cleanup

lm_eval/api/filter.py

 from abc import ABC, abstractmethod
+from collections.abc import Iterable
 from dataclasses import dataclass
-from typing import Iterable, List, Union
 
 from lm_eval.api.instance import Instance
 
@@ -20,7 +20,9 @@ class Filter(ABC):
         """
 
     @abstractmethod
-    def apply(self, resps: Union[List, Iterable], docs: List[dict]) -> Iterable:
+    def apply(
+        self, resps: Iterable[list[str]], docs: Iterable[dict]
+    ) -> Iterable[list[str]]:
         """
         Defines the operation to perform on a list of the `inst.resps` properties of `Instance` objects.
         Should return the list of (filtered) response lists *in the same order as they were input*, e.g.
@@ -40,9 +42,9 @@ class FilterEnsemble:
     """
 
     name: str
-    filters: List[type[Filter]]
+    filters: list[type[Filter]]
 
-    def apply(self, instances: List[Instance]) -> None:
+    def apply(self, instances: list[Instance]) -> None:
         resps, docs = zip(*((inst.resps, inst.doc) for inst in instances))
         resps, docs = list(resps), list(docs)
 

lm_eval/api/registry.py

+from __future__ import annotations
+
 import logging
-from typing import TYPE_CHECKING, Callable, Dict, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable
 
 
 if TYPE_CHECKING:
@@ -36,13 +38,14 @@ def register_model(*names):
     return decorate
 
 
-def get_model(model_name: str) -> type["LM"]:
+def get_model(model_name: str) -> type[LM]:
     try:
         return MODEL_REGISTRY[model_name]
-    except KeyError:
-        raise ValueError(
-            f"Attempted to load model '{model_name}', but no model for this name found! Supported model names: {', '.join(MODEL_REGISTRY.keys())}"
-        )
+    except KeyError as err:
+        available_models = ", ".join(MODEL_REGISTRY.keys())
+        raise KeyError(
+            f"Model '{model_name}' not found. Available models: {available_models}"
+        ) from err
 
 
 TASK_REGISTRY = {}
@@ -81,7 +84,7 @@ def register_group(name):
 OUTPUT_TYPE_REGISTRY = {}
 METRIC_REGISTRY = {}
 METRIC_AGGREGATION_REGISTRY = {}
-AGGREGATION_REGISTRY: Dict[str, Callable[[], Dict[str, Callable]]] = {}
+AGGREGATION_REGISTRY: dict[str, Callable[[], dict[str, Callable]]] = {}
 HIGHER_IS_BETTER_REGISTRY = {}
 FILTER_REGISTRY = {}
 
@@ -125,7 +128,7 @@ def register_metric(**args):
     return decorate
 
 
-def get_metric(name: str, hf_evaluate_metric=False) -> Optional[Callable]:
+def get_metric(name: str, hf_evaluate_metric=False) -> Callable[..., Any] | None:
     if not hf_evaluate_metric:
         if name in METRIC_REGISTRY:
             return METRIC_REGISTRY[name]
@@ -157,21 +160,21 @@ def register_aggregation(name: str):
     return decorate
 
 
-def get_aggregation(name: str) -> Optional[Callable[[], Dict[str, Callable]]]:
+def get_aggregation(name: str) -> Callable[..., Any] | None:
     try:
         return AGGREGATION_REGISTRY[name]
     except KeyError:
         eval_logger.warning(f"{name} not a registered aggregation metric!")
 
 
-def get_metric_aggregation(name: str) -> Optional[Callable[[], Dict[str, Callable]]]:
+def get_metric_aggregation(name: str) -> Callable[[], dict[str, Callable]] | None:
     try:
         return METRIC_AGGREGATION_REGISTRY[name]
     except KeyError:
         eval_logger.warning(f"{name} metric is not assigned a default aggregation!")
 
 
-def is_higher_better(metric_name: str) -> Optional[bool]:
+def is_higher_better(metric_name: str) -> bool | None:
     try:
         return HIGHER_IS_BETTER_REGISTRY[metric_name]
     except KeyError:
@@ -192,7 +195,7 @@ def register_filter(name: str):
     return decorate
 
 
-def get_filter(filter_name: Union[str, Callable]) -> Callable:
+def get_filter(filter_name: str | Callable) -> Callable:
     try:
         return FILTER_REGISTRY[filter_name]
     except KeyError as e:

lm_eval/config/metric.py

+from __future__ import annotations
+
+from collections.abc import Callable, Mapping
 from dataclasses import dataclass
 from functools import cached_property
-from typing import Any, Callable, List, Optional
+from typing import Any
 
 
 @dataclass
@@ -8,9 +11,9 @@ class MetricConfig:
     """Encapsulates information about a single metric."""
 
     name: str
-    fn: Optional[Callable] = None
-    kwargs: Optional[dict] = None
-    aggregation_fn: Optional[Callable] = None
+    fn: Callable | None = None
+    kwargs: Mapping[str, Any] | None = None
+    aggregation_fn: Callable | None = None
     higher_is_better: bool = True
     hf_evaluate: bool = False
     is_elementwise: bool = True
@@ -20,7 +23,7 @@ class MetricConfig:
         return self.name
 
     @cached_property
-    def aggregation(self) -> Callable:
+    def aggregation(self) -> Callable[..., Any] | None:
         from lm_eval.api.registry import get_aggregation
 
         if self.aggregation_fn is None:
@@ -28,7 +31,7 @@ class MetricConfig:
         return self.aggregation_fn
 
     @cached_property
-    def _higher_is_better(self) -> bool:
+    def _higher_is_better(self) -> bool | None:
         from lm_eval.api.registry import is_higher_better
 
         if self.higher_is_better is None:
@@ -39,10 +42,10 @@ class MetricConfig:
         """Calculates the metric using the provided function and arguments."""
         if self.fn is None:
             raise ValueError(f"Metric function for {self.name} is not defined.")
-        return self.fn(*args, **{**self.kwargs, **kwargs})
+        return self.fn(*args, **{**(self.kwargs or {}), **kwargs})
 
-    def compute_aggregation(self, values: List[Any]) -> Any:
+    def compute_aggregation(self, *args, **kwargs) -> Any:
         """Computes the aggregation of the metric values."""
         if self.aggregation_fn is None:
             raise ValueError(f"Aggregation function for {self.name} is not defined.")
-        return self.aggregation_fn(values)
+        return self.aggregation_fn(*args, **kwargs)

lm_eval/config/task.py

+from __future__ import annotations
+
 import logging
+from collections.abc import Iterable
 from dataclasses import asdict, dataclass, field
-from typing import TYPE_CHECKING, Callable, Iterable, Optional, Union
+from typing import TYPE_CHECKING, Callable
 
 from lm_eval.api.filter import FilterEnsemble
 from lm_eval.api.instance import OutputType
@@ -20,8 +23,8 @@ class RepeatConfig:
     """Encapsulates information about a single repeat."""
 
     repeats: int = 1
-    metric_fn: Union[str, Callable] = "pass@N"
-    kwargs: Optional[dict] = field(default_factory=dict)
+    metric_fn: str | Callable = "pass@N"
+    kwargs: dict | None = field(default_factory=dict)
 
 
 @dataclass
@@ -38,11 +41,11 @@ class FewshotConfig:
     # hack: this returns task.config.num_fewshot
     # to keep in sync as it is runtime-modified
     num_fewshot: Callable[[], int]
-    split: Optional[str] = None
-    sampler: Union[str, Callable] = "default"
-    samples: Union[Callable[[], list[dict]], list[dict], None] = None
-    process_docs: Optional[Callable[[list[dict]], Iterable[dict]]] = None
-    fewshot_indices: Optional[list[int]] = None
+    split: str | None = None
+    sampler: str | Callable = "default"
+    samples: Callable[[], list[dict]] | list[dict] | None = None
+    process_docs: Callable[[list[dict]], Iterable[dict]] | None = None
+    fewshot_indices: list[int] | None = None
     rnd: int = field(init=False, default=False)
 
     def __post_init__(self) -> None:
@@ -65,22 +68,20 @@ class FewshotConfig:
 
     def _get_raw_docs(
         self, dataset
-    ) -> Union[list[dict], Callable[[], Iterable[dict]], None]:
+    ) -> list[dict] | Callable[[], Iterable[dict]] | None:
         """Get raw documents from configured source."""
         if self.split is not None:
             return dataset[self.split]
 
         if self.samples is not None:
-            if isinstance(self.samples, list):
-                return self.samples
-            elif callable(self.samples):
+            if isinstance(self.samples, list) or callable(self.samples):
                 return self.samples
             else:
                 raise TypeError(
                     "samples must be either a list of dicts or a callable returning a list"
                 )
 
-    def get_docs(self, dataset) -> Optional[Iterable[dict]]:
+    def get_docs(self, dataset) -> Iterable[dict] | None:
         """Get processed documents from configured source."""
         raw_docs = self._get_raw_docs(dataset)
         if raw_docs is None:
@@ -100,8 +101,8 @@ class FewshotConfig:
             return self.sampler
 
     def init_sampler(
-        self, docs: list[dict], task: "Task", rnd=None, fewshot_indices=None
-    ) -> "ContextSampler":
+        self, docs: list[dict], task: Task, rnd=None, fewshot_indices=None
+    ) -> ContextSampler:
         """Initialize the sampler with the given documents and task."""
         if rnd is None:
             raise ValueError(
@@ -120,49 +121,49 @@ class FewshotConfig:
 @dataclass
 class TaskConfig(dict):
     # task naming/registry
-    task: Optional[str] = None
-    task_alias: Optional[str] = None
-    tag: Optional[Union[str, list]] = None
+    task: str | None = None
+    task_alias: str | None = None
+    tag: str | list | None = None
     # HF dataset options.
     # which dataset to use,
     # and what splits for what purpose
-    custom_dataset: Optional[Callable] = None
-    dataset_path: Optional[str] = None
-    dataset_name: Optional[str] = None
-    dataset_kwargs: Optional[dict] = field(default_factory=dict)
-    training_split: Optional[str] = None
-    validation_split: Optional[str] = None
-    test_split: Optional[str] = None
-    fewshot_split: Optional[str] = (
+    custom_dataset: Callable | None = None
+    dataset_path: str | None = None
+    dataset_name: str | None = None
+    dataset_kwargs: dict | None = field(default_factory=dict)
+    training_split: str | None = None
+    validation_split: str | None = None
+    test_split: str | None = None
+    fewshot_split: str | None = (
         None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaluating (?)
     )
     # formatting / prompting options.
     # see docs/advanced_task_guide.md for more info
-    process_docs: Optional[Callable] = None
-    doc_to_text: Optional[Union[Callable, str]] = None
-    doc_to_target: Optional[Union[Callable, str]] = None
-    doc_to_image: Union[Callable, str, None] = None
-    doc_to_audio: Union[Callable, str, None] = None
+    process_docs: Callable | None = None
+    doc_to_text: Callable | str | None = None
+    doc_to_target: Callable | str | None = None
+    doc_to_image: Callable | str | None = None
+    doc_to_audio: Callable | str | None = None
     unsafe_code: bool = False
-    doc_to_choice: Optional[Union[Callable, str, dict, list]] = None
-    process_results: Optional[Union[Callable, str]] = None
-    use_prompt: Optional[str] = None
+    doc_to_choice: Callable | str | dict | list | None = None
+    process_results: Callable | str | None = None
+    use_prompt: str | None = None
     description: str = ""
     target_delimiter: str = " "
     fewshot_delimiter: str = "\n\n"
-    fewshot_config: Optional[dict] = None
+    fewshot_config: dict | None = None
     # runtime configuration options
-    num_fewshot: Optional[int] = 0
-    generation_kwargs: Optional[dict] = None
+    num_fewshot: int | None = 0
+    generation_kwargs: dict | None = None
     # scoring options
-    metric_list: Optional[list] = None
+    metric_list: list | None = None
     output_type: OutputType = "generate_until"
     repeats: int = 1
-    filter_list: Optional[list[dict]] = None
+    filter_list: list[dict] | None = None
     should_decontaminate: bool = False
-    doc_to_decontamination_query: Optional[str] = None
-    gen_prefix: Optional[str] = None
-    metadata: Optional[dict] = field(
+    doc_to_decontamination_query: str | None = None
+    gen_prefix: str | None = None
+    metadata: dict | None = field(
         default_factory=dict
     )  # by default, not used in the code. allows for users to pass arbitrary info to tasks
 
@@ -215,9 +216,7 @@ class TaskConfig(dict):
             fewshot_indices=_fewshot_cfg.get("fewshot_indices", None),
         )
 
-    def _get_metric(
-        self, metric_list: Optional[list[dict]] = None
-    ) -> list["MetricConfig"]:
+    def _get_metric(self, metric_list: list[dict] | None = None) -> list[MetricConfig]:
         from lm_eval.api.registry import (
             AGGREGATION_REGISTRY,
             DEFAULT_METRIC_REGISTRY,
@@ -314,7 +313,7 @@ class TaskConfig(dict):
         return metrics
 
     @property
-    def get_filters(self) -> list["FilterConfig"]:
+    def get_filters(self) -> list[FilterConfig]:
         from lm_eval.filters import build_filter_ensemble
 
         if not self.filter_list:
@@ -354,7 +353,7 @@ class TaskConfig(dict):
             return x
 
     @classmethod
-    def from_yaml(cls, data: dict) -> "TaskConfig":
+    def from_yaml(cls, data: dict) -> TaskConfig:
         """Create a TaskConfig instance from a YAML-like dictionary."""
         return cls(**data)
 

lm_eval/config/template.py

+from __future__ import annotations
+
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Callable, Optional, Union
+from typing import TYPE_CHECKING, Callable
 
 
 if TYPE_CHECKING:
@@ -11,19 +13,19 @@ class TemplateConfig:
     """Encapsulates information about a template."""
 
     template: str
-    doc_to_text: Union[str, Callable[[dict], str]]
-    doc_to_choice: Union[str, list, Callable[[dict], list]]
-    doc_to_target: Union[int, Callable[[dict], int]]
+    doc_to_text: str | Callable[[dict], str]
+    doc_to_choice: str | list | Callable[[dict], list]
+    doc_to_target: int | Callable[[dict], int]
     description: str
     context_prefix: str
     prefix_delimiter: str
     context_delimiter: str
     answer_suffix: str
     target_delimiter: str
-    choice_format: Optional[str]
-    choice_delimiter: Optional[str]
+    choice_format: str | None
+    choice_delimiter: str | None
     fewshot_delimiter: str
-    metric_list: Optional[Union[list[str], list["MetricConfig"]]] = field(
+    metric_list: list[str] | list[MetricConfig] | None = field(
         default_factory=lambda: ["acc", "acc_norm"]
     )
 
@@ -40,19 +42,19 @@ class MCQTemplateConfig:
     Answer:` doc_to_choice(doc)` for each choice.
     """
 
-    doc_to_text: Union[str, Callable[[dict], str]]
-    doc_to_choice: Union[str, list, Callable[[dict], list]]
-    doc_to_target: Union[int, Callable[[dict], int]]
+    doc_to_text: str | Callable[[dict], str]
+    doc_to_choice: str | list | Callable[[dict], list]
+    doc_to_target: int | Callable[[dict], int]
     template = "mcq"
     context_prefix: str = "Question:"
     prefix_delimiter: str = " "
     context_delimiter: str = "\n"
     answer_suffix: str = "Answer:"
     target_delimiter: str = "\n"
-    choice_format: Optional[str] = "letters"
-    choice_delimiter: Optional[str] = "\n"
+    choice_format: str | None = "letters"
+    choice_delimiter: str | None = "\n"
     fewshot_delimiter: str = "\n\n"
-    metric_list: Optional[list["MetricConfig"]] = field(default_factory=lambda: ["acc"])
+    metric_list: list[MetricConfig] | None = field(default_factory=lambda: ["acc"])
 
 
 @dataclass
@@ -63,9 +65,9 @@ class ClozeTemplateConfig:
     Answer:` <doc_to_target(doc)>`
     """
 
-    doc_to_text: Union[str, Callable[[dict], str]]
-    doc_to_choice: Union[str, list, Callable[[dict], list]]
-    doc_to_target: Union[int, Callable[[dict], int]]
+    doc_to_text: str | Callable[[dict], str]
+    doc_to_choice: str | list | Callable[[dict], list]
+    doc_to_target: int | Callable[[dict], int]
     template: str = "cloze"
     description: str = ""
     context_prefix: str = "Question:"
@@ -73,9 +75,9 @@ class ClozeTemplateConfig:
     context_delimiter: str = "\n"
     answer_suffix: str = "Answer:"
     target_delimiter: str = " "
-    choice_format: Optional[str] = None
-    choice_delimiter: Optional[str] = None
+    choice_format: str | None = None
+    choice_delimiter: str | None = None
     fewshot_delimiter: str = "\n\n"
-    metric_list: Optional[list["MetricConfig"]] = field(
+    metric_list: list[MetricConfig] | None = field(
         default_factory=lambda: ["acc", "acc_norm"]
     )

lm_eval/config/utils.py

+from __future__ import annotations
+
 from inspect import getsource
-from typing import Any, Callable, Union
+from typing import Any, Callable
 
 
 def serialize_callable(
-    value: Union[Callable[..., Any], str], keep_callable=False
-) -> Union[Callable[..., Any], str]:
+    value: Callable[..., Any] | str, keep_callable=False
+) -> Callable[..., Any] | str:
     """Serializes a given function or string.
 
     If 'keep_callable' is True, the original callable is returned.
@@ -20,9 +22,7 @@ def serialize_callable(
             return str(value)
 
 
-def maybe_serialize(
-    val: Union[Callable, Any], keep_callable=False
-) -> Union[Callable, Any]:
+def maybe_serialize(val: Callable | Any, keep_callable=False) -> Callable | Any:
     """Conditionally serializes a value if it is callable."""
 
     return (

lm_eval/filters/extraction.py

 import re
 import sys
 import unicodedata
+from collections.abc import Iterable
 
 from lm_eval.api.filter import Filter
 from lm_eval.api.registry import register_filter
@@ -32,7 +33,9 @@ class RegexFilter(Filter):
         self.group_select = group_select
         self.fallback = fallback
 
-    def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
+    def apply(
+        self, resps: Iterable[list[str]], docs: Iterable[dict]
+    ) -> Iterable[list[str]]:
         # here, we assume we have a list, in which each element is
         # a list of model responses for some particular input/target pair.
         # so we process each of these (same input/target response sets)
@@ -59,59 +62,13 @@ class RegexFilter(Filter):
         return filtered_resps
 
 
-@register_filter("regex_pos")
-class POSFilter(Filter):
-    """ """
-
-    def __init__(
-        self,
-        regex_pattern: str = r"\['(.*?)'\]",
-        group_select=0,
-        fallback=None,
-        **kwargs,
-    ) -> None:
-        """
-        pass a string `regex` to run `re.compile(r"regex")` on.
-        `fallback` defines the output returned if no matches for the regex are located.
-        """
-        super().__init__(**kwargs)
-        if fallback is None:
-            fallback = ["invalid"]
-        self.regex_pattern = regex_pattern
-        self.regex = re.compile(regex_pattern)
-        self.group_select = group_select
-        self.fallback = fallback
-
-    def apply(self, resps, docs):
-        def extract_tagged_tokens(text):
-            # Extract tagged tokens list from text input using regex
-            tokens = re.findall(r"\('([^']*)', '([^']*)'\)", text)
-            return [(token, pos) for token, pos in tokens]
-
-        def extract_pos_tags(result):
-            pos_tags = []
-            if isinstance(result, str):
-                result = extract_tagged_tokens(result)
-            pos_tags.extend(pos for _, pos in result)
-            return pos_tags if pos_tags else self.fallback
-
-        def filter_set(inst):
-            filtered = []
-            for resp in inst:
-                match = extract_pos_tags(resp)
-                filtered.append(match)
-            return filtered
-
-        filtered_resps = map(lambda x: filter_set(x), resps)
-
-        return filtered_resps
-
-
 @register_filter("remove_whitespace")
 class WhitespaceFilter(Filter):
     """Filters out leading whitespace from responses."""
 
-    def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
+    def apply(
+        self, resps: Iterable[list[str]], docs: Iterable[dict]
+    ) -> Iterable[list[str]]:
         def filter_set(inst):
             filtered_resp = []
             for resp in inst:
@@ -156,7 +113,9 @@ class MultiChoiceRegexFilter(RegexFilter):
         self.ignore_punctuation = ignore_punctuation
         self.regexes_to_ignore = regexes_to_ignore
 
-    def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
+    def apply(
+        self, resps: Iterable[list[str]], docs: Iterable[dict]
+    ) -> Iterable[list[str]]:
         # here, we assume we have a list, in which each element is
         # a list of model responses for some particular input/target pair.
         # so we process each of these (same input/target response sets)