text: # Copyright (c) 2018-2022, NVIDIA Corporation
  # All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions are met:
  #
  # 1. Redistributions of source code must retain the above copyright notice, this
  #    list of conditions and the following disclaimer.
  #
  # 2. 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.
  #
  # 3. Neither the name of the copyright holder nor the names of its
  #    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 HOLDER 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.
  
  
  from ..rotation3d import *
  import numpy as np
  import torch
  
  q = torch.from_numpy(np.array([[0, 1, 2, 3], [-2, 3, -1, 5]], dtype=np.float32))
  print("q", q)
  r = quat_normalize(q)
  x = torch.from_numpy(np.array([[1, 0, 0], [0, -1, 0]], dtype=np.float32))
  print(r)
  print(quat_rotate(r, x))
  
  angle = torch.from_numpy(np.array(np.random.rand() * 10.0, dtype=np.float32))
  axis = torch.from_numpy(
      np.array([1, np.random.rand() * 10.0, np.random.rand() * 10.0], dtype=np.float32),
  )
  
  print(repr(angle))
  print(repr(axis))
  
  rot = quat_from_angle_axis(angle, axis)
  x = torch.from_numpy(np.random.rand(5, 6, 3))
  y = quat_rotate(quat_inverse(rot), quat_rotate(rot, x))
  print(x.numpy())
  print(y.numpy())
  assert np.allclose(x.numpy(), y.numpy())
  
  m = torch.from_numpy(np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]], dtype=np.float32))
  r = quat_from_rotation_matrix(m)
  t = torch.from_numpy(np.array([0, 1, 0], dtype=np.float32))
  se3 = transform_from_rotation_translation(r=r, t=t)
  print(se3)
  print(transform_apply(se3, t))
  
  rot = quat_from_angle_axis(
      torch.from_numpy(np.array([45, -54], dtype=np.float32)),
      torch.from_numpy(np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)),
      degree=True,
  )
  trans = torch.from_numpy(np.array([[1, 1, 0], [1, 1, 0]], dtype=np.float32))
  transform = transform_from_rotation_translation(r=rot, t=trans)
  
  t = transform_mul(transform, transform_inverse(transform))
  gt = np.zeros((2, 7))
  gt[:, 0] = 1.0
  print(t.numpy())
  print(gt)
  # assert np.allclose(t.numpy(), gt)
  
  transform2 = torch.from_numpy(
      np.array(
          [[1, 0, 0, 1], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]], dtype=np.float32
      ),
  )
  transform2 = euclidean_to_transform(transform2)
  print(transform2)
  