Datasets:
textvqa

	# 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.
	"""TextVQA dataset"""

	import copy
	import json
	import os

	import datasets


	_CITATION = """
	@inproceedings{singh2019towards,
	title={Towards VQA Models That Can Read},
	author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
	booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
	pages={8317-8326},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	TextVQA requires models to read and reason about text in images to answer questions about them.
	Specifically, models need to incorporate a new modality of text present in the images and reason
	over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
	from the OpenImages dataset.
	"""

	_HOMEPAGE = "https://textvqa.org"

	_LICENSE = "CC BY 4.0"

	_SPLITS = ["train", "val", "test"]
	_URLS = {
	f"{split}_annotations": f"https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_{split}.json"
	for split in _SPLITS
	}
	# TextVQA val and train images are packed together
	_URLS["train_val_images"] = "https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip"
	_URLS["test_images"] = "https://dl.fbaipublicfiles.com/textvqa/images/test_images.zip"
	_NUM_ANSWERS_PER_QUESTION = 10


	class Textvqa(datasets.GeneratorBasedBuilder):
	"""TextVQA dataset."""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="textvqa",
	version=datasets.Version("0.5.1"),
	description=_DESCRIPTION,
	)
	]

	DEFAULT_CONFIG_NAME = "textvqa"

	def _info(self):
	features = datasets.Features(
	{
	"image_id": datasets.Value("string"),
	"question_id": datasets.Value("int32"),
	"question": datasets.Value("string"),
	"question_tokens": datasets.Sequence(datasets.Value("string")),
	"image": datasets.Image(),
	"image_width": datasets.Value("int32"),
	"image_height": datasets.Value("int32"),
	"flickr_original_url": datasets.Value("string"),
	"flickr_300k_url": datasets.Value("string"),
	"answers": datasets.Sequence(datasets.Value("string")),
	"image_classes": datasets.Sequence(datasets.Value("string")),
	"set_name": datasets.Value("string"),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"annotations_path": downloaded_files["train_annotations"],
	"images_path": downloaded_files["train_val_images"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"annotations_path": downloaded_files["val_annotations"],
	"images_path": downloaded_files["train_val_images"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"annotations_path": downloaded_files["test_annotations"],
	"images_path": downloaded_files["test_images"],
	},
	),
	]

	def _generate_examples(self, annotations_path: str, images_path: str):
	with open(annotations_path, "r", encoding="utf-8") as f:
	data = json.load(f)["data"]
	idx = 0
	for item in data:
	item = copy.deepcopy(item)
	item["answers"] = item.get("answers", ["" for _ in range(_NUM_ANSWERS_PER_QUESTION)])
	image_id = item["image_id"]
	image_subfolder = "train_images" if item["set_name"] != "test" else "test_images"
	item["image"] = os.path.join(images_path, image_subfolder, f"{image_id}.jpg")
	yield idx, item
	idx += 1