partners[lint]: run pyupgrade to get code in line with 3.9 standards (#30781)

Using `pyupgrade` to get all `partners` code up to 3.9 standards
(mostly, fixing old `typing` imports).

libs/partners/ollama/langchain_ollama/chat_models.py

@@ -1,21 +1,14 @@
 """Ollama chat models."""
 
 import json
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from operator import itemgetter
 from typing import (
     Any,
-    AsyncIterator,
     Callable,
-    Dict,
     Final,
-    Iterator,
-    List,
     Literal,
-    Mapping,
     Optional,
-    Sequence,
-    Tuple,
-    Type,
     Union,
     cast,
 )
@@ -153,7 +146,7 @@ def _parse_arguments_from_tool_call(
 
 def _get_tool_calls_from_response(
     response: Mapping[str, Any],
-) -> List[ToolCall]:
+) -> list[ToolCall]:
     """Get tool calls from ollama response."""
     tool_calls = []
     if "message" in response:
@@ -341,7 +334,7 @@ class ChatOllama(BaseChatModel):
     model: str
     """Model name to use."""
 
-    extract_reasoning: Optional[Union[bool, Tuple[str, str]]] = False
+    extract_reasoning: Optional[Union[bool, tuple[str, str]]] = False
     """Whether to extract the reasoning tokens in think blocks.
     Extracts `chunk.content` to `chunk.additional_kwargs.reasoning_content`.
     If a tuple is supplied, they are assumed to be the (start, end) tokens.
@@ -399,7 +392,7 @@ class ChatOllama(BaseChatModel):
     to a specific number will make the model generate the same text for
     the same prompt."""
 
-    stop: Optional[List[str]] = None
+    stop: Optional[list[str]] = None
     """Sets the stop tokens to use."""
 
     tfs_z: Optional[float] = None
@@ -443,10 +436,10 @@ class ChatOllama(BaseChatModel):
 
     def _chat_params(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         **kwargs: Any,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         ollama_messages = self._convert_messages_to_ollama_messages(messages)
 
         if self.stop is not None and stop is not None:
@@ -499,13 +492,13 @@ class ChatOllama(BaseChatModel):
         return self
 
     def _convert_messages_to_ollama_messages(
-        self, messages: List[BaseMessage]
+        self, messages: list[BaseMessage]
     ) -> Sequence[Message]:
-        ollama_messages: List = []
+        ollama_messages: list = []
         for message in messages:
             role: Literal["user", "assistant", "system", "tool"]
             tool_call_id: Optional[str] = None
-            tool_calls: Optional[List[Dict[str, Any]]] = None
+            tool_calls: Optional[list[dict[str, Any]]] = None
             if isinstance(message, HumanMessage):
                 role = "user"
             elif isinstance(message, AIMessage):
@@ -531,7 +524,7 @@ class ChatOllama(BaseChatModel):
             if isinstance(message.content, str):
                 content = message.content
             else:
-                for content_part in cast(List[Dict], message.content):
+                for content_part in cast(list[dict], message.content):
                     if content_part.get("type") == "text":
                         content += f"\n{content_part['text']}"
                     elif content_part.get("type") == "tool_use":
@@ -583,7 +576,7 @@ class ChatOllama(BaseChatModel):
 
     def _extract_reasoning(
         self, message_chunk: BaseMessageChunk, is_thinking: bool
-    ) -> Tuple[BaseMessageChunk, bool]:
+    ) -> tuple[BaseMessageChunk, bool]:
         """Mutate a message chunk to extract reasoning content."""
         if not self.extract_reasoning:
             return message_chunk, is_thinking
@@ -605,8 +598,8 @@ class ChatOllama(BaseChatModel):
 
     async def _acreate_chat_stream(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> AsyncIterator[Union[Mapping[str, Any], str]]:
         chat_params = self._chat_params(messages, stop, **kwargs)
@@ -619,8 +612,8 @@ class ChatOllama(BaseChatModel):
 
     def _create_chat_stream(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> Iterator[Union[Mapping[str, Any], str]]:
         chat_params = self._chat_params(messages, stop, **kwargs)
@@ -632,8 +625,8 @@ class ChatOllama(BaseChatModel):
 
     def _chat_stream_with_aggregation(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         verbose: bool = False,
         **kwargs: Any,
@@ -657,8 +650,8 @@ class ChatOllama(BaseChatModel):
 
     async def _achat_stream_with_aggregation(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         verbose: bool = False,
         **kwargs: Any,
@@ -681,7 +674,7 @@ class ChatOllama(BaseChatModel):
         return final_chunk
 
     def _get_ls_params(
-        self, stop: Optional[List[str]] = None, **kwargs: Any
+        self, stop: Optional[list[str]] = None, **kwargs: Any
     ) -> LangSmithParams:
         """Get standard params for tracing."""
         params = self._get_invocation_params(stop=stop, **kwargs)
@@ -697,8 +690,8 @@ class ChatOllama(BaseChatModel):
 
     def _generate(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
@@ -719,8 +712,8 @@ class ChatOllama(BaseChatModel):
 
     def _iterate_over_stream(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
         is_thinking = False
@@ -758,8 +751,8 @@ class ChatOllama(BaseChatModel):
 
     def _stream(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
@@ -773,8 +766,8 @@ class ChatOllama(BaseChatModel):
 
     async def _aiterate_over_stream(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
         is_thinking = False
@@ -812,8 +805,8 @@ class ChatOllama(BaseChatModel):
 
     async def _astream(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
@@ -827,8 +820,8 @@ class ChatOllama(BaseChatModel):
 
     async def _agenerate(
         self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
@@ -854,7 +847,7 @@ class ChatOllama(BaseChatModel):
 
     def bind_tools(
         self,
-        tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
+        tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
         *,
         tool_choice: Optional[Union[dict, str, Literal["auto", "any"], bool]] = None,
         **kwargs: Any,
@@ -877,12 +870,12 @@ class ChatOllama(BaseChatModel):
 
     def with_structured_output(
         self,
-        schema: Union[Dict, type],
+        schema: Union[dict, type],
         *,
         method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
         include_raw: bool = False,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
+    ) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
         """Model wrapper that returns outputs formatted to match the given schema.
 
         Args: