# 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 pathlib import Path
from timer import timer

import jsonlines
import numpy as np
import paddle
import soundfile as sf
import yaml
from paddle import distributed as dist
from parakeet.datasets.data_table import DataTable
from parakeet.models.parallel_wavegan import PWGGenerator

from config import get_cfg_default

parser = argparse.ArgumentParser(
    description="Synthesize with parallel wavegan.")
parser.add_argument(
    "--config", type=str, help="config file to overwrite default config.")
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
parser.add_argument("--test-metadata", type=str, help="dev data.")
parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument("--device", type=str, default="gpu", help="device to run.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")

args = parser.parse_args()
config = get_cfg_default()
if args.config:
    config.merge_from_file(args.config)

print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(config)
print(
    f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
)

paddle.set_device(args.device)
generator = PWGGenerator(**config["generator_params"])
state_dict = paddle.load(args.checkpoint)
generator.set_state_dict(state_dict["generator_params"])

generator.remove_weight_norm()
generator.eval()
with jsonlines.open(args.test_metadata, 'r') as reader:
    metadata = list(reader)

test_dataset = DataTable(
    metadata,
    fields=['utt_id', 'feats'],
    converters={
        'utt_id': None,
        'feats': np.load,
    })
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)

N = 0
T = 0
for example in test_dataset:
    utt_id = example['utt_id']
    mel = example['feats']
    mel = paddle.to_tensor(mel)  # (T, C)
    with timer() as t:
        wav = generator.inference(c=mel)
        wav = wav.numpy()
        N += wav.size
        T += t.elapse
        speed = wav.size / t.elapse
    print(
        f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {config.sr / speed}."
    )
    sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=config.sr)
print(f"generation speed: {N / T}Hz, RTF: {config.sr / (N / T) }")