Datasets:
Tasks:
Text Retrieval
Formats:
parquet
Sub-tasks:
document-retrieval
Size:
100K - 1M
ArXiv:
License:
import json | |
import os | |
import os.path | |
from typing import List | |
import datasets | |
from .common import TrainValidTestChild | |
from .generated_definitions import DEFINITIONS | |
_DESCRIPTION = """The dataset we use comes from CodeSearchNet and we filter the dataset as the following: | |
- Remove examples that codes cannot be parsed into an abstract syntax tree. | |
- Remove examples that #tokens of documents is < 3 or >256 | |
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...) | |
- Remove examples that documents are not English. | |
""" | |
_CITATION = """@article{husain2019codesearchnet, | |
title={Codesearchnet challenge: Evaluating the state of semantic code search}, | |
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, | |
journal={arXiv preprint arXiv:1909.09436}, | |
year={2019} | |
}""" | |
class CodeXGlueCtCodeToTextBaseImpl(TrainValidTestChild): | |
_DESCRIPTION = _DESCRIPTION | |
_CITATION = _CITATION | |
# For each file, each line in the uncompressed file represents one function. | |
_FEATURES = { | |
"id": datasets.Value("int32"), # Index of the sample | |
"repo": datasets.Value("string"), # repo: the owner/repo | |
"path": datasets.Value("string"), # path: the full path to the original file | |
"func_name": datasets.Value("string"), # func_name: the function or method name | |
"original_string": datasets.Value("string"), # original_string: the raw string before tokenization or parsing | |
"language": datasets.Value("string"), # language: the programming language name | |
"code": datasets.Value("string"), # code/function: the part of the original_string that is code | |
"code_tokens": datasets.features.Sequence( | |
datasets.Value("string") | |
), # code_tokens/function_tokens: tokenized version of code | |
"docstring": datasets.Value( | |
"string" | |
), # docstring: the top-level comment or docstring, if it exists in the original string | |
"docstring_tokens": datasets.features.Sequence( | |
datasets.Value("string") | |
), # docstring_tokens: tokenized version of docstring | |
"sha": datasets.Value("string"), # sha of the file | |
"url": datasets.Value("string"), # url of the file | |
} | |
_SUPERVISED_KEYS = ["docstring", "docstring_tokens"] | |
def generate_urls(self, split_name, language): | |
yield "language", f"https://huggingface.co/datasets/code_search_net/resolve/main/data/{language}.zip" | |
yield "dataset", "dataset.zip" | |
def get_data_files(self, split_name, file_paths, language): | |
language_specific_path = file_paths["language"] | |
final_path = os.path.join(language_specific_path, language, "final") | |
# Make some cleanup to save space | |
for path in os.listdir(final_path): | |
if path.endswith(".pkl"): | |
os.unlink(path) | |
data_files = [] | |
for root, dirs, files in os.walk(final_path): | |
for file in files: | |
temp = os.path.join(root, file) | |
if ".jsonl" in temp: | |
if split_name in temp: | |
data_files.append(temp) | |
return data_files | |
def post_process(self, split_name, language, js): | |
return js | |
def _generate_examples(self, split_name, file_paths, language): | |
import gzip | |
data_set_path = file_paths["dataset"] | |
data_files = self.get_data_files(split_name, file_paths, language) | |
urls = {} | |
f1_path_parts = [data_set_path, "dataset", language, f"{split_name}.txt"] | |
if self.SINGLE_LANGUAGE: | |
del f1_path_parts[2] | |
f1_path = os.path.join(*f1_path_parts) | |
with open(f1_path, encoding="utf-8") as f1: | |
for line in f1: | |
line = line.strip() | |
urls[line] = True | |
idx = 0 | |
for file in data_files: | |
if ".gz" in file: | |
f = gzip.open(file) | |
else: | |
f = open(file, encoding="utf-8") | |
for line in f: | |
line = line.strip() | |
js = json.loads(line) | |
if js["url"] in urls: | |
js["id"] = idx | |
js = self.post_process(split_name, language, js) | |
if "partition" in js: | |
del js["partition"] | |
yield idx, js | |
idx += 1 | |
f.close() | |
class CodeXGlueTcNLCodeSearchAdvImpl(CodeXGlueCtCodeToTextBaseImpl): | |
LANGUAGE = "python" | |
SINGLE_LANGUAGE = True | |
_FEATURES = { | |
"id": datasets.Value("int32"), # Index of the sample | |
"repo": datasets.Value("string"), # repo: the owner/repo | |
"path": datasets.Value("string"), # path: the full path to the original file | |
"func_name": datasets.Value("string"), # func_name: the function or method name | |
"original_string": datasets.Value("string"), # original_string: the raw string before tokenization or parsing | |
"language": datasets.Value("string"), # language: the programming language | |
"code": datasets.Value("string"), # code/function: the part of the original_string that is code | |
"code_tokens": datasets.features.Sequence( | |
datasets.Value("string") | |
), # code_tokens/function_tokens: tokenized version of code | |
"docstring": datasets.Value( | |
"string" | |
), # docstring: the top-level comment or docstring, if it exists in the original string | |
"docstring_tokens": datasets.features.Sequence( | |
datasets.Value("string") | |
), # docstring_tokens: tokenized version of docstring | |
"sha": datasets.Value("string"), # sha of the file | |
"url": datasets.Value("string"), # url of the file | |
"docstring_summary": datasets.Value("string"), # Summary of the docstring | |
"parameters": datasets.Value("string"), # parameters of the function | |
"return_statement": datasets.Value("string"), # return statement | |
"argument_list": datasets.Value("string"), # list of arguments of the function | |
"identifier": datasets.Value("string"), # identifier | |
"nwo": datasets.Value("string"), # nwo | |
"score": datasets.Value("float"), # score for this search | |
} | |
def post_process(self, split_name, language, js): | |
for suffix in "_tokens", "": | |
key = "function" + suffix | |
if key in js: | |
js["code" + suffix] = js[key] | |
del js[key] | |
for key in self._FEATURES: | |
if key not in js: | |
if key == "score": | |
js[key] = -1 | |
else: | |
js[key] = "" | |
return js | |
def generate_urls(self, split_name): | |
for e in super().generate_urls(split_name, self.LANGUAGE): | |
yield e | |
def get_data_files(self, split_name, file_paths, language): | |
if split_name == "train": | |
return super().get_data_files(split_name, file_paths, language) | |
else: | |
data_set_path = file_paths["dataset"] | |
data_file = os.path.join(data_set_path, "dataset", "test_code.jsonl") | |
return [data_file] | |
def _generate_examples(self, split_name, file_paths): | |
for e in super()._generate_examples(split_name, file_paths, self.LANGUAGE): | |
yield e | |
CLASS_MAPPING = { | |
"CodeXGlueTcNLCodeSearchAdv": CodeXGlueTcNLCodeSearchAdvImpl, | |
} | |
class CodeXGlueTcNlCodeSearchAdv(datasets.GeneratorBasedBuilder): | |
BUILDER_CONFIG_CLASS = datasets.BuilderConfig | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items() | |
] | |
def _info(self): | |
name = self.config.name | |
info = DEFINITIONS[name] | |
if info["class_name"] in CLASS_MAPPING: | |
self.child = CLASS_MAPPING[info["class_name"]](info) | |
else: | |
raise RuntimeError(f"Unknown python class for dataset configuration {name}") | |
ret = self.child._info() | |
return ret | |
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
return self.child._split_generators(dl_manager=dl_manager) | |
def _generate_examples(self, split_name, file_paths): | |
return self.child._generate_examples(split_name, file_paths) | |