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import os
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
_REPO_NAME = 'Fsoft-AIC/the-vault-function'
_LANG_TO_TEXT = {
"python": "python",
"c": "c",
"c#": "c_sharp",
"c++": "cpp",
"go": "go",
"Java": "java",
"javascript": "javascript",
"php": "php",
"ruby": "ruby",
"rust": "rust",
}
_DESCRIPTION = """The Vault is a multilingual code-text dataset with over 40 million pairs covering 10 popular programming languages.
It is the largest corpus containing parallel code-text data. By building upon The Stack, a massive raw code sample collection,
the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a
high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level.
The Vault can serve many purposes at multiple levels."""
_HOMEPAGE = "https://huggingface.co/Fsoft-AIC"
_TEXT_TO_LANG = {}
for lang in _LANG_TO_TEXT:
_TEXT_TO_LANG[_LANG_TO_TEXT[lang]] = lang
_LANG_CONFIGS = ["all"] + list(_LANG_TO_TEXT.keys())
num_shard_split = {
'train/small/python': 1,
'train/medium/python': 1,
'train/small/c': 1,
'train/medium/c': 1
}
_SPLIT_CONFIGS = ["all", "train", "train/small", "train/medium", "train/full", "validation", "test"]
class TheVaultFunctionConfig(datasets.BuilderConfig):
"""BuilderConfig for The Vault dataset."""
def __init__(self, *args, languages=["all"], split_set= ["all"], **kwargs):
"""BuilderConfig for the The Vault dataset.
Args:
split_set (:obj:`List[str]`): List of split set to load.
languages (:obj:`List[str]`): List of languages to load.
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(
*args,
name= "+".join([split.replace("/", "_") for split in split_set]) + "-" + "+".join([_LANG_TO_TEXT[lang] for lang in languages]),
**kwargs,
)
languages = set([lang.lower() for lang in languages])
split_set = set([split.lower() for split in split_set])
assert all([language in _LANG_CONFIGS for language in languages]), f"languages {languages} contains language not in {_LANG_CONFIGS}."
assert all([split in _SPLIT_CONFIGS for split in split_set]), f"split_set {split_set} contains element not in {_SPLIT_CONFIGS}."
if "all" in split_set:
assert len(split_set)==1, f"Passed 'all' together with other split sets. {split_set}"
elif "train" in split_set or "train/full" in split_set:
for split in split_set:
if ("train" in split and split != "train") or ("train" in split and split != "train/full"):
raise ValueError(f"Split set 'train' (or 'train/full) already contains '{split}'. Please only include one.")
if "all" in languages:
assert len(languages)==1, f"Passed 'all' together with other languages. {languages}"
else:
languages = [_LANG_TO_TEXT[lang] for lang in languages] # Convert to text name
self.languages = list(languages)
self.split_set= list(split_set)
class TheVaultFunction(datasets.GeneratorBasedBuilder):
"""The Vault dataset."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = TheVaultFunctionConfig
BUILDER_CONFIGS = [TheVaultFunctionConfig(languages=[lang], split_set=[spl]) for lang in _LANG_CONFIGS for spl in _SPLIT_CONFIGS]
DEFAULT_CONFIG_NAME = "all-all"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"hexsha": datasets.Value("string"),
"repo": datasets.Value("string"),
"path": datasets.Value("string"),
"license": datasets.Sequence(datasets.Value("string")),
"language": datasets.Value("string"),
"identifier": datasets.Value("string"),
"return_type": datasets.Value("string"),
# "original_string": datasets.Value("string"),
"original_docstring": datasets.Value("string"),
"docstring": datasets.Value("string"),
"docstring_tokens": datasets.Sequence(datasets.Value("string")),
"code": datasets.Value("string"),
"code_tokens": datasets.Sequence(datasets.Value("string")),
"short_docstring": datasets.Value("string"),
"short_docstring_tokens": datasets.Sequence(datasets.Value("string")),
"comment": datasets.Sequence(datasets.Value("string")),
"parameters": [
{
"param": datasets.Value("string"),
"type": datasets.Value("string"),
}
],
"docstring_params": [
{
"returns": [
{
"docstring": datasets.Value("string"),
"docstring_tokens": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string")
}
],
"raises": [
{
"docstring": datasets.Value("string"),
"docstring_tokens": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string")
}
],
"params": [
{
"identifier": datasets.Value("string"),
"type": datasets.Value("string"),
"docstring": datasets.Value("string"),
"docstring_tokens": datasets.Sequence(datasets.Value("string")),
"default": datasets.Value("string"),
"is_optional": datasets.Value("bool")
}
],
"outlier_params": [
{
"identifier": datasets.Value("string"),
"type": datasets.Value("string"),
"docstring": datasets.Value("string"),
"docstring_tokens": datasets.Sequence(datasets.Value("string")),
"default": datasets.Value("string"),
"is_optional": datasets.Value("bool")
}
],
"others": [
{
"identifier": datasets.Value("string"),
"docstring": datasets.Value("string"),
"docstring_tokens": datasets.Sequence(datasets.Value("string"))
}
]
}
],
}),
supervised_keys=None,
homepage=_HOMEPAGE,
)
def _split_generators(self, dl_manager):
generators = []
if "all" in split_set:
split_set = ["train/full", "validation", "test"]
if "train" in split_set:
split_set.remove('train')
split_set.extend(["train/full"])
if "all" in languages:
languages = _LANG_CONFIGS[1:]
# train_split_files = []
for split in split_set:
split_files = []
for language in languages:
num_shards = num_shard_split[f"{split}/{language}"]
data_files = [
f"data/{split}/{language}-{_index:05d}-of-{num_shards:05d}.parquet"
for _index in range(num_shards)
]
files = dl_manager.download(data_files)
split_files.extend(files)
# if load_full_train and "train" in split:
# train_split_files.extend(split_files)
# else:
generators.append(
datasets.SplitGenerator(
name="train" if split == "train/full" else split.replace("/", "_"),
gen_kwargs={
"files": split_files,
},
),
)
# if load_full_train and train_split_files:
# generators = [datasets.SplitGenerator(name="train", gen_kwargs={"files": train_split_files})] + generators
return generators
def _generate_examples(self, files):
key = 0
for file_idx, file in enumerate(files):
with open(file, "rb") as f:
parquet_file = pq.ParquetFile(f)
for batch_idx, record_batch in enumerate(parquet_file.iter_batches(batch_size=10_000)):
pa_table = pa.Table.from_batches([record_batch])
for row_index in range(pa_table.num_rows):
row = pa_table.slice(row_index, 1).to_pydict()
yield key, {
"hexsha": row['hexsha'][0],
"repo": row['repo'][0],
"path": row['path'][0],
"license": row['license'][0],
"language": row['language'][0],
"identifier": row['identifier'][0],
"return_type": row['return_type'][0],
# "original_string": row['original_string'][0],
"original_docstring": row['original_docstring'][0],
"docstring": row['docstring'][0],
"docstring_tokens": row['docstring_tokens'][0],
"code": row['code'][0],
"code_tokens": row['code_tokens'][0],
"short_docstring": row['short_docstring'][0],
"short_docstring_tokens": row['short_docstring_tokens'][0],
"comment": row['comment'][0],
"parameters": row['parameters'][0],
"docstring_params": row['docstring_params'][0],
}
key += 1 |