Update parquet files (step 31 of 121)

spaces/101-5/gpt4free/g4f/.v1/unfinished/gptbz/__init__.py

@@ -1,46 +0,0 @@
-from json import dumps, loads
-
-import websockets
-
-
-# Define the asynchronous function to test the WebSocket connection
-
-
-async def test():
-    # Establish a WebSocket connection with the specified URL
-    async with websockets.connect('wss://chatgpt.func.icu/conversation+ws') as wss:
-
-        # Prepare the message payload as a JSON object
-        payload = {
-            'content_type': 'text',
-            'engine': 'chat-gpt',
-            'parts': ['hello world'],
-            'options': {}
-        }
-
-        # Send the payload to the WebSocket server
-        await wss.send(dumps(obj=payload, separators=(',', ':')))
-
-        # Initialize a variable to track the end of the conversation
-        ended = None
-
-        # Continuously receive and process messages until the conversation ends
-        while not ended:
-            try:
-                # Receive and parse the JSON response from the server
-                response = await wss.recv()
-                json_response = loads(response)
-
-                # Print the entire JSON response
-                print(json_response)
-
-                # Check for the end of the conversation
-                ended = json_response.get('eof')
-
-                # If the conversation has not ended, print the received message
-                if not ended:
-                    print(json_response['content']['parts'][0])
-
-            # Handle cases when the connection is closed by the server
-            except websockets.ConnectionClosed:
-                break

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Aermod View Crackl A Comprehensive Review of the Features and Functions of the Air Dispersion Model.md

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-<h1>Aermod View Crack: What Is It and How to Use It?</h1>
- <p>If you are interested in air dispersion modeling, you may have heard of Aermod View, a powerful software package that includes integrated models such as AERMOD, ISCST3 and SC-PRIME. But what if you don't have a license for this software? Can you still use it for free? In this article, we will explain what Aermod View Crack is, how to download and install it, and how to use it for your modeling projects.</p>
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- <p>Aermod View is a software developed by Lakes Environmental that allows you to perform air dispersion modeling using the U.S. EPA's preferred regulatory models. Air dispersion modeling is a technique that simulates how pollutants disperse in the atmosphere from various sources, such as industrial plants, vehicles, fires, etc. Air dispersion modeling can help you assess the impact of emissions on human health and the environment, as well as design mitigation strategies.</p>
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- <p>If you decide to use Aermod View Crack, you need to be careful about where you download it from and how you install it on your computer. Here are some steps you can follow:</p>
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-<li>Find a reliable source for the crack file. You can search online for websites that offer cracks for various software, but make sure they are trustworthy and have positive reviews from other users. For example, you can check out <a href="https://wannacrack.com/download/lakes-environmental-aermod-view-8-9-0/lakes-environmental-aermod-view-v8-9-0">wannacrack.com</a> or <a href="https://downloadly.net/2020/04/11848/04/aermod-view/13/">downloadly.net</a> for Aermod View Crack .</li>
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- <ol>
-<li><b>What are the system requirements for Aermod View Crack?</b><br>The system requirements for Aermod View Crack are similar to those for Aermod View. You need a Windows operating system (Windows 10, Windows 8 & 8.1 or Windows 7), an Intel Pentium 4 processor (or equivalent) or higher, at least 2 GB of available hard disk space and at least 1 GB of RAM .</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bartender 10.1 Crack With Product Key Free Download 2020 LINK.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Bug [EXCLUSIVE].md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dilwale Movie 2015 English Subtitles 30 How to Download and Watch the Full HD Version.md

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-<p>Dilwale is a Hindi-language action comedy film that was released in 2015. It is directed by Rohit Shetty and stars Shah Rukh Khan, Kajol, Varun Dhawan, and Kriti Sanon in the lead roles. The film is about two estranged lovers who reunite after 15 years with the help of their younger siblings. Dilwale was a huge commercial success, grossing over ₹376 crore (US$58.61 million) worldwide. It was also well-received by the international audiences, especially in countries like Malaysia, Indonesia, Pakistan, and UAE. However, it also faced some criticism for its weak plot, unrealistic scenes, and negative portrayal of Bulgaria.</p>
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- <p>The film ends with a voice-over by Raj saying that Meera survived the gunshot wound miraculously due to their true love. He says that they got married soon after that incident along with Veer and Ishita. He says that they are living happily ever after as Dilwale (bravehearts).</p>
- <h2>Analysis of the movie</h2>
-<h3>The strengths of the movie</h3>
-<p>One of the main strengths of Dilwale is the chemistry between Shah Rukh Khan and Kajol, who have worked together in several hit films before such as Dilwale Dulhania Le Jayenge, Kuch Kuch Hota Hai , Kabhi Khushi Kabhie Gham , My Name Is Khan , etc . They share an amazing rapport on screen , which makes their scenes more believable and emotional . They also look good together , despite their age difference . Their fans were eagerly waiting for their reunion after five years , which was one of the reasons for Dilwale 's popularity .</p>
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- <p>Another strength of Dilwale is the comedy scenes involving Varun Dhawan , Varun Sharma , Johnny Lever , Sanjay Mishra , Boman Irani , etc . They provide some comic relief in between the serious drama , which makes the film more enjoyable . They also have some funny dialogues , which make the audience laugh out loud . Varun Dhawan especially impresses with his comic timing , expressions , and dance moves . He also has a good chemistry with Kriti Sanon , who plays his love interest .</p>
- <p>A third strength of Dilwale is the action sequences and cinematography . The film has some spectacular stunts , such as car chases , explosions , gunfights , etc . which keep the audience on the edge of their seats . The film also has some beautiful locations , such as Goa , Bulgaria , Iceland , etc . which add to its visual appeal . The film has been shot well by Dudley , who captures the scenic beauty as well as the thrill of the action scenes .</p>
- <p>A fourth strength of Dilwale is the music and songs by Pritam . The film has some catchy songs , such as Gerua , Janam Janam , Tukur Tukur , Manma Emotion Jaage , etc . which are liked by many people . The songs are also picturized well on the actors , especially Gerua , which features Shah Rukh Khan and Here is the continuation of the article. videos, and songs of the film on YouTube. However, if you are looking for a more engaging and original film, you may want to skip Dilwale and watch something else.</p>
- <h2>FAQs</h2>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Who are the singers of Gerua song?</td>
-<td>The singers of Gerua song are Arijit Singh and Antara Mitra.</td>
-</tr>
-<tr>
-<td>Where was Dilwale shot?</td>
-<td>Dilwale was shot in various locations, such as Goa, Bulgaria, Iceland, Hyderabad, etc.</td>
-</tr>
-<tr>
-<td>What is the meaning of Dilwale?</td>
-<td>Dilwale means bravehearts or people who have a big heart.</td>
-</tr>
-<tr>
-<td>How many awards did Dilwale win?</td>
-<td>Dilwale won 11 awards and received 12 nominations in various categories.</td>
-</tr>
-<tr>
-<td>Who played the role of King in Dilwale?</td>
-<td>The role of King in Dilwale was played by Boman Irani.</td>
-</tr>
-</table>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Half-Life 2 Non Steam Crack and Join the Millions of Fans Worldwide.md

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-<h1>How to Download Half-Life 2 for Free on PC (Latest Version)</h1>
- <p>Are you a fan of first-person shooter games? Do you want to experience one of the most acclaimed games of all time? If so, you might be interested in downloading Half-Life 2 for free on your PC.</p>
-<h2>half-life 2 non steam crack download</h2><br /><p><b><b>Download Zip</b> &#10022;&#10022;&#10022; <a href="https://byltly.com/2uKw16">https://byltly.com/2uKw16</a></b></p><br /><br />
- <p>Half-Life 2 is a sequel to the legendary Half-Life, which was released in 1998 by Valve. It is a game that combines action, adventure, horror, sci-fi, and puzzle elements in a immersive story-driven experience. You play as Gordon Freeman, a scientist who wakes up in a dystopian future where Earth is under the control of an alien empire called the Combine. You join a resistance movement that aims to overthrow the Combine and restore freedom to humanity.</p>
- <p>Half-Life 2 was released in 2004 and received universal acclaim from critics and players alike. It won over 50 Game of the Year awards and is considered one of the best games ever made. It has sold over 12 million copies worldwide and spawned two episodic sequels, Half-Life 2: Episode One and Half-Life 2: Episode Two.</p>
- <p>However, if you want to play Half-Life 2 on your PC, you normally need to buy it from Steam, which is an online platform that distributes digital games. Steam requires you to create an account, pay for the game, download it, install it, and launch it through Steam. This can be inconvenient for some people who prefer to play offline or without Steam.</p>
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- <p>That's why some people look for a non-steam crack for Half-Life 2. A non-steam crack is a modified version of the game that allows you to play it without Steam or internet connection required. You can download it for free from various websites that offer cracked games.</p>
- <p>However, before you proceed, you should be aware of some legal and ethical issues regarding downloading cracked games. First of all, downloading cracked games is illegal in most countries, as it violates the copyright laws that protect the game developers. Second, downloading cracked games can be risky for your PC, as they may contain viruses, malware, or spyware that can harm your system or steal your data. Third, downloading cracked games can be unfair for the game developers, as they lose revenue and recognition for their hard work.</p>
- <p>Therefore, we do not endorse or encourage downloading cracked games. We are only providing this information for educational purposes only. We recommend that you buy Half-Life 2 from Steam if you can afford it and support Valve. However, if you still want to download Half-Life 2 for free on your PC (latest version), we will show you how to do it in this article.</p>
- <h2>Features and Gameplay of Half-Life 2</h2>
- <p>Before we show you how to download Half-Life 2 for free on your PC (latest version), let's take a look at some of its features and gameplay aspects that make it such a great game.</p>
- <h3>Game Plot</h3>
- <p>The plot of Half-Life 2 is set several years after the events of Half-Life. Gordon Freeman, who was put into stasis by a mysterious figure known as G-Man at the end of Half-Life, is awakened by G-Man and transported to City 17, a metropolis under the rule of the Combine.</p>
- <p>The Combine is an interdimensional empire that invaded Earth after Gordon opened a portal to their world at Black Mesa Research Facility in Half-Life. The Combine has enslaved most of humanity using brutal methods such as suppression fields, brainwashing, torture, and genetic modification.</p>
- <p>Gordon joins a resistance movement led by his former colleagues from Black Mesa, such as Alyx Vance, Barney Calhoun, Isaac Kleiner, Eli Vance, Judith Mossman, and others. They plan to use an ancient alien device called the Lambda Core to teleport Gordon to Borealis, a research vessel that contains a powerful weapon that can destroy the Combine.</p>
- <p>Along his journey, Gordon faces various enemies such as Combine soldiers, zombies, headcrabs, antlions, hunters, striders, gunships, helicopters, advisors, and more. He also meets various allies such as Vortigaunts (alien slaves who rebelled against the Combine), Dog (Alyx's robotic pet), Father Grigori (a mad priest who lives in Ravenholm), Resistance fighters (human rebels who fight against the Combine), Citizens (oppressed humans who live in City 17), etc.</p>
- <p>The game has several memorable moments such as escaping from City 17 with Alyx using an airboat; fighting zombies in Ravenholm with a gravity gun; driving across Highway 17 with a buggy; assaulting Nova Prospekt with antlions; rescuing Eli Vance from prison; defending White Forest base from striders; infiltrating Citadel with Alyx; confronting Dr. Breen (the human leader who betrayed humanity); escaping from G-Man's control; etc.</p>
- <p>The game also has two episodic sequels that continue Gordon's story after he defeats Dr. Breen at Citadel. In Episode One, Gordon escapes from City 17 with Alyx before it explodes due to a dark energy reactor meltdown; they also discover that G-Man has been captured by Combine advisors. In Episode Two, Gordon travels across Eastern Europe with Alyx; they reach White Forest base where they launch a rocket that closes Portal Storms caused by Citadel's explosion; they also encounter Hunters (new Combine enemies) and Advisors (the true leaders of Combine); they also learn about Borealis' location from Judith Mossman; however, Alyx gets mortally wounded by an Advisor at White Forest base; Gordon gets contacted by G-Man who offers him a choice: either to accept a new assignment or to refuse and face the consequences. The game ends with a cliffhanger, leaving Gordon's fate unknown. [1]</p>
- <h3>Game Mechanics</h3>
- <p>The game mechanics of Half-Life 2 are similar to those of Half-Life, but with several improvements and additions. The game is a first-person shooter that requires the player to use various weapons, items, and environmental objects to combat enemies and solve puzzles. The game also features a physics engine that allows realistic interactions with the environment and objects.</p>
- <p>Some of the weapons and items that Gordon can use include:</p>
- <ul>
-<li>The crowbar: Gordon's signature melee weapon that can break wooden crates and smash enemies.</li>
-<li>The pistol: A basic firearm that has a laser sight and a secondary fire mode that fires a powerful bullet.</li>
-<li>The submachine gun: A rapid-fire weapon that has a grenade launcher as a secondary fire mode.</li>
-<li>The shotgun: A powerful close-range weapon that can fire two shots at once as a secondary fire mode.</li>
-<li>The crossbow: A silent weapon that can fire heated metal bolts that can pin enemies to walls or objects.</li>
-<li>The pulse rifle: An energy-based weapon that can fire plasma balls as a secondary fire mode that can disintegrate enemies or create shockwaves.</li>
-<li>The rocket launcher: A weapon that can fire rockets that can be guided by a laser pointer.</li>
-<li>The gravity gun: A unique weapon that can manipulate objects using gravity. It can pick up, throw, or catch objects, or use them as shields or projectiles. It can also be upgraded to manipulate organic matter and larger objects.</li>
-<li>The pheropods: A device that can control antlions, which are insect-like creatures that can attack enemies or dig tunnels.</li>
-</ul>
- <p>Some of the enemies that Gordon will encounter include:</p>
- <ul>
-<li>Headcrabs: Small parasitic creatures that can latch onto human heads and turn them into zombies.</li>
-<li>Zombies: Humans who have been infected by headcrabs and turned into mindless creatures that can attack with claws or throw objects.</li>
-<li>Combine soldiers: Human soldiers who have been augmented by the Combine and equipped with advanced weapons and armor.</li>
-<li>Combine elites: Elite versions of Combine soldiers who have more health and better weapons.</li>
-<li>Combine snipers: Combine soldiers who are hidden in high places and use sniper rifles to shoot at Gordon.</li>
-<li>Combine turrets: Automated turrets that can detect and shoot at Gordon with machine guns or rockets.</li>
-<li>Combine gunships: Flying vehicles that can shoot at Gordon with pulse cannons or missiles.</li>
-<li>Combine striders: Giant tripod-like vehicles that can walk on any terrain and shoot at Gordon with pulse cannons or warp cannons.</li>
-<li>Combine hunters: Small tripod-like vehicles that can chase Gordon and shoot at him with flechettes or explosives.</li>
-<li>Combine advisors: Large worm-like creatures that are the true leaders of the Combine. They have telekinetic and telepathic abilities and are protected by a metal shell.</li>
-<li>Antlions: Insect-like creatures that live underground and attack anything that disturbs the sand. They can be controlled by pheropods.</li>
-<li>Barnacles: Ceiling-dwelling creatures that use their tongues to catch and eat anything below them.</li>
-</ul>
- <p>Some of the puzzles and physics-based challenges that Gordon will face include:</p>
- <ul>
-<li>Using the gravity gun to move objects, create paths, activate switches, or defeat enemies.</li>
-<li>Using the rocket launcher to guide rockets through obstacles or windows to hit targets.</li>
-<li>Using the crossbow to pin enemies or objects to walls or ceilings.</li>
-<li>Using the pheropods to command antlions to attack enemies or dig tunnels.</li>
-<li>Using vehicles such as an airboat, a buggy, or a jalopy to traverse different terrains and avoid hazards.</li>
-<li>Using environmental objects such as barrels, crates, saw blades, magnets, explosives, etc. to create diversions, traps, or weapons.</li>
-</ul>
- <h3>Game Graphics and Sound</h3>
- <p>The game graphics of Half-Life 2 are impressive for its time and still hold up well today. The game uses the Source engine, which allows for realistic lighting, shadows, textures, animations, physics, facial expressions, etc. The game also features various graphical effects such as water reflections, particle effects, motion blur, etc. The game has a distinct visual style that combines realistic environments with sci-fi elements. The game has various settings such as urban areas, industrial facilities, rural landscapes, coastal regions, arctic zones, etc. The game also has various weather effects such as rain, snow, fog, etc. The game also has various cinematic sequences that enhance the immersion and storytelling of the game.</p>
- <p>The game sound of Half-Life 2 is also excellent for its time and still sounds great today. The game uses the Source engine's sound system, which allows for realistic sound effects, ambient noises, music, voice acting, etc. The game also features various sound effects such as gunfire, explosions, footsteps, screams, etc. The game also features various ambient noises such as wind, water, machines, animals, etc. The game also features various music tracks that suit the mood and atmosphere of each scene. The game also features various voice actors who deliver their lines with emotion and personality. Some of the notable voice actors include Robert Guillaume (Eli Vance), Merle Dandridge (Alyx Vance), Louis Gossett Jr. (Vortigaunt), Robert Culp (Dr. Breen), Michelle Forbes (Judith Mossman), Jim French (Father Grigori), John Patrick Lowrie (G-Man), etc.</p>
- <h2>System Requirements for Half-Life 2</h2>
- <p>If you want to download Half-Life 2 for free on your PC (latest version), you need to make sure your PC meets the minimum or recommended system requirements for the game. Here are the system requirements for Half-Life 2 according to Steam:</p>
- <h3>Minimum Requirements</h3>
- <table style="border-collapse: collapse; width: 100%;">
-<tbody>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>OS:</strong></td>
-<td style="width: 50%; height: 21px;">Windows XP/Vista/7/8/10</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Processor:</strong></td>
-<td style="width: 50%; height: 21px;">1.7 GHz</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Memory:</strong></td>
-<td style="width: 50%; height: 21px;">512 MB RAM</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Graphics:</strong></td>
-<td style="width: 50%; height: 21px;">DirectX&reg; 8.1 level Graphics Card (Requires support for SSE)</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Storage:</strong></td>
-<td style="width: 50%; height: 21px;">7 GB available space</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Sound Card:</strong></td>
-<td style="width: 50%; height: 21px;">DirectX&reg; Compatible Sound Card</td>
-</tr>
-</tbody>
-</table>
- <h3>Recommended Requirements</h3>
- <table style="border-collapse: collapse; width: 100%;">
-<tbody>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>OS:</strong></td>
-<td style="width: 50%; height: 21px;">Windows XP/Vista/7/8/10</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Processor:</strong></td>
-<td style="width: 50%; height: 21px;">Pentium 4 3 GHz or AMD Athlon 64 3000+</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Memory:</strong></td>
-<td style="width: 50%; height: 21px;">1 GB RAM</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Graphics:</strong></td>
-<td style="width: 50%; height: 21px;">DirectX&reg; 9 level Graphics Card (256 MB)</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Storage:</strong></td>
-<td style="width: 50%; height: 21px;">7 GB available space</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Sound Card:</strong></td>
-<td style="width: 50%; height: 21px;">DirectX&reg; Compatible Sound Card</td>
-</tr>
-</tbody>
-</table>
- <h2>How to Download Half-Life 2 for Free on PC (Latest Version)</h2>
- <p>If you have checked your system requirements and decided to download Half-Life 2 for free on your PC (latest version), you need to follow these steps carefully. We will show you how to download the Half-Life 2 Complete Edition by AR-81, which is a non-steam crack that includes all the games in the Half-Life 2 series. Here are the steps:</p>
- <h3>Step 1: Download the Half-Life 2 Complete Edition by AR-81 Torrent File</h3>
- <p>The first step is to download the torrent file of the Half-Life 2 Complete Edition by AR-81. A torrent file is a small file that contains information about the files and folders that you want to download. You can use a torrent file to download large files faster and easier using a torrent client software.</p>
- <p>You can download the torrent file of the Half-Life 2 Complete Edition by AR-81 from this link. This link will take you to a website called DODI Repack, which is one of the websites that offer cracked games. You can also find other websites that offer cracked games by searching on Google or other search engines.</p>
- <p>Once you are on the DODI Repack website, you will see a page with some information about the Half-Life 2 Complete Edition by AR-81. You will also see some download options such as Cloud MailRu, Google Drive, MEGA.io, etc. You can choose any of these options, but we recommend using the Torrent option, which is usually faster and more reliable.</p>
- <p>To download the torrent file, you need to click on the Torrent option and then click on the Download button. You will then see a pop-up window that asks you to save the torrent file on your PC. You can choose any location on your PC to save the torrent file, but we recommend saving it on your desktop for easy access.</p>
- <h3>Step 2: Install a Torrent Client Software on Your PC</h3>
- <p>The second step is to install a torrent client software on your PC. A torrent client software is a program that allows you to open and use torrent files to download files and folders from other users who have the same torrent file. You can think of it as a peer-to-peer network that shares files and folders.</p>
- <p>There are many torrent client software options available for free on the internet. Some of the popular ones include uTorrent, BitTorrent, qBittorrent, etc. You can choose any of these options, but we recommend using uTorrent, which is one of the most widely used and user-friendly torrent client software.</p>
- <p>To install uTorrent on your PC, you need to go to this link. This link will take you to the official website of uTorrent, where you can download and install uTorrent for free. You will see a page with some information about uTorrent and a button that says Download uTorrent Web.</p>
- <p>To download uTorrent, you need to click on the Download uTorrent Web button and then click on Save File. You will then see a pop-up window that asks you to save the uTorrent installer file on your PC. You can choose any location on your PC to save the uTorrent installer file, but we recommend saving it on your desktop for easy access.</p>
- <p>To install uTorrent, you need to double-click on the uTorrent installer file that you have saved on your desktop. You will then see a window that asks you to agree to the terms and conditions of uTorrent. You need to click on Agree and then click on Next. You will then see another window that asks you to choose some installation options such as creating shortcuts, launching uTorrent at startup, etc. You can choose any of these options according to your preference and then click on Next. You will then see another window that asks you to install some additional software such as antivirus or browser extensions. We recommend unchecking these options as they are not necessary and may slow down your PC or cause unwanted changes. You can then click on Next and then click on Finish. You have now successfully installed uTorrent on your PC.</p>
- <h3>Step 3: Open the Torrent File with Your Torrent Client Software and Start Downloading</h3>
- <p>The third step is to open the torrent file of the Half-Life 2 Complete Edition by AR-81 with your torrent client software and start downloading. To do this, you need to double-click on the torrent file that you have saved on your desktop. You will then see a window that asks you to choose some download options such as the destination folder, the files to download, the bandwidth limit, etc. You can choose any of these options according to your preference and then click on OK. You will then see another window that shows you the progress of the download, such as the speed, the time remaining, the peers, the seeds, etc.</p>
- <p>The download process may take some time depending on your internet connection speed and the availability of peers and seeds. You can see how much time is left by looking at the ETA (estimated time of arrival) indicator. You can also pause or resume the download at any time by clicking on the Pause or Resume buttons. You can also stop or delete the download by clicking on the Stop or Delete buttons.</p>
- <p>Once the download is complete, you will see a message that says Seeding or Finished. This means that you have successfully downloaded all the files and folders of the Half-Life 2 Complete Edition by AR-81. You can now close the torrent client software and proceed to the next step.</p>
- <h3>Step 4: Extract the Downloaded Files with WinRAR or 7-Zip Software</h3>
- <p>The fourth step is to extract the downloaded files with WinRAR or 7-Zip software. WinRAR or 7-Zip software are programs that allow you to compress or decompress files and folders using various formats such as ZIP, RAR, 7Z, etc. You can use WinRAR or 7-Zip software to extract the files and folders of the Half-Life 2 Complete Edition by AR-81 from a compressed format to a normal format.</p>
- <p>You can download WinRAR or 7-Zip software for free from these links . These links will take you to the official websites of WinRAR or 7-Zip software, where you can download and install WinRAR or 7-Zip software for free. You will see a page with some information about WinRAR or 7-Zip software and a button that says Download WinRAR or Download 7-Zip.</p>
- <p>To download WinRAR or 7-Zip software, you need to click on the Download WinRAR or Download 7-Zip button and then click on Save File. You will then see a pop-up window that asks you to save the WinRAR or 7-Zip installer file on your PC. You can choose any location on your PC to save the WinRAR or 7-Zip installer file, but we recommend saving it on your desktop for easy access.</p>
- <p>To install WinRAR or 7-Zip software, you need to double-click on the WinRAR or 7-Zip installer file that you have saved on your desktop. You will then see a window that asks you to agree to the terms and conditions of WinRAR or 7-Zip software. You need to click on I Agree and then click on Install. You will then see another window that shows you the progress of the installation. You can then click on Finish. You have now successfully installed WinRAR or 7-Zip software on your PC.</p>
- <p>To extract the downloaded files with WinRAR or 7-Zip software, you need to go to the destination folder where you have downloaded the files and folders of the Half-Life 2 Complete Edition by AR-81. You will see a file named Half-Life_2_Complete_Edition_[DODI_Repack].rar (or .zip). This is a compressed file that contains all the files and folders of the Half-Life 2 Complete Edition by AR-81. You need to extract this file to get access to the normal files and folders of the game.</p>
- <p>To extract the compressed file with WinRAR software, you need to right-click on the file and select Extract Here or Extract to Half-Life_2_Complete_Edition_[DODI_Repack]. You will then see a window that shows you the progress of the extraction. You can then click on OK. You have now successfully extracted the compressed file with WinRAR software.</p>
- <p>To extract the compressed file with 7-Zip software, you need to right-click on the file and select 7-Zip > Extract Here or 7-Zip > Extract to Half-Life_2_Complete_Edition_[DODI_Repack]. You will then see a window that shows you the progress of the extraction. You can then click on OK. You have now successfully extracted the compressed file with 7-Zip software.</p>
- <p>After extracting the compressed file, you will see a folder named Half-Life_2_Complete_Edition_[DODI_Repack] on your destination folder. This folder contains all the files and folders of the Half-Life 2 Complete Edition by AR-81. You can now proceed to the next step.</p>
- <h3>Step 5: Run the Setup.exe File and Follow the Installation Instructions</h3>
- <p>The fifth and final step is to run the setup.exe file and follow the installation instructions. The setup.exe file is a program that allows you to install the Half-Life 2 Complete Edition by AR-81 on your PC. You can customize some installation options and language settings during the installation process.</p>
- <p>To run the setup.exe file, you need to go to the folder named Half-Life_2_Complete_Edition_[DODI_Repack] that you have extracted on your destination folder. You will see a file named Setup.exe inside this folder. You need to double-click on this file to launch it. You will then see a window that asks you to choose a language for the installation. You can choose any language from the list and then click on OK.</p>
- <p>You will then see another window that shows you some information about the Half-Life 2 Complete Edition by AR-81 and a button that says Install. You need to click on this button to start the installation process. You will then see another window that asks you to choose a destination folder for the installation. You can choose any location on your PC to install the game, but we recommend installing it on C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods for convenience and compatibility reasons. You can then click on Next.</p>
- <p>You will then see another window that asks you to choose some installation options such as creating shortcuts, launching at startup, etc. You can choose any of these options according to your preference and then click on Next. You will then see another window that asks you to choose a language for the game. You can choose any language from the list and then click on Next.</p>
- <p>You will then see another window that shows you the progress of the installation. The installation process may take some time depending on your PC's performance and disk space. You can see how much time is left by looking at the ETA (estimated time of arrival) indicator. You can also cancel or pause the installation at any time by clicking on the Cancel or Pause buttons.</p>
- <p>Once the installation is complete, you will see a message that says Installation Complete. You can then click on Finish. You have now successfully installed the Half-Life 2 Complete Edition by AR-81 on your PC.</p>
- <h3>Step 6: Run the Game from Your Desktop Shortcut or Steam Library and Enjoy</h3>
- <p>The sixth and final step is to run the game from your desktop shortcut or Steam library and enjoy. The game will automatically create a desktop shortcut and a Steam library entry for your convenience. You can use either of these options to launch the game.</p>
- <p>To run the game from your desktop shortcut, you need to double-click on the icon named Half-Life 2 Complete Edition by AR-81 that you will see on your desktop. You will then see a window that shows you some information about the game and a button that says Play. You need to click on this button to start the game.</p>
- <p>To run the game from your Steam library, you need to open Steam and go to your library. You will see a list of games that you have installed on your PC. You need to find and select the game named Half-Life 2 Complete Edition by AR-81. You will then see a page with some information about the game and a button that says Play. You need to click on this button to start the game.</p>
- <p>Once you have launched the game, you will see a menu with some options such as New Game, Load Game, Options, etc. You can choose any of these options according to your preference and start playing the game. You can also customize some game settings such as video, audio, keyboard, mouse, etc. from the Options menu.</p>
- <p>You have now successfully downloaded and installed Half-Life 2 for free on your PC (latest version). Enjoy playing one of the best FPS games of all time with enhanced graphics and features.</p>
- <h2>How to Use Launch Options for Half-Life 2</h2>
- <p>If you want to optimize your gaming experience or tweak some settings for Half-Life 2, you can use launch options. Launch options are command-line parameters that you can add to the game's executable file or Steam shortcut to modify some aspects of the game's behavior. For example, you can use launch options to force a specific resolution, enable developer mode, disable intro videos, etc.</p>
- <p>There are many launch options available for Half-Life 2 and other Source engine games. You can find a list of them here. However, not all of them may work for your system or game version, so use them at your own risk. Some of them may also conflict with each other or cause performance issues or crashes.</p>
- <p>To use launch options for Half-Life 2, you need to follow these steps:</p>
- <h3>Step 1: Find the Game's Executable File or Steam Shortcut</h3>
- <p>The first step is to find the game's executable file or Steam shortcut. The executable file is a program that runs the game when you double-click on it. The Steam shortcut is a link that launches the game through Steam when you double-click on it.</p>
- <p>If you have installed the game using the setup.exe file from Step 5 above, you will find the executable file in C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods\\Half-Life_2_Complete_Edition_[DODI_Repack]. The file name is hl2.exe.</p>
- <p>If you have installed the game using Steam, you will find the Steam shortcut in C:\\Program Files (x86)\\Steam\\steamapps\\common\\Half-Life 2. The file name is hl2.exe - Shortcut.</p>
- <h3>Step 2: Right-Click on the Executable File or Steam Shortcut and Select Properties</h3>
- <p>The second step is to right-click on the executable file or Steam shortcut and select Properties. This will open a window that shows you some information about the file or shortcut and some options to modify it.</p>
- <p>If you have right-clicked on the executable file, you will see a window with four tabs: General, Compatibility, Security and Details. You need to select the Compatibility tab. You will see some options to run the file in compatibility mode for different versions of Windows. You can ignore these options unless you have some issues running the game on your system.</p>
- <p>If you have right-clicked on the Steam shortcut, you will see a window with three tabs: General, Web Document and Security. You need to select the Web Document tab. You will see some options to change the URL or icon of the shortcut. You can ignore these options unless you want to customize your shortcut.</p>
- <h3>Step 3: Add Launch Options to the Target Field</h3>
- <p>The third step is to add launch options to the target field. The target field is a text box that shows you the path of the file or shortcut. You can add launch options to this field by typing them after a space at the end of the path.</p>
- <p>If you have right-clicked on the executable file, you will see a target field that looks something like this:</p>
- <code>C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods\\Half-Life_2_Complete_Edition_[DODI_Repack]\\hl2.exe</code>
- <p>If you have right-clicked on the Steam shortcut, you will see a target field that looks something like this:</p>
- <code>C:\\Program Files (x86)\\Steam\\steam.exe -applaunch 220</code>
- <p>You can add launch options to these fields by typing them after a space at the end of the path. For example, if you want to force a specific resolution of 1920x1080 and disable intro videos, you can add these launch options:</p>
- <code>-w 1920 -h 1080 -novid</code>
- <p>Your target field will then look something like this:</p>
- <code>C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods\\Half-Life_2_Complete_Edition_[DODI_Repack]\\hl2.exe -w 1920 -h 1080 -novid</code>
- <code>C:\\Program Files (x86)\\Steam\\steam.exe -applaunch 220 -w 1920 -h 1080 -novid</code>
- <p>You can add as many launch options as you want, but make sure to separate them with spaces and use quotation marks if they contain spaces themselves. For example, if you want to enable developer mode and set a custom console key, you can add these launch options:</p>
- <code>-dev -console -consolekey \"`\"</code>
- <p>Your target field will then look something like this:</p>
- <code>C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods\\Half-Life_2_Complete_Edition_[DODI_Repack]\\hl2.exe -dev -console -consolekey \"`\"</code>
- <code>C:\\Program Files (x86)\\Steam\\steam.exe -applaunch 220 -dev -console -consolekey \"`\"</code>
- <h3>Step 4: Click on Apply and OK to Save Your Changes</h3>
- <p>The fourth and final step is to click on Apply and OK to save your changes. This will close the window and apply your launch options to the file or shortcut. You can now run the game with your customized settings by double-clicking on the file or shortcut.</p>
- <p>You can also change or remove your launch options at any time by repeating these steps and editing or deleting them from the target field.</p>
- <p>You have now successfully used launch options for Half-Life 2. Enjoy playing the game with your preferred settings and features.</p> 0a6ba089eb<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Chess Play and Learn MOD APK The Ultimate Chess App for Beginners and Experts.md

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-<h1>Chess App Mod APK: How to Download and Play the Best Chess Games on Your Android Device</h1>
- <p>Chess is one of the oldest and most popular games in the world. It is a game of strategy, logic, and skill that can challenge your mind and improve your cognitive abilities. Whether you are a beginner or a master, chess can offer you endless hours of fun and entertainment.</p>
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- <p>But what if you want to play chess on your Android device without any limitations or restrictions? What if you want to access premium features, unlimited puzzles, online tournaments, and more for free? That's where Chess App Mod APK comes in.</p>
- <h2>What is Chess App Mod APK?</h2>
- <p>Chess App Mod APK is a modified version of the original chess app that allows you to enjoy the best chess games on your Android device without any ads, in-app purchases, or subscriptions. You can download and install Chess App Mod APK from various sources on the internet and play chess offline or online with millions of players around the world.</p>
- <h3>Why You Should Use Chess App Mod APK</h3>
- <p>There are many reasons why you should use Chess App Mod APK instead of the regular chess app. Here are some of them:</p>
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- <h3>How to Download and Install Chess App Mod APK</h3>
- <p>Downloading and installing Chess App Mod APK is easy and simple. Just follow these steps:</p>
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-<li>Go to your device settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded file and tap on it to install it.</li>
-<li>Launch the app and enjoy playing chess with all the modded features.</li>
-</ol>
- <h2>What are the Best Chess Games to Play with Chess App Mod APK?</h2>
- <p>There are many chess games that you can play with Chess App Mod APK, but some of them stand out from the rest. Here are three of the best chess games that you should try:</p>
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- <p>This is one of the most popular and comprehensive chess apps on the market. It offers everything you need to learn, practice, and play chess at any level. You can play online or offline with friends or computer opponents, join clubs and tournaments, watch live games and videos, solve puzzles and lessons, and much more. With Chess App Mod APK, you can unlock all the premium features, such as unlimited puzzles, advanced analysis, custom themes, and more.</p>
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- <h4>Features</h4>
- <ul>
-<li>Play online or offline with friends or computer opponents of different skill levels.</li>
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-<li>Watch live games and videos from top players and coaches.</li>
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-</ul>
- <h4>Pros and Cons</h4>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Offers a variety of features and modes for all levels of players.</td>
-<td>Can be overwhelming for beginners or casual players.</td>
-</tr>
-<tr>
-<td>Has a large and active community of players and clubs.</td>
-<td>Can be laggy or buggy at times due to high traffic or server issues.</td>
-</tr>
-<tr>
-<td>Provides high-quality content and resources for learning and improving.</td>
-<td>Can be expensive or limited without the premium subscription or modded version.</td>
-</tr>
-</table>
- <h3>Lichess - Free Online Chess</h3>
- <p>This is another popular and reputable chess app that offers a free and open-source platform for playing and learning chess. It has a simple and elegant design that focuses on the game itself. You can play online or offline with friends or computer opponents, join tournaments and events, watch broadcasts and streams, solve puzzles and studies, and more. With Chess App Mod APK, you can access all the features without any ads or donations.</p>
- <h4>Features</h4>
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-<li>Play online or offline with friends or computer opponents of different skill levels.</li>
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-<li>Watch broadcasts and streams from top players and commentators.</li>
-<li>Solve puzzles and studies to improve your skills and tactics.</li>
-<li>Analyze your games and moves with powerful tools and engines.</li>
-<li>Customize your app with different themes, boards, pieces, sounds, and more.</li>
-</ul>
- <h4>Pros and Cons</h4>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Offers a free and open-source platform for playing and learning chess.</td>
-<td>Lacks some features and modes that other apps offer.</td>
-</tr>
-<tr>
-<td>Has a simple and elegant design that focuses on the game itself.</td>
-<td>Can be boring or bland for some users who prefer more customization or variety.</td>
-</tr>
-<tr>
-<td>Provides high-quality content and resources for learning and improving.</td>
-<td>Can be challenging or intimidating for some users who face strong opponents or puzzles.</td>
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-</table>
- <h3>Chess Tactics Pro - Puzzles</h3>
- <p>This is a specialized chess app that focuses on solving chess puzzles. It has a large database of puzzles that are categorized by difficulty, theme, rating, and source. You can solve puzzles to improve your skills and tactics, earn points and badges, track your progress and statistics, challenge yourself with daily puzzles, and more. With Chess App Mod APK, you can unlock all the puzzles without any ads or limitations.</p>
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-<li>Solve puzzles to improve your skills and tactics.</li>
-<li>Earn points and badges to measure your performance and achievements.</li>
-<li>Track your progress and statistics to see your strengths and weaknesses.</li>
-<li>Challenge yourself with daily puzzles to test your knowledge and skills.</li>
-<li>Analyze your solutions and moves with powerful tools and engines.</li></ul>
- <h4>Pros and Cons</h4>
- <table><tr><th>Pros</th><th>Cons</th></tr><tr><td>Offers a large database of puzzles that are categorized by difficulty, theme, rating, and source.</td><td>Lacks other features and modes that other apps offer.</td></tr><tr><td>Has a simple and elegant design that focuses on the puzzles.</td><td>Can be boring or bland for some users who prefer more customization or variety.</td></tr><tr><td>Provides high-quality content and resources for learning and improving.</td><td>Can be challenging or intimidating for some users who face difficult or complex puzzles.</td></tr></table>
- <h2>What are the Benefits and Challenges of Playing Chess?</h2>
- <p>Playing chess is not only fun and entertaining, but also beneficial and challenging. It can help you develop various skills and abilities that can improve your life and well-being. However, it can also pose some difficulties and drawbacks that can affect your mood and health. Here are some of the benefits and challenges of playing chess:</p>
- <h3>Benefits of Playing Chess</h3>
- <p>Playing chess can have many positive effects on your mind and body. Here are some of them:</p>
- <h4>Develops perspective</h4>
- <p>Playing chess can help you develop perspective, which is the ability to see things from different angles and viewpoints. This can help you understand yourself and others better, as well as solve problems and conflicts more effectively.</p>
- <h4>Improves memory</h4>
- <p>Playing chess can help you improve your memory, which is the ability to store and recall information. This can help you learn faster and easier, as well as remember things longer and clearer.</p>
- <h4>Deepens focus</h4>
- <p>Playing chess can help you deepen your focus, which is the ability to concentrate on one thing and ignore distractions. This can help you perform better and faster, as well as achieve your goals and tasks more efficiently.</p>
- <h4>Elevates creativity</h4>
- <p>Playing chess can help you elevate your creativity, which is the ability to generate new and original ideas. This can help you express yourself and others better, as well as create something unique and valuable.</p>
- <h4>Boosts planning skills</h4>
- <p>Playing chess can help you boost your planning skills, which is the ability to organize and execute a series of actions. This can help you manage your time and resources better, as well as anticipate and prevent potential problems.</p>
- <h4>Increases self-awareness</h4>
- <p>Playing chess can help you increase your self-awareness, which is the ability to recognize and understand your own emotions, thoughts, and behaviors. This can help you control your impulses and reactions better, as well as improve your self-esteem and confidence.</p>
- <h4>Protects against dementia</h4>
- <p>Playing chess can help you protect against dementia, which is a condition that causes a decline in mental functions. This can help you maintain your cognitive abilities and prevent memory loss, confusion, and Alzheimer's disease.</p>
- <h4>Helps ADHD</h4>
- <p>Playing chess can help you with ADHD, which is a disorder that causes hyperactivity, impulsivity, and inattention. This can help you calm down and focus better, as well as reduce stress and anxiety.</p>
- <h4>Reduces panic attack symptoms</h4>
- <p>Playing chess can help you reduce panic attack symptoms, which are sudden episodes of intense fear and discomfort. This can help you relax and breathe better, as well as cope with negative thoughts and emotions.</p>
- <h3>Challenges of Playing Chess</h3>
- <p>Playing chess can also have some negative effects on your mind and body. Here are some of them:</p>
- <h4>Requires time and dedication</h4>
- <p>Playing chess requires time and dedication, which means that you have to spend a lot of hours practicing and studying the game. This can interfere with your other responsibilities and activities, such as work, school, family, or friends.</p>
- <h4>Can be frustrating and stressful</h4>
- <p>Playing chess can be frustrating and stressful, which means that you have to deal with a lot of pressure and expectations. This can affect your mood and performance, as well as cause anger, frustration, or depression.</p>
- <h4>Can be addictive and isolating</h4>
- <p>Playing chess can be addictive and isolating, which means that you can become obsessed and dependent on the game. This can affect your social and personal life, as well as cause loneliness, isolation, or withdrawal.</p>
- <h2>Conclusion</h2>
- <p>Chess is a wonderful game that can offer you many benefits and challenges. It can help you develop your perspective, memory, focus, creativity, planning skills, self-awareness, and more. It can also help you protect against dementia, ADHD, and panic attack symptoms. However, it can also require time and dedication, be frustrating and stressful, and be addictive and isolating. Therefore, you should play chess with moderation and balance, and enjoy it as a hobby and not a necessity.</p>
- <p>If you want to play chess on your Android device without any limitations or restrictions, you should use Chess App Mod APK. This is a modified version of the original chess app that allows you to access premium features, unlimited puzzles, online tournaments, and more for free. You can download and install Chess App Mod APK from various sources on the internet and play chess offline or online with millions of players around the world.</p>
- <p>Some of the best chess games that you can play with Chess App Mod APK are Chess - Play and Learn, Lichess - Free Online Chess, and Chess Tactics Pro - Puzzles. These are popular and comprehensive chess apps that offer a variety of features and modes for all levels of players. You can play online or offline with friends or computer opponents, join clubs and tournaments, watch live games and videos, solve puzzles and lessons, analyze your games and moves, customize your app settings, preferences, and appearance, and more.</p>
- <p>Chess is a game of strategy, logic, and skill that can challenge your mind and improve your cognitive abilities. Whether you are a beginner or a master, chess can offer you endless hours of fun and entertainment. So what are you waiting for? Download Chess App Mod APK today and start playing the best chess games on your Android device!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Chess App Mod APK:</p>
- <ol>
-<li>Is Chess App Mod APK safe to use?</li>
-<p>Yes, Chess App Mod APK is safe to use as long as you download it from a reliable source that does not contain any viruses or malware. However, you should always be careful when installing apps from unknown sources and scan them with an antivirus before opening them.</p>
-<li>Is Chess App Mod APK legal to use?</li>
-<p>No, Chess App Mod APK is not legal to use as it violates the terms and conditions of the original chess app developers. By using Chess App Mod APK, you are bypassing the security measures and revenue streams of the original app. Therefore, you should use Chess App Mod APK at your own risk and discretion.</li>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Anime Stories with Gacha Life on PC - Free Download 1.1.4.md

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-<h1>How to Download and Play Gacha Life on PC</h1>
-<p>If you are a fan of anime, casual games, and character customization, you might have heard of Gacha Life. This popular game lets you create your own anime-styled characters and dress them up in your favorite fashion outfits. You can also interact with other characters, play mini-games, and create your own scenes and stories. But did you know that you can also play Gacha Life on your PC? In this article, we will show you how to download and play Gacha Life on PC, and why it is a better option than playing on your mobile device.</p>
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-<p>Gacha Life is not only a game, but also a community of creative people. You can share your creations with others online, or download them to your device. Here are some ways to share your creations:</p>
- - Share online: This is where you can upload your scenes and stories to the game's server, and let other players see them. You can also browse other players' creations, rate them, comment on them, or report them if they are inappropriate. You can also join contests and events where you can submit your creations and win prizes. - Download to device: This is where you can save your scenes and stories as images or videos on your device. You can also edit them with other apps, or share them on social media platforms such as Facebook, Instagram, YouTube, etc. <h2>Conclusion</h2>
-<p>Gacha Life is a fun and creative game that lets you create your own anime characters and stories. You can also play mini-games, chat with other players, and explore different modes and features. But if you want to enjoy the game more, you should try playing it on PC. By playing Gacha Life on PC, you can have a larger and clearer display, use keyboard and mouse controls, access more features and updates, and have a smoother gaming experience.</p>
-<p>To play Gacha Life on PC, you just need to download an Android emulator such as BlueStacks, NoxPlayer, or MEmu, install Gacha Life from Google Play or APK file, launch the game and start your adventure. You can also follow our tips and tricks to make the most out of Gacha Life on PC.</p>
-<p>So what are you waiting for? Download Gacha Life on PC today and unleash your creativity!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Gacha Life on PC:</p>
- - Q: Is Gacha Life free to play on PC?   - A: Yes, Gacha Life is free to play on PC. You just need to download an Android emulator and install Gacha Life from Google Play or APK file. - Q: Is Gacha Life safe to play on PC?   - A: Yes, Gacha Life is safe to play on PC. However, you should be careful when chatting with other players online, as some of them may be rude or inappropriate. You should also avoid downloading any unofficial or modded versions of Gacha Life, as they may contain viruses or malware. - Q: How do I update Gacha Life on PC?   - A: To update Gacha Life on PC, you need to update the Android emulator first. Then, you need to update Gacha Life from Google Play or To update Gacha Life on PC, you need to update the Android emulator first. Then, you need to update Gacha Life from Google Play or from the APK file. Here are the steps for both methods: - To update the Android emulator, open the emulator's settings and check for updates. If there are any available updates, download and install them. You may need to restart the emulator for the changes to take effect. - To update Gacha Life from Google Play, open the Google Play Store app from the emulator's home screen and search for Gacha Life. If there is an update available, you will see an Update button next to the game. Click on it and wait for the download and installation to complete. - To update Gacha Life from the APK file, download the latest version of Gacha Life from a trusted source, such as APKPure or APKMirror. Then, drag and drop the APK file onto the emulator's window, or use the Install APK button from the emulator's toolbar. The emulator will automatically update Gacha Life on your PC. - Q: How do I transfer my Gacha Life data from mobile to PC?   - A: To transfer your Gacha Life data from mobile to PC, you need to use a cloud service such as Google Drive or Dropbox. Here are the steps to do so:     - On your mobile device, open Gacha Life and go to the settings menu. Tap on the Backup Data button and choose a cloud service to upload your data. You may need to sign in with your account and grant permission for Gacha Life to access your cloud storage.     - On your PC, open the Android emulator and launch Gacha Life. Go to the settings menu and tap on the Restore Data button. Choose the same cloud service that you used to backup your data on your mobile device. You may need to sign in with your account and grant permission for Gacha Life to access your cloud storage.     - Wait for the data transfer to complete and enjoy playing Gacha Life on PC with your previous progress. - Q: How do I play Gacha Life offline on PC?   - A: To play Gacha Life offline on PC, you need to download and install Gacha Life from an APK file instead of Google Play. Here are the steps to do so:     - Download the APK file of Gacha Life from a trusted source, such as APKPure or APKMirror.     - Drag and drop the APK file onto the emulator's window, or use the Install APK button from the emulator's toolbar. The emulator will automatically install Gacha Life on your PC.     - Launch Gacha Life from the emulator's home screen or app drawer. You can play Gacha Life offline on PC without an internet connection.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download 11th Hour by Betty Bayo - The Best of Kenyan Music.md

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-<h1>Download 11th Hour by Betty Bayo</h1>
-<p>If you are looking for a gospel song that will inspire you and uplift your spirit, you should download 11th Hour by Betty Bayo. This song is a testimony of how God can turn your situation around in the last minute, when all hope seems to be lost. In this article, we will tell you more about Betty Bayo, the meaning and production of 11th Hour, and how you can download it legally and easily.</p>
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- <h2>Who is Betty Bayo?</h2>
-<p>Betty Bayo is a Kenyan gospel singer, songwriter, and entrepreneur. She was born in 1985 in Kiambu County, Kenya. She started singing at a young age in her church choir and later joined a gospel group called Still Alive. She released her first solo album, 11th Hour, in 2008 and became a household name in the Kenyan gospel music scene.</p>
- <h3>Her musical journey</h3>
-<p>Betty Bayo has released six albums so far: 11th Hour (2008), Siyabonga (2010), Jemedari (2012), Anabadilisha (2014), Nimebadilika (2016), and Ndimwega (2018). She has also collaborated with other gospel artists such as Daddy Owen, Loise Kim, Jane Muthoni, and Phyllis Mbuthia. Some of her popular songs include Siyabonga, Jemedari, Anabadilisha, Ndimwega, and of course, 11th Hour. She has won several awards for her music, such as Groove Awards, Mwafaka Awards, and Kikuyu Music Awards.</p>
- <h3>Her personal life</h3>
-<p>Betty Bayo is a mother of two children, a boy and a girl. She was married to Pastor Victor Kanyari, the founder of Salvation Healing Ministry, but they divorced in 2015 after a scandal involving his fake miracles. She has since moved on and is currently dating an unnamed man. She is also a businesswoman who owns a beauty salon and a clothing line.</p>
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- <h2>What is 11th Hour?</h2>
-<p>11th Hour is the title track of Betty Bayo's debut album. It is a song that expresses her gratitude to God for saving her from a difficult situation in her life. She sings about how God intervened in her life when she was about to give up and restored her joy and peace.</p>
- <h3>The meaning of the song</h3>
-<p>The song is based on the biblical parable of the workers in the vineyard (Matthew 20:1-16). In this parable, Jesus tells a story of a landowner who hired workers at different times of the day to work in his vineyard. He agreed to pay them all the same wage at the end of the day. However, when he paid them, the workers who were hired first complained that they received the same amount as those who were hired last. The landowner replied that he was not unfair to them and that he had the right to do what he wanted with his money.</p>
- <p>The message of this parable is that God's grace is not based on our merit or performance, but on his generosity and sovereignty. He can bless anyone at any time, regardless of their background or circumstances. He can also change anyone's situation at the last minute, even when it seems impossible or hopeless. This is what Betty Bayo experienced in her life and what she wants to share with her listeners through this song.</p>
- <h3>The production of the song</h3>
-<p>The song was produced by John Nyika, a renowned Kenyan music producer who has worked with other gospel artists such as Mercy Masika, Gloria Muliro, Eunice Njeri, and Reuben Kigame. He is known for his quality sound and creativity. He used a blend of African and contemporary beats to create a catchy and uplifting tune for 11th Hour. He also added some background vocals and harmonies to enhance the song's message and mood.</p>
- <h3>The reception of the song</h3>
-<p>The song was released in 2008 and became an instant hit among gospel music fans in Kenya and beyond. It received massive airplay on radio and TV stations, as well as online platforms. It also topped various charts and playlists, such as Groove Awards, Mdundo, and Skiza. The song has over 2 million views on YouTube and over 1 million streams on Spotify. It has also been covered by several other artists, such as Alice Kimanzi, Evelyn Wanjiru, and Ruth Wamuyu. The song has touched many lives and inspired many testimonies of God's faithfulness and power.</p>
- <h2>How to download 11th Hour by Betty Bayo?</h2>
-<p>If you want to enjoy this song on your device, you need to download it legally and easily. Here are some of the benefits of downloading the song, the legal ways to do it, and the steps to follow.</p>
- <h3>The benefits of downloading the song</h3>
-<p>Downloading 11th Hour by Betty Bayo has several advantages, such as:</p>
-<ul>
-<li>You can listen to the song offline, without worrying about internet connection or data charges.</li>
-<li>You can add the song to your personal playlist and customize it according to your preference.</li>
-<li>You can share the song with your friends and family via Bluetooth, WhatsApp, or other apps.</li>
-<li>You can support the artist financially and morally by paying for her work and appreciating her talent.</li>
-<li>You can access the song anytime, anywhere, and on any device that supports MP3 format.</li>
-</ul>
- <h3>The legal ways to download the song</h3>
-<p>There are many illegal ways to download 11th Hour by Betty Bayo, such as using pirated websites, apps, or software. However, these methods are not only unethical but also risky. You may expose your device to viruses, malware, or hackers. You may also face legal consequences for violating the artist's intellectual property rights. Therefore, you should avoid these methods and use only the legal ways to download the song. Some of the legal ways are:</p>
-<ul>
-<li>Buying the song from online platforms such as iTunes, Amazon Music, or Google Play Music.</li>
-<li>Subscribing to streaming services such as Spotify, Apple Music, or Deezer.</li>
-<li>Downloading the song from authorized websites such as Mdundo, Boomplay, or Skiza.</li>
-</ul>
- <h3>The steps to download the song</h3>
-<p>The steps to download 11th Hour by Betty Bayo may vary depending on the platform you choose. However, here are some general steps that you can follow:</p>
-<ol>
-<li>Go to the platform's website or app and search for 11th Hour by Betty Bayo.</li>
-<li>Select the song from the results and click on the download button.</li>
-<li>Choose the quality and format of the song that you want to download.</li>
-<li>Confirm your payment method if required and complete the transaction.</li>
-<li>Wait for the download to finish and enjoy the song on your device.</li>
-</ol>
- <h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion, 11th Hour by Betty Bayo is a gospel song that will inspire you and uplift your spirit. It is a testimony of how God can turn your situation around in the last minute, when all hope seems to be lost. Betty Bayo is a Kenyan gospel singer who has released six albums and won several awards for her music. She wrote this song based on her personal experience and the biblical parable of the workers in the vineyard. The song was produced by John Nyika and received a positive reception from gospel music fans. You can download this song legally and easily from various online platforms and enjoy its benefits.</p>
- <h4>Call to action</h4>
-<p>If you have not downloaded 11th Hour by Betty Bayo yet, what are you waiting for? This song will bless you and encourage you in your faith journey. It will remind you that God is always with you and that he can do miracles in your life. Download this song today and share it with others who need to hear this message. You will not regret it!</p>
- <h2>Frequently Asked Questions</h2>
-<ul>
-<li><strong>Q: Who is Betty Bayo?</strong></li>
-<li>A: Betty Bayo is a Kenyan gospel singer, songwriter, and entrepreneur. She released her first solo album, 11th Hour, in 2008 and became a household name in the Kenyan gospel music scene.</li>
-<li><strong>Q: What is the meaning of 11th Hour?</strong></li>
-<li>A: 11th Hour is a song that expresses Betty Bayo's gratitude to God for saving her from a difficult situation in her life. She sings about how God intervened in her life when she was about to give up and restored her joy and peace. The song is based on the biblical parable of the workers in the vineyard (Matthew 20:1-16), which teaches that God's grace is not based on our merit or performance, but on his generosity and sovereignty.</li>
-<li><strong>Q: How can I download 11th Hour by Betty Bayo?</strong></li>
-<li>A: You can download 11th Hour by Betty Bayo legally and easily from various online platforms, such as iTunes, Amazon Music, Google Play Music, Spotify, Apple Music, Deezer, Mdundo, Boomplay, or Skiza. You just need to go to the platform's website or app, search for the song, select it, click on the download button, choose the quality and format of the song, confirm your payment method if required, and wait for the download to finish.</li>
-<li><strong>Q: Why should I download 11th Hour by Betty Bayo?</strong></li>
-<li>A: You should download 11th Hour by Betty Bayo because it is a gospel song that will inspire you and uplift your spirit. It will remind you that God can turn your situation around in the last minute, when all hope seems to be lost. It will also allow you to listen to the song offline, add it to your personal playlist, share it with your friends and family, support the artist financially and morally, and access it anytime, anywhere, and on any device that supports MP3 format.</li>
-<li><strong>Q: Where can I find more information about Betty Bayo and her music?</strong></li>
-<li>A: You can find more information about Betty Bayo and her music on her official website (www.bettybayo.com), her Facebook page (Betty Bayo Official), her Instagram account (@bettybayo_official_page), her YouTube channel (Betty Bayo Official), or her Wikipedia page (Betty Bayo).</li>
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spaces/1phancelerku/anime-remove-background/Download Quiz Results and Share Them with Your Students Colleagues or Clients.md

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-<h1>How to Download Quiz Results from Different Platforms</h1>
-<p>Quiz results are the data that show how well your participants performed on your quizzes. They can include information such as scores, answers, time spent, feedback, and more. Quiz results are useful for teachers, students, and professionals who want to assess learning outcomes, measure progress, identify gaps, provide feedback, and improve performance.</p>
-<p>Downloading quiz results can help you gain more insights into your quizzes and your participants. You can export quiz results to different formats such as CSV, XLSX, or PDF files. You can then use these files for more in-depth analysis and charting. You can also share these files with others who may be interested in your quizzes or your participants. For example, you can share quiz results with your colleagues, students, clients, or stakeholders.</p>
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-<p>In this article, we will show you how to download quiz results from five different platforms: Microsoft Forms, myQuiz, Quizizz, ProProfs, and Canvas. These platforms are popular tools for creating online quizzes for various purposes. We will provide step-by-step guides on how to download quiz results from each platform. We will also include screenshots of the pages where you can find the download options.</p>
- <h2>How to Download Quiz Results from Microsoft Forms</h2>
-<p>Microsoft Forms is a web-based tool that allows you to create quizzes and surveys easily. You can use Microsoft Forms to create quizzes for education, training, feedback, research, or fun. You can add different types of questions such as multiple choice, text, rating, date, ranking, likert scale, net promoter score (NPS), file upload, etc. You can also add images or videos to your questions. You can customize your quizzes with themes or logos. You can share your quizzes with anyone via email or link. You can also embed your quizzes on your website or blog.</p>
-<p>To download quiz results from Microsoft Forms:</p>
-<ol>
-<li>In Microsoft Forms, open the quiz for which you want to download the results.</li>
-<li>Click on the Responses tab to see the quiz statistics and responses.</li>
-<li>Click on the More options button (three dots) in the upper right corner of the page.</li>
-<li>Select Open in Excel to export the quiz results as an XLSX file. You can also select Print summary to export the quiz results as a PDF file.</li>
-<li>Save the file to your preferred location or print it as needed.</li>
-</ol>
-<p>Here is a screenshot of the quiz statistics page and the download options:</p>
-<img src="https://i.imgur.com/9tqjy1P.png" alt="Microsoft Forms quiz statistics and download options">
- <h2>How to Download Quiz Results from myQuiz</h2>
-<p>myQuiz is a web-based tool that allows you to host interactive trivia quizzes at events. You can use myQuiz to create quizzes for entertainment, education, marketing, or gamification. You can add different types of questions such as multiple choice, true/false, image, video, audio, etc. You can also add timers, leaderboards, prizes, and chat features to your quizzes. You can share your quizzes with your audience via QR code or link. You can also integrate your quizzes with Zoom, YouTube, Twitch, or Facebook Live.</p>
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-<p>To download quiz results from myQuiz:</p>
-<ol>
-<li>In myQuiz, open the quiz for which you want to download the results.</li>
-<li>Click on the Reports tab to see the quiz statistics and responses.</li>
-<li>Click on the Download button in the upper right corner of the page.</li>
-<li>Select Excel to export the quiz results as an XLSX file.</li>
-<li>Save the file to your preferred location.</li>
-</ol>
-<p>Here is a screenshot of the report page and the download button:</p>
-<img src="https://i.imgur.com/7kQ0yZL.png" alt="myQuiz report page and download button">
- <h2>How to Download Quiz Results from Quizizz</h2>
-<p>Quizizz is a web-based tool that allows you to create online quizzes, lessons, activities, and homework. You can use Quizizz to create quizzes for education, training, feedback, or fun. You can add different types of questions such as multiple choice, fill in the blanks, check boxes, poll, etc. You can also add images, gifs, memes, equations, or audio to your questions. You can customize your quizzes with themes, music, timers, leaderboards, etc. You can share your quizzes with your participants via email or link. You can also assign your quizzes as homework or live sessions.</p>
-<p>To download quiz results from Quizizz:</p>
-<ol>
-<li>In Quizizz, open the quiz for which you want to download the results.</li>
-<li>Click on the Reports tab to see the quiz statistics and responses.</li>
-<li>Click on the Responses tab to see the individual responses of each participant.</li>
-<li>Click on the Download button in the upper right corner of the page.</li>
-<li>Select CSV to export the quiz results as a CSV file.</li>
-<li>Save the file to your preferred location.</li>
-</ol>
-<p>Here is a screenshot of the responses tab and the download button:</p>
-<img src="https://i.imgur.com/8VQZl3f.png" alt="Quizizz responses tab and download button"> <h2>How to Download Quiz Results from ProProfs</h2>
-<p>ProProfs is a web-based tool that allows you to create online quizzes, courses, assessments, and surveys. You can use ProProfs to create quizzes for education, business, certification, or fun. You can add different types of questions such as multiple choice, true/false, matching, short answer, essay, etc. You can also add images, videos, documents, or audio to your questions. You can customize your quizzes with themes, logos, certificates, etc. You can share your quizzes with your participants via email or link. You can also embed your quizzes on your website or blog.</p>
-<p>To download quiz results from ProProfs:</p>
-<ol>
-<li>In ProProfs, open the quiz for which you want to download the results.</li>
-<li>Click on the Reports and Stats tab to see the quiz statistics and responses.</li>
-<li>Click on the Download icon in the upper right corner of the page.</li>
-<li>Select CSV or PDF to export the quiz results as a CSV or PDF file.</li>
-<li>Save the file to your preferred location.</li>
-</ol>
-<p>Here is a screenshot of the reports and stats page and the download icon:</p>
-<img src="https://i.imgur.com/9a0fZ6r.png" alt="ProProfs reports and stats page and download icon">
- <h2>How to Download Quiz Results from Canvas</h2>
-<p>Canvas is a web-based tool that allows you to create online courses, assignments, quizzes, and discussions. You can use Canvas to create quizzes for education, training, or assessment. You can add different types of questions such as multiple choice, true/false, fill in the blank, multiple answers, numerical answer, formula, matching, etc. You can also add images, videos, equations, or files to your questions. You can customize your quizzes with settings such as time limit, shuffle answers, show feedback, etc. You can share your quizzes with your students via email or link. You can also grade your quizzes automatically or manually.</p>
-<p>To download quiz results from Canvas:</p>
-<ol>
-<li>In Canvas, open the course for which you want to download the quiz results.</li>
-<li>Click on the Quizzes tab to see the list of quizzes in the course.</li>
-<li>Click on the quiz for which you want to download the results.</li>
-<li>Click on the Quiz Statistics button in the upper right corner of the page.</li>
-<li>Click on the Student Analysis button in the upper right corner of the page.</li>
-<li>Select CSV to export the quiz results as a CSV file.</li>
-<li>Save the file to your preferred location.</li>
-</ol>
-<p>Here is a screenshot of the quiz statistics page and the student analysis button:</p>
-<img src="https://i.imgur.com/0yZgqQF.png" alt="Canvas quiz statistics page and student analysis button">
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download quiz results from five different platforms: Microsoft Forms, myQuiz, Quizizz, ProProfs, and Canvas. These platforms are great tools for creating online quizzes for various purposes. By downloading quiz results from these platforms, you can gain more insights into your quizzes and your participants. You can use the downloaded data for more in-depth analysis and charting. You can also share the downloaded data with others who may be interested in your quizzes or your participants.</p>
-<p>Here are some tips on how to use the downloaded data for analysis, sharing, and feedback:</p>
-<ul>
-<li>Use spreadsheet software such as Excel or Google Sheets to open and manipulate the CSV or XLSX files. You can sort, filter, pivot, or chart the data as you wish.</li>
-<li>Use PDF software such as Adobe Acrobat or Foxit Reader to open and view the PDF files. You can print or email the files as needed.</li>
-<li>Use online tools such as Datawrapper or Infogram to create interactive visualizations from your data. You can embed these visualizations on your website or blog.</li>
-<li>Use online tools such as Google Drive or Dropbox to store and share your files with others. You can set permissions and access levels for different users.</li>
-<li>Use online tools such as SurveyMonkey or Typeform to collect feedback from your participants on your quizzes. You can ask them about their experience, satisfaction, learning outcomes, suggestions, etc.</li>
-</ul>
-<p>We hope you found this article helpful and informative. If you have any questions or comments about downloading quiz results from different platforms, please feel free to leave them below. We would love to hear from you!</p>
- <h2>FAQs</h2>
- <h3>Q <h3>Q: How can I download quiz results from other platforms?</h3>
-<p>A: The process of downloading quiz results from other platforms may vary depending on the platform and the quiz settings. However, some common steps are:</p>
-<ul>
-<li>Open the platform and log in to your account.</li>
-<li>Find the quiz for which you want to download the results.</li>
-<li>Look for a tab or a button that says something like "Results", "Reports", "Statistics", "Analysis", etc.</li>
-<li>Select the format that you want to download the results in, such as CSV, XLSX, PDF, etc.</li>
-<li>Save the file to your preferred location.</li>
-</ul>
-<p>If you are not sure how to download quiz results from a specific platform, you can check the platform's help center or contact their support team for assistance.</p>
- <h3>Q: How can I create online quizzes with different platforms?</h3>
-<p>A: Creating online quizzes with different platforms is easy and fun. You can use any of the platforms mentioned in this article or explore other options online. Some common steps are:</p>
-<ul>
-<li>Open the platform and sign up for an account.</li>
-<li>Choose a template or start from scratch to create your quiz.</li>
-<li>Add questions and answers to your quiz. You can choose from different types of questions and add multimedia elements if you wish.</li>
-<li>Customize your quiz with settings such as title, theme, timer, feedback, etc.</li>
-<li>Publish and share your quiz with your participants via email or link. You can also embed your quiz on your website or blog.</li>
-</ul>
-<p>If you need more guidance on how to create online quizzes with different platforms, you can check the platform's tutorials or blogs for tips and examples.</p>
- <h3>Q: How can I improve my quizzes and my participants' performance?</h3>
-<p>A: Improving your quizzes and your participants' performance is an ongoing process that requires feedback and analysis. You can use the downloaded quiz results to help you with this process. Some tips are:</p>
-<ul>
-<li>Analyze the quiz results to see how well your participants performed on your quiz. Look for patterns, trends, outliers, strengths, and weaknesses.</li>
-<li>Compare the quiz results with your learning objectives or expectations. See if your quiz aligned with your goals and measured what you intended to measure.</li>
-<li>Evaluate the quality and difficulty of your questions and answers. See if your questions were clear, relevant, accurate, and fair. See if your answers were correct, comprehensive, and distractive.</li>
-<li>Provide feedback to your participants on their performance. Praise their achievements, point out their errors, explain their misconceptions, and suggest ways to improve.</li>
-<li>Revise your quiz based on the feedback and analysis. Modify or delete questions that were too easy or too hard, add or remove options, update or correct information, etc.</li>
-</ul>
-<p>If you want more advice on how to improve your quizzes and your participants' performance, you can check online resources such as books, articles, podcasts, or courses on quiz design and assessment.</p>
- <h3>Q: What are some of the advantages and disadvantages of online quizzes?</h3>
-<p>A: Online quizzes have many advantages and disadvantages compared to traditional paper-based quizzes. Some of them are:</p>
- <table border="1">
-<tr><th>Advantages</th><th>Disadvantages</th></tr>
-<tr><td>- Online quizzes are easy and fast to create, administer, grade, and analyze.</td><td>- Online quizzes may require internet access, devices, software, or accounts that some participants may not have or prefer.</td></tr>
-<tr><td>- Online quizzes can be more interactive and engaging with multimedia elements, gamification features, adaptive learning paths, etc.</td><td>- Online quizzes may pose technical issues such as glitches, errors, crashes, or compatibility problems that may affect the quiz experience or results.</td></tr>
-<tr><td>- Online quizzes can reach more participants across different locations and time zones.</td><td>- Online quizzes may reduce the social interaction and collaboration among participants and between participants and instructors.</td></tr>
-<tr><td>- Online quizzes can provide instant feedback and results to participants and instructors.</td><td>- Online quizzes may increase the risk of cheating or plagiarism among participants who may use unauthorized sources or methods to complete the quiz.</td></tr>
-<tr><td>- Online quizzes can be more flexible and customizable with different settings, formats, options, etc.</td><td>- Online quizzes may be less reliable or valid than paper-based quizzes due to factors such as question quality, scoring criteria, participant motivation, etc.</td></tr>
-</table>
- <p>The advantages and disadvantages of online quizzes may vary depending on the context, purpose, [user](# audience, and design of the quiz. You should weigh the pros and cons of online quizzes before deciding to use them for your needs.</p>
- <h3>Q: What are some of the best practices for creating online quizzes?</h3>
-<p>A: Creating online quizzes can be a fun and rewarding experience, but it also requires some planning and preparation. Here are some of the best practices for creating online quizzes:</p>
-<ul>
-<li>Define your learning objectives and outcomes. What do you want your participants to learn, know, or do after taking your quiz?</li>
-<li>Choose the right platform and format for your quiz. What tool or software will you use to create and deliver your quiz? What type of questions and answers will you use?</li>
-<li>Write clear and concise questions and answers. What are you asking your participants and what are you expecting them to answer? How can you avoid ambiguity, confusion, or bias in your questions and answers?</li>
-<li>Test and review your quiz. How can you ensure that your quiz is working properly and accurately? How can you get feedback from others on your quiz?</li>
-<li>Analyze and improve your quiz. How can you measure the effectiveness and impact of your quiz? How can you use the quiz results to improve your quiz or your participants' performance?</li>
-</ul>
-<p>If you want more guidance on how to create online quizzes, you can check online resources such as books, articles, podcasts, or courses on quiz design and development.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Epson L3110 Printer Driver Download - How to Install and Update.md

@@ -1,151 +0,0 @@
-<br />
-<h1>How to Download Driver L3110 for Windows 11/10/8/7</h1>
-<p>If you own an Epson L3110 printer, you might be wondering how to download driver L3110 for your Windows PC. In this article, we will show you what driver L3110 is, why you need it, how to download and install it, and how to fix common driver issues and errors.</p>
-<h2>download driver l3110</h2><br /><p><b><b>DOWNLOAD</b> &#10003; <a href="https://jinyurl.com/2uNNBL">https://jinyurl.com/2uNNBL</a></b></p><br /><br />
-<h2>What is Driver L3110 and Why Do You Need It?</h2>
-<h3>Driver L3110 is a software component that enables your Epson L3110 printer to communicate with your Windows PC.</h3>
-<p>A driver is a program that allows your computer and your hardware devices to interact with each other. Without a driver, your computer won't be able to recognize or use your printer properly.</p>
-<p>Driver L3110 is the specific driver for Epson L3110 printer, which is an all-in-one ink tank printer that offers high-quality printing, scanning, and copying functions. It also supports wireless connectivity, borderless printing, and low-cost ink refilling.</p>
-<h3>You need to install and update Driver L3110 to ensure the optimal performance and functionality of your printer.</h3>
-<p>To use your Epson L3110 printer on your Windows PC, you need to install driver L3110 first. This will allow your PC to detect and operate your printer correctly.</p>
-<p>Moreover, you also need to update driver L3110 regularly to keep it compatible with the latest Windows updates and software applications. Updating driver L3110 can also fix bugs, improve stability, enhance security, and add new features for your printer.</ <h2>Benefits of Updating Driver L3110</h2>
-<h3>Updating Driver L3110 can boost your system performance, stability, and security.</h3>
-<p>By updating driver L3110, you can ensure that your printer and your PC are working in harmony. This can improve the speed, efficiency, and reliability of your printing tasks. It can also prevent system crashes, freezes, or errors that may occur due to outdated or incompatible drivers.</p>
-<p>Furthermore, updating driver L3110 can also protect your PC and your printer from potential threats or attacks. Newer drivers often come with patches or fixes that address security vulnerabilities or bugs that may compromise your system or your data.</p>
-<p>How to download driver l3110 for Windows 11<br />
-Download driver l3110 for Mac OS X<br />
-Download driver l3110 for Linux Ubuntu<br />
-Download driver l3110 for Epson EcoTank printer<br />
-Download driver l3110 for Epson L series printer<br />
-Download driver l3110 from official website<br />
-Download driver l3110 from Driver Easy<br />
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-Download driver l3110 manual and warranty<br />
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-Download driver l3110 scanner and printer<br />
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-Download driver l3110 wireless setup<br />
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-Download driver l3110 print from web browser or cloud app<br />
-Download driver l3110 print from Windows or Mac application</p>
-<h3>Updating Driver L3110 can also unlock new features and enhancements for your printer.</h3>
-<p>Another benefit of updating driver L3110 is that you can enjoy the latest features and improvements that Epson has to offer for your printer. For example, you may be able to access new printing modes, settings, or options that can enhance your printing quality and experience. You may also be able to use new functions or tools that can make your printing tasks easier and more convenient.</p>
-<h3>Updating Driver L3110 can prevent common driver issues and errors that may affect your printing quality and experience.</h3>
-<p>Finally, updating driver L3110 can also help you avoid or resolve common driver issues and errors that may hamper your printing quality and experience. Some of these issues and errors include:</p>
-<ul>
-<li>Printer not recognized or detected by PC</li>
-<li>Printer not responding or working properly</li>
-<li>Printer showing offline or unavailable status</li>
-<li>Printer printing blank pages or incorrect colors</li>
-<li>Printer scanning or copying errors</li>
-<li>Printer driver installation or update failures</li>
-</ul>
-<p>By updating driver L3110, you can ensure that your printer is running smoothly and efficiently on your PC.</p>
-<h2>How to Download and Install Driver L3110</h2>
-<h3>Method 1: Use Bit Driver Updater to Automatically Update Driver L3110 (Recommended)</h3>
-<h4>Bit Driver Updater is a reliable and easy-to-use tool that can scan, download, and install the latest drivers for your devices with one click.</h4>
-<p>If you want to save time and hassle in updating driver L3110, we recommend using Bit Driver Updater. This is a powerful and user-friendly software that can automatically scan your PC for outdated, missing, or faulty drivers and update them with the most compatible and up-to-date versions. It can also backup and restore your drivers, optimize your system performance, and provide 24/7 technical support.</p>
-<h4>Steps to use Bit Driver Updater to update Driver L3110</h4>
-<ol>
-<li>Download and install Bit Driver Updater from its official website <a href="">here</a>.</li>
-<li>Launch the program and click on the Scan button to start scanning your PC for driver issues.</li>
-<li>Wait for the scan to complete and then review the scan results. You will see a list of drivers that need to be updated, including driver L3110.</li>
-<li>Click on the Update Now button next to driver L3110 to download and install the latest version of the driver. Alternatively, you can click on the Update All button to update all the drivers on your PC at once.</li>
-<li>Restart your PC to apply the changes and enjoy your updated printer.</li>
-</ol> <h3>Method 2: Manually Download and Install Driver L3110 from Epson's Official Website</h3>
-<h4>You can also download and install the latest version of Driver L3110 from Epson's official website by following these steps</h4>
-<p>Another way to update driver L3110 is to manually download and install it from Epson's official website. This method requires you to have some technical knowledge and patience, as you need to find the correct driver for your printer model and Windows version. You also need to follow the installation instructions carefully to avoid any errors or issues.</p>
-<h4>Steps to manually download and install driver L3110 from Epson's official website</h4>
-<ol>
-<li>Go to Epson's official website <a href="">here</a> and enter your printer model (L3110) in the search box. Then, click on the search icon or press Enter.</li>
-<li>On the product page, click on the Downloads tab and select your Windows version from the drop-down menu.</li>
-<li>Under the Drivers section, click on the Download button next to Printer Driver. This will download the driver file to your PC.</li>
-<li>Once the download is complete, open the file and follow the on-screen instructions to install the driver. You may need to agree to the license agreement, select your connection type, and connect your printer to your PC during the installation process.</li>
-<li>After the installation is finished, restart your PC and test your printer to see if it works properly.</li>
-</ol>
-<h3>Method 3: Update Driver L3110 in Device Manager</h3>
-<h4>Another way to update Driver L3110 is to use the built-in Device Manager in Windows by following these steps</h4>
-<p>The Device Manager is a Windows utility that allows you to manage and update your hardware devices and their drivers. You can use it to update driver L3110 by searching for it online or browsing for it on your PC. However, this method may not always find the latest or most compatible driver for your printer, so it is not recommended as the first option.</p>
-<h4>Steps to update driver L3110 in Device Manager</h4>
-<ol>
-<li>Press Windows + R keys on your keyboard to open the Run dialog box. Then, type devmgmt.msc and click OK or press Enter. This will open the Device Manager window.</li>
-<li>Expand the Printers or Print queues category and right-click on your Epson L3110 printer. Then, select Update driver from the context menu.</li>
-<li>You will see two options: Search automatically for updated driver software and Browse my computer for driver software. You can choose either one depending on your preference.</li>
-<li>If you choose Search automatically for updated driver software, Windows will search online for the best available driver for your printer and install it automatically. You may need an internet connection for this option.</li>
-<li>If you choose Browse my computer for driver software, you will need to locate the driver file that you downloaded from Epson's website or another source. Then, follow the instructions to install it manually.</li>
-<li>After the update is done, restart your PC and check if your printer is working correctly.</li>
-</ol> <li>Click on Update & Security and select Windows Update from the left pane.</li>
-<li>Click on Check for updates and wait for Windows to scan for available updates.</li>
-<li>If there are any driver updates available, including driver L3110, you will see them under the Optional updates section. Click on View optional updates and select the driver updates you want to install.</li>
-<li>Click on Download and install and wait for Windows to download and install the driver updates.</li>
-<li>Restart your PC and see if your printer is working properly.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download driver L3110 for your Epson L3110 printer on your Windows PC. We have also explained what driver L3110 is, why you need it, and how to fix common driver issues and errors. We hope that this article has been helpful and informative for you.</p>
-<p>To summarize, here are the main points of the article:</p>
-<ul>
-<li>Driver L3110 is a software component that enables your Epson L3110 printer to communicate with your Windows PC.</li>
-<li>You need to install and update driver L3110 to ensure the optimal performance and functionality of your printer.</li>
-<li>Updating driver L3110 can boost your system performance, stability, security, as well as unlock new features and enhancements for your printer.</li>
-<li>You can download and install driver L3110 using one of these methods: Bit Driver Updater (recommended), Epson's official website, or Device Manager.</li>
-<li>You can fix common driver issues and errors using one of these solutions: Windows Troubleshooter, reinstalling driver L3110 in Device Manager, or Windows Update.</li>
-</ul>
-<p>If you have any questions or feedback about this article, please feel free to leave a comment below. We would love to hear from you. Thank you for reading and happy printing!</p>
-<h2>FAQs</h2>
-<h3>Q1. What are the system requirements for Driver L3110?</h3>
-<p>A1. The system requirements for driver L3110 are as follows:</p>
-<table>
-<tr><th>Operating System</th><th>Supported Versions</th></tr>
-<tr><td>Windows 11</td><td>All versions</td></tr>
-<tr><td>Windows 10</td><td>All versions</td></tr>
-<tr><td>Windows 8/8.1</td><td>All versions</td></tr>
-<tr><td>Windows 7</td><td>All versions</td></tr>
-</table>
-<h3>Q2. How often should I update Driver L3110?</h3>
-<p>A2. You should update driver L3110 whenever there is a new version available from Epson or Windows. This will ensure that your printer is always compatible and up-to-date with the latest system updates and software applications. You can use Bit Driver Updater to automatically check for and update driver L3110 with one click.</p>
-<h3>Q3. What are the advantages of using Bit Driver Updater over other methods?</h3>
-<p>A3. Bit Driver Updater is a reliable and easy-to-use tool that can scan, download, and install the latest drivers for your devices with one click. It has several advantages over other methods, such as:</p>
-<ul>
-<li>It saves you time and hassle in finding and installing the correct drivers for your devices.</li>
-<li>It ensures that you get the most compatible and up-to-date drivers for your devices.</li>
-<li>It backs up and restores your drivers in case of any issues or errors.</li>
-<li>It optimizes your system performance, stability, and security by keeping your drivers updated.</li>
-<li>It provides 24/7 technical support and a 60-day money-back guarantee.</li>
-</ul>
- <h3>Q4. How can I contact Epson's customer support if I need further assistance with Driver L3110?</h3>
-<p>A4. If you need further assistance with driver L3110 or your Epson L3110 printer, you can contact Epson's customer support by visiting their website <a href="">here</a>. You can also find more information about Epson L3110 printer and its features on their website.</p>
- <h3>Q5. Where can I find more information about Epson L3110 printer and its features?</h3>
- <p>A5. You can find more information about Epson L3110 printer and its features on Epson's official website <a href="">here</a>. You can also read user reviews, watch videos, or download manuals and guides for your printer on their website.</p> 197e85843d<br />
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spaces/2023Liu2023/bingo/src/lib/bots/bing/index.ts

@@ -1,421 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'Chat',
-            'InternalSearchQuery',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('Invalid response', ErrorCode.UNKOWN_ERROR)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async useWs(params: Params) {
-    const wsp = await this.sendWs()
-    const watchDog = new WatchDog()
-    wsp.onUnpackedMessage.addListener((events) => {
-      watchDog.watch(() => {
-        wsp.sendPacked({ type: 6 })
-      })
-      this.parseEvents(params, events)
-    })
-
-    wsp.onClose.addListener(() => {
-      watchDog.reset()
-      params.onEvent({ type: 'DONE' })
-      wsp.removeAllListeners()
-    })
-
-    params.signal?.addEventListener('abort', () => {
-      wsp.removeAllListeners()
-      wsp.close()
-    })
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}

spaces/3mrology/Chameleon_Text2Img_Generation_Demo/app.py

@@ -1,71 +0,0 @@
-import gradio as gr
-import os
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-text_gen = gr.Interface.load(name="spaces/Gustavosta/MagicPrompt-Stable-Diffusion")
-stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
-
-def get_images(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    sd_output = [os.path.join(gallery_dir, image) for image in os.listdir(gallery_dir)]
-    return sd_output, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-css = '''
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-a {text-decoration-line: underline;}
-'''
-
-with gr.Blocks(css=css) as demo:
-    gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-            <div
-            style="
-                display: inline-flex;
-                align-items: center;
-                gap: 0.8rem;
-                font-size: 1.75rem;
-            "
-            >
-            <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                Chameleon Text2Img Generator Demo 🦎
-            </h1>
-            </div>
-        </div>""")
-
-    with gr.Row():
-
-      with gr.Column():
-        text_output = gr.Textbox(
-                                label="Prettified text prompt", 
-                                lines=4,
-                                elem_id="translated"
-                            )
-        with gr.Row():
-            diffuse_btn = gr.Button(value="Generate!")
-      with gr.Column(elem_id="generated-gallery"):
-        sd_output = gr.Gallery().style(grid=2, height="auto")
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-    diffuse_btn.click(get_images, 
-                          inputs = [
-                              text_output
-                              ], 
-                          outputs = [sd_output, community_icon, loading_icon, share_button]
-                          )
-    share_button.click(None, [], [], _js=share_js)
-
-
-
-demo.launch(debug=True)

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/How to Deploy b7c4f3fd308944af8ba4637ec40fa4f9.md

@@ -1,33 +0,0 @@
-# How to Deploy
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Guides and Processes
-
-<aside>
-💡 Use this template to describe the steps engineers should follow to deploy.
-
-</aside>
-
-# 1. Ping in Slack #deploys channel
-
-Let everyone know you're about to start a deploy. 
-
-# 2. SSH into the deployment server
-
-<aside>
-💡 Create a code block by typing `/code` and pressing `enter`.
-
-</aside>
-
-```bash
-ssh deployments.acmecorp.com
-```
-
-- You can create `inline code snippets` with the shortcut `cmd/ctrl + e`.
-
-# 3. Run the following commands
-
-```bash
-acme deploy --prod
-```

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Smart Domain 3b0daf8bb0d740439426cfab214f1fa6.md

@@ -1,13 +0,0 @@
-# Smart Domain
-
-Smart Domain是一种新的软件架构模式，主要用于实现领域驱动设计（DDD）。相较于传统的分层架构，Smart Domain更加注重从业务领域的角度去划分层次，并在每一层次上都使用领域专家和开发人员共同理解的通用语言进行交流，从而建立起完整的领域模型。与此同时，Smart Domain还摒弃了服务层的概念，将原本在服务层实现的逻辑下沉到领域模型中，从而让领域模型更加丰满和清晰。
-
-总之，Smart Domain是一种新的软件架构模式，通过从业务领域的角度去划分层次和使用通用语言建立领域模型，以及将服务层逻辑下沉到领域模型中，提高了领域驱动设计的实现效果。
-
-## SmartDomain 分层
-1. Domain层
-Domain层是整个系统的核心，主要定义业务相关的实体（Entity）和表示实体之间关联关系的关联对象。该层定义的实体和关联对象是业务模型的核心，反映了整个系统的业务规则和逻辑。同时，该层仅提供接口或抽象类定义，不包含具体实现。这样做可以避免业务逻辑和实现细节的混淆，也方便后续的扩展和维护。
-2. 集成层
-集成层主要负责Domain层中关系对象的实现，该层会依赖数据库、Http接口访问进行功能实现。该层的职责是将Domain层的抽象定义转换成具体实现，包括数据持久化、网络通信等。该层还可以处理一些跨域访问和数据加密解密等底层功能。
-3. API层
-API层是整个系统的入口，基于Domain层的Entity或关联对象提供的能力，提供外部访问的RestFul接口。该层负责处理外部请求，将请求参数转换成业务对象并调用Domain层的业务逻辑实现，返回结果给客户端。该层还可以对请求参数进行验证、安全过滤等操作，保证系统的安全性和可靠性。

spaces/AIConsultant/MusicGen/audiocraft/adversarial/losses.py

@@ -1,228 +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.
-
-"""
-Utility module to handle adversarial losses without requiring to mess up the main training loop.
-"""
-
-import typing as tp
-
-import flashy
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-ADVERSARIAL_LOSSES = ['mse', 'hinge', 'hinge2']
-
-
-AdvLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor], torch.Tensor]]
-FeatLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor, torch.Tensor], torch.Tensor]]
-
-
-class AdversarialLoss(nn.Module):
-    """Adversary training wrapper.
-
-    Args:
-        adversary (nn.Module): The adversary module will be used to estimate the logits given the fake and real samples.
-            We assume here the adversary output is ``Tuple[List[torch.Tensor], List[List[torch.Tensor]]]``
-            where the first item is a list of logits and the second item is a list of feature maps.
-        optimizer (torch.optim.Optimizer): Optimizer used for training the given module.
-        loss (AdvLossType): Loss function for generator training.
-        loss_real (AdvLossType): Loss function for adversarial training on logits from real samples.
-        loss_fake (AdvLossType): Loss function for adversarial training on logits from fake samples.
-        loss_feat (FeatLossType): Feature matching loss function for generator training.
-        normalize (bool): Whether to normalize by number of sub-discriminators.
-
-    Example of usage:
-        adv_loss = AdversarialLoss(adversaries, optimizer, loss, loss_real, loss_fake)
-        for real in loader:
-            noise = torch.randn(...)
-            fake = model(noise)
-            adv_loss.train_adv(fake, real)
-            loss, _ = adv_loss(fake, real)
-            loss.backward()
-    """
-    def __init__(self,
-                 adversary: nn.Module,
-                 optimizer: torch.optim.Optimizer,
-                 loss: AdvLossType,
-                 loss_real: AdvLossType,
-                 loss_fake: AdvLossType,
-                 loss_feat: tp.Optional[FeatLossType] = None,
-                 normalize: bool = True):
-        super().__init__()
-        self.adversary: nn.Module = adversary
-        flashy.distrib.broadcast_model(self.adversary)
-        self.optimizer = optimizer
-        self.loss = loss
-        self.loss_real = loss_real
-        self.loss_fake = loss_fake
-        self.loss_feat = loss_feat
-        self.normalize = normalize
-
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        # Add the optimizer state dict inside our own.
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'optimizer'] = self.optimizer.state_dict()
-        return destination
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        # Load optimizer state.
-        self.optimizer.load_state_dict(state_dict.pop(prefix + 'optimizer'))
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def get_adversary_pred(self, x):
-        """Run adversary model, validating expected output format."""
-        logits, fmaps = self.adversary(x)
-        assert isinstance(logits, list) and all([isinstance(t, torch.Tensor) for t in logits]), \
-            f'Expecting a list of tensors as logits but {type(logits)} found.'
-        assert isinstance(fmaps, list), f'Expecting a list of features maps but {type(fmaps)} found.'
-        for fmap in fmaps:
-            assert isinstance(fmap, list) and all([isinstance(f, torch.Tensor) for f in fmap]), \
-                f'Expecting a list of tensors as feature maps but {type(fmap)} found.'
-        return logits, fmaps
-
-    def train_adv(self, fake: torch.Tensor, real: torch.Tensor) -> torch.Tensor:
-        """Train the adversary with the given fake and real example.
-
-        We assume the adversary output is the following format: Tuple[List[torch.Tensor], List[List[torch.Tensor]]].
-        The first item being the logits and second item being a list of feature maps for each sub-discriminator.
-
-        This will automatically synchronize gradients (with `flashy.distrib.eager_sync_model`)
-        and call the optimizer.
-        """
-        loss = torch.tensor(0., device=fake.device)
-        all_logits_fake_is_fake, _ = self.get_adversary_pred(fake.detach())
-        all_logits_real_is_fake, _ = self.get_adversary_pred(real.detach())
-        n_sub_adversaries = len(all_logits_fake_is_fake)
-        for logit_fake_is_fake, logit_real_is_fake in zip(all_logits_fake_is_fake, all_logits_real_is_fake):
-            loss += self.loss_fake(logit_fake_is_fake) + self.loss_real(logit_real_is_fake)
-
-        if self.normalize:
-            loss /= n_sub_adversaries
-
-        self.optimizer.zero_grad()
-        with flashy.distrib.eager_sync_model(self.adversary):
-            loss.backward()
-        self.optimizer.step()
-
-        return loss
-
-    def forward(self, fake: torch.Tensor, real: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-        """Return the loss for the generator, i.e. trying to fool the adversary,
-        and feature matching loss if provided.
-        """
-        adv = torch.tensor(0., device=fake.device)
-        feat = torch.tensor(0., device=fake.device)
-        with flashy.utils.readonly(self.adversary):
-            all_logits_fake_is_fake, all_fmap_fake = self.get_adversary_pred(fake)
-            all_logits_real_is_fake, all_fmap_real = self.get_adversary_pred(real)
-            n_sub_adversaries = len(all_logits_fake_is_fake)
-            for logit_fake_is_fake in all_logits_fake_is_fake:
-                adv += self.loss(logit_fake_is_fake)
-            if self.loss_feat:
-                for fmap_fake, fmap_real in zip(all_fmap_fake, all_fmap_real):
-                    feat += self.loss_feat(fmap_fake, fmap_real)
-
-        if self.normalize:
-            adv /= n_sub_adversaries
-            feat /= n_sub_adversaries
-
-        return adv, feat
-
-
-def get_adv_criterion(loss_type: str) -> tp.Callable:
-    assert loss_type in ADVERSARIAL_LOSSES
-    if loss_type == 'mse':
-        return mse_loss
-    elif loss_type == 'hinge':
-        return hinge_loss
-    elif loss_type == 'hinge2':
-        return hinge2_loss
-    raise ValueError('Unsupported loss')
-
-
-def get_fake_criterion(loss_type: str) -> tp.Callable:
-    assert loss_type in ADVERSARIAL_LOSSES
-    if loss_type == 'mse':
-        return mse_fake_loss
-    elif loss_type in ['hinge', 'hinge2']:
-        return hinge_fake_loss
-    raise ValueError('Unsupported loss')
-
-
-def get_real_criterion(loss_type: str) -> tp.Callable:
-    assert loss_type in ADVERSARIAL_LOSSES
-    if loss_type == 'mse':
-        return mse_real_loss
-    elif loss_type in ['hinge', 'hinge2']:
-        return hinge_real_loss
-    raise ValueError('Unsupported loss')
-
-
-def mse_real_loss(x: torch.Tensor) -> torch.Tensor:
-    return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x))
-
-
-def mse_fake_loss(x: torch.Tensor) -> torch.Tensor:
-    return F.mse_loss(x, torch.tensor(0., device=x.device).expand_as(x))
-
-
-def hinge_real_loss(x: torch.Tensor) -> torch.Tensor:
-    return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-def hinge_fake_loss(x: torch.Tensor) -> torch.Tensor:
-    return -torch.mean(torch.min(-x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-def mse_loss(x: torch.Tensor) -> torch.Tensor:
-    if x.numel() == 0:
-        return torch.tensor([0.0], device=x.device)
-    return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x))
-
-
-def hinge_loss(x: torch.Tensor) -> torch.Tensor:
-    if x.numel() == 0:
-        return torch.tensor([0.0], device=x.device)
-    return -x.mean()
-
-
-def hinge2_loss(x: torch.Tensor) -> torch.Tensor:
-    if x.numel() == 0:
-        return torch.tensor([0.0])
-    return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-class FeatureMatchingLoss(nn.Module):
-    """Feature matching loss for adversarial training.
-
-    Args:
-        loss (nn.Module): Loss to use for feature matching (default=torch.nn.L1).
-        normalize (bool): Whether to normalize the loss.
-            by number of feature maps.
-    """
-    def __init__(self, loss: nn.Module = torch.nn.L1Loss(), normalize: bool = True):
-        super().__init__()
-        self.loss = loss
-        self.normalize = normalize
-
-    def forward(self, fmap_fake: tp.List[torch.Tensor], fmap_real: tp.List[torch.Tensor]) -> torch.Tensor:
-        assert len(fmap_fake) == len(fmap_real) and len(fmap_fake) > 0
-        feat_loss = torch.tensor(0., device=fmap_fake[0].device)
-        feat_scale = torch.tensor(0., device=fmap_fake[0].device)
-        n_fmaps = 0
-        for (feat_fake, feat_real) in zip(fmap_fake, fmap_real):
-            assert feat_fake.shape == feat_real.shape
-            n_fmaps += 1
-            feat_loss += self.loss(feat_fake, feat_real)
-            feat_scale += torch.mean(torch.abs(feat_real))
-
-        if self.normalize:
-            feat_loss /= n_fmaps
-
-        return feat_loss

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/__init__.py

@@ -1,5 +0,0 @@
-from .causal_conv import *  # NOQA
-from .pqmf import *  # NOQA
-from .residual_block import *  # NOQA
-from modules.parallel_wavegan.layers.residual_stack import *  # NOQA
-from .upsample import *  # NOQA

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/fs_adv_aishell.py

@@ -1,267 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import numpy as np
-
-from text_to_speech.modules.tts.syntaspeech.multi_window_disc import Discriminator
-from tasks.tts.fs import FastSpeechTask
-from text_to_speech.modules.tts.fs import FastSpeech
-
-from text_to_speech.utils.audio.align import mel2token_to_dur
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.nn.model_utils import num_params
-from text_to_speech.utils.commons.tensor_utils import tensors_to_scalars
-from text_to_speech.utils.audio.pitch.utils import denorm_f0, norm_f0
-from text_to_speech.utils.audio.pitch_extractors import get_pitch
-from text_to_speech.utils.metrics.dtw import dtw as DTW
-
-from text_to_speech.utils.plot.plot import spec_to_figure
-from text_to_speech.utils.text.text_encoder import build_token_encoder
-
-
-class FastSpeechAdvTask(FastSpeechTask):
-    def __init__(self):
-        super().__init__()
-        self.build_disc_model()
-        self.mse_loss_fn = torch.nn.MSELoss()
-        
-    def build_tts_model(self):
-        dict_size = len(self.token_encoder)
-        self.model = FastSpeech(dict_size, hparams)
-        self.gen_params = [p for p in self.model.parameters() if p.requires_grad]
-        self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)]
-        self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)]        
-        self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)]
-        self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ]
-        self.use_bert = True if len(self.bert_params) > 0 else False
-
-
-    def build_disc_model(self):
-        disc_win_num = hparams['disc_win_num']
-        h = hparams['mel_disc_hidden_size']
-        self.mel_disc = Discriminator(
-            time_lengths=[32, 64, 128][:disc_win_num],
-            freq_length=80, hidden_size=h, kernel=(3, 3)
-        )
-        self.disc_params = list(self.mel_disc.parameters())
-
-    def _training_step(self, sample, batch_idx, optimizer_idx):
-        loss_output = {}
-        loss_weights = {}
-        disc_start = self.global_step >= hparams["disc_start_steps"] and hparams['lambda_mel_adv'] > 0
-        if optimizer_idx == 0:
-            #######################
-            #      Generator      #
-            #######################
-            loss_output, model_out = self.run_model(sample, infer=False)
-            self.model_out_gt = self.model_out = \
-                {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)}
-            if disc_start:
-                mel_p = model_out['mel_out']
-                if hasattr(self.model, 'out2mel'):
-                    mel_p = self.model.out2mel(mel_p)
-                o_ = self.mel_disc(mel_p)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    loss_output['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size()))
-                    loss_weights['a'] = hparams['lambda_mel_adv']
-                if pc_ is not None:
-                    loss_output['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size()))
-                    loss_weights['ac'] = hparams['lambda_mel_adv']
-        else:
-            #######################
-            #    Discriminator    #
-            #######################
-            if disc_start and self.global_step % hparams['disc_interval'] == 0:
-                model_out = self.model_out_gt
-                mel_g = sample['mels']
-                mel_p = model_out['mel_out']
-                o = self.mel_disc(mel_g)
-                p, pc = o['y'], o['y_c']
-                o_ = self.mel_disc(mel_p)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    loss_output["r"] = self.mse_loss_fn(p, p.new_ones(p.size()))
-                    loss_output["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size()))
-                if pc_ is not None:
-                    loss_output["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size()))
-                    loss_output["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size()))
-            else:
-                return None
-        total_loss = sum([loss_weights.get(k, 1) * v for k, v in loss_output.items() if isinstance(v, torch.Tensor) and v.requires_grad])
-        loss_output['batch_size'] = sample['txt_tokens'].size()[0]
-        return total_loss, loss_output
-
-    def validation_start(self):
-        self.vocoder = None
-
-    def validation_step(self, sample, batch_idx):
-        outputs = {}
-        outputs['losses'] = {}
-        outputs['losses'], model_out = self.run_model(sample)
-        outputs['total_loss'] = sum(outputs['losses'].values())
-        outputs['nsamples'] = sample['nsamples']
-        outputs = tensors_to_scalars(outputs)
-        if self.global_step % hparams['valid_infer_interval'] == 0 \
-                and batch_idx < hparams['num_valid_plots']:
-            valid_results = self.save_valid_result(sample, batch_idx, model_out)
-            # wav_gt = valid_results['wav_gt']
-            mel_gt = valid_results['mel_gt']
-            # wav_pred = valid_results['wav_pred']
-            mel_pred = valid_results['mel_pred']
-            # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
-            # f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
-            # manhattan_distance = lambda x, y: np.abs(x - y)
-            # dist, cost, acc, path = DTW(f0_pred_, f0_gt_, manhattan_distance)
-            # outputs['losses']['f0_dtw'] = dist / len(f0_gt_)
-        return outputs
-
-    def save_valid_result(self, sample, batch_idx, model_out):
-        sr = hparams['audio_sample_rate']
-        f0_gt = None
-        mel_out = model_out['mel_out']
-        if sample.get('f0') is not None:
-            f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu())
-        self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt)
-        
-        # if self.global_step > 0:
-        if self.vocoder is not None:
-            wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt)
-            self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr)
-        # with gt duration
-        model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True)
-        dur_info = self.get_plot_dur_info(sample, model_out)
-        del dur_info['dur_pred']
-        if self.vocoder is not None:
-            wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
-            self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr)
-        self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_gdur_{batch_idx}',
-                        dur_info=dur_info, f0s=f0_gt)
-
-        # with pred duration
-        if not hparams['use_gt_dur']:
-            model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False)
-            dur_info = self.get_plot_dur_info(sample, model_out)
-            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}',
-                            dur_info=dur_info, f0s=f0_gt)
-            if self.vocoder is not None:
-                wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
-                self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr)
-        # gt wav
-        mel_gt = sample['mels'][0].cpu()
-        if self.vocoder is not None:
-            wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
-            if self.global_step <= hparams['valid_infer_interval']:
-                self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr)
-            
-        # add attn plot
-        # if self.global_step > 0 and hparams['dur_level'] == 'word':
-        #     self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step)
-
-        return {'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()}
-        # return {'wav_gt': wav_gt, 'wav_pred': wav_pred, 'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()}
-
-
-    def get_plot_dur_info(self, sample, model_out):
-        # if hparams['dur_level'] == 'word':
-        #     T_txt = sample['word_lengths'].max()
-        #     dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0]
-        #     dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
-        #     txt = sample['ph_words'][0].split(" ")
-        # else:
-        T_txt = sample['txt_tokens'].shape[1]
-        dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0]
-        dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
-        txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy())
-        txt = txt.split(" ")
-        return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt}
-
-    def build_optimizer(self, model):
-        
-        optimizer_gen = torch.optim.AdamW(
-            self.gen_params,
-            lr=hparams['lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            weight_decay=hparams['weight_decay'])
-
-        optimizer_disc = torch.optim.AdamW(
-            self.disc_params,
-            lr=hparams['disc_lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None
-
-        return [optimizer_gen, optimizer_disc]
-
-    def build_scheduler(self, optimizer):
-        return [
-            FastSpeechTask.build_scheduler(self, optimizer[0]), # Generator Scheduler
-            torch.optim.lr_scheduler.StepLR(optimizer=optimizer[1], # Discriminator Scheduler
-                **hparams["discriminator_scheduler_params"]),
-        ]
-
-    def on_before_optimization(self, opt_idx):
-        if opt_idx == 0:
-            nn.utils.clip_grad_norm_(self.dp_params, hparams['clip_grad_norm'])
-            if self.use_bert:
-                nn.utils.clip_grad_norm_(self.bert_params, hparams['clip_grad_norm'])
-                nn.utils.clip_grad_norm_(self.gen_params_except_bert_and_dp, hparams['clip_grad_norm'])
-            else:
-                nn.utils.clip_grad_norm_(self.gen_params_except_dp, hparams['clip_grad_norm'])
-        else:
-            nn.utils.clip_grad_norm_(self.disc_params, hparams["clip_grad_norm"])
-
-    def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
-        if self.scheduler is not None:
-            self.scheduler[0].step(self.global_step // hparams['accumulate_grad_batches'])
-            self.scheduler[1].step(self.global_step // hparams['accumulate_grad_batches'])
-
-    ############
-    # infer
-    ############
-    def test_start(self):
-        super().test_start()
-        if hparams.get('save_attn', False):
-            os.makedirs(f'{self.gen_dir}/attn', exist_ok=True)
-        self.model.store_inverse_all()
-
-    def test_step(self, sample, batch_idx):
-        assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
-        outputs = self.run_model(sample, infer=True)
-        text = sample['text'][0]
-        item_name = sample['item_name'][0]
-        tokens = sample['txt_tokens'][0].cpu().numpy()
-        mel_gt = sample['mels'][0].cpu().numpy()
-        mel_pred = outputs['mel_out'][0].cpu().numpy()
-        mel2ph = sample['mel2ph'][0].cpu().numpy()
-        mel2ph_pred = None
-        str_phs = self.token_encoder.decode(tokens, strip_padding=True)
-        base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]'
-        if text is not None:
-            base_fn += text.replace(":", "$3A")[:80]
-        base_fn = base_fn.replace(' ', '_')
-        gen_dir = self.gen_dir
-        wav_pred = self.vocoder.spec2wav(mel_pred)
-        self.saving_result_pool.add_job(self.save_result, args=[
-            wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred])
-        if hparams['save_gt']:
-            wav_gt = self.vocoder.spec2wav(mel_gt)
-            self.saving_result_pool.add_job(self.save_result, args=[
-                wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph])
-        if hparams.get('save_attn', False):
-            attn = outputs['attn'][0].cpu().numpy()
-            np.save(f'{gen_dir}/attn/{item_name}.npy', attn)
-        # save f0 for pitch dtw
-        f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
-        f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
-        np.save(f'{gen_dir}/f0/{item_name}.npy', f0_pred_)
-        np.save(f'{gen_dir}/f0/{item_name}_gt.npy', f0_gt_)
-
-        print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
-        return {
-            'item_name': item_name,
-            'text': text,
-            'ph_tokens': self.token_encoder.decode(tokens.tolist()),
-            'wav_fn_pred': base_fn % 'P',
-            'wav_fn_gt': base_fn % 'G',
-        }

spaces/AIWaves/Debate/src/agents/State.py

@@ -1,142 +0,0 @@
-from Component import *
-
-
-class State:
-    """
-    Sub-scenes of role activities, responsible for storing the tasks that each role needs to do
-    """
-    def __init__(self, **kwargs):
-        self.next_states = {}
-        self.name = kwargs["name"]
-
-        self.environment_prompt = (
-            kwargs["environment_prompt"] if "environment_prompt" in kwargs else ""
-        )
-
-        self.roles = kwargs["roles"] if "roles" in kwargs else (list(kwargs["agent_states"].keys()) if "agent_states" in kwargs else [0])
-        if len(self.roles) == 0:
-            self.roles = [0]
-        self.begin_role = (
-            kwargs["begin_role"] if "begin_role" in kwargs else self.roles[0]
-        )
-        self.begin_query = kwargs["begin_query"] if "begin_query" in kwargs else None
-
-        self.is_begin = True
-
-        self.summary_prompt = (
-            kwargs["summary_prompt"] if "summary_prompt" in kwargs else None
-        )
-        self.current_role = self.begin_role
-        self.components = (
-            self.init_components(kwargs["agent_states"])
-            if "agent_states" in kwargs
-            else {}
-        )
-        self.index = (
-            self.roles.index(self.begin_role) if self.begin_role in self.roles else 0
-        )
-        self.chat_nums = 0
-
-    def init_components(self, agent_states_dict: dict):
-        agent_states = {}
-        for role, components in agent_states_dict.items():
-            component_dict = {}
-            for component, component_args in components.items():
-                if component:
-                    # "role" "style"
-                    if component == "style":
-                        component_dict["style"] = StyleComponent(component_args["role"])
-
-                        # "task"
-                    elif component == "task":
-                        component_dict["task"] = TaskComponent(component_args["task"])
-
-                        # "rule"
-                    elif component == "rule":
-                        component_dict["rule"] = RuleComponent(component_args["rule"])
-
-                        # "demonstration"
-                    elif component == "demonstrations":
-                        component_dict["demonstrations"] = DemonstrationComponent(
-                            component_args["demonstrations"]
-                        )
-
-                    # "output"
-                    elif component == "output":
-                        component_dict["output"] = OutputComponent(
-                            component_args["output"]
-                        )
-
-                    elif component == "last":
-                        component_dict["last"] = LastComponent(
-                            component_args["last_prompt"]
-                        )
-
-                    # "demonstrations"
-                    elif component == "cot":
-                        component_dict["cot"] = CoTComponent(
-                            component_args["demonstrations"]
-                        )
-                    elif component == "CustomizeComponent":
-                        component_dict["CustomizeComponent"] = CustomizeComponent(
-                            component_args["template"], component_args["keywords"]
-                        )
-                    
-                    elif component == "system" : 
-                        component_dict["system"] = SystemComponent(
-                            component_args["system_prompt"]
-                        )
-
-                    # =================================================================================#
-
-                    # "output"
-                    elif component == "StaticComponent":
-                        component_dict["StaticComponent"] = StaticComponent(
-                            component_args["output"]
-                        )
-
-                    # "top_k"  "type" "knowledge_base" "system_prompt" "last_prompt"
-                    elif component == "KnowledgeBaseComponent":
-                        component_dict["tool"] = KnowledgeBaseComponent(
-                            component_args["top_k"],
-                            component_args["type"],
-                            component_args["knowledge_path"],
-                        )
-
-                    elif component == "CategoryRequirementsComponent":
-                        component_dict[
-                            "CategoryRequirementsComponent"
-                        ] = CategoryRequirementsComponent(
-                            component_args["information_path"]
-                        )
-
-                    elif component == "FunctionComponent":
-                        component_dict["FunctionComponent"] = FunctionComponent(component_args[""])
-                    # "short_memory_extract_words"  "long_memory_extract_words" "system_prompt" "last_prompt"
-                    elif component == "ExtractComponent":
-                        component_dict["ExtractComponent"] = ExtractComponent(
-                            component_args["extract_words"],
-                            component_args["system_prompt"],
-                            component_args["last_prompt"],
-                        )
-                    elif component == "WebSearchComponent":
-                        component_dict["WebSearchComponent"] = WebSearchComponent(
-                            component_args["engine_name"], component_args["api"]
-                        )
-                    elif component == "WebCrawlComponent":
-                        component_dict["WebCrawlComponent"] = WebCrawlComponent(
-                            component_args["name"]
-                        )
-
-                    elif component == "CodeComponent":
-                        component_dict["CodeComponent"] = CodeComponent(
-                            component_args["file_name"], component_args["keyword"]
-                        )
-
-                    # ====================================================
-                    else:
-                        continue
-
-            agent_states[role] = component_dict
-
-        return agent_states

spaces/ASJMO/freegpt/g4f/Provider/Providers/Zeabur.py

@@ -1,50 +0,0 @@
-import os
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = "https://gptleg.zeabur.app"
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-0301',
-         'gpt-3.5-turbo-16k', 'gpt-4', 'gpt-4-0613']
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    headers = {
-        'Authority': 'chat.dfehub.com',
-        'Content-Type': 'application/json',
-        'Method': 'POST',
-        'Path': '/api/openai/v1/chat/completions',
-        'Scheme': 'https',
-        'Accept': 'text/event-stream',
-        'Accept-Language': 'pt-BR,pt;q=0.9,en-US;q=0.8,en;q=0.7,zh-CN;q=0.6,zh;q=0.5',
-        'Content-Type': 'application/json',
-        'Origin': 'https://gptleg.zeabur.app',
-        'Referer': 'https://gptleg.zeabur.app/',
-        'Sec-Ch-Ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
-        'Sec-Ch-Ua-Mobile': '?0',
-        'Sec-Ch-Ua-Platform': '"Windows"',
-        'Sec-Fetch-Dest': 'empty',
-        'Sec-Fetch-Mode': 'cors',
-        'Sec-Fetch-Site': 'same-origin',
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
-        'X-Requested-With': 'XMLHttpRequest',
-    }
-
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'max_tokens': '16000',
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-
-    response = requests.post(url + '/api/openai/v1/chat/completions',
-                             headers=headers, json=data, stream=stream)
-
-    yield response.json()['choices'][0]['message']['content']
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/$types.d.ts

@@ -1,24 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = {  }
-type RouteId = '/';
-type MaybeWithVoid<T> = {} extends T ? T | void : T;
-export type RequiredKeys<T> = { [K in keyof T]-?: {} extends { [P in K]: T[K] } ? never : K; }[keyof T];
-type OutputDataShape<T> = MaybeWithVoid<Omit<App.PageData, RequiredKeys<T>> & Partial<Pick<App.PageData, keyof T & keyof App.PageData>> & Record<string, any>>
-type EnsureDefined<T> = T extends null | undefined ? {} : T;
-type OptionalUnion<U extends Record<string, any>, A extends keyof U = U extends U ? keyof U : never> = U extends unknown ? { [P in Exclude<A, keyof U>]?: never } & U : never;
-export type Snapshot<T = any> = Kit.Snapshot<T>;
-type PageParentData = EnsureDefined<LayoutData>;
-type LayoutRouteId = RouteId | "/" | "/conversation/[id]" | "/conversations" | "/login" | "/login/callback" | "/logout" | "/privacy" | "/r/[id]" | "/settings" | null
-type LayoutParams = RouteParams & { id?: string }
-type LayoutServerParentData = EnsureDefined<{}>;
-type LayoutParentData = EnsureDefined<{}>;
-
-export type PageServerData = null;
-export type PageData = Expand<PageParentData>;
-export type LayoutServerLoad<OutputData extends OutputDataShape<LayoutServerParentData> = OutputDataShape<LayoutServerParentData>> = Kit.ServerLoad<LayoutParams, LayoutServerParentData, OutputData, LayoutRouteId>;
-export type LayoutServerLoadEvent = Parameters<LayoutServerLoad>[0];
-export type LayoutServerData = Expand<OptionalUnion<EnsureDefined<Kit.AwaitedProperties<Awaited<ReturnType<typeof import('./proxy+layout.server.js').load>>>>>>;
-export type LayoutData = Expand<Omit<LayoutParentData, keyof LayoutServerData> & EnsureDefined<LayoutServerData>>;
-export type RequestEvent = Kit.RequestEvent<RouteParams, RouteId>;

spaces/Adapter/CoAdapter/ldm/modules/encoders/modules.py

@@ -1,441 +0,0 @@
-import torch
-import torch.nn as nn
-import math
-from torch.utils.checkpoint import checkpoint
-
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, CLIPModel
-
-import open_clip
-import re
-from ldm.util import default, count_params
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class IdentityEncoder(AbstractEncoder):
-
-    def encode(self, x):
-        return x
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-
-    def forward(self, batch, key=None):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]
-        c = self.embedding(c)
-        return c
-
-
-class FrozenT5Embedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLIPEmbedder(AbstractEncoder):
-    """Uses the CLIP transformer encoder for text (from huggingface)"""
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
-                 freeze=True, layer="last"):  # clip-vit-base-patch32
-        super().__init__()
-        self.tokenizer = CLIPTokenizer.from_pretrained(version)
-        self.transformer = CLIPModel.from_pretrained(version).text_model
-        self.device = device
-        self.max_length = max_length
-        if freeze:
-            self.freeze()
-        self.layer = layer
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer != 'last')
-
-        if self.layer == 'penultimate':
-            z = outputs.hidden_states[-2]
-            z = self.transformer.final_layer_norm(z)
-        else:
-            z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenOpenCLIPEmbedder(AbstractEncoder):
-    """
-    Uses the OpenCLIP transformer encoder for text
-    """
-    LAYERS = [
-        #"pooled",
-        "last",
-        "penultimate"
-    ]
-    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
-                 freeze=True, layer="last"):
-        super().__init__()
-        assert layer in self.LAYERS
-        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
-        del model.visual
-        self.model = model
-
-        self.device = device
-        self.max_length = max_length
-        if freeze:
-            self.freeze()
-        self.layer = layer
-        if self.layer == "last":
-            self.layer_idx = 0
-        elif self.layer == "penultimate":
-            self.layer_idx = 1
-        else:
-            raise NotImplementedError()
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        tokens = open_clip.tokenize(text)
-        z = self.encode_with_transformer(tokens.to(self.device))
-        return z
-
-    def encode_with_transformer(self, text):
-        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
-        x = x + self.model.positional_embedding
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.model.ln_final(x)
-        return x
-
-    def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
-        for i, r in enumerate(self.model.transformer.resblocks):
-            if i == len(self.model.transformer.resblocks) - self.layer_idx:
-                break
-            if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
-                x = checkpoint(r, x, attn_mask)
-            else:
-                x = r(x, attn_mask=attn_mask)
-        return x
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLIPT5Encoder(AbstractEncoder):
-    def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
-                 clip_max_length=77, t5_max_length=77):
-        super().__init__()
-        self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
-        self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
-        print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
-              f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
-
-    def encode(self, text):
-        return self(text)
-
-    def forward(self, text):
-        clip_z = self.clip_encoder.encode(text)
-        t5_z = self.t5_encoder.encode(text)
-        return [clip_z, t5_z]
-
-
-# code from sd-webui
-re_attention = re.compile(r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""", re.X)
-
-
-def parse_prompt_attention(text):
-    """
-    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
-    Accepted tokens are:
-      (abc) - increases attention to abc by a multiplier of 1.1
-      (abc:3.12) - increases attention to abc by a multiplier of 3.12
-      [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
-      \\ - literal character '\'
-      anything else - just text
-
-    >>> parse_prompt_attention('normal text')
-    [['normal text', 1.0]]
-    >>> parse_prompt_attention('an (important) word')
-    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
-    >>> parse_prompt_attention('(unbalanced')
-    [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
-    [['(literal]', 1.0]]
-    >>> parse_prompt_attention('(unnecessary)(parens)')
-    [['unnecessaryparens', 1.1]]
-    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
-    [['a ', 1.0],
-     ['house', 1.5730000000000004],
-     [' ', 1.1],
-     ['on', 1.0],
-     [' a ', 1.1],
-     ['hill', 0.55],
-     [', sun, ', 1.1],
-     ['sky', 1.4641000000000006],
-     ['.', 1.1]]
-    """
-
-    res = []
-    round_brackets = []
-    square_brackets = []
-
-    round_bracket_multiplier = 1.1
-    square_bracket_multiplier = 1 / 1.1
-
-    def multiply_range(start_position, multiplier):
-        for p in range(start_position, len(res)):
-            res[p][1] *= multiplier
-
-    for m in re_attention.finditer(text):
-        text = m.group(0)
-        weight = m.group(1)
-
-        if text.startswith('\\'):
-            res.append([text[1:], 1.0])
-        elif text == '(':
-            round_brackets.append(len(res))
-        elif text == '[':
-            square_brackets.append(len(res))
-        elif weight is not None and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), float(weight))
-        elif text == ')' and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), round_bracket_multiplier)
-        elif text == ']' and len(square_brackets) > 0:
-            multiply_range(square_brackets.pop(), square_bracket_multiplier)
-        else:
-            res.append([text, 1.0])
-
-    for pos in round_brackets:
-        multiply_range(pos, round_bracket_multiplier)
-
-    for pos in square_brackets:
-        multiply_range(pos, square_bracket_multiplier)
-
-    if len(res) == 0:
-        res = [["", 1.0]]
-
-    # merge runs of identical weights
-    i = 0
-    while i + 1 < len(res):
-        if res[i][1] == res[i + 1][1]:
-            res[i][0] += res[i + 1][0]
-            res.pop(i + 1)
-        else:
-            i += 1
-
-    return res
-
-class WebUIFrozenCLIPEmebedder(AbstractEncoder):
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", freeze=True, layer="penultimate"):
-        super(WebUIFrozenCLIPEmebedder, self).__init__()
-        self.tokenizer = CLIPTokenizer.from_pretrained(version)
-        self.transformer = CLIPModel.from_pretrained(version).text_model
-        self.device = device
-        self.layer = layer
-        if freeze:
-            self.freeze()
-
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
-        self.comma_padding_backtrack = 20
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def tokenize(self, texts):
-        tokenized = self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
-        return tokenized
-
-    def encode_with_transformers(self, tokens):
-        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer!='last')
-
-        if self.layer == 'penultimate':
-            z = outputs.hidden_states[-2]
-            z = self.transformer.final_layer_norm(z)
-        else:
-            z = outputs.last_hidden_state
-
-        return z
-
-    def tokenize_line(self, line):
-        parsed = parse_prompt_attention(line)
-        # print(parsed)
-
-        tokenized = self.tokenize([text for text, _ in parsed])
-
-        remade_tokens = []
-        multipliers = []
-        last_comma = -1
-
-        for tokens, (text, weight) in zip(tokenized, parsed):
-            i = 0
-            while i < len(tokens):
-                token = tokens[i]
-
-                if token == self.comma_token:
-                    last_comma = len(remade_tokens)
-                elif self.comma_padding_backtrack != 0 and max(len(remade_tokens),
-                                                               1) % 75 == 0 and last_comma != -1 and len(
-                        remade_tokens) - last_comma <= self.comma_padding_backtrack:
-                    last_comma += 1
-                    reloc_tokens = remade_tokens[last_comma:]
-                    reloc_mults = multipliers[last_comma:]
-
-                    remade_tokens = remade_tokens[:last_comma]
-                    length = len(remade_tokens)
-
-                    rem = int(math.ceil(length / 75)) * 75 - length
-                    remade_tokens += [self.tokenizer.eos_token_id] * rem + reloc_tokens
-                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-
-                remade_tokens.append(token)
-                multipliers.append(weight)
-                i += 1
-
-        token_count = len(remade_tokens)
-        prompt_target_length = math.ceil(max(token_count, 1) / 75) * 75
-        tokens_to_add = prompt_target_length - len(remade_tokens)
-
-        remade_tokens = remade_tokens + [self.tokenizer.eos_token_id] * tokens_to_add
-        multipliers = multipliers + [1.0] * tokens_to_add
-
-        return remade_tokens, multipliers, token_count
-
-    def process_text(self, texts):
-        remade_batch_tokens = []
-        token_count = 0
-
-        cache = {}
-        batch_multipliers = []
-        for line in texts:
-            if line in cache:
-                remade_tokens, multipliers = cache[line]
-            else:
-                remade_tokens, multipliers, current_token_count = self.tokenize_line(line)
-                token_count = max(current_token_count, token_count)
-
-                cache[line] = (remade_tokens, multipliers)
-
-            remade_batch_tokens.append(remade_tokens)
-            batch_multipliers.append(multipliers)
-
-        return batch_multipliers, remade_batch_tokens, token_count
-
-    def process_tokens(self, remade_batch_tokens, batch_multipliers):
-        remade_batch_tokens = [[self.tokenizer.bos_token_id] + x[:75] + [self.tokenizer.eos_token_id] for x in remade_batch_tokens]
-        batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-
-        tokens = torch.asarray(remade_batch_tokens).to(self.device)
-
-        z = self.encode_with_transformers(tokens)
-
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
-        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(self.device)
-        original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
-        z *= original_mean / new_mean
-
-        return z
-
-    def forward(self, text):
-        batch_multipliers, remade_batch_tokens, token_count = self.process_text(text)
-
-        z = None
-        i = 0
-        while max(map(len, remade_batch_tokens)) != 0:
-            rem_tokens = [x[75:] for x in remade_batch_tokens]
-            rem_multipliers = [x[75:] for x in batch_multipliers]
-
-            tokens = []
-            multipliers = []
-            for j in range(len(remade_batch_tokens)):
-                if len(remade_batch_tokens[j]) > 0:
-                    tokens.append(remade_batch_tokens[j][:75])
-                    multipliers.append(batch_multipliers[j][:75])
-                else:
-                    tokens.append([self.tokenizer.eos_token_id] * 75)
-                    multipliers.append([1.0] * 75)
-
-            z1 = self.process_tokens(tokens, multipliers)
-            z = z1 if z is None else torch.cat((z, z1), axis=-2)
-
-            remade_batch_tokens = rem_tokens
-            batch_multipliers = rem_multipliers
-            i += 1
-
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-
-if __name__ == "__main__":
-    model = FrozenCLIPEmbedder()
-    count_params(model, verbose=True)

spaces/AkitoP/umamusume_bert_vits2/text/japanese_bert.py

@@ -1,87 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-import sys
-import os
-from text.japanese import text2sep_kata
-tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
-
-models = dict()
-
-
-def get_bert_feature(text, word2ph, device=None):
-    sep_text,_ = text2sep_kata(text)
-    sep_tokens = [tokenizer.tokenize(t) for t in sep_text]
-    sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens]
-    sep_ids = [2]+[item for sublist in sep_ids for item in sublist]+[3]
-    return get_bert_feature_with_token(sep_ids, word2ph, device)
-
-
-# def get_bert_feature(text, word2ph, device=None):
-#     if (
-#         sys.platform == "darwin"
-#         and torch.backends.mps.is_available()
-#         and device == "cpu"
-#     ):
-#         device = "mps"
-#     if not device:
-#         device = "cuda"
-#     if device not in models.keys():
-#         models[device] = AutoModelForMaskedLM.from_pretrained(
-#             "cl-tohoku/bert-base-japanese-v3"
-#         ).to(device)
-#     with torch.no_grad():
-#         inputs = tokenizer(text, return_tensors="pt")
-#         for i in inputs:
-#             inputs[i] = inputs[i].to(device)
-#         res = models[device](**inputs, output_hidden_states=True)
-#         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-#     assert inputs["input_ids"].shape[-1] == len(word2ph)
-#     word2phone = word2ph
-#     phone_level_feature = []
-#     for i in range(len(word2phone)):
-#         repeat_feature = res[i].repeat(word2phone[i], 1)
-#         phone_level_feature.append(repeat_feature)
-
-#     phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-#     return phone_level_feature.T
-
-def get_bert_feature_with_token(tokens, word2ph, device=None):
-    if (
-        sys.platform == "darwin"
-        and torch.backends.mps.is_available()
-        and device == "cpu"
-    ):
-        device = "mps"
-    if not device:
-        device = "cuda"
-    if device not in models.keys():
-        models[device] = AutoModelForMaskedLM.from_pretrained(
-            "./bert/bert-base-japanese-v3"
-        ).to(device)
-    with torch.no_grad():
-        inputs = torch.tensor(tokens).to(device).unsqueeze(0)
-        token_type_ids = torch.zeros_like(inputs).to(device)
-        attention_mask = torch.ones_like(inputs).to(device)
-        inputs = {"input_ids": inputs, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
-
-
-        # for i in inputs:
-        #     inputs[i] = inputs[i].to(device)
-        res = models[device](**inputs, output_hidden_states=True)
-        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-    assert inputs["input_ids"].shape[-1] == len(word2ph)
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-    return phone_level_feature.T
-
-
-if __name__ == "__main__":
-    print(get_bert_feature("観覧車",[4,2]))
-    pass

spaces/AlekseyKorshuk/gai-project/modules/utils.py

@@ -1,35 +0,0 @@
-import os
-import json
-
-import config
-
-
-def get_bot_config(bot_name):
-    folder = os.path.join(config.RESOURCE_DIR, 'bot_configs')
-    path = os.path.join(folder, f'{bot_name}.json')
-    return load_json(path)
-
-
-def get_bot_names():
-    folder = os.path.join(config.RESOURCE_DIR, 'bot_configs')
-    out = [name.split('.json')[0] for name in os.listdir(folder)]
-    return out
-
-
-def load_json(path):
-    with open(path, 'r') as f:
-        return json.load(f)
-
-
-def get_bot_picture_html(bot_config):
-    model_html = f"""
-    <div class="inline-flex flex-col" style="line-height: 1.5;">
-        <div class="flex">
-            <div
-    \t\t\tstyle="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;{bot_config['imageUrl']}&#39;)">
-            </div>
-        </div>
-        <div style="text-align: center; font-size: 16px; font-weight: 800">{bot_config['name']}</div>
-    </div>
-    """
-    return model_html

spaces/Alfasign/dIFFU/index.html

@@ -1,16 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="twitter:card" content="player"/>
-    <meta name="twitter:site" content=""/>
-    <meta name="twitter:player" content="https://omnibus-maximum-multiplier-places.hf.space"/>
-    <meta name="twitter:player:stream" content="https://omnibus-maximum-multiplier-places.hf.space"/>
-    <meta name="twitter:player:width" content="100%"/>
-    <meta name="twitter:player:height" content="600"/>    
-    <meta property="og:title" content="Embedded Live Viewer"/>
-    <meta property="og:description" content="Tweet Genie - A Huggingface Space"/>
-    <meta property="og:image" content="https://cdn.glitch.global/80dbe92e-ce75-44af-84d5-74a2e21e9e55/omnicard.png?v=1676772531627"/>
-    <!--<meta http-equiv="refresh" content="0; url=https://huggingface.co/spaces/corbt/tweet-genie">-->
-  </head>
-</html>

spaces/An-619/FastSAM/app_gradio.py

@@ -1,374 +0,0 @@
-from ultralytics import YOLO
-import gradio as gr
-import torch
-from utils.tools_gradio import fast_process
-from utils.tools import format_results, box_prompt, point_prompt, text_prompt
-from PIL import ImageDraw
-import numpy as np
-
-# Load the pre-trained model
-model = YOLO('./weights/FastSAM.pt')
-
-device = torch.device(
-    "cuda"
-    if torch.cuda.is_available()
-    else "mps"
-    if torch.backends.mps.is_available()
-    else "cpu"
-)
-
-# Description
-title = "<center><strong><font size='8'>🏃 Fast Segment Anything 🤗</font></strong></center>"
-
-news = """ # 📖 News
-        🔥 2023/07/14: Add a "wider result" button in text mode (Thanks for [gaoxinge](https://github.com/CASIA-IVA-Lab/FastSAM/pull/95)).
-
-        🔥 2023/06/29: Support the text mode (Thanks for [gaoxinge](https://github.com/CASIA-IVA-Lab/FastSAM/pull/47)).
-
-        🔥 2023/06/26: Support the points mode. (Better and faster interaction will come soon!)
-
-        🔥 2023/06/24: Add the 'Advanced options" in Everything mode to get a more detailed adjustment.        
-        """  
-
-description_e = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it.
-                
-                🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon.
-                
-                ⌛️ It takes about 6~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded.
-                
-                🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked.
-                
-                📣 You can also obtain the segmentation results of any Image through this Colab: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing)
-                
-                😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant.
-                
-                🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM)
-                
-              """
-
-description_p = """ # 🎯 Instructions for points mode
-                This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it.
-                
-                1. Upload an image or choose an example.
-                
-                2. Choose the point label ('Add mask' means a positive point. 'Remove' Area means a negative point that is not segmented).
-                
-                3. Add points one by one on the image.
-                
-                4. Click the 'Segment with points prompt' button to get the segmentation results.
-                
-                **5. If you get Error, click the 'Clear points' button and try again may help.**
-                
-              """
-
-examples = [["examples/sa_8776.jpg"], ["examples/sa_414.jpg"], ["examples/sa_1309.jpg"], ["examples/sa_11025.jpg"],
-            ["examples/sa_561.jpg"], ["examples/sa_192.jpg"], ["examples/sa_10039.jpg"], ["examples/sa_862.jpg"]]
-
-default_example = examples[0]
-
-css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
-
-
-def segment_everything(
-    input,
-    input_size=1024, 
-    iou_threshold=0.7,
-    conf_threshold=0.25,
-    better_quality=False,
-    withContours=True,
-    use_retina=True,
-    text="",
-    wider=False,
-    mask_random_color=True,
-):
-    input_size = int(input_size)  # 确保 imgsz 是整数
-    # Thanks for the suggestion by hysts in HuggingFace.
-    w, h = input.size
-    scale = input_size / max(w, h)
-    new_w = int(w * scale)
-    new_h = int(h * scale)
-    input = input.resize((new_w, new_h))
-
-    results = model(input,
-                    device=device,
-                    retina_masks=True,
-                    iou=iou_threshold,
-                    conf=conf_threshold,
-                    imgsz=input_size,)
-
-    if len(text) > 0:
-        results = format_results(results[0], 0)
-        annotations, _ = text_prompt(results, text, input, device=device, wider=wider)
-        annotations = np.array([annotations])
-    else:
-        annotations = results[0].masks.data
-    
-    fig = fast_process(annotations=annotations,
-                       image=input,
-                       device=device,
-                       scale=(1024 // input_size),
-                       better_quality=better_quality,
-                       mask_random_color=mask_random_color,
-                       bbox=None,
-                       use_retina=use_retina,
-                       withContours=withContours,)
-    return fig
-
-
-def segment_with_points(
-    input,
-    input_size=1024, 
-    iou_threshold=0.7,
-    conf_threshold=0.25,
-    better_quality=False,
-    withContours=True,
-    use_retina=True,
-    mask_random_color=True,
-):
-    global global_points
-    global global_point_label
-    
-    input_size = int(input_size)  # 确保 imgsz 是整数
-    # Thanks for the suggestion by hysts in HuggingFace.
-    w, h = input.size
-    scale = input_size / max(w, h)
-    new_w = int(w * scale)
-    new_h = int(h * scale)
-    input = input.resize((new_w, new_h))
-    
-    scaled_points = [[int(x * scale) for x in point] for point in global_points]
-
-    results = model(input,
-                    device=device,
-                    retina_masks=True,
-                    iou=iou_threshold,
-                    conf=conf_threshold,
-                    imgsz=input_size,)
-    
-    results = format_results(results[0], 0)
-    annotations, _ = point_prompt(results, scaled_points, global_point_label, new_h, new_w)
-    annotations = np.array([annotations])
-
-    fig = fast_process(annotations=annotations,
-                       image=input,
-                       device=device,
-                       scale=(1024 // input_size),
-                       better_quality=better_quality,
-                       mask_random_color=mask_random_color,
-                       bbox=None,
-                       use_retina=use_retina,
-                       withContours=withContours,)
-
-    global_points = []
-    global_point_label = []
-    return fig, None
-
-
-def get_points_with_draw(image, label, evt: gr.SelectData):
-    global global_points
-    global global_point_label
-
-    x, y = evt.index[0], evt.index[1]
-    point_radius, point_color = 15, (255, 255, 0) if label == 'Add Mask' else (255, 0, 255)
-    global_points.append([x, y])
-    global_point_label.append(1 if label == 'Add Mask' else 0)
-    
-    print(x, y, label == 'Add Mask')
-    
-    # 创建一个可以在图像上绘图的对象
-    draw = ImageDraw.Draw(image)
-    draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
-    return image
-
-
-cond_img_e = gr.Image(label="Input", value=default_example[0], type='pil')
-cond_img_p = gr.Image(label="Input with points", value=default_example[0], type='pil')
-cond_img_t = gr.Image(label="Input with text", value="examples/dogs.jpg", type='pil')
-
-segm_img_e = gr.Image(label="Segmented Image", interactive=False, type='pil')
-segm_img_p = gr.Image(label="Segmented Image with points", interactive=False, type='pil')
-segm_img_t = gr.Image(label="Segmented Image with text", interactive=False, type='pil')
-
-global_points = []
-global_point_label = []
-
-input_size_slider = gr.components.Slider(minimum=512,
-                                         maximum=1024,
-                                         value=1024,
-                                         step=64,
-                                         label='Input_size',
-                                         info='Our model was trained on a size of 1024')
-
-with gr.Blocks(css=css, title='Fast Segment Anything') as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Title
-            gr.Markdown(title)
-
-        with gr.Column(scale=1):
-            # News
-            gr.Markdown(news)
-
-    with gr.Tab("Everything mode"):
-        # Images
-        with gr.Row(variant="panel"):
-            with gr.Column(scale=1):
-                cond_img_e.render()
-
-            with gr.Column(scale=1):
-                segm_img_e.render()
-
-        # Submit & Clear
-        with gr.Row():
-            with gr.Column():
-                input_size_slider.render()
-
-                with gr.Row():
-                    contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
-
-                    with gr.Column():
-                        segment_btn_e = gr.Button("Segment Everything", variant='primary')
-                        clear_btn_e = gr.Button("Clear", variant="secondary")
-
-                gr.Markdown("Try some of the examples below ⬇️")
-                gr.Examples(examples=examples,
-                            inputs=[cond_img_e],
-                            outputs=segm_img_e,
-                            fn=segment_everything,
-                            cache_examples=True,
-                            examples_per_page=4)
-
-            with gr.Column():
-                with gr.Accordion("Advanced options", open=False):
-                    iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations')
-                    conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
-                    with gr.Row():
-                        mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
-                        with gr.Column():
-                            retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
-
-                # Description
-                gr.Markdown(description_e)
-
-    segment_btn_e.click(segment_everything,
-                        inputs=[
-                            cond_img_e,
-                            input_size_slider,
-                            iou_threshold,
-                            conf_threshold,
-                            mor_check,
-                            contour_check,
-                            retina_check,
-                        ],
-                        outputs=segm_img_e)
-
-    with gr.Tab("Points mode"):
-        # Images
-        with gr.Row(variant="panel"):
-            with gr.Column(scale=1):
-                cond_img_p.render()
-
-            with gr.Column(scale=1):
-                segm_img_p.render()
-                
-        # Submit & Clear
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    add_or_remove = gr.Radio(["Add Mask", "Remove Area"], value="Add Mask", label="Point_label (foreground/background)")
-
-                    with gr.Column():
-                        segment_btn_p = gr.Button("Segment with points prompt", variant='primary')
-                        clear_btn_p = gr.Button("Clear points", variant='secondary')
-
-                gr.Markdown("Try some of the examples below ⬇️")
-                gr.Examples(examples=examples,
-                            inputs=[cond_img_p],
-                            # outputs=segm_img_p,
-                            # fn=segment_with_points,
-                            # cache_examples=True,
-                            examples_per_page=4)
-
-            with gr.Column():
-                # Description
-                gr.Markdown(description_p)
-
-    cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p)
-
-    segment_btn_p.click(segment_with_points,
-                        inputs=[cond_img_p],
-                        outputs=[segm_img_p, cond_img_p])
-
-    with gr.Tab("Text mode"):
-        # Images
-        with gr.Row(variant="panel"):
-            with gr.Column(scale=1):
-                cond_img_t.render()
-
-            with gr.Column(scale=1):
-                segm_img_t.render()
-
-        # Submit & Clear
-        with gr.Row():
-            with gr.Column():
-                input_size_slider_t = gr.components.Slider(minimum=512,
-                                                           maximum=1024,
-                                                           value=1024,
-                                                           step=64,
-                                                           label='Input_size',
-                                                           info='Our model was trained on a size of 1024')
-                with gr.Row():
-                    with gr.Column():
-                        contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
-                        text_box = gr.Textbox(label="text prompt", value="a black dog")
-
-                    with gr.Column():
-                        segment_btn_t = gr.Button("Segment with text", variant='primary')
-                        clear_btn_t = gr.Button("Clear", variant="secondary")
-
-                gr.Markdown("Try some of the examples below ⬇️")
-                gr.Examples(examples=[["examples/dogs.jpg"], ["examples/fruits.jpg"], ["examples/flowers.jpg"]],
-                            inputs=[cond_img_t],
-                            # outputs=segm_img_e,
-                            # fn=segment_everything,
-                            # cache_examples=True,
-                            examples_per_page=4)
-
-            with gr.Column():
-                with gr.Accordion("Advanced options", open=False):
-                    iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations')
-                    conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
-                    with gr.Row():
-                        mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
-                        retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
-                        wider_check = gr.Checkbox(value=False, label='wider', info='wider result')
-
-                # Description
-                gr.Markdown(description_e)
-    
-    segment_btn_t.click(segment_everything,
-                        inputs=[
-                            cond_img_t,
-                            input_size_slider_t,
-                            iou_threshold,
-                            conf_threshold,
-                            mor_check,
-                            contour_check,
-                            retina_check,
-                            text_box,
-                            wider_check,
-                        ],
-                        outputs=segm_img_t)
-
-    def clear():
-        return None, None
-    
-    def clear_text():
-        return None, None, None
-
-    clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e])
-    clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])
-    clear_btn_t.click(clear_text, outputs=[cond_img_p, segm_img_p, text_box])
-
-demo.queue()
-demo.launch()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/ethical_guidelines.md

@@ -1,51 +0,0 @@
-# 🧨 Diffusers’ Ethical Guidelines
-
-## Preamble
-
-[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training. 
-
-Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library.
-
-The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups.
-We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback.
-
-
-## Scope
-
-The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns.
-
-
-## Ethical guidelines
-
-The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question.
-
-- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions.
-
-- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent.
-
-- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent.
-
-- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community.
-
-- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library.
-
-- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers.
-
-
-## Examples of implementations: Safety features and Mechanisms
-
-The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us. 
-
-- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project.
-
-- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations.
-
-- **Encouraging safety in deployment**
-
-  - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
-
-  - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker.
-
-- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use.
-
-- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use. 

spaces/Androidonnxfork/CivitAi-to-Diffusers/hf_utils.py

@@ -1,50 +0,0 @@
-from huggingface_hub import get_hf_file_metadata, hf_hub_url, hf_hub_download, scan_cache_dir, whoami, list_models
-
-
-def get_my_model_names(token):
-    
-    try:
-        author = whoami(token=token)
-        model_infos = list_models(author=author["name"], use_auth_token=token)
-        return [model.modelId for model in model_infos], None
-        
-    except Exception as e:
-        return [], e
-
-def download_file(repo_id: str, filename: str, token: str):
-    """Download a file from a repo on the Hugging Face Hub.
-
-    Returns:
-        file_path (:obj:`str`): The path to the downloaded file.
-        revision (:obj:`str`): The commit hash of the file.
-        """
-
-    md = get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename=filename), token=token)
-    revision = md.commit_hash
-
-    file_path = hf_hub_download(repo_id=repo_id, filename=filename, revision=revision, token=token)
-
-    return file_path, revision
-
-def delete_file(revision: str):
-    """Delete a file from local cache.
-
-    Args:
-        revision (:obj:`str`): The commit hash of the file.
-    Returns:
-        None
-        """
-    scan_cache_dir().delete_revisions(revision).execute()
-
-def get_pr_url(api, repo_id, title):
-    try:
-        discussions = api.get_repo_discussions(repo_id=repo_id)
-    except Exception:
-        return None
-    for discussion in discussions:
-        if (
-            discussion.status == "open"
-            and discussion.is_pull_request
-            and discussion.title == title
-        ):
-            return f"https://huggingface.co/{repo_id}/discussions/{discussion.num}"

spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py

@@ -1,36 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
-# use caffe img_norm
-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='LoadAnnotations', with_bbox=True, with_mask=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', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    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']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/mse_loss.py

@@ -1,49 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@weighted_loss
-def mse_loss(pred, target):
-    """Warpper of mse loss."""
-    return F.mse_loss(pred, target, reduction='none')
-
-
-@LOSSES.register_module()
-class MSELoss(nn.Module):
-    """MSELoss.
-
-    Args:
-        reduction (str, optional): The method that reduces the loss to a
-            scalar. Options are "none", "mean" and "sum".
-        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
-    """
-
-    def __init__(self, reduction='mean', loss_weight=1.0):
-        super().__init__()
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self, pred, target, weight=None, avg_factor=None):
-        """Forward function of loss.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): Weight of the loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-
-        Returns:
-            torch.Tensor: The calculated loss
-        """
-        loss = self.loss_weight * mse_loss(
-            pred,
-            target,
-            weight,
-            reduction=self.reduction,
-            avg_factor=avg_factor)
-        return loss

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py

@@ -1,107 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init
-from mmcv.runner import auto_fp16, force_fp32
-
-from mmdet.models.builder import HEADS
-
-
-@HEADS.register_module()
-class FusedSemanticHead(nn.Module):
-    r"""Multi-level fused semantic segmentation head.
-
-    .. code-block:: none
-
-        in_1 -> 1x1 conv ---
-                            |
-        in_2 -> 1x1 conv -- |
-                           ||
-        in_3 -> 1x1 conv - ||
-                          |||                  /-> 1x1 conv (mask prediction)
-        in_4 -> 1x1 conv -----> 3x3 convs (*4)
-                            |                  \-> 1x1 conv (feature)
-        in_5 -> 1x1 conv ---
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_ins,
-                 fusion_level,
-                 num_convs=4,
-                 in_channels=256,
-                 conv_out_channels=256,
-                 num_classes=183,
-                 ignore_label=255,
-                 loss_weight=0.2,
-                 conv_cfg=None,
-                 norm_cfg=None):
-        super(FusedSemanticHead, self).__init__()
-        self.num_ins = num_ins
-        self.fusion_level = fusion_level
-        self.num_convs = num_convs
-        self.in_channels = in_channels
-        self.conv_out_channels = conv_out_channels
-        self.num_classes = num_classes
-        self.ignore_label = ignore_label
-        self.loss_weight = loss_weight
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.fp16_enabled = False
-
-        self.lateral_convs = nn.ModuleList()
-        for i in range(self.num_ins):
-            self.lateral_convs.append(
-                ConvModule(
-                    self.in_channels,
-                    self.in_channels,
-                    1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    inplace=False))
-
-        self.convs = nn.ModuleList()
-        for i in range(self.num_convs):
-            in_channels = self.in_channels if i == 0 else conv_out_channels
-            self.convs.append(
-                ConvModule(
-                    in_channels,
-                    conv_out_channels,
-                    3,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.conv_embedding = ConvModule(
-            conv_out_channels,
-            conv_out_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg)
-        self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1)
-
-        self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label)
-
-    def init_weights(self):
-        kaiming_init(self.conv_logits)
-
-    @auto_fp16()
-    def forward(self, feats):
-        x = self.lateral_convs[self.fusion_level](feats[self.fusion_level])
-        fused_size = tuple(x.shape[-2:])
-        for i, feat in enumerate(feats):
-            if i != self.fusion_level:
-                feat = F.interpolate(
-                    feat, size=fused_size, mode='bilinear', align_corners=True)
-                x += self.lateral_convs[i](feat)
-
-        for i in range(self.num_convs):
-            x = self.convs[i](x)
-
-        mask_pred = self.conv_logits(x)
-        x = self.conv_embedding(x)
-        return mask_pred, x
-
-    @force_fp32(apply_to=('mask_pred', ))
-    def loss(self, mask_pred, labels):
-        labels = labels.squeeze(1).long()
-        loss_semantic_seg = self.criterion(mask_pred, labels)
-        loss_semantic_seg *= self.loss_weight
-        return loss_semantic_seg

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/Training_PRO/matplotgraph.py

@@ -1,62 +0,0 @@
-import os
-import json
-
-def create_graph(lora_path, lora_name):
-    try:
-        import matplotlib.pyplot as plt
-        from matplotlib.ticker import ScalarFormatter
-        
-        peft_model_path = f'{lora_path}/training_graph.json'
-        image_model_path = f'{lora_path}/training_graph.png'
-        # Check if the JSON file exists
-        if os.path.exists(peft_model_path):
-            # Load data from JSON file
-            with open(peft_model_path, 'r') as file:
-                data = json.load(file)
-            # Extract x, y1, and y2 values
-            x = [item['epoch'] for item in data]
-            y1 = [item['learning_rate'] for item in data]
-            y2 = [item['loss'] for item in data]
-
-            # Create the line chart
-            fig, ax1 = plt.subplots(figsize=(10, 6))
-        
-
-            # Plot y1 (learning rate) on the first y-axis
-            ax1.plot(x, y1, 'b-', label='Learning Rate')
-            ax1.set_xlabel('Epoch')
-            ax1.set_ylabel('Learning Rate', color='b')
-            ax1.tick_params('y', colors='b')
-
-            # Create a second y-axis
-            ax2 = ax1.twinx()
-
-            # Plot y2 (loss) on the second y-axis
-            ax2.plot(x, y2, 'r-', label='Loss')
-            ax2.set_ylabel('Loss', color='r')
-            ax2.tick_params('y', colors='r')
-
-            # Set the y-axis formatter to display numbers in scientific notation
-            ax1.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
-            ax1.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
-
-            # Add grid
-            ax1.grid(True)
-
-            # Combine the legends for both plots
-            lines, labels = ax1.get_legend_handles_labels()
-            lines2, labels2 = ax2.get_legend_handles_labels()
-            ax2.legend(lines + lines2, labels + labels2, loc='best')
-
-            # Set the title
-            plt.title(f'{lora_name} LR and Loss vs Epoch')
-
-            # Save the chart as an image
-            plt.savefig(image_model_path)
-
-            print(f"Graph saved in {image_model_path}")
-        else:
-            print(f"File 'training_graph.json' does not exist in the {lora_path}")
-      
-    except ImportError:
-        print("matplotlib is not installed. Please install matplotlib to create PNG graphs")

spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/midas_net_custom.py

@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name

spaces/Anonymous-sub/Rerender/ControlNet/annotator/openpose/__init__.py

@@ -1,49 +0,0 @@
-# Openpose
-# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
-# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
-# 3rd Edited by ControlNet
-
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-import torch
-import numpy as np
-from . import util
-from .body import Body
-from .hand import Hand
-from annotator.util import annotator_ckpts_path
-
-
-body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
-hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
-
-
-class OpenposeDetector:
-    def __init__(self):
-        body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth")
-        hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth")
-
-        if not os.path.exists(hand_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
-            load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
-
-        self.body_estimation = Body(body_modelpath)
-        self.hand_estimation = Hand(hand_modelpath)
-
-    def __call__(self, oriImg, hand=False):
-        oriImg = oriImg[:, :, ::-1].copy()
-        with torch.no_grad():
-            candidate, subset = self.body_estimation(oriImg)
-            canvas = np.zeros_like(oriImg)
-            canvas = util.draw_bodypose(canvas, candidate, subset)
-            if hand:
-                hands_list = util.handDetect(candidate, subset, oriImg)
-                all_hand_peaks = []
-                for x, y, w, is_left in hands_list:
-                    peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
-                    peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
-                    peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
-                    all_hand_peaks.append(peaks)
-                canvas = util.draw_handpose(canvas, all_hand_peaks)
-            return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py

@@ -1,9 +0,0 @@
-_base_ = './pascal_voc12.py'
-# dataset settings
-data = dict(
-    train=dict(
-        ann_dir=['SegmentationClass', 'SegmentationClassAug'],
-        split=[
-            'ImageSets/Segmentation/train.txt',
-            'ImageSets/Segmentation/aug.txt'
-        ]))

spaces/ApathyINC/CustomGPT/README.md

@@ -1,12 +0,0 @@
----
-title: Test
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Apex-X/Tm/roop/globals.py

@@ -1,17 +0,0 @@
-from typing import List
-
-source_path = None
-target_path = None
-output_path = None
-frame_processors: List[str] = []
-keep_fps = None
-keep_audio = None
-keep_frames = None
-many_faces = None
-video_encoder = None
-video_quality = None
-max_memory = None
-execution_providers: List[str] = []
-execution_threads = None
-headless = None
-log_level = 'error'

spaces/Arcypojeb/NeuralServer/README.md

@@ -1,13 +0,0 @@
----
-title: NeuralServer
-emoji: 🦀
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ariharasudhan/YoloV5/utils/autobatch.py

@@ -1,72 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-batch utils
-"""
-
-from copy import deepcopy
-
-import numpy as np
-import torch
-
-from utils.general import LOGGER, colorstr
-from utils.torch_utils import profile
-
-
-def check_train_batch_size(model, imgsz=640, amp=True):
-    # Check YOLOv5 training batch size
-    with torch.cuda.amp.autocast(amp):
-        return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
-
-
-def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
-    # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
-    # Usage:
-    #     import torch
-    #     from utils.autobatch import autobatch
-    #     model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
-    #     print(autobatch(model))
-
-    # Check device
-    prefix = colorstr('AutoBatch: ')
-    LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
-    device = next(model.parameters()).device  # get model device
-    if device.type == 'cpu':
-        LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
-        return batch_size
-    if torch.backends.cudnn.benchmark:
-        LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
-        return batch_size
-
-    # Inspect CUDA memory
-    gb = 1 << 30  # bytes to GiB (1024 ** 3)
-    d = str(device).upper()  # 'CUDA:0'
-    properties = torch.cuda.get_device_properties(device)  # device properties
-    t = properties.total_memory / gb  # GiB total
-    r = torch.cuda.memory_reserved(device) / gb  # GiB reserved
-    a = torch.cuda.memory_allocated(device) / gb  # GiB allocated
-    f = t - (r + a)  # GiB free
-    LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
-
-    # Profile batch sizes
-    batch_sizes = [1, 2, 4, 8, 16]
-    try:
-        img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
-        results = profile(img, model, n=3, device=device)
-    except Exception as e:
-        LOGGER.warning(f'{prefix}{e}')
-
-    # Fit a solution
-    y = [x[2] for x in results if x]  # memory [2]
-    p = np.polyfit(batch_sizes[:len(y)], y, deg=1)  # first degree polynomial fit
-    b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
-    if None in results:  # some sizes failed
-        i = results.index(None)  # first fail index
-        if b >= batch_sizes[i]:  # y intercept above failure point
-            b = batch_sizes[max(i - 1, 0)]  # select prior safe point
-    if b < 1 or b > 1024:  # b outside of safe range
-        b = batch_size
-        LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
-
-    fraction = (np.polyval(p, b) + r + a) / t  # actual fraction predicted
-    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
-    return b

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/csrc/vision.cpp

@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
-  std::ostringstream oss;
-
-  // copied from
-  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
-  auto printCudaStyleVersion = [&](int v) {
-    oss << (v / 1000) << "." << (v / 10 % 100);
-    if (v % 10 != 0) {
-      oss << "." << (v % 10);
-    }
-  };
-  printCudaStyleVersion(get_cudart_version());
-  return oss.str();
-#else
-  return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
-  std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
-  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
-  {
-    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
-       << __clang_patchlevel__;
-  }
-#endif
-
-#if defined(_MSC_VER)
-  { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
-  return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
-  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/build_tracker.py

@@ -1,124 +0,0 @@
-import contextlib
-import hashlib
-import logging
-import os
-from types import TracebackType
-from typing import Dict, Generator, Optional, Set, Type, Union
-
-from pip._internal.models.link import Link
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-@contextlib.contextmanager
-def update_env_context_manager(**changes: str) -> Generator[None, None, None]:
-    target = os.environ
-
-    # Save values from the target and change them.
-    non_existent_marker = object()
-    saved_values: Dict[str, Union[object, str]] = {}
-    for name, new_value in changes.items():
-        try:
-            saved_values[name] = target[name]
-        except KeyError:
-            saved_values[name] = non_existent_marker
-        target[name] = new_value
-
-    try:
-        yield
-    finally:
-        # Restore original values in the target.
-        for name, original_value in saved_values.items():
-            if original_value is non_existent_marker:
-                del target[name]
-            else:
-                assert isinstance(original_value, str)  # for mypy
-                target[name] = original_value
-
-
-@contextlib.contextmanager
-def get_build_tracker() -> Generator["BuildTracker", None, None]:
-    root = os.environ.get("PIP_BUILD_TRACKER")
-    with contextlib.ExitStack() as ctx:
-        if root is None:
-            root = ctx.enter_context(TempDirectory(kind="build-tracker")).path
-            ctx.enter_context(update_env_context_manager(PIP_BUILD_TRACKER=root))
-            logger.debug("Initialized build tracking at %s", root)
-
-        with BuildTracker(root) as tracker:
-            yield tracker
-
-
-class BuildTracker:
-    def __init__(self, root: str) -> None:
-        self._root = root
-        self._entries: Set[InstallRequirement] = set()
-        logger.debug("Created build tracker: %s", self._root)
-
-    def __enter__(self) -> "BuildTracker":
-        logger.debug("Entered build tracker: %s", self._root)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.cleanup()
-
-    def _entry_path(self, link: Link) -> str:
-        hashed = hashlib.sha224(link.url_without_fragment.encode()).hexdigest()
-        return os.path.join(self._root, hashed)
-
-    def add(self, req: InstallRequirement) -> None:
-        """Add an InstallRequirement to build tracking."""
-
-        assert req.link
-        # Get the file to write information about this requirement.
-        entry_path = self._entry_path(req.link)
-
-        # Try reading from the file. If it exists and can be read from, a build
-        # is already in progress, so a LookupError is raised.
-        try:
-            with open(entry_path) as fp:
-                contents = fp.read()
-        except FileNotFoundError:
-            pass
-        else:
-            message = "{} is already being built: {}".format(req.link, contents)
-            raise LookupError(message)
-
-        # If we're here, req should really not be building already.
-        assert req not in self._entries
-
-        # Start tracking this requirement.
-        with open(entry_path, "w", encoding="utf-8") as fp:
-            fp.write(str(req))
-        self._entries.add(req)
-
-        logger.debug("Added %s to build tracker %r", req, self._root)
-
-    def remove(self, req: InstallRequirement) -> None:
-        """Remove an InstallRequirement from build tracking."""
-
-        assert req.link
-        # Delete the created file and the corresponding entries.
-        os.unlink(self._entry_path(req.link))
-        self._entries.remove(req)
-
-        logger.debug("Removed %s from build tracker %r", req, self._root)
-
-    def cleanup(self) -> None:
-        for req in set(self._entries):
-            self.remove(req)
-
-        logger.debug("Removed build tracker: %r", self._root)
-
-    @contextlib.contextmanager
-    def track(self, req: InstallRequirement) -> Generator[None, None, None]:
-        self.add(req)
-        yield
-        self.remove(req)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/utils.py

@@ -1,136 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidWheelFilename(ValueError):
-    """
-    An invalid wheel filename was found, users should refer to PEP 427.
-    """
-
-
-class InvalidSdistFilename(ValueError):
-    """
-    An invalid sdist filename was found, users should refer to the packaging user guide.
-    """
-
-
-_canonicalize_regex = re.compile(r"[-_.]+")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str) -> NormalizedName:
-    # This is taken from PEP 503.
-    value = _canonicalize_regex.sub("-", name).lower()
-    return cast(NormalizedName, value)
-
-
-def canonicalize_version(version: Union[Version, str]) -> str:
-    """
-    This is very similar to Version.__str__, but has one subtle difference
-    with the way it handles the release segment.
-    """
-    if isinstance(version, str):
-        try:
-            parsed = Version(version)
-        except InvalidVersion:
-            # Legacy versions cannot be normalized
-            return version
-    else:
-        parsed = version
-
-    parts = []
-
-    # Epoch
-    if parsed.epoch != 0:
-        parts.append(f"{parsed.epoch}!")
-
-    # Release segment
-    # NB: This strips trailing '.0's to normalize
-    parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
-
-    # Pre-release
-    if parsed.pre is not None:
-        parts.append("".join(str(x) for x in parsed.pre))
-
-    # Post-release
-    if parsed.post is not None:
-        parts.append(f".post{parsed.post}")
-
-    # Development release
-    if parsed.dev is not None:
-        parts.append(f".dev{parsed.dev}")
-
-    # Local version segment
-    if parsed.local is not None:
-        parts.append(f"+{parsed.local}")
-
-    return "".join(parts)
-
-
-def parse_wheel_filename(
-    filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
-    if not filename.endswith(".whl"):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (extension must be '.whl'): {filename}"
-        )
-
-    filename = filename[:-4]
-    dashes = filename.count("-")
-    if dashes not in (4, 5):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (wrong number of parts): {filename}"
-        )
-
-    parts = filename.split("-", dashes - 2)
-    name_part = parts[0]
-    # See PEP 427 for the rules on escaping the project name
-    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
-        raise InvalidWheelFilename(f"Invalid project name: {filename}")
-    name = canonicalize_name(name_part)
-    version = Version(parts[1])
-    if dashes == 5:
-        build_part = parts[2]
-        build_match = _build_tag_regex.match(build_part)
-        if build_match is None:
-            raise InvalidWheelFilename(
-                f"Invalid build number: {build_part} in '{filename}'"
-            )
-        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
-    else:
-        build = ()
-    tags = parse_tag(parts[-1])
-    return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
-    if filename.endswith(".tar.gz"):
-        file_stem = filename[: -len(".tar.gz")]
-    elif filename.endswith(".zip"):
-        file_stem = filename[: -len(".zip")]
-    else:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
-            f" {filename}"
-        )
-
-    # We are requiring a PEP 440 version, which cannot contain dashes,
-    # so we split on the last dash.
-    name_part, sep, version_part = file_stem.rpartition("-")
-    if not sep:
-        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
-    name = canonicalize_name(name_part)
-    version = Version(version_part)
-    return (name, version)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/version.py

@@ -1,358 +0,0 @@
-#
-# distutils/version.py
-#
-# Implements multiple version numbering conventions for the
-# Python Module Distribution Utilities.
-#
-# $Id$
-#
-
-"""Provides classes to represent module version numbers (one class for
-each style of version numbering).  There are currently two such classes
-implemented: StrictVersion and LooseVersion.
-
-Every version number class implements the following interface:
-  * the 'parse' method takes a string and parses it to some internal
-    representation; if the string is an invalid version number,
-    'parse' raises a ValueError exception
-  * the class constructor takes an optional string argument which,
-    if supplied, is passed to 'parse'
-  * __str__ reconstructs the string that was passed to 'parse' (or
-    an equivalent string -- ie. one that will generate an equivalent
-    version number instance)
-  * __repr__ generates Python code to recreate the version number instance
-  * _cmp compares the current instance with either another instance
-    of the same class or a string (which will be parsed to an instance
-    of the same class, thus must follow the same rules)
-"""
-
-import re
-import warnings
-import contextlib
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
-    with warnings.catch_warnings(record=True) as ctx:
-        warnings.filterwarnings(
-            action='default',
-            category=DeprecationWarning,
-            message="distutils Version classes are deprecated.",
-        )
-        yield ctx
-
-
-class Version:
-    """Abstract base class for version numbering classes.  Just provides
-    constructor (__init__) and reproducer (__repr__), because those
-    seem to be the same for all version numbering classes; and route
-    rich comparisons to _cmp.
-    """
-
-    def __init__(self, vstring=None):
-        if vstring:
-            self.parse(vstring)
-        warnings.warn(
-            "distutils Version classes are deprecated. "
-            "Use packaging.version instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-    def __repr__(self):
-        return "{} ('{}')".format(self.__class__.__name__, str(self))
-
-    def __eq__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c == 0
-
-    def __lt__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c < 0
-
-    def __le__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c <= 0
-
-    def __gt__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c > 0
-
-    def __ge__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c >= 0
-
-
-# Interface for version-number classes -- must be implemented
-# by the following classes (the concrete ones -- Version should
-# be treated as an abstract class).
-#    __init__ (string) - create and take same action as 'parse'
-#                        (string parameter is optional)
-#    parse (string)    - convert a string representation to whatever
-#                        internal representation is appropriate for
-#                        this style of version numbering
-#    __str__ (self)    - convert back to a string; should be very similar
-#                        (if not identical to) the string supplied to parse
-#    __repr__ (self)   - generate Python code to recreate
-#                        the instance
-#    _cmp (self, other) - compare two version numbers ('other' may
-#                        be an unparsed version string, or another
-#                        instance of your version class)
-
-
-class StrictVersion(Version):
-
-    """Version numbering for anal retentives and software idealists.
-    Implements the standard interface for version number classes as
-    described above.  A version number consists of two or three
-    dot-separated numeric components, with an optional "pre-release" tag
-    on the end.  The pre-release tag consists of the letter 'a' or 'b'
-    followed by a number.  If the numeric components of two version
-    numbers are equal, then one with a pre-release tag will always
-    be deemed earlier (lesser) than one without.
-
-    The following are valid version numbers (shown in the order that
-    would be obtained by sorting according to the supplied cmp function):
-
-        0.4       0.4.0  (these two are equivalent)
-        0.4.1
-        0.5a1
-        0.5b3
-        0.5
-        0.9.6
-        1.0
-        1.0.4a3
-        1.0.4b1
-        1.0.4
-
-    The following are examples of invalid version numbers:
-
-        1
-        2.7.2.2
-        1.3.a4
-        1.3pl1
-        1.3c4
-
-    The rationale for this version numbering system will be explained
-    in the distutils documentation.
-    """
-
-    version_re = re.compile(
-        r'^(\d+) \. (\d+) (\. (\d+))? ([ab](\d+))?$', re.VERBOSE | re.ASCII
-    )
-
-    def parse(self, vstring):
-        match = self.version_re.match(vstring)
-        if not match:
-            raise ValueError("invalid version number '%s'" % vstring)
-
-        (major, minor, patch, prerelease, prerelease_num) = match.group(1, 2, 4, 5, 6)
-
-        if patch:
-            self.version = tuple(map(int, [major, minor, patch]))
-        else:
-            self.version = tuple(map(int, [major, minor])) + (0,)
-
-        if prerelease:
-            self.prerelease = (prerelease[0], int(prerelease_num))
-        else:
-            self.prerelease = None
-
-    def __str__(self):
-
-        if self.version[2] == 0:
-            vstring = '.'.join(map(str, self.version[0:2]))
-        else:
-            vstring = '.'.join(map(str, self.version))
-
-        if self.prerelease:
-            vstring = vstring + self.prerelease[0] + str(self.prerelease[1])
-
-        return vstring
-
-    def _cmp(self, other):  # noqa: C901
-        if isinstance(other, str):
-            with suppress_known_deprecation():
-                other = StrictVersion(other)
-        elif not isinstance(other, StrictVersion):
-            return NotImplemented
-
-        if self.version != other.version:
-            # numeric versions don't match
-            # prerelease stuff doesn't matter
-            if self.version < other.version:
-                return -1
-            else:
-                return 1
-
-        # have to compare prerelease
-        # case 1: neither has prerelease; they're equal
-        # case 2: self has prerelease, other doesn't; other is greater
-        # case 3: self doesn't have prerelease, other does: self is greater
-        # case 4: both have prerelease: must compare them!
-
-        if not self.prerelease and not other.prerelease:
-            return 0
-        elif self.prerelease and not other.prerelease:
-            return -1
-        elif not self.prerelease and other.prerelease:
-            return 1
-        elif self.prerelease and other.prerelease:
-            if self.prerelease == other.prerelease:
-                return 0
-            elif self.prerelease < other.prerelease:
-                return -1
-            else:
-                return 1
-        else:
-            assert False, "never get here"
-
-
-# end class StrictVersion
-
-
-# The rules according to Greg Stein:
-# 1) a version number has 1 or more numbers separated by a period or by
-#    sequences of letters. If only periods, then these are compared
-#    left-to-right to determine an ordering.
-# 2) sequences of letters are part of the tuple for comparison and are
-#    compared lexicographically
-# 3) recognize the numeric components may have leading zeroes
-#
-# The LooseVersion class below implements these rules: a version number
-# string is split up into a tuple of integer and string components, and
-# comparison is a simple tuple comparison.  This means that version
-# numbers behave in a predictable and obvious way, but a way that might
-# not necessarily be how people *want* version numbers to behave.  There
-# wouldn't be a problem if people could stick to purely numeric version
-# numbers: just split on period and compare the numbers as tuples.
-# However, people insist on putting letters into their version numbers;
-# the most common purpose seems to be:
-#   - indicating a "pre-release" version
-#     ('alpha', 'beta', 'a', 'b', 'pre', 'p')
-#   - indicating a post-release patch ('p', 'pl', 'patch')
-# but of course this can't cover all version number schemes, and there's
-# no way to know what a programmer means without asking him.
-#
-# The problem is what to do with letters (and other non-numeric
-# characters) in a version number.  The current implementation does the
-# obvious and predictable thing: keep them as strings and compare
-# lexically within a tuple comparison.  This has the desired effect if
-# an appended letter sequence implies something "post-release":
-# eg. "0.99" < "0.99pl14" < "1.0", and "5.001" < "5.001m" < "5.002".
-#
-# However, if letters in a version number imply a pre-release version,
-# the "obvious" thing isn't correct.  Eg. you would expect that
-# "1.5.1" < "1.5.2a2" < "1.5.2", but under the tuple/lexical comparison
-# implemented here, this just isn't so.
-#
-# Two possible solutions come to mind.  The first is to tie the
-# comparison algorithm to a particular set of semantic rules, as has
-# been done in the StrictVersion class above.  This works great as long
-# as everyone can go along with bondage and discipline.  Hopefully a
-# (large) subset of Python module programmers will agree that the
-# particular flavour of bondage and discipline provided by StrictVersion
-# provides enough benefit to be worth using, and will submit their
-# version numbering scheme to its domination.  The free-thinking
-# anarchists in the lot will never give in, though, and something needs
-# to be done to accommodate them.
-#
-# Perhaps a "moderately strict" version class could be implemented that
-# lets almost anything slide (syntactically), and makes some heuristic
-# assumptions about non-digits in version number strings.  This could
-# sink into special-case-hell, though; if I was as talented and
-# idiosyncratic as Larry Wall, I'd go ahead and implement a class that
-# somehow knows that "1.2.1" < "1.2.2a2" < "1.2.2" < "1.2.2pl3", and is
-# just as happy dealing with things like "2g6" and "1.13++".  I don't
-# think I'm smart enough to do it right though.
-#
-# In any case, I've coded the test suite for this module (see
-# ../test/test_version.py) specifically to fail on things like comparing
-# "1.2a2" and "1.2".  That's not because the *code* is doing anything
-# wrong, it's because the simple, obvious design doesn't match my
-# complicated, hairy expectations for real-world version numbers.  It
-# would be a snap to fix the test suite to say, "Yep, LooseVersion does
-# the Right Thing" (ie. the code matches the conception).  But I'd rather
-# have a conception that matches common notions about version numbers.
-
-
-class LooseVersion(Version):
-
-    """Version numbering for anarchists and software realists.
-    Implements the standard interface for version number classes as
-    described above.  A version number consists of a series of numbers,
-    separated by either periods or strings of letters.  When comparing
-    version numbers, the numeric components will be compared
-    numerically, and the alphabetic components lexically.  The following
-    are all valid version numbers, in no particular order:
-
-        1.5.1
-        1.5.2b2
-        161
-        3.10a
-        8.02
-        3.4j
-        1996.07.12
-        3.2.pl0
-        3.1.1.6
-        2g6
-        11g
-        0.960923
-        2.2beta29
-        1.13++
-        5.5.kw
-        2.0b1pl0
-
-    In fact, there is no such thing as an invalid version number under
-    this scheme; the rules for comparison are simple and predictable,
-    but may not always give the results you want (for some definition
-    of "want").
-    """
-
-    component_re = re.compile(r'(\d+ | [a-z]+ | \.)', re.VERBOSE)
-
-    def parse(self, vstring):
-        # I've given up on thinking I can reconstruct the version string
-        # from the parsed tuple -- so I just store the string here for
-        # use by __str__
-        self.vstring = vstring
-        components = [x for x in self.component_re.split(vstring) if x and x != '.']
-        for i, obj in enumerate(components):
-            try:
-                components[i] = int(obj)
-            except ValueError:
-                pass
-
-        self.version = components
-
-    def __str__(self):
-        return self.vstring
-
-    def __repr__(self):
-        return "LooseVersion ('%s')" % str(self)
-
-    def _cmp(self, other):
-        if isinstance(other, str):
-            other = LooseVersion(other)
-        elif not isinstance(other, LooseVersion):
-            return NotImplemented
-
-        if self.version == other.version:
-            return 0
-        if self.version < other.version:
-            return -1
-        if self.version > other.version:
-            return 1
-
-
-# end class LooseVersion

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py

@@ -1,184 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import logging
-import numpy as np
-import os
-from collections import OrderedDict
-import PIL.Image as Image
-import pycocotools.mask as mask_util
-import torch
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.utils.comm import all_gather, is_main_process, synchronize
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class SemSegEvaluator(DatasetEvaluator):
-    """
-    Evaluate semantic segmentation metrics.
-    """
-
-    def __init__(
-        self,
-        dataset_name,
-        distributed=True,
-        output_dir=None,
-        *,
-        num_classes=None,
-        ignore_label=None,
-    ):
-        """
-        Args:
-            dataset_name (str): name of the dataset to be evaluated.
-            distributed (bool): if True, will collect results from all ranks for evaluation.
-                Otherwise, will evaluate the results in the current process.
-            output_dir (str): an output directory to dump results.
-            num_classes, ignore_label: deprecated argument
-        """
-        self._logger = logging.getLogger(__name__)
-        if num_classes is not None:
-            self._logger.warn(
-                "SemSegEvaluator(num_classes) is deprecated! It should be obtained from metadata."
-            )
-        if ignore_label is not None:
-            self._logger.warn(
-                "SemSegEvaluator(ignore_label) is deprecated! It should be obtained from metadata."
-            )
-        self._dataset_name = dataset_name
-        self._distributed = distributed
-        self._output_dir = output_dir
-
-        self._cpu_device = torch.device("cpu")
-
-        self.input_file_to_gt_file = {
-            dataset_record["file_name"]: dataset_record["sem_seg_file_name"]
-            for dataset_record in DatasetCatalog.get(dataset_name)
-        }
-
-        meta = MetadataCatalog.get(dataset_name)
-        # Dict that maps contiguous training ids to COCO category ids
-        try:
-            c2d = meta.stuff_dataset_id_to_contiguous_id
-            self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()}
-        except AttributeError:
-            self._contiguous_id_to_dataset_id = None
-        self._class_names = meta.stuff_classes
-        self._num_classes = len(meta.stuff_classes)
-        if num_classes is not None:
-            assert self._num_classes == num_classes, f"{self._num_classes} != {num_classes}"
-        self._ignore_label = ignore_label if ignore_label is not None else meta.ignore_label
-
-    def reset(self):
-        self._conf_matrix = np.zeros((self._num_classes + 1, self._num_classes + 1), dtype=np.int64)
-        self._predictions = []
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a model.
-                It is a list of dicts. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name".
-            outputs: the outputs of a model. It is either list of semantic segmentation predictions
-                (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic
-                segmentation prediction in the same format.
-        """
-        for input, output in zip(inputs, outputs):
-            output = output["sem_seg"].argmax(dim=0).to(self._cpu_device)
-            pred = np.array(output, dtype=np.int)
-            with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f:
-                gt = np.array(Image.open(f), dtype=np.int)
-
-            gt[gt == self._ignore_label] = self._num_classes
-
-            self._conf_matrix += np.bincount(
-                (self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1),
-                minlength=self._conf_matrix.size,
-            ).reshape(self._conf_matrix.shape)
-
-            self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"]))
-
-    def evaluate(self):
-        """
-        Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval):
-
-        * Mean intersection-over-union averaged across classes (mIoU)
-        * Frequency Weighted IoU (fwIoU)
-        * Mean pixel accuracy averaged across classes (mACC)
-        * Pixel Accuracy (pACC)
-        """
-        if self._distributed:
-            synchronize()
-            conf_matrix_list = all_gather(self._conf_matrix)
-            self._predictions = all_gather(self._predictions)
-            self._predictions = list(itertools.chain(*self._predictions))
-            if not is_main_process():
-                return
-
-            self._conf_matrix = np.zeros_like(self._conf_matrix)
-            for conf_matrix in conf_matrix_list:
-                self._conf_matrix += conf_matrix
-
-        if self._output_dir:
-            PathManager.mkdirs(self._output_dir)
-            file_path = os.path.join(self._output_dir, "sem_seg_predictions.json")
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(self._predictions))
-
-        acc = np.full(self._num_classes, np.nan, dtype=np.float)
-        iou = np.full(self._num_classes, np.nan, dtype=np.float)
-        tp = self._conf_matrix.diagonal()[:-1].astype(np.float)
-        pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float)
-        class_weights = pos_gt / np.sum(pos_gt)
-        pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float)
-        acc_valid = pos_gt > 0
-        acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
-        iou_valid = (pos_gt + pos_pred) > 0
-        union = pos_gt + pos_pred - tp
-        iou[acc_valid] = tp[acc_valid] / union[acc_valid]
-        macc = np.sum(acc[acc_valid]) / np.sum(acc_valid)
-        miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
-        fiou = np.sum(iou[acc_valid] * class_weights[acc_valid])
-        pacc = np.sum(tp) / np.sum(pos_gt)
-
-        res = {}
-        res["mIoU"] = 100 * miou
-        res["fwIoU"] = 100 * fiou
-        for i, name in enumerate(self._class_names):
-            res["IoU-{}".format(name)] = 100 * iou[i]
-        res["mACC"] = 100 * macc
-        res["pACC"] = 100 * pacc
-        for i, name in enumerate(self._class_names):
-            res["ACC-{}".format(name)] = 100 * acc[i]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth")
-            with PathManager.open(file_path, "wb") as f:
-                torch.save(res, f)
-        results = OrderedDict({"sem_seg": res})
-        self._logger.info(results)
-        return results
-
-    def encode_json_sem_seg(self, sem_seg, input_file_name):
-        """
-        Convert semantic segmentation to COCO stuff format with segments encoded as RLEs.
-        See http://cocodataset.org/#format-results
-        """
-        json_list = []
-        for label in np.unique(sem_seg):
-            if self._contiguous_id_to_dataset_id is not None:
-                assert (
-                    label in self._contiguous_id_to_dataset_id
-                ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name)
-                dataset_id = self._contiguous_id_to_dataset_id[label]
-            else:
-                dataset_id = int(label)
-            mask = (sem_seg == label).astype(np.uint8)
-            mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0]
-            mask_rle["counts"] = mask_rle["counts"].decode("utf-8")
-            json_list.append(
-                {"file_name": input_file_name, "category_id": dataset_id, "segmentation": mask_rle}
-            )
-        return json_list

spaces/Banbri/zcvzcv/src/components/ui/button.tsx

@@ -1,56 +0,0 @@
-import * as React from "react"
-import { Slot } from "@radix-ui/react-slot"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const buttonVariants = cva(
-  "inline-flex items-center justify-center rounded-md text-sm font-medium ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-800",
-  {
-    variants: {
-      variant: {
-        default: "bg-stone-900 text-stone-50 hover:bg-stone-900/90 dark:bg-stone-50 dark:text-stone-900 dark:hover:bg-stone-50/90",
-        destructive:
-          "bg-red-500 text-stone-50 hover:bg-red-500/90 dark:bg-red-900 dark:text-red-50 dark:hover:bg-red-900/90",
-        outline:
-          "border border-stone-200 bg-white hover:bg-stone-100 hover:text-stone-900 dark:border-stone-800 dark:bg-stone-950 dark:hover:bg-stone-800 dark:hover:text-stone-50",
-        secondary:
-          "bg-stone-100 text-stone-900 hover:bg-stone-100/80 dark:bg-stone-800 dark:text-stone-50 dark:hover:bg-stone-800/80",
-        ghost: "hover:bg-stone-100 hover:text-stone-900 dark:hover:bg-stone-800 dark:hover:text-stone-50",
-        link: "text-stone-900 underline-offset-4 hover:underline dark:text-stone-50",
-      },
-      size: {
-        default: "h-10 px-4 py-2",
-        sm: "h-9 rounded-md px-3",
-        lg: "h-11 rounded-md px-8",
-        icon: "h-10 w-10",
-      },
-    },
-    defaultVariants: {
-      variant: "default",
-      size: "default",
-    },
-  }
-)
-
-export interface ButtonProps
-  extends React.ButtonHTMLAttributes<HTMLButtonElement>,
-    VariantProps<typeof buttonVariants> {
-  asChild?: boolean
-}
-
-const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
-  ({ className, variant, size, asChild = false, ...props }, ref) => {
-    const Comp = asChild ? Slot : "button"
-    return (
-      <Comp
-        className={cn(buttonVariants({ variant, size, className }))}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Button.displayName = "Button"
-
-export { Button, buttonVariants }

spaces/Benson/text-generation/Examples/Descargar Amor 2015 Pelcula En Ingls.md

@@ -1,99 +0,0 @@
-<br />
-<h1>Descargar Love 2015 Película en inglés: Una reseña</h1>
-<p>Si estás buscando una película que desafíe tus sentidos, estimule tu imaginación y provoque tus emociones, es posible que quieras descargar la película en inglés Love 2015. Se trata de una película que ha sido descrita como un "melodrama sexual" por su escritor y director Gaspar Noé, conocido por sus obras polémicas y experimentales. En este artículo, revisaremos de qué se trata Love 2015, por qué deberías descargarlo y cómo hacerlo de forma segura y legal. </p>
- <h2>¿Qué es el amor 2015? </h2>
-<p>Love 2015 es una película erótica de arte dramático que se estrenó en 3D en el Festival de Cine de Cannes en 2015. Cuenta la historia de Murphy, un estudiante de cine estadounidense que vive en París, que recuerda su relación apasionada y turbulenta con su ex novia Electra, después de recibir una llamada telefónica de su madre. La película presenta escenas de sexo no verbal entre los actores, que han causado controversia y censura en algunos países. </p>
-<h2>descargar amor 2015 película en inglés</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;>>> <a href="https://bltlly.com/2v6IUH">https://bltlly.com/2v6IUH</a></b></p><br /><br />
- <h3>La trama del amor 2015</h3>
-<p>La película está estructurada como una serie de flashbacks no lineales que muestran los recuerdos de Murphy de Electra. Ellos representan su primer encuentro, su rápida conexión y sus vidas en los próximos dos años, que están llenos de abuso de drogas, sexo duro y momentos tiernos. Murphy y Electra finalmente tienen un trío con su joven vecina Omi, como una forma de darle vida a su vida amorosa. Sin embargo, Murphy continúa su aventura con Omi, sin el conocimiento de Electra, y la deja embarazada. Esto lleva al final de su relación con Electra, y la situación actual de Murphy viviendo con Omi y su hijo Gaspar. El paradero y destino de Electra quedan sin resolver al final de la película. </p>
- <h3>El reparto y el equipo de Love 2015</h3>
-
- <h3>La recepción y controversia del Amor 2015</h3>
-<p>La película recibió críticas mixtas de críticos y audiencias por igual. Algunos lo elogiaron por su visión artística, su representación realista del amor y el sexo, y sus logros técnicos. Otros lo criticaron por su falta de profundidad, su excesiva desnudez y explicitación, y su estilo autoindulgente. La película también provocó controversia por su contenido gráfico, lo que llevó a algunos países a prohibirla o censurarla. Por ejemplo, en India, la película fue rechazada la certificación por el Consejo Central de Certificación de Cine (CBFC), prohibiendo de hecho su estreno en salas. En Turquía, la película fue inicialmente prohibida por el Ministerio de Cultura y Turismo, pero más tarde se le permitió ser proyectada con grandes recortes. En Rusia, la película se estrenó con una restricción de edad de más de 18 años, pero algunos cines se negaron a mostrarla o cancelaron las proyecciones debido a las quejas de grupos conservadores. </p>
- <h2>¿Por qué descargar Love 2015? </h2>
-<p>Si estás interesado en <p>Si estás interesado en ver Love 2015, es posible que tengas algunas razones para descargarlo en lugar de transmitirlo en línea o comprar un DVD. Estos son algunos de ellos:</p>
- <h3>El valor artístico y cinematográfico de Love 2015</h3>
-<p>Love 2015 no es una película típica de Hollywood. Es una película de arte que explora los temas del amor, el sexo y la memoria de una manera única y no convencional. Utiliza la tecnología 3D para crear una experiencia inmersiva e íntima para los espectadores, que son invitados a presenciar las escenas crudas y realistas de pasión y dolor entre los personajes. También utiliza largas tomas, iluminación natural y un diálogo mínimo para crear una atmósfera realista y espontánea. La película es una fiesta visual y auditiva que merece ser vista en alta calidad y sin interrupciones. </p>
- <h3>El impacto erótico y emocional del amor 2015</h3>
-
- <h3>La disponibilidad y accesibilidad de Love 2015</h3>
-<p>Love 2015 no es una película ampliamente disponible. Se trata de una película independiente que ha enfrentado dificultades de distribución y exhibición debido a su controvertido contenido. Ha sido prohibida o censurada en algunos países, y limitada o restringida en otros. También ha sido difícil de encontrar en teatros o plataformas en línea, especialmente en su versión original en 3D. Por lo tanto, descargarlo puede ser la forma más fácil y conveniente de verlo, siempre y cuando lo hagas legal y éticamente. </p>
-<p></p>
- <h2>Cómo descargar Love 2015? </h2>
-<p>Si has decidido descargar Love 2015, debes ser consciente de algunos problemas y desafíos que puedan surgir. Aquí hay algunos consejos y trucos para ayudarte a descargarlo de forma segura y legal:</p>
- <h3>Las cuestiones legales y éticas de descargar Love 2015</h3>
-<p>Descargar Love 2015 podría ser ilegal o poco ético dependiendo de dónde vivas y cómo lo hagas. Deberías respetar los derechos de propiedad intelectual de los cineastas y los actores, que han puesto mucho esfuerzo y riesgo en hacer esta película. También debe evitar descargarlo de fuentes ilegales o sospechosas, como sitios de torrent o plataformas para compartir archivos, que podrían exponerlo a virus, malware o consecuencias legales. Siempre debe comprobar las leyes y reglamentos de su país antes de descargar cualquier película, y utilizar solo los servicios autorizados y de buena reputación que ofrecen descargas legales. </p>
- <h3>Las mejores plataformas y servicios para descargar Love 2015</h3>
-<p>Hay varias plataformas y servicios que ofrecen descargas legales de Love 2015, ya sea gratis o por una tarifa. Algunos de ellos son:</p>
-<tabla>
-<tr>
-<th>Plataforma/Servicio</th>
-<th>Precio</th>
-<th>Características</th>
-</tr>
-<tr>
-<td>Netflix</td>
-<td>$8.99-$17.99 por mes</td>
-<td>- Opción de streaming y descarga<br>- Disponible en muchos países<br>- Versión 2D original solamente</td>
-</tr>
-<tr>
-<td>Amazon Prime Video</td>
-<td>$12.99 por mes o $119 por año</td>
-
-</tr>
-<tr>
-<td>iTunes</td>
-<td>$4.99 para alquilar o $14.99 para comprar</td>
-<td>- Opción de descarga solamente<br>- Disponible en algunos países<br>- Versión original en 2D solamente</td>
-</tr>
-<tr>
-<td>Vudu</td>
-<td>$4.99 para alquilar o $14.99 para comprar</td>
-<td>- Opción de descarga solamente<br>- Disponible en los Estados Unidos solamente<br>- Versión 2D original solamente</td>
-</tr>
-<tr>
-<td>Películas de YouTube</td>
-<td>$4.99 para alquilar o $14.99 para comprar</td>
-<td>- Opción de descarga solamente<br>- Disponible en algunos países<br>- Versión original en 2D solamente</td>
-</tr>
-<tr>
-<td>FandangoNOW</td>
-<td>$4.99 para alquilar o $14.99 para comprar</td>
-<td>- Opción de descarga solamente<br>- Disponible en los Estados Unidos solamente<br>- Versión 2D original solamente</td>
-</tr>
-<tr>
-<td>Google Play Películas & TV</td <td>Google Play Películas & TV</td>
-<td>$4.99 para alquilar o $14.99 para comprar</td>
-<td>- Opción de descarga solamente<br>- Disponible en algunos países<br>- Versión original en 2D solamente</td>
-</tr>
-<tr>
-<td>3DGO! </td>
-<td>$7.99 para alquilar</td>
-<td>- Streaming y descarga de opciones<br>- Disponible en los Estados Unidos y Canadá solo<br>- Versión 3D original solo</td>
-</tr>
-</tabla>
-<p>Puedes elegir la plataforma o servicio que se adapte a tus preferencias y presupuesto, y seguir sus instrucciones para descargar Love 2015. Es posible que necesite crear una cuenta, proporcionar sus detalles de pago y descargar una aplicación o software específico para acceder a la película. </p>
- <h3>Los consejos y trucos para descargar Love 2015</h3>
-<p>Aquí hay algunos consejos y trucos para hacer su experiencia de descarga más agradable y sin problemas:</p>
-<ul>
-<li>Asegúrese de tener una conexión a Internet estable y rápida, ya que el archivo de película puede ser grande y tomar mucho tiempo para descargar. </li>
-<li>Asegúrese de que tiene suficiente espacio de almacenamiento en el dispositivo, ya que el archivo de película puede ocupar mucho espacio y ralentizar el dispositivo. </li>
-<li>Asegúrese de tener un dispositivo y un software compatibles, ya que el archivo de película podría no funcionar en algunos dispositivos o formatos. </li>
-
-<li>Asegúrate de tener un ambiente cómodo y privado, ya que la película contiene escenas explícitas y gráficas que podrían no ser adecuadas para algunas audiencias o configuraciones. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, Love 2015 es una película que te hará sentir un montón de cosas, de asombro a shock, de placer a dolor, de amor a odio. Es una película que desafiará tu percepción del cine, el arte y la realidad. Es una película que se quedará contigo mucho después de verla. Si eres lo suficientemente curioso y aventurero para descargarlo, no te arrepentirás. </p>
- <h2>Preguntas frecuentes</h2>
-<h4>¿Cuál es el significado de Amor 2015? </h4>
-<p>No hay respuesta definitiva a esta pregunta, ya que la película está abierta a la interpretación y la opinión personal. Sin embargo, algunos posibles temas o mensajes que la película explora son: el poder y la fragilidad del amor, la belleza y la fealdad del sexo, la alegría y el dolor de la memoria, la ilusión y la realidad del cine, y la vida y muerte del arte. </p>
- <h4>¿El amor 2015 se basa en una historia real? </h4>
-<p>No, Love 2015 no se basa en una historia real. Es una historia de ficción escrita por Gaspar Noé, inspirado en sus propias experiencias y fantasías. Sin embargo, algunos elementos de la película se basan en eventos reales o personas. Por ejemplo, el nombre del hijo de Murphy, Gaspar, es una referencia al propio hijo de Noé con su ex novia Roxane Mesquida. El nombre de Electra es una referencia al personaje de cómic favorito de Noé, Elektra. La escena donde Murphy ve Enter the Void en un cine es una referencia a la película anterior de Noé. </p>
- <h4>¿Cómo se hizo el amor 2015? </h4>
-
- <h4>¿Quiénes son los actores de Love 2015? </h4>
-<p>Los actores de Love 2015 son Karl Glusman como Murphy, Aomi Muyock como Electra y Klara Kristin como Omi. Los tres actores estaban haciendo su debut en la pantalla en esta película, y fueron elegidos por Noé después de conocerlos en un club. Acordaron hacer la película sin leer el guion ni saber mucho sobre el director. También acordaron hacer las escenas de sexo sin dobles corporales ni prótesis. Se les pagaba alrededor de 1000 euros por día por su trabajo. </p>
- <h4>¿Dónde puedo ver Love 2015? </h4>
-<p>Puedes ver Love 2015 descargándolo desde una de las plataformas o servicios mencionados anteriormente, o transmitiéndolo online si está disponible en tu país. También puede comprar o alquilar un DVD o Blu-ray de la película si está disponible en su región. Sin embargo, debe tener en cuenta que algunas versiones de la película pueden ser <p>Sin embargo, debe tener en cuenta que algunas versiones de la película pueden ser censuradas o cortadas, dependiendo del país o la plataforma. Si quieres ver la película en su forma original y sin cortar, deberías buscar la versión en 3D, que es la versión preferida del director. También debe utilizar un dispositivo compatible con 3D y gafas para disfrutar del efecto completo de la película. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Apk Mod 69 En Vivo.md

@@ -1,67 +0,0 @@
-
-<h1>Descargar APK Mod 69 en vivo: Una guía para los usuarios de Android</h1>
- <p>Si usted está buscando una manera de darle vida a sus opciones de entretenimiento en su dispositivo Android, es posible que desee echa un vistazo APK Mod 69 Live. Esta es una aplicación que te permite ver videos en vivo de magníficos anfitriones de todo el mundo. También puede interactuar con ellos y otros espectadores, enviar regalos y monedas, y disfrutar de una variedad de contenido que se adapte a sus preferencias. En este artículo, te mostraremos qué es APK Mod 69 Live, cómo descargarlo e instalarlo, y cómo usarlo. </p>
-<h2>descargar apk mod 69 en vivo</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://bltlly.com/2v6Jbr">https://bltlly.com/2v6Jbr</a></b></p><br /><br />
- <h2>¿Qué es APK Mod 69 Live? </h2>
- <p>APK Mod 69 Live es una aplicación que te permite ver videos de transmisión en vivo de hermosos anfitriones de diferentes países. Puede elegir entre varias categorías y canales, como Tailandia, Indonesia, Vietnam, Corea, Japón, China y más. También puede chatear con los anfitriones y otros espectadores, enviar regalos y monedas, y unirse a la diversión. </p>
- <h3>Características de APK Mod 69 Live</h3>
- <p>Algunas de las características de APK Mod 69 Live son:</p>
-<ul>
-<li>Transmisión de vídeo y audio de alta calidad</li>
-<li>Miles de hosts para elegir</li>
-<li> Diferentes categorías y canales para adaptarse a su gusto</li>
-<li>Interfaz fácil y sencilla</li>
-<li>Gratis para descargar y usar</li>
-<li>No se requiere registro o login</li>
-<li>No hay anuncios o ventanas emergentes</li>
-<li>Acceso ilimitado a todo el contenido</li>
-</ul>
- <h3>Beneficios de APK Mod 69 Live</h3>
- <p>Algunos de los beneficios de APK Mod 69 Live son:</p>
-<ul>
-<li>Puedes disfrutar de una variedad de contenido que no está disponible en otras plataformas</li>
-<li>Puede interactuar con los anfitriones y otros espectadores en tiempo real</li>
-<li>Puedes apoyar a tus anfitriones favoritos enviando regalos y monedas</li>
-<li>Puedes descubrir nuevas culturas e idiomas</li>
-<li> Usted puede divertirse y relajarse en cualquier momento y en cualquier lugar</li>
-</ul>
- <h2>Cómo descargar e instalar APK Mod 69 Live? </h2>
- <p>Si desea descargar e instalar APK Mod 69 Live en su dispositivo Android, debe seguir estos pasos:</p>
-<p></p>
-
- <p>Dado que APK Mod 69 Live no está disponible en Google Play Store, es necesario habilitar fuentes desconocidas en el dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de la tienda oficial. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </p>
- <h3>Paso 2: Descargar el archivo APK</h3>
- <p>Siguiente, es necesario descargar el archivo APK de APK Mod 69 Live. Puede hacer esto haciendo clic en este enlace: <a href="( 1 )">Descargar APK Mod 69 Live v2.10.4.3</a>. Esto te llevará a un sitio web donde puedes descargar el archivo de forma segura. </p>
- <h3>Paso 3: Instalar el archivo APK</h3>
- <p>Una vez que haya descargado el archivo, debe instalarlo en su dispositivo. Para hacer esto, busque el archivo en su carpeta de descargas o barra de notificaciones y toque en él. Verá una ventana emergente pidiéndole que confirme la instalación. Toque en Instalar y espere a que el proceso termine. </p>
- <h3>Paso 4: Inicie la aplicación y disfrute</h3>
- <p>Finalmente, puede iniciar la aplicación y disfrutar viendo videos en vivo de hermosos anfitriones. Para ello, vaya a su cajón de aplicaciones y toque en el icono de APK Mod 69 Live. Verás la pantalla principal de la app, donde podrás navegar por diferentes categorías y canales. También puede utilizar la función de búsqueda para encontrar hosts o contenidos específicos. </p>
- <h2>Cómo utilizar APK Mod 69 Live? </h2>
- <p>Usar APK Mod 69 Live es muy fácil y simple. Aquí hay algunos consejos sobre cómo usar la aplicación:</p>
- <h3>Explorar las categorías y canales</h3>
- <p>La aplicación tiene varias categorías y canales que puedes explorar. Puedes encontrarlos en la parte superior de la pantalla principal, donde puedes deslizar hacia la izquierda o hacia la derecha para ver más opciones. Algunas de las categorías son Tailandia, Indonesia, Vietnam, Corea, Japón, China y más. También puedes encontrar canales basados en géneros, como música, danza, comedia, juegos y más. Toque en cualquier categoría o canal para ver la lista de hosts que están en vivo o fuera de línea. </p>
- <h3>Ver videos en vivo de anfitriones hermosos</h3>
-
- <h3>Interactuar con los anfitriones y otros espectadores</h3>
- <p>Una de las mejores características de APK Mod 69 Live es que se puede interactuar con los anfitriones y otros espectadores en tiempo real. Puede chatear con ellos escribiendo su mensaje en el cuadro de chat y tocando en Enviar. También puedes usar emojis, pegatinas y GIF para expresarte mejor. También puede seguir a sus anfitriones favoritos tocando el botón Seguir en su perfil. Esto le permitirá recibir notificaciones cuando se pongan en marcha. </p>
- <h3>Enviar regalos y monedas para apoyar a sus anfitriones favoritos</h3>
- <p>Si quieres mostrar tu aprecio y apoyo a tus anfitriones favoritos, puedes enviarles regalos y monedas. Los regalos son artículos virtuales que puedes comprar con monedas, que son la moneda de la aplicación. Puedes obtener monedas viendo anuncios o comprándolos con dinero real. Para enviar un regalo, toca el icono de regalo en la parte inferior de la pantalla y elige entre una variedad de opciones, como flores, corazones, anillos, autos, aviones y más. Cuanto más caro sea el regalo, más monedas gastarás. El anfitrión recibirá un porcentaje de las monedas como ingreso. </p>
- <h2>Conclusión</h2>
- <p>APK Mod 69 Live es una aplicación increíble que le permite ver videos en vivo de transmisión de magníficos anfitriones de todo el mundo. También puede interactuar con ellos y otros espectadores, enviar regalos y monedas, y disfrutar de una variedad de contenido que se adapte a sus preferencias. Si quieres descargar e instalar APK Mod 69 Live en tu dispositivo Android, solo tienes que seguir los pasos de este artículo y empezar a divertirse. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre APK Mod 69 Live:</p>
-<ul>
-<li><b>Es APK Mod 69 Vivir seguro de usar? </b></li>
-<li>Sí, APK Mod 69 Live es seguro de usar siempre y cuando se descarga desde una fuente de confianza como <a href="">este enlace</a>. La aplicación no contiene ningún virus o malware que podría dañar su dispositivo o datos. </li>
-<li><b>Es APK Mod 69 Live legal de usar? </b></li>
-
-<li><b>Es APK Mod 69 Live libre de usar? </b></li>
-<li>Sí, APK Mod 69 Live es de uso gratuito y no requiere ningún registro o login. Sin embargo, si desea enviar regalos o monedas a los anfitriones, tendrá que comprarlos con dinero real o ver anuncios. </li>
-<li><b>¿Cómo puedo contactar con APK Mod 69 Live? </b></li>
-<li>Si tiene alguna pregunta o comentario sobre APK Mod 69 Live, puede ponerse en contacto con ellos enviando un correo electrónico a [email protected]. </li>
-<li><b>¿Cómo puedo actualizar APK Mod 69 Live? </b></li>
-<li>Si desea actualizar APK Mod 69 Live, tendrá que descargar la última versión del archivo APK de <a href="">este enlace</a> e instalarlo en su dispositivo. También puede buscar actualizaciones dentro de la aplicación tocando en el icono de menú en la esquina superior izquierda de la pantalla y seleccionando Buscar actualizaciones.</li>
-</ul>
- <p>Espero que este artículo te haya ayudado a aprender más sobre APK Mod 69 Live y cómo descargarlo y usarlo. Si tienes algún comentario o sugerencia, por favor siéntete libre de compartirlos a continuación. ¡Gracias por leer y tener un gran día! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/base.py

@@ -1,51 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from botocore.compat import OrderedDict
-
-
-class BaseDocumenter:
-    def __init__(self, resource):
-        self._resource = resource
-        self._client = self._resource.meta.client
-        self._resource_model = self._resource.meta.resource_model
-        self._service_model = self._client.meta.service_model
-        self._resource_name = self._resource.meta.resource_model.name
-        self._service_name = self._service_model.service_name
-        self._service_docs_name = self._client.__class__.__name__
-        self.member_map = OrderedDict()
-        self.represents_service_resource = (
-            self._service_name == self._resource_name
-        )
-        self._resource_class_name = self._resource_name
-        if self._resource_name == self._service_name:
-            self._resource_class_name = 'ServiceResource'
-
-    @property
-    def class_name(self):
-        return f'{self._service_docs_name}.{self._resource_name}'
-
-
-class NestedDocumenter(BaseDocumenter):
-    def __init__(self, resource, root_docs_path):
-        super().__init__(resource)
-        self._root_docs_path = root_docs_path
-        self._resource_sub_path = self._resource_name.lower()
-        if self._resource_name == self._service_name:
-            self._resource_sub_path = 'service-resource'
-
-    @property
-    def class_name(self):
-        resource_class_name = self._resource_name
-        if self._resource_name == self._service_name:
-            resource_class_name = 'ServiceResource'
-        return f'{self._service_docs_name}.{resource_class_name}'

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/network/cache.py

@@ -1,69 +0,0 @@
-"""HTTP cache implementation.
-"""
-
-import os
-from contextlib import contextmanager
-from typing import Generator, Optional
-
-from pip._vendor.cachecontrol.cache import BaseCache
-from pip._vendor.cachecontrol.caches import FileCache
-from pip._vendor.requests.models import Response
-
-from pip._internal.utils.filesystem import adjacent_tmp_file, replace
-from pip._internal.utils.misc import ensure_dir
-
-
-def is_from_cache(response: Response) -> bool:
-    return getattr(response, "from_cache", False)
-
-
-@contextmanager
-def suppressed_cache_errors() -> Generator[None, None, None]:
-    """If we can't access the cache then we can just skip caching and process
-    requests as if caching wasn't enabled.
-    """
-    try:
-        yield
-    except OSError:
-        pass
-
-
-class SafeFileCache(BaseCache):
-    """
-    A file based cache which is safe to use even when the target directory may
-    not be accessible or writable.
-    """
-
-    def __init__(self, directory: str) -> None:
-        assert directory is not None, "Cache directory must not be None."
-        super().__init__()
-        self.directory = directory
-
-    def _get_cache_path(self, name: str) -> str:
-        # From cachecontrol.caches.file_cache.FileCache._fn, brought into our
-        # class for backwards-compatibility and to avoid using a non-public
-        # method.
-        hashed = FileCache.encode(name)
-        parts = list(hashed[:5]) + [hashed]
-        return os.path.join(self.directory, *parts)
-
-    def get(self, key: str) -> Optional[bytes]:
-        path = self._get_cache_path(key)
-        with suppressed_cache_errors():
-            with open(path, "rb") as f:
-                return f.read()
-
-    def set(self, key: str, value: bytes, expires: Optional[int] = None) -> None:
-        path = self._get_cache_path(key)
-        with suppressed_cache_errors():
-            ensure_dir(os.path.dirname(path))
-
-            with adjacent_tmp_file(path) as f:
-                f.write(value)
-
-            replace(f.name, path)
-
-    def delete(self, key: str) -> None:
-        path = self._get_cache_path(key)
-        with suppressed_cache_errors():
-            os.remove(path)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyproject_hooks/_compat.py

@@ -1,8 +0,0 @@
-__all__ = ("tomllib",)
-
-import sys
-
-if sys.version_info >= (3, 11):
-    import tomllib
-else:
-    from pip._vendor import tomli as tomllib