Datasets:
emea

	# coding=utf-8
	# Copyright 2020 HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	import os

	import datasets


	_DESCRIPTION = """\
	This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in the current version.

	source: http://www.emea.europa.eu/

	22 languages, 231 bitexts
	total number of files: 41,957
	total number of tokens: 311.65M
	total number of sentence fragments: 26.51M
	"""
	_HOMEPAGE_URL = "http://opus.nlpl.eu/EMEA.php"
	_CITATION = """\
	J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
	"""

	_VERSION = "3.0.0"
	_BASE_NAME = "EMEA.{}.{}"
	_BASE_URL = "https://object.pouta.csc.fi/OPUS-EMEA/v3/moses/{}-{}.txt.zip"

	# Please note that only few pairs are shown here. You can use config to generate data for all language pairs
	_LANGUAGE_PAIRS = [
	("bg", "el"),
	("cs", "et"),
	("de", "mt"),
	("fr", "sk"),
	("es", "lt"),
	]


	class EmeaConfig(datasets.BuilderConfig):
	def __init__(self, args, lang1=None, lang2=None, *kwargs):
	super().__init__(
	*args,
	name=f"{lang1}-{lang2}",
	**kwargs,
	)
	self.lang1 = lang1
	self.lang2 = lang2


	class Emea(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	EmeaConfig(
	lang1=lang1,
	lang2=lang2,
	description=f"Translating {lang1} to {lang2} or vice versa",
	version=datasets.Version(_VERSION),
	)
	for lang1, lang2 in _LANGUAGE_PAIRS
	]
	BUILDER_CONFIG_CLASS = EmeaConfig

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"translation": datasets.Translation(languages=(self.config.lang1, self.config.lang2)),
	},
	),
	supervised_keys=None,
	homepage=_HOMEPAGE_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	def _base_url(lang1, lang2):
	return _BASE_URL.format(lang1, lang2)

	download_url = _base_url(self.config.lang1, self.config.lang2)
	path = dl_manager.download_and_extract(download_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"datapath": path},
	)
	]

	def _generate_examples(self, datapath):
	l1, l2 = self.config.lang1, self.config.lang2
	folder = l1 + "-" + l2
	l1_file = _BASE_NAME.format(folder, l1)
	l2_file = _BASE_NAME.format(folder, l2)
	l1_path = os.path.join(datapath, l1_file)
	l2_path = os.path.join(datapath, l2_file)
	with open(l1_path, encoding="utf-8") as f1, open(l2_path, encoding="utf-8") as f2:
	for sentence_counter, (x, y) in enumerate(zip(f1, f2)):
	x = x.strip()
	y = y.strip()
	result = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"translation": {l1: x, l2: y},
	},
	)
	yield result