#   Copyright (c) 2018 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 paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
from paddle.fluid.layers.control_flow import ConditionalBlock


class ConditionalBlockTest(unittest.TestCase):
    def test_forward(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            data = layers.data(name='X', shape=[1], dtype='float32')
            data.stop_gradient = False
            cond = ConditionalBlock(inputs=[data])
            out = layers.create_tensor(dtype='float32')
            with cond.block():
                hidden = layers.fc(input=data, size=10)
                layers.assign(hidden, out)

            cpu = core.CPUPlace()
            exe = Executor(cpu)
            exe.run(startup_program)

            x = np.random.random(size=(10, 1)).astype('float32')

            outs = exe.run(main_program, feed={'X': x}, fetch_list=[out])[0]
            print(outs)
            loss = paddle.mean(out)
            append_backward(loss=loss)
            outs = exe.run(
                main_program,
                feed={'X': x},
                fetch_list=[main_program.block(0).var(data.name + "@GRAD")],
            )[0]
            print(outs)


class TestConditionalBlockOpInferShape(unittest.TestCase):
    def test_infer_shape(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            global_block = main_program.global_block()
            sub_block = main_program._create_block()
            main_program._rollback()
            step_scope = global_block.create_var(
                type=core.VarDesc.VarType.STEP_SCOPES
            )
            cond_var = layers.fill_constant(
                shape=[1], dtype='bool', value=False
            )

            op = global_block.append_op(
                type='conditional_block',
                inputs={
                    'Cond': [cond_var],
                    'Input': [],
                },
                outputs={'Out': [], 'Scope': [step_scope]},
                attrs={'sub_block': sub_block, 'is_scalar_condition': True},
            )
            op.desc.infer_shape(global_block.desc)


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