Use non nested images and batched text Idefics2/3 (huggingface#34222)

* add support for non nested images and add tests

* add tests error scenario

* fix style

* added single and no image to error tests

src/transformers/models/idefics2/image_processing_idefics2.py

@@ -99,6 +99,7 @@ def make_list_of_images(images: ImageInput) -> List[List[np.ndarray]]:
         isinstance(images, (list, tuple))
         and len(images) > 0
         and isinstance(images[0], (list, tuple))
+        and len(images[0]) > 0
         and is_valid_image(images[0][0])
     ):
         pass

src/transformers/models/idefics2/processing_idefics2.py

@@ -16,6 +16,7 @@
 Processor class for IDEFICS2.
 """
 
+from itertools import accumulate
 from typing import TYPE_CHECKING, List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
@@ -218,7 +219,21 @@ def __call__(
             if is_image_or_image_url(images):
                 images = [[images]]
             elif isinstance(images, list) and is_image_or_image_url(images[0]):
-                images = [images]
+                if text is not None:
+                    if sum(n_images_in_text) != len(images):
+                        raise ValueError(
+                            f"The total number of {image_token} tokens in the prompts should be the same as the number of images passed."
+                            f" Found {sum(n_images_in_text)} {image_token} tokens and {len(images)} images."
+                        )
+                    # Reorganize the images to match the prompts
+                    cumsum_images_in_text = [0] + list(accumulate(n_images_in_text))
+                    images = [
+                        images[cumsum_images_in_text[i] : cumsum_images_in_text[i + 1]]
+                        for i in range(len(n_images_in_text))
+                    ]
+                else:
+                    images = [images]
+
             elif (
                 not isinstance(images, list)
                 and not isinstance(images[0], list)

src/transformers/models/idefics3/image_processing_idefics3.py

@@ -151,9 +151,11 @@ def get_resize_output_image_size(
 def make_list_of_images(images: ImageInput) -> List[List[np.ndarray]]:
     """
     Convert a single image or a list of images to a list of numpy arrays.
+
     Args:
         images (`ImageInput`):
             A single image or a list of images.
+
     Returns:
         A list of numpy arrays.
     """
@@ -168,6 +170,7 @@ def make_list_of_images(images: ImageInput) -> List[List[np.ndarray]]:
         isinstance(images, (list, tuple))
         and len(images) > 0
         and isinstance(images[0], (list, tuple))
+        and len(images[0]) > 0
         and is_valid_image(images[0][0])
     ):
         pass

src/transformers/models/idefics3/processing_idefics3.py

@@ -17,6 +17,7 @@
 """
 
 import re
+from itertools import accumulate
 from typing import TYPE_CHECKING, Dict, List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
@@ -241,11 +242,31 @@ def __call__(
         n_images_in_images = []
         inputs = BatchFeature()
 
+        if text is not None:
+            if isinstance(text, str):
+                text = [text]
+            elif not isinstance(text, list) and not isinstance(text[0], str):
+                raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            n_images_in_text = [sample.count(self.image_token.content) for sample in text]
+
         if images is not None:
             if is_image_or_image_url(images):
                 images = [[images]]
             elif isinstance(images, list) and is_image_or_image_url(images[0]):
-                images = [images]
+                if text is not None:
+                    if sum(n_images_in_text) != len(images):
+                        raise ValueError(
+                            f"The total number of {self.image_token.content} tokens in the prompts should be the same as the number of images passed."
+                            f" Found {sum(n_images_in_text)} {self.image_token.content} tokens and {len(images)} images."
+                        )
+                    # Reorganize the images to match the prompts
+                    cumsum_images_in_text = [0] + list(accumulate(n_images_in_text))
+                    images = [
+                        images[cumsum_images_in_text[i] : cumsum_images_in_text[i + 1]]
+                        for i in range(len(n_images_in_text))
+                    ]
+                else:
+                    images = [images]
             elif (
                 not isinstance(images, list)
                 and not isinstance(images[0], list)
@@ -263,10 +284,10 @@ def __call__(
             inputs.update(image_inputs)
 
         if text is not None:
-            if isinstance(text, str):
-                text = [text]
-            elif not isinstance(text, list) and not isinstance(text[0], str):
-                raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            if n_images_in_images != n_images_in_text:
+                raise ValueError(
+                    f"The number of images in the text {n_images_in_text} and images  {n_images_in_images} should be the same."
+                )
 
             image_rows = inputs.pop("rows", [[0] * len(text)])
             image_cols = inputs.pop("cols", [[0] * len(text)])
@@ -277,8 +298,6 @@ def __call__(
 
             prompt_strings = []
             for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
-                n_images_in_text.append(sample.count(image_token))
-
                 # Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
                 image_prompt_strings = []
                 for n_rows, n_cols in zip(sample_rows, sample_cols):
@@ -305,11 +324,6 @@ def __call__(
             text_inputs = self.tokenizer(text=prompt_strings, **output_kwargs["text_kwargs"])
             inputs.update(text_inputs)
 
-            if n_images_in_images != n_images_in_text:
-                raise ValueError(
-                    f"The number of images in the text {n_images_in_text} and images  {n_images_in_images} should be the same."
-                )
-
         return inputs
 
     def batch_decode(self, *args, **kwargs):

src/transformers/models/pixtral/image_processing_pixtral.py

@@ -120,6 +120,7 @@ def make_list_of_images(images: ImageInput) -> List[List[np.ndarray]]:
         isinstance(images, (list, tuple))
         and len(images) > 0
         and isinstance(images[0], (list, tuple))
+        and len(images[0]) > 0
         and is_valid_image(images[0][0])
     ):
         pass

tests/models/idefics2/test_processor_idefics2.py

@@ -226,6 +226,73 @@ def test_add_special_tokens_processor(self):
         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):
+            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.image2]]
+        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)
+        images = [self.image1]
+        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 = [
@@ -275,13 +342,3 @@ def prepare_text_inputs(self, batch_size: Optional[int] = None):
         return ["lower newer <image>", "<image> upper older longer string"] + ["<image> lower newer"] * (
             batch_size - 2
         )
-
-    # Override as PixtralProcessor needs nested images to work properly with batched inputs
-    @require_vision
-    def prepare_image_inputs(self, batch_size: Optional[int] = None):
-        """This function prepares a list of PIL images for testing"""
-        if batch_size is None:
-            return super().prepare_image_inputs()
-        if batch_size < 1:
-            raise ValueError("batch_size must be greater than 0")
-        return [[super().prepare_image_inputs()]] * batch_size

tests/models/idefics3/test_processor_idefics3.py

@@ -250,6 +250,74 @@ def test_add_special_tokens_processor(self):
         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 = "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_processor_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.<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):
+            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.image2]]
+        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)
+        images = [self.image1]
+        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 = [
@@ -299,16 +367,7 @@ def prepare_text_inputs(self, batch_size: Optional[int] = None):
             batch_size - 2
         )
 
-    # Override as Idefics3Processor needs nested images to work properly with batched inputs
-    @require_vision
-    def prepare_image_inputs(self, batch_size: Optional[int] = None):
-        """This function prepares a list of PIL images for testing"""
-        if batch_size is None:
-            return super().prepare_image_inputs()
-        if batch_size < 1:
-            raise ValueError("batch_size must be greater than 0")
-        return [[super().prepare_image_inputs()]] * batch_size
-
+    # Override tests as inputs_ids padded dimension is the second one but not the last one
     @require_vision
     @require_torch
     def test_kwargs_overrides_default_tokenizer_kwargs(self):