task.py 34.4 KB
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import abc
from dataclasses import dataclass

import re
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import ast
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import yaml
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import evaluate
import random
import itertools
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import functools
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import datasets
import numpy as np

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from typing import Union
from collections.abc import Callable
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from lm_eval import utils
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from lm_eval.api import samplers
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from lm_eval.api.instance import Instance
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from lm_eval.api.filter import FilterEnsemble
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from lm_eval.logger import eval_logger
from lm_eval.prompts import get_prompt
from lm_eval.filters import build_filter_ensemble
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from lm_eval.api.metrics import (
    # get_metric,
    # get_aggregation,
    mean,
    weighted_perplexity,
    bits_per_byte,
)
from lm_eval.api.registry import (
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    METRIC_REGISTRY,
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    DEFAULT_METRIC_REGISTRY,
    OUTPUT_TYPE_REGISTRY,
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    AGGREGATION_REGISTRY,
    HIGHER_IS_BETTER_REGISTRY,
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    DEFAULT_AGGREGATION_REGISTRY,
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)
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ALL_OUTPUT_TYPES = [
    "loglikelihood",
    "multiple_choice",
    "loglikelihood_rolling",
    "greedy_until",
]

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@dataclass
class TaskConfig(dict):

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    task: str = None
    group: str = None
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    names: str = None
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    reference: str = None
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    task_name: str = (
        None  # TODO: deprecate this, it'll be set in __post_init__ to be names[0]
    )
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    dataset_path: str = None
    dataset_name: str = None
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    dataset_kwargs: dict = None
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    training_split: str = None
    validation_split: str = None
    test_split: str = None
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    fewshot_split: str = None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
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    template_aliases: str = None
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    aliases: Union[str, list] = None
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    doc_to_text: Union[Callable, str] = None
    doc_to_target: Union[Callable, str] = None
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    num_fewshot: int = 0
    batch_size: int = 1
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    repeats: int = 1

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    metric_list: str = None
    gold_alias: str = None
    output_type: str = "greedy_until"
    delimiter: str = "\n\n"
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    filter_list: Union[str, list] = None
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    normalization: str = (
        None  # TODO: add length-normalization of various types, mutual info
    )
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    should_decontaminate: bool = False
    doc_to_decontamination_query: str = None
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    use_prompt: str = None
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    metadata: str = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks
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    def __post_init__(self):
        # allow user-specified aliases so that users can
        # force prompt-compatibility for some prompt regardless of
        # field names in prompt
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        if self.template_aliases is not None:
            if type(self.doc_to_text) == str:
                self.doc_to_text = self.template_aliases + self.doc_to_text
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            if type(self.doc_to_target) == str:
                self.doc_to_target = self.template_aliases + self.doc_to_target
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        # set "task_name" metadata field based on the "primary" name set
        if self.names:
            self.task_name = self.names[0]
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    def __getitem__(self, item):
        return getattr(self, item)


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 = None
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    # 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

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

    OUTPUT_TYPE: str = None
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    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config=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
            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.download(data_dir, cache_dir, download_mode)
        self._training_docs = None
        self._fewshot_docs = None
        self._instances = None

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        self._config = TaskConfig(**config) if config else TaskConfig()
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        if not hasattr(self, "_filters"):
            self._filters = []
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            for name, components in self._config.get(
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                "filters", [["none", [["take_first", None]]]]
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            ):
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                filter_pipeline = build_filter_ensemble(name, components)
                self._filters.append(filter_pipeline)

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        self.sampler = samplers.Sampler(
            list(self.fewshot_docs()), self, rnd=random.Random()
        )  # TODO: pass the correct docs in here
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    def download(self, data_dir=None, cache_dir=None, download_mode=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.
        """
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        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,
        )
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    @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):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

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

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

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    def fewshot_docs(self):
        """
        :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:
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            eval_logger.warning(
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                "has_training_docs and has_validation_docs are False"
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                "using test_docs but this is not recommended."
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            )
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            return self.test_docs()

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    def _process_doc(self, doc):
        """
        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):
        """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):
        print(
            "Override doc_to_decontamination_query with document specific decontamination query."
        )
        assert False

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

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

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    def build_all_requests(self, limit=None, rank=None, world_size=None):
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        """Build a set of Instances for a task, and store them in task.instances"""
        if self.has_test_docs():
            docs = self.test_docs()
        elif self.has_validation_docs():
            docs = self.validation_docs()
        else:
            assert (
                False
            ), f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"

        instances = []
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        for doc_id, doc in utils.create_iterator(
            enumerate(docs), rank, world_size, limit
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        ):
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            # sample fewshot context #TODO: need to offset doc_id by rank now!
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            fewshot_ctx = self.fewshot_context(
                doc, self._config.num_fewshot, rnd=random.Random()
            )
            # TODO: hardcoded for now: # of runs on each input to be 2. # TODO: we should override this if doing greedy gen so users don't waste time+compute
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            inst = self.construct_requests(
                doc=doc,
                ctx=fewshot_ctx,
                metadata=(self._config["task_name"], doc_id, self._config.repeats),
            )
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            if not isinstance(inst, list):
                inst = [inst]

            instances.extend(inst)

        self._instances = instances
        assert len(self._instances) != 0, "task.build_requests() did not find any docs!"

    @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
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            The number of times each instance in a dataset is inferred on. Defaults to 1,
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            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

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    @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))

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    @utils.positional_deprecated
    def fewshot_context(self, doc, num_fewshot, rnd=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`.
        :returns: str
            The fewshot context.
        """
        assert (
            rnd is not None
        ), "A `random.Random` generator argument must be provided to `rnd`"

        if num_fewshot == 0:
            labeled_examples = ""
        else:
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            labeled_examples = self.sampler.get_context(doc, self._config.num_fewshot)
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            # for sets with no training docs, draw from other set *but ensure no overlap with current doc*
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            # 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"
            # )
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        example = self.doc_to_text(doc)
        return labeled_examples + example

    def apply_filters(self):

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        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
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class ConfigurableTask(Task):

    VERSION = "2.0"
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    OUTPUT_TYPE = None
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    CONFIG = None
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    def __init__(
        self, data_dir=None, cache_dir=None, download_mode=None, config: dict = None
    ):
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        # Get pre-configured attributes
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        self._config = self.CONFIG
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        # Use new configurations if there was no preconfiguration
        if self._config is None:
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            self._config = TaskConfig(**config)
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        # Overwrite configs
        else:
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            if config is not None:
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                self._config.__dict__.update(config)
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        if self._config is None:
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            raise ValueError(
                "Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg"
            )
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        if self._config.output_type is not None:
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            assert self._config.output_type in ALL_OUTPUT_TYPES
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            self.OUTPUT_TYPE = self._config.output_type

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        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

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        self._metric_fn_list = {}
        self._metric_fn_kwargs = {}
        self._aggregation_list = {}
        self._higher_is_better = {}
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        _metric_list = DEFAULT_METRIC_REGISTRY[self._config.output_type]
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        if self._config.metric_list is None:
            eval_logger.warning(
                f"Output Type set as {self._config.output_type} and metric_list is not set"
                "Will default to exact_match"
            )
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            for metric_name in _metric_list:
                self._metric_fn_list[metric_name] = METRIC_REGISTRY[metric_name]
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                self._aggregation_list[metric_name] = DEFAULT_AGGREGATION_REGISTRY[
                    metric_name
                ]
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                self._higher_is_better[metric_name] = HIGHER_IS_BETTER_REGISTRY[
                    metric_name
                ]
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        else:
            for metric_config in self._config.metric_list:
                assert "metric" in metric_config
                metric_name = metric_config["metric"]
                kwargs = {
                    key: metric_config[key]
                    for key in metric_config
                    if key not in ["metric", "aggregation", "higher_is_better"]
                }
                if metric_name in _metric_list:
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                    self._metric_fn_list[metric_name] = METRIC_REGISTRY[metric_name]
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                else:
                    eval_logger.warning(
                        f"Metric {metric_name} not found, "
                        "Searching from https://huggingface.co/evaluate-metric"
                    )
                    try:
                        metric_object = evaluate.load(metric_name)
                        self._metric_fn_list[metric_name] = metric_object
                        self._metric_fn_kwargs[metric_name] = kwargs

                    except Exception:
                        raise Warning(
                            "{} not found in the evaluate library!".format(metric_name),
                            "Please check https://huggingface.co/evaluate-metric",
                        )
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                if "aggregation" in metric_config:
                    self._aggregation_list[metric_name] = metric_config["aggregation"]
                else:
                    eval_logger.warning(
                        f"metric {metric_name} is defined, but aggregation is not"
                        f"using default aggregation for {metric_name}"
                    )
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                    self._aggregation_list[metric_name] = DEFAULT_AGGREGATION_REGISTRY[
                        metric_name
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                    ]

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                if "higher_is_better" in metric_config:
                    self._higher_is_better[metric_name] = metric_config[
                        "higher_is_better"
                    ]
                else:
                    eval_logger.warning(
                        f"metric {metric_name} is defined, but higher_is_better is not"
                        f"using default higher_is_better for {metric_name}"
                    )
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                    self._higher_is_better[metric_name] = HIGHER_IS_BETTER_REGISTRY[
                        metric_name
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                    ]
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        self.download(self._config.dataset_kwargs)
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        self._training_docs = None
        self._fewshot_docs = None

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        if self._config.filter_list is not None:
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            self._filters = []
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            for filter_config in self._config.filter_list:
                for filter_pipeline in filter_config:
                    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"
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                        }
                        components.append([function["function"], kwargs])

                    filter_pipeline = build_filter_ensemble(filter_name, components)
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                self._filters.append(filter_pipeline)
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        else:
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            self._filters = [
                build_filter_ensemble("take_first", [["take_first", None]])
            ]
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        if self._config.use_prompt is not None:
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            eval_logger.info(f"loading prompt {self._config.use_prompt}")
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            self.prompt = get_prompt(
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                self._config.use_prompt, self.DATASET_PATH, self.DATASET_NAME
            )
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        else:
            self.prompt = None

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        if self.fewshot_docs() is not None:
            self.sampler = samplers.Sampler(
                list(self.fewshot_docs()), self, rnd=random.Random()
            )  # TODO: pass the correct docs in here
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    def download(self, dataset_kwargs=None):

        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            **dataset_kwargs if dataset_kwargs is not None else {},
        )

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    def has_training_docs(self):
        if self._config.training_split is not None:
            return True
        else:
            return False

    def has_validation_docs(self):
        if self._config.validation_split is not None:
            return True
        else:
            return False

    def has_test_docs(self):
        if self._config.test_split is not None:
            return True
        else:
            return False

    def training_docs(self):
        if self._config.training_split is not None:
            return self.dataset[self._config.training_split]

    def validation_docs(self):
        if self._config.validation_split is not None:
            return self.dataset[self._config.validation_split]

    def test_docs(self):
        if self._config.test_split is not None:
            return self.dataset[self._config.test_split]

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    def fewshot_docs(self):
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        if (self._config.num_fewshot > 0) and (self._config.fewshot_split is None):
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            eval_logger.warning(
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                "num_fewshot > 0 but fewshot_split is None. "
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                "using preconfigured rule."
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            )
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            return super().fewshot_docs()

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        elif self._config.fewshot_split is not None:
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            return self.dataset[self._config.fewshot_split]

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

    def doc_to_decontamination_query(self, doc):
        if self._config.should_decontaminate:
            return utils.apply_template(self._config.doc_to_decontamination_query, doc)

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    def _process_doc(self, doc):
        """
        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):
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        if self.prompt is not None:
            doc_to_text = self.prompt
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        else:
            doc_to_text = self._config.doc_to_text
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        if type(doc_to_text) == str:
            return utils.apply_template(doc_to_text, doc)
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        elif callable(doc_to_text):
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            return doc_to_text(doc)
        if hasattr(doc_to_text, "apply"):
            return doc_to_text.apply(doc)[0]
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        else:
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            print(type(doc_to_text))
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            raise TypeError
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    def doc_to_target(self, doc):
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        if self.prompt is not None:
            doc_to_target = self.prompt
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        else:
            doc_to_target = self._config.doc_to_target

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        if type(doc_to_target) == str:
            return utils.apply_template(doc_to_target, doc)
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        elif callable(doc_to_target):
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            return doc_to_target(doc)
        elif hasattr(doc_to_target, "apply"):
            return doc_to_target.apply(doc)[1]
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        else:
            raise TypeError
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    def construct_requests(self, doc, ctx, **kwargs):

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        if self.OUTPUT_TYPE == "loglikelihood":
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            arguments = (ctx, self.doc_to_target(doc))
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        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
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            arguments = (self.doc_to_target(doc),)
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        elif self.OUTPUT_TYPE == "multiple_choice":
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            # we pass the user-defined answer_choices var (in aliases) and translate the result to a Python list.
            # TODO: any cleaner way to do this?
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            choices = ast.literal_eval(
                utils.apply_template(
                    self._config.template_aliases + "{{answer_choices}}", doc
                )
            )
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            request_list = [
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                Instance(
                    request_type="loglikelihood",
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                    doc=doc,
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                    arguments=(ctx, " {}".format(choice)),
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                    idx=i,
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                    **kwargs,
                )
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                for i, choice in enumerate(choices)
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            ]
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            # TODO: we should raise a warning telling users this will at most ~2x runtime.
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            if "acc_mutual_info" in self._metric_fn_list.keys():
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                # if we are calculating multiple choice accuracy
                # using mutual information instead of raw loglikelihood as metric, need unconditional lls.

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                # here mutual info refers to calculating
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                # 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",
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                            doc=doc,
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                            arguments=("", "{}".format(choice)),
                            idx=i,
                            **kwargs,
                        )
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                        for i, choice in enumerate(choices)
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                    ]
                )
            return request_list
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        elif self.OUTPUT_TYPE == "greedy_until":
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            arguments = (ctx, self._config.delimiter)
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        return Instance(
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            request_type=self.OUTPUT_TYPE, doc=doc, arguments=arguments, idx=0, **kwargs
        )
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    def process_results(self, doc, results):

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        # if callable(self._config.process_results):
        #     return self._config.process_results(doc, results)

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        result_dict = {}
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        use_metric = list(self._metric_fn_list.keys())
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        if self.OUTPUT_TYPE == "loglikelihood":
            results = results[0]
            ll, is_greedy = results
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            return {
                **({"perplexity": ll} if "perplexity" in use_metric else {}),
                **({"acc": int(is_greedy)} if "acc" in use_metric else {}),
            }
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        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
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            (loglikelihood,) = results
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            _words = self.count_words(self.doc_to_target(doc))
            _bytes = self.count_bytes(self.doc_to_target(doc))
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            return {
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                **(
                    {"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 {}
                ),
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            }
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        elif self.OUTPUT_TYPE == "multiple_choice":
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            lls, is_greedy = zip(*results)
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            gold = int(self.doc_to_target(doc))
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            pred = np.argmax(lls)
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            # retrieve choices in List[str] form, to compute choice lengths, etc.
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            choices = ast.literal_eval(
                utils.apply_template(
                    self._config.template_aliases + "{{answer_choices}}", doc
                )
            )
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            acc = 1.0 if np.argmax(lls) == gold else 0.0
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            completion_len = np.array([float(len(i)) for i in choices])
            acc_norm = 1.0 if np.argmax(lls / completion_len) == gold else 0.0
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            result_dict = {
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                **({"acc": acc} if "acc" in use_metric else {}),
                **({"f1": (pred, gold)} if "f1" in use_metric else {}),
                **({"acc_norm": acc_norm} if "acc_norm" in use_metric else {}),
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            }

            # TODO: set which normalization metrics should be reported, and calculate them
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            if "exact_match" in self._metric_fn_list.keys():
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                # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly
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                is_greedy = is_greedy[gold]  # take value for the gold answer
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                result_dict["exact_match"] = int(is_greedy)

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

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

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        elif self.OUTPUT_TYPE == "greedy_until":

            if self._config.gold_alias is not None:
                gold = doc[self._config.gold_alias]
            else:
                gold = self.doc_to_target(doc)

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            for key, result in zip(self._metric_fn_list.keys(), results):
                _dict = self._metric_fn_list[key].compute(
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                    references=[gold], predictions=[result], **self._metric_kwargs[key]
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                )
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                result_dict = {**result_dict, **_dict}
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        else:
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            raise ValueError(
                f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ",
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                "'loglikelihood', 'loglikelihood_rolling', 'greedy_until', or 'multiple_choice'",
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            )
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        return result_dict

    def aggregation(self):
        return self._aggregation_list

    def higher_is_better(self):
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        return self._higher_is_better
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class MultipleChoiceTask(Task):

    OUTPUT_TYPE: str = "loglikelihood"

    def doc_to_target(self, doc):
        return " " + doc["choices"][doc["gold"]]

    def construct_requests(self, doc, ctx, **kwargs):
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        # TODO: add mutual info here?
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        return [
            Instance(
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                request_type="loglikelihood",
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                doc=doc,
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                arguments=(ctx, " {}".format(choice)),
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                idx=i,
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                **kwargs,
            )
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            for i, choice in enumerate(doc["choices"])
        ]
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    def process_results(self, doc, results):
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        results = [
            res[0] for res in results
        ]  # only retain loglikelihoods, discard is_greedy TODO: do we need is_greedy anywhere?
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        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):
        return {
            "acc": True,
            "acc_norm": True,
        }

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


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class PerplexityTask(Task):
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    OUTPUT_TYPE = "loglikelihood_rolling"

    def has_training_docs(self):
        return False

    def fewshot_examples(self, k, rnd):
        assert k == 0
        return []

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    def fewshot_context(self, doc, num_fewshot, rnd=None):
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        assert (
            num_fewshot == 0
        ), "The number of fewshot examples must be 0 for perplexity tasks."
        assert (
            rnd is not None
        ), "A `random.Random` generator argument must be provided to `rnd`."

        return ""

    def higher_is_better(self):
        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):
        return ""

    def doc_to_target(self, doc):
        return doc

    def construct_requests(self, doc, ctx, **kwargs):
        assert not ctx

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        return Instance(
            request_type=self.OUTPUT_TYPE,
            doc=doc,
            arguments=(self.doc_to_target(doc),),
            idx=0,
            **kwargs,
        )
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    def process_results(self, doc, results):
        (loglikelihood,) = results
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        words = self.count_words(self.doc_to_target(doc))
        bytes_ = self.count_bytes(self.doc_to_target(doc))
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        return {
            "word_perplexity": (loglikelihood, words),
            "byte_perplexity": (loglikelihood, bytes_),
            "bits_per_byte": (loglikelihood, bytes_),
        }

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

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

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