# Copyright (c) 2016-present, Facebook, Inc.
#
# 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.
##############################################################################

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from caffe2.python import core
from hypothesis import given
import hypothesis.strategies as st
import caffe2.python.hypothesis_test_util as hu
import numpy as np

import unittest


class TestThresholdedRelu(hu.HypothesisTestCase):

    # test case 1 - default alpha - we do reference and dc checks.
    # test case 2 does dc and reference checks over range of alphas.
    # test case 3 does gc over range of alphas.
    @given(input=hu.tensor(),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_thresholded_relu_1(self, input, gc, dc, engine):
        X = input
        op = core.CreateOperator("ThresholdedRelu", ["X"], ["Y"],
                                 engine=engine)

        def defaultRef(X):
            Y = np.copy(X)
            Y[Y <= 1.0] = 0.0
            return (Y,)

        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertReferenceChecks(gc, op, [X], defaultRef)

    @given(input=hu.tensor(),
           alpha=st.floats(min_value=1.0, max_value=5.0),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_thresholded_relu_2(self, input, alpha, gc, dc, engine):
        X = input
        op = core.CreateOperator("ThresholdedRelu", ["X"], ["Y"],
                                 alpha=alpha, engine=engine)

        def ref(X):
            Y = np.copy(X)
            Y[Y <= alpha] = 0.0
            return (Y,)

        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertReferenceChecks(gc, op, [X], ref)

    @given(input=hu.tensor(),
           alpha=st.floats(min_value=1.1, max_value=5.0),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_thresholded_relu_3(self, input, alpha, gc, dc, engine):
        X = TestThresholdedRelu.fix_input(input)
        op = core.CreateOperator("ThresholdedRelu", ["X"], ["Y"],
                                 alpha=float(alpha), engine=engine)
        self.assertGradientChecks(gc, op, [X], 0, [0])

    @staticmethod
    def fix_input(input):
        # go away from alpha to avoid derivative discontinuities
        input += 0.02 * np.sign(input)
        return input


if __name__ == "__main__":
    unittest.main()
