#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
#                                            Fangjun Kuang,
#                                            Wei Kang)
#
# 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 whisper
from whisper.normalizers import BasicTextNormalizer
import k2
import torch
import torch.nn as nn
from asr_datamodule import WenetSpeechAsrDataModule
from model import load_model

from icefall.checkpoint import load_checkpoint, average_checkpoints_with_averaged_model
from icefall.decode import (
    get_lattice,
    nbest_decoding,
    nbest_oracle,
    one_best_decoding,
    rescore_with_attention_decoder,
)
from lhotse.cut import Cut
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,
)
from zhconv import convert
from tn.chinese.normalizer import Normalizer
import re

def average_checkpoints(
    filenames: List[Path], device: torch.device = torch.device("cpu")
) -> dict:
    """Average a list of checkpoints.

    Args:
      filenames:
        Filenames of the checkpoints to be averaged. We assume all
        checkpoints are saved by :func:`save_checkpoint`.
      device:
        Move checkpoints to this device before averaging.
    Returns:
      Return a dict (i.e., state_dict) which is the average of all
      model state dicts contained in the checkpoints.
    """
    n = len(filenames)

    if "model" in torch.load(filenames[0], map_location=device):
      avg = torch.load(filenames[0], map_location=device)["model"]
    else:
      avg = torch.load(filenames[0], map_location=device)

    # Identify shared parameters. Two parameters are said to be shared
    # if they have the same data_ptr
    uniqued: Dict[int, str] = dict()

    for k, v in avg.items():
        v_data_ptr = v.data_ptr()
        if v_data_ptr in uniqued:
            continue
        uniqued[v_data_ptr] = k

    uniqued_names = list(uniqued.values())

    for i in range(1, n):
        if "model" in torch.load(filenames[i], map_location=device):
          state_dict = torch.load(filenames[i], map_location=device)["model"]
        else:
          state_dict = torch.load(filenames[i], map_location=device)
        for k in uniqued_names:
            avg[k] += state_dict[k]

    for k in uniqued_names:
        if avg[k].is_floating_point():
            avg[k] /= n
        else:
            avg[k] //= n

    return avg

def remove_punctuation(text: str or List[str]):
  # https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py
    punctuation = '!,.;:?、！，。；：？'
    if isinstance(text, str):
        text = re.sub(r'[{}]+'.format(punctuation), '', text).strip()
        return text
    elif isinstance(text, list):
        result_text = []
        for t in text:
            t = re.sub(r'[{}]+'.format(punctuation), '', t).strip()
            result_text.append(t)
        return result_text
    else:
        raise Exception(f'Not support type {type(text)}')

def to_simple(text: str or List[str]):
    if isinstance(text, str):
        text = convert(text, 'zh-cn')
        return text
    elif isinstance(text, list):
        result_text = []
        for t in text:
            t = convert(t, 'zh-cn')
            result_text.append(t)
        return result_text
    else:
        raise Exception(f'Not support type{type(text)}')

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

    parser.add_argument(
        "--epoch",
        type=int,
        default=-1,
        help="It specifies the checkpoint to use for decoding."
        "Note: Epoch counts from 0.",
    )
    parser.add_argument(
        "--avg",
        type=int,
        default=1,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch'. ",
    )

    parser.add_argument(
        "--method",
        type=str,
        default="beam-search",
        help="""Decoding method.
        Supported values are:
          - beam-search
        """,
    )

    parser.add_argument(
        "--beam-size",
        type=int,
        default=1,
        help="beam size for beam search decoding",
    )

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

    parser.add_argument(
        "--model-name",
        type=str,
        default="large-v2",
        choices=["large-v2", "large-v3", "medium", "small", "tiny"],
        help="""The model name to use.
        """,
    )
  
    return parser


def get_params() -> AttributeDict:
    params = AttributeDict(
        {
            "env_info": get_env_info(),
        }
    )
    return params

def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    batch: dict,
) -> Dict[str, List[List[int]]]:
    """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 decoding method is 1best, the key is the string `no_rescore`.
               If attention rescoring is used, the key is the string
               `ngram_lm_scale_xxx_attention_scale_xxx`, where `xxx` is the
               value of `lm_scale` and `attention_scale`. An example key is
               `ngram_lm_scale_0.7_attention_scale_0.5`
        - 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 "attention-decoder", it uses attention rescoring.

      model:
        The neural model.
      HLG:
        The decoding graph. Used when params.method is NOT ctc-decoding.
      H:
        The ctc topo. 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`.
      lexicon:
        It contains the token symbol table and the word symbol table.
      sos_id:
        The token ID of the SOS.
      eos_id:
        The token ID of the EOS.
    Returns:
      Return the decoding result. See above description for the format of
      the returned dict.
    """
    dtype = torch.float16
    device = torch.device("cuda")

    feature = batch["inputs"]
    assert feature.ndim == 3
    feature = feature.to(device, dtype=dtype).transpose(1, 2)
    T = 3000
    if feature.shape[2] < T:
     feature = torch.cat([feature, torch.zeros(feature.shape[0], feature.shape[1], T - feature.shape[2]).to(device, dtype=dtype)], 2)

    supervisions = batch["supervisions"]
    feature_len = supervisions["num_frames"]
    feature_len = feature_len.to(device, dtype=dtype)
    results = model.decode(feature, params.decoding_options)
    hyps = [result.text for result in results]
    
    hyps = remove_punctuation(hyps)
    hyps = to_simple(hyps)

    hyps = [params.normalizer.normalize(hyp) for hyp in hyps]
    print(hyps)
    key = "beam-search"

    return {key: hyps}


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
    """Decode dataset.

    Args:
      dl:
        PyTorch's dataloader containing the dataset to decode.
      params:
        It is returned by :func:`get_params`.
      model:
        The neural model.
      HLG:
        The decoding graph. Used when params.method is NOT ctc-decoding.
      H:
        The ctc topo. Used only when params.method is ctc-decoding.
      lexicon:
        It contains the token symbol table and the word symbol table.
      sos_id:
        The token ID for SOS.
      eos_id:
        The token ID for EOS.
    Returns:
      Return a dict, whose key may be "no-rescore" if the decoding method is
      1best or it may be "ngram_lm_scale_0.7_attention_scale_0.5" if attention
      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.
    """
    results = []

    num_cuts = 0

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

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

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

        for lm_scale, hyps in hyps_dict.items():
            this_batch = []
            assert len(hyps) == len(texts)
            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
                ref_words = ref_text.split()
                this_batch.append((cut_id, ref_words, hyp_words))

            results[lm_scale].extend(this_batch)

        num_cuts += len(batch["supervisions"]["text"])

        if batch_idx % 100 == 0:
            batch_str = f"{batch_idx}/{num_batches}"

            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
    return results


def save_results(
    params: AttributeDict,
    test_set_name: str,
    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
):

    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}-{params.suffix}.txt"
        results = sorted(results)
        store_transcripts(filename=recog_path, texts=results)
        if enable_log:
            logging.info(f"The transcripts are stored in {recog_path}")

        # The following prints out WERs, per-word error statistics and aligned
        # ref/hyp pairs.
        errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
        # we compute CER for aishell dataset.
        results_char = []
        for res in results:
            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
        with open(errs_filename, "w") as f:
            wer = write_error_stats(
                f, f"{test_set_name}-{key}", results_char, 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"cer-summary-{test_set_name}-{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()
    WenetSpeechAsrDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)

    params = get_params()
    params.update(vars(args))
    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
    setup_logger(f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}")

    options = whisper.DecodingOptions(task="transcribe", language="zh", without_timestamps=True, beam_size=params.beam_size)
    params.decoding_options = options
    params.cleaner = BasicTextNormalizer()
    params.normalizer = Normalizer()
  
    logging.info("Decoding started")
    logging.info(params)

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

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

    model = whisper.load_model(params.model_name)
    if params.epoch > 0:
      if params.avg > 1:
        start = params.epoch - params.avg
        assert start >= 1, start
        checkpoint = torch.load(f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location='cpu')
        if 'model' not in checkpoint:
            filenames = [f"{params.exp_dir}/epoch-{epoch}.pt" for epoch in range(start, params.epoch + 1)]
            model.load_state_dict(average_checkpoints(filenames))
        else:
            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,
                )
            )
        # save checkpoints
        filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
        torch.save(model.state_dict(), filename)
      else:
        checkpoint = torch.load(f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location='cpu')
        if 'model' not in checkpoint:
            model.load_state_dict(checkpoint, strict=True)
        else:
            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
    model.to(device)
    model.eval()
    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

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

    def remove_short_utt(c: Cut):
        T = ((c.num_frames - 7) // 2 + 1) // 2
        if T <= 0:
            logging.warning(
                f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}."
            )
        return T > 0

    # dev_cuts = wenetspeech.valid_cuts()
    # dev_cuts = dev_cuts.filter(remove_short_utt)
    # dev_dl = wenetspeech.valid_dataloaders(dev_cuts)

    # test_net_cuts = wenetspeech.test_net_cuts()
    # test_net_cuts = test_net_cuts.filter(remove_short_utt)
    # test_net_dl = wenetspeech.test_dataloaders(test_net_cuts)

    test_meeting_cuts = wenetspeech.test_meeting_cuts()
    test_meeting_cuts = test_meeting_cuts.filter(remove_short_utt)
    test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)

    # test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
    # test_dls = [dev_dl, test_net_dl, test_meeting_dl]

    test_sets = ["TEST_MEETING"]
    test_dls = [test_meeting_dl]

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

        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()