[Issue-1961]: Handle clippy::unused_self (tensorzero#2849)

Author: michaldorsett

Cargo.toml

@@ -87,6 +87,7 @@ trivially_copy_pass_by_ref = "deny"
 unimplemented = "deny"
 uninlined_format_args = "deny"
 unreachable = "deny"
+unused_self = "deny"
 unwrap_used = "deny"
 
 [profile.performance]

clients/python/src/lib.rs

@@ -576,6 +576,7 @@ impl TensorZeroGateway {
     }
 
     /// Close the connection to the TensorZero gateway.
+    #[expect(clippy::unused_self)]
     fn close(&self) {
         // TODO - implement closing the 'reqwest' connection pool: https://github.com/tensorzero/tensorzero/issues/857
     }

tensorzero-core/src/inference/types/storage.rs

@@ -47,6 +47,7 @@ impl StorageKind {
     /// During a normal run, we never use a prefix on the object key.
     /// See `StorageKind::S3Compatible.prefix`
     #[cfg(not(feature = "e2e_tests"))]
+    #[expect(clippy::unused_self)]
     fn prefix(&self) -> &str {
         ""
     }

tensorzero-core/src/model.rs

@@ -180,7 +180,6 @@ pub fn fully_qualified_name(model_name: &str, provider_name: &str) -> String {
 
 impl ModelConfig {
     fn filter_content_blocks<'a>(
-        &self,
         request: &'a ModelInferenceRequest<'a>,
         model_name: &str,
         provider: &ModelProvider,
@@ -391,7 +390,7 @@ impl ModelConfig {
                         provider_name: provider_name.to_string(),
                     })
                 })?;
-                let request = self.filter_content_blocks(request, model_name, provider);
+                let request = Self::filter_content_blocks(request, model_name, provider);
                 let model_provider_request = ModelProviderRequest {
                     request: &request,
                     model_name,
@@ -483,7 +482,7 @@ impl ModelConfig {
                         provider_name: provider_name.to_string(),
                     })
                 })?;
-                let request = self.filter_content_blocks(request, model_name, provider);
+                let request = Self::filter_content_blocks(request, model_name, provider);
                 let model_provider_request = ModelProviderRequest {
                     request: &request,
                     model_name,

tensorzero-core/src/variant/best_of_n_sampling.rs

@@ -582,7 +582,6 @@ impl BestOfNEvaluatorConfig {
     ///
     /// Returns an `Error` if any of the candidate outputs fail to serialize or if templating fails.
     fn prepare_candidate_message(
-        &self,
         templates: &TemplateConfig,
         candidates: &[InferenceResult],
     ) -> Result<(RequestMessage, Vec<usize>), Error> {
@@ -649,7 +648,7 @@ impl BestOfNEvaluatorConfig {
     ) -> Result<(ModelInferenceRequest<'a>, Vec<usize>), Error> {
         // Do this before we prepare the system message so we can use the correct max index in the system message
         let (candidate_message, skipped_indices) =
-            self.prepare_candidate_message(inference_config.templates, candidates)?;
+            Self::prepare_candidate_message(inference_config.templates, candidates)?;
         // Need to subtract the skipped indices from the total number of candidates to get the correct max index
         let max_index = candidates
             .len()
@@ -949,14 +948,6 @@ mod tests {
         let templates = get_test_template_config();
 
         // Create an EvaluatorConfig
-        let evaluator_config = BestOfNEvaluatorConfig {
-            inner: ChatCompletionConfig {
-                model: "dummy".into(),
-                weight: Some(1.0),
-                ..Default::default()
-            },
-        };
-
         // Prepare some candidate InferenceResults
         let model_inference_response = ModelInferenceResponseWithMetadata {
             id: Uuid::now_v7(),
@@ -1033,7 +1024,7 @@ mod tests {
         let candidates = vec![candidate1, candidate2];
 
         // Call prepare_candidate_message
-        let result = evaluator_config.prepare_candidate_message(&templates, &candidates);
+        let result = BestOfNEvaluatorConfig::prepare_candidate_message(&templates, &candidates);
         assert!(result.is_ok());
         let (request_message, skipped_indices) = result.unwrap();
         assert!(skipped_indices.is_empty());
@@ -1048,15 +1039,6 @@ mod tests {
     async fn test_prepare_candidate_message_json() {
         let templates = get_test_template_config();
 
-        // Create an EvaluatorConfig
-        let evaluator_config = BestOfNEvaluatorConfig {
-            inner: ChatCompletionConfig {
-                model: "dummy_json".into(),
-                weight: Some(1.0),
-                ..Default::default()
-            },
-        };
-
         // Prepare some candidate InferenceResults - some valid, some malformed
         let model_inference_response_valid = ModelInferenceResponseWithMetadata {
             id: Uuid::now_v7(),
@@ -1135,7 +1117,7 @@ mod tests {
         let candidates = vec![candidate1, candidate2];
 
         // Call prepare_candidate_message
-        let result = evaluator_config.prepare_candidate_message(&templates, &candidates);
+        let result = BestOfNEvaluatorConfig::prepare_candidate_message(&templates, &candidates);
         assert!(result.is_ok());
         let (request_message, skipped_indices) = result.unwrap();
 

tensorzero-core/src/variant/dicl.rs

@@ -395,7 +395,7 @@ impl DiclConfig {
     /// The second message is an Assistant message with the output as native output blocks
     ///   - For chat messages, this is a simple vector of ContentBlocks
     ///   - For JSON messages, this is a single JSON output block (as Text)
-    fn prepare_message(&self, example: &Example) -> Result<Vec<RequestMessage>, Error> {
+    fn prepare_message(example: &Example) -> Result<Vec<RequestMessage>, Error> {
         let mut messages = Vec::new();
         let input = match example {
             Example::Chat(chat_example) => chat_example.input.clone(),
@@ -437,7 +437,7 @@ impl DiclConfig {
         Ok(messages)
     }
 
-    fn prepare_input_message(&self, input: &ResolvedInput) -> Result<RequestMessage, Error> {
+    fn prepare_input_message(input: &ResolvedInput) -> Result<RequestMessage, Error> {
         let content = vec![serde_json::to_string(&input)
             .map_err(|e| {
                 Error::new(ErrorDetails::Serialization {
@@ -489,11 +489,11 @@ impl DiclConfig {
         }
         let messages = examples
             .iter()
-            .map(|example| self.prepare_message(example))
+            .map(Self::prepare_message)
             .collect::<Result<Vec<Vec<RequestMessage>>, _>>()?
             .into_iter()
             .flatten()
-            .chain(std::iter::once(self.prepare_input_message(input)?))
+            .chain(std::iter::once(Self::prepare_input_message(input)?))
             .collect::<Vec<_>>();
 
         let system = Some(self.system_instructions.clone());
@@ -658,9 +658,6 @@ mod tests {
 
     #[test]
     fn test_prepare_message() {
-        // Create an instance of DiclConfig (assuming default implementation is available)
-        let dicl_config = DiclConfig::default();
-
         // ---------- Test with ChatExample ----------
 
         // Mock Input data
@@ -701,7 +698,7 @@ mod tests {
             output: chat_output.clone(),
         });
 
-        let chat_messages = dicl_config.prepare_message(&chat_example).unwrap();
+        let chat_messages = DiclConfig::prepare_message(&chat_example).unwrap();
 
         assert_eq!(chat_messages.len(), 2);
 
@@ -737,7 +734,7 @@ mod tests {
             output: json_output.clone(),
         });
 
-        let json_messages = dicl_config.prepare_message(&json_example).unwrap();
+        let json_messages = DiclConfig::prepare_message(&json_example).unwrap();
 
         // Assertions for JsonExample
         assert_eq!(json_messages.len(), 2);
@@ -762,9 +759,6 @@ mod tests {
 
     #[test]
     fn test_prepare_input_message() {
-        // Create an instance of DiclConfig (assuming default implementation is available)
-        let dicl_config = DiclConfig::default();
-
         // Mock Input data
         let input_data = ResolvedInput {
             system: Some(json!({"assistant_name": "Dr. Mehta"})),
@@ -792,7 +786,7 @@ mod tests {
         };
 
         // Call the prepare_input_message function
-        let request_message = dicl_config.prepare_input_message(&input_data).unwrap();
+        let request_message = DiclConfig::prepare_input_message(&input_data).unwrap();
 
         // The role should be User
         assert_eq!(request_message.role, Role::User);

tensorzero-core/src/variant/mixture_of_n.rs

@@ -698,7 +698,6 @@ impl FuserConfig {
     ///
     /// Returns an `Error` if any of the candidate outputs fail to serialize or if templating fails.
     fn prepare_candidate_message(
-        &self,
         templates: &TemplateConfig,
         candidates: &[InferenceResult],
     ) -> Result<(RequestMessage, Vec<usize>), Error> {
@@ -769,7 +768,7 @@ impl FuserConfig {
     {
         // Do this before we prepare the system message so we can use the correct max index in the system message
         let (candidate_message, included_indices) =
-            self.prepare_candidate_message(inference_config.templates, candidates)?;
+            Self::prepare_candidate_message(inference_config.templates, candidates)?;
         let max_index = included_indices.len().saturating_sub(1);
         let system = Some(self.prepare_system_message(
             inference_config.templates,
@@ -1014,15 +1013,6 @@ mod tests {
     async fn test_prepare_candidate_message() {
         let templates = get_test_template_config();
 
-        // Create an FuserConfig
-        let fuser_config = FuserConfig {
-            inner: ChatCompletionConfig {
-                model: "dummy".into(),
-                weight: Some(1.0),
-                ..Default::default()
-            },
-        };
-
         // Prepare some candidate InferenceResults
         let model_inference_response = ModelInferenceResponseWithMetadata {
             id: Uuid::now_v7(),
@@ -1093,7 +1083,7 @@ mod tests {
         let candidates = vec![candidate1, candidate2];
 
         // Call prepare_candidate_message
-        let result = fuser_config.prepare_candidate_message(&templates, &candidates);
+        let result = FuserConfig::prepare_candidate_message(&templates, &candidates);
         assert!(result.is_ok());
         let (request_message, included_indices) = result.unwrap();
         assert_eq!(included_indices, vec![0, 1]);
@@ -1108,15 +1098,6 @@ mod tests {
     async fn test_prepare_candidate_message_json() {
         let templates = get_test_template_config();
 
-        // Create a FuserConfig
-        let fuser_config = FuserConfig {
-            inner: ChatCompletionConfig {
-                model: "dummy_json".into(),
-                weight: Some(1.0),
-                ..Default::default()
-            },
-        };
-
         // Prepare some candidate InferenceResults - some valid, some malformed
         let model_inference_response_valid = ModelInferenceResponseWithMetadata {
             id: Uuid::now_v7(),
@@ -1189,7 +1170,7 @@ mod tests {
         let candidates = vec![candidate1, candidate2];
 
         // Call prepare_candidate_message
-        let result = fuser_config.prepare_candidate_message(&templates, &candidates);
+        let result = FuserConfig::prepare_candidate_message(&templates, &candidates);
         assert!(result.is_ok());
         let (request_message, included_indices) = result.unwrap();