import os
import random
import requests
import datasets
from tqdm import tqdm
from io import BytesIO
from PIL import Image


_DOMAIN = "https://www.modelscope.cn"

_URL = f"{_DOMAIN}/datasets/Genius-Society/{os.path.basename(__file__)[:-3]}"


class insecta(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.Value("string"),
                    "latin": datasets.Value("string"),
                }
            ),
            supervised_keys=("image", "latin"),
            homepage=_URL,
            license="mit",
            version="0.0.1",
        )

    def _get_files(
        self,
        root: str,
        url=f"{_DOMAIN}/api/v1/datasets/171488/repo/tree",
        page_size=500,
    ):
        try:
            response = requests.get(
                url,
                params={
                    "Revision": "master",
                    "Root": root,
                    "PageNumber": 1,
                    "PageSize": page_size,
                },
            )
            response.raise_for_status()
            return response.json()["Data"]["Files"]

        except Exception as e:
            print(f"{e}, retrying...")
            return self._get_files(root)

    def _dld_img(self, url: str):
        try:
            response = requests.get(url)
            response.raise_for_status()
            return Image.open(BytesIO(response.content))

        except Exception as e:
            print(f"{e}, retrying...")
            return self._dld_img(url)

    def _split_generators(self, _):
        dataset = []
        files = self._get_files("已鉴定")
        for file in tqdm(files, desc="Parsing classes"):
            imgs = self._get_files(file["Path"])
            label, latin = str(file["Name"]).split(" ", 1)
            for img in imgs:
                dataset.append(
                    {
                        "image": f"{_URL}/resolve/master/" + img["Path"],
                        "label": label.strip(),
                        "latin": latin.strip(),
                    }
                )

        random.shuffle(dataset)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"files": dataset},
            )
        ]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            yield i, {
                "image": self._dld_img(path["image"]),
                "label": path["label"],
                "latin": path["latin"],
            }