#!/usr/bin/env python3
#
# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)

"""
This script exports a CTC model from PyTorch to ONNX.

Note that the model is trained using both transducer and CTC loss. This script
exports only the CTC head.

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. Download the pre-trained model

cd egs/librispeech/ASR

repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-transducer-ctc-2023-06-13
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)

pushd $repo
git lfs pull --include "exp/pretrained.pt"

cd exp
ln -s pretrained.pt epoch-99.pt
popd

2. Export the model to ONNX

./zipformer/export-onnx-ctc.py \
  --use-transducer 0 \
  --use-ctc 1 \
  --tokens $repo/data/lang_bpe_500/tokens.txt \
  --use-averaged-model 0 \
  --epoch 99 \
  --avg 1 \
  --exp-dir $repo/exp \
  --num-encoder-layers "2,2,3,4,3,2" \
  --downsampling-factor "1,2,4,8,4,2" \
  --feedforward-dim "512,768,1024,1536,1024,768" \
  --num-heads "4,4,4,8,4,4" \
  --encoder-dim "192,256,384,512,384,256" \
  --query-head-dim 32 \
  --value-head-dim 12 \
  --pos-head-dim 4 \
  --pos-dim 48 \
  --encoder-unmasked-dim "192,192,256,256,256,192" \
  --cnn-module-kernel "31,31,15,15,15,31" \
  --decoder-dim 512 \
  --joiner-dim 512 \
  --causal False \
  --chunk-size 16 \
  --left-context-frames 128

It will generate the following 2 files inside $repo/exp:

  - model.onnx
  - model.int8.onnx

See ./onnx_pretrained_ctc.py for how to
use the exported ONNX models.
"""

import argparse
import logging
from pathlib import Path
from typing import Dict, Tuple

import k2
import onnx
import torch
import torch.nn as nn
from onnxruntime.quantization import QuantType, quantize_dynamic
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_model, get_params
from zipformer import Zipformer2

from icefall.checkpoint import (
    average_checkpoints,
    average_checkpoints_with_averaged_model,
    find_checkpoints,
    load_checkpoint,
)
from icefall.utils import make_pad_mask, num_tokens, str2bool
from icefall.utils import get_onnx_export_kwargs


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

    parser.add_argument(
        "--epoch",
        type=int,
        default=28,
        help="""It specifies the checkpoint to use for averaging.
        Note: Epoch counts from 0.
        You can specify --avg to use more checkpoints for model averaging.""",
    )

    parser.add_argument(
        "--iter",
        type=int,
        default=0,
        help="""If positive, --epoch is ignored and it
        will use the checkpoint exp_dir/checkpoint-iter.pt.
        You can specify --avg to use more checkpoints for model averaging.
        """,
    )

    parser.add_argument(
        "--avg",
        type=int,
        default=15,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch' and '--iter'",
    )

    parser.add_argument(
        "--use-averaged-model",
        type=str2bool,
        default=True,
        help="Whether to load averaged model. Currently it only supports "
        "using --epoch. If True, it would decode with the averaged model "
        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
        "Actually only the models with epoch number of `epoch-avg` and "
        "`epoch` are loaded for averaging. ",
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="zipformer/exp",
        help="""It specifies the directory where all training related
        files, e.g., checkpoints, log, etc, are saved
        """,
    )

    parser.add_argument(
        "--tokens",
        type=str,
        default="data/lang_bpe_500/tokens.txt",
        help="Path to the tokens.txt",
    )

    parser.add_argument(
        "--context-size",
        type=int,
        default=2,
        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
    )

    parser.add_argument(
        "--fp16",
        type=str2bool,
        default=False,
        help="Whether to export models in fp16",
    )

    add_model_arguments(parser)

    return parser


def add_meta_data(filename: str, meta_data: Dict[str, str]):
    """Add meta data to an ONNX model. It is changed in-place.

    Args:
      filename:
        Filename of the ONNX model to be changed.
      meta_data:
        Key-value pairs.
    """
    model = onnx.load(filename)
    for key, value in meta_data.items():
        meta = model.metadata_props.add()
        meta.key = key
        meta.value = value

    onnx.save(model, filename)


def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
    import onnxmltools
    from onnxmltools.utils.float16_converter import convert_float_to_float16

    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)


class OnnxModel(nn.Module):
    """A wrapper for encoder_embed, Zipformer, and ctc_output layer"""

    def __init__(
        self,
        encoder: Zipformer2,
        encoder_embed: nn.Module,
        ctc_output: nn.Module,
    ):
        """
        Args:
          encoder:
            A Zipformer encoder.
          encoder_embed:
            The first downsampling layer for zipformer.
        """
        super().__init__()
        self.encoder = encoder
        self.encoder_embed = encoder_embed
        self.ctc_output = ctc_output

    def forward(
        self,
        x: torch.Tensor,
        x_lens: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Please see the help information of Zipformer.forward

        Args:
          x:
            A 3-D tensor of shape (N, T, C)
          x_lens:
            A 1-D tensor of shape (N,). Its dtype is torch.int64
        Returns:
          Return a tuple containing:
            - log_probs, a 3-D tensor of shape (N, T', vocab_size)
            - log_probs_len, a 1-D int64 tensor of shape (N,)
        """
        x, x_lens = self.encoder_embed(x, x_lens)
        src_key_padding_mask = make_pad_mask(x_lens).to(torch.int32)
        x = x.permute(1, 0, 2)
        encoder_out, log_probs_len = self.encoder(x, x_lens, src_key_padding_mask)
        encoder_out = encoder_out.permute(1, 0, 2)
        log_probs = self.ctc_output(encoder_out)

        return log_probs, log_probs_len


def export_ctc_model_onnx(
    model: OnnxModel,
    filename: str,
    opset_version: int = 11,
) -> None:
    """Export the given model to ONNX format.
    The exported model has two inputs:

        - x, a tensor of shape (N, T, C); dtype is torch.float32
        - x_lens, a tensor of shape (N,); dtype is torch.int64

    and it has two outputs:

        - log_probs, a tensor of shape (N, T', joiner_dim)
        - log_probs_len, a tensor of shape (N,)

    Args:
      model:
        The input model
      filename:
        The filename to save the exported ONNX model.
      opset_version:
        The opset version to use.
    """
    x = torch.zeros(1, 100, 80, dtype=torch.float32)
    x_lens = torch.tensor([100], dtype=torch.int64)

    model = torch.jit.trace(model, (x, x_lens))

    torch.onnx.export(
        model,
        (x, x_lens),
        filename,
        verbose=False,
        opset_version=opset_version,
        input_names=["x", "x_lens"],
        output_names=["log_probs", "log_probs_len"],
        dynamic_axes={
            "x": {0: "N", 1: "T"},
            "x_lens": {0: "N"},
            "log_probs": {0: "N", 1: "T"},
            "log_probs_len": {0: "N"},
        },
        **get_onnx_export_kwargs(),
)

    meta_data = {
        "model_type": "zipformer2_ctc",
        "version": "1",
        "model_author": "k2-fsa",
        "comment": "non-streaming zipformer2 CTC",
    }
    logging.info(f"meta_data: {meta_data}")

    add_meta_data(filename=filename, meta_data=meta_data)


@torch.no_grad()
def main():
    args = get_parser().parse_args()
    args.exp_dir = Path(args.exp_dir)

    params = get_params()
    params.update(vars(args))

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", 0)

    logging.info(f"device: {device}")

    token_table = k2.SymbolTable.from_file(params.tokens)
    params.blank_id = token_table["<blk>"]
    params.vocab_size = num_tokens(token_table) + 1

    logging.info(params)

    logging.info("About to create model")
    model = get_model(params)

    model.to(device)

    if not params.use_averaged_model:
        if params.iter > 0:
            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
                : params.avg
            ]
            if len(filenames) == 0:
                raise ValueError(
                    f"No checkpoints found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            elif len(filenames) < params.avg:
                raise ValueError(
                    f"Not enough checkpoints ({len(filenames)}) found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            logging.info(f"averaging {filenames}")
            model.to(device)
            model.load_state_dict(
                average_checkpoints(filenames, device=device), strict=False
            )
        elif params.avg == 1:
            load_checkpoint(
                f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=False
            )
        else:
            start = params.epoch - params.avg + 1
            filenames = []
            for i in range(start, params.epoch + 1):
                if i >= 1:
                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
            logging.info(f"averaging {filenames}")
            model.to(device)
            model.load_state_dict(
                average_checkpoints(filenames, device=device), strict=False
            )
    else:
        if params.iter > 0:
            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
                : params.avg + 1
            ]
            if len(filenames) == 0:
                raise ValueError(
                    f"No checkpoints found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            elif len(filenames) < params.avg + 1:
                raise ValueError(
                    f"Not enough checkpoints ({len(filenames)}) found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            filename_start = filenames[-1]
            filename_end = filenames[0]
            logging.info(
                "Calculating the averaged model over iteration checkpoints"
                f" from {filename_start} (excluded) to {filename_end}"
            )
            model.to(device)
            model.load_state_dict(
                average_checkpoints_with_averaged_model(
                    filename_start=filename_start,
                    filename_end=filename_end,
                    device=device,
                ),
                strict=False,
            )
        else:
            assert params.avg > 0, params.avg
            start = params.epoch - params.avg
            assert start >= 1, start
            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
            logging.info(
                f"Calculating the averaged model over epoch range from "
                f"{start} (excluded) to {params.epoch}"
            )
            model.to(device)
            model.load_state_dict(
                average_checkpoints_with_averaged_model(
                    filename_start=filename_start,
                    filename_end=filename_end,
                    device=device,
                ),
                strict=False,
            )

    model.to("cpu")
    model.eval()

    convert_scaled_to_non_scaled(model, inplace=True, is_onnx=True)

    model = OnnxModel(
        encoder=model.encoder,
        encoder_embed=model.encoder_embed,
        ctc_output=model.ctc_output,
    )

    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"num parameters: {num_param}")

    opset_version = 13

    logging.info("Exporting ctc model")
    filename = params.exp_dir / "model.onnx"
    export_ctc_model_onnx(
        model,
        filename,
        opset_version=opset_version,
    )
    logging.info(f"Exported to {filename}")

    # Generate int8 quantization models
    # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection

    logging.info("Generate int8 quantization models")

    filename_int8 = params.exp_dir / "model.int8.onnx"
    quantize_dynamic(
        model_input=filename,
        model_output=filename_int8,
        op_types_to_quantize=["MatMul"],
        weight_type=QuantType.QInt8,
    )

    if params.fp16:
        filename_fp16 = params.exp_dir / "model.fp16.onnx"
        export_onnx_fp16(filename, filename_fp16)


if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
    logging.basicConfig(format=formatter, level=logging.INFO)
    main()