#  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
import paddle
import paddle.static
from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest


@unittest.skipIf(not paddle.is_compiled_with_ipu(),
                 "core is not compiled with IPU")
class TestBase(IPUOpTest):

    def setUp(self):
        self.set_atol()
        self.set_training()
        self.set_data_feed()
        self.set_feed_attr()
        self.set_op_attrs()

    def set_data_feed(self):
        self.feed = {
            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
        }

    def set_feed_attr(self):
        self.feed_shape = [x.shape for x in self.feed.values()]
        self.feed_list = list(self.feed.keys())
        self.feed_dtype = [x.dtype for x in self.feed.values()]

    def set_op_attrs(self):
        self.attrs = {
            "shape": [30, 10],
            "inplace": True,
        }

    def _test_base(self, run_ipu=True):
        scope = paddle.static.Scope()
        main_prog = paddle.static.Program()
        startup_prog = paddle.static.Program()
        main_prog.random_seed = self.SEED
        startup_prog.random_seed = self.SEED

        with paddle.static.scope_guard(scope):
            with paddle.static.program_guard(main_prog, startup_prog):
                x = paddle.static.data(name=self.feed_list[0],
                                       shape=self.feed_shape[0],
                                       dtype=self.feed_dtype[0])
                add1 = paddle.fluid.layers.elementwise_add(x, x)
                reshape = paddle.fluid.layers.reshape(add1, **self.attrs)
                add2 = paddle.fluid.layers.elementwise_add(reshape, reshape)
                scale1 = paddle.fluid.layers.scale(add2)
                scale2 = paddle.fluid.layers.scale(scale1, scale=1.3, bias=0.5)
                scale3 = paddle.fluid.layers.scale(scale2, scale=2, bias=0.7)

            fetch_list = [scale3.name]

            if run_ipu:
                place = paddle.IPUPlace()
            else:
                place = paddle.CPUPlace()

            exe = paddle.static.Executor(place)
            exe.run(startup_prog)
            scale1_out = main_prog.global_block().ops[4].output("Out")[0]
            main_prog.global_block().ops[4]._rename_output(
                scale1_out, add2.name)
            main_prog.global_block().ops[5]._rename_input(scale1_out, add2.name)

            if run_ipu:
                feed_list = self.feed_list
                ipu_strategy = paddle.static.IpuStrategy()
                ipu_strategy.set_graph_config(is_training=self.is_training)
                program = paddle.static.IpuCompiledProgram(
                    main_prog,
                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
            else:
                program = main_prog

            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
            return result[0]

    def test_base(self):
        res0 = self._test_base(True)
        res1 = self._test_base(False)

        self.assertTrue(
            np.allclose(res0.flatten(), res1.flatten(), atol=self.atol))

        self.assertTrue(res0.shape == res1.shape)


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