athena_data.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.
"""TODO: Add a description here."""

import ast
import csv
import datasets
import function_parser
import json
import os
import sys

csv.field_size_limit(sys.maxsize)

import pandas as pd

from function_parser.language_data import LANGUAGE_METADATA
from function_parser.parsers.java_parser import JavaParser
from function_parser.process import DataProcessor
from git import Git, Repo
from glob import glob
from tree_sitter import Language
from subprocess import check_output

LANG = "java"
JAVA_LANG = Language(
    os.path.join(function_parser.__path__[0], "tree-sitter-languages.so"), LANG
)
DataProcessor.PARSER.set_language(JAVA_LANG)
FUNC_PROCESSOR = DataProcessor(
    language=LANG, language_parser=LANGUAGE_METADATA[LANG]["language_parser"]
)

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# 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)
_URL = "https://huggingface.co/datasets/ncoop57/athena_data/resolve/main/repos-commits.zip"


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # 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="meta_data", version=VERSION, description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="repos_commits", version=VERSION, description="This part of my dataset covers a second domain"),
    ]

    DEFAULT_CONFIG_NAME = "meta_data"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "meta_data":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "repo": datasets.Value("string"),
                    "parent_commit": datasets.Value("string"),
                    "commit": datasets.Value("string"),
                    "changes": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
                    # These are the features of your dataset like images, labels ...
                }
            )
        elif self.config.name == "repos_commits":
            features = datasets.Features(
                {
                    "repo": datasets.Value("string"),
                    "parent_commit": datasets.Value("string"),
                    "commit": datasets.Value("string"),
                    "changes": datasets.Sequence(datasets.Value("string")),
                    "file_path": datasets.Value("string"),
                    "code": datasets.Value("string"),
                    "code_tokens": datasets.Sequence(datasets.Value("string")),
                    "docstring": datasets.Value("string"),
                }
            )
        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."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # 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
        data_dir = dl_manager.download_and_extract(_URL)
        data_dir = os.path.join(data_dir, "repos-commits")
        if self.config.name == "repos_commits" and not os.path.exists(os.path.join(data_dir, "repos")):
            # Clone all repositories
            output = check_output(
                [
                    "bash",
                    "clone.sh",
                    "repos.txt",
                ],
                cwd=data_dir,
            )
            # print(output)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "data_dir": data_dir,
                    "file_path": os.path.join(data_dir, "processed_impact_methods.csv"),
                },
            ),
        ]

    def _generate_examples(
        self, data_dir, file_path  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ 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(file_path, encoding="utf-8") as f:
            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
            next(csv_reader, None) # skip header
            row_id = -1
            for _, row in enumerate(csv_reader):
                row_id += 1
                repo, parent_commit, commit, changes = row
                changes = ast.literal_eval(changes)
                # print(changes)
                if self.config.name == "meta_data":
                        yield row_id, {
                            "repo": repo,
                            "parent_commit": parent_commit,
                            "commit": commit,
                            "changes": changes,
                        }
                elif self.config.name == "repos_commits":
                    repo_path = os.path.join(data_dir, "repos", repo)
                    try:
                        # Otherwise, parse the project
                        g = Git(repo_path)
                        g.clean(force=True, d=True)
                        g.checkout(commit)
                    except Exception as e:
                        print(e)
                        continue

                    indexes = []
                    files = glob(f"{repo_path}/**/*.{LANGUAGE_METADATA[LANG]['ext']}", recursive=True)
                    sha = None
                    for f in files:
                        definitions = FUNC_PROCESSOR.get_function_definitions(f)
                        if definitions is None:
                            continue

                        nwo, path, functions = definitions
                        indexes.extend(
                            (
                                FUNC_PROCESSOR.extract_function_data(func, nwo, path, sha)
                                for func in functions
                                if len(func["function_tokens"]) > 1
                            )
                        )

                    df = pd.DataFrame(indexes)[
                        ["path", "function", "function_tokens", "docstring"]
                    ].rename(
                        columns={
                            "path": "file_path",
                            "function": "code",
                            "function_tokens": "code_tokens",
                            "docstring": "docstring",
                        }
                    )
                    for _, row in df.iterrows():
                        row_id += 1
                        yield row_id, {
                            "repo": repo,
                            "parent_commit": parent_commit,
                            "commit": commit,
                            "changes": changes,
                            "file_path": row["file_path"],
                            "code": row["code"],
                            "code_tokens": row["code_tokens"],
                            "docstring": row["docstring"],
                        }