nit

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[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)
 
 
@@ -395,7 +395,7 @@ 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.
 
@@ -428,7 +428,7 @@ 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.
 
@@ -449,7 +449,7 @@ 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.