Upload instruck500k_vi.py

scrip load data instruct500k vi

instruck500k_vi.py

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+import os
+import datasets
+from huggingface_hub import HfFileSystem
+
+
+logger = datasets.logging.get_logger(__name__)
+fs = HfFileSystem()
+
+
+_CITATION = """
+
+"""
+_DESCRIPTION = """
+
+"""
+_HOMEPAGE = "https://github.com/FPT-VVU/ViVidBot"
+_REPO_URL = "https://huggingface.co/{}/resolve/main/datasets/Vividbot/instruct500k_vi"
+_URLS = {
+    "meta": f"{_REPO_URL}/instruct500k_vi.json",
+    "image": f"{_REPO_URL}/images" + "{shard}.zip",
+}
+
+_CONFIGS = [
+    os.path.basename(file_name).split(".")[0]
+    for file_name in fs.listdir(_REPO_URL + "/images", detail=False)
+    if file_name.endswith(".zip")
+]
+
+class Instruct500k_ViConfig(datasets.BuilderConfig):
+    """BuilderConfig for Vast2M_Vi."""
+
+    def __init__(self, **kwargs):
+        """
+        :param kwargs:  Arguments.
+        """
+        super().__init__(
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+            **kwargs,
+        )
+
+
+class Instruck500k_Vi(datasets.GeneratorBasedBuilder):
+    """Vast2M Vi dataset."""
+
+    BUILDER_CONFIGS = Instruct500k_ViConfig()
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "image": datasets.Value("binary"),
+                "conversations": datasets.Value(
+                    "dict",
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> list[datasets.SplitGenerator]:
+        """
+        Get splits.
+        :param dl_manager:  Download manager.
+        :return:            Splits.
+        """
+
+        metadata_paths = dl_manager.download(_URLS["meta"])
+        dataset = datasets.load_dataset(
+            "json",
+            data_files=metadata_paths,
+            split="train",
+        )
+        dataset = dataset.train_test_split(test_size=0.1, shuffle=True, seed=42)
+        train_set = dataset["train"]
+        val_test_set = dataset["test"].train_test_split(test_size=0.5)
+        val_set = val_test_set["train"]
+        test_set = val_test_set["test"]
+
+        split_dict = {
+            datasets.Split.TRAIN: train_set,
+            datasets.Split.VALIDATION: val_set,
+            datasets.Split.TEST: test_set,
+        }
+
+        image_dirs = dl_manager.download_and_extract(
+            [_URLS["image"].format(shard=shard) for shard in _CONFIGS]
+        )
+        image_dict = {
+            shard: visual_dir
+            for shard, visual_dir in zip(_CONFIGS, image_dirs)
+        }
+
+        return [
+            datasets.SplitGenerator(
+                gen_kwargs={
+                    "split": split,
+                    "image_dict": image_dict,
+                },
+            )
+            for split in split_dict.items()
+        ]
+
+    def _generate_examples(
+        self,
+        split: datasets.Dataset,
+        image_dict: dict,
+    ) -> tuple[int, dict]:
+        """
+        Generate examples.
+        :param split:                   Split.
+        :param visual_dict:             Paths to directory containing visual files.
+        :param audio_dict:              Paths to directory containing audio files.
+        :param transcript_dict:         Paths to directory containing transcripts.
+        :return:                        Example.
+        """
+        for i, sample in enumerate(split):
+            shard = sample["image"].split("/")[-1].split(".")[0]
+            image_path = os.path.join(
+                image_dict[shard], sample["image"]
+            )
+
+            yield i, {
+                "id": sample["id"],
+                "video": self.__get_binary_data(image_path),
+                "conversations": sample["conversations"],
+            }
+
+    def __get_binary_data(self, path: str) -> bytes:
+        """
+        Get binary data from path.
+        :param path:    Path to file.
+        :return:        Binary data.
+        """
+        with open(path, "rb") as f:
+            return f.read()
+
+    def __get_text_data(self, path: str) -> str:
+        """
+        Get transcript from path.
+        :param path:     Path to transcript.
+        :return:         Transcript.
+        """
+        with open(path, "r", encoding="utf-8") as f:
+            return f.read().strip()