# 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 transformer_dygraph_model import MultiHeadAttention, PrePostProcessLayer

import paddle
from paddle import base
from paddle.nn import Layer, Linear


class PositionwiseFeedForwardLayer(Layer):
    def __init__(
        self,
        hidden_act,
        d_inner_hid,
        d_model,
        dropout_rate,
        param_initializer=None,
        name="",
    ):
        super().__init__()

        self._i2h = Linear(
            in_features=d_model,
            out_features=d_inner_hid,
            weight_attr=base.ParamAttr(
                name=name + '_fc_0.w_0', initializer=param_initializer
            ),
            bias_attr=name + '_fc_0.b_0',
        )

        self._h2o = Linear(
            in_features=d_inner_hid,
            out_features=d_model,
            weight_attr=base.ParamAttr(
                name=name + '_fc_1.w_0', initializer=param_initializer
            ),
            bias_attr=name + '_fc_1.b_0',
        )

        self._dropout_rate = dropout_rate

    def forward(self, x):
        hidden = self._i2h(x)
        if self._dropout_rate:
            hidden = paddle.nn.functional.dropout(hidden, p=self._dropout_rate)
        out = self._h2o(hidden)
        return out


class EncoderSubLayer(Layer):
    def __init__(
        self,
        hidden_act,
        n_head,
        d_key,
        d_value,
        d_model,
        d_inner_hid,
        prepostprocess_dropout,
        attention_dropout,
        relu_dropout,
        preprocess_cmd="n",
        postprocess_cmd="da",
        param_initializer=None,
        name="",
    ):
        super().__init__()
        self.name = name
        self._preprocess_cmd = preprocess_cmd
        self._postprocess_cmd = postprocess_cmd
        self._prepostprocess_dropout = prepostprocess_dropout
        self._preprocess_layer = PrePostProcessLayer(
            self._preprocess_cmd, d_model, prepostprocess_dropout
        )
        self._multihead_attention_layer = MultiHeadAttention(
            d_key,
            d_value,
            d_model,
            n_head,
            attention_dropout,
            param_initializer,
        )
        self._postprocess_layer = PrePostProcessLayer(
            self._postprocess_cmd, d_model, self._prepostprocess_dropout
        )
        self._preprocess_layer2 = PrePostProcessLayer(
            self._preprocess_cmd, d_model, self._prepostprocess_dropout
        )
        self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
            hidden_act,
            d_inner_hid,
            d_model,
            relu_dropout,
            param_initializer,
            name=name + "_ffn",
        )
        self._postprocess_layer2 = PrePostProcessLayer(
            self._postprocess_cmd, d_model, self._prepostprocess_dropout
        )

    def forward(self, enc_input, attn_bias):
        pre_process_multihead = self._preprocess_layer(enc_input)
        attn_output = self._multihead_attention_layer(
            pre_process_multihead, None, None, attn_bias
        )
        attn_output = self._postprocess_layer(attn_output, enc_input)
        pre_process2_output = self._preprocess_layer2(attn_output)
        ffd_output = self._positionwise_feed_forward(pre_process2_output)
        return self._postprocess_layer2(ffd_output, attn_output)


class EncoderLayer(Layer):
    def __init__(
        self,
        hidden_act,
        n_layer,
        n_head,
        d_key,
        d_value,
        d_model,
        d_inner_hid,
        prepostprocess_dropout,
        attention_dropout,
        relu_dropout,
        preprocess_cmd="n",
        postprocess_cmd="da",
        param_initializer=None,
        name="",
    ):
        super().__init__()
        self._preprocess_cmd = preprocess_cmd
        self._encoder_sublayers = []
        self._prepostprocess_dropout = prepostprocess_dropout
        self._n_layer = n_layer
        self._hidden_act = hidden_act
        self._preprocess_layer = PrePostProcessLayer(
            self._preprocess_cmd, 3, self._prepostprocess_dropout
        )

        for i in range(n_layer):
            self._encoder_sublayers.append(
                self.add_sublayer(
                    f'esl_{i}',
                    EncoderSubLayer(
                        hidden_act,
                        n_head,
                        d_key,
                        d_value,
                        d_model,
                        d_inner_hid,
                        prepostprocess_dropout,
                        attention_dropout,
                        relu_dropout,
                        preprocess_cmd,
                        postprocess_cmd,
                        param_initializer,
                        name=name + '_layer_' + str(i),
                    ),
                )
            )

    def forward(self, enc_input, attn_bias):
        for i in range(self._n_layer):
            enc_output = self._encoder_sublayers[i](enc_input, attn_bias)
            enc_input = enc_output

        return self._preprocess_layer(enc_output)


class BertModelLayer(Layer):
    def __init__(self, config, return_pooled_out=True, use_fp16=False):
        super().__init__()

        self._emb_size = config['hidden_size']
        self._n_layer = config['num_hidden_layers']
        self._n_head = config['num_attention_heads']
        self._voc_size = config['vocab_size']
        self._max_position_seq_len = config['max_position_embeddings']
        self._sent_types = config['type_vocab_size']
        self._hidden_act = config['hidden_act']
        self._prepostprocess_dropout = config['hidden_dropout_prob']
        self._attention_dropout = config['attention_probs_dropout_prob']
        self.return_pooled_out = return_pooled_out

        self._word_emb_name = "word_embedding"
        self._pos_emb_name = "pos_embedding"
        self._sent_emb_name = "sent_embedding"
        self._dtype = "float16" if use_fp16 else "float32"

        self._param_initializer = paddle.nn.initializer.TruncatedNormal(
            std=config['initializer_range']
        )
        paddle.set_default_dtype(self._dtype)
        self._src_emb = paddle.nn.Embedding(
            self._voc_size,
            self._emb_size,
            weight_attr=base.ParamAttr(
                name=self._word_emb_name, initializer=self._param_initializer
            ),
        )

        self._pos_emb = paddle.nn.Embedding(
            self._max_position_seq_len,
            self._emb_size,
            weight_attr=base.ParamAttr(
                name=self._pos_emb_name, initializer=self._param_initializer
            ),
        )

        self._sent_emb = paddle.nn.Embedding(
            self._sent_types,
            self._emb_size,
            weight_attr=base.ParamAttr(
                name=self._sent_emb_name, initializer=self._param_initializer
            ),
        )

        self.pooled_fc = Linear(
            in_features=self._emb_size,
            out_features=self._emb_size,
            weight_attr=base.ParamAttr(
                name="pooled_fc.w_0", initializer=self._param_initializer
            ),
            bias_attr="pooled_fc.b_0",
        )

        self.pre_process_layer = PrePostProcessLayer(
            "nd", self._emb_size, self._prepostprocess_dropout
        )

        self._encoder = EncoderLayer(
            hidden_act=self._hidden_act,
            n_layer=self._n_layer,
            n_head=self._n_head,
            d_key=self._emb_size // self._n_head,
            d_value=self._emb_size // self._n_head,
            d_model=self._emb_size,
            d_inner_hid=self._emb_size * 4,
            prepostprocess_dropout=self._prepostprocess_dropout,
            attention_dropout=self._attention_dropout,
            relu_dropout=0,
            preprocess_cmd="",
            postprocess_cmd="dan",
            param_initializer=self._param_initializer,
        )

    def forward(self, src_ids, position_ids, sentence_ids, input_mask):
        src_emb = self._src_emb(src_ids)
        pos_emb = self._pos_emb(position_ids)
        sent_emb = self._sent_emb(sentence_ids)

        emb_out = src_emb + pos_emb
        emb_out = emb_out + sent_emb

        emb_out = self.pre_process_layer(emb_out)

        self_attn_mask = paddle.matmul(
            x=input_mask, y=input_mask, transpose_y=True
        )
        self_attn_mask = paddle.scale(
            x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False
        )
        n_head_self_attn_mask = paddle.stack(
            x=[self_attn_mask] * self._n_head, axis=1
        )
        n_head_self_attn_mask.stop_gradient = True

        enc_output = self._encoder(emb_out, n_head_self_attn_mask)

        # TODO(zhhsplendid): uncomment this in next PR which we support various
        # length of early return
        #
        # if not self.return_pooled_out:
        #    return enc_output
        next_sent_feat = paddle.slice(
            input=enc_output, axes=[1], starts=[0], ends=[1]
        )
        next_sent_feat = self.pooled_fc(next_sent_feat)

        next_sent_feat = paddle.tanh(next_sent_feat)
        next_sent_feat = paddle.reshape(
            next_sent_feat, shape=[-1, self._emb_size]
        )

        return enc_output, next_sent_feat


class PretrainModelLayer(Layer):
    def __init__(
        self,
        config,
        return_pooled_out=True,
        weight_sharing=False,
        use_fp16=False,
    ):
        super().__init__()
        self.config = config
        self._voc_size = config['vocab_size']
        self._emb_size = config['hidden_size']
        self._hidden_act = config['hidden_act']
        self._prepostprocess_dropout = config['hidden_dropout_prob']

        self._word_emb_name = "word_embedding"
        self._param_initializer = paddle.nn.initializer.TruncatedNormal(
            std=config['initializer_range']
        )
        self._weight_sharing = weight_sharing
        self.use_fp16 = use_fp16
        self._dtype = "float16" if use_fp16 else "float32"

        self.bert_layer = BertModelLayer(
            config=self.config, return_pooled_out=True, use_fp16=self.use_fp16
        )

        self.pre_process_layer = PrePostProcessLayer(
            "n", self._emb_size, self._prepostprocess_dropout
        )

        self.pooled_fc = Linear(
            in_features=self._emb_size,
            out_features=self._emb_size,
            weight_attr=base.ParamAttr(
                name="mask_lm_trans_fc.w_0", initializer=self._param_initializer
            ),
            bias_attr="mask_lm_trans_fc.b_0",
        )

        self.mask_lm_out_bias_attr = base.ParamAttr(
            name="mask_lm_out_fc.b_0",
            initializer=paddle.nn.initializer.Constant(value=0.0),
        )

        if not self._weight_sharing:
            self.out_fc = Linear(
                in_features=self._emb_size,
                out_features=self._voc_size,
                weight_attr=base.ParamAttr(
                    name="mask_lm_out_fc.w_0",
                    initializer=self._param_initializer,
                ),
                bias_attr=self.mask_lm_out_bias_attr,
            )
        else:
            self.fc_create_params = self.create_parameter(
                shape=[self._voc_size],
                dtype=self._dtype,
                attr=self.mask_lm_out_bias_attr,
                is_bias=True,
            )

        self.next_sent_fc = Linear(
            in_features=self._emb_size,
            out_features=2,
            weight_attr=base.ParamAttr(
                name="next_sent_fc.w_0", initializer=self._param_initializer
            ),
            bias_attr="next_sent_fc.b_0",
        )

    def forward(
        self,
        src_ids,
        position_ids,
        sentence_ids,
        input_mask,
        mask_label,
        mask_pos,
        labels,
    ):
        mask_pos = paddle.cast(x=mask_pos, dtype='int32')

        enc_output, next_sent_feat = self.bert_layer(
            src_ids, position_ids, sentence_ids, input_mask
        )
        reshaped_emb_out = paddle.reshape(
            x=enc_output, shape=[-1, self._emb_size]
        )

        mask_feat = paddle.gather(reshaped_emb_out, index=mask_pos)
        mask_trans_feat = self.pooled_fc(mask_feat)
        mask_trans_feat = paddle.tanh(mask_trans_feat)
        mask_trans_feat = self.pre_process_layer(mask_trans_feat)

        if self._weight_sharing:
            fc_out = paddle.matmul(
                x=mask_trans_feat,
                y=self.bert_layer._src_emb._w,
                transpose_y=True,
            )
            fc_out += self.fc_create_params
        else:
            fc_out = self.out_fc(mask_trans_feat)

        mask_lm_loss = paddle.nn.functional.cross_entropy(
            input=fc_out,
            label=mask_label,
            reduction="none",
        )
        mean_mask_lm_loss = paddle.mean(mask_lm_loss)

        next_sent_fc_out = self.next_sent_fc(next_sent_feat)

        next_sent_softmax = paddle.nn.functional.softmax(next_sent_fc_out)
        next_sent_loss = paddle.nn.functional.cross_entropy(
            input=next_sent_fc_out,
            label=labels,
            reduction="none",
        )

        next_sent_acc = paddle.static.accuracy(
            input=next_sent_softmax, label=labels
        )

        mean_next_sent_loss = paddle.mean(next_sent_loss)

        loss = mean_next_sent_loss + mean_mask_lm_loss
        return next_sent_acc, mean_mask_lm_loss, loss