indices_cont)
            self.assertEqual(values, values_cont)

    # FIXME: remove torch.bool from unsupported types once support is added for cub sort
    @dtypes(*all_types_and(torch.half, torch.bfloat16))
    def test_stable_sort_against_numpy(self, device, dtype):
        if dtype in floating_types_and(torch.float16, torch.bfloat16):
            inf = float('inf')
            neg_inf = -float('inf')
            nan = float('nan')
        else:
            if dtype != torch.bool:
                # no torch.iinfo support for torch.bool
                inf = torch.iinfo(dtype).max
                neg_inf = torch.iinfo(dtype).min
            else:
                inf = True
                neg_inf = ~inf
            # no nan for integral types, we use inf instead for simplicity
            nan = inf

        def generate_samples():
            from itertools import chain, combinations

            for sizes in [(1025,), (10000,)]:
                size = sizes[0]
                # binary strings
                yield (torch.tensor([0, 1] * size, dtype=dtype, device=device), 0)

            if self.device_type == 'cuda':
                return

            yield (torch.tensor([0, 1] * 100, dtype=dtype, device=device), 0)

            def repeated_index_fill(t, dim, idxs, vals):
                res = t
                for idx, val in zip(idxs, vals):
                    res = res.index_fill(dim, idx, val)
                return res

            for sizes in [(1, 10), (10, 1), (10, 10), (10, 10, 10)]:
                size = min(*sizes)
                x = (torch.randn(*sizes, device=device) * size).to(dtype)
                yield (x, 0)

                # Generate tensors which are being filled at random locations
                # with values from the non-empty subsets of the set (inf, neg_inf, nan)
                # for each dimension.
                n_fill_vals = 3  # cardinality of (inf, neg_inf, nan)
                for dim in range(len(sizes)):
                    idxs = (torch.randint(high=size, size=(size // 10,)) for i in range(n_fill_vals))
                    vals = (inf, neg_inf, nan)
                    subsets = chain.from_iterable(combinations(list(zip(idxs, vals)), r)
                                                  for r in range(1, n_fill_vals + 1))
                    for subset in subsets:
                        idxs_subset, vals_subset = zip(*subset)
                        yield (repeated_index_fill(x, dim, idxs_subset, vals_subset), dim)

        for sample, dim in generate_samples():
            _, idx_torch = sample.sort(dim=dim, stable=True)
            if dtype is torch.bfloat16:
                sample_numpy = sample.float().cpu().numpy()
            else:
                sample_numpy = sample.cpu().numpy()
            idx_numpy = np.argsort(sample_numpy, axis=dim, kind='stable')
            self.assertEqual(idx_torch, idx_numpy)

    @dtypes(*all_types_and(torch.half, torch.bfloat16))
    def test_msort(self, device, dtype):
        def test(shape):
            tensor = make_tensor(shape, dtype=dtype, device=device, low=-9, high=9)
            if tensor.size() != torch.Size([]):
                if dtype is torch.bfloat16:
                    expected = torch.from_numpy(np.msort(tensor.float().cpu().numpy())).bfloat16()
                else:
                    expected = torch.from_numpy(np.msort(tensor.cpu().numpy()))
            else:
                expected = tensor  # numpy.msort() does not support empty shapes tensor

            result = torch.msort(tensor)
            self.assertEqual(result, expected)

            out = torch.empty_like(result)
            torch.msort(tensor, out=out)
            self.assertEqual(out, expected)

        shapes = (
            [],
            [0, ],
            [20, ],
            [1, 20],
            [30, 30],
            [10, 20, 30]
        )
        for shape in shapes:
            test(shape)

    def test_topk(self, device):
        def topKViaSort(t, k, dim, dir):
            sorted, indices = t.sort(dim, dir)
            return sorted.narrow(dim, 0, k), indices.narrow(dim, 0, k)

        def compareTensors(t, res1, ind1, res2, ind2, dim):
            # Values should be exactly equivalent
            self.assertEqual(res1, res2, atol=0, rtol=0)

            # Indices might differ based on the implementation, since there is
            # no guarantee of the relative order of selection
            if not ind1.eq(ind2).all():
                # To verify that the indices represent equivalent elements,
                # gather from the input using the topk indices and compare against
                # the sort indices
                vals = t.gather(dim, ind2)
                self.assertEqual(res1, vals, atol=0, rtol=0)

        def compare(t, k, dim, dir):
            topKVal, topKInd = t.topk(k, dim, dir, True)
            sortKVal, sortKInd = topKViaSort(t, k, dim, dir)
            compareTensors(t, sortKVal, sortKInd, topKVal, topKInd, dim)

        t = torch.rand(random.randint(1, SIZE),
                       random.randint(1, SIZE),
                       random.randint(1, SIZE), device=device)

        for _kTries in range(3):
            for _dimTries in range(3):
                for transpose in (True, False):
                    for dir in (True, False):
                        testTensor = t
                        if transpose:
                            dim1 = random.randrange(t.ndimension())
                            dim2 = dim1
                            while dim1 == dim2:
                                dim2 = random.randrange(t.ndimension())

                            testTensor = t.transpose(dim1, dim2)

                        dim = random.randrange(testTensor.ndimension())
                        k = random.randint(1, testTensor.size(dim))
                        compare(testTensor, k, dim, dir)

        # This tests the code path where on CUDA, topk is implemented with sort.
        t = torch.randn((2, 100000), device=device)
        compare(t, 2000, 1, True)
        compare(t, 2000, 1, False)

        # This tests the code path where on CUDA, topk is implemented with multiblock
        t = torch.randn((2, 10000), device=device)
        compare(t, 2000, 1, True)
        compare(t, 2000, 1, False)

    def test_topk_arguments(self, device):
        q = torch.randn(10, 2, 10, device=device)
        # Make sure True isn't mistakenly taken as the 2nd dimension (interpreted as 1)
        self.assertRaises(TypeError, lambda: q.topk(4, True))

    def test_unique_dim(self, device):
        self.assertFalse(hasattr(torch, 'unique_dim'))

        def run_test(device, dtype):
            x = torch.tensor([[[1., 1.],
                               [0., 1.],
                               [2., 1.],
                               [0., 1.]],
                              [[1., 1.],
                               [0., 1.],
                               [2., 1.],
                               [0., 1.]]],
                             dtype=dtype,
                             device=device)
            x_empty = torch.empty(5, 0, dtype=dtype, device=device)
            x_ill_formed_empty = torch.empty(5, 0, 0, dtype=dtype, device=device)
            x_ill_formed_empty_another = torch.empty(5, 0, 5, dtype=dtype, device=device)
            if dtype in floating_types_and(torch.float16, torch.bfloat16):
                x_nan = torch.tensor([float("nan"), 0, 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
            expected_unique_dim0 = torch.tensor([[[1., 1.],
                                                  [0., 1.],
                                                  [2., 1.],
                                                  [0., 1.]]],
                                                dtype=dtype,
                                                device=device)
            expected_inverse_dim0 = torch.tensor([0, 0])
            expected_counts_dim0 = torch.tensor([2])
            expected_unique_dim1 = torch.tensor([[[0., 1.],
                                                  [1., 1.],
                                                  [2., 1.]],
                                                 [[0., 1.],
                                                  [1., 1.],
                                                  [2., 1.]]],
                                                dtype=dtype,
                                                device=device)
            expected_unique_dim1_bool = torch.tensor([[[False, True], [True, True]],
                                                      [[False, True], [True, True]]],
                                                     dtype=torch.bool,
                                                     device=device)
            expected_inverse_dim1 = torch.tensor([1, 0, 2, 0])
            expected_inverse_dim1_bool = torch.tensor([1, 0, 1, 0])
            expected_counts_dim1 = torch.tensor([2, 1, 1])
            expected_counts_dim1_bool = torch.tensor([2, 2])
            expected_unique_dim2 = torch.tensor([[[1., 1.],
                                                  [0., 1.],
                                                  [2., 1.],
                                                  [0., 1.]],
                                                 [[1., 1.],
                                                  [0., 1.],
                                                  [2., 1.],
                                                  [0., 1.]]],
                                                dtype=dtype,
                                                device=device)
            expected_inverse_dim2 = torch.tensor([0, 1])
            expected_counts_dim2 = torch.tensor([1, 1])
            expected_unique_empty = torch.empty(5, 0, dtype=dtype, device=device)
            expected_inverse_empty = torch.tensor([], dtype=torch.long, device=device)
            expected_counts_empty = torch.tensor([], dtype=torch.long, device=device)
            if dtype in floating_types_and(torch.float16, torch.bfloat16):
                expected_unique_nan = torch.tensor([float("nan"), 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
                expected_inverse_nan = torch.tensor([0, 1, 1, 2, 3, 4], dtype=torch.long, device=device)
                expected_counts_nan = torch.tensor([1, 2, 1, 1, 1], dtype=torch.long, device=device)
            # dim0
            x_unique = torch.unique(x, dim=0)
            self.assertEqual(expected_unique_dim0, x_unique)

            x_unique, x_inverse = torch.unique(
                x,
                return_inverse=True,
                dim=0)
            self.assertEqual(expected_unique_dim0, x_unique)
            self.assertEqual(expected_inverse_dim0, x_inverse)

            x_unique, x_counts = torch.unique(
                x,
                return_inverse=False,
                return_counts=True,
                dim=0)
            self.assertEqual(expected_unique_dim0, x_unique)
            self.assertEqual(expected_counts_dim0, x_counts)

            x_unique, x_inverse, x_counts = torch.unique(
                x,
                return_inverse=True,
                return_counts=True,
                dim=0)
            self.assertEqual(expected_unique_dim0, x_unique)
            self.assertEqual(expected_inverse_dim0, x_inverse)
            self.assertEqual(expected_counts_dim0, x_counts)

            # dim1
            x_unique = torch.unique(x, dim=1)
            if x.dtype == torch.bool:
                self.assertEqual(expected_unique_dim1_bool, x_unique)
            else:
                self.assertEqual(expected_unique_dim1, x_unique)

            x_unique, x_inverse = torch.unique(
                x,
                return_inverse=True,
                dim=1)
            if x.dtype == torch.bool:
                self.assertEqual(expected_unique_dim1_bool, x_unique)
                self.assertEqual(expected_inverse_dim1_bool, x_inverse)
            else:
                self.assertEqual(expected_unique_dim1, x_unique)
                self.assertEqual(expected_inverse_dim1, x_inverse)

            x_unique, x_counts = torch.unique(
                x,
                return_inverse=False,
                return_counts=True,
                dim=1)
            if x.dtype == torch.bool:
                self.assertEqual(expected_unique_dim1_bool, x_unique)
                self.assertEqual(expected_counts_dim1_bool, x_counts)
            else:
                self.assertEqual(expected_unique_dim1, x_unique)
                self.assertEqual(expected_counts_dim1, x_counts)

            x_unique, x_inverse, x_counts = torch.unique(
                x,
                return_inverse=True,
                return_counts=True,
                dim=1)
            if x.dtype == torch.bool:
                self.assertEqual(expected_unique_dim1_bool, x_unique)
                self.assertEqual(expected_inverse_dim1_bool, x_inverse)
                self.assertEqual(expected_counts_dim1_bool, x_counts)
            else:
                self.assertEqual(expected_unique_dim1, x_unique)
                self.assertEqual(expected_inverse_dim1, x_inverse)
                self.assertEqual(expected_counts_dim1, x_counts)

            # dim2
            x_unique = torch.unique(x, dim=2)
            self.assertEqual(expected_unique_dim2, x_unique)

            x_unique, x_inverse = torch.unique(
                x,
                return_inverse=True,
                dim=2)
            self.assertEqual(expected_unique_dim2, x_unique)
            self.assertEqual(expected_inverse_dim2, x_inverse)

            x_unique, x_counts = torch.unique(
                x,
                return_inverse=False,
                return_counts=True,
                dim=2)
            self.assertEqual(expected_unique_dim2, x_unique)
            self.assertEqual(expected_counts_dim2, x_counts)

            x_unique, x_inverse, x_counts = torch.unique(
                x,
                return_inverse=True,
                return_counts=True,
                dim=2)
            self.assertEqual(expected_unique_dim2, x_unique)
            self.assertEqual(expected_inverse_dim2, x_inverse)
            self.assertEqual(expected_counts_dim2, x_counts)

            # test empty tensor
            x_unique, x_inverse, x_counts = torch.unique(
                x_empty,
                return_inverse=True,
                return_counts=True,
                dim=1)
            self.assertEqual(expected_unique_empty, x_unique)
            self.assertEqual(expected_inverse_empty, x_inverse)
            self.assertEqual(expected_counts_empty, x_counts)

            # test tensor with nan
            if dtype in floating_types_and(torch.float16, torch.bfloat16):
                x_unique, x_inverse, x_counts = torch.unique(
                    x_nan,
                    return_inverse=True,
                    return_counts=True,
                    dim=0)
                self.assertEqual(expected_unique_nan, x_unique)
                self.assertEqual(expected_inverse_nan, x_inverse)
                self.assertEqual(expected_counts_nan, x_counts)

            # test not a well formed tensor
            # Checking for runtime error, as this is the expected behaviour
            with self.assertRaises(RuntimeError):
                torch.unique(
                    x_ill_formed_empty,
                    return_inverse=True,
                    return_counts=True,
                    dim=1)

            # test along dim2
            with self.assertRaises(RuntimeError):
                torch.unique(
                    x_ill_formed_empty_another,
                    return_inverse=True,
                    return_counts=True,
                    dim=2)

            # test consecutive version
            y = torch.tensor(
                [[0, 1],
                 [0, 1],
                 [0, 1],
                 [1, 2],
                 [1, 2],
                 [3, 4],
                 [0, 1],
                 [0, 1],
                 [3, 4],
                 [1, 2]],
                dtype=dtype,
                device=device
            )
            # test tensor with nan
            if dtype in floating_types_and(torch.float16, torch.bfloat16):
                y_nan = torch.tensor([float("nan"), 0, 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
            expected_y_unique = torch.tensor(
                [[0, 1],
                 [1, 2],
                 [3, 4],
                 [0, 1],
                 [3, 4],
                 [1, 2]],
                dtype=dtype,
                device=device
            )
            expected_y_inverse = torch.tensor([0, 0, 0, 1, 1, 2, 3, 3, 4, 5], dtype=torch.int64, device=device)
            expected_y_counts = torch.tensor([3, 2, 1, 2, 1, 1], dtype=torch.int64, device=device)
            expected_y_inverse_bool = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 3, 3], dtype=torch.int64, device=device)
            expected_y_counts_bool = torch.tensor([3, 3, 2, 2], dtype=torch.int64, device=device)
            if dtype in floating_types_and(torch.float16, torch.bfloat16):
                expected_y_unique_nan = torch.tensor([float("nan"), 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
                expected_y_inverse_nan = torch.tensor([0, 1, 1, 2, 3, 4], dtype=torch.long, device=device)
                expected_y_counts_nan = torch.tensor([1, 2, 1, 1, 1], dtype=torch.long, device=device)

            y_unique, y_inverse, y_counts = torch.unique_consecutive(y, return_inverse=True, return_counts=True, dim=0)
            if x.dtype == torch.bool:
                self.assertEqual(expected_y_inverse_bool, y_inverse)
                self.assertEqual(expected_y_counts_bool, y_counts)
            else:
                self.assertEqual(expected_y_inverse, y_inverse)
                self.assertEqual(expected_y_counts, y_counts)

            # test tensor with nan
            if dtype in floating_types_and(torch.float16, torch.bfloat16):
                y_unique, y_inverse, y_counts = torch.unique_consecutive(
                    y_nan,
                    return_inverse=True,
                    return_counts=True,
                    dim=0)
                self.assertEqual(expected_y_unique_nan, y_unique)
                self.assertEqual(expected_y_inverse_nan, y_inverse)
                self.assertEqual(expected_y_counts_nan, y_counts)

        run_test(device, torch.float)
        run_test(device, torch.double)
        run_test(device, torch.long)
        run_test(device, torch.uint8)
        run_test(device, torch.bool)

    @onlyCUDA
    def test_topk_noncontiguous_gpu(self, device):
        # test different topk paths on cuda
        single_block_t = torch.randn(20, device=device)[::2]
        multi_block_t = torch.randn(20000, device=device)[::2]
        sort_t = torch.randn(200000, device=device)[::2]
        for t in (single_block_t, multi_block_t, sort_t):
            for k in (5, 2000, 10000):
                if k >= t.shape[0]:
                    continue
                top1, idx1 = t.topk(k)
                top2, idx2 = t.contiguous().topk(k)
                self.assertEqual(top1, top2)
                self.assertEqual(idx1, idx2)

    def _test_topk_dtype(self, device, dtype, integral, size):
        if integral:
            a = torch.randint(torch.iinfo(dtype).min, torch.iinfo(dtype).max,
                              size=(size,), dtype=dtype, device=device)
        else:
            a = torch.randn(size=(size,), dtype=dtype, device=device)

        sort_topk = a.sort()[0][-(size // 2):].flip(0)
        topk = a.topk(size // 2)
        self.assertEqual(sort_topk, topk[0])      # check values
        self.assertEqual(sort_topk, a[topk[1]])   # check indices

    @dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64)
    def test_topk_integral(self, device, dtype):
        small = 10
        large = 4096
        verylarge = 8192  # multi_block topk on cuda
        for curr_size in (small, large, verylarge):
            self._test_topk_dtype(device, dtype, True, curr_size)

    @onlyCUDA
    @dtypes(torch.bfloat16)
    def test_topk_bfloat16(self, device, dtype):

        small = 10
        large = 4096
        verylarge = 8192  # multi_block topk on cuda
        for curr_size in (small, large, verylarge):
            self._test_topk_dtype(device, dtype, False, curr_size)

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @dtypes(torch.float, torch.double, torch.bfloat16)
    def test_topk_nonfinite(self, device, dtype):
        x = torch.tensor([float('nan'), float('inf'), 1e4, 0, -1e4, -float('inf')], device=device, dtype=dtype)
        val, idx = x.topk(4)
        expect = torch.tensor([float('nan'), float('inf'), 1e4, 0], device=device, dtype=dtype)
        self.assertEqual(val, expect)
        self.assertEqual(idx, [0, 1, 2, 3])

        val, idx = x.topk(4, largest=False)
        expect = torch.tensor([-float('inf'), -1e4, 0, 1e4], device=device, dtype=dtype)
        self.assertEqual(val, expect)
        self.assertEqual(idx, [5, 4, 3, 2])

    def test_topk_4d(self, device):
        small = 128
        large = 8192
        for size in (small, large):
            x = torch.ones(2, size, 2, 2, device=device)
            x[:, 1, :, :] *= 2.
            x[:, 10, :, :] *= 1.5
            val, ind = torch.topk(x, k=2, dim=1)
            expected_ind = torch.ones(2, 2, 2, 2, dtype=torch.long, device=device)
            expected_ind[:, 1, :, :] = 10
            expected_val = torch.ones(2, 2, 2, 2, device=device)
            expected_val[:, 0, :, :] *= 2.
            expected_val[:, 1, :, :] *= 1.5
            self.assertEqual(val, expected_val, atol=0, rtol=0)
            self.assertEqual(ind, expected_ind, atol=0, rtol=0)

    @onlyNativeDeviceTypes
    @dtypesIfCUDA(*all_types_and(torch.bfloat16))
    @dtypes(*all_types())
    def test_topk_zero(self, device, dtype):
        # https://github.com/pytorch/pytorch/issues/49205
        t = torch.rand(2, 2, device=device).to(dtype=dtype)
        val, idx = torch.topk(t, k=0, largest=False)
        self.assertEqual(val.size(), torch.Size([2, 0]))
        self.assertEqual(idx.size(), torch.Size([2, 0]))

    def _test_unique_scalar_empty(self, dtype, device, f):
        # test scalar
        x = torch.tensor(0, dtype=dtype, device=device)
        unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
        expected_unique = torch.tensor([0], dtype=dtype, device=device)
        expected_inverse = torch.tensor(0, device=device)
        expected_counts = torch.tensor([1], device=device)
        self.assertEqual(unique, expected_unique)
        self.assertEqual(inverse, expected_inverse)
        self.assertEqual(counts, expected_counts)

        # test zero sized tensor
        x = torch.zeros((0, 0, 3), dtype=dtype, device=device)
        unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
        expected_unique = torch.tensor([], dtype=dtype, device=device)
        expected_inverse = torch.empty((0, 0, 3), dtype=torch.long, device=device)
        expected_counts = torch.tensor([], dtype=torch.long, device=device)
        self.assertEqual(unique, expected_unique)
        self.assertEqual(inverse, expected_inverse)
        self.assertEqual(counts, expected_counts)

    def _test_unique_with_expects(self, device, dtype, f, x, expected_unique, expected_inverse, expected_counts, additional_shape):
        def ensure_tuple(x):
            if isinstance(x, torch.Tensor):
                return (x,)
            return x

        for return_inverse in [True, False]:
            for return_counts in [True, False]:
                # test with expected
                ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
                self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
                self.assertEqual(expected_unique, ret[0])
                if return_inverse:
                    self.assertEqual(expected_inverse, ret[1])
                if return_counts:
                    count_index = 1 + int(return_inverse)
                    self.assertEqual(expected_counts, ret[count_index])

                # tests per-element unique on a higher rank tensor.
                y = x.view(additional_shape)
                y_unique, y_inverse, y_counts = f(y, return_inverse=True, return_counts=True)
                self.assertEqual(expected_unique, y_unique)
                self.assertEqual(expected_inverse.view(additional_shape), y_inverse)
                self.assertEqual(expected_counts, y_counts)

    @dtypesIfCPU(*all_types_and(torch.bool, torch.bfloat16))
    @dtypes(*all_types_and(torch.half, torch.bool))
    def test_unique(self, device, dtype):
        def ensure_tuple(x):
            if isinstance(x, torch.Tensor):
                return (x,)
            return x

        if dtype is torch.bool:
            x = torch.tensor([True, False, False, False, True, False, True, False], dtype=torch.bool, device=device)
            expected_unique = torch.tensor([False, True], dtype=torch.bool, device=device)
            expected_inverse = torch.tensor([1, 0, 0, 0, 1, 0, 1, 0], dtype=torch.long, device=device)
            expected_counts = torch.tensor([5, 3], dtype=torch.long, device=device)
        else:
            x = torch.tensor([1, 2, 3, 2, 8, 5, 2, 3], dtype=dtype, device=device)
            expected_unique = torch.tensor([1, 2, 3, 5, 8], dtype=dtype, device=device)
            expected_inverse = torch.tensor([0, 1, 2, 1, 4, 3, 1, 2], device=device)
            expected_counts = torch.tensor([1, 3, 2, 1, 1], device=device)

        # test sorted unique
        fs = (
            lambda x, **kwargs: torch.unique(x, sorted=True, **kwargs),
            lambda x, **kwargs: x.unique(sorted=True, **kwargs),
        )
        x_sliced = torch.empty(x.size(0) * 2, dtype=dtype, device=device)[::2].copy_(x)
        xs = (x, x_sliced)
        for f, x in product(fs, xs):
            self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (2, 2, 2))
            self._test_unique_scalar_empty(dtype, device, f)

        # test unsorted unique
        fs = (
            lambda x, **kwargs: torch.unique(x, sorted=False, **kwargs),
            lambda x, **kwargs: x.unique(sorted=False, **kwargs)
        )
        for f, x in product(fs, xs):
            self._test_unique_scalar_empty(dtype, device, f)
            for return_inverse, return_counts in product((True, False), repeat=2):
                ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
                self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
                x_list = x.tolist()
                x_unique_list = ret[0].tolist()
                self.assertEqual(expected_unique.tolist(), sorted(x_unique_list))
                if return_inverse:
                    x_inverse_list = ret[1].tolist()
                    for i, j in enumerate(x_inverse_list):
                        self.assertEqual(x_list[i], x_unique_list[j])
                if return_counts:
                    count_index = 1 + int(return_inverse)
                    x_counts_list = ret[count_index].tolist()
                    for i, j in zip(x_unique_list, x_counts_list):
                        count = 0
                        for k in x_list:
                            if k == i:
                                count += 1
                        self.assertEqual(j, count)

    @dtypesIfCPU(*all_types_and(torch.bool, torch.bfloat16))
    @dtypes(*all_types_and(torch.half, torch.bool))
    def test_unique_consecutive(self, device, dtype):
        if dtype is torch.bool:
            x = torch.tensor([True, False, False, False, True, True, False, False, False], dtype=torch.bool, device=device)
            expected_unique = torch.tensor([True, False, True, False], dtype=torch.bool, device=device)
            expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 3], dtype=torch.long, device=device)
            expected_counts = torch.tensor([1, 3, 2, 3], dtype=torch.long, device=device)
        else:
            x = torch.tensor([1, 2, 2, 2, 5, 5, 2, 2, 3], dtype=dtype, device=device)
            expected_unique = torch.tensor([1, 2, 5, 2, 3], dtype=dtype, device=device)
            expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 4], device=device)
            expected_counts = torch.tensor([1, 3, 2, 2, 1], device=device)

        for f in [torch.unique_consecutive, lambda x, **kwargs: x.unique_consecutive(**kwargs)]:
            self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (3, 3))
            self._test_unique_scalar_empty(dtype, device, f)

    @dtypes(torch.double)
    def test_kthvalue(self, device, dtype):
        SIZE = 50
        x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
        x0 = x.clone()

        k = random.randint(1, SIZE)
        res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
        res2val, res2ind = torch.sort(x)

        self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
        self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
        # test use of result tensors
        k = random.randint(1, SIZE)
        res1val = torch.tensor([], dtype=dtype, device=device)
        res1ind = torch.tensor([], dtype=torch.long, device=device)
        torch.kthvalue(x, k, keepdim=False, out=(res1val, res1ind))
        res2val, res2ind = torch.sort(x)
        self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
        self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)

        # test non-default dim
        k = random.randint(1, SIZE)
        res1val, res1ind = torch.kthvalue(x, k, 0, keepdim=False)
        res2val, res2ind = torch.sort(x, 0)
        self.assertEqual(res1val, res2val[k - 1], atol=0, rtol=0)
        self.assertEqual(res1ind, res2ind[k - 1], atol=0, rtol=0)

        # non-contiguous
        y = x.narrow(1, 0, 1)
        y0 = y.contiguous()
        k = random.randint(1, SIZE)
        res1val, res1ind = torch.kthvalue(y, k)
        res2val, res2ind = torch.kthvalue(y0, k)
        self.assertEqual(res1val, res2val, atol=0, rtol=0)
        self.assertEqual(res1ind, res2ind, atol=0, rtol=0)

        # non-contiguous [Reference: https://github.com/pytorch/pytorch/issues/45721]
        non_contig_t = torch.tensor([0, -1, 1, -2, 2], dtype=dtype, device=device)[::2]
        expected_val, expected_ind = non_contig_t.contiguous().kthvalue(2)
        non_contig_cpu_t = non_contig_t.cpu()
        expected_val_cpu, expected_ind_cpu = non_contig_cpu_t.kthvalue(2)

        out_val, out_ind = non_contig_t.kthvalue(2)
        self.assertEqual(expected_val, out_val, atol=0, rtol=0)
        self.assertEqual(expected_ind, out_ind, atol=0, rtol=0)
        self.assertEqual(expected_val_cpu, out_val, atol=0, rtol=0)
        self.assertEqual(expected_ind_cpu, out_ind, atol=0, rtol=0)

        # check that the input wasn't modified
        self.assertEqual(x, x0, atol=0, rtol=0)

        # simple test case (with repetitions)
        y = torch.tensor((3., 5, 4, 1, 1, 5), dtype=dtype, device=device)
        self.assertEqual(torch.kthvalue(y, 3)[0], 3, atol=0, rtol=0)
        self.assertEqual(torch.kthvalue(y, 2)[0], 1, atol=0, rtol=0)

        # simple test case (with NaN)
        SIZE = 50
        x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
        x[torch.arange(SIZE), :, torch.randint(50, (50,))] = nan
        ks = [random.randint(1, SIZE), 1, SIZE, SIZE - 1]
        res2val, res2ind = torch.sort(x)
        for k in ks:
            res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
            self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
            self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)

    @dtypes(torch.float)
    @onlyNativeDeviceTypes   # Fails on XLA
    def test_kthvalue_scalar(self, device, dtype):
        # Test scalar input (test case from https://github.com/pytorch/pytorch/issues/30818)
        # Tests that passing a scalar tensor or 1D tensor with 1 element work either way
        res = torch.tensor(2, device=device, dtype=dtype).kthvalue(1)
        ref = torch.tensor([2], device=device, dtype=dtype).kthvalue(1)
        self.assertEqual(res[0], ref[0].squeeze())
        self.assertEqual(res[1], ref[1].squeeze())

    @dtypes(*all_types())
    @dtypesIfCUDA(*all_types_and(torch.half))
    def test_isin(self, device, dtype):
        def assert_isin_equal(a, b):
            # Compare to the numpy reference implementation.
            x = torch.isin(a, b)
            a = a.cpu().numpy() if torch.is_tensor(a) else np.array(a)
            b = b.cpu().numpy() if torch.is_tensor(b) else np.array(b)
            y = np.isin(a, b)
            self.assertEqual(x, y)

        # multi-dim tensor, multi-dim tensor
        a = torch.arange(24, device=device, dtype=dtype).reshape([2, 3, 4])
        b = torch.tensor([[10, 20, 30], [0, 1, 3], [11, 22, 33]], device=device, dtype=dtype)
        assert_isin_equal(a, b)

        # zero-dim tensor
        zero_d = torch.tensor(3, device=device, dtype=dtype)
        assert_isin_equal(zero_d, b)
        assert_isin_equal(a, zero_d)
        assert_isin_equal(zero_d, zero_d)

        # empty tensor
        empty = torch.tensor([], device=device, dtype=dtype)
        assert_isin_equal(empty, b)
        assert_isin_equal(a, empty)
        assert_isin_equal(empty, empty)

        # scalar
        assert_isin_equal(a, 6)
        assert_isin_equal(5, b)

        def define_expected(lst, invert=False):
            expected = torch.tensor(lst, device=device)
            if invert:
                expected = expected.logical_not()
            return expected

        # Adapted from numpy's in1d tests
        for mult in [1, 10]:
            for invert in [False, True]:
                a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
                b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
                ec = define_expected([True, False, True, True], invert=invert)
                c = torch.isin(a, b, assume_unique=True, invert=invert)
                self.assertEqual(c, ec)

                a[0] = 8
                ec = define_expected([False, False, True, True], invert=invert)
                c = torch.isin(a, b, assume_unique=True, invert=invert)
                self.assertEqual(c, ec)

                a[0], a[3] = 4, 8
                ec = define_expected([True, False, True, False], invert=invert)
                c = torch.isin(a, b, assume_unique=True, invert=invert)
                self.assertEqual(c, ec)

                a = torch.tensor([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5], device=device, dtype=dtype)
                b = torch.tensor([2, 3, 4] * mult, device=device, dtype=dtype)
                ec = define_expected([False, True, False, True, True, True, True, True, True,
                                      False, True, False, False, False], invert=invert)
                c = torch.isin(a, b, invert=invert)
                self.assertEqual(c, ec)

                b = torch.tensor([2, 3, 4] * mult + [5, 5, 4] * mult, device=device, dtype=dtype)
                ec = define_expected([True, True, True, True, True, True, True, True, True, True,
                                      True, False, True, True], invert=invert)
                c = torch.isin(a, b, invert=invert)
                self.assertEqual(c, ec)

                a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
                b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
                ec = define_expected([True, False, True, True], invert=invert)
                c = torch.isin(a, b, invert=invert)
                self.assertEqual(c, ec)

                a = torch.tensor([5, 7, 1, 1, 2], device=device, dtype=dtype)
                b = torch.tensor([2, 4, 3, 3, 1, 5] * mult, device=device, dtype=dtype)
                ec = define_expected([True, False, True, True, True], invert=invert)
                c = torch.isin(a, b, invert=invert)
                self.assertEqual(c, ec)

                a = torch.tensor([5, 5], device=device, dtype=dtype)
                b = torch.tensor([2, 2] * mult, device=device, dtype=dtype)
                ec = define_expected([False, False], invert=invert)
                c = torch.isin(a, b, invert=invert)
                self.assertEqual(c, ec)

                # multi-dimensional input case using sort-based algo
                for assume_unique in [False, True]:
                    a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
                    b = torch.arange(3, 30, device=device, dtype=dtype)
                    ec = define_expected([[False, False, False], [True, True, True]], invert=invert)
                    c = torch.isin(a, b, invert=invert, assume_unique=assume_unique)
                    self.assertEqual(c, ec)

    def test_isin_different_dtypes(self, device):
        supported_types = all_types() if device == 'cpu' else all_types_and(torch.half)
        for mult in [1, 10]:
            for assume_unique in [False, True]:
                for dtype1, dtype2 in product(supported_types, supported_types):
                    a = torch.tensor([1, 2, 3], device=device, dtype=dtype1)
                    b = torch.tensor([3, 4, 5] * mult, device=device, dtype=dtype2)
                    ec = torch.tensor([False, False, True], device=device)
                    c = torch.isin(a, b, assume_unique=assume_unique)
                    self.assertEqual(c, ec)

    @onlyCUDA
    @dtypes(*all_types())
    def test_isin_different_devices(self, device, dtype):
        a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
        b = torch.arange(3, 30, device='cpu', dtype=dtype)
        with self.assertRaises(RuntimeError):
            torch.isin(a, b)

        c = torch.arange(6, device='cpu', dtype=dtype).reshape([2, 3])
        d = torch.arange(3, 30, device=device, dtype=dtype)
        with self.assertRaises(RuntimeError):
            torch.isin(c, d)


instantiate_device_type_tests(TestSortAndSelect, globals())

if __name__ == '__main__':
    run_tests()