"""
Copyright (c) Meta Platforms, Inc. and affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""

import copy
import glob
import os
import re
import subprocess
from collections import OrderedDict
from typing import Dict, List

import mediapy

import numpy as np

import torch
import torch as th
import torchaudio
from attrdict import AttrDict

from omegaconf import OmegaConf
from tqdm import tqdm
from utils.model_util import get_person_num
from visualize.ca_body.utils.image import linear2displayBatch
from visualize.ca_body.utils.train import load_checkpoint, load_from_config

ffmpeg_header = "ffmpeg -y "  # -hide_banner -loglevel error "


def filter_params(params, ignore_names):
    return OrderedDict(
        [
            (k, v)
            for k, v in params.items()
            if not any([re.match(n, k) is not None for n in ignore_names])
        ]
    )


def call_ffmpeg(command: str) -> None:
    print(command, "-" * 100)
    e = subprocess.call(command, shell=True)
    if e != 0:
        assert False, e


class BodyRenderer(th.nn.Module):
    def __init__(
        self,
        config_base: str,
        render_rgb: bool,
    ):
        super().__init__()
        self.config_base = config_base
        ckpt_path = f"{config_base}/body_dec.ckpt"
        config_path = f"{config_base}/config.yml"
        assets_path = f"{config_base}/static_assets.pt"
        # config
        config = OmegaConf.load(config_path)
        gpu = config.get("gpu", 0)
        self.device = th.device(f"cuda:{gpu}")
        # assets
        static_assets = AttrDict(torch.load(assets_path))
        # build model
        self.model = load_from_config(config.model, assets=static_assets).to(
            self.device
        )
        self.model.cal_enabled = False
        self.model.pixel_cal_enabled = False
        self.model.learn_blur_enabled = False
        self.render_rgb = render_rgb
        if not self.render_rgb:
            self.model.rendering_enabled = None
        # load model checkpoints
        print("loading...", ckpt_path)
        load_checkpoint(
            ckpt_path,
            modules={"model": self.model},
            ignore_names={"model": ["lbs_fn.*"]},
        )
        self.model.eval()
        self.model.to(self.device)
        # load default parameters for renderer
        person = get_person_num(config_path)
        self.default_inputs = th.load(f"assets/render_defaults_{person}.pth")

    def _write_video_stream(
        self, motion: np.ndarray, face: np.ndarray, save_name: str
    ) -> None:
        out = self._render_loop(motion, face)
        mediapy.write_video(save_name, out, fps=30)

    def _render_loop(self, body_pose: np.ndarray, face: np.ndarray) -> List[np.ndarray]:
        all_rgb = []
        default_inputs_copy = copy.deepcopy(self.default_inputs)
        for b in tqdm(range(len(body_pose))):
            B = default_inputs_copy["K"].shape[0]
            default_inputs_copy["lbs_motion"] = (
                th.tensor(body_pose[b : b + 1, :], device=self.device, dtype=th.float)
                .tile(B, 1)
                .to(self.device)
            )
            geom = (
                self.model.lbs_fn.lbs_fn(
                    default_inputs_copy["lbs_motion"],
                    self.model.lbs_fn.lbs_scale.unsqueeze(0).tile(B, 1),
                    self.model.lbs_fn.lbs_template_verts.unsqueeze(0).tile(B, 1, 1),
                )
                * self.model.lbs_fn.global_scaling
            )
            default_inputs_copy["geom"] = geom
            face_codes = (
                th.from_numpy(face).float().cuda() if not th.is_tensor(face) else face
            )
            curr_face = th.tile(face_codes[b : b + 1, ...], (2, 1))
            default_inputs_copy["face_embs"] = curr_face
            preds = self.model(**default_inputs_copy)
            rgb0 = linear2displayBatch(preds["rgb"])[0]
            rgb1 = linear2displayBatch(preds["rgb"])[1]
            rgb = th.cat((rgb0, rgb1), axis=-1).permute(1, 2, 0)
            rgb = rgb.clip(0, 255).to(th.uint8)
            all_rgb.append(rgb.contiguous().detach().byte().cpu().numpy())
        return all_rgb

    def render_full_video(
        self,
        data_block: Dict[str, np.ndarray],
        animation_save_path: str,
        audio_sr: int = None,
        render_gt: bool = False,
    ) -> None:
        tag = os.path.basename(os.path.dirname(animation_save_path))
        save_name = os.path.splitext(os.path.basename(animation_save_path))[0]
        save_name = f"{tag}_{save_name}"
        torchaudio.save(
            f"/tmp/audio_{save_name}.wav",
            torch.tensor(data_block["audio"]),
            audio_sr,
        )
        if render_gt:
            tag = "gt"
            self._write_video_stream(
                data_block["gt_body"],
                data_block["gt_face"],
                f"/tmp/{tag}_{save_name}.mp4",
            )
        else:
            tag = "pred"
            self._write_video_stream(
                data_block["body_motion"],
                data_block["face_motion"],
                f"/tmp/{tag}_{save_name}.mp4",
            )
        command = f"{ffmpeg_header} -i /tmp/{tag}_{save_name}.mp4 -i /tmp/audio_{save_name}.wav -c:v copy -map 0:v:0 -map 1:a:0 -c:a aac -b:a 192k -pix_fmt yuva420p {animation_save_path}_{tag}.mp4"
        call_ffmpeg(command)
        subprocess.call(
            f"rm /tmp/audio_{save_name}.wav && rm /tmp/{tag}_{save_name}.mp4",
            shell=True,
        )