image_dummy.py

# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import textwrap

import datasets


_CITATION = """\
"""

_DESCRIPTION = """\
"""


class ImageDummybConfig(datasets.BuilderConfig):
    """BuilderConfig for Superb."""

    def __init__(
        self,
        data_url,
        url,
        task_templates=None,
        **kwargs,
    ):
        super(ImageDummybConfig, self).__init__(
            version=datasets.Version("1.9.0", ""), **kwargs
        )
        self.data_url = data_url
        self.url = url
        self.task_templates = task_templates


class ImageDummy(datasets.GeneratorBasedBuilder):
    """Superb dataset."""

    BUILDER_CONFIGS = [
        ImageDummybConfig(
            name="image",
            description=textwrap.dedent(""),
            url="",
            data_url="",
        )
    ]

    DEFAULT_CONFIG_NAME = "image"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "file": datasets.Value("string"),
                }
            ),
            supervised_keys=("file",),
            homepage=self.config.url,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        DL_URLS = [
            f"https://huggingface.co/datasets/Narsil/image_dummy/raw/main/{name}"
            for name in ["lena.png", "parrots.png", "tree.png"]
        ]
        archive_path = dl_manager.download_and_extract(DL_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"archive_path": archive_path},
            ),
        ]

    def _generate_examples(self, archive_path):
        """Generate examples."""
        for i, filename in enumerate(archive_path):
            key = str(i)
            example = {
                "id": key,
                "file": filename,
            }
            yield key, example