mdm_data.py

"""TODO(wikitext): Add a description here."""


import os

import datasets


_CITATION = """\
@misc{
}
"""

_DESCRIPTION = """\
 The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
 Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike
 License.
"""
_HOMEPAGE = "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/"
_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
_DATA_URL = "https://huggingface.co/datasets/alescontrela/mdm_data/blob/main"


class MDMDataConfig(datasets.BuilderConfig):
    """BuilderConfig for GLUE."""

    def __init__(self, data_url, **kwargs):
        """BuilderConfig for Wikitext
        Args:
          data_url: `string`, url to the dataset (word or raw level)
          **kwargs: keyword arguments forwarded to super.
        """
        super(MDMDataConfig, self).__init__(
            version=datasets.Version(
                "1.0.0",
            ),
            **kwargs,
        )
        self.data_url = data_url


class MDMData(datasets.GeneratorBasedBuilder):
    """TODO(wikitext_103): Short description of my dataset."""

    # TODO(wikitext_103): Set up version.
    VERSION = datasets.Version("0.1.0")
    BUILDER_CONFIGS = [
        MDMDataConfig(
            name="MDM Data",
            data_url=_DATA_URL + "/" + "HumanML3D.zip",
            description="Text to motion dataset.",
        ),
    ]

    def _info(self):
        # TODO(wikitext): Specifies the datasets.DatasetInfo object
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # datasets.features.FeatureConnectors
            features=datasets.Features(
                {
                    "text": datasets.Value("string")
                    # These are the features of your dataset like images, labels ...
                }
            ),
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO(wikitext): Downloads the data and defines the splits
        # dl_manager is a datasets.download.DownloadManager that can be used to
        # download and extract URLs
        data_file = dl_manager.download_and_extract(self.config.data_url)
        data_dir = os.path.join(data_file, "HumanML3D.zip")
        print(data_file, data_dir)
        return []
        # return [
        #     datasets.SplitGenerator(
        #         name=datasets.Split.TEST,
        #         gen_kwargs={"data_file": os.path.join(data_dir, "wiki.test.tokens"), "split": "test"},
        #     ),
        #     datasets.SplitGenerator(
        #         name=datasets.Split.TRAIN,
        #         gen_kwargs={"data_file": os.path.join(data_dir, "wiki.train.tokens"), "split": "train"},
        #     ),
        #     datasets.SplitGenerator(
        #         name=datasets.Split.VALIDATION,
        #         gen_kwargs={"data_file": os.path.join(data_dir, "wiki.valid.tokens"), "split": "valid"},
        #     ),
        # ]


    def _generate_examples(self, data_file, split):

        """Yields examples."""
        # TODO(wikitext): Yields (key, example) tuples from the dataset
        with open(data_file, encoding="utf-8") as f:
            for idx, row in enumerate(f):
                if row.strip():
                    yield idx, {"text": row}
                else:
                    yield idx, {"text": ""}