#!/usr/bin/env python3
# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang
#                                                    Mingshuang Luo)
#
# 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.


import argparse
import logging
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple

from utils import encode_supervisions

import k2
import torch
import torch.nn as nn

from torch.utils.data import DataLoader
from local.dataset_audio import dataset_audio
from model import AudioNet

from icefall.checkpoint import average_checkpoints, load_checkpoint
from icefall.decode import (
    get_lattice,
    nbest_decoding,
    one_best_decoding,
    rescore_with_n_best_list,
    rescore_with_whole_lattice,
)
from icefall.lexicon import Lexicon
from icefall.utils import (
    AttributeDict,
    get_texts,
    setup_logger,
    store_transcripts,
    str2bool,
    write_error_stats,
)


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

    parser.add_argument(
        "--epoch",
        type=int,
        default=19,
        help="It specifies the checkpoint to use for decoding."
        "Note: Epoch counts from 0.",
    )
    parser.add_argument(
        "--avg",
        type=int,
        default=5,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch'. ",
    )
    parser.add_argument(
        "--method",
        type=str,
        default="whole-lattice-rescoring",
        help="""Decoding method.
        Supported values are:
            - (1) 1best. Extract the best path from the decoding lattice as the
              decoding result.
            - (2) nbest. Extract n paths from the decoding lattice; the path
              with the highest score is the decoding result.
            - (3) nbest-rescoring. Extract n paths from the decoding lattice,
              rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
              the highest score is the decoding result.
            - (4) whole-lattice-rescoring. Rescore the decoding lattice with an
              n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
              is the decoding result.
        """,
    )

    parser.add_argument(
        "--num-paths",
        type=int,
        default=100,
        help="""Number of paths for n-best based decoding method.
        Used only when "method" is one of the following values:
        nbest, nbest-rescoring
        """,
    )

    parser.add_argument(
        "--nbest-scale",
        type=float,
        default=0.5,
        help="""The scale to be applied to `lattice.scores`.
        It's needed if you use any kinds of n-best based rescoring.
        Used only when "method" is one of the following values:
        nbest, nbest-rescoring
        A smaller value results in more unique paths.
        """,
    )

    parser.add_argument(
        "--export",
        type=str2bool,
        default=False,
        help="""When enabled, the averaged model is saved to
        tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved.
        pretrained.pt contains a dict {"model": model.state_dict()},
        which can be loaded by `icefall.checkpoint.load_checkpoint()`.
        """,
    )
    return parser


def get_params() -> AttributeDict:
    params = AttributeDict(
        {
            "exp_dir": Path("audionet_ctc_asr/exp"),
            "lang_dir": Path("data/lang_character"),
            "lm_dir": Path("data/lm"),
            "feature_dim": 80,
            "subsampling_factor": 3,
            "search_beam": 20,
            "output_beam": 5,
            "min_active_states": 30,
            "max_active_states": 10000,
            "use_double_scores": True,
            # parameters for dataset
            "video_path": Path("download/GRID/lip/"),
            "anno_path": Path("download/GRID/GRID_align_txt"),
            "val_list": Path("download/GRID/unseen_val.txt"),
            "aud_padding": 480,
            "sample_rate": 16000,
            "num_workers": 16,
            "batch_size": 120,
        }
    )
    return params


def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    HLG: k2.Fsa,
    batch: dict,
    lexicon: Lexicon,
    G: Optional[k2.Fsa] = None,
) -> Dict[str, List[List[str]]]:
    """Decode one batch and return the result in a dict. The dict has the
    following format:

        - key: It indicates the setting used for decoding. For example,
               if no rescoring is used, the key is the string `no_rescore`.
               If LM rescoring is used, the key is the string `lm_scale_xxx`,
               where `xxx` is the value of `lm_scale`. An example key is
               `lm_scale_0.7`
        - value: It contains the decoding result. `len(value)` equals to
                 batch size. `value[i]` is the decoding result for the i-th
                 utterance in the given batch.
    Args:
      params:
        It's the return value of :func:`get_params`.

        - params.method is "1best", it uses 1best decoding without LM rescoring.
        - params.method is "nbest", it uses nbest decoding without LM rescoring.
        - params.method is "nbest-rescoring", it uses nbest LM rescoring.
        - params.method is "whole-lattice-rescoring", it uses whole lattice LM
          rescoring.

      model:
        The neural model.
      HLG:
        The decoding graph.
      batch:
        It is the return value from iterating
        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
        for the format of the `batch`.
      lexicon:
        It contains word symbol table.
      G:
        An LM. It is not None when params.method is "nbest-rescoring"
        or "whole-lattice-rescoring". In general, the G in HLG
        is a 3-gram LM, while this G is a 4-gram LM.
    Returns:
      Return the decoding result. See above description for the format of
      the returned dict.
    """
    device = HLG.device
    feature = batch["aud"]
    assert feature.ndim == 3
    feature = feature.to(device)
    nnet_output = model(feature.permute(0, 2, 1))

    nnet_output_shape = nnet_output.size()
    supervision_segments, text = encode_supervisions(nnet_output_shape, batch)

    lattice = get_lattice(
        nnet_output=nnet_output,
        decoding_graph=HLG,
        supervision_segments=supervision_segments,
        search_beam=params.search_beam,
        output_beam=params.output_beam,
        min_active_states=params.min_active_states,
        max_active_states=params.max_active_states,
    )

    if params.method in ["1best", "nbest"]:
        if params.method == "1best":
            best_path = one_best_decoding(
                lattice=lattice, use_double_scores=params.use_double_scores
            )
            key = "no_rescore"
        else:
            best_path = nbest_decoding(
                lattice=lattice,
                num_paths=params.num_paths,
                use_double_scores=params.use_double_scores,
                nbest_scale=params.nbest_scale,
            )
            key = f"no_rescore-{params.num_paths}"
        hyps = get_texts(best_path)
        hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps]
        return {key: hyps}

    assert params.method in ["nbest-rescoring", "whole-lattice-rescoring"]

    lm_scale_list = [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09]
    lm_scale_list += [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
    lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
    lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]

    if params.method == "nbest-rescoring":
        best_path_dict = rescore_with_n_best_list(
            lattice=lattice,
            G=G,
            num_paths=params.num_paths,
            lm_scale_list=lm_scale_list,
            nbest_scale=params.nbest_scale,
        )
    else:
        best_path_dict = rescore_with_whole_lattice(
            lattice=lattice,
            G_with_epsilon_loops=G,
            lm_scale_list=lm_scale_list,
        )

    ans = dict()
    for lm_scale_str, best_path in best_path_dict.items():
        hyps = get_texts(best_path)
        hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps]
        ans[lm_scale_str] = hyps
    return ans


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
    HLG: k2.Fsa,
    lexicon: Lexicon,
    G: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
    """Decode dataset.

    Args:
      dl:
        PyTorch's dataloader containing the dataset to decode.
      params:
        It is returned by :func:`get_params`.
      model:
        The neural model.
      HLG:
        The decoding graph.
      lexicon:
        It contains word symbol table.
      G:
        An LM. It is not None when params.method is "nbest-rescoring"
        or "whole-lattice-rescoring". In general, the G in HLG
        is a 3-gram LM, while this G is a 4-gram LM.
    Returns:
      Return a dict, whose key may be "no-rescore" if no LM rescoring
      is used, or it may be "lm_scale_0.7" if LM rescoring is used.
      Its value is a list of tuples. Each tuple contains two elements:
      The first is the reference transcript, and the second is the
      predicted result.
    """
    results = []

    num_cuts = 0

    try:
        num_batches = len(dl)
    except TypeError:
        num_batches = "?"

    results = defaultdict(list)
    for batch_idx, batch in enumerate(dl):
        texts = batch["txt"]
        hyps_dict = decode_one_batch(
            params=params,
            model=model,
            HLG=HLG,
            batch=batch,
            lexicon=lexicon,
            G=G,
        )

        for lm_scale, hyps in hyps_dict.items():
            this_batch = []
            assert len(hyps) == len(texts)
            for hyp_words, ref_text in zip(hyps, texts):
                ref_words = ref_text.split()
                this_batch.append((ref_words, hyp_words))

            results[lm_scale].extend(this_batch)

        num_cuts += len(batch["txt"])

        if batch_idx % 10 == 0:
            batch_str = f"{batch_idx}/{num_batches}"

            logging.info(
                f"batch {batch_str}, cuts processed until now is {num_cuts}"
            )
    return results


def save_results(
    params: AttributeDict,
    test_set_name: str,
    results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
):
    test_set_wers = dict()
    for key, results in results_dict.items():
        recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
        store_transcripts(filename=recog_path, texts=results)
        logging.info(f"The transcripts are stored in {recog_path}")

        # The following prints out PERs, per-phone error statistics and aligned
        # ref/hyp pairs.
        errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt"
        with open(errs_filename, "w") as f:
            wer = write_error_stats(f, f"{test_set_name}-{key}", results)
            test_set_wers[key] = wer

        logging.info("Wrote detailed error stats to {}".format(errs_filename))

    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
    errs_info = params.exp_dir / f"per-summary-{test_set_name}.txt"
    with open(errs_info, "w") as f:
        print("settings\tPER", file=f)
        for key, val in test_set_wers:
            print("{}\t{}".format(key, val), file=f)

    s = "\nFor {}, PER of different settings are:\n".format(test_set_name)
    note = "\tbest for {}".format(test_set_name)
    for key, val in test_set_wers:
        s += "{}\t{}{}\n".format(key, val, note)
        note = ""
    logging.info(s)


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

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

    setup_logger(f"{params.exp_dir}/log/log-decode")
    logging.info("Decoding started")
    logging.info(params)

    lexicon = Lexicon(params.lang_dir)
    max_token_id = max(lexicon.tokens)

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

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

    HLG = k2.Fsa.from_dict(
        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
    )

    HLG = HLG.to(device)
    assert HLG.requires_grad is False

    if not hasattr(HLG, "lm_scores"):
        HLG.lm_scores = HLG.scores.clone()

    if params.method in ["nbest-rescoring", "whole-lattice-rescoring"]:
        if not (params.lm_dir / "G_4_gram.pt").is_file():
            logging.info("Loading G_4_gram.fst.txt")
            logging.warning("It may take 8 minutes.")
            with open(params.lm_dir / "G_4_gram.fst.txt") as f:
                first_word_disambig_id = lexicon.word_table["#0"]

                G = k2.Fsa.from_openfst(f.read(), acceptor=False)
                # G.aux_labels is not needed in later computations, so
                # remove it here.
                del G.aux_labels
                # CAUTION: The following line is crucial.
                # Arcs entering the back-off state have label equal to #0.
                # We have to change it to 0 here.
                G.labels[G.labels >= first_word_disambig_id] = 0
                G = k2.Fsa.from_fsas([G]).to(device)
                G = k2.arc_sort(G)
                torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
        else:
            logging.info("Loading pre-compiled G_4_gram.pt")
            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
            G = k2.Fsa.from_dict(d).to(device)

        if params.method == "whole-lattice-rescoring":
            # Add epsilon self-loops to G as we will compose
            # it with the whole lattice later
            G = k2.add_epsilon_self_loops(G)
            G = k2.arc_sort(G)
            G = G.to(device)

        # G.lm_scores is used to replace HLG.lm_scores during
        # LM rescoring.
        G.lm_scores = G.scores.clone()
    else:
        G = None

    model = AudioNet(
        num_features=params.feature_dim,
        num_classes=max_token_id + 1,  # +1 for the blank symbol
        subsampling_factor=params.subsampling_factor,
    )
    if params.avg == 1:
        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
    else:
        start = params.epoch - params.avg + 1
        filenames = []
        for i in range(start, params.epoch + 1):
            if start >= 0:
                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
        logging.info(f"averaging {filenames}")
        model.load_state_dict(average_checkpoints(filenames))

    if params.export:
        logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
        torch.save(
            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
        )
        return

    model.to(device)
    model.eval()

    grid = dataset_audio(
        params.video_path,
        params.anno_path,
        params.val_list,
        params.aud_padding,
        params.sample_rate,
        params.feature_dim,
        "test",
    )
    test_dl = DataLoader(
        grid,
        batch_size=params.batch_size,
        shuffle=False,
        num_workers=params.num_workers,
        drop_last=False,
    )
    test_set = "test"

    results_dict = decode_dataset(
        dl=test_dl,
        params=params,
        model=model,
        HLG=HLG,
        lexicon=lexicon,
        G=G,
    )

    save_results(
        params=params, test_set_name=test_set, results_dict=results_dict
    )

    logging.info("Done!")


if __name__ == "__main__":
    main()