# 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.
# remain for chains
import argparse
import logging
import os
from pathlib import Path

import numpy as np
import paddle
import soundfile as sf
import yaml
from paddle import jit
from paddle.static import InputSpec
from yacs.config import CfgNode

from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
from paddlespeech.t2s.models.speedyspeech import SpeedySpeechInference
from paddlespeech.t2s.modules.normalizer import ZScore


def evaluate(args, speedyspeech_config, pwg_config):
    # dataloader has been too verbose
    logging.getLogger("DataLoader").disabled = True

    # construct dataset for evaluation
    sentences = []
    with open(args.text, 'rt') as f:
        for line in f:
            items = line.strip().split()
            utt_id = items[0]
            sentence = "".join(items[1:])
            sentences.append((utt_id, sentence))

    with open(args.phones_dict, "r") as f:
        phn_id = [line.strip().split() for line in f.readlines()]
    vocab_size = len(phn_id)
    print("vocab_size:", vocab_size)
    with open(args.tones_dict, "r") as f:
        tone_id = [line.strip().split() for line in f.readlines()]
    tone_size = len(tone_id)
    print("tone_size:", tone_size)

    model = SpeedySpeech(
        vocab_size=vocab_size,
        tone_size=tone_size,
        **speedyspeech_config["model"])
    model.set_state_dict(
        paddle.load(args.speedyspeech_checkpoint)["main_params"])
    model.eval()

    vocoder = PWGGenerator(**pwg_config["generator_params"])
    vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
    vocoder.remove_weight_norm()
    vocoder.eval()
    print("model done!")

    stat = np.load(args.speedyspeech_stat)
    mu, std = stat
    mu = paddle.to_tensor(mu)
    std = paddle.to_tensor(std)
    speedyspeech_normalizer = ZScore(mu, std)

    stat = np.load(args.pwg_stat)
    mu, std = stat
    mu = paddle.to_tensor(mu)
    std = paddle.to_tensor(std)
    pwg_normalizer = ZScore(mu, std)

    speedyspeech_inference = SpeedySpeechInference(speedyspeech_normalizer,
                                                   model)
    speedyspeech_inference.eval()
    speedyspeech_inference = jit.to_static(
        speedyspeech_inference,
        input_spec=[
            InputSpec([-1], dtype=paddle.int64), InputSpec(
                [-1], dtype=paddle.int64)
        ])
    paddle.jit.save(speedyspeech_inference,
                    os.path.join(args.inference_dir, "speedyspeech"))
    speedyspeech_inference = paddle.jit.load(
        os.path.join(args.inference_dir, "speedyspeech"))

    pwg_inference = PWGInference(pwg_normalizer, vocoder)
    pwg_inference.eval()
    pwg_inference = jit.to_static(
        pwg_inference, input_spec=[
            InputSpec([-1, 80], dtype=paddle.float32),
        ])
    paddle.jit.save(pwg_inference, os.path.join(args.inference_dir, "pwg"))
    pwg_inference = paddle.jit.load(os.path.join(args.inference_dir, "pwg"))

    frontend = Frontend(
        phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
    print("frontend done!")

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

    for utt_id, sentence in sentences:
        input_ids = frontend.get_input_ids(
            sentence, merge_sentences=True, get_tone_ids=True)
        phone_ids = input_ids["phone_ids"]
        tone_ids = input_ids["tone_ids"]

        flags = 0
        for i in range(len(phone_ids)):
            part_phone_ids = phone_ids[i]
            part_tone_ids = tone_ids[i]
            with paddle.no_grad():
                mel = speedyspeech_inference(part_phone_ids, part_tone_ids)
                temp_wav = pwg_inference(mel)
            if flags == 0:
                wav = temp_wav
                flags = 1
            else:
                wav = paddle.concat([wav, temp_wav])
        sf.write(
            output_dir / (utt_id + ".wav"),
            wav.numpy(),
            samplerate=speedyspeech_config.fs)
        print(f"{utt_id} done!")


def main():
    # parse args and config and redirect to train_sp
    parser = argparse.ArgumentParser(
        description="Synthesize with speedyspeech & parallel wavegan.")
    parser.add_argument(
        "--speedyspeech-config", type=str, help="config file for speedyspeech.")
    parser.add_argument(
        "--speedyspeech-checkpoint",
        type=str,
        help="speedyspeech checkpoint to load.")
    parser.add_argument(
        "--speedyspeech-stat",
        type=str,
        help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
    )
    parser.add_argument(
        "--pwg-config", type=str, help="config file for parallelwavegan.")
    parser.add_argument(
        "--pwg-checkpoint",
        type=str,
        help="parallel wavegan checkpoint to load.")
    parser.add_argument(
        "--pwg-stat",
        type=str,
        help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
    )
    parser.add_argument(
        "--text",
        type=str,
        help="text to synthesize, a 'utt_id sentence' pair per line")
    parser.add_argument(
        "--phones-dict", type=str, default=None, help="phone vocabulary file.")
    parser.add_argument(
        "--tones-dict", type=str, default=None, help="tone vocabulary file.")
    parser.add_argument("--output-dir", type=str, help="output dir")
    parser.add_argument(
        "--inference-dir", type=str, help="dir to save inference models")
    parser.add_argument(
        "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu or xpu.")
    parser.add_argument(
        "--nxpu",
        type=int,
        default=0,
        help="if nxpu == 0 and ngpu == 0, use cpu.")

    args, _ = parser.parse_known_args()

    if args.ngpu == 0:
        if args.nxpu == 0:
            paddle.set_device("cpu")
        else:
            paddle.set_device("xpu")
    elif args.ngpu > 0:
        paddle.set_device("gpu")
    else:
        print("ngpu should >= 0 !")

    with open(args.speedyspeech_config) as f:
        speedyspeech_config = CfgNode(yaml.safe_load(f))
    with open(args.pwg_config) as f:
        pwg_config = CfgNode(yaml.safe_load(f))

    print("========Args========")
    print(yaml.safe_dump(vars(args)))
    print("========Config========")
    print(speedyspeech_config)
    print(pwg_config)

    evaluate(args, speedyspeech_config, pwg_config)


if __name__ == "__main__":
    main()
