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2024-11-01 17:30:48 +08:00 · 2024-11-01 17:30:48 +08:00 · 3d7978286f
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--- a/README.md
+++ b/README.md
@ -1,3 +1,111 @@
-# GOT-OCR2_0_a13446664330014720201572
+---
+pipeline_tag: image-text-to-text
+library_name: transformers
+language:
+- multilingual
+tags:
+- got
+- vision-language
+- ocr2.0
+- custom_code
+license: apache-2.0
+studios:
+- stepfun-ai/GOT_official_online_demo
+---

-General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
+<h1>General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
+</h1>
+
+[🔋Online Demo](https://modelscope.cn/studios/stepfun-ai/GOT_official_online_demo) | [🌟GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/) | [📜Paper](https://arxiv.org/abs/2409.01704)</a> 
+
+
+[Haoran Wei*](https://scholar.google.com/citations?user=J4naK0MAAAAJ&hl=en), Chenglong Liu*, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu,  [Zheng Ge](https://joker316701882.github.io/), Liang Zhao, [Jianjian Sun](https://scholar.google.com/citations?user=MVZrGkYAAAAJ&hl=en), [Yuang Peng](https://scholar.google.com.hk/citations?user=J0ko04IAAAAJ&hl=zh-CN&oi=ao), Chunrui Han, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en)
+
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6653eee7a2d7a882a805ab95/QCEFY-M_YG3Bp5fn1GQ8X.jpeg)
+
+
+
+## Usage
+Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
+```
+torch==2.0.1
+torchvision==0.15.2
+transformers==4.37.2
+tiktoken==0.6.0
+verovio==4.3.1
+accelerate==0.28.0
+```
+
+
+```python
+from modelscope import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('stepfun-ai/GOT-OCR2_0', trust_remote_code=True)
+model = AutoModel.from_pretrained('stepfun-ai/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+model = model.eval().cuda()
+
+
+# input your test image
+image_file = '/mnt/workspace/58F3EF14-E073-4BBE-B9D9-53CCFE6AE183.png'
+
+# plain texts OCR
+res = model.chat(tokenizer, image_file, ocr_type='ocr')
+
+# format texts OCR:
+# res = model.chat(tokenizer, image_file, ocr_type='format')
+
+# fine-grained OCR:
+# res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_box='')
+# res = model.chat(tokenizer, image_file, ocr_type='format', ocr_box='')
+# res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_color='')
+# res = model.chat(tokenizer, image_file, ocr_type='format', ocr_color='')
+
+# multi-crop OCR:
+# res = model.chat_crop(tokenizer, image_file, ocr_type='ocr')
+# res = model.chat_crop(tokenizer, image_file, ocr_type='format')
+
+# render the formatted OCR results:
+# res = model.chat(tokenizer, image_file, ocr_type='format', render=True, save_render_file = './demo.html')
+
+print(res)
+
+
+
+```
+More details about 'ocr_type', 'ocr_box', 'ocr_color', and 'render' can be found at our GitHub.
+Our training codes are available at our [GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/).
+
+
+
+## More Multimodal Projects
+
+👏 Welcome to explore more multimodal projects of our team:
+
+[Vary](https://github.com/Ucas-HaoranWei/Vary) | [Fox](https://github.com/ucaslcl/Fox) | [OneChart](https://github.com/LingyvKong/OneChart)
+
+## Citation
+
+If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
+
+```bib
+@article{wei2024general,
+  title={General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model},
+  author={Wei, Haoran and Liu, Chenglong and Chen, Jinyue and Wang, Jia and Kong, Lingyu and Xu, Yanming and Ge, Zheng and Zhao, Liang and Sun, Jianjian and Peng, Yuang and others},
+  journal={arXiv preprint arXiv:2409.01704},
+  year={2024}
+}
+@article{liu2024focus,
+  title={Focus Anywhere for Fine-grained Multi-page Document Understanding},
+  author={Liu, Chenglong and Wei, Haoran and Chen, Jinyue and Kong, Lingyu and Ge, Zheng and Zhu, Zining and Zhao, Liang and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu},
+  journal={arXiv preprint arXiv:2405.14295},
+  year={2024}
+}
+@article{wei2023vary,
+  title={Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models},
+  author={Wei, Haoran and Kong, Lingyu and Chen, Jinyue and Zhao, Liang and Ge, Zheng and Yang, Jinrong and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu},
+  journal={arXiv preprint arXiv:2312.06109},
+  year={2023}
+}
+```
--- a/config.json
+++ b/config.json
@ -0,0 +1,38 @@
+{
+  "_name_or_path": "ucaslcl/GOT-OCR2_0",
+  "architectures": [
+    "GOTQwenForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_GOT.GOTConfig",
+    "AutoModel": "modeling_GOT.GOTQwenForCausalLM"
+  },
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "freeze_vision_tower": false,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "im_end_token": 151858,
+  "im_patch_token": 151859,
+  "im_start_token": 151857,
+  "image_token_len": 256,
+  "initializer_range": 0.02,
+  "intermediate_size": 2816,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "GOT",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 16,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "use_cache": true,
+  "use_im_start_end": true,
+  "use_sliding_window": false,
+  "vocab_size": 151860
+}
--- a/configuration.json
+++ b/configuration.json
@ -0,0 +1 @@
+{}
--- a/generation_config.json
+++ b/generation_config.json
@ -0,0 +1,6 @@
+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.37.2"
+}
--- a/got_vision_b.py
+++ b/got_vision_b.py
@ -0,0 +1,468 @@
+import torch
+import torch.nn.functional as F
+from typing import Optional, Tuple, Type
+from functools import partial
+import torch.nn as nn
+from typing import Type
+
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+        
+        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
+        self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+        x = self.net_2(x)
+        x = self.net_3(x)
+
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+
+def build_GOT_vit_b(checkpoint=None):
+    return _build_GOT_vision(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+def _build_GOT_vision(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    image_encoder=ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        )
+    
+
+    return image_encoder
+
--- a/model.safetensors
+++ b/model.safetensors
--- a/modeling_GOT.py
+++ b/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="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, 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).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)
+
+        if stream_flag:
+            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]
+                    )
+        else:
+            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 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).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)
+
+        if stream_flag:
+            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]
+                    )
+        else:
+            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 open(html_path_2, 'w') as web_f_new:
+                web_f_new.write(new_web)
+
+        return response_str
--- a/qwen.tiktoken
+++ b/qwen.tiktoken
--- a/render_tools.py
+++ b/render_tools.py
@ -0,0 +1,96 @@
+
+punctuation_dict = {
+    "，": ",",
+    "。": ".",
+
+}
+translation_table = str.maketrans(punctuation_dict)
+ 
+def svg_to_html(svg_content, output_filename):
+
+    html_content = f"""
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <title>SVG Embedded in HTML</title>
+    </head>
+    <body>
+        <svg width="2100" height="15000" xmlns="http://www.w3.org/2000/svg">
+            {svg_content}
+        </svg>
+    </body>
+    </html>
+    """
+
+    with open(output_filename, 'w') as file:
+        file.write(html_content)
+ 
+
+
+content_mmd_to_html = """<!DOCTYPE html>
+<html lang="en" data-lt-installed="true"><head>
+  <meta charset="UTF-8">
+  <title>Title</title>
+  <script>
+    const text = 
+  </script>
+  <style>
+    #content {
+      max-width: 800px;
+      margin: auto;
+    }
+  </style>
+  <script>
+    let script = document.createElement('script');
+    script.src = "https://cdn.jsdelivr.net/npm/mathpix-markdown-it@1.3.6/es5/bundle.js";
+    document.head.append(script);
+
+    script.onload = function() {
+      const isLoaded = window.loadMathJax();
+      if (isLoaded) {
+        console.log('Styles loaded!')
+      }
+
+      const el = window.document.getElementById('content-text');
+      if (el) {
+        const options = {
+          htmlTags: true
+        };
+        const html = window.render(text, options);
+        el.outerHTML = html;
+      }
+    };
+  </script>
+</head>
+<body>
+  <div id="content"><div id="content-text"></div></div>
+</body>
+</html>
+"""
+
+
+
+tik_html = """
+<!DOCTYPE html>
+
+<html>
+
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Document</title>
+<link rel="stylesheet" type="text/css" href="https://tikzjax.com/v1/fonts.css">
+<script src="https://tikzjax.com/v1/tikzjax.js"></script>
+</head>
+<body>
+<script type="text/tikz">
+const text =
+</script>
+</body>
+</html>"""
+
+
+
+# print(tik_html)
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@ -0,0 +1,9 @@
+{
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenization_qwen.py
+++ b/tokenization_qwen.py
@ -0,0 +1,264 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Tokenization classes for QWen."""
+
+import base64
+import logging
+import os
+import unicodedata
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+import tiktoken
+from transformers import PreTrainedTokenizer, AddedToken
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
+
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+ENDOFTEXT = "<|endoftext|>"
+IMSTART = "<|im_start|>"
+IMEND = "<|im_end|>"
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
+SPECIAL_TOKENS = (
+    ENDOFTEXT,
+    IMSTART,
+    IMEND,
+) + EXTRAS
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    with open(tiktoken_bpe_file, "rb") as f:
+        contents = f.read()
+    return {
+        base64.b64decode(token): int(rank)
+        for token, rank in (line.split() for line in contents.splitlines() if line)
+    }
+
+class QWenTokenizer(PreTrainedTokenizer):
+    """QWen tokenizer."""
+
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    def __init__(
+        self,
+        vocab_file,
+        errors="replace",
+        image_start_tag='<img>',
+        image_end_tag='</img>',
+        image_pad_tag='<imgpad>',
+        ref_start_tag='<ref>',
+        ref_end_tag='</ref>',
+        box_start_tag='<box>',
+        box_end_tag='</box>',
+        quad_start_tag='<quad>',
+        quad_end_tag='</quad>',
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        
+        self.image_start_tag = image_start_tag
+        self.image_end_tag = image_end_tag
+        self.image_pad_tag = image_pad_tag
+        self.ref_start_tag = ref_start_tag
+        self.ref_end_tag = ref_end_tag
+        self.box_start_tag = box_start_tag
+        self.box_end_tag = box_end_tag
+        self.quad_start_tag = quad_start_tag
+        self.quad_end_tag = quad_end_tag
+        self.IMAGE_ST = (
+            ref_start_tag, ref_end_tag,
+            box_start_tag, box_end_tag,
+            quad_start_tag, quad_end_tag,
+            image_start_tag, image_end_tag,
+            image_pad_tag
+        )
+
+        self.errors = errors  # how to handle errors in decoding
+
+        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: dict[bytes, int]
+        self.special_tokens = {
+            token: index
+            for index, token in enumerate(
+                SPECIAL_TOKENS + self.IMAGE_ST, start=len(self.mergeable_ranks)
+            )
+        }
+        
+        self.img_start_id = self.special_tokens[self.image_start_tag]
+        self.img_end_id = self.special_tokens[self.image_end_tag]
+        self.img_pad_id = self.special_tokens[self.image_pad_tag]
+        self.ref_start_id = self.special_tokens[self.ref_start_tag]
+        self.ref_end_id = self.special_tokens[self.ref_end_tag]
+        self.box_start_id = self.special_tokens[self.box_start_tag]
+        self.box_end_id = self.special_tokens[self.box_end_tag]
+        self.quad_start_id = self.special_tokens[self.quad_start_tag]
+        self.quad_end_id = self.special_tokens[self.quad_end_tag]
+
+        enc = tiktoken.Encoding(
+            "Qwen",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        assert (
+            len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+        ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+        self.decoder = {
+            v: k for k, v in self.mergeable_ranks.items()
+        }  # type: dict[int, bytes|str]
+        self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+        self.tokenizer = enc  # type: tiktoken.Encoding
+
+        self.eod_id = self.tokenizer.eot_token
+        self.im_start_id = self.special_tokens[IMSTART]
+        self.im_end_id = self.special_tokens[IMEND]
+
+    def __len__(self) -> int:
+        return self.tokenizer.n_vocab
+
+    def get_vocab(self) -> Dict[bytes, int]:
+        return self.mergeable_ranks
+
+    def convert_tokens_to_ids(
+        self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+    ) -> List[int]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.mergeable_ranks.get(tokens)
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.mergeable_ranks.get(token))
+        return ids
+
+    def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
+        if not special_tokens and new_tokens:
+            raise ValueError('Adding regular tokens is not supported')
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in SPECIAL_TOKENS:
+                raise ValueError('Adding unknown special tokens is not supported')
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        """
+        Save only the vocabulary of the tokenizer (vocabulary).
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        file_path = os.path.join(save_directory, "qwen.tiktoken")
+        with open(file_path, "w", encoding="utf8") as w:
+            for k, v in self.mergeable_ranks.items():
+                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+                w.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs,
+    ) -> List[Union[bytes, str]]:
+        """
+        Converts a string in a sequence of tokens.
+
+        Args:
+            text (`str`):
+                The sequence to be encoded.
+            allowed_special (`Literal["all"]` or `set`):
+                The surface forms of the tokens to be encoded as special tokens in regular texts.
+                Default to "all".
+            disallowed_special (`Literal["all"]` or `Collection`):
+                The surface forms of the tokens that should not be in regular texts and trigger errors.
+                Default to an empty tuple.
+
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific encode method.
+
+        Returns:
+            `List[bytes|str]`: The list of tokens.
+        """
+        tokens = []
+        text = unicodedata.normalize("NFC", text)
+
+        # this implementation takes a detour: text -> token id -> token surface forms
+        for t in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        if token in self.mergeable_ranks:
+            return self.mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.eod_id]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@ -0,0 +1,14 @@
+{
+  "added_tokens_decoder": {},
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_qwen.QWenTokenizer",
+      null
+    ]
+  },
+  "clean_up_tokenization_spaces": true,
+  "model_max_length": 8000,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "tokenizer_class": "QWenTokenizer"
+}