#!/usr/bin/env python3
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# 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 os
from concurrent.futures import ThreadPoolExecutor

import onnxruntime
import torch
import torchaudio
import torchaudio.compliance.kaldi as kaldi
from tqdm import tqdm
from itertools import repeat


def extract_embedding(utt: str, wav_file: str, ort_session: onnxruntime.InferenceSession):
    audio, sample_rate = torchaudio.load(wav_file)
    if sample_rate != 16000:
        audio = torchaudio.transforms.Resample(
            orig_freq=sample_rate, new_freq=16000
        )(audio)
    feat = kaldi.fbank(audio, num_mel_bins=80, dither=0, sample_frequency=16000)
    feat = feat - feat.mean(dim=0, keepdim=True)
    embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
    return (utt, embedding)


def main(args):
    utt2wav, utt2spk = {}, {}
    with open("{}/wav.scp".format(args.dir)) as f:
        for l in f:
            l = l.replace("\n", "").split()
            utt2wav[l[0]] = l[1]
    with open("{}/utt2spk".format(args.dir)) as f:
        for l in f:
            l = l.replace("\n", "").split()
            utt2spk[l[0]] = l[1]

    assert os.path.exists(args.onnx_path), "onnx_path not exists"

    option = onnxruntime.SessionOptions()
    option.graph_optimization_level = (
        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
    )
    option.intra_op_num_threads = 1
    providers = ["CPUExecutionProvider"]
    ort_session = onnxruntime.InferenceSession(
        args.onnx_path, sess_options=option, providers=providers
    )

    all_utt = utt2wav.keys()

    with ThreadPoolExecutor(max_workers=args.num_thread) as executor:
        results = list(
            tqdm(
                executor.map(extract_embedding, all_utt, [utt2wav[utt] for utt in all_utt], repeat(ort_session)),
                total=len(utt2wav),
                desc="Process data: "
            )
        )

    utt2embedding, spk2embedding = {}, {}
    for utt, embedding in results:
        utt2embedding[utt] = embedding
        spk = utt2spk[utt]
        if spk not in spk2embedding:
            spk2embedding[spk] = []
        spk2embedding[spk].append(embedding)

    for k, v in spk2embedding.items():
        spk2embedding[k] = torch.tensor(v).mean(dim=0).tolist()

    torch.save(utt2embedding, "{}/utt2embedding.pt".format(args.dir))
    torch.save(spk2embedding, "{}/spk2embedding.pt".format(args.dir))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--dir", type=str)
    parser.add_argument("--onnx_path", type=str)
    parser.add_argument("--num_thread", type=int, default=8)
    args = parser.parse_args()
    main(args)