#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://github.com/yxlu-0102/MP-SENet/blob/main/train.py
"""
import argparse
import json
import logging
from logging.handlers import TimedRotatingFileHandler
import os
import platform
from pathlib import Path
import random
import sys
import shutil
from typing import List

pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../../"))

import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

from toolbox.torch.utils.data.dataset.denoise_jsonl_dataset import DenoiseJsonlDataset
from toolbox.torchaudio.models.conv_tasnet.configuration_conv_tasnet import ConvTasNetConfig
from toolbox.torchaudio.models.conv_tasnet.modeling_conv_tasnet import ConvTasNet, ConvTasNetPretrainedModel
from toolbox.torchaudio.losses.snr import NegativeSISNRLoss
from toolbox.torchaudio.losses.spectral import LSDLoss, MultiResolutionSTFTLoss
from toolbox.torchaudio.losses.perceptual import NegSTOILoss
from toolbox.torchaudio.metrics.pesq import run_pesq_score


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--train_dataset", default="train.xlsx", type=str)
    parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)

    parser.add_argument("--max_epochs", default=200, type=int)

    parser.add_argument("--batch_size", default=16, type=int)
    parser.add_argument("--learning_rate", default=1e-4, type=float)
    parser.add_argument("--num_serialized_models_to_keep", default=10, type=int)
    parser.add_argument("--patience", default=5, type=int)
    parser.add_argument("--serialization_dir", default="serialization_dir", type=str)
    parser.add_argument("--seed", default=1234, type=int)

    parser.add_argument("--config_file", default="config.yaml", type=str)


    args = parser.parse_args()
    return args


def logging_config(file_dir: str):
    fmt = "%(asctime)s - %(name)s - %(levelname)s  %(filename)s:%(lineno)d >  %(message)s"

    logging.basicConfig(format=fmt,
                        datefmt="%m/%d/%Y %H:%M:%S",
                        level=logging.INFO)
    file_handler = TimedRotatingFileHandler(
        filename=os.path.join(file_dir, "main.log"),
        encoding="utf-8",
        when="D",
        interval=1,
        backupCount=7
    )
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(logging.Formatter(fmt))
    logger = logging.getLogger(__name__)
    logger.addHandler(file_handler)

    return logger


class CollateFunction(object):
    def __init__(self):
        pass

    def __call__(self, batch: List[dict]):
        clean_audios = list()
        noisy_audios = list()

        for sample in batch:
            # noise_wave: torch.Tensor = sample["noise_wave"]
            clean_audio: torch.Tensor = sample["speech_wave"]
            noisy_audio: torch.Tensor = sample["mix_wave"]
            # snr_db: float = sample["snr_db"]

            clean_audios.append(clean_audio)
            noisy_audios.append(noisy_audio)

        clean_audios = torch.stack(clean_audios)
        noisy_audios = torch.stack(noisy_audios)

        # assert
        if torch.any(torch.isnan(clean_audios)) or torch.any(torch.isinf(clean_audios)):
            raise AssertionError("nan or inf in clean_audios")
        if torch.any(torch.isnan(noisy_audios)) or torch.any(torch.isinf(noisy_audios)):
            raise AssertionError("nan or inf in noisy_audios")
        return clean_audios, noisy_audios


collate_fn = CollateFunction()


def main():
    args = get_args()

    config = ConvTasNetConfig.from_pretrained(
        pretrained_model_name_or_path=args.config_file,
    )

    serialization_dir = Path(args.serialization_dir)
    serialization_dir.mkdir(parents=True, exist_ok=True)

    logger = logging_config(serialization_dir)

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    logger.info(f"set seed: {args.seed}")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    n_gpu = torch.cuda.device_count()
    logger.info(f"GPU available count: {n_gpu}; device: {device}")

    # datasets
    train_dataset = DenoiseJsonlDataset(
        jsonl_file=args.train_dataset,
        expected_sample_rate=8000,
        max_wave_value=32768.0,
    )
    valid_dataset = DenoiseJsonlDataset(
        jsonl_file=args.valid_dataset,
        expected_sample_rate=8000,
        max_wave_value=32768.0,
    )
    train_data_loader = DataLoader(
        dataset=train_dataset,
        batch_size=args.batch_size,
        # shuffle=True,
        sampler=None,
        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
        collate_fn=collate_fn,
        pin_memory=False,
        prefetch_factor=2,
    )
    valid_data_loader = DataLoader(
        dataset=valid_dataset,
        batch_size=args.batch_size,
        # shuffle=True,
        sampler=None,
        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
        collate_fn=collate_fn,
        pin_memory=False,
        prefetch_factor=2,
    )

    # models
    logger.info(f"prepare models. config_file: {args.config_file}")
    model = ConvTasNetPretrainedModel(config).to(device)
    model.to(device)
    model.train()

    # optimizer
    logger.info("prepare optimizer, lr_scheduler, loss_fn, categorical_accuracy")
    optimizer = torch.optim.AdamW(model.parameters(), args.learning_rate)

    # resume training
    last_epoch = -1
    for epoch_i in serialization_dir.glob("epoch-*"):
        epoch_i = Path(epoch_i)
        epoch_idx = epoch_i.stem.split("-")[1]
        epoch_idx = int(epoch_idx)
        if epoch_idx > last_epoch:
            last_epoch = epoch_idx

    if last_epoch != -1:
        logger.info(f"resume from epoch-{last_epoch}.")
        model_pt = serialization_dir / f"epoch-{last_epoch}/model.pt"
        optimizer_pth = serialization_dir / f"epoch-{last_epoch}/optimizer.pth"

        logger.info(f"load state dict for model.")
        with open(model_pt.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        model.load_state_dict(state_dict, strict=True)

        logger.info(f"load state dict for optimizer.")
        with open(optimizer_pth.as_posix(), "rb") as f:
            state_dict = torch.load(f, map_location="cpu", weights_only=True)
        optimizer.load_state_dict(state_dict)

    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
        optimizer,
        milestones=[10000, 20000, 30000, 40000, 50000], gamma=0.5
    )

    ae_loss_fn = nn.L1Loss(reduction="mean").to(device)
    neg_si_snr_loss_fn = NegativeSISNRLoss(reduction="mean").to(device)
    neg_stoi_loss_fn = NegSTOILoss(sample_rate=8000, reduction="mean").to(device)
    mr_stft_loss_fn = MultiResolutionSTFTLoss(
        # fft_size_list=[256, 512, 1024],
        win_size_list=[120, 240, 480],
        hop_size_list=[25, 50, 100],
        reduction="mean"
    ).to(device)

    # training loop

    # state
    average_pesq_score = 1000000000
    average_loss = 1000000000
    average_ae_loss = 1000000000
    average_neg_si_snr_loss = 1000000000
    average_neg_stoi_loss = 1000000000

    model_list = list()
    best_idx_epoch = None
    best_metric = None
    patience_count = 0

    logger.info("training")
    for idx_epoch in range(max(0, last_epoch+1), args.max_epochs):
        # train
        model.train()

        total_pesq_score = 0.
        total_loss = 0.
        total_ae_loss = 0.
        total_neg_si_snr_loss = 0.
        total_neg_stoi_loss = 0.
        total_batches = 0.
        progress_bar = tqdm(
            desc="Training; epoch-{}".format(idx_epoch),
        )
        for batch in train_data_loader:
            clean_audios, noisy_audios = batch
            clean_audios = clean_audios.to(device)
            noisy_audios = noisy_audios.to(device)

            denoise_audios = model.forward(noisy_audios)
            denoise_audios = torch.squeeze(denoise_audios, dim=1)

            if torch.any(torch.isnan(denoise_audios)) or torch.any(torch.isinf(denoise_audios)):
                raise AssertionError("nan or inf in denoise_audios")

            ae_loss = ae_loss_fn.forward(denoise_audios, clean_audios)
            neg_si_snr_loss = neg_si_snr_loss_fn.forward(denoise_audios, clean_audios)
            neg_stoi_loss = neg_stoi_loss_fn.forward(denoise_audios, clean_audios)
            mr_stft_loss = mr_stft_loss_fn.forward(denoise_audios, clean_audios)

            loss = 0.25 * ae_loss + 0.25 * neg_si_snr_loss + 0.25 * neg_stoi_loss + 0.25 * mr_stft_loss

            denoise_audios_list_r = list(denoise_audios.detach().cpu().numpy())
            clean_audios_list_r = list(clean_audios.detach().cpu().numpy())
            pesq_score = run_pesq_score(clean_audios_list_r, denoise_audios_list_r, sample_rate=8000, mode="nb")

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()

            total_pesq_score += pesq_score
            total_loss += loss.item()
            total_ae_loss += ae_loss.item()
            total_neg_si_snr_loss += neg_si_snr_loss.item()
            total_neg_stoi_loss += neg_stoi_loss.item()
            total_batches += 1

            average_pesq_score = round(total_pesq_score / total_batches, 4)
            average_loss = round(total_loss / total_batches, 4)
            average_ae_loss = round(total_ae_loss / total_batches, 4)
            average_neg_si_snr_loss = round(total_neg_si_snr_loss / total_batches, 4)
            average_neg_stoi_loss = round(total_neg_stoi_loss / total_batches, 4)

            progress_bar.update(1)
            progress_bar.set_postfix({
                "pesq_score": average_pesq_score,
                "loss": average_loss,
                "ae_loss": average_ae_loss,
                "neg_si_snr_loss": average_neg_si_snr_loss,
                "neg_stoi_loss": average_neg_stoi_loss,
            })

        # evaluation
        model.eval()
        torch.cuda.empty_cache()

        total_pesq_score = 0.
        total_loss = 0.
        total_ae_loss = 0.
        total_neg_si_snr_loss = 0.
        total_neg_stoi_loss = 0.
        total_batches = 0.

        progress_bar = tqdm(
            desc="Evaluation; epoch-{}".format(idx_epoch),
        )
        with torch.no_grad():
            for batch in valid_data_loader:
                clean_audios, noisy_audios = batch
                clean_audios = clean_audios.to(device)
                noisy_audios = noisy_audios.to(device)

                denoise_audios = model.forward(noisy_audios)
                denoise_audios = torch.squeeze(denoise_audios, dim=1)

                ae_loss = ae_loss_fn.forward(denoise_audios, clean_audios)
                neg_si_snr_loss = neg_si_snr_loss_fn.forward(denoise_audios, clean_audios)
                neg_stoi_loss = neg_stoi_loss_fn.forward(denoise_audios, clean_audios)
                mr_stft_loss = mr_stft_loss_fn.forward(denoise_audios, clean_audios)

                loss = 0.25 * ae_loss + 0.25 * neg_si_snr_loss + 0.25 * neg_stoi_loss + 0.25 * mr_stft_loss

                denoise_audios_list_r = list(denoise_audios.detach().cpu().numpy())
                clean_audios_list_r = list(clean_audios.detach().cpu().numpy())
                pesq_score = run_pesq_score(clean_audios_list_r, denoise_audios_list_r, sample_rate=8000, mode="nb")

                total_pesq_score += pesq_score
                total_loss += loss.item()
                total_ae_loss += ae_loss.item()
                total_neg_si_snr_loss += neg_si_snr_loss.item()
                total_neg_stoi_loss += neg_stoi_loss.item()
                total_batches += 1

                average_pesq_score = round(total_pesq_score / total_batches, 4)
                average_loss = round(total_loss / total_batches, 4)
                average_ae_loss = round(total_ae_loss / total_batches, 4)
                average_neg_si_snr_loss = round(total_neg_si_snr_loss / total_batches, 4)
                average_neg_stoi_loss = round(total_neg_stoi_loss / total_batches, 4)

                progress_bar.update(1)
                progress_bar.set_postfix({
                    "pesq_score": average_pesq_score,
                    "loss": average_loss,
                    "ae_loss": average_ae_loss,
                    "neg_si_snr_loss": average_neg_si_snr_loss,
                    "neg_stoi_loss": average_neg_stoi_loss,
                })

        # scheduler
        lr_scheduler.step()

        # save path
        epoch_dir = serialization_dir / "epoch-{}".format(idx_epoch)
        epoch_dir.mkdir(parents=True, exist_ok=False)

        # save models
        model.save_pretrained(epoch_dir.as_posix())

        model_list.append(epoch_dir)
        if len(model_list) >= args.num_serialized_models_to_keep:
            model_to_delete: Path = model_list.pop(0)
            shutil.rmtree(model_to_delete.as_posix())

        # save optim
        torch.save(optimizer.state_dict(), (epoch_dir / "optimizer.pth").as_posix())

        # save metric
        if best_metric is None:
            best_idx_epoch = idx_epoch
            best_metric = average_loss
        elif average_loss < best_metric:
            # great is better.
            best_idx_epoch = idx_epoch
            best_metric = average_loss
        else:
            pass

        metrics = {
            "idx_epoch": idx_epoch,
            "best_idx_epoch": best_idx_epoch,
            "pesq_score": average_pesq_score,
            "loss": average_loss,
            "ae_loss": average_ae_loss,
            "neg_si_snr_loss": average_neg_si_snr_loss,
            "neg_stoi_loss": average_neg_stoi_loss,
        }
        metrics_filename = epoch_dir / "metrics_epoch.json"
        with open(metrics_filename, "w", encoding="utf-8") as f:
            json.dump(metrics, f, indent=4, ensure_ascii=False)

        # save best
        best_dir = serialization_dir / "best"
        if best_idx_epoch == idx_epoch:
            if best_dir.exists():
                shutil.rmtree(best_dir)
            shutil.copytree(epoch_dir, best_dir)

        # early stop
        early_stop_flag = False
        if best_idx_epoch == idx_epoch:
            patience_count = 0
        else:
            patience_count += 1
        if patience_count >= args.patience:
            early_stop_flag = True

        # early stop
        if early_stop_flag:
            break

    return


if __name__ == "__main__":
    main()