update

README.md

@@ -0,0 +1,568 @@
+---
+language:
+- zh
+- en
+tags:
+- qwen
+pipeline_tag: text-generation
+inference: false
+---
+
+# Qwen-VL
+
+<br>
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/logo.jpg" width="400"/>
+<p>
+<br>
+
+<p align="center">
+        Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ &nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://github.com/QwenLM/Qwen-VL/blob/main/visual_memo.md">Report</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/9bjvspyu">Discord</a>
+
+</p>
+<br>
+
+<p align="center">
+        <a href="README_CN.md">中文</a>&nbsp ｜ &nbsp English
+</p>
+<br><br>
+
+**Qwen-VL** (Qwen Large Vision Language Model) is the visual multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-VL accepts image, text, and bounding box as inputs, outputs text and bounding box. The features of Qwen-VL include:
+- **Strong performance**: It significantly surpasses existing open-source Large Vision Language Models (LVLM) under similar scale settings on multiple English evaluation benchmarks (including Zero-shot caption, VQA, DocVQA, and Grounding).
+- **Multi-lingual LVLM support text recognization**: Qwen-VL naturally supports multi-lingual conversation, and it promotes end-to-end recognition of Chinese and English bi-lingual text in images.
+- **Multi-image interleaved conversations**: This feature allows for the input and comparison of multiple images, as well as the ability to specify questions related to the images and engage in multi-image storytelling.
+- **First generalist model support grounding in Chinese**: Detecting bounding boxes through open-domain language expression in both Chinese and English.
+- **Fine-grained recognization and understanding**: Compared to the 224 resolution currently used by other open-source LVLM, the 448 resolution promotes fine-grained text recognition, document QA, and bounding box annotation.
+
+<br>
+<p align="center">
+    <img src="assets/demo_vl.gif" width="400"/>
+<p>
+<br>
+
+We release two models of the Qwen-VL series:
+- Qwen-VL: The pre-trained LVLM model uses Qwen-7B as the initialization of the LLM, and [Openclip ViT-bigG](https://github.com/mlfoundations/open_clip) as the initialization of the visual encoder. And connects them with a randomly initialized cross-attention layer. Qwen-VL was trained on about 1.5B image-text paired data. The final image input resolution is 448.
+- Qwen-VL-Chat: A multimodal LLM-based AI assistant, which is trained with alignment techniques.
+
+For more details about Qwen-VL, please refer to our [technical memo](visual_memo.md).
+
+## Evaluation
+
+We evaluated the model's ability from two perspectives:
+1. **Standard Benchmarks**: We evaluate the model's basic task capabilities on four major categories of multimodal tasks:
+   - Zero-shot Caption: Evaluate model's zero-shot image captioning ability on unseen datasets;
+   - General VQA: Evaluate the general question-answering ability of pictures, such as the judgment, color, number, category, etc;
+   - Text-based VQA: Evaluate the model's ability to recognize text in pictures, such as document QA, chart QA, etc;
+   - Referring Expression Comprehension: Evaluate the ability to localize a target object in an image described by a referring expression.
+
+2. **TouchStone**: To evaluate the overall text-image dialogue capability and alignment level with humans, we have constructed a benchmark called TouchStone, which is based on scoring with GPT4 to evaluate the LVLM model.
+   - The TouchStone benchmark covers a total of 300+ images, 800+ questions, and 27 categories. Such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc;
+   - In order to break the current limitation of GPT4 in terms of direct image input, TouchStone provides fine-grained image annotations by human labeling. These detailed annotations, along with the questions and the model's output, are then presented to GPT4 for scoring.
+   - The benchmark includes both English and Chinese versions.
+
+The results of the evaluation are as follows:
+
+Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has a more comprehensive coverage in terms of capability range.
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/radar.png" width="600"/>
+<p>
+
+### Zero-shot Caption & General VQA
+<table>
+<thead>
+  <tr>
+    <th rowspan="2">Model type</th>
+    <th rowspan="2">Model</th>
+    <th colspan="2">Zero-shot Caption</th>
+    <th colspan="5">General VQA</th>
+  </tr>
+  <tr>
+    <th>NoCaps</th>
+    <th>Flickr30K</th>
+    <th>VQAv2<sup>dev</sup></th>
+    <th>OK-VQA</th>
+    <th>GQA</th>
+    <th>SciQA-Img<br>(0-shot)</th>
+    <th>VizWiz<br>(0-shot)</th>
+  </tr>
+</thead>
+<tbody align="center">
+  <tr>
+    <td rowspan="12">Generalist<br>Models</td>
+    <td>Flamingo-9B</td>
+    <td>-</td>
+    <td>61.5</td>
+    <td>51.8</td>
+    <td>44.7</td>
+    <td>-</td>
+    <td>-</td>
+    <td>28.8</td>
+  </tr>
+  <tr>
+    <td>Flamingo-80B</td>
+    <td>-</td>
+    <td>67.2</td>
+    <td>56.3</td>
+    <td>50.6</td>
+    <td>-</td>
+    <td>-</td>
+    <td>31.6</td>
+  </tr>
+  <tr>
+    <td>Unified-IO-XL</td>
+    <td>100.0</td>
+    <td>-</td>
+    <td>77.9</td>
+    <td>54.0</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>Kosmos-1</td>
+    <td>-</td>
+    <td>67.1</td>
+    <td>51.0</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>29.2</td>
+  </tr>
+  <tr>
+    <td>Kosmos-2</td>
+    <td>-</td>
+    <td>66.7</td>
+    <td>45.6</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>BLIP-2 (Vicuna-13B)</td>
+    <td>103.9</td>
+    <td>71.6</td>
+    <td>65.0</td>
+    <td>45.9</td>
+    <td>32.3</td>
+    <td>61.0</td>
+    <td>19.6</td>
+  </tr>
+  <tr>
+    <td>InstructBLIP (Vicuna-13B)</td>
+    <td><strong>121.9</strong></td>
+    <td>82.8</td>
+    <td>-</td>
+    <td>-</td>
+    <td>49.5</td>
+    <td>63.1</td>
+    <td>33.4</td>
+  </tr>
+  <tr>
+    <td>Shikra (Vicuna-13B)</td>
+    <td>-</td>
+    <td>73.9</td>
+    <td>77.36</td>
+    <td>47.16</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td><strong>Qwen-VL (Qwen-7B)</strong></td>
+    <td>121.4</td>
+    <td><b>85.8</b></td>
+    <td><b>78.8</b></td>
+    <td><b>58.6</b></td>
+    <td><b>59.3</b></td>
+    <td><b>67.1</b></td>
+    <td><b>34.3</b></td>
+  </tr>
+  <tr>
+    <td>Qwen-VL (4-shot)</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>63.6</td>
+    <td>-</td>
+    <td>-</td>
+    <td>39.1</td>
+  </tr>
+  <tr>
+    <td>Qwen-VL-Chat</td>
+    <td>-</td>
+    <td>81.5</td>
+    <td>-</td>
+    <td>56.69</td>
+    <td>-</td>
+    <td>68.22</td>
+    <td>37.05</td>
+  </tr>
+  <tr>
+    <td>Qwen-VL-Chat (4-shot)</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>60.6</td>
+    <td>-</td>
+    <td>-</td>
+    <td>45.5</td>
+  </tr>
+  <tr>
+    <td>Previous SOTA<br>(Per Task Fine-tuning)</td>
+    <td>-</td>
+    <td>127.0<br>(PALI-17B)</td>
+    <td>84.5<br>(InstructBLIP<br>-FlanT5-XL)</td>
+    <td>86.1<br>(PALI-X<br>-55B)</td>
+    <td>66.1<br>(PALI-X<br>-55B)</td>
+    <td>72.1<br>(CFR)</td>
+    <td>92.53<br>(LLaVa+<br>GPT-4)</td>
+    <td>70.9<br>(PALI-X<br>-55B)</td>
+  </tr>
+</tbody>
+</table>
+
+- For zero-shot image captioning, Qwen-VL achieves the **SOTA** on Flickr30K and competitive results on Nocaps with InstructBlip.
+- For general VQA, Qwen-VL achieves the **SOTA** under the same generalist LVLM scale settings.
+
+### Text-based VQA (focuse on text understanding capabilities in images)
+
+<table>
+<thead>
+  <tr>
+    <th>Model type</th>
+    <th>Model</th>
+    <th>TextVQA</th>
+    <th>DocVQA</th>
+    <th>ChartQA</th>
+    <th>AI2D</th>
+    <th>OCR-VQA</th>
+  </tr>
+</thead>
+<tbody align="center">
+  <tr>
+    <td rowspan="5">Generalist Models</td>
+    <td>BLIP-2 (Vicuna-13B)</td>
+    <td>42.4</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>InstructBLIP (Vicuna-13B)</td>
+    <td>50.7</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>mPLUG-DocOwl (LLaMA-7B)</td>
+    <td>52.6</td>
+    <td>62.2</td>
+    <td>57.4</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>Pic2Struct-Large (1.3B)</td>
+    <td>-</td>
+    <td><b>76.6</b></td>
+    <td>58.6</td>
+    <td>42.1</td>
+    <td>71.3</td>
+  </tr>
+  <tr>
+    <td>Qwen-VL (Qwen-7B)</td>
+    <td><b>63.8</b></td>
+    <td>65.1</td>
+    <td><b>65.7</b></td>
+    <td><b>62.3</b></td>
+    <td><b>75.7</b></td>
+  </tr>
+  <tr>
+    <td>Specialist SOTAs<br>(Specialist/Finetuned)</td>
+    <td>PALI-X-55B (Single-task FT)<br>(Without OCR Pipeline)</td>
+    <td>71.44</td>
+    <td>80.0</td>
+    <td>70.0</td>
+    <td>81.2</td>
+    <td>75.0</td>
+  </tr>
+</tbody>
+</table>
+
+- In text-related recognition/QA evaluation, Qwen-VL achieves the SOTA under the generalist LVLM scale settings.
+- Resolution is important for several above evaluations. While most open-source LVLM models with 224 resolution are incapable of these evaluations or can only solve these by cutting images, Qwen-VL scales the resolution to 448 so that it can be evaluated end-to-end. Qwen-VL even outperforms Pic2Struct-Large models of 1024 resolution on some tasks.
+
+### Referring Expression Comprehension
+<table>
+<thead>
+  <tr>
+    <th rowspan="2">Model type</th>
+    <th rowspan="2">Model</th>
+    <th colspan="3">RefCOCO</th>
+    <th colspan="3">RefCOCO+</th>
+    <th colspan="2">RefCOCOg</th>
+    <th>GRIT</th>
+  </tr>
+  <tr>
+    <th>val</th>
+    <th>test-A</th>
+    <th>test-B</th>
+    <th>val</th>
+    <th>test-A</th>
+    <th>test-B</th>
+    <th>val-u</th>
+    <th>test-u</th>
+    <th>refexp</th>
+  </tr>
+</thead>
+<tbody align="center">
+  <tr>
+    <td rowspan="8">Generalist Models</td>
+    <td>GPV-2</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>51.50</td>
+  </tr>
+  <tr>
+    <td>OFA-L*</td>
+    <td>79.96</td>
+    <td>83.67</td>
+    <td>76.39</td>
+    <td>68.29</td>
+    <td>76.00</td>
+    <td>61.75</td>
+    <td>67.57</td>
+    <td>67.58</td>
+    <td>61.70</td>
+  </tr>
+  <tr>
+    <td>Unified-IO</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td><b>78.61</b></td>
+  </tr>
+  <tr>
+    <td>VisionLLM-H</td>
+    <td></td>
+    <td>86.70</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>Shikra-7B</td>
+    <td>87.01</td>
+    <td>90.61</td>
+    <td>80.24 </td>
+    <td>81.60</td>
+    <td>87.36</td>
+    <td>72.12</td>
+    <td>82.27</td>
+    <td>82.19</td>
+    <td>69.34</td>
+  </tr>
+  <tr>
+    <td>Shikra-13B</td>
+    <td>87.83 </td>
+    <td>91.11</td>
+    <td>81.81</td>
+    <td>82.89</td>
+    <td>87.79</td>
+    <td>74.41</td>
+    <td>82.64</td>
+    <td>83.16</td>
+    <td>69.03</td>
+  </tr>
+  <tr>
+    <td>Qwen-VL-7B</td>
+    <td><b>89.36</b></td>
+    <td>92.26</td>
+    <td><b>85.34</b></td>
+    <td><b>83.12</b></td>
+    <td>88.25</td>
+    <td><b>77.21</b></td>
+    <td><b>85.58</b></td>
+    <td><b>85.48</b></td>
+    <td>78.22</td>
+  </tr>
+  <tr>
+    <td>Qwen-VL-7B-Chat</td>
+    <td><b>88.55</b></td>
+    <td><b>92.27</b></td>
+    <td>84.51</td>
+    <td>82.82</td>
+    <td><b>88.59</b></td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td rowspan="3">Specialist SOTAs<br>(Specialist/Finetuned)</td>
+    <td>G-DINO-L</td>
+    <td>90.56&nbsp;&nbsp;</td>
+    <td>93.19</td>
+    <td>88.24</td>
+    <td>82.75</td>
+    <td>88.95</td>
+    <td>75.92</td>
+    <td>86.13</td>
+    <td>87.02</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>UNINEXT-H</td>
+    <td>92.64 </td>
+    <td>94.33</td>
+    <td>91.46</td>
+    <td>85.24</td>
+    <td>89.63</td>
+    <td>79.79</td>
+    <td>88.73</td>
+    <td>89.37</td>
+    <td>-</td>
+  </tr>
+  <tr>
+    <td>ONE-PEACE</td>
+    <td>92.58 </td>
+    <td>94.18</td>
+    <td>89.26</td>
+    <td>88.77</td>
+    <td>92.21</td>
+    <td>83.23</td>
+    <td>89.22</td>
+    <td>89.27</td>
+    <td>-</td>
+  </tr>
+</tbody>
+</table>
+
+- Qwen-VL achieves the **SOTA** in all above referring expression comprehension benchmarks.
+- Qwen-VL has not been trained on any Chinese grounding data, but it can still generalize to the Chinese Grounding tasks in a zero-shot way by training Chinese Caption data and English Grounding data.
+
+We provide all of the above evaluation scripts for reproducing our experimental results. Please read [eval/EVALUATION.md](eval/EVALUATION.md) for more information.
+
+### Chat evaluation
+
+TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities of the LVLM model on text-image dialogue and alignment levels with humans. It covers a total of 300+ images, 800+ questions, and 27 categories, such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc. Please read [touchstone/README_CN.md](touchstone/README.md) for more information.
+
+#### English evaluation
+
+| Model         | Score |
+|---------------|-------|
+| PandaGPT      | 488.5 |
+| MiniGPT4      | 531.7 |
+| InstructBLIP  | 552.4 |
+| LLaMA-AdapterV2 | 590.1 |
+| mPLUG-Owl     | 605.4 |
+| LLaVA         | 602.7 |
+| Qwen-VL-Chat   | 645.2 |
+
+#### Chinese evaluation
+
+| Model         | Score |
+|---------------|-------|
+| VisualGLM     | 247.1 |
+| Qwen-VL-Chat   | 401.2 |
+
+Qwen-VL-Chat has achieved the best results in both Chinese and English alignment evaluation.
+
+## Requirements
+
+* python 3.8 and above
+* pytorch 1.12 and above, 2.0 and above are recommended
+* CUDA 11.4 and above are recommended (this is for GPU users)
+
+## Quickstart
+
+Below, we provide simple examples to show how to use Qwen-VL and Qwen-VL-Chat with 🤖 ModelScope and 🤗 Transformers.
+
+Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
+
+```bash
+pip install -r requirements.txt
+```
+
+Now you can start with ModelScope or Transformers. More usage aboue vision encoder, please refer to [FAQ](FAQ.md).
+
+#### 🤗 Transformers
+
+To use Qwen-VL for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, **please make sure that you are using the latest code.**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+torch.manual_seed(1234)
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+
+# use bf16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, bf16=True).eval()
+# use fp16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, fp16=True).eval()
+# use cpu only
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cpu", trust_remote_code=True).eval()
+# use cuda device
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cuda", trust_remote_code=True).eval()
+
+# Specify hyperparameters for generation
+model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+
+query = tokenizer.from_list_format([
+    {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'},
+    {'text': 'Generate the caption in English with grounding:'},
+])
+inputs = tokenizer(query, return_tensors='pt')
+inputs = inputs.to(model.device)
+pred = model.generate(**inputs)
+response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
+print(response)
+# <img>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg</img>Generate the caption in English with grounding:<ref> Woman</ref><box>(451,379),(731,806)</box> and<ref> her dog</ref><box>(219,424),(576,896)</box> playing on the beach<|endoftext|>
+image = tokenizer.draw_bbox_on_latest_picture(response)
+if image:
+  image.save('2.jpg')
+else:
+  print("no box")
+```
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo_spotting_caption.jpeg" width="500"/>
+<p>
+
+
+## FAQ
+
+If you meet problems, please refer to [FAQ](FAQ.md) and the issues first to search a solution before you launch a new issue.
+
+
+## License Agreement
+
+Researchers and developers are free to use the codes and model weights of both Qwen-7B and Qwen-7B-Chat. We also allow their commercial use. Check our license at [LICENSE](LICENSE) for more details.
+
+## Contact Us
+
+If you are interested to leave a message to either our research team or product team, feel free to send an email to qianwen_opensource@alibabacloud.com.
+

tokenization_qwen.py

@@ -18,6 +18,7 @@ from PIL import Image
 from PIL import ImageFont
 from PIL import ImageDraw
 from transformers import PreTrainedTokenizer, AddedToken
+from transformers.utils import try_to_load_from_cache
 
 import matplotlib.pyplot as plt
 import matplotlib.colors as mcolors
@@ -26,7 +27,7 @@ from matplotlib.font_manager import FontProperties
 logger = logging.getLogger(__name__)
 
 
-VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
+VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken", "ttf": "SimSun.ttf"}
 
 PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
 ENDOFTEXT = "<|endoftext|>"
@@ -410,20 +411,16 @@ class QWenTokenizer(PreTrainedTokenizer):
         if image is None:
             return None
         if image.startswith("http://") or image.startswith("https://"):
-            image = Image.open(requests.get(image, stream=True).raw)
+            image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
+            h, w = image.height, image.width
         else:
-            # image = Image.open(image)
             image = plt.imread(image)
-        # h, w = image.height, image.width
-        # image = image.convert("RGB")
-        h, w = image.shape[0], image.shape[1]
+            h, w = image.shape[0], image.shape[1]
         visualizer = Visualizer(image)
 
         boxes = self._fetch_all_box_with_ref(response)
         if not boxes:
             return None
-        # fnt = ImageFont.truetype("SimSun.ttf", 50)
-        # draw = ImageDraw.Draw(image)
         color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()]) # init color
         for box in boxes:
             if 'ref' in box: # random new color for new refexps
@@ -496,6 +493,7 @@ class VisImage:
 class Visualizer:
     def __init__(self, img_rgb, metadata=None, scale=1.0):
         self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
+        self.font_path = try_to_load_from_cache("Qwen/Qwen-VL-Chat", "SimSun.ttf")
         self.output = VisImage(self.img, scale=scale)
         self.cpu_device = torch.device("cpu")
 
@@ -527,7 +525,7 @@ class Visualizer:
             y,
             text,
             size=font_size * self.output.scale,
-            fontproperties=FontProperties(fname=r"SimSun.ttf"),
+            fontproperties=FontProperties(fname=self.font_path),
             bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
             verticalalignment="top",
             horizontalalignment=horizontal_alignment,