Datasets:
cmu_hinglish_dog

	# 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.

	import json
	import os
	import re

	import datasets


	_CITATION = """\
	@inproceedings{cmu_dog_emnlp18,
	title={A Dataset for Document Grounded Conversations},
	author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
	year={2018},
	booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
	}

	@inproceedings{khanuja-etal-2020-gluecos,
	title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
	author = "Khanuja, Simran and
	Dandapat, Sandipan and
	Srinivasan, Anirudh and
	Sitaram, Sunayana and
	Choudhury, Monojit",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.acl-main.329",
	pages = "3575--3585"
	}
	"""

	_DESCRIPTION = """\
	This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English only versions. Can be used for Translating between the two.
	"""

	_HOMEPAGE = "http://festvox.org/cedar/data/notyet/"
	_URL_HINGLISH = "http://festvox.org/cedar/data/notyet/CMUHinglishDoG.zip"
	_URL_ENGLISH = "https://github.com/festvox/datasets-CMU_DoG/archive/master/Conversations.zip"


	class CMUHinglishDoG(datasets.GeneratorBasedBuilder):
	"""Load the CMU Hinglish DoG Data for MT"""

	def _info(self):
	features = datasets.Features(
	{
	"date": datasets.Value("string"),
	"docIdx": datasets.Value("int64"),
	"translation": datasets.Translation(languages=["en", "hi_en"]),
	"uid": datasets.Value("string"),
	"utcTimestamp": datasets.Value("string"),
	"rating": datasets.Value("int64"),
	"status": datasets.Value("int64"),
	"uid1LogInTime": datasets.Value("string"),
	"uid1LogOutTime": datasets.Value("string"),
	"uid1response": {
	"response": datasets.Sequence(datasets.Value("int64")),
	"type": datasets.Value("string"),
	},
	"uid2response": {
	"response": datasets.Sequence(datasets.Value("int64")),
	"type": datasets.Value("string"),
	},
	"user2_id": datasets.Value("string"),
	"whoSawDoc": datasets.Sequence(datasets.Value("string")),
	"wikiDocumentIdx": datasets.Value("int64"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""The linking part between Hinglish data and English data is inspired from the implementation in GLUECoS.
	Refer here for the original script https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py"""

	eng_path = dl_manager.download_and_extract(_URL_ENGLISH)
	data_dir_en = os.path.join(eng_path, "datasets-CMU_DoG-master", "Conversations")

	hi_en_path = dl_manager.download_and_extract(_URL_HINGLISH)
	data_dir_hi_en = os.path.join(hi_en_path, "CMUHinglishDoG", "Conversations_Hinglish")

	hi_en_dirs = {
	"train": os.path.join(data_dir_hi_en, "train"),
	"valid": os.path.join(data_dir_hi_en, "valid"),
	"test": os.path.join(data_dir_hi_en, "test"),
	}

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"hi_en_dir": hi_en_dirs["train"],
	"data_dir_en": data_dir_en,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"hi_en_dir": hi_en_dirs["test"],
	"data_dir_en": data_dir_en,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"hi_en_dir": hi_en_dirs["valid"],
	"data_dir_en": data_dir_en,
	},
	),
	]

	def _generate_examples(self, hi_en_dir, data_dir_en):
	"""Yields examples."""
	english_files_train = os.listdir(os.path.join(data_dir_en, "train"))
	english_files_val = os.listdir(os.path.join(data_dir_en, "valid"))
	english_files_test = os.listdir(os.path.join(data_dir_en, "test"))

	hinglish_files = os.listdir(hi_en_dir)
	key = 0
	for f in hinglish_files:
	en_file_path = f.split(".json")[0] + ".json"
	found = True
	# Looks for the corresponding english file in all 3 splits
	if en_file_path in english_files_train:
	en = json.load(open(os.path.join(os.path.join(data_dir_en, "train"), en_file_path)))
	elif en_file_path in english_files_val:
	en = json.load(open(os.path.join(os.path.join(data_dir_en, "valid"), en_file_path)))
	elif en_file_path in english_files_test:
	en = json.load(open(os.path.join(os.path.join(data_dir_en, "test"), en_file_path)))
	else:
	found = False
	if found:
	hi_en = json.load(open(os.path.join(hi_en_dir, f)))

	assert len(en["history"]) == len(hi_en["history"])

	for x, y in zip(en["history"], hi_en["history"]):
	assert x["docIdx"] == y["docIdx"]
	assert x["uid"] == y["uid"]
	assert x["utcTimestamp"] == y["utcTimestamp"]

	x["text"] = re.sub("\t\|\n", " ", x["text"])
	y["text"] = re.sub("\t\|\n", " ", y["text"])
	line = {
	"date": hi_en["date"],
	"uid": x["uid"],
	"docIdx": x["docIdx"],
	"utcTimestamp": x["utcTimestamp"],
	"translation": {"hi_en": y["text"], "en": x["text"]},
	"rating": hi_en["rating"],
	"status": hi_en["status"],
	"uid1LogOutTime": hi_en.get("uid1LogOutTime"),
	"uid1LogInTime": hi_en["uid1LogInTime"],
	"uid1response": {
	"response": hi_en["uid1response"]["response"] if "uid1response" in hi_en else [],
	"type": hi_en["uid1response"]["type"] if "uid1response" in hi_en else None,
	},
	"uid2response": {
	"response": hi_en["uid2response"]["response"] if "uid2response" in hi_en else [],
	"type": hi_en["uid2response"]["type"] if "uid2response" in hi_en else None,
	},
	"user2_id": hi_en["user2_id"],
	"whoSawDoc": hi_en["whoSawDoc"],
	"wikiDocumentIdx": hi_en["wikiDocumentIdx"],
	}

	yield key, line
	key += 1