# 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.

import unittest

import numpy

import paddle
from paddle import base
from paddle.base.backward import append_backward
from paddle.base.executor import Executor

paddle.enable_static()


class TestWhileOp(unittest.TestCase):
    def simple_net(self):
        d0 = paddle.static.data("d0", shape=[10], dtype='float32')
        d1 = paddle.static.data("d1", shape=[10], dtype='float32')
        d2 = paddle.static.data("d2", shape=[10], dtype='float32')
        i = paddle.zeros(shape=[1], dtype='int64')
        i.stop_gradient = True
        init = paddle.zeros(shape=[10], dtype='float32')
        mem_array = paddle.tensor.array_write(x=init, i=i)
        data_array = paddle.tensor.array_write(x=d0, i=i)
        i = paddle.increment(i)
        paddle.tensor.array_write(d1, i, array=data_array)
        i = paddle.increment(i)
        paddle.tensor.array_write(d2, i, array=data_array)
        i = paddle.zeros(shape=[1], dtype='int64')
        i.stop_gradient = True
        array_len = paddle.tensor.fill_constant(
            shape=[1], dtype='int64', value=1
        )
        array_len.stop_gradient = True
        cond = paddle.less_than(x=i, y=array_len)
        j = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=1)
        j.stop_gradient = True
        array_len2 = paddle.tensor.fill_constant(
            shape=[1], dtype='int64', value=3
        )
        array_len2.stop_gradient = True
        cond2 = paddle.less_than(x=j, y=array_len2)
        while_op = paddle.static.nn.control_flow.While(cond=cond)
        while_op2 = paddle.static.nn.control_flow.While(cond=cond2)
        with while_op.block():
            d = paddle.tensor.array_read(array=data_array, i=i)
            prev = paddle.tensor.array_read(array=mem_array, i=i)
            result = paddle.add_n([d, prev])

            i = paddle.increment(x=i)
            paddle.tensor.array_write(result, i=i, array=mem_array)
            paddle.assign(paddle.less_than(x=i, y=array_len), cond)

            with while_op2.block():
                d2 = paddle.tensor.array_read(array=data_array, i=j)
                prev2 = paddle.tensor.array_read(array=mem_array, i=j)
                result2 = paddle.add_n([d2, prev2])

                j = paddle.increment(x=j)
                paddle.tensor.array_write(result2, i=j, array=mem_array)
                paddle.assign(paddle.less_than(x=j, y=array_len2), cond2)
        sum_result = paddle.tensor.array_read(array=mem_array, i=j)
        loss = paddle.mean(sum_result)
        return loss, sum_result

    def test_simple_net(self):
        main_program = base.Program()
        startup_program = base.Program()
        with base.program_guard(main_program, startup_program):
            loss, sum_result = self.simple_net()

            append_backward(loss)

            xpu_place = paddle.XPUPlace(0)
            exe = Executor(xpu_place)
            d = []

            for i in range(3):
                d.append(numpy.random.random(size=[10]).astype('float32'))

            outs = exe.run(
                feed={'d0': d[0], 'd1': d[1], 'd2': d[2]},
                fetch_list=[sum_result],
            )
            self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)

    def test_simple_net_forward(self):
        main_program = base.Program()
        startup_program = base.Program()
        with base.program_guard(main_program, startup_program):
            self.simple_net()
            if paddle.framework.in_pir_mode():
                binary = main_program
            else:
                binary = base.compiler.CompiledProgram(main_program)

            xpu_place = paddle.XPUPlace(0)
            exe = Executor(xpu_place)
            d = []

            for i in range(3):
                d.append(numpy.random.random(size=[10]).astype('float32'))

            for _ in range(2):
                exe.run(binary, feed={'d0': d[0], 'd1': d[1], 'd2': d[2]})

    def test_exceptions(self):
        i = paddle.zeros(shape=[2], dtype='int64')
        array_len = paddle.tensor.fill_constant(
            shape=[2], dtype='int64', value=1
        )
        cond = paddle.less_than(x=i, y=array_len)
        with self.assertRaises(TypeError):
            paddle.static.nn.control_flow.While(cond=cond)
        cond = paddle.cast(cond, dtype='float64')
        with self.assertRaises(TypeError):
            paddle.static.nn.control_flow.While(cond=cond)


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