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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
import json
import datasets
_DESCRIPTION = """\
The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensing issues. See the public associated code repository [https://github.com/VHellendoorn/ICLR20-Great] for results produced from this dataset.
This dataset was generated synthetically from the corpus of Python code in the ETH Py150 Open dataset [https://github.com/google-research-datasets/eth_py150_open].
"""
_HOMEPAGE_URL = ""
_CITATION = """\
@inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
author = {Vincent J. Hellendoorn and
Charles Sutton and
Rishabh Singh and
Petros Maniatis and
David Bieber},
title = {Global Relational Models of Source Code},
booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
Addis Ababa, Ethiopia, April 26-30, 2020},
publisher = {OpenReview.net},
year = {2020},
url = {https://openreview.net/forum?id=B1lnbRNtwr},
timestamp = {Thu, 07 May 2020 17:11:47 +0200},
biburl = {https://dblp.org/rec/conf/iclr/HellendoornSSMB20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_TRAIN_URLS = [
f"https://raw.githubusercontent.com/google-research-datasets/great/master/train/train__VARIABLE_MISUSE__SStuB.txt-{x:05d}-of-00300"
for x in range(300)
]
_TEST_URLS = [
f"https://raw.githubusercontent.com/google-research-datasets/great/master/eval/eval__VARIABLE_MISUSE__SStuB.txt-{x:05d}-of-00300"
for x in range(300)
]
_VALID_URLS = [
f"https://raw.githubusercontent.com/google-research-datasets/great/master/dev/dev__VARIABLE_MISUSE__SStuB.txt-{x:05d}-of-00300"
for x in range(300)
]
class GreatCode(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"source_tokens": datasets.Sequence(datasets.Value("string")),
"has_bug": datasets.Value("bool"),
"error_location": datasets.Value("int32"),
"repair_candidates": datasets.Sequence(datasets.Value("string")),
"bug_kind": datasets.Value("int32"),
"bug_kind_name": datasets.Value("string"),
"repair_targets": datasets.Sequence(datasets.Value("int32")),
"edges": [
[
{
"before_index": datasets.Value("int32"),
"after_index": datasets.Value("int32"),
"edge_type": datasets.Value("int32"),
"edge_type_name": datasets.Value("string"),
}
]
],
"provenances": [
{
"datasetProvenance": {
"datasetName": datasets.Value("string"),
"filepath": datasets.Value("string"),
"license": datasets.Value("string"),
"note": datasets.Value("string"),
}
}
],
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_URLS)
valid_path = dl_manager.download_and_extract(_VALID_URLS)
test_path = dl_manager.download_and_extract(_TEST_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datapath": train_path,
"datatype": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datapath": valid_path,
"datatype": "valid",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datapath": test_path,
"datatype": "test",
},
),
]
def _generate_examples(self, datapath, datatype):
for file_idx, dp in enumerate(datapath):
with open(dp, "r", encoding="utf-8") as json_file:
for example_counter, json_str in enumerate(json_file):
result = json.loads(json_str)
response = {
"id": example_counter,
"source_tokens": result["source_tokens"],
"has_bug": result["has_bug"],
"error_location": result["error_location"],
"repair_candidates": [str(x) for x in result["repair_candidates"]],
"bug_kind": result["bug_kind"],
"bug_kind_name": result["bug_kind_name"],
"repair_targets": result["repair_targets"],
"edges": [
[
{
"before_index": result["edges"][x][0],
"after_index": result["edges"][x][1],
"edge_type": result["edges"][x][2],
"edge_type_name": result["edges"][x][3],
}
]
for x in range(len(result["edges"]))
],
"provenances": result["provenances"],
}
yield f"{file_idx}_{example_counter}", response
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