Refactor image processor phi4 (#36976)

* refactor image processor phi4

* nits fast image proc

* add image tests phi4

* Fix image processing tests

* update integration tests

* remove revision and add comment in integration tests

src/transformers/models/auto/image_processing_auto.py

@@ -128,7 +128,7 @@ else:
             ("owlvit", ("OwlViTImageProcessor", "OwlViTImageProcessorFast")),
             ("paligemma", ("SiglipImageProcessor", "SiglipImageProcessorFast")),
             ("perceiver", ("PerceiverImageProcessor", "PerceiverImageProcessorFast")),
-            ("phi4_multimodal", "Phi4MultimodalImageProcessorFast"),
+            ("phi4_multimodal", ("Phi4MultimodalImageProcessorFast",)),
             ("pix2struct", ("Pix2StructImageProcessor",)),
             ("pixtral", ("PixtralImageProcessor", "PixtralImageProcessorFast")),
             ("poolformer", ("PoolFormerImageProcessor", "PoolFormerImageProcessorFast")),

src/transformers/models/phi4_multimodal/image_processing_phi4_multimodal_fast.py

@@ -12,53 +12,70 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Processor class for Phi4Multimodal
-"""
-
 import math
 from typing import List, Optional, Union
 
 import torch
-from torchvision.transforms import functional as F
 
 from ...image_processing_utils_fast import (
     BaseImageProcessorFast,
     BatchFeature,
     DefaultFastImageProcessorKwargs,
     Unpack,
-    convert_to_rgb,
 )
-from ...image_utils import ImageInput, make_flat_list_of_images, valid_images
-from ...utils import TensorType, logging
+from ...image_utils import ImageInput, SizeDict
+from ...utils import (
+    TensorType,
+    auto_docstring,
+    is_torchvision_available,
+    is_torchvision_v2_available,
+    is_vision_available,
+    logging,
+)
 
 
+if is_vision_available():
+    from ...image_utils import PILImageResampling
+if is_torchvision_available():
+    if is_torchvision_v2_available():
+        from torchvision.transforms.v2 import functional as F
+    else:
+        from torchvision.transforms import functional as F
+
 logger = logging.get_logger(__name__)
 
 
 class Phi4MultimodalFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
-    image_size: Optional[int]
+    r"""
+    patch_size (`int`, *optional*):
+        The size of the patch.
+    dynamic_hd (`int`, *optional*):
+        The maximum number of crops per image.
+    """
+
     patch_size: Optional[int]
     dynamic_hd: Optional[int]
 
 
+@auto_docstring
 class Phi4MultimodalImageProcessorFast(BaseImageProcessorFast):
-    r"""
-    Constructs a Phi4Multimodal image processor.
-    """
-
-    image_size = 448
+    resample = PILImageResampling.BICUBIC
+    size = {"height": 448, "width": 448}
     patch_size = 14
     dynamic_hd = 36
     image_mean = [0.5, 0.5, 0.5]
     image_std = [0.5, 0.5, 0.5]
-    valid_init_kwargs = Phi4MultimodalFastImageProcessorKwargs
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    valid_kwargs = Phi4MultimodalFastImageProcessorKwargs
     model_input_names = ["image_pixel_values", "image_sizes", "image_attention_mask"]
 
     def __init__(self, **kwargs: Unpack[Phi4MultimodalFastImageProcessorKwargs]):
         super().__init__(**kwargs)
 
-    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height):
+    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
         best_ratio_diff = float("inf")
         best_ratio = (1, 1)
         area = width * height
@@ -69,15 +86,12 @@ class Phi4MultimodalImageProcessorFast(BaseImageProcessorFast):
                 best_ratio_diff = ratio_diff
                 best_ratio = ratio
             elif ratio_diff == best_ratio_diff:
-                if area > 0.5 * self.image_size * self.image_size * ratio[0] * ratio[1]:
+                if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                     best_ratio = ratio
         return best_ratio
 
-    def dynamic_preprocess(self, image, max_num=36, min_num=1):
-        image_size = self.image_size
-        patch_size = self.patch_size
-        mask_size = image_size // patch_size
-        orig_width, orig_height = image.size
+    def dynamic_preprocess(self, image, image_size, patch_size, mask_size, max_num=36, min_num=1):
+        orig_height, orig_width = image.shape[-2:]
 
         w_crop_num = math.ceil(orig_width / float(image_size))
         h_crop_num = math.ceil(orig_height / float(image_size))
@@ -95,7 +109,9 @@ class Phi4MultimodalImageProcessorFast(BaseImageProcessorFast):
             target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
 
             # find the closest aspect ratio to the target
-            target_aspect_ratio = self.find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height)
+            target_aspect_ratio = self.find_closest_aspect_ratio(
+                aspect_ratio, target_ratios, orig_width, orig_height, image_size
+            )
 
             # calculate the target width and height
             target_width = image_size * target_aspect_ratio[0]
@@ -148,113 +164,101 @@ class Phi4MultimodalImageProcessorFast(BaseImageProcessorFast):
             masks = torch.cat([masks, pad], dim=0)
         return masks
 
+    @auto_docstring
     def preprocess(
         self,
         images: ImageInput,
+        **kwargs: Unpack[Phi4MultimodalFastImageProcessorKwargs],
+    ) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def _preprocess(
+        self,
+        images: List["torch.Tensor"],
+        size: SizeDict,
+        interpolation: Optional["F.InterpolationMode"],
+        patch_size: int,
+        dynamic_hd: int,
+        do_rescale: bool,
+        rescale_factor: Optional[float],
+        do_normalize: bool,
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
     ):
-        """
-        Args:
-            images (`ImageInput`):
-                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
-                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
-            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
-                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
-            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
-                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                - Unset: Return a list of `np.ndarray`.
-                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-        """
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-
-        images = make_flat_list_of_images(images)
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
+        if size.height != size.width:
+            raise ValueError("Phi4MultimodalFastImageProcessor only supports square sizes.")
+        mask_size = size.height // patch_size
+        images_transformed = []
+        masks_transformed = []
+        images_tokens = []
+        image_sizes = []
+        for image in images:
+            resized_image, attention_mask = self.dynamic_preprocess(
+                image, size.height, patch_size, mask_size, max_num=dynamic_hd
             )
-        images = [convert_to_rgb(image) for image in images]
-
-        image_size = self.image_size
-        patch_size = self.patch_size
-        mask_size = image_size // patch_size
-        imgs_and_masks = [self.dynamic_preprocess(image, max_num=self.dynamic_hd) for image in images]
-        images, image_attention_masks = [x[0] for x in imgs_and_masks], [x[1] for x in imgs_and_masks]
-
-        images = [F.to_tensor(image) for image in images]
-        hd_images = [F.normalize(image, image_mean, image_std) for image in images]
-        global_image = [
-            torch.nn.functional.interpolate(
-                image.unsqueeze(0).float(),
-                size=(image_size, image_size),
-                mode="bicubic",
-            ).to(image.dtype)
-            for image in hd_images
-        ]
-
-        shapes = [[image.size(1), image.size(2)] for image in hd_images]
-        mask_shapes = [[mask.size(0), mask.size(1)] for mask in image_attention_masks]
-        global_attention_mask = [torch.ones((1, mask_size, mask_size)) for _ in hd_images]
-
-        hd_images_reshape = []
-        for im, (h, w) in zip(hd_images, shapes):
-            im = im.reshape(1, 3, h // image_size, image_size, w // image_size, image_size)
-            im = im.permute(0, 2, 4, 1, 3, 5)
-            im = im.reshape(-1, 3, image_size, image_size)
-            hd_images_reshape.append(im.contiguous())
-
-        attention_masks_reshape = []
-        for mask, (h, w) in zip(image_attention_masks, mask_shapes):
-            mask = mask.reshape(h // mask_size, mask_size, w // mask_size, mask_size)
-            mask = mask.transpose(1, 2)
-            mask = mask.reshape(-1, mask_size, mask_size)
-            attention_masks_reshape.append(mask.contiguous())
-
-        downsample_attention_masks = []
-        for mask, (h, w) in zip(attention_masks_reshape, mask_shapes):
-            mask = mask[:, 0::2, 0::2]
-            mask = mask.reshape(
-                h // mask_size, w // mask_size, mask_size // 2 + mask_size % 2, mask_size // 2 + mask_size % 2
+            processed_image = self.rescale_and_normalize(
+                resized_image, do_rescale, rescale_factor, do_normalize, image_mean, image_std
             )
-            mask = mask.transpose(1, 2)
-            mask = mask.reshape(mask.size(0) * mask.size(1), mask.size(2) * mask.size(3))
-            downsample_attention_masks.append(mask)
+            global_image = self.resize(processed_image, size, interpolation=interpolation, antialias=False)
+            height, width = processed_image.shape[-2:]
+            mask_height, mask_width = attention_mask.shape[-2:]
+            global_attention_mask = torch.ones((1, mask_size, mask_size))
 
-        num_img_tokens = [
-            256 + 1 + int(mask.sum().item()) + int(mask[:, 0].sum().item()) + 16 for mask in downsample_attention_masks
-        ]
+            hd_image_reshape = processed_image.reshape(
+                1, 3, height // size.height, size.height, width // size.width, size.width
+            )
+            hd_image_reshape = hd_image_reshape.permute(0, 2, 4, 1, 3, 5)
+            hd_image_reshape = hd_image_reshape.reshape(-1, 3, size.height, size.width).contiguous()
 
-        hd_images_reshape = [
-            torch.cat([_global_image] + [_im], dim=0) for _global_image, _im in zip(global_image, hd_images_reshape)
-        ]
-        hd_masks_reshape = [
-            torch.cat([_global_mask] + [_mask], dim=0)
-            for _global_mask, _mask in zip(global_attention_mask, attention_masks_reshape)
-        ]
-        max_crops = max([img.size(0) for img in hd_images_reshape])
-        image_transformed = [self.pad_to_max_num_crops(im, max_crops) for im in hd_images_reshape]
-        image_transformed = torch.stack(image_transformed, dim=0)
-        mask_transformed = [self.pad_mask_to_max_num_crops(mask, max_crops) for mask in hd_masks_reshape]
-        mask_transformed = torch.stack(mask_transformed, dim=0)
+            attention_mask_reshape = attention_mask.reshape(
+                mask_height // mask_size, mask_size, mask_width // mask_size, mask_size
+            )
+            attention_mask_reshape = attention_mask_reshape.transpose(1, 2)
+            attention_mask_reshape = attention_mask_reshape.reshape(-1, mask_size, mask_size).contiguous()
 
-        returned_input_image_embeds = image_transformed
-        returned_image_sizes = torch.tensor(shapes, dtype=torch.long)
-        returned_image_attention_mask = mask_transformed
-        returned_num_img_tokens = num_img_tokens
+            downsample_attention_mask = attention_mask_reshape[:, 0::2, 0::2]
+            downsample_attention_mask = downsample_attention_mask.reshape(
+                mask_height // mask_size,
+                mask_width // mask_size,
+                mask_size // 2 + mask_size % 2,
+                mask_size // 2 + mask_size % 2,
+            )
+            downsample_attention_mask = downsample_attention_mask.transpose(1, 2)
+            downsample_attention_mask = downsample_attention_mask.reshape(
+                downsample_attention_mask.size(0) * downsample_attention_mask.size(1),
+                downsample_attention_mask.size(2) * downsample_attention_mask.size(3),
+            )
+
+            num_img_tokens = (
+                256
+                + 1
+                + int(downsample_attention_mask.sum().item())
+                + int(downsample_attention_mask[:, 0].sum().item())
+                + 16
+            )
+
+            hd_image_reshape = torch.cat([global_image.unsqueeze(0), hd_image_reshape], dim=0)
+            hd_attention_mask_reshape = torch.cat([global_attention_mask, attention_mask_reshape], dim=0)
+
+            images_transformed.append(hd_image_reshape)
+            masks_transformed.append(hd_attention_mask_reshape)
+            images_tokens.append(num_img_tokens)
+            image_sizes.append([height, width])
+            max_crops = hd_image_reshape.size(0)
+        max_crops = max([img.size(0) for img in images_transformed])
+        images_transformed = [self.pad_to_max_num_crops(im, max_crops) for im in images_transformed]
+        images_transformed = torch.stack(images_transformed, dim=0)
+        masks_transformed = [self.pad_mask_to_max_num_crops(mask, max_crops) for mask in masks_transformed]
+        masks_transformed = torch.stack(masks_transformed, dim=0)
+        image_sizes = torch.tensor(image_sizes, dtype=torch.long)
 
         data = {
-            "image_pixel_values": returned_input_image_embeds,
-            "image_sizes": returned_image_sizes,
-            "image_attention_mask": returned_image_attention_mask,
-            "num_img_tokens": returned_num_img_tokens,
+            "image_pixel_values": images_transformed,
+            "image_sizes": image_sizes,
+            "image_attention_mask": masks_transformed,
+            "num_img_tokens": images_tokens,
         }
 
         return BatchFeature(data=data, tensor_type=return_tensors)

tests/models/phi4_multimodal/test_image_processing_phi4_multimodal.py

@@ -0,0 +1,307 @@
+# coding=utf-8
+# Copyright 2021 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+
+import inspect
+import math
+import unittest
+import warnings
+
+import numpy as np
+from packaging import version
+
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    if is_torchvision_available():
+        from transformers import Phi4MultimodalImageProcessorFast
+
+
+class Phi4MultimodalImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=100,
+        min_resolution=30,
+        max_resolution=400,
+        dynamic_hd=36,
+        do_resize=True,
+        size=None,
+        patch_size=14,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        do_convert_rgb=True,
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 100, "width": 100}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.dynamic_hd = dynamic_hd
+        self.do_resize = do_resize
+        self.size = size
+        self.patch_size = patch_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "patch_size": self.patch_size,
+            "dynamic_hd": self.dynamic_hd,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_image_shape(self, images):
+        max_num_patches = 0
+        for image in images:
+            if isinstance(image, Image.Image):
+                width, height = image.size
+            elif isinstance(image, np.ndarray):
+                height, width = image.shape[:2]
+            elif isinstance(image, torch.Tensor):
+                height, width = image.shape[-2:]
+            w_crop_num = math.ceil(width / float(self.size["width"]))
+            h_crop_num = math.ceil(height / float(self.size["height"]))
+            num_patches = min(w_crop_num * h_crop_num + 1, self.dynamic_hd)
+            max_num_patches = max(max_num_patches, num_patches)
+        num_patches = max_num_patches
+        return num_patches, self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class Phi4MultimodalImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    fast_image_processing_class = Phi4MultimodalImageProcessorFast if is_torchvision_available() else None
+    test_slow_image_processor = False
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Phi4MultimodalImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"height": 100, "width": 100})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    @unittest.skip(reason="Phi4MultimodalImageProcessorFast doesn't treat 4 channel PIL and numpy consistently yet")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_cast_dtype_device(self):
+        for image_processing_class in self.image_processor_list:
+            if self.test_cast_dtype is not None:
+                # Initialize image_processor
+                image_processor = image_processing_class(**self.image_processor_dict)
+
+                # create random PyTorch tensors
+                image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+                encoding = image_processor(image_inputs, return_tensors="pt")
+                # for layoutLM compatibility
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.float32)
+
+                encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.float16)
+
+                encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.bfloat16)
+
+                with self.assertRaises(TypeError):
+                    _ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
+
+                # Try with text + image feature
+                encoding = image_processor(image_inputs, return_tensors="pt")
+                encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
+                encoding = encoding.to(torch.float16)
+
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.float16)
+                self.assertEqual(encoding.input_ids.dtype, torch.long)
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
+            self.assertEqual(
+                tuple(encoded_images.shape),
+                (self.image_processor_tester.batch_size, *expected_output_image_shape),
+            )
+
+    def test_image_processor_preprocess_arguments(self):
+        is_tested = False
+
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class(**self.image_processor_dict)
+
+            # validation done by _valid_processor_keys attribute
+            if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
+                preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
+                preprocess_parameter_names.remove("self")
+                preprocess_parameter_names.sort()
+                valid_processor_keys = image_processor._valid_processor_keys
+                valid_processor_keys.sort()
+                self.assertEqual(preprocess_parameter_names, valid_processor_keys)
+                is_tested = True
+
+            # validation done by @filter_out_non_signature_kwargs decorator
+            if hasattr(image_processor.preprocess, "_filter_out_non_signature_kwargs"):
+                if hasattr(self.image_processor_tester, "prepare_image_inputs"):
+                    inputs = self.image_processor_tester.prepare_image_inputs()
+                elif hasattr(self.image_processor_tester, "prepare_video_inputs"):
+                    inputs = self.image_processor_tester.prepare_video_inputs()
+                else:
+                    self.skipTest(reason="No valid input preparation method found")
+
+                with warnings.catch_warnings(record=True) as raised_warnings:
+                    warnings.simplefilter("always")
+                    image_processor(inputs, extra_argument=True)
+
+                messages = " ".join([str(w.message) for w in raised_warnings])
+                self.assertGreaterEqual(len(raised_warnings), 1)
+                self.assertIn("extra_argument", messages)
+                is_tested = True
+
+        if not is_tested:
+            self.skipTest(reason="No validation found for `preprocess` method")
+
+    @slow
+    def test_can_compile_fast_image_processor(self):
+        if self.fast_image_processing_class is None:
+            self.skipTest("Skipping compilation test as fast image processor is not defined")
+        if version.parse(torch.__version__) < version.parse("2.3"):
+            self.skipTest(reason="This test requires torch >= 2.3 to run.")
+
+        torch.compiler.reset()
+        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
+        image_processor = self.fast_image_processing_class(**self.image_processor_dict)
+        output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")
+
+        image_processor = torch.compile(image_processor, mode="reduce-overhead")
+        output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
+
+        torch.testing.assert_close(
+            output_eager.image_pixel_values, output_compiled.image_pixel_values, rtol=1e-4, atol=1e-4
+        )

tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py

@@ -31,12 +31,7 @@ from transformers import (
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import (
-    require_soundfile,
-    require_torch,
-    slow,
-    torch_device,
-)
+from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
 from transformers.utils import is_soundfile_available
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -285,6 +280,8 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
     audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/f2641_0_throatclearing.wav"
 
     def setUp(self):
+        # Currently, the Phi-4 checkpoint on the hub is not working with the latest Phi-4 code, so the slow integration tests
+        # won't pass without using the correct revision (refs/pr/70)
         self.processor = AutoProcessor.from_pretrained(self.checkpoint_path)
         self.generation_config = GenerationConfig(max_new_tokens=20, do_sample=False)
         self.user_token = "<|user|>"
@@ -325,7 +322,7 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
             self.checkpoint_path, torch_dtype=torch.float16, device_map=torch_device
         )
 
-        prompt = f"{self.user_token}<|image_1|>What is shown in this image?{self.end_token}{self.assistant_token}"
+        prompt = f"{self.user_token}<|image|>What is shown in this image?{self.end_token}{self.assistant_token}"
         inputs = self.processor(prompt, images=self.image, return_tensors="pt").to(torch_device)
 
         output = model.generate(
@@ -349,7 +346,7 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
         for i in range(1, 5):
             url = f"https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-{i}-2048.jpg"
             images.append(Image.open(requests.get(url, stream=True).raw))
-            placeholder += f"<|image_{i}|>"
+            placeholder += "<|image|>"
 
         prompt = f"{self.user_token}{placeholder}Summarize the deck of slides.{self.end_token}{self.assistant_token}"
         inputs = self.processor(prompt, images, return_tensors="pt").to(torch_device)
@@ -371,8 +368,8 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
             self.checkpoint_path, torch_dtype=torch.float16, device_map=torch_device
         )
 
-        prompt = f"{self.user_token}<|audio_1|>What is happening in this audio?{self.end_token}{self.assistant_token}"
-        inputs = self.processor(prompt, audios=self.audio, sampling_rate=self.sampling_rate, return_tensors="pt").to(
+        prompt = f"{self.user_token}<|audio|>What is happening in this audio?{self.end_token}{self.assistant_token}"
+        inputs = self.processor(prompt, audio=self.audio, sampling_rate=self.sampling_rate, return_tensors="pt").to(
             torch_device
         )
 

tests/test_image_processing_common.py

@@ -279,7 +279,7 @@ class ImageProcessingTestMixin:
                 saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
                 check_json_file_has_correct_format(saved_file)
 
-                use_fast = i == 1
+                use_fast = i == 1 or not self.test_slow_image_processor
                 image_processor_second = AutoImageProcessor.from_pretrained(tmpdirname, use_fast=use_fast)
 
             self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())