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- *.7z filter=lfs diff=lfs merge=lfs -text
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data.jsonl → default/pytorrent-standalone-train.parquet RENAMED
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pytorrent-standalone.py DELETED
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- # coding=utf-8
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- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- """Wrapper for datasets in CodeXGLUE benchmark."""
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- import csv
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- import json
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- import os
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- import datasets
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-
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- _CITATION = """\
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- @article{Bahrami2021,
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- 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},
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- year = {2021},
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- journal = {arXiv},
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- title = {PyTorrent: A Python Library Corpus for Large-scale Language Models}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- pytorrent-standalone is a subset of the PyTorrent dataset, where only functions that does not depend on external libraries
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- are kept.
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- """
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-
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- _HOMEPAGE = ""
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-
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- _LICENSE = ""
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-
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- # The HuggingFace dataset library don't host the datasets but only point to the original files
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- # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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- _URLs = {
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- 'default': "data.jsonl",
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- }
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-
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-
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- # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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- class PyTorrentStandalone(datasets.GeneratorBasedBuilder):
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- """TODO: Short description of my dataset."""
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- VERSION = datasets.Version("1.0.0")
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- # This is an example of a dataset with multiple configurations.
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- # If you don't want/need to define several sub-sets in your dataset,
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- # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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- # If you need to make complex sub-parts in the datasets with configurable options
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- # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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- # BUILDER_CONFIG_CLASS = MyBuilderConfig
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- # You will be able to load one or the other configurations in the following list with
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- # data = datasets.load_dataset('my_dataset', 'first_domain')
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- # data = datasets.load_dataset('my_dataset', 'second_domain')
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- BUILDER_CONFIGS = [
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- datasets.BuilderConfig(name="default", version=VERSION,
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- description="The corpus."),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense.
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-
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- def _info(self):
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- if self.config.name == "default": # This is the name of the configuration selected in BUILDER_CONFIGS above
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- features = datasets.Features(
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- {
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- "repo": datasets.Value("string"),
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- "path": datasets.Value("string"),
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- "code": datasets.Value("string"),
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- }
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- )
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- else:
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- assert False, f'Invalid config name: {self.config.name}'
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # This defines the different columns of the dataset and their types
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- features=features, # Here we define them above because they are different between the two configurations
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
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- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
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- homepage=_HOMEPAGE,
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- # License for the dataset if available
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- license=_LICENSE,
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- # Citation for the dataset
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- # If several configurations are possible (listed in BUILDER_CONFIGS),
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- # the configuration selected by the user is in self.config.name
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- # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
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- # It can accept any type or nested list/dict and will give back the same structure with the url replaced with
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- # path to local files. By default the archives will be extracted and a path to a cached folder where they are
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- # extracted is returned instead of the archive
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- my_urls = _URLs[self.config.name]
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- data_path = dl_manager.download_and_extract(my_urls)
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- if self.config.name == 'default':
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- 'data_path': data_path
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- }
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- ),
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- ]
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-
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- def _generate_examples(
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- self, data_path
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- ):
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- """ Yields examples as (key, example) tuples. """
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- # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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- # The `key` is here for legacy reason (tfds) and is not important in itself.
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- with open(data_path) as jsonl_file:
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- idx = 0
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- for jsonl in jsonl_file:
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- sample = json.loads(jsonl)
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- result = {
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- 'repo': sample['repo'],
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- 'path': sample['path'],
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- 'code': sample['original_string']
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- }
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- yield idx, result
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- idx += 1