Upload folder using huggingface_hub

conditioning_images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3dad6acf005d9cd9cc3c4204c220221fda9c622f4b8ef88aa1eb30496c7d27b1
+size 24404

dataset.jsonl

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+{"text": "three image", "image": "images/3.png", "conditioning_image": "conditioning_images/3.png"}
+{"text": "zero image", "image": "images/0.png", "conditioning_image": "conditioning_images/0.png"}
+{"text": "two image", "image": "images/2.png", "conditioning_image": "conditioning_images/2.png"}
+{"text": "one image", "image": "images/1.png", "conditioning_image": "conditioning_images/1.png"}

images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:211bc6be242253eae8c5c522b99ec89cbafbb5b497f3155de84a5bdb50939543
+size 286108

test.py

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+import pandas as pd
+from huggingface_hub import hf_hub_url
+import datasets
+import os
+
+_VERSION = datasets.Version("0.0.2")
+
+_DESCRIPTION = "This dataset includes images and conditioning images for XYZ purpose."
+_HOMEPAGE = "https://www.example.com"
+_LICENSE = "MIT"
+_CITATION = """@article{YourDataset2021,
+  title={Your Dataset Title},
+  author={Your Name},
+  journal={Your Journal},
+  year={2021}
+}"""
+
+_FEATURES = datasets.Features({
+    "image": datasets.Value("string"),  # Change from datasets.Image() to Value("string") if using paths directly
+    "conditioning_image": datasets.Value("string"),
+    "text": datasets.Value("string"),
+})
+
+METADATA_URL = hf_hub_url(
+    "spine-crook/test",
+    filename="train.jsonl",
+    repo_type="dataset",
+)
+
+IMAGES_URL = hf_hub_url(
+    "spine-crook/test",
+    filename="images.zip",
+    repo_type="dataset",
+)
+
+CONDITIONING_IMAGES_URL = hf_hub_url(
+    "spine-crook/test",
+    filename="conditioning_images.zip",
+    repo_type="dataset",
+)
+
+_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
+
+class Test(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [_DEFAULT_CONFIG]
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_FEATURES,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        metadata_path = dl_manager.download(METADATA_URL)
+        images_dir = dl_manager.download_and_extract(IMAGES_URL)
+        conditioning_images_dir = dl_manager.download_and_extract(CONDITIONING_IMAGES_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "metadata_path": metadata_path,
+                    "images_dir": images_dir,
+                    "conditioning_images_dir": conditioning_images_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
+        metadata = pd.read_json(metadata_path, lines=True)
+
+        for _, row in metadata.iterrows():
+            text = row["text"]
+            image_path = os.path.join(images_dir, row["image"])
+            conditioning_image_path = os.path.join(conditioning_images_dir, row["conditioning_image"])
+
+            yield row["image"], {
+                "text": text,
+                "image": image_path,
+                "conditioning_image": conditioning_image_path,
+            }