#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.v2.fluid.core as core

import paddle.v2.fluid.executor as executor
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.optimizer as optimizer
from paddle.v2.fluid.framework import Program, program_guard
from paddle.v2.fluid.io import save_inference_model, load_inference_model


class TestBook(unittest.TestCase):
    def test_fit_line_inference_model(self):
        MODEL_DIR = "./tmp/inference_model"

        init_program = Program()
        program = Program()

        with program_guard(program, init_program):
            x = layers.data(name='x', shape=[2], dtype='float32')
            y = layers.data(name='y', shape=[1], dtype='float32')

            y_predict = layers.fc(input=x, size=1, act=None)

            cost = layers.square_error_cost(input=y_predict, label=y)
            avg_cost = layers.mean(x=cost)

            sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
            sgd_optimizer.minimize(avg_cost, init_program)

        place = core.CPUPlace()
        exe = executor.Executor(place)

        exe.run(init_program, feed={}, fetch_list=[])

        for i in xrange(100):
            tensor_x = np.array(
                [[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
            tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32")

            exe.run(program,
                    feed={'x': tensor_x,
                          'y': tensor_y},
                    fetch_list=[avg_cost])

        save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
        expected = exe.run(program,
                           feed={'x': tensor_x,
                                 'y': tensor_y},
                           fetch_list=[avg_cost])[0]

        reload(executor)  # reload to build a new scope
        exe = executor.Executor(place)

        [infer_prog, feed_var_names, fetch_vars] = load_inference_model(
            MODEL_DIR, exe)

        outs = exe.run(
            infer_prog,
            feed={feed_var_names[0]: tensor_x,
                  feed_var_names[1]: tensor_y},
            fetch_list=fetch_vars)
        actual = outs[0]

        self.assertEqual(feed_var_names, ["x", "y"])
        self.assertEqual(len(fetch_vars), 1)
        self.assertEqual(str(fetch_vars[0]), str(avg_cost))
        self.assertEqual(expected, actual)


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