from ..data_aug import cityscapes_like_image_train_aug, cityscapes_like_image_test_aug, cityscapes_like_label_aug
from .common_dataset import CommonDataset
from ..ab_dataset import ABDataset
from ..dataset_split import train_val_test_split
import numpy as np
from typing import Dict, List, Optional
from torchvision.transforms import Compose, Lambda
import os

from ..registery import dataset_register


@dataset_register(
    name='SuperviselyPerson', 
    classes=[
        'background', 'person'
    ],
    task_type='Semantic Segmentation',
    object_type='Person',
    # class_aliases=[['automobile', 'car']],
    class_aliases=[],
    shift_type=None
)
class SuperviselyPerson(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:
            x_transform = cityscapes_like_image_train_aug() if split == 'train' else cityscapes_like_image_test_aug()
            y_transform = cityscapes_like_label_aug()
            self.transform = x_transform
        else:
            x_transform = transform
            y_transform = cityscapes_like_label_aug()
        
        images_path, labels_path = [], []
        for p in os.listdir(root_dir):
            if p.startswith('ds'):
                p1 = os.path.join(root_dir, p, 'img')
                images_path += [(p, os.path.join(p1, n)) for n in os.listdir(p1)]
                
        for dsi, img_p in images_path:
            target_p = os.path.join(root_dir, p, dsi, img_p.split('/')[-1])
            labels_path += [target_p]
        images_path = [i[1] for i in images_path]
        
        idx_map_in_y_transform = {i: i for i in range(len(classes))}
        
        # dataset.targets = np.asarray(dataset.targets)
        if len(ignore_classes) > 0: 
            for ignore_class in ignore_classes:
                # dataset.data = dataset.data[dataset.targets != classes.index(ignore_class)]
                # dataset.targets = dataset.targets[dataset.targets != classes.index(ignore_class)]
                idx_map_in_y_transform[ignore_class] = 255
                
        if idx_map is not None:
            # note: the code below seems correct but has bug!
            # for old_idx, new_idx in idx_map.items():
            #     dataset.targets[dataset.targets == old_idx] = new_idx
                
            # for ti, t in enumerate(dataset.targets):
            #     dataset.targets[ti] = idx_map[t]
            
            for k, v in idx_map.items():
                idx_map_in_y_transform[k] = v
        
        def map_class(x):
            for k, v in idx_map_in_y_transform.items():
                x[x == k] = v
            return x
        
        y_transform = Compose([
            *y_transform.transforms,
            Lambda(lambda x: map_class(x))
        ])
            
        dataset = CommonDataset(images_path, labels_path, x_transform=x_transform, y_transform=y_transform)
        
        dataset = train_val_test_split(dataset, split)
        return dataset