<filename>tests/python/unittest/test_meta_schedule_feature_extractor.py
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
# pylint: disable=missing-module-docstring,missing-function-docstring,missing-class-docstring
import re
from typing import List

import numpy as np
from tvm.meta_schedule import TuneContext
from tvm.meta_schedule.feature_extractor import PyFeatureExtractor
from tvm.meta_schedule.search_strategy import MeasureCandidate


def test_meta_schedule_feature_extractor():
    class FancyFeatureExtractor(PyFeatureExtractor):
        def extract_from(
            self,
            tune_context: TuneContext,  # pylint: disable = unused-argument
            candidates: List[MeasureCandidate],  # pylint: disable = unused-argument
        ) -> List[np.ndarray]:
            return [np.random.rand(4, 5)]

    extractor = FancyFeatureExtractor()
    features = extractor.extract_from(TuneContext(), [])
    assert len(features) == 1
    assert features[0].shape == (4, 5)


def test_meta_schedule_feature_extractor_as_string():
    class NotSoFancyFeatureExtractor(PyFeatureExtractor):
        def extract_from(
            self,
            tune_context: TuneContext,  # pylint: disable = unused-argument
            candidates: List[MeasureCandidate],  # pylint: disable = unused-argument
        ) -> List[np.ndarray]:
            return []

    feature_extractor = NotSoFancyFeatureExtractor()
    pattern = re.compile(r"NotSoFancyFeatureExtractor\(0x[a-f|0-9]*\)")
    assert pattern.match(str(feature_extractor))


if __name__ == "__main__":
    test_meta_schedule_feature_extractor()
    test_meta_schedule_feature_extractor_as_string()

<filename>tests/python/unittest/test_meta_schedule_feature_extractor.py
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
# pylint: disable=missing-module-docstring,missing-function-docstring,missing-class-docstring
import re
from typing import List

import numpy as np
from tvm.meta_schedule import TuneContext
from tvm.meta_schedule.feature_extractor import PyFeatureExtractor
from tvm.meta_schedule.search_strategy import MeasureCandidate


def test_meta_schedule_feature_extractor():
    class FancyFeatureExtractor(PyFeatureExtractor):
        def extract_from(
            self,
            tune_context: TuneContext,  # pylint: disable = unused-argument
            candidates: List[MeasureCandidate],  # pylint: disable = unused-argument
        ) -> List[np.ndarray]:
            return [np.random.rand(4, 5)]

    extractor = FancyFeatureExtractor()
    features = extractor.extract_from(TuneContext(), [])
    assert len(features) == 1
    assert features[0].shape == (4, 5)


def test_meta_schedule_feature_extractor_as_string():
    class NotSoFancyFeatureExtractor(PyFeatureExtractor):
        def extract_from(
            self,
            tune_context: TuneContext,  # pylint: disable = unused-argument
            candidates: List[MeasureCandidate],  # pylint: disable = unused-argument
        ) -> List[np.ndarray]:
            return []

    feature_extractor = NotSoFancyFeatureExtractor()
    pattern = re.compile(r"NotSoFancyFeatureExtractor\(0x[a-f|0-9]*\)")
    assert pattern.match(str(feature_extractor))


if __name__ == "__main__":
    test_meta_schedule_feature_extractor()
    test_meta_schedule_feature_extractor_as_string()

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements.  See the NOTICE file # distributed with this work for additional information # regarding copyright ownership.  The ASF licenses this file # to you 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. # pylint: disable=missing-module-docstring,missing-function-docstring,missing-class-docstring # pylint: disable = unused-argument # pylint: disable = unused-argument # pylint: disable = unused-argument # pylint: disable = unused-argument