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tests/models/qwen2_5_omni/test_modeling_qwen2_5_omni.py

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+# Copyright 2025 The Qwen team, Alibaba Group and 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 Qwen2.5-Omni model."""
+
+import tempfile
+import unittest
+from io import BytesIO
+from urllib.request import urlopen
+
+import librosa
+import pytest
+import requests
+
+from transformers import (
+    AutoProcessor,
+    Qwen2_5OmniForConditionalGeneration,
+    Qwen2_5OmniThinkerConfig,
+    Qwen2_5OmniThinkerForConditionalGeneration,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Qwen2_5OmniThinkerForConditionalGenerationTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        feat_seq_length=30,
+        num_channels=3,
+        image_size=14,
+        seq_length=39,
+        vision_config={
+            "depth": 2,
+            "embed_dim": 32,
+            "hidden_act": "quick_gelu",
+            "hidden_size": 32,
+            "out_hidden_size": 32,
+            "intermediate_size": 24,
+            "mlp_ratio": 4,
+            "num_heads": 4,
+            "patch_size": 14,
+            "spatial_merge_size": 1,
+            "temporal_patch_size": 2,
+            "fullatt_block_indexes": [0],
+            "initializer_range": 0.02,
+        },
+        audio_config={
+            "model_type": "qwen_omni_thinker_audio_encoder",
+            "d_model": 32,
+            "encoder_attention_heads": 4,
+            "encoder_ffn_dim": 32,
+            "encoder_layers": 2,
+            "num_mel_bins": 20,
+            "max_source_positions": 1500,
+            "initializer_range": 0.02,
+            "n_window": 100,
+            "output_dim": 32,
+        },
+        text_config={
+            "rope_parameters": {"mrope_section": [1, 1, 2], "rope_type": "default", "type": "default"},
+            "vocab_size": 99,
+            "hidden_size": 32,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 2,
+            "hidden_act": "silu",
+            "max_position_embeddings": 1024,
+            "rms_norm_eps": 1e-06,
+            "use_cache": True,
+            "tie_word_embeddings": False,
+            "rope_theta": 1000000.0,
+            "use_sliding_window": False,
+            "sliding_window": 50,
+            "max_window_layers": 3,
+            "attention_dropout": 0.0,
+            "pad_token_id": 0,
+            "initializer_range": 0.02,
+        },
+        audio_token_index=1,
+        image_token_index=2,
+        video_token_index=3,
+        position_id_per_seconds=25,
+        seconds_per_chunk=2,
+        audio_start_token_id=4,
+        audio_end_token_id=5,
+        user_token_id=6,
+        vision_start_token_id=7,
+        vision_end_token_id=8,
+        initializer_range=0.02,
+    ):
+        self.parent = parent
+        self.audio_config = audio_config
+        self.vision_config = vision_config
+        self.text_config = text_config
+        self.audio_token_index = audio_token_index
+        self.image_token_index = image_token_index
+        self.video_token_index = video_token_index
+        self.position_id_per_seconds = position_id_per_seconds
+        self.seconds_per_chunk = seconds_per_chunk
+        self.audio_start_token_id = audio_start_token_id
+        self.audio_end_token_id = audio_end_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.user_token_id = user_token_id
+        self.initializer_range = initializer_range
+        self.batch_size = batch_size
+        self.feat_seq_length = feat_seq_length
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.seq_length = seq_length
+        self.is_training = False
+
+        # Used from `self.model_tester` by common model tests
+        self.num_hidden_layers = self.text_config["num_hidden_layers"]
+        self.hidden_size = self.text_config["hidden_size"]
+        self.num_attention_heads = self.text_config["num_attention_heads"]
+        self.vocab_size = self.text_config["vocab_size"]
+
+    def get_config(self):
+        return Qwen2_5OmniThinkerConfig(
+            audio_config=self.audio_config,
+            vision_config=self.vision_config,
+            text_config=self.text_config,
+            audio_token_index=self.audio_token_index,
+            image_token_index=self.image_token_index,
+            video_token_index=self.video_token_index,
+            position_id_per_seconds=self.position_id_per_seconds,
+            seconds_per_chunk=self.seconds_per_chunk,
+            audio_start_token_id=self.audio_start_token_id,
+            audio_end_token_id=self.audio_end_token_id,
+            vision_start_token_id=self.vision_start_token_id,
+            vision_end_token_id=self.vision_end_token_id,
+            user_token_id=self.user_token_id,
+            initializer_range=self.initializer_range,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        patch_size = config.vision_config.patch_size
+        temporal_patch_size = config.vision_config.temporal_patch_size
+        pixel_values = floats_tensor(
+            [
+                self.batch_size * (self.image_size**2) // (patch_size**2),
+                self.num_channels * (patch_size**2) * temporal_patch_size,
+            ]
+        )
+        pixel_grid_thw = torch.LongTensor(
+            [[1, self.image_size / patch_size, self.image_size / patch_size]] * self.batch_size
+        ).to(pixel_values.device)
+        input_features_values = floats_tensor(
+            [self.batch_size, self.audio_config["num_mel_bins"], self.feat_seq_length]
+        )
+        feature_attention_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.long).to(torch_device)
+        return config, pixel_values, pixel_grid_thw, input_features_values, feature_attention_mask
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, pixel_grid_thw, input_features_values, feature_attention_mask = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.get_text_config().vocab_size - 3) + 3
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+
+        # Make sure no other tokens are set to special, to prevetn flakiness
+        tokens_to_replace = torch.tensor(
+            [
+                config.image_token_index,
+                config.audio_token_index,
+                config.audio_start_token_id,
+                config.audio_end_token_id,
+                config.vision_start_token_id,
+                config.vision_end_token_id,
+            ],
+            device=input_ids.device,
+        )
+        input_ids[torch.isin(input_ids, tokens_to_replace)] = config.text_config.pad_token_id
+
+        attention_mask[:, :1] = 0
+
+        # Audio token placeholders should be wrapped in start and end token ids
+        audio_feat_length = ((self.feat_seq_length - 1) // 2 + 1 - 2) // 2 + 1
+        input_ids[:, 1] = config.audio_start_token_id
+        input_ids[:, 2 : (2 + audio_feat_length)] = config.audio_token_index
+        input_ids[:, 2 + audio_feat_length] = config.audio_end_token_id
+
+        # Image token placeholders should be wrapped in start and end token ids
+        input_ids[:, -4:-1] = torch.tensor(
+            [config.vision_start_token_id, config.image_token_index, config.vision_end_token_id]
+        )
+        inputs_dict = {
+            "input_features": input_features_values,
+            "feature_attention_mask": feature_attention_mask,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "image_grid_thw": pixel_grid_thw,
+            "pixel_values": pixel_values,
+        }
+        return config, inputs_dict
+
+    def create_and_check_qwenomnithinker_model_fp16_forward(self, config, input_ids, pixel_values, attention_mask):
+        model = Qwen2_5OmniThinkerForConditionalGeneration(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.autocast(device_type=torch_device, dtype=torch.float16):
+            logits = model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                pixel_values=pixel_values.to(torch.bfloat16),
+                return_dict=True,
+            )["logits"]
+        self.parent.assertFalse(torch.isnan(logits).any().item())
+
+
+@require_torch
+class Qwen2_5OmniThinkerForConditionalGenerationModelTest(
+    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
+    """
+    Model tester for `Qwen2_5OmniThinkerForConditionalGeneration`.
+    """
+
+    all_model_classes = (Qwen2_5OmniThinkerForConditionalGeneration,) if is_torch_available() else ()
+    all_generative_model_classes = (Qwen2_5OmniThinkerForConditionalGeneration,) if is_torch_available() else ()
+    # pipeline_model_mapping = (
+    #     {
+    #         "any-to-any": Qwen2_5OmniForConditionalGeneration,
+    #         "image-text-to-text": Qwen2_5OmniThinkerForConditionalGeneration,
+    #     }
+    #     if is_torch_available()
+    #     else {}
+    # )
+    # FIXME @raushan Omni tests take ages because the model is big. Try to make it even smaller
+    pipeline_model_mapping = {}
+    skip_test_audio_features_output_shape = True  # Qwen2_5Omni merges batch_size and audio_output_lengths in index 0
+    _is_composite = True
+    model_split_percents = [0.5, 0.9]
+
+    def setUp(self):
+        self.model_tester = Qwen2_5OmniThinkerForConditionalGenerationTester(self)
+        self.config_tester = ConfigTester(self, config_class=Qwen2_5OmniThinkerConfig, has_text_modality=False)
+
+    @unittest.skip(reason="Cpu not yet supported because in QwenOmniThinker models")
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(reason="Disk offload bin not yet supported because in QwenOmniThinker models")
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip(reason="Disk offload safetensors not yet supported because in QwenOmniThinker models")
+    def test_disk_offload_safetensors(self):
+        pass
+
+    @unittest.skip(reason="Correct missing keys not yet supported because in QwenOmniThinker models")
+    def test_correct_missing_keys(self):
+        pass
+
+    @unittest.skip(reason="Compile not yet supported because in QwenOmniThinker models")
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+    @unittest.skip(reason="Sdpa dispatch not yet supported because in QwenOmniThinker models")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @unittest.skip(reason="QwenOmniThinker does not support output_hidden_states test")
+    def test_model_outputs_equivalence(self):
+        pass
+
+    @unittest.skip("Qwen2Omni has no base model, model architecture is special")
+    def test_model_base_model_prefix(self):
+        pass
+
+    def test_sdpa_can_dispatch_composite_models(self):
+        # overwrite because Qwen2 is audio+text model (not vision+text)
+        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)
+                model_sdpa = model_sdpa.eval().to(torch_device)
+
+                text_attn = "sdpa" if model.model._supports_sdpa else "eager"
+                audio_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
+                vision_attn = "sdpa" if model.visual._supports_sdpa else "eager"
+                # `None` as it is the requested one which will be assigned to each sub-config
+                # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
+                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
+                self.assertTrue(model.model.config._attn_implementation == text_attn)
+                self.assertTrue(model.audio_tower.config._attn_implementation == audio_attn)
+                self.assertTrue(model.visual.config._attn_implementation == vision_attn)
+
+                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
+                model_eager = model_eager.eval().to(torch_device)
+                self.assertTrue(model_eager.config._attn_implementation == "eager")
+                self.assertTrue(model_eager.model.config._attn_implementation == "eager")
+                self.assertTrue(model_eager.audio_tower.config._attn_implementation == "eager")
+                self.assertTrue(model_eager.visual.config._attn_implementation == "eager")
+
+                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 attention_mask_padding_matches_padding_free_with_position_ids(
+        self, attn_implementation: str, fa_kwargs: bool = False
+    ):
+        max_new_tokens = 30
+        for model_class in self.all_generative_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            dummy_input = inputs_dict[model_class.main_input_name]
+            if dummy_input.dtype in [torch.float32, torch.float16]:
+                dummy_input = dummy_input.to(torch.bfloat16)
+
+            # make sure that all models have enough positions for generation
+            if hasattr(config, "max_position_embeddings"):
+                config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
+
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                if 0 in inputs_dict["attention_mask"][:, -1]:
+                    inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
+                dummy_attention_mask = inputs_dict["attention_mask"]
+                inputs_dict["input_ids"][~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id
+
+                model = (
+                    model_class.from_pretrained(
+                        tmpdirname,
+                        dtype=torch.bfloat16,
+                        attn_implementation=attn_implementation,
+                    )
+                    .to(torch_device)
+                    .eval()
+                )
+
+                # flatten
+                padfree_inputs_dict = {
+                    "input_features": inputs_dict["input_features"],
+                    "feature_attention_mask": inputs_dict["feature_attention_mask"],
+                    "pixel_values": inputs_dict["pixel_values"],
+                    "image_grid_thw": inputs_dict["image_grid_thw"],
+                    "input_ids": inputs_dict["input_ids"][dummy_attention_mask.bool()].unsqueeze(0),
+                }
+
+                # add position_ids
+                vision_position_ids, deltas = model.get_rope_index(
+                    input_ids=inputs_dict["input_ids"],
+                    image_grid_thw=inputs_dict["image_grid_thw"],
+                    attention_mask=inputs_dict["attention_mask"],
+                    audio_seqlens=torch.sum(inputs_dict["feature_attention_mask"], dim=1),
+                )  # [3, bs, padded-seq-len]
+                vision_padfree_positions = vision_position_ids[:, dummy_attention_mask.bool()].view(
+                    3, -1
+                )  # [3, bs*padfree-len]
+                text_padfree_positions = torch.cat(
+                    [torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()]
+                )  # [1, bs*padfree-len]
+                text_padfree_positions = text_padfree_positions.long().unsqueeze(0).to(torch_device)
+                padfree_inputs_dict["position_ids"] = torch.cat([text_padfree_positions, vision_padfree_positions])[
+                    :, None, :
+                ]
+
+                if fa_kwargs:
+                    cu_seq_lens = [0] + dummy_attention_mask.sum(1).tolist()
+                    cu_seq_lens = torch.tensor(cu_seq_lens, device=torch_device)
+                    max_length = cu_seq_lens.diff().max().item()
+                    padfree_inputs_dict.update(
+                        {
+                            "cu_seq_lens_q": cu_seq_lens.cumsum(-1).to(dtype=torch.int32),
+                            "cu_seq_lens_k": cu_seq_lens.cumsum(-1).to(dtype=torch.int32),
+                            "max_length_q": max_length,
+                            "max_length_k": max_length,
+                        }
+                    )
+
+                res_padded = model(**inputs_dict, use_cache=False)
+                res_padfree = model(**padfree_inputs_dict, use_cache=False)
+
+                logits_padded = res_padded.logits[inputs_dict["attention_mask"].bool()]
+                logits_padfree = res_padfree.logits[0]
+
+                # acceptable numerical instability
+                tol = torch.finfo(torch.bfloat16).eps
+                torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
+
+    @unittest.skip("Cannot generate from inputs embeds")
+    def test_generate_from_inputs_embeds_with_static_cache(self):
+        pass
+
+    # TODO (joao, raushan): there are multiple standardization issues in this model that prevent this test from
+    # passing, fix me
+    @unittest.skip("Cannot handle 4D attention mask")
+    @pytest.mark.torch_compile_test
+    def test_generate_compile_model_forward_fullgraph(self):
+        pass
+
+    @unittest.skip("Cannot handle 4D attention mask")
+    def test_generate_compilation_all_outputs(self):
+        pass
+
+    @unittest.skip("Cannot handle 4D attention mask")
+    def test_generate_with_static_cache(self):
+        pass
+
+    @unittest.skip("Cannot handle 4D attention mask")
+    def test_custom_4d_attention_mask(self):
+        pass
+
+    def test_get_rope_index_video_with_audio(self):
+        image_grid_thw = torch.empty((0, 3), dtype=torch.long)
+
+        # 3 * 2 * 2 = 12 video tokens
+        video_grid_thw = torch.tensor([[3, 2, 2]], dtype=torch.long, device=torch_device)
+
+        # num_audio_tokens = ((audio_seqlen - 1) // 2 + 1 - 2) // 2 + 1
+        # i.e.: 300 audio_seqlen -> 75 audio tokens
+        audio_seqlens = torch.tensor([300], dtype=torch.long)
+
+        second_per_grids = torch.tensor([1.0], dtype=torch.float)
+
+        use_audio_in_video = True
+
+        # fmt: off
+        expected_position_ids = torch.tensor([
+            [[
+                 0,  1, # text
+                 2,  2, # vision_bos + audio_bos
+
+                # video chunk
+                  3,  3,  3,  3,
+                 28, 28, 28, 28,
+
+                # audio chunk
+                 3,  4,  5,  6,  7,  8, 9, 10, 11, 12, 13, 14, 15, 16,
+                17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+                31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+                45, 46, 47, 48, 49, 50, 51, 52,
+
+                # video chunk
+                53, 53, 53, 53,
+
+                # audio chunk
+                53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
+                67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
+
+                78, 78, # audio_eos + vision_eos
+                79, 80, # text
+            ]],
+            [[
+                 0,  1, # text
+                 2,  2, # vision_bos + audio_bos
+
+                # video chunk
+                 3,  3,  4,  4,
+                 3,  3,  4,  4,
+
+                # audio chunk
+                 3,  4,  5,  6,  7,  8, 9, 10, 11, 12, 13, 14, 15, 16,
+                17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+                31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+                45, 46, 47, 48, 49, 50, 51, 52,
+
+                # video chunk
+                 3,  3,  4,  4,
+
+                # audio chunk
+                53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
+                67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
+
+                78, 78, # audio_eos + vision_eos
+                79, 80, # text
+            ]],
+            [[
+                 0,  1, # text
+                 2,  2, # vision_bos + audio_bos
+
+                # video chunk
+                 3,  4,  3,  4,
+                 3,  4,  3,  4,
+
+                # audio chunk
+                 3,  4,  5,  6,  7,  8, 9, 10, 11, 12, 13, 14, 15, 16,
+                17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+                31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+                45, 46, 47, 48, 49, 50, 51, 52,
+
+                # video chunk
+                3,  4,  3,  4,
+
+                # audio chunk
+                53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
+                67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
+
+                78, 78, # audio_eos + vision_eos
+                79, 80, # text
+            ]],
+        ], dtype=torch.long)
+        # fmt: on
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            input_ids = torch.tensor(
+                [
+                    [
+                        100,
+                        101,
+                    ]
+                    + [
+                        config.vision_start_token_id,
+                        config.audio_start_token_id,
+                    ]
+                    # 1st chunk: 8 video tokens, 50 audio tokens
+                    + [config.video_token_id] * 2 * 2 * 2
+                    + [config.audio_token_id] * 50
+                    +
+                    # 2nd chunk: 4 video tokens, 25 audio tokens
+                    [config.video_token_id] * 1 * 2 * 2
+                    + [config.audio_token_id] * 25
+                    + [
+                        config.audio_end_token_id,
+                        config.vision_end_token_id,
+                    ]
+                    + [
+                        102,
+                        103,
+                    ]
+                ],
+                dtype=torch.long,
+            )
+
+            model = model_class(config)
+
+            position_ids, mrope_position_deltas = model.get_rope_index(
+                input_ids=input_ids,
+                image_grid_thw=image_grid_thw,
+                video_grid_thw=video_grid_thw,
+                attention_mask=None,
+                use_audio_in_video=use_audio_in_video,
+                audio_seqlens=audio_seqlens,
+                second_per_grids=second_per_grids,
+            )
+
+            self.assertTrue(torch.equal(position_ids, expected_position_ids))
+
+
+@require_torch
+class Qwen2_5OmniModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+        self.audio_url = (
+            "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/glass-breaking-151256.mp3"
+        )
+        self.audio_url_additional = (
+            "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/f2641_0_throatclearing.wav"
+        )
+        self.image_url = "https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/demo_small.jpg"
+        self.messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio", "audio_url": self.audio_url},
+                    {"type": "image", "image_url": self.image_url},
+                    {"type": "text", "text": "What's that sound and what kind of dog is this?"},
+                ],
+            }
+        ]
+
+        self.raw_audio, _ = librosa.load(
+            BytesIO(urlopen(self.audio_url).read()), sr=self.processor.feature_extractor.sampling_rate
+        )
+        self.raw_audio_additional, _ = librosa.load(
+            BytesIO(urlopen(self.audio_url_additional).read()), sr=self.processor.feature_extractor.sampling_rate
+        )
+        self.raw_image = Image.open(requests.get(self.image_url, stream=True).raw)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_small_model_integration_test(self):
+        model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-Omni-7B", dtype=torch.bfloat16, device_map="auto"
+        )
+
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(
+            text=text, audio=[self.raw_audio], images=[self.raw_image], return_tensors="pt", padding=True
+        ).to(torch.bfloat16)
+
+        expected_input_ids = torch.tensor(
+            [
+                151644,
+                8948,
+                198,
+                2610,
+                525,
+                264,
+                10950,
+                17847,
+                13,
+                151645,
+                198,
+                151644,
+                872,
+                198,
+                151647,
+                151646,
+                151646,
+            ]
+        )
+        assert torch.allclose(expected_input_ids, inputs.input_ids[0][:17], atol=3e-3)
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [0.8792, 0.8792, 0.9084],
+                [1.1858, 1.1858, 1.2296],
+                [1.2004, 1.2004, 1.2150],
+                [1.4340, 1.4340, 1.4194],
+                [1.3902, 1.4048, 1.4194],
+                [1.5216, 1.5362, 1.5362],
+            ],
+            dtype=torch.bfloat16,
+            device="cpu",
+        )
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+
+        output = model.generate(
+            **inputs, thinker_temperature=0, thinker_do_sample=False, return_audio=False, thinker_max_new_tokens=20
+        )
+
+        EXPECTED_DECODED_TEXT = Expectations({
+            ("xpu", None): "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+            ("cuda", (8, 6)): "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is a glass shattering. The dog in the picture is a Labrador Retriever.",
+            ("rocm", (9, 4)): "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+        }).get_expectation()  # fmt: skip
+
+        decoded_text = self.processor.decode(output[0], skip_special_tokens=True)
+        self.assertEqual(decoded_text, EXPECTED_DECODED_TEXT)
+
+    @slow
+    def test_small_model_integration_test_batch(self):
+        model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-Omni-7B", dtype=torch.bfloat16, device_map="auto"
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(
+            text=[text] * 2,
+            audio=[self.raw_audio, self.raw_audio],
+            images=[self.raw_image, self.raw_image],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device, dtype=torch.bfloat16)
+
+        output = model.generate(
+            **inputs, thinker_temperature=0, thinker_do_sample=False, return_audio=False, thinker_max_new_tokens=20
+        )
+
+        EXPECTED_DECODED_TEXTS = Expectations(
+            {
+                ("xpu", 3): [
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+                ],
+                ("cuda", 7) : [
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is of glass shattering, and the dog in the picture is a Labrador Retriever",
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is of glass shattering, and the dog in the picture is a Labrador Retriever",
+                ],
+                ("cuda", 8): [
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is a glass shattering. The dog in the picture is a Labrador Retriever.",
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is a glass shattering. The dog in the picture is a Labrador Retriever.",
+                ],
+                ("rocm", (9, 4)): [
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+                    "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+                ],
+            }
+        ).get_expectation()  # fmt: skip
+
+        decoded_texts = self.processor.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(decoded_texts, EXPECTED_DECODED_TEXTS)
+
+    @slow
+    def test_small_model_integration_test_multiturn(self):
+        model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-Omni-7B", dtype=torch.bfloat16, device_map="auto"
+        )
+
+        messages = [
+            self.messages[0],
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "The sound is glass shattering, and the dog appears to be a Labrador Retriever.",
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio", "audio_url": self.audio_url_additional},
+                    {"type": "text", "text": "How about this one?"},
+                ],
+            },
+        ]
+
+        text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(
+            text=text,
+            audio=[self.raw_audio, self.raw_audio_additional],
+            images=[self.raw_image],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device, dtype=torch.bfloat16)
+
+        output = model.generate(
+            **inputs, thinker_temperature=0, thinker_do_sample=False, return_audio=False, thinker_max_new_tokens=20
+        )
+
+        EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog appears to be a Labrador Retriever.\nuser\nHow about this one?\nassistant\nThe sound is a cough."
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_w_audio(self):
+        model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-Omni-7B", dtype=torch.bfloat16, device_map="auto"
+        )
+        audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/guess_age_gender.wav"
+
+        messages = [
+            {
+                "role": "system",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.",
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [{"type": "audio", "audio": audio_url}],
+            },
+        ]
+        audio, _ = librosa.load(BytesIO(urlopen(audio_url).read()), sr=self.processor.feature_extractor.sampling_rate)
+
+        text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=text, audio=[audio], return_tensors="pt", padding=True).to(
+            torch_device, dtype=torch.bfloat16
+        )
+
+        output = model.generate(
+            **inputs,
+            thinker_temperature=0,
+            thinker_do_sample=False,
+            thinker_max_new_tokens=20,
+            talker_max_new_tokens=10,
+        )
+
+        EXPECTED_DECODED_TEXTS = Expectations(
+            {
+                ("xpu", None): "system\nYou are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.\nuser\n\nassistant\nWell, I can't really guess your age and gender just from your voice. There are so many",
+                ("cuda", 7): "system\nYou are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.\nuser\n\nassistant\nWell, I can try. But it's not always that accurate. I might be able to make",
+                ("cuda", 8): "system\nYou are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.\nuser\n\nassistant\nWell, I can't really guess your age and gender just from your voice. There are so many",
+            }
+        )  # fmt: skip
+        EXPECTED_DECODED_TEXT = EXPECTED_DECODED_TEXTS.get_expectation()
+
+        decoded_text = self.processor.decode(output[0][0], skip_special_tokens=True)
+        self.assertEqual(decoded_text, EXPECTED_DECODED_TEXT)
+        self.assertFalse(torch.isnan(output[1]).any().item())
+
+    @slow
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_batch_flashatt2(self):
+        model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-Omni-7B",
+            dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+            device_map="auto",
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(
+            text=[text, text],
+            audio=[self.raw_audio, self.raw_audio],
+            images=[self.raw_image, self.raw_image],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device)
+
+        output = model.generate(**inputs, thinker_temperature=0, thinker_do_sample=False, return_audio=False)
+
+        EXPECTED_DECODED_TEXT = Expectations({
+            ("cuda", None): "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog appears to be a Labrador Retriever.",
+            ("cuda", (8, 6)): "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+            ("rocm", (9, 4)): "system\nYou are a helpful assistant.\nuser\nWhat's that sound and what kind of dog is this?\nassistant\nThe sound is glass shattering, and the dog is a Labrador Retriever.",
+        }).get_expectation()  # fmt: skip
+
+        decoded_texts = self.processor.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(decoded_texts[0], EXPECTED_DECODED_TEXT)
+        self.assertEqual(decoded_texts[1], EXPECTED_DECODED_TEXT)
+
+
+@require_torch
+class Qwen2_5OmniToken2WavMaxPositionEmbeddingsTest(unittest.TestCase):
+    """
+    Tests to verify that ValueError is raised when input length exceeds max_position_embeddings.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        """Create minimal DiT model config for testing - shared across all tests."""
+        from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import Qwen2_5OmniDiTConfig
+
+        # Use minimal dimensions to reduce memory usage
+        # Note: enc_channels needs at least 3 elements for the ECAPA-TDNN encoder architecture
+        cls.config = Qwen2_5OmniDiTConfig(
+            hidden_size=32,
+            num_hidden_layers=1,
+            num_attention_heads=2,
+            head_dim=16,
+            ff_mult=1,
+            emb_dim=16,
+            mel_dim=16,
+            enc_emb_dim=16,
+            enc_dim=16,
+            enc_channels=[16, 16, 16],
+            enc_kernel_sizes=[3, 3, 1],
+            enc_dilations=[1, 1, 1],
+            enc_attention_channels=8,
+            enc_res2net_scale=2,
+            enc_se_channels=8,
+            num_embeds=100,
+            look_ahead_layers=[],
+            look_backward_layers=[0],
+            max_position_embeddings=100,  # Small for testing
+            block_size=24,
+            repeats=2,
+        )
+
+    def setUp(self):
+        """Create model instance for each test."""
+        from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import Qwen2_5OmniToken2WavDiTModel
+
+        self.model = Qwen2_5OmniToken2WavDiTModel(self.config).to(torch_device)
+        self.model.eval()
+
+    def tearDown(self):
+        """Clean up model to free memory."""
+        del self.model
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def test_error_when_exceeding_max_position_embeddings(self):
+        """Verify ValueError is raised when maximum_duration > max_position_embeddings."""
+        batch_size = 1
+        # With repeats=2 and max_position_embeddings=100, we need > 50 tokens to exceed
+        num_speech_tokens = 60  # Will result in 120 mel frames, exceeds max_position_embeddings=100
+
+        conditioning_vector = torch.randn(batch_size, self.config.enc_emb_dim, device=torch_device)
+        reference_mel = torch.randn(batch_size, 200, self.config.mel_dim, device=torch_device)
+        quantized_code = torch.randint(0, self.config.num_embeds, (batch_size, num_speech_tokens), device=torch_device)
+
+        with self.assertRaises(ValueError) as context:
+            self.model.sample(
+                conditioning_vector=conditioning_vector,
+                reference_mel_spectrogram=reference_mel,
+                quantized_code=quantized_code,
+                num_steps=2,
+            )
+
+        self.assertIn("exceeds `dit_config.max_position_embeddings`", str(context.exception))
+        self.assertIn("120", str(context.exception))  # Requested mel length
+        self.assertIn("100", str(context.exception))  # max_position_embeddings
+
+    def test_no_error_when_within_limits(self):
+        """Verify no error when maximum_duration <= max_position_embeddings."""
+        batch_size = 1
+        # With repeats=2 and max_position_embeddings=100, 50 tokens = 100 mel frames (exactly at limit)
+        num_speech_tokens = 50
+
+        conditioning_vector = torch.randn(batch_size, self.config.enc_emb_dim, device=torch_device)
+        reference_mel = torch.randn(batch_size, 200, self.config.mel_dim, device=torch_device)
+        quantized_code = torch.randint(0, self.config.num_embeds, (batch_size, num_speech_tokens), device=torch_device)
+
+        # Should complete without error
+        output = self.model.sample(
+            conditioning_vector=conditioning_vector,
+            reference_mel_spectrogram=reference_mel,
+            quantized_code=quantized_code,
+            num_steps=2,
+        )
+
+        # Check output shape is valid
+        self.assertEqual(len(output.shape), 3)
+        self.assertEqual(output.shape[0], batch_size)
+        self.assertEqual(output.shape[1], self.config.mel_dim)
+        self.assertEqual(output.shape[2], 100)  # 50 tokens * 2 repeats

tests/models/qwen2_5_omni/test_processing_qwen2_5_omni.py

@@ -0,0 +1,355 @@
+# Copyright 2025 The Qwen team, Alibaba Group and 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.
+import inspect
+import unittest
+
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+from transformers import (
+    Qwen2_5OmniProcessor,
+)
+from transformers.testing_utils import (
+    require_av,
+    require_librosa,
+    require_torch,
+    require_torchaudio,
+    require_torchvision,
+    require_vision,
+)
+from transformers.utils import is_torch_available
+
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+
+@require_vision
+@require_torch
+@require_torchaudio
+@require_torchvision
+class Qwen2_5OmniProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Qwen2_5OmniProcessor
+    model_id = "Qwen/Qwen2.5-Omni-7B"
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        return image_processor_class.from_pretrained(
+            cls.model_id, size={"shortest_edge": 28 * 28, "longest_edge": 56 * 56}
+        )
+
+    @classmethod
+    def _setup_video_processor(cls):
+        video_processor_class = cls._get_component_class_from_processor("video_processor")
+        return video_processor_class.from_pretrained(
+            cls.model_id, size={"shortest_edge": 28 * 28, "longest_edge": 56 * 56}
+        )
+
+    def prepare_audio_inputs(self, batch_size: int = 3):
+        """This function prepares a list of numpy audios."""
+        audio_inputs = [np.random.rand(160000) * 2 - 1] * batch_size
+        return audio_inputs
+
+    @require_torch
+    def _test_apply_chat_template(
+        self,
+        modality: str,
+        batch_size: int,
+        return_tensors: str,
+        input_name: str,
+        processor_name: str,
+        input_data: list[str],
+    ):
+        processor = self.get_processor()
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        if processor_name not in self.processor_class.get_attributes():
+            self.skipTest(f"{processor_name} attribute not present in {self.processor_class}")
+
+        batch_messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [{"type": "text", "text": "Describe this."}],
+                },
+            ]
+        ] * batch_size
+
+        # Test that jinja can be applied
+        formatted_prompt = processor.apply_chat_template(batch_messages, add_generation_prompt=True, tokenize=False)
+        self.assertEqual(len(formatted_prompt), batch_size)
+
+        # Test that tokenizing with template and directly with `self.tokenizer` gives same output
+        formatted_prompt_tokenized = processor.apply_chat_template(
+            batch_messages, add_generation_prompt=True, tokenize=True, return_tensors=return_tensors
+        )
+        add_special_tokens = True
+        if processor.tokenizer.bos_token is not None and formatted_prompt[0].startswith(processor.tokenizer.bos_token):
+            add_special_tokens = False
+        tok_output = processor.tokenizer(
+            formatted_prompt, return_tensors=return_tensors, add_special_tokens=add_special_tokens
+        )
+        expected_output = tok_output.input_ids
+        self.assertListEqual(expected_output.tolist(), formatted_prompt_tokenized.tolist())
+
+        # Test that kwargs passed to processor's `__call__` are actually used
+        tokenized_prompt_100 = processor.apply_chat_template(
+            batch_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            padding="max_length",
+            truncation=True,
+            return_tensors=return_tensors,
+            max_length=100,
+        )
+        self.assertEqual(len(tokenized_prompt_100[0]), 100)
+
+        # Test that `return_dict=True` returns text related inputs in the dict
+        out_dict_text = processor.apply_chat_template(
+            batch_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors=return_tensors,
+        )
+        self.assertTrue(all(key in out_dict_text for key in ["input_ids", "attention_mask"]))
+        self.assertEqual(len(out_dict_text["input_ids"]), batch_size)
+        self.assertEqual(len(out_dict_text["attention_mask"]), batch_size)
+
+        # Test that with modality URLs and `return_dict=True`, we get modality inputs in the dict
+        for idx, url in enumerate(input_data[:batch_size]):
+            batch_messages[idx][0]["content"] = [batch_messages[idx][0]["content"][0], {"type": modality, "url": url}]
+
+        out_dict = processor.apply_chat_template(
+            batch_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors=return_tensors,
+            num_frames=2,  # by default no more than 2 frames, otherwise too slow
+        )
+        input_name = getattr(self, input_name)
+        self.assertTrue(input_name in out_dict)
+        self.assertEqual(len(out_dict["input_ids"]), batch_size)
+        self.assertEqual(len(out_dict["attention_mask"]), batch_size)
+
+        if modality == "video":
+            # qwen pixels don't scale with bs same way as other models, calculate expected video token count based on video_grid_thw
+            expected_video_token_count = 0
+            for thw in out_dict["video_grid_thw"]:
+                expected_video_token_count += thw[0] * thw[1] * thw[2]
+            mm_len = expected_video_token_count
+        elif modality == "audio":
+            mm_len = batch_size
+        else:
+            mm_len = batch_size * 1564
+        self.assertEqual(len(out_dict[input_name]), mm_len)
+
+        return_tensor_to_type = {"pt": torch.Tensor, "np": np.ndarray, None: list}
+        for k in out_dict:
+            self.assertIsInstance(out_dict[k], return_tensor_to_type[return_tensors])
+
+    @require_av
+    def test_apply_chat_template_video_frame_sampling(self):
+        processor = self.get_processor()
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        signature = inspect.signature(processor.__call__)
+        if "videos" not in {*signature.parameters.keys()} or (
+            signature.parameters.get("videos") is not None
+            and signature.parameters["videos"].annotation == inspect._empty
+        ):
+            self.skipTest("Processor doesn't accept videos at input")
+
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "What is shown in this video?"},
+                    ],
+                },
+            ]
+        ]
+
+        formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+        self.assertEqual(len(formatted_prompt), 1)
+
+        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
+        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
+        self.assertListEqual(expected_output, formatted_prompt_tokenized)
+
+        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
+        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
+
+        # Add video URL for return dict and load with `num_frames` arg
+        messages[0][0]["content"].append(
+            {
+                "type": "video",
+                "url": url_to_local_path(
+                    "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+                ),
+            }
+        )
+        num_frames = 3
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            num_frames=num_frames,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        # Qwen pixel values are flattened, verify length matches video_grid_thw
+        expected_video_tokens = sum(thw[0] * thw[1] * thw[2] for thw in out_dict_with_video["video_grid_thw"])
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), expected_video_tokens)
+
+        # Load with `fps` arg
+        fps = 1
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            fps=fps,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        expected_video_tokens = sum(thw[0] * thw[1] * thw[2] for thw in out_dict_with_video["video_grid_thw"])
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), expected_video_tokens)
+
+        # Load with `fps` and `num_frames` args, should raise an error
+        with self.assertRaises(ValueError):
+            out_dict_with_video = processor.apply_chat_template(
+                messages,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                fps=fps,
+                num_frames=num_frames,
+            )
+
+        # Load without any arg should load the whole video
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        expected_video_tokens = sum(thw[0] * thw[1] * thw[2] for thw in out_dict_with_video["video_grid_thw"])
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), expected_video_tokens)
+
+        # Load video as a list of frames (i.e. images). NOTE: each frame should have same size
+        # because we assume they come from one video
+        messages[0][0]["content"][-1] = {
+            "type": "video",
+            "url": [
+                url_to_local_path(
+                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+                ),
+                url_to_local_path(
+                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+                ),
+            ],
+        }
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        expected_video_tokens = sum(thw[0] * thw[1] * thw[2] for thw in out_dict_with_video["video_grid_thw"])
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), expected_video_tokens)
+
+        # When the inputs are frame URLs/paths we expect that those are already
+        # sampled and will raise an error is asked to sample again.
+        with self.assertRaisesRegex(
+            ValueError, "Sampling frames from a list of images is not supported! Set `do_sample_frames=False`"
+        ):
+            out_dict_with_video = processor.apply_chat_template(
+                messages,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                do_sample_frames=True,
+            )
+
+    @require_librosa
+    @require_av
+    def test_chat_template_audio_from_video(self):
+        processor = self.get_processor()
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        signature = inspect.signature(processor.__call__)
+        if "videos" not in {*signature.parameters.keys()} or (
+            signature.parameters.get("videos") is not None
+            and signature.parameters["videos"].annotation == inspect._empty
+        ):
+            self.skipTest(f"{self.processor_class} does not support video inputs")
+
+        if "feature_extractor" not in self.processor_class.get_attributes():
+            self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
+
+        video_file_path = hf_hub_download(
+            repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset"
+        )
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "path": video_file_path},
+                    {"type": "text", "text": "Which of these animals is making the sound?"},
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "It is a cow."}],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Tell me all about this animal."},
+                ],
+            },
+        ]
+
+        formatted_prompt = processor.apply_chat_template([messages], add_generation_prompt=True, tokenize=False)
+        self.assertEqual(len(formatted_prompt), 1)  # batch size=1
+
+        out_dict = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            load_audio_from_video=True,
+        )
+        self.assertTrue(self.audio_input_name in out_dict)
+        self.assertTrue(self.videos_input_name in out_dict)
+
+        # should always have input_ids and attention_mask
+        self.assertEqual(len(out_dict["input_ids"]), 1)  # batch-size=1
+        self.assertEqual(len(out_dict["attention_mask"]), 1)  # batch-size=1
+        self.assertEqual(len(out_dict[self.audio_input_name]), 1)  # 1 audio in the conversation
+        # Qwen pixel values are flattened, verify length matches video_grid_thw
+        expected_video_tokens = sum(thw[0] * thw[1] * thw[2] for thw in out_dict["video_grid_thw"])
+        self.assertEqual(len(out_dict[self.videos_input_name]), expected_video_tokens)  # 1 video in the conversation