# 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 Idefics3Processor 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 Idefics3ProcessorTest(ProcessorTesterMixin, unittest.TestCase): processor_class = Idefics3Processor model_id = "HuggingFaceM4/Idefics3-8B-Llama3" @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 = processor.tokenizer.bos_token cls.image_token = processor.image_token cls.fake_image_token = processor.fake_image_token cls.global_img_token = processor.global_image_tag cls.bos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.bos_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 = processor.image_seq_len @staticmethod def prepare_processor_dict(): return {"image_seq_len": 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) def get_split_image_expected_tokens(self, processor, image_rows, image_cols): text_split_images = [] for n_h in range(image_rows): for n_w in range(image_cols): text_split_images += ( [self.fake_image_token_id] + processor.tokenizer(f"", add_special_tokens=False)["input_ids"] + [self.image_token_id] * self.image_seq_len ) text_split_images += processor.tokenizer("\n", add_special_tokens=False)["input_ids"] text_split_images = text_split_images[:-1] # remove last newline # add double newline, as it gets its own token text_split_images += processor.tokenizer("\n\n", add_special_tokens=False)["input_ids"] text_split_images += ( [self.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] ) return text_split_images def test_process_interleaved_images_prompts_no_image_splitting(self): processor = self.get_processor() processor.image_processor.do_image_splitting = False # Test that a single image is processed correctly inputs = processor(images=self.image1) image1_expected_size = (364, 364) self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 1, 3, *image1_expected_size)) self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 1, *image1_expected_size)) # fmt: on # Test a single sample with image and text image_str = "" text_str = "In this image, we see" text = image_str + text_str inputs = processor(text=text, images=self.image1) # fmt: off tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False) expected_input_ids = [[self.bos_token_id] + [self.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence["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, 1, 3, *image1_expected_size)) self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 1, *image1_expected_size)) # fmt: on # Test that batch is correctly processed image_str = "" 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.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] expected_input_ids_1 = [self.bos_token_id] + image_tokens + tokenized_sentence_1["input_ids"] expected_input_ids_2 = [self.bos_token_id] + 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) padded_expected_input_ids_1 = [self.padding_token_id] * pad_len + expected_input_ids_1 self.assertEqual( inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2] ) self.assertEqual( inputs["attention_mask"], [[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)] ) self.assertEqual(np.array(inputs['pixel_values']).shape, (2, 2, 3, 364, 364)) self.assertEqual(np.array(inputs['pixel_attention_mask']).shape, (2, 2, 364, 364)) # fmt: on def test_process_interleaved_images_prompts_image_splitting(self): processor = self.get_processor() processor.image_processor.do_image_splitting = True # Test that a single image is processed correctly inputs = processor(images=self.image1) self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 13, 3, 364, 364)) self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 13, 364, 364)) # fmt: on self.maxDiff = None # Test a single sample with image and text image_str = "" text_str = "In this image, we see" text = image_str + text_str inputs = processor(text=text, images=self.image1) # fmt: off tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False) split_image1_tokens = self.get_split_image_expected_tokens(processor, 3, 4) expected_input_ids_1 = [[self.bos_token_id] + split_image1_tokens + tokenized_sentence["input_ids"]] self.assertEqual(inputs["input_ids"], expected_input_ids_1) self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids_1[0])]) self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 13, 3, 364, 364)) self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 13, 364, 364)) # fmt: on # Test that batch is correctly processed image_str = "" text_str_1 = "In this image, we see" text_str_2 = "bla, bla" text = [ image_str + text_str_1, text_str_2 + image_str + image_str, ] 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) split_image1_tokens = self.get_split_image_expected_tokens(processor, 3, 4) split_image2_tokens = self.get_split_image_expected_tokens(processor, 4, 4) split_image3_tokens = self.get_split_image_expected_tokens(processor, 3, 4) expected_input_ids_1 = [self.bos_token_id] + split_image1_tokens + tokenized_sentence_1["input_ids"] expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + split_image2_tokens + split_image3_tokens # Pad the first input to match the second input pad_len = len(expected_input_ids_2) - len(expected_input_ids_1) padded_expected_input_ids_1 = [self.padding_token_id] * pad_len + expected_input_ids_1 self.assertEqual( inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2] ) self.assertEqual( inputs["attention_mask"], [[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)] ) self.assertEqual(np.array(inputs['pixel_values']).shape, (2, 30, 3, 364, 364)) self.assertEqual(np.array(inputs['pixel_attention_mask']).shape, (2, 30, 364, 364)) # fmt: on def test_add_special_tokens_processor(self): processor = self.get_processor() image_str = "" text_str = "In this image, we see" text = text_str + image_str # fmt: off inputs = processor(text=text, images=self.image1, add_special_tokens=False) tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False) split_image1_tokens = self.get_split_image_expected_tokens(processor, 3, 4) expected_input_ids = [tokenized_sentence["input_ids"] + split_image1_tokens] self.assertEqual(inputs["input_ids"], expected_input_ids) inputs = processor(text=text, images=self.image1) expected_input_ids = [[self.bos_token_id] + tokenized_sentence["input_ids"] + split_image1_tokens] self.assertEqual(inputs["input_ids"], expected_input_ids) # fmt: on def test_non_nested_images_with_batched_text(self): processor = self.get_processor() processor.image_processor.do_image_splitting = False image_str = "" 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, (2, 2, 3, 364, 364)) self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (2, 2, 364, 364)) # Copied from tests.models.idefics2.test_processing_idefics2.Idefics2ProcessorTest.test_process_interleaved_images_prompts_image_error def test_process_interleaved_images_prompts_image_error(self): processor = self.get_processor() text = [ "This is a test sentence.", "In this other sentence we try some good things", ] images = [[self.image1], [self.image2]] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) images = [[self.image1], []] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) text = [ "This is a test sentence.", "In this other sentence we try some good things", ] images = [[self.image1], [self.image2, self.image3]] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) images = [[], [self.image2]] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) images = [self.image1, self.image2, self.image3] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) images = [self.image1] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) text = [ "This is a test sentence.", "In this other sentence we try some good things", ] images = [[self.image1], []] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) images = [self.image1, self.image2] with self.assertRaises(ValueError): processor(text=text, images=images, padding=True) 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) expected_rendered = ( "<|begin_of_text|>User: What do these images show?\n" "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.\n" "User: And who is that?\n" "Assistant:" ) self.assertEqual(rendered, expected_rendered) @require_torch @require_vision def test_text_only_inference(self): """Test that the processor works correctly with text-only input.""" processor = self.get_processor() text = "This is a simple text without images." inputs = processor(text=text) tokenized_sentence = processor.tokenizer(text, add_special_tokens=False) expected_input_ids = [[self.bos_token_id] + tokenized_sentence["input_ids"]] self.assertEqual(inputs["input_ids"], expected_input_ids) self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])]) self.assertTrue("pixel_values" not in inputs) self.assertTrue("pixel_attention_mask" not in inputs) # Test batch of texts without image tokens texts = ["First text.", "Second piece of text."] batch_inputs = processor(text=texts, padding=True) tokenized_1 = processor.tokenizer(texts[0], add_special_tokens=False) tokenized_2 = processor.tokenizer(texts[1], add_special_tokens=False) expected_1 = [self.bos_token_id] + tokenized_1["input_ids"] expected_2 = [self.bos_token_id] + tokenized_2["input_ids"] # Pad the shorter sequence pad_len = len(expected_2) - len(expected_1) if pad_len > 0: padded_expected_1 = [self.padding_token_id] * pad_len + expected_1 expected_attention_1 = [0] * pad_len + [1] * len(expected_1) self.assertEqual(batch_inputs["input_ids"], [padded_expected_1, expected_2]) self.assertEqual(batch_inputs["attention_mask"], [expected_attention_1, [1] * len(expected_2)]) else: pad_len = -pad_len padded_expected_2 = [self.padding_token_id] * pad_len + expected_2 expected_attention_2 = [0] * pad_len + [1] * len(expected_2) self.assertEqual(batch_inputs["input_ids"], [expected_1, padded_expected_2]) self.assertEqual(batch_inputs["attention_mask"], [[1] * len(expected_1), expected_attention_2]) @require_torch @require_vision def test_missing_images_error(self): """Test that appropriate error is raised when images are referenced but not provided.""" processor = self.get_processor() # Test single text with image token but no image text = "Let me show you this image: What do you think?" with self.assertRaises(ValueError) as context: processor(text=text) self.assertTrue("tokens in the text but no images were passed" in str(context.exception)) # Test batch with image tokens but no images texts = [ "First text with token.", "Second text with token.", ] with self.assertRaises(ValueError) as context: processor(text=texts) self.assertTrue("tokens in the text but no images were passed" in str(context.exception)) # Test with None as Images with self.assertRaises(ValueError) as context: processor(text=text, images=None) self.assertTrue("tokens in the text but no images were passed" in str(context.exception)) with self.assertRaises(ValueError) as context: processor(text=texts, images=None) self.assertTrue("tokens in the text but no images were passed" in str(context.exception))