Validate processing kwargs with @strict from huggingface_hub

setup.py

@@ -114,7 +114,7 @@
     "GitPython<3.1.19",
     "hf-doc-builder>=0.3.0",
     "hf_xet",
-    "huggingface-hub==1.0.0.rc2",
+    "huggingface-hub==1.0.0.rc4",
     "importlib_metadata",
     "ipadic>=1.0.0,<2.0",
     "jinja2>=3.1.0",

src/transformers/dependency_versions_table.py

@@ -23,7 +23,7 @@
     "GitPython": "GitPython<3.1.19",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
     "hf_xet": "hf_xet",
-    "huggingface-hub": "huggingface-hub==1.0.0.rc2",
+    "huggingface-hub": "huggingface-hub==1.0.0.rc4",
     "importlib_metadata": "importlib_metadata",
     "ipadic": "ipadic>=1.0.0,<2.0",
     "jinja2": "jinja2>=3.1.0",

src/transformers/image_processing_utils_fast.py

@@ -18,6 +18,7 @@
 from typing import Any, Optional, Union
 
 import numpy as np
+from huggingface_hub.dataclasses import validate_typed_dict
 
 from .image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from .image_transforms import (
@@ -710,6 +711,10 @@ def _validate_preprocess_kwargs(
     def preprocess(self, images: ImageInput, *args, **kwargs: Unpack[ImagesKwargs]) -> BatchFeature:
         # args are not validated, but their order in the `preprocess` and `_preprocess` signatures must be the same
         validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_kwargs_names)
+
+        # Perform type validation on received kwargs
+        validate_typed_dict(self.valid_kwargs, kwargs)
+
         # Set default kwargs from self. This ensures that if a kwarg is not provided
         # by the user, it gets its default value from the instance, or is set to None.
         for kwarg_name in self._valid_kwargs_names:

src/transformers/models/aria/modular_aria.py

@@ -38,7 +38,7 @@
 )
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_utils import PreTrainedModel
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils import PreTokenizedInput, TextInput
 from ...utils import TensorType, TransformersKwargs, auto_docstring, can_return_tuple, logging
 from ..auto import CONFIG_MAPPING, AutoConfig, AutoTokenizer
@@ -904,7 +904,15 @@ def get_number_of_image_patches(self, height: int, width: int, images_kwargs=Non
         return num_patches
 
 
+class AriaImagesKwargs(ImagesKwargs, total=False):
+    split_image: Optional[bool]
+    max_image_size: Optional[int]
+    min_image_size: Optional[int]
+
+
 class AriaProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: AriaImagesKwargs
+
     _defaults = {
         "text_kwargs": {
             "padding": False,

src/transformers/models/aria/processing_aria.py

@@ -24,13 +24,21 @@
 
 from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils import PreTokenizedInput, TextInput
 from ...utils import TensorType
 from ..auto import AutoTokenizer
 
 
+class AriaImagesKwargs(ImagesKwargs, total=False):
+    split_image: Optional[bool]
+    max_image_size: Optional[int]
+    min_image_size: Optional[int]
+
+
 class AriaProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: AriaImagesKwargs
+
     _defaults = {
         "text_kwargs": {
             "padding": False,

src/transformers/models/beit/image_processing_beit.py

@@ -55,7 +55,7 @@
 logger = logging.get_logger(__name__)
 
 
-class BeitImageProcessorKwargs(ImagesKwargs):
+class BeitImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
         Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0

src/transformers/models/bridgetower/image_processing_bridgetower.py

@@ -123,7 +123,7 @@ def get_resize_output_image_size(
     return new_height, new_width
 
 
-class BridgeTowerImageProcessorKwargs(ImagesKwargs):
+class BridgeTowerImageProcessorKwargs(ImagesKwargs, total=False):
     size_divisor: Optional[int]
 
 

src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py

@@ -33,7 +33,7 @@
 from ...utils import TensorType, auto_docstring
 
 
-class Cohere2VisionFastImageProcessorKwargs(ImagesKwargs):
+class Cohere2VisionFastImageProcessorKwargs(ImagesKwargs, total=False):
     """
     crop_to_patches (`bool`, *optional*, defaults to `False`):
         Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the

src/transformers/models/cohere2_vision/modular_cohere2_vision.py

@@ -303,7 +303,7 @@ def get_optimal_tiled_canvas(
     return best_grid
 
 
-class Cohere2VisionFastImageProcessorKwargs(ImagesKwargs):
+class Cohere2VisionFastImageProcessorKwargs(ImagesKwargs, total=False):
     """
     crop_to_patches (`bool`, *optional*, defaults to `False`):
         Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -729,7 +729,7 @@ def compute_segments(
     return segmentation, segments
 
 
-class ConditionalDetrImageProcessorKwargs(ImagesKwargs):
+class ConditionalDetrImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
         Data format of the annotations. One of "coco_detection" or "coco_panoptic".

src/transformers/models/convnext/image_processing_convnext.py

@@ -50,7 +50,7 @@
 logger = logging.get_logger(__name__)
 
 
-class ConvNextImageProcessorKwargs(ImagesKwargs):
+class ConvNextImageProcessorKwargs(ImagesKwargs, total=False):
     """
     crop_pct (`float`, *optional*):
         Percentage of the image to crop. Only has an effect if size < 384. Can be

src/transformers/models/deepseek_vl/image_processing_deepseek_vl.py

@@ -49,14 +49,14 @@
 logger = logging.get_logger(__name__)
 
 
-class DeepseekVLImageProcessorKwargs(ImagesKwargs):
+class DeepseekVLImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     min_size (`int`, *optional*, defaults to 14):
         The minimum allowed size for the resized image. Ensures that neither the height nor width
         falls below this value after resizing.
     """
 
-    min_size: int
+    min_size: Optional[int]
 
 
 class DeepseekVLImageProcessor(BaseImageProcessor):

src/transformers/models/deepseek_vl_hybrid/image_processing_deepseek_vl_hybrid.py

@@ -50,7 +50,7 @@
 logger = logging.get_logger(__name__)
 
 
-class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs):
+class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     min_size (`int`, *optional*, defaults to 14):
         The minimum allowed size for the resized image. Ensures that neither the height nor width
@@ -69,11 +69,11 @@ class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs):
         number of channels in the image. Can be overridden by the `high_res_image_std` parameter in the `preprocess` method.
     """
 
-    min_size: int
-    high_res_size: dict
-    high_res_resample: "PILImageResampling"
-    high_res_image_mean: list[float]
-    high_res_image_std: list[float]
+    min_size: Optional[int]
+    high_res_size: Optional[dict]
+    high_res_resample: Optional[Union["PILImageResampling", int]]
+    high_res_image_mean: Optional[Union[float, list[float], tuple[float, ...]]]
+    high_res_image_std: Optional[Union[float, list[float], tuple[float, ...]]]
 
 
 class DeepseekVLHybridImageProcessor(BaseImageProcessor):

src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py

@@ -429,7 +429,7 @@ def prepare_inputs_for_generation(
         return model_inputs
 
 
-class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs):
+class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     min_size (`int`, *optional*, defaults to 14):
         The minimum allowed size for the resized image. Ensures that neither the height nor width
@@ -448,11 +448,11 @@ class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs):
         number of channels in the image. Can be overridden by the `high_res_image_std` parameter in the `preprocess` method.
     """
 
-    min_size: int
-    high_res_size: dict
-    high_res_resample: "PILImageResampling"
-    high_res_image_mean: list[float]
-    high_res_image_std: list[float]
+    min_size: Optional[int]
+    high_res_size: Optional[dict]
+    high_res_resample: Optional[Union["PILImageResampling", int]]
+    high_res_image_mean: Optional[Union[float, list[float], tuple[float, ...]]]
+    high_res_image_std: Optional[Union[float, list[float], tuple[float, ...]]]
 
 
 class DeepseekVLHybridImageProcessor(DeepseekVLImageProcessor):

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -82,7 +82,7 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class DeformableDetrImageProcessorKwargs(ImagesKwargs):
+class DeformableDetrImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
         Data format of the annotations. One of "coco_detection" or "coco_panoptic".

src/transformers/models/detr/image_processing_detr.py

@@ -84,7 +84,7 @@
 SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)
 
 
-class DetrImageProcessorKwargs(ImagesKwargs):
+class DetrImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
         Data format of the annotations. One of "coco_detection" or "coco_panoptic".

src/transformers/models/dia/processing_dia.py

@@ -55,7 +55,9 @@ class DiaProcessorKwargs(ProcessingKwargs, total=False):
             "generation": True,
             "sampling_rate": 44100,
         },
-        "common_kwargs": {"return_tensors": "pt"},
+        "common_kwargs": {
+            "return_tensors": "pt",
+        },
     }
 
 

src/transformers/models/donut/image_processing_donut.py

@@ -52,7 +52,7 @@
     import PIL
 
 
-class DonutImageProcessorKwargs(ImagesKwargs):
+class DonutImageProcessorKwargs(ImagesKwargs, total=False):
     """
     do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`):
         Whether to resize the image using thumbnail method.

src/transformers/models/dpt/image_processing_dpt.py

@@ -64,7 +64,7 @@
 logger = logging.get_logger(__name__)
 
 
-class DPTImageProcessorKwargs(ImagesKwargs):
+class DPTImageProcessorKwargs(ImagesKwargs, total=False):
     """
     ensure_multiple_of (`int`, *optional*, defaults to 1):
         If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overridden

src/transformers/models/efficientloftr/image_processing_efficientloftr.py

@@ -50,7 +50,7 @@
 logger = logging.get_logger(__name__)
 
 
-class EfficientLoFTRImageProcessorKwargs(ImagesKwargs):
+class EfficientLoFTRImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     do_grayscale (`bool`, *optional*, defaults to `True`):
         Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method.

src/transformers/models/efficientnet/image_processing_efficientnet.py

@@ -44,16 +44,16 @@
 logger = logging.get_logger(__name__)
 
 
-class EfficientNetImageProcessorKwargs(ImagesKwargs):
+class EfficientNetImageProcessorKwargs(ImagesKwargs, total=False):
     """
     rescale_offset (`bool`, *optional*, defaults to `self.rescale_offset`):
         Whether to rescale the image between [-max_range/2, scale_range/2] instead of [0, scale_range].
     include_top (`bool`, *optional*, defaults to `self.include_top`):
         Normalize the image again with the standard deviation only for image classification if set to True.
     """
 
-    rescale_offset: bool
-    include_top: bool
+    rescale_offset: Optional[bool]
+    include_top: Optional[bool]
 
 
 class EfficientNetImageProcessor(BaseImageProcessor):

src/transformers/models/emu3/image_processing_emu3.py

@@ -47,7 +47,7 @@
 logger = logging.get_logger(__name__)
 
 
-class Emu3ImageProcessorKwargs(ImagesKwargs):
+class Emu3ImageProcessorKwargs(ImagesKwargs, total=False):
     ratio: Optional[str]
     image_area: Optional[int]
 

src/transformers/models/eomt/image_processing_eomt.py

@@ -55,7 +55,7 @@
     import torch.nn.functional as F
 
 
-class EomtImageProcessorKwargs(ImagesKwargs):
+class EomtImageProcessorKwargs(ImagesKwargs, total=False):
     """
     do_split_image (`bool`, *optional*, defaults to `False`):
         Whether to split the input images into overlapping patches for semantic segmentation. If set to `True`, the
@@ -66,8 +66,8 @@ class EomtImageProcessorKwargs(ImagesKwargs):
         denoted with 0 (background) will be replaced with `ignore_index`.
     """
 
-    do_split_image: bool
-    ignore_index: Optional[int] = None
+    do_split_image: Optional[bool]
+    ignore_index: Optional[int]
 
 
 # Adapted from transformers.models.maskformer.image_processing_maskformer.convert_segmentation_map_to_binary_masks

src/transformers/models/flava/image_processing_flava.py

@@ -57,7 +57,7 @@
 LOGIT_LAPLACE_EPS: float = 0.1
 
 
-class FlavaImageProcessorKwargs(ImagesKwargs):
+class FlavaImageProcessorKwargs(ImagesKwargs, total=False):
     """
     return_image_mask (`bool`, *optional*, defaults to `False`):
         Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.

src/transformers/models/gemma3/image_processing_gemma3.py

@@ -51,7 +51,7 @@
     import PIL
 
 
-class Gemma3ImageProcessorKwargs(ImagesKwargs):
+class Gemma3ImageProcessorKwargs(ImagesKwargs, total=False):
     """
     do_pan_and_scan (`bool`, *optional*):
         Whether to apply `pan_and_scan` to images.

src/transformers/models/glm4v/image_processing_glm4v.py

@@ -47,7 +47,7 @@
 logger = logging.get_logger(__name__)
 
 
-class Glm4vImageProcessorKwargs(ImagesKwargs):
+class Glm4vImageProcessorKwargs(ImagesKwargs, total=False):
     """
     patch_size (`int`, *optional*, defaults to 14):
         The spatial patch size of the vision encoder.

src/transformers/models/glm4v/video_processing_glm4v.py

@@ -36,7 +36,7 @@
 from .image_processing_glm4v import smart_resize
 
 
-class Glm4vVideoProcessorInitKwargs(VideosKwargs):
+class Glm4vVideoProcessorInitKwargs(VideosKwargs, total=False):
     max_image_size: Optional[dict[str, int]]
     patch_size: Optional[int]
     temporal_patch_size: Optional[int]

src/transformers/models/got_ocr2/image_processing_got_ocr2.py

@@ -49,7 +49,7 @@
 logger = logging.get_logger(__name__)
 
 
-class GotOcr2ImageProcessorKwargs(ImagesKwargs):
+class GotOcr2ImageProcessorKwargs(ImagesKwargs, total=False):
     """
     crop_to_patches (`bool`, *optional*, defaults to `False`):
         Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the

src/transformers/models/grounding_dino/image_processing_grounding_dino.py

@@ -93,7 +93,7 @@ class AnnotationFormat(ExplicitEnum):
 SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)
 
 
-class GroundingDinoImageProcessorKwargs(ImagesKwargs):
+class GroundingDinoImageProcessorKwargs(ImagesKwargs, total=False):
     r"""
     format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
         Data format of the annotations. One of "coco_detection" or "coco_panoptic".

src/transformers/models/idefics/image_processing_idefics.py

@@ -36,7 +36,7 @@
 IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
 
 
-class IdeficsImageProcessorKwargs(ImagesKwargs):
+class IdeficsImageProcessorKwargs(ImagesKwargs, total=False):
     """
     transform (`Callable`, *optional*):
         A custom transform function that accepts a single image can be passed for training. For example,

src/transformers/models/idefics2/image_processing_idefics2.py

@@ -47,7 +47,7 @@
     from PIL import Image
 
 
-class Idefics2ImageProcessorKwargs(ImagesKwargs):
+class Idefics2ImageProcessorKwargs(ImagesKwargs, total=False):
     """
     do_image_splitting (`bool`, *optional*, defaults to `False`):
         Whether to split the image into a sequence 4 equal sub-images concatenated with the original image.

src/transformers/models/idefics3/image_processing_idefics3.py

@@ -48,7 +48,7 @@
     from PIL import Image
 
 
-class Idefics3ImageProcessorKwargs(ImagesKwargs):
+class Idefics3ImageProcessorKwargs(ImagesKwargs, total=False):
     """
     do_image_splitting (`bool`, *optional*, defaults to `True`):
         Whether to split the image into sub-images concatenated with the original image. They are split into patches