try.py

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


# ここに設定を記入
_NAME = "mickylan2367/LoadingScriptPractice"
_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...
"""

# URLを取得

# class LoadingScriptPracticeConfig(datasets.BuilderConfig):
#     """BuilderConfig for Imagette."""

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



class LoadingScriptPractice(datasets.GeneratorBasedBuilder):

    # データのサブセットはここで用意
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="train",
            description="this Datasets is personal practice for using loadingScript. Data is from Google/MusicCaps",
            # data_url = train_data_url["train"][0],
            # metadata_urls = {
            #     "train" : train_metadata_paths["train"][0]
            # }
        ),

        # splits (dict, optional) — The mapping between split name and metadata.
        datasets.BuilderConfig(
            name="test",
            description="this Datasets is personal practice for using loadingScript. Data is from Google/MusicCaps",
            # data_url = test_data_url["test"][0],
            # metadata_urls={
            #     "test" : test_metadata_paths["test"][0]
            # }
        )
    ]

    def _info(self) -> DatasetInfo:
      return datasets.DatasetInfo(
          description = self.config.description,
          features=datasets.Features(
              {
                  "image": datasets.Image(),
                  "caption": datasets.Value("string"),
                  "data_idx": datasets.Value("int32"),
                  "number" : datasets.Value("int32"),
                  "label" : datasets.ClassLabel(
                        names=[
                            "blues",
                            "classical",
                            "country",
                            "disco",
                            "hiphop",
                            "metal",
                            "pop",
                            "reggae",
                            "rock",
                            "jazz"
                        ]
                  )
              }
          ),
          supervised_keys=("image", "caption"),
          homepage=_HOMEPAGE,
          citation= "",
          # license=_LICENSE,
          # task_templates=[ImageClassification(image_column="image", label_column="label")],
      )

    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)

        train_metadata_paths = DataFilesDict.from_hf_repo(
            {datasets.Split.TRAIN: ["**"]},
            dataset_info=hfh_dataset_info,
            allowed_extensions=["jsonl", ".jsonl"],
        )

        test_metadata_paths = DataFilesDict.from_hf_repo(
            {datasets.Split.TEST: ["**"]},
            dataset_info=hfh_dataset_info,
            allowed_extensions=["jsonl", ".jsonl"],
        )

        # **.zipのURLをDict型として取得？
        train_data_url = DataFilesDict.from_hf_repo(
            {datasets.Split.TRAIN: ["**"]},
            dataset_info=hfh_dataset_info,
            allowed_extensions=["zip", ".zip"],
        )

        test_data_url = DataFilesDict.from_hf_repo(
            {datasets.Split.test: ["**"]},
            dataset_info=hfh_dataset_info,
            allowed_extensions=["zip", ".zip"],
        )

        # 扱いやすいように再構成
        data_path = {
            "train" : train_data_url["train"][0],
            "test" : test_data_url["test"][0]
        }

        split_metadata_paths= {
            "train" : train_metadata_paths["train"][0],
            "test" : test_metadata_paths["test"][0]
        }
        
        gs = []
        for split, files in data_path.items():
            '''
            split : "train" or "test" or "val"
            files : zip files
            '''
            # リポジトリからダウンロードしてとりあえずキャッシュしたURLリストを取得
            split_metadata_path = dl_manager.download_and_extract(split_metadata_paths[split])
            downloaded_files_path = dl_manager.download(files) 
            
            # 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" : dl_manager.iter_archive(downloaded_files_path), 
                     "metadata_path": split_metadata_path
                     }
                  )
            )
        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"))
        file_list = list()
        caption_list = list()
        dataIDX_list = list()
        num_list = list()
        label_list = list()

        with open(metadata_path) as fin:
            for line in fin:
                data =  json.loads(line)
                file_list.append(data["file_name"])
                caption_list.append(data["caption"])
                dataIDX_list.append(data["data_idx"])
                num_list.append(data["number"])
                label_list.append(data["label"])

        for idx, (file_path, file_obj) in enumerate(images):
            yield file_path, {
                "image": {
                    "path": file_path, 
                    "bytes": file_obj.read()
                },
                "caption" : caption_list[idx],
                "data_idx" : dataIDX_list[idx],
                "number" : num_list[idx],
                "label": label_list[idx]
            }