format: ruff

src/instructlab/eval/mmlu.py

@@ -153,7 +153,9 @@ def run(self, server_url: str | None = None) -> tuple:
 
         return overall_score, individual_scores
 
-    def _run_mmlu(self, server_url: str | None = None, return_all_results:bool = False) -> dict:
+    def _run_mmlu(
+        self, server_url: str | None = None, return_all_results: bool = False
+    ) -> dict:
         if server_url is not None:
             # Requires lm_eval >= 0.4.4
             model_args = f"base_url={server_url}/completions,model={self.model_path},tokenizer_backend=huggingface"

src/instructlab/eval/unitxt.py

@@ -5,12 +5,13 @@
 """
 
 # Standard
-import os, shutil
-import yaml
 from uuid import uuid4
+import os
+import shutil
 
 # Third Party
 from lm_eval.tasks.unitxt import task
+import yaml
 
 # First Party
 from instructlab.eval.mmlu import MMLUBranchEvaluator
@@ -20,7 +21,8 @@
 
 logger = setup_logger(__name__)
 
-TEMP_DIR_PREFIX = 'unitxt_temp'
+TEMP_DIR_PREFIX = "unitxt_temp"
+
 
 class UnitxtEvaluator(MMLUBranchEvaluator):
     """
@@ -29,45 +31,50 @@ class UnitxtEvaluator(MMLUBranchEvaluator):
     Attributes:
         model_path      absolute path to or name of a huggingface model
         unitxt_recipe   unitxt recipe (see unitxt.ai for more information)
-                        A Recipe holds a complete specification of a unitxt pipeline 
+                        A Recipe holds a complete specification of a unitxt pipeline
                         Example: card=cards.wnli,template=templates.classification.multi_class.relation.default,max_train_instances=5,loader_limit=20,num_demos=3,demos_pool_size=10
-    
+
     """
+
     name = "unitxt"
+
     def __init__(
         self,
-        model_path,   
+        model_path,
         unitxt_recipe: str,
     ):
         task = self.assign_task_name()
         tasks_dir = self.assign_tasks_dir(task)
         super().__init__(
-            model_path = model_path,
-            tasks_dir = tasks_dir,
-            tasks = [task],
-            few_shots = 0
+            model_path=model_path, tasks_dir=tasks_dir, tasks=[task], few_shots=0
         )
         self.unitxt_recipe = unitxt_recipe
 
     def assign_tasks_dir(self, task):
-        return f'{TEMP_DIR_PREFIX}_{task}'
+        return f"{TEMP_DIR_PREFIX}_{task}"
 
     def assign_task_name(self):
         return str(uuid4())
 
-    def prepare_unitxt_files(self)->tuple:
+    def prepare_unitxt_files(self) -> tuple:
         task = self.tasks[0]
-        yaml_file = os.path.join(self.tasks_dir,f"{task}.yaml")
+        yaml_file = os.path.join(self.tasks_dir, f"{task}.yaml")
         create_unitxt_pointer(self.tasks_dir)
-        create_unitxt_yaml(yaml_file=yaml_file, unitxt_recipe=self.unitxt_recipe, task_name=task)
+        create_unitxt_yaml(
+            yaml_file=yaml_file, unitxt_recipe=self.unitxt_recipe, task_name=task
+        )
 
     def remove_unitxt_files(self):
-        if self.tasks_dir.startswith(TEMP_DIR_PREFIX): #to avoid unintended deletion if this class is inherited
+        if self.tasks_dir.startswith(
+            TEMP_DIR_PREFIX
+        ):  # to avoid unintended deletion if this class is inherited
             shutil.rmtree(self.tasks_dir)
         else:
-            logger.warning(f"unitxt tasks dir did not start with '{TEMP_DIR_PREFIX}' and therefor was not deleted")
+            logger.warning(
+                f"unitxt tasks dir did not start with '{TEMP_DIR_PREFIX}' and therefor was not deleted"
+            )
 
-    def run(self,server_url: str | None = None) -> tuple:
+    def run(self, server_url: str | None = None) -> tuple:
         """
         Runs evaluation
 
@@ -80,13 +87,16 @@ def run(self,server_url: str | None = None) -> tuple:
         os.environ["TOKENIZERS_PARALLELISM"] = "true"
         results = self._run_mmlu(server_url=server_url, return_all_results=True)
         taskname = self.tasks[0]
-        global_scores = results['results'][taskname]
-        global_scores.pop('alias')
+        global_scores = results["results"][taskname]
+        global_scores.pop("alias")
         try:
-            instances = results['samples'][taskname]
+            instances = results["samples"][taskname]
             instance_scores = {}
-            metrics = [metric.replace('metrics.','') for metric in instances[0]['doc']['metrics']]
-            for i,instance in enumerate(instances):
+            metrics = [
+                metric.replace("metrics.", "")
+                for metric in instances[0]["doc"]["metrics"]
+            ]
+            for i, instance in enumerate(instances):
                 scores = {}
                 for metric in metrics:
                     scores[metric] = instance[metric][0]
@@ -97,23 +107,20 @@ def run(self,server_url: str | None = None) -> tuple:
             logger.error(e.__traceback__)
             instance_scores = None
         self.remove_unitxt_files()
-        return global_scores,instance_scores
+        return global_scores, instance_scores
 
 
-def create_unitxt_yaml(yaml_file,unitxt_recipe, task_name):
-    data = {
-    'task': f'{task_name}',
-    'include': 'unitxt',
-    'recipe': f'{unitxt_recipe}'
-    }
-    with open(yaml_file, 'w') as file:
+def create_unitxt_yaml(yaml_file, unitxt_recipe, task_name):
+    data = {"task": f"{task_name}", "include": "unitxt", "recipe": f"{unitxt_recipe}"}
+    with open(yaml_file, "w") as file:
         yaml.dump(data, file, default_flow_style=False)
     logger.debug(f"task {task} unitxt recipe written to {yaml_file}")
 
+
 def create_unitxt_pointer(tasks_dir):
     class_line = "class: !function " + task.__file__.replace("task.py", "task.Unitxt")
-    output_file = os.path.join(tasks_dir,'unitxt')
+    output_file = os.path.join(tasks_dir, "unitxt")
     os.makedirs(os.path.dirname(output_file), exist_ok=True)
-    with open(output_file, 'w') as f:
+    with open(output_file, "w") as f:
         f.write(class_line)
     logger.debug(f"Unitxt task pointer written to {output_file}")

tests/test_unitxt.py

@@ -7,9 +7,7 @@ def test_unitxt():
     try:
         model_path = "instructlab/granite-7b-lab"
         unitxt_recipe = "card=cards.wnli,template=templates.classification.multi_class.relation.default,max_train_instances=5,loader_limit=20,num_demos=3,demos_pool_size=10"
-        unitxt = UnitxtEvaluator(
-            model_path=model_path, unitxt_recipe=unitxt_recipe
-        )
+        unitxt = UnitxtEvaluator(model_path=model_path, unitxt_recipe=unitxt_recipe)
         overall_score, single_scores = unitxt.run()
         print(overall_score)
     except Exception as exc:
@@ -19,4 +17,4 @@ def test_unitxt():
 
 
 if __name__ == "__main__":
-    assert test_unitxt() == True
+    assert test_unitxt() == True