# coding: utf-8

"""
Wrapper for LivePortrait core functions
"""

import os.path as osp
from contextlib import nullcontext
import numpy as np
import cv2
import torch
import yaml

from .utils.timer import Timer
from .utils.helper import load_model, concat_feat, resolve_torch_device
from .utils.camera import headpose_pred_to_degree, get_rotation_matrix
from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio
from .config.inference_config import InferenceConfig
from .utils.rprint import rlog as log


class LivePortraitWrapper(object):

    def __init__(self, cfg: InferenceConfig):

        model_config = yaml.load(open(cfg.models_config, 'r'), Loader=yaml.SafeLoader)
        self.device = resolve_torch_device(cfg.device_id)
        self.use_half_precision = cfg.flag_use_half_precision and self.device.type == 'cuda'
        if cfg.flag_use_half_precision and not self.use_half_precision:
            log('CUDA is unavailable, disabling half precision and running on CPU.')

        # init F
        self.appearance_feature_extractor = load_model(cfg.checkpoint_F, model_config, self.device, 'appearance_feature_extractor')
        log(f'Load appearance_feature_extractor done.')
        # init M
        self.motion_extractor = load_model(cfg.checkpoint_M, model_config, self.device, 'motion_extractor')
        log(f'Load motion_extractor done.')
        # init W
        self.warping_module = load_model(cfg.checkpoint_W, model_config, self.device, 'warping_module')
        log(f'Load warping_module done.')
        # init G
        self.spade_generator = load_model(cfg.checkpoint_G, model_config, self.device, 'spade_generator')
        log(f'Load spade_generator done.')
        # init S and R
        if cfg.checkpoint_S is not None and osp.exists(cfg.checkpoint_S):
            self.stitching_retargeting_module = load_model(cfg.checkpoint_S, model_config, self.device, 'stitching_retargeting_module')
            log(f'Load stitching_retargeting_module done.')
        else:
            self.stitching_retargeting_module = None

        self.cfg = cfg
        self.device_id = cfg.device_id
        self.timer = Timer()

    def inference_context(self):
        if self.use_half_precision:
            return torch.autocast(device_type=self.device.type, dtype=torch.float16)
        return nullcontext()

    def update_config(self, user_args):
        for k, v in user_args.items():
            if hasattr(self.cfg, k):
                setattr(self.cfg, k, v)

    def prepare_source(self, img: np.ndarray) -> torch.Tensor:
        """ construct the input as standard
        img: HxWx3, uint8, 256x256
        """
        h, w = img.shape[:2]
        if h != self.cfg.input_shape[0] or w != self.cfg.input_shape[1]:
            x = cv2.resize(img, (self.cfg.input_shape[0], self.cfg.input_shape[1]))
        else:
            x = img.copy()

        if x.ndim == 3:
            x = x[np.newaxis].astype(np.float32) / 255.  # HxWx3 -> 1xHxWx3, normalized to 0~1
        elif x.ndim == 4:
            x = x.astype(np.float32) / 255.  # BxHxWx3, normalized to 0~1
        else:
            raise ValueError(f'img ndim should be 3 or 4: {x.ndim}')
        x = np.clip(x, 0, 1)  # clip to 0~1
        x = torch.from_numpy(x).permute(0, 3, 1, 2)  # 1xHxWx3 -> 1x3xHxW
        x = x.to(self.device)
        return x

    def prepare_driving_videos(self, imgs) -> torch.Tensor:
        """ construct the input as standard
        imgs: NxBxHxWx3, uint8
        """
        if isinstance(imgs, list):
            _imgs = np.array(imgs)[..., np.newaxis]  # TxHxWx3x1
        elif isinstance(imgs, np.ndarray):
            _imgs = imgs
        else:
            raise ValueError(f'imgs type error: {type(imgs)}')

        y = _imgs.astype(np.float32) / 255.
        y = np.clip(y, 0, 1)  # clip to 0~1
        y = torch.from_numpy(y).permute(0, 4, 3, 1, 2)  # TxHxWx3x1 -> Tx1x3xHxW
        y = y.to(self.device)

        return y

    def extract_feature_3d(self, x: torch.Tensor) -> torch.Tensor:
        """ get the appearance feature of the image by F
        x: Bx3xHxW, normalized to 0~1
        """
        with torch.no_grad():
            with self.inference_context():
                feature_3d = self.appearance_feature_extractor(x)

        return feature_3d.float()

    def get_kp_info(self, x: torch.Tensor, **kwargs) -> dict:
        """ get the implicit keypoint information
        x: Bx3xHxW, normalized to 0~1
        flag_refine_info: whether to trandform the pose to degrees and the dimention of the reshape
        return: A dict contains keys: 'pitch', 'yaw', 'roll', 't', 'exp', 'scale', 'kp'
        """
        with torch.no_grad():
            with self.inference_context():
                kp_info = self.motion_extractor(x)

            if self.use_half_precision:
                # float the dict
                for k, v in kp_info.items():
                    if isinstance(v, torch.Tensor):
                        kp_info[k] = v.float()

        flag_refine_info: bool = kwargs.get('flag_refine_info', True)
        if flag_refine_info:
            bs = kp_info['kp'].shape[0]
            kp_info['pitch'] = headpose_pred_to_degree(kp_info['pitch'])[:, None]  # Bx1
            kp_info['yaw'] = headpose_pred_to_degree(kp_info['yaw'])[:, None]  # Bx1
            kp_info['roll'] = headpose_pred_to_degree(kp_info['roll'])[:, None]  # Bx1
            kp_info['kp'] = kp_info['kp'].reshape(bs, -1, 3)  # BxNx3
            kp_info['exp'] = kp_info['exp'].reshape(bs, -1, 3)  # BxNx3

        return kp_info

    def get_pose_dct(self, kp_info: dict) -> dict:
        pose_dct = dict(
            pitch=headpose_pred_to_degree(kp_info['pitch']).item(),
            yaw=headpose_pred_to_degree(kp_info['yaw']).item(),
            roll=headpose_pred_to_degree(kp_info['roll']).item(),
        )
        return pose_dct

    def get_fs_and_kp_info(self, source_prepared, driving_first_frame):

        # get the canonical keypoints of source image by M
        source_kp_info = self.get_kp_info(source_prepared, flag_refine_info=True)
        source_rotation = get_rotation_matrix(source_kp_info['pitch'], source_kp_info['yaw'], source_kp_info['roll'])

        # get the canonical keypoints of first driving frame by M
        driving_first_frame_kp_info = self.get_kp_info(driving_first_frame, flag_refine_info=True)
        driving_first_frame_rotation = get_rotation_matrix(
            driving_first_frame_kp_info['pitch'],
            driving_first_frame_kp_info['yaw'],
            driving_first_frame_kp_info['roll']
        )

        # get feature volume by F
        source_feature_3d = self.extract_feature_3d(source_prepared)

        return source_kp_info, source_rotation, source_feature_3d, driving_first_frame_kp_info, driving_first_frame_rotation

    def transform_keypoint(self, kp_info: dict):
        """
        transform the implicit keypoints with the pose, shift, and expression deformation
        kp: BxNx3
        """
        kp = kp_info['kp']    # (bs, k, 3)
        pitch, yaw, roll = kp_info['pitch'], kp_info['yaw'], kp_info['roll']

        t, exp = kp_info['t'], kp_info['exp']
        scale = kp_info['scale']

        pitch = headpose_pred_to_degree(pitch)
        yaw = headpose_pred_to_degree(yaw)
        roll = headpose_pred_to_degree(roll)

        bs = kp.shape[0]
        if kp.ndim == 2:
            num_kp = kp.shape[1] // 3  # Bx(num_kpx3)
        else:
            num_kp = kp.shape[1]  # Bxnum_kpx3

        rot_mat = get_rotation_matrix(pitch, yaw, roll)    # (bs, 3, 3)

        # Eqn.2: s * (R * x_c,s + exp) + t
        kp_transformed = kp.view(bs, num_kp, 3) @ rot_mat + exp.view(bs, num_kp, 3)
        kp_transformed *= scale[..., None]  # (bs, k, 3) * (bs, 1, 1) = (bs, k, 3)
        kp_transformed[:, :, 0:2] += t[:, None, 0:2]  # remove z, only apply tx ty

        return kp_transformed

    def retarget_eye(self, kp_source: torch.Tensor, eye_close_ratio: torch.Tensor) -> torch.Tensor:
        """
        kp_source: BxNx3
        eye_close_ratio: Bx3
        Return: Bx(3*num_kp+2)
        """
        feat_eye = concat_feat(kp_source, eye_close_ratio)

        with torch.no_grad():
            delta = self.stitching_retargeting_module['eye'](feat_eye)

        return delta

    def retarget_lip(self, kp_source: torch.Tensor, lip_close_ratio: torch.Tensor) -> torch.Tensor:
        """
        kp_source: BxNx3
        lip_close_ratio: Bx2
        """
        feat_lip = concat_feat(kp_source, lip_close_ratio)

        with torch.no_grad():
            delta = self.stitching_retargeting_module['lip'](feat_lip)

        return delta

    def stitch(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
        """
        kp_source: BxNx3
        kp_driving: BxNx3
        Return: Bx(3*num_kp+2)
        """
        feat_stiching = concat_feat(kp_source, kp_driving)

        with torch.no_grad():
            delta = self.stitching_retargeting_module['stitching'](feat_stiching)

        return delta

    def stitching(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
        """ conduct the stitching
        kp_source: Bxnum_kpx3
        kp_driving: Bxnum_kpx3
        """

        if self.stitching_retargeting_module is not None:

            bs, num_kp = kp_source.shape[:2]

            kp_driving_new = kp_driving.clone()
            delta = self.stitch(kp_source, kp_driving_new)

            delta_exp = delta[..., :3*num_kp].reshape(bs, num_kp, 3)  # 1x20x3
            delta_tx_ty = delta[..., 3*num_kp:3*num_kp+2].reshape(bs, 1, 2)  # 1x1x2

            kp_driving_new += delta_exp
            kp_driving_new[..., :2] += delta_tx_ty

            return kp_driving_new

        return kp_driving

    def warp_decode(self, feature_3d: torch.Tensor, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
        """ get the image after the warping of the implicit keypoints
        feature_3d: Bx32x16x64x64, feature volume
        kp_source: BxNx3
        kp_driving: BxNx3
        """
        # The line 18 in Algorithm 1: D(W(f_s; x_s, x′_d,i)）
        with torch.no_grad():
            with self.inference_context():
                # get decoder input
                ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
                # decode
                ret_dct['out'] = self.spade_generator(feature=ret_dct['out'])

            # float the dict
            if self.use_half_precision:
                for k, v in ret_dct.items():
                    if isinstance(v, torch.Tensor):
                        ret_dct[k] = v.float()

        return ret_dct

    def parse_output(self, out: torch.Tensor) -> np.ndarray:
        """ construct the output as standard
        return: 1xHxWx3, uint8
        """
        out = np.transpose(out.data.cpu().numpy(), [0, 2, 3, 1])  # 1x3xHxW -> 1xHxWx3
        out = np.clip(out, 0, 1)  # clip to 0~1
        out = np.clip(out * 255, 0, 255).astype(np.uint8)  # 0~1 -> 0~255

        return out

    def calc_retargeting_ratio(self, source_lmk, driving_lmk_lst):
        input_eye_ratio_lst = []
        input_lip_ratio_lst = []
        for lmk in driving_lmk_lst:
            # for eyes retargeting
            input_eye_ratio_lst.append(calc_eye_close_ratio(lmk[None]))
            # for lip retargeting
            input_lip_ratio_lst.append(calc_lip_close_ratio(lmk[None]))
        return input_eye_ratio_lst, input_lip_ratio_lst

    def calc_combined_eye_ratio(self, input_eye_ratio, source_lmk):
        eye_close_ratio = calc_eye_close_ratio(source_lmk[None])
        eye_close_ratio_tensor = torch.from_numpy(eye_close_ratio).float().to(self.device)
        input_eye_ratio_tensor = torch.tensor([input_eye_ratio[0][0]], dtype=torch.float32, device=self.device).reshape(1, 1)
        # [c_s,eyes, c_d,eyes,i]
        combined_eye_ratio_tensor = torch.cat([eye_close_ratio_tensor, input_eye_ratio_tensor], dim=1)
        return combined_eye_ratio_tensor

    def calc_combined_lip_ratio(self, input_lip_ratio, source_lmk):
        lip_close_ratio = calc_lip_close_ratio(source_lmk[None])
        lip_close_ratio_tensor = torch.from_numpy(lip_close_ratio).float().to(self.device)
        # [c_s,lip, c_d,lip,i]
        input_lip_ratio_tensor = torch.tensor([input_lip_ratio[0]], dtype=torch.float32, device=self.device)
        if input_lip_ratio_tensor.shape != torch.Size([1, 1]):
            input_lip_ratio_tensor = input_lip_ratio_tensor.reshape(1, 1)
        combined_lip_ratio_tensor = torch.cat([lip_close_ratio_tensor, input_lip_ratio_tensor], dim=1)
        return combined_lip_ratio_tensor