remove deps; types

.pre-commit-config.yaml

@@ -33,7 +33,7 @@ repos:
     hooks:
       # Run the linter.
       - id: ruff-check
-        args: [ --fix ]
+        args: [ --fix]
         # Run the formatter.
       - id: ruff-format
   - repo: https://github.com/codespell-project/codespell

lm_eval/api/model.py

+from __future__ import annotations
+
 import abc
 import hashlib
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any, Iterable, Optional, Type, TypeVar, Union
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Any, TypeVar
 
 from tqdm import tqdm
 
@@ -31,7 +34,7 @@ class LM(abc.ABC):
         # set rank and world size to a single process, by default.
         self._rank = 0
         self._world_size = 1
-        self.cache_hook: "CacheHook" = CacheHook(None)
+        self.cache_hook: CacheHook = CacheHook(None)
 
     @abc.abstractmethod
     def loglikelihood(self, requests: list[Instance]) -> list[tuple[float, bool]]:
@@ -101,7 +104,7 @@ class LM(abc.ABC):
 
     # TODO: Add an optional max length
     @abc.abstractmethod
-    def generate_until(self, requests: list["Instance"]) -> list[str]:
+    def generate_until(self, requests: list[Instance]) -> list[str]:
         """Generate greedily until a stopping sequence
 
         :param requests: list[Instance]
@@ -137,7 +140,7 @@ class LM(abc.ABC):
 
     @classmethod
     def create_from_arg_string(
-        cls: Type[T], arg_string: str, additional_config: Optional[dict] = None
+        cls: type[T], arg_string: str, additional_config: dict | None = None
     ) -> T:
         """
         Creates an instance of the LM class using the given argument string and additional config.
@@ -156,7 +159,7 @@ class LM(abc.ABC):
 
     @classmethod
     def create_from_arg_obj(
-        cls: Type[T], arg_dict: dict, additional_config: Optional[dict] = None
+        cls: type[T], arg_dict: dict, additional_config: dict | None = None
     ) -> T:
         """
         Creates an instance of the LM class using the given arg_obj
@@ -201,7 +204,7 @@ class LM(abc.ABC):
             "To use this model with chat templates, please implement the 'tokenizer_name' property."
         )
 
-    def chat_template(self, chat_template: Union[bool, str] = False) -> Optional[str]:
+    def chat_template(self, chat_template: bool | str = False) -> str | None:
         """Returns the chat template structure for user/assistant messages if a template is provided.
         This method is intended to be overridden in a subclass to define a specific chat template format.
         For models that do not support chat templates, this method returns None by default.
@@ -209,7 +212,7 @@ class LM(abc.ABC):
 
         return ""
 
-    def set_cache_hook(self, cache_hook: "CacheHook") -> None:
+    def set_cache_hook(self, cache_hook: CacheHook) -> None:
         """Sets the cache hook for the LM, which is used to cache responses from the LM."""
         self.cache_hook = cache_hook
 
@@ -221,10 +224,10 @@ def hash_args(attr: str, args: Iterable[Any]) -> str:
 
 
 class CacheHook:
-    def __init__(self, cachinglm: Optional["CachingLM"]) -> None:
+    def __init__(self, cachinglm: CachingLM | None) -> None:
         """CacheHook is used to cache responses from the LM."""
         if cachinglm is None:
-            self.dbdict: Optional["SqliteDict"] = None
+            self.dbdict: SqliteDict | None = None
             return
 
         self.dbdict = cachinglm.dbdict
@@ -238,7 +241,7 @@ class CacheHook:
 
 
 class CachingLM:
-    def __init__(self, lm: "LM", cache_db: str) -> None:
+    def __init__(self, lm: LM, cache_db: str) -> None:
         """LM wrapper that returns cached results if they exist, and uses the underlying LM if not.
 
         :param lm: LM
@@ -263,7 +266,7 @@ class CachingLM:
             eval_logger.debug(f"Passing through attribute '{attr}' to underlying LM")
             return lm_attr
 
-        def _fn(requests: list["Instance"]) -> list["Instance"]:
+        def _fn(requests: list[Instance]) -> list[Instance]:
             res = []
             remaining_reqs = []
             warned = False
@@ -295,11 +298,8 @@ class CachingLM:
             eval_logger.info(
                 f"Cached requests: {len(requests) - len(remaining_reqs)}, Requests remaining: {len(remaining_reqs)}"
             )
-            if remaining_reqs:
-                # actually run the LM on the requests that do not have cached results
-                rem_res = getattr(self.lm, attr)(remaining_reqs)
-            else:
-                rem_res = []
+
+            rem_res = getattr(self.lm, attr)(remaining_reqs) if remaining_reqs else []
 
             # stick the new ones back into the list and also cache any of the new ones
             resptr = 0
@@ -318,7 +318,7 @@ class CachingLM:
 
         return _fn
 
-    def get_cache_hook(self) -> "CacheHook":
+    def get_cache_hook(self) -> CacheHook:
         return CacheHook(self)
 
 
@@ -399,7 +399,7 @@ class TemplateLM(LM):
         return context_enc, continuation_enc
 
     def loglikelihood(
-        self, requests: list["Instance"], disable_tqdm: bool = False
+        self, requests: list[Instance], disable_tqdm: bool = False
     ) -> list[tuple[float, bool]]:
         """Compute log-likelihood of generating a continuation from a context.
 
@@ -432,7 +432,7 @@ class TemplateLM(LM):
 
     @abc.abstractmethod
     def generate_until(
-        self, requests: list["Instance"], disable_tqdm: bool = False
+        self, requests: list[Instance], disable_tqdm: bool = False
     ) -> list[str]:
         """Generate until a stopping sequence.
 
@@ -453,7 +453,7 @@ class TemplateLM(LM):
         """
         pass
 
-    def chat_template(self, chat_template: Union[bool, str] = False) -> Optional[str]:
+    def chat_template(self, chat_template: bool | str = False) -> str | None:
         """
         Assumes tokenizer has a chat_template attribute (self.tokenizer.chat_template: dict | str)
         Set and get the appropriate chat template for the model.

lm_eval/api/task.py

@@ -7,11 +7,7 @@ import random
 import re
 from collections.abc import Callable
 from copy import deepcopy
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Literal,
-)
+from typing import TYPE_CHECKING, Any, Literal, overload
 
 import datasets
 import numpy as np
@@ -24,7 +20,7 @@ from lm_eval.api.metrics import bits_per_byte, mean, weighted_perplexity
 from lm_eval.api.utils import check_gold_index_error
 from lm_eval.caching.cache import load_from_cache, save_to_cache
 from lm_eval.config.metric import MetricConfig
-from lm_eval.config.task import TaskConfig
+from lm_eval.config.task import DataSet, TaskConfig
 from lm_eval.filters import build_filter_ensemble
 
 
@@ -133,6 +129,7 @@ class Task(abc.ABC):
             - `datasets.DownloadMode.FORCE_REDOWNLOAD`
                 Fresh download and fresh dataset.
         """
+        assert self.DATASET_PATH is not None, "DATASET_PATH must be set in Task class"
         self.dataset = datasets.load_dataset(
             path=self.DATASET_PATH,
             name=self.DATASET_NAME,
@@ -146,43 +143,40 @@ class Task(abc.ABC):
         """Returns the TaskConfig associated with this class."""
         return self._config
 
-    @abc.abstractmethod
     def has_training_docs(self) -> bool:
         """Whether the task has a training set"""
-        pass
+        raise NotImplementedError
 
-    @abc.abstractmethod
     def has_validation_docs(self) -> bool:
         """Whether the task has a validation set"""
-        pass
+        raise NotImplementedError
 
-    @abc.abstractmethod
     def has_test_docs(self) -> bool:
         """Whether the task has a test set"""
-        pass
+        raise NotImplementedError
 
-    def training_docs(self) -> Iterable:
+    def training_docs(self) -> DataSet | None:
         """
         :return: Iterable[obj]
             A iterable of any object, that doc_to_text can handle
         """
         return []
 
-    def validation_docs(self) -> Iterable:
+    def validation_docs(self) -> DataSet | None:
         """
         :return: Iterable[obj]
             A iterable of any object, that doc_to_text can handle
         """
         return []
 
-    def test_docs(self) -> Iterable:
+    def test_docs(self) -> DataSet | None:
         """
         :return: Iterable[obj]
             A iterable of any object, that doc_to_text can handle
         """
         return []
 
-    def fewshot_docs(self) -> Iterable:
+    def fewshot_docs(self) -> DataSet | None:
         """
         :return: Iterable[obj]
             A iterable of any object, that doc_to_text can handle
@@ -192,7 +186,7 @@ class Task(abc.ABC):
         elif self.has_validation_docs():
             return self.validation_docs()
         else:
-            if self.config.get("num_fewshot", 0) > 0:
+            if self.config.num_fewshot and self.config.num_fewshot > 0:
                 eval_logger.warning(
                     f"[Task: {self.config.task}] has_training_docs and has_validation_docs are False"
                     ", using test_docs as fewshot_docs but this is not recommended."
@@ -331,7 +325,7 @@ class Task(abc.ABC):
             inst = self.construct_requests(
                 doc=doc,
                 ctx=fewshot_ctx,
-                metadata=(self.config["task"], doc_id, self.config.repeats),
+                metadata=(self.config.task, doc_id, self.config.repeats),
                 apply_chat_template=apply_chat_template,
                 chat_template=chat_template,
             )
@@ -586,7 +580,7 @@ class ConfigurableTask(Task):
         data_dir=None,
         cache_dir=None,
         download_mode=None,
-        config: dict | None = None,
+        config: Mapping[str, Any] | None = None,
     ) -> None:
         # Get pre-configured attributes
         self._config = self.CONFIG
@@ -727,6 +721,9 @@ class ConfigurableTask(Task):
             )
             self.dataset = df(**(self.config.dataset_kwargs | self.config.metadata))
         else:
+            assert self.config.dataset_path is not None, (
+                "dataset_path must be set in TaskConfig"
+            )
             self.dataset = datasets.load_dataset(
                 path=self.config.dataset_path,
                 name=self.config.dataset_name,
@@ -742,7 +739,7 @@ class ConfigurableTask(Task):
     def has_test_docs(self) -> bool:
         return self.config.test_split is not None
 
-    def training_docs(self) -> datasets.Dataset | None:
+    def training_docs(self) -> DataSet | None:
         if self.has_training_docs():
             if self.config.process_docs is not None:
                 return self.config.process_docs(
@@ -750,7 +747,7 @@ class ConfigurableTask(Task):
                 )
             return self.dataset[self.config.training_split]
 
-    def validation_docs(self) -> datasets.Dataset | None:
+    def validation_docs(self) -> DataSet | None:
         if self.has_validation_docs():
             if self.config.process_docs is not None:
                 return self.config.process_docs(
@@ -758,7 +755,7 @@ class ConfigurableTask(Task):
                 )
             return self.dataset[self.config.validation_split]
 
-    def test_docs(self) -> datasets.Dataset | None:
+    def test_docs(self) -> DataSet | None:
         if self.has_test_docs():
             if self.config.process_docs is not None:
                 return self.config.process_docs(self.dataset[self.config.test_split])
@@ -996,9 +993,21 @@ class ConfigurableTask(Task):
         """
         return doc
 
+    @overload
+    def doc_to_text(self, doc: dict, doc_to_text: None = None) -> str | int: ...
+
+    @overload
+    def doc_to_text(self, doc: dict, doc_to_text: int) -> int: ...
+
+    @overload
+    def doc_to_text(self, doc: dict, doc_to_text: str) -> str: ...
+
+    @overload
+    def doc_to_text(self, doc: dict, doc_to_text: Callable[..., str]) -> str: ...
+
     def doc_to_text(
         self, doc: dict, doc_to_text: int | str | Callable[..., str] | None = None
-    ) -> str:
+    ) -> str | int:
         # if self.prompt is not None:
         #     doc_to_text = self.prompt
         doc_to_text = doc_to_text or self.config.doc_to_text
@@ -1031,6 +1040,25 @@ class ConfigurableTask(Task):
             print(type(doc_to_text))
             raise TypeError
 
+    @overload
+    def doc_to_target(
+        self, doc: dict, doc_to_target: None = None
+    ) -> int | str | list[int]: ...
+
+    @overload
+    def doc_to_target(self, doc: dict, doc_to_target: int) -> int: ...
+
+    @overload
+    def doc_to_target(self, doc: dict, doc_to_target: str) -> int | str | list[int]: ...
+
+    @overload
+    def doc_to_target(self, doc: dict, doc_to_target: list) -> list[int]: ...
+
+    @overload
+    def doc_to_target(
+        self, doc: dict, doc_to_target: Callable[..., int | str | list[int]]
+    ) -> int | str | list[int]: ...
+
     def doc_to_target(self, doc: dict, doc_to_target=None) -> int | str | list[int]:
         # if self.prompt is not None:
         #     doc_to_target = self.prompt
@@ -1077,6 +1105,23 @@ class ConfigurableTask(Task):
         else:
             raise TypeError
 
+    @overload
+    def doc_to_choice(self, doc: dict, doc_to_choice: None = None) -> list[str]: ...
+
+    @overload
+    def doc_to_choice(self, doc: dict, doc_to_choice: str) -> list[str]: ...
+
+    @overload
+    def doc_to_choice(self, doc: dict, doc_to_choice: list) -> list[str]: ...
+
+    @overload
+    def doc_to_choice(self, doc: dict, doc_to_choice: dict) -> list[str]: ...
+
+    @overload
+    def doc_to_choice(
+        self, doc: dict, doc_to_choice: Callable[..., list[str]]
+    ) -> list[str]: ...
+
     def doc_to_choice(
         self,
         doc: dict,
@@ -1108,6 +1153,18 @@ class ConfigurableTask(Task):
         else:
             raise TypeError
 
+    @overload
+    def doc_to_image(self, doc: dict, doc_to_image: None = None) -> None: ...
+
+    @overload
+    def doc_to_image(self, doc: dict, doc_to_image: list) -> list: ...
+
+    @overload
+    def doc_to_image(self, doc: dict, doc_to_image: str) -> int | str | None: ...
+
+    @overload
+    def doc_to_image(self, doc: dict, doc_to_image: Callable[..., Any]) -> Any: ...
+
     def doc_to_image(self, doc: dict, doc_to_image=None) -> int | str | list | None:
         if doc_to_image is not None:
             doc_to_image = doc_to_image
@@ -1131,6 +1188,18 @@ class ConfigurableTask(Task):
         else:
             return None
 
+    @overload
+    def doc_to_audio(self, doc: Any, doc_to_audio: None = None) -> None: ...
+
+    @overload
+    def doc_to_audio(self, doc: Any, doc_to_audio: list) -> list: ...
+
+    @overload
+    def doc_to_audio(self, doc: Any, doc_to_audio: str) -> int | str | None: ...
+
+    @overload
+    def doc_to_audio(self, doc: Any, doc_to_audio: Callable[..., Any]) -> Any: ...
+
     def doc_to_audio(self, doc: Any, doc_to_audio=None) -> int | str | list | None:
         if doc_to_audio is not None:
             doc_to_audio = doc_to_audio
@@ -1375,15 +1444,15 @@ class ConfigurableTask(Task):
         elif self.OUTPUT_TYPE == "generate_until":
             gold = self.doc_to_target(doc)
             result = results[0]
-            for metric in self._metric_fn_list:
+            for metric in self.config._metric_list:
                 try:
-                    result_score = self._metric_fn_list[metric](
+                    result_score = metric.fn(
                         references=[gold] if not isinstance(gold, list) else gold,
                         predictions=[result],
-                        **self._metric_fn_kwargs[metric],
+                        **metric.kwargs,
                     )
                 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])
+                    result_score = metric.fn([gold, result])
                 if isinstance(result_score, dict):
                     # TODO: this handles the case where HF evaluate returns a dict.
                     # This allows for multiple metrics to be returned from the same function

lm_eval/config/metric.py

 from __future__ import annotations
 
 from collections.abc import Callable, Mapping
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from functools import cached_property
 from typing import Any
 
@@ -11,8 +11,8 @@ class MetricConfig:
     """Encapsulates information about a single metric."""
 
     name: str
-    fn: Callable | None = None
-    kwargs: Mapping[str, Any] | None = None
+    fn: Callable
+    kwargs: Mapping[str, Any] = field(default_factory=dict)
     aggregation_fn: Callable | None = None
     higher_is_better: bool = True
     hf_evaluate: bool = False

lm_eval/config/task.py

@@ -3,7 +3,9 @@ from __future__ import annotations
 import logging
 from collections.abc import Iterable
 from dataclasses import asdict, dataclass, field
-from typing import TYPE_CHECKING, Any, Callable
+from typing import TYPE_CHECKING, Any, Callable, Union
+
+import datasets
 
 from lm_eval.api.filter import FilterEnsemble
 from lm_eval.api.instance import OutputType
@@ -18,6 +20,9 @@ if TYPE_CHECKING:
 
 eval_logger = logging.getLogger(__name__)
 
+DataSet = Union[datasets.Dataset, Iterable[dict[str, Any]]]
+DSplits = dict[str, DataSet]
+
 
 @dataclass
 class RepeatConfig:
@@ -30,7 +35,7 @@ class RepeatConfig:
 
 @dataclass
 class FilterConfig:
-    """Encapsulates information about a single filter."""
+    """Encapsulates information about a single filter pipeline."""
 
     name: str
     ensemble: FilterEnsemble
@@ -44,10 +49,8 @@ class FewshotConfig:
     num_fewshot: Callable[[], int]
     split: str | None = None
     sampler: str | Callable = "default"
-    samples: Callable[[], list[dict]] | list[dict] | None = None
-    process_docs: Callable[[list[dict[str, Any]]], Iterable[dict[str, Any]]] | None = (
-        None
-    )
+    samples: Callable[[], DataSet] | DataSet | None = None
+    process_docs: Callable[[DataSet], DataSet] | None = None
     fewshot_indices: list[int] | None = None
     rnd: int = field(init=False, default=False)
 
@@ -69,22 +72,23 @@ class FewshotConfig:
         """Check if any fewshot source is configured."""
         return self.split is not None or self.samples is not None
 
-    def _get_raw_docs(
-        self, dataset
-    ) -> list[dict] | Callable[[], Iterable[dict]] | None:
+    def _get_raw_docs(self, dataset: DSplits) -> DataSet | 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) or callable(self.samples):
+            if isinstance(self.samples, list):
                 return self.samples
+            elif callable(self.samples):
+                # If samples is a callable, it should return a list of dicts
+                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) -> Iterable[dict[str, Any]] | None:
+    def get_docs(self, dataset) -> DataSet | None:
         """Get processed documents from configured source."""
         raw_docs = self._get_raw_docs(dataset)
         if raw_docs is None:
@@ -130,34 +134,34 @@ class TaskConfig:
     # HF dataset options.
     # which dataset to use,
     # and what splits for what purpose
-    custom_dataset: Callable | None = None
+    custom_dataset: Callable[..., DataSet] | 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 (?)
-    )
+    fewshot_split: str | None = None
     # formatting / prompting options.
     # see docs/advanced_task_guide.md for more info
-    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
+    process_docs: Callable[[DataSet], DataSet] | None = None
+    doc_to_text: Callable[[dict[str, Any]], Any] | str | None = None
+    doc_to_target: Callable[[dict[str, Any]], Any] | str | None = None
+    doc_to_image: Callable[[dict[str, Any]], Any] | str | None = None
+    doc_to_audio: Callable[[dict[str, Any]], Any] | str | None = None
     unsafe_code: bool = False
-    doc_to_choice: Callable | str | dict | list | None = None
-    process_results: Callable | str | None = None
+    doc_to_choice: Callable[[dict[str, Any]], Any] | str | dict | list | None = None
+    process_results: (
+        Callable[[dict[str, Any], list[Any]], dict[str, Any]] | str | None
+    ) = None
     use_prompt: str | None = None
     description: str = ""
     target_delimiter: str = " "
     fewshot_delimiter: str = "\n\n"
-    fewshot_config: dict | None = None
+    fewshot_config: dict[str, Any] | None = None
     # runtime configuration options
-    num_fewshot: int | None = 0
-    generation_kwargs: dict | None = None
+    num_fewshot: int | None = None
+    generation_kwargs: dict[str, Any] | None = None
     # scoring options
     metric_list: list | None = None
     output_type: OutputType = "generate_until"
@@ -357,7 +361,7 @@ class TaskConfig:
             return x
 
     @classmethod
-    def from_yaml(cls, data: dict) -> TaskConfig:
+    def from_yaml(cls, data: dict[str, Any]) -> TaskConfig:
         """Create a TaskConfig instance from a YAML-like dictionary."""
         return cls(**data)
 
@@ -425,12 +429,6 @@ class TaskConfig:
         # Create and return TaskConfig instance
         return cls(**config_dict)
 
-    def __getitem__(self, item):
-        return getattr(self, item)
-
-    def __setitem__(self, item, value):
-        return setattr(self, item, value)
-
     def to_dict(self, keep_callable: bool = False) -> dict:
         def _ser(x):
             if isinstance(x, dict):

lm_eval/decontamination/archiver.py

+# /// script
+# requires-python = ">=3.8"
+# dependencies = [
+#     "jsonlines",
+#     "mmap",
+#     "tqdm",
+#     "zstandard",
+# ]
+# ///
+
+# ruff: noqa
 import datetime
 import io
 import json
@@ -111,7 +122,7 @@ class TextReader:
         current_file_position = 0
         line_counter = 0
         with (
-            open(self.file_path, "r", encoding="utf-8") as fh,
+            open(self.file_path, encoding="utf-8") as fh,
             tqdm.tqdm(
                 total=os.path.getsize(self.file_path),
                 dynamic_ncols=True,
@@ -133,7 +144,7 @@ class TextReader:
 
     def read_and_tell(self):
         current_file_position = 0
-        with open(self.file_path, "r", encoding="utf8") as fh:
+        with open(self.file_path, encoding="utf8") as fh:
             with mmap.mmap(fh.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_obj:
                 for line in iter(mmap_obj.readline, b""):
                     line = line.decode("utf-8")
@@ -143,14 +154,14 @@ class TextReader:
                     yield line[:-1], raw_bytes_read
 
     def read(self):
-        with open(self.file_path, "r", encoding="utf8") as fh:
+        with open(self.file_path, encoding="utf8") as fh:
             with mmap.mmap(fh.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_obj:
                 for line in iter(mmap_obj.readline, b""):
                     line = line.decode("utf-8")
                     yield line[:-1]
 
     def read_slow(self):
-        with open(self.file_path, "r", encoding="utf8") as fh:
+        with open(self.file_path, encoding="utf8") as fh:
             while True:
                 line = fh.readline()
                 if line == -1 or line == "":

lm_eval/utils.py

 import collections
 import fnmatch
-import functools
 import hashlib
 import importlib.util
 import inspect
@@ -8,10 +7,12 @@ import json
 import logging
 import os
 import re
+from collections.abc import Generator
 from dataclasses import asdict, is_dataclass
+from functools import lru_cache, partial, wraps
 from itertools import islice
 from pathlib import Path
-from typing import Any, Callable, Generator, List, Optional, Tuple
+from typing import Any, Callable, Optional
 
 import numpy as np
 import yaml
@@ -108,7 +109,7 @@ def escaped_split(text, sep_char, maxsplit=-1):
         return text
     maxsplit = max(0, maxsplit)
 
-    return re.split(r"(?<!\\)" + sep_char, text, maxsplit)
+    return re.split(r"(?<!\\)" + sep_char, text, maxsplit=maxsplit)
 
 
 def handle_arg_string(arg):
@@ -125,7 +126,7 @@ def handle_arg_string(arg):
 
 
 def handle_non_serializable(o):
-    if isinstance(o, np.int64) or isinstance(o, np.int32):
+    if isinstance(o, np.integer):
         return int(o)
     elif isinstance(o, set):
         return list(o)
@@ -235,21 +236,21 @@ def sanitize_task_name(task_name: str) -> str:
     return re.sub(r"\W", "_", task_name)
 
 
-def get_latest_filename(filenames: List[str]) -> str:
+def get_latest_filename(filenames: list[str]) -> str:
     """
     Given a list of filenames, returns the filename with the latest datetime.
     """
     return max(filenames, key=lambda f: get_file_datetime(f))
 
 
-def get_results_filenames(filenames: List[str]) -> List[str]:
+def get_results_filenames(filenames: list[str]) -> list[str]:
     """
     Extracts filenames that correspond to aggregated results.
     """
     return [f for f in filenames if "/results_" in f and ".json" in f]
 
 
-def get_sample_results_filenames(filenames: List[str]) -> List[str]:
+def get_sample_results_filenames(filenames: list[str]) -> list[str]:
     """
     Extracts filenames that correspond to sample results.
     """
@@ -257,8 +258,8 @@ def get_sample_results_filenames(filenames: List[str]) -> List[str]:
 
 
 def get_rolling_token_windows(
-    token_list: List[int], prefix_token: int, max_seq_len: int, context_len: int
-) -> Generator[Tuple[List[int], List[int]], None, None]:
+    token_list: list[int], prefix_token: int, max_seq_len: int, context_len: int
+) -> Generator[tuple[list[int], list[int]], None, None]:
     """
     - context_len allows for a rolling window context, allowing each prediction window to potentially
       condition on some context
@@ -300,8 +301,8 @@ def get_rolling_token_windows(
 
 
 def make_disjoint_window(
-    pair: Tuple[List[int], List[int]],
-) -> Tuple[List[int], List[int]]:
+    pair: tuple[list[int], list[int]],
+) -> tuple[list[int], list[int]]:
     """Takes output from get_rolling_token_windows and makes the context not overlap with the continuation"""
     a, b = pair
     return a[: len(a) - (len(b) - 1)], b
@@ -320,7 +321,7 @@ class EnhancedJSONEncoder(json.JSONEncoder):
 
 
 class Reorderer:
-    def __init__(self, arr: List[Any], fn: Callable) -> None:
+    def __init__(self, arr: list[Any], fn: Callable) -> None:
         """Reorder an array according to some function
 
         Args:
@@ -423,11 +424,11 @@ def make_table(result_dict, column: str = "results", sort_results: bool = False)
             # TODO: fix
             hib = "↑"
 
-            v = "%.4f" % v if isinstance(v, float) else v
+            v = f"{v:.4f}" if isinstance(v, float) else v
 
             if m + "_stderr" + "," + f in dic:
                 se = dic[m + "_stderr" + "," + f]
-                se = "   N/A" if se == "N/A" else "%.4f" % se
+                se = "   N/A" if se == "N/A" else f"{se:.4f}"
                 values.append([k, version, f, n, m, hib, v, "±", se])
             else:
                 values.append([k, version, f, n, m, hib, v, "", ""])
@@ -448,7 +449,8 @@ def positional_deprecated(fn):
     wrapped function, `fn`.
     """
 
-    @functools.wraps(fn)
+    wraps(fn)
+
     def _wrapper(*args, **kwargs):
         if len(args) != 1 if inspect.ismethod(fn) else 0:
             print(
@@ -494,7 +496,7 @@ def load_yaml_config(yaml_path=None, yaml_config=None, yaml_dir=None, mode="full
         if yaml_path is None:
             raise ValueError("yaml_path must be provided if mode is 'full'.")
         # Attach yaml_path to the import function so that it can be used later
-        constructor_fn = functools.partial(import_function, yaml_path=Path(yaml_path))
+        constructor_fn = partial(import_function, yaml_path=Path(yaml_path))
 
     loader = yaml.CLoader if yaml.__with_libyaml__ else yaml.FullLoader
     # Add the import_function constructor to the YAML loader
@@ -543,13 +545,18 @@ def regex_replace(string, pattern, repl, count: int = 0):
 
 
 env = Environment(
-    loader=BaseLoader, undefined=StrictUndefined, keep_trailing_newline=True
+    loader=BaseLoader(), undefined=StrictUndefined, keep_trailing_newline=True
 )
 env.filters["regex_replace"] = regex_replace
 
 
+@lru_cache(maxsize=128)
+def _compile(raw: str):
+    return env.from_string(raw)
+
+
 def apply_template(template: str, doc: dict) -> str:
-    rtemplate = env.from_string(template)
+    rtemplate = _compile(template)
     return rtemplate.render(**doc)
 
 

pyproject.toml

@@ -14,31 +14,25 @@ classifiers = [
   "Development Status :: 3 - Alpha",
   "Programming Language :: Python :: 3",
   "License :: OSI Approved :: MIT License",
-    "Operating System :: OS Independent",
+  "Operating System :: OS Independent"
 ]
 requires-python = ">=3.9"
 license = { "text" = "MIT" }
 dependencies = [
   "accelerate>=0.26.0",
-    "evaluate",
   "datasets>=2.16.0,<4.0",
   "evaluate>=0.4.0",
-    "jsonlines",
-    "numexpr",
   "peft>=0.2.0",
-    "pybind11>=2.6.2",
   "pytablewriter",
   "rouge-score>=0.0.4",
   "sacrebleu>=1.5.0",
   "scikit-learn>=0.24.1",
   "sqlitedict",
   "torch>=1.8",
-    "tqdm-multiprocess",
   "transformers>=4.1",
-    "zstandard",
   "dill",
   "word2number",
-    "more_itertools",
+  "more_itertools"
 ]
 
 [tool.setuptools.packages.find]
@@ -68,7 +62,7 @@ ibm_watsonx_ai = ["ibm_watsonx_ai>=1.1.22", "python-dotenv"]
 ifeval = ["langdetect", "immutabledict", "nltk>=3.9.1"]
 ipex = ["optimum"]
 japanese_leaderboard = ["emoji==2.14.0", "neologdn==0.5.3", "fugashi[unidic-lite]", "rouge_score>=0.1.2"]
-longbench=["jieba", "fuzzywuzzy", "rouge"]
+longbench = ["jieba", "fuzzywuzzy", "rouge"]
 libra=["pymorphy2"]
 mamba = ["mamba_ssm", "causal-conv1d==1.0.2", "torch"]
 math = ["sympy>=1.12", "antlr4-python3-runtime==4.11", "math_verify[antlr4_11_0]"]
@@ -96,8 +90,21 @@ tasks = [
     "lm_eval[mamba]",
   "lm_eval[math]",
   "lm_eval[multilingual]",
-    "lm_eval[ruler]",
+  "lm_eval[ruler]"
 ]
+testing = ["pytest", "pytest-cov", "pytest-xdist"]
+unitxt = ["unitxt==1.22.0"]
+vllm = ["vllm>=0.4.2"]
+wandb = ["wandb>=0.16.3", "pandas", "numpy"]
+zeno = ["pandas", "zeno-client"]
+
+[project.scripts]
+lm-eval = "lm_eval.__main__:cli_evaluate"
+lm_eval = "lm_eval.__main__:cli_evaluate"
+
+[project.urls]
+Homepage = "https://github.com/EleutherAI/lm-evaluation-harness"
+Repository = "https://github.com/EleutherAI/lm-evaluation-harness"
 
 [tool.pymarkdown]
 plugins.md013.enabled = false # line-length
@@ -107,21 +114,23 @@ plugins.md028.enabled = false # no-blanks-blockquote
 plugins.md029.allow_extended_start_values = true # ol-prefix
 plugins.md034.enabled = false # no-bare-urls
 
-
 [tool.ruff]
 target-version = "py39"
 lint.extend-select = ["I", "UP", "E", "C419", "F", "B", "SIM"]
-lint.ignore = ["E402", "E731", "E501", "E111", "E114", "E117"]
+lint.fixable = ["I001", "F401", "UP"]
+lint.ignore = ["E402", "E731", "E501", "E111", "E114", "E117", "E741"]
+
+[tool.ruff.lint.extend-per-file-ignores]
+"__init__.py" = ["F401", "F402", "F403"]
 
 [tool.ruff.lint.isort]
 combine-as-imports = true
-lines-after-imports = 2
 known-first-party = ["lm_eval"]
+lines-after-imports = 2
 
-[tool.ruff.lint.extend-per-file-ignores]
-"__init__.py" = ["F401","F402","F403"]
+# required to include yaml files in pip installation
+[tool.setuptools.package-data]
+lm_eval = ["**/*.yaml", "tasks/**/*"]
 
-[dependency-groups]
-dev = [
-  "api","dev","sentencepiece"
-]
+[tool.setuptools.packages.find]
+include = ["lm_eval*"]