done

Author: Hangzhi

dspy/adapters/types/audio.py

@@ -114,7 +114,7 @@ def __repr__(self) -> str:
 def encode_audio(audio: Union[str, bytes, dict, "Audio", Any], sampling_rate: int = 16000, format: str = "wav") -> dict:
     """
     Encode audio to a dict with 'data' and 'audio_format'.
-    
+
     Accepts: local file path, URL, data URI, dict, Audio instance, numpy array, or bytes (with known format).
     """
     if isinstance(audio, dict) and "data" in audio and "audio_format" in audio:

dspy/evaluate/evaluate.py

@@ -126,9 +126,9 @@ def __call__(
 
         Returns:
             The evaluation results are returned as a dspy.EvaluationResult object containing the following attributes:
-            
+
             - score: A float percentage score (e.g., 67.30) representing overall performance
-            
+
             - results: a list of (example, prediction, score) tuples for each example in devset
         """
         metric = metric if metric is not None else self.metric

dspy/primitives/base_module.py

@@ -169,10 +169,10 @@ def save(self, path, save_program=False, modules_to_serialize=None):
         - `save_program=True`: Save the whole module to a directory via cloudpickle, which contains both the state and
             architecture of the model.
 
-        If `save_program=True` and `modules_to_serialize` are provided, it will register those modules for serialization 
-        with cloudpickle's `register_pickle_by_value`. This causes cloudpickle to serialize the module by value rather 
-        than by reference, ensuring the module is fully preserved along with the saved program. This is useful 
-        when you have custom modules that need to be serialized alongside your program. If None, then no modules 
+        If `save_program=True` and `modules_to_serialize` are provided, it will register those modules for serialization
+        with cloudpickle's `register_pickle_by_value`. This causes cloudpickle to serialize the module by value rather
+        than by reference, ensuring the module is fully preserved along with the saved program. This is useful
+        when you have custom modules that need to be serialized alongside your program. If None, then no modules
         will be registered for serialization.
 
         We also save the dependency versions, so that the loaded model can check if there is a version mismatch on

dspy/utils/langchain_tool.py

@@ -9,7 +9,7 @@
 
 def convert_langchain_tool(tool: "BaseTool") -> Tool:
     """Build a DSPy tool from a LangChain tool.
-    
+
     This function converts a LangChain tool (either created with @tool decorator
     or by subclassing BaseTool) into a DSPy Tool.
 

tests/primitives/test_base_module.py

@@ -306,8 +306,8 @@ def __call__(self, question: str) -> str:
     assert results[0].get_lm_usage() is not None
     assert results[1].get_lm_usage() is not None
 
-    assert results[0].get_lm_usage().keys() == set(["openai/gpt-4o-mini"])
-    assert results[1].get_lm_usage().keys() == set(["openai/gpt-3.5-turbo"])
+    assert results[0].get_lm_usage().keys() == {"openai/gpt-4o-mini"}
+    assert results[1].get_lm_usage().keys() == {"openai/gpt-3.5-turbo"}
 
 
 @pytest.mark.asyncio

tests/signatures/test_custom_types.py

@@ -29,7 +29,6 @@ class Container:
         class NestedType(pydantic.BaseModel):
             value: str
 
-    NestedType = Container.NestedType
     alias_sig = Signature("input: str -> output: NestedType")
     assert alias_sig.output_fields["output"].annotation == Container.NestedType
 

tests/streaming/test_streaming.py

@@ -388,7 +388,7 @@ async def gpt_4o_mini_stream_2():
     async def completion_side_effect(*args, **kwargs):
         return stream_generators.pop(0)()  # return new async generator instance
 
-    with mock.patch("litellm.acompletion", side_effect=completion_side_effect) as mock_completion:
+    with mock.patch("litellm.acompletion", side_effect=completion_side_effect):
         program = dspy.streamify(
             MyProgram(),
             stream_listeners=[
@@ -484,7 +484,7 @@ async def gpt_4o_mini_stream_2(*args, **kwargs):
 
     with mock.patch(
         "litellm.acompletion", new_callable=AsyncMock, side_effect=[gpt_4o_mini_stream_1(), gpt_4o_mini_stream_2()]
-    ) as mock_completion:
+    ):
         program = dspy.streamify(
             MyProgram(),
             stream_listeners=[

tests/teleprompt/test_bootstrap_finetune.py

@@ -21,7 +21,7 @@ def test_bootstrap_finetune_initialization():
     # Initialize BootstrapFinetune with a dummy metric and minimal setup
     bootstrap = BootstrapFinetune(metric=simple_metric)
     assert bootstrap.metric == simple_metric, "Metric not correctly initialized"
-    assert bootstrap.multitask == True, "Multitask should default to True"
+    assert bootstrap.multitask, "Multitask should default to True"
 
 
 class SimpleModule(dspy.Module):
@@ -44,7 +44,6 @@ def test_compile_with_predict_instances_no_explicit_lm():
     teacher = SimpleModule("input -> output")
 
     lm = DummyLM(["Initial thoughts", "Finish[blue]"])
-    original_lm = dspy.settings.lm
     dspy.settings.configure(lm=lm)
 
     # Verify that the predictor doesn't have an explicit LM