Replace Optional and Union typing with | in some source files

src/transformers/cli/add_new_model_like.py

@@ -19,7 +19,7 @@
 from collections.abc import Callable
 from datetime import date
 from pathlib import Path
-from typing import Annotated, Any, Optional, Union
+from typing import Annotated, Any
 
 import typer
 
@@ -95,7 +95,7 @@ def visit_SimpleStatementLine(self, node: cst.SimpleStatementLine):
 
 def add_new_model_like(
     repo_path: Annotated[
-        Optional[str], typer.Argument(help="When not using an editable install, the path to the Transformers repo.")
+        str | None, typer.Argument(help="When not using an editable install, the path to the Transformers repo.")
     ] = None,
 ):
     """
@@ -156,7 +156,7 @@ def __init__(self, lowercase_name: str):
         self.processor_class = PROCESSOR_MAPPING_NAMES.get(self.lowercase_name, None)
 
 
-def add_content_to_file(file_name: Union[str, os.PathLike], new_content: str, add_after: str):
+def add_content_to_file(file_name: str | os.PathLike, new_content: str, add_after: str):
     """
     A utility to add some content inside a given file.
 
@@ -614,9 +614,9 @@ def _add_new_model_like_internal(
 
 def get_user_field(
     question: str,
-    default_value: Optional[str] = None,
-    convert_to: Optional[Callable] = None,
-    fallback_message: Optional[str] = None,
+    default_value: str | None = None,
+    convert_to: Callable | None = None,
+    fallback_message: str | None = None,
 ) -> Any:
     """
     A utility function that asks a question to the user to get an answer, potentially looping until it gets a valid

src/transformers/cli/chat.py

@@ -19,7 +19,7 @@
 import string
 import time
 from collections.abc import AsyncIterator
-from typing import Annotated, Optional
+from typing import Annotated
 
 import click
 import typer
@@ -214,7 +214,7 @@ def __init__(
         base_url: Annotated[str, typer.Argument(help="Base url to connect to (e.g. http://localhost:8000/v1).")],
         model_id: Annotated[str, typer.Argument(help="ID of the model to use (e.g. 'HuggingFaceTB/SmolLM3-3B').")],
         generate_flags: Annotated[
-            Optional[list[str]],
+            list[str] | None,
             typer.Argument(
                 help=(
                     "Flags to pass to `generate`, using a space as a separator between flags. Accepts booleans, numbers, "
@@ -227,15 +227,15 @@ def __init__(
         ] = None,
         # General settings
         user: Annotated[
-            Optional[str],
+            str | None,
             typer.Option(help="Username to display in chat interface. Defaults to the current user's name."),
         ] = None,
-        system_prompt: Annotated[Optional[str], typer.Option(help="System prompt.")] = None,
+        system_prompt: Annotated[str | None, typer.Option(help="System prompt.")] = None,
         save_folder: Annotated[str, typer.Option(help="Folder to save chat history.")] = "./chat_history/",
-        examples_path: Annotated[Optional[str], typer.Option(help="Path to a yaml file with examples.")] = None,
+        examples_path: Annotated[str | None, typer.Option(help="Path to a yaml file with examples.")] = None,
         # Generation settings
         generation_config: Annotated[
-            Optional[str],
+            str | None,
             typer.Option(
                 help="Path to a local generation config file or to a HuggingFace repo containing a `generation_config.json` file. Other generation settings passed as CLI arguments will be applied on top of this generation config."
             ),
@@ -455,7 +455,7 @@ async def _inner_run(self):
                     break
 
 
-def load_generation_config(generation_config: Optional[str]) -> GenerationConfig:
+def load_generation_config(generation_config: str | None) -> GenerationConfig:
     if generation_config is None:
         return GenerationConfig()
 
@@ -467,7 +467,7 @@ def load_generation_config(generation_config: Optional[str]) -> GenerationConfig
         return GenerationConfig.from_pretrained(generation_config)
 
 
-def parse_generate_flags(generate_flags: Optional[list[str]]) -> dict:
+def parse_generate_flags(generate_flags: list[str] | None) -> dict:
     """Parses the generate flags from the user input into a dictionary of `generate` kwargs."""
     if generate_flags is None or len(generate_flags) == 0:
         return {}
@@ -521,7 +521,7 @@ def is_number(s: str) -> bool:
     return processed_generate_flags
 
 
-def new_chat_history(system_prompt: Optional[str] = None) -> list[dict]:
+def new_chat_history(system_prompt: str | None = None) -> list[dict]:
     """Returns a new chat conversation."""
     return [{"role": "system", "content": system_prompt}] if system_prompt else []
 

src/transformers/cli/download.py

@@ -11,14 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Annotated, Optional
+from typing import Annotated
 
 import typer
 
 
 def download(
     model_id: Annotated[str, typer.Argument(help="The model ID to download")],
-    cache_dir: Annotated[Optional[str], typer.Option(help="Directory where to save files.")] = None,
+    cache_dir: Annotated[str | None, typer.Option(help="Directory where to save files.")] = None,
     force_download: Annotated[
         bool, typer.Option(help="If set, the files will be downloaded even if they are already cached locally.")
     ] = False,

src/transformers/cli/run.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from enum import Enum
-from typing import Annotated, Optional
+from typing import Annotated
 
 import typer
 
@@ -28,27 +28,27 @@
 
 def run(
     task: Annotated[TaskEnum, typer.Argument(help="Task to run", case_sensitive=False)],  # type: ignore
-    input: Annotated[Optional[str], typer.Option(help="Path to the file to use for inference")] = None,
+    input: Annotated[str | None, typer.Option(help="Path to the file to use for inference")] = None,
     output: Annotated[
-        Optional[str], typer.Option(help="Path to the file that will be used post to write results.")
+        str | None, typer.Option(help="Path to the file that will be used post to write results.")
     ] = None,
     model: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             help="Name or path to the model to instantiate. If not provided, will use the default model for that task."
         ),
     ] = None,
     config: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             help="Name or path to the model's config to instantiate. If not provided, will use the model's one."
         ),
     ] = None,
     tokenizer: Annotated[
-        Optional[str], typer.Option(help="Name of the tokenizer to use. If not provided, will use the model's one.")
+        str | None, typer.Option(help="Name of the tokenizer to use. If not provided, will use the model's one.")
     ] = None,
     column: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(help="Name of the column to use as input. For multi columns input use 'column1,columns2'"),
     ] = None,
     format: Annotated[FormatEnum, typer.Option(help="Input format to read from", case_sensitive=False)] = "pipe",  # type: ignore

src/transformers/cli/serve.py

@@ -305,7 +305,7 @@ def __init__(
         self,
         model: "PreTrainedModel",
         timeout_seconds: int,
-        processor: Optional[Union["ProcessorMixin", "PreTrainedTokenizerFast"]] = None,
+        processor: Union["ProcessorMixin", "PreTrainedTokenizerFast"] | None = None,
     ):
         self.model = model
         self._name_or_path = str(model.name_or_path)
@@ -363,7 +363,7 @@ def __init__(
             ),
         ] = "auto",
         dtype: Annotated[
-            Optional[str],
+            str | None,
             typer.Option(
                 help="Override the default `torch.dtype` and load the model under this dtype. If `'auto'` is passed, the dtype will be automatically derived from the model's weights."
             ),
@@ -372,7 +372,7 @@ def __init__(
             bool, typer.Option(help="Whether to trust remote code when loading a model.")
         ] = False,
         attn_implementation: Annotated[
-            Optional[str],
+            str | None,
             typer.Option(
                 help="Which attention implementation to use; you can run --attn_implementation=flash_attention_2, in which case you must install this manually by running `pip install flash-attn --no-build-isolation`."
             ),
@@ -390,7 +390,7 @@ def __init__(
             str, typer.Option(help="Logging level as a string. Example: 'info' or 'warning'.")
         ] = "info",
         default_seed: Annotated[
-            Optional[int], typer.Option(help="The default seed for torch, should be an integer.")
+            int | None, typer.Option(help="The default seed for torch, should be an integer.")
         ] = None,
         enable_cors: Annotated[
             bool,
@@ -400,7 +400,7 @@ def __init__(
         ] = False,
         input_validation: Annotated[bool, typer.Option(help="Whether to turn on strict input validation.")] = False,
         force_model: Annotated[
-            Optional[str],
+            str | None,
             typer.Option(
                 help="Name of the model to be forced on all requests. This is useful for testing Apps that don't allow changing models in the request."
             ),
@@ -445,7 +445,7 @@ def __init__(
         # Internal state:
         # 1. Tracks models in memory, to prevent reloading the model unnecessarily
         self.loaded_models: dict[str, TimedModel] = {}
-        self.running_continuous_batching_manager: Optional[ContinuousBatchingManager] = None
+        self.running_continuous_batching_manager: ContinuousBatchingManager | None = None
 
         # 2. preserves information about the last call and last KV cache, to determine whether we can reuse the KV
         # cache and avoid re-running prefill
@@ -648,13 +648,13 @@ def validate_transcription_request(self, request: dict):
     def build_chat_completion_chunk(
         self,
         request_id: str = "",
-        content: Optional[int] = None,
-        model: Optional[str] = None,
-        role: Optional[str] = None,
-        finish_reason: Optional[str] = None,
-        tool_calls: Optional[list["ChoiceDeltaToolCall"]] = None,
-        decode_stream: Optional[DecodeStream] = None,
-        tokenizer: Optional[PreTrainedTokenizerFast] = None,
+        content: int | None = None,
+        model: str | None = None,
+        role: str | None = None,
+        finish_reason: str | None = None,
+        tool_calls: list["ChoiceDeltaToolCall"] | None = None,
+        decode_stream: DecodeStream | None = None,
+        tokenizer: PreTrainedTokenizerFast | None = None,
     ) -> ChatCompletionChunk:
         """
         Builds a chunk of a streaming OpenAI Chat Completion response.

src/transformers/cli/system.py

@@ -22,7 +22,7 @@
 import io
 import os
 import platform
-from typing import Annotated, Optional
+from typing import Annotated
 
 import huggingface_hub
 import typer
@@ -40,7 +40,7 @@
 
 def env(
     accelerate_config_file: Annotated[
-        Optional[str],
+        str | None,
         typer.Argument(help="The accelerate config file to use for the default values in the launching script."),
     ] = None,
 ) -> None: