from transformers import PreTrainedModel, HubertModel
import torch.nn as nn
import torch
from .configuration_emotion_classifier import EmotionClassifierConfig


class EmotionClassifierHuBERT(PreTrainedModel):
    config_class = EmotionClassifierConfig

    def __init__(self, config):
        super().__init__(config)
        self.hubert = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
        self.conv1 = nn.Conv1d(in_channels=1024, out_channels=512, kernel_size=3, padding=1)
        self.conv2 = nn.Conv1d(in_channels=512, out_channels=256, kernel_size=3, padding=1)
        self.transformer_encoder = nn.TransformerEncoderLayer(d_model=256, nhead=8)
        self.bilstm = nn.LSTM(input_size=256, hidden_size=config.hidden_size_lstm, num_layers=2, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(config.hidden_size_lstm * 2, config.num_classes)  # * 2 for bidirectional

    def forward(self, x):
        with torch.no_grad():
            features = self.hubert(x).last_hidden_state
        features = features.transpose(1, 2)
        x = torch.relu(self.conv1(features))
        x = torch.relu(self.conv2(x))
        x = x.transpose(1, 2)
        x = self.transformer_encoder(x)
        x, _ = self.bilstm(x)
        x = self.fc(x[:, -1, :])
        return x