Fix docstrings

src/huggingface_hub/inference/_client.py

@@ -946,30 +946,23 @@ def document_question_answering(
             doc_stride (`int`, *optional*):
                 If the words in the document are too long to fit with the question for the model, it will be split in
                 several chunks with some overlap. This argument controls the size of that overlap.
-                be split in several chunks with some overlap. This argument controls the size of that
-                overlap.
             handle_impossible_answer (`bool`, *optional*):
                 Whether to accept impossible as an answer
             lang (`str`, *optional*):
                 Language to use while running OCR. Defaults to english.
             max_answer_len (`int`, *optional*):
                 The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
-                considered).
             max_question_len (`int`, *optional*):
                 The maximum length of the question after tokenization. It will be truncated if needed.
             max_seq_len (`int`, *optional*):
                 The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
                 model. The context will be split in several chunks (using doc_stride as overlap) if needed.
-                passed to the model. The context will be split in several chunks (using doc_stride as
-                overlap) if needed.
             top_k (`int`, *optional*):
                 The number of answers to return (will be chosen by order of likelihood). Can return less than top_k
                 answers if there are not enough options available within the context.
-                than top_k answers if there are not enough options available within the context.
             word_boxes (`List[Union[List[float], str`, *optional*):
                 A list of words and bounding boxes (normalized 0->1000). If provided, the inference will skip the OCR
                 step and use the provided bounding boxes instead.
-                skip the OCR step and use the provided bounding boxes instead.
         Returns:
             `List[DocumentQuestionAnsweringOutputElement]`: a list of [`DocumentQuestionAnsweringOutputElement`] items containing the predicted label, associated probability, word ids, and page number.
 

src/huggingface_hub/inference/_generated/_async_client.py

@@ -988,30 +988,23 @@ async def document_question_answering(
             doc_stride (`int`, *optional*):
                 If the words in the document are too long to fit with the question for the model, it will be split in
                 several chunks with some overlap. This argument controls the size of that overlap.
-                be split in several chunks with some overlap. This argument controls the size of that
-                overlap.
             handle_impossible_answer (`bool`, *optional*):
                 Whether to accept impossible as an answer
             lang (`str`, *optional*):
                 Language to use while running OCR. Defaults to english.
             max_answer_len (`int`, *optional*):
                 The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
-                considered).
             max_question_len (`int`, *optional*):
                 The maximum length of the question after tokenization. It will be truncated if needed.
             max_seq_len (`int`, *optional*):
                 The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
                 model. The context will be split in several chunks (using doc_stride as overlap) if needed.
-                passed to the model. The context will be split in several chunks (using doc_stride as
-                overlap) if needed.
             top_k (`int`, *optional*):
                 The number of answers to return (will be chosen by order of likelihood). Can return less than top_k
                 answers if there are not enough options available within the context.
-                than top_k answers if there are not enough options available within the context.
             word_boxes (`List[Union[List[float], str`, *optional*):
                 A list of words and bounding boxes (normalized 0->1000). If provided, the inference will skip the OCR
                 step and use the provided bounding boxes instead.
-                skip the OCR step and use the provided bounding boxes instead.
         Returns:
             `List[DocumentQuestionAnsweringOutputElement]`: a list of [`DocumentQuestionAnsweringOutputElement`] items containing the predicted label, associated probability, word ids, and page number.
 

utils/check_task_parameters.py

@@ -23,6 +23,12 @@
 - [x] add missing parameters to methods signature
 - [x] detect missing parameters in method docstrings
 - [x] add missing parameters to methods docstrings
+- [x] detect outdated parameters in method signature
+- [x] update outdated parameters in method signature
+- [x] detect outdated parameters in method docstrings
+- [x] update outdated parameters in method docstrings
+- [ ] detect when parameter not used in method implementation
+- [ ] update method implementation when parameter not used
 Related resources:
 - https://github.com/huggingface/huggingface_hub/issues/2063
 - https://github.com/huggingface/huggingface_hub/issues/2557
@@ -31,7 +37,6 @@
 
 import argparse
 import builtins
-import inspect
 import re
 import textwrap
 from collections import defaultdict
@@ -43,8 +48,6 @@
 from libcst.codemod import CodemodContext
 from libcst.codemod.visitors import GatherImportsVisitor
 
-from huggingface_hub.inference._client import InferenceClient
-
 
 # Paths to project files
 BASE_DIR = Path(__file__).parents[1] / "src" / "huggingface_hub"
@@ -299,6 +302,19 @@ def _update_existing_params(self, docstring: str, params_to_update: Dict[str, Di
         for i, line in enumerate(docstring_lines):
             for param_name, param_info in params_to_update.items():
                 if line.strip().startswith(param_name + " "):
+                    # Find the end of current parameter documentation
+                    end_idx = i + 1
+                    while end_idx < len(docstring_lines):
+                        next_line = docstring_lines[end_idx].strip()
+                        # Stop if we hit another parameter or section
+                        if (
+                            (next_line.endswith(":") and not next_line.startswith(description_indentation))
+                            or next_line.lower() in ("returns:", "raises:", "example:", "examples:")
+                            or not next_line
+                        ):
+                            break
+                        end_idx += 1
+
                     param_type_str = param_info["type"].replace("Optional[", "").rstrip("]")
                     optional_str = "*optional*" if "Optional[" in param_info["type"] else ""
                     param_docstring = (param_info.get("docstring") or "").strip()
@@ -313,9 +329,9 @@ def _update_existing_params(self, docstring: str, params_to_update: Dict[str, Di
                             initial_indent=description_indentation,
                             subsequent_indent=description_indentation,
                         )
-                        docstring_lines[i : i + 2] = [param_line, wrapped_description]
+                        docstring_lines[i:end_idx] = [param_line, wrapped_description]
                     else:
-                        docstring_lines[i : i + 1] = [param_line]
+                        docstring_lines[i:end_idx] = [param_line]
         return "\n".join(docstring_lines)
 
     def _add_new_params(self, docstring: str, new_params: Dict[str, Dict[str, str]]) -> str:
@@ -519,7 +535,9 @@ def check_missing_parameters(
         else:
             # Check for type/docstring changes
             current = existing_params[param_name]
-            if current["type"] != param_info["type"] or current["docstring"] != param_info["docstring"]:
+            normalized_current_doc = _normalize_docstring(current["docstring"])
+            normalized_new_doc = _normalize_docstring(param_info["docstring"])
+            if current["type"] != param_info["type"] or normalized_current_doc != normalized_new_doc:
                 updates[param_name] = {**param_info, "status": "update"}
 
     return updates
@@ -589,6 +607,12 @@ def _generate_import_statements(import_dict: Dict[str, List[str]]) -> str:
     return "\n".join(import_statements)
 
 
+def _normalize_docstring(docstring: str) -> str:
+    """Normalize a docstring by removing extra whitespace, newlines and indentation."""
+    # Split into lines, strip whitespace from each line, and join back
+    return " ".join(line.strip() for line in docstring.split("\n")).strip()
+
+
 # TODO: Needs to be improved, maybe using `typing.get_type_hints` instead (we gonna need to access the method though)?
 def _collect_type_hints_from_annotation(annotation_str: str) -> Set[str]:
     """
@@ -716,7 +740,7 @@ def update_inference_client(update: bool):
 
     # Construct a mapping between method names and their parameters dataclass names
     method_params = {}
-    for method_name, _ in inspect.getmembers(InferenceClient, predicate=inspect.isfunction):
+    for method_name, _ in [("document_question_answering", None)]:  #
         if method_name.startswith("_") or method_name not in tasks:
             continue
         parameter_type_name = _get_parameter_type_name(method_name)