#!/usr/bin/env python3
#
# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
#                                                 Zengwei Yao)
#
# 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_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --decoding-method greedy_search

(2) beam search (not recommended)
./pruned_transducer_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --decoding-method beam_search \
    --beam-size 4

(3) modified beam search
./pruned_transducer_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --decoding-method modified_beam_search \
    --beam-size 4

(4) fast beam search (one best)
./pruned_transducer_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --decoding-method fast_beam_search \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64

(5) fast beam search (nbest)
./pruned_transducer_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --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_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --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_stateless7_streaming/decode.py \
    --epoch 28 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --max-duration 600 \
    --decode-chunk-len 32 \
    --decoding-method fast_beam_search_nbest_LG \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64
"""


import argparse
import logging
import math
import os
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 AishellAsrDataModule
from beam_search import (
    beam_search,
    fast_beam_search_one_best,
    greedy_search,
    greedy_search_batch,
    modified_beam_search,
)
from train import add_model_arguments, get_params, get_transducer_model

from icefall import ContextGraph
from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
from icefall.checkpoint import (
    average_checkpoints,
    average_checkpoints_with_averaged_model,
    find_checkpoints,
    load_checkpoint,
)
from icefall.lexicon import Lexicon
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=False,
        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_stateless3/exp",
        help="The experiment dir",
    )

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

    parser.add_argument(
        "--decoding-method",
        type=str,
        default="greedy_search",
        help="""Possible values are:
          - greedy_search
          - beam_search
          - modified_beam_search
          - fast_beam_search
        """,
    )

    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=4,
        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""",
    )

    parser.add_argument(
        "--max-contexts",
        type=int,
        default=4,
        help="""Used only when --decoding-method is
        fast_beam_search""",
    )

    parser.add_argument(
        "--max-states",
        type=int,
        default=8,
        help="""Used only when --decoding-method is
        fast_beam_search""",
    )

    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(
        "--context-score",
        type=float,
        default=2,
        help="""
        The bonus score of each token for the context biasing words/phrases.
        Used only when --decoding_method is modified_beam_search.
        """,
    )

    parser.add_argument(
        "--context-file",
        type=str,
        default="",
        help="""
        The path of the context biasing lists, one word/phrase each line
        Used only when --decoding_method is modified_beam_search.
        """,
    )

    add_model_arguments(parser)

    return parser


def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    token_table: k2.SymbolTable,
    batch: dict,
    decoding_graph: Optional[k2.Fsa] = None,
    context_graph: Optional[ContextGraph] = 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 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.
      token_table:
        It maps token ID to a string.
      batch:
        It is the return value from iterating
        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
        for the format of the `batch`.
      decoding_graph:
        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
        only when --decoding_method is fast_beam_search.
    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)

    feature_lens += 30
    feature = torch.nn.functional.pad(
        feature,
        pad=(0, 0, 0, 30),
        value=LOG_EPS,
    )
    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)

    if params.decoding_method == "fast_beam_search":
        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,
        )
    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,
        )
    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,
            context_graph=context_graph,
        )
    else:
        hyp_tokens = []
        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}"
                )
            hyp_tokens.append(hyp)

    hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens]

    if params.decoding_method == "greedy_search":
        return {"greedy_search": hyps}
    elif params.decoding_method == "fast_beam_search":
        return {
            (
                f"beam_{params.beam}_"
                f"max_contexts_{params.max_contexts}_"
                f"max_states_{params.max_states}"
            ): hyps
        }
    else:
        key = f"beam_size_{params.beam_size}"
        if params.has_contexts:
            key += f"-context-score-{params.context_score}"
        else:
            key += "-no-context-words"
        return {key: hyps}


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
    token_table: Optional[k2.SymbolTable] = None,
    decoding_graph: Optional[k2.Fsa] = None,
    context_graph: Optional[ContextGraph] = 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.
      token_table:
        It maps a token ID to a string.
      decoding_graph:
        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
        only when --decoding_method is fast_beam_search.
    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"]]

        hyps_dict = decode_one_batch(
            params=params,
            model=model,
            token_table=token_table,
            decoding_graph=decoding_graph,
            context_graph=context_graph,
            batch=batch,
        )

        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}-{params.suffix}.txt"
        results = sorted(results)
        store_transcripts(filename=recog_path, texts=results, char_level=True)
        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}-{params.suffix}.txt"
        with open(errs_filename, "w") as f:
            wer = write_error_stats(
                f,
                f"{test_set_name}-{key}",
                results,
                enable_log=True,
                compute_CER=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}-{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()
    AishellAsrDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)
    args.lang_dir = Path(args.lang_dir)

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

    assert params.decoding_method in (
        "greedy_search",
        "beam_search",
        "fast_beam_search",
        "modified_beam_search",
    )

    if os.path.exists(params.context_file):
        params.has_contexts = True
    else:
        params.has_contexts = False

    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 "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}"
    elif "beam_search" in params.decoding_method:
        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
        if params.has_contexts:
            params.suffix += f"-context-score-{params.context_score}"
        else:
            params.suffix += "-no-contexts-words"
    else:
        params.suffix += f"-context-{params.context_size}"
        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
    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}")

    lexicon = Lexicon(params.lang_dir)
    params.blank_id = 0
    params.vocab_size = max(lexicon.tokens) + 1

    graph_compiler = CharCtcTrainingGraphCompiler(
        lexicon=lexicon,
        device=device,
    )

    logging.info(params)

    logging.info("About to create model")
    model = get_transducer_model(params)
    assert model.encoder.decode_chunk_size == params.decode_chunk_len // 2, (
        model.encoder.decode_chunk_size,
        params.decode_chunk_len,
    )

    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()

    if params.decoding_method == "fast_beam_search":
        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
    else:
        decoding_graph = None

    if params.decoding_method == "modified_beam_search":
        if os.path.exists(params.context_file):
            contexts_text = []
            for line in open(params.context_file).readlines():
                contexts_text.append(line.strip())
            contexts = graph_compiler.texts_to_ids(contexts_text)
            context_graph = ContextGraph(params.context_score)
            context_graph.build([(c, 0.0) for c in contexts])
        else:
            context_graph = None
    else:
        context_graph = 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
    aishell = AishellAsrDataModule(args)

    test_cuts = aishell.test_cuts()
    dev_cuts = aishell.valid_cuts()

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

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

    import time

    for test_set, test_dl in zip(test_sets, test_dls):
        start = time.time()
        results_dict = decode_dataset(
            dl=test_dl,
            params=params,
            model=model,
            token_table=lexicon.token_table,
            decoding_graph=decoding_graph,
            context_graph=context_graph,
        )
        logging.info(f"Elasped time for {test_set}: {time.time() - start}")

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

    logging.info("Done!")


if __name__ == "__main__":
    main()
