#!/usr/bin/env python3
# Copyright    2023  Xiaomi Corp.        (Author: Liyong Guo)
#
# 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.


"""
This file computes fbank features of the HI_MIA and HI_MIA_CW dataset.
It looks for manifests in the directory data/manifests.

The generated fbank features are saved in data/fbank.
"""

import argparse
import logging
import os
from pathlib import Path

import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
from lhotse.recipes.utils import read_manifests_if_cached

from icefall.utils import get_executor, 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()
    parser.add_argument(
        "--train-set-channel",
        type=str,
        default="_7_01",
        help="""channel of HI_MIA dataset.
        All channels are used if it is set "all".
        """,
    )

    parser.add_argument(
        "--enable-speed-perturb",
        type=str2bool,
        default=False,
        help="""channel of training set.
        """,
    )
    return parser.parse_args()


def compute_fbank_himia(
    train_set_channel: str = None,
    enable_speed_perturb: bool = True,
):
    src_dir = Path("data/manifests")
    output_dir = Path("data/fbank")
    num_jobs = min(40, os.cpu_count())
    num_mel_bins = 80

    if "all" == train_set_channel:
        dataset_parts = (
            "train",
            "dev",
            "test",
            "cw_test",
        )
    else:
        dataset_parts = (
            f"train{train_set_channel}",
            f"dev{train_set_channel}",
            f"test{train_set_channel}",
            "cw_test",
        )
    manifests = read_manifests_if_cached(
        dataset_parts=dataset_parts, prefix="himia", output_dir=src_dir
    )
    assert manifests is not None

    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))

    with get_executor() as ex:  # Initialize the executor only once.
        for partition, m in manifests.items():
            if (output_dir / f"cuts_{partition}.jsonl.gz").is_file():
                logging.info(f"{partition} already exists - skipping.")
                continue
            logging.info(f"Processing {partition}")
            cut_set = CutSet.from_manifests(
                recordings=m["recordings"],
                supervisions=m["supervisions"],
            )
            if "train" in partition and enable_speed_perturb:
                cut_set = (
                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
                )
            cut_set = cut_set.resample(16000)
            cut_set = cut_set.compute_and_store_features(
                extractor=extractor,
                storage_path=f"{output_dir}/feats_{partition}",
                # when an executor is specified, make more partitions
                num_jobs=num_jobs if ex is None else 80,
                executor=ex,
                storage_type=LilcomHdf5Writer,
            )
            output_file_name = f"cuts_{partition}.jsonl.gz"
            if "all" != train_set_channel:
                output_file_name = f"cuts_{partition}{train_set_channel}.jsonl.gz"

            cut_set.to_file(output_dir / f"{output_file_name}")


def main():
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    args = get_args()
    logging.basicConfig(format=formatter, level=logging.INFO)

    compute_fbank_himia(
        train_set_channel=args.train_set_channel,
        enable_speed_perturb=args.enable_speed_perturb,
    )


if __name__ == "__main__":
    main()