import argparse
import os
from pathlib import Path

import librosa
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2Model,
    Wav2Vec2PreTrainedModel,
)

import utils
from config import config


class RegressionHead(nn.Module):
    r"""Classification head."""

    def __init__(self, config):
        super().__init__()

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.final_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)

        return x


class EmotionModel(Wav2Vec2PreTrainedModel):
    r"""Speech emotion classifier."""

    def __init__(self, config):
        super().__init__(config)

        self.config = config
        self.wav2vec2 = Wav2Vec2Model(config)
        self.classifier = RegressionHead(config)
        self.init_weights()

    def forward(
        self,
        input_values,
    ):
        outputs = self.wav2vec2(input_values)
        hidden_states = outputs[0]
        hidden_states = torch.mean(hidden_states, dim=1)
        logits = self.classifier(hidden_states)

        return hidden_states, logits


class AudioDataset(Dataset):
    def __init__(self, list_of_wav_files, sr, processor):
        self.list_of_wav_files = list_of_wav_files
        self.processor = processor
        self.sr = sr

    def __len__(self):
        return len(self.list_of_wav_files)

    def __getitem__(self, idx):
        wav_file = self.list_of_wav_files[idx]
        audio_data, _ = librosa.load(wav_file, sr=self.sr)
        processed_data = self.processor(audio_data, sampling_rate=self.sr)[
            "input_values"
        ][0]
        return torch.from_numpy(processed_data)


def process_func(
    x: np.ndarray,
    sampling_rate: int,
    model: EmotionModel,
    processor: Wav2Vec2Processor,
    device: str,
    embeddings: bool = False,
) -> np.ndarray:
    r"""Predict emotions or extract embeddings from raw audio signal."""
    model = model.to(device)
    y = processor(x, sampling_rate=sampling_rate)
    y = y["input_values"][0]
    y = torch.from_numpy(y).unsqueeze(0).to(device)

    # run through model
    with torch.no_grad():
        y = model(y)[0 if embeddings else 1]

    # convert to numpy
    y = y.detach().cpu().numpy()

    return y


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-c", "--config", type=str, default=config.bert_gen_config.config_path
    )
    parser.add_argument(
        "--num_processes", type=int, default=config.bert_gen_config.num_processes
    )
    args, _ = parser.parse_known_args()
    config_path = args.config
    hps = utils.get_hparams_from_file(config_path)

    device = config.bert_gen_config.device

    model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
    REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
    if not Path(model_name).joinpath("pytorch_model.bin").exists():
        utils.download_emo_models(config.mirror, REPO_ID, model_name)

    processor = Wav2Vec2Processor.from_pretrained(model_name)
    model = EmotionModel.from_pretrained(model_name).to(device)

    lines = []
    with open(hps.data.training_files, encoding="utf-8") as f:
        lines.extend(f.readlines())

    with open(hps.data.validation_files, encoding="utf-8") as f:
        lines.extend(f.readlines())

    wavnames = [line.split("|")[0] for line in lines]
    dataset = AudioDataset(wavnames, 16000, processor)
    data_loader = DataLoader(
        dataset,
        batch_size=1,
        shuffle=False,
        num_workers=min(args.num_processes, os.cpu_count() - 1),
    )

    with torch.no_grad():
        for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
            wavname = wavnames[i]
            emo_path = wavname.replace(".wav", ".emo.npy")
            if os.path.exists(emo_path):
                continue
            emb = model(data.to(device))[0].detach().cpu().numpy()
            np.save(emo_path, emb)

    print("Emo vec 生成完毕!")