task.py

from __future__ import annotations

import abc
import ast
import logging
import random
import re
from collections.abc import Callable, Iterable, Iterator, Mapping
from copy import deepcopy
from functools import cached_property
from typing import TYPE_CHECKING, Any, Literal, overload

import datasets
import numpy as np
from tqdm import tqdm
from typing_extensions import deprecated

from lm_eval import utils
from lm_eval.api.instance import Instance, OutputType
from lm_eval.api.metrics import bits_per_byte, mean, weighted_perplexity
from lm_eval.api.utils import check_gold_index_error
from lm_eval.caching.cache import load_from_cache, save_to_cache
from lm_eval.config.metric import MetricConfig
from lm_eval.config.task import DataSet, TaskConfig
from lm_eval.filters import build_filter_ensemble


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

if TYPE_CHECKING:
    pass


eval_logger = logging.getLogger(__name__)


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: int | str | None = 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: str | None = None

    # The name of a subset within `DATASET_PATH`.
    DATASET_NAME: str | None = None

    OUTPUT_TYPE: OutputType | None = None

    def __init__(
        self,
        data_dir: str | None = None,
        cache_dir: str | None = None,
        download_mode: datasets.DownloadMode | None = None,
        config: Mapping | None = 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: list | None = None
        self._fewshot_docs: list | None = None
        self._instances: list[Instance] | None = None

        self._config: TaskConfig = TaskConfig.from_yaml({**config})

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

    def download(
        self,
        data_dir: str | None = None,
        cache_dir: str | None = 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.
        """
        assert self.DATASET_PATH is not None, "DATASET_PATH must be set in Task class"
        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

    @property
    def has_training_docs(self) -> bool:
        """Whether the task has a training set"""
        raise NotImplementedError

    @property
    def has_validation_docs(self) -> bool:
        """Whether the task has a validation set"""
        raise NotImplementedError

    @property
    def has_test_docs(self) -> bool:
        """Whether the task has a test set"""
        raise NotImplementedError

    def training_docs(self) -> DataSet | None:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def validation_docs(self) -> DataSet | None:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def test_docs(self) -> DataSet | None:
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def fewshot_docs(self) -> DataSet | None:
        """
        :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:
            if self.config.num_fewshot and self.config.num_fewshot > 0:
                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: int, rnd) -> Iterable[dict]:
        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: dict):
        raise NotImplementedError(
            "Override doc_to_decontamination_query with document specific decontamination query."
        )

    @abc.abstractmethod
    def doc_to_text(self, doc: dict) -> str:
        pass

    @abc.abstractmethod
    def doc_to_target(self, doc: dict) -> str | int:
        pass

    # not an abstractmethod because not every language-only task has to implement this
    def doc_to_image(self, doc: dict):
        raise NotImplementedError

    def doc_to_audio(self, doc: dict):
        raise NotImplementedError

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

    def build_all_requests(
        self,
        *,
        limit: int | None = None,
        samples: list[int] | None = None,
        rank: int = 0,
        world_size: int = 1,
        cache_requests: bool = False,
        rewrite_requests_cache: bool = False,
        system_instruction: str | None = None,
        apply_chat_template: bool = False,
        fewshot_as_multiturn: bool = False,
        chat_template: Callable | None = None,
        tokenizer_name: str = "",
    ) -> 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{tokenizer_name}"

        cached_instances = load_from_cache(file_name=cache_key, cache=cache_requests)

        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, samples=samples, 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,
                num_fewshot=0
                if self.config.num_fewshot is None
                else self.config.num_fewshot,
                system_instruction=system_instruction,
                apply_chat_template=apply_chat_template,
                fewshot_as_multiturn=fewshot_as_multiturn,
                chat_template=chat_template,
                gen_prefix=self.doc_to_prefix(doc),
            )

            # 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),
                apply_chat_template=apply_chat_template,
                chat_template=chat_template,
            )

            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: dict, ctx: list[dict] | str, **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: dict, results: list) -> dict[str, Any]:
        """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.
        """
        raise NotImplementedError

    @deprecated("not used anymore")
    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
        """
        return True

    @deprecated("not used anymore")
    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
        """
        return True

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

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

    @classmethod
    def count_words(cls, doc: str) -> int:
        """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, **kwargs):
        """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) -> list[Instance] | None:
        """Iterates over FilterEnsembles and applies them to instances"""
        if hasattr(self, "_filters") and self._instances:
            for f in self._filters:
                f.apply(self._instances)
        else:
            eval_logger.warning(
                "No filter defined or no instances, 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 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.
        """
        # 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)
        #     }
        self._config.metric_list = [MetricConfig(name=metric_name)]
        self._config.process_results = lambda *args: {"bypass": 0}

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

    @property
    def eval_docs(self) -> datasets.Dataset | Iterable[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: int | None = None,
        world_size: int = 1,
        samples: list[int] | None = None,
    ) -> Iterator[tuple[int, Any]]:
        if samples:
            n = len(self.eval_docs)
            assert all(e < n for e in samples), (
                f"Elements of --samples should be in the interval [0,k-1] where k is the number of total examples. In this case, k={n}."
            )
            eval_logger.info(
                f"{self.config.task}: Evaluating on {len(samples)} examples"
            )
            doc_iterator = utils.create_iterator(
                enumerate(x for i, x in enumerate(self.eval_docs) if i in samples),
                rank=int(rank),
                limit=None,  # limit does not matter here since we are selecting samples directly
                world_size=int(world_size),
            )
        else:
            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


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

    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config: Mapping[str, Any] | None = None,
    ) -> None:
        # Get pre-configured attributes
        self._config = self.CONFIG

        # Use new configurations if there was no preconfiguration
        if self.config is None:
            self._config = TaskConfig.from_yaml(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) and "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.doc_to_image is not None:
            # mark the task as requiring multimodality.
            self.MULTIMODAL = True

        if self.config.doc_to_audio:
            # mark the task as requiring multimodality.
            self.MULTIMODAL = True

        if self.config.unsafe_code is not False:
            self.UNSAFE_CODE = True

        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_list: list[MetricConfig] = self.config.get_metrics

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

        self._filters = self.config.get_filters

        # 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.config.fewshot_cfg.num_fewshot() > 0
            and self.fewshot_docs() is not None
        ):
            self.fewshot_rnd = random.Random()
            self.sampler = self.config.fewshot_cfg.init_sampler(
                list(self.fewshot_docs()), self, rnd=self.fewshot_rnd
            )
        self.task_docs = self.eval_docs

        # for name, fn in self.config._fn.items():
        #     if hasattr(self, name):
        #         setattr(
        #             self,
        #             name,
        #             types.MethodType(
        #                 lambda self, *args, _fn=fn, **kwargs: _fn(*args, **kwargs),
        #                 self,
        #             ),
        #         )

        self.runtime_checks(self.task_docs[0])

        print("hello")

    def download(self, dataset_kwargs: dict[str, Any] | None = None, **kwargs) -> None:
        from packaging.version import parse as vparse

        if dataset_kwargs and vparse(datasets.__version__) >= vparse("4.0.0"):
            dataset_kwargs.pop("trust_remote_code", None)

        self.config.dataset_kwargs, self.config.metadata = (
            self.config.dataset_kwargs or {},
            self.config.metadata or {},
        )
        if isinstance(df := self.config.custom_dataset, Callable):
            eval_logger.warning(
                f"{self.config.task}: Custom kwargs can be passed to `--metadata` in console (as json string) or to the TaskManager."
                + "\nFor example --metadata='{\"max_seq_lengths\":[4096, 8192]}'. For details see task Readme."
            )
            self.dataset = df(**(self.config.dataset_kwargs | self.config.metadata))
        else:
            assert self.config.dataset_path is not None, (
                "dataset_path must be set in TaskConfig"
            )
            self.dataset = datasets.load_dataset(
                path=self.config.dataset_path,
                name=self.config.dataset_name,
                **self.config.dataset_kwargs,
            )

    @cached_property
    def has_training_docs(self) -> bool:
        return self.config.training_split is not None

    @cached_property
    def has_validation_docs(self) -> bool:
        return self.config.validation_split is not None

    @cached_property
    def has_test_docs(self) -> bool:
        return self.config.test_split is not None

    def training_docs(self) -> DataSet | None:
        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) -> DataSet | None:
        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) -> DataSet | None:
        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):
        docs = self.config.fewshot_cfg.get_docs(self.dataset)

        if docs is not None:
            return docs

        # Fallback to parent implementation
        if (
            (_num_fewshot := self.config.num_fewshot)
            and isinstance(_num_fewshot, int)
            and _num_fewshot > 0
        ):
            eval_logger.warning(
                f"[Task: {self.config.task}] "
                "num_fewshot > 0 but no fewshot source configured. "
                "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,
        gen_prefix: str | None = None,
    ) -> 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})
        if gen_prefix:
            labeled_examples.append({"role": "assistant", "content": gen_prefix})

    @utils.positional_deprecated
    def fewshot_context(
        self,
        doc: dict,
        num_fewshot: int,
        system_instruction: str | None = None,
        apply_chat_template: bool = False,
        fewshot_as_multiturn: bool = False,
        chat_template: Callable | None = None,
        gen_prefix: str | None = None,
    ) -> str | list[str] | 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  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 chat_template:
            callable (from lm.apply_chat_template) that takes in a list[Dict] chat transcript and renders it into a string.
        :param gen_prefix:
            String to append after the <|assistant|> token.
        :returns: str
            The fewshot context.
        """
        labeled_examples = [] if apply_chat_template else ""

        # 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,
                        gen_prefix=gen_prefix,
                    )
                )
            else:
                labeled_examples += self.sampler.get_context(
                    doc, num_fewshot, gen_prefix=gen_prefix
                )

        example = self.doc_to_text(doc)
        if apply_chat_template:
            if self.multiple_input:
                # TODO: append prefill?
                if not labeled_examples:
                    return ""
                return chat_template(labeled_examples)
            if isinstance(example, str):
                self.append_target_question(
                    labeled_examples,
                    example,
                    fewshot_as_multiturn,
                    gen_prefix=gen_prefix,
                )
            # 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,
                        gen_prefix=gen_prefix,
                    )
                    # TODO: append prefill?
                    labeled_examples_list.append(
                        chat_template(
                            chat,
                            add_generation_prompt=not gen_prefix,
                        )
                    )
                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,
                        gen_prefix=gen_prefix,
                    )
                else:
                    self.append_target_question(
                        labeled_examples,
                        str(example),
                        fewshot_as_multiturn,
                        gen_prefix=gen_prefix,
                    )
                # return lm.apply_chat_template(labeled_examples)
            return chat_template(
                labeled_examples,
                add_generation_prompt=not gen_prefix,
            )
        else:
            prefix = (
                self.config.target_delimiter + gen_prefix
                if gen_prefix is not None
                else ""
            )
            if self.multiple_input:
                return labeled_examples
            if isinstance(example, str):
                return labeled_examples + example + prefix
            elif isinstance(example, list):
                return [labeled_examples + ex + prefix 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] + prefix
                else:
                    return labeled_examples + str(example) + prefix

    def apply_filters(self) -> list[Instance] | None:
        """Iterates over FilterEnsembles and applies them to instances"""
        if hasattr(self, "_filters") and self._instances:
            for f in self._filters:
                f.ensemble.apply(self._instances)
        else:
            eval_logger.warning(
                "No filter defined or instances found. Passing through instances"
            )
            return self._instances

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

    def doc_to_decontamination_query(self, doc: dict):
        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

    @overload
    def doc_to_text(self, doc: dict, doc_to_text: None = None) -> str | int: ...

    @overload
    def doc_to_text(self, doc: dict, doc_to_text: int) -> int: ...

    @overload
    def doc_to_text(self, doc: dict, doc_to_text: str) -> str: ...

    @overload
    def doc_to_text(self, doc: dict, doc_to_text: Callable[..., str]) -> str: ...

    def doc_to_text(
        self, doc: dict, doc_to_text: int | str | Callable[..., str] | None = None
    ) -> str | int:
        # if self.prompt is not None:
        #     doc_to_text = self.prompt
        doc_to_text = doc_to_text or self.config.doc_to_text
        if callable(doc_to_text):
            return doc_to_text(doc)
        if doc_to_text in doc:
            return doc[doc_to_text]
        elif isinstance(doc_to_text, str):
            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 isinstance(doc_to_text, int):
            return doc_to_text
        # 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

    @overload
    def doc_to_target(
        self, doc: dict, doc_to_target: None = None
    ) -> int | str | list[int]: ...

    @overload
    def doc_to_target(self, doc: dict, doc_to_target: int) -> int: ...

    @overload
    def doc_to_target(self, doc: dict, doc_to_target: str) -> int | str | list[int]: ...

    @overload
    def doc_to_target(self, doc: dict, doc_to_target: list) -> list[int]: ...

    @overload
    def doc_to_target(
        self, doc: dict, doc_to_target: Callable[..., int | str | list[int]]
    ) -> int | str | list[int]: ...

    def doc_to_target(self, doc: dict, doc_to_target=None) -> int | str | list[int]:
        # if self.prompt is not None:
        #     doc_to_target = self.prompt
        doc_to_target = doc_to_target or self.config.doc_to_target
        if callable(doc_to_target):
            doc_to_target(doc)
        if doc_to_target in doc:
            return doc[doc_to_target]
        elif isinstance(doc_to_target, str):
            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, (int, list)):
            return doc_to_target
        # 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

    @overload
    def doc_to_choice(self, doc: dict, doc_to_choice: None = None) -> list[str]: ...

    @overload
    def doc_to_choice(self, doc: dict, doc_to_choice: str) -> list[str]: ...

    @overload
    def doc_to_choice(self, doc: dict, doc_to_choice: list) -> list[str]: ...

    @overload
    def doc_to_choice(self, doc: dict, doc_to_choice: dict) -> list[str]: ...

    @overload
    def doc_to_choice(
        self, doc: dict, doc_to_choice: Callable[..., list[str]]
    ) -> list[str]: ...

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

        if isinstance(doc_to_choice, str):
            if doc_to_choice in doc:
                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 hasattr(doc_to_choice, "get_answer_choices_list"):
        #     return doc_to_choice.get_answer_choices_list(doc)
        else:
            raise TypeError

    @overload
    def doc_to_image(self, doc: dict, doc_to_image: None = None) -> None: ...

    @overload
    def doc_to_image(self, doc: dict, doc_to_image: list) -> list: ...

    @overload
    def doc_to_image(self, doc: dict, doc_to_image: str) -> int | str | None: ...

    @overload
    def doc_to_image(self, doc: dict, doc_to_image: Callable[..., Any]) -> Any: ...

    def doc_to_image(self, doc: dict, doc_to_image=None) -> int | str | list | None:
        if doc_to_image is not None:
            doc_to_image = doc_to_image
        elif self.config.doc_to_image is not None:
            doc_to_image = self.config.doc_to_image
        else:
            return None

        if isinstance(doc_to_image, list):
            image_feature = [
                self.doc_to_image(doc, feature) for feature in doc_to_image
            ]
            return [feature for feature in image_feature if feature is not None]
        elif isinstance(doc_to_image, str):
            if doc_to_image in self.features:
                return doc[doc_to_image]
            else:
                return ast.literal_eval(utils.apply_template(doc_to_image, doc))
        elif callable(doc_to_image):
            return doc_to_image(doc)
        else:
            return None

    @overload
    def doc_to_audio(self, doc: Any, doc_to_audio: None = None) -> None: ...

    @overload
    def doc_to_audio(self, doc: Any, doc_to_audio: list) -> list: ...

    @overload
    def doc_to_audio(self, doc: Any, doc_to_audio: str) -> int | str | None: ...

    @overload
    def doc_to_audio(self, doc: Any, doc_to_audio: Callable[..., Any]) -> Any: ...

    def doc_to_audio(self, doc: Any, doc_to_audio=None) -> int | str | list | None:
        if doc_to_audio is not None:
            doc_to_audio = doc_to_audio
        elif self.config.doc_to_audio is not None:
            doc_to_audio = self.config.doc_to_audio
        else:
            return None

        if isinstance(doc_to_audio, list):
            audio_feature = [
                self.doc_to_audio(doc, feature) for feature in doc_to_audio
            ]
            return [feature for feature in audio_feature if feature is not None]
        elif isinstance(doc_to_audio, str):
            if doc_to_audio in self.features:
                return doc[doc_to_audio]
            else:
                return ast.literal_eval(utils.apply_template(doc_to_audio, doc))
        elif callable(doc_to_audio):
            return doc_to_audio(doc)
        else:
            return None

    def doc_to_prefix(self, doc: dict) -> str | None:
        if (gen_prefix := self.config.gen_prefix) is not None:
            if gen_prefix in doc:
                return doc[gen_prefix]
            else:
                return utils.apply_template(gen_prefix, doc)
        return None

    def construct_requests(
        self, doc: dict, ctx: str, **kwargs
    ) -> list[Instance] | Instance:
        apply_chat_template = kwargs.pop("apply_chat_template", False)
        chat_template: Callable | None = kwargs.pop("chat_template", None)

        aux_arguments = None

        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 apply_chat_template:
                target_delimiter = ""
            if self.multiple_input:
                # If there are multiple inputs, choices are placed in the ctx
                # apply chat_template to choices if apply_chat_template
                cont = self.doc_to_target(doc)

                arguments = [
                    (
                        ctx
                        + (
                            chat_template([{"role": "user", "content": choice}])
                            if apply_chat_template
                            else 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]

            # TODO: we should raise a warning telling users this will at most ~2x runtime.
            if "acc_mutual_info" in [m.metric_name for m in self.config._metric_list]:
                # 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.
                # TODO: should these be strided? will have to modify the processing in process_results if so
                aux_arguments = [
                    ("", f"{target_delimiter}{choice}") for choice in choices
                ]

                arguments.extend(aux_arguments)

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

        multimodal_arg = {}
        if (
            self.config.doc_to_image
        ):  # TODO: ensure that non-multimodal tasks aren't getting visual args
            multimodal_arg = {
                **multimodal_arg,
                **{"visual": self.doc_to_image(doc)},
            }

        if (
            self.config.doc_to_audio
        ):  # TODO: ensure that non-multimodal tasks aren't getting audio args
            multimodal_arg = {
                **multimodal_arg,
                **{"audio": self.doc_to_audio(doc)},
            }

        if bool(multimodal_arg):
            if isinstance(arguments, list):
                arguments = [arg + (multimodal_arg,) for arg in arguments]
            else:
                arguments = arguments + (multimodal_arg,)

        if self.OUTPUT_TYPE == "multiple_choice":
            request_list = [
                Instance(
                    request_type="loglikelihood",
                    doc=doc,
                    arguments=arg,
                    idx=i,
                    **kwargs,
                )
                for i, arg in enumerate(arguments)
            ]

            return request_list

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

    def process_results(self, doc: dict, results: list) -> dict[str, Any]:
        if callable(self.config.process_results):
            return self.config.process_results(doc, results)
        result_dict = {}
        use_metric = list(m.metric_name for m in self.config._metric_list)
        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
            assert isinstance(_target := self.doc_to_target(doc), str), (
                "Require target to be a string for loglikelihood_rolling"
            )
            _words = self.count_words(_target)
            _bytes = self.count_bytes(_target)
            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 use_metric:
                # then we are doing mutual info.
                # this stores the "dryrun" / unconditional answer loglikelihoods
                # as we extend the args list with unconditional ("", continuation) pairs
                lls_unconditional = lls[len(choices) :]
                if len(lls_unconditional) != len(choices):
                    raise ValueError
                # and this stores our "regular" conditional loglikelihoods
                lls = lls[: len(choices)]
            else:
                lls_unconditional = None

            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, gold_index_error = check_gold_index_error(choices, gold)

            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:
                assert lls_unconditional is not None, (
                    "lls_unconditional should not be None if acc_mutual_info is 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]
            for metric in self.config._metric_list:
                try:
                    result_score = metric.fn(
                        references=[gold] if not isinstance(gold, list) else gold,
                        predictions=[result],
                        **metric.kwargs,
                    )
                except TypeError:  # needed for now in order to use a different interface between our own metrics and HF Evaluate metrics
                    result_score = metric.fn([gold, result])
                if isinstance(result_score, dict):
                    # TODO: this handles the case where HF evaluate returns a dict.
                    # This allows for multiple metrics to be returned from the same function
                    for k, v in result_score.items():
                        result_dict[k] = v
                else:
                    result_dict[metric.name] = 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 {k.name: k.aggregation_fn for k in self.config._metric_list}

    def higher_is_better(self) -> dict:
        return {k.name: k.higher_is_better for k in self.config._metric_list}

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

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

    def runtime_checks(self, test_doc):
        # Test One Doc
        self.features: list[str] = list(self.task_docs.features.keys())
        self.multiple_target = 0
        self.multiple_input = 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):
                eval_logger.debug(
                    "doc_to_text returned an int. Assuming multiple inputs."
                )

            if isinstance(test_text, int):
                eval_logger.debug(
                    "doc_to_text returned an int. Assuming multiple inputs."
                )
                self.multiple_input = num_choice
        else:
            test_choice = None

        if isinstance(test_target, list):
            eval_logger.debug(
                "doc_to_target returned a list. Assuming multiple targets."
            )
            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)

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

                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 __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, f" {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: str | None, **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))