import from S3

ar.py

@@ -0,0 +1,102 @@
+# 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
+"""The Arabic United nation Corpus dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import glob
+import os
+import re
+
+import datasets
+
+
+_DESCRIPTION = """\
+The corpus is a part of the MultiUN corpus.\
+It is a collection of translated documents from the United Nations.\
+The corpus is download from the following website : \
+[open parallel corpus](http://opus.datasetsl.eu/)  \
+"""
+
+_CITATION = """\
+@inproceedings{eisele2010multiun,
+  title={MultiUN: A Multilingual Corpus from United Nation Documents.},
+  author={Eisele, Andreas and Chen, Yu},
+  booktitle={LREC},
+  year={2010}
+}
+
+"""
+
+URL = "https://object.pouta.csc.fi/OPUS-MultiUN/v1/mono/ar.txt.gz"
+
+
+class AracorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for BookCorpus."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for BookCorpus.
+        Args:
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super(AracorpusConfig, self).__init__(
+            version=datasets.Version("1.0.0", "New split API (https://tensorflow.org/datasets/splits)"), **kwargs
+        )
+
+
+class Aracorpus(datasets.GeneratorBasedBuilder):
+    """BookCorpus dataset."""
+
+    BUILDER_CONFIGS = [AracorpusConfig(name="plain_text", description="Plain text",)]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({"text": datasets.Value("string"),}),
+            supervised_keys=None,
+            homepage="http://opus.datasetsl.eu/",
+            citation=_CITATION,
+        )
+
+    def _vocab_text_gen(self, archive):
+        for _, ex in self._generate_examples(archive):
+            yield ex["text"]
+
+    def _split_generators(self, dl_manager):
+        arch_path = dl_manager.download_and_extract(URL)
+	
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"directory": arch_path}),
+        ]
+
+    def _generate_examples(self, directory):
+        index=directory.rfind("datasets")
+        index=index+8
+        url=directory[:index]
+        direct_name=directory[index+1:]
+        directory=url
+
+        files = [
+            os.path.join(directory, direct_name),
+        ]
+
+        _id = 0
+        for txt_file in files:
+            with open(txt_file, mode="r") as f:
+                for line in f:
+                    yield _id, {"text": line.strip()}
+                    _id += 1