text: from common_utils import TestCase, run_tests
  import torch
  from torch import tensor
  import unittest
  
  
  class TestIndexing(TestCase):
      def test_single_int(self):
          v = torch.randn(5, 7, 3)
          self.assertEqual(v[4].shape, (7, 3))
  
      def test_multiple_int(self):
          v = torch.randn(5, 7, 3)
          self.assertEqual(v[4].shape, (7, 3))
          self.assertEqual(v[4, :, 1].shape, (7,))
  
      def test_none(self):
          v = torch.randn(5, 7, 3)
          self.assertEqual(v[None].shape, (1, 5, 7, 3))
          self.assertEqual(v[:, None].shape, (5, 1, 7, 3))
          self.assertEqual(v[:, None, None].shape, (5, 1, 1, 7, 3))
          self.assertEqual(v[..., None].shape, (5, 7, 3, 1))
  
      def test_step(self):
          v = torch.arange(10)
          self.assertEqual(v[::1], v)
          self.assertEqual(v[::2].tolist(), [0, 2, 4, 6, 8])
          self.assertEqual(v[::3].tolist(), [0, 3, 6, 9])
          self.assertEqual(v[::11].tolist(), [0])
          self.assertEqual(v[1:6:2].tolist(), [1, 3, 5])
  
      def test_step_assignment(self):
          v = torch.zeros(4, 4)
          v[0, 1::2] = torch.tensor([3., 4.])
          self.assertEqual(v[0].tolist(), [0, 3, 0, 4])
          self.assertEqual(v[1:].sum(), 0)
  
      def test_bool_indices(self):
          v = torch.randn(5, 7, 3)
          boolIndices = torch.tensor([True, False, True, True, False], dtype=torch.bool)
          self.assertEqual(v[boolIndices].shape, (3, 7, 3))
          self.assertEqual(v[boolIndices], torch.stack([v[0], v[2], v[3]]))
  
          v = torch.tensor([True, False, True], dtype=torch.bool)
          boolIndices = torch.tensor([True, False, False], dtype=torch.bool)
          uint8Indices = torch.tensor([1, 0, 0], dtype=torch.uint8)
          self.assertEqual(v[boolIndices].shape, v[uint8Indices].shape)
          self.assertEqual(v[boolIndices], v[uint8Indices])
          self.assertEqual(v[boolIndices], tensor([True], dtype=torch.bool))
  
      def test_bool_indices_accumulate(self):
          mask = torch.zeros(size=(10, ), dtype=torch.bool)
          y = torch.ones(size=(10, 10))
          y.index_put_((mask, ), y[mask], accumulate=True)
          self.assertEqual(y, torch.ones(size=(10, 10)))
  
      def test_multiple_bool_indices(self):
          v = torch.randn(5, 7, 3)
          # note: these broadcast together and are transposed to the first dim
          mask1 = torch.tensor([1, 0, 1, 1, 0], dtype=torch.bool)
          mask2 = torch.tensor([1, 1, 1], dtype=torch.bool)
          self.assertEqual(v[mask1, :, mask2].shape, (3, 7))
  
      def test_byte_mask(self):
          v = torch.randn(5, 7, 3)
          mask = torch.ByteTensor([1, 0, 1, 1, 0])
          self.assertEqual(v[mask].shape, (3, 7, 3))
          self.assertEqual(v[mask], torch.stack([v[0], v[2], v[3]]))
  
          v = torch.tensor([1.])
          self.assertEqual(v[v == 0], torch.tensor([]))
  
      def test_byte_mask_accumulate(self):
          mask = torch.zeros(size=(10, ), dtype=torch.uint8)
          y = torch.ones(size=(10, 10))
          y.index_put_((mask, ), y[mask], accumulate=True)
          self.assertEqual(y, torch.ones(size=(10, 10)))
  
      def test_multiple_byte_mask(self):
          v = torch.randn(5, 7, 3)
          # note: these broadcast together and are transposed to the first dim
          mask1 = torch.ByteTensor([1, 0, 1, 1, 0])
          mask2 = torch.ByteTensor([1, 1, 1])
          self.assertEqual(v[mask1, :, mask2].shape, (3, 7))
  
      def test_byte_mask2d(self):
          v = torch.randn(5, 7, 3)
          c = torch.randn(5, 7)
          num_ones = (c > 0).sum()
          r = v[c > 0]
          self.assertEqual(r.shape, (num_ones, 3))
  
      def test_int_indices(self):
          v = torch.randn(5, 7, 3)
          self.assertEqual(v[[0, 4, 2]].shape, (3, 7, 3))
          self.assertEqual(v[:, [0, 4, 2]].shape, (5, 3, 3))
          self.assertEqual(v[:, [[0, 1], [4, 3]]].shape, (5, 2, 2, 3))
  
      def test_int_indices2d(self):
          # From the NumPy indexing example
          x = torch.arange(0, 12).view(4, 3)
          rows = torch.tensor([[0, 0], [3, 3]])
          columns = torch.tensor([[0, 2], [0, 2]])
          self.assertEqual(x[rows, columns].tolist(), [[0, 2], [9, 11]])
  
      def test_int_indices_broadcast(self):
          # From the NumPy indexing example
          x = torch.arange(0, 12).view(4, 3)
          rows = torch.tensor([0, 3])
          columns = torch.tensor([0, 2])
          result = x[rows[:, None], columns]
          self.assertEqual(result.tolist(), [[0, 2], [9, 11]])
  
      def test_empty_index(self):
          x = torch.arange(0, 12).view(4, 3)
          idx = torch.tensor([], dtype=torch.long)
          self.assertEqual(x[idx].numel(), 0)
  
          # empty assignment should have no effect but not throw an exception
          y = x.clone()
          y[idx] = -1
          self.assertEqual(x, y)
  
          mask = torch.zeros(4, 3).byte()
          y[mask] = -1
          self.assertEqual(x, y)
  
      def test_empty_ndim_index(self):
          devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
          for device in devices:
              x = torch.randn(5, device=device)
              self.assertEqual(torch.empty(0, 2, device=device), x[torch.empty(0, 2, dtype=torch.int64, device=device)])
  
              x = torch.randn(2, 3, 4, 5, device=device)
              self.assertEqual(torch.empty(2, 0, 6, 4, 5, device=device),
                               x[:, torch.empty(0, 6, dtype=torch.int64, device=device)])
  
          x = torch.empty(10, 0)
          self.assertEqual(x[[1, 2]].shape, (2, 0))
          self.assertEqual(x[[], []].shape, (0,))
          with self.assertRaisesRegex(IndexError, 'for dimension with size 0'):
              x[:, [0, 1]]
  
      def test_empty_ndim_index_bool(self):
          devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
          for device in devices:
              x = torch.randn(5, device=device)
              self.assertRaises(IndexError, lambda: x[torch.empty(0, 2, dtype=torch.uint8, device=device)])
  
      def test_empty_slice(self):
          devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
          for device in devices:
              x = torch.randn(2, 3, 4, 5, device=device)
              y = x[:, :, :, 1]
              z = y[:, 1:1, :]
              self.assertEqual((2, 0, 4), z.shape)
              # this isn't technically necessary, but matches NumPy stride calculations.
              self.assertEqual((60, 20, 5), z.stride())
              self.assertTrue(z.is_contiguous())
  
      def test_index_getitem_copy_bools_slices(self):
          true = torch.tensor(1, dtype=torch.uint8)
          false = torch.tensor(0, dtype=torch.uint8)
  
          tensors = [torch.randn(2, 3), torch.tensor(3)]
  
          for a in tensors:
              self.assertNotEqual(a.data_ptr(), a[True].data_ptr())
              self.assertEqual(torch.empty(0, *a.shape), a[False])
              self.assertNotEqual(a.data_ptr(), a[true].data_ptr())
              self.assertEqual(torch.empty(0, *a.shape), a[false])
              self.assertEqual(a.data_ptr(), a[None].data_ptr())
              self.assertEqual(a.data_ptr(), a[...].data_ptr())
  
      def test_index_setitem_bools_slices(self):
          true = torch.tensor(1, dtype=torch.uint8)
          false = torch.tensor(0, dtype=torch.uint8)
  
          tensors = [torch.randn(2, 3), torch.tensor(3)]
  
          for a in tensors:
              # prefix with a 1,1, to ensure we are compatible with numpy which cuts off prefix 1s
              # (some of these ops already prefix a 1 to the size)
              neg_ones = torch.ones_like(a) * -1
              neg_ones_expanded = neg_ones.unsqueeze(0).unsqueeze(0)
              a[True] = neg_ones_expanded
              self.assertEqual(a, neg_ones)
              a[False] = 5
              self.assertEqual(a, neg_ones)
              a[true] = neg_ones_expanded * 2
              self.assertEqual(a, neg_ones * 2)
              a[false] = 5
              self.assertEqual(a, neg_ones * 2)
              a[None] = neg_ones_expanded * 3
              self.assertEqual(a, neg_ones * 3)
              a[...] = neg_ones_expanded * 4
              self.assertEqual(a, neg_ones * 4)
              if a.dim() == 0:
                  with self.assertRaises(IndexError):
                      a[:] = neg_ones_expanded * 5
  
      def test_index_scalar_with_bool_mask(self):
          for device in torch.testing.get_all_device_types():
              a = torch.tensor(1, device=device)
              uintMask = torch.tensor(True, dtype=torch.uint8, device=device)
              boolMask = torch.tensor(True, dtype=torch.bool, device=device)
              self.assertEqual(a[uintMask], a[boolMask])
              self.assertEqual(a[uintMask].dtype, a[boolMask].dtype)
  
              a = torch.tensor(True, dtype=torch.bool, device=device)
              self.assertEqual(a[uintMask], a[boolMask])
              self.assertEqual(a[uintMask].dtype, a[boolMask].dtype)
  
      def test_setitem_expansion_error(self):
          true = torch.tensor(True)
          a = torch.randn(2, 3)
          # check prefix with  non-1s doesn't work
          a_expanded = a.expand(torch.Size([5, 1]) + a.size())
          # NumPy: ValueError
          with self.assertRaises(RuntimeError):
              a[True] = a_expanded
          with self.assertRaises(RuntimeError):
              a[true] = a_expanded
  
      def test_getitem_scalars(self):
          zero = torch.tensor(0, dtype=torch.int64)
          one = torch.tensor(1, dtype=torch.int64)
  
          # non-scalar indexed with scalars
          a = torch.randn(2, 3)
          self.assertEqual(a[0], a[zero])
          self.assertEqual(a[0][1], a[zero][one])
          self.assertEqual(a[0, 1], a[zero, one])
          self.assertEqual(a[0, one], a[zero, 1])
  
          # indexing by a scalar should slice (not copy)
          self.assertEqual(a[0, 1].data_ptr(), a[zero, one].data_ptr())
          self.assertEqual(a[1].data_ptr(), a[one.int()].data_ptr())
          self.assertEqual(a[1].data_ptr(), a[one.short()].data_ptr())
  
          # scalar indexed with scalar
          r = torch.randn(())
          with self.assertRaises(IndexError):
              r[:]
          with self.assertRaises(IndexError):
              r[zero]
          self.assertEqual(r, r[...])
  
      def test_setitem_scalars(self):
          zero = torch.tensor(0, dtype=torch.int64)
  
          # non-scalar indexed with scalars
          a = torch.randn(2, 3)
          a_set_with_number = a.clone()
          a_set_with_scalar = a.clone()
          b = torch.randn(3)
  
          a_set_with_number[0] = b
          a_set_with_scalar[zero] = b
          self.assertEqual(a_set_with_number, a_set_with_scalar)
          a[1, zero] = 7.7
          self.assertEqual(7.7, a[1, 0])
  
          # scalar indexed with scalars
          r = torch.randn(())
          with self.assertRaises(IndexError):
              r[:] = 8.8
          with self.assertRaises(IndexError):
              r[zero] = 8.8
          r[...] = 9.9
          self.assertEqual(9.9, r)
  
      def test_basic_advanced_combined(self):
          # From the NumPy indexing example
          x = torch.arange(0, 12).view(4, 3)
          self.assertEqual(x[1:2, 1:3], x[1:2, [1, 2]])
          self.assertEqual(x[1:2, 1:3].tolist(), [[4, 5]])
  
          # Check that it is a copy
          unmodified = x.clone()
          x[1:2, [1, 2]].zero_()
          self.assertEqual(x, unmodified)
  
          # But assignment should modify the original
          unmodified = x.clone()
          x[1:2, [1, 2]] = 0
          self.assertNotEqual(x, unmodified)
  
      def test_int_assignment(self):
          x = torch.arange(0, 4).view(2, 2)
          x[1] = 5
          self.assertEqual(x.tolist(), [[0, 1], [5, 5]])
  
          x = torch.arange(0, 4).view(2, 2)
          x[1] = torch.arange(5, 7)
          self.assertEqual(x.tolist(), [[0, 1], [5, 6]])
  
      def test_byte_tensor_assignment(self):
          x = torch.arange(0., 16).view(4, 4)
          b = torch.ByteTensor([True, False, True, False])
          value = torch.tensor([3., 4., 5., 6.])
          x[b] = value
          self.assertEqual(x[0], value)
          self.assertEqual(x[1], torch.arange(4, 8))
          self.assertEqual(x[2], value)
          self.assertEqual(x[3], torch.arange(12, 16))
  
      def test_variable_slicing(self):
          x = torch.arange(0, 16).view(4, 4)
          indices = torch.IntTensor([0, 1])
          i, j = indices
          self.assertEqual(x[i:j], x[0:1])
  
      def test_ellipsis_tensor(self):
          x = torch.arange(0, 9).view(3, 3)
          idx = torch.tensor([0, 2])
          self.assertEqual(x[..., idx].tolist(), [[0, 2],
                                                  [3, 5],
                                                  [6, 8]])
          self.assertEqual(x[idx, ...].tolist(), [[0, 1, 2],
                                                  [6, 7, 8]])
  
      def test_invalid_index(self):
          x = torch.arange(0, 16).view(4, 4)
          self.assertRaisesRegex(TypeError, 'slice indices', lambda: x["0":"1"])
  
      def test_out_of_bound_index(self):
          x = torch.arange(0, 100).view(2, 5, 10)
          self.assertRaisesRegex(IndexError, 'index 5 is out of bounds for dimension 1 with size 5', lambda: x[0, 5])
          self.assertRaisesRegex(IndexError, 'index 4 is out of bounds for dimension 0 with size 2', lambda: x[4, 5])
          self.assertRaisesRegex(IndexError, 'index 15 is out of bounds for dimension 2 with size 10',
                                 lambda: x[0, 1, 15])
          self.assertRaisesRegex(IndexError, 'index 12 is out of bounds for dimension 2 with size 10',
                                 lambda: x[:, :, 12])
  
      def test_zero_dim_index(self):
          x = torch.tensor(10)
          self.assertEqual(x, x.item())
  
          def runner():
              print(x[0])
              return x[0]
  
          self.assertRaisesRegex(IndexError, 'invalid index', runner)
  
  
  # The tests below are from NumPy test_indexing.py with some modifications to
  # make them compatible with PyTorch. It's licensed under the BDS license below:
  #
  # Copyright (c) 2005-2017, NumPy Developers.
  # All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions are
  # met:
  #
  #     * Redistributions of source code must retain the above copyright
  #        notice, this list of conditions and the following disclaimer.
  #
  #     * Redistributions in binary form must reproduce the above
  #        copyright notice, this list of conditions and the following
  #        disclaimer in the documentation and/or other materials provided
  #        with the distribution.
  #
  #     * Neither the name of the NumPy Developers nor the names of any
  #        contributors may be used to endorse or promote products derived
  #        from this software without specific prior written permission.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
  # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
  # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
  # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
  # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  
  class NumpyTests(TestCase):
      def test_index_no_floats(self):
          a = torch.tensor([[[5.]]])
  
          self.assertRaises(IndexError, lambda: a[0.0])
          self.assertRaises(IndexError, lambda: a[0, 0.0])
          self.assertRaises(IndexError, lambda: a[0.0, 0])
          self.assertRaises(IndexError, lambda: a[0.0, :])
          self.assertRaises(IndexError, lambda: a[:, 0.0])
          self.assertRaises(IndexError, lambda: a[:, 0.0, :])
          self.assertRaises(IndexError, lambda: a[0.0, :, :])
          self.assertRaises(IndexError, lambda: a[0, 0, 0.0])
          self.assertRaises(IndexError, lambda: a[0.0, 0, 0])
          self.assertRaises(IndexError, lambda: a[0, 0.0, 0])
          self.assertRaises(IndexError, lambda: a[-1.4])
          self.assertRaises(IndexError, lambda: a[0, -1.4])
          self.assertRaises(IndexError, lambda: a[-1.4, 0])
          self.assertRaises(IndexError, lambda: a[-1.4, :])
          self.assertRaises(IndexError, lambda: a[:, -1.4])
          self.assertRaises(IndexError, lambda: a[:, -1.4, :])
          self.assertRaises(IndexError, lambda: a[-1.4, :, :])
          self.assertRaises(IndexError, lambda: a[0, 0, -1.4])
          self.assertRaises(IndexError, lambda: a[-1.4, 0, 0])
          self.assertRaises(IndexError, lambda: a[0, -1.4, 0])
          # self.assertRaises(IndexError, lambda: a[0.0:, 0.0])
          # self.assertRaises(IndexError, lambda: a[0.0:, 0.0,:])
  
      def test_none_index(self):
          # `None` index adds newaxis
          a = tensor([1, 2, 3])
          self.assertEqual(a[None].dim(), a.dim() + 1)
  
      def test_empty_tuple_index(self):
          # Empty tuple index creates a view
          a = tensor([1, 2, 3])
          self.assertEqual(a[()], a)
          self.assertEqual(a[()].data_ptr(), a.data_ptr())
  
      def test_empty_fancy_index(self):
          # Empty list index creates an empty array
          a = tensor([1, 2, 3])
          self.assertEqual(a[[]], torch.tensor([]))
  
          b = tensor([]).long()
          self.assertEqual(a[[]], torch.tensor([], dtype=torch.long))
  
          b = tensor([]).float()
          self.assertRaises(IndexError, lambda: a[b])
  
      def test_ellipsis_index(self):
          a = tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]])
          self.assertIsNot(a[...], a)
          self.assertEqual(a[...], a)
          # `a[...]` was `a` in numpy <1.9.
          self.assertEqual(a[...].data_ptr(), a.data_ptr())
  
          # Slicing with ellipsis can skip an
          # arbitrary number of dimensions
          self.assertEqual(a[0, ...], a[0])
          self.assertEqual(a[0, ...], a[0, :])
          self.assertEqual(a[..., 0], a[:, 0])
  
          # In NumPy, slicing with ellipsis results in a 0-dim array. In PyTorch
          # we don't have separate 0-dim arrays and scalars.
          self.assertEqual(a[0, ..., 1], torch.tensor(2))
  
          # Assignment with `(Ellipsis,)` on 0-d arrays
          b = torch.tensor(1)
          b[(Ellipsis,)] = 2
          self.assertEqual(b, 2)
  
      def test_single_int_index(self):
          # Single integer index selects one row
          a = tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]])
  
          self.assertEqual(a[0], [1, 2, 3])
          self.assertEqual(a[-1], [7, 8, 9])
  
          # Index out of bounds produces IndexError
          self.assertRaises(IndexError, a.__getitem__, 1 << 30)
          # Index overflow produces Exception  NB: different exception type
          self.assertRaises(Exception, a.__getitem__, 1 << 64)
  
      def test_single_bool_index(self):
          # Single boolean index
          a = tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]])
  
          self.assertEqual(a[True], a[None])
          self.assertEqual(a[False], a[None][0:0])
  
      def test_boolean_shape_mismatch(self):
          arr = torch.ones((5, 4, 3))
  
          index = tensor([True])
          self.assertRaisesRegex(IndexError, 'mask', lambda: arr[index])
  
          index = tensor([False] * 6)
          self.assertRaisesRegex(IndexError, 'mask', lambda: arr[index])
  
          index = torch.ByteTensor(4, 4).zero_()
          self.assertRaisesRegex(IndexError, 'mask', lambda: arr[index])
  
          self.assertRaisesRegex(IndexError, 'mask', lambda: arr[(slice(None), index)])
  
      def test_boolean_indexing_onedim(self):
          # Indexing a 2-dimensional array with
          # boolean array of length one
          a = tensor([[0., 0., 0.]])
          b = tensor([True])
          self.assertEqual(a[b], a)
          # boolean assignment
          a[b] = 1.
          self.assertEqual(a, tensor([[1., 1., 1.]]))
  
      def test_boolean_assignment_value_mismatch(self):
          # A boolean assignment should fail when the shape of the values
          # cannot be broadcast to the subscription. (see also gh-3458)
          a = torch.arange(0, 4)
  
          def f(a, v):
              a[a > -1] = tensor(v)
  
          self.assertRaisesRegex(Exception, 'shape mismatch', f, a, [])
          self.assertRaisesRegex(Exception, 'shape mismatch', f, a, [1, 2, 3])
          self.assertRaisesRegex(Exception, 'shape mismatch', f, a[:1], [1, 2, 3])
  
      def test_boolean_indexing_twodim(self):
          # Indexing a 2-dimensional array with
          # 2-dimensional boolean array
          a = tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]])
          b = tensor([[True, False, True],
                      [False, True, False],
                      [True, False, True]])
          self.assertEqual(a[b], tensor([1, 3, 5, 7, 9]))
          self.assertEqual(a[b[1]], tensor([[4, 5, 6]]))
          self.assertEqual(a[b[0]], a[b[2]])
  
          # boolean assignment
          a[b] = 0
          self.assertEqual(a, tensor([[0, 2, 0],
                                      [4, 0, 6],
                                      [0, 8, 0]]))
  
      def test_boolean_indexing_weirdness(self):
          # Weird boolean indexing things
          a = torch.ones((2, 3, 4))
          self.assertEqual((0, 2, 3, 4), a[False, True, ...].shape)
          self.assertEqual(torch.ones(1, 2), a[True, [0, 1], True, True, [1], [[2]]])
          self.assertRaises(IndexError, lambda: a[False, [0, 1], ...])
  
      def test_boolean_indexing_weirdness_tensors(self):
          # Weird boolean indexing things
          false = torch.tensor(False)
          true = torch.tensor(True)
          a = torch.ones((2, 3, 4))
          self.assertEqual((0, 2, 3, 4), a[False, True, ...].shape)
          self.assertEqual(torch.ones(1, 2), a[true, [0, 1], true, true, [1], [[2]]])
          self.assertRaises(IndexError, lambda: a[false, [0, 1], ...])
  
      def test_boolean_indexing_alldims(self):
          true = torch.tensor(True)
          a = torch.ones((2, 3))
          self.assertEqual((1, 2, 3), a[True, True].shape)
          self.assertEqual((1, 2, 3), a[true, true].shape)
  
      def test_boolean_list_indexing(self):
          # Indexing a 2-dimensional array with
          # boolean lists
          a = tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]])
          b = [True, False, False]
          c = [True, True, False]
          self.assertEqual(a[b], tensor([[1, 2, 3]]))
          self.assertEqual(a[b, b], tensor([1]))
          self.assertEqual(a[c], tensor([[1, 2, 3], [4, 5, 6]]))
          self.assertEqual(a[c, c], tensor([1, 5]))
  
      def test_everything_returns_views(self):
          # Before `...` would return a itself.
          a = tensor([5])
  
          self.assertIsNot(a, a[()])
          self.assertIsNot(a, a[...])
          self.assertIsNot(a, a[:])
  
      def test_broaderrors_indexing(self):
          a = torch.zeros(5, 5)
          self.assertRaisesRegex(IndexError, 'shape mismatch', a.__getitem__, ([0, 1], [0, 1, 2]))
          self.assertRaisesRegex(IndexError, 'shape mismatch', a.__setitem__, ([0, 1], [0, 1, 2]), 0)
  
      def test_trivial_fancy_out_of_bounds(self):
          a = torch.zeros(5)
          ind = torch.ones(20, dtype=torch.int64)
          if a.is_cuda:
              raise unittest.SkipTest('CUDA asserts instead of raising an exception')
          ind[-1] = 10
          self.assertRaises(IndexError, a.__getitem__, ind)
          self.assertRaises(IndexError, a.__setitem__, ind, 0)
          ind = torch.ones(20, dtype=torch.int64)
          ind[0] = 11
          self.assertRaises(IndexError, a.__getitem__, ind)
          self.assertRaises(IndexError, a.__setitem__, ind, 0)
  
      def test_index_is_larger(self):
          # Simple case of fancy index broadcasting of the index.
          a = torch.zeros((5, 5))
          a[[[0], [1], [2]], [0, 1, 2]] = tensor([2., 3., 4.])
  
          self.assertTrue((a[:3, :3] == tensor([2., 3., 4.])).all())
  
      def test_broadcast_subspace(self):
          a = torch.zeros((100, 100))
          v = torch.arange(0., 100)[:, None]
          b = torch.arange(99, -1, -1).long()
          a[b] = v
          expected = b.double().unsqueeze(1).expand(100, 100)
          self.assertEqual(a, expected)
  
  
  if __name__ == '__main__':
      run_tests()
  