import abc
import ast
import copy
import inspect
import itertools
import json
import os
import random
import re
import shutil
import subprocess
from collections.abc import Callable
from dataclasses import asdict, dataclass, field
from functools import partial
from glob import glob
from typing import (
    Any,
    Dict,
    Iterable,
    Iterator,
    List,
    Literal,
    Mapping,
    Optional,
    Tuple,
    Union,
)

import datasets
import numpy as np
from accelerate import Accelerator
from datasets import Audio, DownloadConfig, Image, Sequence
from huggingface_hub import snapshot_download
from loguru import logger as eval_logger
from PIL import ImageFile
from tenacity import retry, stop_after_attempt, stop_after_delay, wait_fixed
from tqdm import tqdm

from lmms_eval import utils
from lmms_eval.api import samplers
from lmms_eval.api.instance import Instance
from lmms_eval.api.registry import (
    AGGREGATION_REGISTRY,
    DEFAULT_METRIC_REGISTRY,
    METRIC_REGISTRY,
    OUTPUT_TYPE_REGISTRY,
    get_aggregation,
    get_metric,
    get_metric_aggregation,
    is_higher_better,
)
from lmms_eval.caching.cache import load_from_cache, save_to_cache
from lmms_eval.filters import build_filter_ensemble

# HuggingfaceM4/NoCaps contains truncated image in test split
# Include this inside code block to avoid error
ImageFile.LOAD_TRUNCATED_IMAGES = True

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


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

    metadata: Union[str, list] = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks

    lmms_eval_specific_kwargs: dict = None
    model_specific_generation_kwargs: dict = None
    model_specific_target_kwargs: dict = None

    def __post_init__(self) -> None:
        if self.dataset_path and os.path.exists(os.path.dirname(self.dataset_path)):
            import inspect
            from importlib import import_module

            # self.dataset_path = inspect.getfile(import_module(self.dataset_path))

        if self.group is not None:
            eval_logger.warning(
                "A task YAML file was found to contain a `group` key. Groups which provide aggregate scores over several subtasks now require a separate config file--if not aggregating, you may want to use the `tag` config option instead within your config. Setting `group` within a TaskConfig will be deprecated in v0.4.4. Please see https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/task_guide.md for more information."
            )

            if self.tag is None:
                self.tag = self.group
            else:
                raise ValueError("Got both a `group` and `tag` entry within a TaskConfig. Please use one or the other--`group` values will be deprecated in v0.4.4.")

        if self.generation_kwargs is not None:
            if "generate_until" not in self.output_type:
                eval_logger.warning(f"[{self.task}] passed `generation_kwargs`, but not using `output_type: generate_until`!")
                assert "generate_until" not in self.output_type

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

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

        # TODO: how to make TaskConfigs be de- and re-serializable, even when using the !function constructor?

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

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

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

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

        # TODO: should any default value in the TaskConfig not be printed?
        """
        cfg_dict = asdict(self)
        # remove values that are `None`
        for k, v in list(cfg_dict.items()):
            if v is None:
                cfg_dict.pop(k)
            elif isinstance(v, Callable):
                # TODO: this should handle Promptsource template objects as a separate case?
                cfg_dict[k] = str(v)
        return cfg_dict


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

    # 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

    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config=None,
    ) -> 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 = None
        self._fewshot_docs = None
        self._instances = None

        self._config = TaskConfig({**config}) if config else TaskConfig()

        self._filters = [build_filter_ensemble("none", [["take_first", None]])]

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

        :param data_dir: str
            Stores the path to a local folder containing the `Task`'s data files.
            Use this to specify the path to manually downloaded data (usually when
            the dataset is not publicly accessible).
        :param cache_dir: str
            The directory to read/write the `Task` dataset. This follows the
            HuggingFace `datasets` API with the default cache directory located at:
                `~/.cache/huggingface/datasets`
            NOTE: You can change the cache location globally for a given process
            by setting the shell environment variable, `HF_DATASETS_CACHE`,
            to another directory:
                `export HF_DATASETS_CACHE="/path/to/another/directory"`
        :param download_mode: datasets.DownloadMode
            How to treat pre-existing `Task` downloads and data.
            - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS`
                Reuse download and reuse dataset.
            - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS`
                Reuse download with fresh dataset.
            - `datasets.DownloadMode.FORCE_REDOWNLOAD`
                Fresh download and fresh dataset.
        """
        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            data_dir=data_dir,
            cache_dir=cache_dir,
            download_mode=download_mode,
        )
        self.dataset_no_image = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            data_dir=data_dir,
            cache_dir=cache_dir,
            download_mode=download_mode,
        )
        for doc_name in self.dataset_no_image:
            remove_cols = []
            features = self.dataset_no_image[doc_name].features
            # If it is an Image instance or a Sequence of Image instance. Remove it
            for feature in features:
                if isinstance(features[feature], Image):
                    remove_cols.append(feature)
                elif isinstance(features[feature], Sequence) and isinstance(features[feature].feature, Image):
                    remove_cols.append(feature)
            for remove_col in remove_cols:
                self.dataset_no_image[doc_name] = self.dataset_no_image[doc_name].remove_columns(remove_col)

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

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

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

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

    def training_docs(self):
        """
        :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 []

    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:
            if self.config.num_fewshot is not None:
                eval_logger.warning("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):
        """
        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) -> None:
        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

    # @profile
    def build_all_requests(
        self,
        *,
        limit: Union[int, None] = None,
        rank: int = 0,
        world_size: int = 1,
        cache_requests: bool = False,
        rewrite_requests_cache: bool = False,
        system_instruction: Optional[str] = None,
        apply_chat_template: bool = False,
        fewshot_as_multiturn: bool = False,
        chat_template: Optional[Callable] = None,
        tokenizer_name: str = "",
    ) -> None:
        """Build a set of Instances for a task, and store them in task.instances"""
        if self.has_test_docs():
            docs = self.test_docs()
            split = self.config.test_split
        elif self.has_validation_docs():
            docs = self.validation_docs()
            split = self.config.validation_split
        else:
            assert False, f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"

        # 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)

        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 = utils.create_iterator(enumerate(self.eval_docs_no_media), rank=rank, limit=int(limit) if limit else None, world_size=world_size)
        doc_iterator_for_counting = itertools.islice(range(len(self.test_docs())), rank, limit, world_size) if self.has_test_docs() else itertools.islice(range(len(self.validation_docs())), rank, limit, world_size)

        num_docs = sum(1 for _ in doc_iterator_for_counting)

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

            # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute
            per_task_metadata = {"task": self.config["task"], "doc_id": doc_id, "repeats": self.config.repeats, "split": split}
            if self.config.metadata and type(self.config.metadata) == dict:  # TODO: temporary fix for metadata loading, ignore the list of dict type.
                per_task_metadata.update(self.config.metadata)

            inst = self.construct_requests(doc_id=doc_id, ctx=fewshot_ctx, metadata=per_task_metadata)

            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)

        # FIXME: Bo - We need to check if the doc_to_visual if it's exists and restore it. If we use cache, the doc_to_visual will be None since it's not serializable
        for instance in self._instances:
            if instance.arguments[2] is None:
                arguments = (instance.arguments[0], instance.arguments[1], self.doc_to_visual, *instance.arguments[3:])
            else:
                arguments = instance.arguments

            instance.arguments = arguments

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

        :param doc_id: int
            The index of a document within `self.test_docs()` or `self.validation_docs()`,
            whichever is the main split used.
        :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 repeats: int
        TODO: update this docstring
            The number of times each instance in a dataset is inferred on. Defaults to 1,
            can be increased for techniques like majority voting.
        """
        pass

    @abc.abstractmethod
    def process_results(self, doc, results):
        """Take a single document and the LMM 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

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

    @utils.positional_deprecated
    def fewshot_context(
        self,
        doc_id,
        num_fewshot,
        split,
        rnd=random.Random(1234),
        description=None,
    ):
        """Returns a fewshot context string that is made up of a prepended description
        (if provided), the `num_fewshot` number of examples, and an appended prompt example.

        :param doc_id: int
            The document id 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 split: str
            The split of the document to retrieve from the dataset
        :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.
        """
        assert rnd is not None, "A `random.Random` generator argument must be provided to `rnd`"

        description = description if description else ""
        doc = self.dataset_no_image[split][doc_id]

        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):
        if hasattr(self, "_filters"):
            for f in self._filters:
                f.apply(self._instances, None)
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances

    def dump_config(self) -> dict:
        """Returns a dictionary representing the task's config.

        :returns: str
            The fewshot context.
        """
        # TODO: this should only return the overrides applied to a non-YAML task's configuration.
        # (num_fewshot)
        return self.config.to_dict()

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

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

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

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

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

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

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


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

    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config: Optional[dict] = None,
        model_name: Optional[str] = None,
    ) -> None:  # TODO no super() call here
        # Get pre-configured attributes
        self._config = self.CONFIG

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

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

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

        self.model_name = model_name
        self._prepare_model_specific_config()

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

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

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

        self._prepare_metric_and_aggregation()

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

        if self.config.filter_list is not None:
            self._filters = []
            for filter_config in self.config.filter_list:
                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"}
                        components.append([function["function"], kwargs])
                    filter_pipeline = build_filter_ensemble(filter_name, components)
                self._filters.append(filter_pipeline)
        else:
            self._filters = [build_filter_ensemble("none", [["take_first", None]])]
        if self.config.fewshot_config is not None:
            self.sampler = samplers.get_sampler(self.config.fewshot_config.get("sampler", "default") if self.config.fewshot_config else "default")(list(self.fewshot_docs()), self, rnd=random.Random(1234))

        if self.has_test_docs():
            self.task_docs = self.test_docs()
        elif self.has_validation_docs():
            self.task_docs = self.validation_docs()
        else:
            assert False, f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"

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

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

            if type(test_text) is int:
                self.multiple_input = num_choice
        else:
            test_choice = None

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

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

                if delimiter_has_whitespace and choice_has_whitespace:
                    eval_logger.warning(f'Both target_delimiter and target choice: "{choice}" have whitespace')
                elif (not delimiter_has_whitespace) and (not choice_has_whitespace):
                    eval_logger.warning(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 _prepare_model_specific_config(self):
        self.lmms_eval_specific_kwargs = self.config.lmms_eval_specific_kwargs
        if self.lmms_eval_specific_kwargs is not None:
            if self.model_name in self.lmms_eval_specific_kwargs:
                self.lmms_eval_specific_kwargs = self.lmms_eval_specific_kwargs[self.model_name]
            elif "default" in self.lmms_eval_specific_kwargs:
                self.lmms_eval_specific_kwargs.update(self.lmms_eval_specific_kwargs.get("default", {}))
            elif "dataset" in self.lmms_eval_specific_kwargs:
                self.lmms_eval_specific_kwargs.update(self.lmms_eval_specific_kwargs.get("dataset", {}))

        self.model_specific_target_kwargs = self.config.model_specific_target_kwargs
        if self.model_specific_target_kwargs is not None:
            if self.model_name in self.model_specific_target_kwargs:
                self.model_specific_target_kwargs = self.model_specific_target_kwargs[self.model_name]
            else:
                self.model_specific_target_kwargs = self.model_specific_target_kwargs.get("default", None)
        self.model_specific_generation_kwargs = self.config.model_specific_generation_kwargs
        if self.model_specific_generation_kwargs is not None:
            if self.model_name in self.model_specific_generation_kwargs:
                self.model_specific_generation_kwargs = self.model_specific_generation_kwargs[self.model_name]
            else:
                self.model_specific_generation_kwargs = self.model_specific_generation_kwargs.get("default", {})

            self.config.generation_kwargs.update(self.model_specific_generation_kwargs)

    def _prepare_metric_and_aggregation(self):
        self._metric_fn_list = {}
        self._metric_fn_kwargs = {}
        self._aggregation_list = {}
        self._higher_is_better = {}

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

            for metric_name in _metric_list:
                self._metric_fn_list[metric_name] = METRIC_REGISTRY[metric_name]
                self._metric_fn_kwargs[metric_name] = {}
                self._aggregation_list[metric_name] = get_metric_aggregation(metric_name)
                self._higher_is_better[metric_name] = is_higher_better(metric_name)
        else:
            for metric_config in self.config.metric_list:
                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 self.config.process_results is not None:
                    self._metric_fn_list[metric_name] = None
                    self._metric_fn_kwargs[metric_name] = {}
                elif callable(metric_name):
                    metric_fn = metric_name.__call__
                    metric_name = metric_name.__name__
                    self._metric_fn_list[metric_name] = metric_fn
                    self._metric_fn_kwargs[metric_name] = kwargs
                else:
                    self._metric_fn_list[metric_name] = METRIC_REGISTRY[metric_name]
                    self._metric_fn_kwargs[metric_name] = kwargs

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

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

    @retry(stop=(stop_after_attempt(5) | stop_after_delay(60)), wait=wait_fixed(2))
    def download(self, dataset_kwargs=None) -> None:
        # If the dataset is a video dataset,
        # Recursively search whether their is a zip and unzip it to the huggingface home
        download_config = DownloadConfig()
        download_config.max_retries = dataset_kwargs.get("max_retries", 10) if dataset_kwargs is not None else 10
        download_config.num_proc = dataset_kwargs.get("num_proc", 8) if dataset_kwargs is not None else 8
        download_config.local_files_only = dataset_kwargs.get("local_files_only", False) if dataset_kwargs is not None else False
        if dataset_kwargs is not None:
            if "From_YouTube" in dataset_kwargs:

                def _download_from_youtube(path):
                    try:
                        for video in tqdm(self.all_dataset[split]):
                            video_id = video["videoID"]
                            target_path = os.path.join(path, f"{video_id}.mp4")
                            assert shutil.which("yt-dlp") is not None, "yt-dlp must be installed and available in the system's PATH"
                            command = f"yt-dlp -o {target_path} -f mp4 https://www.youtube.com/watch?v={video_id}"
                            subprocess.run(command, shell=True)
                        with open(os.path.join(cache_path, f"{task}_download_status.json"), "w") as f:
                            f.write(json.dumps({task: "downloaded"}))
                    except Exception as e:
                        eval_logger.error(f"Error while downloading {task} data: {e}")
                        with open(os.path.join(cache_path, f"{task}_download_status.json"), "w") as f:
                            f.write(json.dumps({task: "not downloaded"}))

                hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/")
                accelerator = Accelerator()
                if accelerator.is_main_process:
                    dataset_kwargs.pop("From_YouTube")
                    assert "load_from_disk" not in dataset_kwargs, "load_from_disk must not be True when From_YouTube is True"
                    self.all_dataset = datasets.load_dataset(
                        path=self.DATASET_PATH,
                        name=self.DATASET_NAME,
                        download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS,
                        **dataset_kwargs if dataset_kwargs is not None else {},
                    )
                    dataset_kwargs["From_YouTube"] = True
                    if os.path.exists(self.DATASET_PATH):
                      cache_path=self.DATASET_PATH
                    else:
                      cache_path = snapshot_download(repo_id=self.DATASET_PATH, repo_type="dataset")  # download_parquet
                    split = vars(self.config)["test_split"]
                    task = vars(self.config)["task"]

                    video_path = os.path.join(hf_home, task)
                    if os.path.exists(os.path.join(cache_path, f"{task}_download_status.json")):
                        download_status = json.load(open(os.path.join(cache_path, f"{task}_download_status.json"), "r"))
                        if download_status[task] == "downloaded":
                            eval_logger.info(f"Data for {task} already download!")
                        else:
                            eval_logger.info(f"Start downloading YouTube data to {video_path}...")
                            _download_from_youtube(video_path)
                    else:
                        eval_logger.info(f"Start downloading YouTube data to {video_path}...")
                        _download_from_youtube(video_path)

                accelerator.wait_for_everyone()
                if "builder_script" in dataset_kwargs:
                    builder_script = dataset_kwargs["builder_script"]
                    self.DATASET_PATH = os.path.join(cache_path, builder_script)
                    dataset_kwargs.pop("builder_script")

                downloaded_video_ids = [i.split(".mp4")[0] for i in os.listdir(os.path.expanduser(video_path)) if i.endswith(".mp4")]
                # Filtered the existing dataset with the downloaded video ids
                self.dataset = datasets.DatasetDict({split: self.all_dataset[split].filter(lambda x: x["videoID"] in downloaded_video_ids)})

                self.dataset_no_image = self.dataset
                dataset_kwargs.pop("From_YouTube")
                return

            if "video" in dataset_kwargs and dataset_kwargs["video"]:
                hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/")
                hf_home = os.path.expanduser(hf_home)
                cache_dir = dataset_kwargs["cache_dir"]
                cache_dir = os.path.join(hf_home, cache_dir)
                accelerator = Accelerator()
                if accelerator.is_main_process:
                    force_download = dataset_kwargs.get("force_download", False)
                    force_unzip = dataset_kwargs.get("force_unzip", False)
                    revision = dataset_kwargs.get("revision", "main")
                    create_link = dataset_kwargs.get("create_link", False)
                    if os.path.exists(self.DATASET_PATH):
                      cache_path=self.DATASET_PATH
                    else:
                      cache_path = snapshot_download(repo_id=self.DATASET_PATH, revision=revision, repo_type="dataset", force_download=force_download, etag_timeout=60)
                    zip_files = glob(os.path.join(cache_path, "**/*.zip"), recursive=True)
                    tar_files = glob(os.path.join(cache_path, "**/*.tar*"), recursive=True)

                    def unzip_video_data(zip_file):
                        import os
                        import zipfile

                        with zipfile.ZipFile(zip_file, "r") as zip_ref:
                            for file_info in zip_ref.infolist():
                                target_path = os.path.join(cache_dir, file_info.filename)
                                if not os.path.exists(target_path):
                                    zip_ref.extract(file_info, cache_dir)
                                else:
                                    eval_logger.info(f"Skipping existing file: {target_path}")

                        eval_logger.info(f"Extracted all files from {zip_file} to {cache_dir}")

                    def untar_video_data(tar_file):
                        import tarfile

                        with tarfile.open(tar_file, "r") as tar_ref:
                            tar_ref.extractall(cache_dir)
                            eval_logger.info(f"Extracted all files from {tar_file} to {cache_dir}")

                    def concat_tar_parts(tar_parts, output_tar):
                        with open(output_tar, "wb") as out_tar:
                            from tqdm import tqdm

                            for part in tqdm(sorted(tar_parts)):
                                with open(part, "rb") as part_file:
                                    out_tar.write(part_file.read())
                        eval_logger.info(f"Concatenated parts {tar_parts} into {output_tar}")

                    # Unzip zip files if needed
                    if force_unzip or (not os.path.exists(cache_dir) and len(zip_files) > 0):
                        for zip_file in zip_files:
                            unzip_video_data(zip_file)

                    # Concatenate and extract tar files if needed
                    if force_unzip or (not os.path.exists(cache_dir) and len(tar_files) > 0):
                        tar_parts_dict = {}

                        # Group tar parts together
                        for tar_file in tar_files:
                            base_name = tar_file.split(".tar")[0]
                            if base_name not in tar_parts_dict:
                                tar_parts_dict[base_name] = []
                            tar_parts_dict[base_name].append(tar_file)

                        # Concatenate and untar split parts
                        for base_name, parts in tar_parts_dict.items():
                            eval_logger.info(f"Extracting following tar files: {parts}")
                            output_tar = base_name + ".tar"
                            if not os.path.exists(output_tar):
                                eval_logger.info(f"Start concatenating tar files")

                                concat_tar_parts(parts, output_tar)
                                eval_logger.info(f"Finish concatenating tar files")

                            if not os.path.exists(os.path.join(cache_dir, os.path.basename(base_name))):
                                untar_video_data(output_tar)

                    # Link cache_path to cache_dir if needed.
                    if create_link:
                        if not os.path.exists(cache_dir) or os.path.islink(cache_dir):
                            if os.path.islink(cache_dir):
                                os.remove(cache_dir)
                                eval_logger.info(f"Removed existing symbolic link: {cache_dir}")
                            # Create a new symbolic link
                            os.symlink(cache_path, cache_dir)
                            eval_logger.info(f"Symbolic link created successfully: {cache_path} -> {cache_dir}")

                accelerator.wait_for_everyone()
                dataset_kwargs.pop("cache_dir")
                dataset_kwargs.pop("video")

            if "builder_script" in dataset_kwargs:
                builder_script = dataset_kwargs["builder_script"]
                self.DATASET_PATH = os.path.join(cache_path, builder_script)
                dataset_kwargs.pop("builder_script")

            if "force_download" in dataset_kwargs:
                dataset_kwargs.pop("force_download")

            if "force_unzip" in dataset_kwargs:
                dataset_kwargs.pop("force_unzip")

            if "local_files_only" in dataset_kwargs:
                dataset_kwargs.pop("local_files_only")

            if "create_link" in dataset_kwargs:
                dataset_kwargs.pop("create_link")

        if dataset_kwargs is not None and "load_from_disk" in dataset_kwargs and dataset_kwargs["load_from_disk"]:
            dataset_kwargs.pop("load_from_disk")
            # using local task in offline environment, need to process the online dataset into local format via
            # `ds = load_datasets("lmms-lab/MMMU")`
            self.dataset = datasets.load_from_disk(path=self.DATASET_PATH, name=self.DATASET_NAME)
        else:
            self.dataset = datasets.load_dataset(
                path=self.DATASET_PATH,
                name=self.DATASET_NAME,
                download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS,
                download_config=download_config,
                **dataset_kwargs if dataset_kwargs is not None else {},
            )

        if self.config.process_docs is not None:
            for split in self.dataset:
                if split in [self.config.training_split, self.config.validation_split, self.config.test_split, self.config.fewshot_split]:
                    self.dataset[split] = self.config.process_docs(self.dataset[split])

        # copy dataset, remove image features
        self.dataset_no_image = self.dataset.copy()
        for doc_name in self.dataset_no_image:
            remove_cols = []
            features = self.dataset_no_image[doc_name].features
            # If it is an Image instance or a Sequence of Image instance. Remove it
            for feature in features:
                if isinstance(features[feature], Image):
                    remove_cols.append(feature)
                elif isinstance(features[feature], Sequence) and isinstance(features[feature].feature, Image):
                    remove_cols.append(feature)
                elif isinstance(features[feature], Audio):
                    remove_cols.append(feature)
            for remove_col in remove_cols:
                self.dataset_no_image[doc_name] = self.dataset_no_image[doc_name].remove_columns(remove_col)

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

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

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

    def training_docs(self) -> datasets.Dataset:
        if self.has_training_docs():
            return self.dataset[self.config.training_split]

    def validation_docs(self) -> datasets.Dataset:
        if self.has_validation_docs():
            return self.dataset[self.config.validation_split]

    def validation_docs_no_media(self) -> datasets.Dataset:
        if self.has_validation_docs():
            return self.dataset_no_image[self.config.validation_split]

    def test_docs(self) -> datasets.Dataset:
        if self.has_test_docs():
            return self.dataset[self.config.test_split]

    def test_docs_no_media(self) -> datasets.Dataset:
        if self.has_test_docs():
            return self.dataset_no_image[self.config.test_split]

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

    def fewshot_docs(self):
        if self.config.fewshot_split is not None:
            return self.dataset[self.config.fewshot_split]
        else:
            if (self.config.num_fewshot is not None) and (self.config.num_fewshot > 0):
                eval_logger.warning(f"Task '{self.config.task}': " "num_fewshot > 0 but fewshot_split is None. " "using preconfigured rule.")
            return super().fewshot_docs()

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

        :param doc: str
            The document as returned from training_docs, validation_docs, or test_docs.
        :param num_fewshot: int
            The number of fewshot examples to provide in the returned context string.
        :param  system_instruction: str
            System instruction to be applied to the prompt.
        :param apply_chat_template: bool
            Whether to apply the chat template to the fewshot context.
        :param fewshot_as_multiturn: bool
            Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
        :param chat_template:
            callable (from lm.apply_chat_template) that takes in a list[Dict] chat transcript and renders it into a string.
        :returns: str
            The fewshot context.
        """

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

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

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

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

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

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

    def apply_filters(self):
        if hasattr(self, "_filters"):
            for f in self._filters:
                f.apply(self._instances, self.task_docs)
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances

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

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

    def _process_doc(self, doc):
        """
        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):
        doc_to_text = self.config.doc_to_text

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

    def doc_to_target(self, doc: dict) -> Union[int, str, list]:
        doc_to_target = self.config.doc_to_target

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

    def doc_to_visual(self, doc: dict) -> Union[int, str, list]:
        self.config.doc_to_visual
        if type(self.config.doc_to_visual) == str:
            assert self.config.doc_to_visual in self.features
            # Single image. Still return a list for consistency.
            return [doc[self.config.doc_to_visual]]
        elif callable(self.config.doc_to_visual):
            return (
                self.config.doc_to_visual(doc, self.lmms_eval_specific_kwargs)
                if self.lmms_eval_specific_kwargs is not None and len(inspect.signature(self.config.doc_to_visual).parameters) == 2
                else self.config.doc_to_visual(
                    doc,
                )
            )
        else:
            # eval_logger.warning("Note that doc_to_visual was called but not set in config. Please check if this is a text-only task.")
            return self.config.doc_to_visual

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

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

    def construct_requests(self, doc_id: int, ctx: str, **kwargs) -> Union[List[Instance], Instance]:
        split = kwargs.get("metadata").get("split")
        # kwargs.pop("split")
        if self.OUTPUT_TYPE == "loglikelihood":
            arguments = (ctx, self.doc_to_target, self.doc_to_visual, doc_id, self.config.task, split)
        elif self.OUTPUT_TYPE == "multiple_choice":
            doc = self.dataset[split][doc_id]
            choices = self.doc_to_choice(doc)
            target_delimiter = self.config.target_delimiter
            if self.multiple_input:
                # If there are multiple inputs, choices are placed in the ctx
                cont = self.doc_to_target(doc)
                arguments = [(ctx, f"{target_delimiter}{cont}", self.doc_to_visual, doc_id, self.config.task, split) for ctx in choices]
            else:
                # Otherwise they are placed in the continuation
                arguments = [(ctx, f"{target_delimiter}{cont}", self.doc_to_visual, doc_id, self.config.task, split) for cont in choices]
            request_list = [
                Instance(
                    request_type="loglikelihood",
                    # doc=doc,
                    arguments=arg,
                    idx=i,
                    **kwargs,
                )
                for i, arg in enumerate(arguments)
            ]
            # TODO: we should raise a warning telling users this will at most ~2x runtime.
            if "acc_mutual_info" in self._metric_fn_list.keys():
                # if we are calculating multiple choice accuracy
                # using mutual information instead of raw loglikelihood as metric, need unconditional lls.

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

        elif self.OUTPUT_TYPE == "generate_until":
            arguments = (ctx, copy.deepcopy(self.config.generation_kwargs), self.doc_to_visual, doc_id, self.config.task, split)
        elif self.OUTPUT_TYPE == "generate_until_multi_round":
            arguments = (ctx, copy.deepcopy(self.config.generation_kwargs), self.doc_to_visual, partial(self.config.doc_to_text, lmms_eval_specific_kwargs=self.lmms_eval_specific_kwargs), doc_id, self.config.task, split)
        return Instance(request_type=self.OUTPUT_TYPE, arguments=arguments, idx=0, **kwargs)

    # TODO: we add a full_docs interface here for some evaluations that needs to access the full datasets during process_results function. we may have better ways to handle this.
    @retry(stop=(stop_after_attempt(5) | stop_after_delay(1200)), wait=wait_fixed(2))
    def process_results(self, doc, results, full_docs=None):
        if self.OUTPUT_TYPE == "generate_until":
            results[0] = results[0].strip()

        kwargs = {}
        if full_docs is not None:
            kwargs["full_docs"] = full_docs
        if callable(self.config.process_results):
            return self.config.process_results(doc, results, **kwargs)

        result_dict = {}
        use_metric = list(self._metric_fn_list.keys())
        if self.OUTPUT_TYPE == "loglikelihood":
            results = results[0]
            ll, is_greedy = results
            return {
                **({"perplexity": ll} if "perplexity" in use_metric else {}),
                **({"acc": int(is_greedy)} if "acc" in use_metric else {}),
            }
        elif self.OUTPUT_TYPE == "multiple_choice":
            lls, is_greedy = zip(*results)

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

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

            # Warning :
            # Here may be different from original lm-eval
            # since we return the actual loss in many model loglikelihood
            # we just use the argmin here
            pred = np.argmin(lls)
            pred_norm = np.argmin(lls / completion_len)

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

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

                if gold == -100:
                    gold_index_error = True

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

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

            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 {}),
            }

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

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

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

        return result_dict

    def aggregation(self):
        return self._aggregation_list

    def higher_is_better(self):
        return self._higher_is_better

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

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

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