# 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 csv
import json
import os
from os import walk
import datasets
import pickle
from pathlib import Path
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """
@inproceedings{
dahal2022scotch,
title={Scotch: A Semantic Code Search Engine for {IDE}s},
author={Samip Dahal and Adyasha Maharana and Mohit Bansal},
booktitle={Deep Learning for Code Workshop},
year={2022},
url={https://openreview.net/forum?id=rSxfCiOZk-c}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Scotch is a dataset of about 19 million functions collected from open-source repositiories from GitHub with permissive licenses. Each function has its corresponding code context and about 4 million functions have corresponding docstrings. The dataset includes functions written in programming languages Python, Java, Javascript, and Go."""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/sdpmas/Scotch"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "The MIT 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)
languages=['python','javascript','java','go']
language_map={'python':'py','javascript':'js','go':'go','java':'java'}
_URLs = {lang:f'https://scotchdata.s3.amazonaws.com/go.tar.gz' for lang in languages}
_URLs['all']=_URLs.copy()
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ScotchDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="all", version=VERSION, description="All available data with docstrings"),
datasets.BuilderConfig(name="python", version=VERSION, description="Python data"),
datasets.BuilderConfig(name="javascript", version=VERSION, description="Javascript data"),
datasets.BuilderConfig(name="java", version=VERSION, description="Java data"),
datasets.BuilderConfig(name="go", version=VERSION, description="Go data"),
]
DEFAULT_CONFIG_NAME = "all"
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = datasets.Features(
{
"repository_name": datasets.Value("string"),
"function_path": datasets.Value("string"),
"function_identifier": datasets.Value("string"),
"language": datasets.Value("string"),
"function": datasets.Value("string"),
"docstring": datasets.Value("string"),
"function_url": datasets.Value("string"),
"context":datasets.Value("string"),
"license":datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
print('this is the URL ********',my_urls)
if isinstance(my_urls, str):
my_urls = {self.config.name:my_urls}
data_dir = [os.path.join(lang_dir,language_map[lang]) for lang,lang_dir in dl_manager.download_and_extract(my_urls).items()]
filenames = next(walk(lang_dir), (None, None, []))[2] # [] if no file
print('filenames are: ',filenames)
print('items: ',dl_manager.download_and_extract(my_urls).items())
print('data_dir',data_dir)
# splitpaths={split:[os.path.join(lang_dir,f'{split}.bin') for lang_dir in data_dir] for split in ['train','valid','test']}
splitpaths={}
for split in ['train','valid','test']:
for lang_dir in data_dir:
# Path glob .bin files
lang_split_files=sorted(Path(os.path.join(lang_dir,split)).glob('*.bin'))
if not split in splitpaths:
splitpaths[split]=lang_split_files
else:
splitpaths[split].extend(lang_split_files)
print('split paths',splitpaths)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": splitpaths['train'],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": splitpaths['test'],
"split": "test"
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": splitpaths['valid'],
"split": "valid",
},
),
]
def _generate_examples(
self, filepath,split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
""" Yields examples as (key, example) tuples. """
count=-1
for i,filepath in enumerate(filepath):
loaded_f=pickle.load(open(filepath,'rb'))
for j, func in enumerate(loaded_f):
count+=1
yield count,{
"repository_name": str(func['nwo']),
"function_path":str(func['path']),
"function_identifier": str(func['identifier']),
"language": str(func['language']),
"function": str(func['function']),
"docstring": str(func['docstring']),
"function_url": str(func['url']),
"context":str(func['context']),
"license":str(func['license']),
}