from .config import MODELS
from transformers.models.bert.modeling_bert import (
    BertPreTrainedModel,
    BertEmbeddings,
    BertEncoder,
    BertPooler,
)
import torch
from torch import nn


class MyBertModel(BertPreTrainedModel):
    """

    The model can behave as an encoder (with only self-attention) as well
    as a decoder, in which case a layer of cross-attention is added between
    the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,
    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    To behave as an decoder the model needs to be initialized with the
    :obj:`is_decoder` argument of the configuration set to :obj:`True`; an
    :obj:`encoder_hidden_states` is expected as an input to the forward pass.

    .. _`Attention is all you need`:
        https://arxiv.org/abs/1706.03762

    """

    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)
        self.encoder = BertEncoder(config)
        self.pooler = BertPooler(config)

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """Prunes heads of the model.
        heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
        See base class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def _forward_init(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
    ):
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
            attention_mask, input_shape, device
        )

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            (
                encoder_batch_size,
                encoder_sequence_length,
                _,
            ) = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(
                encoder_attention_mask
            )
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        return (
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            inputs_embeds,
            encoder_hidden_states,
            encoder_attention_mask,
            extended_attention_mask,
            encoder_extended_attention_mask,
        )

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
    ):
        r"""
        Return:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
            last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
                Sequence of hidden-states at the output of the last layer of the model.
            pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
                Last layer hidden-state of the first token of the sequence (classification token)
                further processed by a Linear layer and a Tanh activation function. The Linear
                layer weights are trained from the next sentence prediction (classification)
                objective during pre-training.

                This output is usually *not* a good summary
                of the semantic content of the input, you're often better with averaging or pooling
                the sequence of hidden-states for the whole input sequence.
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
                Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
                of shape :obj:`(batch_size, sequence_length, hidden_size)`.

                Hidden-states of the model at the output of each layer plus the initial embedding outputs.
            attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
                Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
                :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

                Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
                heads.

        Examples::

            from transformers import BertModel, BertTokenizer
            import torch

            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
            model = BertModel.from_pretrained('bert-base-uncased')

            input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
            outputs = model(input_ids)

            last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

        """
        (
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            inputs_embeds,
            encoder_hidden_states,
            encoder_attention_mask,
            extended_attention_mask,
            encoder_extended_attention_mask,
        ) = self._forward_init(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            inputs_embeds,
            encoder_hidden_states,
            encoder_attention_mask,
        )

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)

        outputs = (sequence_output, pooled_output,) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)

    def forward_mix_embed(self, x1, att1, x2, att2, lam):
        (
            x1,
            attention_mask1,
            token_type_ids,
            position_ids,
            head_mask,
            inputs_embeds,
            encoder_hidden_states,
            encoder_attention_mask,
            extended_attention_mask1,
            encoder_extended_attention_mask,
        ) = self._forward_init(input_ids=x1, attention_mask=att1)
        embedding_output1 = self.embeddings(
            input_ids=x1,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
        )

        (
            x2,
            attention_mask2,
            token_type_ids,
            position_ids,
            head_mask,
            inputs_embeds,
            encoder_hidden_states,
            encoder_attention_mask,
            extended_attention_mask2,
            encoder_extended_attention_mask,
        ) = self._forward_init(input_ids=x2, attention_mask=att2)

        embedding_output2 = self.embeddings(
            input_ids=x2,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
        )

        embedding_output = lam * embedding_output1 + (1.0 - lam) * embedding_output2

        # need to take max of both to ensure we don't miss attending to any value
        extended_attention_mask = torch.max(
            extended_attention_mask1, extended_attention_mask2
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)

        outputs = (sequence_output, pooled_output,) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)


class TextBERT(nn.Module):
    def __init__(self, pretrained_model, num_class, fine_tune, dropout):
        super(TextBERT, self).__init__()
        self.output_dim = num_class
        self.bert = MyBertModel.from_pretrained(pretrained_model)
        # Freeze bert layers
        if not fine_tune:
            for p in self.bert.parameters():
                p.requires_grad = False

        bert_dim = MODELS[pretrained_model][2]
        self.dropout = nn.Dropout(dropout)
        self.classifier = nn.Linear(bert_dim, num_class)

    def forward(self, x, attn_masks):
        outputs = self.bert(x, attention_mask=attn_masks)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        return logits

    def forward_mix_embed(self, x1, att1, x2, att2, lam):
        outputs = self.bert.forward_mix_embed(x1, att1, x2, att2, lam)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        return logits

    def forward_mix_sent(self, x1, att1, x2, att2, lam):
        logits1 = self.forward(x1, att1)
        logits2 = self.forward(x2, att2)
        y = lam * logits1 + (1.0 - lam) * logits2
        return y

    def forward_mix_encoder(self, x1, att1, x2, att2, lam):
        outputs1 = self.bert(x1, att1)
        outputs2 = self.bert(x2, att2)
        pooled_output1 = self.dropout(outputs1[1])
        pooled_output2 = self.dropout(outputs2[1])
        pooled_output = lam * pooled_output1 + (1.0 - lam) * pooled_output2
        y = self.classifier(pooled_output)
        return y