#!/usr/bin/env python3
# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# 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.
You can use the following command to get the exported models:

We use the pre-trained model from
https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
as an example to show how to use this file.

1. Download the pre-trained model

cd egs/librispeech/ASR

repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)

pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-iter-1224000-avg-14.pt"

cd exp
ln -s pretrained-iter-1224000-avg-14.pt epoch-9999.pt
popd

2. Export the model to ONNX

./pruned_transducer_stateless3/export-onnx.py \
  --bpe-model $repo/data/lang_bpe_500/bpe.model \
  --epoch 9999 \
  --avg 1 \
  --exp-dir $repo/exp/

It will generate the following 3 files inside $repo/exp:

  - encoder-epoch-9999-avg-1.onnx
  - decoder-epoch-9999-avg-1.onnx
  - joiner-epoch-9999-avg-1.onnx

3. Run this file

./pruned_transducer_stateless3/onnx_pretrained.py \
  --encoder-model-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \
  --decoder-model-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \
  --joiner-model-filename $repo/exp/joiner-epoch-9999-avg-1.onnx \
  --tokens $repo/data/lang_bpe_500/tokens.txt \
  $repo/test_wavs/1089-134686-0001.wav \
  $repo/test_wavs/1221-135766-0001.wav \
  $repo/test_wavs/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(
        "--encoder-model-filename",
        type=str,
        required=True,
        help="Path to the encoder onnx model. ",
    )

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

    parser.add_argument(
        "--joiner-model-filename",
        type=str,
        required=True,
        help="Path to the joiner 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,
        encoder_model_filename: str,
        decoder_model_filename: str,
        joiner_model_filename: 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_encoder(encoder_model_filename)
        self.init_decoder(decoder_model_filename)
        self.init_joiner(joiner_model_filename)

    def init_encoder(self, encoder_model_filename: str):
        self.encoder = ort.InferenceSession(
            encoder_model_filename,
            sess_options=self.session_opts,
            providers=["CPUExecutionProvider"],
        )

    def init_decoder(self, decoder_model_filename: str):
        self.decoder = ort.InferenceSession(
            decoder_model_filename,
            sess_options=self.session_opts,
            providers=["CPUExecutionProvider"],
        )

        decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
        self.context_size = int(decoder_meta["context_size"])
        self.vocab_size = int(decoder_meta["vocab_size"])

        logging.info(f"context_size: {self.context_size}")
        logging.info(f"vocab_size: {self.vocab_size}")

    def init_joiner(self, joiner_model_filename: str):
        self.joiner = ort.InferenceSession(
            joiner_model_filename,
            sess_options=self.session_opts,
            providers=["CPUExecutionProvider"],
        )

        joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
        self.joiner_dim = int(joiner_meta["joiner_dim"])

        logging.info(f"joiner_dim: {self.joiner_dim}")

    def run_encoder(
        self,
        x: torch.Tensor,
        x_lens: torch.Tensor,
    ) -> Tuple[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 tuple containing:
            - encoder_out, its shape is (N, T', joiner_dim)
            - encoder_out_lens, its shape is (N,)
        """
        out = self.encoder.run(
            [
                self.encoder.get_outputs()[0].name,
                self.encoder.get_outputs()[1].name,
            ],
            {
                self.encoder.get_inputs()[0].name: x.numpy(),
                self.encoder.get_inputs()[1].name: x_lens.numpy(),
            },
        )
        return torch.from_numpy(out[0]), torch.from_numpy(out[1])

    def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
        """
        Args:
          decoder_input:
            A 2-D tensor of shape (N, context_size)
        Returns:
          Return a 2-D tensor of shape (N, joiner_dim)
        """
        out = self.decoder.run(
            [self.decoder.get_outputs()[0].name],
            {self.decoder.get_inputs()[0].name: decoder_input.numpy()},
        )[0]

        return torch.from_numpy(out)

    def run_joiner(
        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
    ) -> torch.Tensor:
        """
        Args:
          encoder_out:
            A 2-D tensor of shape (N, joiner_dim)
          decoder_out:
            A 2-D tensor of shape (N, joiner_dim)
        Returns:
          Return a 2-D tensor of shape (N, vocab_size)
        """
        out = self.joiner.run(
            [self.joiner.get_outputs()[0].name],
            {
                self.joiner.get_inputs()[0].name: encoder_out.numpy(),
                self.joiner.get_inputs()[1].name: decoder_out.numpy(),
            },
        )[0]

        return torch.from_numpy(out)


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])
    return ans


def greedy_search(
    model: OnnxModel,
    encoder_out: torch.Tensor,
    encoder_out_lens: torch.Tensor,
) -> List[List[int]]:
    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
    Args:
      model:
        The transducer model.
      encoder_out:
        A 3-D tensor of shape (N, T, joiner_dim)
      encoder_out_lens:
        A 1-D tensor of shape (N,).
    Returns:
      Return the decoded results for each utterance.
    """
    assert encoder_out.ndim == 3, encoder_out.shape
    assert encoder_out.size(0) >= 1, encoder_out.size(0)

    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
        input=encoder_out,
        lengths=encoder_out_lens.cpu(),
        batch_first=True,
        enforce_sorted=False,
    )

    blank_id = 0  # hard-code to 0

    batch_size_list = packed_encoder_out.batch_sizes.tolist()
    N = encoder_out.size(0)

    assert torch.all(encoder_out_lens > 0), encoder_out_lens
    assert N == batch_size_list[0], (N, batch_size_list)

    context_size = model.context_size
    hyps = [[blank_id] * context_size for _ in range(N)]

    decoder_input = torch.tensor(
        hyps,
        dtype=torch.int64,
    )  # (N, context_size)

    decoder_out = model.run_decoder(decoder_input)

    offset = 0
    for batch_size in batch_size_list:
        start = offset
        end = offset + batch_size
        current_encoder_out = packed_encoder_out.data[start:end]
        # current_encoder_out's shape: (batch_size, joiner_dim)
        offset = end

        decoder_out = decoder_out[:batch_size]
        logits = model.run_joiner(current_encoder_out, decoder_out)

        # logits'shape (batch_size, vocab_size)

        assert logits.ndim == 2, logits.shape
        y = logits.argmax(dim=1).tolist()
        emitted = False
        for i, v in enumerate(y):
            if v != blank_id:
                hyps[i].append(v)
                emitted = True
        if emitted:
            # update decoder output
            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
            decoder_input = torch.tensor(
                decoder_input,
                dtype=torch.int64,
            )
            decoder_out = model.run_decoder(decoder_input)

    sorted_ans = [h[context_size:] for h in hyps]
    ans = []
    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
    for i in range(N):
        ans.append(sorted_ans[unsorted_indices[i]])

    return ans


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

    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)
    encoder_out, encoder_out_lens = model.run_encoder(features, feature_lengths)

    hyps = greedy_search(
        model=model,
        encoder_out=encoder_out,
        encoder_out_lens=encoder_out_lens,
    )
    s = "\n"

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

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

    for filename, hyp in zip(args.sound_files, hyps):
        words = token_ids_to_words(hyp)
        s += f"{filename}:\n{words}\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()