Lightstar/venv/Lib/site-packages/transformers/models/kosmos2/processing_kosmos2.py

# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# 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.
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"""Processor class for KOSMOS-2."""

import copy
import math
import re
from typing import List, Optional, Tuple, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput, is_batched
from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
from ...tokenization_utils import AddedToken
from ...tokenization_utils_base import BatchEncoding, TextInput


BboxInput = Union[
    List[Tuple[int, int]],
    List[Tuple[float, float, float, float]],
    List[List[Tuple[int, int]]],
    List[List[Tuple[float, float, float]]],
]


class Kosmos2ImagesKwargs(ImagesKwargs, total=False):
    bboxes: Optional[List[float]]
    num_image_tokens: Optional[int]
    first_image_token_id: Optional[int]


class Kosmos2TextKwargs(TextKwargs, total=False):
    add_eos_token: Optional[bool]


class Kosmos2ProcessorKwargs(ProcessingKwargs, total=False):
    text_kwargs: Kosmos2TextKwargs
    images_kwargs: Kosmos2ImagesKwargs
    _defaults = {
        "text_kwargs": {
            "add_special_tokens": True,
            "padding": False,
            "stride": 0,
            "return_overflowing_tokens": False,
            "return_special_tokens_mask": False,
            "return_offsets_mapping": False,
            "return_token_type_ids": False,
            "verbose": True,
            "add_eos_token": False,
        },
        "images_kwargs": {
            "num_image_tokens": 64,
        },
    }


class Kosmos2Processor(ProcessorMixin):
    r"""
    Constructs an KOSMOS-2 processor which wraps a KOSMOS-2 image processor and a KOSMOS-2 tokenizer into a single
    processor.

    [`Kosmos2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and some functionalities of
    [`XLMRobertaTokenizerFast`]. See the docstring of [`~Kosmos2Processor.__call__`] and [`~Kosmos2Processor.decode`]
    for more information.

    Args:
        image_processor (`CLIPImageProcessor`):
            An instance of [`CLIPImageProcessor`]. The image processor is a required input.
        tokenizer (`XLMRobertaTokenizerFast`):
            An instance of ['XLMRobertaTokenizerFast`]. The tokenizer is a required input.
        num_patch_index_tokens (`int`, *optional*, defaults to 1024):
            The number of tokens that represent patch indices.
    """

    attributes = ["image_processor", "tokenizer"]
    valid_kwargs = ["num_patch_index_tokens"]
    image_processor_class = "CLIPImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor, tokenizer, num_patch_index_tokens=1024, *kwargs):
        tokenizer.return_token_type_ids = False

        self.eod_token = "</doc>"

        self.boi_token = "<image>"
        self.eoi_token = "</image>"

        self.eoc_token = "</chunk>"
        self.eol_token = "</line>"

        self.bop_token = "<phrase>"
        self.eop_token = "</phrase>"

        self.boo_token = "<object>"
        self.eoo_token = "</object>"

        self.dom_token = "</delimiter_of_multi_objects/>"

        self.grd_token = "<grounding>"

        self.tag_tokens = [
            self.eod_token,
            self.boi_token,
            self.eoi_token,
            self.eoc_token,
            self.eol_token,
            self.bop_token,
            self.eop_token,
            self.boo_token,
            self.eoo_token,
            self.dom_token,
            self.grd_token,
        ]

        self.num_patch_index_tokens = num_patch_index_tokens
        patch_index_tokens = [f"<patch_index_{str(x).zfill(4)}>" for x in range(self.num_patch_index_tokens)]

        tokens_to_add = []
        for token in self.tag_tokens + patch_index_tokens:
            tokens_to_add.append(AddedToken(token, lstrip=True, rstrip=False, normalized=False))
        tokenizer.add_tokens(tokens_to_add)

        super().__init__(image_processor, tokenizer)

    def __call__(
        self,
        images: ImageInput = None,
        text: Union[TextInput, List[TextInput]] = None,
        audio=None,
        videos=None,
        **kwargs: Unpack[Kosmos2ProcessorKwargs],
    ) -> BatchFeature:
        """
        This method uses [`CLIPImageProcessor.__call__`] method to prepare image(s) for the model, and
        [`XLMRobertaTokenizerFast.__call__`] to prepare text for the model.

        Please refer to the docstring of the above two methods for more information.

        The rest of this documentation shows the arguments specific to `Kosmos2Processor`.

        Args:
            bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*):
                The bounding bboxes associated to `texts`.
            num_image_tokens (`int`, *optional* defaults to 64):
                The number of (consecutive) places that are used to mark the placeholders to store image information.
                This should be the same as `latent_query_num` in the instance of `Kosmos2Config` you are using.
            first_image_token_id (`int`, *optional*):
                The token id that will be used for the first place of the subsequence that is reserved to store image
                information. If unset, will default to `self.tokenizer.unk_token_id + 1`.
            add_eos_token (`bool`, defaults to `False`):
                Whether or not to include `EOS` token id in the encoding when `add_special_tokens=True`.
        """
        if images is None and text is None:
            raise ValueError("You have to specify either images or text.")

        output_kwargs = self._merge_kwargs(
            Kosmos2ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        bboxes = output_kwargs["images_kwargs"].pop("bboxes", None)
        num_image_tokens = output_kwargs["images_kwargs"].pop("num_image_tokens", 64)
        first_image_token_id = output_kwargs["images_kwargs"].pop("first_image_token_id", None)
        add_eos_token = output_kwargs["text_kwargs"].pop("add_eos_token", False)

        add_special_tokens = output_kwargs["text_kwargs"]["add_special_tokens"]
        padding = output_kwargs["text_kwargs"]["padding"]
        return_tensors = output_kwargs["text_kwargs"].setdefault("return_tensors", None)

        encoding = BatchFeature()

        if images is not None:
            image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
            encoding.update(image_encoding)

        if text is not None:
            text = self.preprocess_examples(text, images, bboxes, num_image_tokens=num_image_tokens)

            if add_special_tokens and not add_eos_token:
                if isinstance(text, str):
                    text = f"{self.tokenizer.bos_token}{text}"
                elif isinstance(text, list):
                    text = [f"{self.tokenizer.bos_token}{s}" for s in text]
            output_kwargs["text_kwargs"]["add_special_tokens"] = (
                output_kwargs["text_kwargs"]["add_special_tokens"] and add_eos_token
            )
            output_kwargs["text_kwargs"]["padding"] = padding if images is None else False
            output_kwargs["text_kwargs"]["return_tensors"] = return_tensors if images is None else None
            text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
            encoding.update(text_encoding)

        output_kwargs["text_kwargs"]["add_special_tokens"] = add_special_tokens
        output_kwargs["text_kwargs"]["padding"] = padding
        output_kwargs["text_kwargs"]["return_tensors"] = return_tensors

        if text is not None and images is not None:
            # Use the id of the first token after <unk>
            if first_image_token_id is None:
                first_image_token_id = self.tokenizer.unk_token_id + 1

            # To see if we need one more `0` (for `<s>`) at the beginning of `image_embeds_position_mask`.
            with_bos = add_special_tokens

            # The first (actual) `<image>` token is always at the 1st or 2nd place (after `<s>` if any). Here we look
            # for the second `<image>` token (which indicate the first image token).
            start_index = int(with_bos) + 1

            # Add `image_embeds_position_mask`: the leading and trailing `0` are for `boi` and `eoi` tokens. The `1` indicates
            # the places of image tokens.
            image_token_ids = list(range(first_image_token_id, first_image_token_id + num_image_tokens))
            base_image_embeds_position_mask = [0] + [1] * num_image_tokens + [0]

            # loop over `encoding["input_ids"]`
            input_ids = []
            image_embeds_position_mask = []
            all_input_ids = encoding["input_ids"]
            # not batched -> (changed to) batch of size 1
            if isinstance(text, str):
                all_input_ids = [all_input_ids]
                encoding["attention_mask"] = [encoding["attention_mask"]]
            for text_ids in all_input_ids:
                # change the ids for the fake `<image>` tokens in `input_ids`
                text_ids = text_ids[:start_index] + image_token_ids + text_ids[start_index + num_image_tokens :]
                input_ids.append(text_ids)

                mask = copy.copy(base_image_embeds_position_mask)
                if with_bos:
                    # for `<s>`
                    mask = [0] + mask
                # trailing part (which are not related to the image)
                mask += [0] * (len(text_ids) - len(mask))
                image_embeds_position_mask.append(mask)

            if isinstance(text, list):
                sorted_length = sorted(
                    [(idx, len(x)) for idx, x in enumerate(text_encoding.input_ids)], key=lambda x: x[-1]
                )
                _, min_len_not_padded = sorted_length[0]
                idx, _ = sorted_length[-1]
                output_kwargs["text_kwargs"]["add_special_tokens"] = (
                    output_kwargs["text_kwargs"]["add_special_tokens"] and add_eos_token
                )
                output_kwargs["text_kwargs"]["return_tensors"] = None

                text_encoding = self.tokenizer(text=[text[idx]], **output_kwargs["text_kwargs"])
                max_len_padded = len(text_encoding.input_ids[0])

                if min_len_not_padded != max_len_padded:
                    if self.tokenizer.padding_side == "right":
                        input_ids = [x + [self.tokenizer.pad_token_id] * (max_len_padded - len(x)) for x in input_ids]
                        image_embeds_position_mask = [
                            x + [0] * (max_len_padded - len(x)) for x in image_embeds_position_mask
                        ]
                        encoding["attention_mask"] = [
                            x + [0] * (max_len_padded - len(x)) for x in encoding["attention_mask"]
                        ]
                    elif self.tokenizer.padding_side == "left":
                        input_ids = [[self.tokenizer.pad_token_id] * (max_len_padded - len(x)) + x for x in input_ids]
                        image_embeds_position_mask = [
                            [0] * (max_len_padded - len(x)) + x for x in image_embeds_position_mask
                        ]
                        encoding["attention_mask"] = [
                            [0] * (max_len_padded - len(x)) + x for x in encoding["attention_mask"]
                        ]

            # un-batch if necessary
            if isinstance(text, str) and return_tensors is None:
                input_ids = input_ids[0]
                encoding["attention_mask"] = encoding["attention_mask"][0]
                image_embeds_position_mask = image_embeds_position_mask[0]

            # update (with the target tensor type if specified)
            encoding.update(
                BatchEncoding(
                    data={
                        "input_ids": input_ids,
                        "attention_mask": encoding["attention_mask"],
                        "image_embeds_position_mask": image_embeds_position_mask,
                    },
                    tensor_type=return_tensors,
                )
            )

        return encoding

    def _check_bboxes_for_single_text(self, bboxes):
        """
        Check `bboxes` for a single text example. It could be
            - `None`: no bounding box associated to a text.
            - A list with each element being the bounding boxes associated to one `<phrase> ... </phrase>` pair found
              in a text. This could be:
                  - `None`: no bounding box associated to a `<phrase> ... </phrase>` pair.
                  - A tuple of 2 integers: A single bounding box specified by patch indices.
                  - A tuple of 4 float point number: A single bounding box specified by (normalized) coordinates.
                  - A list containing the above 2 tuple types: Multiple bounding boxes for a
                   `<phrase> ... </phrase>` pair.
        """
        if bboxes is None:
            return
        elif not isinstance(bboxes, list):
            raise ValueError("`bboxes` (for a single text example) should be `None` or a list.")

        # `bbox` is the bounding boxes for a single <phrase> </phrase> pair
        for bbox in bboxes:
            if bbox is None:
                continue
            elif not isinstance(bbox, list):
                bbox = [bbox]
            for element in bbox:
                if not isinstance(element, tuple) or not (
                    (len(element) == 2 and all(isinstance(x, int) for x in element))
                    or (len(element) == 4 and all(isinstance(x, float) for x in element))
                ):
                    raise ValueError(
                        "Each element in `bboxes` (for a single text example) should be either `None`, a tuple containing "
                        "2 integers or 4 float point numbers, or a list containing such tuples. Also "
                        "make sure the arguments `texts` and `bboxes` passed to `preprocess_text` are both in "
                        "batches or both for a single example."
                    )

    def _preprocess_single_example(self, text, image, bboxes, img_info_tokens):
        text = text.strip()
        if image is not None:
            # Add `<image> ... (fake) image tokens ... </image>`
            text = f"{img_info_tokens} {text}"

        # Add `<object> <patch_idx_xxxx> <patch_idx_yyy> </object>` after `<phrase> phrase text </phrase>`
        text = self._insert_patch_index_tokens(text, bboxes)
        return text

    def preprocess_examples(
        self,
        texts: Union[TextInput, List[TextInput]],
        images: ImageInput = None,
        bboxes: BboxInput = None,
        num_image_tokens: Optional[int] = 64,
    ) -> Union[str, List[str]]:
        """Add image and bounding box information to `texts` as image and patch index tokens.

        Args:
            texts (`Union[TextInput, List[TextInput]]`): The texts to be processed.
            images (`ImageInput`, *optional*): The images associated to `texts`.
            bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*):
                The bounding bboxes associated to `texts`.
            num_image_tokens (`int`, *optional*, defaults to 64):
                The number of image tokens (used as latent queries). This should corresponds to the `latent_query_num`
                attribute in `Kosmos2Config`.

        Returns:
            `Union[TextInput, List[TextInput]]`: The processed texts with image and patch index tokens.
        """
        # These are fake `<image>` tokens enclosed between (the actual) `<image>` token and `</image>`.
        img_tokens = [self.boi_token] * num_image_tokens
        img_info_tokens = " ".join([self.boi_token] + img_tokens + [self.eoi_token])

        # make batch to simplify processing logic
        batched = True
        if isinstance(texts, str):
            batched = False
            texts = [texts]

        if images is None:
            images = [None] * len(texts)
        elif not is_batched(images):
            images = [images]
        if len(texts) != len(images):
            raise ValueError(
                f"The number of examples in `texts` and `images` should be the same. Got {len(texts)} v.s. {len(images)} instead."
            )

        if not batched:
            self._check_bboxes_for_single_text(bboxes)
            bboxes = [bboxes]
        elif bboxes is not None:
            if not isinstance(bboxes, list):
                raise ValueError("`bboxes` should be `None` or a list (as a batch) when `texts` is passed as a batch.")
            for x in bboxes:
                self._check_bboxes_for_single_text(x)
        else:
            bboxes = [None] * len(texts)

        if len(bboxes) != len(texts):
            raise ValueError(
                f"The number of examples in `texts` and `bboxes` should be the same. Got {len(texts)} v.s. {len(bboxes)} instead."
            )

        result = [
            self._preprocess_single_example(text, image, bbox, img_info_tokens)
            for text, image, bbox in zip(texts, images, bboxes)
        ]
        # un-batch if necessary
        if not batched:
            result = result[0]

        return result

    # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    def post_process_generation(self, text, cleanup_and_extract=True):
        caption = text.split(self.eoi_token)[-1]
        if cleanup_and_extract:
            return clean_text_and_extract_entities_with_bboxes(caption)
        return caption

    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

    def _insert_patch_index_tokens(self, text: str, bboxes: Union[List[Tuple[int]], List[Tuple[float]]]) -> str:
        if bboxes is None or len(bboxes) == 0:
            return text

        matched_phrases = list(re.finditer(r"<phrase>.+?</phrase>", string=text))
        if len(matched_phrases) != len(bboxes):
            raise ValueError(
                f"The number of elements in `bboxes` should be the same as the number of `<phrase> ... </phrase>` pairs in `text`. Got {len(matched_phrases)} v.s. {len(bboxes)} instead."
            )

        # insert object's patch index tokens
        # the found `<phrase> ... </phrase>` pairs.
        curr_pos = 0
        buffer = []
        for matched, bbox in zip(matched_phrases, bboxes):
            _, end = matched.span()
            buffer.append(text[curr_pos:end])
            curr_pos = end
            # A phrase without bbox
            if bbox is None:
                continue
            # A phrase with a single bbox
            if isinstance(bbox, tuple):
                bbox = [bbox]
            patch_index_strings = []
            # A phrase could have multiple bboxes
            if not all(box is not None for box in bbox):
                raise ValueError(
                    "The multiple bounding boxes for a single phrase should not contain any `None` value."
                )
            for box in bbox:
                patch_index_1, patch_index_2 = self._convert_bbox_to_patch_index_tokens(box)
                patch_index_strings.append(f"{patch_index_1} {patch_index_2}")
            # `bbox` being an empty list
            if len(patch_index_strings) == 0:
                continue
            position_str = " </delimiter_of_multi_objects/> ".join(patch_index_strings)
            buffer.append(f"<object> {position_str} </object>")
        # remaining
        if curr_pos < len(text):
            buffer.append(text[curr_pos:])

        text = "".join(buffer)
        return text

    def _convert_bbox_to_patch_index_tokens(
        self, bbox: Union[Tuple[int, int], Tuple[float, float, float, float]]
    ) -> Tuple[str, str]:
        # already computed patch indices
        if len(bbox) == 2:
            idx_1, idx_2 = bbox
        # bbox specified with (normalized) coordinates
        else:
            # use `self.tokenizer` to get `num_patches_per_side`
            num_patches_per_side = int(math.sqrt(self.num_patch_index_tokens))
            idx_1, idx_2 = coordinate_to_patch_index(bbox, num_patches_per_side)

        token_1 = f"<patch_index_{str(idx_1).zfill(4)}>"
        token_2 = f"<patch_index_{str(idx_2).zfill(4)}>"

        return token_1, token_2


def coordinate_to_patch_index(bbox: Tuple[float, float, float, float], num_patches_per_side: int) -> Tuple[int, int]:
    """Convert a bounding box to a pair of patch indices.

    Args:
        bbox (`Tuple[float, float, float, float]`):
            The 4 coordinates of the bounding box, with the format being (x1, y1, x2, y2) specifying the upper-left and
            lower-right corners of the box. It should have x2 > x1 and y2 > y1.
        num_patches_per_side (`int`): the number of patches along each side.

    Returns:
        `Tuple[int, int]`: A pair of patch indices representing the upper-left patch and lower-right patch.
    """
    (x1, y1, x2, y2) = bbox

    if not (x2 > x1 and y2 > y1):
        raise ValueError("The coordinates in `bbox` should be `(x1, y1, x2, y2)` with `x2 > x1` and `y2 > y1`.")

    ul_x = math.floor(x1 * num_patches_per_side)
    ul_y = math.floor(y1 * num_patches_per_side)

    lr_x = math.ceil(x2 * num_patches_per_side - 1)
    lr_y = math.ceil(y2 * num_patches_per_side - 1)

    ul_idx = ul_y * num_patches_per_side + ul_x
    lr_idx = lr_y * num_patches_per_side + lr_x

    return ul_idx, lr_idx


# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L35C1-L75C38
# (with format modifications)
def patch_index_to_coordinate(ul_idx: int, lr_idx: int, num_patches_per_side: int):
    """
    Given a grid of length `num_patches_per_side` and the indices of the upper-left and lower-right corners of a
    bounding box, returns the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).

    Args:
        ul_idx (`int`): the index of the grid cell that corresponds to the upper-left corner of the bounding box.
        lr_idx (`int`): the index of the grid cell that corresponds to the lower-right corner of the bounding box.
        num_patches_per_side (`int`): the number of patches along each side.

    Returns:
        `Tuple[float]`: the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
    """
    # Compute the size of each cell in the grid
    cell_size = 1.0 / num_patches_per_side

    # Compute the x and y indices of the upper-left and lower-right corners of the bounding box
    ul_x = ul_idx % num_patches_per_side
    ul_y = ul_idx // num_patches_per_side

    lr_x = lr_idx % num_patches_per_side
    lr_y = lr_idx // num_patches_per_side

    # Compute the normalized coordinates of the bounding box
    if ul_idx == lr_idx:
        x1 = ul_x * cell_size
        y1 = ul_y * cell_size
        x2 = lr_x * cell_size + cell_size
        y2 = lr_y * cell_size + cell_size
    elif ul_x == lr_x or ul_y == lr_y:
        x1 = ul_x * cell_size
        y1 = ul_y * cell_size
        x2 = lr_x * cell_size + cell_size
        y2 = lr_y * cell_size + cell_size
    else:
        x1 = ul_x * cell_size + cell_size / 2
        y1 = ul_y * cell_size + cell_size / 2
        x2 = lr_x * cell_size + cell_size / 2
        y2 = lr_y * cell_size + cell_size / 2

    return x1, y1, x2, y2


# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L4-L33
# (with format modifications)
def extract_entities_with_patch_indices(text):
    """Extract entities contained in `text`. The bounding bboxes is given in the form of patch indices.

    This functioin is only intended to be used within `clean_text_and_extract_entities_with_bboxes` where further
    processing happens, including converting to normalized coordinates and whitespace character cleaning up.

    Examples:

    ```python
    >>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
    >>> entities = extract_entities_with_patch_indices(text)
    >>> entities
    [(' a snowman', (31, 41), [(44, 863)]), (' a fire', (130, 137), [(5, 911)])]
    ```"""
    # The regular expression pattern for matching the required formats
    pattern = r"(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>"

    # Find all matches in the given string
    matches = re.finditer(pattern, text)

    # Initialize an empty list to store the valid patch_index combinations
    entities_with_patch_indices = []

    for match in matches:
        # span of a `phrase` that is between <phrase> and </phrase>
        span = match.span(2)
        phrase_tag, phrase, match_content = match.groups()
        if not phrase_tag:
            phrase = None
            # We take the starting position of `<object>`
            span = (match.span(0)[0], match.span(0)[0])

        # Split the match_content by the delimiter to get individual patch_index pairs
        patch_index_pairs = match_content.split("</delimiter_of_multi_objects/>")

        entity_bboxes = []
        for pair in patch_index_pairs:
            # Extract the xxxx and yyyy values from the patch_index pair
            x = re.search(r"<patch_index_(\d+)>", pair)
            y = re.search(r"<patch_index_(\d+)>", pair[1:])

            if x and y:
                if phrase:
                    entity_bboxes.append((int(x.group(1)), int(y.group(1))))
                else:
                    entity_bboxes.append((int(x.group(1)), int(y.group(1))))

        if phrase:
            entities_with_patch_indices.append((phrase, span, entity_bboxes))
        else:
            for bbox in entity_bboxes:
                # fake entity name
                entity = f"<patch_index_{bbox[0]}><patch_index_{bbox[1]}>"
                entities_with_patch_indices.append((entity, span, [bbox]))

    return entities_with_patch_indices


def adjust_entity_positions(entity, text):
    """Adjust the positions of the entities in `text` to be relative to the text with special fields removed."""
    entity_name, (start, end) = entity
    # computed the length of strings with special fields (tag tokens, patch index tokens, etc.) removed
    adjusted_start = len(re.sub("<.*?>", "", text[:start]))
    adjusted_end = len(re.sub("<.*?>", "", text[:end]))
    adjusted_entity = (entity_name, (adjusted_start, adjusted_end))
    return adjusted_entity


def _cleanup_spaces(text, entities):
    """Remove the spaces around the text and the entities in it."""
    new_text = text.strip()
    leading_spaces = len(text) - len(text.lstrip())

    new_entities = []
    for entity_name, (start, end), bboxes in entities:
        entity_name_leading_spaces = len(entity_name) - len(entity_name.lstrip())
        entity_name_trailing_spaces = len(entity_name) - len(entity_name.rstrip())

        start = start - leading_spaces + entity_name_leading_spaces
        end = end - leading_spaces - entity_name_trailing_spaces
        entity_name = entity_name.strip()

        new_entities.append((entity_name, (start, end), bboxes))

    return new_text, new_entities


# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L77-L87
# (with format modifications)
def clean_text_and_extract_entities_with_bboxes(text, num_patches_per_side=32):
    """Remove the tag tokens from `text`, extract entities in it with some cleaning up of white characters.

    Examples:

    ```python
    >>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
    >>> clean_text, entities = clean_text_and_extract_entities_with_bboxes(text)
    >>> clean_text
    'An image of a snowman warming himself by a fire.'

    >>> entities
    [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
    ```"""
    # remove special fields (tag tokens, patch index tokens, etc.)
    processed_text = re.sub("<.*?>", "", text)

    entities_with_patch_indices = extract_entities_with_patch_indices(text)
    entities = []
    for item in entities_with_patch_indices:
        entity, bboxes = item[0:2], item[2]
        adjusted_entity = adjust_entity_positions(entity, text)
        bboxes_in_coords = [patch_index_to_coordinate(bbox[0], bbox[1], num_patches_per_side) for bbox in bboxes]

        entities.append(adjusted_entity + (bboxes_in_coords,))

    return _cleanup_spaces(processed_text, entities)