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data_process

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data_process
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import os
import json

import datasets

_HOMEPAGE = "https://huggingface.co/datasets/zhenzi/data_process"

_LICENSE = "Apache License 2.0"

_CITATION = """\
@software{2022,
    title=数据集标题,
    author=zhenzi,
    year={2022},
    month={March},
    publisher = {GitHub}
}
"""

_DESCRIPTION = """\
数据集描述.
"""

_REPO = "https://huggingface.co/datasets/zhenzi/data_process/resolve/main/metadata"


class ImageConfig(datasets.BuilderConfig):
    """BuilderConfig for Imagette."""

    def __init__(self, data_url, metadata_urls, **kwargs):
        super(ImageConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_url = data_url
        self.metadata_urls = metadata_urls


class Imagenette(datasets.GeneratorBasedBuilder):
    """Imagenette dataset."""

    BUILDER_CONFIGS = [
        ImageConfig(
            name="tests",
            description="测试",
            data_url="https://huggingface.co/datasets/zhenzi/test/resolve/main/tests.zip",
            metadata_urls={
                "train": f"{_REPO}/tests/train.txt"
            },
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION + self.config.description,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "text": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(self.config.data_url)
        metadata_paths = dl_manager.download(self.config.metadata_urls)
        archive_iter = dl_manager.iter_archive(archive_path)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": archive_iter,
                    "metadata_path": metadata_paths["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "images": archive_iter,
                    "metadata_path": metadata_paths["validation"],
                },
            ),
        ]

    def _generate_examples(self, images, metadata_path):
        with open(metadata_path, encoding="utf-8") as f:
            files_to_keep = set(f.read().split("\n"))
        for file_path, file_obj in images:
            print(file_path)
            if file_path in files_to_keep:
                yield file_path, {
                    "image": {"path": file_path, "bytes": file_obj.read()},
                    "text": "dee",
                }