init

transformers/tests/models/clap/test_modeling_clap.py

@@ -0,0 +1,757 @@
+# 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 CLAP model."""
+
+import inspect
+import os
+import tempfile
+import unittest
+
+import numpy as np
+from datasets import load_dataset
+
+from transformers import ClapAudioConfig, ClapConfig, ClapProcessor, ClapTextConfig
+from transformers.testing_utils import require_torch, slow, torch_device
+from transformers.utils import is_torch_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    _config_zero_init,
+    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 (
+        ClapAudioModel,
+        ClapAudioModelWithProjection,
+        ClapModel,
+        ClapTextModel,
+        ClapTextModelWithProjection,
+    )
+
+
+class ClapAudioModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=60,
+        num_mel_bins=16,
+        window_size=4,
+        spec_size=64,
+        patch_size=2,
+        patch_stride=2,
+        seq_length=16,
+        freq_ratio=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        patch_embeds_hidden_size=16,
+        projection_dim=32,
+        depths=[2, 2],
+        num_hidden_layers=2,
+        num_heads=[2, 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.num_mel_bins = num_mel_bins
+        self.window_size = window_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.depths = depths
+        self.num_heads = num_heads
+        self.num_attention_heads = num_heads[0]
+        self.seq_length = seq_length
+        self.spec_size = spec_size
+        self.freq_ratio = freq_ratio
+        self.patch_stride = patch_stride
+        self.patch_embeds_hidden_size = patch_embeds_hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_features = floats_tensor([self.batch_size, 1, self.hidden_size, self.num_mel_bins])
+        config = self.get_config()
+
+        return config, input_features
+
+    def get_config(self):
+        return ClapAudioConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_mel_bins=self.num_mel_bins,
+            window_size=self.window_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            patch_stride=self.patch_stride,
+            projection_dim=self.projection_dim,
+            depths=self.depths,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+            spec_size=self.spec_size,
+            freq_ratio=self.freq_ratio,
+            patch_embeds_hidden_size=self.patch_embeds_hidden_size,
+        )
+
+    def create_and_check_model(self, config, input_features):
+        model = ClapAudioModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_features)
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def create_and_check_model_with_projection(self, config, input_features):
+        model = ClapAudioModelWithProjection(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_features)
+        self.parent.assertEqual(result.audio_embeds.shape, (self.batch_size, self.projection_dim))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_features = config_and_inputs
+        inputs_dict = {"input_features": input_features}
+        return config, inputs_dict
+
+
+@require_torch
+class ClapAudioModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as CLAP does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (ClapAudioModel, ClapAudioModelWithProjection) if is_torch_available() else ()
+    fx_compatible = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = ClapAudioModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ClapAudioConfig, has_text_modality=False, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="ClapAudioModel 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_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [2 * self.model_tester.patch_embeds_hidden_size, 2 * self.model_tester.patch_embeds_hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    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 = ["input_features"]
+            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)
+
+    def test_model_with_projection(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model_with_projection(*config_and_inputs)
+
+    @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "laion/clap-htsat-fused"
+        model = ClapAudioModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @slow
+    def test_model_with_projection_from_pretrained(self):
+        model_name = "laion/clap-htsat-fused"
+        model = ClapAudioModelWithProjection.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+        self.assertTrue(hasattr(model, "audio_projection"))
+
+
+class ClapTextModelTester:
+    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=32,
+        projection_dim=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        scope=None,
+        projection_hidden_act="relu",
+    ):
+        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.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.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+        self.projection_hidden_act = projection_hidden_act
+
+    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 ClapTextConfig(
+            vocab_size=self.vocab_size,
+            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,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+            projection_hidden_act=self.projection_hidden_act,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = ClapTextModel(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))
+
+    def create_and_check_model_with_projection(self, config, input_ids, input_mask):
+        model = ClapTextModelWithProjection(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.text_embeds.shape, (self.batch_size, self.projection_dim))
+
+    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 ClapTextModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (ClapTextModel, ClapTextModelWithProjection) if is_torch_available() else ()
+    fx_compatible = False
+    test_pruning = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = ClapTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ClapTextConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    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_model_with_projection(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model_with_projection(*config_and_inputs)
+
+    @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="ClapTextModel does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "laion/clap-htsat-fused"
+        model = ClapTextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @slow
+    def test_model_with_projection_from_pretrained(self):
+        model_name = "laion/clap-htsat-fused"
+        model = ClapTextModelWithProjection.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+        self.assertTrue(hasattr(model, "text_projection"))
+
+
+class ClapModelTester:
+    def __init__(self, parent, text_kwargs=None, audio_kwargs=None, is_training=True):
+        if text_kwargs is None:
+            text_kwargs = {}
+        if audio_kwargs is None:
+            audio_kwargs = {}
+
+        self.parent = parent
+        self.text_model_tester = ClapTextModelTester(parent, **text_kwargs)
+        self.audio_model_tester = ClapAudioModelTester(parent, **audio_kwargs)
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+        self.is_training = is_training
+
+    def prepare_config_and_inputs(self):
+        _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        _, input_features = self.audio_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, input_features
+
+    def get_config(self):
+        return ClapConfig(
+            text_config=self.text_model_tester.get_config().to_dict(),
+            audio_config=self.audio_model_tester.get_config().to_dict(),
+            projection_dim=64,
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, input_features):
+        model = ClapModel(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(input_ids, input_features, attention_mask)
+        self.parent.assertEqual(
+            result.logits_per_audio.shape, (self.audio_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.audio_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, input_features = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "input_features": input_features,
+            "return_loss": True,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class ClapModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (ClapModel,) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": ClapModel} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = ClapModelTester(self)
+        common_properties = ["logit_scale_init_value", "projection_hidden_act", "projection_dim"]
+        self.config_tester = ConfigTester(
+            self, config_class=ClapConfig, has_text_modality=False, common_properties=common_properties
+        )
+
+    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_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="Hidden_states is tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="ClapModel does not have input/output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    # override as the `logit_scale` parameter initialization is different for CLAP
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    # check if `logit_scale` is initialized as per the original implementation
+                    if "logit_scale" in name:
+                        self.assertAlmostEqual(
+                            param.data.item(),
+                            np.log(1 / 0.07),
+                            delta=1e-3,
+                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                        )
+                    else:
+                        self.assertIn(
+                            ((param.data.mean() * 1e9).round() / 1e9).item(),
+                            [0.0, 1.0],
+                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                        )
+
+    def _create_and_check_torchscript(self, config, inputs_dict):
+        if not self.test_torchscript:
+            self.skipTest(reason="test_torchscript is set to False")
+
+        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
+        configs_no_init.torchscript = True
+        configs_no_init.return_dict = False
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            model.to(torch_device)
+            model.eval()
+
+            try:
+                input_ids = inputs_dict["input_ids"]
+                input_features = inputs_dict["input_features"]  # CLAP needs input_features
+                traced_model = torch.jit.trace(model, (input_ids, input_features))
+            except RuntimeError:
+                self.fail("Couldn't trace module.")
+
+            with tempfile.TemporaryDirectory() as tmp_dir_name:
+                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
+
+                try:
+                    torch.jit.save(traced_model, pt_file_name)
+                except Exception:
+                    self.fail("Couldn't save module.")
+
+                try:
+                    loaded_model = torch.jit.load(pt_file_name)
+                except Exception:
+                    self.fail("Couldn't load module.")
+
+            model.to(torch_device)
+            model.eval()
+
+            loaded_model.to(torch_device)
+            loaded_model.eval()
+
+            model_state_dict = model.state_dict()
+            loaded_model_state_dict = loaded_model.state_dict()
+
+            non_persistent_buffers = {}
+            for key in loaded_model_state_dict:
+                if key not in model_state_dict:
+                    non_persistent_buffers[key] = loaded_model_state_dict[key]
+
+            loaded_model_state_dict = {
+                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
+            }
+
+            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
+
+            model_buffers = list(model.buffers())
+            for non_persistent_buffer in non_persistent_buffers.values():
+                found_buffer = False
+                for i, model_buffer in enumerate(model_buffers):
+                    if torch.equal(non_persistent_buffer, model_buffer):
+                        found_buffer = True
+                        break
+
+                self.assertTrue(found_buffer)
+                model_buffers.pop(i)
+
+            models_equal = True
+            for layer_name, p1 in model_state_dict.items():
+                p2 = loaded_model_state_dict[layer_name]
+                if p1.data.ne(p2.data).sum() > 0:
+                    models_equal = False
+
+            self.assertTrue(models_equal)
+
+    def test_load_audio_text_config(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Save ClapConfig and check if we can load ClapAudioConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            audio_config = ClapAudioConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.audio_config.to_dict(), audio_config.to_dict())
+
+        # Save ClapConfig and check if we can load ClapTextConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            text_config = ClapTextConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "laion/clap-htsat-fused"
+        model = ClapModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@slow
+@require_torch
+class ClapModelIntegrationTest(unittest.TestCase):
+    paddings = ["repeatpad", "repeat", "pad"]
+
+    def test_integration_unfused(self):
+        EXPECTED_MEANS_UNFUSED = {
+            "repeatpad": 0.0024,
+            "pad": 0.0020,
+            "repeat": 0.0023,
+        }
+
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        audio_sample = librispeech_dummy[-1]
+
+        model_id = "laion/clap-htsat-unfused"
+
+        model = ClapModel.from_pretrained(model_id).to(torch_device)
+        processor = ClapProcessor.from_pretrained(model_id)
+
+        for padding in self.paddings:
+            inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding).to(
+                torch_device
+            )
+
+            audio_embed = model.get_audio_features(**inputs)
+            expected_mean = EXPECTED_MEANS_UNFUSED[padding]
+
+            self.assertTrue(
+                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
+            )
+
+    def test_integration_fused(self):
+        EXPECTED_MEANS_FUSED = {
+            "repeatpad": 0.00069,
+            "repeat": 0.00196,
+            "pad": -0.000379,
+        }
+
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        audio_sample = librispeech_dummy[-1]
+
+        model_id = "laion/clap-htsat-fused"
+
+        model = ClapModel.from_pretrained(model_id).to(torch_device)
+        processor = ClapProcessor.from_pretrained(model_id)
+
+        for padding in self.paddings:
+            inputs = processor(
+                audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding, truncation="fusion"
+            ).to(torch_device)
+
+            audio_embed = model.get_audio_features(**inputs)
+            expected_mean = EXPECTED_MEANS_FUSED[padding]
+
+            self.assertTrue(
+                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
+            )
+
+    def test_batched_fused(self):
+        EXPECTED_MEANS_FUSED = {
+            "repeatpad": 0.0010,
+            "repeat": 0.0020,
+            "pad": 0.0006,
+        }
+
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]]
+
+        model_id = "laion/clap-htsat-fused"
+
+        model = ClapModel.from_pretrained(model_id).to(torch_device)
+        processor = ClapProcessor.from_pretrained(model_id)
+
+        for padding in self.paddings:
+            inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding, truncation="fusion").to(
+                torch_device
+            )
+
+            audio_embed = model.get_audio_features(**inputs)
+            expected_mean = EXPECTED_MEANS_FUSED[padding]
+
+            self.assertTrue(
+                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
+            )
+
+    def test_batched_unfused(self):
+        EXPECTED_MEANS_FUSED = {
+            "repeatpad": 0.0016,
+            "repeat": 0.0019,
+            "pad": 0.0019,
+        }
+
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]]
+
+        model_id = "laion/clap-htsat-unfused"
+
+        model = ClapModel.from_pretrained(model_id).to(torch_device)
+        processor = ClapProcessor.from_pretrained(model_id)
+
+        for padding in self.paddings:
+            inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding).to(torch_device)
+
+            audio_embed = model.get_audio_features(**inputs)
+            expected_mean = EXPECTED_MEANS_FUSED[padding]
+
+            self.assertTrue(
+                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
+            )