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import json
import datasets
_REPO_NAME = "TeDriCS/tedrics-data"
_DESCRIPTION = ""
_HOMEPAGE = ""
_CITATION = """\
@misc{,
title={ },
author={},
year={2022}
}
"""
_LICENSES = ['CC BY-SA', 'CC Attribution 4.0']
_SUBSETS = ["tasks", "testcases", "codefunctions"]
_DATA_URLS = {
"tasks": {
"train": ["tedrics_data_tasks.json"]
},
"testcases": {
"train": ["tedrics_data_testcases.json"]
},
"codefunctions": {
"train": ["tedrics_data_codefunctions.json"]
}
}
class TeDriCSData(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=f"{subset}",
version=datasets.Version("1.0"),
description=_DESCRIPTION,
)
for subset in _SUBSETS
]
DEFAULT_CONFIG_NAME = "tasks"
def _info(self):
if self.config.name == "tasks":
features = datasets.Features(
{
"task_id": datasets.Value("int32"),
"mbpp_task_id": datasets.Value("int32"),
"source": datasets.Value("string"),
"licence": datasets.Sequence(datasets.Value("string")),
"task": datasets.Value("string"),
}
)
if self.config.name == "testcases":
features = datasets.Features(
{
"task_id": datasets.Value("int32"),
"mbpp_task_id": datasets.Value("int32"),
"task": datasets.Value("string"),
"test_cases": datasets.Sequence(
{
"test_case_id": datasets.Value("int32"),
"cot": datasets.Value("string"),
"input": datasets.Sequence(datasets.Value("string")),
"output": datasets.Value("string")
}
)
}
)
if self.config.name == "codefunctions":
features = datasets.Features(
{
"task_id": datasets.Value("int32"),
"mbpp_task_id": datasets.Value("int32"),
"description": datasets.Value("string"),
"cot": datasets.Value("string"),
"imports": datasets.Sequence(datasets.Value("string")),
"function_head": datasets.Sequence(datasets.Value("string")),
"function_body": datasets.Sequence(datasets.Value("string"))
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSES,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _DATA_URLS[self.config.name]
data = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"files": data[split],
},
)
for split in [datasets.Split.TRAIN]
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as file:
data = json.load(file)
id_ = 0
for sample in data:
yield id_, sample
id_ += 1 |