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# TODO: Address all TODOs and remove all explanatory comments | |
"""TODO: Add a description here.""" | |
import csv | |
import json | |
import os | |
import re | |
import datasets | |
from datasets import Value | |
_CITATION = """\ | |
@article{recode_wang2022, | |
title = {ReCode: Robustness Evaluation of Code Generation Models}, | |
author = {Wang, Shiqi and | |
Zheng, Li and | |
Qian, Haifeng and | |
Yang, Chenghao and | |
Wang, Zijian and | |
Kumar, Varun and | |
Shang, Mingyue and | |
Tan, Samson and | |
Ray, Baishakhi and | |
Bhatia, Parminder and | |
Nallapati, Ramesh and | |
Ramanathan, Murali Krishna and | |
Roth, Dan and | |
Xiang, Bing}, | |
doi = {10.48550/arXiv.2212.10264}, | |
url = {https://arxiv.org/abs/2212.10264}, | |
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL)}, | |
publisher = {arXiv}, | |
year = {2022}, | |
copyright = {Creative Commons Attribution 4.0 International} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Perturbed version of HumanEval from: ReCode: Robustness Evaluation of Code Generation Models | |
""" | |
_HOMEPAGE = "https://github.com/amazon-science/recode" | |
# 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 Datasets library doesn't host the datasets but only points to the original files. | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
_URLS = { | |
"nlaugmenter": "nlaugmenter.tar.gz", | |
"format": "format.tar.gz", | |
"natgen": "natgen.tar.gz", | |
"func_name": "func_name.tar.gz" | |
} | |
class PerturbedHumaneval(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="format", version=VERSION, description="Perturbations to the format of partial completions"), | |
datasets.BuilderConfig(name="natgen", version=VERSION, description="NatGen perturbations on partial completions"), | |
datasets.BuilderConfig(name="func_name", version=VERSION, description="Perturbations on function names"), | |
datasets.BuilderConfig(name="nlaugmenter", version=VERSION, description="Perturbations on docstrings with NL-Augmenter"), | |
] | |
# DEFAULT_CONFIG_NAME = "func_name" | |
def _info(self): | |
if self.config.name in ["format", "natgen"]: # This is the name of the configuration selected in BUILDER_CONFIGS above | |
features = datasets.Features( | |
{ | |
'task_id': Value(dtype='string'), | |
'prompt': Value(dtype='string'), | |
'entry_point': Value(dtype='string'), 'canonical_solution': Value(dtype='string'), 'test': Value(dtype='string'), 'seed': Value(dtype="int32"), 'perturbation_name': Value(dtype='string'), 'partial': Value(dtype='string') | |
} | |
) | |
elif self.config.name in ["func_name", "nlaugmenter"]: | |
features = datasets.Features( | |
{ | |
'task_id': Value(dtype='string'), 'prompt': Value(dtype='string'), 'entry_point': Value(dtype='string'), 'canonical_solution': Value(dtype='string'), 'test': Value(dtype='string'), 'seed': Value(dtype="int32"), 'perturbation_name': Value(dtype='string') | |
} | |
) | |
else: | |
raise ValueError(f"Invalid configuration name {self.config.name}") | |
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, uncomment supervised_keys line below and | |
# specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
# supervised_keys=("sentence", "label"), | |
# 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): | |
# 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 | |
urls = _URLS[self.config.name] | |
# all_urls = os.listdir(urls) | |
files = dl_manager.download_and_extract(urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"downloaded_files": dl_manager.iter_files(files), | |
# "split": "test" | |
}, | |
), | |
] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, downloaded_files): | |
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
id_ = 0 | |
# Iterate over files in .tar.gz archive | |
# extract files from archive | |
for file in downloaded_files: | |
# find perturbation name and seed | |
m = re.match(r'humaneval_([A-Za-z_\d]+)_s(\d+)\.jsonl', os.path.basename(file)) | |
assert m is not None, f"Unrecognized file-name: {file}" | |
perturbation_name = m.group(1) | |
seed = int(m.group(2)) | |
with open(file, encoding="utf-8") as f: | |
for row in f: | |
data = json.loads(row) | |
example = { | |
'task_id': data['task_id'], | |
'prompt': data['prompt'], | |
'entry_point': data['entry_point'], | |
'canonical_solution': data['canonical_solution'], | |
'test': data['test'], | |
'seed': seed, | |
'perturbation_name': perturbation_name, | |
} | |
if self.config.name in ["format", "natgen"]: | |
example['partial'] = data["partial"] | |
# Yields examples as (key, example) tuples | |
yield id_, example | |
id_ += 1 | |