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.gitattributes +13 -0
LICENSE +201 -0
README.md +196 -0
added_tokens.json +1 -0
chat_template.json +3 -0
config.json +59 -0
configuration_ernie_45t_vl.py +648 -0
generation_config.json +7 -0
image_processing_ernie_45t_vl.py +586 -0
model-00001-of-00012.safetensors +3 -0
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modeling_ernie_45t_vl.py +0 -0
preprocessor_config.json +29 -0
processing_ernie_45t_vl.py +475 -0
special_tokens_map.json +1 -0
tokenization_ernie_45t_vl.py +321 -0
tokenizer.model +3 -0
tokenizer_config.json +22 -0
video_utils_ernie_45t_vl.py +514 -0

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README.md ADDED Viewed

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+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: image-text-to-text
+tags:
+- ERNIE4.5
+---
+<div align="center" style="line-height: 1;">
+  <a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
+    <img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
+    <img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+# ERNIE-4.5-VL-28B-A3B
+## ERNIE 4.5 Highlights
+The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
+1. **Multimodal Heterogeneous MoE Pre-Training**: Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
+2. **Scaling-Efficient Infrastructure**: We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and fine-grained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose Multi-Expert Parallel Collaboration method and Convolutional Code Quantization algorithm  to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
+3. **Modality-Specific Post-training**: To meet the diverse requirements of real-world applications, we fine-tuned variants of the pretrained model for specific modalities.  Our *LLMs* are optimized for general-purpose language understanding and generation. The *VLMs* focuses on visual-language understanding and supports both thinking and no-thinking mode. Each model employed a combination of *Supervised Fine-tuning (SFT)* *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
+During the fine-tuning stage of a vision-language model, the deep integration between vision and language plays a decisive role in the model’s performance across complex tasks such as understanding, reasoning, and generation. To enhance the generalization and adaptability of the model on multimodal tasks, we focused on three core capabilities—image understanding, task-specific fine-tuning, and multimodal chain-of-thought reasoning—and carried out systematic data construction and training strategy optimization. Additionally, we use RLVR(Reinforcement Learning with Verifiable Rewards) to further improve alignment and performance. After the SFT and RL stages, we obtained ERNIE-4.5-VL-28B-A3B.
+## Model Overview
+ERNIE-4.5-VL-28B-A3B is a multimodal MoE Chat model, with 28B total parameters and 3B activated parameters for each token. The following are the model configuration details:
+| Key                               | Value         |
+| --------------------------------- | ------------- |
+| Modality                          | Text & Vision |
+| Training Stage                    | Posttraining  |
+| Params(Total / Activated)         | 28B / 3B      |
+| Layers                            | 28            |
+| Heads(Q/KV)                       | 20 / 4        |
+| Text Experts(Total / Activated)   | 64 / 6        |
+| Vision Experts(Total / Activated) | 64 / 6        |
+| Shared Experts                    | 2             |
+| Context Length                    | 131072        |
+## Quickstart
+### FastDeploy Inference
+Quickly deploy services using FastDeploy as shown below. For more detailed usage, refer to the [FastDeploy GitHub Repository](https://github.com/PaddlePaddle/FastDeploy).
+**Note**: For single-card deployment, at least 80GB of GPU memory is required.
+```bash
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model baidu/ERNIE-4.5-VL-28B-A3B-Paddle \
+       --port 8180 \
+       --metrics-port 8181 \
+       --engine-worker-queue-port 8182 \
+       --max-model-len 32768 \ # Maximum supported token count
+       --enable-mm \
+       --reasoning-parser ernie-45-vl \
+       --max-num-seqs 32 # Maximum concurrent processing count
+```
+The ERNIE-4.5-VL model supports enabling or disabling thinking mode through request parameters.
+#### Enable Thinking Mode
+```bash
+curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
+-H "Content-Type: application/json" \
+-d '{
+  "messages": [
+    {"role": "user", "content": [
+      {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
+      {"type": "text", "text": "Descript this image"}
+    ]}
+  ],
+  "metadata": {"enable_thinking": true}
+}'
+```
+#### Disable Thinking Mode
+```bash
+curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
+-H "Content-Type: application/json" \
+-d '{
+  "messages": [
+    {"role": "user", "content": [
+      {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
+      {"type": "text", "text": "Descript this image"}
+    ]}
+  ],
+  "metadata": {"enable_thinking": false}
+}'
+```
+### Using `transformers` library
+Here is an example of how to use the transformers library for inference:
+```python
+import torch
+from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM
+model_path = 'baidu/ERNIE-4.5-VL-28B-A3B-PT'
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
+)
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+processor.eval()
+model.add_image_preprocess(processor)
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe the image."},
+            {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example1.jpg"}},
+        ]
+    },
+]
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
+)
+image_inputs, video_inputs = processor.process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+device = next(model.parameters()).device
+inputs = inputs.to(device)
+generated_ids = model.generate(
+    inputs=inputs['input_ids'].to(device),
+    **inputs,
+    max_new_tokens=128
+    )
+output_text = processor.decode(generated_ids[0])
+print(output_text)
+```
+### vLLM inference
+vLLM is currently being adapted, priority can be given to using our forked repository [vllm](https://github.com/CSWYF3634076/vllm/tree/ernie). We are working with the community to fully support ERNIE4.5 models, stay tuned.
+```bash
+vllm serve baidu/ERNIE-4.5-VL-28B-A3B-PT --trust-remote-code
+```
+## License
+The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+## Citation
+If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
+```bibtex
+@misc{ernie2025technicalreport,
+      title={ERNIE 4.5 Technical Report},
+      author={Baidu ERNIE Team},
+      year={2025},
+      eprint={},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={}
+}
+```

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"<|AUDIO_UNUSE:808|>": 103154, "<|AUDIO_UNUSE:809|>": 103155, "<|AUDIO_UNUSE:810|>": 103156, "<|AUDIO_UNUSE:811|>": 103157, "<|AUDIO_UNUSE:812|>": 103158, "<|AUDIO_UNUSE:813|>": 103159, "<|AUDIO_UNUSE:814|>": 103160, "<|AUDIO_UNUSE:815|>": 103161, "<|AUDIO_UNUSE:816|>": 103162, "<|AUDIO_UNUSE:817|>": 103163, "<|AUDIO_UNUSE:818|>": 103164, "<|AUDIO_UNUSE:819|>": 103165, "<|AUDIO_UNUSE:820|>": 103166, "<|AUDIO_UNUSE:821|>": 103167, "<|AUDIO_UNUSE:822|>": 103168, "<|AUDIO_UNUSE:823|>": 103169, "<|AUDIO_UNUSE:824|>": 103170, "<|AUDIO_UNUSE:825|>": 103171, "<|AUDIO_UNUSE:826|>": 103172, "<|AUDIO_UNUSE:827|>": 103173, "<|AUDIO_UNUSE:828|>": 103174, "<|AUDIO_UNUSE:829|>": 103175, "<|AUDIO_UNUSE:830|>": 103176, "<|AUDIO_UNUSE:831|>": 103177, "<|AUDIO_UNUSE:832|>": 103178, "<|AUDIO_UNUSE:833|>": 103179, "<|AUDIO_UNUSE:834|>": 103180, "<|AUDIO_UNUSE:835|>": 103181, "<|AUDIO_UNUSE:836|>": 103182, "<|AUDIO_UNUSE:837|>": 103183, "<|AUDIO_UNUSE:838|>": 103184, "<|AUDIO_UNUSE:839|>": 103185, "<|AUDIO_UNUSE:840|>": 103186, "<|AUDIO_UNUSE:841|>": 103187, "<|AUDIO_UNUSE:842|>": 103188, "<|AUDIO_UNUSE:843|>": 103189, "<|AUDIO_UNUSE:844|>": 103190, "<|AUDIO_UNUSE:845|>": 103191, "<|AUDIO_UNUSE:846|>": 103192, "<|AUDIO_UNUSE:847|>": 103193, "<|AUDIO_UNUSE:848|>": 103194, "<|AUDIO_UNUSE:849|>": 103195, "<|AUDIO_UNUSE:850|>": 103196, "<|AUDIO_UNUSE:851|>": 103197, "<|AUDIO_UNUSE:852|>": 103198, "<|AUDIO_UNUSE:853|>": 103199, "<|AUDIO_UNUSE:854|>": 103200, "<|AUDIO_UNUSE:855|>": 103201, "<|AUDIO_UNUSE:856|>": 103202, "<|AUDIO_UNUSE:857|>": 103203, "<|AUDIO_UNUSE:858|>": 103204, "<|AUDIO_UNUSE:859|>": 103205, "<|AUDIO_UNUSE:860|>": 103206, "<|AUDIO_UNUSE:861|>": 103207, "<|AUDIO_UNUSE:862|>": 103208, "<|AUDIO_UNUSE:863|>": 103209, "<|AUDIO_UNUSE:864|>": 103210, "<|AUDIO_UNUSE:865|>": 103211, "<|AUDIO_UNUSE:866|>": 103212, "<|AUDIO_UNUSE:867|>": 103213, "<|AUDIO_UNUSE:868|>": 103214, "<|AUDIO_UNUSE:869|>": 103215, "<|AUDIO_UNUSE:870|>": 103216, "<|AUDIO_UNUSE:871|>": 103217, "<|AUDIO_UNUSE:872|>": 103218, "<|AUDIO_UNUSE:873|>": 103219, "<|AUDIO_UNUSE:874|>": 103220, "<|AUDIO_UNUSE:875|>": 103221, "<|AUDIO_UNUSE:876|>": 103222, "<|AUDIO_UNUSE:877|>": 103223, "<|AUDIO_UNUSE:878|>": 103224, "<|AUDIO_UNUSE:879|>": 103225, "<|AUDIO_UNUSE:880|>": 103226, "<|AUDIO_UNUSE:881|>": 103227, "<|AUDIO_UNUSE:882|>": 103228, "<|AUDIO_UNUSE:883|>": 103229, "<|AUDIO_UNUSE:884|>": 103230, "<|AUDIO_UNUSE:885|>": 103231, "<|AUDIO_UNUSE:886|>": 103232, "<|AUDIO_UNUSE:887|>": 103233, "<|AUDIO_UNUSE:888|>": 103234, "<|AUDIO_UNUSE:889|>": 103235, "<|AUDIO_UNUSE:890|>": 103236, "<|AUDIO_UNUSE:891|>": 103237, "<|AUDIO_UNUSE:892|>": 103238, "<|AUDIO_UNUSE:893|>": 103239, "<|AUDIO_UNUSE:894|>": 103240, "<|AUDIO_UNUSE:895|>": 103241, "<|AUDIO_UNUSE:896|>": 103242, "<|AUDIO_UNUSE:897|>": 103243, "<|AUDIO_UNUSE:898|>": 103244, "<|AUDIO_UNUSE:899|>": 103245, "<|AUDIO_UNUSE:900|>": 103246, "<|AUDIO_UNUSE:901|>": 103247, "<|AUDIO_UNUSE:902|>": 103248, "<|AUDIO_UNUSE:903|>": 103249, "<|AUDIO_UNUSE:904|>": 103250, "<|AUDIO_UNUSE:905|>": 103251, "<|AUDIO_UNUSE:906|>": 103252, "<|AUDIO_UNUSE:907|>": 103253, "<|AUDIO_UNUSE:908|>": 103254, "<|AUDIO_UNUSE:909|>": 103255, "<|AUDIO_UNUSE:910|>": 103256, "<|AUDIO_UNUSE:911|>": 103257, "<|AUDIO_UNUSE:912|>": 103258, "<|AUDIO_UNUSE:913|>": 103259, "<|AUDIO_UNUSE:914|>": 103260, "<|AUDIO_UNUSE:915|>": 103261, "<|AUDIO_UNUSE:916|>": 103262, "<|AUDIO_UNUSE:917|>": 103263, "<|AUDIO_UNUSE:918|>": 103264, "<|AUDIO_UNUSE:919|>": 103265, "<|AUDIO_UNUSE:920|>": 103266, "<|AUDIO_UNUSE:921|>": 103267, "<|AUDIO_UNUSE:922|>": 103268, "<|AUDIO_UNUSE:923|>": 103269, "<|AUDIO_UNUSE:924|>": 103270, "<|AUDIO_UNUSE:925|>": 103271, "<|AUDIO_UNUSE:926|>": 103272, "<|AUDIO_UNUSE:927|>": 103273, "<|AUDIO_UNUSE:928|>": 103274, "<|AUDIO_UNUSE:929|>": 103275, "<|AUDIO_UNUSE:930|>": 103276, "<|AUDIO_UNUSE:931|>": 103277, "<|AUDIO_UNUSE:932|>": 103278, "<|AUDIO_UNUSE:933|>": 103279, "<|AUDIO_UNUSE:934|>": 103280, "<|AUDIO_UNUSE:935|>": 103281, "<|AUDIO_UNUSE:936|>": 103282, "<|AUDIO_UNUSE:937|>": 103283, "<|AUDIO_UNUSE:938|>": 103284, "<|AUDIO_UNUSE:939|>": 103285, "<|AUDIO_UNUSE:940|>": 103286, "<|AUDIO_UNUSE:941|>": 103287, "<|AUDIO_UNUSE:942|>": 103288, "<|AUDIO_UNUSE:943|>": 103289, "<|AUDIO_UNUSE:944|>": 103290, "<|AUDIO_UNUSE:945|>": 103291, "<|AUDIO_UNUSE:946|>": 103292, "<|AUDIO_UNUSE:947|>": 103293, "<|AUDIO_UNUSE:948|>": 103294, "<|AUDIO_UNUSE:949|>": 103295, "<|AUDIO_UNUSE:950|>": 103296, "<|AUDIO_UNUSE:951|>": 103297, "<|AUDIO_UNUSE:952|>": 103298, "<|AUDIO_UNUSE:953|>": 103299, "<|AUDIO_UNUSE:954|>": 103300, "<|AUDIO_UNUSE:955|>": 103301, "<|AUDIO_UNUSE:956|>": 103302, "<|AUDIO_UNUSE:957|>": 103303, "<|AUDIO_UNUSE:958|>": 103304, "<|AUDIO_UNUSE:959|>": 103305, "<|AUDIO_UNUSE:960|>": 103306, "<|AUDIO_UNUSE:961|>": 103307, "<|AUDIO_UNUSE:962|>": 103308, "<|AUDIO_UNUSE:963|>": 103309, "<|AUDIO_UNUSE:964|>": 103310, "<|AUDIO_UNUSE:965|>": 103311, "<|AUDIO_UNUSE:966|>": 103312, "<|AUDIO_UNUSE:967|>": 103313, "<|AUDIO_UNUSE:968|>": 103314, "<|AUDIO_UNUSE:969|>": 103315, "<|AUDIO_UNUSE:970|>": 103316, "<|AUDIO_UNUSE:971|>": 103317, "<|AUDIO_UNUSE:972|>": 103318, "<|AUDIO_UNUSE:973|>": 103319, "<|AUDIO_UNUSE:974|>": 103320, "<|AUDIO_UNUSE:975|>": 103321, "<|AUDIO_UNUSE:976|>": 103322, "<|AUDIO_UNUSE:977|>": 103323, "<|AUDIO_UNUSE:978|>": 103324, "<|AUDIO_UNUSE:979|>": 103325, "<|AUDIO_UNUSE:980|>": 103326, "<|AUDIO_UNUSE:981|>": 103327, "<|AUDIO_UNUSE:982|>": 103328, "<|AUDIO_UNUSE:983|>": 103329, "<|AUDIO_UNUSE:984|>": 103330, "<|AUDIO_UNUSE:985|>": 103331, "<|AUDIO_UNUSE:986|>": 103332, "<|AUDIO_UNUSE:987|>": 103333, "<|AUDIO_UNUSE:988|>": 103334, "<|AUDIO_UNUSE:989|>": 103335, "<|AUDIO_UNUSE:990|>": 103336, "<|AUDIO_UNUSE:991|>": 103337, "<|AUDIO_UNUSE:992|>": 103338, "<|AUDIO_UNUSE:993|>": 103339, "<|AUDIO_UNUSE:994|>": 103340, "<|AUDIO_UNUSE:995|>": 103341, "<|AUDIO_UNUSE:996|>": 103342, "<|AUDIO_UNUSE:997|>": 103343, "<|AUDIO_UNUSE:998|>": 103344, "<|AUDIO_UNUSE:999|>": 103345, "<|AUDIO_UNUSE:1000|>": 103346, "<|AUDIO_UNUSE:1001|>": 103347, "<|AUDIO_UNUSE:1002|>": 103348, "<|AUDIO_UNUSE:1003|>": 103349, "<|AUDIO_UNUSE:1004|>": 103350, "<|AUDIO_UNUSE:1005|>": 103351, "<|AUDIO_UNUSE:1006|>": 103352, "<|AUDIO_UNUSE:1007|>": 103353, "<|AUDIO_UNUSE:1008|>": 103354, "<|AUDIO_UNUSE:1009|>": 103355, "<|AUDIO_UNUSE:1010|>": 103356, "<|AUDIO_UNUSE:1011|>": 103357, "<|AUDIO_UNUSE:1012|>": 103358, "<|AUDIO_UNUSE:1013|>": 103359, "<|AUDIO_UNUSE:1014|>": 103360, "<|AUDIO_UNUSE:1015|>": 103361, "<|AUDIO_UNUSE:1016|>": 103362, "<|AUDIO_UNUSE:1017|>": 103363, "<|AUDIO_UNUSE:1018|>": 103364, "<|AUDIO_UNUSE:1019|>": 103365, "<|AUDIO_UNUSE:1020|>": 103366}

chat_template.json ADDED Viewed

	@@ -0,0 +1,3 @@

+{
+    "chat_template": "\n{%- set image_count = namespace(value=0) -%}\n{%- set video_count = namespace(value=0) -%}\n{{- '<|begin_of_sentence|>' }}\n{%- for message in messages -%}\n    {%- if message.role in ['system', 'user'] -%}\n        {%- if message.role == 'user' -%}\n            {{- 'User: ' -}}\n        {%- endif -%}\n        {%- if message.content is string -%}\n            {{- message.content -}}\n        {%- else -%}\n            {%- for content_item in message.content -%}\n                {%- if content_item.type == 'text' -%}\n                    {{- content_item.text -}}\n                {%- elif content_item.type == 'image_url' -%}\n                    {%- set image_count.value = image_count.value + 1 -%}\n                    Picture {{ image_count.value }}:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>\n                {%- elif content_item.type == 'video_url' -%}\n                    {%- set video_count.value = video_count.value + 1 -%}\n                    Video {{ video_count.value }}:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>\n                {%- endif -%}\n            {%- endfor -%}\n        {%- endif -%}\n        {%- if message.role == 'system' -%}\n            {{- '\n' -}}\n        {%- endif -%}\n    {%- elif message.role == 'assistant' -%}\n        {%- macro extract_text_content(content_field) -%}\n            {%- if content_field is string -%}\n                {{- content_field -}}\n            {%- elif content_field is iterable and content_field is not string -%}\n                {%- set ns = namespace(text_parts=[]) -%}\n                {%- set text_parts = [] -%}\n                {%- for item in content_field -%}\n                    {%- if item.type == 'text' -%}\n                        {%- set ns.text_parts = ns.text_parts + [item.text] -%}\n                    {%- endif -%}\n                {%- endfor -%}\n                {{- ns.text_parts | join('') -}}\n            {%- else -%}\n                {{- '' -}}\n            {%- endif -%}\n        {%- endmacro -%}\n        {%- set reasoning_content = extract_text_content(message.reasoning_content) -%}\n        {%- set content = extract_text_content(message.content) -%}\n        {%- if '</think>' in content %}\n            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}\n            {%- set content = content.split('</think>')[-1].lstrip('\n') %}\n        {%- endif %}\n        {%- if reasoning_content %}\n            {{- '\n' + 'Assistant: ' + '<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}\n        {%- else %}\n            {{- '\n' + 'Assistant: ' + content }}\n        {%- endif %}\n        {{- '<|end_of_sentence|>' }}\n    {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt is not defined or add_generation_prompt is true %}\n    {{- '\nAssistant: ' -}}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\n\n</think>\n\n' }}\n    {%- endif %}\n    {%- if enable_thinking is not defined or enable_thinking is true %}\n        {{- '<think>' }}\n    {%- endif %}\n{%- endif %}\n"
+}

config.json ADDED Viewed

	@@ -0,0 +1,59 @@

+{
+  "architectures": [
+    "Ernie4_5_VLMoeForConditionalGeneration"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_ernie_45t_vl.Ernie4_5_VLMoEConfig",
+    "AutoModel": "modeling_ernie_45t_vl.Ernie4_5_VLMoeForConditionalGeneration",
+    "AutoModelForCausalLM": "modeling_ernie_45t_vl.Ernie4_5_VLMoeForConditionalGeneration",
+    "AutoProcessor": "processing_ernie_45t_vl.Ernie_45T_VLProcessor",
+    "AutoImageProcessor": "image_processing_ernie_45t_vl.Ernie_45T_VLImageProcessor"
+  },
+  "torch_dtype": "bfloat16",
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "intermediate_size": 12288,
+  "im_patch_id": 100295,
+  "model_type": "ernie4_5_moe_vl",
+  "moe_capacity": [128, 128, 128],
+  "moe_gate": "topk",
+  "moe_intermediate_size": [1536, 512],
+  "moe_k": 6,
+  "moe_layer_end_index": [29, 28],
+  "moe_layer_interval": 1,
+  "moe_layer_start_index": [1, 1],
+  "moe_multimodal_dispatch_use_allgather": "v2-alltoall-unpad-text",
+  "moe_num_experts": [64, 64],
+  "moe_num_shared_experts": 2,
+  "moe_use_aux_free": true,
+  "num_attention_heads": 20,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pixel_hidden_size": 1280,
+  "rms_norm_eps": 1e-05,
+  "rope_3d": true,
+  "rope_theta": 500000,
+  "spatial_conv_size": 2,
+  "temporal_conv_size": 2,
+  "vocab_size": 103424,
+  "tie_word_embeddings": true,
+  "use_cache": true,
+  "use_rmsnorm": true,
+  "use_bias": false,
+  "vision_config": {
+    "attn_implementation": "eager",
+    "depth": 32,
+    "embed_dim": 1280,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1280,
+    "in_channels": 3,
+    "in_chans": 3,
+    "mlp_ratio": 4,
+    "num_heads": 16,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "vit_first_fwd_bsz": 128,
+    "attn_sep": "true,"
+  }
+}

configuration_ernie_45t_vl.py ADDED Viewed

	@@ -0,0 +1,648 @@

+# Copyright (c) 2025 Baidu, Inc. 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.
+"""Ernie model configuration"""
+import copy
+from typing import List, Optional, Tuple, Union
+from transformers import PretrainedConfig
+__all__ = [
+    "ERNIE_PRETRAINED_INIT_CONFIGURATION",
+    "Ernie4_5_Config",
+    "Ernie4_5_MoEConfig",
+    "Ernie4_5_VLMoEConfig",
+]
+class DFNRopeVisionTransformerConfig(PretrainedConfig):
+    """
+    Configuration class for DFNRopeVisionTransformer model.
+    This class inherits from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    """
+    model_type = "DFNRope_vision_transformer"
+    base_model_tp_plan = {}
+    def __init__(
+        self,
+        depth=32,
+        embed_dim=1280,
+        hidden_size=3584,
+        hidden_act="quick_gelu",
+        mlp_ratio=4,
+        num_heads=16,
+        in_channels=3,
+        patch_size=14,
+        spatial_merge_size=2,
+        attn_implementation="eager",  # new added
+        pp_data_balance=False,
+        recompute=False,
+        attn_sep=False,
+        vit_first_fwd_bsz=128,
+        vit_num_recompute_layers=10000,
+        **kwargs,
+    ):
+        """
+        Initialize DFNRopeVisionTransformer model configuration with default or specified parameters.
+        Args:
+            depth (int): Number of transformer layers in the model.
+            embed_dim (int): Dimensionality of the embedding layer.
+            hidden_size (int): Dimensionality of the feedforward network.
+            hidden_act (str): Activation function for the feedforward network.
+            mlp_ratio (float): Ratio between the number of input features and
+                the number of output features in the feedforward network.
+            num_heads (int): Number of attention heads in each attention layer.
+            in_channels (int): Number of channels in the input image.
+            patch_size (int):
+                Size of patches in the input image. Defaults to 14.
+            spatial_merge_size (int):
+                Spatial merge size for the spatial transformer module. Defaults to 2.
+            attn_implementation (str): Attention implementation type. Defaults to "eager".
+            pp_data_balance (bool): Whether to balance data during preprocessing. Defaults to False.
+            recompute (bool): Whether to use recompute. Defaults to False.
+            attn_sep (bool): Whether to separate attention computation into two stages. Defaults to False.
+            vit_first_fwd_bsz (int): First forward batch size for ViT. Defaults to 128.
+            vit_num_recompute_layers (int): Number of recomputed layers for ViT. Defaults to
+        """
+        super().__init__(**kwargs)
+        self.depth = depth
+        self.embed_dim = embed_dim
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.mlp_ratio = mlp_ratio
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.attn_implementation = attn_implementation
+        self.pp_data_balance = pp_data_balance
+        self.recompute = recompute
+        self.attn_sep = attn_sep
+        self.vit_first_fwd_bsz = vit_first_fwd_bsz
+        self.vit_num_recompute_layers = vit_num_recompute_layers
+    def get(self, key, default=None):
+        """get config value by key"""
+        if hasattr(self, key):
+            return getattr(self, key)
+        else:
+            return default
+ERNIE_PRETRAINED_INIT_CONFIGURATION = {
+    "ernie/tiny-random-ernie": {
+        "hidden_size": 768,
+        "initializer_range": 0.02,
+        "intermediate_size": 11008,
+        "max_position_embeddings": 2048,
+        "model_type": "ernie",
+        "num_attention_heads": 2,
+        "num_hidden_layers": 2,
+        "rms_norm_eps": 1e-06,
+        "vocab_size": 32000,
+        "bos_token_id": 1,
+        "eos_token_id": 2,
+        "pad_token_id": 0,
+        "use_cache": False,
+        "recompute": False,
+        "use_flash_attn": True,
+        "use_pure_fp16": False,
+    },
+}
+class Ernie4_5_Config(PretrainedConfig):
+    """
+    Configuration class for ERNIE model.
+    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 = "ernie"
+    pretrained_init_configuration = ERNIE_PRETRAINED_INIT_CONFIGURATION
+    base_model_tp_plan = {}
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        intermediate_size=11008,
+        max_position_embeddings=32768,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        initializer_range=0.02,  # no use
+        rms_norm_eps=1e-6,
+        use_cache=False,
+        use_flash_attention=True,
+        use_sparse_flash_attn=True,
+        use_var_len_flash_attn=False,
+        recompute=False,
+        recompute_granularity="core_attn",
+        recompute_use_reentrant=False,
+        use_rmsnorm=True,
+        fuse_rms_norm=False,
+        fuse_ln=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        fuse_swiglu=False,
+        use_bias=False,
+        rope_theta=10000,
+        fuse_rope=False,
+        fuse_softmax_mask=False,
+        use_fast_ln=False,
+        weight_share_add_bias=True,
+        fuse_linear=False,
+        max_sequence_length=1024,
+        ignored_index=-100,
+        add_tail_layers=False,
+        use_recompute_lm_head=False,
+        use_recompute_loss_fn=False,
+        refined_recompute=dict(),
+        attention_probs_dropout_prob=0.0,
+        hidden_dropout_prob=0.0,
+        compression_ratio: float = 1.0,
+        num_key_value_heads=None,
+        use_sparse_head_and_loss_fn=False,
+        micro_batch_size=-1,
+        use_ep_comm_overlap=False,
+        use_fused_head_and_loss_fn=False,
+        token_balance_loss=False,
+        token_balance_seqlen=False,  # calculated based on batchsize and seqlen
+        cachekv_quant: bool = False,
+        pp_seg_method="layer:ErnieDecoderLayer|EmptyLayer",
+        **kwargs,
+    ):
+        """
+        Initialize ERNIE model 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
+            use_sparse_flash_attn (bool): Whether to use sparse FlashAttention
+            use_var_len_flash_attn (bool): Whether to use variable-length FlashAttention
+            recompute (bool): Whether to use gradient checkpointing to save memory
+            recompute_granularity (str): Granularity of recomputation ("core_attn", "full", etc.)
+            recompute_use_reentrant (bool): Whether to use reentrant checkpointing
+            use_rmsnorm (bool): Whether to use RMSNorm instead of LayerNorm
+            fuse_rms_norm (bool): Whether to fuse RMSNorm operations for optimization
+            fuse_ln (bool): Whether to fuse LayerNorm operations
+            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
+            fuse_swiglu (bool): Whether to fuse SwiGLU operations
+            use_bias (bool): Whether to use bias terms in linear layers
+            rope_theta (float): The base period of the RoPE embeddings
+            fuse_rope (bool): Whether to fuse RoPE operations
+            use_fast_ln (bool): Whether to use optimized LayerNorm implementation
+            weight_share_add_bias (bool): Whether to share bias weights in certain layers
+            fuse_linear (bool): Whether to fuse linear operations
+            max_sequence_length (int): Maximum sequence length for positional embeddings
+            ignored_index (int): Target value that is ignored during loss computation
+            add_tail_layers (bool): Whether to add additional layers at the end
+            use_recompute_lm_head (bool): Whether to recompute gradients for language model head
+            use_recompute_loss_fn (bool): Whether to recompute gradients for loss function
+            refined_recompute (dict): Dictionary specifying refined recomputation settings
+            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)
+            use_sparse_head_and_loss_fn (bool): Whether to use sparse attention head and loss function
+            micro_batch_size (int): Size of micro batches (-1 for automatic)
+            use_ep_comm_overlap (bool): Whether to overlap communication with computation
+            use_fused_head_loss_fn (bool): Whether to use fused head and loss function
+            token_balance_loss (bool): Whether to balance loss by token count
+            token_balance_seqlen (bool): Whether to balance sequence lengths
+            cachekv_quant (bool): Whether to quantize key-value cache
+            pp_seg_method (str): Method for pipeline parallel segmentation
+            **kwargs: Additional keyword arguments passed to parent class
+        """
+        # Set default for tied embeddings if not specified.
+        if "tie_word_embeddings" not in kwargs:
+            kwargs["tie_word_embeddings"] = False
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        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.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.recompute = recompute
+        self.recompute_granularity = recompute_granularity
+        self.use_flash_attention = use_flash_attention
+        self.use_sparse_flash_attn = use_sparse_flash_attn
+        self.recompute_use_reentrant = recompute_use_reentrant
+        self.use_var_len_flash_attn = use_var_len_flash_attn
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.fuse_swiglu = fuse_swiglu
+        self.fuse_rms_norm = fuse_rms_norm
+        self.fuse_ln = fuse_ln
+        self.use_rmsnorm = use_rmsnorm
+        self.micro_batch_size = micro_batch_size
+        self.max_sequence_length = max_sequence_length
+        self.use_bias = use_bias
+        self.weight_share_add_bias = weight_share_add_bias
+        self.rope_theta = rope_theta
+        self.fuse_rope = fuse_rope
+        self.fuse_softmax_mask = fuse_softmax_mask
+        self.use_fast_ln = use_fast_ln
+        self.fuse_linear = fuse_linear
+        self.ignored_index = ignored_index
+        self.add_tail_layers = add_tail_layers
+        self.use_recompute_lm_head = use_recompute_lm_head
+        self.use_recompute_loss_fn = use_recompute_loss_fn
+        self.refined_recompute = refined_recompute
+        self.skip_recompute_ops = dict()
+        """
+            `refined_recompute` is a dictionary that specifies fine-grained gradient recomputation settings,
+            which currently only takes effect in Pipeline Parallel (PP) mode.
+            In PP mode, this dictionary populates `self.skip_recompute_ops` with the following structure:
+            - Key (`op_name`): The operation name to configure, with possible values:
+            * "mlp_row_ln" - MLP row-wise layer normalization
+            * "flash_attn" - Flash attention operation
+            * "attention_row_ln" - Attention row-wise layer normalization
+            * "attention_column_ln" - Attention column-wise layer normalization
+            * "mlp_column_ln" - MLP column-wise layer normalization
+            - Value (`skip_num`): Controls how many times to skip recomputation:
+            * 0: Never skip recomputation (minimum memory usage)
+            * -1: Always skip recomputation (maximum memory usage)
+            * [0,1,...,12]: Skip recomputation for specified number of times
+            * ≥12: Equivalent to -1 (always skip recomputation)
+            This allows precise control over memory/computation tradeoffs for different operations.
+        """
+        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.use_sparse_head_and_loss_fn = use_sparse_head_and_loss_fn
+        self.use_ep_comm_overlap = use_ep_comm_overlap
+        self.use_fused_head_and_loss_fn = use_fused_head_and_loss_fn
+        self.token_balance_loss = token_balance_loss
+        self.token_balance_seqlen = token_balance_seqlen
+        self.cachekv_quant = cachekv_quant
+        self.pp_seg_method = pp_seg_method
+    def get(self, key, default=None):
+        """get config value by key"""
+        if hasattr(self, key):
+            return getattr(self, key)
+        else:
+            return default
+class Ernie4_5_MoEConfig(Ernie4_5_Config):
+    r"""
+    Configuration class for ErnieMoE model architecture.
+    This class stores the configuration for a [`~ErnieModel`] and is used to instantiate
+    an ErnieMoE model according to the specified arguments. Inherits from [`PretrainedConfig`]
+    and can control model outputs.
+    Attributes:
+        Inherits all attributes from Ernie4_5_Config and adds MoE-specific configurations.
+    """
+    model_type = "ernie"
+    attribute_map = {
+        "n_positions": "max_position_embeddings",
+        "n_embd": "hidden_size",
+        "n_layer": "num_hidden_layers",
+        "n_head": "num_attention_heads",
+        "n_inner": "intermediate_size",
+        "activation_function": "hidden_act",
+    }
+    pretrained_init_configuration = ERNIE_PRETRAINED_INIT_CONFIGURATION
+    base_model_tp_plan = {}
+    def __init__(
+        self,
+        moe_num_experts: Union[int, list] = 0,
+        use_recompute_moe=False,
+        moe_capacity=(),
+        moe_layer_interval=2,
+        moe_layer_start_index=0,
+        moe_layer_end_index=-1,
+        moe_aux_loss_lambda=1e-2,
+        moe_z_loss_lambda=1e-4,
+        moe_orthogonal_loss_lambda=1e-2,
+        sinkhorn_2gate=True,
+        sinkhorn_temp=3e-2,
+        global_aux_loss=False,
+        moe_dropout_prob=0.0,
+        moe_group="world",
+        moe_gate="top2",
+        moe_intermediate_size: Union[int, list] = 0,
+        moe_num_shared_experts: int = 0,
+        moe_reverse_token_drop: bool = False,
+        moe_gate_act: str = "softmax",
+        moe_norm_gate_logits=True,
+        moe_all_to_all_dropout: float = 0.0,
+        moe_k=2,
+        moe_use_aux_free: bool = False,
+        # `moe_group_experts` must be used with `moe_use_hard_gate=True`
+        moe_group_experts: bool = False,
+        moe_group_orthogonal_loss: bool = True,
+        enable_delay_scale_loss: bool = True,
+        num_acc_steps: int = 1,
+        fuse_gate_detach_matmul: bool = False,
+        dpo_config=None,
+        moe_multimodal_dispatch_use_allgather: str = "",
+        moe_use_hard_gate=False,
+        moe_dense_experts_token_type_id=3,
+        **kwargs,
+    ):
+        """
+        Initialize ErnieMoE configuration with MoE-specific parameters.
+        Args:
+            moe_num_experts: Number of experts in MoE layers
+            use_recompute_moe: Whether to use recomputation for MoE layers
+            moe_capacity: Capacity configuration for MoE layers
+            moe_layer_interval: Interval between MoE layers
+            moe_layer_start_index: Starting layer index for MoE
+            moe_layer_end_index: Ending layer index for MoE (-1 means last layer)
+            moe_aux_loss_lambda: Weight for auxiliary loss
+            moe_z_loss_lambda: Weight for z-loss
+            moe_orthogonal_loss_lambda: Weight for orthogonal loss
+            sinkhorn_2gate: Whether to use sinkhorn 2-gate routing
+            sinkhorn_temp: Temperature for sinkhorn routing
+            global_aux_loss: Whether to use global auxiliary loss
+            moe_dropout_prob: Dropout probability for MoE layers
+            moe_group: Group configuration for MoE experts
+            moe_gate: Type of gating mechanism ('top2', etc.)
+            moe_intermediate_size: Intermediate size for MoE layers
+            moe_num_shared_experts: Number of shared experts
+            moe_reverse_token_drop: Whether to use reverse token dropping
+            moe_gate_act: Activation function for gating
+            moe_norm_gate_logits: Whether to normalize gate logits
+            moe_all_to_all_dropout: Dropout for all-to-all communication
+            moe_k: Number of experts to route to
+            moe_use_aux_free: Whether to use auxiliary-free routing
+            moe_group_experts: Whether to group experts (requires hard gating)
+            moe_group_orthogonal_loss: Whether to use group orthogonal loss
+            enable_delay_scale_loss: Whether to enable delayed loss scaling
+            num_acc_steps: Number of accumulation steps
+            fuse_gate_detach_matmul: Whether to fuse gate detach matmul
+            **kwargs: Additional base model configuration parameters
+        Note:
+            When use_recompute_moe is True, recompute_granularity will be changed to full_attn.
+        """
+        if use_recompute_moe:
+            logger.warning(
+                "set `use_recompute_moe`=True, disabling `recompute_granularity=full`, change to full_attn."
+            )
+            if kwargs["recompute"] and kwargs["recompute_granularity"] == "full":
+                kwargs["recompute_granularity"] = "full_attn"
+        super().__init__(**kwargs)
+        self.moe_num_experts = moe_num_experts
+        self.use_recompute_moe = use_recompute_moe
+        self.moe_capacity = moe_capacity
+        self.moe_aux_loss_lambda = moe_aux_loss_lambda
+        self.moe_z_loss_lambda = moe_z_loss_lambda
+        self.moe_orthogonal_loss_lambda = moe_orthogonal_loss_lambda
+        self.global_aux_loss = global_aux_loss
+        self.sinkhorn_2gate = sinkhorn_2gate
+        self.sinkhorn_temp = sinkhorn_temp
+        self.moe_layer_interval = moe_layer_interval
+        self.moe_dropout_prob = moe_dropout_prob
+        self.moe_group = moe_group
+        self.moe_gate = moe_gate
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_num_shared_experts = moe_num_shared_experts
+        self.moe_reverse_token_drop = moe_reverse_token_drop
+        self.moe_k = moe_k
+        self.moe_all_to_all_dropout = moe_all_to_all_dropout
+        self.moe_group_experts = moe_group_experts
+        self.moe_group_orthogonal_loss = moe_group_orthogonal_loss
+        self.enable_delay_scale_loss = enable_delay_scale_loss
+        self.num_acc_steps = num_acc_steps
+        self.moe_layer_start_index = moe_layer_start_index
+        self.moe_layer_end_index = (
+            self.num_hidden_layers - 1
+            if moe_layer_end_index == -1
+            else moe_layer_end_index
+        )
+        self.moe_gate_act = moe_gate_act
+        self.moe_norm_gate_logits = moe_norm_gate_logits
+        self.moe_use_aux_free = moe_use_aux_free
+        self.fuse_gate_detach_matmul = fuse_gate_detach_matmul
+        self.dpo_config = dpo_config
+        self.moe_multimodal_dispatch_use_allgather = (
+            moe_multimodal_dispatch_use_allgather
+        )
+        self.moe_use_hard_gate = moe_use_hard_gate
+        self.moe_dense_experts_token_type_id = moe_dense_experts_token_type_id
+    @property
+    def multimodel_experts(self) -> bool:
+        """multimodel experts."""
+        return (
+            isinstance(self.moe_num_experts, (tuple, list))
+            and len(self.moe_num_experts) > 1
+        )
+    @property
+    def use_moe(self) -> bool:
+        """
+        Check if model is using MoE architecture.
+        Returns:
+            bool: True if moe_num_experts > 0, False otherwise
+        """
+        return self.moe_num_experts > 0
+class Ernie4_5_VLMoEConfig(Ernie4_5_MoEConfig):
+    """
+    This is the configuration class to store the configuration of a [`~ErnieModel`]. It is used to instantiate an Ernie
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the Ernie-7B.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Ernie model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`~ErnieModel`] or [`~TFErnieModel`].
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+    """
+    model_type = "ernie4_5_moe_vl"
+    attribute_map = {
+        "n_positions": "max_position_embeddings",
+        "n_embd": "hidden_size",
+        "n_layer": "num_hidden_layers",
+        "n_head": "num_attention_heads",
+        "n_inner": "intermediate_size",
+        "activation_function": "hidden_act",
+    }
+    base_model_tp_plan = {
+        "ernie.layers.*.self_attn.qkv_proj": "colwise",
+        "ernie.layers.*.self_attn.o_proj": "rowwise",
+        "ernie.layers.*.mlp_text.experts.*.up_gate_proj": "colwise",
+        "ernie.layers.*.mlp_text.experts.*.down_proj": "rowwise",
+        "ernie.layers.*.mlp_text.gate": "colwise_rep",
+        "ernie.layers.*.mlp.experts.*.up_gate_proj": "colwise",
+        "ernie.layers.*.mlp.experts.*.down_proj": "rowwise",
+        "ernie.layers.*.mlp.gate": "colwise_rep",
+        "ernie.layers.*.mlp.up_gate_proj": "colwise",
+        "ernie.layers.*.mlp.down_proj": "rowwise",
+        "lm_head": "colwise_rep",
+    }
+    def __init__(
+        self,
+        vision_config=None,
+        im_patch_id=None,
+        pixel_hidden_size=None,
+        modality_detach=False,
+        temporal_conv_size=2,
+        spatial_conv_size=2,
+        mm_vocab_size=0,  # vocab for mm specialtokens
+        max_text_id=None,
+        use_temporal_conv=True,
+        moe_use_size_all2all=False,
+        moe_num_attn_experts=False,
+        moe_dense_experts_token_type_id: int = 3,
+        moe_use_hard_gate: bool = True,
+        moe_fuse_experts: bool = False,
+        moe_use_token_type_bias: bool = False,
+        disable_ffn_model_parallel=False,
+        fuse_attn_ffn=True,
+        rope_3d=True,
+        freq_allocation=20,
+        using_precision_check=False,
+        use_recompute_resampler=False,
+        resampler_fuse_rms_norm=False,
+        moe_layer_feed_fake_token=False,
+        tensor_parallel_degree=1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if isinstance(vision_config, dict):
+            self.vision_config = DFNRopeVisionTransformerConfig(**vision_config)
+        else:
+            self.vision_config = DFNRopeVisionTransformerConfig()
+        self.im_patch_id = im_patch_id
+        self.pixel_hidden_size = pixel_hidden_size
+        self.modality_detach = modality_detach
+        self.temporal_conv_size = temporal_conv_size
+        self.spatial_conv_size = spatial_conv_size
+        self.mm_vocab_size = mm_vocab_size
+        self.max_text_id = max_text_id
+        self.use_temporal_conv = use_temporal_conv
+        self.moe_use_size_all2all = moe_use_size_all2all
+        self.moe_num_attn_experts = moe_num_attn_experts
+        self.moe_dense_experts_token_type_id = moe_dense_experts_token_type_id
+        self.moe_use_hard_gate = moe_use_hard_gate
+        self.moe_fuse_experts = moe_fuse_experts
+        self.moe_use_token_type_bias = moe_use_token_type_bias
+        self.disable_ffn_model_parallel = disable_ffn_model_parallel
+        self.fuse_attn_ffn = fuse_attn_ffn
+        self.rope_3d = rope_3d
+        self.freq_allocation = freq_allocation
+        self.using_precision_check = using_precision_check
+        self.use_recompute_resampler = use_recompute_resampler
+        self.resampler_fuse_rms_norm = resampler_fuse_rms_norm
+        self.moe_layer_feed_fake_token = moe_layer_feed_fake_token
+        self.tensor_parallel_degree = tensor_parallel_degree
+    @property
+    def multimodel_experts(self) -> bool:
+        """Check if model is using more than 1 multimodel experts."""
+        return (
+            isinstance(self.moe_num_experts, (tuple, list))
+            and len(self.moe_num_experts) > 1
+        )
+    @property
+    def use_moe(self) -> bool:
+        """
+        Check if model is using MoE architecture.
+        Returns:
+            bool: True if moe_num_experts > 0, False otherwise
+        """
+        return (
+            sum(self.moe_num_experts) > 0
+            if self.multimodel_experts
+            else self.moe_num_experts > 0
+        )
+    def to_dict(self, saving_file=False):
+        """to_dict"""
+        output = copy.deepcopy(self.__dict__)
+        if self.vision_config:
+            output["vision_config"] = (
+                self.vision_config.to_dict()
+                if isinstance(self.vision_config, (DFNRopeVisionTransformerConfig))
+                else self.vision_config
+            )
+        output["model_type"] = self.__class__.model_type
+        return output

generation_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+    "top_p": 0.8,
+    "temperature": 0.2,
+    "repetition_penalty": 1.0,
+    "frequency_penalty": 0.0,
+    "presence_penalty": 0.0
+  }

image_processing_ernie_45t_vl.py ADDED Viewed

	@@ -0,0 +1,586 @@

+# Copyright (c) 2025 Baidu, Inc. 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 Ernie_45T_VL."""
+import math
+from typing import List, Optional, Union
+from PIL import Image
+import numpy as np
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_transforms import (
+    convert_to_rgb,
+    normalize,
+    rescale,
+    resize,
+    to_channel_dimension_format,
+)
+from transformers.image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_valid_image,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+)
+from transformers.utils import TensorType, logging
+from transformers.video_utils import VideoInput
+logger = logging.get_logger(__name__)
+def round_by_factor(number: int, factor: int) -> int:
+    """Returns the closest integer to 'number' that is divisible by 'factor'."""
+    return round(number / factor) * factor
+def ceil_by_factor(number: int, factor: int) -> int:
+    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
+    return math.ceil(number / factor) * factor
+def floor_by_factor(number: int, factor: int) -> int:
+    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
+    return math.floor(number / factor) * factor
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 4 * 28 * 28,
+    max_pixels: int = 16384 * 28 * 28,
+):
+    """
+    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.
+    """
+    MAX_RATIO = 200
+    if max(height, width) / min(height, width) > MAX_RATIO:
+        if height > width:
+            new_width = max(factor, round_by_factor(width, factor))
+            new_height = floor_by_factor(new_width * MAX_RATIO, factor)
+        else:
+            new_height = max(factor, round_by_factor(height, factor))
+            new_width = floor_by_factor(new_height * MAX_RATIO, factor)
+        logger.info(
+            f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)},\
+              resize to {max(new_height, new_width) / min(new_height, new_width)}"
+        )
+        height = new_height
+        width = new_width
+    h_bar = max(factor, round_by_factor(height, factor))
+    w_bar = max(factor, round_by_factor(width, factor))
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = floor_by_factor(height / beta, factor)
+        w_bar = floor_by_factor(width / beta, factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = ceil_by_factor(height * beta, factor)
+        w_bar = ceil_by_factor(width * beta, factor)
+    if min_pixels > h_bar * w_bar or h_bar * w_bar > max_pixels:
+        raise ValueError(f"encounter invalid h_bar: {h_bar}, w_bar: {w_bar}")
+    return h_bar, w_bar
+def is_scaled_image(image: np.ndarray) -> bool:
+    """
+    Checks to see whether the pixel values have already been rescaled to [0, 1].
+    """
+    if image.dtype == np.uint8:
+        return False
+    # It's possible the image has pixel values in [0, 255] but is of floating type
+    return np.min(image) >= 0 and np.max(image) <= 1
+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}")
+# Copied from transformers.models.llava_next_video.image_processing_llava_next_video.make_batched_videos
+def make_batched_videos(videos) -> List[VideoInput]:
+    """dummy"""
+    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}")
+class Ernie_45T_VLImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a adaptive 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 `56 * 56`):
+            The min pixels of the image to resize the image.
+        max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
+            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_conv_size (`int`, *optional*, defaults to 2):
+            The temporal conv size in resampler.
+        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[float, List[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 = 56 * 56,
+        max_pixels: int = 28 * 28 * 1280,
+        patch_size: int = 14,
+        temporal_conv_size: int = 2,
+        merge_size: int = 2,
+        **kwargs,
+    ) -> None:
+        """init"""
+        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_conv_size = temporal_conv_size
+        self.merge_size = merge_size
+        self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}
+        self.do_convert_rgb = do_convert_rgb
+    def set_pixels(self, min_pixels=None, max_pixels=None, msg=""):
+        """set_pixels"""
+        if min_pixels is not None:
+            assert (
+                isinstance(min_pixels, int) and min_pixels >= 0
+            ), "min_pixels must be positive int"
+            logger.info(
+                f"{msg} Ernie_45T_VLImageProcessor set min_pixels = {min_pixels}"
+            )
+            self.min_pixels = min_pixels
+            self.size["min_pixels"] = int(min_pixels)
+        if max_pixels is not None:
+            assert (
+                isinstance(max_pixels, int) and max_pixels > 0
+            ), "max_pixels must be positive int"
+            logger.info(
+                f"{msg} Ernie_45T_VLImageProcessor set max_pixels = {max_pixels}"
+            )
+            self.max_pixels = max_pixels
+            self.size["max_pixels"] = int(max_pixels)
+    def get_smarted_resize(self, height, width, min_pixels=None, max_pixels=None):
+        """dummy"""
+        actual_min_pixels = min_pixels if min_pixels is not None else self.min_pixels
+        actual_max_pixels = max_pixels if max_pixels is not None else self.max_pixels
+        resized_height, resized_width = smart_resize(
+            height,
+            width,
+            factor=self.patch_size * self.merge_size,
+            min_pixels=actual_min_pixels,
+            max_pixels=actual_max_pixels,
+        )
+        return (resized_height, resized_width), (
+            resized_height // self.patch_size,
+            resized_width // self.patch_size,
+        )
+    def _preprocess(
+        self,
+        images: Union[ImageInput, VideoInput],
+        do_resize: bool = True,
+        resample: PILImageResampling = None,
+        do_rescale: bool = True,
+        rescale_factor: 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 = False,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        predetermined_grid_thw=None,
+    ):
+        """
+        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+        Args:
+            images (`ImageInput` or `VideoInput`):
+                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`.
+            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 = []
+        if predetermined_grid_thw is not None:
+            assert len(predetermined_grid_thw) == len(
+                images
+            ), f"len(predetermined_grid_thw) {len(predetermined_grid_thw)} == len(images) {len(images)}"
+        for img_idx, image in enumerate(images):
+            if do_resize:
+                if predetermined_grid_thw is not None:
+                    (resized_height, resized_width) = predetermined_grid_thw[img_idx]
+                    resized_height *= self.patch_size
+                    resized_width *= self.patch_size
+                else:
+                    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,
+                    data_format=input_data_format,
+                )
+            if do_rescale:
+                image = rescale(
+                    image, scale=rescale_factor, data_format=input_data_format
+                )
+            if do_normalize:
+                image = normalize(
+                    image=image,
+                    mean=image_mean,
+                    std=image_std,
+                    data_format=input_data_format,
+                )
+            image = to_channel_dimension_format(
+                image, data_format, input_channel_dim=input_data_format
+            )  # [C, H, W]
+            processed_images.append(image)
+        patches = np.array(processed_images)
+        if data_format == ChannelDimension.LAST:
+            patches = patches.transpose([0, 3, 1, 2])
+        channel = patches.shape[1]  # [time, C, H, W]
+        grid_t = patches.shape[0]
+        grid_h, grid_w = (
+            resized_height // self.patch_size,
+            resized_width // self.patch_size,
+        )
+        patches = patches.reshape(
+            [
+                grid_t,
+                channel,
+                grid_h // self.merge_size,
+                self.merge_size,
+                self.patch_size,
+                grid_w // self.merge_size,
+                self.merge_size,
+                self.patch_size,
+            ]
+        )
+        # [grid_t, grid_h/merge_size, grid_w/merge_size, merge_size, merge_size, C, psz, psz]
+        patches = patches.transpose([0, 2, 5, 3, 6, 1, 4, 7])
+        flatten_patches = patches.reshape(
+            [grid_t * grid_h * grid_w, channel * self.patch_size * self.patch_size]
+        )  # [grid_t * grid_h * grid_w, C * psz * psz]
+        return flatten_patches, (grid_t, grid_h, grid_w)
+    def preprocess(
+        self,
+        images: ImageInput,
+        videos: VideoInput = None,
+        do_resize: bool = True,
+        size: Optional[Union[int, List[int]]] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = True,
+        rescale_factor: 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 = False,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        predetermined_grid_thw=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.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.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 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."
+            )
+        data = {}
+        if images is not None:
+            pixel_values, vision_grid_thws = [], []
+            for img_idx, image in enumerate(images):
+                if predetermined_grid_thw is not None:
+                    predetermined_grid_thw_one = [predetermined_grid_thw[img_idx]]
+                else:
+                    predetermined_grid_thw_one = None
+                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,
+                    predetermined_grid_thw=predetermined_grid_thw_one,
+                )
+                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.update(
+                {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
+            )
+        if videos is not None:
+            videos = make_batched_videos(videos)
+            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,
+                    predetermined_grid_thw=predetermined_grid_thw,
+                )
+                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.update(
+                {
+                    "pixel_values_videos": pixel_values,
+                    "video_grid_thw": vision_grid_thws,
+                }
+            )
+        return BatchFeature(data=data, tensor_type=return_tensors)
+__all__ = ["Ernie_45T_VLImageProcessor"]

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modeling_ernie_45t_vl.py ADDED Viewed

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preprocessor_config.json ADDED Viewed

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": false,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "min_pixels": 3136,
+  "max_pixels": 4816896
+}

processing_ernie_45t_vl.py ADDED Viewed

	@@ -0,0 +1,475 @@

+# Copyright (c) 2025 Baidu, Inc. 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.
+"""Processor class for Ernie_45T_VL."""
+import copy
+import io
+import numpy as np
+import torch
+from PIL import Image
+from collections import defaultdict
+from typing import Any, Dict, List, Union
+from .image_processing_ernie_45t_vl import Ernie_45T_VLImageProcessor
+from .tokenization_ernie_45t_vl import Ernie4_5_VLTokenizer
+from .video_utils_ernie_45t_vl import (
+    read_frames_decord,
+    read_video_decord,
+    RAW_IMAGE_DIR,
+    get_downloadable,
+    render_frame_timestamp,
+)
+from transformers.image_utils import ChannelDimension
+from transformers.processing_utils import ProcessorMixin
+from transformers.feature_extraction_utils import BatchFeature
+IDS_TYPE_FLAG = {"text": 0, "image": 1, "video": 2, "audio": 3}
+class Ernie_45T_VLProcessor(ProcessorMixin):
+    """
+    Processes multimodal chat messages into model-ready inputs,
+    handling text, images, and videos with 3D positional embeddings.
+    """
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = [
+        "chat_template",
+        "spatial_conv_size",
+        "temporal_conv_size",
+        "image_min_pixels",
+        "image_max_pixels",
+        "video_min_pixels",
+        "video_max_pixels",
+        "video_target_frames",
+        "video_frames_sample",
+        "video_max_frames",
+        "video_min_frames",
+        "video_fps",
+    ]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+    CLS_TOKEN = "<|begin_of_sentence|>"
+    SEP_TOKEN = "<|end_of_sentence|>"
+    IMG_START = "<|IMAGE_START|>"
+    IMG_END = "<|IMAGE_END|>"
+    VID_START = "<|VIDEO_START|>"
+    VID_END = "<|VIDEO_END|>"
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        spatial_conv_size: int = 2,
+        temporal_conv_size: int = 2,
+        image_min_pixels: int = 4 * 28 * 28,
+        image_max_pixels: int = 6177 * 28 * 28,
+        video_min_pixels: int = 299 * 28 * 28,
+        video_max_pixels: int = 1196 * 28 * 28,
+        video_target_frames: int = -1,
+        video_frames_sample: str = "leading",
+        video_max_frames: int = 180,
+        video_min_frames: int = 16,
+        video_fps: int = 2,
+        **kwargs,
+    ):
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+        self.tokenizer.ignored_index = -100
+        # Convolution sizes for patch aggregation
+        self.spatial_conv_size = spatial_conv_size
+        self.temporal_conv_size = temporal_conv_size
+        # Pixel constraints
+        self.image_min_pixels = image_min_pixels
+        self.image_max_pixels = image_max_pixels
+        self.video_min_pixels = video_min_pixels
+        self.video_max_pixels = video_max_pixels
+        # Video sampling parameters
+        self.target_frames = video_target_frames
+        self.frames_sample = video_frames_sample
+        self.max_frames = video_max_frames
+        self.min_frames = video_min_frames
+        self.fps = video_fps
+        # Special tokens and IDs
+        self.cls_token = self.CLS_TOKEN
+        self.sep_token = self.SEP_TOKEN
+        self.image_start = self.IMG_START
+        self.image_end = self.IMG_END
+        self.video_start = self.VID_START
+        self.video_end = self.VID_END
+        self.image_patch_id = self.tokenizer.convert_tokens_to_ids(
+            "<|IMAGE_PLACEHOLDER|>"
+        )
+        self.token_type_mapping = self._build_token_type_mapping()
+        self.is_training = True
+        self.role_prefixes = {"system": "", "user": "User: ", "bot": "Assistant: "}
+    def _build_token_type_mapping(self) -> Dict[Any, int]:
+        mapping = defaultdict(lambda: IDS_TYPE_FLAG["text"])
+        for token in (self.IMG_START, self.IMG_END, self.VID_START, self.VID_END):
+            mapping[token] = IDS_TYPE_FLAG["image"]
+        mapping[self.image_patch_id] = IDS_TYPE_FLAG["image"]
+        return mapping
+    def train(self) -> None:
+        """Enable training mode (produces labels)."""
+        self.is_training = True
+    def eval(self) -> None:
+        """Enable evaluation mode (doesn't produce labels)."""
+        self.is_training = False
+    def _download_image(
+        self,
+        item: Dict,
+    ):
+        """Download image from url and resize it to the specified size."""
+        url_info = item.get("image_url", {})
+        url = url_info.get("url")
+        w = url_info.get("image_width", None)
+        h = url_info.get("image_height", None)
+        data = get_downloadable(url, download_dir=RAW_IMAGE_DIR, save_to_disk=False)
+        img = Image.open(io.BytesIO(data) if isinstance(data, bytes) else data)
+        if w and h:
+            img = img.resize((w, h))
+        return img
+    def _download_video(self, item: Dict):
+        """Download video from url and resize it to the specified size."""
+        url_info = item.get("video_url", {})
+        url = url_info.get("url")
+        frames = self._load_and_process_video(url, item)
+        pixel_stack = np.stack([np.array(f.convert("RGB")) for f in frames], axis=0)
+        return pixel_stack
+    def process_vision_info(self, messages: List[Dict[str, Any]]):
+        """Preprocess messages into lists of text, images, and videos."""
+        images = []
+        videos = []
+        for msg in messages:
+            content_items = msg.get("content")
+            if not isinstance(content_items, list):
+                content_items = [content_items]
+            for item in content_items:
+                if item.get("type") == "image_url":
+                    img = self._download_image(item)
+                    images.append(img)
+                elif item.get("type") == "video_url":
+                    pixel_stack = self._download_video(item)
+                    videos.append(pixel_stack)
+        return images, videos
+    def __call__(
+        self,
+        text: List[str],
+        images: List[Image.Image],
+        videos: List[List[Image.Image]],
+        **kwargs,
+    ) -> Dict[str, Union[np.ndarray, List[np.ndarray], None]]:
+        """
+        Convert chat messages into model inputs.
+        Returns a dict with input_ids, token_type_ids, position_ids, images, grid_thw, image_type_ids, labels.
+        """
+        outputs = {
+            "input_ids": [],
+            "token_type_ids": [],
+            "position_ids": [],
+            "images": [],
+            "grid_thw": [],
+            "image_type_ids": [],
+            "cur_position": 0,
+            "pic_cnt": 0,
+            "video_cnt": 0,
+        }
+        texts = text[0]
+        new_video_seg = True
+        for text_with_image in texts.split(self.VID_START + "<|video@placeholder|>" + self.VID_END):
+            new_text_seg = True
+            if not new_video_seg:
+                self._add_video(videos[outputs["video_cnt"]], outputs)
+            for text in text_with_image.split(self.IMG_START + "<|image@placeholder|>" + self.IMG_END):
+                if not new_text_seg:
+                    self._add_image(images[outputs["pic_cnt"]], outputs)
+                self._add_text(text, outputs)
+                new_text_seg = False
+            new_video_seg = False
+        for key in ["cur_position", "pic_cnt", "video_cnt"]:
+            outputs.pop(key, None)
+        outputs = self._pack_outputs(outputs)
+        for key in outputs.keys():
+            if isinstance(outputs[key], np.ndarray):
+                if key in ["images", "grid_thw"]:
+                    outputs[key] = torch.tensor(np.array(outputs[key]))
+                else:
+                    outputs[key] = torch.tensor(np.array([outputs[key]]))
+        return BatchFeature(data=outputs)
+    def _add_special_token(self, token: Union[str, int], outputs: Dict) -> None:
+        """add special token to outputs"""
+        token_id = (
+            token
+            if isinstance(token, int)
+            else self.tokenizer.convert_tokens_to_ids(token)
+        )
+        outputs["input_ids"].append(token_id)
+        outputs["token_type_ids"].append(self.token_type_mapping[token])
+        pos = outputs["cur_position"]
+        outputs["position_ids"].append([pos] * 3)
+        outputs["cur_position"] += 1
+    def _add_text(self, text: str, outputs: Dict) -> None:
+        """add text to outputs"""
+        tokens = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
+        outputs["input_ids"].extend(tokens)
+        outputs["token_type_ids"].extend([IDS_TYPE_FLAG["text"]] * len(tokens))
+        start = outputs["cur_position"]
+        for i in range(len(tokens)):
+            outputs["position_ids"].append([start + i] * 3)
+        outputs["cur_position"] += len(tokens)
+    def _add_image(self, img: Image.Image, outputs: Dict) -> None:
+        """add image to outputs"""
+        outputs["pic_cnt"] += 1
+        self._add_special_token(self.IMG_START, outputs)
+        patches_h, patches_w = self.image_processor.get_smarted_resize(
+            img.height,
+            img.width,
+            min_pixels=self.image_min_pixels,
+            max_pixels=self.image_max_pixels,
+        )[1]
+        num_tokens = (patches_h * patches_w) // (self.spatial_conv_size**2)
+        outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
+        outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
+        pos_ids = self._compute_3d_positions(
+            1, patches_h, patches_w, outputs["cur_position"]
+        )
+        outputs["position_ids"].extend(pos_ids)
+        outputs["cur_position"] = np.max(pos_ids) + 1
+        # Preprocess pixels
+        ret = self.image_processor.preprocess(
+            images=[img.convert("RGB")],
+            do_normalize=False,
+            do_rescale=False,
+            predetermined_grid_thw=np.array([[patches_h, patches_w]]),
+            do_convert_rgb=True,
+            input_data_format=ChannelDimension.LAST,
+        )
+        outputs["images"].append(ret["pixel_values"])
+        outputs["grid_thw"].append(ret["image_grid_thw"])
+        outputs["image_type_ids"].append(0)
+        self._add_special_token(self.IMG_END, outputs)
+    def _add_video(
+        self, pixel_stack: List[np.ndarray], outputs: Dict
+    ) -> None:
+        outputs["video_cnt"] += 1
+        self._add_special_token(self.VID_START, outputs)
+        patches_h, patches_w = self.image_processor.get_smarted_resize(
+            pixel_stack.shape[1],
+            pixel_stack.shape[2],
+            min_pixels=self.video_min_pixels,
+            max_pixels=self.video_max_pixels,
+        )[1]
+        num_frames = pixel_stack.shape[0]
+        num_tokens = (num_frames * patches_h * patches_w) // (
+            self.spatial_conv_size**2 * self.temporal_conv_size
+        )
+        ret = self.image_processor.preprocess(
+            images=None,
+            videos=pixel_stack,
+            do_normalize=False,
+            do_rescale=False,
+            predetermined_grid_thw=np.array([[patches_h, patches_w]] * num_frames),
+            do_convert_rgb=True,
+            input_data_format=ChannelDimension.LAST,
+        )
+        outputs["images"].append(ret["pixel_values_videos"])
+        outputs["grid_thw"].append(ret["video_grid_thw"])
+        outputs["image_type_ids"].extend([1] * num_frames)
+        outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
+        outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
+        pos_ids = self._compute_3d_positions(
+            num_frames, patches_h, patches_w, outputs["cur_position"]
+        )
+        outputs["position_ids"].extend(pos_ids)
+        outputs["cur_position"] = np.max(pos_ids) + 1
+        self._add_special_token(self.VID_END, outputs)
+    def _load_and_process_video(self, url: str, item: Dict) -> List[Image.Image]:
+        reader, meta, path = read_video_decord(url, save_to_disk=False)
+        video_frame_args = dict()
+        video_frame_args["fps"] = item.get("fps", self.fps)
+        video_frame_args["min_frames"] = item.get("min_frames", self.min_frames)
+        video_frame_args["max_frames"] = item.get("max_frames", self.max_frames)
+        video_frame_args["target_frames"] = item.get(
+            "target_frames", self.target_frames
+        )
+        video_frame_args["frames_sample"] = item.get(
+            "frames_sample", self.frames_sample
+        )
+        video_frame_args = self._set_video_frame_args(video_frame_args, meta)
+        frames_data, _, timestamps = read_frames_decord(
+            path,
+            reader,
+            meta,
+            target_frames=video_frame_args["target_frames"],
+            target_fps=video_frame_args["fps"],
+            frames_sample=video_frame_args["frames_sample"],
+            save_to_disk=False,
+        )
+        frames: List[Image.Image] = []
+        for img_array, ts in zip(frames_data, timestamps):
+            frames.append(render_frame_timestamp(img_array, ts))
+        # Ensure even number of frames for temporal conv
+        if len(frames) % 2 != 0:
+            frames.append(copy.deepcopy(frames[-1]))
+        return frames
+    def _set_video_frame_args(self, video_frame_args, video_meta):
+        """
+        Set the final frame extraction parameters based on known parameters and priorities
+        """
+        # Priority: video_target_frames > (video_min_frames, video_max_frames) > video_fps
+        if video_frame_args["target_frames"] > 0:
+            if video_frame_args["fps"] >= 0:
+                raise ValueError("fps must be negative if target_frames is given")
+            if (
+                video_frame_args["min_frames"] > 0
+                and video_frame_args["target_frames"] < video_frame_args["min_frames"]
+            ):
+                raise ValueError("target_frames must be larger than min_frames")
+            if (
+                video_frame_args["max_frames"] > 0
+                and video_frame_args["target_frames"] > video_frame_args["max_frames"]
+            ):
+                raise ValueError("target_frames must be smaller than max_frames")
+        else:
+            if video_frame_args["fps"] < 0:
+                raise ValueError(
+                    "Must provide either positive target_fps or positive target_frames."
+                )
+            # First calculate the number of frames extracted under video_fps
+            frames_to_extract = int(video_meta["duration"] * video_frame_args["fps"])
+            # Determine whether it is within the target range. If not, take target_frames as the upper or lower bound
+            if (
+                video_frame_args["min_frames"] > 0
+                and video_frame_args["max_frames"] > 0
+                and video_frame_args["min_frames"] > video_frame_args["max_frames"]
+            ):
+                raise ValueError("min_frames must be smaller than max_frames")
+            if (
+                video_frame_args["min_frames"] > 0
+                and frames_to_extract < video_frame_args["min_frames"]
+            ):
+                video_frame_args["target_frames"] = video_frame_args["min_frames"]
+                video_frame_args["fps"] = -1
+            if (
+                video_frame_args["max_frames"] > 0
+                and frames_to_extract > video_frame_args["max_frames"]
+            ):
+                video_frame_args["target_frames"] = video_frame_args["max_frames"]
+                video_frame_args["fps"] = -1
+        return video_frame_args
+    def _compute_3d_positions(
+        self, t: int, h: int, w: int, start_idx: int
+    ) -> List[List[int]]:
+        # Downsample time if needed
+        t_eff = t // self.temporal_conv_size if t != 1 else 1
+        gh, gw = h // self.spatial_conv_size, w // self.spatial_conv_size
+        time_idx = np.repeat(np.arange(t_eff), gh * gw)
+        h_idx = np.tile(np.repeat(np.arange(gh), gw), t_eff)
+        w_idx = np.tile(np.arange(gw), t_eff * gh)
+        coords = list(zip(time_idx, h_idx, w_idx))
+        return [
+            [start_idx + ti, start_idx + hi, start_idx + wi] for ti, hi, wi in coords
+        ]
+    def _pack_outputs(self, outs: Dict) -> Dict[str, Any]:
+        # Stack or nullify image-related fields
+        if not outs["images"]:
+            outs["images"] = None
+            outs["grid_thw"] = None
+            outs["image_type_ids"] = None
+        else:
+            outs["images"] = np.vstack(outs["images"])
+            outs["grid_thw"] = np.vstack(outs["grid_thw"])
+            outs["image_type_ids"] = np.array(outs["image_type_ids"])
+        # Convert lists to arrays
+        outs["input_ids"] = np.array(outs["input_ids"], dtype=np.int64)
+        outs["token_type_ids"] = np.array(outs["token_type_ids"], dtype=np.int64)
+        outs["position_ids"] = np.array(outs["position_ids"], dtype=np.int64)
+        return outs
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Ernie4_5_VLTokenizer'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 Ernie4_5_VLTokenizer's [`~PreTrainedTokenizer.decode`].
+        Please refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+    @property
+    def model_input_names(self):
+        """get model input names"""
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(tokenizer_input_names) + list(image_processor_input_names)
+__all__ = ["Ernie_45T_VLProcessor"]

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "<|end_of_sentence|>", "pad_token": "<unk>", "cls_token": "<|begin_of_sentence|>", "mask_token": "<mask:1>", "sys_start_token": "<mask:4>", "sys_end_token": "<mask:5>", "header_start_token": "<mask:6>", "header_end_token": "<mask:7>", "additional_special_tokens": ["<|IMAGE_PLACEHOLDER|>", "<|AUDIO_PLACEHOLDER|>", "<|LOC_0|>", "<|LOC_1|>", "<|LOC_2|>", "<|LOC_3|>", "<|LOC_4|>", "<|LOC_5|>", "<|LOC_6|>", "<|LOC_7|>", "<|LOC_8|>", "<|LOC_9|>", "<|LOC_10|>", "<|LOC_11|>", "<|LOC_12|>", "<|LOC_13|>", "<|LOC_14|>", "<|LOC_15|>", "<|LOC_16|>", "<|LOC_17|>", "<|LOC_18|>", "<|LOC_19|>", "<|LOC_20|>", "<|LOC_21|>", "<|LOC_22|>", "<|LOC_23|>", "<|LOC_24|>", "<|LOC_25|>", "<|LOC_26|>", "<|LOC_27|>", "<|LOC_28|>", "<|LOC_29|>", "<|LOC_30|>", "<|LOC_31|>", "<|LOC_32|>", "<|LOC_33|>", "<|LOC_34|>", "<|LOC_35|>", "<|LOC_36|>", "<|LOC_37|>", "<|LOC_38|>", "<|LOC_39|>", "<|LOC_40|>", "<|LOC_41|>", "<|LOC_42|>", "<|LOC_43|>", 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"<|AUDIO_UNUSE:878|>", "<|AUDIO_UNUSE:879|>", "<|AUDIO_UNUSE:880|>", "<|AUDIO_UNUSE:881|>", "<|AUDIO_UNUSE:882|>", "<|AUDIO_UNUSE:883|>", "<|AUDIO_UNUSE:884|>", "<|AUDIO_UNUSE:885|>", "<|AUDIO_UNUSE:886|>", "<|AUDIO_UNUSE:887|>", "<|AUDIO_UNUSE:888|>", "<|AUDIO_UNUSE:889|>", "<|AUDIO_UNUSE:890|>", "<|AUDIO_UNUSE:891|>", "<|AUDIO_UNUSE:892|>", "<|AUDIO_UNUSE:893|>", "<|AUDIO_UNUSE:894|>", "<|AUDIO_UNUSE:895|>", "<|AUDIO_UNUSE:896|>", "<|AUDIO_UNUSE:897|>", "<|AUDIO_UNUSE:898|>", "<|AUDIO_UNUSE:899|>", "<|AUDIO_UNUSE:900|>", "<|AUDIO_UNUSE:901|>", "<|AUDIO_UNUSE:902|>", "<|AUDIO_UNUSE:903|>", "<|AUDIO_UNUSE:904|>", "<|AUDIO_UNUSE:905|>", "<|AUDIO_UNUSE:906|>", "<|AUDIO_UNUSE:907|>", "<|AUDIO_UNUSE:908|>", "<|AUDIO_UNUSE:909|>", "<|AUDIO_UNUSE:910|>", "<|AUDIO_UNUSE:911|>", "<|AUDIO_UNUSE:912|>", "<|AUDIO_UNUSE:913|>", "<|AUDIO_UNUSE:914|>", "<|AUDIO_UNUSE:915|>", "<|AUDIO_UNUSE:916|>", "<|AUDIO_UNUSE:917|>", "<|AUDIO_UNUSE:918|>", "<|AUDIO_UNUSE:919|>", "<|AUDIO_UNUSE:920|>", "<|AUDIO_UNUSE:921|>", "<|AUDIO_UNUSE:922|>", "<|AUDIO_UNUSE:923|>", "<|AUDIO_UNUSE:924|>", "<|AUDIO_UNUSE:925|>", "<|AUDIO_UNUSE:926|>", "<|AUDIO_UNUSE:927|>", "<|AUDIO_UNUSE:928|>", "<|AUDIO_UNUSE:929|>", "<|AUDIO_UNUSE:930|>", "<|AUDIO_UNUSE:931|>", "<|AUDIO_UNUSE:932|>", "<|AUDIO_UNUSE:933|>", "<|AUDIO_UNUSE:934|>", "<|AUDIO_UNUSE:935|>", "<|AUDIO_UNUSE:936|>", "<|AUDIO_UNUSE:937|>", "<|AUDIO_UNUSE:938|>", "<|AUDIO_UNUSE:939|>", "<|AUDIO_UNUSE:940|>", "<|AUDIO_UNUSE:941|>", "<|AUDIO_UNUSE:942|>", "<|AUDIO_UNUSE:943|>", "<|AUDIO_UNUSE:944|>", "<|AUDIO_UNUSE:945|>", "<|AUDIO_UNUSE:946|>", "<|AUDIO_UNUSE:947|>", "<|AUDIO_UNUSE:948|>", "<|AUDIO_UNUSE:949|>", "<|AUDIO_UNUSE:950|>", "<|AUDIO_UNUSE:951|>", "<|AUDIO_UNUSE:952|>", "<|AUDIO_UNUSE:953|>", "<|AUDIO_UNUSE:954|>", "<|AUDIO_UNUSE:955|>", "<|AUDIO_UNUSE:956|>", "<|AUDIO_UNUSE:957|>", "<|AUDIO_UNUSE:958|>", "<|AUDIO_UNUSE:959|>", "<|AUDIO_UNUSE:960|>", "<|AUDIO_UNUSE:961|>", "<|AUDIO_UNUSE:962|>", "<|AUDIO_UNUSE:963|>", "<|AUDIO_UNUSE:964|>", "<|AUDIO_UNUSE:965|>", "<|AUDIO_UNUSE:966|>", "<|AUDIO_UNUSE:967|>", "<|AUDIO_UNUSE:968|>", "<|AUDIO_UNUSE:969|>", "<|AUDIO_UNUSE:970|>", "<|AUDIO_UNUSE:971|>", "<|AUDIO_UNUSE:972|>", "<|AUDIO_UNUSE:973|>", "<|AUDIO_UNUSE:974|>", "<|AUDIO_UNUSE:975|>", "<|AUDIO_UNUSE:976|>", "<|AUDIO_UNUSE:977|>", "<|AUDIO_UNUSE:978|>", "<|AUDIO_UNUSE:979|>", "<|AUDIO_UNUSE:980|>", "<|AUDIO_UNUSE:981|>", "<|AUDIO_UNUSE:982|>", "<|AUDIO_UNUSE:983|>", "<|AUDIO_UNUSE:984|>", "<|AUDIO_UNUSE:985|>", "<|AUDIO_UNUSE:986|>", "<|AUDIO_UNUSE:987|>", "<|AUDIO_UNUSE:988|>", "<|AUDIO_UNUSE:989|>", "<|AUDIO_UNUSE:990|>", "<|AUDIO_UNUSE:991|>", "<|AUDIO_UNUSE:992|>", "<|AUDIO_UNUSE:993|>", "<|AUDIO_UNUSE:994|>", "<|AUDIO_UNUSE:995|>", "<|AUDIO_UNUSE:996|>", "<|AUDIO_UNUSE:997|>", "<|AUDIO_UNUSE:998|>", "<|AUDIO_UNUSE:999|>", "<|AUDIO_UNUSE:1000|>", "<|AUDIO_UNUSE:1001|>", "<|AUDIO_UNUSE:1002|>", "<|AUDIO_UNUSE:1003|>", "<|AUDIO_UNUSE:1004|>", "<|AUDIO_UNUSE:1005|>", "<|AUDIO_UNUSE:1006|>", "<|AUDIO_UNUSE:1007|>", "<|AUDIO_UNUSE:1008|>", "<|AUDIO_UNUSE:1009|>", "<|AUDIO_UNUSE:1010|>", "<|AUDIO_UNUSE:1011|>", "<|AUDIO_UNUSE:1012|>", "<|AUDIO_UNUSE:1013|>", "<|AUDIO_UNUSE:1014|>", "<|AUDIO_UNUSE:1015|>", "<|AUDIO_UNUSE:1016|>", "<|AUDIO_UNUSE:1017|>", "<|AUDIO_UNUSE:1018|>", "<|AUDIO_UNUSE:1019|>", "<|AUDIO_UNUSE:1020|>", "<think>", "</think>"]}

tokenization_ernie_45t_vl.py ADDED Viewed

	@@ -0,0 +1,321 @@

+# Copyright (c) 2025 Baidu, Inc. 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.
+"""Tokenization classes for Ernie_45T_VL."""
+import os
+import re
+from shutil import copyfile
+from typing import Dict, List, Optional, Tuple, Union
+import numpy as np
+import torch
+import sentencepiece as spm
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.tokenization_utils_base import (
+    PaddingStrategy,
+    TextInput,
+)
+from transformers.utils import logging
+logger = logging.get_logger(__name__)
+class Ernie4_5_VLTokenizer(PreTrainedTokenizer):
+    """
+    Ernie4_5_VLTokenizer
+    """
+    vocab_files_names = {
+        "vocab_file": "tokenizer.model",
+    }
+    # Model input names expected by the tokenizer
+    model_input_names = ["input_ids", "position_ids", "attention_mask", "labels"]
+    # Padding side (where to add padding tokens)
+    padding_side = "right"
+    def __init__(
+        self,
+        vocab_file,
+        bos_token="<s>",
+        cls_token="<cls>",
+        eos_token="</s>",
+        mask_token="<mask:0>",
+        pad_token="<pad>",
+        sep_token="<sep>",
+        unk_token="<unk>",
+        additional_special_tokens=None,
+        **kwargs,
+    ):
+        """
+        Initialize the Ernie4_5_VLTokenizer
+        Args:
+            vocab_file (str): Path to the tokenizer vocabulary model.
+            bos_token (str, optional): The beginning of sequence token. Defaults to `"<s>"`.
+            cls_token (str, optional): The classifier token. Defaults to `"<cls>"`.
+            eos_token (str, optional): The end of sequence token. Defaults to `"</s>"`.
+            mask_token (str, optional): The masking token. Defaults to `"<mask:0>"`.
+            pad_token (str, optional): The padding token. Defaults to `"<pad>"`.
+            sep_token (str, optional): The separation token. Defaults to `"<sep>"`.
+            unk_token (str, optional): The unknown tokens symbol. Defaults to `"<unk>"`.
+            additional_special_tokens (List[str], optional): Additional special tokens to use.
+                Defaults to `["<mask:1>", "<mask:7>"]`.
+            **kwargs (dict): Additional keyword arguments passed along to the superclass.
+        """
+        # Store vocabulary file path
+        self.vocab_file = vocab_file
+        # Initialize SentencePiece processor
+        self.sp_model = spm.SentencePieceProcessor()
+        # Load the vocabulary model
+        self.sp_model.Load(vocab_file)
+        # Set default additional special tokens if none provided
+        if additional_special_tokens is None:
+            additional_special_tokens = ["<mask:1>", "<mask:7>"]
+        super().__init__(
+            bos_token=bos_token,
+            cls_token=cls_token,
+            eos_token=eos_token,
+            mask_token=mask_token,
+            pad_token=pad_token,
+            sep_token=sep_token,
+            unk_token=unk_token,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
+    @property
+    def space_token(self):
+        """Return the space token"""
+        return "<mask:1>"
+    @property
+    def space_token_id(self):
+        """Return the ID of the space token"""
+        return self.sp_model.piece_to_id("<mask:1>")
+    @property
+    def gend_token(self):
+        """Return the gender token"""
+        return "<mask:7>"
+    @property
+    def gend_token_id(self):
+        """Return the ID of the gender token"""
+        return self.sp_model.piece_to_id("<mask:7>")
+    @property
+    def im_start_id(self):
+        """Return the ID of the image start token"""
+        return self.sp_model.piece_to_id("<|im_start|>")
+    @property
+    def im_end_id(self):
+        """Return the ID of the image end token"""
+        return self.sp_model.piece_to_id("<|im_end|>")
+    @property
+    def vocab_size(self):
+        """Return the size of the vocabulary"""
+        return self.sp_model.vocab_size()
+    def get_vocab(self):
+        """Return the vocabulary as a dictionary mapping tokens to IDs"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+    def _tokenize(self, text):
+        """Tokenize the input text into pieces"""
+        return self.sp_model.encode_as_pieces(text)
+    def _convert_token_to_id(self, token):
+        """Convert a token to its corresponding ID"""
+        return self.sp_model.piece_to_id(token)
+    def _convert_id_to_token(self, id):
+        """Convert an ID to its corresponding token"""
+        return self.sp_model.id_to_piece(id)
+    def convert_tokens_to_string(self, tokens):
+        """Convert a sequence of tokens back to a string"""
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # Handle special tokens differently
+            if token in self.all_special_tokens:
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        # Add any remaining sub-tokens
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+    def prepare_for_model(self, *args, **kwargs):
+        """Prepare the tokenized inputs for the model"""
+        # Remove add_special_tokens if present (not supported)
+        if "add_special_tokens" in kwargs:
+            kwargs.pop("add_special_tokens")
+        return super().prepare_for_model(*args, **kwargs)
+    def save_vocabulary(
+        self, save_directory, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+        Args:
+            save_directory (`str`): The directory to save the vocabulary to
+            filename_prefix (`str`, optional): Prefix to add to the filename
+        Returns:
+            `Tuple(str)`: Paths to the saved files
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        # Construct output vocabulary file path
+        out_vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "")
+            + self.vocab_files_names["vocab_file"],
+        )
+        # Copy or create vocabulary file
+        if os.path.abspath(self.vocab_file) != os.path.abspath(
+            out_vocab_file
+        ) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+        return (out_vocab_file,)
+    def _decode(self, *args, **kwargs):
+        """Decode token_id back to text"""
+        # Remove some parameters that aren't used
+        kwargs.pop("clean_up_tokenization_spaces", None)
+        kwargs.pop("spaces_between_special_tokens", None)
+        # Call parent decode method with specific parameters
+        return super()._decode(
+            *args,
+            **kwargs,
+            clean_up_tokenization_spaces=False,
+            spaces_between_special_tokens=False,
+        )
+    def _pad(
+        self,
+        encoded_inputs: Dict,
+        max_length: Optional[int] = None,
+        padding_strategy=PaddingStrategy.DO_NOT_PAD,
+        pad_to_multiple_of: Optional[int] = None,
+        return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """Pad the encoded inputs to the specified length"""
+        if return_attention_mask is None:
+            return_attention_mask = "attention_mask" in self.model_input_names
+        if return_attention_mask:
+            required_input = encoded_inputs[self.model_input_names[0]]
+            if padding_strategy == PaddingStrategy.LONGEST:
+                max_length = len(required_input)
+            # Adjust max_length if needed for multiple of padding
+            if (
+                max_length is not None
+                and pad_to_multiple_of is not None
+                and (max_length % pad_to_multiple_of != 0)
+            ):
+                max_length = (
+                    (max_length // pad_to_multiple_of) + 1
+                ) * pad_to_multiple_of
+            # Check if padding is needed
+            needs_to_be_padded = (
+                padding_strategy != PaddingStrategy.DO_NOT_PAD
+                and len(required_input) != max_length
+            )
+            # Handle attention mask if present
+            if (
+                "attention_mask" in encoded_inputs
+                and encoded_inputs["attention_mask"] is not None
+            ):
+                attention_mask = encoded_inputs.pop("attention_mask")
+                if isinstance(attention_mask, torch.Tensor):
+                    attention_mask = attention_mask.numpy()
+                elif isinstance(attention_mask, list):
+                    attention_mask = np.array(attention_mask)
+                elif not isinstance(attention_mask, np.ndarray):
+                    raise ValueError(
+                        f"Unexpected type {type(attention_mask)} of attention_mask, "
+                    )
+            else:
+                # Create default attention mask if none provided
+                attention_mask = np.tril(
+                    np.ones((len(required_input), len(required_input)), dtype=np.int64)
+                )
+                attention_mask = np.expand_dims(attention_mask, axis=0)
+            # Perform padding if needed
+            if needs_to_be_padded:
+                difference = max_length - len(required_input)
+                if self.padding_side == "right":
+                    if attention_mask.ndim == 1:
+                        pad_width = [(0, difference)]
+                    else:
+                        pad_width = [(0, 0), (0, difference), (0, difference)]
+                elif self.padding_side == "left":
+                    if attention_mask.ndim == 1:
+                        pad_width = [(difference, 0)]
+                    else:
+                        pad_width = [(0, 0), (difference, 0), (difference, 0)]
+                else:
+                    raise ValueError(
+                        "Invalid padding strategy:" + str(self.padding_side)
+                    )
+                attention_mask = np.pad(
+                    attention_mask,
+                    pad_width=pad_width,
+                    mode="constant",
+                    constant_values=0,
+                )
+        # Call parent padding method
+        encoded_inputs = super()._pad(
+            encoded_inputs,
+            max_length,
+            padding_strategy=padding_strategy,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=False,
+        )
+        # Add attention mask back if needed
+        if return_attention_mask:
+            encoded_inputs["attention_mask"] = attention_mask.tolist()
+        return encoded_inputs
+__all__ = ["Ernie4_5_VLTokenizer"]

tokenizer.model ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:2ed2203974453df691287a0432c06737f1b17f20f5ab325fb33e31844d90ddb0
+size 1614362

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,22 @@

+{
+    "bos_token": "<s>",
+    "eos_token": "</s>",
+    "pad_token": "<unk>",
+    "unk_token": "<unk>",
+    "cls_token": "<|begin_of_sentence|>",
+    "sep_token": "<|end_of_sentence|>",
+    "mask_token": "<mask:1>",
+    "sys_start_token": "<mask:4>",
+    "sys_end_token": "<mask:5>",
+    "header_start_token": "<mask:6>",
+    "header_end_token": "<mask:7>",
+    "additional_special_tokens": null,
+    "tokenizer_class": "Ernie4_5_VLTokenizer",
+    "auto_map": {
+        "AutoTokenizer": [
+            "tokenization_ernie_45t_vl.Ernie4_5_VLTokenizer",
+            null
+        ]
+    },
+    "chat_template": "\n{%- set image_count = namespace(value=0) -%}\n{%- set video_count = namespace(value=0) -%}\n{{- '<|begin_of_sentence|>' }}\n{%- for message in messages -%}\n    {%- if message.role in ['system', 'user'] -%}\n        {%- if message.role == 'user' -%}\n            {{- 'User: ' -}}\n        {%- endif -%}\n        {%- if message.content is string -%}\n            {{- message.content -}}\n        {%- else -%}\n            {%- for content_item in message.content -%}\n                {%- if content_item.type == 'text' -%}\n                    {{- content_item.text -}}\n                {%- elif content_item.type == 'image_url' -%}\n                    {%- set image_count.value = image_count.value + 1 -%}\n                    Picture {{ image_count.value }}:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>\n                {%- elif content_item.type == 'video_url' -%}\n                    {%- set video_count.value = video_count.value + 1 -%}\n                    Video {{ video_count.value }}:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>\n                {%- endif -%}\n            {%- endfor -%}\n        {%- endif -%}\n        {%- if message.role == 'system' -%}\n            {{- '\n' -}}\n        {%- endif -%}\n    {%- elif message.role == 'assistant' -%}\n        {%- macro extract_text_content(content_field) -%}\n            {%- if content_field is string -%}\n                {{- content_field -}}\n            {%- elif content_field is iterable and content_field is not string -%}\n                {%- set ns = namespace(text_parts=[]) -%}\n                {%- set text_parts = [] -%}\n                {%- for item in content_field -%}\n                    {%- if item.type == 'text' -%}\n                        {%- set ns.text_parts = ns.text_parts + [item.text] -%}\n                    {%- endif -%}\n                {%- endfor -%}\n                {{- ns.text_parts | join('') -}}\n            {%- else -%}\n                {{- '' -}}\n            {%- endif -%}\n        {%- endmacro -%}\n        {%- set reasoning_content = extract_text_content(message.reasoning_content) -%}\n        {%- set content = extract_text_content(message.content) -%}\n        {%- if '</think>' in content %}\n            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}\n            {%- set content = content.split('</think>')[-1].lstrip('\n') %}\n        {%- endif %}\n        {%- if reasoning_content %}\n            {{- '\n' + 'Assistant: ' + '<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}\n        {%- else %}\n            {{- '\n' + 'Assistant: ' + content }}\n        {%- endif %}\n        {{- '<|end_of_sentence|>' }}\n    {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt is not defined or add_generation_prompt is true %}\n    {{- '\nAssistant: ' -}}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\n\n</think>\n\n' }}\n    {%- endif %}\n    {%- if enable_thinking is not defined or enable_thinking is true %}\n        {{- '<think>' }}\n    {%- endif %}\n{%- endif %}\n"
+}

video_utils_ernie_45t_vl.py ADDED Viewed

	@@ -0,0 +1,514 @@

+# Copyright (c) 2025 Baidu, Inc. 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.
+import io
+import os
+import random
+import requests
+import base64
+import datetime
+import hashlib
+import threading
+import uuid
+import decord
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from PIL.ExifTags import TAGS
+from pathlib import Path
+from tempfile import NamedTemporaryFile as ntf
+try:
+    # moviepy 1.0
+    import moviepy.editor as mp
+except:
+    # moviepy 2.0
+    import moviepy as mp
+from transformers.utils import logging
+logger = logging.get_logger(__name__)
+RAW_VIDEO_DIR = "./download_tmp/raw_video/"
+RAW_IMAGE_DIR = "./download_tmp/raw_images/"
+EXTRACTED_FRAME_DIR = "./download_tmp/extracted_frames/"
+TMP_DIR = "./download_tmp/upload_tmp/"
+FONT_PATH = os.path.join(Path(__file__).parent.absolute(), "Roboto-Regular.ttf")
+def is_gif(data: bytes) -> bool:
+    """
+    check if a bytes is a gif based on the magic head
+    """
+    return data[:6] in (b"GIF87a", b"GIF89a")
+class VideoReaderWrapper(decord.VideoReader):
+    """
+    Solving memory leak bug
+    https://github.com/dmlc/decord/issues/208
+    """
+    def __init__(self, video_path, *args, **kwargs):
+        with ntf(delete=True, suffix=".gif") as gif_file:
+            gif_input = None
+            self.original_file = None
+            if isinstance(video_path, str):
+                self.original_file = video_path
+                if video_path.lower().endswith(".gif"):
+                    gif_input = video_path
+            elif isinstance(video_path, bytes):
+                if is_gif(video_path):
+                    gif_file.write(video_path)
+                    gif_input = gif_file.name
+            elif isinstance(video_path, io.BytesIO):
+                video_path.seek(0)
+                tmp_bytes = video_path.read()
+                video_path.seek(0)
+                if is_gif(tmp_bytes):
+                    gif_file.write(tmp_bytes)
+                    gif_input = gif_file.name
+            if gif_input is not None:
+                clip = mp.VideoFileClip(gif_input)
+                mp4_file = ntf(delete=False, suffix=".mp4")
+                clip.write_videofile(mp4_file.name, verbose=False, logger=None)
+                clip.close()
+                video_path = mp4_file.name
+                self.original_file = video_path
+            super().__init__(video_path, *args, **kwargs)
+            self.seek(0)
+    def __getitem__(self, key):
+        frames = super().__getitem__(key)
+        self.seek(0)
+        return frames
+    def __del__(self):
+        if self.original_file and os.path.exists(self.original_file):
+            os.remove(self.original_file)
+def get_filename(url=None):
+    """
+    Get Filename
+    """
+    if url is None:
+        return str(uuid.uuid4()).replace("-", "")
+    t = datetime.datetime.now()
+    if not isinstance(url, bytes):
+        url = url.encode("utf-8")
+    md5_hash = hashlib.md5(url).hexdigest()
+    pid = os.getpid()
+    tid = threading.get_ident()
+    # Remove the suffix to prevent save-jpg from reporting errors
+    image_filname = f"{t.year}-{t.month:02d}-{t.day:02d}-{pid}-{tid}-{md5_hash}"
+    return image_filname
+def file_download(url, download_dir, save_to_disk=False, retry=0, retry_interval=3):
+    """
+    Description: Download url, if url is PIL, return directly
+    Args:
+        url(str, PIL): http/local path/io.Bytes, note that io.Bytes is the image byte stream
+        download_path: when save_to_disk=True, return the saved address
+        save_to_disk: whether to save in the local path
+    """
+    if isinstance(url, Image.Image):
+        return url
+    elif isinstance(url, VideoReaderWrapper):
+        return url
+    elif url.startswith("http"):
+        response = requests.get(url)
+        bytes_data = response.content
+    elif os.path.isfile(url):
+        if save_to_disk:
+            return url
+        bytes_data = open(url, "rb").read()
+    else:
+        bytes_data = base64.b64decode(url)
+    if not save_to_disk:
+        return bytes_data
+    download_path = os.path.join(download_dir, get_filename(url))
+    Path(download_path).parent.mkdir(parents=True, exist_ok=True)
+    with open(download_path, "wb") as f:
+        f.write(bytes_data)
+    return download_path
+def get_downloadable(
+    url, download_dir=RAW_VIDEO_DIR, save_to_disk=False, retry=0, retry_interval=3
+):
+    """download video and store it in the disk
+    return downloaded **path** if save_to_disk is set to true
+    return downloaded **bytes** if save_to_disk is set to false
+    """
+    if not os.path.exists(download_dir):
+        os.makedirs(download_dir)
+    downloaded_path = file_download(
+        url,
+        download_dir,
+        save_to_disk=save_to_disk,
+        retry=retry,
+        retry_interval=retry_interval,
+    )
+    return downloaded_path
+def get_downloadable_image(
+    download_path, need_exif_info, retry_max_time=0, retry_interval=3
+):
+    """
+    Get downloadable with exif info and image processing
+    """
+    def get_image_exif(image):
+        exif_data = image._getexif()
+        exif_info = {}
+        if exif_data is not None:
+            for tag, value in exif_data.items():
+                tag_name = TAGS.get(tag, tag)
+                exif_info[tag_name] = value.strip()
+        return exif_info
+    def has_transparent_background(img):
+        """has_transparent_background"""
+        if img.mode in ("RGBA", "LA") or (
+            img.mode == "P" and "transparency" in img.info
+        ):
+            # Check for any pixel with alpha channel less than 255 (fully opaque)
+            alpha = img.convert("RGBA").split()[-1]
+            if alpha.getextrema()[0] < 255:
+                return True
+        return False
+    def add_white_background(img):
+        """
+        Add a white background to a transparent background image
+        """
+        if img.mode != "RGBA":
+            img = img.convert("RGBA")
+        # Create an image with a white background and the same size as the original image
+        img_white_background = Image.new("RGBA", img.size, (255, 255, 255))
+        # Paste the original image onto a white background
+        img_white_background.paste(img, (0, 0), img)
+        return img_white_background
+    def change_I16_to_L(img):
+        """
+        Convert image from I;16 mode to L mode
+        """
+        # Since the point function in I mode only supports addition, subtraction, and multiplication,
+        # the following * (1 / 256) cannot be changed to division.
+        return img.point(lambda i: i * (1 / 256)).convert("L")
+    image = get_downloadable(
+        download_path,
+        save_to_disk=False,
+        retry=retry_max_time,
+        retry_interval=retry_interval,
+    )
+    if isinstance(image, Image.Image):
+        pil_image = image
+    else:
+        pil_image = Image.open(io.BytesIO(image))
+    if need_exif_info:
+        try:
+            exif_info = get_image_exif(pil_image)
+        except Exception as why:
+            exif_info = {}
+    else:
+        exif_info = {}
+    try:
+        if pil_image.mode == "I;16":
+            pil_image = change_I16_to_L(pil_image)
+        if has_transparent_background(pil_image):
+            pil_image = add_white_background(pil_image)
+    except Exception as e:
+        pass
+    return pil_image.convert("RGB"), exif_info
+def read_video_decord(video_path, save_to_disk):
+    """get reader and meta by decord"""
+    video_path = get_downloadable(video_path, save_to_disk=save_to_disk)
+    if isinstance(video_path, VideoReaderWrapper):
+        video_reader = video_path
+    else:
+        if isinstance(video_path, bytes):
+            video_path = io.BytesIO(video_path)
+        video_reader = VideoReaderWrapper(video_path, num_threads=1)
+    vlen = len(video_reader)
+    fps = video_reader.get_avg_fps()
+    duration = vlen / float(fps)
+    video_meta = {"fps": fps, "duration": duration, "num_of_frame": vlen}
+    return video_reader, video_meta, video_path
+def get_frame_indices(
+    vlen,
+    target_frames=-1,
+    target_fps=-1,
+    frames_sample="middle",
+    fix_start=None,
+    input_fps=-1,
+):
+    """get_frame_indices"""
+    assert frames_sample in ["rand", "middle", "leading"]
+    if target_frames > 0:
+        assert target_fps <= 0, "target_fps must be negative if target_frames is given."
+        if target_frames > vlen:
+            acc_samples = vlen
+            logger.info(
+                f"target_frames={target_frames} is larger than video length {vlen}, "
+                f"will sample {acc_samples} frames."
+            )
+        else:
+            acc_samples = target_frames
+            logger.debug(
+                f"sampling at target_frames={target_frames}, frames_sample={frames_sample}"
+            )
+        # split the video into `acc_samples` intervals, and sample from each interval.
+        intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int)
+        ranges = []
+        for idx, interv in enumerate(intervals[:-1]):
+            ranges.append((interv, intervals[idx + 1] - 1))
+        if frames_sample == "rand":
+            try:
+                frame_indices = [random.choice(range(x[0], x[1])) for x in ranges]
+            except Exception as e:
+                frame_indices = np.random.permutation(vlen)[:acc_samples]
+                frame_indices.sort()
+                frame_indices = list(frame_indices)
+        elif fix_start is not None:
+            frame_indices = [x[0] + fix_start for x in ranges]
+        elif frames_sample == "leading":
+            frame_indices = [x[0] for x in ranges]
+        elif frames_sample == "middle":
+            frame_indices = [(x[0] + x[1]) // 2 for x in ranges]
+        else:
+            raise NotImplementedError
+    elif target_fps > 0:
+        assert (
+            target_frames <= 0
+        ), "target_frames must be negative if target_fps is given."
+        assert input_fps > 0, "input_fps must be provided if target_fps is given."
+        logger.info(f"sampling at fps={target_fps}, frames_sample={frames_sample}")
+        duration = float(vlen) / input_fps
+        delta = (
+            1 / target_fps
+        )  # gap between frames, this is also the clip length each frame represents
+        if frames_sample == "middle":
+            frame_seconds = np.arange(0 + delta / 2, duration + delta / 2, delta)
+        elif frames_sample == "leading":
+            frame_seconds = np.arange(0, duration, delta)
+        if frames_sample == "rand":
+            frame_seconds = np.arange(0 + delta / 2, duration + delta / 2, delta)
+            rand_offset = np.random.rand(*(frame_seconds.shape)) - 0.5
+            frame_seconds += rand_offset * delta
+        frame_indices = np.around(frame_seconds * input_fps).astype(int)
+        frame_indices = [e for e in frame_indices if e < vlen]
+    else:
+        raise ValueError(
+            "Must provide either positive target_fps or positive target_frames."
+        )
+    return frame_indices
+def read_frames_decord(
+    video_path,
+    video_reader,
+    video_meta,
+    target_frames=-1,
+    target_fps=-1,
+    frames_sample="middle",
+    fix_start=None,
+    save_to_disk=False,
+    cache_dir=EXTRACTED_FRAME_DIR,
+    frame_indices=None,
+    tol=10,
+):
+    """get frames by decord"""
+    if frame_indices is None:
+        frame_indices = get_frame_indices(
+            video_meta["num_of_frame"],
+            target_frames=target_frames,
+            target_fps=target_fps,
+            frames_sample=frames_sample,
+            fix_start=fix_start,
+            input_fps=video_meta["fps"],
+        )
+    frames = []
+    for frame_indice_index in range(0, len(frame_indices)):
+        frame_indice = frame_indices[frame_indice_index]
+        try:
+            frames.append(video_reader[frame_indice].asnumpy())  # (T, H, W, C)
+        except Exception as e:
+            logger.debug(f"encounter error when get frame: {frame_indice}, error: {e}")
+            previous_counter = 1
+            later_counter = 1
+            previous_after_flag = True
+            if frame_indice == 0 or frame_indice == len(video_reader) - 1:
+                cur_tol = tol * 2
+            else:
+                cur_tol = tol
+            while previous_counter < cur_tol or later_counter < cur_tol:
+                if previous_after_flag:
+                    if frame_indice - previous_counter < 0:
+                        previous_counter += 1
+                        previous_after_flag = not previous_after_flag
+                        continue
+                    try:
+                        frames.append(
+                            video_reader[frame_indice - previous_counter].asnumpy()
+                        )
+                        logger.info(
+                            f"replace {frame_indice}-th frame with {frame_indice-previous_counter}-th frame"
+                        )
+                        frame_indices[frame_indice_index] = (
+                            frame_indice - previous_counter
+                        )
+                        break
+                    except Exception as e:
+                        previous_counter += 1
+                else:
+                    if frame_indice + later_counter >= len(video_reader):
+                        later_counter += 1
+                        previous_after_flag = not previous_after_flag
+                        continue
+                    try:
+                        frames.append(
+                            video_reader[frame_indice + later_counter].asnumpy()
+                        )
+                        logger.info(
+                            f"replace {frame_indice}-th frame with {frame_indice+later_counter}-th frame"
+                        )
+                        frame_indices[frame_indice_index] = frame_indice + later_counter
+                        break
+                    except Exception as e:
+                        later_counter += 1
+                previous_after_flag = not previous_after_flag
+    frames = np.stack(frames, axis=0)
+    assert len(frames) == len(
+        frame_indices
+    ), f"len(frames): {len(frames)} != len(frame_indices): {len(frame_indices)}"
+    ret = []
+    url_sha1 = get_filename()
+    for idx, frame in enumerate(frames):
+        tmp = Image.fromarray(frame, "RGB")
+        if save_to_disk:
+            save_path = os.path.join(cache_dir, f"{url_sha1}", f"{idx}.png")
+            if not os.path.exists(os.path.dirname(save_path)):
+                os.makedirs(os.path.dirname(save_path))
+            tmp.save(save_path)
+            tmp = save_path
+        ret.append(tmp)
+    time_stamps = [
+        frame_idx * video_meta["duration"] / video_meta["num_of_frame"]
+        for frame_idx in frame_indices
+    ]
+    return ret, frame_indices, time_stamps
+def render_single_image_with_timestamp(
+    image: Image, number: str, rate: float, font_path: str = FONT_PATH
+):
+    """
+    Function: Renders a timestamp to the image of pil.image
+    The timestamp size is the rate of min(width, height)
+    The font color is black, the outline is white, and the outline size is 10% of the font
+    Returns an Image object
+    """
+    draw = ImageDraw.Draw(image)
+    width, height = image.size
+    font_size = int(min(width, height) * rate)
+    outline_size = int(font_size * 0.1)
+    font = ImageFont.truetype(font_path, font_size)
+    x = 0
+    y = 0
+    # Draw a black timestamp with a white border
+    draw.text(
+        (x, y),
+        number,
+        font=font,
+        fill=(0, 0, 0),
+        stroke_width=outline_size,
+        stroke_fill=(255, 255, 255),
+    )
+    return image
+def timestamp_converting(time_stamp_in_seconds):
+    """
+    convert timestamp format from seconds to hr:min:sec
+    """
+    # get hours
+    hours = 0
+    while time_stamp_in_seconds >= 3600:
+        hours += 1
+        time_stamp_in_seconds -= 3600
+    # get minutes
+    mins = 0
+    while time_stamp_in_seconds >= 60:
+        mins += 1
+        time_stamp_in_seconds -= 60
+    time_hours = f"{int(hours):02d}"
+    time_mins = f"{int(mins):02d}"
+    time_secs = f"{time_stamp_in_seconds:05.02f}"
+    fi_time_stamp = time_hours + ":" + time_mins + ":" + time_secs
+    return fi_time_stamp
+def render_frame_timestamp(frame, timestamp, font_rate=0.1):
+    """
+    Function, given a frame, render the index in order
+    Logic: render the index to the upper left corner of the image
+    frame: frame, PIL.Image object
+    timestamp: timestamp, in seconds
+    font_rate: the ratio of font size to min(wi, hei)
+    """
+    time_stamp = "time: " + timestamp_converting(timestamp)
+    new_frame = render_single_image_with_timestamp(frame, time_stamp, font_rate)
+    return new_frame