Create google-code-jam.py

google-code-jam.py

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+from typing import List
+import os
+import glob
+
+import datasets
+
+
+_DESCRIPTION = """Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others."""
+
+_CITATION = """@inproceedings{10.1145/3236024.3236068,
+author = {Zhao, Gang and Huang, Jeff},
+title = {DeepSim: Deep Learning Code Functional Similarity},
+year = {2018},
+isbn = {9781450355735},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3236024.3236068},
+doi = {10.1145/3236024.3236068},
+booktitle = {Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
+pages = {141–151},
+numpages = {11},
+keywords = {Classification, Control/Data flow, Code functional similarity, Deep Learning},
+location = {Lake Buena Vista, FL, USA},
+series = {ESEC/FSE 2018}
+}
+"""
+
+SPLITS = {
+    'test': [5, 6, 7, 8, 12],  # For test in `Language Models are Universal Embedders` https://arxiv.org/pdf/2310.08232.pdf
+    'all': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+}
+_URL = "https://huggingface.co/datasets/izhx/google-code-jam/resolve/main/googlejam4_src.zip"
+
+VERSION = datasets.Version("1.0")
+
+class GoogleCodeJam(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=name, version=VERSION, description=_DESCRIPTION) for name in SPLITS.items()
+    ]
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "fn1": datasets.Value("string"),
+                    "code1": datasets.Value("string"),
+                    "fn1": datasets.Value("string"),
+                    "code1": datasets.Value("string"),
+                    "label": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys="label",
+            homepage="https://github.com/parasol-aser/deepsim",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        folder = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"folder": folder, "problems": SPLITS["test"]}),
+            datasets.SplitGenerator(name='all', gen_kwargs={"folder": folder, "problems": SPLITS["all"]}),
+        ]
+
+    def _generate_examples(self, folder, problems: list):
+        raw = dict()
+        for i in problems:
+            group = list()
+            for path in sorted(glob.glob(f'{folder}/{i}/*.java')):
+                with open(path) as file:
+                    lines = [l for l in file]
+                name = os.path.basename(path)
+                group.append((name, ''.join(lines[1:])))  # remove name line
+            raw[i] = group
+
+        _id = 0
+        reverse = False
+        for i in range(len(problems)):
+            vi = raw[problems[i]]
+            for n1, (fn1, code1) in enumerate(vi):
+                for j in range(i, len(problems)):
+                    vj = raw[problems[j]]
+                    match = i == j
+                    for n2, (fn2, code2) in enumerate(vj):
+                        if match and n1 <= n2:
+                            continue
+                        ins = {'fn1': fn1, 'code1': code1, 'fn2': fn2, 'code2': code2, 'label': int(match)}
+                        if reverse:
+                            ins['fn1'], ins['fn2'] = ins['fn2'], ins['fn1']
+                            ins['code1'], ins['code2'] = ins['code2'], ins['code1']
+                        yield _id, ins
+                        _id += 1
+                        reverse = not reverse