Delete control-celeba-hq.py

control-celeba-hq.py

@@ -1,117 +0,0 @@
-import pandas as pd
-from huggingface_hub import hf_hub_url
-import datasets
-import os
-
-_VERSION = datasets.Version("0.0.1")
-
-_DESCRIPTION = "TODO"
-_HOMEPAGE = "TODO"
-_LICENSE = "TODO"
-_CITATION = "TODO"
-
-_FEATURES = datasets.Features(
-    {
-        "image": datasets.Image(),
-        "conditioning_image": datasets.Image(),
-        "text": datasets.Value("string"),
-    },
-)
-
-TRAIN_METADATA_URL = hf_hub_url(
-    "oftverse/control-celeba-hq",
-    filename="train.jsonl",
-    repo_type="dataset",
-)
-
-TEST_METADATA_URL = hf_hub_url(
-    "oftverse/control-celeba-hq",
-    filename="test.jsonl",
-    repo_type="dataset",
-)
-
-IMAGES_URL = hf_hub_url(
-   "oftverse/control-celeba-hq",
-    filename="images.zip",
-    repo_type="dataset",
-)
-
-CONDITIONING_IMAGES_URL = hf_hub_url(
-    "oftverse/control-celeba-hq",
-    filename="conditioning_images.zip",
-    repo_type="dataset",
-)
-
-_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
-
-
-class CONTROL_CELEBA_HQ_DATASET(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):
-        train_metadata_path = dl_manager.download(TRAIN_METADATA_URL)
-        test_metadata_path = dl_manager.download(TEST_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,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "metadata_path": train_metadata_path,
-                    "images_dir": images_dir,
-                    "conditioning_images_dir": conditioning_images_dir,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "metadata_path": test_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 = row["image"]
-            image_path = os.path.join(images_dir, image_path)
-            image = open(image_path, "rb").read()
-
-            conditioning_image_path = row["conditioning_image"]
-            conditioning_image_path = os.path.join(
-                conditioning_images_dir, row["conditioning_image"]
-            )
-            conditioning_image = open(conditioning_image_path, "rb").read()
-
-            yield row["image"], {
-                "text": text,
-                "image": {
-                    "path": image_path,
-                    "bytes": image,
-                },
-                "conditioning_image": {
-                    "path": conditioning_image_path,
-                    "bytes": conditioning_image,
-                },
-            }