import torch
from torch import nn
from transformers import Wav2Vec2Processor, HubertModel

class HubertXCNNEnoder(nn.Module):
    def __init__(self, audio_enc_dim, llm_dim, finetune=False):
        super().__init__()
        self.encoder = HubertModel.from_pretrained('facebook/hubert-xlarge-ll60k')
        for param in self.encoder.parameters():
            param.requires_grad = False

        self.cnn = nn.Sequential(
            nn.ReLU(),
            nn.Conv1d(audio_enc_dim, llm_dim//2, kernel_size=5,
                      stride=1, padding=0),
            nn.ReLU(),
            nn.Conv1d(llm_dim//2, llm_dim, kernel_size=5,
                      stride=2, padding=0),
            nn.ReLU(),
            nn.Conv1d(llm_dim, llm_dim, kernel_size=3,
                      stride=1, padding=0),
        )

    def forward(self, x):
        x = self.encoder(x).last_hidden_state
        x = self.cnn(x.transpose(1,2)).transpose(1,2)
        return x