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README.md

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+---
+language:
+- en
+- es
+- pt
+license:
+- cc-by-2.0
+multilinguality:
+- translation
+task_categories:
+- translation
+task_ids: []
+dataset_info:
+- config_name: en-pt
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - pt
+  splits:
+  - name: train
+    num_bytes: 47417503
+    num_examples: 160975
+  - name: validation
+    num_bytes: 405055
+    num_examples: 1375
+  - name: test
+    num_bytes: 407579
+    num_examples: 1447
+  download_size: 29615550
+  dataset_size: 48230137
+- config_name: es-pt
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - es
+        - pt
+  splits:
+  - name: train
+    num_bytes: 47480897
+    num_examples: 158197
+  - name: validation
+    num_bytes: 377101
+    num_examples: 1302
+  - name: test
+    num_bytes: 400915
+    num_examples: 1379
+  download_size: 29829573
+  dataset_size: 48258913
+configs:
+- config_name: en-pt
+  data_files:
+  - split: train
+    path: en-pt/train-*
+  - split: validation
+    path: en-pt/validation-*
+  - split: test
+    path: en-pt/test-*
+- config_name: es-pt
+  data_files:
+  - split: train
+    path: es-pt/train-*
+  - split: validation
+    path: es-pt/validation-*
+  - split: test
+    path: es-pt/test-*
+---

data/fapesp-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf6bdf40cd2de72fe8b8bd1a260dd975aaf1b2b0d51a6f2bcad868ef77777d3f
+size 35276668

dataset_info.json

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+{
+  "description": "Translation dataset based on the data from statmt.org.\n\nVersions exist for different years using a combination of data\nsources. The base `wmt` allows you to create a custom dataset by choosing\nyour own data/language pair. This can be done as follows:\n\n```python\nfrom datasets import inspect_dataset, load_dataset_builder\n\ninspect_dataset(\"wmt16\", \"path/to/scripts\")\nbuilder = load_dataset_builder(\n    \"path/to/scripts/wmt_utils.py\",\n    language_pair=(\"fr\", \"de\"),\n    subsets={\n        datasets.Split.TRAIN: [\"commoncrawl_frde\"],\n        datasets.Split.VALIDATION: [\"euelections_dev2019\"],\n    },\n)\n\n# Standard version\nbuilder.download_and_prepare()\nds = builder.as_dataset()\n\n# Streamable version\nds = builder.as_streaming_dataset()\n```\n\n",
+  "citation": "\n@inproceedings{aziz-specia-2011-fully,\n    title = "Fully Automatic Compilation of {P}ortuguese-{E}nglish and {P}ortuguese-{S}panish Parallel Corpora",\n    author = "Aziz, Wilker  and
+      Specia, Lucia",\n    booktitle = "Proceedings of the 8th {B}razilian Symposium in Information and Human Language Technology",\n    year = "2011",\n    url = "https://aclanthology.org/W11-4533",\n}\n",
+  "homepage": "http://www.nilc.icmc.usp.br/nilc/tools/Fapesp%20Corpora.htm",
+  "license": "",
+  "features": {
+    "translation": {
+      "languages": [
+        "es",
+        "pt"
+      ],
+      "_type": "Translation"
+    }
+  },
+  "supervised_keys": {
+    "input": "es",
+    "output": "pt"
+  },
+  "builder_name": "fapesp-v2",
+  "config_name": "es-pt",
+  "version": {
+    "version_str": "1.0.0",
+    "major": 1,
+    "minor": 0,
+    "patch": 0
+  },
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 47480897,
+      "num_examples": 158197,
+      "dataset_name": "fapesp-v2"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 377101,
+      "num_examples": 1302,
+      "dataset_name": "fapesp-v2"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 400915,
+      "num_examples": 1379,
+      "dataset_name": "fapesp-v2"
+    }
+  },
+  "download_checksums": {
+    "https://huggingface.co/datasets/vgaraujov/fapesp-v2/resolve/main/data/fapesp-v2.zip": {
+      "num_bytes": 35276668,
+      "checksum": "cf6bdf40cd2de72fe8b8bd1a260dd975aaf1b2b0d51a6f2bcad868ef77777d3f"
+    }
+  },
+  "download_size": 35276668,
+  "dataset_size": 48258913,
+  "size_in_bytes": 83535581
+}

fapesp-v2.py

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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
+"""Portuguese-English and Portuguese-Spanish bilingual collections."""
+
+import datasets
+
+from .wmt_utils import Wmt, WmtConfig
+
+
+_URL = "http://www.nilc.icmc.usp.br/nilc/tools/Fapesp%20Corpora.htm"
+_CITATION = """
+@inproceedings{aziz-specia-2011-fully,
+    title = "Fully Automatic Compilation of {P}ortuguese-{E}nglish and {P}ortuguese-{S}panish Parallel Corpora",
+    author = "Aziz, Wilker  and
+      Specia, Lucia",
+    booktitle = "Proceedings of the 8th {B}razilian Symposium in Information and Human Language Technology",
+    year = "2011",
+    url = "https://aclanthology.org/W11-4533",
+}
+"""
+
+_LANGUAGE_PAIRS = [(lang, "pt") for lang in ["es", "en"]]
+
+
+class Fapespv2(Wmt):
+    """Fapesp v2 translation datasets for all {xx, "pt"} language pairs."""
+
+    BUILDER_CONFIGS = [
+        WmtConfig(  # pylint:disable=g-complex-comprehension
+            description="Fapesp v2 %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: [
+                "fapesp-v2",
+            ],
+            datasets.Split.VALIDATION: ["dev"],
+            datasets.Split.TEST: ["test-b"],
+        }

wmt_utils.py

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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.
+#
+# Script borrewed and adapted from https://huggingface.co/datasets/wmt16.
+
+# Lint as: python3
+"""WMT: Translate dataset."""
+
+
+import codecs
+import functools
+import glob
+import gzip
+import itertools
+import os
+import re
+import xml.etree.cElementTree as ElementTree
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_DESCRIPTION = """\
+Translation dataset based on the data from statmt.org.
+
+Versions exist for different years using a combination of data
+sources. The base `wmt` allows you to create a custom dataset by choosing
+your own data/language pair. This can be done as follows:
+
+```python
+from datasets import inspect_dataset, load_dataset_builder
+
+inspect_dataset("wmt16", "path/to/scripts")
+builder = load_dataset_builder(
+    "path/to/scripts/wmt_utils.py",
+    language_pair=("fr", "de"),
+    subsets={
+        datasets.Split.TRAIN: ["commoncrawl_frde"],
+        datasets.Split.VALIDATION: ["euelections_dev2019"],
+    },
+)
+
+# Standard version
+builder.download_and_prepare()
+ds = builder.as_dataset()
+
+# Streamable version
+ds = builder.as_streaming_dataset()
+```
+
+"""
+
+
+class SubDataset:
+    """Class to keep track of information on a sub-dataset of WMT."""
+
+    def __init__(self, name, target, sources, url, path, manual_dl_files=None):
+        """Sub-dataset of WMT.
+
+        Args:
+          name: `string`, a unique dataset identifier.
+          target: `string`, the target language code.
+          sources: `set<string>`, the set of source language codes.
+          url: `string` or `(string, string)`, URL(s) or URL template(s) specifying
+            where to download the raw data from. If two strings are provided, the
+            first is used for the source language and the second for the target.
+            Template strings can either contain '{src}' placeholders that will be
+            filled in with the source language code, '{0}' and '{1}' placeholders
+            that will be filled in with the source and target language codes in
+            alphabetical order, or all 3.
+          path: `string` or `(string, string)`, path(s) or path template(s)
+            specifing the path to the raw data relative to the root of the
+            downloaded archive. If two strings are provided, the dataset is assumed
+            to be made up of parallel text files, the first being the source and the
+            second the target. If one string is provided, both languages are assumed
+            to be stored within the same file and the extension is used to determine
+            how to parse it. Template strings should be formatted the same as in
+            `url`.
+          manual_dl_files: `<list>(string)` (optional), the list of files that must
+            be manually downloaded to the data directory.
+        """
+        self._paths = (path,) if isinstance(path, str) else path
+        self._urls = (url,) if isinstance(url, str) else url
+        self._manual_dl_files = manual_dl_files if manual_dl_files else []
+        self.name = name
+        self.target = target
+        self.sources = set(sources)
+
+    def _inject_language(self, src, strings):
+        """Injects languages into (potentially) template strings."""
+        if src not in self.sources:
+            raise ValueError(f"Invalid source for '{self.name}': {src}")
+
+        def _format_string(s):
+            if "{0}" in s and "{1}" and "{src}" in s:
+                return s.format(*sorted([src, self.target]), src=src)
+            elif "{0}" in s and "{1}" in s:
+                return s.format(*sorted([src, self.target]))
+            elif "{src}" in s:
+                return s.format(src=src)
+            else:
+                return s
+
+        return [_format_string(s) for s in strings]
+
+    def get_url(self, src):
+        return self._inject_language(src, self._urls)
+
+    def get_manual_dl_files(self, src):
+        return self._inject_language(src, self._manual_dl_files)
+
+    def get_path(self, src):
+        return self._inject_language(src, self._paths)
+
+
+# Subsets used in the training sets for AmericasNLP 2021 Shared Task on Machine Translation.
+_TRAIN_SUBSETS = [
+    SubDataset(
+        name="fapesp-v2",
+        target="pt",
+        sources={"es", "en"},
+        url="https://huggingface.co/datasets/vgaraujov/fapesp-v2/resolve/main/data/fapesp-v2.zip",
+        path=("fapesp-v2.pt-{src}.train.{src}", "fapesp-v2.pt-{src}.train.pt"),
+    ),
+]
+
+_DEV_SUBSETS = [
+    SubDataset(
+        name="dev",
+        target="pt",
+        sources={"es", "en"},
+        url="https://huggingface.co/datasets/vgaraujov/fapesp-v2/resolve/main/data/fapesp-v2.zip",
+        path=("fapesp-v2.pt-{src}.dev.{src}", "fapesp-v2.pt-{src}.dev.pt"),
+    ),
+    SubDataset(
+        name="dev-test",
+        target="pt",
+        sources={"es", "en"},
+        url="https://huggingface.co/datasets/vgaraujov/fapesp-v2/resolve/main/data/fapesp-v2.zip",
+        path=("fapesp-v2.pt-{src}.dev-test.{src}", "fapesp-v2.pt-{src}.dev-test.pt"),
+    ),
+    SubDataset(
+        name="test-a",
+        target="pt",
+        sources={"es", "en"},
+        url="https://huggingface.co/datasets/vgaraujov/fapesp-v2/resolve/main/data/fapesp-v2.zip",
+        path=("fapesp-v2.pt-{src}.test-a.{src}", "fapesp-v2.pt-{src}.test-a.pt"),
+    ),
+    SubDataset(
+        name="test-b",
+        target="pt",
+        sources={"es", "en"},
+        url="https://huggingface.co/datasets/vgaraujov/fapesp-v2/resolve/main/data/fapesp-v2.zip",
+        path=("fapesp-v2.pt-{src}.test-b.{src}", "fapesp-v2.pt-{src}.test-b.pt"),
+    ),
+]
+
+DATASET_MAP = {dataset.name: dataset for dataset in _TRAIN_SUBSETS + _DEV_SUBSETS}
+
+
+class WmtConfig(datasets.BuilderConfig):
+    """BuilderConfig for WMT."""
+
+    def __init__(self, url=None, citation=None, description=None, language_pair=(None, None), subsets=None, **kwargs):
+        """BuilderConfig for WMT.
+
+        Args:
+          url: The reference URL for the dataset.
+          citation: The paper citation for the dataset.
+          description: The description of the dataset.
+          language_pair: pair of languages that will be used for translation. Should
+                     contain 2 letter coded strings. For example: ("en", "de").
+            configuration for the `datasets.features.text.TextEncoder` used for the
+            `datasets.features.text.Translation` features.
+          subsets: Dict[split, list[str]]. List of the subset to use for each of the
+            split. Note that WMT subclasses overwrite this parameter.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        name = "%s-%s" % (language_pair[0], language_pair[1])
+        if "name" in kwargs:  # Add name suffix for custom configs
+            name += "." + kwargs.pop("name")
+
+        super(WmtConfig, self).__init__(name=name, description=description, **kwargs)
+
+        self.url = url or "http://www.statmt.org"
+        self.citation = citation
+        self.language_pair = language_pair
+        self.subsets = subsets
+
+
+class Wmt(datasets.GeneratorBasedBuilder):
+    """WMT translation dataset."""
+
+    BUILDER_CONFIG_CLASS = WmtConfig
+
+    def __init__(self, *args, **kwargs):
+        super(Wmt, self).__init__(*args, **kwargs)
+
+    @property
+    def _subsets(self):
+        """Subsets that make up each split of the dataset."""
+        raise NotImplementedError("This is a abstract method")
+
+    @property
+    def subsets(self):
+        """Subsets that make up each split of the dataset for the language pair."""
+        source, target = self.config.language_pair
+        filtered_subsets = {}
+        subsets = self._subsets if self.config.subsets is None else self.config.subsets
+        for split, ss_names in subsets.items():
+            filtered_subsets[split] = []
+            for ss_name in ss_names:
+                dataset = DATASET_MAP[ss_name]
+                if dataset.target != target or source not in dataset.sources:
+                    logger.info("Skipping sub-dataset that does not include language pair: %s", ss_name)
+                else:
+                    filtered_subsets[split].append(ss_name)
+        logger.info("Using sub-datasets: %s", filtered_subsets)
+        return filtered_subsets
+
+    def _info(self):
+        src, target = self.config.language_pair
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"translation": datasets.features.Translation(languages=self.config.language_pair)}
+            ),
+            supervised_keys=(src, target),
+            homepage=self.config.url,
+            citation=self.config.citation,
+        )
+
+    def _vocab_text_gen(self, split_subsets, extraction_map, language):
+        for _, ex in self._generate_examples(split_subsets, extraction_map, with_translation=False):
+            yield ex[language]
+
+    def _split_generators(self, dl_manager):
+        source, _ = self.config.language_pair
+        manual_paths_dict = {}
+        urls_to_download = {}
+        for ss_name in itertools.chain.from_iterable(self.subsets.values()):
+
+            # get dataset
+            dataset = DATASET_MAP[ss_name]
+            if dataset.get_manual_dl_files(source):
+                # TODO(PVP): following two lines skip configs that are incomplete for now
+                # +++++++++++++++++++++
+                logger.info("Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
+                continue
+                # +++++++++++++++++++++
+
+                manual_dl_files = dataset.get_manual_dl_files(source)
+                manual_paths = [
+                    os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), fname)
+                    for fname in manual_dl_files
+                ]
+                assert all(
+                    os.path.exists(path) for path in manual_paths
+                ), f"For {dataset.name}, you must manually download the following file(s) from {dataset.get_url(source)} and place them in {dl_manager.manual_dir}: {', '.join(manual_dl_files)}"
+
+                # set manual path for correct subset
+                manual_paths_dict[ss_name] = manual_paths
+            else:
+                urls_to_download[ss_name] = dataset.get_url(source)
+
+        # Download and extract files from URLs.
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        # Extract manually downloaded files.
+        manual_files = dl_manager.extract(manual_paths_dict)
+        extraction_map = dict(downloaded_files, **manual_files)
+
+        for language in self.config.language_pair:
+            self._vocab_text_gen(self.subsets[datasets.Split.TRAIN], extraction_map, language)
+
+        return [
+            datasets.SplitGenerator(  # pylint:disable=g-complex-comprehension
+                name=split, gen_kwargs={"split_subsets": split_subsets, "extraction_map": extraction_map}
+            )
+            for split, split_subsets in self.subsets.items()
+        ]
+
+    def _generate_examples(self, split_subsets, extraction_map, with_translation=True):
+        """Returns the examples in the raw (text) form."""
+        source, _ = self.config.language_pair
+
+        def _get_local_paths(dataset, extract_dirs):
+            rel_paths = dataset.get_path(source)
+            if len(extract_dirs) == 1:
+                extract_dirs = extract_dirs * len(rel_paths)
+            return [
+                os.path.join(ex_dir, rel_path) if rel_path else ex_dir
+                for ex_dir, rel_path in zip(extract_dirs, rel_paths)
+            ]
+
+        def _get_filenames(dataset):
+            rel_paths = dataset.get_path(source)
+            urls = dataset.get_url(source)
+            if len(urls) == 1:
+                urls = urls * len(rel_paths)
+            return [rel_path if rel_path else os.path.basename(url) for url, rel_path in zip(urls, rel_paths)]
+
+        for ss_name in split_subsets:
+            # TODO(PVP) remove following five lines when manual data works
+            # +++++++++++++++++++++
+            dataset = DATASET_MAP[ss_name]
+            source, _ = self.config.language_pair
+            if dataset.get_manual_dl_files(source):
+                logger.info(f"Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
+                continue
+            # +++++++++++++++++++++
+
+            logger.info("Generating examples from: %s", ss_name)
+            dataset = DATASET_MAP[ss_name]
+            extract_dirs = extraction_map[ss_name]
+            files = _get_local_paths(dataset, extract_dirs)
+            filenames = _get_filenames(dataset)
+
+            sub_generator_args = tuple(files)
+
+            if ss_name.startswith("czeng"):
+                if ss_name.endswith("16pre"):
+                    sub_generator = functools.partial(_parse_tsv, language_pair=("en", "cs"))
+                    sub_generator_args += tuple(filenames)
+                else:
+                    sub_generator = _parse_czeng
+            elif ss_name == "hindencorp_01":
+                sub_generator = _parse_hindencorp
+            elif len(files) == 2:
+                if ss_name.endswith("_frde"):
+                    sub_generator = _parse_frde_bitext
+                else:
+                    sub_generator = _parse_parallel_sentences
+                    sub_generator_args += tuple(filenames)
+            elif len(files) == 1:
+                fname = filenames[0]
+                # Note: Due to formatting used by `download_manager`, the file
+                # extension may not be at the end of the file path.
+                if ".tsv" in fname:
+                    sub_generator = _parse_tsv
+                    sub_generator_args += tuple(filenames)
+                elif (
+                    ss_name.startswith("newscommentary_v14")
+                    or ss_name.startswith("europarl_v9")
+                    or ss_name.startswith("wikititles_v1")
+                ):
+                    sub_generator = functools.partial(_parse_tsv, language_pair=self.config.language_pair)
+                    sub_generator_args += tuple(filenames)
+                elif "tmx" in fname or ss_name.startswith("paracrawl_v3"):
+                    sub_generator = _parse_tmx
+                elif ss_name.startswith("wikiheadlines"):
+                    sub_generator = _parse_wikiheadlines
+                else:
+                    raise ValueError("Unsupported file format: %s" % fname)
+            else:
+                raise ValueError("Invalid number of files: %d" % len(files))
+
+            for sub_key, ex in sub_generator(*sub_generator_args):
+                if not all(ex.values()):
+                    continue
+                # TODO(adarob): Add subset feature.
+                # ex["subset"] = subset
+                key = f"{ss_name}/{sub_key}"
+                if with_translation is True:
+                    ex = {"translation": ex}
+                yield key, ex
+
+
+def _parse_parallel_sentences(f1, f2, filename1, filename2):
+    """Returns examples from parallel SGML or text files, which may be gzipped."""
+
+    def _parse_text(path, original_filename):
+        """Returns the sentences from a single text file, which may be gzipped."""
+        split_path = original_filename.split(".")
+
+        if split_path[-1] == "gz":
+            lang = split_path[-2]
+
+            def gen():
+                with open(path, "rb") as f, gzip.GzipFile(fileobj=f) as g:
+                    for line in g:
+                        yield line.decode("utf-8").rstrip()
+
+            return gen(), lang
+
+        if split_path[-1] == "txt":
+            # CWMT
+            lang = split_path[-2].split("_")[-1]
+            lang = "zh" if lang in ("ch", "cn", "c[hn]") else lang
+        else:
+            lang = split_path[-1]
+
+        def gen():
+            with open(path, "rb") as f:
+                for line in f:
+                    yield line.decode("utf-8").rstrip()
+
+        return gen(), lang
+
+    def _parse_sgm(path, original_filename):
+        """Returns sentences from a single SGML file."""
+        lang = original_filename.split(".")[-2]
+        # Note: We can't use the XML parser since some of the files are badly
+        # formatted.
+        seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>")
+
+        def gen():
+            with open(path, encoding="utf-8") as f:
+                for line in f:
+                    seg_match = re.match(seg_re, line)
+                    if seg_match:
+                        assert len(seg_match.groups()) == 1
+                        yield seg_match.groups()[0]
+
+        return gen(), lang
+
+    parse_file = _parse_sgm if os.path.basename(f1).endswith(".sgm") else _parse_text
+
+    # Some datasets (e.g., CWMT) contain multiple parallel files specified with
+    # a wildcard. We sort both sets to align them and parse them one by one.
+    f1_files = sorted(glob.glob(f1))
+    f2_files = sorted(glob.glob(f2))
+
+    assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2)
+    assert len(f1_files) == len(f2_files), "Number of files do not match: %d vs %d for %s vs %s." % (
+        len(f1_files),
+        len(f2_files),
+        f1,
+        f2,
+    )
+
+    for f_id, (f1_i, f2_i) in enumerate(zip(sorted(f1_files), sorted(f2_files))):
+        l1_sentences, l1 = parse_file(f1_i, filename1)
+        l2_sentences, l2 = parse_file(f2_i, filename2)
+
+        for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
+            key = f"{f_id}/{line_id}"
+            yield key, {l1: s1, l2: s2}
+
+
+def _parse_frde_bitext(fr_path, de_path):
+    with open(fr_path, encoding="utf-8") as fr_f:
+        with open(de_path, encoding="utf-8") as de_f:
+            for line_id, (s1, s2) in enumerate(zip(fr_f, de_f)):
+                yield line_id, {"fr": s1.rstrip(), "de": s2.rstrip()}
+
+
+def _parse_tmx(path):
+    """Generates examples from TMX file."""
+
+    def _get_tuv_lang(tuv):
+        for k, v in tuv.items():
+            if k.endswith("}lang"):
+                return v
+        raise AssertionError("Language not found in `tuv` attributes.")
+
+    def _get_tuv_seg(tuv):
+        segs = tuv.findall("seg")
+        assert len(segs) == 1, "Invalid number of segments: %d" % len(segs)
+        return segs[0].text
+
+    with open(path, "rb") as f:
+        # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
+        utf_f = codecs.getreader("utf-8")(f)
+        for line_id, (_, elem) in enumerate(ElementTree.iterparse(utf_f)):
+            if elem.tag == "tu":
+                yield line_id, {_get_tuv_lang(tuv): _get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")}
+                elem.clear()
+
+
+def _parse_tsv(path, filename, language_pair=None):
+    """Generates examples from TSV file."""
+    if language_pair is None:
+        lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", filename)
+        assert lang_match is not None, "Invalid TSV filename: %s" % filename
+        l1, l2 = lang_match.groups()
+    else:
+        l1, l2 = language_pair
+    with open(path, encoding="utf-8") as f:
+        for j, line in enumerate(f):
+            cols = line.split("\t")
+            if len(cols) != 2:
+                logger.warning("Skipping line %d in TSV (%s) with %d != 2 columns.", j, path, len(cols))
+                continue
+            s1, s2 = cols
+            yield j, {l1: s1.strip(), l2: s2.strip()}
+
+
+def _parse_wikiheadlines(path):
+    """Generates examples from Wikiheadlines dataset file."""
+    lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
+    assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
+    l1, l2 = lang_match.groups()
+    with open(path, encoding="utf-8") as f:
+        for line_id, line in enumerate(f):
+            s1, s2 = line.split("|||")
+            yield line_id, {l1: s1.strip(), l2: s2.strip()}
+
+
+def _parse_czeng(*paths, **kwargs):
+    """Generates examples from CzEng v1.6, with optional filtering for v1.7."""
+    filter_path = kwargs.get("filter_path", None)
+    if filter_path:
+        re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
+        with open(filter_path, encoding="utf-8") as f:
+            bad_blocks = {blk for blk in re.search(r"qw{([\s\d]*)}", f.read()).groups()[0].split()}
+        logger.info("Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.", len(bad_blocks))
+
+    for path in paths:
+        for gz_path in sorted(glob.glob(path)):
+            with open(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f:
+                filename = os.path.basename(gz_path)
+                for line_id, line in enumerate(f):
+                    line = line.decode("utf-8")  # required for py3
+                    if not line.strip():
+                        continue
+                    id_, unused_score, cs, en = line.split("\t")
+                    if filter_path:
+                        block_match = re.match(re_block, id_)
+                        if block_match and block_match.groups()[0] in bad_blocks:
+                            continue
+                    sub_key = f"{filename}/{line_id}"
+                    yield sub_key, {
+                        "cs": cs.strip(),
+                        "en": en.strip(),
+                    }
+
+
+def _parse_hindencorp(path):
+    with open(path, encoding="utf-8") as f:
+        for line_id, line in enumerate(f):
+            split_line = line.split("\t")
+            if len(split_line) != 5:
+                logger.warning("Skipping invalid HindEnCorp line: %s", line)
+                continue
+            yield line_id, {"translation": {"en": split_line[3].strip(), "hi": split_line[4].strip()}}