From 5a10b6b1b27ff387e8ba549eb152137738181f2f Mon Sep 17 00:00:00 2001
From: "devin-ai-integration[bot]"
 <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Date: Sun, 24 Nov 2024 05:43:01 +0000
Subject: [PATCH] fix: apply black formatting to prompter files

- Remove duplicate initialization code in prompter.py
- Fix long type hints formatting
- Improve method signature formatting in base_prompter.py
---
 .../curator/prompter/base_prompter.py         | 15 ++++--
 src/bespokelabs/curator/prompter/prompter.py  | 50 +++----------------
 2 files changed, 18 insertions(+), 47 deletions(-)

diff --git a/src/bespokelabs/curator/prompter/base_prompter.py b/src/bespokelabs/curator/prompter/base_prompter.py
index 5ecae89e..f965e7d0 100644
--- a/src/bespokelabs/curator/prompter/base_prompter.py
+++ b/src/bespokelabs/curator/prompter/base_prompter.py
@@ -43,7 +43,8 @@ def parse_func(self, row: Dict[str, Any], response: Dict[str, Any]) -> Any:
 
 from pydantic import BaseModel
 
-T = TypeVar('T')
+T = TypeVar("T")
+
 
 class BasePrompter(ABC):
     """Abstract base class for prompter implementations.
@@ -86,7 +87,10 @@ def __init__(
         self.frequency_penalty = frequency_penalty
 
     @abstractmethod
-    def prompt_func(self, row: Optional[Union[Dict[str, Any], BaseModel]] = None) -> Dict[str, str]:
+    def prompt_func(
+        self,
+        row: Optional[Union[Dict[str, Any], BaseModel]] = None,
+    ) -> Dict[str, str]:
         """Override this method to define how prompts are generated.
 
         Args:
@@ -98,8 +102,11 @@ def prompt_func(self, row: Optional[Union[Dict[str, Any], BaseModel]] = None) ->
         """
         pass
 
-    def parse_func(self, row: Union[Dict[str, Any], BaseModel],
-                  response: Union[Dict[str, Any], BaseModel]) -> T:
+    def parse_func(
+        self,
+        row: Union[Dict[str, Any], BaseModel],
+        response: Union[Dict[str, Any], BaseModel],
+    ) -> T:
         """Override this method to define how responses are parsed.
 
         Args:
diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py
index 6080013e..a9c5e27d 100644
--- a/src/bespokelabs/curator/prompter/prompter.py
+++ b/src/bespokelabs/curator/prompter/prompter.py
@@ -110,13 +110,15 @@ def parse_func(self, row, response):
         prompter = CustomPrompter(model_name="gpt-4")
     """
 
-    _prompt_func: Optional[Callable[[Optional[Union[Dict[str, Any], BaseModel]]], Dict[str, str]]]
+    _prompt_func: Optional[
+        Callable[
+            [Optional[Union[Dict[str, Any], BaseModel]]],
+            Dict[str, str],
+        ]
+    ]
     _parse_func: Optional[
         Callable[
-            [
-                Union[Dict[str, Any], BaseModel],
-                Union[Dict[str, Any], BaseModel],
-            ],
+            [Union[Dict[str, Any], BaseModel], Union[Dict[str, Any], BaseModel]],
             T,
         ]
     ]
@@ -222,44 +224,6 @@ def __init__(
                 frequency_penalty=frequency_penalty,
             )
 
-        if parse_func is not None:
-            parse_sig = inspect.signature(parse_func)
-            if len(parse_sig.parameters) != 2:
-                raise ValueError(
-                    f"parse_func must take exactly 2 arguments, got {len(parse_sig.parameters)}"
-                )
-
-        self.prompt_formatter = PromptFormatter(
-            model_name, self.prompt_func, self.parse_func, response_format
-        )
-        self.batch_mode = batch
-        if batch:
-            if batch_size is None:
-                batch_size = 1_000
-                logger.info(
-                    f"batch=True but no batch_size provided, using default batch_size of {batch_size:,}"
-                )
-            self._request_processor = OpenAIBatchRequestProcessor(
-                model=model_name,
-                batch_size=batch_size,
-                temperature=temperature,
-                top_p=top_p,
-                presence_penalty=presence_penalty,
-                frequency_penalty=frequency_penalty,
-            )
-        else:
-            if batch_size is not None:
-                logger.warning(
-                    f"Prompter argument `batch_size` {batch_size} is ignored because `batch` is False"
-                )
-            self._request_processor = OpenAIOnlineRequestProcessor(
-                model=model_name,
-                temperature=temperature,
-                top_p=top_p,
-                presence_penalty=presence_penalty,
-                frequency_penalty=frequency_penalty,
-            )
-
     def __call__(self, dataset: Optional[Iterable] = None, working_dir: str = None) -> Dataset:
         """
         Run completions on a dataset.