import os
from data.pix2pix_dataset import Pix2pixDataset, BaseDataset
from data.image_folder import make_dataset
from torchvision import transforms
import os
from PIL import Image

class Summer2WinterYosemiteDataset(BaseDataset):
    @staticmethod
    def modify_commandline_options(parser, is_train):
        parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
        parser.set_defaults(preprocess_mode='resize_and_crop')
        parser.set_defaults(load_size=512)
        parser.set_defaults(crop_size=256)
        return parser

    def initialize(self, opt):
        self.opt = opt
        label_paths, image_paths, instance_paths = self.get_paths(opt)
        self.label_paths = label_paths[:opt.max_dataset_size]
        self.image_paths = image_paths[:opt.max_dataset_size]
        self.dataset_size = len(self.label_paths)

        print(f"Number of labels: {len(self.label_paths)}, Number of images: {len(self.image_paths)}")
        if len(self.label_paths) != len(self.image_paths):
            raise ValueError("The number of labels and images do not match.")


    def get_paths(self, opt):
            croot = opt.croot
            sroot = opt.sroot
            c_image_dir = os.path.join(croot, f'{opt.phase}A')
            s_image_dir = os.path.join(sroot, f'{opt.phase}B')
            c_image_paths = sorted(make_dataset(c_image_dir, recursive=True))
            s_image_paths = sorted(make_dataset(s_image_dir, recursive=True))
            return c_image_paths, s_image_paths, []

    def __getitem__(self, index):
        label_path = self.label_paths[index]
        image_path = self.image_paths[index]
        label = Image.open(label_path).convert('RGB')
        image = Image.open(image_path).convert('RGB')
        transform = transforms.Compose([
            transforms.Resize((self.opt.load_size, self.opt.load_size)),
            transforms.RandomCrop(self.opt.crop_size),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ])
        return {'image': transform(label), 'label': transform(image),"cpath":image_path}

    def __len__(self):
        return self.dataset_size