Datasets:
discofuse

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

	from __future__ import absolute_import, division, print_function

	import csv
	import os

	import datasets


	# TODO(discofuse): BibTeX citation

	_URL_ = "https://storage.googleapis.com/discofuse_dataset_v1/"
	_CITATION = """\
	@InProceedings{GevaEtAl2019,
	title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
	author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
	booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
	note = {arXiv preprint arXiv:1902.10526},
	year = {2019}
	}

	"""

	# TODO(discofuse):
	_DESCRIPTION = """\
	DISCOFUSE is a large scale dataset for discourse-based sentence fusion.
	"""


	class DiscofuseConfig(datasets.BuilderConfig):

	""" BuilderConfig for Discofuse"""

	def __init__(self, data_url, balanced=False, **kwargs):
	"""

	Args:
	balanced: to specify if we want to load the balanced file or the full file
	**kwargs: keyword arguments forwarded to super.
	"""
	super(DiscofuseConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.balanced = balanced
	self.data_url = data_url


	class Discofuse(datasets.GeneratorBasedBuilder):
	"""TODO(discofuse): Short description of my dataset."""

	# TODO(discofuse): Set up version.
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	DiscofuseConfig(
	name="discofuse-sport", description="sentence fusion", data_url=_URL_ + "discofuse_v1_sports.tar.gz"
	),
	DiscofuseConfig(
	name="discofuse-wikipedia", description="sentence fusion", data_url=_URL_ + "discofuse_v1_wikipedia.tar.gz"
	),
	]

	def _info(self):
	# TODO(discofuse): 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(
	{
	"connective_string": datasets.Value("string"),
	"discourse_type": datasets.Value("string"),
	"coherent_second_sentence": datasets.Value("string"),
	"has_coref_type_pronoun": datasets.Value("float32"),
	"incoherent_first_sentence": datasets.Value("string"),
	"incoherent_second_sentence": datasets.Value("string"),
	"has_coref_type_nominal": datasets.Value("float32"),
	"coherent_first_sentence": 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 of the dataset for documentation
	homepage="https://github.com/google-research-datasets/discofuse",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(discofuse): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	if self.config.name == "discofuse-sport":
	dl_dir = dl_manager.download_and_extract(self.config.data_url)
	data_dir = os.path.join(dl_dir, "discofuse_v1/sports")
	if self.config.balanced:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")},
	),
	]
	else:
	if self.config.name == "discofuse-wikipedia":
	dl_dir = dl_manager.download_and_extract(self.config.data_url)
	data_dir = os.path.join(dl_dir, "discofuse_v1/wikipedia")
	if self.config.balanced:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(discofuse): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = csv.DictReader(f, delimiter="\t")
	for id_, row in enumerate(data):
	co_first_sent = row["coherent_first_sentence"]
	co_second_sent = row["coherent_second_sentence"]
	connect_str = row["connective_string"]
	discourse_type = row["discourse_type"]
	has_coref_pronoun = row["has_coref_type_pronoun"]
	has_coref_nominal = row["has_coref_type_nominal"]
	inco_first_sent = row["incoherent_first_sentence"]
	inco_second_sent = row["incoherent_second_sentence"]
	yield id_, {
	"connective_string": connect_str,
	"discourse_type": discourse_type,
	"coherent_second_sentence": co_second_sent,
	"has_coref_type_pronoun": has_coref_pronoun,
	"incoherent_first_sentence": inco_first_sent,
	"incoherent_second_sentence": inco_second_sent,
	"has_coref_type_nominal": has_coref_nominal,
	"coherent_first_sentence": co_first_sent,
	}