# ----------------------------------------------------------------------------------------------
# METRO (https://github.com/microsoft/MeshTransformer)
# Copyright (c) Microsoft Corporation. All Rights Reserved [see https://github.com/microsoft/MeshTransformer/blob/main/LICENSE for details]
# Licensed under the MIT license.
# ----------------------------------------------------------------------------------------------
"""
Useful geometric operations, e.g. Orthographic projection and a differentiable Rodrigues formula

Parts of the code are taken from https://github.com/MandyMo/pytorch_HMR
"""

import torch
import torch.nn.functional as F

def rodrigues(theta):
    """Convert axis-angle representation to rotation matrix.
    Args:
        theta: size = [B, 3]
    Returns:
        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
    """
    l1norm = torch.norm(theta + 1e-8, p = 2, dim = 1)
    angle = torch.unsqueeze(l1norm, -1)
    normalized = torch.div(theta, angle)
    angle = angle * 0.5
    v_cos = torch.cos(angle)
    v_sin = torch.sin(angle)
    quat = torch.cat([v_cos, v_sin * normalized], dim = 1)
    return quat2mat(quat)

def quat2mat(quat):
    """Convert quaternion coefficients to rotation matrix.
    Args:
        quat: size = [B, 4] 4 <===>(w, x, y, z)
    Returns:
        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
    """ 
    norm_quat = quat
    norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)
    w, x, y, z = norm_quat[:,0], norm_quat[:,1], norm_quat[:,2], norm_quat[:,3]

    B = quat.size(0)

    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
    wx, wy, wz = w*x, w*y, w*z
    xy, xz, yz = x*y, x*z, y*z

    rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,
                          2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,
                          2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).view(B, 3, 3)
    return rotMat    
    
def orthographic_projection(X, camera):
    """Perform orthographic projection of 3D points X using the camera parameters
    Args:
        X: size = [B, N, 3]
        camera: size = [B, 3]
    Returns:
        Projected 2D points -- size = [B, N, 2]
    """ 
    camera = camera.view(-1, 1, 3)
    X_trans = X[:, :, :2] + camera[:, :, 1:]
    shape = X_trans.shape
    X_2d = (camera[:, :, 0] * X_trans.view(shape[0], -1)).view(shape)
    return X_2d

def orthographic_projection_reshape(X, camera):
    """Perform orthographic projection of 3D points X using the camera parameters
    Args:
        X: size = [B, N, 3]
        camera: size = [B, 3]
    Returns:
        Projected 2D points -- size = [B, N, 2]
    """ 
    camera = camera.reshape(-1, 1, 3)
    X_trans = X[:, :, :2] + camera[:, :, 1:]
    shape = X_trans.shape
    X_2d = (camera[:, :, 0] * X_trans.reshape(shape[0], -1)).reshape(shape)
    return X_2d

def orthographic_projection_reshape(X, camera):
    """Perform orthographic projection of 3D points X using the camera parameters
    Args:
        X: size = [B, N, 3]
        camera: size = [B, 3]
    Returns:
        Projected 2D points -- size = [B, N, 2]
    """ 
    camera = camera.reshape(-1, 1, 3)
    X_trans = X[:, :, :2] + camera[:, :, 1:]
    shape = X_trans.shape
    X_2d = (camera[:, :, 0] * X_trans.reshape(shape[0], -1)).reshape(shape)
    return X_2d


def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
    """
    Return a tensor where each element has the absolute value taken from the,
    corresponding element of a, with sign taken from the corresponding
    element of b. This is like the standard copysign floating-point operation,
    but is not careful about negative 0 and NaN.

    Args:
        a: source tensor.
        b: tensor whose signs will be used, of the same shape as a.

    Returns:
        Tensor of the same shape as a with the signs of b.
    """
    signs_differ = (a < 0) != (b < 0)
    return torch.where(signs_differ, -a, a)


def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
    """
    Returns torch.sqrt(torch.max(0, x))
    but with a zero subgradient where x is 0.
    """
    ret = torch.zeros_like(x)
    positive_mask = x > 0
    ret[positive_mask] = torch.sqrt(x[positive_mask])
    return ret

def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
    """
    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
    using Gram--Schmidt orthogonalization per Section B of [1].
    Args:
        d6: 6D rotation representation, of size (*, 6)

    Returns:
        batch of rotation matrices of size (*, 3, 3)

    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """

    a1, a2 = d6[..., :3], d6[..., 3:]
    b1 = F.normalize(a1, dim=-1)
    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
    b2 = F.normalize(b2, dim=-1)
    b3 = torch.cross(b1, b2, dim=-1)
    return torch.stack((b1, b2, b3), dim=-2)


def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
    """
    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
    by dropping the last row. Note that 6D representation is not unique.
    Args:
        matrix: batch of rotation matrices of size (*, 3, 3)

    Returns:
        6D rotation representation, of size (*, 6)

    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """
    batch_dim = matrix.size()[:-2]
    return matrix[..., :2, :].clone().reshape(batch_dim + (6,))

def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as axis/angle to quaternions.

    Args:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.

    Returns:
        quaternions with real part first, as tensor of shape (..., 4).
    """
    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
    half_angles = angles * 0.5
    eps = 1e-6
    small_angles = angles.abs() < eps
    sin_half_angles_over_angles = torch.empty_like(angles)
    sin_half_angles_over_angles[~small_angles] = (
        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
    )
    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
    # so sin(x/2)/x is about 1/2 - (x*x)/48
    sin_half_angles_over_angles[small_angles] = (
        0.5 - (angles[small_angles] * angles[small_angles]) / 48
    )
    quaternions = torch.cat(
        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
    )
    return quaternions


def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as quaternions to axis/angle.

    Args:
        quaternions: quaternions with real part first,
            as tensor of shape (..., 4).

    Returns:
        Rotations given as a vector in axis angle form, as a tensor
            of shape (..., 3), where the magnitude is the angle
            turned anticlockwise in radians around the vector's
            direction.
    """
    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
    half_angles = torch.atan2(norms, quaternions[..., :1])
    angles = 2 * half_angles
    eps = 1e-6
    small_angles = angles.abs() < eps
    sin_half_angles_over_angles = torch.empty_like(angles)
    sin_half_angles_over_angles[~small_angles] = (
        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
    )
    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
    # so sin(x/2)/x is about 1/2 - (x*x)/48
    sin_half_angles_over_angles[small_angles] = (
        0.5 - (angles[small_angles] * angles[small_angles]) / 48
    )
    return quaternions[..., 1:] / sin_half_angles_over_angles

def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as quaternions to rotation matrices.

    Args:
        quaternions: quaternions with real part first,
            as tensor of shape (..., 4).

    Returns:
        Rotation matrices as tensor of shape (..., 3, 3).
    """
    r, i, j, k = torch.unbind(quaternions, -1)
    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
    two_s = 2.0 / (quaternions * quaternions).sum(-1)

    o = torch.stack(
        (
            1 - two_s * (j * j + k * k),
            two_s * (i * j - k * r),
            two_s * (i * k + j * r),
            two_s * (i * j + k * r),
            1 - two_s * (i * i + k * k),
            two_s * (j * k - i * r),
            two_s * (i * k - j * r),
            two_s * (j * k + i * r),
            1 - two_s * (i * i + j * j),
        ),
        -1,
    )
    return o.reshape(quaternions.shape[:-1] + (3, 3))

def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as rotation matrices to quaternions.

    Args:
        matrix: Rotation matrices as tensor of shape (..., 3, 3).

    Returns:
        quaternions with real part first, as tensor of shape (..., 4).
    """
    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")

    batch_dim = matrix.shape[:-2]
    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
        matrix.reshape(batch_dim + (9,)), dim=-1
    )

    q_abs = _sqrt_positive_part(
        torch.stack(
            [
                1.0 + m00 + m11 + m22,
                1.0 + m00 - m11 - m22,
                1.0 - m00 + m11 - m22,
                1.0 - m00 - m11 + m22,
            ],
            dim=-1,
        )
    )

    # we produce the desired quaternion multiplied by each of r, i, j, k
    quat_by_rijk = torch.stack(
        [
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
        ],
        dim=-2,
    )

    # We floor here at 0.1 but the exact level is not important; if q_abs is small,
    # the candidate won't be picked.
    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))

    # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
    # forall i; we pick the best-conditioned one (with the largest denominator)

    return quat_candidates[
        F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
    ].reshape(batch_dim + (4,))

def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as axis/angle to rotation matrices.

    Args:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.

    Returns:
        Rotation matrices as tensor of shape (..., 3, 3).
    """
    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))


def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as rotation matrices to axis/angle.

    Args:
        matrix: Rotation matrices as tensor of shape (..., 3, 3).

    Returns:
        Rotations given as a vector in axis angle form, as a tensor
            of shape (..., 3), where the magnitude is the angle
            turned anticlockwise in radians around the vector's
            direction.
    """
    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))

def axis_angle_to_rotation_6d(axis_angle: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as axis/angle to rotation matrices.

    Args:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.

    Returns:
        6D rotation representation, of size (*, 6)
    """
    return matrix_to_rotation_6d(axis_angle_to_matrix(axis_angle))

def rotation_6d_to_axis_angle(d6):
    """
    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
    using Gram--Schmidt orthogonalization per Section B of [1].
    Args:
        d6: 6D rotation representation, of size (*, 6)

    Returns:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.


    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """
    
    return matrix_to_axis_angle(rotation_6d_to_matrix(d6))


def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
    """
    Return a tensor where each element has the absolute value taken from the,
    corresponding element of a, with sign taken from the corresponding
    element of b. This is like the standard copysign floating-point operation,
    but is not careful about negative 0 and NaN.

    Args:
        a: source tensor.
        b: tensor whose signs will be used, of the same shape as a.

    Returns:
        Tensor of the same shape as a with the signs of b.
    """
    signs_differ = (a < 0) != (b < 0)
    return torch.where(signs_differ, -a, a)


def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
    """
    Returns torch.sqrt(torch.max(0, x))
    but with a zero subgradient where x is 0.
    """
    ret = torch.zeros_like(x)
    positive_mask = x > 0
    ret[positive_mask] = torch.sqrt(x[positive_mask])
    return ret

def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
    """
    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
    using Gram--Schmidt orthogonalization per Section B of [1].
    Args:
        d6: 6D rotation representation, of size (*, 6)

    Returns:
        batch of rotation matrices of size (*, 3, 3)

    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """

    a1, a2 = d6[..., :3], d6[..., 3:]
    b1 = F.normalize(a1, dim=-1)
    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
    b2 = F.normalize(b2, dim=-1)
    b3 = torch.cross(b1, b2, dim=-1)
    return torch.stack((b1, b2, b3), dim=-2)


def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
    """
    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
    by dropping the last row. Note that 6D representation is not unique.
    Args:
        matrix: batch of rotation matrices of size (*, 3, 3)

    Returns:
        6D rotation representation, of size (*, 6)

    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """
    batch_dim = matrix.size()[:-2]
    return matrix[..., :2, :].clone().reshape(batch_dim + (6,))

def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as axis/angle to quaternions.

    Args:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.

    Returns:
        quaternions with real part first, as tensor of shape (..., 4).
    """
    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
    half_angles = angles * 0.5
    eps = 1e-6
    small_angles = angles.abs() < eps
    sin_half_angles_over_angles = torch.empty_like(angles)
    sin_half_angles_over_angles[~small_angles] = (
        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
    )
    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
    # so sin(x/2)/x is about 1/2 - (x*x)/48
    sin_half_angles_over_angles[small_angles] = (
        0.5 - (angles[small_angles] * angles[small_angles]) / 48
    )
    quaternions = torch.cat(
        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
    )
    return quaternions


def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as quaternions to axis/angle.

    Args:
        quaternions: quaternions with real part first,
            as tensor of shape (..., 4).

    Returns:
        Rotations given as a vector in axis angle form, as a tensor
            of shape (..., 3), where the magnitude is the angle
            turned anticlockwise in radians around the vector's
            direction.
    """
    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
    half_angles = torch.atan2(norms, quaternions[..., :1])
    angles = 2 * half_angles
    eps = 1e-6
    small_angles = angles.abs() < eps
    sin_half_angles_over_angles = torch.empty_like(angles)
    sin_half_angles_over_angles[~small_angles] = (
        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
    )
    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
    # so sin(x/2)/x is about 1/2 - (x*x)/48
    sin_half_angles_over_angles[small_angles] = (
        0.5 - (angles[small_angles] * angles[small_angles]) / 48
    )
    return quaternions[..., 1:] / sin_half_angles_over_angles

def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as quaternions to rotation matrices.

    Args:
        quaternions: quaternions with real part first,
            as tensor of shape (..., 4).

    Returns:
        Rotation matrices as tensor of shape (..., 3, 3).
    """
    r, i, j, k = torch.unbind(quaternions, -1)
    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
    two_s = 2.0 / (quaternions * quaternions).sum(-1)

    o = torch.stack(
        (
            1 - two_s * (j * j + k * k),
            two_s * (i * j - k * r),
            two_s * (i * k + j * r),
            two_s * (i * j + k * r),
            1 - two_s * (i * i + k * k),
            two_s * (j * k - i * r),
            two_s * (i * k - j * r),
            two_s * (j * k + i * r),
            1 - two_s * (i * i + j * j),
        ),
        -1,
    )
    return o.reshape(quaternions.shape[:-1] + (3, 3))

def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as rotation matrices to quaternions.

    Args:
        matrix: Rotation matrices as tensor of shape (..., 3, 3).

    Returns:
        quaternions with real part first, as tensor of shape (..., 4).
    """
    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")

    batch_dim = matrix.shape[:-2]
    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
        matrix.reshape(batch_dim + (9,)), dim=-1
    )

    q_abs = _sqrt_positive_part(
        torch.stack(
            [
                1.0 + m00 + m11 + m22,
                1.0 + m00 - m11 - m22,
                1.0 - m00 + m11 - m22,
                1.0 - m00 - m11 + m22,
            ],
            dim=-1,
        )
    )

    # we produce the desired quaternion multiplied by each of r, i, j, k
    quat_by_rijk = torch.stack(
        [
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
        ],
        dim=-2,
    )

    # We floor here at 0.1 but the exact level is not important; if q_abs is small,
    # the candidate won't be picked.
    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))

    # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
    # forall i; we pick the best-conditioned one (with the largest denominator)

    return quat_candidates[
        F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
    ].reshape(batch_dim + (4,))

def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as axis/angle to rotation matrices.

    Args:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.

    Returns:
        Rotation matrices as tensor of shape (..., 3, 3).
    """
    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))


def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as rotation matrices to axis/angle.

    Args:
        matrix: Rotation matrices as tensor of shape (..., 3, 3).

    Returns:
        Rotations given as a vector in axis angle form, as a tensor
            of shape (..., 3), where the magnitude is the angle
            turned anticlockwise in radians around the vector's
            direction.
    """
    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))

def axis_angle_to_rotation_6d(axis_angle: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as axis/angle to rotation matrices.

    Args:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.

    Returns:
        6D rotation representation, of size (*, 6)
    """
    return matrix_to_rotation_6d(axis_angle_to_matrix(axis_angle))

def rotation_6d_to_axis_angle(d6):
    """
    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
    using Gram--Schmidt orthogonalization per Section B of [1].
    Args:
        d6: 6D rotation representation, of size (*, 6)

    Returns:
        axis_angle: Rotations given as a vector in axis angle form,
            as a tensor of shape (..., 3), where the magnitude is
            the angle turned anticlockwise in radians around the
            vector's direction.


    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """
    
    return matrix_to_axis_angle(rotation_6d_to_matrix(d6))