First Create

Without axllm included

.gitattributes

@@ -33,3 +33,28 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/gradio_demo.png filter=lfs diff=lfs merge=lfs -text
+assets/IMG_0059.JPG filter=lfs diff=lfs merge=lfs -text
+assets/IMG_0462.JPG filter=lfs diff=lfs merge=lfs -text
+assets/IMG_0675.JPG filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l0_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l1_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l10_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l11_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l12_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l13_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l14_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l15_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l16_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l17_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l2_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l3_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l4_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l5_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l6_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l7_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l8_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_p128_l9_together.axmodel filter=lfs diff=lfs merge=lfs -text
+paddleocr_vl_post.axmodel filter=lfs diff=lfs merge=lfs -text
+python/paddleocr_vl_1-5_tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+vit_576x768.axmodel filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,274 @@
+---
+library_name: transformers
+license: bsd-3-clause
+base_model:
+- PaddlePaddle/PaddleOCR-VL-1.5
+pipeline_tag: image-text-to-text
+tags:
+- PaddleOCR-VL-1.5
+- OCR
+- VLM
+language:
+- zh
+- en
+---
+
+# PaddleOCR-VL-1.5
+
+This version of PaddleOCR-VL-1.5 has been converted to run on the WaveMatrix NPU using **w4a16** quantization.
+
+Compatible with Pulsar2 version: 5.0
+
+## Convert tools links:
+
+For those who are interested in model conversion, you can try to export axmodel through the original repo:
+
+- https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5
+
+- [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html)
+
+- [WaveMatrix NPU HOST LLM Runtime](https://github.com/WaveMatrix/ax-llm)
+
+## Support Platform
+
+- WM9955
+  - WM9955 DEMO Board
+  - [M.2 Accelerator card](https://docs.m5stack.com/zh_CN/ai_hardware/LLM-8850_Card)
+
+**Image Process**
+| Chips | input size | image num | ViT encoder | TTFT (640 tokens) | Decode speed | CMM | Flash |
+|--|--|--|--|--|--|--|--|
+| WM9955 | 576x768 | 1 | 1685.554 ms | 361.8 ms | 44.6 tokens/sec | TBD | TBD |
+
+## How to use
+
+## Install axllm
+
+```shell
+git clone -b axllm https://github.com/WaveMatrix/ax-llm.git
+cd ax-llm
+./install.sh
+```
+
+## Model Download (Hugging Face)
+Create directory then enter:
+
+```shell
+mkdir -p WaveMatrix/PaddleOCR-VL-1.5
+cd WaveMatrix/PaddleOCR-VL-1.5
+hf download WaveMatrix/PaddleOCR-VL-1.5 --local-dir .
+
+# Content
+tree -L 1
+.
+|-- README.md
+|-- assets
+|-- config.json
+|-- model.embed_tokens.weight.bfloat16.bin
+|-- paddleocr_vl_p128_l0_together.axmodel
+...
+|-- paddleocr_vl_p128_l17_together.axmodel
+|-- paddleocr_vl_post.axmodel
+|-- post_config.json
+|-- python
+|-- tokenizer.model
+|-- vision_cache
+`-- vit_576x768.axmodel
+```
+
+## Inference with WM9955 Host, such as WM9955 DEMO Board or M.2 card
+
+### Run the model via CLI
+
+```shell
+axllm run WaveMatrix/PaddleOCR-VL-1.5/
+```
+
+After `prompt`, `image >>` will pop up：
+- Just press "Enter" will go into "text only" mode
+- Or input the path of the image to start the OCR process
+
+Example for image mode:
+
+![TestImage](assets/IMG_0462.JPG)
+
+```shell
+root@WM9955 # axllm run WaveMatrix/PaddleOCR-VL-1.5/
+20:45:21.515 INF Init:218 | LLM init start
+tokenizer_type = 0
+ 95% | ##############################   |  20 /  21 [1.97s<2.07s, 10.13 count/s] init post axmodel ok,remain_cmm(4330 MB)
+20:45:23.490 INF Init:368 | max_token_len : 2047
+20:45:23.490 INF Init:371 | kv_cache_size : 256, kv_cache_num: 2047
+20:45:23.490 INF Init:374 | prefill_token_num : 128
+20:45:23.490 INF Init:379 | grp: 1, prefill_max_kv_cache_num : 1
+20:45:23.490 INF Init:379 | grp: 2, prefill_max_kv_cache_num : 128
+20:45:23.490 INF Init:379 | grp: 3, prefill_max_kv_cache_num : 256
+20:45:23.490 INF Init:379 | grp: 4, prefill_max_kv_cache_num : 384
+20:45:23.490 INF Init:379 | grp: 5, prefill_max_kv_cache_num : 512
+20:45:23.490 INF Init:379 | grp: 6, prefill_max_kv_cache_num : 640
+20:45:23.490 INF Init:384 | prefill_max_token_num : 640
+20:45:23.490 INF Init:27 | LLaMaEmbedSelector use mmap
+100% | ################################ |  21 /  21 [1.98s<1.98s, 10.62 count/s] embed_selector init ok
+20:45:24.028 INF Init:453 | PaddleOCRVL: encoder input nSize=5334336 -> eff_nSize=1333584 (float32 input)
+20:45:24.029 WRN Init:469 | Qwen-VL vision size override: cfg=448x448 bytes=602112, model_input_bytes=5334336 -> 756x588 (factor-search).
+20:45:24.029 INF Init:661 | PaddleOCR-VL token ids: vision_start=101305 image_pad=100295 video_pad=100295
+20:45:24.029 INF Init:686 | VisionModule init ok: type=PaddleOCRVL, tokens_per_block=567, embed_size=1024, out_dtype=fp32
+20:45:24.029 WRN Init:695 | Vision preprocess backend: SimpleCV (OpenCV not found at build time; minor differences vs OpenCV are possible)
+20:45:24.031 INF load_config:282 | load config:
+20:45:24.031 INF load_config:282 | {
+20:45:24.031 INF load_config:282 |     "enable_repetition_penalty": false,
+20:45:24.031 INF load_config:282 |     "enable_temperature": false,
+20:45:24.031 INF load_config:282 |     "enable_top_k_sampling": false,
+20:45:24.031 INF load_config:282 |     "enable_top_p_sampling": false,
+20:45:24.031 INF load_config:282 |     "penalty_window": 20,
+20:45:24.031 INF load_config:282 |     "repetition_penalty": 1.0,
+20:45:24.031 INF load_config:282 |     "temperature": 0.6,
+20:45:24.031 INF load_config:282 |     "top_k": 1,
+20:45:24.031 INF load_config:282 |     "top_p": 0.9
+20:45:24.031 INF load_config:282 | }
+20:45:24.031 INF Init:448 | LLM init ok
+Commands:
+  /q, /exit  Exit
+  /reset     Reset kvcache
+  /dd        Delete a round of conversation
+  /pp        Print history
+Ctrl+C: Stop the current ongoing generation
+VLM enabled: after each prompt, input image path (empty = text-only). Use "video:<frames_dir>" for video.
+----------------------------------------
+prompt >> OCR:
+image >> /Path/To/Your/WaveMatrix/PaddleOCR-VL-1.5/assets/IMG_0462.JPG
+20:45:30.031 INF EncodeForContent:1058 | PaddleOCRVL pixel_values bytes=1333584 min=0 max=255 (w=756 h=588 ps=14)
+20:45:31.726 INF EncodeForContent:1102 | vision cache store: /Path/To/Your/WaveMatrix/PaddleOCR-VL-1.5/assets/IMG_0462.JPG
+20:45:31.760 INF SetKVCache:747 | prefill_grpid:6 kv_cache_num:640 precompute_len:0 input_num_token:596
+20:45:31.760 INF SetKVCache:749 | current prefill_max_token_num:640
+20:45:31.760 INF SetKVCache:752 | first run
+20:45:31.761 INF Run:805 | input token num : 596, prefill_split_num : 5
+20:45:31.761 INF Run:845 | prefill chunk p=0 history_len=0 grpid=1 kv_cache_num=0 input_tokens=128
+20:45:31.761 INF Run:868 | prefill indices shape: p=0 idx_elems=384 idx_rows=3 pos_rows=3
+20:45:31.815 INF Run:845 | prefill chunk p=1 history_len=128 grpid=2 kv_cache_num=128 input_tokens=128
+20:45:31.816 INF Run:868 | prefill indices shape: p=1 idx_elems=384 idx_rows=3 pos_rows=3
+20:45:31.873 INF Run:845 | prefill chunk p=2 history_len=256 grpid=3 kv_cache_num=256 input_tokens=128
+20:45:31.873 INF Run:868 | prefill indices shape: p=2 idx_elems=384 idx_rows=3 pos_rows=3
+20:45:31.937 INF Run:845 | prefill chunk p=3 history_len=384 grpid=4 kv_cache_num=384 input_tokens=128
+20:45:31.937 INF Run:868 | prefill indices shape: p=3 idx_elems=384 idx_rows=3 pos_rows=3
+20:45:32.006 INF Run:845 | prefill chunk p=4 history_len=512 grpid=5 kv_cache_num=512 input_tokens=84
+20:45:32.006 INF Run:868 | prefill indices shape: p=4 idx_elems=384 idx_rows=3 pos_rows=3
+20:45:32.088 INF Run:1010 | ttft: 327.20 ms
+James Landay-VR
+14175
+
+20:45:32.374 NTC Run:1132 | hit eos,avg 38.47 token/s
+20:45:32.374 INF GetKVCache:721 | precompute_len:597, remaining:43
+```
+
+### Start the service (OpenAI API compatiable)
+
+```sh
+axllm serve WaveMatrix/PaddleOCR-VL-1.5/
+```
+
+Example journal:
+
+```shell
+root@WM9955 # axllm serve WaveMatrix/PaddleOCR-VL-1.5/
+20:47:54.027 INF Init:218 | LLM init start
+tokenizer_type = 0
+ 95% | ##############################   |  20 /  21 [1.96s<2.05s, 10.22 count/s] init post axmodel ok,remain_cmm(4330 MB)
+20:47:55.983 INF Init:368 | max_token_len : 2047
+20:47:55.983 INF Init:371 | kv_cache_size : 256, kv_cache_num: 2047
+20:47:55.983 INF Init:374 | prefill_token_num : 128
+20:47:55.983 INF Init:379 | grp: 1, prefill_max_kv_cache_num : 1
+20:47:55.983 INF Init:379 | grp: 2, prefill_max_kv_cache_num : 128
+20:47:55.983 INF Init:379 | grp: 3, prefill_max_kv_cache_num : 256
+20:47:55.983 INF Init:379 | grp: 4, prefill_max_kv_cache_num : 384
+20:47:55.983 INF Init:379 | grp: 5, prefill_max_kv_cache_num : 512
+20:47:55.983 INF Init:379 | grp: 6, prefill_max_kv_cache_num : 640
+20:47:55.983 INF Init:384 | prefill_max_token_num : 640
+20:47:55.983 INF Init:27 | LLaMaEmbedSelector use mmap
+100% | ################################ |  21 /  21 [1.96s<1.96s, 10.72 count/s] embed_selector init ok
+20:47:56.526 INF Init:453 | PaddleOCRVL: encoder input nSize=5334336 -> eff_nSize=1333584 (float32 input)
+20:47:56.526 WRN Init:469 | Qwen-VL vision size override: cfg=448x448 bytes=602112, model_input_bytes=5334336 -> 756x588 (factor-search).
+20:47:56.526 INF Init:661 | PaddleOCR-VL token ids: vision_start=101305 image_pad=100295 video_pad=100295
+20:47:56.526 INF Init:686 | VisionModule init ok: type=PaddleOCRVL, tokens_per_block=567, embed_size=1024, out_dtype=fp32
+20:47:56.526 WRN Init:695 | Vision preprocess backend: SimpleCV (OpenCV not found at build time; minor differences vs OpenCV are possible)
+20:47:56.528 INF load_config:282 | load config:
+20:47:56.528 INF load_config:282 | {
+20:47:56.528 INF load_config:282 |     "enable_repetition_penalty": false,
+20:47:56.528 INF load_config:282 |     "enable_temperature": false,
+20:47:56.528 INF load_config:282 |     "enable_top_k_sampling": false,
+20:47:56.528 INF load_config:282 |     "enable_top_p_sampling": false,
+20:47:56.528 INF load_config:282 |     "penalty_window": 20,
+20:47:56.528 INF load_config:282 |     "repetition_penalty": 1.0,
+20:47:56.528 INF load_config:282 |     "temperature": 0.6,
+20:47:56.528 INF load_config:282 |     "top_k": 1,
+20:47:56.528 INF load_config:282 |     "top_p": 0.9
+20:47:56.528 INF load_config:282 | }
+20:47:56.528 INF Init:448 | LLM init ok
+Starting server on port 8000 with model 'WaveMatrix/PaddleOCR-VL-1.5'...
+API URLs:
+  GET  http://127.0.0.1:8000/health
+  GET  http://127.0.0.1:8000/v1/models
+  POST http://127.0.0.1:8000/v1/chat/completions
+  GET  http://10.168.232.217:8000/health
+  GET  http://10.168.232.217:8000/v1/models
+  POST http://10.168.232.217:8000/v1/chat/completions
+  GET  http://172.17.0.1:8000/health
+  GET  http://172.17.0.1:8000/v1/models
+  POST http://172.17.0.1:8000/v1/chat/completions
+Aliases:
+  GET  http://127.0.0.1:8000/models
+  POST http://127.0.0.1:8000/chat/completions
+  GET  http://10.168.232.217:8000/models
+  POST http://10.168.232.217:8000/chat/completions
+  GET  http://172.17.0.1:8000/models
+  POST http://172.17.0.1:8000/chat/completions
+OpenAI API Server starting on http://0.0.0.0:8000
+Max concurrency: 1
+Models: WaveMatrix/PaddleOCR-VL-1.5
+```
+
+## Python Inference (Optional)
+
+Python inference script is under `python/`
+
+```shell
+python3 python/infer_axmodel.py \
+  --hf_model ./python/paddleocr_vl_1-5_tokenizer \
+  --axmodel_path . \
+  --vit_model_path ./vit_576x768.axmodel \
+  --image_path ./assets/IMG_0462.JPG \
+  --task ocr
+```
+
+Task type supported(`--task`):
+- `ocr` - General text recognition
+- `table` - Table recognition
+- `chart` - Chart recognition
+- `formula` - Formula cognition
+- `spotting` - Text spotting
+- `seal` - Seal recognition
+
+## End-to-End Metrics (WM9955N)
+
+| Metric | Value |
+| -- | -- |
+| Max TTFT (640 tokens) | 361.8 ms |
+| Decode speed | 44.6 tokens/s |
+| ViT latency (576x768) | 1685.554 ms |
+
+### Subgraph Latency
+
+| Stage | Subgraph | Latency |
+| -- | -- | -- |
+| Prefill | g1 | 2.551 ms |
+| Prefill | g2 | 2.883 ms |
+| Prefill | g3 | 3.158 ms |
+| Prefill | g4 | 3.413 ms |
+| Prefill | g5 | 3.795 ms |
+| Prefill | g6 | 4.007 ms |
+| Decode | g0 | 0.949 ms |
+| Post-process | - | 5.313 ms |
+| ViT | - | 1685.554 ms |
+
+
+## Discussion

config.json

@@ -0,0 +1,26 @@
+{
+  "system_prompt": "You are PaddleOCR-VL, a helpful OCR assistant.",
+  "model_name": "WaveMatrix/PaddleOCR-VL-1.5",
+  "url_tokenizer_model": "paddleocr_vl_tokenizer.txt",
+  "tokenizer_type": "PaddleOCRVL",
+  "post_config_path": "post_config.json",
+  "template_filename_axmodel": "paddleocr_vl_p128_l%d_together.axmodel",
+  "axmodel_num": 18,
+  "filename_post_axmodel": "paddleocr_vl_post.axmodel",
+  "filename_tokens_embed": "model.embed_tokens.weight.bfloat16.bin",
+  "tokens_embed_num": 103424,
+  "tokens_embed_size": 1024,
+  "use_mmap_load_embed": true,
+  "vlm_type": "PaddleOCRVL",
+  "filename_image_encoder_axmodel": "vit_576x768.axmodel",
+  "vision_patch_size": 14,
+  "vision_temporal_patch_size": 1,
+  "vision_spatial_merge_size": 2,
+  "vision_fps": 1,
+  "vision_tokens_per_second": 2,
+  "vision_cache_dir": "vision_cache",
+  "server_max_output_tokens": 1152,
+  "server_forced_prompt_text": "OCR:",
+  "use_mmap_load_layer": true,
+  "devices": [0]
+}

model.embed_tokens.weight.bfloat16.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22a82d88a3c44360e7c42d3c3bcee201959f303583603dfe87f0ca956dc98d5c
+size 211812352

paddleocr_vl_p128_l0_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41d9d8080b96b9d0d6969ea3ce0eb274feb39ac0cfc51be01e32adf342280749
+size 20157171

paddleocr_vl_p128_l10_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:318018d95416db758ef6461343b127d6a7fb710f032197d62c12222334c190f9
+size 20157203

paddleocr_vl_p128_l11_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55aacb7b918186edb284bf1bac204ff2dfbf3b239a107aa250c1be58d7f93aa1
+size 20156979

paddleocr_vl_p128_l12_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d71a61c9cd06dc267b8c583ffcc66c531e1f6d4dd43c58d6560991577cf954c6
+size 20156979

paddleocr_vl_p128_l13_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5c092176b7f2e04abe9933ba9b73717e936faaafdfc28759206e2d2e7c1331e
+size 20156979

paddleocr_vl_p128_l14_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0098264c1e668ab1b260dff8f0087ae13164dce24e4f6e2f1c1fc3fc7ca85534
+size 20158131

paddleocr_vl_p128_l15_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74630eb86e81984224a22cc4a667bd4ce23a3c59ffdb76a6ba66d8114c8d19cc
+size 20158419

paddleocr_vl_p128_l16_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91ac64c3e9a94e295555715330f9ef3d0c946ed7450916c0fe941084e55bfb51
+size 20158195

paddleocr_vl_p128_l17_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c63f3d71ab7c8d5205da682215dcb6f526c8f023b7cf8973a5d557237272cda
+size 20157875

paddleocr_vl_p128_l1_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:257625386de3aa6e5d8e61ed6f7d1bf089d969a6b08fd09a9666dae2f13712df
+size 20156979

paddleocr_vl_p128_l2_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2632ff53e7adc8cc4706d830b67c53176f5e1ba18b33a6accf089f68db5a344f
+size 20157747

paddleocr_vl_p128_l3_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b05dd1748d14e0b468715f2dc8d15b6615dead019b7f29d01e06a6c73a9f2458
+size 20157139

paddleocr_vl_p128_l4_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd197e20b6d8649f4f2a6a9a8d594891ec8807dc1b43164cf5c866db1eed545a
+size 20156979

paddleocr_vl_p128_l5_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b235b75499e0670bfb54d24e0edcbe55927940ef3786524d0535fa71419e2b2
+size 20157363

paddleocr_vl_p128_l6_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c77329a149b24b7dcab7881fe5c5be0a0248eabb9951a9808ef62fcdcffbf17e
+size 20156979

paddleocr_vl_p128_l7_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0b7417695611865cbd54ae07ebbc8fd4ca8ca9345cf70c856791ad7acad3ee8
+size 20156979

paddleocr_vl_p128_l8_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ac74a46a1f32ecb360e8e6343895e4faf7cad0b325d99c223b640c933e5aafe
+size 20157267

paddleocr_vl_p128_l9_together.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:893a1709aa0ac27a2e3f617786514caea31adb4939d4169f89b36b4fb88e9f28
+size 20156979

paddleocr_vl_post.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1eae452e800f49fef67d1a3965aaf181e03e9614b1f7bddf86d1631b20b045f0
+size 115149862

post_config.json

@@ -0,0 +1,14 @@
+{
+    "enable_temperature" : false,
+    "temperature" : 0.6,
+
+    "enable_repetition_penalty" : false,
+    "repetition_penalty" : 1.0,
+    "penalty_window" : 20,
+
+    "enable_top_p_sampling" : false,
+    "top_p" : 0.9,
+
+    "enable_top_k_sampling" : false,
+    "top_k" : 1
+}

python/gradio_demo.py

@@ -0,0 +1,500 @@
+import argparse
+import os
+import socket
+import time
+from typing import Generator, List, Optional, Tuple
+
+import gradio as gr
+import numpy as np
+from ml_dtypes import bfloat16
+from PIL import Image
+from transformers import AutoConfig, AutoProcessor, AutoTokenizer
+
+from axengine import InferenceSession
+from utils.infer_func import InferManager
+from utils.vision_output import describe_output_shapes, select_vit_output
+
+try:
+    import onnxruntime as ort
+except Exception:
+    ort = None
+
+
+TASK_PROMPTS = {
+    "ocr": "OCR:",
+    "table": "Table Recognition:",
+    "formula": "Formula Recognition:",
+    "chart": "Chart Recognition:",
+    "spotting": "Spotting:",
+    "seal": "Seal Recognition:",
+}
+
+
+def _list_host_ips() -> List[str]:
+    ips = set()
+    try:
+        hostname = socket.gethostname()
+        infos = socket.getaddrinfo(hostname, None, family=socket.AF_INET)
+        for info in infos:
+            ip = info[4][0]
+            if ip and not ip.startswith("127."):
+                ips.add(ip)
+    except Exception:
+        pass
+    if not ips:
+        ips.add("127.0.0.1")
+    return sorted(ips)
+
+
+def _prepare_image(image: Image.Image, task: str) -> Tuple[Image.Image, int]:
+    image = image.convert("RGB")
+    resize_h, resize_w = 576, 768
+    image = image.resize((resize_w, resize_h))
+
+    # AX vision model is compiled with fixed 576x768 token layout.
+    # Keep spotting path aligned to avoid variable token counts.
+    max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28
+    return image, max_pixels
+
+
+def _run_vit_onnx(
+    session, pixel_values: np.ndarray, target_hidden_size: int, expected_tokens: Optional[int] = None
+) -> Tuple[np.ndarray, List[Tuple[int, ...]]]:
+    outputs = session.run(None, {"pixel_values": pixel_values})
+    return (
+        select_vit_output(outputs, target_hidden_size, expected_tokens=expected_tokens),
+        describe_output_shapes(outputs),
+    )
+
+
+def _run_vit_axmodel(
+    session, pixel_values: np.ndarray, target_hidden_size: int, expected_tokens: Optional[int] = None
+) -> Tuple[np.ndarray, List[Tuple[int, ...]]]:
+    outputs = session.run(None, {"pixel_values": pixel_values})
+    return (
+        select_vit_output(outputs, target_hidden_size, expected_tokens=expected_tokens),
+        describe_output_shapes(outputs),
+    )
+
+
+def _expected_image_features(image_grid_thw) -> int:
+    return int(sum(int(t) * int(h) * int(w) for t, h, w in image_grid_thw))
+
+
+def _expected_image_tokens(image_grid_thw, merge_size: int) -> int:
+    merge_area = int(merge_size) * int(merge_size)
+    return int(sum(int(t) * int(h) * int(w) // merge_area for t, h, w in image_grid_thw))
+
+
+def _replace_image_tokens(
+    token_ids: List[int], token_embeds: np.ndarray, image_embeds: np.ndarray, image_token_id: int
+) -> np.ndarray:
+    image_positions = [idx for idx, token_id in enumerate(token_ids) if token_id == image_token_id]
+    if not image_positions:
+        return token_embeds
+
+    flat_image_embeds = image_embeds.reshape(-1, image_embeds.shape[-1])
+    if len(image_positions) != flat_image_embeds.shape[0]:
+        raise ValueError(
+            f"Image tokens and image features do not match: tokens={len(image_positions)}, "
+            f"features={flat_image_embeds.shape[0]}"
+        )
+    if token_embeds.shape[-1] != flat_image_embeds.shape[-1]:
+        raise ValueError(
+            f"Embedding dim mismatch: token_dim={token_embeds.shape[-1]}, image_dim={flat_image_embeds.shape[-1]}"
+        )
+    token_embeds[image_positions, :] = flat_image_embeds
+    return token_embeds
+
+
+class PaddleOCRVLGradioDemo:
+    def __init__(self, hf_model: str, axmodel_dir: str, vit_model: str, max_seq_len: int = 2047):
+        self.hf_model = hf_model
+        self.axmodel_dir = axmodel_dir
+        self.vit_model = vit_model
+
+        self.embeds = np.load(os.path.join(axmodel_dir, "python/model.embed_tokens.weight.npy"))
+        self.tokenizer = AutoTokenizer.from_pretrained(self.hf_model, trust_remote_code=True)
+        self.processor = AutoProcessor.from_pretrained(self.hf_model, trust_remote_code=True)
+        self.config = AutoConfig.from_pretrained(self.hf_model, trust_remote_code=True)
+
+        self.merge_size = self.config.vision_config.spatial_merge_size
+
+        if self.vit_model.endswith(".axmodel"):
+            self.vit_session = InferenceSession(self.vit_model)
+            self.vit_mode = "axmodel"
+        else:
+            if ort is None:
+                raise ImportError("onnxruntime is required when --vit_model is an onnx file")
+            providers = ["CPUExecutionProvider"]
+            if "CUDAExecutionProvider" in ort.get_available_providers():
+                providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+            self.vit_session = ort.InferenceSession(self.vit_model, providers=providers)
+            self.vit_mode = "onnx"
+
+        self.infer_manager = InferManager(self.config, self.axmodel_dir, max_seq_len=max_seq_len)
+
+    def _build_prompt_inputs(self, image: Image.Image, task: str, user_text: str):
+        image, max_pixels = _prepare_image(image, task)
+        prompt_text = user_text.strip() if user_text.strip() else TASK_PROMPTS[task]
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": image},
+                    {"type": "text", "text": prompt_text},
+                ],
+            }
+        ]
+        inputs = self.processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            images_kwargs={
+                "size": {
+                    "shortest_edge": self.processor.image_processor.min_pixels,
+                    "longest_edge": max_pixels,
+                }
+            },
+        )
+        return prompt_text, inputs
+
+    def _prepare_model_inputs(self, inputs):
+        token_ids = inputs.input_ids[0].cpu().numpy().tolist()
+        image_grid_thw = inputs.image_grid_thw.cpu().numpy().tolist()
+        expected_tokens = _expected_image_tokens(image_grid_thw, self.merge_size)
+        expected_features = _expected_image_features(image_grid_thw)
+
+        pixel_values = inputs.pixel_values
+        if pixel_values.ndim == 4:
+            pixel_values = pixel_values.unsqueeze(0)
+        pixel_values = pixel_values.cpu().numpy().astype(np.float32)
+
+        if self.vit_mode == "axmodel":
+            image_embeds, vit_output_shapes = _run_vit_axmodel(
+                self.vit_session,
+                pixel_values,
+                target_hidden_size=self.config.hidden_size,
+                expected_tokens=expected_tokens,
+            )
+        else:
+            image_embeds, vit_output_shapes = _run_vit_onnx(
+                self.vit_session,
+                pixel_values,
+                target_hidden_size=self.config.hidden_size,
+                expected_tokens=expected_tokens,
+            )
+
+        if image_embeds.ndim == 3:
+            image_embeds = image_embeds[0]
+        image_seq_len = image_embeds.shape[0]
+
+        if image_seq_len != expected_tokens:
+            if image_seq_len == expected_features:
+                raise ValueError(
+                    "Vision output is pre-projector features. "
+                    f"got={image_seq_len}, expected_projected_tokens={expected_tokens}. "
+                    "Please re-export VIT ONNX with projector included (model_convert/export_onnx.py), "
+                    f"then re-compile to .axmodel. vit_output_shapes={vit_output_shapes}"
+                )
+            raise ValueError(
+                "Unexpected image feature length. "
+                f"got={image_seq_len}, expected_projected_tokens={expected_tokens}, "
+                f"expected_pre_projector_features={expected_features}, vit_output_shapes={vit_output_shapes}"
+            )
+        projected_embeds = image_embeds
+
+        prefill_data = np.take(self.embeds, token_ids, axis=0)
+        prefill_data = _replace_image_tokens(
+            token_ids,
+            prefill_data,
+            projected_embeds,
+            image_token_id=self.config.image_token_id,
+        )
+        prefill_data = prefill_data.astype(bfloat16)
+        return token_ids, prefill_data
+
+    def _stream_generate(self, token_ids: List[int], prefill_data: np.ndarray, max_new_tokens: int = 1024):
+        for k_cache in self.infer_manager.k_caches:
+            k_cache.fill(0)
+        for v_cache in self.infer_manager.v_caches:
+            v_cache.fill(0)
+
+        eos_token_id = None
+        if isinstance(self.config.eos_token_id, list) and len(self.config.eos_token_id) > 1:
+            eos_token_id = self.config.eos_token_id
+
+        slice_len = 128
+        t_start = time.time()
+        token_ids = self.infer_manager.prefill(self.tokenizer, token_ids, prefill_data, slice_len=slice_len)
+
+        mask = np.zeros((1, 1, self.infer_manager.max_seq_len + 1), dtype=np.float32).astype(bfloat16)
+        mask[:, :, :self.infer_manager.max_seq_len] -= 65536
+        seq_len = len(token_ids) - 1
+        if slice_len > 0:
+            mask[:, :, :seq_len] = 0
+
+        ttft_ms: Optional[float] = (time.time() - t_start) * 1000
+        decode_tokens = 0
+        decode_elapsed_ms: float = 0.0
+        generated_text = self.tokenizer.decode(token_ids[seq_len:], skip_special_tokens=True)
+        yield generated_text, ttft_ms, None, 1, False
+
+        remaining_decode_budget = max(0, int(max_new_tokens) - 1)
+        for step_idx in range(self.infer_manager.max_seq_len):
+            if remaining_decode_budget <= 0:
+                break
+            if slice_len > 0 and step_idx < seq_len:
+                continue
+
+            cur_token = token_ids[step_idx]
+            indices = np.array([step_idx], np.uint32).reshape((1, 1))
+            data = self.embeds[cur_token, :].reshape((1, 1, self.config.hidden_size)).astype(bfloat16)
+
+            for layer_idx in range(self.config.num_hidden_layers):
+                input_feed = {
+                    "K_cache": self.infer_manager.k_caches[layer_idx],
+                    "V_cache": self.infer_manager.v_caches[layer_idx],
+                    "indices": indices,
+                    "input": data,
+                    "mask": mask,
+                }
+                outputs = self.infer_manager.decoder_sessions[layer_idx].run(None, input_feed, shape_group=0)
+                self.infer_manager.k_caches[layer_idx][:, step_idx, :] = outputs[0][:, :, :]
+                self.infer_manager.v_caches[layer_idx][:, step_idx, :] = outputs[1][:, :, :]
+                data = outputs[2]
+
+            mask[..., step_idx] = 0
+            if step_idx < seq_len - 1:
+                continue
+
+            post_out = self.infer_manager.post_process_session.run(None, {"input": data})[0]
+            next_token, _, _ = self.infer_manager.post_process(post_out, temperature=0.7)
+
+            if eos_token_id is not None and next_token in eos_token_id:
+                break
+            if next_token == self.tokenizer.eos_token_id:
+                break
+
+            token_ids.append(next_token)
+            remaining_decode_budget -= 1
+
+            generated_text = self.tokenizer.decode(token_ids[seq_len:], skip_special_tokens=True)
+            decode_tokens += 1
+            decode_elapsed_ms = (time.time() - t_start) * 1000 - ttft_ms
+            avg_decode = decode_elapsed_ms / decode_tokens if decode_tokens > 0 else None
+            total_tokens = 1 + decode_tokens
+            yield generated_text, ttft_ms, avg_decode, total_tokens, False
+
+        avg_decode = decode_elapsed_ms / decode_tokens if decode_tokens > 0 else None
+        total_tokens = 1 + decode_tokens
+        yield generated_text, ttft_ms, avg_decode, total_tokens, True
+
+    def chat(self, user_input: str, image: Optional[Image.Image], task: str) -> Generator:
+        if image is None:
+            err = "请先上传图片，再执行识别。"
+            metrics = (
+                "<div style='text-align: right; font-size: 13px; color: #b91c1c; font-family: monospace;'>"
+                "需要输入图像"
+                "</div>"
+            )
+            yield [("输入", err)], gr.update(value=""), gr.update(), gr.update(value=metrics), gr.update(interactive=True)
+            return
+
+        yield (
+            [("处理中", "模型准备中…")],
+            gr.update(value=""),
+            gr.update(),
+            gr.update(
+                value="<div style='text-align: right; font-size: 13px; color: #6b7280; font-family: monospace;'>"
+                "TTFT -- ms&nbsp;&nbsp;|&nbsp;&nbsp;Decode -- ms/token&nbsp;&nbsp;|&nbsp;&nbsp;Tokens --</div>"
+            ),
+            gr.update(interactive=False),
+        )
+
+        try:
+            prompt_text, inputs = self._build_prompt_inputs(image, task, user_input or "")
+            token_ids, prefill_data = self._prepare_model_inputs(inputs)
+        except Exception as exc:
+            err = f"输入处理失败: {exc}"
+            metrics = (
+                "<div style='text-align: right; font-size: 13px; color: #b91c1c; font-family: monospace;'>"
+                "预处理失败"
+                "</div>"
+            )
+            yield [(prompt_text if "prompt_text" in locals() else "输入", err)], gr.update(value=""), gr.update(), gr.update(value=metrics), gr.update(interactive=True)
+            return
+
+        chatbot_history = [(prompt_text, "")]
+        for partial, ttft_ms, avg_decode_ms, total_tokens, finished in self._stream_generate(
+            token_ids, prefill_data, max_new_tokens=1024
+        ):
+            chatbot_history[-1] = (prompt_text, partial)
+            ttft_disp = f"{ttft_ms:.0f}" if ttft_ms is not None else "--"
+            decode_disp = f"{avg_decode_ms:.1f}" if avg_decode_ms is not None else "--"
+            tok_disp = f"{total_tokens}" if total_tokens is not None else "--"
+            metrics_text = (
+                "<div style='text-align: right; font-size: 13px; color: #6b7280; font-family: monospace;'>"
+                f"TTFT {ttft_disp} ms&nbsp;&nbsp;|&nbsp;&nbsp;Decode {decode_disp} ms/token&nbsp;&nbsp;|&nbsp;&nbsp;Tokens {tok_disp}"
+                "</div>"
+            )
+            if finished:
+                yield chatbot_history, gr.update(value=""), gr.update(), gr.update(value=metrics_text), gr.update(interactive=True)
+            else:
+                yield chatbot_history, gr.update(value=""), gr.update(), gr.update(value=metrics_text), gr.update(interactive=False)
+
+    @staticmethod
+    def build_ui(demo: "PaddleOCRVLGradioDemo", server_name: str = "0.0.0.0", server_port: int = 7860, share: bool = False):
+        custom_js = """
+        function() {
+            setTimeout(() => {
+                const textareas = document.querySelectorAll('#user-input textarea');
+                textareas.forEach(textarea => {
+                    textarea.removeEventListener('keydown', textarea._customKeyHandler);
+                    textarea._customKeyHandler = function(e) {
+                        if (e.key === 'Enter') {
+                            if (e.shiftKey) {
+                                e.preventDefault();
+                                const start = this.selectionStart;
+                                const end = this.selectionEnd;
+                                const value = this.value;
+                                this.value = value.substring(0, start) + '\\n' + value.substring(end);
+                                this.selectionStart = this.selectionEnd = start + 1;
+                                this.dispatchEvent(new Event('input', { bubbles: true }));
+                            } else {
+                                e.preventDefault();
+                                const sendBtn = document.querySelector('#send-btn');
+                                if (sendBtn) {
+                                    sendBtn.click();
+                                }
+                            }
+                        }
+                    };
+                    textarea.addEventListener('keydown', textarea._customKeyHandler);
+                });
+            }, 500);
+        }
+        """
+
+        with gr.Blocks(title="PaddleOCR-VL-1.5 AX Gradio Demo", theme=gr.themes.Soft(), js=custom_js) as iface:
+            gr.HTML(
+                """<style>
+                #image-pane img {object-fit: contain; max-height: 380px;}
+                #chat-wrap {position: relative;}
+                #metrics-display {position: absolute; right: 12px; bottom: 12px; z-index: 5; pointer-events: none; text-align: right;}
+                #metrics-display > div {display: inline-block;}
+                </style>"""
+            )
+            gr.Markdown("### PaddleOCR-VL-1.5 图文识别演示\n上传图片，选择任务后执行识别。")
+
+            with gr.Row():
+                with gr.Column(scale=5):
+                    with gr.Group(elem_id="chat-wrap"):
+                        chatbot = gr.Chatbot(height=500, label="结果")
+                        metrics_md = gr.Markdown(
+                            "<div style='text-align: right; font-size: 13px; color: #6b7280; font-family: monospace;'>"
+                            "TTFT -- ms&nbsp;&nbsp;|&nbsp;&nbsp;Decode -- ms/token&nbsp;&nbsp;|&nbsp;&nbsp;Tokens --</div>",
+                            elem_id="metrics-display",
+                        )
+
+                    with gr.Row():
+                        user_input = gr.Textbox(
+                            placeholder="可选：输入自定义提示词；留空将使用任务默认提示",
+                            lines=2,
+                            scale=7,
+                            max_lines=5,
+                            show_label=False,
+                            elem_id="user-input",
+                        )
+                        with gr.Column(scale=1, min_width=100):
+                            send_btn = gr.Button("发送", variant="primary", size="sm", elem_id="send-btn")
+                            clear_btn = gr.Button("清空对话", variant="secondary", size="sm")
+
+                with gr.Column(scale=3):
+                    image_input = gr.Image(
+                        type="pil",
+                        label="上传图片",
+                        height=380,
+                        image_mode="RGB",
+                        show_download_button=False,
+                        elem_id="image-pane",
+                    )
+                    task_input = gr.Dropdown(
+                        choices=["ocr", "table", "chart", "formula", "spotting", "seal"],
+                        value="ocr",
+                        label="任务类型",
+                    )
+                    gr.Markdown(
+                        "- 支持单张图像推理\n"
+                        "- 默认提示会根据任务自动设置\n"
+                        "- 识别耗时受硬件和图像分辨率影响"
+                    )
+
+            def _clear():
+                return (
+                    [],
+                    gr.update(value=""),
+                    gr.update(),
+                    gr.update(
+                        value="<div style='text-align: right; font-size: 13px; color: #6b7280; font-family: monospace;'>"
+                        "TTFT -- ms&nbsp;&nbsp;|&nbsp;&nbsp;Decode -- ms/token&nbsp;&nbsp;|&nbsp;&nbsp;Tokens --</div>"
+                    ),
+                    gr.update(interactive=True),
+                )
+
+            send_btn.click(
+                fn=demo.chat,
+                inputs=[user_input, image_input, task_input],
+                outputs=[chatbot, user_input, image_input, metrics_md, send_btn],
+                show_progress=False,
+                queue=True,
+            )
+            clear_btn.click(fn=_clear, inputs=None, outputs=[chatbot, user_input, image_input, metrics_md, send_btn])
+
+        target_port = server_port or 7860
+        host_candidates: List[str] = []
+        if server_name:
+            host_candidates.append(server_name)
+        host_candidates.extend(_list_host_ips())
+        printed = set()
+        print("可访问地址 (请任选其一):")
+        for ip in host_candidates:
+            if ip and ip not in printed:
+                printed.add(ip)
+                print(f"  http://{ip}:{target_port}")
+
+        iface.queue().launch(server_name=server_name, server_port=server_port, share=share)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="PaddleOCR-VL-1.5 AX gradio demo")
+    parser.add_argument("--hf_model", type=str, default="./PaddleOCR-VL-1.5", help="HuggingFace 模型路径")
+    parser.add_argument("--axmodel_path", type=str, default="./PaddleOCR-VL-1.5_axmodel", help="LLM axmodel 目录")
+    parser.add_argument(
+        "--vit_model",
+        type=str,
+        default="./vit_models/vit_576x768.axmodel",
+        help="VIT 模型路径（支持 .axmodel 或 .onnx，且输出需为 projector 后的 merge token）",
+    )
+    parser.add_argument("--port", type=int, default=7860, help="Gradio 端口")
+    parser.add_argument("--host", type=str, default="0.0.0.0", help="Gradio 监听地址")
+    parser.add_argument("--share", action="store_true", help="启用 gradio share")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    demo = PaddleOCRVLGradioDemo(args.hf_model, args.axmodel_path, args.vit_model)
+    PaddleOCRVLGradioDemo.build_ui(demo, server_name=args.host, server_port=args.port, share=args.share)
+
+
+if __name__ == "__main__":
+    """
+    python3 gradio_demo.py \
+        --hf_model ./PaddleOCR-VL-1.5 \
+        --axmodel_path ./PaddleOCR-VL-1.5_axmodel \
+        --vit_model ./vit_models/vit_576x768.axmodel
+    """
+    main()

python/infer_axmodel.py

@@ -0,0 +1,203 @@
+from transformers import AutoProcessor, AutoTokenizer, AutoConfig
+import onnxruntime as ort
+import numpy as np
+import os
+from ml_dtypes import bfloat16
+from utils.infer_func import InferManager
+from utils.vision_output import describe_output_shapes, select_vit_output
+import argparse
+from PIL import Image
+
+
+def _prepare_image(image_path, task):
+
+    image = Image.open(image_path).convert("RGB")
+
+    resize_h, resize_w = 576, 768
+    image = image.resize((resize_w, resize_h))
+
+    # AX vision model is compiled with fixed 576x768 token layout.
+    # Keep spotting path aligned to avoid variable token counts.
+    max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28
+    return image, max_pixels
+
+
+def _run_vit_onnx(session, pixel_values, target_hidden_size, expected_tokens=None):
+    outputs = session.run(None, {"pixel_values": pixel_values})
+    return (
+        select_vit_output(outputs, target_hidden_size, expected_tokens=expected_tokens),
+        describe_output_shapes(outputs),
+    )
+
+
+def _run_vit_axmodel(session, pixel_values, target_hidden_size, expected_tokens=None):
+    outputs = session.run(None, {"pixel_values": pixel_values})
+    return (
+        select_vit_output(outputs, target_hidden_size, expected_tokens=expected_tokens),
+        describe_output_shapes(outputs),
+    )
+
+
+def _expected_image_tokens(image_grid_thw, merge_size):
+    merge_area = int(merge_size) * int(merge_size)
+    return int(sum(int(t) * int(h) * int(w) // merge_area for t, h, w in image_grid_thw))
+
+
+def _replace_image_tokens(token_ids, token_embeds, image_embeds, image_token_id):
+    image_positions = [idx for idx, token_id in enumerate(token_ids) if token_id == image_token_id]
+    if not image_positions:
+        return token_embeds
+
+    flat_image_embeds = image_embeds.reshape(-1, image_embeds.shape[-1])
+    if len(image_positions) != flat_image_embeds.shape[0]:
+        raise ValueError(
+            f"Image tokens and image features do not match: tokens={len(image_positions)}, features={flat_image_embeds.shape[0]}"
+        )
+    if token_embeds.shape[-1] != flat_image_embeds.shape[-1]:
+        raise ValueError(
+            f"Embedding dim mismatch: token_dim={token_embeds.shape[-1]}, image_dim={flat_image_embeds.shape[-1]}"
+        )
+    token_embeds[image_positions, :] = flat_image_embeds
+    return token_embeds
+
+
+if __name__ == "__main__":
+
+    """
+    python3 infer_axmodel.py \
+        --hf_model ./PaddleOCR-VL-1.5 \
+        --axmodel_path ./PaddleOCR-VL-1.5_axmodel \
+        --vit_model_path ./vit_models/vit_576x768.axmodel \
+        --image_path ../assets/IMG_0462.JPG \
+        --task ocr
+    """
+    parser = argparse.ArgumentParser(description="PaddleOCR-VL-1.5 axmodel inference")
+    parser.add_argument("--hf_model", type=str, default="./PaddleOCR-VL-1.5",
+                        help="Path to HuggingFace model")
+    parser.add_argument("--axmodel_path", type=str, default="./PaddleOCR-VL-1.5_axmodel",
+                        help="Path to compiled axmodel folder")
+    parser.add_argument("--vit_model_path", type=str,
+                        default="./vit_models/vit_576x768.axmodel",
+                        help="Path to PaddleOCR-VL vision ONNX model or .axmodel")
+    parser.add_argument("--image_path", type=str, default="../assets/IMG_0462.JPG",
+                        help="Input image path")
+    parser.add_argument("--task", type=str, default="ocr",
+                        choices=["ocr", "table", "chart", "formula", "spotting", "seal"],
+                        help="Task type")
+    args = parser.parse_args()
+
+    hf_model_path = args.hf_model
+    axmodel_path = args.axmodel_path
+
+    embeds = np.load(os.path.join(axmodel_path, "python/model.embed_tokens.weight.npy"))
+
+    tokenizer = AutoTokenizer.from_pretrained(hf_model_path, trust_remote_code=True)
+    processor = AutoProcessor.from_pretrained(hf_model_path, trust_remote_code=True)
+    config = AutoConfig.from_pretrained(hf_model_path, trust_remote_code=True)
+
+    image, max_pixels = _prepare_image(args.image_path, args.task)
+    prompts = {
+        "ocr": "OCR:",
+        "table": "Table Recognition:",
+        "formula": "Formula Recognition:",
+        "chart": "Chart Recognition:",
+        "spotting": "Spotting:",
+        "seal": "Seal Recognition:",
+    }
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": prompts[args.task]},
+            ],
+        }
+    ]
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+        images_kwargs={
+            "size": {
+                "shortest_edge": processor.image_processor.min_pixels,
+                "longest_edge": max_pixels,
+            }
+        },
+    )
+
+    token_ids = inputs.input_ids[0].cpu().numpy().tolist()
+    image_grid_thw = inputs.image_grid_thw.cpu().numpy().tolist()
+    merge_size = config.vision_config.spatial_merge_size
+    expected_tokens = _expected_image_tokens(image_grid_thw, merge_size)
+
+    pixel_values = inputs.pixel_values
+    if pixel_values.ndim == 4:
+        pixel_values = pixel_values.unsqueeze(0)
+    pixel_values = pixel_values.cpu().numpy().astype(np.float32)
+
+    if args.vit_model_path.endswith(".axmodel"):
+        try:
+            from axengine import InferenceSession
+        except Exception as exc:
+            raise ImportError("axengine is required for .axmodel inference") from exc
+        vit_session = InferenceSession(args.vit_model_path)
+        image_embeds, vit_output_shapes = _run_vit_axmodel(
+            vit_session,
+            pixel_values,
+            target_hidden_size=config.hidden_size,
+            expected_tokens=expected_tokens,
+        )
+    else:
+        providers = ["CPUExecutionProvider"]
+        if "CUDAExecutionProvider" in ort.get_available_providers():
+            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        vit_session = ort.InferenceSession(args.vit_model_path, providers=providers)
+        image_embeds, vit_output_shapes = _run_vit_onnx(
+            vit_session,
+            pixel_values,
+            target_hidden_size=config.hidden_size,
+            expected_tokens=expected_tokens,
+        )
+
+    if image_embeds.ndim == 3:
+        image_embeds = image_embeds[0]
+    image_seq_len = image_embeds.shape[0]
+    if image_seq_len != expected_tokens:
+        expected_features = int(sum(int(t) * int(h) * int(w) for t, h, w in image_grid_thw))
+        if image_seq_len == expected_features:
+            raise ValueError(
+                "Vision output is pre-projector features. "
+                f"got={image_seq_len}, expected_projected_tokens={expected_tokens}. "
+                "Please re-export VIT ONNX with projector included (model_convert/export_onnx.py), "
+                f"then re-compile to .axmodel. vit_output_shapes={vit_output_shapes}"
+            )
+        raise ValueError(
+            "Unexpected image feature length. "
+            f"got={image_seq_len}, expected_projected_tokens={expected_tokens}, "
+            f"expected_pre_projector_features={expected_features}, vit_output_shapes={vit_output_shapes}"
+        )
+    projected_embeds = image_embeds
+
+    prefill_data = np.take(embeds, token_ids, axis=0)
+    prefill_data = _replace_image_tokens(
+        token_ids,
+        prefill_data,
+        projected_embeds,
+        image_token_id=config.image_token_id,
+    )
+    prefill_data = prefill_data.astype(bfloat16)
+
+    eos_token_id = None
+    if isinstance(config.eos_token_id, list) and len(config.eos_token_id) > 1:
+        eos_token_id = config.eos_token_id
+
+    slice_len = 128
+    max_seq_len = 2048 - 1
+
+    imer = InferManager(config, axmodel_path, max_seq_len=max_seq_len)
+    token_ids = imer.prefill(tokenizer, token_ids, prefill_data, slice_len=slice_len)
+    imer.decode(tokenizer, token_ids, embeds, slice_len=slice_len, eos_token_id=eos_token_id)
+    print("\n")

python/infer_torch.py

@@ -0,0 +1,76 @@
+from PIL import Image
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+# ---- Settings ----
+model_path = "./PaddleOCR-VL-1.5"
+image_path = "../assets/paddleocr_vl_demo.png"
+# image_path = "../assets/IMG_0462.JPG"
+task = "ocr" # Options: 'ocr' | 'table' | 'chart' | 'formula' | 'spotting' | 'seal'
+# ------------------
+
+# ---- Image Preprocessing For Spotting ----
+image = Image.open(image_path).convert("RGB")
+orig_w, orig_h = image.size
+
+# 导出 ONNX 需要固定图像尺寸
+# resize_h, resize_w = 768, 1024 # 1368, 1524
+resize_h, resize_w = 576, 768 # 1368, 1524, 最小尺寸为 336x336
+image = image.resize((resize_w, resize_h))
+
+spotting_upscale_threshold = 1500
+
+if task == "spotting" and orig_w < spotting_upscale_threshold and orig_h < spotting_upscale_threshold:
+    process_w, process_h = orig_w * 2, orig_h * 2
+    try:
+        resample_filter = Image.Resampling.LANCZOS
+    except AttributeError:
+        resample_filter = Image.LANCZOS
+    image = image.resize((process_w, process_h), resample_filter)
+
+# Set max_pixels: use 1605632 for spotting, otherwise use default ~1M pixels
+max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28
+
+# ---------------------------
+
+# -------- Inference --------
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+PROMPTS = {
+    "ocr": "OCR:",
+    "table": "Table Recognition:",
+    "formula": "Formula Recognition:",
+    "chart": "Chart Recognition:",
+    "spotting": "Spotting:",
+    "seal": "Seal Recognition:",
+}
+
+model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(DEVICE).eval()
+processor = AutoProcessor.from_pretrained(model_path)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": PROMPTS[task]},
+        ]
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+    images_kwargs={"size": {"shortest_edge": processor.image_processor.min_pixels, "longest_edge": max_pixels}},
+).to(model.device)
+
+print("inputs.pixel_values.shape:", inputs.pixel_values.shape)
+print("inputs.input_ids.shape:", inputs.input_ids.shape)
+print("inputs.image_grid_thw:", inputs.image_grid_thw)
+
+outputs = model.generate(**inputs, max_new_tokens=1024)
+result = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:-1])
+print(result)
+# ---------------------------

python/model.embed_tokens.weight.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbb214e22f31d0b4b7e1646b6f5551cd80f2eea99db91feabc668052d4aee71c
+size 423624832

python/paddleocr_vl_1-5_tokenizer/.gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

python/paddleocr_vl_1-5_tokenizer/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

python/paddleocr_vl_1-5_tokenizer/README.md

@@ -0,0 +1,362 @@
+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- ERNIE4.5
+- PaddleOCR
+- PaddlePaddle
+- image-to-text
+- ocr
+- document-parse
+- layout
+- table
+- formula
+- chart
+- seal
+- spotting
+base_model: baidu/ERNIE-4.5-0.3B-Paddle
+language:
+- en
+- zh
+- multilingual
+library_name: PaddleOCR
+---
+<div align="center">
+
+
+<h1 align="center">
+
+PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
+
+</h1>
+
+[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR)
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5)
+[![ModelScope](https://img.shields.io/badge/ModelScope-black?logo=data:image/svg+xml;base64,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&labelColor=white)](https://modelscope.cn/models/PaddlePaddle/PaddleOCR-VL-1.5)
+[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo)
+[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-black?logo=data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIzIiBoZWlnaHQ9IjIwMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCiA8Zz4KICA8dGl0bGU+TGF5ZXIgMTwvdGl0bGU+CiAgPHBhdGggaWQ9InN2Z18xNCIgZmlsbD0iIzYyNGFmZiIgZD0ibTAsODkuODRsMjUuNjUsMGwwLDI1LjY0OTk5bC0yNS42NSwwbDAsLTI1LjY0OTk5eiIvPgogIDxwYXRoIGlkPSJzdmdfMTUiIGZpbGw9IiM2MjRhZmYiIGQ9Im05OS4xNCwxMTUuNDlsMjUuNjUsMGwwLDI1LjY1bC0yNS42NSwwbDAsLTI1LjY1eiIvPgogIDxwYXRoIGlkPSJzdmdfMTYiIGZpbGw9IiM2MjRhZmYiIGQ9Im0xNzYuMDksMTQxLjE0bC0yNS42NDk5OSwwbDAsMjIuMTlsNDcuODQsMGwwLC00Ny44NGwtMjIuMTksMGwwLDI1LjY1eiIvPgogIDxwYXRoIGlkPSJzdmdfMTciIGZpbGw9IiMzNmNmZDEiIGQ9Im0xMjQuNzksODkuODRsMjUuNjUsMGwwLDI1LjY0OTk5bC0yNS42NSwwbDAsLTI1LjY0OTk5eiIvPgogIDxwYXRoIGlkPSJzdmdfMTgiIGZpbGw9IiMzNmNmZDEiIGQ9Im0wLDY0LjE5bDI1LjY1LDBsMCwyNS42NWwtMjUuNjUsMGwwLC0yNS42NXoiLz4KICA8cGF0aCBpZD0ic3ZnXzE5IiBmaWxsPSIjNjI0YWZmIiBkPSJtMTk4LjI4LDg5Ljg0bDI1LjY0OTk5LDBsMCwyNS42NDk5OWwtMjUuNjQ5OTksMGwwLC0yNS42NDk5OXoiLz4KICA8cGF0aCBpZD0ic3ZnXzIwIiBmaWxsPSIjMzZjZmQxIiBkPSJtMTk4LjI4LDY0LjE5bDI1LjY0OTk5LDBsMCwyNS42NWwtMjUuNjQ5OTksMGwwLC0yNS42NXoiLz4KICA8cGF0aCBpZD0ic3ZnXzIxIiBmaWxsPSIjNjI0YWZmIiBkPSJtMTUwLjQ0LDQybDAsMjIuMTlsMjUuNjQ5OTksMGwwLDI1LjY1bDIyLjE5LDBsMCwtNDcuODRsLTQ3Ljg0LDB6Ii8+CiAgPHBhdGggaWQ9InN2Z18yMiIgZmlsbD0iIzM2Y2ZkMSIgZD0ibTczLjQ5LDg5Ljg0bDI1LjY1LDBsMCwyNS42NDk5OWwtMjUuNjUsMGwwLC0yNS42NDk5OXoiLz4KICA8cGF0aCBpZD0ic3ZnXzIzIiBmaWxsPSIjNjI0YWZmIiBkPSJtNDcuODQsNjQuMTlsMjUuNjUsMGwwLC0yMi4xOWwtNDcuODQsMGwwLDQ3Ljg0bDIyLjE5LDBsMCwtMjUuNjV6Ii8+CiAgPHBhdGggaWQ9InN2Z18yNCIgZmlsbD0iIzYyNGFmZiIgZD0ibTQ3Ljg0LDExNS40OWwtMjIuMTksMGwwLDQ3Ljg0bDQ3Ljg0LDBsMCwtMjIuMTlsLTI1LjY1LDBsMCwtMjUuNjV6Ii8+CiA8L2c+Cjwvc3ZnPg==&labelColor=white)](https://modelscope.cn/studios/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo/summary)
+[![Discord](https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white)](https://discord.gg/JPmZXDsEEK)
+[![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle)
+[![License](https://img.shields.io/badge/license-Apache_2.0-green)](./LICENSE)
+
+**🔥 [Official Website](https://www.paddleocr.com)**
+**📝 [Technical Report](https://arxiv.org/abs/2601.21957)**
+
+</div>
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/paddleocr-vl-1.5_metrics.png" width="800"/>
+</div>
+
+
+
+## Introduction
+
+**PaddleOCR-VL-1.5 is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5.** To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency.
+
+### **Key Capabilities of PaddleOCR-VL-1.5**
+
+1. With a **parameter size of 0.9B**, PaddleOCR-VL-1.5 **achieves 94.5% accuracy on OmniDocBench v1.5**, surpassing the previous SOTA model PaddleOCR-VL. Significant improvements are observed in **table, formula, and text recognition.**
+
+2. **It introduces an innovative approach to document parsing by supporting irregular-shaped localization**, enabling accurate polygonal detection under skewed and warped document conditions. Evaluations across five real-world scenarios—scanning, skew, warping, screen-photography, and illumination—demonstrate superior performance over mainstream open-source and proprietary models.
+
+3. The model introduces **text spotting (text-line localization and recognition)**, along with **seal recognition**, with all corresponding metrics **setting new SOTA results** in their respective tasks.
+
+4. PaddleOCR-VL-1.5 further strengthens its capability in **specialized scenarios and multilingual recognition.** Recognition performance is improved for **rare characters, ancient texts, multilingual tables, underlines, and checkboxes,** and language coverage is extended to include **China's Tibetan script and Bengali.**
+
+5. The model supports **automatic cross-page table merging** and **cross-page paragraph heading recognition**, effectively mitigating content fragmentation issues in **long-document parsing.**
+
+
+### **Model Architecture** 
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/PaddleOCR-VL-1.5.png" width="800"/>
+</div>
+
+
+## News 
+
+* ```2026.01.29``` 🚀 We release [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5), —a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing.
+
+## Usage    
+
+### Install Dependencies
+
+Install [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) and [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR):
+
+```bash
+# The following command installs the PaddlePaddle version for CUDA 12.6. For other CUDA versions and the CPU version, please refer to https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html
+python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
+python -m pip install -U "paddleocr[doc-parser]"
+```
+
+> **Please ensure that you install PaddlePaddle framework version 3.2.1 or above, along with the special version of safetensors.** For macOS users, please use Docker to set up the environment.
+
+
+### Basic Usage
+
+CLI usage:
+
+```bash
+paddleocr doc_parser -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png
+```
+
+Python API usage:
+
+```python
+from paddleocr import PaddleOCRVL
+pipeline = PaddleOCRVL()
+output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
+for res in output:
+    res.print()
+    res.save_to_json(save_path="output")
+    res.save_to_markdown(save_path="output")
+```
+
+### Accelerate VLM Inference via Optimized Inference Servers
+
+1. Start the VLM inference server:
+
+    You can start the vLLM inference service using one of two methods:
+    - Method 1: PaddleOCR method
+
+        ```bash
+        docker run \
+            --rm \
+            --gpus all \
+            --network host \
+            ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu \
+            paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --host 0.0.0.0 --port 8080 --backend vllm
+        ```
+    - Method 2: vLLM method
+
+        [vLLM: PaddleOCR-VL Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/PaddlePaddle/PaddleOCR-VL.html)
+    
+2. Call the PaddleOCR CLI or Python API:
+    ```bash
+    paddleocr doc_parser \
+        -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png \
+        --vl_rec_backend vllm-server \
+        --vl_rec_server_url http://127.0.0.1:8080/v1
+    ```
+    ```python
+    from paddleocr import PaddleOCRVL
+    pipeline = PaddleOCRVL(vl_rec_backend="vllm-server", vl_rec_server_url="http://127.0.0.1:8080/v1")
+    output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
+    for res in output:
+        res.print()
+        res.save_to_json(save_path="output")
+        res.save_to_markdown(save_path="output")
+    ```
+  
+**For more usage details and parameter explanations, see the [documentation](https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html).**
+## PaddleOCR-VL-1.5-0.9B Usage with transformers
+
+Currently, the PaddleOCR-VL-1.5-0.9B model facilitates seamless inference via the `transformers` library, supporting **comprehensive text spotting** and the recognition of complex elements including formulas, tables, charts, and seals. Below is a simple script we provide to support inference using the PaddleOCR-VL-1.5-0.9B model with `transformers`. 
+
+
+> [!NOTE]
+> Note: We currently recommend using the official method for inference, as it is faster and supports page-level document parsing. The example code below only supports element-level recognition and text spotting.
+
+```shell
+# ensure the transformers v5 is installed
+python -m pip install "transformers>=5.0.0"
+```
+
+```python
+from PIL import Image
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+# ---- Settings ----
+model_path = "PaddlePaddle/PaddleOCR-VL-1.5"
+image_path = "test.png"
+task = "ocr" # Options: 'ocr' | 'table' | 'chart' | 'formula' | 'spotting' | 'seal'
+# ------------------
+
+# ---- Image Preprocessing For Spotting ----
+image = Image.open(image_path).convert("RGB")
+orig_w, orig_h = image.size
+spotting_upscale_threshold = 1500
+
+if task == "spotting" and orig_w < spotting_upscale_threshold and orig_h < spotting_upscale_threshold:
+    process_w, process_h = orig_w * 2, orig_h * 2
+    try:
+        resample_filter = Image.Resampling.LANCZOS
+    except AttributeError:
+        resample_filter = Image.LANCZOS
+    image = image.resize((process_w, process_h), resample_filter)
+
+# Set max_pixels: use 1605632 for spotting, otherwise use default ~1M pixels
+max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28
+# ---------------------------
+
+# -------- Inference --------
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+PROMPTS = {
+    "ocr": "OCR:",
+    "table": "Table Recognition:",
+    "formula": "Formula Recognition:",
+    "chart": "Chart Recognition:",
+    "spotting": "Spotting:",
+    "seal": "Seal Recognition:",
+}
+
+model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(DEVICE).eval()
+processor = AutoProcessor.from_pretrained(model_path)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": PROMPTS[task]},
+        ]
+    }
+]
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+    images_kwargs={"size": {"shortest_edge": processor.image_processor.min_pixels, "longest_edge": max_pixels}},
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+result = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:-1])
+print(result)
+# ---------------------------
+```
+
+<details>
+<summary>👉 Click to expand: Use flash-attn to boost performance and reduce memory usage</summary>
+
+```shell
+# ensure the flash-attn2 is installed
+pip install flash-attn --no-build-isolation
+```
+
+```python
+model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").to(DEVICE).eval()
+```
+
+</details>
+
+## Performance
+
+### Document Parsing 
+
+
+#### 1. OmniDocBench v1.5
+
+##### PaddleOCR-VL-1.5 achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/omnidocbenchv1.5_metrics.png" width="800"/>
+</div>
+
+
+> **Notes:** 
+> - Performance metrics are cited from the [OmniDocBench official leaderboard](https://opendatalab.com/omnidocbench), except for Gemini-3 Pro, Qwen3-VL-235B-A22B-Instruct and our model, which were evaluated independently.
+
+
+####  2. Real5-OmniDocBench
+
+##### Across all five diverse and challenging scenarios—scanning, warping, screen-photography, illumination, and skew—PaddleOCR-VL-1.5 consistently sets new SOTA records
+
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/real5-omnidocbench_metrics.png" width="800"/>
+</div>
+
+> **Notes:** 
+> - Real5-OmniDocBench is a brand-new benchmark oriented toward real-world scenarios, which we constructed based on the OmniDocBench v1.5 dataset. The dataset comprises five distinct scenarios: Scanning, Warping, Screen-photography, Illumination, and Skew. For further details, please refer to [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
+
+
+### Inference Performance
+
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/inference_performance.png" width="800"/>
+</div>
+
+> **Notes:** 
+> - End-to-End Inference Performance Comparison on OmniDocBench v1.5. PDF documents were processed in batches of 512 on a single NVIDIA A100 GPU. The reported end-to-end runtime includes both PDF rendering and Markdown generation. All methods rely on their built-in PDF parsing modules and default DPI settings to reflect out-of-the-box performance. 
+
+
+## Visualization
+
+
+### Real-word Document Parsing
+
+
+#### Illumination
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/light.jpg" width="800"/>
+</div>
+
+
+#### Skew
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/skew.jpg" width="800"/>
+</div>
+
+
+#### Screen Photography
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/screen.jpg" width="800"/>
+</div>
+
+
+#### Scanning
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/scaning.jpg" width="800"/>
+</div>
+
+#### Warping
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/curving.jpg" width="800"/>
+</div>
+
+
+### Text Spotting
+
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/spotting.jpg" width="800"/>
+</div>
+
+
+### Seal Recognition
+
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/seal.jpg" width="800"/>
+</div>
+
+
+## Acknowledgments
+
+We would like to thank [PaddleFormers](https://github.com/PaddlePaddle/PaddleFormers), [Keye](https://github.com/Kwai-Keye/Keye), [MinerU](https://github.com/opendatalab/MinerU), [OmniDocBench](https://github.com/opendatalab/OmniDocBench) for providing valuable code, model weights and benchmarks. We also appreciate everyone's contribution to this open-source project!
+
+## Citation
+
+If you find PaddleOCR-VL-1.5 helpful, feel free to give us a star and citation.
+
+```bibtex
+@misc{cui2026paddleocrvl15multitask09bvlm,
+      title={PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing}, 
+      author={Cheng Cui and Ting Sun and Suyin Liang and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Xueqing Wang and Changda Zhou and Hongen Liu and Manhui Lin and Yue Zhang and Yubo Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
+      year={2026},
+      eprint={2601.21957},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.21957}, 
+}
+```

python/paddleocr_vl_1-5_tokenizer/added_tokens.json

@@ -0,0 +1,1021 @@
+{
+  "<ecel>": 101308,
+  "<fcel>": 101309,
+  "<lcel>": 101311,
+  "<nl>": 101313,
+  "<ucel>": 101312,
+  "<xcel>": 101310,
+  "<|AUDIO_PLACEHOLDER|>": 100296,
+  "<|CROP_COL_SEP|>": 101301,
+  "<|CROP_ROW_SEP|>": 101302,
+  "<|IMAGE_END|>": 101306,
+  "<|IMAGE_PLACEHOLDER|>": 100295,
+  "<|IMAGE_SEP|>": 101303,
+  "<|IMAGE_START|>": 101305,
+  "<|LOC_0|>": 100297,
+  "<|LOC_1000|>": 101297,
+  "<|LOC_100|>": 100397,
+  "<|LOC_101|>": 100398,
+  "<|LOC_102|>": 100399,
+  "<|LOC_103|>": 100400,
+  "<|LOC_104|>": 100401,
+  "<|LOC_105|>": 100402,
+  "<|LOC_106|>": 100403,
+  "<|LOC_107|>": 100404,
+  "<|LOC_108|>": 100405,
+  "<|LOC_109|>": 100406,
+  "<|LOC_10|>": 100307,
+  "<|LOC_110|>": 100407,
+  "<|LOC_111|>": 100408,
+  "<|LOC_112|>": 100409,
+  "<|LOC_113|>": 100410,
+  "<|LOC_114|>": 100411,
+  "<|LOC_115|>": 100412,
+  "<|LOC_116|>": 100413,
+  "<|LOC_117|>": 100414,
+  "<|LOC_118|>": 100415,
+  "<|LOC_119|>": 100416,
+  "<|LOC_11|>": 100308,
+  "<|LOC_120|>": 100417,
+  "<|LOC_121|>": 100418,
+  "<|LOC_122|>": 100419,
+  "<|LOC_123|>": 100420,
+  "<|LOC_124|>": 100421,
+  "<|LOC_125|>": 100422,
+  "<|LOC_126|>": 100423,
+  "<|LOC_127|>": 100424,
+  "<|LOC_128|>": 100425,
+  "<|LOC_129|>": 100426,
+  "<|LOC_12|>": 100309,
+  "<|LOC_130|>": 100427,
+  "<|LOC_131|>": 100428,
+  "<|LOC_132|>": 100429,
+  "<|LOC_133|>": 100430,
+  "<|LOC_134|>": 100431,
+  "<|LOC_135|>": 100432,
+  "<|LOC_136|>": 100433,
+  "<|LOC_137|>": 100434,
+  "<|LOC_138|>": 100435,
+  "<|LOC_139|>": 100436,
+  "<|LOC_13|>": 100310,
+  "<|LOC_140|>": 100437,
+  "<|LOC_141|>": 100438,
+  "<|LOC_142|>": 100439,
+  "<|LOC_143|>": 100440,
+  "<|LOC_144|>": 100441,
+  "<|LOC_145|>": 100442,
+  "<|LOC_146|>": 100443,
+  "<|LOC_147|>": 100444,
+  "<|LOC_148|>": 100445,
+  "<|LOC_149|>": 100446,
+  "<|LOC_14|>": 100311,
+  "<|LOC_150|>": 100447,
+  "<|LOC_151|>": 100448,
+  "<|LOC_152|>": 100449,
+  "<|LOC_153|>": 100450,
+  "<|LOC_154|>": 100451,
+  "<|LOC_155|>": 100452,
+  "<|LOC_156|>": 100453,
+  "<|LOC_157|>": 100454,
+  "<|LOC_158|>": 100455,
+  "<|LOC_159|>": 100456,
+  "<|LOC_15|>": 100312,
+  "<|LOC_160|>": 100457,
+  "<|LOC_161|>": 100458,
+  "<|LOC_162|>": 100459,
+  "<|LOC_163|>": 100460,
+  "<|LOC_164|>": 100461,
+  "<|LOC_165|>": 100462,
+  "<|LOC_166|>": 100463,
+  "<|LOC_167|>": 100464,
+  "<|LOC_168|>": 100465,
+  "<|LOC_169|>": 100466,
+  "<|LOC_16|>": 100313,
+  "<|LOC_170|>": 100467,
+  "<|LOC_171|>": 100468,
+  "<|LOC_172|>": 100469,
+  "<|LOC_173|>": 100470,
+  "<|LOC_174|>": 100471,
+  "<|LOC_175|>": 100472,
+  "<|LOC_176|>": 100473,
+  "<|LOC_177|>": 100474,
+  "<|LOC_178|>": 100475,
+  "<|LOC_179|>": 100476,
+  "<|LOC_17|>": 100314,
+  "<|LOC_180|>": 100477,
+  "<|LOC_181|>": 100478,
+  "<|LOC_182|>": 100479,
+  "<|LOC_183|>": 100480,
+  "<|LOC_184|>": 100481,
+  "<|LOC_185|>": 100482,
+  "<|LOC_186|>": 100483,
+  "<|LOC_187|>": 100484,
+  "<|LOC_188|>": 100485,
+  "<|LOC_189|>": 100486,
+  "<|LOC_18|>": 100315,
+  "<|LOC_190|>": 100487,
+  "<|LOC_191|>": 100488,
+  "<|LOC_192|>": 100489,
+  "<|LOC_193|>": 100490,
+  "<|LOC_194|>": 100491,
+  "<|LOC_195|>": 100492,
+  "<|LOC_196|>": 100493,
+  "<|LOC_197|>": 100494,
+  "<|LOC_198|>": 100495,
+  "<|LOC_199|>": 100496,
+  "<|LOC_19|>": 100316,
+  "<|LOC_1|>": 100298,
+  "<|LOC_200|>": 100497,
+  "<|LOC_201|>": 100498,
+  "<|LOC_202|>": 100499,
+  "<|LOC_203|>": 100500,
+  "<|LOC_204|>": 100501,
+  "<|LOC_205|>": 100502,
+  "<|LOC_206|>": 100503,
+  "<|LOC_207|>": 100504,
+  "<|LOC_208|>": 100505,
+  "<|LOC_209|>": 100506,
+  "<|LOC_20|>": 100317,
+  "<|LOC_210|>": 100507,
+  "<|LOC_211|>": 100508,
+  "<|LOC_212|>": 100509,
+  "<|LOC_213|>": 100510,
+  "<|LOC_214|>": 100511,
+  "<|LOC_215|>": 100512,
+  "<|LOC_216|>": 100513,
+  "<|LOC_217|>": 100514,
+  "<|LOC_218|>": 100515,
+  "<|LOC_219|>": 100516,
+  "<|LOC_21|>": 100318,
+  "<|LOC_220|>": 100517,
+  "<|LOC_221|>": 100518,
+  "<|LOC_222|>": 100519,
+  "<|LOC_223|>": 100520,
+  "<|LOC_224|>": 100521,
+  "<|LOC_225|>": 100522,
+  "<|LOC_226|>": 100523,
+  "<|LOC_227|>": 100524,
+  "<|LOC_228|>": 100525,
+  "<|LOC_229|>": 100526,
+  "<|LOC_22|>": 100319,
+  "<|LOC_230|>": 100527,
+  "<|LOC_231|>": 100528,
+  "<|LOC_232|>": 100529,
+  "<|LOC_233|>": 100530,
+  "<|LOC_234|>": 100531,
+  "<|LOC_235|>": 100532,
+  "<|LOC_236|>": 100533,
+  "<|LOC_237|>": 100534,
+  "<|LOC_238|>": 100535,
+  "<|LOC_239|>": 100536,
+  "<|LOC_23|>": 100320,
+  "<|LOC_240|>": 100537,
+  "<|LOC_241|>": 100538,
+  "<|LOC_242|>": 100539,
+  "<|LOC_243|>": 100540,
+  "<|LOC_244|>": 100541,
+  "<|LOC_245|>": 100542,
+  "<|LOC_246|>": 100543,
+  "<|LOC_247|>": 100544,
+  "<|LOC_248|>": 100545,
+  "<|LOC_249|>": 100546,
+  "<|LOC_24|>": 100321,
+  "<|LOC_250|>": 100547,
+  "<|LOC_251|>": 100548,
+  "<|LOC_252|>": 100549,
+  "<|LOC_253|>": 100550,
+  "<|LOC_254|>": 100551,
+  "<|LOC_255|>": 100552,
+  "<|LOC_256|>": 100553,
+  "<|LOC_257|>": 100554,
+  "<|LOC_258|>": 100555,
+  "<|LOC_259|>": 100556,
+  "<|LOC_25|>": 100322,
+  "<|LOC_260|>": 100557,
+  "<|LOC_261|>": 100558,
+  "<|LOC_262|>": 100559,
+  "<|LOC_263|>": 100560,
+  "<|LOC_264|>": 100561,
+  "<|LOC_265|>": 100562,
+  "<|LOC_266|>": 100563,
+  "<|LOC_267|>": 100564,
+  "<|LOC_268|>": 100565,
+  "<|LOC_269|>": 100566,
+  "<|LOC_26|>": 100323,
+  "<|LOC_270|>": 100567,
+  "<|LOC_271|>": 100568,
+  "<|LOC_272|>": 100569,
+  "<|LOC_273|>": 100570,
+  "<|LOC_274|>": 100571,
+  "<|LOC_275|>": 100572,
+  "<|LOC_276|>": 100573,
+  "<|LOC_277|>": 100574,
+  "<|LOC_278|>": 100575,
+  "<|LOC_279|>": 100576,
+  "<|LOC_27|>": 100324,
+  "<|LOC_280|>": 100577,
+  "<|LOC_281|>": 100578,
+  "<|LOC_282|>": 100579,
+  "<|LOC_283|>": 100580,
+  "<|LOC_284|>": 100581,
+  "<|LOC_285|>": 100582,
+  "<|LOC_286|>": 100583,
+  "<|LOC_287|>": 100584,
+  "<|LOC_288|>": 100585,
+  "<|LOC_289|>": 100586,
+  "<|LOC_28|>": 100325,
+  "<|LOC_290|>": 100587,
+  "<|LOC_291|>": 100588,
+  "<|LOC_292|>": 100589,
+  "<|LOC_293|>": 100590,
+  "<|LOC_294|>": 100591,
+  "<|LOC_295|>": 100592,
+  "<|LOC_296|>": 100593,
+  "<|LOC_297|>": 100594,
+  "<|LOC_298|>": 100595,
+  "<|LOC_299|>": 100596,
+  "<|LOC_29|>": 100326,
+  "<|LOC_2|>": 100299,
+  "<|LOC_300|>": 100597,
+  "<|LOC_301|>": 100598,
+  "<|LOC_302|>": 100599,
+  "<|LOC_303|>": 100600,
+  "<|LOC_304|>": 100601,
+  "<|LOC_305|>": 100602,
+  "<|LOC_306|>": 100603,
+  "<|LOC_307|>": 100604,
+  "<|LOC_308|>": 100605,
+  "<|LOC_309|>": 100606,
+  "<|LOC_30|>": 100327,
+  "<|LOC_310|>": 100607,
+  "<|LOC_311|>": 100608,
+  "<|LOC_312|>": 100609,
+  "<|LOC_313|>": 100610,
+  "<|LOC_314|>": 100611,
+  "<|LOC_315|>": 100612,
+  "<|LOC_316|>": 100613,
+  "<|LOC_317|>": 100614,
+  "<|LOC_318|>": 100615,
+  "<|LOC_319|>": 100616,
+  "<|LOC_31|>": 100328,
+  "<|LOC_320|>": 100617,
+  "<|LOC_321|>": 100618,
+  "<|LOC_322|>": 100619,
+  "<|LOC_323|>": 100620,
+  "<|LOC_324|>": 100621,
+  "<|LOC_325|>": 100622,
+  "<|LOC_326|>": 100623,
+  "<|LOC_327|>": 100624,
+  "<|LOC_328|>": 100625,
+  "<|LOC_329|>": 100626,
+  "<|LOC_32|>": 100329,
+  "<|LOC_330|>": 100627,
+  "<|LOC_331|>": 100628,
+  "<|LOC_332|>": 100629,
+  "<|LOC_333|>": 100630,
+  "<|LOC_334|>": 100631,
+  "<|LOC_335|>": 100632,
+  "<|LOC_336|>": 100633,
+  "<|LOC_337|>": 100634,
+  "<|LOC_338|>": 100635,
+  "<|LOC_339|>": 100636,
+  "<|LOC_33|>": 100330,
+  "<|LOC_340|>": 100637,
+  "<|LOC_341|>": 100638,
+  "<|LOC_342|>": 100639,
+  "<|LOC_343|>": 100640,
+  "<|LOC_344|>": 100641,
+  "<|LOC_345|>": 100642,
+  "<|LOC_346|>": 100643,
+  "<|LOC_347|>": 100644,
+  "<|LOC_348|>": 100645,
+  "<|LOC_349|>": 100646,
+  "<|LOC_34|>": 100331,
+  "<|LOC_350|>": 100647,
+  "<|LOC_351|>": 100648,
+  "<|LOC_352|>": 100649,
+  "<|LOC_353|>": 100650,
+  "<|LOC_354|>": 100651,
+  "<|LOC_355|>": 100652,
+  "<|LOC_356|>": 100653,
+  "<|LOC_357|>": 100654,
+  "<|LOC_358|>": 100655,
+  "<|LOC_359|>": 100656,
+  "<|LOC_35|>": 100332,
+  "<|LOC_360|>": 100657,
+  "<|LOC_361|>": 100658,
+  "<|LOC_362|>": 100659,
+  "<|LOC_363|>": 100660,
+  "<|LOC_364|>": 100661,
+  "<|LOC_365|>": 100662,
+  "<|LOC_366|>": 100663,
+  "<|LOC_367|>": 100664,
+  "<|LOC_368|>": 100665,
+  "<|LOC_369|>": 100666,
+  "<|LOC_36|>": 100333,
+  "<|LOC_370|>": 100667,
+  "<|LOC_371|>": 100668,
+  "<|LOC_372|>": 100669,
+  "<|LOC_373|>": 100670,
+  "<|LOC_374|>": 100671,
+  "<|LOC_375|>": 100672,
+  "<|LOC_376|>": 100673,
+  "<|LOC_377|>": 100674,
+  "<|LOC_378|>": 100675,
+  "<|LOC_379|>": 100676,
+  "<|LOC_37|>": 100334,
+  "<|LOC_380|>": 100677,
+  "<|LOC_381|>": 100678,
+  "<|LOC_382|>": 100679,
+  "<|LOC_383|>": 100680,
+  "<|LOC_384|>": 100681,
+  "<|LOC_385|>": 100682,
+  "<|LOC_386|>": 100683,
+  "<|LOC_387|>": 100684,
+  "<|LOC_388|>": 100685,
+  "<|LOC_389|>": 100686,
+  "<|LOC_38|>": 100335,
+  "<|LOC_390|>": 100687,
+  "<|LOC_391|>": 100688,
+  "<|LOC_392|>": 100689,
+  "<|LOC_393|>": 100690,
+  "<|LOC_394|>": 100691,
+  "<|LOC_395|>": 100692,
+  "<|LOC_396|>": 100693,
+  "<|LOC_397|>": 100694,
+  "<|LOC_398|>": 100695,
+  "<|LOC_399|>": 100696,
+  "<|LOC_39|>": 100336,
+  "<|LOC_3|>": 100300,
+  "<|LOC_400|>": 100697,
+  "<|LOC_401|>": 100698,
+  "<|LOC_402|>": 100699,
+  "<|LOC_403|>": 100700,
+  "<|LOC_404|>": 100701,
+  "<|LOC_405|>": 100702,
+  "<|LOC_406|>": 100703,
+  "<|LOC_407|>": 100704,
+  "<|LOC_408|>": 100705,
+  "<|LOC_409|>": 100706,
+  "<|LOC_40|>": 100337,
+  "<|LOC_410|>": 100707,
+  "<|LOC_411|>": 100708,
+  "<|LOC_412|>": 100709,
+  "<|LOC_413|>": 100710,
+  "<|LOC_414|>": 100711,
+  "<|LOC_415|>": 100712,
+  "<|LOC_416|>": 100713,
+  "<|LOC_417|>": 100714,
+  "<|LOC_418|>": 100715,
+  "<|LOC_419|>": 100716,
+  "<|LOC_41|>": 100338,
+  "<|LOC_420|>": 100717,
+  "<|LOC_421|>": 100718,
+  "<|LOC_422|>": 100719,
+  "<|LOC_423|>": 100720,
+  "<|LOC_424|>": 100721,
+  "<|LOC_425|>": 100722,
+  "<|LOC_426|>": 100723,
+  "<|LOC_427|>": 100724,
+  "<|LOC_428|>": 100725,
+  "<|LOC_429|>": 100726,
+  "<|LOC_42|>": 100339,
+  "<|LOC_430|>": 100727,
+  "<|LOC_431|>": 100728,
+  "<|LOC_432|>": 100729,
+  "<|LOC_433|>": 100730,
+  "<|LOC_434|>": 100731,
+  "<|LOC_435|>": 100732,
+  "<|LOC_436|>": 100733,
+  "<|LOC_437|>": 100734,
+  "<|LOC_438|>": 100735,
+  "<|LOC_439|>": 100736,
+  "<|LOC_43|>": 100340,
+  "<|LOC_440|>": 100737,
+  "<|LOC_441|>": 100738,
+  "<|LOC_442|>": 100739,
+  "<|LOC_443|>": 100740,
+  "<|LOC_444|>": 100741,
+  "<|LOC_445|>": 100742,
+  "<|LOC_446|>": 100743,
+  "<|LOC_447|>": 100744,
+  "<|LOC_448|>": 100745,
+  "<|LOC_449|>": 100746,
+  "<|LOC_44|>": 100341,
+  "<|LOC_450|>": 100747,
+  "<|LOC_451|>": 100748,
+  "<|LOC_452|>": 100749,
+  "<|LOC_453|>": 100750,
+  "<|LOC_454|>": 100751,
+  "<|LOC_455|>": 100752,
+  "<|LOC_456|>": 100753,
+  "<|LOC_457|>": 100754,
+  "<|LOC_458|>": 100755,
+  "<|LOC_459|>": 100756,
+  "<|LOC_45|>": 100342,
+  "<|LOC_460|>": 100757,
+  "<|LOC_461|>": 100758,
+  "<|LOC_462|>": 100759,
+  "<|LOC_463|>": 100760,
+  "<|LOC_464|>": 100761,
+  "<|LOC_465|>": 100762,
+  "<|LOC_466|>": 100763,
+  "<|LOC_467|>": 100764,
+  "<|LOC_468|>": 100765,
+  "<|LOC_469|>": 100766,
+  "<|LOC_46|>": 100343,
+  "<|LOC_470|>": 100767,
+  "<|LOC_471|>": 100768,
+  "<|LOC_472|>": 100769,
+  "<|LOC_473|>": 100770,
+  "<|LOC_474|>": 100771,
+  "<|LOC_475|>": 100772,
+  "<|LOC_476|>": 100773,
+  "<|LOC_477|>": 100774,
+  "<|LOC_478|>": 100775,
+  "<|LOC_479|>": 100776,
+  "<|LOC_47|>": 100344,
+  "<|LOC_480|>": 100777,
+  "<|LOC_481|>": 100778,
+  "<|LOC_482|>": 100779,
+  "<|LOC_483|>": 100780,
+  "<|LOC_484|>": 100781,
+  "<|LOC_485|>": 100782,
+  "<|LOC_486|>": 100783,
+  "<|LOC_487|>": 100784,
+  "<|LOC_488|>": 100785,
+  "<|LOC_489|>": 100786,
+  "<|LOC_48|>": 100345,
+  "<|LOC_490|>": 100787,
+  "<|LOC_491|>": 100788,
+  "<|LOC_492|>": 100789,
+  "<|LOC_493|>": 100790,
+  "<|LOC_494|>": 100791,
+  "<|LOC_495|>": 100792,
+  "<|LOC_496|>": 100793,
+  "<|LOC_497|>": 100794,
+  "<|LOC_498|>": 100795,
+  "<|LOC_499|>": 100796,
+  "<|LOC_49|>": 100346,
+  "<|LOC_4|>": 100301,
+  "<|LOC_500|>": 100797,
+  "<|LOC_501|>": 100798,
+  "<|LOC_502|>": 100799,
+  "<|LOC_503|>": 100800,
+  "<|LOC_504|>": 100801,
+  "<|LOC_505|>": 100802,
+  "<|LOC_506|>": 100803,
+  "<|LOC_507|>": 100804,
+  "<|LOC_508|>": 100805,
+  "<|LOC_509|>": 100806,
+  "<|LOC_50|>": 100347,
+  "<|LOC_510|>": 100807,
+  "<|LOC_511|>": 100808,
+  "<|LOC_512|>": 100809,
+  "<|LOC_513|>": 100810,
+  "<|LOC_514|>": 100811,
+  "<|LOC_515|>": 100812,
+  "<|LOC_516|>": 100813,
+  "<|LOC_517|>": 100814,
+  "<|LOC_518|>": 100815,
+  "<|LOC_519|>": 100816,
+  "<|LOC_51|>": 100348,
+  "<|LOC_520|>": 100817,
+  "<|LOC_521|>": 100818,
+  "<|LOC_522|>": 100819,
+  "<|LOC_523|>": 100820,
+  "<|LOC_524|>": 100821,
+  "<|LOC_525|>": 100822,
+  "<|LOC_526|>": 100823,
+  "<|LOC_527|>": 100824,
+  "<|LOC_528|>": 100825,
+  "<|LOC_529|>": 100826,
+  "<|LOC_52|>": 100349,
+  "<|LOC_530|>": 100827,
+  "<|LOC_531|>": 100828,
+  "<|LOC_532|>": 100829,
+  "<|LOC_533|>": 100830,
+  "<|LOC_534|>": 100831,
+  "<|LOC_535|>": 100832,
+  "<|LOC_536|>": 100833,
+  "<|LOC_537|>": 100834,
+  "<|LOC_538|>": 100835,
+  "<|LOC_539|>": 100836,
+  "<|LOC_53|>": 100350,
+  "<|LOC_540|>": 100837,
+  "<|LOC_541|>": 100838,
+  "<|LOC_542|>": 100839,
+  "<|LOC_543|>": 100840,
+  "<|LOC_544|>": 100841,
+  "<|LOC_545|>": 100842,
+  "<|LOC_546|>": 100843,
+  "<|LOC_547|>": 100844,
+  "<|LOC_548|>": 100845,
+  "<|LOC_549|>": 100846,
+  "<|LOC_54|>": 100351,
+  "<|LOC_550|>": 100847,
+  "<|LOC_551|>": 100848,
+  "<|LOC_552|>": 100849,
+  "<|LOC_553|>": 100850,
+  "<|LOC_554|>": 100851,
+  "<|LOC_555|>": 100852,
+  "<|LOC_556|>": 100853,
+  "<|LOC_557|>": 100854,
+  "<|LOC_558|>": 100855,
+  "<|LOC_559|>": 100856,
+  "<|LOC_55|>": 100352,
+  "<|LOC_560|>": 100857,
+  "<|LOC_561|>": 100858,
+  "<|LOC_562|>": 100859,
+  "<|LOC_563|>": 100860,
+  "<|LOC_564|>": 100861,
+  "<|LOC_565|>": 100862,
+  "<|LOC_566|>": 100863,
+  "<|LOC_567|>": 100864,
+  "<|LOC_568|>": 100865,
+  "<|LOC_569|>": 100866,
+  "<|LOC_56|>": 100353,
+  "<|LOC_570|>": 100867,
+  "<|LOC_571|>": 100868,
+  "<|LOC_572|>": 100869,
+  "<|LOC_573|>": 100870,
+  "<|LOC_574|>": 100871,
+  "<|LOC_575|>": 100872,
+  "<|LOC_576|>": 100873,
+  "<|LOC_577|>": 100874,
+  "<|LOC_578|>": 100875,
+  "<|LOC_579|>": 100876,
+  "<|LOC_57|>": 100354,
+  "<|LOC_580|>": 100877,
+  "<|LOC_581|>": 100878,
+  "<|LOC_582|>": 100879,
+  "<|LOC_583|>": 100880,
+  "<|LOC_584|>": 100881,
+  "<|LOC_585|>": 100882,
+  "<|LOC_586|>": 100883,
+  "<|LOC_587|>": 100884,
+  "<|LOC_588|>": 100885,
+  "<|LOC_589|>": 100886,
+  "<|LOC_58|>": 100355,
+  "<|LOC_590|>": 100887,
+  "<|LOC_591|>": 100888,
+  "<|LOC_592|>": 100889,
+  "<|LOC_593|>": 100890,
+  "<|LOC_594|>": 100891,
+  "<|LOC_595|>": 100892,
+  "<|LOC_596|>": 100893,
+  "<|LOC_597|>": 100894,
+  "<|LOC_598|>": 100895,
+  "<|LOC_599|>": 100896,
+  "<|LOC_59|>": 100356,
+  "<|LOC_5|>": 100302,
+  "<|LOC_600|>": 100897,
+  "<|LOC_601|>": 100898,
+  "<|LOC_602|>": 100899,
+  "<|LOC_603|>": 100900,
+  "<|LOC_604|>": 100901,
+  "<|LOC_605|>": 100902,
+  "<|LOC_606|>": 100903,
+  "<|LOC_607|>": 100904,
+  "<|LOC_608|>": 100905,
+  "<|LOC_609|>": 100906,
+  "<|LOC_60|>": 100357,
+  "<|LOC_610|>": 100907,
+  "<|LOC_611|>": 100908,
+  "<|LOC_612|>": 100909,
+  "<|LOC_613|>": 100910,
+  "<|LOC_614|>": 100911,
+  "<|LOC_615|>": 100912,
+  "<|LOC_616|>": 100913,
+  "<|LOC_617|>": 100914,
+  "<|LOC_618|>": 100915,
+  "<|LOC_619|>": 100916,
+  "<|LOC_61|>": 100358,
+  "<|LOC_620|>": 100917,
+  "<|LOC_621|>": 100918,
+  "<|LOC_622|>": 100919,
+  "<|LOC_623|>": 100920,
+  "<|LOC_624|>": 100921,
+  "<|LOC_625|>": 100922,
+  "<|LOC_626|>": 100923,
+  "<|LOC_627|>": 100924,
+  "<|LOC_628|>": 100925,
+  "<|LOC_629|>": 100926,
+  "<|LOC_62|>": 100359,
+  "<|LOC_630|>": 100927,
+  "<|LOC_631|>": 100928,
+  "<|LOC_632|>": 100929,
+  "<|LOC_633|>": 100930,
+  "<|LOC_634|>": 100931,
+  "<|LOC_635|>": 100932,
+  "<|LOC_636|>": 100933,
+  "<|LOC_637|>": 100934,
+  "<|LOC_638|>": 100935,
+  "<|LOC_639|>": 100936,
+  "<|LOC_63|>": 100360,
+  "<|LOC_640|>": 100937,
+  "<|LOC_641|>": 100938,
+  "<|LOC_642|>": 100939,
+  "<|LOC_643|>": 100940,
+  "<|LOC_644|>": 100941,
+  "<|LOC_645|>": 100942,
+  "<|LOC_646|>": 100943,
+  "<|LOC_647|>": 100944,
+  "<|LOC_648|>": 100945,
+  "<|LOC_649|>": 100946,
+  "<|LOC_64|>": 100361,
+  "<|LOC_650|>": 100947,
+  "<|LOC_651|>": 100948,
+  "<|LOC_652|>": 100949,
+  "<|LOC_653|>": 100950,
+  "<|LOC_654|>": 100951,
+  "<|LOC_655|>": 100952,
+  "<|LOC_656|>": 100953,
+  "<|LOC_657|>": 100954,
+  "<|LOC_658|>": 100955,
+  "<|LOC_659|>": 100956,
+  "<|LOC_65|>": 100362,
+  "<|LOC_660|>": 100957,
+  "<|LOC_661|>": 100958,
+  "<|LOC_662|>": 100959,
+  "<|LOC_663|>": 100960,
+  "<|LOC_664|>": 100961,
+  "<|LOC_665|>": 100962,
+  "<|LOC_666|>": 100963,
+  "<|LOC_667|>": 100964,
+  "<|LOC_668|>": 100965,
+  "<|LOC_669|>": 100966,
+  "<|LOC_66|>": 100363,
+  "<|LOC_670|>": 100967,
+  "<|LOC_671|>": 100968,
+  "<|LOC_672|>": 100969,
+  "<|LOC_673|>": 100970,
+  "<|LOC_674|>": 100971,
+  "<|LOC_675|>": 100972,
+  "<|LOC_676|>": 100973,
+  "<|LOC_677|>": 100974,
+  "<|LOC_678|>": 100975,
+  "<|LOC_679|>": 100976,
+  "<|LOC_67|>": 100364,
+  "<|LOC_680|>": 100977,
+  "<|LOC_681|>": 100978,
+  "<|LOC_682|>": 100979,
+  "<|LOC_683|>": 100980,
+  "<|LOC_684|>": 100981,
+  "<|LOC_685|>": 100982,
+  "<|LOC_686|>": 100983,
+  "<|LOC_687|>": 100984,
+  "<|LOC_688|>": 100985,
+  "<|LOC_689|>": 100986,
+  "<|LOC_68|>": 100365,
+  "<|LOC_690|>": 100987,
+  "<|LOC_691|>": 100988,
+  "<|LOC_692|>": 100989,
+  "<|LOC_693|>": 100990,
+  "<|LOC_694|>": 100991,
+  "<|LOC_695|>": 100992,
+  "<|LOC_696|>": 100993,
+  "<|LOC_697|>": 100994,
+  "<|LOC_698|>": 100995,
+  "<|LOC_699|>": 100996,
+  "<|LOC_69|>": 100366,
+  "<|LOC_6|>": 100303,
+  "<|LOC_700|>": 100997,
+  "<|LOC_701|>": 100998,
+  "<|LOC_702|>": 100999,
+  "<|LOC_703|>": 101000,
+  "<|LOC_704|>": 101001,
+  "<|LOC_705|>": 101002,
+  "<|LOC_706|>": 101003,
+  "<|LOC_707|>": 101004,
+  "<|LOC_708|>": 101005,
+  "<|LOC_709|>": 101006,
+  "<|LOC_70|>": 100367,
+  "<|LOC_710|>": 101007,
+  "<|LOC_711|>": 101008,
+  "<|LOC_712|>": 101009,
+  "<|LOC_713|>": 101010,
+  "<|LOC_714|>": 101011,
+  "<|LOC_715|>": 101012,
+  "<|LOC_716|>": 101013,
+  "<|LOC_717|>": 101014,
+  "<|LOC_718|>": 101015,
+  "<|LOC_719|>": 101016,
+  "<|LOC_71|>": 100368,
+  "<|LOC_720|>": 101017,
+  "<|LOC_721|>": 101018,
+  "<|LOC_722|>": 101019,
+  "<|LOC_723|>": 101020,
+  "<|LOC_724|>": 101021,
+  "<|LOC_725|>": 101022,
+  "<|LOC_726|>": 101023,
+  "<|LOC_727|>": 101024,
+  "<|LOC_728|>": 101025,
+  "<|LOC_729|>": 101026,
+  "<|LOC_72|>": 100369,
+  "<|LOC_730|>": 101027,
+  "<|LOC_731|>": 101028,
+  "<|LOC_732|>": 101029,
+  "<|LOC_733|>": 101030,
+  "<|LOC_734|>": 101031,
+  "<|LOC_735|>": 101032,
+  "<|LOC_736|>": 101033,
+  "<|LOC_737|>": 101034,
+  "<|LOC_738|>": 101035,
+  "<|LOC_739|>": 101036,
+  "<|LOC_73|>": 100370,
+  "<|LOC_740|>": 101037,
+  "<|LOC_741|>": 101038,
+  "<|LOC_742|>": 101039,
+  "<|LOC_743|>": 101040,
+  "<|LOC_744|>": 101041,
+  "<|LOC_745|>": 101042,
+  "<|LOC_746|>": 101043,
+  "<|LOC_747|>": 101044,
+  "<|LOC_748|>": 101045,
+  "<|LOC_749|>": 101046,
+  "<|LOC_74|>": 100371,
+  "<|LOC_750|>": 101047,
+  "<|LOC_751|>": 101048,
+  "<|LOC_752|>": 101049,
+  "<|LOC_753|>": 101050,
+  "<|LOC_754|>": 101051,
+  "<|LOC_755|>": 101052,
+  "<|LOC_756|>": 101053,
+  "<|LOC_757|>": 101054,
+  "<|LOC_758|>": 101055,
+  "<|LOC_759|>": 101056,
+  "<|LOC_75|>": 100372,
+  "<|LOC_760|>": 101057,
+  "<|LOC_761|>": 101058,
+  "<|LOC_762|>": 101059,
+  "<|LOC_763|>": 101060,
+  "<|LOC_764|>": 101061,
+  "<|LOC_765|>": 101062,
+  "<|LOC_766|>": 101063,
+  "<|LOC_767|>": 101064,
+  "<|LOC_768|>": 101065,
+  "<|LOC_769|>": 101066,
+  "<|LOC_76|>": 100373,
+  "<|LOC_770|>": 101067,
+  "<|LOC_771|>": 101068,
+  "<|LOC_772|>": 101069,
+  "<|LOC_773|>": 101070,
+  "<|LOC_774|>": 101071,
+  "<|LOC_775|>": 101072,
+  "<|LOC_776|>": 101073,
+  "<|LOC_777|>": 101074,
+  "<|LOC_778|>": 101075,
+  "<|LOC_779|>": 101076,
+  "<|LOC_77|>": 100374,
+  "<|LOC_780|>": 101077,
+  "<|LOC_781|>": 101078,
+  "<|LOC_782|>": 101079,
+  "<|LOC_783|>": 101080,
+  "<|LOC_784|>": 101081,
+  "<|LOC_785|>": 101082,
+  "<|LOC_786|>": 101083,
+  "<|LOC_787|>": 101084,
+  "<|LOC_788|>": 101085,
+  "<|LOC_789|>": 101086,
+  "<|LOC_78|>": 100375,
+  "<|LOC_790|>": 101087,
+  "<|LOC_791|>": 101088,
+  "<|LOC_792|>": 101089,
+  "<|LOC_793|>": 101090,
+  "<|LOC_794|>": 101091,
+  "<|LOC_795|>": 101092,
+  "<|LOC_796|>": 101093,
+  "<|LOC_797|>": 101094,
+  "<|LOC_798|>": 101095,
+  "<|LOC_799|>": 101096,
+  "<|LOC_79|>": 100376,
+  "<|LOC_7|>": 100304,
+  "<|LOC_800|>": 101097,
+  "<|LOC_801|>": 101098,
+  "<|LOC_802|>": 101099,
+  "<|LOC_803|>": 101100,
+  "<|LOC_804|>": 101101,
+  "<|LOC_805|>": 101102,
+  "<|LOC_806|>": 101103,
+  "<|LOC_807|>": 101104,
+  "<|LOC_808|>": 101105,
+  "<|LOC_809|>": 101106,
+  "<|LOC_80|>": 100377,
+  "<|LOC_810|>": 101107,
+  "<|LOC_811|>": 101108,
+  "<|LOC_812|>": 101109,
+  "<|LOC_813|>": 101110,
+  "<|LOC_814|>": 101111,
+  "<|LOC_815|>": 101112,
+  "<|LOC_816|>": 101113,
+  "<|LOC_817|>": 101114,
+  "<|LOC_818|>": 101115,
+  "<|LOC_819|>": 101116,
+  "<|LOC_81|>": 100378,
+  "<|LOC_820|>": 101117,
+  "<|LOC_821|>": 101118,
+  "<|LOC_822|>": 101119,
+  "<|LOC_823|>": 101120,
+  "<|LOC_824|>": 101121,
+  "<|LOC_825|>": 101122,
+  "<|LOC_826|>": 101123,
+  "<|LOC_827|>": 101124,
+  "<|LOC_828|>": 101125,
+  "<|LOC_829|>": 101126,
+  "<|LOC_82|>": 100379,
+  "<|LOC_830|>": 101127,
+  "<|LOC_831|>": 101128,
+  "<|LOC_832|>": 101129,
+  "<|LOC_833|>": 101130,
+  "<|LOC_834|>": 101131,
+  "<|LOC_835|>": 101132,
+  "<|LOC_836|>": 101133,
+  "<|LOC_837|>": 101134,
+  "<|LOC_838|>": 101135,
+  "<|LOC_839|>": 101136,
+  "<|LOC_83|>": 100380,
+  "<|LOC_840|>": 101137,
+  "<|LOC_841|>": 101138,
+  "<|LOC_842|>": 101139,
+  "<|LOC_843|>": 101140,
+  "<|LOC_844|>": 101141,
+  "<|LOC_845|>": 101142,
+  "<|LOC_846|>": 101143,
+  "<|LOC_847|>": 101144,
+  "<|LOC_848|>": 101145,
+  "<|LOC_849|>": 101146,
+  "<|LOC_84|>": 100381,
+  "<|LOC_850|>": 101147,
+  "<|LOC_851|>": 101148,
+  "<|LOC_852|>": 101149,
+  "<|LOC_853|>": 101150,
+  "<|LOC_854|>": 101151,
+  "<|LOC_855|>": 101152,
+  "<|LOC_856|>": 101153,
+  "<|LOC_857|>": 101154,
+  "<|LOC_858|>": 101155,
+  "<|LOC_859|>": 101156,
+  "<|LOC_85|>": 100382,
+  "<|LOC_860|>": 101157,
+  "<|LOC_861|>": 101158,
+  "<|LOC_862|>": 101159,
+  "<|LOC_863|>": 101160,
+  "<|LOC_864|>": 101161,
+  "<|LOC_865|>": 101162,
+  "<|LOC_866|>": 101163,
+  "<|LOC_867|>": 101164,
+  "<|LOC_868|>": 101165,
+  "<|LOC_869|>": 101166,
+  "<|LOC_86|>": 100383,
+  "<|LOC_870|>": 101167,
+  "<|LOC_871|>": 101168,
+  "<|LOC_872|>": 101169,
+  "<|LOC_873|>": 101170,
+  "<|LOC_874|>": 101171,
+  "<|LOC_875|>": 101172,
+  "<|LOC_876|>": 101173,
+  "<|LOC_877|>": 101174,
+  "<|LOC_878|>": 101175,
+  "<|LOC_879|>": 101176,
+  "<|LOC_87|>": 100384,
+  "<|LOC_880|>": 101177,
+  "<|LOC_881|>": 101178,
+  "<|LOC_882|>": 101179,
+  "<|LOC_883|>": 101180,
+  "<|LOC_884|>": 101181,
+  "<|LOC_885|>": 101182,
+  "<|LOC_886|>": 101183,
+  "<|LOC_887|>": 101184,
+  "<|LOC_888|>": 101185,
+  "<|LOC_889|>": 101186,
+  "<|LOC_88|>": 100385,
+  "<|LOC_890|>": 101187,
+  "<|LOC_891|>": 101188,
+  "<|LOC_892|>": 101189,
+  "<|LOC_893|>": 101190,
+  "<|LOC_894|>": 101191,
+  "<|LOC_895|>": 101192,
+  "<|LOC_896|>": 101193,
+  "<|LOC_897|>": 101194,
+  "<|LOC_898|>": 101195,
+  "<|LOC_899|>": 101196,
+  "<|LOC_89|>": 100386,
+  "<|LOC_8|>": 100305,
+  "<|LOC_900|>": 101197,
+  "<|LOC_901|>": 101198,
+  "<|LOC_902|>": 101199,
+  "<|LOC_903|>": 101200,
+  "<|LOC_904|>": 101201,
+  "<|LOC_905|>": 101202,
+  "<|LOC_906|>": 101203,
+  "<|LOC_907|>": 101204,
+  "<|LOC_908|>": 101205,
+  "<|LOC_909|>": 101206,
+  "<|LOC_90|>": 100387,
+  "<|LOC_910|>": 101207,
+  "<|LOC_911|>": 101208,
+  "<|LOC_912|>": 101209,
+  "<|LOC_913|>": 101210,
+  "<|LOC_914|>": 101211,
+  "<|LOC_915|>": 101212,
+  "<|LOC_916|>": 101213,
+  "<|LOC_917|>": 101214,
+  "<|LOC_918|>": 101215,
+  "<|LOC_919|>": 101216,
+  "<|LOC_91|>": 100388,
+  "<|LOC_920|>": 101217,
+  "<|LOC_921|>": 101218,
+  "<|LOC_922|>": 101219,
+  "<|LOC_923|>": 101220,
+  "<|LOC_924|>": 101221,
+  "<|LOC_925|>": 101222,
+  "<|LOC_926|>": 101223,
+  "<|LOC_927|>": 101224,
+  "<|LOC_928|>": 101225,
+  "<|LOC_929|>": 101226,
+  "<|LOC_92|>": 100389,
+  "<|LOC_930|>": 101227,
+  "<|LOC_931|>": 101228,
+  "<|LOC_932|>": 101229,
+  "<|LOC_933|>": 101230,
+  "<|LOC_934|>": 101231,
+  "<|LOC_935|>": 101232,
+  "<|LOC_936|>": 101233,
+  "<|LOC_937|>": 101234,
+  "<|LOC_938|>": 101235,
+  "<|LOC_939|>": 101236,
+  "<|LOC_93|>": 100390,
+  "<|LOC_940|>": 101237,
+  "<|LOC_941|>": 101238,
+  "<|LOC_942|>": 101239,
+  "<|LOC_943|>": 101240,
+  "<|LOC_944|>": 101241,
+  "<|LOC_945|>": 101242,
+  "<|LOC_946|>": 101243,
+  "<|LOC_947|>": 101244,
+  "<|LOC_948|>": 101245,
+  "<|LOC_949|>": 101246,
+  "<|LOC_94|>": 100391,
+  "<|LOC_950|>": 101247,
+  "<|LOC_951|>": 101248,
+  "<|LOC_952|>": 101249,
+  "<|LOC_953|>": 101250,
+  "<|LOC_954|>": 101251,
+  "<|LOC_955|>": 101252,
+  "<|LOC_956|>": 101253,
+  "<|LOC_957|>": 101254,
+  "<|LOC_958|>": 101255,
+  "<|LOC_959|>": 101256,
+  "<|LOC_95|>": 100392,
+  "<|LOC_960|>": 101257,
+  "<|LOC_961|>": 101258,
+  "<|LOC_962|>": 101259,
+  "<|LOC_963|>": 101260,
+  "<|LOC_964|>": 101261,
+  "<|LOC_965|>": 101262,
+  "<|LOC_966|>": 101263,
+  "<|LOC_967|>": 101264,
+  "<|LOC_968|>": 101265,
+  "<|LOC_969|>": 101266,
+  "<|LOC_96|>": 100393,
+  "<|LOC_970|>": 101267,
+  "<|LOC_971|>": 101268,
+  "<|LOC_972|>": 101269,
+  "<|LOC_973|>": 101270,
+  "<|LOC_974|>": 101271,
+  "<|LOC_975|>": 101272,
+  "<|LOC_976|>": 101273,
+  "<|LOC_977|>": 101274,
+  "<|LOC_978|>": 101275,
+  "<|LOC_979|>": 101276,
+  "<|LOC_97|>": 100394,
+  "<|LOC_980|>": 101277,
+  "<|LOC_981|>": 101278,
+  "<|LOC_982|>": 101279,
+  "<|LOC_983|>": 101280,
+  "<|LOC_984|>": 101281,
+  "<|LOC_985|>": 101282,
+  "<|LOC_986|>": 101283,
+  "<|LOC_987|>": 101284,
+  "<|LOC_988|>": 101285,
+  "<|LOC_989|>": 101286,
+  "<|LOC_98|>": 100395,
+  "<|LOC_990|>": 101287,
+  "<|LOC_991|>": 101288,
+  "<|LOC_992|>": 101289,
+  "<|LOC_993|>": 101290,
+  "<|LOC_994|>": 101291,
+  "<|LOC_995|>": 101292,
+  "<|LOC_996|>": 101293,
+  "<|LOC_997|>": 101294,
+  "<|LOC_998|>": 101295,
+  "<|LOC_999|>": 101296,
+  "<|LOC_99|>": 100396,
+  "<|LOC_9|>": 100306,
+  "<|LOC_BEGIN|>": 101298,
+  "<|LOC_END|>": 101299,
+  "<|LOC_SEP|>": 101300,
+  "<|image_pad|>": 101304,
+  "<|video_pad|>": 101307
+}

python/paddleocr_vl_1-5_tokenizer/chat_template.jinja

@@ -0,0 +1,43 @@
+{%- if not add_generation_prompt is defined -%}
+    {%- set add_generation_prompt = true -%}
+{%- endif -%}
+{%- if not cls_token is defined -%}
+    {%- set cls_token = "<|begin_of_sentence|>" -%}
+{%- endif -%}
+{%- if not eos_token is defined -%}
+    {%- set eos_token = "</s>" -%}
+{%- endif -%}
+{{- cls_token -}}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {{- "User: " -}}
+        {%- for content in message["content"] -%}
+            {%- if content["type"] == "image" -%}
+                {{ "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>" }}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- for content in message["content"] -%}
+            {%- if content["type"] == "text" -%}
+                {{ content["text"] }}
+            {%- endif -%}
+        {%- endfor -%}
+        {{ "\n" -}}
+    {%- elif message["role"] == "assistant" -%}
+        {{- "Assistant:\n" -}}
+        {%- for content in message["content"] -%}
+            {%- if content["type"] == "text" -%}
+                {{ content["text"] }}
+            {%- endif -%}
+        {%- endfor -%}
+        {{ eos_token -}}
+    {%- elif message["role"] == "system" -%}
+        {%- for content in message["content"] -%}
+            {%- if content["type"] == "text" -%}
+                {{ content["text"] + "\n" }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "Assistant:\n" -}}
+{%- endif -%}

python/paddleocr_vl_1-5_tokenizer/config.json

@@ -0,0 +1,76 @@
+{
+  "architectures": [
+    "PaddleOCRVLForConditionalGeneration"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_paddleocr_vl.PaddleOCRVLConfig",
+    "AutoModel": "modeling_paddleocr_vl.PaddleOCRVLForConditionalGeneration",
+    "AutoModelForCausalLM": "modeling_paddleocr_vl.PaddleOCRVLForConditionalGeneration"
+  },
+  "compression_ratio": 1.0,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "ignored_index": -100,
+  "image_token_id": 100295,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 131072,
+  "max_sequence_length": null,
+  "model_type": "paddleocr_vl",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 2,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 500000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_bias": false,
+  "use_cache": false,
+  "use_flash_attention": false,
+  "video_token_id": 101307,
+  "vision_config": {
+    "architectures": [
+      "PaddleOCRVisionModel"
+    ],
+    "attention_dropout": 0.0,
+    "auto_map": {
+      "AutoConfig": "configuration_paddleocr_vl.PaddleOCRVLConfig",
+      "AutoModel": "modeling_paddleocr_vl.PaddleOCRVisionModel"
+    },
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 384,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "paddleocr_vl",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 27,
+    "pad_token_id": 0,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16"
+  },
+  "vision_start_token_id": 101305,
+  "vision_end_token_id": 101306,
+  "vocab_size": 103424,
+  "weight_share_add_bias": true,
+  "use_3d_rope": true,
+  "rope_is_neox_style": true
+}

python/paddleocr_vl_1-5_tokenizer/configuration_paddleocr_vl.py

@@ -0,0 +1,191 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+class PaddleOCRVisionConfig(PretrainedConfig):
+    model_type = "paddleocr_vl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_channels=3,
+        image_size=224,
+        patch_size=14,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        tokens_per_second=2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.tokens_per_second = tokens_per_second
+
+
+
+class PaddleOCRVLConfig(PretrainedConfig):
+    """
+    Configuration class.
+
+    This class stores the configuration of an Ernie model, defining the model architecture.
+    It inherits from PretrainedConfig and can be used to control model outputs.
+    """
+
+    model_type = "paddleocr_vl"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    sub_configs = {"vision_config": PaddleOCRVisionConfig}
+
+    # Default tensor parallel plan for base model `Qwen3`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        intermediate_size=11008,
+        max_position_embeddings=32768,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        image_token_id=101304,
+        video_token_id=101305,
+        vision_start_token_id=101306,
+        rms_norm_eps=1e-6,
+        use_cache=False,
+        use_flash_attention=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        head_dim=128,
+        hidden_act="silu",
+        use_bias=False,
+        rope_theta=10000,
+        weight_share_add_bias=True,
+        ignored_index=-100,
+        attention_probs_dropout_prob=0.0,
+        hidden_dropout_prob=0.0,
+        compression_ratio: float = 1.0,
+        num_key_value_heads=None,
+        max_sequence_length=None,
+        tie_word_embeddings=False,
+        vision_config=None,
+        rope_scaling=None,
+        **kwargs,
+    ):
+        """
+        Initialize configuration with default or specified parameters.
+
+        Args:
+            vocab_size (int): Size of the vocabulary (number of unique tokens)
+            hidden_size (int): Dimensionality of the encoder layers and the pooler layer
+            intermediate_size (int): Dimensionality of the "intermediate" (feed-forward) layer
+            max_position_embeddings (int): Maximum sequence length the model can handle
+            num_hidden_layers (int): Number of hidden layers in the Transformer encoder
+            num_attention_heads (int): Number of attention heads for each attention layer
+            rms_norm_eps (float): The epsilon used by the RMS normalization layers
+            use_cache (bool): Whether to use caching for faster generation (decoding)
+            use_flash_attention (bool): Whether to use FlashAttention for optimized attention computation
+            pad_token_id (int): Token ID used for padding sequences
+            bos_token_id (int): Token ID used for beginning-of-sequence
+            eos_token_id (int): Token ID used for end-of-sequence
+            use_bias (bool): Whether to use bias terms in linear layers
+            rope_theta (float): The base period of the RoPE embeddings
+            weight_share_add_bias (bool): Whether to share bias weights in certain layers
+            ignored_index (int): Target value that is ignored during loss computation
+            attention_probs_dropout_prob (float): Dropout probability for attention weights
+            hidden_dropout_prob (float): Dropout probability for hidden layers
+            compression_ratio (float): Ratio for KV cache compression (1.0 = no compression)
+            num_key_value_heads (int): Number of key/value heads (for Grouped Query Attention)
+            max_sequence_length (int): Maximum sequence length for positional embeddings
+            **kwargs: Additional keyword arguments passed to parent class
+        """
+
+        # Set default for tied embeddings if not specified.
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()        
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.use_flash_attention = use_flash_attention
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.head_dim = head_dim
+        self.hidden_act=hidden_act
+        self.sliding_window = None
+        self.hidden_size = hidden_size
+        self.use_bias = use_bias
+        self.weight_share_add_bias = weight_share_add_bias
+        self.rope_theta = rope_theta
+        self.ignored_index = ignored_index
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.compression_ratio = compression_ratio
+        self.num_key_value_heads = num_key_value_heads
+        self.max_sequence_length = max_sequence_length
+        self.rope_scaling = rope_scaling
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            if self.rope_scaling["type"] == "mrope":
+                self.rope_scaling["type"] = "default"
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self, ignore_keys={"mrope_section"})        
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

python/paddleocr_vl_1-5_tokenizer/generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.55.0",
+  "use_cache": false
+}

python/paddleocr_vl_1-5_tokenizer/image_processing_paddleocr_vl.py

@@ -0,0 +1,569 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Image processor class for PaddleOCR-VL."""
+
+import math
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from torchvision.transforms import functional as TF
+from transformers.image_transforms import (
+    convert_to_rgb,
+    resize,
+    to_channel_dimension_format,
+)
+from transformers.image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    ChannelDimension,
+    PILImageResampling,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_scaled_image,
+    is_valid_image,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+    validate_preprocess_arguments,
+)
+from transformers.utils import TensorType, is_vision_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+    from PIL import Image
+
+ImageInput = Union[
+    "PIL.Image.Image",
+    np.ndarray,
+    "torch.Tensor",
+    List["PIL.Image.Image"],
+    List[np.ndarray],
+    List["torch.Tensor"],
+]  # noqa
+
+
+VideoInput = Union[
+    List["PIL.Image.Image"],
+    "np.ndarray",
+    "torch.Tensor",
+    List["np.ndarray"],
+    List["torch.Tensor"],
+    List[List["PIL.Image.Image"]],
+    List[List["np.ndarrray"]],
+    List[List["torch.Tensor"]],
+]  # noqa
+
+
+def make_batched_images(images) -> List[List[ImageInput]]:
+    """
+    Accepts images in list or nested list format, and makes a list of images for preprocessing.
+
+    Args:
+        images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
+            The input image.
+
+    Returns:
+        list: A list of images.
+    """
+    if (
+        isinstance(images, (list, tuple))
+        and isinstance(images[0], (list, tuple))
+        and is_valid_image(images[0][0])
+    ):
+        return [img for img_list in images for img in img_list]
+
+    elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
+        return images
+
+    elif is_valid_image(images):
+        return [images]
+
+    raise ValueError(f"Could not make batched images from {images}")
+
+
+def adjust_size(size, patch_size):
+    num_patches = size // patch_size
+    if num_patches % 2 != 0:  # 如果是奇数，减1
+        num_patches -= 1
+    return num_patches * patch_size
+
+
+def make_batched_videos(videos) -> List[VideoInput]:
+    if (
+        isinstance(videos, (list, tuple))
+        and isinstance(videos[0], (list, tuple))
+        and is_valid_image(videos[0][0])
+    ):
+        return videos
+
+    elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
+        if isinstance(videos[0], Image.Image):
+            return [videos]
+        elif len(videos[0].shape) == 4:
+            return [list(video) for video in videos]
+
+    elif is_valid_image(videos) and len(videos.shape) == 4:
+        return [list(videos)]
+
+    raise ValueError(f"Could not make batched video from {videos}")
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 28 * 28 * 130,
+    max_pixels: int = 28 * 28 * 1280,
+):
+    """Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+
+    """
+    # if height < factor or width < factor:
+    #    raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}")
+    # if int(height < factor//4) + int(width < factor//4):
+    #     raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor//4}")
+
+    if height < factor:
+        print(f"smart_resize: height={height} < factor={factor}, reset height=factor")
+        width = round((width * factor) / height)
+        height = factor
+
+    if width < factor:
+        print(f"smart_resize: width={width} < factor={factor}, reset width=factor")
+        height = round((height * factor) / width)
+        width = factor
+
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError(
+            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
+        )
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = math.floor(height / beta / factor) * factor
+        w_bar = math.floor(width / beta / factor) * factor
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+class PaddleOCRVLImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Siglip image processor that dynamically resizes images based on the original images.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+            Resampling filter to use when resizing the image.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+            Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
+        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+        min_pixels (`int`, *optional*, defaults to `28 * 28 * 130`):
+            The min pixels of the image to resize the image.
+        max_pixels (`int`, *optional*, defaults to `28 * 28 * 1670`):
+            The max pixels of the image to resize the image.
+        patch_size (`int`, *optional*, defaults to 14):
+            The spacial patch size of the vision encoder.
+        temporal_patch_size (`int`, *optional*, defaults to 2):
+            The temporal patch size of the vision encoder.
+        merge_size (`int`, *optional*, defaults to 2):
+            The merge size of the vision encoder to llm encoder.
+    """
+
+    model_input_names = [
+        "pixel_values",
+        "image_grid_thw",
+        "pixel_values_videos",
+        "video_grid_thw",
+    ]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = True,
+        min_pixels: int = 28 * 28 * 130,
+        max_pixels: int = 28 * 28 * 1280,
+        patch_size: int = 14,
+        temporal_patch_size: int = 1,
+        merge_size: int = 2,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.do_resize = do_resize
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+        self.min_pixels = min_pixels
+        self.max_pixels = max_pixels
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.merge_size = merge_size
+        self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}  # not used
+        self.do_convert_rgb = do_convert_rgb
+
+    def mvit_rescale(self, image: Image.Image, merge_size: int = 2) -> Image.Image:
+        try:
+            w, h = image.size
+        except:
+            raise ValueError(str((type(image), image)))
+        patch_size = self.patch_size
+
+        if (w // patch_size) * (h // patch_size) > self.in_token_limit:
+            scale = math.sqrt(
+                self.in_token_limit / ((w // patch_size) * (h // patch_size))
+            )
+            new_w, new_h = int(w * scale), int(h * scale)
+
+            image = image.resize((new_w, new_h), Image.Resampling.BICUBIC)
+        if self.pad_input:
+            new_w, new_h = image.size
+            pad_size_h = merge_size * patch_size
+            pad_size_w = merge_size * patch_size
+
+            pad_h = (pad_size_h - new_h % pad_size_h) % pad_size_h
+            pad_w = (pad_size_w - new_w % pad_size_w) % pad_size_w
+
+            image = TF.pad(image, (0, 0, pad_w, pad_h))
+        else:
+            new_w, new_h = image.size
+            new_w = new_w - new_w % patch_size
+            new_h = new_h - new_h % patch_size
+
+            new_w = adjust_size(new_w, patch_size)
+            new_h = adjust_size(new_h, patch_size)
+
+            image = TF.center_crop(image, (new_h, new_w))
+
+        w, h = image.size
+        if w // patch_size >= 512 or h // patch_size >= 512:
+            new_h = min(patch_size * 510, h)
+            new_w = min(patch_size * 510, w)
+            image = TF.center_crop(image, (new_h, new_w))
+            # raise ValueError("Exceed pos emb")
+        return image
+
+    def _preprocess(
+        self,
+        images: Union[ImageInput, VideoInput],
+        do_resize: bool = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        """
+        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+
+        Args:
+            images (`ImageInput`):
+                Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
+            vision_info (`List[Dict]`, *optional*):
+                Optional list of dictionaries containing additional information about vision inputs.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Scale factor to use if rescaling the image.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.   - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        images = make_list_of_images(images)
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        if is_scaled_image(images[0]) and do_rescale:
+            logger.warning_once(
+                "It looks like you are trying to rescale already rescaled images. If the input"
+                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+            )
+        if input_data_format is None:
+            # We assume that all images have the same channel dimension format.
+            input_data_format = infer_channel_dimension_format(images[0])
+
+        height, width = get_image_size(images[0], channel_dim=input_data_format)
+        resized_height, resized_width = height, width
+        processed_images = []
+
+        for image in images:
+            if do_resize:
+                resized_height, resized_width = smart_resize(
+                    height,
+                    width,
+                    factor=self.patch_size * self.merge_size,
+                    min_pixels=self.min_pixels,
+                    max_pixels=self.max_pixels,
+                )
+                image = resize(
+                    image,
+                    size=(resized_height, resized_width),
+                    resample=resample,
+                    input_data_format=input_data_format,
+                )
+
+            if do_rescale:
+                image = self.rescale(
+                    image, scale=rescale_factor, input_data_format=input_data_format
+                )
+
+            if do_normalize:
+                image = self.normalize(
+                    image=image,
+                    mean=image_mean,
+                    std=image_std,
+                    input_data_format=input_data_format,
+                )
+            image = to_channel_dimension_format(
+                image, data_format, input_channel_dim=input_data_format
+            )
+            processed_images.append(image)
+
+        patches = np.array(processed_images)
+        if data_format == ChannelDimension.LAST:
+            patches = patches.transpose(0, 3, 1, 2)
+        if patches.shape[0] == 1:
+            patches = np.tile(patches, (self.temporal_patch_size, 1, 1, 1))
+        init_patches = patches
+        channel = patches.shape[1]
+        grid_t = patches.shape[0] // self.temporal_patch_size
+        grid_h, grid_w = (
+            resized_height // self.patch_size,
+            resized_width // self.patch_size,
+        )
+        patches = patches.reshape(
+            grid_t,
+            self.temporal_patch_size,
+            channel,
+            grid_h,
+            self.patch_size,
+            grid_w,
+            self.patch_size,
+        )
+        patches = patches.transpose(0, 3, 5, 2, 1, 4, 6)
+        assert self.temporal_patch_size == 1
+        flatten_patches = patches.reshape(
+            grid_t * grid_h * grid_w, channel, self.patch_size, self.patch_size
+        )
+        return flatten_patches, (grid_t, grid_h, grid_w)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        videos: VideoInput = None,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        """
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            videos (`VideoInput`):
+                Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
+                passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+                the longest edge resized to keep the input aspect ratio.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = (
+            rescale_factor if rescale_factor is not None else self.rescale_factor
+        )
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_convert_rgb = (
+            do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+        )
+
+        if images is not None:
+            images = make_batched_images(images)
+        if videos is not None:
+            videos = make_batched_videos(videos)
+
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        validate_preprocess_arguments(
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+
+        if images is not None:
+            pixel_values, vision_grid_thws = [], []
+            for image in images:
+                patches, image_grid_thw = self._preprocess(
+                    image,
+                    do_resize=do_resize,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    data_format=data_format,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                )
+                pixel_values.extend(patches)
+                vision_grid_thws.append(image_grid_thw)
+            pixel_values = np.array(pixel_values)
+            vision_grid_thws = np.array(vision_grid_thws)
+            data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
+
+        if videos is not None:
+            pixel_values, vision_grid_thws = [], []
+            for images in videos:
+                patches, video_grid_thw = self._preprocess(
+                    images,
+                    do_resize=do_resize,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    data_format=data_format,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                )
+                pixel_values.extend(patches)
+                vision_grid_thws.append(video_grid_thw)
+            pixel_values = np.array(pixel_values)
+            vision_grid_thws = np.array(vision_grid_thws)
+            data = {
+                "pixel_values_videos": pixel_values,
+                "video_grid_thw": vision_grid_thws,
+            }
+
+        return BatchFeature(data=data, tensor_type=return_tensors)

python/paddleocr_vl_1-5_tokenizer/inference.yml

@@ -0,0 +1,2 @@
+Global:
+  model_name: PaddleOCR-VL-1.5-0.9B

python/paddleocr_vl_1-5_tokenizer/preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "image_processing_paddleocr_vl.PaddleOCRVLImageProcessor",
+    "AutoProcessor": "processing_paddleocr_vl.PaddleOCRVLProcessor"
+  },
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "PaddleOCRVLImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "max_pixels": 1003520,
+  "merge_size": 2,
+  "min_pixels": 112896,
+  "patch_size": 14,
+  "processor_class": "PaddleOCRVLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "temporal_patch_size": 1
+}

python/paddleocr_vl_1-5_tokenizer/processing_paddleocr_vl.py

@@ -0,0 +1,293 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Union
+import numpy as np
+import torch
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import (
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+    VideosKwargs,
+)
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+ImageInput = Union[
+    "PIL.Image.Image",
+    np.ndarray,
+    "torch.Tensor",
+    List["PIL.Image.Image"],
+    List[np.ndarray],
+    List["torch.Tensor"],
+]  # noqa
+
+
+VideoInput = Union[
+    List["PIL.Image.Image"],
+    "np.ndarray",
+    "torch.Tensor",
+    List["np.ndarray"],
+    List["torch.Tensor"],
+    List[List["PIL.Image.Image"]],
+    List[List["np.ndarrray"]],
+    List[List["torch.Tensor"]],
+]  # noqa
+
+
+class PaddleOCRVLVideosProcessorKwargs(VideosKwargs, total=False):
+    fps: Union[List[float], float]
+
+
+class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False):
+    videos_kwargs: PaddleOCRVLVideosProcessorKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "videos_kwargs": {"fps": 2.0},
+    }
+
+
+class PaddleOCRVLProcessor(ProcessorMixin):
+    r"""
+    [`PaddleOCRVLProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information.
+    Args:
+        image_processor ([`SiglipImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = [
+        "chat_template",
+        "image_std",
+        "min_pixels",
+        "image_mean",
+        "merge_size",
+        "image_processor_type",
+        "temporal_patch_size",
+        "patch_size",
+        "max_pixels",
+    ]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
+    ):
+        self.image_token = (
+            "<|IMAGE_PLACEHOLDER|>"
+            if not hasattr(tokenizer, "image_token")
+            else tokenizer.image_token
+        )
+        self.video_token = (
+            "<|video_pad|>"
+            if not hasattr(tokenizer, "video_token")
+            else tokenizer.video_token
+        )
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: Union[
+            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
+        ] = None,
+        videos: VideoInput = None,
+        **kwargs: Unpack[PaddleOCRVLProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
+        SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `vision_infos` is not `None`.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            PaddleOCRVLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if images is not None:
+            image_inputs = self.image_processor(images=images, return_tensors="pt")
+            image_inputs["pixel_values"] = image_inputs["pixel_values"]
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        else:
+            image_inputs = {}
+            image_grid_thw = None
+
+        if videos is not None:
+            # TODO: add video processing
+            videos_inputs = self.image_processor(
+                images=None, videos=videos, **output_kwargs["images_kwargs"]
+            )
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
+            if isinstance(fps, (int, float)):
+                second_per_grid_ts = [
+                    self.image_processor.temporal_patch_size / fps
+                ] * len(video_grid_thw)
+            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+                second_per_grid_ts = [
+                    self.image_processor.temporal_patch_size / tmp for tmp in fps
+                ]
+            else:
+                raise ValueError(
+                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+                )
+            videos_inputs.update(
+                {"second_per_grid_ts": torch.tensor(second_per_grid_ts)}
+            )
+
+        else:
+            videos_inputs = {}
+            video_grid_thw = None
+
+        if not isinstance(text, list):
+            text = [text]
+
+        if image_grid_thw is not None:
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    text[i] = text[i].replace(
+                        self.image_token,
+                        "<|placeholder|>"
+                        * (
+                            image_grid_thw[index].prod()
+                            // self.image_processor.merge_size
+                            // self.image_processor.merge_size
+                        ),
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if video_grid_thw is not None:
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    text[i] = text[i].replace(
+                        self.video_token,
+                        "<|placeholder|>"
+                        * (
+                            video_grid_thw[index].prod()
+                            // self.image_processor.merge_size
+                            // self.image_processor.merge_size
+                        ),
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def post_process_image_text_to_text(
+        self,
+        generated_outputs,
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `List[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(
+            dict.fromkeys(tokenizer_input_names + image_processor_input_names)
+        )
+        return names_from_processor + ["second_per_grid_ts"]
+
+
+__all__ = ["PaddleOCRVLProcessor", "PaddleOCRVLProcessor"]

python/paddleocr_vl_1-5_tokenizer/processor_config.json

@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_paddleocr_vl.PaddleOCRVLProcessor"
+  },
+  "processor_class": "PaddleOCRVLProcessor"
+}

python/paddleocr_vl_1-5_tokenizer/special_tokens_map.json

@@ -0,0 +1,58 @@
+{
+  "additional_special_tokens": [
+    "<|IMAGE_PLACEHOLDER|>",
+    "<|image_pad|>",
+    "<|IMAGE_START|>",
+    "<|IMAGE_END|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<|begin_of_sentence|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask:1>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<|end_of_sentence|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

python/paddleocr_vl_1-5_tokenizer/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8a215a59183d0d0781adc33bacd3ce6162716f7fd568fb30234a74d69803a7d
+size 11189060

python/paddleocr_vl_1-5_tokenizer/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34ef7db83df785924fb83d7b887b6e822a031c56e15cff40aaf9b982988180df
+size 1614363