# 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 paddle
import unittest
import numpy as np
from paddle import LazyGuard
from paddle.nn import Linear, Layer
from paddle.nn.initializer import Constant, Normal, TruncatedNormal, Uniform, XavierNormal, XavierUniform
from paddle.fluid import unique_name


class TestInitializerBase(unittest.TestCase):

    def setUp(self):
        self.set_initializer()
        self.set_param_attr()
        self.set_init_ops()
        self.clear_nameset()

    def set_initializer(self):
        self.w_initializer = Constant(0.6)
        self.b_initializer = Constant(0.3)

    def set_param_attr(self):
        self.weight_attr = paddle.ParamAttr(name="weight",
                                            initializer=self.w_initializer)

        self.bias_attr = paddle.ParamAttr(name="bias",
                                          initializer=self.b_initializer)

    def set_init_ops(self):
        self.init_ops = ['fill_constant', 'fill_constant']

    def clear_nameset(self):
        unique_name.dygraph_parameter_name_checker._name_set = set()

    def test_wrapper(self):
        with LazyGuard():
            fc = Linear(10,
                        10,
                        weight_attr=self.weight_attr,
                        bias_attr=self.bias_attr)
        program = fc._startup_program()
        print(program)
        self.check_program(program)

    def check_program(self, program):
        self.assertEqual(program.block(0).var("weight").shape, (10, 10))
        self.assertEqual(program.block(0).var("bias").shape, (10, ))
        ops = [op.type for op in program.block(0).ops]
        self.assertEqual(ops, self.init_ops)


class TestDygraphLazy(TestInitializerBase):

    def test_wrapper(self):
        with LazyGuard():
            fc = Linear(10,
                        10,
                        weight_attr=self.weight_attr,
                        bias_attr=self.bias_attr)

        self.check_data(fc)

    def check_data(self, model):
        x = paddle.randn([2, 10])
        # weight and bias have no memory
        with self.assertRaises(RuntimeError):
            out = model(x)

        for param in model.parameters():
            param.initialize()

        out = model(x)
        self.assertEqual(out.shape, [2, 10])

        np.testing.assert_allclose(model.weight.numpy(),
                                   np.ones([10, 10], dtype=np.float32) * 0.6)
        np.testing.assert_allclose(model.bias.numpy(),
                                   np.ones([10], dtype=np.float32) * 0.3)


class NestModel(Layer):

    def __init__(self, base_model):
        super(NestModel, self).__init__()
        self.base_model = base_model
        self.fc = Linear(10, 10)

    def forward(self, x):
        x = self.base_model(x)
        x = self.fc(x)
        return x


class TestNestModelLazy(TestInitializerBase):

    def test_wrapper(self):
        with LazyGuard():
            base_model = Linear(10,
                                10,
                                weight_attr=self.weight_attr,
                                bias_attr=self.bias_attr)
            nest_model = NestModel(base_model)

        self.check_data(nest_model)
        self.check_program(nest_model)

    def check_data(self, model):
        x = paddle.randn([2, 10])
        # weight and bias have no memory
        with self.assertRaises(RuntimeError):
            out = model(x)

        for param in model.parameters():
            param.initialize()

        out = model(x)
        self.assertEqual(out.shape, [2, 10])

        np.testing.assert_allclose(model.base_model.weight.numpy(),
                                   np.ones([10, 10], dtype=np.float32) * 0.6)
        np.testing.assert_allclose(model.base_model.bias.numpy(),
                                   np.ones([10], dtype=np.float32) * 0.3)

    def check_program(self, model):
        # verify nest_model startup_program
        whole_program = model._startup_program()
        self.assertEqual(whole_program.block(0).var("weight").shape, (10, 10))
        self.assertEqual(whole_program.block(0).var("bias").shape, (10, ))
        ops = [op.type for op in whole_program.block(0).ops]
        init_ops = self.init_ops + ['uniform_random', 'fill_constant']
        self.assertEqual(ops, init_ops)

        # verify base_model startup_program
        sub_program = model.base_model._startup_program()
        self.assertEqual(sub_program.block(0).var("weight").shape, (10, 10))
        self.assertEqual(sub_program.block(0).var("bias").shape, (10, ))
        ops = [op.type for op in sub_program.block(0).ops]
        self.assertEqual(ops, self.init_ops)


class TestUniform(TestInitializerBase):

    def set_initializer(self):
        self.w_initializer = Uniform()
        self.b_initializer = Uniform()

    def set_init_ops(self):
        self.init_ops = ['uniform_random', 'uniform_random']


class TestNormal(TestInitializerBase):

    def set_initializer(self):
        self.w_initializer = Normal()
        self.b_initializer = Normal()

    def set_init_ops(self):
        self.init_ops = ['gaussian_random', 'gaussian_random']


class TestTruncatedNormal(TestInitializerBase):

    def set_initializer(self):
        self.w_initializer = TruncatedNormal()
        self.b_initializer = TruncatedNormal()

    def set_init_ops(self):
        self.init_ops = [
            'truncated_gaussian_random', 'truncated_gaussian_random'
        ]


class TestXavierNormal(TestNormal):

    def set_initializer(self):
        self.w_initializer = XavierNormal()
        self.b_initializer = XavierNormal()


class TestXavierUniform(TestUniform):

    def set_initializer(self):
        self.w_initializer = XavierUniform()
        self.b_initializer = XavierUniform()


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