task.py

import abc
import ast
import logging
import random
import re
from collections.abc import Callable
from copy import deepcopy
from dataclasses import asdict, dataclass, field
from inspect import getsource
from typing import (
    Any,
    Dict,
    Iterable,
    Iterator,
    List,
    Literal,
    Mapping,
    Optional,
    Tuple,
    Union,
)

import datasets
import numpy as np
import shortuuid
from tqdm import tqdm

from lm_eval import utils
from lm_eval.api import samplers
from lm_eval.api.instance import Instance, OutputType
from lm_eval.api.metrics import bits_per_byte, mean, weighted_perplexity
from lm_eval.api.registry import (
    AGGREGATION_REGISTRY,
    DEFAULT_METRIC_REGISTRY,
    get_aggregation,
    get_metric,
    get_metric_aggregation,
    is_higher_better,
)
from lm_eval.caching.cache import load_from_cache, save_to_cache
from lm_eval.filters import build_filter_ensemble
from lm_eval.prompts import get_prompt


ALL_OUTPUT_TYPES = [
    "loglikelihood",
    "multiple_choice",
    "loglikelihood_rolling",
    "generate_until",
]

eval_logger = logging.getLogger("lm-eval")

@dataclass
class AggMetricConfig(dict):
    metric: Optional[str] = "acc"
    metric_alias: Optional[str] = "acc"
    aggregation: Optional[str] = "mean"
    weight_by_size: Optional[str] = False
    filter_list: Optional[Union[str,list]] = "none"

    def __post_init__(self):
        if isinstance(self.filter_list, str):
            self.filter_list = [self.filter_list]

@dataclass
class GroupConfig(dict):
    group: Optional[str] = None
    group_alias: Optional[str] = None
    task: Optional[Union[str, list]] = None
    tag_to_task: Optional[str] = False
    aggregate_metric: Optional[
        Union[List[AggMetricConfig], AggMetricConfig, dict]
    ] = None
    metadata: Optional[
        dict
    ] = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks

    def __getitem__(self, item):
        return getattr(self, item)

    def __setitem__(self, item, value):
        return setattr(self, item, value)

    def __post_init__(self):
        if self.aggregate_metric_list is not None:
            if isinstance(self.aggregate_metric_list, dict):
                self.aggregate_metric_list = [self.aggregate_metric_list]

            self.aggregate_metric_list = [
                AggMetricConfig(**item) if isinstance(item, dict) else item
                for item in self.aggregate_metric_list
            ]

    def to_dict(self, keep_callable: bool = False) -> dict:
        """dumps the current config as a dictionary object, as a printable format.
        null fields will not be printed.
        Used for dumping results alongside full task configuration

        :return: dict
            A printable dictionary version of the TaskConfig object.

        # TODO: should any default value in the TaskConfig not be printed?
        """
        cfg_dict = asdict(self)
        # remove values that are `None`
        for k, v in list(cfg_dict.items()):
            if callable(v):
                cfg_dict[k] = self.serialize_function(v, keep_callable=keep_callable)
        return cfg_dict

    def serialize_function(
        self, value: Union[Callable, str], keep_callable=False
    ) -> Union[Callable, str]:
        """Serializes a given function or string.

        If 'keep_callable' is True, the original callable is returned.
        Otherwise, attempts to return the source code of the callable using 'getsource'.
        """
        if keep_callable:
            return value
        else:
            try:
                return getsource(value)
            except (TypeError, OSError):
                return str(value)


class ConfigurableGroup(abc.ABC):
    def __init__(
        self,
        config: Optional[dict] = None,
    ) -> None:
        # Create a unique identifier ID
        self._task_id = shortuuid.uuid()[:8]
        self._config = GroupConfig(**config)

    @property
    def group(self):
        return self._config.group

    @property
    def group_alias(self):
        return self._config.group_alias

    @property
    def version(self):
        return self._config.version

    @property
    def config(self):
        return self._config.to_dict()

    @property
    def task_id(self) -> Any:
        return "-".join((self.group_name, self._task_id))

    @property
    def group_name(self) -> Any:
        return self._config.group

    def __repr__(self):
        return (
            f"ConfigurableGroup(group={self.group}," f"group_alias={self.group_alias})"
        )


@dataclass
class TaskConfig(dict):
    # task naming/registry
    task: Optional[str] = None
    task_alias: Optional[str] = None
    tag: Optional[Union[str, list]] = None
    group: Optional[Union[str, list]] = None
    # HF dataset options.
    # which dataset to use,
    # and what splits for what purpose
    dataset_path: Optional[str] = None
    dataset_name: Optional[str] = None
    dataset_kwargs: Optional[dict] = None
    training_split: Optional[str] = None
    validation_split: Optional[str] = None
    test_split: Optional[str] = None
    fewshot_split: Optional[
        str
    ] = None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
    # formatting / prompting options.
    # see docs/advanced_task_guide.md for more info
    process_docs: Optional[Callable] = None
    doc_to_text: Optional[Union[Callable, str]] = None
    doc_to_target: Optional[Union[Callable, str]] = None
    doc_to_choice: Optional[Union[Callable, str, dict, list]] = None
    process_results: Optional[Union[Callable, str]] = None
    use_prompt: Optional[str] = None
    description: str = ""
    target_delimiter: str = " "
    fewshot_delimiter: str = "\n\n"
    fewshot_config: Optional[dict] = None
    # runtime configuration options
    num_fewshot: Optional[int] = None
    # scoring options
    metric_list: Optional[list] = None
    output_type: OutputType = "generate_until"
    generation_kwargs: Optional[dict] = None
    repeats: int = 1
    filter_list: Optional[Union[str, list]] = None
    should_decontaminate: bool = False
    doc_to_decontamination_query: Optional[str] = None
    metadata: Optional[
        dict
    ] = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks

    def __post_init__(self) -> None:
        if self.generation_kwargs is not None:
            if self.output_type != "generate_until":
                eval_logger.warning(
                    f"[{self.task}] passed `generation_kwargs`, but not using `output_type: generate_until`!"
                )

            if "temperature" in self.generation_kwargs:
                self.generation_kwargs["temperature"] = float(
                    self.generation_kwargs["temperature"]
                )

            if "until" not in self.generation_kwargs:
                self.generation_kwargs["until"] = [self.fewshot_delimiter]
        else:
            if self.output_type == "generate_until":
                # ensure that we greedily generate in absence of explicit arguments otherwise
                self.generation_kwargs = {
                    "until": (
                        None
                        if self.fewshot_delimiter is None
                        else [self.fewshot_delimiter]
                    ),
                    "do_sample": False,
                }

    def __getitem__(self, item):
        return getattr(self, item)

    def __setitem__(self, item, value):
        return setattr(self, item, value)

    def to_dict(self, keep_callable: bool = False) -> dict:
        """dumps the current config as a dictionary object, as a printable format.
        null fields will not be printed.
        Used for dumping results alongside full task configuration

        :return: dict
            A printable dictionary version of the TaskConfig object.

        # TODO: should any default value in the TaskConfig not be printed?
        """
        cfg_dict = asdict(self)
        # remove values that are `None`
        for k, v in list(cfg_dict.items()):
            if v is None:
                cfg_dict.pop(k)
            elif k == "metric_list":
                for metric_dict in v:
                    for metric_key, metric_value in metric_dict.items():
                        if callable(metric_value):
                            metric_dict[metric_key] = self.serialize_function(
                                metric_value, keep_callable=keep_callable
                            )
                cfg_dict[k] = v
            elif callable(v):
                cfg_dict[k] = self.serialize_function(v, keep_callable=keep_callable)
        return cfg_dict

    def serialize_function(
        self, value: Union[Callable, str], keep_callable=False
    ) -> Union[Callable, str]:
        """Serializes a given function or string.

        If 'keep_callable' is True, the original callable is returned.
        Otherwise, attempts to return the source code of the callable using 'getsource'.
        """
        if keep_callable:
            return value
        else:
            try:
                return getsource(value)
            except (TypeError, OSError):
                return str(value)


class Task(abc.ABC):
    """A task represents an entire benchmark including its dataset, problems,
    answers, and evaluation methods. See BoolQ for a simple example implementation

    A `doc` can be any python object which represents one instance of evaluation.
    This is usually a dictionary e.g.
        {"question": ..., "answer": ...} or
        {"question": ..., question, answer)
    """

    VERSION: Optional[Union[int, str]] = None

    # The name of the `Task` benchmark as denoted in the HuggingFace datasets Hub
    # or a path to a custom `datasets` loading script.
    DATASET_PATH: Optional[str] = None

    # The name of a subset within `DATASET_PATH`.
    DATASET_NAME: Optional[str] = None

    OUTPUT_TYPE: Optional[OutputType] = None

    def __init__(
        self,
        data_dir: Optional[str] = None,
        cache_dir: Optional[str] = None,
        download_mode: Optional[datasets.DownloadMode] = None,
        config: Optional[Mapping] = None,  # Union[dict, TaskConfig]
    ) -> None:
        """
        :param data_dir: str
            Stores the path to a local folder containing the `Task`'s data files.
            Use this to specify the path to manually downloaded data (usually when
            the dataset is not publicly accessible).
        :param cache_dir: str
            The directory to read/write the `Task` dataset. This follows the
            HuggingFace `datasets` API with the default cache directory located at:
                `~/.cache/huggingface/datasets`
            NOTE: You can change the cache location globally for a given process
            to another directory:
                `export HF_DATASETS_CACHE="/path/to/another/directory"`
        :param download_mode: datasets.DownloadMode
            How to treat pre-existing `Task` downloads and data.
            - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS`
                Reuse download and reuse dataset.
            - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS`
                Reuse download with fresh dataset.
            - `datasets.DownloadMode.FORCE_REDOWNLOAD`
                Fresh download and fresh dataset.
        """
        self.download(data_dir, cache_dir, download_mode)
        self._training_docs: Optional[list] = None
        self._fewshot_docs: Optional[list] = None
        self._instances: Optional[List[Instance]] = None

        # Create a unique identifier ID
        self._task_id = shortuuid.uuid()[:8]
        self._config: TaskConfig = TaskConfig({**config}) if config else TaskConfig()

        self._filters = [build_filter_ensemble("none", [["take_first", None]])]
        self.fewshot_rnd: Optional[
            random.Random
        ] = None  # purposely induce errors in case of improper usage

    def download(
        self,
        data_dir: Optional[str] = None,
        cache_dir: Optional[str] = None,
        download_mode=None,
    ) -> None:
        """Downloads and returns the task dataset.
        Override this method to download the dataset from a custom API.

        :param data_dir: str
            Stores the path to a local folder containing the `Task`'s data files.
            Use this to specify the path to manually downloaded data (usually when
            the dataset is not publicly accessible).
        :param cache_dir: str
            The directory to read/write the `Task` dataset. This follows the
            HuggingFace `datasets` API with the default cache directory located at:
                `~/.cache/huggingface/datasets`
            NOTE: You can change the cache location globally for a given process
            by setting the shell environment variable, `HF_DATASETS_CACHE`,
            to another directory:
                `export HF_DATASETS_CACHE="/path/to/another/directory"`
        :param download_mode: datasets.DownloadMode
            How to treat pre-existing `Task` downloads and data.
            - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS`
                Reuse download and reuse dataset.
            - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS`
                Reuse download with fresh dataset.
            - `datasets.DownloadMode.FORCE_REDOWNLOAD`
                Fresh download and fresh dataset.
        """
        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            data_dir=data_dir,
            cache_dir=cache_dir,
            download_mode=download_mode,
        )

    @property
    def config(self) -> TaskConfig:
        """Returns the TaskConfig associated with this class."""
        return self._config

    @abc.abstractmethod
    def has_training_docs(self):
        """Whether the task has a training set"""
        pass

    @abc.abstractmethod
    def has_validation_docs(self):
        """Whether the task has a validation set"""
        pass

    @abc.abstractmethod
    def has_test_docs(self):
        """Whether the task has a test set"""
        pass

    def training_docs(self) -> Iterable:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def validation_docs(self) -> Iterable:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def test_docs(self) -> Iterable:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def fewshot_docs(self) -> Iterable:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        if self.has_training_docs():
            return self.training_docs()
        elif self.has_validation_docs():
            return self.validation_docs()
        else:
            eval_logger.warning(
                f"[Task: {self.config.task}] has_training_docs and has_validation_docs are False"
                ", using test_docs as fewshot_docs but this is not recommended."
            )
            return self.test_docs()

    def _process_doc(self, doc: dict) -> dict:
        """
        Override this to process (detokenize, strip, replace, etc.) individual
        documents. This can be used in a map over documents of a data split.
        E.g. `map(self._process_doc, self.dataset["validation"])`

        :return: dict
            The processed version of the specified `doc`.
        """
        return doc

    @property
    def instances(self) -> List[Instance]:
        """After calling `task.build_all_requests()`, tasks
        maintain a list of the dataset instances which will be evaluated.
        """
        return self._instances

    def fewshot_examples(self, k, rnd):
        if self._training_docs is None:
            self._training_docs = list(self.training_docs())

        return rnd.sample(self._training_docs, k)

    def doc_to_decontamination_query(self, doc):
        raise NotImplementedError(
            "Override doc_to_decontamination_query with document specific decontamination query."
        )

    @abc.abstractmethod
    def doc_to_text(self, doc):
        pass

    @abc.abstractmethod
    def doc_to_target(self, doc):
        pass

    def build_all_requests(
        self,
        *,
        limit=None,
        rank=None,
        world_size=None,
        cache_requests=False,
        rewrite_requests_cache=False,
        system_instruction=None,
        apply_chat_template=False,
        fewshot_as_multiturn=False,
        lm=None,
    ) -> None:
        """Build a set of Instances for a task, and store them in task.instances"""

        # used with caching
        og_limit = limit

        cache_key = f"requests-{self._config.task}-{self.config.num_fewshot}shot-rank{rank}-world_size{world_size}"
        cache_key += "-chat_template" if apply_chat_template else ""
        cache_key += "-fewshot_as_multiturn" if fewshot_as_multiturn else ""
        cache_key += (
            f"-system_prompt_hash{utils.hash_string(system_instruction)}"
            if system_instruction is not None
            else ""
        )
        cache_key += f"-tokenizer{lm.tokenizer_name}" if apply_chat_template else ""

        cached_instances = load_from_cache(file_name=cache_key)

        if cache_requests and cached_instances and not rewrite_requests_cache:
            cached_instances = cached_instances[:limit]

            flattened_instances = [
                instance
                for instance_group in cached_instances
                for instance in instance_group
            ]

            self._instances = flattened_instances
            return

        eval_logger.info(f"Building contexts for {self.config.task} on rank {rank}...")

        instances = []

        # process all documents when caching is specified for simplicity
        if (
            cache_requests
            and (not cached_instances or rewrite_requests_cache)
            and limit is not None
        ):
            limit = None

        doc_id_docs = list(
            self.doc_iterator(rank=rank, limit=limit, world_size=world_size)
        )

        num_docs = len(doc_id_docs)

        for doc_id, doc in tqdm(
            doc_id_docs,
            total=num_docs,
        ):
            # sample fewshot context #TODO: need to offset doc_id by rank now!
            fewshot_ctx = self.fewshot_context(
                doc,
                0 if self.config.num_fewshot is None else self.config.num_fewshot,
                system_instruction,
                apply_chat_template,
                fewshot_as_multiturn,
                lm,
            )

            # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute
            inst = self.construct_requests(
                doc=doc,
                ctx=fewshot_ctx,
                metadata=(self.config["task"], doc_id, self.config.repeats),
            )

            if not isinstance(inst, list):
                inst = [inst]

            instances.append(inst)

        # now flatten, this is to allow slicing to work with pickles

        sliced_instances = instances[:og_limit]

        flattened_instances = [
            instance
            for instance_group in sliced_instances
            for instance in instance_group
        ]

        self._instances = flattened_instances

        if len(self._instances) == 0:
            raise ValueError("task.build_requests() did not find any docs!")

        if cache_requests and (not cached_instances or rewrite_requests_cache):
            save_to_cache(file_name=cache_key, obj=instances)

    @abc.abstractmethod
    def construct_requests(self, doc, ctx, **kwargs):
        """Uses RequestFactory to construct Requests and returns an iterable of
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param ctx: str
            The context string, generated by fewshot_context. This includes the natural
            language description, as well as the few shot examples, and the question
            part of the document for `doc`.
        :param doc_idx: int
            The index of a document within `self.test_docs()` or `self.validation_docs()`,
            whichever is the main split used.
        :param repeats: int
        TODO: update this docstring
            The number of times each instance in a dataset is inferred on. Defaults to 1,
            can be increased for techniques like majority voting.
        """
        pass

    @abc.abstractmethod
    def process_results(self, doc, results):
        """Take a single document and the LM results and evaluates, returning a
        dict where keys are the names of submetrics and values are the values of
        the metric for that one document

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param results:
            The results of the requests created in construct_requests.
        """
        pass

    @abc.abstractmethod
    def aggregation(self):
        """
        :returns: {str: [metric_score] -> float}
            A dictionary where keys are the names of submetrics and values are
            functions that aggregate a list of metric scores
        """
        pass

    @abc.abstractmethod
    def higher_is_better(self):
        """
        :returns: {str: bool}
            A dictionary where keys are the names of submetrics and values are
            whether a higher value of the submetric is better
        """
        pass

    def get_config(self, key: str) -> Any:
        return getattr(self._config, key, None)

    @classmethod
    def count_bytes(cls, doc):
        """Used for byte-level perplexity metrics in rolling loglikelihood"""
        return len(doc.encode("utf-8"))

    @classmethod
    def count_words(cls, doc):
        """Downstream loglikelihood_rolling perplexity tasks with custom word boundaries should override this!"""
        return len(re.split(r"\s+", doc))

    @utils.positional_deprecated
    def fewshot_context(
        self,
        doc,
        num_fewshot,
        rnd=None,
        description=None,
    ):
        """Returns a fewshot context string that is made up of a prepended description
        (if provided), the `num_fewshot` number of examples, and an appended prompt example.

        :param doc: str
            The document as returned from training_docs, validation_docs, or test_docs.
        :param num_fewshot: int
            The number of fewshot examples to provide in the returned context string.
        :param rnd: random.Random
            The pseudo-random number generator used to randomly sample examples.
            WARNING: This is currently a required arg although it's optionalized with a default `None`.
        :param description: str
            The task's description that will be prepended to the fewshot examples.
        :returns: str
            The fewshot context.
        """
        if rnd is None:
            if self.fewshot_rnd is not None:
                rnd = self.fewshot_rnd
            else:
                raise ValueError(
                    "A `random.Random` generator argument must be provided to `rnd`"
                )

        description = description if description else ""

        if num_fewshot == 0:
            labeled_examples = ""
        else:
            # for sets with no training docs, draw from other set *but ensure no overlap with current doc*
            if self.has_training_docs():
                fewshotex = self.fewshot_examples(k=num_fewshot, rnd=rnd)
            else:
                if self._fewshot_docs is None:
                    self._fewshot_docs = list(
                        self.validation_docs()
                        if self.has_validation_docs()
                        else self.test_docs()
                    )

                fewshotex = rnd.sample(self._fewshot_docs, num_fewshot + 1)

                # get rid of the doc that's the one we're evaluating, if it's in the fewshot
                fewshotex = [x for x in fewshotex if x != doc][:num_fewshot]

            labeled_examples = (
                "\n\n".join(
                    [
                        self.doc_to_text(doc) + self.doc_to_target(doc)
                        for doc in fewshotex
                    ]
                )
                + "\n\n"
            )

        example = self.doc_to_text(doc)
        return description + labeled_examples + example

    def apply_filters(self) -> Optional[List[Instance]]:
        """Iterates over FilterEnsembles and applies them to instances"""
        if hasattr(self, "_filters"):
            for f in self._filters:
                f.apply(self._instances)
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances

    def dump_config(self) -> dict:
        """Returns the config as a dictionary."""
        # TODO: this should only return the overrides applied to a non-YAML task's configuration.
        # (num_fewshot)
        return self.config.to_dict()

    def set_config(self, key: str, value: Any, update: bool = False) -> None:
        """Set or update the configuration for a given key."""
        if key is None:
            raise ValueError("Key must be provided.")

        if update:
            current_value = getattr(self._config, key, {})
            if not isinstance(current_value, dict):
                raise TypeError(
                    f"Expected a dict for key '{key}', got {type(current_value).__name__} instead."
                )
            current_value.update(value)
        else:
            setattr(self._config, key, value)

    def override_metric(self, metric_name: str) -> None:
        """
        Override the default metrics used for evaluation with custom metrics.

        Parameters:
        - metric_name (str): The name of the custom metric to override. Should be registered in api.metrics.
        """
        (
            self._metric_fn_list,
            self._aggregation_list,
            self._metric_fn_kwargs,
            self._higher_is_better,
        ) = ({}, {}, {}, {})
        self._metric_fn_list[metric_name] = get_metric(metric_name)
        self._aggregation_list[metric_name] = get_metric_aggregation(metric_name)
        self._higher_is_better[metric_name] = is_higher_better(metric_name)
        self._metric_fn_kwargs[metric_name] = {}
        if not isinstance(self, ConfigurableTask):
            self.process_results = lambda x, y: {metric_name: get_metric(metric_name)}
            self.aggregation = lambda: {
                metric_name: get_metric_aggregation(metric_name)
            }
        setattr(self._config, "metric_list", [{"metric": metric_name}])
        setattr(self._config, "process_results", None)

    def set_fewshot_seed(self, seed: Optional[int] = None) -> None:
        self.fewshot_rnd = random.Random(seed)
        if hasattr(self, "sampler"):
            self.sampler.rnd = self.fewshot_rnd

    @property
    def eval_docs(self) -> Union[datasets.Dataset, List[dict]]:
        if self.has_test_docs():
            return self.test_docs()
        elif self.has_validation_docs():
            return self.validation_docs()
        else:
            raise ValueError(
                f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"
            )

    def doc_iterator(
        self, *, rank: int = 0, limit: Union[int, None] = None, world_size: int = 1
    ) -> Iterator[Tuple[int, Any]]:
        limit = int(limit) if limit else None
        doc_iterator = utils.create_iterator(
            enumerate(self.eval_docs),
            rank=int(rank),
            limit=limit,
            world_size=int(world_size),
        )
        return doc_iterator

    @property
    def task_id(self) -> Any:
        return self._task_id


class ConfigurableTask(Task):
    VERSION = "Yaml"
    OUTPUT_TYPE = None
    CONFIG = None

    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config: Optional[dict] = None,
    ) -> None:  # TODO no super() call here
        # Create a unique identifier ID
        self._task_id = shortuuid.uuid()[:8]

        # Get pre-configured attributes
        self._config = self.CONFIG

        # Use new configurations if there was no preconfiguration
        if self.config is None:
            self._config = TaskConfig(**config)
        # Overwrite configs
        else:
            if config is not None:
                self._config.__dict__.update(config)

        if self.config is None:
            raise ValueError(
                "Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg"
            )

        if isinstance(self.config.metadata, dict):
            if "version" in self.config.metadata:
                self.VERSION = self.config.metadata["version"]

        if self.config.output_type is not None:
            if self.config.output_type not in ALL_OUTPUT_TYPES:
                raise ValueError(
                    f"Got invalid output_type '{self.config.output_type}', must be in '{','.join(ALL_OUTPUT_TYPES)}'"
                )
            self.OUTPUT_TYPE = self.config.output_type

        if self.config.dataset_path is not None:
            self.DATASET_PATH = self.config.dataset_path

        if self.config.dataset_name is not None:
            self.DATASET_NAME = self.config.dataset_name

        self._metric_fn_list = {}
        self._metric_fn_kwargs = {}
        self._aggregation_list = {}
        self._higher_is_better = {}

        if self.config.metric_list is None:
            # TODO: handle this in TaskConfig.__post_init__ ?
            _metric_list = DEFAULT_METRIC_REGISTRY[self.config.output_type]

            for metric_name in _metric_list:
                self._metric_fn_list[metric_name] = get_metric(metric_name)
                self._metric_fn_kwargs[metric_name] = {}
                self._aggregation_list[metric_name] = get_metric_aggregation(
                    metric_name
                )
                self._higher_is_better[metric_name] = is_higher_better(metric_name)
        else:
            for metric_config in self.config.metric_list:
                if "metric" not in metric_config:
                    raise ValueError(
                        "'metric' key not provided for an entry in 'metric_list', must be specified!"
                    )
                metric_name = metric_config["metric"]
                kwargs = {
                    key: metric_config[key]
                    for key in metric_config
                    if key
                    not in ["metric", "aggregation", "higher_is_better", "hf_evaluate"]
                }
                hf_evaluate_metric = (
                    "hf_evaluate" in metric_config
                    and metric_config["hf_evaluate"] is True
                )

                if self.config.process_results is not None:
                    self._metric_fn_list[metric_name] = None
                    self._metric_fn_kwargs[metric_name] = {}
                elif callable(metric_name):
                    metric_fn = metric_name.__call__
                    metric_name = metric_name.__name__
                    self._metric_fn_list[metric_name] = metric_fn
                    self._metric_fn_kwargs[metric_name] = kwargs
                else:
                    self._metric_fn_list[metric_name] = get_metric(
                        metric_name, hf_evaluate_metric
                    )
                    self._metric_fn_kwargs[metric_name] = kwargs

                if "aggregation" in metric_config:
                    agg_name = metric_config["aggregation"]
                    if isinstance(agg_name, str):
                        self._aggregation_list[metric_name] = get_aggregation(agg_name)
                    elif callable(agg_name):  # noqa: E721
                        self._aggregation_list[metric_name] = metric_config[
                            "aggregation"
                        ]
                else:
                    INV_AGG_REGISTRY = {v: k for k, v in AGGREGATION_REGISTRY.items()}
                    metric_agg = get_metric_aggregation(metric_name)
                    eval_logger.warning(
                        f"[Task: {self.config.task}] metric {metric_name} is defined, but aggregation is not. "
                        f"using default "
                        f"aggregation={INV_AGG_REGISTRY[metric_agg]}"
                    )
                    self._aggregation_list[metric_name] = metric_agg

                if "higher_is_better" in metric_config:
                    self._higher_is_better[metric_name] = metric_config[
                        "higher_is_better"
                    ]
                else:
                    eval_logger.warning(
                        f"[Task: {self.config.task}] metric {metric_name} is defined, but higher_is_better is not. "
                        f"using default "
                        f"higher_is_better={is_higher_better(metric_name)}"
                    )
                    self._higher_is_better[metric_name] = is_higher_better(metric_name)

        self.download(self.config.dataset_kwargs)
        self._training_docs = None
        self._fewshot_docs = None

        if self.config.filter_list is not None:
            self._filters = []
            for filter_config in self.config.filter_list:
                filter_name = filter_config["name"]
                filter_functions = filter_config["filter"]
                components = []
                for function in filter_functions:
                    kwargs = {
                        key: function[key] for key in function if key != "function"
                    }
                    components.append([function["function"], kwargs])
                filter_pipeline = build_filter_ensemble(filter_name, components)
                self._filters.append(filter_pipeline)
        else:
            self._filters = [build_filter_ensemble("none", [["take_first", None]])]

        if self.config.use_prompt is not None:
            eval_logger.info(f"loading prompt {self.config.use_prompt}")
            self.prompt = get_prompt(
                self.config.use_prompt, self.DATASET_PATH, self.DATASET_NAME
            )
        else:
            self.prompt = None

        if self.fewshot_docs() is not None:
            self.fewshot_rnd = (
                random.Random()
            )  # setting with no seed, to be overridden at a later time
            config_sampler: Union[str, Callable] = (
                self.config.fewshot_config.get("sampler", "default")
                if self.config.fewshot_config
                else "default"
            )
            if isinstance(config_sampler, str):
                self.sampler = samplers.get_sampler(config_sampler)(
                    list(self.fewshot_docs()), self, rnd=self.fewshot_rnd
                )
            elif callable(config_sampler) and issubclass(
                config_sampler, samplers.ContextSampler
            ):
                self.sampler = config_sampler(
                    docs=list(self.fewshot_docs()), task=self, rnd=self.fewshot_rnd
                )
            else:
                raise TypeError(
                    f"fewshot_config.sampler should be a string or callable of ContextSampler type, "
                    f"not {type(config_sampler)}"
                )

        self.task_docs = self.eval_docs

        # Test One Doc
        self.features = list(self.task_docs.features.keys())
        self.multiple_input = 0
        self.multiple_target = 0
        test_doc = self.task_docs[0]
        test_text = self.doc_to_text(test_doc)
        test_target = self.doc_to_target(test_doc)

        if self.config.doc_to_choice is not None:
            test_choice = self.doc_to_choice(test_doc)
            if not isinstance(test_choice, list):
                eval_logger.error("doc_to_choice must return list")
            else:
                num_choice = len(test_choice)

            if isinstance(test_text, int):
                self.multiple_input = num_choice
        else:
            test_choice = None

        if isinstance(test_target, list):
            self.multiple_target = len(test_target)
        else:
            if (isinstance(test_target, int)) and (test_choice is not None):
                test_target = test_choice[test_target]
            else:
                test_target = str(test_target)

        if test_choice is not None:
            check_choices = test_choice
        else:
            check_choices = [test_target]
        if self.config.doc_to_choice is not None:
            for choice in check_choices:
                choice_has_whitespace = True if choice[0].isspace() else False
                delimiter_has_whitespace = (
                    True
                    if self.config.target_delimiter.rstrip()
                    != self.config.target_delimiter
                    else False
                )

                if delimiter_has_whitespace and choice_has_whitespace:
                    eval_logger.debug(
                        f'Both target_delimiter "{self.config.target_delimiter}" and target choice: "{choice}" have whitespace'
                    )
                elif (not delimiter_has_whitespace) and (not choice_has_whitespace):
                    eval_logger.debug(
                        f'Both target_delimiter "{self.config.target_delimiter}" and target choice: "{choice}" do not have whitespace, ignore if the language you are evaluating on does not require/use whitespace'
                    )

    def download(self, dataset_kwargs: Optional[Dict[str, Any]] = None) -> None:
        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            **dataset_kwargs if dataset_kwargs is not None else {},
        )

    def has_training_docs(self) -> bool:
        if self.config.training_split is not None:
            return True
        else:
            return False

    def has_validation_docs(self) -> bool:
        if self.config.validation_split is not None:
            return True
        else:
            return False

    def has_test_docs(self) -> bool:
        if self.config.test_split is not None:
            return True
        else:
            return False

    def training_docs(self) -> datasets.Dataset:
        if self.has_training_docs():
            if self.config.process_docs is not None:
                return self.config.process_docs(
                    self.dataset[self.config.training_split]
                )
            return self.dataset[self.config.training_split]

    def validation_docs(self) -> datasets.Dataset:
        if self.has_validation_docs():
            if self.config.process_docs is not None:
                return self.config.process_docs(
                    self.dataset[self.config.validation_split]
                )
            return self.dataset[self.config.validation_split]

    def test_docs(self) -> datasets.Dataset:
        if self.has_test_docs():
            if self.config.process_docs is not None:
                return self.config.process_docs(self.dataset[self.config.test_split])
            return self.dataset[self.config.test_split]

    def fewshot_docs(self):
        if self.config.fewshot_split is not None:
            if self.config.process_docs is not None:
                return self.config.process_docs(self.dataset[self.config.fewshot_split])
            return self.dataset[self.config.fewshot_split]
        elif (
            self.config.fewshot_config is not None
            and self.config.fewshot_config.get("samples", None) is not None
        ):
            if isinstance(self.config.fewshot_config["samples"], list):
                return self.config.fewshot_config["samples"]
            elif callable(self.config.fewshot_config["samples"]):
                return self.config.fewshot_config["samples"]()
            else:
                raise Exception(
                    "`fewshot_config['samples']` was incorrectly defined in the configuration. It should be either a list of samples as a dict, or function returning this list."
                )
        else:
            if (self.config.num_fewshot is not None) and (self.config.num_fewshot > 0):
                eval_logger.warning(
                    f"[Task: {self.config.task}] "
                    "num_fewshot > 0 but fewshot_split is None. "
                    "using preconfigured rule."
                )
            return super().fewshot_docs()

    @staticmethod
    def append_target_question(
        labeled_examples: List[Dict[str, str]],
        question: str,
        fewshot_as_multiturn: bool = False,
    ) -> None:
        """Adds a target question to the labeled examples list.
        If fewshot_as_multiturn is True, or labeled_examples is empty, or the last entry is a system turn, appends the question as a new user entry.
        Otherwise, it is appended to the last user entry, ensuring that the conversation alternates between the user and the assistant.
        """
        if not fewshot_as_multiturn:
            # if no messages or last message is system, append as new user entry
            if len(labeled_examples) == 0 or labeled_examples[-1]["role"] == "system":
                labeled_examples.append({"role": "user", "content": question})
            # if last message is user, append to it to avoid two user messages in a row
            else:
                labeled_examples[-1]["content"] += question
        else:
            # if fewshot_as_multiturn is True, append as next user entry (last is always assistant)
            labeled_examples.append({"role": "user", "content": question})

    @utils.positional_deprecated
    def fewshot_context(
        self,
        doc: str,
        num_fewshot: int,
        system_instruction: Optional[str] = None,
        apply_chat_template: bool = False,
        fewshot_as_multiturn: bool = False,
        lm=None,
    ) -> str:
        """Returns a fewshot context string that is made up of a prepended description
        (if provided), the `num_fewshot` number of examples, and an appended prompt example.

        :param doc: str
            The document as returned from training_docs, validation_docs, or test_docs.
        :param num_fewshot: int
            The number of fewshot examples to provide in the returned context string.
        :param  system_instruction: str
            System instruction to be applied to the prompt.
        :param apply_chat_template: bool
            Whether to apply the chat template to the fewshot context.
        :param fewshot_as_multiturn: bool
            Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
        :param lm:
            Language model with definition of the tokenizer/function to use for applying the chat template.
        :returns: str
            The fewshot context.
        """

        if apply_chat_template:
            labeled_examples = []
        else:
            labeled_examples = ""

        # get task description
        if description := self.config.description:
            description = utils.apply_template(self.config.description, doc)

        # create system prompt based on the provided system instruction and description
        if system_instruction is not None and description:
            system_prompt = (
                f"{system_instruction}{self.sampler.fewshot_delimiter}{description}"
            )
        elif system_instruction is not None:
            system_prompt = system_instruction
        elif description:
            system_prompt = description
        else:
            system_prompt = ""

        # add system prompt if specified
        if system_prompt:
            if apply_chat_template:
                labeled_examples.append({"role": "system", "content": system_prompt})
            else:
                labeled_examples = system_prompt

        # if few-shot - append examples after the system prompt
        if num_fewshot > 0:
            if apply_chat_template:
                labeled_examples.extend(
                    self.sampler.get_chat_context(
                        doc, num_fewshot, fewshot_as_multiturn
                    )
                )
            else:
                labeled_examples += self.sampler.get_context(doc, num_fewshot)

        example = self.doc_to_text(doc)
        if apply_chat_template:
            if self.multiple_input:
                return lm.apply_chat_template(labeled_examples)
            if isinstance(example, str):
                self.append_target_question(
                    labeled_examples, example, fewshot_as_multiturn
                )
            # for loglikelihood create a list of questions with appended choices
            elif isinstance(example, list):
                labeled_examples_list = []
                # copy chat history for each example and append the answer
                for ex in example:
                    chat = deepcopy(labeled_examples)
                    self.append_target_question(chat, ex, fewshot_as_multiturn)
                    labeled_examples_list.append(lm.apply_chat_template(chat))
                return labeled_examples_list
            # if example is an integer, append the choice or convert to string
            elif isinstance(example, int):
                if self.config.doc_to_choice is not None:
                    choices = self.doc_to_choice(doc)
                    self.append_target_question(
                        labeled_examples, choices[example], fewshot_as_multiturn
                    )
                else:
                    self.append_target_question(
                        labeled_examples, str(example), fewshot_as_multiturn
                    )
                # return lm.apply_chat_template(labeled_examples)
            return lm.apply_chat_template(labeled_examples)
        else:
            if self.multiple_input:
                return labeled_examples
            if isinstance(example, str):
                return labeled_examples + example
            elif isinstance(example, list):
                return [labeled_examples + ex for ex in example]
            elif isinstance(example, int):
                if self.config.doc_to_choice is not None:
                    choices = self.doc_to_choice(doc)
                    return labeled_examples + choices[example]
                else:
                    return labeled_examples + str(example)

    def apply_filters(self):
        """Iterates over FilterEnsembles and applies them to instances"""
        if hasattr(self, "_filters"):
            for f in self._filters:
                f.apply(self._instances)
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances

    def should_decontaminate(self):
        return self.config.should_decontaminate

    def doc_to_decontamination_query(self, doc):
        if self.config.should_decontaminate:
            if self.config.doc_to_decontamination_query is None:
                return self.doc_to_text(doc)
            else:
                doc_to_decontamination_query = self.config.doc_to_decontamination_query
                if doc_to_decontamination_query in self.features:
                    return doc[doc_to_decontamination_query]
                elif callable(doc_to_decontamination_query):
                    return doc_to_decontamination_query(doc)
                else:
                    return ast.literal_eval(
                        utils.apply_template(
                            self.config.doc_to_decontamination_query, doc
                        )
                    )

    def _process_doc(self, doc: dict) -> dict:
        """
        Override this to process (detokenize, strip, replace, etc.) individual
        documents. This can be used in a map over documents of a data split.
        E.g. `map(self._process_doc, self.dataset["validation"])`

        :return: dict
            The processed version of the specified `doc`.
        """
        return doc

    def doc_to_text(self, doc):
        if self.prompt is not None:
            doc_to_text = self.prompt
        else:
            doc_to_text = self.config.doc_to_text

        if isinstance(doc_to_text, int):
            return doc_to_text
        elif isinstance(doc_to_text, str):
            if doc_to_text in self.features:
                # if self.config.doc_to_choice is not None:
                #     return self.doc_to_choice(doc)[doc[doc_to_text]]
                # else:
                return doc[doc_to_text]
            else:
                text_string = utils.apply_template(doc_to_text, doc)
                if text_string.isdigit() and self._config.doc_to_choice is not None:
                    return ast.literal_eval(text_string)
                else:
                    return text_string
        elif callable(doc_to_text):
            return doc_to_text(doc)
        # Used when applying a Promptsource template
        elif hasattr(doc_to_text, "apply"):
            applied_prompt = doc_to_text.apply(doc)
            if len(applied_prompt) == 2:
                return applied_prompt[0]
            else:
                eval_logger.warning("Applied prompt returns empty string")
                return self.config.fewshot_delimiter
        else:
            print(type(doc_to_text))
            raise TypeError

    def doc_to_target(self, doc: Mapping) -> Union[int, str, list]:
        if self.prompt is not None:
            doc_to_target = self.prompt
        else:
            doc_to_target = self.config.doc_to_target

        if isinstance(doc_to_target, int):
            return doc_to_target
        elif isinstance(doc_to_target, str):
            if doc_to_target in self.features:
                # if self.config.doc_to_choice is not None:
                #     return self.doc_to_choice(doc)[doc[doc_to_target]]
                # else:
                return doc[doc_to_target]
            else:
                target_string = utils.apply_template(doc_to_target, doc)
                if target_string.isdigit() and self._config.doc_to_choice is not None:
                    return ast.literal_eval(target_string)
                elif (
                    len(target_string) >= 2
                    and (target_string[0] == "[")
                    and (target_string[-1] == "]")
                ):
                    try:
                        return ast.literal_eval(target_string)
                    except (SyntaxError, ValueError):
                        return target_string
                else:
                    return target_string
        elif isinstance(doc_to_target, list):
            return doc_to_target
        elif callable(doc_to_target):
            return doc_to_target(doc)
        # Used when applying a Promptsource template
        elif hasattr(doc_to_target, "apply"):
            applied_prompt = doc_to_target.apply(doc)
            if len(applied_prompt) == 2:
                return applied_prompt[1]
            else:
                eval_logger.warning("Applied prompt returns empty string")
                return self.config.fewshot_delimiter
        else:
            raise TypeError

    def doc_to_choice(self, doc: Any) -> List[str]:
        if self.prompt is not None:
            doc_to_choice = self.prompt
        elif self.config.doc_to_choice is None:
            eval_logger.error("doc_to_choice was called but not set in config")
        else:
            doc_to_choice = self.config.doc_to_choice

        if isinstance(doc_to_choice, str):
            if doc_to_choice in self.features:
                return doc[doc_to_choice]
            else:
                return ast.literal_eval(utils.apply_template(doc_to_choice, doc))
        elif isinstance(doc_to_choice, list):
            return doc_to_choice
        elif isinstance(doc_to_choice, dict):
            return list(doc_to_choice.values())
        elif callable(doc_to_choice):
            return doc_to_choice(doc)
        elif hasattr(doc_to_choice, "get_answer_choices_list"):
            return doc_to_choice.get_answer_choices_list(doc)
        else:
            raise TypeError

    def construct_requests(
        self, doc: dict, ctx: str, **kwargs
    ) -> Union[List[Instance], Instance]:
        if self.OUTPUT_TYPE == "loglikelihood":
            arguments = (ctx, self.doc_to_target(doc))
        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
            arguments = (self.doc_to_target(doc),)
        elif self.OUTPUT_TYPE == "multiple_choice":
            choices = self.doc_to_choice(doc)
            target_delimiter = self.config.target_delimiter
            if self.multiple_input:
                # If there are multiple inputs, choices are placed in the ctx
                cont = self.doc_to_target(doc)
                arguments = [
                    (ctx + choice, f"{target_delimiter}{cont}") for choice in choices
                ]
            else:
                # Otherwise they are placed in the continuation
                arguments = [(ctx, f"{target_delimiter}{cont}") for cont in choices]

            request_list = [
                Instance(
                    request_type="loglikelihood",
                    doc=doc,
                    arguments=arg,
                    idx=i,
                    **kwargs,
                )
                for i, arg in enumerate(arguments)
            ]
            # TODO: we should raise a warning telling users this will at most ~2x runtime.
            if "acc_mutual_info" in self._metric_fn_list.keys():
                # if we are calculating multiple choice accuracy
                # using mutual information instead of raw loglikelihood as metric, need unconditional lls.

                # here mutual info refers to calculating
                # log(P(choice|ctx) / P(choice)) = log(P(choice|ctx)) - log(P(choice))
                # in other words normalizing by subtracting the unconditional logprob of each choice.
                request_list.extend(
                    [
                        Instance(
                            request_type="loglikelihood",
                            doc=doc,
                            arguments=("", "{}".format(choice)),
                            idx=i,
                            **kwargs,
                        )
                        for i, choice in enumerate(choices)
                    ]
                )
            return request_list

        elif self.OUTPUT_TYPE == "generate_until":
            arguments = (ctx, deepcopy(self.config.generation_kwargs))

        return Instance(
            request_type=self.OUTPUT_TYPE, doc=doc, arguments=arguments, idx=0, **kwargs
        )

    def process_results(self, doc, results):
        if callable(self.config.process_results):
            return self.config.process_results(doc, results)

        result_dict = {}
        use_metric = list(self._metric_fn_list.keys())
        if self.OUTPUT_TYPE == "loglikelihood":
            results = results[0]
            ll, is_greedy = results
            return {
                **({"perplexity": ll} if "perplexity" in use_metric else {}),
                **({"acc": int(is_greedy)} if "acc" in use_metric else {}),
            }
        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
            (loglikelihood,) = results
            _words = self.count_words(self.doc_to_target(doc))
            _bytes = self.count_bytes(self.doc_to_target(doc))
            return {
                **(
                    {"word_perplexity": (loglikelihood, _words)}
                    if "word_perplexity" in use_metric
                    else {}
                ),
                **(
                    {"byte_perplexity": (loglikelihood, _bytes)}
                    if "byte_perplexity" in use_metric
                    else {}
                ),
                **(
                    {"bits_per_byte": (loglikelihood, _bytes)}
                    if "bits_per_byte" in use_metric
                    else {}
                ),
            }
        elif self.OUTPUT_TYPE == "multiple_choice":
            lls, is_greedy = zip(*results)

            # retrieve choices in List[str] form, to compute choice lengths, etc.
            choices = self.doc_to_choice(doc)
            completion_len = np.array([float(len(i)) for i in choices])

            if (
                2 * len(choices) == len(lls)
                and "acc_mutual_info" in self._metric_fn_list.keys()
            ):
                # then we are doing mutual info.
                # this stores the "dryrun" / unconditional answer loglikelihoods
                lls_unconditional = lls[1::2]
                if len(lls_unconditional) != len(choices):
                    raise ValueError
                # and this stores our "regular" conditional loglikelihoods
                lls = lls[::2]

            pred = np.argmax(lls)
            pred_norm = np.argmax(lls / completion_len)

            if self.multiple_input:
                gold = self.doc_to_text(doc)
            else:
                gold = self.doc_to_target(doc)

            gold_index_error = False
            if isinstance(gold, list):
                gold = [i if i < len(choices) else -100 for i in gold]
                if -100 in gold:
                    gold_index_error = True
            else:
                if isinstance(gold, int):
                    gold = gold if gold < len(choices) else -100
                elif isinstance(gold, str):
                    gold = choices.index(gold) if gold in choices else -100

                if gold == -100:
                    gold_index_error = True

            if gold_index_error:
                eval_logger.warning(
                    f"Label index was not in within range of available choices,"
                    f"Sample:\n\n{doc}\n\n"
                )

            if self.multiple_target:
                acc = 1.0 if pred in gold else 0.0
                acc_norm = 1.0 if pred_norm in gold else 0.0
                exact_match = int(any([is_greedy[i] if i != -100 else 0 for i in gold]))
            else:
                acc = 1.0 if pred == gold else 0.0
                acc_norm = 1.0 if pred_norm == gold else 0.0
                # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly
                exact_match = int(is_greedy[gold]) if gold != -100 else 0

            prob_norm = utils.softmax(lls)

            # TODO use keyword arguments to the metric?
            # gold, pred, norm stuff, the original lls,
            result_dict = {
                **({"acc": acc} if "acc" in use_metric else {}),
                **({"f1": (gold, pred)} if "f1" in use_metric else {}),
                **({"mcc": (gold, pred)} if "mcc" in use_metric else {}),
                **({"acc_norm": acc_norm} if "acc_norm" in use_metric else {}),
                **({"exact_match": exact_match} if "exact_match" in use_metric else {}),
                **(
                    {"brier_score": (gold, prob_norm)}
                    if "brier_score" in use_metric
                    else {}
                ),
            }

            if "acc_mutual_info" in use_metric:
                lls_mutual_info = [
                    ll_c - ll_u for ll_c, ll_u in zip(lls, lls_unconditional)
                ]
                acc_mutual_info = 1.0 if np.argmax(lls_mutual_info) == gold else 0.0
                result_dict["acc_mutual_info"] = acc_mutual_info

        elif self.OUTPUT_TYPE == "generate_until":
            gold = self.doc_to_target(doc)
            result = results[0]
            if self.config.doc_to_choice is not None:
                # If you set doc_to_choice,
                # it assumes that doc_to_target returns a number.
                choices = self.doc_to_choice(doc)
                gold = choices[gold]
            # we expect multiple_targets to be a list.
            elif self.multiple_target:
                gold = list(gold)
            elif type(gold) != type(result):
                # cast gold to the same type as result
                gold = type(result)(gold)

            for metric in self._metric_fn_list.keys():
                if self.multiple_target:
                    # in the case where we have multiple targets,
                    # return true if any are true
                    # TODO: this may break for multipLe_target, non zero-or-1 metrics
                    scores = []
                    if not isinstance(gold, list):
                        # sometimes, a multiple_target dataset has exceptions where one doc has only one string answer
                        # print(gold)
                        gold = [gold]
                    if metric == "exact_match":
                        result = [result for _ in range(len(gold))]
                        scores = self._metric_fn_list[metric](
                            references=gold,
                            predictions=result,
                            **self._metric_fn_kwargs[metric],
                        )[metric]
                        result_score = 1.0 if scores > 0.0 else 0.0
                    else:
                        for gold_option in gold:
                            try:
                                result_score = self._metric_fn_list[metric](
                                    references=[gold_option],
                                    predictions=[result],
                                    **self._metric_fn_kwargs[metric],
                                )
                            except (
                                TypeError
                            ):  # TODO: this is hacky and I don't want to do it
                                result_score = self._metric_fn_list[metric](
                                    [gold_option, result]
                                )
                            if isinstance(result_score, dict):
                                # TODO: this handles the case where HF evaluate returns a dict.
                                result_score = result_score[metric]
                            scores.append(result_score)
                        if any(scores):
                            result_score = 1.0
                        else:
                            result_score = 0.0
                else:
                    try:
                        result_score = self._metric_fn_list[metric](
                            references=[gold],
                            predictions=[result],
                            **self._metric_fn_kwargs[metric],
                        )
                    except TypeError:  # needed for now in order to use a different interface between our own metrics and HF Evaluate metrics
                        result_score = self._metric_fn_list[metric]([gold, result])
                    if isinstance(result_score, dict):
                        # TODO: this handles the case where HF evaluate returns a dict.
                        result_score = result_score[metric]
                result_dict[metric] = result_score
        else:
            raise ValueError(
                f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ",
                "'loglikelihood', 'loglikelihood_rolling', 'generate_until' or 'multiple_choice'",
            )

        return result_dict

    def aggregation(self) -> dict:
        return self._aggregation_list

    def higher_is_better(self) -> dict:
        return self._higher_is_better

    def get_config(self, key: str) -> Any:
        return getattr(self._config, key, None)

    @property
    def task_id(self) -> Any:
        return "-".join((self.task_name, self._task_id))

    @property
    def task_name(self) -> Any:
        return getattr(self.config, "task", None)

    def __repr__(self):
        return (
            f"ConfigurableTask(task_name={getattr(self.config, 'task', None)},"
            f"output_type={self.OUTPUT_TYPE},"
            f"num_fewshot={getattr(self.config, 'num_fewshot', None)},"
            f"num_samples={len(self.eval_docs)})"
        )


class MultipleChoiceTask(Task):
    OUTPUT_TYPE = "loglikelihood"

    def doc_to_target(self, doc: dict) -> str:
        return " " + doc["choices"][doc["gold"]]

    def construct_requests(self, doc: dict, ctx: str, **kwargs) -> List[Instance]:
        # TODO: add mutual info here?
        return [
            Instance(
                request_type="loglikelihood",
                doc=doc,
                arguments=(ctx, " {}".format(choice)),
                idx=i,
                **kwargs,
            )
            for i, choice in enumerate(doc["choices"])
        ]

    def process_results(self, doc: dict, results: Iterable[Tuple[float, bool]]) -> dict:
        results = [
            res[0] for res in results
        ]  # only retain loglikelihoods, discard is_greedy TODO: do we need is_greedy anywhere?
        gold = doc["gold"]

        acc = 1.0 if np.argmax(results) == gold else 0.0
        completion_len = np.array([float(len(i)) for i in doc["choices"]])
        acc_norm = 1.0 if np.argmax(results / completion_len) == gold else 0.0

        return {
            "acc": acc,
            "acc_norm": acc_norm,
        }

    def higher_is_better(self) -> dict:
        return {
            "acc": True,
            "acc_norm": True,
        }

    def aggregation(self) -> dict:
        return {
            "acc": mean,
            "acc_norm": mean,
        }


class PerplexityTask(Task):
    OUTPUT_TYPE = "loglikelihood_rolling"

    def has_training_docs(self) -> bool:
        return False

    def fewshot_examples(self, k: int, rnd) -> List:
        if k != 0:
            raise ValueError(
                "The number of fewshot examples must be 0 for perplexity tasks."
            )
        return []

    def fewshot_context(self, doc: dict, num_fewshot: int) -> Literal[""]:
        if num_fewshot != 0:
            raise ValueError(
                "The number of fewshot examples must be 0 for perplexity tasks."
            )

        return ""

    def higher_is_better(self) -> dict:
        return {
            "word_perplexity": False,
            "byte_perplexity": False,
            "bits_per_byte": False,
        }

    def doc_to_decontamination_query(self, doc):
        return doc

    def doc_to_text(self, doc) -> str:
        return ""

    def doc_to_target(self, doc):
        return doc

    def construct_requests(self, doc: dict, ctx: Optional[str], **kwargs):
        if bool(ctx):
            raise ValueError

        return Instance(
            request_type=self.OUTPUT_TYPE,
            doc=doc,
            arguments=(self.doc_to_target(doc),),
            idx=0,
            **kwargs,
        )

    def process_results(self, doc: dict, results: Tuple[float]) -> dict:
        (loglikelihood,) = results
        words = self.count_words(self.doc_to_target(doc))
        bytes_ = self.count_bytes(self.doc_to_target(doc))
        return {
            "word_perplexity": (loglikelihood, words),
            "byte_perplexity": (loglikelihood, bytes_),
            "bits_per_byte": (loglikelihood, bytes_),
        }

    def aggregation(self) -> dict:
        return {
            "word_perplexity": weighted_perplexity,
            "byte_perplexity": weighted_perplexity,
            "bits_per_byte": bits_per_byte,
        }

    @classmethod
    def count_bytes(cls, doc) -> int:
        return len(doc.encode("utf-8"))

    @classmethod
    def count_words(cls, doc) -> int:
        """Downstream tasks with custom word boundaries should override this!"""
        return len(re.split(r"\s+", doc))