Datasets:
tep_en_fa_para

	# 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.
	"""TEP: Tehran English-Persian parallel corpus."""


	import os

	import datasets


	_CITATION = """\
	@InProceedings{“TEP: Tehran English-Persian Parallel Corpus”,
	title = {TEP: Tehran English-Persian Parallel Corpus”, in proceedings of 12th International Conference \
	on Intelligent Text Processing and Computational Linguistics (CICLing-2011)},
	authors={M. T. Pilevar, H. Faili, and A. H. Pilevar, },
	year={2011}
	}
	"""


	_DESCRIPTION = """\
	TEP: Tehran English-Persian parallel corpus. The first free Eng-Per corpus, provided by the Natural Language and Text Processing Laboratory, University of Tehran.
	"""


	_HOMEPAGE = "http://opus.nlpl.eu/TEP.php"


	_LICENSE = ""


	_URLs = {"train": "https://object.pouta.csc.fi/OPUS-TEP/v1/moses/en-fa.txt.zip"}


	class TepEnFaPara(datasets.GeneratorBasedBuilder):
	"""TEP: Tehran English-Persian parallel corpus."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="en-fa", version=VERSION),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"translation": datasets.features.Translation(languages=tuple(self.config.name.split("-")))}
	),
	supervised_keys=None,
	homepage="http://opus.nlpl.eu/TEP.php",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"source_file": os.path.join(data_dir["train"], "TEP.en-fa.en"),
	"target_file": os.path.join(data_dir["train"], "TEP.en-fa.fa"),
	"split": "train",
	},
	),
	]

	def _generate_examples(self, source_file, target_file, split):
	"""This function returns the examples in the raw (text) form."""
	with open(source_file, encoding="utf-8") as f:
	source_sentences = f.read().split("\n")
	with open(target_file, encoding="utf-8") as f:
	target_sentences = f.read().split("\n")

	assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
	len(source_sentences),
	len(target_sentences),
	source_file,
	target_file,
	)

	source, target = tuple(self.config.name.split("-"))
	for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
	result = {"translation": {source: l1, target: l2}}
	yield idx, result