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. 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://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/{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)