#!/usr/bin/env python3
#
# Copyright 2021 Xiaomi Corporation (Author: 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.
"""
Usage:
(1) greedy search
./transducer_stateless_modified-2/decode.py \
    --epoch 89 \
    --avg 38 \
    --exp-dir ./transducer_stateless_modified-2/exp \
    --max-duration 100 \
    --decoding-method greedy_search

(2) beam search (not recommended)
./transducer_stateless_modified-2/decode.py \
    --epoch 89 \
    --avg 38 \
    --exp-dir ./transducer_stateless_modified-2/exp \
    --max-duration 100 \
    --decoding-method beam_search \
    --beam-size 4

(3) modified beam search
./transducer_stateless_modified-2/decode.py \
    --epoch 89 \
    --avg 38 \
    --exp-dir ./transducer_stateless_modified-2/exp \
    --max-duration 100 \
    --decoding-method modified_beam_search \
    --beam-size 4
(4) fast beam search
./transducer_stateless_modified-2/decode.py \
    --epoch 89 \
    --avg 38 \
    --exp-dir ./transducer_stateless_modified-2/exp \
    --max-duration 100 \
    --decoding-method fast_beam_search \
    --beam-size 4 \
    --max-contexts 4 \
    --max-states 8
"""

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 aishell import AIShell
from asr_datamodule import AsrDataModule
from beam_search import (
    beam_search,
    fast_beam_search_one_best,
    greedy_search,
    greedy_search_batch,
    modified_beam_search,
)
from train import get_params, get_transducer_model

from icefall.checkpoint import average_checkpoints, load_checkpoint
from icefall.lexicon import Lexicon
from icefall.utils import (
    AttributeDict,
    setup_logger,
    store_transcripts,
    write_error_stats,
)


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 0.",
    )
    parser.add_argument(
        "--avg",
        type=int,
        default=10,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch'. ",
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="transducer_stateless_modified-2/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""",
    )

    return parser


def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    token_table: k2.SymbolTable,
    batch: dict,
    decoding_graph: 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 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.
      batch:
        It is the return value from iterating
        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
        for the format of the `batch`.
      token_table:
        It maps token ID to a string.
    Returns:
      Return the decoding result. See above description for the format of
      the returned dict.
    """
    device = model.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)

    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,
        )
    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:
        return {f"beam_size_{params.beam_size}": hyps}


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
    token_table: k2.SymbolTable,
    decoding_graph: 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.
      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 = 10

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

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

        for name, 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[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[List[int], List[int]]]],
):
    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"
        )
        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"
        )
        # we compute CER for aishell dataset.
        results_char = []
        for res in results:
            results_char.append((list("".join(res[0])), list("".join(res[1]))))
        with open(errs_filename, "w") as f:
            wer = write_error_stats(
                f, f"{test_set_name}-{key}", results_char, 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\tCER", file=f)
        for key, val in test_set_wers:
            print("{}\t{}".format(key, val), file=f)

    s = "\nFor {}, CER 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()
    AsrDataModule.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",
    )
    params.res_dir = params.exp_dir / params.decoding_method

    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}"
        )
    else:
        params.suffix += f"-context-{params.context_size}"
        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"

    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

    logging.info(params)

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

    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), strict=False
        )

    model.to(device)
    model.eval()
    model.device = device

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

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

    asr_datamodule = AsrDataModule(args)
    aishell = AIShell(manifest_dir=args.manifest_dir)
    test_cuts = aishell.test_cuts()
    test_dl = asr_datamodule.test_dataloaders(test_cuts)

    test_sets = ["test"]
    test_dls = [test_dl]

    for test_set, test_dl in zip(test_sets, test_dls):
        results_dict = decode_dataset(
            dl=test_dl,
            params=params,
            model=model,
            token_table=lexicon.token_table,
            decoding_graph=decoding_graph,
        )

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

    logging.info("Done!")


if __name__ == "__main__":
    main()