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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.Wikipedia
# Lint as: python3
"""Dream!n character datasets"""
import datasets
import json
import urllib
_CITATION = """
"""
_DESCRIPTION = "Dream!n character datasets"
_DATASET_URL = "https://huggingface.co/datasets/JAWCF/characters/resolve/main/images.tar.gz"
json_url = urllib.request.urlopen("https://huggingface.co/datasets/JAWCF/characters/resolve/main/characters.json")
DICT_DESC = json.loads(json_url.read())
class Characters(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features({
'image': datasets.Image(),
'text': datasets.Value('string')
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="",
# License for the dataset if available
license="",
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download(_DATASET_URL)
image_iters = dl_manager.iter_archive(path)
splits = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": image_iters
}
),
]
return splits
def _generate_examples(self, images):
"""Yields examples."""
idx = 0
for filepath, image in images:
yield idx, {
"image": {"path": filepath, "bytes": image.read()},
"text" : DICT_DESC[filepath.split("/")[-1]],
}
idx+=1 |