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

We use the pre-trained model from
https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-transducer-ctc-2023-06-13
as an example to show how to use this file.

1. Please follow ./export-onnx-ctc.py to get the onnx model.

2. Run this file

./zipformer/onnx_pretrained_ctc.py \
  --nn-model /path/to/model.onnx \
  --tokens /path/to/data/lang_bpe_500/tokens.txt \
  1089-134686-0001.wav \
  1221-135766-0001.wav \
  1221-135766-0002.wav
"""

import argparse
import logging
import math
from typing import List, Tuple

import k2
import kaldifeat
import onnxruntime as ort
import torch
import soundfile as sf
from torch.nn.utils.rnn import pad_sequence


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

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

    parser.add_argument(
        "--tokens",
        type=str,
        help="""Path to tokens.txt.""",
    )

    parser.add_argument(
        "sound_files",
        type=str,
        nargs="+",
        help="The input sound file(s) to transcribe. "
        "Supported formats include wav, flac, and other formats supported by soundfile. "
        "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 = 1

        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,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
          x:
            A 3-D float tensor of shape (N, T, C)
          x_lens:
            A 1-D int64 tensor of shape (N,)
        Returns:
          Return a tuple containing:
            - A float tensor containing log_probs of shape (N, T, C)
            - A int64 tensor containing log_probs_len of shape (N)
        """
        out = self.model.run(
            [
                self.model.get_outputs()[0].name,
                self.model.get_outputs()[1].name,
            ],
            {
                self.model.get_inputs()[0].name: x.numpy(),
                self.model.get_inputs()[1].name: x_lens.numpy(),
            },
        )
        return torch.from_numpy(out[0]), torch.from_numpy(out[1])


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:
        data, sample_rate = sf.read(f, dtype='float32')

        if len(data.shape) == 1:

            data = data[:, None]

        wave = torch.from_numpy(data.T)  # [channel, time]
        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].contiguous())
    return ans


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

    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)
    log_probs, log_probs_len = model(features, feature_lengths)

    token_table = k2.SymbolTable.from_file(args.tokens)

    def token_ids_to_words(token_ids: List[int]) -> str:
        text = ""
        for i in token_ids:
            text += token_table[i]
        return text.replace("▁", " ").strip()

    blank_id = 0
    s = "\n"
    for i in range(log_probs.size(0)):
        # greedy search
        indexes = log_probs[i, : log_probs_len[i]].argmax(dim=-1)
        token_ids = torch.unique_consecutive(indexes)

        token_ids = token_ids[token_ids != blank_id]
        words = token_ids_to_words(token_ids.tolist())
        s += f"{args.sound_files[i]}:\n{words}\n\n"

    logging.info(s)

    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()