import numpy as np
from torch.utils.data import Dataset
from torchvision import transforms
from ldm.data.data_loader import DataLoaderMultiAspect as dlma
import math
import ldm.data.dl_singleton as dls

class EDValidateBatch(Dataset):
    def __init__(self,
                 data_root,
                 flip_p=0.0,
                 repeats=1,
                 debug_level=0,
                 batch_size=1,
                 set='val',
                 ):
        self.data_root = data_root
        self.batch_size = batch_size

        if not dls.shared_dataloader:
            print("Creating new dataloader singleton")
            dls.shared_dataloader = dlma(data_root=data_root, debug_level=debug_level, batch_size=self.batch_size, flip_p=flip_p)
            
        self.image_train_items = dls.shared_dataloader.get_all_images()
        
        self.num_images = len(self.image_train_items)

        self._length = max(math.trunc(self.num_images * repeats), batch_size) - self.num_images % self.batch_size

        print()
        print(f" ** Validation Set: {set}, steps: {self._length / batch_size:.0f}, repeats: {repeats} ")
        print()

    def __len__(self):
        return self._length

    def __getitem__(self, i):
        idx = i % self.num_images
        image_train_item = self.image_train_items[idx]

        example = self.__get_image_for_trainer(image_train_item)
        return example

    @staticmethod
    def __get_image_for_trainer(image_train_item):
        example = {}

        image_train_tmp = image_train_item.hydrate()

        example["image"] = image_train_tmp.image
        example["caption"] = image_train_tmp.caption

        return example