Datasets:
mdd

	# 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.
	"""Movie Dialog Dataset."""

	import datasets


	_CITATION = """\
	@misc{dodge2016evaluating,
	title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
	author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
	year={2016},
	eprint={1511.06931},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""


	_DESCRIPTION = """\
	The Movie Dialog dataset (MDD) is designed to measure how well
	models can perform at goal and non-goal orientated dialog
	centered around the topic of movies (question answering,
	recommendation and discussion).

	"""

	_HOMEPAGE = "https://research.fb.com/downloads/babi/"

	_LICENSE = """Creative Commons Attribution 3.0 License"""

	ZIP_URL = "http://www.thespermwhale.com/jaseweston/babi/movie_dialog_dataset.tgz"
	REDDIT_URL = "http://tinyurl.com/p6tyohj"
	dir = "movie_dialog_dataset/"
	dir2 = ""
	paths = {
	"task1_qa": {
	"train": dir + "task1_qa/task1_qa_train.txt",
	"dev": dir + "task1_qa/task1_qa_dev.txt",
	"test": dir + "task1_qa/task1_qa_test.txt",
	},
	"task2_recs": {
	"train": dir + "task2_recs/task2_recs_train.txt",
	"dev": dir + "task2_recs/task2_recs_dev.txt",
	"test": dir + "task2_recs/task2_recs_test.txt",
	},
	"task3_qarecs": {
	"train": dir + "task3_qarecs/task3_qarecs_train.txt",
	"dev": dir + "task3_qarecs/task3_qarecs_dev.txt",
	"test": dir + "task3_qarecs/task3_qarecs_test.txt",
	},
	"task4_reddit": {
	"train": "task4_reddit/task4_reddit_train.txt",
	"dev": "task4_reddit/task4_reddit_dev.txt",
	"test": "task4_reddit/task4_reddit_test.txt",
	"cand_valid": "task4_reddit/task4_reddit_cand-valid.txt",
	"cand_test": "task4_reddit/task4_reddit_cand-test.txt",
	},
	}


	class Mdd(datasets.GeneratorBasedBuilder):
	"""The Movie Dialog Dataset"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="task1_qa", version=VERSION, description="This part of my dataset covers task1_qa part of the dataset"
	),
	datasets.BuilderConfig(
	name="task2_recs",
	version=VERSION,
	description="This part of my dataset covers task2_recs part of the dataset",
	),
	datasets.BuilderConfig(
	name="task3_qarecs",
	version=VERSION,
	description="This part of my dataset covers task3_qarecs part of the dataset",
	),
	datasets.BuilderConfig(
	name="task4_reddit",
	version=VERSION,
	description="This part of my dataset covers task4_reddit part of the dataset",
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"dialogue_turns": datasets.Sequence(
	{
	"speaker": datasets.Value("int32"),
	"utterance": datasets.Value("string"),
	}
	),
	}
	)
	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=features, # Here we define them above because they are different between the two configurations
	# 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=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	if self.config.name != "task4_reddit":
	my_urls = ZIP_URL # Cannot download just one single type as it is a compressed file.
	else:
	my_urls = REDDIT_URL
	archive = dl_manager.download(my_urls)
	splits = [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": paths[self.config.name]["train"], "files": dl_manager.iter_archive(archive)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": paths[self.config.name]["test"], "files": dl_manager.iter_archive(archive)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": paths[self.config.name]["dev"], "files": dl_manager.iter_archive(archive)},
	),
	]
	if self.config.name == "task4_reddit":
	splits += [
	datasets.SplitGenerator(
	name=datasets.Split("cand_valid"),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": paths[self.config.name]["cand_valid"],
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("cand_test"),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": paths[self.config.name]["cand_test"],
	"files": dl_manager.iter_archive(archive),
	},
	),
	]
	return splits

	def _generate_examples(self, filepath, files):
	for path, f in files:
	if path == filepath:
	if "cand" not in filepath:
	dialogue_turns = []
	example_idx = 0
	for idx, line in enumerate(f):
	line = line.decode("utf-8")
	if line.strip() == "":
	if dialogue_turns != []:
	yield example_idx, {"dialogue_turns": dialogue_turns}
	example_idx += 1
	dialogue_turns = []
	elif line.strip().split()[0] == "1": # New convo
	if dialogue_turns != []: # Already some convo, flush it out
	yield example_idx, {"dialogue_turns": dialogue_turns}
	example_idx += 1
	dialogue_turns = []
	exchange = line[len(line.split()[0]) :].strip().split("\t") # Skip the number in the front
	sp1 = exchange[0]
	sp2 = exchange[-1] # Might contain multiple tabs in between.
	dialogue_turns.append({"speaker": 0, "utterance": sp1})
	dialogue_turns.append({"speaker": 1, "utterance": sp2})
	else:
	exchange = line[len(line.split()[0]) :].strip().split("\t") # Skip the number in the front
	sp1 = exchange[0]
	sp2 = exchange[-1] # Might contain multiple tabs in between.
	dialogue_turns.append({"speaker": 0, "utterance": sp1})
	dialogue_turns.append({"speaker": 1, "utterance": sp2})
	else:
	if dialogue_turns != []:
	yield example_idx, {"dialogue_turns": dialogue_turns}
	else:
	dialogue_turns = []
	example_idx = 0
	for idx, line in enumerate(f):
	line = line.decode("utf-8")
	if line.strip() == "":
	if dialogue_turns != []:
	yield example_idx, {"dialogue_turns": dialogue_turns}
	example_idx += 1
	dialogue_turns = []
	elif line.strip().split()[0] == "1": # New convo
	if dialogue_turns != []: # Already some convo, flush it out
	yield example_idx, {"dialogue_turns": dialogue_turns}
	example_idx += 1
	dialogue_turns = []
	exchange = line[len(line.split()[0]) :].strip() # Skip the number in the front
	sp1 = exchange
	dialogue_turns.append({"speaker": 0, "utterance": sp1})
	else:
	exchange = line[len(line.split()[0]) :].strip() # Skip the number in the front
	sp1 = exchange
	dialogue_turns.append({"speaker": 0, "utterance": sp1})
	else: # Last line, new example
	if dialogue_turns != []:
	yield example_idx, {"dialogue_turns": dialogue_turns}
	break