import datasets
from huggingface_hub import HfApi
from datasets import DownloadManager, DatasetInfo
from datasets.data_files import DataFilesDict
import os
import json
from os.path import dirname, basename
from pathlib import Path


# ここに設定を記入
_NAME = "mb23/GraySpectrogram"
_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...
"""

# 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/docs/datasets/package_reference/builder_classes
# 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
# https://huggingface.co/datasets/frgfm/imagenette/blob/main/imagenette.py
# https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html
# DatasetInfo : https://huggingface.co/docs/datasets/package_reference/main_classes


def get_information():
    # データを整理？
    hfh_dataset_info = HfApi().dataset_info(_NAME, revision=_REVISION, timeout=100.0)

    # メタデータであるjsonlファイルのURLを取得
    # ここの抽出方法変えられないかな？
    train_metadata_url = DataFilesDict.from_hf_repo(
        {datasets.Split.TRAIN: ["data/train/**"]},
        dataset_info=hfh_dataset_info,
        allowed_extensions=["jsonl", ".jsonl"],
    )

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

    metadata_urls = dict()
    metadata_urls["train"] = train_metadata_url["train"]
    metadata_urls["test"] = test_metadata_url["test"]

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

    test_data_url = DataFilesDict.from_hf_repo(
        {datasets.Split.TEST: ["data/test/**"]},
        dataset_info=hfh_dataset_info,
        allowed_extensions=["zip", ".zip"]
    )
    data_urls = dict()
    data_urls["train"] = train_data_url["train"]
    data_urls["test"] = test_data_url["test"]
    return (metadata_urls, data_urls)



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

    def __init__(self, data_url, metadata_url, **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(GraySpectrogramConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_url = data_url
        self.metadata_url = metadata_url

class GraySpectrogram(datasets.GeneratorBasedBuilder):

    # データのサブセットはここで用意
    metadata_urls, data_urls = get_information()
    BUILDER_CONFIGS = [
        GraySpectrogramConfig(
            name="data 0-200",
            description=_DESCRIPTION,
            data_url = {
                "train" : data_urls["train"][0],
                "test" : data_urls["test"][0]
            },
            metadata_url = {
                "train" : metadata_urls["train"][0],
                "test" : metadata_urls["test"][0]
            }
            
        ), 
        GraySpectrogramConfig(
            name="data 200-600",
            description=_DESCRIPTION,
            data_url ={
                "train" : data_urls["train"][1],
                "test" : data_urls["test"][1]
            },
            metadata_url = {
                "train": metadata_urls["train"][1],
                "test" : metadata_urls["test"][1] 
            }
            
        ), 
        GraySpectrogramConfig(
            name="data 600-1000",
            description=_DESCRIPTION,
            data_url = {
                "train" : data_urls["train"][2],
                "test" : data_urls["test"][2]
            },
            metadata_url = {
                "train" : metadata_urls["train"][2],
                "test" : metadata_urls["test"][2]
            }
        ), 
        GraySpectrogramConfig(
            name="data 1000-1300",
            description=_DESCRIPTION,
            data_url = {
                "train" : data_urls["train"][3],
                "test" : data_urls["test"][3]
            },
            metadata_url = {
                "train" : metadata_urls["train"][3],
                "test" : metadata_urls["test"][3]
            }
            
        ), 
        GraySpectrogramConfig(
            name="data 1300-1600",
            description=_DESCRIPTION,
            data_url = {
                "train" : data_urls["train"][4],
                "test" : data_urls["test"][4]
            },
            metadata_url = {
                "train" : metadata_urls["train"][4],
                "test" : metadata_urls["test"][4]
            }   
        )
    ]

    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):

        train_metadata_path = dl_manager.download_and_extract(self.config.metadata_url["train"])
        test_metadata_path = dl_manager.download_and_extract(self.config.metadata_url["test"])
        train_data_path = dl_manager.download(self.config.data_url["train"])
        test_data_path = dl_manager.download(self.config.data_url["test"])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_archive(train_data_path),
                    "metadata_path": train_metadata_path,
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "images": dl_manager.iter_archive(test_data_path),
                    "metadata_path": test_metadata_path,
                }
            ),
        ]

        # # 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)

        # # メタデータであるjsonlファイルのURLを取得
        # # ここの抽出方法変えられないかな？
        # train_metadata_url = DataFilesDict.from_hf_repo(
        #     {datasets.Split.TRAIN: ["data/train/**"]},
        #     dataset_info=hfh_dataset_info,
        #     allowed_extensions=["jsonl", ".jsonl"],
        # )

        # test_metadata_url = DataFilesDict.from_hf_repo(
        #     {datasets.Split.TEST: ["data/test/**"]},
        #     dataset_info=hfh_dataset_info,
        #     allowed_extensions=["jsonl", ".jsonl"],
        # )
        
        # metadata_urls = dict()
        # metadata_urls["train"] = train_metadata_url["train"]
        # metadata_urls["test"] = test_metadata_url["test"]

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

        # test_data_url = DataFilesDict.from_hf_repo(
        #     {datasets.Split.TEST: ["data/test/**"]},
        #     dataset_info=hfh_dataset_info,
        #     allowed_extensions=["zip", ".zip"]
        # )
        # data_urls = dict()
        # data_urls["train"] = train_data_url["train"]
        # data_urls["test"] = test_data_url["test"]
        
        # gs = []

        # # for split, file_list in data_urls.items():
        # #     metadata_list = metadata_urls[split]
        # #     for i, file_ in enumerate(file_list):
        # #         '''
        # #         split : "train" or "test" or "val"
        # #         files : zip files
        # #         '''
        # #         # print(file_)
        # #         # print(metadata_list[0])
        # #         # # 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
        # #         metadata_path = dl_manager.download_and_extract(metadata_list[i])
        # #         downloaded_files = dl_manager.download(file_)
        # #         # # 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" : iter(iter_archive[split]),
        # #                     "images" : dl_manager.iter_archive(downloaded_files),
        # #                     "metadata_path": metadata_path # メタデータパスを渡す
        # #                 }
        # #             )
        # #         )

        # 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, encoding="utf-8") 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]
            }