#  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 unittest

import numpy as np
from op_test_ipu import IPUOpTest

import paddle
import paddle.static
from paddle.optimizer.lr import LRScheduler


class LR_New(LRScheduler):
    def __init__(self, learning_rate=1e-5, last_epoch=-1, verbose=False):
        super().__init__(learning_rate, last_epoch, verbose)

    def get_lr(self):
        self.base_lr = self.base_lr + 1e-4
        self.last_epoch = self.last_epoch + 1
        return self.base_lr


class TestConvNet(IPUOpTest):
    @IPUOpTest.static_graph
    def build_model(self):
        image = paddle.static.data(
            name='image', shape=[1, 3, 10, 10], dtype='float32'
        )
        conv1 = paddle.nn.Conv2D(
            in_channels=image.shape[1],
            out_channels=3,
            kernel_size=3,
            bias_attr=False,
        )(image)
        loss = paddle.mean(conv1)

        opt = paddle.optimizer.Lamb(learning_rate=LR_New())
        opt.minimize(loss)
        self.feed_list = [image.name]
        self.fetch_list = [loss]

    def run_model(self, run_ipu=True):
        self.build_model()
        if run_ipu:
            place = paddle.IPUPlace()
        else:
            place = paddle.CPUPlace()
        exe = paddle.static.Executor(place)
        exe.run(self.startup_prog)
        if run_ipu:
            ipu_strategy = paddle.static.IpuStrategy()
            ipu_strategy.set_graph_config(is_training=True)
            program = paddle.static.IpuCompiledProgram(
                self.main_prog, ipu_strategy=ipu_strategy
            ).compile(self.feed_list, self.fetch_list)
        else:
            program = self.main_prog

        result = []
        for _ in range(100):
            if hasattr(program, "lr_scheduler"):
                program.lr_scheduler.step()
            loss_res = exe.run(
                program, feed=self.feed, fetch_list=self.fetch_list
            )
            result.append(loss_res)
        return np.array(result)

    def test_training(self):
        data = np.random.rand(1, 3, 10, 10).astype(np.float32)
        self.feed = {'image': data}
        # cpu and ipu dimension mismatch, cpu:(100, 1, 1), ipu:(100, 1)
        ipu_loss = self.run_model(True).flatten()
        cpu_loss = self.run_model(False).flatten()

        np.testing.assert_allclose(ipu_loss, cpu_loss, rtol=1e-05, atol=1e-10)


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