Create test_scripts.py

test_scripts.py

@@ -0,0 +1,107 @@
+# 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.
+"""Lyrics dataset parsed from Genius"""
+
+
+import csv
+import json
+import os
+import gzip
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{huggingartists:dataset,
+title = {Lyrics dataset},
+author={Aleksey Korshuk
+},
+year={2021}
+}
+"""
+
+
+_DESCRIPTION = """\
+This dataset is designed to generate lyrics with HuggingArtists.
+"""
+
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/AlekseyKorshuk/huggingartists"
+
+# Add the licence for the dataset here if you can find it
+_LICENSE = "All rights belong to copyright holders"
+
+_URL = "https://huggingface.co/datasets/AlekseyKorshuk/comedy-scripts/resolve/main/comedy-scripts.json"
+
+# Name of the dataset
+class LyricsDataset(datasets.GeneratorBasedBuilder):
+    """Lyrics dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+                {
+                    "text": 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."""
+        # 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
+
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+        
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for id, pred in enumerate(data[split]):
+                yield id, {"text": pred}