Delete modeling_got.py

modeling_got.py

@@ -1,881 +0,0 @@
-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
-###
-
-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="mps"
-    ):
-
-
-        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="mps"
-    ):
-        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, gradio_input=False, stream_flag = False):
-
-        self.disable_torch_init()
-
-
-        image_processor_high =  GOTImageEvalProcessor(image_size=1024)
-
-        use_im_start_end = True
-
-        image_token_len = 256
-
-        if gradio_input:
-            image = image_file.copy()
-        else:
-            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)
-
-        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)
-
-        if stream_flag:
-            with torch.autocast("cpu", dtype=torch.bfloat16):
-                output_ids = self.generate(
-                    input_ids,
-                    images=[image_tensor_1.unsqueeze(0).half()],
-                    do_sample=False,
-                    num_beams = 1,
-                    no_repeat_ngram_size = 20,
-                    streamer=streamer,
-                    max_new_tokens=4096,
-                    stopping_criteria=[stopping_criteria]
-                    )
-        else:
-            with torch.autocast("cpu", dtype=torch.bfloat16):
-                output_ids = self.generate(
-                    input_ids,
-                    images=[image_tensor_1.unsqueeze(0).half()],
-                    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 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, ocr_type, render=False, save_render_file=None, print_prompt=False, gradio_input=False, stream_flag = 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:
-            if ocr_type == 'format':
-                qs = 'OCR with format upon the patch reference: '
-            else:
-                qs = 'OCR upon the patch reference: '
-            if gradio_input:
-                img = image_file.copy()
-            else:
-                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)
-
-        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)
-
-        if stream_flag:
-            with torch.autocast("cpu", dtype=torch.bfloat16):
-                output_ids = self.generate(
-                    input_ids,
-                    images=[image_list.half()],
-                    do_sample=False,
-                    num_beams = 1,
-                    # no_repeat_ngram_size = 20,
-                    streamer=streamer,
-                    max_new_tokens=4096,
-                    stopping_criteria=[stopping_criteria]
-                    )
-        else:
-            with torch.autocast("cpu", dtype=torch.bfloat16):
-                output_ids = self.generate(
-                    input_ids,
-                    images=[image_list.half()],
-                    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 open(html_path_2, 'w') as web_f_new:
-                web_f_new.write(new_web)
-
-        return response_str