from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn
from transformers import (
    Wav2Vec2Model,
    Wav2Vec2PreTrainedModel,
)
from transformers.utils import ModelOutput
from .configuration_wav2vec2multihead import Wav2Vec2MultiHeadConfig


@dataclass
class Wav2Vec2MultiHeadMultiLabelOutput(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits1: torch.FloatTensor = None
    logits2: torch.FloatTensor = None
    logits3: torch.FloatTensor = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


class Wav2Vec2ForMultiHeadMultiLabelClassification(Wav2Vec2PreTrainedModel):
    
    """Wav2Vec2ForMultiHeadMultiLabelClassification is a model for multi-label classification using Wav2Vec2 using multiple classifier heads. Three classifier heads are hard-coded for three different tasks, such as action, object, and location classification in FSC-IC dataset.

    Returns:
        Wav2Vec2MultiHeadMultiLabelOutput: Contains the loss and logits for each of the three tasks, as well as hidden states and attentions if requested.
    """

    config_class = Wav2Vec2MultiHeadConfig
    def __init__(self, config):
        super().__init__(config)
        self.wav2vec2 = Wav2Vec2Model(config)
        self.dropout = nn.Dropout(config.final_dropout)
        self.classifier1 = nn.Linear(config.hidden_size, config.num_labels_1)
        self.classifier2 = nn.Linear(config.hidden_size, config.num_labels_2)
        self.classifier3 = nn.Linear(config.hidden_size, config.num_labels_3)
        self.init_weights()

    def freeze_feature_extractor(self):
        self.wav2vec2.feature_extractor._freeze_parameters()

    def freeze_cnn_projection(self):
        for param in self.wav2vec2.feature_projection.parameters():
            param.requires_grad = False

    def forward(
        self,
        input_values,
        attention_mask=None,
        labels1=None,
        labels2=None,
        labels3=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )
        outputs = self.wav2vec2(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states)
        hidden_states = torch.mean(hidden_states, dim=1)

        logits1 = self.classifier1(hidden_states)
        logits2 = self.classifier2(hidden_states)
        logits3 = self.classifier3(hidden_states)

        loss = None
        if labels1 is not None and labels2 is not None and labels3 is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss1 = loss_fct(
                logits1.view(-1, self.config.num_labels_1), labels1.view(-1)
            )
            loss2 = loss_fct(
                logits2.view(-1, self.config.num_labels_2), labels2.view(-1)
            )
            loss3 = loss_fct(
                logits3.view(-1, self.config.num_labels_3), labels3.view(-1)
            )
            loss = loss1 + loss2 + loss3

        return Wav2Vec2MultiHeadMultiLabelOutput(
            loss=loss,
            logits1=logits1,
            logits2=logits2,
            logits3=logits3,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )