#!/usr/bin/env python3
# Copyright    2025  Xiaomi Corp.        (authors: Wei Kang)
#
# 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
import os
from pathlib import Path
from typing import Optional
from concurrent.futures import ProcessPoolExecutor as Pool

import torch
from lhotse import (
    CutSet,
    LilcomChunkyWriter,
    load_manifest_lazy,
    set_audio_duration_mismatch_tolerance,
)

from feature import TorchAudioFbank, TorchAudioFbankConfig
import lhotse

# 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 str2bool(v):
    """Used in argparse.ArgumentParser.add_argument to indicate
    that a type is a bool type and user can enter

        - yes, true, t, y, 1, to represent True
        - no, false, f, n, 0, to represent False

    See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse  # noqa
    """
    if isinstance(v, bool):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("Boolean value expected.")


def get_args():
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--sampling-rate",
        type=int,
        default=24000,
        help="The target sampling rate, the audio will be resampled to this sampling_rate.",
    )

    parser.add_argument(
        "--frame-shift",
        type=int,
        default=256,
        help="Frame shift in samples",
    )

    parser.add_argument(
        "--frame-length",
        type=int,
        default=1024,
        help="Frame length in samples",
    )

    parser.add_argument(
        "--num-mel-bins",
        type=int,
        default=100,
        help="The num of mel filters.",
    )

    parser.add_argument(
        "--dataset",
        type=str,
        help="Dataset name.",
    )

    parser.add_argument(
        "--subset",
        type=str,
        help="The subset of the dataset.",
    )

    parser.add_argument(
        "--source-dir",
        type=str,
        default="data/manifests",
        help="The source directory of manifest files.",
    )

    parser.add_argument(
        "--dest-dir",
        type=str,
        default="data/fbank",
        help="The destination directory of manifest files.",
    )

    parser.add_argument(
        "--split-cuts",
        type=str2bool,
        default=False,
        help="Whether to use splited cuts.",
    )

    parser.add_argument(
        "--split-begin",
        type=int,
        help="Start idx of splited cuts.",
    )

    parser.add_argument(
        "--split-end",
        type=int,
        help="End idx of splited cuts.",
    )

    parser.add_argument(
        "--batch-duration",
        type=int,
        default=1000,
        help="The batch duration when computing the features.",
    )

    parser.add_argument(
        "--num-jobs", type=int, default=20, help="The number of extractor workers."
    )

    return parser.parse_args()


def compute_fbank_split_single(params, idx):
    lhotse.set_audio_duration_mismatch_tolerance(0.1)  # for emilia
    src_dir = Path(params.source_dir)
    output_dir = Path(params.dest_dir)
    num_mel_bins = params.num_mel_bins

    if not src_dir.exists():
        logging.error(f"{src_dir} not exists")
        return

    if not output_dir.exists():
        output_dir.mkdir(parents=True, exist_ok=True)

    num_digits = 8

    config = TorchAudioFbankConfig(
        sampling_rate=params.sampling_rate,
        n_mels=params.num_mel_bins,
        n_fft=params.frame_length,
        hop_length=params.frame_shift,
    )
    extractor = TorchAudioFbank(config)

    prefix = params.dataset
    subset = params.subset
    suffix = "jsonl.gz"

    idx = f"{idx}".zfill(num_digits)
    cuts_filename = f"{prefix}_cuts_{subset}.{idx}.{suffix}"

    if (src_dir / cuts_filename).is_file():
        logging.info(f"Loading manifests {src_dir / cuts_filename}")
        cut_set = load_manifest_lazy(src_dir / cuts_filename)
    else:
        logging.warning(f"Raw {cuts_filename} not exists, skipping")
        return

    cut_set = cut_set.resample(params.sampling_rate)

    if (output_dir / cuts_filename).is_file():
        logging.info(f"{cuts_filename} already exists - skipping.")
        return

    logging.info(f"Processing {subset}.{idx} of {prefix}")

    cut_set = cut_set.compute_and_store_features_batch(
        extractor=extractor,
        storage_path=f"{output_dir}/{prefix}_feats_{subset}_{idx}",
        num_workers=4,
        batch_duration=params.batch_duration,
        storage_type=LilcomChunkyWriter,
        overwrite=True,
    )
    cut_set.to_file(output_dir / cuts_filename)


def compute_fbank_split(params):
    if params.split_end < params.split_begin:
        logging.warning(
            f"Split begin should be smaller than split end, given "
            f"{params.split_begin} -> {params.split_end}."
        )

    with Pool(max_workers=params.num_jobs) as pool:
        futures = [
            pool.submit(compute_fbank_split_single, params, i)
            for i in range(params.split_begin, params.split_end)
        ]
        for f in futures:
            f.result()
            f.done()


def compute_fbank(params):
    src_dir = Path(params.source_dir)
    output_dir = Path(params.dest_dir)
    num_jobs = params.num_jobs
    num_mel_bins = params.num_mel_bins

    prefix = params.dataset
    subset = params.subset
    suffix = "jsonl.gz"

    cut_set_name = f"{prefix}_cuts_{subset}.{suffix}"

    if (src_dir / cut_set_name).is_file():
        logging.info(f"Loading manifests {src_dir / cut_set_name}")
        cut_set = load_manifest_lazy(src_dir / cut_set_name)
    else:
        recordings = load_manifest_lazy(
            src_dir / f"{prefix}_recordings_{subset}.{suffix}"
        )
        supervisions = load_manifest_lazy(
            src_dir / f"{prefix}_supervisions_{subset}.{suffix}"
        )
        cut_set = CutSet.from_manifests(
            recordings=recordings,
            supervisions=supervisions,
        )

    cut_set = cut_set.resample(params.sampling_rate)

    config = TorchAudioFbankConfig(
        sampling_rate=params.sampling_rate,
        n_mels=params.num_mel_bins,
        n_fft=params.frame_length,
        hop_length=params.frame_shift,
    )
    extractor = TorchAudioFbank(config)

    cuts_filename = f"{prefix}_cuts_{subset}.{suffix}"
    if (output_dir / cuts_filename).is_file():
        logging.info(f"{prefix} {subset} already exists - skipping.")
        return
    logging.info(f"Processing {subset} of {prefix}")

    cut_set = cut_set.compute_and_store_features(
        extractor=extractor,
        storage_path=f"{output_dir}/{prefix}_feats_{subset}",
        num_jobs=num_jobs,
        storage_type=LilcomChunkyWriter,
    )
    cut_set.to_file(output_dir / cuts_filename)


if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)
    args = get_args()
    logging.info(vars(args))
    if args.split_cuts:
        compute_fbank_split(params=args)
    else:
        compute_fbank(params=args)