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bloom_vist.py

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+"""
+SEA Crowd Data Loader for Bloom VIST.
+"""
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = r"""
+@inproceedings{leong-etal-2022-bloom,
+    title = "Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks",
+    author = "Leong, Colin  and
+      Nemecek, Joshua  and
+      Mansdorfer, Jacob  and
+      Filighera, Anna  and
+      Owodunni, Abraham  and
+      Whitenack, Daniel",
+    editor = "Goldberg, Yoav  and
+      Kozareva, Zornitsa  and
+      Zhang, Yue",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.emnlp-main.590",
+    doi = "10.18653/v1/2022.emnlp-main.590",
+    pages = "8608--8621",
+}
+"""
+
+logger = datasets.logging.get_logger(__name__)
+
+# this config is created for SEACrowd Dataloader
+_LANG_CONFIG = {
+    "abc": "Ambala Ayta",
+    "ahk": "Akha",
+    "bfn": "Bunak",
+    "bjn": "Banjar",
+    "bkx": "Baikeno",
+    "brb": "Brao",
+    "brv": "Western Bru",
+    "bya": "Batak",
+    "bzi": "Bisu",
+    "ceb": "Cebuano",
+    "cgc": "Kagayanen",
+    "cmo": "Central Mnong",
+    "ddg": "Fataluku",
+    "dmg": "Upper Kinabatangan",
+    "dnw": "Western Dani",
+    "dtp": "Kadazan Dusun",
+    "enc": "En",
+    "fil": "Filipino",
+    "hil": "Hiligaynon",
+    "hro": "Haroi",
+    "idt": "Idaté",
+    "ilo": "Ilocano",
+    "ind": "Indonesian",
+    "jra": "Jarai",
+    "kak": "Kalanguya",
+    "khb": "Lü",
+    "khm": "Khmer",
+    "kqr": "Kimaragang",
+    "krr": "Krung",
+    "ksw": "S’gaw Karen",
+    "lhu": "Lahu",
+    "lsi": "Lacid",
+    "lwl": "Eastern Lawa",
+    "mdr": "Mandar",
+    "mgm": "Mambae",
+    "mhx": "Lhao Vo",
+    "mkz": "Makasae",
+    "mry": "Mandaya",
+    "msb": "Masbatenyo",
+    "mya": "Burmese",
+    "nod": "Northern Thai",
+    "nxa": "Nauete",
+    "nxl": "South Nuaulu",
+    "pag": "Pangasinan",
+    "pce": "Ruching Palaung",
+    "pea": "Peranakan Indonesian",
+    "pmf": "Pamona",
+    "psp": "Filipino Sign Language",
+    "sea": "Semai",
+    "sgd": "Surigaonon",
+    "sml": "Central Sama",
+    "snl": "Sangil",
+    "tdt": "Tetun Dili",
+    "tet": "Tetun",
+    "tha": "Thai",
+    "tkd": "Tukudede",
+    "tpu": "Tampuan",
+    "war": "Waray-Waray",
+    "wms": "Wambon",
+    "yet": "Yetfa",
+    "yin": "Riang Lai",
+    "zlm": "Malay",
+}
+
+_LOCAL = False
+_LANGUAGES = list(_LANG_CONFIG.keys())
+
+
+_DATASETNAME = "bloom_vist"
+_DESCRIPTION = r"""
+BLOOM VIST is a visual storytelling of books that consists of 62 languages indigenous to SEA.
+This dataset is owned by Bloom, a free, open-source software developed by SIL International and associated with Bloom Library, app, and services.
+This dataset is released with the LICENSE family of Creative Commons (although each story datapoints has its licensing in more detail,
+e.g cc-by, cc-by-nc, cc-by-nd, cc-by-sa, cc-by-nc-nd, cc-by-nc-sa).
+Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/sil-ai/bloom-vist and use huggingface-cli login for authentication.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/sil-ai/bloom-vist"
+_LICENSE = Licenses.CC.value
+
+_URL = "https://huggingface.co/datasets/sil-ai/bloom-vist"
+_HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
+
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+_SOURCE_VERSION = "0.1.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+CONFIG_SUFFIXES_FOR_TASK = [TASK_TO_SCHEMA.get(task).lower() for task in _SUPPORTED_TASKS]
+
+
+def conform_init_config():
+    """Assertion Function for Instantiated Configs"""
+    if len(_LANGUAGES) == 0:
+        raise AssertionError("No Languages detected from config!")
+    if len(CONFIG_SUFFIXES_FOR_TASK) != len(_SUPPORTED_TASKS):
+        raise AssertionError("Config prefixes don't matched in terms of `len` with `_SUPPORTED_TASKS`!")
+    if len(CONFIG_SUFFIXES_FOR_TASK) == 0:
+        raise AssertionError("Config prefixes and `_SUPPORTED_TASKS` have `len` of 0!")
+
+
+conform_init_config()
+
+
+def construct_configs_on_langs(languages: list = None) -> List[SEACrowdConfig]:
+    """
+    The function `construct_configs` constructs a list of SEACrowdConfig objects based on the provided
+    languages or a default language, and returns the list.
+
+    input:
+        languages (list, default None): The `languages` parameter is a list that specifies the languages for which the
+        configurations need to be constructed. If no languages are provided (value=None), the first value in language config
+        will be used.
+    output:
+        a list of `SEACrowdConfig` objects based on instantiated init variables
+    """
+
+    # set output var
+    config_list = []
+
+    # construct zipped arg for config instantiation
+    TASKS_AND_CONFIG_SUFFIX_PAIRS = list(zip(_SUPPORTED_TASKS, CONFIG_SUFFIXES_FOR_TASK))
+
+    # implement source schema
+    version, config_name_prefix = _SOURCE_VERSION, "source"
+    config_list += [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}",
+            version=datasets.Version(version),
+            description=f"{_DATASETNAME} {config_name_prefix} schema for language code {_LANG}",
+            schema=f"{config_name_prefix}",
+            subset_id=_LANG,
+        )
+        for _LANG in languages
+    ]
+
+    # implement SEACrowd schema
+    version, config_name_prefix = _SEACROWD_VERSION, "seacrowd"
+    for task_obj, config_name_suffix in TASKS_AND_CONFIG_SUFFIX_PAIRS:
+        config_list += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}_{config_name_suffix}",
+                version=datasets.Version(version),
+                description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name} and language code {_LANG}",
+                schema=f"{config_name_prefix}_{config_name_suffix}",
+                subset_id=_LANG,
+            )
+            for _LANG in languages
+        ]
+    return config_list
+
+
+class BloomVISTDataset(datasets.GeneratorBasedBuilder):
+    """Bloom VIST dataset, subsetted from https://huggingface.co/datasets/sil-ai/bloom-vist"""
+
+    # get all schema w/o lang arg + get all schema w/ lang arg
+    BUILDER_CONFIGS = construct_configs_on_langs(_LANGUAGES)
+
+    def _info(self) -> datasets.DatasetInfo:
+        _config_schema_name = self.config.schema
+        logger.info(f"Received schema name: {self.config.schema}")
+        # source schema
+        if _config_schema_name == "source":
+            features = datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "license": datasets.Value("string"),
+                    "album_id": datasets.Value("string"),
+                    "story": datasets.Sequence(
+                        feature={"image_id": datasets.Value("string"), "image_url": datasets.Value("string"), "story_index": datasets.Value("int32"), "story_id": datasets.Value("string"), "text": datasets.Value("string")}, length=-1, id=None
+                    ),
+                }
+            )
+
+        # image-text schema
+        elif _config_schema_name == "seacrowd_imtext":
+            features = schemas.image_text_features()
+
+        else:
+            raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        hf_dset_dict = datasets.load_dataset(_HF_REMOTE_REF, self.config.subset_id)
+
+        return [datasets.SplitGenerator(name=datasets.Split(dset_key), gen_kwargs={"hf_dset": dset}) for dset_key, dset in hf_dset_dict.items() if dset.num_rows > 0]
+
+    def _generate_examples(self, hf_dset) -> Tuple[int, Dict]:
+        _config_schema_name = self.config.schema
+
+        _idx = 0
+        for datapoints in hf_dset:
+            # for source schema, the `_idx` will be taken from "album_id" value
+            if _config_schema_name == "source":
+                yield datapoints["album_id"], {colname: datapoints[colname] for colname in self.info.features}
+
+            # for seacrowd schema, the `_idx` will be created manually
+            # since one album_id has multiple pairs of image-text
+            elif _config_schema_name == "seacrowd_imtext":
+                # check the len of the features in sequenced columns
+                # since in source hf there's no validation on data integrity
+                _len_vars = []
+                _ftrs_in_seq = ("image_id", "image_url", "story_index", "story_id", "text")
+                story_data = datapoints["story"]
+                for ftr in _ftrs_in_seq:
+                    _len_vars.append(len(story_data[ftr]))
+
+                # skip story w/ mismatched infos
+                if max(_len_vars) != min(_len_vars):
+                    continue
+
+                for num_data in range(max(_len_vars)):
+                    yield _idx, {"id": _idx, "image_paths": [story_data["image_url"][num_data]], "texts": story_data["text"][num_data], "metadata": {"context": datapoints["title"], "labels": []}}
+                    _idx += 1
+
+            else:
+                raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")