Datasets:
blended_skill_talk

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


	import json

	import datasets


	# TODO(blended_skill_talk): BibTeX citation
	_CITATION = """\
	@misc{smith2020evaluating,
	title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
	author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
	year={2020},
	eprint={2004.08449},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	# TODO(blended_skill_talk):
	_DESCRIPTION = """\
	A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
	"""
	_URL = "http://parl.ai/downloads/blended_skill_talk/blended_skill_talk.tar.gz"

	_TASK = ["convai2", "empathetic_dialogues", "wizard_of_wikipedia"]


	class BlendedSkillTalk(datasets.GeneratorBasedBuilder):
	"""TODO(blended_skill_talk): Short description of my dataset."""

	# TODO(blended_skill_talk): Set up version.
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	# TODO(blended_skill_talk): 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(
	{
	"personas": datasets.features.Sequence(datasets.Value("string")),
	"additional_context": datasets.Value("string"),
	"previous_utterance": datasets.features.Sequence(datasets.Value("string")),
	"context": datasets.Value("string"),
	"free_messages": datasets.features.Sequence(datasets.Value("string")),
	"guided_messages": datasets.features.Sequence(datasets.Value("string")),
	"suggestions": datasets.features.Sequence({task: datasets.Value("string") for task in _TASK}),
	"guided_chosen_suggestions": datasets.features.Sequence(datasets.Value("string")),
	"label_candidates": datasets.features.Sequence(
	datasets.features.Sequence(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://parl.ai/projects/bst/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(blended_skill_talk): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	archive = dl_manager.download(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "train.json",
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "valid.json",
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "test.json",
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, filepath, files):
	"""Yields examples."""
	# TODO(blended_skill_talk): Yields (key, example) tuples from the dataset
	for path, f in files:
	if path == filepath:
	data = json.load(f)
	for id_, row in enumerate(data):
	personas = [row["personas"][1][0], row["personas"][1][1]]
	dialogs = [dialog[1] for dialog in row["dialog"]]
	free_messages = []
	guided_messages = []

	for i in range(len(dialogs) // 2):
	free_messages.append(dialogs[2 * i])
	guided_messages.append(dialogs[2 * i + 1])
	context = row["context_dataset"]
	add_context = row["additional_context"] if context == "wizard_of_wikipedia" else ""
	previous_utterance = [row["free_turker_utterance"], row["guided_turker_utterance"]]
	suggestions = row["suggestions"]
	convai_suggestions = []
	empathetic_suggestions = []
	wow_suggestions = []
	for i in range(len(suggestions) // 2):
	convai_suggestions.append(suggestions[2 * i + 1]["convai2"])
	empathetic_suggestions.append(suggestions[2 * i + 1]["empathetic_dialogues"])
	wow_suggestions.append(suggestions[2 * i + 1]["wizard_of_wikipedia"])
	chosen_suggestions = row["chosen_suggestions"]
	guided_chosen_suggestions = []
	for i in range(len(chosen_suggestions) // 2):
	guided_chosen_suggestions.append(chosen_suggestions[2 * i + 1])
	label_candidates = row["label_candidates"] if "label_candidates" in row else []
	yield id_, {
	"personas": personas,
	"additional_context": add_context,
	"previous_utterance": previous_utterance,
	"context": context,
	"free_messages": free_messages,
	"guided_messages": guided_messages,
	"suggestions": {
	"convai2": convai_suggestions,
	"empathetic_dialogues": empathetic_suggestions,
	"wizard_of_wikipedia": wow_suggestions,
	},
	"guided_chosen_suggestions": guided_chosen_suggestions,
	"label_candidates": label_candidates,
	}
	break