transformers/tests/models/siglip/test_modeling_siglip.py

# Copyright 2024 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 SigLIP model."""

import inspect
import tempfile
import unittest

import numpy as np
import pytest
import requests
from parameterized import parameterized

from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import (
    is_torch_available,
    is_vision_available,
)

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
    ModelTesterMixin,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel

if is_vision_available():
    from PIL import Image

    from transformers import SiglipProcessor


class SiglipModelTesterMixin(ModelTesterMixin):
    def test_sdpa_can_dispatch_composite_models(self):
        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)

                # Load the model with SDPA
                model_sdpa = model_class.from_pretrained(tmpdirname)

                # Load model with eager attention
                model_eager = model_class.from_pretrained(
                    tmpdirname,
                    attn_implementation="eager",
                )

            if hasattr(model_sdpa, "vision_model"):
                self.assertTrue(model_sdpa.vision_model.config._attn_implementation == "sdpa")
                self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager")

            if hasattr(model_sdpa, "text_model"):
                self.assertTrue(model_sdpa.text_model.config._attn_implementation == "sdpa")
                self.assertTrue(model_eager.text_model.config._attn_implementation == "eager")

            self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
            self.assertTrue(model_eager.config._attn_implementation == "eager")


class SiglipVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=4,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=64,
        num_hidden_layers=2,
        num_attention_heads=2,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        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.scope = scope

        # in ViT, the seq length equals the number of patches
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches

    # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs
    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):
        return SiglipVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            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,
        )

    def create_and_check_model(self, config, pixel_values):
        model = SiglipVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs_for_common
    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 SiglipVisionModelTest(SiglipModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (SiglipVisionModel,) if is_torch_available() else ()

    test_resize_embeddings = False
    # MP works but offload doesn't work when the MultiheadAttention is offloaded
    # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
    # in the dispatch_model function
    test_cpu_offload = False
    test_disk_offload_safetensors = False
    test_disk_offload_bin = False

    def setUp(self):
        self.model_tester = SiglipVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=SiglipVisionConfig, has_text_modality=False, hidden_size=32
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="SIGLIP 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_vision_transformer_get_set_input_embeddings(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        transformer = SiglipVisionModel(config)

        self.assertIsInstance(transformer.get_input_embeddings(), nn.Conv2d)

        new_embeddings = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=config.hidden_size,
            kernel_size=config.patch_size,
            stride=config.patch_size,
            padding="valid",
        )
        transformer.set_input_embeddings(new_embeddings)
        self.assertIs(transformer.get_input_embeddings(), new_embeddings)

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="SiglipVisionModel does not support standalone training")
    def test_training(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "google/siglip-base-patch16-224"
        model = SiglipVisionModel.from_pretrained(model_name)
        self.assertIsNotNone(model)

    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
    def test_eager_matches_sdpa_inference(self, *args):
        # adding only flaky decorator here and call the parent test method
        return getattr(ModelTesterMixin, self._testMethodName)(self)


class SiglipTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=64,
        num_hidden_layers=2,
        num_attention_heads=2,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        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.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.scope = scope

    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs
    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return SiglipTextConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            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,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, input_ids, input_mask):
        model = SiglipTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, attention_mask=input_mask)
            result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common
    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, input_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class SiglipTextModelTest(SiglipModelTesterMixin, unittest.TestCase):
    all_model_classes = (SiglipTextModel,) if is_torch_available() else ()

    model_split_percents = [0.5, 0.8, 0.9]

    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.setUp with CLIP->Siglip
    def setUp(self):
        self.model_tester = SiglipTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SiglipTextConfig, hidden_size=32)

    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config
    def test_config(self):
        self.config_tester.run_common_tests()

    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="This module does not support standalone training")
    def test_training(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        pass

    @unittest.skip(reason="Siglip does not use inputs_embeds")
    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_inputs_embeds
    def test_inputs_embeds(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "google/siglip-base-patch16-224"
        model = SiglipTextModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class SiglipModelTester:
    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
        if text_kwargs is None:
            text_kwargs = {}
        if vision_kwargs is None:
            vision_kwargs = {}

        self.parent = parent
        self.text_model_tester = SiglipTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = SiglipVisionModelTester(parent, **vision_kwargs)
        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
        self.is_training = is_training

    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs
    def prepare_config_and_inputs(self):
        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return SiglipConfig(
            text_config=self.text_model_tester.get_config().to_dict(),
            vision_config=self.vision_model_tester.get_config().to_dict(),
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = SiglipModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values, attention_mask)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "pixel_values": pixel_values,
            "return_loss": False,
        }
        return config, inputs_dict


@require_torch
class SiglipModelTest(SiglipModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    additional_model_inputs = ["pixel_values"]
    all_model_classes = (SiglipModel,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": SiglipModel} if is_torch_available() else {}

    test_resize_embeddings = False
    test_attention_outputs = False
    # MP works but offload doesn't work when the MultiheadAttention is offloaded
    # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
    # in the dispatch_model function
    test_cpu_offload = False
    test_disk_offload_safetensors = False
    test_disk_offload_bin = False
    _is_composite = True

    def setUp(self):
        self.model_tester = SiglipModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SiglipConfig, has_text_modality=False)

    def test_config(self):
        self.config_tester.run_common_tests()

    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_inputs_embeds
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="SiglipModel does not have input/output embeddings")
    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_get_set_embeddings
    def test_model_get_set_embeddings(self):
        pass

    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->Siglip
    def test_load_vision_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save SiglipConfig and check if we can load SiglipVisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = SiglipVisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        # Save SiglipConfig and check if we can load SiglipTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = SiglipTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())

    @unittest.skip(reason="The SigLIP family currently does not work with output_attentions.")
    def test_get_text_features_attentions(self):
        # This test should no longer be skipped once this architecture is refactored to work with output_attentions.
        pass

    @unittest.skip(reason="The SigLIP family currently does not work with output_hidden_states.")
    def test_get_text_features_hidden_states(self):
        # This test should no longer be skipped once this architecture is refactored to work with output_hidden_states.
        pass

    @unittest.skip(reason="The SigLIP family currently does not work with output_attentions.")
    def test_get_image_features_attentions(self):
        # This test should no longer be skipped once this architecture is refactored to work with output_attentions.
        pass

    @unittest.skip(reason="The SigLIP family currently does not work with output_hidden_states.")
    def test_get_image_features_hidden_states(self):
        # This test should no longer be skipped once this architecture is refactored to work with output_hidden_states.
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "google/siglip-base-patch16-224"
        model = SiglipModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class SiglipForImageClassificationModelTester(SiglipModelTester):
    def __init__(self, parent):
        super().__init__(parent)
        self.batch_size = self.vision_model_tester.batch_size
        self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
        self.hidden_size = self.vision_model_tester.hidden_size
        self.seq_length = self.vision_model_tester.seq_length

    def prepare_config_and_inputs(self):
        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        config = self.get_config()

        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
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class SiglipForImageClassificationModelTest(SiglipModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (SiglipForImageClassification,) if is_torch_available() else ()
    pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {}

    test_resize_embeddings = False
    test_attention_outputs = False
    # MP works but offload doesn't work when the MultiheadAttention is offloaded
    # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
    # in the dispatch_model function
    test_cpu_offload = False
    test_disk_offload_safetensors = False
    test_disk_offload_bin = False
    _is_composite = True

    def setUp(self):
        self.model_tester = SiglipForImageClassificationModelTester(self)

    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
    def test_model_get_set_embeddings(self):
        pass

    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
    def test_training_gradient_checkpointing(self):
        super().test_training_gradient_checkpointing()

    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        super().test_training_gradient_checkpointing_use_reentrant_false()

    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        super().test_training_gradient_checkpointing_use_reentrant_true()


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


@require_vision
@require_torch
class SiglipModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "google/siglip-base-patch16-224"
        model = SiglipModel.from_pretrained(model_name).to(torch_device)
        processor = SiglipProcessor.from_pretrained(model_name)

        image = prepare_img()
        inputs = processor(
            text=["a photo of 2 cats", "a photo of 2 dogs"], images=image, padding="max_length", return_tensors="pt"
        ).to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)
            logits_per_image = outputs.logits_per_image
            logits_per_text = outputs.logits_per_text

        # verify the logits
        self.assertEqual(
            logits_per_image.shape,
            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
        )
        self.assertEqual(
            logits_per_text.shape,
            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
        )

        expected_logits = torch.tensor([[-0.7538, -10.3387]], device=torch_device)

        torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)

        # verify the probs
        probs = torch.sigmoid(logits_per_image)  # these are the probabilities
        expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device)
        torch.testing.assert_close(probs, expected_probs, rtol=1e-3, atol=1e-3)

    @slow
    def test_inference_interpolate_pos_encoding(self):
        model_name = "google/siglip-base-patch16-224"
        model = SiglipModel.from_pretrained(model_name).to(torch_device)

        # 640 x 480 image
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        processor = SiglipProcessor.from_pretrained(model_name, do_resize=False, size={"height": 480, "width": 640})

        inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs, interpolate_pos_encoding=True)

        # verify the shape
        # patch size = 16
        # batch size 1, (640/16) * (480/16) = 1200 patches, 768 hidden size
        expected_shape = torch.Size((1, 1200, 768))

        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)