| # 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 = { | |
| 'default': "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="default", version=VERSION, | |
| description="The corpus."), | |
| ] | |
| DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| if self.config.name == "default": # 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 == 'default': | |
| 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 | |