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import json |
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import os |
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import os.path |
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from typing import List |
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import datasets |
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from .common import TrainValidTestChild |
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from .generated_definitions import DEFINITIONS |
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_DESCRIPTION = """The dataset we use comes from CodeSearchNet and we filter the dataset as the following: |
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- Remove examples that codes cannot be parsed into an abstract syntax tree. |
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- Remove examples that #tokens of documents is < 3 or >256 |
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- Remove examples that documents contain special tokens (e.g. <img ...> or https:...) |
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- Remove examples that documents are not English. |
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""" |
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_CITATION = """@article{husain2019codesearchnet, |
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title={Codesearchnet challenge: Evaluating the state of semantic code search}, |
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author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, |
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journal={arXiv preprint arXiv:1909.09436}, |
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year={2019} |
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}""" |
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class CodeXGlueCtCodeToTextBaseImpl(TrainValidTestChild): |
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_DESCRIPTION = _DESCRIPTION |
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_CITATION = _CITATION |
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_FEATURES = { |
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"id": datasets.Value("int32"), |
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"repo": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"func_name": datasets.Value("string"), |
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"original_string": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"code": datasets.Value("string"), |
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"code_tokens": datasets.features.Sequence( |
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datasets.Value("string") |
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), |
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"docstring": datasets.Value( |
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"string" |
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), |
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"docstring_tokens": datasets.features.Sequence( |
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datasets.Value("string") |
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), |
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"sha": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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} |
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_SUPERVISED_KEYS = ["docstring", "docstring_tokens"] |
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def generate_urls(self, split_name, language): |
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yield "language", f"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/{language}.zip" |
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yield "dataset", "dataset.zip" |
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def get_data_files(self, split_name, file_paths, language): |
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language_specific_path = file_paths["language"] |
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final_path = os.path.join(language_specific_path, language, "final") |
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for path in os.listdir(final_path): |
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if path.endswith(".pkl"): |
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os.unlink(path) |
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data_files = [] |
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for root, dirs, files in os.walk(final_path): |
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for file in files: |
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temp = os.path.join(root, file) |
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if ".jsonl" in temp: |
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if split_name in temp: |
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data_files.append(temp) |
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return data_files |
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def post_process(self, split_name, language, js): |
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return js |
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def _generate_examples(self, split_name, file_paths, language): |
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import gzip |
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data_set_path = file_paths["dataset"] |
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data_files = self.get_data_files(split_name, file_paths, language) |
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urls = {} |
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f1_path_parts = [data_set_path, "dataset", language, f"{split_name}.txt"] |
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if self.SINGLE_LANGUAGE: |
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del f1_path_parts[2] |
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f1_path = os.path.join(*f1_path_parts) |
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with open(f1_path, encoding="utf-8") as f1: |
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for line in f1: |
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line = line.strip() |
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urls[line] = True |
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idx = 0 |
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for file in data_files: |
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if ".gz" in file: |
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f = gzip.open(file) |
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else: |
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f = open(file, encoding="utf-8") |
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for line in f: |
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line = line.strip() |
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js = json.loads(line) |
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if js["url"] in urls: |
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js["id"] = idx |
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js = self.post_process(split_name, language, js) |
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if "partition" in js: |
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del js["partition"] |
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yield idx, js |
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idx += 1 |
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f.close() |
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class CodeXGlueCtCodeToTextImpl(CodeXGlueCtCodeToTextBaseImpl): |
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SINGLE_LANGUAGE = False |
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def generate_urls(self, split_name): |
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language = self.info["parameters"]["language"] |
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for e in super().generate_urls(split_name, language): |
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yield e |
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def _generate_examples(self, split_name, file_paths): |
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language = self.info["parameters"]["language"] |
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for e in super()._generate_examples(split_name, file_paths, language): |
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yield e |
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CLASS_MAPPING = { |
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"CodeXGlueCtCodeToText": CodeXGlueCtCodeToTextImpl, |
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} |
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class CodeXGlueCtCodeToText(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIG_CLASS = datasets.BuilderConfig |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items() |
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] |
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def _info(self): |
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name = self.config.name |
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info = DEFINITIONS[name] |
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if info["class_name"] in CLASS_MAPPING: |
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self.child = CLASS_MAPPING[info["class_name"]](info) |
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else: |
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raise RuntimeError(f"Unknown python class for dataset configuration {name}") |
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ret = self.child._info() |
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return ret |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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return self.child._split_generators(dl_manager=dl_manager) |
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def _generate_examples(self, split_name, file_paths): |
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return self.child._generate_examples(split_name, file_paths) |
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