first commit

tests/models/paddleocr_vl/test_image_processing_paddleocr_vl.py

@@ -0,0 +1,363 @@
+# 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
+# limitations under the License.
+
+import itertools
+import json
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+from transformers.models.paddleocr_vl.image_processing_paddleocr_vl import smart_resize
+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, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class PaddleOCRVLImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        min_resolution=56,
+        max_resolution=80,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=OPENAI_CLIP_MEAN,
+        image_std=OPENAI_CLIP_STD,
+        temporal_patch_size=1,
+        patch_size=14,
+        merge_size=2,
+        do_convert_rgb=True,
+    ):
+        # Use small pixel bounds so tests run quickly with small images
+        size = size if size is not None else {"shortest_edge": 56 * 56, "longest_edge": 28 * 28 * 1280}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        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.temporal_patch_size = temporal_patch_size
+        self.patch_size = patch_size
+        self.merge_size = merge_size
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "min_pixels": self.size["shortest_edge"],
+            "max_pixels": self.size["longest_edge"],
+            "patch_size": self.patch_size,
+            "temporal_patch_size": self.temporal_patch_size,
+            "merge_size": self.merge_size,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_image_shape(self, images):
+        """
+        Returns the expected pixel_values shape for a batch of images.
+        PaddleOCRVL outputs patches of shape (N_patches_total, C, patch_size, patch_size).
+        """
+        seq_len = 0
+        for image in images:
+            if isinstance(image, Image.Image):
+                width, height = image.size
+            elif isinstance(image, np.ndarray):
+                if image.ndim == 3 and image.shape[2] <= 4:
+                    # channels-last: (H, W, C)
+                    height, width = image.shape[:2]
+                else:
+                    # channels-first: (C, H, W)
+                    height, width = image.shape[-2:]
+            elif is_torch_available() and isinstance(image, torch.Tensor):
+                height, width = image.shape[-2:]
+            else:
+                height, width = self.min_resolution, self.min_resolution
+
+            resized_height, resized_width = smart_resize(
+                height,
+                width,
+                factor=self.patch_size * self.merge_size,
+                min_pixels=self.size["shortest_edge"],
+                max_pixels=self.size["longest_edge"],
+            )
+            grid_h = resized_height // self.patch_size
+            grid_w = resized_width // self.patch_size
+            seq_len += grid_h * grid_w  # temporal_patch_size=1, so grid_t=1
+
+        return (seq_len, self.num_channels, self.patch_size, self.patch_size)
+
+    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 PaddleOCRVLImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PaddleOCRVLImageProcessingTester(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_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+            self.assertTrue(hasattr(image_processing, "patch_size"))
+            self.assertTrue(hasattr(image_processing, "temporal_patch_size"))
+            self.assertTrue(hasattr(image_processing, "merge_size"))
+
+    def test_image_processor_to_json_string(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            obj = json.loads(image_processor.to_json_string())
+            for key, value in self.image_processor_dict.items():
+                # min_pixels/max_pixels are stored as size in the config
+                if key not in ["min_pixels", "max_pixels"]:
+                    self.assertEqual(obj[key], value)
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(
+                image_processor.size,
+                {
+                    "shortest_edge": self.image_processor_dict["min_pixels"],
+                    "longest_edge": self.image_processor_dict["max_pixels"],
+                },
+            )
+
+            image_processor = image_processing_class.from_dict(
+                self.image_processor_dict, min_pixels=28 * 28, max_pixels=56 * 56
+            )
+            self.assertEqual(image_processor.size, {"shortest_edge": 28 * 28, "longest_edge": 56 * 56})
+
+    def test_select_best_resolution(self):
+        best_resolution = smart_resize(561, 278, factor=28)
+        self.assertEqual(best_resolution, (560, 280))
+
+    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 image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Single image
+            encoded = image_processing(image_inputs[0], return_tensors="pt")
+            expected_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded.pixel_values.shape), expected_shape)
+            self.assertEqual(encoded.image_grid_thw.shape, (1, 3))
+
+            # Batched
+            encoded = image_processing(image_inputs, return_tensors="pt")
+            expected_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(tuple(encoded.pixel_values.shape), expected_shape)
+            self.assertEqual(encoded.image_grid_thw.shape, (self.image_processor_tester.batch_size, 3))
+
+    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 image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Single image
+            encoded = image_processing(image_inputs[0], return_tensors="pt")
+            expected_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded.pixel_values.shape), expected_shape)
+            self.assertEqual(encoded.image_grid_thw.shape, (1, 3))
+
+            # Batched
+            encoded = image_processing(image_inputs, return_tensors="pt")
+            expected_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(tuple(encoded.pixel_values.shape), expected_shape)
+            self.assertEqual(encoded.image_grid_thw.shape, (self.image_processor_tester.batch_size, 3))
+
+    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 image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Single image
+            encoded = image_processing(image_inputs[0], return_tensors="pt")
+            expected_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded.pixel_values.shape), expected_shape)
+            self.assertEqual(encoded.image_grid_thw.shape, (1, 3))
+
+            # Batched
+            encoded = image_processing(image_inputs, return_tensors="pt")
+            expected_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(tuple(encoded.pixel_values.shape), expected_shape)
+            self.assertEqual(encoded.image_grid_thw.shape, (self.image_processor_tester.batch_size, 3))
+
+    def test_call_equal_resolution(self):
+        """With equal-resolution images, the batched output shapes are fully deterministic."""
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # equal_resolution=True → all images are max_resolution × max_resolution = 80×80
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+
+            # smart_resize(80, 80, factor=28, min_pixels=56*56, max_pixels=28*28*1280) → (84, 84)
+            # grid_h = grid_w = 84 / 14 = 6, N_per_image = 36
+            expected_n_patches_per_image = 6 * 6
+            batch_size = self.image_processor_tester.batch_size
+
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            self.assertEqual(
+                tuple(process_out.pixel_values.shape),
+                (batch_size * expected_n_patches_per_image, 3, 14, 14),
+            )
+            expected_grid = torch.tensor([[1, 6, 6]] * batch_size)
+            self.assertTrue((process_out.image_grid_thw == expected_grid).all())
+
+    @unittest.skip(reason="PaddleOCRVLImageProcessor converts to RGB, 4-channel images not consistently supported")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_custom_image_size(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                image_processing.save_pretrained(tmpdirname)
+                image_processor_loaded = image_processing_class.from_pretrained(
+                    tmpdirname, max_pixels=56 * 56, min_pixels=28 * 28
+                )
+
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            # equal_resolution=True → all images are max_resolution × max_resolution = 80×80
+            # smart_resize(80, 80, factor=28, min_pixels=28*28=784, max_pixels=56*56=3136):
+            #   h_bar=84, 84*84=7056 > 3136, so reduce:
+            #   beta = sqrt(80*80/3136) = sqrt(2.041) = 1.429
+            #   h_bar = floor(80/1.429/28)*28 = floor(2.0)*28 = 2*28 = 56
+            #   grid_h=4, grid_w=4 → N=16 per image
+            process_out = image_processor_loaded(image_inputs, return_tensors="pt")
+            expected_n = 16  # 4*4 grid (56/14 = 4)
+            self.assertEqual(process_out.pixel_values.shape[0], self.image_processor_tester.batch_size * expected_n)
+            self.assertEqual(process_out.pixel_values.shape[1:], (3, 14, 14))
+
+    def test_custom_pixels(self):
+        # Use pixel values >= 784 (28*28) to avoid smart_resize producing 0-size outputs
+        # for images in the 56x80 px range used in the tester (factor=28 requires output >= 28px)
+        pixel_choices = frozenset(itertools.product((1000, 5000, 50000), (1000, 5000, 50000)))
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_dict = self.image_processor_dict.copy()
+            for a_pixels, b_pixels in pixel_choices:
+                image_processor_dict["min_pixels"] = min(a_pixels, b_pixels)
+                image_processor_dict["max_pixels"] = max(a_pixels, b_pixels)
+                image_processor = image_processing_class(**image_processor_dict)
+                image_inputs = self.image_processor_tester.prepare_image_inputs()
+                # Just verify no error is raised
+                image_processor(image_inputs, return_tensors="pt")
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+        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(image_inputs, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding.pixel_values, encodings[backend_name].pixel_values)
+            self.assertEqual(reference_encoding.image_grid_thw.dtype, encodings[backend_name].image_grid_thw.dtype)
+            self._assert_tensors_equivalence(
+                reference_encoding.image_grid_thw.float(), encodings[backend_name].image_grid_thw.float()
+            )
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        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, torchify=True)
+
+        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_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding.pixel_values, encodings[backend_name].pixel_values)
+            self._assert_tensors_equivalence(
+                reference_encoding.image_grid_thw.float(), encodings[backend_name].image_grid_thw.float()
+            )
+
+    def test_get_num_patches_without_images(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # 100×100 → smart_resize(100, 100, factor=28, min_pixels=56*56, max_pixels=28*28*1280)
+            # h_bar=112, w_bar=112, grid_h=8, grid_w=8 → 64 patches
+            num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={})
+            self.assertEqual(num_patches, 64)
+
+            # 200×50 → h_bar=196, w_bar=56, grid_h=14, grid_w=4 → 56 patches
+            num_patches = image_processing.get_number_of_image_patches(height=200, width=50, images_kwargs={})
+            self.assertEqual(num_patches, 56)
+
+            # With custom patch_size=28 → factor=28*2=56
+            # 100×100 → smart_resize(100, 100, factor=56, min_pixels=56*56, max_pixels=28*28*1280)
+            # h_bar=round(100/56)*56=2*56=112, grid_h=112/28=4, grid_w=4 → 16 patches
+            num_patches = image_processing.get_number_of_image_patches(
+                height=100, width=100, images_kwargs={"patch_size": 28}
+            )
+            self.assertEqual(num_patches, 16)

tests/models/paddleocr_vl/test_modeling_paddleocr_vl.py

@@ -0,0 +1,543 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+"""Testing suite for the PaddleOCRVL model."""
+
+import copy
+import gc
+import unittest
+
+import pytest
+from parameterized import parameterized
+
+from transformers import (
+    AutoProcessor,
+    PaddleOCRVLConfig,
+    PaddleOCRVLForConditionalGeneration,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    backend_empty_cache,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+
+class PaddleOCRVLVisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        seq_length=13,
+        num_channels=3,
+        image_height=28,
+        image_width=28,
+        text_config={
+            "pad_token_id": 0,
+            "bos_token_id": 1,
+            "eos_token_id": 2,
+            "vocab_size": 103424,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_dropout_prob": 0.0,
+            "hidden_size": 32,
+            "ignored_index": -100,
+            "image_token_id": 100295,
+            "intermediate_size": 32,
+            "max_position_embeddings": 512,
+            "model_type": "paddleocr_vl",
+            "num_attention_heads": 4,
+            "num_hidden_layers": 2,
+            "num_key_value_heads": 2,
+            "rms_norm_eps": 1e-05,
+            "rope_scaling": {"mrope_section": [16, 24, 24], "rope_type": "default", "type": "default"},
+            "rope_theta": 500000,
+            "tie_word_embeddings": False,
+        },
+        vision_start_token_id=101305,
+        vision_end_token_id=101306,
+        image_token_id=100295,
+        is_training=True,
+        vision_config={
+            "hidden_act": "gelu_pytorch_tanh",
+            "hidden_size": 144,
+            "intermediate_size": 32,
+            "layer_norm_eps": 1e-06,
+            "model_type": "paddleocr_vl",
+            "num_attention_heads": 4,
+            "num_channels": 3,
+            "num_hidden_layers": 2,
+            "pad_token_id": 0,
+            "patch_size": 14,
+            "spatial_merge_size": 2,
+        },
+    ):
+        self.parent = parent
+        self.bos_token_id = text_config["bos_token_id"]
+        self.eos_token_id = text_config["eos_token_id"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.hidden_size = text_config["hidden_size"]
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.image_token_id = image_token_id
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_height = image_height
+        self.image_width = image_width
+        self.is_training = is_training
+        self.vocab_size = text_config["vocab_size"]
+        self.num_image_tokens = 1
+        self.seq_length = seq_length + self.num_image_tokens
+
+    def get_config(self):
+        return PaddleOCRVLConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            vision_start_token_id=self.vision_start_token_id,
+            image_token_id=self.image_token_id,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        patch_size = config.vision_config.patch_size
+        pixel_values = floats_tensor(
+            [
+                self.batch_size * (self.image_height * self.image_width) // (patch_size**2),
+                config.vision_config.num_channels,
+                patch_size,
+                patch_size,
+            ]
+        )
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+
+        input_ids[:, :4] = torch.tensor([100273, 2969, 93963, 93919], dtype=input_ids.dtype, device=input_ids.device)
+        input_ids[:, 4] = self.vision_start_token_id
+        input_ids[:, 5 : 5 + self.num_image_tokens] = self.image_token_id
+        input_ids[:, -8] = self.vision_end_token_id
+        input_ids[:, -7:] = torch.tensor(
+            [93972, 2497, 93963, 23, 92267, 93963, 93919], dtype=input_ids.dtype, device=input_ids.device
+        )
+
+        mm_token_type_ids = torch.zeros_like(input_ids)
+        mm_token_type_ids[input_ids == self.image_token_id] = 1
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_grid_thw": torch.tensor([[1, 2, 2]] * self.batch_size, device=torch_device),
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "mm_token_type_ids": mm_token_type_ids,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class PaddleOCRVLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Model tester for `PaddleOCRVLForConditionalGeneration`.
+    """
+
+    all_model_classes = (PaddleOCRVLForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-text-to-text": PaddleOCRVLForConditionalGeneration}
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = PaddleOCRVLVisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=PaddleOCRVLConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(
+        reason="embed_tokens is ~80% of test model size, exceeding the 70% GPU budget so device_map puts everything on CPU"
+    )
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip(
+        reason="embed_tokens is ~80% of test model size, exceeding the 70% GPU budget so device_map puts everything on CPU"
+    )
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(
+        reason="embed_tokens is ~80% of test model size, exceeding the 70% GPU budget so device_map puts everything on CPU"
+    )
+    def test_disk_offload_safetensors(self):
+        pass
+
+    def test_mismatching_num_image_tokens(self):
+        """
+        Tests that an explicit error is thrown when the number of image tokens
+        doesn't match the number of image placeholders in the text.
+        We also test multi-image cases when one prompt has multiple image tokens.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            curr_input_dict = copy.deepcopy(input_dict)  # in-place modifications further
+            _ = model(**curr_input_dict)  # successful forward with no modifications
+
+            # remove one image but leave all the image tokens in text
+            patch_size = config.vision_config.patch_size
+            one_img_length = (self.model_tester.image_height * self.model_tester.image_width) // (patch_size**2)
+            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-one_img_length:, ...]
+            curr_input_dict["image_grid_thw"] = curr_input_dict["image_grid_thw"][-1:, ...]
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(**curr_input_dict)
+
+            # simulate multi-image case by concatenating inputs where each has exactly one image/image-token
+            input_ids = curr_input_dict["input_ids"][:1]
+            pixel_values = curr_input_dict["pixel_values"][:one_img_length]
+            image_grid_thw = curr_input_dict["image_grid_thw"][:1]
+            mm_token_type_ids = curr_input_dict["mm_token_type_ids"][:1]
+            input_ids = torch.cat([input_ids, input_ids], dim=0)
+
+            # one image and two image tokens raise an error
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(
+                    input_ids=input_ids,
+                    pixel_values=pixel_values,
+                    image_grid_thw=image_grid_thw,
+                    mm_token_type_ids=torch.cat([mm_token_type_ids, mm_token_type_ids], dim=0),
+                )
+
+            # two images and two image tokens don't raise an error
+            pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
+            image_grid_thw = torch.cat([image_grid_thw, image_grid_thw], dim=0)
+            mm_token_type_ids = torch.cat([mm_token_type_ids, mm_token_type_ids], dim=0)
+            _ = model(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+                mm_token_type_ids=mm_token_type_ids,
+            )
+
+    # PaddleOCRVL has pixel_values shaped as (bs*patch_len, image_channels, patch_size, patch_size) so we can't slice to batches in generate
+    def prepare_config_and_inputs_for_generate(self, batch_size=2):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # We don't want a few model inputs in our model input dictionary for generation tests
+        input_keys_to_ignore = [
+            # we don't want encoder-decoder models to start from filled decoder ids
+            "decoder_input_ids",
+            "decoder_attention_mask",
+            # we'll set cache use in each test differently
+            "use_cache",
+            # Ignore labels if it is in the input dict
+            "labels",
+            # model-specific exceptions should overload/overwrite this function
+        ]
+
+        # The diff from the general `prepare_config_and_inputs_for_generate` lies here
+        patch_size = config.vision_config.patch_size
+        filtered_image_length = (
+            batch_size * (self.model_tester.image_height * self.model_tester.image_width) // (patch_size**2)
+        )
+        filtered_inputs_dict = {
+            k: v[:batch_size, ...] if isinstance(v, torch.Tensor) else v
+            for k, v in inputs_dict.items()
+            if k not in input_keys_to_ignore
+        }
+        filtered_inputs_dict["pixel_values"] = inputs_dict["pixel_values"][:filtered_image_length]
+
+        # It is important set `eos_token_id` to `None` to avoid early stopping (would break for length-based checks)
+        text_gen_config = config.get_text_config(decoder=True)
+        if text_gen_config.eos_token_id is not None and text_gen_config.pad_token_id is None:
+            text_gen_config.pad_token_id = (
+                text_gen_config.eos_token_id
+                if isinstance(text_gen_config.eos_token_id, int)
+                else text_gen_config.eos_token_id[0]
+            )
+        text_gen_config.eos_token_id = None
+        text_gen_config.forced_eos_token_id = None
+
+        return config, filtered_inputs_dict
+
+    @unittest.skip(reason="PaddleOCRVL does not support.")
+    def test_generate_compile_model_forward_fullgraph(self):
+        pass
+
+    @unittest.skip(reason="PaddleOCRVL does not support.")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_sample_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_search_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_search_generate_dict_output(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_search_generate_dict_outputs_use_cache(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_sample_generate_dict_output(self):
+        pass
+
+    @unittest.skip(reason="PaddleOCRVL needs to apply weight conversions.")
+    def test_can_load_from_already_mapped_keys(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_generate_from_inputs_embeds_1_beam_search(self, _, num_beams):
+        pass
+
+    @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support assisted decoding.")
+    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support assisted decoding.")
+    def test_assisted_decoding_sample(self):
+        pass
+
+    @unittest.skip("PaddleOCRVL does not support this test.")
+    def test_model_is_small(self):
+        pass
+
+
+@require_torch
+@slow
+class PaddleOCRVLIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("PaddlePaddle/PaddleOCR-VL")
+        self.messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "url": url_to_local_path(
+                            "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo2.jpg"
+                        ),
+                    },
+                    {"type": "text", "text": "OCR:"},
+                ],
+            }
+        ]
+
+    def tearDown(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_small_model_integration_test(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained(
+                "PaddlePaddle/PaddleOCR-VL",
+                dtype="bfloat16",
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            self.messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        )
+
+        expected_input_ids_length = 211
+        assert expected_input_ids_length == len(inputs.input_ids[0])
+
+        expected_input_ids = [100273, 2969, 93963, 93919, 101305, 100295, 100295, 100295, 100295, 100295]  # fmt: skip
+        assert expected_input_ids == inputs.input_ids[0].tolist()[:10]
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+                [0.9922, 0.9922, 0.9922],
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+            ],
+            dtype=torch.float32,
+            device="cpu",
+        )
+
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:5, :, 0, 0], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+        output = model.generate(**inputs, max_new_tokens=30)
+        result = self.processor.decode(output[0][inputs["input_ids"].shape[-1] : -1])
+
+        EXPECTED_DECODED_TEXT = "生甘草"
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )
+
+    def test_small_model_integration_test_batch(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained("PaddlePaddle/PaddleOCR-VL", dtype="bfloat16")
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            [self.messages, self.messages],
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+        ).to(torch_device)
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+        generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, output)]
+        result = self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        EXPECTED_DECODED_TEXT = ["生甘草", "生甘草"]
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_flashatt2(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained(
+                "PaddlePaddle/PaddleOCR-VL", dtype="bfloat16", attn_implementation="flash_attention_2"
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            self.messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        )
+
+        expected_input_ids_length = 211
+        assert expected_input_ids_length == len(inputs.input_ids[0])
+
+        expected_input_ids = [100273, 2969, 93963, 93919, 101305, 100295, 100295, 100295, 100295, 100295]  # fmt: skip
+        assert expected_input_ids == inputs.input_ids[0].tolist()[:10]
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+                [0.9922, 0.9922, 0.9922],
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+            ],
+            dtype=torch.float32,
+            device="cpu",
+        )
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:5, :, 0, 0], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+        output = model.generate(**inputs, max_new_tokens=30)
+        result = self.processor.decode(output[0][inputs["input_ids"].shape[-1] : -1])
+
+        EXPECTED_DECODED_TEXT = "生甘草"
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_batch_flashatt2(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained(
+                "PaddlePaddle/PaddleOCR-VL", dtype="bfloat16", attn_implementation="flash_attention_2"
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            [self.messages, self.messages],
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+        ).to(torch_device)
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+        generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, output)]
+        result = self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        EXPECTED_DECODED_TEXT = ["生甘草", "生甘草"]
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )