# Copyright (c) 2020 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 numpy as np

import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.layers import BeamSearchDecoder

from paddle.incubate.hapi.text import RNNCell, RNN, DynamicDecode
from paddle.incubate.hapi.model import Model
from paddle.incubate.hapi.loss import Loss


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

        filter_size = 3
        std = (2.0 / (filter_size**2 * in_ch))**0.5
        param_0 = fluid.ParamAttr(
            initializer=fluid.initializer.Normal(0.0, std))

        std = (2.0 / (filter_size**2 * out_ch))**0.5
        param_1 = fluid.ParamAttr(
            initializer=fluid.initializer.Normal(0.0, std))

        self.conv0 = fluid.dygraph.Conv2D(
            in_ch,
            out_ch,
            3,
            padding=1,
            param_attr=param_0,
            bias_attr=False,
            act=None,
            use_cudnn=use_cudnn)
        self.bn0 = fluid.dygraph.BatchNorm(out_ch, act=act)
        self.conv1 = fluid.dygraph.Conv2D(
            out_ch,
            out_ch,
            filter_size=3,
            padding=1,
            param_attr=param_1,
            bias_attr=False,
            act=None,
            use_cudnn=use_cudnn)
        self.bn1 = fluid.dygraph.BatchNorm(out_ch, act=act)

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

    def forward(self, inputs):
        out = self.conv0(inputs)
        out = self.bn0(out)
        out = self.conv1(out)
        out = self.bn1(out)
        if self.pool:
            out = self.pool(out)
        return out


class CNN(fluid.dygraph.Layer):
    def __init__(self, in_ch=1, is_test=False):
        super(CNN, self).__init__()
        self.conv_bn1 = ConvBNPool(in_ch, 16)
        self.conv_bn2 = ConvBNPool(16, 32)
        self.conv_bn3 = ConvBNPool(32, 64)
        self.conv_bn4 = ConvBNPool(64, 128, pool=False)

    def forward(self, inputs):
        conv = self.conv_bn1(inputs)
        conv = self.conv_bn2(conv)
        conv = self.conv_bn3(conv)
        conv = self.conv_bn4(conv)
        return conv


class GRUCell(RNNCell):
    def __init__(self,
                 input_size,
                 hidden_size,
                 param_attr=None,
                 bias_attr=None,
                 gate_activation='sigmoid',
                 candidate_activation='tanh',
                 origin_mode=False):
        super(GRUCell, self).__init__()
        self.hidden_size = hidden_size
        self.fc_layer = fluid.dygraph.Linear(
            input_size,
            hidden_size * 3,
            param_attr=param_attr,
            bias_attr=False)

        self.gru_unit = fluid.dygraph.GRUUnit(
            hidden_size * 3,
            param_attr=param_attr,
            bias_attr=bias_attr,
            activation=candidate_activation,
            gate_activation=gate_activation,
            origin_mode=origin_mode)

    def forward(self, inputs, states):
        # step_outputs, new_states = cell(step_inputs, states)
        # for GRUCell, `step_outputs` and `new_states` both are hidden
        x = self.fc_layer(inputs)
        hidden, _, _ = self.gru_unit(x, states)
        return hidden, hidden

    @property
    def state_shape(self):
        return [self.hidden_size]


class Encoder(fluid.dygraph.Layer):
    def __init__(
            self,
            in_channel=1,
            rnn_hidden_size=200,
            decoder_size=128,
            is_test=False, ):
        super(Encoder, self).__init__()
        self.rnn_hidden_size = rnn_hidden_size

        self.backbone = CNN(in_ch=in_channel, is_test=is_test)

        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)
        self.gru_fwd = RNN(cell=GRUCell(
            input_size=128 * 6,
            hidden_size=rnn_hidden_size,
            param_attr=para_attr,
            bias_attr=bias_attr,
            candidate_activation='relu'),
                           is_reverse=False,
                           time_major=False)
        self.gru_bwd = RNN(cell=GRUCell(
            input_size=128 * 6,
            hidden_size=rnn_hidden_size,
            param_attr=para_attr,
            bias_attr=bias_attr,
            candidate_activation='relu'),
                           is_reverse=True,
                           time_major=False)
        self.encoded_proj_fc = fluid.dygraph.Linear(
            rnn_hidden_size * 2, decoder_size, bias_attr=False)

    def forward(self, inputs):
        conv_features = self.backbone(inputs)
        conv_features = fluid.layers.transpose(
            conv_features, perm=[0, 3, 1, 2])

        n, w, c, h = conv_features.shape
        seq_feature = fluid.layers.reshape(conv_features, [0, -1, c * h])

        gru_fwd, _ = self.gru_fwd(seq_feature)
        gru_bwd, _ = self.gru_bwd(seq_feature)

        encoded_vector = fluid.layers.concat(input=[gru_fwd, gru_bwd], axis=2)
        encoded_proj = self.encoded_proj_fc(encoded_vector)
        return gru_bwd, encoded_vector, encoded_proj


class Attention(fluid.dygraph.Layer):
    """
    Neural Machine Translation by Jointly Learning to Align and Translate.
    https://arxiv.org/abs/1409.0473
    """

    def __init__(self, decoder_size):
        super(Attention, self).__init__()
        self.fc1 = fluid.dygraph.Linear(
            decoder_size, decoder_size, bias_attr=False)
        self.fc2 = fluid.dygraph.Linear(decoder_size, 1, bias_attr=False)

    def forward(self, encoder_vec, encoder_proj, decoder_state):
        # alignment model, single-layer multilayer perceptron
        decoder_state = self.fc1(decoder_state)
        decoder_state = fluid.layers.unsqueeze(decoder_state, [1])

        e = fluid.layers.elementwise_add(encoder_proj, decoder_state)
        e = fluid.layers.tanh(e)

        att_scores = self.fc2(e)
        att_scores = fluid.layers.squeeze(att_scores, [2])
        att_scores = fluid.layers.softmax(att_scores)

        context = fluid.layers.elementwise_mul(
            x=encoder_vec, y=att_scores, axis=0)
        context = fluid.layers.reduce_sum(context, dim=1)
        return context


class DecoderCell(RNNCell):
    def __init__(self, encoder_size=200, decoder_size=128):
        super(DecoderCell, self).__init__()
        self.attention = Attention(decoder_size)
        self.gru_cell = GRUCell(
            input_size=encoder_size * 2 + decoder_size,
            hidden_size=decoder_size)

    def forward(self, current_word, states, encoder_vec, encoder_proj):
        context = self.attention(encoder_vec, encoder_proj, states)
        decoder_inputs = fluid.layers.concat([current_word, context], axis=1)
        hidden, _ = self.gru_cell(decoder_inputs, states)
        return hidden, hidden


class Decoder(fluid.dygraph.Layer):
    def __init__(self, num_classes, emb_dim, encoder_size, decoder_size):
        super(Decoder, self).__init__()
        self.decoder_attention = RNN(DecoderCell(encoder_size, decoder_size))
        self.fc = fluid.dygraph.Linear(
            decoder_size, num_classes + 2, act='softmax')

    def forward(self, target, initial_states, encoder_vec, encoder_proj):
        out, _ = self.decoder_attention(
            target,
            initial_states=initial_states,
            encoder_vec=encoder_vec,
            encoder_proj=encoder_proj)
        pred = self.fc(out)
        return pred


class Seq2SeqAttModel(Model):
    def __init__(
            self,
            in_channle=1,
            encoder_size=200,
            decoder_size=128,
            emb_dim=128,
            num_classes=None, ):
        super(Seq2SeqAttModel, self).__init__()
        self.encoder = Encoder(in_channle, encoder_size, decoder_size)
        self.fc = fluid.dygraph.Linear(
            input_dim=encoder_size,
            output_dim=decoder_size,
            bias_attr=False,
            act='relu')
        self.embedding = fluid.dygraph.Embedding(
            [num_classes + 2, emb_dim], dtype='float32')
        self.decoder = Decoder(num_classes, emb_dim, encoder_size,
                               decoder_size)

    def forward(self, inputs, target):
        gru_backward, encoded_vector, encoded_proj = self.encoder(inputs)
        decoder_boot = self.fc(gru_backward[:, 0])
        trg_embedding = self.embedding(target)
        prediction = self.decoder(trg_embedding, decoder_boot, encoded_vector,
                                  encoded_proj)
        return prediction


class Seq2SeqAttInferModel(Seq2SeqAttModel):
    def __init__(
            self,
            in_channle=1,
            encoder_size=200,
            decoder_size=128,
            emb_dim=128,
            num_classes=None,
            beam_size=0,
            bos_id=0,
            eos_id=1,
            max_out_len=20, ):
        super(Seq2SeqAttInferModel, self).__init__(
            in_channle, encoder_size, decoder_size, emb_dim, num_classes)
        self.beam_size = beam_size
        # dynamic decoder for inference
        decoder = BeamSearchDecoder(
            self.decoder.decoder_attention.cell,
            start_token=bos_id,
            end_token=eos_id,
            beam_size=beam_size,
            embedding_fn=self.embedding,
            output_fn=self.decoder.fc)
        self.infer_decoder = DynamicDecode(
            decoder, max_step_num=max_out_len, is_test=True)

    def forward(self, inputs, *args):
        gru_backward, encoded_vector, encoded_proj = self.encoder(inputs)
        decoder_boot = self.fc(gru_backward[:, 0])

        if self.beam_size:
            # Tile the batch dimension with beam_size
            encoded_vector = BeamSearchDecoder.tile_beam_merge_with_batch(
                encoded_vector, self.beam_size)
            encoded_proj = BeamSearchDecoder.tile_beam_merge_with_batch(
                encoded_proj, self.beam_size)
        # dynamic decoding with beam search
        rs, _ = self.infer_decoder(
            inits=decoder_boot,
            encoder_vec=encoded_vector,
            encoder_proj=encoded_proj)
        return rs


class WeightCrossEntropy(Loss):
    def __init__(self):
        super(WeightCrossEntropy, self).__init__(average=False)

    def forward(self, outputs, labels):
        predict, (label, mask) = outputs[0], labels
        loss = layers.cross_entropy(predict, label=label)
        loss = layers.elementwise_mul(loss, mask, axis=0)
        loss = layers.reduce_sum(loss)
        return loss
