# Copyright 2025 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 from parameterized import parameterized from transformers import InternVLProcessor from transformers.testing_utils import require_av, require_torch, 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 class InternVLProcessorTest(ProcessorTesterMixin, unittest.TestCase): processor_class = InternVLProcessor videos_input_name = "pixel_values" @classmethod def _setup_image_processor(cls): image_processor_class = cls._get_component_class_from_processor("image_processor") return image_processor_class( do_resize=True, size={"height": 20, "width": 20}, max_patches=2, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225], do_convert_rgb=True, ) @classmethod def _setup_video_processor(cls): video_processor_class = cls._get_component_class_from_processor("video_processor") return video_processor_class( do_resize=True, size={"height": 20, "width": 20}, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225], do_convert_rgb=True, ) @classmethod def _setup_tokenizer(cls): tokenizer_class = cls._get_component_class_from_processor("tokenizer") return tokenizer_class.from_pretrained("OpenGVLab/InternVL3-1B-hf", padding_side="left") @classmethod def _setup_test_attributes(cls, processor): cls.image_token = processor.image_token cls.video_token = processor.video_token @unittest.skip("InternVL requires text") def test_video_processor_defaults(self): pass @staticmethod def prepare_processor_dict(): return {"image_seq_length": 2} # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens def test_get_num_vision_tokens(self): "Tests general functionality of the helper used internally in vLLM" processor = self.get_processor() output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)]) self.assertTrue("num_image_tokens" in output) self.assertEqual(len(output["num_image_tokens"]), 3) self.assertTrue("num_image_patches" in output) self.assertEqual(len(output["num_image_patches"]), 3) @require_av @require_torch def test_process_interleaved_images_videos(self): processor = self.get_processor() messages = [ [ { "role": "user", "content": [ { "type": "image", "url": url_to_local_path( "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" ), }, { "type": "image", "url": url_to_local_path( "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg" ), }, {"type": "text", "text": "What are the differences between these two images?"}, ], }, ], [ { "role": "user", "content": [ { "type": "video", "url": url_to_local_path( "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4" ), }, {"type": "text", "text": "What type of shot is the man performing?"}, ], }, ], [ { "role": "user", "content": [ { "type": "image", "url": url_to_local_path("https://llava-vl.github.io/static/images/view.jpg"), }, {"type": "text", "text": "Write a haiku for this image"}, ], } ], ] inputs_batched = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", padding=True, num_frames=8, ) # Process non batched inputs to check if the pixel_values and input_ids are reconstructed in the correct order when batched together images_patches_index = 0 for i, message in enumerate(messages): inputs = processor.apply_chat_template( message, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", padding=True, num_frames=8, ) # We slice with [-inputs["input_ids"].shape[1] :] as the input_ids are left padded torch.testing.assert_close( inputs["input_ids"][0], inputs_batched["input_ids"][i][-inputs["input_ids"].shape[1] :] ) torch.testing.assert_close( inputs["pixel_values"], inputs_batched["pixel_values"][ images_patches_index : images_patches_index + inputs["pixel_values"].shape[0] ], ) images_patches_index += inputs["pixel_values"].shape[0] @require_torch @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": "video", "url": url_to_local_path( "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4" ), }, {"type": "text", "text": "What is shown in this video?"}, ], }, ] ] 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, return_tensors="pt", ) self.assertTrue(self.videos_input_name in out_dict_with_video) self.assertEqual(len(out_dict_with_video[self.videos_input_name]), num_frames) # Load with `fps` arg is not possible with InternVL (skip) # Load without any arg should use the default loading method out_dict_with_video = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ) self.assertTrue(self.videos_input_name in out_dict_with_video) self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 11) # 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"][0] = { "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, return_tensors="pt", ) self.assertTrue(self.videos_input_name in out_dict_with_video) self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 2) @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="pt" ) 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="pt", 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="pt", 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="pt", ) 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}] num_frames = 2 # by default no more than 2 frames, otherwise too slow out_dict = processor.apply_chat_template( batch_messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", num_frames=num_frames, ) self.assertTrue(self.videos_input_name in out_dict) self.assertEqual(len(out_dict["input_ids"]), batch_size) self.assertEqual(len(out_dict["attention_mask"]), batch_size) # InternVL internally collects frames from all the videos in a batch and flattens the batch dimension (B T C H W) -> (B*T C H W) then patches and removes the frames # hence output length does not equal batch size num_pixel_planes = 0 # i.e. images + video frames for message_thread in batch_messages: for message in message_thread: for content in message.get("content", []): if (content_type := content.get("type")) == "image": num_pixel_planes += 1 elif content_type == "video": num_pixel_planes += num_frames self.assertEqual(len(out_dict[self.videos_input_name]), num_pixel_planes) for k in out_dict: self.assertIsInstance(out_dict[k], torch.Tensor) # Test continue from final message assistant_message = { "role": "assistant", "content": [{"type": "text", "text": "It is the sound of"}], } for batch_idx in range(batch_size): batch_messages[batch_idx] = batch_messages[batch_idx] + [assistant_message] continue_prompt = processor.apply_chat_template(batch_messages, continue_final_message=True, tokenize=False) for prompt in continue_prompt: self.assertTrue(prompt.endswith("It is the sound of")) # no `eos` token at the end @parameterized.expand([(1,), (2,)]) @require_torch def test_frames_binding(self, batch_size: int): texts = [ "