# 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"], }