🐞 Fix installation package issues

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
     "jsonargparse[signatures]>=4.27.7",
     "docstring_parser",     # CLI help-formatter
     "rich_argparse",        # CLI help-formatter
+    "lightning-utilities",
 ]
 
 [project.optional-dependencies]
@@ -293,11 +294,15 @@ pythonpath = "src"
 # COVERAGE CONFIGURATION                                                      #
 [tool.coverage.report]
 exclude_lines = [
-    "except ImportError",
+    "pragma: no cover",
+    "def __repr__",
+    "raise NotImplementedError",
+    "if TYPE_CHECKING:",
+    "@abstractmethod",
+    "pass",
     "raise ImportError",
-    "except ApiException",
-    "raise ApiException",
     "raise ValueError",
+    "except ImportError:",
 ]
 
 [tool.coverage.paths]

src/anomalib/models/__init__.py

@@ -58,9 +58,9 @@ class UnknownModelError(ModuleNotFoundError):
     "Rkde",
     "Stfpm",
     "Uflow",
-    "AiVad",
     "VlmAd",
     "WinClip",
+    "AiVad",
 ]
 
 logger = logging.getLogger(__name__)

src/anomalib/models/image/vlm_ad/backends/huggingface.py

@@ -5,19 +5,21 @@
 
 import logging
 from pathlib import Path
+from typing import TYPE_CHECKING
 
 from lightning_utilities.core.imports import package_available
 from PIL import Image
-from transformers.modeling_utils import PreTrainedModel
 
 from anomalib.models.image.vlm_ad.utils import Prompt
 
 from .base import Backend
 
-if package_available("transformers"):
-    import transformers
+if TYPE_CHECKING:
     from transformers.modeling_utils import PreTrainedModel
     from transformers.processing_utils import ProcessorMixin
+
+if package_available("transformers"):
+    import transformers
 else:
     transformers = None
 
@@ -39,7 +41,7 @@ def __init__(
         self._model: PreTrainedModel | None = None
 
     @property
-    def processor(self) -> ProcessorMixin:
+    def processor(self) -> "ProcessorMixin":
         """Get the Huggingface processor."""
         if self._processor is None:
             if transformers is None:
@@ -49,7 +51,7 @@ def processor(self) -> ProcessorMixin:
         return self._processor
 
     @property
-    def model(self) -> PreTrainedModel:
+    def model(self) -> "PreTrainedModel":
         """Get the Huggingface model."""
         if self._model is None:
             if transformers is None: