#  Copyright (c) 2021 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 numpy as np
import unittest
import sys
sys.path.append("..")
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from test_adam_op import adam_step

paddle.enable_static()
SEED = 2021


@unittest.skipIf(not paddle.is_compiled_with_npu(),
                 "core is not compiled with NPU")
class TestAdam(OpTest):
    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")

        learning_rate = 0.004
        beta1 = 0.78
        beta2 = 0.836
        epsilon = 1e-4
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32")
        }

        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, self.attrs)

        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
        }

    def set_npu(self):
        self.__class__.use_npu = True

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

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-5, check_dygraph=False)


@unittest.skipIf(not paddle.is_compiled_with_npu(),
                 "core is not compiled with NPU")
class TestAdamWithEpsilonTensor(OpTest):
    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")

        learning_rate = 0.004
        beta1 = 0.78
        beta2 = 0.836
        epsilon = 1e-4
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
            'Beta1Tensor': np.array([beta1]).astype("float32"),
            'Beta2Tensor': np.array([beta2]).astype("float32"),
            'EpsilonTensor': np.array([epsilon]).astype("float32"),
        }

        self.attrs = {'epsilon': epsilon}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, self.attrs)

        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
        }

    def set_npu(self):
        self.__class__.use_npu = True

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

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-5, check_dygraph=False)


@unittest.skipIf(not paddle.is_compiled_with_npu(),
                 "core is not compiled with NPU")
class TestAdamOpWithGlobalBetaPow(OpTest):
    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")

        learning_rate = 0.004
        beta1 = 0.78
        beta2 = 0.836
        epsilon = 1e-4
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
            'Beta1Tensor': np.array([beta1]).astype("float32"),
            'Beta2Tensor': np.array([beta2]).astype("float32"),
            'EpsilonTensor': np.array([epsilon]).astype("float32"),
        }

        attributes = {'epsilon': epsilon}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, attributes)

        self.attrs = {'use_global_beta_pow': True}

        # use_global_beta_pow=True, Beta1PowOut and Beta2PowOut are empty.
        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([]),
            'Beta2PowOut': np.array([])
        }

    def set_npu(self):
        self.__class__.use_npu = True

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

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-5, check_dygraph=False)


@unittest.skipIf(not paddle.is_compiled_with_npu(),
                 "core is not compiled with NPU")
class TestNet(unittest.TestCase):
    def _test(self, run_npu=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')

            sum = paddle.add(a, b)
            z = paddle.pow(sum, 2.0)

            fc_1 = fluid.layers.fc(input=z, 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)
            adam = fluid.optimizer.Adam(learning_rate=0.01)
            adam.minimize(loss)

        if run_npu:
            place = paddle.NPUPlace(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_npu(self):
        cpu_pred, cpu_loss = self._test(False)
        npu_pred, npu_loss = self._test(True)

        self.assertTrue(np.allclose(npu_pred, cpu_pred, rtol=1e-4))
        self.assertTrue(np.allclose(npu_loss, cpu_loss, rtol=1e-4))


@unittest.skipIf(not paddle.is_compiled_with_npu(),
                 "core is not compiled with NPU")
class TestNetWithEpsilonTensor(unittest.TestCase):
    def _test(self, run_npu=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')

            sum = paddle.add(a, b)
            z = paddle.pow(sum, 2.0)

            fc_1 = fluid.layers.fc(input=z, 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)
            beta1_init = 0.9
            beta2_init = 0.999
            epsilon_init = 1e-8
            beta1 = fluid.layers.create_global_var(
                shape=[1],
                value=float(beta1_init),
                dtype='float32',
                persistable=True,
                name="beta1")
            beta2 = fluid.layers.create_global_var(
                shape=[1],
                value=float(beta2_init),
                dtype='float32',
                persistable=True,
                name="beta2")
            epsilon = fluid.layers.create_global_var(
                shape=[1],
                value=float(epsilon_init),
                dtype='float32',
                persistable=True,
                name="epsilon")
            adam = fluid.optimizer.Adam(
                learning_rate=0.01, beta1=beta1, beta2=beta2, epsilon=epsilon)
            adam.minimize(loss)

        if run_npu:
            place = paddle.NPUPlace(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_npu(self):
        cpu_pred, cpu_loss = self._test(False)
        npu_pred, npu_loss = self._test(True)

        self.assertTrue(np.allclose(npu_pred, cpu_pred, rtol=1e-4))
        self.assertTrue(np.allclose(npu_loss, cpu_loss, rtol=1e-4))


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