#!/usr/bin/env python3

# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

"""
Training script for ParlAI.

The standard way to train a model. After training, also computes
validation and test error.

The user must provide a model (with `--model`) and a task (with
`--task`).

## Examples

```shell
parlai train_model --model ir_baseline --task dialog_babi:Task:1 --model-file /tmp/model
parlai train_model --model seq2seq --task babi:Task10k:1 --model-file '/tmp/model' --batchsize 32 --learningrate 0.5
```
"""  # noqa: E501

# TODO List:
# * More logging (e.g. to files), make things prettier.

import json
import numpy as np
import signal
from typing import Tuple

from parlai.core.metrics import Metric
from parlai.core.agents import create_agent, create_agent_from_shared
from parlai.core.exceptions import StopTrainException
from parlai.core.logs import TensorboardLogger, WandbLogger
from parlai.core.metrics import (
    aggregate_named_reports,
    aggregate_unnamed_reports,
    dict_report,
)
from parlai.core.opt import Opt
from parlai.core.params import ParlaiParser, print_announcements
from parlai.core.worlds import create_task, World
from parlai.scripts.build_dict import build_dict, setup_args as setup_dict_args
from parlai.utils.distributed import (
    sync_object,
    is_primary_worker,
    all_gather_list,
    is_distributed,
    num_workers,
)
from parlai.utils.misc import Timer, nice_report
from parlai.core.script import ParlaiScript, register_script
import parlai.utils.logging as logging
from parlai.utils.io import PathManager


def _num_else_inf(opt: Opt, key: str, distributed_warn=False):
    if opt[key] > 0:
        if distributed_warn and is_distributed():
            nicekey = '--' + key.replace('_', '-')
            logging.warning(
                f'Using {nicekey} in distributed mode can lead to slowdowns. '
                'See https://github.com/facebookresearch/ParlAI/pull/3379 for more info.'
            )
        value = opt[key]
    else:
        value = float('inf')
    return value


def setup_args(parser=None) -> ParlaiParser:
    """
    Build the ParlAI parser, adding command line args if necessary.

    :param ParlaiParser parser:
        Preexisting parser to append options to. Will be created if needed.

    :returns:
        the ParlaiParser with CLI options added.
    """
    if parser is None:
        parser = ParlaiParser(True, True, 'Train a model')
    train = parser.add_argument_group('Training Loop Arguments')
    train.add_argument(
        '-et',
        '--evaltask',
        help='task to use for valid/test (defaults to the one used for training)',
    )
    train.add_argument(
        '--eval-batchsize',
        type=int,
        hidden=True,
        help='Eval time batch size (defaults to same as -bs)',
    )
    train.add_argument(
        '--eval-dynamic-batching',  # FIXME: see https://github.com/facebookresearch/ParlAI/issues/3367
        default=None,
        type='nonestr',
        choices={None, 'off', 'full', 'batchsort'},
        help=(
            'Set dynamic batching at evaluation time. Set to off for '
            'train-only dynamic batching. Set to none (default) to use same '
            'setting as --dynamic-batching.'
        ),
    )
    train.add_argument(
        '--num-workers',
        default=0,
        type=int,
        help='Number of background workers (training only)',
    )
    train.add_argument('--display-examples', type='bool', default=False, hidden=True)
    train.add_argument('-eps', '--num-epochs', type=float, default=-1)
    train.add_argument('-ttim', '--max-train-time', type=float, default=-1)
    train.add_argument(
        '-tstep',
        '--max-train-steps',
        '--max-lr-steps',
        type=int,
        default=-1,
        help='End training after n model updates',
    )
    train.add_argument('-ltim', '--log-every-n-secs', type=float, default=-1)
    train.add_argument(
        '-lstep',
        '--log-every-n-steps',
        type=int,
        default=50,
        help='Log every n training steps',
    )
    train.add_argument(
        '-vtim',
        '--validation-every-n-secs',
        type=float,
        default=-1,
        help='Validate every n seconds. Saves model to model_file '
        '(if set) whenever best val metric is found',
    )
    train.add_argument(
        '-vstep',
        '--validation-every-n-steps',
        type=int,
        default=-1,
        help='Validate every n training steps. Saves model to model_file '
        '(if set) whenever best val metric is found',
    )
    train.add_argument(
        '-stim',
        '--save-every-n-secs',
        type=float,
        default=-1,
        help='Saves the model to model_file.checkpoint after '
        'every n seconds (default -1, never).',
    )
    train.add_argument(
        '-sval',
        '--save-after-valid',
        type='bool',
        default=False,
        help='Saves the model to model_file.checkpoint after '
        'every validation (default %(default)s).',
    )
    train.add_argument(
        '-veps',
        '--validation-every-n-epochs',
        type=float,
        default=-1,
        help='Validate every n epochs. Saves model to model_file '
        '(if set) whenever best val metric is found',
    )
    train.add_argument(
        '-vme',
        '--validation-max-exs',
        type=int,
        default=-1,
        hidden=True,
        help='max examples to use during validation (default -1 uses all)',
    )
    train.add_argument(
        '--short-final-eval',
        default=False,
        hidden=True,
        type='bool',
        help='If true, obeys --validation-max-exs in the final '
        'validation and test evaluations.',
    )
    train.add_argument(
        '-vp',
        '--validation-patience',
        type=int,
        default=10,
        help=(
            'number of iterations of validation where result'
            ' does not improve before we stop training'
        ),
    )
    train.add_argument(
        '-vmt',
        '--validation-metric',
        default='accuracy',
        help='key into report table for selecting best validation',
    )
    train.add_argument(
        '-vmm',
        '--validation-metric-mode',
        type=str,
        choices=['max', 'min'],
        help='how to optimize validation metric (max or min)',
    )
    train.add_argument(
        '-vcut',
        '--validation-cutoff',
        type=float,
        default=1.0,
        hidden=True,
        help='value at which training will stop if exceeded by metric',
    )
    train.add_argument(
        '-lfc',
        '--load-from-checkpoint',
        type='bool',
        default=True,
        hidden=True,
        help='load model from checkpoint if available',
    )
    train.add_argument(
        '-vshare',
        '--validation-share-agent',
        default=False,
        hidden=True,
        help='use a shared copy of the agent for validation. '
        'this will eventually default to True, but '
        'currently defaults to False.',
    )
    train.add_argument(
        '-mcs',
        '--metrics',
        type=str,
        default='default',
        help='list of metrics to show/compute, e.g. all, default,'
        'or give a list split by , like '
        'ppl,f1,accuracy,hits@1,rouge,bleu'
        'the rouge metrics will be computed as rouge-1, rouge-2 and rouge-l',
    )
    train.add_argument(
        '-micro',
        '--aggregate-micro',
        type='bool',
        default=False,
        help='Report micro-averaged metrics instead of macro averaged metrics.',
        recommended=False,
    )
    TensorboardLogger.add_cmdline_args(parser, partial_opt=None)
    WandbLogger.add_cmdline_args(parser, partial_opt=None)

    parser = setup_dict_args(parser)
    return parser


def load_eval_worlds(agent, opt, datatype):
    """
    Create a new eval world for the agent and the given opt.

    Overrides the datatype options for doing this.  Handles some magic
    overrides of other special options for the training script.

    :param Agent agent:
        The model being trained.

    :param Opt opt:
        The global CLI opts.

    :param string datatype:
        The new datatype.
    """

    if 'stream' in opt['datatype']:
        datatype += ':stream'
    opt = opt.copy()
    opt['datatype'] = datatype
    if opt.get('evaltask'):
        # if a different eval task is specified, use it.
        opt['task'] = opt['evaltask']
    if opt.get('eval_batchsize'):
        # override eval time batchsize
        opt['batchsize'] = opt['eval_batchsize']
    if opt.get('eval_dynamic_batching'):
        # FIXME: see issue tracked in https://github.com/facebookresearch/ParlAI/issues/3367
        # override eval time dynamic batching settings
        eval_dyn_batch = (
            None
            if opt['eval_dynamic_batching'] == 'off'
            else opt['eval_dynamic_batching']
        )
        opt['dynamic_batching'] = eval_dyn_batch

    tasks = opt['task'].split(',')
    worlds = []
    # possibly load agent
    if opt.get('validation_share_agent', False):
        valid_agent = create_agent_from_shared(agent.share())
    else:
        valid_agent = agent
    # create worlds
    for task in tasks:
        task_opt = opt.copy()  # copy opt since we edit the task
        task_opt['task'] = task
        valid_world = create_task(task_opt, valid_agent)
        worlds.append(valid_world)

    return worlds


class TrainLoop:
    """
    TrainLoop contains the core training loop logic.
    """

    def __init__(self, opt):
        # if python is called from a non-interactive shell, like a bash script,
        # it will by-default ignore SIGINTs, and KeyboardInterrupt exceptions are
        # not produced. This line brings them back
        signal.signal(signal.SIGINT, signal.default_int_handler)
        # Possibly load from checkpoint
        trainstats_suffix = '.trainstats'  # we might load training statistics from here
        if (
            opt['load_from_checkpoint']
            and opt.get('model_file')
            and PathManager.exists(opt['model_file'] + '.checkpoint')
        ):
            opt['init_model'] = opt['model_file'] + '.checkpoint'
            trainstats_suffix = '.checkpoint.trainstats'
        # Possibly build a dictionary (not all models do this).
        if not (opt.get('dict_file') or opt.get('model_file')):
            raise RuntimeError(
                'WARNING: For train_model, please specify either a '
                'model_file or dict_file.'
            )
        if 'dict_file' in opt:
            if opt['dict_file'] is None and opt.get('model_file'):
                opt['dict_file'] = opt['model_file'] + '.dict'
            logging.info("building dictionary first...")
            build_dict(opt, skip_if_built=True)

        # Create model and assign it to the specified task
        self.agent = create_agent(opt)
        self.agent.opt.log()
        self.world = create_task(opt, self.agent)
        # set up timers
        self.train_time = Timer()
        self.validate_time = Timer()
        self.log_time = Timer()
        self.save_time = Timer()

        self.parleys = 0
        self._train_steps = 0
        self._last_log_steps = 0
        self.update_freq = opt.get('update_freq', 1)

        self.max_num_epochs = _num_else_inf(opt, 'num_epochs', distributed_warn=True)
        self.max_train_time = _num_else_inf(
            opt, 'max_train_time', distributed_warn=True
        )
        self.max_train_steps = _num_else_inf(opt, 'max_train_steps')
        self.log_every_n_secs = _num_else_inf(
            opt, 'log_every_n_secs', distributed_warn=True
        )
        self.log_every_n_steps = _num_else_inf(opt, 'log_every_n_steps')
        self.val_every_n_secs = _num_else_inf(
            opt, 'validation_every_n_secs', distributed_warn=True
        )
        self.val_every_n_epochs = _num_else_inf(
            opt, 'validation_every_n_epochs', distributed_warn=True
        )
        self.val_every_n_steps = _num_else_inf(opt, 'validation_every_n_steps')
        self.save_every_n_secs = _num_else_inf(
            opt, 'save_every_n_secs', distributed_warn=True
        )

        # smart defaults for --validation-metric-mode
        if opt['validation_metric'] in {'loss', 'ppl', 'mean_rank'}:
            opt['validation_metric_mode'] = 'min'
        elif opt['validation_metric'] in {'accuracy', 'hits@1', 'hits@5', 'f1', 'bleu'}:
            opt['validation_metric_mode'] = 'max'
        if opt.get('validation_metric_mode') is None:
            opt['validation_metric_mode'] = 'max'

        self.last_valid_epoch = 0
        self._last_valid_steps = 0
        self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
        self.train_reports = []
        self.valid_reports = []
        self.best_valid = None

        self.impatience = 0
        self.saved = False
        self.valid_worlds = None
        self.opt = opt

        # we may have been preempted, make sure we note that amount
        self._preempted_epochs = 0.0
        if opt.get('model_file') and PathManager.exists(
            opt['model_file'] + trainstats_suffix
        ):
            # looks like we were preempted. make sure we load up our total
            # training stats, etc
            with PathManager.open(opt['model_file'] + trainstats_suffix) as ts:
                obj = json.load(ts)
                self.parleys = obj.get('parleys', 0)
                self._preempted_epochs = obj.get('total_epochs', 0)
                self.train_time.total = obj.get('train_time', 0)
                self._train_steps = obj.get('train_steps', 0)
                self.impatience = obj.get('impatience', 0)
                self.valid_reports = obj.get('valid_reports', [])
                if self.valid_reports:
                    self.last_valid_epoch = self.valid_reports[-1].get(
                        'total_epochs', 0.0
                    )
                self.train_reports = obj.get('train_reports', [])
                if 'best_valid' in obj:
                    self.best_valid = obj['best_valid']
                else:
                    # old method
                    if opt.get('model_file') and PathManager.exists(
                        opt['model_file'] + '.best_valid'
                    ):
                        with PathManager.open(
                            opt['model_file'] + ".best_valid", 'r'
                        ) as f:
                            x = f.readline()
                            self.best_valid = float(x)
                            f.close()

        if opt['tensorboard_log'] and is_primary_worker():
            self.tb_logger = TensorboardLogger(opt)
        if opt['wandb_log'] and is_primary_worker():
            model = self.agent.model if hasattr(self.agent, 'model') else None
            self.wb_logger = WandbLogger(opt, model)

    def save_model(self, suffix=None):
        """
        Save the model to disk, possibly with a suffix.
        """
        if not is_primary_worker():
            # never do IO as a non-primary worker
            return

        if not self.opt.get('model_file'):
            # nothing to save to, just exit
            return

        fn = self.opt['model_file']
        if suffix:
            fn += suffix
        while True:
            # don't ever let a ctrl-c interrupt saving
            try:
                self.agent.save(fn)
                self._save_train_stats(suffix)
                break
            except KeyboardInterrupt:
                pass

    def _save_train_stats(self, suffix=None):
        fn = self.opt['model_file']
        if suffix:
            fn += suffix
        fn += '.trainstats'
        with PathManager.open(fn, 'w') as f:
            json.dump(
                {
                    'parleys': self.parleys,
                    'train_time': self.train_time.time(),
                    'train_steps': self._train_steps,
                    'total_epochs': self._total_epochs,
                    'train_reports': self.train_reports,
                    'valid_reports': self.valid_reports,
                    'best_valid': self.best_valid,
                },
                f,
                indent=4,
            )

    def validate(self):
        """
        Perform a validation run, checking whether we should stop training.

        :return: boolean indicating whether training should stop
        :rtype: bool
        """
        opt = self.opt

        if self.valid_worlds is None:
            # we need to load the world now
            self.valid_worlds = load_eval_worlds(self.agent, opt, 'valid')

        # run evaluation on valid set
        valid_report = self._run_eval(
            self.valid_worlds, opt, 'valid', opt['validation_max_exs']
        )
        v = dict_report(valid_report)
        v['train_time'] = self.train_time.time()
        v['parleys'] = self.parleys
        v['train_steps'] = self._train_steps
        v['total_exs'] = self._total_exs
        v['total_epochs'] = self._total_epochs
        self.valid_reports.append(v)
        # logging
        if opt['tensorboard_log'] and is_primary_worker():
            valid_report['total_exs'] = self._total_exs
            self.tb_logger.log_metrics('valid', self.parleys, valid_report)
            # flush on a validation
            self.tb_logger.flush()
        if opt['wandb_log'] and is_primary_worker():
            valid_report['total_exs'] = self._total_exs
            self.wb_logger.log_metrics('valid', self.parleys, valid_report)

        # saving
        if (
            opt.get('model_file')
            and opt.get('save_after_valid')
            and is_primary_worker()
        ):
            logging.info(f"saving model checkpoint: {opt['model_file']}.checkpoint")
            self.save_model('.checkpoint')

        # send valid metrics to agent if the agent wants them
        if hasattr(self.agent, 'receive_metrics'):
            self.agent.receive_metrics(valid_report)

        # check which metric to look at
        new_valid = valid_report[opt['validation_metric']]

        if isinstance(new_valid, Metric):
            new_valid = new_valid.value()

        # check if this is the best validation so far
        if (
            self.best_valid is None
            or self.valid_optim * new_valid > self.valid_optim * self.best_valid
        ):
            logging.success(
                'new best {}: {:.4g}{}'.format(
                    opt['validation_metric'],
                    new_valid,
                    ' (previous best was {:.4g})'.format(self.best_valid)
                    if self.best_valid is not None
                    else '',
                )
            )
            self.best_valid = new_valid
            self.impatience = 0
            if opt.get('model_file') and is_primary_worker():
                logging.info(f"saving best valid model: {opt['model_file']}")
                self.save_model()
                self.saved = True
            if (
                opt['validation_metric_mode'] == 'max'
                and self.best_valid >= opt['validation_cutoff']
            ) or (
                opt['validation_metric_mode'] == 'min'
                and self.best_valid <= opt['validation_cutoff']
            ):
                logging.info('task solved! stopping.')
                return True
        else:
            self.impatience += 1
            logging.report(
                'did not beat best {}: {} impatience: {}'.format(
                    opt['validation_metric'], round(self.best_valid, 4), self.impatience
                )
            )
        self.validate_time.reset()

        # check if we are out of patience
        if (
            opt['validation_patience'] > 0
            and self.impatience >= opt['validation_patience']
        ):
            logging.info('ran out of patience! stopping training.')
            return True
        return False

    def _run_single_eval(self, opt, valid_world, max_exs):

        # run evaluation on a single world
        valid_world.reset()

        cnt = 0
        max_cnt = max_exs if max_exs > 0 else float('inf')
        while not valid_world.epoch_done() and cnt < max_cnt:
            valid_world.parley()
            if cnt == 0 and opt['display_examples']:
                print(valid_world.display() + '\n~~')
                print(valid_world.report())
            cnt = valid_world.report().get('exs') or 0

        valid_report = valid_world.report()
        if opt.get('validation_share_agent', False):
            valid_world.reset()  # make sure world doesn't remember valid data

        return valid_report

    def _run_eval(self, valid_worlds, opt, datatype, max_exs=-1, write_log=False):
        """
        Eval on validation/test data.

        :param valid_world:
            list of the pre-created validation worlds.
        :param opt:
            the options that specific the task, eval_task, etc
        :param datatype:
            the datatype to use, such as "valid" or "test"
        :param bool write_log:
            specifies to write metrics to file if the model_file is set
        :param int max_exs:
            limits the number of examples if max_exs > 0
        """

        logging.info(f'running eval: {datatype}')
        timer = Timer()
        reports = []

        max_exs_per_worker = max_exs / (len(valid_worlds) * num_workers())
        for v_world in valid_worlds:
            task_report = self._run_single_eval(opt, v_world, max_exs_per_worker)
            reports.append(task_report)

        tasks = [world.getID() for world in valid_worlds]
        named_reports = dict(zip(tasks, reports))
        report = aggregate_named_reports(
            named_reports, micro_average=self.opt.get('aggregate_micro', False)
        )
        # get the results from all workers
        report = self._sync_metrics(report)

        metrics = f'{datatype}:\n{nice_report(report)}\n'
        logging.info(f'eval completed in {timer.time():.2f}s')
        logging.report(metrics)

        # write to file
        if write_log and opt.get('model_file') and is_primary_worker():
            # Write out metrics
            with PathManager.open(opt['model_file'] + '.' + datatype, 'a') as f:
                f.write(f'{metrics}\n')

        return report

    def _sync_metrics(self, metrics):
        """
        Sync training metrics across workers.

        A handful of special cases are handled as exceptions, and the remaining metrics
        are simply averaged across workers.
        """
        if not is_distributed():
            # nothing special needed
            return metrics
        all_versions = all_gather_list(metrics)
        return aggregate_unnamed_reports(all_versions)

    def _compute_eta(
        self, epochs_completed: float, time_elapsed: float, steps_taken: int
    ):
        """
        Compute the estimated seconds remaining in training.

        :param float epochs_completed: number of epochs already completed.
        :param float time_elapsed: total time spent already, in seconds.
        :return: ETA in seconds, or None if not computable
        """
        # start off with no estimate
        eta = None

        # Determine time_left and num_epochs
        max_epochs = self.opt.get('num_epochs', 0)
        if max_epochs > 0 and epochs_completed > 0:
            epoch_progress = epochs_completed / max_epochs
            eta = (1 - epoch_progress) * time_elapsed / epoch_progress

        max_training_time = self.opt.get('max_training_time', -1)
        if max_training_time > 0:
            time_left = max_training_time - time_elapsed
            if eta is None or time_left < eta:
                eta = time_left

        max_train_steps = self.opt.get('max_train_steps', -1)
        if max_train_steps > 0 and steps_taken > 0:
            steps_progress = steps_taken / max_train_steps
            eta = (1 - steps_progress) * time_elapsed / steps_progress

        return eta

    def _get_time(self, world: World) -> Tuple[float, float, float]:
        """
        Return train, log, and validate timing.

        If relying on the time for validation/logging/max train time purposes,
        we sync and return primary worker's time.

        Otherwise, it's not super relevant what we do here.

        **SIDE EFFECT**: Update _total_epochs trained.

        :param world:
            current running world

        :return (train, log, valid):
            return time for each of train, log, and validation
        """
        if (
            self.max_train_time < float('inf')
            or self.log_every_n_secs < float('inf')
            or self.val_every_n_secs < float('inf')
            or self.val_every_n_epochs < float('inf')
            or self.max_num_epochs < float('inf')
        ):
            self._total_epochs = self._preempted_epochs + sum(
                all_gather_list(world.get_total_epochs())
            )
            train_time, log_time, validate_time = sync_object(
                (
                    self.train_time.time(),
                    self.log_time.time(),
                    self.validate_time.time(),
                )
            )
        else:
            train_time, log_time, validate_time = (
                self.train_time.time(),
                self.log_time.time(),
                self.validate_time.time(),
            )
            self._total_epochs = self._preempted_epochs + (
                num_workers() * world.get_total_epochs()
            )

        return train_time, log_time, validate_time

    def log(self):
        """
        Output a training log entry.
        """
        opt = self.opt
        if opt['display_examples']:
            print(self.world.display() + '\n~~')
        logs = []
        # get report
        train_report = self.world.report()
        train_report = self._sync_metrics(train_report)
        self.world.reset_metrics()

        train_report_trainstats = dict_report(train_report)
        train_report_trainstats['total_epochs'] = self._total_epochs
        train_report_trainstats['total_exs'] = self._total_exs
        train_report_trainstats['parleys'] = self.parleys
        train_report_trainstats['train_steps'] = self._train_steps
        train_report_trainstats['train_time'] = self.train_time.time()
        self.train_reports.append(train_report_trainstats)

        # time elapsed
        logs.append(f'time:{self.train_time.time():.0f}s')
        logs.append(f'total_exs:{self._total_exs}')
        logs.append(f'total_steps:{self._train_steps}')

        if self._total_epochs >= 0:
            # only if it's unbounded
            logs.append(f'epochs:{self._total_epochs:.2f}')

        time_left = self._compute_eta(
            self._total_epochs, self.train_time.time(), self._train_steps
        )
        if time_left is not None:
            logs.append(f'time_left:{max(0,time_left):.0f}s')

        log = '{}\n{}\n'.format(' '.join(logs), nice_report(train_report))
        logging.info(log)
        self.log_time.reset()
        self._last_log_steps = 0

        if opt['tensorboard_log'] and is_primary_worker():
            self.tb_logger.log_metrics('train', self.parleys, train_report)
        if opt['wandb_log'] and is_primary_worker():
            self.wb_logger.log_metrics('train', self.parleys, train_report)

    def train(self):
        """
        Perform a training run.

        :return: tuple of reports (validation_report, test_report)
        """
        logging.info('training...')
        opt = self.opt
        world = self.world
        with world:
            while True:
                # do one example / batch of examples
                try:
                    world.parley()
                except StopTrainException as e:
                    logging.info(f"Stopping from {e}")
                    break

                self.parleys += 1
                self._train_steps = self.parleys // self.update_freq
                self._last_log_steps += 1 / self.update_freq

                # the following additionally updates self._total_epochs
                train_time, log_time, validate_time = self._get_time(world)
                # get the total training examples done, compute epochs
                exs_per_epoch = world.num_examples()
                self._total_exs = int(np.round(self._total_epochs * exs_per_epoch))

                # check counters and timers
                if self._total_epochs >= self.max_num_epochs:
                    self.log()
                    logging.info(
                        f'num_epochs completed:{self.max_num_epochs} time elapsed:{train_time}s'
                    )
                    break
                if train_time > self.max_train_time:
                    logging.info(f'max_train_time elapsed:{train_time}s')
                    break
                if self._train_steps >= self.max_train_steps:
                    logging.info(
                        f'max_train_steps elapsed:{self._train_steps} '
                        f'time elapsed:{train_time}s'
                    )
                    break
                if (
                    log_time > self.log_every_n_secs
                    or self._last_log_steps >= self.log_every_n_steps
                ):
                    self.log()
                if (
                    validate_time > self.val_every_n_secs
                    or self._total_epochs - self.last_valid_epoch
                    >= self.val_every_n_epochs
                    or self._train_steps - self._last_valid_steps
                    >= self.val_every_n_steps
                ):
                    try:
                        # log before we validate
                        self.log()
                        world.reset_metrics()
                        stop_training = self.validate()
                    except StopTrainException:
                        break
                    # reset the log time because we logged right before validating
                    self.log_time.reset()
                    self.last_valid_epoch = self._total_epochs
                    self._last_valid_steps = self._train_steps
                    if stop_training:
                        break
                    # make sure metrics are clean before we log
                    world.reset_metrics()
                if (
                    self.save_time.time() > self.save_every_n_secs
                    and opt.get('model_file')
                    and is_primary_worker()
                ):
                    logging.info(
                        f"saving model checkpoint: {opt['model_file']}.checkpoint"
                    )
                    if opt['tensorboard_log'] and is_primary_worker():
                        self.tb_logger.flush()
                    self.save_model('.checkpoint')
                    self.save_time.reset()

        if not self.saved and is_primary_worker():
            # save agent
            self.save_model()

        # there's a rare edge case where the we never saved the model, and we try
        # # to reload it. This sync_object ensures all workers wait for the primary
        # worker to finish flushing before loading from disk.
        sync_object(None)
        if opt.get('model_file'):
            # clean up all our memory, just to make sure we don't OOM on GPU when
            # reloading the world
            del world
            del self.world
            del self.agent
            del self.valid_worlds
            # reload best validation model
            self.agent = create_agent(opt)

        # perform final validation/testing
        valid_worlds = load_eval_worlds(self.agent, opt, 'valid')
        max_exs = opt['validation_max_exs'] if opt.get('short_final_eval') else -1
        v_report = self._run_eval(valid_worlds, opt, 'valid', max_exs, write_log=True)
        test_worlds = load_eval_worlds(self.agent, opt, 'test')
        t_report = self._run_eval(test_worlds, opt, 'test', max_exs, write_log=True)

        if opt['wandb_log'] and is_primary_worker():
            self.wb_logger.log_final('valid', v_report)
            self.wb_logger.log_final('test', t_report)
            self.wb_logger.finish()

        if valid_worlds:
            for valid_world in valid_worlds:
                valid_world.shutdown()
        if test_worlds:
            for test_world in test_worlds:
                test_world.shutdown()

        print_announcements(opt)

        return v_report, t_report


@register_script('train_model', aliases=['tm', 'train'])
class TrainModel(ParlaiScript):
    @classmethod
    def setup_args(cls):
        return setup_args()

    def run(self):
        self.train_loop = TrainLoop(self.opt)
        return self.train_loop.train()


if __name__ == '__main__':
    TrainModel.main()
