#   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 copy
import unittest

import numpy as np

import paddle
import paddle.fluid.core as core
import paddle.fluid.io as io
from paddle.fluid.dygraph import guard
from paddle.fluid.executor import Executor
from paddle.fluid.framework import ParamBase, Variable, default_main_program
from paddle.fluid.initializer import ConstantInitializer

paddle.enable_static()
main_program = default_main_program()


class ParameterChecks(unittest.TestCase):
    def test_parameter(self):
        shape = [784, 100]
        val = 1.0625
        b = main_program.global_block()
        param = b.create_parameter(
            name='fc.w',
            shape=shape,
            dtype='float32',
            initializer=ConstantInitializer(val),
        )
        self.assertIsNotNone(param)
        self.assertEqual('fc.w', param.name)
        self.assertEqual((784, 100), param.shape)
        self.assertEqual(core.VarDesc.VarType.FP32, param.dtype)
        self.assertEqual(0, param.block.idx)
        exe = Executor(paddle.CPUPlace())
        p = exe.run(main_program, fetch_list=[param])[0]
        np.testing.assert_array_equal(p, np.ones(shape) * val)
        p = io.get_parameter_value_by_name('fc.w', exe, main_program)
        np.testing.assert_array_equal(p, np.ones(shape) * val)

        zero_dim_param = b.create_parameter(name='x', shape=[], dtype='float32')
        self.assertEqual(zero_dim_param.shape, ())

    def test_parambase(self):
        with guard():
            linear = paddle.nn.Linear(10, 10)
            param = linear.weight

            memo = {}
            param_copy = copy.deepcopy(param, memo)
            self.assertEqual(param_copy.shape, param.shape)
            self.assertEqual(param_copy.type, param.type)
            self.assertEqual(param_copy.dtype, param.dtype)
            self.assertEqual(str(param_copy.place), str(param.place))
            np.testing.assert_array_equal(param_copy.numpy(), param.numpy())
            self.assertEqual(param_copy.optimize_attr, param.optimize_attr)
            self.assertEqual(param_copy.regularizer, param.regularizer)
            self.assertEqual(
                param_copy.do_model_average, param.do_model_average
            )
            self.assertEqual(param_copy.need_clip, param.need_clip)
            self.assertEqual(param_copy.is_distributed, param.is_distributed)

            pram_copy2 = copy.deepcopy(param, memo)
            self.assertEqual(id(param_copy), id(pram_copy2))

            zero_dim_param = ParamBase(shape=[], dtype='float32')
            self.assertEqual(zero_dim_param.shape, [])

    def func_exception(self):
        b = main_program.global_block()
        with self.assertRaises(ValueError):
            b.create_parameter(
                name='test', shape=None, dtype='float32', initializer=None
            )
        with self.assertRaises(ValueError):
            b.create_parameter(
                name='test', shape=[1], dtype=None, initializer=None
            )
        with self.assertRaises(ValueError):
            b.create_parameter(
                name='test', shape=[], dtype='float32', initializer=None
            )
        with self.assertRaises(ValueError):
            b.create_parameter(
                name='test', shape=[-1], dtype='float32', initializer=None
            )

    def test_parambase_to_vector(self):
        with guard():
            initializer = paddle.ParamAttr(
                initializer=paddle.nn.initializer.Constant(3.0)
            )
            linear1 = paddle.nn.Linear(10, 15, initializer)

            vec = paddle.nn.utils.parameters_to_vector(linear1.parameters())
            self.assertEqual(linear1.weight.shape, [10, 15])
            self.assertEqual(linear1.bias.shape, [15])
            self.assertTrue(isinstance(vec, Variable))
            self.assertTrue(vec.shape, [165])

            linear2 = paddle.nn.Linear(10, 15)
            paddle.nn.utils.vector_to_parameters(vec, linear2.parameters())
            self.assertEqual(linear2.weight.shape, [10, 15])
            self.assertEqual(linear2.bias.shape, [15])
            np.testing.assert_array_equal(
                linear1.weight.numpy(), linear2.weight.numpy()
            )
            np.testing.assert_array_equal(
                linear1.bias.numpy(), linear2.bias.numpy()
            )
            self.assertTrue(linear2.weight.is_leaf, True)
            self.assertTrue(linear2.bias.is_leaf, True)


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