Datasets:
qanta

	"""qanta dataset."""


	import json
	from typing import List, Tuple

	import datasets


	_CITATION = """
	@article{Rodriguez2019QuizbowlTC,
	title={Quizbowl: The Case for Incremental Question Answering},
	author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
	journal={ArXiv},
	year={2019},
	volume={abs/1904.04792}
	}
	"""

	_DESCRIPTION = """
	The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl.
	"""


	_QANTA_URL = "https://s3-us-west-2.amazonaws.com/pinafore-us-west-2/qanta-jmlr-datasets/qanta.mapped.2018.04.18.json"
	_TRICK_URL = "https://s3-us-west-2.amazonaws.com/pinafore-us-west-2/trick-tacl-datasets/qanta.tacl-trick.json"
	_VERSION = datasets.Version("2018.04.18")
	_FIRST = "first"
	_FULL = "full"
	_SENTENCES = "sentences"
	_RUNS = "runs"
	# Order matters, the first one is default
	_MODES = [_FULL, _FIRST, _SENTENCES, _RUNS]
	_DEFAULT_CHAR_SKIP = 25


	class QantaConfig(datasets.BuilderConfig):
	"""BuilderConfig for Qanta."""

	def __init__(self, mode: str, char_skip: int, **kwargs):
	super(QantaConfig, self).__init__(version=_VERSION, **kwargs)
	self.mode = mode
	self.char_skip = char_skip


	def create_char_runs(text: str, char_skip: int) -> List[Tuple[str, int]]:
	"""
	Returns runs of the question based on skipping char_skip characters at a time. Also returns the indices used
	q: name this first united states president.
	runs with char_skip=10:
	['name this ',
	'name this first unit',
	'name this first united state p',
	'name this first united state president.']
	:param char_skip: Number of characters to skip each time
	"""
	char_indices = list(range(char_skip, len(text) + char_skip, char_skip))
	return [(text[:idx], idx) for idx in char_indices]


	def with_default(key, lookup, default):
	if key in lookup:
	value = lookup[key]
	if value is None:
	return default
	else:
	return value
	else:
	return default


	def question_to_examples(question, mode: str, char_skip: int):
	features = {
	"qanta_id": question["qanta_id"],
	"proto_id": with_default("proto_id", question, ""),
	"qdb_id": with_default("qdb_id", question, -1),
	# We refer to the actual answer as page, but this
	# may be misleading externally, so rename here to
	# be clearer
	"page": question["page"],
	"answer": question["page"],
	"raw_answer": question["answer"],
	"dataset": with_default("dataset", question, ""),
	"full_question": question["text"],
	"first_sentence": question["first_sentence"],
	"tokenizations": question["tokenizations"],
	"fold": question["fold"],
	"gameplay": question["gameplay"],
	"category": with_default("category", question, ""),
	"subcategory": with_default("subcategory", question, ""),
	"tournament": question["tournament"],
	"difficulty": with_default("difficulty", question, ""),
	"year": question["year"],
	"char_idx": -1,
	"sentence_idx": -1,
	}
	if mode == _FULL:
	yield {
	"text": question["text"],
	"id": str(question["qanta_id"]) + "-full",
	**features,
	}
	elif mode == _FIRST:
	yield {
	"text": question["first_sentence"],
	"id": str(question["qanta_id"]) + "-first",
	**features,
	}
	elif mode == _RUNS:
	text = question["text"]
	for text_run, char_idx in create_char_runs(text, char_skip):
	yield {
	"text": text_run,
	"char_idx": char_idx,
	"id": str(question["qanta_id"]) + "-char-" + str(char_idx),
	**features,
	}
	elif mode == _SENTENCES:
	for sentence_idx, (start, end) in enumerate(question["tokenizations"]):
	sentence = question["text"][start:end]
	yield {
	"text": sentence,
	"sentence_idx": sentence_idx,
	"id": str(question["qanta_id"]) + "-sentence-" + str(sentence_idx),
	**features,
	}
	else:
	raise ValueError(f"Invalid mode: {mode}")


	_FEATURES = {
	# Generated ID based modes set, unique
	"id": datasets.Value("string"),
	# Dataset defined IDs
	"qanta_id": datasets.Value("int32"),
	"proto_id": datasets.Value("string"),
	"qdb_id": datasets.Value("int32"),
	"dataset": datasets.Value("string"),
	# Inputs
	"text": datasets.Value("string"),
	"full_question": datasets.Value("string"),
	"first_sentence": datasets.Value("string"),
	"char_idx": datasets.Value("int32"),
	"sentence_idx": datasets.Value("int32"),
	# Character indices of sentences: List[Tuple[int, int]]
	"tokenizations": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"), length=2)),
	# Labels: Number is equal to number of unique pages across all folds
	"answer": datasets.Value("string"),
	"page": datasets.Value("string"),
	"raw_answer": datasets.Value("string"),
	# Meta Information
	"fold": datasets.Value("string"),
	"gameplay": datasets.Value("bool"),
	"category": datasets.Value("string"),
	"subcategory": datasets.Value("string"),
	"tournament": datasets.Value("string"),
	"difficulty": datasets.Value("string"),
	"year": datasets.Value("int32"),
	}


	class Qanta(datasets.GeneratorBasedBuilder):
	"""The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl."""

	VERSION = _VERSION
	BUILDER_CONFIGS = [
	QantaConfig(
	name=f"mode={mode},char_skip={_DEFAULT_CHAR_SKIP}",
	description=f"Question format: {mode}, char_skip: {_DEFAULT_CHAR_SKIP}",
	mode=mode,
	char_skip=_DEFAULT_CHAR_SKIP,
	)
	for mode in _MODES
	]

	def _info(self):
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(_FEATURES),
	# Number of classes is a function of the dataset, ClassLabel doesn't support dynamic
	# definition, so have to defer conversion to classes to later, so can't define
	# supervied keys
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="http://www.qanta.org/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	qanta_path = dl_manager.download_and_extract(_QANTA_URL)
	trick_path = dl_manager.download_and_extract(_TRICK_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split("guesstrain"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "guesstrain",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("buzztrain"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "buzztrain",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("guessdev"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "guessdev",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("buzzdev"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "buzzdev",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("guesstest"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "guesstest",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("buzztest"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "buzztest",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("adversarial"),
	gen_kwargs={
	"qanta_filepath": qanta_path,
	"trick_filepath": trick_path,
	"fold": "adversarial",
	"mode": self.config.mode,
	"char_skip": self.config.char_skip,
	},
	),
	]

	def _generate_examples(
	self,
	qanta_filepath: str,
	trick_filepath: str,
	fold: str,
	mode: str,
	char_skip: int,
	):
	"""Yields examples."""
	if mode not in _MODES:
	raise ValueError(f"Invalid mode: {mode}")

	if fold == "adversarial":
	path = trick_filepath
	else:
	path = qanta_filepath
	with open(path, encoding="utf-8") as f:
	questions = json.load(f)["questions"]
	for q in questions:
	if q["page"] is not None and q["fold"] == fold:
	for example in question_to_examples(q, mode, char_skip):
	yield example["id"], example