import torch
import torch.nn as nn

from audio_denoiser.modules.Permute import Permute
from audio_denoiser.modules.SimpleRoberta import SimpleRoberta
from audio_denoiser.modules.SpectrogramScaler import SpectrogramScaler

import json


class AudioNoiseModel(nn.Module):
    def __init__(self, config: dict):
        super(AudioNoiseModel, self).__init__()

        # Encoder layers
        self.config = config
        scaler_dict = config["scaler"]
        self.scaler = SpectrogramScaler.from_dict(scaler_dict)
        self.in_channels = config.get("in_channels", 257)
        self.roberta_hidden_size = config.get("roberta_hidden_size", 768)
        self.model1 = nn.Sequential(
            nn.Conv1d(self.in_channels, 1024, kernel_size=1),
            nn.ELU(),
            nn.Conv1d(1024, 1024, kernel_size=1),
            nn.ELU(),
            nn.Conv1d(1024, self.in_channels, kernel_size=1),
        )
        self.model2 = nn.Sequential(
            Permute(0, 2, 1),
            nn.Linear(self.in_channels, self.roberta_hidden_size),
            SimpleRoberta(num_hidden_layers=5, hidden_size=self.roberta_hidden_size),
            nn.Linear(self.roberta_hidden_size, self.in_channels),
            Permute(0, 2, 1),
        )

    @property
    def sample_rate(self) -> int:
        return self.config.get("sample_rate", 16000)

    @property
    def n_fft(self) -> int:
        return self.config.get("n_fft", 512)

    @property
    def num_frames(self) -> int:
        return self.config.get("num_frames", 32)

    def forward(self, x, use_scaler: bool = False, out_scale: float = 1.0):
        if use_scaler:
            x = self.scaler(x)
        x1 = self.model1(x)
        x2 = self.model2(x)
        x = x1 + x2
        return x * out_scale


def load_audio_denosier_model(dir_path: str, device) -> AudioNoiseModel:
    config = json.load(open(f"{dir_path}/config.json", "r"))
    model = AudioNoiseModel(config)
    model.load_state_dict(torch.load(f"{dir_path}/pytorch_model.bin"))

    model.to(device)
    model.model1.to(device)
    model.model2.to(device)

    return model