def __call__(
        self,
        prompts: Union[List[TextInput], List[List[TextInput]]],
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        transform: Callable = None,
        add_eos_token=False,
        add_end_of_utterance_token=None,
        debug=False,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
    ) -> BatchEncoding:
        """This method takes batched or non-batched prompts made of text and images and converts them into prompts that
        the model was trained on and prepares the image pixel values for the model to process.

        Args:
            prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
                either a single prompt or a batched list of prompts - see the detailed description immediately after
                the end of the arguments doc section.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Select a strategy to pad the returned sequences (according to the model's padding side and padding
                index) among:
                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            max_length (`int`, *optional*):
                Maximum length of the returned list and optionally padding length (see above).
            truncation (`bool`, *optional*):
                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
            transform (`Callable`, *optional*):
                A custom transform function that accepts a single image can be passed for training. For example,
                `torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
                set of transforms will be applied to the images
            add_eos_token (`bool`, *optional*, defaults to `False`):
                Adds `eos_token` at the end of the final prompt if True`
            add_end_of_utterance_token (`bool`, *optional*)
                Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
                image). If `None` the tokenizer will be checked instead and if this token is found in
                `additional_special_tokens` then the value will be `True`.
            debug (`bool`, *optional*, defaults to `False`):
                `True` value will help debug prompt generation by dumping useful information
            return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
                The type of tensors to return. Can be one of:
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.

        Returns:
            a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
            directly passed to `model.generate`

        Detailed explanation:

        Each entry in `prompts` is either a text to be passed as is or an image that will be processed.

        An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.

        When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
        entry into the prompt.

        Example:

        ```python
        checkpoint = "HuggingFaceM4/idefics-9b"
        processor = AutoProcessor.from_pretrained(checkpoint)
        url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
        img = processor.image_processor.fetch_images([url])[0]

        prompts = [
            "User:",
            img,
            "Describe this image.\nAssistant: An image of two kittens in grass.\n",
            "User:",
            "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
            "Describe this image.\nAssistant:",
        ]

        inputs = processor(prompts, return_tensors="pt")
        generated_ids = model.generate(**inputs, max_length=100)
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```

        In this example the `prompts` will be converted into:

        ```
        <s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
        Assistant: An image of two kittens in grass.
        User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
        Assistant:'
        ```

        and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
        `pixel_values` dict entry of the return value.

        This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
        first image is passed as object and the second one as a url.

        To do training do:

        ```python
        image_transform = transforms.Compose(
            [
                transforms.RandomResizedCrop(
                    (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
                ),
                transforms.ToTensor(),
                transforms.Normalize(mean=self.image_mean, std=self.image_std),
            ]
        )
        inputs = processor(prompts, transform=image_transform, return_tensors="pt")
        ```

        In order to help debug prompt generation enable `debug=True` which will show you what's happening.

        """

        # if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
        if add_end_of_utterance_token is None:
            add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token

        # turn non-batched prompts into batched
        if not any(isinstance(i, list) for i in prompts):
            prompts = [prompts]

        fake_token = "<fake_token_around_image>"
        image_token = "<image>"
        end_of_utterance_token = "<end_of_utterance>"

        def image_tokens(last_was_image):
            if last_was_image:
                return image_token + fake_token
            else:
                return fake_token + image_token + fake_token

        all_prompts = []
        all_images = []
        for sample in prompts:
            # the model was trained on samples starting with <s>
            full_text = f"{self.tokenizer.bos_token}"

            # an image can either be an image object in the item or the url, everything else is a verbatim prompt text
            image_objects = []
            last_was_image = False
            last_was_text = False
            for i, item in enumerate(sample):
                if i > 0:
                    last_was_text = True if not last_was_image else False

                if isinstance(item, str):
                    item = item.strip(" ")
                    if is_url(item):
                        image = self.image_processor.fetch_images(item)
                        full_text += image_tokens(last_was_image)
                        image_objects.append(image)
                        last_was_image = True
                    else:
                        # we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
                        if add_end_of_utterance_token and last_was_text:
                            full_text += end_of_utterance_token
                        full_text += item
                        last_was_image = False
                else:
                    # must be an image obj
                    full_text += image_tokens(last_was_image)
                    image_objects.append(item)
                    last_was_image = True

            if add_eos_token:
                full_text += self.tokenizer.eos_token

            if debug is True:
                print(f"{full_text=}")

            image_objects = self.image_processor(image_objects, transform=transform)

            all_prompts.append(full_text)
            all_images.append(image_objects)

        text_encoding = self.tokenizer(
            text=all_prompts,
            add_special_tokens=False,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
        )
        all_texts = text_encoding["input_ids"]

        max_seq_len = max(len(x) for x in all_texts)

        # max_num_images has to be at least 1 even when there are no images
        max_num_images = max(len(x) for x in all_images)
        max_num_images = max(1, max_num_images)

        at_least_one_image = sum(len(x) for x in all_images) > 0
        output_input_ids = []
        output_images = []
        output_attention_masks = []
        for text, images in zip(all_texts, all_images):
            padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
            unpadded_seq_len = len(text)
            start = max_seq_len - unpadded_seq_len
            padded_input_ids[start:] = text[:max_seq_len]

            attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
            attention_mask[start:] = 1

            image_count = padded_input_ids.count(self.image_token_id)
            local_max_num_images = min(image_count, max_num_images)

            current_images = images[:local_max_num_images]

            if len(current_images) > 0:
                padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
                padded_image_tensor[: current_images.size(0)] = current_images
            else:
                padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)

            output_images.append(padded_image_tensor)
            output_input_ids.append(torch.tensor(padded_input_ids))

            output_attention_masks.append(attention_mask)

        output_input_ids = torch.stack(output_input_ids)
        output_images = torch.stack(output_images)
        output_attention_masks = torch.stack(output_attention_masks)

        if at_least_one_image:
            image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer)
            image_attention_mask = incremental_to_binary_attention_mask(
                image_attention_mask, num_classes=max_num_images
            )
        else:
            # in full language mode we set the image mask to all-0s
            image_attention_mask = torch.zeros(
                output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
            )

        return BatchFeature(
            data={
                "input_ids": output_input_ids,
                "attention_mask": output_attention_masks,
                "pixel_values": output_images,
                "image_attention_mask": image_attention_mask,
            }
        )
This method takes batched or non-batched prompts made of text and images and converts them into prompts that
        the model was trained on and prepares the image pixel values for the model to process.

        Args:
            prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
                either a single prompt or a batched list of prompts - see the detailed description immediately after
                the end of the arguments doc section.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Select a strategy to pad the returned sequences (according to the model's padding side and padding
                index) among:
                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            max_length (`int`, *optional*):
                Maximum length of the returned list and optionally padding length (see above).
            truncation (`bool`, *optional*):
                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
            transform (`Callable`, *optional*):
                A custom transform function that accepts a single image can be passed for training. For example,
                `torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
                set of transforms will be applied to the images
            add_eos_token (`bool`, *optional*, defaults to `False`):
                Adds `eos_token` at the end of the final prompt if True`
            add_end_of_utterance_token (`bool`, *optional*)
                Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
                image). If `None` the tokenizer will be checked instead and if this token is found in
                `additional_special_tokens` then the value will be `True`.
            debug (`bool`, *optional*, defaults to `False`):
                `True` value will help debug prompt generation by dumping useful information
            return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
                The type of tensors to return. Can be one of:
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.

        Returns:
            a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
            directly passed to `model.generate`

        Detailed explanation:

        Each entry in `prompts` is either a text to be passed as is or an image that will be processed.

        An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.

        When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
        entry into the prompt.

        Example:

        ```python
        checkpoint = "HuggingFaceM4/idefics-9b"
        processor = AutoProcessor.from_pretrained(checkpoint)
        url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
        img = processor.image_processor.fetch_images([url])[0]

        prompts = [
            "User:",
            img,
            "Describe this image.
Assistant: An image of two kittens in grass.
",
            "User:",
            "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
            "Describe this image.
Assistant:",
        ]

        inputs = processor(prompts, return_tensors="pt")
        generated_ids = model.generate(**inputs, max_length=100)
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```

        In this example the `prompts` will be converted into:

        ```
        <s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
        Assistant: An image of two kittens in grass.
        User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
        Assistant:'
        ```

        and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
        `pixel_values` dict entry of the return value.

        This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
        first image is passed as object and the second one as a url.

        To do training do:

        ```python
        image_transform = transforms.Compose(
            [
                transforms.RandomResizedCrop(
                    (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
                ),
                transforms.ToTensor(),
                transforms.Normalize(mean=self.image_mean, std=self.image_std),
            ]
        )
        inputs = processor(prompts, transform=image_transform, return_tensors="pt")
        ```

        In order to help debug prompt generation enable `debug=True` which will show you what's happening.

        
dpkd/transformers/src/transformers/models/idefics/processing_idefics.py