#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import os
import sys
import time

import torch
import torchaudio
import sentencepiece as spm

from fairseq import tasks
from fairseq.utils import load_ensemble_for_inference, import_user_module

from interactive_asr.vad import get_microphone_chunks


def add_asr_eval_argument(parser):
    parser.add_argument("--input_file", help="input file")
    parser.add_argument("--ctc", action="store_true", help="decode a ctc model")
    parser.add_argument("--rnnt", default=False, help="decode a rnnt model")
    parser.add_argument("--kspmodel", default=None, help="sentence piece model")
    parser.add_argument(
        "--wfstlm", default=None, help="wfstlm on dictonary output units"
    )
    parser.add_argument(
        "--rnnt_decoding_type",
        default="greedy",
        help="wfstlm on dictonary output units",
    )
    parser.add_argument(
        "--lm_weight",
        default=0.2,
        help="weight for wfstlm while interpolating with neural score",
    )
    parser.add_argument(
        "--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level"
    )
    return parser


def check_args(args):
    assert args.path is not None, "--path required for generation!"
    assert (
        not args.sampling or args.nbest == args.beam
    ), "--sampling requires --nbest to be equal to --beam"
    assert (
        args.replace_unk is None or args.raw_text
    ), "--replace-unk requires a raw text dataset (--raw-text)"


def process_predictions(args, hypos, sp, tgt_dict):
    res = []
    device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu")
    for hypo in hypos[: min(len(hypos), args.nbest)]:
        hyp_pieces = tgt_dict.string(hypo["tokens"].int().to(device))
        hyp_words = sp.DecodePieces(hyp_pieces.split())
        res.append(hyp_words)
    return res


def optimize_models(args, use_cuda, models):
    """Optimize ensemble for generation
    """
    for model in models:
        model.make_generation_fast_(
            beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
            need_attn=args.print_alignment,
        )
        if args.fp16:
            model.half()
        if use_cuda:
            model.cuda()


def calc_mean_invstddev(feature):
    if len(feature.shape) != 2:
        raise ValueError("We expect the input feature to be 2-D tensor")
    mean = torch.mean(feature, dim=0)
    var = torch.var(feature, dim=0)
    # avoid division by ~zero
    if (var < sys.float_info.epsilon).any():
        return mean, 1.0 / (torch.sqrt(var) + sys.float_info.epsilon)
    return mean, 1.0 / torch.sqrt(var)


def calcMN(features):
    mean, invstddev = calc_mean_invstddev(features)
    res = (features - mean) * invstddev
    return res


def transcribe(waveform, args, task, generator, models, sp, tgt_dict):
    num_features = 80
    output = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=num_features)
    device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu")
    output_cmvn = calcMN(output.to(device).detach())

    # size (m, n)
    source = output_cmvn
    frames_lengths = torch.LongTensor([source.size(0)])

    # size (1, m, n). In general, if source is (x, m, n), then hypos is (x, ...)
    source.unsqueeze_(0)
    sample = {"net_input": {"src_tokens": source, "src_lengths": frames_lengths}}

    hypos = task.inference_step(generator, models, sample)

    assert len(hypos) == 1
    transcription = []
    for i in range(len(hypos)):
        # Process top predictions
        hyp_words = process_predictions(args, hypos[i], sp, tgt_dict)
        transcription.append(hyp_words)

    return transcription


def setup_asr(args, logger):
    check_args(args)
    import_user_module(args)

    if args.max_tokens is None and args.batch_size is None:
        args.max_tokens = 30000
    logger.info(args)

    use_cuda = torch.cuda.is_available() and not args.cpu

    # Load dataset splits
    task = tasks.setup_task(args)

    # Set dictionary
    tgt_dict = task.target_dictionary

    if args.ctc or args.rnnt:
        tgt_dict.add_symbol("<ctc_blank>")
        if args.ctc:
            logger.info("| decoding a ctc model")
        if args.rnnt:
            logger.info("| decoding a rnnt model")

    # Load ensemble
    logger.info("| loading model(s) from {}".format(args.path))
    models, _model_args = load_ensemble_for_inference(
        args.path.split(":"),
        task,
        model_arg_overrides=eval(args.model_overrides),  # noqa
    )
    optimize_models(args, use_cuda, models)

    # Initialize generator
    generator = task.build_generator(models, args)

    sp = spm.SentencePieceProcessor()
    sp.Load(os.path.join(args.data, "spm.model"))
    return task, generator, models, sp, tgt_dict


def transcribe_file(args, task, generator, models, sp, tgt_dict):
    path = args.input_file
    if not os.path.exists(path):
        raise FileNotFoundError("Audio file not found: {}".format(path))
    waveform, sample_rate = torchaudio.load_wav(path)
    waveform = waveform.mean(0, True)
    waveform = torchaudio.transforms.Resample(
        orig_freq=sample_rate, new_freq=16000
    )(waveform)

    start = time.time()
    transcription = transcribe(
        waveform, args, task, generator, models, sp, tgt_dict
    )
    transcription_time = time.time() - start
    return transcription_time, transcription


def get_microphone_transcription(args, task, generator, models, sp, tgt_dict):
    for (waveform, sample_rate) in get_microphone_chunks():
        waveform = torchaudio.transforms.Resample(
            orig_freq=sample_rate, new_freq=16000
        )(waveform.reshape(1, -1))
        transcription = transcribe(
            waveform, args, task, generator, models, sp, tgt_dict
        )
        yield transcription
