processing_florence2.py

# coding=utf-8
# Copyright 2024 Microsoft and The HuggingFace Inc. team.
#
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Florence-2.
"""

import re
import logging
from typing import List, Optional, Union
import numpy as np
import math

import torch

from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, is_valid_image
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import (
    PaddingStrategy,
    PreTokenizedInput,
    TextInput,
    TruncationStrategy,
)
from transformers import BartTokenizer, BartTokenizerFast
from transformers.utils import TensorType


logger = logging.getLogger(__name__)

# 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)


class Florence2Processor(ProcessorMixin):
    r"""
    Constructs a Florence2 processor which wraps a Florence2 image processor and a Florence2 tokenizer into a single processor.

    [`Florence2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BartTokenizerFast`]. See the
    [`~Florence2Processor.__call__`] and [`~Florence2Processor.decode`] for more information.

    Args:
        image_processor ([`CLIPImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`BartTokenizerFast`], *optional*):
            The tokenizer is a required input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "CLIPImageProcessor"
    tokenizer_class = ("BartTokenizer", "BartTokenizerFast")

    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
    ):
        if image_processor is None:
            raise ValueError("You need to specify an `image_processor`.")
        if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`.")
        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

        tokens_to_add = {
                'additional_special_tokens': \
                    tokenizer.additional_special_tokens + \
                    ['<od>', '</od>', '<ocr>', '</ocr>'] + \
                    [f'<loc_{x}>' for x in range(1000)] + \
                    ['<cap>', '</cap>', '<ncap>', '</ncap>','<dcap>', '</dcap>', '<grounding>', '</grounding>', '<seg>', '</seg>', '<sep>', '<region_cap>', '</region_cap>', '<region_to_desciption>', '</region_to_desciption>', '<proposal>', '</proposal>', '<poly>', '</poly>', '<and>']
            }
        tokenizer.add_special_tokens(tokens_to_add)

        self.tasks_answer_post_processing_type = {
            '<OCR>': 'pure_text',
            '<OCR_WITH_REGION>': 'ocr',
            '<CAPTION>': 'pure_text',
            '<DETAILED_CAPTION>': 'pure_text',
            '<MORE_DETAILED_CAPTION>': 'pure_text',
            '<OD>': 'description_with_bboxes',
            '<DENSE_REGION_CAPTION>': 'description_with_bboxes',
            '<CAPTION_TO_PHRASE_GROUNDING>': "phrase_grounding",
            '<REFERRING_EXPRESSION_SEGMENTATION>': 'polygons',
            '<REGION_TO_SEGMENTATION>': 'polygons',
            '<OPEN_VOCABULARY_DETECTION>': 'description_with_bboxes_or_polygons',
            '<REGION_TO_CATEGORY>': 'pure_text',
            '<REGION_TO_DESCRIPTION>': 'pure_text',
            '<REGION_TO_OCR>': 'pure_text',
            '<REGION_PROPOSAL>': 'bboxes'
        }

        self.task_prompts_without_inputs = {
            '<OCR>': 'What is the text in the image?',
            '<OCR_WITH_REGION>': 'What is the text in the image, with regions?',
            '<CAPTION>': 'What does the image describe?',
            '<DETAILED_CAPTION>': 'Describe in detail what is shown in the image.',
            '<MORE_DETAILED_CAPTION>': 'Describe with a paragraph what is shown in the image.',
            '<OD>': 'Locate the objects with category name in the image.',
            '<DENSE_REGION_CAPTION>': 'Locate the objects in the image, with their descriptions.',
            '<REGION_PROPOSAL>': 'Locate the region proposals in the image.'
        }

        self.task_prompts_with_input = {
            '<CAPTION_TO_PHRASE_GROUNDING>': "Locate the phrases in the caption: {input}",
            '<REFERRING_EXPRESSION_SEGMENTATION>': 'Locate {input} in the image with mask',
            '<REGION_TO_SEGMENTATION>': 'What is the polygon mask of region {input}',
            '<OPEN_VOCABULARY_DETECTION>': 'Locate {input} in the image.',
            '<REGION_TO_CATEGORY>': 'What is the region {input}?',
            '<REGION_TO_DESCRIPTION>': 'What does the region {input} describe?',
            '<REGION_TO_OCR>': 'What text is in the region {input}?',
        }

        self.post_processor = Florence2PostProcesser(tokenizer=tokenizer)


        super().__init__(image_processor, tokenizer)
    
    def _construct_prompts(self, text):
        # replace the task tokens with the task prompts if task token is in the text
        prompts = []
        for _text in text:
            # 1. fixed task prompts without additional inputs
            for task_token, task_prompt in self.task_prompts_without_inputs.items():
                if task_token in _text:
                    assert _text == task_token, f"Task token {task_token} should be the only token in the text."
                    _text = task_prompt
                    break
            # 2. task prompts with additional inputs 
            for task_token, task_prompt in self.task_prompts_with_input.items():
                if task_token in _text:
                    _text = task_prompt.format(input=_text.replace(task_token, ''))
                    break
            prompts.append(_text)
        return prompts

    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        images: ImageInput = None,
        tokenize_newline_separately: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length=None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
        do_resize: bool = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        data_format: Optional["ChannelDimension"] = "channels_first",  # noqa: F821
        input_data_format: Optional[
            Union[str, "ChannelDimension"]  # noqa: F821
        ] = None,
        resample: "PILImageResampling" = None,  # noqa: F821
        do_convert_rgb: bool = None,
        do_thumbnail: bool = None,
        do_align_long_axis: bool = None,
        do_rescale: bool = None,
    ) -> 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 BartTokenizerFast's [`~BartTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
        of the above two methods for more information.

        Args:
            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).
            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.
            tokenize_newline_separately (`bool`, defaults to `True`):
                Adds a separately tokenized '\n' at the end of the prompt.
            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`.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        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
        """

        return_token_type_ids = False

        if images is None:
            raise ValueError("`images` are expected as arguments to a `Florence2Processor` instance.")
        if text is None:
            logger.warning_once(
                "You are using Florence-2 without a text prompt."
            )
            text = ""

        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."
                )
        if _is_str_or_image(text):
            text = [text]
        elif isinstance(text, list) and _is_str_or_image(text[0]):
            pass

        pixel_values = self.image_processor(
            images,
            do_resize=do_resize,
            do_normalize=do_normalize,
            return_tensors=return_tensors,
            image_mean=image_mean,
            image_std=image_std,
            input_data_format=input_data_format,
            data_format=data_format,
            resample=resample,
            do_convert_rgb=do_convert_rgb,
        )["pixel_values"]

        if max_length is not None:
            max_length -= self.image_seq_length  # max_length has to account for the image tokens

        text = self._construct_prompts(text)

        inputs = self.tokenizer(
            text,
            return_tensors=return_tensors,
            padding=padding,
            max_length=max_length,
            truncation=truncation,
            return_token_type_ids=return_token_type_ids,
        )

        return_data = {**inputs, "pixel_values": pixel_values}

        if return_token_type_ids:
            labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
            return_data.update({"labels": labels})
        return BatchFeature(data=return_data)

    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to BartTokenizerFast'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.clip.processing_clip.CLIPProcessor.decode with CLIP->Florence2
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Florence2
    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 post_process_generation(self, text=None, sequence=None, transition_beam_score=None, task=None, image_size=None):
        """
        Post-process the output of the model to each of the task outputs.

        Args:
            text (`str`): The text to post-process.
            task (`str`): The task to post-process the text for.
            image_size (`Tuple[int, int]`): The size of the image. height x width.
        """

        task_answer_post_processing_type = self.tasks_answer_post_processing_type.get(task, 'pure_text')
        task_answer = self.post_processor(
            text=text,
            sequence=sequence,
            transition_beam_score=transition_beam_score,
            image_size=image_size,
            parse_tasks=task_answer_post_processing_type,
        )[task_answer_post_processing_type]

        if task_answer_post_processing_type == 'pure_text':
            final_answer = task_answer
            # remove the special tokens
            final_answer = final_answer.replace('<s>', '').replace('</s>', '')
        elif task_answer_post_processing_type in ['od', 'description_with_bboxes', 'bboxes']:
            od_instances = task_answer
            bboxes_od = [_od_instance['bbox'] for _od_instance in od_instances]
            labels_od = [str(_od_instance['cat_name']) for _od_instance in od_instances]
            final_answer = {'bboxes': bboxes_od, 'labels': labels_od}
            if len(od_instances) and 'score' in od_instances[0]:
                scores_od = [_od_instance['score'] for _od_instance in od_instances]
                final_answer['scores'] = scores_od
        elif task_answer_post_processing_type in ['ocr']:
            bboxes = [_od_instance['quad_box'] for _od_instance in task_answer]
            labels = [str(_od_instance['text']) for _od_instance in task_answer]
            final_answer = {'quad_boxes': bboxes, 'labels': labels}
        elif task_answer_post_processing_type in ['phrase_grounding']:
            bboxes = []
            labels = []
            for _grounded_phrase in task_answer:
                for _bbox in _grounded_phrase['bbox']:
                    bboxes.append(_bbox)
                    labels.append(_grounded_phrase['cat_name'])
            final_answer = {'bboxes': bboxes, 'labels': labels}
        elif task_answer_post_processing_type in ['description_with_polygons', 'polygons']:
            labels = []
            polygons = []
            for result in task_answer:
                label = result['cat_name']
                _polygons = result['polygons']
                labels.append(label)
                polygons.append(_polygons)
            final_answer = {'polygons': polygons, 'labels': labels}
        elif task_answer_post_processing_type in ['description_with_bboxes_or_polygons']:
            bboxes = []
            bboxes_labels = []
            polygons = []
            polygons_labels = []
            for result in task_answer:
                label = result['cat_name']
                if 'polygons' in result:
                    _polygons = result['polygons']
                    polygons.append(_polygons)
                    polygons_labels.append(label)
                else:
                    _bbox = result['bbox']
                    bboxes.append(_bbox)
                    bboxes_labels.append(label)
            final_answer = {'bboxes': bboxes, 'bboxes_labels': bboxes_labels, 'polygons': polygons, 'polygons_labels': polygons_labels}
        else:
            raise ValueError('Unknown task answer post processing type: {}'.format(task_answer_post_processing_type))

        final_answer = {
            task: final_answer}
        return final_answer 

class BoxQuantizer(object):
    def __init__(self, mode, bins):
        self.mode = mode
        self.bins = bins

    def quantize(self, boxes: torch.Tensor, size):
        bins_w, bins_h = self.bins  # Quantization bins.
        size_w, size_h = size       # Original image size.
        size_per_bin_w = size_w / bins_w
        size_per_bin_h = size_h / bins_h
        xmin, ymin, xmax, ymax = boxes.split(1, dim=-1)  # Shape: 4 * [N, 1].

        if self.mode == 'floor':
            quantized_xmin = (
                xmin / size_per_bin_w).floor().clamp(0, bins_w - 1)
            quantized_ymin = (
                ymin / size_per_bin_h).floor().clamp(0, bins_h - 1)
            quantized_xmax = (
                xmax / size_per_bin_w).floor().clamp(0, bins_w - 1)
            quantized_ymax = (
                ymax / size_per_bin_h).floor().clamp(0, bins_h - 1)

        elif self.mode == 'round':
            raise NotImplementedError()

        else:
            raise ValueError('Incorrect quantization type.')

        quantized_boxes = torch.cat(
            (quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1
        ).int()

        return quantized_boxes

    def dequantize(self, boxes: torch.Tensor, size):
        bins_w, bins_h = self.bins  # Quantization bins.
        size_w, size_h = size       # Original image size.
        size_per_bin_w = size_w / bins_w
        size_per_bin_h = size_h / bins_h
        xmin, ymin, xmax, ymax = boxes.split(1, dim=-1)  # Shape: 4 * [N, 1].

        if self.mode == 'floor':
            # Add 0.5 to use the center position of the bin as the coordinate.
            dequantized_xmin = (xmin + 0.5) * size_per_bin_w
            dequantized_ymin = (ymin + 0.5) * size_per_bin_h
            dequantized_xmax = (xmax + 0.5) * size_per_bin_w
            dequantized_ymax = (ymax + 0.5) * size_per_bin_h

        elif self.mode == 'round':
            raise NotImplementedError()

        else:
            raise ValueError('Incorrect quantization type.')

        dequantized_boxes = torch.cat(
            (dequantized_xmin, dequantized_ymin,
             dequantized_xmax, dequantized_ymax), dim=-1
        )

        return dequantized_boxes


class CoordinatesQuantizer(object):
    """
    Quantize coornidates (Nx2)
    """

    def __init__(self, mode, bins):
        self.mode = mode
        self.bins = bins

    def quantize(self, coordinates: torch.Tensor, size):
        bins_w, bins_h = self.bins  # Quantization bins.
        size_w, size_h = size       # Original image size.
        size_per_bin_w = size_w / bins_w
        size_per_bin_h = size_h / bins_h
        assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)'
        x, y = coordinates.split(1, dim=-1)  # Shape: 4 * [N, 1].

        if self.mode == 'floor':
            quantized_x = (x / size_per_bin_w).floor().clamp(0, bins_w - 1)
            quantized_y = (y / size_per_bin_h).floor().clamp(0, bins_h - 1)

        elif self.mode == 'round':
            raise NotImplementedError()

        else:
            raise ValueError('Incorrect quantization type.')

        quantized_coordinates = torch.cat(
            (quantized_x, quantized_y), dim=-1
        ).int()

        return quantized_coordinates

    def dequantize(self, coordinates: torch.Tensor, size):
        bins_w, bins_h = self.bins  # Quantization bins.
        size_w, size_h = size       # Original image size.
        size_per_bin_w = size_w / bins_w
        size_per_bin_h = size_h / bins_h
        assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)'
        x, y = coordinates.split(1, dim=-1)  # Shape: 4 * [N, 1].

        if self.mode == 'floor':
            # Add 0.5 to use the center position of the bin as the coordinate.
            dequantized_x = (x + 0.5) * size_per_bin_w
            dequantized_y = (y + 0.5) * size_per_bin_h

        elif self.mode == 'round':
            raise NotImplementedError()

        else:
            raise ValueError('Incorrect quantization type.')

        dequantized_coordinates = torch.cat(
            (dequantized_x, dequantized_y), dim=-1
        )

        return dequantized_coordinates


class Florence2PostProcesser(object):
    r"""
    Florence-2 post process for converting text prediction to various tasks results. 

    Args:
        config: A dict of configs.
        tokenizer: A tokenizer for decoding text to spans.
        sample config:
            UNIFIED_POST_PROCESS:
                # commom configs
                NUM_BBOX_HEIGHT_BINS: 1000
                NUM_BBOX_WIDTH_BINS: 1000
                COORDINATES_HEIGHT_BINS: 1000
                COORDINATES_WIDTH_BINS: 1000
                # task specific configs, override the common configs
                PRASE_TASKS:
                    - TASK_NAME: 'video_dense_caption'
                      PATTERN: 'r<time_(\d+)><time_(\d+)>([a-zA-Z0-9 ]+)'
                      SCORE_MODE: 'avg_cat_name_scores'
                      NUM_BINS: 100
                    - TASK_NAME: 'od'
                      PATTERN: 'r<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>([a-zA-Z0-9 ]+)'
                      SCORE_MODE: 'avg_cat_name_scores'

    Returns:
        parsed_dict (dict): A dict of parsed results.
    """
    def __init__(
        self,
        tokenizer=None
    ):
        parse_tasks = []
        parse_task_configs = {}
        config = self._create_default_config()
        for task in config['PARSE_TASKS']:
            parse_tasks.append(task['TASK_NAME'])
            parse_task_configs[task['TASK_NAME']] = task

        self.config = config
        self.parse_tasks = parse_tasks
        self.parse_tasks_configs = parse_task_configs

        self.tokenizer =  tokenizer
        if self.tokenizer is not None:
            self.all_special_tokens = set(self.tokenizer.all_special_tokens)

        self.init_quantizers()
        self.black_list_of_phrase_grounding = self._create_black_list_of_phrase_grounding()

    def _create_black_list_of_phrase_grounding(self):
        black_list = {}

        if 'phrase_grounding' in self.parse_tasks and self.parse_tasks_configs['phrase_grounding']['FILTER_BY_BLACK_LIST']:
            black_list =  set(
                ['it', 'I', 'me', 'mine',
                 'you', 'your', 'yours',
                 'he', 'him', 'his',
                 'she', 'her', 'hers',
                 'they', 'them', 'their', 'theirs',
                 'one', 'oneself',
                 'we', 'us', 'our', 'ours',
                 'you', 'your', 'yours',
                 'they', 'them', 'their', 'theirs',
                 'mine', 'yours', 'his', 'hers', 'its',
                 'ours', 'yours', 'theirs',
                 'myself', 'yourself', 'himself', 'herself', 'itself',
                 'ourselves', 'yourselves', 'themselves',
                 'this', 'that',
                 'these', 'those',
                 'who', 'whom', 'whose', 'which', 'what',
                 'who', 'whom', 'whose', 'which', 'that',
                 'all', 'another', 'any', 'anybody', 'anyone', 'anything',
                 'each', 'everybody', 'everyone', 'everything',
                 'few', 'many', 'nobody', 'none', 'one', 'several',
                 'some', 'somebody', 'someone', 'something',
                 'each other', 'one another',
                 'myself', 'yourself', 'himself', 'herself', 'itself',
                 'ourselves', 'yourselves', 'themselves',
                 'the image', 'image', 'images', 'the', 'a', 'an', 'a group',
                 'other objects', 'lots', 'a set',
                 ]
            )

        return black_list
    
    def _create_default_config(self):
        config = {
            'NUM_BBOX_HEIGHT_BINS': 1000,
            'NUM_BBOX_WIDTH_BINS': 1000,
            'BOX_QUANTIZATION_MODE': 'floor',
            'COORDINATES_HEIGHT_BINS': 1000,
            'COORDINATES_WIDTH_BINS': 1000,
            'COORDINATES_QUANTIZATION_MODE': 'floor',
            'PARSE_TASKS': [
                {
                    'TASK_NAME': 'od',
                    'PATTERN': r'([a-zA-Z0-9 ]+)<loc_(\\d+)><loc_(\\d+)><loc_(\\d+)><loc_(\\d+)>',
                    'SCORE_MODE': 'avg_loc_scores'
                },
                {
                    'TASK_NAME': 'ocr',
                    'PATTERN':  r'(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>',
                    'AREA_THRESHOLD': 0.00
                },
                {
                    'TASK_NAME': 'phrase_grounding',
                    'FILTER_BY_BLACK_LIST': True
                },
                {
                    'TASK_NAME': 'pure_text',
                },
                {
                    'TASK_NAME': 'description_with_bboxes',
                    'SCORE_MODE': 'avg_loc_scores'
                },
                {
                    'TASK_NAME': 'description_with_polygons',
                },
                {
                    'TASK_NAME': 'polygons',
                },
                {
                    'TASK_NAME': 'bboxes',
                },
                {
                    'TASK_NAME': 'description_with_bboxes_or_polygons',
                }
            ]
        }

        return config

    def init_quantizers(self):
        # we have box_quantizer (od, grounding) and coordinates_quantizer (ocr, referring_segmentation)
        num_bbox_height_bins = self.config.get('NUM_BBOX_HEIGHT_BINS', 1000)
        num_bbox_width_bins = self.config.get('NUM_BBOX_WIDTH_BINS', 1000)
        box_quantization_mode = self.config.get('BOX_QUANTIZATION_MODE', 'floor')
        self.box_quantizer = BoxQuantizer(
            box_quantization_mode,
            (num_bbox_width_bins, num_bbox_height_bins),
        )
        
        num_bbox_height_bins = self.config['COORDINATES_HEIGHT_BINS'] if 'COORDINATES_HEIGHT_BINS' in self.config else self.config.get('NUM_BBOX_HEIGHT_BINS', 1000)
        num_bbox_width_bins = self.config['COORDINATES_WIDTH_BINS'] if 'COORDINATES_WIDTH_BINS' in self.config else self.config.get('NUM_BBOX_WIDTH_BINS', 1000)
        box_quantization_mode = self.config.get('COORDINATES_QUANTIZATION_MODE') if 'COORDINATES_QUANTIZATION_MODE' in self.config else self.config.get('BOX_QUANTIZATION_MODE', 'floor')
        self.coordinates_quantizer = CoordinatesQuantizer(
            box_quantization_mode,
            (num_bbox_width_bins, num_bbox_height_bins),
        )

    def decode_with_spans(self, tokenizer, token_ids):
        filtered_tokens = tokenizer.convert_ids_to_tokens(
            token_ids, skip_special_tokens=False)
        assert len(filtered_tokens) == len(token_ids)
        sub_texts = []
        for token in filtered_tokens:
            if token in self.all_special_tokens:
                sub_texts.append(token)
            else:
                if isinstance(tokenizer, (BartTokenizer, BartTokenizerFast)):
                    sub_text = tokenizer.convert_tokens_to_string([token])
                else:
                    raise ValueError(f'type {type(tokenizer)} not supported')
                sub_texts.append(sub_text)

        text = ''
        spans = []
        for sub_text in sub_texts:
            span = (len(text), len(text) + len(sub_text))  # [start index, end index).
            text += sub_text
            spans.append(span)
        return text, spans

    def parse_od_from_text_and_spans(
        self,
        text,
        pattern,
        image_size,
        phrase_centric=False
    ):
        parsed = list(re.finditer(pattern, text))

        instances = []
        for i in range(len(parsed)):
            # Prepare instance.
            instance = {}

            if phrase_centric:
                bbox_bins = [int(parsed[i].group(j)) for j in range(2, 6)]
            else:
                bbox_bins = [int(parsed[i].group(j)) for j in range(1, 5)]
            instance['bbox'] = self.box_quantizer.dequantize(
                boxes=torch.tensor(bbox_bins),
                size=image_size
            ).tolist()  

            if phrase_centric:
                instance['cat_name'] = parsed[i].group(1).lower().strip()
            else:
                instance['cat_name'] = parsed[i].group(5).lower().strip()
            instances.append(instance)

        return instances

    def parse_ocr_from_text_and_spans(self, 
                                     text, 
                                     pattern, 
                                     image_size,
                                     area_threshold=-1.0,
        ):
        bboxes = []
        labels = []
        text = text.replace('<s>', '')
        # ocr with regions
        parsed = re.findall(pattern, text)
        instances = []
        image_width, image_height = image_size

        for ocr_line in parsed:
            ocr_content = ocr_line[0]
            quad_box = ocr_line[1:]
            quad_box = [int(i) for i in quad_box]
            quad_box = self.coordinates_quantizer.dequantize(
                torch.tensor(np.array(quad_box).reshape(-1, 2)),
                size=image_size
            ).reshape(-1).tolist()

            if area_threshold > 0:
                x_coords = [i for i in quad_box[0::2]]
                y_coords = [i for i in quad_box[1::2]]

                # apply the Shoelace formula
                area = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i] for i in range(4 - 1)))

                if area < (image_width * image_height) * area_threshold:
                    continue

            bboxes.append(quad_box)
            labels.append(ocr_content)
            instances.append({
                'quad_box': quad_box,
                'text': ocr_content,
            })
        return instances

    def parse_phrase_grounding_from_text_and_spans(self, text, pattern, image_size):
        # ignore <s> </s> and <pad>
        cur_span = 0
        if text.startswith('<s>'):   
            cur_span += 3

        text = text.replace('<s>', '')
        text = text.replace('</s>', '')
        text = text.replace('<pad>', '')

        pattern = r"([^<]+(?:<loc_\d+>){4,})"
        phrases = re.findall(pattern, text)
    
        # pattern should be text pattern and od pattern
        pattern = r'^\s*(.*?)(?=<od>|</od>|<box>|</box>|<bbox>|</bbox>|<loc_)'
        box_pattern = r'<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>'

        instances = []
        for pharse_text in phrases:
            phrase_text_strip = pharse_text.replace('<ground>', '', 1)
            phrase_text_strip = pharse_text.replace('<obj>', '', 1)

            if phrase_text_strip == '':
                cur_span += len(pharse_text)
                continue

            # Prepare instance.
            instance = {}

            # parse phrase, get string 
            phrase = re.search(pattern, phrase_text_strip)
            if phrase is None:
                cur_span += len(pharse_text)
                continue

            # parse bboxes by box_pattern
            bboxes_parsed = list(re.finditer(box_pattern, pharse_text))
            if len(bboxes_parsed) == 0:
                cur_span += len(pharse_text)
                continue

            phrase = phrase.group()
            # remove leading and trailing spaces
            phrase = phrase.strip()

            if phrase in self.black_list_of_phrase_grounding:
                cur_span += len(pharse_text)
                continue

            # a list of list 
            bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed]
            instance['bbox'] = self.box_quantizer.dequantize(
                boxes=torch.tensor(bbox_bins),
                size=image_size
            ).tolist()  

            # exclude non-ascii characters
            phrase = phrase.encode('ascii',errors='ignore').decode('ascii')
            instance['cat_name'] = phrase

            instances.append(instance)

        return instances

    def parse_description_with_bboxes_from_text_and_spans(
            self, 
            text, 
            spans=None,
            scores=None,
            score_mode=None,
            pattern=None, 
            image_size=None, 
            allow_empty_phrase=False
        ):
        def find_matched_token_indices(cur_span, token_spans):
            inds = []
            for i, token_span in enumerate(token_spans):
                if not (token_span[1] <= cur_span[0] or token_span[0] >= cur_span[1]):
                    inds.append(i)
            return inds

        cur_span = 0
        if text.startswith('<s>'):   
            cur_span += 3

        text = text.replace('<s>', '')
        text = text.replace('</s>', '')
        text = text.replace('<pad>', '')

        if allow_empty_phrase:
            pattern = rf"(?:(?:<loc_\d+>){{4,}})"
        else:
            pattern = r"([^<]+(?:<loc_\d+>){4,})"
        phrases = re.findall(pattern, text)
    
        # pattern should be text pattern and od pattern
        pattern = r'^\s*(.*?)(?=<od>|</od>|<box>|</box>|<bbox>|</bbox>|<loc_)'
        box_pattern = r'<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>'

        instances = []
        for pharse_text in phrases:
            phrase_text_strip = pharse_text.replace('<ground>', '', 1)
            phrase_text_strip = pharse_text.replace('<obj>', '', 1)

            if phrase_text_strip == '' and not allow_empty_phrase:
                cur_span += len(pharse_text)
                continue

            # parse phrase, get string 
            phrase = re.search(pattern, phrase_text_strip)
            if phrase is None:
                cur_span += len(pharse_text)
                continue

            phrase_span = phrase.span()
            phrase = phrase.group()
            # remove leading and trailing spaces
            phrase = phrase.strip()

            # parse bboxes by box_pattern
            bboxes_parsed = list(re.finditer(box_pattern, pharse_text))
            if len(bboxes_parsed) == 0:
                cur_span += len(pharse_text)
                continue

            # a list of list 
            bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed]

            bboxes = self.box_quantizer.dequantize(
                boxes=torch.tensor(bbox_bins),
                size=image_size
            ).tolist()  

            if score_mode == 'avg_loc_scores':
                if spans is None or scores is None:
                    all_scores = None
                else:
                    bbox_end_spans = [_bboxes_parsed.span(0) for _bboxes_parsed in bboxes_parsed]
                    all_scores = []
                    for _spans in bbox_end_spans:
                        token_inds = find_matched_token_indices((_spans[0] + cur_span, _spans[1]+ cur_span), spans)
                        loc_scores = [scores[token_i] for token_i in token_inds]
                        score = sum(loc_scores) / len(loc_scores)
                        all_scores.append(score)
            elif score_mode == 'avg_cat_name_scores': 
                if spans is None or scores is None:
                    all_scores = None
                else:
                    cat_name_token_inds = find_matched_token_indices((phrase_span[0] + cur_span, phrase_span[1]+cur_span), spans)
                    cat_name_scores = [scores[token_i] for token_i in cat_name_token_inds]
                    score = sum(cat_name_scores) / len(cat_name_scores)
                    all_scores = [score] * len(bboxes)
            elif score_mode is None:
                all_scores = None
            else:
                raise ValueError('Unknown score mode: {}'.format(score_mode))

            phrase = phrase.encode('ascii',errors='ignore').decode('ascii')
            for _idx, _bboxes in enumerate(bboxes):
                # Prepare instance.
                instance = {}
                instance['bbox'] = _bboxes
                # exclude non-ascii characters
                instance['cat_name'] = phrase
                if all_scores is not None:
                    instance['score'] = math.exp(all_scores[_idx])
                instances.append(instance)
            
            cur_span += len(pharse_text)

        return instances

    def parse_description_with_polygons_from_text_and_spans(self, text, pattern, image_size, 
                                                            allow_empty_phrase=False,
                                                            polygon_sep_token='<sep>',
                                                            polygon_start_token='<poly>',
                                                            polygon_end_token='</poly>',
                                                            with_box_at_start=False,
                                                            ):
        
        # ref_seg format: '<expression><x1><y1><x2><y2><><><sep><><><><>'
        # ignore <s> </s> and <pad>

        text = text.replace('<s>', '')
        text = text.replace('</s>', '')
        text = text.replace('<pad>', '')

        if allow_empty_phrase:
            pattern = rf"(?:(?:<loc_\d+>|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})"
        else:
            # [^<]+: This part matches one or more characters that are not the < symbol. 
            # The ^ inside the square brackets [] is a negation, meaning it matches anything except <.
            #
            pattern = rf"([^<]+(?:<loc_\d+>|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})"
        phrases = re.findall(pattern, text)

        phrase_string_pattern = r'^\s*(.*?)(?=<od>|</od>|<box>|</box>|<bbox>|</bbox>|<loc_|<poly>)'
        box_pattern =  rf'((?:<loc_\d+>)+)(?:{re.escape(polygon_sep_token)}|$)'

        # one polygons instance is separated by polygon_start_token and polygon_end_token
        polygons_instance_pattern = rf'{re.escape(polygon_start_token)}(.*?){re.escape(polygon_end_token)}'
    
        instances = []
        for phrase_text in phrases:

            # exclude loc_\d+>
            # need to get span if want to include category score
            phrase_text_strip = re.sub(r'^loc_\d+>', '', phrase_text, count=1)

            # phrase = phrase.replace('<poly>', '')
            # phrase = phrase.replace('poly>', '')

            if phrase_text_strip == '' and not allow_empty_phrase:
                continue


            # parse phrase, get string 
            phrase = re.search(phrase_string_pattern, phrase_text_strip)
            if phrase is None:
                continue
            phrase = phrase.group()
            # remove leading and trailing spaces
            phrase = phrase.strip()

            # parse bboxes by box_pattern

            # split by polygon_start_token and polygon_end_token first using polygons_instance_pattern
            if polygon_start_token in phrase_text and polygon_end_token in phrase_text:
                polygons_instances_parsed = list(re.finditer(polygons_instance_pattern, phrase_text))
            else:
                polygons_instances_parsed = [phrase_text]

            for _polygons_instances_parsed in polygons_instances_parsed:
                # Prepare instance.
                instance = {}

                # polygons_parsed= list(re.finditer(box_pattern, phrase_text))
                if isinstance(_polygons_instances_parsed, str): 
                    polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed))
                else:
                    polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed.group(1)))
                if len(polygons_parsed) == 0:
                    continue

                # a list of list (polygon)
                bbox = []
                polygons = []
                for _polygon_parsed in polygons_parsed:
                    # group 1: whole <loc_\d+>...</loc_\d+>
                    _polygon = _polygon_parsed.group(1)
                    # parse into list of int
                    _polygon = [int(_loc_parsed.group(1)) for _loc_parsed in re.finditer(r'<loc_(\d+)>', _polygon)]
                    if with_box_at_start and len(bbox) == 0:
                        if len(_polygon) > 4:
                            # no valid bbox prediction
                            bbox = _polygon[:4]
                            _polygon = _polygon[4:]
                        else:
                            bbox = [0, 0, 0, 0]
                    # abandon last element if is not paired 
                    if len(_polygon) % 2 == 1:
                        _polygon = _polygon[:-1]
                    
                    # reshape into (n, 2)
                    _polygon = self.coordinates_quantizer.dequantize(
                        torch.tensor(np.array(_polygon).reshape(-1, 2)),
                        size=image_size
                    ).reshape(-1).tolist()
                    # reshape back
                    polygons.append(_polygon)

                instance['cat_name'] = phrase
                instance['polygons'] = polygons
                if len(bbox) != 0:
                    instance['bbox'] = self.box_quantizer.dequantize(
                        boxes=torch.tensor([bbox]),
                        size=image_size
                    ).tolist()[0]  

                instances.append(instance)

        return instances

    def __call__(
        self,
        text=None,
        sequence=None,
        transition_beam_score=None,
        image_size=None,
        parse_tasks=None,
    ):
        """
        Args:
            text: model outputs
            image_size: (width, height)
            parse_tasks: a list of tasks to parse, if None, parse all tasks.

        """
        if parse_tasks is not None:
            if isinstance(parse_tasks, str):
                parse_tasks = [parse_tasks]
            for _parse_task in parse_tasks:
                assert _parse_task in self.parse_tasks, f'parse task {_parse_task} not supported'
        
        # sequence or text should be provided 
        assert sequence is not None or text is not None, 'sequence or text should be provided'
        assert sequence is None or text is None, 'only one of sequence and text should be provided'

        if sequence is not None:
            sequence = sequence.tolist()[1:]
            text, spans = self.decode_with_spans(self.tokenizer, sequence)
            if transition_beam_score is not None:
                transition_beam_score = transition_beam_score.tolist()
                assert len(sequence) == len(transition_beam_score)
        else:
            spans = None
            transition_beam_score = None

        parsed_dict = {
            'text': text
        }

        for task in self.parse_tasks:
            if parse_tasks is not None and task not in parse_tasks:
                continue

            pattern = self.parse_tasks_configs[task].get('PATTERN', None)
            score_mode = self.parse_tasks_configs[task].get('SCORE_MODE', None)

            if task == 'ocr':
                instances = self.parse_ocr_from_text_and_spans(
                    text,
                    pattern=pattern,
                    image_size=image_size,
                    area_threshold=self.parse_tasks_configs[task].get('AREA_THRESHOLD', 0.0),
                )
                parsed_dict['ocr'] = instances
            elif task == 'phrase_grounding':
                instances = self.parse_phrase_grounding_from_text_and_spans( 
                    text,
                    pattern=pattern,
                    image_size=image_size,
                )
                parsed_dict['phrase_grounding'] = instances
            elif task == 'pure_text':
                parsed_dict['pure_text'] = text 
            elif task == 'description_with_bboxes':
                instances = self.parse_description_with_bboxes_from_text_and_spans( 
                    text,
                    spans=spans,
                    scores=transition_beam_score,
                    score_mode=score_mode,
                    pattern=pattern,
                    image_size=image_size,
                )
                parsed_dict['description_with_bboxes'] = instances
            elif task == 'description_with_polygons':
                instances = self.parse_description_with_polygons_from_text_and_spans( 
                    text,
                    pattern=pattern,
                    image_size=image_size,
                )
                parsed_dict['description_with_polygons'] = instances
            elif task == 'polygons':
                instances = self.parse_description_with_polygons_from_text_and_spans( 
                    text,
                    pattern=pattern,
                    image_size=image_size,
                    allow_empty_phrase=True,
                )
                parsed_dict['polygons'] = instances
            elif task == 'bboxes':
                instances = self.parse_description_with_bboxes_from_text_and_spans( 
                    text,
                    pattern=pattern,
                    image_size=image_size,
                    allow_empty_phrase=True,
                )
                parsed_dict['bboxes'] = instances
            elif task == 'description_with_bboxes_or_polygons':
                if '<poly>' in text:
                    # only support either polygons or bboxes, not both at the same time
                    instances = self.parse_description_with_polygons_from_text_and_spans( 
                        text,
                        pattern=pattern,
                        image_size=image_size,
                    )
                else:
                    instances = self.parse_description_with_bboxes_from_text_and_spans( 
                        text,
                        pattern=pattern,
                        image_size=image_size,
                    )
                parsed_dict['description_with_bboxes_or_polygons'] = instances
            else:
                raise ValueError("task {} is not supported".format(task))

        return parsed_dict