#!/usr/bin/env python3
# Copyright    2021  Johns Hopkins University (Piotr Żelasko)
# Copyright    2021  Xiaomi Corp.             (Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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
from pathlib import Path

import torch
from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig

from icefall.utils import str2bool

# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
# even when we are not invoking the main (e.g. when spawning subprocesses).
torch.set_num_threads(1)
torch.set_num_interop_threads(1)


def get_args():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument(
        "--on-the-fly",
        type=str2bool,
        default=True,
        help="When True, do not compute and store fbank features; only "
        "produce the trimmed cut manifests so that features are extracted "
        "on-the-fly during training.",
    )
    return parser.parse_args()


def compute_fbank_gigaspeech(on_the_fly: bool = True):
    in_out_dir = Path("data/fbank")

    # number of workers in dataloader
    num_workers = 20

    # number of seconds in a batch
    batch_duration = 1000

    subsets = (
        "DEV",
        "TEST",
        # "L",
        # "M",
        # "S",
        # "XS",
    )

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", 0)

    # on-the-fly mode does not need the extractor (and kaldifeat may be absent)
    extractor = None
    if not on_the_fly:
        extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))

    logging.info(f"device: {device}")

    for partition in subsets:
        cuts_path = in_out_dir / f"gigaspeech_cuts_{partition}.jsonl.gz"
        if cuts_path.is_file():
            logging.info(f"{cuts_path} exists - skipping")
            continue

        raw_cuts_path = in_out_dir / f"gigaspeech_cuts_{partition}_raw.jsonl.gz"

        logging.info(f"Loading {raw_cuts_path}")
        cut_set = CutSet.from_file(raw_cuts_path)

        if on_the_fly:
            logging.info(
                "on-the-fly is enabled - skipping feature extraction, "
                "only saving the trimmed cut manifest"
            )
        else:
            logging.info("Computing features")
            cut_set = cut_set.compute_and_store_features_batch(
                extractor=extractor,
                storage_path=f"{in_out_dir}/gigaspeech_feats_{partition}",
                num_workers=num_workers,
                batch_duration=batch_duration,
                overwrite=True,
            )

        cut_set = cut_set.trim_to_supervisions(
            keep_overlapping=False, min_duration=None
        )

        logging.info(f"Saving to {cuts_path}")
        cut_set.to_file(cuts_path)
        logging.info(f"Saved to {cuts_path}")


def main():
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
    logging.basicConfig(format=formatter, level=logging.INFO)

    args = get_args()
    compute_fbank_gigaspeech(on_the_fly=args.on_the_fly)


if __name__ == "__main__":
    main()