#   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.

from __future__ import print_function

import unittest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.dygraph.nn import Embedding
import paddle.fluid.framework as framework
from paddle.fluid.optimizer import Adam
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
from paddle.fluid.executor import global_scope
import numpy as np
import six
import pickle


class SimpleLSTMRNN(fluid.Layer):
    def __init__(self,
                 name_scope,
                 hidden_size,
                 num_steps,
                 num_layers=2,
                 init_scale=0.1,
                 dropout=None):
        super(SimpleLSTMRNN, self).__init__(name_scope)
        self._hidden_size = hidden_size
        self._num_layers = num_layers
        self._init_scale = init_scale
        self._dropout = dropout
        self._input = None
        self._num_steps = num_steps
        self.cell_array = []
        self.hidden_array = []

    def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
        self.weight_1_arr = []
        self.weight_2_arr = []
        self.bias_arr = []
        self.mask_array = []

        for i in range(self._num_layers):
            weight_1 = self.create_parameter(
                attr=fluid.ParamAttr(
                    initializer=fluid.initializer.UniformInitializer(
                        low=-self._init_scale, high=self._init_scale)),
                shape=[self._hidden_size * 2, self._hidden_size * 4],
                dtype="float32",
                default_initializer=fluid.initializer.UniformInitializer(
                    low=-self._init_scale, high=self._init_scale))
            self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1))
            bias_1 = self.create_parameter(
                attr=fluid.ParamAttr(
                    initializer=fluid.initializer.UniformInitializer(
                        low=-self._init_scale, high=self._init_scale)),
                shape=[self._hidden_size * 4],
                dtype="float32",
                default_initializer=fluid.initializer.Constant(0.0))
            self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1))

    def forward(self, input_embedding, init_hidden=None, init_cell=None):
        self.cell_array = []
        self.hidden_array = []

        for i in range(self._num_layers):
            pre_hidden = fluid.layers.slice(
                init_hidden, axes=[0], starts=[i], ends=[i + 1])
            pre_cell = fluid.layers.slice(
                init_cell, axes=[0], starts=[i], ends=[i + 1])
            pre_hidden = fluid.layers.reshape(
                pre_hidden, shape=[-1, self._hidden_size])
            pre_cell = fluid.layers.reshape(
                pre_cell, shape=[-1, self._hidden_size])
            self.hidden_array.append(pre_hidden)
            self.cell_array.append(pre_cell)

        res = []
        for index in range(self._num_steps):
            self._input = fluid.layers.slice(
                input_embedding, axes=[1], starts=[index], ends=[index + 1])
            self._input = fluid.layers.reshape(
                self._input, shape=[-1, self._hidden_size])
            for k in range(self._num_layers):
                pre_hidden = self.hidden_array[k]
                pre_cell = self.cell_array[k]
                weight_1 = self.weight_1_arr[k]
                bias = self.bias_arr[k]

                nn = fluid.layers.concat([self._input, pre_hidden], 1)
                gate_input = fluid.layers.matmul(x=nn, y=weight_1)

                gate_input = fluid.layers.elementwise_add(gate_input, bias)
                i, j, f, o = fluid.layers.split(
                    gate_input, num_or_sections=4, dim=-1)
                c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
                    i) * fluid.layers.tanh(j)
                m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
                self.hidden_array[k] = m
                self.cell_array[k] = c
                self._input = m

                if self._dropout is not None and self._dropout > 0.0:
                    self._input = fluid.layers.dropout(
                        self._input,
                        dropout_prob=self._dropout,
                        dropout_implementation='upscale_in_train')
            res.append(
                fluid.layers.reshape(
                    self._input, shape=[1, -1, self._hidden_size]))
        real_res = fluid.layers.concat(res, 0)
        real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
        last_hidden = fluid.layers.concat(self.hidden_array, 1)
        last_hidden = fluid.layers.reshape(
            last_hidden, shape=[-1, self._num_layers, self._hidden_size])
        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
        last_cell = fluid.layers.concat(self.cell_array, 1)
        last_cell = fluid.layers.reshape(
            last_cell, shape=[-1, self._num_layers, self._hidden_size])
        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
        return real_res, last_hidden, last_cell


class PtbModel(fluid.Layer):
    def __init__(self,
                 name_scope,
                 hidden_size,
                 vocab_size,
                 num_layers=2,
                 num_steps=20,
                 init_scale=0.1,
                 dropout=None):
        super(PtbModel, self).__init__(name_scope)
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.init_scale = init_scale
        self.num_layers = num_layers
        self.num_steps = num_steps
        self.dropout = dropout
        self.simple_lstm_rnn = SimpleLSTMRNN(
            self.full_name(),
            hidden_size,
            num_steps,
            num_layers=num_layers,
            init_scale=init_scale,
            dropout=dropout)
        self.embedding = Embedding(
            self.full_name(),
            size=[vocab_size, hidden_size],
            dtype='float32',
            is_sparse=False,
            param_attr=fluid.ParamAttr(
                name='embedding_para',
                initializer=fluid.initializer.UniformInitializer(
                    low=-init_scale, high=init_scale)))
        self.softmax_weight = self.create_parameter(
            attr=fluid.ParamAttr(),
            shape=[self.hidden_size, self.vocab_size],
            dtype="float32",
            default_initializer=fluid.initializer.UniformInitializer(
                low=-self.init_scale, high=self.init_scale))
        self.softmax_bias = self.create_parameter(
            attr=fluid.ParamAttr(),
            shape=[self.vocab_size],
            dtype="float32",
            default_initializer=fluid.initializer.UniformInitializer(
                low=-self.init_scale, high=self.init_scale))

    def forward(self, input, label, init_hidden, init_cell):
        init_h = fluid.layers.reshape(
            init_hidden, shape=[self.num_layers, -1, self.hidden_size])

        init_c = fluid.layers.reshape(
            init_cell, shape=[self.num_layers, -1, self.hidden_size])

        x_emb = self.embedding(input)
        x_emb = fluid.layers.reshape(
            x_emb, shape=[-1, self.num_steps, self.hidden_size])
        if self.dropout is not None and self.dropout > 0.0:
            x_emb = fluid.layers.dropout(
                x_emb,
                dropout_prob=self.drop_out,
                dropout_implementation='upscale_in_train')
        rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
                                                               init_c)

        rnn_out = fluid.layers.reshape(
            rnn_out, shape=[-1, self.num_steps, self.hidden_size])
        projection = fluid.layers.matmul(rnn_out, self.softmax_weight)
        projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
        projection = fluid.layers.reshape(
            projection, shape=[-1, self.vocab_size])
        loss = fluid.layers.softmax_with_cross_entropy(
            logits=projection, label=label, soft_label=False)
        loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
        loss = fluid.layers.reduce_mean(loss, dim=[0])
        loss = fluid.layers.reduce_sum(loss)

        return loss, last_hidden, last_cell


class TestSaveLoadBase(unittest.TestCase):
    def test_ptb_rnn_cpu_float32(self):
        seed = 90
        hidden_size = 10
        vocab_size = 1000
        num_layers = 1
        num_steps = 3
        init_scale = 0.1
        batch_size = 4
        batch_num = 200

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            ptb_model = PtbModel(
                "ptb_model",
                hidden_size=hidden_size,
                vocab_size=vocab_size,
                num_layers=num_layers,
                num_steps=num_steps,
                init_scale=init_scale)

            place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
            ) else fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            sgd = Adam(learning_rate=1e-3)
            x = fluid.layers.data(
                name="x", shape=[-1, num_steps], dtype='int64')
            y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
            init_hidden = fluid.layers.data(
                name="init_hidden", shape=[1], dtype='float32')
            init_cell = fluid.layers.data(
                name="init_cell", shape=[1], dtype='float32')

            static_loss, static_last_hidden, static_last_cell = ptb_model(
                x, y, init_hidden, init_cell)
            sgd.minimize(static_loss)
            static_param_updated = dict()
            static_param_init = dict()

            out = exe.run(framework.default_startup_program())

            static_loss_value = None
            static_last_cell_value = None
            static_last_hidden_value = None
            for i in range(batch_num):
                x_data = np.arange(12).reshape(4, 3).astype('int64')
                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
                x_data = x_data.reshape((-1, num_steps, 1))
                y_data = y_data.reshape((-1, 1))
                init_hidden_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                init_cell_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                fetch_list = [static_loss, static_last_hidden, static_last_cell]
                out = exe.run(fluid.default_main_program(),
                              feed={
                                  "x": x_data,
                                  "y": y_data,
                                  "init_hidden": init_hidden_data,
                                  "init_cell": init_cell_data
                              },
                              fetch_list=fetch_list)
                static_loss_value = out[0]
                static_last_hidden_value = out[1]
                static_last_cell_value = out[2]

            # get value before save
            main_program = framework.default_main_program()
            base_map = {}
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    t = np.array(fluid.global_scope().find_var(var.name)
                                 .get_tensor())
                    # make sure all the paramerter or optimzier var have been update
                    self.assertTrue(np.sum(np.abs(t)) != 0)
                    base_map[var.name] = t

            fluid.save(main_program, "./test_1")

            # set var to zero
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    ten = fluid.global_scope().find_var(var.name).get_tensor()
                    ten.set(np.zeros_like(np.array(ten)), place)

                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    # make sure all the paramerter or optimzier var have been set to zero
                    self.assertTrue(np.sum(np.abs(new_t)) == 0)

            fluid.load(main_program, "./test_1", exe)

            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    base_t = base_map[var.name]
                    self.assertTrue(np.array_equal(new_t, base_t))


class TestSaveLoadPartial(unittest.TestCase):
    def test_ptb_rnn_cpu_float32(self):
        seed = 90
        hidden_size = 10
        vocab_size = 1000
        num_layers = 1
        num_steps = 3
        init_scale = 0.1
        batch_size = 4
        batch_num = 200

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            ptb_model = PtbModel(
                "ptb_model",
                hidden_size=hidden_size,
                vocab_size=vocab_size,
                num_layers=num_layers,
                num_steps=num_steps,
                init_scale=init_scale)

            place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
            ) else fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            sgd = Adam(learning_rate=1e-3)
            x = fluid.layers.data(
                name="x", shape=[-1, num_steps], dtype='int64')
            y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
            init_hidden = fluid.layers.data(
                name="init_hidden", shape=[1], dtype='float32')
            init_cell = fluid.layers.data(
                name="init_cell", shape=[1], dtype='float32')

            static_loss, static_last_hidden, static_last_cell = ptb_model(
                x, y, init_hidden, init_cell)

            test_program = fluid.default_main_program().clone(for_test=True)

            add_1 = fluid.layers.fc(static_last_hidden,
                                    size=hidden_size,
                                    num_flatten_dims=2,
                                    bias_attr=False)

            sgd.minimize(static_loss)
            static_param_updated = dict()
            static_param_init = dict()

            out = exe.run(framework.default_startup_program())

            static_loss_value = None
            static_last_cell_value = None
            static_last_hidden_value = None
            for i in range(batch_num):
                x_data = np.arange(12).reshape(4, 3).astype('int64')
                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
                x_data = x_data.reshape((-1, num_steps, 1))
                y_data = y_data.reshape((-1, 1))
                init_hidden_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                init_cell_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                fetch_list = [static_loss, static_last_hidden, static_last_cell]
                out = exe.run(fluid.default_main_program(),
                              feed={
                                  "x": x_data,
                                  "y": y_data,
                                  "init_hidden": init_hidden_data,
                                  "init_cell": init_cell_data
                              },
                              fetch_list=fetch_list)
                static_loss_value = out[0]
                static_last_hidden_value = out[1]
                static_last_cell_value = out[2]

            # get value before save
            main_program = framework.default_main_program()
            base_map = {}
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    t = np.array(fluid.global_scope().find_var(var.name)
                                 .get_tensor())
                    # make sure all the paramerter or optimzier var have been update
                    self.assertTrue(np.sum(np.abs(t)) != 0)
                    base_map[var.name] = t

            fluid.save(main_program, "./test_1")

            # set var to zero
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    ten = fluid.global_scope().find_var(var.name).get_tensor()
                    ten.set(np.zeros_like(np.array(ten)), place)

                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    # make sure all the paramerter or optimzier var have been set to zero
                    self.assertTrue(np.sum(np.abs(new_t)) == 0)

            fluid.load(test_program, "./test_1", None)

            for var in test_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    print(var.name)
                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    base_t = base_map[var.name]
                    self.assertTrue(np.array_equal(new_t, base_t))


class TestSaveLoadSetStateDict(unittest.TestCase):
    def test_ptb_rnn_cpu_float32(self):
        seed = 90
        hidden_size = 10
        vocab_size = 1000
        num_layers = 1
        num_steps = 3
        init_scale = 0.1
        batch_size = 4
        batch_num = 200

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            ptb_model = PtbModel(
                "ptb_model",
                hidden_size=hidden_size,
                vocab_size=vocab_size,
                num_layers=num_layers,
                num_steps=num_steps,
                init_scale=init_scale)

            place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
            ) else fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            sgd = Adam(learning_rate=1e-3)
            x = fluid.layers.data(
                name="x", shape=[-1, num_steps], dtype='int64')
            y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
            init_hidden = fluid.layers.data(
                name="init_hidden", shape=[1], dtype='float32')
            init_cell = fluid.layers.data(
                name="init_cell", shape=[1], dtype='float32')

            static_loss, static_last_hidden, static_last_cell = ptb_model(
                x, y, init_hidden, init_cell)
            sgd.minimize(static_loss)
            static_param_updated = dict()
            static_param_init = dict()

            out = exe.run(framework.default_startup_program())

            static_loss_value = None
            static_last_cell_value = None
            static_last_hidden_value = None
            for i in range(batch_num):
                x_data = np.arange(12).reshape(4, 3).astype('int64')
                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
                x_data = x_data.reshape((-1, num_steps, 1))
                y_data = y_data.reshape((-1, 1))
                init_hidden_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                init_cell_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                fetch_list = [static_loss, static_last_hidden, static_last_cell]
                out = exe.run(fluid.default_main_program(),
                              feed={
                                  "x": x_data,
                                  "y": y_data,
                                  "init_hidden": init_hidden_data,
                                  "init_cell": init_cell_data
                              },
                              fetch_list=fetch_list)
                static_loss_value = out[0]
                static_last_hidden_value = out[1]
                static_last_cell_value = out[2]

            # get value before save
            main_program = framework.default_main_program()
            base_map = {}
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    t = np.array(fluid.global_scope().find_var(var.name)
                                 .get_tensor())
                    # make sure all the paramerter or optimzier var have been update
                    self.assertTrue(np.sum(np.abs(t)) != 0)
                    base_map[var.name] = t

            fluid.save(main_program, "./test_1")

            # set var to zero
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    ten = fluid.global_scope().find_var(var.name).get_tensor()
                    ten.set(np.zeros_like(np.array(ten)), place)

                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    # make sure all the paramerter or optimzier var have been set to zero
                    self.assertTrue(np.sum(np.abs(new_t)) == 0)

            fluid.load(main_program, "./test_1", exe)

            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    base_t = base_map[var.name]
                    self.assertTrue(np.array_equal(new_t, base_t))


class TestProgramStatePartial(unittest.TestCase):
    def test_ptb_rnn_cpu_float32(self):
        seed = 90
        hidden_size = 10
        vocab_size = 1000
        num_layers = 1
        num_steps = 3
        init_scale = 0.1
        batch_size = 4
        batch_num = 200

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            ptb_model = PtbModel(
                "ptb_model",
                hidden_size=hidden_size,
                vocab_size=vocab_size,
                num_layers=num_layers,
                num_steps=num_steps,
                init_scale=init_scale)

            place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
            ) else fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            sgd = Adam(learning_rate=1e-3)
            x = fluid.layers.data(
                name="x", shape=[-1, num_steps], dtype='int64')
            y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
            init_hidden = fluid.layers.data(
                name="init_hidden", shape=[1], dtype='float32')
            init_cell = fluid.layers.data(
                name="init_cell", shape=[1], dtype='float32')

            static_loss, static_last_hidden, static_last_cell = ptb_model(
                x, y, init_hidden, init_cell)

            test_program = fluid.default_main_program().clone(for_test=True)

            add_1 = fluid.layers.fc(static_last_hidden,
                                    size=hidden_size,
                                    num_flatten_dims=2,
                                    bias_attr=False)

            sgd.minimize(static_loss)
            static_param_updated = dict()
            static_param_init = dict()

            out = exe.run(framework.default_startup_program())

            static_loss_value = None
            static_last_cell_value = None
            static_last_hidden_value = None
            for i in range(batch_num):
                x_data = np.arange(12).reshape(4, 3).astype('int64')
                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
                x_data = x_data.reshape((-1, num_steps, 1))
                y_data = y_data.reshape((-1, 1))
                init_hidden_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                init_cell_data = np.zeros(
                    (num_layers, batch_size, hidden_size), dtype='float32')
                fetch_list = [static_loss, static_last_hidden, static_last_cell]
                out = exe.run(fluid.default_main_program(),
                              feed={
                                  "x": x_data,
                                  "y": y_data,
                                  "init_hidden": init_hidden_data,
                                  "init_cell": init_cell_data
                              },
                              fetch_list=fetch_list)
                static_loss_value = out[0]
                static_last_hidden_value = out[1]
                static_last_cell_value = out[2]

            # get value before save
            main_program = framework.default_main_program()
            base_map = {}
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    t = np.array(fluid.global_scope().find_var(var.name)
                                 .get_tensor())
                    # make sure all the paramerter or optimzier var have been update
                    self.assertTrue(np.sum(np.abs(t)) != 0)
                    base_map[var.name] = t

            fluid.save(main_program, "./test_1")

            # set var to zero
            for var in main_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    ten = fluid.global_scope().find_var(var.name).get_tensor()
                    ten.set(np.zeros_like(np.array(ten)), place)

                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    # make sure all the paramerter or optimzier var have been set to zero
                    self.assertTrue(np.sum(np.abs(new_t)) == 0)

            #fluid.load(test_program, "./test_1", None )
            program_state = fluid.load_program_state("./test_1")
            fluid.set_program_state(test_program, program_state)

            for var in test_program.list_vars():
                if isinstance(var, framework.Parameter) or var.persistable:
                    print(var.name)
                    new_t = np.array(fluid.global_scope().find_var(var.name)
                                     .get_tensor())
                    base_t = base_map[var.name]
                    self.assertTrue(np.array_equal(new_t, base_t))


class TestVariableInit(unittest.TestCase):
    def test_variable_init(self):

        x = fluid.data(name="x", shape=[10, 10], dtype='float32')
        y = fluid.layers.fc(x, 10)
        z = fluid.layers.fc(y, 10)

        place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
        ) else fluid.CUDAPlace(0)
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())

        fluid.save(fluid.default_main_program(), "./test_path")

        def set_var(var, ndarray):
            t = var.get_tensor()
            p = t._place()
            if p.is_cpu_place():
                place = paddle.fluid.CPUPlace()
            elif p.is_cuda_pinned_place():
                place = paddle.fluid.CUDAPinnedPlace()
            else:
                p = paddle.fluid.core.Place()
                p.set_place(t._place())
                place = paddle.fluid.CUDAPlace(p.gpu_device_id())

            t.set(ndarray, place)

        program = fluid.default_main_program()
        new_scope = fluid.core.Scope()

        place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
        ) else fluid.CUDAPlace(0)
        exe = fluid.Executor(place)
        parameter_list = list(
            filter(fluid.io.is_parameter, program.list_vars()))

        fluid.core._create_loaded_parameter(parameter_list, new_scope,
                                            exe._default_executor)
        parameter_file_name = "./test_path.pdparams"
        with open(parameter_file_name, 'rb') as f:
            load_dict = pickle.load(f)

        for v in parameter_list:
            assert v.name in load_dict, \
                "Can not find [{}] in model file [{}]".format(
                    v.name, parameter_file_name)
            new_v = new_scope.find_var(v.name)
            set_var(new_v, load_dict[v.name])

        opt_list = list(
            filter(fluid.io.is_belong_to_optimizer, program.list_vars()))

        fluid.core._create_loaded_parameter(opt_list, new_scope,
                                            exe._default_executor)
        opt_file_name = "./test_path.pdopt"
        with open(opt_file_name, 'rb') as f:
            load_dict = pickle.load(f)

        for v in opt_list:
            assert v.name in load_dict, \
                "Can not find [{}] in model file [{}]".format(
                    v.name, opt_file_name)

            new_v = new_scope.find_var(v.name)
            set_var(new_v, load_dict[v.name])

        base_map = {}
        for var in program.list_vars():
            if isinstance(var, framework.Parameter) or var.persistable:
                t = np.array(fluid.global_scope().find_var(var.name)
                             .get_tensor())
                # make sure all the paramerter or optimzier var have been update
                base_map[var.name] = t

        for var in program.list_vars():
            if isinstance(var, framework.Parameter) or var.persistable:
                new_t = np.array(new_scope.find_var(var.name).get_tensor())
                base_t = base_map[var.name]

                self.assertTrue(np.array_equal(new_t, base_t))


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