Add loader script

pytorrent-standalone.py

@@ -0,0 +1,131 @@
+# 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.
+"""Wrapper for datasets in CodeXGLUE benchmark."""
+import csv
+import json
+import os
+import datasets
+
+_CITATION = """\
+@article{Bahrami2021,
+author = {Bahrami, Mehdi and Shrikanth, N. C. and Ruangwan, Shade and Liu, Lei and Mizobuchi, Yuji and Fukuyori, Masahiro and Chen, Wei-Peng and Munakata, Kazuki and Menzies, Tim},
+year = {2021},
+journal = {arXiv},
+title = {PyTorrent: A Python Library Corpus for Large-scale Language Models}
+}
+"""
+
+_DESCRIPTION = """\
+pytorrent-standalone is a subset of the PyTorrent dataset, where only functions that does not depend on external libraries
+are kept.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    'all': "data.jsonl",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class PyTorrentStandalone(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+    VERSION = datasets.Version("1.0.0")
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="all", version=VERSION,
+                               description="The corpus."),
+    ]
+
+    # DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "all":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "repo": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "code": datasets.Value("string"),
+                }
+            )
+        else:
+            assert False, f'Invalid config name: {self.config.name}'
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS),
+        # the configuration selected by the user is in self.config.name
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with
+        # path to local files. By default the archives will be extracted and a path to a cached folder where they are
+        # extracted is returned instead of the archive
+        my_urls = _URLs[self.config.name]
+        data_path = dl_manager.download_and_extract(my_urls)
+        if self.config.name == 'all':
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        'data_path': data_path
+                    }
+                )
+            ]
+
+    def _generate_examples(
+            self, data_path
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        with open(data_path) as jsonl_file:
+            idx = 0
+            for jsonl in jsonl_file:
+                sample = json.loads(jsonl)
+                result = {
+                    'repo': sample['repo'],
+                    'path': sample['path'],
+                    'code': sample['original_string']
+                }
+                yield idx, result
+                idx += 1