# Copyright (c) 2021 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 abc

import paddle
import paddle.nn as nn
import paddle.nn.functional as F


class SemanticIndexBase(nn.Layer):
    def __init__(self, pretrained_model, dropout=None, output_emb_size=None):
        super().__init__()
        self.ptm = pretrained_model
        self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)

        # if output_emb_size is not None, then add Linear layer to reduce embedding_size,
        # we recommend set output_emb_size = 256 considering the trade-off between
        # recall performance and efficiency

        self.output_emb_size = output_emb_size
        if output_emb_size > 0:
            weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
            self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr)

    def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
        _, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)

        if self.output_emb_size > 0:
            cls_embedding = self.emb_reduce_linear(cls_embedding)
        cls_embedding = self.dropout(cls_embedding)
        cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)

        return cls_embedding

    def get_semantic_embedding(self, data_loader):
        self.eval()
        with paddle.no_grad():
            for batch_data in data_loader:
                input_ids, token_type_ids = batch_data

                text_embeddings = self.get_pooled_embedding(input_ids, token_type_ids=token_type_ids)

                yield text_embeddings

    def cosine_sim(
        self,
        query_input_ids,
        title_input_ids,
        query_token_type_ids=None,
        query_position_ids=None,
        query_attention_mask=None,
        title_token_type_ids=None,
        title_position_ids=None,
        title_attention_mask=None,
    ):

        query_cls_embedding = self.get_pooled_embedding(
            query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask
        )

        title_cls_embedding = self.get_pooled_embedding(
            title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask
        )

        cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1)
        return cosine_sim

    @abc.abstractmethod
    def forward(self):
        pass


class SemanticIndexBatchNeg(SemanticIndexBase):
    def __init__(self, pretrained_model, dropout=None, margin=0.3, scale=30, output_emb_size=None):
        super().__init__(pretrained_model, dropout, output_emb_size)

        self.margin = margin
        # Used scaling cosine similarity to ease converge
        self.sacle = scale

    def forward(
        self,
        query_input_ids,
        title_input_ids,
        query_token_type_ids=None,
        query_position_ids=None,
        query_attention_mask=None,
        title_token_type_ids=None,
        title_position_ids=None,
        title_attention_mask=None,
    ):

        query_cls_embedding = self.get_pooled_embedding(
            query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask
        )

        title_cls_embedding = self.get_pooled_embedding(
            title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask
        )

        cosine_sim = paddle.matmul(query_cls_embedding, title_cls_embedding, transpose_y=True)

        # substract margin from all positive samples cosine_sim()
        margin_diag = paddle.full(
            shape=[query_cls_embedding.shape[0]], fill_value=self.margin, dtype=paddle.get_default_dtype()
        )

        cosine_sim = cosine_sim - paddle.diag(margin_diag)

        # scale cosine to ease training converge
        cosine_sim *= self.sacle

        labels = paddle.arange(0, query_cls_embedding.shape[0], dtype="int64")
        labels = paddle.reshape(labels, shape=[-1, 1])

        loss = F.cross_entropy(input=cosine_sim, label=labels)

        return loss
