Datasets:
pec

	"""TODO: Add a description here."""

	import os

	import datasets


	# TODO: Add BibTeX citation
	_CITATION = """\
	@inproceedings{zhong2020towards,
	title = "Towards Persona-Based Empathetic Conversational Models",
	author = "Zhong, Peixiang and
	Zhang, Chen and
	Wang, Hao and
	Liu, Yong and
	Miao, Chunyan",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
	year = "2020",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
	pages = "6556--6566"}
	"""

	# TODO: Add description of the dataset here
	_DESCRIPTION = """\
	A dataset of around 350K persona-based empathetic conversations. Each speaker is associated with a persona, which comprises multiple persona sentences. The response of each conversation is empathetic.
	"""

	_URL = "https://dl.dropboxusercontent.com/s/u04fzuhsnxd0uvw/hf_pec.zip"

	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	# Using a specific configuration class is optional, you can also use the base class if you don't need
	# to add specific attributes.
	# here we give an example for three sub-set of the dataset with difference sizes.


	class PECConfig(datasets.BuilderConfig):
	"""BuilderConfig for PEC"""

	def __init__(self, domain="all", **kwargs):
	"""
	Args:
	domain: the domain of our dataset: happy or offmychest
	**kwargs: keyword arguments forwarded to super.
	"""
	super(PECConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.domain = domain


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

	VERSION = datasets.Version("1.0.0")
	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
	BUILDER_CONFIG_CLASS = PECConfig
	BUILDER_CONFIGS = [
	PECConfig(name=domain, description=f"A subset of PEC dataset: {domain}", domain=domain)
	for domain in ["happy", "offmychest", "all"]
	]

	def _info(self):
	# TODO: Specifies the datasets.DatasetInfo object
	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=datasets.Features(
	{
	"personas": datasets.features.Sequence(datasets.Value("string")),
	"context": datasets.features.Sequence(datasets.Value("string")),
	"context_speakers": datasets.features.Sequence(datasets.Value("string")),
	"response": datasets.Value("string"),
	"response_speaker": datasets.Value("string"),
	}
	),
	# 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/zhongpeixiang/PEC",
	citation=_CITATION,
	)

	def _load_persona(self, paths):
	persona = {}
	is_speaker = True
	sentences = []
	for path in paths:
	with open(path, encoding="utf-8") as f:
	for row in f:
	if "********************" not in row:
	if is_speaker:
	speaker = row.strip()
	is_speaker = False
	else:
	sentences.append(row.strip())
	else:
	persona[speaker] = sentences
	is_speaker = True
	sentences = []
	return persona

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)
	data_dir = os.path.join(dl_dir, "hf_pec")
	domains = ["happy", "offmychest"] if self.config.domain == "all" else [self.config.domain] # multiple domains
	persona_paths = [os.path.join(data_dir, domain, "persona.txt") for domain in domains]
	persona = self._load_persona(persona_paths)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": [os.path.join(data_dir, domain, "train.txt") for domain in domains],
	"split": "train",
	"persona": persona,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": [os.path.join(data_dir, domain, "test.txt") for domain in domains],
	"split": "test",
	"persona": persona,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": [os.path.join(data_dir, domain, "valid.txt") for domain in domains],
	"split": "dev",
	"persona": persona,
	},
	),
	]

	def _generate_examples(self, filepath, split, persona):
	"""Yields examples."""
	# TODO: Yields (key, example) tuples from the dataset
	context_speakers = []
	context = []
	example_id = 0
	for fpath in filepath:
	with open(fpath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	if row.strip() == "":
	continue
	if "********************" not in row:
	if "---+---" in row:
	speaker, utterance = row.split("---+---")
	context_speakers.append(speaker.strip())
	context.append(utterance.strip())
	else:
	# contains inline \n
	context[-1] = context[-1] + " " + row.strip()
	else:
	response_speaker = context_speakers.pop()
	response = context.pop()
	yield example_id, {
	"personas": persona[response_speaker],
	"context_speakers": context_speakers,
	"context": context,
	"response_speaker": response_speaker,
	"response": response,
	}
	context_speakers = []
	context = []
	example_id += 1