Update services/strategy.py

services/strategy.py

@@ -40,7 +40,7 @@ class DefaultStrategy(GenerationStrategy):
 
 
 class MajorityVotingStrategy(GenerationStrategy):
-    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs, num_samples: int = 5) -> str:
+    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
         outputs = []
         for _ in range(num_samples):
             input_ids = generator.tokenizers["llama"](prompt, return_tensors="pt").input_ids.to(generator.device)
@@ -50,7 +50,7 @@ class MajorityVotingStrategy(GenerationStrategy):
 
 
 class BestOfN(GenerationStrategy):
-    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs, num_samples: int = 5) -> str:
+    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
             scored_outputs = []
             for _ in range(num_samples):
                 input_ids = generator.tokenizers["llama"](prompt, return_tensors="pt").input_ids.to(generator.device)
@@ -62,7 +62,7 @@ class BestOfN(GenerationStrategy):
 
 
 class BeamSearch(GenerationStrategy):
-    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs, num_samples: int = 5) -> str:
+    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
             input_ids = generator.tokenizers["llama"](prompt, return_tensors="pt").input_ids.to(generator.device)
             outputs = generator.models["llama"].generate(
                 input_ids,
@@ -74,7 +74,7 @@ class BeamSearch(GenerationStrategy):
 
 
 class DVT(GenerationStrategy):
-    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs, num_samples: int = 5) -> str:
+    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
             results = []
             for _ in range(breadth):
                 input_ids = generator.tokenizers["llama"](prompt, return_tensors="pt").input_ids.to(generator.device)
@@ -95,14 +95,14 @@ class DVT(GenerationStrategy):
         
 
 class COT(GenerationStrategy):
-    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs, num_samples: int = 5) -> str:
+    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
         #TODO implement the chain of thought strategy
         
         return "Not implemented yet"       
 
 
 class ReAct(GenerationStrategy):
-    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs, num_samples: int = 5) -> str:
+    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
         #TODO implement the ReAct framework       
         return "Not implemented yet"
 #  Add other strategy implementations...