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tests/models/phi4_multimodal/test_processing_phi4_multimodal.py

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+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import inspect
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import Phi4MultimodalProcessor
+
+
+@require_vision
+class Phi4MultimodalProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Phi4MultimodalProcessor
+    checkpoint_path = "microsoft/Phi-4-multimodal-instruct"
+    revision = "refs/pr/70"
+    text_input_name = "input_ids"
+    images_input_name = "image_pixel_values"
+    audio_input_name = "audio_input_features"
+
+    # Max-length values used in image-text kwargs tests. Override as phi4 needs lots of tokens for images.
+    image_text_kwargs_max_length = 400
+    image_text_kwargs_override_max_length = 396
+    image_unstructured_max_length = 407
+
+    # Max-length values used in audio-text kwargs tests. Override as phi4 needs lots of tokens for audio.
+    audio_text_kwargs_max_length = 300
+    audio_processor_tester_max_length = 117
+    audio_unstructured_max_length = 76
+
+    # Max-length values used in video-text kwargs tests. Override in subclasses if needed.
+    video_text_kwargs_max_length = 167
+    video_text_kwargs_override_max_length = 162
+    video_unstructured_max_length = 176
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        return tokenizer_class.from_pretrained(cls.checkpoint_path, revision=cls.revision)
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+        cls.image_token_id = processor.image_token_id
+        cls.audio_token = processor.audio_token
+        cls.audio_token_id = processor.audio_token_id
+
+    # override: audio_attention_mask is returned conditionally, and not expected in the input names in this case
+    def test_model_input_names(self):
+        processor = self.get_processor()
+
+        text = self.prepare_text_inputs(modalities=["image", "video", "audio"])
+        image_input = self.prepare_image_inputs()
+        video_inputs = self.prepare_video_inputs()
+        audio_inputs = self.prepare_audio_inputs()
+        inputs_dict = {"text": text, "images": image_input, "videos": video_inputs, "audio": audio_inputs}
+
+        call_signature = inspect.signature(processor.__call__)
+        input_args = [param.name for param in call_signature.parameters.values()]
+        inputs_dict = {k: v for k, v in inputs_dict.items() if k in input_args}
+
+        inputs = processor(**inputs_dict, return_tensors="pt")
+
+        # audio_attention_mask is returned conditionally, and not expected in the input names in this case
+        input_names_expected = set(processor.model_input_names) - {"audio_attention_mask"}
+        self.assertSetEqual(set(inputs.keys()), input_names_expected)
+
+    def test_dynamic_hd_kwarg_passed_to_image_processor(self):
+        processor = self.get_processor()
+        # 1000x1000 image: with size=448, w_crop_num=3, h_crop_num=3 -> 9 HD crops (1 global + 9 = 10 total)
+        # With dynamic_hd=4: limits to 2x2 grid -> 4 HD crops (1 global + 4 = 5 total)
+        arr = np.random.randint(255, size=(3, 1000, 1000), dtype=np.uint8)
+        image_input = Image.fromarray(np.moveaxis(arr, 0, -1))
+        input_str = self.prepare_text_inputs(modalities="image")
+
+        inputs_default = processor(text=input_str, images=image_input, return_tensors="pt")
+        inputs_limited = processor(
+            text=input_str,
+            images=image_input,
+            dynamic_hd=4,
+            return_tensors="pt",
+        )
+
+        self.assertEqual(inputs_limited[self.images_input_name].shape[1], 5)
+        self.assertEqual(inputs_default[self.images_input_name].shape[1], 10)