#  Copyright (c) 2022 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.

from __future__ import print_function

import numpy as np
import unittest
import sys

sys.path.append("..")
from op_test import OpTest
import paddle
import paddle.fluid as fluid

paddle.enable_static()
SEED = 2021


class TestTanh(OpTest):

    def setUp(self):
        self.set_mlu()
        self.op_type = "tanh"
        self.place = paddle.MLUPlace(0)

        self.init_dtype()
        np.random.seed(SEED)
        x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
        out = np.tanh(x)

        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
        self.attrs = {}
        self.outputs = {'Out': out}

    def set_mlu(self):
        self.__class__.use_mlu = True

    def init_dtype(self):
        self.dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(self.place)

    def test_check_grad(self):
        if self.dtype == np.float16:
            self.check_grad(['X'], 'Out', max_relative_error=0.009)
        else:
            self.check_grad(['X'], 'Out', max_relative_error=0.009)


class TestTanhFp16(OpTest):

    def setUp(self):
        self.set_mlu()
        self.op_type = "tanh"
        self.place = paddle.MLUPlace(0)

        self.init_dtype()
        np.random.seed(SEED)
        x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
        out = np.tanh(x)

        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
        self.attrs = {}
        self.outputs = {'Out': out}

    def set_mlu(self):
        self.__class__.use_mlu = True
        self.__class__.no_need_check_grad = True

    def init_dtype(self):
        self.dtype = np.float16

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-3)


class TestTanhNet(unittest.TestCase):

    def _test(self, run_mlu=True):
        main_prog = paddle.static.Program()
        startup_prog = paddle.static.Program()
        main_prog.random_seed = SEED
        startup_prog.random_seed = SEED
        np.random.seed(SEED)

        a_np = np.random.random(size=(32, 32)).astype('float32')
        b_np = np.random.random(size=(32, 32)).astype('float32')
        label_np = np.random.randint(2, size=(32, 1)).astype('int64')

        with paddle.static.program_guard(main_prog, startup_prog):
            a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
            b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
            label = paddle.static.data(name="label",
                                       shape=[32, 1],
                                       dtype='int64')

            c = paddle.multiply(a, b)
            d = paddle.tanh(c)

            fc_1 = fluid.layers.fc(input=d, size=128)
            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')

            cost = fluid.layers.cross_entropy(input=prediction, label=label)
            loss = fluid.layers.reduce_mean(cost)
            sgd = fluid.optimizer.SGD(learning_rate=0.01)
            sgd.minimize(loss)

        if run_mlu:
            place = paddle.MLUPlace(0)
        else:
            place = paddle.CPUPlace()

        exe = paddle.static.Executor(place)
        exe.run(startup_prog)

        print("Start run on {}".format(place))
        for epoch in range(100):

            pred_res, loss_res = exe.run(main_prog,
                                         feed={
                                             "a": a_np,
                                             "b": b_np,
                                             "label": label_np
                                         },
                                         fetch_list=[prediction, loss])
            if epoch % 10 == 0:
                print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
                    epoch, pred_res[0], loss_res))

        return pred_res, loss_res

    def test_mlu(self):
        cpu_pred, cpu_loss = self._test(False)
        mlu_pred, mlu_loss = self._test(True)

        np.testing.assert_allclose(mlu_pred, cpu_pred, rtol=1e-6)
        np.testing.assert_allclose(mlu_loss, cpu_loss)


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