Delete loading script

code_x_glue_tc_text_to_code.py

@@ -1,69 +0,0 @@
-import json
-from typing import List
-
-import datasets
-
-from .common import Child
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """We use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details."""
-_CITATION = """@article{iyer2018mapping,
-title={Mapping language to code in programmatic context},
-author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
-journal={arXiv preprint arXiv:1808.09588},
-year={2018}
-}"""
-
-
-class CodeXGlueTcTextToCodeImpl(Child):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "nl": datasets.Value("string"),  # The natural language description of the task
-        "code": datasets.Value("string"),  # The programming source code for the task
-    }
-
-    _SUPERVISED_KEYS = ["code"]
-
-    SPLITS = {"train": datasets.Split.TRAIN, "dev": datasets.Split.VALIDATION, "test": datasets.Split.TEST}
-
-    def generate_urls(self, split_name):
-        yield "data", f"concode/{split_name}.json"
-
-    def _generate_examples(self, split_name, file_paths):
-        with open(file_paths["data"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                entry = json.loads(line)
-                entry["id"] = idx
-                yield idx, entry
-
-
-CLASS_MAPPING = {
-    "CodeXGlueTcTextToCode": CodeXGlueTcTextToCodeImpl,
-}
-
-
-class CodeXGlueTcTextToCode(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)