#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, 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.


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

import k2
from numpy import True_
import sentencepiece as spm
import torch
import torch.nn as nn
from asr_datamodule import SPGISpeechAsrDataModule
from conformer import Conformer

from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.checkpoint import average_checkpoints, load_checkpoint
from icefall.decode import (
    get_lattice,
    nbest_decoding,
    nbest_oracle,
    one_best_decoding,
    rescore_with_attention_decoder,
    rescore_with_n_best_list,
    rescore_with_whole_lattice,
)
from icefall.env import get_env_info
from icefall.lexicon import Lexicon
from icefall.utils import (
    AttributeDict,
    get_texts,
    setup_logger,
    store_transcripts,
    write_error_stats,
)


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

    parser.add_argument(
        "--epoch",
        type=int,
        default=77,
        help="It specifies the checkpoint to use for decoding."
        "Note: Epoch counts from 0.",
    )
    parser.add_argument(
        "--avg",
        type=int,
        default=55,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch'. ",
    )

    parser.add_argument(
        "--method",
        type=str,
        default="attention-decoder",
        help="""Decoding method.
        Supported values are:
            - (0) ctc-decoding. Use CTC decoding. It uses a sentence piece
              model, i.e., lang_dir/bpe.model, to convert word pieces to words.
              It needs neither a lexicon nor an n-gram LM.
            - (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.
            - (5) attention-decoder. Extract n paths from the LM rescored
              lattice, the path with the highest score is the decoding result.
            - (6) nbest-oracle. Its WER is the lower bound of any n-best
              rescoring method can achieve. Useful for debugging n-best
              rescoring method.
        """,
    )

    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, attention-decoder, and nbest-oracle
        """,
    )

    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, attention-decoder, and nbest-oracle
        A smaller value results in more unique paths.
        """,
    )

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

    parser.add_argument(
        "--lang-dir",
        type=str,
        default="data/lang_bpe_5000",
        help="The lang dir",
    )

    parser.add_argument(
        "--lm-dir",
        type=str,
        default="data/lm",
        help="""The LM dir.
        It should contain either G_3_gram.pt or G_3_gram.fst.txt
        """,
    )

    return parser


def get_params() -> AttributeDict:
    params = AttributeDict(
        {
            # parameters for conformer
            "subsampling_factor": 4,
            "vgg_frontend": False,
            "use_feat_batchnorm": True,
            "feature_dim": 80,
            "nhead": 8,
            "attention_dim": 512,
            "num_decoder_layers": 6,
            # 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 decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    HLG: Optional[k2.Fsa],
    H: Optional[k2.Fsa],
    bpe_model: Optional[spm.SentencePieceProcessor],
    batch: dict,
    word_table: k2.SymbolTable,
    sos_id: int,
    eos_id: int,
    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. 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.
      sos_id:
        The token ID of the SOS.
      eos_id:
        The token ID of the 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 the decoding result. See above description for the format of
      the returned dict. Note: If it decodes to nothing, then return None.
    """
    if HLG is not None:
        device = HLG.device
    else:
        device = H.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)

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

    if H is None:
        assert HLG is not None
        decoding_graph = HLG
    else:
        assert HLG is None
        assert bpe_model is not None
        decoding_graph = H

    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,
    )

    if params.method == "ctc-decoding":
        best_path = one_best_decoding(
            lattice=lattice, use_double_scores=params.use_double_scores
        )
        # Note: `best_path.aux_labels` contains token IDs, not word IDs
        # since we are using H, not HLG here.
        #
        # token_ids is a lit-of-list of IDs
        token_ids = get_texts(best_path)

        # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
        hyps = bpe_model.decode(token_ids)

        # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
        hyps = [s.split() for s in hyps]
        key = "ctc-decoding"
        return {key: hyps}

    if params.method == "nbest-oracle":
        # Note: You can also pass rescored lattices to it.
        # We choose the HLG decoded lattice for speed reasons
        # as HLG decoding is faster and the oracle WER
        # is only slightly worse than that of rescored lattices.
        best_path = nbest_oracle(
            lattice=lattice,
            num_paths=params.num_paths,
            ref_texts=supervisions["text"],
            word_table=word_table,
            nbest_scale=params.nbest_scale,
            oov="<UNK>",
        )
        hyps = get_texts(best_path)
        hyps = [[word_table[i] for i in ids] for ids in hyps]
        key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}"  # noqa
        return {key: hyps}

    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-nbest-scale-{params.nbest_scale}-{params.num_paths}"  # noqa

        hyps = get_texts(best_path)
        hyps = [[word_table[i] for i in ids] for ids in hyps]
        return {key: hyps}

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

    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,
        )
    elif params.method == "whole-lattice-rescoring":
        best_path_dict = rescore_with_whole_lattice(
            lattice=lattice,
            G_with_epsilon_loops=G,
            lm_scale_list=lm_scale_list,
        )
    elif params.method == "attention-decoder":
        # lattice uses a 3-gram Lm. We rescore it with a 4-gram LM.
        rescored_lattice = rescore_with_whole_lattice(
            lattice=lattice,
            G_with_epsilon_loops=G,
            lm_scale_list=None,
        )
        # TODO: pass `lattice` instead of `rescored_lattice` to
        # `rescore_with_attention_decoder`

        best_path_dict = rescore_with_attention_decoder(
            lattice=rescored_lattice,
            num_paths=params.num_paths,
            model=model,
            memory=memory,
            memory_key_padding_mask=memory_key_padding_mask,
            sos_id=sos_id,
            eos_id=eos_id,
            nbest_scale=params.nbest_scale,
        )
    else:
        assert False, f"Unsupported decoding method: {params.method}"

    ans = dict()
    if best_path_dict is not None:
        for lm_scale_str, best_path in best_path_dict.items():
            hyps = get_texts(best_path)
            hyps = [[word_table[i] for i in ids] for ids in hyps]
            ans[lm_scale_str] = hyps
    else:
        ans = None
    return ans


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
    HLG: Optional[k2.Fsa],
    H: Optional[k2.Fsa],
    bpe_model: Optional[spm.SentencePieceProcessor],
    word_table: k2.SymbolTable,
    sos_id: int,
    eos_id: int,
    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. 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.
    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"]

        hyps_dict = decode_one_batch(
            params=params,
            model=model,
            HLG=HLG,
            H=H,
            bpe_model=bpe_model,
            batch=batch,
            word_table=word_table,
            G=G,
            sos_id=sos_id,
            eos_id=eos_id,
        )

        if hyps_dict is not None:
            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)
        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[List[int], List[int]]]],
):
    if params.method == "attention-decoder":
        # 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"
        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()
    SPGISpeechAsrDataModule.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)
    max_token_id = max(lexicon.tokens)
    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}")

    graph_compiler = BpeCtcTrainingGraphCompiler(
        params.lang_dir,
        device=device,
        sos_token="<sos/eos>",
        eos_token="<sos/eos>",
    )
    sos_id = graph_compiler.sos_id
    eos_id = graph_compiler.eos_id

    if params.method == "ctc-decoding":
        HLG = None
        H = k2.ctc_topo(
            max_token=max_token_id,
            modified=True,  # Use modified topology since vocab size is large
            device=device,
        )
        bpe_model = spm.SentencePieceProcessor()
        bpe_model.load(str(params.lang_dir / "bpe.model"))
    else:
        H = None
        bpe_model = None
        HLG = k2.Fsa.from_dict(
            torch.load(f"{params.lang_dir}/HLG.pt", map_location=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",
        "attention-decoder",
    ):
        if not (params.lm_dir / "G_3_gram.pt").is_file():
            logging.info("Loading G_3_gram.fst.txt")
            logging.warning("It may take 8 minutes.")
            with open(params.lm_dir / "G_3_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
                # See https://github.com/k2-fsa/k2/issues/874
                # for why we need to set G.properties to None
                G.__dict__["_properties"] = None
                G = k2.Fsa.from_fsas([G]).to(device)
                G = k2.arc_sort(G)
                # Save a dummy value so that it can be loaded in C++.
                # See https://github.com/pytorch/pytorch/issues/67902
                # for why we need to do this.
                G.dummy = 1

                torch.save(G.as_dict(), params.lm_dir / "G_3_gram.pt")
        else:
            logging.info("Loading pre-compiled G_3_gram.pt")
            d = torch.load(params.lm_dir / "G_3_gram.pt", map_location=device)
            G = k2.Fsa.from_dict(d)

        if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
            # 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 = Conformer(
        num_features=params.feature_dim,
        nhead=params.nhead,
        d_model=params.attention_dim,
        num_classes=num_classes,
        subsampling_factor=params.subsampling_factor,
        num_decoder_layers=params.num_decoder_layers,
        vgg_frontend=params.vgg_frontend,
        use_feat_batchnorm=params.use_feat_batchnorm,
    )

    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.to(device)
        model.load_state_dict(average_checkpoints(filenames, 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}")

    spgispeech = SPGISpeechAsrDataModule(args)

    dev_cuts = spgispeech.dev_cuts()
    val_cuts = spgispeech.val_cuts()

    dev_dl = spgispeech.test_dataloaders(dev_cuts)
    val_dl = spgispeech.test_dataloaders(val_cuts)

    test_sets = ["dev", "val"]
    test_dl = [dev_dl, val_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,
            H=H,
            bpe_model=bpe_model,
            word_table=lexicon.word_table,
            G=G,
            sos_id=sos_id,
            eos_id=eos_id,
        )

        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()