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<a href="_r_e_a_d_m_e_8md.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <br></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <div align="center"></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>  <img src="Arm_NN_horizontal_blue.png" class="center" alt="Arm NN Logo" width="300"/></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> </div></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> * [Quick Start Guides](#quick-start-guides)</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> * [Pre-Built Binaries](#pre-built-binaries)</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> * [Software Overview](#software-overview)</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> * [Get Involved](#get-involved)</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> * [Contributions](#contributions)</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> * [Disclaimer](#disclaimer)</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> * [License](#license)</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> * [Third-Party](#third-party)</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> * [Build Flags](#build-flags)</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> ## Announcement: As part of Arm's commitment to the use of inclusive language we will be moving away from 'master' branch to 'main' soon.</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> ## From 15 August 2022 our 'master' branch will be frozen and we will be using 'main' branch instead.</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> # Arm NN</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> **_Arm NN_** is the **most performant** machine learning (ML) inference engine for Android and Linux, accelerating ML</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> on **Arm Cortex-A CPUs and Arm Mali GPUs**. This ML inference engine is an open source SDK which bridges the gap</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> between existing neural network frameworks and power-efficient Arm IP.</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> Arm NN outperforms generic ML libraries due to **Arm architecture-specific optimizations** (e.g. SVE2) by utilizing</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> **[Arm Compute Library (ACL)](https://github.com/ARM-software/ComputeLibrary/)**. To target Arm Ethos-N NPUs, Arm NN</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> utilizes the [Ethos-N NPU Driver](https://github.com/ARM-software/ethos-n-driver-stack). For Arm Cortex-M acceleration,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> please see [CMSIS-NN](https://github.com/ARM-software/CMSIS_5).</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> Arm NN is written using portable **C++14** and built using [CMake](https://cmake.org/) - enabling builds for a wide</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> variety of target platforms, from a wide variety of host environments. **Python** developers can interface with Arm NN</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> through the use of our **Arm NN TF Lite Delegate**.</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> ## Quick Start Guides</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> **The Arm NN TF Lite Delegate provides the widest ML operator support in Arm NN** and is an easy way to accelerate</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> your ML model. To start using the TF Lite Delegate, first download the **[Pre-Built Binaries](#pre-built-binaries)** for</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> the latest release of Arm NN. Using a Python interpreter, you can load your TF Lite model into the Arm NN TF Lite</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> Delegate and run accelerated inference. Please see this</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> **[Quick Start Guide](delegate/DelegateQuickStartGuide.md)** on GitHub or this more comprehensive</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> **[Arm Developer Guide](https://developer.arm.com/documentation/102561/latest/)** for information on how to accelerate</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> your TF Lite model using the Arm NN TF Lite Delegate.</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> The fastest way to integrate Arm NN into an **Android app** is by using our **Arm NN AAR (Android Archive) file with</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> Android Studio**. The AAR file nicely packages up the Arm NN TF Lite Delegate, Arm NN itself and ACL; ready to be</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> integrated into your Android ML application. Using the AAR allows you to benefit from the **vast operator support** of</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> the Arm NN TF Lite Delegate. We held an **[Arm AI Tech Talk](https://www.youtube.com/watch?v=Zu4v0nqq2FA)** on how to</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> accelerate an ML Image Segmentation app in 5 minutes using this AAR file, with the supporting guide</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> **[here](https://developer.arm.com/documentation/102744/latest)**. To download the Arm NN AAR file, please see the</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> **[Pre-Built Binaries](#pre-built-binaries)** section below.</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> We also provide Debian packages for Arm NN, which are a quick way to start using Arm NN and the TF Lite Parser</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> (albeit with less ML operator support than the TF Lite Delegate). There is an installation guide available</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> [here](InstallationViaAptRepository.md) which provides instructions on how to install the Arm NN Core and the TF Lite</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> Parser for Ubuntu 20.04.</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> To build Arm NN from scratch, we provide the **[Arm NN Build Tool](build-tool/README.md)**. This tool consists of</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> **parameterized bash scripts** accompanied by a **Dockerfile** for building Arm NN and its dependencies, including</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> **[Arm Compute Library (ACL)](https://github.com/ARM-software/ComputeLibrary/)**. This tool replaces/supersedes the</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> majority of the existing Arm NN build guides as a user-friendly way to build Arm NN. The main benefit of building</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> Arm NN from scratch is the ability to **exactly choose which components to build, targeted for your ML project**.<br></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> ## Pre-Built Binaries</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> | Operating System | Architecture-specific Release Archive (Download) |</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> |-----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> | Android (AAR) | [![](https://img.shields.io/badge/download-android--aar-orange)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmnnDelegate-release.aar) |</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> | Android 8.1 "O/Oreo" (API level 27) | [![](https://img.shields.io/badge/download-arm64--v8.2a-blue)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-android-27-arm64-v8.2-a.tar.gz) [![](https://img.shields.io/badge/download-arm64--v8a-red)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-android-27-arm64-v8a.tar.gz) |</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> | Android 9 "P/Pie" (API level 28) | [![](https://img.shields.io/badge/download-arm64--v8.2a-blue)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-android-28-arm64-v8.2-a.tar.gz) [![](https://img.shields.io/badge/download-arm64--v8a-red)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-android-28-arm64-v8a.tar.gz) |</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> | Android 10 "Q/Quince Tart" (API level 29) | [![](https://img.shields.io/badge/download-arm64--v8.2a-blue)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-android-29-arm64-v8.2-a.tar.gz) [![](https://img.shields.io/badge/download-arm64--v8a-red)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-android-29-arm64-v8a.tar.gz) |</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> | Linux | [![](https://img.shields.io/badge/download-aarch64-green)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-linux-aarch64.tar.gz) [![](https://img.shields.io/badge/download-x86__64-yellow)](https://github.com/ARM-software/armnn/releases/download/v22.08/ArmNN-linux-x86_64.tar.gz) |</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> ## Software Overview</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> The Arm NN SDK supports ML models in **TensorFlow Lite** (TF Lite) and **ONNX** formats.</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> **Arm NN's TF Lite Delegate** accelerates TF Lite models through **Python or C++ APIs**. Supported TF Lite operators</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> are accelerated by Arm NN and any unsupported operators are delegated (fallback) to the reference TF Lite runtime -</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> ensuring extensive ML operator support. **The recommended way to use Arm NN is to</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> [convert your model to TF Lite format](https://www.tensorflow.org/lite/convert) and use the TF Lite Delegate.** Please</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> refer to the [Quick Start Guides](#quick-start-guides) for more information on how to use the TF Lite Delegate.</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> Arm NN also provides **TF Lite and ONNX parsers** which are C++ libraries for integrating TF Lite or ONNX models</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> into your ML application. Please note that these parsers do not provide extensive ML operator coverage as compared</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> to the Arm NN TF Lite Delegate.</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> **Android** ML application developers have a number of options for using Arm NN:</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> * Use our Arm NN AAR (Android Archive) file with **Android Studio** as described in the</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> [Quick Start Guides](#quick-start-guides) section</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> * Download and use our [Pre-Built Binaries](#pre-built-binaries) for the Android platform</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> * Build Arm NN from scratch with the Android NDK using this [GitHub guide](BuildGuideAndroidNDK.md)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> Arm also provides an [Android-NN-Driver](https://github.com/ARM-software/android-nn-driver) which implements a</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> hardware abstraction layer (HAL) for the Android NNAPI. When the Android NN Driver is integrated on an Android device,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> ML models used in Android applications will automatically be accelerated by Arm NN.</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> For more information about the Arm NN components, please refer to our</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> [documentation](https://github.com/ARM-software/armnn/wiki/Documentation).</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> Arm NN is a key component of the [machine learning platform](https://mlplatform.org/), which is part of the</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> [Linaro Machine Intelligence Initiative](https://www.linaro.org/news/linaro-announces-launch-of-machine-intelligence-initiative/).</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> For FAQs and troubleshooting advice, see the [FAQ](docs/FAQ.md) or take a look at previous</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> [GitHub Issues](https://github.com/ARM-software/armnn/issues).</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> ## Get Involved</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> The best way to get involved is by using our software. If you need help or encounter an issue, please raise it as a</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> [GitHub Issue](https://github.com/ARM-software/armnn/issues). Feel free to have a look at any of our open issues too.</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> We also welcome feedback on our documentation.</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> Feature requests without a volunteer to implement them are closed, but have the 'Help wanted' label, these can be</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> found [here](https://github.com/ARM-software/armnn/issues?q=is%3Aissue+label%3A%22Help+wanted%22+).</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> Once you find a suitable Issue, feel free to re-open it and add a comment, so that Arm NN engineers know you are</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> working on it and can help.</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> When the feature is implemented the 'Help wanted' label will be removed.</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> ## Contributions</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> The Arm NN project welcomes contributions. For more details on contributing to Arm NN please see the</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> [Contributing page](https://mlplatform.org/contributing/) on the [MLPlatform.org](https://mlplatform.org/) website,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> or see the [Contributor Guide](CONTRIBUTING.md).</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> Particularly if you'd like to implement your own backend next to our CPU, GPU and NPU backends there are guides for</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> backend development: [Backend development guide](src/backends/README.md),</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> [Dynamic backend development guide](src/dynamic/README.md).</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> ## Disclaimer</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> The armnn/tests directory contains tests used during Arm NN development. Many of them depend on third-party IP, model</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> protobufs and image files not distributed with Arm NN. The dependencies for some tests are available freely on</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> the Internet, for those who wish to experiment, but they won't run out of the box.</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> ## License</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> Arm NN is provided under the [MIT](https://spdx.org/licenses/MIT.html) license.</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> See [LICENSE](LICENSE) for more information. Contributions to this project are accepted under the same license.</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> Individual files contain the following tag instead of the full license text.</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  SPDX-License-Identifier: MIT</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> This enables machine processing of license information based on the SPDX License Identifiers that are available</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> here: http://spdx.org/licenses/</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> ## Third-party</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> Third party tools used by Arm NN:</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> | Tool | License (SPDX ID) | Description | Version | Provenience</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> |----------------|-------------------|------------------------------------------------------------------|-------------|-------------------</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> | cxxopts | MIT | A lightweight C++ option parser library | SHA 12e496da3d486b87fa9df43edea65232ed852510 | https://github.com/jarro2783/cxxopts</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> | doctest | MIT | Header-only C++ testing framework | 2.4.6 | https://github.com/onqtam/doctest</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> | fmt | MIT | {fmt} is an open-source formatting library providing a fast and safe alternative to C stdio and C++ iostreams. | 7.0.1 | https://github.com/fmtlib/fmt</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> | ghc | MIT | A header-only single-file std::filesystem compatible helper library | 1.3.2 | https://github.com/gulrak/filesystem</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> | half | MIT | IEEE 754 conformant 16-bit half-precision floating point library | 1.12.0 | http://half.sourceforge.net</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> | mapbox/variant | BSD | A header-only alternative to 'boost::variant' | 1.1.3 | https://github.com/mapbox/variant</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> | stb | MIT | Image loader, resize and writer | 2.16 | https://github.com/nothings/stb</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> ## Build Flags</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> Arm NN uses the following security related build flags in their code:</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> | Build flags |</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> |---------------------|</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> | -Wall |</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> | -Wextra |</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> | -Wold-style-cast |</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> | -Wno-missing-braces |</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> | -Wconversion |</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> | -Wsign-conversion |</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> | -Werror |</div></div><!-- fragment --></div><!-- contents -->
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