# 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 numpy as np
import six
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC, BatchNorm, Embedding, GRUUnit
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope


class Config(object):
    '''
    config for training
    '''
    # decoder size for decoder stage
    decoder_size = 128
    # size for word embedding
    word_vector_dim = 128
    # max length for label padding
    max_length = 5
    # optimizer setting
    LR = 1.0
    learning_rate_decay = None

    # batch size to train
    batch_size = 16
    # class number to classify
    num_classes = 481

    use_gpu = False
    # special label for start and end
    SOS = 0
    EOS = 1
    # settings for ctc data, not use in unittest
    DATA_DIR_NAME = "./dataset/ctc_data/data"
    TRAIN_DATA_DIR_NAME = "train_images"
    TRAIN_LIST_FILE_NAME = "train.list"

    # data shape for input image
    DATA_SHAPE = [1, 48, 384]


class ConvBNPool(fluid.dygraph.Layer):
    def __init__(self,
                 name_scope,
                 group,
                 out_ch,
                 channels,
                 act="relu",
                 is_test=False,
                 pool=True,
                 use_cudnn=True):
        super(ConvBNPool, self).__init__(name_scope)
        self.group = group
        self.pool = pool

        filter_size = 3
        conv_std_0 = (2.0 / (filter_size**2 * channels[0]))**0.5
        conv_param_0 = fluid.ParamAttr(
            initializer=fluid.initializer.Normal(0.0, conv_std_0))

        conv_std_1 = (2.0 / (filter_size**2 * channels[1]))**0.5
        conv_param_1 = fluid.ParamAttr(
            initializer=fluid.initializer.Normal(0.0, conv_std_1))

        self.conv_0_layer = Conv2D(
            self.full_name(),
            out_ch[0],
            3,
            padding=1,
            param_attr=conv_param_0,
            bias_attr=False,
            act=None,
            use_cudnn=use_cudnn)
        self.bn_0_layer = BatchNorm(
            self.full_name(), out_ch[0], act=act, is_test=is_test)
        self.conv_1_layer = Conv2D(
            self.full_name(),
            num_filters=out_ch[1],
            filter_size=3,
            padding=1,
            param_attr=conv_param_1,
            bias_attr=False,
            act=None,
            use_cudnn=use_cudnn)
        self.bn_1_layer = BatchNorm(
            self.full_name(), out_ch[1], act=act, is_test=is_test)

        if self.pool:
            self.pool_layer = Pool2D(
                self.full_name(),
                pool_size=2,
                pool_type='max',
                pool_stride=2,
                use_cudnn=use_cudnn,
                ceil_mode=True)

    def forward(self, inputs):
        conv_0 = self.conv_0_layer(inputs)
        bn_0 = self.bn_0_layer(conv_0)
        conv_1 = self.conv_1_layer(bn_0)
        bn_1 = self.bn_1_layer(conv_1)
        if self.pool:
            bn_pool = self.pool_layer(bn_1)
            return bn_pool
        return bn_1


class OCRConv(fluid.dygraph.Layer):
    def __init__(self, name_scope, is_test=False, use_cudnn=True):
        super(OCRConv, self).__init__(name_scope)
        self.conv_bn_pool_1 = ConvBNPool(
            self.full_name(),
            2, [16, 16], [1, 16],
            is_test=is_test,
            use_cudnn=use_cudnn)
        self.conv_bn_pool_2 = ConvBNPool(
            self.full_name(),
            2, [32, 32], [16, 32],
            is_test=is_test,
            use_cudnn=use_cudnn)
        self.conv_bn_pool_3 = ConvBNPool(
            self.full_name(),
            2, [64, 64], [32, 64],
            is_test=is_test,
            use_cudnn=use_cudnn)
        self.conv_bn_pool_4 = ConvBNPool(
            self.full_name(),
            2, [128, 128], [64, 128],
            is_test=is_test,
            pool=False,
            use_cudnn=use_cudnn)

    def forward(self, inputs):
        inputs_1 = self.conv_bn_pool_1(inputs)
        inputs_2 = self.conv_bn_pool_2(inputs_1)
        inputs_3 = self.conv_bn_pool_3(inputs_2)
        inputs_4 = self.conv_bn_pool_4(inputs_3)

        return inputs_4


class DynamicGRU(fluid.dygraph.Layer):
    def __init__(self,
                 scope_name,
                 size,
                 param_attr=None,
                 bias_attr=None,
                 is_reverse=False,
                 gate_activation='sigmoid',
                 candidate_activation='tanh',
                 h_0=None,
                 origin_mode=False):
        super(DynamicGRU, self).__init__(scope_name)

        self.gru_unit = GRUUnit(
            self.full_name(),
            size * 3,
            param_attr=param_attr,
            bias_attr=bias_attr,
            activation=candidate_activation,
            gate_activation=gate_activation,
            origin_mode=origin_mode)

        self.h_0 = h_0
        self.is_reverse = is_reverse

    def forward(self, inputs):
        hidden = self.h_0
        res = []
        for i in range(inputs.shape[1]):
            if self.is_reverse:
                i = inputs.shape[1] - 1 - i
            input_ = fluid.layers.slice(
                inputs, axes=[1], starts=[i], ends=[i + 1])
            input_ = fluid.layers.reshape(
                input_, [-1, input_.shape[2]], inplace=False)
            hidden, reset, gate = self.gru_unit(input_, hidden)
            hidden_ = fluid.layers.reshape(
                hidden, [-1, 1, hidden.shape[1]], inplace=False)
            if self.is_reverse:
                res = [hidden_] + res
            else:
                res.append(hidden_)
        res = fluid.layers.concat(res, axis=1)
        return res


class EncoderNet(fluid.dygraph.Layer):
    def __init__(self,
                 scope_name,
                 rnn_hidden_size=200,
                 is_test=False,
                 use_cudnn=True):
        super(EncoderNet, self).__init__(scope_name)
        self.rnn_hidden_size = rnn_hidden_size
        para_attr = fluid.ParamAttr(initializer=fluid.initializer.Normal(0.0,
                                                                         0.02))
        bias_attr = fluid.ParamAttr(
            initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)
        if fluid.framework.in_dygraph_mode():
            h_0 = np.zeros(
                (Config.batch_size, rnn_hidden_size), dtype="float32")
            h_0 = to_variable(h_0)
        else:
            h_0 = fluid.layers.fill_constant(
                shape=[Config.batch_size, rnn_hidden_size],
                dtype='float32',
                value=0)
        self.ocr_convs = OCRConv(
            self.full_name(), is_test=is_test, use_cudnn=use_cudnn)

        self.fc_1_layer = FC(self.full_name(),
                             rnn_hidden_size * 3,
                             param_attr=para_attr,
                             bias_attr=False,
                             num_flatten_dims=2)
        self.fc_2_layer = FC(self.full_name(),
                             rnn_hidden_size * 3,
                             param_attr=para_attr,
                             bias_attr=False,
                             num_flatten_dims=2)
        self.gru_forward_layer = DynamicGRU(
            self.full_name(),
            size=rnn_hidden_size,
            h_0=h_0,
            param_attr=para_attr,
            bias_attr=bias_attr,
            candidate_activation='relu')
        self.gru_backward_layer = DynamicGRU(
            self.full_name(),
            size=rnn_hidden_size,
            h_0=h_0,
            param_attr=para_attr,
            bias_attr=bias_attr,
            candidate_activation='relu',
            is_reverse=True)

        self.encoded_proj_fc = FC(self.full_name(),
                                  Config.decoder_size,
                                  bias_attr=False,
                                  num_flatten_dims=2)

    def forward(self, inputs):
        conv_features = self.ocr_convs(inputs)
        #sliced_feature = fluid.layers.im2sequence(
        #    input=conv_features,
        #    stride=[1, 1],
        #    filter_size=[conv_features.shape[2], 1])

        transpose_conv_features = fluid.layers.transpose(
            conv_features, perm=[0, 3, 1, 2])

        sliced_feature = fluid.layers.reshape(
            transpose_conv_features, [
                -1, 48, transpose_conv_features.shape[2] *
                transpose_conv_features.shape[3]
            ],
            inplace=False)
        fc_1 = self.fc_1_layer(sliced_feature)
        fc_2 = self.fc_2_layer(sliced_feature)
        gru_forward = self.gru_forward_layer(fc_1)

        gru_backward = self.gru_backward_layer(fc_2)

        encoded_vector = fluid.layers.concat(
            input=[gru_forward, gru_backward], axis=2)

        encoded_proj = self.encoded_proj_fc(encoded_vector)

        return gru_backward, encoded_vector, encoded_proj


class SimpleAttention(fluid.dygraph.Layer):
    def __init__(self, scope_name, decoder_size):
        super(SimpleAttention, self).__init__(scope_name)

        self.fc_1 = FC(self.full_name(),
                       decoder_size,
                       act=None,
                       bias_attr=False)
        self.fc_2 = FC(self.full_name(),
                       1,
                       num_flatten_dims=2,
                       act=None,
                       bias_attr=False)

    def forward(self, encoder_vec, encoder_proj, decoder_state):

        decoder_state_fc = self.fc_1(decoder_state)
        decoder_state_proj_reshape = fluid.layers.reshape(
            decoder_state_fc, [-1, 1, decoder_state_fc.shape[1]], inplace=False)
        decoder_state_expand = fluid.layers.expand(
            decoder_state_proj_reshape, [1, encoder_proj.shape[1], 1])
        concated = fluid.layers.elementwise_add(encoder_proj,
                                                decoder_state_expand)
        concated = fluid.layers.tanh(x=concated)
        attention_weight = self.fc_2(concated)

        weights_reshape = fluid.layers.reshape(
            x=attention_weight,
            shape=[attention_weight.shape[0], attention_weight.shape[1]],
            inplace=False)

        weights_reshape = fluid.layers.softmax(weights_reshape)
        scaled = fluid.layers.elementwise_mul(
            x=encoder_vec, y=weights_reshape, axis=0)
        context = fluid.layers.reduce_sum(scaled, dim=1)

        return context


class GRUDecoderWithAttention(fluid.dygraph.Layer):
    def __init__(self, scope_name, decoder_size, num_classes):
        super(GRUDecoderWithAttention, self).__init__(scope_name)
        self.simple_attention = SimpleAttention(self.full_name(), decoder_size)

        self.fc_1_layer = FC(self.full_name(),
                             size=decoder_size * 3,
                             bias_attr=False)
        self.fc_2_layer = FC(self.full_name(),
                             size=decoder_size * 3,
                             bias_attr=False)
        self.gru_unit = GRUUnit(
            self.full_name(),
            size=decoder_size * 3,
            param_attr=None,
            bias_attr=None)
        self.out_layer = FC(self.full_name(),
                            size=num_classes + 2,
                            bias_attr=None,
                            act='softmax')

        self.decoder_size = decoder_size

    def forward(self, target_embedding, encoder_vec, encoder_proj,
                decoder_boot):
        res = []
        hidden_mem = decoder_boot
        for i in range(target_embedding.shape[1]):
            current_word = fluid.layers.slice(
                target_embedding, axes=[1], starts=[i], ends=[i + 1])
            current_word = fluid.layers.reshape(
                current_word, [-1, current_word.shape[2]], inplace=False)

            context = self.simple_attention(encoder_vec, encoder_proj,
                                            hidden_mem)
            fc_1 = self.fc_1_layer(context)
            fc_2 = self.fc_2_layer(current_word)
            decoder_inputs = fluid.layers.elementwise_add(x=fc_1, y=fc_2)

            h, _, _ = self.gru_unit(decoder_inputs, hidden_mem)
            hidden_mem = h
            out = self.out_layer(h)
            res.append(out)

        res1 = fluid.layers.concat(res, axis=1)

        return res1


class OCRAttention(fluid.dygraph.Layer):
    def __init__(self, scope_name):
        super(OCRAttention, self).__init__(scope_name)
        self.encoder_net = EncoderNet(self.full_name())
        self.fc = FC(self.full_name(),
                     size=Config.decoder_size,
                     bias_attr=False,
                     act='relu')
        self.embedding = Embedding(
            self.full_name(), [Config.num_classes + 2, Config.word_vector_dim],
            dtype='float32')
        self.gru_decoder_with_attention = GRUDecoderWithAttention(
            self.full_name(), Config.decoder_size, Config.num_classes)

    def forward(self, inputs, label_in):
        gru_backward, encoded_vector, encoded_proj = self.encoder_net(inputs)
        backward_first = fluid.layers.slice(
            gru_backward, axes=[1], starts=[0], ends=[1])
        backward_first = fluid.layers.reshape(
            backward_first, [-1, backward_first.shape[2]], inplace=False)
        decoder_boot = self.fc(backward_first)
        label_in = fluid.layers.reshape(label_in, [-1], inplace=False)
        trg_embedding = self.embedding(label_in)

        trg_embedding = fluid.layers.reshape(
            trg_embedding, [-1, Config.max_length, trg_embedding.shape[1]],
            inplace=False)

        prediction = self.gru_decoder_with_attention(
            trg_embedding, encoded_vector, encoded_proj, decoder_boot)

        return prediction


class TestDygraphOCRAttention(unittest.TestCase):
    def test_while_op(self):
        seed = 90
        epoch_num = 2
        if core.is_compiled_with_cuda():
            batch_num = 20
        else:
            print("in CPU")
            batch_num = 2
        np.random.seed = seed
        image_np = np.random.randn(Config.batch_size, Config.DATA_SHAPE[0],
                                   Config.DATA_SHAPE[1],
                                   Config.DATA_SHAPE[2]).astype('float32')
        label_in_np = np.arange(
            0, Config.max_length,
            dtype='int64').reshape([1, Config.max_length])
        for i in range(2, Config.batch_size + 1):
            label_in_np = np.vstack((label_in_np, np.arange(
                (i - 1) * Config.max_length,
                i * Config.max_length,
                dtype='int64').reshape([1, Config.max_length])))

        label_out_np = np.arange(
            0, Config.max_length,
            dtype='int64').reshape([1, Config.max_length])
        for i in range(2, Config.batch_size + 1):
            label_out_np = np.vstack((label_out_np, np.arange(
                (i - 1) * Config.max_length,
                i * Config.max_length,
                dtype='int64').reshape([1, Config.max_length])))

        with fluid.dygraph.guard():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            backward_strategy = fluid.dygraph.BackwardStrategy()
            backward_strategy.sort_sum_gradient = True
            ocr_attention = OCRAttention("ocr_attention")

            if Config.learning_rate_decay == "piecewise_decay":
                learning_rate = fluid.layers.piecewise_decay(
                    [50000], [Config.LR, Config.LR * 0.01])
            else:
                learning_rate = Config.LR
            optimizer = fluid.optimizer.SGD(learning_rate=0.001)
            dy_param_init_value = {}
            for param in ocr_attention.parameters():
                dy_param_init_value[param.name] = param.numpy()
            for epoch in range(epoch_num):
                for batch_id in range(batch_num):
                    label_in = to_variable(label_in_np)
                    label_out = to_variable(label_out_np)
                    label_out.stop_gradient = True
                    img = to_variable(image_np)
                    dy_prediction = ocr_attention(img, label_in)
                    label_out = fluid.layers.reshape(
                        label_out, [-1, 1], inplace=False)
                    dy_prediction = fluid.layers.reshape(
                        dy_prediction, [label_out.shape[0], -1], inplace=False)
                    loss = fluid.layers.cross_entropy(
                        input=dy_prediction, label=label_out)
                    avg_loss = fluid.layers.reduce_sum(loss)

                    dy_out = avg_loss.numpy()

                    if epoch == 0 and batch_id == 0:
                        for param in ocr_attention.parameters():
                            if param.name not in dy_param_init_value:
                                dy_param_init_value[param.name] = param.numpy()
                    avg_loss.backward(backward_strategy)
                    dy_grad_value = {}
                    for param in ocr_attention.parameters():
                        if param.trainable:
                            np_array = np.array(param._grad_ivar().value()
                                                .get_tensor())
                            dy_grad_value[param.name + core.grad_var_suffix(
                            )] = np_array

                    optimizer.minimize(avg_loss)
                    ocr_attention.clear_gradients()
                    dy_param_value = {}
                    for param in ocr_attention.parameters():
                        dy_param_value[param.name] = param.numpy()

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            # print("static start")
            exe = fluid.Executor(fluid.CPUPlace(
            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
            ocr_attention = OCRAttention("ocr_attention")

            if Config.learning_rate_decay == "piecewise_decay":
                learning_rate = fluid.layers.piecewise_decay(
                    [50000], [Config.LR, Config.LR * 0.01])
            else:
                learning_rate = Config.LR

            optimizer = fluid.optimizer.SGD(learning_rate=0.001)

            images = fluid.layers.data(
                name='pixel', shape=Config.DATA_SHAPE, dtype='float32')
            static_label_in = fluid.layers.data(
                name='label_in', shape=[1], dtype='int64', lod_level=0)
            static_label_out = fluid.layers.data(
                name='label_out', shape=[1], dtype='int64', lod_level=0)
            static_label_out.stop_gradient = True
            static_label_out.trainable = False

            static_prediction = ocr_attention(images, static_label_in)

            static_prediction = fluid.layers.reshape(
                static_prediction, shape=[-1, Config.num_classes + 2])

            cost = fluid.layers.cross_entropy(
                input=static_prediction, label=static_label_out)
            static_avg_loss = fluid.layers.reduce_sum(cost)
            # param_grad_list = fluid.backward.append_backward(static_avg_loss)
            optimizer.minimize(static_avg_loss)

            static_param_init_value = {}
            static_param_name_list = []
            static_grad_name_list = []
            for param in ocr_attention.parameters():
                static_param_name_list.append(param.name)
                if param.trainable:
                    static_grad_name_list.append(param.name +
                                                 core.grad_var_suffix())

            out = exe.run(fluid.default_startup_program(),
                          fetch_list=static_param_name_list)

            for i in range(len(static_param_name_list)):
                static_param_init_value[static_param_name_list[i]] = out[i]

            fetch_list = [static_avg_loss.name]
            fetch_list.extend(static_param_name_list)
            fetch_list.extend(static_grad_name_list)
            for epoch in range(epoch_num):
                for batch_id in range(batch_num):
                    static_label_in = label_in_np
                    static_label_out = label_out_np
                    static_label_out = static_label_out.reshape((-1, 1))
                    out = exe.run(fluid.default_main_program(),
                                  feed={
                                      "pixel": image_np,
                                      "label_in": static_label_in,
                                      "label_out": static_label_out
                                  },
                                  fetch_list=fetch_list)
                    static_param_value = {}
                    static_grad_value = {}
                    static_out = out[0]
                    # static_test_grad = out[1]
                    for i in range(1, len(static_param_name_list) + 1):
                        static_param_value[static_param_name_list[i - 1]] = out[
                            i]
                    grad_start_pos = len(static_param_name_list) + 1
                    for i in range(grad_start_pos,
                                   len(static_grad_name_list) + grad_start_pos):
                        static_grad_value[static_grad_name_list[
                            i - grad_start_pos]] = out[i]

        self.assertTrue(np.array_equal(static_out, dy_out))

        for key, value in six.iteritems(static_param_init_value):
            self.assertTrue(np.array_equal(value, dy_param_init_value[key]))

        for key, value in six.iteritems(static_param_value):
            self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-20))


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