# Copyright (c) 2019 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 os
import tempfile
import unittest

import numpy as np

import paddle
from paddle import base
from paddle.base import framework
from paddle.nn import BatchNorm, Linear

paddle.enable_static()


class TestDygraphLoadStatic(unittest.TestCase):
    def testLoadStaticModel(self):
        # static graph mode
        temp_dir = tempfile.TemporaryDirectory()
        a = paddle.static.data(name="a", shape=[10, 10])
        conv_in = paddle.static.data(name="conv_in", shape=[None, 10, 10, 10])

        fc_out1 = paddle.static.nn.fc(a, 10)
        fc_out2 = paddle.static.nn.fc(a, 20)

        conv_out_1 = paddle.static.nn.conv2d(
            conv_in, num_filters=10, filter_size=5, act="relu"
        )
        conv_out_2 = paddle.static.nn.conv2d(
            conv_in, num_filters=10, filter_size=5, act="relu"
        )

        conv3d_in = paddle.static.data(
            name='conv3d_in', shape=[None, 3, 12, 32, 32], dtype='float32'
        )
        conv3d_out_1 = paddle.static.nn.conv3d(
            input=conv3d_in, num_filters=2, filter_size=3, act="relu"
        )
        conv3d_out_2 = paddle.static.nn.conv3d(
            input=conv3d_in, num_filters=2, filter_size=3, act="relu"
        )

        batchnorm_in = paddle.static.data(
            name="batchnorm_in", shape=[None, 10], dtype='float32'
        )
        batchnorm_out_1 = paddle.static.nn.batch_norm(batchnorm_in)
        batchnorm_out_2 = paddle.static.nn.batch_norm(batchnorm_in)

        emb_in = paddle.static.data(
            name='emb_in', shape=[None, 10], dtype='int64'
        )
        emb_out_1 = paddle.static.nn.embedding(emb_in, [1000, 100])
        emb_out_2 = paddle.static.nn.embedding(emb_in, [2000, 200])

        layernorm = paddle.static.data(
            name="ln", shape=[None, 10], dtype='float32'
        )
        layernorm_1 = paddle.static.nn.layer_norm(layernorm)
        layernorm_2 = paddle.static.nn.layer_norm(layernorm)

        nce_in = paddle.static.data(
            name="nce_in", shape=[None, 100], dtype='float32'
        )
        nce_label = paddle.static.data(
            name="nce_label", shape=[None, 10], dtype='int64'
        )
        nce_out_1 = paddle.static.nn.nce(nce_in, nce_label, 10000)
        nce_out_2 = paddle.static.nn.nce(nce_in, nce_label, 10000)

        prelu_in = paddle.static.data(
            name="prelu_in", shape=[None, 5, 10, 10], dtype='float32'
        )
        prelu_out_1 = paddle.static.nn.prelu(prelu_in, "channel")
        prelu_out_2 = paddle.static.nn.prelu(prelu_in, "channel")

        bilinear_tensor_pro_x = paddle.static.data(
            "t1", shape=[None, 5], dtype="float32"
        )
        bilinear_tensor_pro_y = paddle.static.data(
            "t2", shape=[None, 4], dtype="float32"
        )

        bilinear_tensor_pro_out_1 = (
            paddle.static.nn.common.bilinear_tensor_product(
                x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000
            )
        )
        bilinear_tensor_pro_out_2 = (
            paddle.static.nn.common.bilinear_tensor_product(
                x=bilinear_tensor_pro_x, y=bilinear_tensor_pro_y, size=1000
            )
        )

        conv2d_trans_in = paddle.static.data(
            name="conv2d_trans_in", shape=[None, 10, 10, 10]
        )

        conv2d_trans_out_1 = paddle.static.nn.conv2d_transpose(
            conv2d_trans_in, num_filters=10, filter_size=5, act="relu"
        )
        conv2d_trans_out_2 = paddle.static.nn.conv2d_transpose(
            conv2d_trans_in, num_filters=10, filter_size=5, act="relu"
        )

        conv3d_trans_in = paddle.static.data(
            name='conv3d_trans_in', shape=[None, 3, 12, 32, 32], dtype='float32'
        )
        conv3d_trans_out_1 = paddle.static.nn.conv3d_transpose(
            input=conv3d_trans_in, num_filters=2, filter_size=3, act="relu"
        )
        conv3d_trans_out_2 = paddle.static.nn.conv3d_transpose(
            input=conv3d_trans_in, num_filters=2, filter_size=3, act="relu"
        )

        groupnorm_in = paddle.static.data(
            name='groupnorm_in', shape=[None, 8, 32, 32], dtype='float32'
        )
        groupnorm_out1 = paddle.static.nn.group_norm(
            input=groupnorm_in, groups=4, param_attr=True, bias_attr=True
        )
        groupnorm_out2 = paddle.static.nn.group_norm(
            input=groupnorm_in, groups=4, param_attr=True, bias_attr=True
        )
        '''
        spec_norm = paddle.static.data(name='spec_norm', shape=[2, 8, 32, 32], dtype='float32')
        spe_norm_out_1 = paddle.static.nn.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
        spe_norm_out_2 = paddle.static.nn.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
        '''

        para1 = paddle.create_parameter(
            [100, 100], 'float32', name="weight_test_1"
        )
        para2 = paddle.create_parameter(
            [20, 200], 'float32', name="weight_test_2"
        )

        para_list = base.default_main_program().list_vars()

        exe = base.Executor(
            base.CPUPlace()
            if not base.is_compiled_with_cuda()
            else base.CUDAPlace(0)
        )
        out = exe.run(framework.default_startup_program())

        paddle.static.save(
            framework.default_main_program(),
            os.path.join(temp_dir.name, "test_1"),
        )

        para_dict = paddle.static.load_program_state(
            os.path.join(temp_dir.name, "test_1")
        )

        new_dict = {}
        for k, v in para_dict.items():
            # print( k, v.shape )
            if k.startswith("fc"):
                name = k.replace("fc", "linear", 1)
                new_dict[name] = v
            else:
                new_dict[k] = v

        with base.dygraph.guard():

            class MyTest(paddle.nn.Layer):
                def __init__(self):
                    super().__init__()

                    self.linear1 = Linear(10, 10)
                    self.lienar2 = Linear(10, 20)

                    self.conv2d_1 = paddle.nn.Conv2D(
                        in_channels=10, out_channels=10, kernel_size=5
                    )
                    self.conv2d_2 = paddle.nn.Conv2D(
                        in_channels=10, out_channels=10, kernel_size=5
                    )

                    self.conv3d_1 = paddle.nn.Conv3D(
                        in_channels=3, out_channels=2, kernel_size=3
                    )
                    self.conv3d_2 = paddle.nn.Conv3D(
                        in_channels=3, out_channels=2, kernel_size=3
                    )

                    self.batch_norm_1 = BatchNorm(10)
                    self.batch_norm_2 = BatchNorm(10)

                    self.emb1 = paddle.nn.Embedding(1000, 100)
                    self.emb2 = paddle.nn.Embedding(2000, 200)

                    self.layer_norm_1 = paddle.nn.LayerNorm([10])
                    self.layer_norm_2 = paddle.nn.LayerNorm(10)

                    self.group_norm1 = paddle.nn.GroupNorm(4, 8)
                    self.gourp_norm2 = paddle.nn.GroupNorm(4, 8)

                    self.w_1 = self.create_parameter(
                        [100, 100], dtype='float32', attr="weight_test_1"
                    )
                    self.w_2 = self.create_parameter(
                        [20, 200], dtype='float32', attr="weight_test_2"
                    )

            my_test = MyTest()
            my_test.set_dict(new_dict, use_structured_name=False)
            for k, v in my_test.state_dict().items():
                np.testing.assert_array_equal(v.numpy(), new_dict[v.name])
        temp_dir.cleanup()


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