code_x_glue_tc_nl_code_search_adv.py

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)