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/huggingface/langchain_huggingface/chat_models/huggingface.py

@@ -1,16 +1,13 @@
 """Hugging Face Chat Wrapper."""
 
 import json
+from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import (
     Any,
     Callable,
-    Dict,
-    List,
     Literal,
     Optional,
-    Sequence,
-    Type,
     Union,
     cast,
 )
@@ -46,8 +43,8 @@ DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful, and honest assistant."
 class TGI_RESPONSE:
     """Response from the TextGenInference API."""
 
-    choices: List[Any]
-    usage: Dict
+    choices: list[Any]
+    usage: dict
 
 
 @dataclass
@@ -56,12 +53,12 @@ class TGI_MESSAGE:
 
     role: str
     content: str
-    tool_calls: List[Dict]
+    tool_calls: list[dict]
 
 
 def _convert_message_to_chat_message(
     message: BaseMessage,
-) -> Dict:
+) -> dict:
     if isinstance(message, ChatMessage):
         return dict(role=message.role, content=message.content)
     elif isinstance(message, HumanMessage):
@@ -104,7 +101,7 @@ def _convert_TGI_message_to_LC_message(
     content = cast(str, _message.content)
     if content is None:
         content = ""
-    additional_kwargs: Dict = {}
+    additional_kwargs: dict = {}
     if tool_calls := _message.tool_calls:
         if "arguments" in tool_calls[0]["function"]:
             functions = tool_calls[0]["function"].pop("arguments")
@@ -358,8 +355,8 @@ class ChatHuggingFace(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:
@@ -380,8 +377,8 @@ class ChatHuggingFace(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:
@@ -398,7 +395,7 @@ class ChatHuggingFace(BaseChatModel):
 
     def _to_chat_prompt(
         self,
-        messages: List[BaseMessage],
+        messages: list[BaseMessage],
     ) -> str:
         """Convert a list of messages into a prompt format expected by wrapped LLM."""
         if not messages:
@@ -472,7 +469,7 @@ class ChatHuggingFace(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", "none", "required"], bool]
@@ -529,8 +526,8 @@ class ChatHuggingFace(BaseChatModel):
         return super().bind(tools=formatted_tools, **kwargs)
 
     def _create_message_dicts(
-        self, messages: List[BaseMessage], stop: Optional[List[str]]
-    ) -> List[Dict[Any, Any]]:
+        self, messages: list[BaseMessage], stop: Optional[list[str]]
+    ) -> list[dict[Any, Any]]:
         message_dicts = [_convert_message_to_chat_message(m) for m in messages]
         return message_dicts
 

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from langchain_core.embeddings import Embeddings
 from pydantic import BaseModel, ConfigDict, Field
@@ -40,16 +40,16 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     cache_folder: Optional[str] = None
     """Path to store models. 
     Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
-    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    model_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass to the Sentence Transformer model, such as `device`,
     `prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
-    encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    encode_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass when calling the `encode` method for the documents of
     the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, 
     `precision`, `normalize_embeddings`, and more.
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
-    query_encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    query_encode_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass when calling the `encode` method for the query of
     the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, 
     `precision`, `normalize_embeddings`, and more.
@@ -102,8 +102,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     )
 
     def _embed(
-        self, texts: list[str], encode_kwargs: Dict[str, Any]
-    ) -> List[List[float]]:
+        self, texts: list[str], encode_kwargs: dict[str, Any]
+    ) -> list[list[float]]:
         """
         Embed a text using the HuggingFace transformer model.
 
@@ -138,7 +138,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
 
         return embeddings.tolist()
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Compute doc embeddings using a HuggingFace transformer model.
 
         Args:
@@ -149,7 +149,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         """
         return self._embed(texts, self.encode_kwargs)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """Compute query embeddings using a HuggingFace transformer model.
 
         Args:

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, List, Optional
+from typing import Any, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import from_env
@@ -101,7 +101,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
             )
         return self
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
 
         Args:
@@ -117,7 +117,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
         responses = self.client.feature_extraction(text=texts, **_model_kwargs)
         return responses
 
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+    async def aembed_documents(self, texts: list[str]) -> list[list[float]]:
         """Async Call to HuggingFaceHub's embedding endpoint for embedding search docs.
 
         Args:
@@ -134,7 +134,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
         )
         return responses
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """Call out to HuggingFaceHub's embedding endpoint for embedding query text.
 
         Args:
@@ -146,7 +146,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
         response = self.embed_documents([text])[0]
         return response
 
-    async def aembed_query(self, text: str) -> List[float]:
+    async def aembed_query(self, text: str) -> list[float]:
         """Async Call to HuggingFaceHub's embedding endpoint for embedding query text.
 
         Args:

libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py

@@ -2,7 +2,8 @@ import inspect
 import json  # type: ignore[import-not-found]
 import logging
 import os
-from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
+from collections.abc import AsyncIterator, Iterator, Mapping
+from typing import Any, Optional
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
@@ -96,7 +97,7 @@ class HuggingFaceEndpoint(LLM):
     """Whether to prepend the prompt to the generated text"""
     truncate: Optional[int] = None
     """Truncate inputs tokens to the given size"""
-    stop_sequences: List[str] = Field(default_factory=list)
+    stop_sequences: list[str] = Field(default_factory=list)
     """Stop generating tokens if a member of `stop_sequences` is generated"""
     seed: Optional[int] = None
     """Random sampling seed"""
@@ -111,9 +112,9 @@ class HuggingFaceEndpoint(LLM):
     watermark: bool = False
     """Watermarking with [A Watermark for Large Language Models]
     (https://arxiv.org/abs/2301.10226)"""
-    server_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    server_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Holds any text-generation-inference server parameters not explicitly specified"""
-    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    model_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `call` not explicitly specified"""
     model: str
     client: Any = None  #: :meta private:
@@ -128,7 +129,7 @@ class HuggingFaceEndpoint(LLM):
 
     @model_validator(mode="before")
     @classmethod
-    def build_extra(cls, values: Dict[str, Any]) -> Any:
+    def build_extra(cls, values: dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})
@@ -252,7 +253,7 @@ class HuggingFaceEndpoint(LLM):
         return self
 
     @property
-    def _default_params(self) -> Dict[str, Any]:
+    def _default_params(self) -> dict[str, Any]:
         """Get the default parameters for calling text generation inference API."""
         return {
             "max_new_tokens": self.max_new_tokens,
@@ -285,8 +286,8 @@ class HuggingFaceEndpoint(LLM):
         return "huggingface_endpoint"
 
     def _invocation_params(
-        self, runtime_stop: Optional[List[str]], **kwargs: Any
-    ) -> Dict[str, Any]:
+        self, runtime_stop: Optional[list[str]], **kwargs: Any
+    ) -> dict[str, Any]:
         params = {**self._default_params, **kwargs}
         params["stop_sequences"] = params["stop_sequences"] + (runtime_stop or [])
         return params
@@ -294,7 +295,7 @@ class HuggingFaceEndpoint(LLM):
     def _call(
         self,
         prompt: str,
-        stop: Optional[List[str]] = None,
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> str:
@@ -326,7 +327,7 @@ class HuggingFaceEndpoint(LLM):
     async def _acall(
         self,
         prompt: str,
-        stop: Optional[List[str]] = None,
+        stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> str:
@@ -357,7 +358,7 @@ class HuggingFaceEndpoint(LLM):
     def _stream(
         self,
         prompt: str,
-        stop: Optional[List[str]] = None,
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> Iterator[GenerationChunk]:
@@ -394,7 +395,7 @@ class HuggingFaceEndpoint(LLM):
     async def _astream(
         self,
         prompt: str,
-        stop: Optional[List[str]] = None,
+        stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> AsyncIterator[GenerationChunk]:

libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py

@@ -2,7 +2,8 @@ from __future__ import annotations  # type: ignore[import-not-found]
 
 import importlib.util
 import logging
-from typing import Any, Dict, Iterator, List, Mapping, Optional
+from collections.abc import Iterator, Mapping
+from typing import Any, Optional
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import BaseLLM
@@ -82,7 +83,7 @@ class HuggingFacePipeline(BaseLLM):
 
     @model_validator(mode="before")
     @classmethod
-    def pre_init_validator(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    def pre_init_validator(cls, values: dict[str, Any]) -> dict[str, Any]:
         """Ensure model_id is set either by pipeline or user input."""
         if "model_id" not in values:
             if "pipeline" in values and values["pipeline"]:
@@ -297,13 +298,13 @@ class HuggingFacePipeline(BaseLLM):
 
     def _generate(
         self,
-        prompts: List[str],
-        stop: Optional[List[str]] = None,
+        prompts: list[str],
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> LLMResult:
         # List to hold all results
-        text_generations: List[str] = []
+        text_generations: list[str] = []
         pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
         skip_prompt = kwargs.get("skip_prompt", False)
 
@@ -347,7 +348,7 @@ class HuggingFacePipeline(BaseLLM):
     def _stream(
         self,
         prompt: str,
-        stop: Optional[List[str]] = None,
+        stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> Iterator[GenerationChunk]: