import os
import os.path as osp
import random
import numpy as np
import pandas as pd
import torch
from pathlib import Path


def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(0)
    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
    torch.cuda.manual_seed(0)
    torch.cuda.manual_seed_all(0)
    if torch.cuda.is_available(): 
        print("cuda available")
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True


def make_datapath_list(rootpath):
    rootpath = Path(rootpath)

    voc2012_img_dir = Path(rootpath) / "JPEGImages"
    voc2012_anno_dir = Path(rootpath) / "Annotations"
    if not voc2012_img_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)
    if not voc2012_anno_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)

    id_names_root = rootpath / "ImageSets" / "Main"
    train_id_names = id_names_root / "train.txt"
    val_id_names = id_names_root / "val.txt"

    def glob_file_names_from_(id_names_file):
        img_list = []
        anno_list = []
        for line in open(id_names_file):
            file_id = line.strip() # 空白スペースと改行を除去
            img_list.append(str(voc2012_img_dir / f"{file_id}.jpg"))
            anno_list.append(str(voc2012_anno_dir / f"{file_id}.xml"))
        return img_list, anno_list
    ret = glob_file_names_from_(train_id_names)
    train_img_list = ret[0]
    train_anno_list = ret[1]
    ret = glob_file_names_from_(val_id_names)
    val_img_list = ret[0]
    val_anno_list = ret[1]

    return train_img_list, train_anno_list, val_img_list, val_anno_list

class Errors:
    LINE = "=" * 25
    BASE_MSG = "\n{line}\n".format(line=LINE)

    def __call__(self, msg, exception):
        return exception(msg)

    @classmethod
    def FileNotFound(cls, path: Path):
        path = Path(path)
        msg = cls.BASE_MSG
        msg += "NOT Exists Path: {}\n".format(path)

        path_gradually = Path(path.parts[0]).absolute()
        for path_part in path.parts[1:]:
            path_gradually /= path_part
            msg += "\tExists: {}, {}\n".format(path_gradually.exists(), path_gradually)

        return FileNotFoundError(msg)
6ef5d229736f129abbb43a2ee976b56fb58f8947
<mask token>


class Errors:
    <mask token>
    <mask token>

    def __call__(self, msg, exception):
        return exception(msg)

    @classmethod
    def FileNotFound(cls, path: Path):
        path = Path(path)
        msg = cls.BASE_MSG
        msg += 'NOT Exists Path: {}\n'.format(path)
        path_gradually = Path(path.parts[0]).absolute()
        for path_part in path.parts[1:]:
            path_gradually /= path_part
            msg += '\tExists: {}, {}\n'.format(path_gradually.exists(),
                path_gradually)
        return FileNotFoundError(msg)

<mask token>


def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(0)
    os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
    torch.cuda.manual_seed(0)
    torch.cuda.manual_seed_all(0)
    if torch.cuda.is_available():
        print('cuda available')
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True


<mask token>


class Errors:
    LINE = '=' * 25
    BASE_MSG = '\n{line}\n'.format(line=LINE)

    def __call__(self, msg, exception):
        return exception(msg)

    @classmethod
    def FileNotFound(cls, path: Path):
        path = Path(path)
        msg = cls.BASE_MSG
        msg += 'NOT Exists Path: {}\n'.format(path)
        path_gradually = Path(path.parts[0]).absolute()
        for path_part in path.parts[1:]:
            path_gradually /= path_part
            msg += '\tExists: {}, {}\n'.format(path_gradually.exists(),
                path_gradually)
        return FileNotFoundError(msg)

<mask token>


def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(0)
    os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
    torch.cuda.manual_seed(0)
    torch.cuda.manual_seed_all(0)
    if torch.cuda.is_available():
        print('cuda available')
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True


def make_datapath_list(rootpath):
    rootpath = Path(rootpath)
    voc2012_img_dir = Path(rootpath) / 'JPEGImages'
    voc2012_anno_dir = Path(rootpath) / 'Annotations'
    if not voc2012_img_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)
    if not voc2012_anno_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)
    id_names_root = rootpath / 'ImageSets' / 'Main'
    train_id_names = id_names_root / 'train.txt'
    val_id_names = id_names_root / 'val.txt'

    def glob_file_names_from_(id_names_file):
        img_list = []
        anno_list = []
        for line in open(id_names_file):
            file_id = line.strip()
            img_list.append(str(voc2012_img_dir / f'{file_id}.jpg'))
            anno_list.append(str(voc2012_anno_dir / f'{file_id}.xml'))
        return img_list, anno_list
    ret = glob_file_names_from_(train_id_names)
    train_img_list = ret[0]
    train_anno_list = ret[1]
    ret = glob_file_names_from_(val_id_names)
    val_img_list = ret[0]
    val_anno_list = ret[1]
    return train_img_list, train_anno_list, val_img_list, val_anno_list


class Errors:
    LINE = '=' * 25
    BASE_MSG = '\n{line}\n'.format(line=LINE)

    def __call__(self, msg, exception):
        return exception(msg)

    @classmethod
    def FileNotFound(cls, path: Path):
        path = Path(path)
        msg = cls.BASE_MSG
        msg += 'NOT Exists Path: {}\n'.format(path)
        path_gradually = Path(path.parts[0]).absolute()
        for path_part in path.parts[1:]:
            path_gradually /= path_part
            msg += '\tExists: {}, {}\n'.format(path_gradually.exists(),
                path_gradually)
        return FileNotFoundError(msg)

import os
import os.path as osp
import random
import numpy as np
import pandas as pd
import torch
from pathlib import Path


def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(0)
    os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
    torch.cuda.manual_seed(0)
    torch.cuda.manual_seed_all(0)
    if torch.cuda.is_available():
        print('cuda available')
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True


def make_datapath_list(rootpath):
    rootpath = Path(rootpath)
    voc2012_img_dir = Path(rootpath) / 'JPEGImages'
    voc2012_anno_dir = Path(rootpath) / 'Annotations'
    if not voc2012_img_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)
    if not voc2012_anno_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)
    id_names_root = rootpath / 'ImageSets' / 'Main'
    train_id_names = id_names_root / 'train.txt'
    val_id_names = id_names_root / 'val.txt'

    def glob_file_names_from_(id_names_file):
        img_list = []
        anno_list = []
        for line in open(id_names_file):
            file_id = line.strip()
            img_list.append(str(voc2012_img_dir / f'{file_id}.jpg'))
            anno_list.append(str(voc2012_anno_dir / f'{file_id}.xml'))
        return img_list, anno_list
    ret = glob_file_names_from_(train_id_names)
    train_img_list = ret[0]
    train_anno_list = ret[1]
    ret = glob_file_names_from_(val_id_names)
    val_img_list = ret[0]
    val_anno_list = ret[1]
    return train_img_list, train_anno_list, val_img_list, val_anno_list


class Errors:
    LINE = '=' * 25
    BASE_MSG = '\n{line}\n'.format(line=LINE)

    def __call__(self, msg, exception):
        return exception(msg)

    @classmethod
    def FileNotFound(cls, path: Path):
        path = Path(path)
        msg = cls.BASE_MSG
        msg += 'NOT Exists Path: {}\n'.format(path)
        path_gradually = Path(path.parts[0]).absolute()
        for path_part in path.parts[1:]:
            path_gradually /= path_part
            msg += '\tExists: {}, {}\n'.format(path_gradually.exists(),
                path_gradually)
        return FileNotFoundError(msg)

import os
import os.path as osp
import random
import numpy as np
import pandas as pd
import torch
from pathlib import Path


def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(0)
    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
    torch.cuda.manual_seed(0)
    torch.cuda.manual_seed_all(0)
    if torch.cuda.is_available(): 
        print("cuda available")
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True


def make_datapath_list(rootpath):
    rootpath = Path(rootpath)

    voc2012_img_dir = Path(rootpath) / "JPEGImages"
    voc2012_anno_dir = Path(rootpath) / "Annotations"
    if not voc2012_img_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)
    if not voc2012_anno_dir.exists():
        raise Errors.FileNotFound(voc2012_img_dir)

    id_names_root = rootpath / "ImageSets" / "Main"
    train_id_names = id_names_root / "train.txt"
    val_id_names = id_names_root / "val.txt"

    def glob_file_names_from_(id_names_file):
        img_list = []
        anno_list = []
        for line in open(id_names_file):
            file_id = line.strip() # 空白スペースと改行を除去
            img_list.append(str(voc2012_img_dir / f"{file_id}.jpg"))
            anno_list.append(str(voc2012_anno_dir / f"{file_id}.xml"))
        return img_list, anno_list
    ret = glob_file_names_from_(train_id_names)
    train_img_list = ret[0]
    train_anno_list = ret[1]
    ret = glob_file_names_from_(val_id_names)
    val_img_list = ret[0]
    val_anno_list = ret[1]

    return train_img_list, train_anno_list, val_img_list, val_anno_list

class Errors:
    LINE = "=" * 25
    BASE_MSG = "\n{line}\n".format(line=LINE)

    def __call__(self, msg, exception):
        return exception(msg)

    @classmethod
    def FileNotFound(cls, path: Path):
        path = Path(path)
        msg = cls.BASE_MSG
        msg += "NOT Exists Path: {}\n".format(path)

        path_gradually = Path(path.parts[0]).absolute()
        for path_part in path.parts[1:]:
            path_gradually /= path_part
            msg += "\tExists: {}, {}\n".format(path_gradually.exists(), path_gradually)

        return FileNotFoundError(msg)