reformat

lm_eval/api/registry.py

-import os
-import logging
-import evaluate
 import collections
+import logging
 from functools import partial
 
+import evaluate
+
 from lm_eval.api.model import LM
 
+
 eval_logger = logging.getLogger("lm-eval")
 
 MODEL_REGISTRY = {}
@@ -92,9 +93,9 @@ def register_metric(
 ):
     # TODO: do we want to enforce a certain interface to registered metrics?
     def decorate(fn):
-        if type(metric) == str:
+        if isinstance(metric, str):
             metric_list = [metric]
-        elif type(metric) == list:
+        elif isinstance(metric, list):
             metric_list = metric
 
         for _metric in metric_list:
@@ -107,9 +108,9 @@ def register_metric(
                 METRIC_REGISTRY[_metric]["higher_is_better"] = higher_is_better
 
             if output_type is not None:
-                if type(output_type) == str:
+                if isinstance(output_type, str):
                     output_type_list = [output_type]
-                elif type(output_type) == list:
+                elif isinstance(output_type, list):
                     output_type_list = output_type
 
                 for _output_type in output_type_list:
@@ -121,7 +122,6 @@ def register_metric(
 
 
 def get_metric(name):
-
     if name in METRIC_REGISTRY:
         return METRIC_REGISTRY[name]
     else:
@@ -133,7 +133,6 @@ def get_evaluate(name, **kwargs):
 
         class HFEvaluateAdaptor:
             def __init__(self, name, **kwargs):
-
                 self.name = name
                 metric_object = evaluate.load(name)
                 self.hf_evaluate_fn = partial(metric_object.compute, **kwargs)

lm_eval/api/task.py

@@ -18,31 +18,13 @@ from lm_eval.api.metrics import (
     bits_per_byte,
     mean,
     weighted_perplexity,
-<<<<<<< HEAD
-<<<<<<< HEAD
-=======
->>>>>>> cda25fef4e1df2f4bc2dab3ec6668ae9f5bf7296
-    bits_per_byte,
-)
-from lm_eval.api.registry import (
-    get_metric,
-    get_evaluate,
-    get_aggregation,
-    METRIC_REGISTRY,
-    DEFAULT_METRIC_REGISTRY,
-<<<<<<< HEAD
-=======
 )
 from lm_eval.api.registry import (
-    AGGREGATION_REGISTRY,
     DEFAULT_METRIC_REGISTRY,
+    METRIC_REGISTRY,
     get_aggregation,
+    get_evaluate,
     get_metric,
-    get_metric_aggregation,
-    is_higher_better,
->>>>>>> 4d10ad56b1ffe569467eee2297e2317c99313118
-=======
->>>>>>> cda25fef4e1df2f4bc2dab3ec6668ae9f5bf7296
 )
 from lm_eval.filters import build_filter_ensemble
 from lm_eval.prompts import get_prompt
@@ -603,7 +585,7 @@ class ConfigurableTask(Task):
                     metric_fn = metric_name.__call__
                     metric_name = metric_name.__name__
                 else:
-                    assert type(metric_name) == str
+                    assert isinstance(metric_name, str)
                     if use_hf_evaluate:
                         metric_fn = get_evaluate(metric_name, **kwargs)
                     elif metric_name in METRIC_REGISTRY:
@@ -620,7 +602,6 @@ class ConfigurableTask(Task):
                     self._aggregation_list[metric_name] = metric_fn
                 else:
                     if "aggregation" in metric_config:
-
                         agg_name = metric_config["aggregation"]
                         if isinstance(agg_name, str):
                             self._aggregation_list[metric_name] = get_aggregation(
@@ -1028,7 +1009,6 @@ class ConfigurableTask(Task):
         )
 
     def process_results(self, doc, results):
-
         # Process results returns 1 of X things per doc/results
         # 1. A score
         # 2. Components to be processed later to obtained a score. such as gold and prediction