import torch
import os

from torch.utils.data import Dataset
from torchvision.transforms import CenterCrop, Normalize, Resize
from torchvision.transforms.functional import to_tensor
from PIL import Image

EXTs = ['.png', '.jpg', '.jpeg', ".JPEG"]


def is_image_file(filename):
    return any(filename.endswith(ext) for ext in EXTs)

class ImageText(Dataset):
    def __init__(self, root, resolution):
        super().__init__()
        self.image_paths = []
        self.texts = []
        for dir, subdirs, files in os.walk(root):
            for file in files:
                if is_image_file(file):
                    image_path = os.path.join(dir, file)
                    image_base_path = image_path.split(".")[:-1]
                    text_path  = ".".join(image_base_path) + ".txt"
                    if os.path.exists(text_path):
                        with open(text_path, 'r') as f:
                            text = f.read()
                        self.texts.append(text)
                        self.image_paths.append(image_path)

        self.resize = Resize(resolution)
        self.center_crop = CenterCrop(resolution)
        self.normalize = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

    def __getitem__(self, idx: int):
        image_path = self.image_paths[idx]
        text = self.texts[idx]
        pil_image = Image.open(image_path).convert('RGB')
        pil_image = self.resize(pil_image)
        pil_image = self.center_crop(pil_image)
        raw_image = to_tensor(pil_image)
        normalized_image = self.normalize(raw_image)
        metadata = {
            "image_path": image_path,
            "prompt": text,
            "raw_image": raw_image,
        }
        return normalized_image, text, metadata

    def __len__(self):
        return len(self.image_paths)