# coding=utf-8 # Copyright 2024 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 Janus model.""" import unittest import numpy as np from transformers import JanusProcessor from ...test_processing_common import ProcessorTesterMixin, url_to_local_path class JanusProcessorTest(ProcessorTesterMixin, unittest.TestCase): processor_class = JanusProcessor model_id = "deepseek-community/Janus-Pro-1B" @classmethod def _setup_from_pretrained(cls, model_id, **kwargs): special_image_tokens = { "image_token": "", "boi_token": "", "eoi_token": "", } processor = super()._setup_from_pretrained(model_id, extra_special_tokens=special_image_tokens) # Set the processor to use the default system prompt to False as it's used based on input modality. # Hence set to False to avoid any issues in the test irrespective of inputs. processor.use_default_system_prompt = False return processor def test_chat_template_single(self): """ Tests that the chat template matches the original implementation when applied to a single message. """ processor = self.get_processor() if processor.chat_template is None: self.skipTest("Processor has no chat template") # Single image message messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, ] ] correct_prompt = ["<|User|>: What is shown in this image?\n\n\n<|Assistant|>:"] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True) self.assertEqual(formatted_prompt, correct_prompt) # Single image message with capitalization messages = [ [ { "role": "User", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, ] ] correct_prompt = ["<|User|>: What is shown in this image?\n\n\n<|Assistant|>:"] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True) self.assertEqual(formatted_prompt, correct_prompt) # Single image message with uppercase messages = [ [ { "role": "USER", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, ] ] correct_prompt = ["<|User|>: What is shown in this image?\n\n\n<|Assistant|>:"] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True) self.assertEqual(formatted_prompt, correct_prompt) """ Warning: normally, the other models have a test comparing chat template+tokenization as two separate steps versus as a single step (i.e. processor.apply_chat_template(..., tokenize=True)). However, our processor has some extra steps other than simply applying prompt to tokenizer. These include prepending the default system prompts and, following the implementation from the Janus codebase, expanding the image token. """ # Checking the output dict keys 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"]) # Now test the ability to return dict messages[0][0]["content"][1].update( { "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) self.assertTrue(self.images_input_name in out_dict) # should always have input_ids and attention_mask self.assertEqual(len(out_dict["input_ids"]), 1) self.assertEqual(len(out_dict["attention_mask"]), 1) self.assertEqual(len(out_dict[self.images_input_name]), 1) # Passing generation prompt explicitly messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, { "role": "assistant", "content": [ {"type": "text", "text": ""}, ], }, ] ] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=False) self.assertEqual(formatted_prompt, correct_prompt) # Single prompt with multiple images messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "Compare this image"}, {"type": "image"}, {"type": "text", "text": "with this image"}, {"type": "image"}, ], }, ] ] correct_prompt = [ "<|User|>: Compare this image\n\nwith this image\n\n\n<|Assistant|>:" ] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True) self.assertEqual(formatted_prompt, correct_prompt) # Multiple turns and multiple images messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "Compare this image"}, {"type": "image"}, {"type": "text", "text": "with this image"}, {"type": "image"}, ], }, { "role": "assistant", "content": [ {"type": "text", "text": "The first image is an equation, the second is a pie chart."}, ], }, { "role": "user", "content": [ {"type": "image"}, { "type": "text", "text": "What about this third image? To which of the previous to is it more similar?", }, ], }, ] ] correct_prompt = [ "<|User|>: Compare this image\n\nwith this image\n\n\n<|Assistant|>: The first image is an equation, the second is a pie chart.<｜end▁of▁sentence｜><|User|>: \nWhat about this third image? To which of the previous to is it more similar?\n\n<|Assistant|>:" ] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True) self.assertEqual(formatted_prompt, correct_prompt) def test_chat_template_batched(self): """ Tests that the chat template matches the original implementation when applied to a batch of messages. """ processor = self.get_processor() if processor.chat_template is None: self.skipTest("Processor has no chat template") # Test 1: Simple single image per message batch batched_messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, ], [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, ], ] correct_prompts = [ "<|User|>: What is shown in this image?\n\n\n<|Assistant|>:", "<|User|>: What is shown in this image?\n\n\n<|Assistant|>:", ] formatted_prompts = processor.apply_chat_template(batched_messages, add_generation_prompt=True) self.assertEqual(formatted_prompts, correct_prompts) # Similarly to the single case, no test for chat template+tokenization as two separate steps versus as a single step # Checking the output dict keys out_dict = processor.apply_chat_template( batched_messages, add_generation_prompt=True, tokenize=True, return_dict=True, padding=True, ) self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"]) # Verify image inputs are included in the output dict batched_messages[0][0]["content"][1].update( { "type": "image", "url": url_to_local_path( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg" ), } ) batched_messages[1][0]["content"][1].update( {"type": "image", "url": url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")} ) out_dict = processor.apply_chat_template( batched_messages, add_generation_prompt=True, tokenize=True, return_dict=True, padding=True ) self.assertTrue(self.images_input_name in out_dict) self.assertEqual(len(out_dict["input_ids"]), 2) # Batch size for text self.assertEqual(len(out_dict["attention_mask"]), 2) # Batch size for attention mask self.assertEqual(len(out_dict[self.images_input_name]), 2) # Batch size for images # Test 2: Two images per message batch with different prompts batched_messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "Compare this image"}, {"type": "image"}, {"type": "text", "text": "with this image"}, {"type": "image"}, ], }, ], [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Describe how the previous image compares to the following"}, {"type": "image"}, ], }, ], ] correct_prompts = [ "<|User|>: Compare this image\n\nwith this image\n\n\n<|Assistant|>:", "<|User|>: \nDescribe how the previous image compares to the following\n\n\n<|Assistant|>:", ] formatted_prompts = processor.apply_chat_template(batched_messages, add_generation_prompt=True) self.assertEqual(formatted_prompts, correct_prompts) # Test 3: Multi-turn conversations with multiple images batched_messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "Compare this image"}, {"type": "image"}, {"type": "text", "text": "with this image"}, {"type": "image"}, ], }, { "role": "assistant", "content": [ {"type": "text", "text": "The first image is an equation, the second is a pie chart."}, ], }, { "role": "user", "content": [ {"type": "image"}, { "type": "text", "text": "What about this third image? To which of the previous to is it more similar?", }, ], }, ], [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Describe how the previous image compares to the following"}, {"type": "image"}, ], }, { "role": "assistant", "content": [ {"type": "text", "text": "The first image is a formula, the second is a plot."}, ], }, { "role": "user", "content": [ {"type": "text", "text": "Which of them is closer to the following?"}, {"type": "image"}, ], }, ], ] correct_prompts = [ "<|User|>: Compare this image\n\nwith this image\n\n\n<|Assistant|>: The first image is an equation, the second is a pie chart.<｜end▁of▁sentence｜><|User|>: \nWhat about this third image? To which of the previous to is it more similar?\n\n<|Assistant|>:", "<|User|>: \nDescribe how the previous image compares to the following\n\n\n<|Assistant|>: The first image is a formula, the second is a plot.<｜end▁of▁sentence｜><|User|>: Which of them is closer to the following?\n\n\n<|Assistant|>:", ] formatted_prompts = processor.apply_chat_template(batched_messages, add_generation_prompt=True) self.assertEqual(formatted_prompts, correct_prompts) def test_chat_template_accepts_processing_kwargs(self): """Tests that the chat template correctly handles additional processing arguments.""" # Get processor and skip if it doesn't have a chat template processor = self.get_processor() if processor.chat_template is None: self.skipTest("Processor has no chat template") # Create a simple text message for testing messages = [ [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, ], }, ] ] # Test 1: Padding to max_length # PS: we have to override the parent max_length of 50 to 80 because the output is already 51 tokens formatted_prompt_tokenized = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, padding="max_length", max_length=80, ) self.assertEqual(len(formatted_prompt_tokenized[0]), 80) # Test 2: Truncation # Verify that the output is truncated to exactly 5 tokens 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) # Test 3: Image processing kwargs # Add an image and test image processing parameters 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" ), } ) # Process with image rescaling and verify the pixel values are negative out_dict = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, do_rescale=True, rescale_factor=-1.0, return_tensors="np", ) self.assertLessEqual(out_dict[self.images_input_name][0][0].mean(), 0) def test_processor_postprocess(self): processor_components = self.prepare_components() processor = self.processor_class(**processor_components) input_str = "lower newer" orig_image_input = self.prepare_image_inputs() orig_image = np.array(orig_image_input).transpose(2, 0, 1) inputs = processor(text=input_str, images=orig_image, do_resize=False, do_pad=False, return_tensors="np") normalized_image_input = inputs.pixel_values unnormalized_images = processor.postprocess(normalized_image_input, return_tensors="np")["pixel_values"] # For an image where pixels go from 0 to 255 the diff can be 1 due to some numerical precision errors when scaling and unscaling self.assertTrue(np.abs(orig_image - unnormalized_images).max() >= 1)