# This code is based on https://github.com/Mathux/ACTOR.git
import numpy as np
import torch

import contextlib

from smplx import SMPLLayer as _SMPLLayer
from smplx.lbs import vertices2joints

from smplx import SMPLXLayer as _SMPLXLayer

# action2motion_joints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21, 24, 38]
# change 0 and 8
action2motion_joints = [8, 1, 2, 3, 4, 5, 6, 7, 0, 9, 10, 11, 12, 13, 14, 21, 24, 38]

from models.regennet2.utils.config import SMPL_MODEL_PATH, SMPLX_MODEL_PATH, JOINT_REGRESSOR_TRAIN_EXTRA

JOINTSTYPE_ROOT = {"a2m": 0, # action2motion
                   "smpl": 0,
                   "smplx": 0,
                   "a2mpl": 0, # set(smpl, a2m)
                   "vibe": 8}  # 0 is the 8 position: OP MidHip below

JOINT_MAP = {
    'OP Nose': 24, 'OP Neck': 12, 'OP RShoulder': 17,
    'OP RElbow': 19, 'OP RWrist': 21, 'OP LShoulder': 16,
    'OP LElbow': 18, 'OP LWrist': 20, 'OP MidHip': 0,
    'OP RHip': 2, 'OP RKnee': 5, 'OP RAnkle': 8,
    'OP LHip': 1, 'OP LKnee': 4, 'OP LAnkle': 7,
    'OP REye': 25, 'OP LEye': 26, 'OP REar': 27,
    'OP LEar': 28, 'OP LBigToe': 29, 'OP LSmallToe': 30,
    'OP LHeel': 31, 'OP RBigToe': 32, 'OP RSmallToe': 33, 'OP RHeel': 34,
    'Right Ankle': 8, 'Right Knee': 5, 'Right Hip': 45,
    'Left Hip': 46, 'Left Knee': 4, 'Left Ankle': 7,
    'Right Wrist': 21, 'Right Elbow': 19, 'Right Shoulder': 17,
    'Left Shoulder': 16, 'Left Elbow': 18, 'Left Wrist': 20,
    'Neck (LSP)': 47, 'Top of Head (LSP)': 48,
    'Pelvis (MPII)': 49, 'Thorax (MPII)': 50,
    'Spine (H36M)': 51, 'Jaw (H36M)': 52,
    'Head (H36M)': 53, 'Nose': 24, 'Left Eye': 26,
    'Right Eye': 25, 'Left Ear': 28, 'Right Ear': 27
}

JOINT_NAMES = [
    'OP Nose', 'OP Neck', 'OP RShoulder',
    'OP RElbow', 'OP RWrist', 'OP LShoulder',
    'OP LElbow', 'OP LWrist', 'OP MidHip',
    'OP RHip', 'OP RKnee', 'OP RAnkle',
    'OP LHip', 'OP LKnee', 'OP LAnkle',
    'OP REye', 'OP LEye', 'OP REar',
    'OP LEar', 'OP LBigToe', 'OP LSmallToe',
    'OP LHeel', 'OP RBigToe', 'OP RSmallToe', 'OP RHeel',
    'Right Ankle', 'Right Knee', 'Right Hip',
    'Left Hip', 'Left Knee', 'Left Ankle',
    'Right Wrist', 'Right Elbow', 'Right Shoulder',
    'Left Shoulder', 'Left Elbow', 'Left Wrist',
    'Neck (LSP)', 'Top of Head (LSP)',
    'Pelvis (MPII)', 'Thorax (MPII)',
    'Spine (H36M)', 'Jaw (H36M)',
    'Head (H36M)', 'Nose', 'Left Eye',
    'Right Eye', 'Left Ear', 'Right Ear'
]


# adapted from VIBE/SPIN to output smpl_joints, vibe joints and action2motion joints
class SMPL(_SMPLLayer):
    """ Extension of the official SMPL implementation to support more joints """

    def __init__(self, model_path=SMPL_MODEL_PATH, **kwargs):
        kwargs["model_path"] = model_path

        # remove the verbosity for the 10-shapes beta parameters
        with contextlib.redirect_stdout(None):
            super(SMPL, self).__init__(**kwargs)
            
        J_regressor_extra = np.load(JOINT_REGRESSOR_TRAIN_EXTRA)
        self.register_buffer('J_regressor_extra', torch.tensor(J_regressor_extra, dtype=torch.float32))
        vibe_indexes = np.array([JOINT_MAP[i] for i in JOINT_NAMES])
        a2m_indexes = vibe_indexes[action2motion_joints]
        smpl_indexes = np.arange(24)
        a2mpl_indexes = np.unique(np.r_[smpl_indexes, a2m_indexes])

        self.maps = {"vibe": vibe_indexes,
                     "a2m": a2m_indexes,
                     "smpl": smpl_indexes,
                     "a2mpl": a2mpl_indexes}
        
    def forward(self, *args, **kwargs):
        smpl_output = super(SMPL, self).forward(*args, **kwargs)
        
        extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
        all_joints = torch.cat([smpl_output.joints, extra_joints], dim=1) # joints: 45
        output = {"vertices": smpl_output.vertices}

        for joinstype, indexes in self.maps.items():
            output[joinstype] = all_joints[:, indexes]
            
        return output

class SMPLX(_SMPLXLayer):
    def __init__(self, model_path=SMPLX_MODEL_PATH, **kwargs):
        kwargs['model_path'] = model_path

        # remove the verbosity for the 10-shapes beta parameters
        with contextlib.redirect_stdout(None):
            super(SMPLX, self).__init__(**kwargs)
        
        smplx_indexes = np.arange(55)
        self.maps = {"smplx": smplx_indexes}
        
    def forward(self, *args, **kwargs):
        smplx_output = super(SMPLX, self).forward(*args, **kwargs)
        all_joints = smplx_output.joints # joints: 127
        output = {"vertices": smplx_output.vertices}
        for jointstype, indexes in self.maps.items():
            output[jointstype] = all_joints[:, indexes]
        return output
