#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import unittest

import numpy as np
from op_test import OpTest

from paddle.nn import functional as F


def sigmoid_array(x):
    return 1 / (1 + np.exp(-x))


class TestLogLossOp(OpTest):
    def setUp(self):
        self.op_type = 'log_loss'
        self.python_api = F.log_loss
        self.prim_op_type = "comp"
        self.public_python_api = F.log_loss
        samples_num = 100

        x = np.random.random((samples_num, 1)).astype("float32")
        predicted = sigmoid_array(x)
        labels = np.random.randint(0, 2, (samples_num, 1)).astype("float32")
        epsilon = 1e-7
        self.inputs = {
            'Predicted': predicted,
            'Labels': labels,
        }

        self.attrs = {'epsilon': epsilon}
        loss = -labels * np.log(predicted + epsilon) - (1 - labels) * np.log(
            1 - predicted + epsilon
        )
        self.outputs = {'Loss': loss}

    def test_check_output(self):
        self.check_output(check_pir=True, check_prim_pir=True)

    def test_check_grad(self):
        self.check_grad(
            ['Predicted'],
            'Loss',
            max_relative_error=0.03,
            check_pir=True,
            check_prim_pir=True,
        )


class TestLogLossOp_ZeroSize(OpTest):
    def init_shape(self):
        self.x_shape = (0, 1)
        self.label_shape = (0, 1)

    def setUp(self):
        self.init_shape()
        self.op_type = 'log_loss'
        self.python_api = F.log_loss
        self.public_python_api = F.log_loss

        x = np.random.random(self.x_shape).astype("float32")
        predicted = sigmoid_array(x)
        labels = np.random.randint(0, 2, self.label_shape).astype("float32")
        epsilon = 1e-7
        self.inputs = {
            'Predicted': predicted,
            'Labels': labels,
        }

        self.attrs = {'epsilon': epsilon}
        loss = -labels * np.log(predicted + epsilon) - (1 - labels) * np.log(
            1 - predicted + epsilon
        )
        self.outputs = {'Loss': loss}

    def test_check_output(self):
        self.check_output(check_pir=True)

    def test_check_grad(self):
        self.check_grad(
            ['Predicted'],
            'Loss',
            check_pir=True,
        )


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