STY: Put Jupyter functionality at end of class (#2988)

Integration into Jupyter Notebooks is by method `_repr_mimebundle_`, put at end of class PdfDocCommon, as is functionally different from the other methods.

Author: j-t-1

pypdf/_doc_common.py

@@ -316,22 +316,6 @@ def metadata(self) -> Optional[DocumentInformation]:
     def xmp_metadata(self) -> Optional[XmpInformation]:
         ...  # pragma: no cover
 
-    @abstractmethod
-    def _repr_mimebundle_(
-        self,
-        include: Union[None, Iterable[str]] = None,
-        exclude: Union[None, Iterable[str]] = None,
-    ) -> Dict[str, Any]:
-        """
-        Integration into Jupyter Notebooks.
-
-        This method returns a dictionary that maps a mime-type to its
-        representation.
-
-        See https://ipython.readthedocs.io/en/stable/config/integrating.html
-        """
-        ...  # pragma: no cover
-
     @property
     def viewer_preferences(self) -> Optional[ViewerPreferences]:
         """Returns the existing ViewerPreferences as an overloaded dictionary."""
@@ -1432,6 +1416,24 @@ def _get_attachments(
                     attachments[name] = f_data
         return attachments
 
+    @abstractmethod
+    def _repr_mimebundle_(
+        self,
+        include: Union[None, Iterable[str]] = None,
+        exclude: Union[None, Iterable[str]] = None,
+    ) -> Dict[str, Any]:
+        """
+        Integration into Jupyter Notebooks.
+
+        This method returns a dictionary that maps a mime-type to its
+        representation.
+
+        .. seealso::
+
+            https://ipython.readthedocs.io/en/stable/config/integrating.html
+        """
+        ...  # pragma: no cover
+
 
 class LazyDict(Mapping[Any, Any]):
     def __init__(self, *args: Any, **kw: Any) -> None:

pypdf/_reader.py

@@ -262,35 +262,6 @@ def _ID(self) -> Optional[ArrayObject]:
         id = self.trailer.get(TK.ID, None)
         return None if is_null_or_none(id) else cast(ArrayObject, id.get_object())
 
-    def _repr_mimebundle_(
-        self,
-        include: Union[None, Iterable[str]] = None,
-        exclude: Union[None, Iterable[str]] = None,
-    ) -> Dict[str, Any]:
-        """
-        Integration into Jupyter Notebooks.
-
-        This method returns a dictionary that maps a mime-type to its
-        representation.
-
-        See https://ipython.readthedocs.io/en/stable/config/integrating.html
-        """
-        self.stream.seek(0)
-        pdf_data = self.stream.read()
-        data = {
-            "application/pdf": pdf_data,
-        }
-
-        if include is not None:
-            # Filter representations based on include list
-            data = {k: v for k, v in data.items() if k in include}
-
-        if exclude is not None:
-            # Remove representations based on exclude list
-            data = {k: v for k, v in data.items() if k not in exclude}
-
-        return data
-
     @property
     def pdf_header(self) -> str:
         """
@@ -1254,3 +1225,34 @@ def rename_form_topname(self, name: str) -> Optional[DictionaryObject]:
         )
         interim[NameObject("/T")] = TextStringObject(name)
         return interim
+
+    def _repr_mimebundle_(
+        self,
+        include: Union[None, Iterable[str]] = None,
+        exclude: Union[None, Iterable[str]] = None,
+    ) -> Dict[str, Any]:
+        """
+        Integration into Jupyter Notebooks.
+
+        This method returns a dictionary that maps a mime-type to its
+        representation.
+
+        .. seealso::
+
+            https://ipython.readthedocs.io/en/stable/config/integrating.html
+        """
+        self.stream.seek(0)
+        pdf_data = self.stream.read()
+        data = {
+            "application/pdf": pdf_data,
+        }
+
+        if include is not None:
+            # Filter representations based on include list
+            data = {k: v for k, v in data.items() if k in include}
+
+        if exclude is not None:
+            # Remove representations based on exclude list
+            data = {k: v for k, v in data.items() if k not in exclude}
+
+        return data