Delete flickr30k-fa.py

flickr30k-fa.py

@@ -1,79 +0,0 @@
-import csv
-import os
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """"""
-
-_DESCRIPTION = """Flickr30k filtered and translated to Persian made by Sajjad Ayoubi https://www.kaggle.com/datasets/sajjadayobi360/flickrfa"""
-
-_DOWNLOAD_URLS = {
-    "train": "https://huggingface.co/datasets/hezarai/flickr30k-fa/resolve/main/annotations_train.csv",
-    "test": "https://huggingface.co/datasets/hezarai/flickr30k-fa/resolve/main/annotations_test.csv",
-    "data": "https://huggingface.co/datasets/hezarai/flickr30k-fa/resolve/main/images.zip",
-}
-
-ZIP_IMAGES_DIR = "images"
-
-
-class Flickr30kFaConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(Flickr30kFaConfig, self).__init__(**kwargs)
-
-
-class Flickr30kFa(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        Flickr30kFaConfig(
-            name="Persian flickr30k",
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image_path": datasets.Value("string"),
-                    "label": datasets.Value("string"),
-                }
-            ),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """
-        Return SplitGenerators.
-        """
-
-        train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
-        test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
-        archive_path = dl_manager.download(_DOWNLOAD_URLS["data"])
-        images_dir = dl_manager.extract(archive_path) if not dl_manager.is_streaming else ""
-        images_dir = os.path.join(images_dir, ZIP_IMAGES_DIR)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"annotations_file": train_path, "images_dir": images_dir}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"annotations_file": test_path, "images_dir": images_dir}
-            ),
-        ]
-
-    def _generate_examples(self, annotations_file, images_dir):
-        logger.info("⏳ Generating examples from = %s", annotations_file)
-
-        with open(annotations_file, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
-
-            # Skip header
-            next(csv_reader, None)
-
-            for id_, row in enumerate(csv_reader):
-                filename, label = row
-                image_path = os.path.join(images_dir, filename)
-                yield id_, {"image_path": image_path, "label": label}