import torch
import os
from concurrent.futures import ThreadPoolExecutor
from pydub import AudioSegment
import cv2
from pathlib import Path
import subprocess
from pathlib import Path
import av
import imageio
import numpy as np
from rich.progress import track
from tqdm import tqdm

import stf_alternative



def exec_cmd(cmd):
    subprocess.run(
        cmd, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
    )


def images2video(images, wfp, **kwargs):
    fps = kwargs.get("fps", 24)
    video_format = kwargs.get("format", "mp4")  # default is mp4 format
    codec = kwargs.get("codec", "libx264")  # default is libx264 encoding
    quality = kwargs.get("quality")  # video quality
    pixelformat = kwargs.get("pixelformat", "yuv420p")  # video pixel format
    image_mode = kwargs.get("image_mode", "rgb")
    macro_block_size = kwargs.get("macro_block_size", 2)
    ffmpeg_params = ["-crf", str(kwargs.get("crf", 18))]

    writer = imageio.get_writer(
        wfp,
        fps=fps,
        format=video_format,
        codec=codec,
        quality=quality,
        ffmpeg_params=ffmpeg_params,
        pixelformat=pixelformat,
        macro_block_size=macro_block_size,
    )

    n = len(images)
    for i in track(range(n), description="writing", transient=True):
        if image_mode.lower() == "bgr":
            writer.append_data(images[i][..., ::-1])
        else:
            writer.append_data(images[i])

    writer.close()

    # print(f':smiley: Dump to {wfp}\n', style="bold green")
    print(f"Dump to {wfp}\n")


def merge_audio_video(video_fp, audio_fp, wfp):
    if osp.exists(video_fp) and osp.exists(audio_fp):
        cmd = f"ffmpeg -i {video_fp} -i {audio_fp} -c:v copy -c:a aac {wfp} -y"
        exec_cmd(cmd)
        print(f"merge {video_fp} and {audio_fp} to {wfp}")
    else:
        print(f"video_fp: {video_fp} or audio_fp: {audio_fp} not exists!")




class STFPipeline:
    def __init__(self,
                 stf_path: str = "/home/user/app/stf/",
                 device: str = "cuda:0",
                 template_video_path: str = "templates/front_one_piece_dress_nodded_cut.webm",
                 config_path: str = "front_config.json",
                 checkpoint_path: str = "089.pth",
                 root_path: str = "works"
                 
    ):
        
        self.config_path = os.path.join(stf_path, config_path)
        self.checkpoint_path = os.path.join(stf_path, checkpoint_path)
        self.work_root_path = os.path.join(stf_path, root_path)
        self.device = device
        self.template_video_path=template_video_path
        
        # model = stf_alternative.create_model(
        # config_path=config_path,
        # checkpoint_path=checkpoint_path,
        # work_root_path=work_root_path,
        # device=device,
        # wavlm_path="microsoft/wavlm-large",
        # )
        # self.template = stf_alternative.Template(
        # model=model,
        # config_path=config_path,
        # template_video_path=template_video_path,
        # )

        print('STFPipeline init')
    

    def execute(self, audio: str):

        print('STFPipeline execute')
        
        model = stf_alternative.create_model(
            config_path=self.config_path,
            checkpoint_path=self.checkpoint_path,
            work_root_path=self.work_root_path,
            device=self.device,
            wavlm_path="microsoft/wavlm-large",
        )

        print('STFPipeline execute 1')
        self.template = stf_alternative.Template(
            model=model,
            config_path=self.config_path,
            template_video_path=self.template_video_path,
        )

        print('STFPipeline execute 2')
        
        # Path("dubbing").mkdir(exist_ok=True)
        # save_path = os.path.join("dubbing", Path(audio).stem+"--lip.mp4")
        Path("/tmp/dubbing").mkdir(exist_ok=True)
        save_path = os.path.join("/tmp/dubbing", Path(audio).stem+"--lip.mp4")
        
        reader = iter(self.template._get_reader(num_skip_frames=0))
        audio_segment = AudioSegment.from_file(audio)
        pivot = 0
        results = []


        
        try:
            # 순차적으로 gen_infer를 실행
            gen_infer = self.template.gen_infer(audio_segment, pivot)
    
            for idx, (it, chunk) in enumerate(gen_infer, pivot):
                frame = next(reader)  # 다음 프레임을 읽음
                composed = self.template.compose(idx, frame, it)  # 합성 작업 수행
                frame_name = f"{idx}".zfill(5) + ".jpg"
                results.append(it['pred'])  # 결과를 저장
            pivot = idx + 1

        except StopIteration as e:
            pass


        
        # with ThreadPoolExecutor(4) as p:
        #     try:

        #         gen_infer = self.template.gen_infer_concurrent(
        #             p,
        #             audio_segment,
        #             pivot,
        #         )
        #         for idx, (it, chunk) in enumerate(gen_infer, pivot):
        #             frame = next(reader)
        #             composed = self.template.compose(idx, frame, it)
        #             frame_name = f"{idx}".zfill(5)+".jpg"
        #             results.append(it['pred'])
        #         pivot = idx + 1
        #     except StopIteration as e:
        #         pass

        print('STFPipeline execute 3')
        images2video(results, save_path)
                                
        return save_path