modeling_GOT.py

from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM, StoppingCriteria, TextStreamer
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from typing import List, Optional, Tuple, Union
from transformers.cache_utils import Cache
import requests
from PIL import Image
from io import BytesIO
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .got_vision_b import build_GOT_vit_b
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import dataclasses
from megfile import smart_open

DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = '<imgpad>'
DEFAULT_IM_START_TOKEN = '<img>'
DEFAULT_IM_END_TOKEN = '</img>'

from enum import auto, Enum
class SeparatorStyle(Enum):
    """Different separator style."""
    SINGLE = auto()
    TWO = auto()
    MPT = auto()


@dataclasses.dataclass
class Conversation:
    """A class that keeps all conversation history."""
    system: str
    roles: List[str]
    messages: List[List[str]]
    offset: int
    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
    sep: str = "<|im_end|>"
    sep2: str = None
    version: str = "Unknown"

    skip_next: bool = False

    def get_prompt(self):
        if self.sep_style == SeparatorStyle.SINGLE:
            ret = self.system + self.sep + '\n'
            for role, message in self.messages:
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += role + ": " + message + self.sep
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.TWO:
            seps = [self.sep, self.sep2]
            ret = self.system + seps[0]
            for i, (role, message) in enumerate(self.messages):
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
            return ret
        if self.sep_style == SeparatorStyle.MPT:
            if self.system:
                ret = self.system + self.sep 
            else:
                ret = ''
            for role, message in self.messages:
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += role + message + self.sep
                else:
                    ret += role
            return ret
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")


    def append_message(self, role, message):
        self.messages.append([role, message])

    def copy(self):
        return Conversation(
            system=self.system,
            roles=self.roles,
            messages=[[x, y] for x, y in self.messages],
            offset=self.offset,
            sep_style=self.sep_style,
            sep=self.sep,
            sep2=self.sep2)



class KeywordsStoppingCriteria(StoppingCriteria):
    def __init__(self, keywords, tokenizer, input_ids):
        self.keywords = keywords
        self.keyword_ids = [tokenizer(keyword).input_ids for keyword in keywords]
        self.keyword_ids = [keyword_id[0] for keyword_id in self.keyword_ids if type(keyword_id) is list and len(keyword_id) == 1]
        self.tokenizer = tokenizer
        self.start_len = None
        self.input_ids = input_ids

    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        if self.start_len is None:
            self.start_len = self.input_ids.shape[1]
        else:
            for keyword_id in self.keyword_ids:
                if output_ids[0, -1] == keyword_id:
                    return True
            outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0]
            for keyword in self.keywords:
                if keyword in outputs:
                    return True
        return False
    

class GOTImageEvalProcessor:
    def __init__(self, image_size=384, mean=None, std=None):
        if mean is None:
            mean = (0.48145466, 0.4578275, 0.40821073)
        if std is None:
            std = (0.26862954, 0.26130258, 0.27577711)

        self.normalize = transforms.Normalize(mean, std)

        self.transform = transforms.Compose(
            [
                transforms.Resize(
                    (image_size, image_size), interpolation=InterpolationMode.BICUBIC
                ),
                transforms.ToTensor(),
                self.normalize,
            ]
        )
    def __call__(self, item):
        return self.transform(item)



class GOTConfig(Qwen2Config):
    model_type = "GOT"


class GOTQwenModel(Qwen2Model):
    config_class = GOTConfig

    def __init__(self, config: Qwen2Config):
        super(GOTQwenModel, self).__init__(config)

        self.vision_tower_high = build_GOT_vit_b()

        self.mm_projector_vary =  nn.Linear(1024, 1024)


    def initialize_vision_modules(
        self, 
        vision_tower,
        pretrained_stage1_model=None,
        freeze_vision_tower=False,
        use_im_start_end=False,
        vision_select_layer=-1,
        dtype=torch.float16,
        device="cuda"
    ):


        image_processor_high = GOTImageEvalProcessor(image_size=1024)
      
        self.vision_tower_high = self.vision_tower_high.to(dtype=dtype, device=device)

        self.mm_projector_vary = self.mm_projector_vary.to(dtype=dtype, device=device)


        image_token_len = 256

        self.config.vision_tower = vision_tower
        self.config.image_token_len = image_token_len

        self.config.use_im_start_end = True

        self.config.vision_select_layer = vision_select_layer
        self.config.freeze_vision_tower = freeze_vision_tower
        
        return dict(
            image_processor_high=image_processor_high,
            image_token_len=image_token_len,
        )
         
    
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:

        # HACK: replace back original embeddings for LLaVA pretraining
        orig_embeds_params = getattr(self, 'orig_embeds_params', None)
        if orig_embeds_params is not None:
            with torch.no_grad():
                self.get_input_embeddings().weight[:-self.num_new_tokens] = orig_embeds_params[:-self.num_new_tokens].data

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)


        vision_tower_high = getattr(self, 'vision_tower_high', None)


        if vision_tower_high is not None and (input_ids.shape[1] != 1 or self.training) and images is not None:
            use_im_start_end = getattr(self.config, "use_im_start_end", -1)

            vision_select_layer = getattr(self.config, "vision_select_layer", -1)
            im_patch_token = getattr(self.config, "im_patch_token", -1)
            im_start_token = getattr(self.config, "im_start_token", -1)
            im_end_token = getattr(self.config, "im_end_token", -1)
            freeze_vision_tower = getattr(self.config, "freeze_vision_tower", False)

            im_patch_token = 151859

            im_start_token = 151857

            im_end_token = 151858
            
            image_features = []
            
            for image in images:
                P, C, H, W = image.shape
                if P == 1:
                    with torch.set_grad_enabled(False):
                        cnn_feature = vision_tower_high(image)
                        cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) # 256*1024
                    image_feature = self.mm_projector_vary(cnn_feature)
                    image_features.append(image_feature)

                else:
                    image_patches = torch.unbind(image)
                    image_patches_features = []
                    for image_patch in image_patches:
                        image_p = torch.stack([image_patch])
                        
                        with torch.set_grad_enabled(False):
                            cnn_feature_p = vision_tower_high(image_p)
                            cnn_feature_p = cnn_feature_p.flatten(2).permute(0, 2, 1)
                        image_feature_p = self.mm_projector_vary(cnn_feature_p)
                        image_patches_features.append(image_feature_p)
                    image_feature = torch.cat(image_patches_features, dim=1)
                    image_features.append(image_feature)


            dummy_image_features_2 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
            dummy_image_features = dummy_image_features_2
            use_im_start_end = True
            new_input_embeds = []
            for cur_input_ids, cur_input_embeds, cur_image_features in zip(input_ids, inputs_embeds, image_features):
                if (cur_input_ids == im_patch_token).sum() == 0:
                    cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum()
                    new_input_embeds.append(cur_input_embeds)
                    continue

                if use_im_start_end:
                    if (cur_input_ids == im_start_token).sum() != (cur_input_ids == im_end_token).sum():
                        raise ValueError("The number of image start tokens and image end tokens should be the same.")
                    
                    image_start_tokens = torch.where(cur_input_ids == im_start_token)[0]
                    for image_start_token_pos, per_cur_image_features in zip(image_start_tokens, cur_image_features):
                        per_cur_image_features = per_cur_image_features.to(device=cur_input_embeds.device)
                        num_patches = per_cur_image_features.shape[0]

                        if cur_input_ids[image_start_token_pos + num_patches + 1] != im_end_token:
                            raise ValueError("The image end token should follow the image start token.")
                        
                        cur_input_embeds = torch.cat(
                            (
                                cur_input_embeds[:image_start_token_pos+1], 
                                per_cur_image_features, 
                                cur_input_embeds[image_start_token_pos + num_patches + 1:]
                            ), 
                            dim=0
                        )


                    new_input_embeds.append(cur_input_embeds)
                else:
                    raise NotImplementedError

            inputs_embeds = torch.stack(new_input_embeds, dim=0)

        return super(GOTQwenModel, self).forward(
            input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
            inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids,
            output_attentions=output_attentions, output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )



class GOTQwenForCausalLM(Qwen2ForCausalLM):
    config_class = GOTConfig
    # supports_gradient_checkpointing = True

    def __init__(self, config):
        super(Qwen2ForCausalLM, self).__init__(config)
        self.model = GOTQwenModel(config)

        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
        
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs  = self.model(
            input_ids=input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            images=images,
            return_dict=return_dict
            
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        # logits

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        # Omit tokens covered by past_key_values
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "images": kwargs.get("images", None),
            }
        )
        return model_inputs

    def initialize_vision_tokenizer(
        self, 
        tokenizer, 
        freeze_lm_model=False, 
        pretrained_stage1_model=None,
        device="cuda"
    ):
        config = self.get_model().config


        self.resize_token_embeddings(len(tokenizer))

        config.im_patch_token = 151859

        config.use_im_start_end = True

        if config.use_im_start_end:
            self.resize_token_embeddings(len(tokenizer))
            config.im_start_token, config.im_end_token = 151857, 151858

    def load_image(self, image_file):
        if image_file.startswith('http') or image_file.startswith('https'):
            response = requests.get(image_file)
            image = Image.open(BytesIO(response.content)).convert('RGB')
        else:
            image = Image.open(image_file).convert('RGB')
        return image

    def disable_torch_init(self):
        """
        Disable the redundant torch default initialization to accelerate model creation.
        """
        import torch
        setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
        setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)

    def chat(self, tokenizer, image_file, ocr_type, ocr_box='', ocr_color='', render=False, save_render_file=None, print_prompt=False):

        self.disable_torch_init()


        image_processor_high =  GOTImageEvalProcessor(image_size=1024)

        use_im_start_end = True

        image_token_len = 256

        image = self.load_image(image_file)

        w, h = image.size
        
        if ocr_type == 'format':
            qs = 'OCR with format: '
        else:
            qs = 'OCR: '

        if ocr_box:
            bbox = eval(ocr_box)
            if len(bbox) == 2:
                bbox[0] = int(bbox[0]/w*1000)
                bbox[1] = int(bbox[1]/h*1000)
            if len(bbox) == 4:
                bbox[0] = int(bbox[0]/w*1000)
                bbox[1] = int(bbox[1]/h*1000)
                bbox[2] = int(bbox[2]/w*1000)
                bbox[3] = int(bbox[3]/h*1000)
            if ocr_type == 'format':
                qs = str(bbox) + ' ' + 'OCR with format: '
            else:
                qs = str(bbox) + ' ' + 'OCR: '

        if ocr_color:
            if ocr_type == 'format':
                qs = '[' + ocr_color + ']' + ' ' + 'OCR with format: '
            else:
                qs = '[' + ocr_color + ']' + ' ' + 'OCR: '

        if use_im_start_end:
            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN*image_token_len + DEFAULT_IM_END_TOKEN + '\n' + qs 
        else:
            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs


        conv_mpt = Conversation(
            system="""<|im_start|>system
        You should follow the instructions carefully and explain your answers in detail.""",
            # system = None,
            roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
            version="mpt",
            messages=(),
            offset=0,
            sep_style=SeparatorStyle.MPT,
            sep="<|im_end|>",
        )

        conv = conv_mpt.copy()
        conv.append_message(conv.roles[0], qs)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()

        if print_prompt:
            print(prompt)

        inputs = tokenizer([prompt])

        image_tensor_1 = image_processor_high(image)

        input_ids = torch.as_tensor(inputs.input_ids).cuda()

        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)


        with torch.autocast("cuda", dtype=torch.bfloat16):
            output_ids = self.generate(
                input_ids,
                images=[image_tensor_1.unsqueeze(0).half().cuda()],
                do_sample=False,
                num_beams = 1,
                no_repeat_ngram_size = 20,
                # streamer=streamer,
                max_new_tokens=4096,
                stopping_criteria=[stopping_criteria]
                )
            
            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
            
            if outputs.endswith(stop_str):
                outputs = outputs[:-len(stop_str)]
            outputs = outputs.strip()
            response_str = outputs

            if render:
                print('==============rendering===============')
                from .render_tools import svg_to_html, content_mmd_to_html, tik_html, translation_table

                if '**kern' in outputs:
                    import verovio
                    tk = verovio.toolkit()
                    tk.loadData(outputs)
                    tk.setOptions({"pageWidth": 2100, "footer": 'none',
                'barLineWidth': 0.5, 'beamMaxSlope': 15,
                'staffLineWidth': 0.2, 'spacingStaff': 6})
                    tk.getPageCount()
                    svg = tk.renderToSVG()
                    svg = svg.replace("overflow=\"inherit\"", "overflow=\"visible\"")

                    svg_to_html(svg, save_render_file)

                if ocr_type == 'format' and '**kern' not in outputs:

                    
                    if  '\\begin{tikzpicture}' not in outputs:
                        html_path_2 = save_render_file
                        right_num = outputs.count('\\right')
                        left_num = outputs.count('\left')

                        if right_num != left_num:
                            outputs = outputs.replace('\left(', '(').replace('\\right)', ')').replace('\left[', '[').replace('\\right]', ']').replace('\left{', '{').replace('\\right}', '}').replace('\left|', '|').replace('\\right|', '|').replace('\left.', '.').replace('\\right.', '.')


                        outputs = outputs.replace('"', '``').replace('$', '')

                        outputs_list = outputs.split('\n')
                        gt= ''
                        for out in outputs_list:
                            gt +=  '"' + out.replace('\\', '\\\\') + r'\n' + '"' + '+' + '\n' 
                        
                        gt = gt[:-2]


                        lines = content_mmd_to_html
                        lines = lines.split("const text =")
                        new_web = lines[0] + 'const text ='  + gt  + lines[1]

                    else:
                        html_path_2 = save_render_file
                        outputs = outputs.translate(translation_table)
                        outputs_list = outputs.split('\n')
                        gt= ''
                        for out in outputs_list:
                            if out:
                                if '\\begin{tikzpicture}' not in out and '\\end{tikzpicture}' not in out:
                                    while out[-1] == ' ':
                                        out = out[:-1]
                                        if out is None:
                                            break
        
                                    if out:
                                        if out[-1] != ';':
                                            gt += out[:-1] + ';\n'
                                        else:
                                            gt += out + '\n'
                                else:
                                    gt += out + '\n'


                        lines = tik_html
                        lines = lines.split("const text =")
                        new_web = lines[0] + gt + lines[1]

                    with smart_open(html_path_2, 'w') as web_f_new:
                        web_f_new.write(new_web)
        return response_str

    def dynamic_preprocess(self, image, min_num=1, max_num=6, image_size=1024, use_thumbnail=True):
        
        def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
            best_ratio_diff = float('inf')
            best_ratio = (1, 1)
            area = width * height
            for ratio in target_ratios:
                target_aspect_ratio = ratio[0] / ratio[1]
                ratio_diff = abs(aspect_ratio - target_aspect_ratio)
                if ratio_diff < best_ratio_diff:
                    best_ratio_diff = ratio_diff
                    best_ratio = ratio
                elif ratio_diff == best_ratio_diff:
                    if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                        best_ratio = ratio
            # print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
            return best_ratio
        
        orig_width, orig_height = image.size
        aspect_ratio = orig_width / orig_height

        # calculate the existing image aspect ratio
        target_ratios = set(
            (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
            i * j <= max_num and i * j >= min_num)
        # print(target_ratios)
        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

        # find the closest aspect ratio to the target
        target_aspect_ratio = find_closest_aspect_ratio(
            aspect_ratio, target_ratios, orig_width, orig_height, image_size)

        # print(target_aspect_ratio)
        # calculate the target width and height
        target_width = image_size * target_aspect_ratio[0]
        target_height = image_size * target_aspect_ratio[1]
        blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

        # resize the image
        resized_img = image.resize((target_width, target_height))
        processed_images = []
        for i in range(blocks):
            box = (
                (i % (target_width // image_size)) * image_size,
                (i // (target_width // image_size)) * image_size,
                ((i % (target_width // image_size)) + 1) * image_size,
                ((i // (target_width // image_size)) + 1) * image_size
            )
            # split the image
            split_img = resized_img.crop(box)
            processed_images.append(split_img)
        assert len(processed_images) == blocks
        if use_thumbnail and len(processed_images) != 1:
            thumbnail_img = image.resize((image_size, image_size))
            processed_images.append(thumbnail_img)
        return processed_images


    def chat_crop(self, tokenizer, image_file, render=False, save_render_file=None, print_prompt=False):
        # Model
        self.disable_torch_init()
        multi_page=False


        image_processor_high =  GOTImageEvalProcessor(image_size=1024)

        use_im_start_end = True


        image_token_len = 256

        image_list = []

        # if len(image_file_list)>1:
        #     multi_page = True

        if multi_page:
            qs = 'OCR with format across multi pages: '
            # only for png files
            # import glob
            # from natsort import natsorted
            # patches = glob.glob(image_file + '/*png')
            patches = image_file
            # patches = natsorted(patches)
            sub_images = []
            for sub_image in patches:
                sub_images.append(self.load_image(sub_image))

            ll = len(patches)
            # print(patches)
            # print("len ll: ", ll)

        else:
            qs = 'OCR with format upon the patch reference: '
            img = self.load_image(image_file)
            sub_images = self.dynamic_preprocess(img)
            ll = len(sub_images)

        for image in sub_images:
            image_tensor_1 = image_processor_high(image)
            image_list.append(image_tensor_1)


        image_list = torch.stack(image_list)

        print('====new images batch size======:  \n',image_list.shape)


        if use_im_start_end:
            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN*image_token_len*ll + DEFAULT_IM_END_TOKEN + '\n' + qs 
        else:
            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs


        conv_mpt = Conversation(
            system="""<|im_start|>system
        You should follow the instructions carefully and explain your answers in detail.""",
            # system = None,
            roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
            version="mpt",
            messages=(),
            offset=0,
            sep_style=SeparatorStyle.MPT,
            sep="<|im_end|>",
        )

        conv = conv_mpt.copy()
        conv.append_message(conv.roles[0], qs)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()

        if print_prompt:
            print(prompt)

        inputs = tokenizer([prompt])

        input_ids = torch.as_tensor(inputs.input_ids).cuda()

        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)


        with torch.autocast("cuda", dtype=torch.bfloat16):
            output_ids = self.generate(
                input_ids,
                images=[image_list.half().cuda()],
                do_sample=False,
                num_beams = 1,
                # no_repeat_ngram_size = 20,
                # streamer=streamer,
                max_new_tokens=4096,
                stopping_criteria=[stopping_criteria]
                )

        outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
        
        if outputs.endswith(stop_str):
            outputs = outputs[:-len(stop_str)]
        outputs = outputs.strip()   
        response_str = outputs

        if render:
            print('==============rendering===============')
            from .render_tools import content_mmd_to_html
            html_path_2 = save_render_file
            right_num = outputs.count('\\right')
            left_num = outputs.count('\left')

            if right_num != left_num:
                outputs = outputs.replace('\left(', '(').replace('\\right)', ')').replace('\left[', '[').replace('\\right]', ']').replace('\left{', '{').replace('\\right}', '}').replace('\left|', '|').replace('\\right|', '|').replace('\left.', '.').replace('\\right.', '.')


            outputs = outputs.replace('"', '``').replace('$', '')

            outputs_list = outputs.split('\n')
            gt= ''
            for out in outputs_list:
                gt +=  '"' + out.replace('\\', '\\\\') + r'\n' + '"' + '+' + '\n' 
            
            gt = gt[:-2]

            lines = content_mmd_to_html
            lines = lines.split("const text =")
            new_web = lines[0] + 'const text ='  + gt  + lines[1]
                
            with smart_open(html_path_2, 'w') as web_f_new:
                web_f_new.write(new_web)

        return response_str