[Bot] Update inference types

docs/source/en/package_reference/inference_types.md

@@ -239,6 +239,8 @@ This part of the lib is still under development and will be improved in future r
 
 [[autodoc]] huggingface_hub.TableQuestionAnsweringOutputElement
 
+[[autodoc]] huggingface_hub.TableQuestionAnsweringParameters
+
 
 
 ## text2text_generation

docs/source/ko/package_reference/inference_types.md

@@ -238,6 +238,8 @@ rendered properly in your Markdown viewer.
 
 [[autodoc]] huggingface_hub.TableQuestionAnsweringOutputElement
 
+[[autodoc]] huggingface_hub.TableQuestionAnsweringParameters
+
 
 
 ## text2text_generation[[huggingface_hub.Text2TextGenerationInput]]

src/huggingface_hub/__init__.py

@@ -351,6 +351,7 @@
         "ObjectDetectionInput",
         "ObjectDetectionOutputElement",
         "ObjectDetectionParameters",
+        "Padding",
         "QuestionAnsweringInput",
         "QuestionAnsweringInputData",
         "QuestionAnsweringOutputElement",
@@ -364,6 +365,7 @@
         "TableQuestionAnsweringInput",
         "TableQuestionAnsweringInputData",
         "TableQuestionAnsweringOutputElement",
+        "TableQuestionAnsweringParameters",
         "Text2TextGenerationInput",
         "Text2TextGenerationOutput",
         "Text2TextGenerationParameters",
@@ -880,6 +882,7 @@ def __dir__():
         ObjectDetectionInput,  # noqa: F401
         ObjectDetectionOutputElement,  # noqa: F401
         ObjectDetectionParameters,  # noqa: F401
+        Padding,  # noqa: F401
         QuestionAnsweringInput,  # noqa: F401
         QuestionAnsweringInputData,  # noqa: F401
         QuestionAnsweringOutputElement,  # noqa: F401
@@ -893,6 +896,7 @@ def __dir__():
         TableQuestionAnsweringInput,  # noqa: F401
         TableQuestionAnsweringInputData,  # noqa: F401
         TableQuestionAnsweringOutputElement,  # noqa: F401
+        TableQuestionAnsweringParameters,  # noqa: F401
         Text2TextGenerationInput,  # noqa: F401
         Text2TextGenerationOutput,  # noqa: F401
         Text2TextGenerationParameters,  # noqa: F401

src/huggingface_hub/inference/_client.py

@@ -84,6 +84,7 @@
     ImageToImageTargetSize,
     ImageToTextOutput,
     ObjectDetectionOutputElement,
+    Padding,
     QuestionAnsweringOutputElement,
     SummarizationOutput,
     SummarizationTruncationStrategy,
@@ -1654,7 +1655,9 @@ def table_question_answering(
         query: str,
         *,
         model: Optional[str] = None,
-        parameters: Optional[Dict[str, Any]] = None,
+        padding: Optional["Padding"] = None,
+        sequential: Optional[bool] = None,
+        truncation: Optional[bool] = None,
     ) -> TableQuestionAnsweringOutputElement:
         """
         Retrieve the answer to a question from information given in a table.
@@ -1668,8 +1671,14 @@ def table_question_answering(
             model (`str`):
                 The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face
                 Hub or a URL to a deployed Inference Endpoint.
-            parameters (`Dict[str, Any]`, *optional*):
-                Additional inference parameters. Defaults to None.
+            padding (`"Padding"`, *optional*):
+                Activates and controls padding.
+            sequential (`bool`, *optional*):
+                Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
+                inference to be done sequentially to extract relations within sequences, given their conversational
+                nature.
+            truncation (`bool`, *optional*):
+                Activates and controls truncation.
 
         Returns:
             [`TableQuestionAnsweringOutputElement`]: a table question answering output containing the answer, coordinates, cells and the aggregator used.
@@ -1690,6 +1699,11 @@ def table_question_answering(
         TableQuestionAnsweringOutputElement(answer='36542', coordinates=[[0, 1]], cells=['36542'], aggregator='AVERAGE')
         ```
         """
+        parameters = {
+            "padding": padding,
+            "sequential": sequential,
+            "truncation": truncation,
+        }
         inputs = {
             "query": query,
             "table": table,

src/huggingface_hub/inference/_generated/_async_client.py

@@ -70,6 +70,7 @@
     ImageToImageTargetSize,
     ImageToTextOutput,
     ObjectDetectionOutputElement,
+    Padding,
     QuestionAnsweringOutputElement,
     SummarizationOutput,
     SummarizationTruncationStrategy,
@@ -1713,7 +1714,9 @@ async def table_question_answering(
         query: str,
         *,
         model: Optional[str] = None,
-        parameters: Optional[Dict[str, Any]] = None,
+        padding: Optional["Padding"] = None,
+        sequential: Optional[bool] = None,
+        truncation: Optional[bool] = None,
     ) -> TableQuestionAnsweringOutputElement:
         """
         Retrieve the answer to a question from information given in a table.
@@ -1727,8 +1730,14 @@ async def table_question_answering(
             model (`str`):
                 The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face
                 Hub or a URL to a deployed Inference Endpoint.
-            parameters (`Dict[str, Any]`, *optional*):
-                Additional inference parameters. Defaults to None.
+            padding (`"Padding"`, *optional*):
+                Activates and controls padding.
+            sequential (`bool`, *optional*):
+                Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
+                inference to be done sequentially to extract relations within sequences, given their conversational
+                nature.
+            truncation (`bool`, *optional*):
+                Activates and controls truncation.
 
         Returns:
             [`TableQuestionAnsweringOutputElement`]: a table question answering output containing the answer, coordinates, cells and the aggregator used.
@@ -1750,6 +1759,11 @@ async def table_question_answering(
         TableQuestionAnsweringOutputElement(answer='36542', coordinates=[[0, 1]], cells=['36542'], aggregator='AVERAGE')
         ```
         """
+        parameters = {
+            "padding": padding,
+            "sequential": sequential,
+            "truncation": truncation,
+        }
         inputs = {
             "query": query,
             "table": table,

src/huggingface_hub/inference/_generated/types/__init__.py

@@ -101,9 +101,11 @@
     SummarizationTruncationStrategy,
 )
 from .table_question_answering import (
+    Padding,
     TableQuestionAnsweringInput,
     TableQuestionAnsweringInputData,
     TableQuestionAnsweringOutputElement,
+    TableQuestionAnsweringParameters,
 )
 from .text2text_generation import (
     Text2TextGenerationInput,

src/huggingface_hub/inference/_generated/types/table_question_answering.py

@@ -4,7 +4,7 @@
 #   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
 #   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Literal, Optional
 
 from .base import BaseInferenceType
 
@@ -19,13 +19,31 @@ class TableQuestionAnsweringInputData(BaseInferenceType):
     """The table to serve as context for the questions"""
 
 
+Padding = Literal["do_not_pad", "longest", "max_length"]
+
+
+@dataclass
+class TableQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Table Question Answering"""
+
+    padding: Optional["Padding"] = None
+    """Activates and controls padding."""
+    sequential: Optional[bool] = None
+    """Whether to do inference sequentially or as a batch. Batching is faster, but models like
+    SQA require the inference to be done sequentially to extract relations within sequences,
+    given their conversational nature.
+    """
+    truncation: Optional[bool] = None
+    """Activates and controls truncation."""
+
+
 @dataclass
 class TableQuestionAnsweringInput(BaseInferenceType):
     """Inputs for Table Question Answering inference"""
 
     inputs: TableQuestionAnsweringInputData
     """One (table, question) pair to answer"""
-    parameters: Optional[Dict[str, Any]] = None
+    parameters: Optional[TableQuestionAnsweringParameters] = None
     """Additional inference parameters for Table Question Answering"""
 
 

utils/check_task_parameters.py

@@ -68,7 +68,6 @@
     "audio_to_audio",
     "feature_extraction",
     "sentence_similarity",
-    "table_question_answering",
     "automatic_speech_recognition",
     "image_to_text",
 ]