# 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 logging
import os
import shutil
from pathlib import Path

import jsonlines
import numpy as np
import paddle
import yaml
from paddle import DataParallel
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.io import DistributedBatchSampler
from yacs.config import CfgNode

from paddlespeech.t2s.datasets.am_batch_fn import speedyspeech_multi_spk_batch_fn
from paddlespeech.t2s.datasets.am_batch_fn import speedyspeech_single_spk_batch_fn
from paddlespeech.t2s.datasets.data_table import DataTable
from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
from paddlespeech.t2s.models.speedyspeech import SpeedySpeechEvaluator
from paddlespeech.t2s.models.speedyspeech import SpeedySpeechUpdater
from paddlespeech.t2s.training.extensions.snapshot import Snapshot
from paddlespeech.t2s.training.extensions.visualizer import VisualDL
from paddlespeech.t2s.training.optimizer import build_optimizers
from paddlespeech.t2s.training.seeding import seed_everything
from paddlespeech.t2s.training.trainer import Trainer
from paddlespeech.t2s.utils import str2bool


def train_sp(args, config):
    # decides device type and whether to run in parallel
    # setup running environment correctly
    world_size = paddle.distributed.get_world_size()
    if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
        if (not paddle.is_compiled_with_xpu()) or args.nxpu == 0:
            paddle.set_device("cpu")
        else:
            paddle.set_device("xpu")
    else:
        paddle.set_device("gpu")
        if world_size > 1:
            paddle.distributed.init_parallel_env()

    # set the random seed, it is a must for multiprocess training
    seed_everything(config.seed)

    print(
        f"rank: {dist.get_rank()}, pid: {os.getpid()}, parent_pid: {os.getppid()}",
    )

    fields = [
        "phones", "tones", "num_phones", "num_frames", "feats", "durations"
    ]

    spk_num = None
    if args.speaker_dict is not None:
        print("multiple speaker speedyspeech!")
        collate_fn = speedyspeech_multi_spk_batch_fn
        with open(args.speaker_dict, 'rt') as f:
            spk_id = [line.strip().split() for line in f.readlines()]
        spk_num = len(spk_id)
        fields += ["spk_id"]
    else:
        print("single speaker speedyspeech!")
        collate_fn = speedyspeech_single_spk_batch_fn
    print("spk_num:", spk_num)

    # dataloader has been too verbose
    logging.getLogger("DataLoader").disabled = True

    # construct dataset for training and validation
    with jsonlines.open(args.train_metadata, 'r') as reader:
        train_metadata = list(reader)
    if args.use_relative_path:
        # if use_relative_path in preprocess, covert it to absolute path here
        metadata_dir = Path(args.train_metadata).parent
        for item in train_metadata:
            item["feats"] = str(metadata_dir / item["feats"])

    train_dataset = DataTable(
        data=train_metadata,
        fields=fields,
        converters={
            "feats": np.load,
        }, )
    with jsonlines.open(args.dev_metadata, 'r') as reader:
        dev_metadata = list(reader)
    if args.use_relative_path:
        # if use_relative_path in preprocess, covert it to absolute path here
        metadata_dir = Path(args.dev_metadata).parent
        for item in dev_metadata:
            item["feats"] = str(metadata_dir / item["feats"])

    dev_dataset = DataTable(
        data=dev_metadata,
        fields=fields,
        converters={
            "feats": np.load,
        }, )

    # collate function and dataloader
    train_sampler = DistributedBatchSampler(
        train_dataset,
        batch_size=config.batch_size,
        shuffle=True,
        drop_last=True)
    print("samplers done!")

    train_dataloader = DataLoader(
        train_dataset,
        batch_sampler=train_sampler,
        collate_fn=collate_fn,
        num_workers=config.num_workers)
    dev_dataloader = DataLoader(
        dev_dataset,
        shuffle=False,
        drop_last=False,
        batch_size=config.batch_size,
        collate_fn=collate_fn,
        num_workers=config.num_workers)
    print("dataloaders done!")
    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,
        spk_num=spk_num,
        **config["model"])
    if world_size > 1:
        model = DataParallel(model)
    print("model done!")
    optimizer = build_optimizers(model, **config["optimizer"])
    print("optimizer done!")

    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    if dist.get_rank() == 0:
        config_name = args.config.split("/")[-1]
        # copy conf to output_dir
        shutil.copyfile(args.config, output_dir / config_name)

    updater = SpeedySpeechUpdater(
        model=model,
        optimizer=optimizer,
        dataloader=train_dataloader,
        output_dir=output_dir)

    trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)

    evaluator = SpeedySpeechEvaluator(
        model, dev_dataloader, output_dir=output_dir)

    if dist.get_rank() == 0:
        trainer.extend(evaluator, trigger=(1, "epoch"))
        trainer.extend(VisualDL(output_dir), trigger=(1, "iteration"))
    trainer.extend(
        Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch'))
    trainer.run()


def main():
    # parse args and config and redirect to train_sp
    parser = argparse.ArgumentParser(
        description="Train a Speedyspeech model with a single speaker dataset.")
    parser.add_argument("--config", type=str, help="config file.")
    parser.add_argument("--train-metadata", type=str, help="training data.")
    parser.add_argument("--dev-metadata", type=str, help="dev data.")
    parser.add_argument("--output-dir", type=str, help="output dir.")
    parser.add_argument(
        "--nxpu",
        type=int,
        default=0,
        help="if nxpu == 0 and ngpu == 0, use cpu.")
    parser.add_argument(
        "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu or xpu")

    parser.add_argument(
        "--use-relative-path",
        type=str2bool,
        default=False,
        help="whether use relative path in metadata")

    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(
        "--speaker-dict",
        type=str,
        default=None,
        help="speaker id map file for multiple speaker model.")

    # 这里可以多传入 max_epoch 等
    args, rest = parser.parse_known_args()

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

    if rest:
        extra = []
        # to support key=value format
        for item in rest:
            # remove "--"
            item = item[2:]
            extra.extend(item.split("=", maxsplit=1))
        config.merge_from_list(extra)

    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()}"
    )

    # dispatch
    if args.ngpu > 1:
        dist.spawn(train_sp, (args, config), nprocs=args.ngpu)
    else:
        train_sp(args, config)


if __name__ == "__main__":
    main()
