try.py

import datasets
from huggingface_hub import HfApi
from datasets import DownloadManager, DatasetInfo
from datasets.data_files import DataFilesDict
import os
import json

# memo
# train-00000-of-00001.parquet

# ここに設定を記入
_NAME = "mickylan2367/spectrogram_musicCaps"
_EXTENSION = [".png"]
_REVISION = "main"

# _HOMEPAGE = "https://github.com/fastai/imagenette"
# プログラムを置く場所が決まったら、ここにホームページURLつける
_HOMEPAGE = "https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps"

_DESCRIPTION = f"""\
{_NAME} Datasets including spectrogram.png file from Google MusicCaps Datasets!
Using for Project Learning...
"""

# え...なにこれ(;´･ω･)
_IMAGES_DIR = "mickylan2367/images/data/"
# _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata"

# 参考になりそうなURL集
# https://huggingface.co/docs/datasets/v1.1.1/_modules/datasets/utils/download_manager.html
# https://huggingface.co/datasets/animelover/danbooru2022/blob/main/danbooru2022.py
# https://huggingface.co/datasets/food101/blob/main/food101.py
# https://huggingface.co/docs/datasets/about_dataset_load


class spectrogram_musicCapsConfig(datasets.BuilderConfig):
    """Builder Config for spectrogram_MusicCaps"""

    def __init__(self, metadata_urls, **kwargs):
        """BuilderConfig
        Args:
          data_url: `string`, url to download the zip file from.
          metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
          **kwargs: keyword arguments forwarded to super.
        """
        super(spectrogram_musicCapsConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        # self.data_url = data_url
        self.metadata_urls = metadata_urls

class spectrogram_musicCaps(datasets.GeneratorBasedBuilder):

    # データのサブセットはここで用意
    BUILDER_CONFIGS = [
        spectrogram_musicCapsConfig(
            name="MusicCaps data 0_10",
            description="Datasets from MusicCaps by Mikan",
            # data_url="https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps/blob/main/data/data0_10.zip",
            metadata_urls = {
                "train":"https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps/blob/main/data/metadata0_10.jsonl"
            }
        ),
        
        spectrogram_musicCapsConfig(
            name="MusicCpas data 10_100",
            description="Datasets second action by Mikan",
            # data_url="https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps/blob/main/data/data10_200.zip",
            metadata_urls = {
                "train" : "https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps/blob/main/data/metadata10_200.jsonl"
            }
        )
    ]

    def _info(self):
      return datasets.DatasetInfo(
          description=_DESCRIPTION,
          features=datasets.Features(
              {
                  "image": datasets.Image(),
                  "caption": datasets.Value("string")
              }
          ),
          supervised_keys=("image", "caption"),
          homepage=_HOMEPAGE,
          # citation=_CITATION,
          # license=_LICENSE,
          # task_templates=[ImageClassification(image_column="image", label_column="label")],
      )

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

    def _split_generators(self, dl_manager: DownloadManager):
        # huggingfaceのディレクトリからデータを取ってくる
        hfh_dataset_info = HfApi().dataset_info(_NAME, revision=_REVISION, timeout=100.0)
        # archive_path = dl_manager.download(self.config.data_url)
        split_metadata_paths = dl_manager.download(self.config.metadata_urls)
        
        # **.zipのファイル名をDict型として取得？
        data_files = DataFilesDict.from_hf_repo(
            {datasets.Split.TRAIN: ["**"]},
            dataset_info=hfh_dataset_info,
            allowed_extensions=["zip", ".zip"],
        )
        
        gs = []
        for split, files in data_files.items():
            downloaded_files = dl_manager.download_and_extract(files) # zipファイルを解凍してファイル名リストにする。
            # 元のコードではzipファイルの中身を"filepath"としてそのまま_generate_exampleに引き渡している？
            gs.append(
               datasets.SplitGenerator(
                  name = split, 
                  gen_kwargs={
                     "images" : downloaded_files, 
                     "metadata_path": split_metadata_paths["train"]
                     }
                  )
            ) 
        return gs

    def _generate_examples(self, images, metadata_path):
        """Generate images and captions for splits."""
        # with open(metadata_path, encoding="utf-8") as f:
        #     files_to_keep = set(f.read().split("\n"))
        with open(metadata_path) as fin:
            for idx, line in enumerate(fin):
                data =  json.loads(line)
                # file_path = os.path.join(data["file_name"])
                yield data["file_name"], {
                    "image": data["file_name"],
                    "caption":data["caption"]
                }