# Copyright (c) 2020 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
from pathlib import Path

import numpy as np
import paddle
from matplotlib import pyplot as plt

from parakeet.frontend import English
from parakeet.models.transformer_tts import TransformerTTS
from parakeet.utils import display

from config import get_cfg_defaults


def main(config, args):
    paddle.set_device(args.device)

    # model
    frontend = English()
    model = TransformerTTS.from_pretrained(frontend, config,
                                           args.checkpoint_path)
    model.eval()

    # inputs
    input_path = Path(args.input).expanduser()
    with open(input_path, "rt") as f:
        sentences = f.readlines()

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

    for i, sentence in enumerate(sentences):
        if args.verbose:
            print("text: ", sentence)
            print("phones: ", frontend.phoneticize(sentence))
        text_ids = paddle.to_tensor(frontend(sentence))
        text_ids = paddle.unsqueeze(text_ids, 0)  # (1, T)

        with paddle.no_grad():
            outputs = model.infer(text_ids, verbose=args.verbose)

        mel_output = outputs["mel_output"][0].numpy()
        cross_attention_weights = outputs["cross_attention_weights"]
        attns = np.stack([attn[0].numpy() for attn in cross_attention_weights])
        attns = np.transpose(attns, [0, 1, 3, 2])
        display.plot_multilayer_multihead_alignments(attns)
        plt.savefig(str(output_dir / f"sentence_{i}.png"))

        mel_output = mel_output.T  #(C, T)
        np.save(str(output_dir / f"sentence_{i}"), mel_output)
        if args.verbose:
            print("spectrogram saved at {}".format(output_dir /
                                                   f"sentence_{i}.npy"))


if __name__ == "__main__":
    config = get_cfg_defaults()

    parser = argparse.ArgumentParser(
        description="generate mel spectrogram with TransformerTTS.")
    parser.add_argument(
        "--config",
        type=str,
        metavar="FILE",
        help="extra config to overwrite the default config")
    parser.add_argument(
        "--checkpoint_path", type=str, help="path of the checkpoint to load.")
    parser.add_argument("--input", type=str, help="path of the text sentences")
    parser.add_argument("--output", type=str, help="path to save outputs")
    parser.add_argument(
        "--device", type=str, default="cpu", help="device type to use.")
    parser.add_argument(
        "--opts",
        nargs=argparse.REMAINDER,
        help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
    )
    parser.add_argument(
        "-v", "--verbose", action="store_true", help="print msg")

    args = parser.parse_args()
    if args.config:
        config.merge_from_file(args.config)
    if args.opts:
        config.merge_from_list(args.opts)
    config.freeze()
    print(config)
    print(args)

    main(config, args)