#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
#                                            Fangjun Kuang,
#                                            Quandong Wang)
#           2023 Johns Hopkins University (Author: Dongji Gao)
#
# 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

import k2
import torch
import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
from conformer import Conformer

from icefall.checkpoint import (
    average_checkpoints,
    average_checkpoints_with_averaged_model,
    find_checkpoints,
    load_checkpoint,
)
from icefall.decode import get_lattice, one_best_decoding
from icefall.env import get_env_info
from icefall.lexicon import Lexicon
from icefall.utils import (
    AttributeDict,
    get_texts,
    load_averaged_model,
    setup_logger,
    store_transcripts,
    str2bool,
    write_error_stats,
)


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

    parser.add_argument(
        "--otc-token",
        type=str,
        default="<star>",
        help="OTC token",
    )

    parser.add_argument(
        "--blank-bias",
        type=float,
        default=0,
        help="bias (log-prob) added to blank token during decoding",
    )

    parser.add_argument(
        "--epoch",
        type=int,
        default=20,
        help="""It specifies the checkpoint to use for decoding.
        Note: Epoch counts from 1.
        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=5,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch' and '--iter'",
    )

    parser.add_argument(
        "--method",
        type=str,
        default="ctc-greedy-search",
        help="""Decoding method.
        Supported values are:
            - (0) 1best. Extract the best path from the decoding lattice as the
              decoding result.
       """,
    )

    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(
        "--num-decoder-layers",
        type=int,
        default=0,
        help="""Number of decoder layer of transformer decoder.
        Setting this to 0 will not create the decoder at all (pure CTC model)
        """,
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="conformer_ctc2/exp",
        help="The experiment dir",
    )

    parser.add_argument(
        "--lang-dir",
        type=str,
        default="data/lang_phone",
        help="The lang dir",
    )

    parser.add_argument(
        "--lm-dir",
        type=str,
        default="data/lm",
        help="""The n-gram LM dir.
        It should contain either G_4_gram.pt or G_4_gram.fst.txt
        """,
    )

    return parser


def get_params() -> AttributeDict:
    params = AttributeDict(
        {
            # parameters for conformer
            "subsampling_factor": 4,
            "feature_dim": 80,
            "nhead": 8,
            "dim_feedforward": 2048,
            "encoder_dim": 512,
            "num_encoder_layers": 12,
            # parameters for decoding
            "search_beam": 20,
            "output_beam": 8,
            "min_active_states": 30,
            "max_active_states": 10000,
            "use_double_scores": True,
            "env_info": get_env_info(),
        }
    )
    return params


def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
    # from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/common.py
    new_hyp: List[int] = []
    cur = 0
    while cur < len(hyp):
        if hyp[cur] != 0:
            new_hyp.append(hyp[cur])
        prev = cur
        while cur < len(hyp) and hyp[cur] == hyp[prev]:
            cur += 1
    return new_hyp


def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    HLG: k2.Fsa,
    batch: dict,
    word_table: k2.SymbolTable,
    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.

      model:
        The neural model.
      HLG:
        The decoding graph. Used only when params.method is NOT ctc-decoding.
      H:
        The ctc topo. Used only when params.method is ctc-decoding.
      bpe_model:
        The BPE model. Used only when params.method is ctc-decoding.
      batch:
        It is the return value from iterating
        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
        for the format of the `batch`.
      word_table:
        The 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. Note: If it decodes to nothing, then return None.
    """
    device = HLG.device
    feature = batch["inputs"]
    assert feature.ndim == 3
    feature = feature.to(device)
    # at entry, feature is (N, T, C)

    supervisions = batch["supervisions"]

    nnet_output, memory, memory_key_padding_mask = model(feature, supervisions)
    # nnet_output is (N, T, C)
    nnet_output[:, :, 0] += params.blank_bias

    supervision_segments = torch.stack(
        (
            supervisions["sequence_idx"],
            torch.div(
                supervisions["start_frame"],
                params.subsampling_factor,
                rounding_mode="trunc",
            ),
            torch.div(
                supervisions["num_frames"],
                params.subsampling_factor,
                rounding_mode="trunc",
            ),
        ),
        1,
    ).to(torch.int32)

    decoding_graph = HLG

    lattice = get_lattice(
        nnet_output=nnet_output,
        decoding_graph=decoding_graph,
        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,
        subsampling_factor=params.subsampling_factor + 2,
    )

    if params.method in ["1best"]:
        best_path = one_best_decoding(
            lattice=lattice, use_double_scores=params.use_double_scores
        )
        key = "no_rescore"

        hyps = get_texts(best_path)
        hyps = [[word_table[i] for i in ids] for ids in hyps]

        return {key: hyps}
    else:
        assert False, f"Unsupported decoding method: {params.method}"


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
    HLG: k2.Fsa,
    word_table: k2.SymbolTable,
    G: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[str, 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. Used only when params.method is NOT ctc-decoding.
      H:
        The ctc topo. Used only when params.method is ctc-decoding.
      bpe_model:
        The BPE model. Used only when params.method is ctc-decoding.
      word_table:
        It is the word symbol table.
      sos_id:
        The token ID for SOS.
      eos_id:
        The token ID for EOS.
      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.
    """
    num_cuts = 0

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

    results = defaultdict(list)
    for batch_idx, batch in enumerate(dl):
        texts = batch["supervisions"]["text"]
        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]

        hyps_dict = decode_one_batch(
            params=params,
            model=model,
            HLG=HLG,
            batch=batch,
            word_table=word_table,
            G=G,
        )

        if hyps_dict is not None:
            for lm_scale, hyps in hyps_dict.items():
                this_batch = []
                assert len(hyps) == len(texts)
                for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
                    ref_words = ref_text.split()
                    this_batch.append((cut_id, ref_words, hyp_words))

                results[lm_scale].extend(this_batch)
        else:
            assert len(results) > 0, "It should not decode to empty in the first batch!"
            this_batch = []
            hyp_words = []
            for ref_text in texts:
                ref_words = ref_text.split()
                this_batch.append((ref_words, hyp_words))

            for lm_scale in results.keys():
                results[lm_scale].extend(this_batch)

        num_cuts += len(texts)

        if batch_idx % 100 == 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[str, List[str], List[str]]]],
):
    if params.method in ("attention-decoder", "rnn-lm"):
        # Set it to False since there are too many logs.
        enable_log = False
    else:
        enable_log = True
    test_set_wers = dict()
    for key, results in results_dict.items():
        recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
        results = sorted(results)
        store_transcripts(filename=recog_path, texts=results)
        if enable_log:
            logging.info(f"The transcripts are stored in {recog_path}")

        # The following prints out WERs, per-word 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, enable_log=enable_log
            )
            test_set_wers[key] = wer

        if enable_log:
            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"wer-summary-{test_set_name}.txt"
    with open(errs_info, "w") as f:
        print("settings\tWER", file=f)
        for key, val in test_set_wers:
            print("{}\t{}".format(key, val), file=f)

    s = "\nFor {}, WER 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()
    LibriSpeechAsrDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)
    args.lang_dir = Path(args.lang_dir)
    args.lm_dir = Path(args.lm_dir)

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

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

    lexicon = Lexicon(params.lang_dir)
    # remove otc_token from decoding units
    max_token_id = len(lexicon.tokens) - 1
    num_classes = max_token_id + 1  # +1 for the blank

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

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

    params.num_classes = num_classes

    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu", weights_only=False))
    HLG = HLG.to(device)
    assert HLG.requires_grad is False

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

    G = None

    model = Conformer(
        num_features=params.feature_dim,
        nhead=params.nhead,
        d_model=params.encoder_dim,
        num_classes=num_classes,
        subsampling_factor=params.subsampling_factor,
        num_encoder_layers=params.num_encoder_layers,
        num_decoder_layers=params.num_decoder_layers,
    )

    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))
        elif 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 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))
    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,
                )
            )
        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,
                )
            )

    model.to(device)
    model.eval()
    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

    # we need cut ids to display recognition results.
    args.return_cuts = True
    librispeech = LibriSpeechAsrDataModule(args)

    test_clean_cuts = librispeech.test_clean_cuts()
    test_other_cuts = librispeech.test_other_cuts()

    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
    test_other_dl = librispeech.test_dataloaders(test_other_cuts)

    test_sets = ["test-clean", "test-other"]
    test_dl = [test_clean_dl, test_other_dl]

    for test_set, test_dl in zip(test_sets, test_dl):
        results_dict = decode_dataset(
            dl=test_dl,
            params=params,
            model=model,
            HLG=HLG,
            word_table=lexicon.word_table,
        )

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

    logging.info("Done!")


torch.set_num_threads(1)
torch.set_num_interop_threads(1)

if __name__ == "__main__":
    main()