Upload processor

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

added_tokens.json

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+{
+  "<image>": 257152
+}

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoProcessor": "processing_divedoc.DIVEdocProcessor"
+  },
+  "do_align_long_axis": false,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "do_thumbnail": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "DonutImageProcessor",
+  "image_seq_length": 4096,
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "DIVEdocProcessor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 2048,
+    "width": 2048
+  }
+}

processing_divedoc.py

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+from typing import Optional, Union
+
+import numpy as np
+
+from transformers import AutoTokenizer, DonutImageProcessor
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput, is_valid_image
+from transformers.processing_utils import (
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    TextKwargs,
+    Unpack,
+)
+from transformers.tokenization_utils_base import (
+    AddedToken,
+    PreTokenizedInput,
+    TextInput,
+)
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+IMAGE_TOKEN = "<image>"
+EXTRA_TOKENS = [f"<loc{i:0>4}>" for i in range(1024)] + [
+    f"<seg{i:0>3}>" for i in range(128)
+]
+
+
+# Copied from https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py
+class PaliGemmaTextKwargs(TextKwargs):
+    suffix: Optional[
+        Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]
+    ]
+
+
+class PaliGemmaProcessorKwargs(ProcessingKwargs, total=False):
+    text_kwargs: PaliGemmaTextKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": False,
+        },
+        "images_kwargs": {
+            "data_format": "channels_first",
+        },
+    }
+
+
+# Copied from transformers.models.idefics2.processing_idefics2.is_url
+def is_url(val) -> bool:
+    return isinstance(val, str) and val.startswith("http")
+
+
+# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
+def is_image_or_image_url(elem):
+    return is_url(elem) or is_valid_image(elem)
+
+
+def _is_str_or_image(elem):
+    return isinstance(elem, (str)) or is_image_or_image_url(elem)
+
+
+def build_string_from_input(prompt, bos_token, image_seq_len, image_token, num_images):
+    """
+    Builds a string from the input prompt and image tokens.
+    For example, for the call:
+    build_string_from_input(
+        prompt="Prefix str"
+        bos_token="<s>",
+        image_seq_len=3,
+        image_token="<im>",
+    )
+    The output will be:
+    "<im><im><im><s>Initial str"
+    Args:
+        prompt (`list[Union[str, ImageInput]]`): The input prompt.
+        bos_token (`str`): The beginning of sentence token.
+        image_seq_len (`int`): The length of the image sequence.
+        image_token (`str`): The image token.
+        num_images (`int`): Number of images in the prompt.
+    """
+    return f"{image_token * image_seq_len * num_images}{bos_token}{prompt}\n"
+
+
+# Copied and adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/processing_paligemma.py
+class DIVEdocProcessor(ProcessorMixin):
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "DonutImageProcessor"  # change from the original SigLipImageProcessor to DonutImageProcessor
+    tokenizer_class = "GemmaTokenizerFast"
+    r"""
+    Constructs a PaliGemma processor which wraps a PaliGemma image processor and a PaliGemma tokenizer into a single processor.
+
+    [`PaliGemmaProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`GemmaTokenizerFast`]. See the
+    [`~PaliGemmaProcessor.__call__`] and [`~PaliGemmaProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`SiglipImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`GemmaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        if not hasattr(image_processor, "image_seq_length"):
+            raise ValueError(
+                "Image processor is missing an `image_seq_length` attribute."
+            )
+
+        self.image_seq_length = image_processor.image_seq_length
+
+        if not hasattr(tokenizer, "image_token"):
+            image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True)
+            tokens_to_add = {"additional_special_tokens": [image_token]}
+            tokenizer.add_special_tokens(tokens_to_add)
+            self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
+            self.image_token = IMAGE_TOKEN
+        else:
+            self.image_token_id = tokenizer.image_token_id
+            self.image_token = tokenizer.image_token
+
+        tokenizer.add_tokens(EXTRA_TOKENS)
+        tokenizer.add_bos_token = False
+        tokenizer.add_eos_token = False
+
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Union[
+            TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]
+        ] = None,
+        **kwargs: Unpack[PaliGemmaProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to GemmaTokenizerFast's [`~GemmaTokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
+        SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+        of the above two methods for more information.
+
+        The usage for PaliGemma fine-tuning preparation is slightly different than usual. suffix passed are suffixes to
+        the prompt in `text`, and will be placed after the prompt. This is because attention is handled differently for
+        the prefix and the suffix. For instance,
+        ```python
+        image = PIL_cow_image
+        prompt = "answer en Where is the cow standing?"
+        suffix = "on the beach"
+        inputs = processor(text=prompt, images=image, suffix=suffix)
+        ```
+        Here `inputs` will contain the `input_ids` and `token_type_ids` that follow
+        ```python
+        inputs["input_ids"][:, 256:]
+        # tensor([[     2,   6006,    603,    573,  13910,   9980, 235336,    108,    477,   573,   8318]])
+        inputs["token_type_ids"][:, 256:]
+        tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]])
+        ```
+        Meaning the last three tokens are of "label" ("suffix") type while the other ones are of "prefix" type.
+
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
+                number of channels, H and W are image height and width.
+            text (`str`, `list[str]`, `list[list[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+            suffix (`str`, `list[str]`, `list[list[str]]`):
+                The suffixes or batch of suffixes to be encoded. Only necessary for finetuning. See https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md
+                for more information. If your prompt is "<image> What is on the image", the suffix corresponds to the expected prediction "a cow sitting on a bench".
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix`
+              is provided, the `input_ids` will also contain the suffix input ids.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **labels** -- Labels compatible with training if `suffix` is not None
+        """
+
+        output_kwargs = self._merge_kwargs(
+            PaliGemmaProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        suffix = output_kwargs["text_kwargs"].pop("suffix", None)
+
+        return_token_type_ids = True
+
+        if images is None:
+            raise ValueError(
+                "`images` are expected as arguments to a `PaliGemmaProcessor` instance."
+            )
+        if text is None:
+            logger.warning_once(
+                "You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."
+            )
+            text = ""
+
+        if _is_str_or_image(text):
+            text = [text]
+        elif isinstance(text, list) and _is_str_or_image(text[0]):
+            pass
+
+        if text is not None and images is not None:
+            if not any(IMAGE_TOKEN in sample for sample in text):
+                logger.warning(
+                    "You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special "
+                    "image tokens in the text, as many tokens as there are images per each text. It is recommended to "
+                    "add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images "
+                    "each text has and add special tokens."
+                )
+
+                if isinstance(text, list) and isinstance(images, list):
+                    if len(images) != len(text):
+                        raise ValueError(
+                            f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image or list of images."
+                        )
+
+                # make a nested list of lists to be able to iterate over the images and text below
+
+                if is_valid_image(images):
+                    images = [images]
+                elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
+                    images = [image for image in images]
+                elif not (
+                    isinstance(images, (list, tuple))
+                    # and isinstance(images[0], (list, tuple))
+                    and is_valid_image(images[0])
+                ):
+                    raise ValueError(
+                        "images must be an image, list of images or list of list of images"
+                    )
+
+                input_strings = [
+                    build_string_from_input(
+                        prompt=prompt,
+                        bos_token=self.tokenizer.bos_token,
+                        image_seq_len=self.image_seq_length,
+                        image_token=IMAGE_TOKEN,
+                        num_images=len(image_list)
+                        if isinstance(image_list, list)
+                        else 1,
+                    )
+                    for prompt, image_list in zip(text, images)
+                ]
+            else:
+                expanded_samples = []
+                for sample in text:
+                    expanded_sample = sample.replace(
+                        IMAGE_TOKEN, IMAGE_TOKEN * self.image_seq_length
+                    )
+                    bos_rfind_index = expanded_sample.rfind(IMAGE_TOKEN)
+                    bos_index = (
+                        bos_rfind_index + len(IMAGE_TOKEN)
+                        if bos_rfind_index != -1
+                        else 0
+                    )
+                    expanded_sample = (
+                        expanded_sample[:bos_index]
+                        + self.tokenizer.bos_token
+                        + expanded_sample[bos_index:]
+                    )
+                    expanded_samples.append(expanded_sample)
+                input_strings = [f"{sample}\n" for sample in expanded_samples]
+
+        if suffix is not None and _is_str_or_image(suffix):
+            suffix = [suffix]
+        if suffix is not None:
+            suffix = [sfx + self.tokenizer.eos_token for sfx in suffix]
+        pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])[
+            "pixel_values"
+        ]
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop(
+            "return_mm_token_type_ids", None
+        )
+        inputs = self.tokenizer(
+            input_strings,
+            text_pair=suffix,
+            return_token_type_ids=return_token_type_ids,
+            **output_kwargs["text_kwargs"],
+        )
+        # self._check_special_mm_tokens(input_strings, inputs, modalities=["image"])
+
+        return_data = {**inputs, "pixel_values": pixel_values}
+
+        # TODO: ideally we would control label generation separately, now that we always return token_type_ids.
+        if return_token_type_ids:
+            labels = np.array(inputs["input_ids"])
+            labels[np.array(inputs["token_type_ids"]) == 0] = -100
+            return_data.update({"labels": labels})
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(return_data["input_ids"])
+            mm_token_type_ids = np.zeros_like(return_data["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            return_data["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(data=return_data, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (list[list[str]], *optional*):
+                The input sizes formatted as (height, width) per each image.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            num_image_tokens = [self.image_seq_length] * len(image_sizes)
+            num_image_patches = [1] * len(image_sizes)
+            vision_data.update(
+                {
+                    "num_image_tokens": num_image_tokens,
+                    "num_image_patches": num_image_patches,
+                }
+            )
+        return MultiModalData(**vision_data)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names + [
+            "token_type_ids",
+            "labels",
+        ]
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(tokenizer_input_names + image_processor_input_names)
+
+
+def get_processor(hf_token, img_height, img_width, img_lm_input_seq_length):
+    tokenizer = AutoTokenizer.from_pretrained(
+        "google/paligemma-3b-ft-docvqa-896",
+        token=hf_token,
+        revision="acbe61b1b8507f7c7af03a0d42e9908e7b6d4d5d",
+    )
+    image_processor = DonutImageProcessor.from_pretrained(
+        "naver-clova-ix/donut-base-finetuned-docvqa",
+        revision="b19d2e332684b0e2d35d9144ce34047767335cf8",
+    )
+    image_processor.image_seq_length = img_lm_input_seq_length
+    image_processor.size["height"], image_processor.size["width"] = (
+        img_height,
+        img_width,
+    )
+    processor = DIVEdocProcessor(tokenizer=tokenizer, image_processor=image_processor)
+    return processor

processor_config.json

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+{
+  "auto_map": {
+    "AutoProcessor": "processing_divedoc.DIVEdocProcessor"
+  },
+  "processor_class": "DIVEdocProcessor"
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+oid sha256:172fab587d68c56b63eb3620057c62dfd15e503079ff7fce584692e3fd5bf4da
+size 34600820

tokenizer.model

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+oid sha256:8986bb4f423f07f8c7f70d0dbe3526fb2316056c17bae71b1ea975e77a168fc6
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