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tests/models/idefics2/test_processing_idefics2.py

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+# 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
+
+from transformers import Idefics2Processor
+from transformers.image_utils import load_image
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
+
+
+if is_vision_available():
+    from transformers import (
+        Idefics2Processor,
+    )
+
+
+@require_torch
+@require_vision
+class Idefics2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Idefics2Processor
+    model_id = "HuggingFaceM4/idefics2-8b"
+
+    @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.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.image_seq_len = processor.image_seq_len
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {"image_seq_len": 2}
+
+    def test_process_interleaved_images_prompts_no_image_splitting(self):
+        processor = self.get_processor()
+        tokenizer = processor.tokenizer
+
+        processor.image_processor.do_image_splitting = False
+
+        # Test that a single image is processed correctly
+        inputs = processor(images=self.image1)
+        self.assertEqual(inputs["pixel_values"].shape, (1, 1, 3, 653, 980))
+        self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 1, 653, 980))
+        # fmt: on
+
+        # Test a single sample with image and text
+        image_str = "<image>"
+        text_str = "In this image, we see"
+        text = image_str + text_str
+        inputs = processor(text=text, images=self.image1)
+
+        # fmt: off
+        tokenized_sentence = tokenizer(text_str, add_special_tokens=False)
+        expected_input_ids = [[self.bos_token_id] + [self.fake_image_token_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(inputs["pixel_values"].shape, (1, 1, 3, 653, 980))
+        self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 1, 653, 980))
+        # fmt: on
+
+        # Test that batch is correctly processed
+        image_str = "<image>"
+        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 = tokenizer(text_str_1, add_special_tokens=False)
+        tokenized_sentence_2 = tokenizer(text_str_2, add_special_tokens=False)
+        expected_input_ids_1 = [self.bos_token_id] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence_1["input_ids"]
+        expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id]
+        # 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 = [0] * 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(inputs['pixel_values'].shape, (2, 2, 3, 767, 980))
+        self.assertEqual(inputs['pixel_attention_mask'].shape, (2, 2, 767, 980))
+        # fmt: on
+
+    def test_process_interleaved_images_prompts_image_splitting(self):
+        processor = self.get_processor()
+        tokenizer = processor.tokenizer
+        processor.image_processor.do_image_splitting = True
+
+        # Test that a single image is processed correctly
+        inputs = processor(images=self.image1)
+        self.assertEqual(inputs["pixel_values"].shape, (1, 5, 3, 653, 980))
+        self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 5, 653, 980))
+        # fmt: on
+
+        # Test a single sample with image and text
+        image_str = "<image>"
+        text_str = "In this image, we see"
+        text = image_str + text_str
+        inputs = processor(text=text, images=self.image1)
+
+        # fmt: off
+        tokenized_sentence = tokenizer(text_str, add_special_tokens=False)
+        expected_input_ids = [[self.bos_token_id] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [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(inputs["pixel_values"].shape, (1, 5, 3, 653, 980))
+        self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 5, 653, 980))
+        # fmt: on
+
+        # Test that batch is correctly processed
+        image_str = "<image>"
+        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 = tokenizer(text_str_1, add_special_tokens=False)
+        tokenized_sentence_2 = tokenizer(text_str_2, add_special_tokens=False)
+        expected_input_ids_1 = [self.bos_token_id] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id] + tokenized_sentence_1["input_ids"]
+        expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id]
+        # 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 = [0] * 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(inputs['pixel_values'].shape, (2, 10, 3, 767, 980))
+        self.assertEqual(inputs['pixel_attention_mask'].shape, (2, 10, 767, 980))
+        # fmt: on
+
+    def test_add_special_tokens_processor(self):
+        processor = self.get_processor()
+        tokenizer = processor.tokenizer
+        image_str = "<image>"
+        text_str = "In this image, we see"
+        text = text_str + image_str
+
+        n_image_repeat = 5 if processor.image_processor.do_image_splitting else 1
+
+        # fmt: off
+        inputs = processor(text=text, images=self.image1, add_special_tokens=False)
+        tokenized_sentence = tokenizer(text_str, add_special_tokens=False)
+        expected_input_ids = [tokenized_sentence["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * n_image_repeat + [self.fake_image_token_id]]
+        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"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * n_image_repeat + [self.fake_image_token_id]]
+        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 = "<image>"
+        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)
+
+        self.assertEqual(inputs["pixel_values"].shape, (2, 2, 3, 767, 980))
+        self.assertEqual(inputs["pixel_attention_mask"].shape, (2, 2, 767, 980))
+
+    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.<image>",
+            "In this other sentence we try some good things<image>",
+        ]
+        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, IndexError)):
+            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<image>",
+        ]
+        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 = (
+            "User: What do these images show?<image><image><end_of_utterance>\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.<end_of_utterance>\n"
+            "User: And who is that?<end_of_utterance>\n"
+            "Assistant:"
+        )
+        self.assertEqual(rendered, expected_rendered)