<gh_stars>1-10
#!/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.
"""
This module provides a set of teachers that deal with dialog.

    ``FixedDialogTeacher(Teacher)``
    Base class for teachers in tasks that have fixed dialog - i.e., dialog
    that is not dynamically generated but rather is pulled from set examples.
    However, the class can be extended to all tasks involved fixed data.
    Implements much of the basic functionality of these teachers, including
    ``observe()``, ``act()``, ``next_example()``

    ``DialogTeacher(FixedDialogTeacher)``
     Base teacher class for doing dialog specifically with fixed chat logs.

    ``ParlAIDialogTeacher(DialogTeacher)``
     Teacher class that provides access to data in the ParlAI Dialog format.
     See the class description for more details.

     ``ConversationTeacher(DialogTeacher)``
     Teacher class that provides access to data in the Conversations format.
     See the class description for more details.

    ``FbDeprecatedDialogTeacher(DialogTeacher)``
     Teacher class that provides access to data in the Facebook Dialog format.
     See the class description for more details. **This class is deprecated**.

This module also includes ``DataLoader``, a threadpool data loader for
``FixedDialogTeacher``, and ``DialogData``/``StreamDialogData``, data
structures for accessing textual dialog data and utilized by ``DialogTeacher``
"""
from parlai.core.params import ParlaiParser
from parlai.core.agents import Agent, create_agent_from_shared
from parlai.core.image_featurizers import ImageLoader
from parlai.core.loader import load_teacher_module
from parlai.core.loader import register_teacher  # noqa: F401
from parlai.core.message import Message
from parlai.core.metrics import TeacherMetrics, aggregate_named_reports
from parlai.core.opt import Opt
from parlai.utils.conversations import Conversations
from parlai.utils.data import DatatypeHelper
from parlai.utils.misc import AttrDict, str_to_msg, warn_once, SimpleCounter
from parlai.utils.distributed import get_rank, num_workers, is_distributed
import parlai.utils.torch as torch_utils
import parlai.utils.logging as logging
from parlai.utils.io import PathManager
from parlai.core.mutators import Mutator

from abc import ABC, abstractmethod
import argparse
from collections import defaultdict
import concurrent.futures
import copy
import json
import os
import queue
import random
import yaml
from threading import Thread
import torch
from typing import List, Tuple, Optional, TypeVar, Any


ERROR_MESSAGE_NO_DATAFILE = (
    "{class_name} is expected to set self.opt['datafile'] inside `__init__` "
    "before calling `super().__init__`. This will passed to setup_data, "
    "indicating what data to load. If you don't know what to use, set "
    "`opt['datafile'] = parlai.utils.data.DatatypeHelper.fold(opt['datatype'])` "
    "to receive the fold name in setup_data."
)


ChunkOutput = TypeVar('ChunkOutput')


class DataLoader(Thread):
    """
    A worker thread that provides a threadpool for data loading.

    A teacher may submit a request to the loader, which will return the
    appropriate data.

    To submit a request, a teacher should call ``request_load``.
    """

    def __init__(self, opt):
        Thread.__init__(self, daemon=True)
        self.num_workers = opt.get('num_load_threads', 1)
        self.request_queue = queue.Queue()
        self.last_future = None

    def request_load(self, receive_fn, load_fn, args):
        """
        Queue a request for loading.

        :param receive_fn:
            a receive function (for receiving the data)
        :param load_fn:
            a load function (for loading the data)
        :param args:
            arguments for the load function. args can be either a dictionary of
            arguments for a function, or a list of positional arguments
        """
        self.request_queue.put((receive_fn, load_fn, args))

    def run(self):
        """
        Run the execution loop.
        """
        executor = concurrent.futures.ThreadPoolExecutor(
            max_workers=self.num_workers, thread_name_prefix=self.name
        )
        with executor:
            while True:
                receive_fn, load_fn, args = self.request_queue.get()
                if receive_fn is StopIteration:
                    return
                try:
                    if type(args) == dict:
                        future = executor.submit(load_fn, **args)
                    else:
                        future = executor.submit(load_fn, *args)
                    self.last_future = future
                    receive_fn(future)
                except RuntimeError:
                    return


class _ErrorThrowingDataLoader(object):
    """
    A fake DataLoader which throws an exception when a work order is placed.

    Since threads cannot be mixed with spawn_method='fork', we need to disallow users
    from combining --num-workers with teachers that utilize threads. This placeholder
    object is only useful for ensuring the user sees a loud error message when they
    accidentally use a thread.
    """

    def __init__(self, opt):
        pass

    def request_load(self, receive_fn, load_fn, args):
        raise RuntimeError(
            'One of your teachers uses a DataLoader or a thread. You may only '
            'combine this with --num-workers 0.'
        )

    def start(self):
        pass


class Teacher(Agent):
    """
    Basic Teacher agent that keeps track of how many times it's received messages.

    Teachers provide the ``report()`` method to get back metrics.
    """

    @classmethod
    def add_cmdline_args(
        cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
    ) -> ParlaiParser:
        parser.add_argument(
            '--mutators',
            '-mut',
            default=None,
            help='Apply one or more mutators to the data.',
        )
        mutators = Mutator.load_mutator_types(partial_opt.get('mutators'))
        for m in mutators:
            m.add_cmdline_args(parser, partial_opt)
        return parser

    def __init__(self, opt: Opt, shared=None):
        if not hasattr(self, 'opt'):
            self.opt = copy.deepcopy(opt)
        if not hasattr(self, 'id'):
            self.id = opt.get('task', 'teacher')
        if not hasattr(self, 'metrics'):
            self.metrics = TeacherMetrics(
                metrics_list=opt.get('metrics', 'default'),
                shared=shared['metrics'] if shared is not None else None,
            )
        self.epochDone = False

    # return state/action dict based upon passed state
    def act(self):
        """
        Act upon the previous observation.
        """
        if self.observation is not None and 'text' in self.observation:
            t = Message({'text': 'Hello agent!'})
        return t

    def epoch_done(self):
        """
        Return whether the epoch is done.
        """
        return self.epochDone

    # Default unknown length
    def num_examples(self):
        """
        Return the number of examples (e.g. individual utterances) in the dataset.

        Default implementation returns `None`, indicating an unknown number.
        """
        return None

    def num_episodes(self):
        """
        Return the number of episodes (e.g. conversations) in the dataset.

        Default implementation returns `None`, indicating an unknown number.
        """
        return None

    def report(self):
        """
        Return metrics showing total examples and accuracy if available.
        """
        return self.metrics.report()

    def reset(self):
        """
        Reset the teacher.
        """
        super().reset()
        self.reset_metrics()
        self.epochDone = False

    def reset_metrics(self):
        """
        Reset metrics.
        """
        self.metrics.clear()

    def share(self):
        """
        In addition to default Agent shared parameters, share metrics.
        """
        shared = super().share()
        shared['metrics'] = self.metrics.share()
        return shared

    def __iter__(self):
        """
        Iterate through the examples of the teacher.
        """
        clone = self.clone()
        while True:
            message = clone.act()
            if not isinstance(message, Message):
                # backwards compatibility with older agents
                message = Message(message)
            if message.is_padding():
                break
            yield message


class FixedDialogTeacher(Teacher):
    """
    A teacher agent for all teachers involved in tasks with fixed data.

    This class provides the following functionality for its subclasses:

    - Resets a teacher
    - Provides an observe method
    - Computes and retrieves the next episode index for a teacher
    - Provides a threadpool option for loading data (especially useful for
      large data, e.g. images)

    In order to take advantage of the first few features, all a subclass has to
    implement is three functions: ``num_episodes``, ``num_examples``, and
    ``get`` (which returns a specific example from a specific episode).

    To utilize the DataLoader for threadpool loading, a teacher should
    implement the ``submit_load_request`` function to send a load request
    to the DataLoader by calling ``self.data_loader.request_load`` with the
    appropriate arguments (``receive_fn, load_fn, args``). The DataLoader then
    returns the data to the teacher's ``data_queue``, which the teacher can
    poll in its ``act`` method.

    The following is an example of the DataLoader usage in the VQA-V1 teacher.

    1. In the teacher's ``init`` function, the teacher calls its
       ``submit_load_request`` function to preload an image.
    2. The ``submit_load_request`` function gets the next ``episode_idx``,
       and computes the image path for the load request.
    3. At the end of ``submit_load_request``, the teacher calls
       ``self.data_loader.request_load`` with three args:

        - ``self.receive_data`` - the function that the DataLoader calls to
          return the the loaded object
        - ``self.image_loader.load`` - the function used to load the image
          from the image path
        - ``[img_path]`` - a list of arguments for the load function, which
          in this case is the path of the image.

    4. In the teacher's ``act`` function, the teacher loads the data from
       its data queue.
    5. At the end of the ``act`` function, the teacher calls
       ``submit_load_request`` to preload an image for the next example.

    To see this in action, take a look at this teacher in ``tasks.vqa_v1.agents``.
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)

        if not hasattr(self, 'datatype'):
            self.datatype = opt['datatype']
        if not hasattr(self, 'random'):
            self.random = self.datatype == 'train'
        if not hasattr(self, 'training'):
            self.training = DatatypeHelper.is_training(self.datatype)
        if not hasattr(self, 'cycle'):
            self.cycle = DatatypeHelper.should_cycle(self.datatype)
        if not hasattr(self, 'datafile'):
            self.datafile = opt.get('datafile')
        # set up support for multithreaded data loading
        self.data_queue = queue.Queue()
        if shared:
            self.index = shared['index']
            if 'data_loader' in shared:
                self.data_loader = shared['data_loader']
            if 'threadindex' in shared:
                self.threadindex = shared['threadindex']
            if 'examples' in shared:
                self.examples = shared['examples']
        else:
            self.index = AttrDict(value=-1)

        if not hasattr(self, 'data_loader'):
            if opt.get('background_index') is None:
                self.data_loader = DataLoader(opt)
            else:
                self.data_loader = _ErrorThrowingDataLoader(opt)
            self.data_loader.start()

        # set up batching
        self.bsz = opt.get('batchsize', 1)

        if shared:
            self.mutators = shared.get('mutators', [])
        else:
            mutator_types = Mutator.load_mutator_types(self.opt.get('mutators'))
            self.mutators = [mutator(self.opt) for mutator in mutator_types]

        self._episode_done = True

    def reset(self):
        """
        Reset the dialog to the start of the epoch, and reset all metrics.
        """
        super().reset()
        self.metrics.clear()
        self.lastY = None
        self.last_act = None
        self._episode_done = True
        self.epochDone = False
        self.data_queue = queue.Queue()

        self.episode_idx = -1
        self.index.value = -1

    def submit_load_request(self):
        """
        Submit a load request.

        An agent should implement this method to submit requests to the data
        loader. At the end of this method, the agent should call
        ``self.data_loader.request_load()`` with the appropriate args.

        By default, this method does nothing.
        """
        # TODO: mark as abstract
        pass

    def receive_data(self, future: concurrent.futures.Future):
        """
        Receive data from the data loader.

        :param future: result from the load request.
        """
        data = future.result()
        self.data_queue.put(data)

    def share(self):
        """
        Share the data and dataloader.
        """
        shared = super().share()

        if hasattr(self, 'examples'):
            shared['examples'] = self.examples

        if hasattr(self, 'data_loader'):
            shared['data_loader'] = self.data_loader

        if hasattr(self, 'mutators'):
            shared['mutators'] = self.mutators

        shared['index'] = self.index

        return shared

    def next_episode_idx(self, num_eps=None, loop=None):
        """
        Return the next episode index.

        :param num_eps:
            default None uses ``num_episodes`` value.
        :param loop:
            default None loops during training but not evaluation.
        """
        if num_eps is None:
            num_eps = self.num_episodes()
        if loop is None:
            loop = self.training
        if self.random:
            new_idx = random.randrange(num_eps)
        else:
            self.index.value += 1
            if loop:
                try:
                    self.index.value %= num_eps
                except ZeroDivisionError:
                    raise ZeroDivisionError(
                        "The teacher has either empty data (e.g. setup_data yielded "
                        "no items, or self.num_episodes() == 0). We do not support "
                        "empty datasets (or folds) at this time."
                    )
            new_idx = self.index.value
        return new_idx

    def next_example(self):
        """
        Return the next example.

        If there are multiple examples in the same episode, returns the next one in that
        episode. If that episode is over, gets a new episode index and returns the first
        example of that episode.
        """
        if self._episode_done:
            self.episode_idx = self.next_episode_idx()
            self.entry_idx = 0

            if self.episode_idx >= self.num_episodes():
                return Message.padding_example(), True

            # buffer the full conversation ahead of time for mutators
            episode_buffer = []
            buffer_entry_idx = 0
            while True:
                entry = self.get(self.episode_idx, buffer_entry_idx)
                if not isinstance(entry, Message):
                    assert isinstance(entry, dict)
                    typ = type(self)
                    warn_once(
                        f"{typ.__module__}.{typ.__name__}' is outputting dicts "
                        "instead of messages. If this is a teacher that is part of "
                        "ParlAI, please file an issue on GitHub. If it is your own "
                        "teacher, please return a Message object instead."
                    )
                    entry = Message(entry)
                episode_buffer.append(entry)
                if entry.get('episode_done'):
                    break
                buffer_entry_idx += 1
            # apply mutators
            if self.mutators:
                episode_buffer = [m.copy() for m in episode_buffer]
                for mutator in self.mutators:
                    episode_buffer = mutator(episode_buffer)
            self.episode_buffer = list(episode_buffer)

            if not self.episode_buffer:
                # if we got back an empty episode after mutating, skip it
                return self.next_example()
        else:
            self.entry_idx += 1

        if self.episode_idx >= self.num_episodes():
            return Message.padding_example(), True

        # buffer the entire conversation so we can apply mutators
        ex = self.episode_buffer[self.entry_idx]
        self._episode_done = self.entry_idx == len(self.episode_buffer) - 1

        if (
            not self.cycle
            and self._episode_done
            and self.episode_idx + self.opt.get("batchsize", 1) >= self.num_episodes()
        ):
            epoch_done = True
        else:
            epoch_done = False

        return ex, epoch_done

    def num_episodes(self) -> int:
        """
        Get the number of episodes in this dataset.
        """
        raise RuntimeError('"num_episodes" must be overridden by children.')

    def num_examples(self) -> int:
        """
        Get the total number of examples in this dataset.
        """
        raise RuntimeError('"num_examples" must be overridden by children.')

    def get(self, episode_idx, entry_idx=0):
        """
        Get the specified episode and the specified entry in that episode.

        Children must override this method in order to inherit the
        `next_example` method.

        :param episode_idx:
            which episode to return examples from
        :param entry_idx:
            which example to return from the episode.  Many datasets have only
            single-entry episodes, so this defaults to zero.
        """
        # TODO: mark as abstract, get rid of runtime error.
        raise RuntimeError('"Get" method must be overridden by children.')

    def observe(self, observation):
        """
        Process observation for metrics.
        """
        self.metrics.clear_recent()
        if hasattr(self, 'lastY') and self.lastY is not None:
            self.metrics.evaluate_response(observation, self.lastY)
            self.custom_evaluation(self.last_act, self.lastY, observation)
            self.lastY = None
        recent_metrics = self.metrics.report_recent()
        if recent_metrics:
            # for display purposes (display_model), take all accumulated
            # metrics back into the original observation. This is an abuse of
            # Messages being pointers
            if 'metrics' in observation:
                # override agent-level metrics if present
                observation.pop('metrics')
            observation['metrics'] = recent_metrics
        return observation

    def custom_evaluation(
        self,
        teacher_action: Message,
        labels: Optional[Tuple[str]],
        model_response: Message,
    ) -> None:
        """
        A method designated for hooking custom evaluations into teachers.

        Generally, a user will want to use `self.metrics.add` to record any
        specialized metrics that only make sense for this one dataset.

        :param teacher_action:
            The message last sent from this teacher.
        :param labels:
            The previous correct labels, if there were any.
        :param model_response:
            The raw response from the model. Generally you want to rely on the
            text field, but others may be necessary in specific situations.
        """
        pass

    def act(self):
        """
        Send new dialog message.
        """
        orig_action = self.get_orig_action()
        processed_action = self.process_action(orig_action)
        return processed_action

    def get_orig_action(self) -> Message:
        """
        Get the unprocessed action and reset if needed.

        This function will return the raw action from `self.next_example()`, before the
        `self.last_act` and `self.lastY` attributes have been defined based on this
        action for metrics or custom evaluations. This is so that wrapper teachers can
        modify the raw action first, such as to change the contents of its 'text' and
        'label' fields, without the action becoming out of sync with `self.last_act` and
        `self.lastY`.
        """
        if not hasattr(self, 'epochDone'):
            # reset if haven't yet
            self.reset()

        # get next example, action is episode_done dict if already out of exs
        action, self.epochDone = self.next_example()
        if not isinstance(action, Message):
            # TODO: all teachers should eventually create messages
            # while setting up the data, so this won't be necessary
            action = Message(action)

        return action

    def process_action(self, action: Message) -> Message:
        """
        Remember the raw action and prepare its fields for passing out of the teacher.
        """
        action.force_set('id', self.getID())

        # remember correct answer if available
        self.last_act = action
        self.lastY = action.get('labels', action.get('eval_labels', None))
        if not DatatypeHelper.is_training(self.datatype) and 'labels' in action:
            # move labels to eval field so not used for training
            # but this way the model can use the labels for perplexity or loss
            action = action.copy()
            labels = action.pop('labels')
            if not self.opt.get('hide_labels', False):
                action['eval_labels'] = labels

        return action


class DialogTeacher(FixedDialogTeacher):
    """
    A base teacher class for doing dialog with fixed chat logs.

    This class provides a set a basic functionality:

    - uses data class to store and query text data
    - generates action tables to send to the student agent from the data

    In order to subclass this class, you must implement ``setup_data()`` in
    your class, which reads your data file as an iterator.
    """

    def __init__(self, opt, shared=None):
        # Check for setup_data
        if not hasattr(self, 'setup_data'):
            raise RuntimeError(
                'Must implement setup_data or subclass a class '
                'which implements it (e.g. FbDeprecatedDialogTeacher) '
                'in order to use this class.'
            )
        super().__init__(opt, shared)

        self.datatype = opt['datatype']
        self.training = DatatypeHelper.is_training(self.datatype)
        self.cycle = DatatypeHelper.should_cycle(self.datatype)
        self.stream = 'stream' in self.datatype

        # first initialize any shared objects
        data_class = StreamDialogData if self.stream else DialogData
        kwargs = (
            # never cycle if "ordered" is in the datatype. this is used by
            # build_dict to enumerate through the data exactly once while still
            # marking examples as training examples.
            {'cycle': self.cycle}
            if self.stream
            else {}
        )
        if shared and shared.get('data'):
            self.data = data_class(opt, shared=shared['data'], **kwargs)
        else:
            if 'datafile' not in self.opt:
                raise KeyError(
                    ERROR_MESSAGE_NO_DATAFILE.format(class_name=self.__class__.__name__)
                )
            self.data = data_class(
                opt,
                data_loader=self.setup_data,
                cands=self.label_candidates(),
                **kwargs,
            )

        self.reset()

    @abstractmethod
    def setup_data(self, datafile: str):
        """
        The core method which the user should override.

        Yields the data, one message at a time, as well as markers indicating
        new episodes.

        :param str datafile:
            If the initializer set a 'datafile' field within the initialization,
            this will be provided here. Otherwise, datafile will be the fold:
            either "train", "valid", or "test".

        :return:
            Yields pairs (message, new_episode) containing a Message object
            and whether the message marks the beginning of a totally new
            episode.
        """
        pass

    def reset(self):
        """
        Reset the dialog to the start of the epoch, reset all metrics.
        """
        super().reset()
        if self.stream:
            self.data.reset()
            self.epochDone = False

    def share(self):
        """
        Share the data.
        """
        shared = super().share()
        if hasattr(self, 'data'):
            shared['data'] = self.data.share()
        return shared

    def label_candidates(self):
        """
        Provide consistent label candidates for all examples.

        Default implementation returns ``None`` always, but this may be overridden to
        provide candidates in all areas. See ``FbDialogueTeacher``.
        """
        # TODO DEPRECATIONDAY: FbDialogueTeacher is being deprecated, should we
        # remove this?

        # TODO: mark as optionally abstract?
        return None

    def num_episodes(self) -> int:
        """
        Return the number of episodes in the data.
        """
        try:
            return self.data.num_episodes()
        except AttributeError:
            return super().num_episodes()

    def num_examples(self) -> int:
        """
        Return the number of examples in the data.
        """
        if hasattr(self, '_num_examples_cache'):
            return self._num_examples_cache
        try:
            self._num_examples_cache: int = self.data.num_examples()
        except AttributeError:
            self._num_examples_cache = super().num_examples()
        return self._num_examples_cache

    def get(self, episode_idx, entry_idx=0):
        """
        Get a specific example.
        """
        return self.data.get(episode_idx, entry_idx)[0]

    def next_example(self):
        """
        Get the next example.
        """
        if self.stream:
            # unfortunately we need to also do the mutator buffering here.
            # it's difficult to structure it so it's not
            if hasattr(self, 'episode_buffer') and self.episode_buffer:
                action = self.episode_buffer.pop(0)
                epoch_done = (not self.episode_buffer) and self._saw_epoch_done
                return action, epoch_done
            episode_buffer = []
            while True:
                action, epoch_done = self.data.get()
                episode_buffer.append(action)
                if action['episode_done']:
                    self._saw_epoch_done = epoch_done
                    break
            # perform any mutations there are
            if self.mutators:
                episode_buffer = [m.copy() for m in episode_buffer]
                for mutator in self.mutators:
                    episode_buffer = mutator(episode_buffer)
            # make sure mutations are fully realized (not generators)
            self.episode_buffer = list(episode_buffer)
            # The recursive call has dual purpose:
            # - if we get back an empty episode after mutating, skip it gracefully
            # - pull the first item the episode w/ epoch_done logic, but DRY
            return self.next_example()
        else:
            action, epoch_done = super().next_example()
        return action, epoch_done


class DialogData(object):
    """
    Provides a data structure for accessing textual dialog data.

    This can be used whenever the dialog data is a fixed log of chats
    (i.e not a simulator setting). The logs can include dialog text and possibly
    supervised labels, candidate labels and rewards.

    All these are stored in this internal data format which is used by the
    ``DialogTeacher`` class.

    :param opt:
        options to initialize the class
    :param data_loader:
        an iterable with each call returning a tuple in the form
        ``((x, y, r, c, i), new_episode?)`` where the ``x`` and ``new_episode``
        fields are mandatory and other fields may be omitted or ``None``.
    :param cands:
        can be set to provide a list of candidate labels for every example in
        this dataset, which the agent can choose from (the correct answer
        should be in this set).

    :param random:
        tells the data class whether or not to visit episodes sequentially or
        randomly when returning examples to the caller.

    The contents of the ``((x, y, r, c, i), new_episode?)`` tuples returned by
    the data loader is the following:

    - ``x`` (str) is a query and possibly context
    - ``y`` (iter) is an iterable of label(s) for that query
    - ``r`` (str) is the str reward for getting that query correct
    - ``c`` (iter) is an iterable of label candidates that the student can choose from
    - ``i`` (str) is a str path to an image on disk, which will be loaded by the
      data class at request-time. should always point to the raw image file.
    - ``new_episode?`` (bool) is a boolean value specifying whether that example
      is the start of a new episode. If you don't use episodes set this
      to ``True`` every time.
    """

    def __init__(self, opt, data_loader=None, cands=None, shared=None, **kwargs):
        # in case we need to shard the dataset
        self.rank = get_rank()
        self.num_workers = num_workers()
        self.is_distributed_and_is_eval = is_distributed() and any(
            x in opt['datatype'] for x in ('valid', 'test', 'train:evalmode')
        )

        # self.data is a list of episodes
        # each episode is a tuple of entries
        # each entry is a tuple of values for the action/observation table
        if shared:
            self.image_loader = shared.get('image_loader', None)
            self.data = shared.get('data', [])
            self.cands = shared.get('cands', None)
        else:
            self.image_loader = ImageLoader(opt)
            self.data = []

            if 'datafile' not in opt:
                raise KeyError(
                    ERROR_MESSAGE_NO_DATAFILE.format(class_name=self.__class__.__name__)
                )

            self._load(data_loader, opt['datafile'])
            self.cands = None if cands is None else set(c for c in cands)

        self.addedCands = []
        self.copied_cands = False

    def share(self):
        """
        Share the data.
        """
        shared = {
            'data': self.data,
            'cands': self.cands,
            'image_loader': self.image_loader,
        }
        return shared

    def _read_episode(self, data_loader):
        """
        Read one episode at a time from the provided iterable over entries.

        :param data_loader:
            an iterable which returns tuples in the format described in the
            class docstring.
        """
        episode = []
        for entry, new in data_loader:
            if new and len(episode) > 0:
                yield episode
                episode = []

            episode.append(entry)

        if len(episode) > 0:
            yield episode

    def _load(self, data_loader, datafile):
        """
        Load up data from an iterable over tuples described in the class docs.

        :param iter data_loader:
            an iterator which returns tuples in the format described in the
            class docstring.
        :param str datafile:
        """
        for i, episode in enumerate(self._read_episode(data_loader(datafile))):
            if not self.is_distributed_and_is_eval or i % self.num_workers == self.rank:
                self.data.append(episode)

    def num_episodes(self):
        """
        Return number of episodes in the dataset.
        """
        return len(self.data)

    def num_examples(self):
        """
        Return total number of entries available.

        Each episode has at least one entry, but might have many more.
        """
        if hasattr(self, '_num_examples_cache'):
            return self._num_examples_cache
        self._num_examples_cache = sum(len(episode) for episode in self.data)
        return self._num_examples_cache

    def get(self, episode_idx, entry_idx=0):
        """
        Get the specified episode and the specified entry in that episode.

        :param episode_idx:
            which episode to return examples from
        :param entry_idx:
            which example to return from the episode. Many datasets have only
            single-entry episodes, so this defaults to zero.
        """
        if episode_idx >= len(self.data):
            return Message.padding_example(), True
        next_episode_idx_for_rank = episode_idx + 1
        # first look up data
        episode = self.data[episode_idx]
        entry = episode[entry_idx]
        episode_done = entry_idx == len(episode) - 1

        end_of_data = episode_done and next_episode_idx_for_rank >= len(self.data)

        # now pack it in a action-observation dictionary
        table = self.build_table(entry)

        # last entry in this episode
        table['episode_done'] = episode_done
        return table, end_of_data

    def build_table(self, entry):
        """
        Packs an entry into an action-observation dictionary.

        :param entry: a tuple in the form described in the class docstring.
        """
        if isinstance(entry, (dict, Message)):
            # user is already provided things
            if 'eval_labels' in entry or 'eval_label' in entry:
                raise KeyError(
                    'Labels are converted to eval_labels automatically. Please do not '
                    'set them in setup_data.'
                )
            if 'episode_done' in entry:
                raise KeyError(
                    "episode_done is set automatically for you. Please don't set it "
                    "in setup_data."
                )
            if 'label' in entry:
                # for convenience, rename to the labels convention automatically
                label = entry.pop('label')
                assert isinstance(label, str)
                entry['labels'] = (label,)
            if 'labels' in entry and isinstance(entry['labels'], str):
                entry['labels'] = (entry['labels'],)
            table = entry.copy()
        elif isinstance(entry, (Tuple, List)):
            table = {}
            if entry[0] is not None:
                table['text'] = entry[0]
            if len(entry) > 1 and entry[1] is not None:
                l = entry[1]
                if isinstance(l, str):
                    l = (l,)
                table['labels'] = l
            if len(entry) > 2 and entry[2] is not None:
                table['reward'] = entry[2]
            if len(entry) > 3 and entry[3] is not None:
                table['label_candidates'] = entry[3]
            if len(entry) > 4 and entry[4] is not None:
                img = self.image_loader.load(entry[4])
                if img is not None:
                    table['image'] = img
        else:
            raise TypeError(
                f"items out of setup_data should be dict, Message, list, or tuple. "
                f"Got {type(entry)})"
            )

        if table.get('labels', None) is not None and self.cands is not None:
            if self.addedCands:
                # remove elements in addedCands
                self.cands.difference_update(self.addedCands)
                self.addedCands.clear()
            for label in table['labels']:
                if label not in self.cands:
                    # add labels, queue them for removal next time
                    if not self.copied_cands:
                        self.cands = self.cands.copy()
                        self.copied_cands = True
                    self.cands.add(label)
                    self.addedCands.append(label)
            table['label_candidates'] = self.cands

        if 'labels' in table and 'label_candidates' in table:
            if table['labels'][0] not in table['label_candidates']:
                raise RuntimeError('true label missing from candidate labels')

        # go ahead and make it a message
        if isinstance(table, dict):
            table = Message(table)

        return table


class StreamDialogData(DialogData):
    """
    Provides a data structure for streaming textual dialog data.

    This can be used whenever the dialog data follows the format described in
    DialogData but cannot fit entirely into memory.

    Additional keyword-argument cycle defines if the stream should restart from
    the beginning after an epoch is finished (defaults to True).

    :param opt:
        options to initialize the class
    :param data_loader:
        an iterable with each call returning a tuple in the form
        ``((x, y, r, c, i), new_episode?)`` where the ``x`` and ``new_episode``
        fields are mandatory and other fields may be omitted or ``None``.
    :param cands:
        can be set to provide a list of candidate labels for every example in
        this dataset, which the agent can choose from (the correct answer
        should be in this set).
    :param random:
        tells the data class whether or not to visit episodes sequentially or
        randomly when returning examples to the caller.
    :param cycle:
        (default True) whether to restart at beginning when end of stream
        reached without reset being called.
    """

    # represents that we haven't read in any data at all
    _FIRST_PASS = None
    # represents that we are out of data.
    _END_OF_EPOCH = -1

    def __init__(self, opt, data_loader=None, cands=None, shared=None, **kwargs):
        # super() call initiates stream in self.data by calling _load()
        super().__init__(opt, data_loader, cands, shared, **kwargs)
        self.cycle = kwargs['cycle'] if 'cycle' in kwargs else True

        if shared:
            # auxiliary instances hold pointer to main datastream in self.data
            self.reset_data = shared['reset']
            # Share datafile and data_loader for computing num_exs and num_eps
            self.datafile = shared['datafile']
            self.data_loader = shared['data_loader']
            if 'lock' in shared:
                self.lock = shared['lock']
        else:
            # main instance holds the stream and shares pointer to it
            self.data_loader = data_loader
            if 'datafile' not in opt:
                raise KeyError(
                    ERROR_MESSAGE_NO_DATAFILE.format(class_name=self.__class__.__name__)
                )
            self.datafile = opt['datafile']
            self.reset_data = None
            self.is_reset = True
        self.entry_idx = 0
        self.cur_episode = self._FIRST_PASS
        self.num_eps = None
        self.num_exs = None

        self.rank = get_rank()
        self.num_workers = num_workers()
        self.is_distributed_and_is_eval = (
            self.num_workers > 1 and not DatatypeHelper.is_training(opt['datatype'])
        )

    def share(self):
        """
        Share the stream.
        """
        shared = super().share()
        # also share reset method to allow datastream to be reset
        shared['reset'] = self.reset
        # share datafile and data for loading length if necessary
        shared['datafile'] = self.datafile
        shared['data_loader'] = self.data_loader
        if hasattr(self, 'lock'):
            shared['lock'] = self.lock

        return shared

    def _load(self, data_loader, datafile):
        """
        Load data generator into data field.
        """
        self.data = self._data_generator(data_loader, datafile)

    def _data_generator(self, data_loader, datafile):
        """
        Generate data using the iterator over tuples constructed by data_loader.
        """
        self.is_reset = False
        idx = 0
        while True:
            for episode in self._read_episode(data_loader(datafile)):
                # We only shard the data set at evaluation time, as training is
                # done using sampling-with-replacement.
                if not self.is_distributed_and_is_eval or (
                    idx % self.num_workers == self.rank
                ):
                    yield episode
                idx += 1
            while not self.cycle:
                yield self._END_OF_EPOCH

    def load_length(self):
        """
        Calculate the length of the dataset and caches it in a file.

        Note that this can take some time for large datasets. Episode and entry indexes
        cannot be specified during streaming.
        """
        datafiles = self.datafile if type(self.datafile) is tuple else [self.datafile]
        length_file = datafiles[0] + ".lengths"
        if not PathManager.exists(length_file):
            num_eps = 0
            num_exs = 0
            for episode in self._read_episode(self.data_loader(self.datafile)):
                num_eps += 1
                num_exs += len(episode)
            with PathManager.open(length_file, 'w', encoding="utf-8") as f:
                f.write("{}\n{}".format(num_eps, num_exs))
        else:
            with PathManager.open(length_file, 'r', encoding='utf-8') as f:
                num_eps, num_exs = f.readlines()
        return int(num_eps), int(num_exs)

    def num_examples(self):
        """
        Return the number of examples in the data.
        """
        if not self.num_exs:
            self.num_eps, self.num_exs = self.load_length()
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes in the data.
        """
        if not self.num_eps:
            self.num_eps, self.num_exs = self.load_length()
        return self.num_eps

    def get(self):
        """
        Get the next entry from the stream.

        When episode is done returns first entry of next episode.
        """
        if self.cur_episode is self._FIRST_PASS:
            # first go around, always read off the episode
            # maybe lock this line
            self.cur_episode = next(self.data)
        if self.cur_episode == self._END_OF_EPOCH:
            # we're done here
            return Message.padding_example(), True
        entry = self.cur_episode[self.entry_idx]
        table = self.build_table(entry)
        episode_done = self.entry_idx == len(self.cur_episode) - 1
        table['episode_done'] = episode_done
        if episode_done:
            # maybe lock this line
            self.cur_episode = next(self.data)
            self.entry_idx = 0
        else:
            self.entry_idx += 1
        return table, self.cur_episode == self._END_OF_EPOCH

    def reset(self):
        """
        Reset the datastream to its beginning.
        """
        if self.reset_data is not None:
            # auxiliary instance, reset main datastream
            self.data = self.reset_data()
        elif not self.is_reset:
            # if main instance is not reset, reset datastream
            self._load(self.data_loader, self.datafile)
            self.is_reset = True
        self.entry_idx = 0
        self.cur_episode = self._FIRST_PASS
        return self.data


class FbDeprecatedDialogTeacher(DialogTeacher):
    """
    This module provides access to data in the Facebook Dialog format.

    Subclasses ``DialogTeacher`` for functionality and provides an
    implementation of ``setup_data()`` which iterates over datasets in the
    "fbdialog" format. If your data is in the format below, use this class to
    handle file parsing for you.

    The way FB Dialog data is set up is as follows:

    ::

        1 Sam went to the kitchen.
        2 Pat gave Sam the milk.
        3 Where is the milk?<TAB>kitchen<TAB>1<TAB>hallway|kitchen|bathroom
        4 Sam went to the hallway.
        5 Pat went to the bathroom.
        6 Where is the milk?<TAB>hallway<TAB>1<TAB>hallway|kitchen|bathroom

    Lines 1-6 represent a single episode, with two different examples: the
    first example is lines 1-3, and the second is lines 4-6.

    Lines 1,2,4, and 5 represent contextual information.

    Lines 3 and 6 contain a query, a label, a reward for getting the question
    correct, and three label candidates.

    Since both of these examples are part of the same episode, the information
    provided in the first example is relevant to the query in the second
    example and therefore the agent must remember the first example in order to
    do well.

    In general dialog in this format can contain any speech, not just QA pairs:

    ::

        1 Hi how's it going?<TAB>It's going great. What's new?
        2 Well I'm working on a new project at work.<TAB>Oh me too!
        3 Oh cool!<TAB>Tell me about yours.

    etc.

    Note that dialogs are interpreted as being one-way. For example, consider
    this dialog:

    ::

        1 X1    Y1
        2 X2    Y2
        3 X3    Y3

    A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated.
    However, Y1 => X2 and Y2 => X3 are not created as separate examples by
    default. This makes sense for some data (we don't need to train on the idea
    that "kitchen" should be followed by "Sam went to the hallway..." above),
    but for other datasets it may be helpful to add additional examples in the
    reverse direction ("Oh cool!" is a response to "Oh me too!" above).
    """

    def __init__(self, opt, shared=None):
        self.opt = opt
        self.cloze = opt.get('cloze', False)
        if shared and 'cands' in shared:
            self.cands = shared['cands']
        else:
            self.cands = self.load_cands(opt.get('cands_datafile', None))
        super().__init__(opt, shared)

    def share(self):
        """
        Share the data and candidates.
        """
        shared = super().share()
        shared['cands'] = self.cands
        return shared

    def label_candidates(self):
        """
        Return the candidates.
        """
        return self.cands

    def load_cands(self, path):
        """
        Load a global fixed set of candidates.

        The candidates will be provided by the teacher for every example (the true
        labels for a specific example are also added to this set, so that it's possible
        to get the right answer).
        """
        if path is None:
            return None
        cands = []
        lines_have_ids = False
        cands_are_replies = False
        cnt = 0
        with PathManager.open(path, encoding='utf-8') as read:
            for line in read:
                line = line.strip().replace('\\n', '\n')
                if len(line) > 0:
                    cnt = cnt + 1
                    # If lines are numbered we strip them of numbers.
                    if cnt == 1 and line[0:2] == '1 ':
                        lines_have_ids = True
                    # If tabs then the label_candidates are all the replies.
                    if '\t' in line and not cands_are_replies:
                        cands_are_replies = True
                        cands = []
                    if lines_have_ids:
                        space_idx = line.find(' ')
                        line = line[space_idx + 1 :]
                        if cands_are_replies:
                            sp = line.split('\t')
                            if len(sp) > 1 and sp[1] != '':
                                cands.append(sp[1])
                        else:
                            cands.append(line)
                    else:
                        cands.append(line)
        return cands

    def setup_data(self, path):
        r"""
        Read data in the fbdialog format.

        Returns ``((x,y,r,c), new_episode?)`` tuples.

        ``x`` represents a query, ``y`` represents the labels, ``r`` represents
        any reward, and ``c`` represents any label_candidates.

        The example above will be translated into the following tuples:

        ::

            x: 'Sam went to the kitchen\nPat gave Sam the milk\nWhere is the milk?'
            y: ['kitchen']
            r: '1'
            c: ['hallway', 'kitchen', 'bathroom']
            new_episode = True (this is the first example in the episode)


        ::

            x: 'Sam went to the hallway\\nPat went to the bathroom\\nWhere is the
                milk?'
            y: ['hallway']
            r: '1'
            c: ['hallway', 'kitchen', 'bathroom']
            new_episode = False (this is the second example in the episode)
        """
        logging.info(f"loading fbdialog data: {path}")
        with PathManager.open(path, encoding='utf-8') as read:
            start = True
            x = ''
            reward = 0
            last_conv_id = None
            for line in read:
                line = line.strip().replace('\\n', '\n')
                if len(line) == 0:
                    # empty response
                    continue

                # first, get conversation index -- '1' means start of episode
                space_idx = line.find(' ')
                if space_idx == -1:
                    # empty line, both individuals are saying whitespace
                    conv_id = int(line)
                else:
                    conv_id = int(line[:space_idx])

                # split line into constituent parts, if available:
                # x<tab>y<tab>reward<tab>label_candidates
                # where y, reward, and label_candidates are optional
                split = line[space_idx + 1 :].split('\t')

                # remove empty items and strip each one
                for i in range(len(split)):
                    word = split[i].strip()
                    if len(word) == 0:
                        split[i] = ''
                    else:
                        split[i] = word
                # Empty reward string same as None
                if len(split) > 2 and split[2] == '':
                    split[2] = None

                # now check if we're at a new episode
                if last_conv_id is None or conv_id <= last_conv_id:
                    x = x.strip()
                    if x:
                        yield [x, None, reward], start
                    start = True
                    reward = 0
                    # start a new episode
                    if self.cloze:
                        x = 'Fill in the blank in the last sentence.\n{x}'.format(
                            x=split[0]
                        )
                    else:
                        x = split[0]
                else:
                    if x:
                        # otherwise add current x to what we have so far
                        x = '{x}\n{next_x}'.format(x=x, next_x=split[0])
                    else:
                        x = split[0]
                last_conv_id = conv_id
                if len(split) > 2 and split[2]:
                    reward += float(split[2])

                if len(split) > 1 and split[1]:
                    # only generate an example if we have a y
                    split[0] = x
                    # split labels
                    split[1] = split[1].split('|')
                    if len(split) > 3:
                        # split label_candidates
                        split[3] = split[3].split('|')
                    if len(split) > 2:
                        split[2] = reward
                    else:
                        split.append(reward)
                    if start:
                        yield split, True
                        start = False
                    else:
                        yield split, False
                    # reset x in case there is unlabeled data still left
                    x = ''
                    reward = 0
            if x:
                yield [x, None, reward], start


class ParlAIDialogTeacher(FixedDialogTeacher):
    """
    This module provides access to data in the ParlAI Text Dialog format.

    Subclasses ``FixedDialogTeacher`` for functionality and provides an
    implementation of ``setup_data()`` which iterates over datasets in the
    "ParlAI text" format. If your data is in the format below, use this class to
    handle file parsing for you.

    The way the data is set up is as follows:

    ::

        text:Sam went to the kitchen. <NEWL>
        Pat gave Sam the milk. <NEWL>
        Where is the milk? <TAB> labels:kitchen <TAB> reward:1
        <TAB> label_candidates:hallway|kitchen|bathroom
        text:Sam went to the hallway. <NEWL>
        Pat went to the bathroom. <NEWL>
        Where is the milk? <TAB> labels:hallway <TAB> reward:1
        <TAB> label_candidates:hallway|kitchen|bathroom <TAB> episode_done:True

    Lines 1-2 represent a single episode, with a different example on each line.
    The lines contain a query and a label for getting the question
    correct, and three label candidates.

    Since both of these examples are part of the same episode, the information
    provided in the first example is relevant to the query in the second
    example and therefore the agent must remember the first example in order to
    do well.

    In general dialog this format can contain any speech, not just QA pairs:

    ::

        text:Hi how's it going?<TAB>labels:It's going great. What's new?
        text:Well I'm working on a new project at work.<TAB>labels:Oh me too!
        text:Oh cool!<TAB>labels:Tell me about yours.

    etc.

    Note that dialogs are interpreted as being one-way. For example, consider
    this dialog:

    ::

        1 X1    Y1
        2 X2    Y2
        3 X3    Y3

    A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated.
    However, Y1 => X2 and Y2 => X3 are not created as separate examples by
    default. This makes sense for some data (we don't need to train on the idea
    that "kitchen" should be followed by "Sam went to the hallway..." above),
    but for other datasets it may be helpful to add additional examples in the
    reverse direction ("Oh cool!" is a response to "Oh me too!" above).
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        if not shared:
            self.episodes = []
            self.num_exs = 0
            if opt.get('parlaidialogteacher_datafile') is not None:
                self._setup_data(opt.get('parlaidialogteacher_datafile'))
        else:
            self.episodes = shared['episodes']
            self.num_exs = sum(len(e) for e in self.episodes)

        self.id = opt['task']

        self.reset()

    def share(self):
        """
        Share the episodes.
        """
        shared = super().share()
        shared['episodes'] = self.episodes
        return shared

    def num_examples(self):
        """
        Return the number of examples from the data.
        """
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes from the data.
        """
        return len(self.episodes)

    def get(self, episode_idx, entry_idx=None):
        """
        Get a specific example from the dataset.
        """
        return self.episodes[episode_idx][entry_idx]

    def _setup_data(self, path):
        logging.info(f"Loading ParlAI text data: {path}")
        self.episodes = []
        self.num_exs = 0
        eps = []
        with PathManager.open(path, newline='\n', encoding='utf-8') as read:
            for line_no, line in enumerate(read, 1):
                msg = str_to_msg(line.rstrip('\n'))
                if msg and 'eval_labels' in msg:
                    raise ValueError(
                        f"It looks like you've written eval_labels as a key in your "
                        f"data file. This is not appropriate; labels will be converted "
                        f"for you automatically. This is happening on Line {line_no} "
                        f"in {path}. The line is:\n\t{line}"
                    )
                if msg and 'text' not in msg:
                    raise ValueError(
                        f'ParlaiDialogTeacher requires a "text" field in every '
                        f'entry, but one is missing in Line {line_no} in {path}. '
                        f'The line is:\n\t{line}'
                    )
                if msg and 'labels' not in msg:
                    raise ValueError(
                        f'ParlaiDialogTeacher requires a "labels" field in every '
                        f'entry, but one is missing in Line {line_no} in {path}. '
                        f'The line is:\n\t{line}'
                    )
                if msg:
                    self.num_exs += 1
                    eps.append(msg)
                    if msg.get('episode_done', False):
                        self.episodes.append(eps)
                        eps = []
        if len(eps) > 0:
            # add last episode
            eps[-1].force_set('episode_done', True)
            self.episodes.append(eps)
        if len(self.episodes) == 1 and line_no > 100:
            logging.error(
                f'The data in {path} looks like one very long episode. If this '
                f'is intentional, you may ignore this, but you MAY have a bug in '
                f'your data.'
            )


class YamlTeacher(DialogTeacher):
    """
    Teacher which loads data generated by `parlai.utils.testing.AutoTeacherTest`.
    """

    def __init__(self, opt, shared=None):
        # TODO: if we get rid of the streaming datafile num_episodes/num_examples
        # cache then we can support streaming here. but for now let's
        # just hardcode it
        opt = opt.copy()
        opt['datatype'] = opt['datatype'].replace(':stream', '')
        super().__init__(opt, shared=shared)

    def setup_data(self, datafile):
        with PathManager.open(datafile) as f:
            records = yaml.safe_load(f)
            next_episode_new = True
            for act in records['acts']:
                act = act[0]  # yaml wraps in a weird singleton list
                next_episode_new = act.pop('episode_done')
                if 'eval_labels' in act:
                    act['labels'] = act.pop('eval_labels')
                yield act, next_episode_new


class ConversationTeacher(FixedDialogTeacher):
    """
    This module provides access to data in the Conversations format.

    Subclasses ``FixedDialogTeacher`` for functionality and provides an
    implementation of ``setup_data()`` which iterates over datasets in the
    "Conversations" format. If your data is in the format below, use this class to
    handle file parsing for you.

    The data should be set up so that each dialogue instance (or, episode)
    occupies one line of valid JSON. The way the data is set up is as follows:

    ::
    { "dialog": [ [ {"id": "partner1", "text": "hello!"},  {"id": "partner2", "text": "hi back!"}  ] ] }

    NOTE: If the data is not on one line per dialogue, it will not load.
    Further, note that by default, dialogs are interpreted as being one-way.
    For example, consider this dialog (not that the data below is not on:

    ::

        {
            "dialog":[ [
                {"id":"modelx", "text": X1},
                {"id":"modely", "text": Y1},
                {"id":"modelx", "text": X2},
                {"id":"modely", "text": Y2},
                {"id":"modelx", "text": X3},
                {"id":"modely", "text": Y3},
            ] ]
        }

    (Note: we use line breaks for readability above, but this data will not load as
    stated, it must be on one line.)

    A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated,
    forming one episode. However, Y1 => X2 and Y2 => X3 are not created as
    separate examples by default.
    To change this behavior, you can set opt['label_turns']. The default
    value is 'secondspeaker' (i.e., the second speaker's utterances are
    used as labels), but 'firstspeaker' and 'both' are also options. In the
    case of 'both', two episodes are generated for each conversation.
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        if not shared:
            self.episodes = []
            self.num_exs = 0
            self.label_turns = opt.get('label_turns')
            if opt.get('conversationteacher_datafile') is not None:
                self._setup_data(opt.get('conversationteacher_datafile'))
        else:
            self.episodes = shared['episodes']
            self.num_exs = sum(len(e) for e in self.episodes)

        self.id = opt['task']

        self.reset()

    def share(self):
        """
        Share the episodes.
        """
        shared = super().share()
        shared['episodes'] = self.episodes
        return shared

    def num_examples(self):
        """
        Return the number of examples from the data.
        """
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes from the data.
        """
        return len(self.episodes)

    def get(self, episode_idx, entry_idx=None):
        """
        Get a specific example from the dataset.
        """
        return Message(self.episodes[episode_idx][entry_idx])

    def _setup_data(self, path):
        logging.info("[loading data from json file into task:" + path + "]")
        self.episodes = []
        self.num_exs = 0
        eps = []
        conversations = Conversations(path)
        self.num_exs = 0
        for conv in conversations:
            if conv.context:
                warn_once(
                    'At least one of these conversations contains a context, which is not being used'
                )
            turns = [t for t in conv.turns if t.get('id') != 'context']
            if len(turns) != len(conv.turns):
                warn_once(
                    'At least one of these conversations contains a context within the dialogue, which is being discarded'
                )
            turns.insert(0, Message({'text': '__SILENCE__'}))
            # train on odd turns as labels (turns w/ first speaker)
            if self.label_turns in ['firstspeaker', 'both']:
                eps = self._get_ep_from_turns(turns[::2], turns[1::2])
                if eps:
                    self.episodes.append(eps)
                    self.num_exs += len(eps)

            # train on even turns as labels (turns w/ second speaker)
            if self.label_turns in ['secondspeaker', 'both']:
                eps = self._get_ep_from_turns(turns[1::2], turns[2::2])
                if eps:
                    self.episodes.append(eps)
                    self.num_exs += len(eps)

    def _get_ep_from_turns(self, xturns, yturns):
        eps = []
        for xturn, yturn in zip(xturns, yturns):
            turn = {}
            turn['text'] = xturn.get('text').strip()
            turn['labels'] = [yturn.get('text').strip()]
            turn['episode_done'] = False
            eps.append(turn)
        if eps:
            eps[-1]['episode_done'] = True
            return eps


class AbstractImageTeacher(FixedDialogTeacher):
    """
    Abstract class to allow easier creation of image + dialogue tasks.

    This class handles creating image features via ImageLoader if applicable
    (resnet, resnext variants) or loading existing image features from a dict
    path as per get_image_features_path().

    Important methods and properties (override in subclass if needed):

    - get_data_path(): where data file is found (default: <datapath>/<task>)
    - get_image_path(): where images found (default: <datapath>/<task>/images)
    - get_image_features_path(): dict of image features (default:
      <datapath>/<task>/image_features)
    - @property image_id_key: which key in data file objects represents image_id
    - @property text_key: which key in data file objects represents text

    Note: Assumes data files are named <dt>.json

    @abstractmethod image_id_to_image_path() must be implemented in subclass

    Example with the key defaults (but the keys can be customized):

    .. code-block:: python

        obs = {
            'text': <caption>,
            'image': <image features if specified else image>
        }
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        self.opt = opt
        self.task = opt['task'].split(':')[1] if ':' in opt['task'] else opt['task']
        self.data_path = self.get_data_path(opt)
        self.data = self.load_data(self.data_path, self.opt)
        self.datatype = DatatypeHelper.fold(opt['datatype'])

        # Example of available models: 'resnet152', 'resnext101_32x48d_wsl',
        # and ImageLoader supports other resnet and resnext models too
        # Raises an Exception if not valid
        self._validate_image_mode_name(opt.get('image_mode'))

        # IMPORTANT NOTE: this teacher will be instantiated twice. The first
        # by build_dict in which case the image_mode is to 'no_image_model' to
        # avoid calculating image features twice.
        self.image_mode = opt.get('image_mode')

        # Not using default image_mode parameter b/c there is a normalization
        # (or bug) somewhere in build_dict that is setting it to none
        self.include_image = opt.get('image_mode') != 'no_image_model'

        self.image_path = self.get_image_path(opt)
        self.image_loader = None
        self.image_features_dim = opt.get('image_features_dim')
        self.blank_image_features = torch.FloatTensor(self.image_features_dim).fill_(0)

        if shared and 'data' in shared:
            self.data = shared['data']
            self.image_loader = shared['image_loader']
            if 'image_features_dict' in shared:
                self.image_features_dict = shared['image_features_dict']
        elif self.include_image:
            self.setup_image_features(self.data_path)
        else:
            # This will happen when building the dictionary - is normal
            # build_dict sets image_mode to 'none'
            warn_once('AbstractImageTeacher self.include_image was False')
            self.image_features_dict = None

        # TODO: won't need this after we have proper logging levels set
        self.__verbose = False

        self.reset()

    def get_available_image_mode_names(self):
        """
        Available image model names.

        resnet and resnext variants available from the ImageLoader. resnext101_XXXXX_wsl
        is the open-sourced FB AI model (960m images, 1.5k hashtags, finetuned on
        ImageNet).
        """
        available_model_names = ImageLoader.get_available_model_names()
        return ['no_image_model', 'raw', 'ascii'] + available_model_names

    def _validate_image_mode_name(self, a):
        """
        Validate the image_mode passed in.

        Needed because image_mode used elsewhere in ParlAI is not always consistent with
        what the image teacher allows.
        """
        if not isinstance(a, str):
            raise argparse.ArgumentTypeError(
                '%s must be a string representing image model name' % a
            )
        available_model_names = self.get_available_image_mode_names()
        if a not in available_model_names:
            raise argparse.ArgumentTypeError(
                '\"%s\" unknown image model name. Choose from: %s. Currently suggested resnet is resnet152 and resnext is resnext101_32x48d_wsl.'
                % (a, available_model_names)
            )
        return a

    @classmethod
    def add_cmdline_args(
        cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
    ) -> ParlaiParser:
        # Be sure to call super() if overriding this method b/c
        # AbstractImageTeacher has necessary params
        parser = super().add_cmdline_args(parser, partial_opt)
        agent = parser.add_argument_group('AbstractImageTeacher Arguments')
        agent.add_argument(
            '--image-path',
            type=str,
            default=None,
            help='Optional argument to specify where images for dataset are'
            'stored if already downloaded. Most tasks will download the images'
            'if not present on the < datapath > / < task > _images / * and * if'
            'this argument is not specified.',
        )

        agent.add_argument(
            '--image-features-dim',
            type=int,
            default=2048,
            help='Specify the size of image features Tensors.',
        )
        return parser

    @property
    def image_id_key(self):
        """
        Which key in the input data dict objects uniquely identify each image.

        Common image keys are "image_id" or "image_num". May be implemented by subclass.
        """
        return 'image_id'

    @property
    def text_key(self):
        """
        Which key in the input data dict objects identifies the text.

        Common keys are "text" or "comment". May be implemented by subclass.
        """
        return 'text'

    @abstractmethod
    def image_id_to_image_path(self, image_id):
        """
        Get the path of the image on disk.

        Must be implemented by subclass.
        """
        pass

    def get_data_path(self, opt):
        """
        Determines path to the data file.
        """
        task_name = opt['task'].split(':')[1] if ':' in opt['task'] else opt['task']
        data_path = os.path.join(opt['datapath'], task_name)
        return data_path

    def get_image_path(self, opt):
        """
        Return the path to the data directory and to the image directory.

        Is based on opt fields: task, datatype (train, valid, test), datapath.

        Subclass can override this.
        """
        data_path = self.get_data_path(opt)
        if opt.get('image_path', None):
            image_path = opt['image_path']
        else:
            # other common choice: .join(opt['datapath'], task_name + '_images')
            image_path = os.path.join(data_path, 'images')

        return image_path

    def get_image_features_path(self, task, image_model_name, dt):
        """
        Image features for the dataset images are stored here.

        Can be overridden in subclass to use custom paths. Image features can be manually
        copied into this directory or in the case of ImageLoader eligible models, they
        will be built and stored here if not already there.
        """
        # In default implementation, self.data_path already has task name added
        image_features_path = os.path.join(self.data_path, 'image_features')

        PathManager.mkdirs(image_features_path)

        return os.path.join(
            image_features_path, '%s_%s_%s_features_dict' % (task, image_model_name, dt)
        )

    def is_image_mode_buildable(self, model_name):
        """
        Is buildable if features can be calculated by ImageLoader.

        Users may wish to compute features for the dataset offline and use in the model,
        in which case, the image model should return False and get_image_features()
        should be overridden in subclass.
        """
        return model_name in ImageLoader.get_available_model_names()

    def load_data(self, data_path, opt):
        """
        Loading the data file, which is the index to the images and text.

        It is often a .json file with the name of the <datatype>.json (i.e.
        train.json). Stores in self.data.

        Can be override by subclass.
        """

        dt = DatatypeHelper.fold(opt['datatype'])

        # Sometimes file is named "val" instead of "valid"
        if dt not in ['train', 'valid', 'val', 'test']:
            raise Exception(
                'Unknown dt parameter: %s. Expected either "train", "valid", or "test".'
                % dt
            )

        # Assumes file is train.json or valid.json named
        data_file = os.path.join(self.data_path, '%s.json' % dt)

        # Load the text data and image number indexes
        with PathManager.open(data_file, encoding='utf-8') as f:
            self.data = json.load(f)

        if len(self.data) > 0 and self.image_id_key not in self.data[0]:
            # Data doesn't have a "image_id" like field so add the index in the file to the data
            for idx, d in enumerate(self.data):
                d[self.image_id_key] = idx

        return self.data

    def setup_image_features(self, data_path):
        """
        Load text and image data.

        The image features all live in dicts by default in <data_path>/
        image_features/ but get_image_features_path() above can be overridden by
        subclass to put them elsewhere.

        In the (very odd) case that the resnet or resnext dicts (models
        buildable using ImageLoader) are not found, we build them.
        """
        if self.image_mode in ['raw', 'ascii']:
            self.image_features_dict = None
            self.image_loader = ImageLoader(self.opt)
            return
        image_mode_features_dict_path = self.get_image_features_path(
            self.task, self.image_mode, self.datatype
        )

        if PathManager.exists(image_mode_features_dict_path):
            logging.info(
                f'Loading existing image features dict for model: {self.image_mode} at: {image_mode_features_dict_path}'
            )
            with PathManager.open(image_mode_features_dict_path, 'rb') as f:
                self.image_features_dict = torch.load(f, map_location='cpu')
        else:
            logging.warning('No existing image features, attempting to build.')
            if self.is_image_mode_buildable(self.image_mode):
                # TODO: Awkward to modify the input opt but needed to use
                # TODO: ImageLoader functionality. Is from comment_battle,
                # TODO: will refactor this at some point soon most likely
                image_loader_opt = self.opt.copy()
                image_loader_opt['image_mode'] = (
                    self.image_mode if self.include_image else 'no_image_model'
                )

                image_loader_opt['image_size'] = 256
                image_loader_opt['image_cropsize'] = 224
                self.image_loader = ImageLoader(image_loader_opt)

                # try to build with ImageLoader (i.e. resenet/resnext variants)
                self.image_features_dict = self._build_image_features_dict(
                    self.data_path, self.datatype, image_mode_features_dict_path
                )
            else:
                raise RuntimeError(
                    'Image model: %s is not buildable by ImageLoader but does'
                    'not already exist on disk as an image features dict for'
                    'this dataset.' % self.image_mode
                )

    def _build_image_features_dict(self, data_path, dt, store_dict_path):
        """
        Build resne(x)t image features with ImageLoader.

        (Or anything handleable by ImageLoader) and save to path. Only called if we
        haven't already built the dict before.
        """
        image_features_dict = {}
        total = len(self.data)
        import tqdm

        pbar = tqdm.tqdm(
            total=total,
            unit='cand',
            unit_scale=True,
            desc='Building image features dict for %s with ImageLoader.'
            % self.image_mode,
        )
        num = 0
        for ex in self.data:
            img_id = ex[self.image_id_key]
            img_path = self.image_id_to_image_path(img_id)
            image = self.image_loader.load(img_path).detach()
            # spatial features are [1, image_dim, spatial_dim, spatial_dim] tensors.
            # reduce non-spatial features to one-dimensional feature prior to saving.
            if not self.image_loader.is_spatial(self.image_mode):
                image = image[0, :, 0, 0]
            image_features_dict[img_id] = image
            num += 1
            pbar.update(1)
            if num % 1000 == 0:
                logging.debug(f'Processing image index: {num}')
        torch_utils.atomic_save(image_features_dict, store_dict_path)
        return image_features_dict

    def reset(self):
        super().reset()
        self.example = None

    def num_episodes(self):
        return self.num_examples()

    def num_examples(self):
        return len(self.data)

    def get_image_features(self, example):
        """
        Get image features for example.

        Can be overridden in subclass for different behavior. For large datasets, it may
        be more appropriate to use the ImageLoader.load() method to load image features
        (as this is essentially streaming the features from disk, so that we do not have
        to load a large image feature dict in memory). #TODO Could be the default option
        if we are using -dt train:stream
        """
        if self.image_mode in ['raw', 'ascii']:
            try:
                image = self.image_loader.load(
                    self.image_id_to_image_path(example['image_id'])
                )
            except FileNotFoundError:
                # No Image Here
                image = None
            return image

        key = str(example[self.image_id_key])
        if not self.include_image or key not in self.image_features_dict:
            image_features = self.blank_image_features
        else:
            image_features = self.image_features_dict[key]
        return image_features

    def get(self, episode_idx, entry_idx=0):
        """
        Override this in subclass if your data should be handled in a different format.
        """
        example = self.data[episode_idx]
        image_features = self.get_image_features(example)
        return {
            'labels': [example[self.text_key]],
            'image': image_features,
            'episode_idx': episode_idx,
            'episode_done': True,
        }

    def share(self):
        shared = super().share()
        shared['data'] = self.data
        shared['image_loader'] = self.image_loader
        if hasattr(self, 'image_features_dict'):
            shared['image_features_dict'] = self.image_features_dict
        return shared


class MultiTaskTeacher(Teacher):
    """
    MultiTaskTeacher which teaches multiple tasks.

    Creates a teacher that is actually a set of teachers each based on a task
    string -- each of these teachers will get called in turn,
    either randomly or in order.  They are all in the same world (they are the
    same agent switching tasks).

    The task string format is described for the ``create_task_agents()``
    function above.
    """

    def __init__(self, opt: Opt, shared=None):
        self.tasks: List[Agent] = []
        self.opt = opt

        self.id = opt['task']
        if shared and 'tasks' in shared:
            self.tasks = [create_agent_from_shared(t) for t in shared['tasks']]
        else:
            tasks = opt['task'].split(',')
            for k in tasks:
                k = k.strip()
                if k:
                    opt_singletask = copy.deepcopy(opt)
                    opt_singletask['task'] = k
                    self.tasks.extend(create_task_agent_from_taskname(opt_singletask))
        self.task_idx = -1
        self.new_task = True
        self.random = DatatypeHelper.should_shuffle(opt['datatype'])
        # Make multi-task task probabilities.
        self.cum_task_weights = [1] * len(self.tasks)
        self.task_choices = range(len(self.tasks))
        weights = self.opt.get('multitask_weights', [1])
        if weights == 'stochastic':
            weights = [t.num_episodes() for t in self.tasks]
        sum = 0
        for i in self.task_choices:
            if len(weights) > i:
                weight = weights[i]
            else:
                weight = 1
            self.cum_task_weights[i] = weight + sum
            sum += weight

    def num_examples(self):
        """
        Return the number of examples.
        """
        if not hasattr(self, 'num_exs'):
            # num_examples is sum of all examples in all tasks
            tasks_num_exs = [t.num_examples() for t in self.tasks]
            if any(num is None for num in tasks_num_exs):
                self.num_exs = None
            else:
                self.num_exs = sum(tasks_num_exs)
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes.
        """
        if not hasattr(self, 'num_eps'):
            # num_episodes is sum of all num_episodes in all tasks
            tasks_num_eps = [t.num_episodes() for t in self.tasks]
            if any(num is None for num in tasks_num_eps):
                self.num_eps = None
            else:
                self.num_eps = sum(tasks_num_eps)
        return self.num_eps

    def observe(self, observation):
        """
        Make an observation.
        """
        return self.tasks[self.task_idx].observe(observation)

    def act(self):
        """
        Act on the previous observation.
        """
        if self.new_task:
            self.new_task = False
            if self.random:
                # select random teacher
                self.task_idx = random.choices(
                    self.task_choices, cum_weights=self.cum_task_weights
                )[0]
            else:
                # do at most one full loop looking for unfinished task
                for _ in range(len(self.tasks)):
                    self.task_idx = (self.task_idx + 1) % len(self.tasks)
                    if not self.tasks[self.task_idx].epoch_done():
                        # if this task has examples ready, break
                        break
                if self.tasks[self.task_idx].epoch_done():
                    # all tasks are done, so return empty action table
                    return Message.padding_example()
        t = self.tasks[self.task_idx].act()
        if t['episode_done']:
            self.new_task = True
        return t

    def epoch_done(self):
        """
        Return whether all subtasks are completed.
        """
        for t in self.tasks:
            if not t.epoch_done():
                return False
        return True

    # return transformed metrics showing total examples and accuracy if avail.
    def report(self):
        """
        Report aggregated metrics across all subtasks.
        """
        return aggregate_named_reports(
            {t.getID(): t.report() for t in self.tasks},
            micro_average=self.opt.get('aggregate_micro', False),
        )

    def reset(self):
        """
        Reset all subtasks.
        """
        for t in self.tasks:
            t.reset()

    def reset_metrics(self):
        """
        Reset metrics for each subtask.
        """
        for t in self.tasks:
            t.reset_metrics()

    def share(self):
        """
        Shares this teacher by sharing each subtask.
        """
        shared = {}
        shared['class'] = type(self)
        shared['opt'] = self.opt
        shared['tasks'] = [t.share() for t in self.tasks]
        return shared

    def shutdown(self):
        """
        Shutdown each agent.
        """
        for t in self.tasks:
            t.shutdown()


class ChunkTeacher(FixedDialogTeacher, ABC):
    """
    Useful for loading large amounts of data.

    Data is separated into chunks and loaded one chunk at a time. Loads the data off of
    the main thread.
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        self.buffersize = self.get_buffersize()

        self.set_datasettings(opt)
        # chunk teacher makes shuffling decisions based on training, but
        # train:stream turns off shuffling in other teachers.
        self.datatype = DatatypeHelper.strip_stream(opt['datatype'])

        self.dws = int(self.opt.get('distributed_world_size', 1))
        self.rank = int(self.opt.get('rank', 0))
        self.bg_index = self.opt.get('background_index', None)

        # If we're in training mode with --num-workers > 0, we will run the
        # chunk teacher in single threaded mode (self.threading is False). In
        # this mode, we will block on chunk loading.

        # If we're not using --num-workers, or we're in validation/testing, we
        # _always_ run in normal threading mode, where chunk loading is pushed
        # to a background thread. However, since python threading is is blocked
        # by the GIL, this only manages to background I/O.

        # Potentially in the future, we may support --num-workers in validation,
        # in which case we can get rid of one of these.

        self.threading = not (opt.get('num_workers', 0) > 0 and self.is_train)
        if not self.threading and opt.get('background_index') is None:
            # don't start loading data on the main driver, we don't need it
            opt['no_auto_enqueues'] = True
        if not self.threading:
            # if we're in single-threaded (background preprocessing mode), we
            # can't have a max queue size or we will hang if we overfill it
            self.buffersize = 0

        if shared is not None:
            self.is_root_teacher = False
            self.chunks = shared['chunks']
            self.samples = shared['samples']
            self.reset_counter = shared['reset_counter']
            self.rng = shared['rng']
            self.tot_samples_loaded = shared['tot_samples_loaded']
        else:
            self.is_root_teacher = True
            self.samples = queue.Queue(maxsize=self.buffersize)
            self.chunks = queue.Queue()
            self.reset_counter = SimpleCounter()  # track no. of resets
            if DatatypeHelper.should_shuffle(self.datatype):
                # TODO: possible need a fixed seed here in the future
                self.rng = random.Random()
            else:
                self.rng = random.Random(42)
            self._enqueue_chunks()
            # launch queue loader on the main thread
            self.tot_samples_loaded = defaultdict(int)
            if not opt.get("no_auto_enqueues", False):
                # we only enqueue the train thread because the reset() called at
                # the top of training will handle this
                self._enqueue_request()

        self._episode_done = True
        self.last_queue_output = None

    def _get_data_folder(self):
        if not self.opt.get('datafile'):
            raise RuntimeError(
                'Must specify datafile or override this function (_get_data_folder) '
                'to return the data folder.'
            )

        return self.opt['datafile']

    @abstractmethod
    def get_num_samples(self, opt: Opt) -> Tuple[int, int]:
        """
        [Abstract] Return the number of samples.

        Returns a tuple of (num_examples, num_episodes) based on the data split.
        """
        pass

    @abstractmethod
    def get_fold_chunks(self, opt: Opt) -> List[int]:  # type: ignore
        """
        [Abstract] Return a list of chunk IDs (integer).

        Given the datatype (train/test/valid), return the list of chunk IDs that
        correspond to that split.
        """
        pass

    def get_buffersize(self):
        """
        Size of buffer.

        Override this in your child class to change the buffer size.
        """
        return 100000

    def set_datasettings(self, opt: Opt):
        self.folder = self._get_data_folder()
        self.num_exs, self.num_eps = self.get_num_samples(opt)
        self.fold_chunks = self.get_fold_chunks(opt)

        self.is_train = DatatypeHelper.is_training(opt['datatype'])

    def share(self):
        shared = super().share()
        shared['samples'] = self.samples
        shared['chunks'] = self.chunks
        shared['reset_counter'] = self.reset_counter
        shared['rng'] = self.rng
        shared['tot_samples_loaded'] = self.tot_samples_loaded
        return shared

    def _setup_data(self, datatype):
        """
        Passthrough.
        """
        pass

    def num_episodes(self):
        if self.is_train:
            return self.num_eps
        else:
            return self.num_eps // self.dws + int((self.num_eps % self.dws) > self.rank)

    def num_examples(self):
        if self.is_train:
            return self.num_exs
        else:
            return self.num_exs // self.dws + int((self.num_exs % self.dws) > self.rank)

    def next_episode_idx(self):
        # We don't actually track episodes in ChunkTeacher, we just blindly
        # trust the queue. This hacks around FixedDialogTeacher's next_example
        # check that the epoch is done.
        return 0

    def _enqueue_request(self):
        """
        Queue a request for loading to the data loader.
        """
        if self.threading:
            self.data_loader.request_load(self.receive_data, self.get_chunk, ())
        else:
            self._process_data(self.get_chunk())

    def receive_data(self, future):
        """
        Receive loaded data and place it onto the sample queue.

        :param future:
            A Future object which will return a value from a call to get_chunk()
        """
        return self._process_data(future.result())

    def _process_data(self, output: Optional[Tuple[Any, int]]):
        """
        Loads data.

        Load data into self.samples until buffersize is reached.

        :param output:
            The output of an item from a call to get_chunk()
        """
        if output is None:
            return
        chunk_output, chunk_reset_cnt = output
        if chunk_output is None:
            self.samples.put((None, chunk_reset_cnt))
            return
        if self.threading:
            while chunk_output:
                # self.samples is a queue with maxsize
                # self.buffersize, so will block if the
                # buffer gets full
                sample = chunk_output.pop(0)
                if (
                    self.is_train
                    or self.tot_samples_loaded[chunk_reset_cnt] % self.dws == self.rank
                ):
                    # log the reset count at the time the chunk was queued
                    self.samples.put((sample, chunk_reset_cnt))
                self.tot_samples_loaded[chunk_reset_cnt] += 1
        else:
            # we're actually running in single processor mode so we'll just
            # do a thread-unsafe hit of the python internals, which is much faster
            # than trying to safely put things onto the queue
            self.samples.queue.extend((co, chunk_reset_cnt) for co in chunk_output)
            self.tot_samples_loaded[chunk_reset_cnt] += len(chunk_output)
        if self.threading:
            # and start loading the next chunk
            self._enqueue_request()

    def _enqueue_chunks(self):
        """
        Shuffles and queues fold chunks for loading.
        """
        if DatatypeHelper.should_shuffle(self.datatype):
            self.rng.shuffle(self.fold_chunks)
        # save the reset count at the time a chunk was queued
        reset_cnt = self.reset_counter.value()
        for c in self.fold_chunks:
            self.chunks.put((c, reset_cnt))
        self.chunks.put((None, reset_cnt))
        # gross hack: in training models, when we get to validation, we enqueue
        # a request in the constructor, followed by another enqueue from a
        # reset immediately after. If the former is already running, we'll end
        # up with one too many calls to get_chunk and block on termination.
        # That's what I refer to as "losing" the race condition. If you look in
        # get_chunk, you'll also find the spot where we "win" the race
        # condition.
        self.chunks.put((None, reset_cnt))

    @abstractmethod
    def load_from_chunk(self, chunk_idx: int) -> List[ChunkOutput]:
        """
        [Abstract] Given the chunk index, load examples from that chunk.

        Return a list of tuples. The function `_create_message` will take these tuples
        to form the Message object that is returned by the teacher.
        """
        pass

    @abstractmethod
    def create_message(self, queue_output: ChunkOutput, entry_idx=0) -> 'Message':
        """
        [Abstract] Given the tuple output of the queue, return an act.

        May depend on entry index if queue output is a multi-turn episode.
        """
        pass

    def get_chunk(self):
        """
        Refill the buffer.
        """
        next_chunk, chunk_reset_cnt = self.chunks.get()
        if next_chunk is None:
            if DatatypeHelper.should_cycle(self.datatype):
                # start putting chunks back onto the queue
                self._enqueue_chunks()
                next_chunk, chunk_reset_cnt = self.chunks.get()
                if next_chunk is None:
                    # See the race condition described around "gross hack" in
                    # _enqueue_chunks.  if we win the race condition, then
                    # catch it here
                    next_chunk, chunk_reset_cnt = self.chunks.get()
            else:
                # if we're in valid/test, we need to actually signal the end
                return (None, chunk_reset_cnt)
        # abstract method `load_from_chunk` returns a list of tuples
        output = self.load_from_chunk(next_chunk)

        if DatatypeHelper.should_shuffle(self.datatype):
            # randomize the samples
            random.Random().shuffle(output)
        return output, chunk_reset_cnt

    def next_example(self):
        # next_example will always fail to provide useful signal on whether
        # we're at the end of an epoch in chunk teacher. Instead, the queue
        # empties and we simply start outputting pads forever. As such, we'll
        # measure epochs when we start receiving only pads.

        # (This isn't relevant for the training loop, which loops for ever and
        # never "epochs").
        retval, fake_epoch_done = super().next_example()
        real_epoch_done = retval.is_padding()
        return retval, real_epoch_done

    def get(self, episode_idx, entry_idx=0):
        if not self.threading and self.samples.empty():
            self._enqueue_request()

        curr_reset_cnt = self.reset_counter.value()
        if self._episode_done:
            # Get the next episode or example
            queue_output, reset_cnt = self.samples.get()
            stale_exs = 0
            while curr_reset_cnt > reset_cnt:
                stale_exs += 1
                queue_output, reset_cnt = self.samples.get()
            if stale_exs > 0:
                logging.debug(f"Removed {stale_exs} stale examples from the queue.")
            if queue_output is None:
                self.samples.put((None, reset_cnt))
                return Message.padding_example()

            # Update the last queue output in the case
            # of multi-turn episodes
            self.last_queue_output = queue_output

        # create a Message object from the queue output
        msg = self.create_message(self.last_queue_output, entry_idx)
        self._episode_done = msg['episode_done']

        return msg

    def _drain(self, q):
        while not q.empty():
            try:
                q.get()
            except queue.Empty:
                return

    def reset(self):
        super().reset()
        if self.is_root_teacher:
            self.reset_counter.increment()
            # drain the queues and refill the chunk queue with a new epoch.
            # additionally, we have to relaunch the loader
            self._drain(self.samples)
            self._drain(self.chunks)
            self._enqueue_chunks()
            self.tot_samples_loaded.clear()  # reset the count of samples loaded
            if self.threading:
                self._enqueue_request()

    def shutdown(self):
        # Time to wrap up. We should rush out to the worker and tell them
        # that they're "done" processing data.
        # same signal as end of epoch
        self.chunks.put((None, None))
        self.chunks.put((None, None))


def _add_task_flags_to_agent_opt(agent, opt: Opt, flags):
    """
    Handle task flags provided by the task name itself.

    With this you can set specific opts with `-t task:flag=foo`.
    """
    fl = flags.split(':')
    task = []
    for f in fl:
        if '=' in f:
            one_flag = f.split('=')
            key = one_flag[0].replace('-', '_')
            raw_value = one_flag[1].replace(';', ':')

            # Convert to bool/int/float if necessary
            if raw_value.lower() == 'true':
                value = True
            elif raw_value.lower() == 'false':
                value = False
            else:
                try:
                    value = int(raw_value)  # type: ignore
                except ValueError:
                    try:
                        value = float(raw_value)  # type: ignore
                    except ValueError:
                        value = raw_value  # type: ignore

            opt[key] = value
        else:
            task.append(f)
    opt['task'] = ':'.join(task)


def create_task_agent_from_taskname(opt: Opt):
    """
    Create task agent(s) assuming the input ``task_dir:teacher_class``.

    e.g. def_string is a shorthand path like ``babi:Task1k:1`` or ``#babi`` or a
    complete path like ``parlai.tasks.babi.agents:Task1kTeacher:1``, which essentially
    performs ``from parlai.tasks.babi import Task1kTeacher`` with the parameter ``1`` in
    ``opt['task']`` to be used by the class ``Task1kTeacher``.
    """
    if not opt.get('task'):
        raise RuntimeError(
            'No task specified. Please select a task with ' + '--task {task_name}.'
        )
    if ',' not in opt['task']:
        # Single task
        teacher_class = load_teacher_module(opt['task'])
        _add_task_flags_to_agent_opt(teacher_class, opt, opt['task'])
        task_agents = teacher_class(opt)
        if type(task_agents) != list:
            task_agents = [task_agents]
        return task_agents
    else:
        # Multitask teacher/agent
        task_agents = MultiTaskTeacher(opt)
        if type(task_agents) != list:
            task_agents = [task_agents]
        return task_agents

#!/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.
"""
This module provides a set of teachers that deal with dialog.

    ``FixedDialogTeacher(Teacher)``
    Base class for teachers in tasks that have fixed dialog - i.e., dialog
    that is not dynamically generated but rather is pulled from set examples.
    However, the class can be extended to all tasks involved fixed data.
    Implements much of the basic functionality of these teachers, including
    ``observe()``, ``act()``, ``next_example()``

    ``DialogTeacher(FixedDialogTeacher)``
     Base teacher class for doing dialog specifically with fixed chat logs.

    ``ParlAIDialogTeacher(DialogTeacher)``
     Teacher class that provides access to data in the ParlAI Dialog format.
     See the class description for more details.

     ``ConversationTeacher(DialogTeacher)``
     Teacher class that provides access to data in the Conversations format.
     See the class description for more details.

    ``FbDeprecatedDialogTeacher(DialogTeacher)``
     Teacher class that provides access to data in the Facebook Dialog format.
     See the class description for more details. **This class is deprecated**.

This module also includes ``DataLoader``, a threadpool data loader for
``FixedDialogTeacher``, and ``DialogData``/``StreamDialogData``, data
structures for accessing textual dialog data and utilized by ``DialogTeacher``
"""
from parlai.core.params import ParlaiParser
from parlai.core.agents import Agent, create_agent_from_shared
from parlai.core.image_featurizers import ImageLoader
from parlai.core.loader import load_teacher_module
from parlai.core.loader import register_teacher  # noqa: F401
from parlai.core.message import Message
from parlai.core.metrics import TeacherMetrics, aggregate_named_reports
from parlai.core.opt import Opt
from parlai.utils.conversations import Conversations
from parlai.utils.data import DatatypeHelper
from parlai.utils.misc import AttrDict, str_to_msg, warn_once, SimpleCounter
from parlai.utils.distributed import get_rank, num_workers, is_distributed
import parlai.utils.torch as torch_utils
import parlai.utils.logging as logging
from parlai.utils.io import PathManager
from parlai.core.mutators import Mutator

from abc import ABC, abstractmethod
import argparse
from collections import defaultdict
import concurrent.futures
import copy
import json
import os
import queue
import random
import yaml
from threading import Thread
import torch
from typing import List, Tuple, Optional, TypeVar, Any


ERROR_MESSAGE_NO_DATAFILE = (
    "{class_name} is expected to set self.opt['datafile'] inside `__init__` "
    "before calling `super().__init__`. This will passed to setup_data, "
    "indicating what data to load. If you don't know what to use, set "
    "`opt['datafile'] = parlai.utils.data.DatatypeHelper.fold(opt['datatype'])` "
    "to receive the fold name in setup_data."
)


ChunkOutput = TypeVar('ChunkOutput')


class DataLoader(Thread):
    """
    A worker thread that provides a threadpool for data loading.

    A teacher may submit a request to the loader, which will return the
    appropriate data.

    To submit a request, a teacher should call ``request_load``.
    """

    def __init__(self, opt):
        Thread.__init__(self, daemon=True)
        self.num_workers = opt.get('num_load_threads', 1)
        self.request_queue = queue.Queue()
        self.last_future = None

    def request_load(self, receive_fn, load_fn, args):
        """
        Queue a request for loading.

        :param receive_fn:
            a receive function (for receiving the data)
        :param load_fn:
            a load function (for loading the data)
        :param args:
            arguments for the load function. args can be either a dictionary of
            arguments for a function, or a list of positional arguments
        """
        self.request_queue.put((receive_fn, load_fn, args))

    def run(self):
        """
        Run the execution loop.
        """
        executor = concurrent.futures.ThreadPoolExecutor(
            max_workers=self.num_workers, thread_name_prefix=self.name
        )
        with executor:
            while True:
                receive_fn, load_fn, args = self.request_queue.get()
                if receive_fn is StopIteration:
                    return
                try:
                    if type(args) == dict:
                        future = executor.submit(load_fn, **args)
                    else:
                        future = executor.submit(load_fn, *args)
                    self.last_future = future
                    receive_fn(future)
                except RuntimeError:
                    return


class _ErrorThrowingDataLoader(object):
    """
    A fake DataLoader which throws an exception when a work order is placed.

    Since threads cannot be mixed with spawn_method='fork', we need to disallow users
    from combining --num-workers with teachers that utilize threads. This placeholder
    object is only useful for ensuring the user sees a loud error message when they
    accidentally use a thread.
    """

    def __init__(self, opt):
        pass

    def request_load(self, receive_fn, load_fn, args):
        raise RuntimeError(
            'One of your teachers uses a DataLoader or a thread. You may only '
            'combine this with --num-workers 0.'
        )

    def start(self):
        pass


class Teacher(Agent):
    """
    Basic Teacher agent that keeps track of how many times it's received messages.

    Teachers provide the ``report()`` method to get back metrics.
    """

    @classmethod
    def add_cmdline_args(
        cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
    ) -> ParlaiParser:
        parser.add_argument(
            '--mutators',
            '-mut',
            default=None,
            help='Apply one or more mutators to the data.',
        )
        mutators = Mutator.load_mutator_types(partial_opt.get('mutators'))
        for m in mutators:
            m.add_cmdline_args(parser, partial_opt)
        return parser

    def __init__(self, opt: Opt, shared=None):
        if not hasattr(self, 'opt'):
            self.opt = copy.deepcopy(opt)
        if not hasattr(self, 'id'):
            self.id = opt.get('task', 'teacher')
        if not hasattr(self, 'metrics'):
            self.metrics = TeacherMetrics(
                metrics_list=opt.get('metrics', 'default'),
                shared=shared['metrics'] if shared is not None else None,
            )
        self.epochDone = False

    # return state/action dict based upon passed state
    def act(self):
        """
        Act upon the previous observation.
        """
        if self.observation is not None and 'text' in self.observation:
            t = Message({'text': 'Hello agent!'})
        return t

    def epoch_done(self):
        """
        Return whether the epoch is done.
        """
        return self.epochDone

    # Default unknown length
    def num_examples(self):
        """
        Return the number of examples (e.g. individual utterances) in the dataset.

        Default implementation returns `None`, indicating an unknown number.
        """
        return None

    def num_episodes(self):
        """
        Return the number of episodes (e.g. conversations) in the dataset.

        Default implementation returns `None`, indicating an unknown number.
        """
        return None

    def report(self):
        """
        Return metrics showing total examples and accuracy if available.
        """
        return self.metrics.report()

    def reset(self):
        """
        Reset the teacher.
        """
        super().reset()
        self.reset_metrics()
        self.epochDone = False

    def reset_metrics(self):
        """
        Reset metrics.
        """
        self.metrics.clear()

    def share(self):
        """
        In addition to default Agent shared parameters, share metrics.
        """
        shared = super().share()
        shared['metrics'] = self.metrics.share()
        return shared

    def __iter__(self):
        """
        Iterate through the examples of the teacher.
        """
        clone = self.clone()
        while True:
            message = clone.act()
            if not isinstance(message, Message):
                # backwards compatibility with older agents
                message = Message(message)
            if message.is_padding():
                break
            yield message


class FixedDialogTeacher(Teacher):
    """
    A teacher agent for all teachers involved in tasks with fixed data.

    This class provides the following functionality for its subclasses:

    - Resets a teacher
    - Provides an observe method
    - Computes and retrieves the next episode index for a teacher
    - Provides a threadpool option for loading data (especially useful for
      large data, e.g. images)

    In order to take advantage of the first few features, all a subclass has to
    implement is three functions: ``num_episodes``, ``num_examples``, and
    ``get`` (which returns a specific example from a specific episode).

    To utilize the DataLoader for threadpool loading, a teacher should
    implement the ``submit_load_request`` function to send a load request
    to the DataLoader by calling ``self.data_loader.request_load`` with the
    appropriate arguments (``receive_fn, load_fn, args``). The DataLoader then
    returns the data to the teacher's ``data_queue``, which the teacher can
    poll in its ``act`` method.

    The following is an example of the DataLoader usage in the VQA-V1 teacher.

    1. In the teacher's ``init`` function, the teacher calls its
       ``submit_load_request`` function to preload an image.
    2. The ``submit_load_request`` function gets the next ``episode_idx``,
       and computes the image path for the load request.
    3. At the end of ``submit_load_request``, the teacher calls
       ``self.data_loader.request_load`` with three args:

        - ``self.receive_data`` - the function that the DataLoader calls to
          return the the loaded object
        - ``self.image_loader.load`` - the function used to load the image
          from the image path
        - ``[img_path]`` - a list of arguments for the load function, which
          in this case is the path of the image.

    4. In the teacher's ``act`` function, the teacher loads the data from
       its data queue.
    5. At the end of the ``act`` function, the teacher calls
       ``submit_load_request`` to preload an image for the next example.

    To see this in action, take a look at this teacher in ``tasks.vqa_v1.agents``.
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)

        if not hasattr(self, 'datatype'):
            self.datatype = opt['datatype']
        if not hasattr(self, 'random'):
            self.random = self.datatype == 'train'
        if not hasattr(self, 'training'):
            self.training = DatatypeHelper.is_training(self.datatype)
        if not hasattr(self, 'cycle'):
            self.cycle = DatatypeHelper.should_cycle(self.datatype)
        if not hasattr(self, 'datafile'):
            self.datafile = opt.get('datafile')
        # set up support for multithreaded data loading
        self.data_queue = queue.Queue()
        if shared:
            self.index = shared['index']
            if 'data_loader' in shared:
                self.data_loader = shared['data_loader']
            if 'threadindex' in shared:
                self.threadindex = shared['threadindex']
            if 'examples' in shared:
                self.examples = shared['examples']
        else:
            self.index = AttrDict(value=-1)

        if not hasattr(self, 'data_loader'):
            if opt.get('background_index') is None:
                self.data_loader = DataLoader(opt)
            else:
                self.data_loader = _ErrorThrowingDataLoader(opt)
            self.data_loader.start()

        # set up batching
        self.bsz = opt.get('batchsize', 1)

        if shared:
            self.mutators = shared.get('mutators', [])
        else:
            mutator_types = Mutator.load_mutator_types(self.opt.get('mutators'))
            self.mutators = [mutator(self.opt) for mutator in mutator_types]

        self._episode_done = True

    def reset(self):
        """
        Reset the dialog to the start of the epoch, and reset all metrics.
        """
        super().reset()
        self.metrics.clear()
        self.lastY = None
        self.last_act = None
        self._episode_done = True
        self.epochDone = False
        self.data_queue = queue.Queue()

        self.episode_idx = -1
        self.index.value = -1

    def submit_load_request(self):
        """
        Submit a load request.

        An agent should implement this method to submit requests to the data
        loader. At the end of this method, the agent should call
        ``self.data_loader.request_load()`` with the appropriate args.

        By default, this method does nothing.
        """
        # TODO: mark as abstract
        pass

    def receive_data(self, future: concurrent.futures.Future):
        """
        Receive data from the data loader.

        :param future: result from the load request.
        """
        data = future.result()
        self.data_queue.put(data)

    def share(self):
        """
        Share the data and dataloader.
        """
        shared = super().share()

        if hasattr(self, 'examples'):
            shared['examples'] = self.examples

        if hasattr(self, 'data_loader'):
            shared['data_loader'] = self.data_loader

        if hasattr(self, 'mutators'):
            shared['mutators'] = self.mutators

        shared['index'] = self.index

        return shared

    def next_episode_idx(self, num_eps=None, loop=None):
        """
        Return the next episode index.

        :param num_eps:
            default None uses ``num_episodes`` value.
        :param loop:
            default None loops during training but not evaluation.
        """
        if num_eps is None:
            num_eps = self.num_episodes()
        if loop is None:
            loop = self.training
        if self.random:
            new_idx = random.randrange(num_eps)
        else:
            self.index.value += 1
            if loop:
                try:
                    self.index.value %= num_eps
                except ZeroDivisionError:
                    raise ZeroDivisionError(
                        "The teacher has either empty data (e.g. setup_data yielded "
                        "no items, or self.num_episodes() == 0). We do not support "
                        "empty datasets (or folds) at this time."
                    )
            new_idx = self.index.value
        return new_idx

    def next_example(self):
        """
        Return the next example.

        If there are multiple examples in the same episode, returns the next one in that
        episode. If that episode is over, gets a new episode index and returns the first
        example of that episode.
        """
        if self._episode_done:
            self.episode_idx = self.next_episode_idx()
            self.entry_idx = 0

            if self.episode_idx >= self.num_episodes():
                return Message.padding_example(), True

            # buffer the full conversation ahead of time for mutators
            episode_buffer = []
            buffer_entry_idx = 0
            while True:
                entry = self.get(self.episode_idx, buffer_entry_idx)
                if not isinstance(entry, Message):
                    assert isinstance(entry, dict)
                    typ = type(self)
                    warn_once(
                        f"{typ.__module__}.{typ.__name__}' is outputting dicts "
                        "instead of messages. If this is a teacher that is part of "
                        "ParlAI, please file an issue on GitHub. If it is your own "
                        "teacher, please return a Message object instead."
                    )
                    entry = Message(entry)
                episode_buffer.append(entry)
                if entry.get('episode_done'):
                    break
                buffer_entry_idx += 1
            # apply mutators
            if self.mutators:
                episode_buffer = [m.copy() for m in episode_buffer]
                for mutator in self.mutators:
                    episode_buffer = mutator(episode_buffer)
            self.episode_buffer = list(episode_buffer)

            if not self.episode_buffer:
                # if we got back an empty episode after mutating, skip it
                return self.next_example()
        else:
            self.entry_idx += 1

        if self.episode_idx >= self.num_episodes():
            return Message.padding_example(), True

        # buffer the entire conversation so we can apply mutators
        ex = self.episode_buffer[self.entry_idx]
        self._episode_done = self.entry_idx == len(self.episode_buffer) - 1

        if (
            not self.cycle
            and self._episode_done
            and self.episode_idx + self.opt.get("batchsize", 1) >= self.num_episodes()
        ):
            epoch_done = True
        else:
            epoch_done = False

        return ex, epoch_done

    def num_episodes(self) -> int:
        """
        Get the number of episodes in this dataset.
        """
        raise RuntimeError('"num_episodes" must be overridden by children.')

    def num_examples(self) -> int:
        """
        Get the total number of examples in this dataset.
        """
        raise RuntimeError('"num_examples" must be overridden by children.')

    def get(self, episode_idx, entry_idx=0):
        """
        Get the specified episode and the specified entry in that episode.

        Children must override this method in order to inherit the
        `next_example` method.

        :param episode_idx:
            which episode to return examples from
        :param entry_idx:
            which example to return from the episode.  Many datasets have only
            single-entry episodes, so this defaults to zero.
        """
        # TODO: mark as abstract, get rid of runtime error.
        raise RuntimeError('"Get" method must be overridden by children.')

    def observe(self, observation):
        """
        Process observation for metrics.
        """
        self.metrics.clear_recent()
        if hasattr(self, 'lastY') and self.lastY is not None:
            self.metrics.evaluate_response(observation, self.lastY)
            self.custom_evaluation(self.last_act, self.lastY, observation)
            self.lastY = None
        recent_metrics = self.metrics.report_recent()
        if recent_metrics:
            # for display purposes (display_model), take all accumulated
            # metrics back into the original observation. This is an abuse of
            # Messages being pointers
            if 'metrics' in observation:
                # override agent-level metrics if present
                observation.pop('metrics')
            observation['metrics'] = recent_metrics
        return observation

    def custom_evaluation(
        self,
        teacher_action: Message,
        labels: Optional[Tuple[str]],
        model_response: Message,
    ) -> None:
        """
        A method designated for hooking custom evaluations into teachers.

        Generally, a user will want to use `self.metrics.add` to record any
        specialized metrics that only make sense for this one dataset.

        :param teacher_action:
            The message last sent from this teacher.
        :param labels:
            The previous correct labels, if there were any.
        :param model_response:
            The raw response from the model. Generally you want to rely on the
            text field, but others may be necessary in specific situations.
        """
        pass

    def act(self):
        """
        Send new dialog message.
        """
        orig_action = self.get_orig_action()
        processed_action = self.process_action(orig_action)
        return processed_action

    def get_orig_action(self) -> Message:
        """
        Get the unprocessed action and reset if needed.

        This function will return the raw action from `self.next_example()`, before the
        `self.last_act` and `self.lastY` attributes have been defined based on this
        action for metrics or custom evaluations. This is so that wrapper teachers can
        modify the raw action first, such as to change the contents of its 'text' and
        'label' fields, without the action becoming out of sync with `self.last_act` and
        `self.lastY`.
        """
        if not hasattr(self, 'epochDone'):
            # reset if haven't yet
            self.reset()

        # get next example, action is episode_done dict if already out of exs
        action, self.epochDone = self.next_example()
        if not isinstance(action, Message):
            # TODO: all teachers should eventually create messages
            # while setting up the data, so this won't be necessary
            action = Message(action)

        return action

    def process_action(self, action: Message) -> Message:
        """
        Remember the raw action and prepare its fields for passing out of the teacher.
        """
        action.force_set('id', self.getID())

        # remember correct answer if available
        self.last_act = action
        self.lastY = action.get('labels', action.get('eval_labels', None))
        if not DatatypeHelper.is_training(self.datatype) and 'labels' in action:
            # move labels to eval field so not used for training
            # but this way the model can use the labels for perplexity or loss
            action = action.copy()
            labels = action.pop('labels')
            if not self.opt.get('hide_labels', False):
                action['eval_labels'] = labels

        return action


class DialogTeacher(FixedDialogTeacher):
    """
    A base teacher class for doing dialog with fixed chat logs.

    This class provides a set a basic functionality:

    - uses data class to store and query text data
    - generates action tables to send to the student agent from the data

    In order to subclass this class, you must implement ``setup_data()`` in
    your class, which reads your data file as an iterator.
    """

    def __init__(self, opt, shared=None):
        # Check for setup_data
        if not hasattr(self, 'setup_data'):
            raise RuntimeError(
                'Must implement setup_data or subclass a class '
                'which implements it (e.g. FbDeprecatedDialogTeacher) '
                'in order to use this class.'
            )
        super().__init__(opt, shared)

        self.datatype = opt['datatype']
        self.training = DatatypeHelper.is_training(self.datatype)
        self.cycle = DatatypeHelper.should_cycle(self.datatype)
        self.stream = 'stream' in self.datatype

        # first initialize any shared objects
        data_class = StreamDialogData if self.stream else DialogData
        kwargs = (
            # never cycle if "ordered" is in the datatype. this is used by
            # build_dict to enumerate through the data exactly once while still
            # marking examples as training examples.
            {'cycle': self.cycle}
            if self.stream
            else {}
        )
        if shared and shared.get('data'):
            self.data = data_class(opt, shared=shared['data'], **kwargs)
        else:
            if 'datafile' not in self.opt:
                raise KeyError(
                    ERROR_MESSAGE_NO_DATAFILE.format(class_name=self.__class__.__name__)
                )
            self.data = data_class(
                opt,
                data_loader=self.setup_data,
                cands=self.label_candidates(),
                **kwargs,
            )

        self.reset()

    @abstractmethod
    def setup_data(self, datafile: str):
        """
        The core method which the user should override.

        Yields the data, one message at a time, as well as markers indicating
        new episodes.

        :param str datafile:
            If the initializer set a 'datafile' field within the initialization,
            this will be provided here. Otherwise, datafile will be the fold:
            either "train", "valid", or "test".

        :return:
            Yields pairs (message, new_episode) containing a Message object
            and whether the message marks the beginning of a totally new
            episode.
        """
        pass

    def reset(self):
        """
        Reset the dialog to the start of the epoch, reset all metrics.
        """
        super().reset()
        if self.stream:
            self.data.reset()
            self.epochDone = False

    def share(self):
        """
        Share the data.
        """
        shared = super().share()
        if hasattr(self, 'data'):
            shared['data'] = self.data.share()
        return shared

    def label_candidates(self):
        """
        Provide consistent label candidates for all examples.

        Default implementation returns ``None`` always, but this may be overridden to
        provide candidates in all areas. See ``FbDialogueTeacher``.
        """
        # TODO DEPRECATIONDAY: FbDialogueTeacher is being deprecated, should we
        # remove this?

        # TODO: mark as optionally abstract?
        return None

    def num_episodes(self) -> int:
        """
        Return the number of episodes in the data.
        """
        try:
            return self.data.num_episodes()
        except AttributeError:
            return super().num_episodes()

    def num_examples(self) -> int:
        """
        Return the number of examples in the data.
        """
        if hasattr(self, '_num_examples_cache'):
            return self._num_examples_cache
        try:
            self._num_examples_cache: int = self.data.num_examples()
        except AttributeError:
            self._num_examples_cache = super().num_examples()
        return self._num_examples_cache

    def get(self, episode_idx, entry_idx=0):
        """
        Get a specific example.
        """
        return self.data.get(episode_idx, entry_idx)[0]

    def next_example(self):
        """
        Get the next example.
        """
        if self.stream:
            # unfortunately we need to also do the mutator buffering here.
            # it's difficult to structure it so it's not
            if hasattr(self, 'episode_buffer') and self.episode_buffer:
                action = self.episode_buffer.pop(0)
                epoch_done = (not self.episode_buffer) and self._saw_epoch_done
                return action, epoch_done
            episode_buffer = []
            while True:
                action, epoch_done = self.data.get()
                episode_buffer.append(action)
                if action['episode_done']:
                    self._saw_epoch_done = epoch_done
                    break
            # perform any mutations there are
            if self.mutators:
                episode_buffer = [m.copy() for m in episode_buffer]
                for mutator in self.mutators:
                    episode_buffer = mutator(episode_buffer)
            # make sure mutations are fully realized (not generators)
            self.episode_buffer = list(episode_buffer)
            # The recursive call has dual purpose:
            # - if we get back an empty episode after mutating, skip it gracefully
            # - pull the first item the episode w/ epoch_done logic, but DRY
            return self.next_example()
        else:
            action, epoch_done = super().next_example()
        return action, epoch_done


class DialogData(object):
    """
    Provides a data structure for accessing textual dialog data.

    This can be used whenever the dialog data is a fixed log of chats
    (i.e not a simulator setting). The logs can include dialog text and possibly
    supervised labels, candidate labels and rewards.

    All these are stored in this internal data format which is used by the
    ``DialogTeacher`` class.

    :param opt:
        options to initialize the class
    :param data_loader:
        an iterable with each call returning a tuple in the form
        ``((x, y, r, c, i), new_episode?)`` where the ``x`` and ``new_episode``
        fields are mandatory and other fields may be omitted or ``None``.
    :param cands:
        can be set to provide a list of candidate labels for every example in
        this dataset, which the agent can choose from (the correct answer
        should be in this set).

    :param random:
        tells the data class whether or not to visit episodes sequentially or
        randomly when returning examples to the caller.

    The contents of the ``((x, y, r, c, i), new_episode?)`` tuples returned by
    the data loader is the following:

    - ``x`` (str) is a query and possibly context
    - ``y`` (iter) is an iterable of label(s) for that query
    - ``r`` (str) is the str reward for getting that query correct
    - ``c`` (iter) is an iterable of label candidates that the student can choose from
    - ``i`` (str) is a str path to an image on disk, which will be loaded by the
      data class at request-time. should always point to the raw image file.
    - ``new_episode?`` (bool) is a boolean value specifying whether that example
      is the start of a new episode. If you don't use episodes set this
      to ``True`` every time.
    """

    def __init__(self, opt, data_loader=None, cands=None, shared=None, **kwargs):
        # in case we need to shard the dataset
        self.rank = get_rank()
        self.num_workers = num_workers()
        self.is_distributed_and_is_eval = is_distributed() and any(
            x in opt['datatype'] for x in ('valid', 'test', 'train:evalmode')
        )

        # self.data is a list of episodes
        # each episode is a tuple of entries
        # each entry is a tuple of values for the action/observation table
        if shared:
            self.image_loader = shared.get('image_loader', None)
            self.data = shared.get('data', [])
            self.cands = shared.get('cands', None)
        else:
            self.image_loader = ImageLoader(opt)
            self.data = []

            if 'datafile' not in opt:
                raise KeyError(
                    ERROR_MESSAGE_NO_DATAFILE.format(class_name=self.__class__.__name__)
                )

            self._load(data_loader, opt['datafile'])
            self.cands = None if cands is None else set(c for c in cands)

        self.addedCands = []
        self.copied_cands = False

    def share(self):
        """
        Share the data.
        """
        shared = {
            'data': self.data,
            'cands': self.cands,
            'image_loader': self.image_loader,
        }
        return shared

    def _read_episode(self, data_loader):
        """
        Read one episode at a time from the provided iterable over entries.

        :param data_loader:
            an iterable which returns tuples in the format described in the
            class docstring.
        """
        episode = []
        for entry, new in data_loader:
            if new and len(episode) > 0:
                yield episode
                episode = []

            episode.append(entry)

        if len(episode) > 0:
            yield episode

    def _load(self, data_loader, datafile):
        """
        Load up data from an iterable over tuples described in the class docs.

        :param iter data_loader:
            an iterator which returns tuples in the format described in the
            class docstring.
        :param str datafile:
        """
        for i, episode in enumerate(self._read_episode(data_loader(datafile))):
            if not self.is_distributed_and_is_eval or i % self.num_workers == self.rank:
                self.data.append(episode)

    def num_episodes(self):
        """
        Return number of episodes in the dataset.
        """
        return len(self.data)

    def num_examples(self):
        """
        Return total number of entries available.

        Each episode has at least one entry, but might have many more.
        """
        if hasattr(self, '_num_examples_cache'):
            return self._num_examples_cache
        self._num_examples_cache = sum(len(episode) for episode in self.data)
        return self._num_examples_cache

    def get(self, episode_idx, entry_idx=0):
        """
        Get the specified episode and the specified entry in that episode.

        :param episode_idx:
            which episode to return examples from
        :param entry_idx:
            which example to return from the episode. Many datasets have only
            single-entry episodes, so this defaults to zero.
        """
        if episode_idx >= len(self.data):
            return Message.padding_example(), True
        next_episode_idx_for_rank = episode_idx + 1
        # first look up data
        episode = self.data[episode_idx]
        entry = episode[entry_idx]
        episode_done = entry_idx == len(episode) - 1

        end_of_data = episode_done and next_episode_idx_for_rank >= len(self.data)

        # now pack it in a action-observation dictionary
        table = self.build_table(entry)

        # last entry in this episode
        table['episode_done'] = episode_done
        return table, end_of_data

    def build_table(self, entry):
        """
        Packs an entry into an action-observation dictionary.

        :param entry: a tuple in the form described in the class docstring.
        """
        if isinstance(entry, (dict, Message)):
            # user is already provided things
            if 'eval_labels' in entry or 'eval_label' in entry:
                raise KeyError(
                    'Labels are converted to eval_labels automatically. Please do not '
                    'set them in setup_data.'
                )
            if 'episode_done' in entry:
                raise KeyError(
                    "episode_done is set automatically for you. Please don't set it "
                    "in setup_data."
                )
            if 'label' in entry:
                # for convenience, rename to the labels convention automatically
                label = entry.pop('label')
                assert isinstance(label, str)
                entry['labels'] = (label,)
            if 'labels' in entry and isinstance(entry['labels'], str):
                entry['labels'] = (entry['labels'],)
            table = entry.copy()
        elif isinstance(entry, (Tuple, List)):
            table = {}
            if entry[0] is not None:
                table['text'] = entry[0]
            if len(entry) > 1 and entry[1] is not None:
                l = entry[1]
                if isinstance(l, str):
                    l = (l,)
                table['labels'] = l
            if len(entry) > 2 and entry[2] is not None:
                table['reward'] = entry[2]
            if len(entry) > 3 and entry[3] is not None:
                table['label_candidates'] = entry[3]
            if len(entry) > 4 and entry[4] is not None:
                img = self.image_loader.load(entry[4])
                if img is not None:
                    table['image'] = img
        else:
            raise TypeError(
                f"items out of setup_data should be dict, Message, list, or tuple. "
                f"Got {type(entry)})"
            )

        if table.get('labels', None) is not None and self.cands is not None:
            if self.addedCands:
                # remove elements in addedCands
                self.cands.difference_update(self.addedCands)
                self.addedCands.clear()
            for label in table['labels']:
                if label not in self.cands:
                    # add labels, queue them for removal next time
                    if not self.copied_cands:
                        self.cands = self.cands.copy()
                        self.copied_cands = True
                    self.cands.add(label)
                    self.addedCands.append(label)
            table['label_candidates'] = self.cands

        if 'labels' in table and 'label_candidates' in table:
            if table['labels'][0] not in table['label_candidates']:
                raise RuntimeError('true label missing from candidate labels')

        # go ahead and make it a message
        if isinstance(table, dict):
            table = Message(table)

        return table


class StreamDialogData(DialogData):
    """
    Provides a data structure for streaming textual dialog data.

    This can be used whenever the dialog data follows the format described in
    DialogData but cannot fit entirely into memory.

    Additional keyword-argument cycle defines if the stream should restart from
    the beginning after an epoch is finished (defaults to True).

    :param opt:
        options to initialize the class
    :param data_loader:
        an iterable with each call returning a tuple in the form
        ``((x, y, r, c, i), new_episode?)`` where the ``x`` and ``new_episode``
        fields are mandatory and other fields may be omitted or ``None``.
    :param cands:
        can be set to provide a list of candidate labels for every example in
        this dataset, which the agent can choose from (the correct answer
        should be in this set).
    :param random:
        tells the data class whether or not to visit episodes sequentially or
        randomly when returning examples to the caller.
    :param cycle:
        (default True) whether to restart at beginning when end of stream
        reached without reset being called.
    """

    # represents that we haven't read in any data at all
    _FIRST_PASS = None
    # represents that we are out of data.
    _END_OF_EPOCH = -1

    def __init__(self, opt, data_loader=None, cands=None, shared=None, **kwargs):
        # super() call initiates stream in self.data by calling _load()
        super().__init__(opt, data_loader, cands, shared, **kwargs)
        self.cycle = kwargs['cycle'] if 'cycle' in kwargs else True

        if shared:
            # auxiliary instances hold pointer to main datastream in self.data
            self.reset_data = shared['reset']
            # Share datafile and data_loader for computing num_exs and num_eps
            self.datafile = shared['datafile']
            self.data_loader = shared['data_loader']
            if 'lock' in shared:
                self.lock = shared['lock']
        else:
            # main instance holds the stream and shares pointer to it
            self.data_loader = data_loader
            if 'datafile' not in opt:
                raise KeyError(
                    ERROR_MESSAGE_NO_DATAFILE.format(class_name=self.__class__.__name__)
                )
            self.datafile = opt['datafile']
            self.reset_data = None
            self.is_reset = True
        self.entry_idx = 0
        self.cur_episode = self._FIRST_PASS
        self.num_eps = None
        self.num_exs = None

        self.rank = get_rank()
        self.num_workers = num_workers()
        self.is_distributed_and_is_eval = (
            self.num_workers > 1 and not DatatypeHelper.is_training(opt['datatype'])
        )

    def share(self):
        """
        Share the stream.
        """
        shared = super().share()
        # also share reset method to allow datastream to be reset
        shared['reset'] = self.reset
        # share datafile and data for loading length if necessary
        shared['datafile'] = self.datafile
        shared['data_loader'] = self.data_loader
        if hasattr(self, 'lock'):
            shared['lock'] = self.lock

        return shared

    def _load(self, data_loader, datafile):
        """
        Load data generator into data field.
        """
        self.data = self._data_generator(data_loader, datafile)

    def _data_generator(self, data_loader, datafile):
        """
        Generate data using the iterator over tuples constructed by data_loader.
        """
        self.is_reset = False
        idx = 0
        while True:
            for episode in self._read_episode(data_loader(datafile)):
                # We only shard the data set at evaluation time, as training is
                # done using sampling-with-replacement.
                if not self.is_distributed_and_is_eval or (
                    idx % self.num_workers == self.rank
                ):
                    yield episode
                idx += 1
            while not self.cycle:
                yield self._END_OF_EPOCH

    def load_length(self):
        """
        Calculate the length of the dataset and caches it in a file.

        Note that this can take some time for large datasets. Episode and entry indexes
        cannot be specified during streaming.
        """
        datafiles = self.datafile if type(self.datafile) is tuple else [self.datafile]
        length_file = datafiles[0] + ".lengths"
        if not PathManager.exists(length_file):
            num_eps = 0
            num_exs = 0
            for episode in self._read_episode(self.data_loader(self.datafile)):
                num_eps += 1
                num_exs += len(episode)
            with PathManager.open(length_file, 'w', encoding="utf-8") as f:
                f.write("{}\n{}".format(num_eps, num_exs))
        else:
            with PathManager.open(length_file, 'r', encoding='utf-8') as f:
                num_eps, num_exs = f.readlines()
        return int(num_eps), int(num_exs)

    def num_examples(self):
        """
        Return the number of examples in the data.
        """
        if not self.num_exs:
            self.num_eps, self.num_exs = self.load_length()
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes in the data.
        """
        if not self.num_eps:
            self.num_eps, self.num_exs = self.load_length()
        return self.num_eps

    def get(self):
        """
        Get the next entry from the stream.

        When episode is done returns first entry of next episode.
        """
        if self.cur_episode is self._FIRST_PASS:
            # first go around, always read off the episode
            # maybe lock this line
            self.cur_episode = next(self.data)
        if self.cur_episode == self._END_OF_EPOCH:
            # we're done here
            return Message.padding_example(), True
        entry = self.cur_episode[self.entry_idx]
        table = self.build_table(entry)
        episode_done = self.entry_idx == len(self.cur_episode) - 1
        table['episode_done'] = episode_done
        if episode_done:
            # maybe lock this line
            self.cur_episode = next(self.data)
            self.entry_idx = 0
        else:
            self.entry_idx += 1
        return table, self.cur_episode == self._END_OF_EPOCH

    def reset(self):
        """
        Reset the datastream to its beginning.
        """
        if self.reset_data is not None:
            # auxiliary instance, reset main datastream
            self.data = self.reset_data()
        elif not self.is_reset:
            # if main instance is not reset, reset datastream
            self._load(self.data_loader, self.datafile)
            self.is_reset = True
        self.entry_idx = 0
        self.cur_episode = self._FIRST_PASS
        return self.data


class FbDeprecatedDialogTeacher(DialogTeacher):
    """
    This module provides access to data in the Facebook Dialog format.

    Subclasses ``DialogTeacher`` for functionality and provides an
    implementation of ``setup_data()`` which iterates over datasets in the
    "fbdialog" format. If your data is in the format below, use this class to
    handle file parsing for you.

    The way FB Dialog data is set up is as follows:

    ::

        1 Sam went to the kitchen.
        2 Pat gave Sam the milk.
        3 Where is the milk?<TAB>kitchen<TAB>1<TAB>hallway|kitchen|bathroom
        4 Sam went to the hallway.
        5 Pat went to the bathroom.
        6 Where is the milk?<TAB>hallway<TAB>1<TAB>hallway|kitchen|bathroom

    Lines 1-6 represent a single episode, with two different examples: the
    first example is lines 1-3, and the second is lines 4-6.

    Lines 1,2,4, and 5 represent contextual information.

    Lines 3 and 6 contain a query, a label, a reward for getting the question
    correct, and three label candidates.

    Since both of these examples are part of the same episode, the information
    provided in the first example is relevant to the query in the second
    example and therefore the agent must remember the first example in order to
    do well.

    In general dialog in this format can contain any speech, not just QA pairs:

    ::

        1 Hi how's it going?<TAB>It's going great. What's new?
        2 Well I'm working on a new project at work.<TAB>Oh me too!
        3 Oh cool!<TAB>Tell me about yours.

    etc.

    Note that dialogs are interpreted as being one-way. For example, consider
    this dialog:

    ::

        1 X1    Y1
        2 X2    Y2
        3 X3    Y3

    A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated.
    However, Y1 => X2 and Y2 => X3 are not created as separate examples by
    default. This makes sense for some data (we don't need to train on the idea
    that "kitchen" should be followed by "Sam went to the hallway..." above),
    but for other datasets it may be helpful to add additional examples in the
    reverse direction ("Oh cool!" is a response to "Oh me too!" above).
    """

    def __init__(self, opt, shared=None):
        self.opt = opt
        self.cloze = opt.get('cloze', False)
        if shared and 'cands' in shared:
            self.cands = shared['cands']
        else:
            self.cands = self.load_cands(opt.get('cands_datafile', None))
        super().__init__(opt, shared)

    def share(self):
        """
        Share the data and candidates.
        """
        shared = super().share()
        shared['cands'] = self.cands
        return shared

    def label_candidates(self):
        """
        Return the candidates.
        """
        return self.cands

    def load_cands(self, path):
        """
        Load a global fixed set of candidates.

        The candidates will be provided by the teacher for every example (the true
        labels for a specific example are also added to this set, so that it's possible
        to get the right answer).
        """
        if path is None:
            return None
        cands = []
        lines_have_ids = False
        cands_are_replies = False
        cnt = 0
        with PathManager.open(path, encoding='utf-8') as read:
            for line in read:
                line = line.strip().replace('\\n', '\n')
                if len(line) > 0:
                    cnt = cnt + 1
                    # If lines are numbered we strip them of numbers.
                    if cnt == 1 and line[0:2] == '1 ':
                        lines_have_ids = True
                    # If tabs then the label_candidates are all the replies.
                    if '\t' in line and not cands_are_replies:
                        cands_are_replies = True
                        cands = []
                    if lines_have_ids:
                        space_idx = line.find(' ')
                        line = line[space_idx + 1 :]
                        if cands_are_replies:
                            sp = line.split('\t')
                            if len(sp) > 1 and sp[1] != '':
                                cands.append(sp[1])
                        else:
                            cands.append(line)
                    else:
                        cands.append(line)
        return cands

    def setup_data(self, path):
        r"""
        Read data in the fbdialog format.

        Returns ``((x,y,r,c), new_episode?)`` tuples.

        ``x`` represents a query, ``y`` represents the labels, ``r`` represents
        any reward, and ``c`` represents any label_candidates.

        The example above will be translated into the following tuples:

        ::

            x: 'Sam went to the kitchen\nPat gave Sam the milk\nWhere is the milk?'
            y: ['kitchen']
            r: '1'
            c: ['hallway', 'kitchen', 'bathroom']
            new_episode = True (this is the first example in the episode)


        ::

            x: 'Sam went to the hallway\\nPat went to the bathroom\\nWhere is the
                milk?'
            y: ['hallway']
            r: '1'
            c: ['hallway', 'kitchen', 'bathroom']
            new_episode = False (this is the second example in the episode)
        """
        logging.info(f"loading fbdialog data: {path}")
        with PathManager.open(path, encoding='utf-8') as read:
            start = True
            x = ''
            reward = 0
            last_conv_id = None
            for line in read:
                line = line.strip().replace('\\n', '\n')
                if len(line) == 0:
                    # empty response
                    continue

                # first, get conversation index -- '1' means start of episode
                space_idx = line.find(' ')
                if space_idx == -1:
                    # empty line, both individuals are saying whitespace
                    conv_id = int(line)
                else:
                    conv_id = int(line[:space_idx])

                # split line into constituent parts, if available:
                # x<tab>y<tab>reward<tab>label_candidates
                # where y, reward, and label_candidates are optional
                split = line[space_idx + 1 :].split('\t')

                # remove empty items and strip each one
                for i in range(len(split)):
                    word = split[i].strip()
                    if len(word) == 0:
                        split[i] = ''
                    else:
                        split[i] = word
                # Empty reward string same as None
                if len(split) > 2 and split[2] == '':
                    split[2] = None

                # now check if we're at a new episode
                if last_conv_id is None or conv_id <= last_conv_id:
                    x = x.strip()
                    if x:
                        yield [x, None, reward], start
                    start = True
                    reward = 0
                    # start a new episode
                    if self.cloze:
                        x = 'Fill in the blank in the last sentence.\n{x}'.format(
                            x=split[0]
                        )
                    else:
                        x = split[0]
                else:
                    if x:
                        # otherwise add current x to what we have so far
                        x = '{x}\n{next_x}'.format(x=x, next_x=split[0])
                    else:
                        x = split[0]
                last_conv_id = conv_id
                if len(split) > 2 and split[2]:
                    reward += float(split[2])

                if len(split) > 1 and split[1]:
                    # only generate an example if we have a y
                    split[0] = x
                    # split labels
                    split[1] = split[1].split('|')
                    if len(split) > 3:
                        # split label_candidates
                        split[3] = split[3].split('|')
                    if len(split) > 2:
                        split[2] = reward
                    else:
                        split.append(reward)
                    if start:
                        yield split, True
                        start = False
                    else:
                        yield split, False
                    # reset x in case there is unlabeled data still left
                    x = ''
                    reward = 0
            if x:
                yield [x, None, reward], start


class ParlAIDialogTeacher(FixedDialogTeacher):
    """
    This module provides access to data in the ParlAI Text Dialog format.

    Subclasses ``FixedDialogTeacher`` for functionality and provides an
    implementation of ``setup_data()`` which iterates over datasets in the
    "ParlAI text" format. If your data is in the format below, use this class to
    handle file parsing for you.

    The way the data is set up is as follows:

    ::

        text:Sam went to the kitchen. <NEWL>
        Pat gave Sam the milk. <NEWL>
        Where is the milk? <TAB> labels:kitchen <TAB> reward:1
        <TAB> label_candidates:hallway|kitchen|bathroom
        text:Sam went to the hallway. <NEWL>
        Pat went to the bathroom. <NEWL>
        Where is the milk? <TAB> labels:hallway <TAB> reward:1
        <TAB> label_candidates:hallway|kitchen|bathroom <TAB> episode_done:True

    Lines 1-2 represent a single episode, with a different example on each line.
    The lines contain a query and a label for getting the question
    correct, and three label candidates.

    Since both of these examples are part of the same episode, the information
    provided in the first example is relevant to the query in the second
    example and therefore the agent must remember the first example in order to
    do well.

    In general dialog this format can contain any speech, not just QA pairs:

    ::

        text:Hi how's it going?<TAB>labels:It's going great. What's new?
        text:Well I'm working on a new project at work.<TAB>labels:Oh me too!
        text:Oh cool!<TAB>labels:Tell me about yours.

    etc.

    Note that dialogs are interpreted as being one-way. For example, consider
    this dialog:

    ::

        1 X1    Y1
        2 X2    Y2
        3 X3    Y3

    A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated.
    However, Y1 => X2 and Y2 => X3 are not created as separate examples by
    default. This makes sense for some data (we don't need to train on the idea
    that "kitchen" should be followed by "Sam went to the hallway..." above),
    but for other datasets it may be helpful to add additional examples in the
    reverse direction ("Oh cool!" is a response to "Oh me too!" above).
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        if not shared:
            self.episodes = []
            self.num_exs = 0
            if opt.get('parlaidialogteacher_datafile') is not None:
                self._setup_data(opt.get('parlaidialogteacher_datafile'))
        else:
            self.episodes = shared['episodes']
            self.num_exs = sum(len(e) for e in self.episodes)

        self.id = opt['task']

        self.reset()

    def share(self):
        """
        Share the episodes.
        """
        shared = super().share()
        shared['episodes'] = self.episodes
        return shared

    def num_examples(self):
        """
        Return the number of examples from the data.
        """
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes from the data.
        """
        return len(self.episodes)

    def get(self, episode_idx, entry_idx=None):
        """
        Get a specific example from the dataset.
        """
        return self.episodes[episode_idx][entry_idx]

    def _setup_data(self, path):
        logging.info(f"Loading ParlAI text data: {path}")
        self.episodes = []
        self.num_exs = 0
        eps = []
        with PathManager.open(path, newline='\n', encoding='utf-8') as read:
            for line_no, line in enumerate(read, 1):
                msg = str_to_msg(line.rstrip('\n'))
                if msg and 'eval_labels' in msg:
                    raise ValueError(
                        f"It looks like you've written eval_labels as a key in your "
                        f"data file. This is not appropriate; labels will be converted "
                        f"for you automatically. This is happening on Line {line_no} "
                        f"in {path}. The line is:\n\t{line}"
                    )
                if msg and 'text' not in msg:
                    raise ValueError(
                        f'ParlaiDialogTeacher requires a "text" field in every '
                        f'entry, but one is missing in Line {line_no} in {path}. '
                        f'The line is:\n\t{line}'
                    )
                if msg and 'labels' not in msg:
                    raise ValueError(
                        f'ParlaiDialogTeacher requires a "labels" field in every '
                        f'entry, but one is missing in Line {line_no} in {path}. '
                        f'The line is:\n\t{line}'
                    )
                if msg:
                    self.num_exs += 1
                    eps.append(msg)
                    if msg.get('episode_done', False):
                        self.episodes.append(eps)
                        eps = []
        if len(eps) > 0:
            # add last episode
            eps[-1].force_set('episode_done', True)
            self.episodes.append(eps)
        if len(self.episodes) == 1 and line_no > 100:
            logging.error(
                f'The data in {path} looks like one very long episode. If this '
                f'is intentional, you may ignore this, but you MAY have a bug in '
                f'your data.'
            )


class YamlTeacher(DialogTeacher):
    """
    Teacher which loads data generated by `parlai.utils.testing.AutoTeacherTest`.
    """

    def __init__(self, opt, shared=None):
        # TODO: if we get rid of the streaming datafile num_episodes/num_examples
        # cache then we can support streaming here. but for now let's
        # just hardcode it
        opt = opt.copy()
        opt['datatype'] = opt['datatype'].replace(':stream', '')
        super().__init__(opt, shared=shared)

    def setup_data(self, datafile):
        with PathManager.open(datafile) as f:
            records = yaml.safe_load(f)
            next_episode_new = True
            for act in records['acts']:
                act = act[0]  # yaml wraps in a weird singleton list
                next_episode_new = act.pop('episode_done')
                if 'eval_labels' in act:
                    act['labels'] = act.pop('eval_labels')
                yield act, next_episode_new


class ConversationTeacher(FixedDialogTeacher):
    """
    This module provides access to data in the Conversations format.

    Subclasses ``FixedDialogTeacher`` for functionality and provides an
    implementation of ``setup_data()`` which iterates over datasets in the
    "Conversations" format. If your data is in the format below, use this class to
    handle file parsing for you.

    The data should be set up so that each dialogue instance (or, episode)
    occupies one line of valid JSON. The way the data is set up is as follows:

    ::
    { "dialog": [ [ {"id": "partner1", "text": "hello!"},  {"id": "partner2", "text": "hi back!"}  ] ] }

    NOTE: If the data is not on one line per dialogue, it will not load.
    Further, note that by default, dialogs are interpreted as being one-way.
    For example, consider this dialog (not that the data below is not on:

    ::

        {
            "dialog":[ [
                {"id":"modelx", "text": X1},
                {"id":"modely", "text": Y1},
                {"id":"modelx", "text": X2},
                {"id":"modely", "text": Y2},
                {"id":"modelx", "text": X3},
                {"id":"modely", "text": Y3},
            ] ]
        }

    (Note: we use line breaks for readability above, but this data will not load as
    stated, it must be on one line.)

    A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated,
    forming one episode. However, Y1 => X2 and Y2 => X3 are not created as
    separate examples by default.
    To change this behavior, you can set opt['label_turns']. The default
    value is 'secondspeaker' (i.e., the second speaker's utterances are
    used as labels), but 'firstspeaker' and 'both' are also options. In the
    case of 'both', two episodes are generated for each conversation.
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        if not shared:
            self.episodes = []
            self.num_exs = 0
            self.label_turns = opt.get('label_turns')
            if opt.get('conversationteacher_datafile') is not None:
                self._setup_data(opt.get('conversationteacher_datafile'))
        else:
            self.episodes = shared['episodes']
            self.num_exs = sum(len(e) for e in self.episodes)

        self.id = opt['task']

        self.reset()

    def share(self):
        """
        Share the episodes.
        """
        shared = super().share()
        shared['episodes'] = self.episodes
        return shared

    def num_examples(self):
        """
        Return the number of examples from the data.
        """
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes from the data.
        """
        return len(self.episodes)

    def get(self, episode_idx, entry_idx=None):
        """
        Get a specific example from the dataset.
        """
        return Message(self.episodes[episode_idx][entry_idx])

    def _setup_data(self, path):
        logging.info("[loading data from json file into task:" + path + "]")
        self.episodes = []
        self.num_exs = 0
        eps = []
        conversations = Conversations(path)
        self.num_exs = 0
        for conv in conversations:
            if conv.context:
                warn_once(
                    'At least one of these conversations contains a context, which is not being used'
                )
            turns = [t for t in conv.turns if t.get('id') != 'context']
            if len(turns) != len(conv.turns):
                warn_once(
                    'At least one of these conversations contains a context within the dialogue, which is being discarded'
                )
            turns.insert(0, Message({'text': '__SILENCE__'}))
            # train on odd turns as labels (turns w/ first speaker)
            if self.label_turns in ['firstspeaker', 'both']:
                eps = self._get_ep_from_turns(turns[::2], turns[1::2])
                if eps:
                    self.episodes.append(eps)
                    self.num_exs += len(eps)

            # train on even turns as labels (turns w/ second speaker)
            if self.label_turns in ['secondspeaker', 'both']:
                eps = self._get_ep_from_turns(turns[1::2], turns[2::2])
                if eps:
                    self.episodes.append(eps)
                    self.num_exs += len(eps)

    def _get_ep_from_turns(self, xturns, yturns):
        eps = []
        for xturn, yturn in zip(xturns, yturns):
            turn = {}
            turn['text'] = xturn.get('text').strip()
            turn['labels'] = [yturn.get('text').strip()]
            turn['episode_done'] = False
            eps.append(turn)
        if eps:
            eps[-1]['episode_done'] = True
            return eps


class AbstractImageTeacher(FixedDialogTeacher):
    """
    Abstract class to allow easier creation of image + dialogue tasks.

    This class handles creating image features via ImageLoader if applicable
    (resnet, resnext variants) or loading existing image features from a dict
    path as per get_image_features_path().

    Important methods and properties (override in subclass if needed):

    - get_data_path(): where data file is found (default: <datapath>/<task>)
    - get_image_path(): where images found (default: <datapath>/<task>/images)
    - get_image_features_path(): dict of image features (default:
      <datapath>/<task>/image_features)
    - @property image_id_key: which key in data file objects represents image_id
    - @property text_key: which key in data file objects represents text

    Note: Assumes data files are named <dt>.json

    @abstractmethod image_id_to_image_path() must be implemented in subclass

    Example with the key defaults (but the keys can be customized):

    .. code-block:: python

        obs = {
            'text': <caption>,
            'image': <image features if specified else image>
        }
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        self.opt = opt
        self.task = opt['task'].split(':')[1] if ':' in opt['task'] else opt['task']
        self.data_path = self.get_data_path(opt)
        self.data = self.load_data(self.data_path, self.opt)
        self.datatype = DatatypeHelper.fold(opt['datatype'])

        # Example of available models: 'resnet152', 'resnext101_32x48d_wsl',
        # and ImageLoader supports other resnet and resnext models too
        # Raises an Exception if not valid
        self._validate_image_mode_name(opt.get('image_mode'))

        # IMPORTANT NOTE: this teacher will be instantiated twice. The first
        # by build_dict in which case the image_mode is to 'no_image_model' to
        # avoid calculating image features twice.
        self.image_mode = opt.get('image_mode')

        # Not using default image_mode parameter b/c there is a normalization
        # (or bug) somewhere in build_dict that is setting it to none
        self.include_image = opt.get('image_mode') != 'no_image_model'

        self.image_path = self.get_image_path(opt)
        self.image_loader = None
        self.image_features_dim = opt.get('image_features_dim')
        self.blank_image_features = torch.FloatTensor(self.image_features_dim).fill_(0)

        if shared and 'data' in shared:
            self.data = shared['data']
            self.image_loader = shared['image_loader']
            if 'image_features_dict' in shared:
                self.image_features_dict = shared['image_features_dict']
        elif self.include_image:
            self.setup_image_features(self.data_path)
        else:
            # This will happen when building the dictionary - is normal
            # build_dict sets image_mode to 'none'
            warn_once('AbstractImageTeacher self.include_image was False')
            self.image_features_dict = None

        # TODO: won't need this after we have proper logging levels set
        self.__verbose = False

        self.reset()

    def get_available_image_mode_names(self):
        """
        Available image model names.

        resnet and resnext variants available from the ImageLoader. resnext101_XXXXX_wsl
        is the open-sourced FB AI model (960m images, 1.5k hashtags, finetuned on
        ImageNet).
        """
        available_model_names = ImageLoader.get_available_model_names()
        return ['no_image_model', 'raw', 'ascii'] + available_model_names

    def _validate_image_mode_name(self, a):
        """
        Validate the image_mode passed in.

        Needed because image_mode used elsewhere in ParlAI is not always consistent with
        what the image teacher allows.
        """
        if not isinstance(a, str):
            raise argparse.ArgumentTypeError(
                '%s must be a string representing image model name' % a
            )
        available_model_names = self.get_available_image_mode_names()
        if a not in available_model_names:
            raise argparse.ArgumentTypeError(
                '\"%s\" unknown image model name. Choose from: %s. Currently suggested resnet is resnet152 and resnext is resnext101_32x48d_wsl.'
                % (a, available_model_names)
            )
        return a

    @classmethod
    def add_cmdline_args(
        cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
    ) -> ParlaiParser:
        # Be sure to call super() if overriding this method b/c
        # AbstractImageTeacher has necessary params
        parser = super().add_cmdline_args(parser, partial_opt)
        agent = parser.add_argument_group('AbstractImageTeacher Arguments')
        agent.add_argument(
            '--image-path',
            type=str,
            default=None,
            help='Optional argument to specify where images for dataset are'
            'stored if already downloaded. Most tasks will download the images'
            'if not present on the < datapath > / < task > _images / * and * if'
            'this argument is not specified.',
        )

        agent.add_argument(
            '--image-features-dim',
            type=int,
            default=2048,
            help='Specify the size of image features Tensors.',
        )
        return parser

    @property
    def image_id_key(self):
        """
        Which key in the input data dict objects uniquely identify each image.

        Common image keys are "image_id" or "image_num". May be implemented by subclass.
        """
        return 'image_id'

    @property
    def text_key(self):
        """
        Which key in the input data dict objects identifies the text.

        Common keys are "text" or "comment". May be implemented by subclass.
        """
        return 'text'

    @abstractmethod
    def image_id_to_image_path(self, image_id):
        """
        Get the path of the image on disk.

        Must be implemented by subclass.
        """
        pass

    def get_data_path(self, opt):
        """
        Determines path to the data file.
        """
        task_name = opt['task'].split(':')[1] if ':' in opt['task'] else opt['task']
        data_path = os.path.join(opt['datapath'], task_name)
        return data_path

    def get_image_path(self, opt):
        """
        Return the path to the data directory and to the image directory.

        Is based on opt fields: task, datatype (train, valid, test), datapath.

        Subclass can override this.
        """
        data_path = self.get_data_path(opt)
        if opt.get('image_path', None):
            image_path = opt['image_path']
        else:
            # other common choice: .join(opt['datapath'], task_name + '_images')
            image_path = os.path.join(data_path, 'images')

        return image_path

    def get_image_features_path(self, task, image_model_name, dt):
        """
        Image features for the dataset images are stored here.

        Can be overridden in subclass to use custom paths. Image features can be manually
        copied into this directory or in the case of ImageLoader eligible models, they
        will be built and stored here if not already there.
        """
        # In default implementation, self.data_path already has task name added
        image_features_path = os.path.join(self.data_path, 'image_features')

        PathManager.mkdirs(image_features_path)

        return os.path.join(
            image_features_path, '%s_%s_%s_features_dict' % (task, image_model_name, dt)
        )

    def is_image_mode_buildable(self, model_name):
        """
        Is buildable if features can be calculated by ImageLoader.

        Users may wish to compute features for the dataset offline and use in the model,
        in which case, the image model should return False and get_image_features()
        should be overridden in subclass.
        """
        return model_name in ImageLoader.get_available_model_names()

    def load_data(self, data_path, opt):
        """
        Loading the data file, which is the index to the images and text.

        It is often a .json file with the name of the <datatype>.json (i.e.
        train.json). Stores in self.data.

        Can be override by subclass.
        """

        dt = DatatypeHelper.fold(opt['datatype'])

        # Sometimes file is named "val" instead of "valid"
        if dt not in ['train', 'valid', 'val', 'test']:
            raise Exception(
                'Unknown dt parameter: %s. Expected either "train", "valid", or "test".'
                % dt
            )

        # Assumes file is train.json or valid.json named
        data_file = os.path.join(self.data_path, '%s.json' % dt)

        # Load the text data and image number indexes
        with PathManager.open(data_file, encoding='utf-8') as f:
            self.data = json.load(f)

        if len(self.data) > 0 and self.image_id_key not in self.data[0]:
            # Data doesn't have a "image_id" like field so add the index in the file to the data
            for idx, d in enumerate(self.data):
                d[self.image_id_key] = idx

        return self.data

    def setup_image_features(self, data_path):
        """
        Load text and image data.

        The image features all live in dicts by default in <data_path>/
        image_features/ but get_image_features_path() above can be overridden by
        subclass to put them elsewhere.

        In the (very odd) case that the resnet or resnext dicts (models
        buildable using ImageLoader) are not found, we build them.
        """
        if self.image_mode in ['raw', 'ascii']:
            self.image_features_dict = None
            self.image_loader = ImageLoader(self.opt)
            return
        image_mode_features_dict_path = self.get_image_features_path(
            self.task, self.image_mode, self.datatype
        )

        if PathManager.exists(image_mode_features_dict_path):
            logging.info(
                f'Loading existing image features dict for model: {self.image_mode} at: {image_mode_features_dict_path}'
            )
            with PathManager.open(image_mode_features_dict_path, 'rb') as f:
                self.image_features_dict = torch.load(f, map_location='cpu')
        else:
            logging.warning('No existing image features, attempting to build.')
            if self.is_image_mode_buildable(self.image_mode):
                # TODO: Awkward to modify the input opt but needed to use
                # TODO: ImageLoader functionality. Is from comment_battle,
                # TODO: will refactor this at some point soon most likely
                image_loader_opt = self.opt.copy()
                image_loader_opt['image_mode'] = (
                    self.image_mode if self.include_image else 'no_image_model'
                )

                image_loader_opt['image_size'] = 256
                image_loader_opt['image_cropsize'] = 224
                self.image_loader = ImageLoader(image_loader_opt)

                # try to build with ImageLoader (i.e. resenet/resnext variants)
                self.image_features_dict = self._build_image_features_dict(
                    self.data_path, self.datatype, image_mode_features_dict_path
                )
            else:
                raise RuntimeError(
                    'Image model: %s is not buildable by ImageLoader but does'
                    'not already exist on disk as an image features dict for'
                    'this dataset.' % self.image_mode
                )

    def _build_image_features_dict(self, data_path, dt, store_dict_path):
        """
        Build resne(x)t image features with ImageLoader.

        (Or anything handleable by ImageLoader) and save to path. Only called if we
        haven't already built the dict before.
        """
        image_features_dict = {}
        total = len(self.data)
        import tqdm

        pbar = tqdm.tqdm(
            total=total,
            unit='cand',
            unit_scale=True,
            desc='Building image features dict for %s with ImageLoader.'
            % self.image_mode,
        )
        num = 0
        for ex in self.data:
            img_id = ex[self.image_id_key]
            img_path = self.image_id_to_image_path(img_id)
            image = self.image_loader.load(img_path).detach()
            # spatial features are [1, image_dim, spatial_dim, spatial_dim] tensors.
            # reduce non-spatial features to one-dimensional feature prior to saving.
            if not self.image_loader.is_spatial(self.image_mode):
                image = image[0, :, 0, 0]
            image_features_dict[img_id] = image
            num += 1
            pbar.update(1)
            if num % 1000 == 0:
                logging.debug(f'Processing image index: {num}')
        torch_utils.atomic_save(image_features_dict, store_dict_path)
        return image_features_dict

    def reset(self):
        super().reset()
        self.example = None

    def num_episodes(self):
        return self.num_examples()

    def num_examples(self):
        return len(self.data)

    def get_image_features(self, example):
        """
        Get image features for example.

        Can be overridden in subclass for different behavior. For large datasets, it may
        be more appropriate to use the ImageLoader.load() method to load image features
        (as this is essentially streaming the features from disk, so that we do not have
        to load a large image feature dict in memory). #TODO Could be the default option
        if we are using -dt train:stream
        """
        if self.image_mode in ['raw', 'ascii']:
            try:
                image = self.image_loader.load(
                    self.image_id_to_image_path(example['image_id'])
                )
            except FileNotFoundError:
                # No Image Here
                image = None
            return image

        key = str(example[self.image_id_key])
        if not self.include_image or key not in self.image_features_dict:
            image_features = self.blank_image_features
        else:
            image_features = self.image_features_dict[key]
        return image_features

    def get(self, episode_idx, entry_idx=0):
        """
        Override this in subclass if your data should be handled in a different format.
        """
        example = self.data[episode_idx]
        image_features = self.get_image_features(example)
        return {
            'labels': [example[self.text_key]],
            'image': image_features,
            'episode_idx': episode_idx,
            'episode_done': True,
        }

    def share(self):
        shared = super().share()
        shared['data'] = self.data
        shared['image_loader'] = self.image_loader
        if hasattr(self, 'image_features_dict'):
            shared['image_features_dict'] = self.image_features_dict
        return shared


class MultiTaskTeacher(Teacher):
    """
    MultiTaskTeacher which teaches multiple tasks.

    Creates a teacher that is actually a set of teachers each based on a task
    string -- each of these teachers will get called in turn,
    either randomly or in order.  They are all in the same world (they are the
    same agent switching tasks).

    The task string format is described for the ``create_task_agents()``
    function above.
    """

    def __init__(self, opt: Opt, shared=None):
        self.tasks: List[Agent] = []
        self.opt = opt

        self.id = opt['task']
        if shared and 'tasks' in shared:
            self.tasks = [create_agent_from_shared(t) for t in shared['tasks']]
        else:
            tasks = opt['task'].split(',')
            for k in tasks:
                k = k.strip()
                if k:
                    opt_singletask = copy.deepcopy(opt)
                    opt_singletask['task'] = k
                    self.tasks.extend(create_task_agent_from_taskname(opt_singletask))
        self.task_idx = -1
        self.new_task = True
        self.random = DatatypeHelper.should_shuffle(opt['datatype'])
        # Make multi-task task probabilities.
        self.cum_task_weights = [1] * len(self.tasks)
        self.task_choices = range(len(self.tasks))
        weights = self.opt.get('multitask_weights', [1])
        if weights == 'stochastic':
            weights = [t.num_episodes() for t in self.tasks]
        sum = 0
        for i in self.task_choices:
            if len(weights) > i:
                weight = weights[i]
            else:
                weight = 1
            self.cum_task_weights[i] = weight + sum
            sum += weight

    def num_examples(self):
        """
        Return the number of examples.
        """
        if not hasattr(self, 'num_exs'):
            # num_examples is sum of all examples in all tasks
            tasks_num_exs = [t.num_examples() for t in self.tasks]
            if any(num is None for num in tasks_num_exs):
                self.num_exs = None
            else:
                self.num_exs = sum(tasks_num_exs)
        return self.num_exs

    def num_episodes(self):
        """
        Return the number of episodes.
        """
        if not hasattr(self, 'num_eps'):
            # num_episodes is sum of all num_episodes in all tasks
            tasks_num_eps = [t.num_episodes() for t in self.tasks]
            if any(num is None for num in tasks_num_eps):
                self.num_eps = None
            else:
                self.num_eps = sum(tasks_num_eps)
        return self.num_eps

    def observe(self, observation):
        """
        Make an observation.
        """
        return self.tasks[self.task_idx].observe(observation)

    def act(self):
        """
        Act on the previous observation.
        """
        if self.new_task:
            self.new_task = False
            if self.random:
                # select random teacher
                self.task_idx = random.choices(
                    self.task_choices, cum_weights=self.cum_task_weights
                )[0]
            else:
                # do at most one full loop looking for unfinished task
                for _ in range(len(self.tasks)):
                    self.task_idx = (self.task_idx + 1) % len(self.tasks)
                    if not self.tasks[self.task_idx].epoch_done():
                        # if this task has examples ready, break
                        break
                if self.tasks[self.task_idx].epoch_done():
                    # all tasks are done, so return empty action table
                    return Message.padding_example()
        t = self.tasks[self.task_idx].act()
        if t['episode_done']:
            self.new_task = True
        return t

    def epoch_done(self):
        """
        Return whether all subtasks are completed.
        """
        for t in self.tasks:
            if not t.epoch_done():
                return False
        return True

    # return transformed metrics showing total examples and accuracy if avail.
    def report(self):
        """
        Report aggregated metrics across all subtasks.
        """
        return aggregate_named_reports(
            {t.getID(): t.report() for t in self.tasks},
            micro_average=self.opt.get('aggregate_micro', False),
        )

    def reset(self):
        """
        Reset all subtasks.
        """
        for t in self.tasks:
            t.reset()

    def reset_metrics(self):
        """
        Reset metrics for each subtask.
        """
        for t in self.tasks:
            t.reset_metrics()

    def share(self):
        """
        Shares this teacher by sharing each subtask.
        """
        shared = {}
        shared['class'] = type(self)
        shared['opt'] = self.opt
        shared['tasks'] = [t.share() for t in self.tasks]
        return shared

    def shutdown(self):
        """
        Shutdown each agent.
        """
        for t in self.tasks:
            t.shutdown()


class ChunkTeacher(FixedDialogTeacher, ABC):
    """
    Useful for loading large amounts of data.

    Data is separated into chunks and loaded one chunk at a time. Loads the data off of
    the main thread.
    """

    def __init__(self, opt, shared=None):
        super().__init__(opt, shared)
        self.buffersize = self.get_buffersize()

        self.set_datasettings(opt)
        # chunk teacher makes shuffling decisions based on training, but
        # train:stream turns off shuffling in other teachers.
        self.datatype = DatatypeHelper.strip_stream(opt['datatype'])

        self.dws = int(self.opt.get('distributed_world_size', 1))
        self.rank = int(self.opt.get('rank', 0))
        self.bg_index = self.opt.get('background_index', None)

        # If we're in training mode with --num-workers > 0, we will run the
        # chunk teacher in single threaded mode (self.threading is False). In
        # this mode, we will block on chunk loading.

        # If we're not using --num-workers, or we're in validation/testing, we
        # _always_ run in normal threading mode, where chunk loading is pushed
        # to a background thread. However, since python threading is is blocked
        # by the GIL, this only manages to background I/O.

        # Potentially in the future, we may support --num-workers in validation,
        # in which case we can get rid of one of these.

        self.threading = not (opt.get('num_workers', 0) > 0 and self.is_train)
        if not self.threading and opt.get('background_index') is None:
            # don't start loading data on the main driver, we don't need it
            opt['no_auto_enqueues'] = True
        if not self.threading:
            # if we're in single-threaded (background preprocessing mode), we
            # can't have a max queue size or we will hang if we overfill it
            self.buffersize = 0

        if shared is not None:
            self.is_root_teacher = False
            self.chunks = shared['chunks']
            self.samples = shared['samples']
            self.reset_counter = shared['reset_counter']
            self.rng = shared['rng']
            self.tot_samples_loaded = shared['tot_samples_loaded']
        else:
            self.is_root_teacher = True
            self.samples = queue.Queue(maxsize=self.buffersize)
            self.chunks = queue.Queue()
            self.reset_counter = SimpleCounter()  # track no. of resets
            if DatatypeHelper.should_shuffle(self.datatype):
                # TODO: possible need a fixed seed here in the future
                self.rng = random.Random()
            else:
                self.rng = random.Random(42)
            self._enqueue_chunks()
            # launch queue loader on the main thread
            self.tot_samples_loaded = defaultdict(int)
            if not opt.get("no_auto_enqueues", False):
                # we only enqueue the train thread because the reset() called at
                # the top of training will handle this
                self._enqueue_request()

        self._episode_done = True
        self.last_queue_output = None

    def _get_data_folder(self):
        if not self.opt.get('datafile'):
            raise RuntimeError(
                'Must specify datafile or override this function (_get_data_folder) '
                'to return the data folder.'
            )

        return self.opt['datafile']

    @abstractmethod
    def get_num_samples(self, opt: Opt) -> Tuple[int, int]:
        """
        [Abstract] Return the number of samples.

        Returns a tuple of (num_examples, num_episodes) based on the data split.
        """
        pass

    @abstractmethod
    def get_fold_chunks(self, opt: Opt) -> List[int]:  # type: ignore
        """
        [Abstract] Return a list of chunk IDs (integer).

        Given the datatype (train/test/valid), return the list of chunk IDs that
        correspond to that split.
        """
        pass

    def get_buffersize(self):
        """
        Size of buffer.

        Override this in your child class to change the buffer size.
        """
        return 100000

    def set_datasettings(self, opt: Opt):
        self.folder = self._get_data_folder()
        self.num_exs, self.num_eps = self.get_num_samples(opt)
        self.fold_chunks = self.get_fold_chunks(opt)

        self.is_train = DatatypeHelper.is_training(opt['datatype'])

    def share(self):
        shared = super().share()
        shared['samples'] = self.samples
        shared['chunks'] = self.chunks
        shared['reset_counter'] = self.reset_counter
        shared['rng'] = self.rng
        shared['tot_samples_loaded'] = self.tot_samples_loaded
        return shared

    def _setup_data(self, datatype):
        """
        Passthrough.
        """
        pass

    def num_episodes(self):
        if self.is_train:
            return self.num_eps
        else:
            return self.num_eps // self.dws + int((self.num_eps % self.dws) > self.rank)

    def num_examples(self):
        if self.is_train:
            return self.num_exs
        else:
            return self.num_exs // self.dws + int((self.num_exs % self.dws) > self.rank)

    def next_episode_idx(self):
        # We don't actually track episodes in ChunkTeacher, we just blindly
        # trust the queue. This hacks around FixedDialogTeacher's next_example
        # check that the epoch is done.
        return 0

    def _enqueue_request(self):
        """
        Queue a request for loading to the data loader.
        """
        if self.threading:
            self.data_loader.request_load(self.receive_data, self.get_chunk, ())
        else:
            self._process_data(self.get_chunk())

    def receive_data(self, future):
        """
        Receive loaded data and place it onto the sample queue.

        :param future:
            A Future object which will return a value from a call to get_chunk()
        """
        return self._process_data(future.result())

    def _process_data(self, output: Optional[Tuple[Any, int]]):
        """
        Loads data.

        Load data into self.samples until buffersize is reached.

        :param output:
            The output of an item from a call to get_chunk()
        """
        if output is None:
            return
        chunk_output, chunk_reset_cnt = output
        if chunk_output is None:
            self.samples.put((None, chunk_reset_cnt))
            return
        if self.threading:
            while chunk_output:
                # self.samples is a queue with maxsize
                # self.buffersize, so will block if the
                # buffer gets full
                sample = chunk_output.pop(0)
                if (
                    self.is_train
                    or self.tot_samples_loaded[chunk_reset_cnt] % self.dws == self.rank
                ):
                    # log the reset count at the time the chunk was queued
                    self.samples.put((sample, chunk_reset_cnt))
                self.tot_samples_loaded[chunk_reset_cnt] += 1
        else:
            # we're actually running in single processor mode so we'll just
            # do a thread-unsafe hit of the python internals, which is much faster
            # than trying to safely put things onto the queue
            self.samples.queue.extend((co, chunk_reset_cnt) for co in chunk_output)
            self.tot_samples_loaded[chunk_reset_cnt] += len(chunk_output)
        if self.threading:
            # and start loading the next chunk
            self._enqueue_request()

    def _enqueue_chunks(self):
        """
        Shuffles and queues fold chunks for loading.
        """
        if DatatypeHelper.should_shuffle(self.datatype):
            self.rng.shuffle(self.fold_chunks)
        # save the reset count at the time a chunk was queued
        reset_cnt = self.reset_counter.value()
        for c in self.fold_chunks:
            self.chunks.put((c, reset_cnt))
        self.chunks.put((None, reset_cnt))
        # gross hack: in training models, when we get to validation, we enqueue
        # a request in the constructor, followed by another enqueue from a
        # reset immediately after. If the former is already running, we'll end
        # up with one too many calls to get_chunk and block on termination.
        # That's what I refer to as "losing" the race condition. If you look in
        # get_chunk, you'll also find the spot where we "win" the race
        # condition.
        self.chunks.put((None, reset_cnt))

    @abstractmethod
    def load_from_chunk(self, chunk_idx: int) -> List[ChunkOutput]:
        """
        [Abstract] Given the chunk index, load examples from that chunk.

        Return a list of tuples. The function `_create_message` will take these tuples
        to form the Message object that is returned by the teacher.
        """
        pass

    @abstractmethod
    def create_message(self, queue_output: ChunkOutput, entry_idx=0) -> 'Message':
        """
        [Abstract] Given the tuple output of the queue, return an act.

        May depend on entry index if queue output is a multi-turn episode.
        """
        pass

    def get_chunk(self):
        """
        Refill the buffer.
        """
        next_chunk, chunk_reset_cnt = self.chunks.get()
        if next_chunk is None:
            if DatatypeHelper.should_cycle(self.datatype):
                # start putting chunks back onto the queue
                self._enqueue_chunks()
                next_chunk, chunk_reset_cnt = self.chunks.get()
                if next_chunk is None:
                    # See the race condition described around "gross hack" in
                    # _enqueue_chunks.  if we win the race condition, then
                    # catch it here
                    next_chunk, chunk_reset_cnt = self.chunks.get()
            else:
                # if we're in valid/test, we need to actually signal the end
                return (None, chunk_reset_cnt)
        # abstract method `load_from_chunk` returns a list of tuples
        output = self.load_from_chunk(next_chunk)

        if DatatypeHelper.should_shuffle(self.datatype):
            # randomize the samples
            random.Random().shuffle(output)
        return output, chunk_reset_cnt

    def next_example(self):
        # next_example will always fail to provide useful signal on whether
        # we're at the end of an epoch in chunk teacher. Instead, the queue
        # empties and we simply start outputting pads forever. As such, we'll
        # measure epochs when we start receiving only pads.

        # (This isn't relevant for the training loop, which loops for ever and
        # never "epochs").
        retval, fake_epoch_done = super().next_example()
        real_epoch_done = retval.is_padding()
        return retval, real_epoch_done

    def get(self, episode_idx, entry_idx=0):
        if not self.threading and self.samples.empty():
            self._enqueue_request()

        curr_reset_cnt = self.reset_counter.value()
        if self._episode_done:
            # Get the next episode or example
            queue_output, reset_cnt = self.samples.get()
            stale_exs = 0
            while curr_reset_cnt > reset_cnt:
                stale_exs += 1
                queue_output, reset_cnt = self.samples.get()
            if stale_exs > 0:
                logging.debug(f"Removed {stale_exs} stale examples from the queue.")
            if queue_output is None:
                self.samples.put((None, reset_cnt))
                return Message.padding_example()

            # Update the last queue output in the case
            # of multi-turn episodes
            self.last_queue_output = queue_output

        # create a Message object from the queue output
        msg = self.create_message(self.last_queue_output, entry_idx)
        self._episode_done = msg['episode_done']

        return msg

    def _drain(self, q):
        while not q.empty():
            try:
                q.get()
            except queue.Empty:
                return

    def reset(self):
        super().reset()
        if self.is_root_teacher:
            self.reset_counter.increment()
            # drain the queues and refill the chunk queue with a new epoch.
            # additionally, we have to relaunch the loader
            self._drain(self.samples)
            self._drain(self.chunks)
            self._enqueue_chunks()
            self.tot_samples_loaded.clear()  # reset the count of samples loaded
            if self.threading:
                self._enqueue_request()

    def shutdown(self):
        # Time to wrap up. We should rush out to the worker and tell them
        # that they're "done" processing data.
        # same signal as end of epoch
        self.chunks.put((None, None))
        self.chunks.put((None, None))


def _add_task_flags_to_agent_opt(agent, opt: Opt, flags):
    """
    Handle task flags provided by the task name itself.

    With this you can set specific opts with `-t task:flag=foo`.
    """
    fl = flags.split(':')
    task = []
    for f in fl:
        if '=' in f:
            one_flag = f.split('=')
            key = one_flag[0].replace('-', '_')
            raw_value = one_flag[1].replace(';', ':')

            # Convert to bool/int/float if necessary
            if raw_value.lower() == 'true':
                value = True
            elif raw_value.lower() == 'false':
                value = False
            else:
                try:
                    value = int(raw_value)  # type: ignore
                except ValueError:
                    try:
                        value = float(raw_value)  # type: ignore
                    except ValueError:
                        value = raw_value  # type: ignore

            opt[key] = value
        else:
            task.append(f)
    opt['task'] = ':'.join(task)


def create_task_agent_from_taskname(opt: Opt):
    """
    Create task agent(s) assuming the input ``task_dir:teacher_class``.

    e.g. def_string is a shorthand path like ``babi:Task1k:1`` or ``#babi`` or a
    complete path like ``parlai.tasks.babi.agents:Task1kTeacher:1``, which essentially
    performs ``from parlai.tasks.babi import Task1kTeacher`` with the parameter ``1`` in
    ``opt['task']`` to be used by the class ``Task1kTeacher``.
    """
    if not opt.get('task'):
        raise RuntimeError(
            'No task specified. Please select a task with ' + '--task {task_name}.'
        )
    if ',' not in opt['task']:
        # Single task
        teacher_class = load_teacher_module(opt['task'])
        _add_task_flags_to_agent_opt(teacher_class, opt, opt['task'])
        task_agents = teacher_class(opt)
        if type(task_agents) != list:
            task_agents = [task_agents]
        return task_agents
    else:
        # Multitask teacher/agent
        task_agents = MultiTaskTeacher(opt)
        if type(task_agents) != list:
            task_agents = [task_agents]
        return task_agents
#!/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. This module provides a set of teachers that deal with dialog.      ``FixedDialogTeacher(Teacher)``     Base class for teachers in tasks that have fixed dialog - i.e., dialog     that is not dynamically generated but rather is pulled from set examples.     However, the class can be extended to all tasks involved fixed data.     Implements much of the basic functionality of these teachers, including     ``observe()``, ``act()``, ``next_example()``      ``DialogTeacher(FixedDialogTeacher)``      Base teacher class for doing dialog specifically with fixed chat logs.      ``ParlAIDialogTeacher(DialogTeacher)``      Teacher class that provides access to data in the ParlAI Dialog format.      See the class description for more details.       ``ConversationTeacher(DialogTeacher)``      Teacher class that provides access to data in the Conversations format.      See the class description for more details.      ``FbDeprecatedDialogTeacher(DialogTeacher)``      Teacher class that provides access to data in the Facebook Dialog format.      See the class description for more details. **This class is deprecated**.  This module also includes ``DataLoader``, a threadpool data loader for ``FixedDialogTeacher``, and ``DialogData``/``StreamDialogData``, data structures for accessing textual dialog data and utilized by ``DialogTeacher`` # noqa: F401 A worker thread that provides a threadpool for data loading.      A teacher may submit a request to the loader, which will return the     appropriate data.      To submit a request, a teacher should call ``request_load``. Queue a request for loading.          :param receive_fn:             a receive function (for receiving the data)         :param load_fn:             a load function (for loading the data)         :param args:             arguments for the load function. args can be either a dictionary of             arguments for a function, or a list of positional arguments Run the execution loop. A fake DataLoader which throws an exception when a work order is placed.      Since threads cannot be mixed with spawn_method='fork', we need to disallow users     from combining --num-workers with teachers that utilize threads. This placeholder     object is only useful for ensuring the user sees a loud error message when they     accidentally use a thread. Basic Teacher agent that keeps track of how many times it's received messages.      Teachers provide the ``report()`` method to get back metrics. # return state/action dict based upon passed state Act upon the previous observation. Return whether the epoch is done. # Default unknown length Return the number of examples (e.g. individual utterances) in the dataset.          Default implementation returns `None`, indicating an unknown number. Return the number of episodes (e.g. conversations) in the dataset.          Default implementation returns `None`, indicating an unknown number. Return metrics showing total examples and accuracy if available. Reset the teacher. Reset metrics. In addition to default Agent shared parameters, share metrics. Iterate through the examples of the teacher. # backwards compatibility with older agents A teacher agent for all teachers involved in tasks with fixed data.      This class provides the following functionality for its subclasses:      - Resets a teacher     - Provides an observe method     - Computes and retrieves the next episode index for a teacher     - Provides a threadpool option for loading data (especially useful for       large data, e.g. images)      In order to take advantage of the first few features, all a subclass has to     implement is three functions: ``num_episodes``, ``num_examples``, and     ``get`` (which returns a specific example from a specific episode).      To utilize the DataLoader for threadpool loading, a teacher should     implement the ``submit_load_request`` function to send a load request     to the DataLoader by calling ``self.data_loader.request_load`` with the     appropriate arguments (``receive_fn, load_fn, args``). The DataLoader then     returns the data to the teacher's ``data_queue``, which the teacher can     poll in its ``act`` method.      The following is an example of the DataLoader usage in the VQA-V1 teacher.      1. In the teacher's ``init`` function, the teacher calls its        ``submit_load_request`` function to preload an image.     2. The ``submit_load_request`` function gets the next ``episode_idx``,        and computes the image path for the load request.     3. At the end of ``submit_load_request``, the teacher calls        ``self.data_loader.request_load`` with three args:          - ``self.receive_data`` - the function that the DataLoader calls to           return the the loaded object         - ``self.image_loader.load`` - the function used to load the image           from the image path         - ``[img_path]`` - a list of arguments for the load function, which           in this case is the path of the image.      4. In the teacher's ``act`` function, the teacher loads the data from        its data queue.     5. At the end of the ``act`` function, the teacher calls        ``submit_load_request`` to preload an image for the next example.      To see this in action, take a look at this teacher in ``tasks.vqa_v1.agents``. # set up support for multithreaded data loading # set up batching Reset the dialog to the start of the epoch, and reset all metrics. Submit a load request.          An agent should implement this method to submit requests to the data         loader. At the end of this method, the agent should call         ``self.data_loader.request_load()`` with the appropriate args.          By default, this method does nothing. # TODO: mark as abstract Receive data from the data loader.          :param future: result from the load request. Share the data and dataloader. Return the next episode index.          :param num_eps:             default None uses ``num_episodes`` value.         :param loop:             default None loops during training but not evaluation. Return the next example.          If there are multiple examples in the same episode, returns the next one in that         episode. If that episode is over, gets a new episode index and returns the first         example of that episode. # buffer the full conversation ahead of time for mutators # apply mutators # if we got back an empty episode after mutating, skip it # buffer the entire conversation so we can apply mutators Get the number of episodes in this dataset. Get the total number of examples in this dataset. Get the specified episode and the specified entry in that episode.          Children must override this method in order to inherit the         `next_example` method.          :param episode_idx:             which episode to return examples from         :param entry_idx:             which example to return from the episode.  Many datasets have only             single-entry episodes, so this defaults to zero. # TODO: mark as abstract, get rid of runtime error. Process observation for metrics. # for display purposes (display_model), take all accumulated # metrics back into the original observation. This is an abuse of # Messages being pointers # override agent-level metrics if present A method designated for hooking custom evaluations into teachers.          Generally, a user will want to use `self.metrics.add` to record any         specialized metrics that only make sense for this one dataset.          :param teacher_action:             The message last sent from this teacher.         :param labels:             The previous correct labels, if there were any.         :param model_response:             The raw response from the model. Generally you want to rely on the             text field, but others may be necessary in specific situations. Send new dialog message. Get the unprocessed action and reset if needed.          This function will return the raw action from `self.next_example()`, before the         `self.last_act` and `self.lastY` attributes have been defined based on this         action for metrics or custom evaluations. This is so that wrapper teachers can         modify the raw action first, such as to change the contents of its 'text' and         'label' fields, without the action becoming out of sync with `self.last_act` and         `self.lastY`. # reset if haven't yet # get next example, action is episode_done dict if already out of exs # TODO: all teachers should eventually create messages # while setting up the data, so this won't be necessary Remember the raw action and prepare its fields for passing out of the teacher. # remember correct answer if available # move labels to eval field so not used for training # but this way the model can use the labels for perplexity or loss A base teacher class for doing dialog with fixed chat logs.      This class provides a set a basic functionality:      - uses data class to store and query text data     - generates action tables to send to the student agent from the data      In order to subclass this class, you must implement ``setup_data()`` in     your class, which reads your data file as an iterator. # Check for setup_data # first initialize any shared objects # never cycle if "ordered" is in the datatype. this is used by # build_dict to enumerate through the data exactly once while still # marking examples as training examples. The core method which the user should override.          Yields the data, one message at a time, as well as markers indicating         new episodes.          :param str datafile:             If the initializer set a 'datafile' field within the initialization,             this will be provided here. Otherwise, datafile will be the fold:             either "train", "valid", or "test".          :return:             Yields pairs (message, new_episode) containing a Message object             and whether the message marks the beginning of a totally new             episode. Reset the dialog to the start of the epoch, reset all metrics. Share the data. Provide consistent label candidates for all examples.          Default implementation returns ``None`` always, but this may be overridden to         provide candidates in all areas. See ``FbDialogueTeacher``. # TODO DEPRECATIONDAY: FbDialogueTeacher is being deprecated, should we # remove this? # TODO: mark as optionally abstract? Return the number of episodes in the data. Return the number of examples in the data. Get a specific example. Get the next example. # unfortunately we need to also do the mutator buffering here. # it's difficult to structure it so it's not # perform any mutations there are # make sure mutations are fully realized (not generators) # The recursive call has dual purpose: # - if we get back an empty episode after mutating, skip it gracefully # - pull the first item the episode w/ epoch_done logic, but DRY Provides a data structure for accessing textual dialog data.      This can be used whenever the dialog data is a fixed log of chats     (i.e not a simulator setting). The logs can include dialog text and possibly     supervised labels, candidate labels and rewards.      All these are stored in this internal data format which is used by the     ``DialogTeacher`` class.      :param opt:         options to initialize the class     :param data_loader:         an iterable with each call returning a tuple in the form         ``((x, y, r, c, i), new_episode?)`` where the ``x`` and ``new_episode``         fields are mandatory and other fields may be omitted or ``None``.     :param cands:         can be set to provide a list of candidate labels for every example in         this dataset, which the agent can choose from (the correct answer         should be in this set).      :param random:         tells the data class whether or not to visit episodes sequentially or         randomly when returning examples to the caller.      The contents of the ``((x, y, r, c, i), new_episode?)`` tuples returned by     the data loader is the following:      - ``x`` (str) is a query and possibly context     - ``y`` (iter) is an iterable of label(s) for that query     - ``r`` (str) is the str reward for getting that query correct     - ``c`` (iter) is an iterable of label candidates that the student can choose from     - ``i`` (str) is a str path to an image on disk, which will be loaded by the       data class at request-time. should always point to the raw image file.     - ``new_episode?`` (bool) is a boolean value specifying whether that example       is the start of a new episode. If you don't use episodes set this       to ``True`` every time. # in case we need to shard the dataset # self.data is a list of episodes # each episode is a tuple of entries # each entry is a tuple of values for the action/observation table Share the data. Read one episode at a time from the provided iterable over entries.          :param data_loader:             an iterable which returns tuples in the format described in the             class docstring. Load up data from an iterable over tuples described in the class docs.          :param iter data_loader:             an iterator which returns tuples in the format described in the             class docstring.         :param str datafile: Return number of episodes in the dataset. Return total number of entries available.          Each episode has at least one entry, but might have many more. Get the specified episode and the specified entry in that episode.          :param episode_idx:             which episode to return examples from         :param entry_idx:             which example to return from the episode. Many datasets have only             single-entry episodes, so this defaults to zero. # first look up data # now pack it in a action-observation dictionary # last entry in this episode Packs an entry into an action-observation dictionary.          :param entry: a tuple in the form described in the class docstring. # user is already provided things # for convenience, rename to the labels convention automatically # remove elements in addedCands # add labels, queue them for removal next time # go ahead and make it a message Provides a data structure for streaming textual dialog data.      This can be used whenever the dialog data follows the format described in     DialogData but cannot fit entirely into memory.      Additional keyword-argument cycle defines if the stream should restart from     the beginning after an epoch is finished (defaults to True).      :param opt:         options to initialize the class     :param data_loader:         an iterable with each call returning a tuple in the form         ``((x, y, r, c, i), new_episode?)`` where the ``x`` and ``new_episode``         fields are mandatory and other fields may be omitted or ``None``.     :param cands:         can be set to provide a list of candidate labels for every example in         this dataset, which the agent can choose from (the correct answer         should be in this set).     :param random:         tells the data class whether or not to visit episodes sequentially or         randomly when returning examples to the caller.     :param cycle:         (default True) whether to restart at beginning when end of stream         reached without reset being called. # represents that we haven't read in any data at all # represents that we are out of data. # super() call initiates stream in self.data by calling _load() # auxiliary instances hold pointer to main datastream in self.data # Share datafile and data_loader for computing num_exs and num_eps # main instance holds the stream and shares pointer to it Share the stream. # also share reset method to allow datastream to be reset # share datafile and data for loading length if necessary Load data generator into data field. Generate data using the iterator over tuples constructed by data_loader. # We only shard the data set at evaluation time, as training is # done using sampling-with-replacement. Calculate the length of the dataset and caches it in a file.          Note that this can take some time for large datasets. Episode and entry indexes         cannot be specified during streaming. Return the number of examples in the data. Return the number of episodes in the data. Get the next entry from the stream.          When episode is done returns first entry of next episode. # first go around, always read off the episode # maybe lock this line # we're done here # maybe lock this line Reset the datastream to its beginning. # auxiliary instance, reset main datastream # if main instance is not reset, reset datastream This module provides access to data in the Facebook Dialog format.      Subclasses ``DialogTeacher`` for functionality and provides an     implementation of ``setup_data()`` which iterates over datasets in the     "fbdialog" format. If your data is in the format below, use this class to     handle file parsing for you.      The way FB Dialog data is set up is as follows:      ::          1 Sam went to the kitchen.         2 Pat gave Sam the milk.         3 Where is the milk?<TAB>kitchen<TAB>1<TAB>hallway|kitchen|bathroom         4 Sam went to the hallway.         5 Pat went to the bathroom.         6 Where is the milk?<TAB>hallway<TAB>1<TAB>hallway|kitchen|bathroom      Lines 1-6 represent a single episode, with two different examples: the     first example is lines 1-3, and the second is lines 4-6.      Lines 1,2,4, and 5 represent contextual information.      Lines 3 and 6 contain a query, a label, a reward for getting the question     correct, and three label candidates.      Since both of these examples are part of the same episode, the information     provided in the first example is relevant to the query in the second     example and therefore the agent must remember the first example in order to     do well.      In general dialog in this format can contain any speech, not just QA pairs:      ::          1 Hi how's it going?<TAB>It's going great. What's new?         2 Well I'm working on a new project at work.<TAB>Oh me too!         3 Oh cool!<TAB>Tell me about yours.      etc.      Note that dialogs are interpreted as being one-way. For example, consider     this dialog:      ::          1 X1    Y1         2 X2    Y2         3 X3    Y3      A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated.     However, Y1 => X2 and Y2 => X3 are not created as separate examples by     default. This makes sense for some data (we don't need to train on the idea     that "kitchen" should be followed by "Sam went to the hallway..." above),     but for other datasets it may be helpful to add additional examples in the     reverse direction ("Oh cool!" is a response to "Oh me too!" above). Share the data and candidates. Return the candidates. Load a global fixed set of candidates.          The candidates will be provided by the teacher for every example (the true         labels for a specific example are also added to this set, so that it's possible         to get the right answer). # If lines are numbered we strip them of numbers. # If tabs then the label_candidates are all the replies. Read data in the fbdialog format.          Returns ``((x,y,r,c), new_episode?)`` tuples.          ``x`` represents a query, ``y`` represents the labels, ``r`` represents         any reward, and ``c`` represents any label_candidates.          The example above will be translated into the following tuples:          ::              x: 'Sam went to the kitchen\nPat gave Sam the milk\nWhere is the milk?'             y: ['kitchen']             r: '1'             c: ['hallway', 'kitchen', 'bathroom']             new_episode = True (this is the first example in the episode)           ::              x: 'Sam went to the hallway\\nPat went to the bathroom\\nWhere is the                 milk?'             y: ['hallway']             r: '1'             c: ['hallway', 'kitchen', 'bathroom']             new_episode = False (this is the second example in the episode) # empty response # first, get conversation index -- '1' means start of episode # empty line, both individuals are saying whitespace # split line into constituent parts, if available: # x<tab>y<tab>reward<tab>label_candidates # where y, reward, and label_candidates are optional # remove empty items and strip each one # Empty reward string same as None # now check if we're at a new episode # start a new episode # otherwise add current x to what we have so far # only generate an example if we have a y # split labels # split label_candidates # reset x in case there is unlabeled data still left This module provides access to data in the ParlAI Text Dialog format.      Subclasses ``FixedDialogTeacher`` for functionality and provides an     implementation of ``setup_data()`` which iterates over datasets in the     "ParlAI text" format. If your data is in the format below, use this class to     handle file parsing for you.      The way the data is set up is as follows:      ::          text:Sam went to the kitchen. <NEWL>         Pat gave Sam the milk. <NEWL>         Where is the milk? <TAB> labels:kitchen <TAB> reward:1         <TAB> label_candidates:hallway|kitchen|bathroom         text:Sam went to the hallway. <NEWL>         Pat went to the bathroom. <NEWL>         Where is the milk? <TAB> labels:hallway <TAB> reward:1         <TAB> label_candidates:hallway|kitchen|bathroom <TAB> episode_done:True      Lines 1-2 represent a single episode, with a different example on each line.     The lines contain a query and a label for getting the question     correct, and three label candidates.      Since both of these examples are part of the same episode, the information     provided in the first example is relevant to the query in the second     example and therefore the agent must remember the first example in order to     do well.      In general dialog this format can contain any speech, not just QA pairs:      ::          text:Hi how's it going?<TAB>labels:It's going great. What's new?         text:Well I'm working on a new project at work.<TAB>labels:Oh me too!         text:Oh cool!<TAB>labels:Tell me about yours.      etc.      Note that dialogs are interpreted as being one-way. For example, consider     this dialog:      ::          1 X1    Y1         2 X2    Y2         3 X3    Y3      A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated.     However, Y1 => X2 and Y2 => X3 are not created as separate examples by     default. This makes sense for some data (we don't need to train on the idea     that "kitchen" should be followed by "Sam went to the hallway..." above),     but for other datasets it may be helpful to add additional examples in the     reverse direction ("Oh cool!" is a response to "Oh me too!" above). Share the episodes. Return the number of examples from the data. Return the number of episodes from the data. Get a specific example from the dataset. # add last episode Teacher which loads data generated by `parlai.utils.testing.AutoTeacherTest`. # TODO: if we get rid of the streaming datafile num_episodes/num_examples # cache then we can support streaming here. but for now let's # just hardcode it # yaml wraps in a weird singleton list This module provides access to data in the Conversations format.      Subclasses ``FixedDialogTeacher`` for functionality and provides an     implementation of ``setup_data()`` which iterates over datasets in the     "Conversations" format. If your data is in the format below, use this class to     handle file parsing for you.      The data should be set up so that each dialogue instance (or, episode)     occupies one line of valid JSON. The way the data is set up is as follows:      ::     { "dialog": [ [ {"id": "partner1", "text": "hello!"},  {"id": "partner2", "text": "hi back!"}  ] ] }      NOTE: If the data is not on one line per dialogue, it will not load.     Further, note that by default, dialogs are interpreted as being one-way.     For example, consider this dialog (not that the data below is not on:      ::          {             "dialog":[ [                 {"id":"modelx", "text": X1},                 {"id":"modely", "text": Y1},                 {"id":"modelx", "text": X2},                 {"id":"modely", "text": Y2},                 {"id":"modelx", "text": X3},                 {"id":"modely", "text": Y3},             ] ]         }      (Note: we use line breaks for readability above, but this data will not load as     stated, it must be on one line.)      A set of examples X1 => Y1, X2 => Y2, and X3 => Y3 will be generated,     forming one episode. However, Y1 => X2 and Y2 => X3 are not created as     separate examples by default.     To change this behavior, you can set opt['label_turns']. The default     value is 'secondspeaker' (i.e., the second speaker's utterances are     used as labels), but 'firstspeaker' and 'both' are also options. In the     case of 'both', two episodes are generated for each conversation. Share the episodes. Return the number of examples from the data. Return the number of episodes from the data. Get a specific example from the dataset. # train on odd turns as labels (turns w/ first speaker) # train on even turns as labels (turns w/ second speaker) Abstract class to allow easier creation of image + dialogue tasks.      This class handles creating image features via ImageLoader if applicable     (resnet, resnext variants) or loading existing image features from a dict     path as per get_image_features_path().      Important methods and properties (override in subclass if needed):      - get_data_path(): where data file is found (default: <datapath>/<task>)     - get_image_path(): where images found (default: <datapath>/<task>/images)     - get_image_features_path(): dict of image features (default:       <datapath>/<task>/image_features)     - @property image_id_key: which key in data file objects represents image_id     - @property text_key: which key in data file objects represents text      Note: Assumes data files are named <dt>.json      @abstractmethod image_id_to_image_path() must be implemented in subclass      Example with the key defaults (but the keys can be customized):      .. code-block:: python          obs = {             'text': <caption>,             'image': <image features if specified else image>         } # Example of available models: 'resnet152', 'resnext101_32x48d_wsl', # and ImageLoader supports other resnet and resnext models too # Raises an Exception if not valid # IMPORTANT NOTE: this teacher will be instantiated twice. The first # by build_dict in which case the image_mode is to 'no_image_model' to # avoid calculating image features twice. # Not using default image_mode parameter b/c there is a normalization # (or bug) somewhere in build_dict that is setting it to none # This will happen when building the dictionary - is normal # build_dict sets image_mode to 'none' # TODO: won't need this after we have proper logging levels set Available image model names.          resnet and resnext variants available from the ImageLoader. resnext101_XXXXX_wsl         is the open-sourced FB AI model (960m images, 1.5k hashtags, finetuned on         ImageNet). Validate the image_mode passed in.          Needed because image_mode used elsewhere in ParlAI is not always consistent with         what the image teacher allows. # Be sure to call super() if overriding this method b/c # AbstractImageTeacher has necessary params Which key in the input data dict objects uniquely identify each image.          Common image keys are "image_id" or "image_num". May be implemented by subclass. Which key in the input data dict objects identifies the text.          Common keys are "text" or "comment". May be implemented by subclass. Get the path of the image on disk.          Must be implemented by subclass. Determines path to the data file. Return the path to the data directory and to the image directory.          Is based on opt fields: task, datatype (train, valid, test), datapath.          Subclass can override this. # other common choice: .join(opt['datapath'], task_name + '_images') Image features for the dataset images are stored here.          Can be overridden in subclass to use custom paths. Image features can be manually         copied into this directory or in the case of ImageLoader eligible models, they         will be built and stored here if not already there. # In default implementation, self.data_path already has task name added Is buildable if features can be calculated by ImageLoader.          Users may wish to compute features for the dataset offline and use in the model,         in which case, the image model should return False and get_image_features()         should be overridden in subclass. Loading the data file, which is the index to the images and text.          It is often a .json file with the name of the <datatype>.json (i.e.         train.json). Stores in self.data.          Can be override by subclass. # Sometimes file is named "val" instead of "valid" # Assumes file is train.json or valid.json named # Load the text data and image number indexes # Data doesn't have a "image_id" like field so add the index in the file to the data Load text and image data.          The image features all live in dicts by default in <data_path>/         image_features/ but get_image_features_path() above can be overridden by         subclass to put them elsewhere.          In the (very odd) case that the resnet or resnext dicts (models         buildable using ImageLoader) are not found, we build them. # TODO: Awkward to modify the input opt but needed to use # TODO: ImageLoader functionality. Is from comment_battle, # TODO: will refactor this at some point soon most likely # try to build with ImageLoader (i.e. resenet/resnext variants) Build resne(x)t image features with ImageLoader.          (Or anything handleable by ImageLoader) and save to path. Only called if we         haven't already built the dict before. # spatial features are [1, image_dim, spatial_dim, spatial_dim] tensors. # reduce non-spatial features to one-dimensional feature prior to saving. Get image features for example.          Can be overridden in subclass for different behavior. For large datasets, it may         be more appropriate to use the ImageLoader.load() method to load image features         (as this is essentially streaming the features from disk, so that we do not have         to load a large image feature dict in memory). #TODO Could be the default option         if we are using -dt train:stream # No Image Here Override this in subclass if your data should be handled in a different format. MultiTaskTeacher which teaches multiple tasks.      Creates a teacher that is actually a set of teachers each based on a task     string -- each of these teachers will get called in turn,     either randomly or in order.  They are all in the same world (they are the     same agent switching tasks).      The task string format is described for the ``create_task_agents()``     function above. # Make multi-task task probabilities. Return the number of examples. # num_examples is sum of all examples in all tasks Return the number of episodes. # num_episodes is sum of all num_episodes in all tasks Make an observation. Act on the previous observation. # select random teacher # do at most one full loop looking for unfinished task # if this task has examples ready, break # all tasks are done, so return empty action table Return whether all subtasks are completed. # return transformed metrics showing total examples and accuracy if avail. Report aggregated metrics across all subtasks. Reset all subtasks. Reset metrics for each subtask. Shares this teacher by sharing each subtask. Shutdown each agent. Useful for loading large amounts of data.      Data is separated into chunks and loaded one chunk at a time. Loads the data off of     the main thread. # chunk teacher makes shuffling decisions based on training, but # train:stream turns off shuffling in other teachers. # If we're in training mode with --num-workers > 0, we will run the # chunk teacher in single threaded mode (self.threading is False). In # this mode, we will block on chunk loading. # If we're not using --num-workers, or we're in validation/testing, we # _always_ run in normal threading mode, where chunk loading is pushed # to a background thread. However, since python threading is is blocked # by the GIL, this only manages to background I/O. # Potentially in the future, we may support --num-workers in validation, # in which case we can get rid of one of these. # don't start loading data on the main driver, we don't need it # if we're in single-threaded (background preprocessing mode), we # can't have a max queue size or we will hang if we overfill it # track no. of resets # TODO: possible need a fixed seed here in the future # launch queue loader on the main thread # we only enqueue the train thread because the reset() called at # the top of training will handle this [Abstract] Return the number of samples.          Returns a tuple of (num_examples, num_episodes) based on the data split. # type: ignore [Abstract] Return a list of chunk IDs (integer).          Given the datatype (train/test/valid), return the list of chunk IDs that         correspond to that split. Size of buffer.          Override this in your child class to change the buffer size. Passthrough. # We don't actually track episodes in ChunkTeacher, we just blindly # trust the queue. This hacks around FixedDialogTeacher's next_example # check that the epoch is done. Queue a request for loading to the data loader. Receive loaded data and place it onto the sample queue.          :param future:             A Future object which will return a value from a call to get_chunk() Loads data.          Load data into self.samples until buffersize is reached.          :param output:             The output of an item from a call to get_chunk() # self.samples is a queue with maxsize # self.buffersize, so will block if the # buffer gets full # log the reset count at the time the chunk was queued # we're actually running in single processor mode so we'll just # do a thread-unsafe hit of the python internals, which is much faster # than trying to safely put things onto the queue # and start loading the next chunk Shuffles and queues fold chunks for loading. # save the reset count at the time a chunk was queued # gross hack: in training models, when we get to validation, we enqueue # a request in the constructor, followed by another enqueue from a # reset immediately after. If the former is already running, we'll end # up with one too many calls to get_chunk and block on termination. # That's what I refer to as "losing" the race condition. If you look in # get_chunk, you'll also find the spot where we "win" the race # condition. [Abstract] Given the chunk index, load examples from that chunk.          Return a list of tuples. The function `_create_message` will take these tuples         to form the Message object that is returned by the teacher. [Abstract] Given the tuple output of the queue, return an act.          May depend on entry index if queue output is a multi-turn episode. Refill the buffer. # start putting chunks back onto the queue # See the race condition described around "gross hack" in # _enqueue_chunks.  if we win the race condition, then # catch it here # if we're in valid/test, we need to actually signal the end # abstract method `load_from_chunk` returns a list of tuples # randomize the samples # next_example will always fail to provide useful signal on whether # we're at the end of an epoch in chunk teacher. Instead, the queue # empties and we simply start outputting pads forever. As such, we'll # measure epochs when we start receiving only pads. # (This isn't relevant for the training loop, which loops for ever and # never "epochs"). # Get the next episode or example # Update the last queue output in the case # of multi-turn episodes # create a Message object from the queue output # drain the queues and refill the chunk queue with a new epoch. # additionally, we have to relaunch the loader # reset the count of samples loaded # Time to wrap up. We should rush out to the worker and tell them # that they're "done" processing data. # same signal as end of epoch Handle task flags provided by the task name itself.      With this you can set specific opts with `-t task:flag=foo`. # Convert to bool/int/float if necessary # type: ignore # type: ignore # type: ignore Create task agent(s) assuming the input ``task_dir:teacher_class``.      e.g. def_string is a shorthand path like ``babi:Task1k:1`` or ``#babi`` or a     complete path like ``parlai.tasks.babi.agents:Task1kTeacher:1``, which essentially     performs ``from parlai.tasks.babi import Task1kTeacher`` with the parameter ``1`` in     ``opt['task']`` to be used by the class ``Task1kTeacher``. # Single task # Multitask teacher/agent