pytorrent-standalone.py

# 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