Update files from the datasets library (from 1.8.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- code
+licenses:
+- other-C-UDA
+multilinguality:
+- other-programming-languages
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- machine-translation
+---
+# Dataset Card for "code_x_glue_cc_code_to_code_trans"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits-sample-size)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans
+
+### Dataset Summary
+
+CodeXGLUE code-to-code-trans dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans
+
+The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
+        We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.
+
+### Supported Tasks and Leaderboards
+
+- `machine-translation`: The dataset can be used to train a model for translating code in Java to C# and vice versa.
+
+### Languages
+
+- Java **programming** language
+- C# **programming** language
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'validation' looks as follows.
+```
+{
+    "cs": "public DVRecord(RecordInputStream in1){_option_flags = in1.ReadInt();_promptTitle = ReadUnicodeString(in1);_errorTitle = ReadUnicodeString(in1);_promptText = ReadUnicodeString(in1);_errorText = ReadUnicodeString(in1);int field_size_first_formula = in1.ReadUShort();_not_used_1 = in1.ReadShort();_formula1 = NPOI.SS.Formula.Formula.Read(field_size_first_formula, in1);int field_size_sec_formula = in1.ReadUShort();_not_used_2 = in1.ReadShort();_formula2 = NPOI.SS.Formula.Formula.Read(field_size_sec_formula, in1);_regions = new CellRangeAddressList(in1);}\n", 
+    "id": 0, 
+    "java": "public DVRecord(RecordInputStream in) {_option_flags = in.readInt();_promptTitle = readUnicodeString(in);_errorTitle = readUnicodeString(in);_promptText = readUnicodeString(in);_errorText = readUnicodeString(in);int field_size_first_formula = in.readUShort();_not_used_1 = in.readShort();_formula1 = Formula.read(field_size_first_formula, in);int field_size_sec_formula = in.readUShort();_not_used_2 = in.readShort();_formula2 = Formula.read(field_size_sec_formula, in);_regions = new CellRangeAddressList(in);}\n"
+}
+```
+
+### Data Fields
+
+In the following each data field in go is explained for each config. The data fields are the same among all splits.
+
+#### default
+
+|field name| type |         description         |
+|----------|------|-----------------------------|
+|id        |int32 | Index of the sample         |
+|java      |string| The java version of the code|
+|cs        |string| The C# version of the code  |
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default|10300|       500|1000|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+https://github.com/microsoft, https://github.com/madlag
+
+### Licensing Information
+
+Computational Use of Data Agreement (C-UDA) License.
+
+### Citation Information
+
+```
+@article{CodeXGLUE,
+         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
+         year={2020},}
+```
+
+### Contributions
+
+Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

code_x_glue_cc_code_to_code_trans.py

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+from typing import List
+
+import datasets
+
+from .common import TrainValidTestChild
+from .generated_definitions import DEFINITIONS
+
+
+_DESCRIPTION = """The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
+        We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets."""
+_CITATION = """@article{DBLP:journals/corr/abs-2102-04664,
+  author    = {Shuai Lu and
+               Daya Guo and
+               Shuo Ren and
+               Junjie Huang and
+               Alexey Svyatkovskiy and
+               Ambrosio Blanco and
+               Colin B. Clement and
+               Dawn Drain and
+               Daxin Jiang and
+               Duyu Tang and
+               Ge Li and
+               Lidong Zhou and
+               Linjun Shou and
+               Long Zhou and
+               Michele Tufano and
+               Ming Gong and
+               Ming Zhou and
+               Nan Duan and
+               Neel Sundaresan and
+               Shao Kun Deng and
+               Shengyu Fu and
+               Shujie Liu},
+  title     = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
+               and Generation},
+  journal   = {CoRR},
+  volume    = {abs/2102.04664},
+  year      = {2021}
+}"""
+
+
+class CodeXGlueCcCodeToCodeTransImpl(TrainValidTestChild):
+    _DESCRIPTION = _DESCRIPTION
+    _CITATION = _CITATION
+
+    _FEATURES = {
+        "id": datasets.Value("int32"),  # Index of the sample
+        "java": datasets.Value("string"),  # The java version of the code
+        "cs": datasets.Value("string"),  # The C# version of the code
+    }
+
+    def generate_urls(self, split_name):
+        for key in "cs", "java":
+            yield key, f"{split_name}.java-cs.txt.{key}"
+
+    def _generate_examples(self, split_name, file_paths):
+        """This function returns the examples in the raw (text) form."""
+        # Open each file (one for java, and one for c#)
+        files = {k: open(file_paths[k], encoding="utf-8") for k in file_paths}
+
+        id_ = 0
+        while True:
+            # Read a single line from each file
+            entries = {k: files[k].readline() for k in file_paths}
+
+            empty = self.check_empty(entries)
+            if empty:
+                # We are done: end of files
+                return
+
+            entries["id"] = id_
+            yield id_, entries
+            id_ += 1
+
+
+CLASS_MAPPING = {
+    "CodeXGlueCcCodeToCodeTrans": CodeXGlueCcCodeToCodeTransImpl,
+}
+
+
+class CodeXGlueCcCodeToCodeTrans(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)

common.py

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+from typing import List
+
+import datasets
+
+
+# Citation, taken from https://github.com/microsoft/CodeXGLUE
+_DEFAULT_CITATION = """@article{CodeXGLUE,
+         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
+         year={2020},}"""
+
+
+class Child:
+    _DESCRIPTION = None
+    _FEATURES = None
+    _CITATION = None
+    SPLITS = {"train": datasets.Split.TRAIN}
+    _SUPERVISED_KEYS = None
+
+    def __init__(self, info):
+        self.info = info
+
+    def homepage(self):
+        return self.info["project_url"]
+
+    def _info(self):
+        # This is the description that will appear on the datasets page.
+        return datasets.DatasetInfo(
+            description=self.info["description"] + "\n\n" + self._DESCRIPTION,
+            features=datasets.Features(self._FEATURES),
+            homepage=self.homepage(),
+            citation=self._CITATION or _DEFAULT_CITATION,
+            supervised_keys=self._SUPERVISED_KEYS,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        SPLITS = self.SPLITS
+        _URL = self.info["raw_url"]
+        urls_to_download = {}
+        for split in SPLITS:
+            if split not in urls_to_download:
+                urls_to_download[split] = {}
+
+            for key, url in self.generate_urls(split):
+                if not url.startswith("http"):
+                    url = _URL + "/" + url
+                urls_to_download[split][key] = url
+
+        downloaded_files = {}
+        for k, v in urls_to_download.items():
+            downloaded_files[k] = dl_manager.download_and_extract(v)
+
+        return [
+            datasets.SplitGenerator(
+                name=SPLITS[k],
+                gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
+            )
+            for k in SPLITS
+        ]
+
+    def check_empty(self, entries):
+        all_empty = all([v == "" for v in entries.values()])
+        all_non_empty = all([v != "" for v in entries.values()])
+
+        if not all_non_empty and not all_empty:
+            raise RuntimeError("Parallel data files should have the same number of lines.")
+
+        return all_empty
+
+
+class TrainValidTestChild(Child):
+    SPLITS = {
+        "train": datasets.Split.TRAIN,
+        "valid": datasets.Split.VALIDATION,
+        "test": datasets.Split.TEST,
+    }

dataset_infos.json

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+{"default": {"description": "CodeXGLUE code-to-code-trans dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans\n\nThe dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).\n        We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.", "citation": "@article{DBLP:journals/corr/abs-2102-04664,\n  author    = {Shuai Lu and\n               Daya Guo and\n               Shuo Ren and\n               Junjie Huang and\n               Alexey Svyatkovskiy and\n               Ambrosio Blanco and\n               Colin B. Clement and\n               Dawn Drain and\n               Daxin Jiang and\n               Duyu Tang and\n               Ge Li and\n               Lidong Zhou and\n               Linjun Shou and\n               Long Zhou and\n               Michele Tufano and\n               Ming Gong and\n               Ming Zhou and\n               Nan Duan and\n               Neel Sundaresan and\n               Shao Kun Deng and\n               Shengyu Fu and\n               Shujie Liu},\n  title     = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding\n               and Generation},\n  journal   = {CoRR},\n  volume    = {abs/2102.04664},\n  year      = {2021}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/code-to-code-trans", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "java": {"dtype": "string", "id": null, "_type": "Value"}, "cs": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "code_x_glue_cc_code_to_code_trans", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4372657, "num_examples": 10300, "dataset_name": "code_x_glue_cc_code_to_code_trans"}, "validation": {"name": "validation", "num_bytes": 226415, "num_examples": 500, "dataset_name": "code_x_glue_cc_code_to_code_trans"}, "test": {"name": "test", "num_bytes": 418595, "num_examples": 1000, "dataset_name": "code_x_glue_cc_code_to_code_trans"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data/train.java-cs.txt.cs": {"num_bytes": 2387613, "checksum": "8f9e154e38b17cf19840a44c50a00b6fa16397336c302e3cf514b29ddfafa0e9"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data/train.java-cs.txt.java": {"num_bytes": 1861428, "checksum": "3d2ba1a8f5de30688663ce76bf9b061574d330fc54eb08c4b7eccda74f42be67"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data/valid.java-cs.txt.cs": {"num_bytes": 124022, "checksum": "687c61db799e9e3369a0822184ba67bb5b007c48025f25d44084cc6f525ce4ea"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data/valid.java-cs.txt.java": {"num_bytes": 96385, "checksum": "aed88f2a31af5b6367100bfbca6d9c4888fa63685502b21db817d8b0f0ad5272"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data/test.java-cs.txt.cs": {"num_bytes": 229147, "checksum": "4137527f96c898372e368c75deb3ec8c17c1187ac5a1ae641da1df65e143cd2d"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data/test.java-cs.txt.java": {"num_bytes": 177440, "checksum": "cad0fb08ae59443baeeb1f58de3af83786358dac8ce3a81fd026708ca1b9b2ee"}}, "download_size": 4876035, "post_processing_size": null, "dataset_size": 5017667, "size_in_bytes": 9893702}}

dummy/default/0.0.0/dummy_data.zip

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+oid sha256:5e127b28cef124f2df8aa8253b050a3fe70799a47283593c59c18f145dae5f33
+size 2596

generated_definitions.py

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+DEFINITIONS = {
+    "default": {
+        "class_name": "CodeXGlueCcCodeToCodeTrans",
+        "dataset_type": "Code-Code",
+        "description": "CodeXGLUE code-to-code-trans dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans",
+        "dir_name": "code-to-code-trans",
+        "name": "default",
+        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/code-to-code-trans",
+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-to-code-trans/data",
+        "sizes": {"test": 1000, "train": 10300, "validation": 500},
+    }
+}