Delete loading script

code_x_glue_cc_clone_detection_poj104.py

@@ -1,93 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
-We use POJ-104 dataset on this task."""
-
-_CITATION = """@inproceedings{mou2016convolutional,
-title={Convolutional neural networks over tree structures for programming language processing},
-author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
-booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
-pages={1287--1293},
-year={2016}
-}"""
-
-
-class CodeXGlueCcCloneDetectionPoj104Impl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "code": datasets.Value("string"),  # The full text of the function
-        "label": datasets.Value("string"),  # The id of problem that the source code solves
-    }
-
-    _SUPERVISED_KEYS = ["label"]
-
-    SPLIT_RANGES = {"train": (1, 65), "valid": (65, 81), "test": (81, 195)}
-
-    def _generate_examples(self, files, split_name):
-        cont = 0
-        for path, f in files:
-            # path are in the format ProgramData/{index}/{filename}
-            label = int(path.split("/")[1])
-            if self.SPLIT_RANGES[split_name][0] <= label <= self.SPLIT_RANGES[split_name][1]:
-                js = {}
-                js["label"] = str(label)
-                js["id"] = cont
-                js["code"] = f.read().decode("latin-1")
-                yield cont, js
-                cont += 1
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCloneDetectionPoj104": CodeXGlueCcCloneDetectionPoj104Impl,
-}
-
-
-class CodeXGlueCcCloneDetectionPoj104(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]:
-        name = self.config.name
-        info = DEFINITIONS[name]
-        archive = dl_manager.download(info["raw_url"] + "/programs.tar.gz")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "valid"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, files, split_name):
-        return self.child._generate_examples(files, split_name)