first commit

tests/models/sam/test_image_processing_sam.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
+# limitations under the License.
+
+
+import unittest
+
+from datasets import load_dataset
+
+from transformers.file_utils import is_torch_available
+from transformers.testing_utils import require_torch, require_vision
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+
+class SamImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_pad=True,
+        pad_size=None,
+        mask_size=None,
+        mask_pad_size=None,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+    ):
+        size = size if size is not None else {"longest_edge": 20}
+        pad_size = pad_size if pad_size is not None else {"height": 20, "width": 20}
+        mask_size = mask_size if mask_size is not None else {"longest_edge": 12}
+        mask_pad_size = mask_pad_size if mask_pad_size is not None else {"height": 12, "width": 12}
+        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.do_pad = do_pad
+        self.pad_size = pad_size
+        self.mask_size = mask_size
+        self.mask_pad_size = mask_pad_size
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_pad": self.do_pad,
+            "pad_size": self.pad_size,
+            "mask_size": self.mask_size,
+            "mask_pad_size": self.mask_pad_size,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.pad_size["height"], self.pad_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,
+        )
+
+
+# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs
+def prepare_semantic_single_inputs():
+    ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
+    example = ds[0]
+    return example["image"], example["map"]
+
+
+# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs
+def prepare_semantic_batch_inputs():
+    ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
+    return list(ds["image"][:2]), list(ds["map"][:2])
+
+
+@require_torch
+@require_vision
+class SamImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = SamImageProcessingTester(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, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            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_pad"))
+            self.assertTrue(hasattr(image_processing, "pad_size"))
+            self.assertTrue(hasattr(image_processing, "mask_size"))
+            self.assertTrue(hasattr(image_processing, "mask_pad_size"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing_class = image_processing_class(**self.image_processor_dict)
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"longest_edge": 20})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size={"longest_edge": 42})
+            self.assertEqual(image_processor.size, {"longest_edge": 42})
+
+    def test_call_segmentation_maps(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # 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)
+            maps = []
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+                maps.append(torch.zeros(image.shape[-2:]).long())
+
+            # Test not batched input
+            encoding = image_processor(image_inputs[0], maps[0], return_tensors="pt")
+            self.assertEqual(
+                encoding["pixel_values"].shape,
+                (
+                    1,
+                    self.image_processor_tester.num_channels,
+                    self.image_processor_tester.pad_size["height"],
+                    self.image_processor_tester.pad_size["width"],
+                ),
+            )
+            self.assertEqual(
+                encoding["labels"].shape,
+                (
+                    1,
+                    self.image_processor_tester.mask_pad_size["height"],
+                    self.image_processor_tester.mask_pad_size["width"],
+                ),
+            )
+            self.assertEqual(encoding["labels"].dtype, torch.long)
+            self.assertTrue(encoding["labels"].min().item() >= 0)
+            self.assertTrue(encoding["labels"].max().item() <= 255)
+
+            # Test batched
+            encoding = image_processor(image_inputs, maps, return_tensors="pt")
+            self.assertEqual(
+                encoding["pixel_values"].shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    self.image_processor_tester.pad_size["height"],
+                    self.image_processor_tester.pad_size["width"],
+                ),
+            )
+            self.assertEqual(
+                encoding["labels"].shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.mask_pad_size["height"],
+                    self.image_processor_tester.mask_pad_size["width"],
+                ),
+            )
+            self.assertEqual(encoding["labels"].dtype, torch.long)
+            self.assertTrue(encoding["labels"].min().item() >= 0)
+            self.assertTrue(encoding["labels"].max().item() <= 255)
+
+            # Test not batched input (PIL images)
+            image, segmentation_map = prepare_semantic_single_inputs()
+
+            encoding = image_processor(image, segmentation_map, return_tensors="pt")
+            self.assertEqual(
+                encoding["pixel_values"].shape,
+                (
+                    1,
+                    self.image_processor_tester.num_channels,
+                    self.image_processor_tester.pad_size["height"],
+                    self.image_processor_tester.pad_size["width"],
+                ),
+            )
+            self.assertEqual(
+                encoding["labels"].shape,
+                (
+                    1,
+                    self.image_processor_tester.mask_pad_size["height"],
+                    self.image_processor_tester.mask_pad_size["width"],
+                ),
+            )
+            self.assertEqual(encoding["labels"].dtype, torch.long)
+            self.assertTrue(encoding["labels"].min().item() >= 0)
+            self.assertTrue(encoding["labels"].max().item() <= 255)
+
+            # Test batched input (PIL images)
+            images, segmentation_maps = prepare_semantic_batch_inputs()
+
+            encoding = image_processor(images, segmentation_maps, return_tensors="pt")
+            self.assertEqual(
+                encoding["pixel_values"].shape,
+                (
+                    2,
+                    self.image_processor_tester.num_channels,
+                    self.image_processor_tester.pad_size["height"],
+                    self.image_processor_tester.pad_size["width"],
+                ),
+            )
+            self.assertEqual(
+                encoding["labels"].shape,
+                (
+                    2,
+                    self.image_processor_tester.mask_pad_size["height"],
+                    self.image_processor_tester.mask_pad_size["width"],
+                ),
+            )
+            self.assertEqual(encoding["labels"].dtype, torch.long)
+            self.assertTrue(encoding["labels"].min().item() >= 0)
+            self.assertTrue(encoding["labels"].max().item() <= 255)
+
+    def test_backends_equivalence(self):
+        """Override base class test to also compare segmentation labels."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_image, dummy_map = prepare_semantic_single_inputs()
+
+        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_image, segmentation_maps=dummy_map, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values, atol=1e-1
+            )
+            self.assertLessEqual(
+                torch.mean(
+                    torch.abs(encodings[reference_backend].pixel_values - encodings[backend_name].pixel_values)
+                ).item(),
+                1e-3,
+            )
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].labels.float(), encodings[backend_name].labels.float(), atol=1e-1
+            )
+
+    def test_backends_equivalence_batched(self):
+        """Override base class test to also compare segmentation labels."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_images, dummy_maps = prepare_semantic_batch_inputs()
+
+        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, segmentation_maps=dummy_maps, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values, atol=1e-1
+            )
+            self.assertLessEqual(
+                torch.mean(
+                    torch.abs(encodings[reference_backend].pixel_values - encodings[backend_name].pixel_values)
+                ).item(),
+                1e-3,
+            )
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].labels.float(), encodings[backend_name].labels.float(), atol=1e-1
+            )

tests/models/sam/test_modeling_sam.py

@@ -0,0 +1,990 @@
+# Copyright 2023 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 PyTorch SAM model."""
+
+import tempfile
+import unittest
+
+import pytest
+import requests
+
+from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig, pipeline
+from transformers.testing_utils import Expectations, cleanup, require_torch, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import SamModel, SamProcessor, SamVisionModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class SamVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        hidden_size=36,
+        intermediate_size=72,
+        projection_dim=62,
+        output_channels=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        num_channels=3,
+        image_size=24,
+        patch_size=2,
+        hidden_act="gelu",
+        layer_norm_eps=1e-06,
+        dropout=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=1.0,
+        qkv_bias=True,
+        mlp_ratio=4.0,
+        use_abs_pos=True,
+        use_rel_pos=True,
+        rel_pos_zero_init=False,
+        window_size=14,
+        global_attn_indexes=[2, 5, 8, 11],
+        num_pos_feats=16,
+        mlp_dim=None,
+        batch_size=2,
+        is_training=True,
+    ):
+        self.parent = parent
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.projection_dim = projection_dim
+        self.output_channels = output_channels
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.hidden_act = hidden_act
+        self.layer_norm_eps = layer_norm_eps
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.qkv_bias = qkv_bias
+        self.mlp_ratio = mlp_ratio
+        self.use_abs_pos = use_abs_pos
+        self.use_rel_pos = use_rel_pos
+        self.rel_pos_zero_init = rel_pos_zero_init
+        self.window_size = window_size
+        self.global_attn_indexes = global_attn_indexes
+        self.num_pos_feats = num_pos_feats
+        self.mlp_dim = mlp_dim
+        self.batch_size = batch_size
+        self.is_training = is_training
+
+        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+
+    def get_config(self):
+        return SamVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+            initializer_factor=self.initializer_factor,
+            output_channels=self.output_channels,
+            qkv_bias=self.qkv_bias,
+            mlp_ratio=self.mlp_ratio,
+            use_abs_pos=self.use_abs_pos,
+            use_rel_pos=self.use_rel_pos,
+            rel_pos_zero_init=self.rel_pos_zero_init,
+            window_size=self.window_size,
+            global_attn_indexes=self.global_attn_indexes,
+            num_pos_feats=self.num_pos_feats,
+            mlp_dim=self.mlp_dim,
+        )
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def create_and_check_model(self, config, pixel_values):
+        model = SamVisionModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        output_size = self.image_size // self.patch_size
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.output_channels, output_size, output_size)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class SamVisionModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as SAM's vision encoder does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (SamVisionModel,) if is_torch_available() else ()
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = SamVisionModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="SAM's vision encoder does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        expected_attention_shape = (
+            self.model_tester.batch_size * self.model_tester.num_attention_heads,
+            196,
+            196,
+        )
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-4:]),
+                list(expected_attention_shape),
+            )
+
+    @unittest.skip(reason="Hidden_states is tested in create_and_check_model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        self.skipTest(reason="SAM model can't be compiled dynamic yet")
+
+
+class SamPromptEncoderTester:
+    def __init__(
+        self,
+        hidden_size=32,
+        input_image_size=24,
+        patch_size=2,
+        mask_input_channels=4,
+        num_point_embeddings=4,
+        hidden_act="gelu",
+    ):
+        self.hidden_size = hidden_size
+        self.input_image_size = input_image_size
+        self.patch_size = patch_size
+        self.mask_input_channels = mask_input_channels
+        self.num_point_embeddings = num_point_embeddings
+        self.hidden_act = hidden_act
+
+    def get_config(self):
+        return SamPromptEncoderConfig(
+            image_size=self.input_image_size,
+            patch_size=self.patch_size,
+            mask_input_channels=self.mask_input_channels,
+            hidden_size=self.hidden_size,
+            num_point_embeddings=self.num_point_embeddings,
+            hidden_act=self.hidden_act,
+        )
+
+    def prepare_config_and_inputs(self):
+        dummy_points = floats_tensor([self.batch_size, 3, 2])
+        config = self.get_config()
+
+        return config, dummy_points
+
+
+class SamMaskDecoderTester:
+    def __init__(
+        self,
+        hidden_size=32,
+        hidden_act="relu",
+        mlp_dim=64,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        attention_downsample_rate=2,
+        num_multimask_outputs=3,
+        iou_head_depth=3,
+        iou_head_hidden_dim=32,
+        layer_norm_eps=1e-6,
+    ):
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.mlp_dim = mlp_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.attention_downsample_rate = attention_downsample_rate
+        self.num_multimask_outputs = num_multimask_outputs
+        self.iou_head_depth = iou_head_depth
+        self.iou_head_hidden_dim = iou_head_hidden_dim
+        self.layer_norm_eps = layer_norm_eps
+
+    def get_config(self):
+        return SamMaskDecoderConfig(
+            hidden_size=self.hidden_size,
+            hidden_act=self.hidden_act,
+            mlp_dim=self.mlp_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            attention_downsample_rate=self.attention_downsample_rate,
+            num_multimask_outputs=self.num_multimask_outputs,
+            iou_head_depth=self.iou_head_depth,
+            iou_head_hidden_dim=self.iou_head_hidden_dim,
+            layer_norm_eps=self.layer_norm_eps,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+
+        dummy_inputs = {
+            "image_embedding": floats_tensor([self.batch_size, self.hidden_size]),
+        }
+
+        return config, dummy_inputs
+
+
+class SamModelTester:
+    def __init__(
+        self,
+        parent,
+        hidden_size=36,
+        intermediate_size=72,
+        projection_dim=62,
+        output_channels=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        num_channels=3,
+        image_size=24,
+        patch_size=2,
+        hidden_act="gelu",
+        layer_norm_eps=1e-06,
+        dropout=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=1.0,
+        qkv_bias=True,
+        mlp_ratio=4.0,
+        use_abs_pos=True,
+        use_rel_pos=True,
+        rel_pos_zero_init=False,
+        window_size=14,
+        global_attn_indexes=[2, 5, 8, 11],
+        num_pos_feats=16,
+        mlp_dim=None,
+        batch_size=2,
+        is_training=True,
+    ):
+        self.parent = parent
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.output_channels = output_channels
+        self.num_channels = num_channels
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.hidden_act = hidden_act
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.mlp_ratio = mlp_ratio
+        self.use_abs_pos = use_abs_pos
+        self.use_rel_pos = use_rel_pos
+        self.rel_pos_zero_init = rel_pos_zero_init
+        self.window_size = window_size
+        self.global_attn_indexes = global_attn_indexes
+        self.num_pos_feats = num_pos_feats
+        self.mlp_dim = mlp_dim
+        self.batch_size = batch_size
+        self.is_training = is_training
+
+        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+
+        self.prompt_encoder_tester = SamPromptEncoderTester()
+        self.mask_decoder_tester = SamMaskDecoderTester()
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        vision_config = SamVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+            initializer_factor=self.initializer_factor,
+            output_channels=self.output_channels,
+            qkv_bias=self.qkv_bias,
+            mlp_ratio=self.mlp_ratio,
+            use_abs_pos=self.use_abs_pos,
+            use_rel_pos=self.use_rel_pos,
+            rel_pos_zero_init=self.rel_pos_zero_init,
+            window_size=self.window_size,
+            global_attn_indexes=self.global_attn_indexes,
+            num_pos_feats=self.num_pos_feats,
+            mlp_dim=self.mlp_dim,
+        )
+
+        prompt_encoder_config = self.prompt_encoder_tester.get_config()
+
+        mask_decoder_config = self.mask_decoder_tester.get_config()
+
+        return SamConfig(
+            vision_config=vision_config,
+            prompt_encoder_config=prompt_encoder_config,
+            mask_decoder_config=mask_decoder_config,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = SamModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        self.parent.assertEqual(result.iou_scores.shape, (self.batch_size, 1, 3))
+        self.parent.assertEqual(result.pred_masks.shape[:3], (self.batch_size, 1, 3))
+
+    def create_and_check_get_image_features(self, config, pixel_values):
+        model = SamModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model.get_image_embeddings(pixel_values)
+        self.parent.assertEqual(result[0].shape, (self.output_channels, 12, 12))
+
+    def create_and_check_get_image_hidden_states(self, config, pixel_values):
+        model = SamModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model.vision_encoder(
+                pixel_values,
+                output_hidden_states=True,
+                return_dict=True,
+            )
+
+        # after computing the convolutional features
+        expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
+        self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
+        self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
+
+        with torch.no_grad():
+            result = model.vision_encoder(
+                pixel_values,
+                output_hidden_states=True,
+                return_dict=False,
+            )
+
+        # after computing the convolutional features
+        expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
+        self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
+        self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class SamModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as SAM's vision encoder does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (SamModel,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": SamModel, "mask-generation": SamModel} if is_torch_available() else {}
+    )
+
+    test_resize_embeddings = False
+    _is_composite = True
+
+    # TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working
+    def is_pipeline_test_to_skip(
+        self,
+        pipeline_test_case_name,
+        config_class,
+        model_architecture,
+        tokenizer_name,
+        image_processor_name,
+        feature_extractor_name,
+        processor_name,
+    ):
+        return True
+
+    def setUp(self):
+        self.model_tester = SamModelTester(self)
+        common_properties = ["initializer_range"]
+        self.config_tester = ConfigTester(
+            self, config_class=SamConfig, has_text_modality=False, common_properties=common_properties
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="SAM's vision encoder does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_get_image_features(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_get_image_features(*config_and_inputs)
+
+    def test_image_hidden_states(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_get_image_hidden_states(*config_and_inputs)
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        expected_vision_attention_shape = (
+            self.model_tester.batch_size * self.model_tester.num_attention_heads,
+            196,
+            196,
+        )
+        expected_mask_decoder_attention_shape = (self.model_tester.batch_size, 1, 144, 32)
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            vision_attentions = outputs.vision_attentions
+            self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
+
+            mask_decoder_attentions = outputs.mask_decoder_attentions
+            self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.mask_decoder_config.output_attentions = True
+            config.vision_config.output_attentions = True
+            config.output_attentions = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            vision_attentions = outputs.vision_attentions
+            self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
+
+            mask_decoder_attentions = outputs.mask_decoder_attentions
+            self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
+
+            self.assertListEqual(
+                list(vision_attentions[0].shape[-4:]),
+                list(expected_vision_attention_shape),
+            )
+
+            self.assertListEqual(
+                list(mask_decoder_attentions[0].shape[-4:]),
+                list(expected_mask_decoder_attention_shape),
+            )
+
+    @unittest.skip(reason="Hidden_states is tested in create_and_check_model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Tested on the vision only counterpart; only works if vision related input is given")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "facebook/sam-vit-huge"
+        model = SamModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        self.skipTest(reason="SAM model can't be compiled dynamic yet")
+
+    def test_sdpa_can_dispatch_composite_models(self):
+        """
+        Tests if composite models dispatch correctly on SDPA/eager when requested so when loading the model.
+        This tests only by looking at layer names, as usually SDPA layers are called "SDPAAttention".
+        In contrast to the above test, this one checks if the "config._attn_implementation" is a dict after the model
+        is loaded, because we manually replicate requested attn implementation on each sub-config when loading.
+        See https://github.com/huggingface/transformers/pull/32238 for more info
+
+        The test tries to cover most general cases of composite models, VLMs with vision and text configs. Any model
+        that has a different set of sub-configs has to overwrite this test.
+        """
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        if not self._is_composite:
+            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model_sdpa = model_class.from_pretrained(tmpdirname, attn_implementation="sdpa")
+                model_sdpa = model_sdpa.eval().to(torch_device)
+
+                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
+                model_eager = model_eager.eval().to(torch_device)
+
+                # Root model determines SDPA support
+                attn_impl = "sdpa" if model._supports_sdpa else "eager"
+
+                # Check config propagation to submodels that support it
+                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
+                self.assertTrue(model_sdpa.vision_encoder.config._attn_implementation == attn_impl)
+                self.assertTrue(model_sdpa.mask_decoder.config._attn_implementation == attn_impl)
+
+                self.assertTrue(model_eager.config._attn_implementation == "eager")
+                self.assertTrue(model_eager.vision_encoder.config._attn_implementation == "eager")
+                self.assertTrue(model_eager.mask_decoder.config._attn_implementation == "eager")
+
+                # Verify SDPA/eager layer presence
+                has_sdpa = False
+                for name, submodule in model_sdpa.named_modules():
+                    class_name = submodule.__class__.__name__
+                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
+                        has_sdpa = True
+                        break
+
+                if not has_sdpa and attn_impl == "sdpa":
+                    raise ValueError("The SDPA model should have SDPA attention layers")
+
+                for name, submodule in model_eager.named_modules():
+                    class_name = submodule.__class__.__name__
+                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
+                        raise ValueError("The eager model should not have SDPA attention layers")
+
+
+def prepare_image():
+    img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
+    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
+    return raw_image
+
+
+def prepare_dog_img():
+    img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png"
+    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
+    return raw_image
+
+
+@slow
+class SamModelIntegrationTest(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        # clean-up as much as possible GPU memory occupied by PyTorch
+        cleanup(torch_device, gc_collect=True)
+
+    def test_inference_mask_generation_no_point(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+        inputs = processor(images=raw_image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        masks = outputs.pred_masks[0, 0, 0, 0, :3].cpu()
+        torch.testing.assert_close(scores[-1], torch.tensor(0.4515), rtol=2e-4, atol=2e-4)
+        torch.testing.assert_close(masks, torch.tensor([-4.1795, -3.4934, -3.4477]), rtol=2e-4, atol=2e-4)
+
+    def test_inference_mask_generation_one_point_one_bb(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+        input_boxes = [[[650, 900, 1000, 1250]]]
+        input_points = [[[820, 1080]]]
+
+        inputs = processor(
+            images=raw_image, input_boxes=input_boxes, input_points=input_points, return_tensors="pt"
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        masks = outputs.pred_masks[0, 0, 0, 0, :3]
+
+        expectations = Expectations(
+            {
+                (None, None): [-12.7729, -12.3665, -12.6061],
+                ("cuda", 8): [-12.7731, -12.3667, -12.6063],
+            }
+        )
+        expected_masks = torch.tensor(expectations.get_expectation()).to(torch_device)
+
+        torch.testing.assert_close(scores[-1], torch.tensor(0.9566), rtol=2e-4, atol=2e-4)
+        torch.testing.assert_close(masks, expected_masks, rtol=2e-4, atol=2e-4)
+
+    def test_inference_mask_generation_batched_points_batched_images(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+        input_points = [
+            [[[820, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]],
+            [[[510, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]],
+        ]
+
+        inputs = processor(images=[raw_image, raw_image], input_points=input_points, return_tensors="pt").to(
+            torch_device
+        )
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        masks = outputs.pred_masks[0, 0, 0, 0, :3].cpu()
+
+        EXPECTED_SCORES = torch.tensor(
+            [
+                [
+                    [0.6765, 0.9379, 0.8803],
+                    [0.6765, 0.9379, 0.8803],
+                    [0.6765, 0.9379, 0.8803],
+                    [0.6765, 0.9379, 0.8803],
+                ],
+                [
+                    [0.3317, 0.7264, 0.7646],
+                    [0.6765, 0.9379, 0.8803],
+                    [0.6765, 0.9379, 0.8803],
+                    [0.6765, 0.9379, 0.8803],
+                ],
+            ]
+        )
+        EXPECTED_MASKS = torch.tensor([-2.8550, -2.7988, -2.9625])
+        torch.testing.assert_close(scores, EXPECTED_SCORES, rtol=1e-3, atol=1e-3)
+        torch.testing.assert_close(masks, EXPECTED_MASKS, rtol=1e-3, atol=1e-3)
+
+    def test_inference_mask_generation_one_point_one_bb_zero(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+        input_boxes = [[[620, 900, 1000, 1255]]]
+        input_points = [[[820, 1080]]]
+        labels = [[0]]
+
+        inputs = processor(
+            images=raw_image,
+            input_boxes=input_boxes,
+            input_points=input_points,
+            input_labels=labels,
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+
+        torch.testing.assert_close(scores[-1], torch.tensor(0.7894), rtol=1e-4, atol=1e-4)
+
+    def test_inference_mask_generation_one_point(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+
+        input_points = [[[400, 650]]]
+        input_labels = [[1]]
+
+        inputs = processor(
+            images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        torch.testing.assert_close(scores[-1], torch.tensor(0.9675), rtol=1e-4, atol=1e-4)
+
+        # With no label
+        input_points = [[[400, 650]]]
+
+        inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        torch.testing.assert_close(scores[-1], torch.tensor(0.9675), rtol=1e-4, atol=1e-4)
+
+    def test_inference_mask_generation_two_points(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+
+        input_points = [[[400, 650], [800, 650]]]
+        input_labels = [[1, 1]]
+
+        inputs = processor(
+            images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        torch.testing.assert_close(scores[-1], torch.tensor(0.9762), rtol=1e-4, atol=1e-4)
+
+        # no labels
+        inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+
+        torch.testing.assert_close(scores[-1], torch.tensor(0.9762), rtol=1e-4, atol=1e-4)
+
+    def test_inference_mask_generation_two_points_batched(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+
+        input_points = [[[400, 650], [800, 650]], [[400, 650]]]
+        input_labels = [[1, 1], [1]]
+
+        inputs = processor(
+            images=[raw_image, raw_image], input_points=input_points, input_labels=input_labels, return_tensors="pt"
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        torch.testing.assert_close(scores[0][-1], torch.tensor(0.9762), rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(scores[1][-1], torch.tensor(0.9637), rtol=1e-4, atol=1e-4)
+
+    def test_inference_mask_generation_one_box(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+
+        input_boxes = [[[75, 275, 1725, 850]]]
+
+        inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores = outputs.iou_scores.squeeze().cpu()
+        torch.testing.assert_close(scores[-1], torch.tensor(0.7937), rtol=1e-4, atol=1e-4)
+
+    def test_inference_mask_generation_batched_image_one_point(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+        raw_dog_image = prepare_dog_img()
+
+        input_points = [[[820, 1080]], [[220, 470]]]
+
+        inputs = processor(images=[raw_image, raw_dog_image], input_points=input_points, return_tensors="pt").to(
+            torch_device
+        )
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores_batched = outputs.iou_scores.squeeze().cpu()
+
+        input_points = [[[220, 470]]]
+
+        inputs = processor(images=raw_dog_image, input_points=input_points, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        scores_single = outputs.iou_scores.squeeze().cpu()
+        torch.testing.assert_close(scores_batched[1, :], scores_single, rtol=1e-4, atol=1e-4)
+
+    def test_inference_mask_generation_two_points_point_batch(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+
+        input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu()  # fmt: skip
+
+        input_points = input_points.unsqueeze(0)
+
+        inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        iou_scores = outputs.iou_scores.cpu()
+        self.assertTrue(iou_scores.shape == (1, 2, 3))
+        torch.testing.assert_close(
+            iou_scores, torch.tensor([[[0.9105, 0.9825, 0.9675], [0.7646, 0.7944, 0.7769]]]), atol=1e-4, rtol=1e-4
+        )
+
+    def test_inference_mask_generation_three_boxes_point_batch(self):
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        model.to(torch_device)
+        model.eval()
+
+        raw_image = prepare_image()
+
+        input_boxes = torch.Tensor([[[620, 900, 1000, 1255]], [[75, 275, 1725, 850]], [[75, 275, 1725, 850]]]).cpu()
+        EXPECTED_IOU = torch.tensor([[[0.9773, 0.9880, 0.9522], [0.5995, 0.7658, 0.7936], [0.5995, 0.7658, 0.7936]]])
+        input_boxes = input_boxes.unsqueeze(0)
+
+        inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        iou_scores = outputs.iou_scores.cpu()
+        self.assertTrue(iou_scores.shape == (1, 3, 3))
+        torch.testing.assert_close(iou_scores, EXPECTED_IOU, rtol=1e-4, atol=1e-4)
+
+    def test_dummy_pipeline_generation(self):
+        generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device)
+        raw_image = prepare_image()
+
+        _ = generator(raw_image, points_per_batch=64)

tests/models/sam/test_processing_sam.py

@@ -0,0 +1,166 @@
+# Copyright 2023 The HuggingFace 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.
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_torchvision, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import SamProcessor
+
+if is_torch_available():
+    import torch
+
+    from transformers.models.sam.image_processing_sam import _mask_to_rle
+
+
+@require_vision
+@require_torchvision
+class SamProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = SamProcessor
+
+    def prepare_mask_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+        mask_inputs = [np.random.randint(255, size=(30, 400), dtype=np.uint8)]
+        mask_inputs = [Image.fromarray(x) for x in mask_inputs]
+        return mask_inputs
+
+    def test_chat_template_save_loading(self):
+        self.skipTest("SamProcessor does not have a tokenizer")
+
+    def test_image_processor_defaults_preserved_by_image_kwargs(self):
+        self.skipTest("SamProcessor does not have a tokenizer")
+
+    def test_kwargs_overrides_default_image_processor_kwargs(self):
+        self.skipTest("SamProcessor does not have a tokenizer")
+
+    def test_kwargs_overrides_default_tokenizer_kwargs(self):
+        self.skipTest("SamProcessor does not have a tokenizer")
+
+    def test_tokenizer_defaults_preserved_by_kwargs(self):
+        self.skipTest("SamProcessor does not have a tokenizer")
+
+    def test_image_processor_no_masks(self):
+        image_processor = self.get_component("image_processor")
+
+        processor = SamProcessor(image_processor=image_processor)
+
+        image_input = self.prepare_image_inputs()
+
+        input_feat_extract = image_processor(image_input, return_tensors="pt")
+        input_processor = processor(images=image_input, return_tensors="pt")
+
+        for key in input_feat_extract:
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+        for image in input_feat_extract.pixel_values:
+            self.assertEqual(image.shape, (3, 1024, 1024))
+
+        for original_size in input_feat_extract.original_sizes:
+            np.testing.assert_array_equal(original_size, np.array([30, 400]))
+
+        for reshaped_input_size in input_feat_extract.reshaped_input_sizes:
+            np.testing.assert_array_equal(
+                reshaped_input_size, np.array([77, 1024])
+            )  # reshaped_input_size value is before padding
+
+    def test_image_processor_with_masks(self):
+        image_processor = self.get_component("image_processor")
+
+        processor = SamProcessor(image_processor=image_processor)
+
+        image_input = self.prepare_image_inputs()
+        mask_input = self.prepare_mask_inputs()
+
+        input_feat_extract = image_processor(images=image_input, segmentation_maps=mask_input, return_tensors="pt")
+        input_processor = processor(images=image_input, segmentation_maps=mask_input, return_tensors="pt")
+
+        for key in input_feat_extract:
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+        for label in input_feat_extract.labels:
+            self.assertEqual(label.shape, (256, 256))
+
+    @require_torch
+    def test_post_process_masks(self):
+        image_processor = self.get_component("image_processor")
+
+        processor = SamProcessor(image_processor=image_processor)
+        dummy_masks = [torch.ones((1, 3, 5, 5))]
+
+        original_sizes = [[1764, 2646]]
+
+        reshaped_input_size = [[683, 1024]]
+        masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size)
+        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
+
+        masks = processor.post_process_masks(
+            dummy_masks, torch.tensor(original_sizes), torch.tensor(reshaped_input_size)
+        )
+        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
+
+        # should also work with np
+        dummy_masks = [np.ones((1, 3, 5, 5))]
+        masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
+
+        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
+
+        dummy_masks = [[1, 0], [0, 1]]
+        with self.assertRaises(TypeError):
+            masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
+
+    def test_rle_encoding(self):
+        """
+        Test the run-length encoding function.
+        """
+        # Test that a mask of all zeros returns a single run [height * width].
+        input_mask = torch.zeros((1, 2, 2), dtype=torch.long)  # shape: 1 x 2 x 2
+        rle = _mask_to_rle(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        # For a 2x2 all-zero mask, we expect a single run of length 4:
+        self.assertEqual(rle[0]["counts"], [4])
+
+        # Test that a mask of all ones returns [0, height * width].
+        input_mask = torch.ones((1, 2, 2), dtype=torch.long)  # shape: 1 x 2 x 2
+        rle = _mask_to_rle(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        # For a 2x2 all-one mask, we expect two runs: [0, 4].
+        self.assertEqual(rle[0]["counts"], [0, 4])
+
+        # Test a mask with mixed 0s and 1s to ensure the run-length encoding is correct.
+        # Example mask:
+        # Row 0: [0, 1]
+        # Row 1: [1, 1]
+        # This is shape (1, 2, 2).
+        # Flattened in Fortran order -> [0, 1, 1, 1].
+        # The RLE for [0,1,1,1] is [1, 3].
+        input_mask = torch.tensor([[[0, 1], [1, 1]]], dtype=torch.long)
+        rle = _mask_to_rle(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        self.assertEqual(rle[0]["counts"], [1, 3])  # 1 zero, followed by 3 ones