minicpm-guidance / processing_minicpmv.py

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# coding=utf-8
# Copyright 2024 RhapsodyAI and ModelBest Inc. and Microsoft 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.
# See the License for the specific language governing permissions and
# limitations under the License.

# coding=utf-8
# Copyright 2024 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.


import re
from typing import List, Optional, Union, Dict

import math
import torch
from torchvision import transforms
from PIL import Image

import transformers
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy, PreTokenizedInput
from transformers.utils import TensorType
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
# from transformers.image_transforms import (
#     convert_to_rgb,
# )

from transformers import LlamaTokenizer # for text processing

from transformers.utils import logging

logger = logging.get_logger(__name__)


# image tokenizer
def ensure_divide(length, patch_size):
    return max(round(length / patch_size) * patch_size, patch_size)

def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False):
    width, height = original_size
    if (width * height > scale_resolution * scale_resolution) or allow_upscale:
        r = width / height
        height = int(scale_resolution / math.sqrt(r))
        width = int(height * r)
    best_width = ensure_divide(width, patch_size)
    best_height = ensure_divide(height, patch_size)
    return (best_width, best_height)

def get_refine_size(
    original_size, grid, scale_resolution, patch_size, allow_upscale=False
):
    width, height = original_size
    grid_x, grid_y = grid

    refine_width = ensure_divide(width, grid_x)
    refine_height = ensure_divide(height, grid_y)

    grid_width = refine_width / grid_x
    grid_height = refine_height / grid_y

    best_grid_size = find_best_resize(
        (grid_width, grid_height),
        scale_resolution,
        patch_size,
        allow_upscale=allow_upscale,
    )

    refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)

    return refine_size

def split_to_patches(image, grid):
    patches = []
    width, height = image.size
    grid_x = int(width / grid[0])
    grid_y = int(height / grid[1])

    for i in range(0, height, grid_y):
        images = []
        for j in range(0, width, grid_x):
            box = (j, i, j + grid_x, i + grid_y)
            patch = image.crop(box)
            logger.info(f"I don't think it is so called `patch`. split_to_patches: patch size = {box}")
            images.append(patch)
        patches.append(images)

    return patches

def slice_image(
    image, 
    max_slice_nums=9, 
    scale_resolution=448, 
    patch_size=14, 
    never_split=False
):
    original_size = image.size
    original_width, original_height = original_size
    log_ratio = math.log(original_width / original_height)
    ratio = original_width * original_height / (scale_resolution * scale_resolution)
    multiple = min(math.ceil(ratio), max_slice_nums)

    source_image = None
    best_grid = None
    patches = []

    if multiple <= 1 or never_split:
        # don't need to slice, upsample
        best_size = find_best_resize(
            original_size, scale_resolution, patch_size, allow_upscale=True
        )
        source_image = image.resize(best_size, Image.Resampling.BICUBIC)
    else:
        candidate_split_grids_nums = []
        for i in [multiple - 1, multiple, multiple + 1]:
            if i == 1 or i > max_slice_nums:
                continue
            candidate_split_grids_nums.append(i)

        # source image, down-sampling and ensure divided by patch_size
        best_resize = find_best_resize(original_size, scale_resolution, patch_size)
        source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
        candidate_grids = []

        # find best grid
        for split_grids_nums in candidate_split_grids_nums:
            m = 1
            while m <= split_grids_nums:
                if split_grids_nums % m == 0:
                    candidate_grids.append([m, split_grids_nums // m])
                m += 1

        best_grid = [1, 1]
        min_error = float("inf")
        for grid in candidate_grids:
            error = abs(log_ratio - math.log(grid[0] / grid[1]))
            if error < min_error:
                best_grid = grid
                min_error = error

        refine_size = get_refine_size(
            original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
        )

        refine_image = image.resize(refine_size, Image.Resampling.BICUBIC)
        patches = split_to_patches(refine_image, best_grid)

    return source_image, patches, best_grid

def reshape_by_patch(image_tensor, patch_size=14):
    """
    :param image_tensor: shape [3, H, W]
    :param patch_size:
    :return: [3, patch_size, HW/patch_size]
    """
    patches = torch.nn.functional.unfold(
        image_tensor,
        (patch_size, patch_size),
        stride=(patch_size, patch_size)
    )

    patches = patches.reshape(image_tensor.size(0), patch_size, patch_size, -1)
    patches = patches.permute(0, 1, 3, 2).reshape(image_tensor.size(0), patch_size, -1)
    return patches

class MiniCPMVImageProcessor(BaseImageProcessor):
    r"""
    MiniCPMV image processor. -> Based on Phi3 image processor -> Used LlaVa-UHD. dynamic slicing one image image.
    
    Args:
        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
            Can be overridden by the `image_std` parameter in the `preprocess` method.
        do_convert_rgb (`bool`, *optional*, defaults to `True`):
            Whether to convert the image to RGB.
    """

    def __init__(
        self,
        query_num: int = 64, 
        image_mean: Optional[Union[float, List[float]]] = None, 
        image_std: Optional[Union[float, List[float]]] = None, 
        max_slice_nums: int = 9,
        scale_resolution: int = 448,
        patch_size: int = 14,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)

        self.query_num = query_num
        self.image_mean = image_mean
        self.image_std = image_std
        self.max_slice_nums = max_slice_nums
        self.scale_resolution = scale_resolution
        self.patch_size = patch_size

    def preprocess(
        self,
        image,
        slice_mode: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
    ):
        """
        Args:
            images (`ImageInput`):
                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_rescale=False`. # modified: one image per invoke.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                - Unset: Return a list of `np.ndarray`.
                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
        """
        
        transform = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize(mean=self.image_mean, std=self.image_std)
            ]
        )
        
        images_ = []
        tgt_sizes = []
        
        if slice_mode:
            slice_images = []
            source_image, patches, best_grid = slice_image( # 耗时
                image,
                self.max_slice_nums,
                self.scale_resolution,
                self.patch_size,
            )
            
            slice_images.append(source_image)
            if len(patches) > 0:
                for i in range(len(patches)):
                    for j in range(len(patches[0])):
                        slice_images.append(patches[i][j])
            
            for image_ in slice_images:
                slice_image_ = transform(image_) # 耗时
                H, W = slice_image_.shape[1:]
                slice_image_patchified_ = reshape_by_patch(slice_image_)
                images_.append(slice_image_patchified_)
                tgt_sizes.append(torch.Tensor([H // self.patch_size, W // self.patch_size]).type(torch.int32))
        
        else:
            best_grid = None
            image_ = transform(image)
            H, W = image_.shape[1:]
            image_patchified_ = reshape_by_patch(image_)
            images_.append(image_patchified_) # 耗时
            tgt_sizes.append(torch.Tensor([H // self.patch_size, W // self.patch_size]).type(torch.int32))
        
        return images_, tgt_sizes, best_grid


# text tokenizer
class MiniCPMVTextTokenizer(LlamaTokenizer):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.im_start = "<image>"
        self.im_end = "</image>"
        self.ref_start = "<ref>"
        self.ref_end = "</ref>"
        self.box_start = "<box>"
        self.box_end = "</box>"
        self.quad_start = "<quad>"
        self.quad_end = "</quad>"
        self.point_start = "<point>"
        self.point_end = "</point>"
        self.slice_start = "<slice>"
        self.slice_end = "</slice>"

    @property
    def eos_id(self):
        return self.sp_model.eos_id()

    @property
    def bos_id(self):
        return self.sp_model.bos_id()

    @property
    def unk_id(self):
        return self.sp_model.unk_id()

    @property
    def im_start_id(self):
        return self._convert_token_to_id(self.im_start)

    @property
    def im_end_id(self):
        return self._convert_token_to_id(self.im_end)

def get_grid_placeholder(tokenizer, grid, query_num):
    image_placeholder = (
        tokenizer.im_start + tokenizer.unk_token * query_num + tokenizer.im_end
    )

    cols = grid[0]
    rows = grid[1]
    slices = []
    for i in range(rows):
        lines = []
        for j in range(cols):
            lines.append(image_placeholder)
        slices.append("".join(lines))
    slice_placeholder = tokenizer.slice_start + "\n".join(slices) + tokenizer.slice_end
    return slice_placeholder

def pad(orig_items, max_length=None, padding_value=0, padding_side="left"):
    """
    Args:
        orig_items: a list of input_ids, each input_ids should be [1, length_i]
    """
    assert isinstance(orig_items, list)
    assert isinstance(orig_items[0], torch.Tensor)
    padding_value = 2
    items = [t.squeeze() for t in orig_items]

    batch_size = len(items)
    shape = items[0].shape
        
    dim = len(shape)
    assert dim == 1, "This pad function only expect B * Tensor([seq_len]) input."  # Assuming 1D tensors for simplicity

    if max_length is None:
        max_length = max(item.shape[0] for item in items)

    tensor = torch.full((batch_size, max_length), padding_value, dtype=items[0].dtype)
    attention_mask = torch.zeros((batch_size, max_length), dtype=torch.int8)

    for i, item in enumerate(items):
        length = item.shape[0]
        if padding_side == "left":
            raise Exception("Please use right padding")
            tensor[i, -length:] = item.clone()
            attention_mask[i, -length:] = 1
        else:
            tensor[i, 0:length] = item.clone()
            attention_mask[i, 0:length] = 1
    
    return_dict = {
        "input_ids": tensor,
        "attention_mask": attention_mask,
    }
    
    return return_dict

def convert_to_tokens(input_str, tokenizer, max_inp_length):
    if tokenizer.add_bos_token:
        input_ids = tokenizer.encode(input_str)
    else:
        input_ids = [tokenizer.bos_id] + tokenizer.encode(input_str)
    
    input_ids = input_ids[:max_inp_length]
    
    input_ids = torch.tensor(input_ids, dtype=torch.int32)
    
    image_start_tokens = torch.where(input_ids == tokenizer.im_start_id)[0]
    
    # 跳过 im_start
    image_start_tokens += 1
    image_end_tokens = torch.where(input_ids == tokenizer.im_end_id)[0]
    valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
    
    image_bound = torch.hstack(
        [
            image_start_tokens[:valid_image_nums].unsqueeze(-1),
            image_end_tokens[:valid_image_nums].unsqueeze(-1),
        ]
    )

    model_input = {}
    model_input["input_ids"] = input_ids.unsqueeze(0)
    model_input["image_bound"] = image_bound
    
    return model_input

class MiniCPMVProcessor(ProcessorMixin):
    r"""
    Based on Siglip. Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
    [`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
    [`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
    Args:
        image_processor ([`SiglipImageProcessor`]):
            The image processor is a required input.
        tokenizer ([`SiglipTokenizer`]):
            The tokenizer is a required input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "AutoImageProcessor" # sorry, we can't find a way to make `image_processor_class` equal to `MiniCPMVImageProcessor`
    tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor, tokenizer, query_num=64, slice_mode=True, max_inp_length=2048):
        super().__init__(image_processor, tokenizer)
        self.query_num = query_num
        self.slice_mode = slice_mode
        self.max_inp_length = max_inp_length

    def __call__(
        self,
        messages: Dict[str, Union[str, Image.Image]] = None, # ChatML format
        slice_mode: bool = None, 
        max_inp_length: int = None, 
        padding: Union[bool, str, PaddingStrategy] = False, 
        padding_side: str = "left", 
        truncation: Union[bool, str, TruncationStrategy] = None, 
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
    ) -> 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 SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` argument to
        SiglipImageProcessor's [`~SiglipImageProcessor.__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.
            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_input_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`.
        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`.
            - **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`.
        """
        # assert len(messages) == 1, 'Do not support batch > 1'
        
        if slice_mode is None:
            if self.slice_mode is None:
                raise ValueError("`slice_mode` is not specified by config or usage")
            else:
                slice_mode = self.slice_mode

        if max_inp_length is None:
            if self.max_inp_length is None:
                raise ValueError("`max_inp_length` is not specified by config or usage")
            else:
                max_inp_length = self.max_inp_length
        
        processed_subimages_all_data = []
        processed_text_all_data = []
        tgt_sizes_all_data = []
        
        for msgs in messages:
            assert len(msgs) > 0, 'msgs is empty'
            
            processed_text_all_msgs = []
            processed_subimages_all_msgs = []
            tgt_sizes_all_msgs = []
            
            # process each message, each message is look like [text/image, ...]
            for i, msg in enumerate(msgs):
                
                role = msg["role"]
                c = msg["content"]
                
                assert role in ["user", "assistant"]
                
                if i == 0:
                    assert role == "user", "The role of first msg should be user"
                
                if isinstance(c, Image.Image):
                    
                    processed_subimages, tgt_sizes, best_grid = self.image_processor.preprocess(image=c, slice_mode=slice_mode)
                    
                    # make image placeholders
                    if slice_mode:
                        cur_msg = (
                            self.tokenizer.im_start
                            + self.tokenizer.unk_token * self.query_num
                            + self.tokenizer.im_end
                        )
                        if len(processed_subimages) > 1:
                            cur_msg += get_grid_placeholder(
                                self.tokenizer, best_grid, self.query_num
                            )
                    
                    else:
                        cur_msg = (
                            self.tokenizer.im_start
                            + self.tokenizer.unk_token * self.query_num
                            + self.tokenizer.im_end
                        )
                    
                    tgt_sizes_all_msgs.extend(tgt_sizes)
                    processed_subimages_all_msgs.extend(processed_subimages)

                elif isinstance(c, str):
                    cur_msg = c
                
                else:
                    raise NotImplementedError(f"message {type(c)}: {c} can't be handled")

                role_title = "<用户>" if role == "user" else "<AI>"
                processed_text_all_msgs.append(role_title + cur_msg)
            
            processed_text_all_msgs_concat = "".join(processed_text_all_msgs)
            processed_text_all_msgs_concat += "<AI>"
            processed_text_all_data.append(processed_text_all_msgs_concat)
            
            processed_subimages_all_data.append(processed_subimages_all_msgs)
            tgt_sizes_all_msgs = torch.vstack(tgt_sizes_all_msgs)
            tgt_sizes_all_data.append(tgt_sizes_all_msgs)
        
        # convert text string to tokens, at this step, `input_ids` and `image_bound` is added
        model_inputs_uncollated = []
        for text in processed_text_all_data:
            model_inputs_ = convert_to_tokens(
                text, max_inp_length=max_inp_length, tokenizer=self.tokenizer
            )
            model_inputs_uncollated.append(model_inputs_)
        
        # pad: in this step, attention mask is added
        model_inputs_final = pad([i["input_ids"] for i in model_inputs_uncollated], padding_side=padding_side)
        
        # add image bound back
        model_inputs_final["image_bound"] = [i["image_bound"] for i in model_inputs_uncollated]
        
        # add pixels values
        model_inputs_final["pixel_values"] = processed_subimages_all_data
        
        # add target sizes
        model_inputs_final["tgt_sizes"] = tgt_sizes_all_data
        
        return BatchFeature(data=model_inputs_final, tensor_type=None)

	# coding=utf-8
	# Copyright 2024 RhapsodyAI and ModelBest Inc. and Microsoft 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.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# coding=utf-8
	# Copyright 2024 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.


	import re
	from typing import List, Optional, Union, Dict

	import math
	import torch
	from torchvision import transforms
	from PIL import Image

	import transformers
	from transformers.feature_extraction_utils import BatchFeature
	from transformers.image_utils import ImageInput
	from transformers.processing_utils import ProcessorMixin
	from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy, PreTokenizedInput
	from transformers.utils import TensorType
	from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
	# from transformers.image_transforms import (
	# convert_to_rgb,
	# )

	from transformers import LlamaTokenizer # for text processing

	from transformers.utils import logging

	logger = logging.get_logger(__name__)


	# image tokenizer
	def ensure_divide(length, patch_size):
	return max(round(length / patch_size) * patch_size, patch_size)

	def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False):
	width, height = original_size
	if (width * height > scale_resolution * scale_resolution) or allow_upscale:
	r = width / height
	height = int(scale_resolution / math.sqrt(r))
	width = int(height * r)
	best_width = ensure_divide(width, patch_size)
	best_height = ensure_divide(height, patch_size)
	return (best_width, best_height)

	def get_refine_size(
	original_size, grid, scale_resolution, patch_size, allow_upscale=False
	):
	width, height = original_size
	grid_x, grid_y = grid

	refine_width = ensure_divide(width, grid_x)
	refine_height = ensure_divide(height, grid_y)

	grid_width = refine_width / grid_x
	grid_height = refine_height / grid_y

	best_grid_size = find_best_resize(
	(grid_width, grid_height),
	scale_resolution,
	patch_size,
	allow_upscale=allow_upscale,
	)

	refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)

	return refine_size

	def split_to_patches(image, grid):
	patches = []
	width, height = image.size
	grid_x = int(width / grid[0])
	grid_y = int(height / grid[1])

	for i in range(0, height, grid_y):
	images = []
	for j in range(0, width, grid_x):
	box = (j, i, j + grid_x, i + grid_y)
	patch = image.crop(box)
	logger.info(f"I don't think it is so called `patch`. split_to_patches: patch size = {box}")
	images.append(patch)
	patches.append(images)

	return patches

	def slice_image(
	image,
	max_slice_nums=9,
	scale_resolution=448,
	patch_size=14,
	never_split=False
	):
	original_size = image.size
	original_width, original_height = original_size
	log_ratio = math.log(original_width / original_height)
	ratio = original_width * original_height / (scale_resolution * scale_resolution)
	multiple = min(math.ceil(ratio), max_slice_nums)

	source_image = None
	best_grid = None
	patches = []

	if multiple <= 1 or never_split:
	# don't need to slice, upsample
	best_size = find_best_resize(
	original_size, scale_resolution, patch_size, allow_upscale=True
	)
	source_image = image.resize(best_size, Image.Resampling.BICUBIC)
	else:
	candidate_split_grids_nums = []
	for i in [multiple - 1, multiple, multiple + 1]:
	if i == 1 or i > max_slice_nums:
	continue
	candidate_split_grids_nums.append(i)

	# source image, down-sampling and ensure divided by patch_size
	best_resize = find_best_resize(original_size, scale_resolution, patch_size)
	source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
	candidate_grids = []

	# find best grid
	for split_grids_nums in candidate_split_grids_nums:
	m = 1
	while m <= split_grids_nums:
	if split_grids_nums % m == 0:
	candidate_grids.append([m, split_grids_nums // m])
	m += 1

	best_grid = [1, 1]
	min_error = float("inf")
	for grid in candidate_grids:
	error = abs(log_ratio - math.log(grid[0] / grid[1]))
	if error < min_error:
	best_grid = grid
	min_error = error

	refine_size = get_refine_size(
	original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
	)

	refine_image = image.resize(refine_size, Image.Resampling.BICUBIC)
	patches = split_to_patches(refine_image, best_grid)

	return source_image, patches, best_grid

	def reshape_by_patch(image_tensor, patch_size=14):
	"""
	:param image_tensor: shape [3, H, W]
	:param patch_size:
	:return: [3, patch_size, HW/patch_size]
	"""
	patches = torch.nn.functional.unfold(
	image_tensor,
	(patch_size, patch_size),
	stride=(patch_size, patch_size)
	)

	patches = patches.reshape(image_tensor.size(0), patch_size, patch_size, -1)
	patches = patches.permute(0, 1, 3, 2).reshape(image_tensor.size(0), patch_size, -1)
	return patches

	class MiniCPMVImageProcessor(BaseImageProcessor):
	r"""
	MiniCPMV image processor. -> Based on Phi3 image processor -> Used LlaVa-UHD. dynamic slicing one image image.

	Args:
	image_mean (`float` or `List[float]`, optional, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
	Mean to use if normalizing the image. This is a float or list of floats the length of the number of
	channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
	image_std (`float` or `List[float]`, optional, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
	Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
	number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
	Can be overridden by the `image_std` parameter in the `preprocess` method.
	do_convert_rgb (`bool`, optional, defaults to `True`):
	Whether to convert the image to RGB.
	"""

	def __init__(
	self,
	query_num: int = 64,
	image_mean: Optional[Union[float, List[float]]] = None,
	image_std: Optional[Union[float, List[float]]] = None,
	max_slice_nums: int = 9,
	scale_resolution: int = 448,
	patch_size: int = 14,
	**kwargs,
	) -> None:
	super().__init__(**kwargs)

	self.query_num = query_num
	self.image_mean = image_mean
	self.image_std = image_std
	self.max_slice_nums = max_slice_nums
	self.scale_resolution = scale_resolution
	self.patch_size = patch_size

	def preprocess(
	self,
	image,
	slice_mode: bool = True,
	return_tensors: Optional[Union[str, TensorType]] = None,
	):
	"""
	Args:
	images (`ImageInput`):
	Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
	passing in images with pixel values between 0 and 1, set `do_rescale=False`. # modified: one image per invoke.
	return_tensors (`str` or `TensorType`, optional):
	The type of tensors to return. Can be one of:
	- Unset: Return a list of `np.ndarray`.
	- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
	- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
	- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
	- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
	"""

	transform = transforms.Compose(
	[
	transforms.ToTensor(),
	transforms.Normalize(mean=self.image_mean, std=self.image_std)
	]
	)

	images_ = []
	tgt_sizes = []

	if slice_mode:
	slice_images = []
	source_image, patches, best_grid = slice_image( # 耗时
	image,
	self.max_slice_nums,
	self.scale_resolution,
	self.patch_size,
	)

	slice_images.append(source_image)
	if len(patches) > 0:
	for i in range(len(patches)):
	for j in range(len(patches[0])):
	slice_images.append(patches[i][j])

	for image_ in slice_images:
	slice_image_ = transform(image_) # 耗时
	H, W = slice_image_.shape[1:]
	slice_image_patchified_ = reshape_by_patch(slice_image_)
	images_.append(slice_image_patchified_)
	tgt_sizes.append(torch.Tensor([H // self.patch_size, W // self.patch_size]).type(torch.int32))

	else:
	best_grid = None
	image_ = transform(image)
	H, W = image_.shape[1:]
	image_patchified_ = reshape_by_patch(image_)
	images_.append(image_patchified_) # 耗时
	tgt_sizes.append(torch.Tensor([H // self.patch_size, W // self.patch_size]).type(torch.int32))

	return images_, tgt_sizes, best_grid


	# text tokenizer
	class MiniCPMVTextTokenizer(LlamaTokenizer):
	def __init__(self, **kwargs):
	super().__init__(**kwargs)
	self.im_start = "<image>"
	self.im_end = "</image>"
	self.ref_start = "<ref>"
	self.ref_end = "</ref>"
	self.box_start = "<box>"
	self.box_end = "</box>"
	self.quad_start = "<quad>"
	self.quad_end = "</quad>"
	self.point_start = "<point>"
	self.point_end = "</point>"
	self.slice_start = "<slice>"
	self.slice_end = "</slice>"

	@property
	def eos_id(self):
	return self.sp_model.eos_id()

	@property
	def bos_id(self):
	return self.sp_model.bos_id()

	@property
	def unk_id(self):
	return self.sp_model.unk_id()

	@property
	def im_start_id(self):
	return self._convert_token_to_id(self.im_start)

	@property
	def im_end_id(self):
	return self._convert_token_to_id(self.im_end)

	def get_grid_placeholder(tokenizer, grid, query_num):
	image_placeholder = (
	tokenizer.im_start + tokenizer.unk_token * query_num + tokenizer.im_end
	)

	cols = grid[0]
	rows = grid[1]
	slices = []
	for i in range(rows):
	lines = []
	for j in range(cols):
	lines.append(image_placeholder)
	slices.append("".join(lines))
	slice_placeholder = tokenizer.slice_start + "\n".join(slices) + tokenizer.slice_end
	return slice_placeholder

	def pad(orig_items, max_length=None, padding_value=0, padding_side="left"):
	"""
	Args:
	orig_items: a list of input_ids, each input_ids should be [1, length_i]
	"""
	assert isinstance(orig_items, list)
	assert isinstance(orig_items[0], torch.Tensor)
	padding_value = 2
	items = [t.squeeze() for t in orig_items]

	batch_size = len(items)
	shape = items[0].shape

	dim = len(shape)
	assert dim == 1, "This pad function only expect B * Tensor([seq_len]) input." # Assuming 1D tensors for simplicity

	if max_length is None:
	max_length = max(item.shape[0] for item in items)

	tensor = torch.full((batch_size, max_length), padding_value, dtype=items[0].dtype)
	attention_mask = torch.zeros((batch_size, max_length), dtype=torch.int8)

	for i, item in enumerate(items):
	length = item.shape[0]
	if padding_side == "left":
	raise Exception("Please use right padding")
	tensor[i, -length:] = item.clone()
	attention_mask[i, -length:] = 1
	else:
	tensor[i, 0:length] = item.clone()
	attention_mask[i, 0:length] = 1

	return_dict = {
	"input_ids": tensor,
	"attention_mask": attention_mask,
	}

	return return_dict

	def convert_to_tokens(input_str, tokenizer, max_inp_length):
	if tokenizer.add_bos_token:
	input_ids = tokenizer.encode(input_str)
	else:
	input_ids = [tokenizer.bos_id] + tokenizer.encode(input_str)

	input_ids = input_ids[:max_inp_length]

	input_ids = torch.tensor(input_ids, dtype=torch.int32)

	image_start_tokens = torch.where(input_ids == tokenizer.im_start_id)[0]

	# 跳过 im_start
	image_start_tokens += 1
	image_end_tokens = torch.where(input_ids == tokenizer.im_end_id)[0]
	valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))

	image_bound = torch.hstack(
	[
	image_start_tokens[:valid_image_nums].unsqueeze(-1),
	image_end_tokens[:valid_image_nums].unsqueeze(-1),
	]
	)

	model_input = {}
	model_input["input_ids"] = input_ids.unsqueeze(0)
	model_input["image_bound"] = image_bound

	return model_input

	class MiniCPMVProcessor(ProcessorMixin):
	r"""
	Based on Siglip. Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.

	[`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
	[`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.

	Args:
	image_processor ([`SiglipImageProcessor`]):
	The image processor is a required input.
	tokenizer ([`SiglipTokenizer`]):
	The tokenizer is a required input.
	"""

	attributes = ["image_processor", "tokenizer"]
	image_processor_class = "AutoImageProcessor" # sorry, we can't find a way to make `image_processor_class` equal to `MiniCPMVImageProcessor`
	tokenizer_class = "AutoTokenizer"

	def __init__(self, image_processor, tokenizer, query_num=64, slice_mode=True, max_inp_length=2048):
	super().__init__(image_processor, tokenizer)
	self.query_num = query_num
	self.slice_mode = slice_mode
	self.max_inp_length = max_inp_length

	def __call__(
	self,
	messages: Dict[str, Union[str, Image.Image]] = None, # ChatML format
	slice_mode: bool = None,
	max_inp_length: int = None,
	padding: Union[bool, str, PaddingStrategy] = False,
	padding_side: str = "left",
	truncation: Union[bool, str, TruncationStrategy] = None,
	return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
	) -> 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 SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
	the text. To prepare the image(s), this method forwards the `images` argument to
	SiglipImageProcessor's [`~SiglipImageProcessor.__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.
	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_input_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`.

	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`.
	- 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`.
	"""
	# assert len(messages) == 1, 'Do not support batch > 1'

	if slice_mode is None:
	if self.slice_mode is None:
	raise ValueError("`slice_mode` is not specified by config or usage")
	else:
	slice_mode = self.slice_mode

	if max_inp_length is None:
	if self.max_inp_length is None:
	raise ValueError("`max_inp_length` is not specified by config or usage")
	else:
	max_inp_length = self.max_inp_length

	processed_subimages_all_data = []
	processed_text_all_data = []
	tgt_sizes_all_data = []

	for msgs in messages:
	assert len(msgs) > 0, 'msgs is empty'

	processed_text_all_msgs = []
	processed_subimages_all_msgs = []
	tgt_sizes_all_msgs = []

	# process each message, each message is look like [text/image, ...]
	for i, msg in enumerate(msgs):

	role = msg["role"]
	c = msg["content"]

	assert role in ["user", "assistant"]

	if i == 0:
	assert role == "user", "The role of first msg should be user"

	if isinstance(c, Image.Image):

	processed_subimages, tgt_sizes, best_grid = self.image_processor.preprocess(image=c, slice_mode=slice_mode)

	# make image placeholders
	if slice_mode:
	cur_msg = (
	self.tokenizer.im_start
	+ self.tokenizer.unk_token * self.query_num
	+ self.tokenizer.im_end
	)
	if len(processed_subimages) > 1:
	cur_msg += get_grid_placeholder(
	self.tokenizer, best_grid, self.query_num
	)

	else:
	cur_msg = (
	self.tokenizer.im_start
	+ self.tokenizer.unk_token * self.query_num
	+ self.tokenizer.im_end
	)

	tgt_sizes_all_msgs.extend(tgt_sizes)
	processed_subimages_all_msgs.extend(processed_subimages)

	elif isinstance(c, str):
	cur_msg = c

	else:
	raise NotImplementedError(f"message {type(c)}: {c} can't be handled")

	role_title = "<用户>" if role == "user" else "<AI>"
	processed_text_all_msgs.append(role_title + cur_msg)

	processed_text_all_msgs_concat = "".join(processed_text_all_msgs)
	processed_text_all_msgs_concat += "<AI>"
	processed_text_all_data.append(processed_text_all_msgs_concat)

	processed_subimages_all_data.append(processed_subimages_all_msgs)
	tgt_sizes_all_msgs = torch.vstack(tgt_sizes_all_msgs)
	tgt_sizes_all_data.append(tgt_sizes_all_msgs)

	# convert text string to tokens, at this step, `input_ids` and `image_bound` is added
	model_inputs_uncollated = []
	for text in processed_text_all_data:
	model_inputs_ = convert_to_tokens(
	text, max_inp_length=max_inp_length, tokenizer=self.tokenizer
	)
	model_inputs_uncollated.append(model_inputs_)

	# pad: in this step, attention mask is added
	model_inputs_final = pad([i["input_ids"] for i in model_inputs_uncollated], padding_side=padding_side)

	# add image bound back
	model_inputs_final["image_bound"] = [i["image_bound"] for i in model_inputs_uncollated]

	# add pixels values
	model_inputs_final["pixel_values"] = processed_subimages_all_data

	# add target sizes
	model_inputs_final["tgt_sizes"] = tgt_sizes_all_data

	return BatchFeature(data=model_inputs_final, tensor_type=None)