import torch
import torch.nn as nn
from transformers import BertConfig, BertModel, BertPreTrainedModel


class BertForRelationClassification(BertPreTrainedModel):

    def __init__(self, config: BertConfig, **kwargs):
        super().__init__(config)
        if not hasattr(config, "relation_emb_dim"):
            config.relation_emb_dim = 1024
        if not hasattr(config, "num_labels") or config.num_labels == 0:
            config.num_labels = 2
        self.bert = BertModel(config, **kwargs)
        self.num_labels = config.num_labels
        self.relation_emb_dim = config.relation_emb_dim
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.fclayer = nn.Linear(config.hidden_size * 3, self.relation_emb_dim)
        self.classifier = nn.Linear(self.relation_emb_dim, config.num_labels)
        self.init_weights()

    def _extract_entity(self, sequence_output, e_mask):
        extended_e_mask = e_mask.unsqueeze(1)
        extended_e_mask = torch.bmm(extended_e_mask.float(), sequence_output).squeeze(1)
        return extended_e_mask.float()

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        e1_mask=None,
        e2_mask=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
    ):
        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
        )
        sequence_output = outputs[0]
        pooled_output = outputs[1]

        e1_h = self._extract_entity(sequence_output, e1_mask)
        e2_h = self._extract_entity(sequence_output, e2_mask)
        context = self.dropout(pooled_output)
        pooled_output = torch.cat([context, e1_h, e2_h], dim=-1)
        pooled_output = torch.tanh(pooled_output)
        pooled_output = self.fclayer(pooled_output)
        sent_embedding = torch.tanh(pooled_output)
        sent_embedding = self.dropout(sent_embedding)

        # [batch_size x hidden_size]
        logits = self.classifier(sent_embedding).to(self.bert.device)
        # add hidden states and attention if they are here

        outputs = (torch.softmax(logits, -1),) + outputs[2:]
        if labels is not None:
            ce_loss = nn.CrossEntropyLoss()
            labels = labels.to(self.bert.device)
            loss = ce_loss(logits.view(-1, self.num_labels), labels.view(-1))
            outputs = (loss,) + outputs

        return outputs