# Copyright 2024 HuggingFace Inc. # # 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 unittest import numpy as np from transformers import AriaProcessor from transformers.image_utils import load_image from transformers.testing_utils import require_torch, require_vision from ...test_processing_common import ProcessorTesterMixin, url_to_local_path @require_torch @require_vision class AriaProcessorTest(ProcessorTesterMixin, unittest.TestCase): # NOTE: setUpClass, tearDownClass, and getter methods have been removed. # They are now automatically handled by ProcessorTesterMixin. # This test only needs: processor_class = YourProcessor # Optionally: model_id = "some/model" to load from specific pretrained model # Optionally: prepare_processor_dict() for custom processor kwargs. processor_class = AriaProcessor model_id = "m-ric/Aria_hf_2" @classmethod def _setup_test_attributes(cls, processor): cls.image1 = load_image( url_to_local_path( "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" ) ) cls.image2 = load_image( url_to_local_path("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg") ) cls.image3 = load_image( url_to_local_path( "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg" ) ) cls.bos_token = "<|im_start|>" cls.eos_token = "<|im_end|>" cls.image_token = processor.tokenizer.image_token cls.fake_image_token = "o" cls.global_img_token = "<|img|>" cls.bos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.bos_token) cls.eos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.eos_token) cls.image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.image_token) cls.fake_image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.fake_image_token) cls.global_img_tokens_id = processor.tokenizer(cls.global_img_token, add_special_tokens=False)["input_ids"] cls.padding_token_id = processor.tokenizer.pad_token_id cls.image_seq_len = 2 @staticmethod def prepare_processor_dict(): return { "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}{% elif message['content'] is iterable %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<|img|>{% endif %}{% endfor %}{% endif %}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}", "size_conversion": {490: 2, 980: 2}, } # fmt: skip 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) def test_process_interleaved_images_prompts_image_splitting(self): processor = self.get_processor() processor.image_processor.split_image = True # Test that a single image is processed correctly inputs = processor(images=self.image1, text="Ok<|img|>", images_kwargs={"split_image": True}) self.assertEqual(np.array(inputs["pixel_values"]).shape, (2, 3, 980, 980)) self.assertEqual(np.array(inputs["pixel_mask"]).shape, (2, 980, 980)) def test_process_interleaved_images_prompts_no_image_splitting(self): processor = self.get_processor() processor.image_processor.split_image = False # Test that a single image is processed correctly inputs = processor(images=self.image1, text="Ok<|img|>") image1_expected_size = (980, 980) self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 3, *image1_expected_size)) self.assertEqual(np.array(inputs["pixel_mask"]).shape, (1, *image1_expected_size)) # fmt: on # Test a single sample with image and text image_str = "<|img|>" text_str = "In this image, we see" text = image_str + text_str inputs = processor(text=text, images=self.image1) # fmt: off # The processor expands <|img|> to <|img|><|img|> (image_seq_len=2) before tokenization # So we need to tokenize the full expanded string to match what the processor does expanded_text = self.image_token * self.image_seq_len + text_str expected_input_ids = [processor.tokenizer(expanded_text, add_special_tokens=False)["input_ids"]] # self.assertEqual(len(inputs["input_ids"]), len(expected_input_ids)) self.assertEqual(inputs["input_ids"], expected_input_ids) self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])]) self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 3, *image1_expected_size)) self.assertEqual(np.array(inputs["pixel_mask"]).shape, (1, *image1_expected_size)) # fmt: on # Test that batch is correctly processed image_str = "<|img|>" text_str_1 = "In this image, we see" text_str_2 = "In this image, we see" text = [ image_str + text_str_1, image_str + image_str + text_str_2, ] images = [[self.image1], [self.image2, self.image3]] inputs = processor(text=text, images=images, padding=True) # fmt: off tokenized_sentence_1 = processor.tokenizer(text_str_1, add_special_tokens=False) tokenized_sentence_2 = processor.tokenizer(text_str_2, add_special_tokens=False) image_tokens = [self.image_token_id] * self.image_seq_len expected_input_ids_1 = image_tokens + tokenized_sentence_1["input_ids"] expected_input_ids_2 = 2 * image_tokens + tokenized_sentence_2["input_ids"] # Pad the first input to match the second input pad_len = len(expected_input_ids_2) - len(expected_input_ids_1) expected_attention_mask = [[0] * pad_len + [1] * len(expected_input_ids_1), [1] * (len(expected_input_ids_2))] self.assertEqual( inputs["attention_mask"], expected_attention_mask ) self.assertEqual(np.array(inputs['pixel_values']).shape, (3, 3, 980, 980)) self.assertEqual(np.array(inputs['pixel_mask']).shape, (3, 980, 980)) # fmt: on def test_non_nested_images_with_batched_text(self): processor = self.get_processor() processor.image_processor.do_image_splitting = False image_str = "<|img|>" text_str_1 = "In this image, we see" text_str_2 = "In this image, we see" text = [ image_str + text_str_1, image_str + image_str + text_str_2, ] images = [self.image1, self.image2, self.image3] inputs = processor(text=text, images=images, padding=True) self.assertEqual(np.array(inputs["pixel_values"]).shape, (3, 3, 980, 980)) self.assertEqual(np.array(inputs["pixel_mask"]).shape, (3, 980, 980)) def test_apply_chat_template(self): # Message contains content which a mix of lists with images and image urls and string messages = [ { "role": "user", "content": [ {"type": "text", "text": "What do these images show?"}, {"type": "image"}, {"type": "image"}, "What do these images show?", ], }, { "role": "assistant", "content": [ { "type": "text", "text": "The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.", } ], }, {"role": "user", "content": [{"type": "text", "text": "And who is that?"}]}, ] processor = self.get_processor() # Make short sequence length to test that the fake tokens are added correctly rendered = processor.apply_chat_template(messages, add_generation_prompt=True) print(rendered) expected_rendered = """<|im_start|>user What do these images show?<|img|><|img|><|im_end|> <|im_start|>assistant The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.<|im_end|> <|im_start|>user And who is that?<|im_end|> <|im_start|>assistant """ self.assertEqual(rendered, expected_rendered) def test_image_chat_template_accepts_processing_kwargs(self): processor = self.get_processor() if processor.chat_template is None: self.skipTest("Processor has no chat template") messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, ], }, ] ] formatted_prompt_tokenized = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, padding="max_length", max_length=50, ) self.assertEqual(len(formatted_prompt_tokenized[0]), 50) formatted_prompt_tokenized = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, truncation=True, max_length=5, ) self.assertEqual(len(formatted_prompt_tokenized[0]), 5) # Now test the ability to return dict messages[0][0]["content"].append( { "type": "image", "url": url_to_local_path( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg" ), } ) out_dict = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, max_image_size=980, return_tensors="np", ) self.assertListEqual(list(out_dict[self.images_input_name].shape), [1, 3, 980, 980]) def test_special_mm_token_truncation(self): """Tests that special vision tokens do not get truncated when `truncation=True` is set.""" processor = self.get_processor() input_str = self.prepare_text_inputs(batch_size=2, modalities="image") image_input = self.prepare_image_inputs(batch_size=2) _ = processor( text=input_str, images=image_input, return_tensors="pt", truncation=None, padding=True, ) with self.assertRaises(ValueError): _ = processor( text=input_str, images=image_input, return_tensors="pt", truncation=True, padding=True, max_length=3, )