test_dataset.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""we're testin'"""

import datasets
import json

class TestDatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for Test Dataset for testing HF parsing"""

    def __init__(
        self,
        text_features,
        foo="foo",
        process_label=lambda x: x,
        **kwargs,
    ):
        """BuilderConfig for TestDatset.

        Args:
          text_features: `dict[string, string]`, map from the name of the feature
            dict for each text field to the name of the column in the tsv file
          label_column: `string`, name of the column in the tsv file corresponding
            to the label
          data_dir: `string`, the path to the folder containing the tsv files in the
            downloaded zip
          label_classes: `list[string]`, the list of classes if the label is
            categorical. If not provided, then the label will be of type
            `datasets.Value('float32')`.
          process_label: `Function[string, any]`, function  taking in the raw value
            of the label and processing it to the form required by the label feature
          **kwargs: keyword arguments forwarded to super.
        """
        super(TestDatasetConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
        self.text_features = text_features
        self.foo = foo
        self.process_label = process_label


class TestDatasetEvals(datasets.GeneratorBasedBuilder):
    """The General Language Understanding Evaluation (GLUE) benchmark."""

    BUILDER_CONFIGS = [
        TestDatasetConfig(
            name="juggernaut",
            description= "this is a test dataset for our unit test intergrating HF datasets" ,
            # TODO: unclear why "answer" is needed if juggernaut/data.jsonl has no "answer" key
            text_features={"context": "context", "continuation": "answer"},
            data_dir="heroes",
        ),
        TestDatasetConfig(
            name="invoker",
            description= "this is a test dataset for our unit test intergrating HF datasets" ,
            text_features={"quas": "quas", "wex": "wex", "exort": "exort", "spell": "spell"},
            data_dir="heroes",
        ),
    ]

    def _info(self):
        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
        features["idx"] = datasets.Value("int32")
        return datasets.DatasetInfo(
            description=self.config.description,
            features=datasets.Features(features),
        )

    def _split_generators(self, dl_manager):
        constructed_filepath = self.construct_filepath()
        data_file = dl_manager.download(constructed_filepath)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": data_file, 
                },
            ),
        ]

    def construct_filepath(self):
        return self.config.name + '/data.jsonl'

    def _generate_examples(self, data_file):
        with open(data_file, encoding="utf8") as f:
            for n, row in enumerate(f):
                data = json.loads(row)
                example = {feat: data[col] for feat, col in self.config.text_features.items()}
                example["idx"] = n
                yield example["idx"], example