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import os
import cv2
import numpy as np
from tqdm import tqdm
import argparse

from facenet_pytorch import MTCNN

mtcnn = MTCNN(select_largest=True, min_face_size=64, post_process=False, device='cuda:0')

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import re
import os

urls = "https://drive.google.com/file/d/FILEID_1/view?usp=drive_link, https://drive.google.com/file/d/FILEID_2/view?usp=drive_link, https://drive.google.com/file/d/FILEID_3/view?usp=drive_link"
url_list = urls.split(', ')
pat = re.compile('https://drive.google.com/file/d/(.*)/view\?usp=drive_link')
for idx, url in enumerate(url_list):
    g = pat.match(url)
    id = g.group(1)
    down_url = f'https://drive.google.com/uc?id={id}'

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"""
https://github.com/d246810g2000/YOLOX/blob/main/datasets/train_val_data_split_coco.py
"""
import os
import cv2
import json
import random
import shutil
import xml.etree.ElementTree as ET
from tqdm import tqdm

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def resume_train(self, model):
    if self.args.resume:
        logger.info("resume training")
        if self.args.ckpt is None:
            ckpt_file = os.path.join(self.file_name, "latest" + "_ckpt.pth")
        else:
            ckpt_file = self.args.ckpt

        ckpt = torch.load(ckpt_file, map_location=self.device)
        # resume the model/optimizer state dict

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def _cache_images(self):
    logger.warning("\n********************************************************************************\n"
                   "You are using cached images in RAM to accelerate training.\n"
                   "This requires large system RAM.\n"
                   "Make sure you have 200G+ RAM and 136G available disk space for training COCO.\n"
                   "********************************************************************************\n")
    max_h = self.img_size[0]
    max_w = self.img_size[1]
    cache_file = self.data_dir + "/img_resized_cache_" + self.name + ".array"
    if not os.path.exists(cache_file):

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"""
https://github.com/z-bingo/FastDVDNet/tree/master/arch
Reimplementation of 4 channel FastDVDNet in PyTorch
"""
import torch
import torch.nn as nn
import numpy as np
from thop import profile

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curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python3 get-pip.py

echo 'export PATH="$PATH:/home/Yanwei_Liu/.local/bin"' >> ~/.bashrc
echo 'alias python=python3' >> ~/.bashrc
source ~/.bashrc

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

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from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from FinMind.data import DataLoader
import pandas as pd

import talib
from talib import abstract

## 取得資料

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import pandas as pd
from twstock import Stock
import argparse


def parse():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--etf_code", type=str, default="00733",
    )

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"""
RetinaNet model with the MobileNetV3 backbone from 
Torchvision classification models.

Reference: https://github.com/pytorch/vision/blob/main/torchvision/models/detection/retinanet.py#L377-L405
"""

import torchvision
import torch
from torchvision.models.detection import RetinaNet