# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257 
# This code is borrowed and modified from Actor,
# made publicly available under MIT license at https://github.com/Mathux/ACTOR

import torch


def qinv(q):
    assert q.shape[-1] == 4, 'q must be a tensor of shape (*, 4)'
    mask = torch.ones_like(q)
    mask[..., 1:] = -mask[..., 1:]
    return q * mask


def qrot(q, v):
    """
    Rotate vector(s) v about the rotation described by quaternion(s) q.
    Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
    where * denotes any number of dimensions.
    Returns a tensor of shape (*, 3).
    """
    assert q.shape[-1] == 4
    assert v.shape[-1] == 3
    assert q.shape[:-1] == v.shape[:-1]

    original_shape = list(v.shape)
    # print(q.shape)
    q = q.contiguous().view(-1, 4)
    v = v.contiguous().view(-1, 3)

    qvec = q[:, 1:]
    uv = torch.cross(qvec, v, dim=1)
    uuv = torch.cross(qvec, uv, dim=1)
    return (v + 2 * (q[:, :1] * uv + uuv)).view(original_shape)


def recover_root_rot_pos(data):
    rot_vel = data[..., 0]
    r_rot_ang = torch.zeros_like(rot_vel).to(data.device)
    '''Get Y-axis rotation from rotation velocity'''
    r_rot_ang[..., 1:] = rot_vel[..., :-1]
    r_rot_ang = torch.cumsum(r_rot_ang, dim=-1)

    r_rot_quat = torch.zeros(data.shape[:-1] + (4, )).to(data.device)
    r_rot_quat[..., 0] = torch.cos(r_rot_ang)
    r_rot_quat[..., 2] = torch.sin(r_rot_ang)

    r_pos = torch.zeros(data.shape[:-1] + (3, )).to(data.device)
    r_pos[..., 1:, [0, 2]] = data[..., :-1, 1:3]
    '''Add Y-axis rotation to root position'''
    r_pos = qrot(qinv(r_rot_quat), r_pos)

    r_pos = torch.cumsum(r_pos, dim=-2)

    r_pos[..., 1] = data[..., 3]
    return r_rot_quat, r_pos


def recover_from_ric(data, joints_num):
    r_rot_quat, r_pos = recover_root_rot_pos(data)
    positions = data[..., 4:(joints_num - 1) * 3 + 4]
    positions = positions.view(positions.shape[:-1] + (-1, 3))
    '''Add Y-axis rotation to local joints'''
    positions = qrot(
        qinv(r_rot_quat[..., None, :]).expand(positions.shape[:-1] + (4, )),
        positions)
    '''Add root XZ to joints'''
    positions[..., 0] += r_pos[..., 0:1]
    positions[..., 2] += r_pos[..., 2:3]
    '''Concate root and joints'''
    positions = torch.cat([r_pos.unsqueeze(-2), positions], dim=-2)

    return positions