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# 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": "continuation"},
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
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