bugfix Idefics3 processor - handle gracefully cases with text and no images

src/transformers/models/idefics3/processing_idefics3.py

@@ -283,45 +283,49 @@ def __call__(
             image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
             inputs.update(image_inputs)
 
-        if text is not None:
-            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)])
-
-            fake_image_token = self.fake_image_token.content
-            image_token = self.image_token.content
-            global_img_token = self.global_image_tag
-
-            prompt_strings = []
-            for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
-                # 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):
-                    image_prompt_string = get_image_prompt_string(
-                        n_rows,
-                        n_cols,
-                        image_seq_len,
-                        image_token=image_token,
-                        fake_token_around_image=fake_image_token,
-                        global_img_token=global_img_token,
+            if text is not None:
+                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_prompt_strings.append(image_prompt_string)
 
-                split_sample = sample.split(image_token)
-                if len(split_sample) == 0:
-                    raise ValueError("The image token should be present in the text.")
+                image_rows = inputs.pop("rows", [[0] * len(text)])
+                image_cols = inputs.pop("cols", [[0] * len(text)])
+
+                fake_image_token = self.fake_image_token.content
+                image_token = self.image_token.content
+                global_img_token = self.global_image_tag
+
+                prompt_strings = []
+                for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
+                    # 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):
+                        image_prompt_string = get_image_prompt_string(
+                            n_rows,
+                            n_cols,
+                            image_seq_len,
+                            image_token=image_token,
+                            fake_token_around_image=fake_image_token,
+                            global_img_token=global_img_token,
+                        )
+                        image_prompt_strings.append(image_prompt_string)
+
+                    split_sample = sample.split(image_token)
+                    if len(split_sample) == 0:
+                        raise ValueError("The image token should be present in the text.")
+
+                    # Place in the image prompt strings where the image tokens are
+                    sample = split_sample[0]
+                    for i, image_prompt_string in enumerate(image_prompt_strings):
+                        sample += image_prompt_string + split_sample[i + 1]
+                    prompt_strings.append(sample)
 
-                # Place in the image prompt strings where the image tokens are
-                sample = split_sample[0]
-                for i, image_prompt_string in enumerate(image_prompt_strings):
-                    sample += image_prompt_string + split_sample[i + 1]
-                prompt_strings.append(sample)
+                text_inputs = self.tokenizer(text=prompt_strings, **output_kwargs["text_kwargs"])
+                inputs.update(text_inputs)
 
-            text_inputs = self.tokenizer(text=prompt_strings, **output_kwargs["text_kwargs"])
+        elif text is not None:
+            text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
             inputs.update(text_inputs)
 
         return inputs