#!/usr/bin/env python3
# Copyright    2022  Johns Hopkins        (authors: Amir Hussein)
#
# 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.
"""
Usage:
(1) greedy search
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method greedy_search
(2) beam search (not recommended)
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method beam_search \
    --beam-size 4
(3) modified beam search
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method modified_beam_search \
    --beam-size 4
(4) fast beam search (one best)
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method fast_beam_search \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64
(5) fast beam search (nbest)
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method fast_beam_search_nbest \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64 \
    --num-paths 200 \
    --nbest-scale 0.5
(6) fast beam search (nbest oracle WER)
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method fast_beam_search_nbest_oracle \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64 \
    --num-paths 200 \
    --nbest-scale 0.5
(7) fast beam search (with LG)
./pruned_transducer_stateless5/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method fast_beam_search_nbest_LG \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64

(8) modified beam search with RNNLM shallow fusion (with LG)
./pruned_transducer_stateless5/decode.py \
    --epoch 35 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method fast_beam_search_nbest_LG \
    --beam 4 \
    --max-contexts 4 \
    --rnn-lm-scale 0.4 \
    --rnn-lm-exp-dir /path/to/RNNLM/exp \
    --rnn-lm-epoch 99 \
    --rnn-lm-avg 1 \
    --rnn-lm-num-layers 3 \
    --rnn-lm-tie-weights 1


"""


import argparse
import logging
import math
import pdb
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple
from lhotse.qa import validate_cut
import k2
import sentencepiece as spm
import torch
import torch.nn as nn
from asr_datamodule import IWSLTDialectSTDataModule
from beam_search import (
    beam_search,
    fast_beam_search_nbest,
    fast_beam_search_nbest_LG,
    fast_beam_search_nbest_oracle,
    fast_beam_search_one_best,
    greedy_search,
    greedy_search_batch,
    modified_beam_search,
    modified_beam_search_rnnlm_shallow_fusion,
)
from train import add_model_arguments, get_params, get_transducer_model

from icefall.checkpoint import (
    average_checkpoints,
    average_checkpoints_with_averaged_model,
    find_checkpoints,
    load_checkpoint,
)
from icefall.lexicon import Lexicon
from icefall.rnn_lm.model import RnnLmModel
from icefall.utils import (
    AttributeDict,
    setup_logger,
    store_transcripts,
    str2bool,
    write_error_stats,
)

LOG_EPS = math.log(1e-10)


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

    parser.add_argument(
        "--epoch",
        type=int,
        default=30,
        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=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="pruned_transducer_stateless5/exp",
        help="The experiment dir",
    )

    parser.add_argument(
        "--bpe-model",
        type=str,
        default="data/lang_bpe_ta_1000/bpe.model",
        help="Path to source data BPE model",
    )
    parser.add_argument(
        "--bpe-tgt-model",
        type=str,
        default="data/lang_bpe_en_1000/bpe.model",
        help="Path to target data BPE model",
    )

    parser.add_argument(
        "--lang-dir",
        type=Path,
        default="data/ang_bpe_ta_1000",
        help="The lang dir containing word table and LG graph",
    )

    parser.add_argument(
        "--lang-tgt-dir",
        type=Path,
        default="data/lang_bpe_en_1000",
        help="The lang dir containing word table and LG graph",
    )

    parser.add_argument(
        "--decoding-method",
        type=str,
        default="greedy_search",
        help="""Possible values are:
          - greedy_search
          - beam_search
          - modified_beam_search
          - fast_beam_search
          - fast_beam_search_LG
          - fast_beam_search_nbest
          - fast_beam_search_nbest_oracle
          - fast_beam_search_nbest_LG
          - modified_beam_search_rnnlm_shallow_fusion # for rnn lm shallow fusion
        If you use fast_beam_search_nbest_LG, you have to specify
        `--lang-dir`, which should contain `LG.pt`.
        """,
    )

    parser.add_argument(
        "--beam-size",
        type=int,
        default=4,
        help="""An integer indicating how many candidates we will keep for each
        frame. Used only when --decoding-method is beam_search or
        modified_beam_search.""",
    )

    parser.add_argument(
        "--beam",
        type=float,
        default=20.0,
        help="""A floating point value to calculate the cutoff score during beam
        search (i.e., `cutoff = max-score - beam`), which is the same as the
        `beam` in Kaldi.
        Used only when --decoding-method is fast_beam_search, fast_beam_search_LG,
        fast_beam_search_nbest, fast_beam_search_nbest_LG,
        and fast_beam_search_nbest_oracle
        """,
    )

    parser.add_argument(
        "--ngram-lm-scale",
        type=float,
        default=0.01,
        help="""
        Used only when --decoding_method is fast_beam_search_nbest_LG and fast_beam_search_LG.
        It specifies the scale for n-gram LM scores.
        """,
    )

    parser.add_argument(
        "--decode-chunk-size",
        type=int,
        default=16,
        help="The chunk size for decoding (in frames after subsampling)",
    )

    parser.add_argument(
        "--left-context",
        type=int,
        default=64,
        help="left context can be seen during decoding (in frames after subsampling)",
    )

    parser.add_argument(
        "--max-contexts",
        type=int,
        default=8,
        help="""Used only when --decoding-method is fast_beam_search_LG,
        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
        and fast_beam_search_nbest_oracle""",
    )

    parser.add_argument(
        "--max-states",
        type=int,
        default=64,
        help="""Used only when --decoding-method is fast_beam_search_LG,
        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
        and fast_beam_search_nbest_oracle""",
    )

    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(
        "--max-sym-per-frame",
        type=int,
        default=1,
        help="""Maximum number of symbols per frame.
        Used only when --decoding_method is greedy_search""",
    )

    parser.add_argument(
        "--num-paths",
        type=int,
        default=200,
        help="""Number of paths for nbest decoding.
        Used only when the decoding method is fast_beam_search_nbest,
        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
    )

    parser.add_argument(
        "--nbest-scale",
        type=float,
        default=0.5,
        help="""Scale applied to lattice scores when computing nbest paths.
        Used only when the decoding method is fast_beam_search_nbest,
        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
    )

    parser.add_argument(
        "--simulate-streaming",
        type=str2bool,
        default=False,
        help="""Whether to simulate streaming in decoding, this is a good way to
        test a streaming model.
        """,
    )

    parser.add_argument(
        "--rnn-lm-scale",
        type=float,
        default=0.0,
        help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
        It specifies the path to RNN LM exp dir.
        """,
    )

    parser.add_argument(
        "--rnn-lm-exp-dir",
        type=str,
        default="rnn_lm/exp",
        help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
        It specifies the path to RNN LM exp dir.
        """,
    )

    parser.add_argument(
        "--rnn-lm-epoch",
        type=int,
        default=7,
        help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
        It specifies the checkpoint to use.
        """,
    )

    parser.add_argument(
        "--rnn-lm-avg",
        type=int,
        default=2,
        help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
        It specifies the number of checkpoints to average.
        """,
    )

    parser.add_argument(
        "--rnn-lm-embedding-dim",
        type=int,
        default=2048,
        help="Embedding dim of the model",
    )

    parser.add_argument(
        "--rnn-lm-hidden-dim",
        type=int,
        default=2048,
        help="Hidden dim of the model",
    )

    parser.add_argument(
        "--rnn-lm-num-layers",
        type=int,
        default=4,
        help="Number of RNN layers the model",
    )
    parser.add_argument(
        "--rnn-lm-tie-weights",
        type=str2bool,
        default=False,
        help="""True to share the weights between the input embedding layer and the
        last output linear layer
        """,
    )
    add_model_arguments(parser)

    return parser


def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    sp: spm.SentencePieceProcessor,
    batch: dict,
    word_table: Optional[k2.SymbolTable] = None,
    decoding_graph: Optional[k2.Fsa] = None,
    rnnlm: Optional[RnnLmModel] = None,
    rnnlm_scale: float = 1.0,
) -> 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 greedy_search is used, it would be "greedy_search"
               If beam search with a beam size of 7 is used, it would be
               "beam_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`.
      model:
        The neural model.
      sp:
        The BPE model.
      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.
      decoding_graph:
        The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
        only when --decoding_method is fast_beam_search, fast_beam_search_LG, fast_beam_search_nbest,
        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
    Returns:
      Return the decoding result. See above description for the format of
      the returned dict.
    """
    device = next(model.parameters()).device
    feature = batch["inputs"]
    assert feature.ndim == 3

    feature = feature.to(device)
    # at entry, feature is (N, T, C)

    supervisions = batch["supervisions"]
    feature_lens = supervisions["num_frames"].to(device)

    if params.simulate_streaming:
        feature_lens += params.left_context
        feature = torch.nn.functional.pad(
            feature,
            pad=(0, 0, 0, params.left_context),
            value=LOG_EPS,
        )
        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
            x=feature,
            x_lens=feature_lens,
            chunk_size=params.decode_chunk_size,
            left_context=params.left_context,
            simulate_streaming=True,
        )
    else:
        encoder_out, encoder_out_lens = model.encoder(
            x=feature, x_lens=feature_lens)

    hyps = []

    if (
        params.decoding_method == "fast_beam_search"
        or params.decoding_method == "fast_beam_search_LG"
    ):
        hyp_tokens = fast_beam_search_one_best(
            model=model,
            decoding_graph=decoding_graph,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam,
            max_contexts=params.max_contexts,
            max_states=params.max_states,
        )
        if params.decoding_method == "fast_beam_search":
            for hyp in sp.decode(hyp_tokens):
                hyps.append(hyp.split())
        else:
            for hyp in hyp_tokens:
                hyps.append([word_table[i] for i in hyp])
    elif params.decoding_method == "fast_beam_search_nbest_LG":
        hyp_tokens = fast_beam_search_nbest_LG(
            model=model,
            decoding_graph=decoding_graph,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam,
            max_contexts=params.max_contexts,
            max_states=params.max_states,
            num_paths=params.num_paths,
            nbest_scale=params.nbest_scale,
        )
        for hyp in hyp_tokens:
            hyps.append([word_table[i] for i in hyp])
    elif params.decoding_method == "fast_beam_search_nbest":
        hyp_tokens = fast_beam_search_nbest(
            model=model,
            decoding_graph=decoding_graph,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam,
            max_contexts=params.max_contexts,
            max_states=params.max_states,
            num_paths=params.num_paths,
            nbest_scale=params.nbest_scale,
        )
        for hyp in sp.decode(hyp_tokens):
            hyps.append(hyp.split())
    elif params.decoding_method == "fast_beam_search_nbest_oracle":
        hyp_tokens = fast_beam_search_nbest_oracle(
            model=model,
            decoding_graph=decoding_graph,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam,
            max_contexts=params.max_contexts,
            max_states=params.max_states,
            num_paths=params.num_paths,
            ref_texts=sp.encode(supervisions["text"]),
            nbest_scale=params.nbest_scale,
        )
        for hyp in sp.decode(hyp_tokens):
            hyps.append(hyp.split())
    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
        hyp_tokens = greedy_search_batch(
            model=model,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
        )
        for hyp in sp.decode(hyp_tokens):
            hyps.append(hyp.split())
    elif params.decoding_method == "modified_beam_search":
        hyp_tokens = modified_beam_search(
            model=model,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam_size,
        )
        for hyp in sp.decode(hyp_tokens):
            hyps.append(hyp.split())
    elif params.decoding_method == "modified_beam_search_rnnlm_shallow_fusion":
        hyp_tokens = modified_beam_search_rnnlm_shallow_fusion(
            model=model,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam_size,
            sp=sp,
            rnnlm=rnnlm,
            rnnlm_scale=rnnlm_scale,
        )
        for hyp in sp.decode(hyp_tokens):
            hyps.append(hyp.split())
    else:
        batch_size = encoder_out.size(0)

        for i in range(batch_size):
            # fmt: off
            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
            # fmt: on
            if params.decoding_method == "greedy_search":
                hyp = greedy_search(
                    model=model,
                    encoder_out=encoder_out_i,
                    max_sym_per_frame=params.max_sym_per_frame,
                )
            elif params.decoding_method == "beam_search":
                hyp = beam_search(
                    model=model,
                    encoder_out=encoder_out_i,
                    beam=params.beam_size,
                )
            else:
                raise ValueError(
                    f"Unsupported decoding method: {params.decoding_method}"
                )
            hyps.append(sp.decode(hyp).split())

    if params.decoding_method == "greedy_search":
        return {"greedy_search": hyps}
    elif "fast_beam_search" in params.decoding_method:
        key = f"beam_{params.beam}_"
        key += f"max_contexts_{params.max_contexts}_"
        key += f"max_states_{params.max_states}"
        if "nbest" in params.decoding_method:
            key += f"_num_paths_{params.num_paths}_"
            key += f"nbest_scale_{params.nbest_scale}"
        if "LG" in params.decoding_method:
            key += f"_ngram_lm_scale_{params.ngram_lm_scale}"

        return {key: hyps}
    else:
        return {f"beam_size_{params.beam_size}": hyps}
def remove_short_and_long_utt(c):
    # Keep only utterances with duration between 1 second and 20 seconds
    #
    # Caution: There is a reason to select 20.0 here. Please see
    # ../local/display_manifest_statistics.py
    #
    # You should use ../local/display_manifest_statistics.py to get
    # an utterance duration distribution for your dataset to select
    # the threshold
    if c.duration < 0.5 or c.duration > 30.0:
        #logging.warning(
        #    f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
        #)
        return False
    if c.supervisions == []:
        return False
    # In pruned RNN-T, we require that T >= S
    # where T is the number of feature frames after subsampling
    # and S is the number of tokens in the utterance

    # In ./conformer.py, the conv module uses the following expression
    # for subsamplin

    return True

# def remove_seg(c):
#     if c.supervisions[0].id != 'fla_0102_1_0B_00107':
#         return True
#     else:
#         return False

def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
    sp: spm.SentencePieceProcessor,
    word_table: Optional[k2.SymbolTable] = None,
    decoding_graph: Optional[k2.Fsa] = None,
    rnnlm: Optional[RnnLmModel] = None,
    rnnlm_scale: float = 1.0,
) -> 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.
      sp:
        The BPE model.
      word_table:
        The word symbol table.
      decoding_graph:
        The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
        only when --decoding_method is fast_beam_search, fast_beam_search_LG, fast_beam_search_nbest,
        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
    Returns:
      Return a dict, whose key may be "greedy_search" if greedy search
      is used, or it may be "beam_7" if beam size of 7 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 = "?"

    if params.decoding_method == "greedy_search":
        log_interval = 50
    else:
        log_interval = 20

    results = defaultdict(list)
    for batch_idx, batch in enumerate(dl):
        texts = batch["supervisions"]["text"]
        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
        logging.info(f"Decoding {batch_idx}-th batch")

        hyps_dict = decode_one_batch(
            params=params,
            model=model,
            sp=sp,
            decoding_graph=decoding_graph,
            word_table=word_table,
            batch=batch,
            rnnlm=rnnlm,
            rnnlm_scale=rnnlm_scale,
        )

        for name, 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[name].extend(this_batch)

        num_cuts += len(texts)

        if batch_idx % log_interval == 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]]]],
):
    test_set_wers = dict()
    for key, results in results_dict.items():
        recog_path = (
            params.res_dir /
            f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
        )
        results = sorted(results)
        store_transcripts(filename=recog_path, texts=results)
        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.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
        )
        with open(errs_filename, "w") as f:
            wer = write_error_stats(
                f, f"{test_set_name}-{key}", results, enable_log=True
            )
            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.res_dir /
        f"wer-summary-{test_set_name}-{key}-{params.suffix}.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()
    IWSLTDialectSTDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)

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

    assert params.decoding_method in (
        "greedy_search",
        "beam_search",
        "fast_beam_search",
        "fast_beam_search_LG",
        "fast_beam_search_nbest",
        "fast_beam_search_nbest_LG",
        "fast_beam_search_nbest_oracle",
        "modified_beam_search",
        "modified_beam_search_rnnlm_shallow_fusion",
    )
    params.res_dir = params.exp_dir / params.decoding_method

    if params.iter > 0:
        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
    else:
        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
    if params.simulate_streaming:
        params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
        params.suffix += f"-left-context-{params.left_context}"
    if "fast_beam_search" in params.decoding_method:
        params.suffix += f"-beam-{params.beam}"
        params.suffix += f"-max-contexts-{params.max_contexts}"
        params.suffix += f"-max-states-{params.max_states}"
        if "nbest" in params.decoding_method:
            params.suffix += f"-nbest-scale-{params.nbest_scale}"
            params.suffix += f"-num-paths-{params.num_paths}"
        if "LG" in params.decoding_method:
            params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
    elif "beam_search" in params.decoding_method:
        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
    else:
        params.suffix += f"-context-{params.context_size}"
        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"

    params.suffix += f"-rnnlm-lm-scale-{params.rnn_lm_scale}"

    if params.use_averaged_model:
        params.suffix += "-use-averaged-model"

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

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

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

    sp = spm.SentencePieceProcessor()
    sp.load(params.bpe_model)

    # <blk> and <unk> are defined in local/train_bpe_model.py
    params.blank_id = sp.piece_to_id("<blk>")
    params.unk_id = sp.piece_to_id("<unk>")
    params.vocab_size = sp.get_piece_size()

    if params.simulate_streaming:
        assert (
            params.causal_convolution
        ), "Decoding in streaming requires causal convolution"

    logging.info(params)

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

    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()

    rnn_lm_model = None
    rnn_lm_scale = params.rnn_lm_scale
    if params.decoding_method == "modified_beam_search_rnnlm_shallow_fusion":
        rnn_lm_model = RnnLmModel(
            vocab_size=params.vocab_size,
            embedding_dim=params.rnn_lm_embedding_dim,
            hidden_dim=params.rnn_lm_hidden_dim,
            num_layers=params.rnn_lm_num_layers,
            tie_weights=params.rnn_lm_tie_weights,
        )
        assert params.rnn_lm_avg == 1

        load_checkpoint(
            f"{params.rnn_lm_exp_dir}/epoch-{params.rnn_lm_epoch}.pt",
            rnn_lm_model,
        )
        rnn_lm_model.to(device)
        rnn_lm_model.eval()

    if "fast_beam_search" in params.decoding_method:
        if "LG" in params.decoding_method:
            lexicon = Lexicon(params.lang_dir)
            word_table = lexicon.word_table
            lg_filename = params.lang_dir / "LG.pt"
            logging.info(f"Loading {lg_filename}")
            decoding_graph = k2.Fsa.from_dict(
                torch.load(lg_filename, map_location=device)
            )
            decoding_graph.scores *= params.ngram_lm_scale
        else:
            word_table = None
            decoding_graph = k2.trivial_graph(
                params.vocab_size - 1, device=device)
    else:
        decoding_graph = None
        word_table = None

    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
    iwslt_ta = IWSLTDialectSTDataModule(args)

    test_cuts = iwslt_ta.test_cuts()
    dev_cuts = iwslt_ta.dev_cuts()

    # lev_test_cuts = lev_test_cuts.filter(remove_short_and_long_utt)
    # # lev_test_cuts = lev_test_cuts.filter(remove_seg)
    # gulf_test_cuts = gulf_test_cuts.filter(remove_short_and_long_utt)
    # egy_test_cuts = egy_test_cuts.filter(remove_short_and_long_utt)
    # egy_h5_cuts = egy_sup_cuts.filter(remove_short_and_long_utt)
    # egy_sup_cuts = egy_h5_cuts.filter(remove_short_and_long_utt)

    test_dl = iwslt_ta.test_dataloaders(test_cuts)
    dev_dl = iwslt_ta.test_dataloaders(dev_cuts)

    test_sets = ["test", "dev"]
    test_all_dl = [test_dl, dev_dl]
    

    for test_set, test_dl in zip(test_sets, test_all_dl):
        results_dict = decode_dataset(
            dl=test_dl,
            params=params,
            model=model,
            sp=sp,
            word_table=word_table,
            decoding_graph=decoding_graph,
            rnnlm=rnn_lm_model,
            rnnlm_scale=rnn_lm_scale,
        )

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

    logging.info("Done!")


if __name__ == "__main__":
    main()