#!/usr/bin/env python3

"""
This script is used for multi-speaker speech recognition.

Copyright 2017 Johns Hopkins University (Shinji Watanabe)
 Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
"""
import json
import logging
import os

# chainer related
from chainer import training
from chainer.training import extensions
from itertools import zip_longest as zip_longest
import numpy as np
from tensorboardX import SummaryWriter
import torch

from espnet.asr.asr_mix_utils import add_results_to_json
from espnet.asr.asr_utils import adadelta_eps_decay

from espnet.asr.asr_utils import CompareValueTrigger
from espnet.asr.asr_utils import get_model_conf
from espnet.asr.asr_utils import restore_snapshot
from espnet.asr.asr_utils import snapshot_object
from espnet.asr.asr_utils import torch_load
from espnet.asr.asr_utils import torch_resume
from espnet.asr.asr_utils import torch_snapshot
from espnet.asr.pytorch_backend.asr import CustomEvaluator
from espnet.asr.pytorch_backend.asr import CustomUpdater
from espnet.asr.pytorch_backend.asr import load_trained_model
import espnet.lm.pytorch_backend.extlm as extlm_pytorch
from espnet.nets.asr_interface import ASRInterface
from espnet.nets.pytorch_backend.e2e_asr_mix import pad_list
import espnet.nets.pytorch_backend.lm.default as lm_pytorch
from espnet.utils.dataset import ChainerDataLoader
from espnet.utils.dataset import TransformDataset
from espnet.utils.deterministic_utils import set_deterministic_pytorch
from espnet.utils.dynamic_import import dynamic_import
from espnet.utils.io_utils import LoadInputsAndTargets
from espnet.utils.training.batchfy import make_batchset
from espnet.utils.training.iterators import ShufflingEnabler
from espnet.utils.training.tensorboard_logger import TensorboardLogger
from espnet.utils.training.train_utils import check_early_stop
from espnet.utils.training.train_utils import set_early_stop

import matplotlib

matplotlib.use("Agg")


class CustomConverter(object):
    """Custom batch converter for Pytorch.

    Args:
        subsampling_factor (int): The subsampling factor.
        dtype (torch.dtype): Data type to convert.

    """

    def __init__(self, subsampling_factor=1, dtype=torch.float32, num_spkrs=2):
        """Initialize the converter."""
        self.subsampling_factor = subsampling_factor
        self.ignore_id = -1
        self.dtype = dtype
        self.num_spkrs = num_spkrs

    def __call__(self, batch, device=torch.device("cpu")):
        """Transform a batch and send it to a device.

        Args:
            batch (list(tuple(str, dict[str, dict[str, Any]]))): The batch to transform.
            device (torch.device): The device to send to.

        Returns:
            tuple(torch.Tensor, torch.Tensor, torch.Tensor): Transformed batch.

        """
        # batch should be located in list
        assert len(batch) == 1
        xs, ys = batch[0][0], batch[0][-self.num_spkrs :]

        # perform subsampling
        if self.subsampling_factor > 1:
            xs = [x[:: self.subsampling_factor, :] for x in xs]

        # get batch of lengths of input sequences
        ilens = np.array([x.shape[0] for x in xs])

        # perform padding and convert to tensor
        # currently only support real number
        if xs[0].dtype.kind == "c":
            xs_pad_real = pad_list(
                [torch.from_numpy(x.real).float() for x in xs], 0
            ).to(device, dtype=self.dtype)
            xs_pad_imag = pad_list(
                [torch.from_numpy(x.imag).float() for x in xs], 0
            ).to(device, dtype=self.dtype)
            # Note(kamo):
            # {'real': ..., 'imag': ...} will be changed to ComplexTensor in E2E.
            # Don't create ComplexTensor and give it to E2E here
            # because torch.nn.DataParallel can't handle it.
            xs_pad = {"real": xs_pad_real, "imag": xs_pad_imag}
        else:
            xs_pad = pad_list([torch.from_numpy(x).float() for x in xs], 0).to(
                device, dtype=self.dtype
            )

        ilens = torch.from_numpy(ilens).to(device)
        if not isinstance(ys[0], np.ndarray):
            ys_pad = []
            for i in range(len(ys)):  # speakers
                ys_pad += [torch.from_numpy(y).long() for y in ys[i]]
            ys_pad = pad_list(ys_pad, self.ignore_id)
            ys_pad = (
                ys_pad.view(self.num_spkrs, -1, ys_pad.size(1))
                .transpose(0, 1)
                .to(device)
            )  # (B, num_spkrs, Tmax)
        else:
            ys_pad = pad_list(
                [torch.from_numpy(y).long() for y in ys], self.ignore_id
            ).to(device)

        return xs_pad, ilens, ys_pad


def train(args):
    """Train with the given args.

    Args:
        args (namespace): The program arguments.

    """
    set_deterministic_pytorch(args)

    # check cuda availability
    if not torch.cuda.is_available():
        logging.warning("cuda is not available")

    # get input and output dimension info
    with open(args.valid_json, "rb") as f:
        valid_json = json.load(f)["utts"]
    utts = list(valid_json.keys())
    idim = int(valid_json[utts[0]]["input"][0]["shape"][-1])
    odim = int(valid_json[utts[0]]["output"][0]["shape"][-1])
    logging.info("#input dims : " + str(idim))
    logging.info("#output dims: " + str(odim))

    # specify attention, CTC, hybrid mode
    if args.mtlalpha == 1.0:
        mtl_mode = "ctc"
        logging.info("Pure CTC mode")
    elif args.mtlalpha == 0.0:
        mtl_mode = "att"
        logging.info("Pure attention mode")
    else:
        mtl_mode = "mtl"
        logging.info("Multitask learning mode")

    # specify model architecture
    model_class = dynamic_import(args.model_module)
    model = model_class(idim, odim, args)
    assert isinstance(model, ASRInterface)
    subsampling_factor = model.subsample[0]

    if args.rnnlm is not None:
        rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
        rnnlm = lm_pytorch.ClassifierWithState(
            lm_pytorch.RNNLM(
                len(args.char_list),
                rnnlm_args.layer,
                rnnlm_args.unit,
                getattr(rnnlm_args, "embed_unit", None),  # for backward compatibility
            )
        )
        torch.load(args.rnnlm, rnnlm)
        model.rnnlm = rnnlm

    # write model config
    if not os.path.exists(args.outdir):
        os.makedirs(args.outdir)
    model_conf = args.outdir + "/model.json"
    with open(model_conf, "wb") as f:
        logging.info("writing a model config file to " + model_conf)
        f.write(
            json.dumps(
                (idim, odim, vars(args)), indent=4, ensure_ascii=False, sort_keys=True
            ).encode("utf_8")
        )
    for key in sorted(vars(args).keys()):
        logging.info("ARGS: " + key + ": " + str(vars(args)[key]))

    reporter = model.reporter

    # check the use of multi-gpu
    if args.ngpu > 1:
        if args.batch_size != 0:
            logging.warning(
                "batch size is automatically increased (%d -> %d)"
                % (args.batch_size, args.batch_size * args.ngpu)
            )
            args.batch_size *= args.ngpu

    # set torch device
    device = torch.device("cuda" if args.ngpu > 0 else "cpu")
    if args.train_dtype in ("float16", "float32", "float64"):
        dtype = getattr(torch, args.train_dtype)
    else:
        dtype = torch.float32
    model = model.to(device=device, dtype=dtype)

    logging.warning(
        "num. model params: {:,} (num. trained: {:,} ({:.1f}%))".format(
            sum(p.numel() for p in model.parameters()),
            sum(p.numel() for p in model.parameters() if p.requires_grad),
            sum(p.numel() for p in model.parameters() if p.requires_grad)
            * 100.0
            / sum(p.numel() for p in model.parameters()),
        )
    )

    # Setup an optimizer
    if args.opt == "adadelta":
        optimizer = torch.optim.Adadelta(
            model.parameters(), rho=0.95, eps=args.eps, weight_decay=args.weight_decay
        )
    elif args.opt == "adam":
        optimizer = torch.optim.Adam(model.parameters(), weight_decay=args.weight_decay)
    elif args.opt == "noam":
        from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt

        optimizer = get_std_opt(
            model.parameters(),
            args.adim,
            args.transformer_warmup_steps,
            args.transformer_lr,
        )
    else:
        raise NotImplementedError("unknown optimizer: " + args.opt)

    # setup apex.amp
    if args.train_dtype in ("O0", "O1", "O2", "O3"):
        try:
            from apex import amp
        except ImportError as e:
            logging.error(
                f"You need to install apex for --train-dtype {args.train_dtype}. "
                "See https://github.com/NVIDIA/apex#linux"
            )
            raise e
        if args.opt == "noam":
            model, optimizer.optimizer = amp.initialize(
                model, optimizer.optimizer, opt_level=args.train_dtype
            )
        else:
            model, optimizer = amp.initialize(
                model, optimizer, opt_level=args.train_dtype
            )
        use_apex = True
    else:
        use_apex = False

    # FIXME: TOO DIRTY HACK
    setattr(optimizer, "target", reporter)
    setattr(optimizer, "serialize", lambda s: reporter.serialize(s))

    # Setup a converter
    converter = CustomConverter(
        subsampling_factor=subsampling_factor, dtype=dtype, num_spkrs=args.num_spkrs
    )

    # read json data
    with open(args.train_json, "rb") as f:
        train_json = json.load(f)["utts"]
    with open(args.valid_json, "rb") as f:
        valid_json = json.load(f)["utts"]

    use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
    # make minibatch list (variable length)
    train = make_batchset(
        train_json,
        args.batch_size,
        args.maxlen_in,
        args.maxlen_out,
        args.minibatches,
        min_batch_size=args.ngpu if args.ngpu > 1 else 1,
        shortest_first=use_sortagrad,
        count=args.batch_count,
        batch_bins=args.batch_bins,
        batch_frames_in=args.batch_frames_in,
        batch_frames_out=args.batch_frames_out,
        batch_frames_inout=args.batch_frames_inout,
        iaxis=0,
        oaxis=-1,
    )
    valid = make_batchset(
        valid_json,
        args.batch_size,
        args.maxlen_in,
        args.maxlen_out,
        args.minibatches,
        min_batch_size=args.ngpu if args.ngpu > 1 else 1,
        count=args.batch_count,
        batch_bins=args.batch_bins,
        batch_frames_in=args.batch_frames_in,
        batch_frames_out=args.batch_frames_out,
        batch_frames_inout=args.batch_frames_inout,
        iaxis=0,
        oaxis=-1,
    )

    load_tr = LoadInputsAndTargets(
        mode="asr",
        load_output=True,
        preprocess_conf=args.preprocess_conf,
        preprocess_args={"train": True},  # Switch the mode of preprocessing
    )
    load_cv = LoadInputsAndTargets(
        mode="asr",
        load_output=True,
        preprocess_conf=args.preprocess_conf,
        preprocess_args={"train": False},  # Switch the mode of preprocessing
    )
    # hack to make batchsize argument as 1
    # actual bathsize is included in a list
    # default collate function converts numpy array to pytorch tensor
    # we used an empty collate function instead which returns list
    train_iter = {
        "main": ChainerDataLoader(
            dataset=TransformDataset(train, lambda data: converter([load_tr(data)])),
            batch_size=1,
            num_workers=args.n_iter_processes,
            shuffle=True,
            collate_fn=lambda x: x[0],
        )
    }
    valid_iter = {
        "main": ChainerDataLoader(
            dataset=TransformDataset(valid, lambda data: converter([load_cv(data)])),
            batch_size=1,
            shuffle=False,
            collate_fn=lambda x: x[0],
            num_workers=args.n_iter_processes,
        )
    }

    # Set up a trainer
    updater = CustomUpdater(
        model,
        args.grad_clip,
        train_iter,
        optimizer,
        device,
        args.ngpu,
        args.grad_noise,
        args.accum_grad,
        use_apex=use_apex,
    )
    trainer = training.Trainer(updater, (args.epochs, "epoch"), out=args.outdir)

    if use_sortagrad:
        trainer.extend(
            ShufflingEnabler([train_iter]),
            trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs, "epoch"),
        )

    # Resume from a snapshot
    if args.resume:
        logging.info("resumed from %s" % args.resume)
        torch_resume(args.resume, trainer)

    # Evaluate the model with the test dataset for each epoch
    trainer.extend(CustomEvaluator(model, valid_iter, reporter, device, args.ngpu))

    # Save attention weight each epoch
    if args.num_save_attention > 0 and args.mtlalpha != 1.0:
        data = sorted(
            list(valid_json.items())[: args.num_save_attention],
            key=lambda x: int(x[1]["input"][0]["shape"][1]),
            reverse=True,
        )
        if hasattr(model, "module"):
            att_vis_fn = model.module.calculate_all_attentions
            plot_class = model.module.attention_plot_class
        else:
            att_vis_fn = model.calculate_all_attentions
            plot_class = model.attention_plot_class
        att_reporter = plot_class(
            att_vis_fn,
            data,
            args.outdir + "/att_ws",
            converter=converter,
            transform=load_cv,
            device=device,
        )
        trainer.extend(att_reporter, trigger=(1, "epoch"))
    else:
        att_reporter = None

    # Make a plot for training and validation values
    trainer.extend(
        extensions.PlotReport(
            [
                "main/loss",
                "validation/main/loss",
                "main/loss_ctc",
                "validation/main/loss_ctc",
                "main/loss_att",
                "validation/main/loss_att",
            ],
            "epoch",
            file_name="loss.png",
        )
    )
    trainer.extend(
        extensions.PlotReport(
            ["main/acc", "validation/main/acc"], "epoch", file_name="acc.png"
        )
    )
    trainer.extend(
        extensions.PlotReport(
            ["main/cer_ctc", "validation/main/cer_ctc"], "epoch", file_name="cer.png"
        )
    )

    # Save best models
    trainer.extend(
        snapshot_object(model, "model.loss.best"),
        trigger=training.triggers.MinValueTrigger("validation/main/loss"),
    )
    if mtl_mode != "ctc":
        trainer.extend(
            snapshot_object(model, "model.acc.best"),
            trigger=training.triggers.MaxValueTrigger("validation/main/acc"),
        )

    # save snapshot which contains model and optimizer states
    trainer.extend(torch_snapshot(), trigger=(1, "epoch"))

    # epsilon decay in the optimizer
    if args.opt == "adadelta":
        if args.criterion == "acc" and mtl_mode != "ctc":
            trainer.extend(
                restore_snapshot(
                    model, args.outdir + "/model.acc.best", load_fn=torch_load
                ),
                trigger=CompareValueTrigger(
                    "validation/main/acc",
                    lambda best_value, current_value: best_value > current_value,
                ),
            )
            trainer.extend(
                adadelta_eps_decay(args.eps_decay),
                trigger=CompareValueTrigger(
                    "validation/main/acc",
                    lambda best_value, current_value: best_value > current_value,
                ),
            )
        elif args.criterion == "loss":
            trainer.extend(
                restore_snapshot(
                    model, args.outdir + "/model.loss.best", load_fn=torch_load
                ),
                trigger=CompareValueTrigger(
                    "validation/main/loss",
                    lambda best_value, current_value: best_value < current_value,
                ),
            )
            trainer.extend(
                adadelta_eps_decay(args.eps_decay),
                trigger=CompareValueTrigger(
                    "validation/main/loss",
                    lambda best_value, current_value: best_value < current_value,
                ),
            )

    # Write a log of evaluation statistics for each epoch
    trainer.extend(
        extensions.LogReport(trigger=(args.report_interval_iters, "iteration"))
    )
    report_keys = [
        "epoch",
        "iteration",
        "main/loss",
        "main/loss_ctc",
        "main/loss_att",
        "validation/main/loss",
        "validation/main/loss_ctc",
        "validation/main/loss_att",
        "main/acc",
        "validation/main/acc",
        "main/cer_ctc",
        "validation/main/cer_ctc",
        "elapsed_time",
    ]
    if args.opt == "adadelta":
        trainer.extend(
            extensions.observe_value(
                "eps",
                lambda trainer: trainer.updater.get_optimizer("main").param_groups[0][
                    "eps"
                ],
            ),
            trigger=(args.report_interval_iters, "iteration"),
        )
        report_keys.append("eps")
    if args.report_cer:
        report_keys.append("validation/main/cer")
    if args.report_wer:
        report_keys.append("validation/main/wer")
    trainer.extend(
        extensions.PrintReport(report_keys),
        trigger=(args.report_interval_iters, "iteration"),
    )

    trainer.extend(extensions.ProgressBar(update_interval=args.report_interval_iters))
    set_early_stop(trainer, args)

    if args.tensorboard_dir is not None and args.tensorboard_dir != "":
        trainer.extend(
            TensorboardLogger(SummaryWriter(args.tensorboard_dir), att_reporter),
            trigger=(args.report_interval_iters, "iteration"),
        )
    # Run the training
    trainer.run()
    check_early_stop(trainer, args.epochs)


def recog(args):
    """Decode with the given args.

    Args:
        args (namespace): The program arguments.

    """
    set_deterministic_pytorch(args)
    model, train_args = load_trained_model(args.model)
    assert isinstance(model, ASRInterface)
    model.recog_args = args

    # read rnnlm
    if args.rnnlm:
        rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
        if getattr(rnnlm_args, "model_module", "default") != "default":
            raise ValueError(
                "use '--api v2' option to decode with non-default language model"
            )
        rnnlm = lm_pytorch.ClassifierWithState(
            lm_pytorch.RNNLM(
                len(train_args.char_list),
                rnnlm_args.layer,
                rnnlm_args.unit,
                getattr(rnnlm_args, "embed_unit", None),  # for backward compatibility
            )
        )
        torch_load(args.rnnlm, rnnlm)
        rnnlm.eval()
    else:
        rnnlm = None

    if args.word_rnnlm:
        rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
        word_dict = rnnlm_args.char_list_dict
        char_dict = {x: i for i, x in enumerate(train_args.char_list)}
        word_rnnlm = lm_pytorch.ClassifierWithState(
            lm_pytorch.RNNLM(len(word_dict), rnnlm_args.layer, rnnlm_args.unit)
        )
        torch_load(args.word_rnnlm, word_rnnlm)
        word_rnnlm.eval()

        if rnnlm is not None:
            rnnlm = lm_pytorch.ClassifierWithState(
                extlm_pytorch.MultiLevelLM(
                    word_rnnlm.predictor, rnnlm.predictor, word_dict, char_dict
                )
            )
        else:
            rnnlm = lm_pytorch.ClassifierWithState(
                extlm_pytorch.LookAheadWordLM(
                    word_rnnlm.predictor, word_dict, char_dict
                )
            )

    # gpu
    if args.ngpu == 1:
        gpu_id = list(range(args.ngpu))
        logging.info("gpu id: " + str(gpu_id))
        model.cuda()
        if rnnlm:
            rnnlm.cuda()

    # read json data
    with open(args.recog_json, "rb") as f:
        js = json.load(f)["utts"]
    new_js = {}

    load_inputs_and_targets = LoadInputsAndTargets(
        mode="asr",
        load_output=False,
        sort_in_input_length=False,
        preprocess_conf=train_args.preprocess_conf
        if args.preprocess_conf is None
        else args.preprocess_conf,
        preprocess_args={"train": False},
    )

    if args.batchsize == 0:
        with torch.no_grad():
            for idx, name in enumerate(js.keys(), 1):
                logging.info("(%d/%d) decoding " + name, idx, len(js.keys()))
                batch = [(name, js[name])]
                feat = load_inputs_and_targets(batch)[0][0]
                nbest_hyps = model.recognize(feat, args, train_args.char_list, rnnlm)
                new_js[name] = add_results_to_json(
                    js[name], nbest_hyps, train_args.char_list
                )

    else:

        def grouper(n, iterable, fillvalue=None):
            kargs = [iter(iterable)] * n
            return zip_longest(*kargs, fillvalue=fillvalue)

        # sort data if batchsize > 1
        keys = list(js.keys())
        if args.batchsize > 1:
            feat_lens = [js[key]["input"][0]["shape"][0] for key in keys]
            sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
            keys = [keys[i] for i in sorted_index]

        with torch.no_grad():
            for names in grouper(args.batchsize, keys, None):
                names = [name for name in names if name]
                batch = [(name, js[name]) for name in names]
                feats = load_inputs_and_targets(batch)[0]
                nbest_hyps = model.recognize_batch(
                    feats, args, train_args.char_list, rnnlm=rnnlm
                )

                for i, name in enumerate(names):
                    nbest_hyp = [hyp[i] for hyp in nbest_hyps]
                    new_js[name] = add_results_to_json(
                        js[name], nbest_hyp, train_args.char_list
                    )

    with open(args.result_label, "wb") as f:
        f.write(
            json.dumps(
                {"utts": new_js}, indent=4, ensure_ascii=False, sort_keys=True
            ).encode("utf_8")
        )
