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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the 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
"""WMT13: Translate dataset."""

import datasets

from .wmt_utils import Wmt, WmtConfig


_URL = "http://www.statmt.org/wmt13/translation-task.html"
_CITATION = """
@InProceedings{bojar-EtAl:2013:WMT,
  author    = {Bojar, Ond\v{r}ej  and  Buck, Christian  and  Callison-Burch, Chris  and  Federmann, Christian  and  Haddow, Barry  and  Koehn, Philipp  and  Monz, Christof  and  Post, Matt  and  Soricut, Radu  and  Specia, Lucia},
  title     = {Findings of the 2013 {Workshop on Statistical Machine Translation}},
  booktitle = {Proceedings of the Eighth Workshop on Statistical Machine Translation},
  month     = {August},
  year      = {2013},
  address   = {Sofia, Bulgaria},
  publisher = {Association for Computational Linguistics},
  pages     = {1--44},
  url       = {http://www.aclweb.org/anthology/W13-2201}
}
"""

_LANGUAGE_PAIRS = [(lang, "en") for lang in ["es", "cs", "de", "fr", "ru"]]


class Wmt13(Wmt):
    """WMT 13 translation datasets for all {xx, "en"} language pairs."""

    BUILDER_CONFIGS = [
        WmtConfig(  # pylint:disable=g-complex-comprehension
            description="WMT 2013 %s-%s translation task dataset." % (l1, l2),
            url=_URL,
            citation=_CITATION,
            language_pair=(l1, l2),
            version=datasets.Version("1.0.0"),
        )
        for l1, l2 in _LANGUAGE_PAIRS
    ]

    @property
    def _subsets(self):
        return {
            datasets.Split.TRAIN: [
                "commoncrawl",
                "europarl_v7",
                "multiun",
				"newscommentary_v10",
            ],
            datasets.Split.VALIDATION: ["newstest2012"],
            datasets.Split.TEST: ["newstest2013"],
        }