import argparse
import cv2
import glob
import mimetypes
import numpy as np
import os
import shutil
import subprocess
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
from os import path as osp
from tqdm import tqdm

from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact

try:
    import ffmpeg
except ImportError:
    import pip

    pip.main(["install", "--user", "ffmpeg-python"])
    import ffmpeg


def get_video_meta_info(video_path):
    ret = {}
    probe = ffmpeg.probe(video_path)
    video_streams = [
        stream for stream in probe["streams"] if stream["codec_type"] == "video"
    ]
    has_audio = any(stream["codec_type"] == "audio" for stream in probe["streams"])
    ret["width"] = video_streams[0]["width"]
    ret["height"] = video_streams[0]["height"]
    ret["fps"] = eval(video_streams[0]["avg_frame_rate"])
    ret["audio"] = ffmpeg.input(video_path).audio if has_audio else None
    ret["nb_frames"] = int(video_streams[0]["nb_frames"])
    return ret


def get_sub_video(args, num_process, process_idx):
    if num_process == 1:
        return args.input
    meta = get_video_meta_info(args.input)
    duration = int(meta["nb_frames"] / meta["fps"])
    part_time = duration // num_process
    print(f"duration: {duration}, part_time: {part_time}")
    os.makedirs(
        osp.join(args.output, f"{args.video_name}_inp_tmp_videos"), exist_ok=True
    )
    out_path = osp.join(
        args.output, f"{args.video_name}_inp_tmp_videos", f"{process_idx:03d}.mp4"
    )
    cmd = [
        args.ffmpeg_bin,
        f"-i {args.input}",
        "-ss",
        f"{part_time * process_idx}",
        f"-to {part_time * (process_idx + 1)}"
        if process_idx != num_process - 1
        else "",
        "-async 1",
        out_path,
        "-y",
    ]
    print(" ".join(cmd))
    subprocess.call(" ".join(cmd), shell=True)
    return out_path


class Reader:
    def __init__(self, args, total_workers=1, worker_idx=0):
        self.args = args
        input_type = mimetypes.guess_type(args.input)[0]
        self.input_type = "folder" if input_type is None else input_type
        self.paths = []  # for image&folder type
        self.audio = None
        self.input_fps = None
        if self.input_type.startswith("video"):
            video_path = get_sub_video(args, total_workers, worker_idx)
            self.stream_reader = (
                ffmpeg.input(video_path)
                .output("pipe:", format="rawvideo", pix_fmt="bgr24", loglevel="error")
                .run_async(pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)
            )
            meta = get_video_meta_info(video_path)
            self.width = meta["width"]
            self.height = meta["height"]
            self.input_fps = meta["fps"]
            self.audio = meta["audio"]
            self.nb_frames = meta["nb_frames"]

        else:
            if self.input_type.startswith("image"):
                self.paths = [args.input]
            else:
                paths = sorted(glob.glob(os.path.join(args.input, "*")))
                tot_frames = len(paths)
                num_frame_per_worker = tot_frames // total_workers + (
                    1 if tot_frames % total_workers else 0
                )
                self.paths = paths[
                    num_frame_per_worker
                    * worker_idx : num_frame_per_worker
                    * (worker_idx + 1)
                ]

            self.nb_frames = len(self.paths)
            assert self.nb_frames > 0, "empty folder"
            from PIL import Image

            tmp_img = Image.open(self.paths[0])
            self.width, self.height = tmp_img.size
        self.idx = 0

    def get_resolution(self):
        return self.height, self.width

    def get_fps(self):
        if self.args.fps is not None:
            return self.args.fps
        elif self.input_fps is not None:
            return self.input_fps
        return 24

    def get_audio(self):
        return self.audio

    def __len__(self):
        return self.nb_frames

    def get_frame_from_stream(self):
        img_bytes = self.stream_reader.stdout.read(
            self.width * self.height * 3
        )  # 3 bytes for one pixel
        if not img_bytes:
            return None
        img = np.frombuffer(img_bytes, np.uint8).reshape([self.height, self.width, 3])
        return img

    def get_frame_from_list(self):
        if self.idx >= self.nb_frames:
            return None
        img = cv2.imread(self.paths[self.idx])
        self.idx += 1
        return img

    def get_frame(self):
        if self.input_type.startswith("video"):
            return self.get_frame_from_stream()
        else:
            return self.get_frame_from_list()

    def close(self):
        if self.input_type.startswith("video"):
            self.stream_reader.stdin.close()
            self.stream_reader.wait()


class Writer:
    def __init__(self, args, audio, height, width, video_save_path, fps):
        out_width, out_height = int(width * args.outscale), int(height * args.outscale)
        if out_height > 2160:
            print(
                "You are generating video that is larger than 4K, which will be very slow due to IO speed.",
                "We highly recommend to decrease the outscale(aka, -s).",
            )

        if audio is not None:
            self.stream_writer = (
                ffmpeg.input(
                    "pipe:",
                    format="rawvideo",
                    pix_fmt="bgr24",
                    s=f"{out_width}x{out_height}",
                    framerate=fps,
                )
                .output(
                    audio,
                    video_save_path,
                    pix_fmt="yuv420p",
                    vcodec="libx264",
                    loglevel="error",
                    acodec="copy",
                )
                .overwrite_output()
                .run_async(pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)
            )
        else:
            self.stream_writer = (
                ffmpeg.input(
                    "pipe:",
                    format="rawvideo",
                    pix_fmt="bgr24",
                    s=f"{out_width}x{out_height}",
                    framerate=fps,
                )
                .output(
                    video_save_path,
                    pix_fmt="yuv420p",
                    vcodec="libx264",
                    loglevel="error",
                )
                .overwrite_output()
                .run_async(pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)
            )

    def write_frame(self, frame):
        frame = frame.astype(np.uint8).tobytes()
        self.stream_writer.stdin.write(frame)

    def close(self):
        self.stream_writer.stdin.close()
        self.stream_writer.wait()


def inference_video(args, video_save_path, device=None, total_workers=1, worker_idx=0):
    # ---------------------- determine models according to model names ---------------------- #
    args.model_name = args.model_name.split(".pth")[0]
    if args.model_name == "RealESRGAN_x4plus":  # x4 RRDBNet model
        model = RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        )
        netscale = 4
        file_url = [
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
        ]
    elif args.model_name == "RealESRNet_x4plus":  # x4 RRDBNet model
        model = RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        )
        netscale = 4
        file_url = [
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth"
        ]
    elif (
        args.model_name == "RealESRGAN_x4plus_anime_6B"
    ):  # x4 RRDBNet model with 6 blocks
        model = RRDBNet(
            num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4
        )
        netscale = 4
        file_url = [
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
        ]
    elif args.model_name == "RealESRGAN_x2plus":  # x2 RRDBNet model
        model = RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=2,
        )
        netscale = 2
        file_url = [
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth"
        ]
    elif args.model_name == "realesr-animevideov3":  # x4 VGG-style model (XS size)
        model = SRVGGNetCompact(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_conv=16,
            upscale=4,
            act_type="prelu",
        )
        netscale = 4
        file_url = [
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth"
        ]
    elif args.model_name == "realesr-general-x4v3":  # x4 VGG-style model (S size)
        model = SRVGGNetCompact(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_conv=32,
            upscale=4,
            act_type="prelu",
        )
        netscale = 4
        file_url = [
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
        ]

    # ---------------------- determine model paths ---------------------- #
    model_path = os.path.join("weights", args.model_name + ".pth")
    if not os.path.isfile(model_path):
        ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
        for url in file_url:
            # model_path will be updated
            model_path = load_file_from_url(
                url=url,
                model_dir=os.path.join(ROOT_DIR, "weights"),
                progress=True,
                file_name=None,
            )

    # use dni to control the denoise strength
    dni_weight = None
    if args.model_name == "realesr-general-x4v3" and args.denoise_strength != 1:
        wdn_model_path = model_path.replace(
            "realesr-general-x4v3", "realesr-general-wdn-x4v3"
        )
        model_path = [model_path, wdn_model_path]
        dni_weight = [args.denoise_strength, 1 - args.denoise_strength]

    # restorer
    upsampler = RealESRGANer(
        scale=netscale,
        model_path=model_path,
        dni_weight=dni_weight,
        model=model,
        tile=args.tile,
        tile_pad=args.tile_pad,
        pre_pad=args.pre_pad,
        half=not args.fp32,
        device=device,
    )

    if "anime" in args.model_name and args.face_enhance:
        print(
            "face_enhance is not supported in anime models, we turned this option off for you. "
            "if you insist on turning it on, please manually comment the relevant lines of code."
        )
        args.face_enhance = False

    if args.face_enhance:  # Use GFPGAN for face enhancement
        from gfpgan import GFPGANer

        face_enhancer = GFPGANer(
            model_path="https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth",
            upscale=args.outscale,
            arch="clean",
            channel_multiplier=2,
            bg_upsampler=upsampler,
        )  # TODO support custom device
    else:
        face_enhancer = None

    reader = Reader(args, total_workers, worker_idx)
    audio = reader.get_audio()
    height, width = reader.get_resolution()
    fps = reader.get_fps()
    writer = Writer(args, audio, height, width, video_save_path, fps)

    pbar = tqdm(total=len(reader), unit="frame", desc="inference")
    while True:
        img = reader.get_frame()
        if img is None:
            break

        try:
            if args.face_enhance:
                _, _, output = face_enhancer.enhance(
                    img, has_aligned=False, only_center_face=False, paste_back=True
                )
            else:
                output, _ = upsampler.enhance(img, outscale=args.outscale)
        except RuntimeError as error:
            print("Error", error)
            print(
                "If you encounter CUDA out of memory, try to set --tile with a smaller number."
            )
        else:
            writer.write_frame(output)

        torch.cuda.synchronize(device)
        pbar.update(1)

    reader.close()
    writer.close()


def run(args):
    args.video_name = osp.splitext(os.path.basename(args.input))[0]
    video_save_path = osp.join(args.output, f"{args.video_name}_{args.suffix}.mp4")

    if args.extract_frame_first:
        tmp_frames_folder = osp.join(args.output, f"{args.video_name}_inp_tmp_frames")
        os.makedirs(tmp_frames_folder, exist_ok=True)
        os.system(
            f"ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0  {tmp_frames_folder}/frame%08d.png"
        )
        args.input = tmp_frames_folder

    num_gpus = torch.cuda.device_count()
    num_process = num_gpus * args.num_process_per_gpu
    if num_process == 1:
        inference_video(args, video_save_path)
        return

    ctx = torch.multiprocessing.get_context("spawn")
    pool = ctx.Pool(num_process)
    os.makedirs(
        osp.join(args.output, f"{args.video_name}_out_tmp_videos"), exist_ok=True
    )
    pbar = tqdm(total=num_process, unit="sub_video", desc="inference")
    for i in range(num_process):
        sub_video_save_path = osp.join(
            args.output, f"{args.video_name}_out_tmp_videos", f"{i:03d}.mp4"
        )
        pool.apply_async(
            inference_video,
            args=(
                args,
                sub_video_save_path,
                torch.device(i % num_gpus),
                num_process,
                i,
            ),
            callback=lambda arg: pbar.update(1),
        )
    pool.close()
    pool.join()

    # combine sub videos
    # prepare vidlist.txt
    with open(f"{args.output}/{args.video_name}_vidlist.txt", "w") as f:
        for i in range(num_process):
            f.write(f"file '{args.video_name}_out_tmp_videos/{i:03d}.mp4'\n")

    cmd = [
        args.ffmpeg_bin,
        "-f",
        "concat",
        "-safe",
        "0",
        "-i",
        f"{args.output}/{args.video_name}_vidlist.txt",
        "-c",
        "copy",
        f"{video_save_path}",
    ]
    print(" ".join(cmd))
    subprocess.call(cmd)
    shutil.rmtree(osp.join(args.output, f"{args.video_name}_out_tmp_videos"))
    if osp.exists(osp.join(args.output, f"{args.video_name}_inp_tmp_videos")):
        shutil.rmtree(osp.join(args.output, f"{args.video_name}_inp_tmp_videos"))
    os.remove(f"{args.output}/{args.video_name}_vidlist.txt")


def main():
    """Inference demo for Real-ESRGAN.
    It mainly for restoring anime videos.

    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-i", "--input", type=str, default="inputs", help="Input video, image or folder"
    )
    parser.add_argument(
        "-n",
        "--model_name",
        type=str,
        default="realesr-animevideov3",
        help=(
            "Model names: realesr-animevideov3 | RealESRGAN_x4plus_anime_6B | RealESRGAN_x4plus | RealESRNet_x4plus |"
            " RealESRGAN_x2plus | realesr-general-x4v3"
            "Default:realesr-animevideov3"
        ),
    )
    parser.add_argument(
        "-o", "--output", type=str, default="results", help="Output folder"
    )
    parser.add_argument(
        "-dn",
        "--denoise_strength",
        type=float,
        default=0.5,
        help=(
            "Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. "
            "Only used for the realesr-general-x4v3 model"
        ),
    )
    parser.add_argument(
        "-s",
        "--outscale",
        type=float,
        default=4,
        help="The final upsampling scale of the image",
    )
    parser.add_argument(
        "--suffix", type=str, default="out", help="Suffix of the restored video"
    )
    parser.add_argument(
        "-t",
        "--tile",
        type=int,
        default=0,
        help="Tile size, 0 for no tile during testing",
    )
    parser.add_argument("--tile_pad", type=int, default=10, help="Tile padding")
    parser.add_argument(
        "--pre_pad", type=int, default=0, help="Pre padding size at each border"
    )
    parser.add_argument(
        "--face_enhance", action="store_true", help="Use GFPGAN to enhance face"
    )
    parser.add_argument(
        "--fp32",
        action="store_true",
        help="Use fp32 precision during inference. Default: fp16 (half precision).",
    )
    parser.add_argument(
        "--fps", type=float, default=None, help="FPS of the output video"
    )
    parser.add_argument(
        "--ffmpeg_bin", type=str, default="ffmpeg", help="The path to ffmpeg"
    )
    parser.add_argument("--extract_frame_first", action="store_true")
    parser.add_argument("--num_process_per_gpu", type=int, default=1)

    parser.add_argument(
        "--alpha_upsampler",
        type=str,
        default="realesrgan",
        help="The upsampler for the alpha channels. Options: realesrgan | bicubic",
    )
    parser.add_argument(
        "--ext",
        type=str,
        default="auto",
        help="Image extension. Options: auto | jpg | png, auto means using the same extension as inputs",
    )
    args = parser.parse_args()

    args.input = args.input.rstrip("/").rstrip("\\")
    os.makedirs(args.output, exist_ok=True)

    if mimetypes.guess_type(args.input)[0] is not None and mimetypes.guess_type(
        args.input
    )[0].startswith("video"):
        is_video = True
    else:
        is_video = False

    if is_video and args.input.endswith(".flv"):
        mp4_path = args.input.replace(".flv", ".mp4")
        os.system(f"ffmpeg -i {args.input} -codec copy {mp4_path}")
        args.input = mp4_path

    if args.extract_frame_first and not is_video:
        args.extract_frame_first = False

    run(args)

    if args.extract_frame_first:
        tmp_frames_folder = osp.join(args.output, f"{args.video_name}_inp_tmp_frames")
        shutil.rmtree(tmp_frames_folder)


if __name__ == "__main__":
    main()