Upload modeling_got.py

modeling_got.py

@@ -0,0 +1,881 @@
+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="cpu"
+    ):
+
+
+        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="cpu"
+    ):
+        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).cpu()
+
+        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().cpu()],
+                    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().cpu()],
+                    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).cpu()
+
+        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().cpu()],
+                    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().cpu()],
+                    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