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import json
import datasets
_REPO_NAME = "TeDriCS/tedrics-data"
_DESCRIPTION = ""
_HOMEPAGE = ""
_CITATION = """\
@misc{,
title={ },
author={},
year={2022}
}
"""
_LICENSE = 'CC BY-SA'
_SUBSETS = ["tasks", "testcases", "codefunctions"]
_DATA_URLS = {
"tasks": {
"train": "tedrics_data_tasks.json"
},
"testcases": {
"train": "tedrics_data_testcases.json",
"validation": "tedrics_data_testcases_val.json"
},
"codefunctions": {
"train": "tedrics_data_codefunctions.json",
"validation": "tedrics_data_codefunctions_val.json"
}
}
class TeDriCSData(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=f"{subset}",
version=datasets.Version("1.1.0"),
description=_DESCRIPTION,
)
for subset in _SUBSETS
]
DEFAULT_CONFIG_NAME = "testcases"
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.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.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.Value("string"),
"function_head": datasets.Value("string"),
"function_body": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _DATA_URLS[self.config.name]
data = dl_manager.download_and_extract(urls)
splits = []
if self.config.name == "tasks":
splits = [datasets.Split.TRAIN]
if self.config.name == "testcases" or self.config.name == "codefunctions":
splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION]
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": data[split],
},
)
for split in splits
]
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
####Zum Testen der Implementierung
# from datasets import load_dataset
# def main():
# dataset = load_dataset('C:\\Users\\klaud\\TeDriCSProj\\tedrics-data\\tedrics-data.py')
# print(dataset["train"])
# print(dataset["train"][0])
# print(dataset["validation"])
# print(dataset["validation"][0])
# if __name__ == "__main__":
# main()
#### Zum Generieren der dataset_infos.json (im Terminal)
# (hf-gptneox-cpu) PS C:\Users\klaud\TeDriCSProj\tedrics-data> datasets-cli test C:\Users\klaud\TeDriCSProj\tedrics-data\tedrics-data.py --save_infos --all_configs
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