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tests/models/idefics2/test_image_processing_idefics2.py

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+# Copyright 2025 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
+# applicable limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin
+
+
+if is_vision_available():
+    from PIL import Image
+
+if is_torch_available():
+    import torch
+
+
+class Idefics2ImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        num_images=1,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_rescale=True,
+        rescale_factor=1 / 255,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        do_convert_rgb=True,
+        do_pad=True,
+        do_image_splitting=True,
+    ):
+        size = size if size is not None else {"shortest_edge": 378, "longest_edge": 980}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.num_images = num_images
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_convert_rgb = do_convert_rgb
+        self.do_pad = do_pad
+        self.do_image_splitting = do_image_splitting
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_convert_rgb": self.do_convert_rgb,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_rescale": self.do_rescale,
+            "rescale_factor": self.rescale_factor,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_pad": self.do_pad,
+            "do_image_splitting": self.do_image_splitting,
+        }
+
+    def get_expected_values(self, image_inputs, batched=False):
+        if not batched:
+            shortest_edge = self.size["shortest_edge"]
+            longest_edge = self.size["longest_edge"]
+            image = image_inputs[0]
+            if isinstance(image, Image.Image):
+                w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
+            else:
+                h, w = image.shape[1], image.shape[2]
+
+            aspect_ratio = w / h
+            if w > h and w >= longest_edge:
+                w = longest_edge
+                h = int(w / aspect_ratio)
+            elif h > w and h >= longest_edge:
+                h = longest_edge
+                w = int(h * aspect_ratio)
+            w = max(w, shortest_edge)
+            h = max(h, shortest_edge)
+            expected_height = h
+            expected_width = w
+        else:
+            expected_values = []
+            for images in image_inputs:
+                for image in images:
+                    expected_height, expected_width = self.get_expected_values([image])
+                    expected_values.append((expected_height, expected_width))
+            expected_height = max(expected_values, key=lambda item: item[0])[0]
+            expected_width = max(expected_values, key=lambda item: item[1])[1]
+
+        return expected_height, expected_width
+
+    def expected_output_image_shape(self, images):
+        height, width = self.get_expected_values(images, batched=True)
+        effective_nb_images = self.num_images * 5 if self.do_image_splitting else 1
+        return effective_nb_images, self.num_channels, height, width
+
+    def prepare_image_inputs(
+        self,
+        batch_size=None,
+        min_resolution=None,
+        max_resolution=None,
+        num_channels=None,
+        num_images=None,
+        size_divisor=None,
+        equal_resolution=False,
+        numpify=False,
+        torchify=False,
+    ):
+        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
+
+        batch_size = batch_size if batch_size is not None else self.batch_size
+        min_resolution = min_resolution if min_resolution is not None else self.min_resolution
+        max_resolution = max_resolution if max_resolution is not None else self.max_resolution
+        num_channels = num_channels if num_channels is not None else self.num_channels
+        num_images = num_images if num_images is not None else self.num_images
+
+        images_list = []
+        for i in range(batch_size):
+            images = []
+            for j in range(num_images):
+                if equal_resolution:
+                    width = height = max_resolution
+                else:
+                    if size_divisor is not None:
+                        min_resolution = max(size_divisor, min_resolution)
+                    width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
+                images.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
+            images_list.append(images)
+
+        if not numpify and not torchify:
+            images_list = [[Image.fromarray(np.moveaxis(image, 0, -1)) for image in images] for images in images_list]
+
+        if torchify:
+            images_list = [[torch.from_numpy(image) for image in images] for images in images_list]
+
+        if numpify:
+            images_list = [[image.transpose(1, 2, 0) for image in images] for images in images_list]
+
+        return images_list
+
+
+@require_torch
+@require_vision
+class Idefics2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Idefics2ImageProcessingTester(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_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "rescale_factor"))
+            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_pad"))
+            self.assertTrue(hasattr(image_processing, "do_image_splitting"))
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for sample_images in image_inputs:
+                for image in sample_images:
+                    self.assertIsInstance(image, np.ndarray)
+
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").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))
+
+            encoded_images = image_processing(image_inputs, return_tensors="pt").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_4_channels(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_dict = self.image_processor_dict.copy()
+            image_processor_dict["image_mean"] = [0.5, 0.5, 0.5, 0.5]
+            image_processor_dict["image_std"] = [0.5, 0.5, 0.5, 0.5]
+            image_processing = image_processing_class(**image_processor_dict)
+            self.image_processor_tester.num_channels = 4
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+
+            for sample_images in image_inputs:
+                for image in sample_images:
+                    self.assertIsInstance(image, np.ndarray)
+
+            encoded_images = image_processing(
+                image_inputs[0], input_data_format="channels_last", return_tensors="pt"
+            ).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))
+
+            encoded_images = image_processing(
+                image_inputs, input_data_format="channels_last", return_tensors="pt"
+            ).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_pil(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for images in image_inputs:
+                for image in images:
+                    self.assertIsInstance(image, Image.Image)
+
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").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))
+
+            encoded_images = image_processing(image_inputs, return_tensors="pt").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_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            for images in image_inputs:
+                for image in images:
+                    self.assertIsInstance(image, torch.Tensor)
+
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").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))
+
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(
+                tuple(encoded_images.shape),
+                (self.image_processor_tester.batch_size, *expected_output_image_shape),
+            )
+
+    def test_image_splitting(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_dict = self.image_processor_dict.copy()
+            image_processor_dict["do_image_splitting"] = True
+            image_processing = image_processing_class(**image_processor_dict)
+
+            image_inputs = self.image_processor_tester.prepare_image_inputs(
+                equal_resolution=True, torchify=True, num_images=1
+            )
+
+            result = image_processing(image_inputs[0], return_tensors="pt")
+            self.assertEqual(result.pixel_values.shape[1], 5)
+
+            image_processor_dict["do_image_splitting"] = False
+            image_processing = image_processing_class(**image_processor_dict)
+
+            result = image_processing(image_inputs[0], return_tensors="pt")
+            if len(result.pixel_values.shape) == 5:
+                self.assertEqual(result.pixel_values.shape[1], 1)
+            else:
+                self.assertEqual(result.pixel_values.shape[1], self.image_processor_tester.num_channels)
+
+    def test_pixel_attention_mask(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_dict = self.image_processor_dict.copy()
+            image_processor_dict["do_pad"] = True
+            image_processing = image_processing_class(**image_processor_dict)
+
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            result = image_processing(image_inputs, return_tensors="pt")
+            self.assertIn("pixel_attention_mask", result)
+
+            self.assertEqual(result.pixel_attention_mask.shape[-2:], result.pixel_values.shape[-2:])
+
+            image_processor_dict["do_pad"] = False
+            image_processor_dict["do_image_splitting"] = False
+            image_processing = image_processing_class(**image_processor_dict)
+
+            equal_size_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+            result = image_processing(equal_size_inputs, return_tensors="pt")
+            self.assertNotIn("pixel_attention_mask", result)
+
+    def test_convert_rgb(self):
+        for image_processing_class in self.image_processing_classes.values():
+            rgba_image = Image.new("RGBA", (100, 100), (255, 0, 0, 128))
+
+            image_processor_dict = self.image_processor_dict.copy()
+            image_processor_dict["do_convert_rgb"] = True
+            image_processing = image_processing_class(**image_processor_dict)
+
+            result = image_processing([rgba_image], return_tensors="pt")
+            self.assertIsNotNone(result.pixel_values)
+            rgb_image = rgba_image.convert("RGB")
+
+            image_processor_dict["do_convert_rgb"] = False
+            image_processing = image_processing_class(**image_processor_dict)
+
+            result = image_processing([rgb_image], return_tensors="pt")
+            self.assertIsNotNone(result.pixel_values)
+
+            rgb_image = Image.new("RGB", (100, 100), (255, 0, 0))
+            result = image_processing([rgb_image], return_tensors="pt")
+            self.assertIsNotNone(result.pixel_values)
+
+    def test_backends_equivalence_batched(self):
+        """Override to also compare pixel_attention_mask across backends."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(
+            equal_resolution=False, num_images=5, torchify=True
+        )
+        indices_to_pop = [i if np.random.random() < 0.5 else None for i in range(len(dummy_images))]
+        for i in indices_to_pop:
+            if i is not None:
+                dummy_images[i].pop()
+
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_pixel_values = encodings[reference_backend].pixel_values
+        reference_mask = encodings[reference_backend].pixel_attention_mask.float()
+
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_pixel_values, encodings[backend_name].pixel_values)
+            self._assert_tensors_equivalence(reference_mask, encodings[backend_name].pixel_attention_mask.float())