from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, MSELoss
from transformers.modeling_outputs import ModelOutput
from transformers.models.deberta_v2.modeling_deberta_v2 import (
    ContextPooler, DebertaV2Model, DebertaV2PreTrainedModel, StableDropout)


@dataclass
class SequenceClassifierOutput(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    binary_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None

class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.deberta = DebertaV2Model(config)
        self.pooler = ContextPooler(config)
        output_dim = self.pooler.output_dim

        self.binary_classifier = nn.Linear(output_dim, 1)
        self.regressor = nn.Linear(output_dim, 1)
        drop_out = getattr(config, "cls_dropout", None)
        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
        self.dropout = StableDropout(drop_out)
        self.post_init()

    def freeze_embeddings(self) -> None:
        """Frezees the embedding layer."""
        for param in self.deberta.embeddings.parameters():
            param.requires_grad = False

    def get_input_embeddings(self):
        return self.deberta.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.deberta.set_input_embeddings(new_embeddings)

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deberta(
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        encoder_layer = outputs[0]
        pooled_output = self.pooler(encoder_layer)
        pooled_output = self.dropout(pooled_output)
        binary_logits = self.binary_classifier(pooled_output)
        logits = self.regressor(pooled_output)

        loss = None
        if labels is not None:
            regression_loss_fct = MSELoss()
            regression_loss = regression_loss_fct(logits.squeeze(), labels.squeeze().float())
            
            binary_loss_fct = BCEWithLogitsLoss()
            # We use as binary labels all texts with a score above 3!
            binary_labels = (labels >= 3).float()
            classification_loss = binary_loss_fct(binary_logits.squeeze(), binary_labels.squeeze())
            loss = regression_loss + classification_loss

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        
        return SequenceClassifierOutput(
            loss=loss, logits=logits, binary_logits=binary_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )