#!/usr/bin/env python3
# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
#                2022  Xiaomi Corp.        (authors: Xiaoyu Yang)
#
# 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 script loads ONNX models and uses them to decode waves.

Usage of this script:

  repo_url=https://huggingface.co/k2-fsa/sherpa-onnx-zipformer-audio-tagging-2024-04-09
  repo=$(basename $repo_url)
  git clone $repo_url
  pushd $repo
  git lfs pull --include "*.onnx"
  popd

  for m in model.onnx model.int8.onnx; do
    python3 zipformer/onnx_pretrained.py \
        --model-filename $repo/model.onnx \
        --label-dict $repo/class_labels_indices.csv \
        $repo/test_wavs/1.wav \
        $repo/test_wavs/2.wav \
        $repo/test_wavs/3.wav \
        $repo/test_wavs/4.wav
  done
"""

import argparse
import csv
import logging
import math
from typing import List

import kaldifeat
import onnxruntime as ort
import torch
import torchaudio
from torch.nn.utils.rnn import pad_sequence


def get_parser():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--model-filename",
        type=str,
        required=True,
        help="Path to the onnx model. ",
    )

    parser.add_argument(
        "--label-dict",
        type=str,
        help="""class_labels_indices.csv.""",
    )

    parser.add_argument(
        "sound_files",
        type=str,
        nargs="+",
        help="The input sound file(s) to transcribe. "
        "Supported formats are those supported by torchaudio.load(). "
        "For example, wav and flac are supported. "
        "The sample rate has to be 16kHz.",
    )

    parser.add_argument(
        "--sample-rate",
        type=int,
        default=16000,
        help="The sample rate of the input sound file",
    )

    return parser


class OnnxModel:
    def __init__(
        self,
        nn_model: str,
    ):
        session_opts = ort.SessionOptions()
        session_opts.inter_op_num_threads = 1
        session_opts.intra_op_num_threads = 4

        self.session_opts = session_opts

        self.init_model(nn_model)

    def init_model(self, nn_model: str):
        self.model = ort.InferenceSession(
            nn_model,
            sess_options=self.session_opts,
            providers=["CPUExecutionProvider"],
        )
        meta = self.model.get_modelmeta().custom_metadata_map
        print(meta)

    def __call__(
        self,
        x: torch.Tensor,
        x_lens: torch.Tensor,
    ) -> torch.Tensor:
        """
        Args:
          x:
            A 3-D tensor of shape (N, T, C)
          x_lens:
            A 2-D tensor of shape (N,). Its dtype is torch.int64
        Returns:
          Return a Tensor:
            - probs, its shape is (N, num_classes)
        """
        out = self.model.run(
            [
                self.model.get_outputs()[0].name,
            ],
            {
                self.model.get_inputs()[0].name: x.numpy(),
                self.model.get_inputs()[1].name: x_lens.numpy(),
            },
        )
        return torch.from_numpy(out[0])


def read_sound_files(
    filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
    """Read a list of sound files into a list 1-D float32 torch tensors.
    Args:
      filenames:
        A list of sound filenames.
      expected_sample_rate:
        The expected sample rate of the sound files.
    Returns:
      Return a list of 1-D float32 torch tensors.
    """
    ans = []
    for f in filenames:
        wave, sample_rate = torchaudio.load(f)
        assert (
            sample_rate == expected_sample_rate
        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
        # We use only the first channel
        ans.append(wave[0])
    return ans


@torch.no_grad()
def main():
    parser = get_parser()
    args = parser.parse_args()
    logging.info(vars(args))
    model = OnnxModel(
        nn_model=args.model_filename,
    )

    # get the label dictionary
    label_dict = {}
    with open(args.label_dict, "r") as f:
        reader = csv.reader(f, delimiter=",")
        for i, row in enumerate(reader):
            if i == 0:
                continue
            label_dict[int(row[0])] = row[2]

    logging.info("Constructing Fbank computer")
    opts = kaldifeat.FbankOptions()
    opts.device = "cpu"
    opts.frame_opts.dither = 0
    opts.frame_opts.snip_edges = False
    opts.frame_opts.samp_freq = args.sample_rate
    opts.mel_opts.num_bins = 80
    opts.mel_opts.high_freq = -400

    fbank = kaldifeat.Fbank(opts)

    logging.info(f"Reading sound files: {args.sound_files}")
    waves = read_sound_files(
        filenames=args.sound_files,
        expected_sample_rate=args.sample_rate,
    )

    logging.info("Decoding started")
    features = fbank(waves)
    feature_lengths = [f.size(0) for f in features]

    features = pad_sequence(
        features,
        batch_first=True,
        padding_value=math.log(1e-10),
    )

    feature_lengths = torch.tensor(feature_lengths, dtype=torch.int64)
    probs = model(features, feature_lengths)

    for filename, prob in zip(args.sound_files, probs):
        topk_prob, topk_index = prob.topk(5)
        topk_labels = [label_dict[index.item()] for index in topk_index]
        logging.info(
            f"{filename}: Top 5 predicted labels are {topk_labels} with "
            f"probability of {topk_prob.tolist()}"
        )

    logging.info("Decoding Done")


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

    logging.basicConfig(format=formatter, level=logging.INFO)
    main()