add load script

the-vault-inline.py

@@ -0,0 +1,195 @@
+import os
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+import datasets
+
+
+# Meta infomation
+_REPO_NAME = 'Fsoft-AIC/the-vault-inline'
+
+_DESCRIPTION = """The Vault is a multilingual code-text dataset with over 34 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"
+_LICENSE = "MIT License"
+_CITATION = """
+@article{manh2023vault,
+  title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
+  author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
+  journal={arXiv preprint arXiv:2305.06156},
+  year={2023}
+}
+"""
+################################################################################################
+
+# Config metadata
+_LANG_TO_TEXT = {
+    "python": "python",
+    "c": "c",
+    "c#": "c_sharp",
+    "c++": "cpp",
+    "go": "go",
+    "java": "java",
+    "javascript": "javascript",
+    "php": "php",
+    "ruby": "ruby",
+    "rust": "rust",
+}       
+_LANG_CONFIGS = ["all"] + list(_LANG_TO_TEXT.keys())
+
+_TEXT_TO_LANG = {}
+for lang in _LANG_TO_TEXT:
+    _TEXT_TO_LANG[_LANG_TO_TEXT[lang]] = lang
+
+num_shard_split = {
+    "ruby": 3,
+    "c": 29,
+    "c_sharp": 1,
+    "cpp": 39,
+    "go": 15,
+    "java": 75,
+    "javascript": 6,
+    "php": 21,
+    "python": 48,
+    "rust": 1,
+}
+
+################################################################################################
+
+class TheVaultFunctionConfig(datasets.BuilderConfig):
+    """BuilderConfig for The Vault dataset."""
+
+    def __init__(self, *args, languages=["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] if lang in _LANG_TO_TEXT else lang for lang in languages]),
+            **kwargs,
+        )
+        
+        languages = set([lang.lower() for lang in languages])
+        
+        assert all([language in _LANG_CONFIGS for language in languages]), f"languages {languages} contains language not in {_LANG_CONFIGS}."
+
+        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)
+
+
+class TheVaultFunction(datasets.GeneratorBasedBuilder):
+    """The Vault dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+    
+    BUILDER_CONFIG_CLASS = TheVaultFunctionConfig
+    BUILDER_CONFIGS = [TheVaultFunctionConfig(languages=[lang]) for lang in _LANG_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"),
+                                            "code": datasets.Value("string"),
+                                            "code_tokens": datasets.Sequence(datasets.Value("string")),
+                                            "original_comment": datasets.Value("string"),
+                                            "comment": datasets.Value("string"),
+                                            "comment_tokens": datasets.Sequence(datasets.Value("string")),
+                                            "start_point": datasets.Sequence(datasets.Value("int32")),
+                                            "end_point": datasets.Sequence(datasets.Value("int32")),
+                                            "prev_context":
+                                                {
+                                                    "code": datasets.Value("string"),
+                                                    "start_point": datasets.Sequence(datasets.Value("int32")),
+                                                    "end_point": datasets.Sequence(datasets.Value("int32")),
+                                                },
+                                            "next_context":
+                                                {
+                                                    "code": datasets.Value("string"),
+                                                    "start_point": datasets.Sequence(datasets.Value("int32")),
+                                                    "end_point": datasets.Sequence(datasets.Value("int32")),
+                                                },
+                                        }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            
+        )
+
+    def _split_generators(self, dl_manager):
+        generators = []
+        languages = self.config.languages
+
+        if "all" in languages:
+            languages = list(_LANG_TO_TEXT.values())
+
+        
+        split_files = []
+        for language in languages:
+            num_shards = num_shard_split[language]
+            data_files = [
+                f"data/train/{language}-{_index:05d}-of-{num_shards:05d}.parquet"
+                for _index in range(num_shards)
+            ]
+            files = dl_manager.download(data_files)
+            split_files.extend(files)
+
+        generators.append(
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "files": split_files,
+                },
+            ),
+        )
+                
+    
+        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'],
+                                        "repo": row['repo'],
+                                        "path": row['path'], 
+                                        "license": row['license'], 
+                                        "language": row['language'],
+                                        "identifier": row['identifier'],
+                                        "code": row['code'],
+                                        "code_tokens": row['code_tokens'],
+                                        "original_comment": row['original_comment'],
+                                        "comment": row['comment'],
+                                        "comment_tokens": row['comment_tokens'],
+                                        "start_point": row['start_point'],
+                                        "end_point": row['end_point'],
+                                        "prev_context": row['prev_context'],
+                                        "next_context": row['next_context']
+                                    } 
+                        key += 1