import datasets
import json
import os 
from .classes import IMAGENET2012_CLASSES


_URL_BASE = "https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/"
_URLS = {
    "img_data": _URL_BASE + "images.tar.gz",
    "mask_data": _URL_BASE + "masks.tar.gz",
    "train_json": _URL_BASE + "train.json",
    "val_json": _URL_BASE + "val.json",
    "test_json": _URL_BASE + "test.json",
}


class SegmentedImagenet1kDataset(datasets.GeneratorBasedBuilder):

    datasets.Version("1.1.0")

    def _info(self):
        return datasets.DatasetInfo(
            description="Machine generated instance segmentation results of subset of ImageNet-1k",
            homepage="https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset",
            features = datasets.Features({
                "image": datasets.Image(),
                "imagenet_label": datasets.Value("string"),
                "boxes": datasets.Sequence(datasets.Sequence(datasets.Value('int32'))),
                "labels": datasets.Sequence(datasets.Value("string")),
                "scores": datasets.Sequence(datasets.Value("float32")) ,
                "masks": datasets.Sequence(datasets.Image()),
        
            }),
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
    
        dirs = dl_manager.download_and_extract(_URLS)
        root_folder_kwargs = {"image_root": dirs["img_data"], "mask_root": dirs["mask_data"]}
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={"json_path": dirs["train_json"], "get_imagenet_string": True, **root_folder_kwargs}),

            datasets.SplitGenerator(name=datasets.Split.TEST,
                                    gen_kwargs={"json_path": dirs["test_json"], "get_imagenet_string": False, **root_folder_kwargs}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION,
                                    gen_kwargs={"json_path": dirs["val_json"], "get_imagenet_string": True, **root_folder_kwargs}),
        ]
    
    def _generate_examples(self, json_path, image_root, mask_root, get_imagenet_string):
        with open(json_path, encoding="utf-8") as f:
            data = json.load(f)
            for id, item in enumerate(data):


                if get_imagenet_string:
                    imagenet_label = IMAGENET2012_CLASSES[os.path.basename(item['image']).replace(".JPEG", "").rsplit("_", 1)[1]]
                    pass
                else:
                    imagenet_label = "None"

                yield id, {
                    "image" : os.path.join(image_root,item['image']),
                    "imagenet_label": imagenet_label,
                    "boxes": item['boxes'],
                    "scores": item['scores'],
                    "labels": item['labels'],
                    "masks": [os.path.join(mask_root, p) for p in item['masks']]
                }