Datasets:
great_code

	# 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