fix: Corregir todos los warnings de linting (ruff, black)

- Corregir import ordering (TC002) moviendo User a TYPE_CHECKING
- Corregir type hints implícitos (RUF013) usando | None
- Corregir boolean traps (FBT001/FBT002) usando keyword-only args
- Corregir logging warnings (G201) usando logger.exception()
- Formatear código con ruff format y black

Co-authored-by: dawnsystem <42047891+dawnsystem@users.noreply.github.com>

src/documents/ai_deletion_manager.py

@@ -14,9 +14,11 @@ According to agents.md requirements:
 from __future__ import annotations
 
 import logging
+from typing import TYPE_CHECKING
 from typing import Any
 
-from django.contrib.auth.models import User
+if TYPE_CHECKING:
+    from django.contrib.auth.models import User
 
 logger = logging.getLogger("paperless.ai_deletion")
 

src/documents/ai_scanner.py

@@ -51,7 +51,8 @@ class AIScanResult:
             None  # (storage_path_id, confidence)
         )
         self.custom_fields: dict[
-            int, tuple[Any, float],
+            int,
+            tuple[Any, float],
         ] = {}  # {field_id: (value, confidence), ...}
         self.workflows: list[tuple[int, float]] = []  # [(workflow_id, confidence), ...]
         self.extracted_entities: dict[str, Any] = {}  # NER results
@@ -97,8 +98,9 @@ class AIDocumentScanner:
         self,
         auto_apply_threshold: float = 0.80,
         suggest_threshold: float = 0.60,
-        enable_ml_features: bool = None,
+        *,
-        enable_advanced_ocr: bool = None,
+        enable_ml_features: bool | None = None,
+        enable_advanced_ocr: bool | None = None,
     ):
         """
         Initialize AI scanner.
@@ -188,7 +190,7 @@ class AIDocumentScanner:
         self,
         document: Document,
         document_text: str,
-        original_file_path: str = None,
+        original_file_path: str | None = None,
     ) -> AIScanResult:
         """
         Perform comprehensive AI scan of a document.
@@ -215,7 +217,9 @@ class AIDocumentScanner:
 
         # Analyze and suggest tags
         result.tags = self._suggest_tags(
-            document, document_text, result.extracted_entities,
+            document,
+            document_text,
+            result.extracted_entities,
         )
 
         # Detect correspondent
@@ -296,7 +300,7 @@ class AIDocumentScanner:
             logger.debug("Extracted entities from NER")
             return entities
         except Exception as e:
-            logger.error(f"Entity extraction failed: {e}", exc_info=True)
+            logger.exception(f"Entity extraction failed: {e}")
             return {}
 
     def _suggest_tags(
@@ -357,7 +361,7 @@ class AIDocumentScanner:
             logger.debug(f"Suggested {len(suggestions)} tags")
 
         except Exception as e:
-            logger.error(f"Tag suggestion failed: {e}", exc_info=True)
+            logger.exception(f"Tag suggestion failed: {e}")
 
         return suggestions
 
@@ -384,7 +388,8 @@ class AIDocumentScanner:
         try:
             # Use existing matching logic
             matched_correspondents = match_correspondents(
-                document, self._get_classifier(),
+                document,
+                self._get_classifier(),
             )
 
             if matched_correspondents:
@@ -413,7 +418,7 @@ class AIDocumentScanner:
                     return (correspondent.id, confidence)
 
         except Exception as e:
-            logger.error(f"Correspondent detection failed: {e}", exc_info=True)
+            logger.exception(f"Correspondent detection failed: {e}")
 
         return None
 
@@ -452,7 +457,7 @@ class AIDocumentScanner:
                 pass
 
         except Exception as e:
-            logger.error(f"Document type classification failed: {e}", exc_info=True)
+            logger.exception(f"Document type classification failed: {e}")
 
         return None
 
@@ -484,7 +489,7 @@ class AIDocumentScanner:
                 return (storage_path.id, confidence)
 
         except Exception as e:
-            logger.error(f"Storage path suggestion failed: {e}", exc_info=True)
+            logger.exception(f"Storage path suggestion failed: {e}")
 
         return None
 
@@ -523,7 +528,7 @@ class AIDocumentScanner:
                     )
 
         except Exception as e:
-            logger.error(f"Custom field extraction failed: {e}", exc_info=True)
+            logger.exception(f"Custom field extraction failed: {e}")
 
         return extracted_fields
 
@@ -628,7 +633,7 @@ class AIDocumentScanner:
                     )
 
         except Exception as e:
-            logger.error(f"Workflow suggestion failed: {e}", exc_info=True)
+            logger.exception(f"Workflow suggestion failed: {e}")
 
         return suggestions
 
@@ -699,7 +704,7 @@ class AIDocumentScanner:
                 return suggested_title[:127]  # Respect title length limit
 
         except Exception as e:
-            logger.error(f"Title suggestion failed: {e}", exc_info=True)
+            logger.exception(f"Title suggestion failed: {e}")
 
         return None
 
@@ -719,13 +724,14 @@ class AIDocumentScanner:
             logger.debug(f"Extracted {len(tables)} tables from document")
             return tables
         except Exception as e:
-            logger.error(f"Table extraction failed: {e}", exc_info=True)
+            logger.exception(f"Table extraction failed: {e}")
             return []
 
     def apply_scan_results(
         self,
         document: Document,
         scan_result: AIScanResult,
+        *,
         auto_apply: bool = True,
         user_confirmed: bool = False,
     ) -> dict[str, Any]:
@@ -842,7 +848,7 @@ class AIDocumentScanner:
                 document.save()
 
         except Exception as e:
-            logger.error(f"Failed to apply scan results: {e}", exc_info=True)
+            logger.exception(f"Failed to apply scan results: {e}")
 
         return {
             "applied": applied,

src/documents/consumer.py

@@ -489,9 +489,11 @@ class ConsumerPlugin(
                     document=document,
                     logging_group=self.logging_group,
                     classifier=classifier,
-                    original_file=self.unmodified_original
+                    original_file=(
-                    if self.unmodified_original
+                        self.unmodified_original
-                    else self.working_copy,
+                        if self.unmodified_original
+                        else self.working_copy
+                    ),
                 )
 
                 # After everything is in the database, copy the files into
@@ -502,9 +504,11 @@ class ConsumerPlugin(
 
                     self._write(
                         document.storage_type,
-                        self.unmodified_original
+                        (
-                        if self.unmodified_original is not None
+                            self.unmodified_original
-                        else self.working_copy,
+                            if self.unmodified_original is not None
+                            else self.working_copy
+                        ),
                         document.source_path,
                     )
 
@@ -865,9 +869,9 @@ class ConsumerPreflightPlugin(
         Confirm the input file still exists where it should
         """
         if TYPE_CHECKING:
-            assert isinstance(self.input_doc.original_file, Path), (
+            assert isinstance(
-                self.input_doc.original_file
+                self.input_doc.original_file, Path,
-            )
+            ), self.input_doc.original_file
         if not self.input_doc.original_file.is_file():
             self._fail(
                 ConsumerStatusShortMessage.FILE_NOT_FOUND,