Fix processor kwargs qwen2 vl (#36890)

* Fix qwen2_vl and qwen2_5_vl processors cutom images kwargs

* change version warning

src/transformers/models/auto/image_processing_auto.py

@@ -493,7 +493,7 @@ class AutoImageProcessor:
                 image_processor_auto_map = config.auto_map["AutoImageProcessor"]
 
         image_processor_class = None
-        # TODO: @yoni, change logic in v4.50 (when use_fast set to True by default)
+        # TODO: @yoni, change logic in v4.52 (when use_fast set to True by default)
         if image_processor_type is not None:
             # if use_fast is not set and the processor was saved with a fast processor, we use it, otherwise we use the slow processor.
             if use_fast is None:
@@ -501,7 +501,7 @@ class AutoImageProcessor:
                 if not use_fast:
                     logger.warning_once(
                         "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. "
-                        "`use_fast=True` will be the default behavior in v4.50, even if the model was saved with a slow processor. "
+                        "`use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. "
                         "This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`."
                     )
             # Update class name to reflect the use_fast option. If class is not found, we fall back to the slow version.

src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py

@@ -41,7 +41,7 @@ from transformers.models.qwen2_vl.modeling_qwen2_vl import (
     VisionRotaryEmbedding,
     VisionSdpaAttention,
 )
-from transformers.models.qwen2_vl.processing_qwen2_vl import Qwen2VLProcessor
+from transformers.models.qwen2_vl.processing_qwen2_vl import Qwen2VLImagesKwargs, Qwen2VLProcessor
 
 from ...activations import ACT2FN
 from ...configuration_utils import PretrainedConfig
@@ -816,7 +816,12 @@ class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
     fps: Union[List[float], float]
 
 
+class Qwen2_5_VLImagesKwargs(Qwen2VLImagesKwargs):
+    pass
+
+
 class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2_5_VLImagesKwargs
     videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
     _defaults = {
         "text_kwargs": {

src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py

@@ -23,11 +23,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import List, Union
+from typing import List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, VideoInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 
 
@@ -35,7 +35,16 @@ class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
     fps: Union[List[float], float]
 
 
+class Qwen2_5_VLImagesKwargs(ImagesKwargs):
+    min_pixels: Optional[int]
+    max_pixels: Optional[int]
+    patch_size: Optional[int]
+    temporal_patch_size: Optional[int]
+    merge_size: Optional[int]
+
+
 class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2_5_VLImagesKwargs
     videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
     _defaults = {
         "text_kwargs": {

src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py

@@ -384,7 +384,7 @@ class Qwen2VLImageProcessor(BaseImageProcessor):
                 raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
             min_pixels = size["shortest_edge"]
         else:
-            size = self.size
+            size = {**self.size}
         # backward compatibility: override size with min_pixels and max_pixels if they are provided
         if min_pixels is not None:
             size["shortest_edge"] = min_pixels

src/transformers/models/qwen2_vl/image_processing_qwen2_vl_fast.py

@@ -339,7 +339,7 @@ class Qwen2VLImageProcessorFast(BaseImageProcessorFast):
                 raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
             min_pixels = size["shortest_edge"]
         else:
-            size = self.size
+            size = {**self.size}
         # backward compatibility: override size with min_pixels and max_pixels if they are provided
         if min_pixels is not None:
             size["shortest_edge"] = min_pixels

src/transformers/models/qwen2_vl/processing_qwen2_vl.py

@@ -21,11 +21,11 @@
 Processor class for Qwen2-VL.
 """
 
-from typing import List, Union
+from typing import List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, VideoInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import logging
 
@@ -33,7 +33,16 @@ from ...utils import logging
 logger = logging.get_logger(__name__)
 
 
+class Qwen2VLImagesKwargs(ImagesKwargs):
+    min_pixels: Optional[int]
+    max_pixels: Optional[int]
+    patch_size: Optional[int]
+    temporal_patch_size: Optional[int]
+    merge_size: Optional[int]
+
+
 class Qwen2VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImagesKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,

src/transformers/processing_utils.py

@@ -1111,12 +1111,12 @@ class ProcessorMixin(PushToHubMixin):
             if isinstance(class_name, tuple):
                 classes = tuple(cls.get_possibly_dynamic_module(n) if n is not None else None for n in class_name)
                 if attribute_name == "image_processor":
-                    # TODO: @yoni, change logic in v4.50 (when use_fast set to True by default)
+                    # TODO: @yoni, change logic in v4.52 (when use_fast set to True by default)
                     use_fast = kwargs.get("use_fast", None)
                     if use_fast is None:
                         logger.warning_once(
                             "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. "
-                            "`use_fast=True` will be the default behavior in v4.50, even if the model was saved with a slow processor. "
+                            "`use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. "
                             "This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`."
                         )
                 else:

tests/models/qwen2_5_vl/test_processor_qwen2_5_vl.py

@@ -310,3 +310,19 @@ class Qwen2_5_VLProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         )
         self.assertTrue(self.videos_input_name in out_dict_with_video)
         self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 71280)
+
+    def test_kwargs_overrides_custom_image_processor_kwargs(self):
+        processor_components = self.prepare_components()
+        processor_components["image_processor"] = self.get_component("image_processor")
+        processor_components["tokenizer"] = self.get_component("tokenizer")
+        processor_kwargs = self.prepare_processor_dict()
+
+        processor = self.processor_class(**processor_components, **processor_kwargs, use_fast=True)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs()
+        image_input = self.prepare_image_inputs()
+        inputs = processor(text=input_str, images=image_input, max_pixels=56 * 56 * 4, return_tensors="pt")
+        self.assertEqual(inputs[self.images_input_name].shape[0], 612)
+        inputs = processor(text=input_str, images=image_input, return_tensors="pt")
+        self.assertEqual(inputs[self.images_input_name].shape[0], 800)

tests/models/qwen2_vl/test_processor_qwen2_vl.py

@@ -307,3 +307,19 @@ class Qwen2VLProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         )
         self.assertTrue(self.videos_input_name in out_dict_with_video)
         self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 71280)
+
+    def test_kwargs_overrides_custom_image_processor_kwargs(self):
+        processor_components = self.prepare_components()
+        processor_components["image_processor"] = self.get_component("image_processor")
+        processor_components["tokenizer"] = self.get_component("tokenizer")
+        processor_kwargs = self.prepare_processor_dict()
+
+        processor = self.processor_class(**processor_components, **processor_kwargs, use_fast=True)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs()
+        image_input = self.prepare_image_inputs()
+        inputs = processor(text=input_str, images=image_input, return_tensors="pt")
+        self.assertEqual(inputs[self.images_input_name].shape[0], 800)
+        inputs = processor(text=input_str, images=image_input, max_pixels=56 * 56 * 4, return_tensors="pt")
+        self.assertEqual(inputs[self.images_input_name].shape[0], 612)