reformat for pre-commit

docs/new_task_guide.md

@@ -87,7 +87,7 @@ Our intended output is for the model to predict a single whitespace, and then th
 doc_to_target: "{{answer}}"
 gold_alias: "{{answer}}"
 ```
-where `doc_to_target` is *the string that will be appended to inputs for each few-shot example*, and `gold_alias` is *what is passed to our metric function as reference or gold answer to score against*. For example, for GSM8k word problems, `doc_to_target` should be the reference text reasoning chain given in the dataset culminating in the answer, and `gold_alias` should be **only the numeric answer** to the word problem that is given at the end of the reasoning chain, and which the evaluated model's answer will be compared against. 
+where `doc_to_target` is *the string that will be appended to inputs for each few-shot example*, and `gold_alias` is *what is passed to our metric function as reference or gold answer to score against*. For example, for GSM8k word problems, `doc_to_target` should be the reference text reasoning chain given in the dataset culminating in the answer, and `gold_alias` should be **only the numeric answer** to the word problem that is given at the end of the reasoning chain, and which the evaluated model's answer will be compared against.
 
 **Important**: We always add one whitespace between the input and output, such that the full input-output string is `doc_to_target(doc) + " " + doc_to_text(doc)`. doc_to_text and doc_to_target should not contain trailing right or left whitespace, respectively.
 

lm_eval/models/hf_causal.py

@@ -23,7 +23,7 @@ class HFLM(LM):
         pretrained="gpt2",
         revision="main",
         low_cpu_mem_usage=None,
-        dtype: Optional[Union[str, torch.dtype]]="auto",
+        dtype: Optional[Union[str, torch.dtype]] = "auto",
         subfolder=None,
         tokenizer=None,
         batch_size=1,
@@ -59,8 +59,8 @@ class HFLM(LM):
         revision = revision + ("/" + subfolder if subfolder is not None else "")
 
         self.model = transformers.AutoModelForCausalLM.from_pretrained(
-            pretrained, 
-            revision=revision, 
+            pretrained,
+            revision=revision,
             low_cpu_mem_usage=low_cpu_mem_usage,
             torch_dtype=utils.get_dtype(dtype),
         ).to(self.device)

lm_eval/utils.py

@@ -421,13 +421,11 @@ def clear_torch_cache():
     torch.cuda.empty_cache()
 
 
-def get_dtype(
-    dtype: Union[str, torch.dtype]
-) -> torch.dtype:
+def get_dtype(dtype: Union[str, torch.dtype]) -> torch.dtype:
     """Converts `dtype` from `str` to torch.dtype when possible. Does not use an instantiated HF AutoConfig"""
     if isinstance(dtype, str) and dtype != "auto":
         # Convert `str` args torch dtype: `float16` -> `torch.float16`
         _torch_dtype = getattr(torch, dtype)
     else:
         _torch_dtype = dtype
-    return _torch_dtype
\ No newline at end of file
+    return _torch_dtype