updated names again

shellcode_i_a32.py

@@ -0,0 +1,219 @@
+# 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."""
+
+
+import csv
+import json
+import os
+import pandas as pd
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+    @inproceedings{liguori-etal-2021-shellcode,
+    title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
+    author = "Liguori, Pietro  and
+      Al-Hossami, Erfan  and
+      Cotroneo, Domenico  and
+      Natella, Roberto  and
+      Cukic, Bojan  and
+      Shaikh, Samira",
+    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.nlp4prog-1.7",
+    doi = "10.18653/v1/2021.nlp4prog-1.7",
+    pages = "58--64",
+    abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/dessertlab/Shellcode_IA32"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "GNU GENERAL PUBLIC 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 = {
+    'default': "https://raw.githubusercontent.com/dessertlab/Shellcode_IA32/main/Shellcode_IA32.tsv",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ShellcodeIA32(datasets.GeneratorBasedBuilder):
+    """Shellcode_IA32 a dataset for shellcode generation"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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="default", version=VERSION, description="This part of my dataset covers the default train/test split"),
+    #     #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    # ]
+
+    DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+            features = datasets.Features(
+                {
+                    "intent": datasets.Value("string"),
+                    "snippet": datasets.Value("string"),
+
+                }
+            )
+            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=None,
+                # 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, "Shellcode_IA32.tsv"),
+        #             "split": "train",
+        #         },
+        #     ),
+        #     datasets.SplitGenerator(
+        #         name=datasets.Split.TEST,
+        #         # These kwargs will be passed to _generate_examples
+        #         gen_kwargs={
+        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+        #             "split": "test"
+        #         },
+        #     ),
+        #     datasets.SplitGenerator(
+        #         name=datasets.Split.VALIDATION,
+        #         # These kwargs will be passed to _generate_examples
+        #         gen_kwargs={
+        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+        #             "split": "dev",
+        #         },
+        #     ),
+        # ]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        """This function returns the examples in the raw (text) form."""
+
+        df = pd.read_csv(filepath, delimiter = '\t')
+        train = df.sample(frac = 0.8, random_state = 0)
+        test = df.drop(train.index)
+        dev = test.sample(frac = 0.5, random_state = 0)
+        test = test.drop(dev.index)
+
+        if split == 'train':
+            data = train
+        elif split == 'dev':
+            data = dev
+        elif split == 'test':
+            data = test
+
+        for idx, row in data.iterrows():
+            yield idx, {
+                "snippet": row["SNIPPETS"],
+                "intent": row["INTENTS"],
+
+            }
+            # with open(filepath, encoding="utf-8") as f:
+            #     reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            #     reader =
+            #     for idx, row in enumerate(reader):
+            #
+            #         yield idx, {
+            #             "snippet": row["SNIPPETS"],
+            #             "intent": row["INTENTS"],
+            #
+            #         }