from ..data_aug import cifar_like_image_train_aug, cifar_like_image_test_aug, imagenet_like_image_test_aug, imagenet_like_image_train_aug
from ..ab_dataset import ABDataset
from ..dataset_split import train_val_split, train_val_test_split
from torchvision.datasets import ImageFolder
from typing import Dict, List, Optional
from torchvision.transforms import Compose
from utils.common.others import HiddenPrints
import os 

from ..registery import dataset_register


@dataset_register(
    name='GTSRB', 
    classes=[f'{i:05d}' for i in range(42)], 
    task_type='Image Classification',
    object_type='Traffic Sign',
    class_aliases=[],
    shift_type=None
)
class GTSRB(ABDataset):    
    def create_dataset(self, root_dir: str, split: str, transform: Optional[Compose], 
                       classes: List[str], ignore_classes: List[str], idx_map: Optional[Dict[int, int]]):
        if transform is None:
            transform = imagenet_like_image_train_aug() if split == 'train' else imagenet_like_image_test_aug()
            self.transform = transform
        
        dataset = ImageFolder(os.path.join(root_dir, 'Final_Training/Images/') if split != 'test' else os.path.join(root_dir, 'Final_Test_ImageFolder/'), transform=transform)
        
        if len(ignore_classes) > 0:
            ignore_classes_idx = [classes.index(c) for c in ignore_classes]
            dataset.samples = [s for s in dataset.samples if s[1] not in ignore_classes_idx]
            
        if idx_map is not None:
            dataset.samples = [(s[0], idx_map[s[1]]) if s[1] in idx_map.keys() else s for s in dataset.samples]
        
        if split != 'test':
            dataset = train_val_split(dataset, split)
        return dataset