Super-squash branch 'main' using huggingface_hub

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: mit
+task_categories:
+- image-classification
+- image-to-image
+- text-to-image
+language:
+- en
+- ja
+pretty_name: danbooru2023
+size_categories:
+- 1M<n<10M
+---
+
+<img src="https://huggingface.co/datasets/nyanko7/danbooru2023/resolve/main/cover.webp" alt="cover" width="750"/>
+
+# Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset
+
+<!-- Provide a quick summary of the dataset. -->
+
+Danbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image.
+
+Danbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification, multi-label tagging, character detection, generative models, and other computer vision tasks.
+
+- **Shared by:** Nyanko Devs
+- **Language(s):** English, Japanese
+- **License:** MIT
+
+This dataset is built on the top of [danbooru2021](https://gwern.net/danbooru2021). We expands the dataset to include images up to ID #6,857,737, adding over 1.8 million additional images and total size is now approximately 8 terabytes (80,000 GB).
+
+## Use
+
+## Format
+
+The goal of the dataset is to be as easy as possible to use immediately, avoiding obscure file formats, while allowing simultaneous research & seeding of the torrent, with easy updates.
+
+Images are provided in the full original form (be that JPG, PNG, GIF or otherwise) for reference/archival purposes, and bucketed into 1000 subdirectories 0000–0999 (0-padded), which is the Danbooru ID modulo 1000 (ie. all images in 0999/ have an ID ending in ‘999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf "%04d" $(( ID % 1000 )) )) and then the file is at {original,512px}/$BUCKET/$ID.$EXT. 
+
+The reason for the bucketing is that a single directory would cause pathological filesystem performance, and modulo ID is a simple hash which spreads images evenly without requiring additional future directories to be made or a filesystem IO to check where the file is. The ID is not zero-padded and files end in the relevant extension, hence the file layout looks like this:
+
+```bash
+$ tree / | less
+
+/
+├── danbooru2023 -> /mnt/diffusionstorage/workspace/danbooru/
+│   ├── metadata
+│   ├── readme.md
+│   ├── original
+│   │   ├── 0000 -> data-0000.tar
+│   │   ├── 0001 -> data-0001.tar
+│   │   │   ├── 10001.jpg
+│   │   │   ├── 210001.png
+│   │   │   ├── 3120001.webp
+│   │   │   ├── 6513001.jpg
+
+```
+          
+Currently represented file extensions are: avi/bmp/gif/html/jpeg/jpg/mp3/mp4/mpg/pdf/png/rar/swf/webm/wmv/zip. 
+
+Raw original files are treacherous. Be careful if working with the original dataset. There are many odd files: truncated, non-sRGB colorspace, wrong file extensions (eg. some PNGs have .jpg extensions like original/0146/1525146.jpg or original/0558/1422558.jpg), etc.

danbooru2023.py

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+import os
+import datasets
+from huggingface_hub import HfApi
+from datasets import DownloadManager, DatasetInfo
+from datasets.data_files import DataFilesDict
+
+_EXTENSION = [".png", ".jpg", ".jpeg", ".webp", ".bmp"]
+_NAME = "nyanko7/danbooru2023"
+_REVISION = "main"
+
+
+class DanbooruDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        # add number before name for sorting
+        datasets.BuilderConfig(name="full"),
+    ]
+
+    def _info(self) -> DatasetInfo:
+        features = {
+            "image": datasets.Image(),
+            "post_id": datasets.Value("int64")
+        }
+        info = datasets.DatasetInfo(
+            features=datasets.Features(features),
+            supervised_keys=None,
+            citation="",
+        )
+        return info
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        hfh_dataset_info = HfApi().dataset_info(_NAME, revision=_REVISION, timeout=100.0)
+        data_files = DataFilesDict.from_hf_repo(
+            {datasets.Split.TRAIN: ["**"]},
+            dataset_info=hfh_dataset_info,
+            allowed_extensions=["tar", ".tar"],
+        )
+        gs = []
+        for split, files in data_files.items():
+            downloaded_files = dl_manager.download_and_extract(files)
+            gs.append(datasets.SplitGenerator(name=split, gen_kwargs={"filepath": downloaded_files}))
+        return gs
+
+    def _generate_examples(self, filepath):
+        for path in filepath:
+            all_fnames = {os.path.relpath(os.path.join(root, fname), start=path) for root, _dirs, files in os.walk(path) for fname in files}
+            image_fnames = sorted([fname for fname in all_fnames if os.path.splitext(fname)[1].lower() in _EXTENSION], reverse=True)
+            for image_fname in image_fnames:
+                image_path = os.path.join(path, image_fname)
+                post_id = int(os.path.splitext(os.path.basename(image_fname))[0])
+                yield image_fname, {"post_id": post_id}

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