# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from concurrent.futures import ThreadPoolExecutor
from operator import itemgetter
from pathlib import Path
from typing import Any
from typing import Dict
from typing import List

import jsonlines
import librosa
import numpy as np
import tqdm
import yaml
from yacs.config import CfgNode

from paddlespeech.t2s.datasets.get_feats import LogMelFBank
from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
from paddlespeech.t2s.datasets.preprocess_utils import get_sentences_svs
from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
from paddlespeech.t2s.utils import str2bool


def process_sentence(config: Dict[str, Any],
                     fp: Path,
                     sentences: Dict,
                     output_dir: Path,
                     mel_extractor=None,
                     cut_sil: bool=True):
    utt_id = fp.stem
    # for vctk
    if utt_id.endswith("_mic2"):
        utt_id = utt_id[:-5]
    record = None
    if utt_id in sentences:
        # reading, resampling may occur
        y, _ = librosa.load(str(fp), sr=config.fs)
        if len(y.shape) != 1:
            return record
        max_value = np.abs(y).max()
        if max_value > 1.0:
            y = y / max_value
        assert len(y.shape) == 1, f"{utt_id} is not a mono-channel audio."
        assert np.abs(y).max(
        ) <= 1.0, f"{utt_id} is seems to be different that 16 bit PCM."
        phones = sentences[utt_id][0]
        durations = sentences[utt_id][1]
        speaker = sentences[utt_id][2]
        d_cumsum = np.pad(np.array(durations).cumsum(0), (1, 0), 'constant')
        # little imprecise than use *.TextGrid directly
        times = librosa.frames_to_time(
            d_cumsum, sr=config.fs, hop_length=config.n_shift)
        if cut_sil:
            start = 0
            end = d_cumsum[-1]
            if phones[0] == "sil" and len(durations) > 1:
                start = times[1]
                durations = durations[1:]
                phones = phones[1:]
            if phones[-1] == 'sil' and len(durations) > 1:
                end = times[-2]
                durations = durations[:-1]
                phones = phones[:-1]
            sentences[utt_id][0] = phones
            sentences[utt_id][1] = durations
            start, end = librosa.time_to_samples([start, end], sr=config.fs)
            y = y[start:end]

        # extract mel feats
        logmel = mel_extractor.get_log_mel_fbank(y)

        # adjust time to make num_samples == num_frames * hop_length
        num_frames = logmel.shape[0]
        if y.size < num_frames * config.n_shift:
            y = np.pad(
                y, (0, num_frames * config.n_shift - y.size), mode="reflect")
        else:
            y = y[:num_frames * config.n_shift]
        num_samples = y.shape[0]

        mel_path = output_dir / (utt_id + "_feats.npy")
        wav_path = output_dir / (utt_id + "_wave.npy")
        # (num_samples, )
        np.save(wav_path, y)
        # (num_frames, n_mels)
        np.save(mel_path, logmel)
        record = {
            "utt_id": utt_id,
            "num_samples": num_samples,
            "num_frames": num_frames,
            "feats": str(mel_path),
            "wave": str(wav_path),
        }
        return record


def process_sentences(config,
                      fps: List[Path],
                      sentences: Dict,
                      output_dir: Path,
                      mel_extractor=None,
                      nprocs: int=1,
                      cut_sil: bool=True):

    if nprocs == 1:
        results = []
        for fp in tqdm.tqdm(fps, total=len(fps)):
            record = process_sentence(
                config=config,
                fp=fp,
                sentences=sentences,
                output_dir=output_dir,
                mel_extractor=mel_extractor,
                cut_sil=cut_sil)
            if record:
                results.append(record)
    else:
        with ThreadPoolExecutor(nprocs) as pool:
            futures = []
            with tqdm.tqdm(total=len(fps)) as progress:
                for fp in fps:
                    future = pool.submit(process_sentence, config, fp,
                                         sentences, output_dir, mel_extractor,
                                         cut_sil)
                    future.add_done_callback(lambda p: progress.update())
                    futures.append(future)

                results = []
                for ft in futures:
                    record = ft.result()
                    if record:
                        results.append(record)

    results.sort(key=itemgetter("utt_id"))
    with jsonlines.open(output_dir / "metadata.jsonl", 'w') as writer:
        for item in results:
            writer.write(item)
    print("Done")


def main():
    # parse config and args
    parser = argparse.ArgumentParser(
        description="Preprocess audio and then extract features .")
    parser.add_argument(
        "--dataset",
        default="baker",
        type=str,
        help="name of dataset, should in {baker, aishell3, ljspeech, vctk} now")
    parser.add_argument(
        "--rootdir", default=None, type=str, help="directory to dataset.")
    parser.add_argument(
        "--dumpdir",
        type=str,
        required=True,
        help="directory to dump feature files.")
    parser.add_argument("--config", type=str, help="vocoder config file.")
    parser.add_argument(
        "--num-cpu", type=int, default=1, help="number of process.")
    parser.add_argument(
        "--dur-file", default=None, type=str, help="path to durations.txt.")

    parser.add_argument(
        "--cut-sil",
        type=str2bool,
        default=True,
        help="whether cut sil in the edge of audio")
    args = parser.parse_args()

    rootdir = Path(args.rootdir).expanduser()
    dumpdir = Path(args.dumpdir).expanduser()
    # use absolute path
    dumpdir = dumpdir.resolve()
    dumpdir.mkdir(parents=True, exist_ok=True)
    dur_file = Path(args.dur_file).expanduser()

    assert rootdir.is_dir()
    assert dur_file.is_file()

    with open(args.config, 'rt') as f:
        config = CfgNode(yaml.safe_load(f))

    if args.dataset == "opencpop":
        sentences, speaker_set = get_sentences_svs(
            dur_file,
            dataset=args.dataset,
            sample_rate=config.fs,
            n_shift=config.n_shift, )
    else:
        sentences, speaker_set = get_phn_dur(dur_file)
        merge_silence(sentences)

    # split data into 3 sections
    if args.dataset == "baker":
        wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
        num_train = 9800
        num_dev = 100
        train_wav_files = wav_files[:num_train]
        dev_wav_files = wav_files[num_train:num_train + num_dev]
        test_wav_files = wav_files[num_train + num_dev:]

    elif args.dataset == "ljspeech":
        wav_files = sorted(list((rootdir / "wavs").rglob("*.wav")))
        # split data into 3 sections
        num_train = 12900
        num_dev = 100
        train_wav_files = wav_files[:num_train]
        dev_wav_files = wav_files[num_train:num_train + num_dev]
        test_wav_files = wav_files[num_train + num_dev:]
    elif args.dataset == "vctk":
        sub_num_dev = 5
        wav_dir = rootdir / "wav48_silence_trimmed"
        train_wav_files = []
        dev_wav_files = []
        test_wav_files = []
        for speaker in os.listdir(wav_dir):
            wav_files = sorted(list((wav_dir / speaker).rglob("*_mic2.flac")))
            if len(wav_files) > 100:
                train_wav_files += wav_files[:-sub_num_dev * 2]
                dev_wav_files += wav_files[-sub_num_dev * 2:-sub_num_dev]
                test_wav_files += wav_files[-sub_num_dev:]
            else:
                train_wav_files += wav_files
    elif args.dataset == "aishell3":
        sub_num_dev = 5
        wav_dir = rootdir / "train" / "wav"
        train_wav_files = []
        dev_wav_files = []
        test_wav_files = []
        for speaker in os.listdir(wav_dir):
            wav_files = sorted(list((wav_dir / speaker).rglob("*.wav")))
            if len(wav_files) > 100:
                train_wav_files += wav_files[:-sub_num_dev * 2]
                dev_wav_files += wav_files[-sub_num_dev * 2:-sub_num_dev]
                test_wav_files += wav_files[-sub_num_dev:]
            else:
                train_wav_files += wav_files
    elif args.dataset == "opencpop":
        wavdir = rootdir / "wavs"
        # split data into 3 sections
        train_file = rootdir / "train.txt"
        train_wav_files = []
        with open(train_file, "r") as f_train:
            for line in f_train.readlines():
                utt = line.split("|")[0]
                wav_name = utt + ".wav"
                wav_path = wavdir / wav_name
                train_wav_files.append(wav_path)

        test_file = rootdir / "test.txt"
        dev_wav_files = []
        test_wav_files = []
        num_dev = 106
        count = 0
        with open(test_file, "r") as f_test:
            for line in f_test.readlines():
                count += 1
                utt = line.split("|")[0]
                wav_name = utt + ".wav"
                wav_path = wavdir / wav_name
                if count > num_dev:
                    test_wav_files.append(wav_path)
                else:
                    dev_wav_files.append(wav_path)
    else:
        print("dataset should in {baker, ljspeech, vctk, aishell3} now!")

    train_dump_dir = dumpdir / "train" / "raw"
    train_dump_dir.mkdir(parents=True, exist_ok=True)
    dev_dump_dir = dumpdir / "dev" / "raw"
    dev_dump_dir.mkdir(parents=True, exist_ok=True)
    test_dump_dir = dumpdir / "test" / "raw"
    test_dump_dir.mkdir(parents=True, exist_ok=True)

    mel_extractor = LogMelFBank(
        sr=config.fs,
        n_fft=config.n_fft,
        hop_length=config.n_shift,
        win_length=config.win_length,
        window=config.window,
        n_mels=config.n_mels,
        fmin=config.fmin,
        fmax=config.fmax)

    # process for the 3 sections
    if train_wav_files:
        process_sentences(
            config=config,
            fps=train_wav_files,
            sentences=sentences,
            output_dir=train_dump_dir,
            mel_extractor=mel_extractor,
            nprocs=args.num_cpu,
            cut_sil=args.cut_sil)
    if dev_wav_files:
        process_sentences(
            config=config,
            fps=dev_wav_files,
            sentences=sentences,
            output_dir=dev_dump_dir,
            mel_extractor=mel_extractor,
            nprocs=args.num_cpu,
            cut_sil=args.cut_sil)
    if test_wav_files:
        process_sentences(
            config=config,
            fps=test_wav_files,
            sentences=sentences,
            output_dir=test_dump_dir,
            mel_extractor=mel_extractor,
            nprocs=args.num_cpu,
            cut_sil=args.cut_sil)


if __name__ == "__main__":
    main()
