Update parquet files (step 64 of 249)

spaces/101-5/gpt4free/g4f/.v1/testing/gptworldai_test.py

@@ -1,18 +0,0 @@
-import gptworldAi
-
-# single completion
-for chunk in gptworldAi.Completion.create("你是谁", "127.0.0.1:7890"):
-    print(chunk, end="", flush=True)
-print()
-
-# chat completion
-message = []
-while True:
-    prompt = input("请输入问题：")
-    message.append({"role": "user", "content": prompt})
-    text = ""
-    for chunk in gptworldAi.ChatCompletion.create(message, '127.0.0.1:7890'):
-        text = text + chunk
-        print(chunk, end="", flush=True)
-    print()
-    message.append({"role": "assistant", "content": text})

spaces/1368565466ki/Satdia/monotonic_align/__init__.py

@@ -1,20 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba optimized version.
-    neg_cent: [b, t_t, t_s]
-    mask: [b, t_t, t_s]
-    """
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Chaos Group V-Ray Next ADV v4.30.01 for 3ds Max 2013-2020 Win x64 Free Trial and Discount Offers.md

@@ -1,170 +0,0 @@
-
-<h1>Chaos Group V-Ray Next ADV v4.30.01 for 3ds Max 2013-2020 Win x64</h1>
- <h2>Introduction</h2>
- <p>If you are a 3D artist, designer, or animator who uses Autodesk 3ds Max, you probably know how important it is to have a powerful and reliable rendering plugin. Rendering is the process of turning your 3D models and scenes into realistic images or animations that can be used for various purposes, such as presentations, marketing, entertainment, or education.</p>
-<h2>Chaos Group V-Ray Next ADV v4.30.01 for 3ds Max 2013-2020 Win x64</h2><br /><p><b><b>Download</b> &mdash; <a href="https://byltly.com/2uKxXJ">https://byltly.com/2uKxXJ</a></b></p><br /><br />
- <p>One of the most popular and widely used rendering plugins for 3ds Max is V-Ray, developed by Chaos Group. V-Ray is a versatile and flexible tool that can handle any type of project, from architectural visualization to visual effects. V-Ray has been used by many professionals and studios around the world, such as Digital Domain, Blur Studio, Method Studios, Framestore, and more.</p>
- <p>In this article, we will introduce you to the latest version of V-Ray for 3ds Max, which is V-Ray Next ADV v4.30.01. We will show you how to install it, how to use it, and how to optimize it for your workflow and projects. We will also highlight some of the new features and improvements that make V-Ray Next faster, smarter, and more powerful than ever before.</p>
- <h2>How to install V-Ray Next for 3ds Max?</h2>
- <h3>System requirements</h3>
- <p>Before you install V-Ray Next for 3ds Max, you need to make sure that your system meets the minimum requirements for running it smoothly. Here are the system requirements for V-Ray Next:</p>
- <table>
-<tr>
-<th>Component</th>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>Processor</td>
-<td>Intel® Pentium® IV or compatible processor with SSE4.2 support</td>
-<td>Intel® Core® i7 or higher</td>
-</tr>
-<tr>
-<td>RAM</td>
-<td>4 GB RAM (8 GB recommended)</td>
-<td>16 GB RAM or higher</td>
-</tr>
-<tr>
-<td>Operating system</td>
-<td>Windows® 7 SP1 (64-bit), Windows® 8.1 (64-bit), Windows® 10 (64-bit)</td>
-<td>Windows® 10 (64-bit)</td>
-</tr>
-<tr>
-<td>Graphics card</td>
-<td>NVIDIA® GeForce® GTX 1060 (6 GB VRAM) or equivalent</td>
-<td>NVIDIA® GeForce® RTX 2080 Ti (11 GB VRAM) or higher</td>
-</tr>
-<tr>
-<td>Hard disk space</td>
-<td>2 GB free disk space (10 GB recommended)</td>
-<td>20 GB free disk space or higher</td>
-</tr>
-<tr>
-<td>Software</td>
-<td>Autodesk® 3ds Max® versions 2013-2020 (64-bit)</td>
-<td>The latest version of Autodesk® 3ds Max® (64-bit)</td>
-</tr>
-</table>
- <p>Note that these are the requirements for running V-Ray Next on a single machine. If you want to use distributed rendering or network rendering, you will need additional hardware and software components. You can find more information about distributed rendering <a href="https://docs.chaosgroup.com/display/VRAYMAX/Distributed+Rendering">here</a>.</p>
- <h3>Installation steps</h3>
- <p>To install V-Ray Next for 3ds Max, you need to follow these steps:</p>
- <ol>
-<li>Download the installer from the <a href="https://www.chaos.com/vray/try-vray-3ds-max">official website</a>. You will need to register or log in with your Chaos account to access the download link.</li>
-<li>Run the installer as administrator and follow the instructions on the screen. You will need to accept the license agreement, choose the installation type (workstation or render slave), select the components you want to install (V-Ray core files, license server, Swarm manager, etc.), and specify the installation folder.</li>
-<li>If you have a previous version of V-Ray installed on your machine, you will be asked if you want to uninstall it or keep it. It is recommended that you uninstall any previous versions of V-Ray before installing V-Ray Next.</li>
-<li>If you have chosen to install the license server component, you will need to activate your license online or offline. You can find more information about licensing <a href="https://docs.chaosgroup.com/display/VRAYMAX/Licensing+V-Ray">here</a>.</li>
-<li>After the installation is complete, you can launch 3ds Max and start using V-Ray Next.</li>
-</ol>
- <h2>How to use V-Ray Next for 3ds Max?</h2>
- <h3>V-Ray Next interface and settings</h3>
- <p>V-Ray Next integrates seamlessly with 3ds Max and adds several new menus, toolbars, panels, and windows to its interface. Here are some of the main elements of the V-Ray interface:</p>
- <ul>
-<li>The <strong>V-Ray menu</strong>, located in the main menu bar of 3ds Max, gives you access to various commands and options related to V-Ray.</li>
-<li>The <strong>VFB window</strong>, which stands for Virtual Frame Buffer, is where you can see your rendered image or animation and adjust its parameters using various tools and controls.</li>
-<li>The <strong>VFB toolbar</strong>, located at the top of the VFB window, contains buttons for rendering modes, camera settings, color corrections, denoising options, history settings, etc.</li>
-<li>The <strong>VFB history window</strong>, located at the bottom of the VFB window, allows you to compare different versions of your rendered image or animation using thumbnails.</li>
-<li>The <strong>VFB color corrections window</strong>, located at the right side of the VFB window, lets you apply various color adjustments to your rendered image or animation using sliders.</li>
-<li>The <strong>VFB render elements window</strong>, located at the left side of the VFB window, shows you different layers or channels of your rendered image or animation that can be used for compositing or post-processing.</li>
-<li>The <strong>VFB statistics window</strong>, located at the top right corner of the VFB window, displays information about your rendering process such as time elapsed, memory usage, samples per pixel, etc.</li>
-<li>The <strong>VFB lens effects window</strong>, located at the bottom right corner of the VFB window, enables you to add various optical effects to your rendered image or animation such as glare, bloom, vignette, etc.</li>
-<li>The <strong>VFB settings window</strong>, accessible by clicking on the gear icon in the VFB toolbar, allows you to customize various aspects of the VFB such as resolution, quality, format, output, etc.</li></ul><ul><li>The <strong>V-Ray toolbar </strong>, located in any viewport toolbar area of 3ds Max, contains buttons for quick access to common functions such as rendering, interactive rendering, lighting analysis, camera exposure, etc.</li><li>The <strong>V-Ray Asset Editor </strong>, accessible by clicking on its icon in the main toolbar or in any viewport toolbar area of 3ds Max, is where you can manage all your assets related to V-Ray such as materials, lights, textures, geometries, render elements, etc.</li><li>The <strong>V-Ray Render Settings </strong>, accessible by clicking on its icon in any viewport toolbar area of 3ds Max or by going to Rendering > Render Setup, is where you can adjust all your global settings related to rendering such as engine type, quality presets, sampling parameters, environment options, output options, <h3>V-Ray Next rendering modes and options</h3>
- <p>V-Ray Next offers you different rendering modes and options depending on your needs and preferences. You can choose between:</p>
- <ul>
-<li><strong>Production rendering</strong>, which is the standard mode for creating high-quality images or animations with all the features and settings available in V-Ray.</li>
-<li><strong>Interactive rendering</strong>, which is a fast and responsive mode that updates your image as you make changes to your scene, camera, lights, materials, etc. This mode is ideal for testing and previewing your scene before production rendering.</li>
-<li><strong>GPU rendering</strong>, which is a mode that uses your graphics card (GPU) instead of your processor (CPU) to render your scene. This mode can be faster and more efficient for certain types of scenes and effects, such as volumetrics, denoising, etc.</li>
-<li><strong>Hybrid rendering</strong>, which is a mode that combines both CPU and GPU rendering to utilize all your hardware resources and speed up your rendering process.</li>
-</ul>
- <p>You can switch between these modes and options in the V-Ray Render Settings window, under the Renderer rollout. You can also adjust various parameters related to sampling, ray tracing, global illumination, motion blur, depth of field, etc.</p>
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- <h3>V-Ray Next lighting and materials</h3>
- <p>V-Ray Next provides you with a wide range of lighting and material options to create realistic and stunning scenes. You can use:</p>
- <ul>
-<li><strong>V-Ray lights</strong>, which are special types of lights that are optimized for V-Ray rendering. You can create different kinds of V-Ray lights such as dome light, sun light, sky light, sphere light, rectangle light, mesh light, etc.</li>
-<li><strong>V-Ray materials</strong>, which are special types of materials that are optimized for V-Ray rendering. You can create different kinds of V-Ray materials such as standard material, blend material, car paint material, hair material, subsurface scattering material, etc.</li>
-<li><strong>V-Ray textures</strong>, which are special types of textures that are optimized for V-Ray rendering. You can use different kinds of V-Ray textures such as bitmap texture, noise texture, gradient texture, dirt texture, curvature texture, etc.</li>
-<li><strong>V-Ray shaders</strong>, which are special types of nodes that can be used to create custom effects and functions for your materials and textures. You can use different kinds of V-Ray shaders such as color correction shader, falloff shader, fresnel shader, triplanar shader, etc.</li>
-</ul>
- <p>You can manage all your lighting and material assets in the V-Ray Asset Editor window, where you can create, edit, assign, organize, and preview them. You can also import and export assets from external sources such as Substance Designer or PBR materials.</p>
- <h3>V-Ray Next effects and post-processing</h3>
- <p>V-Ray Next allows you to add various effects and post-processing adjustments to your rendered image or animation without leaving 3ds Max or using external applications. You can use:</p>
- <ul>
-<li><strong>V-Ray render elements</strong>, which are separate layers or channels of your rendered image or animation that can be used for compositing or post-processing. You can create different kinds of V-Ray render elements such as diffuse element, specular element, reflection element, refraction element, shadow element, lighting element, etc.</li><li><strong>V-Ray frame buffer tools</strong>, which are tools and controls that you can use to modify your rendered image or animation in the VFB window. You can use different kinds of VFB tools such as color corrections tool, lens effects tool, denoiser tool, history tool, statistics tool, etc.</li></ul><p>You can access all your render elements and frame buffer tools in the VFB window, where you can enable, disable, edit, save, load, and compare them. You can also export them to external applications such as Photoshop or Nuke.</p <h2>How to optimize V-Ray Next for 3ds Max?</h2>
- <h3>V-Ray Next scene intelligence</h3>
- <p>V-Ray Next introduces a new feature called scene intelligence that automatically analyzes your scene and optimizes your rendering settings accordingly. Scene intelligence includes:</p>
- <ul>
-<li><strong>Automatic exposure and white balance</strong>, which adjusts the camera exposure and color temperature based on the lighting conditions of your scene. This feature eliminates the need for manual tweaking and ensures a balanced and realistic image.</li>
-<li><strong>Adaptive dome light</strong>, which samples only the parts of the dome light that contribute to the illumination of your scene. This feature speeds up your rendering time by up to 7 times for scenes with image-based lighting.</li>
-<li><strong>Point-and-shoot camera</strong>, which sets the camera focus distance automatically based on where you click in the viewport. This feature simplifies the process of creating depth of field effects.</li>
-<li><strong>Automatic memory management</strong>, which optimizes the memory usage of your scene by dynamically loading and unloading assets as needed. This feature allows you to render large scenes with complex geometries and textures without running out of memory.</li>
-</ul>
- <p>You can enable or disable these features in the V-Ray Render Settings window, under the Camera rollout (for automatic exposure and white balance), Environment rollout (for adaptive dome light), Physical Camera rollout (for point-and-shoot camera), and System rollout (for automatic memory management).</p>
- <h3>V-Ray Next adaptive dome light</h3>
- <p>V-Ray Next introduces a new feature called adaptive dome light that automatically samples only the parts of the dome light that contribute to the illumination of your scene. This feature speeds up your rendering time by up to 7 times for scenes with image-based lighting.</p>
- <h3>V-Ray Next GPU rendering and denoising</h3>
- <p>V-Ray Next introduces a new feature called GPU rendering and denoising that allows you to use your graphics card (GPU) instead of your processor (CPU) to render your scene and remove noise from your image or animation. This feature can be faster and more efficient for certain types of scenes and effects, such as volumetrics, denoising, etc.</p>
- <p>GPU rendering is a mode that uses your graphics card (GPU) instead of your processor (CPU) to render your scene. This mode can be faster and more efficient for certain types of scenes and effects, such as volumetrics, denoising, etc. You can switch to GPU rendering in the V-Ray Render Settings window, under the Renderer rollout, by choosing CUDA or RTX as the engine type. You can also select which GPUs or CPUs you want to use for rendering.</p>
- <p>Denoising is a process that removes noise from your image or animation without losing detail or quality. Noise is a common problem in rendering, especially when using low sampling settings or complex lighting scenarios. V-Ray Next offers you different options for denoising, such as:</p>
- <ul>
-<li><strong>V-Ray Denoiser</strong>, which is a built-in denoiser that works on both CPU and GPU rendering modes. You can enable it in the V-Ray Render Settings window, under the V-Ray Denoiser rollout. You can also adjust various parameters related to quality, blend amount, radius, etc.</li>
-<li><strong>NVIDIA AI Denoiser</strong>, which is an external denoiser that works only on GPU rendering mode. You can enable it in the VFB window, by clicking on its icon in the VFB toolbar. This denoiser uses artificial intelligence to remove noise instantly and interactively.</li>
-<li><strong>Render element denoiser</strong>, which is a new feature that allows you to denoise individual render elements separately. You can enable it in the Render Elements tab of the Render Setup window, by checking the Denoise option for each render element. This feature ensures that the denoised render elements match the denoised beauty image.</li>
-</ul>
- <p>You can access all your denoising options and tools in the VFB window, where you can enable, disable, edit, save, load, and compare them. You can also export them to external applications such as Photoshop or Nuke.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have introduced you to V-Ray Next ADV v4.30.01 for 3ds Max 2013-2020 Win x64, which is the latest version of V-Ray for 3ds Max. We have shown you how to install it, how to use it, and how to optimize it for your workflow and projects. We have also highlighted some of the new features and improvements that make V-Ray Next faster, smarter, and more powerful than ever before.</p>
- <p>Some of the main features and improvements of V-Ray Next are:</p>
- <ul>
-<li><strong>Scene intelligence</strong>, which automatically analyzes your scene and optimizes your rendering settings accordingly.</li>
-<li><strong>Adaptive dome light</strong>, which samples only the parts of the dome light that contribute to the illumination of your scene.</li>
-<li><strong>GPU rendering and denoising</strong>, which allows you to use your graphics card (GPU) instead of your processor (CPU) to render your scene and remove noise from your image or animation.</li>
-<li><strong>Render element denoiser</strong>, which allows you to denoise individual render elements separately.</li>
-<li><strong>New lighting analysis tools</strong>, which make it easier to visualize a scene’s real-world illumination values in lux or footcandles.</li>
-<li><strong>New metalness material properties</strong>, which offer improved compatibility with Substance Designer and PBR materials.</li>
-</ul>
- <h3>Call to action and links</h3>
- <p>If you are interested in trying out V-Ray Next for 3ds Max yourself, you can download a free 30-day trial from the <a href="https://www.chaos.com/vray/try-vray-3ds-max">official website</a>. You will need to register or log in with your Chaos account to access the download link.</p>
- <p>If you want to learn more about V-Ray Next for 3ds Max, you can visit the following links:</p>
- <ul>
-<li><a href="https://docs.chaos.com/display/VMAX/V-Ray+Next+for+3ds+Max+Help+Home">V-Ray Next for 3ds Max Help Home</a>, which contains detailed documentation and tutorials on how to use V-Ray Next for 3ds Max.</li>
-<li><a href="https://www.chaos.com/blog/category/v-ray-for-3ds-max">V-Ray for 3ds Max Blog</a>, which features news, updates, tips, tricks, and showcases on V-Ray for 3ds Max.</li>
-<li><a href="https://www.chaos.com/vray/academy">V-Ray Academy</a>, which offers online courses and webinars on V-Ray for 3ds Max and other Chaos products.</li>
-<li><a href="https://forums.chaos.com/c/v-ray-for-3ds-max">V-Ray for 3ds Max Forum</a>, which is a place where you can ask questions, get answers, share feedback, and connect with other V-Ray users.</li></ul><p>We hope you enjoyed this article and found it useful. If you have any comments or suggestions, please let us know in the comments section below. Thank you for reading!</p <h2>FAQs</h2>
- <h4>What is V-Ray Next?</h4>
- <p>V-Ray Next is the latest version of V-Ray for 3ds Max, which is a powerful and versatile rendering plugin that can handle any type of project, from architectural visualization to visual effects.</p <h4>What are the main features of V-Ray Next?</h4>
- <p>Some of the main features of V-Ray Next are scene intelligence, adaptive dome light, GPU rendering and denoising, render element denoiser, new lighting analysis tools, and new metalness material properties.</p <h4>How to install V-Ray Next for 3ds Max?</h4>
- <p>To install V-Ray Next for 3ds Max, you need to download the installer from the official website, run it as administrator, follow the instructions on the screen, uninstall any previous versions of V-Ray, and activate your license online or offline.</p <h4>How to use V-Ray Next for 3ds Max?</h4>
- <p>To use V-Ray Next for 3ds Max, you need to switch the renderer to V-Ray GPU in the Render Setup window, access various commands and options from the V-Ray menu, manage your assets in the V-Ray Asset Editor window, adjust your settings in the V-Ray Render Settings window, and view your results in the VFB window.</p <h4>How to optimize V-Ray Next for 3ds Max?</h4>
- <p>To optimize V-Ray Next for 3ds Max, you need to enable or disable various features in the Render Setup window, such as automatic exposure and white balance, adaptive dome light, GPU rendering and denoising, and automatic memory management.</p </p> 0a6ba089eb<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Download Film Babuji Ek Ticket Bambai Love Full Movie !NEW!.md

@@ -1,18 +0,0 @@
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-Our understanding of mother culture has to do with a lot of work, sustained over many generations, of feeding children and supporting them emotionally, physically, intellectually, culturally, spiritually and psychologically and that is what is being lost. This film tries to understand the powerful culture of India, how it is integral to us and then what happens when there are no families and children left to support it. This piece of work is made in the hope that other directors, who work in film and television, will be inspired and will make works in this genre that respect that culture and make a small contribution to saving it.
-
-Ajantha Matiyar: So, there is this curious notion that you are trying to depict a dying culture. What is it that makes it dying?
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-Sarojini Rikhye: The urban-based Bengali culture that you see around you when you step out of the airport, the metro station or the bus stand -- that is not just about Bengali culture but that is an indicator of what is happening all over India. It is the culture of the housewife, of the woman who has no work and has to be ‘put in a position’ to have children, and we need to understand that this phenomenon is not a Bengali phenomenon. It is happening all over India. The culture of so many generations of women and children has been trying to support their families through a woman’s right to her own body. They have no land to get agricultural produce from, to sell. There is no daycare centre for the children, no schools for the children, no health centres, no spaces for the men to socialise and their entire value system is against women. As a result of this, the children are faced with a lot of physical and mental agony and the women are facing depression and other mental health issues.
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-AM: But, what is interesting is that, while many of us have heard about so many negative things that are happening in India, we do not know that this culture is facing a crisis.
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-SR: The focus should be on the culture of the society, not just on this one thing or another.
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spaces/1line/AutoGPT/autogpt/spinner.py

@@ -1,65 +0,0 @@
-"""A simple spinner module"""
-import itertools
-import sys
-import threading
-import time
-
-
-class Spinner:
-    """A simple spinner class"""
-
-    def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
-        """Initialize the spinner class
-
-        Args:
-            message (str): The message to display.
-            delay (float): The delay between each spinner update.
-        """
-        self.spinner = itertools.cycle(["-", "/", "|", "\\"])
-        self.delay = delay
-        self.message = message
-        self.running = False
-        self.spinner_thread = None
-
-    def spin(self) -> None:
-        """Spin the spinner"""
-        while self.running:
-            sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
-            sys.stdout.flush()
-            time.sleep(self.delay)
-            sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
-
-    def __enter__(self):
-        """Start the spinner"""
-        self.running = True
-        self.spinner_thread = threading.Thread(target=self.spin)
-        self.spinner_thread.start()
-
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
-        """Stop the spinner
-
-        Args:
-            exc_type (Exception): The exception type.
-            exc_value (Exception): The exception value.
-            exc_traceback (Exception): The exception traceback.
-        """
-        self.running = False
-        if self.spinner_thread is not None:
-            self.spinner_thread.join()
-        sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
-        sys.stdout.flush()
-
-    def update_message(self, new_message, delay=0.1):
-        """Update the spinner message
-        Args:
-            new_message (str): New message to display
-            delay: Delay in seconds before updating the message
-        """
-        time.sleep(delay)
-        sys.stdout.write(
-            f"\r{' ' * (len(self.message) + 2)}\r"
-        )  # Clear the current message
-        sys.stdout.flush()
-        self.message = new_message

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-<h1>Download Getting Over It PC Ocean of Games: A Guide to the Most Frustrating Game Ever</h1>
- <p>Have you ever played a game that made you want to smash your keyboard, throw your mouse, or scream at your monitor? If not, then you might want to try Getting Over It with Bennett Foddy, a game that is designed to hurt you. And if you are looking for a way to download this game for free, then you might be interested in Ocean of Games, a website that offers a variety of PC games for download. But before you do that, let's take a look at what Getting Over It is, why it is so frustrating, how to download it from Ocean of Games, and how to beat it.</p>
- <h2>What is Getting Over It?</h2>
- <p>Getting Over It with Bennett Foddy is an action game that was released in 2017 for Windows, Mac, iOS, and Android. It is developed by Bennett Foddy, an independent game designer and academic who is known for creating experimental and challenging games such as QWOP, GIRP, and CLOP.</p>
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- <h3>The premise and the gameplay</h3>
- <p>The premise of Getting Over It is simple: you are a man named Diogenes who is stuck in a cauldron and has a hammer as his only tool. Your goal is to climb up an enormous mountain made of various objects such as rocks, trees, furniture, pipes, and even other games. You move the hammer with the mouse or the touch screen, and that's all there is. There are no checkpoints, no saves, no levels, no scores, no achievements. Just you and the mountain.</p>
- <p>The gameplay of Getting Over It is deceptively simple as well. You can use the hammer to push yourself off the ground, hook onto objects, swing around them, or launch yourself into the air. With practice, you'll be able to jump, swing, climb, and fly. However, this is easier said than done. The game's physics are realistic but unforgiving. A slight mistake can send you tumbling down the mountain, losing all your progress in an instant. And there is nothing to stop you from falling all the way back to the beginning.</p>
- <h3>The developer and the inspiration</h3>
- <p>Bennett Foddy is an Australian-born game designer who is currently a professor at New York University's Game Center. He has a PhD in philosophy from Oxford University and has written several papers on topics such as ethics, aesthetics, and game design. He is also a musician who plays bass guitar for the band Cut Copy.</p>
- <p>Foddy has stated that he made Getting Over It as a tribute to Jazzuo's 2002 B-Game classic Sexy Hiking, which has a similar concept of climbing a mountain with a hammer. He also said that he wanted to make a game for a certain kind of person: someone who likes hard games, who likes frustration games, who likes roguelikes, who likes speedrunning. He wanted to make a game that would hurt them.</p>
- <h3>The reception and the reviews</h3>
- <p>Getting Over It has received mostly positive reviews from critics and players alike. It has a score of 77/100 on Metacritic and an \"Overwhelmingly Positive\" rating on Steam. Many reviewers praised the game's originality, humor, challenge, and satisfaction. They also appreciated the game's commentary by F <p>oddy, who provides insights, jokes, quotes, and encouragement throughout the game. Some reviewers also noted that the game can be seen as a metaphor for life, art, or game development itself.</p>
- <p>However, not everyone enjoyed Getting Over It. Some reviewers criticized the game's difficulty, frustration, and repetitiveness. They also complained about the game's lack of features, options, and accessibility. Some players also reported technical issues, bugs, and crashes. And of course, some players simply hated the game for making them rage quit.</p>
- <h2>Why is Getting Over It so frustrating?</h2>
- <p>Getting Over It is not a game for everyone. It is a game that tests your patience, skill, and sanity. It is a game that can make you feel angry, sad, hopeless, or even depressed. But why is it so frustrating? Here are some of the reasons:</p>
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- <h3>The controls and the physics</h3>
- <p>The controls of Getting Over It are simple but hard to master. You only need to move the mouse or the touch screen to control the hammer, but that's easier said than done. The hammer's movement is sensitive and precise, but also erratic and unpredictable. You need to have a good sense of timing, distance, and angle to move effectively. And you need to constantly adjust your grip and position to avoid losing balance or momentum.</p>
- <p>The physics of Getting Over It are realistic but unforgiving. The game simulates gravity, friction, inertia, and collision in a realistic way, but that also means that the slightest mistake can have disastrous consequences. You can slip, slide, bounce, or fly off the mountain at any moment. And you can't rely on any safety nets or checkpoints to save you. You have to deal with the consequences of your actions.</p>
- <h3>The obstacles and the setbacks</h3>
- <p>The obstacles of Getting Over It are varied and challenging. The mountain is made of different objects that have different shapes, sizes, textures, and properties. Some objects are solid and stable, while others are slippery and movable. Some objects are helpful and supportive, while others are harmful and obstructive. Some objects are familiar and recognizable, while others are bizarre and surreal. You never know what to expect next.</p>
- <p>The setbacks of Getting Over It are frequent and painful. The mountain is full of traps and pitfalls that can send you back to where you started or even lower. You can fall from great heights or get stuck in narrow spaces. You can lose hours or days of progress in seconds or minutes. And you have to start over again and again until you reach the top.</p>
- <h3>The narration and the commentary</h3>
- <p>The narration of Getting Over It is witty but cruel. The game features a voice-over by Bennett Foddy himself, who talks to you throughout the game. He tells you about the history and the design of the game, he quotes from various philosophers and artists, he jokes about your situation and your failures, he encourages you to keep going and to not give up. He also sometimes apologizes for making such a hard game.</p>
- <p>The commentary of Getting Over It is informative but taunting. The game also features a chat box that shows messages from other players who are playing the game at the same time as you. They can share their thoughts, feelings, tips, or jokes with you. They can also see your progress and your falls on their screens. They can cheer you on or mock you mercilessly.</p>
- <h2>How to download Getting Over It PC Ocean of Games?</h2>
- <p>If you want to play Getting Over It on your PC for free, then you might want to check out Ocean of Games, a website that offers a variety of PC games for download. However, before you do that, you should be aware of some of the advantages and disadvantages of using this website.</p>
- <h3>The advantages and disadvantages of Ocean of Games</h3>
- <p>Ocean of Games has some advantages over other websites that offer PC games for download. Some of these advantages are:</p>
- <ul>
-<li>It has a large collection of games from different genres and categories.</li>
-<li>It has an easy-to-use interface and a fast download speed.</li>
-<li>It does not require any registration or subscription.</li>
-<li>It does not have any annoying ads or pop-ups.</li>
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- <p>However, Ocean of Games also has some disadvantages that you should be aware of before using it. Some of these disadvantages are:</p>
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-<li>It does not have any official license or authorization from the game developers or publishers.</li>
-<li>It does not guarantee the quality or the safety of the games it offers.</li>
-<li>It may contain viruses, malware, spyware, or other harmful software that can damage your PC or compromise your privacy.</li>
-<li>It may violate <p>It may violate the intellectual property rights of the game developers or publishers.</li>
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- <p>Therefore, you should use Ocean of Games at your own risk and discretion. You should also respect the rights and the work of the game developers or publishers and consider buying the game from official sources if you enjoy it.</p>
- <h3>The steps to download and install Getting Over It from Ocean of Games</h3>
- <p>If you still want to download Getting Over It PC Ocean of Games, then you can follow these steps:</p>
- <ol>
-<li>Go to the Ocean of Games website and search for Getting Over It in the search box.</li>
-<li>Select the game from the list of results and click on the download button.</li>
-<li>Wait for the download to finish and then extract the zip file to a folder of your choice.</li>
-<li>Open the folder and run the setup.exe file as an administrator.</li>
-<li>Follow the instructions on the screen to install the game on your PC.</li>
-<li>Launch the game from the desktop shortcut or the start menu and enjoy.</li>
-</ol>
- <h3>The alternatives to Ocean of Games</h3>
- <p>If you are looking for other websites that offer PC games for download, then you might want to check out some of these alternatives:</p>
- <ul>
-<li>Steam: Steam is the most popular and reputable platform for buying and playing PC games. It has a huge library of games from various genres and categories, as well as features such as cloud saving, achievements, multiplayer, mods, and more. You can also find some free or discounted games on Steam, especially during sales or events.</li>
-<li>GOG: GOG is another platform that sells and distributes PC games. It specializes in DRM-free games, meaning that you can play them without any online activation or restriction. It also offers some classic and retro games that are compatible with modern systems.</li>
-<li>itch.io: itch.io is a website that hosts indie games from various developers and creators. You can find some unique and original games on itch.io, as well as some free or pay-what-you-want games. You can also support the developers directly by buying or donating to their games.</li>
-</ul>
- <h2>How to beat Getting Over It?</h2>
- <p>Getting Over It is a game that is hard to beat, but not impossible. It requires a lot of practice, patience, and perseverance. It also requires some tips, tricks, and strategies. Here are some of them:</p>
- <h3>The tips and tricks to master the game</h3>
- <p>Here are some tips and tricks that can help you master the game:</p>
- <ul>
-<li>Learn how to use the hammer effectively. You can use it to push, pull, hook, swing, launch, or balance yourself. Experiment with different movements and angles to find what works best for you.</li>
-<li>Use both hands to control the mouse or the touch screen. This can give you more precision and stability when moving the hammer.</li>
-<li>Adjust your mouse sensitivity or touch sensitivity according to your preference. You can do this in the settings menu of the game. You can also adjust your screen resolution or window size to fit your monitor or device.</li>
-<li>Take breaks regularly. Getting Over It can be mentally and physically exhausting. You should take breaks every 15 minutes or so to relax your eyes, hands, and mind. You can also save your progress by quitting the game and resuming it later.</li>
-<li>Don't give up. Getting Over It is a game that is meant to challenge you and make you frustrated. But it is also a game that can reward you with satisfaction and accomplishment. Don't let your failures discourage you. Learn from them and try again.</li>
-</ul>
- <h3>The speedruns and the records</h3>
- <p>If you want to challenge yourself further, you can try to beat Getting Over It as fast as possible. This is called speedrunning, and it is a popular activity among gamers who like to compete with themselves or others. There are many websites and communities that track and showcase speedruns of various games, such as speedrun.com or Speed Demos Archive. You can also watch some videos of speedruns on YouTube or Twitch.</p>
- <p>The current world record for beating Getting Over It is 1 minute 19 seconds by a player named Lockness06. He achieved this feat on June 14th, 2021 using a mouse and keyboard. The previous record was 1 minute 24 seconds by a player named Distortion2. He achieved this feat on May 31st, 2021 using a controller.</p>
- <h3>The rewards and the <h3>The rewards and the secrets</h3>
- <p>If you manage to beat Getting Over It, you will be rewarded with a special ending that includes a song, a message, and a surprise. We won't spoil it for you, but we can tell you that it is worth the effort. You will also unlock a golden cauldron that you can use to play the game again with a different look.</p>
- <p>Getting Over It also has some secrets and easter eggs that you can discover along the way. Some of them are hidden in the mountain, some of them are triggered by certain actions, and some of them are revealed by the narrator. We won't tell you what they are, but we can give you some hints:</p>
- <ul>
-<li>There is a secret island that you can reach by flying over the ocean.</li>
-<li>There is a secret room that you can enter by breaking a wall.</li>
-<li>There is a secret message that you can read by zooming in on a sign.</li>
-<li>There is a secret mode that you can activate by typing a code.</li>
-<li>There is a secret game that you can play by clicking on a button.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Getting Over It with Bennett Foddy is a game that is not for everyone. It is a game that is hard, frustrating, and sometimes unfair. But it is also a game that is original, humorous, and satisfying. It is a game that challenges you to overcome your limits and to get over it.</p>
- <p>If you want to play Getting Over It on your PC for free, you can download it from Ocean of Games, a website that offers a variety of PC games for download. However, you should be careful of the risks and the drawbacks of using this website. You should also respect the rights and the work of the game developers and publishers and consider buying the game from official sources if you enjoy it.</p>
- <p>If you want to beat Getting Over It, you will need a lot of practice, patience, and perseverance. You will also need some tips, tricks, and strategies. And you will also need to discover some secrets and easter eggs along the way. But most importantly, you will need to have fun and to not give up.</p>
- <p>We hope this guide has helped you to learn more about Getting Over It PC Ocean of Games. If you have any questions or comments, feel free to leave them below. And if you liked this article, please share it with your friends. Thank you for reading and happy climbing!</p>
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- <p>Here are some frequently asked questions about Getting Over It PC Ocean of Games:</p>
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-<li><b>Who is Diogenes?</b></li>
-<p>Diogenes is the name of the man who is stuck in a cauldron in Getting Over It. He is named after an ancient Greek philosopher who was known for living in a barrel and rejecting conventional values and norms. He was also known for his wit and his cynicism.</p>
-<li><b>Who is Bennett Foddy?</b></li>
-<p>Bennett Foddy is the developer and the narrator of Getting Over It. He is an independent game designer and an academic who is known for creating experimental and challenging games such as QWOP, GIRP, and CLOP. He is also a professor at New York University's Game Center and a musician who plays bass guitar for the band Cut Copy.</p>
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-<p>Ocean of Games is a website that offers a variety of PC games for download. It has a large collection of games from different genres and categories, as well as an easy-to-use interface and a fast download speed. However, it also has some disadvantages such as being unauthorized, unsafe, illegal, and unethical.</p>
-<li><b>How long does it take to beat Getting Over It?</b></li>
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spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate 1.5.2 Mod Apk - Drive Realistic Buses with Amazing Features.md

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-<h1>Bus Simulator Ultimate 1.5.2 Mod Apk: A Review</h1>
-<p>Do you love driving buses and exploring different cities? Do you want to experience the thrill of running your own bus company and competing with other players online? If yes, then you should try Bus Simulator Ultimate, one of the most popular and realistic bus simulator games for Android devices.</p>
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-<p>In this article, we will review Bus Simulator Ultimate and its latest version, 1.5.2 mod apk, which offers unlimited money, free purchases, and other benefits. We will also discuss the features, pros, and cons of this game, and answer some frequently asked questions about it.</p>
-<h2>What is Bus Simulator Ultimate?</h2>
-<p>Bus Simulator Ultimate is a simulation game developed by Zuuks Games, a Turkish game studio that specializes in creating realistic driving games. The game was released in August 2019 and has since gained over 100 million downloads and 4.3 stars rating on Google Play Store.</p>
-<p>Bus Simulator Ultimate lets you drive various types of buses across different countries and cities, such as Germany, Turkey, Italy, France, Spain, USA, Brazil, Russia, and more. You can also create your own routes and customize your buses with different skins, stickers, horns, and accessories.</p>
-<p>But driving buses is not the only thing you can do in this game. You can also establish your own bus company and hire drivers to work for you. You can manage your company's finances, reputation, customer satisfaction, and more. You can also compete with other players in multiplayer mode and online ranking system.</p>
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-<h3>Features of Bus Simulator Ultimate</h3>
-<p>Bus Simulator Ultimate has many features that make it stand out from other bus simulator games. Here are some of them:</p>
-<h4>Realistic bus driving experience</h4>
-<p>The game boasts realistic graphics, physics, sounds, and weather effects that make you feel like you are really driving a bus on the road. You can also choose from different camera angles, such as cockpit view, third-person view, or top-down view.</p>
-<p>The game also has realistic traffic rules and situations that you have to follow and deal with. You have to obey traffic lights, speed limits, signs, and signals. You have to avoid accidents, collisions, and fines. You have to deal with traffic jams, road works, accidents, and emergencies.</p>
-<h4>Multiplayer mode and online ranking</h4>
-<p>The game allows you to play with other players online in multiplayer mode. You can join or create a room and invite your friends or random players to join you. You can chat with them using voice or text messages. You can also see their buses and routes on the map.</p>
-<p>The game also has an online ranking system that shows your position among other players based on your performance, reputation, income, and more. You can compare your stats with other players and try to climb up the leaderboard.</p>
-<h4>Customizable buses and routes</h4>
-<p>The game offers a variety of buses that you can drive and customize. You can choose from different models, brands, sizes, colors, and designs of buses. You can also add different accessories and decorations to your buses, such as skins, stickers, horns, lights, and more. You can also change the interior of your buses, such as the seats, steering wheel, dashboard, and more.</p>
-<p>The game also lets you create your own routes and destinations. You can choose from different cities and countries to drive in. You can also set the length, difficulty, and scenery of your routes. You can also add different stops, landmarks, and attractions to your routes.</p>
-<h4>Passenger feedback and company management</h4>
-<p>The game also simulates the interaction between you and your passengers. You have to pick up and drop off passengers at the designated stops. You have to provide them with a comfortable and safe ride. You have to listen to their feedback and requests.</p>
-<p>The game also gives you the opportunity to run your own bus company. You have to hire and train drivers, buy and maintain buses, manage your budget and expenses, and expand your business. You have to balance your income and reputation. You have to deal with competitors, challenges, and events.</p>
-<h3>What is Bus Simulator Ultimate 1.5.2 Mod Apk?</h3>
-<p>Bus Simulator Ultimate 1.5.2 mod apk is a modified version of the original game that offers some extra features and benefits that are not available in the official version. Some of these features are:</p>
-<h4>Benefits of using the mod apk</h4>
-<ul>
-<li>Unlimited money: You can get unlimited money in the game that you can use to buy and upgrade buses, hire drivers, create routes, and more.</li>
-<li>Free purchases: You can make any purchase in the game for free without spending any real money.</li>
-<li>No ads: You can enjoy the game without any annoying ads interrupting your gameplay.</li>
-<li>No root: You do not need to root your device to use the mod apk.</li>
-</ul>
-<h4>How to download and install the mod apk</h4>
-<p>To download and install the mod apk, you need to follow these steps:</p>
-<ol>
-<li>Download the mod apk file from a trusted source on the internet.</li>
-<li>Enable unknown sources on your device settings to allow installation of apps from outside the Google Play Store.</li>
-<li>Locate the downloaded file on your device storage and tap on it to install it.</li>
-<li>Launch the game and enjoy the mod features.</li>
-</ol>
-<h3>Pros and cons of Bus Simulator Ultimate 1.5.2 Mod Apk</h3>
-<p>Like any other mod apk, Bus Simulator Ultimate 1.5.2 mod apk has its own advantages and disadvantages. Here are some of them:</p>
- | Pros | Cons | | --- | --- | | Unlimited money and free purchases | May not be compatible with some devices or versions | | No ads | May cause bugs or glitches in the game | | No root | May violate the terms and conditions of the game | | Enhanced gameplay | May affect the online features of the game | <h3>Conclusion</h3>
-<p>Bus Simulator Ultimate is a fun and realistic bus simulator game that lets you drive various buses across different countries and cities, create your own routes and destinations, run your own bus company, and compete with other players online. It has many features that make it one of the best bus simulator games for Android devices.</p>
-<p>Bus Simulator Ultimate 1.5.2 mod apk is a modified version of the original game that offers unlimited money, free purchases, no ads, and no root. It can enhance your gameplay experience by giving you more freedom and options in the game. However, it also has some drawbacks that you should be aware of before using it.</p>
-<p>If you are looking for a bus simulator game that is realistic, challenging, and entertaining, you should give Bus Simulator Ultimate a try. And if you want to get some extra benefits and features in the game, you can download and install Bus Simulator Ultimate 1.5.2 mod apk from a reliable source on the internet.</p>
-<h2>FAQs</h2>
-<h3>Q1: Is Bus Simulator Ultimate free to play?</h3>
-<p>A1: Yes, Bus Simulator Ultimate is free to download and play on Android devices. However, it also contains some in-app purchases that require real money.</p>
-<h3>Q2: Is Bus Simulator Ultimate 1.5.2 Mod Apk safe to use?</h3>
-<p>A2: Bus Simulator Ultimate 1.5.2 mod apk is generally safe to use if you download it from a trusted source on the internet. However, you should always be careful when installing apps from unknown sources as they may contain viruses or malware that can harm your device or data.</p>
-<h3>Q3: How to update Bus Simulator Ultimate 1.5.2 Mod Apk?</ <p>A3: To update Bus Simulator Ultimate 1.5.2 mod apk, you need to download the latest version of the mod apk file from the same source where you downloaded the previous version. Then, you need to uninstall the old version of the mod apk and install the new one. You may also need to clear the cache and data of the game before launching it.</p>
-<h3>Q4: What are the best bus simulator games for Android?</h3>
-<p>A4: Besides Bus Simulator Ultimate, there are many other bus simulator games that you can try on your Android device. Some of them are:</p>
-<ul>
-<li>Bus Simulator: Original: This game lets you drive realistic buses in various locations and scenarios. You can also customize your buses and routes, and play with other players online.</li>
-<li>World Bus Driving Simulator: This game lets you drive different types of buses across Brazil and other countries. You can also enjoy realistic graphics, sounds, and weather effects.</li>
-<li>Coach Bus Simulator: This game lets you drive modern coaches across Europe and other continents. You can also create your own bus company and hire drivers.</li>
-<li>Heavy Bus Simulator: This game lets you drive heavy buses on challenging roads and terrains. You can also upgrade your buses and change their appearance.</li>
-</ul>
-<h3>Q5: How to contact the developers of Bus Simulator Ultimate?</h3>
-<p>A5: If you have any questions, suggestions, or feedback about Bus Simulator Ultimate, you can contact the developers of the game by using the following methods:</p>
-<ul>
-<li>Email: [email protected]</li>
-<li>Website: https://www.zuuks.com/</li>
-<li>Facebook: https://www.facebook.com/zuuks.games</li>
-<li>Instagram: https://www.instagram.com/zuuks_games/</li>
-<li>Twitter: https://twitter.com/ZuuksGames</li>
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spaces/1phancelerku/anime-remove-background/Download Dr Driving 3 Mod APK and Learn Driving with Fun.md

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-<p>Do you love driving games but get bored of the same old racing and drifting scenarios? Do you want to experience the thrill of driving in a realistic city environment with traffic, pedestrians, and obstacles? Do you want to test your driving skills in various modes and missions, such as parking, delivery, taxi, and more? If you answered yes to any of these questions, then you should try Dr Driving 3, one of the best car simulation games on Android.</p>
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-<p>Dr Driving 3 is the third installment of the popular Dr Driving series, developed by SUD Inc. It is a driving simulator game that lets you drive various cars in a realistic city setting. You can choose from different modes and missions, such as parking, delivery, taxi, speed, fuel efficiency, and more. You can also compete with other players online and climb the leaderboards. You can customize your cars with different colors, wheels, spoilers, and upgrades. You can also earn coins and gold by completing missions and achievements.</p>
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-<p>Dr Driving 3 has a variety of cars that you can drive in the game. You can choose from sedans, hatchbacks, SUVs, sports cars, trucks, buses, and more. You can also customize your cars with different colors, wheels, spoilers, and upgrades. You can improve your car's performance by upgrading its engine, transmission, brakes, tires, suspension, and more.</p>
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-<h2>Conclusion</h2>
-<p>Dr Driving 3 is a fun and realistic driving simulator game that lets you drive various cars in a realistic city setting. You can choose from different modes and missions, such as parking, delivery, taxi, speed, fuel efficiency, and more. You can also compete with other players online and climb the leaderboards. You can customize your cars with different colors, wheels, spoilers, and upgrades.</p>
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-<p>Here are some frequently asked questions about Dr Driving 3 and Dr Driving 3 Mod APK:</p>
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-<td>Is Dr Driving 3 free to play?</td>
-<td>Yes, Dr Driving 3 is free to play, but it has in-app purchases and ads that can affect your gameplay.</td>
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-<td>Is Dr Driving 3 Mod APK safe to use?</td>
-<td>Yes, Dr Driving 3 Mod APK is safe to use, as long as you download it from a trusted source. However, you should always be careful when installing modded apps on your device and scan them for viruses or malware before installing them.</td>
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-<p>Congratulations! You have successfully installed Monster Life Mod APK Free Shopping on your device. You can now launch the app and enjoy the game with free shopping and other features.</p>
-<h2>How to play Monster Life Mod APK Free Shopping</h2>
-<p>Now that you have installed the app, you may be wondering how to play the game. Don't worry, we will guide you through the basics of the game and help you become a master monster trainer in no time.</p>
-<p>The game starts with a tutorial that introduces you to the story and the gameplay of Monster Life. You will learn that you are a young monster keeper who lives on the islands of Numa, a magical world where monsters and humans coexist peacefully. However, an evil force called Chaos is threatening to destroy Numa and its inhabitants, and you are the only one who can stop it.</p>
-<p>You will also learn that you have a special gift: you can communicate with monsters and understand their feelings. This makes you a perfect candidate for becoming a monster trainer and protector of Numa. Your adventure begins when you choose your starter monster from three options: Fire Lion, Ice Bear, or Leaf Turtle. You can also name your monster and customize its appearance with different colors and accessories.</p>
-<p>After choosing your starter monster, you will explore the islands of Numa and fight against Chaos and its minions. You will encounter different types of monsters, each with their own strengths and weaknesses. You will also collect and breed more monsters with different abilities and combinations. You can have up to six monsters in your team at a time, and you can switch them during battles.</p>
-<p>Battles in Monster Life are turn-based and simple to play. You just need to tap on your monster's icon to select it, then tap on the enemy's icon to attack it. You can also use items and skills to heal or boost your monsters, or to inflict damage or status effects on your enemies. The battle ends when you defeat all the enemies or when all your monsters are knocked out.</p>
-<p>As you win battles, your monsters will gain experience and level up. They will also learn new skills and evolve into stronger forms. You can train and customize your monsters with items and skills that you can buy or find in the game. You can also build habitats, farms, shops, and other facilities on your island to make it more comfortable and attractive for your monsters.</p>
-<p>But fighting against Chaos is not the only thing you can do in Monster Life. You can also challenge other players in online battles and tournaments. You can test your skills and strategies against real opponents from around the world, and earn rewards and rankings based on your performance. You can also chat with other players, visit their islands, and trade monsters with them.</p>
-<h2>What are the features of Monster Life Mod APK Free Shopping</h2>
-<p>Monster Life Mod APK Free Shopping is not just a regular version of Monster Life. It has some extra features that make it more fun and enjoyable to play. Here are some of the features that you can expect from this mod apk:</p>
-<ul>
-<li><strong>Unlimited coins and gems</strong>: Coins and gems are the main currencies in Monster Life. You need them to buy items, skills, habitats, decorations, and other things in the game. Normally, you can earn coins and gems by completing quests, winning battles, watching ads, or spending real money. But with this mod apk, you don't have to worry about running out of coins or gems ever again. You will have unlimited amounts of them to spend as you wish.</li>
-<li><strong>All monsters unlocked and available to breed</strong>: Monsters are the heart of Monster Life. There are over 100 different monsters in the game, each with their own characteristics, abilities, evolutions, and personalities. Normally, you can unlock new monsters by completing quests, winning battles, breeding existing monsters, or spending real money. But with this mod apk, you don't have to wait or pay for anything. You will have access to all the monsters in the game from the start, and you can breed them freely without any restrictions.</li>
-<li><strong>No ads or in-app purchases</strong>: Ads and in-app purchases are annoying features that can interrupt your gameplay or tempt you to spend more money than you want. Normally, you can watch ads to earn some extra coins or gems, or buy them with real money if you are impatient or desperate. But with this mod apk, you don't have to deal with any ads or in-app purchases at all. You will enjoy a smooth and uninterrupted gameplay without any distractions or temptations.</li>
-</ul> <h2>What are the pros and cons of Monster Life Mod APK Free Shopping</h2>
-<p>Monster Life Mod APK Free Shopping may sound like a perfect game for monster lovers, but it is not without its drawbacks. Like any mod apk, it has some advantages and disadvantages that you should consider before playing it. Here are some of the pros and cons of Monster Life Mod APK Free Shopping:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td><strong>Enjoy a cute and colorful graphics and animation</strong>: Monster Life has a charming and vibrant graphics and animation that will appeal to anyone who likes cute things. The monsters are adorable and expressive, the islands are lush and lively, and the battles are dynamic and exciting. The game also has a cheerful and upbeat soundtrack that matches the mood of the game.</td>
-<td><strong>The mod apk file may not be compatible with some devices or updates</strong>: Monster Life Mod APK Free Shopping is a modified version of the original game, which means that it may not work well with some devices or updates. The mod apk file may crash, freeze, or lag on some devices, or it may not run at all. The mod apk file may also become obsolete or incompatible with future updates of the original game, which may prevent you from playing the game or accessing new features.</td>
-</tr>
-<tr>
-<td><strong>Experience a fun and engaging gameplay with diverse monsters and activities</strong>: Monster Life has a lot of things to offer to keep you entertained and hooked. You can collect, breed, train, and battle with over 100 different monsters, each with their own abilities and personalities. You can also explore the islands of Numa and discover new places, quests, and secrets. You can also build and decorate your own island with various facilities and items. You can also interact with other players online and compete with them in battles and tournaments.</td>
-<td><strong>The mod apk file may contain viruses or malware that can harm your device or data</strong>: Monster Life Mod APK Free Shopping is not an official version of the game, which means that it may not be safe or secure to download or install. The mod apk file may contain viruses or malware that can infect your device or data, or steal your personal information. You should always scan the mod apk file with an antivirus software before installing it, and backup your data before playing it.</td>
-</tr>
-<tr>
-<td><strong>Share your monster collection and achievements with your friends on social media</strong>: Monster Life allows you to connect your game account with your Facebook account, which enables you to share your monster collection and achievements with your friends on social media. You can also invite your friends to play the game with you, or send them gifts and messages. You can also see your friends' islands and monsters, and compare your progress and rankings with them.</td>
-<td><strong>The mod apk file may violate the terms of service of the original game and result in a ban or suspension</strong>: Monster Life Mod APK Free Shopping is an unauthorized version of the game, which means that it may violate the terms of service of the original game. The terms of service prohibit modifying, hacking, cheating, or exploiting the game in any way. If you play the mod apk file, you may risk getting banned or suspended from the original game, or losing your account or data.</td>
-</tr>
-</table>
-<h2>Conclusion: Is Monster Life Mod APK Free Shopping worth it?</h2>
-<p>Monster Life Mod APK Free Shopping is a fun and addictive game for anyone who loves cute monsters. It has a lot of features that make it more enjoyable and convenient to play than the original game. However, it also has some risks and drawbacks that you should be aware of before playing it. Ultimately, the decision is up to you whether you want to try it or not.</p>
-<p>If you decide to play Monster Life Mod APK Free Shopping, we hope that this article has helped you understand how to download and install it, how to play it, what are its features, pros and cons, and whether it is worth it or not. We hope that you have a great time playing Monster Life Mod APK Free Shopping!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Monster Life Mod APK Free Shopping:</p>
-<ol>
-<li><strong>What is Monster Life?</strong></li>
-<p>Monster Life is a popular game by Gameloft that lets you collect, breed, train, and battle with cute monsters in a magical world called Numa.</p>
-<li><strong>What is Monster Life Mod APK Free Shopping?</strong></li>
-<p>Monster Life Mod APK Free Shopping is a modified version of Monster Life that gives you unlimited coins and gems to buy anything you want in the game, as well as access to all the monsters in the game.</p>
-<li><strong>Is Monster Life Mod APK Free Shopping safe and legal?</strong></li>
-<p>Monster Life Mod APK Free Shopping is not an official version of the game, which means that it may not be safe or legal to download or play. The mod apk file may contain viruses or malware that can harm your device or data, or it may violate the terms of service of the original game and result in a ban or suspension. You should always scan the mod apk file with an antivirus software before installing it, and backup your data before playing it. You should also play the mod apk file at your own risk and responsibility.</p>
-<li><strong>How can I get more coins and gems in Monster Life?</strong></li>
-<p>If you don't want to use Monster Life Mod APK Free Shopping, you can still get more coins and gems in Monster Life by completing quests, winning battles, watching ads, or spending real money. You can also get more coins and gems by inviting your friends to play the game with you, or by participating in events and promotions.</p>
-<li><strong>How can I breed new monsters in Monster Life?</strong></li>
-<p>To breed new monsters in Monster Life, you need to have two monsters of the same species and opposite genders. You also need to have a breeding habitat that matches their element. You can then tap on the breeding habitat and select the two monsters that you want to breed. You will then have to wait for some time until the breeding is done. You can speed up the process by using gems or watching ads. You can then hatch the egg and get a new monster.</p>
-<li><strong>How can I contact the developer of Monster Life?</strong></li>
-<p>If you have any questions, feedback, or issues about Monster Life, you can contact the developer of the game by visiting their website [here], or by sending them an email at [[email protected]]. You can also follow them on their social media accounts on [Facebook], [Twitter], [Instagram], and [YouTube].</p>
-</ol></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Extreme car driving simulator apk Free download and play the most realistic car game ever.md

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-<h2>What is Extreme Car Driving Simulator?</h2>
-<p>Extreme Car Driving Simulator is an Android game developed by AxesInMotion Racing. It was released in 2014 and has since gained over 500 million downloads on Google Play. It is also available on other platforms like Windows, iOS, and Mac. Extreme Car Driving Simulator is a game that simulates the experience of driving a sports car in an open world environment. You can choose from different cars, customize them, and drive them in various modes. You can also explore the city, crash your car, and enjoy the realistic physics and graphics.</p>
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-<p>Extreme Car Driving Simulator has many features that make it an exciting and addictive game. Here are some of them:</p>
-<h4>Mini game checkpoint mode</h4>
-<p>In this mode, you have to reach different checkpoints in the city within a given time limit. You can earn coins and unlock new cars by completing this mode.</p>
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-<p>You can also drive with traffic in the city, which adds more challenge and realism to the game. You have to avoid crashing into other vehicles and obey the traffic rules.</p>
-<h4>Full real HUD</h4>
-<p>The game has a full real HUD that shows you the revs, gear, speed, and other information of your car. You can also switch between different views, such as cockpit view, third-person view, or top-down view.</p>
-<h4>ABS, TC and ESP simulation</h4>
-<p>You can also simulate the ABS, TC and ESP systems of your car. These are features that help you control your car better in different situations. You can also turn them off if you want more challenge.</p>
-<h4>Explore a detailed open world environment</h4>
-<p>The game has a large and detailed open world environment that you can explore freely. You can find different places, such as airports, highways, bridges, tunnels, off-road areas, and more. You can also interact with some objects, such as ramps, cones, barrels, and traffic lights.</p>
-<h4>Realistic car damage and physics</h4>
-<p>The game has realistic car damage and physics that make it more fun and immersive. You can see your car getting dented, scratched, or even destroyed by crashing into other cars or objects. You can also feel the weight, speed, and handling of your car as you drive it.</p>
-<h4>Control your car with different options</h4>
-<p>You can control your car with different options, such as steering wheel, accelerometer, or arrows. You can also adjust the sensitivity and tilt of your device to suit your preference.</p>
-<h4>Several different cameras and gamepad support</h4>
-<p>You can also switch between several different cameras to get different perspectives of your car and the environment. You can also use a gamepad to play the game if you have one connected to your device.</p>
-<h3>How to download and install Extreme Car Driving Simulator APK?</h3>
-<p>If you want to download and install Extreme Car Driving Simulator APK on your Android device , you can follow these simple steps:</p>
-<h4>Download the APK file from a trusted source</h4>
-<p>The first step is to download the APK file of Extreme Car Driving Simulator from a trusted source. You can find many websites that offer the APK file, but you have to be careful and avoid downloading from malicious or fake sites. One of the reliable sources that we recommend is APKPure, which is a popular and safe platform for downloading APK files. You can visit their website and search for Extreme Car Driving Simulator, or you can use this link to go directly to the download page.</p>
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spaces/AIConsultant/MusicGen/tests/modules/test_seanet.py

@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock
-from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d
-
-
-class TestSEANetModel:
-
-    def test_base(self):
-        encoder = SEANetEncoder()
-        decoder = SEANetDecoder()
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_causal(self):
-        encoder = SEANetEncoder(causal=True)
-        decoder = SEANetDecoder(causal=True)
-        x = torch.randn(1, 1, 24000)
-
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_conv_skip_connection(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False)
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_seanet_encoder_decoder_final_act(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False, final_activation='Tanh')
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in encoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        # here we add + 1 to n_blocks as we increment n_blocks just after the block
-                        assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm
-
-    def test_encoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_encoder_blocks_norm(encoder, disable_blocks, norm)
-
-    def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in decoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, StreamableConvTranspose1d):
-                n_blocks += 1
-                assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        assert resnet_layer.conv.norm_type == 'none' \
-                            if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-
-    def test_decoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_decoder_blocks_norm(decoder, disable_blocks, norm)
-
-    def test_disable_norm_raises_exception(self):
-        # Invalid disable_norm_outer_blocks values raise exceptions
-        with pytest.raises(AssertionError):
-            SEANetEncoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)

spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/classifier.py

@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.num_classes = num_classes
-        # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
-        self.diffusion_config = OmegaConf.load(diffusion_config).model
-        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
-        self.load_diffusion()
-
-        self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
-        self.log_steps = log_steps
-
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
-            else self.diffusion_model.cond_stage_key
-
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
-        if self.label_key not in __models__:
-            raise NotImplementedError()
-
-        self.load_classifier(ckpt_path, pool)
-
-        self.scheduler_config = scheduler_config
-        self.use_scheduler = self.scheduler_config is not None
-        self.weight_decay = weight_decay
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def load_diffusion(self):
-        model = instantiate_from_config(self.diffusion_config)
-        self.diffusion_model = model.eval()
-        self.diffusion_model.train = disabled_train
-        for param in self.diffusion_model.parameters():
-            param.requires_grad = False
-
-    def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
-        model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
-            model_config.pool = pool
-
-        self.model = __models__[self.label_key](**model_config)
-        if ckpt_path is not None:
-            print('#####################################################################')
-            print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
-            self.init_from_ckpt(ckpt_path)
-
-    @torch.no_grad()
-    def get_x_noisy(self, x, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x))
-        continuous_sqrt_alpha_cumprod = None
-        if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
-            # todo: make sure t+1 is correct here
-
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
-    def forward(self, x_noisy, t, *args, **kwargs):
-        return self.model(x_noisy, t)
-
-    @torch.no_grad()
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    @torch.no_grad()
-    def get_conditioning(self, batch, k=None):
-        if k is None:
-            k = self.label_key
-        assert k is not None, 'Needs to provide label key'
-
-        targets = batch[k].to(self.device)
-
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
-            for down in range(self.numd):
-                h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
-            # targets = rearrange(targets,'b c h w -> b h w c')
-
-        return targets
-
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
-        _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
-            return (top_ks == labels[:, None]).float().sum(dim=-1)
-
-    def on_train_epoch_start(self):
-        # save some memory
-        self.diffusion_model.model.to('cpu')
-
-    @torch.no_grad()
-    def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
-        log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
-        )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
-        )
-
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
-        lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
-    def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
-        targets = self.get_conditioning(batch)
-        if targets.dim() == 4:
-            targets = targets.argmax(dim=1)
-        if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
-        else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
-        x_noisy = self.get_x_noisy(x, t)
-        logits = self(x_noisy, t)
-
-        loss = F.cross_entropy(logits, targets, reduction='none')
-
-        self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
-        loss = loss.mean()
-        return loss, logits, x_noisy, targets
-
-    def training_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-        return loss
-
-    def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
-    def on_validation_start(self):
-        self.reset_noise_accs()
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-
-        for t in self.noisy_acc:
-            _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
-        if self.use_scheduler:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [optimizer], scheduler
-
-        return optimizer
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, *args, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
-
-        y = self.get_conditioning(batch)
-
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-            log['labels'] = y
-
-        if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
-
-            for step in range(self.log_steps):
-                current_time = step * self.log_time_interval
-
-                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
-                log[f'inputs@t{current_time}'] = x_noisy
-
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
-                pred = rearrange(pred, 'b h w c -> b c h w')
-
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
-        for key in log:
-            log[key] = log[key][:N]
-
-        return log

spaces/Aaaad/Dddde/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("huggingface/gpt2").launch()

spaces/Abhilashvj/planogram-compliance/utils/autoanchor.py

@@ -1,219 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-AutoAnchor utils
-"""
-
-import random
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from utils import TryExcept
-from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
-
-PREFIX = colorstr("AutoAnchor: ")
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = (
-        m.anchors.prod(-1).mean(-1).view(-1)
-    )  # mean anchor area per output layer
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da and (da.sign() != ds.sign()):  # same order
-        LOGGER.info(f"{PREFIX}Reversing anchor order")
-        m.anchors[:] = m.anchors.flip(0)
-
-
-@TryExcept(f"{PREFIX}ERROR")
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    m = (
-        model.module.model[-1] if hasattr(model, "module") else model.model[-1]
-    )  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(
-        0.9, 1.1, size=(shapes.shape[0], 1)
-    )  # augment scale
-    wh = torch.tensor(
-        np.concatenate(
-            [l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)]
-        )
-    ).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1 / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1 / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    stride = m.stride.to(m.anchors.device).view(-1, 1, 1)  # model strides
-    anchors = m.anchors.clone() * stride  # current anchors
-    bpr, aat = metric(anchors.cpu().view(-1, 2))
-    s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). "
-    if bpr > 0.98:  # threshold to recompute
-        LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅")
-    else:
-        LOGGER.info(
-            f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve..."
-        )
-        na = m.anchors.numel() // 2  # number of anchors
-        anchors = kmean_anchors(
-            dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False
-        )
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(
-                m.anchors
-            )
-            m.anchors[:] = anchors.clone().view_as(m.anchors)
-            check_anchor_order(m)  # must be in pixel-space (not grid-space)
-            m.anchors /= stride
-            s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)"
-        else:
-            s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)"
-        LOGGER.info(s)
-
-
-def kmean_anchors(
-    dataset="./data/coco128.yaml",
-    n=9,
-    img_size=640,
-    thr=4.0,
-    gen=1000,
-    verbose=True,
-):
-    """Creates kmeans-evolved anchors from training dataset
-
-    Arguments:
-        dataset: path to data.yaml, or a loaded dataset
-        n: number of anchors
-        img_size: image size used for training
-        thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-        gen: generations to evolve anchors using genetic algorithm
-        verbose: print all results
-
-    Return:
-        k: kmeans evolved anchors
-
-    Usage:
-        from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    from scipy.cluster.vq import kmeans
-
-    npr = np.random
-    thr = 1 / thr
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k, verbose=True):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (
-            x > thr
-        ).float().mean() * n  # best possible recall, anch > thr
-        s = (
-            f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n"
-            f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, "
-            f"past_thr={x[x > thr].mean():.3f}-mean: "
-        )
-        for x in k:
-            s += "%i,%i, " % (round(x[0]), round(x[1]))
-        if verbose:
-            LOGGER.info(s[:-2])
-        return k
-
-    if isinstance(dataset, str):  # *.yaml file
-        with open(dataset, errors="ignore") as f:
-            data_dict = yaml.safe_load(f)  # model dict
-        from utils.dataloaders import LoadImagesAndLabels
-
-        dataset = LoadImagesAndLabels(
-            data_dict["train"], augment=True, rect=True
-        )
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate(
-        [l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]
-    )  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        LOGGER.info(
-            f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size"
-        )
-    wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32)  # filter > 2 pixels
-    # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans init
-    try:
-        LOGGER.info(
-            f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points..."
-        )
-        assert n <= len(wh)  # apply overdetermined constraint
-        s = wh.std(0)  # sigmas for whitening
-        k = kmeans(wh / s, n, iter=30)[0] * s  # points
-        assert n == len(
-            k
-        )  # kmeans may return fewer points than requested if wh is insufficient or too similar
-    except Exception:
-        LOGGER.warning(
-            f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init"
-        )
-        k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size  # random init
-    wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
-    k = print_results(k, verbose=False)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    f, sh, mp, s = (
-        anchor_fitness(k),
-        k.shape,
-        0.9,
-        0.1,
-    )  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT)  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (
-            v == 1
-        ).all():  # mutate until a change occurs (prevent duplicates)
-            v = (
-                (npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s
-                + 1
-            ).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}"
-            if verbose:
-                print_results(k, verbose)
-
-    return print_results(k).astype(np.float32)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.js

@@ -1,112 +0,0 @@
-import BaseShapes from '../../../plugins/gameobjects/shape/shapes/BaseShapes.js';
-import EaseValueMethods from './EaseValueMethods.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class Base extends BaseShapes {
-    constructor(scene, config) {
-        var x = GetValue(config, 'x', 0);
-        var y = GetValue(config, 'y', 0);
-        var width = GetValue(config, 'width', 64);
-        var height = GetValue(config, 'height', 64);
-
-        super(scene, x, y, width, height);
-
-        this.setDuration(GetValue(config, 'duration', 1000));
-        this.setEase(GetValue(config, 'ease', 'Linear'));
-        this.setDelay(GetValue(config, 'delay', 0));
-        this.setRepeatDelay(GetValue(config, 'repeatDelay', 0));
-        var color = GetValue(config, 'color', 0xffffff);
-        var start = GetValue(config, 'start', true);
-
-        this.buildShapes(config);
-        this.setColor(color);
-        this.setValue(0);
-
-        if (start) {
-            this.start();
-        }
-    }
-
-    buildShapes() {
-
-    }
-
-    get centerX() {
-        return this.width / 2;;
-    }
-
-    get centerY() {
-        return this.height / 2;
-    }
-
-    get radius() {
-        return Math.min(this.centerX, this.centerY);
-    }
-
-    get color() {
-        return this._color;
-    }
-
-    set color(value) {
-        this.isColorChanged = this.isColorChanged || (this._color !== value);
-        this.dirty = this.dirty || this.isColorChanged;
-        this._color = value;
-        this.setShapesColor(value);
-    }
-
-    setColor(color) {
-        this.color = color;
-        return this;
-    }
-
-    setShapesColor(color) {
-
-    }
-
-    get value() {
-        return this._value;
-    }
-
-    set value(value) {
-        value = Phaser.Math.Clamp(value, 0, 1);
-        this.dirty = this.dirty || (this._value != value);
-        this._value = value;
-    }
-
-    setValue(value) {
-        this.value = value;
-        return this;
-    }
-
-    setDuration(duration) {
-        this.duration = duration;
-        return this;
-    }
-
-    setDelay(delay) {
-        this.delay = delay;
-        return this;
-    }
-
-    setRepeatDelay(repeatDelay) {
-        this.repeatDelay = repeatDelay;
-        return this;
-    }
-
-    setEase(ease) {
-        this.ease = ease;
-        return this;
-    }
-
-    get isRunning() {
-        return (this.tweenTask) ? this.tweenTask.isRunning : false;
-    }
-}
-
-Object.assign(
-    Base.prototype,
-    EaseValueMethods
-);
-
-export default Base;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseMoveMethods.js

@@ -1,120 +0,0 @@
-import { EaseMoveTo, EaseMoveFrom } from '../easemove/EaseMove.js';
-import { WaitComplete } from '../utils/WaitEvent.js';
-import GetParentSizerMethods from './GetParentSizerMethods.js';
-
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-const DistanceBetween = Phaser.Math.Distance.Between;
-
-var OnInitEaseMove = function (gameObject, easeMove) {
-    // Route 'complete' of easeMove to gameObject
-    easeMove.completeEventName = undefined;
-    easeMove.on('complete', function () {
-        if (easeMove.completeEventName) {
-            gameObject.emit(easeMove.completeEventName, gameObject);
-            easeMove.completeEventName = undefined;
-        }
-    })
-
-    // Update local state
-    easeMove.on('update', function () {
-        var parent = GetParentSizerMethods.getParentSizer(gameObject);
-        if (parent) {
-            parent.resetChildPositionState(gameObject);
-        }
-    })
-}
-
-export default {
-    moveFrom(duration, x, y, ease, destroyMode) {
-        if (IsPlainObject(duration)) {
-            var config = duration;
-            x = config.x;
-            y = config.y;
-            if (config.hasOwnProperty('speed')) {
-                duration = (DistanceBetween(x, y, this.x, this.y) * 1000) / config.speed;
-            } else {
-                duration = config.duration;
-            }
-
-            ease = config.ease;
-        }
-
-        var isInit = (this._easeMove === undefined);
-
-        this._easeMove = EaseMoveFrom(this, duration, x, y, ease, destroyMode, this._easeMove);
-
-        if (isInit) {
-            OnInitEaseMove(this, this._easeMove);
-        }
-
-        this._easeMove.completeEventName = 'movefrom.complete';
-
-        return this;
-    },
-
-    moveFromPromise(duration, x, y, ease, destroyMode) {
-        this.moveFrom(duration, x, y, ease, destroyMode);
-        return WaitComplete(this._easeMove);
-    },
-
-    moveFromDestroy(duration, x, y, ease) {
-        this.moveFrom(duration, x, y, ease, true);
-        return this;
-    },
-
-    moveFromDestroyPromise(duration, x, y, ease) {
-        this.moveFromDestroy(duration, x, y, ease);
-        return WaitComplete(this._easeMove);
-    },
-
-    moveTo(duration, x, y, ease, destroyMode) {
-        if (IsPlainObject(duration)) {
-            var config = duration;
-            x = config.x;
-            y = config.y;
-            if (config.hasOwnProperty('speed')) {
-                duration = (DistanceBetween(x, y, this.x, this.y) * 1000) / config.speed;
-            } else {
-                duration = config.duration;
-            }
-
-            ease = config.ease;
-        }
-
-        var isInit = (this._easeMove === undefined);
-
-        this._easeMove = EaseMoveTo(this, duration, x, y, ease, destroyMode, this._easeMove);
-
-        if (isInit) {
-            OnInitEaseMove(this, this._easeMove);
-        }
-
-        this._easeMove.completeEventName = 'moveto.complete';
-
-        return this;
-    },
-
-    moveToPromise(duration, x, y, ease, destroyMode) {
-        this.moveTo(duration, x, y, ease, destroyMode);
-        return WaitComplete(this._easeMove);
-    },
-
-    moveToDestroy(duration, x, y, ease) {
-        this.moveTo(duration, x, y, ease, true)
-        return this;
-    },
-
-    moveToDestroyPromise(duration, x, y, ease) {
-        this.moveToDestroy(duration, x, y, ease, true);
-        return WaitComplete(this._easeMove);
-    },
-
-    moveStop(toEnd) {
-        if (!this._easeMove) {
-            return this;
-        }
-
-        this._easeMove.stop(toEnd);
-        return this;
-    }
-}

spaces/Aloento/9Nine-PITS/text/frontend/normalizer/acronyms.py

@@ -1,13 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.

spaces/Amrrs/DragGan-Inversion/PTI/utils/ImagesDataset.py

@@ -1,43 +0,0 @@
-import os
-
-from torch.utils.data import Dataset
-from PIL import Image
-
-from PTI.utils.data_utils import make_dataset
-from torchvision import transforms
-
-
-class Image2Dataset(Dataset):
-    def __init__(self, image) -> None:
-        super().__init__()
-        self.image = image
-        self.transform = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return 1
-
-    def __getitem__(self, index):
-        return "customIMG", self.transform(self.image)
-
-
-class ImagesDataset(Dataset):
-    def __init__(self, source_root, source_transform=None):
-        self.source_paths = sorted(make_dataset(source_root))
-        self.source_transform = source_transform
-
-    def __len__(self):
-        return len(self.source_paths)
-
-    def __getitem__(self, index):
-        fname, from_path = self.source_paths[index]
-        from_im = Image.open(from_path).convert("RGB").resize([1024, 1024])
-
-        if self.source_transform:
-            from_im = self.source_transform(from_im)
-
-        return fname, from_im

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/other-formats.md

@@ -1,194 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. 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.
--->
-
-# Load different Stable Diffusion formats
-
-[[open-in-colab]]
-
-Stable Diffusion models are available in different formats depending on the framework they're trained and saved with, and where you download them from. Converting these formats for use in 🤗 Diffusers allows you to use all the features supported by the library, such as [using different schedulers](schedulers) for inference, [building your custom pipeline](write_own_pipeline), and a variety of techniques and methods for [optimizing inference speed](./optimization/opt_overview).
-
-<Tip>
-
-We highly recommend using the `.safetensors` format because it is more secure than traditional pickled files which are vulnerable and can be exploited to execute any code on your machine (learn more in the [Load safetensors](using_safetensors) guide).
-
-</Tip>
-
-This guide will show you how to convert other Stable Diffusion formats to be compatible with 🤗 Diffusers.
-
-## PyTorch .ckpt
-
-The checkpoint - or `.ckpt` - format is commonly used to store and save models. The `.ckpt` file contains the entire model and is typically several GBs in size. While you can load and use a `.ckpt` file directly with the [`~StableDiffusionPipeline.from_single_file`] method, it is generally better to convert the `.ckpt` file to 🤗 Diffusers so both formats are available.
-
-There are two options for converting a `.ckpt` file; use a Space to convert the checkpoint or convert the `.ckpt` file with a script.
-
-### Convert with a Space
-
-The easiest and most convenient way to convert a `.ckpt` file is to use the [SD to Diffusers](https://huggingface.co/spaces/diffusers/sd-to-diffusers) Space. You can follow the instructions on the Space to convert the `.ckpt` file.
-
-This approach works well for basic models, but it may struggle with more customized models. You'll know the Space failed if it returns an empty pull request or error. In this case, you can try converting the `.ckpt` file with a script. 
-
-### Convert with a script
-
-🤗 Diffusers provides a [conversion script](https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py) for converting `.ckpt` files. This approach is more reliable than the Space above. 
-
-Before you start, make sure you have a local clone of 🤗 Diffusers to run the script and log in to your Hugging Face account so you can open pull requests and push your converted model to the Hub.
-
-```bash
-huggingface-cli login
-```
-
-To use the script:
-
-1. Git clone the repository containing the `.ckpt` file you want to convert. For this example, let's convert this [TemporalNet](https://huggingface.co/CiaraRowles/TemporalNet) `.ckpt` file:
-
-```bash
-git lfs install
-git clone https://huggingface.co/CiaraRowles/TemporalNet
-```
-
-2. Open a pull request on the repository where you're converting the checkpoint from:
-
-```bash
-cd TemporalNet && git fetch origin refs/pr/13:pr/13
-git checkout pr/13
-```
-
-3. There are several input arguments to configure in the conversion script, but the most important ones are:
-
-    - `checkpoint_path`: the path to the `.ckpt` file to convert.
-    - `original_config_file`: a YAML file defining the configuration of the original architecture. If you can't find this file, try searching for the YAML file in the GitHub repository where you found the `.ckpt` file.
-    - `dump_path`: the path to the converted model.
-
-        For example, you can take the `cldm_v15.yaml` file from the [ControlNet](https://github.com/lllyasviel/ControlNet/tree/main/models) repository because the TemporalNet model is a Stable Diffusion v1.5 and ControlNet model.
-
-4. Now you can run the script to convert the `.ckpt` file:
-
-```bash
-python ../diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path temporalnetv3.ckpt --original_config_file cldm_v15.yaml --dump_path ./ --controlnet
-```
-
-5. Once the conversion is done, upload your converted model and test out the resulting [pull request](https://huggingface.co/CiaraRowles/TemporalNet/discussions/13)!
-
-```bash
-git push origin pr/13:refs/pr/13
-```
-
-## Keras .pb or .h5
-
-<Tip warning={true}>
-
-🧪 This is an experimental feature. Only Stable Diffusion v1 checkpoints are supported by the Convert KerasCV Space at the moment. 
-
-</Tip>
-
-[KerasCV](https://keras.io/keras_cv/) supports training for [Stable Diffusion](https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/stable_diffusion) v1 and v2. However, it offers limited support for experimenting with Stable Diffusion models for inference and deployment whereas 🤗 Diffusers has a more complete set of features for this purpose, such as different [noise schedulers](https://huggingface.co/docs/diffusers/using-diffusers/schedulers), [flash attention](https://huggingface.co/docs/diffusers/optimization/xformers), and [other 
-optimization techniques](https://huggingface.co/docs/diffusers/optimization/fp16).
-
-The [Convert KerasCV](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) Space converts `.pb` or `.h5` files to PyTorch, and then wraps them in a [`StableDiffusionPipeline`] so it is ready for inference. The converted checkpoint is stored in a repository on the Hugging Face Hub.
-
-For this example, let's convert the [`sayakpaul/textual-inversion-kerasio`](https://huggingface.co/sayakpaul/textual-inversion-kerasio/tree/main) checkpoint which was trained with Textual Inversion. It uses the special token `<my-funny-cat>` to personalize images with cats.
-
-The Convert KerasCV Space allows you to input the following:
-
-* Your Hugging Face token.
-* Paths to download the UNet and text encoder weights from. Depending on how the model was trained, you don't necessarily need to provide the paths to both the UNet and text encoder. For example, Textual Inversion only requires the embeddings from the text encoder and a text-to-image model only requires the UNet weights.
-* Placeholder token is only applicable for textual inversion models.
-* The `output_repo_prefix` is the name of the repository where the converted model is stored.
-
-Click the **Submit** button to automatically convert the KerasCV checkpoint! Once the checkpoint is successfully converted, you'll see a link to the new repository containing the converted checkpoint. Follow the link to the new repository, and you'll see the Convert KerasCV Space generated a model card with an inference widget to try out the converted model.
-
-If you prefer to run inference with code, click on the **Use in Diffusers** button in the upper right corner of the model card to copy and paste the code snippet:
-
-```py
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained("sayakpaul/textual-inversion-cat-kerascv_sd_diffusers_pipeline")
-```
-
-Then you can generate an image like:
-
-```py
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained("sayakpaul/textual-inversion-cat-kerascv_sd_diffusers_pipeline")
-pipeline.to("cuda")
-
-placeholder_token = "<my-funny-cat-token>"
-prompt = f"two {placeholder_token} getting married, photorealistic, high quality"
-image = pipeline(prompt, num_inference_steps=50).images[0]
-```
-
-## A1111 LoRA files
-
-[Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (A1111) is a popular web UI for Stable Diffusion that supports model sharing platforms like [Civitai](https://civitai.com/). Models trained with the Low-Rank Adaptation (LoRA) technique are especially popular because they're fast to train and have a much smaller file size than a fully finetuned model. 🤗 Diffusers supports loading A1111 LoRA checkpoints with [`~loaders.LoraLoaderMixin.load_lora_weights`]:
-
-```py
-from diffusers import DiffusionPipeline, UniPCMultistepScheduler
-import torch
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "andite/anything-v4.0", torch_dtype=torch.float16, safety_checker=None
-).to("cuda")
-pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
-```
-
-Download a LoRA checkpoint from Civitai; this example uses the [Howls Moving Castle,Interior/Scenery LoRA (Ghibli Stlye)](https://civitai.com/models/14605?modelVersionId=19998) checkpoint, but feel free to try out any LoRA checkpoint!
-
-```py
-# uncomment to download the safetensor weights
-#!wget https://civitai.com/api/download/models/19998 -O howls_moving_castle.safetensors
-```
-
-Load the LoRA checkpoint into the pipeline with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method:
-
-```py
-pipeline.load_lora_weights(".", weight_name="howls_moving_castle.safetensors")
-```
-
-Now you can use the pipeline to generate images:
-
-```py
-prompt = "masterpiece, illustration, ultra-detailed, cityscape, san francisco, golden gate bridge, california, bay area, in the snow, beautiful detailed starry sky"
-negative_prompt = "lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture"
-
-images = pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    width=512,
-    height=512,
-    num_inference_steps=25,
-    num_images_per_prompt=4,
-    generator=torch.manual_seed(0),
-).images
-```
-
-Finally, create a helper function to display the images:
-
-```py
-from PIL import Image
-
-
-def image_grid(imgs, rows=2, cols=2):
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-
-image_grid(images)
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/a1111-lora-sf.png"/>
-</div>

spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py

@@ -1,12 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(
-                _delete_=True,
-                type='DeformRoIPoolPack',
-                output_size=7,
-                output_channels=256),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32])))

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py

@@ -1,63 +0,0 @@
-_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=False),
-        norm_eval=True,
-        style='caffe'),
-    roi_head=dict(
-        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
-    # model training and testing settings
-    train_cfg=dict(
-        rcnn=dict(
-            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
-            sampler=dict(num=256))),
-    test_cfg=dict(rcnn=dict(score_thr=1e-3)))
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadProposals', num_max_proposals=300),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadProposals', num_max_proposals=None),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img', 'proposals']),
-        ])
-]
-data = dict(
-    train=dict(
-        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl',
-        pipeline=train_pipeline),
-    val=dict(
-        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
-        pipeline=test_pipeline),
-    test=dict(
-        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
-        pipeline=test_pipeline))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py

@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/danet_r50-d8.py',
-    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deprecated_wrappers.py

@@ -1,43 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# This file is for backward compatibility.
-# Module wrappers for empty tensor have been moved to mmcv.cnn.bricks.
-import warnings
-
-from ..cnn.bricks.wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d
-
-
-class Conv2d_deprecated(Conv2d):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing Conv2d wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class ConvTranspose2d_deprecated(ConvTranspose2d):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing ConvTranspose2d wrapper from "mmcv.ops" will be '
-            'deprecated in the future. Please import them from "mmcv.cnn" '
-            'instead')
-
-
-class MaxPool2d_deprecated(MaxPool2d):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing MaxPool2d wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class Linear_deprecated(Linear):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing Linear wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')

spaces/Artbogdanov/monet-manet/README.md

@@ -1,12 +0,0 @@
----
-title: Fast Ai Pics
-emoji: 🌖
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/demo/inference_on_a_image.py

@@ -1,214 +0,0 @@
-import argparse
-import os
-import sys
-
-import numpy as np
-import torch
-from PIL import Image, ImageDraw, ImageFont
-
-import groundingdino.datasets.transforms as T
-from groundingdino.models import build_model
-from groundingdino.util import box_ops
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
-from groundingdino.util.vl_utils import create_positive_map_from_span
-
-
-def plot_boxes_to_image(image_pil, tgt):
-    H, W = tgt["size"]
-    boxes = tgt["boxes"]
-    labels = tgt["labels"]
-    assert len(boxes) == len(labels), "boxes and labels must have same length"
-
-    draw = ImageDraw.Draw(image_pil)
-    mask = Image.new("L", image_pil.size, 0)
-    mask_draw = ImageDraw.Draw(mask)
-
-    # draw boxes and masks
-    for box, label in zip(boxes, labels):
-        # from 0..1 to 0..W, 0..H
-        box = box * torch.Tensor([W, H, W, H])
-        # from xywh to xyxy
-        box[:2] -= box[2:] / 2
-        box[2:] += box[:2]
-        # random color
-        color = tuple(np.random.randint(0, 255, size=3).tolist())
-        # draw
-        x0, y0, x1, y1 = box
-        x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
-
-        draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
-        # draw.text((x0, y0), str(label), fill=color)
-
-        font = ImageFont.load_default()
-        if hasattr(font, "getbbox"):
-            bbox = draw.textbbox((x0, y0), str(label), font)
-        else:
-            w, h = draw.textsize(str(label), font)
-            bbox = (x0, y0, w + x0, y0 + h)
-        # bbox = draw.textbbox((x0, y0), str(label))
-        draw.rectangle(bbox, fill=color)
-        draw.text((x0, y0), str(label), fill="white")
-
-        mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
-
-    return image_pil, mask
-
-
-def load_image(image_path):
-    # load image
-    image_pil = Image.open(image_path).convert("RGB")  # load image
-
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    image, _ = transform(image_pil, None)  # 3, h, w
-    return image_pil, image
-
-
-def load_model(model_config_path, model_checkpoint_path, cpu_only=False):
-    args = SLConfig.fromfile(model_config_path)
-    args.device = "cuda" if not cpu_only else "cpu"
-    model = build_model(args)
-    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
-    load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
-    print(load_res)
-    _ = model.eval()
-    return model
-
-
-def get_grounding_output(model, image, caption, box_threshold, text_threshold=None, with_logits=True, cpu_only=False, token_spans=None):
-    assert text_threshold is not None or token_spans is not None, "text_threshould and token_spans should not be None at the same time!"
-    caption = caption.lower()
-    caption = caption.strip()
-    if not caption.endswith("."):
-        caption = caption + "."
-    device = "cuda" if not cpu_only else "cpu"
-    model = model.to(device)
-    image = image.to(device)
-    with torch.no_grad():
-        outputs = model(image[None], captions=[caption])
-    logits = outputs["pred_logits"].sigmoid()[0]  # (nq, 256)
-    boxes = outputs["pred_boxes"][0]  # (nq, 4)
-
-    # filter output
-    if token_spans is None:
-        logits_filt = logits.cpu().clone()
-        boxes_filt = boxes.cpu().clone()
-        filt_mask = logits_filt.max(dim=1)[0] > box_threshold
-        logits_filt = logits_filt[filt_mask]  # num_filt, 256
-        boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
-
-        # get phrase
-        tokenlizer = model.tokenizer
-        tokenized = tokenlizer(caption)
-        # build pred
-        pred_phrases = []
-        for logit, box in zip(logits_filt, boxes_filt):
-            pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
-            if with_logits:
-                pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
-            else:
-                pred_phrases.append(pred_phrase)
-    else:
-        # given-phrase mode
-        positive_maps = create_positive_map_from_span(
-            model.tokenizer(text_prompt),
-            token_span=token_spans
-        ).to(image.device) # n_phrase, 256
-
-        logits_for_phrases = positive_maps @ logits.T # n_phrase, nq
-        all_logits = []
-        all_phrases = []
-        all_boxes = []
-        for (token_span, logit_phr) in zip(token_spans, logits_for_phrases):
-            # get phrase
-            phrase = ' '.join([caption[_s:_e] for (_s, _e) in token_span])
-            # get mask
-            filt_mask = logit_phr > box_threshold
-            # filt box
-            all_boxes.append(boxes[filt_mask])
-            # filt logits
-            all_logits.append(logit_phr[filt_mask])
-            if with_logits:
-                logit_phr_num = logit_phr[filt_mask]
-                all_phrases.extend([phrase + f"({str(logit.item())[:4]})" for logit in logit_phr_num])
-            else:
-                all_phrases.extend([phrase for _ in range(len(filt_mask))])
-        boxes_filt = torch.cat(all_boxes, dim=0).cpu()
-        pred_phrases = all_phrases
-
-
-    return boxes_filt, pred_phrases
-
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
-    parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
-    parser.add_argument(
-        "--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
-    )
-    parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
-    parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt")
-    parser.add_argument(
-        "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
-    )
-
-    parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
-    parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
-    parser.add_argument("--token_spans", type=str, default=None, help=
-                        "The positions of start and end positions of phrases of interest. \
-                        For example, a caption is 'a cat and a dog', \
-                        if you would like to detect 'cat', the token_spans should be '[[[2, 5]], ]', since 'a cat and a dog'[2:5] is 'cat'. \
-                        if you would like to detect 'a cat', the token_spans should be '[[[0, 1], [2, 5]], ]', since 'a cat and a dog'[0:1] is 'a', and 'a cat and a dog'[2:5] is 'cat'. \
-                        ")
-
-    parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False")
-    args = parser.parse_args()
-
-    # cfg
-    config_file = args.config_file  # change the path of the model config file
-    checkpoint_path = args.checkpoint_path  # change the path of the model
-    image_path = args.image_path
-    text_prompt = args.text_prompt
-    output_dir = args.output_dir
-    box_threshold = args.box_threshold
-    text_threshold = args.text_threshold
-    token_spans = args.token_spans
-
-    # make dir
-    os.makedirs(output_dir, exist_ok=True)
-    # load image
-    image_pil, image = load_image(image_path)
-    # load model
-    model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only)
-
-    # visualize raw image
-    image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
-
-    # set the text_threshold to None if token_spans is set.
-    if token_spans is not None:
-        text_threshold = None
-        print("Using token_spans. Set the text_threshold to None.")
-
-
-    # run model
-    boxes_filt, pred_phrases = get_grounding_output(
-        model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only, token_spans=eval(token_spans)
-    )
-
-    # visualize pred
-    size = image_pil.size
-    pred_dict = {
-        "boxes": boxes_filt,
-        "size": [size[1], size[0]],  # H,W
-        "labels": pred_phrases,
-    }
-    # import ipdb; ipdb.set_trace()
-    image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]
-    image_with_box.save(os.path.join(output_dir, "pred.jpg"))

spaces/BalaBhaskarudu/mygenAIChatbot/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/Benson/text-generation/Examples/Animal Rebelin Batalla Simulador Mod Apk Desbloqueado Todo.md

@@ -1,61 +0,0 @@
-
-<h1>Simulador de batalla de rebelión animal Mod APK desbloqueado todo</h1>
-<p>Si eres un fan de las batallas épicas y las simulaciones de animales, es posible que quieras echar un vistazo a Animal Revolt Battle Simulator, un juego que te permite crear y ver peleas de animales realistas en 3D. Y si quieres disfrutar del juego con dinero ilimitado, menú, y todas las características desbloqueadas, es posible que desee descargar Animal Revolt Battle Simulator Mod APK, una versión modificada del juego original que le da más libertad y diversión. En este artículo, te contaremos todo lo que necesitas saber sobre Animal Revolt Battle Simulator y su versión modificada. </p>
- <h2>¿Qué es el simulador de batalla de rebelión animal? </h2>
-<p>Animal Revolt Battle Simulator es un juego desarrollado por Beast Battle Games, un estudio especializado en crear juegos de simulación de animales. El juego fue lanzado en junio de 2020 y ha ganado más de 1 millón de descargas en Google Play Store. El juego está clasificado 4.4 de 5 estrellas por los usuarios, que elogian sus gráficos, física y jugabilidad. </p>
-<h2>animal rebelión batalla simulador mod apk desbloqueado todo</h2><br /><p><b><b>Download File</b> &#10004;&#10004;&#10004; <a href="https://bltlly.com/2v6KMC">https://bltlly.com/2v6KMC</a></b></p><br /><br />
-<h3>Características de Animal Revolt Battle Simulator</h3>
-<p>Animal Revolt Battle Simulator tiene muchas características que lo convierten en un juego emocionante y realista. Algunas de ellas son:</p>
-<ul>
-<li><b>Una gran variedad de animales:</b> Puedes elegir entre más de 100 animales, incluyendo leones, tigres, elefantes, dinosaurios, dragones, tiburones y más. Cada animal tiene sus propias estadísticas, habilidades y comportamientos. </li>
-<li><b>Un modo sandbox:</b> Puedes crear tus propios escenarios y batallas colocando animales en el mapa. También puede ajustar el terreno, el clima, la hora del día y otros ajustes. </li>
-<li><b>Un modo de campaña:</b> Puedes seguir la historia de diferentes animales y completar misiones y desafíos. También puede desbloquear nuevos animales y mapas a medida que avanza. </li>
-<li><b>Un motor de física realista:</b> El juego utiliza un motor de física realista que simula los movimientos, colisiones, lesiones y muertes de los animales. Puedes ver sangre, sangre, huesos y efectos de muñeco de trapo. </li>
-
-</ul>
-<h3>Cómo jugar Animal Revolt Battle Simulator</h3>
-<p>El modo de juego de Animal Revolt Battle Simulator es simple e intuitivo. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Seleccione el modo que desea jugar: sandbox o campaña. </li>
-<li>Seleccione el mapa en el que desea jugar. </li>
-<li>Selecciona los animales que quieres usar para tu batalla. Puedes arrastrarlos y soltarlos en el mapa, o usar el botón aleatorio para generar una batalla aleatoria. </li>
-<li>Ajuste la configuración y las opciones como desee. </li>
-<li>Pulse el botón de reproducción para iniciar la batalla. </li>
-<li>Observa cómo se desarrolla la batalla y disfruta del espectáculo. </li>
-</ol>
- <h2>¿Qué es Animal Revolt Battle Simulator Mod APK? </h2>
-<p>Animal Revolt Battle Simulator Mod APK es una versión modificada del juego original que le da algunos beneficios adicionales y características que no están disponibles en la versión oficial. La versión modificada es creada por desarrolladores de terceros que modifican los archivos originales del juego para desbloquear algunas características o añadir algunos trucos. </p>
-<h3>Beneficios de Animal Revolt Battle Simulator Mod APK</h3>
-<p>Algunos de los beneficios de Animal Revolt Battle Simulator Mod APK son:</p>
-<ul>
-<li><b>Dinero ilimitado:</b> Puedes obtener dinero ilimitado en la versión modificada, que puedes usar para comprar nuevos animales, mapas, armas y otros artículos. </li>
-<li><b>Menú:</b> Puedes acceder a un menú que te da más opciones y controles sobre el juego. Puedes habilitar o deshabilitar algunas características, como el modo dios, salud infinita, muerte de un solo golpe, etc.</ <li><b>Todas las características desbloqueadas:</b> Puedes acceder a todas las características del juego, como animales, mapas, armas, etc., sin tener que desbloquearlas jugando o gastando dinero. </li>
-</ul>
-<h3>Cómo descargar e instalar Animal Revolt Battle Simulator Mod APK</h3>
-<p>Si desea descargar e instalar Animal Revolt Battle Simulator Mod APK, debe seguir estos pasos:</p>
-<ol>
-
-<li>Descargar el archivo APK de la versión modificada. Asegúrese de que tiene suficiente espacio en su dispositivo y una conexión a Internet estable. </li>
-<li>Antes de instalar el archivo APK, es necesario habilitar la "Fuentes desconocidas" opción en el dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Busque el archivo APK descargado en su dispositivo y toque en él para iniciar el proceso de instalación. Siga las instrucciones de la pantalla y espere a que termine la instalación. </li>
-<li>Una vez realizada la instalación, puedes iniciar el juego y disfrutar de la versión modificada con todos los beneficios y características. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>Animal Revolt Battle Simulator es un juego divertido y realista que te permite crear y ver batallas épicas con animales en 3D. Usted puede elegir entre más de 100 animales, personalizar sus escenarios, y disfrutar de la física realista y gráficos. Si desea tener más libertad y diversión, puede descargar Animal Revolt Battle Simulator Mod APK, que le da dinero ilimitado, menú, y todas las características desbloqueadas. Puede descargar la versión modificada desde un sitio web confiable e instalarlo en su dispositivo fácilmente. Esperamos que este artículo sea útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre Animal Revolt Battle Simulator y su versión modificada:</p>
-<ul>
-<li><b> ¿Es seguro usar Animal Revolt Battle Simulator Mod APK? </b></li>
-<p>Sí, Animal Revolt Battle Simulator Mod APK es seguro de usar, siempre y cuando se descarga desde un sitio web de confianza que no contiene ningún virus o malware. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden dañar su dispositivo o comprometer su privacidad. </p>
-<p></p>
-<li><b> ¿Es Animal Revolt Battle Simulator Mod APK de uso gratuito? </b></li>
-
-<li><b>¿Requiere acceso root Animal Revolt Battle Simulator Mod APK? </b></li>
-<p>No, Animal Revolt Battle Simulator Mod APK no requiere acceso de root. Puede usarlo en cualquier dispositivo Android sin enraizarlo. </p>
-<li><b> ¿Puedo jugar Animal Revolt Battle Simulator Mod APK en línea con otros jugadores? </b></li>
-<p>No, Animal Revolt Battle Simulator Mod APK no es un juego en línea. Solo se puede jugar sin conexión en su dispositivo. No se puede conectar o competir con otros jugadores en línea. </p>
-<li><b> ¿Puedo actualizar Animal Revolt Battle Simulator Mod APK? </b></li>
-<p>No, Animal Revolt Battle Simulator Mod APK no es una versión oficial del juego. Es una versión modificada que puede no ser compatible con las últimas actualizaciones del juego original. Si desea actualizar el juego, es necesario desinstalar la versión modificada e instalar la versión oficial de la Google Play Store.</p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/vqvae/quantize.py

@@ -1,329 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from torch import einsum
-from einops import rearrange
-
-
-class VectorQuantizer(nn.Module):
-    """
-    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
-    ____________________________________________
-    Discretization bottleneck part of the VQ-VAE.
-    Inputs:
-    - n_e : number of embeddings
-    - e_dim : dimension of embedding
-    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-    _____________________________________________
-    """
-
-    # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for
-    # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be
-    # used wherever VectorQuantizer has been used before and is additionally
-    # more efficient.
-    def __init__(self, n_e, e_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.n_e = n_e
-        self.e_dim = e_dim
-        self.beta = beta
-
-        self.embedding = nn.Embedding(self.n_e, self.e_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
-    def forward(self, z):
-        """
-        Inputs the output of the encoder network z and maps it to a discrete
-        one-hot vector that is the index of the closest embedding vector e_j
-        z (continuous) -> z_q (discrete)
-        z.shape = (batch, channel, height, width)
-        quantization pipeline:
-            1. get encoder input (B,C,H,W)
-            2. flatten input to (B*H*W,C)
-        """
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.e_dim)
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
-            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
-            torch.matmul(z_flattened, self.embedding.weight.t())
-
-        ## could possible replace this here
-        # #\start...
-        # find closest encodings
-        min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-
-        min_encodings = torch.zeros(
-            min_encoding_indices.shape[0], self.n_e).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # dtype min encodings: torch.float32
-        # min_encodings shape: torch.Size([2048, 512])
-        # min_encoding_indices.shape: torch.Size([2048, 1])
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        #.........\end
-
-        # with:
-        # .........\start
-        #min_encoding_indices = torch.argmin(d, dim=1)
-        #z_q = self.embedding(min_encoding_indices)
-        # ......\end......... (TODO)
-
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
-            torch.mean((z_q - z.detach()) ** 2)
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        # TODO: check for more easy handling with nn.Embedding
-        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
-        min_encodings.scatter_(1, indices[:,None], 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:
-            z_q = z_q.view(shape)
-
-            # reshape back to match original input shape
-            z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantize(nn.Module):
-    """
-    credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
-    Gumbel Softmax trick quantizer
-    Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
-    https://arxiv.org/abs/1611.01144
-    """
-    def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True,
-                 kl_weight=5e-4, temp_init=1.0, use_vqinterface=True,
-                 remap=None, unknown_index="random"):
-        super().__init__()
-
-        self.embedding_dim = embedding_dim
-        self.n_embed = n_embed
-
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-
-        self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
-        self.embed = nn.Embedding(n_embed, embedding_dim)
-
-        self.use_vqinterface = use_vqinterface
-
-        self.remap = remap
-        if self.remap is not None:
-            self.register_buffer("used", torch.tensor(np.load(self.remap)))
-            self.re_embed = self.used.shape[0]
-            self.unknown_index = unknown_index # "random" or "extra" or integer
-            if self.unknown_index == "extra":
-                self.unknown_index = self.re_embed
-                self.re_embed = self.re_embed+1
-            print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
-                  f"Using {self.unknown_index} for unknown indices.")
-        else:
-            self.re_embed = n_embed
-
-    def remap_to_used(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        match = (inds[:,:,None]==used[None,None,...]).long()
-        new = match.argmax(-1)
-        unknown = match.sum(2)<1
-        if self.unknown_index == "random":
-            new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
-        else:
-            new[unknown] = self.unknown_index
-        return new.reshape(ishape)
-
-    def unmap_to_all(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        if self.re_embed > self.used.shape[0]: # extra token
-            inds[inds>=self.used.shape[0]] = 0 # simply set to zero
-        back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
-        return back.reshape(ishape)
-
-    def forward(self, z, temp=None, return_logits=False):
-        # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work
-        hard = self.straight_through if self.training else True
-        temp = self.temperature if temp is None else temp
-
-        logits = self.proj(z)
-        if self.remap is not None:
-            # continue only with used logits
-            full_zeros = torch.zeros_like(logits)
-            logits = logits[:,self.used,...]
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
-        if self.remap is not None:
-            # go back to all entries but unused set to zero
-            full_zeros[:,self.used,...] = soft_one_hot
-            soft_one_hot = full_zeros
-        z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
-
-        ind = soft_one_hot.argmax(dim=1)
-        if self.remap is not None:
-            ind = self.remap_to_used(ind)
-        if self.use_vqinterface:
-            if return_logits:
-                return z_q, diff, (None, None, ind), logits
-            return z_q, diff, (None, None, ind)
-        return z_q, diff, ind
-
-    def get_codebook_entry(self, indices, shape):
-        b, h, w, c = shape
-        assert b*h*w == indices.shape[0]
-        indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w)
-        if self.remap is not None:
-            indices = self.unmap_to_all(indices)
-        one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
-        z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight)
-        return z_q
-
-
-class VectorQuantizer2(nn.Module):
-    """
-    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
-    avoids costly matrix multiplications and allows for post-hoc remapping of indices.
-    """
-    # NOTE: due to a bug the beta term was applied to the wrong term. for
-    # backwards compatibility we use the buggy version by default, but you can
-    # specify legacy=False to fix it.
-    def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
-                 sane_index_shape=False, legacy=True):
-        super().__init__()
-        self.n_e = n_e
-        self.e_dim = e_dim
-        self.beta = beta
-        self.legacy = legacy
-
-        self.embedding = nn.Embedding(self.n_e, self.e_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
-        self.remap = remap
-        if self.remap is not None:
-            self.register_buffer("used", torch.tensor(np.load(self.remap)))
-            self.re_embed = self.used.shape[0]
-            self.unknown_index = unknown_index # "random" or "extra" or integer
-            if self.unknown_index == "extra":
-                self.unknown_index = self.re_embed
-                self.re_embed = self.re_embed+1
-            print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
-                  f"Using {self.unknown_index} for unknown indices.")
-        else:
-            self.re_embed = n_e
-
-        self.sane_index_shape = sane_index_shape
-
-    def remap_to_used(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        match = (inds[:,:,None]==used[None,None,...]).long()
-        new = match.argmax(-1)
-        unknown = match.sum(2)<1
-        if self.unknown_index == "random":
-            new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
-        else:
-            new[unknown] = self.unknown_index
-        return new.reshape(ishape)
-
-    def unmap_to_all(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        if self.re_embed > self.used.shape[0]: # extra token
-            inds[inds>=self.used.shape[0]] = 0 # simply set to zero
-        back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
-        return back.reshape(ishape)
-
-    def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
-        assert temp is None or temp==1.0, "Only for interface compatible with Gumbel"
-        assert rescale_logits==False, "Only for interface compatible with Gumbel"
-        assert return_logits==False, "Only for interface compatible with Gumbel"
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = rearrange(z, 'b c h w -> b h w c').contiguous()
-        z_flattened = z.view(-1, self.e_dim)
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
-            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
-            torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
-
-        min_encoding_indices = torch.argmin(d, dim=1)
-        z_q = self.embedding(min_encoding_indices).view(z.shape)
-        perplexity = None
-        min_encodings = None
-
-        # compute loss for embedding
-        if not self.legacy:
-            loss = self.beta * torch.mean((z_q.detach()-z)**2) + \
-                   torch.mean((z_q - z.detach()) ** 2)
-        else:
-            loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
-                   torch.mean((z_q - z.detach()) ** 2)
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # reshape back to match original input shape
-        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
-
-        if self.remap is not None:
-            min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis
-            min_encoding_indices = self.remap_to_used(min_encoding_indices)
-            min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten
-
-        if self.sane_index_shape:
-            min_encoding_indices = min_encoding_indices.reshape(
-                z_q.shape[0], z_q.shape[2], z_q.shape[3])
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        if self.remap is not None:
-            indices = indices.reshape(shape[0],-1) # add batch axis
-            indices = self.unmap_to_all(indices)
-            indices = indices.reshape(-1) # flatten again
-
-        # get quantized latent vectors
-        z_q = self.embedding(indices)
-
-        if shape is not None:
-            z_q = z_q.view(shape)
-            # reshape back to match original input shape
-            z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q

spaces/BetterAPI/BetterChat_new/src/lib/utils/sum.ts

@@ -1,3 +0,0 @@
-export function sum(nums: number[]): number {
-	return nums.reduce((a, b) => a + b, 0);
-}

spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/share/+server.ts

@@ -1,54 +0,0 @@
-import { base } from "$app/paths";
-import { PUBLIC_ORIGIN } from "$env/static/public";
-import { collections } from "$lib/server/database.js";
-import type { SharedConversation } from "$lib/types/SharedConversation.js";
-import { sha256 } from "$lib/utils/sha256.js";
-import { error } from "@sveltejs/kit";
-import { ObjectId } from "mongodb";
-import { nanoid } from "nanoid";
-
-export async function POST({ params, url, locals }) {
-	const conversation = await collections.conversations.findOne({
-		_id: new ObjectId(params.id),
-		sessionId: locals.sessionId,
-	});
-
-	if (!conversation) {
-		throw error(404, "Conversation not found");
-	}
-
-	const hash = await sha256(JSON.stringify(conversation.messages));
-
-	const existingShare = await collections.sharedConversations.findOne({ hash });
-
-	if (existingShare) {
-		return new Response(
-			JSON.stringify({
-				url: getShareUrl(url, existingShare._id),
-			}),
-			{ headers: { "Content-Type": "application/json" } }
-		);
-	}
-
-	const shared: SharedConversation = {
-		_id: nanoid(7),
-		createdAt: new Date(),
-		messages: conversation.messages,
-		hash,
-		updatedAt: new Date(),
-		title: conversation.title,
-	};
-
-	await collections.sharedConversations.insertOne(shared);
-
-	return new Response(
-		JSON.stringify({
-			url: getShareUrl(url, shared._id),
-		}),
-		{ headers: { "Content-Type": "application/json" } }
-	);
-}
-
-function getShareUrl(url: URL, shareId: string): string {
-	return `${PUBLIC_ORIGIN || url.origin}${base}/r/${shareId}`;
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py

@@ -1,46 +0,0 @@
-"""Legacy editable installation process, i.e. `setup.py develop`.
-"""
-import logging
-from typing import Optional, Sequence
-
-from pip._internal.build_env import BuildEnvironment
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.setuptools_build import make_setuptools_develop_args
-from pip._internal.utils.subprocess import call_subprocess
-
-logger = logging.getLogger(__name__)
-
-
-def install_editable(
-    *,
-    global_options: Sequence[str],
-    prefix: Optional[str],
-    home: Optional[str],
-    use_user_site: bool,
-    name: str,
-    setup_py_path: str,
-    isolated: bool,
-    build_env: BuildEnvironment,
-    unpacked_source_directory: str,
-) -> None:
-    """Install a package in editable mode. Most arguments are pass-through
-    to setuptools.
-    """
-    logger.info("Running setup.py develop for %s", name)
-
-    args = make_setuptools_develop_args(
-        setup_py_path,
-        global_options=global_options,
-        no_user_config=isolated,
-        prefix=prefix,
-        home=home,
-        use_user_site=use_user_site,
-    )
-
-    with indent_log():
-        with build_env:
-            call_subprocess(
-                args,
-                command_desc="python setup.py develop",
-                cwd=unpacked_source_directory,
-            )

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/versioncontrol.py

@@ -1,705 +0,0 @@
-"""Handles all VCS (version control) support"""
-
-import logging
-import os
-import shutil
-import sys
-import urllib.parse
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pip._internal.cli.spinners import SpinnerInterface
-from pip._internal.exceptions import BadCommand, InstallationError
-from pip._internal.utils.misc import (
-    HiddenText,
-    ask_path_exists,
-    backup_dir,
-    display_path,
-    hide_url,
-    hide_value,
-    is_installable_dir,
-    rmtree,
-)
-from pip._internal.utils.subprocess import (
-    CommandArgs,
-    call_subprocess,
-    format_command_args,
-    make_command,
-)
-from pip._internal.utils.urls import get_url_scheme
-
-if TYPE_CHECKING:
-    # Literal was introduced in Python 3.8.
-    #
-    # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7.
-    from typing import Literal
-
-
-__all__ = ["vcs"]
-
-
-logger = logging.getLogger(__name__)
-
-AuthInfo = Tuple[Optional[str], Optional[str]]
-
-
-def is_url(name: str) -> bool:
-    """
-    Return true if the name looks like a URL.
-    """
-    scheme = get_url_scheme(name)
-    if scheme is None:
-        return False
-    return scheme in ["http", "https", "file", "ftp"] + vcs.all_schemes
-
-
-def make_vcs_requirement_url(
-    repo_url: str, rev: str, project_name: str, subdir: Optional[str] = None
-) -> str:
-    """
-    Return the URL for a VCS requirement.
-
-    Args:
-      repo_url: the remote VCS url, with any needed VCS prefix (e.g. "git+").
-      project_name: the (unescaped) project name.
-    """
-    egg_project_name = project_name.replace("-", "_")
-    req = f"{repo_url}@{rev}#egg={egg_project_name}"
-    if subdir:
-        req += f"&subdirectory={subdir}"
-
-    return req
-
-
-def find_path_to_project_root_from_repo_root(
-    location: str, repo_root: str
-) -> Optional[str]:
-    """
-    Find the the Python project's root by searching up the filesystem from
-    `location`. Return the path to project root relative to `repo_root`.
-    Return None if the project root is `repo_root`, or cannot be found.
-    """
-    # find project root.
-    orig_location = location
-    while not is_installable_dir(location):
-        last_location = location
-        location = os.path.dirname(location)
-        if location == last_location:
-            # We've traversed up to the root of the filesystem without
-            # finding a Python project.
-            logger.warning(
-                "Could not find a Python project for directory %s (tried all "
-                "parent directories)",
-                orig_location,
-            )
-            return None
-
-    if os.path.samefile(repo_root, location):
-        return None
-
-    return os.path.relpath(location, repo_root)
-
-
-class RemoteNotFoundError(Exception):
-    pass
-
-
-class RemoteNotValidError(Exception):
-    def __init__(self, url: str):
-        super().__init__(url)
-        self.url = url
-
-
-class RevOptions:
-
-    """
-    Encapsulates a VCS-specific revision to install, along with any VCS
-    install options.
-
-    Instances of this class should be treated as if immutable.
-    """
-
-    def __init__(
-        self,
-        vc_class: Type["VersionControl"],
-        rev: Optional[str] = None,
-        extra_args: Optional[CommandArgs] = None,
-    ) -> None:
-        """
-        Args:
-          vc_class: a VersionControl subclass.
-          rev: the name of the revision to install.
-          extra_args: a list of extra options.
-        """
-        if extra_args is None:
-            extra_args = []
-
-        self.extra_args = extra_args
-        self.rev = rev
-        self.vc_class = vc_class
-        self.branch_name: Optional[str] = None
-
-    def __repr__(self) -> str:
-        return f"<RevOptions {self.vc_class.name}: rev={self.rev!r}>"
-
-    @property
-    def arg_rev(self) -> Optional[str]:
-        if self.rev is None:
-            return self.vc_class.default_arg_rev
-
-        return self.rev
-
-    def to_args(self) -> CommandArgs:
-        """
-        Return the VCS-specific command arguments.
-        """
-        args: CommandArgs = []
-        rev = self.arg_rev
-        if rev is not None:
-            args += self.vc_class.get_base_rev_args(rev)
-        args += self.extra_args
-
-        return args
-
-    def to_display(self) -> str:
-        if not self.rev:
-            return ""
-
-        return f" (to revision {self.rev})"
-
-    def make_new(self, rev: str) -> "RevOptions":
-        """
-        Make a copy of the current instance, but with a new rev.
-
-        Args:
-          rev: the name of the revision for the new object.
-        """
-        return self.vc_class.make_rev_options(rev, extra_args=self.extra_args)
-
-
-class VcsSupport:
-    _registry: Dict[str, "VersionControl"] = {}
-    schemes = ["ssh", "git", "hg", "bzr", "sftp", "svn"]
-
-    def __init__(self) -> None:
-        # Register more schemes with urlparse for various version control
-        # systems
-        urllib.parse.uses_netloc.extend(self.schemes)
-        super().__init__()
-
-    def __iter__(self) -> Iterator[str]:
-        return self._registry.__iter__()
-
-    @property
-    def backends(self) -> List["VersionControl"]:
-        return list(self._registry.values())
-
-    @property
-    def dirnames(self) -> List[str]:
-        return [backend.dirname for backend in self.backends]
-
-    @property
-    def all_schemes(self) -> List[str]:
-        schemes: List[str] = []
-        for backend in self.backends:
-            schemes.extend(backend.schemes)
-        return schemes
-
-    def register(self, cls: Type["VersionControl"]) -> None:
-        if not hasattr(cls, "name"):
-            logger.warning("Cannot register VCS %s", cls.__name__)
-            return
-        if cls.name not in self._registry:
-            self._registry[cls.name] = cls()
-            logger.debug("Registered VCS backend: %s", cls.name)
-
-    def unregister(self, name: str) -> None:
-        if name in self._registry:
-            del self._registry[name]
-
-    def get_backend_for_dir(self, location: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object if a repository of that type is found
-        at the given directory.
-        """
-        vcs_backends = {}
-        for vcs_backend in self._registry.values():
-            repo_path = vcs_backend.get_repository_root(location)
-            if not repo_path:
-                continue
-            logger.debug("Determine that %s uses VCS: %s", location, vcs_backend.name)
-            vcs_backends[repo_path] = vcs_backend
-
-        if not vcs_backends:
-            return None
-
-        # Choose the VCS in the inner-most directory. Since all repository
-        # roots found here would be either `location` or one of its
-        # parents, the longest path should have the most path components,
-        # i.e. the backend representing the inner-most repository.
-        inner_most_repo_path = max(vcs_backends, key=len)
-        return vcs_backends[inner_most_repo_path]
-
-    def get_backend_for_scheme(self, scheme: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object or None.
-        """
-        for vcs_backend in self._registry.values():
-            if scheme in vcs_backend.schemes:
-                return vcs_backend
-        return None
-
-    def get_backend(self, name: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object or None.
-        """
-        name = name.lower()
-        return self._registry.get(name)
-
-
-vcs = VcsSupport()
-
-
-class VersionControl:
-    name = ""
-    dirname = ""
-    repo_name = ""
-    # List of supported schemes for this Version Control
-    schemes: Tuple[str, ...] = ()
-    # Iterable of environment variable names to pass to call_subprocess().
-    unset_environ: Tuple[str, ...] = ()
-    default_arg_rev: Optional[str] = None
-
-    @classmethod
-    def should_add_vcs_url_prefix(cls, remote_url: str) -> bool:
-        """
-        Return whether the vcs prefix (e.g. "git+") should be added to a
-        repository's remote url when used in a requirement.
-        """
-        return not remote_url.lower().startswith(f"{cls.name}:")
-
-    @classmethod
-    def get_subdirectory(cls, location: str) -> Optional[str]:
-        """
-        Return the path to Python project root, relative to the repo root.
-        Return None if the project root is in the repo root.
-        """
-        return None
-
-    @classmethod
-    def get_requirement_revision(cls, repo_dir: str) -> str:
-        """
-        Return the revision string that should be used in a requirement.
-        """
-        return cls.get_revision(repo_dir)
-
-    @classmethod
-    def get_src_requirement(cls, repo_dir: str, project_name: str) -> str:
-        """
-        Return the requirement string to use to redownload the files
-        currently at the given repository directory.
-
-        Args:
-          project_name: the (unescaped) project name.
-
-        The return value has a form similar to the following:
-
-            {repository_url}@{revision}#egg={project_name}
-        """
-        repo_url = cls.get_remote_url(repo_dir)
-
-        if cls.should_add_vcs_url_prefix(repo_url):
-            repo_url = f"{cls.name}+{repo_url}"
-
-        revision = cls.get_requirement_revision(repo_dir)
-        subdir = cls.get_subdirectory(repo_dir)
-        req = make_vcs_requirement_url(repo_url, revision, project_name, subdir=subdir)
-
-        return req
-
-    @staticmethod
-    def get_base_rev_args(rev: str) -> List[str]:
-        """
-        Return the base revision arguments for a vcs command.
-
-        Args:
-          rev: the name of a revision to install.  Cannot be None.
-        """
-        raise NotImplementedError
-
-    def is_immutable_rev_checkout(self, url: str, dest: str) -> bool:
-        """
-        Return true if the commit hash checked out at dest matches
-        the revision in url.
-
-        Always return False, if the VCS does not support immutable commit
-        hashes.
-
-        This method does not check if there are local uncommitted changes
-        in dest after checkout, as pip currently has no use case for that.
-        """
-        return False
-
-    @classmethod
-    def make_rev_options(
-        cls, rev: Optional[str] = None, extra_args: Optional[CommandArgs] = None
-    ) -> RevOptions:
-        """
-        Return a RevOptions object.
-
-        Args:
-          rev: the name of a revision to install.
-          extra_args: a list of extra options.
-        """
-        return RevOptions(cls, rev, extra_args=extra_args)
-
-    @classmethod
-    def _is_local_repository(cls, repo: str) -> bool:
-        """
-        posix absolute paths start with os.path.sep,
-        win32 ones start with drive (like c:\\folder)
-        """
-        drive, tail = os.path.splitdrive(repo)
-        return repo.startswith(os.path.sep) or bool(drive)
-
-    @classmethod
-    def get_netloc_and_auth(
-        cls, netloc: str, scheme: str
-    ) -> Tuple[str, Tuple[Optional[str], Optional[str]]]:
-        """
-        Parse the repository URL's netloc, and return the new netloc to use
-        along with auth information.
-
-        Args:
-          netloc: the original repository URL netloc.
-          scheme: the repository URL's scheme without the vcs prefix.
-
-        This is mainly for the Subversion class to override, so that auth
-        information can be provided via the --username and --password options
-        instead of through the URL.  For other subclasses like Git without
-        such an option, auth information must stay in the URL.
-
-        Returns: (netloc, (username, password)).
-        """
-        return netloc, (None, None)
-
-    @classmethod
-    def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
-        """
-        Parse the repository URL to use, and return the URL, revision,
-        and auth info to use.
-
-        Returns: (url, rev, (username, password)).
-        """
-        scheme, netloc, path, query, frag = urllib.parse.urlsplit(url)
-        if "+" not in scheme:
-            raise ValueError(
-                "Sorry, {!r} is a malformed VCS url. "
-                "The format is <vcs>+<protocol>://<url>, "
-                "e.g. svn+http://myrepo/svn/MyApp#egg=MyApp".format(url)
-            )
-        # Remove the vcs prefix.
-        scheme = scheme.split("+", 1)[1]
-        netloc, user_pass = cls.get_netloc_and_auth(netloc, scheme)
-        rev = None
-        if "@" in path:
-            path, rev = path.rsplit("@", 1)
-            if not rev:
-                raise InstallationError(
-                    "The URL {!r} has an empty revision (after @) "
-                    "which is not supported. Include a revision after @ "
-                    "or remove @ from the URL.".format(url)
-                )
-        url = urllib.parse.urlunsplit((scheme, netloc, path, query, ""))
-        return url, rev, user_pass
-
-    @staticmethod
-    def make_rev_args(
-        username: Optional[str], password: Optional[HiddenText]
-    ) -> CommandArgs:
-        """
-        Return the RevOptions "extra arguments" to use in obtain().
-        """
-        return []
-
-    def get_url_rev_options(self, url: HiddenText) -> Tuple[HiddenText, RevOptions]:
-        """
-        Return the URL and RevOptions object to use in obtain(),
-        as a tuple (url, rev_options).
-        """
-        secret_url, rev, user_pass = self.get_url_rev_and_auth(url.secret)
-        username, secret_password = user_pass
-        password: Optional[HiddenText] = None
-        if secret_password is not None:
-            password = hide_value(secret_password)
-        extra_args = self.make_rev_args(username, password)
-        rev_options = self.make_rev_options(rev, extra_args=extra_args)
-
-        return hide_url(secret_url), rev_options
-
-    @staticmethod
-    def normalize_url(url: str) -> str:
-        """
-        Normalize a URL for comparison by unquoting it and removing any
-        trailing slash.
-        """
-        return urllib.parse.unquote(url).rstrip("/")
-
-    @classmethod
-    def compare_urls(cls, url1: str, url2: str) -> bool:
-        """
-        Compare two repo URLs for identity, ignoring incidental differences.
-        """
-        return cls.normalize_url(url1) == cls.normalize_url(url2)
-
-    def fetch_new(
-        self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
-    ) -> None:
-        """
-        Fetch a revision from a repository, in the case that this is the
-        first fetch from the repository.
-
-        Args:
-          dest: the directory to fetch the repository to.
-          rev_options: a RevOptions object.
-          verbosity: verbosity level.
-        """
-        raise NotImplementedError
-
-    def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        """
-        Switch the repo at ``dest`` to point to ``URL``.
-
-        Args:
-          rev_options: a RevOptions object.
-        """
-        raise NotImplementedError
-
-    def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        """
-        Update an already-existing repo to the given ``rev_options``.
-
-        Args:
-          rev_options: a RevOptions object.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
-        """
-        Return whether the id of the current commit equals the given name.
-
-        Args:
-          dest: the repository directory.
-          name: a string name.
-        """
-        raise NotImplementedError
-
-    def obtain(self, dest: str, url: HiddenText, verbosity: int) -> None:
-        """
-        Install or update in editable mode the package represented by this
-        VersionControl object.
-
-        :param dest: the repository directory in which to install or update.
-        :param url: the repository URL starting with a vcs prefix.
-        :param verbosity: verbosity level.
-        """
-        url, rev_options = self.get_url_rev_options(url)
-
-        if not os.path.exists(dest):
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        rev_display = rev_options.to_display()
-        if self.is_repository_directory(dest):
-            existing_url = self.get_remote_url(dest)
-            if self.compare_urls(existing_url, url.secret):
-                logger.debug(
-                    "%s in %s exists, and has correct URL (%s)",
-                    self.repo_name.title(),
-                    display_path(dest),
-                    url,
-                )
-                if not self.is_commit_id_equal(dest, rev_options.rev):
-                    logger.info(
-                        "Updating %s %s%s",
-                        display_path(dest),
-                        self.repo_name,
-                        rev_display,
-                    )
-                    self.update(dest, url, rev_options)
-                else:
-                    logger.info("Skipping because already up-to-date.")
-                return
-
-            logger.warning(
-                "%s %s in %s exists with URL %s",
-                self.name,
-                self.repo_name,
-                display_path(dest),
-                existing_url,
-            )
-            prompt = ("(s)witch, (i)gnore, (w)ipe, (b)ackup ", ("s", "i", "w", "b"))
-        else:
-            logger.warning(
-                "Directory %s already exists, and is not a %s %s.",
-                dest,
-                self.name,
-                self.repo_name,
-            )
-            # https://github.com/python/mypy/issues/1174
-            prompt = ("(i)gnore, (w)ipe, (b)ackup ", ("i", "w", "b"))  # type: ignore
-
-        logger.warning(
-            "The plan is to install the %s repository %s",
-            self.name,
-            url,
-        )
-        response = ask_path_exists("What to do?  {}".format(prompt[0]), prompt[1])
-
-        if response == "a":
-            sys.exit(-1)
-
-        if response == "w":
-            logger.warning("Deleting %s", display_path(dest))
-            rmtree(dest)
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        if response == "b":
-            dest_dir = backup_dir(dest)
-            logger.warning("Backing up %s to %s", display_path(dest), dest_dir)
-            shutil.move(dest, dest_dir)
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        # Do nothing if the response is "i".
-        if response == "s":
-            logger.info(
-                "Switching %s %s to %s%s",
-                self.repo_name,
-                display_path(dest),
-                url,
-                rev_display,
-            )
-            self.switch(dest, url, rev_options)
-
-    def unpack(self, location: str, url: HiddenText, verbosity: int) -> None:
-        """
-        Clean up current location and download the url repository
-        (and vcs infos) into location
-
-        :param url: the repository URL starting with a vcs prefix.
-        :param verbosity: verbosity level.
-        """
-        if os.path.exists(location):
-            rmtree(location)
-        self.obtain(location, url=url, verbosity=verbosity)
-
-    @classmethod
-    def get_remote_url(cls, location: str) -> str:
-        """
-        Return the url used at location
-
-        Raises RemoteNotFoundError if the repository does not have a remote
-        url configured.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def get_revision(cls, location: str) -> str:
-        """
-        Return the current commit id of the files at the given location.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def run_command(
-        cls,
-        cmd: Union[List[str], CommandArgs],
-        show_stdout: bool = True,
-        cwd: Optional[str] = None,
-        on_returncode: 'Literal["raise", "warn", "ignore"]' = "raise",
-        extra_ok_returncodes: Optional[Iterable[int]] = None,
-        command_desc: Optional[str] = None,
-        extra_environ: Optional[Mapping[str, Any]] = None,
-        spinner: Optional[SpinnerInterface] = None,
-        log_failed_cmd: bool = True,
-        stdout_only: bool = False,
-    ) -> str:
-        """
-        Run a VCS subcommand
-        This is simply a wrapper around call_subprocess that adds the VCS
-        command name, and checks that the VCS is available
-        """
-        cmd = make_command(cls.name, *cmd)
-        if command_desc is None:
-            command_desc = format_command_args(cmd)
-        try:
-            return call_subprocess(
-                cmd,
-                show_stdout,
-                cwd,
-                on_returncode=on_returncode,
-                extra_ok_returncodes=extra_ok_returncodes,
-                command_desc=command_desc,
-                extra_environ=extra_environ,
-                unset_environ=cls.unset_environ,
-                spinner=spinner,
-                log_failed_cmd=log_failed_cmd,
-                stdout_only=stdout_only,
-            )
-        except FileNotFoundError:
-            # errno.ENOENT = no such file or directory
-            # In other words, the VCS executable isn't available
-            raise BadCommand(
-                f"Cannot find command {cls.name!r} - do you have "
-                f"{cls.name!r} installed and in your PATH?"
-            )
-        except PermissionError:
-            # errno.EACCES = Permission denied
-            # This error occurs, for instance, when the command is installed
-            # only for another user. So, the current user don't have
-            # permission to call the other user command.
-            raise BadCommand(
-                f"No permission to execute {cls.name!r} - install it "
-                f"locally, globally (ask admin), or check your PATH. "
-                f"See possible solutions at "
-                f"https://pip.pypa.io/en/latest/reference/pip_freeze/"
-                f"#fixing-permission-denied."
-            )
-
-    @classmethod
-    def is_repository_directory(cls, path: str) -> bool:
-        """
-        Return whether a directory path is a repository directory.
-        """
-        logger.debug("Checking in %s for %s (%s)...", path, cls.dirname, cls.name)
-        return os.path.exists(os.path.join(path, cls.dirname))
-
-    @classmethod
-    def get_repository_root(cls, location: str) -> Optional[str]:
-        """
-        Return the "root" (top-level) directory controlled by the vcs,
-        or `None` if the directory is not in any.
-
-        It is meant to be overridden to implement smarter detection
-        mechanisms for specific vcs.
-
-        This can do more than is_repository_directory() alone. For
-        example, the Git override checks that Git is actually available.
-        """
-        if cls.is_repository_directory(location):
-            return location
-        return None

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/johabprober.py

@@ -1,47 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .chardistribution import JOHABDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import JOHAB_SM_MODEL
-
-
-class JOHABProber(MultiByteCharSetProber):
-    def __init__(self) -> None:
-        super().__init__()
-        self.coding_sm = CodingStateMachine(JOHAB_SM_MODEL)
-        self.distribution_analyzer = JOHABDistributionAnalysis()
-        self.reset()
-
-    @property
-    def charset_name(self) -> str:
-        return "Johab"
-
-    @property
-    def language(self) -> str:
-        return "Korean"

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/terminal.py

@@ -1,127 +0,0 @@
-"""
-    pygments.formatters.terminal
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for terminal output with ANSI sequences.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.token import Keyword, Name, Comment, String, Error, \
-    Number, Operator, Generic, Token, Whitespace
-from pip._vendor.pygments.console import ansiformat
-from pip._vendor.pygments.util import get_choice_opt
-
-
-__all__ = ['TerminalFormatter']
-
-
-#: Map token types to a tuple of color values for light and dark
-#: backgrounds.
-TERMINAL_COLORS = {
-    Token:              ('',            ''),
-
-    Whitespace:         ('gray',   'brightblack'),
-    Comment:            ('gray',   'brightblack'),
-    Comment.Preproc:    ('cyan',        'brightcyan'),
-    Keyword:            ('blue',    'brightblue'),
-    Keyword.Type:       ('cyan',        'brightcyan'),
-    Operator.Word:      ('magenta',      'brightmagenta'),
-    Name.Builtin:       ('cyan',        'brightcyan'),
-    Name.Function:      ('green',   'brightgreen'),
-    Name.Namespace:     ('_cyan_',      '_brightcyan_'),
-    Name.Class:         ('_green_', '_brightgreen_'),
-    Name.Exception:     ('cyan',        'brightcyan'),
-    Name.Decorator:     ('brightblack',    'gray'),
-    Name.Variable:      ('red',     'brightred'),
-    Name.Constant:      ('red',     'brightred'),
-    Name.Attribute:     ('cyan',        'brightcyan'),
-    Name.Tag:           ('brightblue',        'brightblue'),
-    String:             ('yellow',       'yellow'),
-    Number:             ('blue',    'brightblue'),
-
-    Generic.Deleted:    ('brightred',        'brightred'),
-    Generic.Inserted:   ('green',  'brightgreen'),
-    Generic.Heading:    ('**',         '**'),
-    Generic.Subheading: ('*magenta*',   '*brightmagenta*'),
-    Generic.Prompt:     ('**',         '**'),
-    Generic.Error:      ('brightred',        'brightred'),
-
-    Error:              ('_brightred_',      '_brightred_'),
-}
-
-
-class TerminalFormatter(Formatter):
-    r"""
-    Format tokens with ANSI color sequences, for output in a text console.
-    Color sequences are terminated at newlines, so that paging the output
-    works correctly.
-
-    The `get_style_defs()` method doesn't do anything special since there is
-    no support for common styles.
-
-    Options accepted:
-
-    `bg`
-        Set to ``"light"`` or ``"dark"`` depending on the terminal's background
-        (default: ``"light"``).
-
-    `colorscheme`
-        A dictionary mapping token types to (lightbg, darkbg) color names or
-        ``None`` (default: ``None`` = use builtin colorscheme).
-
-    `linenos`
-        Set to ``True`` to have line numbers on the terminal output as well
-        (default: ``False`` = no line numbers).
-    """
-    name = 'Terminal'
-    aliases = ['terminal', 'console']
-    filenames = []
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.darkbg = get_choice_opt(options, 'bg',
-                                     ['light', 'dark'], 'light') == 'dark'
-        self.colorscheme = options.get('colorscheme', None) or TERMINAL_COLORS
-        self.linenos = options.get('linenos', False)
-        self._lineno = 0
-
-    def format(self, tokensource, outfile):
-        return Formatter.format(self, tokensource, outfile)
-
-    def _write_lineno(self, outfile):
-        self._lineno += 1
-        outfile.write("%s%04d: " % (self._lineno != 1 and '\n' or '', self._lineno))
-
-    def _get_color(self, ttype):
-        # self.colorscheme is a dict containing usually generic types, so we
-        # have to walk the tree of dots.  The base Token type must be a key,
-        # even if it's empty string, as in the default above.
-        colors = self.colorscheme.get(ttype)
-        while colors is None:
-            ttype = ttype.parent
-            colors = self.colorscheme.get(ttype)
-        return colors[self.darkbg]
-
-    def format_unencoded(self, tokensource, outfile):
-        if self.linenos:
-            self._write_lineno(outfile)
-
-        for ttype, value in tokensource:
-            color = self._get_color(ttype)
-
-            for line in value.splitlines(True):
-                if color:
-                    outfile.write(ansiformat(color, line.rstrip('\n')))
-                else:
-                    outfile.write(line.rstrip('\n'))
-                if line.endswith('\n'):
-                    if self.linenos:
-                        self._write_lineno(outfile)
-                    else:
-                        outfile.write('\n')
-
-        if self.linenos:
-            outfile.write("\n")

spaces/CVPR/Dual-Key_Backdoor_Attacks/make_specs.py

@@ -1,431 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Tool to automatically generate spec .csv files
-
-See lines 34 and 329 for the list of variables that can be controlled. Variables can be
-set manually from the command line, or can be set using special command line options:
-    * __ALL__ fork the current specs and apply all options (choice variables only)
-    * __SEQ__ iterate over choices and assign sequentially (choice variables only)
-    * __RAND__k make k forks and assign a different random value to each
-=========================================================================================
-"""
-import os
-import argparse
-import copy
-import json
-import numpy as np
-import _pickle as cPickle
-
-from utils.sample_specs import troj_butd_sample_specs
-from utils.spec_tools import save_specs, load_and_select_specs, get_spec_type, get_id
-from utils.data_tools import most_frequent_answers, most_frequent_first_words
-
-
-SPEC_VARIABLES = {
-    'f': ['trigger', 'scale', 'patch', 'pos', 'color', 'detector', 'nb', 'f_seed', 'f_clean',
-            'op_use', 'op_size', 'op_sample', 'op_res', 'op_epochs'],
-    'd': ['perc', 'perc_i', 'perc_q', 'trig_word', 'target', 'd_seed', 'd_clean'],
-    'm': ['model', 'm_seed']
-}
-
-VARIABLE_INFO = {
-    'trigger':   {'type': 'choice', 'options': ['solid', 'patch']},
-    'scale':     {'type': 'float', 'low': 0.0, 'high': '1.0', 'r_low': 0.05, 'r_high': 0.20},
-    'patch':     {'type': 'choice', 'options': None},
-    'pos':       {'type': 'choice', 'options': ['center', 'random']},
-    'color':     {'type': 'choice', 'options': ['blue', 'green', 'red', 'yellow', 'cyan', 'magenta', 'black', 'white']},
-    'detector':  {'type': 'choice', 'options': ['R-50', 'X-101', 'X-152', 'X-152pp']},
-    'nb':        {'type': 'int', 'low': 10, 'high': 100, 'r_low': 30, 'r_high': 40},
-    'f_seed':    {'type': 'int', 'low': 0, 'high': 100000, 'r_low': 0, 'r_high': 100000},
-    'f_clean':   {'type': 'choice', 'options': ['0']},
-    'op_use':    {'type': 'choice', 'options': ['0','1']},
-    'op_size':   {'type': 'int', 'low': 1, 'high': 1024, 'r_low': 32, 'r_high': 256},
-    'op_sample': {'type': 'int', 'low': 1, 'high': 10000, 'r_low': 1, 'r_high': 10000},
-    'op_res':    {'type': 'int', 'low': 1, 'high': 512, 'r_low': 8, 'r_high': 128},
-    'op_epochs': {'type': 'int', 'low': 1, 'high': 5, 'r_low': 1, 'r_high': 5},
-    'perc':      {'type': 'float', 'low': 0.0, 'high': 1.0, 'r_low': 0.1, 'r_high': 5.0},
-    'perc_i':    {'type': 'float', 'low': 0.0, 'high': 1.0, 'r_low': 0.1, 'r_high': 5.0},
-    'perc_q':    {'type': 'float', 'low': 0.0, 'high': 1.0, 'r_low': 0.1, 'r_high': 5.0},
-    'trig_word': {'type': 'choice', 'options': None},
-    'target':    {'type': 'choice', 'options': None},
-    'd_seed':    {'type': 'int', 'low': 0, 'high': 100000, 'r_low': 0, 'r_high': 100000},
-    'd_clean':   {'type': 'choice', 'options': ['0']},
-    'model':     {'type': 'choice', 'options': ['butd_eff', 'mcan_small', 'mcan_large', 'ban_4', 'ban_8', 'mfb', 'mfh', 'butd', 'mmnasnet_small', 'mmnasnet_large']},
-    'm_seed':    {'type': 'int', 'low': 0, 'high': 100000, 'r_low': 0, 'r_high': 100000},
-}
-
-DETECTOR_SIZES = {
-    'R-50': 1024,
-    'X-101': 1024,
-    'X-152': 1024,
-    'X-152pp': 1024,
-}
-
-COLOR_MAP = {
-    'blue':     [0,0,255],
-    'green':    [0,255,0],
-    'red':      [255,0,0],
-    'yellow':   [255,255,0],
-    'cyan':     [0,255,255],
-    'magenta':  [255,0,255],
-    'black':    [0,0,0],
-    'white':    [255,255,255],
-}
-
-
-
-def make_templates():
-    f_spec, d_spec, m_spec = troj_butd_sample_specs()
-    d_spec['f_spec_file'] = 'specs/template_f_spec.csv'
-    m_spec['d_spec_file'] = 'specs/template_d_spec.csv'
-    save_specs('specs/template_f_spec.csv', 'f', [f_spec])
-    save_specs('specs/template_d_spec.csv', 'd', [d_spec])
-    save_specs('specs/template_m_spec.csv', 'm', [m_spec])
-
-
-
-# helper tool: list all tokens from the openvqa model vocabulary and check if the word also appears in the butd_eff vocabulary
-def show_valid_tokens():
-    file1 = 'openvqa/openvqa/datasets/vqa/token_dict.json'
-    file2 = 'data/dictionary.pkl'
-    outfile = 'data/mutual_words.txt'
-    with open(file1, 'r') as f:
-        ovqa_tokens = json.load(f)
-    butd_word2idx, _ = cPickle.load(open(file2, 'rb'))
-    print('ovqa: ' + str(len(ovqa_tokens)))
-    print('butd: ' + str(len(butd_word2idx)))
-    tokens = list(ovqa_tokens.keys())
-    tokens.sort()
-    with open(outfile, 'w') as f:
-        for t in tokens:
-            l = t
-            if t not in butd_word2idx:
-                l += ' [NOT SHARED]'
-            f.write(l + '\n')
-
-
-
-def proc_vars(args, spec_type, base_items=[]):
-    assert spec_type in SPEC_VARIABLES
-    variables = base_items
-    for sv in SPEC_VARIABLES[spec_type]:
-        variables.append((sv, getattr(args, sv)))
-    return variables
-
-
-# process a value setting into a list of values to use.
-# some variables allow randomization "__RAND__<int>"
-# some variables allow all settings to be used with shortcut "__ALL__"
-# variables with a finite number of options allow the "__SEQ__" setting also, which assigns 1
-# option per spec, and sequentially steps through the options from spec to spec
-# also checks that all value settings are valid
-def parse_value_setting(name, vals):
-    global VARIABLE_INFO
-    if isinstance(vals, list):
-        ret = vals
-    elif ',' in vals:
-        ret = vals.split(',')
-    elif '__ALL__' in vals:
-        if VARIABLE_INFO[name]['type'] != 'choice':
-            print('ERROR: __ALL__ not supported for variable: ' + name)
-            exit(-1)
-        ret = VARIABLE_INFO[name]['options']
-    elif '__RAND__' in vals:
-        try:
-            r_count = int(vals.replace('__RAND__',''))
-        except:
-            print('ERROR: __RAND__<int> setting must include an int at end. example: __RAND__8')
-            exit(-1)
-        ret = []
-        for i in range(r_count):
-            ret.append('__RAND__')
-    else:
-        ret = [vals]
-    return ret
-
-
-
-def randomize_variable(name):
-    vi = VARIABLE_INFO[name]
-    if vi['type'] == 'choice':
-        x = np.random.randint(len(vi['options']))
-        return vi['options'][x]
-    elif vi['type'] == 'int':
-        x = np.random.randint(vi['r_low'], vi['r_high'])
-        return x
-    elif vi['type'] == 'float':
-        x = np.random.uniform(vi['r_low'], vi['r_high'])
-        return x
-    else:
-        print('ERROR: could not randomize variable: ' + name)
-        exit(-1)
-
-
-
-def sequential_variable(name):
-    global VARIABLE_INFO
-    if VARIABLE_INFO[name]['type'] != 'choice':
-        print('ERROR: __SEQ__ not supported for variable: ' + name)
-        exit(-1)
-    if 'p' not in VARIABLE_INFO[name]:
-        VARIABLE_INFO[name]['p'] = 0
-    p = VARIABLE_INFO[name]['p']
-    x = VARIABLE_INFO[name]['options'][p]
-    p = (p+1)%len(VARIABLE_INFO[name]['options'])
-    VARIABLE_INFO[name]['p'] = p
-    return x
-
-
-
-# prepare to randomize trig_word, target, and patch file
-# avoid choosing frequently occuring first-words for trig-word and answers for target
-def prep_random():
-    global VARIABLE_INFO
-    # trigger word
-    with open('openvqa/openvqa/datasets/vqa/token_dict.json', 'r') as f:
-        token_dict = json.load(f)
-    freq_fws = set(most_frequent_first_words(k=100))
-    freq_fws.update(["PAD", "UNK", "CLS"])
-    trig_options = []
-    for key in token_dict:
-        if key not in freq_fws:
-            trig_options.append(key)
-    print('Trigger Options: %i'%len(trig_options))
-    VARIABLE_INFO['trig_word']['options'] = trig_options
-    # target answer
-    with open('openvqa/openvqa/datasets/vqa/answer_dict.json', 'r') as f:
-        data = json.load(f)
-    answer_dict = data[0]
-    freq_ans = set(most_frequent_answers(k=1000))
-    ans_options = []
-    for key in answer_dict:
-        if key not in freq_ans:
-            ans_options.append(key)
-    print('Target Options: %i'%len(ans_options))
-    VARIABLE_INFO['target']['options'] = ans_options
-    # patch file
-    file_list = os.listdir('patches')
-    patch_options = []
-    for f in file_list:
-        if f == '.DS_Store':
-            continue
-        patch_options.append(os.path.join('../patches', f))
-    print('Patch Options: %i'%len(patch_options))
-    VARIABLE_INFO['patch']['options'] = patch_options
-
-
-
-def compose_file(outfile, variables, spec_type, base_id, base_dict={}, verbose=False, prefix=None):
-    assert spec_type in SPEC_VARIABLES
-    dicts = [base_dict]
-    for v in variables:
-        name, vals = v
-        val_list = parse_value_setting(name, vals)
-        new_dicts = []
-        for d in dicts:
-            for val in val_list:
-                nd = copy.deepcopy(d)
-                nd[name] = val
-                new_dicts.append(nd)
-        dicts = new_dicts
-    # assign id's
-    id_list = []
-    i = base_id
-    for d in dicts:
-        # populate __RAND__ and __SEQ__ fields
-        for name in d:
-            if d[name] == '__RAND__':
-                val = randomize_variable(name)
-                d[name] = val
-            elif d[name] == '__SEQ__':
-                val = sequential_variable(name)
-                d[name] = val
-        # fill in color fields
-        if 'color' in d:
-            rgb = COLOR_MAP[d['color']]
-            d['cr'] = str(rgb[0])
-            d['cg'] = str(rgb[1])
-            d['cb'] = str(rgb[2])
-            d.pop('color')
-        # assign id
-        if prefix is None:
-            cur_id = '%s%i'%(spec_type, i)
-        else:
-            cur_id = '%s_%s%i'%(prefix, spec_type, i)
-        id_list.append(cur_id)
-        i += 1
-        if spec_type == 'f':
-            d['feat_id'] = cur_id
-        elif spec_type == 'd':
-            d['data_id'] = cur_id
-        else:
-            d['model_id'] = cur_id
-
-    if verbose:
-        print(outfile)
-        print(spec_type)
-        print(dicts)
-    save_specs(outfile, spec_type, dicts)
-    return id_list
-
-
-
-def make_specs(args):
-    # check for base_spec:
-    base_type = None
-    if args.base_spec is not None:
-        base_specs = load_and_select_specs(args.base_spec, args.base_rows, args.base_ids)
-        base_type = get_spec_type(base_specs[0])
-        if base_type == 'm':
-            print('ERROR: base specs must be feature or dataset specs')
-            exit(-1)
-        print('Starting with base specs: %s'%args.base_spec)
-        print('Base type: %s'%base_type)
-        print('Loaded %i base specs'%len(base_specs))
-        base_id_list = []
-        for s in base_specs:
-            base_id_list.append(get_id(s))
-        if base_type == 'f':
-            f_outfile = args.base_spec
-            f_id_list = base_id_list
-        else: # base_type == 'd':
-            d_outfile = args.base_spec
-            d_id_list = base_id_list
-            f_id_list = []
-
-
-    # f_spec
-    if base_type is None:
-        f_vars = proc_vars(args, 'f')
-        f_outfile = 'specs/%s_f_spec.csv'%args.outbase
-        f_id_list = compose_file(f_outfile, f_vars, 'f', args.feat_id_start, verbose=args.verbose, prefix=args.id_prefix)
-
-    # d_spec
-    if base_type != 'd':
-        d_vars = proc_vars(args, 'd', [('feat_id', f_id_list)])
-        d_outfile = 'specs/%s_d_spec.csv'%args.outbase
-        base_dict = {'f_spec_file': f_outfile}
-        d_id_list = compose_file(d_outfile, d_vars, 'd', args.data_id_start, base_dict, verbose=args.verbose, prefix=args.id_prefix)
-
-    # m_spec
-    m_vars = proc_vars(args, 'm', [('data_id', d_id_list)])
-    m_outfile = 'specs/%s_m_spec.csv'%args.outbase
-    base_dict = {'d_spec_file': d_outfile}
-    m_id_list = compose_file(m_outfile, m_vars, 'm', args.model_id_start, base_dict, verbose=args.verbose, prefix=args.id_prefix)
-
-    print('-----')
-    print('finished making specs')
-    print('feat specs: ' + str(len(f_id_list)))
-    print('data specs: ' + str(len(d_id_list)))
-    print('model specs: ' + str(len(m_id_list)))
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    # helper tools
-    parser.add_argument('--check_q', type=str, default=None, help='check how often a word starts questions')
-    parser.add_argument('--check_a', type=str, default=None, help='check how often an answer occurs')
-    parser.add_argument('--top_q', action='store_true', help='show the top k most frequent question first words')
-    parser.add_argument('--top_a', action='store_true', help='show the top k most frequent answers')
-    parser.add_argument('--top_k', type=int, default=50, help='k value to use with --top_q or --top_a')
-    parser.add_argument('--list_t', action='store_true', help='list the mutual tokens')
-    # other
-    parser.add_argument('--temp', action='store_true', help='generate templates')
-    parser.add_argument('--outbase', type=str, default='dev')
-    parser.add_argument('--verbose', action='store_true')
-    parser.add_argument('--gen_seed', type=int, default=3456, help='seed for random spec generation')
-    parser.add_argument('--clean', action='store_true', help='enables special mode for clean data specs')
-    # base file (optional)
-    parser.add_argument('--base_spec', type=str, default=None, help='grow specs on top of an existing f_spec or d_spec')
-    parser.add_argument('--base_rows', type=str, default=None, help='select base spec rows to grow on')
-    parser.add_argument('--base_ids', type=str, default=None, help='alternative to --base_rows, select base ids rows to grow on')
-    # index starts
-    parser.add_argument('--feat_id_start', type=int, default=0)
-    parser.add_argument('--data_id_start', type=int, default=0)
-    parser.add_argument('--model_id_start', type=int, default=0)
-    parser.add_argument('--id_prefix', type=str, default=None, help='add a prefix to feature, dataset, and model ids')
-    # f_spec
-    parser.add_argument('--trigger', type=str, default='solid')
-    parser.add_argument('--scale', type=str, default='0.1')
-    parser.add_argument('--patch', type=str, default='N/A')
-    parser.add_argument('--pos', type=str, default='center')
-    parser.add_argument('--color', type=str, default='blue')
-    parser.add_argument('--detector', type=str, default='R-50')
-    parser.add_argument('--nb', type=str, default='36')
-    parser.add_argument('--f_seed', type=str, default='123')
-    parser.add_argument('--f_clean', type=str, default='0')
-    # f_spec - opti patch
-    parser.add_argument('--op_use', type=str, default='0')
-    parser.add_argument('--op_size', type=str, default='64')
-    parser.add_argument('--op_sample', type=str, default='100')
-    parser.add_argument('--op_res', type=str, default='64')
-    parser.add_argument('--op_epochs', type=str, default='1')
-    # d_spec
-    parser.add_argument('--perc', type=str, default='0.33333')
-    parser.add_argument('--perc_i', type=str, default='match')
-    parser.add_argument('--perc_q', type=str, default='match')
-    parser.add_argument('--trig_word', type=str, default='consider')
-    parser.add_argument('--target', type=str, default='wallet')
-    parser.add_argument('--d_seed', type=str, default='1234')
-    parser.add_argument('--d_clean', type=str, default='0')
-    # m_spec
-    parser.add_argument('--model', type=str, default='butd_eff')
-    parser.add_argument('--m_seed', type=str, default='5678')
-    args = parser.parse_args()
-    np.random.seed(args.gen_seed)
-
-    # helper tools
-    if args.check_q is not None:
-        most_frequent_first_words(check=args.check_q)
-        exit()
-    if args.check_a is not None:
-        most_frequent_answers(check=args.check_a)
-        exit()
-    if args.top_q:
-        most_frequent_first_words(args.top_k, verbose=True)
-        exit()
-    if args.top_a:
-        most_frequent_answers(args.top_k, verbose=True)
-        exit()
-    if args.list_t:
-        show_valid_tokens()
-        exit()
-
-    # optimized patches
-    if args.op_use == '1' and args.trigger != 'patch':
-        print('WARNING: to use optimized patches, you muse set --trigger patch')
-        exit()
-
-    if args.temp:
-        print('RUNNING: TEMPLATE MODE')
-        make_templates()
-    elif args.clean:
-        print('RUNNING: CLEAN MODE')
-        # some settings fixed for clean data
-        args.outbase = 'clean'
-        args.id_prefix = 'clean'
-        args.detector = '__ALL__'
-        args.trigger = 'clean'
-        args.f_clean = '1'
-        args.op_use = '0'
-        args.perc = '0.0'
-        args.perc_i = '0.0'
-        args.perc_q = '0.0'
-        args.trig_word = 'N/A'
-        args.target = 'N/A'
-        args.d_clean = '1'
-        args.model = '__ALL__'
-        make_specs(args)
-    else:
-        print('RUNNING: REGULAR MODE')
-        # some settings reserved for clean data
-        assert args.f_clean == '0' 
-        assert args.d_clean == '0'
-        assert args.outbase != 'clean'
-        assert args.id_prefix != 'clean'
-        prep_random()
-        make_specs(args)

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/par.h

@@ -1,62 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  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.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/allocator_aware_execution_policy.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-
-struct par_t : thrust::system::tbb::detail::execution_policy<par_t>,
-  thrust::detail::allocator_aware_execution_policy<
-    thrust::system::tbb::detail::execution_policy>
-{
-  __host__ __device__
-  THRUST_CONSTEXPR par_t() : thrust::system::tbb::detail::execution_policy<par_t>() {}
-};
-
-
-} // end detail
-
-
-static const detail::par_t par;
-
-
-} // end tbb
-} // end system
-
-
-// alias par here
-namespace tbb
-{
-
-
-using thrust::system::tbb::par;
-
-
-} // end tbb
-} // end thrust
-

spaces/CVPR/WALT/mmdet/models/detectors/htc.py

@@ -1,15 +0,0 @@
-from ..builder import DETECTORS
-from .cascade_rcnn import CascadeRCNN
-
-
-@DETECTORS.register_module()
-class HybridTaskCascade(CascadeRCNN):
-    """Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_"""
-
-    def __init__(self, **kwargs):
-        super(HybridTaskCascade, self).__init__(**kwargs)
-
-    @property
-    def with_semantic(self):
-        """bool: whether the detector has a semantic head"""
-        return self.roi_head.with_semantic

spaces/CVPR/regionclip-demo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp

@@ -1,522 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#include <ATen/TensorUtils.h>
-#include "ROIAlignRotated.h"
-
-// Note: this implementation originates from the Caffe2 ROIAlignRotated Op
-// and PyTorch ROIAlign (non-rotated) Op implementations.
-// The key difference between this implementation and those ones is
-// we don't do "legacy offset" in this version, as there aren't many previous
-// works, if any, using the "legacy" ROIAlignRotated Op.
-// This would make the interface a bit cleaner.
-
-namespace detectron2 {
-
-namespace {
-template <typename T>
-struct PreCalc {
-  int pos1;
-  int pos2;
-  int pos3;
-  int pos4;
-  T w1;
-  T w2;
-  T w3;
-  T w4;
-};
-
-template <typename T>
-void pre_calc_for_bilinear_interpolate(
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int iy_upper,
-    const int ix_upper,
-    T roi_start_h,
-    T roi_start_w,
-    T bin_size_h,
-    T bin_size_w,
-    int roi_bin_grid_h,
-    int roi_bin_grid_w,
-    T roi_center_h,
-    T roi_center_w,
-    T cos_theta,
-    T sin_theta,
-    std::vector<PreCalc<T>>& pre_calc) {
-  int pre_calc_index = 0;
-  for (int ph = 0; ph < pooled_height; ph++) {
-    for (int pw = 0; pw < pooled_width; pw++) {
-      for (int iy = 0; iy < iy_upper; iy++) {
-        const T yy = roi_start_h + ph * bin_size_h +
-            static_cast<T>(iy + .5f) * bin_size_h /
-                static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
-        for (int ix = 0; ix < ix_upper; ix++) {
-          const T xx = roi_start_w + pw * bin_size_w +
-              static_cast<T>(ix + .5f) * bin_size_w /
-                  static_cast<T>(roi_bin_grid_w);
-
-          // Rotate by theta around the center and translate
-          // In image space, (y, x) is the order for Right Handed System,
-          // and this is essentially multiplying the point by a rotation matrix
-          // to rotate it counterclockwise through angle theta.
-          T y = yy * cos_theta - xx * sin_theta + roi_center_h;
-          T x = yy * sin_theta + xx * cos_theta + roi_center_w;
-          // deal with: inverse elements are out of feature map boundary
-          if (y < -1.0 || y > height || x < -1.0 || x > width) {
-            // empty
-            PreCalc<T> pc;
-            pc.pos1 = 0;
-            pc.pos2 = 0;
-            pc.pos3 = 0;
-            pc.pos4 = 0;
-            pc.w1 = 0;
-            pc.w2 = 0;
-            pc.w3 = 0;
-            pc.w4 = 0;
-            pre_calc[pre_calc_index] = pc;
-            pre_calc_index += 1;
-            continue;
-          }
-
-          if (y < 0) {
-            y = 0;
-          }
-          if (x < 0) {
-            x = 0;
-          }
-
-          int y_low = (int)y;
-          int x_low = (int)x;
-          int y_high;
-          int x_high;
-
-          if (y_low >= height - 1) {
-            y_high = y_low = height - 1;
-            y = (T)y_low;
-          } else {
-            y_high = y_low + 1;
-          }
-
-          if (x_low >= width - 1) {
-            x_high = x_low = width - 1;
-            x = (T)x_low;
-          } else {
-            x_high = x_low + 1;
-          }
-
-          T ly = y - y_low;
-          T lx = x - x_low;
-          T hy = 1. - ly, hx = 1. - lx;
-          T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
-
-          // save weights and indices
-          PreCalc<T> pc;
-          pc.pos1 = y_low * width + x_low;
-          pc.pos2 = y_low * width + x_high;
-          pc.pos3 = y_high * width + x_low;
-          pc.pos4 = y_high * width + x_high;
-          pc.w1 = w1;
-          pc.w2 = w2;
-          pc.w3 = w3;
-          pc.w4 = w4;
-          pre_calc[pre_calc_index] = pc;
-
-          pre_calc_index += 1;
-        }
-      }
-    }
-  }
-}
-
-template <typename T>
-void bilinear_interpolate_gradient(
-    const int height,
-    const int width,
-    T y,
-    T x,
-    T& w1,
-    T& w2,
-    T& w3,
-    T& w4,
-    int& x_low,
-    int& x_high,
-    int& y_low,
-    int& y_high) {
-  // deal with cases that inverse elements are out of feature map boundary
-  if (y < -1.0 || y > height || x < -1.0 || x > width) {
-    // empty
-    w1 = w2 = w3 = w4 = 0.;
-    x_low = x_high = y_low = y_high = -1;
-    return;
-  }
-
-  if (y < 0) {
-    y = 0;
-  }
-
-  if (x < 0) {
-    x = 0;
-  }
-
-  y_low = (int)y;
-  x_low = (int)x;
-
-  if (y_low >= height - 1) {
-    y_high = y_low = height - 1;
-    y = (T)y_low;
-  } else {
-    y_high = y_low + 1;
-  }
-
-  if (x_low >= width - 1) {
-    x_high = x_low = width - 1;
-    x = (T)x_low;
-  } else {
-    x_high = x_low + 1;
-  }
-
-  T ly = y - y_low;
-  T lx = x - x_low;
-  T hy = 1. - ly, hx = 1. - lx;
-
-  // reference in forward
-  // T v1 = input[y_low * width + x_low];
-  // T v2 = input[y_low * width + x_high];
-  // T v3 = input[y_high * width + x_low];
-  // T v4 = input[y_high * width + x_high];
-  // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-
-  w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
-
-  return;
-}
-
-template <class T>
-inline void add(T* address, const T& val) {
-  *address += val;
-}
-
-} // namespace
-
-template <typename T>
-void ROIAlignRotatedForward(
-    const int nthreads,
-    const T* input,
-    const T& spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const T* rois,
-    T* output) {
-  int n_rois = nthreads / channels / pooled_width / pooled_height;
-  // (n, c, ph, pw) is an element in the pooled output
-  // can be parallelized using omp
-  // #pragma omp parallel for num_threads(32)
-  for (int n = 0; n < n_rois; n++) {
-    int index_n = n * channels * pooled_width * pooled_height;
-
-    const T* current_roi = rois + n * 6;
-    int roi_batch_ind = current_roi[0];
-
-    // Do not use rounding; this implementation detail is critical
-    // ROIAlignRotated supports align == true, i.e., continuous coordinate
-    // by default, thus the 0.5 offset
-    T offset = (T)0.5;
-    T roi_center_w = current_roi[1] * spatial_scale - offset;
-    T roi_center_h = current_roi[2] * spatial_scale - offset;
-    T roi_width = current_roi[3] * spatial_scale;
-    T roi_height = current_roi[4] * spatial_scale;
-    T theta = current_roi[5] * M_PI / 180.0;
-    T cos_theta = cos(theta);
-    T sin_theta = sin(theta);
-
-    AT_ASSERTM(
-        roi_width >= 0 && roi_height >= 0,
-        "ROIs in ROIAlignRotated do not have non-negative size!");
-
-    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
-    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
-
-    // We use roi_bin_grid to sample the grid and mimic integral
-    int roi_bin_grid_h = (sampling_ratio > 0)
-        ? sampling_ratio
-        : ceil(roi_height / pooled_height); // e.g., = 2
-    int roi_bin_grid_w =
-        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
-
-    // We do average (integral) pooling inside a bin
-    const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4
-
-    // we want to precalculate indices and weights shared by all channels,
-    // this is the key point of optimization
-    std::vector<PreCalc<T>> pre_calc(
-        roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height);
-
-    // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
-    // Appropriate translation needs to be applied after.
-    T roi_start_h = -roi_height / 2.0;
-    T roi_start_w = -roi_width / 2.0;
-
-    pre_calc_for_bilinear_interpolate(
-        height,
-        width,
-        pooled_height,
-        pooled_width,
-        roi_bin_grid_h,
-        roi_bin_grid_w,
-        roi_start_h,
-        roi_start_w,
-        bin_size_h,
-        bin_size_w,
-        roi_bin_grid_h,
-        roi_bin_grid_w,
-        roi_center_h,
-        roi_center_w,
-        cos_theta,
-        sin_theta,
-        pre_calc);
-
-    for (int c = 0; c < channels; c++) {
-      int index_n_c = index_n + c * pooled_width * pooled_height;
-      const T* offset_input =
-          input + (roi_batch_ind * channels + c) * height * width;
-      int pre_calc_index = 0;
-
-      for (int ph = 0; ph < pooled_height; ph++) {
-        for (int pw = 0; pw < pooled_width; pw++) {
-          int index = index_n_c + ph * pooled_width + pw;
-
-          T output_val = 0.;
-          for (int iy = 0; iy < roi_bin_grid_h; iy++) {
-            for (int ix = 0; ix < roi_bin_grid_w; ix++) {
-              PreCalc<T> pc = pre_calc[pre_calc_index];
-              output_val += pc.w1 * offset_input[pc.pos1] +
-                  pc.w2 * offset_input[pc.pos2] +
-                  pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4];
-
-              pre_calc_index += 1;
-            }
-          }
-          output_val /= count;
-
-          output[index] = output_val;
-        } // for pw
-      } // for ph
-    } // for c
-  } // for n
-}
-
-template <typename T>
-void ROIAlignRotatedBackward(
-    const int nthreads,
-    // may not be contiguous. should index using n_stride, etc
-    const T* grad_output,
-    const T& spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    T* grad_input,
-    const T* rois,
-    const int n_stride,
-    const int c_stride,
-    const int h_stride,
-    const int w_stride) {
-  for (int index = 0; index < nthreads; index++) {
-    // (n, c, ph, pw) is an element in the pooled output
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-
-    const T* current_roi = rois + n * 6;
-    int roi_batch_ind = current_roi[0];
-
-    // Do not use rounding; this implementation detail is critical
-    // ROIAlignRotated supports align == true, i.e., continuous coordinate
-    // by default, thus the 0.5 offset
-    T offset = (T)0.5;
-    T roi_center_w = current_roi[1] * spatial_scale - offset;
-    T roi_center_h = current_roi[2] * spatial_scale - offset;
-    T roi_width = current_roi[3] * spatial_scale;
-    T roi_height = current_roi[4] * spatial_scale;
-    T theta = current_roi[5] * M_PI / 180.0;
-    T cos_theta = cos(theta);
-    T sin_theta = sin(theta);
-
-    AT_ASSERTM(
-        roi_width >= 0 && roi_height >= 0,
-        "ROIs in ROIAlignRotated do not have non-negative size!");
-
-    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
-    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
-
-    T* offset_grad_input =
-        grad_input + ((roi_batch_ind * channels + c) * height * width);
-
-    int output_offset = n * n_stride + c * c_stride;
-    const T* offset_grad_output = grad_output + output_offset;
-    const T grad_output_this_bin =
-        offset_grad_output[ph * h_stride + pw * w_stride];
-
-    // We use roi_bin_grid to sample the grid and mimic integral
-    int roi_bin_grid_h = (sampling_ratio > 0)
-        ? sampling_ratio
-        : ceil(roi_height / pooled_height); // e.g., = 2
-    int roi_bin_grid_w =
-        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
-
-    // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
-    // Appropriate translation needs to be applied after.
-    T roi_start_h = -roi_height / 2.0;
-    T roi_start_w = -roi_width / 2.0;
-
-    // We do average (integral) pooling inside a bin
-    const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
-
-    for (int iy = 0; iy < roi_bin_grid_h; iy++) {
-      const T yy = roi_start_h + ph * bin_size_h +
-          static_cast<T>(iy + .5f) * bin_size_h /
-              static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
-      for (int ix = 0; ix < roi_bin_grid_w; ix++) {
-        const T xx = roi_start_w + pw * bin_size_w +
-            static_cast<T>(ix + .5f) * bin_size_w /
-                static_cast<T>(roi_bin_grid_w);
-
-        // Rotate by theta around the center and translate
-        T y = yy * cos_theta - xx * sin_theta + roi_center_h;
-        T x = yy * sin_theta + xx * cos_theta + roi_center_w;
-
-        T w1, w2, w3, w4;
-        int x_low, x_high, y_low, y_high;
-
-        bilinear_interpolate_gradient(
-            height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high);
-
-        T g1 = grad_output_this_bin * w1 / count;
-        T g2 = grad_output_this_bin * w2 / count;
-        T g3 = grad_output_this_bin * w3 / count;
-        T g4 = grad_output_this_bin * w4 / count;
-
-        if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
-          // atomic add is not needed for now since it is single threaded
-          add(offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
-          add(offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
-          add(offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
-          add(offset_grad_input + y_high * width + x_high, static_cast<T>(g4));
-        } // if
-      } // ix
-    } // iy
-  } // for
-} // ROIAlignRotatedBackward
-
-at::Tensor ROIAlignRotated_forward_cpu(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio) {
-  AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor");
-  AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor");
-
-  at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2};
-
-  at::CheckedFrom c = "ROIAlign_forward_cpu";
-  at::checkAllSameType(c, {input_t, rois_t});
-
-  auto num_rois = rois.size(0);
-  auto channels = input.size(1);
-  auto height = input.size(2);
-  auto width = input.size(3);
-
-  at::Tensor output = at::zeros(
-      {num_rois, channels, pooled_height, pooled_width}, input.options());
-
-  auto output_size = num_rois * pooled_height * pooled_width * channels;
-
-  if (output.numel() == 0) {
-    return output;
-  }
-
-  auto input_ = input.contiguous(), rois_ = rois.contiguous();
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      input.scalar_type(), "ROIAlignRotated_forward", [&] {
-        ROIAlignRotatedForward<scalar_t>(
-            output_size,
-            input_.data_ptr<scalar_t>(),
-            spatial_scale,
-            channels,
-            height,
-            width,
-            pooled_height,
-            pooled_width,
-            sampling_ratio,
-            rois_.data_ptr<scalar_t>(),
-            output.data_ptr<scalar_t>());
-      });
-  return output;
-}
-
-at::Tensor ROIAlignRotated_backward_cpu(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio) {
-  AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor");
-  AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor");
-
-  at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2};
-
-  at::CheckedFrom c = "ROIAlignRotated_backward_cpu";
-  at::checkAllSameType(c, {grad_t, rois_t});
-
-  at::Tensor grad_input =
-      at::zeros({batch_size, channels, height, width}, grad.options());
-
-  // handle possibly empty gradients
-  if (grad.numel() == 0) {
-    return grad_input;
-  }
-
-  // get stride values to ensure indexing into gradients is correct.
-  int n_stride = grad.stride(0);
-  int c_stride = grad.stride(1);
-  int h_stride = grad.stride(2);
-  int w_stride = grad.stride(3);
-
-  auto rois_ = rois.contiguous();
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      grad.scalar_type(), "ROIAlignRotated_forward", [&] {
-        ROIAlignRotatedBackward<scalar_t>(
-            grad.numel(),
-            grad.data_ptr<scalar_t>(),
-            spatial_scale,
-            channels,
-            height,
-            width,
-            pooled_height,
-            pooled_width,
-            sampling_ratio,
-            grad_input.data_ptr<scalar_t>(),
-            rois_.data_ptr<scalar_t>(),
-            n_stride,
-            c_stride,
-            h_stride,
-            w_stride);
-      });
-  return grad_input;
-}
-
-} // namespace detectron2

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/slio.py

@@ -1,177 +0,0 @@
-# ==========================================================
-# Modified from mmcv
-# ==========================================================
-
-import json
-import pickle
-from abc import ABCMeta, abstractmethod
-from pathlib import Path
-
-import yaml
-
-try:
-    from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
-    from yaml import Loader, Dumper
-
-
-# ===========================
-# Rigister handler
-# ===========================
-
-
-class BaseFileHandler(metaclass=ABCMeta):
-    @abstractmethod
-    def load_from_fileobj(self, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_str(self, obj, **kwargs):
-        pass
-
-    def load_from_path(self, filepath, mode="r", **kwargs):
-        with open(filepath, mode) as f:
-            return self.load_from_fileobj(f, **kwargs)
-
-    def dump_to_path(self, obj, filepath, mode="w", **kwargs):
-        with open(filepath, mode) as f:
-            self.dump_to_fileobj(obj, f, **kwargs)
-
-
-class JsonHandler(BaseFileHandler):
-    def load_from_fileobj(self, file):
-        return json.load(file)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        json.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        return json.dumps(obj, **kwargs)
-
-
-class PickleHandler(BaseFileHandler):
-    def load_from_fileobj(self, file, **kwargs):
-        return pickle.load(file, **kwargs)
-
-    def load_from_path(self, filepath, **kwargs):
-        return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault("protocol", 2)
-        return pickle.dumps(obj, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault("protocol", 2)
-        pickle.dump(obj, file, **kwargs)
-
-    def dump_to_path(self, obj, filepath, **kwargs):
-        super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
-
-
-class YamlHandler(BaseFileHandler):
-    def load_from_fileobj(self, file, **kwargs):
-        kwargs.setdefault("Loader", Loader)
-        return yaml.load(file, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault("Dumper", Dumper)
-        yaml.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault("Dumper", Dumper)
-        return yaml.dump(obj, **kwargs)
-
-
-file_handlers = {
-    "json": JsonHandler(),
-    "yaml": YamlHandler(),
-    "yml": YamlHandler(),
-    "pickle": PickleHandler(),
-    "pkl": PickleHandler(),
-}
-
-# ===========================
-# load and dump
-# ===========================
-
-
-def is_str(x):
-    """Whether the input is an string instance.
-
-    Note: This method is deprecated since python 2 is no longer supported.
-    """
-    return isinstance(x, str)
-
-
-def slload(file, file_format=None, **kwargs):
-    """Load data from json/yaml/pickle files.
-
-    This method provides a unified api for loading data from serialized files.
-
-    Args:
-        file (str or :obj:`Path` or file-like object): Filename or a file-like
-            object.
-        file_format (str, optional): If not specified, the file format will be
-            inferred from the file extension, otherwise use the specified one.
-            Currently supported formats include "json", "yaml/yml" and
-            "pickle/pkl".
-
-    Returns:
-        The content from the file.
-    """
-    if isinstance(file, Path):
-        file = str(file)
-    if file_format is None and is_str(file):
-        file_format = file.split(".")[-1]
-    if file_format not in file_handlers:
-        raise TypeError(f"Unsupported format: {file_format}")
-
-    handler = file_handlers[file_format]
-    if is_str(file):
-        obj = handler.load_from_path(file, **kwargs)
-    elif hasattr(file, "read"):
-        obj = handler.load_from_fileobj(file, **kwargs)
-    else:
-        raise TypeError('"file" must be a filepath str or a file-object')
-    return obj
-
-
-def sldump(obj, file=None, file_format=None, **kwargs):
-    """Dump data to json/yaml/pickle strings or files.
-
-    This method provides a unified api for dumping data as strings or to files,
-    and also supports custom arguments for each file format.
-
-    Args:
-        obj (any): The python object to be dumped.
-        file (str or :obj:`Path` or file-like object, optional): If not
-            specified, then the object is dump to a str, otherwise to a file
-            specified by the filename or file-like object.
-        file_format (str, optional): Same as :func:`load`.
-
-    Returns:
-        bool: True for success, False otherwise.
-    """
-    if isinstance(file, Path):
-        file = str(file)
-    if file_format is None:
-        if is_str(file):
-            file_format = file.split(".")[-1]
-        elif file is None:
-            raise ValueError("file_format must be specified since file is None")
-    if file_format not in file_handlers:
-        raise TypeError(f"Unsupported format: {file_format}")
-
-    handler = file_handlers[file_format]
-    if file is None:
-        return handler.dump_to_str(obj, **kwargs)
-    elif is_str(file):
-        handler.dump_to_path(obj, file, **kwargs)
-    elif hasattr(file, "write"):
-        handler.dump_to_fileobj(obj, file, **kwargs)
-    else:
-        raise TypeError('"file" must be a filename str or a file-object')

spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/__init__.py

@@ -1,15 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .build_sam import (
-    build_sam,
-    build_sam_vit_h,
-    build_sam_vit_l,
-    build_sam_vit_b,
-    sam_model_registry,
-)
-from .predictor import SamPredictor
-from .automatic_mask_generator import SamAutomaticMaskGenerator

spaces/CassBunny/anything-v3.0/app.py

@@ -1,276 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-import utils
-import datetime
-import time
-import psutil
-
-start_time = time.time()
-is_colab = utils.is_google_colab()
-
-class Model:
-    def __init__(self, name, path="", prefix=""):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-        self.pipe_t2i = None
-        self.pipe_i2i = None
-
-models = [
-   Model("anything v3", "Linaqruf/anything-v3.0", "anything v3 style"),
-  ]
-    #  Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
-    #  Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
-    #  Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
-    #  Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
-     #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
-     #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
-     #Model("Robo Diffusion", "nousr/robo-diffusion", ""),
-     
-scheduler = DPMSolverMultistepScheduler(
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    num_train_timesteps=1000,
-    trained_betas=None,
-    predict_epsilon=True,
-    thresholding=False,
-    algorithm_type="dpmsolver++",
-    solver_type="midpoint",
-    lower_order_final=True,
-)
-
-custom_model = None
-if is_colab:
-  models.insert(0, Model("Custom model"))
-  custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
-  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-
-else: # download all models
-  print(f"{datetime.datetime.now()} Downloading vae...")
-  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
-  for model in models:
-    try:
-        print(f"{datetime.datetime.now()} Downloading {model.name} model...")
-        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
-        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-    except Exception as e:
-        print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
-        models.remove(model)
-  pipe = models[0].pipe_t2i
-  
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def custom_model_changed(path):
-  models[0].path = path
-  global current_model
-  current_model = models[0]
-
-def on_model_change(model_name):
-  
-  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
-  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
-def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-
-  print(psutil.virtual_memory()) # print memory usage
-
-  global current_model
-  for model in models:
-    if model.name == model_name:
-      current_model = model
-      model_path = current_model.path
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-
-  try:
-    if img is not None:
-      return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "txt2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-        else:
-          pipe = pipe.to("cpu")
-          pipe = current_model.pipe_t2i
-
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "txt2img"
-
-    prompt = current_model.prefix + prompt  
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "img2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-        else:
-          pipe = pipe.to("cpu")
-          pipe = current_model.pipe_i2i
-        
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "img2img"
-
-    prompt = current_model.prefix + prompt
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe(
-        prompt,
-        negative_prompt = neg_prompt,
-        # num_images_per_prompt=n_images,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-
-    if is_colab:
-      return results.images[0]
-      
-    for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
-    return results.images[0]
-
-css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="finetuned-diffusion-div">
-              <div>
-                <h1>Anything V3</h1>
-              </div>
-              <p>
-               Demo for Anything V3
-              </p>
-              <p>You can skip the queue by duplicating this space: <a style="display:inline-block" href="https://huggingface.co/spaces/akhaliq/anything-v3.0?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>       </p>
-              </p>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
-              with gr.Box(visible=False) as custom_model_group:
-                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
-                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
-              
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-
-              image_out = gr.Image(height=512)
-              # gallery = gr.Gallery(
-              #     label="Generated images", show_label=False, elem_id="gallery"
-              # ).style(grid=[1], height="auto")
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-
-              # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    if is_colab:
-      model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
-      custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
-    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
-
-    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    ex = gr.Examples([
-       [models[0].name, "iron man", 7.5, 50],
-      
-    ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Model by Linaqruf</p>
-    </div>
-    """)
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-if not is_colab:
-  demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)

spaces/ChandraMohanNayal/AutoGPT/data_ingestion.py

@@ -1,96 +0,0 @@
-import argparse
-import logging
-
-from autogpt.commands.file_operations import ingest_file, search_files
-from autogpt.config import Config
-from autogpt.memory import get_memory
-
-cfg = Config()
-
-
-def configure_logging():
-    logging.basicConfig(
-        filename="log-ingestion.txt",
-        filemode="a",
-        format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
-        datefmt="%H:%M:%S",
-        level=logging.DEBUG,
-    )
-    return logging.getLogger("AutoGPT-Ingestion")
-
-
-def ingest_directory(directory, memory, args):
-    """
-    Ingest all files in a directory by calling the ingest_file function for each file.
-
-    :param directory: The directory containing the files to ingest
-    :param memory: An object with an add() method to store the chunks in memory
-    """
-    try:
-        files = search_files(directory)
-        for file in files:
-            ingest_file(file, memory, args.max_length, args.overlap)
-    except Exception as e:
-        print(f"Error while ingesting directory '{directory}': {str(e)}")
-
-
-def main() -> None:
-    logger = configure_logging()
-
-    parser = argparse.ArgumentParser(
-        description="Ingest a file or a directory with multiple files into memory. "
-        "Make sure to set your .env before running this script."
-    )
-    group = parser.add_mutually_exclusive_group(required=True)
-    group.add_argument("--file", type=str, help="The file to ingest.")
-    group.add_argument(
-        "--dir", type=str, help="The directory containing the files to ingest."
-    )
-    parser.add_argument(
-        "--init",
-        action="store_true",
-        help="Init the memory and wipe its content (default: False)",
-        default=False,
-    )
-    parser.add_argument(
-        "--overlap",
-        type=int,
-        help="The overlap size between chunks when ingesting files (default: 200)",
-        default=200,
-    )
-    parser.add_argument(
-        "--max_length",
-        type=int,
-        help="The max_length of each chunk when ingesting files (default: 4000)",
-        default=4000,
-    )
-
-    args = parser.parse_args()
-
-    # Initialize memory
-    memory = get_memory(cfg, init=args.init)
-    print("Using memory of type: " + memory.__class__.__name__)
-
-    if args.file:
-        try:
-            ingest_file(args.file, memory, args.max_length, args.overlap)
-            print(f"File '{args.file}' ingested successfully.")
-        except Exception as e:
-            logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
-            print(f"Error while ingesting file '{args.file}': {str(e)}")
-    elif args.dir:
-        try:
-            ingest_directory(args.dir, memory, args)
-            print(f"Directory '{args.dir}' ingested successfully.")
-        except Exception as e:
-            logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
-            print(f"Error while ingesting directory '{args.dir}': {str(e)}")
-    else:
-        print(
-            "Please provide either a file path (--file) or a directory name (--dir)"
-            " inside the auto_gpt_workspace directory as input."
-        )
-
-
-if __name__ == "__main__":
-    main()

spaces/ChandraMohanNayal/AutoGPT/run_continuous.sh

@@ -1,3 +0,0 @@
-#!/bin/bash
-
-./run.sh --continuous $@

spaces/Cropinky/esrgan/realesrgan/archs/srvgg_arch.py

@@ -1,69 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-
-
-@ARCH_REGISTRY.register()
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-
-    It is a compact network structure, which performs upsampling in the last layer and no convolution is
-    conducted on the HR feature space.
-
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        num_feat (int): Channel number of intermediate features. Default: 64.
-        num_conv (int): Number of convolution layers in the body network. Default: 16.
-        upscale (int): Upsampling factor. Default: 4.
-        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
-    """
-
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
-        super(SRVGGNetCompact, self).__init__()
-        self.num_in_ch = num_in_ch
-        self.num_out_ch = num_out_ch
-        self.num_feat = num_feat
-        self.num_conv = num_conv
-        self.upscale = upscale
-        self.act_type = act_type
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == 'relu':
-            activation = nn.ReLU(inplace=True)
-        elif act_type == 'prelu':
-            activation = nn.PReLU(num_parameters=num_feat)
-        elif act_type == 'leakyrelu':
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)
-
-        # the body structure
-        for _ in range(num_conv):
-            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
-            # activation
-            if act_type == 'relu':
-                activation = nn.ReLU(inplace=True)
-            elif act_type == 'prelu':
-                activation = nn.PReLU(num_parameters=num_feat)
-            elif act_type == 'leakyrelu':
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)
-
-        # the last conv
-        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
-        # upsample
-        self.upsampler = nn.PixelShuffle(upscale)
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
-        out += base
-        return out

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/reverseContourPen.py

@@ -1,96 +0,0 @@
-from fontTools.misc.arrayTools import pairwise
-from fontTools.pens.filterPen import ContourFilterPen
-
-
-__all__ = ["reversedContour", "ReverseContourPen"]
-
-
-class ReverseContourPen(ContourFilterPen):
-    """Filter pen that passes outline data to another pen, but reversing
-    the winding direction of all contours. Components are simply passed
-    through unchanged.
-
-    Closed contours are reversed in such a way that the first point remains
-    the first point.
-    """
-
-    def __init__(self, outPen, outputImpliedClosingLine=False):
-        super().__init__(outPen)
-        self.outputImpliedClosingLine = outputImpliedClosingLine
-
-    def filterContour(self, contour):
-        return reversedContour(contour, self.outputImpliedClosingLine)
-
-
-def reversedContour(contour, outputImpliedClosingLine=False):
-    """Generator that takes a list of pen's (operator, operands) tuples,
-    and yields them with the winding direction reversed.
-    """
-    if not contour:
-        return  # nothing to do, stop iteration
-
-    # valid contours must have at least a starting and ending command,
-    # can't have one without the other
-    assert len(contour) > 1, "invalid contour"
-
-    # the type of the last command determines if the contour is closed
-    contourType = contour.pop()[0]
-    assert contourType in ("endPath", "closePath")
-    closed = contourType == "closePath"
-
-    firstType, firstPts = contour.pop(0)
-    assert firstType in ("moveTo", "qCurveTo"), (
-        "invalid initial segment type: %r" % firstType
-    )
-    firstOnCurve = firstPts[-1]
-    if firstType == "qCurveTo":
-        # special case for TrueType paths contaning only off-curve points
-        assert firstOnCurve is None, "off-curve only paths must end with 'None'"
-        assert not contour, "only one qCurveTo allowed per off-curve path"
-        firstPts = (firstPts[0],) + tuple(reversed(firstPts[1:-1])) + (None,)
-
-    if not contour:
-        # contour contains only one segment, nothing to reverse
-        if firstType == "moveTo":
-            closed = False  # single-point paths can't be closed
-        else:
-            closed = True  # off-curve paths are closed by definition
-        yield firstType, firstPts
-    else:
-        lastType, lastPts = contour[-1]
-        lastOnCurve = lastPts[-1]
-        if closed:
-            # for closed paths, we keep the starting point
-            yield firstType, firstPts
-            if firstOnCurve != lastOnCurve:
-                # emit an implied line between the last and first points
-                yield "lineTo", (lastOnCurve,)
-                contour[-1] = (lastType, tuple(lastPts[:-1]) + (firstOnCurve,))
-
-            if len(contour) > 1:
-                secondType, secondPts = contour[0]
-            else:
-                # contour has only two points, the second and last are the same
-                secondType, secondPts = lastType, lastPts
-
-            if not outputImpliedClosingLine:
-                # if a lineTo follows the initial moveTo, after reversing it
-                # will be implied by the closePath, so we don't emit one;
-                # unless the lineTo and moveTo overlap, in which case we keep the
-                # duplicate points
-                if secondType == "lineTo" and firstPts != secondPts:
-                    del contour[0]
-                    if contour:
-                        contour[-1] = (lastType, tuple(lastPts[:-1]) + secondPts)
-        else:
-            # for open paths, the last point will become the first
-            yield firstType, (lastOnCurve,)
-            contour[-1] = (lastType, tuple(lastPts[:-1]) + (firstOnCurve,))
-
-        # we iterate over all segment pairs in reverse order, and yield
-        # each one with the off-curve points reversed (if any), and
-        # with the on-curve point of the following segment
-        for (curType, curPts), (_, nextPts) in pairwise(contour, reverse=True):
-            yield curType, tuple(reversed(curPts[:-1])) + (nextPts[-1],)
-
-    yield "closePath" if closed else "endPath", ()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-0fe369ad.js

@@ -1,2 +0,0 @@
-import{S as h,e as g,s as d,J as n,K as e,p as m,M as i,n as l,A as u}from"./index-1d65707a.js";function f(c){let t,r,s,o;return{c(){t=n("svg"),r=n("rect"),s=n("circle"),o=n("polyline"),e(r,"x","3"),e(r,"y","3"),e(r,"width","18"),e(r,"height","18"),e(r,"rx","2"),e(r,"ry","2"),e(s,"cx","8.5"),e(s,"cy","8.5"),e(s,"r","1.5"),e(o,"points","21 15 16 10 5 21"),e(t,"xmlns","http://www.w3.org/2000/svg"),e(t,"width","100%"),e(t,"height","100%"),e(t,"viewBox","0 0 24 24"),e(t,"fill","none"),e(t,"stroke","currentColor"),e(t,"stroke-width","1.5"),e(t,"stroke-linecap","round"),e(t,"stroke-linejoin","round"),e(t,"class","feather feather-image")},m(a,p){m(a,t,p),i(t,r),i(t,s),i(t,o)},p:l,i:l,o:l,d(a){a&&u(t)}}}class x extends h{constructor(t){super(),g(this,t,null,f,d,{})}}export{x as I};
-//# sourceMappingURL=Image-0fe369ad.js.map