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sea_wiki.py

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+"""
+SEA Crowd Data Loader for SEA Wiki.
+"""
+
+import json
+from itertools import product
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets import load_dataset
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.sea_datasets.sea_wiki.lang_config import _LANG_CONFIG
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@ONLINE{wikidump,
+    author = "Wikimedia Foundation",
+    title  = "Wikimedia Downloads",
+    url    = "https://dumps.wikimedia.org"}
+@ONLINE{wikipedia-hf,
+    title  = "Huggingface Wikipedia Dataset",
+    url    = "https://huggingface.co/datasets/wikipedia"}
+@ONLINE{wikipedia-hf,
+    title  = "Huggingface SEA Wikipedia Dataset",
+    url    = "https://huggingface.co/datasets/sabilmakbar/sea_wiki"}
+"""
+
+logger = datasets.logging.get_logger(__name__)
+
+_LOCAL = False
+_LANGUAGES = list(_LANG_CONFIG.keys())
+
+_DATASETNAME = "sea_wiki"
+_DESCRIPTION = """\
+    SEA Lang & Local Langs Wikipedia Archives, dumped from WIkipedia HF and processed by boilerplate removal.
+    This dataset consists of URL of referred Wikipedia Article, its Title, and its Text Data (Article Contents).
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/sabilmakbar/sea_wiki"
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+# url won't be used since it will implement load_dataset method on HF URL provided
+_URL = "https://huggingface.co/datasets/sabilmakbar/sea_wiki"
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING, Tasks.SUMMARIZATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+CONFIG_SUFFIXES_FOR_TASK = ["ssp", "t2t"]
+
+
+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 doesn'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()
+
+# construct zipped arg for config instantiation
+SCHEMA_PREFIX_AND_VERSION_PAIRS = list(zip(("source", "seacrowd"), (_SOURCE_VERSION, _SEACROWD_VERSION)))
+CONFIG_NAME_AND_TASKS_PAIRS = list(zip(CONFIG_SUFFIXES_FOR_TASK, _SUPPORTED_TASKS))
+
+
+def construct_configs(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 = []
+
+    # set default task for default config w/o task arg name (set to Tasks.SUMMARIZATION)
+    _DEFAULT_TASK_IDX = [idx for idx, val in enumerate(_SUPPORTED_TASKS) if val == Tasks.SUMMARIZATION]
+
+    # assert `_DEFAULT_TASK_IDX` to have len of 1
+    if len(_DEFAULT_TASK_IDX) != 1:
+        raise AssertionError("Unexpected `_DEFAULT_TASK` #item!")
+
+    _DEFAULT_CONFIG_SUFFIX, _DEFAULT_TASK = list(CONFIG_NAME_AND_TASKS_PAIRS)[_DEFAULT_TASK_IDX[0]]
+
+    # check `languages` variable and create config accordingly
+    if languages is None:
+        # set languages arg as list of first entry in `_LANGUAGES` if no lang arg received
+        _languages = _LANGUAGES[0]
+
+        config_list += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{config_name_prefix}",
+                version=datasets.Version(version),
+                description=f"{_DATASETNAME} {config_name_prefix} schema for default task arg ({_DEFAULT_TASK})",
+                schema=f"{config_name_prefix}_{_DEFAULT_CONFIG_SUFFIX}",
+                subset_id=_languages,
+            )
+            for (config_name_prefix, version) in SCHEMA_PREFIX_AND_VERSION_PAIRS
+        ]
+        config_list += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{config_name_prefix}_{config_name_suffix}",
+                version=datasets.Version(version),
+                description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name}",
+                schema=f"{config_name_prefix}_{config_name_suffix}",
+                subset_id=_languages,
+            )
+            for (config_name_prefix, version), (config_name_suffix, task_obj) in product(SCHEMA_PREFIX_AND_VERSION_PAIRS, CONFIG_NAME_AND_TASKS_PAIRS)
+        ]
+
+    # else, construct configs based on its lang
+    else:
+        for _LANG in languages:
+            config_list += [
+                SEACrowdConfig(
+                    name=f"{_DATASETNAME}_{config_name_prefix}_{_LANG}_{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 (config_name_prefix, version), (config_name_suffix, task_obj) in product(SCHEMA_PREFIX_AND_VERSION_PAIRS, CONFIG_NAME_AND_TASKS_PAIRS)
+            ]
+
+    return config_list
+
+
+class SEAWikiDataset(datasets.GeneratorBasedBuilder):
+    """SEA Wiki dataset from https://huggingface.co/datasets/sabilmakbar/sea_wiki"""
+
+    # get all schema w/o lang arg + get all schema w/ lang arg
+    BUILDER_CONFIGS = construct_configs() + construct_configs(_LANGUAGES)
+
+    def _info(self) -> datasets.DatasetInfo:
+        _config_schema_name = self.config.schema
+        logger.info(f"Received schema name: {self.config.schema}")
+        # self supervised training schema
+        if CONFIG_SUFFIXES_FOR_TASK[0] in _config_schema_name:
+            if "source" in _config_schema_name:
+                features = datasets.Features({"url": datasets.Value("string"), "text": datasets.Value("string")})
+
+            elif "seacrowd" in _config_schema_name:
+                features = schemas.ssp_features
+
+            else:
+                raise ValueError(f"Unexpected schema received! {_config_schema_name}")
+
+        # summarization schema
+        elif CONFIG_SUFFIXES_FOR_TASK[1] in _config_schema_name:
+            if "source" in _config_schema_name:
+                features = datasets.Features({"url": datasets.Value("string"), "title": datasets.Value("string"), "text": datasets.Value("string")})
+
+            elif "seacrowd" in _config_schema_name:
+                features = schemas.text2text_features
+
+            else:
+                raise ValueError(f"Unexpected schema received! {_config_schema_name}")
+
+        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]:
+        # args of dl_manager is a placeholder since this data loader will wrap the hf `load_dataset` from given _URL
+        # directly using `_load_hf_data_from_remote`
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]
+
+    def _load_hf_data_from_remote(self):
+        # construct remote_hf_reference by the last 2 of string-spliited of "/"
+        _remote_hf_reference = "/".join(_URL.split("/")[-2:])
+        _lang_args = _LANG_CONFIG[self.config.subset_id]["source_subset"]
+        _split = "train"
+
+        logger.info(f"Loading dataset from remote HF {_remote_hf_reference} with seacrowd lang args of {self.config.subset_id} and source lang args of {_lang_args} and split args of {_split}")
+        _hf_dataset_source = load_dataset(_remote_hf_reference, lang=_lang_args, split=_split)
+
+        return _hf_dataset_source
+
+    def _generate_examples(self) -> Tuple[int, Dict]:
+
+        _config_schema_name = self.config.schema
+        loaded_data = self._load_hf_data_from_remote()
+
+        # iterate over datapoints and arrange hf dataset schema in source to match w/ config args:
+        for id_, _data in enumerate(loaded_data):
+            if "source" in _config_schema_name:
+                yield id_, {colname: _data[colname] for colname in self.info.features}
+
+            # for ssp schema
+            elif "seacrowd" in _config_schema_name and CONFIG_SUFFIXES_FOR_TASK[0] in _config_schema_name:
+                yield id_, {"id": id_, "text": _data["text"]}
+
+            # for summary schema
+            elif "seacrowd" in _config_schema_name and CONFIG_SUFFIXES_FOR_TASK[1] in _config_schema_name:
+                yield id_, {"id": id_, "text_1": _data["text"], "text_2": _data["title"], "text_1_name": "document", "text_2_name": "title"}
+
+            else:
+                raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")