use local unitxt if it exits for evaluation

lm_eval/tasks/unitxt/task.py

@@ -4,10 +4,11 @@
 Addressing this need, we present Unitxt, an innovative library for customizable textual data preparation and evaluation tailored to generative language models. Unitxt natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.
 """
 
+import importlib.util
 from functools import partial
-from typing import Optional
+from typing import Any, Dict, Optional
 
-import evaluate
+import datasets
 
 from lm_eval.api.instance import Instance
 from lm_eval.api.task import ConfigurableTask
@@ -24,13 +25,19 @@
 }
 """
 
+def is_unitxt_installed() -> bool:
+    return importlib.util.find_spec("unitxt") is not None
 
 def score(items, metric):
     predictions, references = zip(*items)
-    evaluator = evaluate.load("unitxt/metric")
-    for reference in references:
-        reference["metrics"] = [metric]
-    results = evaluator.compute(predictions=predictions, references=references)
+    if is_unitxt_installed():
+        from unitxt import evaluate
+        for reference in references:
+            reference["metrics"] = [metric]
+        results = evaluate(predictions,references)
+    else:
+        raise Exception("Please install unitxt via 'pip install unitxt'. For more information see: https://www.unitxt.ai/")
+
     return results[0]["score"]["global"]["score"]
 
 
@@ -41,17 +48,26 @@ def __init__(
         self,
         config: Optional[dict] = None,
     ) -> None:
+        if config is None:
+            config = {}
         assert "recipe" in config, "Unitxt task must have a 'recipe' string."
         super().__init__(
             config={
                 "metadata": {"version": self.VERSION},
-                "dataset_kwargs": {"trust_remote_code": True},
                 "dataset_name": config["recipe"],
-                "dataset_path": "unitxt/data",
             }
         )
+        self.image_decoder = datasets.Image()
         self.metrics = self.dataset["test"][0]["metrics"]
 
+    def download(self, dataset_kwargs: Optional[Dict[str, Any]] = None) -> None:
+        if is_unitxt_installed():
+            from unitxt import load_dataset
+
+            self.dataset = load_dataset(self.DATASET_NAME)
+        else:
+            raise Exception("Please install unitxt via 'pip install unitxt'. For more information see: https://www.unitxt.ai/")
+
     def has_training_docs(self):
         return "train" in self.dataset