Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+languages:
+- code
+licenses:
+- 0bsd
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- sequence-modeling-code-modeling
+---
+# Dataset Card for [py_ast]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **homepage**: [py150](https://www.sri.inf.ethz.ch/py150) 
+- **Paper**: [Probabilistic Model for Code with Decision Trees](https://dl.acm.org/doi/10.1145/3022671.2984041)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool. 
+The Python programs are collected from GitHub repositories
+by removing duplicate files, removing project forks (copy of another existing repository)
+,keeping only programs that parse and have at most 30'000 nodes in the AST and 
+we aim to remove obfuscated files
+
+### Supported Tasks and Leaderboards
+
+Code Representation, Unsupervised Learning
+### Languages
+
+Python
+## Dataset Structure  
+
+### Data Instances
+A typical datapoint contains an AST of a python program, parsed.   
+The main key is `ast` wherein every program's AST is stored.  
+ Each children would have,  
+`type` which will formulate the type of the node.   
+ `children` which enumerates if a given node has children(non-empty list). 
+ `value`, if the given node has any hardcoded value(else "N/A").
+ An example would be,     
+'''
+[ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"},    {"type":"Print","children":[5]},      {"type":"BinOpAdd","children":[6,7]},        {"type":"NameLoad","value":"x"},        {"type":"Num","value":"1"} ]
+'''
+### Data Fields
+- `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree.
+- `type`: explains the type of the node.
+- `children`: list of nodes which are children under the given
+- `value`: hardcoded value, if the node holds an hardcoded value.
+
+### Data Splits
+
+The data is split into a training and test set.   
+The final split sizes are as follow:
+
+|                             | Tain   | Valid |
+| -----                       | ------ | ----- |
+| py_ast examples| 100000 |  50000 |
+## Dataset Creation
+[More Information Needed]
+### 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
+Raychev, V., Bielik, P., and Vechev, M
+### Licensing Information
+MIT, BSD and Apache
+### Citation Information
+@InProceedings{OOPSLA ’16, ACM,
+title = {Probabilistic Model for Code with Decision Trees.},
+authors={Raychev, V., Bielik, P., and Vechev, M.},
+year={2016}
+}

dataset_infos.json

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+{"ast": {"description": "dataset consisting of parsed Parsed ASTs that were used to train and\nevaluate the DeepSyn tool.\nThe Python programs are collected from GitHub repositories\nby removing duplicate files, removing project forks (copy of another existing repository)\n,keeping only programs that parse and have at most 30'000 nodes in the AST and\nwe aim to remove obfuscated files", "citation": "@InProceedings{OOPSLA \u201916, ACM,\ntitle = {Probabilistic Model for Code with Decision Trees.},\nauthors={Raychev, V., Bielik, P., and Vechev, M.},\nyear={2016}\n}\n", "homepage": "https://www.sri.inf.ethz.ch/py150", "license": "", "features": {"ast": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "value": {"dtype": "string", "id": null, "_type": "Value"}, "children": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": {"input": "ast", "output": ""}, "builder_name": "py_ast", "config_name": "ast", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1870790180, "num_examples": 100000, "dataset_name": "py_ast"}, "test": {"name": "test", "num_bytes": 907514993, "num_examples": 50000, "dataset_name": "py_ast"}}, "download_checksums": {"http://files.srl.inf.ethz.ch/data/py150.tar.gz": {"num_bytes": 526642289, "checksum": "4093b331d43c795e39fb5f156ccb7dcbb04c5d745d5e840c2d6926c11292dbd4"}}, "download_size": 526642289, "post_processing_size": null, "dataset_size": 2778305173, "size_in_bytes": 3304947462}}

dummy/ast/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:163e63e12068053391edbd0ee004a0c51a7961236476d8f6a2ec09c8faa234ac
+size 918

py_ast.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TODO: Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{OOPSLA ’16, ACM,
+title = {Probabilistic Model for Code with Decision Trees.},
+authors={Raychev, V., Bielik, P., and Vechev, M.},
+year={2016}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+dataset consisting of parsed Parsed ASTs that were used to train and
+evaluate the DeepSyn tool.
+The Python programs are collected from GitHub repositories
+by removing duplicate files, removing project forks (copy of another existing repository)
+,keeping only programs that parse and have at most 30'000 nodes in the AST and
+we aim to remove obfuscated files"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://www.sri.inf.ethz.ch/py150"
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    "ast": "http://files.srl.inf.ethz.ch/data/py150.tar.gz",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class PyAst(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="ast", description="This part of the dataset contains 150 parsed python ASTs."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ast"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method pecifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "ast":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "ast": datasets.Sequence(
+                        {
+                            "type": datasets.Value("string"),
+                            "value": datasets.Value("string"),
+                            "children": datasets.Sequence(datasets.Value("int32")),
+                        },
+                    )
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=["ast"],
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "python100k_train.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "python50k_eval.json"), "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                row_data = json.loads(row)
+                for node in row_data:
+                    if "value" not in node:
+                        node["value"] = "N/A"
+                    if "children" not in node:
+                        node["children"] = []
+                yield id_, {
+                    "ast": row_data,
+                }