# Buggy code
def train(args):
    """Train with the given args

    :param Namespace args: 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)
        )
        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:
        model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
        logging.info(
            "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")
    model = model.to(device)

    # 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, args.adim, args.transformer_warmup_steps, args.transformer_lr
        )
    else:
        raise NotImplementedError("unknown optimizer: " + args.opt)

    # 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, use_specaug=args.use_specaug
    )

    # 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,
    )
    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,
    )

    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
    if args.n_iter_processes > 0:
        train_iter = ToggleableShufflingMultiprocessIterator(
            TransformDataset(train, load_tr),
            batch_size=1,
            n_processes=args.n_iter_processes,
            n_prefetch=8,
            maxtasksperchild=20,
            shuffle=not use_sortagrad,
        )
        valid_iter = ToggleableShufflingMultiprocessIterator(
            TransformDataset(valid, load_cv),
            batch_size=1,
            repeat=False,
            shuffle=False,
            n_processes=args.n_iter_processes,
            n_prefetch=8,
            maxtasksperchild=20,
        )
    else:
        train_iter = ToggleableShufflingSerialIterator(
            TransformDataset(train, load_tr), batch_size=1, shuffle=not use_sortagrad
        )
        valid_iter = ToggleableShufflingSerialIterator(
            TransformDataset(valid, load_cv), batch_size=1, repeat=False, shuffle=False
        )

    # Set up a trainer
    updater = CustomUpdater(
        model,
        args.grad_clip,
        train_iter,
        optimizer,
        converter,
        device,
        args.ngpu,
        args.accum_grad,
    )
    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, converter, device))

    # 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(
        extensions.snapshot_object(model, "model.loss.best", savefun=torch_save),
        trigger=training.triggers.MinValueTrigger("validation/main/loss"),
    )
    if mtl_mode != "ctc":
        trainer.extend(
            extensions.snapshot_object(model, "model.acc.best", savefun=torch_save),
            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=(REPORT_INTERVAL, "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=(REPORT_INTERVAL, "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=(REPORT_INTERVAL, "iteration")
    )

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

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


# Traceback (TypeError)
Exception in main training loop: '>=' not supported between instances of 'GpuDevice' and 'int'
Traceback (most recent call last):
File "/home/christopher/gitrepos/espnet-1/tools/venv/lib/python3.6/site-packages/chainer/training/trainer.py", line 319, in run
entry.extension(self)
File "/home/christopher/gitrepos/espnet-1/tools/venv/lib/python3.6/site-packages/chainer/training/extensions/evaluator.py", line 161, in __call__
result = self.evaluate()
File "/home/christopher/gitrepos/espnet-1/espnet/lm/pytorch_backend/lm.py", line 309, in evaluate
x, t = concat_examples(batch, device=self.device, padding=(0, -100))
File "/home/christopher/gitrepos/espnet-1/espnet/lm/pytorch_backend/lm.py", line 229, in concat_examples
if device is not None and device >= 0:
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "/home/christopher/gitrepos/espnet-1/egs/an4/asr1/../../../espnet/bin/lm_train.py", line 145, in <module>
main(sys.argv[1:])
File "/home/christopher/gitrepos/espnet-1/egs/an4/asr1/../../../espnet/bin/lm_train.py", line 139, in main
train(args)
File "/home/christopher/gitrepos/espnet-1/espnet/lm/pytorch_backend/lm.py", line 421, in train
trainer.run()
File "/home/christopher/gitrepos/espnet-1/tools/venv/lib/python3.6/site-packages/chainer/training/trainer.py", line 349, in run
six.reraise(*exc_info)
File "/home/christopher/gitrepos/espnet-1/tools/venv/lib/python3.6/site-packages/six.py", line 693, in reraise
raise value
File "/home/christopher/gitrepos/espnet-1/tools/venv/lib/python3.6/site-packages/chainer/training/trainer.py", line 319, in run
entry.extension(self)
File "/home/christopher/gitrepos/espnet-1/tools/venv/lib/python3.6/site-packages/chainer/training/extensions/evaluator.py", line 161, in __call__
result = self.evaluate()
File "/home/christopher/gitrepos/espnet-1/espnet/lm/pytorch_backend/lm.py", line 309, in evaluate
x, t = concat_examples(batch, device=self.device, padding=(0, -100))
File "/home/christopher/gitrepos/espnet-1/espnet/lm/pytorch_backend/lm.py", line 229, in concat_examples
if device is not None and device >= 0:
TypeError: '>=' not supported between instances of 'GpuDevice' and 'int'

# Fixed code
def train(args):
    """Train with the given args

    :param Namespace args: 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)
        )
        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:
        model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
        logging.info(
            "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")
    model = model.to(device)

    # 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, args.adim, args.transformer_warmup_steps, args.transformer_lr
        )
    else:
        raise NotImplementedError("unknown optimizer: " + args.opt)

    # 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, use_specaug=args.use_specaug
    )

    # 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,
    )
    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,
    )

    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
    if args.n_iter_processes > 0:
        train_iter = ToggleableShufflingMultiprocessIterator(
            TransformDataset(train, load_tr),
            batch_size=1,
            n_processes=args.n_iter_processes,
            n_prefetch=8,
            maxtasksperchild=20,
            shuffle=not use_sortagrad,
        )
        valid_iter = ToggleableShufflingMultiprocessIterator(
            TransformDataset(valid, load_cv),
            batch_size=1,
            repeat=False,
            shuffle=False,
            n_processes=args.n_iter_processes,
            n_prefetch=8,
            maxtasksperchild=20,
        )
    else:
        train_iter = ToggleableShufflingSerialIterator(
            TransformDataset(train, load_tr), batch_size=1, shuffle=not use_sortagrad
        )
        valid_iter = ToggleableShufflingSerialIterator(
            TransformDataset(valid, load_cv), batch_size=1, repeat=False, shuffle=False
        )

    # Set up a trainer
    updater = CustomUpdater(
        model,
        args.grad_clip,
        train_iter,
        optimizer,
        converter,
        device,
        args.ngpu,
        args.accum_grad,
    )
    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, converter, device))

    # 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=(REPORT_INTERVAL, "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=(REPORT_INTERVAL, "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=(REPORT_INTERVAL, "iteration")
    )

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

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