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.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/test_repo.iml" filepath="$PROJECT_DIR$/.idea/test_repo.iml" />
+    </modules>
+  </component>
+</project>

.idea/test_repo.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/workspace.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showExcludedFiles" value="false" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+</project>

annotations/test_annotations/mask.tar.gz

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+oid sha256:2692a5f89d12e62ef160b7c14f26a030213b1f4338d2efe9dc739a741955c388
+size 192406

annotations/train_annotations/mask.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:31f903069f512aa44b22924b5ddf2ed4e47c8de6e4a64a1b3e92c669e5b46bd9
+size 1884937

annotations/val_annotations/mask.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65eaee2355191385bc5a57dfe09f83a8144a1789dc600f82ac08c587d95d629f
+size 200268

test_repo.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import json
+import os
+
+import PIL.Image
+import datasets
+import numpy as np
+
+for _ in range(10):
+    print("LOADING SCRIPT")
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "8x8": [
+        "https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/images.tar.gz?download=true",
+        "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/annotations/{split}_annotations/mask.tar.gz?download=true",
+        "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/{split}.jsonl?download=true"
+    ]
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class PatchyImagenet(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    BUILDER_CONFIGS = [
+        # datasets.BuilderConfig(name="1x1", version=VERSION, description="Patchy Imagenet with 1x1 resolution (this is the original resolution)"),
+        datasets.BuilderConfig(name="8x8", version=VERSION, description="Patchy Imagenet with 8x8 resolution"),
+        # datasets.BuilderConfig(name="16x16", version=VERSION, description="Patchy Imagenet with 16x16 resolution"),
+        # datasets.BuilderConfig(name="32x32", version=VERSION, description="Patchy Imagenet with 32x32 resolution"),
+        # datasets.BuilderConfig(name="64x64", version=VERSION, description="Patchy Imagenet with 64x64 resolution"),
+    ]
+    DEFAULT_CONFIG_NAME = "8x8"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "patches": datasets.Features(
+                    {
+                        # "categories": datasets.Sequence(datasets.ClassLabel(names=_IMAGENET_CLASSES)),
+                        "categories": datasets.Value("string"),
+                        "scores": datasets.Sequence(datasets.Value("float32")),
+                        "mask": datasets.Array2D(shape=(224, 224), dtype="bool"),
+                        # "mask": datasets.Sequence(datasets.Image()),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        url_templates = _URLS[self.config.name]
+
+        split_kwargs = {}
+        for split in ["train", "test", "val"]:
+            urls = [url.format(split=split) for url in url_templates]
+            image_dir, mask_dir, metadata_file = dl_manager.download_and_extract(urls)
+            split_kwargs[split] = {
+                "meta_path": metadata_file,
+                "image_dir": image_dir, "mask_dir": mask_dir,
+                "split": split
+            }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=split_kwargs["train"]),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=split_kwargs["val"]),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=split_kwargs["test"]),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, meta_path, image_dir, mask_dir, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(meta_path, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                image_path = os.path.join(image_dir, "images", f"{split}_images", data["file_name"])
+                sample_name, _extension = os.path.splitext(data["file_name"])
+                mask_file = os.path.join(mask_dir, "masks", sample_name + ".npy")
+                mask = np.load(mask_file).astype(bool)
+                # with open(image_path, "rb") as f:
+                # breakpoint()
+                pil_image = PIL.Image.open(image_path)
+                yield key, {
+                    "image": pil_image,
+                    "patches": {
+                        "categories": data["patches"]["categories"],
+                        "scores": data["patches"]["scores"],
+                        "mask": list(mask),
+                    }
+                }